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In order to study whether college scholarships can be an effective tool in raising students’ performance in secondary school, we use one aspect of the Kalamazoo Promise that resembles a quasi-experiment. The surprise announcement of the scholarship created a large change in expected college tuition costs that varied across different groups of students based on past enrollment decisions. This variation is arguably exogenous to unobserved student characteristics. We estimate the effects of this change by a set of “difference-in-differences” regressions where we compare the change in student outcomes in secondary school across time for different student “length of enrollment” groups. We also control for student fixed effects. We find positive effects of the Kalamazoo Promise on Promise-eligible students large enough to be deemed important – about a 9 percent increase in the probability of earning any credits and one less suspension day per year. We also find large increases in GPA among African American students.

Hausman (1978) represented a tectonic shift in inference related to the specification of econometric models. The seminal insight that one could compare two models which were both consistent under the null spawned a test which was both simple and powerful. The so-called ‘Hausman test’ has been applied and extended theoretically in a variety of econometric domains. This paper discusses the basic Hausman test and its development within econometric panel data settings since its publication. We focus on the construction of the Hausman test in a variety of panel data settings, and in particular, the recent adaptation of the Hausman test to semiparametric and nonparametric panel data models. We present simulation experiments which show the value of the Hausman test in a nonparametric setting, focusing primarily on the consequences of parametric model misspecification for the Hausman test procedure. A formal application of the Hausman test is also given focusing on testing between fixed and random effects within a panel data model of gasoline demand.

This chapter demonstrates that fixed-effects and first-differences models often understate the effect of interest because of the variation used to identify the model. In particular, the within-unit time-series variation often reflects transitory fluctuations that have little effect on behavioral outcomes. The data in effect suffer from measurement error, as a portion of the variation in the independent variable has no effect on the dependent variable. Two empirical examples are presented: one on the relationship between AFDC and fertility and the other on the relationship between local economic conditions and AFDC expenditures.

This chapter investigates which factors contribute to (small) shareholder attendance using a hand-collected panel data set with information about turnout rates, voting behaviour and ownership structures of companies that are listed in seven Member States. We document how ownership concentration positively affects total shareholder turnout, but has a negative effect on small shareholder turnout. Voting power also affects small shareholder turnout rates; the greater small shareholder voting power, the greater their eagerness to vote. In addition, total and small shareholder turnout is higher the more important the meeting agenda. And, small shareholders tend to free-ride on large institutional shareholders and corporate insiders, but the magnitude of the free-rider effect is larger for the latter category of blockholders. Our results provide some important insights for the debate on shareholder rights and the role of the AGM in corporate governance. The results show that, despite the criticism, the AGM still plays an important role in small shareholder monitoring. Some topics seem to clearly motivate small shareholders to attend, while others are less relevant. Policy makers can stimulate shareholder monitoring by focusing on the factors that are determined in this study, but it is important to consider possible endogeneity issues as well.

Purpose: The present study investigates how the performance of Greek bank branching varies when the external environment causes dramatic changes that are reflected in recession and capital control effects.

Design/Methodology: A unique dataset of accounting Profit and Loss statements of retail branches of a systemic Greek commercial bank, closely supervised by the European Central Bank (ECB), is utilized. A profit bootstrap Data Envelopment Analysis (DEA) model is selected to measure the bank branch efficiency. The derived efficiency estimates are analyzed through a second-stage panel data regression analysis against a set of efficiency drivers related to branch profitability, diversification of income, branch size, and branch activity.

Findings: The results indicate that recession negatively affects branch efficiency in the short and long run. The occurrence of recession significantly intensifies the efficiency premium of branch profitability, reduces the efficiency premium of diversification of income (i.e., a negative efficiency effect is recorded during the early recession period), while mitigating the generally negative efficiency effect of branch size. The analysis of efficiency effects from the deep recession period that encompasses capital controls reveals the importance of diversification of income for the improvement of profit efficiency at bank branch level.

Originality/Value: This is the first branch banking study that explores branch efficiency alteration and the dynamic of branch efficiency drivers when the economy suddenly enters recession and afterwards when conditions are becoming extremely difficult and consequently capital controls are imposed on the economy.

Previous research provides evidence of a negative effect of body mass on women's economic outcomes. We extend this research by using a much older sample of individuals from the Panel Study of Income Dynamics and by using a body mass measure that is lagged by 15 years instead of the traditional 7 years. One of the main contributions of this paper is a replication of previous research findings given our differing samples and measures. We compare OLS estimates with sibling fixed effects estimates and find that obesity is associated with an 18% reduction in women's wages, a 25% reduction in women's family income, and a 16% reduction in women's probability of marriage. These effects are robust – they persist much longer than previously understood and they persist across the life course, affecting older women as well as younger women.

This chapter develops a set of two-step identification methods for social interactions models with unknown networks, and discusses how the proposed methods are connected to the identification methods for models with known networks. The first step uses linear regression to identify the reduced forms. The second step decomposes the reduced forms to identify the primitive parameters. The proposed methods use panel data to identify networks. Two cases are considered: the sample exogenous vectors span Rn (long panels), and the sample exogenous vectors span a proper subspace of Rⁿ (short panels). For the short panel case, in order to solve the sample covariance matrices’ non-invertibility problem, this chapter proposes to represent the sample vectors with respect to a basis of a lower-dimensional space so that we have fewer regression coefficients in the first step. This allows us to identify some reduced form submatrices, which provide equations for identifying the primitive parameters.

Panel data, where observations of entities are repeated over time, are common in strategic management research. However, explorations of the role of time on predictors of interest are often unexplored. In this chapter, we illustrate how the use of mixed-effect growth models can enhance theory and research in strategic management by exploring changes in outcomes of interest over time. Mixed-effects models allow for testing both within and between effects, while also calculating specific intercepts (firm average values) and slopes (trajectories of specific firms over time) using empirical Bayes estimates. We also illustrate how a discontinuous growth model could be used to assess differences in firm intercepts and slopes surrounding exogenous events (e.g., global pandemics) without requiring a control group.