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We investigate how career disruptions in terms of job loss may impact morbidity for individuals diagnosed with type 2 diabetes (T2D). Combining unique, high-quality longitudinal data from the Swedish National Diabetes Register (NDR) with matched employer–employee data, we focus on individuals diagnosed with T2D, who are established on the labor market and who lose their job in a mass layoff. Using a conditional difference-in-differences evaluation approach, our results give limited support for job loss having an impact on health behavior, diabetes progression, and cardiovascular risk factors.

This paper is concerned with estimation and inference for difference-in-difference regressions with errors that exhibit high serial dependence, including near unit roots, unit roots, and linear trends. We propose a couple of solutions based on a parametric formulation of the error covariance. First stage estimates of autoregressive structures are obtained by using the Han, Phillips, and Sul (2011, 2013) X-differencing transformation. The X-differencing method is simple to implement and is unbiased in large N settings. Compared to similar parametric methods, the approach is computationally simple and requires fewer restrictions on the permissible parameter space of the error process. Simulations suggest that our methods perform well in the finite sample across a wide range of panel dimensions and dependence structures.

This paper looks at the changes in the time allocation of welfare recipients in the United States following the 1996 welfare reform and other changes in their economic environment. Time use is a major determinant of well-being, and for policymakers to understand the broad influences that their policies can have on a population they ought to consider changes in all activities, not simply paid work. While an increase in market work of the welfare population has been well documented, little is known on the evolution of the balance of their time. Using the Current Population Survey to model the propensity to receive welfare, together with a multiple imputation procedure, I replicate previous difference-in-differences estimates that found an increase in child care and a decline in nonmarket work. However when additional data sources are used, I find that time spent providing child care does not increase. This is especially relevant as welfare recipients are overwhelmingly poor single mothers and the welfare reform increased time at work with ambiguous effects on time spent with children. I also find that time at work follows business cycles, with dramatic increases in work time throughout the strong economy of the late 1990s, accompanied by less time in leisure activities.

This chapter examines the effects that airports have had on economic development in cities from 1950 to 2010. It uses a novel dataset consisting of previously unexploited data on the origins and history of the aviation system in the United States. Applying the method of synthetic controls to a set of medium and small airports, I examine both the overall impacts and the heterogeneity within the outcomes of various airports. Then, I use regression analysis to determine key factors differentiating successful airports from less successful ones, as it pertains particularly to population and employment growth. I find that, first, on average, cities have benefited from airports over this period. Airports, overall, provided a causal contribution of 0.2– 0.6% per year on population and employment growth over the time period. Second, I show that city-level factors contributing to airport success include: (1) closer proximity to a major research university, (2) a capital city location, and (3) climate factors, particularly higher January mean temperatures and/or hours of sunshine. City size is a consideration as well; cities in larger metropolitan areas, with larger shares of employment in nontradables in the 1950s, were also better positioned to reap the benefits that airports provided on city growth. Significant differences were not found across regions, airport governance structures, or other factors.

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.

In Germany, the economic crisis 2008/09 was restricted to export-oriented industries such as automotive, chemistry, and mechanical engineering and hence to industries with a high proportion of qualified employees. Therefore, we expect the most current crisis to have a reversed effect on the relative earnings position between more and less qualified in contrast to a development that favored the more qualified since the beginning of the 1980s. Our empirical study is based on the Institute for Employment Research (IAB) Establishment Panel, a representative German establishment level panel data set that surveys information from almost 16,000 personal interviews with high ranked managers.

Despite the “German Job Miracle,” conditional difference-in-differences estimations to control for observed and unobserved heterogeneity reveal substantial employment reductions in establishments affected by the economic crisis. Falls in employment are strongest in plants with a relatively low proportion of qualified workers. Furthermore, our results indicate that the economic crisis is associated with a decline in wages, but only in those establishments that do not operate working time accounts. In sum, we do not find evidence for the current crisis having a reversed effect on the relative earnings position. Obviously once again, the higher qualified are better off than the lower qualified.

Education researchers have been urged to utilize causal inference methods to estimate the policy effect more rigorously. While randomized controlled trials (RCTs) are the gold standard for assessing causality, RCTs are infeasible in some educational settings, particularly when ethical concerns or high cost are involved. Quasi-experimental research designs are the best alternative approach to study educational topics not amenable to RCTs, as they mimic experimental conditions and use statistical techniques to reduce bias from variables omitted in the empirical models. In this chapter, we introduce and discuss the core concepts, applicability, and limitations of three quasi-experimental methods in higher education research (i.e., difference-in-differences, instrumental variables, and regression discontinuity). By introducing each of these techniques, we aim to expand the higher education researcher's toolbox and encourage the use of these quasi-experimental methods to evaluate educational interventions.

A number of states have recently either adopted, or have considered adopting, combined reporting accounting for state income tax purposes. Proponents claim that this policy increases state revenues by obviating certain tax panning techniques, while critics claim this policy causes firms to avoid locating in a state, or to downsize. There has been mixed empirical evidence to support either position. The purpose of this paper is to provide more convincing empirical evidence, which is enabled by a new dataset.

The study uses regression analysis and a new dataset available through Dun & Bradstreet. The analysis employs a firm-specific, difference-in-differences design which controls for trends and specifically identifies multistate firms which might be affected by combined reporting. Specifically, the study examines the economic impacts of the recent adoption of combined reporting by four states in terms of sales and employment changes, moves, births, and deaths. The theoretical scope of the paper uses the economics literature on location choice, combined with traditional tax optimization concepts from the accounting and economics literature.

The results suggest that combined reporting does in fact reduce investment in a state in terms of employment and births, deaths, and moves, and this effect is largest for in-state-based firms. From a policy perspective, this may imply that (ceteris paribus) there is an incentive for firms to move their headquarters/major operations out of combined reporting states and into separate reporting states. Given the recent trend for states to adopt combined reporting, the findings may be important. While imposition of combined reporting may increase state tax revenues, states should also consider that such policies may hurt locally based firms and reduce employment, much more so than for out-of-state-based firms. While firms’ location/expansion decisions are clearly also a function of nontax factors, the results here are broadly consistent with literature reviews which conclude that state business taxes do have an impact on business decision-making.

In addition to contributing to the literature on the economic effects of combined reporting for state income tax purposes, this study also introduces the tax research community to a newly available dataset from Dun and Bradstreet that contains precise locational firm and establishment data for public and private firms, as well as data on births, deaths, and moves. The data allows clear identification of firms that are multistate, as well as affiliate information (including exact name and location of parent); type of legal entity; employment; sales; CEO minority information; government contract data; import/export status; foreign ownership; credit data from D&B and Paydex; and other useful data.

This paper empirically examines the impact of corporate governance reforms on the financial performance of Indian state-owned enterprises (SOEs) for the period 2003–2011.

The findings indicate that the various corporate governance reforms collectively exhibited a statistically significant positive impact on performance when a difference in difference estimation process is used. However, the performance of SOEs is less than that of publicly listed companies, which is consistent with prior research. When the SOEs are compared with a matched pairing of publicly listed companies of similar size and same industry, their performance was comparable and in many instances superior. This is indicative of the regulatory constraints on competitors and preferential access to resources and markets given to the SOEs. As SOEs move towards a more mixed ownership model with more of them listed on the stock exchange and greater public ownership of shares the corporate governance issues will increase in importance.

The controlled sell down of shares in SOEs presents a need for continuing governance reforms and ongoing research to track progress.

The most striking observation from the study is that changes that were introduced as a corporate governance reform, such greater professionalism in boards, did not gain traction and enhance performance, rather the process of director selection and the concentrated bureaucratic and political interference stymied what was asserted to be conceptually sound reforms.

The manner in which publicly traded companies’ management teams handle their firm’s free cash flows (FCF) has been an issue for many decades, because it is difficult to determine whether these management teams work for their own benefit or for that of their shareholders. Recent financial scandals have heightened mistrust of management. This mistrust, in turn, may have increased the pressure to reduce the portion of FCF left under management’s control. Boards of directors control dividend payout decisions, thus determining the portion of FCF available to corporate management. This paper examines whether the 2002 legal response to corporate financial reporting scandals, which came in the form of many new initiatives and requirements imposed by the Sarbanes–Oxley Act of 2002 (SOX) on all publicly traded firms, was relevant to dividend payouts. This question is investigated by noting that the impact of these new requirements differed among firms. Some firms had already introduced the use of independent directors and fully independent committees prior to SOX making them compulsory in 2002. This paper examines whether these “pre-adopters” experienced less change in their dividend payout policies than those firms that were forced to change the composition of their board and committees.

This investigation examines the effect on dividend payouts for listed firms attributable to the SOX and concurrent changes in stock exchange regulations that compelled increased use of independent directors and fully independent committees. To study the impact of SOX and the associated, required, changes in the composition of boards of directors for many firms, the difference-in-differences methodology is employed to overcome the endogeneity concerns that have consistently challenged prior governance studies. This was accomplished by examining the effects on dividend payouts associated with the exogenously forced addition of independent directors to the boards of publicly listed firms. The results reveal that there is a significant positive relationship between firms that were compelled by law to change their boards and increases in average changes in dividend payouts and percentage changes in dividends paid, when compared to firms that had pre-adopted the Sarbanes–Oxley corporate board composition requirements. A further exploratory analysis showed that the same significant positive relationship is detected for increases in average changes in total dollars distributed, where stock repurchase dollars are combined with dividend payouts. These findings imply that these board composition changes led to decisions that increased dividend payouts in percentage terms, as well as dividend payouts and total dollars distributed in aggregate dollar amount terms.