ANALISIS FAKTOR-FAKTOR YANG MEMPENGARUHI PENYERAPAN TENAGA KERJA PADA INDUSTRI KECIL DI PROVINSI JAMBI TAHUN 2000-2012

Abstract

Development Goals in developing countries, among others, to create economic development that the results can be equally felt by the people, and promote economic growth, increase employment, income distribution, reducing regional dispariti es. One of the indicators to assess the success of the economic development of a country can be seen from the employment opportunities created capable. This study aims to determine the ability of a small industrial sector in increasing the labor absorption rate is influenced by several factors, among others, the value of production, the number of business units and the provincial minimum wage rate. In this study, the authors define the scope of the study to examine the level of employment in small industrial sector in the province of Jambi. The research was conducted by using secondary data, derived from Statistics Jambi and related institutions. The data is the time series data over a period of 13 years. The method used is multiple linear regression using EVIEWS6. Based on the results of the regression analysis, factors that partial business unit, and the provincial minimum wage rate of significant positive effect on the employment of small industry in Jambi. While the production value factors, no significant effect. F test results obtained F value of 52 852 count. F table value is 3.86 with a level of α = 0.05 level. Calculated F value (52.852) is greater than the F table (3.86) shows jointly Production value (X1), Total Business Unit (X2), and the Minimum Wage Rate (X3) significantly affect industry sectors Manpower Absorption (Y). The coefficient of determination (R2) is equal to 0.946 = 94.62%, which means that a variable amount of production value (X1) and a variable number of business units (X2), and the Minimum Wage Rate (X3) has been able to explain the amount of labor absorbed (Y ) in small industry in Jambi Province was 94.6% and the remaining 5.4% is explained by some or other variables that are not described in the model. The results of the calculation of correlation coefficient (R) shows the correlation value of 0.93 indicates a close relationship between the variable X with the variable Y.