Christophe J Nordman
email: nordman@dial.prd.fr

François-Charles Wolff

On-the-job learning and earnings: Comparative evidence from Morocco and Senegal #

Keywords: Earnings functions, informal training, learning-by-watching, learning-by-doing, returns to tenure, Morocco, Senegal JEL Classification: J24, J31, O12

In this paper, we consider a model of on-the-job learning where workers learn informally by watching and imitating colleagues. We estimate the rate of knowledge diffusion inside the firm using two matched worker-firm data sets from Morocco and Senegal. We rely on non-linear least squares to estimate the structural parameters of the informal learning model and account for firm heterogeneity using firm factors derived from a principal component analysis. We find that the rate of knowledge diffusion is around 7 percent in Morocco and Senegal, but part of the learning-by-watching returns stems from firm heterogeneity. Informal training significantly affects the shape of returns to tenure in these two countries. Finally, we estimate an extended model with both learning-by-watching and learning-by-doing and find significant benefits from imitating colleagues in Morocco.

Résumé: Dans cet article, nous considérons un modèle d'apprentissage où les travailleurs apprennent de façon informelle en observant et en imitant leurs collègues de travail. Nous estimons le taux de diffusion du savoir au sein des entreprises en utilisant deux bases de données appariant les employeurs à leurs employés, respectivement au Maroc et au Sénégal. Nous estimons les paramètres structurels du modèle théorique en prenant en compte l'hétérogénéité des entreprises à l'aide d'une analyse factorielle. Nos résultats montrent que le taux de diffusion du savoir s'élève à environ 7% au Maroc et au Sénégal, mais une partie des rendements de l'apprentissage par l'imitation provient de l'hétérogénéité des entreprises. La formation informelle affecte significativement la nature des rendements de l'ancienneté dans ces deux pays. Finalement, nous estimons un modèle augmenté incluant deux types d'apprentissage informel, celui par imitation et celui par la pratique, et observons que le premier bénéficie grandement aux travailleurs marocains.

Introduction

Within the academic literature in sociology, there has been a growing interest in workplaces as learning environments and the importance of on-the-job learning in skill formation processes (Garrick, 1998, Boud and[START_REF] Boud | Understanding Learning at Work[END_REF]. In particular, many workplace learning processes are variously described as 'informal' or 'nonformal' [START_REF] Billett | Learning in the Workplace: Strategies for Effective Practice[END_REF], Colley et alii, 2003[START_REF] Hayward | Balancing the Skills Equation: Key Issues and Challenges for Policy and Practice[END_REF]. The perceived importance of informal processes in workplace learning is captured by Coffield's image of the learning iceberg (2000, p. 1): "If all learning were to be represented by an iceberg, then the section above the surface of the water would be sufficient to cover formal learning, but the submerged two thirds of the structure would be needed to convey the much greater importance of informal learning".

It is important to appreciate that interest in workplace learning, through both formal and informal processes, is still a relatively recent phenomenon and that evidence about effective practices is still relatively scarce (Battu et alii, 2003). Research on workplace learning in economics is still in late infancy at best, albeit [START_REF] Mincer | Job Training: Costs, Returns and Wage Profiles[END_REF] was claiming years ago that informal training may constitute the essential part of training provided by firms. If workplace learning, and in particular informal training, is as important in developing vocational knowledge and skills as research is beginning to suggest, then it is also important to understand the ways in which (and of course the extent to which) skill formation resulting from situated learning affects workers' productivity and wages.

While the benefits of investments in human capital are clearly established in the economic profession, the accurate calculation of rates of return to informal training remains complex. One reason is that the usual on-the-job training variables are often affected by measurement errors. Some authors have shown that these errors are likely to bias the estimates of the rates of return to training (Barron et alii, 1997[START_REF] Loewenstein | Formal and Informal Training: Evidence from the NLSY[END_REF][START_REF] Frazis | Reexamining the Returns to Training: Functional Form, Magnitude, and Interpretation[END_REF]. Moreover, for reasons that are inherent in the very nature of informal training, the few direct measures of informal training available today in data sets are even more imperfect (see the discussion in Barron et alii, 1997, Loewenstein and[START_REF] Loewenstein | Formal and Informal Training: Evidence from the NLSY[END_REF]. Finally, informal training appears inextricably part of the employee's productive activity [START_REF] Brown | Empirical Evidence on Private Training[END_REF]. The modeling of a process of informal learning susceptible to be submitted to an empirical test requires the availability of micro data containing information both on workers and on their firm.

In this contribution, we rely on two matched worker-firm data sets for Morocco and Senegal to study informal training in Africa. With a method similar to that of [START_REF] Mincer | Schooling, Experience and Earnings[END_REF], our approach consists of estimating the returns to informal training using the individual earnings profiles. For that purpose, a structural model of on-the-job learning is developed to conform to the structure of our data 1 . The model accounts for on-the-job learning. Workers learn informally on the job by watching others performing their tasks 2 . They may also learn by themselves, i.e. by a sort of learning-by-doing process. Structural parameters of the model are estimated using non-linear least squares.

We extend the previous results on the learning model in the following way. First, we structurally take into account a flexible form of the returns to schooling. Indeed, the previous estimated model did not consider the possibility of convex returns to education (Destré et alii, 2008). Yet, constant rates of return to education are more and more challenged in developed and developing countries [START_REF] Card | The Causal Effect of Education on Earnings[END_REF], especially in Africa 3 . Second, we introduce controls for the firm's heterogeneity component using a factor analysis of the firms' characteristics and show the impact of these firm factors on the structural parameters of the model. Finally, our estimates are innovative in the context of developing countries.

The issue of informal training is especially important among African countries.

Mechanisms for closing the skill gap across categories of workers in Africa have long been articulated in terms of supply-side reforms. There is a recent recognition among policy makers that the demand side of the skills equation also needs attention. More effort needs to be directed towards the development of the skills of the existing workforce, in addition to improving the educational outcomes of learners in schools, colleges and higher education. This is all the more important given that public vocational education and training systems have generally failed in providing young workers, which are massively found in the informal sector, with the necessary human capital to access stable and decent jobs in the formal sector. [START_REF] Johanson | Skills Development in Sub-Saharan Africa[END_REF] show that in Sub-Saharan Africa training by traditional learning (mostly informal) is the most frequent form of training in the informal sector. Traditional learning probably makes a far greater contribution to develop the workers' skills than all the training providers taken together. It is then interesting to gauge the informal training potential of the formal private sector in these economies as well.

Our empirical results suggest that informal learning may be of importance in African firms. We find that the rate of knowledge diffusion is around 7% in the Moroccan and Senegalese firms of our samples. However, part of the learning-by-watching returns stems 1 The first presentation of the model appears in the original work of [START_REF] Lévy-Garboua | Formation sur le tas et rendements de l'expérience : un modèle de diffusion du savoir[END_REF]. It has been extended and successively estimated by Chennouf et alii (1997), [START_REF] Nordman | La formation sur le tas par diffusion du savoir : estimation sur données marocaines et mauriciennes[END_REF], [START_REF] Destré | Les effets de la formation informelle sur les gains : une comparaison sur données appariées françaises, marocaines et tunisiennes[END_REF], [START_REF] Destré | Fonctions de gains et diffusion du savoir: une estimation sur données françaises appariées[END_REF] and Destré et alii (2008). 2 Employees who are getting informal training may not always be conscious that they are doing so. 3 See Bigsten et alii (2000), [START_REF] Schultz | Evidence of Returns to Schooling in Africa from Household Surveys: Monitoring and Restructuring the Market for Education[END_REF], Söderbom et alii (2006) and Kuepie et alii (2009). from firm heterogeneity. The remainder of this paper proceeds as follows. In section 2, we present the on-the-job learning model. Section 3 describes the two matched worker-firm surveys together with the information collected from workers and firms respectively for Morocco and Senegal. In Section 4, we present the econometric strategy to recover the structural parameters of the model. Our results are discussed in Section 5. Section 6 concludes.

A model of learning-by-watching

Workers may improve their skills by learning informally, simply while being in their firm and watching other workers performing their tasks. Unlike formal training, this knowledge acquisition process seems not really costly as a firm does not have to provide specific resources for it. More productive workers may not necessarily devote time to explain other workers how to improve their own productivity. All the training effort remains informal, in the sense that less productive workers are simply expected to watch those who have more knowledge and experience, and then to replicate what they have observed. This imitation process acts as a positive externality whose benefits extend over time 4 .

In what follows, we draw on the model of learning-by-watching first described in [START_REF] Lévy-Garboua | Formation sur le tas et rendements de l'expérience : un modèle de diffusion du savoir[END_REF] and extended in Destré et alii (2008). Consider a competitive industry where wage rates are equal to the true marginal product of labour, so that earnings reflect pure human capital. Using a discrete-time framework, we denote by t h the amount of human capital for a worker at date t . Assume that the worker enters the firm at date 0, so that t corresponds to tenure. Then, 0 h is the value of the worker's human capital when starting his activity in the firm. Each individual has presumably accumulated some experience while working in previous firms. Let x be the number of working years spent outside the current firm, so that individual total experience is t x + .

In the firm, each worker is supposed to learn from colleagues who have more human capital than him/her. Let t H be the highest level of human capital embodied in colleagues.

We assume that the firm's knowledge is invariant, meaning that H H t = . Owing to the imitation process, a worker's human capital is expected to increase over time by learning from others. The following equation describes the dynamics of human capital formation for a worker (see [START_REF] Lévy-Garboua | Formation sur le tas et rendements de l'expérience : un modèle de diffusion du savoir[END_REF]):

(

)

1 1 1 - - - + = - t t t h H n n h h (1)
where n is the rate of knowledge diffusion inside the firm. The rate of learning-by-watching is the same for each worker and is time-invariant. For a given n , human capital will increase faster when the worker has a lot to learn from the most qualified worker. Hence, at the period t , the level of human capital is a weighted sum of human capital in 1 t and of human capital of the most capable worker:

1 1 1 1 - + + + = t t h n H n n h (2)
From the recurrence equation ( 2), we get the following solution for t h :

0 ) 1 ( 1 ) 1 ( 1 1 h n H n h t t t + +       + - = (3)
which we can also be expressed as:

              -         + - + = 1 ) 1 ( 1 1 1 0 0 h H n h h t t (4)
The human capital of a worker the number of periods spent within the firm (

0 / > ∂ ∂ t h t ).
Also, as time goes by, the individual level of human capital converges towards the firm's jobspecific knowledge (

H h t t = ∞ →

lim

). In this model, the central interest lies in the estimation of the parameter n .

From (2), all the job-specific information is learnt from colleagues and the highest level of human capital remains constant. A more realistic framework, considered in Destré et alii (2008) and estimated in [START_REF] Destré | Les effets de la formation informelle sur les gains : une comparaison sur données appariées françaises, marocaines et tunisiennes[END_REF], is to assume that workers learn both by themselves through their own experience and by watching others5 . In such setting, the human capital of a worker is both increasing with tenure and it converges towards the firm's job-specific knowledge. However, the latter component is no longer fixed within the firm.

Since all workers are expected to learn by themselves, the level of human capital of the most qualified worker is continuously growing.

Let g be a measure of the impact of self-learning. The dynamics of human capital formation may be expressed as:

( )

1 1 1 1 1 - - - - - + + = - t t t t t h H n n h g h h (5)
By definition, there is no learning-by-watching for the most capable worker, meaning that the highest level of human capital will increase inside the firm only owing to self-learning. This

implies that 1 ) 1 ( - + = t t H g H
. Using (5) and after some calculations, we finally deduce the following value for t h (Destré et alii, 2008):

                          + + + + - +         + + + + + = 0 0 ) 1 )( 1 ( ) 1 ( 1 1 ) 1 )( 1 ( ) 1 ( 1 ) 1 ( H n g n g h n g n g g h t t t t (6) When 0 = g
, equation ( 6) is equivalent to (3), which is the pure learning-by-watching case.

two comments are in order. First, it is unclear whether the firm's job specific knowledge t H may really be seen as a moving target, increasing at steady state. Imagine a manufacturing firm, with a young worker and a very experienced, older worker. In a context where the technology remains fixed, the latter has certainly nothing more to learn even by him/herself6 .

As a consequence, we estimate first the learning-by-watching model in our empirical analysis and then examine the consequences of self-learning. Second, as shown by ( 4) and ( 6), the expression of t h is a non-linear function of both g and n . A non-linear estimation is hence needed to estimate the structural parameters of informal training. programme.

Data and descriptive statistics

These two surveys are based on the notion that the workplace is the microdata unit where labour supply and demand are resolved. In that spirit, the ICA and FACS surveys collected data both on the firm characteristics and on a sample of employees in each workplace. The survey instrument was then a written questionnaire addressed to both employers and employees. The questionnaires are specifically tailored for each country, but they enable cross-country comparisons as they are made of very similar questions.

In Senegal, the firms have been randomly selected among the population of formal establishments and belong to the following ten sectors of production: agro-industry, chemicals and paints, construction materials, food, furniture, metal, paper and publishing, plastics, textile and leather and wood industry. There is no constraint on the size of the firms which were selected in the sample. Conversely, in Morocco, the focus is restricted to formal companies which have at least ten employees. The selected firms are in seven industries:

electronics, textiles, garments, food, pharmaceuticals, leather and shoes products, and plastics.

Clearly, there is less heterogeneity in the firm sample of Morocco.

Let us describe more precisely the two samples7 . In Senegal, a sample of 262 manufacturing firms has been surveyed based on a sampling plan made of 1645 formal companies. These firms have been randomly selected using a stratification based on sector, size and localisation and represented 59.6% of the formal manufacturing firms in 2003 and 68.9% of its formal permanent jobs. For Morocco, the Moroccan Census of Manufactures was used as the establishment sampling frame, with 1933 formal firms of more than ten employees in the seven sectors mentioned above. The sample includes data from 859 manufacturing plants which are representative of the sampling plan in terms of employment, production and exportation.

In each surveyed country, up to ten employees were randomly sampled in each firm following the idea advocated by [START_REF] Mairesse | Using Employee Level Data in a Firm Level Econometric Study[END_REF]. All the employees of small firms have been interviewed, while the sampling rate decreases with the size of the firms. The number of workers interviewed in Senegal and Morocco are respectively equal to 1645 and 8561.

Descriptive statistics of the workers

To estimate the structural parameters of the on-the-job learning model, we need several observations of workers in each firm. Recall that we assume that the more capable worker in a given firm takes up the teaching role. We make the following selections to the initial samples (similar for each country).

First, we restrict the samples to firms having information on at least four workers.

Second, as our modelling framework is in discrete time, we decide to exclude all the workers having less than one year of tenure in the current firm. In so doing, it may be that we underestimate the rate of knowledge diffusion within the firm, if we assume that the learningby-watching process is very efficient once entering the firm (and there is less to learn from colleagues a couple of months later). However, we also argue that the level of earnings is unlikely to increase just after being hired and before reaching one year of tenure, even if there is a rise in the worker's productivity due to learning from others. Finally, we drop from the sample all the observations with missing values or outliers.

The final samples are described in Table 1. This leaves us with samples comprising 7622 workers and 822 firms in Morocco, and 1284 workers and 180 firms in Senegal. Owing to the large size of its sample, the FACS Moroccan data are expected to be much more informative. More than 75% of workers are employed in firms with 10 completed individual questionnaires. The same proportion is equal to 31.9% in Senegal. Meanwhile, the proportion of workers belonging to firms with information on less than 7 respondents is much higher in Senegal than in Morocco (respectively 30.3% vs. 4.6%)8 .

Insert Table 1 here

The questionnaires of the two surveys allow us to construct identical human capital indicators for the workers in Morocco and Senegal. We compute for each respondent the number of years of completed schooling, the number of years of experience off the current firm and the number of years of tenure in the incumbent firm. All these variables provide good controls for the potential advantage on the labour markets. We further construct a dummy variable which is equal to one when the worker has received formal on-the-job training in the past. Owing to data constraints, this training is off the current job in the case of Morocco and in the current firm for Senegal. Finally, we add two demographic variables, i.e.

a dummy for gender and a dummy for whether the individual is married or not9 .

In Table 2, we present some descriptive statistics related to the different covariates introduced into the earnings equations. On average, Moroccan workers are less educated than Senegalese employees (8.7 years of education against 10.3 years). This is surprising because Sub-Saharan African countries are often believed to be less endowed in human capital as compared to North African ones. An explanation is that an overwhelming proportion of poorly educated individuals actually work in the informal sector of Senegal (see [START_REF] Dial | Youth and Labour Markets in Africa: A Critical Review of Literature[END_REF].

The formal private sector in Senegal, highly selective, might be in fact reserved to the most educated workers. This is probably less true for Morocco where uneducated workers are also found in significant proportion in garment firms for instance.

Insert Table 2 here Also, we find a much higher proportion of women in Morocco compared to Senegal (40% against 16%). At the same time, the specific gender composition of the Moroccan subsample is not so influential when explaining the lower education observed in that country.

Indeed, while the mean number of years of schooling is equal to 8.8 among men, it is only slightly lower among women, equal to 8.5 years.

In terms of work experience, the workers of the two samples have the same amount of potential experience off the current firm, which stands at 12.7 years. Tenure in the current firm is on average higher for the Senegalese workers (8.9 versus 7.4 years). Finally, we note that 35% of the Senegalese workers received formal job training in the incumbent firm.

As returns to on-the-job learning are expected to depend on both workers' and firms' characteristics, we now investigate the differences in the composition of the firms.

Firm heterogeneity

For the sake of comparability, it matters to know whether there are any differences in the characteristics of the firms. In Table 3, we summarize the descriptive statistics of the final samples of selected firms for each country.

Insert Table 3 here

For Morocco, 40.9% of the 822 firms are small and medium-sized plants with less than 50 permanent employees. Firms with more than 150 permanent employees represent 23% of the firm sample. Most of these firms are found in the textile and garment sectors (60.3%). More than half of these firms are exporting companies, therefore submitted to strong market competition, especially in the garment sector. However, less than 5% of the sample of firms can be described as 'multinationals', i.e. with more than 75% foreign capital. Note that 62% of the firms have positive profits (hereafter 'profitable' firms).

The Senegalese sample represents well the actual distribution of jobs and firms in the manufacturing sector of Senegal (World Bank, 2005). The firm size is quite similar to that of the Moroccan sample, with 52% of the 180 plants being small and medium-sized companies.

Large-sized firms are also found in relatively fair proportion (21%). Firms in agro-industry are predominant (34%), the second most important sector being the industry of paper, closely followed by the textile and leather firms (10%). There are slightly more foreign owned companies in the Senegalese sample (15%), while profitable firms are also well represented (84%).

Econometric specification

We turn to a structural econometric analysis to recover the values of the different parameters of interest. We first consider equation ( 4) and then show how to add the impact of self-learning into the estimation strategy. By taking the logarithm of t h , we get:

              -         + - + + = 1 ) 1 ( 1 1 1 ln ln ln 0 0 h H n h h t t (7)
In a setting with only learning-by-watching, we get a human capital earnings function which depends on the human capital of both the worker's initial stock and the most qualified worker.

It is also non-linear in both the rate of knowledge diffusion and tenure, so that the appropriate econometric approach is to rely on non-linear least squares (NLSQ).

Suppose that the initial earning (when entering the firm) is not observed. We can then approximate the level 0 h using a Mincerian earnings function. We introduce into the earnings function both years of education and years of experience outside the firm in a quadratic way.

Several studies have indeed shown that returns to education are convex in African countries10 . We denote by s , e and t respectively years of education, years of experience off the firm and tenure. We express 0 h as:

( )

2 4 3 2 2 1 0 0 exp e e s s h α α α α α + + + + = (8) since 0 = t
by definition when entering the firm. Assume now that we can perfectly observe the most qualified worker to whom each individual is exposed. Following the same approach, we can rely on a Mincerian earnings function to approximate the level H . With S , E and T respectively years of education, years of experience outside the firm and tenure for the most qualified worker and using quadratic profiles for these three covariates, it follows:

( )

2 6 5 2 4 3 2 2 1 0 exp T T E E S S H α α α α α α α + + + + + + = (9)
A difficulty with the data is that we have no information on the most productive worker who may be imitated by each individual. Such observation would require a description of "student-teacher" interactions within establishments.

To overcome this shortcoming, we follow the method of [START_REF] Destré | Les effets de la formation informelle sur les gains : une comparaison sur données appariées françaises, marocaines et tunisiennes[END_REF] and Destré et alii (2008). There are then two important assumptions. First, as we have matched employer-employee data and observe a random sample of employees from the same firm, we consider the whole set of employees for each firm and suppose that the most qualified worker within the firm is the one with the highest characteristics recorded in the survey 11 . Second, as we are not sure that an individual is really subject to the influence of the most qualified worker (as measured with the data), we account for a distance indicator between the maximum position and the individual situation.

Formally, this means that for an exogenous covariate denoted by X for the most productive worker and by x for the selected individual, we suppose that the characteristic of the teacher is such that:

x X X x x ) 1 ( δ δ - + = (10)
with x δ a parameter to be estimated (

1 0 ≤ ≤ x δ
). It measures the relative distance between the individual and the most capable worker within the firm. x δ takes the value 0 if the individual has no possibility of learning from others and the value 1 if his/her most qualified teacher corresponds effectively to the worker having the biggest X of the firm's subsample 12 . This implies that there are three parameters of relative distance to estimate, i.e. s δ , e δ and t δ . After some calculations, we finally deduce the following non-linear form for the earnings equation under learning-by-watching:

ε β α α α α α δ α δ α δ α δ α δ α δ α δ α δ α + + + + + + +                         + + - + - + - + - + - + -         + - + = Z e e
) ( 2 ) ( ) ( ) ( 2 ) ( ) ( exp ) 1 ( 1 1 1 ln ln ( 11
)
where Z is a set of control variables, β is the corresponding vector of estimates, and ε is a random perturbation. We estimate equation ( 11) using non-linear least squares (NLSQ) to get 11 In that sense, this means that we tend to underestimate the rate of learning-by-watching. Indeed, there may be even more productive workers within the firms, who have not been interviewed during the survey process.

12 Implicitly, the distance method makes the assumption that any firm includes several knowledge pyramids and that workers can only acquire the knowledge of their work group. Alternatively, Chennouf et alii (1997) made use of firm average human capital variables corrected by a variation index of either education or tenure inside the firm. [START_REF] Nordman | La formation sur le tas par diffusion du savoir : estimation sur données marocaines et mauriciennes[END_REF] and [START_REF] Destré | Les effets de la formation informelle sur les gains : une comparaison sur données appariées françaises, marocaines et tunisiennes[END_REF], who applied both methods on the same dataset, show that the alternative techniques provide a negligible difference in the estimation of the parameters of interest. However, the distance indicator seems to be more accurate because the relative knowledge between the "teacher" and the new entrant is then not diluted among mean variables. Ideally, of course, one would like to consider externalities within each occupation, which seems far beyond the possibilities of most available matched employer-employee data on Africa.

the coefficients of both the parameters and the explanatory variables. Let us briefly discuss identification issues. Clearly, the parameters 0

( ) ( )( ) ε β α α α α α δ α δ α δ α δ α δ α δ α δ α δ α + + + + + + +                         + + - + - + - + - + - + -                 + + + + - + + = Z e e
) ( 2 ) ( ) ( ) ( 2 ) ( ) ( exp 1 1 1 1 1 1 ln ln (12)
If n equals zero, meaning there is no possibility of learning by watching others inside the firm, (12) reduces to a simple linear-in-tenure Mincerian earnings function with the parameter g reflecting a 'gross' effect of tenure on wages (including informal learning effects on wages but admittedly also other aspects of this relationship). One may then interpret specification (12) as a generalisation of the Mincerian model, including a sum of a linear-intenure earnings function and a correction factor considering what firms have to offer to their employees in terms of learning 13 .

Owing to the importance of the work environment in which workers are placed, which is more or less favourable to learning by watching other colleagues, it seems important to control for firm heterogeneity. However, accounting for firm heterogeneity is complex when estimating (11) since the extended earnings equations are intrinsically highly non linear. Thus, we cannot control for firm heterogeneity using firm fixed effects. In this paper, we rely on a 13 The Mincerian model actually offers an accurate description of gross earnings for new entrants, i.e. for t=0, and in two other specific circumstances: i) if firms offer no extra knowledge to their employees; and ii) if the rate of knowledge diffusion within firms approaches zero. Both assumptions are clearly unrealistic, but the Mincerian earnings function stands as a first-order approximation insofar as each firm can only make a small contribution to an individual's stock of human capital.

factor analysis following [START_REF] Muller | Which Human Capital Matters for Rich and Poor's Wages? Evidence from Matched Worker-Firm Data from Tunisia[END_REF] and Jellal et alii (2008). We summarize the main statistical information on the firms' characteristics using first a multivariate analysis and then introduce the computed principal components (factors) into the earnings functions deriving from this analysis.

Using factors may be seen as a further step with respect to studies that have added mean firm variables into earnings functions, individual characteristics being controlled for.

With respect to firm fixed effects, the factors are expected to pick up the impact of more qualitative characteristics of the firms. Specifically, we use a principal component analysis (PCA) to summarize the information about the surveyed enterprises. This method is based on the calculation of the inertia axes for a cloud of points that represents the data in table format.

As long as the computed factors account for most of the firm heterogeneity, this approach allows us to obtain consistent estimates close to those of the fixed effect estimator.

In the case of Morocco, the first ten inertia axes, defined as the estimated factors which are linear components of all the firm's characteristics, concentrate a large proportion of the total variance of the original variables (63%). This reflects therefore a fair amount of the relevant information about the firm's characteristics. For Senegal, firm heterogeneity seems to be greater according to very basic descriptive statistics. We thus choose to rely on twelve factors which concentrate 55% of the total variance of the firm variables. In both cases, the selected factors reflect a wide range of firm characteristics that can mainly be summarised by the sector affiliation, size, performances, and workforce composition 14 .

Econometric results

Basic earnings regressions

We begin by estimating earnings equations with a set of individual demographic and labour characteristics as control variables. We wonder then whether controlling for firm heterogeneity has an impact or not on our findings. Under the assumption that both 0 = n and 0 = g , we estimate the earnings functions using simple OLS regressions. The dependent variable is defined as the log of the hourly earnings, which is computed as the ratio of monthly earnings (including additional salaries and premiums) divided by the number of worked hours per month. The corresponding results are in Table 3.

Insert Table 3 here 14 To save space, the detailed results of the two factor analyses (including the complete list, definitions and descriptive statistics of the firms' characteristics) are not shown but available from the authors upon request.

In panel A, we describe the estimates obtained without control for firm heterogeneity.

Models (1A) and (2A), respectively for Morocco and Senegal, include as regressors education and off the firm work experience along with their squared values, and three dummy variables for being female, married and the receipt of formal job training15 . For both countries, the results exhibit a convex profile in years of education. This finding contradicts much of the comparative studies on the rates of return to education across countries which often use a linear in education specification of the earnings function (see Trostel et alii, 2002). However, constant or decreasing rates are more and more challenged in both developed and developing countries and non-linearities (mostly convexity) in the returns to education have been recently

put forward by some studies on Africa (Bigsten et alii, 2000[START_REF] Schultz | Evidence of Returns to Schooling in Africa from Household Surveys: Monitoring and Restructuring the Market for Education[END_REF], Söderbom et alii, 2006, Kuepie et alii, 2009).

Concerning the other covariates, we find that the gender dummy is only significant in Morocco. In that country, [START_REF] Nordman | Is there A Glass Ceiling in Morocco? Evidence from Matched Worker-Firm Data[END_REF] have evidenced the presence of a glass ceiling effect using quantile regressions. The fact that there is no gender difference in Senegal is somewhat surprising, but this result may be due to the low number of female workers in the sample. Being married has a positive effect in the two countries, and the receipt of formal training (treated as exogenous) only really matters in Morocco. Finally, we note that the values of the R² in the regressions are reasonably high (around 0.4), but there are less significant explanatory variables in Senegal.

In columns (1B) and (2B), we add in the list of covariates the years of tenure in the firm and rely on a cubic for. Several comments are in order. First, this additional covariate does not really affect the previous estimates, except the marital status whose effect is now much lower. Second, the squared and cubic tenure terms are only significant in Morocco, while they turn to be insignificant in Senegal. Third, we evidence a U-shaped profile for the returns to years of tenure in the current firm without control for firm heterogeneity in Morocco, while the shape is continuously decreasing in Senegal. This result is of interest as it stands in contrast with the standard Mincerian earnings function, which relies on a quadratic profile for years in tenure (the marginal returns to tenure are thus linearly decreasing).

Fourth, we find lower returns to years of tenure in Morocco than in Senegal. The returns to tenure remain rather flat in the former country, equal to 2% after either 5, 10 or 20 years of tenure. These returns amount to 4%, 3% and 2% in Senegal respectively after 5, 10 and 20 years of tenure 16 . Fifth, we find that it matters to control for the firm heterogeneity. In the two countries, we evidence that the returns to tenure are lower when being calculated with either firm fixed effects or firm factors models. Both in Morocco and in Senegal, the shape of the returns curve is not really affected by the use of either fixed effects or firm factors. In what follows, we rely on the firm factor strategy when turning to the structural estimation.

Estimation of the learning-by-watching model

We now turn to the estimation of the structural model of learning-by-watching. For each country, the model is estimated twice using NLSQ, once with individual covariates only and once with inclusion of firm factors in order to pick up the impact of firm unobserved heterogeneity. The corresponding estimates are in Table 4.

A first remark is that introducing the possibility of learning-by-watching colleagues does not really affect the coefficients obtained through Mincerian equations for education, experience off the firm, gender or marital status. For instance, being a woman reduces the hourly earnings in Morocco by 7.8% with the Mincerian specification (without firm controls), and by 7.3% with the possibility of informal on-the-job training. We still find a convex profile for years of schooling in both countries, at least when firm heterogeneity is controlled for 17 .

Conversely, years of experience off the firm always exhibit a concave profile.

Let us focus on the values of the structural parameters of the model. When firm heterogeneity is not controlled for, we find a significant value for n in Morocco and Senegal (at the 1 percent level). This suggests that the learning-by-watching mechanism described in the theoretical section is probably operative in the selected countries. These results are in accordance with the previous findings reported in Chennouf et alii (1997) for Algeria and Canada, [START_REF] Nordman | La formation sur le tas par diffusion du savoir : estimation sur données marocaines et mauriciennes[END_REF] for Morocco and Mauritius, [START_REF] Destré | Les effets de la formation informelle sur les gains : une comparaison sur données appariées françaises, marocaines et tunisiennes[END_REF] for Morocco, Tunisia and France and [START_REF] Destré | Fonctions de gains et diffusion du savoir: une estimation sur données françaises appariées[END_REF] for France. The rates of knowledge diffusion within firm amount to 6.3% and 7.6% respectively in Morocco and Senegal. Our rate of knowledge diffusion for Morocco is lower than the estimated values found in [START_REF] Nordman | La formation sur le tas par diffusion du savoir : estimation sur données marocaines et mauriciennes[END_REF] and [START_REF] Destré | Les effets de la formation informelle sur les gains : une comparaison sur données appariées françaises, marocaines et tunisiennes[END_REF], around 15% 18 . 16 It is interesting to compare our results with those found in Anglophone African countries with similar data sets. For instance, Bigsten et alii (2000) obtain lower rates of return to tenure, though it is difficult to compare their estimates with ours with accuracy as their specifications differ somewhat from ours in that they use a quadratic term in tenure only. From their sample statistics and estimates, however, we can evaluate their rates at 1.8% for Ghana at the sample mean (after 4 years of tenure), 0.2% for Kenya (after 7 years), 3.4% for Zambia (after 6 years) and 0.9% for Zimbabwe (after 9 years). 17 Without firm factors, the squared term for education is not significant in Senegal. 18 The sample used for Morocco in [START_REF] Nordman | La formation sur le tas par diffusion du savoir : estimation sur données marocaines et mauriciennes[END_REF] and [START_REF] Destré | Les effets de la formation informelle sur les gains : une comparaison sur données appariées françaises, marocaines et tunisiennes[END_REF] is a non-representative sample of workers in only two manufacturing sectors. Interestingly, results reported in [START_REF] Destré | Fonctions de gains et diffusion du savoir: une estimation sur données françaises appariées[END_REF] for a

In Figure 1, we show how the diffusion of the firm specific knowledge is sensitive to the parameter n . Specifically, we calculate the number of years of tenure which is requested to assimilate a given proportion of the knowledge of the firm. Denoting by ϑ the share of the firm knowledge, the number of years t to assimilate ϑ is

) 1 ln( / ) 1 ln( n t + - - = ϑ
. Hence, a worker will get faster a given proportion of the firm knowledge when n is important. For the sake of illustration, suppose that we seek the number of years in tenure to assimilate half of the firm knowledge. We find that t is equal to 11.3 years in Morocco and 9.5 years in Senegal. The requested years in tenure are respectively 26.3 and 18.9 when 8 . 0 = ϑ . It is of interest to have a look at s δ and e δ . These parameters measure the relative distance which separates the average worker from the most qualified teacher inside the firm, respectively in terms of years of education and in terms of experience off the firm. We find that the educational distance is much lower in Morocco than in Senegal (respectively 0.069 and 0.430). This explains in part the slightly lower value found for n in Morocco. As the average worker is rather close to his/her most qualified teacher, the potential of learning-bywatching is less important. At the same time, the relative distance in terms of experience off the firm is not significant in Morocco, while it is in Senegal.

So far, we have neglected the influential role of firm characteristics so that we may overestimate the rate of job-specific knowledge diffusion. This would be the case if there are some differences in wage policies among firms related to sectors of activity or to the size of the firm for instance. In models (1B) and (2B) of Table 4, we add in the country-specific regressions a set of firm factors obtained by the PCAs. The main conclusion is that controlling for firm characteristics does significantly reduce the value of the rate of knowledge diffusion.

The magnitude of this coefficient is now twice lower both in Morocco and Senegal. It amounts to 3.6% for the former country and to 3.5% for the latter, but the parameter is only significant in Morocco (at the 10 percent level).

Insert Table 4 here Finally, we calculate the marginal returns to tenure in the learning-by-watching model.

Of course, if workers have the opportunity to learn a lot from colleagues, they are expected to improve quickly their earnings and then the returns to tenure should exhibit a more convex profile. Doing as if the time variable t is continuous, we first express equation ( 11) as representative sample of French workers in the private sector, with n standing at about 5%, are closer to our estimates for Morocco and Senegal.

( ) [ ] B A n h t t + + - + = - ) 1 ( 1 1 ln ln
, A being the exponential term in (11) and B a constant (these two terms are independent of t ). The derivative

t h t ∂ ∂ / ln is: ( ) A n n n A t h t t t - - + - + + + = ∂ ∂ ) 1 ( 1 1 ) 1 )( 1 ln( ln (13)
which is clearly not linear in t as with Mincerian earnings functions. We describe in Figure 2 the profiles of returns to tenure. For each country, we compare the results from earnings regression with a cubic profile in tenure to those of learning-by-watching models, both without and with firm factors.

Insert Figure 2 here

Both in Morocco and Senegal, we find that the benefits from learning-by-watching others are essentially reaped by workers during the first ten years of their career. The returns to tenure with the learning-by-watching model are strongly decreasing till about the 15 th years of tenure. Owing to the opportunities of imitating others, the returns are more important just after being hired with this model compared with the Mincerian specification. In Morocco, the returns curves of the Mincerian and learning-by-watching model cross at around 20 years in tenure 19 . Finally, in Senegal, the returns to tenure are more convex with the Mincerian specification, so that there are fewer differences with the learning-by-watching model. The possibility of imitating other colleagues conveys again an economic benefit in the first years of the career (at least with no firm factors).

Learning from others or learning by oneself?

In the previous estimations, we have done as if workers were unable to acquire any job knowledge by themselves. We now relax this constraint and estimate the extended model of on-the-job learning given by ( 12), with both learning from both others and oneself. The model is again estimated using non-linear least squares, respectively without and with firm factors.

For the sake of comparison, we begin by considering a simple model of human capital formation with no possibility of learning-by-watching ( 0 = n

). The dynamics of human capital is

t t h g h ) 1 ( 1 + = + , so that 0 ) 1 ( h g h t t + =
. Taking the logarithm of this expression and assuming that ( )

2 4 3 2 2 1 0 0 exp e e s s h α α α α α + + + + =
as in (8), we estimate the following regression20 :

ε β α α α α α + + + + + + + + = Z e e s s g t h t 2 4 3 2 2 1 0 ) 1 ln( ln (14)
It is straightforward to estimate this model with learning-by-oneself only. Our estimates are in Table 5. We find very similar values for the parameter g in both countries. It is equal to 1.3% per year in Morocco and to 1.1% in Senegal. Results lead to somewhat different findings once accounting for the possibility of imitation within the firm.

Insert Table 5 here

In the case of Senegal, we find a more important value for g with the extended learning model (columns 2B and 2C). Indeed, this parameter takes a value of 2.1% without firm factors and 1.8% with firm factors. At the same time, we fail to evidence a significant value for the rate of knowledge diffusion. In models (2B) and (2C), the parameter n is still positive and is equal to 0.041 once controlling for firm heterogeneity. A similar result was found with the pure learning-by-watching specification, with an insignificant parameter of 0.035. A last remark, suggesting that there is still something to learn from colleagues (despite the insignificant rate of job-specific knowledge diffusion), is that the relative distance separating the average worker from the most qualified worker remains highly significant. In the meantime, the distance is quite low, around 0.16 in presence of firm factors.

In Morocco, introducing the possibility of learning-by-imitation leads to higher returns to learning-by-oneself, just like in the Senegalese case. The parameter g is equal to 3.2% without firm factors and to 5% with firm factors. At the same time, while n is insignificant in model (1B), it appears significant at the 1 percent level and equal to 4% in model (1C) which controls for firm heterogeneity. Among Moroccan firms, there is thus a real potential benefit of learning from colleagues and this learning-by-watching process also conveys higher benefits to self-learning. Finally, the rate of diffusion of 4% in the learning-by-oneself and others model (1C) of Table 5 is very close to the value of 3.5% found in the pure learning-bywatching model (1B) of Table 4.

It is worth comparing our results with those of [START_REF] Destré | Les effets de la formation informelle sur les gains : une comparaison sur données appariées françaises, marocaines et tunisiennes[END_REF]. For

Morocco, these authors have found that the learning-by-oneself process was the only component of informal training influencing earnings. Conversely, they exhibit both a high rate of knowledge diffusion and a significant impact of learning-by-oneself (1.9%) for Tunisian workers. The argument advocated to explain this difference is that the distance with the most educated ( s S -) in the Tunisian firms is on average higher than in the Moroccan ones, while the number of years of schooling is equivalent in both countries. These statistics may justify the possibilities that learning by imitation is much more important among Tunisian employees.

In our case, however, this does not seem to be a relevant explanation for the divergence of learning effects in the Moroccan and Senegalese cases. While s Samounts on average to 5.5 years for the Moroccan workers, it is 6.7 years for the Senegalese. Besides, the average education is slightly higher for the Senegalese. Similarly, the distance to the most experienced worker is respectively 13.9 versus 14.3 years, again higher for the Senegalese where the benefits of the imitation process are found to be null. Hence, the fact that there are no benefits from learning-by-watching in the Senegalese case should be explained by other factors, beyond the workers' human capital endowments.

A first explanation could lie in strong rigidity in the fixation of wages. Another explanation may refer to the work organisational features within firms, the environment of employees contributing to intensification of the learning-by-watching process. For instance, more compartmentalized firms may leave fewer places to the emergence of peer effects.

Unfortunately, we lack relevant information on the firms to know whether Senegalese firms are more partitioned21 . Finally, the important presence of temporary workers in firms, or firms with high labour turnover, could also explain why the diffusion of knowledge is not efficient.

While the ratio of the number of full time temporary workers to the total number of full time permanent employees amounts to 27% in the Moroccan firms, it is much higher in the Senegalese firms (67%). The underlying higher turnover may well explain the divergence of knowledge diffusion efficiency between these two countries.

A last comment is whether we can interpret estimates of the structural model as pure measures of learning capacities or knowledge externalities. Given the first-term of the righthand-side of (12), the estimated parameters of the learning model do not only pick up the tenure effects on earnings since we are able to disentangle the role of g and n when estimating the model. Nevertheless, that observed tenure effects on wages reflect pure learning is challenged by alternative interpretations of this relationship.

A first possibility is that there is a positive correlation between recorded tenure and time invariant worker productivity, because high-ability workers get paid well and tend to keep their jobs. Second, job transitions may affect the relationship between tenure and wages if young workers are likely to have higher exit rates, as a result of searching for better paid jobs. The quality of the match, which determines earnings, should be better amongst middleaged workers than for young workers. So, the latter will have short spells of tenure partly because they are young and partly because they switch jobs a lot. It is clear that recorded tenure may be positively correlated with wage for other reasons than pure learning effects.

However, since we only have at our disposal a cross-section of workers, we cannot investigate the potential bias introduced by unobserved worker heterogeneity.

Concluding comments

Using matched worker-firm data from Morocco and Senegal, we have developed and estimated in this paper a model of on-the-job learning which accounts for two forms of informal learning within firms, i.e. learning-by-watching and learning-by-oneself. With a pure learning-by-watching model without controls for firm heterogeneity, we note little difference in the estimated rates of knowledge diffusion for Morocco and Senegal, (between 6% and 8%). Controlling for firm characteristics significantly reduces the values of the rate of knowledge diffusion. Both in Morocco and Senegal, the benefits of learning-by-oneself are revealed, but the potential benefit of learning from colleagues disappears in the latter country.

Moroccan estimates still exhibit significant economic returns to both learning-by-watching and self-learning.

From our results, it turns out that the overall return to human capital explaining the remuneration of a given worker involves personal skill characteristics, including individual abilities to learn, but also firms' knowledge characteristics. It is then important to consider these two sources of returns from human capital simultaneously because education policies and policies promoting vocational training may affect both worker's human capital and firm's human capital environment. In particular, assessing policies without accounting for educational and knowledge externalities within firms may largely under-estimate the benefits of such policies. Regressions (A) and (B) are estimated using non-linear least squares, models (B) including firm factors. Absolute value of t statistics are in parentheses, significance levels being respectively equal to 1% (***), 5% (**) and 10% (*). All regressions also include a set of dummies for occupation. All regressions are estimated using non-linear least squares, models (A) and (C) including firm factors. Absolute value of t statistics are in parentheses, significance levels being respectively equal to 1% (***), 5% (**) and 10% (*). All regressions also include a set of dummies for occupation.

Figure

  Figure 1. Time needed to accumulate the firm knowledge

Figure 2 .

 2 Figure 2. Rates of return of tenure -learning-by-watching modelsA. Morocco

Table 1 . Composition of the sample

 1

	Number of		Morocco			Senegal
	employees per	Workers	Firms	Workers	Firms
	firm	N	%	N	%	N	%	N	%
	4	40	0.5	10	1.2	128	10.0	32	17.8
	5	130	1.7	26	3.2	105	8.2	21	11.7
	6	180	2.4	30	3.6	156	12.1	26	14.4
	7	224	2.9	32	3.9	105	8.2	15	8.3
	8	320	4.2	40	4.9	200	15.6	25	13.9
	9	1008	13.2	112	13.6	180	14.0	20	11.1
	10	5720	75.0	572	69.6	410	31.9	41	22.8
	All	7622	100.0	822	100.0	1284	100.0	180	100.0
	Sources: ICA Senegal, FACS Morocco.

Table 2 . Descriptive statistics of the workers

 2

	Variables	Morocco	Senegal
	Log of hourly earnings	4.050	6.590
		(0.616)	(0.885)
	Years of education	8.679	10.322
		(5.428)	(5.661)
	Years of experience off the firm	12.766	12.747
		(9.300)	(9.280)
	Years of tenure in the firm	7.435	8.981
		(6.305)	(7.870)
	Female	0.398	0.160
		(0.490)	(0.367)
	Married	0.518	0.673
		(0.500)	(0.469)
	Formal training	0.041	0.355
		(0.199)	(0.479)
	Number of observations	7622	1284
	Sources: ICA Senegal, FACS Morocco.
	Standard deviations are in parentheses.

Table 3 . Estimates of the log of hourly earnings

 3 ) and (B) are OLS, (C) are fixed effects models, and (D) are OLS estimates with firm factors. Absolute value of t statistics are in parentheses, significance levels being respectively equal to 1% (***), 5% (**) and 10% (*). All regressions also include a set of dummies for occupation.

	A. Without controls of firm heterogeneity
	Variables	Morocco	Senegal
		(1A)	(1B)	(2A)	(2B)
	Constant	3.443***	3.177***	5.594***	5.087***
		(34.55)	(32.37)	(52.73)	(44.16)
	Years of education	-0.018***	-0.006*	0.011	0.027**
		(4.85)	(1.80)	(0.92)	(2.39)
	Years of education ² (/10)	0.026***	0.026***	0.014***	0.017***
		(11.43)	(11.61)	(2.72)	(3.33)
	Years of experience off the firm	0.005**	0.016***	0.007	0.025***
		(2.51)	(8.27)	(1.15)	(4.08)
	Years of experience off the firm ² (/10)	-0.002***	-0.003***	-0.002	-0.004**
		(3.86)	(6.38)	(1.27)	(2.51)
	Female	-0.105***	-0.078***	-0.074	-0.016
		(9.00)	(6.84)	(1.21)	(0.28)
	Married	0.171***	0.062***	0.407***	0.169***
		(14.12)	(4.79)	(9.45)	(3.65)
	Receipt of formal training	0.260***	0.263***	0.086*	0.058
		(9.50)	(9.89)	(1.86)	(1.30)
	Years of tenure in the firm		0.034***		0.050***
			(7.35)		(3.16)
	Years of tenure in the firm ² (/10)		-0.012***		-0.009
			(3.47)		(0.83)
	Years of tenure in the firm 3 (/100)		0.003***		0.001
			(3.53)		(0.44)
	Observations	7622	7622	1284	1284
	R-squared	0.43	0.46	0.42	0.48
	B. With controls of firm heterogeneity
	Variables	Morocco	Senegal
		(1C)	(1D)	(2C)	(2D)
	Constant	3.242***	3.292***	5.421***	5.153***
		(41.21)	(35.63)	(54.05)	(46.13)
	Years of education	-0.002	-0.001	0.011	0.020*
		(0.71)	(0.26)	(1.12)	(1.89)
	Years of education ² (/10)	0.018***	0.018***	0.013***	0.017***
		(8.93)	(8.64)	(3.06)	(3.39)
	Years of experience off the firm	0.013***	0.013***	0.013**	0.024***
		(8.62)	(7.45)	(2.54)	(4.16)
	Years of experience off the firm ² (/10)	-0.002***	-0.002***	-0.001	-0.004***
		(5.74)	(5.38)	(0.94)	(2.59)
	Female	-0.066***	-0.065***	-0.032	0.035
		(6.79)	(5.89)	(0.65)	(0.61)
	Married	0.048***	0.059***	0.081**	0.171***
		(4.64)	(4.85)	(2.11)	(3.82)
	Receipt of formal training	0.024	0.134***	0.089**	0.043
		(0.86)	(5.27)	(2.30)	(1.00)
	Years of tenure in the firm	0.036***	0.029***	0.034**	0.048***
		(8.47)	(6.58)	(2.31)	(3.10)
	Years of tenure in the firm ² (/10)	-0.014***	-0.011***	-0.003	-0.014
		(4.58)	(3.32)	(0.35)	(1.31)
	Years of tenure in the firm 3 (/100)	0.002***	0.002***	-0.000	0.002
		(3.86)	(3.36)	(0.23)	(0.91)
	Observations	7622	7622	1284	1284
	R-squared	0.51	0.52	0.46	0.52
	Sources: ICA Senegal, FACS Morocco.
	Regressions (A

Table 4 . Structural estimates of the learning-by-watching model

 4

	Variables	Morocco	Senegal
		(1A)	(1B)	(2A)	(2B)
	Constant	3.260***	3.365***	4.854***	5.216***
		(33.42)	(36.47)	(39.36)	(45.94)
	Years of education	-0.021***	-0.008*	0.065***	0.004
		(4.95)	(1.95)	(7.52)	(0.32)
	Years of education ² (/10)	0.033***	0.021***	0.002	0.025***
		(13.13)	(8.96)	(1.07)	(4.14)
	Years of experience off the firm	0.015***	0.011***	0.029***	0.030***
		(7.24)	(5.88)	(4.40)	(4.87)
	Years of experience off the firm ² (/10)	-0.003***	-0.002***	-0.004***	-0.005***
		(5.67)	(4.29)	(2.89)	(3.30)
	Female	-0.073***	-0.064***	0.009	0.056
		(6.38)	(5.82)	(0.15)	(1.00)
	Married	0.074***	0.074***	0.191***	0.183***
		(5.74)	(6.14)	(4.28)	(4.22)
	Receipt of formal training	0.243***	0.129***	0.049	0.044
		(9.13)	(5.06)	(1.13)	(1.05)
	δ s	0.069***	0.061***	0.430***	0.142***
		(5.83)	(2.69)	(3.74)	(3.07)
	δ e	0.026	0.001	0.150***	0.120**
		(1.19)	(0.02)	(2.75)	(2.05)
	α 5 δ t	0.014***	0.019**	0.023**	0.025
		(3.53)	(2.42)	(2.25)	(1.41)
	α 6 δ t ²	0.000	-0.000	-0.000	-0.000
		(0.51)	(0.79)	(0.20)	(0.70)
	n	0.063***	0.036*	0.076***	0.035
		(3.50)	(1.90)	(2.61)	(1.45)
	Observations	7622	7622	1284	1284
	R-squared	0.46	0.52	0.50	0.53
	Sources: ICA Senegal, FACS Morocco.

Table 5 . Structural estimates of the learning by oneself and others model

 5

	Variables		Morocco			Senegal
		(1A)	(1B)	(1C)	(2A)	(2B)	(2C)
	Constant	2.324***	3.187***	3.298***	4.200***	5.028***	5.190***
		(25.26)	(32.54)	(35.81)	(45.48)	(46.52)	(51.32)
	Years of education	-0.001	-0.008**	-0.004	0.033***	0.028*	0.017
		(0.19)	(2.12)	(1.08)	(3.03)	(1.94)	(1.22)
	Years of education ² (/10)	0.018***	0.027***	0.020***	0.015***	0.021***	0.024***
		(8.51)	(11.69)	(9.04)	(3.09)	(3.32)	(3.93)
	Years of experience off the firm	0.013***	0.015***	0.014***	0.028***	0.037***	0.032***
		(7.13)	(7.79)	(7.47)	(4.68)	(5.67)	(5.19)
	Years of experience off the firm ² (/10)	-0.002***	-0.003***	-0.003***	-0.004***	-0.006***	-0.005***
		(5.07)	(6.23)	(5.59)	(2.82)	(3.82)	(3.31)
	Female	-0.064***	-0.077***	-0.064***	0.185***	0.168***	0.197***
		(5.82)	(6.77)	(5.81)	(3.63)	(3.24)	(3.93)
	Married	0.063***	0.061***	0.061***	0.187***	0.171***	0.170***
		(5.24)	(4.73)	(4.99)	(4.16)	(3.72)	(3.82)
	Receipt of formal training	0.136***	0.265***	0.134***	0.119***	0.127***	0.115***
		(5.32)	(9.94)	(5.26)	(2.81)	(2.96)	(2.74)
	δ s		0.376***	0.228**		0.070***	0.161**
			(4.32)	(1.98)		(3.26)	(2.40)
	δ e		0.761***	0.247		0.084***	0.107
			(3.31)	(1.50)		(2.91)	(1.55)
	α 5 δ t		0.050	-0.239***		-0.001	-0.018
			(0.23)	(2.88)		(0.09)	(0.66)
	α 6 δ t ²		-0.015	0.005***		0.000	0.000
			(1.12)	(2.86)		(1.46)	(0.79)
	n		0.012	0.040***		0.242	0.041
			(1.11)	(2.78)		(1.37)	(1.28)
	g	0.013***	0.032***	0.050***	0.011***	0.021***	0.018**
		(19.42)	(3.08)	(3.60)	(8.88)	(3.65)	(1.96)
	Observations	7622	7622	7622	1284	1284	1284
	R-squared	0.52	0.46	0.52	0.50	0.48	0.52
	Sources: ICA Senegal, FACS Morocco.

Workers receiving at a given time some informal training from others will be later in a supply position, showing in turn informally to new incumbents how they can enhance their own productivity.

By repeating tasks within the firm, a worker is expected to improve his/her own productivity and hence human capital.

In fact, the worker may even become less productive as time goes by, and could thus be concerned by a decrease in earnings. We rule out this possibility by assuming that there exists some downward wage rigidity.

Further details of the surveys and their methodology can be found in WorldBank (2005) for Senegal and in WorldBank (2002) for Morocco.

The distribution of firms by number of employees is characterized by a U-shaped profile in Senegal.

This variable is approximated in the case of Morocco where the marital status was not collected from the workers. Instead, we use the fact of having declared children. In Morocco like in many African countries, it is reasonable to assume that all individuals who have declared children are (or have been) married because of the social norms in force.

The assumption of convex returns to human capital seems important. Taking into account a linear form for the returns to education when the "true" profile is convex is likely to lead to an overestimated value of the rate of knowledge diffusion, since workers will benefit less from the rewards of their own personal characteristics when estimating the model.

The different regressions in Table3also include dummy variables related to occupations (not reported).

We also note that in Morocco the returns to tenure are slightly lower with the learning-by-watching model with firm factors. As the rate of knowledge diffusion is lower in that case, there are fewer opportunities for workers to learn quickly from others (and then less economic benefits).

Clearly, equation (14) is a restrictive case of (11). It is obtained when the rate of knowledge diffusion is equal to zero, meaning that the characteristics of the most qualified worker are in fact those of the considered worker (there is nothing to learn from others).

However, indicators of the supervision rates in firms are informative. Note that while the proportion of firms with a share of managers higher than 10 percent of the total employees amounts to 11% for Morocco, it is much higher for Senegal with 39%.

# We gratefully acknowledge valuable comments from Francis Teal and participants at the IZA/


Delphine Decruyenaere

Marjan Mashkour

Kevin Lidour

Karyne Debue

Thomas Sagory

Maria Paola Pellegrino

Julien Charbonnier

Anne Benoist

Late Bronze and Iron Age animal exploitation in Masafi, Fujairah, UAE

Keywords: archaeozoology, Masāfī, bovids, dromedary, mollusc

à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

Archaeozoological studies concerning the Late Bronze and Iron Age human occupations in the United Arab Emirates, although still limited in number, provide precious insights into the human-animal relationship (Fig. 1). They have allowed a better understanding of the subsistence economies of the agro-pastoral societies that inhabited this area and their livestock management, hunting practices, ritual practices, and interactions through exchanges and trade between coastal and inland sites.

The number of Late Bronze Age sites investigated is half that of the Iron Age. These sites are located in a variety of environments: coastal areas, foothills, oases, and dune hinterlands (Fig. 1). However, despite this difference in environment, in either the Late Bronze Age or the Iron Age, the studies show almost the same pattern: a subsistence economy based on domesticated mammals, predominantly sheep and goat and, to a lesser extent, cattle. These animals were the main meat/dairy component of human diet. Equids, canids, and camelids whose domestic status is not always obvious are also documented [START_REF] Magee | When was the dromedary domesticated?[END_REF][START_REF] Uerpmann | The Iron Age site of Muweilah (Sharjah, UAE) and the problems of dromedary domestication[END_REF].

Alongside domesticates, a wide variety of wild terrestrial mammals are also represented, although usually by a limited number of remains. Among the wild taxa, gazelle is the most common but we also find oryx, fox, wild canids and felids, hyena, and hare. A large number of species of fish and molluscs have been identified at both coastal and inland sites. Their presence at inland sites such as Saruq al-Hadid [START_REF] Roberts | The exploitation of marine resources at Saruq al-Hadid: Insight into the movement of people and resources in Bronze and Iron Age south-eastern Arabia[END_REF] or al-Madam [START_REF] Cerro | Biological remains at al-Madam (Sharjah, UAE). Archaeological, archaeobotanical and archaeozoological studies in an Iron Age farming-stockbreeding village[END_REF] attests not only to the existence of contacts with the coast but also to the importance of marine resources in the diet of these protohistoric societies. Marine mammals such as dugongs and dolphins or birds such as cormorants are also mentioned.

The general trend shows a very diversified spectrum of fauna. A perfect example is Tell Abraq where E. [START_REF] Benoist | Preliminary report on the faunal remains of the first two seasons of Tell Abraq, Umm al-Quwain, United Arab Emirates[END_REF] and M. [START_REF] Uerpmann | Remarks on the animal economy of Tell Abraq (Emirates of Sharjah and Umm al-Qaywayn)[END_REF], in addition to domestic species, describe a long list of wild animals including Oryx leucoryx, Gazella sp., Canis lupus, Vulpes sp., Delphinus sp., Dugong dugon, birds, rodents, reptiles, invertebrates, and fish. This is also the case at Saruq al-Hadid where the faunal spectrum includes domesticated herbivores, gazelle, oryx, lagomorphs, dugongs, reptiles, birds, and fish [START_REF] Roberts | The role of wild terrestrial animals in late prehistoric societies of south-eastern Arabia: New insights from Saruq al-Hadid[END_REF][START_REF] Roberts | The exploitation of marine resources at Saruq al-Hadid: Insight into the movement of people and resources in Bronze and Iron Age south-eastern Arabia[END_REF]2020). The site of Muweilah is remarkable for the diversity of its carnivores with remains of Canis lupus, Vulpes rueppelli, Hyaena hyaena, Felis silvestris, Lynx caracal, and Panthera pardus, besides the importance of its domestic dromedaries, not to mention molluscs, marine mammals, and fish [START_REF] Uerpmann | The Iron Age site of Muweilah (Sharjah, UAE) and the problems of dromedary domestication[END_REF]. One could also mention Shimal [START_REF] Vogt | Shimal 1985/1986: Excavations of the German mission in Ras al-Khaimah[END_REF] and Hamriya [START_REF] Magee | Multi-disciplinary research on the past human ecology of the east Arabian coast: Excavations at Hamriya and Tell Abraq (Emirate of Sharjah, United Arab Emirates)[END_REF]). However, some sites are exceptions to this rule. The pits dug in the vicinity of the public building at Bithnah 44 only yielded remains of Caprinae, Bos sp., and a small number of gazelles [START_REF] Skorupka | Analyses des vestiges osseux de Bithnah-44: Des sacrifices animaux[END_REF]. The site of al-Madam is also an exception with a spectrum composed exclusively of domestic mammals and a few gazelles. However, there is a high diversity of molluscs [START_REF] Cerro | Biological remains at al-Madam (Sharjah, UAE). Archaeological, archaeobotanical and archaeozoological studies in an Iron Age farming-stockbreeding village[END_REF]. On the other hand, al-Sufouh2 produced predominantly wild camelids [START_REF] Driesch Von Den | The hunt for wild dromedaries during the 3rd and 2nd millennia BC on the United Arab Emirates coast. The camel bones from the excavations at al-Sufouh 2, Dubai/ UAE[END_REF]. This paper aims to describe the faunal remains discovered at the MSF-1, -2, -3, and -5, in particular the remains of terrestrial mammals, as they represent 85% of the material.

The sites of Masāfī

Presently, Masāfī is located on the border between the emirates of Ras al-Khaimah and Fujairah, in the north-western part of the Hajar Mountains. It appears to be a strategic area, standing at the junction of three significant valleys that connect the western part of the UAE to the eastern coast. Wādī Ham, to the south-east, provides access to the present-day city of Fujairah and its surroundings, Wādī Abadilah, to the north-east, connects the site to the Dibba region, while Wādī Sidji, to the west, leads to the western coast [START_REF] Benoist | A small green paradise. Economic strategies, ritual practices and local ancestors of the Iron Age community of Masāfī (Fujairah, United Arab Emirates)[END_REF]. In addition, it is an area with perennial freshwater sources (Charbonnier et al. 2020: 486), a boon in a region where arid climatic conditions prevail, and it is located within an area particularly rich in outcrops of copper ore, which were exploited at various times, as witnessed by the smelting sites still visible today [START_REF] Goy | Archaeometallurgical survey in the area of Masāfī (Fujairah, UAE): Preliminary data from an integrated programme of survey, excavation, and physicochemical analyses[END_REF].

Archaeological excavations and surveys have been carried out since 2006 by the French Archaeological Mission in collaboration with the Fujairah Tourism and Antiquities Authority. Five sites, dating from the Middle and Late Bronze Age and Iron Age II/Early III, were identified:

-MSF-1, -2, and -3, excavated under the supervision of A. Benoist, is an Iron Age II/Early III occupation that is divided into three clusters. MSF-1 and -3, respectively a public and a cultic building, occupy the centre of the oasis while the fortified settlement of MSF-2 is situated on a hill overlooking the palm grove. It should also be noted that Islamic-period occupations were uncovered on top of MSF-1 and -3 [START_REF] Benoist | The Iron Age occupation at Masāfī: Report on two seasons of excavations[END_REF][START_REF] Putelat | Archéologie des dépôts animaux et mortalité extraordinaire du cheptel bovin au premier Moyen Âge. Plaidoyer pour une redynamisation et une mise en perspective de la recherche[END_REF][START_REF] Benoist | A small green paradise. Economic strategies, ritual practices and local ancestors of the Iron Age community of Masāfī (Fujairah, United Arab Emirates)[END_REF]).

-MSF-5, excavated in the past few years under the supervision of J. Charbonnier, is located in the south of the oasis. It is a Late Bronze Age settlement standing on the top of a hill overlooking the palm grove (Degli Esposti & Benoist 2015). Both settlements, MSF-2 and MSF-5, were located outside the irrigated areas that were dedicated to cultivation. Only buildings of a public and ritual nature were tolerated in the centre of the oasis. The hillside occupations also enjoyed a very good position from which to defend the oasis.

-Finally, MSF-4, not yet excavated, is a settlement probably dating to the Middle Bronze Age.

Material and methodology

A total of 1657 faunal remains were examined. They were excavated by hand and came from the excavated sites of MSF-1, -2, -3, and -5. The bones were not equally distributed among the four sites (Fig. 2). MSF-1 and -5 yielded 829 and 634 remains respectively, while MSF-2 and -3 yielded only twenty-nine and twenty-seven remains for the protohistoric periods. Despite being outside our period of interest, we thought it would also be interesting to document the 138 skeletal remains collected from an Islamic-period well in MSF-3.

As mentioned above, the majority of the remains belong to terrestrial mammals (85%). While fish are almost absent from the faunal spectrum, the MSF-5 excavation yielded a significant number of mollusc shells that will be discussed only briefly here since a specific paper is being prepared (Lidour et al., in preparation).

A faunal spectrum was established for each site and context (Figs 3 & 4), presented in number of remains (NR) and weight of remains (WR). The mean weight of the remains was used to assess the degree of fragmentation of each assemblage. This is also a proxy to evaluate meat weight [START_REF] Bartosiewicz | Skin and bone: Taphonomy of a medieval tannery in Hungary[END_REF][START_REF] Casteel | Faunal assemblages and the 'Wiegemethods' or weight method[END_REF][START_REF] Uerpmann | Animal bone finds and economic archaeology: A critical study of 'osteoarchaeological' method[END_REF][START_REF] Vigne | The meat and offal weight (MOW) method and the relative proportion of ovicaprines in some ancient meat diets of the north-western Mediterranean[END_REF] but in the case of the faunal assemblage of MSF the weight of remains is certainly affected by demineralization due to extremearidity. For some contexts, the MNE (Minimum Number of Elements) was calculated in order to establish the MNI (Minimum Number of Individuals) of each species represented.

The anatomical and taxonomic identification of the mammal remains was carried out at the National Museum of Natural History in Paris (MNHN) within the Archaeozoology & Archaeobotany Research Unit, using the modern comparative comparison collections of the archaeozoology laboratory and the patrimonial collections of the National Museum of Natural History. We also used several atlases [START_REF] Barone | Anatomie comparée des mammifères domestiques[END_REF][START_REF] Hillson | Teeth[END_REF][START_REF] Schmid | Atlas of animal bones: For prehistorians, archaeologists and quaternary geologists[END_REF].

In this paper, the Caprinae category is used when a bone can be identified as sheep or goat but with insufficient diagnostic morphological features to allow a distinction between the two genera [START_REF] Boessneck | Osteological differences between sheep (Ovis aries Linné) and goat (Capra hircus Linné)[END_REF][START_REF] Fernandez | Ostéologie comparée des petits ruminants eurasiatiques sauvages et domestiques (Rupicapra, Ovis, Capra et Capreolus): Diagnose différentielle du squelette appendiculaire[END_REF][START_REF] Halstead | Sorting the sheep from the goats: Morphological distinctions between the mandibles and mandibular teeth of adult Ovis and Capra[END_REF][START_REF] Helmer | Atlas du squelette appendiculaire des principaux genres holocènes de petits ruminants du Nord de la Méditerranée et du Proche-Orient (Capra, Ovis, Rupicapra, Capreolus, Gazella). Fiche d'ostéologie animale pour l'archéologie[END_REF][START_REF] Zeder | Assessing the reliability of criteria used to identify postcranial bones in sheep, Ovis, and goats, Capra[END_REF][START_REF] Zeder | Assessing the reliability of criteria used to identify mandibles and mandibularteeth in sheep, Ovis, and goats, Capra[END_REF], while SR category (Small Ruminants) includes caprines and gazelles. LM (Large Mammals) is used for unidentified bones of the size of cattle to camelids, since these taxa were present at MSF sites.

The Bronze Age site of Masāfī-5 MSF-5, which dates from 1600 to 1200 BC, is located on a small rocky hill overlooking the oasis and cultivable land. The settlement is organized in terraces built on the hillside including small domestic facilities such as fireplaces, stone benches, and temporary shelters. In the eastern part, a large vertical cavity (C1421) was excavated during the 2018 and 2020 campaigns. Its function is not clear and is still under debate.

The faunal remains of MSF-5 were collected in two different contexts, the settlement and the filling of C1421. Since the latter is a closed context, the study of these two assemblages was carried out separately. We immediately noted a huge difference in taxonomic diversity between the settlement and C1421 (Fig. 3). The latter yielded only mammalian remains, whereas the bulk of the assemblage from the settlement consists of molluscs and curiously, only nine fragments belonging to caprines or small herbivores were identified.

Faunal remains of the settlement

Molluscs constitute a set of 250 fragments of shells weighing 2.4 kg, with 99% of them belonging to marine species. It includes a total of twenty-three species distributed between twenty-two genera and sixteen families. Bivalves are represented mostly by the black-lipped pearl oyster (Pinctada persica) (13.2% of the whole shell assemblage, based on the MNI calculation), large venus clams (Callista spp.) (11.8%), ark shells (Arcidae) (5.9%), and the violet asaphis (Asaphis violascens) (2.8%). Gastropods are composed essentially of the giantmangrove whelk (Terebralia palustris) (29.6%) and cones (Conidae) (7.1%).

All of these molluscs attest to the contacts and exchanges that exist between coastal and inland territories. Most of the taxa identified in the shell assemblage could have been gathered along the coast of the Gulf of Oman, in particular in the Dibba-Khor Kalba area which is reachable within a few hours on foot by following the wadi roads to the east (35 km to Murbah and 37 km to Qurayyah) or the southeast (30 km to Fujairah through Bithnah). The joint occurrence of many ark shells, large cones, and some fragments of the giant frog snail (Tutufa bardeyi) in the assemblage suggests connections with the coast of the Gulf of Oman. Indeed, ark shells have been extensively exploited in the past in the Kalba area, including during the Bronze and Iron Age periods at Kalba 4 [START_REF] Phillips | Sustaining change: The emerging picture of the Neolithic to Iron Age subsistence economy at Kalba, Sharjah Emirate, UAE[END_REF]. Furthermore, great quantities of large Conus shells (mostly belonging to Conus betulinus) can be found washed up on the beaches near Khor Fakkan. The giant frog snail is not reported from the Persian Gulf by Bosch et al. (1995: 102) whereas it is encountered in the Gulf ofOman, down to southern Oman and Yemen.

Only 1% of the assemblage is made up of freshwater molluscs including the red-rimmed melania (Melanoides tuberculata) and an unidentified mud snail belonging to the genus Hydrobia. The irrigation systems of the oasis would explain the presence of these freshwater molluscs in the MSF-5 shell assemblage. The favoured consumption of the giant mangrove whelk is supported by the high frequency of fragmentation and combustion marks observed on shell fragments of this species. It is likely that the giant mangrove whelks were brought to the site alive before being cooked there. Most clam and ark shells were potentially consumed at the site as well, but some specimens could have been reused as small containers or raw material for adornments and appropriate tools.

Indeed, an important part of the assemblage consists of worked elements. Several large valves bear retouching and may have been used as multipurpose tools such as scrapers or knives. Shells were also used for ornamental purposes, such as the manufacture of adornments. Several rough-outs of Conus rings were found, as well as thick polished mother-of-pearl plates made from Pinctada persica valves. Despite the presence of a few broken Conus rings, the absence of finished products in the assemblage is noticeable. The presence of shell workshops at MSF-5 must be contextualized on a regional scale, where many other Late Bronze and Iron Age sites have yielded Conus and mother-of-pearl elements of adornment. A few valves (mainly of bittersweet clams, Glycymeris sp.) were potentially used as containers for cosmetic products (probably metal oxide-based). Chemical composition analyses are currently being conducted to confirm this hypothesis.

Faunal remains of C1421

The large cavity C1421 occupies a strange location on the top of the rocky hill of MSF-5 and its function is not clear. The hypothesis of a water well has been ruled out because of the inappropriate location of the structure dug on top of the hill. It could also be a mining gallery but we do not have any evidence for this. Further excavations and analyses are needed. This feature was entirely dug into the bedrock; it was excavated to a depth of 6 m without reaching a natural base. The pit yielded only Late Bronze Age ceramic sherds, so there is no doubt about its chronological allocation. One of the aims of this analysis is to provide a better understanding and establish a chronology of the accumulation of deposits.

The bones were found in two separate layers: UF816 and UF 819 (Fig. 5) and we noticed a quantitative and qualitative imbalance between them. The former yielded only thirty-nine very poorly preserved remains, of which only one distal metacarpal could be positively identified as belonging to a domestic bovine. The assemblage of UF 819 is much more abundant and relatively more diversified since, in addition to bovines, we also found remains of domestic caprines and only four bones -a distal humerus, a proximal radius, and two tali -were identified as belonging to Ovis aries (see Fig. 3).

The bones from UF819 were in a very poor state of preservation with numerous flakes and cracks. The loss of organic matter made the bone remains extremely brittle and it was very difficult, if not impossible, to collect them without destroying them. In these conditions, it was decided to collect the identified pieces during excavation in individual bags in order to refit them together later on and avoid a large number of unidentified fragments. Of the traces observed on the bones, we noted some cut marks but no traces of fire that could be directly or indirectly associated with consumption. In addition, one bone bore oxidation traces from copper.

During the excavation, because of the bone fragmentation and preservation explained above, it was not possible to make anatomical connections. For this reason, we plotted the bones and projected the scatter on a zenithal projection of UF 819.

On more careful examination of the cattle bones (Fig. 6/a), it became evident that some parts of the carcass were in association. Lower limb sections could be identified: radius/ulna-metacarpus or tibiametatarsus with nearby areas of phalanx concentrations. The humerus and femur appear to be isolated. Ribs and vertebrae are scattered on the ground with no apparent coherence, but the fragmentation was such that it is not possible to reconstruct the axial skeleton.

The MNI is three, taking into account the MNE, which is one for all bones (almost all represented) except for three right metatarsals. It should be noted that the phalanges could belong to different individuals. In fact, the number of second phalanges identified as being abaxial is five (Fig. 6/b). According to the fusion stage, the three bovids present are at most 2 years old. The fused bones include one humerus and first and second phalanges that fuse before the age of 2 years. The remaining bones are not fused, notably vertebrae, distal ends of metapodials, and distal and proximal ends of tibia and femur that fuse after two years [START_REF] Barone | Anatomie comparée des mammifères domestiques[END_REF]). The coxal bone is poorly preserved and does not allow identification of the sex. Consequently, we determine that we are dealing with one bovid plus two right lower limbs belonging to different individuals. Some phalanges could have been lost during the processing of the carcass.

Although almost all the bones are represented, the skull (including the mandibles) and the scapulae are missing. This absence is not the result of post-depositional phenomena, as the teeth and the proximal part of the scapula are usually well preserved. It seems unlikely that they were present at the time of deposition and we can therefore confidently assume that the animal was dismembered and cut into anatomical segments before being thrown into the cavity, perhaps because of the limited space available in the bottom of C1421 (Fig. 5). Cut marks were identified on some of the vertebrae and ribs, suggesting that the axial skeleton was cut into segments. Incisions on the head of the femur result from disarticulation. Finally, fine incisions were found on the distal ends of two metatarsals. They are suggestive of skinning.

The scatter of goat bones also reveals connected skeletal elements (Fig. 7/a). Nevertheless, these are not exactly the same carcass parts as those described for the cattle. There are still lower limb connections (radius/ ulna-metacarpus and tibia-metatarsus) but also upper limb connections such as a femur-tibia and humerus-radius/ulna. The parts of the carcass appear to have been discarded, rather than deposited, as evidenced by the disorganized remains.

The MNI is also three and among these three caprines, at least two have been identified as sheep based on two left tali [START_REF] Fernandez | Ostéologie comparée des petits ruminants eurasiatiques sauvages et domestiques (Rupicapra, Ovis, Capra et Capreolus): Diagnose différentielle du squelette appendiculaire[END_REF][START_REF] Zeder | Assessing the reliability of criteria used to identify postcranial bones in sheep, Ovis, and goats, Capra[END_REF] (Fig. 7/b). The age of these caprines was calculated using teeth from a hemi-maxillary and the age of fusion of the epiphyses. These factors indicate that they must have been between 4 and 6 years old. Again, some skeletal parts are missing. Only a hemi-maxillary was found, the remaining skull and the mandibles are missing. Moreover, no phalanges were discovered. As with the cattle, the absence of these bones cannot be explained by a post-depositional process. No anthropogenic traces were found, but the poor preservation of the assemblage could explain this observation.

Unfortunately, the faunal analysis does not allow us to make any assumptions about the function of C1421. Nevertheless, it can be assumed that this was a single deposition if one refers to the preservation and intermingled state of the bones that must have been covered quite quickly. Due to the low number of anthropogenic traces, it seems difficult to hypothesize about the removal of the meat. Nevertheless, the absence of butchery marks does not necessarily mean that those bones are not remains of consumption; it is possible that the meaty parts were deboned without marking the bones or that only certain cuts were removed. The absence of burning or roasting marks can be explained by the simple fact that the meat was boiled.

Nevertheless, in the case of cattle, it seems unlikely that segments as large as a tibia-tarsus-metatarsus would have been cooked together. For caprines, this is not so easy to discount -an entire haunch of sheep/ goat with femur-tibia or humerus-radius articulation is more likely.

A hypothesis to explain this animal bone deposit could be the rendering of the carcases following a natural death [START_REF] Putelat | Archéologie des dépôts animaux et mortalité extraordinaire du cheptel bovin au premier Moyen Âge. Plaidoyer pour une redynamisation et une mise en perspective de la recherche[END_REF][START_REF] Barme | La pratique urbaine de l'équarrissage à la charnière du Moyen Âge et de l'époque moderne[END_REF]. Disease may explain why the meat does not seem to have been used, at least for the cattle. The incisions on the bones may be related to the action of rendering. If we follow this hypothesis, it is possible that people threw extra waste, such as the two lower legs, into the cavity at the time of deposit.

On the other hand, the lack of certain skeletal elements of both species, particularly skulls/mandibles, raises the question of ritual deposits. Following the discovery of Iron Age foot-braziers, including one decorated with a snake at the top of MSF-5, Benoist et al. (2015: 26) speculated on the symbolic importance of the site that is the highest point of the oasis, and the possibility of the presence of an Iron Age altar there. However, excavation of this area did not reveal any evidence of a Bronze Age or Iron Age ritual building. In the meantime, the site was seriously damaged by the construction of a highway and a pipeline.

On the subject of ritual spaces, it should be remembered that the nearby sites of Bithnah 44 (Fujairah), and al-Madam 1-Tuqeibah (Sharjah) have both delivered ritual contexts where remains of cattle and caprines were buried in pits or cenotaphs [START_REF] Skorupka | Analyses des vestiges osseux de Bithnah-44: Des sacrifices animaux[END_REF][START_REF] Cerro | Biological remains at al-Madam (Sharjah, UAE). Archaeological, archaeobotanical and archaeozoological studies in an Iron Age farming-stockbreeding village[END_REF]). Could it be a ritual deposit and offering, or simply the remains of food or a rendering to get rid of sick animals? The little data in our possession do not allow us to come to a definitive conclusion.

The Iron Age II and Early III sites of Masāfī-1, -2, and -3

The Iron Age II/Early III occupation (1300-600 BC) is distributed around a central area of cultivated land dominated to the north-east by MSF-2, a hillside fortress. To the south-east lies a collective area, including a building with a large, pillared meeting hall, a possible market, a canal [START_REF] Charbonnier | Ancient agricultural landscapes in Southeast Arabia: Approach and first results of an archaeological, geo-archaeological, and spatial study of the Masāfī Palm Grove, Emirate of Fujairah[END_REF]) and an altar associated with several vertical pits (MSF-1). Not far from there, researchers have uncovered a cultic site dedicated to a divinity represented as a snake (MSF-3).

The preservation of faunal remains is far from satisfactory and bones are highly fragmented and brittle (Fig. 8). On the sites located in the oasis (MSF-1 and MSF-3), the percentage of identified fragments is about 50% of the material if we consider the NR. The percentage of the WR appears to be consistently higher and reveals that the unidentified remains are small and light fragments. The bones found at MSF-2 appear somewhat better preserved. However, it should be kept in mind that three quarters of the material was assigned to the small ruminant (SR) category (see Fig. 4).

Apart from the Iron Age deposits in MSF-3, we should also note the presence of a pit (C302) that dates to the late Islamic period and contained 138 bones comprising small bovids, camelids, birds, fish, and molluscs (see Fig. 4). The preservation of the bones here is far better than that for the Iron Age levels (Fig. 8). The bulk of the remains recovered from the three Iron Age sites belongs to mammals. Molluscs and fish (including a shark vertebra) are represented by only a few remains at MSF-1. Among the mammals, caprines -sheep and goats -are the most abundant. At MSF-1,goats outnumber sheep with a ratio of 2:1. No sheep were identified at either MSF-2 or MSF-3. It should be noted that even if they are present in the region, sheep are less well adapted than goats to the hyper-arid climate.

The four bones identified as camelids were found in the sites of MSF-1 and MSF-3. A single vertebral fragment was attributed to domestic cattle. However, it is likely that other cattle bones were present, but since they could not be distinguished precisely they are listed in the large mammals (LM) category that also comprises other large mammals such as camelids.

Recalling the introduction and the general trend of faunal spectra at that time, one cannot but notice the absence of wild taxa. Even if the presence of gazelle is highly probable, no bone could be attributed to it with certainty. However, there are several poorly preserved fragments that are in doubt which were allocated to 'possibly Gazella sp.' In the absence of medium-sized mammals other than caprines and gazelle, we would expect the MM (Medium Mammals) category to be composed of these species.

The distribution of faunal remains in the excavated areas

The three construction phases of the large pillared building of MSF-1 are shown in Figure 9. With the exception of a few remains buried in a pit in the first phase of the public building, no bones were found in the main room. Instead, they were found in the storage rooms to the west, in the small northern room that housed a podium, and outside the building (Fig. 9/a).

During Phase II, the bone remains are concentrated in the western storage rooms (Fig. 9/b). During Phase III all faunal remains were found to the north of the public building, in an area rich in ceramics and artefacts that has been interpreted as a market place. It seems consistent that animals were also found there. The species are varied: goats, cattle, dromedaries, fish (both bony and cartilaginous fish), and marine molluscs (Fig. 9/c).

MSF-1 must have been a place for ceremonies and banquets [START_REF] Benoist | Une occupation de l'âge du Fer à Masāfī. Travaux récents de la Mission archéologique française aux[END_REF]. The preparation of the former may have taken place in the open air or in the secondary rooms of the public building. The absence of bones associated with the last phase of the pillared building could be explained by the poor preservation of the bones if they had been processed outside. Another possible explanation is that the cooking or refuse area was located to the north-east, but Islamic graves have obliterated much of this area.

Not far from MSF-1 stands the small cultic structure of MSF-3 (Fig. 10/a). Except for a single goat fragment, the temple did not yield any bones, which were concentrated in the eastern outer area.

The faunal remains at MSF-2 (Fig. 10/b) were mainly found in the rooms of the orthogonal structure associated with a settlement or production area.

Discussion

MSF-1 and MSF-3 probably functioned in a complementary way. In the area of MSF-1, meetings and various ceremonies were held, while MSF-3 was used as a devotional area. The cultic character is corroborated by the discovery of some objects or ceramics related to the cult of the snake: incense burners, ceramics decorated with snakes, copper snake figurines, and votive weapons. The buildings and artefacts found at MSF-1 and MSF-3 are echoed at a number of other sites that also yielded faunal remains associated with ritual activities: sacrifice and offering [START_REF] Benoist | Snakes, copper and water in south-eastern Arabian religion during the Iron Age: The Bithnah and Masāfī evidence[END_REF]. For example, at Bithnah, excavators found a large number of pits filled with bones belonging to caprines and domestic cattle [START_REF] Skorupka | Analyses des vestiges osseux de Bithnah-44: Des sacrifices animaux[END_REF]. At Tell Abraq, a large quantity of bones filled the pit of a possible altar (Locus 48 -Potts 1991: 78;[START_REF] Benoist | A small green paradise. Economic strategies, ritual practices and local ancestors of the Iron Age community of Masāfī (Fujairah, United Arab Emirates)[END_REF]. At Mudhmar East, in present-day Oman, the filling of Building 2 contained burnt bones of young domestic caprines and cattle (Decruyenaere, Jean& Gernez, in preparation). At Al-Madam, there were no human skeletons in the cenotaphs, only specific parts of young goats [START_REF] Cerro | Biological remains at al-Madam (Sharjah, UAE). Archaeological, archaeobotanical and archaeozoological studies in an Iron Age farming-stockbreeding village[END_REF]; no heads, teeth, or vertebrae were found. Saruq al-Hadid is more difficult to interpret, but it seems that a small quantity of the camel remains may have been deposited in ritual contexts (Roberts et al., forthcoming). It is true that the Iron Age levels of Masāfī did not yield any such deposits, but two observations deserve to be made. Firstly, the vertical pits associated with the altar to the east of MSF-1 are similar to those found at Bithnah. Some were found empty and interpreted as libation pits. Subsequently, the deposit in the Late Bronze Age pit at MSF-5 echoes the above. Secondly, the species present are similar to those found in the Iron Age pits of Bithnah, in the cenotaphs of al-Madam or in Building 2 of Mudhmar East. In the latter two cases, selection of the bones had also been made. Are we observing the beginnings of a ritual tradition involving banqueting practices that will continue until the Iron Age?

In conclusion, we have presented the faunal evidence found in the oasis of Masāfī from the Late Bronze Age to the Early Iron Age III and some later settlements during the Islamic period. In Masāfī, sheep and goats occupy a predominant place in the diet of the inhabitants with a ratio of 1:2. Cattle are not abundant, but they are present and must have been a complementary resource for meat and dairy products alongside sheep and goat. The rare remains of camels were only found in public areas during the Iron Age and they were also found in deposits of the Islamic period.

Wild taxa are only attested by a small number of bones potentially attributable to gazelle. This lack of diversity is the major difference from most Late Bronze and Iron Age sites in south-eastern Arabia where wild animals have economic and social importance alongside the domesticates. However, this is also a feature that brings it closer to the sites of Bithnah or al-Madam. Fish remains are rare at Masāfī, but this could be explained by the lack of sieving. Conversely, marine molluscs are surprisingly abundant in MSF-5.

While a more detailed paper on the MSF-5 shell assemblage is currently in preparation, the preliminary information provided in the present study highlights the importance of marine resources both in the diet of the inhabitants of the site, and in the economic activity such as the manufacture of shell adornments and the use of shell tools. The large proportion of worked shell suggests the presence of a workshop during the Late Bronze Age. Further studies are needed to assess the use of shell tools (such as scrapers or knives) at the site. Shell taxa, which have been identified, show contacts with sites located on the coast of the Gulf of Oman.

Acknowledgements

We are very grateful to our collaborators from the Fujairah Tourism and Antiquities Authority: S. Al-Samahi, A. Al-Shamsi, S. Ali Hasan, and A.K. Late Wahid Ali. We also thank the French Archaeological Mission in the United Arab Emirates headed by S. Méry and the French Ministry of Europe and Foreign Affairs for their support. Finally, we wish to express our gratitude to the Muséum national d'Histoire naturelle de Paris and the UMR 7209 for allowing us access to their collections.