Abstract
This paper attempts to empirically investigate the link between institutional quality and economic performance in a group of 72 countries during 1980–2001, using dynamic panel data analysis. Five institutional indicators that represent the overall institutional infrastructures of an economy are employed, namely corruption, rule of law, bureaucracy, repudiation of contracts and risk of expropriation. The empirical results demonstrate that institutions’ variables are statistically significant determinants of economic performance. The findings also suggest that the effects of institutional quality vary according to the level of economic development, where institutions are more responsive in the low-income and middle-income countries. In terms of specific effect of institutional development, the results reveal that the higher level of rule of law is most potent in delivering long-run economic benefits.
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Notes
Blomstrom et al. (1996) show that increases in capital stock is generally not the igniting source of economic growth. Easterly and Levine (1997) find that the conventional factors of growth such as capital, labour, human capital and so on do not fully explain Africa’s experience and have turned to institutional explanations. Bils and Klenow (1998) point out that human capital does not have a causal impact on output growth.
According to their study, social infrastructure can be defined as the institutions and government policies that provide the incentives for individuals and firms in an economy. Incentives can encourage productive activities or instead encourage predatory behavior such as rent-seeking, corruption and theft.
In the neoclassical growth model only labour augmenting technological change is consistent with the existence of the steady-state. See Barro and Sala-i-Martin (1995).
While cross-sectional estimation methods may, in principle, capture the long-run relationship between the variables concerned, they do not take advantage of the time-series variation in the data, which could increase the efficiency of estimation. It is, therefore, preferable to estimate the growth model using panel data techniques.
The list of countries is presented in the Appendix.
For robustness check, this study also used different weight for each indicator to construct the aggregate index and the estimations are similar.
The lag structure is (1,0,0,0) for Eq. (7) and the order of variables is as follows: RGDPC, Capital Stock Per Capita, Time Trend and institutions.
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Law, S.H., Bany-Ariffin, A.N. Institutional Infrastructure and Economic Performance: Dynamic Panel Data Evidence. Transit Stud Rev 15, 542–557 (2008). https://doi.org/10.1007/s11300-008-0026-1
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DOI: https://doi.org/10.1007/s11300-008-0026-1