Arbeitspapier
Why Guarantee Employment? Evidence from a Large Indian Public-Works Program
Most countries around the world implement some form of a safety net program for poor households. A widespread concern is that such programs crowd out private-sector jobs. But they could also improve workers' welfare by allowing them to take on more risk, for example through self-employment. This paper analyzes the employment impacts of the world's largest public-works program using a novel regression-discontinuity design. The analysis exploits detailed institutional information to describe the allocation formula of the program and to construct a benefit calculator that predicts early and late treatment of districts. The results show that there is little evidence of a crowding out of private-sector jobs. Instead, the scheme functions as a safety net after a bad rainfall shock. Male workers also take on more risk by moving into family employment. This self-revealed preference for a different type of job suggests other potential benefits of safety net programs which so far have received little attention in the literature.
- Sprache
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Englisch
- Erschienen in
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Series: GLO Discussion Paper ; No. 504
- Klassifikation
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Wirtschaft
National Government Expenditures and Welfare Programs
State and Local Government: Health; Education; Welfare; Public Pensions
Welfare, Well-Being, and Poverty: Government Programs; Provision and Effects of Welfare Programs
Time Allocation and Labor Supply
Wages, Compensation, and Labor Costs: Public Policy
- Thema
-
public-works program
anti-poverty program
National Rural Employment Guarantee Scheme
NREGA
NREGS
India
regression discontinuity design
safety net
crowding out
risk coping
risk mitigation
insurance
- Ereignis
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Geistige Schöpfung
- (wer)
-
Zimmermann, Laura
- Ereignis
-
Veröffentlichung
- (wer)
-
Global Labor Organization (GLO)
- (wo)
-
Essen
- (wann)
-
2020
- Handle
- Letzte Aktualisierung
-
10.03.2025, 11:44 MEZ
Datenpartner
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Objekttyp
- Arbeitspapier
Beteiligte
- Zimmermann, Laura
- Global Labor Organization (GLO)
Entstanden
- 2020