Nonfarm Employment and Poverty in
Rural El Salvador
PETER LANJOUW
*The World Bank, Washington, DC, USA
Summary. ÐThis paper analyzes two complementary data sets to study poverty and the nonfarm sector in rural El Salvador. We ®nd that rural poverty in El Salvador remains acute and signi®cantly higher than in urban areas. While the rural poor are mainly agricultural laborers and marginal farmers, some nonfarm activities are also of importance to the poor. In fact, nonfarm activities in El Salvador account for a signi®cant share of rural employment and income for both the poor and the nonpoor. The poor, on the one hand, are engaged in ``last resort'' nonfarm activities that are not associated with high levels of labor productivity. The nonpoor, on the other, are engaged in productive nonfarm activities which are likely to present a potent force for upward mobility. Signi®cant correlates of these high-productivity occupations include education, infra-structure, location, and gender. While most of the analysis is at the household level, the data also permit some focus on small-scale rural enterprise activities. It appears that in El Salvador very few rural enterprises report utilizing formal credit in setting up their activities. In addition, a signi®cant proportion of enterprises are engaged in subcontracting arrangements with some larger, often urban-based, ®rm. Ó 2001 Elsevier Science Ltd. All rights reserved.
Key words Ðnonfarm employment, poverty, El Salvador
1. INTRODUCTION
Poverty is the subject of much discussion in El Salvador. There is a sense that without concerted attention to poverty issues, a full and sustained transition from decades of violence will remain elusive. It is also feared that equity-motivated programs, such as the agrarian reforms, have not succeeded in eliminating poverty altogetherÐeither because implemen-tation has not been as eective as hoped, or because at least part of the poverty problem is linked to issues beyond access to land and tenure security alone. There is a feeling that more must be done.
While fewdispute the importance of ad-dressing poverty in El Salvador, there is con-siderable debate surrounding certain key aspects of the poverty problem. There is, for example, no universal consensus on how widespread poverty is in El Salvador, where poverty is concentrated, and which household characteristics are most closely linked to pov-erty. Much of this debate is prompted by dif-ferences in methodological approaches, and by shortcomings in available data sources. None-theless, there does appear to be broad agree-ment that poverty in rural areas deserves
particularly close attention. Most approaches to the measurement of poverty tend to indicate that the rural poverty problem is particularly pressing.
In order to understand the causes of rural poverty and to design policies that address these, one must examine in some detail the operation of the rural economy. It becomes quickly apparent that the rural economy ex-tends well beyond agriculture. The nonfarm sector in rural areas is highly heterogeneousÐ encompassing the full spectrum of economic activities which occur in rural areas but which are not directly associated with agricultureÐ and can represent a very important part of the rural economy in terms of incomes and em-ployment generated. While this sector has not received the same level of attention as the ag-riculture sector, there is a growing appreciation of its potential in terms of both poverty
2001 Elsevier Science Ltd. All rights reserved Printed in Great Britain 0305-750X/01/$ - see front matter
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*I am grateful for the comments and suggestions of Alberto Valdes, Ramon Lopez, and three anonymous referees. I remain responsible for all outstanding errors. The views in this paper are my own and should not be taken to re¯ect the views of the World Bank or any aliated institution.
alleviation and growth more generally. Recent analyses of the sources of growth in East Asia have stressed the central role played by the nonfarm sector in rural areas.1 A question of
considerable interest is whether the nonfarm sector can play a similar role in stimulating rural growth in El Salvador, and Latin America more broadly.
This paper examines data from two recent household surveys in El Salvador to assess to what extent the nonfarm sector might be able to contribute to rural poverty alleviation. The layout of the paper is as follows. The next section brie¯y attempts to organize our think-ing about poverty and the nonfarm sector. This is followed in Section 3 with a discussion of poverty in El Salvador, and some tentative es-timates of poverty based on consumption ex-penditures. Section 4 introduces some quantitative evidence on the size of the non-farm sector in rural El Salvador, and the range of activities that comprise this sector. This section also considers what relationship exists between poverty and nonfarm employment. Section 5 turns to an examination of the factors that appear to in¯uence the involvement of rural households in the nonfarm sector and also the earnings associated with those activities. In Section 6 we summarize the main ®ndings and oer some remarks on policy implications of the ®ndings.
Before proceeding, we make a brief note about the data sources underlying the analysis. Two sources of data are being used in this pa-per. The Encuesta de Hogares de Propositos Multiples, 1994-III, (EHPM) is a nationally representative household survey ®elded by the Ministerio de Plani®cacion y Coordinacion del Desarollo Economico y Social (MIPLAN) in El Salvador. The EHPM is an annual survey, ®elded throughout the year in four ``waves.'' In total, roughly 20,000 households are covered. The analysis in this paper is based only on the third wave of the 1994 survey (the waves are designed to be amenable to self-standing anal-ysis) covering 4,229 households in total (1,743 in rural areas) and ®elded during the period July-September, 1994. This wave is somewhat special in that it contained a detailed con-sumption module (for a subsample of house-holds) which permits an analysis of poverty based on consumption expenditure rather than income (see below). 2
The second source of data comes from a ru-ral household survey ®elded by Fundacion Salvadore~na Para el Desarollo Economico y
Social (FUSADES) in 1996. The survey cov-ered a sample of about 630 rural households from all regions of El Salvador, strati®ed on households' characteristics according to their main economic activities, i.e. the self-employed, agricultural workers, and nonfarm workers. The survey was designed to be representative of the rural population at the 10% level of signif-icance (Lopez, 1996). The FUSADES survey obtained information on a wide range of de-mographic characteristics, location, and in-come variables. The level of detail in the information collected has permitted the calcu-lation of a comprehensive measure of income which is less likely to suer from important omissions than is conventionally the case with income surveys. The FUSADES survey pro-vides a useful complement to the EHPM in that while it is smaller in sample size it provides greater detail about rural livelihoods and ac-tivities.
2. A QUICK OVERVIEW OF THE POVERTY±NONFARM LINKS
Rural o-farm employment has been tradi-tionally seen as a low-productivity sector, pro-ducing lowquality goods. The sector, in this view, is expected to wither away as a country develops and incomes rise. There is thus no obvious rationale for governments to promote the sector, nor be concerned about negative repercussions on the rural nonagricultural sec-tor arising from government policies directed at other objectives.
In recent years, opinion has been swinging away from this view, however, and there are a number of arguments that suggest that neglect of the sector is socially costly. For example, it has been argued that the sector has a positive role in absorbing a growing rural labor force, in slowing rural-urban migration, in contributing to national income growth and in promoting a more equitable distribution of income.3
is employed in nonfarm activities. This can be compared to 28% when only rural settlements are included. Hazell and Haggblade also high-light the importance of female participation in nonagricultural activities: 79% of women in the Latin American rural wage-labor force are es-timated to be employed in nonagricultural ac-tivities.
Nonagricultural activities can be broadly di-vided into two groups of occupations: high la-bor productivity/high income activities and low labor productivity activities that serve as a re-sidual source of employmentÐa ``last-resort'' source of income (Lanjouwand Lanjouw, forthcoming). These latter activities are com-mon acom-mong the very poor, particularly acom-mong women. Such employment may nevertheless be very important from a social welfare perspec-tive for the following reasons: (a) o-farm
em-ployment income may serve to reduce
aggregate income inequality; (b) where there exists seasonal or longer-term unemployment in agriculture, households may bene®t even from lownonagricultural earnings; and (c) for cer-tain subgroups of the population who are un-able to participate in the agricultural wage labor market, notably women in many parts of the developing world, nonagricultural incomes oer some means to economic security.
It is dicult to say whether nonfarm em-ployment is income inequality increasing or decreasing without information about what the situation would have been in the absence of such occupations. One important consideration remains that although aggregate income in-equality may widen as rural nonagricultural incomes increase, this may occur alongside a decline in absolute poverty (if, for example, all households bene®t from o-farm income, but the rich bene®t proportionately more).4
Empirical evidence in many countries sup-ports the notion that agricultural wages are not perfectly ¯exible, and that rural agricultural labor markets are segmentedÐwith certain subgroups of the population such as women and children unable to obtain employment at the market wage. Lanjouw (1995) found some evidence that small farms in Ecuador obtained higher yields than large farms. A possible ex-planation for this observation could be that small farmers apply more labor per unit of land than large farmers.5 Family labor is applied beyond the level where the marginal product of labor is equal to the market wage, because for at least some family members the market wage is not the opportunity cost of labor. If indeed
agricultural wage employment is not an option for certain family members, then rural nonag-ricultural employment opportunities, even if they are not highly remunerative can make a real dierenceÐespecially for those households which do not possess farm land.
In sum, the existing literature points to a potentially strong relationship between the ru-ral nonagriculturu-ral sector and ruru-ral poverty. Because of market imperfections and distor-tions, nonfarm activities are likely to employ labor beyond the point where the marginal product of labor is equal to the prevailing av-erage agricultural or urban wage. The wide range of nonagricultural activities in terms of labor productivity suggests that for some households and individuals these activities provide a last resort safety-net function, while for others they oer a genuine opportunity for sustained upward mobility. In this paper we attempt to shed some empirical light on at least some aspects of the relationship between pov-erty and the nonagricultural sector in the con-text of rural El Salvador.
3. RURAL POVERTY IN EL SALVADOR
Poverty has been the focus of attention in many studies in El Salvador (recent examples include FUSADES, 1993, World Bank, 1994a and MIPLAN, 1995a). But to date, there is no clear consensus as to the magnitude and di-mensions of the poverty problem in El Salva-dor. There are numerous methodological and data-related issues that stand in the way of precise, quanti®ed poverty rate calculations in El Salvador: for the country as a whole, and among various population subgroups. These issues are brie¯y noted below, but space pre-vents discussing them in detail.6
designed to measure household earnings. Such surveys are generally weak in capturing in-comes from nonwage labor sources (such as remittances, transfers, and self-employment) and from agricultural activities. These omis-sions may be particularly pertinent to the measurement of rural poverty.
The next step in measuring poverty is to re-late household-level welfare indicators to some poverty thresholdÐthe poverty line. In con-structing a poverty line, many assumptions are generally required, and these can at times be-come quite contentious. Issues that must be addressed, for example, relate to the nutritional cut-o point to be applied, whether account is to be taken of dierent requirements between adults and children or between males and fe-males, and what kind of adjustment must be made to allowfor nonfood items in the basic consumption basket. While various poverty lines have been formulated for El Salvador, their treatment of these issues has not always been very clearly documented (see, IIES-UCA, 1993, and also World Bank, 1994a). Nor has the treatment been the same across the dierent calculations.
Yet further issues relate to the partial geo-graphic coverage of many household surveys in El Salvador (missing certain regions, or focusing only on urban areas) and the nonavailability of up-to-date population expansion factors that may lead to biased assessments of the distribu-tion of poverty across populadistribu-tion subgroups. Finally, there is an important issue associated with the nonavailability of a spatial cost-of-liv-ing index. Such an index adjusts for the fact that to reach a given standard of living in Metro-politan San Salvador might cost quite a dierent amount from other urban areas or rural areas.
Combined, these factors issue a strong warning against eorts to provide detailed cal-culations of poverty rates in El Salvador. While such calculations would undoubtedly be of great value, one might wish to ®rst concentrate on reaching agreement on the appropriate methodology to apply and in securing the req-uisite data. At the same time, the lack of quantitative poverty measures need not impede unduly one's ability to focus on poverty-related questions. If one is prepared to con®ne one's remarks to broad comparisons of poverty across population subgroups (say, between ru-ral and urban areas) and to be satis®ed in stating that poverty among one group is higher or lower than among the other (without at-tempting to state by howmuch), then
meth-odological dierences and uncertainties may be less of a constraint.
It is in this spirit that we present in Table 1 some tentative estimates of the incidence of poverty in El Salvador in 1994, based on the consumption expenditure collected by the EHPM. These ®gures should not be accepted without question (just as all other attempts to measure poverty in El Salvador are likely to be contentious), because they embody a range of strong (and controversial) assumptions. First, a speci®c methodology was applied to estimate a robust incidence of poverty for the two subs-amples of the EHPM for which highly dier-ence consumption modules were ®elded (for a detailed exposition, see Lanjouwand Lanjouw, 1996). Second, it was assumed that the rural cost of living was lower than in urban areas, in proportion to the ratio of the MIPLAN (1995b) urban poverty line to rural poverty line. Third, the poverty line which was taken was simply the one published in MIPLAN (1995b) for urban areas without any attempt to establish its validity. Fourth, we have made no adjustments for dierences in needs across family members (via equivalence scales) and make no allowances for economies of scale in household consumption.
than in either the East or West. We shall see belowthat the most productive nonfarm ac-tivities tend to be concentrated in the two central regions of the country.
4. THE NONFARM SECTOR IN RURAL EL SALVADOR
A study of rural nonfarm employment in Latin America based on census data suggests
that in 1975 roughly 20% of the economically active population in rural El Salvador was employed in the nonfarm sector (Klein, 1993). This can be compared with ®gures of 16%, 18% and 40% for Honduras, Guatemala and Costa Rica, respectively, in the early to mid-1970s. More recent census ®gures for El Sal-vador have not been calculated, no doubt for data-related reasons associated with the polit-ical and military instability of the intervening period.
Table 1. The incidence ofpoverty in El Salvadora;b
High poverty line Lowpoverty line
Incidence of poverty Persons poor Incidence of poverty Persons poor West
Urban 0.68 312,343 0.28 127,241 Rural 0.75 449,050 0.38 223,726 All 0.72 761,393 0.33 350,967 Central 1
Urban 0.74 329,117 0.31 138,726 Rural 0.76 560,313 0.33 240,980 All 0.75 889,430 0.32 379,706 Central 2
Urban 0.70 131,827 0.36 66,983 Rural 0.79 237,550 0.32 96,925 All 0.76 369,377 0.34 163,908 East
Urban 0.67 287,074 0.30 129,960 Rural 0.79 513,079 0.38 247,231 All 0.74 800,153 0.35 377,191 Metropolitan
San Salvador 0.40 518,926 0.08 100,751 National
Urban 0.56 1,579,287 0.20 563,661 Rural 0.77 1,759,992 0.35 808,862 Total 0.66 3,339,279 0.27 1,372,523
a
Source: Encuesta de Hogares de Propositos Multiples, 1994-III.
b(i) The Encuesta de Hogares de Propositos Multiples 1994-III yields potentially problematic consumption ®gures as
In Tables 2 and 3 we present estimates based on the EHPM household survey. In 1994, 36.4% of the economically active rural popu-lation was employed in the nonfarm sector, nearly twice as many as in the mid-1970s (Table 2). The range of activities in which the rural population is engaged includes both manufac-turing and services. Nearly 30% of all rural nonfarm employment is engaged in some form of manufacturing activity (combining textiles and carpentry with the generic manufacturing entry). Commerce represents nearly an addi-tional 25%, construction about 13%, domestic service just over 10%, and transport nearly 6%. The remaining activities are largely various service sector activities.
As a proportion of the economically active population, women are far more likely to be active in the nonfarm market than men (Table 2). Seventy-two precent of economically active women are employed in the nonfarm sector, compared to just about 25% of men. But in the EHPM survey, only about 22.5% of all women of working age are counted among the eco-nomically active population (compared to 73.6% of men).9The activities in which women
are heavily engaged include ®rst of all com-merce, and are followed by manufacturing and domestic service. For men, commerce and do-mestic service are of far less signi®cance, but
construction, manufacturing and also transport are important sectors.
There is geographic variation in El Salva-dor in the signi®cance of the rural nonfarm sector (Table 3). In the Central 1 region (which includes the departments of Chala-tenango, La Libertad, San Salvador, and Cuscatlan) nearly 50% of the economically active population is employed in the nonfarm sector. This contrasts with only 23.2% in the East (Usulutan, San Miguel, Morazan, and La Union). The spectrum of activities across regions is fairly uniform in terms of shares of employment. For example, while manufac-turing appears to be relatively more impor-tant in the West than in the other regions: about 30% of all nonfarm employment occurs in the textile, carpentry and manufacturing sectors in the West (Ahuachapan, Santa Ana and Sonsonate), it is as high as 24±26% in the other regions.
In Table 4 we observe that involvement by households in the nonfarm sector is broadly correlated with lower rates of poverty.10 The highest incidence of rural poverty in the EHPM survey is observed among households that en-gage in both agricultural labor and farming. In fact, agricultural labor appears to be particu-larly closely linked to rural poverty in that of eight possible household economic status
cate-Table 2. Nonfarm activities in rural El Salvador (percentage of persons aged above 10 years engaged in remunerated labor)a
Percentage of populationbwith primary occupation Male Female Total
Fishing 0.7 (2.8)c 0.2 (0.3) 0.6 (1.6)
Manufacture 4.2 (17.0) 10.0 (13.8) 5.6 (15.4) Textiles/garments 1.0 (4.0) 8.0 (11.1) 2.7 (7.4) Wood/straw/leatherware 1.4 (5.7) 3.7 (5.1) 1.9 (5.2) Utilities 0.3 (1.2) 0.0 (0.0) 0.3 (0.8) Construction 6.1 (24.7) 0.4 (0.6) 4.7 (12.9) Commerce 2.1 (8.5) 28.1 (38.9) 8.4 (23.1) Restaurant/hotel 0.2 (0.8) 1.8 (2.5) 0.6 (1.6) Transport 2.8 (11.3) 0.0 (0.0) 2.1 (5.8) Finance 0.1 (0.4) 0.5 (0.7) 0.2 (0.5) Administration 0.2 (0.8) 0.2 (0.3) 0.2 (0.5) Teaching 0.4 (1.6) 1.6 (2.2) 0.7 (1.9) Health 0.0 (0.0) 1.3 (1.7) 0.4 (1.1) Domestic service 0.9 (3.6) 12.4 (17.2) 3.7 (10.2) Other service 4.3 (17.4) 4.1 (5.7) 4.3 (11.8) Total nonfarm 24.7 (100.0) 72.3 (100.0) 36.4 (100.0)
Farming 54.4 11.4 44.0
Agricultural labor 20.8 16.1 19.6 No. of observations 2,230 685 2,915
a
Source: Republica de El Salvador: Encuesta de Hogares de Propositos Multiples, 1994-III.
b
Column percentages provided in brackets.
gories, the three associated with highest inci-dence of poverty include agricultural labor amongst the household economic activities. Such a pattern has also been noted in the context of rural north India where agricultural labor is widely viewed with distaste, in which households participate only when faced with acute hardship and no alternative sources of income (Drezeet al., 1992). For this reason the likelihood that agricultural labor households are poor is signi®cantly higher than for many other household types. Such a perspective might also apply in rural El Salvador. Of course, there are dierent types of agricultural labor. The category applied in Table 4 encom-passes both casual daily wage labor and long-term, permanent employment on a farm, plantation, or ranch. As a result, not all agri-cultural labor is likely to be unattractive as a source of income. This is possibly re¯ected in Table 4 in that households which engage in both agricultural labor as well as nonfarm la-bor run only about an average ``risk'' of pov-erty (35%Ðsee Table 1).
Nonfarm labor, we have already seen, is also not homogeneous. In Table 4 one household category engaging in nonfarm employment is relatively highly exposed to poverty (house-holds simultaneously engaging in farming, ag-ricultural labor, and nonfarm labor). But households reliant only on nonfarm labor are signi®cantly less likely to be poor than all other
rural households. This observation illustrates the important point raised in Section 2, namely that the nonfarm sector typically comprises two distinct sets of activities. On the one hand there is a set of activities which are reasonably pro-ductive, relatively well-paid, and which have the appealing feature of being comparatively less exposed than agriculture to climatic varia-tions and uncertainties. On the other hand, there are a group of activities undertaken by persons who are unable even to secure an ag-ricultural laboring position; persons who are perhaps old or disabled, or who may be pro-hibited by custom from participating in the agricultural labor market (for example, women and children). This second set of nonfarm jobs plays a very dierent role to the ®rst set. One way to perceive these two is to regard the ®rst set as a source of upward mobilityÐa route out of poverty, and the second as a type of ``safety net'' which helps to prevent poor persons from falling into even greater destitution.11
Both sets of nonfarm jobs have a very im-portant role to play in reducing, or relieving, poverty. But the types of policies that can be pursued to help realize their potential are quite dierent. In the Indian state of Maharashtra, the state government supports a large public-works program oering nonfarm employment at a wage below the prevailing agricultural wage rate to anyone who presents himself at the worksite (see Dreze, 1995 and Datt and
Rav-Table 3. Nonfarm activities in rural el salvador (% of persons aged above 10 years engaged in remunerated labor)a
Percentage of population with primary occupation West Central 1 Central 2 East Fishing 0.0 (0.0)b 0.9 (1.9) 1.6 (4.7) 0.3 (1.3)
Manufacture 6.3 (18.8) 6.5 (13.4) 5.3 (15.7) 3.5(15.1) Textiles/garments 2.0 (6.0) 3.8 (7.8) 3.3 (10.0) 1.5 (6.5) Wood/straw/leatherware 2.3 (6.8) 3.1 (6.4) 0.5 (1.5) 0.6 (2.6) Utilities 0.4 (1.2) 0.4 (0.8) 0.2 (0.6) 0.0 (0.0) Construction 4.1 (12.2) 6.5 (13.4) 4.9 (14.5) 2.8 (12.1) Commerce 7.1 (21.2) 9.9 (20.4) 9.7 (28.8) 7.1 (30.6) Restaurant/hotel 0.1 (0.3) 1.3 (2.7) 0.6 (1.8) 0.0 (0.0) Transport 2.2 (6.5) 2.8 (5.8) 0.6 (1.8) 1.9 (8.2) Finance 0.2 (0.6) 0.2 (0.4) 0.3 (0.9) 0.2 (0.9) Administration 0.5 (1.5) 0.1 (0.2) 0.1 (0.3) 0.1 (0.4) Teaching 0.8 (2.4) 0.4 (0.8) 0.9 (2.7) 0.9 (3.9) Health 0.5 (1.5) 0.5 (1.0) 0.0 (0.0) 0.2 (0.9) Domestic service 3.0 (8.9) 5.8 (12.0) 2.4 (7.1) 2.0 (8.6) Other services 4.1 (12.2) 6.3 (13.0) 3.3 (9.8) 2.1 (9.1) Total nonfarm 33.6 (100.0) 48.5 (100.0) 33.7 (100.0) 23.2 (100.0)
Farming 43.7 33.1 50.1 56.6
Agricultural labor 22.7 18.3 16.1 20.2 No. of observations 802 830 635 648
a
Source: Republica de El Salvador: Encuesta de Hogares de Propositos Multiples, 1994-III.
allion, 1993). This program provides nonfarm employment to poor persons for whom agri-cultural wage employment is not an option (perhaps because of cultural restrictions, or because climatic conditions have sharply re-duced demand for labor). It therefore exploits the ``safety-net'' function of the nonfarm sec-tor. The attraction of this approach is that the poor are ``self-targeted,'' i.e., only those who have no other viable employment alternative present themselves at the worksite. Thereby the costly administration of a government targeting scheme is avoided. Policies aimed to expand access of the poor to the high-income nonfarm jobs are likely to look very dierent. In these cases, the emphasis is typically on removing constraints and bottlenecks to such employ-ment by providing training, supporting infra-structure, etc.
In Table 4, while we see evidence of both types of nonfarm employment (i.e. associated both with a higher risk of poverty and also with a lower risk), the numerical importance of the latter is far greater. Only 2.7% of the rural population falls in the category of engaging simultaneously in farming, agricultural labor, and nonfarm labor. By contrast, nearly 30% of the rural population is engaged only in non-farm activities. And this segment is the least poor of the entire rural population. It seems possible therefore, that the rural nonfarm sec-tor in El Salvador functions as a route out of poverty as well as a safety net to those who experience acute distress.12
We turn next to an oft-overlooked segment of the nonfarm sector. Table 5 examines the FUSADES data to consider the role of small
enterprises in rural areas. While the EHPM survey did not inquire speci®cally into house-hold enterprises, the FUSADES rural survey included a separate questionnaire on such ac-tivities. In Table 5 we see that of the 300 workers active in rural enterprises, about 40% were family members. Over 50% of all rural enterprises covered in the FUSADES survey were home-based. Commerce was by far the most common form of rural enterprise, al-though on average such enterprises were smaller in terms of employment per ®rm than pottery and brick-making enterprises. It is in-teresting to note that only about 5% of all rural enterprises covered in the FUSADES survey reported having received training.
Textile enterprises stand out among the more common enterprises in that they drawparticu-larly heavily on family labor and are most fre-quently home-based. Nearly a quarter of these enterprises are engaged in a relationship with some larger ®rm, in which they receive inputs from the larger ®rm, assemble them, and then re-sell their output to the same contractor. Such subcontracting arrangements has been ob-served elsewhere in Latin America (see, for example, Lanjouw, 1995), and are argued by Hayami (1995) to have been common in rural areas of East Asia during the earlier stages of economic development. Hayami (1995) argues that these arrangements are useful to both parties in that they provide to the contractor access to cheap labor, while the home-based ®rms are able to choose howand when to al-locate their family labor, and do not have to concern themselves with bringing the ®nal goods to the market (which, in the case of
Table 4. Poverty and rural household activitiesa
Household characteristicsb Percent of population
(%)
Incidence of extreme poverty (%)c
Agricultural labor and farming 5.0 54.7 Agricultural labor only 9.6 48.7 Agricultural labor farming and nonfarm employment 2.7 43.9
Farming only 26.1 41.5
Farming and nonfarm employment 19.9 35.9 Agricultural labor and nonfarm employment 9.1 35.2 Nonfarm employment only 26.1 20.3 Nonfarm income from non-wage sources 1.6 16.3
aSource: Encuesta de Hogares de Propositos Multiples, 1994-III. b
Agricultural labor households are de®ned as such if at least one household member is employed as a salaried or casual wage laborer in agriculture. Farming households refer to those households where at least one household member is engaged in cultivation. Nonfarm households correspond to those households where at least one household member is employed in a nonfarm occupation.
c
clothing or shoes for example, might be very far away).
The range of both nonfarm employment as well as rural enterprise activities that are en-gaged in provides some clues as to the rela-tionship between the nonagriculture and the agriculture sector in rural areas. This broader relationship has received considerable attention in the literature. Mellor and Lele (1972), Mellor (1976), and Johnston and Kilby (1975) have argued that a virtuous cycle between agricul-tural intensi®cation and nonfarm activity can emerge on the basis of production and con-sumption linkages. Production linkages emerge, for example, when demand of agri-culturalists for inputs such as plows and ma-chinery repair stimulate nonfarm activity via ``backward'' linkages or where agricultural goods require processing in spinning, milling, or canning factories (``forward'' linkages). Consumption linkages emerge as rising agri-cultural incomes feed primarily into increased demands for goods and services produced in nearby towns and villages. While it is dicult to test the strength of such linkages with the available data sources, the fact that a large fraction of nonfarm activities center around commerce, food processing, transport, and re-pair activities, suggests that these linkages are certainly present in the El Salvador case. 13
It is also interesting to note the importance of rural manufacturing and the existence of sub-contracting arrangements between rural home-based enterprises and large, perhaps ur-ban-based, supplier companies. The existence of such rural nonfarm activities can be very important to the rural economy because they introduce a source of rural income that is less
closely linked to agricultural ¯uctuations. This is in contrast to the activities that are directly linked to agricultural production and incomes. In rural areas, insurance and credit markets often do not operate well, or are missing alto-gether. This means that, in order to avoid ®nding themselves in a position where they might need to take consumption loans, farmers' production decisions are often aimed at crop-ping patterns which minimize the risk of har-vest failure, but which have lower-value expected yields (Murdoch, 1995). Access of certain family members to noncyclical sources of income from manufacturing activities might help to encourage higher-value agricultural production decisions.
5. CORRELATES OF NONFARM EMPLOYMENT AND EARNINGS
We turn nowto a closer examination of the correlates of nonfarm employment in El Sal-vador by presenting, in Table 6, results from a Probit model considering the likelihood of nonfarm employment for the working-age rural population. In light of the discussion in the previous section we look not only at all non-farm jobs together, but also distinguish be-tween nonfarm jobs that can be considered as ``low-productivity'' jobs and those that are ``high-productivity.'' The distinction is based on whether hourly earnings from these jobs are lower, or higher, than average hourly earnings from agricultural labor, respectively. Rather than report the parameter estimates from the Probit models, we report in Tables 6±8 the marginal eects.14
Table 5. Rural enterprises in El Salvadora
Sector Number of ®rms
Number of workers
Percentage family members (%)
Percentage home-based
(%)
Percentage with training
(%)
Percentage supplying contractor (%)
Transport 1 1 100 0 0 0
Other services 3 3 100 33 0 0 Other industry 5 6 100 100 0 20
Repair shop 6 16 44 33 0 0
Restaurant/bar 5 19 16 20 0 0
Textiles 13 25 73 92 8 23
Wood/work 10 37 22 60 10 30
Food proc. 13 53 28 54 8 0
Pottery/bricks 14 63 13 7 7 7
Commerce 31 77 64 58 3 0
Total 101 300 40 52 5 8
In the ®rst column of regression results in Table 6, we ®nd that women are signi®cantly less likely than men to ®nd employment in the nonfarm sector (a probability 18 percentage points lower than for a male, other variables at their means). We shall return to this ®nding below. As a person get older, he or she is sig-ni®cantly less likely to be employed in the nonfarm sector. Compared to the uneducated, all persons who have been educated are signif-icantly more likely to ®nd employment in the nonfarm sector. There appears to be a strengthening of the eect of education on the probability of employment as education levels improve.
Households with larger per capita landhold-ings are less likely to be employed in the non-farm sector. This is not the case in all contexts, because where highly desirable nonfarm jobs are rationed, it is the wealthier households (those with more land) who might be better placed to secure such jobs. In El Salvador, while some family members of the larger landowning households are indeed likely to be working in the nonfarm sector, they probably reside in San Salvador and therefore do not feature in the FUSADES sample. Cultivating households (those reporting actual involvement in agricul-ture) are also less likely to have family members employed in the nonfarm sector. For these households, the ®rst claim on family members' labor is apparently for assistance in the ®elds rather than nonfarm sources of income.
The proximity of a household to a paved road signi®cantly improves the likelihood that a family member will be engaged in nonfarm employment. A similar, but insigni®cant, in-¯uence is observed for distance to nearest sec-ondary schoolÐintended to proxy distance to the nearest town or settlement. Whether the household has a power connection is strongly signi®cant in increasing the likelihood that a family member will be engaged in some form of nonfarm employment. Certainly, home-based activities such as tailoring, food preparation, or carpentry are much more attractive if the household is connected to the electricity grid.
In the FUSADES sample, dummy variables for dierent departments in El Salvador are signi®cant only in the case of the department of Sonsonate, La Libertad, San Salvador, La Paz, and San Miguel. In these departments the probability of nonfarm employment is signi®-cantly higher than in the department of Mora-zan (in the east of the country). For all other departments (with the exception of
Ahuacha-pan) the point estimate is positive, indicating a particularly lowprobability of nonfarm em-ployment in Morazan. These point estimates are not, however, signi®cantly dierent from zero.
In the second and third columns of Table 6 we consider the same speci®cation against a binary dependent variable indicating, in turn, whether the nonfarm job is a high-productivity one or one which yields a return below the average agricultural wage. The negative impact of age is not signi®cant for high-productivity jobs, while for low-productivity jobs, it seems that household size is one factor increasing the likelihood that a family member will seek an outside job. For high-productivity jobs, the eect of higher levels of education is particu-larly strong and positive, while for low-pro-ductivity jobs the education variables are all insigni®cant. The per capita land variable and cultivation dummy both remain negative and signi®cant in the two respective cases. The ac-cess to infrastructure variables are broadly similar, although in the high-productivity case, the distance variables are not signi®cant, while in low-productivity case, it is distance to the nearest secondary school which is signi®cant (once again indicating that nonfarm activities appear to be more plentiful nearer to towns or settlements). Connection to the electricity grid is strongly signi®cant for both high and low-productivity employment.
Of the regional dummies, Chalatenango and San Salvador are the two departments in which high-productivity jobs are concentrated. Rela-tive to Morazan, low-productivity jobs are more common in Sonsonate, Chalatenango, La Libertad, San Salvador, Cabanas, Usulutan,~ San Miguel, and La Union.
In Table 8, we retain our focus on the eco-nomically active population but apply the sig-ni®cantly larger EHPM data set to roughly the
same speci®cation. The only dierence is that we are unable to include the same infrastructure access variables. In this data set, women are
Table 6. Probability ofnonfarm employment as a primary occupationa
Probit modelb Any nonfarm occupation High-productivity job Low-productivity job
Obs: 2738; at 1: 481; at 0: 2257 Obs: 2738; at 1: 331; at 0: 2407 Obs: 2738; at 1: 150; at 0: 2588 Variable Marginal eect Prob valuec Marginal
eect
Prob. value Marginal eect
Prob. value Household
size
)0.0004 0.8785 )0.003 0.1244 0.002 0.0790
Female )0.177 0.0001 )0.130 0.0001 )0.028 0.0001
Age (years) )0.001 0.0137 0.000 0.3555 )0.001 0.0001 Education(highest level reached)
Primary 0.070 0.0002 0.058 0.0003 0.011 0.2287 Middle school 0.066 0.0054 0.074 0.0003 )0.001 0.9194
High school 0.179 0.0001 0.203 0.0001 )0.005 0.6280
Tertiary level 0.273 0.0030 0.373 0.0001 n/a )
Per capita land
)0.059 0.0001 )0.036 0.0017 )0.018 0.0359
Cultivating HH.
)0.130 0.0001 )0.086 0.0001 )0.026 0.0016
Distance to road
)0.002 0.0979 )0.001 0.2067 )0.001 0.2976
Distance to school
)0.002 0.3041 )0.001 0.4751 )0.002 0.0080
Electricity connec.
0.044 0.0021 0.024 0.0359 0.012 0.0956 Ahuachapan )0.011 0.7940 )0.023 0.3698 0.065 0.1894
Santa Ana 0.058 0.2218 )0.006 0.8521 0.139 0.2261
Sonsonate 0.152 0.0041 0.057 0.1382 0.167 0.0102 Chalatenango 0.008 0.8722 )0.032 0.0061 0.118 0.0591
La Libertad 0.127 0.0102 0.055 0.1247 0.128 0.0258 San Salvador 0.247 0.0001 0.114 0.0061 0.204 0.0035 Cuscatlan 0.065 0.2295 0.033 0.4090 0.064 0.2259 La Paz 0.094 0.0744 0.065 0.1067 0.039 0.4100 Caba~nas 0.086 0.1448 )0.029 0.4513 0.212 0.0068 San Vicente 0.032 0.5800 )0.010 0.8242 0.095 0.1206
Usulutan 0.073 0.1469 0.022 0.5379 0.113 0.0558 San Miguel 0.116 0.0226 0.059 0.1187 0.102 0.0655 La Union 0.085 0.0893 0.009 0.7963 0.156 0.0148 Observed
probability
0.176 0.121 0.055
Predicted probability
0.131 0.081 0.035
Log likelihood
Model )1058.84 )833.34 )515.92
Constant )1272.55 )1009.49 )581.47
LR test (model)
427.42 352.30 131.10 Degrees of
freedom
25 25 25
Critical2 37.65 37.65 37.65 aSource: Rural survey, FUSADES (1996).
bDomain: Entire rural population aged above 14. c
signi®cantly more likely to be employed in the nonfarm sector irrespective of whether the jobs are high- or low-productivity ones. As in Table 7,
it appears that the greatest probability is women being employed in the low-productivity occu-pations, controlling for all other characteristics.
Table 7. Probability ofnonfarm employment as a primary occupationa
Probit modelb Any nonfarm occupation High-productivity job Low-productivity job
Obs: 1592; at 1: 481; at 0: 1111 Obs: 1592; at 1: 331; at 0: 1261 Obs: 1592; at 1: 150; at 0: 1442 Variable Marginal eectc Prob. value Marginal
eect
Prob. value Marginal eect
Prob. value Household
size
0.003 0.6109 )0.004 0.2507 0.004 0.0397
Female 0.031 0.3025 )0.031 0.1720 0.045 0.0025
Age (years) )0.002 0.0087 0.001 0.3593 )0.002 0.0001 Education(highest level reached)
Primary 0.101 0.0018 0.083 0.0022 0.013 0.3841 Middle
school
0.097 0.0147 0.107 0.0020 )0.004 0.8135
High school 0.330 0.0001 0.342 0.0001 )0.007 0.6875
Tertiary level 0.562 0.0007 0.673 0.0001 n/a Per capita
land )
0.052 0.0456 )0.032 0.1223 )0.014 0.3090
Cultivating
HH )
0.336 0.0001 )0.235 0.0001 )0.070 0.0001
Distance to road )
0.003 0.1322 )0.002 0.2524 )0.001 0.4263
Distance to school )
0.002 0.5729 0.002 0.2675 )0.003 0.0148
Electricity connec.
0.094 0.0002 0.047 0.0220 0.022 0.0588 Ahuachapan )0.065 0.3839 )0.067 0.2163 0.085 0.2917
Santa Ana 0.007 0.9287 )0.053 0.3270 0.175 0.0651
Sonsonate 0.132 0.1048 0.027 0.6580 0.201 0.0390 Chalatenango )0.018 0.8423 )0.077 0.2014 0.200 0.0652
La Libertad 0.125 0.1149 0.042 0.4851 0.167 0.0640 San Salvador 0.329 0.0002 0.141 0.0418 0.267 0.0134 Cuscatlan 0.034 0.7013 0.009 0.8907 0.081 0.3355 La Paz 0.130 0.1456 0.103 0.1559 0.058 0.4606 Caba~nas 0.159 0.1079 )0.048 0.4893 0.352 0.0068
San Vicente 0.096 0.3452 0.002 0.9827 0.191 0.0877 Usulutan 0.093 0.2751 0.021 0.7429 0.172 0.0802 San Miguel 0.217 0.0124 0.116 0.0947 0.177 0.0670 La Union 0.181 0.0396 0.029 0.6541 0.281 0.0130 Observed
probability
0.302 0.208 0.095
Predicted probability
0.244 0.155 0.055
Log likelihood
Model )739.82 )649.27 )414.86
Constant )975.36 )813.80 )497.02
LR test (model)
471.08 329.06 164.32
Degrees of freedom
25 25 25
Critical2 37.65 37.65 37.65 a
Source: Rural survey, FUSADES (1996).
b
Domain: Rural population aged above 14 and engaged in remunerated work.
cA prob. value of 0.05 indicates that with 95% con®dence one can reject the hypothesis that the parameter estimate is
Age is positively associated with nonfarm em-ployment, particularly for the high-productivity jobs. As in the previous tables, education is positively associated with high-productivity nonfarm jobs, but not with low-productivity jobs. The eect of the land-ownership variable and cultivating dummy is unchanged.
Among the regional dummies, Sonsonate, Chalatenango, La Libertad, San Salvador, and La Paz are strongly associated with high-pro-ductivity jobs (relative to Morazan) while low-productivity jobs appear particularly numerous in Santa Ana, Sonsonate, Cuscatlan, La Paz, San Miguel, and La Union. The combination
Table 8. Probability ofnonfarm employment as a primary occupationa
Probit modelb Any nonfarm occupation High-productivity job Low-productivity job
Obs: 2914; at 1: 1035; at 0: 1879 Obs: 2914; at 1: 544; at 0: 2370 Obs: 2914; at 1: 491; at 0: 2423 Variable Marginal eect Prob valuec Marginal
eect
Prob value Marginal eect
Prob value Household
size
)0.006 0.0990 0.000 0.9737 )0.005 0.0508
Female 0.495 0.0001 0.073 0.0001 0.367 0.0001 Age (years) 0.001 0.0300 0.001 0.0190 0.000 0.5350
Education(highest level reached)
Primary 0.136 0.0001 0.111 0.0001 0.012 0.3813 Middle
school
0.487 0.0001 0.477 0.0001 0.008 0.8133 High school 0.569 0.0001 0.616 0.0001 )0.072 0.0476
Tertiary level n/a 0.847 0.0001 )0.114 0.0292
Per capita
land )
0.001 0.0070 )0.000 0.1848 )0.0002 .0858
Cultivating
HH )
0.112 0.0001 )0.058 0.4363 )0.035 0.0439
Ahuachapan 0.140 0.0718 0.060 0.3416 0.090 0.1193 Santa Ana 0.203 0.0071 0.072 0.2431 0.129 0.0258 Sonsonate 0.339 0.0001 0.168 0.0137 0.198 0.0016
Chalatenan-go
0.313 0.0001 0.185 0.0112 0.138 0.0293 La Libertad 0.266 0.0005 0.206 0.0032 0.077 0.1634 San Salvador 0.492 0.0001 0.399 0.0001 0.101 0.0766 Cuscatlan 0.263 0.0018 0.130 0.0742 0.144 0.0294 La Paz 0.347 0.0001 0.242 0.0007 0.116 0.0436 Caba~nas 0.017 0.8414
)0.002 0.9718 )0.000 0.9944
San Vicente 0.207 0.0096 0.126 0.0697 0.088 0.1361 Usulutan 0.053 0.5035 0.027 0.6636 0.034 0.5418 San Miguel 0.154 0.0564 0.030 0.6416 0.123 0.0478 La Union 0.197 0.0158 0.056 0.3939 0.132 0.0366 Observed
probability
0.351 0.187 0.168
Predicted probability
0.319 0.147 0.131
Log likelihood
Model )1401.40 )1080.48 )1131.35
Constant )1895.83 )1402.78 )1321.48
LR test (model)
988.86 644.60 380.26
Degrees of freedom
22 22 22
Critical2 33.92 33.92 33.92
aSource: Encuesta de Hogares de Propositos Multiples, MIPLAN, 1994-III. b
Domain: Rural population aged above 14 and engaged in remunerated work.
c
of ®ndings from these regressions suggests that nonfarm employment opportunities are clus-tered principally in the departments of Son-sonate, Chalatenango, La Libertad, San Salvador, and La Paz. The more remote de-partments, particularly those in the northeast and the far west appear to be less well-served. It is interesting to note that in Table 8 more of the regional dummies are statistically signi®cant than in Table 7, and in absolute value the pa-rameter estimates are generally higher. This could be due to the smaller sample size of the FUSADES survey, but is also likely to be the consequence of being able to control better for infrastructure variables in Table 7. The dum-mies in Table 8 are therefore likely to be proxying, at least in part, for dierential access to infrastructure, such that regional dierences diminish once infrastructure is controlled for.
In Table 9 we turn to the correlates of earn-ings from nonfarm employment, basing our analysis once again on the FUSADES data. We present results for an OLS regression which includes an adjustment for sample selection (Heckman, 1979). Simply estimating the rela-tionship between household characteristics and
earnings over the subsample of persons en-gaged in nonfarm activities could result in bi-ased estimates. This would be the case if such persons diered in some fundamental way from the population at large, and if these dierences led to them obtaining dierent returns from their characteristics.
To control for this sample selection issue we use the Mill's ratio method (Heckman, 1979). We ®rst estimate a Probit model for the prob-ability an individual is employed in the non-farm sector. We use this to construct a new regressorÐthe inverse Mill's ratioÐwhich we then include (in addition to the other explana-tory variables) in the estimation of the earnings. Since the Mill's ratio is basically a function of the exogenous variables in the Probit model, this corrects for potential selectivity or non-random sampling bias. The procedure involves the selection of an identifying variable: one that is related to the decision to work in the non-farm sector, but does not aect the expenditure outcome. The identifying variable used here is the percentage of the working population em-ployed in the nonfarm sector in the department in which the individual resides.15 The notion
Table 9. Non-agricultural labor earningsa
OLS Modelb With adjustment for sample selection
Variables Estimate Prob. valuec
Constant 8.968 0.0001
Household size )0.006 0.6338
Female )0.344 0.0001
Age (years) 0.007 0.0179
Education(highest level achieved)
Primary 0.174 0.1345
Middle school 0.370 0.0018
High school 0.596 0.0001
Tertiary 1.160 0.0003
Per capita land 0.086 0.4222 Cultivating household (dummy) )0.624 0.0001
Distance from paved road )0.003 0.6521
Distance from secondary school 0.005 0.5629 Electricity connection )0.079 0.3086
West )0.166 0.1100
Central 1 )0.064 0.5066
Central 2 )0.148 0.2413
Mills ratio (1,00,000) )0.180 0.9334
AdjustedR2 0.1515
Number of observations 481
aSource: Rural Survey, FUSADES (1996). b
Dependent variable: (log) annual nonfarm labor income; domain: all persons aged 14 and above with nonfarm employment.
cA prob. value of 0.05 indicates that with 95% con®dence one can reject the hypothesis that the parameter estimate is
here is that this variable should in¯uence the probability of an individual's employment in the sector (as it re¯ects the availability of nonfarm jobs), but that conditional on em-ployment it is not clear why this factor should in¯uence earnings. In the event, our inverse Mill's ratio term is not statistically signi®cant, indicating that the sample-selection issue is of relatively minor concern in our setting.
Females earn signi®cantly less than men. A woman can expect to earn 29% less than a man holding all other characteristics constant. 16 Earnings are sharply higher with higher edu-cation levels. While a person with primary schooling would expect to earn about 19% more than an uneducated person from nonfarm work (although this is not statistically signi®-cant), a person with middle schooling would receive about 45% more, a person with high-school level education would receive 79% more, and a person with education at the tertiary level would receive about 223% more.
Although the coecient is not signi®cant, in this regression, a person from a wealthier household would expect to earn more from nonfarm employment than a person from a household with less per capita land. If the household is a cultivating one, then a family member employed in some nonfarm job would be earning about 47% less than one from non-cultivating household. While the infrastructure variables in¯uenced signi®cantly whether a person was likely to be employed in the non-farm sector, these variables do not seem to in-¯uence earnings signi®cantly. Earnings, given employment in a nonfarm occupation, are also not signi®cantly in¯uenced by geographic lo-cation.
Before ending this section, we consider brie¯y some of the factors that may in¯uence the establishment of rural nonfarm enterprises. We focused on the 101 rural enterprises covered in the FUSADES survey to inquire into their access to infrastructure, as well as any dicul-ties they report with infrastructure services. It is often argued that the key to rural development is not so much the provision of state-of-the-art components of infrastructure as ensuring access to a basic package which combines simple, or even rudimentary, communications, transport, power, and water services (World Bank, 1994b).
About two-thirds of rural enterprises have access to electricity. In El Salvador, very few households without connection appear to pro-vide for their electricity requirements via the
purchase of generators. In the FUSADES sur-vey, no rural enterprise without an electricity connection reported such private provision of electricity. Water provision was most common via private sources such as a well or river, al-though nearly 47% of enterprises were served by the public network. Of all rural enterprises, nearly 19% reported shortages of water during at least part of the year. Very fewrural enter-prises have a telephone connection, and 35% of enterprises reported transport-related dicul-ties associated with the poor state of road in-frastructure. Thus, for most infrastructure sectors a sizeable fraction of rural enterprises report not having access to the services, or having problems with the service. While the situation in rural El Salvador is perhaps not as critical as in other, larger and less densely populated countries, it does appear that access to rural infrastructure is far from perfect. Per capita costs of rural infrastructure provision, in a country such as El Salvador, are presumably not nearly as high as they would be in other, less densely populated, countries.
real returns on such savings. The net impact of such reforms on the number of rural nonfarm ®rms is not clear, but average productivity levels would be expected to rise.
6. CONCLUSIONS
The preceding analysis has demonstrated that the nonfarm sector in El Salvador, while not the dominant sector in rural areas, is im-portant in terms of both employment rates as well as incomes. Roughly 36% of the econom-ically active population in rural El Salvador is employed in the nonfarm sector. Women are particularly highly represented among the eco-nomically active population employed in this sector; nearly three-quarters of all such women are employed in the nonfarm sector. Of course, the proportion of women in the conventionally de®ned ``economically active'' population is only about a third that of men.
The range of activities in which the rural population is engaged extends from manufac-turing to services. A substantial number of nonfarm activities are linked to agriculture in the sense that they feed into agricultural pro-duction, transport or transform agricultural goods, or provide goods and services which agricultural households are likely to be pur-chasing. There is also a sizeable manufacturing component that is not obviously linked to ag-ricultural production. Here, household ®rms are engaged in subcontracting relationships with large, probably urban-based, ®rms. Be-cause these ®rms are not closely linked to the agricultural sector, the incomes derived from
such activities may introduce an important source of liquidity into rural areas which does not covary with agricultural incomes, much the same as remittance incomes from abroad.
The analysis has shown that rural poverty in El Salvador is acute, with some evidence that extreme poverty (de®ned in the context of this study as the incidence of poverty associated with the lower poverty line) is higher in the relatively more remote eastern and western regions of the country. The evidence suggests that while the rural poor are highly repre-sented among agricultural laborers and mar-ginal farmers in particular, there is some suggestion that at least some nonfarm activi-ties are also of importance to the poor. These activities are in the nature of ``last resort'' activities and are not associated with high levels of labor productivty. Although it is dicult to viewthis subsector of the nonfarm sector as oering great prospects of lifting the poor above the poverty line, it is important to recognize that even these activities have a considerable distributional impact: they pre-vent the poor from falling into even greater destitution. Policy makers should be alert to this function of these nonfarm activities, and refrain from taking measures that would un-dermine this safety-net role.
The nonfarm sector could also present a potent force for upward mobility. The least poor in rural areas are households that are heavily engaged in the nonfarm sector. An important challenge is to increase access of the poor to nonfarm activities that yield these high and stable incomes. While data constraints preclude making strong statements about
cau-Table 10. Rural enterprises and start-up ®nancea
Sector Principal source of start-up ®nance (percentage of ®rms) Number of
®rms
Number of workers
Personal savings (%)
Friends and relatives (%)
Informal sources (%)
Formal sources (%)
Transport 1 1 100 0 0 0
Other services 3 3 67 33 0 0 Other industry 5 6 100 0 0 0
Repair shop 6 16 100 0 0 0
Restaurant/bar 5 19 60 20 0 20
Textiles 13 25 85 0 15 0
Wood/work 10 37 30 30 40 0
Food proc. 13 53 46 8 23 23
Pottery/bricks 14 63 85 7 7 0
Commerce 31 77 68 16 6 10
Total 101 300 70 11 12 7
a
sality, the analysis in this paper points to a number of areas to which further attention may be directed when addressing the design of pol-icies.
First, there is strong evidence to suggest that higher income nonfarm jobs go to those with higher levels of education. While it is not the case that the uneducated are unable to secure any nonfarm employment, the kind of jobs obtained tend to be low-productivity activities, which while no doubt of great value in relieving poverty somewhat, do not, in all likelihood, raise the standard of living of these households markedly. As individuals become better edu-cated, their earnings rise sharply. Having said this, it is important to point out that at a given level of education, women tend to earn signi®-cantly less then men from nonfarm employ-ment.
Infrastructure services appear to exercise signi®cant in¯uence on the likelihood of ®nd-ing nonfarm employment. Persons resid®nd-ing in remote, inaccessible areas are far less likely to ®nd employment in the nonfarm sector. Households connected to the electricity net-work are far more likely to have family members engaged in the nonfarm sector. It is not certain whether the electricity services themselves stimulate nonfarm activities, or whether connection to the electricity network acts as a proxy for location near a large(ish) conurbation, where nonfarm jobs are likely to be relatively more common. Rural enterprises in El Salvador are fairly poorly serviced by infrastructure services. In the FUSADES rural survey, only 7% of ®rms had a telephone connection, more than one-third of all ®rms had no access to electricity, more than a third of all ®rms reported transport-related dicul-ties, and more than half were reliant on their own sources of water (and a sizeable fraction reported periods of non-availability). While the cost of infrastructure provision in rural areas is higher than in urban areas, in El Salvador the rural population is fairly dense which would help to reduce per capita costs. As argued for example in World Bank (1994b) the focus of infrastructure providers should perhaps be on a basic package of simple rural infrastructure services rather than on the in-troduction of highest quality (but high-cost) infrastructure.
Regionally, the distribution of nonfarm oc-cupations is not uniform. The corridor of Sonsonate, La Libertad, Chalatenango, San Salvador, and La Paz appear to contain the
highest concentration of nonfarm activities, including in particular the high-productivity jobs which represent the greatest source of upward mobility. The remote departments of Morazan and Ahuachapan appear to be par-ticularly poorly endowed with nonfarm activ-ities. It is an important priority to try to understand better what factors can help to induce the emergence of more nonfarm activ-ities in these relatively under-served depart-ments.
A very lowpercentage of rural enterprises report having obtained ®nancing from formal sector credit sources in setting up their enter-prises. The overwhelmingly dominant source of ®nance came from personal services. This im-plies, ®rst of all, that the poorest in rural areas are not those most likely to set up rural enter-prises (although, of course, they may ®nd em-ployment in such ®rms). It might also tell us something about the availability of rural sav-ings facilities. The emphasis in rural ®nancial reform is often on increasing the availability of credit in rural areas. The foregoing comments indicate that attention might also be usefully focused on improving rural savings mobiliza-tion.
It is interesting to note that in rural El Sal-vador, very fewenterprises report having ben-e®ted from special training in conducting their aairs. This is despite there being a substantial experience in El Salvador with training and special programs for microenterprises. It thus seems important to consider focusing addi-tional eorts to provide such assistance to ®rms in rural areas.
NOTES
1. Aokiet al. (1995, p. 40) argue that the East Asian success in utilizing cheap labor in rural areas, in sectors outside of traditional farming, was ``one of the most important elements of East Asian development'' (see also Hayami, 1995).
2. The EHPM contains household weights that allow one to aggregate up to population totals. Although the sample frame was based on the Census from the early 1970s, the household weights have reportedly been adjusted with reference to the 1992 Labor Force Census, so adding up to population should be valid (personal communication from the Director of the Departamento de Investigaciones Muestrales).
3. Lanjouwand Lanjouw(forthcoming) provide a recent survey of the literature.
4. See Elbers and Lanjouw(2001) for evidence in Ecuador that an expanding nonfarm sector reduces absolute poverty while it increases inequality.
5. Lanjouw(1995) tries to control for land quality dierences by holding crops constant. In any case, anecdotal evidence for Ecuador would suggest that large farmers possess land of better quality than small farmers.
6. See Ravallion (1994) for a good survey of these issues.
7. In contrast, the poor often have myriad income sources, as they try to bring together revenues from all kinds of activities to be able to meet their subsistence needs.
8. Note that the cost-of-living adjustment mentioned above, reduces the gap between Metropolitan San Salvador and the rest of the country. Failure to make such an adjustment would have resulted in an even wider gap.
9. ``Economically active'' is de®ned rather restrictively, it refers to activities which receive some kind of direct monetary remuneration, and thereby excludes domestic activities within the household, or nonremunerated activities on the family farm or enterprise.
10. We emphasize here that we are not making any statements about directions of causality. While nonfarm activities could serve as a means to lift the poor above the poverty line, it is also possible that one must ®rst attain a certain degree of auence before gaining access to nonfarm jobs.
11. For further discussion of this point, see Lanjouw and Lanjouw(forthcoming).
12. Although it should be stressed, again, that we are not able to dismiss the converse conjecture: that only only the nonpoor are in some sense able to ``aord'' to be represented in the nonfarm sector. Once again, care must be taken not to infer causality from the simple statistical associations presented here.
13. De Janvry and Sadoulet (1993) suggest that such linkages might not be so important in Latin America as a whole. With a highly skewed distribution of land and income, a fewlandowners bene®t from the bulk of the income eects of agricultural growth, and these land-owners are often absentee and therefore do not demand locally produced goods. This point indicates that one of the additional bene®ts of land reform may be the stimulus that is given to the local nonfarm economy. 14. These indicate the change in the probability of nonfarm employment associated with a marginal change in given explanatory variable, when all explanatory values are taken at their respective means. In the case of a dummy explanatory variable the calculation is in terms of the change in probability associated with the dummy variable changing in value from zero to one.
15. Using this as our identifying variable implies that we cannot use the departmental regional dummies in the second stage OLS regression. To capture spatial eects in the second stage we thus include dummies at the regional level (see Table 1).
16. A coecientcmultiplying a dummy variable can be interpreted as a percentage change in the endogenous variable only as long asc is close to zero. For larger values, in absolute terms, the percent change in the endogenous variable is given by 100exp c 1.
REFERENCES
Aoki, M., Murdock, K., & Okuno-Fujiwara, M. (1995). Beyond the East Asian miracle: Introducing the market-enhancing view. In M. Aoki, H. Kim, & M. Fujiwara, The role ofgovernment in East Asian
Banerjee, A. (1996).Notes towards a theory ofindustri-alization in the developing world. MIT: Unprocessed. Datt, G., & Ravallion, M. (1993). Transfer bene®ts from public works employment: bene®ts for rural India.
Economic Journal,104(427), 1346±1369.
De Janvry, A., & Sadoulet, E. (1993). Rural develop-ment in Latin America: Relinking poverty reduction to growth. In M. Lipton, & J. van der Gaag,
Including the poor(pp. 249±305). Washington, DC: The World Bank.
Dreze, J. P. (1995). Famine prevention in India. In A. K. Dreze, J. Sen, & A. Hussain (Eds.), The political economy ofhunger: Selected essays (pp. 69±177). Oxford: Oxford University Press.
Dreze, J. P., Lanjouw, P., & Stern, N. H. (1992). Economic mobility and agricultural labour in rural India: A case study.Indian Economic Review(Special number in Memory of Sukhamoy Chakravarti), Vp; XXVII, 25±54.
Elbers, C., & Lanjouw, P. (2001). Intersectoral transfer, growth, and inequality in rural Ecuador. World Development,29(3), 481±496.
FUSADES (1993). Empleo, ingreso y pobreza rural.
Boletin Economico y Social, 91.
Hayami, Y. (1995).In search ofrural entrepreneurship in Asia: Concept and approach. Paper presented at a World Bank Conference on Rural Development in Latin America, September.
Hazell, P., & Haggblade, S. (1993). Farm±nonfarm growth linkages and the welfare of the poor. In M. Liptan, & J. Van der Gaag (Eds.),Including the poor. Washingtan, DC: The World Bank.
Heckman, J. (1979). Sample selection bias as a speci®-cation error.Econometrica,47, 153±161.
IIES-UCA (1993).La reduccion de la pobreza: Comen-tarios al informe de miplan. Unprocessed. IIES-UCA. Johnston, B., & Kilby, P. (1975). Agriculture and structural transformation: Economic strategies in late developing countries. London: Oxford University Press.
Klein, E. (1993). El empleo rural no-agricola en America Latina. InCEPLAES, Latinoamerica Agraria Hacia el Siglo XXI. Quito, Ecuador: CEPLAES.
Lanjouw, P. (1995).Ecuador, Pobreza rural. Cuestiones Economica, Banco Central del Ecvador, No. 27, December.
Lanjouw, J. O., & Lanjouw, P. (1996).Poverty compar-isons with non-compatible data: theory and illustra-tions. Unprocessed. Policy Research Department, the World Bank.
Lanjouw, J. O., & Lanjouw, P. (forthcoming). The rural nonfarm sector: Issues and evidence from developing countries.Agricultural Economics.
Lopez, R. (1996). Rural poverty in El Salvador: A quantitative analysis. Unprocessed. Department of Agricultural and Resource Economics, University of Maryland at College Park.
Mellor, J., & Lele, U. (1972). Growth linkages of the newfood grain technologies. Indian Journal of Agricultural Economics,18(1), 35±55.
Mellor, J. (1976). The new economics ofgrowth: A strategy for india and the developing world. Ithaca, NY: Cornell University Press.
MIPLAN (1995a).El Salvador: Evaluacion de los fact-ores de persistencia de la pobreza en los hogares pobres. Unprocessed. Ministerio de Plani®cacion, San Salvador.
MIPLAN (1995b). Principales resultados: Encuesta de hogares de propositos multiples. Unprocessed. MI-PLAN and Ministerio de Relaciones Exteriores, San Salvador.
Murdoch, J. (1995). Income smoothing and consump-tion smoothing. Journal ofEconomic Perspectives,
9(3), 103±114.
Ravallion, M. (1994).Poverty comparisons. Chur: Har-wood Press.
Vijverberg, W. (1988). Pro®ts from self-employment. LSMS Working Paper No. 43, the World Bank. World Bank. (1994a). El Salvador: The challenge of
poverty alleviation. Report No. 12315-ES, the World Bank.
