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"difference-in-differences"
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Corporate social responsibility as a defense against knowledge spillovers
Research Summary We examine whether companies respond to the threat of knowledge leakage by strategically increasing their engagement in corporate social responsibility (CSR). To obtain exogenous variation in the threat of knowledge leakage, we exploit a natural experiment provided by the rejection of the inevitable disclosure doctrine (IDD) by several U.S. states. Using a difference‐in‐differences methodology we find that, following the rejection of the IDD, companies significantly increase their CSR. Our proposed rationale is that CSR helps mitigate knowledge leakage by (i) reducing employees' propensity to join a rival firm, and (ii) reducing employees' propensity to disclose the firm's valuable knowledge even if they join a rival firm. Evidence from a laboratory experiment, an online experiment, and a survey of knowledge workers is supportive of these arguments. Managerial Summary We study the role of CSR in companies' response to the threat of knowledge leakage—a major managerial challenge that has important implications for firms' innovation and competitiveness. We use three different research designs (an analysis of companies' CSR policies in response to an increased threat of knowledge leakage; a survey of knowledge workers; and an experiment conducted both online and in a laboratory setting). The results show that CSR is perceived to mitigate the threat of knowledge leakage. In particular, (i) CSR reduces knowledge workers' propensity to join rival firms (i.e., they are less likely to “walk”) and, even if they do, (ii) CSR reduces their propensity to disclose the firm's valuable knowledge to their new employer (i.e., they are less likely to “talk”).
Journal Article
Does product market competition foster corporate social responsibility? Evidence from trade liberalization
This study examines whether product market competition affects corporate social responsibility (CSR). To obtain exogenous variation in product market competition, I exploit a quasi-natural experiment provided by large import tariff reductions that occurred between 1992 and 2005 in the U.S. manufacturing sector. Using a difference-in-differences methodology, I find that domestic companies respond to tariff reductions by increasing their engagement in CSR. This finding supports the view of \"CSR as a competitive strategy\" that allows companies to differentiate themselves from their foreign rivals. Overall, my results highlight that trade liberalization is an important factor that shapes CSR practices.
Journal Article
WHEN IS PARALLEL TRENDS SENSITIVE TO FUNCTIONAL FORM?
This paper assesses when the validity of difference-in-differences depends on functional form. We provide a novel characterization: the parallel trends assumption holds under all strictly monotonic transformations of the outcome if and only if a stronger “parallel trends”-type condition holds for the cumulative distribution function of untreated potential outcomes. This condition for parallel trends to be insensitive to functional form is satisfied if and essentially only if the population can be partitioned into a subgroup for which treatment is effectively randomly assigned and a remaining sub-group for which the distribution of untreated potential outcomes is stable over time. These conditions have testable implications, and we introduce falsification tests for the null that parallel trends is insensitive to functional form.
Journal Article
Synthetic controls with imperfect pretreatment fit
We analyze the properties of the Synthetic Control (SC) and related estimators when the pre-treatment fit is imperfect. In this framework, we show that these estimators are generally biased if treatment assignment is correlated with unobserved confounders, even when the number of pre-treatment periods goes to infinity. Still, we show that a demeaned version of the SC method can improve in terms of bias and variance relative to the difference-in-difference estimator. We also derive a specification test for the demeaned SC estimator in this setting with imperfect pre-treatment fit. Given our theoretical results, we provide practical guidance for applied researchers on how to justify the use of such estimators in empirical applications.
Journal Article
Comparative Politics and the Synthetic Control Method
In recent years, a widespread consensus has emerged about the necessity of establishing bridges between quantitative and qualitative approaches to empirical research in political science. In this article, we discuss the use of the synthetic control method as a way to bridge the quantitative/qualitative divide in comparative politics. The synthetic control method provides a systematic way to choose comparison units in comparative case studies. This systematization opens the door to precise quantitative inference in small-sample comparative studies, without precluding the application of qualitative approaches. Borrowing the expression from Sidney Tarrow, the synthetic control method allows researchers to put \"qualitative flesh on quantitative bones.\" We illustrate the main ideas behind the synthetic control method by estimating the economic impact of the 1990 German reunification on West Germany.
Journal Article
SYNTHETIC CONTROL AS ONLINE LINEAR REGRESSION
This paper notes a simple connection between synthetic control and online learning. Specifically, we recognize synthetic control as an instance of Follow-The-Leader (FTL). Standard results in online convex optimization then imply that, even when outcomes are chosen by an adversary, synthetic control predictions of counterfactual outcomes for the treated unit perform almost as well as an oracle weighted average of control units’ outcomes. Synthetic control on differenced data performs almost as well as oracle weighted difference-in-differences, potentially making it an attractive choice in practice. We argue that this observation further supports the use of synthetic control estimators in comparative case studies.
Journal Article
The wild bootstrap for few (treated) clusters
Inference based on cluster-robust standard errors in linear regression models, using either the Student's t-distribution or the wild cluster bootstrap, is known to fail when the number of treated clusters is very small. We propose a family of new procedures called the subcluster wild bootstrap, which includes the ordinary wild bootstrap as a limiting case. In the case of pure treatment models, where all observations within clusters are either treated or not, the latter procedure can work remarkably well. The key requirement is that all cluster sizes, regardless of treatment, should be similar. Unfortunately, the analogue of this requirement is not likely to hold for difference-in-differences regressions. Our theoretical results are supported by extensive simulations and an empirical example.
Journal Article
Will emission trading promote enterprise diversification? Evidence from China
This study examines the impact of the Chinese regional emission trading system (ETS) pilots on enterprise transformation from the perspective of diversification. We use data on Chinese A-share listed companies from 2004 to 2021, and adopt the staggered difference-in-differences (DID) and difference-in-difference-in-differences (DDD) models. The empirical results show that first, the ETS significantly increases the product quantity and revenue diversification of regulated firms. Second, the ETS promotes enterprise diversification through three channels: emission cost, emission risk, and market efficiency. Third, the ETS has a greater impact on the diversification of state-owned enterprises, firms with high business concentration, and firms with low innovation investment. Fourth, the ETS-driven diversification has not been successful as it has increased firms' costs and reduced their profitability. We recommend introducing industrial policies to guide the transformation of enterprises, encourage them to improve their innovation capabilities, and choose appropriate transformation strategies.
Journal Article
On the Use of Two-Way Fixed Effects Regression Models for Causal Inference with Panel Data
The two-way linear fixed effects regression (2FE) has become a default method for estimating causal effects from panel data. Many applied researchers use the 2FE estimator to adjust for unobserved unit-specific and time-specific confounders at the same time. Unfortunately, we demonstrate that the ability of the 2FE model to simultaneously adjust for these two types of unobserved confounders critically relies upon the assumption of linear additive effects. Another common justification for the use of the 2FE estimator is based on its equivalence to the difference-in-differences estimator under the simplest setting with two groups and two time periods. We show that this equivalence does not hold under more general settings commonly encountered in applied research. Instead, we prove that the multi-period difference-in-differences estimator is equivalent to the weighted 2FE estimator with some observations having negative weights. These analytical results imply that in contrast to the popular belief, the 2FE estimator does not represent a design-based, nonparametric estimation strategy for causal inference. Instead, its validity fundamentally rests on the modeling assumptions.
Journal Article