Non-Farm Employment and Rural Household’s consumption expenditures in Neyshabour county: Propensity Score Matching Approach

Rural non-farm sector(RNFS) comprises all economic activities –such as manufacturing, transport, commerce, banking, service and so on) in rural areas expect cropping, agriculture, livestock, husbandry, hunting, fishing, and forestry. So, RNFE means employment of rural household members in the nonfarm sector, and RNFI is the income thereby generated. Various studies have shown that nonfarm activities play an important role in reducing rural poverty, increasing incomes and improving rural household’s well-being. The present study tries to investigate the effect of non-farm employment on rural household’s consumption expenditures in Neyshabour county. 

The data is gathered through a questionnaire survey carried out on 380 rural households from four districts (Zabarkhan, Markazi, Sarvelayat and Mianjolgeh) in the Neyshabour county. The survey is conducted between September 2017 to February 2018. The household was chosen through a multi-stage stratified random sampling. The propensity score matching approach is used to examine the impact of participation in non-farm activities on householdconsumption expenditures. The method compares the consumption expenditures of non-farm households with their counterfactual group that did not diversify into such activities, depending only on  farm activities. The propensity score is defined P(Xi) as the conditional probability of receiving a treatment (employment in nonfarm activities) given pre-treatment characteristics: (1)   Where Xi denotes a vector of pre-treatment characteristics of household i such as household head characteristics, agricultural characteristics and household characteristics ; b(xi) is the logistic conditional function and F(b(xi)) represents logistic cumulative distribution frequency. In propensity score matching analysis, the average treatment effect on the treated (ATET) is important. It is computed by matching farm and non-farm households that are closest in terms of their propensity scores and calculated as follows. (2) ATET (X) = E(Y1i | Ti = 1) - E (Y0i | Ti = 1) Where, E(Y1i | Ti =1) represents the expected consumption expenditures outcome of households engaged in non-farm activities and E(Y0i | Ti=1) denotes the counterfactual  consumption expenditures of households without these activities. The counterfactual estimates represent what the consumption expenditures outcome of  households engaged in non-farm activities would be if they have not engaged in non-farm activities.

Survey of 380 households showed that 267 households were engaged in non-farm employment and 113 households had only agricultural activity. The results showed that household's characteristics such as the age of household head, household education, farmland size, livestock ownership, amount of credits and agricultural assets (water value and agricultural machinery) and household asset for the households engaged in non-farm activities and they have not engaged this activities were significantly different but after matching propensity scores of two groups, differences were removed. In other words, the mean of independent variables in the two groups wasn't significantly different. Also, results indicated that participation in non-farm employment has a positive effect on household consumption expenditure as a measure of welfare. So that, based on the average treatment effect on the treated (ATET), annual consumption expenditure of the households were engaged in non-farm employment was 1420 thousand Rials more than consumption expenditure of households without these activities. 

The result of examining the impact of participation in non-farm activities on household consumption expenditure showed that participation in these activities has a positive effect on household consumption expenditures. Therefore, in order to, non-farm activities have more sustainable income and less risk than farm income, it is suggested that non-farm activities be expanded by strengthening and expanding existing industrial parks.