NREGS: FINDINGS AND STRATEGIES FOR THE NEXT LEVEL

As NREGA Document says… “..The primary objective of the Act is augmenting wage employment. Its auxiliary objective is strengthening natural resource management through works address causes of chronic poverty, like drought, deforestation, and soil erosion and so encourage sustainable development. The process outcomes include strengthening grass-root processes of democracy and infusing transparency and accountability in governance” Source: The National Rural Employment Guarantee Act (NREGA): Design, Process & Impact: UNDP: Chapter 1, p.9

Objectives of the study  To identify the proximate factors influencing NREGA employment among two types of households  Those receiving employment supports  Those not receiving employment supports  To identify the perceived impact of NREGA in rural India Findings on process part being fairly well-known, emphasis is placed on rigorous statistical relations to extract some explanatory power

Sampling Design  Districts with the highest proportion of job- card holders under SC/ST category chosen.  Dangs in Gujarat (100%) and Jalpaiguri in West Bengal (72.19%)  Blocks with highest proportion of ST job-card holders identified in West Bengal – Nagrakata (58.4%) & Kalchini (57.6%). Dangs have only one Block -- Ahwa

Sampling Design (Contd..)  GPs in the blocks chosen: Sulkapara (48.85% ST) from Nagrakata Block and Mendabari (90.31% ST) from Kalchini Block  Two contiguous villages chosen randomly from the identified GPs – Sulkapara IV & V and Mendabari II & III.  50 households chosen from each GP through stratified sampling: 40 out of those possessing job cards and getting employment and 10 out of job card holders, presumably not getting employment as per muster roll available on website for West Bengal as on  HHs arranged in ascending order of cumulative employment provided till with 10 HHs chosen randomly from each quartile.  As data were not available on the website for Dangs, two villages chosen through careful consideration after visits to 7-8 villages – namely, Linga and Shakarpatal. However, similar exercise carried out to identify sample households based on muster rolls.

Data Analysis  Carried out at two levels –  Using secondary data for 486 districts for the year (till 31 st December, 2008) as downloaded from NREGA website, supplemented by land use data from indiastat.com & Agricultural Census or state govt. websites  Using primary data from 196 households (4 HHs found short in one category in Linga) collected during March 2009 from Dangs in Gujarat and Jalpaiguri in West Bengal

Statistically Significant Results at Macro Level: Secondary Data for 480 districts  Cropping intensity has a negative incidence on NREGA employment, as expected => emphasis on traditional policy tools  Larger potential in terms of potential land use leads to larger employment => use of macro & regional planning for NREGA & also going beyond  Larger % of households demanding work among cardholders increases average employment per job card => need for awareness creation & proper registering of demand  Minimum wage exhibits a positive significant effect on NREGA employment => need for concerns over distortions in labor market & possible non- viability of small farm agriculture  Successful districts identified by MoRD fared comparatively better than the rest => enthusiastic, efficient & transparent administration pays  States ruled by non-UPA governments apparently more efficient in creating more employment days per job card. Also expenditure per employment day – especially on non-wage component - turned out to be smaller in such states. Corroborates earlier findings by Dreze & Oldiges (2007) using data  Results stable & significant even after White- corrections for hetroscedasticity.

RESULTS: Micro Household Level Primary Data  Jalpaiguri created higher employment per job card than that in the Dangs.  Closeness in relation to Panchayat ensures larger employment  ST/SC/OBC & BPL households receive relatively more employment  Families having more dependence on animal husbandry receive relatively more employment  Families with greater availability of labor receive more employment – consistent with results from district level data, though availability is an endogenous rather than an exogenous variable.  Availability of non-NREGA employment & higher non-NREGA wages reduce NREGA employment  Human and non-human assets like schooling, literacy, house type, land ownership, irrigation, and even income has no significant explanatory power over employment creation

RESULTS: Primary Data  For households having job cards but not recording significant employment per job card:  8.33% in either place found not interested in getting job  2.33% in Dangs found the jobs not acceptable because of social stigma  One third of the job card holders in Dangs and in Jalpaiguri found better jobs at higher wages  One third of job card holders not offered employment opportunities in Jalpaiguri, even though they were interested. None exist under this category in Dangs

Indirect Impact Analysis based on respondents’ perceptions variables% of hhs (with positive response) in Dangs % of hhs (with positive response) in Jalpaiguri New crop from to 07-08* New crop from to 08-09* Started saving* Reduction in school drop-out Improvement in health facilities Reduction in alcoholism Reduction in women trafficking* Reduction in village crime* Reduction in child labor* * Difference across places statistically significant as per t-test with equal/unequal variance

Further studies needed for Sustainable Rural Development Population that will be part of NREG Poverty distribution on a geographical spread Present assets in the area Assets needed in the area Job creating ability of the area Farm level facilities available in the area Traveling radius as per the NREG guidelines Land holding pattern and average farm size Past trends of participation in NREGA Land use Hydro- geomorphology Existing Assets Drainage Road map Poverty Census data Water bodies Village maps Development of areas through value generating assets creation at suitable locations MAJOR FACTORS IN DECISION MAKINGLAYERS