assets, activities and income generation in rural mexico ... · ter income-generating activities....

Agricultural Economics 27 (2002) 139–156

Assets, activities and income generation in rural Mexico:factoring in social and public capital�

Paul Wintersa,∗, Benjamin Davisb, Leonardo Corralca Graduate School of Agricultural & Resource Economics, School of Economics,

University of New England, Armidale, NSW 2351, Australiab ESAE/Room C-309, Food and Agricultural Organisation, Viale delle Terme de Caracalla, 00100 Rome, Italy

c Inter-American Development Bank, 1300 New York Avenue, NW, Washington, DC 20577, USA

Received 19 June 2001; accepted 14 November 2001

Abstract

In recent years, there has been increasing emphasis in the rural development literature on the multiple income-generatingactivities undertaken by rural households and the importance of assets in determining the capacity to undertake these activities.Controlling for the endogeneity of activity choice and applying Lee’s generalisation of Amemiya’s two-step estimator to asimultaneous equation model, household returns to assets from multiple activities are explored for the Mexico ejido sector. Toincorporate the multiple variables representing social and public capital into the analysis, factor analysis is used. The resultsindicate that the asset position of the household has a significant effect on household participation in income-generatingactivities and returns to those activities. Furthermore, the inclusion of measures of social and public capital into the analysisshow that these assets play an important role in income-generating activities and that the influence is dependent on the typeof social and public capital as well as the particular activity.© 2002 Elsevier Science B.V. All rights reserved.

JEL classification: O12; O13

Keywords: Livelihoods; Mexico; Social capital; Public capital; Agricultural households; Censored regression

1. Introduction

In recent years, there has been increasing emphasiswithin the rural development literature on what isreferred to as rural livelihoods and livelihood diver-sification. A key feature of the concept of livelihoodsis the link between assets, activities and income and

� Paper prepared for the Food and Agriculture OrganizationRegional Office for Latin America and the Caribbean for theproject: Social Capital and Rural Livelihood Strategies in LatinAmerica: Implications for Project Implementation.

∗ Corresponding author.E-mail address: [email protected] (P. Winters).

the role of the institutional context in determining theuse of and returns to assets. For example,Ellis (2000)defines a livelihood as comprising “the assets (natu-ral, physical, human, financial and social capital), theactivities and the access to these (mediated by institu-tions and social relations) that together determine theliving gained by an individual or household”. Liveli-hood diversification is then the process by whichhouseholds construct a diverse portfolio of activitiesand assets to survive and improve their standard ofliving (Ellis, 2000).

The livelihoods approach has played an importantrole in highlighting the multiple activities undertaken

0169-5150/02/$ – see front matter © 2002 Elsevier Science B.V. All rights reserved.PII: S0169-5150(02)00030-0

140 P. Winters et al. / Agricultural Economics 27 (2002) 139–156

by rural households, the importance of assets in de-termining the capacity to undertake activities, the dy-namic nature of the actions of rural households and thelink between the diversification of assets and activities(Barrett and Reardon, 2000). Evidence from develop-ing countries indicates that rural households rely ona number of assets and employ multiple activities togenerate income. In particular, there has been strongevidence indicating the increasing importance of ruralnon-farm activities.Reardon et al. (2001), for exam-ple, show that, based on a set of studies from LatinAmerica, rural non-farm activities make up 40% ofrural household income. Furthermore, the trend is to-ward greater non-farm activities for rural inhabitants.

While the livelihoods approach has contributed toour understanding of rural household behaviour, therehas been little empirical examination of the approach.A number of recent studies have recognised the roleof a diverse set of assets in income-generating activ-ities. For example,Lanjouw (1999, 2001)examinesnon-farm income in Ecuador and El Salvador usingassets such as human capital (education, literacy), nat-ural capital (land) and public capital (electrification,water connection) as explanatory variables.Winterset al. (2001)explore the role of assets, including socialand public capital, in the decision to migrate.Corraland Reardon (2001), and De Janvry and Sadoulet(2001) explore the importance of natural, human,social and public capital in the generation of incomein Nicaragua and Mexico, respectively. While recog-nising the importance of assets in income-generatingactivities, these studies do not consider both the simul-taneous nature of asset allocation and the selectivitybias created by the decision to participate in an activity.

One recent study that does consider these issues isby Taylor and Yunez-Naude (2000). Their paper fo-cuses on the returns to schooling from a diverse set ofactivities in rural Mexico broadening consideration toboth household head and family schooling. Taylor andYunez-Naude’s paper is interesting from an economet-ric point of view in that they carefully address issuesaffecting bias and inefficiency in parameter estimates.By controlling for endogeneity of activity choice andusing Lee’s generalisation of Amemiya’s two-step es-timator in a simultaneous-equation model, the result-ing estimators are asymptotically more efficient thanother two-stage estimators, such as the commonly usedHeckman procedure.

In this study, we use the same econometric ap-proach as Taylor and Yunez-Naude to explore liveli-hood strategies in the Mexican ejido sector. UnlikeTaylor and Yunez-Naude, our main interest lies inexploring the importance of community level factors,particularly social and public capital variables, in theparticipation in and returns to income-generating ac-tivities. Empirical evidence suggests that along withother forms of capital, social and public capital playan important role in household income-generation.For example, an analysis of Tanzanian householdsshows that higher levels of social capital are associ-ated with greater levels of expenditure (Narayan andPritchett, 1999). Understanding the role of social andpublic capital in income generation has importantpolicy implications for governments. If governmentsdecide to invest resources into alleviating poverty,it is essential to know how social and public capi-tal influence income generation. Incorporating socialcapital and community level variables representingpublic capital into the analysis of assets and activitieshas proved problematic since these assets are gener-ally difficult to measure in a way that truly representsthe specific attributes of these assets and reflects howhouseholds secure benefits from them. We addressthis issue by using factor analysis to identify specificfactors to represent social and public capital.

The remainder of this paper is divided into four sec-tions.Section 2provides a conceptual background forthe relationship between assets, activities and incomeand discusses the empirical strategy for examining theuse of assets in income-generating activities.Section3 uses the Mexico data to define the set of assets ruralMexican household use for generating income. Partic-ipation in activities and returns to assets are examinedusing theTaylor and Yunez-Naude (2000)empiricalapproach. InSection 4, the issue of measuring and in-cluding social and public capital is addressed. Mea-sures of these assets, calculated using factor analysis,are then included in the activity equations. Finally, inSection 5conclusions are discussed.

2. Rural livelihood strategies: conceptualframework and empirical approach

The basis of a livelihood strategy is the asset posi-tion of the household at a given point in time. Note

P. Winters et al. / Agricultural Economics 27 (2002) 139–156 141

that, as has become standard practice, household assetsare defined broadly to include natural, physical, hu-man, financial, public, household and social capital.1

These assets are stocks, which may depreciate overtime or may be expanded through investment. Basedon access to a particular set of assets for a given pe-riod, the household must decide which activities itwill employ and the intensity of involvement in thatactivity. For purposes of this paper, activities are ac-tions taken by the household to produce income. Theyinvolve the use of a single asset or a set of assets.Agricultural production, for example, may use naturalcapital in the form of land and water, human capital,physical capital such as tractors, financial capital forthe purchase of inputs and social capital in the formof labour assistance by community members. Alterna-tively, non-farm wage employment may only use hu-man capital. The intensity of an activity depends onthe degree to which assets are used. A strategy of agri-cultural intensification is likely to involve a greateruse of human capital and financial capital (for the pur-chase of inputs) than a strategy that focuses on otheractivities or in less intensive agricultural production.The decision on the set of activities a household willemploy and the intensity of those activities is condi-tioned on the context in which the household operates.The context influences household decisions throughnatural forces, markets, state activity and societal in-stitutions.

Conceptually, the mapping of assets to incomethrough activities can be viewed as similar to a pro-duction process, with assets corresponding to factorsof production and income as the output of the process(Barrett and Reardon, 2000). The return to assets de-pends on the parameters of the functions, which aredetermined by prices of inputs and outputs as wellas other characteristics of the context. The allocationof assets to each activity is expected to maximisehousehold income subject to a number of constraints.Households will allocate assets in a manner thatequates the marginal value product across activities

1 The types of capital that households access can be categorisedin a variety of manners. One common categorisation is to definefour types of capital: natural, human-made (physical), human andsocial (Serageldin and Steer, 1994). Our categorisation reflects adesire to provide greater detail in the types of capital used byhouseholds although each of these categories can be subsumed inone of the four categories commonly presented.

or will allocate assets entirely to one activity that hasa superior return.

One of the key features of this approach is thathouseholds simultaneously determine the allocation ofassets to different activities. Failure to consider thesimultaneous nature of the decision can lead to cu-rious and often contradictory results across differentstudies. For example,Taylor and Yunez-Naude (2000)note that research on the returns to schooling in ruraleconomies range from high to negative. They arguethat the reason for this is that researchers have failed totake into account the technological changes and sec-toral diversification that characterise agricultural trans-formation in developing countries. Households mayreap the rewards from schooling through abandoningor limiting one activity in favour of another. A low ornegative return to schooling in crop production, for ex-ample, may mean that the more educated have shiftedto other activities such as non-farm employment. Sim-ilarly, this logic holds for all assets. If the govern-ment puts a paved road through to a community, thismay cause some households in that community to al-ter income-generating activities. Researchers focusingon a single activity may find that investment in in-frastructure reduces income from that activity, whichmay lead to perverse policy implications. While thismay be the case, analysis of a single activity ignoresthe benefits of infrastructure in other activities. Onlythrough considering all income-generating activitiessimultaneously can this problem be avoided. The is-sue we want to examine in this paper is the relativeimportance of different assets in the activities chosenby rural Mexican households, with particular empha-sis on social and public assets.

The household’s decision on the allocation of assetsacross activities is a simultaneous decision and onethat is censored by the fact that households do notnecessarily participate in all possible activities. Somehouseholds will therefore have zero income from, forexample, self-employment.2 Examining the allocationand returns to assets requires a simultaneous equationmodel in which the dependent income variables arecensored by unobservable latent variables influencing

2 Table 1notes the participation rates for each activity. Less thanhalf of households participate in non-agricultural wage employ-ment, agricultural wage employment, self-employment and migra-tion.


Table 1Income and activities

Household incomeMean household income 10888

Crop incomeMean income for all households 3331Percent of total household income 30.6%Percent participating 93.7%Mean income for participating households 3554Percent of income for participating households 32.6%

Livestock incomeMean income for all households 2071Percent of total household income 19.0%Percent participating 75.3%Mean income for participating households 2750Percent of income for participating households 24.9%

Self-employment incomeMean income for all households 1234Percent of total household income 11.3%Percent participating 28.2%Mean income for participating households 4378Percent of income for participating households 33.0%

Non-agricultural wage incomeMean income for all households 2946Percent of total household income 27.1%Percent participating 42.3%Mean income for participating households 6968Percent of income for participating households 52.8%

Agricultural wage incomeMean income for all households 582Percent of total household income 5.3%Percent participating 17.2%Mean income for participating households 3388Percent of income for participating households 31.0%

Remittance incomeMean income for all households 724Percent of total household income 6.6%Percent participating 15.8%Mean income for participating households 4570Percent of income for participating households 32.1%

Number of households= 972.

the activity participation decision. Households obtainincome from an activity only if they participate in thatactivity. Participation in an income-generating activityoccurs if the household believes the expected returnsto that activity are greater than the alternative of notparticipating.

The econometric approach follows Lee’s gen-eralisation of an estimation principle ofAmemiya(1977a,b)in a general simultaneous equation model.

Lee (1978)compared Amemiya’s generalised two-stage estimators with other two-stage estimators,and showed that Amemiya’s estimators are moreefficient in all cases, including those proposed byHeckman. This econometric specification allows forcensored dependent variables, where unobservablelatent variables influence the decision to participate inan activity. Recently,Taylor and Yunez-Naude (2000)utilised Lee’s generalisation in their study of returnsto schooling in both crop and non-crop activities inrural Mexico and we follow their empirical approach.In the first step, probits using the complete set of assetvariables as explanatory variables are estimated. Theprobit on each income-generating activity measuresthe effects of assets on household participation in thatactivity. The probits are also used to calculate inverseMill’s ratios (IMR), which are included in the secondstep to control for selectivity bias. The second step isto estimate the income equations, including an IMRas a right-hand side variable in each of the corre-sponding income equations. The income equations areestimated jointly, to reflect the simultaneous natureof the decision, for the full household sample usingthree stage least squares which allows the inclusionof information contained in the cross-equation errorcorrelations. For the income equations, only assetsthat are expected to affect the income level of thatactivity are included as right-hand side variables.

The data used in this study comes from a nationallyrepresentative sample of Mexican ejido, or land re-form, households.3 Data were collected at two pointsin time during the Spring and early summer of 1994and 1997, for 972 households.4 The survey covereda wide array of household assets as well as household

3 The ejido is the land reform mechanism utilised by the Mexicangovernment from the 1930s to 1992. Land and water resourceswere granted to a community or a group of producers, or ejido,with each producer obtaining usufruct rights over a parcel andaccess to common lands. A 1992 constitutional reform ended thedistribution of land and established a process by which individualtitles may be provided to ejidatarios, and by which ejidos maydecide to privatise individual parcels. The ejido sector covers75% of all agricultural producers in Mexico (roughly 3 millionhouseholds), and over half of the country’s irrigated and rainfedland.

4 The Secretariat of Agrarian Reform and the World Bank car-ried out the surveys with assistance from the University of Cal-ifornia, Berkeley. A detailed description of the Mexico data andits sampling properties can be found inCord et al. (1998).


demographics, income-generating activities and par-ticipation in organisations. Community-level data wasalso collected on characteristics and organisation ofthe ejido. Most of the data used in this analysis, in-cluding the income data, is from the 1997 survey ex-cept in some cases where 1994 data was used to avoidendogeneity problems.

3. Assets and income-generating activities

Household activities can be divided into sixcategories: crop production, livestock production,self-employment, non-agricultural wage employ-ment, agricultural wage employment and migration.Table 1 presents data on these activities. On aver-age households earned 10,888 pesos in 1997. Notall households participate in every activity. A ma-jority of households participate in crop (93.7%) andlivestock (75.3%) production but few participate inself-employment (17.2%) and migration (15.8%).Agricultural activities (crops, livestock and agricul-tural employment) make up 55% of total rural house-hold income showing that nearly half of income isgenerated by non-agricultural activities. Crop incomerepresents about 30% of total income in 1997 andis the most important source of income followed bynon-agricultural wage income (27.1%). However, only42.3% of households participate in non-agriculturalwage employment and for those that do, it is the mostimportant income-generating activity (52.8% of to-tal income). Similarly, households that participate inself-employment and migration receive, on average,one-third of their income from this activity. A num-ber of agricultural households have thus shifted awayfrom agricultural to non-agricultural activities.

Asset ownership varies significantly across theejido sector. InTable 2, explanatory assets are groupedinto the following categories—natural (land), physi-cal (livestock, equipment), human (education, gender,labour, labour experience and ethnicity), financial(credit), and migration (US and Mexico migration net-works) capital. Regional variables are also includedto control for unobserved factors. On average, house-holds have substantially less irrigated land than eitherrainfed or pasture land. For the 25% of householdswith irrigated land, average size is 5.2 ha. Althoughoverall households hold an average of 6.4 heads of

Table 2Assets

Capital Variable Mean or percent

Natural Irrigated land (ha) 1.3Rainfed land (ha) 7.5Pasture land (ha) 4.3

Physical Livestock ownership, 1994 6.4Tractor ownership, 1994 7.5%Truck ownership, 1994 17.1%

Human Males education—literate 0.7Males education—primary 0.5Males education—secondary 0.3Males education—tertiary 0.1Females education—literate 0.6Females education—primary 0.5Females education—secondary 0.2Females education—tertiary 0.1Age of head 51Male head of household 97.0%Males, 15–34 years 0.9Females, 15–34 years 0.9Males, 35–59 years 0.6Females, 35–59 years 0.6Non-agricultural wage earner, 1994 34.0%Agricultural wage earner, 1994 16.0%Self-employed, 1994 9.7%HYV seed used, 1994 21.9%Chemicals used, 1994 49.2%Indigenous household 21.2%

Financial Formal credit access, 1994 28.8%

Migration Migrant network in US 1.8Migrant network in Mexico 11.0

Regional North 20.9%North Pacific 11.5%Centre 26.7%Gulf 17.0%South 24.0%


cattle, over half own no livestock. Those householdswith livestock own on average 13.5 heads. Educationlevel is divided by gender as we hypothesise that thereturns to human capital vary by this differentiation.As would be expected, the average number of maleand female household members decreases with in-creasing levels of education. Labour composition andexperience vary significantly across households.

Table 3presents the results of the probits on activ-ity participation for 1997. Coefficients with a level ofsignificance greater than 90% are in bold. Almost allhouseholds have some crop production, so results for

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that activity are not particularly interesting. Rainfedland and livestock ownership positively influence par-ticipation in livestock production while irrigated landand tractor ownership are negatively associated withlivestock participation. This is not surprising sincethese latter assets are likely to have higher returns forcrop production. For a similar reason, tractor ownersare less likely to participate in self-employment. Nat-ural and physical capital reduces participation in bothforms of wage employment. The positive associationbetween rainfed land and remittance income may in-dicate that participation in migration requires at leasta minimal wealth level. Education, particularly amongmales, plays a strong role in non-agricultural wage em-ployment. Minimal male education, or literacy, seemsto play a similar role in livestock production. Higherlevels of male education are negatively associatedwith receipt of remittances. Younger households (asindicated by age of household head) are more likelyto participate in agricultural wage employment, andolder households in receiving remittances. The pres-ence of young males, controlling for education, leadsto increased participation in agricultural wage labour,while the presence of older males leads to greaterparticipation in crop production. Previous experi-ence in non-agricultural activities (self-employmentand non-agricultural wage earning), not surprisingly,leads to a greater probability of participation in eachrespective activity. However, previous experience inagricultural wage employment leads to greater partic-ipation in crop production, agricultural wage earningand migration. Somewhat surprisingly, access to for-mal credit in 1994 does not influence participationin any activities in 1997. This could be becausecredit is primarily tied to crop production. Indigenoushouseholds are more likely to participate in on-farmactivities and less likely to migrate. A number ofregional variables are associated with particular activ-ities suggesting there are certain regional factors thatinfluence participation that are not controlled for inthe regression. Finally, migration networks are mostimportant for receiving remittance income.

Table 4presents the results of the selectivity cor-rected system estimates of the income equations.While the probits evaluated the probability of partic-ipation in activities, the income equations examinethe factors influencing the level of income fromeach activity. Note that in all estimates, except for

self-employment, the IMR is significant at the 95%level or above. This indicates that self-selection intoactivities is an important factor in considering theincome-generated from a particular activity and fail-ure to control for this would have led to biased results.

Assets play a variety of roles in terms of the gen-eration of income by activity. Land size, particularlyirrigated land, is important for on-farm categories aswell as self-employment and remittances. Rainfedland is important for livestock income, as it servesas an input into livestock production. Rainfed land isalso important for self-employment suggesting thatfarmers with more land are able to earn more throughself-employment than others. Education provides themost interesting story. Education is most important,not surprisingly, for non-agricultural wage labour,though this varies by gender. All levels of male edu-cation lead to higher off-farm income, while only thehighest level of female education has a positive—andsignificant—coefficient. On the other hand, the num-ber of females aged 15–34 years, holding educationconstant, has a positive and significant effect, sig-nalling that lower levels of female education are notvalued on the labour market. This is presumably dueto the kind of entry level employment rural womencan choose from, such as domestic servant. Femaleeducation is also important for self-employment in-come, and surprisingly, livestock income. Both higherlevels of male and female education are associatedwith higher agricultural income, suggesting that ed-ucation only becomes important when it reaches acertain technical level.

4. Adding social and public capital

The purpose of this section is to discuss a methodfor the inclusion of community variables, includ-ing social and public capital, in the analysis ofincome-generating activities and to present the resultsof the analysis. There has been some debate overthe precise definition and measure of social capital(Woolcock and Narayan, 2000). Social capital canbe defined as a variety of different entities with twocommon elements: they all consist of some aspect ofsocial structure and they facilitate actions of actorswithin that structure (Coleman, 1988). For our pur-poses, social capital constitutes the social relations,both vertical and horizontal, that help facilitate the


generation of income. Since our interest is in examin-ing the community factors that facilitate income gen-eration, we focus on relations at that level. Householdsmay belong to a set of formal and informal organi-sations that may provide direct or indirect assistancein economic activities. For example, membership in awomen’s group may provide information on marketopportunities for non-farm products. In addition tomembership in individual organisations, the ejidos inwhich households live may be well or poorly organ-ised which will also affect the households’ ability togenerate income. Finally, vertical ties to, for example,organisations that provide technical assistance willinfluence income-generation. Each of these is con-sidered as part of social capital. Another asset thatis difficult to measure and plays an important role inincome generation is public capital, which involvesaccess to public goods and services. These include arange of services and infrastructure from health careand telephone access to electricity and paved roads.

Calculating the value of social and public capitalis complicated by the diversity and multiplicity of in-dicators. Households belong to a range of organisa-tions both formal and informal and ejidos may havedifferent types of organisational structure. Infrastruc-ture and services differ in terms of their presence andimportance in a community.Table 5lists a variety ofcommunity variables that may be used as public andsocial capital indicators. They range from measures ofejido organisation and household participation in or-ganisations to the distance to various public services,

Table 5Social and public capital variables

Mean orpercent

Kilometers to health centre 8.8Kilometers to post office 19.7Kilometers to telegraph 27.8Kilometers to fax machine 27.4Time to nearest urban centre (h) 53.6Distance to nearest urban centre (h) 25.2Number of rural centres within 1 h 2.6Number of urban centres within 1 h 1.6Share of roads that are paved 50.7%Ejido has a DICONSA store 50.3%Ejido has a meeting room 49.7%Ejido has public lighting 74.7%Ejido has a secondary school 45.7%

Table 5 (Continued)

Mean orpercent

Ejido has a high school 7.3%Household has access to LICONSA store 18.6%Household water accessHousehold has access to emergency work 11.0%

No piped water access 34.7%Access to piped water outside the house 14.8%Access to piped water inside the hoouse 50.5%

Household sewage accessNo sewage system access 43.8%Access to whole in the ground 9.6%Access to septic system 33.9%Access to a piped sewage system 12.8%

Household latrine/bathroom accessNo access to latrine or bathroom 23.6%Access to a latrine 43.5%Access to a bathroom 32.9%

Household has access to electricity 87.7%

Household telephone accessNo telephone access 41.1%Access to a public phone 53.6%Access to a phone at home 5.4%

Ejido belongs to aric 3.1%Ejido belongs to a ejido union 23.4%Ejido belongs to a campesino organization 72.1%

Stage of PROCEDE reformNot intitiated 35.1%In process 17.3%Completed 47.6%

Household participates in family project 2.9%Household participates in production organisation 11.3%Household participates in irrigation project 4.3%Household participates in UAIM 2.3%Household participates in other formal organisation 7.6%Household participates in informal organisation 16.4%Household is in a production contract 6.2%Household receives technical assistance 14.9%Share of households speaking indigenous language 20.8%Ejido has environmental problems 44.5%Frequency of household particpation in assemblies

Rarely/never particpate 4.2%Sometimes participate 16.6%Always participate 79.2%

Degree of household respect for community agreementsRarely/never respect agreements 5.5%Sometimes respect agreements 20.8%Always respect agreements 73.8%

Household agrees with use of common lands 72.6%Communal lands per capita (ha) 25.2Total ejido population 200.9



the level of community infrastructure and direct house-hold access to publicly provided infrastructure.

The challenge for analysing the benefits of socialand public capital in income-generating activities isdetermining a method for using the information con-tained in this large set of variables. Two options havegenerally been used. The first is to create a singlenumerical index that is a weighted average of therelevant set of variables. A higher value for the indeximplies, for example, greater social capital. The indexis then used in subsequent analysis such as regressionanalysis of income generation.Narayan and Pritchett(1999) use this approach to examine household in-come generation in rural Tanzania. The problem withthis approach is that it requires strong and somewhatarbitrary assumptions about the weights for eachvariable in the aggregation. Furthermore, this methodassumes that a single numerical index is sufficient torepresent social or public capital. However, as withother forms of capital, social and public capital arenot homogenous entities. TheWorld Bank (2000), forexample, identifies three types of social capital: bond-ing, bridging and linking. A single index ignores thispossibility. Even if multiple indexes were to be cre-ated to measure the various types of social or publiccapital, defining the different indexes would still bearbitrary.

The second approach to including social and publiccapital variables in income analysis is to use all ora subset of the variables directly in income regres-sions. For example,De Janvry and Sadoulet (2001)in evaluating the determinants of income in the ejidosector include social and institutional assets (indige-nous household and access to technical assistance)and locational characteristics (number of urban cen-tres within 1 h, number of rural centres within anhour and regional dummies). One problem with thisapproach is in deciding which variables adequatelyrepresent the presence of social and public capital.Which variable inTable 5, for example, representsthe level of infrastructure in a community? The prob-lem with including all or even a significant numberof variables in regression analysis is that this specifi-cation can lead to problems with degrees of freedom,if the sample is not sufficiently large, and multicol-inearity if, as is likely, the multiple social and publiccapital variables are highly correlated. In the presenceof multicolinearity, identification is not a problem

but with highly correlated variables estimates are lessprecise (Greene, 1997).

Given the shortcomings of these two methods inthis paper, a third method, factor analysis, is used toincorporate social and public capital into the analysisof income generation. The primary purpose of factoranalysis is to describe the relationships among manyvariables in terms of a few underlying, but unobserv-able, factors (Johnson and Wichern, 1988). Factoranalysis groups sets of variables by their correlationsand each group of variables represents a single un-derlying construct or factor.5 For example,Klineand Wichelns (1996)use factor analysis to evaluaterespondents’ perceptions of farmland preservationprograms. Based on reasons provided for preservingfarmland, they identify four factors that seem to ex-plain preferences: environmental objectives, aestheticobjectives, agrarian objectives and anti-growth objec-tives. Similarly, using the multiple measures of socialand public capital, the underlying factors that repre-sent the important characteristics of these assets canbe determined. In one such study,Onyx and Bullen(2000), using a set of 36 measures of social capitalfrom five Australian communities, identify eight fac-tors that represent social capital including communityparticipation, agency and trust. While factor analysisdoes assist in identifying underlying factors repre-sented by a set of variables, the method is subjectiveand requires interpretation of the factors to give themmeaning. This interpretation relies on previous knowl-edge and intuition about underlying relationships.

For this study, factor analysis was used to identifyfactors that represent the important elements of socialand public capital. Social and public capital are notnecessarily homogeneous and more than one factorwas expected to emerge for each set of variables. So-cial and public capital variables were analysed usingthe principal component factor method and rotatedusing the varimax rotation method.6 Typically, factors

5 Factor analysis is similar to principal components analysisin that both are attempts to approximate the covariance matrix.However, factor analysis is more elaborate and the primary questionit seeks to ask is whether the data are consistent with someunderlying structure (Johnson and Wichern, 1988).

6 As recommended as a first step byJohnson and Wichern(1988). Additional methods including the iterated principal factormethod and the maximum likelihood factor method were exam-ined. Since these approaches tended to bring about similar solu-tions the original principal components factor method was used.


are retained in the analysis if their eigenvalues aregreater than one. To limit the number of factors re-tained the cut-off value was increased to 1.25, whichallows for a more straightforward interpretation offactors. Even with this limitation, as will be seenbelow, interpreting some of the factors is difficult.At that cut-off, eight factors were retained whichrepresented 44% of the variance.7 Results of the fac-tor analysis are presented inTable 6. As is standardpractice, only factor loadings greater than 0.25 areretained for interpretation.8

4.1. Factor 1: proximity

This factor indicates the proximity of the commu-nity to infrastructure, services and urban areas. This isevidenced by the high negative loadings on distance toa health clinic (−0.45), post office (−0.84), telegraph(−0.80) and a fax (−0.88), and the time (−0.76) anddistance to an urban centre (−0.84). Households witha positive association with this factor tend to be less re-mote than those negatively associated with this factor.

4.2. Factor 2: ejido population size

This factor is associated with ejidos that have a sub-stantial population, which is accompanied by access tosubstantial community services and infrastructure. Thepositive associations with DICONSA and LICONSAstores,9 as well as with the presence of a meetingroom, a secondary school and a high school show this.The negative associations with distance to a health cen-tre and to a post office also indicate a large communitywith access to those services. The negative associationwith PROCEDE reforms and the positive associationwith environmental degradation are indicators of theco-operation problems associated with having a large

7 This corresponds to the 49% of the variance explained by theeight factors identified byOnyx and Bullen (2000).

8 While only one set of results is presented, a number of methodsof factor analysis were explored. Although the order of the factorsdiffered across method, the results were largely consistent acrossmethods. That is, the loadings and relevance of certain variablestended to be the same across methods suggesting these factors dorepresent some underlying structure in the data.

9 LICONSA is a federal program that provides subsidised milkto extremely poor families, urban and rural, with children under12 years of age, and tortillas to extremely poor families, urbanand rural.

ejido—difficulty in organising the reform process anddifficulty in managing the resource base.

4.3. Factor 3: semi-urban

This factor appears to represent ejidos that aresemi-urban. The number of urban centres within anhour has a large coefficient (0.62) as does the numberof rural centres within an hour (0.35). Hence, it is notsurprising to see a positive association with share ofroads that are paved, DICONSA store, meeting room,secondary school and telephone access. An ejido nearan urban or rural centre is more likely to have greaterinfrastructure and services. There is also a positiveassociation with the PROCEDE reform. Since thisreform leads to the allocation of individual propertyrights, it is likely to be the case that the reform pro-cess will occur in areas where land can be more easilysold or rented, as is generally the case in semi-urbanareas. Finally, there is a positive relationship withinformal organisation participation. Again this couldbe associated with a semi-urban setting where popu-lations tend to be denser and organising for culturaland economic reasons is easier.

4.4. Factor 4: co-operation

This factor clearly represents co-operation withinthe ejido as evidenced by the strong loading on house-holds frequently participating in assemblies (0.72), re-specting community agreements (0.71) and belongingto informal organisations (0.40).

4.5. Factor 5: lack of formal productionarrangements

This factor describes the lack of formal productionorganisation in the ejido, as there is a strong nega-tive association with an ejido belonging to an ARIC10

(−0.63). Furthermore, a substantial number of house-holds associated with this factor do not participate in‘other’ formal organisations (−0.54), production con-tracts (−0.48) and the government technology trans-fer program (−0.48). There is a somewhat peculiar

10 ARIC, the Spanish acronym for Rural Associations of Collec-tive Interests, are organisations of ejidos that promote commongoals.

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positive association with household agreement on theuse of common lands.

4.6. Factor 6: household access to infrastructure

This factor represents the degree to which house-holds have access to services as indicated by the strongloadings on water (0.46), sewage (0.75), bathrooms(0.72) and telephone (0.48) access. Households pos-itively associated with this factor also tend to have alower share of the population speaking an indigenouslanguage and lower environmental problems. This par-tially reflects that indigenous households tend to livein more marginalised communities.

4.7. Factor 7: infrastructure

This factor shows the degree of access to infrastruc-ture. This is evident through the strong loadings onpublic lighting (0.71), electricity (0.66), share of pavedroads (0.48) and water access (0.35). The results indi-cate that ejidos that have formal organisational struc-tures tend to have this type of infrastructure and thosewith informal organisations are less likely to have thecorresponding infrastructure.

4.8. Factor 8: formal ejido organisation

This factor represents an ejido that has a formalorganisational structure, indicated by the factor load-ings on ejido membership in a campesino organisation(0.59) and ejido union (0.33) as well as the participa-tion in production contracts (0.36). The high loadingon communal lands per capita (0.32) may indicate acommunity with a need for formal structures. Accessto emergency work and paved roads may also be anindicator of the community’s ability to organise andlobby government officials for assistance.

The results suggest that social and public capitalare not homogenous and a number of factors representtheir role in income generation. This calls into ques-tion the use of single indicators of social or publiccapital. Using these factor loadings and the regressionmethod suggested byThomson (1951),11 a new set ofvariables representing each of the eight factors can be

11 Cited in the STATA reference manual.

generated. As is standard practice, the factors are nor-malised to have a mean value of 0 and standard devia-tion 1. Using these measures of social and public cap-ital, the two-step econometric procedure outlined inSection 2and presented inTables 3 and 4was repeated.Table 7presents the new results of the probits for ac-tivity participation. The majority of the results remainthe same as the preliminary estimations, suggestingtheir robustness. Results that differ with respect to sta-tistical significance fromTable 3are set in italics.

Participation in activities appears to be influencedby a number of social and public capital factors. Prox-imity to an urban centre is found to be negatively as-sociated with agricultural wage employment as wellas remittance income. This most likely reflects the factthat isolated households have few off-farm optionsother than agricultural wage employment or migration.Households in ejidos with large populations appear tobe less likely to participate in livestock production,reflecting problems in managing the resource base.Not surprisingly, households in a semi-urban settingare more likely to participate in self-employment andnon-agricultural wage employment, as the opportuni-ties for households living in these settings are muchhigher. Receipt of remittance income is less likely ina semi-urban setting and is also negatively associatedwith infrastructure and formal organisation again sug-gesting that dependence on external transfers may bethe only alternative in the face of limited opportunities.Household access to infrastructure is positively associ-ated with self-employment, as many microenterprisesrequire minimal infrastructure access. Finally, formalorganisation in the ejido is positively associated withparticipation in crop production, livestock productionand non-agricultural wage employment. The ejido or-ganisational structure clearly influences the choice ofincome-generating activities.

Table 8presents the results of the selectivity cor-rected system estimates of the income equationsincluding the social and public factors. As with theprobits, the majority of the results remain the same asin Table 4. One notable exception relates to male ed-ucation levels. The results fromTable 8suggest thathigher levels of male education are negatively associ-ated with livestock income. Taken together the resultssuggest that households with significant male educa-tion concentrate on non-agricultural wage employmentand crop income (for those with tertiary education).

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Moving to the examination of the social and publiccapital variables, a number of these variables signif-icantly influence the level of income generated fromeach activity. Proximity to an urban centre positivelyinfluences the level of crop income while semi-urbanstatus is negatively associated with crop income. Thissuggests a non-linear relationship in the benefits ofurban access. Households that are very near urbanand rural centres are likely to earn less crop incomethan households further out on the periphery of thesecentres. However, households that are very far awayearn significantly less income. The results also indi-cate that households with access to basic infrastruc-ture such as water, sewage, bathrooms and telephonesearn higher income from crop production. A similarresult is found for livestock income, self-employmentand non-agricultural wage employment. These resultsshow that a community with substantial household ac-cess to these types of infrastructure is likely to havegreater income-generating opportunities.

Lack of formal production arrangements appearsto limit crop, livestock and self-employment income(meaning that the presence of formal productionarrangements would improve income from thesesources). These formal production arrangements seemto play an important role in increasing the level of in-come received by households. However, formal ejidoorganisation is negatively associated with livestockincome. These results provide evidence for the viewthat the types of social and public capital matter. Bothof these variables are indicators of social capital asthey measure associational activity. However, onlyan association with productive oriented organisationshas a positive influence on livestock income. Bothnon-agricultural and agricultural wage income arepositively related to the level of infrastructure. Ejidoswith access to electricity, public lighting, water andpaved roads, all general indicators of economic de-velopment, provide higher wage employment incomethan those without.

5. Conclusion

Our analysis of households in the Mexico ejido sec-tor shows that a household’s asset position has a sig-nificant effect on its participation in specific activitiesas well as on the level of income earned from those

activities. Households choose a portfolio of activitiesand the intensity of involvement in those activitiesbased on their asset position. These results support thelivelihoods approach to household behaviour, whichargues the importance of assets in determining thecapacity of households to undertake certain activities.Furthermore, the results show that partial analysis ofincome generation—that is, the analysis of a singleincome-generating activity—can potentially lead toincorrect conclusions about the role of a particularasset in income generation. For example, our resultsindicate that high levels of male education result ingreater participation and returns to non-agriculturalwage employment but lower income from livestockproduction. While these results mirror the generalconclusion ofTaylor and Yunez-Naude (2000)thatreturns to schooling vary across activity, we findthat education participation and returns differ acrossgender and that the return to education is primarilythrough non-agricultural wage employment and cropproduction. While the returns for crop income for menand women at the highest level of education are simi-lar, lower levels of female education are not valued onthe labour market. This is due primarily to the type ofemployment to which young rural women are limited:domestic help.

Our analysis also shows the critical role that socialand public capital play in household participation inactivities and the level of income generated by eachactivity. By using factor analysis, the forty communityvariables are reduced to four public capital factors—proximity, semi-urban status, household access toinfrastructure and general infrastructure; three socialcapital factors—co-operation, lack of formal produc-tion arrangements and formal ejido organisation; andone factor—ejido size—that shows the effect of ejidopopulation on both public and social capital. Thesefactors represent the key underlying community char-acteristics that are likely to influence household be-haviour. Their heterogeneity suggests that each willinfluence that behaviour in a unique manner, andinclusion of these factors in the regression analysisshows this factor-specific influence. Creation of asingle index of social or public capital would have ig-nored the importance of this heterogeneity, and usinga subset of social and public capital variables wouldhave led to ambiguity regarding the reason for theeffect since each indicator might represent multiple


components of social and public capital. Factor anal-ysis provides an alternative to these methods andidentifies the particular manner in which social andpublic capital influence income generation. Failureto adequately incorporate social and public capitalvariables can lead to inaccurate or ambiguous results.

The results from the regression analysis indicatethat ejido organisation is important in determin-ing participation in crop and livestock production,non-agricultural wage employment and remittance in-come. The location of the household relative to urbanareas (proximity and semi-urban status) is also impor-tant in influencing participation in self-employment,non-agricultural and agricultural wage employmentand remittance income. In terms of income levels,lack of formal production arrangements hinders agri-cultural and livestock income generation, as well asself-employment. The type of infrastructure accessi-ble by the household affects income, with householdaccess to infrastructure associated with greater levelsof all kinds of income, with the exception of agricul-tural wage and remittance, and general infrastructureaccess associated with higher levels of both types ofwage labour. Finally, while being very close to urbanor rural centres limits crop production income, in-termediate proximity to urban centres improves cropproduction income.

The findings presented in this paper have a num-ber of implications for Mexican government policytowards the ejido sector. First, although all ejiditarioshave access to land, many are no longer dependenton agricultural production for most of their incomeand are using the assets at their disposal for a numberof income-generating activities. Policies designed toimprove agricultural production will not necessarilyhave the desired or expected effect since householdsmay respond by shifting assets to other activities.Government interventions should recognise the po-tential fungibility of assets and incorporate this intothe design of the intervention. In this regard, Mexicangovernment interventions consisting of cash transfersto agricultural producers in the ejido sector, throughthe PROCAMPO program, give beneficiary house-holds some flexibility in the use of the monetary trans-fers and have been shown to be effective in alleviatingpoverty (Sadoulet et al., 2001). Second, social andpublic capital variables have an important influence onincome generation. In designing policies to improve

the income-generating capacity of ejiditarios, such aspolicies to alleviate poverty, the government needsto recognise the role of these factors. In particular,interventions need to recognise and respect the socialfabric of the community and include activities to en-hance the role of formal and informal organisations.For example, based on the results presented in this pa-per, if the Mexican government wished to embark ona strategy of improving non-agricultural employmentopportunities for ejiditarios, it should begin by un-derstanding the role ejido relationships play and takeactions to improve ejido organisation (this might alsoinduce participation in crop and livestock production).Additionally, it should improve household access toboth household level and general infrastructure sinceeach of these enhances the level of income earnedfrom non-agricultural employment. Similarly, if thegovernment decided to commit resources to generallyimproving income levels, investing in household ac-cess to infrastructure is likely to have the broadest andgreatest impact. Ignoring the role of social and publiccapital in the ejido sector would constitute a missedopportunity and could prove costly if interventionsside-step the social structure of the community.

Acknowledgements

The authors are grateful to Dr. Chris O’Donnell ofthe University of New England for helpful commentson the data analysis and econometrics.

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