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This article was downloaded by: [University Of Maryland]On: 15 October 2014, At: 13:28Publisher: RoutledgeInforma Ltd Registered in England and Wales Registered Number: 1072954Registered office: Mortimer House, 37-41 Mortimer Street, London W1T 3JH,UK
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Conditional Cash Transfersand Agricultural Production:Lessons from the OportunidadesExperience in MexicoJessica Erin Todd a , Paul C. Winters b & Tom Hertz aa Economic Research Service, US Department ofAgriculture , Washington, DC, USAb American University , Washington, DC, USAPublished online: 16 Dec 2009.
To cite this article: Jessica Erin Todd , Paul C. Winters & Tom Hertz (2010) ConditionalCash Transfers and Agricultural Production: Lessons from the OportunidadesExperience in Mexico, The Journal of Development Studies, 46:1, 39-67, DOI:10.1080/00220380903197945
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Conditional Cash Transfers andAgricultural Production: Lessons fromthe Oportunidades Experience in Mexico
JESSICA ERIN TODD*, PAUL C. WINTERS** & TOM HERTZ**Economic Research Service, US Department of Agriculture, Washington, DC, USA, **American
University, Washington, DC, USA
ABSTRACT This paper explores whether cash transfer programmes conditioned on human capitaloutcomes can influence agricultural production. Programme impact on food consumption from ownproduction, land use, livestock ownership, and agricultural spending are evaluated using firstdifference and weighted estimators, in which weights are constructed from propensity scores. Theprogramme is found to increase the value and variety of food consumed from own production and toincrease land use, livestock ownership and crop spending. Impact estimates are found to differ acrossland use categories and PROCAMPO participation. Results support the hypothesis that transfersinfluence agricultural production and impacts are greater for households invested in agriculture.
I. Oportunidades and Productive Investment
Oportunidades is a conditional cash transfer (CCT) programme that began operatingin rural areas in Mexico in 1997, targeting extremely poor households through acombination of geographic and household targeting.1 The programme delivers cashtransfers for nutrition and health to all eligible households, while education transfersare granted to children between ages eight and 18. The total amount a household canreceive is capped; in 1998, the maximum was 585 pesos, which is about 20 per cent ofthe value of total household consumption (Skoufias, 2005). By granting cash to poorhouseholds, Oportunidades can reduce current poverty by increasing income andtherefore, consumption. In addition, the programme may decrease future poverty byincreasing the human capital of children through conditioning receipt of the cashupon compliance with a set of activities related to health and/or education ofchildren in beneficiary households.
There has been some criticism of CCT programmes because they tend to focuson avoiding the intergenerational transmission of poverty by investing in the
Correspondence Address: Jessica Erin Todd, Economic Research Service, US Department of Agriculture,
1800 M Street NW, Washington, DC, 20036 USA. Email: [email protected]
Journal of Development Studies,Vol. 46, No. 1, 39–67, January 2010
ISSN 0022-0388 Print/1743-9140 Online/10/010039-29 ª 2010 Taylor & Francis
DOI: 10.1080/00220380903197945
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children of the poor rather than improving the productivity of poor adults. Thisargument posits that while the cash provided may help alleviate poverty, it doesnot, at least in the short-run, provide an exit out of poverty. Although increasingproductivity in agricultural activities is not a specific goal of CCT programmes,the cash received by households is fungible and households are free to use theextra resources as they wish, so long as programme conditions related to humancapital outcomes are met. Liquidity and credit constraints are often cited as mainfactors limiting productive investments, and therefore income-generating activities,in poor rural households (Rosenzweig and Wolpin, 1993; Fenwick and Lyne,1999; Lopez and Romano, 2000; Barrett et al., 2001; Imai, 2003; Winter-Nelsonand Temu, 2005). Beneficiaries of Oportunidades are most likely to use cashtransfers for productive purposes if they are liquidity-constrained and the extracash helps overcome this constraint.There is evidence to suggest that liquidity-constraints limit the productivity of
rural households in Mexico. Sadoulet et al. (2001) find that PROCAMPO transferslead to increased productivity and income among households in the ejido sector inMexico. Cash transfers offered by the Oportunidades programme also have thepotential to relax liquidity constraints, and therefore, to influence productiveactivities in beneficiary households. The average size of the cash transfers is largerelative to mean household income and the transfers are distributed regularly(bimonthly), making them a possible form of collateral. If households use part of theadditional income received to invest in income generating activities, the programmemay also reduce future poverty among beneficiary households by helping provide anexit out of poverty.This paper explores the impact of Oportunidades on agricultural production. We
focus on indirect measures of production since the evaluation surveys did not collectagricultural production directly. Earlier impact evaluations have found that theprogramme had a large positive impact on food consumption, especially theconsumption of highly nutritious foods such as fruits, vegetables and animal proteins(Hoddinott et al., 2000). Thus, we examine the impact on household consumption ofsix food groups from own production, separately for the autumn and springagricultural seasons, as the ability to consume from own production is likely to behighly seasonable. The question of whether CCT programmes lead to increasedagricultural production, or at least the pattern of consumption from ownproduction, is particularly salient given the recent increases in international foodprices. An increase in consumption from own production may help shelter extremelypoor households from the risk of food insecurity and hunger.Gertler et al. (2006) examine the impact of Oportunidades on land use and
livestock ownership for four types of rural households. They find that impacts vary,but in general, Oportunidades is associated with increased land use and animalownership. We also estimate the impact on land use and livestock ownership, butparallel to the investigation of the impact on consumption from own production, weestimate the programme impact separately for autumn and spring to exploreseasonal variation. In addition, we examine whether the programme increased theintensity of production by estimating the impact on the amount spent on variableinputs for crop production per hectare for both autumn and spring. We also test forheterogeneity of programme impact for all outcomes according to the household’s
40 J. E. Todd et al.
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area of land used prior to the programme and on household receipt ofPROCAMPO, a cash transfer programme targeted to staple producers and one ofthe government’s largest agricultural programmes. Examining land use prior to theprogramme and the importance of the PROCAMPO programme allows anassessment of whether households historically tied to agricultural production aremore likely to use cash transfers to invest in agriculture.
The remainder of the paper is structured as follows. The next section describes thedesign of the evaluation of Oportunidades in rural areas as well as the data used inthe analysis. Section III presents the identification strategy for determining theimpact of Oportunidades. This is followed by the presentation of the results inthe fourth section. The final section concludes with a discussion of these results andthe implications of the analysis for programme design.
II. Oportunidades Evaluation Design and Data
When Oportunidades was initiated in rural areas, an evaluation was designed to takeadvantage of the fact that the programme was phased in across the country overtime. In total, 506 localities in seven central states were selected for the evaluation,with 320 randomly selected to serve as the treatment group, and the remaining 186localities selected to serve as the control group. Eligibility in both the treatment andcontrol groups was determined at the start of the evaluation by means of amarginality index constructed from household level characteristics. Eligible house-holds in the treatment group began to receive cash transfers in March of 1998, whileeligible households in the control group began receiving transfers in September/November of 1999. Thus, the pure experimental phase of the evaluation lasted onlyuntil November 1999. We use household panel data from four surveys collectedbetween 1997 and May 1999. The first two survey rounds collected baseline data;the first in 1997 collected the information used to construct the householdmarginality index that determined household eligibility, and a second survey inMarch 1998 collected additional information about health, education andconsumption.2 Information on consumption from own production, agriculturalspending, land use and livestock ownership was collected in the October 1998 andMay 1999 surveys.
The sample is composed of 9936 households (3655 in the control group and 6281in the treatment group) that were originally determined to be eligible (‘the originalpoor’)3 and that responded to both baseline surveys as well as to both the October1998 and May 1999 surveys. The experimentally designed evaluation should providemeans to obtain an unbiased estimate of the programme impact. The randomassignment at the locality level should limit the differences across treatment groupsat the household level; this can be tested by examining characteristics of the twogroups. Even if randomisation was initially successful in creating two indistinguish-able groups, including only those households that appear in every survey round forwhich outcomes are examined, provides a way for differential attrition associatedwith earlier participation in the programme to introduce bias in the sample. Notethat we found no significant difference between the treatment and control groups inthe proportion of originally eligible households that appear in the final sample,suggesting attrition was similar in both control and treatment groups.
Cash Transfers and Agricultural Production 41
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Baseline Characteristics
Table 1 presents summary statistics of baseline characteristics for the originally eligiblehouseholds in both treatment and control groups, along with the results of the tests fordifferences in means across groups. Before proceeding, some explanation of thevariables is required. The information about ownership of various consumer durablesand property (dwelling and plot) was collapsed using principal component analysis onthe full set of households in the baseline survey. Three principal components wereextracted and identified as: (1) basic consumer durables, (2) extended consumerdurables and (3) property. The basic consumer durables component places largepositive weights on ownership of a blender, refrigerator, stove, radio and television,the extended consumer durables component places large positive weights onownership of a water heater, a CD or record player, a VCR and a washing machine,while the property component places weight on ownership of the household’s dwellingand on ownership of the lot on which the household’s dwelling is located. Using thedata from theMarch 1998 survey, indicators related to the variety and source of foodsconsumed in the past week were constructed. Specifically, we constructed variablesmeasuring the total number of foods and food groups consumed from own productionin the past week, as well as indicators for consumption from own production forseparate food groups: cereals; beans; fruits and vegetables; meat; and eggs.The first three columns of figures present unweighted sample means for both the
treatment and control groups, along with the p-values from the tests of theirdifferences. Statistically significant differences exist for many of the householdcharacteristics, including household composition, consumption from own produc-tion, assets, economic activities, characteristics of the locality and geographiclocation. In addition, there are slight differences in age and education of thehousehold head that are significant (p5 0.10).Also presented in Table 1 are weighted means of the characteristics, where the
weights are constructed from estimated propensity scores. Testing for differences inthese weighted means across the treatment and control groups reveals few significantdifferences. In fact, most of the p-values increase dramatically over tests fordifferences in the unweighted means. As will be explained in greater detail in the nextsection, the fact that the weights can balance the baseline characteristics across thetwo groups provides motivation for their use to obtain an unbiased estimate of theprogramme impact.
Outcomes
The agricultural outcomes collected in the October 1998 and May 1999 surveys canbe separated into three sets. The first set provides insight into whether beneficiaryhouseholds in general shifted their production relative to non-beneficiaries, andincludes information on whether they consumed any food from own production(a binary outcome) as well as the per capita value of the amount, the number of foodgroups, and the number of different foods consumed from own production. Theexamination of these latter two measures is motivated by the strong associationbetween diet diversity and economic and nutritional status and food security foundin developing countries (Hoddinott and Yohannes, 2002; Arimond and Ruel, 2004).
42 J. E. Todd et al.
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Table
1.Summary
statistics
Unweightedmeans
Weightedmeans
Control
Treatm
ent
Test(p-value)
Control
Treatm
ent
Test(p-value)
Headcharacteristics
Ageofhousehold
head
42.71
42.29
0.183
42.41
42.40
0.975
Head’seducation(years)
2.72
2.80
0.128
2.78
2.78
0.989
Headhasnoeducation
0.31
0.30
0.887
0.31
0.31
0.823
Headhassomeprimary
education
0.49
0.47
0.140
0.47
0.48
0.749
Headcompletedprimary
school
0.17
0.18
0.260
0.18
0.18
0.866
Headcompleted3years
ofsecondary
0.027
0.033
0.093
0.03
0.03
0.956
Headspeaksindigenouslanguage
0.44
0.43
0.682
0.45
0.44
0.467
Household
structure,dwellingcharacteristics
andassets
Household
size
6.13
6.09
0.374
6.11
6.10
0.858
Mem
bers15–59
2.79
2.80
0.774
2.81
2.80
0.652
Males15–59
1.35
1.38
0.095
1.38
1.37
0.619
Children0–4
1.00
1.01
0.677
1.01
1.00
0.960
Children5–10
1.35
1.34
0.578
1.34
1.34
0.976
Children11–14
0.81
0.77
0.054
0.78
0.78
0.925
Household
receives
PROCAMPO
0.33
0.37
0.000
0.35
0.35
0.794
Dwellinghaspiped
water
0.24
0.31
0.000
0.28
0.29
0.364
Dwellinghasearthfloor
0.74
0.72
0.039
0.74
0.73
0.574
Dwellinghaselectricity
0.64
0.59
0.000
0.60
0.61
0.579
Hectaresoflandused
1.80
1.78
0.838
1.81
1.79
0.831
Livestock
(cow-equivalentunits)
1.29
1.36
0.267
1.35
1.33
0.849
HH
marginality
index
(nationalmodel)
3.22
3.21
0.517
3.22
3.21
0.650
Index
ofbasicHH
durables
70.61
70.73
0.000
70.70
70.69
0.730
Index
ofextended
HH
durables
70.33
70.31
0.318
70.32
70.32
0.988
Index
ofproperty
ownership
70.07
0.02
0.001
70.02
70.01
0.991
Migrantnetwork
0.03
0.04
0.132
0.04
0.04
0.891
Internationalmigrantnetwork
0.01
0.02
0.363
0.01
0.01
0.935
Locality
marginality
index
0.64
0.59
0.002
0.63
0.61
0.295
(continued)
Cash Transfers and Agricultural Production 43
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Table
1.(C
ontinued)
Unweightedmeans
Weightedmeans
Control
Treatm
ent
Test(p-value)
Control
Treatm
ent
Test(p-value)
Employment
Adultsem
ployed
last
week
1.31
1.35
0.071
1.36
1.34
0.350
Adultmalesem
ployed
last
week
1.13
1.14
0.494
1.15
1.14
0.675
Adultsem
ployed
aspeon
0.80
0.77
0.051
0.79
0.78
0.780
Adultsem
ployed
innon-agwage
0.19
0.15
0.001
0.17
0.17
0.974
Adultsselfem
ployed
last
week
0.14
0.17
0.000
0.16
0.16
0.776
Adultsem
ployed
infamilybusiness/land
0.09
0.14
0.000
0.14
0.13
0.488
Adultspaid
inkindlast
week
0.21
0.31
0.000
0.28
0.27
0.481
Household
reportsnocash
income
0.03
0.04
0.086
0.04
0.04
0.993
Fooddiversity
andownproduction(March1998)
Number
offoodsfrom
ownproduction
2.90
2.81
0.053
2.85
2.84
0.909
Number
offoodgroupsfrom
ownproduction
1.78
1.70
0.006
1.73
1.73
0.885
consumed
cerealsfrom
ownproduction
0.67
0.66
0.725
0.67
0.66
0.244
Consumed
beansfrom
ownproduction
0.13
0.12
0.169
0.13
0.12
0.383
Consumed
fruitsandvegetablesfrom
ownproduction
0.39
0.35
0.000
0.37
0.37
0.843
Consumed
meatfrom
ownproduction
0.13
0.11
0.087
0.12
0.12
0.689
Consumed
eggsfrom
ownproduction
0.27
0.27
0.662
0.26
0.27
0.085
States
Guerrero
0.05
0.09
0.000
0.08
0.07
0.287
Hidalgo
0.12
0.19
0.000
0.15
0.16
0.195
Michoacan
0.10
0.12
0.043
0.11
0.11
0.867
Puebla
0.15
0.16
0.833
0.16
0.16
0.800
Queretaro
0.04
0.04
0.580
0.04
0.04
0.908
SanLuisPotosi
0.14
0.15
0.465
0.15
0.15
0.929
Veracruz
0.39
0.25
0.000
0.30
0.30
0.988
OBSERVATIO
NS
3397
5781
3397
5781
Notes:Weights
constructed
from
estimatedpropensity
score,p(X
).Householdsare
weightedby1/p(X
)in
thetreatm
entgroupandby1/[1-p(X
)]in
thecontrolgroup;p-values
inbold
indicate
significance
at90per
centorgreater.
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The 36 specific foods reported in each consumption module are classified into 11food groups. There is a maximum of 21 foods and 10 food groups from whichhouseholds can report consuming from own production.4
The second set of agricultural outcomes considers specific food groups consumedfrom own production. The food groups for which impacts are estimated separatelyare cereals and grains; beans; fruits; vegetables; meat; and eggs. A very smallproportion of households report consuming tubers, fish, dairy and fats from ownproduction; for this reason, these four food groups are not analysed separately. Onepossible approach to analysing consumption from own production of these itemswould be to estimate the impact on the value of consumption for each food group.However, the distributions of the per capita value of consumption from ownproduction of cereals, beans, meat, fruits, vegetables and eggs have large spikes atzero, which suggests that estimates of the impact would be highly sensitive to thefunctional form chosen.5 Therefore, we focus on the impact on the probability ofconsuming each food group from own production (a binary variable), rather than onthe value of consumption.
The final set of indicators looks more specifically at the scale and intensificationof agricultural production. These indicators include the use of land (a binaryvariable), the total amount of land used per capita, the ownership of livestock (abinary variable), the amount of livestock owned per capita, spending onagricultural inputs (a binary variable) and the amount spent, per hectare andper capita. In the interest of space, the control group means of all outcomes arepresented and discussed along with the estimated impacts in Tables 3 through 5.Heterogeneity of impact is explored in two different ways. Since the expectation isthat land ownership will critically influence household behaviour, we also examinethe programme’s impact separately for households in different land ownershipcategories, defined by land use at the baseline survey: no land used, use of threeor fewer hectares, and use of more than three hectares. Secondly, since wehypothesise that Oportunidades may reduce liquidity constraints which limitproduction, we test for differences in impact for households that receive anothercash transfer programme, PROCAMPO.
III. Methodology
To assess the impact of Oportunidades on agricultural outcomes, average treatmenteffects are estimated. Following Dehejia (2004), let Y0i be the outcome for householdi when not exposed to the programme and let Y1i be the outcome for household iwhen exposed. The treatment effect for household i is ti¼Y1i7Y0i, and the averagetreatment effect is t¼E(Y1i)7E(Y0i). However, Y0i is not observed for householdsin the treatment group and similarly, Y1i is not observed for households in thecontrol group. When households are randomly assigned to treatment and controlgroups, as in experimentally designed evaluations, treatment status is independentof both potential outcomes (Y1i, Y0i ? Ti), and of pre-treatment characteristics,Xi, (Xi ? Ti), where Ti¼ 1 if the household is in the treatment group and Ti¼ 0 if thehousehold is in the control group. Thus, the average treatment effect is:
t ¼ EðYijTi ¼ 1Þ � EðYijTi ¼ 0Þ ð1Þ
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and can be estimated with a regression of the form:
Yi ¼ a0 þ t � Ti þ ei ð2Þ
For a pure experiment, estimating equation (2) is considered reasonable.Generally, thestandard approach to identifying the impacts of Oportunidades has been to estimateequation (2)with additional covariates since the randomisationwas at the locality level,as opposed to the household level and controlling for X in the regressions can enhancethe precision of estimates. However, even when (2) is expanded to include conditioningvariables, there remains a concern that this may not be sufficient to remove potentialbias arising from the fact that covariates are not balanced between the two groups, asobserved in Table 1. Rosenbaum and Rubin (1983) show that treatment can still beconditionally random (unconfoundedness assumption),
ðY1i; Y0i ? TiÞjXi ð3Þ
and that conditioning on the propensity score p(Xi), is equivalent to conditioning onXi:
fY1i; Y0i ? Tig j Xi , fY1i; Y0i ? Tig j pðXiÞ ð4Þ
Conditioning on the propensity score eliminates bias in the estimated impact whenit results in independence of treatment status and pre-treatment characteristics, Xi ?Ti j p(Xi), or in otherwords, it results in the balancing of covariates across the treatmentand control groups. Rosenbaum (2004) highlights the fact that achieving this covariatebalance is the main goal when using propensity scores.6 Figure 1 displays the kernel
Figure 1. Distribution of propensity scores, treatment and control group. Note: The meanpropensity score in the Treatment group is 0.650 (solid vertical line) and the mean in theControl group is 0.601 (dashed vertical line). This difference is statistically significant
(p50.000), even when accounting for clustering.
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density estimates of the distribution of estimated propensity scores for both the controland treatment groups.7 The scores in both groups are almost entirely in the range ofcommon support, which suggests that standard regression techniques for assessingimpact would be applicable. Yet, the difference in mean propensity score acrossthe treatment and control groups (mean of 0.650 in the treatment group versus 0.601 inthe control group, p5 0.000) implies thatmerely conditioning onXmight not yield thecorrect average treatment effect if this effect is indeed heterogenous.
To address this potential problem, in addition to standard regression approaches,we use a method that, in this situation, is superior to propensity score matchingmethods, which is to weight by the inverse of the propensity score. Weighting by theinverse of the estimated propensity score can also achieve covariate balance and, incontrast to matching and stratification/blocking, uses all of the observations in thesample (Sacerdote, 2004). Parallel to estimators used for stratified sampling, weightscan be used to recover E[Y1] and E[Y0] (Hirano and Imbens, 2001), according to:
E½Y1� ¼ EYi � Ti
pðXiÞ
� �ð5Þ
and
E½Y0� ¼ EYi � ð1� TiÞ1� pðXiÞ
� �ð6Þ
To determine how well weighting by these estimated propensity scores balances thebaseline covariates, weighted means and tests of the differences between thetreatment and control groups are reported in the three right-most columns ofTable 1. As discussed earlier, the weighting dramatically reduces the number ofsignificant differences between the two groups. It is especially important to note thatthe weighting results in an equal distribution of households within each group acrossthe states.
Following from (5) and (6), the weighting estimator, tw, gives a consistent estimateof the average treatment effect and is therefore an alternative to matching orstratifying on the propensity score (Hirano et al., 2000; Sacerdote, 2004):
tw ¼ E½Y1� � E½Y0� ¼1
N
XNi¼1
Ti � Yi
pðXiÞ� ð1� TiÞ � Yi
1� pðXiÞ
� �ð7Þ
While this estimator is consistent, it may be less efficient than full covariate matching(Hahn, 1998; Heckman et al., 1998; Hirano et al., 2000), however, as pointed outabove, the advantage is that it can be estimated even when full covariate matching isimpossible or impractical, as long as the propensity score is bounded away from zeroand one (05 p(Xi)5 1). In this case, the use of the weighting estimator is preferredto fixed-number matching (for example, nearest-neighbour matching) or othermatching methods, as the balancing of covariates is readily confirmed and allobservations are used.
The weighting estimator can also be combined with regression in which X (or asubset of X) is included as a set of covariates (Robins and Rotnitzky, 1995; Hiranoand Imbens, 2001). Imbens (2004: 11) points out that any one method (regression,
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covariate matching, or use of the propensity score for weighting, blocking/stratifying, regression on or matching on) can eliminate bias from observeddifferences, but combining two methods ‘may lead to more robust inference’.Therefore, following Hirano and Imbens (2001), the average treatment effect is alsoestimated using a weighted regression of each of the outcomes of interest on thetreatment indicator and other covariates, with the weights constructed from theestimated propensity scores as in (7):
Yi ¼ a0 þ t � Ti þ a1 � Xi þ ei ð8Þ
Imbens (2004) points out that the weights ensure that there is no correlation betweenthe treatment and the covariates and leads to a consistent estimate of the averagetreatment effect, but that without the weights, equation (8) is inconsistent. Thus,estimating equation (8) using weights as described above serves as a robustness checkof the estimated impacts when simply controlling for observable characteristicsdirectly in a regression approach.In sum, four models are estimated. First, equation (2) is estimated by way of OLS.
Second, equation (8) is estimated by way of OLS, which allows for an assessment ofthe importance of including the covariates when estimating the programme impact.Third, equation (8) is estimated by way of a weighted OLS, where the weights are asdescribed above. This provides an assessment of how well the standard modelestimates the programme impact, even though some differences in the control andtreatment groups remain (as seen in Table 1 and Figure 1). As a final a robustnessand sensitivity check, programme impacts are also estimated using unweightednonlinear models. Specifically, censored regression models are estimated for thevalue of per capita consumption of own production, land used, livestock ownershipand agricultural spending, a poisson model is used for the number of foods andnumber of food groups consumed from own production, and probits are used forwhether a household consumes from own production, consumes certain foods fromown production, uses land, owns livestock or spends on agricultural inputs.
IV. Estimation and Results
Consumption from Own Production
Table 2 presents the estimated impacts from all four models for the first set ofvariables that focus on own production, separately for both October 1998 and May1999. In addition, the mean value in the control group is reported for each outcome.In October 1998, 70 per cent of all control households consumed at least one foodfrom own production, while in May 1999, 68 per cent consumed from ownproduction. The average monthly value of food consumed from own production inOctober 1998 was 25 pesos per capita, and in May 1999, it was slightly less, at 24pesos per capita; this is approximately 20 per cent of the value of all food consumed.In October 1998, the average number of food groups and foods consumed from ownproduction was 1.16 and 1.26, respectively, and in May 1999, the averages were 1.13and 1.17, respectively. Approximately half of the households consumed two or morefood groups from own production and nearly 70 per cent consumed two or more
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Table
2.Im
pact
ofOportunidades
onconsumptionfrom
ownproduction
October
1998
May1999
Control
group
mean
OLSno
controls
OLS
with
controls
OLS
weighted
with
controls
Nonlinear
with
controls
Control
group
mean
OLSno
controls
OLS
with
controls
OLS
weighted
with
controls
Nonlinear
with
controls
Allhouseholds(N¼9936)
Household
consumes
from
ownproduction
70.01%
0.035
(0.113)
0.038**
(0.037)
0.038**
(0.035)
0.039**
(0.036)
67.61%
0.047*
(0.055)
0.043**
(0.017)
0.039**
(0.023)
0.043**
(0.022)
Per
capitavalue
ownproduction
25.11
2.741**
(0.040)
2.961***
(0.009)
3.000***
(0.007)
3.001***
(0.008)
23.61
1.619
(0.371)
1.192
(0.441)
0.967
(0.517)
1.762
(0.204)
Number
offoodgroupsfrom
ownproduction
1.16
0.096*
(0.059)
0.113***
(0.007)
0.118***
(0.004)
0.095***
(0.006)
1.13
0.148**
(0.021)
0.127***
(0.008)
0.118**
(0.011)
0.110***
(0.005)
Number
offoodsfrom
ownproduction
1.26
0.103*
(0.087)
0.135***
(0.006)
0.141***
(0.003)
0.104***
(0.005)
1.17
0.165**
(0.019)
0.149***
(0.005)
0.139***
(0.008)
0.124***
(0.004)
Landless
households(N¼4135)
Household
consumes
from
ownproduction
63.88%
0.038
(0.162)
0.039*
(0.094)
0.040*
(0.080)
0.042*
(0.082)
62.88%
0.061**
(0.040)
0.049**
(0.017)
0.048**
(0.018)
0.052**
(0.020)
Per
capitavalueown
production
21.34
2.869*
(0.053)
2.754**
(0.028)
2.775**
(0.026)
2.730**
(0.031)
21.63
1.436
(0.489)
0.452
(0.796)
0.213
(0.902)
1.349
(0.373)
Number
offoodgroupsfrom
ownproduction
1.01
0.078
(0.190)
0.094*
(0.052)
0.099**
(0.036)
0.092**
(0.040)
1.02
0.154**
(0.043)
0.127**
(0.018)
0.126**
(0.018)
0.120**
(0.015)
Number
offoodsfrom
ownproduction
1.10
0.091
(0.192)
0.119**
(0.034)
0.126**
(0.022)
0.107**
(0.025)
1.07
0.164*
(0.054)
0.143**
(0.022)
0.143**
(0.020)
0.128**
(0.020)
Householdswith�3hectares(N¼4435)
household
consumes
from
ownproduction
74.07%
0.035
(0.178)
0.044*
(0.055)
0.046**
(0.043)
0.046**
(0.048)
69.68%
0.047
(0.114)
0.045*
(0.054)
0.043*
(0.055)
0.045*
(0.063)
Per
capitavalue
ownproduction
27.36
2.750*
(0.099)
3.146**
(0.041)
3.306**
(0.028)
3.288**
(0.033)
23.84
2.501
(0.245)
2.912
(0.129)
2.705
(0.127)
3.049*
(0.083)
(continued)
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Table
2.(C
ontinued)
October
1998
May1999
Control
group
mean
OLSno
controls
OLS
with
controls
OLS
weighted
with
controls
Nonlinear
with
controls
Control
group
mean
OLSno
controls
OLS
with
controls
OLS
weighted
with
controls
Nonlinear
with
controls
Number
offoodgroupsfrom
ownproduction
1.24
0.107*
(0.074)
0.143***
(0.007)
0.148***
(0.004)
0.109***
(0.007)
1.17
0.160**
(0.041)
0.152**
(0.011)
0.138**
(0.018)
0.125***
(0.010)
Number
offoodsfrom
ownproduction
1.35
0.104
(0.151)
0.156**
(0.013)
0.161***
(0.008)
0.1010**
(0.014)
1.21
0.185**
(0.028)
0.184***
(0.005)
0.165***
(0.009)
0.144***
(0.004)
Householdswith43hectares(N¼1366)
Household
consumes
from
ownproduction
76.77%
0.0004
(0.991)
0.024
(0.367)
0.023
(0.394)
0.030
(0.282)
75.68%
70.002
(0.947)
70.001
(0.971)
70.004
(0.893)
0.005
(0.864)
Per
capitavalue
ownproduction
29.97
1.147
(0.633)
2.959
(0.133)
2.967
(0.125)
2.860
(0.130)
28.71
70.782
(0.773)
70.376
(0.866)
70.811
(0.710)
70.314
(0.880)
Number
offoodgroupsfrom
ownproduction
1.38
0.075
(0.414)
0.111
(0.163)
0.115
(0.151)
0.080
(0.155)
1.34
0.077
(0.425)
0.075
(0.316)
0.059
(0.423)
0.059
(0.266)
Number
offoodsfrom
ownproduction
1.48
0.090
(0.364)
0.148*
(0.097)
0.149*
(0.090)
0.098*
(0.087)
1.39
0.086
(0.407)
0.094
(0.250)
0.075
(0.344)
0.071
(0.201)
Notes:Robust
p-values
inparentheses;Marginaleff
ects
reported;*significantat90per
cent,**significantat95per
cent,***significantat99per
cent.
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foods. Thus, a substantial portion of households relied on food from ownproduction to help meet their dietary needs.
The estimated impacts presented in Table 2 are robust across the four models. Themagnitude of the marginal effects remains similar across the models, as does thesignificance of results. The biggest differences in results tend to be between the OLSwithout and with controls, with the impacts estimated without controls sometimesinsignificant and of different magnitude. This suggests that, despite the experimentaldesign, it is important to control for observable characteristics. The lack of differencebetween the OLS and weighted results, however, suggests that adding the covariatesin the estimations is sufficient to deal with the initial observable differences betweenthe two groups. Finally, note that the nonlinear specifications also yield remarkablysimilar marginal effects (calculated at the sample mean) and significance as the OLSestimates. Given the robustness of the results, except in certain circumstances, wefocus on the general results from the different specifications that include controls.
The results indicate that Oportunidades increased the probability of consumingfrom own production by approximately 4 percentage points in both October 1998and May 1999 (an increase of 5.4% and 6.2%, respectively). The per capita value ofconsumption from own production significantly increased in October, by about threepesos per month (or about 12%), although it did not increase in May. The number offood groups and the number of foods consumed from own production increased inboth periods by around 0.10, or slightly less than 10 per cent. This is equivalent toone in 10 Oportunidades households adding another food group or food item to theirbasket of foods consumed from own production.
The bottom three panels of Table 2 report the estimated impacts for the three landuse groups: landless, smallholders (less than three hectares of land) and largelandholders (three or more hectares of land). Although the absolute magnitude ofimpact on the probability of consuming from own production might be slightlyhigher for the smallholders versus the landless in October 1998, the per cent increaseis similar (6%). However, the increase in the per capita value of consumption fromown production is relatively largest for the landless households, at 13 per cent of themean in the control group. In May 1999, the programme lead to larger increases inthe probability of consuming from own production: 7.8 per cent among landlesshouseholds, and 6.5 per cent for smallholders. In addition, the impact on the numberof foods and food groups consumed from own production is greater in May 1999, at12.5 per cent and 13.3 per cent among the landless and 13 per cent and 15.2 per centamong smallholders. In contrast, the results for large landholders, with the exceptionof number of foods in October, are not significantly different than zero. These resultscorrespond to what might be expected if liquidity constraints to agriculturalproduction are greatest among the asset poor.
Table 3 presents the estimated impacts on the probability of consuming six specificfood groups from own production. Similar to Table 2, themean of each outcome in thecontrol group is also reported. The estimates and significance are robust across themodels that include controls, therefore, the focus of the discussion is on these threemodels. Cereals are the most common food group consumed from own production,with 53 per cent of households consuming them inOctober 1998 and 50 per cent inMay1999. Fruits are the secondmost common home-produced food in October (24%), butvegetables are the second most common in May (23%). The difference in production
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Table
3.Im
pact
onconsumptionofownproductionbyfoodgroup
October
1998
May1999
Control
group
mean
OLSno
controls
OLSwith
controls
OLS
weighted
withcontrols
Nonlinear
with
controls
Control
group
mean
OLSno
controls
OLSwith
controls
OLS
weighted
withcontrols
Nonlinear
with
controls
Allhouseholds(N¼9936)
Consumecerealsfrom
ownproduction
52.97%
0.028
(0.301)
0.022
(0.295)
0.023
(0.278)
0.023
(0.312)
50.31%
0.041
(0.224)
0.033
(0.102)
0.030
(0.124)
0.038
(0.123)
Consumebeansfrom
ownproduction
5.85%
0.021*
(0.076)
0.014
(0.125)
0.017*
(0.080)
0.012
(0.146)
5.55%
0.009
(0.497)
70.002
(0.810)
70.001
(0.895)
70.003
(0.700)
Consumefruitsfrom
ownproduction
24.10%
0.017
(0.529)
0.044**
(0.039)
0.047**
(0.024)
0.045**
(0.041)
15.87%
0.013
(0.587)
0.025
(0.211)
0.021
(0.282)
0.026
(0.161)
Consumevegetablesfrom
ownproduction
11.57%
0.005
(0.729)
0.002
(0.896)
0.001
(0.964)
0.001
(0.926)
22.60%
0.051**
(0.011)
0.037**
(0.031)
0.037**
(0.031)
0.039**
(0.032)
Consumemeatfrom
ownproduction
6.78%
0.017*
(0.099)
0.022**
(0.013)
0.021**
(0.012)
0.021***
(0.008)
5.28%
0.015*
(0.069)
0.014*
(0.054)
0.012
(0.113)
0.014**
(0.027)
Consumeeggsfrom
ownproduction
10.25%
0.007
(0.583)
0.008
(0.475)
0.008
(0.455)
0.007
(0.483)
10.72%
0.007
(0.589)
0.008
(0.497)
0.007
(0.536)
0.008
(0.468)
Landless
households(N¼4135)
Consumecerealsfrom
ownproduction
46.25%
0.023
(0.460)
0.010
(0.681)
0.009
(0.715)
0.011
(0.683)
45.68%
0.041
(0.288)
0.016
(0.491)
0.014
(0.546)
0.017
(0.558)
Consumebeansfrom
ownproduction
2.94%
0.023**
(0.022)
0.013*
(0.074)
0.013*
(0.074)
0.011*
(0.074)
2.56%
0.012**
(0.045)
0.007
(0.291)
0.008
(0.322)
0.005
(0.239)
Consumefruitsfrom
ownproduction
23.75%
0.003
(0.920)
0.031
(0.212)
0.034
(0.161)
0.030
(0.226)
14.88%
0.013
(0.620)
0.031
(0.173)
0.031
(0.164)
0.030
(0.137)
Consumevegetablesfrom
ownproduction
11.13%
70.003
(0.882)
0.005
(0.737)
0.006
(0.701)
0.006
(0.672)
23.00%
0.036
(0.105)
0.034*
(0.091)
0.035*
(0.085)
0.035*
(0.088)
Consumemeatfrom
ownproduction
5.69%
0.019
(0.115)
0.020**
(0.043)
0.020**
(0.045)
0.019**
(0.024)
4.13%
0.012
(0.140)
0.010
(0.184)
0.009
(0.268)
0.011
(0.113)
Consumeeggsfrom
ownproduction
8.81%
0.006
(0.646)
0.007
(0.558)
0.008
(0.511)
0.005
(0.621)
9.75%
0.011
(0.477)
0.010
(0.449)
0.011
(0.394)
0.010
(0.413)
(continued)
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Table
3.(C
ontinued)
October
1998
May1999
Control
group
mean
OLSno
controls
OLSwith
controls
OLS
weighted
withcontrols
Nonlinear
with
controls
Control
group
mean
OLSno
controls
OLSwith
controls
OLS
weighted
withcontrols
Nonlinear
with
controls
Householdswith�3hectares(N¼4435)
Consumecerealsfrom
ownproduction
46.25%
0.038
(0.249)
0.041
(0.142)
0.045
(0.103)
0.042
(0.144)
53.39%
0.048
(0.208)
0.057**
(0.032)
0.053**
(0.036)
0.065**
(0.038)
Consumebeansfrom
ownproduction
2.94%
0.029*
(0.072)
0.029**
(0.024)
0.031**
(0.015)
0.021**
(0.050)
6.52%
0.005
(0.788)
70.002
(0.900)
70.001
(0.919)
70.004
(0.631)
Consumefruitsfrom
ownproduction
23.75%
0.023
(0.464)
0.050**
(0.042)
0.052**
(0.028)
0.051**
(0.045)
16.56%
0.011
(0.685)
0.019
(0.429)
0.013
(0.588)
0.020
(0.367)
Consumevegetablesfrom
ownproduction
11.12%
0.009
(0.625)
70.001
(0.953)
70.003
(0.868)
0.0003
(0.987)
21.48%
0.065**
(0.017)
0.040*
(0.061)
0.041*
(0.054)
0.044*
(0.055)
Consumemeatfrom
ownproduction
5.70%
0.012
(0.364)
0.022*
(0.056)
0.022**
(0.044)
0.020**
(0.047)
5.92%
0.017
(0.149)
0.020**
(0.049)
0.018*
(0.092)
0.019**
(0.029)
Consumeeggsfrom
ownproduction
8.81%
70.001
(0.939)
0.003
(0.830)
0.002
(0.909)
0.002
(0.865)
11.10%
0.004
(0.831)
0.008
(0.632)
0.005
(0.739)
0.007
(0.655)
Householdswith43hectares(N¼1366)
Consumecerealsfrom
ownproduction
58.17%
70.021
(0.683)
0.013
(0.691)
0.010
(0.767)
0.013
(0.733)
55.35%
70.003
(0.962)
0.020
(0.468)
0.016
(0.570)
0.026
(0.496)
Consumebeansfrom
ownproduction
6.45%
70.011
(0.744)
70.012
(0.624)
70.008
(0.751)
70.005
(0.778)
11.62%
70.014
(0.658)
70.023
(0.387)
70.024
(0.363)
70.019
(0.346)
Consumefruitsfrom
ownproduction
24.26%
0.038
(0.367)
0.059*
(0.065)
0.060*
(0.052)
0.068**
(0.038)
17.06%
0.012
(0.715)
0.028
(0.342)
0.024
(0.411)
0.033
(0.229)
Consumevegetablesfrom
ownproduction
11.90%
0.014
(0.576)
0.010
(0.654)
0.012
(0.598)
0.004
(0.841)
24.50%
0.054*
(0.086)
0.029
(0.309)
0.025
(0.382)
0.029
(0.329)
Consumemeatfrom
ownproduction
7.58%
0.026
(0.184)
0.031
(0.122)
0.028
(0.153)
0.027*
(0.083)
6.90%
0.012
(0.481)
0.011
(0.530)
0.009
(0.615)
0.013
(0.362)
Consumeeggsfrom
ownproduction
11.17%
0.030
(0.153)
0.013
(0.521)
0.013
(0.507)
0.017
(0.368)
12.52%
0.006
(0.788)
70.0002
(0.993)
70.003
(0.866)
0.001
(0.955)
Notes:Robustp-values
inparentheses;Marginaleff
ectsreported;*sign
ificantat90per
cent,**significantat
95per
cent,***sign
ificantat99per
cent.
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across the two seasons – fruit goes from 24 to 16 per cent and vegetables from 12 to 23per cent – highlights the seasonality of production. Beans and eggs are consumed fromown production at similar levels in both periods, while meat slightly declines betweenOctober and May (from 7 to 5%). Looking at the results in the three lower panels ofTable 3, it is clear that these seasonal patterns are similar across the different landcategories, but there are differences across the groups. Landless households tend toconsume less cereals and beans fromownproduction than smallholders, particularly inMay 1999. They also appear to consume lessmeat and eggs fromownproduction in thespring. Large landholders tend to consume more staple crops (cereals and beans) fromown production, and slightly more meat and eggs.From Table 3, it is clear that programme impacts are also seasonal; increases in
the consumption of fruits and meat from own production occur in the autumn, whileincreases in the consumption of cereals and vegetables (and perhaps meat) from ownproduction occur in the spring. The 4.5 percentage point increase in the probabilityof consuming fruits from own production in October 1998 is large, at 18 per centabove that in the control group. In May 1999, a 4 percentage point increase in theprobability is observed for vegetables, which is an increase of more than 16 per centover that in control group. The relative increase in consumption of meat from ownproduction is even greater, at 32 per cent in October 1998 and 26.5 per cent in May1999. In sum, these results indicate that the programme increases entry into theproduction of foods of higher market and nutritional value.The lower three panels of Table 3 indicate there is some heterogeneity in the impact
of the programme. While for the landless, the impact is mainly on the consumption ofbeans and meat from own production in the autumn and on the consumption of ownproduced vegetables in the spring, there is no significant impact on the probability ofconsuming fruits from own production in either season. On the other hand, theimpact on the probability of consuming fruits from own production for householdsthat used some land at baseline is large, at more than 20 per cent over the controlgroup. In general, impacts are greater among smallholders, with increases in the entryinto consumption of beans, fruit and meat from own production in the autumn andinto that of cereals, vegetables and meat in the spring. In contrast, the only impactobserved among large landholders is on the consumption of fruit from ownproduction in the autumn. Again, this indicates the potential role of Oportunidades inovercoming liquidity constraints for the asset poor.
Intensification of Agricultural Production
Table 4 presents the estimated impacts for land use, livestock ownership andspending on inputs for crops, with the format and emphasis in discussion similar tothat for Tables 2 and 3. In October 1998, 59 per cent of households report usingland, with an average of 0.26 hectares per capita (1.42 hectares per household). Theper cent using land and average area were slightly lower in May 1999, at 55 per centand 0.24 hectares per capita (1.30 hectares per household). Looking across the threeland use groups we see that, not surprisingly, those that did not use land in 1997 areless likely to use land in October (32%) and on average, have smaller plots (0.14hectares per capita) than the landed categories. The changes in per cent use do,however, show a degree of flexibility in land use patterns.
54 J. E. Todd et al.
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Table
4.Im
pact
onlanduse,livestock
ownership
andagriculturalinputspending
October
1998
May1999
Control
group
mean
OLSno
controls
OLS
with
controls
OLS
weighted
with
controls
Nonlinear
with
controls
Control
group
mean
OLSno
controls
OLS
with
controls
OLS
weighted
with
controls
Nonlinear
with
controls
Allhouseholds(N¼9936)
Household
usesland
58.55%
0.041
(0.155)
0.037**
(0.034)
0.034**
(0.048)
0.043**
(0.042)
55.13%
0.029
(0.301)
0.025
(0.188)
0.023
(0.203)
0.026
(0.237)
Per
capitahectaresof
landused
0.26
0.022
(0.374)
0.030
(0.127)
0.032*
(0.094)
0.038**
(0.024)
0.24
0.012
(0.584)
0.016
(0.355)
0.014
(0.378)
0.022
(0.119)
Household
owns
livestock
76.22%
0.026
(0.197)
0.031*
(0.062)
0.031*
(0.060)
0.031*
(0.055)
72.50%
0.034*
(0.076)
0.035**
(0.033)
0.033**
(0.037)
0.035**
(0.032)
Per
capitalivestock
owned
0.14
0.028*
(0.093)
0.016*
(0.098)
0.016*
(0.099)
0.017**
(0.037)
0.14
0.044**
(0.013)
0.034***
(0.008)
0.033***
(0.007)
0.030***
(0.007)
Household
reportsag
spending#
43.33%
0.060**
(0.018)
0.048**
(0.013)
0.049**
(0.013)
0.056**
(0.010)
34.23%
0.009
(0.699)
0.019
(0.334)
0.019
(0.321)
0.023
(0.264)
Per
hectare
agricultural
spending
210.18
36.138
(0.262)
20.370
(0.515)
21.072
(0.478)
63.333*
(0.060)
181.07
24.795
(0.540)
24.792
(0.513)
30.050
(0.403)
45.262
(0.209)
Per
capitaagricultural
spending
53.03
9.418
(0.145)
6.626
(0.294)
6.289
(0.293)
12.456*
(0.056)
49.96
71.319
(0.875)
72.102
(0.777)
70.834
(0.908)
6.180
(0.385)
Landless
households(N¼4135)
Household
usesland
31.68%
0.066**
(0.027)
0.053**
(0.021)
0.049**
(0.029)
0.060**
(0.017)
30.75%
0.056*
(0.060)
0.037*
(0.097)
0.039*
(0.076)
0.042*
(0.087)
Per
capitahectaresof
landused
0.14
70.006
(0.805)
70.006
(0.801)
70.005
(0.841)
0.029*
(0.083)
0.11
0.007
(0.655)
70.001
(0.962)
70.005
(0.775)
0.016
(0.201)
Household
ownslivestock
69.81%
0.044*
(0.069)
0.043**
(0.024)
0.040**
(0.034)
0.042**
(0.030)
66.25%
0.038
(0.126)
0.026
(0.218)
0.030
(0.155)
0.028
(0.198)
Per
capitalivestock
owned
0.09
0.023*
(0.054)
0.011
(0.211)
0.007
(0.516)
0.013*
(0.055)
0.09
0.043*
(0.054)
0.037
(0.124)
0.038
(0.100)
0.030
(0.104)
(continued)
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Table
4.(C
ontinued)
October
1998
May1999
Control
group
mean
OLSno
controls
OLS
with
controls
OLS
weighted
with
controls
Nonlinear
with
controls
Control
group
mean
OLSno
controls
OLS
with
controls
OLS
weighted
with
controls
Nonlinear
with
controls
Household
reportsag
spending#
24.06%
0.050**
(0.044)
0.030
(0.146)
0.030
(0.138)
0.033
(0.126)
20.75%
0.017
(0.470)
0.014
(0.463)
0.013
(0.488)
0.015
(0.441)
Per
hectare
agricultural
spending
149.73
75.645
(0.874)
725.228
(0.509)
725.337
(0.499)
13.924
(0.462)
114.94
43.605
(0.309)
61.075
(0.215)
55.461
(0.228)
37.072
(0.292)
Per
capitaagricultural
spending
25.50
6.809
(0.141)
3.364
(0.385)
4.246
(0.261)
4.997
(0.124)
27.85
70.581
(0.956)
1.248
(0.903)
1.001
(0.917)
3.841
(0.546)
Householdswith�3hectares(N¼4435)
Household
usesland
77.59%
70.003
(0.897)
0.010
(0.614)
0.009
(0.630)
0.010
(0.604)
72.21%
70.013
(0.619)
0.007
(0.736)
0.008
(0.711)
0.007
(0.772)
Per
capitahectaresof
landused
0.27
0.048*
(0.074)
0.055***
(0.010)
0.058***
(0.007)
0.038**
(0.028)
0.25
0.037*
(0.070)
0.052***
(0.005)
0.050***
(0.005)
0.037*
(0.021)
Household
owns
livestock
79.85%
0.005
(0.845)
0.012
(0.620)
0.015
(0.525)
0.011
(0.617)
74.67%
0.040
(0.108)
0.051**
(0.012)
0.047**
(0.019)
0.054***
(0.008)
Per
capitalivestock
owned
0.14
0.040*
(0.052)
0.030**
(0.046)
0.030**
(0.045)
0.022*
(0.074)
0.13
0.046***
(0.010)
0.035***
(0.002)
0.033***
(0.004)
0.032***
(0.001)
Household
reportsag
spending#
56.25%
0.045
(0.123)
0.050*
(0.055)
0.051**
(0.047)
0.054**
(0.045)
42.82%
70.004
(0.887)
0.027
(0.312)
0.029
(0.268)
0.030
(0.293)
Per
hectare
agricultural
spending
270.18
64.157
(0.254)
53.198
(0.342)
48.842
(0.346)
87.636
(0.108)
248.69
718.144
(0.811)
721.683
(0.738)
70.762
(0.990)
38.164
(0.477)
Per
capitaagricultural
spending
62.50
12.152
(0.206)
11.947
(0.285)
9.768
(0.337)
16.106
(0.125)
52.78
3.481
(0.778)
1.684
(0.869)
4.942
(0.629)
9.562
(0.362)
(continued)
56 J. E. Todd et al.
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Table
4.(C
ontinued)
October
1998
May1999
Control
group
mean
OLSno
controls
OLS
with
controls
OLS
weighted
with
controls
Nonlinear
with
controls
Control
group
mean
OLSno
controls
OLS
with
controls
OLS
weighted
with
controls
Nonlinear
with
controls
Householdswith43hectares(N¼1366)
Household
usesland
84.57%
0.010
(0.722)
0.015
(0.539)
0.019
(0.452)
0.013
(0.558)
79.31%
70.007
(0.833)
70.001
(0.978)
70.004
(0.874)
70.003
(0.917)
Per
capitahectaresof
landused
0.57
0.033
(0.702)
0.052
(0.511)
0.051
(0.499)
0.047
(0.438)
0.60
70.046
(0.580)
70.054
(0.442)
70.058
(0.385)
70.040
(0.473)
Household
owns
livestock
84.94%
0.030
(0.204)
0.035*
(0.095)
0.031
(0.123)
0.032*
(0.055)
84.75%
70.005
(0.840)
70.009
(0.668)
70.013
(0.532)
70.013
(0.483)
Per
capitalivestock
owned
0.31
0.010
(0.853)
70.012
(0.741)
70.006
(0.862)
0.001
(0.964)
0.30
0.059
(0.271)
0.029
(0.364)
0.028
(0.361)
0.018
(0.444)
Household
reportsag
spending#
64.07%
0.074**
(0.048)
0.050
(0.164)
0.055
(0.129)
0.052
(0.149)
49.91%
70.016
(0.672)
0.017
(0.619)
0.015
(0.654)
0.019
(0.597)
Per
hectare
agricultural
spending
221.94
25.823
(0.485)
16.084
(0.714)
18.490
(0.652)
30.077
(0.409)
188.51
75.766
(0.508)
49.115
(0.615)
62.040
(0.519)
40.383
(0.584)
Per
capitaagricultural
spending
107.11
5.178
(0.789)
72.923
(0.872)
71.728
(0.920)
6.050
(0.671)
106.41
718.764
(0.496)
722.122
(0.378)
721.095
(0.394)
76.075
(0.735)
Notes:Robustp-values
inparentheses;Marginaleff
ectsreported;*significantat90per
cent,**significantat95per
cent,***significantat99per
cent
#spendingisforpast
12monthsin
October
1998andpast
6monthsforMay1999.
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Our estimates indicate that Oportunidades had a significant impact on theprobability of using land, and on the per capita hectares of land used, in October,but not in May. Looking across the land use groups, this impact appears to be duemostly to the increase in the use of land by the landless category. Specifically, theprogramme increased the likelihood of using land by 5 to 6 percentage points inOctober (an increase of 16%), and around 4 to 5 percentage points in May (anincrease of 12%), among households landless in 1997. For smallholders, we don’tfind a significant increase in the probability of use, but do observe an increase ofabout 20 per cent in the per capita area used. For the largest landholders, there areno significant effects of the programme. Thus, the programme increases entry intoland use for those with previously limited land use and increases the scale ofproduction for those with initially small areas of land.Total livestock is measured in cow-equivalent units, where the conversion rates to
cow-equivalents are calculated as the ratio of the price for one animal to the price forone cow.8 The prices for each type of livestock were calculated as the median sellingprices reported in the October 1998 and May 1999 survey; Gertler et al. (2006)employed a similar method to convert livestock into cow-equivalent units.9 Theimpact of Oportunidades on owning livestock, and the amount per capita, areexamined. In October 1998, 76 per cent of control households owned livestock, withan average of 0.14 cow-equivalent units of livestock per capita (0.84 per household).Slightly lower ownership levels (73%) are observed in May 1999, although similarper capita values are found. As might be expected, livestock ownership is positivelyassociated with land ownership; those with land holdings are more likely to own, andto own more per capita, than the landless.Oportunidades had a positive impact on both the probability of owning livestock
as well as the quantity owned. This result is found in both seasons, although theimpact on per capita ownership in May 1999 (0.034 units, an increase of 24%) ismore than double that in October 1998 (0.016 units or 11.7%). This may indicatethat recipient households are accumulating livestock over time, thereby improvingtheir asset position. For the landless, the programme led to increased entry intolivestock production of 4 percentage points (an increase of 6%) in October 1998. Forsmallholders, the programme increased entry in May 1999, and the per capitaamount of livestock owned in both periods (21.6% in October and 26.9% in May).Again, we find the programme influencing the scale of involvement for smallholders.For large landholders, there is a small increase in the probability of livestockownership in October 1998 (3.5 percentage points), but no increase in the amountowned.Finally, we examine the impact on household spending on variable inputs for crop
production. Specifically, the impact on three variables is estimated: a dummyindicating that the household had non-zero spending on crop production, the totalamount spent during the reference period per hectare of land used during the sameperiod and the total expenditure per capita. In the case of the October 1998 survey,the reference period is the past 12 months, while in the May 1999 survey, thereference period is the past six months. In October 1998, 43 per cent of controlhouseholds reported having invested in crop production in the past year. Theseproportions are much lower in May 1999 (34%), but may be due to the shorterreference period. In October 1998, the average amount spent during the past year in
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all control households was 270 pesos per hectare (53 pesos per capita) while in May1999, the average for the past six months was 181 pesos per hectare (50 pesos percapita). Interestingly, while the reporting of agricultural spending is higher for thosewith greater land holdings, the amount spent per hectare is greater for smallholders(270 pesos versus 222 pesos in October, and 249 pesos versus 189 pesos in May). Thisindicates that smallholders may be using land more intensely or may be producinghigher value, and thus higher cost, crops.
The results indicate that in October 1998, the programme led to an increase of 4.8percentage points (an 11% increase) in the probability of spending on variableinputs. In addition, the impacts estimated from the tobit model indicate a positiveimpact on spending per hectare and per capita. However, no impact is found forMay 1999. For the landless and large landholders, there are no clearly significantimpacts in either period on the probability of spending on variable inputs, or on theamount spent per hectare or per capita. The results do indicate, however, thatthe programme increased the probability of spending for smallholders in October1998.
Impact on PROCAMPO Households
The results presented above indicate that access to land plays a critical role in theimpact of Oportunidades. Those with no land in the baseline appear to enter intoproduction and become involved in more activities. But it is the smallholders, withthree or fewer hectares of land, that are more likely to substantially expandconsumption from own production and to increase the amount of land area used andthe amount of livestock owned. Those with larger land areas expand production to acertain degree, but the impacts are not as large as for the landless and smallholders.These results suggest that the impact of Oportunidades on agricultural production islinked to how involved in agriculture the household was prior to the initiation of theprogramme.
We also wish to assess whether the programme has a different impact onhouseholds that receive cash transfers from PROCAMPO. PROCAMPO is anothercash transfer programme that was implemented in the winter 1994 agriculturalseason to compensate staple producers for potential losses following the com-mencement of the North American Free Trade Agreement (NAFTA). The maximumamount a farmer is eligible for through PROCAMPO is determined by the area ofland that was used for staple production in the agricultural seasons prior to theimplementation of NAFTA, and thus, the eligibility roster is composed of all plots ofland used for staple production during that time. Those that receive PROCAMPOare then households with a history of staple production and are closely tied toagriculture. Although PROCAMPO was scheduled to end in 2008, it has continuedwith the expectation that it will be modified at some future date. One possibility is aprogramme that mirrors the conditionality of Oportunidades but focuses onagriculture (Winters and Davis, 2007). Comparing the two programmes thusprovides insight into how they might differ in their impact.
In the baseline data, 34 per cent of households report they receive PROCAMPO,and of those, about two-thirds (22% of all households) receive both PROCAMPOand Oportunidades. To determine if PROCAMPO recipients differ from other
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households, it is possible to compare differences across agricultural outcomes forPROCAMPO and non-PROCAMPO households prior to the initiation ofOportunidades; results of the analysis are presented in Table 5. Not all the indicatorsused in later surveys are available in the baseline period, so we use the subset that isavailable in March 1998. The comparison of PROCAMPO to non-PROCAMPOhouseholds follows a standard first difference procedure, where PROCAMPO isincluded in the regressions on each of the agricultural outcomes as a dummy variablealong with other covariates. It is not possible to determine if the coefficients on thePROCAMPO dummy are a result of an impact of PROCAMPO or are instead dueto differences in unobservable characteristics of PROCAMPO households. However,this is not the purpose of the exercise; we only wish to establish if PROCAMPOrecipients are different from other households. The results show that PROCAMPOrecipients clearly are more agriculturally oriented than nonrecipients in that theyconsume more food groups and more foods from own production than nonrecipientsand in particular, are more likely to consume cereals, beans and meat from ownproduction. They are also much more likely to have reported using land in 1997 (andto use a greater area), and are also more likely to report owning livestock in 1997(and to own a larger amount). The only insignificant result is in the consumption offruits and vegetables from own production, which is not surprising given thatPROCAMPO is targeted to staple producers.To determine if Oportunidades had a different impact on PROCAMPO house-
holds, we estimate equation (8), but distinguish four types of households: (1) thosethat receive only Oportunidades, (2) those that receive only PROCAMPO, (3) thosethat receive both programmes, and (4) those that receive neither programme. Thespecification is then run including dummy variables for each of the programmeoptions, with no programmes as the default category. Comparisons are then madebetween the impact for those with only Oportunidades and those with bothprogrammes, to those that only received PROCAMPO. This combination of tests,allows us to determine if the programme had a different effect on households
Table 5. Differences between PROCAMPO and non-PROCAMPO recipients prior toOportunidades
PROCAMPO
March 1998-Baseline Marginal effect p-value
Number of food groups from own production 0.150*** 0.00Number of foods from own production 0.154*** 0.00Cereals from own production 0.079*** 0.00Beans from own production 0.063*** 0.00Fruits/vegetables from own production 0.024 0.20Meat from own production 0.038*** 0.00Land used 0.493*** 0.00Per capita area land used 0.630*** 0.00Livestock owned 0.058*** 0.00Per capita livestock owned 0.146*** 0.00
Notes: Robust p-values reported; ***significant at 99 per cent.
60 J. E. Todd et al.
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Table
6.Im
pact
ofOportunidades,PROCAMPO
onconsumptionfrom
ownproductionandagriculturalproductionindicators
October
1998
May1999
OPORTUN
IDADES
BOTH
PRO
CAMPO
OPORT¼
BOTH
PROC¼
BOTH
OPORTUN
IDADES
BOTH
PRO
CAMPO
OPORT¼
BOTH
PROC¼
BOTH
Marginal
effect
Marginal
effect
Marginal
effect
p-value
p-value
Marginal
effect
Marginal
effect
Marginal
effect
p-value
p-value
Household
consumes
from
ownproduction
0.045**
(0.028)
0.073***
(0.001)
0.050**
(0.017)
0.085*
0.356
0.052***
(0.006)
0.067***
(0.002)
0.046**
(0.023)
0.326
0.439
Pcvalueownproduction
3.856***
(0.003)
5.088***
(0.000)
3.722***
(0.008)
0.241
0.382
2.379*
(0.094)
4.252***
(0.008)
3.696**
(0.020)
0.084*
0.783
Number
offoodgroups
from
ownproduction
0.124***
(0.003)
0.149***
(0.000)
0.103**
(0.013)
0.414
0.291
0.117***
(0.007)
0.172***
(0.000)
0.074*
(0.093)
0.075*
0.064*
Number
offoodsfrom
ownproduction
0.138***
(0.002)
0.154***
(0.001)
0.107**
(0.018)
0.636
0.331
0.130***
(0.006)
0.182***
(0.000)
0.071
(0.130)
0.101
0.043*
Consumecerealsfrom
ownproduction
0.033
(0.178)
0.073***
(0.006)
0.071**
(0.011)
0.049**
0.949
0.037
(0.164)
0.069**
(0.020)
0.030
(0.245)
0.127
0.229
Consumebeansfrom
ownproduction
0.013
(0.164)
0.026**
(0.021)
0.013
(0.262)
0.122
0.343
70.0003
(0.968)
0.013
(0.139)
0.020**
(0.044)
0.032**
0.343
Consumefruitsfrom
ownproduction
0.045*
(0.071)
0.049*
(0.070)
70.001
(0.978)
0.884
0.081*
0.039*
(0.060)
0.030
(0.171)
0.026
(0.229)
0.466
0.864
Consumevegetablesfrom
ownproduction
0.002
(0.865)
0.018
(0.281)
0.019
(0.201)
0.155
0.972
0.027
(0.180)
0.046**
(0.046)
70.021
(0.320)
0.222
0.010***
Consumemeatfrom
ownproduction
0.024**
(0.014)
0.006
(0.593)
70.013
(0.228)
0.036**
0.112
0.013*
(0.081)
0.028***
(0.007)
0.007
(0.448)
0.111
0.080*
Household
usesland
0.045**
(0.037)
0.301***
(0.000)
0.262***
(0.000)
0.000***
0.304
0.037
(0.104)
0.259***
(0.000)
0.251***
(0.000)
0.000***
0.984
Per
capitahectares
oflandused
0.042**
(0.046)
0.227***
(0.000)
0.197***
(0.000)
0.000***
0.171
0.028*
(0.093)
0.185***
(0.000)
0.171***
(0.000)
0.000***
0.516
Household
ownslivestock
0.036**
(0.021)
0.062***
(0.003)
0.048**
(0.032)
0.078*
0.609
0.024
(0.170)
0.076***
(0.000)
0.021
(0.366)
0.000***
0.018**
(continued)
Cash Transfers and Agricultural Production 61
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Table
6.(C
ontinued)
October
1998
May1999
OPORTUN
IDADES
BOTH
PRO
CAMPO
OPORT¼
BOTH
PROC¼
BOTH
OPORTUN
IDADES
BOTH
PRO
CAMPO
OPORT¼
BOTH
PROC¼
BOTH
Marginal
effect
Marginal
effect
Marginal
effect
p-value
p-value
Marginal
effect
Marginal
effect
Marginal
effect
p-value
p-value
Per
capitalivestock
owned
0.022**
(0.012)
0.044***
(0.000)
0.035***
(0.005)
0.025**
0.552
0.028**
(0.027)
0.063***
(0.000)
0.029**
(0.016)
0.000***
0.014**
Household
reportsag
spending
0.039*
(0.095)
0.277***
(0.000)
0.191***
(0.000)
0.000***
0.005***
0.026
(0.246)
0.181***
(0.000)
0.164***
(0.000)
0.000***
0.518
Per
hectare
agricultural
spending
38.7
(0.278)
318.6***
(0.001)
217.4***
(0.001)
0.001***
0.031**
70.0
(0.121)
260.0***
(0.000)
252.4***
(0.000)
0.000***
0.871
Per
capitaagricultural
spending
10.13
(0.126)
65.02***
(0.000)
48.98***
(0.000)
0.001***
0.089*
9.2266
(0.302)
52.30***
(0.000)
50.78***
(0.000)
0.000***
0.864
Notes:Robust
p-values
formarginaleff
ects
inparentheses,*significantat90per
cent,**significantat95per
cent,***significantat99per
cent.
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receiving both programmes relative to those only receiving one. Given thesimilarities across the models observed previously, only the non-linear model isused. Results are presented in Table 6.
The results indicate that Oportunidades did have the effects highlighted earlier,even for those households that were not part of PROCAMPO. Most of the keyresults remain the same, indicating a broad effect of the programme on agriculturalproduction. Tests of the difference of the coefficients on those that only receivedOportunidades and those that received both programmes, however, show someinteresting differences. First, in October 1998 those that receive both programmesappear to have increased consumption from own production of staples (cereals andbeans) more than those that only receive Oportunidades, but less in meat. Note,however, that households that only receive PROCAMPO also are more likely toconsume cereals from own production at this time and that there is no significantdifference between those that receive both programmes and those that receive onlyPROCAMPO. This result appears again in May of 1999, further reflecting the stapleorientation of PROCAMPO recipients.
The impact on consumption of fruit from own production (October) andconsumption of vegetables from own production (May) is greater for those that getboth programmes versus just PROCAMPO recipients indicating, that Oportunidadesinduced an increase in fruit and vegetable production, even among established stapleproducers. In May 1999, this results in a greater impact on the number of foodgroups consumed from own production for recipients of both programmes relativeto households that receive only one programme.
The strongest and most compelling results appear to come from the analysis of thelast set of variables, those directly related to agricultural production. The impact onthe probability of land use and on the per capita amount of land used is significantlyhigher for recipients of both programmes, compared to Oportunidades-onlyrecipients. However, those with both programmes are not significantly differentthan PROCAMPO only households, suggesting that this additional impact is notdue to Oportunidades. Results found for livestock are similar, with the estimategreater for those getting both programmes versus those with only Oportunidades.The results are not significantly different between recipients of both programmes andthose that only receive PROCAMPO in October, but are significantly different inMay. This indicates that Oportunidades is having a stronger effect (beyondPROCAMPO) in the accumulation of livestock. The results on agricultural spendingprovides additional evidence of a specific effect on recipients of both programmes,with the impact on spending significantly higher for those receiving both than forthose receiving just one in October. In May, the impact on recipients of bothprogrammes is significantly different from that on Oportunidades-only recipients.These results indicate that households that were dedicated to staple production tendto use the cash from Oportunidadesmore for investment in livestock and spending onagricultural inputs, and thus intensification of production, than other households,and this effect is beyond what would have occurred with just PROCAMPO in place.In sum, we find that while the effects of Oportunidades and PROCAMPO arecompensatory for some outcomes (for example, cereal production and land use),they are complementary for others (for example, fruit production, livestock, andspending on variable crop inputs).
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V. Conclusions
In this paper, we investigate the impact of the Oportunidades programme onagricultural production indirectly by looking at the impact on consumption of foodfrom own production, as well as directly, by looking at land use, livestock ownershipand spending on crop production. The programme increased not only theprobability that a household consumes food from its own production, but also thevalue of the consumption (in October 1998) and the variety of foods and foodgroups. Specifically, the programme increased the probability of consuming anumber of highly nutritious foods from own production, including fruits, vegetablesand meat, ranging from 16 to 32 per cent. This is the first insight into how the overallimprovements in diet quality documented previously were achieved. Given that thesehouseholds are poor and tend to rely on staple production for food, the increase innutritious foods – even if in some cases small in absolute terms – suggests that theprogramme is pushing households in the right direction. Seasonality appears to playan important role in determining the specific food groups that were impacted;consumption of fruits and meats from own production increased during the autumnwhile consumption of vegetables and meats from own production increased duringthe spring. Looking more directly at the information about agricultural production,there is evidence that Oportunidades increased overall land use, livestock ownershipand spending on crop production. The probability of spending on agricultural inputsincreased in the autumn, while increases in livestock ownership due to theprogramme were observed in the spring.The impact of the programme varies substantially by previous land use, indicating
the importance of land assets in household response to the programme. Entry intonew agricultural activities, as indicated by consumption from own production, islimited for those who had access to more than three hectares of land prior to theinitiation of the programme. This indicates that these larger landholders were notconstrained in agricultural production choices and that the impacts on consumptionfrom own production were concentrated among smallholders and previously landlesshouseholds. Similarly, the increase in the area of land used and the number oflivestock owned was largest for smallholders, at 20 per cent and 27 per cent,respectively. Combined, these results indicate that Oportunidades increased theability of smallholders and landless households to ensure diet diversity andnutritional quality through agricultural production. Such an effect is particularlynotable in light of the recent increases in international food prices, which is likely toaffect poor households disproportionately. Thus, access to Oportunidades may helpshelter poor households from the risk of food insecurity during such crises, not onlythrough direct cash assistance but also by bolstering consumption from ownproduction among poor households.In general, the findings are consistent with the idea that a cash transfer programme
can relax liquidity constraints and lead to increased investment in productiveactivities. However, it is possible that these impacts may be due to factors other thanan increase in liquidity. Stecklov et al. (2005) find that Oportunidades can reduce outmigration to the United States; perhaps a decrease in migration left treatmenthouseholds with a larger supply of labour relative to the control households, andtherefore, lead to an increase in agricultural production. In addition, the information
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about diet and nutrition obtained through a component of the programme (periodichealth and nutrition lectures) may have had an impact on the types of foodshouseholds produced, and therefore, consumed. Thus, although a relaxation ofliquidity constraints from the receipt of cash transfers is the most likely factordriving the results, other factors may also be important.
The investigation into whether PROCAMPO households experienced differentimpacts than non-PROCAMPO households indicates that the two programmes weresubstitutable with regard to some outcomes. However, complementarities also existas PROCAMPO households appear to invest the additional cash received from theOportunidades programme to diversify agricultural activities. The combination ofresults indicates the importance of the type of household in the ultimate impact of aprogramme. Eligibility criteria in designing programmes can be very important inensuring programme objectives are met. A programme seeking to enhanceagricultural outcomes may want to provide cash to poor farmers.
Looking beyond the Oportunidades programme, the results of this investigationemphasise that cash transfers to poor households can have awide range of impacts thatextend far beyond programme objectives. Conditions may direct the use of a portion(or even a large majority) of the cash to specific uses, but households also makedecisions about how to use the additional resources. Questions that remain includewhether such impacts are the result of just receiving the cash, exposure to educationallectures that emphasised increasing diversity of food consumption or the fact that themajority of recipients were women. In addition, it is not clear how important theOportunidades programme will become to agricultural production among the poor inMexico if PROCAMPO transfers end and a new set of agricultural programmes areinitiated by the Mexican government.
Acknowledgement
The authors thank the anonymous reviewer, and editors, Ben Davis and GeroCarletto, for providing valuable feedback. We are grateful to the staff atOportunidades and Carola Alvarez at the Inter-American Development Bank forproviding access to the data and explaining many details about the programme. Allremaining errors and omissions are the sole responsibility of the authors.
The views expressed in this paper are those of the authors and do not necessarilyreflect the views of the Economic Research Service or the US Department ofAgriculture.
Notes
1. When it began, Oportunidades was called PROGRESA, but was renamed in 2000 by the Fox
administration. The programme is referred to by its current name in this paper.
2. The first baseline survey was the locality census that was conducted prior to the start of the programme
in all programme localities across the country. The purpose of this survey was to identify eligible
households. This survey was conducted in the control localities at the same time as the treatment
localities to provide a baseline observation. However, many health and education outcomes were not
collected in the census as such characteristics did not affect eligibility. Thus, the March 1998 survey
collected supplementary baseline data on these outcomes.
3. The process of ‘densification’ that began late in 1998 used adjusted models to incorporate many more
households than were initially determined to be eligible. However, the experimental design is
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compromised when these ‘densificados’ are included, as most are different than the originally eligible
households. The ‘densificados’ have on average fewer children and more elderly members and, they
entered the programme later than the originally eligible households in the treatment group, but not in
the control group (Skoufias et al., 1999).
4. The 10 food groups that can be reported as consumed from own production are: i) cereals and grain
(tortillas and rice), ii) tubers (potatoes and carrots), iii) fruits (oranges, bananas/plantains, apples and
lemons/limes) iv) legumes (beans), v) dairy (milk and cheese), vi) vegetables (tomatoes, onions, leafy
vegetables, and nopales), vii) meat (chicken, beef/pork, and goat/sheep). viii) fish, ix) eggs and x) oils/
fats (butter).
5. Details can be found in Todd (2007).
6. Rosenbaum and Rubin’s original work relied on known propensity scores and not estimated propensity
scores, but many since have demonstrated that estimated propensity scores are better at balancing the
covariates than the true propensity scores (Rosenbaum, 2004).
7. Propensity scores were estimated using logistic regression and the full set of variables (no higher-order
terms or interactions) listed in Table 1. Alternative methods, such as boosted regression, yielded no
improvement in the balancing properties of the estimated propensity scores.
8. The conversation rates are cow¼ 1, oxen¼ 1.318, horse¼ 0.564, mule¼ 0.125, goat/sheep¼ 0.150,
pigs¼ 0.150, poultry¼ 0.015 and rabbits¼ 0.011.
9. An alternative method is to convert all livestock owned to tropical livestock units (TLU), in which one
TLU equals 250 kilograms of body weight. The value of livestock constructed using TLUs was
compared to that using cow-equivalent prices and was found to be very similar.
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