Theme 1:Targeting and implementation

Implementation of MGNREGA in India:A Review of Impacts for Future Learning

Featuring work completed by IFPRI, Cornell University, and IGIDR with funding from 3ie

Introduction• MGNREGA is ostensibly a demand-driven program with

local level implementation at its core• “Self targeting” mechanism, but which individuals actually

work on MGNREGA is an indication of how well the program is reaching out to the intended beneficiaries (poor and marginalized)

• Flows of money to particular geographic areas also tell us something about the quality of implementation and governance record

• Are the safeguard measures put in place to bolster transparency of implementation (social audits, publicly available data, etc.) actually working?

Review of literature• Targeting and rationing

• Dutta et al. (2012)• Liu and Barrett (2013)• Narayanan, Das, Liu, Barrett (2015) • Narayanan and Das (2014)

• Implementation and governance issues • Niehaus and Sukhtankar (2013 a, b)• Gupta and Mukhopadhyay (2014)• Zimmermann (2013)• Maiorano (2014) • Sheahan, Liu, Barrett, Narayanan (2014)• Chau, Liu, Soundararajan (in progress)• Narayanan and Ranaware (in progress)

• Overcoming implementation challenges • Afridi and Iversen (2014) • Muralidharan, Niehaus, Sukhtankar (2014)• Raabe et al. (2010)

Review of literature: Targeting and rationing

• Some degree of rationing in unavoidable given fixed budgets at the highest levels, however widespread rationing implies implementation issues and low quality service delivery

• Dutta et al. (2012) look at MGNREGA admin data and NSSO data from 2009-10 to study targeting and rationing• Individuals in the poorest states have the highest demand for MGNREGA work• But poorest states also have highest rationing levels. Why?

• Weaker governance capacity• Less empowerment of the poor

• Participation rates are still high for the poor, implying that rationing isn’t completely undermining the self-targeting of the marginalized (STs and OBCs)

• Find a massive discrepancy in rationing rates between data sources• MGNREGA admin data: 99% of demand was met• NSSO data: 56% of demand was met

• Lots of variation across states (80% of the individuals who demand work in Himachal Pradesh, Rajasthan, and Tamil Nadu receive it)

Liu and Barrett (2013)• Extend the Dutta et al. (2012) story with specific interest

in the extent to which the program is operated as “pro-poor” across all of India and specifically by individual states

• Use the same data (NSSO 2009-10) to estimate the relationship between per capita expenditures and 3 MGNREGA targeting measures1. Participation

2. Job-seeking

3. Rationing

Project paper

Liu and Barrett (2013)• Results: rationing profile1. Greater rates of self-selection

into the program by poorer and disadvantaged households

2. Rationing of MGNREGA jobs is not pro-poor but rather exhibits a “middle-class bias”

3. Self-selection dominates rationing, therefore program is more pro-poor than not

4. Does not reach poor female-headed households, both self-selection and rationing

Project paper

Liu and Barrett (2013)

Project paper

• Results: rationing profile• Considerable differences

across states• Using participation and

rationing both as metrics, find that about half of the states exhibit pro-poor targeting while the other half do not

• 5 states with exemplary pro-poor targeting (mostly in the NE): Manipur, Mizoram, Rajasthan, Sikkim, and Tripura

• 8 states with almost exemplary pro-poor targeting: Andhra Pradesh, Chhattisgarh, Himachal Pradesh, Madhya Pradesh, Meghalaya, Nagaland, Tamil Nadu, and West Bengal

Liu and Barrett (2013)• Appropriate policy responses

• Limited participation by the poor due to low rates of MGNREGA job-seeking • Address lack of knowledge about “right to work”• Identify and address administrative impediments• Free labor supply constraints (for example, for women)• Tackle worker discouragement

• High rates of rationing among poor • Identify and address administrative failures

“Clearly, there is room for improvement and perhaps much to be learned from an in-depth comparative analysis of MGNREGA programme implementation across states that have demonstrated greater or lesser success in targeting the poor with job opportunities.” (p. 53)

Project paper

Narayanan, Das, Liu, Barrett (2015)

Project paper

Revisits rationing and targeting in light of the decline in the scale of the MGNREGA between 2009-10 (66th Round of NSS) and 2011-12 (68th Round of NSS)- What explains this

trend?- What is the extent of

rationing in 2011-12?- Is rationing pro-poor?- What explains

rationing itself?

Narayanan, Das, Liu, Barrett (2015)• The “collapse” of the

MGNREGA• Two views:

• MGNREGA “has done its job” and no longer relevant.

• Unmet demand, poor implementation discourages workers from seeking work.

• Research questions• Demand side issues

(drivers of worker interest)• Supply side issues (poor

implementation – rationing and delay in payments)

66th Round

(2009-10) %

68th Round

(2011-12) %

“Demand” 43.5 30

Rationing Rate 44.4 23.1

Participation Rate (total worked/total hhds)

24.2 23

Project paper

Evidence on unmet demand• Drèze and Khera (2011):

•Only 13% of the survey households in the six Hindi speaking states secured 100 days of work.

• Dreze and Khera (2014): PEEP Survey in ten states• When job card holders were asked how many days of employment they

would like to have over the year, assuming that they are paid on time, an overwhelming majority (83 per cent) answered ‘100 days’—the maximum entitlement.

• Himanshu, Mukhopadhyay and Sharan (2015): Primary survey in Rajasthan

•Decline in MGNREGA demand in Rajasthan, mostly due to loss of worker interest.

•Das (2015) and Dey (2010) • Unmet demand in parts of West Bengal. Finds workers get only 10% of the desired number of days.

•Srinivasan, et. al (in progress) In Surguja, 32.74% of sample reported that they faced problems getting work.

In both, rationing is on the intensive margin.Project paper

Narayanan, Das, Liu, Barrett (2015)• A strong “discouraged worker effect”

• Comes mainly from administrative rationing• To a lesser extent from delay in wage payments (not robust)

• Data is incomplete• People factor in the delays and are not discouraged as long as payment is

certain.

• Competing explanations matter, but not entirely responsible for lower demand• Favourable weather conditions matter• Better opportunities in the labour market do not matter• Wage differentials between MGNREGA and alternatives do not matter• Growth in MPCE associated with lower demand.

Conclusion: Poor implementation, especially administrative rationing is a deterrent. Where improvements in incomes are higher, demand has declined but there is no relationship with the growth in non-agricultural work participation.

Project paper

Narayanan, Das, Liu, Barrett (2015)

Project paper

Narayanan, Das, Liu, Barrett (2015)

Correlates of rationing and pro-poor rationing• Political economy factors

• Clientelism

• Poor implementation capacity• Overwhelming demand (covariate shocks /catastrophic

shocks)• Rationing is more related to shocks and perhaps

administrative infrastructure rather than political affiliation• Post election year rationing is higher.• Pro-poor rationing is correlated with the identity of the party.

Project paper

Narayanan and Das (2014)• Patterns of Rationing for Women (NSS 68th Round, 2011-

12)• The MGNREGA has been considered a “women’s programme”

• One-third mandate• Equal wages• Other programme features supportive of women’s participation

• In general, the performance has been good with women’s participation consistently above the mandated one-third of all workers.

• Disaggregated analysis of rationing rates too suggest women are not disadvantaged.

• Yet, vulnerable groups among women face problems at all stages – possessing job cards, seeking work and being rationed out. • For example, widows, single women households, mothers of young

children.• Considerable variation across states.

Project paper

Narayanan and Das (2014)

Project paper

Groups Rationing rate (All-India)

Female headed households 0.19

Female headed households with no adult males

0.19

Widows 0.20

Females from households belonging to the Scheduled Castes or Tribes

0.26

Females from households with children (0-5 years)

0.26

Narayanan and Das (2014)Conclusion• Differentiated nature of

women’s experience in accessing the MGNREGA

• Where there is pro-women rationing, states need to play supporting role and address higher order concerns.

• Wwomen’s participation is weak and rationing indicates some sort of administrative discrimination, policies have to focus on enabling women to access work and sensitizing implementing staff.

Project paper

States Rationing rate for males

Rationing Rate for females

Andhra Pradesh

0.19 0.16

Rajasthan 0.40 0.26

Tamil Nadu 0.14 0.07

Chattisgarh 0.10 0.10

Karnataka 0.41 0.41

Maharashtra 0.65 0.65

Gujarat 0.46 0.48

Bihar 0.43 0.58

Madhya Pradesh

0.39 0.42

India 0.28 0.25

Narayanan and Ranaware (in progress)• Delay in payments

• Occur at many points in the workflow

• Worker experienceChhattisgarh (Nov, 2014): In Surguja, proportion who reported that they faced problems regarding timely payments 47.6%

• 41.8 in “low” GPs• 50.2 in “medium” GPs• 51.5 in “high” GPs

PEEP Survey (2013)

66% over waited over 15 days• 59.7% in “Leaders”• 79% in “Learners”• 61.5% In “Laggards”

Project paper

Narayanan and Ranaware (in progress)

• Proximate correlates of delays in wage payments

• Data• Delay in payments from MIS (variable quality, dynamic, April 2014)• Compute average delay, using proportion of musters delayed as

weights.• Combine data on rainfall, banks, post offices, elections, etc. from

various sources.

• Qualitative research on the last mile problem.

Project paper

Banks and post offices

Narayanan and Ranaware (in progress)

Project paper

Review of literature: Implementation

• Governance (rent-seeking, leakages) • When statutory daily wages increased in 2007, officials report more fictitious work on

wage projects in Orissa, however theft on piece-rate projects declined (with Andhra Pradesh as control state) (Niehaus and Sukhtankar 2013 a)

• Very high leakage rate among wages paid at that time: wages in official government data increased as expected, but households report receiving the same wages as before (Niehaus and Sukhtankar 2013 b)

• Election-effects (politics) • In Rajasthan, funds allocated were 22 percent higher in blocks where the INC seat share

was less than 39 percent in the previous election (Gupta and Mukhopadhyay 2014)• Only true when the MP of the district, who approves the block fund allocation, is from INC

• Government benefited from MGNREGA in 2009 (Zimmermann 2013)• However only talking about improving the plight of the poor is not sufficient to ensure electoral

success, also requires good implementation • Also finds that districts were not perfectly align with the phase they should have been assigned

based on the known classification system/algorithm

• Political commitment to results has been key to good implementation in Andhra Pradesh, but evolved into a top-down system instead of a demand-driven one as a result, e.g. role of Field Assistants (Maiorano 2014)

Sheahan, Liu, Barrett, Narayanan (2014)

• Extend qualitative findings from Maiorano (2014) to study the relationship between fund allocated at the mandal level by fiscal year and election outcomes in Andhra Pradesh

• All data from publicly available sources (1063 mandals across 22 districts)• MGNREGA spending amounts from government website• Indian Population Census from 2001• Indian Agricultural Census from 2005/06• Geo-referenced rainfall data via NASA• Disaggregated voting data from assembly constituency level via

Election Commission of India (2004 and 2009 elections)

Project paper

Sheahan, Liu, Barrett, Narayanan (2014)

• Allocation of funds before 2009 elections:• Fiscal years include 2006/07, 2007/08, and 2008/09• No evidence that fund allocation was correlated with political

variables in initial years of program implementation• We define political variables relative to voting patterns in the 2004

election (the baseline political affiliation of mandals)

• Allocation of funds well correlated with various “need based” measures• People: Illiteracy, scheduled caste and tribes, agricultural laborers

(assumed to be casual laborers)• Place: Unirrigated land, poorer infrastructure (paved road, ag credit

societies, etc.), remoteness

• This suggests no manipulation of approved funds in an effort to win elections

Project paper

Sheahan, Liu, Barrett, Narayanan (2014)

• Allocation of funds after 2009 elections:• Fiscal years include 2010/11, 2011/12, and 2012/13• Consistent evidence that fund allocation was correlated with

political variables in years of program implementation directly following 2009 election• We define with respect to INC specifically but also UPA-affiliated parties• However, effects are very small in magnitude and economically

insignificant

• At the same time, still well-correlated with needs-based measures too, although less strongly than before 2009 election • For example, not well-correlated with rainfall shock, suggesting that

implementation was not very flexible in dealing with labor market dynamics

Project paper

Sheahan, Liu, Barrett, Narayanan (2014)

  (1)All years

(2)Pre-2009

(3)Post-2009

Clientelism 1.0 0.1 2.5Needs-based: labor-related 14.2 9.9 22.9Needs-based: land-related 11.3 11.6 16.7Needs-based: infrastructure-related 14.2 12.0 20.2Needs-based: rainfall-variability 2.5 2.9 3.9Election controls 2.2 2.3 3.1District and year dummies 54.6 61.2 30.7

Project paper

Decomposition of R2 for MGNREGS fund expenditure models

• Even when politics may have influenced fund allocation, correlation with needs-based measures far exceeds political measures, even in post-2009 years

• Like Zimmerman (2013), we also find aggregate MGNREGA spending in the pre-election years is positive and statistically significantly correlated with the movement of voters towards UPA candidates

Chau, Liu, Soundararajan (in progress)

• Hypothesis: targeting at the household level by the village local leaders in Andhra Pradesh is less related to need and more related to politics

• If targeting is not well correlated with needs, then analyze household political/voting responses to being allocated work preferentially• Do higher levels of MGNREGA benefits affect the likelihood of

households shifting parties between elections?• Does this vary with past political affiliation and intensity of political

participation?

• Use household survey data from 2006 and 2008 matched with administrative voting records

Project paper

Das (2015)• Hypothesis:

• Explores if political clientelism affect allocation of benefits to the households under the programme in West Bengal.

• Examines if households, whose heads attend political meetings and rallies regularly get more benefits out of the programme.

• Survey of 556 households in four GPs of the Cooch Behar district of West Bengal.

• Findings• Households that support the local ruling political party have significantly higher

probability of getting work after seeking relative to those supporting the opposition party.

• They get more days of work and therefore earn more.• Need to generate awareness and reduce rationing.

Review of literature: Overcoming implementation issues

• Social audits• In Andhra Pradesh, positive but insignificant impact of audits on employment

generation and a modest decline in the leakage amount per labour related irregularity (Afridi and Iversen 2014) • However, increase in more sophisticated and harder to detect material-related

irregularities• Need for follow up and punishment/correction mechanisms

• Biometric smartcards• In Andhra Pradesh, the new system delivered a faster, more predictable, and

less corrupt payments process without adversely affecting program access (Muralidharan, Niehaus, Sukhtankar 2014)• Program participants happy with change too

• Process-influence mapping exercise (Raabe et al. 2010)• Implemented by a group of researchers 2 districts in Bihar • Found many nodes of where the details of program implementation were not

working • A useful process for other areas, especially where there are known

challenges?

More information about project outputs can be found at:

http://www.igidr.ac.in/mgnrega/