Land Inequality and Workfare Policies

Abstract

This paper contributes to the relatively scant literature on the impacts of inequality on the efficacy of public works programmes. We study this in the context of India. In particular, we examine the effect of land inequality on the implementation of the world’s largest workfare programme – the National Rural Employment Guarantee Act (NREGA). Our OLS estimates demonstrate that the concentration of land ownership reduces the efficacy of NREGA. An instrumental variable (IV) analysis, where we use the historical land tenure system as an IV for contemporaneous land inequality, further corroborates our findings. This negative relationship is consistent with the hypothesis that public work schemes raise agricultural wages in the private labour market, thereby incentivising big landlords to use their political power to oppose such programmes. We exclude the possibility that the higher provision of public jobs in more equal areas is driven by a higher demand for public jobs or by caste or religious differences. This study suggests that the concentration of land ownership, a proxy for power asymmetries, could hinder effective implementation of development policies.

KEYWORDS:

• Inequality
• land ownership
• public works

Disclosure statement

No potential conflict of interest was reported by the author(s).

Supplementary Material

Supplementary Materials are available for this article which can be accessed via the online version of this journal available at https://doi.org/10.1080/00220388.2021.2008362.

Notes

1. There is a widespread criticism that NREGA does not create productive durable assets that could generate sustained increases in income. According to a Bank (2011), many public works are said to be ‘washed away the next monsoon’. The report attributes the inferior quality of public works to the inadequate attention given to the objective of asset creation in NREGA. However, it is difficult to draw a unified picture for the entire country as there is large heterogeneity in the type/nature and quality of assets across different parts of the country. There are no reliable nationally representative datasets that provide information on quality of the assets created under NREGA – hence our analysis cannot provide any empirical evidence on the heterogeneity between landlord and nonlandlord regions in NREGA asset creation.

2. According to the Agriculture Census in India, ‘an operational holder is the person who has the responsibility for the operation of the agricultural holding and who exercises the technical initiative and is responsible for its operation’. An operational unit could include multiple plots. The operated areas comprise of i) land owned and self operated; ii) land leased in; iii) land otherwise operated. Constructing the measure on owned land holdings would have been preferable. But since more than 97 per cent of holdings are owned and operated by the same individual/family (p. 29, Agriculture Census Report 2005), using operational land holdings is not a major issue.

3. We use the information on ‘sub-total’ land holdings, including both individual holding and joint holdings, to measure district level land distribution. The ratio of joint holdings to individual holdings is 1:6.5 in terms of numbers and 1:5 in terms of areas (Agriculture census report 2005, p. 121). Land operated by institutions constituted less than 0.5 per cent of the total area, hence excluded from the data.

4. http://nregarep2.nic.in/netnrega/dynamic2/dynamicreportnew4.aspx.

5. By taking the logarithm, Phase 2 and 3 districts in 2006 and Phase 3 districts in 2007 are automatically dropped from the regressions. This makes sure that we do not assess the implementation of NREGA prior to the rollout of the programme in these districts. The advantage of taking the logarithm lies in the convenience of making comparisons across different specifications.

6. This ratio is large compared to other studies applying the same method (for example, Gehring & Schneider, 2018; Nunn & Wantchekon, 2011).

7. Banerjee and Iyer (2005) explain why the choice of landlord revenue system had a strong effect on the distribution of land and wealth in the British India period. ‘Under landlord-based systems, the landlords were given a more or less free hand to set the terms for the tenants and, as a result, they were in a position to appropriate most of the gains in productivity.’

8. We first plot the numbers of land reforms over time in major landlord and non-landlord states in Figure A.6. It provides consistent evidence with the literature that landlord areas enacted more frequent land reforms than non-landlord areas, especially after 1970 (based on data from Besley & Burgess, 2000). To depict since when landlord dominated districts started to have lower land ownership inequality than non-landlord dominated districts, we further plot the trends of land inequality, measured by the share of land owned by the top 10 per cent land holdings, for major landlord and non-landlord districts in Figure A.7. It shows that the shift of landlord districts from having relatively high land inequality to relatively low land inequality occurred around 1970. Therefore, interestingly, the turning point of relative inequality in landlord versus non-landlord dominated districts coincides with the time when land reforms in major landlord states started to outnumber those in non-landlord states. Put together this further adds credibility to our first- stage estimates.

9. This method has been used in a number of other studies to examine the sensitivity of estimation results to the violations of exogeneity conditions (for example, Azar, Marinescu, & Steinbaum, 2020; Calvi & Mantovanelli, 2018; Ding, Lehrer, Rosenquist, & Audrain-mcgovern, 2009; Nunn & Wantchekon, 2011).

10. The overall reduced-form effect is obtained by estimating the following model: $Y_{ist}={\alpha }_0+\rho Z_{is}+\alpha X_{ist}+{\alpha }_{st}{D}_{st}+{\eta }_{ist}$, where all variables are the same as in Equation (1)(1) $Y_{ist}={\alpha }_0+\beta \ast IN{E}_{is,2005}+\alpha X_{ist}+{\alpha }_{st}{D}_{st}+{\epsilon }_{ist}$(1) and $Z_{is}$ is the indicator for historical landlord districts.

11. For example, ‘In the summer of 2011, farmers in East and West Godavari districts even declared a “crop holiday” protesting against low profitability mainly due – according to them – to the increase in rural wages witnessed in recent years’ (Maiorano, 2014).

Additional information

Funding

This work was supported by the Beijing Social Science Fund [21JCC120].