poverty alleviation and consumption insurance: evidence from progresa in mexico

The Journal of Socio-Economics 36 (2007) 630–649

Poverty alleviation and consumption insurance:Evidence from PROGRESA in Mexico�

Emmanuel Skoufias ∗The World Bank, 1818 H Street NW, Washington, DC 20433, United States

Abstract

This study uses three rounds of panel data between October 1998 and November 1999 and covering 506villages and 24,000 households in rural Mexico, to examine how the replacement of pre-existing subsidyprograms by a conditional cash transfer program such as PROGRESA (the Health, Education and NutritionProgram) affects the consumption insurance of households.

The results obtained are consistent with the prevalence of formal or informal insurance arrangementsaimed at protecting household consumption from fluctuations in income. Yet, total consumption, as well asfood and nonfood consumption, are significantly correlated with idiosyncratic changes in income suggestingthat insurance is incomplete. A comparison of the results between villages covered and not yet covered byPROGRESA (treatment versus control villages) suggests that PROGRESA did not replace or reinforceany pre-existing risk sharing among households within villages or lead to any substantial changes in howhouseholds cope with shocks.

The analysis also revealed that households eligible for the PROGRESA benefits in the treatment villageswere able to insulate their consumption from fluctuations in income better than their counterparts in controlvillages. Thus, a poverty alleviation program providing cash transfers conditioned on households investingin their human capital is associated with a reduction of household vulnerability to risk.© 2007 Elsevier Inc. All rights reserved.

JEL classification: D1; R2; P5

Keywords: Credit; Consumption; Income; Insurance; Mexico; PROGRESA; Risk sharing; Risk coping; Shocks

1. Introduction and motivation

During the recent years there has been increasing recognition that poverty and the ability ofhouseholds to protect themselves from the prevalence of risk and its adverse consequences on

� The findings, interpretations, and conclusions expressed in this paper are entirely mine. They do not necessarily reflectthe views of the World Bank.

∗ Tel.: +1 202 458 7539; fax: +1 202 522 3134.E-mail address: [email protected].

1053-5357/$ – see front matter © 2007 Elsevier Inc. All rights reserved.doi:10.1016/j.socec.2006.12.020

mailto:[email protected]

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E. Skoufias / The Journal of Socio-Economics 36 (2007) 630–649 631

their welfare are intimately related. While a clear consensus has yet to emerge as to whethervulnerability to risk is a primary cause of poverty or the other way around (Alderman and Paxson,1992), there is a fairly wide agreement that social programs combining successfully povertyalleviation with risk reduction can be more effective at improving household welfare in the short-run as well as in the long-run (Holzmann and Jorgensen, 2000).

This paper contributes to this debate by examining the extent to which the consumptioninsurance of rural households is affected by the presence of a new poverty alleviation pro-gram in their communities. The program examined is the Health, Education and NutritionProgram (PROGRESA), one of the major poverty alleviation programs of the Mexican gov-ernment. Targeting its benefits directly to the population in extreme poverty in rural areas,the program aims to alleviate current poverty through monetary and in-kind benefits, aswell as reduce future levels of poverty by encouraging investments in education, health andnutrition.1

With a cash transfer close to 20% of pre-program consumption, the PROGRESA program hasthe potential of not only complementing but also displacing pre-existing risk-sharing arrange-ments among households. The social differentiation generated by the targeting of programs at thehousehold level within poor rural communities such as those covered by PROGRESA may leadto the weakening or even dissolution of pre-existing informal risk-sharing arrangements and thuslimiting the options available to households in the event of an economic shock (e.g., Attanasio andRios-Rull, 2000). In fact, the latter possibility is of central concern to policy makers concernedwith the crowding out effects of government insurance and income transfer programs (Cox andJimenez, 1990).

The empirical analysis uses panel data from households surveyed for the purposes of evaluat-ing the impact of PROGRESA on basic indicators of household investment in human capital. Adistinguishing feature of the PROGRESA data is that they are based on an experimental design,with randomization of the coverage of the program at the locality level. A considerable part ofthe empirical analysis consists of comparing whether there are any significant differences in thecorrelation of household consumption and income from villages covered by PROGRESA (treat-ment villages) and households from comparable villages that are out of the program (controls).The analysis of the control villages allows one to examine the extent of consumption smoothingthat takes place among rural households in Mexico in the absence (or prior to the introduction)of a specific poverty alleviation program. One of the requirements for participation in the PRO-GRESA program is that households benefiting from PROGRESA were to stop receiving benefitsfrom other pre-existing programs, such as a price subsidy on tortillas and milk, and educationalscholarships. Thus, analysis of the villages covered by PROGRESA reveals whether and how thereplacement of pre-existing poverty alleviation programs by a conditional cash transfer programsuch as PROGRESA affects household consumption smoothing. Given that the introduction ofPROGRESA may also result in fundamental changes in how household scope with economicshocks I also investigate how coping to shocks might differ as a consequence of the new program.Following an examination of the sensitivity of the results, the last section of the paper summarizesthe findings.

1 Skoufias (2005) provides a detailed discussion of PROGRESA, the evaluation design and the estimated impacts ofthe program. Closely related work on the impact of PROGRESA on risk sharing among households is contained in thedissertation of Garcia-Verdu (2002).

632 E. Skoufias / The Journal of Socio-Economics 36 (2007) 630–649

2. A brief description of PROGRESA and of the data used

PROGRESA, initially implemented by the Mexican Federal government in 1997, adopts anintegrated approach to combating the different causes of poverty by intervening simultaneouslyin the areas of health, education and nutrition. By the year 2004, the program which was renamedOportunidades, included nearly 5 million families in 72,345 localities in all 31 states. The totalannual budget of the program in 2004 was around US$ 2.5 billion, or 0.3% of the gross domesticproduct.

The education component of PROGRESA is designed to increase school enrollment amongyouth in Mexico’s poor rural communities by making education grants available to pupils’ moth-ers, who then are required to have their children attend school regularly. In localities wherePROGRESA currently operates, households that have been characterized as poor, and have chil-dren enrolled in grades 3–9, are eligible to receive these educational grants every 2 months. Thelevels of these grants were determined taking into account, among other factors, what a childwould earn in the labor force or contribute to family production. The educational grants areslightly higher at the secondary level for girls, given their propensity to drop out at earlier ages.Every 2 months, confirmation of whether children of beneficiary families attend school more than85% of the time is submitted to PROGRESA by school teachers and directors, and this triggersthe receipt of bi-monthly cash transfer for school attendance.

In the area of health and nutrition, PROGRESA brings basic attention to health issues andpromotes health care through free preventative interventions, such as nutritional supplements,and education on hygiene and nutrition as well as monetary transfers for the purchase of food.Receipt of monetary transfers and nutritional supplements are tied to mandatory health care visitsto public clinics. This aspect of the program emphasizes targeting its benefits to children underfive, and pregnant and lactating women, and is administered by the Ministry of Health and byIMSS-Solidaridad, a branch of the Mexican Social Security Institute, which provides benefits touninsured individuals in rural areas.

Nutritional supplements are given to children between the ages of 4 months and 2 years, andto pregnant and breast-feeding women. If signs of malnutrition are detected in children betweenthe ages of 2 and 5, nutritional supplements will also be administered. The nutritional status ofbeneficiaries is monitored by mandatory visits to the clinic and is more frequently monitored forchildren 5 years of age and under, pregnant women and lactating women. Upon each visit, youngchildren and lactating women are measured for wasting (weight-for-height), stunting (height-for-age), and weight-for-age. An appointment monitoring system is set up and a nurse or doctor verifiesadherence. The health care professionals submit every 2 months certification of beneficiary visitsto PROGRESA, which triggers the receipt of the cash transfer for food support.

The average monthly payment (received every 2 months) by a beneficiary family amounts to20% of the value of monthly consumption expenditures prior to the initiation of the program.2

One additional requirement of the PROGRESA program is that households benefiting fromPROGRESA were to stop receiving benefits from other pre-existing programs such as Ninosde Solidaridad, Abasto Social de Leche, de Tortilla and the National Institute of Indigenous peo-

2 The average monthly transfers during the 12-month period from November 1998 to October 1999 are around 197pesos per beneficiary household per month (expressed in November 1998 pesos). The calculation of this average includeshouseholds that did not receive any benefits due to nonadherence to the conditions of the program, or delays in theverification of the requirements of the program or in the delivery of the monetary benefits. On average, households receive99 pesos for food support (alimento), and 91 pesos for the educational grant (beca).


ple (INI).3 This requirement of the PROGRESA program represents the short-run objective of thenew poverty alleviation strategy of the Mexican government to minimize duplication of benefitsto poor families. A longer run objective is to absorb the variety of poverty alleviation programswithin one program such as PROGRESA that represents an integrated approach to poverty alle-viation. In fact, Skoufias (2005) confirms that the incidence of benefits received from DIF, Ninosde Solidaridad and Abasto Social de Leche, decreased dramatically in the localities covered byPROGRESA after families started receiving PROGRESA benefits.

The data used in this paper consists of the sample of communities and households surveyedbetween October 1997 and November 1999, for the purposes of the evaluation of the PROGRESAprogram. In order to obtain a credible evaluation of the potential impact of the program the PRO-GRESA administration decided to adopt an experimental design that allows one to comparehouseholds before and after and the initiation of PROGRESA with similar households that werenot yet covered by the program. Specifically, the full sample used in the evaluation of PROGRESAconsists of repeated observations (panel data) collected for 24,000 households from 506 localitiesin the seven states of Guerrero, Hidalgo, Michoacan, Puebla, Queretaro, San Luis Potosi andVeracruz. Of the 506 localities, 320 localities were assigned to the treatment group (where PRO-GRESA was in operation) and 186 localities were assigned as controls. As originally planned thelocalities serving the role of a control group started receiving PROGRESA benefits by December1999.

In November 1997 PROGRESA conducted a census of the households (Encuesta de Carac-teristicas Socioeconomicas de los Hogares or ENCASEH) in the 506 evaluation communitiesto determine which households would be eligible for benefits. Using PROGRESA’s beneficiaryselection methods (see Skoufias et al., 2001) in the localities pre-assigned to the treatment groupall eligible households are offered the opportunity to be formally incorporated into PROGRESA.On average in the evaluation sample, 78% of the households were classified as eligible for programbenefits. However, the fraction of households that actually ended up receiving the PROGRESAcash transfers during the 2-year interval covered by the evaluation sample is just under 65%, dueto administrative errors and delays in the final registration of beneficiary households (for moredetails see Skoufias, 2005).

The initial household census, was followed by a number of socio-economic household surveys(Encuesta de Evaluacion de los Hogares or ENCEL) designed to collect information for the eval-uation of PROGRESA The first evaluation survey took place in March 1998 before the initiationof benefits in May 1998. The remaining surveys were conducted after beneficiary householdsin treatment villages started receiving benefits from PROGRESA. One round of surveys tookplace in October 1998, which was well after most households received some benefits as part oftheir participation in the program. The next two waves took place in June 1999 and November1999. A number of core questions about the demographic composition of households and theirsocio-economic status were applied in each round of the survey.

Data used in this paper are drawn from the November 1998 (ENCEL98O), June 1999(ENCEL99J), and November 1999 (ENCEL99N) surveys where comparable consumption andincome information across rounds was collected. Unfortunately, there were a number of flaws

3 Before the establishment of PROGRESA, previous government interventions in the areas of education, health andnutrition in the rural sector of the country consisted of many programs each intervening separately in health, education ornutrition with little prior coordination or consideration of the potential synergies that could result from a better coordinatedand simultaneous intervention.


with the design of the module on food consumption used in the March 1998 survey that made itunusable for the purposes of this investigation.

All of the variables used in all of the regressions are at the household level. The main vari-ables used include the value of total consumption, the value of food consumed, the value ofnonfood expenditures, and income, and variables characterizing the coping strategies of house-holds to income shocks. The construction of each of these variables is discussed in more detailnext.

The value of food consumed (purchased + self produced) consists of the sum of the value ofconsumption on fruits and vegetables, cereals and grains, meats and animal products and otherfoods, as well as the value of food eaten out of the household. Expenditures on durables and otherluxury items are excluded from the calculation of nonfood expenditures. As with food, the value oftotal nonfood consumption (purchased and home-produced) is expressed in October 1998 pricesby dividing by the national consumer price index.4

Household income per month is constructed by summing reported income from work, selfemployment, pension, interest, rents and community profits, government transfers (Ninos de Sol-idaridad, INI, PROBECAT, Empleo Temporal, PROCAMPO), credit, crop sales, sales of animalsand sales of dairy products.5 Income transfers and remittances received from neighbors, friendsand relatives, are excluded from total income, as these sources of income are likely to reflectex-post adjustments to shocks.6 For beneficiary households receiving PROGRESA benefits thehousehold income measure includes the average PROGRESA cash transfer received by the house-hold per month over a 5–7-month interval.7 The latter amount was obtained for the administrativedata files of PROGRESA that contained a record of the receipts of payments by each beneficiaryhousehold in all of the localities covered by the program.

The variables used to identify the various shocks experienced by households are obtained fromdirect questions asked of the main respondent in the household questionnaire. In each of the threesurvey rounds the household was asked whether during the last 6 months it had experienced adrought, flood, freeze, fire, plague, and hurricane and whether as a consequence of these naturalshocks, the household lost land, harvests, animals, its home, or other items. In the November1999 round (round 5), households were asked, in addition, how they responded to these shocks.The list of alternative responses included, selling land, selling animals, selling household items,receiving help (money) from the government, borrowing money, getting additional work, andreceiving help from family members.

Table 1 provides the means and standard deviations of the main variables used in the analysisfor all three rounds of the survey for the sample of households in the control villages and in thePROGRESA treatment villages of the evaluation sample.

4 For more details on the construction of the consumption related variables see the report by Hoddinott et al. (2000).5 Since the question related to income covered a wide variety of reference in intervals (from last month to last year)

care was taken to express household income into a monthly basis and to deflate it into October 1998 prices.6 I have also tried excluding all government transfers from household income but this did not lead to any noticeable

changes in the results report here.7 Beneficiary households in the treatment localities begun receiving PROGRESA benefits in May 1998 with payments

made approximately very 2 months since then. The average monthly benefit received by beneficiary households is 124pesos in round 3 (October 1998), 189 in round 4 (June 1999), and 240 in round 5 (November 1999). The amounts havebeen deflated to October 1998 levels. The average benefit appears to increase because initially there were delays indistributing the cash transfers. With higher payments over the later months PROGRESA eventually caught up with theoriginal payment plan.


Table 1Means and standard deviations of main variables

Control villages(nobs = 23,321)

PROGRESA-treatmentvillages (nobs = 36,113)

Mean S.D. Mean S.D.

Income p.c. (incl PROGRESA transfers) 255 1378 256 1038Consumption p.c. 209 523 228 1245Food consumption p.c. 157 515 174 1241Nonfood consumption p.c. 52 64 54 66Lost any land? 0.11 0.31 0.09 0.29Lost harvest? 0.35 0.48 0.32 0.47Lost animals? 0.03 0.17 0.03 0.17Lost home? 0.01 0.09 0.01 0.09Lost other items? 0.00 0.06 0.01 0.07Household size 6 3 6 2Household head is female 0.10 0.30 0.10 0.30Age of household head (years) 47 16 47 16Head’s education: <primary 0.35 0.48 0.35 0.48Head’s education: =primary 0.59 0.49 0.58 0.49Head is indigenous 0.33 0.47 0.33 0.47Head belongs in ejido 0.09 0.29 0.09 0.28Head is self-employed 0.12 0.32 0.13 0.34Eligible/receiving PROGRESA 0.79 0.41 0.79 0.41

Questions asked on round 5 onlySold animals? 0.017 0.13 0.019 0.14Sold land? 0.004 0.06 0.002 0.05Sold other items 0.002 0.05 0.002 0.04Borrowed money? 0.030 0.17 0.026 0.16Received help from government 0.012 0.11 0.021 0.14Worked more? 0.095 0.29 0.094 0.29Received help from family? 0.027 0.16 0.021 0.14

Notes: Averages based on three rounds (October/November 1998, June 1999 and November 1999). Consumption andincome deflated to October 1998 price level.

3. Empirical analysis

The theoretical model underlying much of the empirical analysis is based on the consumer’soptimization problem in the context of a complete market for state contingent commodities (e.g.,see Deaton, 1992). The assumption of a complete market for state contingent commodities maybe considered as a simple approximation to all the formal and informal arrangements across spaceand over time that households can enter into in order to protect themselves from risk.8

One of the key predictions of this model is that the growth rate of household consumptionwithin an insurance community between time t − 1 and t will be a function only of the growth ratein aggregate shocks affecting the community. Specifically an equation that is more commonlyencountered in the literature (e.g., Cochrane, 1991; Mace, 1991; Townsend, 1994, 1995) is of

8 More recent developments in this literature, working with the weaker assumption of limited commitment, includeLigon (1998), Ligon et al. (2002), and Albarran and Attanasio (2003).


the form

�ln chtv = ατt + β�ln yhtv + φXhvt + �εhtvt (1)

where �ln chtv denotes the change in log consumption or the growth rate in total consumptionper capita of household h, in period t (i.e., between round t and round t − 1), in community v,�ln yhtv the growth rate of income, X a vector of household or household head’s characteristics, α,β, and φ parameters to be estimated, �εhtvt a household-specific error term capturing changes inthe unobservable components of household preferences, and τt denotes the aggregate or covariateshock for the insurance group inperiod t.

Whatever the actual combination of strategies used by households in order to cope with risk,the extent to which households manage to insure their consumption from shocks that affecttheir income or their income potential can be ascertained by the parameter β which provides anestimate of the extent to which idiosyncratic income changes play a significant role in explainingthe household-specific consumption growth rate.9 Much of the focus of the empirical literatureon risk sharing in developing and developed countries alike has focused on testing the predictionderived under complete risk sharing which states that β = 0 (e.g., see Townsend, 1994; Mace,1991; Jacoby and Skoufias, 1998). Although frequently complete risk sharing is rejected, it istypically observed that the estimated values of β are generally low (or close to zero) whichimplies that the growth rate of consumption is related to the (contemporaneous) growth rate ofincome, but certainly less so than what one would expect under an alternative hypothesis (e.g.,β = 1) as implied by complete autarky and the complete lack of any risk sharing tools. Thesefindings provide strong indications that households engage in risk management strategies aimedat insulating, at least partially, consumption changes from income changes. As in Amin et al.(2003), the measure of consumption insurance adopted here takes this idea to the next logicalstep by interpreting lower (higher) estimated values of β as signifying a higher (lower) degree ofconsumption insurance and thus a lower (higher) vulnerability of consumption to income risk.

For the purposes of the empirical analysis, an insurance group is defined to be the full setof households within a locality. On average there are 50 households in a locality. Generally,insurance arrangements are easier to organize and enforce in small or closely knit communitiesthan in larger groups, where the moral hazard, incentive, enforcement and information difficultiesare more severe. In the terms of Eq. (1) the covariate shock τt is replaced by a set binary variablesD identifying each community separately by survey round (round and community interactionterms), i.e.

ατt =∑

tv

atvDtv

Including the community/round interaction dummies is equivalent to deviating all variables fromtheir respective community/round mean (Ravallion and Chaudhuri, 1997).10

The identification of an insurance group with a village implicitly assumes that rural villagesin Mexico are completely isolated and that financial and insurance markets pooling risks across

9 As Alderman and Paxson (1992) argue, empirical evidence that idiosyncratic income changes do not have a significanteffect on household consumption growth may also be consistent with the permanent income hypothesis, especially ifaggregate shocks to income are largely permanent and idiosyncratic shocks are transitory. For two studies distinguishingbetween the permanent and transitory nature of shocks see Jacoby and Skoufias (1998).10 Note that since consumption and income are in logarithms, they also account for potential differences in the round to

round inflation rate across communities.


space and time are not functioning at all. In Mexico, in particular, where migration of familymembers from the village to other towns or even neighboring countries (such as the US) is a longestablished risk management practice, this may be too restrictive. For this purpose and as a testof the sensitivity of the results, I also replicate the analysis using as an insurance group the set ofhouseholds in the whole sample. In this case, the covariate shock τt is identified by one dummyvariable identifying the fifth round of the survey.

3.1. Consumption and household income

One way of testing the hypothesis of complete risk sharing is to examine whether the growthrate of household food consumption is independent of the growth rate in household income (aftercontrolling for aggregate or uninsured shocks). This test implicitly assumes that shocks impacton household consumption indirectly only through their effect on household income. For thispurpose I estimate a slightly different version of Eq. (1) such as

�ln chtv = ατt + β�ln yhtv + PROv(γP + βP�ln yhtv) + φXhtv + �εhtv (2)

where PROv is a binary variable taking the value of 1 for households in PROGRESA/treatmentvillages and 0 for households in control villages. It is important to keep in mind that the binaryvariable PROv identifies the villages covered by PROGRESA and not just the households receivingbenefits from PROGRESA. As mentioned earlier, about 22% of the households in the villagescovered by PROGRESA were determined to be ineligible for the program’s benefits.11

In this specification, which is identical to the “cross-sectional difference estimator” used in theprogram evaluation literature (e.g., see Heckman et al., 1999), the coefficient βP is the differencein the vulnerability to risk between households in the PROGRESA villages and households in thecontrol villages. A negative estimate of βP implies that PROGRESA is associated with a decreasedvulnerability to risk in the treatment villages. In other words, β provides an estimate of the partialcorrelation between income and consumption growth in the control villages, while the sum β + βPprovides an estimate of the partial correlation between income and consumption growth in thePROGRESA villages.12 Assuming the more plausible case that there is incomplete risk sharing inthe control villages, i.e., the estimate of β is positive and significant, then a significantly negativeestimate of βP would suggest that the PROGRESA program reduces the risks faced by householdsin the PROGRESA villages. In the extreme case that PROGRESA is successful at completelyeliminating vulnerability to risk among households in the villages covered by the program (thetreatment villages), then β + βP = 0 or βP = −β.

The OLS estimates of Eq. (2) for total consumption, and separately for food and nonfoodconsumption are presented in Table 2. The estimates presented in panel (a) assume that theinsurance group consists of all the households in a village, whereas those in panel (b) use asinsurance group the set of all households in the sample. The significant coefficients of β in panel(a) reveal that neither total consumption nor its two main two components (i.e., food and nonfoodconsumption) are completely insured from income shocks. For example, in the control villages,a 10% drop in real income is accompanied by a 0.37% drop in household total consumption,a slightly lower (0.28%) decrease in food consumption and a higher (0.62%) drop in nonfood

11 In the latter part of the paper, I also compare the extent of risk sharing among eligible households in the treat-ment/PROGRESA villages relative to eligible households in the control villages.12 Note that complete risk sharing among households in the control villages implies that β = 0.


Table 2The impact of idiosyncratic changes in the log of household income on consumption: OLS estimates

Households in Dependent variable is round to round change in

ln(total cons) ln(food cons) ln(nonfood exp)

Coefficient t-Value Coefficient t-Value Coefficient t-Value

(a) Insurance group: all households in the villageControl villages (β in Eq. (2)) 0.037* 7.3 0.028* 5.54 0.062* 6.89PROGRESA villages (βP in Eq. (2)) −0.006 −1.01 −0.004 −0.63 −0.008 −0.76

(b) Insurance group: all households in the sampleControl villages (β in Eq. (2)) 0.040* 5.59 0.034* 5.13 0.061* 4.59PROGRESA villages (βP in Eq. (2)) −0.007 −0.81 −0.003 −0.41 −0.012 −0.76

Notes: Additional regressors included but not reported—a constant term, family size in round t and t − 1, the age of thehead, whether the head is a female, and set of community and round interaction dummy variables. The t-values reportedare based on Huber–White standard errors.

* The coefficient has a p-value ≤ 0.10.

expenditures. The relatively higher income coefficients for nonfood than for food consumptionsuggest that the consumption of food may be better insured than the consumption of nonfood.However, the differences in the income coefficients for food and nonfood might also be explainedin terms of underlying household preferences. Ceteris paribus, an increase in household incomewill increase the quantity demanded for luxury goods (nonfood) more than for necessities (suchas food). As it is shown below, there are fairly strong indications that the differences in the incomecoefficients can be reasonably well attributed to lack in insurance rather than preferences.

The insignificant coefficients of the interaction of income changes with the dummy variableidentifying the villages where PROGRESA operates (i.e., the βP coefficients) suggest that thereare no significant differences in the level of consumption insurance between control villages andtreatment villages after the initiation of PROGRESA.13 However, it should be noted that thisfinding does not necessarily imply that PROGRESA has no impact on the level of consumptioninsurance in treatment villages. For example, it is possible that the apparent absence in any post-program differences in the level of insurance among households in the PROGRESA villages is thedirect result of PROGRESA, particularly if consumption was less insured in the treatment villagesrelative to the control villages prior to the implementation of the PROGRESA program. Unfor-tunately, the absence of any reliable consumption data in treatment and control villages beforethe implementation of PROGRESA, prevent one from applying the “difference-in-differences”estimator for the evaluation of the impact of PROGRESA on consumption insurance. However, acareful and detailed investigation of the extent to which the selection of localities into treatmentand control groups can be considered as random did not reveal any significant differences betweenvillage means for more than 300 variables in treatment and controls (Behrman and Todd, 1999).14

These findings suggest that for all practical purposes differences in the level of consumption

13 This also agrees with the result of Garcia-Verdu (2002) although he did not test for statistically significant differencesbetween treatment ad control villages.14 Randomized assignment to treatment implies that the distribution of all the variables for treatments and controls should

be equal prior to the administration of the program. To check whether randomization has been successfully implemented,Behrman and Todd compared the treatment and control samples in two key dimensions. First, by comparing the meansof key variables transformed into locality means in control and treatment localities. Second, by comparing the meansof the same variables with household level data. When these comparisons and tests were performed at the locality level


Table 3Evidence on partial risk insurance; the effect of mean community/round income—insurance group: all households in thevillage


ln(total cons) ln(food cons) ln(nonfood cons)


Control villages (γ in Eq. (3)) 0.077* 1.73 0.117* 2.41 −0.048* −0.65PROGRESA villages (γP in Eq. (3)) −0.018 −0.35 −0.005 −0.10 −0.041 −0.49

Notes: Additional regressors included but not reported—see text for details. The t-values reported are based onHuber–White standard errors.


insurance between control and treatment villages prior to the implementation of PROGRESAwere highly unlikely. Then this also implies that the coefficients obtained from the specificationof Eq. (2) are reliable estimates of the “impact” of the program on consumption smoothing.

The estimates in panel (b) using as insurance group the full sample of households did not yieldany significant changes in the main results obtained from panel (a). In fact the point estimates of thecoefficients of income changes turned out to be very close to the estimates in panel (a). Based onthese results the rest of the analysis is conducted by using the village as the insurance community.

3.2. Partial consumption insurance

In order to investigate whether partial insurance and risk sharing is in fact taking place amonghouseholds within the same insurance community I also estimate an alternative version of Eq.(2), as suggested by Deaton (1997) and Ravallion and Chaudhuri (1997). The equation

�ln chtv = α + β�ln yhtv + γ�(ln yvt)

+ PROv(αP + βP�ln yhtv + γP�(ln yvt)) + φXhvt + �εhvt (3)

allows the growth rate in household consumption to be determined by the growth rate in householdincome as well as the growth rate in average community income denoted by �(ln yvt). In a purelyautarkic world, the growth rate in the average community income should have no impact onthe growth rate of consumption of any one household. Evidence that the growth rate in averagecommunity income has a significant role in the growth rate of household consumption (i.e.,γ �= 0) is consistent with the hypothesis that some risk sharing is taking place within the controlcommunities.

The estimated coefficients of the growth rate in average community income (i.e., of the param-eters γ and γP) are reported in Table 3. The estimates provide evidence in favor of partialinsurance and community risk sharing in total consumption. Also, no significant differences

(i.e., comparing locality means of age, education, income, access to health care, etc.) the hypothesis that the means areequal between treatment and control localities is not rejected. Performing the same comparison using household leveldata, it was found that the null hypothesis was rejected more frequently than would be expected by chance given standardsignificance levels. While this rejection of random assignment into control and treatment is somewhat alarming, theresearchers interpreted it as being due to the fact that the samples are large which means that even minor differences couldlead to rejection.


are found regarding the effect of mean village growth rate between households in control andPROGRESA/treatment villages. Interestingly, the investigation of whether partial insurance is ineffect for both food and nonfood consumption suggests that partial risk sharing takes place onlyfor food consumption. Changes in the growth rate of average community income seem to havea positive and significant role in the growth rate of food consumption of individual householdssuggesting that there is risk sharing in food consumption within communities but no risk sharingin nonfood expenditures. This finding also favors the interpretation of the relatively higher incomecoefficients obtained for nonfood compared to food in Table 2, in terms of insurance rather thanjust preferences.

3.3. Differences in household vulnerability by observable characteristics

The analysis so far has investigated whether risk sharing is prevalent among rural Mexicanhouseholds in control and treatment communities overall. For example, Table 4 provides estimatesof the overall vulnerability to risk by pooling together different types of households within avillage. While informative about the overall level of risk sharing and vulnerability to risk at thevillage level, such estimates may mask substantial differences in the vulnerability to risk amonghouseholds with certain characteristics (or even from household to household). Along similarlines, the potential effect of PROGRESA on reducing vulnerability to risk may be higher or lowerfor specific types of households. In an effort to examine this issue, I re-estimate a variant of Eq. (2)on the sub-sample of households with the same characteristic. For example, consider the subsetof households with a specific characteristic Z (e.g., Z = household head speaks an indigenouslanguage, or household head has low level of education, etc.). Limiting the sample to the subsetof households with the same Z characteristic, I estimate a regression, such as

�ln chtv = ατt + β�ln yhtv + Tv(γP + βP�ln yhtv) + φXhtv + �εhtv (4)

where Tv is a binary variable identifying whether the household resides in a treatment village(i.e., a village covered by PROGRESA). In this specification the sign and size of the parameter β

identifies the covariance between income and consumption changes in the group of householdswith characteristic Z = 1 residing in the control villages while the parameter βP dentifies whetherhouseholds with the same characteristic Z in the treatment villages are more or less insured thancomparable households in the control villages.

Table 4 presents the estimates of the parameters β and βP obtained from households in controland PROGRESA treatment villages using the village as an insurance group.15 The characteristicsidentified by the variable Z include whether the household head is a female, or older than 50 yearsof age, or with less than primary schooling, or indigenous, or self-employed. Additional householdcharacteristics identified by Z include whether the household has a low level of assets (less thanthe median value of the asset index constructed using pre-program household assets), whetherthe household resides in “marginal” locality (as identified by the statistical methods employed bythe PROGRESA administration for the targeting of the program in poor localities, Skoufias et al.,2001), the poverty status of the household,16 and the eligibility status of the household for theconditional benefits of the PROGRESA program.

15 All of the findings reported below remained unchanged, at least qualitatively, when the insurance group was assumedto be the full set of households in the sample.16 I have classified a household as poor if its per capita consumption in any round 3 is less than or equal to the 50th

percentile of per capita consumption in round 3 (October/November 1998).

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Table 4The effect of idiosyncratic income shocks on consumption by characteristics of the household or the household head—insurance group: all households in the village

Households in control villages (β in Eq. (4)) Households in PROGRESA/treatment villages (βP in Eq. (4))

(1) ln(total cons) (2) ln(food cons) (3) ln(nonfood cons) (4) ln(total cons) (5) ln(food cons) (6) ln(nonfood cons)

Coefficient t-Value Coefficient t-Value Coefficient t-Value Coefficient t-Value Coefficient t-Value Coefficient t-Value

Hh with head who is afemale

0.036* 1.82 0.035* 1.75 0.059* 1.72 −0.006 −0.23 −0.009 −0.37 −0.011 −0.25

Hh with head who is old(>50 years old)

0.033* 4.18 0.023* 3.00 0.055* 4.00 −0.004 −0.38 0.003 0.26 −0.013 −0.73

Hh w/less than primaryeducation (head)

0.059* 6.87 0.049* 5.49 0.092* 5.51 −0.022* −2.02 −0.017 −1.55 −0.038* −1.85

Hh is indigenous (head) 0.047* 5.08 0.036* 3.75 0.090* 5.60 −0.011 −0.91 −0.011 −0.91 −0.017 −0.85Hh w/self-employed head 0.049* 3.16 0.022 1.36 0.122* 4.27 −0.033 −1.59 −0.003 −0.16 −0.095* −2.74Hh with low assets 0.053* 5.95 0.047* 5.11 0.082* 5.19 −0.013 −1.21 −0.014 −1.24 −0.008 −0.43Hh in marginal/poor

localities0.045* 7.08 0.039* 5.84 0.067* 6.07 −0.006 −0.78 −0.010 −1.18 0.002 0.13

Hh is poor 0.037* 5.75 0.028* 4.09 0.077* 6.05 0.003 0.41 0.005 0.53 −0.009 −0.58Hh is eligible for

PROGRESA0.040* 6.63 0.032* 5.09 0.069* 6.58 −0.014* −1.83 −0.011 −1.42 −0.020 −1.52

Hh is not eligible forPROGRESA

0.018* 1.83 0.009 0.94 0.033* 1.73 0.024* 1.79 0.026* 1.95 0.030 1.27

Notes: Additional regressors included but not reported—a constant term, family size in round t and t − 1, the age of the head, whether the head is a female, and set of communityand round interaction dummy variables. The t-values reported are based on robust standard errors.



Columns 1–3 of Table 4 provide a number of interesting findings particularly with respect tohow certain household characteristics correlate with consumption insurance in the control villages.For example, comparing the coefficient estimates in column of Table 4 households in the controlvillages with a head that has a lower level of education (less than primary level) appear to be morevulnerable to risk. The correlation between income and consumption changes for this group ofhouseholds in the control communities is 0.059, the highest among all other groups. Householdswith low assets are the second group most vulnerable to risk with a coefficient of 0.053, followedby households where the head is self-employed (0.049) and indigenous households (0.047). Thegroup of household with the lowest estimated correlation between consumption and income andincome changes are the households that are classified as noneligible for the PROGRESA programin the control villages, with an estimate of β = 0.018. Note that, in the control villages, householdsthat would be eligible for PROGRESA benefits, had PROGRESA covered these villages, do notappear to be particularly more or less vulnerable than the rest of the households in the controlsample as they have an estimate of β = 0.040.

A comparison of columns 1–3 and 4–6 of Table 4 also yields interesting findings on the poten-tial impact of the PROGRESA program towards reducing household vulnerability to risk. In themajority of cases, the coefficients of the household characteristics in the PROGRESA/treatmentvillages are negative, suggesting that the presence of the PROGRESA program enhances theinsurance possibilities relative to control villages. However, it is only in few cases that these dif-ferences are statistically significant. In the PROGRESA/treatment villages, significant reductionsin vulnerability to risk (i.e., significantly negative estimates of βP) are observed for only two char-acteristics: households with a head that is self-employed and households receiving PROGRESAbenefits.17 Thus, even though the village level estimates of Table 2 suggest that the presence ofPROGRESA does not have an impact on the extent of vulnerability to risk among households inthe treated villages, Table 4 suggests that there is heterogeneity in how the program affected thevulnerability to risk of different groups. Further evidence of this is provided by the estimates ofthe impact of the program on households that are noneligible for the PROGRESA benefits. As theestimates in the last raw of column 4 of Table 4 reveal, the program appears to be associated witha significantly higher vulnerability to risk among noneligible households in the villages coveredby PROGRESA.

3.4. Shocks, risk coping strategies and the role of PROGRESA

The analysis so far has implicitly assumed that a low covariance between household con-sumption and income implies a fairly effective sharing of risks among households. However, itis important to point out that a low covariance between consumption and income may be theconsequence of costly self-insurance strategies instead of risk sharing among households withina community. Households, for example, may be able to smooth their consumption through self-insurance strategies that deplete their assets, such as selling their livestock (Rosenzweig andWolpin, 1993), adjusting their labor supply (Kochar, 1998), and curtailing investments in theirhuman capital, such as withdrawing their children from school when there are shortfalls in income(Jacoby and Skoufias, 1997). In addition, households may avoid taking risky but profitable oppor-

17 I use the terms “being eligible for” and “receiving” PROGRESA benefits interchangeably here. In fact, the regressionis based on the subset of households classified as eligible for PROGRESA benefits by the program’s beneficiary selectionmethod (see Skoufias et al., 2001).


tunities and practice income smoothing as a substitute for consumption smoothing (Morduch,1994).

As a consequence of these self-insurance actions, households may appear to be more insuredand less vulnerable to risk, when in fact their vulnerability to future poverty may be increasing.Clearly, it is critical to have knowledge on the extent to which households use potentially costlyself-insurance strategies and whether coverage by a conditional cash transfer program such asPROGRESA alters the ways in which households cope with shocks. The empirical approach usedto analyze household responses is similar in spirit to that used for consumption. I construct anumber of binary variables, signifying yes or no responses to questions or actions, and examinewhether the incidence of these shocks is associated with increased likelihood of these actions.Ideally one would like to control for the potential role of unobserved household heterogeneity indetermining how households respond to shocks. Unfortunately this possibility is eliminated bythe fact that information on how households might respond to these shocks is collected in onlyone round (November 1999) of the survey.

Mindful of the limitations associated with using cross-sectional data, I estimate a number ofprobit models of the form

Prob(Yi = 1) = F (α + βSi + PRO(αP + βPSi) + φXt) (5)

where F is the cumulative normal distribution and the variable Y is used to denote any one of thefollowing responses: (i) the household sold animals; (ii) other household items including land;(iii) borrowed money; (iv) received help from the government; (v) household member workedmore; and (vi) received help from family members. S is a vector of dummy variables denotingthe incidence of any of the following shocks: (i) the loss of land; (ii) the loss of harvest; (iii) theloss of animals; and (iv) the loss of its home or other household items. X is a vector of other timevarying household characteristics such as the age and gender composition of the household, theage of the household head, the age of the household head, whether the household is headed by afemale, the education level of the household head, binary variables for the type of occupation ofthe head, an index summarizing the asset holdings of the household, the eligibility status of thehousehold for PROGRESA benefits, and binary variables describing whether other governmentprograms operate in the locality (such as DIF, LICONSA, PROBECAT, Tortilla Solidaridad,Empleo Temporal, Educational Scholarships).18

As with the earlier regressions, the focus of my investigation is on whether the incidence of ashock increases the likelihood that the dependent variable Y equals 1 in the control villages andthe extent to which the incidence of the same shock entails a stronger or opposing reaction in thevillages covered by PROGRESA (summarized by the coefficients βP).

The estimated marginal effects of the various shock variables on the probability of adopting aspecific response are reported in Table 5 separately for households in the control villages (panel a)and in the PROGRESA/treatment villages (panel b).19 Overall the results for households in controlvillages reveal that there is no single strategy that is used most frequently by households. Harvestloss, for example, appears to trigger multiple household responses including the selling of animals,borrowing and receiving help from government and relatives. Moreover, more frequently than not,

18 The asset index was constructed by using the method of principal components on all pre-program (collected by the1997 ENCASEH) assets owned by households.19 All shock dummy variables were included simultaneously in the probit regression. Estimation using random effect (at

the village level) probit did not lead to any substantive change in the results obtained using simple probit.

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Table 5Household responses to shocks

Sold animals? Sold other/land? Borrowed? Received helpfrom government?

Worked more? Received helpfrom family?

Coefficient Z-value Coefficient Z-value Coefficient Z-value Coefficient Z-value Coefficient Z-value Coefficient Z-value

(a) Households in control villages (β in Eq. (5))Lost land? 0.013* 4.29 0.002* 2.13 0.003 1.12 0.003 0.93 0.059* 6.20 0.008* 2.24Lost harvest? 0.055* 11.18 0.022* 5.15 0.096* 15.97 0.032* 9.35 0.287* 30.19 0.083* 16.17Lost animals? 0.058* 6.56 0.003* 2.25 0.020* 2.78 0.001 0.25 −0.007 −0.72 −0.004 −1.03Lost other/home? 0.004 0.83 0.010* 3.06 0.134* 7.85 −0.001 −0.24 0.206* 7.54 0.028* 2.81

(b) Households in PROGRESA/treatment villages (βP in Eq. (5))Lost land? 0.001 0.49 0.000 −0.04 0.003 0.64 0.000 0.03 −0.013* −1.84 0.001 0.31Lost harvest? −0.001 −0.64 −0.001 −1.61 −0.002 −1.03 0.001 0.36 0.015* 2.47 −0.003 −1.18Lost animals? −0.004* −4.65 −0.001 −1.38 −0.007* −2.90 0.017 1.47 −0.017 −1.62 0.030* 2.30Lost other/home? 0.014 1.70 0.000 −0.87 0.001 0.13 0.130* 3.59 −0.019* −2.11 0.000 0.02

Notes: All shock variables are included at the same time in the regression. Additional regressors included but not reported: a constant term, variables describing the age andgender composition of the household in each round, the age of the household head whether the household is headed by a female, the education level of the household head, binaryvariables for the type of occupation of the head, an index summarizing the asset holdings of the household, the eligibility status of the household for PROGRESA benefits, andbinary variables describing whether other government programs operate in the locality (DIF, LICONSA, PROBECAT, Tortilla Solidaridad, Empleo Temporal, Educ. Scholarship)prior to October 1997. All coefficients reported are in terms of marginal effects on the probability of the respective outcome (dF/dx).



there does not appear to be any significant differences in how households in PROGRESA villagesrespond to these shocks. The only notable difference is that households in PROGRESA villagesseem to respond differently than households in control villages when there is shock leading tothe loss of animals. Relative to households in control villages, they are less likely to respond byselling animals or borrowing, or working more, and more likely to receive help from relatives.Also, the loss of other household items or the loss of a home is more likely to result in receivinghelp from the government. There also indications that the presence of the PROGRESA programinduces households to use adjustments in their labor supply less frequently than households incontrol villages for coping with the incidence of some shocks.

In sum, it seems that households do not rely exclusively on risk-sharing arrangements nor self-insurance strategies such as adjustments in labor supply, selling of animals and assets. Rather,households appear to complement informal risk sharing strategies with self-insurance strategies.

3.5. Examining the sensitivity of the findings

One potential shortcoming of the OLS estimates presented and discussed so far is that they maybe biased due to measurement error in the income variable and imputation errors in the calculationof the food consumption of households. By itself, measurement error in the income variable givesrise to “attenuation bias” that biases coefficients towards zero. Given that the income coefficientsare significantly different from zero in the majority of cases one can be reasonably confidentthat the hypothesis of complete insurance is justifiably rejected and that the significant incomecoefficients in Table 2 provide a lower bound estimate of the true elasticity of consumption toidiosyncratic income.

However, it is possible that imputation errors in the construction of the food consumptionvariable may bias the income coefficients upwards (Deaton, 1997). This is especially the casefor households in rural areas. For many of these households a significant share of income andconsumption is accounted by food that is produced and consumed by the household and neithersold nor bought in the market. As mentioned earlier, for the food produced at home a value isimputed using local prices for the specific food item produced. Errors in this imputation proceduremay be positively correlated with measurement errors in the income variable, and for positivecoefficients, this upward bias may work in the opposite direction to the standard downwardattenuation bias produced by the measurement errors in the income variable alone (Deaton, 1997).Given that the net effect cannot be signed in advance it is prudent to make an effort to control forthese sources of bias in the estimates.

Table 6 presents the income coefficient estimates using instrumental variables for the changesin household income. The list of instruments used includes the shock variables discussed earlieridentifying whether the household lost land, or its crop harvest; or animals or its home and otherhousehold items. In addition, the list of variables used in the first stage regressions included theset of binary variables summarizing community/round effects, whether the household head is afemale, the age of the household head, and the change in the number of family members betweenrounds t and t − 1.20

Unfortunately, the instrumental variable (IV) estimates presented in Table 6 did not yieldany significant estimates. One likely explanation for the lack of any significant results is the

20 The first stage regressions of the income growth rate on the excluded instruments (i.e. the shock variables) and theother exogenous household characteristics are available upon request.


Table 6The impact of idiosyncratic changes in the log of household income on consumption: estimates using instrumental variables


ln(total cons) ln(food cons) ln(nonfood exp)


(a) Insurance group: all households in the villageControl villages (β in Eq. (2)) −0.158 −0.53 −0.180 −0.55 0.750 1.29PROGRESA villages (βP in Eq. (2)) 0.134 0.89 0.225 1.36 0.274 1.22

(b) Insurance group: all households in the sampleControl villages (β in Eq. (2)) 0.247 1.17 0.314 1.32 0.461 1.29PROGRESA villages (βP in Eq. (2)) 0.034 0.22 0.019 0.12 0.393 1.48

Notes: Additional regressors included but not reported—a constant term, family size in round t and t − 1, the age of thehead, whether the head is a female, and set of community and round interaction dummy variables. The t-values reportedare based on Huber–White standard errors. See text for details on the variables used as instruments.

fact that the shocks variables turned out to be very weak instruments for the growth rate ofincome.

3.6. Consumption and shocks

The potential of measurement error in income and imputation errors in food consumption andthe biases that these entail suggests that it may be preferable to investigate differences in risksharing using the shocks variables in the place of income. As a further test of the sensitivity ofthe estimates presented earlier, I have estimates regressions of the form

�ln chtv = αTt + βShtv + PROv(βPShtv) + φXhvt + �εhvt (6)

where, as before, S is a vector denoting the incidence of household idiosyncratic shocks betweenround t and round t − 1 such as harvest or animal loss is included in the pace of the growth rate ofincome. As was the case for income in Eq. (2), under the null hypothesis of complete insuranceβ = 0, idiosyncratic shocks should have no role in explaining household-specific consumptiongrowth rates.

Table 7 presents the estimated coefficients of the idiosyncratic shocks on the growth rate of totalconsumption per month, and on the growth rate of food and nonfood consumption, separately.As discussed above, these estimates are obtained under the assumption that an insurance groupconsists of all households in a village. Each coefficient is estimated by running a regressionwith all shock variables included simultaneously in the regression. The standard errors of theestimated coefficients are corrected for unknown forms of heteroskedasticity in the error term ofthe regressions using the formula of White (1980).

The estimates in panels (a) suggest that controlling for the impact of such shocks at the villagelevel in each round, the idiosyncratic effect of these shocks at the household level is insignificant.The loss of land is the only shock that appears to have negative effect on the growth of nonfoodconsumption in control villages. The same shock does not have a bigger or smaller impact in thevillages covered by PROGRESA.

One possible interpretation of these finding is that household food consumption, at least, iscompletely insured from idiosyncratic shocks of this nature, although consumption overall is notfully insured from changes in income. Yet, at least two other possible explanations are likely. The


Table 7The impact of idiosyncratic shocks on household consumption: insurance group—all households within a village

During last 6 months Dependent variable: round to round change in

ln(total consumption) ln(food consumption) ln(nonfood consumption)

Coefficient t-Value p-Value Coefficient t-Value p-Value Coefficient t-Value p-Value

(a) Households in control villages (β in Eq. (6))Lost land? 0.003 0.19 0.85 0.011 0.62 0.54 −0.062 −1.89 0.06Lost harvest? 0.011 0.90 0.37 0.009 0.70 0.48 0.014 0.64 0.52Lost animals? 0.004 0.13 0.90 0.007 0.20 0.85 −0.038 −0.64 0.53Lost home/other? −0.033 −0.66 0.51 −0.036 −0.68 0.50 −0.056 −0.62 0.53

(b) Households in PROGRESA (treatment) villages (βP in Eq. (6))Lost land? −0.013 −0.53 0.60 −0.025 −1.00 0.32 0.056 1.31 0.19Lost harvest? 0.010 0.61 0.54 0.003 0.18 0.86 0.040 1.38 0.17Lost animals? 0.038 0.92 0.36 0.008 0.19 0.85 0.141 1.84 0.07Lost home/other? 0.043 0.71 0.48 0.027 0.43 0.67 0.088 0.81 0.42

Notes: All shock variables are included at the same time in the regression. Additional regressors included but not reported:a constant term, family size in round t and t − 1, the age of the head, and whether the head is female. The t-values reportedare based on Huber–White standard errors.

first explanation may be due to differences in the reference period for the incidence of the shockvariables (6 months) and the food consumption (last 7 days). The second explanation may bethat these results are driven by the absence of any significant variation in the incidence of theseshocks within villages. In order investigate the latter possibility further I have also re-estimatingEq. (6) by excluding the village-round interaction dummy variables controlling for the aggregateeffect and replacing them with one dummy variable for the fifth round of the survey. Since therewas no substantial change at least qualitatively from the results reported in Table 7, I am inclinedto reject the interpretation that households are completely insured from idiosyncratic shocks andfavor the first of the two alternative explanations proposed.

4. Concluding remarks

This study used three rounds of panel data collected between October 1998 and November1999 and covering 506 villages and 24,000 households in rural Mexico, to examine how thereplacement of pre-existing subsidy programs by a conditional cash transfer program such asPROGRESA (the Health, Education and Nutrition Program) affects the consumption smoothingof households.

A comparison of the estimates between villages covered and not yet covered by PROGRESA(treatment versus control villages) suggests the program did not replace or reinforce any pre-existing risk sharing among households within villages or result in any substantial changes inhow households cope with shocks. It is possible that households do not perceive PROGRESA asa permanent program. In fact, at the start of the program, households were initially told that theireligibility status was going to be reconsidered after a period of 3 years. Although a formal reviewof the eligibility status of beneficiary households has yet to take place after 4 years since thestart of PROGRESA, the remote possibility of this happening may act as a deterrent for adoptingdrastic changes in pre-existing arrangements and strategies developed by rural communities overtime for the purposes of dealing with risk.


Overall the results obtained are consistent with the prevalence of formal or informal insurancearrangements aimed at protecting household consumption from fluctuations in income. Yet, totalconsumption as well as food and nonfood consumption separately are found to be significantlycorrelated with idiosyncratic changes in income suggesting that insurance is incomplete.

The analysis also revealed that households eligible for the PROGRESA benefits in the treatmentvillages were able to insulate their consumption from fluctuations in income better than theircounterparts in control villages. Thus, a poverty alleviation program providing cash transfersconditioned on households investing in their human capital is associated with a reduction ofhousehold vulnerability to risk. The extent to which reductions in household vulnerability to riskcan lead to poverty alleviation remains a policy question worthy of further investigation.

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poverty alleviation and consumption insurance: evidence from progresa in mexico

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