Direct and indirect effects of cash transfer on entrepreneurship

Introduction Method Results Conclusion

Direct and Indirect Effects of

Cash Transfer on Entrepreneurship

Rafael P. Ribas

University of Illinois

GDN 14th Annual Global Development Conference,June 20, 2013

Introduction Method Results Conclusion

Introduction

Capital is essential for starting a business.

Limited access to credit may lessen entrepreneurialactivity in developing countries.

The role of a liquidity shock in supportingentrepreneurship among poor households.

Large-scale Conditional Cash Transfer (CCT) programin Brazil, Bolsa Famı́lia.

Conditional on school attendance and health care.Small but steady income to poor households.No rule over business investment or labor supply.

Introduction Method Results Conclusion

Introduction

Capital is essential for starting a business.

Limited access to credit may lessen entrepreneurialactivity in developing countries.

The role of a liquidity shock in supportingentrepreneurship among poor households.

Large-scale Conditional Cash Transfer (CCT) programin Brazil, Bolsa Famı́lia.

Conditional on school attendance and health care.Small but steady income to poor households.No rule over business investment or labor supply.

Introduction Method Results Conclusion

Introduction

Capital is essential for starting a business.

Limited access to credit may lessen entrepreneurialactivity in developing countries.

The role of a liquidity shock in supportingentrepreneurship among poor households.

Large-scale Conditional Cash Transfer (CCT) programin Brazil, Bolsa Famı́lia.

Conditional on school attendance and health care.Small but steady income to poor households.No rule over business investment or labor supply.

Introduction Method Results Conclusion

Introduction

Capital is essential for starting a business.

Limited access to credit may lessen entrepreneurialactivity in developing countries.

The role of a liquidity shock in supportingentrepreneurship among poor households.

Large-scale Conditional Cash Transfer (CCT) programin Brazil, Bolsa Famı́lia.

Conditional on school attendance and health care.Small but steady income to poor households.No rule over business investment or labor supply.

Introduction Method Results Conclusion

Introduction

Capital is essential for starting a business.

Limited access to credit may lessen entrepreneurialactivity in developing countries.

The role of a liquidity shock in supportingentrepreneurship among poor households.

Large-scale Conditional Cash Transfer (CCT) programin Brazil, Bolsa Famı́lia.

Conditional on school attendance and health care.Small but steady income to poor households.No rule over business investment or labor supply.

Introduction Method Results Conclusion

Introduction

Capital is essential for starting a business.

Limited access to credit may lessen entrepreneurialactivity in developing countries.

The role of a liquidity shock in supportingentrepreneurship among poor households.

Large-scale Conditional Cash Transfer (CCT) programin Brazil, Bolsa Famı́lia.

Conditional on school attendance and health care.Small but steady income to poor households.No rule over business investment or labor supply.

Introduction Method Results Conclusion

Introduction

The impacts often interpreted as individual responses ofparticipants (direct effects),

I investigate not only the individual effect on participants,but also the social effects.

There are two possible indirect effects:

1 Shifting the aggregate demand andincreasing investment opportunities.

2 Stimulating informal credit and private transfers.

Introduction Method Results Conclusion

Introduction

The impacts often interpreted as individual responses ofparticipants (direct effects), ignoring local spill-overs(indirect effects).

I investigate not only the individual effect on participants,but also the social effects.

There are two possible indirect effects:

1 Shifting the aggregate demand andincreasing investment opportunities.

2 Stimulating informal credit and private transfers.

Introduction Method Results Conclusion

Introduction

The impacts often interpreted as individual responses ofparticipants (direct effects), ignoring local spill-overs(indirect effects).

I investigate not only the individual effect on participants,but also the social effects.

There are two possible indirect effects:

1 Shifting the aggregate demand andincreasing investment opportunities.

2 Stimulating informal credit and private transfers.

Introduction Method Results Conclusion

Introduction

The impacts often interpreted as individual responses ofparticipants (direct effects), ignoring local spill-overs(indirect effects).

I investigate not only the individual effect on participants,but also the social effects.

There are two possible indirect effects:

1 Shifting the aggregate demand andincreasing investment opportunities.

2 Stimulating informal credit and private transfers.

Introduction Method Results Conclusion

Introduction

The impacts often interpreted as individual responses ofparticipants (direct effects), ignoring local spill-overs(indirect effects).

I investigate not only the individual effect on participants,but also the social effects.

There are two possible indirect effects:

1 Shifting the aggregate demand andincreasing investment opportunities.

2 Stimulating informal credit and private transfers.

Introduction Method Results Conclusion

Introduction

The impacts often interpreted as individual responses ofparticipants (direct effects), ignoring local spill-overs(indirect effects).

I investigate not only the individual effect on participants,but also the social effects.

There are two possible indirect effects:

1 Shifting the aggregate demand andincreasing investment opportunities.

2 Stimulating informal credit and private transfers.

Introduction Method Results Conclusion

Introduction

0

.1

.2

.3

.4

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obab

ility

of t

rans

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1 2 3 4 5 6 7 8 9 10Income Decile

CCT beneficiaries

non−beneficiaries

share of beneficiaries

Participants are more likely to make transfers to other households.

With more cash flowing, the individual intervention becomes social.

Introduction Method Results Conclusion

Introduction

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.1

.2

.3

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0

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.15

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obab

ility

of t

rans

ferin

g

1 2 3 4 5 6 7 8 9 10Income Decile

CCT beneficiaries

non−beneficiaries

share of beneficiaries

Participants are more likely to make transfers to other households.

With more cash flowing, the individual intervention becomes social.

Introduction Method Results Conclusion

Data

National Household Survey (PNAD) from 2001, 2004, 2006.

It is a cross-sectional survey, but with apanel of neighborhoods (census tracts).

Low-educated men between 25 and 45 years,living in urban areas.

Entrepreneurs are those either self-employedor small business owners.

Also required that they contribute to social security.

Informal workers’ earnings cannot be tracked.

Introduction Method Results Conclusion

Data

National Household Survey (PNAD) from 2001, 2004, 2006.

It is a cross-sectional survey, but with apanel of neighborhoods (census tracts).

Low-educated men between 25 and 45 years,living in urban areas.

Entrepreneurs are those either self-employedor small business owners.

Also required that they contribute to social security.

Informal workers’ earnings cannot be tracked.

Introduction Method Results Conclusion

Data

National Household Survey (PNAD) from 2001, 2004, 2006.

It is a cross-sectional survey, but with apanel of neighborhoods (census tracts).

Low-educated men between 25 and 45 years,living in urban areas.

Entrepreneurs are those either self-employedor small business owners.

Also required that they contribute to social security.

Informal workers’ earnings cannot be tracked.

Introduction Method Results Conclusion

Data

National Household Survey (PNAD) from 2001, 2004, 2006.

It is a cross-sectional survey, but with apanel of neighborhoods (census tracts).

Low-educated men between 25 and 45 years,living in urban areas.

Entrepreneurs are those either self-employedor small business owners.

Also required that they contribute to social security.

Informal workers’ earnings cannot be tracked.

Introduction Method Results Conclusion

Data

National Household Survey (PNAD) from 2001, 2004, 2006.

It is a cross-sectional survey, but with apanel of neighborhoods (census tracts).

Low-educated men between 25 and 45 years,living in urban areas.

Entrepreneurs are those either self-employedor small business owners.

Also required that they contribute to social security.

Informal workers’ earnings cannot be tracked.

Introduction Method Results Conclusion

Identification Strategy

The identification assumption is inspired by the program design.

Each municipality (city or village) has a maximum number ofbenefits to be offered, given by the 2000-2001 poverty rate.

R2 = 0.768

0

.2

.4

.6

.8

1

CC

T c

over

age

0 .2 .4 .6 .8 1poverty headcount

2004 coverage vs. 2000 poverty

R2 = 0.916

0

.2

.4

.6

.8

1

CC

T c

over

age

0 .2 .4 .6 .8 1poverty headcount

2006 coverage vs. 2000 poverty

2000 Census and Official Record

R2 = 0.742

0

.2

.4

.6

.8

1

CC

T c

over

age

0 .2 .4 .6 .8 1poverty headcount

2004 coverage vs. 2001 poverty

R2 = 0.801

0

.2

.4

.6

.8

1

CC

T c

over

age

0 .2 .4 .6 .8 1poverty headcount

2006 coverage vs. 2001 poverty

R2 = 0.767

0

.2

.4

.6

.8

1

CC

T c

over

age

0 .2 .4 .6 .8 1poverty headcount

2006 coverage vs. 2004 poverty

National Household Survey

Introduction Method Results Conclusion

Identification Strategy

The identification assumption is inspired by the program design.

The growth of coverage mostly driven by the previous povertyrate, rather than the increasing demand from poor households.

R2 = 0.768

0

.2

.4

.6

.8

1

CC

T c

over

age

0 .2 .4 .6 .8 1poverty headcount

2004 coverage vs. 2000 poverty

R2 = 0.916

0

.2

.4

.6

.8

1

CC

T c

over

age

0 .2 .4 .6 .8 1poverty headcount

2006 coverage vs. 2000 poverty

2000 Census and Official Record

R2 = 0.742

0

.2

.4

.6

.8

1

CC

T c

over

age

0 .2 .4 .6 .8 1poverty headcount

2004 coverage vs. 2001 poverty

R2 = 0.801

0

.2

.4

.6

.8

1

CC

T c

over

age

0 .2 .4 .6 .8 1poverty headcount

2006 coverage vs. 2001 poverty

R2 = 0.767

0

.2

.4

.6

.8

1

CC

T c

over

age

0 .2 .4 .6 .8 1poverty headcount

2006 coverage vs. 2004 poverty

National Household Survey

Introduction Method Results Conclusion

Identification Strategy

The decision of being an entrepreneur, yivt, is a function of theindividual benefit, divt, and the local coverage, dvt:

yivt = β0 + β1divt + β2dvt + µv + µt + uivt, (1)

The local coverage, dvt, is assumed to be independent of theindividual propensity to be an entrepreneur, uivt.

Not ignoring the fact that some households are more likelyto go after the program than others

divt is still endogenous due to self-selectioninto the program.

I actually estimate the overall effect, τ = β1 + β2:

yivt = β0 + τdvt + µv + µt + uivt (2)

Introduction Method Results Conclusion

Identification Strategy

The decision of being an entrepreneur, yivt, is a function of theindividual benefit, divt, and the local coverage, dvt:

yivt = β0 + β1divt + β2dvt + µv + µt + uivt, (1)

The local coverage, dvt, is assumed to be independent of theindividual propensity to be an entrepreneur, uivt.

Not ignoring the fact that some households are more likelyto go after the program than others

divt is still endogenous due to self-selectioninto the program.

I actually estimate the overall effect, τ = β1 + β2:

yivt = β0 + τdvt + µv + µt + uivt (2)

Introduction Method Results Conclusion

Identification Strategy

The decision of being an entrepreneur, yivt, is a function of theindividual benefit, divt, and the local coverage, dvt:

yivt = β0 + β1divt + β2dvt + µv + µt + uivt, (1)

The local coverage, dvt, is assumed to be independent of theindividual propensity to be an entrepreneur, uivt.

Not ignoring the fact that some households are more likelyto go after the program than others

divt is still endogenous due to self-selectioninto the program.

I actually estimate the overall effect, τ = β1 + β2:

yivt = β0 + τdvt + µv + µt + uivt (2)

Introduction Method Results Conclusion

Identification Strategy

The decision of being an entrepreneur, yivt, is a function of theindividual benefit, divt, and the local coverage, dvt:

yivt = β0 + β1divt + β2dvt + µv + µt + uivt, (1)

The local coverage, dvt, is assumed to be independent of theindividual propensity to be an entrepreneur, uivt.

Not ignoring the fact that some households are more likelyto go after the program than others

divt is still endogenous due to self-selectioninto the program.

I actually estimate the overall effect, τ = β1 + β2:

yivt = β0 + τdvt + µv + µt + uivt (2)

Introduction Method Results Conclusion

Separating Direct and Indirect Effects

If the indirect effect, β2, is different for participantsand non-participants, then the overall effect, τ , must benonlinear.

Then the sample of non-participants is usedto estimate the indirect effect.

The direct effect is the biased estimate minus expected bias:

β̃1 = β̂1 −(

τ̂(d=0) − β̂2

)

.

All coefficients are estimated using seemingly unrelatedregressions (SUR).

Introduction Method Results Conclusion

Separating Direct and Indirect Effects

If the indirect effect, β2, is different for participantsand non-participants, then the overall effect, τ , must benonlinear.

Then the sample of non-participants is usedto estimate the indirect effect.

The direct effect is the biased estimate minus expected bias:

β̃1 = β̂1 −(

τ̂(d=0) − β̂2

)

.

All coefficients are estimated using seemingly unrelatedregressions (SUR).

Introduction Method Results Conclusion

Separating Direct and Indirect Effects

If the indirect effect, β2, is different for participantsand non-participants, then the overall effect, τ , must benonlinear.

Then the sample of non-participants is usedto estimate the indirect effect.

The direct effect is the biased estimate minus expected bias:

β̃1 = β̂1 −(

τ̂(d=0) − β̂2

)

.

All coefficients are estimated using seemingly unrelatedregressions (SUR).

Introduction Method Results Conclusion

Separating Direct and Indirect Effects

If the indirect effect, β2, is different for participantsand non-participants, then the overall effect, τ , must benonlinear.

Then the sample of non-participants is usedto estimate the indirect effect.

The direct effect is the biased estimate minus expected bias:

β̃1 = β̂1 −(

τ̂(d=0) − β̂2

)

.

All coefficients are estimated using seemingly unrelatedregressions (SUR).

Introduction Method Results Conclusion

Overall Effect

OLS IV

(1) (2) (3) (4) (5) (6)

coverage, d −0.013∗ 0.042∗∗∗ 0.040∗∗∗ 0.053∗∗ 0.051∗∗

(0.01) (0.01) (0.01) (0.02) (0.02)

indiv. benefit, d 0.057∗∗

(0.02)

Control variables

Municipality FE X

Census Tract FE X X X X

Year dummies X X X X X X

Demographic X X X X X X

Social outcomes X X

With municipality fixed effects, results become consistently positive.

10 p.p. in program coverage increases the entrepreneurship ratein 0.4-0.5 p.p. (Baseline = 7 p.p.).

Introduction Method Results Conclusion

Overall Effect

OLS IV

(1) (2) (3) (4) (5) (6)

coverage, d −0.013∗ 0.042∗∗∗ 0.040∗∗∗ 0.053∗∗ 0.051∗∗

(0.01) (0.01) (0.01) (0.02) (0.02)

indiv. benefit, d 0.057∗∗

(0.02)

Control variables

Municipality FE X

Census Tract FE X X X X

Year dummies X X X X X X

Demographic X X X X X X

Social outcomes X X

With municipality fixed effects, results become consistently positive.

10 p.p. in program coverage increases the entrepreneurship ratein 0.4-0.5 p.p. (Baseline = 7 p.p.).

Introduction Method Results Conclusion

Direct and Indirect Effects

All indiv. benefit = 0 All sample

sample OLS IV OLS IV

(1) (2) (3) (4) (5)

coverage, d 0.048∗ 0.063∗∗∗ 0.076∗∗∗ 0.063∗∗∗ 0.078∗∗∗

(0.03) (0.02) (0.02) (0.01) (0.02)

squared coverage, d2

−0.004

(0.044)

indiv. benefit, d −0.026∗∗∗ −0.027∗∗∗

(0.00) (0.00)

Control variables

Census Tract FE X X X X X

Year dummies X X X X X

Demographic X X X X X

Economic sectors X X X X X

Total effect is linear effect, so indirect effect is assumed to be constant.

Indirect effect is greater than the total effect, 0.6-0.7 p.p.

Then the direct effect is negative.

Introduction Method Results Conclusion

Direct and Indirect Effects

All indiv. benefit = 0 All sample

sample OLS IV OLS IV

(1) (2) (3) (4) (5)

coverage, d 0.048∗ 0.063∗∗∗ 0.076∗∗∗ 0.063∗∗∗ 0.078∗∗∗

(0.03) (0.02) (0.02) (0.01) (0.02)

squared coverage, d2

−0.004

(0.044)

indiv. benefit, d −0.026∗∗∗ −0.027∗∗∗

(0.00) (0.00)

Control variables

Census Tract FE X X X X X

Year dummies X X X X X

Demographic X X X X X

Economic sectors X X X X X

Total effect is linear effect, so indirect effect is assumed to be constant.

Indirect effect is greater than the total effect, 0.6-0.7 p.p.

Then the direct effect is negative.

Introduction Method Results Conclusion

Explaining Direct Effects

Additional analyses show that:

Negative effect on entrepreneurship followed by a reductionin the formal sector participation.

While labor supply in the informal sector increases.

Introduction Method Results Conclusion

Explaining Direct Effects

Additional analyses show that:

Negative effect on entrepreneurship followed by a reductionin the formal sector participation.

While labor supply in the informal sector increases.

Introduction Method Results Conclusion

Explaining Direct Effects

Additional analyses show that:

Negative effect on entrepreneurship followed by a reductionin the formal sector participation.

While labor supply in the informal sector increases.

Introduction Method Results Conclusion

Explaining Direct Effects

Additional analyses show that:

Negative effect on entrepreneurship followed by a reductionin the formal sector participation.

While labor supply in the informal sector increases.

Participants don’t want to lose the benefit, so they look forways of not having their earnings tracked.

Introduction Method Results Conclusion

Explaining Indirect Effects

Additional analyses show that:

No indirect effect on job creation.

No indirect effect on other entrepreneurs.

Positive effects on the probability of non-participantsreceiving private transfers.

Introduction Method Results Conclusion

Explaining Indirect Effects

Additional analyses show that:

No indirect effect on job creation.

No indirect effect on other entrepreneurs.

Positive effects on the probability of non-participantsreceiving private transfers.

Introduction Method Results Conclusion

Explaining Indirect Effects

Additional analyses show that:

No indirect effect on job creation.

No indirect effect on other entrepreneurs.

The hypothesis of increasing investmentopportunities is not supported.

Positive effects on the probability of non-participantsreceiving private transfers.

Introduction Method Results Conclusion

Explaining Indirect Effects

Additional analyses show that:

No indirect effect on job creation.

No indirect effect on other entrepreneurs.

The hypothesis of increasing investmentopportunities is not supported.

Positive effects on the probability of non-participantsreceiving private transfers.

It supports the hypothesis of promoting informal credit.

Introduction Method Results Conclusion

Conclusions

Negative direct effect on labor supply, reducing the probabilityof beneficiaries to start their own business.

The amount of cash transfered to poor communities stimulatesprivate transfers among poor households.

The way the liquidity shock spills over the whole community,increasing the overall entrepreneurship rate.

CCTs can also play a role in the promotion of informal creditand small business creation.

However, eligibility rules might discourage program participantsto look for opportunities in the formal sector.

Introduction Method Results Conclusion

Conclusions

Negative direct effect on labor supply, reducing the probabilityof beneficiaries to start their own business.

The amount of cash transfered to poor communities stimulatesprivate transfers among poor households.

The way the liquidity shock spills over the whole community,increasing the overall entrepreneurship rate.

CCTs can also play a role in the promotion of informal creditand small business creation.

However, eligibility rules might discourage program participantsto look for opportunities in the formal sector.

Introduction Method Results Conclusion

Conclusions

Negative direct effect on labor supply, reducing the probabilityof beneficiaries to start their own business.

The amount of cash transfered to poor communities stimulatesprivate transfers among poor households.

The way the liquidity shock spills over the whole community,increasing the overall entrepreneurship rate.

CCTs can also play a role in the promotion of informal creditand small business creation.

However, eligibility rules might discourage program participantsto look for opportunities in the formal sector.

Introduction Method Results Conclusion

Conclusions

Negative direct effect on labor supply, reducing the probabilityof beneficiaries to start their own business.

The amount of cash transfered to poor communities stimulatesprivate transfers among poor households.

The way the liquidity shock spills over the whole community,increasing the overall entrepreneurship rate.

CCTs can also play a role in the promotion of informal creditand small business creation.

However, eligibility rules might discourage program participantsto look for opportunities in the formal sector.

Introduction Method Results Conclusion

Thank you for your attention

Rafael P. Ribas

rpribas.rs@gmail.com

http://publish.illinois.edu/ribas1