Income Strategies Among Rural Households in
Mexico: The Role of O-farm Activities
ALAIN DE JANVRY and ELISABETH SADOULET
University of California at Berkeley, USA
Summary. ÐO-farm activities generate on average more than half of farm households' incomes in the Mexicanejidosector. Participation in these activities helps reduce poverty and contributes to greater equality in the distribution of income. This paper analyzes the determinants of access to o-farm sources of income across households. We ®nd that education plays a major role in accessing better remunerated nonagricultural employment. Adults of indigenous ethnic origin suer from an educational lag and have less access to o-farm nonagricultural employment than non-indigenous adults at identical educational levels. The regional availability of o-farm employment strongly aects participation. In addition, women are dierentially limited by distance to urban centers in their ability to gain o-farm employment. Ó 2001 Elsevier Science Ltd. All rights reserved.
Key words ÐMexico, rural households, income, o-farm employment, education, ethnicity
1. NEW APPROACHES TO RURAL POVERTY REDUCTION
Worldwide, the rural sector harbors the vast majority of the poor, likely accounting for more than 70% of the total (World Bank, 1999). Even in countries as highly urbanized as Mexico, where 75% of the population is urban, rural poverty still represents 32% of total poverty and 46% of extreme poverty (ECLAC, 1999). With many of the immigrants from rural areas adding to the ranks of the urban poor, the contribution of rural poverty to total poverty is far greater than these percentages indicate (Ravallion, 2000). Reducing rural poverty has for this reason been a long-standing concern, motivating an array of initiatives by govern-ments, nongovernmental organizations (NGOs), and international development agen-cies. The traditional approach has been through redistributive land reforms and inte-grated rural development programs to increase the productivity in agriculture of the assets controlled by the rural poor. In general, inte-grated rural development programs, focused on agriculture as the solution to rural poverty and on the role of the government in delivering services to enhance productivity, have met with limited success and have not been sustainable once government subsidies were removed (World Bank, 1997). The approach also underestimated the great degree of
heteroge-neity in asset positions across households and the multiplicity of activities in which they are engaged to generate income. In particular, o-farm activities are typically pursued by a majority of the rural poor, both because they lack access to sucient land to make agricul-ture a viable income strategy, and because market failures for credit and insurance push them into o-farm activities to diversify their risks and seek sources of liquidity to be used in agriculture.
Failures of the integrated rural development approach, and deep changes in the context where rural development is being pursued (typically including trade liberalization, descaling of public subsidies, elimination of parastatal services to agriculture, political democratization, decentralization of gover-nance, the proliferation of civil society organi-zations, and ideological shifts toward greater recognition of the role of markets and the importance of competitiveness for peasant households), have led to experimenting with a widely dierent approach to rural develop-ment, characterized by the roles of decentral-ization, local organizations, participation, and a demand-driven approach to the allocation of public resources (de Janvry et al., forthcom-ing). A distinguishing feature of this new approach is that it takes a comprehensive view of the multiplicity of sources of income households are relying upon in a particular
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regional setting. In so doing, detailed analyses of sources of income for rural households have revealed the tremendous importance of o-farm, and among those, nonagricultural sour-ces of employment and income (Reardonet al., 1998). Promoting the generation of o-farm income-earning opportunities and seeking to enhance access for the rural poor to these sources of income is thus a particularly important aspect of this new approach to rural development. Gaining a good understanding of the determinants of participation in o-farm activities and of the levels of income achieved in these activities by dierent categories of farm households is thus essential for the design of the new approach to rural development and the focus of this paper.
We analyze here the income strategies of households in the Mexican ejido sector. The ejido sector was created by the sweeping land reform that followed the Mexican revolution of 1910. Land in that sector was allocated to peasant communities called ejidos. Today, the ejidosector contains approximately 60±65% of the rural population, half of the country's agricultural land, and also half of the irrigated area (de Janvry et al., 1997). It is a major reservoir of rural poverty, and consequently has been the focus of rural development eorts. Mexico is at the forefront of the new approach to rural development, in particular through the ambitious Pronasol (Cordera & Vanegas, 1999) and Progresa (Scott, 1999) programs.
In the ejido population, all households are landed as a result of allocations through the land reform (Lamartine Yates, 1981). They have access to a plot of land in usufruct culti-vated individually, and to common property resources from which extraction is generally individual (grazing, forest products) and sometimes collective (social forestry). Because of incomplete property rights, theejidosector is severely constrained in accessing commercial credit. This may induce these households to seek liquidity for farm inputs and investments by participating in o-farm activities as a complement to their farm activities. At the same time, as a consequence of structural adjustment policies, public services to the sector have been curtailed. As a consequence, the pro®tability of agriculture has been held low, also inducing participation in o-farm activi-ties, but this time as a substitute for farm incomes.
The data we use come from a nationwide survey of theejidosector conducted in 1997 by
the Mexican Ministry of Land Reform and the World Bank (World Bank, 1998). The survey is representative of the ejido sector at both the national and the state levels. It consists in a set of 250 ejido-level and 928 ejidatario-level observations within the selectedejidos.
In this paper, we analyze the importance of o-farm activities in these households' income strategies. The questions of interest in analyz-ing the role of o-farm incomes are the following: (a) Can o-farm activities substitute for low access to land in helping households generate income, and do o-farm incomes help redress income inequality among ejidatarios? (b) On the demand side, what are the determi-nants of individual participation in o-farm activities and of the household levels of income derived from each of these activities? (c) On the supply side, are there locational dierences in the availability to households of o-farm activities?
2. THE IMPORTANCE OF OFF-FARM INCOMES FOR LANDED HOUSEHOLDS
We start by analyzing in Table 1 the sources of income for households classi®ed by farm size measured in rainfed-equivalent hectares. In the ejido sector, land endowments are exogenous since there is no land market. Land is accessed through the land reform process and through subsequent inheritances to only one child. There are three surprises in the data relative to conventional wisdom.
The ®rst surprise is that, on average, o-farm income accounts for 55% of total household income, increasing from 38% on the largest farms to 77% on the smallest. Hence, in Mexico, with a relatively well-integrated labor market, a fair degree of economic decentral-ization toward secondary towns (Rello, 1996), and intense migration patterns, o-farm activ-ities are very important for farm households. The ``viable'' family farms created by the land reform in fact comprise households that, on average, are more nonfarmers than farmers. As expected, total farm income and the share of income derived from farm activities (agriculture and livestock) increase with farm size. As expected as well, the share of total household income derived from o-farm activities falls with farm size: with the exception of remit-tances, which is most important for medium farms, all categories of o-farm activities are relatively more important for households with
fewer land assets. Hence, the ability to partici-pate in o-farm activities is fundamental for the land-poor.
The second surprise is that, among o-farm incomes, nonagricultural incomes are far larger than agricultural wage incomes. This diers from the classical view in agrarian studies according to which semi-proletarian peasants ®nd complementary o-farm incomes through employment in large farms (Kautsky, 1899). To the contrary, in modern Mexico, nonagricul-tural employment, other incomes (dominated by government transfers through Procampo, SAGAR, 1998, and welfare programs), and remittances are the main sources of o-farm income.
The third surprise is that the absolute value of income derived from o-farm sources increases with farm size. Comparing in partic-ular households with less than ®ve ha to those with 5±10 ha, we observe an increase with farm size for the income derived from nonagricul-tural wages, remittances, and other sources. These sources of income consequently do not dierentially compensate for lack of access to land. The only source of income that dieren-tially bene®ts the land poor is agricultural employment, but with low levels of remunera-tion and consequently weak compensatory power.
Do o-farm sources of income increase or decrease income inequality across farm
house-holds? To answer this question, we decompose total income inequality, measured by the coef-®cient of variation (CV), between the compo-nent sources of income i. The percentage decomposition of total inequality is given by (Pyatt, Chen, & Fei, 1980):
X
i
wiriCVi
CV 1;
wherewili=lis the weight of income source
i, withlithe mean income from sourceiandl
the mean total income, ricorr yi;y is the correlation between income yi from source i and total incomey, andCViis the coecient of variation of income from source i. Results in Table 2 show that agriculture has by far the largest weight in total income (45%). But the largest coecients of variation by income source across households are for remittances (4.9), agricultural wage income (4.9), nonagri-cultural wage income (3.5), and self-employ-ment income (3.1). Least correlated with total income, as measured by ri, are agricultural wages (0.07), remittances (0.21), and self-em-ployment (0.22). Sources of income with a relative concentration coecient, riCVi=CV, larger than one contribute to increasing total inequality. This is the case for agriculture (1.35) and nonagricultural wage income (1.17). Hence, not all o-farm sources of income help reduce total inequality. Sources of income with
Table 1. Sources of income in the Mexican ejido by farm size, 1997
Farm size in rain-fed equivalent hectares
All <2 2±5 5±10 10±18 P18
Number of households (%) 928 131 244 239 179 135
Total income in pesos 25,953 12,474 17,314 28,368 30,564 44,255 Total farm income 11,697 2,855 4,869 11,856 15,377 27,454 Total o-farm income 14,256 9,619 12,444 16,512 15,187 16,801
Wages 6,397 5,022 6,393 8,620 5,568 4,898
Agricultural wages 1,235 1,245 1,300 1,197 1,732 515 Nonagricultural wages 5,162 3,777 5,094 7,424 3,836 4,383 Self-employment 2,442 2,138 2,464 1,312 3,707 3,020
Remittances 1,683 325 942 2,523 1,845 2,636
Other 3,735 2,133 2,644 4,057 4,067 6,247
Shares in total income
Total farm income 5.1 22.9 28.1 41.8 50.3 62.0 Total o-farm income 54.9 77.1 71.9 58.2 49.7 38.0
Wages 24.6 40.3 36.9 30.4 18.2 11.1
Agricultural wages 4.8 10.0 7.5 4.2 5.7 1.2
Nonagricultural wages 19.9 30.3 29.4 26.2 12.5 9.9
Self-employment 9.4 17.1 14.2 4.6 12.1 6.8
Remittances 6.5 2.6 5.4 8.9 6.0 6.0
a relative concentration coecient smaller than one help reduce total inequality. This is the case for agricultural wage income (0.24), other incomes (0.41), self-employment income (0.47), and remittances (0.71). All together, including nonagricultural wage income, o-farm sources of income help reduce total inequality. It is thus true that o-farm incomes help mitigate the income inequality associated with agriculture.
Weighting these three factors in the decom-position of total income inequality by source shows that agriculture is the largest contributor to income inequality, explaining 60% of the total. Next comes nonagricultural wage income explaining 23% of total income inequality. The other sources of income only make minor contributions, including remittances. Clearly,
the most egalitarian source of income is agri-cultural wage employment, an easy entry low-paying option, followed by self-employment, a highly dual economic activity that also includes many easy entry, low-remuneration activities.
The data in Table 2 also show that, among households that receive income from the corresponding o-farm source, nonagricultural wage income is the most remunerative, followed by remittances, self-employment, and agricultural wages. Important will thus be to establish what are the determinants of access to nonagricultural employment as the most remunerative o-farm option.
The role of o-farm incomes as total house-hold income rises is seen in Fig. 1, where each point represents a cluster of 10 observations
Figure 1.Nonfarm income and share in total income.
Table 2. Decomposition of income inequality by sources of income (coecient of variation)
Concept Agricul-tural
Agricul-tural wage
Nonag-ric. wage
Self-em-ployment
Remit-tances
Other Total
Weight of income source wI 0.45 0.06 0.20 0.09 0.07 0.15 1.01 Coecient of variation CVi 2.5 4.9 3.5 3.1 4.9 1.9 1.44 Correlation yi;y rI 0.78 0.07 0.48 0.22 0.21 0.31
Relative variation CVi=CV 1.74 3.40 2.43 2.15 3.40 1.32 1.00 Relative concentration ciriCVi=CV 1.35 0.24 1.17 0.47 0.71 0.41 Decomposition ofCV wici 0.60 0.01 0.23 0.04 0.05 0.06 1.00 % of households with income from the
source
98 17 29 28 16 96 100
Mean income from the source among households with income from that source
11,967 7,299 18,076 8,650 10,411 3,877 25,953 WORLDDEVELOPMENT
ranked by income level and the ®t is a nonparametric kernel estimate. We can observe that the share of o-farm income in total income falls as income rises, indicating the progressive role of these incomes in total income. The absolute level of income obtained o-farm rises monotonically with income, but less than proportionately with total income. The more remunerative source of o-farm income is nonagricultural wage employment. As can be seen in Fig. 2, income from this source also rises with total income, but its share rises slowly across household income levels. It consequently does not help correct for inequality in the distribution of income and, as was shown in Table 2, does contribute mildly to increasing income inequality. As we shall see, this is because there are speci®c demands to access this source of income which the poor are not well placed to meet.
We conclude by observing that o-farm sources of income oer eective strategies to combat poverty and inequality.
3. PARTICIPATION IN OFF-FARM ACTIVITIES
Since participation in o-farm activities is an important source of income for ejido house-holds, we need to establish who, among members of such households, is able to gain
access to these activities, particularly to the more remunerative nonagricultural employ-ment. Data from the ejido survey show that households have on average 1.04 members engaging in o-farm activities as their primary or secondary occupation. Of these members, 40% engage in nonagricultural wage employ-ment and 37% in self-employemploy-ment, while agri-cultural wage employment only occupies 25%. In nonagricultural employment, construction work (8%) dominates over manufacture (5%) and commerce (4%), but the large category of other types of employment (23%) indicates that nonagricultural employment is highly varied. In self-employment, commerce (17%) is the main activity.
The activities of speci®c categories of household members are analyzed in Table 3. Data show that 92% of male household heads have farming as their main occupation. But, even these men are engaged o-farm as 32% have wage employment and self-employment in nonagricultural activities as secondary occupations. For young males (less than 35 years old) who are not household heads, 55% engage in o-farm activities either as their primary occupation (31%) or as their secondary occupation when their ®rst occu-pation is agriculture (24%). For young women in the household, nonagricultural wage employment (15%) is as important as for young men (16%).
The role of education in accessing nonagri-cultural wage employment across categories of household members is quite clear. Among spouses of the household head, those who access nonagricultural wage employment have 11.6 years of education compared to an average of 3.3 years of education. They are also younger, indicating that younger adults are better educated than older adults. Among young males and females with nonagricultural employment as their primary activity, educa-tion is also clearly higher: 7.3 years for men and 8.2 years for women. This is in contrast with those who participate in the agricultural wage labor market as their primary activity, where years of education are only 5.3 for men and 3.0 for women. Older household members who are not household heads display the same regular-ity: those in the nonagricultural wage labor
market are more educated (7.0 years) than those in the agricultural wage labor market (5.0 years). Nonagricultural self-employment is also clearly related to educational levels for the young (7.1 years for males and 7.8 for women). The conclusion is hence that there are strong positive relations between education and both nonagricultural wage employment and self-employment.
Ethnicity also plays an important role in determining participation in o-farm activities. This is analyzed in Table 4 where households are split by ethnicity, age, and educational levels. Two observations emerge from the contrasted participation to o-farm activities across these groups of households. One is that, for household members older than 35 years, levels of education are very low, and equally low across ethnic groups. Participation in
o-Table 3. Activities by position in household
Main activity unless otherwise indicated Number Main activity
(%)
Secondary activity
(%)
Age (yr)
Education (yr)
Head of household 927 51.7 3.3
On-farm 857 92.4 51.6 3.2
Agricultural wage (second activity) 91 10.6 45.1 3.2 Nonagricultural wage (second activity) 99 11.6 44.2 4.3 Nonagriculture self-employment (second activity) 86 10.0 52.3 3.3
Agricultural wage 10 1.1 50.9 2.7
Nonagricultural wage 16 1.7 48.1 4.3
Nonagriculture self-employment 20 2.2 47.6 4.6
Spouse of head of household 822 45.2 3.3
On-farm 10 1.2 44.1 4.3
Agricultural wage 1 0.1 50.0 1.0
Nonagricultural wage 13 1.6 38.8 11.6
Nonagriculture self-employment 17 2.1 46.7 2.7
Other non-students male younger than 35 years 631 20.8 6.3
On-farm 399 63.2 20.6 6.1
Agricultural wage (second activity) 56 14.0 20.9 6.0 Nonagricultural wage (second activity) 18 4.5 21.8 7.0 Nonagriculture self-employment (second activity) 21 5.3 19.2 5.7
Agricultural wage 48 7.6 20.9 5.3
Nonagricultural wage 100 15.8 22.6 7.3
Nonagriculture self-employment 48 7.6 20.6 7.1
Other non-students female younger than 35 years 599 20.4 6.4
On-farm 13 2.2 21.2 6.3
Agricultural wage 8 1.3 20.5 3.0
Nonagricultural wage 92 15.4 21.9 8.2
Nonagriculture self-employment 26 4.3 19.6 7.8
Other members between 35 and 60 years old 119 44.0 4.1
On-farm (second activity) 39 32.8 44.0 3.6
Agricultural wage 3 2.5 40.7 5.0
Nonagricultural wage 13 10.9 40.8 7.0
Nonagriculture self-employment 4 3.4 37.8 7.5 WORLDDEVELOPMENT
farm activities is aected by education, with the more educated more involved in nonagricul-tural employment. But, at a given educational level, there is no systematic dierence in participation in o-farm activities according to ethnicity.
The second observation is that this changes markedly for household members younger than 35 years. First, while average education levels are much higher for the young, non-in-digenous households gained more than six years of education, while indigenous house-holds only gained between three and six years. Hence, there is an educational lag for indige-nous households. In addition, at a given edu-cational level, we see a large and signi®cant overall dierence (joint test) in participation in o-farm activities between indigenous and non-indigenous households to the advantage of the latter. For households with 3±6 years of education, 14.7% of the non-indigenous ®nd nonagricultural employment against 8.3% for the ethnic. We thus conclude that young indigenous adults suer from a double disad-vantage for income generation: they lag in educational progress, and they derive lower bene®ts from education in accessing more remunerative nonagricultural employment.
4. DETERMINANTS OF PARTICIPATION IN OFF-FARM ACTIVITIES
Participation by individuals in o-farm activities is analyzed econometrically in Table 5 as a function of the characteristics of the indi-vidual, the asset position and the characteristics of the household to which he/she belongs, and the community and regional characteristics of the community where he/she is located. The ®ve o-farm activities are agricultural wage labor, construction work, other nonagricultural wage labor, self-employment outside of agriculture, and seasonal migration to the United States. By working with the choices made by individual adults in 928 households, we have 3,188 observations. Because virtually all individuals specialize in only one o-farm activity, we use a multinomial estimation where no participation in o-farm work is the choice comparison. Individual specialization indicates that the great diversity of sources of income observed at the household level results from diversi®cation among the individual members of a household, not diversi®cation by individuals. In the multinomial regression, coecients are to be interpreted as relative risks. For instance, a coecient of 0.04 for the spouse of a household
Table 4. Participation in o-farm activity by ethnicity and age
Number (%) Participation in o-farm activities (in %) Joint test of dierence
Indig./non-in-dig.P-value None
Agricul-tural wage
Nonagric. wage
Nonagric.
self-employ-ment
Household member younger than 35 years
1,562
Non-indigenousÐby education of 1,268
3 years or less 211 16.6 71.6 10.4 10.9 7.1 Between 3 and 6 years 631 49.8 66.5 8.2 14.7 10.6 More than 6 years 426 33.6 55.0 7.0 26.5 11.5 IndigenousÐby education of 294
3 years or less 90 30.6 90.0 17.8 4.4 10.0 0.09
Between 3 and 6 years 156 53.1 74.4 10.9 8.3 14.7 0.08
More than 6 years 48 16.3 54.2 10.4 20.8 14.6 0.67
Household member older than 35 years
1,626
Non-indigenousÐby education of 1,335
3 years or less 930 69.7 78.8 5.0 6.0 10.2 Between 3 and 6 years 330 24.7 77.0 3.9 7.6 11.5 More than 6 years 75 5.6 54.7 5.3 22.7 17.3 IndigenousÐby education of 291
3 years or less 225 77.3 79.0 6.7 3.6 10.7 0.39 Between 3 and 6 years 58 19.9 63.8 8.6 13.8 13.8 0.12
More than 6 years 8 2.7 62.5 0.0 12.5 25.0 0.78
Table 5. Determinant of participation in o-farm activities. Multinomial estimation, with no o-farm work as the comparison choicea Mean
valueb
Agric. wage labor Construction worker
Other nonag. wage labor
Self-employment US seasonal mi-grant
Relative riskc
P-value Relative risk
P-value Relative risk
P-value Relative risk
P-value Relative risk
P-value
Individual characteristics
Head of household (reference group) 29
Spouse of head of household 26 0.04 0.00 0.04 0.00 0.14 0.00 0.80 0.23 0.14 0.01 Male younger than 35 years old 20 1.92 0.00 0.55 0.05 1.36 0.16 0.74 0.13 2.47 0.02 Female younger than 35 years old 19 0.40 0.06 0.10 0.00 0.70 0.20 0.55 0.02 0.23 0.02 Male 35 years old or older 3 0.57 0.26 0.40 0.23 2.32 0.01 0.39 0.07 1.19 0.82 Female 35 years old or older 4 0.04 0.00 0.04 0.00 0.11 0.03 0.66 0.27 0.14 0.01 Education of less than 3 years (reference level) 31
Education P3 years and <6 years 23 0.97 0.90 2.35 0.02 2.83 0.00 1.65 0.01 1.82 0.26 Education P6 years and <9 years 31 0.80 0.43 2.25 0.03 3.79 0.00 1.82 0.00 1.67 0.41 Education of 9 years or more 15 0.97 0.93 1.15 0.80 10.33 0.00 2.58 0.00 3.49 0.05
Assets and characteristics of the household
Land assets per adult (ha of RFE) 31 0.96 0.13 0.76 0.02 1.00 0.83 1.00 0.97 1.00 0.94 Community land per member (100 ha) 25.8 0.99 0.02 1.00 0.81 1.00 0.79 1.00 0.47 0.99 0.13 Access to technical assistance 6 0.31 0.02 0.52 0.36 1.06 0.87 1.18 0.59 0.31 0.24 Access to formal credit 18 0.70 0.21 1.61 0.20 0.87 0.53 0.96 0.87 0.49 0.17 US migration assets (number of persons) 1.88 0.92 0.06 1.00 0.98 0.95 0.11 1.00 0.99 1.10 0.03 Mexico migration assets (number of persons) 5.54 0.99 0.64 0.98 0.50 0.95 0.01 1.00 0.93 0.97 0.54 Age of household head (years) 52.6 0.98 0.00 0.98 0.10 0.99 0.11 1.00 0.72 0.98 0.04
Indigenous 22 1.29 0.38 0.96 0.92 0.59 0.03 1.83 0.01 0.22 0.05
Locational characteristics
Access to urban centers
Number of urban centers within 1 h 1.57 0.98 0.84 1.00 0.97 1.03 0.69 1.01 0.90 0.86 0.33 Number of urban centers within 1 hfemale 0.77 0.66 0.03 0.69 0.10 1.27 0.01 0.92 0.35 0.84 0.64 Number of rural centers within 1 h 2.61 1.07 0.35 1.12 0.35 1.08 0.26 0.91 0.16 1.12 0.27 Regions
North (reference region)
North Paci®c 9 0.58 0.21 0.38 0.23 0.91 0.80 1.05 0.89 0.06 0.01
Center 30 0.53 0.03 1.74 0.17 1.00 0.99 1.84 0.01 0.28 0.00
Gulf 17 1.06 0.87 1.06 0.91 0.90 0.73 1.18 0.62 0.36 0.07
South 21 0.31 0.00 1.09 0.85 0.51 0.02 0.82 0.47 0.11 0.00
Number of observations in the category 3188 228 76 283 336 71
PseudoR2 0.14
aRobust standard error adjusted for clustering by ejidos.
b
Percentages unless otherwise indicated.
cRelative risk is the exponential value of the coecient. It gives the ratio of the relative probability of the choice for a one unit increase in the exogenous variable:
Pr choiceIjx1= Pr base choicejx1=Pr choiceIjx= Pr base choicejx.P-values are the tests of the underlying coecients being equal to 0, i.e., of the relative risk being equal to 1.
WORLDD
EVELO
PMENT
head in participating to agricultural wage labor means that the probability for such a spouse to participate in agricultural wage labor versus not participating in o-farm work is 96% lower for her than for the household head (the reference group for individual characteristics). In the estimation, we allow for cluster eects on residuals for individuals within a same house-hold since the choices made by these individu-als are part of a single household income strategy.
Results show that individual characteristics aect participation in o-farm activities. The spouse of a household head (97% of whom are women) has a much lower likelihood of participating in wage labor, construction work, nonagricultural wage labor, and seasonal migration than does the household head. Only in self-employment are they equally likely to be engaged. Hence, married women are basically con®ned to on-farm work and to self-employ-ment activities (principally commerce and microenterprises). Younger males (less than 35 years old) are more engaged in agricultural wage work and seasonal migration than household heads, while they are less engaged in construction work. They comprise the category most able to work o-farm. Younger females do not have these advantages. They participate less in all o-farm activities, except nonagri-cultural wage labor where they participate as much as do household heads. Older males (more than 35 years old) participate in o-farm activities as much as do household heads, except for their greater participation in nonagricultural wage labor, and less in self-employment. Like younger females, older females are less engaged in all types of o-farm activities, being at par with household heads only for self-employment activities.
Education has no role to play in access to agricultural wage labor employment. By contrast, it is a key factor in determining participation in the more remunerative o-farm activities. Compared to individuals with less than three years of schooling, those with 3±9 years of education participate more in con-struction work, the nonagricultural labor market, and self-employment. For those who went beyond secondary education (more than nine years of schooling), gains are the greatest: they have a signi®cantly higher likelihood of participating in remunerative nonagricultural employment and seasonal migration to the United States compared to those with only pri-mary schooling. They are also more often
engaged in self-employment than household heads.
The household's asset position also aects individual members' participation in o-farm activities. Greater access to land reduces participation in construction work, an easy-entry, low-paying activity. Better access to technical assistance lowers the need to partici-pate in the agricultural labor market. Rural development interventions that enhance access to technical assistance could thus help retain more labor on the farm. Higher endowments in US migration assets (de®ned as the number of persons with US migration experience among the brothers and sisters of the household head) increase participation in seasonal migration to the United States, while helping decrease participation in the local agricultural wage labor market. Finally, ethnicity (de®ned as speaking an indigenous language) lowers access to nonagricultural employment and to seasonal migration, and as a corollary encourages self-employment. Indigenous populations are thus disfavored in accessing the more remunerative o-farm activities, contributing to their poverty.
Location plays a role. The density of urban centers to which an individual has access (de®ned as the number of urban centers within 1 h of travel by public transportation) makes no dierence across all individuals, but increa-ses female participation in nonagricultural wage labor while decreasing their participation in the agricultural labor market and construc-tion. For women, ease of access is thus key to participate in remunerative nonagricultural employment, while it does not aect men. Regional location also matters as it aects the supply of opportunities. Compared to the North, there is less agricultural wage labor in the North Paci®c and the South, less nonagri-cultural wage labor in the South, and less participation to seasonal US migration in all regions. Individuals in the South, with a less dynamic region and weaker tradition in migration, are thus quite ill-placed for access-ing o-farm income opportunities, makaccess-ing the issue of access to land fundamental for house-hold welfare in that region.
But education has a larger participation-in-ducement eect for women than for men in other nonagricultural wage employment and in self-employment. In these two activities, we ®nd a dierence between men and women with nine years or more of education (see Table 6).
By contrast, there is no gender dierence in these activities at lower levels of education. These results show that higher educational levels are particularly bene®cial for women in enabling them to participate in the more remunerative o-farm employment opportuni-ties. Education, by contrast, is not an impor-tant factor for men or women in the easy entry, low-remuneration activities such as agricultural wage labor and construction work.
We thus conclude that individual, household, and locational characteristics all play a role in explaining participation in o-farm activities. Key among the determinants of participation in nonagricultural employment are gender (with lower participation by wives of the household head and mature females), age (positively for men but negatively for women), education (with higher rewards to higher levels of educa-tion), ethnicity (negatively), for women prox-imity to urban centers, and location by region (with de®cits in opportunities for individuals in the South).
5. DETERMINANTS OF HOUSEHOLD INCOME
We now turn to an analysis of the determi-nants of income at the household level, both total income and income by source, in partic-ular to understand why some households are better able to derive income from speci®c o-farm activities than others. Since all households obtain income from land and livestock, these two income equations are estimated by ordi-nary least squares (OLS). By contrast, since many households do not derive income from agricultural wage labor, nonagricultural wage labor, self-employment, and remittances, these income equations are estimated as Tobits. Household assets are classi®ed as land, human capital, migration assets, and social and
insti-tutional assets. Income levels are also aected by locational characteristics.
Regarding the role of land entitlements, results in Table 7 show that exogenous ejido land rights are an important determinant of total income. Irrigated land raises crop income and total household income, with a marginal hectare adding 10% to average household income and 29% to average crop income. Rainfed land raises total income and livestock income, while reducing incomes from the agri-cultural and nonagriagri-cultural labor markets as labor is reallocated to the farm. Rainfed land (which corresponds to the land endowment of the vast majority of households) also raises remittance income, suggesting that participa-tion in migraparticipa-tion may be seen as a source of cash earnings to give value to the land. Hold-ings under pasture raises livestock income, although the modest increase indicates that most of this land has very low productivity. Access to more common property resources lowers agricultural wage income and remit-tances, also as a consequence of labor reallo-cation toward theejido.
We analyze the role of human capital by considering ®rst the contribution of dierent categories of household members by position, gender, and age at educational levels between 0 and 3 years, and then the additional contribu-tion of educacontribu-tion by years of schooling for adult household members. Gender and age aect income strategies and earnings. Older household heads earn more livestock income, participate less in the agricultural wage labor market, and receive more remittances from abroad, indicating what to expect as a life cycle matures. When this household head is a man, the household derives signi®cantly more income from livestock. Presence of a spouse (89% of the households) adds signi®cantly to crop and remittance income, indicating the importance of married women in agriculture, and of their immigrated children in remitting from abroad.
The educational level of adults in the household aects income strategies and adds signi®cantly to total household income. Edu-cation decreases earnings from low-paying agricultural wage employment through labor reallocation, and increases income from crops, self-employment, and especially nonagricul-tural wage labor. In these activities, the return to education increases with educational level, and the payo is highest in nonagricultural wage labor. Hence, the most remunerative
Table 6. Education eect: men and women
Relative risk Other nonag. labor Self-employment
Men 7 1.87
Women 17 3.69
No. of observation 928 Mean value
Household in-come
Crop income Livestock in-come
Ag. wage in-come
Nonag. wage income
Self-employ-ment income
Remittances
OLS OLS OLS Tobit Tobit Tobit Tobit
Param-eter
P-value Param-eter
P-value Param-eter
P- value Param-eter
P-value Param-eter
P-value Param-eter
P-value Param-eter
P-value
Land assets
Irrigated area owned (ha) 1.2 2478 0.02 2043 0.04 175 0.30 )247 0.44 )535 0.43 131 0.51 360 0.43
Rainfed area owned (ha) 7.7 535 0.00 109 0.36 251 0.00 )508 0.02 )569 0.01 125 0.12 235 0.08
Pasture area owned (ha) 4.2 77 0.29 15 0.79 54 0.04 )47 0.46 55 0.62 )12 0.83 )103 0.23
Common property land per ejidatario (ha)
25.8 15 0.23 )2 0.84 8 0.22 )49 0.06 20 0.30 6 0.59 )79 0.05
Human capital assets
Gender of household head (dummy) 0.97 )10030 0.29 )10314 0.23 2278 0.04 )6950 0.42 2174 0.82 1422 0.70 )3051 0.66
Age of household head (years) 51.7 141 0.08 15 0.80 45 0.08 )226 0.02 )209 0.11 )3 0.96 723 0.00
Members other than the head,with06education<3(number of individuals)
Presence of a spouse (dummy) 0.89 7071 0.06 7492 0.01 52 0.97 4915 0.20 640 0.91 )3436 0.19 12985 0.03
Males younger than 35 years old 0.80 )2801 0.08 )3458 0.00 )734 0.08 5850 0.00 4002 0.09 )1298 0.20 )135 0.94
Females younger than 35 years old 0.73 )1515 0.47 )1613 0.18 )1185 0.03 3587 0.02 1055 0.73 95 0.94 3552 0.09
Males 35 years old or older 0.90 5170 0.13 1408 0.56 )24 0.98 3942 0.12 10263 0.03 )1498 0.53 5876 0.09
Females 35 years old or older 14 )429 0.89 503 0.82 )616 0.44 )1665 0.59 1032 0.82 )2759 0.17 45 0.99
Additional eect of education(number of individuals)
Adults with 36education<6 0.77 2417 0.14 1703 0.10 386 0.37 )1802 0.09 2258 0.35 1454 0.13 )3618 0.10
Adults with 66education<9 1.09 4829 0.01 2316 0.02 856 0.02 )2809 0.01 3967 0.15 2195 0.02 )1718 0.21
Adults with educationP9 0.72 8293 0.00 3301 0.00 1017 0.01 )4399 0.00 8394 0.00 4067 0.00 )4243 0.02
MEXICO
Table 7Ðcontinued
No. of observation 928 Mean value
Household in-come
Crop income Livestock in-come
Ag. wage in-come
Nonag. wage income
Self-employ-ment income
Remittances
OLS OLS OLS Tobit Tobit Tobit Tobit
Param-eter
P-value Param-eter
P-value Param-eter
P- value Param-eter
P-value Param-eter
P-value Param-eter
P-value Param-eter
P-value
Migration assets(number of individuals)
Mexico migration assets 5.5 )118 0.62 )135 0.46 98 0.21 51 0.80 )471 0.18 12 0.94 437 0.16
US migration assets 2.0 1797 0.00 316 0.32 391 0.00 )681 0.03 416 0.51 43 0.86 3753 0.00
Social and institutional assets
Access to technical assistance (dummy) 0.7 6031 0.18 5031 0.15 151 0.92 )6134 0.14 )1306 0.83 987 0.76 3089 0.51
Access to formal credit (dummy) 0.18 2430 0.54 4410 0.20 )547 0.55 )3181 0.18 947 0.83 )51 0.98 )1595 0.67
Indigenous household (dummy) 0.22 )2363 0.28 )1372 0.35 238 0.81 171 0.94 )6454 0.12 1640 0.39 )9339 0.03
Locational characteristics
Number of urban center within 1 h 1.5 1449 0.27 1159 0.25 )88 0.80 )764 0.42 1218 0.43 68 0.92 )2489 0.06
* women share in adults 0.6 )3182 0.12 )2619 0.11 87 0.88 670 0.63 2069 0.37 )583 0.59 2049 0.30
Number of rural center within 1 h 2.6 )1048 0.19 )1120 0.05 )367 0.18 770 0.20 2203 0.06 8 0.99 509 0.56
North Paci®c 0.09 )7187 0.21 1425 0.74 1152 0.46 )6341 0.22 )17376 0.03 )1529 0.68 )11218 0.06
Center 0.30 )7510 0.06 506 0.85 234 0.80 )10053 0.01 )7696 0.14 3083 0.18 )3885 0.38
Gulf 0.17 )3569 0.49 )43 0.99 1159 0.42 )3077 0.33 )8752 0.16 5522 0.03 )4517 0.49
South 0.21 )4171 0.33 3418 0.24 1753 0.20 )14852 0.01 )11834 0.05 868 0.73 )4629 0.31
Intercept 1 7794 0.44 4877 0.57 )4010 0.05 7519 0.41 )29395 0.03 )17885 0.00 )77996 0.00
Endogenous variable(mean income) 25953 7110 4586 1235 5162 2442 1683
Quality of ®t
R2 0.19 0.13 0.11
Waldv2(26) 43.6 52.8 36.8 84.2
Left-censored observations 771 663 666 778
a
P-value computed from robust standard errors.
WORLDD
EVELO
PMENT
employment opportunities are captured by those with the highest educational levels. Households with more educated adults are wealthier, and they receive lower transfers from US migrants, suggesting that transfers are motivated by altruism (Becker, 1974) and not by exchange (Cox, Eser, & Jimenez, 1998).
To appreciate the role of education across sources of income, we can compare the marginal contribution (in pesos of 1997 when the exchange rate was 7.9 pesos to the US dollar) of one adult with more than nine years of education compared to less than three years (see Table 8).
Education has a negative role on agricultural wage income since educated household members seek employment on more remuner-ative markets. The higher household income derived from education deters remittances from US migrants. Education has little role in cattle raising, but it has a return in agriculture. Self-employment income is quite heterogeneous, but provides opportunities for higher education to increase income. It is, however, in nonagricul-tural employment that education is by far the most important. The type of education that has the highest payo in rural areas should thus prepare adults to access nonagricultural employment (see Lopez & Valdes, 1997 for similar results for other Latin American coun-tries). While historically badly neglected in the ejido sector, but increasing rapidly among the young, education is thus an important asset to escape poverty for those with little land and few migration assets if nonfarm activities are to compensate for these asset disadvantages.
US migration assets add importantly to household income and to remittances, but also to livestock income as remittances are invested in animals. These migration assets reduce the need to derive income from the agricultural labor market. Mexico migration assets are, by contrast, inconsequential in income and income strategies, indicating that they are not impor-tant in creating income opportunities among the activities considered here. Among social assets, ethnicity negatively aects both nonag-ricultural wage earnings (at an 88% signi®cance level) and remittances, showing the special diculties of these households in gaining access to the most lucrative sources of o-farm income. Indeed, much rural poverty in Mexico is tied to ethnicity.
Finally, geographical location is also a determinant of o-farm incomes. The number of rural centers within 1 h traveling distance
enhances nonagricultural wage income. Regions aect household income and speci®c sources of income, even after controlling for the dierential asset positions of households. In the Center region, household income and income from agricultural wages are lower. In the South, incomes derived from both agricultural and nonagricultural wage work are lower, showing again the importance of the role of land for household welfare in that region. If o-farm activities are to serve as an element of a poverty reduction strategy, the regional supply of such opportunities needs to be an integral component of regional development initiatives. Clearly, focusing on the individual determi-nants of access would not be sucient.
6. CONCLUSION
opportunities also matters, with sharp dier-ences across regions and proximity to urban centers enhancing women's access to nonagri-cultural employment. By contrast, women located far from urban centers are con®ned to agricultural labor markets and to construction work with low levels of remuneration.
Any strategy that aims at raising incomes among ejido households needs to focus importantly on participation in nonagricul-tural activities, particularly for households with little access to land. Yet, we have seen
that households with low access to land are not in a better position to derive more income from nonagricultural activities because they also control less of the other assets needed for this purpose. We identi®ed human capital (for nonagricultural wage employment and self-employment), migration assets (for remit-tances), and ethnicity (playing a negative role in nonagricultural wage income and remit-tances) as the key determinants of nonagri-cultural incomes. Besides enhancing access to land, getting rural households out of poverty thus requires massive eorts to keep the young in school through secondary level, and programs targeted at indigenous households to help reduce educational gaps and enhance their access to nonagricultural employment opportunities. On the supply side, rural development must be part of eorts at promoting regional development to accelerate the economic growth of regions and enhance the availability of o-farm income opportuni-ties for rural households.
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Table 8. Marginal contribution of one adult withmore than nine years of education compared to less than three
years
Income source Marginal contribution
Agricultural wage income )4,399
Remittance income )4,243
Livestock income 1,017
Crop income 3,301
Self-employment income 4,067 Nonagricultural wage income 8,394
