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Comparing ols and rank-based estimation techniques for production analysis: An application to Ghanaian maize farms.
125-130Views:338This paper introduces the rank-based estimation method to modelling the Cobb-Douglas production function as an alternative to the least squares approach. The intent is to demonstrate how a nonparametric regression based on a rank-based estimator can be used to estimate a Cobb-Douglas production function using data on maize production from Ghana. The nonparametric results are compared to common parametric specification using the ordinary least squares regression. Results of the study indicate that the estimated coefficients of the CobbDouglas Model using the Least squares method and the rank-based regression analysis are similar. Findings indicated that in both estimation techniques, land and Equipment had a significant and positive influence on output whilst agrochemicals had a significantly negative effect on output. Additionally, seeds which also had a negative influence on output was found to be significant in the robust rank-based estimation, but insignificant in the ordinary least square estimation. Both the least squares and rank-based regression suggest that the farmers were operating at an increasing returns to scale. In effect this paper demonstrate the usefulness of the rank-based estimation in production analysis.
JEL CODE: Q18, D24, Q12, C1 and C67
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Comparing parametric and semiparametric error correction models for estimation of long run equilibrium between exports and imports
19-23Views:350This paper introduces the semiparametric error correction model for estimation of export-import relationship as an alternative to the least squares approach. The intent is to demonstrate how semiparametric error correction model can be used to estimate the relationship between Ghana’s export and import within the context of a generalized additive modelling (GAM) framework. The semiparametric results are compared to common parametric specification using the ordinary least squares regression. The results from the semiparametric and parametric error correction models (ECM) indicate that the error correction term and import variable are significant determinants of Ghana’s exports. On the basis of Akaike Information Criteria and Generalized Cross-Validation (GCV) scores, it is found that the semiparametric error correction model provides a better fit than the widely used parametric error correction model for modeling Ghana’s export-import relationship. The results of the analysis of variance provide further evidence of nonlinearity in Ghana’s export and import relationship. In effect, this paper demonstrates the usefulness of semiparametric error correction model in the estimation of export – import relationship.
JEL code: C14, C18, C22, F10, F14
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The connenction between global innovation index and economic well-being indexes
87-92Views:927We study the connection of innovation in 126 countries by different well-being indicators and whether there are differences among geographical regions with respect to innovation index score. We approach and define innovation based on Global Innovation Index (GII). The following well-being indicators were emphasized in the research: GDP per capita measured at purchasing power parity, unemployment rate, life expectancy, crude mortality rate, human development index (HDI). Innovation index score was downloaded from the joint publication of 2018 of Cornell University, INSEAD and WIPO, HDI from the website of the UN while we obtained other well-being indicators from the database of the World Bank. Non-parametric hypothesis testing, post-hoc tests and linear regression were used in the study.
We concluded that there are differences among regions/continents based on GII. It is scarcely surprising that North America is the best performer followed by Europe (with significant differences among countries). Central and South Asia scored the next places with high standard deviation. The following regions with significant backwardness include North Africa, West Asia, Latin America, the Caribbean Area, Central and South Asia, and sub-Saharan Africa. Regions lagging behind have lower standard deviation, that is, they are more homogeneous therefore there are no significant differences among countries in the particular region.
In the regression modelling of the Global Innovation Index, it was concluded that GDP per capita, life expectancy and human development index are significant explanatory indicators. In the multivariable regression analysis, HDI remained the only explanatory variable in the final model. It is due to the fact that there was significant multicollinearity among the explanatory variables and the HDI aggregates several non-economic indicators like GII.JEL Classification: B41, I31, O31, Q55
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A Quantitative Assessment of the Rurality and an Efficiency Analysis of Emigration in Romania
39-46Views:358In Romania, as in many other Eastern European countries, the early 1990s were marked by a significant emigration from the countryside as a consequence of the transition from a centralised economy to an open one and due to key changes in the political framework. The permanent emigration has predominantly been concentrated in rural areas where multiple socio-economic variables such as GDP per capita, unemployment, and public financial subsidies aimed at supporting people at risk of severe deprivation and poverty have all had a direct effect on rural depopulation. The rurality is a complex theoretical construct comprising many items and variables and is, therefore, difficult to define in a concise manner. The aim of this paper is to assess the evolution of emigration in Romania between 2001 and 2016 through a quantitative approach, estimating an index of rurality for the same period composed of a set of socio-economic variables having a direct or indirect nexus to it. In the first phase of research, a matrix of correlation and a multiple regression model has been used in order to estimate the direct links among all investigated variables. Following the quantitative methodology, in the second phase Partial Least Square Structural Equation Modelling (PLS-SEM) has been used in order to assess the main cause-effect relationships among a few selected endogenous variables and a set of socio-economic items. Furthermore, using a non-parametric Data Envelopment Analysis (DEA) output-oriented model, this research has assessed the efficiency in terms of permanent emigration from Romania estimated as an output to minimise and not as an output to maximise, as investigated by traditional efficiency approaches. In terms of efficiency, financial subsidies allocated by national authorities and the level of per capita Gross Domestic Product have acted directly on the level of emigration. The index of rurality in 2016 has been influenced in particular by he pluriactivity in farms in terms of agritourism, the dimension of farms in terms of land capital endowment, and the level of GDP per capita.
JEL Classification: Q10; Q18
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