the impact of improved maize varieties on poverty in mexico: a propensity score-matching approach

World Development Vol. 38, No. 7, pp. 1024–1035, 2010� 2009 Elsevier Ltd. All rights reserved

0305-750X/$ - see front matter

www.elsevier.com/locate/worlddevdoi:10.1016/j.worlddev.2009.11.017

The Impact of Improved Maize Varieties on Poverty in Mexico:

A Propensity Score-Matching Approach

JAVIER BECERRILAutonomous University of Yucatan, Mexico

and

AWUDU ABDULAI *

University of Kiel, Kiel, Germany

Summary. — This study examines the adoption of improved maize germplasm in Oaxaca and Chiapas in Mexico. It employs a propen-sity score-matching approach to analyze the impact of the adoption of improved maize varieties on household income and povertyreduction, using cross-sectional data of 325 farmers from the two regions. The findings reveal a robust positive and significant impactof improved maize variety adoption on farm household welfare measured by per capita expenditure and poverty reduction. Specifically,the empirical results suggest that adoption of improved maize varieties helped raise household per capita expenditure by an average of136–173 Mexican pesos, thereby reducing their probability of falling below the poverty line by roughly 19–31%.� 2009 Elsevier Ltd. All rights reserved.

Key words — Mexico, maize, technology adoption, impact assessment, propensity score-matching

* Becerril acknowledges the grant from CONACYT and FIDERH,

Mexico for his Ph.D. studies in Kiel. The authors are grateful to CIM-

MYT for full permission to use the dataset. The authors thank three

anonymous reviewers and the journal editor for their comments and su-

ggestions that significantly improved the paper. The usual disclaimer ap-

plies. Final revision accepted: November 11, 2009.

1. INTRODUCTION

The role of agricultural technology change in reducing ruralpoverty and fostering overall economic development has beenwidely documented in the economic literature. Although quitecomplex, the relationship between the adoption of new tech-nology and poverty reduction has been perceived to be posi-tive (Bellon, Adato, Becerril, & Mindek, 2006; Binswanger &von Braun, 1991; Evenson & Gollin, 2003; Just & Zilberman,1988). Productivity-improving technologies reduce poverty byreducing food prices, facilitating the growth of nonfarm sec-tors, and by stimulating the transition from low productivitysubsistence agriculture to a high productivity agro-industrialeconomy (Just & Zilberman, 1988). However, the potentialfor poverty reduction through reduced food prices, growthin the nonfarm sector and agricultural commercialization de-pends to a large extent on the magnitude of productivity gainsin agriculture.

The effects of new agricultural technology on poverty maybe direct or indirect. The direct effects of new agriculturaltechnology on poverty reduction are the productivity bene-fits enjoyed by the farmers who actually adopt the technol-ogy. These benefits usually manifest themselves in the formof higher farm incomes. The indirect effects are productiv-ity-induced benefits passed on to others by the adopters ofthe technology. These may comprise lower food prices, high-er nonfarm employment levels or increases in consumptionfor all farmers (de Janvry & Sadoulet, 2001). However, pro-ductivity-enhancing agricultural technology involves a bun-dle of innovations rather than just a single technology.Hence, if farmers adopt only one technique such as im-proved maize variety rather than a package that includesapplying new types of fertilizer, improved ways of plantingand weeding, then the productivity-improving effect of thenew maize variety may not be realized (Karanja, Renkow,& Crawford, 2003).

1024

The package nature of new agricultural technology makesthe evaluation of its welfare effects quite difficult. Most ofthe studies on the impact of agricultural technology on farmincomes and poverty have usually relied on fairly macro ap-proaches, with very few analyses at the micro-level. Some ofthe few household level studies include Morris (2002), Karanjaet al. (2003), Evenson and Gollin (2003), Mendola (2007) andMojo, Norton, Alwang, Rhinehart, and Deom (2007). Thus,the literature appears to document overall positive impacts,with far less evidence at the individual household level thatspecifically show the effects of the adoption of agriculturaltechnologies on farm productivity and household welfare.This is in contrast to the plethora of empirical work on factorsaffecting the innovation adoption decisions of farm house-holds. 1

Although improved maize varieties have been available inMexico for more than 40 years, their dissemination has beenquite limited. These improved varieties include hybrids, openpollinated and creolized varieties. These varieties are culti-vated alongside the local varieties. Despite intensive effortsby the government to promote the use of improved seed, onlyabout 31% of the total maize area in the country is devoted toimproved maize varieties (CIMMYT, 2007). As pointed outby Bellon et al. (2006), this relatively low rate of adoptionmay provide a misleading impression of actual benefits or wel-fare that accrue from using improved varieties. A number ofstudies have documented the use of improved maize varietiesand how poor farmers use and perceive benefits from different

http://dx.doi.org/10.1016/j.worlddev.2009.11.017

THE IMPACT OF IMPROVED MAIZE VARIETIES ON POVERTY IN MEXICO: A PROPENSITY SCORE-MATCHING APPROACH 1025

types of maize germplasm (e.g., Bellon & Risopoulos, 2001;Bellon et al., 2006; Morris, 2002; Morris, Tripp, & Dankyi,1999; Smale et al., 2003).

The present study contributes to the literature by analyzingthe impact of adoption of the improved maize germplasm onhousehold welfare in Chiapas and Oaxaca. In a first step, weexamine the effects of the adoption of improved maize germ-plasm—hybrids, creolized and open pollinated varieties—onper capita expenditure and poverty status in both Chiapasand Oaxaca. Given that hybrids are widely used in Chiapaswhile creolized maize is used in Oaxaca, we separately investi-gate the impact of hybrid maize on per capita expenditure andpoverty status in Chiapas, as well as the impact of creolizedmaize on per capita expenditure and poverty status in Oax-aca. 2 We also provide separate estimates for various landhold-ing categories in order to examine the differential welfarebenefits for land-poor and land-rich households. The dataemployed consist of 325 farm households that were inter-viewed in 2001. To account for self-selection that normallyarises when technology adoption is not randomly assignedand self-selection into adoption occurs, we employ a propen-sity score-matching model. A better understanding of theadoption decisions, as well as the impact of the adoption ofnew technologies on the welfare of farm households is criticalto understanding how policy interventions can help reducepoverty among farm households.

2. ADOPTION OF IMPROVED MAIZE VARIETIES INMEXICO

The recent literature on improved maize germplasm revealsthat many farmers have taken up improved varieties andplanted them alongside their local varieties known as land-races (Bellon & Risopoulos, 2001; Bellon et al., 2006). Im-proved maize germplasm include hybrids, opened pollinatedvarieties (OPV) and creolized varieties. They have essentialattributes such as high yields, desired maturity and height, of-fer resistance against diseases and insects, as well as resistanceto water lodging.

A maize hybrid can be defined as the result of the crossing oftwo or more inbred lines. If hybrid seed is replanted, it wouldnot be as productive as the original seed. Open pollinated vari-eties are populations that breeders have selected for a very spe-cific set of traits (no inbred lines or parents), consequently theseed can be replanted usually up to three years without majordrops in yield. A creolized maize seed is defined as a hybrid be-tween an improved maize variety (hybrid or OPV) and a localvariety (Bellon et al., 2006). Creolization is actually a processthat is recognized and named by Mexican farmers. Accordingto Bellon and Risopoulos (2001), creolized varieties are appre-ciated because they combine desirable traits of improved vari-eties with those of landraces, and also allow more constrained,poorer farmers to benefit from the useful traits of improvedgermplasm.

Despite the desirable traits of improved germplasm that arenot found in landraces, they tend to lack certain importanttraits that landraces possess (Bellon et al., 2006). For example,Smale et al. (2003) note that maize landraces that compose theBolita racial complex are known for their tolerance todrought, as well as for other traits related to agronomic per-formance. In addition, modern maize types are not able tocompete with local landraces except under irrigated condi-tions. Given the advantages of both improved germplasmand landraces, many farmers tend to cultivate both varieties,as choosing one or the other presents a trade-off for them.

In their study on improved maize germplasm in Chiapas,Bellon and Risopoulos (2001) found that creolized maizevariety provides farmers nearly the same level of advantageouscharacteristics as improved varieties. An earlier studyby Bellon and Taylor (1993) concluded that policy interven-tions to encourage a more uniform adoption of existing tech-nologies may decrease, rather than increase, productionefficiency. In the present study, we specifically investigate theimpact of the adoption of improved germplasm—hybrids, cre-olized and open pollinated varieties—on household per capitaexpenditure and poverty reduction in both Chiapas and Oax-aca. As indicated earlier hybrids are widely used in Chiapas,while creolized maize is used in Oaxaca, we therefore presentseparate estimates of the impact of hybrid maize adoptionon per capita expenditure and poverty status in Chiapas, aswell as the impact of creolized maize on per capita expenditureand poverty status in Oaxaca.

3. CONCEPTUAL FRAMEWORK AND ESTIMATIONTECHNIQUE

(a) Technology adoption and household welfare

As rightly noted by Smale, Just, and Leathers (1994), com-peting theories are available for explaining farmers’ allocationof plots to different varieties, rather than totally adopting aparticular variety. The major theoretical approaches that ex-plain incomplete adoption of new varieties include input fixityor rationing, risk and uncertainty, and various forms of mar-ket imperfections (Just & Zilberman, 1988; Sadoulet & de Jan-vry, 1995; Smale et al., 1994). In many developing countrieswhere the supply of inputs or credit is rationed, farmers maychoose to cultivate both new and traditional varieties eventhough the optimal choice would be to cultivate one type. Riskattitude of a farmer may also influence his decision on the ex-tent to which the new variety is cultivated. In particular, a risk-averse farmer may choose to grow both improved and tradi-tional varieties.

The safety first behavior postulates that farmers normallyallocate their land to different varieties or crops to achievewell-defined goals. The choice of technology therefore tendsto vary with the sociodemographic characteristics of the farmhousehold when the farmer’s goal is to secure returns large en-ough to cover subsistence needs (Sadoulet & de Janvry, 1995).Employing nested models within a general model to test com-peting explanations provides statistical evidence that a combi-nation of explanations rather than any single theoreticalapproach best describes land allocation to new varieties. Giventhat the primary goal of the current study is to analyze the im-pact of the adoption of improved germplasm on householdwelfare, we employ a framework that simply captures thefarmer’s decision to adopt improved varieties. 3

The basic relationship we consider in examining the impactof the adoption of new technology on household welfare as-sumes that welfare, measured by household income, is a linearfunction of a vector of explanatory variables (Xi) and an adop-tion dummy variable (Ri). The linear regression equation canbe specified as

Y i ¼ X 0ibþ dRi þ li; ð1Þwhere Yi is the mean income of the household, li is a normalrandom disturbance term and Ri is a 0 or 1 dummy variablefor the use of the new technology; Ri ¼ 1 if the technology isadopted and Ri ¼ 0 otherwise. The vector Xi represents house-hold and farm-level characteristics. Whether farmers adopt

1026 WORLD DEVELOPMENT

improved varieties or not is dependent on the characteristics offarmers and farms, hence the decision of a farmer to adopt im-proved variety is based on each farmer’s self-selection insteadof random assignment.

Assuming a risk-neutral farmer, the index function to esti-mate the adoption of improved variety can be expressed as:

R�i ¼ X 0icþ ei; ð2Þwhere R�i is a latent variable denoting the difference betweenutility from adopting improved varieties U iA and the utilityfrom not adopting the technology (UiN ). The farmer will adoptthe new technology if R�i ¼ UiA � UiN > 0. The term X 0ic pro-vides an estimate of the difference in utility from adoptingthe technology (UiA � UiN ), using the household and farm-le-vel characteristics, Xi, as explanatory variables, while ei is anerror term. In estimating Eqns. (1) and (2), it needs to be notedthat the relationship between a new technology and an out-come such as income could be interdependent. Thus, technol-ogy can help increase output and as such household incomewhile richer households may be better disposed toward theadoption of new technologies. Thus, treatment assignment isnot random, with the group of adopters being systematicallydifferent. Specifically, selection bias occurs if unobservable fac-tors influence both the error terms of the income equation, ðlÞ,and the technology choice equation ðeÞ, thus resulting in cor-relation of the error terms of the outcome and technologychoice specifications. Hence, estimating Eqn. (1) with ordinaryleast squares will lead to biased estimates.

Some authors have employed the Heckman two-step meth-od to address selection bias, when the correlation betweenthe two error terms is greater than zero. However, the ap-proach depends on the restrictive assumption of normallydistributed errors. Another way of controlling for selectionbias is to employ instrumental variable approach (IV). A ma-jor limitation of the approach is the difficulty in finding andidentifying instruments in the estimation. Moreover, bothOLS and IV procedures tend to impose a linear functionalform assumption implying that the coefficients on the controlvariables are similar for adopters and nonadopters. As indi-cated by Jalan and Ravallion (2003), this assumption maynot hold, since the coefficients could differ. Unlike the para-metric methods mentioned above, propensity score-matchingrequires no assumption about the functional form in specify-ing the relationship between outcomes and predictors of out-come. The drawback of the approach is the ConditionalIndependence Assumption (CIA), which states that for a gi-ven set of covariates participation is independent of potentialoutcomes. Smith and Todd (2005) rightly note that theremay be systematic differences between the outcomes ofadopters and nonadopters, even after conditioning onobservables. Such differences may arise because of selectioninto treatment based on unmeasured characteristics. How-ever, Jalan and Ravallion (2003) argue that the assumptionof selection on observables is no more restrictive than assum-ing away problems of weak instruments, when Heckmantwo-step or the IV approach is employed in cross-sectionaldata analysis.

(b) The evaluation problem and matching methods

An important issue in evaluating the impact of technologyadoption on income is the specification of the average treat-ment effect. Rosenbaum and Rubin (1983) defined the averagetreatment effect (Di) in a counterfactual framework as

Di ¼ Y Ai � Y N

i ; ð3Þ

where Y Ai and Y N

i denote income of household i that adopts thetechnology and the household that does not adopt the technol-ogy, respectively. In estimating the impact from Eqn. (3), aproblem that arises is due to that fact that either Y A

i or Y Ni is

normally observed, but not both of them for each household.What is normally observed can be expressed as

Y i ¼ DiY iA þ ð1� DiÞY iN D ¼ 0; 1: ð4ÞDenoting P as the probability of observing a household withD ¼ 1, the average treatment effect, s, can be specified as

s ¼ P � EðY A D ¼ 1Þ � EðY N D ¼ 1jj Þ½ �þ ð1� PÞ � EðY N D ¼ 0Þ � EðY N D ¼ 0Þjj½ �: ð5Þ

Eqn. (5) implies that the effect of adoption for the entire sam-ple is the weighted average of the effect of adoption on theadopters (treated) and nonadopters (controls), with eachweighted by its relative frequency. The main problem of causalinference stems from the fact that the unobserved counterfac-tuals, EðY A D ¼ 0Þj and EðY N D ¼ 1Þj cannot be estimated(Smith & Todd, 2005).

Particularly when the data available provide no informationon the counterfactual situation, a missing data problem arises,which requires estimating the direct effect of technology adop-tion from the variation in outcomes across the farm house-holds using statistical matching (Blundell & Costa-Dias,2000). The present study addresses this problem by using thepropensity score-matching (PSM) method that summarizesthe pre-treatment characteristics of each subject into a singleindex variable, and then uses the propensity score to matchsimilar individuals (Rosenbaum & Rubin, 1983). The PSM,which is the probability of assignment to treatment condi-tional on pre-treatment variables, is given by:

pðX Þ ¼ Pr½D ¼ 1jX � ¼ E½DjX �; pðX Þ ¼ F fhðX iÞg; ð6Þ

where F f�g can be normal or logistic cumulative distributionand X is a vector of pre-treatment characteristics. 4

Estimating the treatment effects based on the propensityscore requires two assumptions. The first is the CIA men-tioned earlier. A second condition is that the average treat-ment effect for the treated (ATT) is only defined within theregion of common support. This assumption ensures that per-sons with the same X values have a positive probability ofbeing both participants and nonparticipants (Heckman,Ichimura, & Todd, 1997). 5

Once the propensity score is computed, the ATT effect canthen be estimated as follows:

ATT ¼ E Yf iA � Y iN jD ¼ 1g;ATT ¼ E½E Yf iA � Y iN jDi ¼ 1; pðX Þg�;ATT ¼ E½EfY iAjDi ¼ 1; pðX Þg � EfðY iN jDi ¼ 0; pðX ÞgjD ¼ 1�:

ð7ÞA number of methods have been proposed in the literature tomatch similar adopters and nonadopters. The most commonlyused approaches are the nearest neighbor matching (NNM)and kernel-based matching (KBM) methods. The nearestneighbor method consists of matching each treated individualwith the control individual that has the closest propensityscore. It is usually applied with replacement in the controlunits. The second step is to compute the differences of eachpair of matched units, and finally the ATT is obtained asthe average of all these differences. In the kernel-based meth-od, all treated subjects are matched with a weighted averageof all controls, using weights that are inversely proportional


to the distance between the propensity scores of treated andcontrol groups.

Given that the analysis does not condition on all covariates,but on the propensity score, there is the need to check if thematching procedure is able to balance the distribution of therelevant variables in the control and treatment groups. The ba-sic idea is to compare the situation before and after matchingand then check if there is any remaining differences after con-ditioning on the propensity score (Caliendo & Kopeinig,2008). Sianesi (2004) suggests re-estimating the propensityscore on the matched sample, only on adopters and matchednonadopters and then comparing the pseudo-R2’s before andafter matching. The pseudo-R2 is supposed to indicate howwell the regressors X explain the adoption probability. Aftermatching there should be no systematic differences in the dis-tribution of covariates between both groups and therefore, thepseudo-R2 should be fairly low. The test should not be rejectedbefore, but should be rejected after matching. Given that “hid-den bias” may still arise with the PSM if there are unobservedvariables that simultaneously affect adoption and wellbeing ofhouseholds, there is the need to check for hidden bias aftermatching. Rosenbaum (2002) has suggested the use of a sensi-tivity analysis called bounding approach to address this prob-lem. 6

4. DATA AND DESCRIPTIVE ANALYSIS

The data used in this study were obtained from a survey of325 farmers randomly selected from 12 communities located intwo regions (Oaxaca and Chiapas) of Mexico. The selection ofthe 12 communities from the two regions was not random, butrather systematic, aimed at sampling communities with house-holds of different levels of the adoption of improved germ-plasm, different levels of poverty status and ethnicbackgrounds, as well as different land holdings. The selectionwas aided by CIMMYT’s geographic information system(GIS) Lab. The GIS was developed to define potential loca-

Table 1. Definition of variable

Variable Description

Dependent variable

Improve maizevariety

1 if farmer plants any improved variety, 0 othe

Hybrid variety 1 if farmer plants hybrid variety, 0 otherwiCreolized variety 1 if farmer plants creolized variety, 0 otherwPoverty Headcount index is used to estimate household p

Independent variables

Age Age of the maize farmer in number of yearEducation Number of years of schooling of maize farmEthnicity 1 = if farmer speaks Spanish, 0 otherwiseHousehold size Number of people residing in householdShare male Share of male members residing in householdStaple crops Number of staple crops grown by househoHorse Number of horses owned by householdRemittances 1 if household received remittances from abroad, 0Distance Distance to the permanent market in kilometLandowner 1 if farmer is landlord, 0 otherwiseNumber plots Number of plots under cultivationArea maize Area planted with maize (hectares)Farm size Total land owned by farmer (hectares)Area red Area red-color good quality (%)Area slope 1 if plot is on a slope, 0 otherwise

tions for field work. In each community, 27 households wererandomly drawn for the interviews to ensure that the 12 com-munities were equally represented. The survey was conductedbetween October and December in 2001. Information from theselected households was gathered by a survey of farm house-holds. 7 The questionnaire contained questions about the kindof maize varieties that are planted by farmers, a key variable inthe analysis.

For the purpose of this study, adopters are classified asfarmers who used improved maize germplasm (creolized, hy-brids and open pollinated), while nonadopters are referredto as farmers who planted only traditional varieties or land-races. As shown in Table 1, about 80% of farmers in Chia-pas adopted improved varieties, compared to 32% inOaxaca. Improved germplasm, and in particular, hybrids ap-pear to be predominant in Chiapas while creolized varietypredominate in Oaxaca. For example, about 48% of thefarmers adopted hybrids in Chiapas, compared to only 4%of farmers in Oaxaca. While less than 10% of farmersadopted open pollinated variety in Chiapas, the correspond-ing figure for Oaxaca was about 1%. The development ofagricultural markets has contributed to the adoption of im-proved varieties and particularly the use of hybrids in Chia-pas (Keleman, Hellin, & Bellon, 2009). The empiricalanalysis therefore considers adoption of all improved varie-ties (creolized, hybrids and open pollinated) against land-races for both Chiapas and Oaxaca and then individualanalyses for hybrids in Chiapas and creolized maize in Oax-aca.

Additional information collected included data on farm andnonfarm activities, as well as demographic, location, and plotcharacteristics. Information on farm activities included totalland holding, traditional and improved maize varieties, farmlabor, capital assets, and input and output prices. Demo-graphic factors collected included household size, years ofschooling and age of household members, self-reported accessto credit and contact with extension agents, and the locationof the nearest market.

s and descriptive statistics

Chiapas Oaxaca

Samplemean

Standarddeviation

Samplemean

StandardDeviation

rwise 0.802 0.399 0.319 0.468

se 0.475 0.501 – –ise – – 0.239 0.428overty 0.463 0.500 0.589 0.494

s 48.66 14.38 50.32 13.84er 3.12 2.96 2.77 2.50

– – 0.51 0.505.31 2.27 5.73 2.55

(%) 12.35 14.33 13.43 14.50ld 0.86 1.20 0.49 0.88

0.62 0.93 0.60 1.28otherwise 0.06 0.23 0.04 0.20ers 30.33 19.53 39.97 21.47

0.90 0.30 0.82 0.391.98 1.07 1.13 0.425.04 3.87 1.12 1.3010.58 9.26 9.86 14.250.65 1.74 0.39 0.910.45 0.50 0.73 0.45


We employ per capita expenditure (PCE) in our analysisrather than household income. 8 The PCE was calculatedand adjusted to adult equivalents (Skoufias, Davis, & Behr-man, 1999). Furthermore, household expenditure in Oaxacawas adjusted to make it equivalent to the purchasing powerin Chiapas since prices for similar goods were higher in Oax-aca than in Chiapas. Poverty rates were calculated with dataon households consumption obtained from the survey. Thefood poverty line was constructed from the Mexican food-bas-ket. Based on this poverty line, the poor households are re-ferred to as those households with per capita expendituresbelow the food poverty line. The food poverty line is the valueof food standard basket equivalent to 332.52 Mexican pesos(US$ 36) per capita per month. The headcount index showsthat poverty is pervasive in both areas, with most farminghouseholds living under the poverty line. Specifically, theseare 67.2% and 56.6% in Oaxaca and Chiapas, respectively.

Differences in the features of adopters and nonadopters arepresented in Table 2, alongside their t-values. The t-values sug-gest that there are some differences between adopters and non-

Table 2. Differences in characteristics of ad

Variable characteristic C

Adopters

Per capita expenditure (MX$) 452.77Poverty level (share under poverty line) 0.59Age of farmer in years 47.83Education 1 = formal education, 0 otherwise 0.81Ethnicity 1 = if farmer is Spanish, 0 otherwise 0.98Family size (number of household members) 5.14Share of male members (%) 12.71Number of staple crops 0.74Horse holding 0.67Remittances 0.04Distance (km) 29.95Land holding 0.92Number of plots 1.96Area planted with maize (hectares) 5.21Farm size (hectares) 11.05Area red-color good quality (%) 0.67Area slope 1 = slope; 0 otherwise 0.40Seed = 1 if farmer received improved seed, 0 otherwise 0.41Extension = 1 if farmer received extension visits, 0 otherwise 0.50Number of farmers 130

Note: MX$ refers to Mexican pesos.

Table 3. Distribution of sample

Category Land owned (hectares) A

Frequency

Chiapas

Landless 0 Hectares 10Small <5 Hectares 33Large >5 Hectares 87

Total 130

Oaxaca

Landless 0 Hectares 8Small <4 Hectares 16Large >4 Hectares 28

Total 52

adopters with respect to farm-level and householdcharacteristics. For farmers in Chiapas, there appear to be dif-ferences in family size, education level, number of crops grownand access to remittances, as well as land holdings. In partic-ular, adopters generally own more land than nonadopters. Thedifferences between adopters and nonadopters in Oaxaca aremainly in area planted with maize, distance from markets, eth-nicity, and proportion of males in the family. There are alsosignificant differences in household per capita expendituresand poverty status between adopters and nonadopters in Chi-apas and Oaxaca.

Table 3 also presents the distribution of sample householdsaccording to land holdings. The differences between adoptersand nonadopters in both Chiapas and Oaxaca suggest a posi-tive correlation between the incidence of adoption and land-asset ownership, with the incidence of adoption higher amonglarger farmers, compared to landless and smaller farmers. Gi-ven that farm income remains a major source of income in rur-al Mexico, allocation of land, in turn is one of the importantdeterminants of household income and, hence, of expenditure

opters and nonadopters (sample mean)

hiapas t-Values Oaxaca t-Values

Nonadopters Adopters Nonadopters

320.73 2.38 493.84 383.47 1.620.84 2.70 0.60 0.76 2.1152.03 �1.49 51.12 49.95 0.500.53 3.34 0.79 0.71 1.030.97 0.59 0.71 0.41 3.666.03 �2.01 5.52 5.83 �0.7210.88 0.65 17.06 11.73 2.211.38 �2.73 0.44 0.51 �0.480.41 1.44 1.13 0.35 3.790.13 �1.92 0.08 0.03 1.4731.91 �0.51 33.14 43.17 �2.840.81 1.89 0.85 0.80 0.682.03 �0.33 1.19 1.10 1.334.36 1.11 1.44 0.97 2.188.70 1.28 11.32 9.18 0.890.59 0.21 0.21 0.12 1.590.66 �2.65 0.69 0.75 �0.740.16 2.70 0.35 0.22 1.770.22 1.86 0.31 0.19 1.6932 52 111

households by land holding

dopters Nonadopters

Percentage Frequency Percentage

7.69 6 18.7525.38 16 50.0066.92 10 31.25

32

15.38 22 19.8230.77 43 38.7453.85 46 41.44

111


levels. Thus, the differences in land ownership between adopt-ers and nonadopters may be contributing to the disparities inthe incidence of poverty among the two groups.

A detailed presentation of differences in the incidence ofpoverty for adopters and nonadopters in the two regions is gi-ven in Table 4. As is evident from the table, three poverty mea-sures are employed in the analysis. The poverty measuresinclude the headcount index, the poverty gap, and the squaredpoverty gap. The headcount index is the percentage of thepopulation living in households with income per capita belowthe poverty line. However, the headcount index ignores theamounts by which the expenditures of the poor fall short ofthe poverty line. Hence, the poverty gap index which givesthe mean distance below the poverty line as a proportion ofthe poverty line is also computed. The squared poverty gap in-dex which indicates the severity of poverty is computed byweighting the individual poverty gaps by the gaps themselves,so as to reflect inequality among the poor. 9 All three povertymeasures indicate that poverty is more prevalent and severeamong nonadopters compared to adopters.

The results from comparing mean differences in per capitaexpenditure, poverty rates, and other household characteris-tics between adopters and nonadopters appear to indicate thatadopters are better off than nonadopters. However, these com-parisons of mean differences do not account for the effect ofother characteristics of the households and thus may confoundthe impact of technology adoption on expenditure and povertystatus with the influence of other characteristics. To investi-gate the impact of the adoption of maize technologies on percapita expenditure and poverty levels, multivariate approachesthat account for selection bias arising from the fact that adopt-ers and nonadopters may be systematically different are essen-tial.

Smith and Todd (2005) have argued that matching shouldbe based on variables that influence both treatment assign-ment and outcomes and are not affected by the treatment.The choice of variables in the current study is based on pre-vious studies on the determinants of adoption of improvedvarieties. It is widely documented that adoption and dissem-ination of new technologies depends to a large extent on thehousehold resource endowments, characteristics of the house-hold head, household locational characteristics, and the nat-ure of the technology (Abdulai & Huffman, 2005; Federet al., 1985). Empirical evidence from studies by Bellon andTaylor (1993) as well as by Bellon and Risopoulos (2001),show that micro-environments such as soil type also tendsto influence adoption decisions. However, soil type was notsignificantly related to adoption decisions in our estimatesand was therefore dropped from the propensity score specifi-cations. 10

5. EMPIRICAL RESULTS

The empirical analysis of the impact of adoption on per ca-pita expenditure (PCE) and poverty status involved four setsof estimations: adoption of all improved maize varieties versus

Table 4. Poverty measures for adopters an

Poverty Oaxaca

Adopters Nonadopters

Headcount index 0.595 0.708Poverty gap 0.229 0.231Severity of poverty 0.108 0.111

nonadoption in both Oaxaca and Chiapas, adoption of hybridvarieties versus nonadoption in Chiapas, and then adoption ofcreolized varieties in Oaxaca. The impact analysis was pre-ceded by a specification of the propensity scores for the treat-ment variables. A logit model was employed to predict theprobability of adopting an improved variety. 11 Results ofthe propensity scores are reported in Tables A1 and A2 inthe appendix. As noted by Lee (2008), the propensity scoresonly serve as a device to balance the observed distribution ofcovariates across the treated and the untreated groups. Thesuccess of propensity score estimation is therefore assessedby the resultant balance. Although a detailed interpretationof the propensity score estimates is not undertaken in thisstudy, most of the variables included have the expected signs.

The common support condition is imposed in the estimationby matching in the region of common support. 12 The distribu-tion of the propensity scores and the region of common sup-port are shown in Figure 1. The figure shows the bias in thedistribution of the propensity scores between the groups ofadopters and nonadopters, and clearly reveals the significanceof proper matching, as well as the imposition of the commonsupport condition to avoid bad matches.

The effect of improved varieties on PCE and poverty statusof the households is estimated with the nearest neighbor(NNM) and kernel-based matching (KBM) models. 13 Theempirical results for all improved maize varieties and thoseof hybrids in Chiapas and creolized in Oaxaca are given in Ta-bles 5 and 6. The results in Table 5 show that the adoption ofimproved maize varieties exerts a positive and significant im-pact on the per capita expenditure in Chiapas. Specifically,the NNM estimates suggest that the causal effect for all im-proved maize varieties adoption on household welfare is aboutMX$ 136 (US$ 14.6) in Chiapas and MX$ 173 (US$ 18.6) inOaxaca. This is the average difference in per capita expenditurebetween similar pairs of households that belong to differenttechnological status. In terms of causal effects, the estimatesof the KBM appear to be similar to those of the NNM. TheNNM estimates on the probability of households falling belowthe poverty line were 31% and 27% less than those of non-adopters in Chiapas and Oaxaca, respectively.

The estimates for hybrid maize adoption in Chiapas andcreolized maize adoption in Oaxaca are also presented inTables 5 and 6, respectively, and they also show some interest-ing results. The causal effect of adoption of maize hybrid onthe per capita expenditure in Chiapas is positive and statisti-cally significant and ranges between MX$ 116 (US$ 12.5)and MX$ 123 (US$ 13). This significant increase in PCEhelped adopters reduce poverty levels by 38%. With regardsto the impact of creolized maize variety adoption in Oaxaca,both NNM and KBM estimates also show that the adoptionof improved germplasm contributed positively to an improve-ment in the welfare of households. In particular, PCE ofadopters was almost MX$ 200 higher than those of nonadop-ters, while the poverty level was almost 20% lower than that ofnonadopters. These results suggest that the causal effect of hy-brid variety adoption on poverty reduction is greater in Chia-pas than the creolized variety in Oaxaca. The differential

d nonadopters in Oaxaca and Chiapas

Chiapas

Overall Adopters Nonadopters Overall

0.672 0.543 0.648 0.5660.230 0.168 0.219 0.1800.109 0.075 0.100 0.080

1a Effect on All maize types adoption, Chiapas. 1b Effect on Maize Hybrid adoption, Chiapas

1c Effect on all maize types adoption, Oaxaca 1d Effect on Maize Creolized, Oaxaca

Figure 1. Propensity score distribution and common support for propensity score estimation. Source: Own calculation.


impact of increases in the per capita expenditure on povertyreduction in the two regions is actually consistent with the re-sults of the poverty analysis presented in Table 4. The povertygap, which measures the mean distance below the poverty lineas a proportion of the poverty line, is much higher in Oaxacathan in Chiapas, indicating that for the same income gains,households in Chiapas are more likely to escape out of povertythan their counterparts in Oaxaca.

Results from the sensitivity analysis on hidden bias, whichshow the critical levels of gamma, C, at which the causal infer-ence of significant adoption impact may be questioned are alsopresented in Tables 5 and 6. For example, the value of 1.55–

Table 5. Average treatment effects (ATT) a

Matching algorithm Outcome ATT Crit

Nearest neighbor Per capita expenditure

Matching All improved maize adoption 136.28***

(2.46)Maize hybrid adoption 122.62**

(2.16)

Poverty

All improved maize adoption �30.88***

(�3.07)Maize hybrid adoption �38.46**

(�2.17)

Kernel-based matching Per capita expenditure

All improved maize adoption 119.33**

(2.18)Maize hybrid adoption 116.06**

(2.06)

Poverty

All improved maize adoption �26.19**

(�2.37)Maize hybrid adoption �35.71**

(�2.04)

Note: t-Values in parentheses.** Significant at 5% level.

*** Significant at 1% level.

160 for improved varieties in Chiapas implies that if individu-als that have the same X-vector differ in their odds of adoptionby a factor of 55–60%, the significance of the adoption effecton output may be questionable. 14 Balancing powers of theestimations are ascertained by considering the reduction inthe mean absolute standardized bias between the matchedand unmatched models. The median absolute standardizedbias before and after matching are presented in the last columnof Table 7. The estimates show that standardized difference be-fore matching is in the range of 17% and 20%, while theremaining standardized difference after randomization rangesbetween 5% and 15%, with substantial bias reductions.

nd results of sensitivity analysis, Chiapas

ical level of hidden bias (C) Number of treated Number of control

1.55–1.60 130 32

1.05–1.10 77 76

1.45–1.50 130 32

1.35–1.40 77 76

1.25–1.30 130 32

1.00–1.05 77 76

1.25–1.30 130 32

1.15–1.20 77 76


The third and fourth columns in Table 7 present the pseudo-R2 from the propensity score estimation before and aftermatching. The likelihood-ratio test of the joint significanceof all the regressors in the probit model of propensity scoreestimation before and after matching and their correspondingp-values are presented in the fifth and sixth columns of the Ta-ble. The corresponding p-values of the likelihood-ratio testshow that the joint significance of regressors on treatment sta-

Table 6. Average treatment effects (ATT) a

Matching algorithm Outcome ATT Criti

Nearest neighbor Per capita expenditure

Matching All improved maize adoption 173.44**

(2.23)Maize creolized adoption 281.42***

(2.97)

Poverty

All improved maize adoption �26.92**

(�2.33)Maize creolized adoption �25.64**

(�2.37)

Kernel-based matching Per capita expenditure

All improved maize adoption 170.07**

(2.05)Maize creolized adoption 200.29**

(2.07)

Poverty

All improved maize adoption �18.77*

(�1.90)Maize creolized adoption �20.11**

(�2.07)

Note: t-Values in parentheses.* Significant at 10% level.

** Significant at 5% level.*** Significant at 1% level.

Table 7. PSM quality indicators before and after matching and sens

Improved maizevariety adoption

Pseudo R2 beforematching

Pseudo R2 aftermatching

All improved maizevarieties adoptionin Chiapas

NNMa 0.166 0.045

KBMd 0.166 0.048All improved maizevarieties adoptionin Oaxaca

NNMb 0.117 0.034

KBMc 0.177 0.026Maize hybrid adoptionin Chiapas

NNMb 0.118 0.020

KBMc 0.118 0.002Maize creolized adoptionin Oaxaca

NNMb 0.109 0.054

KBMc 0.109 0.019

Algorithm selected:a NNM (5) and common support.b NNM with replacement and Caliper 0.05.c KBM with bandwidth 0.03.d KBM with bandwidth 0.06.

tus could always be rejected after matching. It was, however,never rejected before matching. The relatively low pseudo-R2

after matching and the p-values of the likelihood-ratio testof joint significance of the regressors imply that there is no sys-tematic difference in the distribution of covariates betweenboth groups after matching.

To gain further insights into the differential impact of adop-tion on farmers belonging to different land ownership catego-

nd results of sensitivity analysis, Oaxaca

cal level of hidden bias (C) Number of treated Number of control

1.00–1.05 52 111

1.95–2.00 39 124

1.15–1.20 52 111

1.60–1.65 39 124

1.00–1.05 52 111

1.00–1.05 39 124

1.15–1.20 52 111

1.35–1.40 39 124

itivity analysis (adoption effect on per capita expenditure MX$)

p > v2 beforematching

p > v2 aftermatching

Meanstandardizedbias beforematching

Meanstandardized

bias aftermatching

(Total)% |bias|reduction

0.005 0.189 19.619 14.750 24.815

0.005 0.144 19.619 15.423 21.3870.021 0.966 20.166 7.454 63.037

0.021 0.989 20.166 6.038 70.0590.005 0.977 17.911 5.681 68.281

0.005 1.000 17.911 3.108 82.6480.076 0.930 18.904 13.655 27.767

0.076 0.999 18.904 5.098 73.032


ries, we also analyzed the causal impacts of adoption on PCEand poverty status for different categories of land ownership. 15

The nearest neighbor estimates, which are presented in Tables 8and 9 for Chiapas and Oaxaca, respectively, generally revealthat even within the different farm size groups, adoption tendsto positively and significantly impact on PCE and negatively onpoverty level. 16 Specifically, the results indicate that adoption

Table 8. Nearest neighbor estimates of ATT and sensiti

Farming category Outcome ATT C

Small Per capita expenditure MX$ 501.19*

(<5 Hectares) (All improved maize adoption) (1.79)

Poverty �66.67**

(All improved maize adoption) (�2.00)

Per capita expenditure MX$ 110.39**

(Maize hybrid adoption) (2.01)

Poverty �50.00*

(Maize hybrid adoption) (�1.73)

Large Per capita expenditure MX$ 160.18**

(>5 Hectares) (All improved maize adoption) (2.37)

Poverty �55.56***


Per capita expenditure MX$ 170.66**

(Maize hybrid adoption) (2.06)

Poverty �35.48***

(Maize hybrid adoption) (�2.61)

Notes: ATT indicates average treatment effects for the treated.t-Values in parentheses.

* Significant at 10% levels.** Significant at 5% levels.

*** Significant at 1% levels.

Table 9. Nearest neighbor estimates of ATT and sensiti

Farming category Outcome ATT Cr

Small Per capita expenditure MX$ 198.13**

(<4 Hectares) (All improved maize adoption) (2.14)

Poverty �35.00**


Per capita expenditure MX$ 175.63*

(Maize creolized adoption) (1.80)Poverty �29.41*

(Maize creolized adoption) (�1.80)

Large Per capita expenditure MX$ 173.50*

(>4 Hectares) (All improved maize adoption) (1.69)

Poverty �26.31*


Per capita expenditure MX$ 262.99*

(Maize creolized adoption) (1.95)

Poverty �33.33**

(Maize creolized adoption) (�2.05)

Notes: ATT indicates average treatment effects for the treated.t-Values in parentheses* Significant at 10% levels.

** Significant at 5% levels.

of germplasm exerts a positive and significant impact on PCEand negative impact on poverty, with declining impact as landownership increases in both Chiapas and Oaxaca. This findingis consistent with the notion that poorer farmers tend to benefitmore from new agricultural technologies.

When hybrids in Chiapas and creolized maize varieties inOaxaca are considered separately, the poverty reduction ef-

vity analysis according to land ownership in Chiapas

ritical value of Gamma Number of treated Number of control

1.55–1.60 33 14

1.00–1.05 33 14

1.35–1.40 20 30

1.00 – 1.05 20 30

1.70–1.75 89 18

1.90–1.95 89 18

1.20–1.25 57 46

1.55–1.60 57 46

vity analysis according to land ownership in Oaxaca

itical value of Gamma Number of treated Number of control

1.25–1.30 22 61

1.65–1.70 22 61

1.00–1.05 19 64

1.05–1.10 19 64

1.05–1.10 28 50

1.25–1.30 28 50

1.30–1.35 20 60

1.45–1.50 20 60


fects of technology appear to be higher for the small farmers inChiapas, although slightly higher for large farmers in Oaxaca.Specifically, the adoption of hybrids in Chiapas results inreducing the probability of households falling below the pov-erty line by 50% for small farmers and by 35% for large farm-ers. Similarly, the adoption of creolized maize in Oaxacacontributes to a reduction in the probability of falling belowthe poverty line by 29% for small farmers and by 33% for largefarmers. Overall, these findings are consistent with the previ-ous results presented in Tables 5 and 6, where the adoptionof improved germplasm was found to exert a significantly po-sitive effect on household welfare, supporting the widely heldview that new agricultural technology can have positive wel-fare implications for farmers in developing countries. 17

6. CONCLUSIONS

This study examined the adoption of different types of maizevarieties and its impact on household welfare, measured by percapita expenditure and poverty status in two regions of Mex-ico. Given the nonexperimental nature of the data used in theanalysis, a propensity score-matching model was used to ac-count for selectivity bias. The results did suggest the presenceof bias in the distribution of covariates between groups ofadopters and nonadopters, indicating that accounting forselection bias is a significant issue.

The empirical analysis was conducted for the adoption of im-proved varieties in Chiapas and Oaxaca, and then specificallyfor the adoption of hybrid varieties in Chiapas and creolizedvarieties in Oaxaca. The results indicate that adoption of im-proved varieties helped raise farmers’ per capita expendituresand thereby increasing their probability of escaping poverty.Specifically, the average income of adopters of improved vari-eties in Chiapas was about 136 Mexican pesos higher than non-adopters, while the corresponding figure for Oaxaca was about174 Mexican pesos. On average, the probability of farmers whoadopted improved varieties in Chiapas falling below the pov-erty line was about 31% less than that of nonadopters, whilethe corresponding figure for Oaxaca was about 27%.

The findings, differentiated by regional preferences for vari-eties, also revealed that farmers in Chiapas, who adopted hy-brid varieties, where these varieties are generally preferred,also had much higher per capita expenditures and lower pov-erty rates than their counterparts who did not adopt thesevarieties. Similarly, households in Oaxaca who planted creol-ized varieties were found to have higher per capita expenditureand lower probability of falling below the poverty line. Esti-mates across land ownership categories also indicate thatadoption of improved varieties exerts positive and significantimpacts on the welfare of both small and large farmers, withsmall farmers appearing to be benefitting more than largefarmers. Moreover, for both regions, the impact of the adop-tion of improved germplasm on reducing poverty appears tobe greater for small farmers than large farmers indicating thattargeting new technologies toward small farmers can have far-reaching welfare implications.

Overall, the findings in this study confirm the widely heldview that productivity-enhancing agricultural innovationscan contribute to raising incomes of farm households, povertyalleviation, and food security in developing countries. Devel-oping mechanisms to help extend the high yielding maize vari-eties to areas with high poverty rates is therefore a reasonablepolicy instrument to raise incomes in these areas, althoughcomplementary measures are needed. As noted by Morriset al. (1999), improved technology is certainly a requirementfor changing farming practices, but elements such as effectiveextension services, improved access to land, an efficient inputdistribution system, and appropriate economic incentives mustalso be present.

Finally, it is significant to mention that the results from thisstudy, as well as observations from other studies such as Bellonand Risopoulos (2001) show that farmers in these regions gen-erally continue to use the traditional maize varieties, alongsidethe improved germplasm. This suggests that intervention pro-grams could also offer farmers appropriate pools of germplasmthat contain useful traits such as high yields, drought resistantand storability, and then help them incorporate these impor-tant traits into their local varieties to generate superior varie-ties.

NOTES

1. The literature on adoption is too large to be reviewed in this study. Foran excellent review see, for example, Feder, Just, and Zilberman (1985).

2. As explained later in the paper, only very few households in the sampleused open pollinated variety.

3. An alternative framework would be to use the partial adoptionframework that allows for simultaneous estimation of the farmers’allocation decisions. Smale et al. (1994) employed this framework in theirstudy on Malawi, while Bellon and Taylor (1993) used it to analyzeadoption in Mexico. Given the focus of the present study and lack ofdetailed data on land allocation across various varieties, we leave thisanalysis to future research work.

4. Propensity score matching (PSM) controls for self-selection bycreating the counterfactual for the group of adopters. PSM constructs astatistical comparison group by matching every individual observation onadopters with individual observations from the group of nonadopters withsimilar characteristics. Thus, the matching process tries to create anexperimental dataset in that, conditional on observed characteristics, theselection process is random.

5. The common support condition is defined as 0 < PðD ¼ 1jX Þ < 1. Bythe overlap condition, the propensity score is bounded away from 1 and 0,excluding the details of the distribution of p(X).

6. By comparing the Rosenbaum bounds on treatment effects at differentlevels of C, it is possible to examine the strength unmeasured influenceswould require in order to change inference about the treatment effect.Sensitivity analysis for insignificant effects is not meaningful and shouldnormally be omitted.

7. The database is a component of one of CIMMYT’s projects. Detailscan be found in Bellon et al. (2006).

8. While household income indicates the ability of the household topurchase its basic needs of life, per capita expenditure reflects the effectiveconsumption of households and therefore provides information on thefood security status of households.

9. For a detailed description of these poverty measures, refer to Sadouletand de Janvry (1995).


10. As pointed out by Wooldridge (2005), including too many factors inestimating propensity scores may result in a violation of the keyignorability assumption.

11. In the empirical analysis the STATA� and psmatch2 programs wereused, the latest developed by Leuven and Sianesi (2003).

12. The matching procedure which is performed in the region of commonsupport follows the suggestion by Leuven and Sianesi (2003), by droppingobservations from the adopters whose p-scores are higher than themaximum, or less than the minimum p-score of the nonadopters.

13. Matching was performed with replacement. As noted by Smith andTodd (2005), matching with replacement involves a tradeoff between biasand variance. Matching with replacement increases the average quality ofthe matches, but reduces the number of distinct nonadopter observationsused to construct the counterfactual mean, thereby increasing thevariance. Dehejia and Wahba (2002) show in their study that matchingwithout replacement results in many bad matches in the sense that manyparticipants get matched to nonparticipants with very different propensityscores.

14. As noted by DiPrete and Gangl (2004), the Rosenbaum bounds are aworst-case scenario.

15. A higher cutting point of 5 hectares was selected for Chiapas becausethe average landholding is much higher than in Oaxaca.

16. Estimates of the kernel based method, which are not presented in theinterest of brevity, also show similar results.

17. An interesting issue that arises with the results presented is whetherthe welfare benefits resulting from the adoption of improved maizevarieties justify the research and development costs of developingimproved varieties and the extension costs associated with disseminatingimproved varieties to the farmers. Addressing this issue requires quanti-tative analysis that is beyond the scope of the present study. Moreover, theindirect nature of the link between investments made today in agriculturalresearch and changes realized tomorrow for the welfare of the poor peoplemake any attempt to measure and quantify research impacts incomplete insome respect (Morris et al., 1999).

REFERENCES

Abdulai, A., & Huffman, W. E. (2005). The diffusion of newagricultural technologies: The case of crossbred cow technology inTanzania. American Journal of Agricultural Economics, 87(3), :645–659.

Bellon, M. R., & Taylor, J. E. (1993). “Folk” soil taxonomy and thepartial adoption of new seed varieties. Economic Development andCultural Change, 41(4), : 763–786.

Bellon, M. R., & Risopoulos, J. (2001). Small-scale farmers expand thebenefits of improved maize germplasm: A case study from Chiapas,Mexico. World Development, 29(5), 799–811.

Bellon, M. R., Adato, M., Becerril, J., & Mindek, D. (2006). Poor farmers’perceived benefits from different types of maize germplasm: The case ofcreolization in lowland tropical Mexico. World Development, 34(1),113–129.

Binswanger, H. P., & von Braun, J. (1991). Technological change andcommercialization in agriculture: The effect on the poor. World BankResearch Observer, 6(1), 57–80.

Blundell, R., & Costa-Dias, M. (2000). Evaluation methods for non-experimental data. Fiscal Studies, 21, 427–468.

Caliendo, M., & Kopeinig, S. (2008). Some practical guidance for theimplementation of propensity score matching. Journal of EconomicSurveys, 22(1), 31–72.

CIMMYT (International Maize and Wheat Improvement Center) (2007).World maize facts and trends 2006–2007 unpublished manuscript.Mexico, DF: Mexico.

de Janvry, A., & Sadoulet, E. (2001). World poverty and the role ofagricultural technology: Direct and indirect effects. Journal of Devel-opment Studies, 38(4), 1–26.

Dehejia, R. H., & Wahba, S. (2002). Propensity score-matching methodsfor nonexperimental causal studies. The Review of Economics andStatistics, 84(1), 151–161.

DiPrete, T. A., & Gangl, M. (2004). Assessing bias in the estimation ofcausal effects: Rosenbaum bounds of matching estimators and instru-mental variables estimation with imperfect instruments (p. 41). Berlin:Wissenschaftszentrum Berlin fur Sozialforschung.

Evenson, R. E., & Gollin, D. (2003). Crop variety improvement and itseffect on productivity: The impact of international agricultural research.Oxon: CABI.

Feder, G., Just, R. E., & Zilberman, D. (1985). Adoption of agriculturalinnovations in developing countries: A survey. Economic Developmentand Cultural Change, 33(2), : 255–298.

Heckman, J., Ichimura, H., & Todd, P. (1997). Matching as aneconometric evaluation estimator. Review of Economic Studies, 65(2),261–294.

Jalan, J., & Ravallion, M. (2003). Does piped water reducediarrhea for children in rural India?. Journal of Econometrics, 112,153–173.

Just, R. E., & Zilberman, D. (1988). The effects of agriculturaldevelopment policies on income distribution and technologicalchange in agriculture. Journal of Development Economics, 28(2), :193–216.

Karanja, D. D., Renkow, M., & Crawford, E. W. (2003). Welfare effectsof maize technologies in marginal and high potential regions of Kenya.Agricultural Economics, 29, 331–341.

Keleman, A., Hellin, J., & Bellon, M. R. (2009). Maize diversity, ruraldevelopment policy, and farmers’ practices: Lessons from Chiapas,Mexico. The Geographical Journal, 175(1), : 52–70.

Lee, W. (2008). Propensity score matching and variations on the balancingtest. In Third conference on policy evaluation, ZEW, Mannheim,Germany, 27–28 October.

Leuven, E., & Sianesi, B. (2003). PSMATCH2 Stata Module to PerformFull Mahalanobis and Propensity Score Matching, Common SupportGraphing, and Covariate Imbalance Testing. URL <http://ideas.repe-c.org/c/boc/bocode/s432001.html>.

Mendola, M. (2007). Agricultural technology adoption and povertyreduction: A propensity-score matching analysis for rural Bangladesh.Food Policy, 32(3), 372–393.

Mojo, S., Norton, G. W., Alwang, J., Rhinehart, I., & Deom, C. M.(2007). Peanut research and poverty reduction: Impacts of varietyimprovement to control peanut viruses in Uganda. American Journal ofAgricultural Economics, 89(2), : 448–460.

Morris, M. L. (2002). Impacts of international maize breeding research indeveloping countries, 1966–1997. Mexico, DF: CIMMYT.

Morris, M. L., Tripp, R., & Dankyi, A. A. (1999). Adoption and impactsof improved maize production technology: A case study of the grainsdevelopment project. CIMMYT Economics Working Paper, 99-01,CIMMYT, Mexico, DF.

Rosenbaum, P. R. (2002). Observational studies. New York: Springer.Rosenbaum, P. R., & Rubin, D. B. (1983). The central role of the

propensity score in observational studies for causal effects. Biometrica,70(1), 41–55.

Sadoulet, E., & de Janvry, A. (1995). Quantitative development policyanalysis. USA: The Johns Hopkins University Press.

Sianesi, B. (2004). An evaluation of the Swedish system of active labormarket programs in the 1990s. The Review of Economics and Statistics,86(1), 133–155.

Skoufias, E., Davis, B., & Behrman, J. R. (1999). An evaluation of theselection of beneficiary households in the education, health, and nutritionprogram (PROGRESA) of Mexico. Washington, DC: IFPRI.

Smale, M., Bellon, M. R., Aguirre, J. A., Rosas, M. I., Mendoza, J.,Solano, A. M., Martinez, R., Ramirez, A., & Berthud, J. (2003). Theeconomic costs and benefits of a participatory project to conservemaize landraces on farms in Oaxaca, Mexico. Agricultural Economics,29, 265–275.

http://ideas.repec.org/c/boc/bocode/s432001.html

http://ideas.repec.org/c/boc/bocode/s432001.html

THE IMPACT OF IMPROVED MAIZE VARIETIES ON POVERTY I

Smale, M., Just, R. E., & Leathers, H. D. (1994). Land allocation in HYVadoption models: An investigation of alternative explanations. Amer-ican Journal of Agricultural Economics, 76(4), : 535–546.

Smith, J. A., & Todd, P. E. (2005). Does matching overcome LaLonde’scritique of nonexperimental estimators?. Journal of Econometrics,125(1–2), 305–353.

Wooldridge, J. M. (2005). Violating ignorability of treatment by control-ling for too many factors. Econometric Theory, 21, 1026–1028.

Table A1. Propensity score for ma

Variable All impr

Coef. St

Age �0.0299 0Education (dummy) 0.7816 0Total family (number) �0.3016 0Share of male members (%) 0.0477 0Staple crops (number) �0.5531 0Horse holding 1.0968 0Remittances �0.9911 0Distance (km) �0.0306 0Land holding 2.0298 1Number of plots �0.4119 0Area planted with maize (hectares) 0.1961 0Area red-color good quality (%) 0.2109 0Area slope 1 = slope; 0 = otherwise �1.1430 0Subsidy 1 = if the farmer received subsidy �1.8840 1Seed 1 = if the Farmer received improved seed 0.6931 0Extension visit 0.5044 0Constant 4.3752 1Number of observations 162Pseudo-R2 0.3121

Source: Own calculation.

Table A2. Propensity score for ma

Variable All maize types

Coef. Std. err.

Age (years) �0.0008 0.0148Education (years) 0.0522 0.0950Total family (number) �0.0828 0.0908Share of male members (%) �4.7660 1.7525Household education average 0.1942 0.0969Land holding 0.4470 0.4946Good soil quality 0.6314 0.4013Number of maize uses �0.1997 0.2908Distance (km) �0.0236 0.0090Remittances 1.2296 0.8271

Constant 0.5001 1.2648Number of observations 163Pseudo-R2 0.1286

Source: Own calculation.

APPENDIX

See Tables A1 and A2.

N MEXICO: A PROPENSITY SCORE-MATCHING APPROACH 1035

ize adoption in Chiapas (logit)

oved maize Maize hybrid

d. err. z Coef. Std. err. z

.0179 �1.67 0.0096 0.0147 0.66

.6148 1.27 0.0652 0.5426 0.12

.1334 �2.26 �0.1135 0.0988 �1.15

.0222 2.15 �0.0050 0.0143 �0.35

.1818 �3.04 �0.5364 0.2305 �2.33

.4314 2.54 0.3375 0.3565 0.95

.6935 �1.43 0.00 0.00 0�00

.0157 �1.96 �0.0015 0.0120 �0.13

.2002 1.69 0.6862 0.6713 1.02

.2998 �1.37 0.0426 0.2595 0.16

.0954 2.06 0.2378 0.0865 2.75

.2890 0.73 �0.0316 0.1786 �0.18

.5803 �1.97 0.8688 0.3960 2.19

.2425 �1.52 0.1245 0.5795 0.21

.5672 1.22 1.1349 0.4543 2.50

.4537 1.11 �0.0105 0.3043 �0.03

.6954 2.58 2.2680 1.1415 1.99162

0.1966

ize adoption in Oaxaca (logit)

Creolized maize

z Coef. Std. err. z

�0.06 �0.0011 0.0161 �0.070.55 0.0488 0.1026 0.48�0.91 �0.0459 0.0931 �0.49�2.72 �3.0380 1.5931 �1.912.00 0.1573 0.0972 1.620.90 0.4140 0.5331 0.781.57 0.3765 0.4206 0.9�0.69 �0.0890 0.2883 �0.31�2.62 �0.0264 0.0094 �2.791.49 1.0245 0.9076 1.13

0.40 �0.2109 1.3405 �0.16163

0.0913

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the impact of improved maize varieties on poverty in mexico: a propensity score-matching approach

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