Productivity gains from reallocation of talent in Brazil and India

Productivity gains from reallocation of talent in Brazil and India

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Productivity gains from reallocation of talent in Brazil and India Kanat Abdulla PII: DOI: Reference:

S0164-0704(19)30144-2 https://doi.org/10.1016/j.jmacro.2019.103160 JMACRO 103160

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Journal of Macroeconomics

Received date: Revised date: Accepted date:

5 April 2019 12 September 2019 14 September 2019

Please cite this article as: Kanat Abdulla, Productivity gains from reallocation of talent in Brazil and India, Journal of Macroeconomics (2019), doi: https://doi.org/10.1016/j.jmacro.2019.103160

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Productivity gains from reallocation of talent in Brazil and India

†

Kanat Abdulla

Abstract This paper investigates labor market outcomes and their effects on aggregate productivity in Brazil and India. The empirical evidence points to the inefficient allocation of talent across occupations in both countries. Two main factors are identified as causes of the inefficiency: frictions in human capital accumulation and frictions in the labor markets. The resulting distribution of talent negatively affects aggregate productivity, which is examined by using an augmented Roy model. The model predicts that the elimination of barriers to human capital accumulation and in the labor markets in Brazil and India increases the output on average by 22–52% and 38–53%, respectively. Key words: allocation of talent, occupational distribution, aggregate productivity. JEL codes: J24, J70, O11, O40

∗ Graduate School of Public Policy, Nazarbayev University Address: Kabanbay batyr 53, Nur-Sultan 010000, Republic of Kazakhstan, E-mail: [email protected] † A am grateful for thoughtful and considered comments from an anonymous reviewer and an editor. I owe a special thanks to my advisor for his advice and encouragement.

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Introduction The question of allocation of talent in Brazil and India is well studied in the literature, where the

allocation of talent refers to the occupational distribution of groups categorized by gender and social groups – caste in India, and race in Brazil. However, the question of how this distribution affects the aggregate productivity in both countries is largely unaddressed. This paper intends to fill this gap. In 1888, Brazil was one of the last countries to abolish slavery in the Americas (Bergad (2007)). During the three centuries of slavery in the region, it was the largest importer of slaves from Africa, and today, Brazil is home to the world’s largest population of African origin. The 2010 census reveals that about 50 percent of nearly 190 million Brazilians are considered black or brown1 . The colonial past of Brazil has affected the gap in socio-economic opportunities among population groups and created barriers to social mobility. As shown by a number of studies the racial prejudice and segregation in the country is more or less standard practice (e.g., Telles (1992, 1995); Lovell and Wood (1998)). Black or mixedrace people tend to earn less than white Brazilians and work in low-skill occupations (Lovell (1993); Telles (2004); Loureiro et al. (2004)). Moreover, there is a gap between the country’s white population and African descent in such an essential component of human capital as education. Until 2001, for example, the country’s top universities were primarily restricted to white Brazilians. Affirmative action in 2001, which implemented quotas in university admissions, gave non-whites the preferred access to higher education. Despite this, the labor market still favors the whites. In particular, the prestigious positions in management and administration remain the preserve of white men. Historically, the unjust or prejudicial treatment of certain categories of people has affected the Indian population as well. It was mainly based on caste, a social stratification that divides society into hierarchically ranked groups. The ethnic groups known as Dalits were excluded from the caste system and were considered as untouchables. Today, Dalits are classified as Scheduled Castes (SC’s), and other socially and economically disadvantaged indigenous ethnic groups are regarded as Scheduled Tribes (STs). By instituting affirmative action in 1950, the Indian government has tried to decrease the socio-economic gap among social groups in India. These actions were maintained by introducing quotas in the government sector jobs and admission to higher education for historically excluded groups (SC/STs). Despite these efforts, the living standards, health status, educational attainment, and labor market outcomes of SC/STs 1 The

term “brown” is used to refer to Brazilians of mixed ethnic ancestries and sometimes known as “parda” in the Brazilian censuses.

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fall behind those of the so-called upper castes (Madeshwaran and Attewell (2007); Thorat and Attewell (2007)). Differences in key socio-economic dimensions by gender are also significant in Brazil and India. Although there is evidence that male-female gaps in education have declined in recent years for both countries, it has not been translated into equal opportunities in the labor market. Many studies show that female labor force participation and wage rates lag behind those of male (Arabsheibani et al. (2003); Klasen and Lamanna (2009); Madalozzo (2010); Deshpande et al. (2018)). To some extent, these disparities in the labor market can be attributable to social norms or stereotypes that restrain women to unpaid housework, child care, and taking care of elderly or sick people (Morrison et al. (2007)). For instance, in India, women devote ten times more hours than men to unpaid care responsibilities, out of which 85% goes into housework (Ferrant et al. (2014)). The gender and social disparities in the labor market outcomes show that a substantial part of the population in Brazil and India is not pursuing its comparative advantage, resulting in the talent misallocation. Recent studies have documented that the distortions in the allocation of talent have adverse aggregate effects. An example is Cuberes and Teignier (2016), who showed the negative impact of gender gaps in entrepreneurship on the allocation of resources and then on aggregate productivity. Hsieh et al. (2013) conduct time series analysis to address the question of whether an improved allocation of workers according to their talents is an important source of productivity growth in the US. Their study is motivated by substantial differences in the occupational choices between men/women and blacks/whites. They argue that the decline in the differences in the occupational distribution results in high productivity growth in the US. This study is most directly related to Hsieh et al. (2013), and it differs from their study in that it conducts the cross-sectional analysis to assess the effect of allocation of talent on aggregate productivity in Brazil and India. The main forces driving the differences in the labor market outcomes between gender and social groups are the frictions in the labor market, frictions in the acquisition of human capital. An example of frictions in Brazil is that browns face unequal access to jobs and experience discrimination before labor market entry, and before affirmative action in 2001, they were restricted from elite colleges. If the frictions in Brazil are based on skin color, in India, they are based on other individual characteristics. For example, job applicants with upper-caste names are significantly more likely to be called for an interview than equally qualified applicants with lower caste names (Thorat and Attewell 3

(2007)) or networks play an essential role in securing a job, they even influence the educational choice of people (Munshi and Rosenzweig (2005, 2016, 2006)). Additional forces that drive the wedge between female and male labor market outcomes are social norms or traditions, which retrict women to stay at home and discourage to invest in education. The purpose of this study is to investigate the effects of the economic and social disparities among groups in Brazil and India resulting from frictions on the aggregate productivity of the countries. These two countries are given special attention both because they are large and because they are well known for the high prevalence of prejudice and discrimination towards certain groups. The study employs micro-level survey data from Brazil and India with detailed information on individual socio-economic and occupational characteristics. The analysis is performed on four groups (white men, white women, brown men, and brown women) in Brazil and four groups (other men, other women, SC/ST men, SC/ST women) in India. Following the approach of Hsieh et al. (2013), the paper measures the differences in the occupational distribution across groups resulting from frictions to human capital accumulation and frictions in the labor market. Further, by using the Roy model of occupational choice, the paper evaluates the potential gains to output from decreasing the frictions in Brazil and India. The findings suggest that there are significant adverse impacts of the frictions in these countries. When the frictions are reduced by half the aggregate productivity increases by 9–13% in Brazil and by 14–15% in India. Eliminating the frictions increases the aggregate productivity by 22–52% in Brazil and by 38–53% in India. The paper is organized as follows. Section 2 describes the data obtained from the Integrated Public Use Microdata Series. Section 3 discusses the model. Section 4 provides empirical evidence on earnings of various population groups and their occupational distribution. Section 5 presents the results of the model. Section 6 concludes. 2

The Data and Variables The study is based on Brazilian and Indian survey data available at the Integrated Public Use

Microdata Series (IPUMS). The Brazilian data are from the census that spans 1991, 2000, and 2010 survey years with a total sample size of about 5–10 million individuals per period. The Indian data are from the socio-economic survey conducted by the National Sample Survey Organization of India every

4

5–6 years with a sample size of 500–600 thousand individuals. The variable names, coding schemes, and documentation are consistent for most samples. The analysis uses an individual’s primary occupation, which is classified according to the system used by the respective country censuses. Brazilian and Indian surveys use different classification systems to identify occupations. Moreover, the classifications used in different years in the Brazilian survey are not directly comparable with each other. This consistency problem is addressed by harmonizing the occupational coding to the 1990 occupational classification system used by Hsieh et al. (2013), which consists of 66 different occupational categories2 . Some related occupation categories are merged into one sub-heading. For instance, management-related occupations include administrative support occupations, and the computer and communications equipment operator occupation consists of communication equipment operators and computer and peripheral equipment operators. Earnings are measured by the total income from labor (wages, income from a business or a farm) in the previous month or year. Then hourly earnings are calculated by dividing total annual labor income by the total annual work hours3 . A person’s educational attainment is identified by “edattaind” and shows a person’s educational attainment in terms of the level of schooling completed; for instance, a person attending the final year of college receives the code for having achieved only a secondary degree. Years of potential experience (e) are based on an individual’s age (a) and years of schooling completed (s) as e = a − s − 6. Table A3 in the online Appendix reports summary statistics for the key variables used in

the analysis.

The following restrictions are made to the data: 1) only brown4 and white are chosen out of 5 race groups for Brazil, 2) the analysis is restricted to individuals whose ages are between 25 and 60, 3) individuals who are on active military, unable to work due to a disability, retired or at school are excluded from the analysis.

2 The

detailed information on occupational coding is provided in the Appendix. Appendix and supplementary material associated with this article can be found online. 3A

person’s hours worked per week are identified by “hrswork” and weeks worked per year by “wkswork”.

4 Brown

and white constitute the largest share of the population.

5

2.1

Data from Brazilian census Table 1 reports sample sizes for 1991, 2000, and 2010 samples stratified by race-gender group. The

number of observations, as shown in the table, has increased considerably over time, with the sample size rising by 60 % from 969,000 to 1,530,000 observations over the 20-years. Most of the sample is composed of whites: they account for 59% and 55% of the 1991 and 2010 samples, respectively. The percentage of browns has slightly increased from 42% to 46% over the period. Table 1: Sample statistics (Brazil) Sample size white men white women brown men brown women

1991 969,833 28% 30% 21% 21%

2000 1,204,718 29% 32% 20% 20%

2010 1,531,081 26% 28% 23% 23%

Table A4 in the online Appendix reports the share of college-educated individuals by group and census year. Educational differences among the groups are substantial, with the racial difference being more apparent than gender. Both brown men and women have very low levels of college enrollment: the percentage of college-educated brown men and women in 1991-2010 is only 1.8%-3.8% and 1.9-6.8%, respectively. White women’s educational levels are comparable to those of white men (even exceeds in 2010). The education levels for all population groups have significantly increased over the 20-years. If college-educated people account for only 5.5% of the total population in 1991, by 2010, their shares have risen to 10.4%. The highest growth of the measure belongs to white women: 7.7% in 1991 and 17.1% in 2010. 2.2

Data from the Indian survey For India, the IPUMS provides consistent data for the following sample periods: 1993, 1999, and

2004. It should be noted that there is a lack of data comparability across different survey periods regarding caste identities. Before 1999 “other backward castes” (OBC) and “others” are treated as one group; however, in the 1999 and 2004 surveys, OBC is treated separately. For comparability, these castes are treated as one group in this study for the 1999 and 2004 sample periods. There are four main caste classifications in India: scheduled caste (SC), scheduled tribe (ST), OBC, 6

and others. The most disadvantaged castes in socio-economic terms are SC and ST. In the analysis section, I group SC and ST as one disadvantaged group5 . Table 2 reports the summary statistics for the samples. As it is shown in the table others form the majority of the workforce in India, comprising 72–75% of the sample. SC and ST are minority groups, consisting, respectively, 13% and 16% of the sample. Table 2: Sample statistics (India) Sample size Other men Other women ST men ST women SC men SC women

1993 244,514 38% 38% 5% 5% 7% 7%

1999 256,948 37% 37% 5% 6% 8% 8%

2004 269,067 35% 36% 6% 7% 8% 8%

A majority of the Indian working population do not have a college degree. As shown in Table A4, only 6.8% and 7.8% of the sample hold at least a bachelor’s degree in 1999 and 2004, respectively. The education attainment differs across gender and caste groups. While 11.5% of other men had a college degree in 1999, the share of college-educated other women was only 5.5%. The proportion of graduates among the SC and ST is lower than the national average in 1993; just over four percent of the ST are graduates, while for the SC, it is only three percent, and lower still for women. There is an increase in the college attainment for all castes over time. In particular, in the 1999-2004 period SC and ST men demonstrate a noticeable increase in the share of college-educated individuals, from 3.5% to 7% and from 2.5% to 5.1%, respectively. Some of the increase in the college attainment by low-caste groups is possibly due to the affirmative action policy. 2.3

Home sector and sample selection An important factor taken into account in the sample selection of the study is the fact that a

substantial part of the working population in developing countries is occupied in the informal sector. The IPUMS provides information about the employment status of individuals in the sample, according to which individuals not in the labor force are classified as working in the home sector (housework), 5 The

results are robust to treating SC and ST as separate groups. See section 5.2.2

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unable to work, being at school, or retired and living on rents. Table A5 in the online Appendix provides detailed information on the number of observations in each category. The analysis excludes individuals who are at school, unable to work or retired and keeps those in the home sector. Therefore, in addition to the 66 occupations defined, another category is created for the home sector. Table 3 presents information on the share of employed individuals in the market and home sectors for Brazil and India. Approximately 1/3 of the working age population in Brazil and India are classified as employed in the home sector. In India, the proportion of women working in the home sector is higher than in Brazil: 64%, 65%, and 60.5%, respectively in 1993, 1999, and 2004. Taking the most recent data, men working at home in Brazil constituted 15.7% of the male population in 2010, while in India this proportion amounted to 3.8% in 2004. Table 3: Share of employed individuals by sector 1991 Number of obs. market sector All home sector market sector Men home sector market sector Women home sector

964,173 71% 29% 99.8% 0.2% 42.7% 57.3%

2000 Brazil 1,204,520 70% 30% 88% 11.7% 51.8% 48.2%

2010

1993

1,530,715 72% 28% 84% 15.7% 59.2% 40.8%

251,690 66% 34% 96.2% 3.8% 36.0% 64.0%

1999 India 266,404 65% 35% 95% 4.6% 34.5% 65.5%

2004 275,405 68% 32% 96% 3.8% 39.5% 60.5%

Wages for individuals in the home sector are imputed by assigning them the predicted wages of workers in the market sector with similar observed characteristics. The observed characteristics include the region where an individual resides, the group to which an individual belongs, his/her years of schooling and experience. The relationship between earnings and these characteristics are assumed to be the same for the home and the market sectors. To estimate selection-corrected wage gaps across genders and ethnic groups, I follow the familiar two-step Heckman procedure. The details are provided in the online Appendix. 3

The Model The study is built upon an augmented Roy (1951) model developed by Hsieh et al. (2013). Here I

summarize the key equations and theoretical results of the model. The detailed description of the model is given in the online Appendix. 8

The economy consists of an infinite number of individuals and a representative firm. Individuals consume goods, accumulate human capital, rent labor, and choose an occupation that delivers the highest utility. A representative firm hires labor inputs to produce goods. Individuals, each belonging to a group g based on gender and race, maximize the following utility: Uig = cβig (1 − sig )dig

(3.1)

and accumulate human capital from education sig and expenditure eig according to the production function: ¯ ig sφi eη h(e, s) = h ig ig

(3.2)

where i refers to occupation, cig is consumption, dig is home preference factor and β is a parameter showing the trade-off between consumption and leisure, φi is an elasticity of human capital with respect ¯ ig is a parameter that refers to the efficiency in human capital. to schooling and h At birth, individuals are endowed with a random skill i from an extreme value distribution as in McFadden (1973) and Eaton and Kortum (2002):

Fg (1 , ..., N ) = exp{−[

N X

i=1

1−ρ (Tig −θ } i )]

(3.3)

where θ determines the skill dispersion, ρ determines the correlation of skills across occupations, and Tig defines occupation-group specific ability. A representative firm produces aggregate output Y = (

N P

i=1

(Ai Hi )

σ−1 σ

σ

) σ−1 from labor in N different

occupations by hiring Hi , total efficiency labor units, in each occupation and taking Ai , exogenous productivity in occupation i, as given. 3.1

Occupational Sorting Given skills, an individual will choose the occupation that yields the highest value of Uig . By

aggregating the optimal occupation choices for all individuals in the model, the following equation is obtained, which is the overall occupational share of a group g:

9

where w˜ig =

1/θ

φ

Tig wi si i (1−si ) τig

1−η β d˜ ig

θ w˜ig pig = PN θ ˜sg s=1 w

(3.4)

1−η

, d˜ig =digβ , and pig is the fraction of people in group g that work in

occupation i. Equation (3.4) indicates that the occupational sorting depends on w˜ig , which is the overall reward that someone with mean talents from group g working in occupation i receives, relative to the power mean of w˜ for the group over all occupations. This means that the occupational distribution is driven by the relative reward, not the absolute reward. The occupational wage gap between any two groups is given by: P

θ 1 1 ˜sg w¯ig sw = ( P θ ) θ 1−η w¯ig0 ˜sg0 sw

(3.5)

Equation (3.5) indicates that the wage gap between group g and group g 0 is same across occupations, i.e., the friction that affects one occupation at the same time affects the wage gap in other occupations. The intuition behind this is that changes in frictions experienced by the groups result in the changes in the quality of the groups across all occupation. Combining equation (3.4) and equation (3.5) yields the propensity of a group g to work in an occupation relative to group g 0 : Tig0 τig −θ w¯g −θ(1−η) pig ( = ) ( ) pig0 Tig τig0 w¯g0

(3.6)

From equation (3.6) one can see that the propensity for a member of the group g to work in an occupation i compared to group g 0 is affected by three factors: the relative mean talent frictions

τig τig0 ,

and the wage gap

w ¯g w ¯g 0 .

Tig0 Tig ,

the relative

The propensity for a group to work in an occupation is increasing

in relative mean talent and decreasing in relative frictions and the relative wage gap. Home preference factor Individuals, based on their gender, decide to work at home or in the market sector. The model allows for differences across gender groups in the extent to which they want to work in the home sector. By using equations (3.4) and (3.5) and normalizing d˜ = 1, τ w = 0 and τ h = 0 for the home sector, the following share of individuals of a group g who choose to work at home relative to a group g 0 is derived:

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θ 2 (1−η) θ(1−η) θ(1−η) θ 2 (1−η) pH d˜ig0 θ(1−η)+1 w¯g0 θ(1−η)+1 pig θ(1−η)+1 τig θ(1−η)+1 g ) ) ( ( ( ) ) = ( w¯g pig0 τig0 pH d˜ig g0

(3.7)

d˜ig0 d˜ig

is the home preference

where pH g is the fraction of people in group g who chose to stay at home and

factor of a group g 0 relative to group g. I also normalize the home preference parameter of group g 0 such that d˜ig0 = 1 in each occupation, to imply that d˜ig is the relative preference of group g relative to group g0. This preference for the home sector captures not only traditions or social norms that restrict some women to work outside their home, but also the preference for fertility. For example, studies find that higher fertility is associated with lower female labor force participation (see Bloom et al. (2009)). 4

Empirical findings As the model predicts in equation (3.6), frictions faced by each group can be derived from the wage

gaps and occupational distributions of the groups. This section begins with investigating the occupational distributions of groups in Brazil and India. Then the section provides simple estimates of wage gaps of the groups in the countries. One purpose of this section is to illustrate with most recent data the differences in the labor market outcomes between the groups in Brazil and India. 4.1

Occupational distribution The study defines four groups for Brazil: white women, white men, brown men, and brown women;

and four groups for India: other men, other women, SC/ST men, and SC/ST women. It is assumed that white men in Brazil and other men in India face fewer frictions than other groups, which is a reasonable assumption based on the occupational distributions shown in Figure 1. Wage gap estimations also indicate that white men in Brazil and other men in India earn more than other groups with similar characteristics (Section 4.2). Therefore, these groups are defined as privileged groups. Figure 1 exhibits the share of each group in high-skill occupations6 in 2010 for Brazil and 2004 for India. As can be seen from the graphs, there are striking differences between groups in both countries. White men in Brazil and other men in India are more likely to work in high-skill occupations. The most

6 Executives,

architects, engineers, mathematicians, doctors, lawyers, and judges.

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disadvantaged groups in terms of this measure are brown men and women in Brazil and other and SC/ST women in India. The members of these groups are less likely to work as executives, architects, engineers, mathematicians, doctors, and lawyers.

(a) Brazil

(b) India

Figure 1: Share of groups in high-skill occupations Further is presented another way of comparing the occupational distributions across groups. For these purposes, the Euclidean norm is used to compare the occupational choices of the groups relative to white men in Brazil and other men in India.

1 − N orm(Pg0 , Pg

v uN uX ) = 1 − t (p i=1

2 i,g 0 − pi,g )

(4.1)

where Pg is a vector of the occupational shares of group g and pig is an occupational share of group g in occupation i. A value of the measure closer to zero implies that the occupational distribution of the group is not similar to that of the privileged group. A detailed distribution of the measure across groups is presented in Table 4. Panels A and B show the similarity of the occupational choices of the groups in Brazil and India, respectively. The magnitude of the measure varies substantially across groups. The value of 0.41 for white women in 1991 indicates that the occupational distribution of white women is not similar to that of white men. The value of 0.89 for brown men in 1991 shows that brown men are closer to white men in their occupational choices. In India, there is the same pattern of occupational distribution across groups as for Brazil. Occupational choice of women in India is more likely to differ from that of other men. The relative occupational distribution of the groups in India fluctuates from year to year but grows 12

very little over time; while in Brazil there is a significant convergence in the measure, especially for women – 0.21 and 0.17 percentage points increase for white and brown women, respectively. Table 4: Occupational similarity measure Panel A: Relative to white men in Brazil 1991 2000 2010 0.41 0.50 0.62 white women 0.89 0.89 0.93 brown men 0.36 0.45 0.53 brown women 4.2

Panel B: Relative to other 1993 other women 0.30 SC/ST men 0.82 SC/ST women 0.49

men in India 1999 2004 0.28 0.35 0.81 0.87 0.48 0.51

Overview of wage gap in Brazil and India The previous section shows that there are differences in the occupational distribution between the

groups. This section examines wage disparity between the groups in Brazil and India. The following Mincer earnings function summarizes the general functional form of earnings: wagej = α + G + β2 Educj + β3 Expj + β4 Exp2j + Ox + εj

(4.2)

where j indexes the individual, wagej stands for the logarithm of hourly earnings, G is a dummy representing groups, Expj denotes years of experience, Educj denotes years of schooling, Ox is a dummy variable representing occupations that takes the value of 1 if an individual works in occupation x; it captures the earnings of a worker in occupation x relative to a worker in the reference occupation, and εj is an i.i.d. error term. Table 5 shows the estimates of the wage gaps across groups in the two countries, and how they have changed over time. These estimates are from regressions of logarithm of hourly wages on dummy variables for groups, including controls for education, experience, and occupation. In Brazil, the value of -0.29 indicates that white women in 1991 received on average 71% of white men’s wage. Brown women earn even less than white women, only half of what white men earn over the period: 0.51 log difference in 1991, 0.50 in 2000, and 0.46 in 2010. The differences in earnings are less among Brazilian brown and white males, but they are still substantial. Wages of brown men are 22%, 25%, and 19% lower than those of white men in 1991, 2000, and 2010, respectively. The trends in the wage gap exhibit continued but smaller gains in subsequent decades. Over the period, white and brown women experienced 0.03 and 0.05 log points wage convergence, respectively, indicating that over time, the earnings of these groups 13

are moving closer to those of white men. The wage gap of brown men has fluctuated over time but still shows a downward trend. Table 5b shows that the earnings in India are always in favor of other men. Other women earned about two-thirds of that made by other men in 1993. The difference in earnings between other men and SC/ST women is even larger. SC/ST women earn half of what other men earn over the period: 0.48-0.49 log difference in 1993-2004. The earnings of SC/ST men are 19-21% less than those of other men. The table further highlights that the groups in India have experienced little wage convergence over the period, with the average value moving from -0.34 in 1993 to -0.31 in 2004 for other women. In the case of wage gaps for SC/ST men and women, the patterns remain relatively unchanged over time. Table 5: Conditional log difference in wages (b) Relative to other men in India

(a) Relative to white men in Brazil

white women brown men brown women

1991 -0.29 (0.006) -0.22 (0.005) -0.51 (0.006)

2000 -0.29 (0.005) -0.25 (0.005) -0.50 (0.006)

2010 -0.26 (0.005) -0.19 (0.005) -0.46 (0.005)

other women SC/ST men SC/ST women

1993 -0.34 (0.003) -0.21 (0.004) -0.49 (0.004)

1999 -0.33 (0.003) -0.19 (0.003) -0.49 (0.004)

2004 -0.31 (0.003) -0.21 (0.003) -0.48 (0.003)

Notes: All coefficients are statistically significant at the 1% level. Standard errors are in parentheses.

The model predicts that the wage gaps are similar across occupations and independent of propensities (equation (3.5)). This means that changes in frictions faced by a group in one occupation, resulting in a change in relative propensities, do not affect the average wage of the group. It is because an increase (a decrease) of friction will deter (attract) less qualified workers, thus increasing (lowering) the average quality of the group. Table 6 shows the results of the regression of the occupational wage gap and relative propensities. The regression is weighted by the share of the workers in each group across occupations. The slope and the R2 from the regression of the wage gap on propensities are small for all groups, which is an indication that there is little to no correlation between these variables, which supports the model version of the equation.

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Table 6: Relationship of wage gaps and propensities (a) Brazil

1991 white women brown men brown women

slope -0.021 0.009 -0.041

2000

st.error 0.020 0.026 0.022

R2

0.016 0.002 0.052

slope -0.023 0.026 0.010

st.error 0.020 0.031 0.021

2010 R2

0.019 0.010 0.004

slope 0.021 0.015 -0.003

st.error 0.015 0.022 0.019

R2 0.031 0.007 0.000

(b) India

1993 other women SC/ST men SC/ST women

slope -0.014 0.059 -0.014

1999

st.error 0.025 0.036 0.028

R2 0.005 0.040 0.004

slope -0.035 0.008 -0.016

st.error 0.018 0.034 0.023

2004 R2 0.059 0.001 0.008

slope -0.018 -0.026 -0.030

st.error 0.021 0.034 0.026

R2 0.011 0.009 0.019

Notes: Results from regressions of occupational wage gaps by relative propensities.

4.3

Estimation of Frictions The frictions faced by the groups in Brazil and India are derived from the available data on the

fraction of people in group g who work in occupation i (pig ) and the wage of group g relative to the ¯g privileged group ( ww ¯ 0 ). So, by rearranging equation (3.6), the following estimate of the composite friction g

τˆig for each group and occupation is derived:

τˆig =

τig Tig0 1 pig − 1 w¯g −(1−η) ( )θ = ( ) θ( ) τig0 Tig pig0 w¯g0

The composite friction is negatively related to the relative propensity gap

w ¯g w ¯g 0 .

(4.3) pig pig0

and positively to the wage

This means that if the group is underrepresented in the occupation or if it faces high wage gap,

then the friction faced by the group will be high. The right-hand side of equation (4.3) is observed in the data, so we can use it to determine τˆig . The evaluation of the friction requires the estimates of θ (the parameter that governs the dispersion of talent) and η (the elasticity of human capital with respect to expenditure on human capital). The estimations of the parameters are discussed below. 4.3.1

Estimation of θ and η Following Hsieh et al. (2013), θ and η are estimated from wage dispersion across people within an

occupation and a group, where wages of people obey Frechet distribution with the parameter θ(1 − η). 15

In particular, the higher the parameter, the lower the wage dispersion within an occupation and a group. For each country, θ(1 − η) is estimated by fitting the distribution of the residuals from a cross-sectional

regression of wages on occupation and group dummies in each year. The resulting estimates of θ(1 − η)

for Brazil and India across available years are given in Table 7.

Table 7: Estimates for θ(1 − η)

θ(1 − η) θ(1 − η)

1991 2.9 1993 4.1

Brazil 2000 2.9 India 1999 3.8

2010 3.2

Average 3.0

2004 3.8

Average 3.9

The parameter η is equal to the fraction of output spent on human capital accumulation. The data on expenditure on education as a share of GDP and the labor share of countries are used to estimate this parameter. Table 8 presents the spending on education as a share of GDP and labor shares for Brazil and India for corresponding years. Expenditure on education for Brazil is obtained from OECD and for India - from UNESCO Institute for Statistics, and the data on labor shares are from Penn World Table. With these data I get estimates of η and with estimates of θ(1 − η) (table 7) I compute θ. As shown, the

estimates of η are more or less similar across countries, it ranges from 0.06 to 0.08, which implies that the fraction of output spent on human capital is between 6% and 8%. The value of the parameter θ ranges from 3.10 to 3.54 in Brazil and from 4.11 to 4.12 in India. The value of the parameter is lower in Brazil than India, meaning that the wage dispersion in Brazil is higher than in India.

16

Table 8: Estimates for η and θ (a) Brazil

Spending on education (% of GDP) Labor share η θ

1991

2000

2010

Average

0.034

0.035

0.056

0.042

0.51 0.07 3.13

0.54 0.06 3.10

0.56 0.10 3.54

0.54 0.08 3.26

1993

1999

2004

Average

-

0.044

0.034

0.039

0.64 -

0.58 0.08 4.11

0.50 0.07 4.12

0.57 0.07 4.20

(b) India

Spending on education (% of GDP) Labor share η θ

4.3.2

Frictions Table 9 shows the means and standard deviations of occupation and group-specific frictions τig in

Brazil and India computed by using the estimates of θ and η. A value of the friction equal to one means that a group does not face friction relative to the privileged group. If the value is more than 1, then a group faces friction, while a value less than 1 acts as a subsidy for that group in that occupation. Table 9: Summary statistics of frictions across countries (a) Brazil

white women brown men brown women

1991 mean st.error 2.34 0.48 1.41 0.17 3.38 0.65

2000 mean st.error 2.38 0.45 1.58 0.17 3.51 0.61

2010 mean st.error 2.02 0.37 1.27 0.13 2.67 0.50

(b) India

other women SC/ST men SC/ST women

1993 mean st.error 2.63 0.35 1.40 0.14 3.64 0.42

1999 mean st.error 2.73 0.36 1.47 0.15 3.41 0.43

2004 mean st.error 2.52 0.34 1.32 0.13 3.35 0.43

In Brazil, the friction faced by brown women is the highest; the average friction in 1991 is 3.38. 17

The variance of the frictions faced by the group is also the highest: in 1991 the standard deviation is 0.65. Over twenty years, the friction experienced by brown women in Brazil has decreased: in 2010, the mean and standard deviation are 2.67 and 0.50, respectively. Of the three groups in Brazil, brown men experience the least frictions. In 1991 the average friction for this group is 1.41, which has decreased by only 0.14 to 1.27 in twenty years. The standard deviation of the frictions reduced slightly over the period from 0.17 to 0.13. The variance of frictions faced by white and brown women shows that the frictions for these groups are highly dispersed across occupations. In India, the frictions experienced by women are higher than those faced by SC/ST men. The frictions faced by SC/ST and other women are 3.64 and 2.63 in 1993, respectively. Women face a high dispersion of frictions as well. The dispersion is 0.42 for SC/ST women versus 0.35 for other women in 1993. Frictions faced by the groups did not change significantly over time, but they have decreased since 1993. Frictions experienced by SC/ST male workers have fallen from 1.40 to 1.32 over the period, while those of female workers decreased from 2.63 to 2.52 and 3.64 to 3.35, respectively for other and SC/ST women. From equation (3.7) we can infer the relative home preference parameter d˜ig . Table 10 shows the mean values of d˜ig for women in Brazil and India. If the value of the parameter is close to 1, then the market sector is more preferred than the home sector. The mean of d˜ig for Brazilian white women in 1991 is about 0.39. Over time, the model predicts that white women’s preference for working in the market relative to the home sector has increased. Likewise, for brown women, the relative home preference factor increased over time, from 0.45 in 1991 to 0.71 in 2010. This indicates that women in Brazil over time prefer working more in the market sector than home or it has become more acceptable for some women to work outside their home. Home preference of Indian women shows a different trajectory. Although the levels of d˜ig in 1993 are comparable to those of Brazil in 1991, over time, they did not change, implying that women in India were discouraged from working in the market sector. This is in line with studies showing that women do not take advantage of new opportunities that open up. Rodgers et al. (2013) show that Indian women find it difficult to migrate from rural to urban areas due to the restrictions imposed by society.

18

Table 10: Estimates for home preference factor d˜ (a) Brazil

white women brown women

1991 0.39 0.45

2000 0.32 0.35

2010 0.65 0.71

1999 0.41 0.49

2004 0.44 0.52

(b) India

other women SC/ST women

5

1993 0.45 0.51

Results There are 9 exogenous parameters: Ai (occupational technology), φi (elasticity of human capital

with respect to schooling), τig (frictions), d˜ig (home preference parameter), qg (total number of people in each group), θ (the parameter that governs the dispersion of talent), η (the elasticity of human capital with respect to expenditure on human capital), σ (elasticity of substitution between occupations), and β (weight on consumption relative to time in the utility function). The baseline values of θ and η are given in Table 8. The baseline parameter estimates of σ=3 and β=0.693 are taken from Hsieh et al. (2013), and robustness checks are conducted later. h captures the The number of people in each group qg is taken from the data. Assuming that τig

¯ ig is set to one. The mean talent across groups for each efficiency in human capital accumulation, h occupation is normalized as Tig = 1. The normalization assumes that there are differences in mean skill between men and women but that it is the same across occupations within groups. The elasticity of human capital with respect to schooling, φi , is estimated by using the equations of average wage gaps and equilibrium condition for schooling and matching the wage gaps to the data. The technology parameter across occupations Ai is estimated from market clearing conditions of human capital. The price of efficiency units of human capital wi is obtained by matching equation (3.4) to the data. 5.1

Model fit This section checks if the model is appropriate to the data and fits it closely. For these purposes, the

model and data counterparts of mean earnings and occupational shares across groups and occupations are compared. 19

The model is calibrated to the occupational shares of white men in each period. The model counterpart of the occupational shares is produced from equation (3.4). Table 11 compares the occupational shares generated by the model with the data for five occupational categories with the highest shares for each group. For example, according to the data and the model, 10.2 % of white men in Brazil work as farm non-managers. For other groups in Brazil, the model produces close results. The data show that 40.6% of white women and 49.7% of brown women work in the home sector. The model suggests that 43% of white women and 47.7% of brown women work in the home sector. In India, most men work as farm non-managers, and a majority of women work in the home sector. The data show that 33% of other men and 42% of SC/ST men in 2004 are occupied in farming. The model counterparts of these shares are 33% and 34%, respectively for other and SC/ST men. In the same period, women predominantly work in the home sector (63.4% of other women and 48.6% of SC/ST women). The model predicts that 57.2% of other women and 40.4% of SC/ST women work at home.

20

Table 11: Occupational shares (data vs. model) (b) India

(a) Brazil

Data white men farm non-managers 0.102 construction 0.101 motor vehicle op. 0.096 sales 0.086 home 0.072 white women home 0.406 sales 0.074 private occupations 0.068 teachers 0.060 farm non-managers 0.035 brown men construction 0.149 farm non-managers 0.110 motor vehicle op. 0.089 related agriculture 0.088 home 0.068 brown women home 0.497 private occupations 0.096 sales 0.056 teachers 0.046 cleaning 0.033

Model

Data other men farm non-managers 0.330 sales 0.148 executives 0.050 construction 0.044 motor vehicle op. 0.040 other women home 0.634 farm non-managers 0.219 teachers 0.022 sales 0.021 precision, textile 0.019 SC/ST men farm non-managers 0.422 sales 0.071 freight handler 0.067 construction 0.067 teachers 0.046 SC/ST women home 0.486 farm non-managers 0.354 sales 0.025 teachers 0.018 private occupations 0.016

0.102 0.101 0.096 0.086 0.072 0.430 0.045 0.065 0.055 0.031 0.164 0.073 0.093 0.097 0.053 0.477 0.092 0.036 0.047 0.056

Model 0.330 0.148 0.050 0.044 0.040 0.572 0.246 0.014 0.048 0.027 0.340 0.079 0.076 0.057 0.077 0.404 0.348 0.059 0.024 0.004

To check if the earnings produced by the model fit the data, I regress the earnings data on the model counterpart for each group and period. Table 12 exhibits the regression results for Brazil and India. For Brazilian white men in 1991, a value of 1.551 indicates that a 1 percent increase in the model counterpart of mean earnings corresponds to a 1.551 percent increase in mean earnings given by the data. Overall, the model generates lower earnings than data. The model generates the highest fit for white men in 2010 with the R2 of 0.894. The lowest fit corresponds to the earnings of brown women in 1991 with the R2 of 0.550. The mean earnings in India produced by the model fit the data better in terms of the slope. Overall, a 1 percent increase in mean earnings generated by the model corresponds to a 0.8–1.15 percent increase in mean earnings given by the data. However, the percentage of the variation in the data that the model explains is lower for India than for Brazil. The fit for the SC/ST women is the weakest with the R2 of 21

0.230 and 0.346 in 1991 and 1999, respectively. Table 12: Mean earnings across groups (data vs. model) (a) Brazil

white men white women brown men brown women white men white women brown men brown women white men white women brown men brown women

5.2

slope st.error 1991 1.551 0.107 1.412 0.149 1.613 0.117 1.532 0.176 2000 1.623 0.080 1.591 0.129 1.552 0.093 1.716 0.128 2010 1.323 0.057 1.418 0.063 1.299 0.058 1.440 0.081

(b) India

R2 0.765 0.585 0.747 0.550

other men other women SC/ST men SC/ST women

0.864 0.704 0.812 0.744

other men other women SC/ST men SC/ST women

0.894 0.886 0.885 0.831

other men other women SC/ST men SC/ST women

slope 1993 0.884 1.096 0.729 0.906 1999 0.993 1.150 0.926 1.098 2004 0.983 1.156 0.959 1.164

st.error

R2

0.105 0.182 0.110 0.220

0.524 0.364 0.401 0.230

0.102 0.165 0.117 0.196

0.592 0.441 0.496 0.346

0.086 0.128 0.100 0.169

0.669 0.563 0.584 0.445

Output gains This section investigates the main question asked at the beginning of the paper: What is the effect

of allocation of talent on productivity in Brazil and India? The allocation of talent in the model is affected h , the labor markets τ w , and the home preference parameter by frictions in human capital accumulation τig ig

d˜ig . Here, I explore the effects of these forces on output. The model does not allow to separately identify w and τ h . It is possible to evaluate only the aggregate frictions τ = the effects of τig ig ig

h η (1+τig ) w . 1−τig

Thus,

throughout, the analysis is conducted for two different cases: a case in which frictions in the acquisition h are allowed, and a case with frictions in the labor market τ w . of human capital τig ig w , τ h and d˜ for Brazil and India. The first Table 13 decomposes the contribution to output of τig ig ig w and τ h ) and second - the effects of removing column shows the effects of removing only frictions ( τig ig

both frictions and setting d˜ig = 1, in other words equating social norms between men and women. Thus, in the case of frictions in the labor market, removing frictions results in 51.6%, 47.1% and 32.2% increase in the productivity in Brazil, respectively in 1991, 2000 and 2010. Removing frictions and setting d˜ig = 1 increases the productivity even more, by 60.4%, 56.8 and 34.7% respectively in the corresponding periods. In India, there is a similar pattern, but also a larger gain than in Brazil. 22

Table 13: Counterfactual output gains (b) India

(a) Brazil w = 0, τ h = 0, τig ig d˜ig = 1 due to τ w 60.4% 56.8% 34.7% h due to τ 47.7% 44.4% 25.4%

w = 0, τ h = 0 τig ig

1991 2000 2010

51.6% 47.1% 32.2%

1991 2000 2010

38.0% 33.9% 22.5%

w = 0, τ h = 0, τig ig d˜ig = 1 due to τ w 61.8% 62.6% 58.6% h due to τ 50.4% 51.8% 47.0%

w = 0, τ h = 0 τig ig

1993 1999 2004

52.9% 52.4% 49.3%

1993 1999 2004

41.7% 41.7% 37.9%

Looking across the results, it is clear that the vast majority of the gain in output is due to τig . Removing frictions accounts for more than 3/4 of the combined effects of removing both τig ’s and d˜ig ’s in the case of frictions in the labor market and human capital accumulation. In the interest of space and given that frictions account for the majority of the productivity gains, in the next sections, I will focus on the effects from frictions by keeping d˜ig ’s fixed. The sections that follow will examine the role of frictions in the productivity growth by analyzing several counterfactuals. In a baseline case, the aggregate output in each period is computed by plugging the estimated frictions of each period. Then the following counterfactuals are examined: setting frictions to one period, reducing frictions by half, eliminating them, and replacing them with those of the US. The analysis is primarily aimed at answering a counterfactual question: ‘How much would aggregate productivity be increased if the frictions were reduced or replaced?’ In a robustness check section, the counterfactuals with different parameter values are tested. 5.2.1

Counterfactual output gains in Brazil Table 14 gives the results for Brazil for different scenarios separately. The first row of the table

w , replacing shows the results of replacing frictions in all periods with the frictions in 2010. In the case of τig

frictions in 1991 and 2000 by those of 2010 increases the output by 8.4% and 7.6%, respectively. This reflects the impact of the reduced frictions in recent periods, as it was shown in the previous section, which affects the output positively. The second row shows the counterfactual gains in Brazil with the barriers faced by the groups in 23

w , replacing the frictions experienced by browns and women by those experienced the US. In the case of τig

by blacks and women in the US would increase the output by 22.9%, 21.5%, and 14.4%, respectively, in 1991, 2000, and 2010. The positive gains associated with the US frictions imply that the groups in the US experience less frictions than those in Brazil. Another interesting counterfactual experiment would be to investigate the effects of reducing frictions to a specific level. The last two rows of Table 14 show the counterfactual output gains from reducing the frictions to half and removing them in the corresponding years. Reducing the frictions by half across all occupations increases the output by 13%, 12%, and 9.3%. Removing them entirely increases it even h are also significant. more, by 51.6%, 47.1%, and 32.2%. Output gains due to τig

Table 14: Counterfactual output gains: Brazil 1991

Brazil 2010 friction US 2010 friction Frictions halved No friction

2000 2010 w due to τ 8.4% 7.6% 0.0% 22.9% 21.5% 14.4% 13.0% 12.0% 9.3% 51.6% 47.1% 32.2%

1991

2000 due to τ h 8.5% 6.7% 19.7% 17.0% 13.0% 11.3% 38.0% 33.9%

2010 0.0% 8.9% 9.8% 22.5%

The impact of segregation How much of the labor market differences between browns and whites are due to regional disparities? Browns are more concentrated in the North and Northeast, the poor and underdeveloped regions of Brazil (Arcand and DHombres (2004)). The majority of whites reside in the industrialized and relatively wealthy region, the South (Telles (1992)). This regional segregation could contribute to economic and social outcomes of the population by impairing their access to better schools, jobs, and health services. Table 15 reports the sample population of the five regions in Brazil (North, Northeast, Southeast, South, Midwest) broken down into whites and browns. The most populous region in Brazil is Southeast with the population in sample 411 263 - 565 784, and the least populous - North with a population of 46 497 - 82 409. The composition of ethnic groups differs significantly by region, for example, the share of browns in the North and Northeast is 75.6 - 71.3%, in the South and Southeast it is 13.3 - 31.4% in 1991-2010.

24

Table 15: Regional distribution of Brazilian population by race Number of obs. Total Whites Browns Total Whites Browns Total Whites Browns Total Whites Browns Total Whites Browns

1991 2000 937 046 1 127 845 North 46 497 63 835 24.4% 31.3% 75.6% 68.7% Northeast 239 453 289 357 28.7% 36.3% 71.3% 63.7% Southeast 411 263 483 931 68.6% 69.1% 31.4% 30.9% South 167 257 199 043 86.7% 88.2% 13.3% 11.8% Midwest 72 576 91 679 47.2% 52.0% 52.8% 48.0%

2010 1 401 391 82 409 26.5% 73.5% 350 619 32.6% 67.4% 565 784 61.2% 38.8% 278 223 82.9% 17.1% 124 356 43.2% 56.8%

Table 16 looks at the geographical dimension of the effect of frictions experienced by browns. The table considers the robustness of the productivity gains to the segregation. In particular, the analysis examines the possibility that the clustering of browns in undeveloped regions skews the allocation of talent. The second column presents the baseline productivity gains from removing frictions for 1991, 2000, and 2010. Columns from three to five show gains estimated separately for each region. Both in the τ w and τ h cases, the productivity gains from removing frictions in the North, Southeast and Midwest are comparable to the baseline. The output gains are largest in the Northeast, in 1991, 2000 and 2010 the gains are respectively, 56.7%, 49.5%, 38.5%. In the South, the gains are the least, but still significant, 35.2%, 32.8%, and 24.3% in the corresponding periods. The analysis suggests that the results are robust to the segregation effects because we see significant increases in the productivity even within regions.7

7 It should be noted that these results do not identify intraregional segregation effects. It is not feasible to estimate productivity gains at the level of states or districts in Brazil for statistical reasons. This is because the resulting sub-samples become too small for one to be able to get reliable results.

25

Table 16: Regional output gains in Brazil Baseline 1991 2000 2010 1991 2000 2010 5.2.2

51.6% 47.1% 32.2% due 38.0% 33.9% 22.5%

North Northeast Southeast South due to frictions in the labor market 45.2% 56.7% 45.4% 35.2% 48.0% 49.5% 41.2% 32.8% 39.2% 38.5% 26.9% 24.3% to frictions in acquisition of human capital 37.8% 43.7% 34.1% 28.4% 33.4% 35.8% 31.6% 26.3% 26.9% 28.9% 19.0% 18.9%

Midwest 45.5% 44.5% 38.1% 35.8% 33.7% 27.4%

Counterfactual output gains in India The sample size with detailed caste and detailed occupation categories is small. This makes it

difficult to estimate earnings for all groups. Given a trade-off between the number of caste categories and the number of occupation categories, the analysis is undertaken for two cases. First, I show the results of the model with the limited number of caste categories but detailed occupational categories. Then, then I present the results with more detailed caste but broader occupational categories. Broader caste categories. Here the analysis is conducted on two caste categories (other and SC/ST) and 67 occupation categories. Table 17 presents counterfactual output gains in India due to labor market w ) and frictions in human capital (τ h ). The following four cases are investigated: output gains frictions (τig ig

if frictions were replaced by 2004 Indian frictions, gains with the US 2010 frictions, gains if the frictions w , replacing the 1993 and 1999 frictions in were halved, and gains with zero frictions. In the case of τig

India with those in 2004 would increase productivity in 1993 and 1999 by 3.2% and 2.6%, respectively. If the frictions faced by the groups were replaced by those in the US in 2010 the output would increase by 28.3%, 28.3%, and 25.8%, respectively in 1993, 1999, and 2004. A significant increase in the output with the US frictions shows that the groups in India are more disadvantaged relative to the privileged group than those in the US. Cutting the frictions to half in all groups across all occupations increases the output by 15.3%, 14.3%, and 14.4% in the corresponding years. Removing frictions increases aggregate output even more, by 52.9%, 52.4%, and 49.3%, respectively for 1993, 1999 and 2004. As in Brazil, the gains are significant h and τ w cases. both with τig ig

26

Table 17: Counterfactual output gains: India 1993

Indian 2004 friction US 2010 friction Frictions halved No friction

1999 2004 due to τ w 3.2% 2.6% 0.0% 28.3% 28.3% 25.8% 15.3% 14.3% 14.4% 52.9% 52.4% 49.3%

1993

1999 due to τ h 3.2% 3.2% 20.4% 22.3% 15.2% 14.5% 41.7% 41.7%

2004 0.0% 19.1% 13.9% 37.9%

Detailed caste categories. Here, the analysis is conducted with three caste categories (“other”, “ST”, and “SC”) and 19 occupation categories. The detailed information on these 19 occupation categories is given in Table A2 in the online Appendix. Table 18 presents the effects of reducing frictions by number of caste categories. The column headings refer to the number of caste categories. Column 2 shows the output gains for three caste categories and column 3 - for two caste categories. The counterfactual output gains from removing frictions with detailed castes are 51%, 49.9%, and 46.8% in 1993, 1999, and 2004, respectively. The counterfactual output gains from removing frictions with broad caste categories are 53%, 52.5%, and 49.6% in 1993, 1999, and 2004, respectively. This shows that the results with detailed caste categories do not differ much from those with broader categories. The gains due to removing frictions in the case h are also substantial and do not differ across the number of caste categories. of τig

Table 18: Counterfactual output gains in India with detailed and broad caste categories (b) due to human capital

(a) due to labor market

1993 1999 2004

with detailed castes 51.0% 49.9% 46.8%

with broad castes 53.0% 52.5% 49.6%

1993 1999 2004

with detailed castes 39.7% 39.2% 35.2%

with broad castes 41.8% 41.8% 38.1%

Gains in Brazil vs. India The output gains are more substantial for India than for Brazil. Why is the impact of frictions more significant in India? To answer this question, the sub-section proceeds with the experiment where the characteristics of both countries are analyzed and compared. This analysis is aimed at answering a counterfactual question: How would aggregate productivity of India be affected by removing the frictions 27

if it’s country-specific characteristics were replaced by those of Brazil? According to the model, three forces vary across countries and affect productivities of countries: occupational shares, wage gaps, and population shares. What would have happened to Indian output had these been replaced with those of Brazil? In particular, I run the experiment by replacing the forces one by one and study the changes in output from removing the frictions. Table 19 shows the gains in output due to removing the frictions for the cases of the τ w and τ h . The first row presents the baseline scenario with occupational shares, wage gaps and population shares in India. The second row of the table illustrates the counterfactual in which wage gaps of the groups in India are replaced by those of the groups in Brazil. As can be seen, had the wage gaps in India been replaced by those in Brazil, output in India would have been increased by 52.9%, 53.3%, 48.5% in 1993, 1999, and 2004, respectively. The effects of changing the wage gaps in the case of the τ w and τ h are similar to the baseline effects. This indicates that wage gaps faced by the groups in Brazil and India are similar in the corresponding years. The third row of the table would show the counterfactual gains if the Brazilian population shares replaced the Indian population shares. That is, in this counterfactual experiment, the population shares of the four groups for each period in India are replaced with those of the four groups in Brazil in the corresponding periods, holding everything else fixed. This produces the following gains in output: in the w - 62.4%, 62.5%, and 62.2%, and in the case of τ h - 47.4%, 48.6%, and 45.9% in 1993, 1999, case of τig ig

and 2004, respectively. The gains are more significant with the Brazilian population shares than with the Indian population shares. This is not surprising since the share of the disadvantaged groups in India is smaller than the share of the disadvantaged groups in Brazil, and reducing the frictions for the groups with larger population share will have a more substantial effect on output. In contrast to the previous two cases, replacing the occupational shares in India with those in Brazil results in less gain in productivity. Aggregate productivity increases by 34%, 35.1%, and 22.8% in the w , and by 22.1%, 24.7%, and 6.3% in the case of τ h , in 1993, 1999 and 2004. The gains in output case of τig ig

are smaller with the Brazilian occupational shares, implying that the allocation of talent is distorted more in India than in Brazil.

28

Table 19: Counterfactual output gains: India (a) due to frictions in the labor market

Baseline with Brazilian wage gaps with Brazilian population shares with Brazilian occupational shares

1993 52.9% 52.9% 62.4% 34.0%

1999 52.4% 53.3% 62.5% 35.1%

2004 49.3% 48.5% 62.2% 22.8%

1999 41.7% 41.8% 48.6% 24.7%

2004 37.9% 37.8% 45.9% 6.3%

(b) due to frictions in human capital

Baseline with Brazilian wage gaps with Brazilian population shares with Brazilian occupational shares

1993 41.7% 41.7% 47.4% 22.1%

Affirmative action policy in India Affirmative action policy in India is addressed at improving the economic and social opportunities of historically disadvantaged minorities by reserving job positions in the public sector and maintaining quotas in higher education. In particular, 15% and 7.5% of the places in the higher education institutions and government jobs are reserved for members of SC and ST, respectively (Desai and Kulkarni (2008)). Many studies have documented that this regulation has helped to decrease the social and economic disparities between groups in India (see e.g., Borooah et al. (2007); Desai and Kulkarni (2008)). In this sub-section I will examine the role of the affirmative action regulation in reducing the labor market disparities between groups. In particular, I consider that the regulation plays an important role in closing of the output gaps across groups. To see the importance of this story, I conduct the counterfactual experiment where I compare productivity gains for two cases: with public sector and without public sector. The information about the economic sector in which the person is employed is available at National Sample Survey of India. It is used to identify workers in the public sector. Table 20 reports the total number of observations and number of the public sector workers in the sample. The number of public sector workers in the sample is 17 902, 14 531, 19 352, respectively in 1993, 1999, and 2004. The table also shows that the share of SC/STs in public sector has increased over the period - 22.2%, 26.5%, and 33.6%. 29

Table 20: Number of public sector workers in the sample Number of obs. Public sector workers of which: others SC/STs

1993 231 008 17 902

1999 241 615 14 531

2004 258 752 19 352

77.8% 22.2%

73.5% 26.5%

66.4% 33.6%

The results in Table 21 support the findings in other studies that job reservation policy plays an important role in improving the economic opportunities of disadvantaged groups in India. The productivity gains from eliminating frictions in the absence of the public sector workers are higher than with all workers included. The model says, in the τ w case, that the gains with all workers are 51.6%, 50.5%, and 47.5%, and in the absence of public sector workers - 61.7%, 59.6%, 58.5% in 1993, 1999, and 2004, respectively. In the τ h case, the analysis shows similar results. Put differently, by offering better opportunities for lower-caste workers the reservation policy was a possible cause of the reduction in the gap in labor market outcomes between groups. This analysis does not take into account the effect of quotas in university admissions on reducing gaps in human capital across groups or possible spillovers from the public to other sectors of the economy. Thus, it needs to be emphasized that the effect of the policy measured in this study possibly understates the true impact. Table 21: Productivity gains in India (Impact of affirmative action policy) (b) due to frictions in human capital

(a) due to friction in the labor market

All workers 1993 1999 2004

51.6% 50.5% 47.5%

excluding public sector workers 61.7% 59.6% 58.5%

All workers 1993 1999 2004

43.3% 42.7% 38.8%

excluding public sector workers 53.0% 51.5% 49.5%

Notes: Entries in the table show the percentage increase of productivity due to removing frictions. The third column of each table shows the results with excluding public sector workers.

5.3

Robustness analysis This section provides several robustness checks which are undertaken by varying the values of η, θ,

and σ. The exercise is done separately by allowing the frictions in the labor market and the acquisition

30

of human capital. The results in Tables 22 and 23 show the gains in output from removing frictions in 2010 for Brazil and 2004 for India. The first column of Table 22 shows the results with baseline parameter values and rows refer to the results with different parameter values. For instance, the output gains due to removing the frictions with varying η, and holding other parameters fixed are 32.2%, 32.5%, 36.5%, and 32.2%. As can be seen, the results are robust to the variation in η. The gains do not change much with varying σ, as well. However, the output is sensitive to the variation in θ; the difference from the baseline case is the highest at θ = 8.4. A similar pattern can be observed with frictions in human capital. Table 22: Sensitivity of the results to variation in parameters: Brazil (b) due to human capital frictions

(a) due to labor market frictions

η σ θ

η = 0.08 32.2% σ=3 32.2% θ = 3.25 32.2%

η = 0.15 32.5% σ = 4.5 33.9% θ = 4.16 31.1%

η = 0.5 36.5% σ = 15 36.2% θ = 5.6 25.6%

η = 0.1 32.2% σ = 2.75 31.7% θ = 8.4 17.2%

η σ θ

η = 0.08 22.5% σ=3 22.5% θ = 3.25 22.5%

η = 0.15 23.4% σ = 4.5 23.6% θ = 4.16 23.8%

η = 0.5 32.5% σ = 15 25.2% θ = 5.6 20.9%

η = 0.1 22.5% σ = 2.75 22.2% θ = 8.4 13.6%

Notes: Table reports the output gains due to removed frictions. Each row represents a result in which the named parameter varies while others are held fixed.

The output gains in India with different parameter values are displayed in Table 23. For the case with the labor market frictions, the variation in η does not affect the output relative to the baseline case. When changing σ the output varies from the baseline by 17% at σ = 15. Varying θ results in significant differences in the output, the gain is smaller with higher values of the parameter. The pattern of output gains in the case of the frictions in human capital accumulation is comparable to those of labor market frictions. The gains differ from the baseline by 4% at η = 0.5, by 15% at σ = 15 and by as much as 28% when the value of θ is set to 8.4.

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Table 23: Sensitivity of the results to variation in parameters: India (b) due to human capital frictions

(a) due to labor market frictions

η σ θ

η = 0.07 49.3% σ=3 49.3% θ = 4.2 49.3%

η = 0.15 49.2% σ = 4.5 49.9% θ = 4.16 49.1%

η = 0.5 50.2% σ = 15 32.5% θ = 5.6 33.8%

η = 0.1 49.3% σ = 2.75 49.2% θ = 8.4 13.7%

η σ θ

η = 0.07 37.9% σ=3 37.9% θ = 4.2 37.9%

η = 0.15 38.4% σ = 4.5 38.3% θ = 4.16 37.9%

η = 0.5 42.2% σ = 15 23.4% θ = 5.6 28.4%

η = 0.1 38.1% σ = 2.75 37.8% θ = 8.4 10.1%

Notes: Table reports the output gains due to removed frictions. Each row represents a result in which the named parameter varies while others are held fixed.

6

Conclusion The purpose of the paper is to investigate the labor market outcomes by gender and social groups

in Brazil and India and document their effects on aggregate productivity. In both of these countries, there are significant differences in labor market outcomes by gender and social groups. The share of the groups other than white men in Brazil and other men in India in high-skill occupations is low. Only 1–2 % of brown men and women in Brazil, and 1% of women in India are occupied in high-skill occupations. The model predicts that these differences in occupational distribution are the result of the frictions in human capital accumulation and the labor markets. The model allows measuring these frictions from the observed occupational shares and wage gaps between the groups in these countries. The effect of the resulting occupational choice from frictions is significantly negative. The augmented Roy model allows estimating the potential gains to output from reducing the frictions. In particular, the results suggest that lowering the frictions by half would increase the aggregate productivity by 9–13% in Brazil and by 14–15% in India. Removing the frictions increases aggregate productivity by 22–52% in Brazil and 38–53% in India. This is consistent with the findings that the gaps in the labor market outcomes between groups of the population have a strongly negative impact on aggregate productivity. A feature which both countries share is that a significant part of the population is disadvantaged and faces barriers in labor markets and the acquisition of human capital. The analysis above highlights that the gains in output from reducing these barriers are large. This result has important policy implications, suggesting that policies and regulations designed to decrease barriers in the labor markets and the acquisition of human capital can have high social returns and increase the overall well-being of the population. 32

References Acemoglu, D., 1995. Reward structures and the allocation of talent. European Economic Review 39 (1), 17–33. Altonji, J., Blank, R., 12 1999. Race and gender in the labor market. Handbook of Labor Economics 3, 3143–3259. Antecol, H., Bedard, K., 2004. The racial wage gap: The importance of labor force attachment differences across brown mexican, and white men. Journal of Human Resources 39 (2), 564–583. Arabsheibani, R., Carneiro, F., Henley, A., 2003. Gender wage differentials in Brazil: Trends over a turbulent era. World Bank working paper. Arcand, J., DHombres, B., 2004. Racial discrimination in the Brazilian labour market: Wage, employment and segregation effects. Journal of International Development 16 (8), 1053–1066. Bandyopadhyaya, D., King, I., Tang, X., 2019. Human capital misallocation, redistributive policies, and TFP. Journal of Macroeconomics 60, 309–324. Baumol, W., 1990. Entrepreneurship: Productive and unproductive and destructive. Journal of Political Economy 98 (5), 893–921. Bergad, L., 2007. The comparative histories of slavery in Brazil, Cuba, and the United States. Cambridge University Press. Bloom, D., Canning, D., Fink, G., Finlay, J., 2009. Fertility, female labor force participation, and the demographic dividend. Journal of Economic Growth 14 (2), 79–101. Borooah, V., Dubey, A., Iyer, S., 2007. The effectiveness of jobs reservation: Caste, religion, and economic status in India. Development and Change 38 (3), 423–455. Cavalcanti, T., Tavares, J., 2015. The output cost of gender discrimination: A model-based macroeconomic estimate. The Economic Journal 126, 109–134. Center, M., 01 2017. Integrated public use microdata series, international: Version 6.5 [dataset]. Cuberes, D., Teignier, M., 2016. Aggregate effects of gender gaps in the labor market: A quantitative estimate. Journal of Human Capital 10 (1), 1–32. Desai, S., Kulkarni, V., May 2008. Changing educational inequalities in india in the context of affirmative action. Demography 45 (2), 245–270. Deshpande, A., 2011. The grammar of caste: Economic discrimination in contemporary India. Oxford University Press. Deshpande, A., Goel, D., Khanna, S., 2018. Bad karma or discrimination? Male-women wage gaps among salaried workers in India. World Development 102, 331–344. Deshpande, M., 2010. History of the Indian caste system and its impact on India today. Social sciences. URL https://digitalcommons.calpoly.edu/socssp/44 Eaton, J., Kortum, S., 2002. Technology, geography and trade. Econometrica 70 (5), 1741–1779. Ferrant, G., Luca Maria, P., Keiko, N., 2014. Unpaid care work: The missing link in the analysis of gender gaps in labour outcomes. OECD Development Centre. URL https://www.oecd.org/dev/development-gender/Unpaid_care_work.pdf Heckman, J., 1979. Sample selection bias as a specification error. Econometrica 47 (1), 153–161. 33

Hsieh, C., Hurst, E., Jones, C., Klenow, P., 2013. The allocation of talent and U.S. economic growth. In: NBER Working Paper. Hsieh, C., Klenow, P., 2009. Misallocation and manufacturing TFP in China and India. Quarterly Journal of Economics 124 (4), 1403–1448. Klasen, S., 2002. Low schooling for girls, slower growth for all? Cross-country evidence on the effect of gender inequality in education on economic development. World Bank Economic Review 16, 345–373. Klasen, S., Lamanna, F., 2009. The impact of gender inequality in education and employment on economic growth: New evidence for a panel of countries. Feminist Economics 15 (3), 91–132. Kumar, J., 07 2011. Blocked by caste: economic discrimination in modern india, edited by Sukhdeo Thorat and Katherine S. Newman. South Asian History and Culture 2, 431–433. Lagerlof, N., 2003. Gender equality and long-run growth. Journal of Economic Growth 8, 403–426. Loureiro, P., Carneiro, F., Sachsida, A., 2004. Race and gender discrimination in the labor market: An urban and rural sector analysis for Brazil. Journal of Economic Studies 31 (2), 129–143. Lovell, P., 1993. The geography of economic development and racial discrimination in Brazil. Development and Change 24 (1), 83–102. Lovell, P., Wood, C., 1998. Skin color, racial identity, and life chances in Brazil. Latin American Perspectives 25 (3), 90–109. Macinko, J., Mullachery, P., Proietti, F., Lima-Costa, L., 2012. Who experiences discrimination in Brazil? Evidence from a large metropolitan region. International Journal for Equity Health 11 (1), 80. Madalozzo, R., 2010. Occupational segregation and the gender wage gap in Brazil: An empirical analysis. Economia Aplicada 14 (2), 147–168. Madeshwaran, S., Attewell, P., 2007. Caste discrimination in the Indian urban labour market: Evidence from the national sample survey. Economic and Political Weekly 42 (41), 4146–4153. Maity, B., 2017. Comparing health outcomes across scheduled tribes and castes in India. World Development 96, 163–181. McFadden, D., 1973. Conditional logit analysis of qualitative choice behavior. Frontiers of Econometrics, 105–142. URL https://eml.berkeley.edu/reprints/mcfadden/zarembka.pdf Morrison, A., Raju, D., Sinha, N., 2007. Gender equality, poverty and economic growth. In: Policy Research working paper. Mulligan, C., Rubinstein, Y., 2008. Selection investment, and women’s relative wages over time. Quarterly Journal of Economics 123 (3), 1061–1110. Munshi, K., Rosenzweig, M., 2005. Economic development and the decline of rural and urban communitybased networks. Economics of Transition 13 (3), 427–443. Munshi, K., Rosenzweig, M., 2006. Traditional institutions meet the modern world: Caste, gender and schooling choice in a globalizing economy. American Economic Review 96 (4), 1225–52. Munshi, K., Rosenzweig, M., 2016. Networks and misallocation: Insurance, migration, and the ruralurban wage gap. American Economic Review 106 (1), 46–98.

34

Rodgers, G., Amrita, D., Janine, R., Sunil, K., Alakh, N., 2013. The challenge of inclusive development in rural Bihar. Institute of Human Development. Roy, A., 1951. Some thoughts on the distribution of earnings. Oxford economic papers 3 (2), 135–146. Telles, E., 1992. Residential segregation by skin color in Brazil. American Sociological Review 57 (2), 186–197. Telles, E., 1995. The structural sources of socioeconomic segregation in Brazil. American Journal of Sociology 100 (5), 1199–1223. Telles, E., 2004. Race in another America: The significance of skin color in Brazil. Princeton University Press. Thorat, S., Attewell, P., 2007. The legacy of social exclusion: A correspondence study of job discrimination in India. Economic and Political Weekly 42 (41), 4141–4145.

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