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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.

Panel data-based demand forecasting models have been widely adopted in various industrial settings over the past few decades. Despite being a highly versatile and intuitive method, in the literature, there is a lack of comprehensive review examining the strengths, the weaknesses, and the industrial applications of panel data-based demand forecasting models. The purpose of this paper is to fill this gap by reviewing and exploring the features of various main stream panel data-based demand forecasting models. A novel process, in the form of a flowchart, which helps practitioners to select the right panel data models for real world industrial applications, is developed. Future research directions are proposed and discussed.

It is a review paper. A systematically searched and carefully selected number of panel data-based forecasting models are examined analytically. Their features are also explored and revealed.

This paper is the first one which reviews the analytical panel data models specifically for demand forecasting applications. A novel model selection process is developed to assist decision makers to select the right panel data models for their specific demand forecasting tasks. The strengths, weaknesses, and industrial applications of different panel data-based demand forecasting models are found. Future research agenda is proposed.

This review covers most commonly used and important panel data-based models for demand forecasting. However, some hybrid models, which combine the panel data-based models with other models, are not covered.

The reviewed panel data-based demand forecasting models are applicable in the real world. The proposed model selection flowchart is implementable in practice and it helps practitioners to select the right panel data-based models for the respective industrial applications.

This paper is the first one which reviews the analytical panel data models specifically for demand forecasting applications. It is original.

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.

The purpose of this paper is to estimate the impact of one productive development program (PROPYME) in a developing nation like Costa Rica. This program seeks to increase the capacity of small and medium-sized firms (SMEs) to innovate.

Impacts have been estimated assuming that beneficiary firms are trying to maximize their profits and that PROPYME aims to increase these firms productivity. The impacts were measured in terms of three result variables real average wages employment demand and the probability of exporting. A combination of fixed effects and propensity score matching techniques was used in estimations to correct for any selection bias. The authors worked with panel data companies treated and untreated for the period 2001-2011.

PROPYME’s beneficiaries performed better than other firms in terms of labor demand and their probability of exporting. In addition, the dose and the duration of the effects of the treatment (timing effects) are important.

The authors study the impact in ways that go beyond the average treatment effects on the treated (ATT) usually estimated in the existing literature. Specifically, the research focusses on the identification of the timing or dynamic effects (i.e. how long should we wait to see results?) and treatment intensity (dosage effects).

Se estima el impacto de un programa de desarrollo productivo (Propyme) en un país en vías de desarrollo como Costa Rica. El Propyme busca incrementar la capacidad innovadora de las pequeñas y medidas empresas (pymes) costarricenses.

el impacto se ha estimado y evaluado asumiendo que las pymes beneficiaras buscan maximizar sus beneficios y que Propyme se enfoca en incrementar la productividad de esas empresas. El impacto se valoró en función de tres variables: salarios reales medios, empleo demandado y la probabilidad de exportar. Se utilizó una combinación de técnicas de efectos fijos y emparejamiento en las estimaciones con el fin de prevenir sesgos de selección. Se trabajó con un panel de datos, incluyendo empresas tratadas (beneficiarias de Propyme) así como no tratadas para el periodo 2001-2011.

los beneficiarios de Propyme tuvieron mejor desempeño que las restantes empresas en términos de empleo demandado y su posibilidad de exportar. Adicionalmente los efectos dinámicos (dosis y duración) de los tratamientos son importantes.

este artículo evalúa el impacto de una forma que va más allá de lo usual en la literatura por medio de los efectos promedios de los tratamientos sobre los beneficiarios. Esto por cuanto se enfoca en efectos dinámicos como la duración así como la intensidad.

This paper aims to test the significance of unobservable firm-specific effects on a capital structure model.

The paper employs the restricted least squares method to test the significance of unobservable firm-specific effects in a fixed effects model that includes unobservable effects against a pooled ordinary least squares model that excludes unobservable effects.

The empirical findings indicate that models that include unobservable firm-specific effects are correctly specified.

The limitation of this study comes from lack of data to measure unobservable effects such as managerial ability or managerial skills. Future research can develop index measures of managerial ability or managerial skills and borrow from management theory to explain the connection between managerial ability or managerial skills and firms' capital structure.

The findings imply that a capital structure model that excludes firm-specific effects could be mis-specified because such a model does not control for unobservable firm-specific factors such as managerial ability or managerial skills which have significant effects on firms' capital structure decisions.

The findings are important because the paper applies the restricted least squares method to test the significance of unobservable firm-specific effects. This technique has not been applied previously. The paper contributes to capital structure research in the fast growing South Africa.

The purpose of this paper is to explore the relationship between the ratification of the United Nations’ (UN’s) Convention on the Elimination of All Forms of Discrimination against Women (CEDAW) and women’s and girls’ health outcomes using a unique longitudinal data set of 192 UN-member countries that encompasses the years from 1980 to 2011.

The authors focus on the impact of CEDAW ratification, number of reports submitted after ratification, years passed since ratification, and the dynamic impact of CEDAW ratification by utilizing ordinary least squares (OLS) and panel fixed effects methods. The study investigates the following women’s and girls’ health outcomes: total fertility rate, adolescent fertility rate, infant mortality rate, maternal mortality ratio, neonatal mortality rate, female life expectancy at birth (FLEB), and female to male life expectancy at birth.

The OLS and panel country and year fixed effects models provide evidence that the impact of CEDAW ratification on women’s and girls’ health outcomes varies by global regions. While the authors find no significant gains in health outcomes in European and North-American countries, the countries in the Northern Africa, sub-Saharan Africa, Southern Africa, Caribbean and Central America, South America, Middle-East, Eastern Asia, and Oceania regions experienced the biggest gains from CEDAW ratification, exhibiting reductions in total fertility, adolescent fertility, infant mortality, maternal mortality, and neonatal mortality while also showing improvements in FLEB. The results provide evidence that both early commitment to CEDAW as measured by the total number of years of engagement after the UN’s 1980 ratification and the timely submission of mandatory CEDAW reports have positive impacts on women’ and girls’ health outcomes. Several sensitivity tests confirm the robustness of main findings.

This study is the first comprehensive attempt to explore the multifaceted relationships between CEDAW ratification and female health outcomes. The study significantly expands on the methods of earlier research and presents novel methods and findings on the relationship between CEDAW ratification and women’s health outcomes. The findings suggest that the impact of CEDAW ratification significantly depends on the country’s region. Furthermore, stronger engagement with CEDAW (as indicated by the total number of years following country ratification) and the submission of the required CEDAW reports (as outlined in the Convention’s guidelines) have positive impacts on women’s and girls’ health outcomes.

The New Zealand (NZ) dairy industry faces the challenge of increasing productivity and dealing with public concerns over nutrient pollution. The effective policy needs to address regional differences in productivity and fertilizer use. The purpose of this paper is to investigate how spatial effects influence the relationship between dairy yields and intensive farming practices across regions in NZ.

This paper employs spatial panel data models to establish whether unobserved spatial effects exist in the relationship between dairy yields and nutrient inputs regionally and nationally using 2002, 2007 and 2012 data from Statistics NZ and DairyNZ.

The results show positive spatial spillovers for most intensive inputs. The high level of effluent use and estimated negative yield response to nitrogen suggests that an opportunity exists for greater use of effluent as a substitute for nitrogenous fertilizer. Substitution has the potential to reduce dependence on fertilizer and contribute to a reduction in the nutrient pollution.

This paper is the first empirical application of spatial econometric methods to examine the spatial relevance of dairy yields and intensive farming in NZ. In particular, the spatial panel data model accounts for cross-sectional dependence and controls for heterogeneity. The results contribute to an understanding of how farmers can improve their management of intensive inputs and contribute to the formation of regional environmental policy that recognizes regional heterogeneity.

This study aims to investigate the influence and impact mechanism of capital tax incentives on firm innovation.

This study employs the difference-in-differences (DID) method, in conjunction with the exogenous impact of accelerated depreciation (AD) pilot policy. This study selects Chinese listed companies from 2010 to 2017 as the research sample.

Firstly, AD exerts a substantial positive effect on the quantity and quality of the innovation output of firms, and the positive impact results primarily from heightened investment in fixed assets, particularly, machinery and equipment. Secondly, the influence of the policy is pronounced in non-state-owned enterprises, mature enterprises, less capital-intensive enterprises and non-high-tech industries, which all exhibit strong innovation incentives. Lastly, the tax incentive policy significantly stimulates firm innovation in the short term, but its long-term impact on innovation incentives lacks statistical significance.

This study highlights the significance of capital tax incentives in facilitating the innovation process in firms.

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.