Directory UMM :Data Elmu:jurnal:UVW:World Development:Vol29.Issue3.2001:
World Development Vol. 29, No. 3, pp. 529±547, 2001
Ó 2001 Elsevier Science Ltd. All rights reserved
Printed in Great Britain
0305-750X/01/$ - see front matter
www.elsevier.com/locate/worlddev
PII: S0305-750X(00)00105-4
Nonfarm Employment and Poverty in
Rural El Salvador
PETER LANJOUW *
The World Bank, Washington, DC, USA
Summary. Ð This 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, infrastructure, 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 Ð nonfarm 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 equitymotivated programs, such as the agrarian
reforms, have not succeeded in eliminating
poverty altogetherÐeither because implementation 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 few dispute the importance of addressing poverty in El Salvador, there is considerable 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 poverty. Much of this debate is prompted by differences in methodological approaches, and by
shortcomings in available data sources. Nonetheless, there does appear to be broad agreement that poverty in rural areas deserves
529
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 extends 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 employment generated. While this sector has not
received the same level of attention as the agriculture sector, there is a growing appreciation
of its potential in terms of both poverty
*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.
530
WORLD DEVELOPMENT
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 thinking about poverty and the nonfarm sector. This
is followed in Section 3 with a discussion of
poverty in El Salvador, and some tentative estimates of poverty based on consumption expenditures. Section 4 introduces some
quantitative evidence on the size of the nonfarm 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 paper. 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 analysis) 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 consumption module (for a subsample of households) which permits an analysis of poverty
based on consumption expenditure rather than
income (see below). 2
The second source of data comes from a rural household survey ®elded by Fundacion
Salvadore~
na Para el Desarollo Economico y
Social (FUSADES) in 1996. The survey covered 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 significance (Lopez, 1996). The FUSADES survey
obtained information on a wide range of demographic characteristics, location, and income variables. The level of detail in the
information collected has permitted the calculation 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 provides a useful complement to the EHPM in that
while it is smaller in sample size it provides
greater detail about rural livelihoods and activities.
2. A QUICK OVERVIEW OF THE
POVERTY±NONFARM LINKS
Rural o-farm employment has been traditionally seen as a low-productivity sector, producing low quality 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 sector 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
Lanjouw and Lanjouw (forthcoming) indicate that while de®nitional and data-related
uncertainties remain, the rural nonagricultural
sector is both large and, on aggregate, has been
growing over time. Hazell and Haggblade
(1993) emphasize that when rural towns are
included in employment calculations, the share
of the rural labor force employed primarily in
nonagricultural activities rises sharply. They
calculate that in Latin America, 47% of the
labor force in rural settlements and rural towns
EL SALVADOR
is employed in nonfarm activities. This can be
compared to 28% when only rural settlements
are included. Hazell and Haggblade also highlight the importance of female participation in
nonagricultural activities: 79% of women in the
Latin American rural wage-labor force are estimated to be employed in nonagricultural activities.
Nonagricultural activities can be broadly divided into two groups of occupations: high labor productivity/high income activities and low
labor productivity activities that serve as a residual source of employmentÐa ``last-resort''
source of income (Lanjouw and Lanjouw,
forthcoming). These latter activities are common among the very poor, particularly among
women. Such employment may nevertheless be
very important from a social welfare perspective for the following reasons: (a) o-farm employment 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
low nonagricultural earnings; and (c) for certain subgroups of the population who are unable 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 employment 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 inequality 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 supports 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 explanation 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
531
agricultural wage employment is not an option
for certain family members, then rural nonagricultural 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 rural nonagricultural sector and rural poverty.
Because of market imperfections and distortions, nonfarm activities are likely to employ
labor beyond the point where the marginal
product of labor is equal to the prevailing average 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 poverty and the nonagricultural sector in the context 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 dimensions of the poverty problem in El Salvador. 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 prevents discussing them in detail. 6
In El Salvador, as in many other Latin
American countries, poverty analysis has generally been carried out on the basis of income as
the household-level indicator of well-being.
This is in contrast with conventional practice in
other parts of the world, where consumption
expenditures are commonly taken as the welfare indicator. The principal reason for choosing consumption is that experience has shown
that these are measured with greater accuracy
than incomesÐparticularly for the poor, who
are most likely to consume a relatively narrow
range of goods and services. 7 Many of the
household surveys ®elded in Latin American
countries are modeled on labor force surveys
532
WORLD DEVELOPMENT
designed to measure household earnings. Such
surveys are generally weak in capturing incomes from nonwage labor sources (such as
remittances, transfers, and self-employment)
and from agricultural activities. These omissions may be particularly pertinent to the
measurement of rural poverty.
The next step in measuring poverty is to relate household-level welfare indicators to some
poverty thresholdÐthe poverty line. In constructing a poverty line, many assumptions are
generally required, and these can at times become 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 females, and what kind of adjustment must be
made to allow for 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 geographic 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 distribution of poverty across population subgroups.
Finally, there is an important issue associated
with the nonavailability of a spatial cost-of-living index. Such an index adjusts for the fact that
to reach a given standard of living in Metropolitan 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 calculations 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 requisite 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 rural and urban areas) and to be satis®ed in
stating that poverty among one group is higher
or lower than among the other (without attempting to state by how much), 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 subsamples of the EHPM for which highly dierence consumption modules were ®elded (for a
detailed exposition, see Lanjouw and 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.
The purpose of the poverty estimates in Table 1 is to shed light on some of the broad
geographic patterns of poverty in El Salvador.
At the level of the country as a whole, poverty
is much higher in rural areas than in urban
areas. This seems to be driven in particular by
the relatively low level of poverty in Metropolitan San Salvador. 8 Across the other four
broad geographic regions of the country, the
evidence is less strong that there exists a clear
distinction between rural and urban areas. El
Salvador is unique in its de®nition of urban
areas in that it counts as urban all municipal
centers (cabeceras municipales) without taking
into account the actual population of those
centers. This is in contrast with many other
countries (although it is not unique in Latin
America, see Klein, 1993) and is likely to result
in higher estimates of the incidence of urban
poverty than would obtain if only large conurbations were designated as urban. There is
rather little evidence of a concentration of
poverty within a speci®c broad region (sample
size constraints prevent a further geographic
breakdown within broad regions). Based on the
``low'' poverty line, there is some indication
that rural poverty in the central regions (located more closely to San Salvador) is lower
EL SALVADOR
533
Table 1. The incidence of poverty in El Salvadora ;b
High poverty line
Incidence of poverty
Low poverty line
Persons poor
Incidence of poverty
Persons poor
West
Urban
Rural
All
0.68
0.75
0.72
312,343
449,050
761,393
0.28
0.38
0.33
127,241
223,726
350,967
Central 1
Urban
Rural
All
0.74
0.76
0.75
329,117
560,313
889,430
0.31
0.33
0.32
138,726
240,980
379,706
Central 2
Urban
Rural
All
0.70
0.79
0.76
131,827
237,550
369,377
0.36
0.32
0.34
66,983
96,925
163,908
East
Urban
Rural
All
0.67
0.79
0.74
287,074
513,079
800,153
0.30
0.38
0.35
129,960
247,231
377,191
Metropolitan
San Salvador
0.40
518,926
0.08
100,751
National
Urban
Rural
0.56
0.77
1,579,287
1,759,992
0.20
0.35
563,661
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
two sharply divergent consumption questionnaires were ®elded to two non-overlapping subsamples of total sample.
The methodology developed in Lanjouw and Lanjouw (1996) was implemented in order to ensure comparability. For
more details, see Lanjouw and Lanjouw (1996). (ii) The ``high'' poverty line refers to a monthly per capita expenditure ®gure of 667 Colones (approximately US$75), and the ``low'' poverty line (which can be interpreted as a
measure of ``extreme poverty'') refers to a monthly per capita expenditure ®gure of 334 Colones (approximately
US$36). These poverty lines have been calculated from the data based on the per capita cost of a basic food bundle
(for urban areas) as calculated by the Ministerio de Plani®cacion in San Salvador, and adding a nonfood expenditure
allowance in accordance with the average amount spent on nonfood items by households with food expenditures
equal in value to the MIPLAN food basket. The ``low'' poverty line is simply 50% of the ``high'' line, and corresponds loosely to the $1 per day poverty line that underpins global poverty comparisons. (iii) Rural expenditures
have been in¯ated by a factor of 60% to account for MIPLAN's calculation that the basic urban food basket costs
60% more than a basic rural food basket. (iv) The broad regions presented above break down into the following
departments: West (Santa Ana, Ahuachapan, Sonsonate); Central 1 (La Libertad, Rural San Salvador, Chalatenango, Cuscatlan); Central 2 (San Vicente, La Paz, Cabanas); East (Usulutan San Miguel, Morazan, La Union). (v)
Overall aggregate populations vary slightly across the high poverty line column and low poverty line column, because
of rounding in the reported headcount rates.
than in either the East or West. We shall see
below that the most productive nonfarm activities 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 mid1970s. More recent census ®gures for El Salvador have not been calculated, no doubt for
data-related reasons associated with the political and military instability of the intervening
period.
534
WORLD DEVELOPMENT
In Tables 2 and 3 we present estimates based
on the EHPM household survey. In 1994,
36.4% of the economically active rural population 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 manufacturing 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 additional 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 economically active population (compared to
73.6% of men). 9 The activities in which women
are heavily engaged include ®rst of all commerce, and are followed by manufacturing and
domestic service. For men, commerce and domestic service are of far less signi®cance, but
construction, manufacturing and also transport
are important sectors.
There is geographic variation in El Salvador in the signi®cance of the rural nonfarm
sector (Table 3). In the Central 1 region
(which includes the departments of Chalatenango, 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 manufacturing appears to be relatively more important 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 engage in both agricultural labor and farming. In
fact, agricultural labor appears to be particularly 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 populationb with primary occupation
Fishing
Manufacture
Textiles/garments
Wood/straw/leatherware
Utilities
Construction
Commerce
Restaurant/hotel
Transport
Finance
Administration
Teaching
Health
Domestic service
Other service
Total nonfarm
Farming
Agricultural labor
No. of observations
a
Male
0.7
4.2
1.0
1.4
0.3
6.1
2.1
0.2
2.8
0.1
0.2
0.4
0.0
0.9
4.3
24.7
54.4
20.8
2,230
(2.8)c
(17.0)
(4.0)
(5.7)
(1.2)
(24.7)
(8.5)
(0.8)
(11.3)
(0.4)
(0.8)
(1.6)
(0.0)
(3.6)
(17.4)
(100.0)
Female
0.2
10.0
8.0
3.7
0.0
0.4
28.1
1.8
0.0
0.5
0.2
1.6
1.3
12.4
4.1
72.3
11.4
16.1
685
(0.3)
(13.8)
(11.1)
(5.1)
(0.0)
(0.6)
(38.9)
(2.5)
(0.0)
(0.7)
(0.3)
(2.2)
(1.7)
(17.2)
(5.7)
(100.0)
Source: Republica de El Salvador: Encuesta de Hogares de Propositos Multiples, 1994-III.
Column percentages provided in brackets.
c
Primary employment refers to self-reported principal activity.
b
Total
0.6
5.6
2.7
1.9
0.3
4.7
8.4
0.6
2.1
0.2
0.2
0.7
0.4
3.7
4.3
36.4
44.0
19.6
2,915
(1.6)
(15.4)
(7.4)
(5.2)
(0.8)
(12.9)
(23.1)
(1.6)
(5.8)
(0.5)
(0.5)
(1.9)
(1.1)
(10.2)
(11.8)
(100.0)
EL SALVADOR
535
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
Fishing
Manufacture
Textiles/garments
Wood/straw/leatherware
Utilities
Construction
Commerce
Restaurant/hotel
Transport
Finance
Administration
Teaching
Health
Domestic service
Other services
Total nonfarm
Farming
Agricultural labor
No. of observations
a
b
West
0.0
6.3
2.0
2.3
0.4
4.1
7.1
0.1
2.2
0.2
0.5
0.8
0.5
3.0
4.1
33.6
43.7
22.7
802
(0.0)b
(18.8)
(6.0)
(6.8)
(1.2)
(12.2)
(21.2)
(0.3)
(6.5)
(0.6)
(1.5)
(2.4)
(1.5)
(8.9)
(12.2)
(100.0)
Central 1
0.9
6.5
3.8
3.1
0.4
6.5
9.9
1.3
2.8
0.2
0.1
0.4
0.5
5.8
6.3
48.5
33.1
18.3
830
(1.9)
(13.4)
(7.8)
(6.4)
(0.8)
(13.4)
(20.4)
(2.7)
(5.8)
(0.4)
(0.2)
(0.8)
(1.0)
(12.0)
(13.0)
(100.0)
Central 2
1.6
5.3
3.3
0.5
0.2
4.9
9.7
0.6
0.6
0.3
0.1
0.9
0.0
2.4
3.3
33.7
50.1
16.1
635
(4.7)
(15.7)
(10.0)
(1.5)
(0.6)
(14.5)
(28.8)
(1.8)
(1.8)
(0.9)
(0.3)
(2.7)
(0.0)
(7.1)
(9.8)
(100.0)
East
0.3 (1.3)
3.5(15.1)
1.5 (6.5)
0.6 (2.6)
0.0 (0.0)
2.8 (12.1)
7.1 (30.6)
0.0 (0.0)
1.9 (8.2)
0.2 (0.9)
0.1 (0.4)
0.9 (3.9)
0.2 (0.9)
2.0 (8.6)
2.1 (9.1)
23.2 (100.0)
56.6
20.2
648
Source: Republica de El Salvador: Encuesta de Hogares de Propositos Multiples, 1994-III.
Column percentages provided in brackets.
gories, the three associated with highest incidence 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 (Dreze et 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 encompasses both casual daily wage labor and longterm, permanent employment on a farm,
plantation, or ranch. As a result, not all agricultural 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 labor run only about an average ``risk'' of poverty (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 (households simultaneously engaging in farming, agricultural 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 productive, relatively well-paid, and which have
the appealing feature of being comparatively
less exposed than agriculture to climatic variations and uncertainties. On the other hand,
there are a group of activities undertaken by
persons who are unable even to secure an agricultural laboring position; persons who are
perhaps old or disabled, or who may be prohibited 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 important 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 publicworks 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-
536
WORLD DEVELOPMENT
Table 4. Poverty and rural household activitiesa
b
Household characteristics
Agricultural labor and farming
Agricultural labor only
Agricultural labor farming and nonfarm employment
Farming only
Farming and nonfarm employment
Agricultural labor and nonfarm employment
Nonfarm employment only
Nonfarm income from non-wage sources
Percent of population
(%)
Incidence of extreme poverty
(%)c
5.0
9.6
2.7
26.1
19.9
9.1
26.1
1.6
54.7
48.7
43.9
41.5
35.9
35.2
20.3
16.3
a
Source: Encuesta de Hogares de Propositos Multiples, 1994-III.
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
Extreme poverty is associated with per capita consumption levels falling below the ``low'' poverty line.
b
allion, 1993). This program provides nonfarm
employment to poor persons for whom agricultural wage employment is not an option
(perhaps because of cultural restrictions, or
because climatic conditions have sharply reduced demand for labor). It therefore exploits
the ``safety-net'' function of the nonfarm sector. 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 employment by providing training, supporting infrastructure, 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 nonfarm activities. And this segment is the least
poor of the entire rural population. It seems
possible therefore, that the rural nonfarm sector 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 household enterprises, the FUSADES rural survey
included a separate questionnaire on such activities. 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, although on average such enterprises were
smaller in terms of employment per ®rm than
pottery and brick-making enterprises. It is interesting 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 draw particularly heavily on family labor and are most frequently 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 observed 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 how and when to allocate their family labor, and do not have to
concern themselves with bringing the ®nal
goods to the market (which, in the case of
EL SALVADOR
537
Table 5. Rural enterprises in El Salvadora
Sector
Transport
Other services
Other industry
Repair shop
Restaurant/bar
Textiles
Wood/work
Food proc.
Pottery/bricks
Commerce
Total
a
Number
of ®rms
Number
of workers
Percentage
Percentage
family
home-based
members (%)
(%)
Percentage
with training
(%)
Percentage
supplying
contractor (%)
1
3
5
6
5
13
10
13
14
31
1
3
6
16
19
25
37
53
63
77
100
100
100
44
16
73
22
28
13
64
0
33
100
33
20
92
60
54
7
58
0
0
0
0
0
8
10
8
7
3
0
0
20
0
0
23
30
0
7
0
101
300
40
52
5
8
Source: Rural survey, FUSADES (1996).
clothing or shoes for example, might be very far
away).
The range of both nonfarm employment as
well as rural enterprise activities that are engaged in provides some clues as to the relationship 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 agricultural intensi®cation and nonfarm activity can
emerge on the basis of production and consumption linkages. Production linkages
emerge, for example, when demand of agriculturalists for inputs such as plows and machinery 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 agricultural 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 repair 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 urban-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 altogether. 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 cropping patterns which minimize the risk of harvest 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 now to a closer examination of the
correlates of nonfarm employment in El Salvador 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 nonfarm jobs together, but also distinguish between 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
538
WORLD DEVELOPMENT
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 signi®cantly less likely to be employed in the
nonfarm sector. Compared to the uneducated,
all persons who have been educated are significantly 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 landholdings are less likely to be employed in the nonfarm 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 agriculture) 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 secondary 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 Morazan (in the east of the country). For all other
departments (with the exception of Ahuacha-
pan) the point estimate is positive, indicating a
particularly low probability of nonfarm employment 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 particularly strong and positive, while for low-productivity 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 access 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 lowproductivity employment.
Of the regional dummies, Chalatenango and
San Salvador are the two departments in which
high-productivity jobs are concentrated. Relative to Morazan, low-productivity jobs are
more common in Sonsonate, Chalatenango, La
Libertad, San Salvador, Caba~
nas, Usulutan,
San Miguel, and La Union.
A somewhat puzzling ®nding from Table 6
was the observation that females were signi®cantly less likely to be employed in the nonfarm
sector than men. This ®nding is due partly to
the fact that in Table 6 the relevant domain was
taken to include all persons of working age in
rural areas. We have already noted that women
are far less likely to be ``economically active''
than men, as it is common practice to not include nonremunerated domestic activities
amongst ``economic'' activities. In Table 7 we
con®ne our attention to the ``economically active'' population, sticking with the FUSADES
data for the time being. Now, women are no
longer signi®cantly less likely to be employed in
nonfarm jobs. In fact, for low-productivity
jobs, women are signi®cantly more likely to be
employed in such occupations.
EL SALVADOR
539
Table 6. Probability of nonfarm employment as a primary occupationa
b
Probit model
Variable
Household
size
Female
Age (years)
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
Prob valuec
Marginal
eect
Marginal
eect
)0.0004
0.8785
)0.003
0.1244
0.002
0.0790
)0.177
)0.001
0.0001
0.0137
)0.130
0.000
0.0001
0.3555
)0.028
)0.001
0.0001
0.0001
0.0002
0.0054
0.0001
0.0030
0.058
0.074
0.203
0.373
0.0003
0.0003
0.0001
0.0001
0.011
)0.001
)0.005
n/a
0.2287
0.9194
0.6280
)
)0.059
0.0001
)0.036
0.0017
)0.018
0.0359
)0.130
0.0001
)0.086
0.0001
)0.026
0.0016
)0.002
0.0979
)0.001
0.2067
)0.001
0.2976
)0.002
0.3041
)0.001
0.4751
)0.002
0.0080
0.044
0.0021
0.024
0.0359
0.012
0.0956
)0.011
0.058
0.152
0.008
0.127
0.247
0.065
0.094
0.086
0.032
0.073
0.116
0.085
0.176
0.7940
0.2218
0.0041
0.8722
0.0102
0.0001
0.2295
0.0744
0.1448
0.5800
0.1469
0.0226
0.0893
)0.023
)0.006
0.057
)0.032
0.055
0.114
0.033
0.065
)0.029
)0.010
0.022
0.059
0.009
0.121
0.3698
0.8521
0.1382
0.0061
0.1247
0.0061
0.4090
0.1067
0.4513
0.8242
0.5379
0.1187
0.7963
0.065
0.139
0.167
0.118
0.128
0.204
0.064
0.039
0.212
0.095
0.113
0.102
0.156
0.055
0.1894
0.2261
0.0102
0.0591
0.0258
0.0035
0.2259
0.4100
0.0068
0.1206
0.0558
0.0655
0.0148
Marginal eect
Education (highest level reached)
Primary
0.070
Middle school
0.066
High school
0.179
Tertiary level
0.273
Per capita
land
Cultivating
HH.
Distance to
road
Distance to
school
Electricity
connec.
Ahuachapan
Santa Ana
Sonsonate
Chalatenango
La Libertad
San Salvador
Cuscatlan
La Paz
Caba~
nas
San Vicente
Usulutan
San Miguel
La Union
Observed
probability
Predicted
probability
0.131
Log likelihood
Model
)1058.84
Constant
)1272.55
LR test
427.42
(model)
Degrees of
25
freedom
2
37.65
Critical
0.081
Prob. value
Prob. value
0.035
)833.34
)1009.49
352.30
)515.92
)581.47
131.10
25
25
37.65
37.65
a
Source: Rural survey, FUSADES (1996).
Domain: Entire rural population aged above 14.
c
A prob value of 0.05 indicates that with 95% con®dence one can reject the hypothesis that the parameter estimate is
zero.
b
In Table 8, we retain our focus on the economically active population but apply the signi®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
540
WORLD DEVELOPMENT
Table 7. Probability of nonfarm employment as a primary occupationa
b
Any nonfarm occupation
Probit model
Obs: 1592; at 1: 481; at 0: 1111
Variable
Household
size
Female
Age (years)
Marginal eectc
Low-productivity job
Marginal
eect
Prob. value
Marginal
eect
Prob. value
0.003
0.6109
)0.004
0.2507
0.004
0.0397
0.031
)0.002
0.3025
0.0087
)0.031
0.001
0.1720
0.3593
0.045
)0.002
0.0025
0.0001
level reached)
0.101
0.097
0.0018
0.0147
0.083
0.107
0.0022
0.0020
0.013
)0.004
0.3841
0.8135
0.330
0.562
0.0001
0.0007
0.342
0.673
0.0001
0.0001
)0.007
n/a
0.6875
)0.052
0.0456
)0.032
0.1223
)0.014
0.3090
)0.336
0.0001
)0.235
0.0001
)0.070
0.0001
)0.003
0.1322
)0.002
0.2524
)0.001
0.4263
)0.002
0.5729
0.002
0.2675
)0.003
0.0148
0.094
0.0002
0.047
0.0220
0.022
0.0588
)0.065
0.007
0.132
)0.018
0.125
0.329
0.034
0.130
0.159
0.096
0.093
0.217
0.181
0.302
0.3839
0.9287
0.1048
0.8423
0.1149
0.0002
0.7013
0.1456
0.1079
0.3452
0.2751
0.0124
0.0396
)0.067
)0.053
0.027
)0.077
0.042
0.141
0.009
0.103
)0.048
0.002
0.021
0.116
0.029
0.208
0.2163
0.3270
0.6580
0.2014
0.4851
0.0418
0.8907
0.1559
0.4893
0.9827
0.7429
0.0947
0.6541
0.085
0.175
0.201
0.200
0.167
0.267
0.081
0.058
0.352
0.191
0.172
0.177
0.281
0.095
0.2917
0.0651
0.0390
0.0652
0.0640
0.0134
0.3355
0.4606
0.0068
0.0877
0.0802
0.0670
0.0130
Education (highest
Primary
Middle
school
High school
Tertiary level
Per capita
land
Cultivating
HH
Distance to
road
Distance to
school
Electricity
connec.
Ahuachapan
Santa Ana
Sonsonate
Chalatenango
La Libertad
San Salvador
Cuscatlan
La Paz
Caba~
nas
San Vicente
Usulutan
San Miguel
La Union
Observed
probability
Predicted
probability
Prob. value
High-productivity job
Obs: 1592; at 1: 331; at 0: 1261 Obs: 1592; at 1: 150; at 0: 1442
0.244
Log likelihood
Model
)739.82
Constant
)975.36
LR test
471.08
(model)
Degrees of
25
freedom
2
37.65
Critical
0.155
0.055
)649.27
)813.80
329.06
)414.86
)497.02
164.32
25
25
37.65
37.65
a
Source: Rural survey, FUSADES (1996).
Domain: Rural population aged above 14 and engaged in remunerated work.
c
A prob. value of 0.05 indicates that with 95% con®dence one can reject the hypothesis that the parameter estimate is
zero.
b
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 occupations, controlling for all other characteristics.
EL SALVADOR
541
Table 8. Probability of nonfarm employment as a primary occupationa
b
Probit model
Any nonfarm occupation
Obs: 2914; at 1: 1035; at 0: 1879
Variable
Household
size
Female
Age (years)
Marginal eect
Low-productivity job
Marginal
eect
Prob value
Marginal
eect
Prob value
)0.006
0.0990
0.000
0.9737
)0.005
0.0508
0.495
0.001
0.0001
0.0300
0.073
0.001
0.0001
0.0190
0.367
0.000
0.0001
0.5350
0.0001
0.0001
0.111
0.477
0.0001
0.0001
0.012
0.008
0.3813
0.8133
0.0001
0.847
0.616
0.0001
0.0001
)0.114
)0.072
0.0292
0.0476
Education (highest level reached)
Primary
0.136
Middle
0.487
school
High school
0.569
Tertiary level
n/a
Per capita
land
Cultivating
HH
Ahuachapan
Santa Ana
Sonsonate
Chalatenango
La Libertad
San Salvador
Cuscatlan
La Paz
Caba~
nas
San Vicente
Usulutan
San Miguel
La Union
Observed
probability
Predicted
probability
Prob valuec
High-productivity job
Obs: 2914; at 1: 544; at 0: 2370 Obs: 2914; at 1: 491; at 0: 2423
)0.001
0.0070
)0.000
0.1848
)0.0002
.0858
)0.112
0.0001
)0.058
0.4363
)0.035
0.0439
0.140
0.203
0.339
0.313
0.0718
0.0071
0.0001
0.0001
0.060
0.072
0.168
0.185
0.3416
0.2431
0.0137
0.0112
0.090
0.129
0.198
0.138
0.1193
0.0258
0.0016
0.0293
0.266
0.492
0.263
0.347
0.017
0.207
0.053
0.154
0.197
0.351
0.0005
0.0001
0.0018
0.0001
0.8414
0.0096
0.5035
0.0564
0.0158
0.206
0.399
0.130
0.242
)0.002
0.126
0.027
0.030
0.056
0.187
0.0032
0.0001
0.0742
0.0007
0.9718
0.0697
0.6636
0.6416
0.3939
0.077
0.101
0.144
0.116
)0.000
0.088
0.034
0.123
0.132
0.168
0.1634
0.0766
0.0294
0.0436
0.9944
0.1361
0.5418
0.0478
0.0366
0.319
Log likelihood
Model
)1401.40
Constant
)1895.83
LR t
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PII: S0305-750X(00)00105-4
Nonfarm Employment and Poverty in
Rural El Salvador
PETER LANJOUW *
The World Bank, Washington, DC, USA
Summary. Ð This 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, infrastructure, 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 Ð nonfarm 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 equitymotivated programs, such as the agrarian
reforms, have not succeeded in eliminating
poverty altogetherÐeither because implementation 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 few dispute the importance of addressing poverty in El Salvador, there is considerable 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 poverty. Much of this debate is prompted by differences in methodological approaches, and by
shortcomings in available data sources. Nonetheless, there does appear to be broad agreement that poverty in rural areas deserves
529
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 extends 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 employment generated. While this sector has not
received the same level of attention as the agriculture sector, there is a growing appreciation
of its potential in terms of both poverty
*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.
530
WORLD DEVELOPMENT
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 thinking about poverty and the nonfarm sector. This
is followed in Section 3 with a discussion of
poverty in El Salvador, and some tentative estimates of poverty based on consumption expenditures. Section 4 introduces some
quantitative evidence on the size of the nonfarm 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 paper. 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 analysis) 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 consumption module (for a subsample of households) which permits an analysis of poverty
based on consumption expenditure rather than
income (see below). 2
The second source of data comes from a rural household survey ®elded by Fundacion
Salvadore~
na Para el Desarollo Economico y
Social (FUSADES) in 1996. The survey covered 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 significance (Lopez, 1996). The FUSADES survey
obtained information on a wide range of demographic characteristics, location, and income variables. The level of detail in the
information collected has permitted the calculation 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 provides a useful complement to the EHPM in that
while it is smaller in sample size it provides
greater detail about rural livelihoods and activities.
2. A QUICK OVERVIEW OF THE
POVERTY±NONFARM LINKS
Rural o-farm employment has been traditionally seen as a low-productivity sector, producing low quality 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 sector 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
Lanjouw and Lanjouw (forthcoming) indicate that while de®nitional and data-related
uncertainties remain, the rural nonagricultural
sector is both large and, on aggregate, has been
growing over time. Hazell and Haggblade
(1993) emphasize that when rural towns are
included in employment calculations, the share
of the rural labor force employed primarily in
nonagricultural activities rises sharply. They
calculate that in Latin America, 47% of the
labor force in rural settlements and rural towns
EL SALVADOR
is employed in nonfarm activities. This can be
compared to 28% when only rural settlements
are included. Hazell and Haggblade also highlight the importance of female participation in
nonagricultural activities: 79% of women in the
Latin American rural wage-labor force are estimated to be employed in nonagricultural activities.
Nonagricultural activities can be broadly divided into two groups of occupations: high labor productivity/high income activities and low
labor productivity activities that serve as a residual source of employmentÐa ``last-resort''
source of income (Lanjouw and Lanjouw,
forthcoming). These latter activities are common among the very poor, particularly among
women. Such employment may nevertheless be
very important from a social welfare perspective for the following reasons: (a) o-farm employment 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
low nonagricultural earnings; and (c) for certain subgroups of the population who are unable 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 employment 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 inequality 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 supports 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 explanation 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
531
agricultural wage employment is not an option
for certain family members, then rural nonagricultural 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 rural nonagricultural sector and rural poverty.
Because of market imperfections and distortions, nonfarm activities are likely to employ
labor beyond the point where the marginal
product of labor is equal to the prevailing average 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 poverty and the nonagricultural sector in the context 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 dimensions of the poverty problem in El Salvador. 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 prevents discussing them in detail. 6
In El Salvador, as in many other Latin
American countries, poverty analysis has generally been carried out on the basis of income as
the household-level indicator of well-being.
This is in contrast with conventional practice in
other parts of the world, where consumption
expenditures are commonly taken as the welfare indicator. The principal reason for choosing consumption is that experience has shown
that these are measured with greater accuracy
than incomesÐparticularly for the poor, who
are most likely to consume a relatively narrow
range of goods and services. 7 Many of the
household surveys ®elded in Latin American
countries are modeled on labor force surveys
532
WORLD DEVELOPMENT
designed to measure household earnings. Such
surveys are generally weak in capturing incomes from nonwage labor sources (such as
remittances, transfers, and self-employment)
and from agricultural activities. These omissions may be particularly pertinent to the
measurement of rural poverty.
The next step in measuring poverty is to relate household-level welfare indicators to some
poverty thresholdÐthe poverty line. In constructing a poverty line, many assumptions are
generally required, and these can at times become 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 females, and what kind of adjustment must be
made to allow for 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 geographic 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 distribution of poverty across population subgroups.
Finally, there is an important issue associated
with the nonavailability of a spatial cost-of-living index. Such an index adjusts for the fact that
to reach a given standard of living in Metropolitan 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 calculations 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 requisite 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 rural and urban areas) and to be satis®ed in
stating that poverty among one group is higher
or lower than among the other (without attempting to state by how much), 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 subsamples of the EHPM for which highly dierence consumption modules were ®elded (for a
detailed exposition, see Lanjouw and 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.
The purpose of the poverty estimates in Table 1 is to shed light on some of the broad
geographic patterns of poverty in El Salvador.
At the level of the country as a whole, poverty
is much higher in rural areas than in urban
areas. This seems to be driven in particular by
the relatively low level of poverty in Metropolitan San Salvador. 8 Across the other four
broad geographic regions of the country, the
evidence is less strong that there exists a clear
distinction between rural and urban areas. El
Salvador is unique in its de®nition of urban
areas in that it counts as urban all municipal
centers (cabeceras municipales) without taking
into account the actual population of those
centers. This is in contrast with many other
countries (although it is not unique in Latin
America, see Klein, 1993) and is likely to result
in higher estimates of the incidence of urban
poverty than would obtain if only large conurbations were designated as urban. There is
rather little evidence of a concentration of
poverty within a speci®c broad region (sample
size constraints prevent a further geographic
breakdown within broad regions). Based on the
``low'' poverty line, there is some indication
that rural poverty in the central regions (located more closely to San Salvador) is lower
EL SALVADOR
533
Table 1. The incidence of poverty in El Salvadora ;b
High poverty line
Incidence of poverty
Low poverty line
Persons poor
Incidence of poverty
Persons poor
West
Urban
Rural
All
0.68
0.75
0.72
312,343
449,050
761,393
0.28
0.38
0.33
127,241
223,726
350,967
Central 1
Urban
Rural
All
0.74
0.76
0.75
329,117
560,313
889,430
0.31
0.33
0.32
138,726
240,980
379,706
Central 2
Urban
Rural
All
0.70
0.79
0.76
131,827
237,550
369,377
0.36
0.32
0.34
66,983
96,925
163,908
East
Urban
Rural
All
0.67
0.79
0.74
287,074
513,079
800,153
0.30
0.38
0.35
129,960
247,231
377,191
Metropolitan
San Salvador
0.40
518,926
0.08
100,751
National
Urban
Rural
0.56
0.77
1,579,287
1,759,992
0.20
0.35
563,661
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
two sharply divergent consumption questionnaires were ®elded to two non-overlapping subsamples of total sample.
The methodology developed in Lanjouw and Lanjouw (1996) was implemented in order to ensure comparability. For
more details, see Lanjouw and Lanjouw (1996). (ii) The ``high'' poverty line refers to a monthly per capita expenditure ®gure of 667 Colones (approximately US$75), and the ``low'' poverty line (which can be interpreted as a
measure of ``extreme poverty'') refers to a monthly per capita expenditure ®gure of 334 Colones (approximately
US$36). These poverty lines have been calculated from the data based on the per capita cost of a basic food bundle
(for urban areas) as calculated by the Ministerio de Plani®cacion in San Salvador, and adding a nonfood expenditure
allowance in accordance with the average amount spent on nonfood items by households with food expenditures
equal in value to the MIPLAN food basket. The ``low'' poverty line is simply 50% of the ``high'' line, and corresponds loosely to the $1 per day poverty line that underpins global poverty comparisons. (iii) Rural expenditures
have been in¯ated by a factor of 60% to account for MIPLAN's calculation that the basic urban food basket costs
60% more than a basic rural food basket. (iv) The broad regions presented above break down into the following
departments: West (Santa Ana, Ahuachapan, Sonsonate); Central 1 (La Libertad, Rural San Salvador, Chalatenango, Cuscatlan); Central 2 (San Vicente, La Paz, Cabanas); East (Usulutan San Miguel, Morazan, La Union). (v)
Overall aggregate populations vary slightly across the high poverty line column and low poverty line column, because
of rounding in the reported headcount rates.
than in either the East or West. We shall see
below that the most productive nonfarm activities 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 mid1970s. More recent census ®gures for El Salvador have not been calculated, no doubt for
data-related reasons associated with the political and military instability of the intervening
period.
534
WORLD DEVELOPMENT
In Tables 2 and 3 we present estimates based
on the EHPM household survey. In 1994,
36.4% of the economically active rural population 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 manufacturing 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 additional 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 economically active population (compared to
73.6% of men). 9 The activities in which women
are heavily engaged include ®rst of all commerce, and are followed by manufacturing and
domestic service. For men, commerce and domestic service are of far less signi®cance, but
construction, manufacturing and also transport
are important sectors.
There is geographic variation in El Salvador in the signi®cance of the rural nonfarm
sector (Table 3). In the Central 1 region
(which includes the departments of Chalatenango, 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 manufacturing appears to be relatively more important 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 engage in both agricultural labor and farming. In
fact, agricultural labor appears to be particularly 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 populationb with primary occupation
Fishing
Manufacture
Textiles/garments
Wood/straw/leatherware
Utilities
Construction
Commerce
Restaurant/hotel
Transport
Finance
Administration
Teaching
Health
Domestic service
Other service
Total nonfarm
Farming
Agricultural labor
No. of observations
a
Male
0.7
4.2
1.0
1.4
0.3
6.1
2.1
0.2
2.8
0.1
0.2
0.4
0.0
0.9
4.3
24.7
54.4
20.8
2,230
(2.8)c
(17.0)
(4.0)
(5.7)
(1.2)
(24.7)
(8.5)
(0.8)
(11.3)
(0.4)
(0.8)
(1.6)
(0.0)
(3.6)
(17.4)
(100.0)
Female
0.2
10.0
8.0
3.7
0.0
0.4
28.1
1.8
0.0
0.5
0.2
1.6
1.3
12.4
4.1
72.3
11.4
16.1
685
(0.3)
(13.8)
(11.1)
(5.1)
(0.0)
(0.6)
(38.9)
(2.5)
(0.0)
(0.7)
(0.3)
(2.2)
(1.7)
(17.2)
(5.7)
(100.0)
Source: Republica de El Salvador: Encuesta de Hogares de Propositos Multiples, 1994-III.
Column percentages provided in brackets.
c
Primary employment refers to self-reported principal activity.
b
Total
0.6
5.6
2.7
1.9
0.3
4.7
8.4
0.6
2.1
0.2
0.2
0.7
0.4
3.7
4.3
36.4
44.0
19.6
2,915
(1.6)
(15.4)
(7.4)
(5.2)
(0.8)
(12.9)
(23.1)
(1.6)
(5.8)
(0.5)
(0.5)
(1.9)
(1.1)
(10.2)
(11.8)
(100.0)
EL SALVADOR
535
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
Fishing
Manufacture
Textiles/garments
Wood/straw/leatherware
Utilities
Construction
Commerce
Restaurant/hotel
Transport
Finance
Administration
Teaching
Health
Domestic service
Other services
Total nonfarm
Farming
Agricultural labor
No. of observations
a
b
West
0.0
6.3
2.0
2.3
0.4
4.1
7.1
0.1
2.2
0.2
0.5
0.8
0.5
3.0
4.1
33.6
43.7
22.7
802
(0.0)b
(18.8)
(6.0)
(6.8)
(1.2)
(12.2)
(21.2)
(0.3)
(6.5)
(0.6)
(1.5)
(2.4)
(1.5)
(8.9)
(12.2)
(100.0)
Central 1
0.9
6.5
3.8
3.1
0.4
6.5
9.9
1.3
2.8
0.2
0.1
0.4
0.5
5.8
6.3
48.5
33.1
18.3
830
(1.9)
(13.4)
(7.8)
(6.4)
(0.8)
(13.4)
(20.4)
(2.7)
(5.8)
(0.4)
(0.2)
(0.8)
(1.0)
(12.0)
(13.0)
(100.0)
Central 2
1.6
5.3
3.3
0.5
0.2
4.9
9.7
0.6
0.6
0.3
0.1
0.9
0.0
2.4
3.3
33.7
50.1
16.1
635
(4.7)
(15.7)
(10.0)
(1.5)
(0.6)
(14.5)
(28.8)
(1.8)
(1.8)
(0.9)
(0.3)
(2.7)
(0.0)
(7.1)
(9.8)
(100.0)
East
0.3 (1.3)
3.5(15.1)
1.5 (6.5)
0.6 (2.6)
0.0 (0.0)
2.8 (12.1)
7.1 (30.6)
0.0 (0.0)
1.9 (8.2)
0.2 (0.9)
0.1 (0.4)
0.9 (3.9)
0.2 (0.9)
2.0 (8.6)
2.1 (9.1)
23.2 (100.0)
56.6
20.2
648
Source: Republica de El Salvador: Encuesta de Hogares de Propositos Multiples, 1994-III.
Column percentages provided in brackets.
gories, the three associated with highest incidence 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 (Dreze et 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 encompasses both casual daily wage labor and longterm, permanent employment on a farm,
plantation, or ranch. As a result, not all agricultural 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 labor run only about an average ``risk'' of poverty (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 (households simultaneously engaging in farming, agricultural 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 productive, relatively well-paid, and which have
the appealing feature of being comparatively
less exposed than agriculture to climatic variations and uncertainties. On the other hand,
there are a group of activities undertaken by
persons who are unable even to secure an agricultural laboring position; persons who are
perhaps old or disabled, or who may be prohibited 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 important 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 publicworks 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-
536
WORLD DEVELOPMENT
Table 4. Poverty and rural household activitiesa
b
Household characteristics
Agricultural labor and farming
Agricultural labor only
Agricultural labor farming and nonfarm employment
Farming only
Farming and nonfarm employment
Agricultural labor and nonfarm employment
Nonfarm employment only
Nonfarm income from non-wage sources
Percent of population
(%)
Incidence of extreme poverty
(%)c
5.0
9.6
2.7
26.1
19.9
9.1
26.1
1.6
54.7
48.7
43.9
41.5
35.9
35.2
20.3
16.3
a
Source: Encuesta de Hogares de Propositos Multiples, 1994-III.
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
Extreme poverty is associated with per capita consumption levels falling below the ``low'' poverty line.
b
allion, 1993). This program provides nonfarm
employment to poor persons for whom agricultural wage employment is not an option
(perhaps because of cultural restrictions, or
because climatic conditions have sharply reduced demand for labor). It therefore exploits
the ``safety-net'' function of the nonfarm sector. 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 employment by providing training, supporting infrastructure, 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 nonfarm activities. And this segment is the least
poor of the entire rural population. It seems
possible therefore, that the rural nonfarm sector 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 household enterprises, the FUSADES rural survey
included a separate questionnaire on such activities. 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, although on average such enterprises were
smaller in terms of employment per ®rm than
pottery and brick-making enterprises. It is interesting 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 draw particularly heavily on family labor and are most frequently 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 observed 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 how and when to allocate their family labor, and do not have to
concern themselves with bringing the ®nal
goods to the market (which, in the case of
EL SALVADOR
537
Table 5. Rural enterprises in El Salvadora
Sector
Transport
Other services
Other industry
Repair shop
Restaurant/bar
Textiles
Wood/work
Food proc.
Pottery/bricks
Commerce
Total
a
Number
of ®rms
Number
of workers
Percentage
Percentage
family
home-based
members (%)
(%)
Percentage
with training
(%)
Percentage
supplying
contractor (%)
1
3
5
6
5
13
10
13
14
31
1
3
6
16
19
25
37
53
63
77
100
100
100
44
16
73
22
28
13
64
0
33
100
33
20
92
60
54
7
58
0
0
0
0
0
8
10
8
7
3
0
0
20
0
0
23
30
0
7
0
101
300
40
52
5
8
Source: Rural survey, FUSADES (1996).
clothing or shoes for example, might be very far
away).
The range of both nonfarm employment as
well as rural enterprise activities that are engaged in provides some clues as to the relationship 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 agricultural intensi®cation and nonfarm activity can
emerge on the basis of production and consumption linkages. Production linkages
emerge, for example, when demand of agriculturalists for inputs such as plows and machinery 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 agricultural 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 repair 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 urban-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 altogether. 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 cropping patterns which minimize the risk of harvest 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 now to a closer examination of the
correlates of nonfarm employment in El Salvador 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 nonfarm jobs together, but also distinguish between 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
538
WORLD DEVELOPMENT
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 signi®cantly less likely to be employed in the
nonfarm sector. Compared to the uneducated,
all persons who have been educated are significantly 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 landholdings are less likely to be employed in the nonfarm 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 agriculture) 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 secondary 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 Morazan (in the east of the country). For all other
departments (with the exception of Ahuacha-
pan) the point estimate is positive, indicating a
particularly low probability of nonfarm employment 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 particularly strong and positive, while for low-productivity 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 access 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 lowproductivity employment.
Of the regional dummies, Chalatenango and
San Salvador are the two departments in which
high-productivity jobs are concentrated. Relative to Morazan, low-productivity jobs are
more common in Sonsonate, Chalatenango, La
Libertad, San Salvador, Caba~
nas, Usulutan,
San Miguel, and La Union.
A somewhat puzzling ®nding from Table 6
was the observation that females were signi®cantly less likely to be employed in the nonfarm
sector than men. This ®nding is due partly to
the fact that in Table 6 the relevant domain was
taken to include all persons of working age in
rural areas. We have already noted that women
are far less likely to be ``economically active''
than men, as it is common practice to not include nonremunerated domestic activities
amongst ``economic'' activities. In Table 7 we
con®ne our attention to the ``economically active'' population, sticking with the FUSADES
data for the time being. Now, women are no
longer signi®cantly less likely to be employed in
nonfarm jobs. In fact, for low-productivity
jobs, women are signi®cantly more likely to be
employed in such occupations.
EL SALVADOR
539
Table 6. Probability of nonfarm employment as a primary occupationa
b
Probit model
Variable
Household
size
Female
Age (years)
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
Prob valuec
Marginal
eect
Marginal
eect
)0.0004
0.8785
)0.003
0.1244
0.002
0.0790
)0.177
)0.001
0.0001
0.0137
)0.130
0.000
0.0001
0.3555
)0.028
)0.001
0.0001
0.0001
0.0002
0.0054
0.0001
0.0030
0.058
0.074
0.203
0.373
0.0003
0.0003
0.0001
0.0001
0.011
)0.001
)0.005
n/a
0.2287
0.9194
0.6280
)
)0.059
0.0001
)0.036
0.0017
)0.018
0.0359
)0.130
0.0001
)0.086
0.0001
)0.026
0.0016
)0.002
0.0979
)0.001
0.2067
)0.001
0.2976
)0.002
0.3041
)0.001
0.4751
)0.002
0.0080
0.044
0.0021
0.024
0.0359
0.012
0.0956
)0.011
0.058
0.152
0.008
0.127
0.247
0.065
0.094
0.086
0.032
0.073
0.116
0.085
0.176
0.7940
0.2218
0.0041
0.8722
0.0102
0.0001
0.2295
0.0744
0.1448
0.5800
0.1469
0.0226
0.0893
)0.023
)0.006
0.057
)0.032
0.055
0.114
0.033
0.065
)0.029
)0.010
0.022
0.059
0.009
0.121
0.3698
0.8521
0.1382
0.0061
0.1247
0.0061
0.4090
0.1067
0.4513
0.8242
0.5379
0.1187
0.7963
0.065
0.139
0.167
0.118
0.128
0.204
0.064
0.039
0.212
0.095
0.113
0.102
0.156
0.055
0.1894
0.2261
0.0102
0.0591
0.0258
0.0035
0.2259
0.4100
0.0068
0.1206
0.0558
0.0655
0.0148
Marginal eect
Education (highest level reached)
Primary
0.070
Middle school
0.066
High school
0.179
Tertiary level
0.273
Per capita
land
Cultivating
HH.
Distance to
road
Distance to
school
Electricity
connec.
Ahuachapan
Santa Ana
Sonsonate
Chalatenango
La Libertad
San Salvador
Cuscatlan
La Paz
Caba~
nas
San Vicente
Usulutan
San Miguel
La Union
Observed
probability
Predicted
probability
0.131
Log likelihood
Model
)1058.84
Constant
)1272.55
LR test
427.42
(model)
Degrees of
25
freedom
2
37.65
Critical
0.081
Prob. value
Prob. value
0.035
)833.34
)1009.49
352.30
)515.92
)581.47
131.10
25
25
37.65
37.65
a
Source: Rural survey, FUSADES (1996).
Domain: Entire rural population aged above 14.
c
A prob value of 0.05 indicates that with 95% con®dence one can reject the hypothesis that the parameter estimate is
zero.
b
In Table 8, we retain our focus on the economically active population but apply the signi®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
540
WORLD DEVELOPMENT
Table 7. Probability of nonfarm employment as a primary occupationa
b
Any nonfarm occupation
Probit model
Obs: 1592; at 1: 481; at 0: 1111
Variable
Household
size
Female
Age (years)
Marginal eectc
Low-productivity job
Marginal
eect
Prob. value
Marginal
eect
Prob. value
0.003
0.6109
)0.004
0.2507
0.004
0.0397
0.031
)0.002
0.3025
0.0087
)0.031
0.001
0.1720
0.3593
0.045
)0.002
0.0025
0.0001
level reached)
0.101
0.097
0.0018
0.0147
0.083
0.107
0.0022
0.0020
0.013
)0.004
0.3841
0.8135
0.330
0.562
0.0001
0.0007
0.342
0.673
0.0001
0.0001
)0.007
n/a
0.6875
)0.052
0.0456
)0.032
0.1223
)0.014
0.3090
)0.336
0.0001
)0.235
0.0001
)0.070
0.0001
)0.003
0.1322
)0.002
0.2524
)0.001
0.4263
)0.002
0.5729
0.002
0.2675
)0.003
0.0148
0.094
0.0002
0.047
0.0220
0.022
0.0588
)0.065
0.007
0.132
)0.018
0.125
0.329
0.034
0.130
0.159
0.096
0.093
0.217
0.181
0.302
0.3839
0.9287
0.1048
0.8423
0.1149
0.0002
0.7013
0.1456
0.1079
0.3452
0.2751
0.0124
0.0396
)0.067
)0.053
0.027
)0.077
0.042
0.141
0.009
0.103
)0.048
0.002
0.021
0.116
0.029
0.208
0.2163
0.3270
0.6580
0.2014
0.4851
0.0418
0.8907
0.1559
0.4893
0.9827
0.7429
0.0947
0.6541
0.085
0.175
0.201
0.200
0.167
0.267
0.081
0.058
0.352
0.191
0.172
0.177
0.281
0.095
0.2917
0.0651
0.0390
0.0652
0.0640
0.0134
0.3355
0.4606
0.0068
0.0877
0.0802
0.0670
0.0130
Education (highest
Primary
Middle
school
High school
Tertiary level
Per capita
land
Cultivating
HH
Distance to
road
Distance to
school
Electricity
connec.
Ahuachapan
Santa Ana
Sonsonate
Chalatenango
La Libertad
San Salvador
Cuscatlan
La Paz
Caba~
nas
San Vicente
Usulutan
San Miguel
La Union
Observed
probability
Predicted
probability
Prob. value
High-productivity job
Obs: 1592; at 1: 331; at 0: 1261 Obs: 1592; at 1: 150; at 0: 1442
0.244
Log likelihood
Model
)739.82
Constant
)975.36
LR test
471.08
(model)
Degrees of
25
freedom
2
37.65
Critical
0.155
0.055
)649.27
)813.80
329.06
)414.86
)497.02
164.32
25
25
37.65
37.65
a
Source: Rural survey, FUSADES (1996).
Domain: Rural population aged above 14 and engaged in remunerated work.
c
A prob. value of 0.05 indicates that with 95% con®dence one can reject the hypothesis that the parameter estimate is
zero.
b
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 occupations, controlling for all other characteristics.
EL SALVADOR
541
Table 8. Probability of nonfarm employment as a primary occupationa
b
Probit model
Any nonfarm occupation
Obs: 2914; at 1: 1035; at 0: 1879
Variable
Household
size
Female
Age (years)
Marginal eect
Low-productivity job
Marginal
eect
Prob value
Marginal
eect
Prob value
)0.006
0.0990
0.000
0.9737
)0.005
0.0508
0.495
0.001
0.0001
0.0300
0.073
0.001
0.0001
0.0190
0.367
0.000
0.0001
0.5350
0.0001
0.0001
0.111
0.477
0.0001
0.0001
0.012
0.008
0.3813
0.8133
0.0001
0.847
0.616
0.0001
0.0001
)0.114
)0.072
0.0292
0.0476
Education (highest level reached)
Primary
0.136
Middle
0.487
school
High school
0.569
Tertiary level
n/a
Per capita
land
Cultivating
HH
Ahuachapan
Santa Ana
Sonsonate
Chalatenango
La Libertad
San Salvador
Cuscatlan
La Paz
Caba~
nas
San Vicente
Usulutan
San Miguel
La Union
Observed
probability
Predicted
probability
Prob valuec
High-productivity job
Obs: 2914; at 1: 544; at 0: 2370 Obs: 2914; at 1: 491; at 0: 2423
)0.001
0.0070
)0.000
0.1848
)0.0002
.0858
)0.112
0.0001
)0.058
0.4363
)0.035
0.0439
0.140
0.203
0.339
0.313
0.0718
0.0071
0.0001
0.0001
0.060
0.072
0.168
0.185
0.3416
0.2431
0.0137
0.0112
0.090
0.129
0.198
0.138
0.1193
0.0258
0.0016
0.0293
0.266
0.492
0.263
0.347
0.017
0.207
0.053
0.154
0.197
0.351
0.0005
0.0001
0.0018
0.0001
0.8414
0.0096
0.5035
0.0564
0.0158
0.206
0.399
0.130
0.242
)0.002
0.126
0.027
0.030
0.056
0.187
0.0032
0.0001
0.0742
0.0007
0.9718
0.0697
0.6636
0.6416
0.3939
0.077
0.101
0.144
0.116
)0.000
0.088
0.034
0.123
0.132
0.168
0.1634
0.0766
0.0294
0.0436
0.9944
0.1361
0.5418
0.0478
0.0366
0.319
Log likelihood
Model
)1401.40
Constant
)1895.83
LR t