Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 3

economic indicators Related Abstracts

3 Migrants and Non Migrants: Class Level Distinctions from a Village Level Analysis of Mahabubnagar District

Authors: T. P. Muhammed Jamsheer


This paper tries to explains some of differences between migrants and non-migrants households by taking ten indicators like land ownership, land distribution, lease in land, lease out land, demand of labour, supply of labour, land operational potential, holding of agriculture implements and livestock’s, irrigation potential of households and credit holding by the households of highly dry, drought affected, poverty stricken, multi caste and pluralistic sub castes village in very backward Mahabubnagar district of Andhra Pradesh. The paper is purely field work based research and conducted census survey of field work among the 298 households in highly dry village called Keppatta from Bhoothpur mandel. One of the main objectives of the paper is that, to find out the factors which differentiate migrants and non-migrants households and what are distress elements which forced the poor peasants migrants to outside the village. It concludes that among the migrants and non-migrants households and among the differences between the categories wise of both types of households, there are differences, except two indicators like lease in and lease out, all other indicators like land holding pattern, demand and supply of labour, land operation, irrigation potential, implements and livestock and credit facilities of migrants and non-migrants households shows that non-migrants have high share than the migrants households. This paper also showing the landed households are more migrants, means among the BC and FC households landed households are migrants while SC landless are more migrants which is contradictory to general/existing literatures conclusion that, landless are more migrant than landed households, here also showing that when the number of land in acres increases the share of SC is declining while the share of FC is increasing among the both migrants and non-migrants households. In the class wise SC households are more in distress situation than any other class and that might be leading to the highest share of migrants from the respective village. In the logistic econometric model to find out the relation between migration and other ten variables, the result shows that supply of labour, lease in of the land and size of the family are statically significantly related with migration and all other variables not significant relation with migration although the theoretical explanation shows the different results.

Keywords: Class, migrants, non migrants, economic indicators, distress factors

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2 Analysis of the Evolution of Social and Economic Indicators of the Mercosur´s Members: 1980-2012

Authors: L. Aparecida Bastos, J. Leige Lopes, J. Crepaldi, R. Monteiro da Silva


The objective of this study is to analyze the evolution of some social and economic indicators of Mercosur´s economies from 1980 to 2012, based on the statistics of the Latin American Integration Association (LAIA). The objective is to observe if after the accession of these economies to Mercosur (the first accessions occurred in 1994) these indicators showed better performance, in order to demonstrate if economic integration contributed to improved trade, macroeconomic performance, and level of social and economic development of member countries. To this end, the methodologies used will be a literature review and descriptive statistics. The theoretical framework that guides the work are the theories of Integration: Classical Liberal, Marxist and structural-proactive. The results reveal that most social and economic indicators showed better performance in those economies that joined Mercosur after 1994. This work is the result of an investigation already completed.

Keywords: Economic Integration, economic indicators, mercosur, social indicators

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1 Determination of the Effective Economic and/or Demographic Indicators in Classification of European Union Member and Candidate Countries Using Partial Least Squares Discriminant Analysis

Authors: Esra Polat


Partial Least Squares Discriminant Analysis (PLSDA) is a statistical method for classification and consists a classical Partial Least Squares Regression (PLSR) in which the dependent variable is a categorical one expressing the class membership of each observation. PLSDA can be applied in many cases when classical discriminant analysis cannot be applied. For example, when the number of observations is low and when the number of independent variables is high. When there are missing values, PLSDA can be applied on the data that is available. Finally, it is adapted when multicollinearity between independent variables is high. The aim of this study is to determine the economic and/or demographic indicators, which are effective in grouping the 28 European Union (EU) member countries and 7 candidate countries (including potential candidates Bosnia and Herzegovina (BiH) and Kosova) by using the data set obtained from database of the World Bank for 2014. Leaving the political issues aside, the analysis is only concerned with the economic and demographic variables that have the potential influence on country’s eligibility for EU entrance. Hence, in this study, both the performance of PLSDA method in classifying the countries correctly to their pre-defined groups (candidate or member) and the differences between the EU countries and candidate countries in terms of these indicators are analyzed. As a result of the PLSDA, the value of percentage correctness of 100 % indicates that overall of the 35 countries is classified correctly. Moreover, the most important variables that determine the statuses of member and candidate countries in terms of economic indicators are identified as 'external balance on goods and services (% GDP)', 'gross domestic savings (% GDP)' and 'gross national expenditure (% GDP)' that means for the 2014 economical structure of countries is the most important determinant of EU membership. Subsequently, the model validated to prove the predictive ability by using the data set for 2015. For prediction sample, %97,14 of the countries are correctly classified. An interesting result is obtained for only BiH, which is still a potential candidate for EU, predicted as a member of EU by using the indicators data set for 2015 as a prediction sample. Although BiH has made a significant transformation from a war-torn country to a semi-functional state, ethnic tensions, nationalistic rhetoric and political disagreements are still evident, which inhibit Bosnian progress towards the EU.

Keywords: classification, European Union, economic indicators, demographic indicators, partial least squares discriminant analysis

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