Spatial Econometric Analysis Of The Transfer And Employment Of Rural Labour Of Our Country

The contradiction between people and land is obvious in our country. The rurallabor which can be absorbed in agriculture is limited to a small number; and theaccelerated process of urbanization is also crowding the already small space of theagricultural labor. What’s more the rapid development of the third industry requires alot of labor supply. All these have promoted a large number of rural labor to transferto non-agricultural industries. They abandoned the original employ track but failed tointegrate into the new labor market stably. The problem of the transformation andemployment of rural labors has become the most important issue in the way of oursocial development. In the process of solving this problem, we must clearlyunderstand the key factors resulting in the transfer labor force and its direction ofaction and size, so that we can target the macro-control and construct the integralurban and rural labor employment market.This paper is biased on the mobility of the transfer of rural labor, exploring thespatial effects of the transfer of rural labor employment behavior and establishing thespatial panel data model of the transfer of rural labor employment of our country, andthen we come to the conclusions as follows:Firstly, the transfer of rural labor employment of our country goes hand in handwith obviously spatial dependence. Both the simple cross-section data model and thepanel data model show that the transfer of rural labor employment of our country iswith spatial dependence. In the results of panel data analysis, the neighborhoodtransfer of rural labor employment behaviors influences not only the localtransformation behaviors by the spatial lag, but also the local transformationbehaviors by impacting the error of local transformation behaviors. The estimationresults (see5.4.1) show that the absolute value of the coefficients of the spatial lagfactor is0.3780, and the absolute value of the coefficients of the spatial error factor is0.3790, which are both different from zero, and both have passed the1%significancelevel test. Secondly, the three facts such as the increase in the proportion ofnon-agricultural products consumption、the reduction of the level of urbanization and the increase in the proportion of the non-state sector are the basic reason of thetransfer of rural labor. Specially, when the proportion of non-agricultural consumptionis increased by one unit, the transfer of rural labor employment will increase by1.4251%. When the level of urbanization is reduced by one unit, the transfer of rurallabor employment will increase by2.6623%. When the non-state sector’s share of theincrease is in the proportion of one unite, the transfer of rural labor employment willalso increased by5.5687%. As a result, income leve、education level and fiscalpolicies are not in their place. In other words, the coefficient values of these threefactors are small. Thirdly, both the results of the ordinary panel regression and thespatial panel data analysis show that the particularity of the transfer of rural laboremployment of our country leads to the minus spatial lag effect and the minus spatialerror effect: that is, the high transfer number of the area is surrounded by the lowtransfer number of the area——the coefficient of the、 is negative. Additionally,the estimated coefficient of the variable (including g、h、l) in the spatial lag panel datemodel (SLPDM) and the spatial error panel data model (SEPDM) are bigger than theestimated results of the ordinary regression. And we can infer that the ordinaryregression underestimates the effect of these three factors. Therefore, in the followingwork, we are supposed to pay more attention to the effect of non-agricultural productsconsumption、urbanization and related management system, and to lay stress on theeffect of education、income and other factors in the employment of rural labor. Thispaper introduces the spatial effects in the analysis of the transfer of rural laboremployment behavior, which enriches the spatial analysis in the field of labormobility and provides a new perspective for future research.