Abstract
We construct a comprehensive database of educational quality by cohort for 92 countries from 1970 to 2015 and analyze its impact on disparities in income and growth worldwide. To estimate educational quality, we utilize secondary students’ scores on international mathematics and science tests. Additionally, we impute unobserved test scores for individual countries in non-participating survey years. Wage regressions using individual earnings data reveal considerable returns to educational quality. We estimate human capital stock by incorporating differences in educational quantity and quality by age group across countries and over time. Our newly-constructed human capital dataset enabled us to explore the role of educational quality and human capital in understanding cross-country income disparities. The findings from development and growth accounting exercises indicated a discernible contribution of educational quality to per capita income and its growth rate.
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16 February 2024
The original online version of this article was revised: Supplementary file 3 has been included.
Notes
Human capital is determined by schooling, labor market experience, and innate abilities. It has many complex human attributes that are difficult to quantify. This study measures one component of human capital, educational attainment that can be compared across many countries. Educational quantity is often measured as the estimated average years of schooling for the adult population (Barro & Lee, 2013; Lee & Lee, 2016).
These studies rescaled the test scores for regional assessments using data for a common sample of countries that participated in international and regional assessments. However, the information derived from this limited sample may not be appropriate for rescaling test scores for African and Latin American countries that participated only in regional assessments. For example, Botswana is the only African country with international and regional assessment scores at the secondary level. Botswana participated in the Southern and Eastern Africa Consortium for Monitoring Educational Quality and TIMSS in 2007.
Reading test scores are available from PISA and the International Association for the Evaluation of Educational Achievement’s (IEA) international tests starting in the 1980s. Angrist et al. (2021) use Early Grade Reading Assessment (EGRA) scores. However, EGRA, which measures the most basic foundational skills for literacy acquisition in early grades in about 15 min, may not be fully comparable with other international assessments, which are subject to more careful participation choices, testing regimes, language considerations, and score scaling.
Several other datasets contain individual wage information. The International Income Distribution Database used by Islam et al. (2019) and Jedwab et al. (2022) covers more than 1,000 surveys for 145 countries from 1990 to 2016. However, this dataset is not publicly available. Lagakos et al. (2018b) use Integrated Public Use Microdata Series (IPUMS) data to estimate the wage equation. Although these data are publicly available, they do not include recent surveys. We need recent data on workers’ wages by age (cohort) that we can match to the estimated qualities of their secondary education.
We also use cross-country data from the Programme for the International Assessment of Adult Competencies (PIAAC) survey as an alternative sample. This dataset provides internationally-comparable data but covers only Organisation for Economic Cooperation and Development (OECD) member-countries. Our estimation of the wage equation shows significant returns to educational quality, although lower than those from the US immigrant sample, for this PIAAC sample. This result is available from the authors upon request.
We exclude China and India because the surveys were taken in a few select provinces.
The following 19 countries participated only in PISA: Albania, Argentina, Azerbaijan, Brazil, Costa Rica, Croatia, Kyrgyz Republic, Liechtenstein, Luxembourg, Macao Special Administrative Region, Mauritius, Mexico, Montenegro, Panama, Peru, Poland, Trinidad and Tobago, Uruguay, and Vietnam.
Patel and Sandefur (2020) develop a new methodology by combining two international assessments, the TIMSS and the Progress in International Reading Literacy Study (PIRLS), and two regional assessments, Laboratorio Latinoamericano de Evaluación de la Calidad de la Educación (LLECE) and Programme d'analyse des systèmes éducatifs de la CONFEMEN (PASEC). They estimate the conversion functions among different tests using the results of these exams for a single sample of primary school students in Bihar, India. This methodology is not applicable to our data construction, which does not combine international and regional assessments or math and science (TIMSS) and reading (PIRLS) scores. We also focus on test scores of secondary rather than primary school students.
We rescale the scores for earlier assessments using mean NAEP scores for 13-year-old US students. Using NAEP scores for 17-year-old US students does not affect the main results.
The results in Sections III and IV are robust to using individual mathematics or science test scores to measure educational quality.
The estimation results of the wage equation and estimates of human capital stock measures using alternative test scores, which were imputed through a machine-learning technique, are nearly identical to those reported in the following sections.
For comprehensive discussions on econometric issues, particularly those pertaining to endogeneity in estimating returns to educational quantity and quality in wage regressions, refer to the surveys conducted by Card (1999), Gunderson and Oreopoulos (2020), and Hoekstra (2020). These surveys also provide insights into existing studies that have addressed these issues.
The ordinary least squares estimator \(\widehat{\beta }\) is given by \(cov(\overline{Q },\alpha +\beta \overline{Q }+\beta {\mu }_{i}+{u}_{i})/cov(\overline{Q },\overline{Q })=\beta +cov(\overline{Q },\beta {\mu }_{i})/cov(\overline{Q },\overline{Q })=\beta (1+cov(\overline{Q },{\mu }_{i})/cov(\overline{Q },\overline{Q }))\). By replacing \(\overline{Q }\) with \({Q}_{i}-{\mu }_{i}\), we have \(\widehat{\beta }=\beta (1+cov({Q}_{i}-{\mu }_{i},{\mu }_{i})/cov({Q}_{i}-{\mu }_{i},{Q}_{i}-{\mu }_{i}))\). Under the assumption of no correlation between \({Q}_{i}\) and \({\mu }_{i}\), we obtain \(\widehat{\beta }=\beta (var{Q}_{i}/(var{Q}_{i}+var{\mu }_{i}))\). Therefore, \(\widehat{\beta }\) is smaller than the true \(\beta .\) Note that \(var{Q}_{i}/(var{Q}_{i}+var{\mu }_{i})=(var\overline{Q }+var\left({Q}_{i}-\overline{Q }\right))/(var\overline{Q }+2var\left({Q}_{i}-\overline{Q }\right)).\) Accordingly, the bias measure \(\beta /\widehat{\beta }\) becomes larger as the variance of the individual-level test score relative to the country-level average test score increases.
Previous research has demonstrated that differences in abilities play an important role in accounting for the variation in students' academic achievements, although the magnitude of the bias is considered minor (Card, 1999; Gunderson & Oreopolous, 2020). Additionally, studies show that some cross-country variations in student achievement are also attributed to unobserved factors including intellectual abilities (Hanushek & Woessmann, 2011). Lynn (1982) contends that the intellectual abilities of East Asian students, rather than their efforts or other school factors, are connected to their individual success in mathematics. Various researchers, including Flynn (1987), have observed secular gains in IQ, even among preschool children, in various countries. These gains may be indicative of environmental factors such as improvements in nutrition and medical care, as well as reductions in fertility.
The standard deviation of country average scores is 66.8 in the sample of wage regression.
We also perform a two-step estimation, in line with Schoellman (2012) and Li and Sweetman (2014), that estimates country-specific returns to educational quantity and then estimates the impact of educational quality (i.e., test scores) on these returns. The quantitative impact of educational quality on wages in this two-step estimation is much smaller than that from our reduced-form estimation. The results are available on request.
We also test whether returns to educational quality vary by the duration of residence, that is, the number of years that the worker spent in the US since the year of immigration. We find that these returns decline with the duration of residence in the US.
This equation implies that human capital per worker across all educational levels is the weighted sum of the shares of workers multiplied by their marginal products (or wage rates). Wages are determined by educational level and quality. This equation implies that the wage rate of a person with no schooling is normalized to equal one.
This specification does not include returns to experience. Some recent studies, such as Lagakos et al. (2018a) and Jedwab et al. (2022), attempt to estimate wage-experience profiles and returns to experience across countries. They estimate different returns to experience in developed and developing economies. We leave the measurement of human capital stock incorporating work experience for future study.
This measure of aggregate human capital stock assumes perfect substitution between workers with different educational attainments. Assuming a lower elasticity of substitution between high-educated and low-educated workers tends to increase differences in the human capital stock over time and across economies (Lee & Lee 2016). This issue seems less important for incorporating educational quality into our aggregate human capital stock because it is assumed to be equal across all education levels in a given year and country.
Hanushek et al. (2017) construct an estimate of quality-adjusted human capital stock for 47 US states considering returns to education quantity and quality of around 8% and 17%, respectively, based on previous studies using US labor data.
We use the latest version of the Barro-Lee dataset that estimates educational attainment for people between 15 and 64 years old, disaggregated by 10-year age group, in 146 countries at five-year intervals from 1950 to 2015. The dataset is available from www.barrolee.com.
We appreciate the anonymous reviewer for suggesting this example.
We also apply the growth accounting decomposition from 1985 to 2015 to the 62 countries with complete data in that period. The estimated contributions of educational quality and human capital to per-worker GDP growth are similar.
When we use the alternative measure of human capital stock that is based on the estimated returns to educational quantity and quality using the sample with imputed test scores, the estimated fractions of the average per-worker GDP growth rate explained by quality-adjusted human capital per worker are similar to the baseline estimates.
The augmented Mincer-type specification in Eq. (6) shows that the growth rate of quality-adjusted human capital is the sum of two components—the growth rate of educational quantity (i.e., quantity-based human capital) and the growth rate of educational quality.
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Acknowledgements
We sincerely thank Robert Barro, Wei Chi, So Kubota, Jong-Suk Han, Eric Hanushek, Do Won Kwak, Kwanho Shin, and Eunbi Song for their comments and discussions, with special gratitude to the editor and associate editor for their constructive comments and invaluable suggestions. This research was supported by a Grant from Korea University (K2110021).
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All authors contributed to the conception and design of the study. HL performed the data collection, which was thoroughly reviewed by J-WL. The statistical analysis was conducted by both J-WL and HL. J-WL wrote the first draft of the manuscript with the support of HL. Finally, all authors read and approved the final manuscript.
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Lee, H., Lee, JW. Educational quality and disparities in income and growth across countries. J Econ Growth (2024). https://doi.org/10.1007/s10887-023-09239-3
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DOI: https://doi.org/10.1007/s10887-023-09239-3