# **4. Econometric results**

We find evidence of significant program-induced wage increases but no support for claims of MNREGS having crowded out private employment; in fact, although casual labor moves from the farm to the nonagricultural sector, lower casual labor demand in agriculture is more than outweighed by higher family labor input, especially at harvest, pointing towards a program-induced increase of labor use in agriculture. Together with evidence on higher intensity of fertilizer, irrigation and machinery use and some diversification away from rice and wheat, this suggests that program-induced resource transfers affected outcomes both directly and indirectly. This is reinforced by our finding of a significant productivity increase due to the program, mostly as a result of increased income rather than efficiency in water use.

# **4.1 Labor supply and wage effects**

Labor market outcomes available from the survey are, for every household member, the number of days spent in salaried employment and wages received in casual work or self-employment in farm- and nonfarm sectors. The latter includes detail on the number of days devoted to crop cultivation by male and female family members and hired workers at the pre-harvest (i.e., land preparation, sowing/transplanting, weeding) and harvest stage.<sup>16</sup> These allow us to assess MNREGS impacts on three sets of outcomes, namely (i) casual wages in agricultural and non-agriculture; (ii) changes in labor supply to self-employment or casual work in different sectors; and (iii) for farmers use of family and hired labor across different seasons. We explore heterogeneity across males and females and by land ownership. The survey provides evidence on total value of output, use of purchased inputs and machinery or irrigation, and number or type -high value vs. paddy and wheat- of crops grown. We use these to test for productivity effects that may have been triggered by program-induced risk-reductions, noting that, as the program targets the poorest groups, direct program effects should be most pronounced for small farmers.

Table 4 reports estimated impacts on wages received, overall (panel A) and allowing for heterogeneous program effects by gender (panel B). We find MNREGS-induced wage increases of some 10%, well above what is reported elsewhere. Most of this increase can be attributed to wages in the agricultural sector which are estimated to have increased by 11%, compared to a positive but insignificant coefficient for wages in the non-farm sector. Gender disaggregation suggests that program-induced wage increases affected males and females more or less equally though estimated rates were marginally higher for females who also seem to have experienced a marginally significant increase in non-agricultural wages.

> 16 While the data are extraordinarily rich, they suffer from two shortcomings. First, lack of information on the source (i.e. public or private) of nonfarm work makes it impossible to distinguish work performed under MNREGS from other types of non-farm casual employment. Second, as data on on-farm labor supply in 1999/2000 was not disaggregated by season, a direct test of the hypothesis that MNREGS-induced increases in dry season labor demand were key to smoothing consumption (Imbert and Papp 2015b; Klonner and Oldiges 2014) is not possible.

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