Title of Abstract

Empirical analysis of Infrastructures impact on the Economy

Poster Number

29

Faculty Mentor

Danko Tarabar, Ph.D.; tarabard@winthrop.edu

College

College of Business Administration

Department

Accounting, Finance & Economics

Faculty Mentor

Danko Tarabar, Ph.D.

Abstract

This paper empirically investigates the relationship between infrastructure and economic well-being across approximately 160 countries observed between 1999 and 2018. Five separate outcome variables are considered: trade volume, patent density, real GDP per capita growth, manufacturing share of GDP, and services share of GDP. Then, in cross-sectional and panel fixed effects multiple regressions, I relate both road and railroad lengths and densities, quality, and amount of goods transported, to the above-named dependent variables. The argument that we put to the econometric test is that increased quantity and quality of infrastructure is associated at a statistically significant level with higher GDP per capita growth, increased patent density, and a higher international Trade volume. The intuition behind this argument is that increased access to transportation decreases the costs of said transportation whether it be information, goods, or services. To account for other predictors of dependent variables, I control for Human capital, continent, government stability, and population in most of my regression models. The implication of a statistically significant and positive correlation between dependent and main independent variables is that countries should devote more resources to infrastructure projects in order to promote economic wealth and the well-being of their citizens.

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Course Assignment

ECON 306 - Tarabar

Type of Presentation

Poster presentation

Start Date

16-4-2021 12:30 PM

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Apr 16th, 12:30 PM

Empirical analysis of Infrastructures impact on the Economy

This paper empirically investigates the relationship between infrastructure and economic well-being across approximately 160 countries observed between 1999 and 2018. Five separate outcome variables are considered: trade volume, patent density, real GDP per capita growth, manufacturing share of GDP, and services share of GDP. Then, in cross-sectional and panel fixed effects multiple regressions, I relate both road and railroad lengths and densities, quality, and amount of goods transported, to the above-named dependent variables. The argument that we put to the econometric test is that increased quantity and quality of infrastructure is associated at a statistically significant level with higher GDP per capita growth, increased patent density, and a higher international Trade volume. The intuition behind this argument is that increased access to transportation decreases the costs of said transportation whether it be information, goods, or services. To account for other predictors of dependent variables, I control for Human capital, continent, government stability, and population in most of my regression models. The implication of a statistically significant and positive correlation between dependent and main independent variables is that countries should devote more resources to infrastructure projects in order to promote economic wealth and the well-being of their citizens.