Optimum government size and economic growth in case of Indian states: Evidence from panel threshold model

This paper investigates the relationship between optimum government size and economic growth using data of Indian states during 1990-91 to 2017–18. Our results derived from panel threshold regression model show a positive and significant impact of government size on economic growth within the estimated thresholds for both aggregate and sub-panels based on income and regions. Once the government size moves above the upper threshold level, then its impact declines and turns to be insignificant. Thus, our findings suggest the policymakers for maintaining the government size within the thresholds limit.     

Does economic policy uncertainty affect cryptocurrency markets? Evidence from Twitter-based uncertainty measures

Using daily data from August 9, 2015, to July 7, 2020, this study examines the effects of economic policy uncertainty (EPU) on the returns of four cryptocurrencies: Bitcoin, Ethereum, Litecoin, and Ripple. To this end, two new measures of EPU (Twitter-based economic uncertainty and Twitter-based market uncertainty) are considered. A Granger causality test using the recursive evolving window approach shows a significant causality between the Twitter-based EPU measures and the BTC/USD exchange rate from October 2016 to July 2017. Moreover, a significant causality was noted from the EPU measures to the ETH/USD exchange rate from June 2019 to February 2020 and from the EPU measures to the XRP/USD exchange rate from January 2020 to February 2020. The Twitter-based EPU measures primarily positively affect the returns of the related cryptocurrencies during these periods. These results are robust to different measures of Twitter-based EPU and different econometric techniques. Potential implications, including the COVID-19 era, are also discussed.     

Impacts of seasonal patterns of climate on recurrent fluctuations in tourism demand: Evidence from Aruba

This study estimates the effect of seasonal patterns of pull and push climate elements (rainfall, temperature, wind, and cloud coverage) on recurrent fluctuations in tourism demand from the United States (USA) and Venezuela to Aruba. The seasonal patterns were first isolated from the series using the Census X-12 decomposition method, after which the analysis included panel data unit root testing, panel data regression, and Euclidean distance calculation. The results show that both pull and push seasonal factors of climate were relevant in determining the seasonal variations in tourism demand from both countries. The study derives two theoretical propositions: (1) climate is a significant push and pull factor affecting tourism demand; and (2) tourism demand and climate are bounded by intertemporal climate constraints.     

Migration and imperfect labor markets: Theory and cross-country evidence from Denmark,Germany and the UK

We investigate the labor market effects of immigration in Denmark, Germany and the UK, three countries which are characterized by considerable differences in labor market institutions and welfare states. Institutions such as collective bargaining, minimum wages, employment protection and unemployment benefits affect the way in which wages respond to labor supply shocks, and, hence, the labor market effects of immigration. We employ a wage-setting approach which assumes that wages decline with the unemployment rate, albeit imperfectly. We find that the wage and employment effects of immigration depend on wage flexibility and the composition of the labor supply shock. In Germany immigration involves only moderate wage, but large unemployment effects, since immigrants are concentrated in labor market segments with low wage flexibility. The reverse is true for the UK and Denmark.     

The determinants of credit spreads changes in global shipping bonds

This paper investigates whether bond, issuer, industry and macro-specific variables account for the observed variation of credit spreads’ changes of global shipping bond issues before and after the onset of the subprime financial crisis. Results show that conclusions as to the significant variables of spreads depend significantly on whether two-way cluster-adjusted standard errors are utilized, thus rendering results in the extant literature ambigious. The main determinants of global cargo-carrying companies’ shipping bond spreads are found in this paper to be: the liquidity of the bond issue, the stock market’s volatility, the bond market’s cyclicality, freight earnings and the credit rating of the bond issue.     

签订双边投资协定对中国吸收FDI影响的实证分析

本文以引力模型为基础,利用1990~2011年40个与中国签订双边投资协定国家的面板数据,采用混合效应模型,实证分析了签订双边投资协定对中国吸收外商直接投资的影响。研究结果表明：双边投资协定的签订显著地促进了各国对中国的投资,中国与发达国家签订双边投资协定的投资促进效果更好。     

Supply chain management executives in corporate upper echelons

Supply chain strategies and their implementation have been recognized as a source of competitive advantage. According to the principle “structure follows strategy”, we expect the number of firms having supply chain management (SCM) functions represented on their top management team (TMT) to have increased in the past years. However, little is known about the degree to which executives responsible for SCM functions (i.e., Chief Supply Chain Officers) are present or absent in TMTs and if their presence is related to firm performance. Therefore, we study the TMTs of large US corporations and show that SCM is present in upper echelons, either through executives whose responsibilities explicitly include SCM or indirectly by executives, especially CEOs, who had acquired SCM experience in their previous positions. However, firms׳ operating margins are lower when a Chief Supply Chain Officer is present in the TMT.     

NONPARAMETRIC AND SEMIPARAMETRIC PANEL DATA MODELS: RECENT DEVELOPMENTS

In this paper, we provide an intensive review of the recent developments for semiparametric and fully nonparametric panel data models that are linearly separable in the innovation and the individual-specific term. We analyze these developments under two alternative model specifications: fixed and random effects panel data models. More precisely, in the random effects setting, we focus our attention in the analysis of some efficiency issues that have to do with the so-called working independence condition. This assumption is introduced when estimating the asymptotic variance–covariance matrix of nonparametric estimators. In the fixed effects setting, to cope with the so-called incidental parameters problem, we consider two different estimation approaches: profiling techniques and differencing methods. Furthermore, we are also interested in the endogeneity problem and how instrumental variables are used in this context. In addition, for practitioners, we also show different ways of avoiding the so-called curse of dimensionality problem in pure nonparametric models. In this way, semiparametric and additive models appear as a solution when the number of explanatory variables is large.     

The long-term role of non-traditional banking in profitability and risk profiles: Evidence from a panel of U.S. banking institutions

The goal of this empirical study is to identify empirically and on a panel basis how non-traditional bank activities affect directly the profitability and risk profiles of the financial institutions involved in such activities. Through a dataset that covers 1725 U.S. financial institutions involved in non-traditional bank activities spanning the period 2000–2013 and the methodology of panel cointegration, the empirical findings document that non-traditional bank activities exert a positive effect on both the profitability and the insolvency risk. The results could be important for regulators given they could serve as a pre-warning signal that sends a clear message to regulators about the potential systemic risk that exists within the financial markets.     

Exploring the relationship between truck load capacity and traffic accidents in the European Union

Applying econometric techniques to EU28 panel data and controlling for explanatory variables such as road types, we find that increased truck load capacity does not necessarily aggravate road traffic safety. Specifically, heavy trucks do not seem to be linked with greater numbers of traffic fatalities/accidents, medium trucks appear to be the worst performers in terms of fatalities, and light trucks seem to be the worst for accidents. In summary, our results clarify the complex relationship between truck load capacity and road safety, pointing to the existence of a negative correlation for accidents per capita and an inverse U-shaped curve for fatalities per capita.