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New Evidence on Linear Regression and Treatment Effect Heterogeneity

Author

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  • Sloczynski, Tymon

    (Brandeis University)

Abstract
It is standard practice in applied work to rely on linear least squares regression to estimate the effect of a binary variable ("treatment") on some outcome of interest. In this paper I study the interpretation of the regression estimand when treatment effects are in fact heterogeneous. I show that the coefficient on treatment is identical to the outcome of the following three-step procedure: first, calculate the linear projection of treatment on the vector of other covariates ("propensity score"); second, calculate average partial effects for both groups of interest ("treated" and "controls") from a regression of outcome on treatment, the propensity score, and their interaction; third, calculate a weighted average of these two effects, with weights being inversely related to the unconditional probability that a unit belongs to a given group. Each of these steps is potentially problematic, but this last property – the reliance on implicit weights which are inversely related to the proportion of each group – can have particularly severe consequences for applied work. To illustrate the importance of this result, I perform Monte Carlo simulations as well as replicate two applied papers: Berger, Easterly, Nunn and Satyanath (2013) on the effects of successful CIA interventions during the Cold War on imports from the US; and Martinez-Bravo (2014) on the effects of appointed officials on village-level electoral results in Indonesia. In both cases some of the conclusions change dramatically after allowing for heterogeneity in effects.

Suggested Citation

  • Sloczynski, Tymon, 2015. "New Evidence on Linear Regression and Treatment Effect Heterogeneity," IZA Discussion Papers 9491, Institute of Labor Economics (IZA).
    • Handle: RePEc:iza:izadps:dp9491
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    Cited by:

    1. Sloczynski, Tymon, 2013. "Population Average Gender Effects," IZA Discussion Papers 7315, Institute of Labor Economics (IZA).
    2. Boris Kaiser, 2016. "Decomposing differences in arithmetic means: a doubly robust estimation approach," Empirical Economics, Springer, vol. 50(3), pages 873-899, May.
    3. Marenya, Paswel & Kassie, Menale & Jaleta, Moti & Rahut, Dil Bahadur, 2015. "Does gender of the household head explain smallholder farmers' maize market positions? Evidence from Ethiopia," 2015 Conference, August 9-14, 2015, Milan, Italy 212229, International Association of Agricultural Economists.
    4. Himaz, Rozana, 2020. "Sweet are the fruit of adversity? The impact of fathers’ death on child non-cognitive outcomes in Ethiopia," Economics & Human Biology, Elsevier, vol. 38(C).

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    More about this item

    Keywords

    heterogeneity; linear regression; ordinary least squares; propensity score; treatment effects;
    All these keywords.

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • D72 - Microeconomics - - Analysis of Collective Decision-Making - - - Political Processes: Rent-seeking, Lobbying, Elections, Legislatures, and Voting Behavior
    • F14 - International Economics - - Trade - - - Empirical Studies of Trade
    • O17 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Formal and Informal Sectors; Shadow Economy; Institutional Arrangements

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