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“The Elephant in the Corner: A Cautionary Tale About Measurement Error in Treatment Effects Models” by Daniel L. Millimet discusses the current use of the unobserved-outcome framework to estimate population-averaged treatment effects, and it exposes the sensitivity of these estimators to assumption of no measurement error. The Monte Carlo simulation evidence in this chapter indicates that “nonclassical measurement error in the covariates, mean-reverting measurement error in the outcome, and simultaneous measurement errors in the outcome, treatment assignment, and covariates have a dramatic, adverse effect on the performance of the various estimators even with relatively small and infrequent errors” (Millimet article, p. 1–39). To some extent, all the estimators analyzed by Millimet are based on weak functional form assumptions and use semiparametric or nonparametric methods. Millimet's results indicate the need for measurement error models be they parametric or nonparametric models, see Schennach (2007), Hu and Schennach (2008), and Matzkin (2007) for some recent research in nonparametric approaches. Chapter 7 develops a Bayesian estimator that can handle some of the measurement errors discussed in this chapter.

Although the theoretical trade-off between the quantity and quality of children is well established, empirical evidence supporting such a causal relationship is limited. This chapter applies a recently developed nonparametric estimator of the conditional local average treatment effect to assess the sensitivity of the quantity–quality trade-off to functional form and parametric assumptions. Using data from the Indonesia Family Life Survey and controlling for the potential endogeneity of fertility, we find mixed evidence supporting the trade-off.

Supposing that decisionmakers in any country and at any point in time tolerate a certain fixed level of perceived poverty, differences in poverty aversion are called for to explain observed international and intertemporal variations in poverty statistics. Under the Natural Rate of Subjective Poverty hypothesis advanced in this paper, variations in the degree of poverty aversion are estimable and can be explained by political and socioeconomic factors. The methodology is applied to US data from 1975 to 1998 and across nations using cross-section data from the mid-1990s. Factors such as the political affiliation of government officials, public expenditure, per capita income, and economic growth account for much of the variation in poverty aversion implied by our hypothesis. The relationship between inequality aversion and poverty aversion is also explored, with the aid of a parallel “natural rate” hypothesis for inequality (Lambert et al., 2003). Our findings provide a new framework in which to interpret observed correlations between poverty, inequality, and social welfare.

The estimation of the effects of treatments – endogenous variables representing everything from child participation in a pre-kindergarten program to adult participation in a job-training program to national participation in a free trade agreement – has occupied much of the theoretical and applied econometric research literatures in recent years. This volume brings together a diverse collection of papers on this important topic by leaders in the field from around the world. This collection draws attention to several key facets of the recent evolution in this literature.

Researchers in economics and other disciplines are often interested in the causal effect of a binary treatment on outcomes. Econometric methods used to estimate such effects are divided into one of two strands depending on whether they require unconfoundedness (i.e., independence of potential outcomes and treatment assignment conditional on a set of observable covariates). When this assumption holds, researchers now have a wide array of estimation techniques from which to choose. However, very little is known about their performance – both in absolute and relative terms – when measurement error is present. In this study, the performance of several estimators that require unconfoundedness, as well as some that do not, are evaluated in a Monte Carlo study. In all cases, the data-generating process is such that unconfoundedness holds with the ‘real’ data. However, measurement error is then introduced. Specifically, three types of measurement error are considered: (i) errors in treatment assignment, (ii) errors in the outcome, and (iii) errors in the vector of covariates. Recommendations for researchers are provided.

In this chapter, we characterise the selection into parenthood for men and women separately and estimate the effects of motherhood and fatherhood on wages. We apply propensity score matching exploiting an extensive high-quality register-based data set augmented with family background information. We estimate the net effects of parenthood and find that mothers receive 7.4% lower average wages compared to non-mothers, whereas fathers gain 6.0% in terms of average wages from fatherhood.