Empirical Strategies in Labor Economics

By Joshua D. Angrist, Alan B. Krueger. 1998

This chapter provides an overview of the methodological and practical issues that arise when estimating causal relationships that are of interest to labor economists. The subject matter includes identification, data collection, and measurement problems. Four identification strategies are discussed, and five empirical examples -- the effects of schooling, unions, immigration, military service, and class size -- illustrate the methodological points. In discussing each example, we adopt an experimentalist perspective that emphasizes the distinction between variables that have causal effects, control variables, and outcome variables. The chapter also discusses secondary datasets, primary data collection strategies, and administrative data. The section on measurement issues focuses on recent empirical examples, presents a summary of empirical findings on the reliability of key labor market data, and briefly reviews the role of survey sampling weights and the allocation of missing values in empirical research.

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Citations in the corpus

The State of Applied Econometrics - Causality and Policy Evaluation (2016, neutral): “These strategies are often referred to as “identification strategies,” or “empirical strategies” (Angrist and Krueger [2000]) because they are strategies for “identifying” the causal effect.”
Synthetic Control Methods for Comparative Case Studies: Estimating the Effect of California’s Tobacco Control Program (2010, background): “Card (1990) studies the impact of the 1980 Mariel Boatlift, a large and sudden Cuban migratory influx in Miami, using other cities in the southern United States as a comparison group.”
Nonparametric Tests for Treatment Effect Heterogeneity (2008, neutral): “Angrist and Krueger (2000) , Heckman, Lalonde and Smith (2000), Blundell and Costa-Dias (2002), and Imbens (2004) for general surveys of this literature.”
The labour market impact of immigration (2008, background): “While not without problems in the specific context considered by Card (see Angrist and Krueger, 1999), this idea has been hugely influential in raising awareness about the fundamental endogeneity problem of immigration flows and motivated a number of studies following a similar methodological approa”
Do China's high-speed-rail projects promote local economy?—New evidence from a panel data approach (2017, methodology): “17 We follow the idea of Angrist and Krueger (1999) to use refutability test in the consideration of robustness.”
Investing in Schooling In Chile: The Role of Information about Financial Aid for Higher Education (2011, unknown): “As with any instrumental variables (IV) approach, equation (2) identifies the impact of watching the DVD at home for students who are ‘‘compliers’’ (Angrist & Krueger, 1999) and may not be representative of the average student.”
Vertical integration and technology: theory and evidence (2004, background): “22See, for example, Angrist and Krueger (2000) on the weighting function of the OLS with heterogeneous coefficients.”
Causal Inference and Omitted Variable Bias in Financial Aid Research: Assessing Solutions (2008, methodology): “See Angrist & Krueger, 1999; Card, 1999.)”
International Migration, Remittances, and Household Investment: Evidence from Philippine Migrants' Exchange Rate Shocks (2008, unknown): “Of course, the migrant return rate and (potentially) migrant job loss are household choice variables, so including them as independent variables can lead to biased estimates of the coefficient on the exchange rate shock (Angrist and Krueger, 1999), adding additional ambiguity to the interpretation o”
Estimation of Nonlinear Models with Measurement Error Using Marginal Information1 (2004, unknown): “We do not observed x∗, but x with x = x∗ + ε (2)”
Estimation of Limited Dependent Variable Models With Dummy Endogenous Regressors (2001, methodology): “Although the regression weighting scheme differs from that in (14), in practice, treatment-effect heterogeneity may be limited enough that alternative weighting schemes have little impact on the overall estimate (see Angrist and Krueger 1999 for more on this point).”
Estimation of Limited-Dependent Variable Models with Dummy Endogenous Regressors: Simple Strategies for Empirical Practice (2000, unknown): “Although the regression weighting scheme differs from that in (14), in practice, treatment-effect heterogeneity may be limited enough that alternative weighting schemes have little impact on the overall estimate (see Angrist and Krueger 1999 for more on this point).”