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The article aims at examination of the shape of relationship between income inequality and the level of economic development measured by GDP per capita in 27 European Union countries in the period of 2004-2014. It also aims at identification of determinants of income inequality. Specifically, we test for the existence of an inverted U-shaped curve, as it is predicted by the standard Kuznets hypothesis, and J-shaped curve following the approach adopted by Deutsch and Silber (2004) and Anand and Kanbur (1993). The data come from Eurostat EU-SILC database (European Union Statistics on Income and Living Conditions), World Bank and International Monetary Fund. In the EU-27 group of countries we contradict the Kuznets hypothesis – our results provide evidence for a U-shaped, rather than the inverted U relationship. It also follows from our analysis that our data cover only the descending part of the U, that is a shape of inverted J.

Many questions in urban and regional economics can be characterized as including both a spatial and a time dimension. However, often one of these dimensions is neglected in empirical work. This paper highlights the danger of methodological inertia, investigating the effect of neglecting the spatial or the time dimension when in fact both are important. A tale of two research teams, one living in a purely dynamic and the other in a purely spatial world of thinking, sets the scene. Because the researcher teams' choices to omit a dimension change the assumed optimal estimation strategies, the issue is more difficult to analyze than a typical omitted variables problem. First, the bias of omitting a relevant dimension is approximated analytically. Second, Monte Carlo simulations show that the neglected dimension projects onto the other, with potentially disastrous results. Interestingly, dynamic models are bound to overestimate autoregressive behavior whenever the spatial dimension is important. The same holds true for the opposite case. An application using the well-known, openly available cigarette demand data supports these findings.

Dynamic panel models are a popular approach to study interrelationships between repeatedly measured variables. Often, dynamic panel models are specified and estimated within a structural equation modeling (SEM) framework. An endemic problem threatening the validity of such models is unmodelled heterogeneity. Recently, individual parameter contribution (IPC) regression was proposed as a flexible method to study heterogeneity in SEM parameters as a function of observed covariates. In the present paper, we derive how IPCs can be calculated for general maximum likelihood estimates and evaluate the performance of IPC regression to estimate group differences in dynamic panel models in discrete and continuous time. We show that IPC regression can be slightly biased in samples with large group differences and present a bias correction procedure. IPC regression showed generally promising results for discrete time models. However, due to highly nonlinear parameter constraints, caution is indicated when applying IPC regression to continuous time models.

This study examines the determinants of firm profitability for 114 firms listed on the Nigerian Stock Exchange (NSE) from 1998 to 2012, using the system Generalized Method of Moments (GMM). The results show that lagged profitability exerts significant positive effect on contemporaneous firm profitability. However, short-term leverage, inflation rate, interest rate and financial risk have significant negative effects on firm profitability. The study therefore suggests, among other recommendations, that the cost of borrowing to the real sector of the economy should be reduced in order to minimize costs of production, enhance productivity and profitability while necessary macroeconomic policies should be put in place by the government to curb inflationary pressure in the economy.

Celem artykułu jest omówienie kierunku rozwoju metodologii badania konwergencji gospodarczej, wskazującego na potrzebę uwzględniania w modelach wzrostu regionów powiązań przestrzennych między nimi. Artykuł prezentuje empiryczne modele β-konwergencji dotyczące wzrostu gospodarczego regionów Europy, otrzymane przy wykorzystaniu różnych koncepcji metodologicznych. W artykule rekomenduje się modele z zakresu ekonometrii przestrzennej. Dane empiryczne dotyczą PKB per capita w regionach NUTS-2 27 państw europejskich, będących członkami Unii Europejskiej. Zakres czasowy analizy obejmuje lata 1995–2009 (dane roczne). ; The aim of the paper is to discuss the course of development of methodology of economic convergence analyses, which points up the necessity of taking into consideration spatial connections among regions in regional growth models. It presents empirical models of β-convergence concerning the economic growth of European regions using various methodological conceptions. In the paper the models offered by spatial econometrics are recommended. The empirical data refer to per capita GDP across the European Union regions at a NUTS-2 level over the period 1995–2009 (annual data).

Conventional OLS fixed-effects and GLS random-effects estimators of dynamic models that control for individual-effects are known to be biased when applied to short panel data (T ≤ 10). GMM estimators are the most used alternative but are known to have drawbacks. Transformed-likelihood estimators are unused in political science. Of these, orthogonal reparameterization estimators are only tangentially referred to in any discipline. We introduce these estimators and test their performance, demonstrating that the unused orthogonal reparameterization estimator in particular performs very well and is an improvement on the commonly used GMM estimators. When T and/or N are small, it provides efficiency gains and overcomes the issues GMM estimators encounter in the estimation of long-run effects when the coefficient on the lagged dependent variable is close to one.

Whether democratic and non-democratic regimes perform differently in social provision policy is an important issue to social scientists and policy makers. As political regimes are rarely changing, their long-term or dynamic effects on the outcome are of concern to researchers when they evaluate how political regimes affect social policy. However, estimating the dynamic effects of rarely changing variables in the analysis of time-series cross-sectional data by conventional estimators may be problematic when the unit effects are included in the model specification. This article proposes a model to account for and estimate the correlation between the unit effects and explanatory variables. Applying the proposed model to 18 Latin American countries, this article finds evidence that democracy has a positive effect on social spending both in the short and long term.