ABSTRACT: This paper studies alternative empirical strategies for estimating the effects of organizational design practices on performance, as well as the factors which determine organizational design, in a cross-section of firms. In particular, we propose an approach for estimating the parameters of an “organizational design production function. ” Further, we identify consistent tests for two classes of hypotheses: first, that some sets of organizational design practices are mutually complementary; and second, that adoption patterns are consistent with static optimization of the organization’s profit. We develop an economic model where multiple organizational design practices are endogenously determined. The model includes exogenous variation in the costs and returns to each of the individual practices, which is the source of the heterogeneity among organizations. In many empirical applications, some of these variables will be unobserved to the econometrician. The model is used to evaluate how different econometric strategies can be interpreted under alternative assumptions about the economic and statistical environment. Of particular interest are a set of results which demonstrate that, under plausible hypotheses about the joint distribution of the unobservables, different reduced-form approaches used in the existing literature to test

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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.

Four alternative but related approaches to empirical evaluation of policy interventions are studied: social experiments, natural experiments, matching methods, and instrumental variables. In each case the necessary assumptions and the data requirements are considered for estimation of a number of key parameters of interest. These key parameters include the average treatment effect, the treatment of the treated and the local average treatment effect. Some issues of implementation and interpretation are discussed drawing on the labour market programme evaluation literature.

This paper provides an introduction to the concepts and methods of impact evaluation. Familiarity with basic statistics and econometrics is assumed. To provide an intuitive understanding, in the

In many field settings, participants sort among environments based on their preferences, beliefs, and skills. Experiments, however, often ignore the potential impact of such sorting. We demonstrate the importance of sorting for experiments, in the domain of social preferences. When individuals are constrained to play a dictator game, 61 % of the subjects share. But when subjects are allowed to avoid the situation altogether, only 23 % share. This reversal of proportions illustrates the importance of sorting for drawing inferences from experimental findings to the field. In a second experiment, we subsidize participation in the dictator game with higher payoffs relative to opting out. We find that the subjects whom the increased payoffs attract back into the sorting environment are primarily those who share the least. Thus, even with subsidization, the portion shared by participants remains much lower than without sorting. A second consequence of sorting, then, is that incentives aimed at enticing pro-social behavior can induce adverse selection. Both experiments also shed light on the motives for sharing. While much sharing is consistent with other-regarding preferences, the majority of sharers prefer to avoid the sharing environment.

This essay reviews progress in empirical economics since Leamer’s (1983) critique. Leamer highlighted the benefits of sensitivity analysis, a procedure in which researchers show how their results change with changes in specification or functional form. Sensitivity analysis has had a salutary but not a revolutionary effect on econometric practice. As we see it, the credibility revolution in empirical work can be traced to the rise of a design-based approach that emphasizes the identification of causal effects. Design-based studies typically feature either real or natural experiments and are distinguished by their prima facie credibility and by the attention investigators devote to making the case for a causal interpretation of the findings their designs generate. Design-based studies are most often found in the microeconomic fields of Development, Education, Environment, Labor, Health, and Public Finance, but are still rare in Industrial Organization and Macroeconomics. We explain why IO and Macro would do well to embrace a design-based approach. Finally, we respond to the charge that the design-based revolution has overreached.

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This paper examines the relationship between two alternative ap- proaches, namely instrumental variables and control function proce- dures, for estimating the impact of endogenous treatment effects. While it is well known that the two approaches generate comparable estimates the relationship between the estimators, and their accompanying en- dogeneity tests, appears to not be well understood. We show that the two procedures are closely related. We also examine the implications of the two procedures for the underlying economic sorting behavior.