Small Sample Bias in Synthetic Cohort Models of Labor Supply

In synthetic cohort models (cross-sectional data grouped at the cohort and year level), researchers often ignore potential biases induced by sampling error because they have 100 or 200 observations per group. I investigate small sample biases in the context of two synthetic cohort labor supply applications ? a model of intertemporal labor supply of men (similar to that of Browning, Deaton, and Irish, 1985) and a female labor supply model (similar to that of Blundell, Duncan, and Meghir, 1998). My approach is to use the Current Population Survey to compare the estimates when group sizes are extremely large to those that arise from randomly drawing subsamples of observations from the large groups. This provides a natural framework for examining the extent of small sample biases and the group sizes required so that small sample biases are negligible. I augment this approach with Monte Carlo analysis so as to precisely quantify biases and coverage rates. I find that, in these two applications, thousands of observations per group are required before small sample issues can be ignored in estimation. In these applications, sampling error leads one to underestimate intertemporal labor supply elasticities for men, and conclude that the income response of female labor supply is zero or tiny when in fact it is quite large.

Improved Errors-in-Variables Estimators for Grouped Data

Grouping models are widely used in economics but are subject to finite sample bias. I show that the standard errors-in-variables estimator (EVE) is exactly equivalent to the Jackknife Instrumental Variables Estimator (JIVE), and use this relationship to develop an estimator which, unlike EVE, is unbiased in finite samples. The theoretical results are demonstrated using Monte Carlo experiments. Finally, I implement a model of intertemporal male labor supply using microdata from the United States Census. There are sizeable differences in the wage elasticity across estimators, showing the practical importance of the theoretical issues even when the sample size is quite large.psuedo-panel, small sample bias, labor supply

The effect of high school rank in English and math on college major choice. ESRI Working Paper No. 650 January 2020

Using unique data on preference rankings for all high school students who apply for college in Ireland, we investigate whether, conditional on absolute achievement, within school-cohort rank in English and math affects choice of college major. We find that higher rank in math increases the likelihood of choosing STEM and decreases the likelihood of choosing Arts and Social Sciences. Similarly, a higher rank in English leads to an increase in the probability of choosing Arts and Social Sciences and decreases the probability of choosing STEM. The rank effects are substantial, being about one third as large as the effects of absolute performance in math and English. We identify subject choice in school as an important mediator – students who rank high in math are more likely to choose STEM subjects in school and this can partly explain their subsequent higher likelihood of choosing STEM for college. We also find that English and math rank have significant explanatory power for the gender gap in the choice of STEM as a college major--they can explain about 36% as much as absolute performance in English and math. Overall, the tendency for girls to be higher ranked in English and lower ranked in math within school-cohorts can explain about 6% of the STEM gender gap in mixedsex schools and about 16% of the difference in the STEM gender gap between mixedsex schools and same-sex schools. Notably, these effects occur even though within-school rank plays no role whatsoever in college admissions decisions

Optimally combining Censored and Uncensored Datasets

We develop a simple semiparametric framework for combining censored and uncensored samples so that the resulting estimators are consistent, asymptotically normal, and use all information optimally. No nonparametric smoothing is required to implement our estimators. To illustrate our results in an empirical setting, we show how to estimate the effect of changes in compulsory schooling laws on age at first marriage, a variable that is censored for younger individuals. Results from a small simulation experiment suggest that the estimator proposed in this paper can work very well in finite samples.

Earnings Returns to the British Education Expansion

We study the effects of the large expansion in British educational attainment that took place for cohorts born between 1970 and 1975. Using the Quarterly Labour Force Survey, we find that the expansion caused men to increase education by about a year on average and gain about 8% higher wages; women obtained a slightly greater increase in education and a similar increase in wages. Clearly, there was a sizeable gain from being born late enough to take advantage of the greater educational opportunities offered by the expansion. Treating the expansion as an exogenous increase in educational attainment, we obtain instrumental variables estimates of returns to schooling of about 6% for both men and women.return to education; higher education expansion

Recent Developments in Intergenerational Mobility

Economists and social scientists have long been interested in intergenerational mobility, and documenting the persistence between parents and children's outcomes has been an active area of research. However, since Gary Solon's 1999 Chapter in the Handbook of Labor Economics, the literature has taken an interesting turn. In addition to focusing on obtaining precise estimates of correlations and elasticities, the literature has placed increased emphasis on the causal mechanisms that underlie this relationship. This chapter describes the developments in the intergenerational transmission literature since the 1999 Handbook Chapter. While there have been some important contributions in terms of measurement of elasticities and correlations, we will focus primarily on advances in our understanding of the forces driving the relationship and less on the precision of the correlations themselves.Intergenerational Transmission, Educational Mobility

Forced to be Rich? Returns to Compulsory Schooling in Britain

Do students benefit from compulsory schooling? In an important article, Oreopoulos (2006) studied the 1947 British compulsory schooling law change and found large returns to schooling of about 15% using the General Household Survey (GHS). Reanalysing this dataset, we find much smaller returns of about 3% on average with no evidence of any positive return for women and a return for men of 4-7%. Additionally, we utilize the New Earnings Survey Panel Data-set (NESPD) that has earnings information superior to that in the GHS and find similar estimates: zero returns for women and returns of 3 to 4% for men.

Forced to be Rich? Returns to Compulsory Schooling in Britain

Do students benefit from compulsory schooling? In an important article, Oreopoulos (2006) studied the 1947 British compulsory schooling law change and found large returns to schooling of about 15% using the General Household Survey (GHS). Reanalysing this dataset, we find much smaller returns of about 3% on average with no evidence of any positive return for women and a return for men of 4-7%. Additionally, we utilize the New Earnings Survey Panel Data-set (NESPD) that has earnings information superior to that in the GHS and find similar estimates: zero returns for women and returns of 3 to 4% for men.Compulsory Schooling, Returns to Education

Improved Jive Estimators for Overidentified Linear Models with and without Heteroskedasticity

We introduce two simple new variants of the Jackknife Instrumental Variables (JIVE) estimator for overidentified linear models and show that they are superior to the existing JIVE estimator, signifi- cantly improving on its small sample bias properties. We also compare our new estimators to existing Nagar (1959) type estimators. We show that, in models with heteroskedasticity, our estimators have superior properties to both the Nagar estimator and the related B2SLS estimator suggested in Donald and Newey (2001). These theoretical results are verified in a set of Monte-Carlo experiments and then applied to estimating the returns to schooling using actual data.