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Analysis of organizational decline has become central to the study of economy and society. Further advances in this area may fail however, because two major literatures on the topic remain disintegrated and because both lack a sophisticated account of how social structure and interdependencies among organizations affect decline. This paper develops a perspective which tries to overcome these problems. The perspective explains decline through an understanding of how social ties and resource dependencies among firms affect market structure and the resulting behavior of firms within it. Evidence is furnished that supports the assumptions of the perspective and provides a basis for specifying propositions about the effect of network structure on organizational survival. I conclude by discussing the perspective’s implications for organizational theory and economic sociology.

Advances in Econometrics is a series of research volumes first published in 1982 by JAI Press. The authors present an update to the history of the Advances in Econometrics series. The initial history, published in 2012 for the 30th Anniversary Volume, describes key events in the history of the series and provides information about key authors and contributors to Advances in Econometrics. The authors update the original history and discuss significant changes that have occurred since 2012. These changes include the addition of five new Senior Co-Editors, seven new AIE Fellows, an expansion of the AIE conferences throughout the United States and abroad, and the increase in the number of citations for the series from 7,473 in 2012 to over 25,000 by 2022.

“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.

This chapter proposes a simple procedure to estimate average derivatives in nonparametric regression models with incomplete responses. The method consists of replacing the responses with an appropriately weighted version and then use local polynomial estimation for the average derivatives. The resulting estimator is shown to be asymptotically normal, and an estimator of its asymptotic variance–covariance matrix is also shown to be consistent. Monte Carlo experiments show that the proposed estimator has desirable finite sample properties.

This chapter uses the nonlinear difference-in-difference (NL-DID) methodology developed by Athey and Imbens (2006) to estimate the effects of a treatment program on the entire distribution of an outcome variable. The NL-DID estimates the entire counterfactual distribution of an outcome variable that would have occurred in the absence of treatment. This chapter extends the Monte Carlo results in Athey and Imbens's (2006) to assess the efficacy of the NL-DID estimators in finite samples. Furthermore, the NL-DID methodology recovers the entire outcome distribution in the absence of treatment. Further, we consider the empirical size and power of tests statistics for equality of mean, medians, and complete distributions as suggested by Abadie (2002). The results show that the NL-DID estimator can effectively be used to recover the average treatment effect, as well as the entire distribution of the treatment effects when there is no selection during the treatment period in finite samples.