Estimating the impact of economic conditions on the likelihood of civil conflict is difficult because of endogeneity and omitted variable bias. We use rainfall variation as an instrumental variable for economic growth in 41 African countries during 1981–99. Growth is strongly negatively related to civil conflict: a negative growth shock of five percentage points increases the likelihood of conflict by one-half the following year. We attempt to rule out other channels through which rainfall may affect conflict. Surprisingly, the impact of growth shocks on conflict is not significantly different in richer, more democratic, or more ethnically diverse countries. I.

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A rchimedes said, “Give me the place to stand, and a lever long enough, andI will move the Earth ” (Hirsch, Kett, and Trefil, 2002, p. 476). Economistshave their own powerful lever: the instrumental variable estimator. The instrumental variable estimator can avoid the bias that ordinary least squares suffers when an explanatory variable in a regression is correlated with the regression’s disturbance term. But, like Archimedes ’ lever, instrumental variable estimation requires both a valid instrument on which to stand and an instrument that isn’t too short (or “too weak”). This paper briefly reviews instrumental variable estimation, discusses classic strategies for avoiding invalid instruments (instruments themselves correlated with the regression’s disturbances), and describes recently developed strategies for coping with weak instruments (instruments only weakly correlated with the offending explanator). As an example of biased ordinary least squares, consider whether incarcerating more criminals reduces crime. To estimate the effect of increased incarceration on crime, an economist might specify a regression with the crime rate as the depen-dent variable and the incarceration rate as an explanatory variable. In this regres-sion, the naı̈ve ordinary least squares regression could misleadingly indicate that high rates of incarceration are causing high rates of crime if the actual pattern is that more crime leads to more incarceration. Ordinary least squares provides a biased estimate of the effect of incarceration rates on crime rates in this case because the incarceration rate is correlated with the regression’s disturbance term. As another example, consider estimating consumption’s elasticity of intertem-poral substitution (which measures the responsiveness of consumption patterns to changes in intertemporal prices). To estimate this elasticity, economists typically specify a linear relationship between the rate of growth in consumption and the

This paper uses a unique dataset from Denmark to investigate the impact of family characteristics in corporate decision making, and the consequences of these decisions on firm performance. We focus on the decision to appoint either a family or an external chief executive officer (CEO). The paper uses variation in CEO succession decisions that result from the gender of a departing CEO’s first-born child. This is a plausible instrumental variable (IV) as male firstchild firms are more likely to pass on control to a family CEO relative to female first-child firms, but the gender of a first child is unlikely to affect firms ’ outcomes. We find that family successions have a large negative causal impact on firm performance: operating profitability on assets falls by at least four percentage points around CEO transitions. Our IV estimates are significantly larger than those obtained using ordinary least squares. Furthermore, we show that family-CEO underperformance is particularly large for firms in high-growth industries and for relatively large firms. Overall, the empirical results demonstrate that professional non-family CEOs provide extremely valuable services to the organizations they head.

Abstract: The paper critically reviews the methods available for the ex-post counterfactual analysis of programs that are assigned exclusively to individuals, households or locations. The discussion covers both experimental and non-experimental methods (including propensity-score matching, discontinuity designs, double and triple differences and instrumental variables). Two main lessons emerge: Firstly, despite the claims of advocates, no single method dominates; rigorous, policy-relevant evaluations should be open-minded about methodology. Secondly, future efforts to draw more useful lessons from evaluations will call for more policy-relevant measures and deeper explanations of measured impacts than are possible from the classic (“black box”) assessment of mean impact.

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Research has found that season of birth is associated with later health and professional outcomes; what drives this association remains unclear. We consider a new explanation—that children born in different seasons have mothers with different socioeconomic characteristics. We document large seasonal changes in the characteristics of women giving birth in the United States; these changes explain nearly half of the relationship between season of birth and adult outcomes. Our findings suggest that using season of birth as an instrumental variable may produce inconsistent estimates. Finally, seasonality in maternal characteristics is partly due to weather differentially affecting fertility across socioeconomic groups. 1 Research across the social and natural sciences has consistently found that the month of a child’s birth is associated with later outcomes involving health, educational attainment, earnings and mortality. Much of this work shows that on average individuals born in the winter have worse outcomes (less schooling, lower wages) than other individuals. What drives this association remains unclear. Some prior work has speculated that this association may be driven

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I quantify the price effect of a low-cost entrant on retail prices using a case-study approach. I consider the effect of Wal-Mart entry on average city-level prices of various consumer goods by exploiting variation in the timing of store entry. The analysis combines two unique data sets, one containing opening dates of all US Wal-Mart stores and the other containing average quarterly retail prices of several narrowly-defined commonly-purchased goods over the period 1982-2002. I focus on 10 specific items likely to be sold at Wal-Mart stores and analyze their price dy-namics in 165 US cities before and after Wal-Mart entry. An instrumental-variables specification corrects for measurement error in Wal-Mart entry dates. I find robust price effects for several products, including shampoo, toothpaste, and laundry de-tergent; magnitudes vary by product and specification, but generally range from 1.5-3 % in the short run and four times as much in the long-run.