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This study aims to empirically explore whether there is causality and in which direction, i.e. whether financial development generates economic growth or whether financial development merely follows economic growth in transition European countries, including Russian Federation and Turkey, during 1998-2015.

The study uses different techniques such as pooled OLS, fixed and random effects and the Hausman–Taylor model with instrumental variables.

The regression results show a positive relationship between financial development indicators and real GDP per capita growth, thus supporting the hypothesis that finance leads economic growth. The result also shows that financial crisis has a negative effect on real GDP per capita growth. Furthermore, these findings show that government spending and inflation have a negative impact on real GDP per capita growth. The study also shows that financial development plays growth-supporting role in real GDP per capita growth in 20 European countries in transition, including Russian Federation and Turkey.

As financial development generates real GDP per capita growth, on the basis of the results of the study, a course of action that involves institutional improvement and incentivizing competition in the financial sector is recommended to the Central Banks’ policymakers in transition economies. These will in turn lead to higher real GDP per capita growth.

The study is original in nature and makes effort to promote financial development in transition European countries, including Russian Federation and Turkey. The findings of this study will be of value to Central Banks and other policymakers.

This study aims to explore whether globalization and technology are harmful to health using a global panel data set of 52 countries over the period 1990–2019.

The study focused on four continents: Africa, the Americas, Asia/Oceania and Europe. The authors used four advanced econometric methodologies, which include the standard panel fixed effect (FE), Arellano–Bover/Blundell–Bond dynamic panel, Hausman–Taylor specification and two-stage least squares (FE-2SLS)/Lewbel-2SLS approaches.

The empirical evidence highlights the significance of globalization and technology in promoting global health. The findings suggest that globalization has various impacts on global health indicators and that technology is useful in tracking, monitoring and promoting global health. In addition, the empirical evidence indicates that a truly health-centred process of globalization and technological innovation can only be realized by ensuring that the interests of countries and vulnerable populations to health risks are adequately considered in international decision-making regarding global economic integration.

The authors suggest that achieving the aspiration of global health will entail the use of globalization and information technology to extend human activities and provide equal access to global health.

The purpose of this paper is to analyze the role of innovation on the export behavior of firms.

Using two waves of Swedish Community Innovation Survey merged with register data on firm-level, the authors estimate the influence of the innovation output and innovation input of firms on their export propensity and intensity.

The authors find that the innovation output of firms (measured as sales due to innovative products) has a positive and significant effect on their subsequent export behavior, particularly on export intensity. The results also show that there is no direct effect of innovation input (innovative efforts) on export behavior.

This is one of the first paper that clearly distinguishes between input and output parts of innovation process and empirically investigate their differentiated impact on export behavior of a representative sample of firms in an entire economy.

The purpose of this paper is to study how proximity affects the trade-migration link. By focusing on two case studies, France and Egypt, the authors explore if migrants promote and help to deal with market heterogeneity in international markets. Using an ethnic network approach the authors also test for interactions between the characteristics of migrants and proximity issues.

The paper builds on two case studies to illustrate the main working hypotheses. The main framework is that of ethnic networks, proximity ties, and market heterogeneity. Static and dynamic panel data methods are employed when estimating extended gravity trade equations. The authors account for country-pair fixed effects and instrument by lagged stocks of migrants, in order to deal with bilateral commonalities and endogeneity issues in the estimation procedure.

The paper provides evidence on how proximity enhances trade. Additional trade effects are found for countries sharing closer ties. Networks of migrants appear to help firms to deal with fixed trade costs, also generating some market heterogeneity that at the end influences the trade-migration linkage. Characteristics of migrants also seem to matter, interacting with proximity issues, and resulting in specific trade effects.

Proximity issues seem to matter in the trade creation effects of networks of migrants. In this way integration processes between countries would be showing some positive externalities in the side of trade flows. Characteristics of emigrants should be taken into account when defining migratory policies, mainly for the education and assimilation issues.

The paper get deeper insights in some emerging issues in the trade-migration literature by focusing in two relevant case studies.

Since the literature on the effect of the unemployment rate as reflection of economic fluctuations on crime shows an empirically ambiguous effect, the purpose of this paper is to argue that a new way of modeling the dynamics of unemployment and crime by focussing on the transitory and persistent effect of unemployment on crime helps resolve this ambiguity.

Panel data for US states from 1965 to 2006 are examined using the Mundlak (1978) methodology to incorporate the dynamic interactions between crime and unemployment into the estimation.

After decomposing the unemployment effect on crime into a transitory and persistent effect, evidence of a strong positive correlation between unemployment and almost all types of crime rates is unearthed. This evidence is robust to endogeneity and the controlling for cross-panel correlation and indicators for state imprisonment.

The paper is the first to examine the dynamics of the interaction of crime and economic fluctuations using the temporary and persistent effects framework of Mundlak (1978). In one set of estimates, one can evaluation both the short- and long-run effects of changes of unemployment on crime.

Field experiments have raised important issues of interpretation of bargaining behavior. There is evidence that bargaining behavior appears to vary across groups of populations, such as nationality, ethnicity and sex. Differences have been observed with respect to initial behavior and with respect to the adjustment pattern over time. Often, such behavioral differences are referred to as cultural, although the delineation of the cultural group has been confined to one or other observable characteristic in isolation. We show that this way of characterizing cultural differences is overly simplistic: at best, it leads to unreliable claims; at worst, it leads to erroneous conclusions. We reconsider the evidence provided by previous experiments using ultimatum game rules, and undertake new experiments that expand the controls for demographics. The lesson from our demonstration is that the task of designing experiments for the field offers many challenges if one wants to define and control for cultural impacts, but that field experiments also offer potential for providing new insights into these issues.

This chapter extends the work of Baltagi, Bresson, Chaturvedi, and Lacroix (2018) to the popular dynamic panel data model. The authors investigate the robustness of Bayesian panel data models to possible misspecification of the prior distribution. The proposed robust Bayesian approach departs from the standard Bayesian framework in two ways. First, the authors consider the ε-contamination class of prior distributions for the model parameters as well as for the individual effects. Second, both the base elicited priors and the ε-contamination priors use Zellner’s (1986) g-priors for the variance–covariance matrices. The authors propose a general “toolbox” for a wide range of specifications which includes the dynamic panel model with random effects, with cross-correlated effects à la Chamberlain, for the Hausman–Taylor world and for dynamic panel data models with homogeneous/heterogeneous slopes and cross-sectional dependence. Using a Monte Carlo simulation study, the authors compare the finite sample properties of the proposed estimator to those of standard classical estimators. The chapter contributes to the dynamic panel data literature by proposing a general robust Bayesian framework which encompasses the conventional frequentist specifications and their associated estimation methods as special cases.

This chapter revisits the Hausman (1978) test for panel data. It emphasizes that it is a general specification test and that rejection of the null signals misspecification and is not an endorsement of the fixed effects estimator as is done in practice. Non-rejection of the null provides support for the random effects estimator which is efficient under the null. The chapter offers practical tips on what to do in case the null is rejected including checking for endogeneity of the regressors, misspecified dynamics, and applying a nonparametric Hausman test, see Amini, Delgado, Henderson, and Parmeter (2012, chapter 16). Alternatively, for the fixed effects die hard, the chapter suggests testing the fixed effects restrictions before adopting this estimator. The chapter also recommends a pretest estimator that is based on an additional Hausman test based on the difference between the Hausman and Taylor estimator and the fixed effects estimator.

This chapter suggests a robust Hausman and Taylor (1981), hereafter HT, estimator that deals with the possible presence of outliers. This entails two modifications of the classical HT estimator. The first modification uses the Bramati and Croux (2007) robust Within MS estimator instead of the Within estimator in the first stage of the HT estimator. The second modification uses the robust Wagenvoort and Waldmann (2002) two-stage generalized MS estimator instead of the 2SLS estimator in the second step of the HT estimator. Monte Carlo simulations show that, in the presence of vertical outliers or bad leverage points, the robust HT estimator yields large gains in MSE as compared to its classical Hausman–Taylor counterpart. We illustrate this robust version of the HT estimator using an empirical application.

Debt burdens have risen for US households over the last several decades. As a result, several studies have investigated potential ethnic and gender differences in these debt burdens, along with the risks they pose. However, such estimations can be biased without correctly controlling for individual unobserved heterogeneity, and standard methods to deal with this, such as fixed effects, remove any time-invariant variables from the analysis. In this paper, I use the Hausman–Taylor (HT) estimator to estimate the relationship between these time-invariant demographics and debt burdens, allowing for potential correlation between some variables and the unobserved heterogeneity. I also consider some guidelines in determining the appropriateness of the HT estimation, both in terms of exogeneity assumptions as well as potential problems due to weak instruments. Using data from the National Longitudinal Survey of Youth 1979, the resulting estimates differ substantially from those of a typical random effects GLS estimator. In particular, the HT results find that after controlling for other variables, women are more likely to take on debt, especially nonhousing debt, but those who do take on debt tend to take on a lower amount than their male counterparts. No differences are found for black or Hispanic individuals with regard to the amount of debt, though black individuals are found to be slightly less likely to have debt.