Christine Meunier
email: meunier@lpl.univ-aix.fr

Cheryl Frenck-Mestre

Taïssia Lelekov-Boissard

Martine Le Besnerais
email: martine@prosodia.uab.es

Production and perception of vowels: does the density of the system play a role?

The objective of the present paper is to understand whether the "density" of the vowel system of a language, in other words the large or small number of vowels in the language, plays a significant role in the production and perception of vowels. Three languages (French, English and Spanish) are analysed. This work rests upon the comparison between speech production and the cognitive processing of linguistic units. Two different methodologies were employed in this aim : observation of phonetic productions and analyses of behavioural measures. First results lead us to hypothese that perception units can be larger than production units. In other words, the density of a vowel system seems to influence vowel perception but not vowel production.

INTRODUCTION

Each language has its own more or less fixed inventory of phonemic units (vowels and consonants). Given this, each language differs as concerns the number of phonemes in its inventory. The study performed by Manuel and Krakow [START_REF] Manuel | Universal and Language particular aspects of vowel-to-vowel coarticulation[END_REF] showed that the tolerance for variation in the production of a vowel is lesser in a language with a filled vocalic system Hence, the density of the vocalic system apparently has an impact upon vowel perception. This hypothesis is weakened, however, by the results obtained by Manuel [START_REF] Manuel | The role of contraste in limiting vowel-to-vowel coarticulation in different languages[END_REF] in a comparative study of three languages which varied little between each other as concerns their vocalic space. As concerns perception, the majority of studies have dealt with the more global problem of perceptual assimilation rather than with the specific issue of density. On the other hand, Flege and Munro [START_REF] Goto | Auditory perception by normal Japanese adults of the sounds /l/ and /r[END_REF] argue in favor of a universal perceptual process, based upon a purely auditory component which is independent of any given phonological system.

The specificity of the organisation of vowels within each language is directly related to questions concerning the cognitive processing of linguistic sounds : do the listeners of languages which possess only a few vowels perceive larger phonetic categories than listeners of languages with a dense vocalic system? Moreover, is the phonetic production of a vowel affected by the density of the system? Last, is there a relationship between the acoustic space associated with the production of a vowel and the perceptual space that is representative of its vocalic category?

In the present work, we plan to compare the processing of vowels by speakers of French, Spanish and English. In English, there are between 13 and 15 oral vowels while French distinguishes some 10 or 12. The density of these two systems is therefore similar, but the specific vowels in each differ considerably. In contrast, Spanish has a relatively sparse inventory, with only 5 vowels, but within this inventory we find the same vowels as in French and English (/a/, /e/, /i/, /o/ and /u/). These three languages thus offer the opportunity to distinguish the effects of density, without a concomitant difference in the phonological nature of the vowels present in the two systems (French and Spanish), from the effect of differing inventories of vowels within comparably dense systems (French and English). This work rests upon the comparison between speech production and the cognitive processing of linguistic units. Two different methodologies were employed in this aim : observation of phonetic production and analyses of behavioural measures.

HYPOTHESES

Whether the question is production or perception, various hypotheses can be envisage. The vowel space may be a function of the density of the system, in which case the space covered by a phoneme of language A takes up the space of two vowels in language B (Hyp. 1). The vowel space may be constant, independent of the density of the vowel system, in which case the acoustic-articulatory space between two vowels is unexploited (Hyp. 2). Last, the vowel space may be fixed, but the production and/or perception of vowels which are phonologically similar may differ, in which case the /a/ in Spanish may differ from the /a/ in French (Hyp. 3). These three hypotheses will be

LINGUISTIC MATERIAL

For each vowel two lists were created, each having three-word groups that contained the vowel of interest. To avoid ambiguity (the pronunciation of certain vowels in isolation in French and English is rather artificial), speakers were requested to produce vowels after having repeated all three words.

Examples :

English : "he, me, be ... i/" French:

"si, lit, nid ... /i/" Spanish:

"si, mi, ti ... /i/" Three speakers (2 women, 1 man) of each language were recorded. Each speaker repeated the lists corresponding to his/her language 5 times (3 speakers * 5 repetitions * 2 lists = 30 exemplars per vowel et per language).

This corpus allowed us to evaluate the variability of vowel production in the absence of context. The controlled nature of the situation should considerably reduce variation. Moreover, the present corpus of isolated vowels provided us with the necessary materials for the preparation of acoustic stimuli for later behavioural experiments.

The two corpuses were manually marked to identify each vowel. Thereafter, an automatic procedure enabled the analysis of F1 and F2 formants of each vowel (the measurement was taken at one-third of the duration of each vowel, due to English speakers tendency to produce dipthongs).

The formant measurements were then normalised to attenuate inter-individual differences, which were not considered in the present project.

ACOUSTIC RESULTS

English and Spanish systems occupy approximately the same space as concerns F1/F2 (see figure 2). The French system, however, was slightly different : it appears to be more closed and more posterior than the other two systems. The size of the system is comparable for the three languages : hypothesis 3 can be refuted as concerns production; Spanish system is not reduced or centralized. The Spanish and French speakers produced distinct vowels which showed little to no overlap between categories, quite unlike the English speakers (figures 3, 4 and 5)

Nonetheless, the size of the production spaces appeared relatively similar for the three languages, although the vowel spaces allocated for French and Spanish were somewhat more restricted than those for English (figure 5).

It can be further noted that the production space of back vowels /u/ et /o/ was highly restricted for all three languages. In opposition, low front vowels were given a relatively large space. In English, certain vowel categories were completely contained within the space of other vowels (/I/ is contained within /e/). For Spanish speakers, there was basically no overlap between vowels as their number is lesser within the same global space : the density of the vowel system does not appear to have an impact upon the production of vowels. This observation goes against hypothesis 1, which predicted a larger production area for systems with fewer vowels.

IDENTIFICATION EXPERIMENT

In this experiment, the listeners from each language group were asked to transcribe both vowels which are foreign to their system and those of their own language. Subjects were requested to provide an obligatory response (transcribed), even for vowels which are absent from their vocalic system. The results of this experiment provide information concerning the assimilation of vowels which are absent in a system Three groups of subjects (20 Spanish, 22 English and 22 French) participated in the experiment. The listeners of each language were requested to identify auditorily presented isolated vowels (listeners were first presented the vowels of their own system, as a control, then those of the two foreign languages), and to mark their choice on designated response sheets. The stimuli were taken from the corpus used for acoustic analyses. Each vowel was produced 5 times by 2 different speakers (1 male and 1 female) for each language.

Spanish subjects :

Spanish listeners identified the vowels of their native language without ambiguity. Their perception of French stimuli corresponded perfectly to the obtained acoustic results : French vowels were perceived as globally higher and more posterior, with the exception of the vowel /a/, which did not produce a change in category. The central vowels that are absent from Spanish (/y/ /O/ /¿/) were most often assimilated to back vowels for /y/ and /O/, which corresponds to the acoustic re-positioning described above. The vowel /¿/ was predominantly assimilated to /a/. The English vowels corresponding to Spanish ones (/a/ /e/ /i/ /o/ /u/) were correctly identified. This is in line with our acoustic results inasmuch as the area covered by the two languages was similar. As concerns the English vowels absent from Spanish, their assimilation appeared to pose little difficulty, except for the vowel /E/ which Spanish subjects categorized equally as /a/ and /e/. Spanish subjects experienced little difficulty with the categorisation task, even for vowels outside of their own system. They seldom proposed different vowels for a same stimulus (figure 6), and do not hesitate when they choose a category (figure 7).

French subjects:

The French subjects categorized vowels from their own system without ambiguity, with the exception of the vowels /E/ /¿/ and /•/ who presence is generally contextual. The Spanish vowels were globally perceived as lower, which corresponds to the acoustic results for Spanish subjects as concerns French stimuli. The English vowels were perceived as lower in some cases, which also corresponds to our acoustic results although not as closely as in the case of Spanish. Indeed, given that the English system is as full as the French one, with a slightly different distribution, categorization is rather complex for vowels that are absent In particular, the vowel /I/ posed considerable difficulty and was categorized across three or even four vowels. The vowels /U/ and /Ã/ were principally assimilated to interior French vowels, /O/ and /¿/ respectively. The French subjects did not appear to have difficulties identifying the English stimuli, which is hardly surprising as those vowels that are specifically English are both similar to each other and distinct from French vowels (/I/, /Q/, etc.). The number of categories proposed for each stimulus was globally rather small (figures 6 and 7).

English subjects:

The results of the present experiment reveal English subjects difficulty in accomplishing the task, as indeed did the self-reports by the subjects following the session. For each stimulus, English subjects demonstrated considerable variability as concerns their categorisations. This was true not only for French and Spanish stimuli but for stimuli for their own language as well. Spanish vowels were globally perceived as lower, which is quite surprising given our acoustic results. French vowels produced a large degree of uncertainty. However, the vowels /U/ -/O/ and /Ã/ -/¿/ produced good overlap, as was the case for French subjects.

In Figure 6 we can see that for each stimulus English subjects proposed a large number of different vowels, reflecting the difficulty these subjects had with the task. In like manner, the best identified vowel obtained a relatively low score in relation to those obtained by French subjects, and even more so in relation to Spanish subjects (figure 7).

DISCUSSION

The first hypothesis was that subjects would adapt their productions as a function of the number of vowels in their system, to occupy the entire space (H1, figure 1). Our results do not support this hypothesis. We find, rather, that the space occupied by different vowels is fairly constant across systems, i.e. not affected by the density of the system.

Theses observations tend to confirm the second hypothese.

As concerns the position of vowels within the system, we found a displacement of the French system (higher and farther back). This variation as concerns position cannot, however, be considered as a centralizing and thus is not to be taken as evidence of an impact of density. The third hypothese is not then supported.

On the other hand, our behavioral experiment tends to show that Spanish subjects are able, without hesitation, to categorize stimuli which do not form part of their space of production. Thus, space perception seem to be larger than production ones. This suggests that the first hypothese would be more appropriate for perception process.

Nevertheless, these results should be confirm with another experiment.

We observed a great coherence between acoustical and perceptual results: Spanish and French produce distinct vowel categories and did not find difficulty to categorize vowel stimuli even if they do not form part of their vowel phonological system. On the other hand, the production of English speakers show a very broad overlapping between the categories and their experimental results show a very great difficulty in the task of categorization. Some works [START_REF] Vallée | Systèmes vocaliques : De la typologie aux prédictions[END_REF] show that, beyond nine vowels, the vowel systems profit from secondary systems of features. This is the case of English: duration makes it possible English listeners to distinguish some vowels. Thus, in our experiment, English subjects misses indices to achieve the task correctly. In the same way, their production is confused because our results only represent F1/F2 values.

In conclusion, our results tend to show that density 1/ may not play a role on vowel production (but this should be confirmed with larger and different linguistic material) and 2/ may play a role on perception but only for languages whose formant values are the only distinctive features between vowels.

  the production and in the perception of speech.

Figure 1 :

 1 Figure 1: Three hypotheses representing the possible phonetic realizations of vowels regarding to the density of the systems (here two systems with 3 or 10 vowels).

Figure 2 :

 2 Figure 2: Acoustic position and surface of the three languages.

Figure 3 :

 3 Figure 3: Spanish vocalic spaces.

Figure 4 :

 4 Figure 4: French vocalic spaces.

Figure 5 :

 5 Figure 5: English vocalic spaces.

Figure 6 :

 6 Figure 6: Mean number of identified vowels for each stimulus.

Figure 7 :

 7 Figure 7: Mean percentage of the best identified vowels.


Renaud Bourlès
email: renaud.bourles@centrale-marseille.fr

Anastasia Cozarenco
email: a.cozarenco@montpellier-bs.com

Mohamed Belhaj

Olivier Chanel

Dominique Henriet

Xavier Joutard

Frank Lasch

Bernard Sinclair-Desgagné

Roy Thurik

Peter Van Der

Keywords: opportunity and necessity entrepreneurs, business microcredit, loan repayment, business survival JEL classication C30, C41, G21, L26, M13

This article examines the link between entrepreneurial motivation and business performance in the French micronance context. Using hand-collected data on business microcredits from a Micronance Institution (MFI), we provide an indirect measure of entrepreneurial success through loan repayment performance. Controlling for the endogeneity of entrepreneurial motivation in a bivariate probit model, we nd that necessity entrepreneurs are more likely to have diculty repaying their microcredits than opportunity entrepreneurs. However, type of motivation does not appear to make a dierence to business survival. We build a stylized model to develop formal arguments supporting this outcome. We test for the robustness of our results using parametric duration models, and show that necessity entrepreneurs experience diculties in loan repayment earlier than their opportunity counterparts, corroborating our initial ndings.

Introduction

The microcredit is an innovative way to address nancial and social exclusion. Microcredits are particularly small loans to poor individuals who cannot easily access mainstream nancial markets.

We distinguish between two main categories of microcredit, business and personal loans.

1 Business microcredits are intended for business start-up and development, to foster self-employment. Personal loans target personal and family development projects. In this paper, we use a hand-collected data set on business microcredits from a French Micronance Institution (MFI) to study the relationship between entrepreneurial motivation and business performance, in terms of loan repayment and business survival.

Despite strong enthusiasm among the proponents of the microcredit, the evidence regarding its impact has so far been mixed [START_REF] Armendáriz | The Economics of Micronance[END_REF]. Findings from a number of rigorous studies based on randomized controlled trials on the statistical impact of the microcredit are inconclusive [START_REF] Banerjee | Six randomized evaluations of microcredit: Introduction and further steps[END_REF]. However, [START_REF] Newman | How does micronance enhance entrepreneurial outcomes in emerging economies? The mediating mechanisms of psychological and social capital[END_REF], performing a qualitative study, argue that microcredits can generate sound entrepreneurial activity and improved business outcomes. The European Union subsidizes business microcredit, seen as a stimulus to entrepreneurial initiatives that will combat unemployment. In France, where the state actively promotes entrepreneurship as a way out of unemployment, micronance is perceived as a valuable tool.

It is widely recognized that entrepreneurship generates economic growth [START_REF] Reynolds | Global entrepreneurship monitor: Data collection design and implementation 19982003[END_REF] Audretsch and Thurik, 2001). In the European Union in 2012, almost 93% of all businesses were microenterprises (employing fewer than 10 people). Micro-enterprises made the second highest contribution in terms of employment and value added, after large rms (employing more than 250 people) (Eurostat, 2015).

Considerable research eort has been directed toward identifying the determinants of entrepreneurial performance. One important aspect of this research is distinguishing among dierent typologies of entrepreneurs [START_REF] Wennekers | Linking entrepreneurship and economic growth[END_REF]. Entrepreneurial typologies can, for instance, be dened according to owners' motivation. Since 2001, the Global Entrepreneurship Monitor (GEM) distinguishes between two dierent types of entrepreneurial motivation, translating into necessity and opportunity entrepreneurship. Distinguishing between these two types is important, both from theoretical and practical standpoints [START_REF] Block | Necessity and opportunity entrepreneurs in Germany: Characteristics and earnings dierentials[END_REF]. Opportunity entrepreneurs start a business voluntarily, to take advantage of new opportunities. In contrast, necessity entrepreneurs get involved in a business because they have diculties remaining in the paid job market. They can be described as requirementbased [START_REF] Block | Necessity and opportunity entrepreneurs and their duration in self-employment: Evidence from German micro-data[END_REF] or against-their-will [START_REF] Korunka | The entrepreneurial personality in the context of resources, environment, and the startup process A congurational approach[END_REF] entrepreneurs. According to the existing literature, there are signicant dierences between the two types of entrepreneurs. First, in terms of individual characteristics, necessity entrepreneurs are generally older [START_REF] Wagner | Nascent necessity and opportunity entrepreneurs in Germany evidence from the Regional Entrepreneurship Monitor (REM)[END_REF][START_REF] Bhola | Explaining engagement levels of opportunity and necessity entrepreneurs[END_REF][START_REF] Block | Necessity and opportunity entrepreneurs and their duration in self-employment: Evidence from German micro-data[END_REF][START_REF] Block | Necessity and opportunity entrepreneurs in Germany: Characteristics and earnings dierentials[END_REF], either less [START_REF] Bhola | Explaining engagement levels of opportunity and necessity entrepreneurs[END_REF] or equally [START_REF] Block | Necessity and opportunity entrepreneurs and their duration in self-employment: Evidence from German micro-data[END_REF]) educated and less likely to have a parent involved in entrepreneurship [START_REF] Wagner | Nascent necessity and opportunity entrepreneurs in Germany evidence from the Regional Entrepreneurship Monitor (REM)[END_REF][START_REF] Bhola | Explaining engagement levels of opportunity and necessity entrepreneurs[END_REF].

As suggested by Esther Duo, the micronance industry now needs to distinguish between necessity and opportunity entrepreneurs, in order to provide them with appropriate services [START_REF] Duo | Statistiques structurelles sur les entreprises -vue d'ensemble[END_REF].

According to the GEM survey in 2003, two-thirds of entrepreneurs report opportunistic motivation, while one-third say they are acting out of necessity. However, there is great heterogeneity across countries, with developed countries having a larger proportion of opportunity entrepreneurs [START_REF] Reynolds | Global entrepreneurship monitor: Data collection design and implementation 19982003[END_REF].

Micronance is a new and relatively under-researched domain in the entrepreneurship literature [START_REF] Newman | How does micronance enhance entrepreneurial outcomes in emerging economies? The mediating mechanisms of psychological and social capital[END_REF]. Micronance is often viewed in Europe as a social policy aimed at encouraging self-employment as a way out of unemployment (push reasons). In particular, poor individuals facing low-paid employment are more likely to start a business out of necessity, to meet basic human needs [START_REF] Naudé | Entrepreneurship, developing countries, and development economics: New approaches and insights[END_REF]. [START_REF] Jaouen | A new typology of micro-rm owner-managers[END_REF] argue that the subsistence type of entrepreneur is specic to micro-rms, the main target of MFIs. However, traditional banks are reluctant to nance microentrepreneurs willing to start a business for pull reasons but lacking any initial capital or credit history. These riskier projects are typical of MFI clients. Consequently, using a data set from an MFI to study entrepreneurial motivation is particularly relevant. We expect our data set to be balanced in terms of the proportions of necessity and opportunity entrepreneurs.

There are dierent ways to assess entrepreneurial performance. Existing studies have mainly focused on such measures of performance as business survival, employment or turnover growth. Using growth as a sign of performance is particularly tricky, since a meaningful proportion of entrepreneurs choose not to grow [START_REF] Jaouen | A new typology of micro-rm owner-managers[END_REF]. But many micro start-ups require access to credit to get going. 2 Therefore, loan repayment is a reasonable alternative measure of business performance, that is not aected by individual choice not to grow. Interestingly, to the best of our knowledge, none of the existing studies have focused on the loan repayment performance of dierent types of microentrepreneurs. We ll this gap by investigating whether necessity entrepreneurs are dierent from their opportunity counterparts in terms of loan repayment. To link our ndings with the existing literature, we will perform parallel business survival analysis, exploring whether necessity entrepreneurs are more likely than opportunity entrepreneurs to close their businesses, as some previous studies suggest [START_REF] Millán | Determinants of self-employment survival in Europe[END_REF][START_REF] Oberschachtsiek | The experience of the founder and self-employment duration: A comparative advantage approach[END_REF][START_REF] Andersson | Do the unemployed become successful entrepreneurs? A comparison between unemployed, inactive and wage-earners[END_REF], or whether there is no signicant dierence between them [START_REF] Block | Necessity and opportunity entrepreneurs and their duration in self-employment: Evidence from German micro-data[END_REF].

The literature on micronance in Europe is scarce, mainly because the industry is relatively young and there is not yet a clear distinction between microcredits and other types of loans (European Commission, 2012). Moreover, MFIs in Europe serve a niche market. 3 According to [START_REF] Bendig | Overview of the microcredit sector in the European Union 20102011[END_REF] the ve main goals of MFIs in Europe are job creation, promotion of micro-and small enterprises, nancial and social inclusion and, to a lesser extent, the empowerment of particular target groups (women, migrants, etc.).

The major nancial product is the microcredit, with a special focus on the business microcredit.

4

Non-nancial services, mainly in the form of business support, are the second major product provided by European MFIs.

Most MFIs in Europe are subsidized by national or local governments. This is not surprising, since subsidies are crucial for MFIs, especially during their start-up phase [START_REF] Hudon | On the eciency eects of subsidies in micronance: An empirical inquiry[END_REF].

Historically, even major MFIs in developing countries have widely beneted from direct or indirect subsidies (see [START_REF] Morduch | The role of subsidies in micronance: Evidence from the Grameen Bank[END_REF], for the Grameen Bank case). Moreover, lack of subsidies can result in socially harmful trade-os (D'Espallier et al., 2013). Nevertheless, the relationship between nonnancial services, and subsidization is tricky, since indirect subsidies can decrease the incentives for MFIs to provide business training to their clients [START_REF] Bourlès | State intervention and the microcredit market: The role of business development services[END_REF]. The abundance of subsidies may also explain the scarcity of complementary nancial products such as microsavings (Cozarenco et al., 2016).

In this context, studying the performance of MFI clients is crucial in terms of policy implications.

Block and Sandner (2009) contest the suggestion that necessity entrepreneurs are less desirable from an economic standpoint, as in [START_REF] Acs | Entrepreneurship, agglomeration and technological change[END_REF] for instance. Insofar as they do not exhibit unequal survival rates, [START_REF] Millán | Determinants of self-employment survival in Europe[END_REF] and [START_REF] Block | Necessity and opportunity entrepreneurs and their duration in self-employment: Evidence from German micro-data[END_REF] argue that public support to initially unemployed or necessity entrepreneurs is justied. However, these studies do not look at the quality of the surviving businesses. In this paper, we use an indirect strategy to infer business success through the loan repayment behavior of micro-borrowers from a French MFI.

We rst sketch a stylized model illustrating the link between entrepreneurial motivation and business performance. In the empirical model, we focus on two dierent measures of performance, i.e. microcredit repayment and business survival, using hand-collected data from a French MFI. This paper is novel in addressing the link between entrepreneurial motivation and performance in the micronance context.

The rest of the paper proceeds as follows. In the next section we develop a stylized model to formalize this relationship. Then our data set and the descriptive statistics are introduced, followed by the presentation of the econometric model and results. We then test for robustness of our results.

Finally, we discuss our main ndings and conclude.

Necessity versus opportunity entrepreneurs: A theoretical framework

In this section, we analyze the link between entrepreneurial motivation and business performance from a theoretical point of view. To do so, we rst examine the key dierences between necessity and opportunity entrepreneurs. We argue that these dierences mainly lie in the non-pecuniary benets from entrepreneurship and external (employment) opportunities (i.e. the opportunity cost of entrepreneurship), leading to an ambiguous eect of entrepreneurial motivation on business performance.

Several empirical papers point out the non-pecuniary benets from entrepreneurship (or selfemployment). [START_REF] Hamilton | Does entrepreneurship pay? An empirical analysis of the returns to selfemployment[END_REF] notably shows that the dierence in earnings between employed and self-employed individuals cannot be explained by self-selection. In the same vein, [START_REF] Benz | Being independent is a great thing: Subjective evaluations of selfemployment and hierarchy[END_REF] show that pay (or material) dierences cannot fully explain the discrepancies in job satisfaction between the two groups (this was also highlighted by [START_REF] Blanchower | Self-employment in oecd countries[END_REF].

These non-pecuniary benets seem to be linked to independence, autonomy or skill utilization (see [START_REF] Benz | Being independent is a great thing: Subjective evaluations of selfemployment and hierarchy[END_REF][START_REF] Hundley | Why and when are the self-employed more satised with their work? Industrial Relations[END_REF][START_REF] Lange | Job satisfaction and self-employment: Autonomy or personality?[END_REF]. From a theoretical point of view, such benets can be related to the concept of intrinsic motivation introduced by [START_REF] Benabou | Intrinsic and extrinsic motivation[END_REF]. From our perspective, in line with the above discussion, intrinsic motivation or non-pecuniary benets should be rather limited for necessity entrepreneurs, who are by denition mainly driven by monetary motives.

At least, intrinsic motives should be stronger for opportunity entrepreneurs, for whom entrepreneurship is clearly a choice.

This dierence is expected to lead to better performance for opportunity entrepreneurs. However, this has to be balanced with the fact that they also enjoy better external opportunities. As previously pointed out, opportunity entrepreneurs appear to be better educated [START_REF] Bhola | Explaining engagement levels of opportunity and necessity entrepreneurs[END_REF]) and also to benet from better (or more) alternative employment opportunities [START_REF] Bergmann | The changing face of entrepreneurship in Germany[END_REF].

As a result, businesses founded by opportunity entrepreneurs could close earlier, as they might be oered better job opportunities or expect higher returns for remaining in business.

Therefore, on the one hand, businesses run by opportunity entrepreneurs should perform better because of higher (intrinsic) motivation; but, on the other hand, they could close more often because of higher opportunity cost of entrepreneurship.

To that extent, the theoretical predictions concerning the link between entrepreneurial motivation and business performance seem to be ambiguous. We formalize this argument in a stylized model presented in the Appendix where we adapt the canonical model of [START_REF] Tirole | The Theory of Corporate Finance[END_REF] to account for microcredit specicity and dierences between necessity and opportunity entrepreneurs. We show that the probability of success of a business increases in the entrepreneur's intrinsic motivation and decreases in her external opportunity. This result suggests an ambiguous eect of entrepreneurial motivation on business survival and call for deeper empirical examination. This is the aim of the remainder of this paper.

3 Methodology The MFI does not require any collateral or guarantees from its clients. Each entrepreneur can apply only once for a microcredit. The MFI aims for nancial inclusion of all borrowers after granting the rst microcredit. 5 It operates in collaboration with public organizations which provide business development services and follow-up at dierent stages. We focus exclusively on business microcredits in our analysis.

Most of the MFI's clients are (long-term) unemployed, have low education and income levels and are starting a business for the rst time. Many of the clients are becoming self-employed to avoid unemployment and/or poverty. Therefore, we expect to observe a relatively large proportion of necessity entrepreneurs in our sample compared to previous studies (see [START_REF] Block | Necessity and opportunity entrepreneurs and their duration in self-employment: Evidence from German micro-data[END_REF], Table 6 for studies in the German context where necessity entrepreneurs represent between 6.7% and 45.5%). In this context, it is particularly relevant to investigate the performance of both types of entrepreneurs (necessity and opportunity) in terms of loan repayment and business survival.

The average loan granted in our sample is EUR 8,250, for a period of 52 months (repayments take place monthly) at an average annual interest rate of 4.4%. The interest rate is xed by the MFI and does not vary according to personal or project characteristics, which is a specic feature of micronance worldwide.

Our data set is built using several sources. It includes ve dierent types of information:

1. Individual and business characteristics.

2. Repayment history within the MFI.

3. Business survival status and date of closure when applicable.

5 As a consequence, the MFI does not provide dynamic incentives through progressive lending (Armendáriz and Morduch, 2010).

4. Information on entrepreneurial motivation.

Data on business cycles at PACA-region level by sector of activity.

Individual and business characteristics and repayment history were provided directly by the MFI.

Information on repayment histories was last updated in January 2016. To distinguish necessity from opportunity entrepreneurs, we asked participants the following question Overall, did you create your business to seize an opportunity, or by necessity, to create your own job?. 95% of respondents could classify themselves in one of the two categories. This trend is consistent with survey data from GEM [START_REF] Reynolds | Global entrepreneurship monitor: Data collection design and implementation 19982003[END_REF]. 143 (49%) replied that they had started their business by necessity, to create their own job. We term these respondents necessity entrepreneurs. 134 (46%) declared that they had started a business to seize an opportunity. We term these respondents opportunity entrepreneurs. Finally, 17 (5%) declared both types of motivation. We exclude these 5% of respondents from our analysis, since their motivations are not clearly dierentiated. Unsurprisingly, the proportion of necessity entrepreneurs among the respondents in our data set is considerably larger than in existing studies. As mentioned before, this bias toward necessity entrepreneurship in our data set is useful for comparison, and is explained by the context of micronance in France. The microcredit is viewed here as a tool for public policy encouraging self-employment, especially for unemployed individuals.

Finally, the data on business cycles was collected from the French National Institute of Statistics and Economic Studies (INSEE). We provide the descriptive statistics of all the variables used in the analysis in Table 1.

[Insert Table 1 here]

Dependent variables

The dependent variables in our analysis represent dierent measures of entrepreneurial success. In our baseline model, we use two main measures of performance.

problematic borrowers, who have diculty paying their debts. 57% of our sample had less than three late payments in their credit history at the time of the analysis. The second performance measure is the dummy variable Closed, taking value one if the business is closed as of March 2016. 43% of businesses in our sample closed during the observation period.

Explanatory variables

The main explanatory variable is the N ecessity dummy (see 2012), the inclusion of macroeconomic sources of variance considerably improves the quality of outcomes attributable to individual eects. Hence, we additionally control for quarterly rates of increase in business failures (as a measure of economic health) and in new business start-ups (as a measure of competition) at the time the loan is granted (as well as one and two quarters later) for each micro-enterprise in our sample, according to its sector of activity. Data for business cycles exclusively cover the French PACA Region, the region where the MFI providing data operates (Source: INSEE).

See Table 1 for the denitions of all the variables used in our empirical analysis. Finally, to explore the dierences between opportunity and necessity entrepreneurs, we present the results of a simple probit regression in Table 2, where the dependent variable is the dummy N ecessity. We nd that necessity entrepreneurs are more likely to take up self-employment in order to avoid unemployment, tend to be older and come from poorer households. These ndings are in line with [START_REF] Block | Necessity and opportunity entrepreneurs and their duration in self-employment: Evidence from German micro-data[END_REF]. We also reveal that necessity entrepreneurs are less likely to set up limited liability companies, meaning that they are more likely to set up sole proprietorships.

[Insert Table 2 here]

Model estimation and results

In this paper, we are interested in the eect of entrepreneurial motivation on repayment performance and business survival. As explained in the previous section, the main explanatory variable here is N ecessity. Our bivariate probit model writes as follows:

y = 1[αNecessity + z 1 β 1 + e 1 ]
(1)

N ecessity = 1[zβ 2 + e 2 ] (2)
where (e 1 , e 2 ) are independent of z and distributed as bivariate normal with mean zero, unit variance, and ρ = Corr(e 1 , e 2 ). If ρ = 0 then estimating equation (1) using a simple probit model would not lead to consistent estimators of parameters α and β 1 . We will use the LR test to test the null hypothesis ρ = 0. This is appropriate to test for exogeneity of binary variables, as suggested by Wooldridge (2010). Variable y is the dependent variable, representing the performance of the entrepreneur. As mentioned before, we use two measures of entrepreneurial performance, the rst being the dummy variable Repaying and the second being the dummy variable Closed.

The vector z 1 includes the constant term and all the explanatory variables dened in Table 1, except the variable Avoid_unemployment. The latter is used as an instrument in our model and is only included in vector z, which also includes vector z 1 .

We present the results of the estimation of the bivariate probit model in Table 3. Columns (1) and

(2) report the results for loan repayment estimations and columns (3) and (4) report the results for business survival. Business cycles are included in columns (2) and (4).

In terms of loan repayment, we nd a negative eect of necessity motivation on the likelihood of having less than 3 late payments. This result suggests that necessity entrepreneurs are signicantly more likely to have diculty with loan repayment. While our paper is innovative in exploiting the relationship between entrepreneurial motivation and loan repayment, this result corroborates previous studies nding that necessity entrepreneurs undertake less protable projects [START_REF] Block | Necessity and opportunity entrepreneurs in Germany: Characteristics and earnings dierentials[END_REF] or are unwilling to grow [START_REF] Jaouen | A new typology of micro-rm owner-managers[END_REF]. This result is also in line with [START_REF] Andersson | Do the unemployed become successful entrepreneurs? A comparison between unemployed, inactive and wage-earners[END_REF], who nd that initially unemployed or inactive entrepreneurs earn lower incomes than entrepreneurs who were initially wage-earners.

In terms of business survival, we nd a non-signicant eect of necessity motivation on business survival. This result is in line with [START_REF] Block | Necessity and opportunity entrepreneurs and their duration in self-employment: Evidence from German micro-data[END_REF]. While opportunity entrepreneurs perform better, enjoying greater intrinsic benets from running a business, this positive eect on eort is counterbalanced by greater external opportunities. These opportunities may lead to them closing their business despite better nancial performance. The coexistence of these two eects may explain the non-signicant eect of entrepreneurial motivation on business survival.

[Insert Table 3 here] Concerning the control variables, we note that loan repayment and business survival are not impacted by exactly the same set of determinants. We nd that older borrowers are more likely to successfully 8 Mimicking a two-step procedure would not lead to consistent parameter estimations, according to Wooldridge (2010). manage their loan repayment and less likely to quit self-employment. The latter result is supported by existing studies. 9 We nd a negative relationship between the male dummy and successful loan repayment, although this result is signicant at 10% level in the specication without business cycles alone. This nding is standard in the micronance literature (see D'Espallier et al., 2011, for instance). However, gender does not signicantly impact business survival, and the literature does not reach consensus on the relationship between gender and business survival. Our insignicant eect is in line with Oberschachtsiek (2012), while others nd a negative impact of the male dummy on business closure (see [START_REF] Millán | Determinants of self-employment survival in Europe[END_REF].

We nd a non-signicant eect of education on loan repayment. In contrast, there is a signicantly negative relationship between education and business closure. This is in line with [START_REF] Millán | Determinants of self-employment survival in Europe[END_REF] and [START_REF] Bates | Entrepreneur human capital inputs and small business longevity[END_REF]. The latter study reveals that businesses started by highly educated owners are more likely to be viable and long-lasting than those started by less educated owners.

We nd no signicant relationship between household income and loan repayment and business survival. The literature mainly supports a positive relationship between initial wealth and business survival, although insignicant results are also reported.

We nd that the size of the project has no signicant impact, in contrast to [START_REF] Oberschachtsiek | The experience of the founder and self-employment duration: A comparative advantage approach[END_REF].

However, that author uses a dummy variable to distinguish between businesses larger than EUR 25,000, whereas we use a continuous variable.

Interestingly, having other debts from nancing institutions improves loan repayment performance and business survival. It is important to mention that the MFI will only disburse funds if all other co-nancing parties agree to nance the client. Therefore, the eect is probably due to the screening complementarities between the MFI and other nancing institutions, which can be commercial banks or other public partners (Cozarenco and Szafarz, 2016).

Although positive, the start-up dummy is not signicant in the business survival equations, in contrast to [START_REF] Bates | Entrepreneur human capital inputs and small business longevity[END_REF], who argues that start-ups are less likely to survive compared to takeovers.

Conversely, [START_REF] Oberschachtsiek | The experience of the founder and self-employment duration: A comparative advantage approach[END_REF] nds that the takeover dummy has no eect on business closure. However, our data set contains a large proportion of start-up businesses, which are also the main target of the microcredit.

The trade sector dummy has a negative impact on business survival and no impact on loan repayment. The limited liability company dummy has no impact in either equation. Results on business cycles are available from the authors upon request.

Finally, ρ corresponding to the correlation between the error terms in equations (1) and ( 2) is only signicant for loan repayment estimation. We conclude that using a bivariate probit model where the N ecessity dummy is endogeneous is appropriate for loan repayment, as opposed to a simple probit equation.

As the literature on credit scoring models [START_REF] Roszbach | Bank lending policy, credit scoring, and the survival of loans[END_REF]) indicates, it is not just credit default itself that is important, but when the default occurs. An early default is more costly for the bank than a default occurring towards the end of the loan period. The survival time of businesses too is now commonly analyzed in the literature on entrepreneurship [START_REF] Oberschachtsiek | The experience of the founder and self-employment duration: A comparative advantage approach[END_REF]. Therefore, to check for the robustness of our results we perform a duration analysis on loan repayment and business survival. However, in this alternative approach we are no longer able to deal with the endogeneity of the N ecessity dummy, since instrumental variable methods are not advanced enough for regression analysis of survival time with censoring (Tchetgen Tchetgen et al., 2015).

To get around this, we adopt an alternative approach using a dierent denition of necessity entrepreneurs. Instead of using the variable N ecessity, we directly include the variable Avoid_unemployment to proxy entrepreneurial motivation. This alternative variable identies respondents with push motives, who become self-employed to avoid unemployment. The main advantage of this approach is that it is relatively objective compared to the previous measure. Furthermore, this variable is less likely to be plagued with endogeneity. Finally, the number of observations used increases from 276 to 293 due to this change in the denition of entrepreneurial motivation.

Estimation of survival time

In Fig. 1, we give the survivor function S(t) and the hazard function h(t), where t represents the time before the third late payment in the upper panel and the time before business closure in the lower panel. For censored observations (borrowers with strictly less than three late payments in their credit history and operating businesses), t is the observation period. The survivor function is the probability of surviving beyond time t and the hazard function is the probability of the failure event occurring within a given time interval, provided that the subject has survived until the beginning of that interval, divided by the width of the interval [START_REF] Cleves | An Introduction to Survival Analysis Using Stata[END_REF].

We tested for equality of the survivor functions across necessity and opportunity entrepreneurs using a log-rank test (see Cleves et al., 2010, pp. 122124 for more details). We nd a signicant (at 10%) dierence between the survivor functions for time before three late payments in the credit history according to borrower motivation. However, no signicant dierence is detected for business survival time.

[Insert Figure 1 here]

We use parametric duration models in accelerated failure-time (AFT) metric.

11 Business survival analysis is close to [START_REF] Oberschachtsiek | The experience of the founder and self-employment duration: A comparative advantage approach[END_REF], who also uses parametric AFT models for exits from selfemployment for initially unemployed individuals. In such models, a parametric distribution is assumed for the expected duration τ i , which is a function of the survival time t i and a vector of explanatory variables x, which includes the Avoid_unemployment dummy, our main variable of interest:

τ i = exp(-x i β)t i (3)
Therefore, the estimated models take the following form:

ln(t i ) = x i β + ln(τ i ) (4)
11 We additionally use the Cox proportional hazards model [START_REF] Cox | Regression models and life-tables (with discussion)[END_REF], which is a semi-parametric model, since the proportionality assumption cannot be rejected in our data [START_REF] Schoenfeld | The asymptotic properties of nonparametric tests for comparing survival distributions[END_REF]. This model yields similar results. They are available from the authors upon request.

where ln(τ i ) has a distribution determined by what is assumed about the distribution of τ i (Cleves et al., 2010) and vector β contains the parameters to be estimated. We tested dierent specications for the distribution of τ i (exponential, Weibull, lognormal and log-logistic). According to the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC), the best distribution according to the underlying data is the lognormal distribution where:

τ i ∼ lognormal(β 0 , σ)
One salient feature of the lognormal distribution is that the hazard function is non-monotonic in time.

It is rst increasing and then decreasing. This feature is particularly relevant for our data, as illustrated in Fig. 1, for the hazard rate function, which seems to follow a non-monotonic pattern. Nevertheless, we additionally report the results for Weibull distribution (τ i ∼ W eibull(β 0 , p)), since it allows for a monotonic hazard function, which also seems to be supported by the hazard function for necessity entrepreneurs in terms of business survival (see Fig. 1 the lower right graph).

The parameters β presented in Table 4 illustrate the relationship between the explanatory variable and the survival time: a positive coecient means that the higher the explanatory variable, the longer before a third late repayment (columns (1) and ( 2)) or business closure (columns (3) and ( 4)).

Alternatively, the results can be interpreted in terms of time ratios, so that for an explanatory variable

x j , exp(β j ) is the factor by which the expected time-to-failure is multiplied as a result of a one-unit increase in x j (see Cleves et al., 2010, pp. 240241 for more details).

Results

In terms of duration models, we nd that respondents who gave a Avoid_unemployment reason for business start-up have a 0.74-times shorter expected time before third late payment (β = -0.3, exp(β) = 0.74) than those who did not give this reason. However, length of business survival is not signicantly dierent across the two groups. This latter result challenges Oberschachtsiek (2012)'s nding of a positive relationship between pull motivation (corresponding to opportunity entrepreneurs) and business survival. Overall, the results for the duration model corroborate our previous ndings using bivariate probit models. Necessity entrepreneurs seem to experience signicantly more diculties with loan repayment, and these diculties arise earlier. Yet, they do not seem to close their businesses more often or earlier than opportunity entrepreneurs.

A few results concerning other controls are worth mentioning. One dierence from Table 3 consists in the signicant coecient for household income in the lognormal specication for late repayment, suggesting that a larger household income increases the time before third late repayment.

Long-term unemployment is associated with a shorter time to business closure, whereas other debts have no signicant impact, in contrast to the results in Table 3. Interestingly, the start-up dummy is negatively associated with time to business closure in the lognormal specication, whereas it has no signicant eect on the probability of business closure in Table 3.

Finally, we note that the Weibull shape parameter is signicant at 1% level, with ln(p)>0, meaning that p > 1 and the Weibull hazard is monotone increasing in time. σ, which is the lognormal distribution parameter, is also signicantly dierent from zero at 5% level, suggesting the presence of a non-monotonic hazard function.

[Insert Table 4 here]

Conclusion

This article analyzes the link between entrepreneurial motivation and business performance. Our contribution to the extant literature is twofold. First, we analyze entrepreneurial motivation in a particular context: French micronance. We nd that, in our sample, almost 52% of micro-entrepreneurs are necessity entrepreneurs. This feature of our data set makes the analysis of entrepreneurial motivation particularly relevant, since we have a relatively heterogeneous data set compared to previous studies [START_REF] Block | Necessity and opportunity entrepreneurs and their duration in self-employment: Evidence from German micro-data[END_REF]. Such heterogeneity is not surprising, given that micronance in Europe is a policy tool encouraging self-employment as a way out of unemployment [START_REF] Bendig | Overview of the microcredit sector in the European Union 20102011[END_REF]. In line with this denition, target micronance clients are expected to be necessity entrepreneurs. That said, micronance also plays a more general role, serving the rejected applicants of mainstream banks.

Start-ups whose founders have limited personal asset to invest are typically rejected by the mainstream nancial market due to lack of credit history or of sucient levels of collateral. Therefore, rejected opportunity micro-entrepreneurs are just as likely to apply for a microcredit. Importantly, [START_REF] Duo | Statistiques structurelles sur les entreprises -vue d'ensemble[END_REF] argues that dierentiating between entrepreneurial motivation is one of the major challenges for the micronance sector.

Second, using a hand-collected data set from a French MFI, containing detailed individual information on credit history, we exploit a novel indirect measure of business performance, namely loan repayment. Its advantage over previous performance measures such as employment and turnover growth relies on the argument that not all entrepreneurs wish to grow [START_REF] Jaouen | A new typology of micro-rm owner-managers[END_REF].

Following this argument, we interpret diculty in loan repayment as an indirect way to measure poor business performance, getting around the limitation of voluntary unwillingness to grow. We nd that necessity entrepreneurs have signicantly more diculty repaying their loans.

In parallel, we perform a business survival analysis, which is particularly relevant since there is still no consensus on the relationship between entrepreneurial motivation and business survival. Our paper nds a non-signicant relationship between necessity motivation and business survival, challenging the recent nding by Oberschachtsiek (2012) of a negative relationship. Our stylized theoretical model, where opportunity entrepreneurs enjoy both higher external opportunities and larger non-pecuniary benets from creating a business, provides formal arguments supporting this non-signicant relationship.

In terms of policy implications, the literature suggests that having received a start-up subsidy decreases the risk of exiting self-employment [START_REF] Millán | Determinants of self-employment survival in Europe[END_REF]. In France, the micronance sector benets from strong government support in the form of direct or indirect subsidies [START_REF] Bendig | Overview of the microcredit sector in the European Union 20122013[END_REF]. Our paper shows that necessity entrepreneurs are just as likely as opportunity entrepreneurs to stay in business. From this perspective, the micronance sector, where necessity micro-entrepreneurs are overrepresented, is worth subsidizing. However, our results additionally show that necessity entrepreneurs have signicantly more diculty repaying their loans. Despite equal chances of surviving, necessity micro-entrepreneurs struggle to face their nancial commitments, most likely due to the limited growth potential of their businesses [START_REF] Jaouen | A new typology of micro-rm owner-managers[END_REF]. The regulator should be aware of this caveat when designing programs targeting necessity entrepreneurs. One way of tackling this problem is by subsidizing business development services [START_REF] Bourlès | State intervention and the microcredit market: The role of business development services[END_REF].

Owners of businesses that close might continue to be self-employed in some other rm or become paid employees, unemployed or inactive [START_REF] Millán | Determinants of self-employment survival in Europe[END_REF]. Using a competing risk model, we could identify any potential dierence in exit state between opportunity and necessity entrepreneurs. One of the limitations of our data set is the fact that we do not observe the exit state when there is business closure. If necessity entrepreneurs are just as likely as opportunity entrepreneurs to switch to paid employment, subsidizing business start-ups through micronance makes perfect sense. Further research would be useful to explore whether professional experience (human capital) acquired through self-employment or the business development services provided by most European MFIs can lead to a stable wage-earning position in the future.

Another limitation is that we observe entrepreneurial motivation at the time of the survey, which coincides with dierent stages in the respondents' business life-cycle. This is problematic, since entrepreneurs are subject to dynamic motivations [START_REF] Jayawarna | Entrepreneur motivations and life course[END_REF]. To get around this, we use an alternative measure of entrepreneurial motivation in the robustness checks. A more rigorous way to deal with this limitation would be to administer the survey at the time of loan application. This procedure would also help control for selection bias [START_REF] Heckman | Sample selection bias as a specication error[END_REF]) by using data on rejected applicants from the MFI.

Finally, survival models using the instrumental variables technique could be implemented to control for the endogeneity of entrepreneurial motivation. One step in this direction is provided by Tchetgen Tchetgen et al. (2015). However, the technique is still in development phase and would need adapting.

Under this setting, an entrepreneur with intrinsic motivation b i and external opportunity o i chooses the level of eort e * i that maximizes her expected utility:

E [u(b i , o i )] = p(e i ) [u(R -r) + b i ] + (1 -p)u(o i ) -ψ(e i )
Her optimal eort therefore solves: Therefore, the next proposition holds:

f (e * i , o i , b i ) = p (e * i ) [u(R -r) + b i -u(o i )] -ψ (e * i ) = 0 provided that u(R -r) + b i > u(o i ) (otherwise,
Proposition 1 The eort chosen by an entrepreneur and the subsequent probability of success is increasing in her intrinsic motivation ∂e * i ∂bi > 0 and decreasing in her external opportunity ∂e * i ∂oi < 0 .

. Thus, the eect of entrepreneurial motivation on business performance appears to be ambiguous when opportunity entrepreneurship is understood as involving both higher intrinsic motivation and greater external opportunity.

3 . 1

 31 Institutional background and data Our sample contains micro-rms started by the clients of a French MFI, created in 2006 and operating in the Provence-Alpes-Côte-d'Azur (PACA) region as a non-prot Non-Governmental Organization (NGO). It was created at the initiative of a commercial bank under its corporate social responsibility policy. Its target clients are individuals who have diculty accessing credit from mainstream banks.

  the optimal eort is nil, as the entrepreneur is better o failing). This allows us to analyze how the optimal eort (and therefore the probability of success) varies with the intrinsic motivation b i and the external opportunity o i using partial derivatives of f (•).Due to our assumptions on p(•), ψ(•) and u(•), we have ∂f ∂e * i (•) < 0, ∂f ∂oi (•) < 0 and ∂f ∂bi (•) > 0.

Figure 1 :

 1 Figure 1: Survivor and hazard functions

  Information on business survival was collected and updated in March 2016 from the French website www.societe.com, containing general information about both closed and active French rms. Interest-

	ingly, the 3-year survival rate in our sample is 83%, whereas it is 68% 6 for the PACA region, according
	to national statistics. Similarly, the 5-year survival rate is higher in our sample (66% versus 55% for
	PACA region).
	Further, to dierentiate between types of entrepreneur (necessity or opportunity), we conducted a
	survey among the clients of the MFI. Of the 574 granted loans (between April 2008 and April 2012),
	294 micro-entrepreneurs agreed to participate in the survey, a 51% response rate. A combination of
	web (20% of respondents) and telephone (80% of respondents) survey methods were used to collect
	data on entrepreneurial motivation between July 1st 2012 and September 15th 2012.

  Table1for denition). As expected, more than 50% of our sample identify themselves as necessity entrepreneurs. The second most important variable, which we use as an instrument (see next section), is Avoid_unemployment. It is a dummy variable taking value one if respondents answered To avoid unemployment to the question What was your main reason for business start-up?, multiple answers being possible. Other possible answers to this question include: to accomplish a life project or dream, to increase personal revenue, to have more independence, to benet from subsidies for business start-up, or other. Interestingly, only 32% of respondents answered Avoid_unemployment to this question, meaning that not all necessity entrepreneurs take up self-employment in order to avoid unemployment.

Other control variables include age, gender, education level, household income, a dummy variable for long-term unemployment, size of the project, having other debts, start-up, trade sector and limited liability company dummies. Most of the respondents are start-ups. This is consistent with Shahriar et al. (

2016

) who argue that not-for-prot MFIs are more likely to nance business start-ups compared to their for-prot counterparts.

According to

Millán et al. (

  However, this variable can be endogenously determined. For instance, ex-ante necessity entrepreneurs can shift to opportunity status over time.[START_REF] Jayawarna | Entrepreneur motivations and life course[END_REF] argue that entrepreneurial motivation are not static and are inuenced by career, household and business life.In our case, this dynamic aspect of motivation is particularly important, since we do not observe motivation at the time of business start-up. Instead, we observe entrepreneurial motivation at the time of the survey, which corresponds to dierent stages in the life of the businesses, ranging from start-ups to well-established rms. Econometrically, the unobserved changes in individual life-courses correspond to the problem of omitted variables. To deal with this caveat, we use a bivariate probit model appropriate to binary endogenous variables (see Wooldridge, 2010, pp. 594599). which does not account for the endogeneity of the N ecessity dummy would lead to inconclusive results[START_REF] Block | Education and entrepreneurial choice: An instrumental variables analysis[END_REF]. The instrument we use to correct for the endogeneity of the N ecessity variable is the dummy variable Avoid_unemployment. This variable does not have the dynamic nature highlighted by[START_REF] Jayawarna | Entrepreneur motivations and life course[END_REF].

	8	Using a
	simple probit equation

Table 2 :

 2 Determinants of entrepreneurial motivation

		Dependent variable:
		Necessity dummy
	Explanatory variables:
	Avoid unemployment	1.53***	(0.21)
	Age	0.02*	(0.01)
	Male	0.14	(0.19)
	Education	-0.03	(0.09)
	HH income	-0.19**	(0.08)
	Unemployed more 6	-0.13	(0.19)
	Project size	-0.00	(0.00)
	Other debts	-0.14	(0.18)
	Start-up	-0.02	(0.24)
	Trade	0.26	(0.21)
	LLC	-0.64***	(0.20)
	Constant	-0.28	(0.50)
	Observations	276
	Standard errors in parentheses
	*** p<0.01, ** p<0.05, * p<0.1

Table 3 :

 3 Determinants of Entrepreneurial Performance

			Dependent variable:
		Repaying	Closed
		(1)	(2)	(3)	(4)
	Explanatory variables:
	Necessity	-0.95***	-0.98***	0.05	-0.04
		(0.31)	(0.33)	(0.35)	(0.36)
	Age	0.02**	0.02**	-0.02**	-0.02**
		(0.01)	(0.01)	(0.01)	(0.01)
	Male	-0.27*	-0.23	-0.25	-0.25
		(0.16)	(0.17)	(0.17)	(0.18)
	Education	0.04	0.07	-0.21**	-0.21**
		(0.08)	(0.09)	(0.09)	(0.09)
	HH income	0.01	0.02	0.02	0.03
		(0.07)	(0.07)	(0.08)	(0.08)
	Unemployed more 6	-0.01	0.03	0.15	0.16
		(0.16)	(0.16)	(0.17)	(0.17)
	Project size	-0.00	-0.00	-0.00	-0.00
		(0.00)	(0.00)	(0.00)	(0.00)
	Other debts	0.24	0.35**	-0.43***	-0.33*
		(0.16)	(0.17)	(0.16)	(0.17)
	Start-up	-0.09	-0.14	0.25	0.25
		(0.21)	(0.21)	(0.22)	(0.23)
	Trade	0.09	0.12	0.32*	0.34*
		(0.17)	(0.18)	(0.18)	(0.18)
	LLC	-0.11	-0.12	-0.02	-0.03
		(0.19)	(0.20)	(0.20)	(0.20)
	Constant	0.11	-0.19	1.08**	0.91*
		(0.47)	(0.49)	(0.51)	(0.51)
	ρ	0.76**	0.75**	0.11	0.15
		(0.35)	(0.37)	(0.23)	(0.23)
	Business Cycles	No	Yes	No	Yes
	Observations	276	276	276	276
	Standard errors in parentheses
	*** p<0.01, ** p<0.05, * p<0.1

Table 4 :

 4 Alternative Measure of Entrepreneurial Motivation: Duration Models

	Estimation method:		Parametric duration models
	Dependent variables:	Time to 3rd late payment	Time to business closure
		Weibull	Lognormal	Weibull	Lognormal
		(1)	(2)	(3)	(4)
	Explanatory variables:
	Avoid unemployment	-0.34**	-0.30*	0.08	0.04
		(0.17)	(0.18)	(0.14)	(0.14)
	Age	0.02*	0.01	0.02***	0.02***
		(0.01)	(0.01)	(0.01)	(0.01)
	Male	-0.26	-0.31*	0.16	0.21
		(0.17)	(0.18)	(0.13)	(0.13)
	Education	0.08	0.13	0.13*	0.12*
		(0.08)	(0.09)	(0.07)	(0.07)
	HH income	0.00	0.00*	-0.00	-0.00
		(0.00)	(0.00)	(0.00)	(0.00)
	Unemployed more 6	0.02	-0.03	-0.28**	-0.23*
		(0.16)	(0.18)	(0.13)	(0.13)
	Project size	-0.00	-0.00	0.00	0.00
		(0.00)	(0.00)	(0.00)	(0.00)
	Other debts	0.42***	0.40**	0.10	0.18
		(0.16)	(0.18)	(0.13)	(0.13)
	Start-up	-0.27	-0.24	-0.21	-0.35**
		(0.22)	(0.23)	(0.17)	(0.17)
	Trade	-0.03	-0.06	-0.41***	-0.45***
		(0.17)	(0.19)	(0.13)	(0.14)
	LLC	0.02	0.07	-0.03	0.05
		(0.18)	(0.19)	(0.14)	(0.14)
	ln(p)	0.21***		0.44***
		(0.08)		(0.07)
	ln(σ)		0.17**		-0.14**
			(0.07)		(0.07)
	Constant	7.00***	6.71***	7.35***	7.19***
		(0.45)	(0.48)	(0.35)	(0.37)
	Business Cycles	Yes	Yes	Yes	Yes
	Observations	293	293	293	293
	Nb. of failures	126	126	124	124
	Standard errors in parentheses
	*** p<0.01, ** p<0.05, * p<0.1

Sometimes, Micronance Institutions do not dierentiate between two types of products, since the boundary between the business and the household is not clearly drawn.

In France in the rst semester of 2002, almost

36% of all business start-ups were (partly) nanced by commercial bank loans (source: authors' own computations using data from the French National Institute of Statistics and Economic Studies). Furthermore, commercial banks are the main providers of small-business nance, according to Berger and Udell (2002).3[START_REF] Reed | Resilience: The state of the microcredits summit campaign report[END_REF] report that MFIs in the industrialized world represent 4.4% of total MFIs reporting to the Microcredit Summit Campaign, serving less then 3% of micronance clients.4 The European Commission denes the microcredit as a loan of up to EUR 25,000 tailored for micro-enterprises employing less than 10 people (91% of all European rms), and unemployed or inactive people who want to go into self-employment but do not have access to traditional banking services (European Commission, 2009).

The rst dependent variable, Repaying, is a dummy taking value one if a client has strictly less than three late payments in his or her credit history as of January 2016. These late payments can be either outstanding or repaid at the time of analysis. This denition is close to that used by the MFI for written-o loans, corresponding to loans with three or more consecutive late payments. We do not focus exclusively on consecutive late payments, since it is not our aim to capture exclusively written-o loans. Our aim is to capture 6 The 3-year survival rate for businesses created in 2010 in PACA region. Source: the French National Institute of Statistics and Economic Studies website, accessed on June 14, 2016.7 Note that both measures of performance are consistent with the measure of success used in our theoretical reasoning.

Overall, we conclude that our ndings are in line with the existing literature and the mechanism highlighted in the stylized model. Necessity entrepreneurs have signicantly more diculty repaying their loans, corroborating previous studies reporting smaller and less protable projects. However, their businesses are just as likely to survive, because opportunity entrepreneurs tend to seize better external opportunities, despite higher intrinsic benets from running a business. 9 In many cases, a non-linear relationship between age and performance is tested. We added a quadratic term for age in the model. We detect a non-linear eect in the loan repayment equation, where beyond a threshold, the positive eect of age becomes negative. However, no such eect is found in the survival equation. These results are available from the authors upon request.10 A simple probit model for the business survival dummy produces similar results. 9

This is consistent with data used in the empirical part of the paper, in which the MFI xes the same interest rate for all borrowers, also common practice for MFIs worldwide.

Note that our result still holds with risk-neutral agents, as in[START_REF] Tirole | The Theory of Corporate Finance[END_REF].

Grunenberger for research assistance. Anastasia Cozarenco beneted from LabEx Entrepreneurship (This laboratory of excellence is funded by the French government in recognition of high-level research initiatives in the human and natural sciences.

Appendix A stylized model linking entrepreneurial motivation and business performance

To analyze the theoretical link between entrepreneurial motivation and business performance, we adapt the canonical model of [START_REF] Tirole | The Theory of Corporate Finance[END_REF] to account for features specic to the microcredit and dierences between opportunity and necessity entrepreneurship. We assume that (i) entrepreneurs own no collateral and apply for full nancing of their project, (ii) the MFI oers a xed interest rate regardless of the borrower's type 12 and (iii) entrepreneurs dier with respect to their external opportunity and their intrinsic motivation.

Entrepreneurs. Formally, we consider entrepreneurs who need one unit of nancing to run their planned business. Depending on eort and luck, this business can either succeed, giving a monetary return R, or fail, giving a 0-return. The probability of success p(e) is partly governed by the eort e ∈ R + that is provided by the entrepreneur (with p (•) > 0 and p (•) < 0). We assume that this eort is costly and represent its cost by an increasing and convex function ψ(e). In the event of success, on top of the monetary return, the entrepreneur receives a non-pecuniary benet b. To disentangle monetary and non-monetary benets, we assume that entrepreneurs derive a concave utility from the monetary component, through the utility function u(•), with u(•) > 0 and u(•) < 0. 13 Conversely, in the event of failure, the entrepreneur must turn to her external opportunity, which provides her with a wage o.

The MFI. As noted above the entrepreneur neither making a personal investment nor having collateral, and in line with micronance practices in developed countries the MFI that lends the monetary unit gets the (xed) interest rate r in the event of success and nothing in the event of failure.

The aim of this stylized model is to understand how the non-pecuniary benet (intrinsic motivation)

b and the external opportunity o inuence borrowers' eort e and consequently their business survival.

Tables and Figures


Cunjian Chen
email: cunjian@msu.edu

Antitza Dantcheva
email: antitza.dantcheva@inria.fr

Thomas Swearingen
email: swearin3@msu.edu

Arun Ross
email: rossarun@msu.edu

Spoofing Faces Using Makeup: An Investigative Study

HAL is

Introduction

Biometrics refers to the automated recognition of individuals based on their biological traits such as face, fingerprints and iris. A typical biometric system acquires the biometric data of an individual using a sensor; extracts a set of salient features from the data; and uses these features to determine or verify the identity of an individual [START_REF] Jain | Introduction to Biometrics[END_REF]. In spite of its advantages, a biometric system is vulnerable to spoofing, where an adversary can spoof the biometric trait of another individual in order to circumvent the system [START_REF] Maatta | Face spoofing detection from single images using texture and local shape analysis[END_REF][START_REF] Pan | Eyeblink-based antispoofing in face recognition from a generic webcamera[END_REF][START_REF] Chakka | Competition on counter measures to 2-D facial spoofing attacks[END_REF][START_REF] Yoon | Altered fingerprints: Analysis and detection[END_REF]. Unlike obfuscation, which entails deliberately obscuring one's own identity, spoofing entails taking on another person's identity, with the purpose of accessing privileges and resources associated with that person [START_REF] Marcel | Handbook of Biometric Anti-Spoofing -Trusted Biometrics under Spoofing Attacks[END_REF][START_REF] Patel | Secure face unlock: Spoof detection on smartphones[END_REF]. Spoofing, in the context of face recognition, Figure 1. Examples of typical spoof attacks previously considered in the literature (images obtained from [START_REF] Marcel | Handbook of Biometric Anti-Spoofing -Trusted Biometrics under Spoofing Attacks[END_REF]). An attacker presents a photograph (a) or a mask (b) to the biometric system. can be accomplished by presenting photographs [START_REF] Anjos | Counter-measures to photo attacks in face recognition: A public database and a baseline[END_REF][START_REF] Patel | Secure face unlock: Spoof detection on smartphones[END_REF], videos [START_REF] Anjos | Counter-measures to photo attacks in face recognition: A public database and a baseline[END_REF][START_REF] Pan | Eyeblink-based antispoofing in face recognition from a generic webcamera[END_REF][START_REF] Patel | Secure face unlock: Spoof detection on smartphones[END_REF] or 2D and 3D-masks to a face recognition system [START_REF] Kose | Countermeasure for the protection of face recognition systems against mask attacks[END_REF] (as seen in Figure 1).

In this work we determine whether facial cosmetics can be used by an adversary to launch a spoof attack. Unlike spoof attacks based on photographs, videos and masks, makeup-induced spoofing can be relatively difficult to detect since makeup is widely used for cosmetic purposes. Thus, it is necessary to understand if makeup-induced spoofing can confound an automated face recognition system, i.e., is the recognition performance of automated face matchers impacted by this type of spoof attack?

In order to conduct our analysis, we first assemble a dataset consisting of face images of female subjects who apply makeup to transform their appearance in order to resemble celebrities. These images are extracted from videos available on YouTube. The subjects here are not trying to deliberately deceive an automated face recognition system; rather, their intention is to impersonate a target celebrity from a human vision perspective.

Besides assembling the dataset, the contributions of this work include the following: (a) We define two spoofing indices to quantify the potential of using makeup for face spoofing; (b) We test the vulnerability of face recognition systems to makeup induced spoofing based on these indices; (c) We conduct an identification experiment to demonstrate the potential of spoofing a target face through the use of makeup. To the best of our knowledge, this is the first work to systematically study this effect.

Background and related work

Recent work has demonstrated the impact of commonly used facial makeup on automated face recognition systems [START_REF] Dantcheva | Can facial cosmetics affect the matching accuracy of face recognition systems?[END_REF][START_REF] Eckert | Facial cosmetics database and impact analysis on automatic face recognition[END_REF][START_REF] Guo | Face authentication with makeup changes[END_REF][START_REF] Kose | Facial makeup detection technique based on texture and shape analysis[END_REF][START_REF] Chen | An ensemble of patchbased subspaces for makeup-robust face recognition[END_REF], and automated face-based gender and age estimation systems [START_REF] Chen | Impact of facial cosmetics on automatic gender and age estimation algorithms[END_REF]. Makeup can be used to alter the perceived (a) facial shape; (b) nose, mouth, eye and eye brow shape; (c) nose, mouth, eye and eye brow size; (d) facial contrast and (e) facial skin quality and color. It can also be used to conceal wrinkles, dark shadows and circles underneath the eyes, and camouflage birth moles, scars and tattoos [START_REF] Mee | Medical makeup for concealing facial scars[END_REF].

Considering the widespread use of makeup and its implications in altering facial appearance (e.g., facial aesthetics [START_REF] Dantcheva | Female facial aesthetics based on soft biometrics and photo-quality[END_REF]), in this work we focus on the use of makeup for spoofing. Unlike previous work [START_REF] Dantcheva | Can facial cosmetics affect the matching accuracy of face recognition systems?[END_REF][START_REF] Eckert | Facial cosmetics database and impact analysis on automatic face recognition[END_REF][START_REF] Guo | Face authentication with makeup changes[END_REF], where commonly used makeup was observed to affect face recognition systems by obscuring a person's identity, here we consider the scenario where makeup is used by an individual to mimic the facial appearance of another individual (see Figure 2).

Current face spoof detection schemes either rely on physiological cues such as eye blinking, mouth movements, and macro-and micro-expression changes [START_REF] Pan | Eyeblink-based antispoofing in face recognition from a generic webcamera[END_REF][START_REF] Maatta | Face spoofing detection from single images using texture and local shape analysis[END_REF], or textural attributes of the face image [START_REF] Maatta | Face spoofing detection from single images using texture and local shape analysis[END_REF][START_REF] Wei | Counterfeit iris detection based on texture analysis[END_REF][START_REF] Tan | Face liveness detection from a single image with sparse low rank bilinear discriminative model[END_REF][START_REF] Li | Live face detection based on the analysis of fourier spectra[END_REF]. But none of these methods represent a viable mechanism for detecting makeup induced spoofing (especially since makeup is widely used). Also, in contrast to other face alteration techniques such as plastic surgery, makeup is non-permanent and cost efficient. This makes makeup-based spoofing a realistic threat to the integrity of a face recognition system.

The rest of the paper is organized as follows. Section 2 discusses the assembled spoofing dataset. Section 3 introduces the two spoofing indices for quantifying the effect of makeup on automated face recognition. Section 4 discusses Figure 2. The subject on the top attempts to resemble identities in the bottom row (labeled "Target Subjects") through the use of makeup. The result of these attempts can be seen in the second row. Images were obtained from the WWW. face recognition methods used in this study to evaluate the impact of makeup induced spoofing. Section 5 presents the related experiments. Results of the experiments are discussed in Section 6, followed by a summary of the paper in Section 7.

Makeup Induced Face Spoofing (MIFS) Dataset

In order to investigate the problem of makeup induced face spoofing, we first assemble a dataset consisting of 107 makeup-transformations taken from random YouTube makeup video tutorials. We refer to this dataset as the Makeup Induced Face Spoofing (MIFS) dataset. 1 There are two before-makeup and two after-makeup images per subject. Since each subject is attempting to spoof a target identity, we also have two face images of the target identity from the Web. Thus, this dataset has three sets of face images: images of a subject before makeup; images of the same subject after makeup with the intention of spoofing; and images of the target subject who is being spoofed. However, it is important to note that the target images are not necessarily those used by the spoofer as a reference during the makeup transformation process. 2 This is important to point out because the spoofed celebrities can often change their facial appearance and this will have an effect on the match score between the after-makeup image of the impersonator and the target image of the celebrity. When we searched the Web for face images of the target identity, we tried to select images that most resembled the after-makeup image.

All the acquired images are subjected to face cropping. This routine eliminates hair and accessories [START_REF] Dantcheva | Can facial cosmetics affect the matching accuracy of face recognition systems?[END_REF]. Examples of cropped images, based on a Commercial Off-The-Shelf (COTS) face detector, are shown in Figure 3.

We make the following observations about this dataset: (a) the subjects in the before and after makeup sets do not overlap with subjects in the target set; (b) there are duplicate identities of subjects attempting to spoof -this is because there are subjects attempting to spoof different target identities (see Figure 2); (c) there are duplicate identities in the target set -this is because there are multiple subjects attempting to spoof the same target identity (see Figure 4); (d) the images in the dataset include variations in expression, illumination, pose, resolution and quality.

Makeup-transformation: In the MIFS dataset, subjects use different types of makeup to alter their appearance and resemble a target identity. While the makeup application process varies across the dataset (depending upon the sub- Here, (a), (b) and (c) represent three spoofing attempts. In each case, the image on the left shows the subject before makeup, the one in the middle is the subject after makeup, and the image on the right is the target identity that the subject is attempting to impersonate (see text for explanation).

ject's face image and the target face image), we make a few general observations here. Generally, makeup-foundation is applied on the face to create a complexion that is similar to the skin color of the target subject. Face powder is then used to fix the foundation and prevent shininess, allowing for an even, uniform appearance of the face. In the next crucial step, a contouring technique is used to mimic the key characteristic features of the target face (e.g., high cheek bones, slim face, presence of beard). Specifically, brush strokes of very dark powder (e.g., bronzer) create the effect of shadows or concave facial features (e.g., underneath the cheek bones or at the periphery of the face), while brush strokes of very bright cream (e.g., highlighter) create the illusion of convex and prominent facial features. The contours are then blended with the foundation using a brush or the fingers. Facial hair, such as beard and moustache, are usually painted with brown or black eye pencils. The mouth area is then altered to resemble that of the target face by either augmenting it (painting the area around the mouth using the target's lip color and drawing new contours around it) or minimizing it (covering part of the lips with foundation and drawing new contours resembling the target). Similarly, the shape and size of the eye region is altered by using dark eye-pencils and white highlighter-pencils, which can either extend the eyes by painting new eye-contours around the initial ones or minimize the eyes by painting within the waterline. The periocular region is then contoured using dark and/or bright eye-shadow or cream to capture the shape of the target's eye (e.g., hooded or deep set eyes).

In general, such transformation requires extensive quantities of different cosmetic products compared to what is commonly used. Therefore, makeup-palettes with a variety of colors and shades are expected to be used in such makeup-transformations.

Face Recognition and Spoofing Metrics

To quantify the impact of makeup-based identity transformation on face recognition, we propose and define two spoofing indices (SIs). The following notations are used: the complete set of subjects in the dataset is denoted as P , the set of before-makeup images as B P , the set of after-makeup images as A P , and the set of target identities as T P . We note that T P does not include identities from A P (and thus B P ). For each subject p, we have the following image samples: {B p 1 , B p 2 } ∈ B P , {A p 1 , A p 2 } ∈ A P , and {T p 1 , T p 2 } ∈ T P . Let φ(x, y) denote the similarity match score between two images x and y as computed by a matcher (the greater the value, the higher the similarity between two faces). The similarity scores are normalized in the [0, 1] interval.

A spoofing attack can be deemed to be successful for subject p, when the similarity score between the aftermakeup images, A p i , and the target images, T p j , (i.e., φ A p i , T p j ) increases. However, it is not easy to make this assessment, since any change in match score has to be viewed with respect to the entire score distribution of the matcher (and not just the absolute change in value). Hence, we consider two spoofing indices.

The two spoofing indices that we introduce below describe the similarity score φ(A p i , T p j ) with respect to two types of genuine scores: (a) reference genuine scores φ(T p 1 , T p 2 ), where the similarity between two samples of the same target identity is computed (spoofing index 1, SI 1 ), and (b) reference genuine scores φ(A p 1 , A p 2 ), where the similarity between two samples of the after-makeup images of a subject is computed (spoofing index 2, SI 2 ). The two spoofing indices are described below.

Spoofing Index 1: SI 1 is defined as follows:

SI 1 = 1 -min i,j |φ(A p i , T p j ) -φ(T p 1 , T p 2 )|, (1)
where i, j ∈ {1, 2}. Here, we examine if the similarity score between the after-makeup image and the target image is within the range of the score between two samples of the target identity. Specifically, φ(A p i , T p j ) ≈ φ(T p 1 , T p 2 ) suggests that spoofing is successful and the output of SI 1 ≈ 1. For the case φ(A p i , T p j ) << φ(T p 1 , T p 2 ), spoofing is not successful and the output of SI 1 ≈ 0.

Spoofing Index 2: SI 2 is based on the difference in similarity score between the after-makeup and target images with respect to the after-makeup images:

SI 2 = 1 -min i,j |φ(A p i , T p j ) -φ(A p 1 , A p 2 )|. (2)
This index is very similar to SI 1 . However, here, we examine if the similarity score between the after-makeup image and the target image is within the range of the score between two after-makeup samples of the same person. Specifically, φ(A p i , T p j ) ≈ φ(A p 1 , A p 2 ) suggests that spoofing is successful and the output of SI 2 ≈ 1. For the case φ(A p i , T p j ) << φ(A p 1 , A p 2 ), spoofing is not successful and the output of SI 2 ≈ 0.

Automated Face Recognition

The following two face matchers were used in this study: a Commercial Off-The-Shelf (COTS) face software and the VGG face matcher.

The VGG face descriptor [START_REF] Parkhi | Deep face recognition[END_REF] is computed using a Convolutional Neural Network (CNN) implementation based on the VGG-Very-Deep-16 architecture as characterized in [START_REF] Parkhi | Deep face recognition[END_REF]. It is developed based on a triplet-loss training scheme to learn a face embedding that has a similar principle as metric learning.

The effectiveness of these two face matchers was verified using the BLUFR protocol [START_REF] Liao | A benchmark study of large-scale unconstrained face recognition[END_REF] on the LFW dataset3 [START_REF] Huang | Labeled faces in the wild: A database for studying face recognition in unconstrained environments[END_REF] (as seen in Figure 5). Compared to the original LFW protocol, the BLUFR protocol contains both verification and open-set identification scenarios to fully exploit the potential of the LFW dataset, with focus on low FARs. The results are reported using the "Verification µ -σ Rate" as Table 1. Performance of the COTS and VGG face matchers when using the BLUFR protocol on the LFW dataset. The mean of the Verification Rate over the 10 folds is given by µ and the standard deviation is given by σ. The "Verification µ -σ Rate" reports the average performance across the 10 folds and subtracts the standard deviation of that performance to yield a lower bound on performance.

Matcher

described in the BLUFR protocol. Table 1 shows the Verification Rate at 0.1% FAR. Both matchers exceed the baseline performance, 4 with the COTS matcher surpassing the baseline by a factor of nearly two. Thus, both matchers are good candidates for use in this work.

Experiments

In this section, we conduct experiments on the MIFS dataset. By performing these experiments, we seek to understand a) whether makeup can be used to spoof faces from the perspective of an automated face matcher, and b) the extent to which makeup has the ability to spoof a face recognition system. We reiterate that the subjects represented in the MIFS dataset were not deliberately trying to spoof an automated face recognition system.

Match Score Analysis

To study the possibility of spoofing, we first analyze the score changes after the application of makeup. For spoofing to be successful, the similarity score between the aftermakeup and target images should increase compared to that of the before-makeup and target images. Match scores were generated according to the following protocol: 1. Match B p i against T p j (B-T ): the image before makeup is matched against the target image. 2. Match A p i against T p j (A-T ): the image after makeup is matched against the target image.

Match B p

i against A p j (B-A): the image before makeup is matched against the image after makeup.

Figure 6 illustrates the boxplots of score distributions corresponding to B-T , A-T and B-A. We note an evident upward shift in A-T scores when makeup is applied (compared to B-T ). This means the application of makeup has increased the chances of a subject being matched against the target image, as reflected by the change in similarity score. Figure 7 visualizes the differences between A-T and B-T as a histogram. As can be seen, the majority of the differences are positive.

Spoofing Indices

Next, we observe the values of the proposed spoofing indices. As shown in Figure 8, both spoofing indices demonstrate the extent to which makeup has the ability to spoof a particular face recognition system. The higher the value, the more likely that spoofing has occurred. Both face matchers are observed to be vulnerable to spoof attacks via makeup.

Histogram Shift

In addition, we visualize the histograms of match scores that would allow us to see the improvement in makeup induced genuine scores in the context of general genuine and impostor score distributions of the matcher. More specifically, we plot the score distributions of B-T , A-T , and the LFW dataset in Figure 9. The LFW scores are derived from images 5 in the LFW dataset [START_REF] Huang | Labeled faces in the wild: A database for studying face recognition in unconstrained environments[END_REF]. In Figure 9(a), we see that the B-T scores fall in the [0.03, 0.61] range and the A-T scores fall in the [0.13, 0.77] range. This indicates that the match scores generally increase after the application of makeup and the histogram shifts to the right. The same phenomenon can be seen in Figure 9(b), but to a lesser extent.

Identification Experiment

In this setting, the after-makeup and before-makeup images are used as probes, and the target images to be spoofed are placed in the gallery. The goal is to determine whether 5 113 of the 13,233 LFW images failed the template extraction stage of the COTS matcher. In order for a fair comparison to the OxfordVGG matcher, we withheld these 113 images from the Oxford VGG matcher as well. target images match at a better rank with after-makeup images than with before-makeup images. We also populate the gallery with 13,120 "background" images from the LFW dataset [START_REF] Huang | Labeled faces in the wild: A database for studying face recognition in unconstrained environments[END_REF]. We match the probe images against the gallery images and compute the ranks at which the target spoofs are successfully matched. In this context, rank-k denotes the probability that the target identity to be spoofed occurs among the top k matches for a given probe image. The results are summarized in Figure 10, from which we observe that the identification accuracy associated with after-makeup images is significantly higher than that of the before-makeup images. Figure 11 shows examples of subjects being matched at better and worse ranks after the application of makeup. This clearly illustrates that makeup is a viable spoofing method in the context of automated face recognition.

Observations and Future Work

In this section we summarize the main findings of this research.

• Based on our experiments with two different face matchers, we observe that the similarity scores between after-makeup and target face images are higher than those between before-makeup and target-images. This indicates that makeup can be strategically used to spoof a face.

• The identification experiment shows that the target identity being spoofed is likely to match at a better rank after the application of makeup by the impostor.

• Spoofing was successful in some cases and not in others. This is expected as the success of spoofing depends on the source face and target face as well as the makeup procedure used.

Even though subjects in the MIFS dataset were not deliberately launching a spoof attack against an automated face matcher, the results presented here demonstrate the potential of using makeup to spoof a face recognition system (see Figure 11). These observations point out the necessity for developing face recognition methods that are less impacted by the application of makeup. There are several ways to potentially address this issue: • As indicated in the work of Short et al. [START_REF] Short | Effects of surface materials on polarimetric-thermal measurements: applications to face recognition[END_REF], polarimetric thermal images are minimally impacted by the application of cosmetic paints. Specifically, their work demonstrated the efficacy of recognizing faces in the presence of makeup by using polarimetric thermal imaging.

• Extracting features that are invariant to the application of makeup can help mitigate the presented type of attack [START_REF] Chen | An ensemble of patchbased subspaces for makeup-robust face recognition[END_REF]. In addition, makeup detection schemes [START_REF] Chen | Automatic facial makeup detection with application in face recognition[END_REF] can be employed for detecting and preprocessing face images prior to face matching.

Summary

In this work, we presented preliminary results on makeup-induced face spoofing. We observed in Section 5.1 that similarity scores between a face image and the target face to be spoofed did indeed increase after the application of makeup. In Sections 5.2 and 5.3, we noted that the increase in match score is significant. Finally, in Section 5.4, we showed that a face image is matched with the target identity at a better rank after applying makeup to the former. With the increasing use of face recognition systems in authentication applications, this research suggests that the issue of makeup has to be accounted for in the context of spoof attacks. It is not difficult to envision scenarios where a malicious individual may strategically employ makeup to deceive the system.

Figure 3 .

 3 Figure 3. Examples of images in the MIFS dataset after cropping.Here, (a), (b) and (c) represent three spoofing attempts. In each case, the image on the left shows the subject before makeup, the one in the middle is the subject after makeup, and the image on the right is the target identity that the subject is attempting to impersonate (see text for explanation).

Figure 4 .

 4 Figure 4. Examples of images in the MIFS dataset after cropping. Here, (a) and (b) represent two spoofing attempts where two different subjects (left) apply makeup (middle) to resemble the same target identity (right).

Figure 5 .

 5 Figure 5. ROC curves averaged over 10 folds of the BLUFR protocol. The "Verification µ -σ Rate" reports the average performance across the 10 folds and subtracts the standard deviation of that performance to yield a lower bound on performance.

Figure 6 .Figure 7 .

 67 Figure 6. Boxplots displaying similarity score distributions of B-T , A-T and B-A, as computed by the two face matchers. For each boxplot, the central red line is the median value and the edges of the box correspond to the 25 th and 75 th percentiles.

Figure 8 .Figure 9 .

 89 Figure 8. Boxplots showing both spoofing indices for the (a) COTS and (b) VGG face matchers. The higher the value, the more likely that spoofing has occurred.

Figure 10 .

 10 Figure10. Comparison of target identification results before and after the application of makeup. The gallery set consists of the target images to be spoofed as well as 13,120 images from the LFW dataset. The probe set consists of the before-makeup and after-makeup images. The application of makeup for spoofing purposes significantly increases the chance of target identities being matched at higher (i.e., better) ranks.

Figure 11 .

 11 Figure 11. Examples of spoofing attempts with respect to the COTS face matcher. The target images are successfully retrieved at better ranks when using after-makeup images as probes in (a) -(d) (green border) and at worse ranks in (e) and (f) (red border). Note that (c) and (d) contain the same subject but different target identities.

The MIFS dataset is available at www.antitza.com/ makeup-datasets.html

Note that the makeup video tutorials do not include images of the target identity, if any, used by the subject during the spoofing process. In fact, it is likely that some subjects are attempting to resemble the target identity from memory.

COTS template extraction failed for 113 images of the 12,896 images in the BLUFR protocol. For a fair comparison with VGG, we withheld these images from the VGG matcher as well. Note that the BLUFR protocol only uses 12,896 images of the 13,233 images in LFW.

http://www.cbsr.ia.ac.cn/users/scliao/ projects/blufr