A job guarantee would provide a ‘safety net’ that increases farmer’s productivity – and incentive to keep their farm open

Previous research suggests that farmers in developing countries are constrained in their production and investment decisions. Evidence of delayed technology adoption, low investment in fixed capital, a preference for conservative crop choices and, more generally, a lack of innovative capacity is by now well established (Foster and Rosenzweig, 2010; Duflo et al., 2008; Suri, 2011). This has potentially severe and long-lasting effects on income and well-being in developing countries as a large share of their populations still rely on agricultural production as a major source of income.

Empirical evidence points to different explanations for the low propensity to innovate. Learning processes have been shown to discourage technology adoption (Besley and Case, 1993; Munshi, 2004; Conley and Udry, 2010), as have time-inconsistent preferences (Duflo et al., 2011) and low levels of human capital (Foster and Rosenzweig, 1996). In addition, market imperfections such as the lack of insurance mechanisms, dysfunctional labour markets and limited access to credit are often cited explanations. On the relative importance of these constraints, however, the literature is much less conclusive. Rosenzweig and Binswanger (1993) and Dercon (1996) provide evidence that uninsured risks prevent farmers from holding profitable asset portfolios and planting profitable crops. On the other hand, Rosenzweig and Wolpin (1993), Fafchamps and Pender (1997) and Gine and Klonner (2005) identify the lack of credit as a major explanation for foregone profits. One of the major challenges in disentangling both constraints is to find exogenous variation in one or both. Observational studies mainly rely on proxies for both constraints, but face the challenge that indicators representing a household’s ability to cope with shocks (such as wealth, human or social capital) are typically the same indicators that predict access to credit and or own financing possibilities.

More recent literature has made important progress in dealing with these methodological challenges. Dercon and Christiaensen (2011), for example, assess the relative importance of risk versus credit constraints by constructing an indicator of household risk exposure that combines a household’s probability of facing a rainfall shock with its ability to cope with such a shock. They thereby circumvent the attribution problem of using only wealth as a proxy for a household’s capacity to smooth consumption. The authors show that Ethiopian households with lower expected consumption outcomes - due to high risk exposure and low savings - are less likely to invest in fertilizer. Other authors have used randomized variation in the availability of insurance mechanisms and/or access to finance to estimate the relative importance of each of the interventions. These articles find that crop insurance is critical in stimulating fertilizer application (Karlan et al., 2012) and risky crop choice (Cole et al., 2013). Karlan et al. (2012) also found that uninsured risk is a more important determinant of low investment rates than are constraints in access to capital. This paper aims at contributing to the empirical evidence on the importance of risk constraints in farmers’ production decisions. But instead of exploring variance in the availability of insurance, as do the studies cited above, it examines variation in the access to an alternative mechanism that could improve a household’s risk management: an employment guarantee. The main argument is that public works programmes or employment guarantees could help households to cope with income shocks by providing additional employment opportunities. This idea is not new; the potential of public works schemes in helping households to smooth income in the case of shocks has been highlighted inter alia by Barrett et al. (2005) and Binswanger-Mkhize (2012). However, to the best of my knowledge, no empirical evidence on the insurance effect of an employment guarantee on households’ production decisions has been provided so far.

In this paper I test the extent to which the introduction of the National Rural Employment Guarantee Scheme (NREGS) reduces households’ uncertainty about future income streams and enables them to produce a higher share of high-risk, high-profit crops. The National Rural Employment Guarantee Act (NREGA) was passed in India in September 2005; the implementation thereof began in 2006. The NREGA entitles every rural household to up to a 100 days of work per year at the state minimum wage, which is to be provided by the block officer within 14 days of the application for work being made.1 Today the NREGS is the largest public works programme in the world. In the financial year 2010/11 it provided work to close to 55 million rural households (MoRD-GoI 2012). A total of 2.5 billion person-days of employment were generated in the same year.

The question outlined above is tested using a household-level panel data set that is representative of the state of Andhra Pradesh in southern India. The quality of implementation of the NREGS has been shown to vary immensely across India (Dutta et al., 2012). In most states the provision of work under NREGS is far too unpredictable to completely offset the effects of a shock. Under such circumstances, the NREGS would not affect households’ risk expectations. Andhra Pradesh, however, is one of the states with the highest number of days of employment generated per rural household. I find that the provision of work in Andhra Pradesh does effectively respond to changes in household demand and thus supports households in managing agricultural production risks.

The estimation strategy employed here builds on the sequenced introduction of the NREGS. Using the introduction of the NREGS at district level, it explores the fact that the scheme was introduced in four out of the six survey districts in 2006 and in the remaining two districts in 2008 and 2009. Because this approach relies heavily on the parallel trends assumption, I perform a number of robustness checks. The use of alternative treatment variables (e.g. block-level spending and employment days generated under the NREGS, as well as households’ registration with NREGS) does not change the results. The results are also robust to a range of alternative specifications, to the inclusion of weather data and to changes in household income and wealth. I find that the key innovation of the Indian public works programme (i.e. giving households the right to work) encourages agricultural households to increase the share of risky but profitable crops, in particular cotton, in their portfolios. The results of this paper suggest that employment guarantees can trigger important gains in agricultural productivity in the medium term. These gains go far beyond the direct income effect that the provision of employment in agricultural lean seasons has on the wellbeing of rural households. That increases in productivity and, in turn, in households’ incomes can be triggered solely through the insurance effect of an employment guarantee is a very important lesson for other countries with planned or ongoing public works programmes.

The remainder of this paper proceeds as follows: Section 2 introduces a theoretical framework for analysing the effects of an employment guarantee on crop choice. Sections 3 presents the data and summary statistics. Section 4 outlines the estimation strategy. Section 5 presents the empirical results, while section 6 concludes.