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.

In this paper we propose a new variance estimator for OLS as well as for nonlinear estimators such as logit, probit and GMM. This variance estimator enables cluster-robust inference when there is two-way or multi-way clustering that is nonnested. The variance estimator extends the standard cluster-robust variance estimator or sandwich estimator for one-way clustering (e.g. Liang and Zeger (1986), Arellano (1987)) and relies on similar relatively weak distributional assumptions. Our method is easily implemented in statistical packages, such as Stata and SAS, that already offer cluster-robust standard errors when there is one-way clustering. The method is demonstrated by a Monte Carlo analysis for a two-way random effects model; a Monte Carlo analysis of a placebo law that extends the state-year effects example of Bertrand et al. (2004) to two dimensions; and by application to two studies in the empirical public/labor literature where two-way clustering is present.

What is the effect of increasing life expectancy on economic growth? To answer this question, we exploit the international epidemiological transition, the wave of international health innovations and improvements that began in the 1940s. We obtain estimates of mortality by disease before the 1940s from the League of Nations and national public health sources. Using these data, we construct an instrument for changes in life expectancy, referred to as predicted mortality, which is based on the pre-intervention distribution of mortality from various diseases around the world and dates of global interventions. We document that predicted mortality has a large and robust effect on changes in life expectancy starting in 1940, but no effect on changes in life expectancy before the interventions. The instrumented changes in life expectancy have alargeeffect on population; a 1 % increase in life expectancy leads to an increase in population of about 1.5%. Life expectancy has a much smaller effect on total GDP both initially and over a 40-year horizon, however. Consequently, there is no evidence that the large exogenous increase in life expectancy led to a significant increase in per capita economic growth. These results confirm that global efforts to combat poor health conditions in less developed countries can be highly effective, but also shed doubt on claims that unfavorable health conditions are the root cause of the poverty of some nations.

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Bilateral, product-level data exhibit a number of strong patterns that can be used to evaluate international trade theories, notably the spatial incidence of “export zeros ” (correlated with distance and importer size), and of export unit values (positively related to distance). We show that leading theoretical trade models fail to explain at least some of these facts, and propose a variant of the Melitz model that can account for all the facts. In our model, high quality firms are the most competitive, with heterogeneous quality increasing with firms ’ heterogeneous cost. (JEL F11, F14, F40) The gravity equation relates bilateral trade volumes to distance and country size. Countless gravity equations have been estimated, usually with “good ” results, and trade theorists have proposed various theoretical explanations for gravity’s success. However, the many potential explanations for the success of the gravity equation make it a problematic tool for discriminating among trade models. 1 As a matter of arithmetic, the value of trade depends on the number of goods

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Are There Civic Returns to Education? “…since the achievement of American Independence, the universal and ever-repeated argument in favor of Free Schools has been, that the general intelligence which they are capable of diffusing, and which can be imparted by no other human instrumentality, is indispensable to a republican form of government.” Horace Mann (1846)

I exploit sharply nonlinear funding rules for defined benefit pension plans in order to identify the dependence of corporate investment on internal financial resources in a large sample. Capital expenditures decline with mandatory contributions to defined benefit pension plans, even when controlling for correlations between the pension funding status itself and the firm’s unobserved investment opportunities. The effect is particularly evident among firms that face financing constraints based on observable variables such as credit ratings. Investment also displays strong negative correlations with the part of mandatory contributions resulting solely from unexpected asset market movements.

We investigate whether differences in individuals’ experiences of macro-economic shocks affect longterm risk attitudes, as is often suggested for the generation that experienced the Great Depression. Using data from the Survey of Consumer Finances from 1964-2004, we find that birth-cohorts that have experienced high stock market returns throughout their life are more likely to be stock market participants, and, if they participate, invest a higher fraction of liquid wealth in stocks. They also report lower aversion to risk. These results are estimated controlling for age effects, year effects, and a broad set of household characteristics. Our estimates indicate that stock market returns early in life affect risktaking several decades later. However, more recent returns have a stronger effect, which fades away slowly as time progresses. Thus, the experience of risky asset payoffs over the course of an individuals’ life affects subsequent risk-taking. Our results explain, for example, the relatively low rates of stock market participation among young households in the early 1980s (following the disappointing stock market returns in the 1970s depression) and the relatively high participation rates of young investors in the late 1990s (following the boom years in the 1990s).

This paper builds on the structural equations, treatment effect, and machine learning literatures to provide a causal framework that permits the identification and estimation of causal effects from observational studies. We begin by providing a causal interpretation for standard exogenous regressors and standard “valid” and “relevant” instrumental variables. We then build on this interpretation to characterize extended instrumental variables (EIV) methods, that is methods that make use of variables that need not be valid instruments in the standard sense, but that are nevertheless instrumental in the recovery of causal effects of interest. After examining special cases of single and double EIV methods, we provide necessary and sufficient conditions for the identification of causal effects by means of EIV and provide consistent and asymptotically normal estimators for the effects of interest.