Silviase/jgov_v1.4_full_ann huggingface dataset:
twin-fixed effect model are considered as the causal effect of college quality and

selectivity conditional on individual observed and unobserved characteristics. As

already explained, MZ twins are supposed to share the same genetic materials and

family environments, which is the ideal situation to precisely control for unobserved

family components $ (A_{j}) $ and to isolate the pure effect of college quality and selectivity.

The estimates drawn from the sample restricted to MZ twins are more accurate than the

those drawn from the entire sample of twins. Therefore, we will mainly show the results

from the MZ twins’ sample, thus following the previous literature.

## Data

We combined three data sets to estimate the returns to higher education by each college. The first data set is the twins' data set that the authors collected through a web-based survey (see Nakamuro & Inui, 2012 and Nakamuro et al., 2013 for more detailed information on the survey). This survey is designed to gather information on over 4,700 individuals in over 2,300 households. The second data set is the microdata from the School Basic Survey collected by the Ministry of Education, Culture, Sports, Science and Technology (MEXT) in Japan. The School Basic Survey is time-series data, started in 1948, based on school characteristics, such as the numbers of applicants, enrolled students, and teaching and administrative staff in school. The third data set is
deviation values (called hensachi in Japanese) calculated by Kawai juku, one of the largest scale cram schools in Japan. It releases Kawai juku's Ranking of Deviation Values every year on its website (see http://www.keinet.ne.jp/rank/index.html). A series of deviation values represent college selectivity, or relative competitiveness to get an admission to each institution, for all colleges and universities in Japan. The interpretation of the deviation value is that the larger the institution, the more selective or competitive it is.

A noteworthy feature of the web-based twins' survey is to include the name of the institution and department that a respondent actually graduated from. Thus, we are able to match the name of institutions with information on the college characteristics provided in the School Basic Survey and college selectivity measured by the Kawai juku's Ranking of Deviation Values.

Our survey was implemented during the fiscal year of 2010. This survey is one of the largest twins' data sets in Japan, with a wide variety of sociodemographic characteristics. More specifically, we conducted the survey through Rakuten Research, which is affiliated with Rakuten, a major Internet shopping site (similar to Amazon.com or eBay, for example), and monitors over 2.2 million people. To analyze the effect of education on earnings, our sample targeted twins who were nonstudents between the
ages of 20 and 60. $ ^{3} $ Through this web-based survey, one member of a twin-pair is

responsible for reporting on both himself and his twin sibling at the same time. In our

estimations, the information on the respondents’ twin sibling is treated as though it were

directly provided by that twin sibling themselves. $ ^{4} $ The entire sample in this survey is

2,360 complete pairs of twins (4,720 individuals), including 1,371 pairs (2,742

individuals) of MZ twins and 882 pairs (1,764 individuals) of DZ twins. Therefore, for

this study, we completely restrict the sample to MZ twin-pairs, both of whom completed

four-year tertiary education. $ ^{5} $ As illustrated in Table 1, the numbers of college graduates
in our sample are 373 complete pairs of MZ twins (746 individuals). Out of those twins, only 61 pairs of MZ twins (122 individuals) attended the same college, indicating that a majority of twin-pairs chose different colleges to attend.

To identify the determinants of a within-pair variance in earnings, which is solely determined by postsecondary education, we further restrict the sample to twin-pairs who attended the same or similar ranking high schools which is defined as a difference in deviation values between two institutions of less than five (in Model V) and three (in Model VI). This setting enables us to assume that they were exposed to the same quality of primary and secondary education. Out of those twins, 139 complete pairs of MZ twins attended the same high school.

Our measure of income is the natural logarithm of annual wage earned during the fiscal year of 2009 before tax deduction. The response category in the original questionnaire ranged from one (no income or less than 0.5 million JPY) through 16 (more than 15 million JPY). We set the minimum (1 = no income and less than 0.5 million JPY) to zero and maximum (16 = more than 15 million JPY) to 15 million JPY. Then, we took the median value for categories between two (0.5 million to 0.99 million JPY) and 15 (10 million to 14.99 million JPY). The control variables deemed to affect productivity, and hence earnings are included in the models: they are age, gender (1 =
male), marital status (1 = married), hours worked per day, and number of years in current employment.

In the School Basic Survey, we can retrieve information on approximately 1,170 colleges and universities nationwide in Japan. According to Long (2008), college quality is classified into three general types: (i) institutional quality; (ii) quality of peers; and (iii) quality of faculty members. Therefore, we use the following information as a proxy for college quality: as a component of institutional quality, (1) a type of college (1 = private) and (2) location of college (1 = Tokyo metropolitan areas $ ^{6} $ and Osaka); as a component of quality of peers, (3) the total number of undergraduate students, (4) the ratio of Ph.D. students to the total number of students, and (5) the ratio of foreign to Japanese students; and as a quality of faculty members and administrative staff, (6) the ratio of students to full-time faculty members, (7) the ratio of students to administrative staff, (8) the ratio of full-time professors to the total number of full-time faculty members, (9) the ratio of full-time to part-time faculty members, and (10) the ratio of foreign to Japanese faculty members.

The striking feature of these statistics is that there is information on school financing; the data is only available for public institutions though. It contains the
following: (11) the average salary of full-time faculty members; (12) tuition fees per student; and (13) the amount of subsidy received from national and local governments per student. The subsidy received from local and national governments is for operating support funds, which is different from research grants, such as Grant-in-Aid for Scientific Research, generally called “Kaken-hi.” It is thus appropriate to use this measurement as a proxy of college quality because this is mainly spent on school resources. The greatest drawback of this data is that we are not able to access anything before 2003. We thus take the mean value for each period between 2003 and 2010, assuming that school characteristics were relatively stable over the years. It is worth mentioning that the variables in the earliest year are highly correlated with the latest.

The descriptive statistics summarized in Table 2 show that the average age of MZ twins in our sample is 40 years, with 4.77 million JPY annual earnings, which is according to the Basic Survey on Wage Structure released by the Ministry of Health, Labor and Welfare, very close to the national average of 4.71 million JPY for 2009, among all employees with a college degree and engaged in their current employment of 11 years, working 8 h a day. They graduated with a major in humanities, social sciences, arts, and music from a relatively large private university. Both foreign faculty members and students are relatively small. Of the full-time faculty members, almost half may be
experienced and senior full-time professors. On an average, the student–faculty ratio is 26, and the student–administrative staff ratio is 37. Surprisingly, the salary, tuition fees, as well as subsidy received at national and municipal universities vary significantly between institutions.

## Empirical Results

As already explained, the twin-fixed effect estimates drawn from the sample of MZ twins are more accurate to fully control for the unobserved family components. Therefore, we restricted our study to the sample of MZ twins and compared the results between the conventional estimates (OLS) and twin-fixed effect estimates (FE).

Looking at the difference in the coefficients on college quality and its statistical significance between OLS and FE estimates, we can find whether the unobserved family component confounds the impact of college quality as well as individual characteristics on labor market outcomes.

Our analysis begins with the Model I, which is reported in the first column of Table 3-1 (OLS), along with the heteroskedasticity robust standard errors. Model I included both men and women, while Model II included only men. The conventional OLS estimate suggests that college quality does not matter, but college selectivity measured by the deviation value does. The coefficient on the respondents' major,
whether or not humanities-related, is negatively significant at a 10% level, indicating that students who majored in math, science, and engineering are more likely to obtain higher earnings in subsequent life. However, once we employed the twin-fixed effect to account for the unobserved family component, as shown in the second column of Table 3-1 (FE), it can be seen that the coefficients both on college selectivity and the major become statistically insignificant. Instead, the ratios of students to full-time faculty members, professors to the total number of teaching staff, and full- to part-time faculty members are systematically related with graduates' subsequent earnings with expected signs. In general, the full-time professors at college are more senior and experienced, and the full-time faculty members make stronger commitments to management and other duties. Therefore, this result may suggest that the quality of teaching staff plays an important role for students in effectively accumulating their skills and knowledge.

Then, we run a separate regression based on male-only samples because there is quite a gender disparity in earnings. Our results, which are presented in Model II of Table 3-1, do not show any significant differences between the male sample and the entire sample. However, one noteworthy feature of Model II is that the OLS estimates suggested that the ratio of foreign to Japanese students is negative and statistically significant, but once the twin-fixed effect model was employed, the coefficient turned
into positive and statistically significant, indicating that the more the number of foreign students relative to native Japanese students, the better are the subsequent earnings later in life. This is supporting evidence for the Government of Japan, which has strongly pushed forward and prioritized global education on the policy agenda. However, we should keep in mind that the composition of foreign students has been dramatically increased since the Government of Japan stipulated the amendment of the Immigration Control and Refugee Recognition Act of 1989. It is therefore difficult to say that the composition of foreign students in 2003 through 2010 on average is the same as that at the time when a majority of respondents in our survey attended college. As in Model I, the ratios of professors to the total number of teaching staff and full- to part-time faculty members are positive and statistically significant for the results drawn from FE. Both Models I and II suggested that the composition of full-time professors is a crucial determinant of graduates' labor market outcomes after graduation.

Dale & Krueger (2002) concluded that, although the effect of college quality is indistinguishable from zero, the effect is positive for students from low-income families. Hence, the effect may be heterogeneous across income. We thus run a separate regression restricted to twin-pairs from low-income families in spite of the sample size being quite small (see Model III of Table 3-2). In the twins' survey, we asked
respondents about their living standards when they were 15 years old, measured the

responses on a five-point scale (1 = very good; 2 = good; 3 = average; 4 = bad; 5 = very bad). We ran a separate regression with the sample restricted to those who answered “bad” (4) or “very bad” (5).

While OLS estimates in Model III show that college quality does not matter, the FE estimates, coupled with the positive coefficients on college quality, clearly support Dale & Krueger (2002): we find evidence of a positive payoff from attending national or municipal colleges or universities with a larger number of Ph.D. students and greater proportion of full-time professors, while the student–faculty ratio is smaller than its counterpart. This estimation also complements the view on global education: the numbers of foreign students as well as foreign faculties have a positive impact on earnings. Most of the coefficients have sizable magnitudes.

The reason behind this finding that college quality matters, particularly for graduates from low-income families, is that the returns to investments in college quality would be higher than those for their counterparts from middle- or high-income families. Perhaps, students from low-income families were merely exposed by low-quality of secondary education before they entered college, including the opportunity to access
shadow education, in part due to the financial constraints of the household $ ^{7} $ . Therefore,

when one of the twin pairs from the poor family was admitted by a college with high

level of quality while his/her twin sibling wasn’t and went to different college with low

level of quality, the return to college quality between twins would become quite larger

as compared with twins from rich families who received high quality of secondary

education, which may reduce the difference in earnings potential between twins before

entering college.

In the School Basic Survey, there is much financial information, such as salary, tuition fees, and subsidy. Unfortunately, this information is only available for public institutions, although we investigate the effect of these expenditures on labor market outcomes of graduates. One significance of this study is that we are able to separately analyze the effect of college quality and college selectivity. In western countries, it is apparent that college selectivity is strongly correlated with resources, and hence, much of the previous literature used the type of college, such as top private, middle private, and top public, as an indicator of college quality that combined the measure of college
selectivity with resources. However, in Japan, college selectivity is not correlated with resources. In fact, in our data set, college selectivity measured by deviation value is not correlated with, or is weakly correlated with, any type of financial resource.

College selectivity is almost solely determined by performance in written exams, and admission committees do not freely choose students on the basis of individual characteristics beyond their accomplishments in the entrance exam. Moreover, resources, especially salary, tuition fees, and subsidy, are largely affected by government policy; the national and prefectural public universities are competitive educational entities, sometimes more competitive than even private universities, where a substantial amount of subsidies has been allocated. Therefore, in this context, Japan is a highly suitable subject to explore the effect of college quality, separate from college selectivity conditional on individual characteristics. Many studies just simply address whether college selectivity matters. However, the effectiveness of spending on college education is a more critical issue for public policy. This research must provide a clue on how to allocate a limited amount of public expenditure for education to colleges.

In Model IV of Table 3-2, we run a separate regression that is restricted to twin-pairs who graduated from a public university in order to look at the effects of financial resources conditional on college selectivity and individual characteristics. The
conventional OLS estimate indicates tuition has a significant positive effect on earnings, but once corrected for selection, the effect becomes indistinguishable from zero. Other variables, such as the salary of full-time faculty members and the subsidy received from national and local governments, also have no positive impact on earnings.

Finally, to identify the determinants of a within-pair variance in earnings, which is solely determined by postsecondary education, we further restrict the sample to twin-pairs who attended the same or similar ranking high schools, which is defined as a difference in deviation values between two high schools as less than five (in Model V) and three (in Model VI). In our dataset, we asked to the respondents the name of high schools from which they graduated. We matched the name of institutions with information on the deviation value at high school provided by Kanjuku, one of the large-scale cramming schools in Japan. In Model V, 194 complete pairs of MZ twins and in Model VI, 153 complete pairs of MZ twins are included while, out of those twins, 139 complete pairs of MZ twins attended the same high school. This setting enables us to assume that they were exposed to the same quality of primary and secondary education. The result is more striking than that for other models; after accounting for potential bias, almost no quality variables are statistically significant, except for the type of college in Model VI. Taken as a whole, it can be said that college quality does not
matter after controlling for college selectivity and individual characteristics. We note that the computed Variance Inflation Factor (VIF) suggests no severe colinearity among college quality measures included in Models I through VI.

## Conclusion

This study attempts to answer the question of why there is a variation in earnings among college graduates. In fact, graduates from more selective colleges are more likely to have higher earnings than graduates from less selective colleges. There are two possible explanations: first, the variation may reflect the quality of education, which is often measured by expenditure on educational resources, such as faculty per student, tuition fees, and student enrollments; second, it may be the signaling effect of education. If signaling is true, it cannot be seen as a positive effect of college quality on earnings. Furthermore, one methodological problem in estimating the returns to college quality is the selection bias arising when part of labor market outcomes can be explained by worker’s unobserved characteristics that are also correlated with their college choice. Economists disagree on the causal effect of college quality on the subsequent earnings of graduates.

This study thus addresses the nonrandom selection of students on different

college quality by analyzing a sample of twins to eliminate the effect of genetic makeup
and family environments. This is a unique twin data set wherein the college from which a twin pair is actually graduated is matched to the college characteristics as well as resources retrieved from official statistics, namely the School Basic Survey, from 2003 through 2010. In addition, in this study, we place a greater emphasis on college characteristics and resources than the college selectivity measured by the deviation values. As colleges and universities in Japan, especially public institutions, are largely supported by government funding, policymakers must be keen to know what type of college characteristics and resources lead to higher labor market premiums. Much of the policy debate in this topic hinges on the effect of college quality on graduates' subsequent earnings, leading to how effectively a college has spent money on education. In particular, Behrman et al (1996) found a positive and significant labor market return from attending small, private, and Ph.D.-granting universities with highly rewarded, experienced faculty members. We basically follow the line of previous research conducted by Behrman et al (1996) and Lindahl & Regner (2003).

The results drawn from empirical analyses show that once we control for the selection of unobserved genetic and family endowments that are constant over time by using the sample of identical twins and twin-fixed effects model, in some empirical specifications, the composition of full-time faculty members, for example, student–
faculty ratio, professors–teaching staff ratio, and full-time to part-time faculty ratio, is regarded as an important determinant of the variation in subsequent earnings among college graduates. Furthermore, although the sample is quite small, college quality has a significantly positive effect on students from low-income families.

However, once we restricted our sample with twin-pairs who may be exposed by similar high school quality, the effect of college quality on earnings became indistinguishable from zero. This result is more relevant to evaluate the college quality because it is ruled out that the effect of college quality is confounded by the effect of high school quality. Therefore, college quality does not matter. This implies that outcomes measured in subsequent life, such as earnings, were more likely to be determined prior to college enrollments. The conclusion in this paper thus is that we find no strong evidence of the positive returns to college quality in Japan.
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