The historical contribution of Transport Infrastructure Investment on Economic Growth in Costa Rica: An Econometric Analysis (1950-2017)

Professor: Vasyl Golosnoy
Student: Camilo Santa Cruz
Email:
Matrikelnummer: 235633
Msc. Econometrics
Seminar in Econometrics
June - Sommer Semester 2023

Abstract

Transportation infrastructure has been historically recognized as a key determinant of economic development in regions and nations. Despite the fact that from the economic literature there is an apparent consensus of the positive contribution that investment in transport infrastructure has, the empirical validation through econometric techniques of the contribution that this has in the case of Costa Rica is scarce. That is why the main purpose of this study is to verify if the investment in transport infrastructure has an impact on the economic growth of Costa Rica for a period that includes up to 7 decades (1950 - 2017). To this end, an extended production function is built in which, in addition to the productive factors: Employment, Capital and Human Capital is incorporated the capital stock of transport infrastructure. Through the application of an ordinary least squares (OLS) model in first differences (FD), it is verified that investment in transport infrastructure has a positive incidence on the economic growth of the country with values that fluctuate between 0.16 and 0.23 p.p close to what empirical literature suggests.

Keywords

Transport Economics, Investment, Public expenditure, Economic development, Competitiveness

Cite: Santa Cruz - Camacho (2023). The historical contribution of Transport Infrastructure Investment on Economic Growth in Costa Rica: An Econometric Analysis (1950-2019). Seminar in Econometrics. Ruhr Universität Bochum.

Preliminary findings

Extended Production function
Dependent variable:
diff(log(GDP/L))
(1) (2) (3)
diff(log(K/L)) 0.439** 0.362* 0.332
(0.197) (0.185) (0.202)
diff(log(TK/L)) 0.163** 0.233*** 0.162**
(0.070) (0.069) (0.069)
d1956[2:68] -0.119***
(0.037)
d1982[2:68] -0.072*
(0.038)
Constant 0.003 0.003 0.006
(0.006) (0.006) (0.006)
Observations 67 67 67
R2 0.171 0.289 0.215
Adjusted R2 0.145 0.255 0.177
Residual Std. Error 0.037 (df = 64) 0.035 (df = 63) 0.036 (df = 63)
F Statistic 6.608*** (df = 2; 64) 8.540*** (df = 3; 63) 5.739*** (df = 3; 63)
Note: p<0.1; p<0.05; p<0.01