School quality and returns to education of U.S. immigrants.

Bratsberg, Bernt ; Terrell, Dek

Dek Terrell (*)

I. INTRODUCTION

Economists agree that human capital is an important factor of production, but considerable controversy exists over how public investments create human capital (Heckman, 2000). In particular, the relation between expenditures per student in schools and the performance of students educated in those schools remains an open question. Using the U.S. labor market as a common point of reference, this study investigates the linkages between cross-country differences in school resources and postschooling labor market outcomes. We first estimate the rates of return to education for U.S. immigrants from 67 countries using 1980 and 1990 census data. We next analyze the relationship between attributes of the source country's educational system and the U.S. return to education for individuals educated under those systems. The primary focus of the article is to examine the relation between school resources in the source country and the rate of return to education earned by U.S. immigrants, but the article also contributes to the immigration and growth literatures.

Hanushek (1986) summarizes a literature that appeared to be heading toward a consensus that school attributes, such as expenditures per pupil and pupil-teacher ratios, had little to do with the performance of students. However, Card and Krueger's (1992a) study of U.S. males educated between 1920 and 1949 showed a strong relationship between pupil-teacher ratios of states and the wages of workers educated in those states. The Card and Krueger study has led to renewed debate on the relationship between school resources and the performance of students. Some studies (Card and Krueger, 1992b, 1996b; Altonji and Dunn, 1996; Angrist and Lavy, 1999; Kreuger, 1999) support Card and Krueger's (1992a) findings, whereas others (Betts, 1995, 1996a; Grogger, 1996a) find little or no relation between commonly used quality attributes of the educational system and the performance of students measured by test scores or wages. (1)

Most previous studies focus on U.S. education, but this work investigates the relation between attributes of educational systems in foreign countries and the return to that education in the U.S. labor market. The approach closely follows Card and Krueger's two-step estimation procedure. In the first step, we estimate the rates of return to education for immigrants from 67 countries using microdata from the 1980 and 1990 censuses. In the second step, we regress returns to education on attributes of the source country's educational system such as expenditures per pupil and the teacher-pupil ratio. The results generally support Card and Krueger's (1992a) finding that pupil-teacher ratios and expenditures per pupil have important impacts on the wages of students educated in those school systems. For example, our results predict that decreasing the number of pupils per teacher by 10% increases the wage of a high school educated immigrant by 1.7% to 3.1%. Similarly, a 10% increase in expenditures per pupil leads to a 0.9% to 1.0% increase in the U.S. wage of a high school--educated immigrant.

An important advantage of this study is the substantial variation in the attributes of the educational systems across nations compared to that observed across states or school districts in the United States. Another important feature is that the first-step results supply the rates of return to education from 67 nations measured in a single labor market. These results may be useful for both the study of immigration and empirical tests of growth models.

Economists studying immigration have long noted the links between education and the labor market outcomes of immigrants in the United States. For example, Chiswick (1978) reports that the effect of an additional year of education on earnings is lower for foreign-born men than for native-born men. Similarly, Butcher (1994) finds that black immigrant groups receive lower rates of return to education than native blacks. Although Chiswick and Butcher propose a number of explanations for these stylized empirical findings (among them that the quality of schooling may be lower in foreign countries), no prior study offers a comparative analysis of the variation in rates of return to education across a large number of immigrant groups, and no study addresses the linkages between the returns to education received by immigrants in the United States and the characteristics of the educational system in the source country.

The article is organized as follows. Section II contains a description of the methodology. Section III presents estimates of the rates of return to education for 67 countries and describes the data. Section IV examines the relationship between the rates of return to education and attributes of educational systems. Section V examines the robustness of results to several methodological issues, and section VI concludes.

II. METHODOLOGY

The major objective of this article consists of assessing the relations between attributes of educational systems and the rates of return to education received by workers. We accomplish this goal by examining the relation between the quality of the educational system in foreign countries and the wages of immigrants in the United States. Implicitly, our empirical specification assumes that immigrants receive the same rate of return to human capital acquired through education but allows the quality of education to vary by country. In particular, the specification uses cross-country differences in attributes of the educational system to identify differences in quality-adjusted education among U.S. immigrants.

Our empirical methodology broadly follows the two-step framework of Card and Krueger (1992a). In the first step, we use microlevel data on U.S. immigrants from the 1980 and 1990 censuses and estimate the rate of return to education by country of birth in the wage regression:

(1) ln [w.sub.ijt] = [[beta]'.sub.t][x.sub.it] + [summation over (j)] [[gamma].sub.jt][D.sub.ij][S.sub.it] + [u.sub.jt] + [[epsilon].sub.it],

where [w.sub.ijt] denotes the weekly wage of immigrant i born in country j and observed in census t; x is a vector of socioeconomic characteristics (specifically, age and its square, English fluency, marital status, residence in a standard metropolitan statistical area (SMSA), health status, year of immigration, and census division); [D.sub.j] is an indicator variable set to unity if the immigrant is born in country j; and s is the years of schooling the immigrant obtained in the source country. (2) The error term of the wage regression consists of a country-specific component (u) and an individual-specific component ([epsilon]). To address the sensitivity of results to accounting for unobserved differences between source countries that may be correlated with educational attainment, we include results with and without country-specific fixed effects in this first step, imposing the restriction [u.sub.jt] = 0 in the models without country-specific fixed effects. The parameter [[gamma].sub.jt] measures the value the U.S. labor market places on a year of schooling from country j. It is instructive to think of [[gamma].sub.jt], as the multiplicative of two components (Welch, 1966; Behrman and Birdsall, 1983):

(2) [[gamma].sub.jt] = [[gamma].sup.*.sub.t][Q.sub.jt],

where [[gamma].sup.*.sub.t] denotes a common return to quality-adjusted education return earned by all immigrants in census t, and [Q.sub.jt] is an index reflecting the quality of the educational system in country j at the time when immigrants in census t undertook their schooling. The second step of the two-step methodology addresses the relationships between the quality index, [Q.sub.jt], and characteristics of the educational system in the source country. Specifically, we model these relationships as

(3) [Q.sub.jt] = [[alpha].sub.t] + [pi]'[z.sub.jt] + [v.sub.j],

where [z.sub.jt] is a vector of characteristics influencing the quality of education and [v.sub.j] is a country-specific component of Q. Thus, in the second step, we estimate [pi] (identified up to a constant, [[gamma].sup.*.sub.t]) by regressing estimates of [gamma].sub.jt] obtained in the first step on a set of characteristics describing the educational system in the source country at the time immigrants in census t attended school. As in the first step, we estimate the second-step equation with and without a country fixed effect ([v.sub.j]).

An advantage of the two-step procedure is that estimates of [[gamma].sub.jt] from the first step provide an index of the quality of schooling for countries in our sample. Because the index is constructed on the basis of returns to education in a single market economy, it supplies a productivity-based estimate of the quality of educational institutions in foreign countries.

III. COUNTRY-OF-ORIGIN-SPECIFIC RETURNS TO EDUCATION

In this section, we present average rates of return to education in the United States for immigrants from 67 countries. The average rates of return are estimated based on samples of immigrant males drawn from the 5/100 public use samples of the 1980 and 1990 U.S. Censuses of Population. (3) To avoid including immigrants who undertook some of their schooling in the United States, the samples exclude individuals whose birth year plus six plus years of schooling exceeds the year of immigration. (4) The regression samples also exclude persons younger than 25 or older than 64 and those currently enrolled in school. The sample from the 1980 census includes 86,728 immigrants; the 1990 sample consists of 125,503 immigrants.

Table 1 reports results from estimation of equation (1) in the samples of immigrant males drawn from the 1980 and 1990 censuses. The results reveal substantial differences in rates of return to education obtained in different nations. For example, the largest rate of return in 1990 was 8.2% to one year of education from Japan, and the smallest rate of return was 2.0% to one year of education obtained in Haiti. To understand the magnitude of this difference compare the impact of education on the wages of two high school graduates, identical in every respect except that one individual was educated in Haiti and the other in Japan. The estimated returns to education indicate that the worker from Japan doubles his earnings (exp[12(.0822 - .0202)] = 2.10) by obtaining education in Japan rather than Haiti.

The estimated rate of return to education from each country supplies a market-based measure of the productivity of education from a cross-section of countries measured in a common market. Thus, the numbers complement recently assembled international databases on human capital stock (Barro and Lee, 1993, 1996; Nehru et al., 1995; Psacharopoulus and Arriagada, 1986) and may be used to adjust for quality of education in studies examining differences in growth rates across nations. For example, the differences in productivity of education help better quantify the differences in human capital that may explain the high growth rates of Asian nations (such as Japan and Singapore) and much lower growth of poorer nations (such as Haiti and Sierra Leone).

The results in Table 1 indicate similar general patterns across countries for the two census years; the simple correlation coefficient between the two series equals .920. In both 1980 and 1990, the table shows high rates of return to education obtained in northern Europe, Australia, and Canada and low rates of return to education obtained in Central America. The largest improvements between 1980 and 1990 were for education obtained in Japan or New Zealand. We also constructed similar regression samples of native-born workers and estimated the return to education for the United States. Results show that the average rates of return to one year of schooling obtained in the United States were .0565 in the 1980 census and .0776 in the 1990 census. In a ranking with the 67 nations listed in Table 1, these returns place the United States sixth (between Australia and United Kingdom) in 1980 and third (between Norway and Sweden) in 1990.

The mean of the average returns to education across the 67 countries rose from .0389 in 1980 to .0482 in 1990. The rise in the mean reflects in part an economy-wide increase in the returns to education in the U.S. economy over that period found in previous studies (Katz and Murphy, 1992; Juhn et al., 1993; Buchinsky, 1994) but may also indicate an improvement in the global quality of education. (5)

The model presented in section II hypothesizes that differences in average returns to education across countries in this study reflect differences in school quality across source countries. To examine this possibility, we compiled a data set that links the estimated rates of return with quality measures of the educational system in the source country. We lag the educational quality data 20 years to better capture differences in school quality at the time immigrants undertook their schooling; that is, we match 1980 and 1990 census data with school characteristics from 1960 and 1970, respectively. The measures of school quality include the pupil-teacher ratio in primary schools, relative expenditures per pupil, (6) and years of compulsory education. The first two measures reflect the resources devoted to education, and the last is intended to capture the commitment to education. The data appendix contains a detailed description of data sources and the construction of variables.

Unfortunately, reliable measures of educational quality were unavailable for at least one of the sample years for Switzerland and China, leaving a sample of 65 countries for the empirical analysis. Matching the 1980 census returns with 1960 school quality attributes and the 1990 census returns with 1970 school attributes supplies 2 observations per nation and a total of 130 observations for returns to education and attributes of the educational system generating those returns.

Are the school resource variables correlated with estimated rates of return to education? Figure 1 contains graphs of the average return to education versus the pupil-teacher ratio and relative education expenditure. Panel A plots the average return to education in 1980 against the pupil-teacher ratio in 1960. This panel reveals a strong negative correlation between the pupil-teacher ratio and the estimated rate of return to education. Panel B contains a similar plot of the 1980 return to education versus the 1960 relative education expenditure and shows a positive relationship between the two variables. Panels C and D contain plots for 1990 returns to education versus 1970 school attributes and reveal similar patterns. Although the figures suggest relations between school quality and the rates of return to education across educational systems, a more detailed analysis is necessary to verify the robustness of the results.

IV. SOURCES OF VARIATION IN RATES OF RETURN TO EDUCATION

We now turn to the results from second-step regressions in which we regress the average returns to education on the attributes of the educational system in the source country. Table 2 contains summary statistics for the school quality measures and other variables used in the analysis. An important feature of this study is that the variation in school quality across countries is much larger than the variation in these measures across U.S. states. For example, in the international data the standard deviation of the pupil-teacher ratio is 8.6 in 1960 and 8.9 in 1970 (see appendix Table A3) compared to standard deviations of 4.8 (the 1920s), 3.9 (the 1930s), and 3.1 (the 1940s) in Card and Krueger's (1992a) data set of U.S. states. Such greater variation should allow more precise estimation of the impact of the quality measures on returns to education.

The bottom of Table 2 displays the correlation coefficients between the rate of return to education and the measures of school resources. These results reveal the same relationships depicted in Figure 1 and also indicate a positive correlation between years of compulsory education and the U.S. return to education. Not surprisingly, the results also show a negative correlation between the pupil-teacher ratio and relative education expenditure. Countries that spend more on education also tend to have lower pupil-teacher ratios. The correlations also suggest that educational systems of wealthier nations tend to be better with regard to all three attributes and that education from these nations earns a higher return in the U.S. labor market. The next step is to separate the impact of GDP from that of the educational attributes.

Table 3 contains results for regressions of the rate of return to education regressed on attributes of the educational systems. The table lists results of the second-step regression with and without country fixed effects in both the first and second steps. Across all specifications, the regressions reveal a very robust negative relationship between the pupil-teacher ratio and the rate of return to education. Overall these results also indicate a positive relationship between relative education expenditures and the rate of return to education, though this result is not robust across specifications. Consider first the coefficient on the log pupil-teacher ratio, which gives the change in the rate of return to education for a proportionate change in the pupil-teacher ratio. The coefficient of the log pupil-teacher ratio ranges from -.0392 to -.0144 across all specifications and is significant at the 1% level in all models. Column (5), which includes the largest set of country-specific variables and country fixed effects in the first step, yields a coefficient of -.0261, implying that for an immigrant with ten years of schooling (the sample mean; see Table Al) the elasticity of the U.S. wage with respect to the pupil-teacher ratio in the source country is -.261. Thus, a 10% reduction in the pupil-teacher ratio raises the expected wage of a high-school educated immigrant from the country by 3.1%.

The results also predict a positive relationship between the relative education expenditures and the rate of return to education. Focusing again on the specification in column (5), a 10% increase in the relative expenditure on education leads to a predicted .75% increase in the wages of an immigrant with ten years of schooling.

The remaining variables in the extended regression models also likely pick up variation in school quality across source countries or may reflect differences in transferability of schooling to the U.S. labor market and therefore serve as important control variables for isolating the impact of the school quality measures. The signs of the coefficients on these variables are mostly as expected. Compulsory schooling generally has a positive (although statistically insignificant) impact on the rate of return to education, and immigrants from English-speaking countries earn a higher rate of return to their education than immigrants from non-English-speaking countries, other things equal. Because the first-step regression controls for English-speaking ability of the immigrant, the latter result likely reflects greater transferability of schooling from these countries. Greater income inequality, communist regimes, and political turmoil are all associated with lower returns to education--reflecting either lower school quality or less transferability of schooling under such conditions. The coefficient of log per capita GDP is positive and significant in one of four specifications and negative in two specifications. Thus, our results are probably best interpreted as inconclusive on the impact of source country development on the U.S. return to education holding educational attributes constant.

How do our results compare to previous studies? As a general summary, we note that our predicted effects of quality of education attributes are similar to estimates from a number of studies based on U.S. school quality data. For example, our estimates of the change in the rate of return to education from a proportionate change in the pupil-teacher ratio range between -1.44 and -3.92, whereas the summary of results from previous studies (that control for state-of-birth effects) in table 5.3 of Card and Krueger (1996a) range from --1.07 to --1.81. Further, Betts (1996b) computes the elasticity of earnings with respect to school spending per pupil from 23 studies, and although he emphasizes the range of these elasticities, most estimates are near the mean elasticity across studies, which is .1041. These studies do not control for the effect of the pupil-teacher ratio and are therefore not directly comparable to ours. When we exclude the pupil-teacher ratio from the specifications in Table 3, the coefficient of log expenditures per pupil becomes .0099 in column 2 and .0106 in column 6. (7) Evaluated at sample mean educational attainment (ten years), these estimates generate elasticities that are remarkably close to those summarized by Betts.

Of course, our estimates generally exceed those of Betts (1995), Grogger 1996a), and others who find small or zero impact of these quality measures on earnings. This discrepancy has been explained in many ways. First, a difference in samples may explain the results. Card and Krueger's (1992a, 1992b) studies find a strong relationship between quality attributes and wages in samples of workers educated before 1960, whereas studies focusing on U.S. workers educated after 1960 more often find a weaker relation or no relation at all. Burtless (1996) hypothesizes that the difference may be due to nonlinearities in the relation between school inputs and the rate of return to schooling. The variation in school attributes across states and school districts in the U.S. has dropped markedly over time (Heckman et al., 1996a). Both Card and Krueger's samples and those used in the present study have much more variation in quality measures, which may allow detection of nonlinear relationships.

Hoxby (1996) argues that teachers' unions may explain the discrepancy. Her results show that strong teachers' unions increase resources devoted to education but may reduce student achievement. Thus studies focusing on students educated after the onset of collective bargaining in the public sector (early 1960s) will find no substantial relation between school inputs and student achievement, but studies based on those educated before 1960 find an important relation. Because very little of the variation in school attributes in our sample would be attributable to unionization, Hoxby's argument suggests that this study should find estimates similar to Card and Krueger (1992a), as we do. (8)

Grogger (1996b) and Hanushek et al. (1996) suggest another explanation of the discrepancy in results that focuses on the Card and Krueger's use of aggregate data. In particular, Hanushek et al. (1996) argue that the omission of regulations affecting the operations of schools, primarily state-level regulations, leads to more severe misspecification bias in aggregate studies and thus an upward bias in the estimated impacts of school resources on achievement in these studies. (9) Because the organization of school systems differs greatly across nations, the bias suggested by Hanushek et al. (1996) may apply to the present study. For example, highly developed countries might have lower pupil teacher ratios and better organized school systems than poorer nations. However, although the aggregation bias could plausibly generate correlation in a cross-section of nations, the organization of school systems should vary much less within nations over time. The results in columns (3) and (6) of Table 3 address this issue by including fixed effects in the second-step regressions. The coefficient of the pupil-teacher ratio is slightly larger in these formulations, suggesting that this form of aggregation bias does not affect our results.

Given the international data used in our study, several other issues emerge. In section V, we examine the sensitivity of results to selective immigration, birth-cohort restrictions, and convexity of the education-earnings profile.

V. SENSITIVITY ANALYSIS

Selective Immigration

Though the immigrant data offer the advantage of large variation in educational characteristics, they have the drawback that the selection mechanism guiding the immigration decision could introduce selectivity bias into the estimation of the parameters of equation (1). Indeed, one of the chief criticisms of the Card and Krueger methodology focuses on selective migration (Heckman et al., 1996a, 1996b). (10) As pointed out by Burtless (1996), it is not clear whether or how nonrandom migration biases the estimates of school resource effects in the two-step procedure. To shed some light on this issue, we consider a simplified version of the Roy model (Borjas, 1987, 1991) focusing on the role of schooling in wage determination. Suppose the wages a potential immigrant could earn in the source country ([w.sub.0]) and in the United States ([w.sub.1]) are determined by

(4) ln [w.sub.0] = [[micro].sub.0] + [[gamma].sub.0]S + [v.sub.0], and

(5) ln [w.sub.1] = [[micro].sub.1] + [[gamma].sub.1]S + [v.sub.1],

where s denotes the years of schooling of the individual; and [v.sub.0] and [v.sub.1] measure the contributions to wages of unobservable skills--known to the individual but unknown to the researcher. Assume that the population distribution of [v.sub.0] and [v.sub.1] is bivariate normal with zero means, standard deviations [[sigma].sub.0] and [[sigma].sub.1], and correlation coefficient [rho]. Also, [v.sub.0] and [v.sub.1] are uncorrelated with s.

If migration costs are given by c, income-maximizing behavior generates the migration condition I = ln [w.sub.1] - ln [w.sub.0] - c > 0. Thus, the emigration rate from the source country to the United States is given by

(6) p = Pr{I > 0} = Pr{([v.sub.1] - [v.sub.0]) > [[micro].sub.0] - [[micro].sub.1] + c - ([[gamma].sub.1] - [[gamma].sub.0])s},

and, in a random sample of immigrants, the expectation of the log wage is

(7) E{ln [w.sub.1]\s, I > 0} = [[micro].sub.1] + [[gamma].sub.1]s + E{[v.sub.1]\([v.sub.1] - [v.sub.0]) > [[micro].sub.0] - [[micro].sub.1] + c - ([[gamma].sub.1] - [[gamma].sub.0])s}.

Thus the Roy model predicts that the error term in the regression of log wages on years of schooling in a random sample of immigrants is truncated and correlated with the regressor, s, causing biased and inconsistent ordinary least squares (OLS) estimates of the parameters in the wage regression.

To continue, we make the simplifying assumption that [v.sub.0] and [v.sub.1] are perfectly correlated in the population. (11) The conditional expectation of the log wage then becomes

(8) E{ln [w.sub.1]\s, I > 0} = [[micro].sub.1] + [[gamma].sub.1]s + E{[v.sub.1]\([[sigma].sub.1] - [[sigma].sub.0])[v.sub.1]/[[sigma].sub.1] > [[micro].sub.0] - [[micro].sub.1] + c - ([[gamma].sub.1] - [[gamma].sub.0])s}.

With the additional assumption of normality of [v.sub.1], the last term simplifies to

(9) E{[v.sub.1]\s, I > 0} = {[PHI](z)/p if [[sigma].sub.0] < [[sigma].sub.1]/-[PHI](z)/p if [[sigma].sub.0] > [[sigma].sub.1],

where [PHI] denotes the standard normal density function and z = [[[micro].sub.0] - [[micro].sub.1] + c - ([[gamma].sub.1] - [[gamma].sub.0])s]/([[sigma].sub.1] - [[sigma].sub.0]). (12) Equations (8) and (9) show that the truncation of [v.sub.1] is strictly from below when [[sigma].sub.0] exceeds [[sigma].sub.1] (the immigrant pool is characterized by "positive sorting" in unobservables), and strictly from above when [[sigma].sub.0] exceeds [[sigma].sub.1] ("negative sorting").

The OLS bias in equations (7) and (8) takes the sign of the correlation between s and the truncated error term. If U.S. immigration is characterized by positive sorting (in education and unobservable skills), this correlation is negative as selectivity in unobservables intensifies with lower levels of schooling. Under such conditions, OLS estimates of the rate of return to education are downward biased. This is exactly the bias discussed in Chiswick (1978) and Butcher (1994). Unfortunately, to assess the bias in estimates of school resource effects additional assumptions on the linkages between school resources and the parameters of the Roy model are needed.

Perhaps more important for the present study, however, is that equations (8) and (9) suggest that parameters of the wage regression can be estimated consistently if we account for the truncation of the error term. To accomplish this, we adapt a variant of Heckman's (1979) method of controlling for sample selectivity, treating the bias in the OLS estimator as omitted variable bias stemming from omission of the expectation of the truncated error term, which is conditional on the level of schooling of the immigrant.

The first step in the sample selectivity procedure requires estimating the probability of migration to the United States conditional on education. In particular, migration rates were computed for male immigrants from each country in our sample at three levels of schooling (corresponding to primary, secondary, and higher education levels in international data): fewer than 7 years, 7 to 12 years, and more than 12 years of education. We use census data to estimate the number of individuals at each level of schooling living in the United States. For each country in our sample, a combination of the population and the proportion of the population of each country with each level of education supplies the number of individuals in that nation in each education category. The resulting migration rates are reported in Table A2, and the data appendix provides further detail on the construction and on data sources.

Based on the estimated migration rates, we compute proxies for the conditional expectation of v according to equation (9), which we then add to the first-step regression model in equation (1) to control for sample selectivity. (13) Results from the first-step model incorporating sample selectivity controls largely parallel results based on OLS. The correlation coefficients between the selectivity adjusted series and those reported in Table 1 are very high--.988 in the 1980 data and .976 in the 1990 data--and the mean rates of return are only slightly higher than those in Table 2-3.9812 in 1980 and 5.1159 in 1990. The first three columns of Table 4 contain a replication of earlier second-step regressions using rates of return to education estimated with selectivity corrections. Comparing these results to Table 3 reveals that the selectivity controls do not substantially alter the results. (14)

Age-Restricted First-Step Samples

Another potential problem with the earlier results lies in the assumption that attributes of the 1960 educational system apply to all individuals in the 1980 census and that 1970 attributes apply to those from the 1990 census. An obvious solution to this problem is to restrict the first-step regression samples according to age at the time of the census. To focus on this issue, equation (1) was reestimated for narrowly defined birth cohorts. The cohorts were defined by associating the 1960 school attributes with immigrants born between 1945 and 1955 and 1970 attributes with immigrants born between 1955 and 1965. An important drawback of this approach is that sample sizes became quite small for a number of source countries, triggering large sampling variances for some first-step parameter estimates. Nevertheless, the rates of return to education estimated from the restricted first-step samples exhibit high correlations with the returns in Table 2 (simple correlation coefficients are .923 for 1980 and .943 for 1990).

The last three columns of Table 4 report second-step regression results based on the restricted birth cohort data. A comparison of these results to comparable results based on the full sample of male immigrants reveals very similar parameter estimates for the pupil-teacher ratio but slightly smaller effects of expenditures per pupil than in previous tables. The finding of smaller resource effects in samples that are restricted to young workers is consistent with Card and Krueger's (1996a) observation that school quality effects are likely understated in samples of young workers. (15) Finally, a closer look also reveals larger standard errors in columns (4)-(6) of Table 4 than in Table 3--a result caused by the smaller sample sizes in the first-step regression.

Nonlinear Returns to Education

Results thus far indicate large effects of school resources in the source country on the returns to education earned by immigrants in the United States. But the evidence is based on the restrictive assumption that the schooling-log wage profile is linear, that is, that returns to education are not related to levels of education. In some human capital investment models, the relationship between schooling and log wages is convex--returns increase with educational attainment. If this relationship is convex for U.S. immigrants, it is possible that our findings reflect that immigrants from countries with higher school quality have more educational attainment and earn higher returns because they are farther out a common schooling-log wage function. Although the higher attainment in this scenario may be the consequence of school quality, the higher returns are not, which affects the interpretation of the relationship between school quality and wages.

Heckman et al. (1996) offer evidence from the United States that the impact of school quality differs by level of education. When they estimate nonlinear schooling-log wage models they find that the effects of school resources on returns to education are concentrated at high levels of education and that such effects are strongest for those with at least a college education. In this section, therefore, we relax the assumption of linear returns and allow marginal returns to education to differ after 9 and 12 years of schooling. In the two-step approach this implies a very highly parameterized first-step model--indeed, there are at least 390 separate returns to be estimated--so we instead substitute equations (3) and (2) into equation (1) and estimate school-quality effects directly in the microsamples based on the equation

(10) ln [w.sub.ijt] = [[beta]'.sub.t][x.sub.it] + [[alpha].sub.t][s.sub.it] + [phi]'[z.sub.it][s.sub.it] + [gamma]'[z.sub.jt] + [u.sub.j] + [[epsilon].sub.it].

We further augment the regression model with a three-segment spline function in education with splines at 9 and 12 years of schooling. School quality impacts on the education slope are captured by interaction terms between the school quality characteristics and schooling ([z.sub.jt][s.sub.it]). To facilitate interpretation of main schooling effects, interactions use sample mean deviates of continuous variables (such as the log pupil-teacher ratio). Results appear in Table 5.

Consider first the results in columns (1) and (2), in which we maintain the linear assumption of prior sections and estimate the log wage regression first without, and then with, source-country fixed effects. These columns offer a robustness check of the two-step estimator, as, in the absence of specification errors, the coefficients of the interaction terms in column (2) should be equivalent to those in Table 3, column (5). As a comparison of the two tables reveals, coefficient estimates are very close.

In columns (3) and (4), we introduce the spline specification of educational attainment. Results support the notion of convexity of the schooling-log wage profile for U.S. immigrants. According to the estimates in column (3), each year of schooling raises wages of the baseline group by .0078 log points for the first nine years of schooling. Returns then increase by a significant .0186 log points after 9 years and an additional .0516 log points after 12 years of schooling. In other words, in the immigrant sample the return to each year of schooling beyond high school is 8.1% (exp[.0078 + .0186 + .0516] -1). Allowing for a nonlinear education-wage profile reduces the magnitudes of school quality effects, but estimates remain statistically significant and within the range of estimates obtained from the two-step approach.

The specification in columns (5) and (6) allows for differential school quality effects in each of the three segments of the spline function. Although estimates of column (5) suggest that the pupil-teacher ratio has the largest impact on returns to the first nine years of education, we do not uncover significant differences in the pupil-teacher ratio effect across segments of the spline. Expenditures per pupil, on the other hand, have a significantly larger impact on returns at midrange levels of education than at lower or higher levels of educational attainment. In summary, results in Table 5 show that the finding that school quality affects the returns to education is not the consequence of failure to account for convexity of the schooling-wage relationship. (16)

Educational Attainment and Returns to Education

We conclude this section with some observations on the relation between educational attainment and returns to education across groups. First, there appears to be a discrepancy between the international and the U.S. evidence on this relationship. Psacharopoulos (1994) finds a negative correlation between returns to education and attainment across countries and attributes this to diminishing marginal returns to investments in education. In contrast, across states of birth and birth cohorts in the United States the association is positive. In the models of Card and Kreuger (1996a) and Heckman et al. (1996), for example, the positive relation arises because higher market returns provide an incentive for students to attend school longer. The U.S. empirical evidence also points to a positive effect of school quality on educational attainment (Johnson and Stafford, 1973; Card and Krueger, 1992a; Heckman et al., 1996a).

Interestingly, our estimates of rates of return to education are negatively correlated with returns to investment in education calculated within each nation such as those compiled by Psacharopoulos (1985, 1994). For example, table A2 of Psacharopoulos (1994) lists coefficients of schooling from log wage regressions for 62 separate countries, most based on microsamples drawn between 1980 and 1990. For the countries that overlap, the correlation coefficients between source-country estimates of the rate of return to schooling and the U.S. estimates listed in Table 1 are -.54 for the 1980 data and -.57 for the 1990 data. Furthermore, average educational attainment in our samples, average schooling in the source population, and enrollment in postsecondary education are all positively related to our school quality measures and to U.S. returns to education but are negatively related to returns to education in the source country.

On further consideration, the contrast between our results and those of Psacharopoulos was quite predictable. As Schultz (1988) observes, returns to education within any one nation are primarily driven by the aggregate quantity of educated workers and other factors of production. However, the supply of educated workers in the U.S. labor market is mainly determined by U.S. natives--educational attainment in other nations has little impact on the quantity of education available in the U.S. labor market. Thus our measures of returns to education for each nation are influenced by very different factors (such as quality of education) than those reported by Psacharopoulos. The positive correlation between our returns and attainment is consistent with the argument that better-quality education leads to increases in attainment, though further research is needed to provide any conclusive evidence on this issue. (17)

VI. CONCLUSION

This article examines the relationship between attributes of a country's educational system and the rate of return to education received by U.S. immigrants from that country. Results reveal that differences in the attributes of educational systems account for the most of the variation in rates of return to education earned by immigrants applying their source-country education in the U.S. labor market. We find a particularly robust inverse relationship between the rate of return to education and the pupil-teacher ratio in primary schools in the source country, and similarly robust direct relationships between the rate of return and relative teacher wages and expenditures per pupil in the source country. The methodology applied in the study also yields several other interesting results.

The results from the first-step regressions estimating rates of return to education for immigrants also supply an index of the quality of a nations education system. As such, Table 1 shows that Japan, Australia, Canada, and northern European nations provide the highest-quality education, with the lowest-quality education coming from educational systems of Caribbean nations. A potentially important application of such rankings is that they complement educational attainment in cross-country studies of the relation between human capital and economic growth.

The study also makes important contributions to the immigration literature. Because the valuation of an immigrant's education in the U.S. labor market depends on the investments made in the educational system in the source country, differences in educational investments create disparities in U.S. earnings across immigrant groups. Indeed, the immigration literature has long recognized source-country effects in labor market outcomes of U.S. immigrants (Chiswick, 1978, 1986; Jasso and Rosenzweig, 1986, 1990; Borjas, 1987, 1993; Borjas and Bratsberg, 1996); the link between school quality and the rate of return to education provides another explanation of the existence of source-country effects.

Cross-country growth regressions, development economists, and World Bank policies continue to stress quality education as a key to economic development. The results of this study affirm the linkage between the attributes of a nation's educational system and the productivity of workers educated in that system. These results provide evidence of potential productivity gains from increases in expenditures per pupil and improvements in pupil-teacher ratios and also provide estimates of the return to such investments in educational systems. As most economists have long maintained, improving the quality of the educational system enhances the productivity of workers receiving that education even when the education in applied in a very different environment from where it was obtained.

[Figure 1 omitted]

TABLE 1

U.S. Rate of Return to Education by Country of Birth

 1980 Census 1990 Census
 Rate of Standard Rate of
Country Return Error Observations Return

Europe
 Austria .0533 .0023 360 .0699
 Belgium .0584 .0032 170 .0690
 Czechoslovakia .0442 .0018 637 .0534
 Denmark .0590 .0031 213 .0692
 Finland .0490 .0044 114 .0671
 France .0531 .0017 632 .0645
 Germany .0509 .0009 3,314 .0635
 Greece .0300 .0014 1,963 .0429
 Hungary .0400 .0017 753 .0482
 Ireland .0429 .0017 955 .0587
 Italy .0442 .0010 5,270 .0542
 Netherlands .0511 .0018 600 .0654
 Norway .0632 .0029 264 .0789
 Poland .0398 .0012 2,544 .0431
 Portugal .0433 .0019 1,892 .0446
 Romania .0414 .0021 485 .0501
 Spain .0424 .0021 587 .0518
 Pakistan .0317 .0022 304 .0379
 Sweden .0543 .0029 220 .0739
 Switzerland .0630 .0025 273 .0716
 UK .0560 .0008 3,860 .0703
 USSR .0339 .0011 1,916 .0450
 Yugoslavia .0432 .0015 1,520 .0522

Asia
 China .0247 .0010 2,732 .0274
 Hong Kong .0316 .0030 209 .0407
 India .0382 .0009 2,082 .0476
 Indonesia .0402 .0025 288 .0508
 Iran .0477 .0018 500 .0491
 Iraq .0303 .0030 241 .0431
 Israel .0386 .0021 457 .0562
 Japan .0522 .0011 1,548 .0822
 Korea .0333 .0010 1,774 .0449
 Lebanon .0398 .0026 338 .0479
 Malaysia .0317 .0056 48 .0439
 Pakistan .0317 .0022 304 .0379
 Philippines .0269 .0008 4,356 .0344
 Singapore .0456 .0078 24 .0622
 Sri Lanka .0497 .0048 56 .0556
 Taiwan .0336 .0020 358 .0463
 Thailand .0252 .0027 235 .0341
 Turkey .0434 .0025 325 .0544

Africa
 Egypt .0408 .0017 495 .0469
 Kenya .0440 .0055 43 .0560
 Morocco .0394 .0046 90 .0402
 Sierra Leone .0293 .0129 9 .0314
 Tanzania .0281 .0071 29 .0439
 Uganda .0382 .0071 30 .0472

Oceania
 Australia .0566 .0026 236 .0703
 New Zealand .0440 .0038 106 .0729
North America
North America
 Canada .0555 .0008 4,754 .0685
 Costa Rica .0296 .0036 207 .0377
 Cuba .0302 .0009 5,262 .0330
 Dominican Republic .0122 .0019 1,324 .0210
 El Salvador .0182 .0023 749 .0221
 Guatemala .0200 .0026 566 .0214
 Haiti .0119 .0017 862 .0202
 Honduras .0254 .0034 283 .0234
 Jamaica .0246 .0014 1,611 .0350
 Mexico .0248 .0009 20,455 .0203
 Panama .0372 .0020 495 .0364
 Trinidad and Tobago .0270 .0021 592 .0375

South America
 Argentina .0436 .0018 704 .0506
 Brazil .0496 .0028 246 .0417
 Chile .0406 .0023 352 .0438
 Colombia .0283 .0015 1,287 .0332
 Ecuador .0220 .0020 783 .0277
 Peru .0301 .0019 581 .0320
 Uruguay .0322 .0040 160 .0461
Mean (67 countries) .0389 .0482
Standard deviation .0119 .0156

 1990 Census
 Standard
Country Error Observations

Europe
 Austria .0032 194
 Belgium .0033 160
 Czechoslovakia .0020 430
 Denmark .0032 181
 Finland .0046 89
 France .0017 623
 Germany .0011 2,149
 Greece .0016 1,454
 Hungary .0021 541
 Ireland .0016 1,030
 Italy .0012 3,182
 Netherlands .0021 440
 Norway .0032 168
 Poland .0010 2,461
 Portugal .0018 1,967
 Romania .0017 733
 Spain .0021 603
 Pakistan .0014 951
 Sweden .0027 237
 Switzerland .0023 301
 UK .0008 4,025
 USSR .0012 1,457
 Yugoslavia .0017 1,078

Asia
 China .0009 4,213
 Hong Kong .0019 634
 India .0007 4,500
 Indonesia .0025 297
 Iran .0012 1,337
 Iraq .0025 377
 Israel .0017 654
 Japan .0010 2,037
 Korea .0008 3,448
 Lebanon .0019 613
 Malaysia .0032 185
 Pakistan .0014 951
 Philippines .0006 7,404
 Singapore .0057 54
 Sri Lanka .0033 141
 Taiwan .0010 1,605
 Thailand .0021 456
 Turkey .0025 342

Africa
 Egypt .0014 853
 Kenya .0039 103
 Morocco .0035 169
 Sierra Leone .0056 54
 Tanzania .0056 58
 Uganda .0053 60

Oceania
 Australia .0024 297
 New Zealand .0033 160
North America
North America
 Canada .0009 3,100
 Costa Rica .0032 295
 Cuba .0009 5,480
 Dominican Republic .0014 2,102
 El Salvador .0012 3,951
 Guatemala .0016 1,922
 Haiti .0014 1,832
 Honduras .0024 701
 Jamaica .0013 2,108
 Mexico .0006 41,412
 Panama .0023 418
 Trinidad and Tobago .0019 722

South America
 Argentina .0016 875
 Brazil .0019 659
 Chile .0021 514
 Colombia .0012 2,269
 Ecuador .0017 1,120
 Peru .0014 1,275
 Uruguay .0034 243
Mean (67 countries)
Standard deviation

Notes: Rates of return to education are estimated using the 5/100 public
use samples of the 1980 and 1990 censuses of population. Samples are
limited to immigrant males age 25-64 who completed their schooling
before migrating to the United States; see text for other sample
restrictions. Sample sizes are 86,728 for the 1980 sample and 125,503
for the 1990 sample. Additional regressors include age and its square
and indicator variables for English fluency, married with spouse
present, residence in SMSA, health limiting work, eight census
divisions, and five (nine in 1990 sample) immigrant cohorts.
TABLE 2

Sample Statistics--Second Step Regression Samples

Variable Mean SD

A. Descriptive statistics
 U.S. Rate of Return to Education .0359 .0151
 In(Pupil-teacher Ratio) 3.5609 .2864
 In(Expenditure per Pupil/Per-capita GDP) -1.8737 .4645
 Years of Compulsory Schooling 6.8757 1.8222
 In(Per-capita GDP) 7.9566 .6623
 Income Inequality (Top 10 to Bottom 20 9.5169 6.3960
 Percentiles Wealth)
 English Official Language (Indicator) .1155 .3208
 Communist Regime (Indicator) .1113 .3157
 Coup or Revolution During Decade (Indicator) .2961 .4583
 Assassinations During Decade per Million .1941 .6436
 Population
 Americas (Indicator) .5484 .4996
 Asia (Indicator) .1865 .3910
 Africa (Indicator) .0097 .0986
 U.S. Return to In(Pupil-
 Education teacher Ratio)

B. Correlation matrix
 In(Pupil-teacher Ratio) -.7073
 In(Expenditure per Pupil) .5571 -.5615
 Compulsory Schooling .5846 -.5424
 In(Per-capita GDP) .4005 -.3465

 In(Expenditure Compulsory
 per Pupil) Schooling

B. Correlation matrix
 In(Pupil-teacher Ratio)
 In(Expenditure per Pupil)
 Compulsory Schooling .4028
 In(Per-capita GDP) .1021 .5318

Notes: Sample size is 130. Country-of-birth characteristics are lagged
20 years from the census estimate of returns to education; i.e., rates
of return based on the 1980 census are matched with country data from
1960 and rates of return based on the 1990 census with country data from
1970. Observations are weighted by cell count of first step.
TABLE 3

Determinants of U.S. Returns to Education

 (1) (2) (3)

In(Pupil-teacher Ratio) -.0239 (**) -.0144 (**) -.0271 (**)
 (.0040) (.0032) (.0070)
In(Expediture per Pupil) .0067 (**) .0082 (**) -.0012
 (.0023) (.0017) (.0032)
Compulsory Schooling .0022 (**) .0008 .0000
 (.0006) (.0005) (.0008)
In(Per-capita GDP) .0047 (**) .0079
 (.0018) (.0063)
Income Inequality -.0003 (*) -.0013
 (.0001) (.0014)
English Official .0105 (*)
 Language (.0021)
Communist -.0063
 (.0032)
Coup or Revolution .0033 .0022
 (.0018) (.0029)
Assassinations -.0027 (*) .0008
 (.0011) (.0016)
Americas -.0069 (**)
 (.0021)
Asia .0058 (*)
 (.0024)
Africa .0042
 (.0065)
1980 Observation -.0024 -.0020 .0002
 (.0018) (.0014) (.0023)
Constant .1194 (**) .0656 (**)
 (.0154) (.0221)
Country fixed effect in No No No
 first step?
Country fixed effect in No No Yes
 second step?
[R.sup.2] .5894 .8360 .9577

 (4) (5) (6)

In(Pupil-teacher Ratio) -.0234 (**) -.0261 (**) -.0392 (**)
 (.0070) (.0060) (.0112)
In(Expediture per Pupil) .0068 .0075 (*) -.0048
 (.0040) (.0031) (.0052)
Compulsory Schooling .0009 .0010 -.0009
 (.0010) (.0009) (.0013)
In(Per-capita GDP) -.0048 -.0151
 (.0032) (.0101)
Income Inequality -.0004 -.0022
 (.0003) (.0022)
English Official .0236 (**)
 Language (.0039)
Communist -.0127 (*)
 (.0058)
Coup or Revolution .0028 .0040
 (.0033) (.0046)
Assassinations -.0054 (**) .0009
 (.0019) (.0025)
Americas .0057
 (.0038)
Asia .0217 (**)
 (.0044)
Africa .0291 (*)
 (.0119)
1980 Observation -.0033 -.0047 -.0067
 (.0031) (.0025) (.0037)
Constant .1284 (**) .1726 (**)
 (.0269) (.0406)
Country fixed effect in Yes Yes Yes
 first step?
Country fixed effect in No No Yes
 second step?
[R.sup.2] .2520 .6148 .9353

Notes: Sample size is 130. Standard errors are reported in parentheses.
Observations are weighted by cell count of first step.

(*)Statistically significant at the 5% level (two-tailed test).

(**)Statistically significant at the 1% level (two-tailed test).
TABLE 4

Sensitivity Analyses

 Selectivity Adjusted Returns
 (1) (2) (3)

In (Pupil-teacher Ratio) -.0114 (**) -.0347 (**) -.0266 (**)
 (.0039) (.0080) (.0055)
In (Expediture per Pupil) .0100 (**) -.0009 .0050
 (.0020) (.0037) (.0029)
Compulsory Schooling .0011 -.0001 .0009
 (.0006) (.0010) (.0008)
In (Per-capita GDP) .0040 .0095 -.0034
 (.0021) (.0072) (.0030)
Income Inequality -.0005 (**) -.0012 .0000
 (.0002) (.0016) (.0002)
English Official Language .0133 (**) .0219 (**)
 (.0026) (.0036)
Communist -.0041 -.0144 (**)
 (.0038) (.0054)
Coup or Revolution .0027 -.0001 .0028
 (.0022) (.0033) (.0031)
Assassinations -.0030 (*) .0006 -.0030
 (.0013) (.0018) (.0018)
Americas -.0082 (**) .0060
 (.0025) (.0035)
Asia .0064 (*) .0203 (**)
 (.0029) (.0041)
Africa .0041 .0302 (**)
 (.0078) (.0110)
1980 Observation -.0042 (*) -.0008 -.0046
 (.0016) (.0026) (.0023)
Constant .0659 (*) .1577 (**)
 (.0265) (.375)
Country fixed effect in No No Yes
 first step?
Country fixed effect in No Yes No
 second step?
[R.sup.2] .8229 .9583 .6397

 Restricted Birth Cohorts
 (4) (5) (6)

In (Pupil-teacher Ratio) -.0152 (**) -.0392 (**) -.0291 (**)
 (.0034) (.0081) (.0080)
In (Expediture per Pupil) .0061 (**) -.0042 .0029
 (.0019) (.0036) (.0045)
Compulsory Schooling .0006 .0000 .0000
 (.0004) (.0008) (.0010)
In (Per-capita GDP) .0049 (**) .0137 (*) -.0033
 (.0018) (.0067) (.0043)
Income Inequality -.0002 -.0020 -.0002
 (.0001) (.0018) (.0003)
English Official Language .0114 (**) .0208 (**)
 (.0024) (.0055)
Communist -.0055 -.0039
 (.0039) (.0092)
Coup or Revolution .0030 .0014 .0025
 (.0019) (.0032) (.0044)
Assassinations -.0023 (*) .0010 -.0031
 (.0009) (.0013) (.0021)
Americas -.0103 (**) .0037
 (.0025) (.0058)
Asia .0026 .0151 (*)
 (.0025) (.0059)
Africa -.0003 .0208
 (.0065) (.0153)
1980 Observation -.0001 .0038 -.0023
 (.0013) (.0024) (.0031)
Constant .0570 (*) .1586 (**)
 (.0228) (.0535)
Country fixed effect in No No Yes
 first step?
Country fixed effect in No Yes No
 second step?
[R.sup.2] .8275 .9526 .4429

Notes: Sample size is 130. Standard errors are reported in parentheses.
Observations are weighted by cell count of first step.

(*)Statistically significant at the 5% level (two-tailed test).

(**)Statistically significant at the 1% level (two-tailed test).
TABLE 5

Log Wage Regreeions with Education-School Quality Interactions

 Linear Spline
 (1) (2) (3)

Education .0291 (**) .0292 (**) .0078 (**)
 (.0011) (.0012) (.0013)
Education> 9 .0186 (**)
 (.0021)
Education> 12 .0516 (**)
 (.0020)
Education (*) -.0207 (**) -.0246 (**) -.0111 (**)
 ln(Pupil-teachcr Ratio) (.0019) (.0020) (.0019)
Education> 9 (*)
 ln(Pupil-tcachcr Ratio)
Education> 12 (*)
 ln(Pupil-tcachcr Ratio)
Education (*) .0135 .0091 (**) .0080 (**)
 ln(Expcnditure per Pupil) (.0010) (.0010) (.0010)
Education > 9 (*)
 ln(Expenditurc pcr Pupil)
Education> 12 (*)
 ln(Expenditure per Pupil)
Education (*) .0015 (**) .0006 (*) .0001
 Compulsoiy Schooling (.0002) (.0003) (.0002)
Education (*) .0002 -.0028 (**) .0014
 ln(Per-capita GDP) (.0009) (.0010) (.0009)
Education (*) -.0005 (**) -.0004 (**) -.0005 (**)
 Incomc Inequality (.0001) (.0001) (.0001)
Education (*) .0157 (**) .0258 (**) -.0006
 English Official Lang. (.0013) (.0014) (.0013)
Education (*) -.0148 (**) -.0115 (**) -.0206 (**)
 Communist (.0017) (.0019) (.0017)
Education (*) -.0001 .0024 (*) -.0040 (**)
 Coup or Revolution (.0010) (.0010) (.0010)
Education (*) -.0053 (**) -.0055 (**) -.0031 (**)
 Assassinations (.0006) (.0006) (.0006)
Education (*) .0120 (**) .0068 (**) .0160 (**)
 Americas (.0012) (.0012) (.0012)
Education (*) .0305 (**) .0243 (*) .0105 (**)
 Asia (.0013) (.0015) (.0014)
Education (*) .0313 (**) .0273 (**) .0080
 Africa (.0043) (.0045) (.0043)
Education (*) -.0077 (**) -.0084 (**) -.0099 (**)
 1980 Observation (.0008) (.0008) (.0008)
ln(Pupil-teacher Ratio) .1178 (**) .1712 (**) .0347
 (.0253) (.0345) (.0251)
ln(Expenditure per Pupil) -.0520 (**) -.0926 (**) .0004
 (.0129) (.0168) (.0128)
Compulsory Schooling -.0069 (*) -.0097 (*) .0103 (**)
 (.0032) (.0044) (.0032)
ln(Per-capita GDP) .0701 (**) .1733 (**) .0617 (**)
 (.0125) (.0242) (.0124)
Income Inequality .0025 (**) -.0082 .0029 (**)
 (.0010) (.0048) (.0010)
English Official Lang. -.1002 (**) .1049 (**)
 (.0172) (.0175)
Communist .0969 (**) .1736 (**)
 (.0219) (.0218)
Coup or Revolution .0140 -.0199 .0521 (**)
 (.0123) (.0159) (.0122)
Assassinations .0350 (**) .0524 (**) .0125 (*)
 (.0058) (.0074) (.0058)
Americas -.2306 (**) -.2695 (**)
 (.0146) (.0145)
Asia -.3413 (**) -.1097 (**)
 (.0181) (.0184)
Africa -.4099 (**) -.1049
 (.0657) (.0654)
1980 Observation -.3606 (**) -.4086 (**) -.3140 (**)
 (.0502) (.0506) (.0498)
Constant 4.3053 (**) 4.526 (**)
 (.0350) (.0351)
Regression includes No Yes No
 country fixed effects?
[R.sup.2] .3394 .3463 .3498

 Spline
 (4) (5) (6)

Education .0092 (**) .0064 (**) .0073 (**)
 (.0013) (.0013) (.0014)
Education> 9 .0128 (**) .0210 (**) .0163 (**)
 (.0021) (.0022) (.0022)
Education> 12 .0555 (**) .0496 (**) .0522 (**)
 (.0020) (.0021) (.0021)
Education (*) -.0143 (**) -.0166 (**) -.0107 (*)
 ln(Pupil-teachcr Ratio) (.0020) (.0049) (.0049)
Education> 9 (*) .0166 -.0069
 ln(Pupil-tcachcr Ratio) (.0109) (.0110)
Education> 12 (*) -.0150 -.0019
 ln(Pupil-tcachcr Ratio) (.0088) (.0088)
Education (*) .0033 (**) -.0040 -.0059 (*)
 ln(Expcnditure per Pupil) (.0010) (.0028) (.0028)
Education > 9 (*) .0303 (**) .0206 (**)
 ln(Expenditurc pcr Pupil) (.0066) (.0067)
Education> 12 (*) -.0178 (**) -.0081
 ln(Expenditure per Pupil) (.0055) (.0055)
Education (*) -.0009 (**) .0001 -.0009 (**)
 Compulsoiy Schooling (.0003) (.0002) (.0003)
Education (*) -.0015 .0010 -.0023 (*)
 ln(Per-capita GDP) (.0010) (.0009) (.0010)
Education (*) -.0004 (**) -.0005 (**) -.0005 (**)
 Incomc Inequality (.0001) (.0001) (.0001)
Education (*) .0090 (**) -.0003 .0094 (**)
 English Official Lang. (.0014) (.0013) (.0015)
Education (*) -.0166 (**) -.0209 (**) -.0172 (**)
 Communist (.0018) (.0017) (.0018)
Education (*) -.0011 -.0037 (**) -.0004
 Coup or Revolution (.0010) (.0010) (.0011)
Education (*) -.0032 (**) -.0029 (**) -.0031 (**)
 Assassinations (.0006) (.0006) (.0006)
Education (*) .0114 (**) .0159 (**) .0111 (**)
 Americas (.0012) (.0012) (.0013)
Education (*) .0046 (**) .0109 (**) .0050 (**)
 Asia (.0015) (.0014) (.0015)
Education (*) .0043 .0078 .0026
 Africa (.0045) (.0043) (.0045)
Education (*) -.0101 (**) -.0100 (**) -.0102 (**)
 1980 Observation (.0008) (.0008) (.0008)
ln(Pupil-teacher Ratio) .0442 .0579 .0323
 (.0344) (.0363) (.0429)
ln(Expenditure per Pupil) -.0281 .0598 (**) .0167
 (.0168) (.0198) (.0222)
Compulsory Schooling .0103 (*) .0102 (**) .0099 (*)
 (.0043) (.0032) (.0043)
ln(Per-capita GDP) .1546 (**) .0649 (**) .1644 (**)
 (.0240) (.0125) (.0241)
Income Inequality -.0056 .0030 (**) -.0047
 (.0047) (.0010) (.0047)
English Official Lang. .1023 (**)
 (.0175)
Communist .1786 (**)
 (.0218)
Coup or Revolution .0366 (*) .0486 (**) .0283
 (.0159) (.0123) (.0160)
Assassinations .0270 (**) .0111 .0255 (**)
 (.0074) (.0058) (.0074)
Americas -.2683 (**)
 (.0146)
Asia -.1132 (**)
 (.0185)
Africa -.1067
 (.0659)
1980 Observation -.3693 (**) -.3165 (**) -.3692 (**)
 (.0502) (.0499) (.0503)
Constant 4.5311 (**)
 (.0351)
Regression includes Yes No Yes
 country fixed effects?
[R.sup.2] .3561 .3499 .3563

Notes: Sample size is 204,712. Standard errors are reported in
parentheses. Regressions also include age and its square, marital
status, English fluency, SMSA, health. eight census divisions, nine
immigrant cohorts, and interactions between each of these variables and
the 1980 indicator.

(*)Statistically significant at the 5% level (two-tailed test).

(*)Statistically significant at the 1% level (two-tailed test).

(*.) We are grateful to Michael Baker, David Card, William Carrington, Matthew Cushing, Daniel Hamermesh, Mary McGarvey, James Ragan, Stephen Trejo, and three anonymous referees for helpful comments.

(1.) See Card and Krueger (1996a, 1996b), Hanushek (1996), or Betts (1996b) for recent reviews of this literature and also the assessments of the current state of the empirical evidence in Blau (1996), Burtless (1996), and Moffitt (1996).

(2.) To avoid confusing schooling obtained in the United States and in the source country, in the empirical analysis we exclude immigrants who received some of their education after arriving in the United States.

(3.) The data appendix contains detailed descriptions of sample restrictions and variable definitions. Also, Table Al gives descriptive statistics of the regression samples and lists control variables included in the first-step regression model.

(4.) The 1980 census reports year of immigration only in five-year intervals, and the 1990 census reports two-year intervals for recent immigrants and five-year intervals for older immigrants. We apply the most restrictive interpretation of the data and assume that all immigrants in each immigration interval arrived in the United States the earliest year of the interval. For example, we impose sample restrictions as if all immigrants who report arriving between 1975 and 1979 immigrated in 1975.

(5.) Another possible explanation for the rise in returns is Jaeger's (1997) assertion that the change in wording of the census question about level of education in the 1990 census leads to higher estimates of returns to education in studies that linearize education in the 1990 census as we do.

(6.) Relative expenditures per pupil are expenditures per student divided by per capita gross domestic product (GDP). We use this variable rather than nominal expenditures because it better measures the proportion of resources devoted to education and is not sensitive to exchange rates or differences in prices of non-traded goods across countries.

(7.) Results are not reported in Table 3 but are available on request.

(8.) Although these studies focus on school resource effects on earnings, Loeb and Bound (1996) also find larger effects of school inputs on student achievement in older birth cohorts than studies based on more recent birth cohorts, suggesting "that both earnings and achievement effects may simply have diminished over time" (Moffit, 1996) in U.S. data.

(9.) More specifically, suppose the omitted quality of the educational regulations is positively correlated with the expenditures per pupil and also positively correlated wages. Regressions omitting the quality of the educational regulations would find a positive relation between expenditure per pupil and wages, even if no such relation exists. Under more restrictive assumptions, Hanushek et al. (1996) show that if key regulations are state-specific, the bias in the estimates of the impact of pupil-teacher ratios and other resources on wages will he largest in studies using state-level attributes of the education system. In our case, the criticism applies if omitted institutional features are country-specific.

(10.) This criticism centers on the lack of a pattern and frequent sign reversals in correlations between earnings and school quality when the earnings of workers residing in a given census division are compared to the school quality in the state where they grew up. When we follow Heckman et al.'s approach, we find that the immigrant data reveal a consistent pattern in rankings of school quality and earnings across census divisions, with signs according to the schooling quality hypothesis (results are available on request). The implication is that regional variation in demand for skill is less important for the Settlement pattern of immigrants across regions than it is for native-born migrants. Moreover, the consistent sign patterns acoss census divisions confirm our approach in which we view the U.S. labor market as a common point of reference for assessing educational quality in international data.

(11.) This assumption rules out the refugee sorting scenario in Borjas (1987).

(12.) Note that p is itself a function of z. In particular, p=[PHI](z) when [[sigma].sub.0] > [[sigma].sub.1], and p=1-[PHI](z) when [[sigma].sub.0] < [[sigma].sub.1].

(13.) Because this procedure is sensitive to the assumption of normality, we also used a procedure that adds a cubic polynomial of the migration rate to the first-step wage regression. Results from this alternative procedure were very close to OLS outcomes.

(14.) All results were estimated using rates of return estimated with selectivity controls as the dependent variable in the second step. There were no cases where selectivity control altered results in a substantial manner.

(15.) Betts (1996b), however, finds no significant age dependence in school quality effects.

(16.) We reach similar conclusions--the effect of the pupil-teacher ratio is greatest at low levels of attainment and the effect of expenditures per pupil increases with attainment--when we introduce nonlinearity in the schooling-wage profile through discrete intercept shifts rather than rotation of the slope as in the spline function.

(17.) An alternative explanation is that both attainment and quality of education are positively correlated with real GDP. Increases in income may lead individuals to choose more education and to improve the quality of education as well.

REFERENCES

Altonji, J., and T. A. Dunn. "Using Siblings to Estimate the Effect of School Quality on Wages." Review of Economics and Statistics, 78(4), 1996, 665-71.

Angrist, J. D., and V. Lavy. "Using Maimonides' Rule to Estimate the Effect of Class Size on Scholastic Achievement." Quarterly Journal of Economics, 114(2), 1999, 533-75.

Banks, A. S., ed. Political Handbook of the World: 1984-1995. Binghamton, NY: CSA Publications, various years.

Barro, R. J., and J. W. Lee. "International Comparisons of Educational Attainment." Journal of Monetary Economics, 32(3), 1993, 363-94.

_____. "International Measures of Schooling Years and School Quality." American Economic Review, 86(2), 1996. 218-23.

Behrman, J. R., and N. Birdsall. "The Quality of Schooling: Quantity Alone is Misleading." American Economic Review, 73(5), 1983, 928-46.

Betts, J. R. "Does School Quality Matter? Evidence from the National Longitudinal Survey of Youth." Review of Economics and Statistics, 77(3), 1995, 231-50.

_____. "Do School Resources Matter Only for Older Workers?" Review of Economics and Statistics, 78(4), 1996a, 638-52.

_____. "Is There a Link between School Inputs and Earning? Fresh Scrutiny of an Old Literature," in Does Money Matter? The Effect of School Resources on Student Achievement and Adult Success, edited by G. Burtless. Washington, DC: Brookings, 1996b.

Blau, F. D. "Symposium on Primary and Secondary Education." Journal of Economic Perspectives, 10(4), 1996, 3-8.

Borjas, G. J. "Self-Selection and Earnings of Immigrants." American Economic Review, 77(4), 1987, 531-53.

_____. "Immigration and Self-Selection," in Immigration, Trade and the Labor Market, edited by J. M. Abowd and R. B. Freeman. Chicago, IL: University of Chicago Press, 1991.

_____. "The Intergenerational Mobility of Immigrants." Journal of Labor Economics, 11(1), 1993, 113-35.

Borjas, G. J., and B. Bratsberg. "Who Leaves? The Out-migration of the Foreign-Born." Review of Economics and Statistics, 78(1) 1996, 165-76.

Buchinsky, M. "Changes in the U.S. Wage Structure 1963-1987: Application of Quantile Regression." Econometrica, 62(2), 1994, 405-58.

Burtless, G., ed. Does Money Matter? The Effect of School Resources on Student Achievement and Adult Success. Washington, DC: Brookings, 1996.

Butcher, K. F. "Black Immigrants in the United States: A Comparison with Native Blacks and Other Immigrants." Industrial and Labor Relations Review, 47(2), 1994, 265-84.

Card, D., and A. B. Krueger. "Does School Quality Matter? Returns to Education and the Characteristics of Public Schools in the United States." Journal of Political Economy, 100(1), 1992a, 1-40.

-----. "School Quality and Black-White Relative Earnings: A Direct Assessment." Quarterly Journal of Economics, 107(1), 1992b, 152-200.

-----. "Labor Market Effects of School Quality: Theory and Evidence," in Does Money Matter? The Effect of School Resources on Student Achievement and Adult Success, edited by G. Burtless. Washington, DC: Brookings, 1996a.

-----. "School Resources and Student Outcomes: An Overview of the Literature and New Evidence from North and South Carolina." Journal of Economic Perspectives, 10(4), 1996b, 31-50.

Chiswick, B. R. "The Effects of Americanization on the Earnings of Foreign-Born Men." Journal of Political Economy, 86(5), 1978, 897-921.

-----. "Is the New Immigration Less Skilled than the Old?" Journal of Labor Economics, 4(2), 1986, 168-92.

Grogger, J. "Does School Quality Explain the Recent Black/White Wage Trend?" Journal of Labor Economics, 14(2), 1996a, 231-53.

-----. "School Expenditures and Post-School Earnings: Evidence from High School and Beyond." Review of Economics and Statistics, 78(4), 1996b, 628-37.

Hanushek, E. A. "The Economics of Schooling: Production and Efficiency in Public Schools." Journal of Economic Literature, 24(3), 1986, 1141-77.

-----. "Measuring Investment in Education." Journal of Economic Perspectives, 10(4), 1996, 9-30.

Hanushek, E. A., S. G. Rivkin, and L. L. Taylor. "Aggregation and the Estimated Effects of School Resources." Review of Economics and Statistics, 78(4), 1996, 611-27.

Heckman, J. "Sample Selection Bias as a Specification Error." Econometrica, 47(1), 1979, 153-61.

-----. "Policies to Foster Human Capital." Research in Economics, 54(1), 2000, 3-56.

Heckman, J., A. Layne-Farrar, and P. Todd. "Human Capital Pricing Equations with an Application to Estimating the Effect of School Quality on Earnings." Review of Economics and Statistics, 78(4), 1996a, 562-610.

-----. "Does Measured School Quality Really Matter? An Examination of the Earnings-Quality Relationship," in Does Money Matter? The Effect of School Resources on Student Achievement and Adult Success, edited by G. Burtless. Washington, DC: Brookings, 1996b.

Hoxby, C. M. "How Teachers' Unions Affect Education Production." Quarterly Journal of Economics, 111(3), 1996, 671-718.

Jaeger, D. A. "Reconciling the Old and New Census Bureau Education Questions: Recommendations for Researchers." Journal of Business and Economic Statistics, 15(3), 1997, 300-09.

Jain, S., Size Distribution of Income. Washington, DC: World Bank, 1975.

Jasso, G., and M. R. Rosenzweig. "What's in a Name? Country-of-Origin Influences on Earnings of Immigrants in the United States." Research in Human Capital and Development, 4, 1986, 75-106.

-----. The New Chosen People: Immigrants in the United States. New York: Russell Sage, 1990.

Johnson, G., and F. Stafford. "Social Returns to Quantity and Quality of Schooling." Journal of Human Resources, 8(2), 1973, 139-55.

Juhn, C., K. M. Murphy, and B. Pierce. "Wage Inequality and the Rise in the Returns to Skill." Journal of Political Economy, 101(3), 1993, 410-42.

Katz, L. F., and K. M. Murphy. "Changes in Relative Wages, 1963-87: Supply and Demand Factors." Quarterly Journal of Economics, 107(1), 1992, 35-78.

Kreuger, A. B. "Experimental Estimates of Education Production Functions." Quarterly Journal of Economics, 114(2), 1999, 497-532.

Loeb, S., and J. Bound. "The Effect of Measured School Inputs on Academic Achievement: Evidence from the 1920s, 1930s, and 1940s Birth Cohorts." Review of Economics and Statistics, 78(4), 1996, 653-64.

Moffitt, R. A. "Symposium on School Quality and Educational Outcomes: Introduction." Review of Economics and Statistics, 78(4), 1996, 559-61.

Nehru, V., E. Swanson, and A. Dubey. "A New Data Base on Human Capital Stock: Sources, Methodology, and Results." Journal of Development Economics, 46(2), 1995, 379-401.

Psacharopoulos, G. "Returns to Education: A Further Update and Implications." Journal of Human Resources, 20(4), 1985, 584-604.

-----. "Returns to Investment in Education: A Global Update." World Development, 22(9), 1994, 1325-43.

Psacharopoulos, G., and A. M. Arriagada. "The Educational Composition of the Labor Force: An International Comparison." International Labor Review, 1225(5), 1986, 561-74.

Schultz, T. P. "Education Investments and Returns," in Handbook of Development Economics, edited by H. Chenery and T. N. Srinivasan. Amsterdam: Elsevier, 1988.

Summers, R., and A. Heston. "The Penn World Table (Mark 5): An Expanded Set of International Comparisons, 1950-1988." Quarterly Journal of Economics, 106(2), 1991, 327-68.

Taylor, C. L., and D. A. Jodice. World Handbook of Political and Social Indicators, 3d ed. New Haven, CT: Yale University Press, 1983.

United Nations Educational, Scientific, and Cultural Organization (UNESCO). Statistical Yearbook. Paris: Imprimerie de la Manutention, Mayenne, various years.

U.S. Arms Control and Disarmament Agency. World Military Expenditures and Arms Transfers. Washington, DC: Government Printing Office, 1984.

U.S. Bureau of the Census. Statistical Abstract of the United States: 1996. Washington, DC: 1996.

Welch, F. "Measurement of the Quality of Schooling." American Economic Review, 56(2), 1966, 379-92.

World Bank. World Development Report. New York: Oxford University Press, various years.

RELATED ARTICLE: ABBREVIATIONS

GDP: Gross Domestic Product

GNP: Gross National Product

OLS: Ordinary Least Squares

SMSA: Standard Metropolitan Statistical Area

APPENDIX A: DATA

This appendix details data sources and the construction of variables used in the empirical analyses.

Rates of Return to Education by Country of Origin

We estimate rates of return to education using wage regressions in microdata samples drawn from the 5/100 public use samples of the 1980 and 1990 censuses of population. In the two-step analysis, we run separate regressions for each census, thereby allowing every parameter of the wage model to change between census years. The dependent variable of the wage regression is the natural log of the weekly wage, constructed as 1979 or 1989 wages or salary income divided by the number of weeks worked that year. The wage regressions include a standard set of control variables: age and its square and dummy variables for English fluency (speak English well or very well), married with spouse present, residence in an SMSA, health limiting work, eight census divisions, and five (nine in 1990 sample) immigrant cohorts. We obtain the estimate of the country-of-birth specific rate of return to education as the coefficient on the interaction term between a country-specific dummy variable and years of schooling of the individua l.

Samples are restricted to immigrant males who arrived in the United States after completing their schooling. During the initial phase of the project, we focused on immigrants from 67 countries chosen on the basis of cell sizes in census data and availability of school quality characteristics. We later dropped two countries--China and Switzerland--from the second-step analyses because we expanded the set of school quality characteristics to include variables unavailable for these countries. Because the census questionnaire does not ask the year of graduation of the individual, we infer year of graduation as year of birth plus six plus years of schooling. Also, the census data only gives the year of immigration in five-year intervals (with the exception of immigrants who arrived during the 1980s for whom year of immigration is known in two- or three-year intervals).

We exclude persons from the regression sample if the inferred year of graduation falls within or after the five-year immigration interval. We also exclude persons who report being enrolled in school during the census year or earned less than $1,000 during the year preceding the census. Finally, we exclude persons less than 25 years of age and alternately impose two upper age restrictions: 64 and 35. The latter age group is designed to match up (i.e., they would have been 5-15 years of age) with the years for which we collect school quality characteristics, 1960 for immigrants in the 1980 census and 1970 for those in the 1990 census.

The sample restrictions leave sample sizes of 86,728 (1980) and 125,503 (1990) for the full sample and 26,414 (1980) and 42,459 (1990) for the restricted age group sample. Descriptive statistics for the full samples are presented in Table A1.

In the 1980 census data, we base years of schooling on the "highest year of schooling attended" question, and subtract one year if the respondent did not finish the highest grade attended, In the 1990 data, we convert educational attainment to years of schooling using the following rule: years of schooling equals zero if educational attainment is less than first grade; 2.5 if first through fourth; 6.5 if fifth through eighth, educational attainment if ninth, tenth, eleventh, or twelth; 12 if GED earned; 13 if some college, but no degree; 14 if associate degree; 16 if bachelor's degree; 18 if master's degree; 19 if professional degree; and 20 if doctorate degree. See Jaeger (1997) for a discussion of alternative conversion rules.

Immigration Rates by Educational Level

To form variables that allow us to control for immigration selectivity in the first-step regression models, we compute immigration rates for three levels of schooling (corresponding to the primary, secondary, and post-secondary levels). The computation uses the number of male immigrants with the level of schooling in the 5/100 public use sample of the census ([I.sub.jlt], where j subscripts country of birth, I level of schooling, and census year), the percentage in the male source country population having attained the level of schooling ([p.sub.jlt]), and the source country population ([pop.sub.jt]). We compute the migration rate ([m.sub.jlt] as

(A1) [m.sub.jlt] = 20 * [I.sub.jlt]/(20 * [I.sub.jlt] + [P.sub.jlt] * .5 * [pop.sub.jt])

We collect data on [p.sub.jlt] from Barro and Lee (1996). For seven countries not included in the Barro and Lee data set, we compute [p.sub.jlt], from enrollment ratios lagged 20 years. The enrollment data are drawn from UNESCO (various years). Finally, we collect population figures from Summers and Heston (1991), Banks (various years), and U.S. Bureau of the Census (1996). The computed migration rates are listed in Table A2. The table also contains summary statistics.

Source-Country School Quality Measures

We collect data on school quality characteristics from 1960 and 1970 (to be linked with estimated returns to education from 1980 and 1990, respectively). Descriptive statistics are presented in Table A3.

The pupil-teacher ratios in primary schools are collected from UNESCO (various years). For 1970, the data Source lists the pupil-teacher ratio, and for 1960 we compute the ratio from enrollment in primary schools and the number of primary-school teachers. These data cover both private and public schools.

We base the measure of expenditures per pupil on government educational expenditures as percentage of GDP. The educational expenditure data refer to recurring expenditures over the five-year period following 1960 or 1970 and are collected from Barro and Lee (1993). For countries not included in the Barro and Lee data set, we apply their method and compute recurring educational expenditure percentage based on data drawn from UNESCO (various years). We calculate nominal expenditures per pupil as educational expenditures as percentage of GDP multiplied by GDP divided by total student enrollment. GDP is computed from per capita GDP (in constant $ chain indexed 1985 international prices) and population size. The GDP and population data for 1960 and 1970 are collected from Summers and Heston (1991), except for two countries not included in the Summers and Heston data (Cuba and Lebanon) and three observations from 1960 missing in these data. For these data points, we collect population and per capita gross national product (GDP) figures from U.S. Arms Control and Disarmament Agency (1984), and impute per capita GDP from per capita GNP figures and sample means of per capita GDP and per capita GNP for countries with nonmissing GDP figures in the Summers and Heston data set. Empirical results presented herein are not sensitive to the exclusion of data points, for which we were forced to impute GDP figures.

Finally, we collect the duration (in years) of compulsory education from UNESCO (various years).

Other Source-Country Characteristics

Other source country characteristics used in the empirical analyses include a measure of income inequality, indicator variables for English being the official language, communist regime, and coup or revolution, the number of assassinations per million population, and indicator variables for continent. Summary statistics are presented in Table 2.

We construct the measure of income inequality as the ratio of income accruing to the top 10% of house-holds to income accruing to the bottom 20% of house-holds These data are drawn from Jain (1975), Taylor and Jodice (1983) and the World Bank (various years). Because these data are unavailable for the early 1960s for a large number of countries in our sample, we use data from around 1970 and 1980.

Data on official language and political status are collected from Banks (various years), data on coups and revolutions from Taylor and Jodice (1983) and Banks (various years), and data on assassinations from Barro and Lee (1993) and Banks (various years). The assassinations variable reflects the number of politically motivated murders or attempted murders of high government officials or politicians during the 1960s or 1970s, respectively. We construct the variable by adding up the number of assassinations per million population for each year during the decade.

TABLE A1

Descriptive Statistics--First-Step Regression Sample

 1980 Census
 (Sample Size = 86,728)
 Mean SD

In (Weekly Wage) 5.616 .678
Years of Schooling 10.399 4.998
Age 43.204 11.067
Age Squared 1,989.090 980.293
Speaks English Well or Very Well .705 .456
Married Spouse Present .841 .365
SMSA .904 .294
Health Limiting Work .035 .184

Region
 New England .063 .244
 Mid-Atlantic .254 .435
 East North Central .127 .333
 West North Central .015 .123
 South Atlantic .109 .312
 East South Atlantic .006 .077
 West South Atlantic .076 .265
 Mountain .031 .174

Year of Immigration
 1985-86
 1982-84
 1980-81
 1975-79
 1970-74 .227 .419
 1965-69 .176 .381
 1960-64 .117 .322
 1950-59 .128 .334
 Pre-1950 .068 .251

 1990 Census
 (Sample Size = 125,503)
 Mean SD

In (Weekly Wage) 6.019 .756
Years of Schooling 10.138 5.349
Age 41.755 10.586
Age Squared 1,855.530 925.507
Speaks English Well or Very Well .660 .474
Married Spouse Present .777 .416
SMSA .947 .225
Health Limiting Work .032 .176

Region
 New England .048 .213
 Mid-Atlantic .201 .401
 East North Central .081 .273
 West North Central .010 .101
 South Atlantic .134 .340
 East South Atlantic .005 .069
 West South Atlantic .100 .301
 Mountain .038 .190

Year of Immigration
 1985-86 .108 .310
 1982-84 .117 .322
 1980-81 .137 .344
 1975-79 .175 .380
 1970-74 .140 .347
 1965-69 .091 .288
 1960-64 .057 .231
 1950-59 .038 .192
 Pre-1950 .002 .047
TABLE A2

Estimated Migration Rate for U.S. Immigrant Males by Schooling and
Country of Birth

 1980 Census 1990
 Census
 Weighted
Country/Schooling 0-6 7-12 13-20 Average 0-6

Europe
 Austria .0050 .0141 .0989 .0149 .0014
 Belgium .0011 .0037 .0110 .0033 .0007
 Czechoslovakia .0015 .0158 .0206 .0064 .0005
 Denmark .0018 .0117 .0146 .0081 .0007
 Finland .0019 .0061 .0102 .0046 .0004
 France .0003 .0046 .0094 .0023 .0003
 Germany .0037 .0433 .0437 .0148 .0037
 Greece .0098 .0498 .0482 .0232 .0027
 Hungary .0156 .0075 .0484 .0123 .0007
 Ireland .0089 .0725 .1015 .0420 .0040
 Italy .0087 .0216 .0370 .0147 .0025
 Netherlands .0021 .0074 .0206 .0075 .0008
 Norway .0590 .0116 .0289 .0153 .0006
 Poland .0040 .0153 .0335 .0105 .0018
 Portugal .0103 .0639 .0252 .0178 .0067
 Romania .0008 .0038 .0099 .0028 .0008
 Spain .0010 .0076 .0058 .0025 .0005
 Sweden .0033 .0125 .0121 .0088 .0009
 Switzerland .0023 .0062 .0166 .0069 .0009
 UK .0023 .0165 .0248 .0104 .0016
 USSR .0011 .0019 .0021 .0017 .0008
 Yugoslavia .0028 .0116 .0172 .0069 .0010

Asia
 China .0003 .0001 .0189 .0003 .0003
 Hong Kong .0048 .0160 .0803 .0161 .0040
 India .0001 .0002 .0065 .0003 .0000
 Indonesia .0000 .0005 .0113 .0002 .0000
 Iran .0005 .0057 .0741 .0040 .0004
 Iraq .0008 .0075 .0172 .0027 .0002
 Israel .0093 .0180 .0370 .0199 .0066
 Japan .0013 .0021 .0048 .0023 .0012
 Korea .0046 .0044 .0197 .0066 .0080
 Lebanon .0071 .0544 .0382 .0226 .0033
 Malaysia .0002 .0008 .0221 .0008 .0002
 Pakistan .0001 .0006 .0108 .0005 .0001
 Philippines .0042 .0172 .0306 .0111 .0022
 Singapore .0008 .0030 .0250 .0024 .0012
 Sri Lanka .0001 .0002 .0208 .0004 .0000
 Taiwan .0014 .0031 .0205 .0043 .0017
 Thailand .0004 .0027 .0144 .0011 .0006
 Turkey .0004 .0043 .0085 .0013 .0002

Africa
 Egypt .0002 .0010 .0102 .0012 .0001
 Kenya .0001 .0006 .0488 .0005 .0000
 Morocco .0001 .0024 .0207 .0007 .0000
 Sierra Leone .0001 .0017 .0690 .0008 .0001
 Tanzania .0000 .0023 .0054 .0002 .0000
 Uganda .0001 .0022 .0382 .0003 .0000

Oceania
 Australia .0013 .0019 .0045 .0023 .0010
 New Zealand .0049 .0025 .0057 .0038 .0011

North America
 Canada .0156 .0494 .0270 .0318 .0106
 Cost Rica .0032 .0504 .0355 .0114 .0021
 Cuba .0144 .1478 .3450 .0575 .0095
 Dominican Republ .0089 .1172 .0634 .0252 .0079
 El Salvador .0066 .1099 .1120 .0180 .0218
 Guatemala .0033 .0513 .0478 .0086 .0059
 Haiti .0030 .0885 .3505 .0164 .0052
 Honduras .0027 .0565 .0638 .0087 .0039
 Jamaica .0182 .2539 .4614 .0763 .0125
 Mexico .0234 .1160 .0332 .0339 .0257
 Panama .0103 .0627 .1410 .0358 .0084
 Trinidad and Tobago .0093 .1314 .2842 .0540 .0072

South America
 Argentina .0005 .0053 .0156 .0025 .0003
 Brazil .0001 .0019 .0023 .0003 .0001
 Chile .0008 .0052 .0166 .0033 .0006
 Colombia .0013 .0148 .0272 .0053 .0012
 Ecuador .0026 .0377 .0295 .0107 .0017
 Peru .0008 .0060 .0116 .0034 .0008
 Uruguay .0014 .0100 .0242 .0048 .0009
Mean (unweighted) .0047 .0281 .0507 .0112 .0029
SD .0084 .0451 .0848 .0148 .0047

 1990 Census
 Weighted
Country/Schooling 7-12 13-20 Average

Europe
 Austria .0069 .0450 .0094
 Belgium .0033 .0111 .0034
 Czechoslovakia .0087 .0204 .0049
 Denmark .0063 .0182 .0068
 Finland .0048 .0088 .0035
 France .0024 .0098 .0022
 Germany .0249 .0459 .0153
 Greece .0289 .0609 .0180
 Hungary .0152 .0411 .0100
 Ireland .0541 .0999 .0385
 Italy .0184 .0229 .0104
 Netherlands .0059 .0184 .0068
 Norway .0153 .0274 .0107
 Poland .0099 .0361 .0092
 Portugal .0800 .0432 .0190
 Romania .0035 .0179 .0038
 Spain .0054 .0088 .0026
 Sweden .0067 .0159 .0064
 Switzerland .0040 .0213 .0064
 UK .0117 .0347 .0111
 USSR .0008 .0034 .0012
 Yugoslavia .0082 .0154 .0055

Asia
 China .0002 .0065 .0005
 Hong Kong .0151 .1072 .0236
 India .0006 .0060 .0006
 Indonesia .0004 .0092 .0003
 Iran .0039 .0630 .0042
 Iraq .0056 .0134 .0024
 Israel .0233 .0346 .0202
 Japan .0018 .0060 .0027
 Korea .0075 .0299 .0121
 Lebanon .0691 .0653 .0284
 Malaysia .0011 .0344 .0018
 Pakistan .0012 .0139 .0009
 Philippines .0295 .0358 .0139
 Singapore .0037 .0471 .0048
 Sri Lanka .0005 .0265 .0008
 Taiwan .0061 .0440 .0111
 Thailand .0129 .0074 .0018
 Turkey .0027 .0077 .0011

Africa
 Egypt .0014 .0101 .0015
 Kenya .0012 .0383 .0007
 Morocco .0029 .0190 .0009
 Sierra Leone .0045 .0977 .0016
 Tanzania .0024 .0620 .0003
 Uganda .0019 .0483 .0005

Oceania
 Australia .0021 .0051 .0027
 New Zealand .0082 .0067 .0051

North America
 Canada .0219 .0550 .0280
 Cost Rica .0662 .0347 .0134
 Cuba .1177 .1892 .0635
 Dominican Republ .1708 .0701 .0360
 El Salvador .5871 .1204 .0802
 Guatemala .2206 .0602 .0231
 Haiti .1133 .4875 .0303
 Honduras .1029 .0603 .0176
 Jamaica .1883 .5142 .0954
 Mexico .1335 .0488 .0562
 Panama .0534 .1072 .0384
 Trinidad and Tobago .1105 .3967 .0645

South America
 Argentina .0045 .0123 .0029
 Brazil .0060 .0033 .0006
 Chile .0076 .0178 .0042
 Colombia .0355 .0336 .0081
 Ecuador .1194 .0178 .0117
 Peru .0214 .0155 .0063
 Uruguay .0151 .0211 .0068
Mean (unweighted) .0393 .0558 .0140
SD .0839 .0961 .0196

Notes: The migration rate is computed for each education level as U.S.
male immigrants/ (country-of-birth male birth male population + U.S.
male immigrants). Data sources are Barro and Lee (1996), Summers and
Heston (1991), UNESCO (various years), U.S. Bureau of the Census (1996),
and tabulations from 5/100 public use samples of the 1980 and 1990
censuses of population.
TABLE A3

Descriptive Statistics--School Quality Characteristics

 1960 Data 1970 Data

Variable Mean SD Mean SD

Pupil-teacher Ratio 33.7 8.6 30.8 8.9
Expenditure per Pupil/ .212 .112 .221 .104
 per-capita GDP
Compulsory Schooling 6.1 3.0 6.8 2.9
Per-capita GDP 2,798.6 2,613.5 4,072.0 3,067.5

 Correlation
 between 1960
Variable and 1970 Data

Pupil-teacher Ratio .895
Expenditure per Pupil/ .716
 per-capita GDP
Compulsory Schooling .822
Per-capita GDP .973

Note: Sample size is 65.

Bratsberg: Professor, Department of Economics, Kansas State University, Manhattan, KS 66506. Phone 1-785-532-7357, Fax 1-785-532-6919, E-mail bernt@ksu.edu

Terrell: Associate Professor, Department of Economics, 2114 CEBA, Louisiana State University, Baton Rouge, LA 70806. Phone 1-225-578-3785, Fax 1-225-578-3808,E-mail mdterre@lsu.edu