加纳的种族特性和工资确定

pS ps*XSO/4 POLICY RESEARCH WORKING PAPER 2506 Ethnicity and Wage In Ghana's manufacturing sector, workers tend to be Determination in Ghana employed by members of their own ethnic group, and Abigail Barr different ethnic groups run Abena Oduro very different types of enterprises. Employers favor their relatives in pay and in job allocation, possibly because they are more productive. There is no evidence of pay discrimination between ethnic groups. The World Bank Development Research Group Macroeconomics and Growth December 2000  | PoiiCy RESEARCH WORKING PAPER 2506 Summary findings Barr and Oduro look at earnings differentials between earn much more than the relatively low-earning Asante, members of different ethnic groups and between Fante, and Ewe. employers' relatives, unrelated members of the same There is no evidence of discrimination between ethnic ethnic group, and other workers in Ghana's groups, although there is evidence of discrimination in manufacturing sector. favor of inexperienced workers from the same ethnic They find that a significant proportion of the earnings group, who can be assessed and matched with jobs more differentials identified between ethnic groups can be easily than similar workers from other ethnic groups. explained with reference to a fairly standard set of Finally, workers who are related to their employers observations about workers' characteristics. Labor earn a considerable premium, possibly because they market segregation along ethnic lines-combined with contribute more to productivity than their fellow considerable variation in employers' characteristics workers (perhaps through an effect on esprit de corps). (especially educational attainment and family The authors' results draw attention to some startling background, possibly because of discrimination in other differences in educational and labor market attainment markets)-accounts for most of the remaining between groups. A strong case can be made for including differentials. such issues in the policy debate. Northerners earn considerably less than other groups mainly because they are less educated. The Other Akan This paper-a product of Macroeconomics and Growth, Development Research Group-is part of a larger effort in the group to understand the role of ethnicity in labor market outcomes and entrepreneurial success in Africa. The study was funded by the Bank's Research Support Budget under the research project "The Economics of Ethnicity and Entrepreneurship in Africa." Copies of the paper are available free from theWorld Bank, 1818 H Street NW, Washington, DC 20433. Please contact Rina Bonfield, room MC3-354, telephone 202-473-1248, fax 202-522-3518, email address abonfieldCaworldbank.org. Policy Research Working Papers are also posted on the Web at www.worldbank.org/research/ workingpapers. Abigail Barr may be contacted at abigail.barr@economics.ox.ac.uk. December 2000. (42 pages) The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors, or the countries they represent. Produced by the Poticy Research Dissemination Center  Ethnicity and Wage Determination in Ghana by Abigail Barr Centre for the Study of African Economies University of Oxford and Abena Oduro Centre for Policy Analysis Ghana   Ethnicity and Wage Determination in Ghana 1. Introduction Discussions about economic policy between international organisations and African governments rarely touch upon issues relating to ethnicity. And yet recent contributions to the literature on cross-country differences in economic performance indicate that ethnic diversity is associated with very high economic costs, in terms of lower rates of economic growth due to the adoption of dysfunctional macroeconomic policies (Easterly and Levine (1997)) and lower levels of trust and weak norms of civic cooperation (Knack and Keefer (1997)), and increased probabilities of civil war (Collier and Hoeffier (1998)).' Given these findings, surely the time has come to place the economics of ethnicity on the agenda for policy debate. The two most commonly raised arguments against placing ethnic issues on this agenda are that ethnic diversity is pre-determined and cannot be manipulated by economic policy and that ethnic issues are politically sensitive.2 With respect to the first of these, we suggest that, while levels of ethnic diversity cannot be changed, there may be ways of changing their effect on economic outcomes. A necessary prerequisite for identifying policy interventions that might achieve this objective is a deeper understanding of the role and effects of ethnic diversity at the micro-level. We need to know how and why ethnic identity and ethnic boundaries affect the economic decisions that people make during their everyday lives. Through such an investigation we may be able to identify the conditions under which the negative effects of ethnic diversity on economic outcomes might be minimised. In addition and with respect to the second argument, by increasing our understanding of why ethnicity matters and, wherever possible, linking it to rational choice, we may start to depoliticise the topic. ' Easterly and Levine (1996) and Collier and Hoeffler (1998) use an index of ethnolinguistic fractionalization, defined as the probability of two randomly drawn individuals for the same country belonging to different ethnic groups, Knack and Keefer (1997) use a measure of ethnic homogeneity, defined as the proportion of the population belonging to the largest ethnic group. 2 Ethnicity became an important political issue in Africa after independence, as pressure grew for the new leaders to create opportunities for indigenous capital, mediate between conflicting ethnic claims on public resources, and enable lagging groups to catch up to those that had secured early economic advantages (Apter (1965), Cohen (1969), Bates (1974), Rothchild and Oluonsola (1983)). Around this time a number of African countries including Ghana attempted to promote indigenous African business by introducing regulations that pressured Lebanese and Indian entrepreneurs to vacate trading and small-scale services (leaving these for African entrepreneurs) and move 3  The following analysis contributes to this effort by investigating the effects of ethnic identity and ethnic boundaries on labour market outcomes in the Ghanaian manufacturing sector. The analysis draws from the literature on the economics of discrimination witiin labour markets. This literature, with its strong empirical component, provides us with a well developed conceptual framework and a set of tools for identifying, categorising, and quantifying the effects of Ghana's ethnic diversity on manufacturing workers' earnings. However, this literature focuses almost exclusively on discrimination against black relative to white workers and women relative to men in OECD labour markets. While most of the models proposed do not rule out the possibility that employers may come from 'disadvantaged' as well as 'advantaged' groups, throughout the literature and especially its empirical dimension, there is an implicit assumption that employers are predominantly white and male, i.e., from the advantaged group. In the Ghanaian context it would be entirely inappropriate to assume that employers come predominantly from one ethnic group. Indeed, our data from the manufacturing sector indicates that the distributions of employers and employees across ethnic groups are very similar. Thus, in our investigation we need to take account both of discrimination between different ethnic groups and of discrimination between own and other ethnic groups.3 In each case our objective, wherever possible, is to identify and discern between taste-based discrimination, statistical discrimination, and discriminatory outcomes that are due to networking and other factors. The paper has six sections. Following this introduction, Section 2 contains a brief review of the literature on labour market discrimination. Then, in Section 3 we set out our methodology for identifying and testing various hypotheses about the origins of ethnic earnings differentials in the Ghanaian manufacturing sector. In Section 4 we describe our data. We present our results in Section 5 and in Section 6 we draw our conclusions. 2. Review of the Literature on Labour Market Discrimination their capital into manufacturing. More recently democratisation has often been accompanied by an increase in the politicisation of ethnicity (Glickman (1995)). 3Collier and Garg (1999) find evidence of discrimination in favour of the dominant kin group in the Ghanaian public sector. 4  Following Altonji and Blank (1999) we define labour market discrimination as 'a situation in which persons who provide labour market services and who are equally productive in a physical or material sense are treated unequally in a way that is related to ... ethnicity' (p. 3168). Further, we endeavour to distinguish between current labour market discrimination, given predetermined worker characteristics, and the effects of prior discrimination on those characteristics. Such pre-market discrimination can take two forms. First, discrimination may occur in other markets. So, for example, the quality of the schooling that is accessible to different groups may vary (O'Neill (1990), Maxwell (1994) and Neal and Johnson (1996)). Second, past labour market discrimination may affect current labour market outcomes to the extent that it affects how workers from different groups prepare for entry into the labour market. So, for example, it may affect their chosen level of investment in human capital (Loury (1977, 1981), Durlauf (1992), Benabou (1994, 1996), Lundberg and Startz (1998)). Discrimination can be motivated in several ways. Becker (1971) focused on the effects of a taste for discrimination. In his model discriminating employers behave as if the price associated with hiring a worker from the less favoured group is their wage plus an additional amount which he calls the 'coefficient of discrimination'. As a result, workers are segregated with those from the less favoured group being hired by the less prejudiced employers and suffering a wage differential that is determined by the preferences of their most prejudiced employer. Further, discriminating employers earn lower profits, so with free entry the effects of discrimination on earnings disappear in the long run. In the US and Europe this has not happened. A similar and similarly problematic prediction derives from Becker's (1971) model in which the employers' disutility is associated with placing the less favoured group in a certain occupation with occupational segregation and a short run earnings differential as the outcome.4 However, Coate and Loury (1993a) present an alternative model in which all employers have the same preferences, thereby removing the tendency for the earnings gap to disappear in the long run. This tendency can also be eliminated by the introducing imperfect information in the form of search costs and thereby rendering segregation costly (Borjas and Bronars (1989), Black (1995), Bowlus and Eckstein (1998)). These models do not predict segregation unless it is the employers and not the workers who are conducting the search (Bowlus and Eckstein (1998)). 4 Becker (1971) also presents models of employee and consumer discrimination. 5  In the Ghanaian context a taste for discrimination could lead to a premium for workers employed by members of their own ethnic group. However, given that no particular group dominates the role of employer, we do not expect discrimination motivated by taste to lead directly to a wage premium for any particular group.5 This notwithstanding, to the extent that (1) a taste for discrimination in favour of co-ethnics leads to segregation and (2) discrimination in credit and other markets leads to variations in the labour demand curves of different types of employer, we may observe earnings differentials between groups. Imperfect information also provides the foundations for models of statistical discrimination. Building on the pioneering work of Phelps (1972) and Arrow (1973) this literature explores the consequences of firms having limited information about the skills and reliability of job applicants, especially young and inexperienced ones, and therefore using correlated and easily observable characteristics such as ethnicity to discriminate between them; One particular finding in this literature is that, due to feedback via, for example, investments in human capital, biased stereotypes might be self confirming (Arrow (1973), Coate and Loury (1993b)). In the Ghanaian context statistical discrimination of this form could be leading to both current labour market discrimination and feedback effects and consequent earnings differentials between members of different ethnic groups. Another form of statistical discrimination may affect the earnings of co-ethnic and related workers. Aigner and Cain (1977) and subsequently Lundberg and Startz (1983) and Lundberg (1991) have explored the effects of group differences in the precision of the information that employers have about individual productivity when that productivity depends on the quality of the match between worker skills and the requirements of the job. Those groups for which more precise information is available will earn a premium. However, if employers learn as workers gain more exposure to the market and if there is no underlying difference in productivity, the premium will be eroded by worker experience. In the literature this form of discrimination may also lead to ex post differences in productivity across groups. However, this does not apply in our context where the favoured workers are defined by their sameness to their employers rather than by some dimension of individual identity. 5 To the extent that all employers, regardless of their own ethnicity prefer to employ any particular ethnic group it is more likely to be due to some form of statistical discrimination. 6  There is a close conceptual link between this work on statistical discrimination and Montgomery's (1991) work on the effect of social networks on labour market outcomes. In both cases some dimension of social structure is associated with a variation in the amount or accuracy of the information available to employers about prospective employees and vice versa. In the former, the agents have better information about others with whom they share a particular aspect of social identity which may be linked to language, culture, or some other determinant of cognition. In the latter, agents have better information about others with whom they share a social connection. Building on Granovetter (1973) and Rees and Schultz (1970), Montgomery (1991) shows that, if social networks are important for this reason, 'workers who are well connected might fare better than poorly connected workers (p. 1408)'. Arrow (1998) makes the connection between this literature and racial discrimination. He cites Kranton and Minehart (1997), who show that a sufficiently dense network will mimic a perfect market, and argues that evidence of statistical discrimination should be viewed as evidence that networks are both important and imperfect in the sense that they are not sufficiently dense. 3. Methodology 3.1 Investigating the variations in earnings between ethnic groups Given a sample of workers, the extent of the variation in earnings between ethnic groups can be established by estimating the following equation:- lnwi = aO + axle, + i(1) where Inwi is the log of earningsfor worker i, ao is a constant term ei is a vector of dummies, one corresponding to each ethnic group represented in the sample, a, is the vector of coefficients associated with those ethnic dummies, and &,1 is the error term. The joint significance of al tells us whether there is variation across the ethnic groups, the sign and significance of specific elements of a, tells us whether particular groups eam significantly more or less than the group chosen as a basis for comparison, and the signs and significance of differences between the elements of a, provide us with similar information about other pairwise comparisons. 7  In an effort to establish how much of the identified earnings differentials are due to variations in predetermined personal characteristics, we then add a vector of worker personal characteristics, xi, to the function lnw, = aO + alei + a2Xi + 42i. (2) Further, to investigate whether the returns associated with various personal characteristics vary across ethnic groups, we introduce a series of interaction terms between es and xi, Inwi = cao + alei + a2xi + a3eixi + 43i- (3) Traditionally, the significance of al and a3 are interpreted as evidence of current labour market discrimination, while a2xi is assumed to be absorbing the effects of variations in personal characteristics, some of which may be due to pre-market discrimination. However, we must be aware of potential omitted variable bias. Omissions of particular concern include controls for innate ability, school quality, worker preferences and comparative advantages.6 In accordance with the literature on labour market discrimination, we use equations (2) and (3) as a basis for our conclusions about whether discrimination is causing ethnic earnings differentials in the Ghanaian manufacturing sector. We then build on equation (2) in our efforts to identify the form that this discrimination takes. Recall that, while a taste for discrimination is unlikely to lead directly to earnings differentials between ethnic groups, a taste for employing co-ethnics combined with discrimination in other markets could lead to segregation and, as a consequence, to such earnings differentials.7 To test whether this is indeed the case, and establish the extent to which ethnic earnings differentials are the result of such a mechanism, we introduce a vector of dummies, gj, dummies corresponding to the ethnicity of the workers' employers into the earnings function:- lnwi = xo + alei + a2xi + C4 gi + 44i (4) 6 Variations in preferences for particular job characteristics across ethnic groups could provide an alternative explanation for both earnings differentials and sorting. It is, however, encouraging to note that variations in preferences have been less the concern of those interested in black-white differentials than those focusing on male- female differentials. Similarly, the discussion about variations in comparative advantages between groups has primarily been limited to male-female comparisons (e.g. Becker (1991)). 7 Fafchamps (2000) shows that members of different ethnic groups have differential access to suppliers credit, although his analysis focuses on the distinction between African and non-African entrepreneurs rather than finer distinctions between African entrepreneurs from different ethnic groups. 8  A significant vector of coefficients, a4, combined with declines in the magnitude and significance of elements of a, would indicate that ethnic segregation due primarily to the employment of co-ethnics is a source earnings differentials. We can take this line of analysis one step further by introducing another vector of employer's characteristics, hi, that includes variables such as size and capital-labour ratio into the function, lnwi=, xoG + ale, + a2xi + a4gi + a5hi +i (5) To the extent that the inclusion of hi reduces the significance of a4 we gain some indication as to why employers from different ethnic groups pay differently. Further, workers may be segregated not only on the basis of co-ethnicity with their employer. Some ethnic groups may be preferentially employed by larger or more capital intensive enterprises, or by public or foreign owned enterprises. If this is the case the introduction of hi will cause declines in the magnitude and significance of a(.8 Our data set is unusual in that it contains both worker and employer characteristics. However, the range of employer characteristics is limited. Thus, in order to fully control for segregation we also estimate a version of (5) in which gi and hi are replaced with employer's fixed effects, di (fixed across workers not time), lnwi = aO + ales + a2xi + a6 di + 46- (6) Having fully controlled for segregation effects we can focus entirely on within- enterprise variations in earnings between ethnic groups. So, to this final with-fixed- effects specification we first, rather circumspectly, introduce a vector of occupational dummies, oi, lnwi = cO + alei + a2xi + a6 di + c7o0i + ,7i (7) and monitor the effect on cc,. Significant elements in a7 combined with a reduction in the magnitude and significance of elements in ccl, could indicate that there is job crowding for some ethnic groups. However, it could also indicate that the observed personal characteristics previously entered into the earnings function are failing to pick up some important aspects of human capital which are correlated with job type. 8 Even in the US, where we might expect labour markets to function better, employer characteristics such as sector and size are found to be important determinants of earnings (e.g., Krueger and Sunmmers (1988) and Brown and Medoff (1989)). 9  We also endeavour to establish whether observed earnings differentials are due to statistical discrimination. Altonji and Pierrot (1997) test for statistical discrimination under the assumption that employers learn about workers as the latter's exposure to the labour market increases. As the employers learn, workers' pay becomes more dependent on actual productivity and less dependent on easily observable characteristics such as ethnicity. Thus, in a wage equation that contains interactions between experience and both ethnicity and a hard-to-observe variable that is correlated with productivity, the coefficient on the former will be such that ethnic earnings differentials decline with experience, while the coefficient on the latter will be such that the effect of the hard-to-observe characteristic increases with experience. Adopting this approach, taking mother's years of education as our hard-to-observe variable, and using (2) and (6) as alternative base functions, we arrive at the following two empirical formulations:- lnw1 = cO + alei + a2Xi + a8aeiJ(k1) + cx9amj(ki) + g8ax, (8a) and lnwi = co + alei + a2xi + a6 di + asbeij(k1) + CL9bmij(ki) + ,8bi (8b) where ki is years of experience, ](ki) is the experience profile of earnings, and mi is mother's years of education. 3.2 Investigating the variations in earnings between employers' kin, co-ethnics and other workers In order to establish whether employers' relatives and co-ethnics earn more than other workers we estimate the following:- lnwi= o+Pj31ri+ P2Ci+fP3Zi+P4gi+P5h1+49ai (9a) and with employer fixed effects, lnw =Po+Pjri+ ,B2Ci+133Zi,+,6di+ 9bi (9b) where r, is a dummy that takes the value one if the worker is related to the employer, ci is a dummy that takes the value one if the worker is from the same ethnic group as the employer, and zi is a vector of other worker characteristics, i.e., it is the 10  combination of ei and xi, but excluding ri and c,. Significant and positive coefficients on ri and ci indicate that relatives and co-ethnics respectively receive a positive earnings premium relative to other workers. Co-ethnicity is defined with respect to shared ethnic identity, while relatedness is defined with reference to an known social linkage. While co-ethnic and even non-co- ethnic workers may have a social linkage with their employer, its is only relatives that definitely have such a linkage. Thus, a significant positive coefficient on ri should be taken as evidence of a network effect associated either with superior information relating to job matching or a more sustained productivity effect due perhaps to reduced moral hazard or greater esprit de corps (Clague (1993)). In contrast, a significant positive coefficient on ci should be taken as evidence of either a taste for discrimination in favour of co-ethnics or statistical discrimination based on shared ethnic identity. In an endeavour to establish whether any identified earnings premiums are due to statistical discrimination or a similar networking effect, we adapt Altonji and Pierrot's (1997) approach. In this context, the growing importance of the hard-to-observe characteristic is confounded by the fact that it will be easier to observe for the related and co-ethnic employees. For this reason we introduce only the interaction between experience and the relationship dummies, lnwi = Po + Piri + P2 Ci + P3 Zi + P4 gi + PA + P7rif(k1) + P8ci A(ki) + 410ai, (lOa) and lnwi = Po+ f3ri + P2 Ci + 03Zi + f6di + 07ri A(k1) + c1i 1(ki) + IObi- (lOb) If 07 and/or P8 are such that the effects of being related or co-ethnic with one's employer decline with experience, then we may conclude that the source of any earnings premium afforded to these groups is due to statistical discrimination or a similar networking effect. If either or both premiums do not decline with experience, then we must look for other explanations such as a taste for discrimination or a productivity effect. 11  4. Data Our data is drawn from the fifth wave of the Ghanaian Manufacturing Enterprise Survey (GMES).9 The sample of enterprises is drawn from four cities in southern Ghana. Each of these cities could be described as potential melting pots, i.e., as environments within which Gluckman (1961) expected to see ethnicity decline in importance over time. Approximately one third of these enterprises are in Kumasi, a city to the north-west of the capital, Accra. Less than five percent of the sample are in either Cape Coast or Takoradi, on the coast to the west of Accra. All the remaining enterprises in the sample are situated in Accra. For each enterprise there is a corresponding sample of up to 10 waged workers and up to 10 apprentices. In the fifth wave of the GMES the questionnaires for the entrepreneurs, defined to include owner- managers and general managers or managing directors of corporate enterprises, and the workers and apprentices contained questions about ethnic identity and the incidence of blood relations between workers and apprentices and their employers. The ethnic structure of the Ghanaian population is complex. There are over one hundred distinct ethnic groups some of which combine to make up larger groups. The Akan, for example, is made up of around twenty groups, including the Asante, the Fante, the Akyem, the Akuapem, the Kwahu, and the Brong. Many of the ethnic groups have distinct languages. Others, while sharing their languages consider themselves to be distinct for cultural or historical reasons. Our approach during the survey was to ask each entrepreneur, worker and apprentice which ethnic group they were from. Coding then took place after the fieldwork was complete. Thus, our data captures the ethnic identities that the individuals ascribe to themselves. It is worth noting that none of the respondents had any difficulty deciding on the ethnic group to which they belonged. In the case of corporate enterprises, if the general manager or managing director was not available, although the questions relating to the enterprises accounts and operations were asked of other managers, the ethnicity questions were not asked. As a results some observations had to be dropped from this analysis. The data required for this analysis was collected from a sample of 1045 workers and 294 apprentices (see Table 1). A total of 35 Ghanaian ethnic groups are represented in 9 The first three waves of this survey were conducted as part of the World Bank's Regional Program for Enterprise Development. The last two were conducted as part of a project on labour markets in sub-Saharan Africa. All five 12  this sample along with two other West African groups that have been present in Ghana for several generations (Hausa and Kokomba) and are described as Ghanaian throughout the analysis below. For the purposes of the analysis the workers and apprentices are allocated to six ethnic categorisations (see Table 2): the Asante, a sub- group of the Akan; the Fante, another sub-group of the Akan; Other Akan; the Ga and Adangbe, which are combined throughout and referred to as the Ga-Adangbe as a reminded; the Ewe; and Northern, which includes the two migrant groups as well as thirteen groups indigenous to Ghana. Tables 1 and 2 are constructed in such a way that it is easy for the reader to see how the allocations are done.'0 The samples of workers and apprentices are spread across 189 employers. The distribution of these employers with respect to ethnicity is presented in disaggregated form in the final column of Table 1 and in the aggregated form to be used in the analysis in the final column of Table 2. Note that, while we have excluded non- Ghanaians from the sample of workers and apprentices, 12.7 percent of the employer sample are Middle Eastern, Asian or European." Excluding these employers from the analysis would greatly reduce the proportion of larger enterprises in our sample. Note that, as we mentioned above, the distributions of workers, apprentices and employers across the six Ghanaian ethnic categorisations are very similar. In all cases the Asante make up the largest proportion (between 21 and 39 percent), with the Fante as second largest (between 17 and 23 percent). The Other Akan, Ga-Adangbe and Ewe groups assume quite similar proportions (between 11 and 17 percent), while only the Northern group accounts for less than 10 percent in each sample. In part, this ethnic distribution reflects the geographical focus of the survey. Kumasi is the capital of the Asante region, while Cape Coast and Takoradi are the two largest towns in the Fante region. A relatively small ethnic group, the Ga, are indigenous to the capital, Accra, while the Adangbe traditionally occupy the area to the east of Accra. The Other Akan groups come from the area to the north of Accra and surrounding Kumasi. The Ewe are from the south-eastern part of the country, but have been present in Accra and waves were funded by the Department for international Development and conducted by the Centre for the Study of African Economies in collaboration with the University of Ghana and the Ghana Statistical Service. '