National Culture, Families, or Education Policies: What Determines National Test Scores?

Los análisis convencionales atribuyen las diferencias entre países en las puntuaciones promedio de los estudiantes a las características de la familia, los recursos escolares y las características del sistema escolar, pero los economistas institucionales y los antropólogos culturales afirman que las creencias culturales y las instituciones son los determinantes fundamentales del capital humano de una sociedad. Examino los efectos de las creencias culturales, las características institucionales, las características de la familia y las diversas características de las políticas educativas y escolares en las puntuaciones PISA promedio en matemáticas. Encuentro que las características culturales e institucionales nacionales explican más del 80% de la variación en las puntuaciones medias en 58 países. Cuando las características de la familia, la escuela y el sistema escolar se incluyen como causas, las características culturales e institucionales siguen explicando la mayor parte de la variación en las puntuaciones promedio. Más recursos financieros para las escuelas siguen aumentando puntajes promedio, la existencia de un examen de salida central tiene un efecto pequeño, y la participación de la matrícula privada en el sistema escolar no tiene ningún efecto en estas puntuaciones.Conventional analyses attribute cross-country differences in students’ average test scores to family characteristics, school resources, and school system characteristics, but institutional economists and cultural anthropologists argue that cultural beliefs and institutions are the fundamental determinants of a society’s level of human capital. I examine the effects of cultural beliefs, institutional characteristics, family characteristics, and various school and education policy characteristics on average PISA scores in mathematics. I find that national cultural and institutional characteristics explain over 80% of the variation in average scores across 58 countries. When family, school, and school system characteristics are included as causes, cultural and institutional characteristics continue to explain most of the variation in average scores. More financial resources for schools continue to raise average scores, the existence of a central exit exam has a small effect, and the share of private enrollment in the school system has no effect on these scores

An Estimate of Unidentified and Total U.S. Coronavirus Cases by State on April 21, 2020

I use data on cumulative tests, positive tests, and deaths for the coronavirus in South Korea and the U.S. lower-48 states during April 2020 to estimate the extent of infection and the unidentified share of the infected population in each state and in the U.S. as a whole on April 21, 2020. I find that 3.8 million people, or 1.2% of the population, have been infected in the U.S., with rates of infection that range from 0.1% in more rural states to 7.0% in New York state. I estimate that only 20% of all U.S. cases have been identified through testing. The unidentified share of total cases ranges from 61% to 83% across the states. I estimate that 38% of all cases are asymptomatic, which is consistent with the high shares of unidentified cases

The Effect of Temperature on the Spread of the Coronavirus in the U.S.

I investigate whether the cross-sectional data on cumulative (symptomatic) cases of coronavirus in the 48 contiguous states of the U.S. at the end of March 2020 provide any evidence that the rate of transmission of the virus declines at higher temperatures. Average temperatures in March varied from 30 to 71 degrees Fahrenheit in the 48 states. Controlling for other relevant factors, including population density and the availability of testing, I find no evidence that a higher average temperature in a state reduced the incidence of cumulative cases/capita of the virus in the state. These results provide no indication that seasonal increases in temperature will cause the coronavirus epidemic to disappear in the summer

World Total Factor Productivity Growth and the Steady-State Rate in the 20th Century

I estimate a Solow model augmented with human capital in 42 countries for 1910-2000. Estimated TFP growth is 0.3%/year, and the steady-state rate for GDP/capita is 1.0%/year. Implicitly for high-income countries maintaining growth above this rate will be increasingly difficult

Higher Test Scores or More Schooling? Another Look at the Causes of Economic Growth

I use a dynamic augmented Solow model to estimate the effects of students’ test scores and investment in schooling on economic growth rates in 49 countries during 1985-2005. In the complete data set, either average test scores or investment in schooling explain economic growth rates, and more of either causes growth. Further analysis reveals that higher test scores only raised growth rates in countries with low average levels of schooling. In countries with more than 7.5 years of schooling attainment in 1985, more investment in schooling raised growth rates, but higher average test scores did not

Increases in human capital and growth: new data, more conclusive results.

Using a new data set for human capital/adult, I show that changes in human capital cause economic growth in 56 countries over the 1985 to 2005 period. I show that these results are superior to results using average schooling attainment.Using a new data set for human capital/adult, I show that changes in human capital cause economic growth in 56 countries over the 1985 to 2005 period. I show that these results are superior to results using average schooling attainment.Using a new data set for human capital/adult, I show that changes in human capital cause economic growth in 56 countries over the 1985 to 2005 period. I show that these results are superior to results using average schooling attainment

A Revision to the Theory and the Practice of Development Accounting

Los profesionales de la contabilidad del desarrollo asumen que el rendimiento privado de la educación es igual a su producto marginal social. Se Muestra que este supuesto es incompatible con la estructura matemática de una función multiplicativa de producción, que especifica que el rendimiento privado es sólo una fracción del producto marginal social. A continuación, se muestra que los resultados empíricos de la contabilidad del desarrollo en la literatura subestiman considerablemente la contribución del capital humano para la producción nacional y sobreestiman las diferencias de productividad nacional.Practitioners of development accounting assume that the private return on schooling equals its social marginal product. I show that this assumption is inconsistent with the mathematical structure of a multiplicative production function, which specifies that the private return is only a fraction of the social marginal product. I then show that the empirical results from development accounting in the literature substantially underestimate human capital’s contribution to national output and overestimate national productivity differences

Cognitive Skills, Schooling Attainment, and Schooling Resources: What Drives Economic Growth?

This paper presents evidence that students’ test scores at ages 9 to 15 are not a good proxy for a nation’s stock of human capital. Across countries test scores rise with increases in human capital up to $40,000/adult (2000$), but then decline as human capital increases up to $125,000/adult. Schooling attainment is a better proxy for the human capital stock than test scores, but it is not very useful for statistical analysis because it is not a precise measure. The nation’s stock of human capital, calculated from cumulative investment in schooling, is the schooling measure most correlated with national income.This paper presents evidence that students’ test scores at ages 9 to 15 are not a good proxy for a nation’s stock of human capital. Across countries test scores rise with increases in human capital up to$40,000/adult (2000$), but then decline as human capital increases up to$125,000/adult. Schooling attainment is a better proxy for the human capital stock than test scores, but it is not very useful for statistical analysis because it is not a precise measure. The nation’s stock of human capital, calculated from cumulative investment in schooling, is the schooling measure most correlated with national income

Evidence that class size matters in 4th grade mathematics an analysis of TIMSS 2007 data for Colombia.

Like students in most developing countries, Colombian students in 4th grade performed poorly in the TIMSS 2007 test of mathematics skills, achieving an average score of 355 relative to an international mean of 500. After controlling for other factors and misreporting error, I find that large classes have substantial adverse effects on student achievement. Increases in class size from 20 to 53 students reduce test scores by about 80 points, or 2.4 points for each additional student in the class. Most likely this is the cumulative effect of class size in grades one to four on achievement in 4th grade.Like students in most developing countries, Colombian students in 4th grade performed poorly in the TIMSS 2007 test of mathematics skills, achieving an average score of 355 relative to an international mean of 500. After controlling for other factors and misreporting error, I find that large classes have substantial adverse effects on student achievement. Increases in class size from 20 to 53 students reduce test scores by about 80 points, or 2.4 points for each additional student in the class. Most likely this is the cumulative effect of class size in grades one to four on achievement in 4th grade

Penn world table 7.0: are the data flawed?

Penn World Table (PWT) 7.0 is the newest PWT data set, based in part on benchmarked prices collected in 2005. In theory the data in PWT 7.0 should be more accurate than the data in PWT 6.3 since 1996 and similar in earlier years. I show that PWT 7.0 GDP/capita and price data for 1970 to 1996 deviate substantially from prior PWT data that are benchmarked to prices across countries for those years. I conclude that overall the PWT 7.0 data are much less accurate than the data in PWT 6.2 and 6.3.Penn World Table (PWT) 7.0 is the newest PWT data set, based in part on benchmarked prices collected in 2005. In theory the data in PWT 7.0 should be more accurate than the data in PWT 6.3 since 1996 and similar in earlier years. I show that PWT 7.0 GDP/capita and price data for 1970 to 1996 deviate substantially from prior PWT data that are benchmarked to prices across countries for those years. I conclude that overall the PWT 7.0 data are much less accurate than the data in PWT 6.2 and 6.3