Cesar Del Pozo

Brasilia, September 10th – 11th 2014

The impact of linking conditional cash transfers

to agricultural credit on productive assets

accumulation of rural households in Peru

Social protection, entrepreneurship and labor market activation; International Seminar and Policy Forum.

CCT´s can have effects on agricultural outcomes (Todd et al. 2010; Gertler et al.

2006); CCT´s can reduce liquity constraints, CCT´s are a relevant, stable and

regular source of non-labor income, CCT´s can serves as a form of colateral for

credit.

Credit can increase income- generating activities and can improve assets position

of poor households (Karlan et al., 2007: Banerjee et al., 2009; Dong et al., 2010).

• Can CCT´s increase the stock of productive assets of rural and poor

households in Peru?

• Can the linking of CCT´s to agricultural credit improve assets position of rural

and poor households in Peru?

• Wich is the magnitude of these impacts?

• The linking of CCT´s to agricultural credit is a valid public policy option to

promote rural development?

Motivation and Research questions

Programa Juntos: relevant policy instrument to poverty alleviation in Peru:

• Start in 2005

• Operating at national level, mainly in rural areas

• Covers around 700000 households

• Fixed transfers UDS 71 bimonthly for at least 4 year.

• Conditionals: use of health services, school asistance.

• Targeting mechanism poor distritcs and poor households.

Agricultural credit

• Lack access to credit by rural households: 8% of total rural producers has credit

• Several types of credit lenders: informal, private banks, public bank (Agrobanco),

Microfinance Institutions

• Microfinace Institutions are the most relevant credit provider (66%)

Context background

• Juntos’ was not randomly assigned

• Credit access is a endogenous decision of households

• Programa Juntos is not formarly linked with any credit program at

national level

• The linking of CCT´s to agricultural credit is based on own decisions

of rural households.

• In households surveys does not exist enough information about

productive assets or agricultural credit.

• Census data is available.

Methodology: empirical challenges

Data:

• Agricultural Census: 1994 and 2012

• Around 2 million farming households.

Dependent variables:

• Agricultural assets: cultivated, land, rrrigated land, rate of cultivated land over total

land, rate of irrigated land over cultivated land, accumulation of productive

equipment, productive infraestructure.

• Livestock assets: number of cows, number of sheeps, small animals (guinea pigs

«cuyes»), poultry.

Independent variables:

• Household belong to CCTP in Peru: Programa Juntos.

• Household has agricultural credit by type of credit lender.

• Socioeconomics and geographical characteristics at district and household level.

Methodology: data and variables

0

.01

.02

.03

10 20 30 40 50 60 70 80 90 100 10 20 30 40 50 60 70 80 90 100

Distritos no coberturados Distritos coberturados

Incidencia

Graphs by Distritos coberturados por el Programa Juntos

Explore targeting rules of Programa Juntos

Methodology: identification strategy

Target, treated Target, Untreated

Districts with a Poverty headcount index > 40% is useful to identify poor

districts that are not yet incorporated by Juntos. It is caused by

unobservables (assumed exogenous).

How deal with endogenous decisions of rural household about

agricultural credit? In this study, I categorizing rural households based

on they agricultural credit decisions:

Methodology: identification strategy

Programa Juntos status Did not request

agricultural credit

Yes they request

agricultural credit (and

obtain it)

Target, treated Beneficiaries of CCT´s

without credit

Beneficiaries of CCT´s

with credit

Target, untreated Non-beneficiaries

without credit

Non-beneficiaries with

credit

Methodology: identification strategy

Final sample is aprox. 400.000 rural

households in 561 target districts:

• 450 target, treated districts: 300.000

households (treatment group)

• 111 target, untreated districts: 100.000

households (control group)

Baseline 1994, “Middleline” 2012

• Panel data at district level

• Pooled cross-sectional data at household

level

Technical note: to improve comparison among districts and rural

household I apply a Propensity Score Matching in both districts and

household level to replicate targeting process and to reduce initial

observable differences.

Them, I apply a Differences in Differences model:

, , = � + � , + � + ��− − � , ∗ � + , ,′ + � , ,

C is the households’ decision about agricultural credit: C=0, Did not request agricultural credit

C=1, Yes they request agricultural credit (and obtain it)

C=2…5, credit by lender type (informal credit, private banks, public bank, Microfinance Institutions)

Methodology: quasi-experimental approach

• CCT increase cultivated land

• CCT+Credit increase cultivated land, increase poultry, and generate a type

of assets specialization

• CCT or CCT+credit no effects on productive equipment and/or productive

infraestructure

Results 1: Average Effects

C=0 C=1Informal

credit

Private

banks

Public

banksMFI

Cultivated land 2.06 0.33*** 0.64*** 0.57 -0.04 0.98*** 0.73**

Cow 2.5 0.04 -0.75*** 0.37 -0.66 -0.19 -1.20***

Sheep 5.97 -0.54 -1.37* -0.56 -1.89 -2.60* -0.12

Small animals 5.62 0.34 1.08 2.95 0.20 0.73 0.24

Poultry 7.52 1.32 3.92*** 7.49*** 5.41*** 4.98*** 1.98***

Obs 345931 22043 1095 2169 5126 11810

Dependent

variablesMean

Average Treatment Effects on the treated (ATT)

***, ** and *; denote significancy at 1%, 5% and 10%, respectively

Source: Own estimations

• CCT reduce cultivated land and the acumulation of small animals if

household head is female.

• CCT+Credit no effects if household head is female.

Results 1.1: Effects by gender

C=0 C=1Public

banksMFI

Cultivated land -0.12*** 0.11 0.03 -0.22

Small animals -0.77*** -0.82 1.76 0.53

Poultry -0.70 -1.93 -2.11 2.54

Obs 345931 22043 5126 11810

***, ** and *; denote significancy at 1%, 5% and 10%, respectively

Source: Own estimations

Dependent

variables

Average Treatment Effects on the treated (ATT) on

female household head

Technical note: I apply an additional empirical approach to estimate the impact of

agricultural credit on beneficiaries of Programa Juntos. For deal with edogenous

decision of access to credit employ a IV approach.

First stage (Stiglitz y Weis, 1981; Carter y Olinto, 2000; Guirkinger et al., 2007; Cámara

et al., 2013): access to agricultural credit dependent of credit supply at local level (both

offices and «cajeros corresponsales»), own land, entrepreneurship trainning, technical

assistance (+). Educational level, age of household head, gender of household head

(female), isolation, population density and altitude (-).

Second stage:

Results 2: The impact of agricultural credit on Juntos households

Access to

agricultural credit

Access to agricultural

credit MFI

Cultivated land 1.28*** 1.10***

Small animals 13.18*** 22.08***

Poultry 5.82*** 6.92***

Obs 301368 301368

Variables

Average Treatment Effects on the treated

(ATT) on Juntos households

***, ** and *; denote significancy at 1%, 5% and 10%, respectively

Source: Own estimations

• Evidence of that the link of CCT´s to agricutlural credit improve assets

position of rural and poor households in Peru (cultivated land,

livestock accumulation.

• The magnitude of these impacts are relevant: increase 31% cultivated

land and 52% accumulation of poultry.

• The linking of CCT´s to agricultural credit can be a valid public policy

option to promote rural development, rural credit market have little

coverage and have few specific products, more dicuss about it.

Initial conclusions

The heroes

Thanks