Schooling and Labor Market Impacts of a Natural Policy Experiment

Abstract. We use a nationally representative household survey to estimate returns to schooling in Venezuela from instrumental variables based on a supply‐side intervention in the education market. These estimates apply to a subgroup of individuals, in the spirit of the local average treatment effect (LATE) literature. Returns to schooling estimates that apply to a subgroup of individuals affected by the policy intervention may be more interesting from a policy perspective than the return to the ‘average’ individual. We use an instrument based on the 1980 education reform (the Organic Law of Education), which provided for 9 years of compulsory basic education. Alternative estimates derived from interacting the education reform with father's education are also obtained. The estimates are consistent with recent findings suggesting that the effect of education, at least for certain subgroups affected by policy intervention, is as large as or larger than what is suggested by ordinary least squares estimates.     

Returns to Schooling in Spain: How Reliable are Instrumental Variable Estimates?

This paper studies the ordinary least–squares (OLS) and instrumental variable (IV) estimates of the returns to schooling for male workers in Spain. OLS estimates are often biased due to the endogeneity of schooling, measurement errors or omitted variables. Proper IV estimates correct this bias. The reliability of family background, natural experiments (based on changes in the education system and season of birth) and the availability of a college in the province is checked using Spanish data. The results suggest that background and college availability are valid instruments and that the IV estimates of the returns to schooling are higher than OLS estimates. These results are in line with the majority of previous results in the literature.     

The Effect of Having More Children on Women's Labour Force Participation in Korea: An Analysis Using Instrument Variables

The objective of this study was to examine the causal relations between having more children and women's labour force participation using Korean data. Given the strong preference for sons in Korea, variables regarding the number of daughters were used as instrument variables for having more children. The results using 1980s data showed that having a third child had a significantly positive impact on women's labour force participation in the ordinary least squares analysis, whereas the coefficient value was significantly negative in the two‐stage least squares analysis. Such results imply that, unlike in the western societies such as the USA and the UK, the ordinary least squares analysis results might underestimate the negative correlations between having more children and women's labour force participation in a developing country such as Korea.     

THE PRIVATE AND FISCAL RETURNS TO SCHOOLING IN THE EUROPEAN UNION

We present estimates of the private and fiscal returns to schooling in 14 European Union countries. Estimates of the private returns to post‐compulsory formal education take into account the effects of schooling on wages and employment probabilities and allow for academic failure rates, the direct and opportunity costs of education, and the impact of personal taxes, social security contributions, and unemployment and pension benefits on lifetime earnings. Estimates of fiscal returns capture the long‐term effects of a marginal increase in average educational attainment on public finances under conditions that approximate general equilibrium. (JEL: I20, J31, H60).     

Random Effects Estimators with many Instrumental Variables

In this paper we propose a new estimator for a model with one endogenous regressor and many instrumental variables. Our motivation comes from the recent literature on the poor properties of standard instrumental variables estimators when the instrumental variables are weakly correlated with the endogenous regressor. Our proposed estimator puts a random coefficients structure on the relation between the endogenous regressor and the instruments. The variance of the random coefficients is modelled as an unknown parameter. In addition to proposing a new estimator, our analysis yields new insights into the properties of the standard two‐stage least squares (TSLS) and limited‐information maximum likelihood (LIML) estimators in the case with many weak instruments. We show that in some interesting cases, TSLS and LIML can be approximated by maximizing the random effects likelihood subject to particular constraints. We show that statistics based on comparisons of the unconstrained estimates of these parameters to the implicit TSLS and LIML restrictions can be used to identify settings when standard large sample approximations to the distributions of TSLS and LIML are likely to perform poorly. We also show that with many weak instruments, LIML confidence intervals are likely to have under‐coverage, even though its finite sample distribution is approximately centered at the true value of the parameter. In an application with real data and simulations around this data set, the proposed estimator performs markedly better than TSLS and LIML, both in terms of coverage rate and in terms of risk.     

How Responsive Is Investment in Schooling to Changes in Redistributive Policies and in Returns?

This paper uses an unusual pay reform to test the responsiveness of investment in schooling to changes in redistribution schemes that increase the rate of return to education. We exploit an episode where different Israeli kibbutzim shifted from equal sharing to productivity‐based wages in different years and find that students in kibbutzim that reformed earlier invested more in high school education and, in the long run, also in post‐secondary schooling. We further show that the effect is mainly driven by students in kibbutzim that reformed to a larger degree. Our findings support the prediction that education is highly responsive to changes in the redistribution policy.     

The Effect of Early Cognitive Ability on Earnings Over the Life‐Cycle

This paper utilizes information on cognitive ability at age 10 and earnings information from age 20 to 65 to estimate the return to ability over the life‐cycle. Cognitive ability measured at an early age is not influenced by the individual's choices of schooling. We find that most of the unconditional return to early cognitive ability goes through educational choice. The conditional return is increasing for low levels of experience and non‐increasing for experience above about 15–25 years. The return is similar for men and women, and highest for individuals with academic education. Only a small part of the return can be explained by higher probability of having a supervisory position.     

Cointegration in Fractional Systems with Unknown Integration Orders

Cointegrated bivariate nonstationary time series are considered in a fractional context, without allowance for deterministic trends. Both the observable series and the cointegrating error can be fractional processes. The familiar situation in which the respective integration orders are 1 and 0 is nested, but these values have typically been assumed known. We allow one or more of them to be unknown real values, in which case Robinson and Marinucci (2001, 2003) have justified least squares estimates of the cointegrating vector, as well as narrow‐band frequency‐domain estimates, which may be less biased. While consistent, these estimates do not always have optimal convergence rates, and they have nonstandard limit distributional behavior. We consider estimates formulated in the frequency domain, that consequently allow for a wide variety of (parametric) autocorrelation in the short memory input series, as well as time‐domain estimates based on autoregressive transformation. Both can be interpreted as approximating generalized least squares and Gaussian maximum likelihood estimates. The estimates share the same limiting distribution, having mixed normal asymptotics (yielding Wald test statistics with χ² null limit distributions), irrespective of whether the integration orders are known or unknown, subject in the latter case to their estimation with adequate rates of convergence. The parameters describing the short memory stationary input series are √n‐consistently estimable, but the assumptions imposed on these series are much more general than ones of autoregressive moving average type. A Monte Carlo study of finite‐sample performance is included.     

The Cohort Size‐Wage Relationship in Europe

This paper estimates the impact of cohort size on wages using data on young males in European regions covering 2004–10. The effect of cohort size on wages is identified through an instrumental variables strategy which, in contrast to previous analyses of European data, addresses self‐selection into geographical areas as well as into educational groups. The results suggest that cohort size has a significant negative effect on male wages for individuals with secondary education — the largest group — but not for individuals with less than secondary education or tertiary education. This effect is underestimated if self‐selection into geographical areas is not addressed.     

Large Market Asymptotics for Differentiated Product Demand Estimators With Economic Models of Supply

IO economists often estimate demand for differentiated products using data sets with a small number of large markets. This paper addresses the question of consistency and asymptotic distributions of instrumental variables estimates as the number of products increases in some commonly used models of demand under conditions on economic primitives. I show that, in a Bertrand–Nash equilibrium, product characteristics lose their identifying power as price instruments in the limit in certain cases, leading to inconsistent estimates. The reason is that product characteristic instruments achieve identification through correlation with markups, and, depending on the model of demand, the supply side can constrain markups to converge to a constant quickly relative to sampling error. I find that product characteristic instruments can yield consistent estimates in many of the cases I consider, but care must be taken in modeling demand and choosing instruments. A Monte Carlo study confirms that the asymptotic results are relevant in market sizes of practical importance.     

Choosing the Number of Instruments

Properties of instrumental variable estimators are sensitive to the choice of valid instruments, even in large cross‐section applications. In this paper we address this problem by deriving simple mean‐square error criteria that can be minimized to choose the instrument set. We develop these criteria for two‐stage least squares (2SLS), limited information maximum likelihood (LIML), and a bias adjusted version of 2SLS (B2SLS). We give a theoretical derivation of the mean‐square error and show optimality. In Monte Carlo experiments we find that the instrument choice generally yields an improvement in performance. Also, in the Angrist and Krueger (1991) returns to education application, when the instrument set is chosen in the way we consider, it turns out that both 2SLS and LIML give similar (large) returns to education.     

Occupational Upgrading and the Business Cycle

Many economics theories suggest that the assignment of workers to occupations changes over the business cycle: expansions allow workers to upgrade to occupations that pay higher wages and require more skill. This paper provides some empirical evidence from the USA that such upgrading does occur and that, as predicted, it has greater effects on less‐skilled individuals. Furthermore, the skill composition of new hires changes over the business cycle, even within occupations. Consistent with a job competition model, the education levels of new hires within occupations are higher when the unemployment rate is high and this effect is more pronounced in lower‐paying occupations. The changes in assignment imply that low‐skilled individuals suffer most from recessions in terms of occupation quality and unemployment. The results are consistent with employers responding to a greater supply of educated workers by increasing hiring standards, and so imply that the social return to education may be lower than the private return. However, the results are also consistent with more neo‐classical models of the labor market.     

Adapting to Unknown Disturbance Autocorrelation in Regression with Long Memory

We show that it is possible to adapt to nonparametric disturbance autocorrelation in time series regression in the presence of long memory in both regressors and disturbances by using a smoothed nonparametric spectrum estimate in frequency–domain generalized least squares. When the collective memory in regressors and disturbances is sufficiently strong, ordinary least squares is not only asymptotically inefficient but asymptotically non–normal and has a slow rate of convergence, whereas generalized least squares is asymptotically normal and Gauss–Markov efficient with standard convergence rate. Despite the anomalous behavior of nonparametric spectrum estimates near a spectral pole, we are able to justify a standard construction of frequency–domain generalized least squares, earlier considered in case of short memory disturbances. A small Monte Carlo study of finite sample performance is included.     

Quantile Regression under Misspecification,with an Application to the U.S. Wage Structure

Quantile regression (QR) fits a linear model for conditional quantiles just as ordinary least squares (OLS) fits a linear model for conditional means. An attractive feature of OLS is that it gives the minimum mean‐squared error linear approximation to the conditional expectation function even when the linear model is misspecified. Empirical research using quantile regression with discrete covariates suggests that QR may have a similar property, but the exact nature of the linear approximation has remained elusive. In this paper, we show that QR minimizes a weighted mean‐squared error loss function for specification error. The weighting function is an average density of the dependent variable near the true conditional quantile. The weighted least squares interpretation of QR is used to derive an omitted variables bias formula and a partial quantile regression concept, similar to the relationship between partial regression and OLS. We also present asymptotic theory for the QR process under misspecification of the conditional quantile function. The approximation properties of QR are illustrated using wage data from the U.S. census. These results point to major changes in inequality from 1990 to 2000.     

Informing Ex Ante Event Studies with Macro‐Econometric Evidence on the Structural and Policy Impacts of Terrorism

Economic consequence analysis is one of many inputs to terrorism contingency planning. Computable general equilibrium (CGE) models are being used more frequently in these analyses, in part because of their capacity to accommodate high levels of event‐specific detail. In modeling the potential economic effects of a hypothetical terrorist event, two broad sets of shocks are required: (1) physical impacts on observable variables (e.g., asset damage); (2) behavioral impacts on unobservable variables (e.g., investor uncertainty). Assembling shocks describing the physical impacts of a terrorist incident is relatively straightforward, since estimates are either readily available or plausibly inferred. However, assembling shocks describing behavioral impacts is more difficult. Values for behavioral variables (e.g., required rates of return) are typically inferred or estimated by indirect means. Generally, this has been achieved via reference to extraneous literature or ex ante surveys. This article explores a new method. We elucidate the magnitude of CGE‐relevant structural shifts implicit in econometric evidence on terrorist incidents, with a view to informing future ex ante event assessments. Ex post econometric studies of terrorism by Blomberg et al . yield macro econometric equations that describe the response of observable economic variables (e.g., GDP growth) to terrorist incidents. We use these equations to determine estimates for relevant (unobservable) structural and policy variables impacted by terrorist incidents, using a CGE model of the United States. This allows us to: (i) compare values for these shifts with input assumptions in earlier ex ante CGE studies; and (ii) discuss how future ex ante studies can be informed by our analysis.     

Identification in Differentiated Products Markets Using Market Level Data

We present new identification results for nonparametric models of differentiated products markets, using only market level observables. We specify a nonparametric random utility discrete choice model of demand allowing rich preference heterogeneity, product/market unobservables, and endogenous prices. Our supply model posits nonparametric cost functions, allows latent cost shocks, and nests a range of standard oligopoly models. We consider identification of demand, identification of changes in aggregate consumer welfare, identification of marginal costs, identification of firms' marginal cost functions, and discrimination between alternative models of firm conduct. We explore two complementary approaches. The first demonstrates identification under the same nonparametric instrumental variables conditions required for identification of regression models. The second treats demand and supply in a system of nonparametric simultaneous equations, leading to constructive proofs exploiting exogenous variation in demand shifters and cost shifters. We also derive testable restrictions that provide the first general formalization of Bresnahan's (1982) intuition for empirically distinguishing between alternative models of oligopoly competition. From a practical perspective, our results clarify the types of instrumental variables needed with market level data, including tradeoffs between functional form and exclusion restrictions.     

Dropping the Books and Working Off the Books

The paper empirically tests the relationship between underground labour and schooling achievement for Italy, a country ranking badly in both respects when compared with other high‐income economies, with a marked duality between North and South. In order to identify underground workers, we exploit the information on individuals' social security positions available from the Bank of Italy's Survey on Household Income and Wealth. After controlling for a wide range of sociodemographic and economic variables and addressing potential endogeneity and selection issues, we show that a low level of education sizeably and significantly increases the probability of working underground. Switching from completing compulsory school to graduating at college more than halves this probability for both men and women. The gain is slightly higher for individuals completing the compulsory track with respect to those having no formal education at all. The different probabilities found for self‐employed and dependent workers support the view of a dual informal sector, in which necessity and desirability coexist.     

Skill Acquisition in the Informal Economy and Schooling Decisions: Evidence from Emerging Economies

Informal jobs offer skill acquisition opportunities that may facilitate a future switch to formal employment for young workers. In this sense, informal training on the job may be a viable alternative to formal schooling in an economy with a large and diverse informal sector. In this paper, I investigate if these considerations are relevant for the schooling decisions of young individuals using panel data for 17 Latin American countries and micro‐level data for Turkey. Specifically, I ask if the prevalence of informal jobs distorts schooling attainment. I concentrate on three measures of schooling outcomes: (1) secondary education enrollment rate; (2) out‐of‐school rate for lower secondary school; and (3) tertiary education graduation rate. I find that the secondary education enrollment rate is negatively correlated with the size of the informal economy, whereas the out‐of‐school rate is positively correlated. Moreover, the tertiary education graduation rates tend to fall as the informal employment opportunities increase. This means that informal training on the job may be crowding out school education in developing countries. Policies that can potentially affect the size of the informal sector should take into consideration these second‐round effects on aggregate schooling outcomes.     

Life Expectancy,Schooling, and Lifetime Labor Supply: Theory and Evidence Revisited

This paper presents a theoretical and empirical analysis of the role of life expectancy for optimal schooling and lifetime labor supply. The results of a simple prototype Ben‐Porath model with age‐specific survival rates show that an increase in lifetime labor supply is not a necessary, or a sufficient, condition for greater life expectancy to increase optimal schooling. The observed increase in survival rates during working ages that follows from the “rectangularization” of the survival function is crucial for schooling and labor supply. The empirical results suggest that the relative benefits of schooling have been increasing across cohorts of U.S. men born between 1840 and 1930. A simple quantitative analysis shows that a realistic shift in the survival function can lead to an increase in schooling and a reduction in lifetime labor hours.     

Designing Supply Contracts Considering Profit Targets and Risk

Managers at all stages of a supply chain are concerned about meeting profit targets. We study contract design for a buyer–supplier supply chain, with each party maximizing expected profit subject to a chance constraint on meeting his respective profit target. We derive the optimal contract form (across all contract types) with randomized and deterministic payments. The best contract has the property that, if the chance constraints are binding, at most one party fails to satisfy his profit target for any given demand realization. This implies that “least risk sharing,”that is, minimizing the probability of outcomes for which both parties fail to achieve their profit targets, is optimal, contrary to the usual expectations of “risk sharing.” We show that an optimal contract can possess only two of the following three properties simultaneously: (i) supply chain coordination, (ii) truth‐telling, and (iii) non‐randomized payments. We discuss methods to mitigate the consequent implementation challenges. We also derive the optimal contract form when chance constraints are incorporated into several simpler and easier‐to‐implement contracts. From a numerical study, we find that an incremental returns contract (in which the marginal rebate rate depends on the return quantity) performs quite well across a relatively broad range of conditions.