Can Public Employment Scheme Increase Equilibrium Wages? Evidence From a Natural Experiment (NREG) in India

Authors

• Erlend Berg• Sambit Bhattacharyya• D Rajashekhar• R Manjula

Presenters• Devesh Bansal• Akash Kalyani• Astha Malhotra• Avinash Totla

INTRODUCTION TO THE PAPER

Research Question/ Aim

• Study the impact of National Rural Employment Generation (NREG) Scheme on agriculture wages.

Motivation

1) Workfare programmes (NREG) are an alternate poverty reduction tool (NREG—Wages--Poverty).

2) NREG itself with the expenditure close to 0.5% of India’s GDP (2010) is worth studying.

3) Recent political debate – letter to PM Modi4) Obviously....part of syllabus

What’s NREG? As if u guys don’t know...• NREG Scheme is a rural employment programme which aims to

provide wage employment to un-under employed landless agricultural labourers, locally, specially during agriculture slack season.

• Important point......NREG was rolled out in three phases from 2006 to 2008 with initial focus on poorest districts of India.

USP of this Paper

• Studies the largest workfare programme with the size of $7.88bn, coverage of 55mn households, and varied experience of ALL states and rural districts. Thus conclusions derived will not be NREGA specific but quite general in nature.

• Data: Uses a very detailed..monthly, district wise, gender wise, labour category wise data. This helps us to achieve robust results

DATA USED

• Agriculture Wages in India (AWI) series has been used for the period 2000-2011.• CPI for rural labourer has been used to fix the

base at Jan 2000.

DATA CATEGORIES

AWI

Unskilled

Field Labour

Ploughing Reaping Sowing Weeding

Herding Other Labour

Skilled (Only Male)

Carpenter Cobbler Blacksmith

Variable of Interest

• Focus of the paper is on Field Wage Labour category (both male and female) because-

a. Huge proportion of agricultural wage labour undertaken in India falls under this category.

b. Data quality and measurement error issues associated specially with herding data.

c. Falls under Unskilled labour category which is expected to get influenced by NREG

AWI Data Collection

• 1 centre is selected for data collection from every district• Advantages of AWI

1. Detailed Data - month wise, district wise, gender, labour category wise data set

• Disadvantages of AWI1. Village/Centre chosen may not be representative of district and officials may

not be trained enough.• Still AWI is good data source ASSUMING no relation b/w measurement

errors (or non randomness of centre selected) and NREG rollout.

EXAMPLE

• Suppose for District(1), January the wages are as follows –

• Average Male Wages = 96.7 • Average Female Wages = 77.5• Wages = (77.5+96.7)/2 = 87.1• Real Wages = 87.1/CPI

CATEGORY WAGES (MALE) WAGES(FEMALE)

Ploughing 80 70

Reaping . 50

Sowing 110 100

Weeding 100 90

Does AWI simply report NREG statutory wages?• If it is the case then our estimation of impact of NREG on

agricultural wages won’t hold.• To check this – Statutory NREG wages were compared

with Wages (just computed) at a random points of time (Jan-March 2009). • Seven districts which had same wages were dropped

from the data set, though it is plausible to have same wages.

Summary Statistics

EMPIRICAL STRATEGY

Empirical Strategy (Fixed Effects with DID)

Yit : the natural logarithm of real daily wages in district i in month tTit : Binary variable for NREG treatment for district i in month tα, and Θ are constants

Issues

Tit does not capture the intensity of

NREGA in month t

We cannot differentiate between NREGA wage

increase from other macroeconomic wage

increases

Selection bias for districts in respective

phases

Autocorrelation

Our ideal experiment would be to calculate the difference between treatment and counterfactual

Empirical Strategy – Published Paper(Fixed Effects with DID)

Yit : the natural logarithm of real daily wages in district i in month tαi : district fixed effects βy: yearly fixed effects γm: monthly fixed effects Tit: Treatment dummyEit : number of months for which Nrega is active in the district [Trends] : district-wise time trendεit : random error term

Autocorrelation: Every regression incorporates robust standard errors, clustered at the district level

*We think that the district wise time trend effects are accounted for by Zit or Zi*t

Empirical Strategy – Published Paper(Fixed Effects with DID)

y1(1)

y1(0)

y0(0)

t=0 t=1

Wages

Graphic Explanation

y1(0’)

RESULTS

Main Results:The Impact of NREG on wage rates for unskilled field labour

• The dependent variable is log daily field wages in fixed January 2000 prices, observed between May 2000 and June 2011

• ‘Treated’ is a binary variable equal to 1 if NREG was active in that district at that time

• In (1), positive and significant coefficient indicates that real wages in NREG-treated districts are (approx) 7% higher

• In (2), coefficient becomes insignificant

• In (3) coefficient is positive and significant (different interpretation)

• District trends take account of any bias relating to wage growth rates that vary across districts independently of NREG

• On average, introduction of NREG in a district increases the growth rate of agricultural wages by 0.4% per month, on top of any underlying trends

• In (5) checking if the impact of NREG diminishes over time (not significant)

c (3)Field labour wages

(4)Field labour wages

(5)Field labour wages

Exposure

[Exposure]2

District, Year & month fixed effects

District trends

0.003***(0.001)

Yes

No

0.004***(0.001)

Yes

Yes

0.003***(0.001)

2X10-5

(2.7X10-5)Yes

Yes

ObservationsDistrictsStates

1527220918

1527220918

State-wise estimates

• To study regional heterogeneity, the main regression is also estimated state by state

• When wages are regressed on the binary variable, the coefficient is not significant at the 5% level for any state

• When wages are regressed on the Exposure variable:• Positive and significant effects for

Rajasthan and Andhra Pradesh (among star performers in Dreze and Khera(2009))

• Positive and significant for Kerala and West Bengal (tend to do well on many social metrics)

• Positive and marginally significant effects in Bihar and Haryana

• For the rest the effect is not positive and significant 21

Seasonal Variation

• To study whether the effect of NREG is uniform throughout the year, wage rates are regressed on the exposure variable interacted with 12 monthly fixed effects

• (regression equation)• it appears NREG exposure pushes

up wages only when labour is relatively scarce

• While it is reasonable that wages should increase more when the demand for labour is high, it is also plausible that the effect on earnings is stronger in the slack season

Impact on the Skilled Labour

• Table 4 presents results of regression of wages in skilled labour categories on NREG Exposure

• The intended beneficiaries of NREG are unskilled agricultural labourers

• To the extent that skilled and unskilled labourers operate in separate labour markets, the wages of skilled labourers are insulated from the effect of the programme

07/1

1/20

14

23

(1)Carpenter’s

wages

(2)Blacksmith’s

wages

(3)Cobbler’s

wages

Exposure

District, year & month fixed effects

District Trends

0.001(0.001)

Yes

Yes

0.001(0.002)

Yes

Yes

0.001(0.002)

Yes

Yes

ObservationsDistrictsStates

1694620618

1433118115

1084215612

• No evidence of strong correlation between wages of the skilled categories and field labour wages- corroborates hypothesis of separate markets

07/1

1/20

14

24

Impact on Gender Wage Gap

• On average, Female field labourers are paid 79% of male field labourers wage

• Differential effect of NREG exposure on women’s wages relative to men’s wages is very small and insignificant

• Analysis is repeated with binary treatment variable and no discrete jump in wages is identified

• NREG Exposure neither diminishes nor enlarges the gender wage gap

Phase-wise Effects

• NREG effect is positive and strongly significant for all three phases

Jan 2000

April 2007 NT=1;NE=1

Dec 2007 NT=1;NE=9

April 2008 NT=1;NE=13

Dec 2008 NT=1;NE=21

Jan 2000

April 2004 PT=1;PE=1

Dec 2004 PT=1;PE=9

April 2005 PT=1;PE=13

Dec 2005 PT=1;PE=21

NREGA ExperimentDistrict i in Phase-2

Placebo ExperimentDistrict i in Phase-2

NT= NREG Treatment VariableNE= NREG Exposure VariablePT=Placebo Treatment VariablePE=Placebo Exposure Variable

Robustness Check: Placebo Test

Robustness Check: Placebo Test

• Estimated effect of Placebo Treatment is not significant• Coefficient of Placebo Exposure is close to zero and insignificant• Placebo test validates the result that the estimated effect on real wages

were caused by NREG

Conclusion

• NREG boosts the growth rate of average real daily agricultural wages @4.8% per annum

• Effect of NREG appears to be gender neutral and concentrated in main agricultural season

• NREG appears well targeted programme as it mainly effects unskilled wages• Effect is mainly concentrated in following states: AP,BH,HR,KL,RJ &WB• Paper have not assessed the cost-effectiveness of NREG programme• Net welfare effects are ambiguous; didn’t account the effect of wage

increase on employers and effect of workfare on the labour supply of poor