Data collected in surveys of more than 700 Salvadoran households carried out in 1996 and 1998 are used in an econometric analysis of linkages between land and labor use. Particular emphasis is offered on how poor households allocated labor resources in response to the El Nino weather phenomenon of 1997. Implications for resource conservation are offered.International Development, Land Economics/Use,

Gonzalez-Vega, Claudio

Hopkins, Jeffrey W.

Rodriguez-Meza, Jorge

Southgate, Douglas

English

Research Papers in Economics

Rural Poverty, Income Shocks, and Land Management:
An Analysis of the Linkages in El Salvador
Douglas Southgate, Jeﬀrey Hopkins,
Claudio Gonz´ alez-Vega, and Jorge Rodr´ ıguez-Meza1
1Selected Paper, AAEA Annual Meeting, Chicago, August 5-8 2001. Southgate,
Gonz´ alez-Vega and Rodr´ iguez-Meza are with the Agricultural, Environmental and Devel-
opment Economics Department at The Ohio State University. Hopkins is an agricultural
economist with Economic Research Service, USDA. Opinions are those of the authors and
do not reﬂect the opinions or policies of Ohio State University or the U.S. Department of
Agriculture.
1Among those involved in watershed management and soil conservation
projects in the developing world, there is considerable discussion of the rela-
tionship between rural poverty, on the one hand, and land degradation, on
the other. This concern is appropriate for two reasons. First, poverty is es-
pecially acute in the African, Asian, and Latin American countryside (IFAD,
2001). Second, the rural poor tend to concentrate in fragile environments, in
part because of policy-induced distortions that cause more aﬄuent people to
enjoy numerous advantages in the competition over prime farmland (Heath
and Binswanger, 1996).
Systematic investigation of the linkages between rural poverty and en-
vironmental deterioration remains at an incipient stage, with a lot of the
existing literature being qualitative, anecdotal, or both. As a result, much
of what is written applies only to speciﬁc settings or even appears contradic-
tory. One investigator claims that poor people in one place are being driven
to exhaust every resource in sight. Someone else points out that commer-
cial farmers elsewhere have grown wealthy by depleting renewable resources.
What general lessons, if any, can be drawn from observations like these?
This paper addresses major elements of the relationship between rural
poverty and land degradation. In particular, we focus on a key facet of
poverty emphasized in the recent literature, which is the high susceptibility
of poor households to changes in income (Ravallion, 1994), and examine the
implications of this susceptibility for natural resource use at the household
level.
The empirical research on which this paper is based was carried out in El
Salvador, where most of the rural population is poor and where deforestation
and other forms of environmental deterioration have reached an advanced
cumulative stage. Moreover, the country suﬀered from climatic disruptions
in the late 1990s, associated with the El Ni˜ no phenomenon. Since there
was some foreknowledge of this shock, an unusual opportunity was created
to examine ex ante decisions made by rural households, including some key
environmental consequences of these decisions.
To set the stage for statistical analysis, this paper begins with a descrip-
tion of El Ni˜ no’s impacts on El Salvador and the country’s agriculture. Next,
the model developed to analyze poverty-environment linkages and the results
of statistical analysis are presented. The paper concludes with a discussion
of policy implications as well as suggestions for additional investigation.
El Ni˜ no and El Salvador’s Farmers
2At least as much as in other parts of the world, weather risk is a con-
stant problem for Central American agriculture. The region is devastated
periodically by hurricanes sweeping in from the Caribbean. In addition, an
upwelling of warm water in the eastern Paciﬁc, which occurs every few years,
causes El Ni˜ no to appear.
Events in 1997 and 1998 were characteristic of the latter phenomenon.
Along with a general rise in temperatures, rainfall increased along much of
Central America’s Caribbean coast. Meanwhile, droughts were experienced
along the Paciﬁc coast, where El Salvador is located. Even within the coun-
try, El Ni˜ no’s eﬀects varied. While precipitation was close to normal in the
central provinces, the wet season started late and rainfall was below normal
in other parts of the country. The drought was particularly severe in eastern
El Salvador.
Yield impacts varied from crop to crop. While per-hectare production
of beans only declined 3 percent, yields of sugar, coﬀee, and rice fell 9, 10,
and 17 percent, respectively. Especially hard-hit was white corn, which is
the main product of small farms, both for market and home consumption.
Since the wet season did not begin on time in many parts of El Salvador,
planting was delayed. In addition, diminished precipitation hampered crop
growth. As a result, yields declined by one-fourth, which was devastating for
the many campesinos who, for reasons explained below, had planted more
land to white corn.
The varied impacts of climatic disturbance on household income have
been investigated by Conning, Olinto, and Trigueros (2000), who used the
same data set employed in this study. Among their ﬁndings is that more
prosperous rural dwellers, apparently responding to predictions of El Ni˜ no,
devoted more time to non-agricultural work and cut back on their farming
operations. In turn, this latter response reduced opportunities for agricul-
tural employment, on which the rural poor depend (L´ opez, 1998). The living
standards of the poorest of the poor, who generally lack land, were dealt a
severe blow since they rely more on agricultural wages than other segments
of the rural population do. Small farmers, who also supply labor to others’
holdings, responded to the decline in agricultural labor demand by cultivat-
ing more land, either not knowing about impending changes in the weather or
not sensing that there was any alternative (Conning, Olinto, and Trigueros,
2000).
Analysis of data collected from rural households by the Fundaci´ on Sal-
vadore˜ na para el Desarrollo Econ´ omico y Social (FUSADES) sheds light on
3the farm-level environmental impacts of these adjustments. Especially inter-
esting are the changes that farmers, small and large, made in land use and
labor intensity between 1995, when climatic conditions were normal, and
1997, when El Ni˜ no struck. This comparison is possible since FUSADES
surveyed a sample of 489 rural landowners on two occasions, once in early
1996 and again in early 1998. At each time, interviewees were questioned
about their activities during the previous calendar year.
Though modest in terms of total agricultural area, farm-level changes
in land use were consistent with preceding observations about how diﬀerent
sorts of rural households responded to El Ni˜ no and ensuing economic adjust-
ments. Between 1995 and 1997, the area planted to crops on large holdings
declined. The owners of these holdings tend to be more prosperous than
the rest of the rural population. Since these people also were more likely to
learn of impending climatic disturbance by early 1997 and had better access
to non-agricultural employment and business opportunities, they rearranged
productive activities in ways that de-emphasized farming. Meanwhile, there
was an increase in agricultural land use on small farms. The owners of these
farms were ﬁnding less work on larger holdings and many of them did not
receive advanced warning of El Ni˜ no.
Changes in land use are a valid indicator of diﬀerent degrees of farm-level
pressure on the environment. If a wealthier farmer decided to use less land in
1997 than he had done in 1995, the area taken out of production was likely
to be inferior – in terms of yield, degradation risks, or both – than the area
still farmed in 1997. Similarly, the additional land used by small farmers in
1997 tended to be less productive and/or more fragile than what they had
been using already.
Labor intensity, measured here as person-days devoted to farming divided
by farmed area, is also a good measure of human pressure on the environment
since the time spent plowing, weeding, and so forth tends to raise erosion
risks. [Additional time spent on conservation practices would, of course, be
beneﬁcial; however, there was no evidence of this behavior in the data.] As
is the case with changes in farm-level land use, diﬀerences between 1995 and
1997 in farm-level labor intensities are consistent with preceding observations
about El Ni˜ no’s impacts. To be speciﬁc, labor intensity declined on large
farms, which is consistent with more prosperous households devoting more
eﬀort to non-agricultural work. Labor intensity rose on small holdings, at-
tributed smallholders experiencing a decline in oﬀ-farm employment options
as well as their imperfect information regarding impending adverse weather
4patterns.
Regression Model and Results
The data collected in FUSADES’s 1996 and 1998 surveys have been used in
a multiple regression analysis of households’ susceptibility to income shocks
and the impact of this susceptibility on natural resource management at the
farm level.
The structure of the regression model is recursive. The ﬁrst equation
relates changes in household income, either positive or negative, to a number
of variables, most of which have been included in the empirical study by
Conning, Olinto, and Trigueros (2000). The model’s second equation focuses
on environmental impacts, as measured either by changes in land use or
changes in labor intensity. Right-hand side variables include the predicted
value of the ﬁrst equation’s dependent variable as well as various factors found
to inﬂuence soil conservation in previous research (Hopkins, Southgate, and
Gonz´ alez-Vega, 1999).
Panel regression techniques were employed to take advantage of the cross-
sectional and time-series components of our data, because repeated observa-
tions were available for most of the households in the sample. We estimated
a model of the form
(1) yit = ® + xit¯ + Ài + "it
where looking for estimates of the vector of coeﬃcients ¯ or variables x
that inﬂuence the dependent variable y. The ﬁnal two terms of the model
are random unobservable or omitted variables that determine the dependent
variable. The ﬁrst part of this residual to estimation, À, is the household-
speciﬁc residual while "t is the usual residual with the standard properties
(mean 0, uncorrelated with itself or À and homoskedastic). In the Salvadoran
dataset, we have two time periods, 1995 and 1997, so (1) is transformed
by taking diﬀerences and we use the “within” or ﬁxed-eﬀects estimator for
¯;¯w,resulting in
(2) yi;1997 ¡ yi;1995 = (xi;1997 ¡ xi;1995)¯w + "i;1997 ¡ "i;1995 :
Note that by taking the diﬀerences of the two years we have eliminated the
time-invariant household-speciﬁc residual and therefore have eliminated any
5correlation between the explanatory variables that we include in the model
and household-level ﬁxed eﬀects, allowing for straightforward OLS estimation
(Deaton, 1997). Additional information can be introduced through the use
of a random eﬀects model, which includes (2) as well as a special case of (1),
where observations take their mean values over all time periods and we can
use a “between” estimator for ¯;¯b, resulting in
(1
0) ¯ yi = ® + ¯ xi¯b + Ài + ¯ "i
The random eﬀects ordinary least squares estimator is a matrix weighted
average of within- and between-household estimators. Since the random
eﬀects model includes additional information, it can be considered more ef-
ﬁcient. However, its small-sample properties are unknown and the potential
for omitted-variable bias in estimation is present because the random ef-
fects model is based on the assumption that household-level ﬁxed eﬀects are
uncorrelated with the regressors (Deaton, 1997). Estimation of a random
eﬀects model is still in progress. Accordingly, we present the results of the
ﬁxed-eﬀects model in this paper.
Table 1 provides descriptive statistics for the variables used in the study.
Table 2 shows the results for the per-capita income equation. In this model,
income from all sources was regressed against household-level variables that
varied over the two time periods. The variables in the model explained about
14 percent of the overall variation in the dataset. Since we took the logarithm
of the dependent variable before estimation, each coeﬃcient estimate should
be interpreted as the percentage change in per-capita income resulting from
a unitary change in the corresponding dependent variable.
Model results indicate that having an additional family member with
paid employment increased income by 28 percent. The relationship between
income and cultivated land area was positive over much of the sample consid-
ered in this study. However, this relationship became negative after farmed
land exceeded 22 manzanas (the standard measure of area in El Salvador, a
manzana is equal to 0.7 hectares), a very large landholding for the farms sur-
veyed. Land ownership negatively aﬀected total ousehold incomes, changes
in the value of this dummy variable between 1995 and 1997 perhaps indicat-
ing that households that sold real estate realized capital gains in the survey
year and therefore had higher incomes. However, the negative coeﬃcient also
reﬂects the low returns to agricultural production for much of the sample;
farm households often have low incomes but high levels of wealth compared
to their non-farm neighbors.
6Also statistically signiﬁcant is the negative eﬀect of the value of livestock
holdings at the end of each calendar year. This is plausible because large
herds indicate that not much livestock was sold in order to maintain con-
sumption in the face of economic diﬃculty. Two other variables were found
to have no eﬀect on per-capita income: the number of people in the house-
hold below the age of 16 and the number of people in the household who
contributed unpaid labor, either in farm or non-farm related enterprises.
Taken jointly, all coeﬃcients on variables were signiﬁcantly diﬀerent from
zero according to an F-test.
The second part of our econometric model is an auxiliary regression for
estimating the labor intensity of agricultural production per manzana, which
as mentioned already is a farm-level indicator of land degradation. Labor
intensity changed drastically between the two periods for all households, and
we hypothesize that this development was due to diminished employment
opportunities oﬀ the farm. Our model explained 16 percent of the variability
in labor hours per manzana, initial results indicating that lower household
incomes were indeed associated with higher labor inputs per unit of land
(Table 3). For example, for a one percent change in incomes, labor intensity
per manzana increased by almost 10 hours (.01*961.64). Since the dependent
variable in this equation is not in logarithmic form, this marginal eﬀect is
constant across the range of values taken for labor intensity. Note that the
eﬀect of conservation practices and technical assistance from the government
or some other outside resource had no eﬀect on eﬀect on labor intensity. Ap-
parently, even when labor was available for on-farm work it was not devoted
either to working with extension or other personnel to improve crop manage-
ment, or to the construction or maintenance of conservation structures.
Conclusions and Directions for Future Work
Our econometric analysis of linkages between rural households’ susceptibility
to income shocks and their use of natural resources is still at an early stage.
Among other tasks that must still be completed is to consult the available
literature on farmers’ soil management decisions in El Salvador and neigh-
boring countries so that the second part of the model, which addresses land
degradation at the farm level, can be reﬁned. Also, the option of estimating a
random (rather than ﬁxed) eﬀects model remains a primary goal. In addition,
data collected in the third FUSADES survey or rural households, which was
7carried out in early 2000, will soon be incorporated into the analysis, thereby
yielding a richer and more robust data set.
Although this study is still in process, results obtained to date are of sub-
stantial interest. Conﬁrming the ﬁndings of Conning, Olinto, and Trigueros
(2000), our analysis suggests that, due to lack of non-agricultural employ-
ment oﬀ the farm among other reasons, poor households in the countryside
are particularly susceptible to adverse income shocks resulting from climatic
disturbance and other events. Moreover, one can argue that these households
are apt to respond, both ex ante and ex post, to income shocks by using land
in a more depletive fashion. This response almost certainly outweighs the
eﬀects of measures, such as extension initiatives, aimed speciﬁcally at pro-
moting soil conservation.
References
Conning, J., P. Olinto, and A. Trigueros. 2000. “Managing Economic
Insecurity in Rural El Salvador” (draft), USAID Broadening Access and
Strengthening Input Market Systems Collaborative Research Support Pro-
gram (BASIS CRSP).
Deaton, A. 1997. The Analysis of Household Surveys: A Microeconomet-
ric Approach to Development Policy. Baltimore: Johns Hopkins University
Press.
Heath, J. and H. Binswanger. 1996. “Natural Resource Degradation
Eﬀects of Poverty and Population Growth Are Largely Policy Induced: The
Case of Colombia,” Environment and Development Economics, 1:1, pp. 65-
84.
Hopkins, J., D. Southgate, and C. Gonz´ alez-Vega. 1999. “Rural Poverty
and Land Degradation in El Salvador” (selected paper), Annual Meeting of
the American Agricultural Economics Association, Nashville.
International Fund for Agricultural Development (IFAD). 2001. Rural
Poverty Report 2001: The Challenge. Oxford: Oxford University Press.
L´ opez, R. 1998. “Rural Poverty in El Salvador: A Quantitative Analysis,”
World Bank, Washington.
Ravallion, M. 1994. Poverty Comparisons. Philadelphia: Harwood Aca-
demic Publishers.
8Table 1: Descriptive Statistics, 1996 and 1998 Salvador Data Set
Variable Deﬁnition Obs Mean Std.
ln pcinc logged per capita income 1367 7.50 1.51
income Income, all sources 1367 18117 21780
under16 Number of household members under 16 1367 2.50 2.06
personas Number of household members 1367 5.99 2.62
endvalue Ending value of animal stocks 1367 333933 12200000
invchange Change in value of animal stocks 1367 -75.67 5499
domwork Workers employed on farm and in other home activities 1367 3.27 1.95
landcult Land farmed 1367 0.95 1.81
landown Land owner dummy 1367 0.34 0.47
totagaway Wage labor hours outside home 1365 989 1785
totaghome Family and hired farm labor hours 1365 1488 2139
totag Total agricultural labor work hours 1365 2478 2550
techassist Agricultural technical assistance 1367 0.06 0.23
con Conservation practice dummy 1367 0.25 0.43
labint Labor intensity per manzana on home production 660 1790 2069
9Table 2: Dependent Variable: Logarithm of per-capita household income
Independent Variable Coeﬃcient Std. Error t P > jtj
personas -0.11 0.06 -1.87 0.062
landcult 0.31 0.07 4.24 0
landcult squared -0.01 0.00 -2.66 0.008
landown -1.47 0.29 -5.13 0
endvalue -1.1E-08 0.00 -2.77 0.006
under16 0.04 0.08 0.52 0.605
domwork -0.03 0.04 -0.87 0.384
paidwork 0.28 0.06 4.81 0
constant 8.00 0.25 32.01 0
R-sq: 0.1401
Table 3: Dependent variable: Labor intensity per manzana
Independent Variable Coeﬃcient Std. Error t P> jtj
Estimated per capita income -961.643 211.6163 -4.54 0
domwork 391.9577 89.6431 4.37 0
con -92.1033 208.2367 -0.44 0.659
techassist -71.6286 437.6781 -0.16 0.87
constant 7190.52 1589.286 4.52 0
R-sq: 0.1647
10

for Agricultural Development (IFAD).

Natural Resource Degradation Eﬀects of Poverty and Population Growth Are Largely Policy Induced: The Case of Colombia,”

Poverty Comparisons.

Rural Poverty and Land Degradation in El Salvador” (selected paper),

Rural Poverty in El Salvador: A Quantitative Analysis,” World Bank,

The Analysis of Household Surveys: A Microeconometric Approach to Development Policy.

RURAL POVERTY, INCOME SHOCKS, AND LAND MANAGEMENT: AN ANALYSIS OF THE LINKAGES IN EL SALVADOR

RURAL POVERTY, INCOME SHOCKS, AND LAND MANAGEMENT: AN ANALYSIS OF THE LINKAGES IN EL SALVADOR

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