7 resultados para empirical determinants of economic growth

em University of Connecticut - USA


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We examine the effects of technology on productivity growth by disaggregating total output into sectoral components, exploring the roles of investment and technology on productivity growth for countries in different income groups. We find that for low-income countries, investment is the most important determinant of productivity growth. While investment plays an important role in determining productivity growth in middle-income countries, additional effects resulting from technological change also emerge. Investment ceases to have a significant effect on productivity growth in high-income countries.

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This paper examines cross-country patterns of economic growth by estimating a stochastic frontier production function for 80 developed and developing countries and decomposing output change into factor accumulation, total factor productivity growth, and production efficiency improvement. In addition, this paper incorporates the quality of inputs in analyzing output growth, where the productivity of capital depends on its average age, while the productivity of labor depends on its average level of education. Our growth decomposition involves five geographic regions - Africa, East Asian, Latin America, South Asia, and the West. Factor growth, especially capital accumulation, generally proves much more important than either the improved quality of factors or total factor productivity growth in explaining output growth. The quality of capital positively and significantly affects output growth in all groups. The quality of labor, however, only possesses a positive and significant effect on output growth in Africa, East Asia, and the West. Labor quality owns a negative and significant effect in Latin America and South Asia.

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Previous studies (e.g., Hamori, 2000; Ho and Tsui, 2003; Fountas et al., 2004) find high volatility persistence of economic growth rates using generalized autoregressive conditional heteroskedasticity (GARCH) specifications. This paper reexamines the Japanese case, using the same approach and showing that this finding of high volatility persistence reflects the Great Moderation, which features a sharp decline in the variance as well as two falls in the mean of the growth rates identified by Bai and Perronâs (1998, 2003) multiple structural change test. Our empirical results provide new evidence. First, excess kurtosis drops substantially or disappears in the GARCH or exponential GARCH model that corrects for an additive outlier. Second, using the outlier-corrected data, the integrated GARCH effect or high volatility persistence remains in the specification once we introduce intercept-shift dummies into the mean equation. Third, the time-varying variance falls sharply, only when we incorporate the break in the variance equation. Fourth, the ARCH in mean model finds no effects of our more correct measure of output volatility on output growth or of output growth on its volatility.

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Recent theoretical work has examined the spatial distribution of unemployment using the efficiency wage model as the mechanism by which unemployment arises in the urban economy. This paper extends the standard efficiency wage model in order to allow for behavioral substitution between leisure time at home and effort at work. In equilibrium, residing at a location with a long commute affects the time available for leisure at home and therefore affects the trade-off between effort at work and risk of unemployment. This model implies an empirical relationship between expected commutes and labor market outcomes, which is tested using the Public Use Microdata sample of the 2000 U.S. Decennial Census. The empirical results suggest that efficiency wages operate primarily for blue collar workers, i.e. workers who tend to be in occupations that face higher levels of supervision. For this subset of workers, longer commutes imply higher levels of unemployment and higher wages, which are both consistent with shirking and leisure being substitutable.

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In this paper, we extend the debate concerning Credit Default Swap valuation to include time varying correlation and co-variances. Traditional multi-variate techniques treat the correlations between covariates as constant over time; however, this view is not supported by the data. Secondly, since financial data does not follow a normal distribution because of its heavy tails, modeling the data using a Generalized Linear model (GLM) incorporating copulas emerge as a more robust technique over traditional approaches. This paper also includes an empirical analysis of the regime switching dynamics of credit risk in the presence of liquidity by following the general practice of assuming that credit and market risk follow a Markov process. The study was based on Credit Default Swap data obtained from Bloomberg that spanned the period January 1st 2004 to August 08th 2006. The empirical examination of the regime switching tendencies provided quantitative support to the anecdotal view that liquidity decreases as credit quality deteriorates. The analysis also examined the joint probability distribution of the credit risk determinants across credit quality through the use of a copula function which disaggregates the behavior embedded in the marginal gamma distributions, so as to isolate the level of dependence which is captured in the copula function. The results suggest that the time varying joint correlation matrix performed far superior as compared to the constant correlation matrix; the centerpiece of linear regression models.

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Most monetary models make use of the quantity theory of money along with a Phillips curve. This implies a strong correlation between money growth and output in the short run (with little or no correlation between money and prices) and a strong long run correlation between money growth and inflation and inflation (with little or no correlation between money growth and output). The empirical evidence between money and inflation is very robust, but the long run money/output relationship is ambiguous at best. This paper attempts to explain this by looking at the impact of money growth on firm financing.

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This paper integrates investments in health to a standard growth model where physical and human capital investments are the combined engines of growth. It shows the existence of two distinct health regimes separated by an 'Epidemiological Transition'. The various patterns of the transition identified in the epidemiological literature can be mapped into the model. The model also leads to the important hypothesis that the epidemiological transition may induce an economy to switch to a modern growth regime.