International Capital Mobility and Factor Reallocation in a Multisector Economy

This paper examines the effects of international capital flows in a small open econ omy utilizing a dynamic general equilibrium framework based on a three-sector Ramsey growth model. In order to analyze the impact of international capital mobility on production, consumption and allocation of resources across three sectors ,two different economic environments are modelled. The first model represents an open economy with capital mobility (a more comprehensive environment),and the second model introduces a closed economy with no capital mobility. Numerical applications of the models use data from the Turkish economy for the year 2002. The numerical results demonstrate that the presence of capital mobility, despite being limited by a borrowing constraint, reverses the impact of economic growth on production and resource allocation. The results also show that while production in the closed economy model simply adjusts to domestic demand, that of the open economy model is not constrained by it. Results further point that although there is positive growth in income and output in both environments, income growth in the capital mobility environment falls short of that in the no capital mobility environment. This result can be attributed to the relatively slower accumulation of capi tal in the former, which may be compensated by a positive rate of technological progress to accompany international capital flows.International Capital Flows,Human Capital, Multisector economy,Borrowing Constraint

Modeling, Empirics and Policy Implications of Firm Heterogeneity in International Trade

Computable General Equilibrium (CGE) models are essential computational tools for trade policy analysis. While traditional CGE models based on the Armington assumption of national product differentiation have been successfully applied to various policy scenarios, they also have significant limitations in explaining the firm-level information prevalent in the recent international trade literature. The pioneering work of Melitz (2003) has provided a firm heterogeneity theory that can help address the shortcomings of Armington-based CGE models by introducing additional productivity mechanisms and extensive margin effects. Incorporation of firm heterogeneity in mainstream CGE models offers great potential to improve computational policy analysis. Even though there have been some efforts to incorporate firm heterogeneity into CGE modeling, a readily accessible Global Trade Analysis Project (GTAP) implementation is currently not available. This dissertation addresses this gap by a combination of theory, calibration, estimation and simulation to develop and implement a firm heterogeneity module executed within the GTAP environment. ^ Chapter 2 presents the newly developed firm heterogeneity module with a stylized tariff removal scenario and compares the model predictions with those of monopolistic competition and perfect competition frameworks previously established in the standard GTAP model. Chapter 3 proposes a theoretically-consistent way to parameterize the firm heterogeneity module with a focus on the elasticity of substitution across varieties. Results show that the elasticity values that are consistent with the firm heterogeneity theory are considerably lower than Armington elasticities used in the standard GTAP model. Finally, Chapter 4 applies this newly developed module and parameterization to policy analysis in order to investigate the implications of reducing non-tariff measures associated with the beef hormone ban imposed by the European Union on imports from the United States based on the negotiations taking place for the Transatlantic Trade and Investment Partnership (TTIP) Agreement. The firm heterogeneity module predictions of welfare changes in the United States are distinctly different from those predicted by the standard GTAP model. This is explained by the endogenous productivity and variety effects implied by the firm heterogeneity theory. Results also suggest that the choice of policy instrument is an important factor in determining which one of these effects dominates in the final welfare outcome. This dissertation introduces the first implementation of firm heterogeneity into the standard GTAP model which I hope will serve as a powerful tool for policy analysis with improved abilities in tracing out the productivity changes and entry/exit of firms following trade liberalization episodes

Climate change and its extensions in infectious diseases: South-Eastern Europe under focus

Climate change is a result of natural processes and human-made activities influencing the atmosphere. Many infectious diseases are climate-sensitive and their nature and epidemiology are changing in parallel with the change in climatic conditions and global warming. Increased replication rates of pathogens at higher temperatures, extended transmission seasons, migration of vectors or human populations are some outcomes of the changing climate, to trigger new concerns including new epidemics with old or new pathogens. Climate change is presenting itself today as an urgent global health threat and it requires immediate international action with high priority. Infectious diseases in relation to changing climatic conditions are reviewed with predominating current examples, keeping a focus on Europe with particular emphasis on South-Eastern European and Eurasian regions

Using Power Laws to Identify the Structural Parameters of Trade Models with Firm Heterogeneity

While various firm heterogeneity models of trade have recently emerged in the CGE literature, their mainstream adoption in trade policy analysis has been limited partly due to lack of available parameter estimates at the disaggregated sector level. In particular, the productivity dispersion and substitution elasticity parameters need to be estimated in a manner consistent with the theoretical underpinnings of the firm heterogeneity framework. In this paper we address this gap by estimating the productivity dispersion parameter by using ORBIS firm-level data and imputing substitution elasticities by fitting the firm size distribution and productivity distribution to the Pareto distribution

Using Power Laws to Identify the Structural Parameters of Trade Models with Firm Heterogeneity

While various firm heterogeneity models of trade have recently emerged in the CGE literature, their mainstream adoption in trade policy analysis has been limited partly due to lack of available parameter estimates at the disaggregated sector level. In particular, the productivity dispersion and substitution elasticity parameters need to be estimated in a manner consistent with the theoretical underpinnings of the firm heterogeneity framework. In this paper we address this gap by estimating the productivity dispersion parameter by using ORBIS firm-level data and imputing substitution elasticities by fitting the firm size distribution and productivity distribution to the Pareto distribution

Structural Parameters of Trade Models with Firm Heterogeneity

While various firm heterogeneity models of trade have recently emerged in the CGE literature, their mainstream adoption in trade policy analysis has been limited partly due to lack of available parameter estimates at the disaggregate sector level. The firm heterogeneity theory in quantitative trade models assume firm productivity as well as firm size distributions are characterized by power-laws, especially in the upper tail of the distribution where rare events such as exporting occurs. In recent empirical studies, it is reported that power-laws may not be suitable for the entire range of the distribution. Therefore, it is paramount to identify a minimum threshold above which the power-law provides a good fit for the data. This is especially important in estimating the structural parameters of the firm heterogeneity model for use in policy analysis as biased estimates may distort trade volumes and welfare responses. In this paper, we use maximum-likelihood and Kolmogorov-Smirnov (KS) statistic to estimate the minimum threshold for truncating the data which then allows us to estimate the shape parameter of firm size and productivity distributions based on power-law and ORBIS firm-level data. We then impute the elasticity of substitution across varieties that are appropriate to use in firm heterogeneity models of trade. We provide parameter values for productivity distribution and substitution elasticity for 19 GTAP manufacturing sectors

Structural Parameters of Trade Models with Firm Heterogeneity

While various firm heterogeneity models of trade have recently emerged in the CGE literature, their mainstream adoption in trade policy analysis has been limited partly due to lack of available parameter estimates at the disaggregate sector level. The firm heterogeneity theory in quantitative trade models assume firm productivity as well as firm size distributions are characterized by power-laws, especially in the upper tail of the distribution where rare events such as exporting occurs. In recent empirical studies, it is reported that power-laws may not be suitable for the entire range of the distribution. Therefore, it is paramount to identify a minimum threshold above which the power-law provides a good fit for the data. This is especially important in estimating the structural parameters of the firm heterogeneity model for use in policy analysis as biased estimates may distort trade volumes and welfare responses. In this paper, we use maximum-likelihood and Kolmogorov-Smirnov (KS) statistic to estimate the minimum threshold for truncating the data which then allows us to estimate the shape parameter of firm size and productivity distributions based on power-law and ORBIS firm-level data. We then impute the elasticity of substitution across varieties that are appropriate to use in firm heterogeneity models of trade. We provide parameter values for productivity distribution and substitution elasticity for 19 GTAP manufacturing sectors

Climate change and its extensions in infectious diseases: South-Eastern Europe under focus

Climate change is a result of natural processes and human-made activities influencing the atmosphere. Many infectious diseases are climate-sensitive and their nature and epidemiology are changing in parallel with the change in climatic conditions and global warming. Increased replication rates of pathogens at higher temperatures, extended transmission seasons, migration of vectors or human populations are some outcomes of the changing climate, to trigger new concerns including new epidemics with old or new pathogens. Climate change is presenting itself today as an urgent global health threat and it requires immediate international action with high priority. Infectious diseases in relation to changing climatic conditions are reviewed with predominating current examples, keeping a focus on Europe with particular emphasis on South-Eastern European and Eurasian regions