gonzalezzaira_writingsample

The impact of PROGRESA, PROCAMPO and the Word

Credit Program on Consumption in Mexico: A Propensity

Score Matching Analysis

Zaira Gonzalez

This research paper was written for the graduate Economics of Development course I took at

Oklahoma State University in the spring of 2015. It also served to fulfill the “creative

component” requirement for my Master of Science in International Agriculture degree.

1

1. INTRODUCTION

In the 1990s Latin America experienced a current in social policy that sought to move

away from discretionary aid towards more pro-poor and pro-democratic forms of welfare

(Gonzalez 2011). In Mexico, the expenditure in social development from 1990 to 2007 had a real

growth of 276%: from 1990 to 1994 it grew by 91%, from 1994 to 1995 it fell by 23%, but had a

subsequent recovery to 537 billion pesos in 1996. In 2007, this amount reached 1,136 billion

dollars (CONEVAL, 2008).

The budget for poverty alleviation in Mexico for 2014 was 1.575 billion pesos (around

101 billion dollars), amounting to 1.2% of the country’s GDP. Prospera (formerly

Progresa/Oportunidades) serves as the federal government’s main tool for poverty alleviation:

with 75 billion pesos in 2014 (around 4,818 million dollars), this program has the biggest budget

compared to any other federal program in Mexico (SEDESOL 2010, CNN.com 2014). Serving

5.8 million families, this program alone benefits around 25% of the Mexican population

(WorldBank.com). However, in spite of the coverage and the expenditure the Mexican

government devotes to poverty alleviation, the lack of correspondence between this expenditure

and their results has given rise to strong criticism to welfare government programs’

implementation and management.

According to the World Bank, the percentage of the population in Mexico living under

poverty amounted to 52.3% in 2012—more than half of the country’s population. This figure is

exacerbated when referring to rural poverty, since it affects about 63.6% of people living in rural

areas (WorldBank.org). The high incidence in poverty that is focalized in rural communities

brings the need of a through and comprehensive strategy to optimize the welfare expenditure on

policies designed to combat poverty.

The objective of this paper is to analyze and compare the impact on welfare of three

governmental programs in Mexico aimed at alleviating poverty, particularly in rural areas:

2

PROGRESA, PROCAMPO, and the Word Credit Program (WCP). Although the WCP was

replaced by Financiera Rural in 2003, which operates under different rules and has a wider target

population, information of the new program is not available. For this reason, this study will be

limited to the year 2002, allowing for the analysis and comparison of the three programs. The

research’s focus is to identify households’ characteristics that are decisive on the programs’

impact size on welfare. The initial hypothesis is that participation in these programs will

increase household consumption.

The key finding of this paper is that most programs seem to have a negative impact on

food expenditure at the household level. This result, although unexpected, is discussed in the last

section of the paper. The remainder of this research paper is organized as follows: the

Background section gives an overview of the operations and history for each of the three

programs analyzed, the Literature Review section goes over some relevant literature used in the

design of this research, the Data section describes the data used, the Methodology section

explains the strategy used to evaluate the impact of the three different government programs, the

Results section presents the findings, and in the Conclusions and Discussion section

interpretations for the results are presented as well as suggestions for future studies of the

programs.

2. BACKGROUND ON WELFARE PROGRAMS IN MEXICO

2.1 PROGRESA

In August 1997 the conditional cash transfer program (CCT) Progresa began operating

with the objective of addressing extreme poverty in rural areas, focusing on the welfare

indications of education, health, and nutrition. The transfer of cash was thus conditioned on

regular school attendance and visits to health care centers for medical checkups, as well as on

“platicas,” which are informational meetings discussing health-related topics, such as nutrition.

Gender plays an important role in this program, since the cash transfer is higher for families that

3

have girls enrolled in school in comparison to boys. In addition, the disbursement is given

directly to the mother in the household (Skoufias 2005). Before its dissolution in 2002 and its

replacement with Oportunidades, Progresa covered approximately 2.6 million families, which is

the equivalent to 40% of all rural families. With a budget size of $777 million in 1999, this

program received the equivalent of Mexico’s 0.2% total GDP (IFPRI.org).

2.2 PROCAMPO

Implemented in 1993 following the commencement of the North America Free Trade

Agreement (NAFTA), PROCAMPO was created to aid farmers compete against the lower-

priced, subsidized American and Canadian agricultural production. Although the program was

initially designed to operate for only 15 years, it is still operating thanks to political lobbying by

the agriculture sector, turning it into the federal program with the highest amount of rural

beneficiaries (Sagarpa.gob.mx). PROCAMPO also substituted agricultural programs that

consisted in government minimum price payment for agricultural production. Instead, this new

program provides fixed payments per hectare to eligible agricultural producers. Land is eligible

to receive PROCAMPO’s benefits if it was used to cultivate safflower, barley, corn, beans,

soybeans, wheat, sorghum, rice, or cotton between August 1992 and August 1993. Most of the

rural farmers that benefit from the program are low-income, and their production is mostly used

for self-consumption (Altamirano et al. 1997, Ruiz-Arranz 2006).

2.3 WORD CREDIT PROGRAM

In 1990, the federal government created the Word Credit Program aimed at low

productivity and rain-fed land farmers, who had no access to formal credit, and who owned no

more than 20 hectares of land. Beneficiaries could continue in the program as long as they did

not default on the previous year’s loan. The credits’ objective was to increasing beneficiaries’

production, particularly basic grains production, and thus improve their quality of life. These

loans were given at zero interest rate and had no collateral requirement. In 2002, the amount that

4

each farmer received was 550 pesos per hectare, which is equivalent to 35 USD (Stanton 2002,

Favela 2003).

In 2003 Financiera Rural replaced the Word Credit Program, among other government

financial institutions (Grammont 2001, FinancieraRural.gob.mx). Financiera Rural operates

with the mission to "develop rural areas through first and second floor financing for any

economic activity undertaken in communities of fewer than 50,000 inhabitants, resulting in an

improved quality of life" (Financiera Rural, 2013).

3. LITERATURE REVIEW

3.1 INDICATORS AND PROXIES FOR WELFARE

The most common proxy for welfare programs impact evaluations in developing

countries, at either household or community level, is consumption (Crépon et al. 2011, Khandker

2005). There is strong evidence that the use of a consumption-based poverty measure is

preferable to any income-based poverty measure for identifying the most disadvantaged sectors

of a population (Meyer and Sullivan, 2012). Waheed (2009) conducted a survey to families

participating in a microcredit program, which included questions about income, assets, education

and family size. The simple consumption model showed that among the variables that improved

the well-being of households, education and microcredits were the most significant ones.

Consumption as a measure of welfare can more efficiently be used if other baseline

characteristics are taken into account. Crépon et al. (2011) performed a randomized experiment

to measure the impact of microcredits using a control and a treatment group, where he found “a

substantial effect [of microcredits] on sales (3,305 MAD increase, Morocco’s currency),

expenses (2,297 MAD, or 14%), in-kind savings (11%) and self-consumption (11.8%).” Since

treatment households reduced their supply on external work to work on their agricultural

projects, Crépon did not observe any change in net income, even though these other indicators

improved. However, when he analyzed the data by households with and without a self-

5

employment activity at baseline, he observed that although households with a pre-existing

activity reduced consumption, they experienced a large increase in sales, expenditures and

savings. On the other hand, households without a baseline activity “had no significant increase

in their activities and had an increase in their consumption.” Thus, acknowledging other factors

that shape the impact of a program can be crucial to successfully understand the interaction

between the program and these impacts.

Another popular, more specific alternative to welfare impact evaluation is using food

consumption as the measure of welfare. For example, Jensen and Miller (2011) proposed

analyzing food consumption for households that benefited from subsidized goods in China,

finding no overall evidence of an effect of subsidies on nutrition, measured as caloric intake.

Interestingly, it is possible that these subsidies reduced the amount of calories consumed in one

of the provinces.

3.2 MICROFINANCE VS. CASH TRANSFERS AS A POLICY FOR POVERTY ALLEVIATION

An option to evaluate the effectiveness of the government’s poverty alleviation programs

is to compare the size of their impacts. Pantelić (2011) found that the effectiveness of CCT’s

compared to microfinance programs is affected by the recipient’s income level: households

living on US$2 or more per day will benefit better from microloans, whereas CCTs may be

better suited for individuals living in extreme poverty. Although both programs have a positive

effect on consumption, the evidence of CCT’s impact on health and education is more evident

compared to microloans’. This success can be attributed to CCT’s conditionality: participant

families are conditioned on sending their children to school and attending regular medical

checkups to receive benefits from Progresa, whereas PROCAMPO beneficiaries are conditioned

on the farming of their land (Pantelić 2011).

An analysis on Progresa and PROCAMPO welfare impact performed in 2006 by Ruiz-

Arranz provides further evidence on the different impact these programs have depending on the

6

recipients’ characteristics. Progresa is a short run consumption based program for nutrition

improvements, since beneficiaries improve their food security through purchases. In contrast,

given the PROCAMPO’s operating dynamics, this program impacts households’ agricultural

investment and home production, serving as a production based, medium term policy tool for

food security (Ruiz-Arranz, 2006).

4. DATA

This papers uses data obtained from household surveys given by the first round of the

Mexican Family Life Survey (MxFLS-1) conducted in 2002. The first round was designed by

the National Institute of Statistics and Geography (INEGI, by its initials in Spanish). The

baseline sample is probabilistic, stratified, multi-staged, and independent at every phase of the

study, and the population is comprised by Mexican households in 2002. According to the

MxFLS website, “primary sampling units were selected under criterions of national, urban-rural

and regional representation on pre-established demographic and economic variables.”

The first round or baseline survey (MXFLS-1) collected information on a sample of

35,000 individuals from 8,400 households in 150 communities throughout the country. The

survey included questions on socioeconomic and demographic indicators at the individual,

household and community level, such as on education, government programs’ benefits, labor and

non-labor income, credits and loans, food expenditure, etc. The survey’s individual observations

were collapsed into average or sum observations, creating a household level dataset with 8,400

observations.

The treatment group consisted of 682 households receiving PROCAMPO benefits, 1163

receiving Progresa benefits, and 56 WCP participating households. In the second part of the

analysis a propensity score matching was performed, where in order to avoid biased caused by

7

household participating in two or more programs 239 household were dropped from the

PROCAMPO analysis, 720 from Progresa, and 50 from the WCP.1

5. METHODOLOGY

In order to account for income (which is an endogenous variable to the model), this paper

uses other variables determinants of households’ consumption. These include measures of

human capital, household assets, regional dummies, age, gender of the head of the household,

and education. Food consumption is measured as calorie intake. The proposed initial model

thus looks as follows:

Consumptioni = β1PROCAMPOi + β2PROGRESAi + β3WCPi + β4Xi + εi

where different proxies are used to account for Consumptioni, the amount of consumption

experimented per year in household i. These include Total Expenditures, Food Expenditures,

and Meat Expenditures. The last proxy was included to understand how the three programs

affect expenditure by food group.

Due to dropped observations, the danger of selection bias is present for each of the three

program analysis. To check whether there are certain observable characteristics that affect

selection into each of the three programs, an initial t-test is run between beneficiaries and non-

beneficiaries in the survey. A propensity score model is calculated afterwards, which controls for

observable heterogeneity by creating a counterfactual outcome to estimate outcomes without the

program. Subsequently, this outcome is compared to the outcome from individuals with similar

propensity to participate in the program given their characteristics. Finally, a test to check that

the covariates are balanced in the matched sample is performed. State is controlled for in all

regressions and models, except for WCP, due to the low number of control observations.

1 These observations were not dropped for the OLS regression due to the low number of observations this regression

relied on.

8

6. RESULTS

6.1 OLS Model

The first simple regression run gives an initial assessment of the impact each of the three

programs had on consumption, measured as total expenditure, expenditure on food, and meat

expenditure. The independent variable is the disbursement amount received by the household

from each of the three programs. None of these programs had a statistically significant effect on

the consumption measures, but the coefficients for PROCAMPO and the WCP were positive,

whereas Progresa’s coefficient was negative. When the regression was run using dummy

variables for household program participation, the results indicated that participating in Progresa

has a significant negative impact on a household’s total expenditures and food expenditures.

Although not statistically significant, the impact of WCP was positive for the three expenditure

measures. Procampo participation had a negative impact for total expenditures and meat, and a

positive impact for food expenditure, all non-significant.

6.2 Propensity Score Matching

The t-test results to check for statistical differences between the beneficiary and the non-

beneficiary groups are shown on table 3, after dropping observations for households participating

in two or more programs. Although the null hypothesis stating that the groups are not different

was rejected for very few observations in the Progresa and WCP t-tests, five characteristics

(household size, household assets, number of people in the household aged 16-21/22-27, and

number of females) out of 17 presented a p-value above .10, and thus the null hypothesis could

not be rejected. Following Azam’s methodology (2013), a Probit model is estimated to calibrate

the propensity score on the sample of individuals surveyed, both beneficiaries and non-

beneficiaries (table 4). The explanatory power of the model is relatively high for Progresa and

Procampo, which have a pseudo R2

of 0.3144 and 0.2808. The WCP has an R2 of 0.0801. Most

explanatory variables are statistically significant, except for the WCP model. The overlap in

9

support between the treatment and control groups for Progresa and Procampo is noticeably better

than for the WCP, where the matching was very low since the covariates were not balanced in

the matched sample. The overlap is shown figure 1, where homoskedasticity is assumed for the

Food Expenditure variable within the treated and control groups. The graphs for

homoskedasticity for Total Expenditure and Meat Expenditure are very similar and not included

in this paper.

The same t-test from table 3 was performed after matching to test for the differences

between treatment and control groups’ characteristics for each program (Progresa, Procampo,

and WCP), and for each welfare measure (Total Expenditure, Food Expenditure, and Meat

Expenditure). No significant differences between the treatment and control groups’

characteristics were found for the Progresa and WCP (Table 5). That means that the matching

method was indeed successful at controlling for the differences observed in the unmatched data.

Table 6 shows the three programs’ average treatment effect on the treated (ATT), which

are negative and not significant, with the exception of Food Expenditure under the Progresa

program, which is significant at a 99% confidence level. The results contrast with those from the

OLS regression on table 1 and 2. However, the only statistically significant coefficients in the

OLS regressions were found on table 2 for the Progresa Participation dummy, and they were

negative.

7. CONCLUSIONS AND DISCUSSION

Although theory would have predicted that the ATT values on Table 6 would be positive,

the ATT results for Progresa can be interpreted theoretically. Boccia et al. (2011) shows that

“there exists some substitution of quantity for quality as cash transfers rise, reflected by the fact

that food expenditure elasticities are higher than calorie elasticities.” This could very much be

the case in the Progresa program: there is a significant reduction on Total Expenditure and Food

Expenditure, but not significant for Meat Expenditure. Although most coefficients in table 6 are

10

not statistically significant, the difference between treatment and control groups is the smallest

under the meat expenditure category. Boccia mentions that the shift from high calorie to low

calorie/better tasting foods is a major policy concern in the development literature. However, the

context in Mexico is different given the high prevalence in obesity in the population. Mexico is

the country with the highest amount of obese children in the world, and the Mexican government

has implemented an array of public policy initiatives to approach this health problem, including

promoting a more balanced diet and the consumption of more fruits and vegetables.

Following the interpretations from Boccia, this research’s findings could reflect families’

substitution of staple goods, such as corn and beans, for meat and vegetables: “since poor

nutritional status can be caused not only by insufficient intake of calories but also by lack of diet

diversity, the observed shift can be considered beneficial” (Boccia et al., 2011). However, these

findings could also be the result of omitted variable bias, which happens when the model does

not incorporate unobserved characteristics that differ between the two groups and that account

for the probability to participate in the program. These worries are dismissed, since the

regressions and models’ results are logical assuming that low-income people consume less.

Although the results are not conclusive, this paper provides a framework for a future

study that could incorporate data from MXFLS-2 (2005) and MXFLS-3 (2009) after information

from the WCP becomes available later this year. Other suggestions for a future study are to

include more food expenditure categories, such as dairy and vegetables, and to incorporate

consumption of homegrown food to the model.

11

REFERENCES

Azam, M., Ferré, C., & Ajwad, M. 2013. Can public works programs mitigate the impact of

crises in Europe? The case of Latvia. IZA Journal of European Labor Studies, 2, 1-21.

Boccia, D., Hargreaves, J., Lönnroth, K., Jaramillo, E., Weiss, J., Uplekar, M., Porter, J.D.H.,

and Evans, C.A. 2011. Cash transfer and microfinance interventions for tuberculosis control:

review of the impact evidence and policy implications. The international journal of tuberculosis

and lung disease: the official journal of the International Union against Tuberculosis and Lung

Disease, 15(Suppl 2), S37.

CONEVAL, 2008. Informe de Evaluación de la Política de Desarrollo Social en México 2008.

CONEVAL, pp. 116.

Crépon, B., Devoto, F., Duflo, E., & Parienté, W. 2011. Impact of Microcredit in Rural Areas of

Morocco. Working Paper. International Growth Center. London School of Economic and

Political Science.

Favela, A. 2003. El combate a la pobreza en el sexenio de Zedillo. Plaza y Valdés.

Financiera Rural. 2013. Historia. Accessed August 6, 2014, from

http://www.financierarural.gob.mx/fr/Paginas/Historia.aspx

Financiera Rural. 2013. Misión y Visión. Accessed August 6, 2014, from

http://www.financierarural.gob.mx/fr/Paginas/MisionVision.aspx.

González, Z. 2011. Rewarding Voters through Welfare Transfers in Mexico and Brazil. Carleton

College. Available at http://people. carleton. edu/~ amontero/Zaira% 20Gonzalez. pdf.

Grammont, H. C. 2001. El Barzón: clase media, ciudadanía y democracia. Plaza y Valdes.

IFPRI. n.d. PROGRESA. http://www.ifpri.org/book-766/ourwork/program/progresa

Khandker, S. R. 2005. Microfinance and poverty: Evidence using panel data from Bangladesh.

The World Bank Economic Review, 19(2), 263-286.

Luna, C. 2014, October 2. De Oportunidades a Prospera, ¿solo un cambio de nombre? CNN

Expansión. Retrieved from

http://www.cnnexpansion.com/economia/2014/10/02/de-oportunidades-a-prospera-un-cambio-

fundamental

Meyer, B. D., & Sullivan, J. X. 2012. Identifying the disadvantaged: Official poverty,

consumption poverty, and the new supplemental poverty measure. The Journal of Economic

Perspectives, 111-135.

Pantelić, A. 2011. “A comparative analysis of microfinance and conditional cash transfers in

Latin America.” Development in Practice, 21:6, 790-805.

Rubalcava, L., y Teruel, G. 2006. “Mexican Family Life Survey, First Wave”, Working Paper,

www.ennvih-mxfls.org

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Ruiz-Arranz, M., Davis, B., Handa, S., Stampini, M., & Winters, P. 2006. Program

conditionality and food security: The impact of PROGRESA and PROCAMPO transfers in rural

Mexico. Revista Economia, 7(2), 249-278.

Sagarpa. 2013. PROCAMPO Productivo - Antecedentes.

http://www.sagarpa.gob.mx/agricultura/Programas/proagro/procampo/Paginas/Antecedentes.asp

x

Santoyo, H., Reyes, J., and Manrubio, R. 1997. Tendencias del financiamiento rural en México,

Revista de Comercio Exterior, p. 1014.

SEDESOL. 2015, February 16. PROSPERA fomenta las capacidades productivas, la mejor

forma de acabar con la pobreza.

http://www.sedesol.gob.mx/en/SEDESOL/Comunicados/2872/prospera-fomenta-las-

capacidades-productivas-la-mejor-forma-de-acabar-con-la-pobreza

Skoufias, E. 2005. PROGRESA and its impacts on the welfare of rural households in Mexico

(Vol. 139). Intl Food Policy Res Inst.

Stanton, J., Zeller, M., & Meyer, R. L. (Eds.). 2002. The triangle of microfinance: Financial

sustainability, outreach, and impact. Intl Food Policy Res Inst.

Waheed, S. 2009. “Does Rural Micro Credit Improve Well-being of Borrowers in the Punjab

(Pakistan)?” Pakistan Economic and Social Review, 31-47.

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https://www.worldbank.org/en/news/feature/2014/11/19/un-modelo-de-mexico-para-el-mundo

13

APPENDIX

Table 1. OLS for Program Impact on Household Expenditure

Variable Total Exp. Food Meat

PROCAMPO -0.1454 (.659) 0.2119 (.1431) -0.0201 (.1137)

WCP 0.9262 (1.17) 0.125 (.2542) 0.0329 (.2019)

Progresa -0.6084 (.6534) -0.0705 (.1419) -0.0634 (.1128)

hhsize

10326.72

(2186.49)*** 2337.665 (474.9)*** 475.7921 (377.33)

primary -8888.20 (3914.73)** 1333.306 (850.44) 792.495 (675.58)

coll 11138.5 (5675.53)** 10785.14 (1232.97)*** 4238.246 (979.45)***

secondary -4897.27 (4883.18) 3770.517 (1060.83)*** 1371.611 (842.71)

high 588.82 (5967.95) 7841.53 (1296.49)*** 1835.088 (1029.91)*

rural -7172.95 (2811.85)** -3097.131 (610.85)***

-1383.339

(485.25)***

pcincome 0.38 (.0438)*** 0.40005 (.0095)*** 0.0018 (.0076)

HHAssets 0.0002 (.0001)* 0.00003 (.00003) 9.92E-06 (.00002)

age -450.49 (148.57)*** -71.3261 (32.2875)** 28.0237 (25.64)

nage015

-13232.05

(2595.03)*** -2218.744 (563.571)*** -690.4924 (447.83)

nage1621

-10741.6

(3119.84)*** -1148.702 (677.76)* -139.4504 (538.40)

nage2227

-10319.97

(3160.24)*** -840.0254 (686.538) 313.6092 (545.38)

nage2835 -7154.74 (2896.11)* -433.2102 (629.157) 409.2961 (499.79)

nage3645 54.4783 (2472.29) 59.7457 (537.0856) 774.5937 (426.65)*

nfemales 1184.702 (1529.18) 464.3349 (332.204) 152.599 (263.90)

R2 0.0342 0.2518 0.0125

Figures represent unstandardized OLS Regression parameters; standard errors in parentheses; * p< .10, **

P< .05, *** p< .01

14

Table 2. OLS for Program Participation Impact on Household Expenditure

Variable Total Exp. Food Meat

dPROCAMPO -5231.99 (4945.5) 492.1406 (1074.40) -765.4642 (853.62)

dWCP 10735.72 (15053.56) 4376.81 (3270.35) 103.2523 (2598.30)

dProgresa -8858.65 (4061.02)** -1987.217 (882.25)** -882.6329 (700.95)

pcincome 0.3845 (.0438)*** 0.4000669 (.0095)*** 0.0018 (.008)

hhsize

10455.35

(2195.45)*** 2332.68 (476.956)*** 501.81 (378.94)

primary -9407.58 (3920.91)** 1239.352 (851.81) 742.18 (676.76)

coll 10072.47 (5695.53)* 10561.04 (1237.34)*** 4131.89 (983.07)***

secondary -5918.58 (4907.27) 3543.037 (1066.09)*** 1272.75 (847.01)

high -514.51 (5990.30) 7604.209 (1301.38)*** 1730.03 (1033.95)*

rural -4954.06 (2989.55)* -2688.564 (649.47)*** -1126.29 (516.006)**

HHAssets 0.0002 (.0001 )* 0.00003 (.00003) 9.89E-06 (.00002)

age -448.49 (148.56) *** -71.1575 (32.2734)** 28.50 (25.64)

nage015

-13081.23

(2606.58)*** -2136.016 (566.27)*** -688.59 (449.91)

nage1621

-10744.89

(3124.13)*** -1116.604 (678.71) -151.78 (539.24)

nage2227

-10509.16

(3164.51)*** -843.6824 (687.48) 284.18 (546.21)

nage2835

-7280.94

(2899.002)** -440.7857 (629.80) 385.12 (500.38)

nage3645 -52.01 (2475.04) 66.54938 (537.7) 755.72 (427.2)*

nfemales 1159.66 (1528.22) 449.4444 (332.003) 150.49 (263.78)

R2 0.0347 0.2522 0.0127

Figures represent unstandardized OLS Regression parameters; standard errors in parentheses; * p< .10, **

P< .05, *** p< .01

15

Table

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f pe

op

le in

HH

age 28-35

.4785.4983

.4886.3363

***.4758

.3333

Nu

mb

er o

f pe

olp

e in

HH

age 36-45

.4943.6117

***.5168

.3769***

.5090.3333

Nu

mb

er o

f Fem

ales

2.1072.681

***2.181

2.099*

2.1933.166

**

Pe

r Cap

ita HH

Asse

ts 231495.1

212118225684.3

289635.1223678.2

56866.37

Sta

ndard

erro

rs in p

are

nth

ese

s; * p

< .1

0, *

* P

< .0

5, *

** p

< .0

1

Pro

gresa

Pro

camp

oW

CP

16

Table 4. Probit for Calibrating Propensity Score

Progresa Procampo WCP

Household Size -.1004 (.04)** .4371 (.04)*** .0745 (.21)

Finished Primary School? -.3011 (.06)*** .2507 (.08)*** -.2205 (.29)

Finished College? -.9448 (.15)*** -.1600 (.18) 0 (omitted)

Finished Secondary School? -.7055 (.09)*** .2275 (.11)** 0 (omitted)

Finished High School? -1.000 (.14)*** .2227 (.16) 0 (omitted)

Lives in Rural Area 1.232 (.05)*** .9679 (.06)*** 0 (omitted)

Per Capita Income -0000 (.000)*** .000 (.000) -.000 (.00)

Household Assets -.000 (.000) .000 (.000) -.000 (.00)

Age of HH Head .0048 (.00)* .0071 (.00)** -.0036 (.01)

Number of people in HH age 0-15 .2813 (.05)*** -.4366 (.05)*** -.1484 (.23)

Number of people in HH age 16-21 .1477 (.05)*** -.3609 (.06) *** -.1305 (.30)

Number of people in HH age 22-27 .0528 (.06) -.3150 (.07)*** -.3746 (.37)

Number of people in HH age 28-35 .0780 (.05) -.2993 (.06)*** -.1552 (.28)

Number of peolpe in HH age 36-45 .1315 (.04)*** -.2204 (.05)*** -.2575 (.27)

Number of Females -.0238 (.02) -.0649 (.03)* .2023 (.16)

Per Capita HH Assets .000 (.000)* .000 (.000) -.000 (.00)

R2 0.3144 0.2808 0.0801

Standard errors in parentheses; * p< .10, ** P< .05, *** p< .01

17

Figure 1. Overlapping support in the distribution of the propensity score, homoskedasticity

assumed for the Food Expenditure variable within the treated and within the control

groups

a) Treatment effect: Participation in PROGRESA.

b) Treatment effect: Participation in PROCAMPO.

0 .2 .4 .6 .8Propensity Score

Untreated Treated: On support

Treated: Off support


Untreated Treated

18

c) Treatment effect: Participation in WCP.


Untreated Treated

19

Table 5. Difference in ex-ante variables, after matching

Variables Treatment Control Treat = Control Treatment Control Treat = Control Treatment Control Treat = Control

Household Size 5.168 5.20 4.252 4.40 5 5.33

Finished Primary School? .5989 .60 .6522 .53 *** .5 .83

Finished College? .0161 .02 .0181 .05 *** 0 0

Finished Secondary School? .1064 .10 .0954 .27 *** 0 0

Finished High School? .0204 .02 .0272 .06 * 0 0

Lives in Rural Area .8763 .88 .825 .61 *** 1 1

Per Capita Income 3726.2 3983.7 6615.7 10.466 1739 4697.8

Household Assets .0000 .00 .0000 .00 .0000 76133

Age of HH Head 27.91 27.37 41.00 31.92 *** 36.31 24.34

Number of people in HH age 0-15 2.414 2.42 1.147 1.71 *** 2 2.33

Number of people in HH age 16-21 .5602 .59 .5136 .45 .6666 .66


Number of people in HH age 28-35 .4989 .48 .3386 .52 *** .3333 .33

Number of peolpe in HH age 36-45 .6118 .60 .3795 .55 *** .3333 .5

Number of Females 2.672 2.67 2.106 2.20 3.166 3.33

Per Capita HH Assets .0000 .00 .0000 99507 5686 11453


Household Size 5.168 5.20 4.252 4.40 5 5.33








Age of HH Head 27.91 27.37 41.00 31.92 *** 36.31 24.34




Number of people in HH age 28-35 .4989 .48 .3386 .52 *** .3333 3.33





Household Size 51.688 5.20 4.252 4.40 5 5.33








Age of HH Head 27.91 27.37 41.00 31.92 *** 36.31 24.34




Number of people in HH age 28-35 .4989 .48 .3386 .52 *** .3333 .33






MEAT

EXP

FOOD



20

Table 6. Average impact of programs on expenditure.


Expenditure Treated Control Difference

Total 24376.68 36702.32 -12325.64 (4784.51)

Food 8768.38 11160.26 -2391.88 (944.47)***

Meat 3966.42 4492.51 -526.08 (521.38)


Total 29899.18 31155.032 -1255.85 (4060.36)

Food 11631.60 15051.28 -3419.67 (2900.91)

Meat 4352.03 4366.49 -14.457 (312.36)


Total 15711.82 22113.19 -6401.38 (6294.89)

Food 5622.43 7464.71 -1842.28 (2868.52)

Meat 2346.30 2998.05 -651.75 (1367.74)

WCP

Progresa

Procampo

gonzalezzaira_writingsample

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