Anne-Claude Dori

Mme Thion Dei Pfdp

Alessandro Parigi

Abhijit Banerjee

Matteo Bobba

Lorenzo Casaburi

Denis Cogneau

Paolo Falco

Jonathan Lehne

Thierry Son

Karen Ly

Imran Macours

Roland Rasul

Liam Rathelot

Wren-Lewis

Luc Behaghel

Gary Fields

David Margolis

Patrizio Piraino

Sara Tonini

Peng Zhang

The Lasting Labor Market Effects of Cash Transfers

Keywords: J46, J64, O17, I38 unconditional cash transfers, job quality, informal sector, South Africa L26, J62, I38 self-employment, cash transfers, South Africa, intergenerational transmission

Can cash with "no strings attached" have long-term benefits on the employment outcomes of adults in developing countries? This paper studies the impact of a largescale, unconditional cash transfer in South Africa targeted to Black and Coloured women, a group with both low employment and high informality. I use discontinuous exposure to the Child Support Grant for mothers whose children were born one year apart to identify the short and long-term labor market effects of the grant. As a job search model would predict, in the short term, mothers are more likely to be unemployed and less likely to be working. Five years after the transfer was received, the employment rate is back to the same level as it is for ineligible mothers; the mothers who benefited for at least one year, however, are more likely to work in the formal sector. This appears to be the result of mothers targeting better jobs in the presence of high search costs.

I am grateful to my supervisor, Luc Behaghel, for guiding me all the way from the very beginning of my master dissertation to the end of the PhD. Luc has been the first, most insightful (and toughest) reader of my work. I can safely say that most (if not all) of what I have learned about research, I've learned from him. From complicated proofs or models to the number of tables and figures that should go in the main text of a paper, his influence on the way I work and generally think about research cannot be overstated. Yet, I still enjoyed great independence in developing and pursuing my own research ideas, as shown by the focus of this dissertation on South Africa. This was, I believe, important to develop the degree of autonomy that is essential in this line of work. A supervisor who provides constant and precise feedback and encouragement is already a privilege; one who does so while also making the effort to follow you on your own subjects is more unique than rare. For this double privilege, I am truly thankful. I would also like to thank my co-supervisor, Jérôme Gautié, for his kindness, feedback and support throughout the PhD, and David Margolis, for his constant comments throughout the years as a member of my PhD committee and final jury.

Moreover, my gratitude goes to the three other members of the jury: Abhijit Banerjee, and in particular the two reviewers, Imran Rasul and Roland Rathelot.

I am indebted to my co-authors on these and other projects. Paul Dutronc-Postel for our collaboration on the second chapter of this thesis; Patrizio Piraino for the preliminary work on which I built the third chapter on self-employment in South Africa; Cyprien Batut for our project on working hours, and the countless times he has helped me in these past years. v PSE is a fantastic environment in many aspects. Within this, the development group is maybe the pinnacle; and that is, without doubt, mostly thanks to Sylvie Lambert and Karen Macours. Their dedication to the common good, and their feedback, advice and support to each student are remarkable. Similarly, I would also like to thank Denis Cogneau, Marc Gurgand, Oliver Vanden Eynde, and Liam Wren-Lewis, who have all taken considerable time to give me advice and feedback on my work in these past years. Also at PSE, I am particularly grateful to Véronique Guillotin, her enthusiasm and the help she provides to PhD students are simply unmatched. I greatly enjoyed being TA for Philipp Ketz both this year and the past, Most of all, I am thankful for the many wonderful friends I have made at PSE before and during the PhD, among many: Laura, Clara, Pepe, Clement, Paul, Matthew, Oscar, Justine, Jonathan, Leo, Rozenn, Nolwenn, Mariona and Yaz. The "Ritalnomics" group: Paolo, Sara, Francesco (che risate organizzare la conferenza con voi quest'anno). And, in particular, my friends Quentin and Cyprien.

A tutti i "ragazzi" di Bergamo, amici che mi porto dietro da sempre, ormai abituati a vedermi andare e tornare (in ordine sparso): Pol, Matty, Gio maffo, Gioca, Riki, Calde, Piè, Zambo, Leo, Edo, Persa. E anche alle sciure: Fra, Mary, Laura, Chiara, Giulia.

Alle varie persone che hanno arricchito la mia vita negli ultimi quattro anni: la

Introduction General Introduction

This dissertation gathers evidence on employment responses to cash transfers in South Africa with the aim to draw general lessons on the functioning of labor markets in middle-income countries. At this stage of development, labor markets are particularly intricate, as they combine multiple sectors where formal and informal jobs coexist. This segmentation requires to think about why and how workers place themselves across different sectors. Similarly, policy design and evaluation need to take into account the presence of an unregulated informal sector, and how this might distort incentives when interacting with social programs. The relevance of this concern, and more generally which policies can lead to welfare-improvements, strongly depend on the nature of this "shadow" sector of the labor market. By analyzing the effects of South Africa's unique cash transfer programs, which offer practical policy experiments to test the role of constraints and incentives in the allocation of workers, I argue that a few important conclusions can be drawn.

First, it is important to explain why this dissertation focuses on South Africa, and to what extent its findings are South Africa-specific. South Africa's labor market is unlike that of a "standard" middle-income country. Contrary to what is usually observed in countries at a similar level of development, it is characterized by a persistently high unemployment rate, a symmetrically low employment rate, and a relatively small informal sector. These well-known issues have been extensively documented since the end of the Apartheid period, yet it is difficult to find in the literature explanations or hypotheses for these anomalies. With a few exceptions [START_REF] Kingdon | Unemployment in South Africa: The Nature of the Beast[END_REF], [START_REF] Banerjee | Why has Unemployment Risen in the New South Africa?[END_REF]), most of the economic literature on South Africa has tried to draw lessons of more general interest, rather than addressing the issues that are specific or unique to its labor market. The country's innovative social programs provide interesting policy experiments, with features that are difficult to find in other middle income countries. Paradoxically, while this has led to a large research output in South Africa, there is much less about South Africa. This dissertation is not entirely exempt from this shortcoming. Most of what is presented in the following chapters are attempts to use South Africa's uncommon cash transfer system to draw lessons of more general interest, and policy implications that are not necessarily country-specific. However, focusing entirely and extensively on one country has allowed me to elaborate an explanation for some of the distinct dysfunctionalities of the South African labor market, and how they interact with issues usually present in other middle-income countries. I mostly elaborate this reasoning in the third chapter, albeit in a more descriptive way, but building onto some of the reduced-form results of the first two chapters.

Overall, the dissertation attempts to contribute to two strands of the economic literature. First, it expands on the large literature on cash transfers, which have received growing attention given their pervasiveness in developing countries (Molina Millán et al. (forthcoming), [START_REF] Haushofer | The Short-Term Impact of Unconditional Cash Transfers to the Poor: Experimental Evidence from Kenya[END_REF]). However, while many of these programs have been extensively studied along different aspects, we still know very little about their labor market effects on adult recipients. Results from several randomized interventions suggest that disincentive effects on work are likely to be small [START_REF] Banerjee | Debunking the Stereotype of the Lazy Welfare Recipient: Evidence from Cash Transfer Programs[END_REF]). Moreover, there are theoretical reasons to believe that cash transfers might actually have positive effects on employment outcomes of adults [START_REF] Baird | The Effects of Cash Transfers on Adult Labor market Outcomes[END_REF]). From a policy perspective, it is key to know whether the shock induced by a cash transfer can have lasting benefits on the income-generating activities of recipients, especially their labor market outcomes. This connects to the debate on the presence of liquidity constraints in developing countries, and more generally to the concerns about poverty traps, i.e. the inability of people to make investments that could lead to large and lasting benefits. South Africa has arguably the most extensive and generous cash transfer system in the developing world, and hence it is particularly suited to answer these questions. Moreover, these cash transfers are often unconditional, meaning that they do not impose any conditionalities on recipients' behavior. Because of these unique features, the labor market effects of these programs can be more easily interpreted as a consequence of the extra income that they provide, which is important both to understand and to generalize these findings.

Also, this dissertation relates to the branch of the literature studying labor market segmentation and informality across middle-income countries. This is the stage of development where the issue of segmentation is most pressing. The formal sector is usually very limited in poor countries, and omnipresent in rich ones, which makes this concern less relevant. There is a longstanding debate on the nature of this segmentation, which is as old as the literature itself [START_REF] Harris | Migration, Unemployment and Development: A Two-Sector Analysis[END_REF], [START_REF] Hart | Informal Income Opportunities and Urban Employment in Ghana[END_REF]). This has been centered around the question of whether the driving force of sectoral allocation is necessity or choice [START_REF] Günther | Informal Employment in Developing Countries, Opportunity or Last resort?[END_REF]). An optimistic view of the informal sector proposes that workers allocate themselves according to comparative advantage within a dual labor market where regulations are weakly enforced. From this perspective, the informal sector is the result of workers' choices, who have higher returns (or utility) in the unregulated sector of the economy. Instead, a negative view of segmentation portrays it as the result of barriers and frictions that impede an optimal allocation of workers, or of a limited stock of jobs in either sector. In this view, workers would have higher earnings in another sector, but do not manage to enter it for whatever reason. In terms of policy implications, distinguishing between these views has important repercussions on what is the most effective way to counter segmentation, or whether it should be countered at all. For example, if the decision of holding an informal job comes from an individual unconstrained choice, then policies that play on the incentives margin will have a greater impact. On the other hand, if informality provides a "job of last resort" when there are constraints to enter either sector, then policies that relax these constraints could be welfare improving.

This dissertation uses labor market responses to cash transfers as a way to disentangle these two views. In spirit, this is a "revealed preferences" approach to the question of segmentation, as I argue that workers' responses to these cash transfer shocks are indirectly revealing of the drivers of segmentation. If liquidity constraints are one of the reasons behind labor market segmentation in the presence of fixed costs to enter either sector, then an unconditional cash transfer would have lasting impacts on sectoral allocation. If, instead, only relative payoffs across sectors matter, then a policy that makes a large amount conditional on a means-test on earnings only in one sector might also induce some type of re-allocation.

The first chapter of this dissertation studies the labor market effects, both in the short and long term, of an unconditional cash transfer program targeted at mothers, the Child Support Grant (CSG). An empirical analysis of this program is particularly informative for several reasons. On the one hand, it is similar to other child grants observed in most developing countries, both in terms of real amount per child, and the labelling of the transfer. On the other hand, the CSG is a purely unconditional transfer, which is not the case for most child grants programs. It has no requirements attached to the grant, and its means-test is not actually applied in practice. These features allow to interpret its effect on labor market outcomes of mothers as a pure wealth shock, but framed in the same way as similar programs across the developing world. With respect to the drivers of workers' allocation in a segmented labor market, the CSG can be seen as an "instrument" to test the importance of the "necessity" channel; an unconditional cash transfer relaxes liquidity constraints, while not affecting relative payoffs across sectors. In practice, it allows to test whether workers who are slightly richer as a result of the transfer, but similar in everything else, end up in different sectors.

In labor and public economics, the impact of more cash-on-hand on job search, and subsequent job quality, is a central topic. A large literature has studied how more generous unemployment benefits or severance pay lengthen job search, with contrasting empirical results on whether longer search leads to better or worse jobs [START_REF] Card | Cash-on-Hand and Competing Models of Intertemporal Behavior: New Evidence from the Labor Market[END_REF], [START_REF] Nekoei | Does Extending Unemployment Benefits Improve Job Quality?[END_REF]). For the most part, this literature has focused on developed countries, while little is known about the impact of social programs on job search and job quality in developing countries. However, this question may be even more relevant in these contexts, exactly because of the co-existence of different sectors with good and very bad jobs within the same labor market. Also, a key take-away from the unemployment insurance (UI) literature is the importance of taking a dynamic look: "How can we distinguish whether UI subsidizes unproductive leisure or productive job search? The best way to do so is to study the quality of post-UI job matches" [START_REF] Gruber | Public Finance and Public Policy[END_REF]). The same logic can be applied to the labor market effects of cash transfers: to disentangle whether a cash transfer is subsidizing leisure or search, one can look at the jobs recipients find after. Empirically, the challenge lies in estimating the long-term effects of cash transfers, which I argue is possible with the identification strategy I employ for the Child Support Grant.

In this chapter, I exploit discontinuous exposure across children's birth cohorts caused by reforms in the age threshold, because a child is eligible to the grant only up to a certain age. As this identification allows me to track cohorts over time, I am able to estimate both immediate and persistent effects. In the short term, mothers who receive the Child Support Grant search for a job longer than mothers who are never eligible for it. Five years after the transfer was received, this longer search has translated into substantial job quality gains. Indeed, I show that mothers who have received at least one year of grant are significantly more likely to work in the formal sector, and symmetrically less likely to work in the informal sector. These gains are concentrated on single mothers, which I argue is consistent with the liquidity explanation. The impact on total employment in the long term is null, suggesting that cash transfers do not lead to higher employment at the extensive margin.

These persistent job quality gains appear as the result of different targeting during job search, when mothers are less likely to pick up low-quality jobs in the formal sector, in industries and occupations with low wages and retention rates. This is also consistent with the observation that mothers with eligible children spend more on transport when looking for a job. Overall, these findings are in line with the main predictions of the job search literature, where an exogenous cash grant should unambiguously increase unemployment length. To explain the effect on job quality, I develop a stationary, directed search model with exogenous effort that shows how the impact on job quality is ambiguous: on the one hand, richer individuals gain less from targeting better jobs; on the other hand, they find it less costly to target better jobs as they have more resources to do so, and can search for longer. It is intuitive that this second channel will be of greater relevance the more severe liquidity constraints are.

Overall, the main contribution of this chapter is to show that unconditional cash transfers can have persistent positive effects on job quality in a segmented labor market, which is a new result in the literature. There is, to my knowledge, no other paper showing long-term effects of cash transfers on the labor market outcomes of adult recipients1 , mostly due to difficulty in having exogenous variation that persists in the long term. The other main take-away is that workers who receive this shock are more likely to end up in the formal sector. This provides evidence in favor of a more negative view of informality, where constraints are in part at play in driving workers across sectors.

The second chapter of this dissertation (co-authored with P. Dutronc-Postel) explores the role of incentives for sectoral allocation in a segmented labor market.

In the same spirit as the first chapter, it exploits a policy shock to identify workers' labor responses, from which it tries to draw conclusions about their preferences. A recent literature has raised concerns about the distortions that social programs might introduce in a labor market where there is an important informal sector [START_REF] Azuara | Informality and the Expansion of Social Protection Programs: Evidence from Mexico[END_REF], [START_REF] Bergolo | Work and Tax Evasion Incentive Effects of Social Insurance Programs: Evidence from an Employment-Based Benefit Extension[END_REF], [START_REF] Gerard | Informal Labor and the Efficiency Cost of Social Programs: Evidence from the Brazilian Unemployment Insurance Program[END_REF]).

If workers allocate themselves freely according to expected earnings across sectors, means-tested social policies may trigger movement towards the unregulated, informal sector. To tackle this question, the chapter analyzes the largest social program in South Africa, the Old Age Pension, and investigates whether there is any evidence of this. As earnings from an informal job de facto do not enter the means-test, individuals have the implicit incentive to cumulate the pension with hidden wages from informal work. The relevance of this channel will depend on why workers located in either sector in the first place, their preferences, and their potential wage in the informal sector. As the means-test is located in a portion of the wage distribution where (monthly) wages across sectors largely overlap, there are reasons to believe that this concern might be relevant.

To study this question in detail, we take advantage of a reform of the public, means-tested pension system of South Africa, which lowered the pension age for men from 65 to 60, while leaving it unchanged for women. We show that correct identification of the labor market effects of this program requires the extra variation introduced by the reform. Bias from other private pension would otherwise lead to estimate responses that are twice as large, and that would also drastically change the qualitative interpretation across the formal/informal dimension. Our results show a large extensive margin response in informal employment, as workers quit their informal job when they become eligible to the pension. Contrary to what a standard leisure-work model would predict, this response is of similar size at all levels of the informal wage distribution, suggesting it is not only workers at the lowest level of wages who drop out. On the contrary, the extensive margin response for formal employment is small and insignificant: only formal workers at wages equivalent to the 1st quartile of the informal wage distribution quit their jobs, while those with higher wages do not respond. This implies that differential employment responses across formal/informal are not driven by lower wages in the informal sector. At the same level of wages, informal workers quit their job, and formal workers do not, which we interpret either as an indication of heterogeneous preferences/characteristics of the worker, or that these jobs are different across other dimensions. Overall, we find no evidence of any significant substitution from formal to informal, suggesting that concerns about the distortions introduced by means-tested programs might be less relevant.

Lastly, we address whether lowering (public) pension age can free up jobs for Introduction younger workers. As this is motivation often enters the policy debate, even in developing countries, it is a question with important policy implications. Our results suggest that this is unlikely to be the case. Although the extensive margin response from the elderly is fairly large, this group is too small to make a significant difference.

We calculate that the reform has freed up at most between 20 000 and 30 000 jobs.

By exploiting the differential effect of the reform across occupation and industries, we are able to reject a one-to-one substitution on the closest substitute workers.

However, we lack the statistical power to judge whether these jobs have been "lost" or picked up by other workers. In any way, we conclude that, in countries with a similar demographic structure to South Africa, this kind of reform unlikely to have any significant impact on the stock of jobs available to younger workers.

The third chapter addresses an issue that is South Africa-specific, and arguably the main anomaly of the South African labor market: the pervasive absence of selfemployment activities. The self-employment rate in South Africa is astonishingly low, only a fraction of what is observed in countries at a similar level of development. In fact, missing self-employment can account for virtually all of the gap with the average employment rate of countries with the same GDP per capita. This peculiarity of the South African labor market is well-known [START_REF] Kingdon | Unemployment in South Africa, 1995-2003: Causes, Problems and Policies[END_REF], [START_REF] Banerjee | Why has Unemployment Risen in the New South Africa?[END_REF], [START_REF] Grabrucker | Does Crime Deter South Africans from Self-Employment?[END_REF]), but it is not yet clear what is the reason behind it. Common explanations, such as the high crime rate, or strict labor market regulation, have failed to provide evidence that can be reconciled with the magnitude of this phenomenon [START_REF] Grabrucker | Does Crime Deter South Africans from Self-Employment?[END_REF], [START_REF] Magruder | High Unemployment Yet Few Small Firms: the Role of Centralized Bargaining in South Africa[END_REF]). It is also unlikely to be a measurement problem, as the rate remains low across surveys and time, despite alternative measurements of self-employment and significant effort from the statistical agency to get an accurate count. Moreover, the absence of self-employment is limited to the native Black and Coloured population, while the self-employment rate among migrants from other African countries is high. This shows that it is not impossible to be self-employed in South Africa; the question is then why most South Africans do not consider or cannot enter this occupation.

Building onto the results of Chapter 1 and 2, I show that households do not Introduction increase their self-employment when receiving large, unconditional cash transfers. This contrasts with an established finding in the literature in development economics, which has shown that cash transfers increase entry into self-employment [START_REF] Bianchi | Liquidity, Risk, and Occupational Choices[END_REF], [START_REF] Blattman | Generating Skilled Self-Employment in Developing Countries: Experimental Evidence from Uganda[END_REF]). In theory, a cash transfer might promote self-employment by relaxing both liquidity and insurance constraints, as it provides extra income while also insuring against the higher earnings volatility of running a business (relative to wage-employment). Instead, this response is not observed in South Africa, where cash transfer recipients are as likely to be selfemployed as non-recipients. For these reasons, I argue that liquidity constraints and risk aversion are unlikely to be the main barrier to self-employment in South Africa.

Importantly, this also shows that self-employment is not simply absorbed by the welfare state.

In this chapter, I put forward the hypothesis that the lack of self-employment is likely to have historical roots. During Apartheid, black South Africans were prevented to be self-employed, either in the formal or informal sector. Repression of informal self-employment activities during this period is extensively documented [START_REF] Rogerson | The Absorptive Capacity of the Informal Sector in the South African City[END_REF]). With the end of Apartheid, and of these restrictions, policy-makers hoped that informal self-employment would flourish, and absorb most of the growing labor supply [START_REF] Rogerson | Emerging from Apartheid's Shadow: South Africa's Informal Economy[END_REF]). In the post-Apartheid years, the official government policy switched to one of promotion of the growth of micro business enterprises, which were believed to be a key component of economic growth in South Africa. This, however, did not happen. Self-employment in South Africa has remained remarkably low since 1994, with no visible growth in the number of self-employed jobs to this day. The hypothesis that the lack of self-employment in South Africa stems from Apartheid is not new. [START_REF] Kingdon | Unemployment in South Africa: The Nature of the Beast[END_REF] already underlined the potentially long-lasting consequences of the Apartheid repression that would have inhibited the development of the social capital necessary to enter this occupation.

I build upon this argument with insights from the literature in socio-economics, which points to the strong inter-generational component in the transmission of self-employment. Indeed, a constant empirical observation is that the children of entrepreneurs are much more likely to be entrepreneurs themselves. This correlation is mostly attributed to a role-modelling effect: by observing an entrepreneur parent, children become more aware of this occupational possibility. Intuitively, this would offer an explanation of why younger generations, who have not been directly touched by the Apartheid years, still do not enter self-employment. Simply, role-modelling could not be at play for young South Africans today, as their parents were not allowed to be self-employed. In the chapter, I present some evidence along these lines for migrants to South Africa: those arrived after Apartheid are more likely to be self-employed, and so are their children.

These conclusions give some justification for policy action, suggesting that an intervention may potentially compensate for these missing inter-generational channels.

In the chapter, I discuss potential avenues for future research, and what mechanisms, in my opinion, remain to be tested. Whether policy-makers should incentive entry into self-employment in South Africa, as opposed to trying to increase wage-employment, is not uncontroversial. However, wage-employment in South Africa is not low. The wage-employment rate is actually roughly comparable to the average of other middleincome countries. If the policy goal is to increase employment, it seems therefore likely that this will have to come from self-employment, at least in part.

In conclusion, the goal of this PhD dissertation is to bring together an analysis of employment responses to cash transfer programs in South Africa to draw lessons about the functioning of labor markets in middle-income countries. In my reading of these results, the main take-aways of general interest are the following: i) unconditional cash transfers can have lasting consequences on labor market outcomes of adult recipients, in particular my findings show that they can improve job quality, through longer search and presumably targeting of better jobs; also, ii) cash transfer recipients are more likely to end up in the formal sector, which is indirectly revealing of the drivers of segmentation in the labor market of a middle-income country. With respect to means-tested, large public pensions, I find evidence that iii) a means-test does not push people to switch to informal jobs; iv) at a given wage, informal workers quit their jobs, while formal workers do not, when they become eligible for the C'est à ce stade de développement économique d'un pays que la question de la segmentation des marchés du travail est la plus cruciale. En effet, le secteur formel est généralement très limité dans les pays pauvres et omniprésent dans les pays riches, ce qui y rend cette problématique moins pertinente. Il y a un débat de longue date sur la nature de cette segmentation, qui remonte aux origines de cette littérature elle-même [START_REF] Harris | Migration, Unemployment and Development: A Two-Sector Analysis[END_REF], [START_REF] Hart | Informal Income Opportunities and Urban Employment in Ghana[END_REF]). Ce débat a pour coeur l'origine de l'allocation sectorielle des individus, et plus précisément, le fait que l'allocation sectorielle se fasse par "nécessité ou [par] choix" [START_REF] Günther | Informal Employment in Developing Countries, Opportunity or Last resort?[END_REF]). Deux conceptions du secteur informel s'opposent dans ce débat. La [START_REF] Card | Cash-on-Hand and Competing Models of Intertemporal Behavior: New Evidence from the Labor Market[END_REF], [START_REF] Nekoei | Does Extending Unemployment Benefits Improve Job Quality?[END_REF]). Cette littérature s'est surtout concentrée sur les pays développés, alors que l'on sait peu de choses sur l'impact des programmes sociaux sur la recherche d'emploi et la qualité des emplois dans les pays en développement. Cependant, cette question est peut-être encore plus pertinente dans ces pays, précisément en raison de la coexistence de différents secteurs avec de bons et de très mauvais emplois au sein d'un seul et même marché du travail. De plus, l'un des principaux points à retenir de la littérature sur l'assurance-chômage (AC) est l'importance d'adopter une perspective dynamique : "Comment peut-on distinguer si l'assurance-chômage subventionne le loisir improductif ou la recherche d'emploi productive ? La meilleure façon de le faire est d'étudier la qualité des appariements après l'assurance-chômage" [START_REF] Gruber | Public Finance and Public Policy[END_REF]). La même logique peut s'appliquer aux effets des transferts monétaires sur le marché du travail : pour déterminer si un transfert monétaire subventionne le loisir ou la recherche d'emploi, on peut examiner les emplois que les bénéficiaires trouvent à la suite de ce transfert. Empiriquement, le défi consiste à estimer les effets à long terme des transferts monétaires, ce qui est possible grâce à la stratégie d'identification que j'emploie pour l'étude du Child Support Grant.

Introduction Générale

Un enfant n'est éligible à la subvention que jusqu'à un certain âge. Dans ce chapitre, j'exploite l'exposition discontinue des cohortes de naissance des enfants due à l'existence d'un seuil d'âge qui a fait l'objet d'une réforme. Comme cette identification me permet de suivre les cohortes dans le temps, je suis en mesure d'estimer à la fois les effets immédiats et persistants de la réforme. À court terme, les mères qui reçoivent le Child Support Grant cherchent un emploi plus longtemps que les mères qui n'y sont jamais éligibles. Cinq ans après la réception du transfert, cette recherche plus longue donne lieu à des gains substantiels en termes de qualité de l'emploi.

En effet, je montre que les mères qui ont reçu au moins un an d'allocations sont beaucoup plus susceptibles de travailler dans le secteur formel et, symétriquement, moins susceptibles de travailler dans le secteur informel. Ces gains sont concentrés sur les mères seules, ce qui est cohérent avec l'hypothèse d'un choc de liquidité.

L'impact sur la probabilité d'emploi total à long terme est nul, ce qui donne à penser que les transferts monétaires n'entraînent pas une hausse du volume d'emploi.

Ces gains persistants sur le plan de la qualité de l'emploi s'expliquent par un comportement différent au cours de la recherche d'emploi: les mères sont moins susceptibles de rechercher des emplois de faible qualité dans le secteur formel, dans les industries et les professions où les salaires et les taux de rétention sont faibles.

Cette conclusion concorde également avec l'observation selon laquelle les mères ayant des enfants éligibles dépensent davantage dans les transports lorsqu'elles cherchent un emploi. Dans l'ensemble, ces résultats sont conformes aux principales prédictions de la littérature sur la recherche d'emploi, selon lesquelles une subvention exogène devrait sans ambiguïté augmenter la durée du chômage. Pour expliquer l'effet sur la qualité de l'emploi, j'ai mis au point un modèle de recherche stationnaire, dit de la "recherche orientée", qui montre à quel point l'impact sur la qualité de l'emploi est ambigu : d'une part, les personnes plus riches gagnent moins à privilégier de meilleurs emplois; d'autre part, il est moins coûteux pour elles de privilégier de meilleurs emplois car elles ont plus de ressources pour ce faire et peuvent demeurer en situation de recherche d'emploi pendant plus longtemps. Intuitivement, plus les contraintes de liquidité seront fortes, plus ce second canal sera important. Dans l'ensemble, la principale contribution de ce chapitre est de montrer que les transferts monétaires inconditionnels peuvent avoir des effets positifs persistants sur la qualité de l'emploi dans un marché du travail segmenté, ce qui est un résultat nouveau dans la littérature. Il n'existe, à ma connaissance, aucune autre étude montrant les effets à long terme des transferts monétaires sur les emplois de bénéficiaires adultes2 , principalement en raison de la difficulté qu'il existe à observer des variations exogènes qui persistent sur le long terme. L'autre principale conclusion est que les travailleurs qui reçoivent ce choc sont plus susceptibles de se retrouver dans le secteur formel. Enfin, nous examinons si l'abaissement de l'âge de la retraite (publique) peut libérer des emplois pour les jeunes travailleurs. Comme cette motivation entre souvent dans le débat politique, même dans les pays en développement, il s'agit d'une question aux implications politiques importantes. Nos résultats suggèrent qu'il est peu probable que ce soit le cas. Bien que la réponse des personnes âgées soit assez importante au sein du groupe des personnes concernées par la réforme, ce groupe est trop petit pour faire une différence significative à l'échelle du marché du travail dans son ensemble. Nous estimons que la réforme a libéré tout au plus entre 20 000 et 30 000 emplois. En exploitant les différences de magnitude de l'effet de la réforme sur les différentes professions et les différents secteurs d'activité, nous sommes en mesure de rejeter l'hypothèse que la réforme ait substitué un travailleur aux caractéristiques très similaires aux bénéficiaires pour chaque travailleur qui quitte le marché du travail en conséquence de la réforme. Cependant, la magnitude de l'effet de la réforme, par rapport aux variations temporelles de l'emploi sud-africain, n'offre pas un pouvoir statistique suffisant pour juger si ces emplois ont été "perdus" ou s'ils ont été repris par d'autres travailleurs. En tout état de cause, nous concluons que, dans les pays ayant une structure démographique similaire à celle de l'Afrique du Sud, il est peu probable que ce type de réforme ait un impact significatif sur le stock d'emplois accessibles aux jeunes travailleurs.

Cela confirme une vision

Le troisième chapitre traite d'une question spécifique à l'Afrique du Sud, qui constitue sans doute la principale anomalie du marché du travail sud-africain: l'absence généralisée de travailleurs indépendants. Le taux des travailleurs indépendants en Afrique du Sud est remarquablement bas, une fraction seulement de ce que l'on observe dans des pays ayant un niveau de développement économique similaire.

L'absence de travail indépendant peut expliquer la quasi-totalité de l'écart avec le taux d'emploi moyen des pays ayant le même PIB par habitant. Cette particularité du marché du travail sud-africain est bien connue [START_REF] Kingdon | Unemployment in South Africa, 1995-2003: Causes, Problems and Policies[END_REF], [START_REF] Banerjee | Why has Unemployment Risen in the New South Africa?[END_REF], [START_REF] Grabrucker | Does Crime Deter South Africans from Self-Employment?[END_REF]), mais les pistes d'explications restent limitées. Des explications communes, comme le taux de criminalité élevé ou la réglementation stricte du marché du travail, ne sont pas conciliables avec l'ampleur de ce phénomène [START_REF] Grabrucker | Does Crime Deter South Africans from Self-Employment?[END_REF] Dans ce chapitre, j'avance l'hypothèse que l'absence de travail indépendant a vraisemblablement des racines historiques. Pendant l'apartheid, les Sud-Africains noirs n'ont pas pu exercer une activité indépendante, que ce soit dans le secteur formel ou informel. La répression des activités de travail indépendant informel pendant cette période est abondamment documentée [START_REF] Rogerson | The Absorptive Capacity of the Informal Sector in the South African City[END_REF]). Avec la fin de l'apartheid et de ces restrictions, les responsables politiques espéraient que le travail indépendant informel prospère et absorbe une grande partie de la main d'oeuvre croissante [START_REF] Rogerson | Emerging from Apartheid's Shadow: South Africa's Informal Economy[END_REF]). Dans les années qui ont suivi la fin de l'apartheid, la politique officielle du gouvernement s'est orientée vers la promotion des micro-entreprises, qui étaient considérées comme un élément clé pour la croissance économique en Afrique du Sud.

Cependant, cette évolution ne s'est pas produite. La proportion de travailleurs indépendants en Afrique du Sud est restée remarquablement basse depuis 1994. L'hypothèse selon laquelle le manque de travailleurs indépendants en Afrique du Sud serait dû à l'apartheid n'est pas nouvelle. [START_REF] Kingdon | Unemployment in South Africa: The Nature of the Beast[END_REF]

Chapter 1

The Lasting Labor Market Effects of Cash Transfers: Evidence from South Africa's Child Support Grant *

Introduction

Improving access to good jobs is a key issue in the labor market of developing countries, where a significant portion of the workforce is often employed in lowquality, informal jobs. The broad concept of "job quality" is then particularly relevant, as simple measures of employment (and unemployment) may be a poor indication of labor market performance. In these contexts, a fundamental question is to what extent low-quality jobs are the result of frictions and barriers in the labor market, which could be addressed by specific policies. For example, the presence of search frictions, as a result of high search costs, would suggest that relaxing liquidity constraints for the unemployed may result in persistent benefits. This could be achieved by cash transfers, a widespread policy instrument in developing countries.

However, empirical evidence on whether these types of programs can lead to lasting improvements in the labor market outcomes of adult recipients is still very limited.

In order to shed more light on the topic, this paper analyzes the labor market effects of a child grant in a dynamic way. This form of cash transfer is very common in developing countries, yet the South African Child Support Grant (CSG) has some unique features that make its analysis particularly informative. I argue that this policy provides a pure income shock, without introducing incentives or conditionalities that might complicate the interpretation of the effect. At the same time, its labeling, targeting, and the size of its income component are comparable to that of many similar policies across the developing world, which adds to the external validity of this analysis. For identification, I exploit exogenous variation in eligibility to the grant across children's birth years. In the main specification, this allows me to pin down the labor market effects on mothers who have received roughly one year of grant 1 , both during the period of receipt and after the grant has stopped. As this specific shock occurs when the child is already quite old, I argue that direct effects on children are likely to be very limited. I find that recipient mothers are more likely to be unemployed, and less likely to take up low-quality, formal sector jobs, when receiving the transfer. Five years after the grant has stopped, the employment rate is the same between mothers who have received the CSG and those who haven't.

The employment composition, however, is not the same: "treated" mothers are significantly more likely to be working in the formal sector, and less likely to be working informally, which indicates a persistent positive effect of the grant on job quality. This appears to be the result of targeting better, more-stable jobs while the grant is received. Significantly longer periods of eligibility to the grant do not seem to cause further job quality gains, which suggests a non-linear effect. Lastly, the effect on overall income is positive but far from statistically significant. Due to data constraints, the minimum detectable effect is well above what could realistically be identified.

This paper connects with the debate in labor and development economics on the nature of segmented labor markets in developing countries. This literature has been characterized by a dichotomy of views, also referred to as the "segmentation" and the "comparative advantage" hypotheses [START_REF] Günther | Informal Employment in Developing Countries, Opportunity or Last resort?[END_REF]). 2 The essence of the dispute is whether the segmentation observed in the labor market of developing countries is the result of frictions and barriers that are impeding an optimal allocation of workers, or the result of revealed preferences. The recent consensus is that each explanation holds for some portion of the informal workforce.

While South Africa's level of informality is relatively low [START_REF] Kingdon | Unemployment in South Africa: The Nature of the Beast[END_REF], [START_REF] Banerjee | Why has Unemployment Risen in the New South Africa?[END_REF]), the informal sector remains one of the main employers of Black and Coloured3 women -mostly as domestic wage workers. Recent papers have attempted to identify the impact of specific policies on workers' allocation in a segmented labor market [START_REF] Dinkelman | Evidence on the Impact of Minimum Wage Laws in an Informal Sector: Domestic Workers in South Africa[END_REF], [START_REF] Azuara | Informality and the Expansion of Social Protection Programs: Evidence from Mexico[END_REF], [START_REF] Bergolo | Work and Tax Evasion Incentive Effects of Social Insurance Programs: Evidence from an Employment-Based Benefit Extension[END_REF], [START_REF] Garganta | The Impact of a Social Program on Labor Informality: The Case of AUH in Argentina[END_REF], [START_REF] Gerard | Informal Labor and the Efficiency Cost of Social Programs: Evidence from the Brazilian Unemployment Insurance Program[END_REF]). 4 The main conceptual difference from the Child Support Grant in South Africa is that these are all policies that change relative payoffs across sectors, while an unconditional cash transfer does not. In other words, while these policies have both an income and a substitution effect, I argue that with the CSG, one is able to capture whether a pure income shock has lasting effects on both job quantity and job quality. This is partly in line with the work by [START_REF] Bianchi | Liquidity, Risk, and Occupational Choices[END_REF] on Progresa in Mexico, who show that recipients of a conditional cash transfer are more likely to become self-employed.5

The interaction of social programs and job search is at the heart of labor and public economics. Many studies have examined the impact of severance pay or unemployment benefits on unemployment duration, and the quality of the subsequent job [START_REF] Card | Cash-on-Hand and Competing Models of Intertemporal Behavior: New Evidence from the Labor Market[END_REF], [START_REF] Nekoei | Does Extending Unemployment Benefits Improve Job Quality?[END_REF]). In the context of developed countries, this has led to a variety of different findings. 6 While there is virtually unanimous empirical evidence that more generous assistance during job search usually increases unemployment duration, the evidence on the effect on job quality is more diverse. Moreover, this literature has largely ignored the context of developing countries, where this question may be even more relevant because of the high segmentation within the labor market. This may be because the programs usually analyzed for this type of evaluation, such as unemployment benefits should make formal employment relatively less attractive, their results show that it had "no effect on informality in the overall population." In a similar fashion, [START_REF] Bergolo | Work and Tax Evasion Incentive Effects of Social Insurance Programs: Evidence from an Employment-Based Benefit Extension[END_REF] look at the extension of health insurance to children of formal workers in Uruguay. Their results show that there is an increase formal employment, "mainly due to an increase in labor force participation rather than to movement from unregistered to registered employment." [START_REF] Garganta | The Impact of a Social Program on Labor Informality: The Case of AUH in Argentina[END_REF] look at an "Universal Child Allowance" in Argentina, a cash transfer available only to those outside formal employment. They find that this transfer decreases incentives to become formal, but does not reallocate workers from the formal to the informal sector. [START_REF] Gerard | Informal Labor and the Efficiency Cost of Social Programs: Evidence from the Brazilian Unemployment Insurance Program[END_REF] examine the efficiency cost to unemployment insurance in an economy with high informality, where potentially there is the perverse incentive to draw unemployment benefits and hold an informal job at the same time. However, they estimate this cost to be relatively small. Last but not least, [START_REF] Dinkelman | Evidence on the Impact of Minimum Wage Laws in an Informal Sector: Domestic Workers in South Africa[END_REF] study the impact of the introduction of a sectoral minimum wage for domestic workers in South Africa, a sector that is largely informal and the main employer of Black, South African women. They show a strong positive impact on wages and formality rates within the sector, yet this sector remains largely informal even after the introduction of the minimum wage.

or severance pay, are often less relevant in the context of developing countries.7 Therefore, whether an exogenous cash transfer can raise future job quality is a key question with important policy implications. As [START_REF] Card | Cash-on-Hand and Competing Models of Intertemporal Behavior: New Evidence from the Labor Market[END_REF] say, "testing this prediction sheds light on whether improvements in future job outcomes provide a rationale for temporary income support programs."8 This literature also provides a very useful framework to think about the mechanisms behind the possible effects of a cash grant. As I will discuss later, a simple adaptation of a directed job search model helps to frame the results of this paper.

Despite the large literature on cash transfers, the labor market effects on adult recipients are still poorly understood. Overall, contrary to what a canonical model would suggest, the literature shows little evidence of a leisure effect, meaning that disincentive effects on work appear to be minor and concentrated on specific populations [START_REF] Banerjee | Debunking the Stereotype of the Lazy Welfare Recipient: Evidence from Cash Transfer Programs[END_REF], [START_REF] Baird | The Effects of Cash Transfers on Adult Labor market Outcomes[END_REF] for summary). However, while the long-term effects of cash transfers on children have received significant attention (see Molina Millán et al. (forthcoming) for review), similar evidence for adults is rare [START_REF] Baird | The Effects of Cash Transfers on Adult Labor market Outcomes[END_REF]). 9 The main contribution of this paper is to show that an unconditional cash transfer can have lasting effects on the labor market outcomes of adults, and that these effects can be sizable for a relatively small amount of money. This finding also helps to better understand the short-term responses in a dynamic way. In this study, the decrease in employment and longer search as a result of the cash grant leads recipients to better-quality jobs five years after. In this light, the negative, short-term employment effect is the result of more productive job search rather than leisure. Indeed, I show direct evidence that the grant does modify the job search behavior of recipients, who search for longer, target jobs in better occupations, and who also, in the process, spend more money.

The paper proceeds as follows: Section 2 introduces the institutional context and various reforms. Section 3 presents the conceptual framework, and predictions of the possible effects of the grant in a directed-search framework. The data used and descriptive statistics are found in Section 4. In Section 5, I present the empirical strategy and the results. I discuss the possible channels in Section 6. Section 7 concludes.

The South African Child Support Grant

The Child Support Grant is the largest social program in South Africa in terms of participants, with a number that reached around 10 million children in 2010 (roughly 20% of the population); it is also the second largest program in terms of government spending [START_REF] Gomersall | The Performance of the Child Support Grant: Review and Research Priorities[END_REF]).10 First implemented in April 1998 in post-Apartheid South Africa with the aim of reducing poverty and inequality, it is generally considered to be the main anti-poverty policy of the South African government. The other two main social grants are the Disability Grant and the Old Age Pension, which cover either individuals who cannot work or who have reached pension age without a private pension.11

The CSG was proposed by the Lund committee as replacement for the support system existing at the time, the State Maintenance Grant (SMG). The SMG was subject to very strict requirements, such that "one parent had to be deceased or maintenance had to be petitioned for in court" [START_REF] Mcewen | Social Assistance Grants: Analysis of the NIDS Wave 1 Dataset[END_REF]). Moreover, having been designed during Apartheid South Africa, this system had a significant racial bias. African children de facto did not have access to the grant, which was attributed almost exclusively to Coloured and Indian children (and, to a lesser extent, White).12 For both these reasons, overall coverage of the SMG in the early 1990s was lower than 1%. The CSG is an unconditional, means-tested, cash transfer program, where the only eligibility requirements are: a) having children of a certain age; and b) an income below a certain threshold. At the end of the month, when the child surpasses the age threshold, the grant is no longer paid. The CSG is paid per child, with no limitation on the number of grants a person can receive. 13 Obtaining access to the grant requires very few documents: an identity card, a birth certificate, and proof of earnings (but this last requirement is flexible, as discussed in the next paragraphs).

The grant is paid to the "primary caregiver" of the child; hence, it is not exclusive to the parents (contrary to the SMG system in place before). This allows members of the households other than the parents to access the grant, given that they can provide an official document showing they are taking care of the child. The amount of the grant is generally considered to be small [START_REF] Lund | Changing Social Policy: the Child Support Grant in South Africa[END_REF]), especially when compared to the less extended but more generous disability and pension grants.

However, this does not seem to be true by either national or international standards.

The size of the CSG is significant when compared to median earnings, especially in the informal sector, as I will discuss later. The average amount per child over the period is around 50 $ '10 PPP per month, and its 1998 amount is comparable to that of Progresa in Mexico for the same year [START_REF] Bianchi | Liquidity, Risk, and Occupational Choices[END_REF]). In real terms, the amount of the transfer has increased by slightly less than 40% since its implementation. 16 The opposite is true for the means-test of the grant, which was initially set at 1100 R in rural areas and 800 R in urban areas, and kept fixed for the entire period from 1998 to 2008.17 Finally, in 2008, the means-test was harmonizedto 10 times the grant for individual incomes, and to 20 times the grant for the pooled income of the caregiver and his/her spouse -and it has not changed since.

While the age threshold is strictly applied through the birth certificate requirement, the means-test has not always been. [START_REF] Lund | Changing Social Policy: the Child Support Grant in South Africa[END_REF], head of the committee behind the creation of this program, states clearly that since its inception the meanstest was put in place to discourage applications from richer individuals, rather than as a strict threshold. Consistently, there is very little evidence of its application, in particular before 2008. Qualitative research has found "that the various elements of the means test are not generally enforced, understood or relevant." Moreover, "an affidavit stating that the 'primary care-giver' and his or her spouse are not earning an income above the means test threshold will generally suffice" [START_REF] Goldblatt | Implementation of the Child Support Grant: A Study of Four Provinces and Recommendations for Improved Service Delivery[END_REF]), which make this constraint de facto non-binding. In Figure 1.A.2, I show that in early 2008, before the means-test was reformed, take-up as a function of earnings was similar for both formal wage, which should enter the means-test, and informal wage, which should not. This reinforces the argument that, at least before the reform, the means-test was not binding, nor perceived as such. Despite being officially introduced in April 1998, the CSG took some time to be fully implemented. [START_REF] Lund | Changing Social Policy: the Child Support Grant in South Africa[END_REF] links this slow start of the CSG with administrative difficulties and overall confusion, but also to a lack of political will to truly implement the grant as it was intended. The coverage of the CSG did not really take off before the year 2000, when take-up for eligible children began increasing dramatically. The steepest increase in coverage occurs from 2003, when the age-eligibility threshold doubled in only three years from 7 to 14. These fast-paced reforms made more children eligible for the grant. The age threshold was stable from 2005 to 2009, and then gradually increased from 14 to 18 on January 1st of every year. Contrary to the previous increase, these reforms only increased duration for cohorts that were already receiving the grant. The massive means-test reform that occurred at the end of 2008 does not seem to have led to a discontinuous jump in take-up, which was increasing smoothly from 2006 and then stabilizes around 2010. This is consistent with the view that the means-test was not strictly applied, and that age-eligibility was often the only binding criteria. 18

There is a lack of quantitative evidence on the impacts of Child Support Grant, mostly due to difficulties in setting up a robust empirical strategy to capture its effects. Several attempts have been made to look at how the CSG affects children's education and health [START_REF] Coetzee | Finding the Benefits: Estimating the Impact of The South African Child Support Grant[END_REF]; for a full review, see [START_REF] Eyal | School Enrolment and the Child Support Grant: Evidence from South Africa[END_REF]), who find positive but limited effects on children's outcomes. The absence of significant effects on children might be due to the lack of conditionalities attached to the grant, but further research is needed. A recent paper looks at the impact on food security at the household level (d'Agostino, Scarlato and Napolitano (2018)).

Only a few papers have begun to investigate the effects of this program on the labor market outcomes of the parents. 19 More attention has been given to the labor market effects of other cash transfer programs in South Africa, such as the Old Age Pension [START_REF] Ardington | Labor Supply Responses to Large Social Transfers: Longitudinal Evidence from South Africa[END_REF], [START_REF] Abel | Unintended Labour Supply Effects of Cash Transfer Programmes: Evidence from South Africa's Old Age Pension[END_REF]), but evidence on working-age adults is mixed. 20

Conceptual Framework

The job search literature provides a useful way to frame the results of this

18 Further evidence of the non-strict appliance of the means-test is that, prior to 2008, the means-test was significantly more binding in urban areas and for married couples, as in theory, the pooled income of both spouses enters the computation. After 2008, when the means-test is equalized across urban/rural regions and doubled for married couples, we might have expected to observe very different evolutions for these subgroups, but that is not the case (Figure 1.A.3). The four groups who experienced different changes in the nominal means-test are: urban married (+475%), urban non-married (+190%), rural married (+320%), and rural non-married (+110%).

19 [START_REF] Eyal | Throwing the Book at the CSG[END_REF], OECD (2011) find some evidence that the CSG might increase employment by comparing mothers of children of a given age, before and after the reforms. On the contrary, [START_REF] Bengtsson | The Marginal Propensity to Earn and Consume out of Unearned Income: Evidence Using an Unusually Large Cash Grant Reform[END_REF] compares the marginal effect on earnings of having a child in the household before and after the CSG was implemented. He finds that CSG receipt lowers the marginal propensity to earn (through lower labor supply) and increases consumption and expenditure. [START_REF] Berg | Are Poor People Credit-Constrained or Myopic? Evidence from a South African Panel[END_REF] looks at how households respond in terms of expenditure when the grant lapses, finding no decrease in expenditure when the child reaches the age eligibility threshold. 20 The Old Age Pension is a public pension system in South Africa that provides an unconditional cash transfer, paid to individuals of a certain age regardless of previous pension contributions. [START_REF] Ardington | Labor Supply Responses to Large Social Transfers: Longitudinal Evidence from South Africa[END_REF] find that, contrary to what previous cross-sectional analyses had suggested, the positive income shock that occurs when an older member of the household reaches pension age leads to a significant increase in employment for working-age individuals in the household. Moreover, more recent work by [START_REF] Abel | Unintended Labour Supply Effects of Cash Transfer Programmes: Evidence from South Africa's Old Age Pension[END_REF], using nationally representative panel data, does not confirm these results, and finds again a negative result on employment of working-age adults.

paper. While this literature has mostly focused on unemployment benefits, these are often not relevant in the context of developing countries. Conceptually, while unemployment benefits only increase the value of unemployment, an unconditional cash transfer raises both the value of employment and unemployment. [START_REF] Card | Cash-on-Hand and Competing Models of Intertemporal Behavior: New Evidence from the Labor Market[END_REF] show that in a job search model incorporating assets and effort decisions (a simplified version of [START_REF] Lentz | Job Search and Savings: Wealth Effects and Duration Dependence[END_REF]), an exogenous cash grant should result in less search effort and longer unemployment, if individuals cannot perfectly smooth consumption between states. They find a null effect on subsequent wage of more cash-on-hand, in line with the empirical literature on unemployment benefits that has found impacts all across the range from positive to negative. [START_REF] Nekoei | Does Extending Unemployment Benefits Improve Job Quality?[END_REF] show that these diverse findings can be reconciled with a directed search model, where two opposing forces drive the impact on job quality. On the one hand, by making people more selective, higher benefits result in a higher target wage (positive effect on job quality). On the other hand, duration dependence makes people less likely to find good work as unemployment lengthens. Intuitively, these channels make the sign of the effect of a positive shock in assets on subsequent job quality theoretically ambiguous, raising an empirically interesting question. From a policy standpoint, understanding whether cash transfers can lead to persistent job quality gains is key, both as an additional rationale for this type of policy, and, more generally, to know whether relaxing liquidity constraints can be a way to better labor market outcomes for recipients. This is not obvious. If there are barriers other than financial constraints (such as low human capital, for example) that prevent access to "good" jobs, then the labor market effects of such a policy, at least through the job search channel, should be null or small. In order to formalize this intuition, I propose a simple job search framework with the same set-up as [START_REF] Card | Cash-on-Hand and Competing Models of Intertemporal Behavior: New Evidence from the Labor Market[END_REF], but following a directed search framework à la [START_REF] Nekoei | Does Extending Unemployment Benefits Improve Job Quality?[END_REF]. This implies that individuals choose w, i.e. what wage to target, which they then receive indefinitely when they get a job (no subsequent job destruction). In the model, for simplicity, wage is the only dimension of job quality. The job arrival rate, s t (w), is a decreasing function of the wage targeted, i.e. better jobs automatically take a longer search (effort is exogenous).

Similarly, the monetary cost of the search, m t (w), is an increasing function of the target wage: better jobs require higher search costs. 21 Similarly to [START_REF] Card | Cash-on-Hand and Competing Models of Intertemporal Behavior: New Evidence from the Labor Market[END_REF], individuals pay the cost of the search at the beginning of the period, and immediately begin their job if the search is successful, or stay unemployed otherwise.

Optimal Target Wage

Following similar notation to [START_REF] Card | Cash-on-Hand and Competing Models of Intertemporal Behavior: New Evidence from the Labor Market[END_REF], one can write the value function of finding a job at the beginning of period t as:

V t (A t , w) = max A t+1 ≥L u (A t -m t (w) -A t+1 /(1 + r) + w) + 1 1 + δ V t+1 (A t+1 , w) (1.1)
where A t are assets at time t, r is the interest rate in the economy, w is the wage, and δ is the time discount factor. L is a potentially binding lower bound on assets.

The value of being unemployed at the beginning of period t for those who do not find a job can be expressed as:

U t (A t , w) = max A t+1 ≥L u (A t -m t (w) -A t+1 /(1 + r)) + 1 1 + δ J t+1 (A t+1 ) (1.2)
Contrary to a standard job search model, unemployment benefits do not enter the unemployment value function, as only a small fraction of unemployed workers actually receives them; this better reflects the reality of the labor market of a developing economy. 22 I assume that U and V are concave. 23

Again, keeping the same notation as in [START_REF] Card | Cash-on-Hand and Competing Models of Intertemporal Behavior: New Evidence from the Labor Market[END_REF], but with the important 21 Better jobs take longer to search implies a negative first derivative with respect to wage for the job arrival rate (s � t (w) < 0), and a positive one for search costs (m � t (w) > 0). 22 According to the 2007 Labour Force Survey, less than 1% of unemployed workers, irrespective of whether they were previously employed, receive support from the Unemployment Insurance Fund.

23 For discussion of the possible convexity of U , see [START_REF] Lentz | Job Search and Savings: Wealth Effects and Duration Dependence[END_REF].

difference that individuals maximize over w, one can write J t , the expected value at the beginning of period t without a job and with assets A t , as:

J t (A t ) = max w H t (A t , w) = max w s t (w)V t (A t , w) + (1 -s t (w))U t (A t , w) (1.3)
from which it is possible to derive the following first-order condition for the optimal target wage, w * :

s t (w * ) ∂V t ∂w * + (1 -s t (w * )) ∂U t ∂w * + s � t (w * )(V t -U t ) = 0 (1.4)
Intuitively, the optimal target wage, w * , is the one where the marginal gains from targeting better jobs are equal to the marginal costs of depleting assets during search 24 , plus the cost from longer search (s � t (w * ) is negative by assumption).

Effect of an Exogenous Cash Grant

The main prediction of interest is how the optimal target wage, w * , changes with respect to a positive shock in assets, as the result of an exogenous cash transfer. The expression for the effect of a shock in assets on the optimal target wage is:

dw * dA t = - ∂ 2 H t (A t , w * ) ∂w * ∂A t / ∂ 2 H t (A t , w * ) ∂w * 2 (1.5)
where the denominator is negative according to the second order condition of the maximization problem. The expression for the numerator is:

∂ 2 H t (A t , w * ) ∂w * ∂A t = s(w * ) ∂V t ∂w * ∂A t + (1 -s t (w * )) ∂U t ∂w * ∂A t + s � t (w * )( ∂V t ∂A t - ∂U t ∂A t ) (1.6)
By applying the envelop theorem and writing c e t for consumption when employed and c u t when unemployed, one can derive the following expressions from Equations 24 As shown in Equations 1.1 and 1.2, these costs enter both U t and V t , so that ∂Vt ∂w includes both the gains from a higher wage and the cost of higher search:

∂Vt ∂w = (-m � t (w) + 1)u � (A t -m t (w) - A t+1 /(1 + r) + w) + 1 1+δ ∂Vt+1 ∂w
1.1 and 1.2:

∂V t ∂w * ∂A t = (-m � t (w) + 1)u �� (c e t ) (1.7)
and:

∂U t ∂w * ∂A t = -m � t (w)u �� (c u t ) (1.8)
Plugging 1.7 and 1.8 into Equation 1.6, and rearranging:

∂ 2 H t (A t , w * ) ∂w * ∂A t = s t (w * )u �� (c e t ) � �� � (1), <0 (2), >0 � �� � -m � t (w * ) {s t (w * )u �� (c e t ) + (1 -s t (w * ))u �� (c u t )} + (3), >0 � �� � s � (w * ) {u � (c e t ) -u � (c u t )}
(1.9) Equation 1.9 puts forward three mechanisms at play. The first term captures that the positive shock in assets reduces the utility gain associated with a better-paying job, which lowers the target wage. On the contrary, the second term points out that a wealth effect reduces the utility loss deriving from depleting assets to pay for the monetary cost of search, which has a positive impact on the target wage.

Similarly, the third term implies that a positive shock in assets decreases the utility loss associated with longer search, which again has a positive effect on the target wage. This channel will be at play if individuals cannot perfectly smooth consumption between employment and unemployment. Therefore, the sign of Equation 1.5 is ambiguous, and will be positive if second and third term dominate. The ambiguity of this prediction is intuitive. On the one hand, richer individuals gain less, in relative terms, from getting a better paid job. On the other hand, they also find it less costly in terms of utility to look longer for better jobs, as they have more resources to do so.

Moreover, one can also obtain the expressions for the effect of a positive wealth shock on search duration, and search costs, which are, respectively:

∂s t (w * ) ∂A t = ∂s t (w * ) ∂w * × ∂w * ∂A t (1.10) ∂m t (w * ) ∂A t = ∂m t (w * ) ∂w * × ∂w * ∂A t (1.11)
Therefore, given the sign of Equation 1.9, we can derive two subsequent predictions, which follow mechanically from the set-up of the model. If the effect of the positive shock in assets on the target wage is positive, we should observe lower job finding rates, and therefore, longer unemployment. Furthermore, as it will lead to target better jobs, and better jobs require higher search costs by assumption, this should also be associated with a higher cost of search.

In sum, the model's prediction of the effect of an exogenous cash transfer is ambiguous. However, we can say that the positive effect on the target wage will be larger if individuals cannot perfectly smooth consumption between the employed and unemployed state, and, more generally, if consumption in unemployment is low.

Hence, we can derive from this an indirect (and imperfect) test for the presence of liquidity constraints during job search. 25 If the liquidity effect dominates, we would expect cash transfer recipients to target better jobs, and search for longer. On the other hand, if the disincentive effect dominates, we would observe people searching for shorter periods and settling for lesser-quality jobs.

Anticipating the empirical results, we can examine how the effect should evolve as a function of the size of the positive wealth shock, as the treatment intensity dA t varies with the duration of CSG receipt. Intuitively, under some assumptions about the concavity of the utility function, the model is consistent with a non-linearity of the effect of assets on the target wage. 26 The liquidity effect would increase less than proportionally as dA t increases. The opposite is true for the negative impact on the target wage: the larger the positive asset shock, the greater the reduction in the utility gain associated with better-paying jobs.

Alternative Channels

There are channels other than investment in job search that could lead to lasting labor market effects of an unconditional cash transfer. [START_REF] Baird | The Effects of Cash Transfers on Adult Labor market Outcomes[END_REF] mention three other potential channels: 1) a health productivity effect, where workers become more productive as a result of better nutrition. This specific channel is less pertinent in a middle-income country like South Africa. We could think, however, that any positive effect on human and/or physical capital could lead to benefits in labor market outcomes that outlast the grant; 2) a self-employment liquidity effect:

in the presence of capital investments necessary to enter self-employment, a cash transfer could provide the positive income shock necessary to enter this sector; 3)

an insurance effect: as some occupations and behaviors are more risky than otherssuch as self-employment and migration, for example -a cash transfer could increase willingness to bear risk (as shown by [START_REF] Bianchi | Liquidity, Risk, and Occupational Choices[END_REF]). Moreover, it has been shown that transfers targeted at women can have beneficial effects on their bargaining power within the household [START_REF] Ambler | Bargaining with Grandma: The Impact of the South African Pension on Household Decision-Making[END_REF] in South Africa, Almas et al.

(2018) for review). While plausible, I show that none of these channels match the evidence to the extent of job search. I provide a discussion of these alternative mechanisms in Section 6.

Data and Descriptive Statistics

Descriptive Statistics

In South Africa, employment in the informal sector accounts for around 30 percent of total employment, slightly less than 15 percent of working-age population (Source:

LFS (2002)(2003)(2004)(2005)(2006)(2007)). The informal sector remains where many employment opportunities are concentrated for Black and Coloured women. Indeed, despite a very low employment rate, women account for more than half the employment in the informal sector. This often occurs as domestic workers in private households, the most common type of informal work in South Africa. In Figure 1.A.4, we observe that at the beginning of the period, informal sector employment made up almost half of women's total employment, with only marginal improvements over time. In terms of the composition of formal jobs, the public sector accounts for less than 15%

of the total stock of formal jobs. On the other hand, more than 30% of formal sector employment is unionized, reflecting the importance of labor unions in South Africa (Table 1.A.1).29

Table 1.A.2 presents the median monthly earnings and hourly wages in the formal and informal sectors. Overall, wages are around three times higher for formal than informal workers, but this gap is even larger for women. Similar magnitudes hold for hourly earnings, suggesting that this is not due to the higher prevalence of part-time in the informal sector. In proportion to wages, the CSG is comparable to around 30% of the median informal wage, and only to 10% of the median wage in the formal sector. This indicates how the amount of the CSG cannot be considered small, in particular relative to returns to informal employment for women. However, it also points out that one CSG grant is not sufficiently large to substitute for labor income at the extensive margin, neither formal nor informal: virtually no formal worker earns less than one CSG grant, and less than 5% of workers in the informal sector do. These orders of magnitude are key to interpret the results that I present in the next section.

1.5 Empirical Analysis

Identification Strategy

Despite its many appealing features, putting in place a sound identification strategy for the Child Support Grant is not an easy task, as shown by the relatively little attention this important policy has received in the literature. However, the age eligibility criteria and its reforms provide a valuable source of variation to evaluate the effects of the CSG. Identification relies on the fact that some cohorts are always older than the age threshold and can never receive the grant. Instead, there are other cohorts who were not initially eligible, but, because of the reforms, become eligible for some periods. As shown in Figure 1.2, exposure to the grant is largely determined by the cohort of birth of the child, and varies drastically even for children born only one year apart. 30 As I show in Figure 1.A.5, this large variation in take-up is clearly the result of changes in age eligibility. The large spike that we observe for cohort 1993 in 2006 matches the peak in eligibility. We also observe that cohort 1991 and 1992 are eligible for some months, 5 and 14 respectively, but in practice never receive the CSG to a significant extent. Instead, cohort 1993 is eligible for 30 months overall, and is the first one eligible for a full year. In this setting, what matters is that the large variation across adjacent cohorts is the result of exogenous changes in policy, which I argue is the case.

It is key to understand here that these differences in CSG exposure occur, on average, for cohorts in adjacent years, not adjacent months: individuals born one year apart can have large differences in the eligibility and take-up of the CSG, but

probably not individuals born one month before or after. In the same way, individuals born in the same year, but in months very far apart (January vs. December, for example), may also experience drastically different eligibility and presumably take-up, too. Unfortunately, to the best of my knowledge, there is no data that allows to reconstruct CSG take-up by month of birth. Therefore, I will always focus on average differences across individuals born in different, discrete years.

Given this setup, a first logical option would be to perform a "Difference-in-Differences" estimation, comparing the outcomes of mothers of exposed and unexposed cohorts before and after CSG receipt. However, a diff-in-diff with cohorts can be problematic. This estimation would rely on a "common trend" assumption, which is unlikely to hold. By definition, for a given period of time, we cannot observe the same age evolution for different cohorts. If there are effects of the age of the child on the mother's labor market outcomes, which is most likely the case, and if these age effects are non-linear, then identification fails by construction. I show a simple proof outlining this issue in Appendix 1.A.2.1.

To solve this problem, I exploit the large, discontinuous spike in take-up observed in Figure 1.2 of cohort 1993 relative to 1992. In the spirit of a Regression Discontinuity (RD) design, I estimate a regression where the forcing variable is the discrete year of birth of the child and the threshold is set at 1993. The advantage of this approach, compared to a diff-in-diff, is that any age effect should be captured away by the functional form on both sides of the threshold; hence, the "common trend" assumption among treated and non-treated cohorts is not required. In this setting, identification relies on the fact that there is a last untreated cohort, i.e. those children born in 1992, and a first treated cohort, i.e. 1993, which, for at least one year, receives the grant to a significant extent. This estimation is made possible by the large take-up differential between cohort 1992 and 1993, in the order of 0. 2-0.3 in 2006. 31 With this estimation, identification relies on the assumption that, had the CSG not been implemented, we should not observe any discontinuity at the threshold with respect to the labor market outcomes of the mother. This can be easily checked

Y i = f (c i -1993)+�{c i � 1993}×f (c i -1993)+β 1 �{c i � 1993}+X � +ε i (1.12)
where Y i is the outcome of interest for the mother of a child born in a given year; f is a function of the cohort of the youngest child centered at the cut-off point. I then introduce a binary variable for individuals whose youngest child is born after 1993, and interact it with the cohort of birth of the youngest child born. β 1 should capture in 2007 the short-term effect of the positive CSG shock, and in 2011 the persistent effect of having received the grant five years before.32 X � is a vector of covariates including a control for household size and dummies for age, education, race, marital status, and municipality. Standard errors are clustered at the household level.33

All estimations are performed on the sample of Black and Coloured mothers.

As White mothers do not receive the CSG, this sub-population is well suited for a placebo test. Unless otherwise indicated, the forcing variable is always the youngest child born to a given mother, regardless of whether this child is observed in the household or not. One advantage of this approach, which is possible using fertility information in the Census, is that I do not need to observe a child in the household to know whether or not the mother was treated. This allows to define treatment status much more accurately than if only observing children in the household, which would be particularly problematic for the long-term results when the children at the threshold are already quite old. It also takes away the concern that the grant might lead to changes in household composition [START_REF] Ardington | Labor Supply Responses to Large Social Transfers: Longitudinal Evidence from South Africa[END_REF],

Hamoudi and Thomas ( 2014)), which could introduce selection bias. A disadvantage is that fertility information is only asked to women under 50 years of age, so there is no information for women older than this threshold. 34 Therefore, I run all estimations on the sub-sample of women born between 1960 and 1985, for whom I have both fertility and labor market information in the three Census waves. 35 In this way, the underlying population over time is roughly the same, unless for those mothers who have other children between 2007 and 2011, who drop out of the sample. Given the discreteness of the forcing variable, and the few support points near the threshold, I cannot run a local linear regression using the optimal bandwidth. Instead, I choose the preferred bandwidth according to Akaike Information Criterion (AIC) as the one that maximizes the window for a quadratic fit (in order to avoid the use of higher-order polynomials). Results are robust to both a linear and a quadratic functional form, and to different windows.

household level and not at child cohort level. 34 This makes the data censored at age 50. I consider this not to be an issue as long as the probability of being over 50 is not discontinuous at the threshold. Further discussion of this issue can be found later in the robustness checks.

35 Labor market information is only collected in the Census for individuals older than 15 years. Encouragingly, we observe that in 2001, before the full roll-out of the CSG, there is no discontinuity at the threshold, neither in the employment level nor its composition.

This suggests that mothers of cohorts at the threshold were comparable in terms of labor market outcomes before the grant was received. It also works as a placebo test, as it suggests that the proposed specification does not pick up effects where there should be none. In the next subsections, I comment on the effects in 2007, therefore as the CSG was received by cohort 1993, and in 2011, five years after. Graphical evidence of these effects is presented in Figures 1.A.6 to 1.A.9.

Short-Term Effects of Receiving the CSG

The short-term results are presented in the middle panel of Table 1.2. For mothers, we observe a significant and large jump in unemployment at the threshold, matched by a symmetric drop in formal sector employment. The drop in overall employment is around 2 pp., although not statistically significant. Other household members, on average, do not respond to the CSG in terms of labor market outcomes, as shown in Table 1.A.4. The lack of any effect on other adult members may come as surprising, as it contrasts with a unitary household model where the grant is be pooled into household income, but it is in line with the fact that mothers are almost always the direct recipients of the grant.

These results indicate that receiving the CSG increases mothers' unemployment.

While in theory, a higher unemployment rate can be the result of a lower flow out of unemployment or a higher flow into unemployment, I argue that it is most likely due to the former. Indeed, the increase is entirely explained by mothers who report not finding work/suitable work as the reason for unemployment, which is (more) consistent with longer search as an explanation. These results imply that those mothers who search more would have been formally employed in the absence of the CSG shock. This may appear counter-intuitive as, in the previous section, I have shown that formal jobs pay significantly more on average than informal jobs. Possibly, the higher flexibility of informal jobs allows mothers to search while also being informally employed.37 Alternatively, this heterogeneity might reflect that workers in formal and informal jobs are inherently different across other dimensions.

Overall, these results are in line with predictions of a job search model, both in the directed search framework outlined before or in a more classic framework where workers choose effort. Without perfect consumption smoothing, an exogenous cash grant should have a positive effect on the level of unemployment, through longer duration, which is indeed what is observed.38

Persistent Effects of the CSG

The main question of interest is whether the longer unemployment induced by the grant translates into job quality gains for recipients. The persistent results are presented in the bottom panel of Table 1.2. In 2011, mothers whose child was born in 1993 are as likely to be employed as mothers who did not have any access to the CSG.

Any effect on total employment did not outlast the grant. However, the composition of employment between mothers of exposed and non-exposed cohorts is not the same.

Among the employed, those who had access to the CSG are around 1.1 percentage point less likely to be working informally, and, symmetrically, around 1.4 pp. more likely to be working in the formal sector. A clear indication of the effect on the composition of employment appears already when looking at the share of informal employment (Figure 1. 3). While in 2001, we observe no significant difference in the composition of employment, in 2011 mothers of exposed cohorts are around 5-6% less likely to be holding an informal job rather than a formal one. This decrease in the share of informal employment is significantly more marked for single mothers, as can be seen in Figure 1.4. For this subgroup, the decrease in informality is almost 10% at the threshold. This heterogeneity is not surprising. Single mothers are likely to be the most liquidity constrained and unable to stay unemployed, given that they cannot count on any income support from a spouse. Again, effects are concentrated on the direct recipients. Consistently with the previous results, there is no evidence of persistent effects on other household members.39

The are two main take-aways from these persistent results of the grant. The "glass half empty" is that the CSG did not have a persistent positive (or negative) impact on the employment rate. This finding could be an indication that the grant is not sufficient to overcome fixed cost to access employment, or that liquidity constraints are not so severe as to prevent entry into employment at the extensive margin, or that other barriers are binding. As overall employment for Black and Coloured women is very low, one could argue that increasing participation at the extensive margin is the first order concern, but this largely depends on whether unemployment/inactivity is voluntary or not. On the contrary, the "glass half full" is that the grant appears to lead to persistent and sizable job quality improvements for employed mothers, as

shown by the higher rate of formal sector employment among treated mothers. The fact that we observe these differences five years after the CSG has stopped for the cohort 1993 should be emphasized. This suggests that the positive effects on job quality are potentially long-lasting. The stronger effect among single mothers also suggests that these benefits might be concentrated on those who are more vulnerable.

In order to test to what extent the grant has led to an actual change in the types of jobs that mothers hold, in Tables 1.A.6 and 1.A.5, I decompose the effect by employer, occupational status, and sector. The long-term increase in formal wage employment comes in equal parts from informal wage-and self-employment.

Contrary to [START_REF] Bianchi | Liquidity, Risk, and Occupational Choices[END_REF], I do not find any evidence that access to a cash transfer increases self-employment in South Africa. This is an indication that credit/liquidity constraints might not the main reason behind the stunningly low rate of self-employment in South Africa. Table 1.A.5 shows that, of the total decrease of 2.5 pp. in the share of informal employment, more than half is a change in industry, as mothers are less likely to be employed by private households. The rest of the effect occurs instead within occupation. To summarize: the CSG makes mothers less likely to be employed by private households and more likely to hold a formal job in other industries. Also, for a given occupation outside private households, the grant makes it more likely to be in a formal sector job rather than in an informal one. be sure that the underlying population is the same in the three years, and explore whether there is a match between the size of the "during" and "after" effect by sub-population. Overall, it confirms the effect dynamics presented before: for both groups, we observe a large increase in unemployment; in 2011, formal employment is higher, and informal employment is lower. Both "during" and "after", the effect of the grant is particularly strong on the Coloured population, a small subgroup of the population, around 5% overall, with significantly better labor market outcomes on average. Indeed, the very large increase in unemployment for Coloureds in 2007 leads to a long-term effect three times larger than the one for the Black population. What these results show is that the subgroup for whom the "during" effect is stronger is also the one for whom the persistent effect is larger. This hints that we are not observing two separate responses, in 2007 and 2011, but rather a dynamic, sequential effect, where higher formal employment after the grant comes from higher unemployment when the grant is received.

Heterogeneous Effects by Years of Eligibility

Having established the main results, I relax the identification strategy to exploit the large variation across treated cohorts in the length of their eligibility. As shown in the Appendix, a difference-in-differences estimation likely suffers from a bias due to age effects when comparing different cohorts over time. With some caution, I still recur to this estimation both as a robustness check and to test the importance of treatment intensity. This allows me to check whether treatment effects vary with the length of exposure to the CSG. To do so, I now estimate the following equation:

Y it = α 0 +α 1 �{c i � c}+ 2 � t=1 δ t �{year = t}+�{c i � c}× 2 � t=1 β t �{year = t}+X � +ε it (1.13)
where �{c i � c} is a binary variable equal to one if the youngest child is born in or above a certain threshold value, c. I then interact this variable with the year to get the evolution of the relative difference over time. The list of covariates is the same as in the previous estimations. Intuitively, what Equation 1.13 does is to compare the relative evolution of mothers of treated and untreated cohorts over the three time periods, or of cohorts treated for longer or shorter periods.

The results of this estimation, for different cohort windows, are presented in Table 1.A.7. For the smallest possible window, comparing the evolution of mothers with a youngest child born in 1992 vs. 1993 (c = 1993), the results replicate for the most part those presented before. Treated mothers are more likely to be unemployed in 2007. The main difference concerns the persistent results. With this estimation, the increase in formal employment appears as an increase in net employment, rather than as a decrease in informal employment. This is a slightly "stronger" result,

which would indicate that one year of CSG had a positive effect on the employment rate. However, for the reasons mentioned before, an RDD-like estimator is more trustworthy in this setting. 40Relaxing the constraints of the RDD estimation allows to further explore how mothers respond to longer exposure to the grant. I compare the relative evolution of cohort 1993 vs. cohort 1994 (c = 1994). These cohorts experience dramatically different exposures. While those mothers whose child is born in 1993 can receive the grant for 30 months in total on average, those whose child is born just a year after experience on average 60 months of eligibility, twice as long (Figure 1.A.5). Despite this variation in treatment intensity, which also leads to large differences in take-up, I do not observe a statistically different evolution in terms of labor market outcomes over the period. Similar evidence is presented in Figure 1.A.10: these findings hold true when comparing cohorts further away from the threshold, who experience even longer treatment duration. These results suggest that the effect might be strongly non-linear, and that longer exposure to the grant does not lead to greater job quality gains.

Another important take-away concerns the size of the treated population. In Figure 1.2, we see that longer eligibility also leads to greater take-up: a larger share of mothers is treated in later cohorts. The fact that the effects are not larger in later cohorts suggests that those "extra" treated mothers did not benefit from the grant in terms of labor market outcomes. In other words, this would indicate that those mothers who self-select into take-up with only one year of eligibility are also the ones with the higher potential gains from receiving the transfer. This conclusion is important for the interpretation of the effect as an average treatment effect (ATE).

Although take-up is only 0.2-0.3 for the first treated cohort 1993, if the grant was received by the full population of mothers, the overall positive effect on job quality would likely remain of the same magnitude as those indicated in Table 1.2. meaning those with low retention rates and low wages. On the contrary, the persistent effect comes from higher employment in mid-quality, formal sector industries and occupations. The jobs that mothers do not take when receiving the grant are not the same jobs they have five years after. This is consistent with a different targeting as a result of the grant, where a cash transfer leads to looking for better jobs. The persistent decrease in informality suggests that, in the absence of the grant, some of these mothers would have ended up in the informal sector, which is not surprising, given the very low retention rates of low-quality formal sector jobs.

Persistent Effect on Income

Arguably, the most important dimension of job quality is the wage. Wages in the formal sector are significantly higher than in the informal sector. This is particularly true for Black and Coloured women in South Africa, who make three times more on average when working a formal job. Census data do not have a direct measure of wages. Only information on overall income, irrespective of the source and recorded in brackets, is collected.

If everybody was paid the median wage in each sector, I calculate that the observable wage increase associated with the increase in job quality should be in the order of magnitude of R 30 (6$) over the total population, and around R 50 (11$) for those employed. 43 In absolute terms, these numbers appear quite small. However, these "predicted" wage effects are sizable when compared to the amount of the grant (around 12% of the monthly value of one grant). Furthermore, as the take-up differential between the first and last unexposed cohort is relatively small (0.2-0.3), effects on the treated are potentially large. Assuming the CSG has not affected other income generating activities, we may expect an effect of similar magnitude on income. In the absence of other adjustments, the effect on income should be a noisier estimate of the effect on wages. Indeed, the estimated coefficient on income is positive, and in line with the magnitudes presented before, but far from statistical significance. The interval-coded nature of the income data in the Census, with fairly large brackets, and the lack of precise wage data, is behind the low statistical power. Given these data constraints, the minimum detectable effect on monthly income would be in the order of 23$ (or 43$

43 Dollar amounts are always in 2010 PPP. Source: OECD.stat

for the employed only). This would require the average wage gains as a result of the CSG to be significantly larger than the overall gap in median wages between the formal and informal sector, which seems unrealistic. With this in mind, the lack of a significant effect on income should not be surprising, and it is not necessarily an indication that there are no wage gains as a result of receiving the grant.

Robustness Checks

I perform several robustness checks for these estimations, presented in the Appendix.

One concern is that fertility decisions might be impacted by the grant. Even though the roll-out of the grant occurs after the youngest child is born for the cohorts in this analysis, exposed mothers might be more or less likely to have an additional child because of the grant, which would lead to selection. To alleviate this concern, I check the log density of cohorts around the threshold in the spirit of [START_REF] Mccrary | Manipulation of the Running Variable in the Regression Discontinuity Design: A Density Test[END_REF].

Graphical evidence, presented in Figure 1.A.14, shows that no discontinuity exists at the threshold, neither before, during, nor after the roll-out of the grant. Fertility data in the Census is censored at age 50, meaning that older women are not asked about their birth history. This should not lead to selection as long as the probability of being 50 or older is not discontinuous at the cut-off point. Again, the smoothness of the density around the threshold suggests that this is not the case.

I also look at the distribution of pre-determined observables around the threshold. This is a standard check in an RDD-like analysis and should serve as an additional confirmation that individuals are comparable on each side of the discontinuity. Figures there is no jump in relevant covariates around the threshold: the share of mothers who are Black, married, and have migrated, as well as their age, education, and household size, evolve continuously around the cut-off point, which suggests that we should not expect any discontinuity in labor market outcomes if not for the CSG. These graphs also show that in 2011, the "after" period, the composition of those employed has not changed: observable characteristics are comparable at the threshold for the sub-population that is employed. This indicates that the persistent effect is not a result of a change in the composition of the workforce, where informal workers would drop out of the labor force and formal workers "drop-in".

Results are robust to changing the bandwidth size and functional specification, by gradually reducing the number of cohorts included in the estimation with a quadratic and linear fit, respectively. These checks are presented in Figures 1.A.15 and 1.A.16.

Moreover, I perform a placebo test, exploiting the fact that CSG take-up for Whites is virtually zero. 44 I estimate Equation 1.12 on White mothers only and check that their labor market outcomes are not significantly affected by the CSG eligibility (Figure 1.A.17). 45 One concern could be that there are discontinuities in mothers' employment (formal and/or informal) that are due to the age of the child. In Figure 1 It is difficult to know from survey data whether this occurs because they do not apply or because there is an implicit rule that they should not access the CSG. Regardless, this group provides a good placebo check. 45 The validity of this placebo check is limited, as the smaller size of the White population would not allow in any case to significantly capture effects of the magnitude presented.

46 Census 2001 and 2011 take place in the same month, October. 47 It should be noted that, in this setting, estimations with placebo threshold still contain the "correct" threshold, which could potentially lead to misspecification and coefficients that are further away from zero. possible combinations.

Mechanisms

This paper provides causal evidence that an unconditional cash transfer program can have lasting, positive effects on job quality for women in a strongly segmented labor market. We also observe that these improvements come from longer unemployment as a result of receiving the transfer. In the directed search framework presented in Section 3, I show that mothers might respond to this positive shock in assets by becoming more selective, and targeting better jobs. The NIDS data allows me to test, more in detail, the various mechanisms at play. Note: These graphs plot the correlation between reported transport cost when looking for a job in the past week (in logs + 1), and CSG take-up by cohort of birth of the youngest child, both with a quadratic fit. The left panel plots the unconditional value, while the right panel plots the residuals controlling for a set of observable characteristics listed in Table 1.2. The sample is limited to unemployed mothers. Source: NIDS (2010-2014).

Self-reported reservation wages, which are available in NIDS data, do not appear to be correlated with eligibility to the Child Support Grant. As shown in Figure 1.A.20, reservation wages are flat with respect to the year of birth of the youngest child, while CSG receipt is a strongly positive function. Between 2010 and 2014, unemployed mothers who receive the CSG report roughly the same reservation wages as those who do not. Target wages and reservation wages are not necessarily related, and generally, there are concerns about the measurement of reservation wages, which are a "theoretical construct" (see [START_REF] Nekoei | Does Extending Unemployment Benefits Improve Job Quality?[END_REF] for discussion). NIDS data has no information on target wages. However, an indication that the grant changes how mothers search is the strong correlation with transport expenses when looking for a job. As can be observed in Figure 1.6, over the same period, mothers whose child is eligible for the grant tend to spend significantly more on transport when unemployed. This correlation, which is robust to the introduction of several controls, suggests that CSG and non-CSG mothers search differently. This matches the prediction that targeting better jobs should be associated with higher transport expenses during job search. High search costs are a well-known issue in the labor market of developing countries; this is particularly true in South Africa, where, for historical reasons pertaining to Apartheid, people are often located far away from jobs and pay significant fees reaching them [START_REF] Kerr | Tax(i)ing the Poor? Commuting Costs in South African Cities[END_REF]). Importantly, the results of this paper are qualitatively similar to those of experimental studies on transport subsidies in other African labor markets [START_REF] Franklin | Location, Search Costs and Youth Unemployment: Experimental Evidence from Transport Subsidies[END_REF], Abebe et al. ( 2017)), which find persistent positive effects on job quality for this type of intervention.48

As mentioned in Section 3, other mechanisms may explain labor market effects of cash transfers on adult recipients [START_REF] Baird | The Effects of Cash Transfers on Adult Labor market Outcomes[END_REF]). A cash transfer could increase willingness to bear risk by providing insurance, and push workers towards self-employment or riskier behaviors, such as migration. I can easily exclude the self-employment channel: the grant does not make mothers, or other household members, more likely to be self-employed. Self-employment in South Africa is very low, and barriers other than liquidity constraints or risk are likely to be binding. Contrary to [START_REF] Ardington | Labor Supply Responses to Large Social Transfers: Longitudinal Evidence from South Africa[END_REF], who study how households respond to receipt of the Old Age pension, I do not find any evidence that the grant induces labor migration. Mothers who have received the transfer are as likely to have migrated as those who have never been eligible to the CSG. This should not be surprising, given the size of the income effect provided by the grant, which is probably not large enough to induce migration responses.

It is harder to tackle the "competitiveness" argument, i.e. whether the grant has changed these mothers or their environment in a way that could explain these longterm gains in job quality. In terms of what can be observed in the data, this does not seem to be the case. For example, the predicted share of informal employment, based on all of the mother, child, and household's characteristics, is smooth around the threshold, suggesting that treated and non-treated mothers should be comparable in terms of their labor market outcomes and in terms of their observables. An advantage of the specific CSG shock used is that it is received when the child is quite old, so it is less likely that the grant has a direct effect on the child that would explain these results. Similarly, while it has been shown that cash transfers can have positive effects on women's empowerment and bargaining power, this is hard to reconcile with the evidence that the effect is significantly stronger on single mothers.

Conclusion

This paper has studied how an unconditional cash transfer, the South African Child Support Grant, impacts labor market outcomes for women, both in the short and long term. By exploiting eligibility across birth years of children, I find that mothers who are eligible for at least one year are more likely to look for a job when receiving the grant, and less likely to work in low-quality formal sector jobs. Five years after the grant was received, the employment rate is the same, but those mothers who have received the grant are more likely to be employed in better formal sector jobs, and less likely to be informal. This persistent effect on job quality is particularly large among single mothers, who seem to benefit the most in terms of gaining access to better employment. Similarly, the persistent effect on overall income is positive, in line with the magnitude of the job quality gains, but far from statistical significance.

This paper contributes to the growing empirical literature on search frictions in developing countries, showing that unconditional cash transfers can be a way to overcome them. The results are theoretically consistent with the key insights from the job search literature, and with recent empirical evidence on the effects of transport subsidies. A cash transfer increases unemployment length and the quality of the subsequent jobs. The mechanism I put forward is that mothers search longer and for better jobs, increasing the chance of finding a more stable formal job and reducing the chance of falling into informality. In line with this mechanism, I also observe that those mothers who receive the grant tend to spend more on transport to look for a job. The positive effect on job quality is strongly non-linear in the number of years a mother is eligible to the grant, which reinforces the liquidity explanation.

On the contrary, I do not find that the grant increases the long-term employment rate, which suggests that a cash transfer of this magnitude does not help to overcome barriers to entry into employment at the extensive margin, or that these barriers are not related to liquidity constraints and search frictions.

The results of this paper also connect to the debate on the nature of informal employment. By showing that cash transfer recipients are less likely to hold an informal job after the transfer has stopped, these results indirectly support the "segmentation" hypothesis, at least within the context of the South African labor market, where informal work mostly takes the form of wage jobs, and where selfemployment is rare. Those who are slightly richer because of the transfer are more likely to end up in the formal sector, which indirectly reflects a preference for these types of jobs. One unique feature are the characteristics of the transfer: its complete lack of conditionalities and eligibility conditions other than age of the child. This allows a clean interpretation of its income effect, avoiding potentially confounding factors on behavior. A transfer as universal as the CSG is rare in developing countries, as reflected by its large coverage. For these reasons, these results on the short and long-term effects of an unconditional grant are more generally informative for the debate around transfers "no strings attached".

Lastly, an important question that remains unanswered is to what extent these results would carry through at the economy-wide level. Simple descriptive analysis shows that labor market outcomes for women have not improved significantly, despite dramatic growth in CSG coverage in the early 2000s. Naively, this would suggest that reducing search frictions through a cash transfer is not sufficient, on its own, to increase formal sector employment in the aggregate. Intuitively, there are many possible explanations why that would be the case, such as a fixed stock of formal jobs. This would suggest combining policies that ease job search with some that increase the pool of formal jobs available, but further research is needed to draw clearer policy implications.

1.A Appendix For unbiasedness, this estimator relies on the "common trend" identification assumption, which states that, in the absence of the treatment, the evolution of treated and non-treated cohorts would have been the same. Formally, using y 0 to express the potential outcome in the absence of the treatment, the condition needed for correct identification is the following: We can then split this difference into the age evolution for those age values that we observe for both cohorts over the 10-year period, and those age values that we do not observe for both cohorts, therefore: Therefore, the previous equation shows that the difference-in-differences estimator the true effect of the CSG, the DiD estimators will suffer from the following bias:

1.A.2 Empirical Analysis

DiD = β + [∆ 18-19 -∆ 8-9 ]
where ∆ 18-19 denotes the age effect from 18 to 19 years, and ∆ 8-9 denotes the age effect from 8 to 9 years. Note: These graphs report the estimations of Equation 1.12 in graphical form. The residuals are plotted after controlling for the same variables as listed in Table 1.2. In the upper panel, a quadratic function is fitted on both sides of the threshold for the three separate years. The window, 1981 to 2004, is chosen according to an Akaike Information Criterion (AIC) that maximizes the window for a quadratic function. In the lower panel, a linear function is fitted on both sides of the threshold for the three separate years. The window, 1987 to 1998, is chosen according to an Akaike Information Criterion (AIC) that maximizes the window for a linear function. Note: These graphs report the estimations of Equation 1.12 in graphical form. The residuals are plotted after controlling for the same variables as listed in Table 1.2. In the upper panel, a quadratic function is fitted on both sides of the threshold for the three separate years. The window, 1981 to 2004, is chosen according to an Akaike Information Criterion (AIC) that maximizes the window for a quadratic function. In the lower panel, a linear function is fitted on both sides of the threshold for the three separate years. The window, 1987 to 1998, is chosen according to an Akaike Information Criterion (AIC) that maximizes the window for a linear function. Note: These graphs report the estimations of Equation 1.12 in graphical form. The residuals are plotted after controlling for the same variables as listed in Table 1.2. In the upper panel, a quadratic function is fitted on both sides of the threshold for the three separate years. The window, 1981 to 2004, is chosen according to an Akaike Information Criterion (AIC) that maximizes the window for a quadratic function. In the lower panel, a linear function is fitted on both sides of the threshold for the three separate years. The window, 1987 to 1998, is chosen according to an Akaike Information Criterion (AIC) that maximizes the window for a linear function. Note: These graphs report the estimations of Equation 1.12 in graphical form. The residuals are plotted after controlling for the same variables as listed in Table 1.2. In the upper panel, a quadratic function is fitted on both sides of the threshold for the three separate years. The window, 1981 to 2004, is chosen according to an Akaike Information Criterion (AIC) that maximizes the window for a quadratic function. In the lower panel, a linear function is fitted on both sides of the threshold for the three separate years. The window, 1987 to 1998, is chosen according to an Akaike Information Criterion (AIC) that maximizes the window for a linear function. 1.12 on mothers' occupational status (Columns (1) and ( 2)), also decomposed by sector (Columns (3) to ( 6)). Self-employed are individuals who run their own business. The coefficients add up in the following way: (2)=(3)+( 5), (1)=(4)+(6), and ( 1)+(2)=( 3)+(4)+( 5)+(6). In the upper panel, the estimation is in 2007, when the CSG is still received by cohort 1993. In the lower panel, the estimation is in 2011, 5 years after the grant has stopped for the cohort at the threshold. Standard errors are clustered at the household level. Source: Author's calculations on Community Survey (2007) and Census (2011). Note: These graphs give the estimates of Equation 1.12 on informal and formal employment for White mothers, who do not receive the CSG, in 2007 (upper panel) and 2011 (lower panel). Results are similar with a linear window. For details on the specification, see the note on Table 1. Note: These graphs plot the correlation between reported reservation wages (in logs) for the unemployed, and CSG take-up by cohort of birth of the youngest child, both with a quadratic fit.

The Lasting Labor Market Effects of Cash Transfers

1.A.3 Robustness Checks

The left panel plots the unconditional value, while the right panel plots the residuals controlling for a set of observable characteristics listed in Table 1.2. The sample is limited to unemployed mothers. Source: NIDS (2010-2014).

or payment in kind (excluding domestic worker) even for only one hour?

and In the last 7 days, did (the person) do any work as a domestic worker for a wage, salary or payment in kind even for only one hour?

and In the last 7 days, did (the person) help unpaid in a household business of any kind even for only one hour?

and In the last 7 days, did (the person) do any work on his/her own or the household's plot, farm, food garden, cattle post or kraal, or help in growing farm produce or in looking after animals for the household even for only one hour?

and In the last 7 days, did (the person) do any construction or major repair work on his/her own home, plot, cattle post or business even for only one hour?

and In the last 7 days, did (the person) catch any fish, prawns, shell fish, wild animals either as food for sale or for household use, even for only one hour?

and Even though (the person) did not do any of these activities in the last seven days, does he/she have a job, business, or other economic or farming activity that he/she will definitely return to? works (main economic activity) for?

Occupation: What is the main occupation of (the person) in this workplace?

Fertility: When was (the person)'s last child born, even if the child died soon after birth? (women aged 12 to 50 years) Employment status is measured in a similar way to the Census; the reference period is the last seven days for employment and past four weeks for unemployment. Measurement of the informal sector is identical to the Census.

Informality Status: Is the organisation / business / enterprise/branch where.... works, in the formal or informal sector?

1=In the formal sector, 2=In the informal sector (including domestic work), 3=Don't know Alternatively, this data also allows to measure informal employment with questions on the presence of a written contract or whether the business is registered for VAT.

General Household Surveys, 2002-2010

The General Household Survey has information on which household members receive the Child Support Grant. In 2002, this information was linked to the child. As from 2002, it is linked to the person actually receiving the grant.

Child Support Grant: Does ... receive any of the following Welfare grants? c) Child support grant

National Income Dynamics Survey, 2008Survey, , 2010Survey, -11, 2012Survey, , 2014 The reference period in the NIDS is the same to define employment (4 weeks) and unemployment. Information on informal employment is available through whether the worker has a written contract or not, affiliation to social security (pension, unemployment, health contributions), or whether the business is registered for VAT (self-employed only). The NIDS also has information on the full birth history of every woman in the household.

Reservation Wages: What is the absolute lowest take-home wage that you would accept for any permanent, full-time work (per month)? (unemployed only)

Transport Costs for Job Search: How much did you spend on travel costs associated with looking for work last week? (unemployed only)

1.A.5 Measuring Informality

The measurement of informality often poses some challenges. This is not necessarily a problem of data. Informality is not a sharply defined concept, but rather a blurry status with different shades of intensity (famously defined as the "murky" sector by [START_REF] Fields | Rural-Urban Migration, Urban Unemployment and Underemployment, and Job-Search Activity in LDCs[END_REF]). There is no consensual definition of what exactly defines an informal job. The first theoretical distinction is between informal employment and the informal sector. A worker in informal employment is one for whom labor market legislation does not apply, while a worker in the informal sector is one employed by a firm operating informally, i.e., which does not follow labor market legislation.

This distinction does not apply to the self-employed, for whom the two definitions coincide. For employees, informal employment and informal sector clearly overlap, but not perfectly. In theory, an informal firm cannot have a formal employee, but a worker can be informally employed in a formal firm; for example, if a registered firm does not pay social security contributions for this employee. The trend in the literature has been to measure informal employment by affiliation to social security and the informal sector by whether or not the business is registered.

Census data has information on whether the sector of employment is formal or informal (based on whether the firm is registered), or whether the employer is a private household. There are some inconsistencies in the coding of this information in 2011 Census data. 49 The Labour Force Surveys have information on both the informal sector, similarly to the census, and informal employment, namely contract status (the presence of a written contract), social security affiliation for employees;

firm size, and VAT tax registration for both employees and self-employed workers.

This allows me test the correlation and the overlap between the informal sector and informal employment, which I present in Table 1.A.8. Overall, the theoretical distinction presented before seems to hold in the data. Individuals employed in the informal sector (non-registered firms) report not being affiliated to social security and not having a written contract in around 80% of the cases. Given that this is all self-reported data on the side of the worker, we can imagine that the remaining gap is due to measurement error, as respondents might not have clear information about the registration of the business or whether the employer is paying social security contributions. Consistently, informal employment is significantly larger than employment in the informal sector (i.e. we can think of informal sector employment as a subset of informal employment). With respect to the self-employed, we also observe that own-account workers are almost entirely in the informal sector, which is, however, also composed by some employers (i.e. self-employed individuals whose business employs other people).

1. Informal Sector Employment (Census/LFS)= Employment in Non-Agricultural Unregistered Businesses + Employment in Private Households + Employment 49 Workers are asked separately their industry of employment and their sector with three options: 1) Formal sector, 2) Informal Sector, and 3) Private Households. This leads to a share of workers reporting to work in a private household, but whose reported industry of employment is not a private household. For these individuals, who account for around 10% of total employment, an informality status is not defined and reported as missing. In my main estimations, I will impute their informality status based on their industry and occupation. To be read as: 78.89% of employees in the informal sector are not affiliated to social security. Note: Informal sector refers to individuals employed in non-registered businesses. Social security refers to individuals whose employer does not pay any social security contributions (pension, medical, or unemployment insurance). Written contract refers to the presence of a written agreement between the employer and the employee. VAT tax refers to the business being registered for Value-Added Tax. Own account workers are self-employed workers with no employees. Source: Author's calculations on LFS (2002LFS ( -2007)).

Introduction

Understanding and quantifying labor supply responses to income shocks is a central question for economic research. It is also a key parameter for policy-makers, essential for the "calibration" of social programs, such as those non-contributory, public pensions schemes that are prevalent across developing countries. What is unique and relevant in these contexts is the concern that social assistance might distort incentives due to the segmented nature of the labor market, and create inefficiencies (Gerard and Gonzaga ( 2016)). Indeed, when a large portion of the workforce escapes labor market regulation, social assistance programs providing large income benefits conditional on means may discourage work, but also incentivize entry into the informal sector.

This paper exploits a reform of the public, non-contributory pension system of South Africa, which significantly lowered the age threshold necessary to access the pension, for men only. The focus of this paper is to document in detail the labor market effects on direct recipients, i.e. the elderly. By making use of this reform, we are able to precisely identify the labor market effects of the public pension, while avoiding potential confounders from other pension schemes, which we show to be sizable. We provide evidence that the disincentive effects of the OAP on its direct recipients are large, but almost exclusively concentrated on informal employment, as informal workers drop out of the labor force regardless of the level of hourly wages.

Instead, formal workers are affected only at very low level of hourly wages. We believe these results have implications for both economic research and policy design. These results speak (indirectly) to the potentially large welfare effects of a public pension in a developing country: providing individuals with an external source of income results in large, informal labor supply adjustments at the extensive margin, suggesting that this type of program might relieve subsistence-level constraints. Moreover, in our results, we find no evidence that the pension gives the incentive to combine earnings from informal labor, which do not enter the means-test, with the pension transfer. This has important implication for policy design, as it indicates that this potentially concerning "perverse" effect is small, despite the means-test being located in an area where the two distributions largely overlap. Lastly, we also argue that, in the aggregate, the jobs freed up by the elderly are unlikely to make a difference for the young. While employment responses of the elderly are relatively large, the affected group is too small to significantly affect the stock of available jobs. Although with limited statistical power, by exploiting differential effects of the reform across industries and occupations, we are able to reject a one-to-one substitution with those workers with most similar characteristics to the affected population.

Non-contributory, old-age pension programs have a long history in developing countries, and have been generally found to have negative, yet relatively small effects on employment of the elderly [START_REF] Juárez | The Effects of a Noncontributory Pension Program on Labor Force Participation: the Case of 70 y Mas in Mexico[END_REF], [START_REF] De Carvalho Filho | Old-Age Benefits and Retirement Decisions of Rural Elderly in Brazil[END_REF], [START_REF] Kaushal | How Public Pension Affects Elderly Labor Supply and Well-Being: Evidence from India[END_REF]), but these programs are usually less universal and provide lower amounts than the South African Old Age Pension. Contrary to [START_REF] Jung | The Extension of Social Security Coverage in Developing Countries[END_REF], who model the general equilibrium effects of extending social security programs to informal workers, our analysis is focused on the direct effects on recipients.

The results of this paper are informative for the broader literature about informality in the labor market of developing countries. The main debate remains whether workers choose to work informally rather than in the formal sector, or whether informal employment provides subsistence-level jobs when better employment is lacking. These diverging views have led to the development of different hypotheses on the nature of the informal sector, also known as the "comparative advantage" and the "segmentation" hypotheses (summarized in [START_REF] Günther | Informal Employment in Developing Countries, Opportunity or Last resort?[END_REF]).

Until recently, less attention has been paid to the interaction between social security programs and informality, more specifically on how different social policies shape the size and composition of the informal sector, and on how the presence of a significant informal sector might distort incentives within the labor market [START_REF] Azuara | Informality and the Expansion of Social Protection Programs: Evidence from Mexico[END_REF] and [START_REF] Bergolo | Work and Tax Evasion Incentive Effects of Social Insurance Programs: Evidence from an Employment-Based Benefit Extension[END_REF] for non-contributory health insurance to workers and/or relatives; [START_REF] Garganta | The Impact of a Social Program on Labor Informality: The Case of AUH in Argentina[END_REF] for cash transfers to those not in formal employment; [START_REF] Gerard | Informal Labor and the Efficiency Cost of Social Programs: Evidence from the Brazilian Unemployment Insurance Program[END_REF] for how informality plays a role in unemployment insurance). We contribute to this growing literature by studying the effects of a strong income effect for workers near retirement, interacted with the "perverse" incentive to switch to informal work. Within the context of older workers in the South African labor market, our results support the view of informality as mostly out of necessity.

There is a large literature on the South African Old Age Pension, which can be categorized in two main branches. The first branch of the literature relates to the impact of the OAP on children's outcomes and, more generally, to the intra-household allocation of resources [START_REF] Duflo | Child Health and Household Resources in South Africa: Evidence from the Old Age Pension Program[END_REF][START_REF] Duflo | Grandmothers and Granddaughters: Old-Age Pensions and Intrahousehold Allocation in South Africa[END_REF], [START_REF] Jensen | Do Private Transfers Displace the Benefits of Public Transfers? Evidence from South Africa[END_REF], Ambler ( 2016)). 1 The labor market effects of the OAP have also been at the center of significant empirical research. [START_REF] Ranchhod | The Effect of the South African Old Age Pension on Labour Supply of the Elderly[END_REF] estimates the discontinuity in labor supply and employment for individuals at the age cut-off point, finding large disincentive effects for both men and women. We are able to significantly improve on this identification by taking advantage of the latest reforms in age eligibility. 2 Evidence on the labor market impacts on other household members is mixed and significantly more complex, mostly because of the issue of selection when household composition changes as a result of pension receipt [START_REF] Hamoudi | Endogenous Coresidence and Program Incidence: South Africa's Old Age Pension[END_REF]). Cross-sectional evidence from [START_REF] Bertrand | Public Policy and Extended Families: Evidence from Pensions in South Africa[END_REF] revealed the presence of disincentive effects for other members of the household who were not the direct recipients, but migration is also impacted by the grant, as members of OAP recipient households are more likely to migrate [START_REF] Posel | Labour Migration and Households: A Reconsideration of the Effects of the Social Pension on Labour Supply in South Africa[END_REF]). To solve this selection issue, Ardington, Case and Hosegood (2009) make use of panel data to show that households that receive the OAP actually experience an increase in employment, which "occurs primarily through labor migration." However, recent evidence by [START_REF] Abel | Unintended Labour Supply Effects of Cash Transfer Programmes: Evidence from South Africa's Old Age Pension[END_REF], who also uses panel data but at the country level, has challenged these results. Thus, evidence on the employment effects for member of the same household is still unclear.

Incorporating the reform within our empirical strategy allows us to better identify the effects of the Old Age Pension. The main issue with the existing literature is that, in the cross-section, it is difficult to disentangle the effect of the OAP from 1 Duflo (2000, 2003) finds that the extension of the OAP to the African population in the early 90s has led to higher health and nutrition outcomes for girls who live in the same household as their grandmother. [START_REF] Ambler | Bargaining with Grandma: The Impact of the South African Pension on Household Decision-Making[END_REF] expands on this argument to show that this is the result of a change in bargaining power within the household upon pension receipt. [START_REF] Jensen | Do Private Transfers Displace the Benefits of Public Transfers? Evidence from South Africa[END_REF] shows that the public pension partly crowds out private transfers from other household members, such as remittances.

other private schemes that might have the same age threshold, and that create discontinuities in the age profile regardless of the social pension. Indeed, we show that ignoring other pension scheme with similar age threshold leads to overestimate the employment response of the elderly by a factor of two, which could potentially confound the impact on other outcomes and affect the computation of income elasticities [START_REF] Berg | Are Poor People Credit-Constrained or Myopic? Evidence from a South African Panel[END_REF], [START_REF] Ambler | Bargaining with Grandma: The Impact of the South African Pension on Household Decision-Making[END_REF]).

The outline of the paper is the following: Section 2 describes the South African Old Age Pension, and the latest reforms. Section 3 provides a simple conceptual framework to better frame the results of the paper. Section 4 presents the empirical strategy, showing the bias from not accounting for other pension schemes, and the results. Section 6 discusses the market level results. Section 7 concludes.

The South African Old Age Pension

The Old Age Pension (OAP) is a non-contributory pension system in South Africa, originally put in place in the 1920s to provide a minimal level of income to those who were not covered by a retirement plan [START_REF] Duflo | Child Health and Household Resources in South Africa: Evidence from the Old Age Pension Program[END_REF], [START_REF] Woolard | The Evolution and Impact of Unconditional Cash Transfers in South Africa[END_REF]).

During the Apartheid period, Black South Africans were consistently excluded from most social transfers, and, to a large extent, from public pensions. This occurred in several different ways: the means-test was set at different levels for different races, and was significantly lower for Black and Coloured people. Moreover, the benefits paid when actually eligible only made up one tenth of the amount paid to Whites [START_REF] Duflo | Grandmothers and Granddaughters: Old-Age Pensions and Intrahousehold Allocation in South Africa[END_REF]). Several other administrative loopholes were exploited to keep groups other than White to fully access the grant (for a full account of the history of the OAP in Apartheid South Africa refer to [START_REF] Lund | State Social Benefits in South Africa[END_REF] and [START_REF] Woolard | The Evolution and Impact of Unconditional Cash Transfers in South Africa[END_REF]). The means-tests were equalised in 1992, and full "legal" equality was achieved as from 1993, about one year before the first democratic election [START_REF] Duflo | Grandmothers and Granddaughters: Old-Age Pensions and Intrahousehold Allocation in South Africa[END_REF], [START_REF] Woolard | The Evolution and Impact of Unconditional Cash Transfers in South Africa[END_REF]).

The Old age Pension scheme is the largest social program in South Africa in terms of spending (National Budget Reviews (2007-2013)). Access to the grant is subject to two criteria: (1) an age threshold, where the recipient has to be older than a certain age; and (2) a means-test, where the recipient's earnings (and overall wealth) have to be lower than a certain threshold. There are no requirements with respect to past contributions or past employment, so it can be characterized as a non-contributory, means-tested pension scheme. Since the extension of the grant to the Black population, the age criterion for women has always remained fixed at 60 years old. Instead, the threshold for men has been set at 65 until July 2008, and then gradually lowered to 60 in the two following years. We present the reforms in the age threshold and the amount paid by the pension over time in Table 2.A.1. The amount of the pension in nominal terms has been constantly increased since 1993 to keep up with inflation. The real amount of the grant is fairly constant, around R 1000 (2010), which is equivalent to approximately 200 $ PPP per month. This is a remarkably high transfer when compared to median income [START_REF] Woolard | The Evolution and Impact of Unconditional Cash Transfers in South Africa[END_REF]), but also to wages, as we will show in the following sections.

The means-test was always set at very high levels. Initially, the proportion of the grant paid was supposed to be a decreasing function of the recipient's income, which would reach zero at a given threshold. However, it was always understood in a "binary fashion" (i.e. an individual either makes the means-test or he/she does not), hence with a 100% marginal tax rate [START_REF] Ranchhod | The Effect of the South African Old Age Pension on Labour Supply of the Elderly[END_REF]). Since the reform, the means-test has been relaxed further. In the next Section, we show how it compares to monthly earnings in the formal and informal sector. It is not clear to what extent the administration can verify applicants' income, and probably relies for the most part on what is self-reported at the application stage (Case andDeaton (1998), Ranchhod (2006)). Importantly, private occupational pensions enter the computation for the means-test, and this is likely to be an important discriminating factor. Together with proof of income, individuals have to provide proof of their private pension (if any) and assets. 3 As informal jobs, by definition, do not provide any proof of income, we consider informal labor supply decision (for workers already in the informal sector) to be independent from the means-test. Source: Authors' calculations on GHS.

An important, complementary feature of the Old Age Pension is the Disability Grant (DG). This transfer is received by individuals who have not yet reached the age threshold for the public pension, and carry some sort of physical and/or mental disability. 4 The Disability Grant has exactly the same means-test as the OAP, and pays the same amount. Moreover, it is automatically converted into the pension once the appropriate age is reached [START_REF] Abel | Unintended Labour Supply Effects of Cash Transfer Programmes: Evidence from South Africa's Old Age Pension[END_REF]). The two transfers cannot be received simultaneously, as one condition to get the Disability Grant is to be younger than the pension age threshold. Therefore, they have to be thought of as complementary, in that the DG provides some form of income support to people who cannot work before they reach pension age. Figure 2.A.1 gives the number of beneficiaries of the OAP and Disability Grant, and their sum, over time.5

In Figure 2.1, we plot the share receiving the Disability Grant and the Old Age

Pension by age in the years before and after the reforms in age eligibility (Source:

General Household Survey). The percentage of men aged 60 to 64 receiving the DG before 2008 was already fairly high before the reform. However, we do observe a large spike between 60 and 64 for the post-reform years. Consistently, this only occurs for men and not for women, for whom everything remains virtually unchanged. Overall, the share of people receiving the OAP is very high, reaching peaks of over 80% for women older than 65, and around 70% for men of a similar age. These rates are even higher when excluding the White and Indian population, who are generally less covered by social grants (Figure 2.A.2.) We also observe that, in proportion, men receive the pension less than for women, which is true both before and after the equalization of the age threshold. The explanation presumably lies in the higher prevalence of formal employment among men, who are therefore more likely to be covered by occupational pension schemes rather than the public pension.

Data and Descriptive Statistics

Data

Our analysis relies on the use of two datasets: the General Household Survey (GHS)

and the Post-Apartheid Labour Market Series (PALMS). The GHS is an annual, nationally representative survey that covers the period 2002 to 2015, run by the South African statistical agency (STATSA). It includes information on the Old Age Pension, the Disability grant, but only some basic information about employment and salary. Its labor market section is not detailed enough for the purpose of our analysis, so we only use this dataset to document the effect of the reforms on the share of people receiving the Old Age Pension.

The PALMS consists of several appended cross-sections from 1994 to 2015. Over our period of interest (2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015), the PALMS is made up of two similar surveys: the Labour Force Survey (2002Survey ( -2007) ) and the Quaterly Labour Force Survey (2008Survey ( -2015)).

These surveys are generally considered to be of high quality, and are the main source of labor market information in South Africa. They are also run by STATSA, but the PALMS dataset, which combines them in a coherent way, is put together by researchers at Data First of the University of Cape Town. 6 From 2000 to 2007, the LFS was bi-annual, i.e. interviews were carried out in March and September. As from 2008 to now, together with a change in the sampling framework, the QLFS began to be run each quarter. Overall, both the LFS and the QLFS have much more detailed information on employment, informality and wages (except for the period 2008-2009, where salary information was not asked). The sampling frame is the same as the GHS. The disadvantage of the PALMS is that information about social transfers (including the public pension) is asked only to individuals who are either inactive or unemployed. This makes it impossible to calculate accurately the share of people receiving the OAP on the PALMS, given that employed people can in theory also access it. The advantage of using the PALMS, rather than the original waves from the LFS and the QLFS, is that variables are coded consistently over the whole period, and sampling weights are adjusted to be coherent over time, but the underlying data is the same. To ensure the maximal comparability across the GHS and the PALMS, we exclude the initial waves of the PALMS and focus only on the period 2002-2015. 7

Informality Definition

Given its detailed labor market section, PALMS data allows for an accurate measurement of informal employment. Throughout the paper, we employ the most objective, and most conservative, measurement of informality. For employees, we consider informal workers those who work without a written contract. The advantage of this measure is its lack of ambiguity, as it is easily known to the worker who answers the survey questions. 8 For self-employed workers, who, by definition, do not have a work contract, we use information about business registration. These are both standard ways to measure informality in the literature. Throughout the paper, we

6 Detailed information about how the PALMS 3.2 was put together can be found here: https: //www.datafirst.uct.ac.za/dataportal/index.php/catalog/434 7 Moreover, it has been reported that the initial waves of the LFS significantly over-measured informal employment. See [START_REF] Kerr | Sampling Methodology and Fieldwork Changes in the October Household Surveys and Labour Force Surveys[END_REF] for discussion.

8 Questions about whether the employer pays social contributions may be more difficult to re-collect, or might not be known to the worker.

use the terms informal employment and informal sector interchangeably, but the definition is always based on the presence/absence of work contracts (employees) and business registration (self-employed). Importantly, earnings are measured in gross amounts, before tax and deductions. This is irrelevant for informal workers (for whom net wage=gross wage), and low-paid formal workers, but it certainly enhances the differences between formal and informal wages, in particular at the top of the wage distribution where taxes and social contributions are more relevant.

Descriptive Statistics

Men aged 60-64 make-up between 1.5%-2% of the working-age population (15-65)9 , and account for a similar share of working-age employment. Around half of employed elderly male were in the informal sector between 2002 and 2007, while this share decreased to one fourth after the reform. The importance of private pension schemes is shown in Figure 2.A.3, where we plot the joint probability to be employed and have an occupational pension in the years before the reform. A significant share of people contributes to some type of pension fund (through the employer). More importantly, this probability drops discontinuously at age 60, suggesting that workers tend to retire at that age. In the next sections, we show that not accounting for these private pension schemes leads to a significant over-estimation of the effects of the Old Age Pension, and to drastically different results about the heterogeneous impacts on formal and informal employment. 2.A.1). Thus, it can be considered a large amount when compared to median monthly salaries in the informal sector (slightly below the median in the pre-reform period). These orders of magnitude are important to interpret the results of the next sections.

Working hours, on average, are similar between the formal and informal sector, where people usually work around 45 hours, with a slight decline over the period.

However, this masks a large heterogeneity in the informal sector, where many workers are employed part-time (15-20%) at less than 30 hours a week. On the contrary, part-time is very rare in the formal sector, where less than 5% work less than 30 hours. This suggests that one of the advantages of informal work might be its higher flexibility. In Figure 2.A.4, we show the distribution of occupations in informal employment by categories of age, for men and women respectively. Elementary occupations make up the most of the informal employment of elderly men. Overall, informality among the elderly appears to be relatively similar to that of their younger counterparts, in the sense that is similarly distributed across occupations and sectors.

The position of the means-test, after the reform, with respect to earnings in the formal and informal sectors is given in Figure 2.A.5. We focus on the period after the reform as this is the relevant one to our analysis, and for which we have better information. Moreover, qualitative evidence suggests that the means-test is likely to be more strictly applied in later years, than in the earlier stages of the program.

As we can see from Figure 2.A.5, the monthly value of the means-test is located around the median of the distribution of formal monthly salaries, and this remains true across years, despite changes in the nominal level. Importantly, the means-test is located in an area where there is a large overlap between formal and informal wages, suggesting that, at that level of wages, there are (potentially) corresponding jobs in the informal sector that pay a similar amount.

Conceptual Framework

We use a simple model of sectoral choice and labor supply to help formalize the potential effects of an unconditional pension reform on labor market outcomes. The purpose of this simple framework is to elaborate a few predictions on the behavior of individuals faced with an exogenous non-labor income shock. The set-up is a classic leisure-consumption trade-off, where individual i chooses how much to consume (C), how much to work (l = T -L) and in which sector to work (k ∈ {F, I}). For simplicity, utility is given by u i (C, L, k) = α i ln(C) + (1 -α i ) ln(L) -µ i (k); people are all endowed with the same amount of time, T , and are faced with wages that may differ for each individual and across sectors, w i,k . These wages are fixed, which at least in the short to medium term, is a reasonable assumption. In the absence of pension, non-labor income, m, is zero. The reform consists in offering m > 0 to all individuals whose labor earnings (w(T -L(w, m))) are below a certain threshold W . We further assume working in the formal sector implies a negative utility component (µ i (F ) = µ i ) which is not encountered when working in the informal sector (µ i (I) = 0). This is meant to reflect some type of fixed cost to enter the formal sector, while we model the informal sector as "free entry". The introduction of this parameter does not qualitatively change the predictions of interest of this simple framework, and it is an analytically simple way to model the constraints vs.

choice debate in the literature on informality. A more detailed treatment of this framework is given in Appendix 2.A.1.

Individual labor supply

Prior to m > 0 being implemented, agents allocate in the formal or in the informal sector. These are people for whom, respectively,

µ i < α i ln w i,F
w i,I (formal-sector workers), and µ i > α i ln w i,F w i,I (informal-sector workers). In words, people chose to work in the formal sector when the relative monetary benefits of doing so (as opposed to working in the informal sector), weighted by their preference for consumption (the utility associated with this gap in expected earnings), is larger than the (utility) costs associated with it. The difference in the choice of sector is driven by (i) differences in w i,F w i,I , (ii) differences in µ i , or (iii) differences in α i (of course, several mechanisms might be simultaneously at play).

As a result of the pension reform, the non-labor income m is given to everyone choosing to work in the informal sector I, and to people earning less than W in the formal sector. The condition for choosing to work in the formal sector rather than the informal one becomes more demanding as the pension reform makes the informal sector more attractive as compared to the formal one (since the means-test does not concern revenue from informal work).

Individuals who would have chosen to work in the informal sector absent the pension either keep working in the informal sector and decrease their hours, or drop out of the labor force when receiving the pension. In other words, the effect on the labor supply of those individuals will be that of an income effect. The magnitude of the drop in the labor supply, relative to its baseline level, is ∆ α l(m) = -1-α i α i m w i,I T . This expression implies that workers with lower hourly wages will react more to the income shock.

Individuals who would have chosen to work in the formal sector absent the pension split in two subcategories that might react differently to the reform. The first are individuals whose optimal labor income in the absence of the pension, w i,F l * (0), is smaller than the means-test threshold. Because m is alleviating the budget constraint, it is making the formal sector's wage premium relatively less important, thereby potentially inducing switching from the formal sector to the informal sector; in other words, the magnitude of w i,F w i,I might not be enough to overturn the costs of working in the formal sector anymore. If individuals do not switch, their formal labor supply will nonetheless decrease, as a result of the income effect created by the pension.

Second, individuals whose optimal labor supply in the absence of the pension is located above the means-test will either (i) be left unaffected by the reform, or (ii) decrease their formal labor supply to locate below the means-test, or (iii) switch to the informal sector (and decrease their labor supply). High levels of w i,F will make m negligible enough that labor supply (and sector choice) is unaffected, with no take-up of the pension. For lower levels of w i,F , it is the ratio between w i,F and w i,I that determines whether individuals will decrease their formal labor supply, or switch to the informal sector and decrease their labor supply. This implies that the share of individuals who would switch to the informal sector will largely depend on the counterfactual wage that formal workers would have in the informal sector.

Aggregate labor supply In summary, we expect that the formal labor supply curve should move unambiguously downward. Whether it should be affected at all, remains to be investigated. Even with no change at the extensive margin, we would still expect a response in terms of hours worked. Because of the means-test threshold, it could be that some individuals work a few hours less, so as to qualify for the means-test. For workers at the lower-end of the formal wage distribution, where agents are anyway eligible to the pension as they are under the means-test, we would also expect a decrease in the number of hours worked. Failure to observe either response could be because the pension amount is not sufficient to make people drop out of the labor force, and because hours would not be able to adjust, say if contracts cannot easily be changed.

Instead, the direction in which the aggregate informal supply curve will react is ambiguous. If there is no switching from formal employment, then any drop in the informal employment rate is entirely attributable to the income effect for informal workers. If there is switching from formal employment, any drop in the informal employment rate observed at 60 would be the sum of a drop in the (informal) labor supply of individuals who would have worked informally in the absence of the pension, and of an increase in the informal labor supply of individuals who would have worked formally in the absence of the pension, but switch to informal work as a consequence of it.

Disentangling the effect of wages The distributions of wages in formal employment and in informal employment are very different (see Figures 2.A.6 and 2.A.7).

Absent any substitution from formal to informal employment, differences between the labor supply reactions of both sectors are likely to be driven by wage differences: for smaller expected hourly wages, the non-labor income m that the pension represents has a relatively larger income effect. However, to control for these differences, we can compare the formal and informal labor supply responses at similar level of hourly wages. The labor supply response of informal workers (A), and formal workers (B) are respectively equal to

∆ α A l(m) = -1-α A α A m w A,I and ∆ α B l(m) = -1-α B α B m w B,F .
At the same level of wages (i.e. for w A,I = w B,F ), the differences in labor supply responses are thus attributable to differences between informal and formal labor markets; either with regards to the characteristics of workers who select into them (the preference parameter α and µ in our framework), or with regards to intrinsic characteristics of the jobs in question. We are able to take this prediction to the data as the distribution of hourly wages in the informal and formal employment overlap, although only partly. We go back to the empirical challenges of this heterogeneity analysis in the next section.

Empirical Analysis

Identification Strategy

In order to capture the labor market effects of the Old Age Pension, we make use of the latest reform in eligibility, which only directly affected men. In our estimations, we employ a "modified" Regression Discontinuity Design (RDD), conceptually similar to the "diff-in-disc" estimator proposed in [START_REF] Grembi | Do Fiscal Rules Matter?[END_REF]. In this context, the main concern is to avoid bias from other pension schemes with a similar age threshold, which we show to be sizable. With this in mind, we extend the RDD framework to incorporate the time variation in the age threshold introduced by the reform. Therefore, rather than simply estimating the jump at a given threshold as in a traditional RDD setting, we estimate the difference in the jump before and after the reform. This relaxes considerably the "classic" RDD assumption that, in the absence of the treatment, there should be no discontinuity at the cut-off point.

Instead, by performing this estimation, the requirement for identification is that the discontinuity at the threshold would have remained the same in the absence of the reform. This design can be thought of as combining both a "Difference-in-Differences" (DID) and an RDD, but requires significantly weaker assumptions than any of the two methodologies applied independently. We exclude the reform years (2008)(2009), and only focus on the before/after period.

As we also want to derive the magnitude of the bias in the simpler, cross-sectional RD estimator, we also compare the estimates in both strategies. This also allows us to have some results for women, for whom the threshold is not reformed. Formally, we begin by estimating the following equations, separately for men and women, before and after the reform:

Y i,t = δ t + f (age i ) + f (age i ) × Age (60+) + β RD Age (60+) + ε i,t (2.1)
where Y i,t is the outcome of interest. δ t indicates year effects. f (Age i ) is a function of age, and we test the sensitivity of our results to both a linear and quadratic function.

Following [START_REF] Gelman | Why High-Order Polynomials Should Not Be Used in Regression Discontinuity Designs[END_REF], we avoid the use of higher-order polynomials.

Age (60+) is a dummy variable indicating whether the individual is older than 60, which is the cut-off point after the reform. β RD is the discontinuity estimated with the classic RD framework, of which we obtain four values: before and after the reform, for men and for women.

To incorporate the reform in the age threshold into the RD strategy, we modify Equation 2.1 in the following form:

Y i,t =
+ ε i,t (2.2)
where the difference is that we interact the function of age with the P ost t and Age (60+) variables. P ost t is a binary variable equal to 1 for the years after the reform (2010)(2011)(2012)(2013)(2014)(2015) and equal to 0 for the years before (2002)(2003)(2004)(2005)(2006)(2007). In this way, we allow for four different functions of age, on both sides of the threshold and before/after. We also allow for two different discontinuities: Age (60+) , and P ost t × Age (60+) . β DiDRD captures the before/after difference in the discontinuity at age 60. It is important to underline that we observe age as a discrete variable, so our design suffers from the limitations of an RD with a discrete forcing variable in terms of inference [START_REF] Lee | Regression Discontinuity Inference with Specification Error[END_REF], [START_REF] Lee | Regression Discontinuity Designs in Economics[END_REF], [START_REF] Kolesár | Inference in Regression Discontinuity Designs with a Discrete Running Variable[END_REF]). As suggested by [START_REF] Kolesár | Inference in Regression Discontinuity Designs with a Discrete Running Variable[END_REF], we do not cluster by the running variable (i.e. age).

Instead, when running Equation 2.2, we obtain robust standard errors by clustering at the race-cohort level. In this way, we want to account for the serial correlation arising from observing some of the same cohorts over time at different points of the age profile.

One of the advantages of this estimation is that it offers a practical solution to "age heaping", meaning the tendency among survey respondents to round age to the closest multiple of 5 or 10, as already pointed out by [START_REF] Ranchhod | The Effect of the South African Old Age Pension on Labour Supply of the Elderly[END_REF]. In our setting, this does not pose a problem unless heaping is more or less severe before or after the reform, which can be checked easily by looking at the change in density.

More generally, under the assumptions stated before, comparing our estimate for β RD and β DiDRD will give us the effect of the pension "purged" of the bias from other pension schemes, and age heaping. In the results Section, we discuss the sign and magnitude of this bias.

Another concern is the possible presence of anticipation effects. This is a common problem when dealing with age as a forcing variable. Individuals are aware of the age threshold, and can anticipate or postpone their retirement decision before reaching the age threshold. Given that we focus on the discontinuity at the threshold, our estimation ignores changes that may occur before the threshold as a response to the pension reform. With this in mind, our estimates have to be understood as capturing the anticipated income effect of receiving the pension (relative to not receiving it) rather than as the "absolute" effect of the pension reform. In theory, the effect of anticipation could go either way: if individuals younger than 60 anticipate their retirement decision because of the lower age threshold, then our estimates will be a lower bound. Instead, if individuals postpone their retirement to reach the public pension age, then we would overestimate the effect of the pension. However, the gender dimension of the reform allows to tackle this openly. As women are not affected by the change, we can compare men and women at unaffected age values close to the threshold to measure anticipation. Overall, we do not find any evidence of employment responses occurring before eligibility kicks in.

Results

The results of the RD estimation are presented in Table 2.1, and those of the "diffin-disc" in Table 2.2. Graphical evidence of the drop in total employment and by sector is presented in Figure 2.2. Before the reform, we estimate a significant and large drop in formal employment at age 60, while informal employment is smooth around the threshold. As men were only receiving the pension from age 65 before the reform, these estimates cannot be the result of the Old Age Pension. After the reform, when men are "treated" as from age 60, the drop in employment is more than double that of the pre-reform period, coming in equal parts from formal and informal employment. We observe qualitatively similar evidence for women, who also experience a sizable drop in employment at 60, but constant over the period, again split in roughly equal parts from formal and informal employment. This is in line with the fact that they are not affected by the reform, and are always eligible to the pension from the same age. 2) informally employed, (3) formally employed, such that (1)=( 2)+( 3), and (4)=( 5)+( 6). Mean Y at Age 59 refers to the value of the dependent variable at age 59 in the years after the reform. Robust standard errors in parentheses.

Source: Authors' calculations on PALMS 3.2 (2002-2007 and 2010-2015). Source: Authors' calculations on PALMS 3.2 (2002PALMS 3.2 ( -2007PALMS 3.2 ( and 2010PALMS 3.2 ( -2015)).

Importantly, these results show that the "simple" RD results suffer from a significant bias. This bias is concentrated on formal employment because this is where the jobs with private pension schemes are. Ignoring the confounding effects from other pension schemes leads to mistakenly attribute a negative effect of the Old Age Pension on formal employment. Overall, this brings to an over-estimation of the total employment effect of the pension by a factor between 1.5 and 2. 11 Intuitively, the coefficients suggest that the size of the bias is roughly the same for women, but this cannot be tested directly as the age threshold for women is left unchanged.12

When we correct for this bias, by employing the estimator of Equation 2.2, the point estimate on total employment decreases significantly (Table 2.2). Not surprisingly, our estimate of the employment effects of the pension on direct recipients is more conservative than previous studies, which found effects around twice as large [START_REF] Ranchhod | The Effect of the South African Old Age Pension on Labour Supply of the Elderly[END_REF], Ambler ( 2016)). The effect on informal employment is unchanged from the previous estimation, between -4 and -5pp., which is relatively large drop: more than a third of informal workers leave their jobs as they become age-eligible to the pension. Looking at Table 2.1, we can speculate that the effect is of similar magnitude for women, although the lack of a reform does not allow us to control for potential confounders.

The effect on formal employment is much smaller, and insignificant, but the point estimate is not zero. We show in the next section that some formal workers at the very bottom of the wage distribution stop working. Formals workers also do not seem to significantly respond at the intensive margin, as average working hours in the formal sector stay stable when men become eligible to the Old Age Pension (Table 2.2, Column 4). 13 This partly contradicts the prediction of our conceptual framework in Section 4, which suggested some kind of adjustment by formal workers.

However, this prediction was based on the assumption that formal workers can freely adjust their hours, which is unlikely to be the case. If formal jobs are not flexible in terms of working hours, as the absence of part-time jobs would suggest, this could explain the lack of an adjustment at both the extensive and intensive margin.

Overall, the results match the prediction that there should be a net decrease in employment. This decrease comes almost entirely from informal employment, while formal workers do not significantly respond. Importantly, we do not observe a "perverse" effect at play where workers reallocate from formal to informal employment.

This would suggest that those in formal employment strictly prefer it to informal work, for example because their counterfactual wages in informal jobs would be significantly lower, so that this change in payoffs across sectors is not large enough to generate any switching. Alternatively, one could posit that there may be barriers that impede this re-allocation across sector. We have assumed that workers can simply enter the informal sector, by modelling it as a "free-entry" sector as is standard in the literature, but this might not be the case.

Heterogeneous Effects by Wage

We now look at how eligibility to the pension has affected workers at different points of the wage distribution. The goal is to test the prediction that the labor supply response of workers should be larger at lower levels of hourly wages. Moreover, we also want to examine differential responses in formal and informal employment at the same level of wages. As wages in the informal sector are, on average, lower than in the formal sector, this observed heterogeneity may simply derive from the fact that the OAP is a relatively larger income shock for informal workers.

Ideally, we would estimate the discontinuity at 60 of the conditional labor supply at various levels of potential wages. However, only realized wages are observed, i.e.

for people who are employed. Alternatively, as outcome variables, we construct different indicator variables equal to 1 if the individual is observed in employment at given levels of wages, and 0 otherwise. The "diff-in-disc" estimator is not well suited for the purpose of this analysis, as the distribution of real wages changes over time.14 Differences in the wage distribution over time, weighted by the discontinuity at baseline, would bias our estimates. However, under the assumption that the distribution of wages around the threshold is continuous, and in the absence of any discontinuity prior to the reform, the RD estimator causally identifies the discontinuity in the conditional labor supply (scaled by the density of wages). We show this formally in Appendix 2.A. 3. 15 As there was no discontinuity pre-reform in Note: These graphs plot the coefficients of Equation 2.1 on the probability to be informally employed within each quartile of the informal hourly wage distribution before and after the OAP reform.

Quartiles are defined according the distribution of informal hourly wages for the 55-59 years old population in each period. The x-axis is labelled with the bounds of the quartiles for the monthly salary equivalent at a 43 hours working week. The sample includes Black and Coloured men only.

Source: Authors' calculations on PALMS 3.2 (2002PALMS 3.2 ( -2007PALMS 3.2 ( and 2010PALMS 3.2 ( -2015)). Note: These graphs plot the coefficients of Equation 2.1 on the probability to be informally employed within each quartile of the informal hourly wage distribution before and after the OAP reform.

Quartiles are defined according the distribution of informal hourly wages for the 55-59 years old population in each period. The x-axis is labelled with the bounds of the quartiles for the monthly salary equivalent at a 43 hours working week. The sample includes Black and Coloured women only.

Source: Authors' calculations on PALMS 3.2 (2002PALMS 3.2 ( -2007PALMS 3.2 ( and 2010PALMS 3.2 ( -2015)). Note: These graphs plot the coefficients of Equation 2.1 on the probability to be formally employed within each quartile of the informal hourly wage distribution before and after the OAP reform.

Quartiles are defined according the distribution of informal hourly wages for the 55-59 years old population in each period. The x-axis is labelled with the bounds of the quartiles for the monthly salary equivalent at a 43 hours working week. The sample includes Black and Coloured men only.

Source: Authors' calculations on PALMS 3.2 (2002PALMS 3.2 ( -2007PALMS 3.2 ( and 2010PALMS 3.2 ( -2015)).

Moreover, we are interested in whether the differential response by formal/informal is the result of wages being higher in the formal sector. The question is whether formal workers, for the same wage, respond differently. In order to test this, we look at the effect of the reform on the probability to be formally employed at each quartile of the informal wage distribution. Indicatively, the top quartile of the informal distribution roughly begins at the median of the formal wage distribution. The effects on formal employment by wage for men are plotted in Figure 2.5, before and after the reform. Interpretation here is complicated by the effects of private pension schemes with the same age threshold. However, these are concentrated in the top quartile of the distribution, which is where we observe a large drop in formal employment, of identical size, both before and after the reform.

With this caveat, we estimate a negative effect for men at the very bottom of the wage distribution, meaning those formal workers whose wage is within the first quartile of the informal wage distribution. 16 Instead, workers formally employed in the 2nd and 3rd quartile of informal wages do not respond to the reform. The coefficient is slightly negative and insignificant, but identical in size to the response at the same quartile before the reform. The coefficient at the top quartile of informal wages (equivalent to formal workers above the median of formal wages) is large and negative, but constant across periods, and easily attributable to private pension schemes.

Importantly, the difference in the total response between formal and informal employment is coming from workers employed in 2nd and 3rd quartile of the informal wage distribution. At these level of wages, informal workers quit their jobs, while formal workers do not. This implies that the heterogeneous effect by sector is not the result of wages being higher in the formal sector, but of differential responses for the same level of wages. As mentioned before, this could be interpreted in two different ways. On the one hand, this could be the result of intrinsic characteristics of the jobs in question at those level of wages. The same worker, with the same characteristics, would react differently at the same wage in a formal or informal job. Alternatively, this can be interpreted as evidence that formal and informal workers are different across other dimensions (for example, in our simple framework, preferences for consumption), which in turn cause these differential responses.

Robustness Checks

Regression discontinuity designs lend themselves to a wide and well-documented array of robustness checks, which we can easily adapt to the difference-in-discontinuities framework. As mentioned before, "age heaping" is problematic when dealing with age as a forcing variable. In our empirical setting, this is an issue only if age heaping is more or less severe before or after the reform. Therefore, we look at the change in density around the threshold. In the spirit of a McCrary (2008) test, we run Equation 2.2 on the log number of individuals within each cell (age x year). We find no evidence of discontinuous change at the threshold, which suggests this is unlikely to be an issue in our estimations. A standard check in an RD design is also to examine the distribution of pre-determined observables around the threshold.

In our modified version, we again focus on the changes in observables before and after the reform, which may indicate selection. For a set of covariates that includes education, race, province, household size and marital status, we find that they evolve smoothly around the threshold (Table 2.A.5).

Lastly, we check the sensitivity of our results to the selected window. In our main estimations, we have shown that the results are very similar with ± 10 window with a quadratic fit, and a ± 5 window with a linear fit (Tables 2.2 and2.A.4). In Figure 2.A.9, we test the sensitivity of the estimates of Equation 2.2 with linear function as we gradually restrict the bandwidth size between ± 10 and ± 5. For formal employment, the point estimate is negative when the window is large, but then converges to -1pp. as we restrict the window, which is equivalent to the estimate with a quadratic function on a larger window. On the contrary, the point estimate on informal employment remains stable, and roughly varies between -7pp. to -5pp.

Another way to employ our Diff-in-Disc estimator of Equation 2.2 is to exploit the change in discontinuity at 65, which is where the threshold was set for men before the reform. We show the results of this estimation in Table 2.A.6, with the note that coefficients should be interpreted with the opposite sign, as here we capture the effect of a negative discontinuity in the (change in the) share of people receiving the pension. Consistently, the results are very similar (and with opposite sign) of those presented before. The effect on employment is positive and significant, slightly larger but again mostly concentrated on informal employment. The coefficient on formal employment is slightly larger than before, but insignificant and imprecisely estimated.

Market-Level Effects

Analyzing the effects of a pension reform that concerns several hundreds of thousands of potential beneficiaries raises the question of its potential impact on labor markets in general. Addressing these potential spillovers helps understanding the trade-offs that a wide-scope public policy entails. Indeed, a common motivation for reforming pension schemes is to allow for the replacement of older generations of workers and to provide jobs for younger generations. In the case of South Africa, previous studies have focused their attention on within-household effects of receiving the pension, with mixed results and conclusions [START_REF] Ardington | Labor Supply Responses to Large Social Transfers: Longitudinal Evidence from South Africa[END_REF]Hosegood (2009), Abel (2013)). More generally, evidence on the question of substitution between old and young workers, which has received thorough empirical investigation in developed countries [START_REF] Salem | Labor Force Participation by the Elderly and Employment of the Young: The case of France[END_REF], [START_REF] Banks | Releasing Jobs for the Young? Early Retirement and Youth Unemployment in the United Kingdom[END_REF], [START_REF] Bovini | Labor Substitutability and the Impact of Raising the Retirement Age[END_REF]), is largely absent in middle-income countries.

If all ages between the ages of 60 and 64 were treated equally, our estimate suggests that the reform drove 25,500 individuals out of the labor force; these are individuals who leave informal and low-paying formal jobs. In this section we address whether some of those jobs are picked up by people who are not directly concerned by the reform. In order to answer this question, we leverage heterogeneity in the effect of the pension by sector of employment. This follows naturally the heterogeneous results on informal and formal employment rates, as we can expect sectors with more informal employment to be more affected.

However, a very important obstacle for this strategy to yield meaningful results is the limited statistical power provided by the natural experiment we exploit.

South African employment is large relative to the direct employment effects of the pension. In the first quarter of 2008, 14.4 million people of all characteristics are employed in South Africa. The effect of the pension reform on the employment of non-beneficiaries only represents, by the largest estimate, 0.2% of the total labor force. More importantly, this also means that those potential effects are small with regards to sampling variability and to the natural temporal variability in the level of employment in the various sectors; in other words, this suggests that the natural experiment we study does not necessarily provide us with enough statistical power to uncover the relevant general equilibrium effects.

We address this difficulty by focusing our attention on the parts of the employment pool that are the closest potential substitutes to workers directly affected, i.e. males aged 55 to 59. The intuition behind this strategy is that people who are very similar in terms of observable characteristics to the treated population may be the ones most likely to be hired as substitutes for them once they exit the labor market.

With this in mind, we carry out the same analysis as before, distinguishing employment across 100 sectors by industry and occupations, and identify the ones where the employment effects are the more pronounced. Figure 2.6 shows that a few key sectors of the economy concentrate a large share of the total drop in employment. This heterogeneity allows us to classify the most heavily affected cells as "treated" and the others as "control" by splitting cells into groups according to the weighted quartiles of their point estimate. We define the first quartile (the sectors with the most negative coefficients) as "treated" and the fourth quartile as "control".17 Here, identification relies on the assumption that employment in sectors that were differently affected by the reform of the OAP scheme would have evolved in a similar way absent the reform, after the date it was effectively implemented. Note: This graph plots the point estimate of Equation 2.2 on the number of elderly workers employed in each industry×occupation cell (black dots, left axis), and the share that the corresponding industry×occupation cell represents in total elderly employment (grey bars, right axis). Cells are ordered from left to right by the magnitude of the effect of the pension on the number of employed workers.

Source: Authors' calculations on PALMS 3.2 (2002PALMS 3.2 ( -2007PALMS 3.2 ( and 2010PALMS 3.2 ( -2015)).

The shock in the aggregate labor supply that is induced by the pension reform is essentially a decrease in the supply curve on a given labor market. According to standard labor economic theory, this could translate into three different responses for individuals who are not directly affected by the shock, but can still be deemed as part of the same aggregate labor supply. The first is a change in the labor supply of people who are not working (extensive margin); the second is a change in the labor supply of individuals who are already working (intensive margin), and a third is a change in wages. Therefore, the outcomes of interest are the employment rate, the number of hours worked per employed person, and the wages, for the subgroups of the population we are interested in. and Coloured men, we find that on aggregate the treated cells do exhibit a sizable drop in employment, by about 20,000 workers from the third quarter of 2008 to the first quarter of 2012; while the control is rather stable over the same period.

The effect seems to partly disappear over time, as the gap between both groups of sectors widens after a few years. Importantly, the pre-reform evolution of trends in employment is also parallel. After the reform, the employment of the 55-59 year old does not seem to evolve deferentially in treated and non-treated sector, which we take as evidence that the jobs left by pension recipients are not being picked up by these workers. Moreover, there is no visible adjustment neither in terms of hours worked nor wages (see Figure 2.A.10).

To confirm these results, we estimate a simple difference-in-difference model on each of these three outcomes. The estimating equation is:

Y c,t = κ c + τ t + θ 1 T × {2010q2 < t < 2014} + θ 2 T × {2014 < t} + � c,t (2.3)
where Y c,t is the outcome of interest in cell c at time t, κ c and τ t are cell and timeperiod fixed effects, T is and indicator variable for being a heavily affected cell, and � c,t is an error term, clustered at the cell-level. As the graphical evidence indicates that the effect of the reform might not be constant, we allow the treatment effect θ to be flexible over time and split it in two components, a short-and a medium-term effect.

Results are displayed in Table 2.A.7. We recover the negative estimates on the treated groups, with a magnitude of about 22,000 Black and Coloured 60-64 y.-o. men, a result that is very compatible with the extrapolation of our diff-in-disc results.18 This is mirrored by a large negative drop in the total number of hours worked per week, by about 930,000 hours in the treatment. Assuming no effect at the intensive margin for those still working, this would mean that the people who stop working as a result of the pension were working 45 hours per week, a number that is the mode of the distribution of hours per week in the sample. Weighting each cell by its total size in employment in the pre-reform period does not qualitatively affect these conclusions.

Estimates on the 55-59 y.-o. men do not show any sizable nor statistically significant effect of the reform in sectors where men aged 60-64 were more affected.

The positive estimates in the unweighted case -which would be compatible with younger men picking up the jobs that the 60-64 are leaving -are very imprecise, and disappear when weighting each sector for its total employment size. As we are limited in our statistical power to disentangle whether the jobs left by the elderly were picked up by younger individuals, we can only reject a one-to-one substitution with the closest workers (55-59 males). Nonetheless, this indicates that, overall, the number of jobs freed up by the reform is unlikely to have any significant impact on the stock of jobs available to younger workers.

Conclusion

This paper uses a decrease in the age eligibility threshold for men in the public pension scheme of South Africa to study its effect on the employment of the elderly.

We show that not properly accounting for other private schemes leads to significantly overestimate the effects of the Old Age Pension. Despite no explicit requirement to retire when receiving the pension, we provide causal evidence that this reform triggered a large adjustment in old-age male employment at the extensive margin.

This occurs because informal workers at all levels of informal wages quit their informal jobs, while formal workers respond only at very bottom of the wage distribution. Our results indirectly suggest that there might be welfare gains for recipients larger than the income effect, as it seems that the pension relieves some sort of subsistence-level constraint for people in informal employment.

These results contribute to the literature on informality in the labor market. The main debate in this literature has been whether workers choose informal employment, or take it due to the lack of better alternatives and as jobs of last resort. The findings of this paper support the second view, although within the context of old age workers in the South African labor market. For workers at age 60 or more, a transfer that is roughly equal to the median wage in the informal sector decreases informal employment by 30-40%; it also does not seem to cause any significant reallocation from formal to informal employment, suggesting that there are no workers for whom this change in incentives is large enough to induce any switching. This relaxes concerns about how the presence of a large informal sector might cause efficiency losses in means-tested programs such as public pensions.

Lastly, this paper aims to contribute to the design of pension systems in developing corresponding utility. Deriving L * (k) and C * (k) follows from first order conditions:

L * (k) = (1 -α i )T C * (k) = α i w i,k T
Conditions (2.4) and (2.5) thus translate into:

α i ln(α i )+(1 -α i ) ln(1 -α i ) + α i ln(w i,I ) + ln(T ) ≶ α i ln(α i ) + (1 -α i ) ln(1 -α i ) + α i ln(w i,F ) + ln(T ) -µ i
Selection into sectors is thus determined by:

µ i < α i ln � w i,F w i,I � (formal-sector workers) (2.6) or µ i > α i ln � w i,F w i,I � (informal-sector workers) (2.7)
In words, people choose to work in the formal sector when the relative monetary benefits of doing so (as opposed to work in the informal sector), weighted by their preference for consumption (the utility associated with this gap in expected earnings), is larger than the (utility) costs associated with it. The difference in their choice of sector is driven by (i) differences in

w i,F
w i,I , (ii) differences in µ i , or (iii) differences in α i (of course, several mechanisms might be simultaneously at play). We'll examine the consequences of (i), (ii) and (iii) on the effect of the introduction of m.

Let's focus on the case where w i,F > w i,I , so that there is a potential incentive to chose the formal sector in the first place (absent any costs, everybody would chose the formal sector because now, α i ln( w F w I ) > 0). If we don't assume that w i,F > w i,I , then trivially, the informal sector is more attractive than the formal sector on all accounts (expected earnings, costs of entry), and adding non-labor income is not going to change sectoral choice (but is going to have a negative effect on labor supply).

We next detail the effects of introducing m > 0 on people's choice of sector. The effects are different according to the characteristics of people (who are defined by the vector {w i,F , w i,I , α i , µ i }):

• people who chose to work in the informal sector when m = 0 (A).

• people who chose to work in the formal sector when m = 0 and whose (actual) formal earnings are below the means-test (B).

• people who chose to work in the formal sector when m = 0 and whose (actual) formal earnings are above the means-test (C);

(A) Informal workers These workers satisfy condition (2.7)

µ A > α A ln � w A,F w A,I

�

In the case where their pre-reform potential formal earnings are under the means-test, these workers face a new inequality,

µ A > α A ln � w A,F w A,I � -ln � w A,F w A,I × w A,I T + m w A,F T + m � (2.8)
This inequality comes from the new optimal levels of leisure and consumption:

L * (k, m) = (1 -α i )(T + m w i,k ) C * (k, m) = α i (w i,k T + m)
We want to compare u(C * (F, m), L * (F, m), F ) and u(C * (I, m), L * (I, m), I), which translate into:

α i ln(α i )+(1 -α i ) ln(1 -α i ) + α i ln(w i,I ) + ln(T + m w i,I ) > α i ln(α i ) + (1 -α i ) ln(1 -α i ) + α i ln(w i,F ) + ln(T + m w i,F ) -µ i which yields µ A > α A ln � w A,F w A,I � + ln � T + m w A,I T + m w A,F
� and after some rearranging, (2.8). However, trivially, α A ln(

w A,F w A,I ) > α A ln( w A,F w A,I ) - ln( w A,F w A,I × w A,I T +m w A,F T +m ), because w A,F
w A,I × w A,I T +m w A,F T +m > 1, as w A,F w A,I T +mw A,F > w A,F w A,I T + mw A,I ; so (2.7) implies (2.8), and informal workers will keep working in the informal sector. Note that the same can be said of informal workers for whom pre-reform potential earnings are above the means-test, since (2.8), for these workers, becomes:

µ A > α A ln � w A,F w A,I � -ln � w A,F w A,I × w A,I T + m w A,F T � (2.8bis)
which is also trivially implied by (2.7).

Labor supply will drop for those workers, as a consequence of the income effect, as leisure goes from

L(0) = (1 -α A )T to L(m) = (1 -α A )(T + m w A,I )
thus the labor supply drops from:

l(0) = T -L(0) = α A T to l(m) = T -L(m) = α A T -(1 -α A ) m w A,I
So that:

∆ α l(m) = l(m) -l(0) l(0) = - 1 -α A α A m w A,I < 0
(B) Formal workers with w B,F l * (F ) < W These workers satisfy (2.6)

µ B < α B ln � w B,F w B,I �
And face a new inequality

µ B < α B ln � w B,F w B,I � -ln � w B,F w B,I × w B,I T + m w B,F T + m � (2.9)
This comes straightforwardly from the fact that, in the absence of the reform, workers locate on a segment of the (formal-labor related) budget constraint that will be shifted by the additional non-labor income. The relevant part of the formal-wage budget constraint (and the whole informal-wage budget constraint) thus both include a shift by m.

Labor supply will drop for those workers, as a consequence of a income effect, as leisure goes from

L(0) = (1 -α B )T to L k (m) = (1 -α B )(T + m w B,k )
if condition (2.9) still holds, then these workers will stay in the formal sector and work less hours:

∆ m (l) = - 1 -α B α B m w B,F < 0 (2.10)
if condition (2.9) does not hold, then these workers will switch to the informal sector and still work less hours:

∆ m (l) = - 1 -α B α B m w B,I < 0 (2.11)
Note that whether the drop in labor supply for "stayers" (2.10) is larger than that of "switchers" (2.11) is not unequivocal. Indeed, stayers might differ from switchers because of a larger expected wage ratios,

w B,F
w B,I , because of a larger α B , or because of a smaller µ B . All other things equal, if switching to informal work as a result of the pension m is due to a smaller α B , then the drop in the labor supply of switchers is larger than that of stayers. On the contrary, if it is due to a smaller w B,F w B,I , the drop in labor supply of switchers is likely to be smaller than that of stayers.

(C) Formal workers with w C,F l * (F ) > W These workers also satisfy (2.6)

µ C < α C ln � w C,F w C,I �
However, the effect of the pension reform is somewhat more intricate. Indeed the means-tested non-labor income induces a discontinuity in the formal-wage budget constraint. Whether this makes workers prefer to locate under the means-test is determined by whether this discontinuity is large with regards to the utility of the pre-reform optimum, and whether the means-test is close to the pre-reform optimal formal labor supply. We call l W the labor supply that satisfies:

w i,F l W = W . If u( W + m, T -l W , F ) > u * 0 (F )
, where u * 0 (F ) is the pre-reform formal-sector optimum, then workers, conditional on choosing to work in the formal sector, will prefer to decrease their labor supply to meet the means-test. Note that this does does not necessarily imply that the new formal optimum is a corner solution at the meanstest: for some workers whose pre-reform optimum was near the means-test, the income-effect of m is likely to kick in and push them towards a new interior solution.

In the case the new formal optimum is a corner solution at the means-test, whether workers prefer this new formal-sector optimum to switching to informal sector is determined by:

u * m (I) ≶ u( W + m, T -l W , F ) (2.12)
where, similarly to above, u * m (I) refers to the informal-sector optimum with the pension. On the case the new formal-sector optimum is an interior solution, this inequality becomes:

µ C ≶ α C ln � w C,F w C,I � -ln � w C,F w C,I × w C,I T + m w C,F T + m � (2.13)
Note that (2.13) is the same as (2.9). If on the other hand, conditional on choosing to work in the formal sector, workers keep with their pre-reform optimum, this means that the size of the pension is not enough to outweigh the amount of formal wage they would have to give up on to meet the means-test. In this case the formal sector optimum is left unchanged by the introduction of the pension. This is more likely when formal hourly wages are large. The condition to switch to the informal sector is given by: the number of years since the start of the current job. Self-employed is the share of the people running their own business. Firm size is a categorical variable for the number of co-workers.

µ C ≷ α C ln � w C,F w
Source: Authors' calculations on PALMS 3.2 (2002PALMS 3.2 ( -2007)). Source: Authors' calculations on PALMS 3.2 (2002PALMS 3.2 ( -2007PALMS 3.2 ( and 2010PALMS 3.2 ( -2015)). Source: Authors' calculations on PALMS 3.2 (2002PALMS 3.2 ( -2007PALMS 3.2 ( and 2010PALMS 3.2 ( -2015)). Source: Authors' calculations on PALMS 3.2 (2002PALMS 3.2 ( -2007PALMS 3.2 ( and 2010PALMS 3.2 ( -2015)). Source: Authors' calculations on PALMS 3.2 (2002PALMS 3.2 ( -2007PALMS 3.2 ( and 2010PALMS 3.2 ( -2015)). Note: These graphs plot the coefficients of Equation 2.1 on the probability to be formally employed within each quartile of the informal hourly wage distribution before and after the OAP reform.

Quartiles are defined according the distribution of informal hourly wages for the 55-59 years old population in each period. The x-axis is labelled with the bounds of the quartiles for the monthly salary equivalent at a 43 hours working week. The sample includes Black and Coloured women only.

Source: Authors' calculations on PALMS 3.2 (2002PALMS 3.2 ( -2007PALMS 3.2 ( and 2010PALMS 3.2 ( -2015)). Source: Authors' calculations on PALMS 3.2 (2002PALMS 3.2 ( -2007PALMS 3.2 ( and 2010PALMS 3.2 ( -2015)). Source: Authors' calculations on PALMS 3.2 (2002PALMS 3.2 ( -2007PALMS 3.2 ( and 2010PALMS 3.2 ( -2015)). Source: Authors' calculations on PALMS 3.2 (2002PALMS 3.2 ( -2017)).

Large Means-Tested Pensions with Informal Labor Markets difference-in-discontinuities -lies in the fact that (�) might not be verified: there could be a discontinuity at the threshold in the absence of the pension reform, i.e.

in the absence of a change in D. However, at the price of making two assumptions, we can leverage the data from the pre-reform period, which yields information on this discontinuity. These assumptions (��) and (���) write down as:

(��) E[Y 10 |T = 1, D = 0, X = 60] -lim x→60 -E[Y 10 |T = 1, D = 0, X = x] = E[Y 00 |T = 0, D = 0, X = 60] -lim x→60 -E[Y 00 |T = 0, D = 0, X = x] (���) lim x→60 -E[Y 11 -Y 10 |T = 1, X = x] = lim x→60 + E[Y 11 -Y 10 |T = 1, X = x]
(provided those limits exist.) Assumption (��) means that the discontinuity in the absence of treatment is constant over time: absent the reform, the same discontinuity, if any, would be observed in T = 1 as is observed in T = 0; it is similar to the continuity assumption (�). Assumption (���) expresses the fact that the discontinuity for the treated and control group are equal.

And this leads to the now well-known difference-in-discontinuities strategy:

τ DiDisc =E[Y |T = 1, D = 1, X = 60] -lim x→60 -E[Y |T = 1, D = 0, X = x] -(E[Y |T = 0, D = 0, X = 60] -lim x→60 -E[Y |T = 0, D = 0, X = x])
as documented for instance in [START_REF] Grembi | Do Fiscal Rules Matter?[END_REF].

Suppose now that we wish to distinguish the employment response by the levels of hourly wages:

τ w = E[Y 11 -Y 10 |T = 1, X = 60, w = w]
. We do not observe potential wages but only realized ones. In other words, rather than observing the conditional labor supply at certain levels of hourly wages, we observe the joint distribution of wages and labor supply. Naively running the difference-in-discontinuities on a dummy equal to 1 if an individual is employed at the level of wage w and 0 otherwise is equivalent to taking the following estimand for τ w: As can be seen in the expression above, the issue with this strategy is that the naive estimand is not equal to τ w, nor to τ wP (w T =1 = w), which is arguably an object of interest, as it is equal to τ w, weighted by the density of the corresponding level of wages19 . The pre-reform discontinuity enters as a second term, weighted by the difference in densities of the level of wage of interest. Unless we can make the very strong assumption that the distribution of wages is stable over time, this term is different from 0 and biases our estimates of τ w.

τ naive = E[Y, w T =1 = w|T = 1, D = 1, X = 60] -lim x→60 -E[Y, w T =1 = w|T = 1, D = 0, X = x] - � E[Y, w T =0 = w|T = 0, D = 0, X = 60] -lim x→60 -E[Y, w T =0 = w|T = 0, D = 0, X = x] � = P (Y, w T =1 = w|T = 1, D = 1, X = 60) -lim x→60 -P (Y, w T =1 = w|T = 1, D = 0, X = x) -P (Y, w T =0 = w|T = 0, D = 0, X = 60) + lim x→60 -P (Y, w T =0 = w|T = 0, D = 0, X = x) = P (Y |T = 1, X = 60, w T =1 = w)P (w T =1 = w|T = 1, X = 60) -lim x→60 -P (Y |T = 1, X = x, w T =1 = w)P (w T =1 = w|T = 1, X = x) -P (Y |T = 0, X =
However, the existence of a pre-reform discontinuity is something we can check.

Therefore, under the assumption the discontinuity in the absence of the reform, if any, is time-invariant, whether the difference-in-discontinuity strategy is required to estimate τ can be decided upon by looking at the pre-reform discontinuity. Provided that we can assume that there is no discontinuity in the absence of the reform, the regression discontinuity framework is enough to estimate the aggregate treatment effect. This is helpful, as using the joint distribution of employment and wages in a simpler regression discontinuity framework is less problematic than in the difference-in-discontinuity framework. Indeed, it writes down as:

τ RDD, w = E[Y, w T =1 = w|T = 1, X = 60] -lim x→60 -E[Y, w T =1 = w|T = 1, X = x] = P (Y, w T =1 = w|T = 1, X = 60) -lim x→60 -P (Y, w T =1 = w|T = 1, X = x) = P (Y |T = 1, X = 60, w T =1 = w)P (w T =1 = w|T = 1, X = 60) -lim x→60 -P (Y |T = 1, X = x, w T =1 = w)P (w T =1 = w|T = 1, X = x)
Assuming that lim x→60 -P (w T =1 = w|T = 1, X = x) = P (w T =1 = w|T = 1, X = 60), then:

= � P (Y |T = 1, X = 60, w T =1 = w) -lim x→60 -P (Y |T = 1, X = x, w T =1 = w) � × P (w T =1 = w|T = 1, X = 60) = τ w × P (w T =1 = w|T = 1, X = 60)
Continuity of the potential wage distribution at the threshold achieves identification of τ w.

Finally, note that in the case the difference-in-discontinuity is warranted (that is, when a pre-period discontinuity is observed), then both the RDD and naive difference-in-discontinuity strategies on the joint distribution of labor supply and wages will fail to yield a consistent estimate of the conditional treatment effect.

Introduction

The labor market of developing countries is often characterized by a high rate of self-employment, usually concentrated in the informal sector. With a few exceptions, this stylized fact is true virtually all over the developing world. However, South Africa is an outlier. With very low overall employment and high unemployment, its level of self-employment is among the lowest in developing countries (and also lower than many developed economies). To put this self-employment "gap" in perspective, South

Africa's has one-fourth of the self-employment rate of Brazil, despite similar GDP per capita, and one-third of the self-employment rate of Namibia, its neighboring country (see Figures barriers might be binding. This paper also explores, descriptively, other potential causes for the low self-employment of South Africa, and puts forward an explanation that is consistent with the available evidence.

The literature on the labor market of developing countries has pointed out that self-employment can be a desirable occupational status, often more so than informal or even formal wage employment (see [START_REF] Maloney | Does Informality Imply Segmentation in Urban Labor Markets? Evidence from Sectoral Transitions in Mexico[END_REF], [START_REF] Maloney | Informality Revisited[END_REF], [START_REF] Falco | Heterogeneity in Subjective Well-Being: An Application to Occupational Allocation in Africa[END_REF], [START_REF] Falco | Entrepreneurship versus Joblessness: Explaining the Rise in Self-employment[END_REF]). Another important finding is that liquidity constraints can be a barrier to entry into bushiness ownership in developing countries. [START_REF] Blattman | Generating Skilled Self-Employment in Developing Countries: Experimental Evidence from Uganda[END_REF] identify this channel through a policy experiment in Uganda, where they show that recipients of an "unsupervised" grant are more likely to start their own businesses. [START_REF] Bianchi | Liquidity, Risk, and Occupational Choices[END_REF] show that a conditional cash transfer targeted at children in Mexico increases parents'

self-employment rate. These findings directly contrast with the results of this paper, where I show that this mechanism is not at play: South Africans do not respond to large, unconditional cash transfer by increasing entry into entrepreneurship.

Several explanations have been put forward to justify this well-known peculiarity of the South African labor market. South Africans perceive widespread crime as one of the main barriers to owning and running a business [START_REF] Cichello | Perceived Barriers to Entry into Self-Employment in Khayelitsha, South Africa: Crime, Risk, and Start-Up Capital Dominate Profit Concerns[END_REF]).

However, recent empirical evidence has shown that this explanation has little support in the data: the correlation between crime rates and self-employment, both across time and geographical areas, is weak and small [START_REF] Grabrucker | Does Crime Deter South Africans from Self-Employment?[END_REF]).

Similarly, many have pointed to strict labor market regulation as a reason that prevents South Africans from entering self-employment activities. [START_REF] Magruder | High Unemployment Yet Few Small Firms: the Role of Centralized Bargaining in South Africa[END_REF] has investigated the role played by unions and centralized bargaining in discouraging (self-) employment. While he does find that strictly enforced collective bargaining rules decrease employment and self-employment, these effects remain marginal.

Moreover, the self-employment rate among immigrants and Whites is three to four times higher than among Black or Coloured individuals born in South Africa (Figure 3.A.3), and this is true within and across municipalities, which is where these regulations are usually set. These facts suggest that overall labor market conditions are unlikely to be the only explanation behind the lack of self-employment activities among native South Africans.

By looking at South Africa's two largest cash transfer programs, the Old Age Pension (OAP) and the Child Support Grant (CSG), I am able to rule out even small positive effects on the probability to be self-employed. Because both programs have age-thresholds that have been significantly reformed over time, one can exploit exogenous variation to causally identify their labor market effects on recipients. There is no self-employment response for direct recipients (mothers or elderly), but also for other working-age adults of the same household. Cash transfers relax liquidity constraints, but may also have an "insurance" effect, and therefore increase willingness to bear risk, and enter self-employment [START_REF] Baird | The Effects of Cash Transfers on Adult Labor market Outcomes[END_REF]). [START_REF] Falco | Does Risk Matter for Occupational Choices? Experimental Evidence from an African Labour Market[END_REF] shows that more risk averse workers tend to queue for formal jobs instead of entering self-employment, because of the higher earnings volatility when owning a business. Similarly, [START_REF] Bianchi | Liquidity, Risk, and Occupational Choices[END_REF] underline the role of this mechanism by showing that entry into self-employment responds more to future cash transfers than to current ones. In the South African case, I am not able to cleanly disentangle the role of liquidity constraints from the effect through risk aversion. However, I

show that both anticipated (OAP) and unanticipated (CSG) cash transfers do not result in higher self-employment. Intuitively, while an anticipated income shock should only capture the role of liquidity constraints, an unanticipated shock would combine both the effect on risk and liquidity. Hence, showing that neither seems to matter points against both channels as explanations for the low self-employment rate.

Instead, I present evidence, discussed in Section 6, that the lack of self-employment in South Africa possibly has historical roots. Informal self-employment was strongly repressed during the Apartheid years; through the mechanisms of inter-generational transmission, this is likely to still have consequences today. Descriptive evidence on migrants to South Africa seems to support this hypothesis. I also discuss avenues for future research on this phenomenon, and potential policy implications.

The paper is organized as follows: Section 2 describes South Africa's two main cash transfer programs. Section 3 summarizes the channels suggested in the literature through which a cash transfer should impact self-employment. Section 4 provides descriptive statistics about self-employment in South Africa, and the data sources at hand. Section 5 presents the empirical analysis and the results. Section 6 discusses other potential explanations for the lack of self-employment in South Africa, and policy implications, while Section 7 concludes.

South Africa's Cash Transfer Programs

Since the end of the Apartheid period, South Africa has put in place (or significantly extended) two main cash transfer programs, the Child Support Grant (CSG) and the Old Age Pension (OAP). Together, these programs account for a significant portion of government spending [START_REF] Gomersall | The Performance of the Child Support Grant: Review and Research Priorities[END_REF]). The CSG provides a small cash amount, around 50$ 2010 PPP, per month for each child below a certain age threshold. This cash transfer is very extended, and covers a large portion of the population. In 2010, around 10 million children were beneficiaries of the CSG every month, and 5 million mothers received it (in a country, back then, of roughly 50 million people). 1 Although it is formally targeted to the "primary caregiver of the child", in practice the transfer is always paid to women, and a large majority of the time to the biological mother. Both programs are unconditional, meaning there are no conditionalities attached to these grants, and means-tested. However, the means-test has not always been consistently applied across programs. In particular for the CSG, there is no evidence that it was enforced during the first decade of the program (at least until the reform in 2008, when the means-test was set at 10 times the grant). How strict the application of the means-test has been for the OAP is also unclear, given that it is the applicant who has to provide proof of income. It has also been dramatically increased in more recent years, so that most of the population is eligible in any case. Both programs are also age-tested, and this criteria has always been binding and strictly applied.

The age-thresholds, for both the CSG and the OAP, have been changed over time.

This is what provides the sources of variation that are used for identification.

Conceptual Framework

To better understand the results of this paper, it is useful to think in terms of the model developed by [START_REF] Bianchi | Liquidity, Risk, and Occupational Choices[END_REF], who show formally how cash transfers impact entry into self-employment. I do not report here the model in full, but just its main conclusions, which are intuitive and helpful to think about the (absence of) results in the South African case.

Bianchi and Bobba (2013) consider agents in a two-period model who are heterogeneous in terms of their initial wealth and risk aversion. Individuals make occupational choice decision (wage-vs. self-employment) and savings decisions, with borrowing constraints. Self-employment has higher returns on average, but also higher variance; it is therefore the more profitable but also riskier occupation.

Moreover, self-employment requires an initial capital investment, individuals who cannot make this investment end up either in wage jobs or unemployed.

In their model, the goal is to formalize how cash transfers impact entry into self-employment. There are two channels at play. On the one hand, cash transfers relax a liquidity constraint: individuals who were unable to make the fixed investment necessary to enter self-employment are more able to do so in the presence of a cash transfer. On the other hand, cash transfers also relax an insurance constraint: risk averse individuals are more likely to enter self-employment because cash transfers provide a guaranteed stream of resources. In order to disentangle these two channels, they show that individuals should respond more to the size of current cash transfers if liquidity constraints bind, or to the size of future cash transfers if insurance constraints bind, because those transfers insure against future earnings volatility. They use variation in the timing of a cash transfer in Mexico (Progresa) to disentangle these two channels, and show that future transfers have a larger effect on the probability to be self-employed.

In South Africa, there is no variation in the timing of the transfers that I can exploit in a similar way. However, I argue that differences in the effects of the CSG and OAP shocks could be interpreted along the same lines. The CSG shock for the relevant cohorts is unexpected, as mothers whose child was not eligible to the transfer suddenly become so as a consequence of the age eligibility reforms. In the light of the aforementioned framework, it is a shock to both current and future transfers. As we are comparing mothers who are never eligible with mothers who are eligible for a few years, any positive effect on self-employment might go through both the liquidity and insurance channels. On the other hand, the OAP shock is fully expected. People know about the age eligibility threshold, hence what is captured is simply the fact of becoming eligible, net of previous adjustments. People just before the threshold know that they will soon receive the pension, but do not yet receive the transfer.

Therefore, by comparing people just before and just after the threshold one should capture only the liquidity effect, as individuals/households who are about to become eligible have already internalized the insurance effect coming from the public pension.

The difficulty in this setting is that, contrary to [START_REF] Bianchi | Liquidity, Risk, and Occupational Choices[END_REF], we are looking at two separate transfers, which are different across many dimensions (size, targeting within the household, labelling). This significantly complicates the interpretation. If the two programs were identical, other than one was expected and one was not, it would be possible to net out the effect of relaxing only the insurance constraint. However, I argue the lack of a positive effect on self-employment for both transfers suggests that neither liquidity nor insurance seem to be the binding constraint preventing entry into self-employment in South Africa.

Data and Descriptive Statistics

For the analysis of the impacts of the two cash transfers programs on self-employment, I make use of Census to present evidence about the direct and intergenerational impacts of Apartheid on self-employment. Therefore, Census data is suited for the majority of this analysis. To further document employment trends, I make use of the Post-Apartheid Labour Market Series (PALMS), an appended cross-section of all South Africa's main labor force surveys since 1994 to 2015, consistently coded and weighted.

Measurement of Self-Employment

Self-employment is generally measured by asking individuals whether they ran any kind of business in the past week. Alternatively, other surveys ask individuals directly about who is their employer (and an option is to work for one's self), or to identify directly as self-employed. In Table 3.A.1 in the Appendix, I show the level of selfemployment according to the various survey sources in South Africa. All give very low levels of self-employment in any year, regardless of the definition of self-employment, or whether the surveys are run by the national statistical agency. This suggests that the lack of self-employment in South Africa is unlikely to be a measurement issue. Even in years when informal self-employment was significantly over-measured (2001 of the PALMS)5 , the rate was never above 10%. Self-employment is almost entirely concentrated in the informal sector, but, in South Africa, it makes up less than half of it, as informal wage-employment is larger. Those in the formal sector, i.e. registered businesses, are an even smaller portion of employment, around 1% of the working-age population.

Empirical Analysis

This paper exploits exogenous variation in eligibility to the CSG and the OAP to test whether recipients (or members of recipients' households) are more likely to enter self-employment. The identification strategies are different for the two programs, but similarly exploit variation created by changes in age conditions for eligibility.

Conceptually, the important difference in the liquidity shocks introduced by these two programs is whether they are expected or not. While the age threshold for the OAP is "expected", meaning that households know exactly when a family member becomes eligible for the pension, the shock used to identify the effect of the CSG was unexpected. Therefore, while we could interpret the effect of the OAP as a pure liquidity effect at the household level, the effect of CSG will combine both the liquidity effect, and the "insurance" effect, meaning that it might also increase willingness to bear risk.

Effect of the Child Support Grant on Self-Employment

As in the first chapter of this thesis, to identify the effect on the Child Support Grant, I exploit the large variation in terms of eligibility across birth years of the child.

Mothers whose youngest child was born before 1993 are virtually never eligible to the grant, while those whose youngest child is born in 1993 or after are eligible for at least one year. Children born after 1993 are "treated" for increasingly longer periods of time; those born in 1994, for example, are eligible on average for around 3 years between 2001 and 2011. grant:

SE i = f (c i -1993) + �{c i � 1993} × f (c i -1993) + β CSG �{c i � 1993} + ε i (3.1)
where SE i is a binary variable equal to one if individual i is self-employed; f is a function of the cohort of the youngest child centered at the cut-off point. In order to maximize power, and be able to observe an effect on self-employment, the preferred window is the one of cohorts going from 1987 to 1998, which is the largest window where a linear function is preferable to a quadratic according to an Akaike Information Criterion (AIC). �{c i � 1993} is a binary variable equal to 1 when: 1) the mother's youngest child is born in 1993 or later, when restricting the specification to mothers only; 2) the latest birth in the household occurred in 1993 or later, when running the regression on other working-age household members.

since the early 2000s. It is possible that one year of grant is not a large enough shock to induce a selfemployment response. The coefficients of the Diff-in-Diff estimation of Equation 3.2 are shown in Figure 3.A.5. If we exploit the large variation in duration across treated cohorts to test whether longer duration has a positive effect on self employment, the results are virtually identical. Households with children treated for several years do not increase their self-employment any more than household that are never eligible to the grant. For example, households with a youngest child born in 19956 , who is eligible for the CSG uninterruptedly between 2004 and 2011, have the same evolution of the self-employment rate as households who never receive the grant.

Effect of the Old Age Pension on Self-Employment

To identify the effect of the OAP, I exploit the 2008-2010 reform in the age-eligibility for men, in the same way as outlined in the second chapter of this dissertation.

Starting from 2008, the age threshold for men was lowered from 65 to 60 in 2010, while it was left unchanged for women. The effect of this reform can be clearly observed in Figure 3.5: the share of men between 60 and 64 receiving the pension increases dramatically. The reform allows to estimate the effect of the OAP without mixing in the effect of other, private pension schemes with the same age thresholds.

In order to capture the effect of becoming eligible on self-employment, I run the following specification on data from the 2001 (before) and 2011 (after) census:

SE i = f (AgeOM i ) + f (AgeOM i ) × �{AgeOM i � 60} + β OAP �{AgeOM i � 60} + ε i (3.3)
where SE i is again a dummy for self-employment. The forcing variable, AgeOM , is a linear function of the age of the oldest man within the household. As the oldest man is virtually always older than the oldest woman, this is what determines whether the household receives at least one OAP. Therefore, the coefficient of interest is the β OAP estimated in 2011, after the reform. One could alternatively run a specification that takes into account the change in threshold, and estimate directly the difference in the discontinuities before and after. As this is significantly more demanding in terms of statistical power, to increase the chances of being able to observe an effect on self-employment, I recur here to a simpler RDD specification, with the age of the oldest man as a discrete running variable, and qualitatively compare the coefficients in the different years.

Alternatively, I also compare the relative difference, before and after, of unaffected and affected age groups. This is the estimation that allows the maximum statistical power, while relying still on a reasonable identification assumption: that the evolution of affected and unaffected age groups would have been the same in the absence of the reform. Formally, I estimate the following specification for people aged between 55 and 65 (or for those other adults in households where the oldest male is between 55 and 65):

SE it = α 0 + α 1 �{AgeOM i � 60} + α 2 �{year = 2011} + β OAP �{AgeOM i � 60} × �{year = 2011} + ε it (3.4)
where �{AgeOM i � 60} indicates the treated age groups, and �{year = 2011} the only year after the reform in Census data. The coefficient of the interaction term, β OAP , captures the relative evolution in the self-employment rate of the treated group.

The lack of an effect of the OAP on self-employment is clear in Figure 3.6. After the reform, self-employment among elderly individuals drops for those who become eligible to the pension, but, at the same time, it does not increase for those other adult members in pension-eligible households. The findings indicate that: similarly to evidence on the CSG, 1) the relaxation of liquidity constraints at the household level that comes with the pension does not lead to higher self-employment; additionally, 2)

those jobs "left" by older South Africans who receive the pension are not necessarily picked-up by younger household individuals. However, I can only reject effects slightly smaller than a one-to-one substitution within the household of these self-employment jobs. It is still possible that there are smaller increases that I do not have sufficient statistical power to identify. Results in regression form are in Table 3.2 for the RD estimation, and in Table 3.3 for the DiD. Both specifications show that treated elderly men decrease their probability of being self-employed by between 0.7 and 1 pp., which is around one fourth of the total employment response of the elderly presented in Chapter 2. Again, we observe no evidence of a statistically significant increase in self-employment for other adult members within the household.

I present several robustness checks for the results both on the CSG and OAP in the Appendix, in the same spirit as those of Chapter 1 and 2. For the RD estimations, I check the robustness to different bandwidths, while for the DiD I check whether changing the composition of the treatment group affects the results. The point estimates are always small and centered around zero. For either program, there is no specification that shows a positive impact on self-employed. The only estimated effect is the decrease in the self-employment rate among pension-eligible elderly, which is robust across specifications and bandwidths.

Further Evidence

The results of this paper leave the question open of why South Africans do not enter self-employment: what barriers are there (if any) that impede the start of small informal businesses? This remains a key question to solve (some of) the issues of the South African labor market, namely its persistently high unemployment rate.

This paper provides evidence that liquidity constraints are unlikely to be the main barrier to self-employment in South Africa. Large cash transfers to the household do not increase self-employment. Importantly, they also do not significantly decrease it, implying that self-employment in South Africa is not simply absorbed by the Welfare state. Indeed, we observe that households/individuals who do not receive any of these cash transfers still report very low levels of entrepreneurial activities.

To recapitulate, we can list three many facts about self-employment in South Africa, which help to frame the following discussion: 1) First, the absence of selfemployment activities is confined to the native Black and Coloured population (which makes up around 80% of the total population of South Africa, hence the low rate overall). Other groups of the population have much higher levels of self-employment. This is generally true for the White population, but also for migrants from other African countries. In general, this shows that there are individuals for whom it is possible to be self-employed in the labor market of South Africa. While the high-end entrepreneurship among the White population might be of a completely different nature, the self-employment that we observe among non-native South Africans is often the kind of low-earning, informal self-employment prevalent in several developing countries. Therefore, any convincing explanation for this phenomenon has to reconcile why migrants from other African countries are able to enter self-employment activities, and native South Africans are not.

2) The lack of self-employment among Black and Coloured is a nation-wide phenomenon. The rate of entrepreneurial activities is low across provinces and municipalities (Figure 3.A.9), suggesting that local labor market conditions or regulations are not a satisfactory explanation. 3) Lastly, selfemployment is low regardless of the level of (formal) human capital. Indeed, we observe that the probability of having a business is a decreasing function of years of education (Figure 3.A.8). More precisely, the probability of owning a business is increasing in absolute terms, but less than wage-employment, so that the share of self-employment actually decreases with education. This is the opposite of what we observe for the White or for the migrant population, for whom the relative probability of being self-employed increases in the number of years of schooling.

Apartheid and Self-Employment

The fact that self-employment is not increasing in physical or human capital is puzzling, and suggests that barriers to entry into self-employment are of a different nature. It is natural to look at South Africa's troubled political and economic history for an explanation. Indeed, Apartheid potentially provides an explanation that is both nation-wide, yet confined to the native Black and Coloured population, to whom restrictions on political and economic activities were applied. Indeed, self-employment, both formal and informal, was a largely inaccessible occupational choice for Africans during the Apartheid years [START_REF] Rogerson | Johannesburg's Informal Sector: Historical Continuity and Change[END_REF], [START_REF] Rogerson | The Absorptive Capacity of the Informal Sector in the South African City[END_REF]). [START_REF] Rogerson | Emerging from Apartheid's Shadow: South Africa's Informal Economy[END_REF] states that "policymakers either largely neglected the SMME economy or, in the case of black-owned informal enterprise, actively discouraged the SMME economy through negative or repressive measures. Historically, under apartheid, the minority white-run government, at both national and local levels, opposed the informal economy, especially the black informal entrepreneur." The figure of the informal black entrepreneur was therefore largely absent from the South African labor market during the Apartheid period. Moreover, the many legal restrictions to the general freedoms of the black population also made formal business ownership extremely complicated [START_REF] Fauvelle | Histoire de l'Afrique du Sud[END_REF]). For these reasons, the hypothesis has been put forward that the lack of self-employment among the Black and Coloured population today is a long-term consequence of this past repression. As stated by [START_REF] Kingdon | Unemployment in South Africa, 1995-2003: Causes, Problems and Policies[END_REF]:

"The lack of African self-employment is, to some extent, a legacy of (2015)). Children of entrepreneurs are systematically more likely to be self-employed themselves [START_REF] Lindquist | Why do Entrepreneurial Parents Have Entrepreneurial Children?[END_REF]).7 Moreover, there is evidence that inheritance of the business or wealth effects are not the most important factors in explaining this correlation [START_REF] Parker | The Economics of Entrepreneurship[END_REF]), and that post-birth transmission factors are twice as important as biological ones [START_REF] Lindquist | Why do Entrepreneurial Parents Have Entrepreneurial Children?[END_REF]). [START_REF] Sørensen | Closure and Exposure: Mechanisms in the Intergenerational Transmission of Self-Employment[END_REF] identifies three channels in the intergenerational transmission of entrepreneurship: an 1) aspirations/awareness (role modelling) channel, whereby children of entrepreneurs are more likely to consider this form of employment than those who have not been exposed to an entrepreneurial role model. Moreover, higher 2) physical or social capital, as those with entrepreneurial parents have higher resources necessary to start a business, or directly inherit the business itself. This channel does not only include the direct transmission of wealth, but also those networks that might facilitate entry and/or increase returns to self-employment.

Lastly, 3) entrepreneurial skills, as children of entrepreneurs might posses skills (human capital) that make them more successful in running a business, which in turn may also incentivize entry at the extensive margin. [START_REF] Sørensen | Closure and Exposure: Mechanisms in the Intergenerational Transmission of Self-Employment[END_REF] and [START_REF] Lindquist | Why do Entrepreneurial Parents Have Entrepreneurial Children?[END_REF] find evidence of all these channels at play, but both point to role modelling and awareness as the most important channel.8

These findings are limited to the context of developed countries, where entrepreneurship (self-employment) is of a different nature. The extent to which these conclusions carry through to developing countries, where much less is known about the mechanism of inter-generational transmission, is unclear. Nonetheless, if we acknowledge that these mechanisms might be at play to a certain extent in the South African context, the persistent absence of self-employment up to this day should not be surprising. The importance of a the inter-generational component in the transmission of self-employment necessarily implies a strong correlation over time.

Almost mechanically, if self-employment was pushed to zero, or to very low levels, for several generations, recovery would not occur automatically. Another way of thinking about this is in terms of the role-modelling mechanism, which has been shown to be central. Most young South Africans today did not have self-employed parents, for the simple reason that their parents could not be self-employed. It follows that this channel could not have been at play for the current generations, which lack, at the very least, this direct role-modelling channel through the parents.

Evidence on Migrants to South Africa

These two propositions, that i) the low self-employment rate is a long-term consequence of Apartheid and that ii) inter-generational transmission plays a role, can partly be examined by looking at migrants to South Africa. In fact, this is the only group that has some variation in terms of their exposure to Apartheid, defined by the timing of migration. Migrants who arrived in South Africa before the 1990s entered a labor market where informal self-employment was still actively discouraged [START_REF] Kingdon | Unemployment in South Africa, 1995-2003: Causes, Problems and Policies[END_REF]). Instead, as from the 1990s, these restrictions were Whites, through what has been called a "two-gates" policy [START_REF] Siddique | South African Migration Policy: A Critical Review[END_REF]).

Indeed, we observe that migrants who entered South Africa after the 1990s have a higher self-employment rate (Figure 3.7 and Table 3.4), and this is not explained by socio-demographic observables, including the country of migration or district of current residence. While this cannot be interpreted as more of a correlation, it matches the prediction that migrants who arrived after Apartheid (hence after the end of the self-employment restrictions) have, to this day, a higher self-employment rate. There is certainly the issue that the year of migration is a strongly endogenous variable, as individuals with a high self-employment "potential" might have decided to migrate after the restrictions were lifted. Alternatively, if instead of the year of migration one uses the year of birth, which is a strong predictor of having migrated before 1990s (Figure 3.A.10), this pattern still holds. Indeed, in Figure 3.A.12, we African natives.

Although only descriptively, this evidence reinforces the two hypotheses mentioned before about the direct effect of Apartheid on self-employment, but also of its lasting consequence on younger generations. Migrants who arrived as from the 1990s are more likely to self-employed, and so are their children.

Policy Implications

This conclusion has important policy implications, first because it suggests that there is room (and a justification) for policy action. Although the restrictions of the Apartheid years have been lifted, one implication of this argument is that the self-employment rate in South Africa is unlikely to recover on its own to a "normal" level. Without some sort of policy intervention, meant to supplement for the lack of inter-generational transmission, the level of self-employment in South Africa is likely to remain low. The trend since the end of Apartheid seems to confirm this intuition:

there is no evidence of any growth in the number of self-employed individuals in the past 20 years. This despite a doubling of GDP per capita since 1994, and the entry into the labor market of generations of young workers born and educated entirely after the end of Apartheid.

This gloomy view, while unfortunately consistent with the stagnation in selfemployment, contrasts with the optimistic predictions at the turn of the century, which forecast upcoming growth of the informal sector and, in particular, selfemployment. Indeed, it was widely believed that the informal sector would flourish as the anti-micro business environment of Apartheid was wiped away: This did not happen. The only outcome that dramatically increased during the period, alongside participation in the labor market, is unemployment [START_REF] Banerjee | Why has Unemployment Risen in the New South Africa?[END_REF]). Despite the formal policy shift towards promoting and encouraging Black micro-entrepreneurship, self-employment has remained low, and the informal sector small. The fundamental flaw in this strategy was the belief that switching to a pro (micro-) business strategy would be enough to undo the damage of the Apartheid years. As argued before, the mechanism of inter-generational transmission implies that it should not necessarily be the case. The staggering lack of growth in the number of self-employed jobs over the past 20 years, which drastically contradicts this optimistic prediction, supports this intuition.

Implications for Future Research

Among the channels listed by [START_REF] Sørensen | Closure and Exposure: Mechanisms in the Intergenerational Transmission of Self-Employment[END_REF], it is challenging to identify through observational data exactly what component of inter-generational transmission might be missing for South Africans. Future research on self-employment in South Africa should try to disentangle which are (if any) the binding constraints that are likely to make a difference. The empirical evidence presented in this paper helps to rule out liquidity constraints as the only barrier to entry into self-employment, but it is very possible that these constraints are nonetheless binding; a conservative interpretation is that relaxing them is not enough to induce self-employment responses because other constraints are also binding.

Policy interventions promoting self-employment in South Africa should focus on understanding whether South Africans do not consider self-employment as an option when looking for a job, or whether they do but do not manage to enter it. This would suggest a bundled intervention that would simultaneously relax: i) the lack of awareness of self-employment as a potential occupational choice due to a lack of role models; ii) the lack of pre-existing networks that help entry into self-employment; iii) the lack of entrepreneurial skills, meaning those basic skills necessary to run a business or that increase returns to business ownership. On top of this, iv) relaxing liquidity constraints once these other constraints are lifted could enhance the (presumed) effectiveness of these interventions. Most interventions on self-employment/entrepreneurship, which are extremely common in development economics, usually focus on people who already own a business (or aspire to do so). This is not the main margin of interest in South Africa, or at least not what is specific about the South African case.

Nonetheless, the questions remains of whether self-employment promotion in South Africa is a priority, or other interventions, for example easing access to good formal jobs, are more relevant. I argue that the results of this paper provide at least some evidence that it should be a prime concern. If one agrees with the premise that the lack of self-employment in South Africa has historical roots, this also implies that the absence of entry into self-employment is likely to be largely involuntary.

Another main motivation is that the lack of self-employment has not translated into higher wage-employment, but into higher unemployment. South Africa's level of wage employment is not higher than that of countries at similar levels of GDP per capita. Therefore, it is unlikely that a significant component of growth in the employment rate, which remains unusually low, would come from wage employment, which is already at "normal" levels (see Figure 3.A.2 for comparison of the wageemployment rate across countries). With this in mind, if the alternative is the high unemployment rate that has plagued South Africa for the past 20 years, with the associated negative repercussions, nudging people towards self-employment (as an alternative to unemployment) could potentially be positive for welfare.

Conclusion

This paper shows that large, positive income shocks, through unconditional cash transfers, do not increase self-employment in South Africa. Through the use of large Census data, I can rule out even small increases in self-employment among cash transfer recipients. This contradicts a common finding in the empirical literature in development economics, and the theoretical prediction that cash transfers should promote entry into self-employment by relaxing liquidity constraints and increasing willingness to bear risk. Consistently with this micro-level evidence, self-employment in South Africa has stagnated since the end of Apartheid, with no visible growth in the number of self-employed individuals.

This finding suggests that other barriers are likely to prevent entry into selfemployment in South Africa. A look at the country's troubled political and economic past shows that Black and Coloured South Africans were prevented from being self-employed for several generations. Given the importance of inter-generational transmission of self-employment, this may explain the stagnation of self-employment in the post-Apartheid era. This suggests that the self-employment rate would not recover to normal levels without some sort of policy intervention. Exactly which one remains to be tested. 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008

  and the students (M1 PPD 2018 & 2019) have just been impressive. During my PhD, I benefitted from a year-long stay at the University of Cape Town, thanks to the financing of the "Policy Design and Evaluation Research in Developing Countries" (PODER), funded under the Marie Curie Actions of the EU. I would like to thank Ingrid Woolard for making this stay possible. And my Cape Town friends: Tania, Sara, Peng, Tim and Janina among others. Part of the work on which this thesis is based started with an internship at the OECD, for that I would like to thank Paolo Falco, with whom I have also exchanged greatly about my work throughout the years.

  Cette thèse regroupe trois études portant sur les réponses des travailleurs aux transferts monétaires en Afrique du Sud. Elle a pour but d'en tirer des leçons générales sur le fonctionnement des marchés du travail dans les pays à revenu intermédiaire. À ce stade de développement d'une économie, les marchés du travail sont particulièrement complexes, car ils combinent de multiples secteurs où des emplois formels et informels coexistent. Cette segmentation impose de réfléchir à pourquoi et à comment les travailleurs choisissent de se porter vers les différents secteurs. De même, la formulation et l'évaluation des politiques publiques doivent tenir compte de la présence d'un secteur informel non réglementé et de la manière dont l'existence de celui-ci pourrait modifier les incitations des agents lorsqu'ils sont visés par des programmes sociaux.. La pertinence de cette préoccupation, et la question plus générale des politiques publiques qui peuvent conduire à des améliorations du bien-être des travailleurs, dépendent fortement de la nature de ce secteur "parallèle" du marché du travail. Les programmes de transferts monétaires exceptionnels de l'Afrique du Sud offrent des cadres empiriques d'"expériences naturelles" propices pour tester le rôle des contraintes et des incitations dans la répartition des travailleurs par secteur. En les analysant, je tire plusieurs conclusions importantes. Tout d'abord, il faut expliquer pourquoi cette thèse se concentre sur l'Afrique du Sud, et dans quelle mesure les résultats qu'elle présente sont spécifiques à ce pays. Le marché du travail sud-africain est différent de celui d'un pays à revenu intermédiaire "typique". Contrairement à ce que l'on observe habituellement dans des pays ayant un niveau de développement économique similaire, il est caractérisé par un taux de chômage constamment élevé, un taux d'emploi symétriquement bas et un secteur informel relativement petit. Ces problèmes sont bien connus et documentés depuis la fin de l'apartheid, mais il est difficile de trouver dans la littérature des explications de ces anomalies. À part quelques exceptions (Kingdon and Knight (2004), Banerjee et al. (2008)), la plupart des études économiques sur l'Afrique du Sud ont tenté de tirer des leçons d'intérêt plus général, au lieu de traiter les questions qui sont spécifiques ou uniques à son marché du travail. Les programmes sociaux innovants du pays fournissent des contextes intéressants qui se prêtent aux méthodes économétriques d'évaluation, avec des caractéristiques qui sont difficiles à trouver dans d'autres pays à revenu intermédiaire. Paradoxalement, si cela a donné lieu à un important volume de recherches en Afrique du Sud, le contexte sud-africain en soi est beaucoup moins bien connu. Cette thèse n'est pas entièrement exempte de cette défaillance. La plupart des analyses présentées dans les chapitres suivants tentent d'utiliser le système peu commun de transferts monétaires de l'Afrique du Sud pour en tirer des leçons d'intérêt plus général et des implications politiques qui ne sont pas nécessairement spécifiques au pays. Toutefois, l'avantage de se concentrer entièrement et largement sur un pays est que cela m'a permis d'expliquer certains des dysfonctionnements spécifiques au marché du travail sud-africain, et comment ils interagissent avec les problèmes généralement présents dans d'autres pays à revenu intermédiaire. Je développe ce raisonnement principalement dans le troisième chapitre, quoique de manière plus descriptive, mais en m'appuyant sur certains des résultats des deux premiers chapitres. Dans l'ensemble, cette thèse de doctorat contribue à deux domaines de la littérature économique. Tout d'abord, elle vise à étendre l'abondante littérature sur les transferts monétaires. Ils ont reçu une attention croissante en raison de leur omniprésence dans les pays en développement (Molina Millán et al. (forthcoming), Haushofer and Shapiro (2016)). Cependant, bien que plusieurs de ces programmes aient fait l'objet d'études approfondies sous différents aspects, nous en savons encore très peu sur leurs effets sur le marché du travail des bénéficiaires adultes. Les résultats de plusieurs interventions, menées sur la base d'expériences aléatoires contrôlées, suggèrent que les effets dissuasifs sur le travail sont faibles (Banerjee et al. (2017)). En outre, il y a des raisons théoriques de croire que les transferts monétaires pourraient effectivement avoir des effets positifs sur les emplois des adultes (Baird, McKenzie and Özler (2018)). Du point de vue des politiques publiques, il est essentiel de savoir si le choc induit par un transfert monétaire peut avoir des avantages durables sur l'activité des bénéficiaires, notamment à travers le marché du travail. Cette question est étroitement liée au débat sur l'existence de contraintes de liquidité dans les pays Introduction en développement, et plus généralement aux préoccupations concernant les trappes à pauvreté, c'est-à-dire l'incapacité des agents à engager des investissements qui pourraient entraîner des bénéfices importants et durables, du fait d'une impossibilité de financer ces investissements. L'Afrique du Sud possède sans doute le système de transferts monétaires le plus étendu et le plus généreux du monde en développement. Elle est donc un objet d'étude particulièrement indiqué pour répondre à ces questions. De plus, ces transferts sont souvent inconditionnels, ce qui signifie qu'ils n'imposent aucune condition sur le comportement des bénéficiaires. En raison de ces caractéristiques uniques, les effets de ces programmes sur le marché du travail peuvent être interprétés plus facilement comme une conséquence du revenu supplémentaire qu'ils procurent, ce qui est important tant pour comprendre que pour généraliser ces résultats. Cette thèse s'inscrit également au sein d'une littérature académique qui étudie la segmentation du marché du travail et l'informalité dans les pays à revenu intermédiaire.

  première, optimiste, suppose que les travailleurs se répartissent en fonction de leur avantage comparatif au sein d'un marché du travail dual où les réglementations sont asymétriquement appliquées. De ce point de vue, le secteur informel naît du choix des travailleurs qui ont un rendement (ou une utilité) plus élevé dans le secteur non réglementé de l'économie. Au contraire, une deuxième conception, négative, de la segmentation la présente comme le résultat d'obstacles et de frictions qui empêchent une répartition optimale des travailleurs ou d'un stock limité d'emplois dans les deux secteurs. De ce point de vue, les travailleurs auraient des revenus plus élevés dans le secteur formel, mais ne parviennent pas à y entrer pour une raison ou une autre. En termes d'implications pour les politiques publiques, la distinction entre ces points de vue a des répercussions importantes sur la manière la plus efficace de lutter contre la segmentation, ou même sur la pertinence de s'en préoccuper. Par exemple, si la décision de travailler dans le secteur informel provient d'un choix individuel sans contrainte, alors les politiques qui jouent sur la marge des incitations auront un impact plus important. D'un autre côté, si l'emploi informel constitue un "emploi de dernier recours" lorsqu'il y a des contraintes pour entrer dans l'un ou l'autre secteur, alors les politiques qui assouplissent ces contraintes pourraient potentiellement améliorer le bien-être des travailleurs. Cette thèse utilise les réponses du marché du travail aux transferts monétaires pour faire la distinction entre ces deux points de vue. Dans l'esprit, il s'agit d'une approche de la question de la segmentation dite de "préférences révélées". Je soutiens que les réponses des travailleurs à ces chocs de transfert monétaire sont indirectement révélatrices des déterminants de cette segmentation. Si les contraintes de liquidité sont l'une des raisons de la segmentation du marché du travail en présence de coûts fixes pour entrer dans l'un ou l'autre secteur, alors un transfert monétaire inconditionnel aurait des effets durables sur la répartition sectorielle. Si, au contraire, seuls les gains relatifs d'un secteur par rapport à l'autre comptent, une politique qui subordonne un montant important à un critère de ressources sur les gains d'un seul secteur pourrait également induire un certain type de réaffectation. Le premier chapitre de cette thèse étudie les effets sur le marché du travail, à court et à long terme, d'un programme de transferts monétaires inconditionnels destinés aux mères, le Child Support Grant (CSG). L'analyse empirique de ce programme est particulièrement intéressante pour plusieurs raisons. D'une part, cette aide est similaire à d'autres allocations familiales observées dans la plupart des pays en développement, tant en termes de montant réel par enfant qu'en ce qui concerne la dénomination du transfert. D'autre part, le CSG est un transfert purement inconditionnel, ce qui n'est pas le cas de la plupart des programmes de subventions pour enfants. La subvention n'est subordonnée à aucune exigence particulière et sa condition de ressources n'est pas réellement appliquée en pratique. Ces caractéristiques permettent d'interpréter son effet sur l'emploi des mères comme un choc purement monétaire, mais de le formuler de la même manière que des programmes similaires dans le monde en développement. En ce qui concerne les déterminants de l'allocation des travailleurs dans un marché du travail segmenté, le CSG peut être considéré comme un "instrument" pour tester l'importance des contraintes monétaires; un transfert sans condition permet d'atténuer les contraintes de liquidités, sans pour autant affecter les gains relatifs entre secteurs. En pratique, elle permet donc de vérifier si des travailleurs légèrement plus riches du fait du transfert, mais similaires dans tous les autres domaines, se retrouvent dans des secteurs différents. En économie du travail et en économie publique, l'impact de ressources monétaires supplémentaires sur la recherche d'emploi et la qualité de l'emploi qui en découle est un sujet central. Un grand nombre d'études ont montré comment des allocations de chômage ou des indemnités de départ plus généreuses allongent la durée de la recherche d'emploi, avec des résultats empiriques contradictoires sur le fait que la recherche prolongée permet ou non l'obtention d'un meilleur emploi

  plus négative de l'informalité, où l'allocation sectorielle des travailleurs est déterminée par des contraintes exogènes. Le deuxième chapitre de cette thèse (co-écrit avec P. Dutronc-Postel) explore le rôle des incitations dans la répartition sectorielle sur un marché du travail segmenté. Dans le même esprit que le premier chapitre, il exploite un choc dérivant d'une politique publique pour identifier les réponses des travailleurs et en tirer des conclusions sur leurs préférences. Une littérature récente souligne les distorsions que les programmes sociaux pourraient introduire sur un marché du travail où il existe un secteur informel important (Azuara and Marinescu (2013), Bergolo and Cruces (2014), Gerard and Gonzaga (2016)). Si les travailleurs se répartissent librement en fonction des revenus anticipés dans chaque secteur, les politiques sociales soumises à une condition de ressources peuvent entraîner un mouvement vers le secteur informel non réglementé. Pour aborder cette question, le chapitre analyse le plus grand programme social d'Afrique du Sud, la Old Age Pension, et examine s'il existe effectivement un effet d'entraînement vers le secteur informel. Comme les revenus d'un emploi informel de facto n'entrent pas dans le critère des ressources, les individus sont implicitement incités à cumuler la pension avec les salaires cachés du travail informel. La pertinence de ce canal dépendra de la raison pour lesquelles les travailleurs se trouvent en premier lieu dans chaque secteur, de leurs préférences et de leur salaire potentiel dans le secteur informel. Étant donné que le seuil du critère des ressources se situe dans une partie de la distribution des salaires où les salaires des secteurs formel et informel coexistent, il y a lieu de croire que cette préoccupation pourrait être pertinente. Pour étudier cette question en détail, nous nous appuyons sur une réforme du système public de retraites sud-africain, qui a abaissé l'âge d'éligibilité à la retraite des hommes de 65 à 60 ans, tout en le laissant inchangé pour les femmes. Nous montrons que l'identification correcte des effets de ce programme sur le marché du travail nécessite la variation supplémentaire introduite par la réforme. Autrement, le simple examen de la discontinuité des comportements au seuil de 60 ans serait soumis au biais généré par l'existence d'autres régimes de retraite privés au même seuil, et mènerait à estimer des réponses deux fois plus importantes. Cela modifierait aussi radicalement l'interprétation qualitative dans la dimension formelle/informelle. Nos résultats montrent une forte réaction de l'emploi informel: il semble que les travailleurs quittent leur emploi informel lorsqu'ils deviennent éligibles à la pension. Contrairement à ce qu'un modèle standard d'arbitrage entre consommation et loisir prédirait, cette réponse est quantitativement similaire à tous les niveaux de la distribution des salaires informels. Cela suggère que ce ne sont pas seulement les travailleurs au niveau le plus bas des salaires qui abandonnent leur travail. Au contraire, la réponse de l'emploi formel est faible et non-significative : seuls les travailleurs formels gagnant des salaires équivalents au 1er quartile de la distribution salariale informelle quittent leur emploi, alors que ceux dont les salaires sont plus élevés ne réagissent pas. Cela signifie que les différences de réponses entre les secteurs formel et informel ne sont pas dues à des salaires plus bas dans le secteur informel. À niveau de salaire égal, les travailleurs informels quittent leur emploi, et les travailleurs formels ne le font pas. Nous interprétons cette asymétrie soit comme une indication de préférences et de caractéristiques hétérogènes parmi les travailleurs de ces deux secteurs, soit comme une différence entre ces emplois dans d'autres dimensions. Dans l'ensemble, nous n'avons trouvé aucune indication d'une substitution importante entre formel et informel, ce qui donne à penser que les préoccupations concernant les distorsions introduites par les programmes imposant une condition du revenu pourraient être peu pertinentes.

  , Magruder (2012)). Il est également peu probable qu'il s'agisse d'un problème de mesure, car le taux reste faible dans toutes les données de différentes enquêtes et à travers le temps, malgré les différentes mesures du travail indépendant et les efforts considérables de l'agence statistique sud-africaine pour obtenir un compte exact. En outre, l'absence de travail indépendant est limitée à la population noire native, alors que le taux de travail indépendant parmi les migrants d'autres pays africains est élevé. Cela montre qu'il n'est pas impossible d'être travailleur indépendant en Afrique du Sud ; la question est alors de savoir pourquoi la plupart des Sud-Africains n'envisagent pas ou ne peuvent pas exercer cette activité. En m'appuyant sur les résultats des chapitres 1 et 2, je montre que les ménages ne travaillent pas plus à leur compte lorsqu'ils reçoivent des transferts monétaires substantiels et inconditionnels. Cette observation contraste avec un résultat établi dans la littérature de l'économie du développement, qui a montré que les transferts monétaires augmentent l'entrée dans le travail indépendant (Bianchi and Bobba (2013), Blattman, Fiala and Martinez (2013)). En théorie, un transfert monétaire pourrait favoriser le travail indépendant en assouplissant à la fois les contraintes de liquidité et d'assurance, car il procure un revenu supplémentaire tout en permettant de se prémunir contre la plus grande volatilité des revenus liés à la gestion d'une entreprise (par rapport au travail salarié). Cette réaction n'est pas observée en Afrique du Sud, où les bénéficiaires de transferts monétaires sont aussi susceptibles d'être des travailleurs indépendants que les non-bénéficiaires. Pour ces raisons, je soutiens que les contraintes de liquidité et l'aversion pour le risque ne sont probablement pas le principal obstacle au travail indépendant en Afrique du Sud. Ces résultats montrent aussi que le système de protection sociale sud-africain n'a pas d'effet dissuasif fort sur le travail indépendant.

  soulignait déjà les conséquences potentiellement durables de la répression durant l'apartheid, qui aurait empêché le développement du capital social nécessaire pour accéder à cette profession. Je m'appuie sur cet argument pour tirer des enseignements de la littérature socio-économique, qui met en lumière la forte composante intergénérationnelle de la transmission de la propension à exercer une activité indépendante. En effet, une observation empirique constante est que les enfants d'entrepreneurs sont beaucoup plus susceptibles d'être eux-mêmes des entrepreneurs. Cette corrélation est surtout attribuée à un effet d'imitation : en observant un parent entrepreneur, les enfants deviennent plus conscients de cette possibilité. Intuitivement, cela expliquerait pourquoi les jeunes générations, qui n'ont pas été directement touchées par les années de l'apartheid, ne se lancent toujours pas dans le travail indépendant. Tout simplement, les jeunes Sud-Africains d'aujourd'hui ne peuvent pas imiter leurs parents, car ceux-ci ne pouvaient pas exercer une activité indépendante. Dans le chapitre, je présente quelques éléments qui vont dans ce sens, en m'intéressant aux migrants en Afrique du Sud et à leur descendance : ceux qui sont arrivés dans le pays après l'apartheid sont plus susceptibles d'être des travailleurs indépendants, tout comme leurs enfants. spécifiques à l'Afrique du Sud, à savoir que v) le taux d'emploi indépendant en Afrique du Sud n'augmente pas en conséquence des programmes de transferts monétaires, ce qui indique que les barrières à l'entrée sont de nature différente et vi) qu'il est peu probable que le taux d'activité indépendante en Afrique du Sud augmente sans intervention politique qui compenserait les restrictions passées qui ont pesé sur son marché du travail.

Figure 1 . 1 :

 11 Figure 1.1: Number of CSG Recipients, South Africa 1999-2010

Figure 1 .

 1 Figure 1.1 plots the number of grants and recipients between 1999 and 2010.

1.4. 1 Data

 1 This paper combines several data sources to study in detail the labor market impact of the CSG. The main part of the analysis is conducted on Census data(2001 & 2011) and the Community Survey (2007). For simplicity, I refer to both as Census data, as they are highly comparable and have a large sample size.27 Both also contain questions on the date of birth of the youngest child, which allows to have information on CSG exposure regardless of whether or not the child is observed in the household. This is particularly important following research by[START_REF] Hamoudi | Endogenous Coresidence and Program Incidence: South Africa's Old Age Pension[END_REF], among others, who show how household composition in South Africa is endogenous to the receipt of social grants. Labor market outcomes in the Census are limited to information on informality, occupation, and sector of work. Wage information is lacking, and income is recorded as a categorical variable. In the Appendix, I outline how the informal sector is measured in the Census (non-registered businesses and private households), and how it compares to other surveys. Despite its limitations, Census data is the only source with information on the child's birth year, which is key to i) compare the same population over time, and to ii) clearly identify which mothers are treated, and has a large enough sample to realistically capture the causal impact of the grant.For more descriptive purposes, I take advantage of other sources of data: 1) for detailed information CSG receipt over time, and other social grants, I make use of the General Household Survey (GHS); 2) for detailed information on wages, and the composition of formal/informal jobs, I employ the South African Labour Force Survey (LFS) from 2002 to 2007. 28 Lastly, 3) to further study how the grant impacts job search, I use the National Income Dynamics Survey (NIDS). This panel dataset has a very detailed labor market section, a longitudinal dimension, but only covers the time period(2008)(2009)(2010)(2011)(2012)(2013)(2014) and at the cost of a significantly smaller sample size. Importantly, this data source has information on aspects of job search such as transport expenses and reservation wages. In the Appendix, I outline how each outcome is measured in the different data sources.

Figure 1 . 2 :

 12 Figure 1.2: CSG Take-Up by Cohort of Birth, 2002-2011

  by using the three cross-sections at my disposal: one in late 2001, before any CSG; one in early 2007, just after the large spike; and one in 2011, after. By estimating the effect separately in 2007 and 2011, I am able to disentangle the short-term effect of receiving the CSG from its long-term effect five years after. Formally, I estimate the following equation, separately for 2001, 2007, and 2011:

Figure 1 . 3 :

 13 Figure 1.3: Share of Informal Employment by Birth Cohort of Youngest Child, 2001 & 2011 (a) Year 2001 -"Before" (b) Year 2011 -"After"

Figure 1 . 4 :

 14 Figure 1.4: Share of Informal Employment by Birth Cohort of Youngest Child, Single Mothers Only, 2001 & 2011 (a) Year 2001 -"Before" (b) Year 2011 -"After"

Figure 1 .

 1 Figure 1.5: Job Quality Dimensions of Formal Jobs by Occupation and Industry, NIDS Data

  1.A.11 to 1.A.13 in the Appendix show that the observables are well-balanced, and

  .A.19, I show the share of informal employment around a threshold set for cohort 1983 in 2001 (hence, exactly the same age as cohort 1993 in 2011) 46 . Clearly, in 2001, we do not observe the drop in informal employment that we observe in 2011. More generally, I run a set of placebo estimations by varying both the bandwidth and the position of the threshold around cohort 1993. This should give an idea of the size of the jumps estimated at the "correct" threshold relative to other, "misplaced" thresholds. I present these in Figure 1.A.18 in the Appendix for the three relevant outcomes: unemployment, informal employment, and formal employment. For all three outcomes, the distributions of coefficients with placebo threshold are centered around zero, and the coefficients estimated with the "correct" threshold are, in most cases, above (below) the 95th (5th) percentile of the distribution. 47 This supports both the internal validity of the estimation, and the statistical significance of the results: the estimated coefficients with the correct threshold in the correct year are generally at the extremes of the distributions relative to all other years × threshold44

Figure 1

 1 Figure 1.6: Correlation between Weekly Transport Cost to Look for a Job and CSG a) Without Controls b) With Controls

Figure 1 .A. 1 :

 11 Figure 1.A.1: Share of Households Receiving Social Grants in South Africa, 2002-2014

Figure 1 .

 1 Figure 1.A.2: Take-Up by Wage in the Formal and Informal Sectors, Mothers, NIDS 2008

Figure 1 .

 1 Figure 1.A.3: Share of Mothers Receiving the CSG by Urban/Rural and Married/Non-Married, GHS, 2002-2014

Figure 1 .

 1 Figure 1.A.4: Female Employment Rates in the Formal and Informal Sectors, South Africa 2002-2007

Figure 1 .

 1 Figure 1.A.5: CSG Eligibility and Take-Up by Cohort of Birth, 2002-2011

[

  E(y 0 |c = 1993, year = 2011) -E(y 0 |c = 1993, year = 2001)] = [E(y 0 |c = 1992, year = 2011) -E(y 0 |c = 1992, year = 2001)] which means that the time evolution of labor market oucomes for mothers of treated and untreated cohorts would have been the same without the CSG. One can replace cohort with yearcohort, in order to obtain the discrete age value in a given year, we obtain that the identification assumption equally implies: [E(y 0 |age = 18, year = 2011) -E(y 0 |age = 8, year = 2001)] = [E(y 0 |age = 19, year = 2011) -E(y 0 |age = 9, year = 2001)] We can write the total difference between the age value in 2011 and the one in 2001 as the sum of the differences between each subsequent age value, such that the previous expression becomes: 10 � j=0 [E(y 0 |age = 8 + j + 1, year = 2001 + j + 1) -E(y 0 |age = 8 + j, year = 2001 + j)] y 0 |age = 9 + j + 1, year = 2001 + j + 1) -E(y 0 |age = 9 + j, year = 2001 + j)]

  y 0 |age = 8 + j + 1, year = 2001 + j + 1) -E(y 0 |age = 8 + j, year = 2001 + j)] +[E(y 0 |age = 9, year = 2002) -E(y 0 |age = 8, year = 2001)] y 0 |age = 9 + j + 1, year = 2001 + j + 1) -E(y 0 |age = 9 + j, year = 2001 + j)] +[E(y 0 |age = 19, year = 2011) -E(y 0 |age = 18, year = 2010)]

Figure 1 .

 1 Figure 1.A.6: CSG Effect on Employment Rate (Residuals) (a) 2001 (b) 2007 (c) 2011

Figure 1 .

 1 Figure 1.A.7: CSG Effect on Unemployment Rate (Residuals) (a) 2001 (b) 2007 (c) 2011

Figure 1 .

 1 Figure 1.A.8: CSG Effect on Informal Sector Employment Rate (Residuals) (a) 2001 (b) 2007 (c) 2011

Figure 1 .

 1 Figure 1.A.9: CSG Effect on Formal Sector Employment Rate (Residuals) (a) 2001 (b) 2007 (c) 2011

Figure 1 .

 1 Figure 1.A.10: Difference-in-Differences Estimates with Varying Windows Formal Employment (a) 2007 (b) 2011

Figure 1 .

 1 Figure 1.A.11: Observable Characteristics (All Mothers and Employed only), 2001

Figure 1 .

 1 Figure 1.A.12: Observable Characteristics (All Mothers and Employed only), 2007

Figure 1 .

 1 Figure 1.A.13: Observable Characteristics (All Mothers and Employed only), 2011

Figure 1

 1 Figure 1.A.14: Density, 2001, 2007, & 2011 (a) 2001

Figure 1

 1 Figure 1.A.15: Bandwidth Sensitivity, 2007 Linear (a) Informal (b) Formal

Figure 1

 1 Figure 1.A.16: Bandwidth Sensitivity, 2011 Linear (a) Informal (b) Formal

Figure 1

 1 Figure 1.A.17: Placebo Test -White Mothers Only, 2007 & 2011 2007 Informal Formal

2 .

 2 Source: Author's calculations on Census (2011) and Community Survey (2007).

Figure 1

 1 Figure 1.A.18: Placebo Thresholds (a) Unemployment

Figure 1 .

 1 Figure 1.A.19: Share of Informal Employment by Birth Cohort of Youngest Child (Cohorts of the Same Age), 2001 & 2011 (a) Year 2001 -Cohort 1983 (b) Year 2011 -Cohort 1993

Figure 1 .

 1 Figure 1.A.20: Correlation between Reported Reservation Wages and CSG a) Without Controls b) With Controls

Unemployment Status:

  During the past four weeks, has (the person) taken any action to look for any kind of work? and During the past four weeks, has (the person) taken any action to start any kind of business? and If a suitable job is offered, how soon can (the person) start work? 1 = Within one week, 2 = More than 1 week, up to 2 weeks, 3 = More than 2 weeks, up to 4 weeks, 4 = Some time after 4 weeks, 5 = Does not choose to work Informality Status: Is the organisation / company / business / enterprise/branch where (the person) works, in the formal or informal sector? 1=In the formal sector, 2=In the informal sector (including domestic work), 3=Do not know Work Status: How can one describe (the person)'s main activity or work status best? 1 = Paid employee, 2 = Paid family worker, 3 = Self-employed, 4 = Employer, 5 = Unpaid family worker Industry: What is the main business/function of the company that (the person)

Figure 2 . 1 :

 21 Figure 2.1: Share of People Receiving the Old Age Pension or Disability Grant by Age, Before and After Pension Reform

  These graphs plot the share of individuals receiving the Old Age Pension or the Disability Grant within each age bin, for men and women separately. The solid line is for the years before the reform(2002)(2003)(2004)(2005)(2006)(2007), while the dotted line is for the years after the reform(2010)(2011)(2012)(2013)(2014)(2015).

Table 2 .

 2 A.2 gives average characteristics by labor market status. The median monthly salary for men aged 60-64 before the reform was R 4462 (R 23 per hour) in the formal sector, and R 1373 ( R 7 per hour) in the informal sector. After the reform, it was R 5613 (R 30 per hour) in the formal sector, and R 2004 (R 12 per hour) in the informal sector (in 2010 Rand). The amount of the OAP is around R 1000, and roughly stable over time time in real terms (Table

  10 All our estimations are run on the subsample of Black and Coloured individuals, during the years 2002 to 2007 and 2010 to 2015.

Figure 2 . 2 :

 22 Figure 2.2: Total, Formal and Informal Employment, Before and After, Men & Women (a) Men: Employment (b) Women: Employment

Figure 2 . 3 :

 23 Figure 2.3: Effect on Probability to be Informally Employed by Quartile of Informal Hourly Wage, Quadratic Fit 50-70, RDD, Before and After, Men(a) Men -Before (b) Men -After

Figure 2 . 4 :

 24 Figure 2.4: Effect on Probability to be Informally Employed by Quartile of Informal Hourly Wage, RDD, Before and After, Women

Figure 2 . 5 :

 25 Figure 2.5: Effect on Probability to be Formally Employed by Quartile of Informal Hourly Wage, RDD, Before and After, Men(a) Men -Before (b) Men -After

Figure 2 .

 2 Figure 2.6: Heterogeneous Effects of the Pension Reform by Industry × Occupation Cells

Figure 2 . 7 :

 27 Figure 2.7: Effect of the Reform on the Employment of 60-64 and 55-59, Top vs. Bottom Quartile (a) 60-64 y.-o. men, Black and Coloured

Figure 2 .

 2 Figure 2.7 displays the result on employment rate. With regards to 60-64 Black

Figure 2 .A. 1 :

 21 Figure 2.A.1: Number of Old Age Pension and Disability Grant Beneficiaries, 2003-2013

Figure 2 .

 2 Figure 2.A.2: Share of People Receiving the Old Age Pension or Disability Grant by Age, Before and After Pension Reform, Black and Coloured Only

  These graphs plot the share of individuals receiving the Old Age Pension or the Disability Grant within each age bin, for men and women separately. The sample is restricted to the Black and Coloured population. The solid line is for the years before the reform(2002)(2003)(2004)(2005)(2006)(2007), while the dotted line is for the years after the reform(2010)(2011)(2012)(2013)(2014)(2015). Source: Authors' calculations on GHS.

Figure 2 .

 2 Figure 2.A.3: Joint Probability to Be Employed and Contribute to a Private Pension, Before Reform

Figure 2 .

 2 Figure 2.A.4: Informal Employment by Occupation and Age Groups, Men and Women, PALMS

Figure 2 .

 2 Figure 2.A.5: Position of the Means-Test with Respect to Formal and Informal Monthly Earnings, 2010, 2012, 2014 (a) 2010

Figure 2 .

 2 Figure 2.A.6: Distribution of Informal Hourly Wages, 55-59 vs. 60-64, Before and After(a) Before (b) After

Figure 2 .

 2 Figure 2.A.7: Distribution of Formal Hourly Wage, 55-59 vs. 60-64, Before and After(a) Before (b) After

Figure 2 .

 2 Figure 2.A.8: Effect on Probability to be Formally Employed by Quartile of Informal Hourly Wage, RDD, Before and After, Women(a) Women -Before (b) Women -After

Figure 2 .

 2 Figure 2.A.9: Impact on Formal and Informal Employment, Bandwidth Sensitivity, Linear Function, PALMS (a) Formal Employment (b) Informal Employment

Figure 2 .

 2 Figure 2.A.10: Effect of the Reform on Hours Worked and Wages of 55-59 y.-o. Black and Coloured Men (a) Hours worked

�P

  60, w T =0 = w)P (w T =0 = w|T = 0, X = 60) + lim x→60 -P (Y |T = 0, X = x, w T =0 = w)P (w T =0 = w|T = 0, X = x)Let's assume that lim x→60 -P (w T =0 = w|T = 0, X = x) = P (w T =0 = w|T = 0, X = 60) on the one hand, and lim x→60 -P (w T =1 = w|T = 1, X = x) = P (w T =1 = w|T = 1, X = 60) on the other, so that wage distributions in each time period are continuous at the threshold. Then:τ naive = (P (Y |T = 1, X = 60, w T =1 = w) -lim x→60 -P (Y |T = 1, X = x, w T =1 = w)) × P (w T =1 = w|T = 1, X = 60) -(P (Y |T = 0, X = 60, w T =0 = w) -lim x→60 -P (Y |T = 0, X = x, w T =0 = w)) × P (w T =0 = w|T = 0, X = 60) = � P (Y |T = 1, X = 60, w T =1 = w) -lim x→60 -P (Y |T = 1, X = x, w T =1 = w) -� P (Y |T = 0, X = 60, w T =0 = w) -lim x→60 -P (Y |T = 0, X = x, w T =0 = w) � � × P (w T =1 = w|T = 1, X = 60) + � P (Y |T = 0, X = 60, w T =0 = w) -lim x→60 -P (Y |T = 0, X = x, w T =0 = w) � × (w T =1 = w|T =1, X = 60) -P (w T =0 = w|T = 0, X = 60) � = τ w × P (w T =1 = w|T = 1, X = 60) + � P (Y |T = 0, X = 60, w T =0 = w) -lim x→60 -P (Y |T = 0, X = x, w T =0 = w) � × � P (w T =1 = w|T = 1, X = 60) -P (w T =0 = w|T = 0, X = 60) �

Figure 3 . 1 :

 31 Figure 3.1: Wage-and Self-Employment Rates, 1994-2015 (a) Wage (b) Self

Figure 3 .

 3 1 shows the evolution of the wage-and self-employment rates since the end of Apartheid until 2015, restricted to the Black and Coloured population.

Figure 3 .

 3 2 gives the share of mothers receiving the Child Support Grant depending on the year the child was born, between 2002 and 2010.As outlined in Chapter 1, the identification strategy relies on the fact that there is a last unexposed cohort, those born in 1992, and a first exposed cohort, those born in 1993, which have large differences in CSG receipt. I run the following specification on data from the 2001 census (before), 2007 Community Survey (during), and 2011 census (after), which is meant to capture the effect of receiving at least one year of

Figure 3 . 2 :

 32 Figure 3.2: Share of Mothers Receiving the CSG by Cohort of Birth, 2002-2011

Figure 3 . 3 :

 33 Figure 3.3: Self-Employment Rates and the Child Support Grant, Mothers, 2001 and 2011 (a) 2001 -"Before" (b) 2007 -"During" (c) 2011 -"After"

Figure 3 .

 3 Figure 3.4: Self-Employment Rates and the Child Support Grant, Other Household Members, 2001 and 2011 (a) 2001 -"Before" (b) 2007 -"During" (c) 2011 -"After"

Figure 3 .

 3 Figure 3.5: Share Receiving Old Age Pension or Disability Grant by Age, Before and After Pension Reform

  These graphs plot the share of individuals receiving the Old Age Pension or the Disability Grant within each age bin, for men and women separately. The solid line plots the share receiving before the reform (2007), while the dotted line for the years after the reform (2011). Source: Authors' calculations onGHS (2007GHS ( & 2011)).

Figure 3 .

 3 Figure 3.6: Self-Employment by Age of Oldest Man in the Household, Before and After Reform (a) Elderly (b) Other Adult Household Members

  apartheid. Historically the apartheid system repressed the informal activities of black South Africans through such restrictive legislation as the Group Areas Act, harsh licensing, strict zoning regulations, and effective detection and prosecution of offenders. Bouts of slum clearance and other periodic attacks on the illegal spaces within which informal enterprise thrived, served to rid South African cities of black-dominated informal sector niches... Moreover, repression and disempowerment of Africans under apartheid would have inhibited the development of entrepreneurial and social skills and of social networks. These factors are important for confidence in entering the self-employed sector and for success in it."There are several reasons to believe that the effects of this past repression would persist onto younger generations, who have not been directly affected by the Apartheid years. The literature in socio-economics has relentlessly pointed out the importance of the intergenerational component in the transmission of entrepreneurship and self-employment[START_REF] Sørensen | Closure and Exposure: Mechanisms in the Intergenerational Transmission of Self-Employment[END_REF],[START_REF] Parker | The Economics of Entrepreneurship[END_REF],Lindquist, Sol and Van Praag

Figure 3 .

 3 Figure 3.7: Self-Employment Rate in 2011 by Year of Migration to South Africa

"

  ..South Africa's informal economy is in the process of coming out of the dark shadows cast by forty years of apartheid planning. The reconstruction initiatives of post apartheid South Africa, promoting and supporting the development of the small, medium and micro-enterprise (SMME) sector, including the informal economy, have been of major policy significance." "The changing informal economy of Gauteng signals that South Africa's informal economy is set to emerge from the shadows cast by the restrictions on entrepreneurship imposed during the apartheid period." (Rogerson (2000))

Figure 3 .

 3 Figure 3.A.2: Self-and Wage-Employment Rates across Several Countries, 2010

Figure 3 .

 3 Figure 3.A.3: Self-Employment Rate by Gender and Native Status

Figure 3 .

 3 Figure 3.A.4: Share of Population Receiving CSG or OAP, 2003 to 2015

  working-age population (15-65), over time and across several surveys: Post-Apartheid Labor Market Series (PALMS), General Household Survey (GHS), Census 2001 and 2011, Community Survey (CS) 2007, National Income Dynamics Survey (NIDS). In the lower panel, I report the definition on which the measurement of self-employment is based in the different data sources. Issues in the measurement of self-employment in the PALMS for the years 2000 and 2001 are documented in[START_REF] Kerr | Sampling Methodology and Fieldwork Changes in the October Household Surveys and Labour Force Surveys[END_REF] and[START_REF] Neyens | Changes in Self-Employment in the Agricultural Sector, South Africa: 1994-2012[END_REF].

Figure 3 .

 3 Figure 3.A.5: Diff-in-Diff Estimates, Effect of the CSG 2007 (a) Mothers (b) Other HH Members

Figure 3

 3 Figure 3.A.6: Bandwidth Sensitivity, Effect of the CSG 2007 (a) Mothers (b) Other HH Members

Figure 3 .

 3 Figure 3.A.7: Bandwidth Sensitivity, Effect of the OAP 2011 (a) Elderly Men (b) Other HH Members

Figure 3 .

 3 Figure 3.A.8: Wage-and Self-Employment by Education, Natives vs Migrants

  These graphs give the probability of being wage-employed (left panel), and self-employed (right panel) by number of years of completed schooling for Black and Coloured South Africans with the dashed line, and migrants from other African countries with the solid line. Source: Census (2011).

Figure 3 .

 3 Figure 3.A.9: Self-Employment Rate Across Provinces and Municipalities

  These graphs give the distribution of the self-employment rates across provinces (left panel), and municipalities (right panel). The sample only includes Black and Coloured individuals born in South Africa. Source: Census (2011).

Figure 3 .

 3 Figure 3.A.10: Share of Migrants who Moved before 1990 by Year of Birth

Figure 3 .

 3 Figure 3.A.11: Number of People by Year of Migration to South Africa

Figure 3 .

 3 Figure 3.A.12: Self-Employment by Age, 1996, 2001 & 2011(a) Natives (b) Migrants

Figure 3 .

 3 Figure 3.A.13: Wage-Employment by Age, 1996, 2001 & 2011 (a) Natives (b) Migrants

Figure 3 .

 3 Figure 3.A.14: Share of Self-Employment by Father's Year of Birth

  These graphs plot the share of self-employment (conditional on being employed) by father's year of birth for both natives (panel (a)), and migrants from other African countries (panel (b)). The fathers born earlier were more likely to have migrated to South Africa before 1990s (Figure 3.A.10), and more likely to have experienced the Apartheid restrictions. Source: Census (2011).

Table 1 .

 1

			1: Evolution of the CSG
	Reform dates Age limit Amount Amount ('10 R)	Means test
	Q2 1998	7	100 R	185 R	1100 R rural, 800 R urban
	Q2 2003	9	160 R	218 R	1100 R rural, 800 R urban
	Q2 2004	11	170 R	234 R	1100 R rural, 800 R urban
	Q2 2005	14	180 R	242 R	1100 R rural, 800 R urban
	Q3 2008	14	230 R	257 R	2300 R
	Q1 2009	15	240 R	250 R	2400 R
	Q1 2010	16	250 R	250 R	2500 R
	Q1 2011	17	260 R	248 R	2600 R
	Q1 2012	18	280 R	252 R	2800 R
	Note: The grant was introduced in April 1998. Column 4 gives the value of the grant in 2010
	Rand, adjusting for inflation measured as CPI (Source: OECD.stat). The means test was fixed
	until 2008, when it was then set at 10 times the grant for individuals and 20 times the grant for
	married couples.
	Source: Gomersall (2013) and Eyal and Woolard (2013).

Table 1 .

 1 1 shows the date of introduction of the CSG, the amount of the grant in nominal and real terms, the level of the means test, and the reforms in age eligibility.

	14 In practice,

Table 1 .

 1 2 presents the main results with the preferred specification and window.36

1.5.2 Effects of the CSG on Employment and Sectoral Allocation

Table 1 .

 1 2: Labor Market Effects of the CSG onMothers, 2001Mothers, , 2007Mothers, , & 2011 Note: *** p<0.01, ** p<0.05, * p<0.1. This table gives the OLS estimates of Equation1.12 on mothers' labor market outcomes in2001, 2007, and 2011, respectively. Only Black and Coloured mothers born between 1960 and 1985 are included. The forcing variable is the cohort of birth of the youngest child ever born to a given mother. The functional form is quadratic for the window of cohorts born between 1981

			Year 2001 -"Before"
		(1)	(2)	(3)	(4)	(5)
		Active Unemployed Employed Informal	Formal
	CSG	-0.0018	-0.0029	0.0011	0.0016	0.0002
		(0.0046)	(0.0051)	(0.0051)	(0.0040)	(0.0042)
	Mean Y at Threshold	0.7397	0.3561	0.3835	0.1548	0.2286
	Weighted n at Threshold 194,868	194,868	194,868	194,868	194,868
	Observations	477,466	477,466	477,466	477,466	477,466
	R-squared	0.1495	0.0656	0.1786	0.0840	0.2085
			Year 2007 -"During"
		(1)	(2)	(3)	(4)	(5)
		Active Unemployed Employed Informal	Formal
	CSG	0.0078	0.0267**	-0.0190	0.0122	-0.0312***
		(0.0114)	(0.0115)	(0.0133)	(0.0110)	(0.0117)
	Mean Y at Threshold	0.7600	0.2231	0.5369	0.2101	0.3268
	Weighted n at Threshold 133,611	133,611	133,611	133,611	133,611
	Observations	90,084	90,084	90,084	90,084	90,084
	R-squared	0.1160	0.0857	0.1520	0.0619	0.2050
			Year 2011 -"After"
		(1)	(2)	(3)	(4)	(5)
		Active Unemployed Employed Informal	Formal
	CSG	-0.0026	-0.0053	0.0027	-0.0114** 0.0141**
		(0.0069)	(0.0056)	(0.0071)	(0.0057)	(0.0063)
	Mean Y at Threshold	0.6517	0.1660	0.4857	0.1821	0.3036
	Weighted n at Threshold 92,079	92,079	92,079	92,079	92,079
	Observations	247,032	247,032	247,032	247,032	247,032
	R-squared	0.1288	0.0421	0.1499	0.0374	0.1838

to 2004. CSG is a binary variable for the child being born in or after 1993, which indicates being part of a cohort that had access to the CSG. Mean Y at Threshold gives the mean of the outcome for the cohort 1992 (last unexposed cohort). Weighted n at Threshold gives the size of the underlying weighted population for cohort 1992. All estimations include controls for: age (cubic), education, race, marital status, municipality, and household size. Standard errors are clustered at the household level. Source: Author's calculations on

Census 2001 & 2011, and Community Survey (2007)

.

Table 1 .

 1 3: Labor Market Effects of the CSG by Population Group, 2007 and 2011

	1.5.2.3 Heterogeneous Effects by Population Group
		Black Mothers		Coloured Mothers
		Unemployed Informal Formal Unemployed Informal Formal
		in 2007	in 2011 in 2011	in 2007	in 2011	in 2011
		(1)	(2)	(3)	(4)	(5)	(6)
	CSG	0.0224*	-0.0080	0.0108	0.0573**	-0.0287** 0.0306*
		(0.0128)	(0.0062) (0.0066)	(0.0243)	(0.0142) (0.0184)
	Observations	78,343	215,765 215,765	11,741	31,267	31,267
	R-squared	0.0787	0.0407	0.1856	0.0923	0.0394	0.1432
	One may wonder to what extent the 2007 and 2011 effects can be interpreted
	as linked, sequential responses. In Table 1.3, I explore heterogeneous effects by
	population groups, dividing between Black and Coloured mothers. The advantage
	of focusing on this heterogeneity is that this categorization, by definition, is not

Note: *** p<0.01, ** p<0.05, * p<0.1. This table gives the estimates of Equation

1

.12 on mothers' labor market status, by population group. In the left panel, the sample is limited to Black mothers, and in the right panel, to Coloured mothers. The dependent variable in Columns (

1

) & (

4

) is the probability of being unemployed in 2007; in Columns (

2

) & (

5

), the probability of being informally employed in 2011; and in Columns (

3

) and (

6

), of being formally employed in 2011. The forcing variable is the cohort of birth of the youngest child ever born to a given mother. The functional form is quadratic for the window of cohorts from 1981 to 2004. CSG is a binary variable for the child being born in or after 1993, which indicates being part of a cohort that had access to the CSG. All estimations include controls for: age (cubic), education, marital status, municipality, and household size. Standard errors are clustered at the household level. Source: Author's calculations on Community Survey (2007) and Census

(2011)

.

time-changing, as opposed to other observables. Using this dimension allows me to

Table 1

 1

.4 reproduces Table

1

.2, splitting formal sector jobs by job quality. In 2007, mothers who receive the CSG are less likely to be in low-quality, formal sector jobs,

Table 1

 1

			.4: CSG & Job Quality, 2007 & 2011
			Year 2007 -"During"
		(1)	(2)	(3)	(4)	(5)	(6)
					Formal	Formal	Formal
		Unemployed Informal	Formal	Low Quality Mid-Quality High Quality
	CSG	0.0242*	0.0145	-0.0275**	-0.0184**	-0.0077	-0.0014
		(0.0124)	(0.0113)	(0.0120)	(0.0079)	(0.0084)	(0.0083)
	Mean Y at Threshold	0.2469	0.1908	0.2975	0.0826	0.0963	0.1185
	Observations	82,634	82,634	82,634	82,634	82,634	82,634
	R-squared	0.0893	0.0613	0.2018	0.0227	0.0501	0.2293
			Year 2011 -"After"
		(1)	(2)	(3)	(4)	(5)	(6)
					Formal	Formal	Formal
		Unemployed Informal	Formal	Low Quality Mid-Quality High Quality
	CSG	-0.0054	-0.0115** 0.0142**	-0.0006	0.0116**	0.0032
		(0.0056)	(0.0057)	(0.0063)	(0.0037)	(0.0051)	(0.0041)
	Mean Y at Threshold	0.1661	0.1820	0.3028	0.0661	0.1468	0.0897
	Observations	246,522	246,522	246,522	246,522	246,522	246,522
	R-squared	0.0421	0.0374	0.1831	0.0103	0.0744	0.1190

Note: *** p<0.01, ** p<0.05, * p<0.1. This table gives the OLS estimates of Equation

1

.12 on mothers' labor market outcomes in

2007 and 2011, respectively. Only Black and Coloured mothers born between 1960 and 1985

are included. The forcing variable is the cohort of birth of the youngest child ever born to a given mother. The functional form is quadratic for the window of cohorts from 1981 to 2004. CSG is a binary variable for the child being born in or after 1993, which indicates being part of a cohort that had access to the CSG. Mean Y at Threshold gives the mean of the outcome for the cohort 1992 (last unexposed cohort). Low-, mid-, and high-quality formal sector jobs are defined according to their level of log-wages, as shown in Figure

1

.5. The table is constructed such that the coefficients of Columns (4), (

5

), and (6) add up to Column

(3)

. Observations for whom occupation and industry information are missing are dropped from the sample. All estimations include controls for: age (cubic), education, race, marital status, municipality, and household size. Standard errors are clustered at the household level. Source: Author's calculations on

Census 2011 and Community Survey (2007)

.

Table 1

 1

		.5: CSG & Income, 2011
		(1)	(2)	(3)	(4)
		Predicted		Predicted
		∆ Wage	Income	∆ Wage	Income
	CSG	30.8480	16.9560	52.5816	30.5555
			(53.3776)		(98.2905)
	Sample	All	All	Employed Employed
	Observations		238,322		107,782

Note: *** p<0.01, ** p<0.05, * p<0.1. This table gives the estimates of Equation

1

.12 on mothers' income in 2011. Columns (

2

) & (4) are estimated with a censored regression to account for the interval-coded nature of the data. Only Black and Coloured mothers born between 1960 and 1985 are included, and only those employed in Columns (

3

) & (4). Predicted ∆ Wage is predicted, observable change in wage given the median wage by formal and informal sector, calculated for the relevant sub-population in the NIDS. Income is a categorical variable for monthly income as asked in the Census questionnaire. Source: Author's calculations on Census 2011; wage data is from the NIDS.

Table 1 .

 1 A.1: Characteristics by Labor Market Status, 2002-2007 To be read as: 89.00 percent of informal workers are Black, while 59.96 percent of formal workers are Black. Note: Informal sector refers to individuals employed in non-registered businesses. Young is a binary variable indicating individuals less than 30 years old. No schooling indicates an individual with no educational attainment. Self indicates selfemployed workers. The sample is restricted to the working-age population aged 18 to 60. Public Job refers to an individual being employed by the national or local government, or by a government agency. Union refers to the worker belonging to a worker union. Part Time workers are those working less than 30 hours per week on average. Source: Author's calculations onLFS (2002LFS ( -2007)).

	Characteristics (pop %)	Informal Formal Inactive Unemployed
	Socio-Demographics
	Black (76.71%)	89.00	59.96	82.79	88.27
	White (10.85%)	3.44	21.90	6.99	2.38
	Women (51.79%)	56.29	37.41	63.75	53.39
	Young (<30) (40.74%)	26.94	28.03	50.74	56.84
	No Schooling (27.30%)	44.12	17.55	33.21	22.32
	Job Characteristics
	Agriculture (4.48%)	12.47	8.15	-	-
	Self (8.79%)	48.92	6.55	-	-
	Public job (6.93%)	1.18	19.58	-	-
	Union (11.96%)	1.88	33.88	-	-
	Part Time (8.21%)	21.67	3.13	-	-
	Temporary Contract (6.61%)	-	20.04	-	-

Table 1 .

 1 A.2: Median Wages by Sector in South Africa, 2010 Rand Informal sector refers to individuals employed in non-registered businesses. Median wages are in 2010 Rand. Employers are self-employed individuals with at least one employee, while own-account workers have no employees. Wages are averaged over the reference period 2002 to 2007 for individuals aged 18 to 60. One CSG grant is R 250.

	Median Monthly	Formal Sector	Informal Sector	Gap
	(Hourly) Wages
	All	2660 R (13,3 R/h) 875 R (5,1 R/h)	1785 R (8,2 R/h)
	Employees	2608 R (13,1 R/h) 875 R (5,0 R/h)	1733 R (8,1 R/h)
	Self-Employed	6821 R (32,1 R/h) 893 R (5,2 R/h) 5928 R (26,9 R/h)
	Employers	7823 R (38,8 R/h) 1412 R (7,9 R/h) 6411 R (30,9 R/h)
	Own-Account	3547 R (19,6 R/h) 782 R (4,6 R/h) 2765 R (15,0 R/h)
	Men	2728 R (13,4 R/h) 1166 R (6,1 R/h) 1526 R (7,3 R/h)
	Women	2457 R (13,2 R/h) 722 R (4,5 R/h)	1735 R (8,7 R/h)

Note: Source: Author's calculations on

LFS (2002LFS ( -2007))

.

  will give an unbiased estimate of the effect of the CSG only if: 1) age effects are constant over time, and do not change from year to year; 2) the age effect for those age values that we do not observe for both cohorts are equal. The age values that we do not observe for both cohorts are the oldest one for the older cohort, and the

youngest one for the younger cohort. The estimator will be unbiased if there are no age effects of the child's on the mother's labor market outcomes, if age effects are linear, if these age effects somehow cancel out. Otherwise, even assuming that age effects are constant over time, and therefore equal for each cohort, and setting β as

Table 1 .

 1 A.3: Labor Market Effects of the CSG on Mothers, 2001, 2007, & 2011, Linear Fit, Window: 1987[START_REF] Case | Large Cash Transfers to the Elderly in South Africa[END_REF] This table gives the OLS estimates of Equation1.12 on mothers' labor market outcomes in2001, 2007, and 2011, respectively. Only Black and Coloured mothers born between 1960 and 1985 are included. The forcing variable is the cohort of birth of the youngest child ever born to a given mother. The functional form is linear for the window of cohorts born between 1987 to 1998.

			Year 2001 -"Before"
		(1)	(2)	(3)	(4)	(5)
		Active Unemployed Employed Informal	Formal
	CSG	0.0023	0.0021	0.0003	0.0046	-0.0049
		(0.0039)	(0.0043)	(0.0043)	(0.0033)	(0.0035)
	Mean Y at Threshold	0.7397	0.3561	0.3835	0.1548	0.2286
	Weighted n at Threshold 194,868	194,868	194,868	194,868	194,868
	Observations	266,587	266,587	266,587	266,587	266,587
	R-squared	0.1268	0.0682	0.1565	0.0784	0.2048
			Year 2007 -"During"
		(1)	(2)	(3)	(4)	(5)
		Active Unemployed Employed Informal	Formal
	CSG	0.0094	0.0266***	-0.0172	0.0125	-0.0297***
		(0.0100)	(0.0101)	(0.0117)	(0.0097)	(0.0103)
	Mean Y at Threshold	0.7600	0.2231	0.5369	0.2101	0.3268
	Weighted n at Threshold 133,611	133,611	133,611	133,611	133,611
	Observations	35,321	35,321	35,321	35,321	35,321
	R-squared	0.1157	0.0771	0.1390	0.0631	0.2116
			Year 2011 -"After"
		(1)	(2)	(3)	(4)	(5)
		Active Unemployed Employed Informal	Formal
	CSG	-0.0003	-0.0024	0.0021	-0.0112** 0.0133**
		(0.0060)	(0.0049)	(0.0062)	(0.0050)	(0.0055)
	Mean Y at Threshold	0.6517	0.1660	0.4857	0.1821	0.3036
	Weighted n at Threshold 92,079	92,079	92,079	92,079	92,079
	Observations	106,255	106,255	106,255	106,255	106,255
	R-squared	0.1302	0.0366	0.1493	0.0423	0.1904
	Note: *** p<0.01, ** p<0.05, * p<0.1.

CSG is a binary variable for the child being born in or after 1993, which indicates being part of a cohort that had access to the CSG. Mean Y at Threshold gives the mean of the outcome for the cohort 1992 (last unexposed cohort). Weighted n at Threshold gives the size of the underlying weighted population for cohort 1992. All estimations include controls for: age (cubic), education, race, marital status, municipality, and household size. Standard errors are clustered at the household level. Source: Author's calculations on

Census 2001 & 2011, and Community Survey (2007)

.

Table 1 .

 1 A.4: Labor Market Effects of the CSG on Other Adult Household Members, 2001, 2007, & 2011, Quadratic Fit, Window: 1981-2004 This table gives the OLS estimates of Equation1.12 on other adult household members' labor market outcomes in2001, 2007, and 2011, respectively (Black and Coloured population only, born between 1960 and 1985). The forcing variable is the cohort of birth of the youngest child ever born to a mother in the same household. The functional form is quadraticThe Lasting Labor Market Effects of Cash Transfers Table1.A.5: Effects on the Employment Composition ofMothers, 2007Mothers, & 2011

			Year 2001 -"Before"
		(1)	(2)	(3)	(4)	(5)
		Active Unemployed Employed Informal Formal
	CSG	-0.0033	-0.0048	0.0016	0.0008	0.0007
		(0.0053)	(0.0050)	(0.0042) (0.0029) (0.0041)
	Mean Y at Threshold	0.4932	0.2461	0.2471	0.0654	0.1816
	Weighted n at Threshold 245,446	245,446	245,446	245,446 245,446
	Observations	558,740	558,740	558,740	558,740 558,740
	R-squared	0.4327	0.1121	0.3854	0.1365	0.3160
			Year 2007 -"During"
		(1)	(2)	(3)	(4)	(5)
		Active Unemployed Employed Informal Formal
	CSG	0.0038	-0.0070	0.0109	0.0070	0.0038
		(0.0156)	(0.0173)	(0.0165) (0.0122) (0.0158)
	Mean Y at Threshold	0.7716	0.2991	0.4724	0.1311	0.3413
	Weighted n at Threshold 92,183	92,183	92,183	92,183	92,183
	Observations	62,737	62,737	62,737	62,737	62,737
	R-squared	0.1508	0.0958	0.2669	0.0772	0.2310
			Year 2011 -"After"
		(1)	(2)	(3)	(4)	(5)
		Active Unemployed Employed Informal Formal
	CSG	-0.0085	0.0051	-0.0136	-0.0075	-0.0061
		(0.0115)	(0.0104)	(0.0120) (0.0083) (0.0115)
	Mean Y at Threshold	0.7240	0.1903	0.5336	0.1310	0.4026
	Weighted n at Threshold 38,942	38,942	38,942	38,942	38,942
	Observations	114,687	114,687	114,687	114,687 114,687
	R-squared	0.1367	0.0525	0.1947	0.0505	0.1756
	Note: *** p<0.01, ** p<0.05, * p<0.1.

for the window of cohorts born between 1981 to 2004. CSG is a binary variable for the child being born in or after 1993, which indicates being part of a cohort that had access to the CSG. Mean Y at Threshold gives the mean of the outcome for the cohort 1992 (last unexposed cohort). Weighted n at Threshold gives the size of the underlying weighted population for cohort 1992. All estimations include controls for: age (cubic), education, race, marital status, municipality, gender, and household size. Standard errors are clustered at the household level. Source: Author's calculations on

Census 2001 & 2011, and Community Survey (2007)

.

Table 1 .

 1 A.6: Labor Market Effects of the CSG on Mothers, Within and Across Jobs, 2011

		(1)	(2)	(3)	(4)	(5)
		Informal	Informal in	Informal outside Within-Occupation Across-Occupation
			Private Household Private Household	Variation	Variation
	CSG	-0.0251***	-0.0147*	-0.0105	-0.0111	0.0006
		(0.0097)	(0.0087)	(0.0072)	(0.0072)	(0.0009)
	Observations	111,463	111,463	111,463	111,463	111,463
	R-squared	0.1469	0.1294	0.0591	0.0534	0.0386

Note: *** p<0.01, ** p<0.05, * p<0.1. I decompose the decrease in informality for employed mothers in 2011 in the following way: The total effect is in Column

(1)

. The probability of working for a private household is in Column

(2)

. The probability of working informally outside a private household is in Column

(3)

. Then, I decompose Column (3) in the following way: Inf ormal i = occupation i + � i , and then separately predict the fitted values and the residuals of this equation. I then estimate Equation

1

.12 having as a dependent variable the residuals (Column (4)) and the fitted values (Column (

5

)). The coefficients add up in the following way: (1) = (

2

) + (

3

) and (3)=(4) +(5). All estimations include controls for: age of the mother (cubic), education, race, marital status, province, household size, and municipality. Standard errors are clustered at the household level. Source: Author's calculations on Census 2011.

Table 1 .

 1 A.7: Difference-in-Differences Estimates of the Effect of the CSG on Mothers Note: *** p<0.01, ** p<0.05, * p<0.1. This table gives the estimates of Equation1.13 on mothers' labor market outcomes. CSG is a binary variable for the child being born in or after1993, upper panel; 1994, middle panel; 1993 or 1994, lower panel. All estimations include controls for: age (cubic), education, race, marital status, municipality, and household size. Standard errors are clustered at the household level. Source: Author's calculations onCensus 2001 & 2011, and Community Survey (2007).

		(1)	(2)	(3)	(4)	(5)
		Active Unemployed Employed Informal	Formal
	Window: 1992 vs. 1993
	CSG×2007	0.0022	0.0322**	-0.0300**	0.0080 -0.0379***
		(0.0121)	(0.0127)	(0.0142) (0.0116)	(0.0124)
	CSG×2011	0.0032	-0.0091	0.0123	-0.0022	0.0145*
		(0.0082)	(0.0074)	(0.0086) (0.0068)	(0.0075)
	Observations 59,614	59,614	59,614	59,614	59,614
	R-squared	0.1203	0.0960	0.1480	0.0670	0.1982
	Window: 1993 vs. 1994
	CSG×2007	0.0012	-0.0185	0.0196	0.0075	0.0122
		(0.0114)	(0.0122)	(0.0133) (0.0109)	(0.0114)
	CSG×2011	0.0005	0.0075	-0.0070	-0.0037	-0.0033
		(0.0077)	(0.0070)	(0.0080) (0.0063)	(0.0070)
	Observations 70,566	70,566	70,566	70,566	70,566
	R-squared	0.1197	0.0941	0.1498	0.0655	0.2007
	Window: 1991-1992 vs. 1993-1994
	CSG×2007	0.0130	0.0213**	-0.0082	0.0130	-0.0212**
		(0.0088)	(0.0090)	(0.0102) (0.0084)	(0.0089)
	CSG×2011	0.0081	-0.0077	0.0158**	-0.0022 0.0180***
		(0.0059)	(0.0054)	(0.0062) (0.0049)	(0.0054)
	Observations 118,669	118,669	118,669	118,669	118,669
	R-squared	0.1182	0.0914	0.1434	0.0622	0.1972

  What are the main goods or services produced at (name's) place of work or its main functions?

	Occupation: What kind of work does (name) usually do in his/her main job/business?
	Fertility: When was (name's) last child born, even if the child died soon after birth?
	Personal Income (Categorical): What is the income category that best describes the (women aged 12 to 50 years)
	gross monthly or annual income of (the person) before deductions and including all
	sources of income? Personal Income (Categorical): What is the income category that best describes
	the gross monthly or annual income of (name) before deductions and including all
	Census 2011 sources of income?
	Labour Force Surveys, 2002-2007
	Unemployment Status: In the four weeks before 10 October was (name) looking for
	any kind of job or trying to start any kind of business?
	and Would (name) have liked to work in the SEVEN DAYS before 10 October?
	and If a suitable job had been offered or circumstances had allowed, would (name)

Employment Status: In the SEVEN DAYS before 10 October ...Did (name) work for a wage, salary, commission or any payment in kind (including paid domestic work), even if it was for only one hour? and In the SEVEN DAYS before 10 October ...Did (name) run or do any kind of business, big or small, for herself/himself or with one or more partners, even if it was for only one hour? and In the SEVEN DAYS before 10 October ...Did (name) help without being paid in any kind of business run by her/his household, even if it was for only one hour?

and Even though (name) did not do any work for pay, profit or did not help without pay in a household business in the SEVEN DAYS before 10 October, did he/she have a paid job or business that he/she would definitely return to? have been able to start work or a business in the SEVEN DAYS before 10 October? Informality Status: Is (name's) place of work ..........? 1=In the formal sector, 2=In the informal sector, 3=Private household, 4= Do not know Industry:

Table 1 .

 1 A.8: Overlap between Informal Sector and Informal Employment

	in Agriculture 50
	2. Informal Employment (NIDS/LFS) = Employees without a Written Contract
	+ Self-Employed without VAT registration
	Employees		Informal Employment
		Informal Sector Social Security Written Contract
	Social Security	78.89	1	64.52
	Written Contract	77.88	74.99	1
	Informal Sector	1	50.49	42.77
	Self-employed		Informal Employment
		Informal Sector	VAT Tax	Own Account
	VAT Tax	98.21	1	92.95
	Own Account	67.06	66.72	1
	Informal Sector	1	95.49	90.75

Table 2 .

 2 1: Old Age Pension and Employment, RDD Results, Quadratic Fit 50-70, PALMS

		Before Reform		After Reform
		(1)	(2)	(3)	(4)	(5)	(6)
		Employed	Informal	Formal	Employed	Informal	Formal
	a. Men
	Age (60+)	-0.0278 (0.0195)	0.0083 (0.0151)	-0.0361** -0.0907*** -0.0427*** -0.0480*** (0.0170) (0.0115) (0.0084) (0.0108)
	Mean Y at Age 59	0.41	0.18	0.23	0.46	0.15	0.31
	Observations	47,034	47,034	47,034	93,030	93,030	93,030
	R-squared	0.1037	0.0214	0.0668	0.1521	0.0242	0.1009
	b. Women
	Age (60+)	-0.0720*** -0.0427*** -0.0293*** -0.0536*** -0.0360*** -0.0176** (0.0136) (0.0114) (0.0096) (0.0090) (0.0067) (0.0074)
	Mean Y at Age 59	0.25	0.15	0.09	0.31	0.14	0.17
	Observations	67,803	67,803	67,803	137,631	137,631	137,631
	R-squared	0.1071	0.0539	0.0460	0.1251	0.0419	0.0689
	Note: *** p<0.01, ** p<0.05, * p<0.1. This table gives the results of Equation 2.1 with a quadratic

function, for men (upper panel), and women(lower panel), on the age window 50-70. The sample is limited to Black and Coloured men and women. We only report the coefficient of interest, β RD . The dependent variables are binary variables for: (1) employed, (

Table 2 .

 2 2: Old Age Pension and Employment, PALMS

		Employed Informal	Formal
				Extensive Intensive
		(1)	(2)	(3)	(4)
	a. Men
	P ost × Age (60+)	-0.0639** (0.0260)	-0.0511** (0.0195)	-0.0128 (0.0261) (1.2691) -0.4621
	Mean Y at Age 59	0.52	0.14	0.38	13.9
	Observations	140,064	140,064	140,064	140,064
	R-squared	0.1365	0.0264	0.0956	0.0843
	b. Women
	P ost × Age (60+)	0.0183 (0.0131)	0.0067 (0.0134)	0.0116 (0.0137) (0.5951) 0.4686
	Mean Y at Age 59	0.34	0.12	0.21	6.5
	Observations	205,434	205,434	205,434	205,434
	R-squared	0.1242	0.0463	0.0743	0.0656

Note: *** p<0.01, ** p<0.05, * p<0.1. This table gives the results of Equation

2

.2 with a quadratic function, for men (upper panel) and women (lower panel) on the age window 50-70. The sample is limited to Black and Coloured men and women. We only report the coefficient of interest, β DiDRD . The dependent variables are binary variables for: (1) employed, (2) informally employed, (3) formally employed, such that (1)=(

2

)+(3), and (4) hours in formal employment. Mean Y at Age 59 refers to the value of the dependent variable at age 59 in the years after the reform. Robust standard errors clustered by race-cohort group.

Table 2 .

 2 A.2: Characteristics by Labor Market Status, Men aged 60-64, Before Reform, 2002-2007 This table gives average characteristics by labor market status, for men aged 60-64 between 2002 and 2007. On average over the period, 13% of men are informally employed, 15% are formally employed, and 72% do not work. Among those informally employed, 83.59% are Black. Education is measured in years of schooling, and household size in number of household members. Salary and wage information is in 2010 Rand, tenure is

	Characteristics (pop %)	Informal Formal Non-Employed
	Proportions	0.13	0.15	0.72
	Socio-Demographics
	Black (67.18%)	83.59	65.34	64.52
	Married (80.11%)	82.32	87.70	78.09
	Education (6.02yrs)	4.68	6.98	6.07
	Household Size (4.66 ppl)	4.54	4.11	4.79
	Job Characteristics
	Average Weekly Hours	45.7	47.3
	Part-time	15.69	3.01
	Median Monthly Salary (R'10)	1373	4662
	Median Hourly Wage (R'10)	7.3	22.9
	Median Tenure (yrs)	5	16
	Self-Employed	36.08	12.49
	Median Firm Size (ppl)	2-4	20-49

Table 2 .

 2 A.3: Old Age Pension and Employment, RDD Results,, PALMS Mean Y at Age 59 refers to the value of the dependent variable at age 59 in the years after the reform. Robust standard errors in parentheses.

		Before Reform		After Reform
		(1)	(2)	(3)	(4)	(5)	(6)
		Employed	Informal	Formal	Employed	Informal	Formal
	a. Men
	Age (60+)	-0.0573*** (0.0182)	-0.0007 (0.0140)	-0.0566*** -0.1097*** -0.0471*** -0.0626*** (0.0157) (0.0107) (0.0077) (0.0100)
	Mean Y at Age 59	0.41	0.18	0.23	0.46	0.15	0.31
	Observations	22,405	22,405	22,405	46,764	46,764	46,764
	R-squared	0.0295	0.0069	0.0279	0.0827	0.0176	0.0557
	b. Women
	Age (60+)	-0.0812*** -0.0510*** -0.0302*** -0.0720*** -0.0430*** -0.0290*** (0.0124) (0.0104) (0.0084) (0.0083) (0.0061) (0.0068)
	Mean Y at Age 59	0.25	0.15	0.09	0.31	0.14	0.17
	Observations	33,026	33,026	33,026	68,977	68,977	68,977
	R-squared	0.0609	0.0325	0.0277	0.0677	0.0247	0.0385
	Note: *** p<0.01, ** p<0.05, * p<0.1. This table gives the results of Equation 2.1 with a linear function,
	for men (upper panel), and women (lower panel), on the age window 55-64. The sample is limited to Black
	and Coloured men and women. We only report the coefficient of interest, β RD . The dependent variables are
	binary variables for: (1) employed, (2) informally employed, (3) formally employed, such that (1)=(2)+(3),
	and (4)=(5)+(6).

Table 2 .

 2 A.4: Old Age Pension and Employment,in-Disc, PALMS Mean Y at Age 59 refers to the value of the dependent variable at age 59 in the years after the reform. Robust standard errors clustered at the race-cohort group.

		Employed Informal	Formal
				Extensive Intensive
		(1)	(2)	(3)	(4)
	a. Men
	P ost × Age (60+)	-0.0539** (0.0214)	-0.0465** (0.0179)	-0.0074 (0.0216) (1.0786) -0.1027
	Mean Y at Age 59	0.52	0.14	0.38	16.70
	Observations	69,169	69,169	69,169	69,169
	R-squared	0.0652	0.0177	0.0514	0.0444
	b. Women
	P ost × Age (60+)	0.0092 (0.0129)	0.0080 (0.0145)	0.0012 (0.0116) (0.5178) 0.1025
	Mean Y at Age 59	0.34	0.12	0.21	8.10
	Observations	102,003	102,003	102,003	102,003
	R-squared	0.0698	0.0276	0.0462	0.0400
	Note: *** p<0.01, ** p<0.05, * p<0.1. This table gives the results of Equation 2.2
	with a linear function, for men (upper panel) and women (lower panel) on the
	age window 55-64. The sample is limited to Black and Coloured men and women.
	We only report the coefficient of interest, β DiDRD . The dependent variables are
	binary variables for: (1) employed, (2) informally employed, (3) formally employed,
	such that (1)=(2)+(3), and (4) hours in formal employment.

Table 2 .

 2 A.5: Density and Balancing Test, PALMS, Men Only

		Diff-in-Disc
		(1)
	a. Density
	Log of Individuals
		-0.1444
		(0.1085)
	Observations	240
	b. Balancing Test
	Y Variable
	Black	0.0016
		(0.0221)
	White	-0.0307
		(0.0204)
	Married	0.0026
		(0.0183)
	Education	-0.1354
		(0.2080)
	Cape Province	0.0011
		(0.0175)
	Household Size	-0.0223
		(0.1280)
	Observations	166,599
	Note: *** p<0.01, ** p<0.05, * p<0.1. The sample is
	restricted to men. This table reports the coefficient β DiDRD
	of Equation 2.2 with a quadratic function, on the age window
	50 to 70. In the upper panel, observations are collapsed
	at age×year cell level. The dependent variable is the log
	of individuals within each cell. In the lower panel, the
	variables aligned vertically are the dependent variables in
	the regression. Education is equal to the completed years of
	schooling. Cape Province is a binary variable equal to one
	for an individual residing in either the Western, Eastern,
	or Northern Cape provinces. Robust standard errors in
	parentheses.

Table 2 .

 2 A.6: Old Age Pension and Employment, Linear Fit 60-70, Diff-in-Disc with Threshold at 65, PALMS

		Employed Informal	Formal
				Extensive Intensive
		(1)	(2)	(3)	(4)
	a. Men
	P ost × Age (65+)	0.0629** (0.0295)	0.0389*** (0.0126)	0.0240 (0.0252) (1.1366) 1.6033
		66,903	66,903	66,903	66,903
	R-squared	0.0764	0.0193	0.0929	0.0815
	b. Women
	P ost × Age (65+)	-0.0096 (0.0122)	-0.0097 (0.0080)	0.0002 (0.0097) (0.4305) -0.0355
	Observations	99,982	99,982	99,982	99,982
	R-squared	0.0532	0.0146	0.0613	0.0545
	Note: *** p<0.01, ** p<0.05, * p<0.1. This table gives the results of Equation
	2.2 with a linear function, for men (upper panel) and women (lower panel) on
	the age window 60-70. The threshold is set at 65, rather than 60. We only
	report the coefficient of interest, β DiDRD . The dependent variables are binary
	variables for: (1) employed, (2) informally employed, (3) formally employed, such
	that (1)=(2)+(3), and (4) hours in formal employment. Robust standard errors
	clustered at the race-cohort group.

Table 2 .

 2 A.7: Labor Outcomes of 55-59 Men; Employment, Hours and Wages Stars indicate the statistical significance of the coefficients. * : p < 0.1 , * * : p < 0.05 , * * * : p < 0.01 . Each column corresponds to the estimation of Equation 2.3 relative to the population indicated in each column. Treatment status is defined by the magnitude of the industry × occupation-specific labor supply response of the 60-64 y.-o. Black and Coloured men. The weights in columns (4) to (6) correspond to the size of the industry× occupation cells in terms of employment of non-treated groups in 2007. Standard errors are clustered at the industry× occupation level.

		Unweighted			Weighted
	60-64	60-64	55-59	60-64	60-64	55-59
	Black & Col.	All	Black & Col. Black & Col.	All	Black & Col.

  3.A.1 and 3.A.2 for more cross-country comparisons). Its level of self-employment is comparable only to ex-Soviet countries. This pervasive absence of entrepreneurial activities, in particular among the native African population, is puzzling, and may be one reason behind the country's low employment rate.This paper shows that, contrary to an established finding in other countries, relaxing liquidity constraints in South Africa does not lead to increases in self-

employment. Indeed, large cash transfers to the household, either in the form of child grants or old age pensions, do not result in higher entrepreneurship among working-age individuals. These findings rule out liquidity constraints as the main reason for the absence of self-employment in South Africa, and suggest that other

  Instead, the OAP provides a large transfer, around 250$ 2010 PPP, per month to elderly individuals above a certain age threshold. In 2010, around 2.5 million elderly received the OAP every month. The Old Age Pension was formally introduced in the early 1920s, but extended to the Black and Coloured population only in the 1990s, while the Child Support Grant was introduced in 1998, and rolled-out in the subsequent years. More than 30% of households in South Africa receive at least one of these transfers, and many receive both.2

  data, both the 2001 and 2011 wave, complemented by the Community Survey in 2007. This survey, carried out on purpose in between twoCensus waves, has a questionnaire that is virtually identical to the census and a large sample size. For simplicity, I refer to Census data as both the actual census and the Community Survey, given their high comparability. These are the only sources of data that allow to identify, at the same time, the impact of both the Child Support Grant and the Old Age Pension. Identification of the pension only requires data with a discrete age variable, this is available in most South African datasets. By contrast, identification of the effects of the CSG requires information on fertility (and a large sample size), which is only available in the Census. Information on fertility (specifically, year of birth of the youngest child) is what determines whether a mother is treated or not. One could alternatively link child and mother in the household, but this would provide a much noisier measure of treatment status, as the probability of co-residence between mother and child declines as the child gets older, and could be endogenously determined by the grant itself. Moreover, the Census is the only source that allows to identify migrants to South Africa. I use this information in Section 7

3

  While wage-employment has increased over the period by around 17%, self-employment has stagnated. The complete absence of growth in the number of self-employed is striking, given the very low self-employment rate in 1994. When considering that during the same period, South Africa experiences a dramatic growth in the number of cash transfer recipients 4 , the results from the next section should come as no surprise. The roll-out of large, unconditional cash transfers in South Africa has not led to a higher level of micro-entrepreneurial activities, and this is true in the aggregate, as it is at the individual level.

Table 3 .

 3 1: Child Support Grant and Self-Employment, Linear Fit 1987-1998, RD Results Note: *** p<0.01, ** p<0.05, * p<0.1. This table gives the results of Equation3.1 with a linear function on the cohort window 1987-1998. I only report the coefficient of interest, β CSG . The dependent variable is always a binary variable equal to one for individuals who are self-employed. Robust standard errors clustered at the household level in parentheses.

			Self-Employed
		(1)	(2)	(3)	(4)
	CSG	0.0028	-0.0012	0.0032	-0.0019
		(0.0051) (0.0032) (0.0055) (0.0037)
	Year	2007	2011	2007	2011
	Sample	Direct Recipients Other HH Members
	Observations 43,927	112,093	49,985	124,158
	R-squared	0.0176	0.0094	0.0148	0.0123

Source: Author's calculations on Community Survey (2007) & Census (2011).

Table 3 . 2 :

 32 Old Age Pension and Self-Employment, Linear Fit 55-64, RD Results, Census Note: *** p<0.01, ** p<0.05, * p<0.1. This table gives the results of Equation 3.3 on the PALMS with a linear function on the age window 55-64. I only report the coefficient of interest, β OAP . The dependent variable is always a binary variable equal to one if the individual is self-employed. Robust standard errors clustered at the household level in parentheses. Source: Author's calculations on Census (2001 & 2011).

			Self-Employed
		(1)	(2)	(3)	(4)
	�{AgeOM i � 60} -0.0006 -0.0066** 0.0027	0.0011
		(0.0029) (0.0031) (0.0021) (0.0029)
	Year	Before	After	Before	After
	Sample	Direct Recipients	Other HH Members
	Observations	59,922	89,956	62,446	85,000
	R-squared	0.0010	0.0028	0.0003	0.0006

Table 3 . 3 :

 33 Old Age Pension and Self-Employment, 55-65, DiD Results, Census Note: *** p<0.01, ** p<0.05, * p<0.1. This table gives the coefficient β OAP of Equation3.4 on direct recipients in Column(1), and other adult household members in Column(2). The dependent variable is always a binary variable equal to one if the individual is self-employed. Robust standard errors clustered at the household level in parentheses.

	Self-Employed
	(1)	(2)

Source: Author's calculations on Census (2011) & Community Survey (2007).

Table 3 .

 3 A.1: Measurement of Self-Employment in South Africa Across Surveys and Years

	Measurement
	Year

  DefinitionsPALMSDefinition based on Individuals who Report Working for Themselves GHS Definition based on Individuals who Report Running Any Kind of Business in the Last Week Census/CS Census 2001: Definition based on Individuals who Identify Directly as Self-Employed CS 2007 & Census 2011: Definition based on Individuals who Report Running Any Kind of Business in the Last Week NIDS Definition based on Individuals who Report Having Engaged in Self-Employment Activities in the Past 30 Days In the upper panel, this table gives the level of self-employment in South Africa for the Black and Coloured

													2009 2010 2011 2012 2013 2014 2015
	PALMS
	5.7 5.9	2.0	2.9	3.5	5.3 10.8 8.9	7.4	6.4	6.4	7.3	8.2	7.2	6.3	5.8	5.6	5.6	5.6	5.3	5.1	5.3
	GHS
						4.9	4.2	4.5	3.1	3.0	3.7	2.6	5.1	5.0	5.0	5.1	5.3	5.4	5.6
	Census/CS
					1.8						3.9				4.9
	NIDS
												6.2		4.2	5.4	4.8		7.5	5.6

The only exception is a recent extension of[START_REF] Haushofer | The Short-Term Impact of Unconditional Cash Transfers to the Poor: Experimental Evidence from Kenya[END_REF] at the two-year horizon.

La seule exception est une extension récentede Haushofer and Shapiro (2016), mais seulement à un horizon de deux ans.

Ces conclusions justifient en partie une action de politique publique, en suggérant qu'une intervention peut potentiellement compenser ce manque de transmission intergénérationnelle. Dans ce chapitre, je discute des pistes de recherche possibles et des mécanismes qui, selon moi, doivent encore être mis à l'essai. La question de savoir si les décideurs politiques devraient encourager l'entrée dans l'entreprenariat en Afrique du Sud, plutôt que d'essayer d'augmenter l'emploi salarié, n'est pas sans controverse. Cependant, le taux d'emploi salarié en Afrique du Sud n'est pas faible, il est même à peu près comparable à la moyenne des autres pays à revenu intermédiaire.Si la priorité est d'accroître l'emploi, il semble donc probable qu'il devra provenir, au moins en partie, du travail indépendant.En conclusion, l'objectif de cette thèse de doctorat est de proposer une analyse des réponses aux programmes de transferts monétaires en Afrique du Sud pour tirer des leçons sur le fonctionnement des marchés du travail dans les pays à revenu intermédiaire. D'après ma lecture de ces résultats, les principales conclusions d'intérêt général sont les suivantes: i) les transferts monétaires non conditionnels peuvent avoir des conséquences durables sur les emplois des bénéficiaires adultes sur le marché du travail; en particulier, mes conclusions montrent qu'ils peuvent améliorer la qualité des emplois, grâce à une recherche prolongée qui mène à de meilleurs emplois; ii) en outre, les bénéficiaires des transferts monétaires ont une plus forte probabilité de travailler dans le secteur formel, ce qui révèle indirectement certains facteurs de segmentation du marché du travail dans un pays à revenu intermédiaire. En ce qui concerne les pensions publiques soumises à conditions de ressources, mes résultats montrent que iii) un transfert sous condition de revenu ne pousse pas les gens à se tourner vers l'emploi informel; iv) à salaire donné, les travailleurs informels quittent leur emploi, alors que les travailleurs formels ne le font pas, quand ils deviennent éligibles à la pension publique. De ces résultats, je tire également des conclusions

This is roughly equivalent to 400 $

2010, or 650 $ 2010 in PPP terms.

The debate can be traced back to[START_REF] Todaro | A Model of Labor Migration and Urban Unemployment in Less Developed Countries[END_REF], immediately challenged by[START_REF] Hart | Small-Scale Entrepreneurs in Ghana and Development Planning[END_REF][START_REF] Hart | Informal Income Opportunities and Urban Employment in Ghana[END_REF], presumably the first to define and openly ask the question about the nature of the informal sector: "The question to be answered is this: Does the 'reserve army of urban unemployed and underemployed' really constitute a passive, exploited majority... or do their informal economic activities possess some autonomous capacity for generating growth in the incomes of the urban (and rural) poor?"

This terminology reflects the classification of population groups in all the official statistics in South Africa: Black Africans, Coloured, Indian/Asians, Whites.

[START_REF] Azuara | Informality and the Expansion of Social Protection Programs: Evidence from Mexico[END_REF] study the labor market repercussions of the introduction of Seguro Popular, a non-contributory health insurance program, in Mexico. While such a policy

One possible interpretation for these different findings is the diverse nature of informal employment, as shown by the drastically different importance of self-employment in the two economies. The lack of self-employment in South Africa is possibly a long-term consequence of its troubled political and economic history. The Apartheid regime strongly repressed all types of self-employment activities among the native African population.

See Nekoei and Weber (2017) for a detailed summary of the empirical evidence.

One notable exception is[START_REF] Gerard | Informal Labor and the Efficiency Cost of Social Programs: Evidence from the Brazilian Unemployment Insurance Program[END_REF], who study how informality in Brazil impacts the efficiency cost of unemployment insurance, but do not look at subsequent job quality.

Improvements in labor market outcomes of recipient mothers is certainly not the main policy justification behind child support grants. However, this is an important aspect, in particular for cash transfers that are targeted to children only in name, without any actual conditionalities attached.

[START_REF] Bianchi | Liquidity, Risk, and Occupational Choices[END_REF] find no long-term effects on the total population of a cash transfer program in Mexico on the probability of self-employment. In a recent extension to[START_REF] Haushofer | The Short-Term Impact of Unconditional Cash Transfers to the Poor: Experimental Evidence from Kenya[END_REF], the authors have shown that an unconditional cash transfer can have a persistent positive effect on assets at the two-year horizon.

The Old Age Pension, i.e. the public pension system, accounts for slightly more than the CSG in terms of yearly government spending.

Coverage of these other grants at the household level is presented in Figure1.A.1 in the Appendix, and does not vary significantly over time.

"Kruger (1998) states 0.2% of African children, 1.5% of White children, 4% of Indian children and 4.8% of Coloured children received the state maintenance grant in 1990" (McEwen, Kannemeyer and Woolard (2009))

This is true for biological children. For non-biological children, only up to 6 grants can be paid.

The South African government lists the following documents: "If you are not the child's parent, you must provide proof that you are the child's primary caregiver through an affidavit from a police official, a social worker's report, an affidavit from the biological parent or a letter from the principal of the school attended by the child." Source: South African Social Security Agency (http://www.gov.za/services/child-care-social-benefits/child-support-grant)

In only 4% of the cases, a man reports receiving the grant.

The amount of the grant in real terms is obtained by adjusting for inflation, measured by CPI at the national level. CPI data is obtained from OECD.stat.

The means test threshold was set 30% higher in rural than urban areas, apparently in order to compensate for a lack of access to health and education services in those areas[START_REF] Lund | Changing Social Policy: the Child Support Grant in South Africa[END_REF]).

This test is imperfect because it also depends on the magnitude of the first term of Equation1.9, therefore whether the disincentive effect to look for better jobs as one gets richer dominates. Also, individuals might want to voluntary keep consumption low when unemployed as in a buffer stock model[START_REF] Card | Cash-on-Hand and Competing Models of Intertemporal Behavior: New Evidence from the Labor Market[END_REF]).

More precisely, this would be the case if the third-order derivative of the utility function is positive, as in a Decreasing Absolute Risk Aversion (DARA) utility function, such as u(c) = ln(c).

The stated purpose of the Community Survey is to be a smaller census, in order to have some information in between two census waves, which are 10 years apart, without having to carry out a full census. The questionnaires are very similar. The publicly available Census waves are a 10% subsample of the overall Census in the years 2001 and 2011; the Community Survey is a large survey of roughly 2% of the population in 2007.

I exclude the two initial years of the LFS, 2000-2001, because of the problems of comparability in measurement of informal employment as pointed out in[START_REF] Kerr | Sampling Methodology and Fieldwork Changes in the October Household Surveys and Labour Force Surveys[END_REF].

See Magruder (2012) for an interesting analysis of the employment effects of labor unions in South Africa.

The GHS does not have a measure of year of birth, but only age. Therefore, information on the year of birth can only be obtained by taking yearage. Given that the GHS takes place in July, those born in the first half of the year have the correct year of birth, yearage = yob, while those born in the second half appear as born one year later, yearage = yob + 1. Therefore, in the GHS, each cohort is composed by those born before July of the correct year, and those born after July of the previous year. This implies that the median month in each cohort is January of the indicated year. So, these graphs should be interpreted as indicating take-up for mothers whose youngest child, on average, is born in January.

One could, in theory, exclude some cohorts in order to increase this differential. For example, excluding cohort 1992 or excluding cohort 1993. While this estimation would certainly give a larger first stage, it would also compare mothers whose child's cohorts are further apart, and hence less likely to be similar. However, taking out cohort 1992 does not qualitatively change the results.

Children born in 1993 gradually lose eligibility in 2007, depending on the month of birth. Given that the Community Survey takes place in early 2007, most children are still receiving the transfer.

[START_REF] Lee | Regression Discontinuity Inference with Specification Error[END_REF] initially suggested that in an RD design with a discrete forcing variable, clustering of the standard errors should occur over the discrete values of the running variable, while in a more recent development,[START_REF] Kolesár | Inference in Regression Discontinuity Designs with a Discrete Running Variable[END_REF] strongly advise against this practice, in particular when the number of clusters is small. In my estimations, the standard errors always drop significantly when clustering by the running variable. For this reason, I only cluster at the

This is a quadratic function on the 1981-2004 window, chosen according to the AIC criterion. Results are virtually identical with a linear window on the 1987-1998 window, chosen with the same criterion. See Table1.A.3.

I do not have information on working hours or on-the-job search, so I cannot test this hypothesis directly.

This prediction is unambiguous in[START_REF] Card | Cash-on-Hand and Competing Models of Intertemporal Behavior: New Evidence from the Labor Market[END_REF] in a job search model with assets where workers choose effort. In the model proposed in Section 3, this prediction is ambiguous, but is more likely to hold as liquidity constraints become more severe.

As shown in Table1.A.4, the issue with estimating effects on other household members is that this population, which is defined as the one sharing the household with a treated mother, changes significantly over time. This complicates the interpretation of the results on other household members in a dynamic way, but the absence of significant effects in any period suggests they are not affected.

The lack of a negative coefficient on informal employment (or, symmetrically, the presence of a positive and significant effect on employment) is likely the result of age bias in the estimator; as can be seen in Figure1.A.6, the child cohort function is much more "steep" in 2001 than 2011, because children are younger, and age effects are stronger.

There are 10 occupations and 10 sectors, for 100 combinations. However, some of these are empty cells, or with too few people to be statistically relevant.

The South African labor market is characterized by large worker flows and by high churning, meaning worker flows that are not justified by job reallocation[START_REF] Banerjee | Why has Unemployment Risen in the New South Africa?[END_REF]). This stylized fact holds true even when focusing only on formal jobs, as[START_REF] Kerr | Job Flows, Worker Flows and Churning in South Africa[END_REF] confirms using administrative data on the universe of formal jobs in South Africa.

However, it should be underlined that, although they might play on the same channel, transport subsidies are conceptually very different from an unconditional cash transfer. A transport subsidy lowers the marginal cost of search and also provides an income effect, while an unconditional cash transfer only provides an income effect, and changes job search only in the presence of liquidity constraints.

For simplicity, when constructing a measure of informal sector employment, I include employment in agriculture in the informal sector. The advantage of this approach is that it allows me to divide total employment in just two sectors, formal and informal. This simplification does not change the results in any way.

To our knowledge, the only other paper to use these reforms for an analysis of the effects of public pension is[START_REF] Matsuda | Large-Scale Social Transfer and Labor Market Outcomes: The Case of the South African Pension Program[END_REF].

This is clearly stated in the guidelines published by the South African Government: http: //www.gov.za/services/social-benefits-retirement-and-old-age/old-age-pension

Information about the Disability Grant can be found here: http://www.gov.za/services/ social-benefits/disability-grant

This figure is calculated on administrative data from the Social Security administration (SOCPEN database) found in the yearly National Budget Reviews. As from 2008 when retirement age is lowered, there is a large increase in the number of OAP recipients. This positive trend is partly offset by a decrease in the number of disability grants, because the two transfers are not cumulative. The total number of beneficiaries increases during the period of the reform but not at a faster (nor slower) pace than the previous years, because the faster growth in the number of pensions paid is offset by a decline in the number of disability grants.

This share grows slightly during the period of our study(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015), as life expectancy, which which was well below 60 over the period in South Africa, increased.

The assumption of the DID is that the affected and unaffected age-groups would have evolved in the same way over the period in the absence of the reform.

The RD overestimates the employment effect by a factor of 2 when using a linear function on the 55-64 window, see Table2.A.3.

The drop in total employment is roughly of the same magnitude as for men, and split in equal parts between formal and informal. If, as for men, there is little or no effect of the pension on formal employment, we can also speculate that the RD estimation over-estimate the effect on employed by a similar magnitude.

Estimating an effect on the intensive margin of informal employment is more complicated because of the large extensive margin response.

Because of growth in real wages over time, the identification assumption that the magnitude of the discontinuity would have stayed the same over time at each level of wages is unlikely to hold. For more details seeAppendix 2.A.3.

The intuition is that the joint density of labor supply and wages is the product of two continuous densities: the conditional labor supply and the distribution of wages; if the latter is continuous, the discontinuity in the joint density identifies the discontinuity in the conditional labor supply.

This group is only 10% of formal employment, but it is as large as the first quartile of informal workers, given the greater size of formal employment overall.

Weighting the distribution of coefficients by the size of the employment of each sector guarantees that treatment and control groups are of equal size.

The small discrepancy could come from the fact that we are focusing here only on the two most extreme quartiles of sectors.

Note:The age threshold was different for men and women until it was equalized between 2008-10. Amount is presented in current Rand and 2010 Rand separately, CPI data is taken from OECD.stat. Source: The main sources on OAP amounts are[START_REF] Eyal | Throwing the Book at the CSG[END_REF] and the South African government (http://www.gov.za/services/social-benefits-retirementand-old-age/old-age-pension). Reform dates are from the National Budget Reviews(2007)(2008)(2009)(2010)(2011)(2012)(2013). Information on the means-test for 2009 and 2010 is collected from US(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015);[START_REF] Ranchhod | The Effect of the South African Old Age Pension on Labour Supply of the Elderly[END_REF] for 2003;Case and Deaton (1998) for 1994; and from SASSA (2010 and from SASSA ( , 2013 and from SASSA ( , 2014 and from SASSA ( , 2016) ) for all remaining years.

In any case, the latter density is observed, and can thus be estimated.

Beneficiary is the individual who is eligible for the grant, recipient is the person actually being paid the grant.

For a full description of these programs and their history refer to the first chapter of this thesis for the Child Support Grant, and the second chapter for the Old Age Pension.

Including other population groups in South Africa (slightly) changes the levels but not the trend.

The OAP is extended to the Black and Coloured Population in the early 1990s, while the CSG is formally introduced in 1999. There is a dramatic increase in the share of the population receiving the CSG in the early 2000s, as can be seen in Figure3.A.4.

This was the result of financial incentives for enumerators to find self-employed individuals[START_REF] Kerr | Sampling Methodology and Fieldwork Changes in the October Household Surveys and Labour Force Surveys[END_REF]).

For one child, this is equivalent to roughly 4200$ '10 PPP over

years.

"Having an entrepreneur for a parent increases the probability that a child ends up as an entrepreneur by a factor of 1.3 to 3.0" Lindquist, Sol and Van Praag (2015).

[START_REF] Lindquist | Why do Entrepreneurial Parents Have Entrepreneurial Children?[END_REF] uses the gender component to disentangle the role modelling mechanism.[START_REF] Sørensen | Closure and Exposure: Mechanisms in the Intergenerational Transmission of Self-Employment[END_REF] uses the timing of the entrepreneurship spell in the parents' life.[START_REF] Sørensen | Closure and Exposure: Mechanisms in the Intergenerational Transmission of Self-Employment[END_REF]: "The results suggest that parental role modelling is an important source of the transmission of self-employment. However, there is little evidence to suggest that children of the self-employed enter self-employment because they have privileged access to their parent's financial or social capital, or because they have superior entrepreneurial abilities."

Acknowledgements

public pension. From these findings, I also draw some conclusions that are South Africa-specific, namely that v) self-employment in South Africa does not increase as result of cash transfer programs, which indicates that barriers to entry are of a different nature; and that vi) the self-employment rate in South Africa is unlikely to recover by itself, without some sort of policy intervention to compensate for past restrictions in its labor market. Industry: What does the business do (main economic activity)?

Occupation: What is the main occupation of (the person) in this workplace?

Fertility: When was (the person's) last child born? (women aged 12 to 50 years) Personal Income (Categorical): What is the income category that best describes the gross income of (this person) before tax?

Community Survey 2007

Employment Status: In the last 7 days, did (the person) run or do any kind of business, big or small, for himself/herself or with one or more partners even for only one hour? and In the last 7 days, did (the person) do any work for a wage, salary, commission

Abstract

Do means-tested social programs push workers into informality? This paper investigates the impact of a reform of the old-age, non-contributory and means-tested pension system in South Africa, which lowered the age of retirement from 65 to 60 for men only. By employing a difference-in-discontinuities ("diff-in-disc") approach, we show that this reform triggered a large drop in elderly male employment. This response at the extensive margin comes largely from informal workers, who drop out of the labor force, while formal employment is mostly unaffected. This heterogeneity is not the result of lower wages in the informal sector; at the same level of wages, informal workers drop out, while formal workers do not. This occurs despite the implicit incentive to draw benefits and work informal jobs at the same time, and even if the means-test is located where formal and informal wages largely overlap.

In total, the pension reform has driven about 25,000 individuals aged 60 to 64 out of the labor market. By exploiting the differential effects this reform has had across sectors, we can reject one-to-one substitution with the most similar workers.

JEL Codes: J26, J46, H55, O17 Keywords: old-age pensions; difference-in-discontinuities; informal employment; South Africa informal employment, informal labor supply responses by wage can be interpreted directly; with some caveats, we argue one can also interpret the effects on formal employment by wage.

To obtain a counter-factual of the distribution of wages absent the pension reform, we focus on the closest unaffected age group. The wage distribution of 55-59 years old is virtually identical to that of 60-64 years old before the reform (Figures 2.A.6 and 2.A.7). Therefore, we use the distribution of informal wages for 55-59 years old to construct quartiles, separately before and after the reform, and estimate heterogeneous effects by wages.

The extensive margin response of informal employment by wage quartile is plotted in Figure 2.3 for men, and in Figure 2.4 for women. The intuitive prediction of our simple framework is that the effect should be stronger for lower levels of wages, but this is not what we observe. For men, the negative effect on informal employment is constant up to the third quartile of the distribution. Only in the top quartile of the informal wage distribution the effect seems to converge to zero. We observe very similar patterns for women, keeping in mind that they are treated at 60 in both periods. Indeed, extensive margin response by wage is quantitatively and qualitatively similar, with an effect of similar size across the wage distribution, both before and after.

Importantly, the magnitude of the response is the same even for those workers whose full-time equivalent monthly salary, i.e. what they would make in a month given their hourly wage and 43 hours working week, is larger than the amount of the pension (R 1000). In other words, some informal workers are willing to give up more earnings than what they get with the pension. One interpretation of the stability of the effect with respect to hourly wage is that the pension might relieve a subsistence-level constraint. A given share of workers at each point of the informal wage distribution cannot afford not to work. Once this constraint is lifted, they stop working entirely. This would have potentially important implications in terms of welfare, as it suggests that any positive effect on utility might be greater than just the income effect. countries. From these results, one could derive two main policy implications, which may apply to different extents to countries other than South Africa: 1) while there are strong disincentive effects on employment when expanding a public, non-contributory pension scheme, these mostly impact informal jobs. We do not observe any significant crowding out of formal jobs, despite the non-negligible amount of the public transfer;

2) for countries with similar demographic and employment structures to South Africa, pension reforms of this kind are unlikely to free-up a significant number of jobs for the young.

2.A Appendix

2.A.1 Conceptual framework

An individual i chooses how much to consume (C), how much to work (l = T -L) and in which sector to work (k ∈ {F, I}). Utility is given by:

We assume for simplicity that µ i (I) = 0 and µ i (F ) = µ i . Working in the formal sector implies a negative utility component that is not encountered when working in the informal sector. Prior to the reform, we assume that non-labor income is 0. This does not alter the qualitative results we derive but simplifies the notation. As a result of the reform, the non-labor income m is given to everyone in the informal sector I, and to people earning less than W in the formal sector.

Prior to m being implemented, we observe people in the formal and in the informal sector. These are people for whom, respectively,

, k ∈ {I, F } refer respectively to the optimal levels of leisure and consumption, given that sector k has been chosen, and the We detail in this Appendix the issues with carrying out a difference-in-discontinuities estimation procedure on employment dummies for different levels of wages, and why and under which assumptions a simpler regression discontinuity design is more appropriate and easier to implement.

2.A.2 Tables and Figures

We call Y 01 , Y 00 , Y 11 and Y 10 the potential outcomes of the individual when he receives the treatment in T = 0, when he does not (still in T = 0), and when he receives the treatment in T = 1, and when he does not. Let us define the treatment as being eligible to the pension. D is a dummy variable for receiving the treatment, T is a time variable equal to 1 if the period is post-reform, and 0 if it is pre-reform, and X represents the age of the individual:

] measures the effect that we are interested in, which is the local effect of the pension at 60, for people who are 60 after the reform is implemented.

We do not observe Y 01 , Y 00 , Y 11 and Y 10 for all X and T, but only:

The classical RDD estimand is defined as:

which, under the continuity assumption (�) that lim

A potential problem with the RDD estimand -and justification for using the The proposed specification captures the effects of being eligible to the CSG for at least one year, and has the advantage of limiting the bias from age effects.

Alternatively, one can run a diff-in-diff specification that allows to explore the large variation in the length of eligibility across cohorts. This allows to test whether being eligible for more years has a larger (or any) impact on the probability to be self-employed. In this case, the specification is the following:

where �{c i = c} is a binary variable equal to one if the youngest child is born in a given cohort, c. As a control group, I arbitrarily choose those whose youngest child is born in 1991 and 1992, hence the last cohorts not to receive the CSG. I then interact this with a variable indicating the year to get the evolution of the relative difference over time.

Graphical evidence that the CSG did not create a discontinuity in the selfemployment rate is presented in Figures 3.3 and 3.4. Table 3.1 gives the RD coefficient of Equation 3.1 on the probability of being self-employed. Those mothers who have received at least one year of the grant are as likely to be self-employed as mothers who never have. There is no evidence of neither a short-term, nor a long-term effect. The lack of an effect on recipient mothers might be specific to this group, which, as shown in Chapter 1, is impacted in terms of their labor market outcomes, but not in terms of self-employment. However, I also find no evidence that the CSG increases entry into self-employment for other household members who are not the direct recipients of the grant. Adult members of households that receive (or have received) the grant are not more likely to engage in self-employment activities.

It should be noted that the particularly low level of self-employment in the 2001

Census is the result of different measurement from the 2011 wave and the Community Survey. In the first Census wave, individuals self classify as self-employed, rather than being asked whether they run their own business or not. As Figure 3.1 shows, when consistently measured, we observe no significant increase in self-employment see that the self-employment rate tends to be lower for migrants born before 1970s, and that this difference persists over time. Interestingly, this is not a pattern that we observe for natives, or for wage-employment, which is instead a smooth function of age (Figure 3.A.13). 2) presents the OLS estimates of regressing Year Moved on self-and wage-employment, with no controls. In column ( 3) & (4), controls for age, sex, education, race, and urban status are included. In column ( 5) and ( 6), controls for country of birth and district of residence are included. The bottom panel only includes individuals over 15 years old who share an household with their father. Robust standard errors in parentheses.

Source: Author's calculations on Census 2011.

In a similar way, if the mechanism of inter-generational transmission is at play, we would expect children of people who migrated later to enter more into selfemployment. This is indeed the case, as shown in Table 3.4, and in Figure 3.A.14 when using the year of birth of the father. There is a strong positive correlation between the self-employment probability and the year of birth of the father (which implies later migration), which is again a pattern that is not observed for South Note: This graph gives the share of self-employment in selected countries with similar GDP per capita to South Africa in 2010, over working-age population (grey bar) and total employment (black bar) respectively. The data is from 2010, or the closet available year. Source: World Bank.

3.A Appendix

List of Tables

List of Figures This dissertation studies the employment effects of cash transfers in a segmented labor market.

The first and main chapter shows that an unconditional cash transfer program targeted at mothers has lasting positive impacts on job quality. Five years after having received the cash transfer, treated mothers are more likely to be employed in the formal sector. This appears to be the result of changes in the way recipients search for a job, as treated mothers are unemployed for longer and target better jobs. The second chapter shows the employment effects of a reform in the means-tested, non-contributory pension system of South Africa, which lowered the age of retirement from 65 to 60 for men. The reform caused a large extensive-margin response, as informal workers stop working when they become eligible to the pension. Instead, formal workers do not quit their jobs nor switch to the informal sector to become eligible to the pension.

Lastly, this dissertation discusses the lack of self-employment in South Africa. Building on the results of the first two chapters, the last chapter shows that South Africans do not increase entry to self-employment as a result of cash transfers. This indicates that liquidity constraints are not the main reason for the lack of self-employment in South Africa, which is likely to have historical roots stemming from Apartheid. The chapter discusses evidence and potential policy implications of this explanation, alongside possible avenues for future research on this phenomenon.

Keywords