درآمدهای سرمایه انسانی، کارآفرینی و مزرعه خانوار

The allocation of resources between agriculture and non-agriculture is a central decision of the farm household. In this paper, we formulate a profit-maximization model in which human capital enhances efficiency through both within-sector effects and across-sector allocation of quasi-fixed inputs. The model is estimated using Chinese household data that contain detailed information on production activities. We find that schooling and experience improve the sectoral uses of household-supplied inputs, accounting for 27% of their total contribution to earnings. The evidence suggests that conventional estimates of human capital returns obtained within sectors would undervalue the role of human capital in development.

Farm households in modern environments engage in multiple productive activities. In addition to farming, rural workers also participate in wage-earning or self-employed activities, such as processing, manufacturing, construction, transportation, and services. Farmers are entrepreneurs in the sense that, in addition to performing tasks of given activities, they respond to changing conditions by reallocating their labor and physical resources across activities (see, for example, Hymer and Resnick, 1969 and Rosenzweig, 1980). Schultz (1975) hypothesizes that the entrepreneurial ability to deal with economic disequilibria is a part of the stock of human capital. From his notion of entrepreneurship, human capital can enhance farmers' abilities both in selecting activities and in performing individual operations. A large body of literature assesses the effects of human capital on rural household earnings (see surveys by Jamison and Lau, 1982, Schultz, 1988 and Phillips, 1994).1 However, this literature has largely neglected the role of schooling and work experience in sectoral factor allocation, despite ample evidence that human capital raises efficiency either in agricultural or non-agricultural activities. In agriculture, for instance, Welch (1970) and Huffman (1977) find that human capital enhanced farmers' technical skills and managerial ability of using inputs, and during the green revolution, better educated farmers achieved higher profitability with high-yielding seed varieties (e.g., Pitt and Sumodiningrat, 1991 and Foster and Rosenzweig, 1995). In rural non-agriculture, there is also evidence of significant returns to schooling in wages and self-employment (e.g., Vijverberg, 1993). While these studies provide sector-specific estimates, an important component of human capital returns has not been assessed. As farm households engage in increasingly diverse activities, it is no longer admissible to omit the returns to human capital in determining sectoral participation. A number of studies (e.g., Huffman, 1980, Yang, 1997a, Fafchamps and Quisumbling, 1999 and Taylor and Yunez-Naude, 2000) find that farmers respond to higher returns to education in the non-farm sector by reallocating labor away from agriculture.2 However, these studies omit the effect of education on capital investments. Furthermore, human capital has been treated as an investment good similar to physical capital that simply receives returns, as opposed to a causal variable whose function, in part, is to optimally allocate household resources. Consequently, these studies provide estimates of returns to human capital in the two sectors, but not its contribution to sectoral factor allocation.3 In this paper, we formulate a two-sector framework of household profit maximization in which human capital may enhance within-sector profits through the purchase of variable inputs and worker productivity effects and, more importantly, to determine the allocation of quasi-fixed factors across sectors. We propose a strategy to decompose these sources of human capital returns by estimating three profit functions: one family-level aggregate profit and two sector-specific profit equations that condition on the allocation of quasi-fixed inputs. The implementation of this proposal yields estimates on human capital's contribution to the household's overall earnings, to the two specific sectors, and to the inter-sectoral gains of input allocation, which is computed as the residual of the total less sector-specific gains. For empirical analysis, we fit the model to a unique cross-sectional data set of Chinese farm households. In addition to detailed economic and demographic variables, this data set contains records of sectoral time allocation for every working member of the households and for every piece of capital equipment. These provide crucial information for activity-specific profit estimation. Our empirical results indicate that, while schooling enhances agricultural and non-agricultural profits, about 14% of its total returns arises from optimally choosing activities through the allocation of household-supplied inputs. Experience also contributes significantly to agricultural production and input allocation. These findings are robust to sensitivity tests. Our results suggest that conventional estimates of human capital returns obtained within sectors would undervalue the role of human capital in development.

In rural settings where farm households engage in multiple lines of production, human capital may enhance the efficiency of individual activities and the selection of activities. In this paper, we have clarified these roles of human capital and suggested a strategy to identify the activity-specific effects and the inter-activity factor allocation effects. By distinguishing farm and non-farm productions, we have decomposed the returns to human capital into its components in agriculture, non-agriculture, and factor allocation between the sectors. Based on cross-sectional Chinese farm data, our empirical results indicate that schooling and experience have significant impacts on rural household earnings. While schooling and experience improve agricultural and non-agricultural profits, these human capital attainments also enhance the efficiency of inter-sector factor allocation, accounting for a combined 27.2% of their total returns. These results are robust to sensitivity tests. We can compare our findings on returns to human capital with the results that would have been obtained using the conventional methodology in the related literature. The standard approach of assessing the effects of education on farm efficiency, reviewed by Jamison and Lau (1982) and Phillips (1994), is to regress agricultural earnings on household productive inputs and schooling variables. When such a model is fitted to our data, the linear and quadratic coefficients for the household aggregate schooling are respectively 0.0095 (t=0.80) and −0.000055 (t=−0.20), indicating no significant returns to education. These results differ noticeably from our findings in a framework that takes into account both agricultural and non-agricultural activities. Without considering other aspects of earnings, these results would represent a puzzling situation in which farmers invest in education, even if there are no economic incentives to do so. In an alternatively approach, we could estimate the empirical earning function that is widely used for assessing the rate of returns to schooling in developing countries (e.g., Psacharopoulos, 1985). A total of 619 individuals in our sample engaged in wage employment in the year of the survey. Treating their log daily wage as the regressand and using schooling, schooling squared, experience, experience squared, and gender and township dummies as the regressors, the estimated coefficients for schooling and schooling squared are respectively 0.046 (t=1.85) and −0.0026 (t=−1.22). These values imply a 1.79% of marginal rate of return at the mean level of 5.4 years of education. Assuming that a full-time employee works 283.4 days a year as implied by the data, one additional year of schooling above the sample mean would increase the individual's annual income by 38 yuan, see table 1. In contrast to this result, the marginal rate of return to education estimated from the aggregate profit function is 2.6% around the sample means, which suggests 99.9 yuan of additional earnings. This estimate is equivalent to a 4.7% in the rate of return measures, which is more than double of the rate based on wage information alone. Although our estimates are based on a specific sample of small Chinese farms and the estimates may differ from those of other developing countries, the findings of this paper suggest that studies concentrating on specific sectors or activities are prone to underestimate the effects of human capital on household earnings. The appropriate approach should take into account the role of human capital in intersectoral resource allocation.