metadata: authors: 0: author: G. Reza Arabsheibani; Francisco Galrao Carneiro;
count: Brazil
docna: 0: docna: Human capital and earnings inequality in
            Brazil, 1988-98 : quantile regression evidence
docty: Policy Research Working Paper
subtopic: Labor Markets,Education Reform and Management,Educational Sciences,International Trade and Trade Rules,Rural Labor Markets
teratopic: Education
abstracts: cdata!: The authors undertake an empirical
            examination of rates of return to human capital for men in
            Brazil, through the period of macroeconomic stabilization
            and trade liberalization, using data from the 1988, 1992,
            and 1998 Brazilian household surveys (Pesquisa Nacional por
            Amostra de Domic�os, PNAD). The authors estimate
            simultaneous quantile equations to gain an insight on the
            impact of human capital on wages across the hourly earnings
            distribution. They conclude that there is evidence of
            growing inequality in rates of return to education in
            Brazil. But the authors find evidence that education is no
            longer used as a screening device in the labor market, but
            rather rewarded for its innate association with higher
            productivity. Although increases in rates of return to
            education have been more pronounced at the top of the
            earnings distribution, this has not led to increased
            inequality. This is because the levels of education and
            other labor market-rewarded endowments have increased and
            offset the rate of return effect.
geo_reg_key: 119228,517191
repnme: repnme: Human capital and earnings inequality in
            Brazil, 1988-98 : quantile regression evidence
display_title: Human capital and earnings
            inequality in Brazil, 1988-98 : quantile regression evidence
listing_relative_url: /research/2003/10/2693914/human-capital-earnings-inequality-brazil-1988-98-quantile-regression-evidence
topicv3: Education, Skills Development and Labor
            Market m1326135 1484,Levels of Education m1326138 478,Labor Markets m1326488 855,Trade m1326924 698,Education m1327048 470
publishtoextweb_dt: 2010-07-01T00:00:00Z
historic_topic: Education
no_of_pages: 21
model_extractions: # WPS3147

## Human Capital and Earnings Inequality in Brazil, 1988–1998: Quintile Regression Evidence

### G. Reza Arabsheibani,<sup>†</sup> Francisco Galrão Carneiro,<sup>*</sup> and Andrew Henley<sup>†</sup>

#### **Abstract**

This paper undertakes an empirical examination of rates of return to human capital for men in Brazil through the period of macroeconomic stabilization and trade liberalization, using data from the PNAD household surveys. Simultaneous quintile equations are estimated to gain a picture of the impact of human capital on earnings across the hourly earnings distribution. We conclude that there is evidence for growing inequality in rates of return to education in Brazil. However we find evidence that education is no longer used as a screening device in the labor market, but rather rewarded for its innate association with higher productivity. Although increases in rates of return to education have been more pronounced at the top of the earnings distribution, this has not led to increased inequality. This is because levels of education and other labor market-rewarded endowments have increased and offset the rate of return effect.

Keywords: Earnings, Human Capital, Inequality, Quintile Regression JEL Classification: J31, I20, C14

- School of Management and Business, University of Wales Aberystwyth, UK.

- The World Bank – Poverty Reduction and Economic Management (PREM).

World Bank Policy Research Working Paper 3147, October 2003 _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. Policy Research Working Papers are available online at http://econ.worldbank.org._
1

# **1. Introduction**

A far-reaching program of macroeconomic stabilization and rapid liberalization of trade in a developing economy are likely to have important and widespread consequences for the labor market. However there appears to be little evidence from a number of recent examples for the Stolpher-Samuelson theorem that trade liberalization in a developing economy will increase the relative demand for relatively abundant low-skilled labor, and so reduce income inequality.<sup>1</sup> The Brazilian case has been particularly well-researched and there is an emerging conventional wisdom that, despite the upheavals of price and exchange rate stabilization and tariff reduction, earnings inequality in Brazil has remained stubbornly high between 1980 and the present (Dickerson et al., 2001; Green et al., 2001). One explanation for this is that steadily rising average levels of schooling have offset increased demand for skilled labor. At first glance this is a persuasive suggestion. In the sample of Brazilian male workers used in the present paper, average years of education for men have risen by almost one year between 1988 and 1998, and the rate of illiteracy has fallen from 16.6 to 11.6 per cent. Research has also established that rates of return to education in Brazil are low for primary schooling but very much higher for advanced levels of education. This may contribute significantly to Brazil’s highly unequal distribution of earnings (Lam and Levinson, 1992). However while this may be true at the mean of the earnings distribution, little is known about rates of return across the range of this wide distribution. Furthermore, there is evidence that inequality peaked in the late 1980s and has been falling since, in contrast to what has happened elsewhere in the developing world and in particular in Latin America. If so this points to the possibility that important changes in the levels of and rates of return to education have taken place since the late 1980s. Consequently the reform experience may have been a very different one for workers in different positions in the income distribution.

Protected product markets in the developing world prior to the early 1990s appear to have been associated with protected labor markets in Brazil in particular (Carneiro and Henley, 1998; Carneiro, 1998). Liberalization of the economy to overseas trade exposes domestic producers significantly to greater product market competition and this in turn serves to introduce much stronger forces of competition to the labor market. An important consequence of this process is that education ceases to serve as a device for rationing (screening) workers’ access to economic rents. Rather education, if it reflects the acquisition of internationally marketable skills, begins to be associated with inherent productive potential. Whether the benefits of this process accrue more to those with lower or higher levels of education is a matter for empirical investigation. In common with other Latin American cases, such as Mexico, there is evidence that tariff protection was highest in sectors where employment of the least skilled was dominant (Mollick, 2002; Arbache and Corseuil, 2001). Overall there appears to have been little or no assessment of whether apparently high rates of return to formal education represent a genuine return to valuable skills acquired at school, or simply reflect the ability of the more educated to signal innately higher productivity to the labor market.

This paper undertakes an empirical examination of these questions through the estimation of simultaneous quintile human capital equations. This is in order to gain a picture of the impact of human capital on earnings across different point of the distribution of earnings in Brazil. This exercise is performed a sample of male workers drawn from 1 See discussion and further references in Green et al., 2001.
2 household survey data from before, during and after the stabilization and liberalization program of the early 1990s. We conclude that there is evidence for growing inequality in rates of return to education in Brazil, but evidence that education is no longer used as a screening device in the labor market, but rather rewarded for its innate association with higher productivity. Although increases in rates of return to education have been more pronounced at the top of the earnings distribution, this has not led to increased inequality. This is because levels of education and other labor market-rewarded endowments have increased and offset the rate of return effect.

The remainder of the paper is structured as follows. Section 2 discusses the empirical method used. Section 3 describes the data source used. Section 4 presents results and section 5 concludes.

# **2. Empirical Approach**

An important issue on the productivity of education concerns whether formal education acts as a screen, separating more able (and educated) individuals from the less able (and educated). The screening hypothesis (Arrow, 1973) observes that at the point of hiring workers’ productivity is unknown to employers and argues therefore that employers use education as a proxy for latent productivity. In competitive sectors of the labor market productivity will matter and so returns to education will be higher. In non-competitive sectors of the labor market returns to subsequent education after hiring will be lower. It is therefore possible that the value of education as a screen may vary across the earnings distribution because of differing degrees of competition. In particular screening may be more important in the top of the distribution, where insider power may be more important.

The empirical literature on screening distinguishes between the weak form and strong form of the hypothesis (Psacharopoulos, 1979; Arabsheibani and Rees, 1998). The weak form states that employers will pay a higher initial salary to recruits with higher levels of education, but is agnostic about the shape of the subsequent experience-earnings profile. The strong form states that employers will continue to pay high salaries even after observing working on the job, because education continues to enhance productivity as experience on the job rises. However the experience-earnings profiles of an educated worker will converge over time with that of a non-educated worker, as the original hiring “mistake” is gradually corrected. Psacharopoulos (1979) proposes what has become known as the P(sacharopoulos) test as a method of empirical investigation.

Assume that log hourly earnings for individual _i_ , _yi_ , are determined according a Mincerian earnings function of the following form: where _S_ is years of education, _E_ is years of experience, _Z_ are other socio-economic variables affecting earnings, _aj_ and _b_ are coefficients and _u_ is a disturbance term. The inclusion of the interaction term between years of education and years of experience provides a straightforward test of convergent experience-earnings profiles under the strong screening hypothesis (Lee, 1980). If the hypothesis holds then _a_ 5 < 0, otherwise _a_ 5 ≥ 0.

Previous research has shown that modeling average earnings (i.e. OLS) fails to reveal that the effect of education on earnings is non-constant across the conditional wage
3 distribution (Buchinsky, 1994, 1998; Machado and Mata, 2001; Bauer and Haisken-DeNew, 2001; Hartog, Pereira and Vieira, 2001). This reinforces the need to investigate the screening hypothesis across the earnings distribution. An appropriate empirical strategy is to fit the earnings model across different points in the conditional sample distribution, using the quintile regression method. This was first introduced by Koenker and Bassett (1978). Assume _yi_ , _i_ = 1,..., _n_ , is a sample of observations on log earnings, and that _Xi_ is a _K_ x 1 vector comprising the education, experience and other control characteristics contained on the righthand side of equation (1). The quintile regression model can be expressed as: where Quint _θ_ ( _yi_ | _Xi_ ) denotes the quintile _θ_ of log earnings conditional on the regressor vector. Following Koenker and Bassett (1978), the regression quintile _θ_ can be defined as the solution to the problem:

where _ρθ_ (.) is known as the “check function” and is defined as: Estimation is by minimizing the sum of weighted absolute deviations and can be performed using linear programming methods (Buchinsky 1998). An estimated variancecovariance matrix for the chosen system of quintile regressions is obtained using a bootstrap re-sampling method using Stata Release 7 (StataCorp 2001). Quintile regression coefficients can be interpreted by considering the partial derivative of the conditional quintile with respect to a particular regressor. This equates to the marginal change in the _θ_ th conditional quintile due to a marginal change in the regressor. It is however important to note that a sample individual who is in the _θ_ th conditional quintile may no longer remain in that quintile if his or her characteristic measured by the particular regressor changes. So, for example, rates of return to additional years of schooling or experience as captured by the estimated coefficients apply to an individual remaining in a particular conditional quintile.

# **3. Data Source and Description**

The present paper uses data drawn from the 1988, 1992 and 1998 Brazilian household surveys (Pesquisa Nacional por Amostra de Domicílios, PNAD). The PNADs are a series of nationally representative household surveys conducted more or less annually since 1976, using a consistent methodology by the Instituto Brasileiro de Geografia e Estastistica (IBGE). All members of each household over ten years in age are asked detailed questions concerning their labor market activity during one week in September. Each survey covers a nationally representative sample of households. The sample size has risen progressively from 69,066 households in 1988, to 94,171 in 1992 and 112,434 in 1998. The present paper draws from these households the sample of employed men between the ages of 18 and 65, who report earnings and hours of work data and information on human capital and the other controls used for estimation purposes. This results in a sample of 65,002 in 1988, 67,880 in 1992 and 74,003 in 1998. The 63% increase in the number of households sampled over the decade
4 appears, therefore, to have resulted in better coverage of households with fewer or no employed males.

Hourly earnings are defined as reported monthly earnings divided by 4.33 and then divided by reported weekly hours of work. Table 1 reports summary descriptive information for each year on log (hourly earnings), along with descriptive statistics on years of education, years of experience<sup>2</sup> (defined as age - (6 + years of schooling)) and the regional, metropolitan, urban and racial composition of the samples. Real hourly earnings fall between 1988 and 1992 but rise thereafter. The standard deviation in hourly earnings drops somewhat over the period, particularly between 1988 and 1992. This narrowing in the male earnings distribution is further apparent in the movement in the gap between the 75<sup>th</sup> and 25<sup>th</sup> percentiles, and in particular in the narrowing in the gap between the 90<sup>th</sup> and 10<sup>th</sup> percentiles, shown at the bottom of the Table.

Sample average years of schooling increase by 0.9 years between 1988 and 1998. Undoubtedly this is the result of younger more educated male cohorts entering the labor force. There is also a reduction in average experience of just over 1 year between 1988 and 1998. In part this is consistent with the increase of the average length of time spent in education. However the mean and median product of education and experience both show a marked increase.

Figures 1 to 3 describe the log hourly earnings distribution in each year through the estimation of univariate Kernel density functions.<sup>3</sup> Vertical lines of each figure identify the position of the 10<sup>th</sup> , 25<sup>th</sup> , 50<sup>th</sup> , 75<sup>th</sup> and 90<sup>th</sup> percentiles of the distribution in each case. Visual inspection reveals that the dispersion of these percentile points narrows over time. A further feature of note is that in 1988 and in 1992 in particular the shape of the distribution reveals a mode at almost exactly the 25<sup>th</sup> percentile. However by 1998 the shape of the distribution changes considerably, with the mode moving much closer to the median.

# **4. Empirical Results**

Key results (coefficients _a_ 1 to _a_ 5) for earnings function estimates for 1988, 1992 and 1998 are presented in Table 2. For each year the table firstly shows OLS estimates and then simultaneous quintile regression estimates for the 10<sup>th</sup> , 25<sup>th</sup> , 50<sup>th</sup> , 75<sup>th</sup> and 90<sup>th</sup> quintiles. For each year a Wald test for the null hypothesis of equality of the coefficients across the quintiles is decisively rejected. The reported coefficients suggest considerable variation in the education-earnings and experience-earnings profiles at the different points in the earnings distribution. We shall discuss rates of return to education and experience shortly.

The education-experience interaction coefficient is negatively signed and statistically significant at the sample mean (OLS) and for the 10<sup>th</sup> , 75<sup>th</sup> and 90<sup>th</sup> conditional quintiles for 1988 and 1992. However at the sample mean (OLS) and for every quintile below the 90<sup>th</sup> the coefficient is positively signed and statistically significant. This suggests that prior to the mid 1990s for those at the very bottom and those in the higher part of the earnings distribution 2 The 1992 and 1998 surveys provide information on experience with the current employer. However the explanatory power of the earnings equations are substantially lower using this definition.

3 Estimated using the Epanechnikov kernel estimator with 200 evaluation points over range of the distribution.
5 formal education appears to have acted as a signal for innate ability rather than provided human capital. Experience-earnings profiles appear to converge, albeit slowly, after initial hiring. However by 1998 there appears to have been a shift in the way the labor market functions, with education having more inherent productivity-enhancing value.<sup>4</sup> Tables 3 and 4 report rates of return to additional years of education and experience respectively, for each year at the mean (OLS) and for each conditional quintile. Marginal rates of return are calculated for levels of education and experience spread across the sample range. The estimates reveal that rates of return to additional years of education are much higher at already high levels of education. So for example an additional year of education for someone with only 2 years of formal education will yield a 12 to 13 percent increase in earnings at the mean in 1988, a 10 to 11 percent increase in 1992 and only a 7 to 8 percent increase in 1998. By contrast at the higher levels of initial education the rates of return to additional education are much higher: 23 to 24 percent at the mean in 1988, 20 to 22 percent in 1992 and rising to 26 percent in 1998. Clearly education to college level has become significantly more valuable in the labor market by the late 1990s. The “stretching out” of returns at the top end of the distribution, and at the top of the range of education and experience arises because of the change in the sign of the interaction effect between the two, indicative of a move from non-competitive screening behavior for more highly paid jobs, to a genuinely competitive labor market in which education is rewarded because of its association with productivity.

Table 3 also reveals that rates of return to an additional year of education are between 4 and 7 percentage points higher at the 90<sup>th</sup> quintile compared to the 10<sup>th</sup> . If this fact is combined with the change over time in the marginal rate of return to education, it makes for growing inequality in the returns to education and points to a very important force for greater earnings inequality. However set against this we must also note, from Table 1, that average levels of education in the male population have been rising over the period in question. An increased supply of educated workers does not appear to have compressed earnings. Rather the increase in demand for skills appears to have outstripped the increase in supply, with the effect that the price has risen. The Brazilian economy appears, in the mid 1990s, to have moved from one in which at the top of the earnings distribution, education is used to signal inherent ability to one where education has become genuinely productive.

Table 4 shows how marginal rates of return to experience change over the sample range. There are three features to the Table. Firstly rates of return fall sharply as experience increases – an indication of the quantitative importance of the statistically significant squared term in experience in all the reported regressions. Between 30 and 40 years of accumulated experience additional experience starts to attract a negative rate of return, albeit a very small one. Secondly there is a progressive, though again small, decline in the marginal return to experience over time, particularly between 1988 and 1992. So for example a man in with 11 years of education and 10 years of experience at the median of the earnings distribution saw the marginal return to the 11<sup>th</sup> year of experience fall from 5.3 percent to 4.2 percent between 1988 and 1992. At the lower quintiles the fall appears to have persisted beyond 1992. This may be indicative of rapid change in the economy, as new skills become important and experience less so. Thirdly a marginal gain in experience is more highly rewarded at the top of the earnings distribution compared to the bottom. So for example a man with 11 years of 4 1998 does not appear to be an “rogue” observation in this respect. We find identically signed and sized coefficients using data from the 1997 PNAD.
6 education and 10 years of experience at the 10<sup>th</sup> quintile enjoyed a marginal return of 4.2 percent to an additional year of experience in 1988. At the 90<sup>th</sup> quintile the return would have been 6.5 percent. There is a similar difference across the distribution, although at lower absolute levels, in 1992 and 1998.

In Table 5 we report the different quintiles of the earnings distribution for each year, along with various inter-quintile differences. Actual values from each empirical distribution are reported in columns (1), (3) and (6). Between 1988 and 1992 the distribution shifts leftwards – at all points reported in the distribution, except the 25<sup>th</sup> percentile, workers experienced falls in real earnings. Between 1992 and 1998 the reverse is true. Columns (2), (4) and (7) report conditional quintiles computed from the regression model estimates, setting years of education, experience and the other controls to their average values. In effect these columns show a hypothetical earnings distribution under conditions in which all individuals have identical human capital and other characteristics. The difference between the actual and corresponding conditional distribution shows the extent to which inequality arises due to differences in endowed characteristics. A comparison of the 90-50 gaps with the 50-10 gaps shows that differences in endowed characteristics are much more important in the top half of the earnings distribution. Columns (5), (8) and (9) report points on counterfactual distributions that show how earnings would have looked in 1992 and 1998 if average levels of human capital and other characteristics had remained unchanged from the early year(s). The purpose of this is to decompose shifts in the predicted distribution into a component caused by changes in endowed characteristics from that caused by changes in the rates of return to education, experience and the other characteristics. If all men had 1988 average endowed characteristics in 1992 then the 90-10 gap would have been 1.87 rather than 1.82, with the difference more pronounced in the top half of the distribution (90-50 gap) than in the bottom half (50-10 gap). If all men had 1988 average endowed characteristics in 1998 then the 90-10 gap would have been 1.92 rather than 1.71. Here the difference is apparent in both the top and the bottom of the distribution, much is still greater in the 50-10 gap. Consequently we see that improved human capital has contributed to the narrowing in earnings inequality over the ten-year period.

Table 6 illustrates this decomposition exercise further by showing the changes in the actual (empirical) distributions and in the conditional distributions between 1988 and 1992, 1992 and 1998 and 1988 and 1998. Between 1988 and 1992 the real negative growth in earnings is sharpest at the top of the distribution (column 1), and this is what causes inequality to fall between these years. Column (2) shows that the same is true in the movement in the conditional distribution. The change in average levels of endowed characteristics hits those at the top of the distribution hardest. On the other hand if we hold mean characteristics fixed at their 1988 levels, as in column (3), then we find that improved rates of return to human capital and attributes benefit workers across the distribution. However, this benefit is larger at the bottom than at the top. Between 1992 and 1998 all points in the distribution experience real earnings growth of between 17 and 25 per cent. However the changes in the conditional distribution, reported in column (5), are largest at the bottom, and fall steadily as we move up. Consequently improvements in human capital and other characteristics have, since 1992, had most benefit for the lowest paid workers. On the other hand column (6) reveals that higher rates of return to characteristics have most benefit on earnings for those higher up the earnings distribution. Over the full ten-year period those at the bottom of the distribution have enjoyed fastest real earnings growth (column 7). The reason for this in clear in column (8) – they have benefited most from the general improvement in human capital and other remunerated characteristics in the economy. They
7 have also seen some improvement in rates of return, particularly those around the 25<sup>th</sup> percentile, and these improvements have not been enjoyed by those at the top of the earnings distribution.

# **5. Conclusions**

This paper has conducted an empirical investigation of the impact of education and experience across the earnings distribution for Brazilian men over the period of recent macroeconomic and trade reform. Rates of return to additional years of schooling are very high in Brazil and at the average vary between 7 and 26 per cent per year depending on levels of experience and prior education. However, rates of return to additional education are typically 4 to 7 percentage points higher at the 90<sup>th</sup> percentile of the earnings distribution compared to the 10<sup>th</sup> percentile. We find strong evidence of growing inequality in _returns_ to additional years of education through the period under investigation. Since the 1980s skills have become more highly rewarded and seniority (experience) less so. Overall our results point to improved forces of competition in the labor market particular since 1992. This is particularly so because there appears to have been a shift in the role of educational qualifications from rationing or screening workers into better paid jobs towards education being rewarded because of their inherent association with higher productivity. This appears to be particularly the case in the top and bottom, as opposed to the center, of the distribution.

If the story ended there then we would expect to find strong evidence for growing earnings inequality. However this has not been a consequence of economic reform in Brazil. The reason is because significant improvements in levels of human capital have taken place, and these have contributed to a narrowing of earnings inequality. Between 1988 and 1992 during the period of high inflation and abortive stabilization attempts real earnings fell for almost all. These falls were most severe further up the earnings distribution. Improvements in _levels_ of human capital and other remunerated characteristics were of most benefit to the less well paid, particularly since 1992. These improvements were significant enough to offset the adverse distributional impact of increased rates of return to education at the top of the distribution. Trade liberalization appears to have stimulated the acquisition of human capital for the less well paid. Higher rates of return, combined with an increased recognition that educational qualifications are of inherent value rather than of use purely as a signaling device, may well have stimulated increased human capital investment, alongside government and private willingness to pay.
8

# **References**

- Arabsheibani, G.R. and Rees, H. (1998), “On the weak versus strong version of the screening hypothesis: a re-examination of the _P_ -test for the U.K.”, _Economics of Education Review_ , 17(2): 189-192.

- Arbache, J.S. and Corseuil, C.H. (2001), “Liberalização Comercial e Estruturas de Emprego e Salário”, IPEA Working Paper No. 801, Rio de Janeiro.

Arrow, K. (1973), “Higher education as a filter”, _Journal of Public Economics_ , 2: 193-216

- Bauer, T.K. and Haisken-DeNew, J.P. (2001), “Employer learning and the returns to schooling”, _Labor Economics_ , 8(2): 161-180.

- Buchinsky, M. (1994) “Changes in the U.S. wage structure 1963-1987: application of quintile regression”, _Econometrica_ , 62(2): 405-458.

- Buchinsky, M. (1998), “Recent advances in quintile regression models: a practical guide for empirical research”, _Journal of Human Resources_ , 33(1): 88-126.

- Carneiro, F.G. (1998), “Productivity effects in Brazilian wage determination”, _World Development_ , 26(1): 139-153.

- Carneiro, F.G. and Henley A. (1998), “Wage determination in Brazil: the growth of union bargaining power and informal employment”, _Journal of Development Studies_ , 34(4): 117-138.

- Dickerson, A., Green, F. and Arbache, J.S. (2001), “Returns to education and trade liberalization: a pseudo-panel approach”, _mimeo_ , University of Kent at Canterbury.

- Green, F., Arbache, J.S. and Dickerson, A. (2001), “A picture of wage inequality and the allocation of labor through a period of trade liberalization: the case of Brazil.”, _World Development_ , 29(11), 1923-1940.

- Hartog. J.,  Pereira, P.T. and Vieira, J.A.C. (2001), “Changing returns to education in Portugal during the 1980s and early 1990s: OLS and quintile regression estimators”, _Applied Economics_ , 33(8): 1021-1037.

Koenker, R. and Bassett, G. (1978), “Regression quintiles”, _Econometrica_ , 50:43-61.

- Lam, D. and Levinson, D. (1992), “Declining inequality in schooling in Brazil and its effects on the inequality of earnings”, _Journal of Development Economics_ , 37: 199-225.

- Lee, K-H (1980), “Screening, ability and the productivity of education in Malaysia”, _Economics Letters_ , 5: 189-193.

- Machado, J.A.F. and Mata, J. (2001), “Earnings functions in Portugal 1982-1994: evidence from quintile regressions”, _Empirical Economics_ , 26(1): 115-134.

- Mollick, A.V. (2002), “Rising Relative Wages, Technology and Trade in Mexican Manufacturing Sector, 1987-1995”, _mimeo_ , ITESM, Monterrey, Mexico.
9

- Psacharopoulos, G. (1979), “On the weak versus the strong version of the screening hypothesis”, _Economics Letters_ , 4: 181-185.

- StataCorp (2001), _Stata Statistical Software: Release 7.0_ , College Station TX: Stata Corporation.
10

# **Table 1 Descriptive Statistics**

||||1988<br>N=65002|||1992<br>N=67880|||1998<br>N=74003||
|---|---|---|---|---|---|---|---|---|---|---|
|||Mean|S.D.|Median|Mean|S.D.|Median|Mean|S.D.|Median|
|Log (hourly|earnings)|0.447|1.090|0.351|0.375|1.015|0.294|0.596|1.008|0.498|
|Education (|years)|5.440|4.368|4.000|5.651|4.310|5.000|6.332|4.357|5.000|
|Experience|(years)|23.839|13.654|21.000|22.938|13.292|21.000|22.845|13.206|21.000|
|Education*|Experience|99.411|93.775|78.000|101.661|93.570|81.000|117.259|102.804|96.000|
|Region:|North|0.082|0.274|0|0.065|0.246|0|0.071|0.257|0|
||Centre East|0.129|0.335|0|0.114|0.317|0|0.118|0.322|0|
||South|0.161|0.368|0|0.186|0.389|0|0.184|0.387|0|
||North East|0.286|0.452|0|0.270|0.444|0|0.282|0.450|0|
|Metropolita|n|0.407|0.491|0|0.397|0.489|0|0.397|0.489|0|
|Urban||0.777|0.416|1|0.814|0.389|1|0.820|0.384|1|
|Race:|Black|0.055|0.228|0|0.058|0.235|0|0.062|0.242|0|
||Mixed|0.417|0.493|0|0.406|0.491|0|0.410|0.492|0|
|Log (hourly<br>– Log (hour|earnings)q90<br>ly earnings)q10||2.7081|||2.5107|||2.4849||
|Log (hourly<br>– Log (hour|earnings)q75<br>ly earnings)q25||1.3732|||1.2432|||1.2585||

Notes: Earnings are in constant 1998 consumer prices. The base region is the South East, and the base racial group is white.
11

# **Table 2 Regression Estimates**

||||19|88|||||19|92|||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||_OLS_|Q10|Q25|Q50|Q75|Q90|_OLS_|Q10|Q25|Q50|Q75|Q90|
|Education|0.1185*|0.0891*|0.0686*|0.0959*|0.1301*|0.1644*|0.0960*|0.0840*|0.0703*|0.0767*|0.1059*|0.1127*|
||(0.0043)|(0.0065)|(0.0058)|(0.0048)|(0.0057)|(0.0081)|(0.0041)|(0.0077)|(0.0066)|(0.0046)|(0.0050)|(0.0079)|
|Education<sup>2</sup>|0.0040*|0.0038*|0.0055*|0.0052*|0.0041*|0.0026*|0.0039*|0.0028*|0.0043*|0.0049*|0.0042*|0.0043*|
||(0.0002)|(0.0003)|(0.0003)|(0.0002)|(0.0002)|(0.0004)|(0.0002)|(0.0003)|(0.0003)|(0.0002)|(0.0002)|(0.0004)|
|Experience|0.0778*|0.0669*|0.0622*|0.0725*|0.0843*|0.0948*|0.0632*|0.0556*|0.0539*|0.0582*|0.0684*|0.0744*|
||(0.0016)|(0.0026)|(0.0019)|(0.0018)|(0.0022)|(0.0029)|(0.0015)|(0.0028)|(0.0022)|(0.0018)|(0.0016)|(0.0026)|
|Experience<sup>2</sup>|-0.0010*|-0.0009*|-0.0009*|-0.0010*|-0.0011*|-0.0012*|-0.0008*|-0.0008*|-0.0008*|-0.0008*|-0.0009*|-0.0009*|
||(0.00002)|(0.00004)|(0.00003)|(0.00003)|(0.00003)|(0.00004)|(0.00002)|(0.00004)|(0.00003)|(0.00002)|(0.00002)|(0.00004)|
|Education*|-0.0004*|-0.0006*|0.0001|0.00001|-0.0003*|-0.0006*|-0.0004*|-0.0006*|-0.0002|-0.0001|-0.0004*|-0.0004*|
|experience|(0.0001)|(0.0001)|(0.0001)|(0.0001)|(0.0001)|(0.0002)|(0.0001)|(0.0002)|(0.0001)|(0.0001)|(0.0001)|(0.0002)|
|N|||650|02|||||67|880|||
|R<sup>2</sup>/Pseudo R<sup>2</sup><br>|0.484|0.229|0.248|0.291|0.327|0.339|0.448|0.227|0.230|0.270|0.296|0.306|
|Wald χ<sup>2</sup>(19)||||846.09||||||1440.61|||
12

**Table 2 (continued)**

||||19<br>|98<br>|||
|---|---|---|---|---|---|---|
||OLS|Q10|Q25|Q50|Q75|Q90|
|Education<br>|0.0410*<br>(0.0038)|0.0197*<br>(0.0060)|0.0175*<br>(0.0054)|0.0254*<br>(0.0047)|0.0552*<br>(0.0045)|0.0749*<br>(0.0053)|
|Education<sup>2</sup>|0.0067*<br>(0.0002)|0.0061*<br>(0.0003)|0.0067*<br>(0.0002)|0.0074*<br>(0.0002)|0.0069*<br>(0.0002)|0.0063*<br>(0.0003)|
|Experience<br>|0.0544*<br>(0.0014)|0.0429*<br>(0.0022)|0.0452*<br>(0.0017)|0.0515*<br>(0.0016)|0.0613*<br>(0.0014)|0.0672*<br>(0.0024)|
|Experience<sup>2</sup>|-0.0007*<br>(0.00002)|-0.0007*<br>(0.00003)|-0.0007*<br>(0.00002)|-0.0007*<br>(0.00002)|-0.0008*<br>(0.00002)|-0.0008*<br>(0.00004)|
|Education*<br>experience|0.00024*<br>(0.00007)|0.00025+<br>(0.00013)|0.00053*<br>(0.00010)|0.00062*<br>(0.00010)|0.00027*<br>(0.00008)|0.00007<br>(0.00013)|
|N<br>|||74|003|||
|R<sup>2</sup>/Pseudo R<sup>2</sup><br>|0.490|0.217|0.264|0.303|0.335|0.341|
|Wald χ<sup>2</sup>(19)||||155.84|||

Notes: All equations also include four regional dummy variables, dummy variables for metropolitan and urban status, dummy variables for racial group (black, mixed raced) and interactions of the race dummies with schooling and with experience. Full results available on request. Simultaneous quintile regression standard errors obtained by bootstrapping (100 replications). Wald is a test for combined equality of the reported schooling and experience coefficients across all quintiles. * denotes significance at 5% or less; + denotes significance at 10%.

Source: authors’ calculations from PNAD 1988, 1992 and 1998
13

**Table 3**

**Estimated Rates of Return to an Additional Year of Schooling**

|Years:||||19|88|||||19|92|||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Education|Experience|_OLS_|Q10|Q25|Q50|Q75|Q90|_OLS_|Q10|Q25|Q50|Q75|Q90|
|2|10|_0.130_|0.099|0.092|0.117|0.146|0.169|_0.107_|0.090|0.086|0.095|0.119|0.126|
|2|20|_0.126_|0.093|0.093|0.117|0.144|0.164|_0.103_|0.084|0.084|0.095|0.116|0.122|
|2|30|_0.122_|0.088|0.095|0.117|0.141|0.158|_0.099_|0.079|0.082|0.094|0.112|0.118|
|2|40|_0.118_|0.082|0.096|0.118|0.138|0.152|_0.095_|0.073|0.081|0.093|0.109|0.114|
|6|10|_0.162_|0.129|0.136|0.159|0.179|0.190|_0.139_|0.113|0.120|0.134|0.153|0.160|
|6|20|_0.158_|0.123|0.137|0.159|0.177|0.185|_0.135_|0.107|0.118|0.134|0.149|0.156|
|6|30|_0.154_|0.118|0.139|0.159|0.174|0.179|_0.130_|0.102|0.117|0.133|0.146|0.152|
|6|40|_0.150_|0.112|0.140|0.159|0.172|0.173|_0.126_|0.096|0.115|0.132|0.142|0.149|
|11|10|_0.202_|0.167|0.191|0.211|0.221|0.217|_0.178_|0.141|0.163|0.183|0.195|0.203|
|11|20|_0.198_|0.161|0.192|0.211|0.218|0.211|_0.174_|0.136|0.161|0.182|0.191|0.199|
|11|30|_0.194_|0.156|0.193|0.212|0.216|0.205|_0.170_|0.130|0.160|0.181|0.188|0.195|
|11|40|_0.190_|0.150|0.195|0.212|0.213|0.200|_0.165_|0.125|0.158|0.181|0.184|0.192|
|16|10|_0.243_|0.205|0.245|0.264|0.262|0.243|_0.217_|0.170|0.206|0.232|0.237|0.246|
|16|20|_0.238_|0.199|0.247|0.264|0.260|0.237|_0.213_|0.164|0.205|0.231|0.234|0.242|
|16|30|_0.234_|0.194|0.248|0.264|0.257|0.232|_0.209_|0.159|0.203|0.230|0.230|0.238|
|16|40|_0.230_|0.188|0.249|0.264|0.254|0.226|_0.205_|0.153|0.201|0.229|0.227|0.234|
14

**Table 3 (continued)**

|Years:<br>||||19|98|||
|---|---|---|---|---|---|---|---|
|Education|Experience|_OLS_|Q10|Q25|Q50|Q75|Q90|
|2|10|_0.070_|0.047|0.050|0.061|0.085|0.101|
|2|20|_0.072_|0.049|0.055|0.067|0.088|0.101|
|2|30|_0.075_|0.051|0.060|0.074|0.091|0.102|
|2|40|_0.077_|0.054|0.066|0.080|0.093|0.103|
|6|10|_0.123_|0.095|0.103|0.120|0.140|0.151|
|6|20|_0.126_|0.098|0.109|0.127|0.143|0.152|
|6|30|_0.128_|0.100|0.114|0.133|0.146|0.153|
|6|40|_0.131_|0.103|0.119|0.139|0.148|0.153|
|11|10|_0.190_|0.157|0.171|0.194|0.209|0.214|
|11|20|_0.193_|0.159|0.176|0.201|0.212|0.215|
|11|30|_0.195_|0.162|0.181|0.207|0.215|0.216|
|11|40|_0.197_|0.164|0.187|0.213|0.217|0.216|
|16|10|_0.257_|0.218|0.238|0.268|0.278|0.277|
|16|20|_0.259_|0.220|0.243|0.275|0.281|0.278|
|16|30|_0.262_|0.223|0.248|0.281|0.284|0.279|
|16|40|_0.264_|0.225|0.254|0.287|0.286|0.279|

Source: computed from results reported in Table 2.
15

**Table 4**

**Estimated Rates of Return to an Additional Year of Experience**

|Years:||||19|88|||||19|92|||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Education|Experience|_OLS_|Q10|Q25|Q50|Q75|Q90|_OLS_|Q10|Q25|Q50|Q75|Q90|
|2|10|_0.057_|0.047|0.045|0.053|0.062|0.070|_0.046_|0.039|0.038|0.042|0.050|0.056|
|2|20|_0.036_|0.028|0.028|0.034|0.041|0.047|_0.029_|0.023|0.023|0.027|0.033|0.039|
|2|30|_0.016_|0.009|0.010|0.014|0.019|0.024|_0.013_|0.007|0.008|0.011|0.015|0.021|
|2|40|_-0.004_|-0.010|-0.007|-0.005|-0.003|0.001|_-0.003_|-0.009|-0.007|-0.005|-0.002|0.003|
|6|10|_0.055_|0.045|0.046|0.053|0.061|0.068|_0.044_|0.036|0.038|0.042|0.049|0.054|
|6|20|_0.035_|0.026|0.028|0.034|0.040|0.045|_0.028_|0.021|0.022|0.026|0.032|0.037|
|6|30|_0.014_|0.007|0.011|0.014|0.018|0.022|_0.011_|0.005|0.007|0.011|0.014|0.019|
|6|40|_-0.006_|-0.012|-0.007|-0.005|-0.004|-0.001|_-0.005_|-0.011|-0.008|-0.005|-0.003|0.002|
|11|10|_0.053_|0.042|0.046|0.053|0.060|0.065|_0.042_|0.034|0.037|0.042|0.047|0.053|
|11|20|_0.033_|0.023|0.029|0.034|0.038|0.042|_0.026_|0.018|0.022|0.026|0.030|0.035|
|11|30|_0.012_|0.004|0.011|0.015|0.017|0.019|_0.009_|0.002|0.006|0.010|0.012|0.017|
|11|40|_-0.008_|-0.015|-0.006|-0.005|-0.005|-0.004|_-0.007_|-0.014|-0.009|-0.005|-0.005|0.000|
|16|10|_0.051_|0.039|0.047|0.053|0.059|0.062|_0.040_|0.031|0.036|0.041|0.045|0.051|
|16|20|_0.031_|0.020|0.030|0.034|0.037|0.039|_0.024_|0.015|0.021|0.026|0.028|0.033|
|16|30|_0.010_|0.001|0.012|0.015|0.015|0.016|_0.007_|-0.001|0.006|0.010|0.011|0.015|
|16|40|_-0.010_|-0.018|-0.005|-0.005|-0.006|-0.007|_-0.009_|-0.016|-0.010|-0.006|-0.007|-0.002|
16

**Table 4 (continued)**

|Years:<br>||||19|98|||
|---|---|---|---|---|---|---|---|
|Education|Experience|_OLS_|Q10|Q25|Q50|Q75|Q90|
|2|10|_0.040_|0.030|0.033|0.038|0.046|0.052|
|2|20|_0.026_|0.017|0.019|0.024|0.031|0.037|
|2|30|_0.011_|0.003|0.005|0.010|0.015|0.021|
|2|40|_-0.003_|-0.010|-0.008|-0.005|0.000|0.006|
|6|10|_0.041_|0.031|0.035|0.041|0.047|0.052|
|6|20|_0.027_|0.018|0.021|0.026|0.032|0.037|
|6|30|_0.012_|0.004|0.008|0.012|0.016|0.022|
|6|40|_-0.002_|-0.009|-0.006|-0.002|0.001|0.006|
|11|10|_0.043_|0.032|0.037|0.044|0.049|0.053|
|11|20|_0.028_|0.019|0.024|0.030|0.033|0.037|
|11|30|_0.014_|0.006|0.010|0.015|0.018|0.022|
|11|40|_-0.001_|-0.008|-0.003|0.001|0.002|0.007|
|16|10|_0.044_|0.034|0.040|0.047|0.050|0.053|
|16|20|_0.029_|0.020|0.027|0.033|0.035|0.038|
|16|30|_0.015_|0.007|0.013|0.018|0.019|0.022|
|16|40|_0.000_|-0.006|-0.001|0.004|0.003|0.007|

Source: computed from results reported in Table 2.
17

# **Table 5 Actual and Conditional Log Hourly Earnings Distributions**

||1<br>(1)<br>_Actual_|988<br>(2)<br>Conditional|(3)<br>_Actual_|1992<br>(4)<br>Conditional|(5)<br>Conditional at<br>1988 means|(6)<br>_Actual_|19<br>(7)<br>Conditional|98<br>(8)<br>Conditional at<br>1988 means|(9)<br>Conditional at<br>1992 means|
|---|---|---|---|---|---|---|---|---|---|
|Q10|_-0.788_|-0.478|_-0.805_|-0.526|-0.444|_-0.550_|-0.246|-0.461|-0.369|
|Q25|_-0.293_|-0.043|_-0.256_|-0.084|-0.013|_-0.086_|0.148|-0.010|0.073|
|Q50|_0.351_|0.431|_0.294_|0.368|0.458|_0.498_|0.577|0.453|0.560|
|Q75|_1.080_|0.924|_0.987_|0.833|0.949|_1.173_|1.019|0.927|1.062|
|Q90|_1.920_|1.408|_1.706_|1.293|1.430|_1.935_|1.464|1.393|1.550|
|Q90-Q10|_2.708_|1.886|_2.511_|1.819|1.874|_2.485_|1.711|1.854|1.919|
|Q75-Q25|_1.373_|0.963|_1.243_|0.917|0.962|_1.258_|0.871|0.937|0.988|
|Q90-Q50|_1.569_|0.977|_1.412_|0.925|0.971|_1.437_|0.888|0.940|0.990|
|Q50-Q10|_1.139_|0.909|_1.099_|0.894|0.903|_1.048_|0.823|0.914|0.929|

Source: computed from results reported in Table 2
18

**Table 6**

**Earnings Growth Across the Distribution**

||(1)<br>_Actual_|1988-1992<br>(2)<br>Conditional|(3)<br>Conditional<br>at 1988|(4)<br>_Actual_|1992-1998<br>(5)<br>Conditional|(6)<br>Conditional<br>at 1992|(7)<br>_Actual_|1988-1998<br>(8)<br>Conditional|(9)<br>Conditional<br>at 1988|
|---|---|---|---|---|---|---|---|---|---|
||||means|||means|||means|
|Q10|_-0.016_|-0.048|0.034|_0.254_|0.279|0.157|_0.238_|0.232|0.017|
|Q25|_0.037_|-0.040|0.030|_0.170_|0.231|0.157|_0.207_|0.191|0.033|
|Q50|_-0.057_|-0.063|0.027|_0.203_|0.209|0.192|_0.147_|0.146|0.022|
|Q75|_-0.093_|-0.091|0.025|_0.186_|0.186|0.229|_0.093_|0.095|0.003|
|Q90|_-0.214_|-0.115|0.021|_0.229_|0.171|0.257|_0.015_|0.056|-0.015|

Source: computed from results reported in Table 2.
19

**Figure 1: Univariate Kernel Density Estimates for Log Hourly Earnings - 1988**

<!-- Start of picture text --> .6<br>.4<br>.2<br>0<br>-10 -5 0 5 10<br>Kernel Density Estimate<br><!-- End of picture text --> Note: vertical lines indicate positions of 10<sup>th</sup> , 25<sup>th</sup> , 50<sup>th</sup> , 75<sup>th</sup> and 90<sup>th</sup> percentiles.

**Figure 2: Univariate Kernel Density Estimates for Log Hourly Earnings - 1992**

<!-- Start of picture text --> .6<br>.4<br>.2<br>0<br>-10 -5 0 5 10<br>Kernel Density Estimate<br><!-- End of picture text --> Note: vertical lines indicate positions of 10<sup>th</sup> , 25<sup>th</sup> , 50<sup>th</sup> , 75<sup>th</sup> and 90<sup>th</sup> percentiles.
20

**Figure 3: Univariate Kernel Density Estimates for Log Hourly Earnings – 1998**

<!-- Start of picture text --> .6<br>.4<br>.2<br>0<br>-10 -5 0 5 10<br>Kernel Density Estimate<br><!-- End of picture text --> Note: vertical lines indicate positions of 10<sup>th</sup> , 25<sup>th</sup> , 50<sup>th</sup> , 75<sup>th</sup> and 90<sup>th</sup> percentiles.