The Determinants of Nonfarm Activities
and Incomes of Rural Households in Mexico,
with Emphasis on Education
ANTONIO Y
UNEZ-NAUDE
El Colegio de M
e
xico, Mexico
and
J. EDWARD TAYLOR
*University of California at Davis, USA
Summary. ÐThis paper presents the main results of a study of the eects of education (as well as other household assets) on the choice of activities and incomes of rural Mexican households. Our study examines the various income sources, as well as the education of the household's head and its members. Implications are drawn for rural education and development policies to promote rural nonfarm incomes and employment. Ó 2001 Elsevier Science Ltd. All rights reserved.
Key words Ð development, farm and nonfarm activities, subsistence and commercial crops, education, migration, diversi®cation, selectivity
1. INTRODUCTION
The economic reforms applied by the gov-ernments of Latin America in the recent past have highlighted the need to develop human capital in the region. Education is crucial to raising economic productivity and competi-tiveness and to combating poverty. The issue is especially pertinent in Mexico, due to the opening of trade with its neighbors to the north as well as with other countries with strong economies, and to its extensive poverty and income inequality and its poor record in edu-cation when judged by international and re-gional standards (see Singh & Santiago, 1997). Mexico's education problems are worst in its rural areas where poverty is concentrated. In their study on the determinants of poverty and inequality in Latin America, Attanasio and Szekely (1999) estimate that the rural sector accounts for 12.2% of poverty and in Mexico, for 30.2% (the highest share among the 14 countries included in the calculations). They found that dierences in education (years of schooling) accounted for 28.6% of Latin American poverty; while the ®gures for Mexico
and Chile are the highest among the countries, 46.9% and 47.8%, respectively.
Moreover, a fundamental characteristic of rural households is diversi®cation of income. This is especially true in countries at an inter-mediate level of development such as Mexico, where there are dual agricultural sectors. Rural households in these situations continue to pro-duce staples for home consumption and earn incomes from other sources (such as production of cash crops and nonfarm activities). This is due to their poverty and risk aversion as well as to missing or failed markets for staple foods, factors, and credit.1 Recent development lit-erature tends to depict income diversi®cation into nonfarm sources as favorable to develop-ment, and education as contributing to diver-si®cation by rural households in developing Ó2001 Elsevier Science Ltd. All rights reserved Printed in Great Britain 0305-750X/01/$ - see front matter
PII: S0305-750X(00)00108-X
www.elsevier.com/locate/worlddev
*We are grateful for funding from Ford, Hewlett, and McKnight Foundations and comments from the guest editors as well as three anonymous reviewers. We are also thankful for the support we received from Eric van Dusen, George Dyer, Xochitl Juarez, Angel Pita, and Luis Gabriel Rojas in this research.
countries (for reviews of literature and evidence see Ellis, 1998; Reardon, 1997; Reardon & Stamoulis, 1998).
Given that study of the role of education in the rural economy is critical for economic de-velopment in Mexico and that fact has been recognized by recent Mexican governments, and given the importance of nonfarm income diversi®cation to Mexican rural households, it is surprising that there is a dearth of empirical research on the returns to education for rural households active in both the nonfarm and the farm sectors. To contribute to ®lling this gap, we undertook research on impacts of education on rural Mexican household incomes and ac-tivity choice in the farm and nonfarm sectors. We de®ne ``rural'' as population concentrations of less than 5000. In a previous article we dis-cussed in detail the methodology used in this research (Taylor & Yunez-Naude, 2000).
The present study extends the latter analysis by presenting data and regressions concerning the impacts of dierent levels of schooling (as well as other household assets such as migra-tion) on household participation in nonfarm and farm activities and on incomes from those activities. We disaggregate by education of dierent household members, but our unit of labor allocation and income analysis is the household. This is because in poor rural com-munities of Mexico, it is at the household rather than the individual level that decisions are made concerning family labor allocation to farming, nonfarm activities, schooling, and so on. The data come from a survey of rural households in eight rural areas of Mexico.
We proceed as follows. In Section 2, we present our model and place our study in the context of recent literature. In Section 3 we describe the education levels and other socio-economic characteristics of the sample house-holds and their communities. In Section 4 we present econometric results. Section 5 con-cludes the paper.
2. THE MODEL
The empirical evidence concerning returns to education in rural areas does not support un-ambiguously the generally accepted argument that education spurs development. For exam-ple, Phillips (1987) criticizes the conclusion of Jamison and Lau (1982) that the results of 37 studies indicate that on average farm produc-tivity increases 8.7% when farmers complete
four years of primary education. Phillips's cri-tique was that many of the studies showed nonsigni®cant and even negative impacts of education on production and the net income of certain crops. Even in recent evidence one ®nds a mix of results, with some studies showing positive and signi®cant returns to education and others the contrary.2
We hypothesize that researchers in this do-main can err should they not take into account the technological change and household income diversi®cation that characterizes rural trans-formation in developing countries. That is, se-lectivity and activity choice is frequently ignored in agricultural economics literature on returns to education.3 Studies that focus on one crop or activity ignore the self-selection by households into speci®c activities (or out of them).
Moreover, it is common for extant studies to only include the schooling of the household head, ignoring the eects of education of other household members on production or incomes of the household. Yet current schooling as well as accumulated years of schooling of the vari-ous hvari-ousehold members can in¯uence hvari-ouse- house-hold investment allocations to the various activities, as well as returns to those invest-ments per activity. To ignore the endogeneity of activity selection creates selectivity bias and to omit household education variables leads to underidenti®cation bias in the estimated pa-rameters.
From an analytical perspective, activity choice by rural households is equivalent to technology choice. Farm households can realize the bene®ts of education by dropping an ac-tivity, for example, traditional agriculture, in which the returns to education are low, and taking up another activity, such as modern agriculture or wage employment, where the payo to education is higher.
product of the probability of participation and the expected income, conditioned on partici-pation. Potentially that probability as well as the expected income are in¯uenced by educa-tion and other variables, that thus ®gure in our model.
That many households do not earn any in-come from certain activities can create selec-tivity bias. For example, the households that participate in migration may have a compara-tive advantage relacompara-tive to others in this activity. This means that only using the subsample that participates in a given activity would produce biased results. To avoid the latter, we include data from all households surveyed. To correct for selectivity bias, we use a Probit model in which the dependent variable is a 0/1 variable for participation, and the regressors are the variables that aect net incomes to these ac-tivities. The coecients estimated with these k1;. . .;K Probits are used to test for the
eects of schooling (and other variables) on participation in the various activities. The re-sults of the Probits are used to correct for se-lectivity bias in the estimation of the returns to schooling and other variables in the activity income equations.
The selection of households who participate or do not participate in a particular activity is not random. Thus, the returns to schooling (and other variables) are estimated based on the households who chose to participate in some of the activities is not representative of returns to education for all the households in the sample. The procedure to correct for this potential se-lectivity bias is a generalization of the Lee two-stage estimator proposed by Amemiya (see Taylor & Yunez-Naude, 2000) in which we present a version of the formal model used in the regressions the results of which are pre-sented below.4
3. HOUSEHOLD CHARACTERISTICS
The data come from a survey of 391 house-holds (with 2,960 members) between 1992 and 1995. The data cover household characteristics, assets, the elements to calculate net incomes and labor time allocation to the principal eco-nomic activities, expenditures, commercial and noncommercial transactions, as well as the lo-cation of household activities as to whether they are local, regional, national, or interna-tional.
The households were selected at random in eight villages in four dierent states of Mexico: Coahuila, Jalisco, Michoacan, and Puebla (see Figure 1). The choice of these eight communi-ties is motivated by the desire to re¯ect the socioeconomic diversity of Mexican small vil-lages and rural households in order to be rep-resentative in a general way of small rural producers in Mexico.
Concordia, a village in the northern state of Coahuila, produces maize for the market, and its inhabitants are very involved in nonrural local and regional labor markets; we will call Concordia the ``wage employees community.'' The residents of El Chante, located in the central state of Jalisco, are mainly engaged in cash cropping (especially of sugar cane), but they also produce maize for own consumption and for the market. We call El Chante the ``commercial agricultural community.'' Eron-garõcuaro, Napõzaro, Urichu, and Puacuaro together form the Municipality of Erongar õcu-aro in the central state of Michoacan. The households of this zone produce maize for own consumption, livestock, undertake o-farm activities, and a large proportion migrate to the United States. We call these four villages the ``migrants community.'' Finally, Naupan and Reyesoghpan are villages populated by indige-nous populations in the Sierra Norte of the Center-East state of Puebla. The households in this area are very poor, migrate little to the United States, produce maize for home con-sumption and several cash crops, chiles in
Naupan and coee in Reyesoghpan cafe. We
call this are the ``indigenous community.'' See Table 1 for the list and characteristics of these communities.
The eight rural towns are closely linked to the nonrural economy, as 60% of the incomes of their households come from nonfarm activ-ities. Most of the latter are generated by wage employment in local and regional labor mar-kets, in villages, rural towns, and intermediate cities.
Mexico and to the US, as well as in nonfarm self employment (in manufactures and services, including commerce). The average size of the maize farms in Mexico is about 2.5 ha, a bit above the average of 1.9 ha in our sample. 6
The average schooling of the sample house-hold members (4.36 years) is close to the na-tional rural average (4.3 years) The share of individuals without education is also similar (22% in rural Mexicovs25% in the sample) as is the share with only primary education, up to six years of schooling (53.6% for rural Mexico
vs55.5% for our sample), and with secondary education, up to 12 years of schooling. The only notable dierence between our sample and national rural averages pertains to those with higher education, more than 12 years of schooling, where the national rural average is 2% while the sample's is 1%. The discrepancy may be due to the villages in the sample being
small and lacking schools beyond the 12th year level (see Bracho & Zamudio, 1994, and Table 2).
4. DETERMINANTS OF RURAL NONFARM INCOMES AND
PARTICIPATION
The dependent variables used in the regres-sions are the net incomes for each of six activ-ities commonly undertaken by rural households in Mexico: production of staple crops; pro-duction of cash crops and livestock; nonfarm self-employment; wage employment in local and regional labor markets; migratory wage employment in Mexico and in the United States.7
The explanatory variables include: years of schooling of the head of household and the
household members; experience of the head of household (ageless schooling less ®ve); national and international migration network of the household, a variable that re¯ects accumulated capital from migration, de®ned by the number of immediate family members who are migrants who were in migration at the beginning of the survey year; family assets (farm size and value of livestock holdings); other characteristics of the household that can in¯uence the decision to participate in various activities and thus that
determine incomes (family size and age of the household head).
(a) Determinants of participation
The results of the Probit regressions are presented in Table 3, which shows percentage changes in probabilities of participation asso-ciated with a unit change in the explanatory variables. A key pattern that emerges is the positive relation between primary education
Table 2. Sample statisticsa
Dependent variables (for all households)
Median S.D. Independent variables
(per household)
Median S.D.
Incomes Education
Total 15,866 20,187 Median 4.36 2.14
Agriculture From 1 to 3 years 1.74 1.76
Commercial crops and livestock 3,712 11,811 From 4 to 6 years 2.36 2.17
Staples 758 1,779 From 7 to 9 years 1.17 1.54
Nonfarm 3,599 12,032 More than 9 years 0.45 0.90
Wage employment 5,716 9,376 Of the household head 4.01 3.88
Migration remittances Number of migrants
International 1,446 4,893 To the US 1.14 2.03
National 636 2,110 To elsewhere in Mexico 0.80 1.40
Community subsample Size of the household 7.31 3.59
Commercial farming community 12.5% Age of the household
head
41.68 16.77
Wage employees community 15.3% Cultivated landb 3.11 6.45
Migrants community 50.0% Livestockc 4,344 13,020
Indigenous Community 22.2%
a
Source: Authors' calculations.
b
Constant 1994 pesos.
cHectares.
Table 1. Activity income levels and shares (1994 pesos and shares)a
All communities
Commercial farm-ing communityb
Wage employees communityc
Migrants communityd
Indigenous communitye
Incime per capita activitiesf
2,066 6,596 2,715 1,660 1,220
Commercial farming 23.4 52.0 0.5 9.6 42.9
Subsistence staples 4.8 2.6 7.2 5.5 1.3
Nonfarmg 58.7 42.7 81.9 64.9 46.7
International remittances
9.1 2.7 3.0 16.2 0.0
National remittances 4.0 0.0 7.4 3.7 9.1
Total 100.0 100.0 100.0 100.0 100.0
a
Source: Authors' estimates.
bEl Chante, Jalisco.
cConcordia, Coahuila.
d
Erongaricuaro, Napizaro, Puacuaro and Uricho, Michoacan.
eNaupan and Reyesoghpan, Puebla.
fConstant pesos of 1994.
g
(from one to six years of schooling) and sec-ondary education (seven to nine years of schooling) and the likelihood of participation both in nonfarm self-employment and wage employment. The only exception is that one additional member with incomplete primary education (one to three years of schooling) is associated with a signi®cant and positive probability of participation in basic staples production (5.62%, with a t1:74). This is
because those members of school age who do
not ®nish primary school (with at most three years of schooling) have no better alternative than to engage in a traditional activity such as maize production.
Another result that is noteworthy is the positive association between incomplete pri-mary education (from one to three years of schooling), complete primary education (four to six years), complete secondary education (seven to nine years of schooling), and the likelihood that the household participates in
Table 3. Probit regressions for participation in activitiesa;b;c
Variable Equations/activities
Farm sector Nonfarm
self-employ-ment
Wage employment
Migration
Staples Cash crops
and livestock
International National
From 1 to 3 years of schooling
5.62 )0.16 4.00 2.72 )4.00 4.12
(1.74) (0.05) (1.45) (0.94) (1.07) (1.28)
From 4 to 6 years schooling
3,84 )0.82 4.76 6.03 3.41 1.66
(1.31) (0.26) (1.95) (2.47) (1.00) (0.54)
From 7 to 9 years of
schooling )
0.48 )0.99 5.44 7.32 3.34 )0.42
(0.15) (0.30) (1.99) (2.54) (0.15) (0.13)
More than 9 years
of schooling )
0.16 0.35 )0.76 5.24 )0.82 )2.09
(0.34) (0.08) (0.17) (1.22) (0.91) (0.42)
Education of the
household head )
1.53 0.36 )0.35 )0.84 1.04 )1.53
(1.15) (0.27) (0.29) (0.65) (0.15) (0.94)
Experience of the household head
0.46 0.38 0.55 )0.15 0.02 )0.54
(1.30, 1.11) (0.30,0.23) (1.17, 0.76) (2.08, 1.62) (1.96, 1.57) (0.62, 0.87)
Family members in the US
2.55 1.64 )7.27 )4.26 13.18 )4.01
(1.07) (0.74) (4.02) (2.32) (6.26) (1.59)
Family members elsewhere in Mexico
)3.0 5)1.54 )2.22 )2.41 )1.7 16.89
(1.05) (0.54) (0.93) (0.99) (0.62) (7.26)
Landholdings 5.96 3.42 0.01 )1.8 1.01 )2.5
(5.74) (3.07) (0.01) (1.97) (1.14) (2.02)
Family size )2,45 1.40 )1.09 )1.2 0.14 0.96
(1.21) (0.57) (0.56) (0.59) (0.05) (0.41)
Value of Livestock holdings
0.42 3.860.39 0.3 )0.62 0.68
(1.18) (4.28) (1.35) (0.91) (0.14) (1.58)
Wage employees
communityd )
18.15 9.43 )34.06 25.99 )18.71 27.83
(1.81) (0.93) (4.32) (2.51) (1.52) (2.76)
Indigenous commu-nity
30.67 18.64 )2.78 19.62 )12.69 15.47
(3.84) (5.94) (0.31) (2.27) (0.02) (1.54)
Commercial farm-ing community
)21.65 5.10 )2.42 )17.7 )14.38 )25.57
(1.79) (0.37) (0.12) (1.59) (1.03) (1.32)
Constant )16.11 )13.38 )13.93 7.1 0.09 )9.38
(0.97) (1.34) (1.50) (2.06) (2.10) (1.63)
a
The numbers in the table are percentages changes in the estimated probabilities associated with a unit change in the explanatory variable, evaluated at the medians of the rest of the variables in the Probit. For the regional dummy variables, the table reports the dierence in the estimated probabilities betweenD1 andD0, evaluated based on the medians of the rest of the variables with the exception of the other regional dummy variables, which are set at 0.
b
Notes: Thetratios are between parentheses and those appearing below the experience variable correspond to the variable itself and to the squared term, respectively.
cSource: Authors' calculations.
dFixed eects. The migrants community is used as the default case. That applies to the other tables as well.
*
Signi®cance level 0.10.
nonfarm activities (4% with t1:45, 4.8%,
with t1:95, and 5.4% witht1:99,
respec-tively). To interpret the ®ndings, note that in small villages in Mexico, most of the manu-factures sector is constituted by production of simple construction materials and handicrafts; the other main nonfarm activity is commerce (undertaken by adults). Keeping in mind these facts, the ®ndings can be explained as follows. It is common for girls and young women to produce handicrafts, and the young who have not ®nished primary schooling who have no better alternatives who work in these activities. A similar argument can be made regarding brick-making, as it is common for boys and young men who have not completed primary education to help the adults in this activity.
An additional member of the family with complete primary education or complete sec-ondary education is associated with a positive and signi®cant probability of participating in the wage-labor market (6% and 7.3%, respec-tively). An additional member with more than nine years of schooling has a positive eect (5.2%) on the probability of participation in the wage-labor market, although the eect is not signi®cant at the 10% level (t1:22). These
results agree with those of Evans and Ngau (1991) for a rural zone in Kenya, as their re-gressions show that more household education (beyond ®ve years of education) means greater nonfarm income.
An increase of one year of education of the household head does not have signi®cant eects on the participation of the household in any of the activities. This result varies from that of Evans and Ngau, and may be due to the fact that the household heads are relatively old, as many of the young have migrated out of the small villages.
Schooling does not have signi®cant eects on the probability of migration to other parts of Mexico or to the United States. The result for national migration may be due to the stagna-tion of urban labor demand in Mexico in the past decade. But our results concerning inter-national migration vary from those of other studies (see, for example, the Mexico±United States Binational Migration Study, 1998). Nevertheless, if we consider average years of education of the members of the household, the eect on the probability of migration to the United States becomes positive and signi®cant (see Taylor & Yunez-Naude, 2000).
An additional year of experience of the household head reduces the household's
par-ticipation in wage employment and spurs its participation in migration; however, despite the statistical signi®cance of the eect, the per-centage eects are slight, 0.15% and 0.02%, respectively.
With respect to the other family assets, the results are as expected. For example, as in other studies (Massey, Arango, Kouaouci, Pellegri-noy, & Taylor, 1999), we ®nd that family con-tacts strongly explain international migration. An increase of one unit in family contacts in the United States. reduces household participation in local, regional, and national nonfarm self-employment and wage self-employment. Moreover, an additional hectare of land spurs signi®cantly household participation in farming, and re-duces participation in labor markets and mi-gration to the rest of Mexico. An increase in livestock holdings spurs participation in cash cropping and in nonfarm activities.
Finally, taking into account that the ``mi-grants community'' is the reference point for comparisons of ®xed eects, it is not surprising that residence in the ``wage employees com-munity'' increases by nearly 26% the probabil-ity (t2:51) that the households participate in
local and regional labor markets, that the probability of participation in the migration labor market in the United States is negative ( 19%) and that of participation in migration to the rest of Mexico is positive (28%).
(b) Determinants of income levels by activity
Table 4 shows the results of the regression of income sources on family assets. We made the calculations based on a system of net income equations correcting for selectivity, and the ®gures in the table show the absolute eects of a unit change in the explanatory variables on incomes per activity, given the households participation in the activity.
The estimated returns to basic education (one to three years) are positive in income to maize production incomes and remittance incomes from migrants within Mexico. Controlling for other variables, an additional member with basic education of one to three years, relative to one with no schooling is associated with an in-crease of 210 pesos in income from production of basic staples (about US$ 70, witht2:48).
education in traditional farming and in within-Mexico migration.
As with primary education, an additional member with higher primary education (four to six years) has positive and signi®cant eects on household income from production of basic staples (199 pesos witht2:57). The dierence
between the two strata of primary education is that for the higher stratum, an additional member increases household incomes by the
channel of international migration remittances (351 pesos,t2:92) but not of within-Mexico
migration.
Secondary education (from seven to nine years of schooling) and preparatory, technical, or secondary education (more than nine years of schooling) also bring substantial gains in incomes from basic staples, which also includes commercial production of these crops. An ad-ditional member with more than nine years of
Table 4. Eects of education and other variables on net income per activity (in 1994 pesos)a;b
Variable Equations/activities
Farm sector Nonfarm
self-employ-ment
Wage-em-ployment
Migration
Staples Cash crops
and livestock
International National
From 1 to 3 years of schooling
210 )303 486 )200 )38 96
(2.48) (0.65) (0.86) (0.52) (0.26) (1.49)
From 4 to 6 years of schooling
199 )251 126 )438 351 )45
(2.57) (0.59) (0.24) (1.20) (2.92) (0.88)
From 7 to 9 years of schooling
156 )183 695 27 )72 9
(1.89) (0.40) (1.25) (0.07) (0.43) (0.13)
More than 9 years of schooling
260 794 )667 2,334 )116 )228
(2.12) (1.12) (0.81) (4.22) (0.42) (1.87)
Education of the household head
9 372 227 579 50 7
(0.24) (1.86) (0.93) (3.50) (0.56) (0.18)
Experience of the household head
)32 217 )130 )9 )10 22
(1.09) (1.37) (0.67) (0.06) (0.13) (0.68)
Family members in
the US ) ) ) )
863 )
Family members in
the rest of Mexico ) ) ) )
(6.27) 595
Landholdings 4.4 538 ) ) (7.55)
(0.31) (5.36)
Family size )127 305 292 458 ) )
(2.07) (0.90) (0.71) (1.65)
) )
Value of livestock holdings (1000)
) 0 ) ) )
(3.36)
Wage employees community
285 )1,481 )2,559 2,420 )344c 1,158
(0.97) (0.91) (1.30) (1.83) (0.60) (3.66)
Indigenous community
)356 3,833 )1,813 )1,834 ) 358
(1.34) (2.62) (1.02) (1.53) (1.21)
Commercial farming community
213 14,384 5,002 )226 )1,556 )116
(0.62) (7.50) (2.20) (0.15) (1.79) (0.32)
Inverse Mills Ratio 826 1,543 5,806 5,398 2,283 1,193
(6.21) (2.06) (7.37) (9.69) (5.58) (7.64)
Constant 898 )8,196 2,782 1,355 )200 )375
(1.18) (1.95) (0.54) (0.39) (0.11) (0.47)
R2d 0.18 0.44 0.19 0.40 0.28 0.29
aThe procedure corrected for selectivity bias in the systems of equations, using Lee's extension of Amemiya
esti-mation method. The ®gures in the table are absolute changes in incomes by activity. Thetratios are between pa-rentheses.
bSource: Authors' estimates.
cIncludes the indigenous community, whose members do not migrate to the United States.
d
The systemR20:89, thev2709:17, the degrees of freedom are 74 and the size of the sample is 328. *Signi®cance levels 0.10.
schooling produces a large increase in the sal-ary income of the rural household (of 2,334 pesos or almost US$780, with at4:22).
By contrast with the results for years of schooling of household members, an additional year of education of the household head does not produce an increase in basic staples in-comes. Nevertheless, and similar to the results for secondary education and above, one addi-tional year of schooling for the household head increases incomes from wage employment (559 pesos or US$186, with a t3:5). Moreover,
increasing the schooling of the household head generates income gains in commercial agricul-ture and livestock husbandry (372 pesos with t1:86).
An additional year of experience of the household head only generates gains in incomes from commercial agriculture and livestock husbandry (217 pesos with at1:37).
Beyond the positive eect of an additional member migrating to the United States or the rest of Mexico (863 pesos witht6:27 and 595
pesos, witht7:55, respectively), other family
assets have positive eects on incomes. That is the case with the estimated eect of an addi-tional hectare of landholdings on incomes coming from commercial agriculture and live-stock husbandry, and an additional family member on wage employment income. By contrast, the impact of an additional hectare does not have an eect on incomes from pro-duction of staples; note that increasing family size by one memberÐwithout adding educa-tionÐactually decreases farm income from staples.
These latter results require further discussion. The average farm size of farm households in small villages is quite small (around two hect-ares) and typically only a part of this land is dedicated to maize for home consumption. As farm size increases, the likelihood increases that the farm produces commercial crops and live-stock (see Table 3) and farm incomes from these sources increase. As for staples, even though more land increases the probability of producing these crops, incomes from these crops do not rise. The latter might be because maize is raised in small ®elds and usually for home consumption, and the net income from this activity is very low or sometimes even negative. That may also explain why income from production of staples declines as family size increases (remember that Table 3 shows that an additional family member does not in-crease the likelihood that the household
pro-duces staples). That is, given land constraints and the use maize for home consumption, an additional family member reduces income from production of staples. Although family size does not aect the probability of household participation in wage employment, it is proba-ble that an additional family member raises family labor time in the labor market. That is re¯ected in the positive impact of family size in wage income, in turn implying that family labor is diverted from production of basic grains into wage employment.
The results arising from comparison of communities are as expected. For example, and recalling that the ``migrant community'' is the reference point for comparisons, it is not sur-prising that income received from nonmigra-tory labor markets by the ``wage employees community'' households are much higher than those received by households in the ``migrants community.''
The Inverse Mills Ratio (IMR) is signi®cant for all the activities. This indicates the impor-tance of self-selection of households in partici-pation in a given activity and in determining their incomes in that activity. The latter is due to the fact that the variables that aect choice of activity, via the selectivity variables, also aect household incomes from speci®c activi-ties.
(c) Returns to education in total incomes of
households
We ran an additional regression using ordi-nary least squares (OLS) of the Mincerian type in order to complete our study of returns to education, as well as to compare our results with the only other study of which we are aware concerning the eects of education in rural ar-eas of Mexico. The dependent variable is the logarithm of household total net income, and the explanatory variables are the same as we used in the regressions in Table 5.
members in the United States (10.6%) and the value of livestock holdings (20.7%). Finally, the experience of the household head has the highest eect in the ``commercial agriculture community'' and the ``wage employees com-munity.''
Our results concerning the returns to
schooling of household heads are very similar to those estimated by Singh and Santiago (1997) in their study on returns to education in farm activities in a rural region of Mexico. According to their calculations, the returns are between 9% (for the head of household) and 8% (for the wife).
5. CONCLUSIONS
Our study shows that education and years of schooling aect activity choice of rural households. Similar to other analyses in Mexico, our results support the argument that the returns to education in rural incomes are high and statistically signi®cant, independent of the level of schooling. Our study allows elaboration on the details of schooling eects as follows.
Primary, secondary, and preparatory educa-tion have positive eects on income from basic grains for those who produce them, indicating positive returns to schooling in Mexican tradi-tional agriculture. Poverty and persistent mar-ket failures make it so that small farmers
continue to produce maize and beans for home consumption and to manage risk, incorporat-ing new knowledge through skills acquired in school. At higher levels of schooling, it is to be expected that returns to education for staples production will be higher in commercial farm-ing as compared to subsistence farmfarm-ing. Our
analysis indeed shows that post-primary
schooling has positive and signi®cant impacts on commercial farm incomes.
The nonsigni®cant returns shown by primary and secondary education on family nonfarm enterprise incomes in the local area or in the region may be due to two factors: (a) the con-tent of public education (universal and stan-dardized over the entire country) does not provide young farmers with the necessary knowledge to incorporate improved technolo-gies in production of cash crops, livestock, and nonfarm products and services; and (b) that that level of education does not lead to in-creased wages. The former is the argument made by Reardon and Schejtman (1999) and the second is made by Attanasio and Szekely (1999). According to the latter, the returns to education in Mexican wage incomes only begin to grow when they go beyond secondary edu-cation. Their estimates indicate that, during 1986±96, the returns to primary and secondary education in wage employment actually di-minished, and that the returns to advanced schooling (beyond nine years of schooling) rose considerably during the same period (from
Table 5. Total income results for the OLS regressions
Variable The estimated eect on the log of total net income
(Mincerian form)
From 1 to 3 years of schooling 0.057
From 4 to 6 years of schooling 0.014
From 7 to 9 years of schooling 0.054
More than 9 years of schooling 0.118
Education of the household head 0.074
Experience of the household head 0.016
Experience squared )0.027
Family members in the US 0.106
Family members in the rest of Mexico )0.133
Value of livestock holdings (1000) 0.207
Landholdings )0.003
Family size 0.052
Wage employees community 0.360
Indigenous community )0.151
Cash crop community 0.459
Constant 7.720
Sample size 391
R2 0.252
*
Signi®cance level of 0.10.
190% to 243%). This result is similar to ours, in that we ®nd that returns to advanced schooling are high for wage employment.
The latter result, combined with a similar result concerning the positive eects of educa-tion of the household head on wage income, suggests that the establishment of manufactures ®rms in rural areas will increase household in-comes. At the same time, the high returns to preparatory and technical education in local and regional labor markets indicates that this type of education increases labor productivity in the nonrural economy of villages and small cities. This implies that development policies in Mexico should not only focus on primary education (as has been the case to present) but also strive to increase the access of the rural young to schooling beyond the nine-year level. In general, and despite the insistence by Mexican politicians of the importance of education, eorts in that direction are still in-sucient. For example, faced with the recom-mendation of UNESCO that investment in
education in developing countries should con-stitute at least 8% of GNP, until 1998 the highest percentage achieved by Mexico was only 5% (Romero Hicks, 2000; Singh & Santiago, 1997). Within this gloomy panorama, the rural areas are in the worst situation, as there one ®nds the highest rates of illiteracy, the lowest average education, the least years of schooling and the least access to post-primary education (Bracho, 1998).
Mexico should make a priority of rural ed-ucation, not only with more investment, but also by making its present investment more eective and ecient. Our results suggest that rural education needs to incorporate the teaching of practical and technical skills that are needed in the rural context, to make rural activities more productive. In that sense, the federalization of education put in place in 1992 may provide an incentive to state and munici-pal authorities to include in their education plans the perspective of rural reality so that rural education performs better.
NOTES
1. In Mexico, most entrepreneurs live in urban centers.
2. The studies that report positive impacts of education include those of Yang (1997) for agreggate agricultural value added in China, and Jollie (1996) for incomes in Ghana, and Jacoby (1991) for own-farm and livestock husbandry incomes in Peru, and Singh and Santiago (1997) for farming activities in Mexico. Examples of studies that report negative or nonsigni®cant impacts of education include Adams (1995) for aggregate gross value added in the production of wheat, sugar cane, and rice in Pakistan; that of Rosegrant and Evenson (1992), which estimated total factor productivity in India, and that of Adams (1993) for total household incomes excluding migration remittances in Egypt. See details in Taylor and Yunez-Naude (2000).
3. An exception is the study of Jollie cited above. There is another group of studies that treat the theme of portfolio diversifcation in rural households, but their objective is not to evaluate the eects of education on rural productivity and incomes. We refer to those that analyze the factors that determine diversi®cation (among which education) as well as the eects of diversi®cation (see below and the review in Ellis, 1998).
4. It should also be mentioned that the data also make it possible to run regressions of the determinants of
diversi®cation of sources of incomes by type of house-hold, as is done in Leones and Feldman (1998) for the Philippines and other authors for various regions of developing countries (see a review in Ellis, 1998). The data base is available from the network PRECESAM, whose website is http://www.colmex.mx/centros/cee/ precesam/precesam.htm
5. We do not make a distinction between them
because, with the exception of land property rights, the characteristics of ejidoproducers are very similar to private smallholders. Moreover, with the ejido
reform of 1991 the land property rights of ejido
producers are being transformed, as the reformed made possible sale and purchase of ejidal land, and with that change, the characteristics ofejidoproducers are converging yet further with those of private producers.
6. The dierence is expected because our sample only includes small producers and in Mexico there are also medium farmers that produce basic grains (see, for example, Hernandez Estrada, 2000 and Yunez-Naude & Guevara, 1998).
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