least squares (OLS), fixedeffects panel and first-differences general method of moment (GMM) model tests were executed in EViews on a dataset for 25 EU countries covering 1996–2017. The research question was as follows: Do the various appropriations (i
The primary intent of this paper is to statistically test whether Buddhist countries tend to contribute to global warming mitigation in comparison with other religious groups of countries. A sample of 160 countries were classified into seven groups coded as ‘Buddhist’, ‘Hindu’, ‘Muslim’, ‘Catholic’, ‘Protestant’, ‘Christian mixed’ and ‘None of the above’. This study modelled the religious heritage of a nation into the IPAT equation (Environmental Impact = Population × Affluence × Technology), religion being as a cultural proxy of the technology factor. ‘Buddhist’ countries were found likely to emit lower CO2 compared with ‘Protestant’ and ‘Christian mixed’ countries, although likely to emit higher CO2 compared than ‘Hindu’, ‘Muslim’ and ‘Catholic’ countries, all other factors being held equal. The relatively low group effect of ‘Buddhist’ countries on CO2 emissions can be interpreted to support the argument that teaching Buddhist economics and ecology could be a useful ingredient to curb ever-increasing global CO2 emissions. Thus, further study is warranted as to how teachings from Buddhism can translate into lower CO2 emissions.
negative impact in OLS and fixedeffects econometric models. With American data, this significant negative impact has been confirmed by other authors as well, including Vafeas (1999) with Tobin's Q in OLS models, Cheng et al. (2008) with Tobin's Q and
This paper examines the impact of firm-specific and industry characteristics on capital structure during a sample period spanning from 2007 to 2013. We used panel regression with fixed effects and found strong evidence that capital structure is most affected by firm-specific factors such as tangibility, non-debt tax shields, liquidity, firm size, taxes paid, profitability, Tobin’s Q ratio, and growth assets. In addition, the empirical results indicate that firms operating in different industries have dissimilar capital structures.
This paper revisits the empirical trade literature on East-West trade in the early 1990s and provides a replication of the traditional gravity findings of that period with the Baier-Bergstrand version of the model, providing thereby better estimates of the trade hindering impact of the Cold War by including multilateral and world resistance factors and simultaneously considering country fixed effects. Breaking down the Cold War Walls increased world trade by 2.7% of world GDP. The replication with the Baier-Bergstrand model also reveals that Cold War trade distortions also significantly impacted China’s trade with the West.
Payment systems make a significant contribution to the flow of transactions and financial stability. In this paper, we start by applying the principles of the gravity model to explain the TARGET flows of banking transactions between Portugal and other eurozone countries. The main explanatory variables tested are a composite indicator of economic and financial activities, distance, membership of the Eurozone (EZ), and country risk measured by treasury bond yields. The results indicate that Portugal has a high level of integration in the European banking market as distance is not statistically significant, and that the membership of the EZ facilitates the financing of the economy. The economic size of the partner country becomes non-significant after controlling for country fixed effects. The increase in the Portuguese country risk during the European sovereign debt crisis led to a marked decline in external financing, indicating that this is an important channel of transmission of crises.
This paper tests a neo-Heckscher-Ohlin versus a neo-Ricardian framework for explaining vertical intra-industry trade. The study applies panel techniques with instrument variables to analyse trade between ‘old’ EU and 10 Central-East European countries in their post-transition period. Results show country-pair fixed effects to be of high relevance for explaining vertical intra-industry trade. Technology differences are positively, while differences in factor endowment, measured in GDP per capita, are negatively correlated with vertical intra-industry trade, and confirm the relevance of the neo-Ricardian approach. In addition, changing bilateral differences in personal income distribution during the transition of Central-East European countries towards a market economy contribute to changes in vertical intra-industry trade.
Authors:Ernesto R. Ferreira, João D. Monteiro and José R. Pires Manso
Can socioeconomic fluctuations explain variations in European Union suicide mortality? To answer this question, we investigate the effect of socioeconomic and demographic factors on (agespecific) male and female suicide rates using a fixed-effects technique and panel data for 15 EU countries, over a time period that leads up to, coincides with, and follows the recession of 2008. The findings show that suicide rates for young and working-age populations are more sensitive to general economic conditions than suicide rates for other age groups, and that male suicide behavior is more responsive than female behavior. In this setting, suicide rates are likely to be higher in countries with lower income, higher unemployment, higher divorce rates, and, most importantly, weaker systems of social protection. Our results, however, raise serious doubts about government involvement in crisis-related mental illness prevention and mental health promotion.
This article attempts to estimate the total factor productivity (TFP) for 35 NUTS-2 regions of the Visegrad Group countries and to identify its determinants. The TFP values are estimated on the basis of the Cobb-Douglas production function, with the assumption of regional differences in productivity. The parameters of the productivity function were analysed with panel data, using a fixed effects model.
There are many economic variables that influence the TFP level. Some of them are highly correlated, and therefore the factor analysis was applied to extract the common factors – the latent variables that capture the common variance among those observed variables that have similar patterns of responses. This statistical procedure uses an orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of linearly uncorrelated variables called principal components. Each component is interpreted using the contributions of variables to the respective component.
I estimated a dynamic panel data model describing TFP formation by regions. An attempt was made to incorporate the common factors among the model’s explanatory variables. One of them, representing the effects of research activity, proved to be significant.