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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.

The economic growth of any country depends largely on the entrance to international capital inflows, that is, external investment and its optimum allotment to components of different economic sectors. In several ways, foreign direct investment (FDI) helps by creating employment opportunities and rapid economic growth in emerging countries through capital flows in the developed countries and under developed countries. Many factors are affecting the FDI inflows in emerging countries among such determinants environmental issues are play a crucial role. Pollution control, air cleaner, water cleanness, etc., are the part of the environmental regulation in any country. Carbon dioxide (CO₂) emission and sulphur dioxide (SO₂) emission are major components of air pollution that have been widely used in empirical studies. The study intends to explore the impact of environmental regulations on FDI inflows in emerging countries along with governance factors and the macroeconomic fundamentals like per capita power consumption, trade openness, per capita GDP, etc. Based on the statistical data of 15 emerging countries from 2000 to 2015, the study follows the static panel data approach to empirically find the impact of environmental issues on FDI inflows. The results reveal that significant bonding realise between environmental regulations and FDI inflows in emerging countries. Based on the statistical evaluation however best our knowledge FDI is more attractive where lower regulations are established. For sake of simplicity environmental regulations are crucial to the multinational corporations (MNCs) for investment.

Adoption of digital technology at different levels of economic activity is an important indicator of development. Countries are adopting digital technology at business and government levels to increase efficiency and accountability of service delivery to appropriate user groups. Many countries are using digital technology in banking, education, and many other sectors and recasting the relationship among customers, workers, and employers. The digital transformation progressively changes productivity across all sectors and industries. The empirical investigation shows that mainly the role of per capita income (state of living) and supply-side factors explain cross-country variations in Digital Adoption Index (DAI) in business using a static panel data model with fixed effect (FE) approach for 169 countries over the period 2014–2016. In this context, this study makes an attempt to understand the state of digital adoption in business across 169 countries in 2016 using World Bank data. The empirical results state that only major variable like labour force are positively and statistically significant with DAI across the countries. Although there are caveats that the International Monetary Fund has made about the digital economy, they state that we should think carefully about how to devise policies that will allow us to fully exploit the digital revolution’s benefits while minimizing job dislocation.

Financial sustainability in emerging market economies crucially depends on stable foreign capital inflows as these countries lack adequate domestic capital and sophisticated technology. This study attempts to examine the impact of major political risk factors in the emerging market economies along with basic economic fundamentals such as institutional variables like per capita electric consumption, trade openness, and real rate of interest. We have followed a static panel data approach in studying the impact of these crucial variables in Foreign Direct Investment (FDI) inflows in 15 major emerging economies for the period 2000–2014. Risk perceptions, i.e., political risk data, have been collected from the International Country Risk Guide (ICRG) provided by the Political Risk Services (PRS) Group. In our research purpose, we have considered dependent variable as FDI inflows for 15 emerging countries during the period 2000–2014, which are drawn from the United Nations Conference on Trade and Development (UNCTAD, 2014, 2015) FDI database. Our results demonstrate that there are six subcomponents of risk perception (political risk) which are statistically significant in explaining variation in FDI inflows of the major emerging countries. The results show that government stability, socioeconomic conditions, religious tension, and bureaucracy quality have a positive impact on FDI inflows of emerging countries, whereas internal conflict and law and order have a negative impact on FDI inflows of these countries. Stable government is more attractive to foreign investors. Again, an improvement in the socioeconomic conditions is positively related with FDI inflows in emerging countries. Decreasing bureaucracy leads to a reduction in corruption, and assists expanding FDI flows in the emerging country.

In this paper, we study a partially linear dynamic panel data model with fixed effects, where either exogenous or endogenous variables or both enter the linear part, and the lagged-dependent variable together with some other exogenous variables enter the nonparametric part. Two types of estimation methods are proposed for the first-differenced model. One is composed of a semiparametric GMM estimator for the finite-dimensional parameter θ and a local polynomial estimator for the infinite-dimensional parameter m based on the empirical solutions to Fredholm integral equations of the second kind, and the other is a sieve IV estimate of the parametric and nonparametric components jointly. We study the asymptotic properties for these two types of estimates when the number of individuals N tends to ∞ and the time period T is fixed. We also propose a specification test for the linearity of the nonparametric component based on a weighted square distance between the parametric estimate under the linear restriction and the semiparametric estimate under the alternative. Monte Carlo simulations suggest that the proposed estimators and tests perform well in finite samples. We apply the model to study the relationship between intellectual property right (IPR) protection and economic growth, and find that IPR has a non-linear positive effect on the economic growth rate.

In this study, we empirically investigate the effect of military expenditure on economic growth in the five South Asian countries of Bangladesh, India, Pakistan, Nepal, and Sri Lanka over the period of 1990–2006. By applying a Solow Growth Model, empirical evidences derived from panel estimation methods indicate that defense has a negative effect on economic growth in the region.

The aim of the chapter is to identify the pattern of city–region relations in Polish spatial and economic development. Based on the regression analysis of GDP per capita growth in chosen cities and their regions, conclusions are drawn on: differences in urban development (measured by GDP per capita growth) between cities and their surrounding regions and the direction of the core-periphery mechanism (backwash vs. spread). Findings of the research enabled us to answer the questions of whether urban Poland follows the core-periphery model of development, and to identify the stage of this theoretical approach at which Polish cities might be. For the majority of studies, cities and regions follow the backwash mechanism in their relations with the surrounding regions; however, the trend of this impact differs. Warsaw is a city where the dependence on its region’s resources is decreasing; nevertheless, other findings presented in the chapter allow the statement that Polish cities reach for their region’s resources and the core-periphery mechanism is characterised by the backwash effect. The following chapter fills in the gap in urban competitiveness studies, as they usually concentrate on specific case studies and rarely use larger panels for analysis. Such approach enables us to draw conclusions for the overall urban development patterns in Poland. Also, the issue of growth spread and city–region relations is rarely found in the Polish urban research.

This chapter focuses to study the aspect of dynamic profitability of the Indian computer industry in the post tariff rationalization period, i.e., complete elimination of tariff on imported computers parts and component after implementation of Information Technology Agreement (ITA) in 2004. If trade liberalization affects profitability, then it also interrupts the firm's financial structure because a firm reduces its short-run debts when it generates huge profit. On the contrary, higher marginal return or profitability of asset encourages the debtor to invest more. In fact, trade liberalization may affect investment through marginal profitability of asset by varying projected sales and costs of imported inputs, i.e., by altering the imported input price. This study examines the viable relationship between dynamic profitability and directives of the ITA. The sample selected from 51 Indian computer firms (14 hardware firms and 37 software firms) level data ranging from 2000–2001 to 2018–2019 and by application of dynamic panel data, the results are analyzed in this research work. This chapter observes that return on asset is negatively significant with the ratio between short-term liability and total liability for both the software and hardware sector of Indian computer industry in post-ITA policy timeline.