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World Development Vol. 29, No. 3, pp. 411±425, 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)00102-9
Rural Nonfarm Employment and Incomes in Chile
JULIO A. BERDEGUEÂ
Red Internacional de Metodologõa de Investigaci
on de Sistemas de Producci
on
(RIMISP), Santiago, Chile
EDUARDO RAMIÂREZ
Ministerio de Plani®caci
on y Cooperaci
on, Santiago, Chile
THOMAS REARDON
Michigan State University, East Lansing, USA
and
ESCOBAR *
GERMAN
Red Internacional de Metodologõa de Investigaci
on de Sistemas de Producci
on
(RIMISP), Santiago, Chile
Summary. Ð This article analyzes the evolution of rural nonfarm employment (RNFE) and income
in Chile during 1990±96. The data used come from the National Socioeconomic Survey (CASEN),
and from a household survey undertaken by the authors in two municipalities in 1999. The latter
contrasted two zones, very dierent in terms of economic dynamism and rural poverty. We show
that during the period, RNFE and incomes increased 10% and 18%, respectively, in 1996, reaching
39% of rural employment and 41% of rural incomes. The rate of multiactivity (the share of
households participating in more than one sector) was only 20%, lower than expected, indicating a
tendency toward economic specialization in rural income strategies. The determinants of such
employment are mainly household characteristics, in particular variables related to human capital,
such as the age and gender of the household head, and the schooling of the household members,
although also important are access to credit and physical capital. The level of nonfarm income of
rural households is determined mainly by the economic context, in particular the economic level
and dynamism of the overall zone and the quality of the roads. It is proposed that policies to
develop RNFE should be geared to zone characteristics, and should in general favor investments in
education, in roads, and in access to credit. Moreover, households headed by women should be the
object of special attention. To promote such policies, it will be necessary to address important gaps
and weaknesses in the public institutional structure. Ó 2001 Elsevier Science Ltd. All rights
reserved.
Key words Ð Latin America, Chile, rural nonfarm employment, incomes, rural poverty,
development
* This
research is based on generous grants from the
Inter-American Development Bank (IADB) and the
United Nations Food and Agriculture Organization
(FAO). The authors thank Drs. Ruben EcheverrõÂa
(IADB), Gustavo Gordillo De Anda, Kostas Stamoulis, and Alexander Schejtman (FAO) for valuable
support and comments, and three anonymous peer
411
reviewers for useful comments. The authors also
acknowledge the support of the Planning and Cooperation Ministry of Chile (MIDEPLAN), that facilitated
access to the CASEN survey data. The authors
acknowledge the work of Ms. Ximena Milicevic in
the organization and analysis of the municipal-level
surveys.
412
WORLD DEVELOPMENT
1. INTRODUCTION
There is growing evidence that rural nonfarm
employment (RNFE) is an important source of
income for rural households in Latin American
and the Caribbean (LAC), including for the
landless and other poor rural groups (Berdegue, Reardon, & Escobar, 2000; Reardon,
Berdegue, & Escobar, 2001). Nevertheless, rural development policies, in particular those
aiming at rural poverty alleviation, generally
concentrate on agricultural development. After
many decades of rural development policies
based on the agricultural sector, it is now clear
that many rural zones and households are
®nding few opportunities in agriculture for
sustainable increase in incomes, in sucient
degree to substantially alleviate poverty (Berdegue, 2000).
Although the principal instruments of agricultural development in Chile directed at small
farmers have been successful in raising incomes, the impact of these interventions has
not been signi®cant in the poorest strata, and
indeed there has been little impact on incomes
of rural households not participating in ownfarming (Comite Interministerial de Desarrollo
Productivo, 1998). Thus, to reduce the poverty
that aects a large share of rural households in
Chile, the focus should be not only on smallscale agricultural production, but also on
employment and incomes in the nonfarm
sector.
RNFE can contribute to agricultural development by providing peasants with cash incomes that can be invested in improvements in
agricultural productivity. A substantial share of
rural nonfarm activity is concentrated in the
broad agrifood system (commerce in agricultural inputs and outputs, equipment service
provision, and so on). By this means it can increase the pro®tability of agriculture via the
better linking of agriculture to other sectors
and markets. In turn, the development of agriculture stimulates growth in commerce, industry, and other rural services. These farm±
nonfarm links are crucial for rural regional
development to be balanced, dynamic, and
sustainable (Banco Interamericano de Desarrollo (BID), 1998).
2. APPROACH
The research is based on two sources of information: (a) for the countrywide analysis, we
used data from the National Socioeconomic
Survey (CASEN) of the Ministry of Planning
and Cooperation (MIDEPLAN) for the years
1990 and 1996; (b) For the zone (``comuna'' or
municipality) level analysis, RIMISP (International Farming Systems Research Network in
Santiago, Chile) undertook a survey in two
comunas in March 1999. The comunas were
Portezuelo, to represent zones with extensive
rural poverty and a dearth of agricultural
modernization, and Molina, to represent situations of little rural poverty and rapid economic growth and agricultural modernization,
which in the case of Molina is in fresh fruit and,
in particular, vineyards and wineries of high
quality, oriented toward export markets.
The CASEN surveys provided data on the
socioeconomic conditions of the various socioeconomic groups in the country, problems in
their living and economic situations, the degree
and nature of their poverty, the distribution of
incomes over households, and the geographic
and socioeconomic strata coverage of social
programs and their contributions to monetary
and nonmonetary incomes of households (MIDEPLAN, 1996). The sampling and survey unit
is the residence, while the unit of analysis is the
household, whether a single person or several,
with or without family links among themselves,
who live in the same residence and have a
common food budget. Members of a household
are only the permanent residents of the residence, de®ned as not being absent more than
two months of the year (MIDEPLAN, 1990).
We used the CASEN surveys of 1990 and 1996.
We did not use the 1987 CASEN because of
various changes in methods and de®nitions
between that survey and the later ones, which
restricted comparison, nor did we use the 1998
survey because disaggregated data from that
survey were not available at the time of the
writing of this paper.
The 1990 sample comprised 25,793 households, of which 18,549 urban and 7,244 rural.
In 1996 the sample comprised 35,730 households of which 25,640 urban and 10,090 rural.
The sample in each case is nationally and regionally representative both for urban and rural areas, and the sampling error is 5% with a
con®dence interval of 95% (MIDEPLAN,
1990, 1996).
In 1990, CASEN de®ned rural as population
concentrations of less than 2,000 inhabitants.
In 1996, the cuto changed to 1,000 inhabitants
or 1,001±2,000 inhabitants involved mainly in
primary sector activities. In practice, only 85 of
CHILE
the 37,618 rural localities were aected by this
change in the classi®cation system. The survey
focuses on the location of the household to
determine whether it is rural, and does not
furnish data on the location of the households'
economic activities or whether they migrate or
commute to jobs in urban areas. The employment data indicate sector but not location, and
thus, RNFE refers to nonfarm jobs undertaken
in either urban or rural areas by rural households. Data limitations, therefore, restrict us
from useful analyses of job location and thus,
whether rural households commute or migrate
to urban jobs, or nonfarm jobs in rural areas
undertaken by urban households, or incomes of
households which are today urban but were
recently in rural areas. Moreover, the CASEN
survey generates employment information for
one month of the year, and thus, it is not possible to know whether the employment pro®le
changes over the year, which is of course important to ascertaining accurately the degree of
multiactivity of the household.
The study of RNFE in the comunas of
Portezuelo and Molina is not meant to be
representative in a statistical sense of the situation in all Chile. Rather, these are case studies
that are meant to be illustrative of dierent
situations of rural poverty, economic dynamism, and agricultural modernization, in order
to examine several themes and issues that cannot be studied using the national CASEN data.
The determination of the sample size for
Portezuelo was calculated using the two-step
method of Stein, using the variance and mean
of the incomes of rural households for the
rainfed agriculture zone of Region VIII based
on observations from a survey of 2,900 households in Chile of which 188 households in that
zone. The sample size for our survey in Portezuelo was 200 households. For Molina, the size
of the sample was limited for budgetary reasons
to 75 households, and thus, the sampling error
is higher for that comuna. In Portezuelo, the
200 households were distributed over 22 rural
localities (e.g., villages, small rural towns), in
proportion to the number of residences in the
localities. In Molina, we selected at random 18
of the 47 rural localities, and the number of
households per district was selected in proportion to the number of residences in the districts.
In each district, households were chosen at
random based on geographic sampling. It is
important to note that the observations on incomes and employment covered all households
and their members over the whole year.
413
3. COUNTRY-LEVEL RESULTS
(a) Agricultural incomes
Table 1 shows that during 1990±96 the
number of households whose principal income
was from agriculture, hunting, and ®shing 1
did not change signi®cantly. Nevertheless, urban households principally engaged in agriculture increased 37%, while rural households
thus, engaged fell 15%. 2 This change of residence of households thus, engaged 3 involved
all occupational categories in agriculture: employers/owners, wage-earners, and self-employed farmers, 4 althoughÐas expectedÐthe
change was greatest among employers/owners.
The upshot is that in 1996, 41% of households
depending on agriculture had their residences
in urban areas, a share much greater than the
31% reported in 1990. Our hypothesis is that,
this change is due to improvements in rural
roads.
Table 1 also shows that agricultural income
stayed at about the same level over 1990±96,
but that this is a result of a reduction in agricultural income among rural households and
an increase among urban households. This
occurred because of the change of residence
discussed above, but also, more fundamentally, because the households that shifted to
the towns and cities were households with
greater incomes, in all occupational categories. The average monthly income of households whose principal income comes from
agriculture did not vary signi®cantly over
1990±96. This average, however, masks a
sharp drop in the monthly income of those
who maintained their rural residence (in particular owners and employers with rural residence, whose incomes fell nearly 7% per year
over the period), and an increase in the average monthly incomes of those who migrated
to urban centers, especially in the category of
small producers (the incomes of whom increased at nearly 9.5% per year) (MIDEPLAN, 1998).
The reduction in the number of rural
households with members employed in agriculture, hunting, and ®shing, occurred in all
regions of the country, with the exception of
the Metropolitan Region (around the capital,
Santiago) and the region of Bõo Bõo. That is to
say, it would appear that the process of urbanization of the households of agricultural
workers is a generalized phenomenon occurring
in most of the country.
414
WORLD DEVELOPMENT
Table 1. Farm employment and income
a
Total monthly income Ch$a
Households employed
in agriculture
Households
1990
1996
1996/1990
1990
1996
1996/1990
Rural
Self-employed
Wage workers
Owners and employers
131,110
259,399
17,194
113,569
222,512
11,454
0.87
0.86
0.66
24,128
28,440
19,601
19,735
24,556
8,153
0.82
0.86
0.42
Total
387,037
331,000
0.85
72,169
52,444
0.73
Urban
Self-employed
Wage workers
Owners and employers
31,451
132,527
8,519
46,201
178,623
12,099
1.47
1.35
1.42
6,845
18,600
13,771
15,806
30,689
13,108
2.31
1.65
0.95
Total
169,974
233,194
1.37
39,216
59,602
1.52
Total national
Self-employed
Wage workers
Owners and employers
162,561
391,926
25,713
159,770
401,135
23,553
0.98
1.02
0.92
30,973
47,040
33,372
35,541
52,245
21,261
1.14
1.17
0.64
Total
557,011
564,194
1.02
111,385
112,046
1.01
Chilean Pesos of March 1999106 . US$1 483.3 Chilean Pesos of March 1999.
(b) Rural employment and incomes
These trends oset the decline in agricultural
employment and incomes of rural households
during the period, implying an increase in the
weight of RNFE and RNF incomes in the total
income of rural households, with the result that
in 1996 nonfarm sources constituted 41% of
incomes and 39% of the employment of rural
households, ®gures that are in the range of
those estimated by Reardon et al. (1998) and
Berdegue et al. (2000) as averages for Latin
America.
During 1990±96, RNF income in Chile grew
due to an increase in the number of rural inhabitants working in manufactures and services, as well as to an increase in the average
monthly income of those employed in those
sectors. The number of rural households with
members whose principal income comes from
RNFE increased 10% over 1990±96, coming to
account for nearly 40% of rural households in
1996 (see Table 2). Moreover, the average
monthly income generated by RNFE increased
7% during the same period. These two trends
combined to produce an increase of 18% in
RNF income during that period (MIDEPLAN,
1999a).
(c) Evolution of rural nonfarm incomes by
subsector and occupation category
In 1996, commerce was the main subsector of
the RNF economy, constituting 24% of RNF
Table 2. Nonfarm employment and income
Households
Rural households
1990
Main
ment
Main
ment
Total
a
employ farm
employ nonfarm
Household average monthly
income Ch$a
1996
1996/
1990
1990
1996
1996/
1990
Number
Percentage
Number
Percentage
387,037
78
331,000
74
0.86
186,466
158,438
0.85
161,072
32
177,332
39
1.10
192,719
205,891
1.07
496,616
100
449,075
100
0.91
208,247
198,084
0.9578
Chilean Pesos of March 1999106 . US$1 483.3 Chilean Pesos of March 1999.
CHILE
415
incomes. Manufactures represented 17% of
RNF incomes, although this was below its
contribution to RNF incomes of 23% in 1990.
By contrast, construction increased substantially its share of RNF incomes from 8% in
1990 to 12% in 1996.
Table 3 shows that during 1990±96, there was
an increase in the number of households in all
the occupational categories of RNFE, with the
exception of the self-employed. Nevertheless,
the latter, along with domestic service workers,
experienced a substantial increase in their average monthly incomes, as did the other occupational categories with the exception of
owners and employers, who suered monthly
income declines (MIDEPLAN, 1999b).
households, there is relative specialization. Incomes from secondary occupations of household members contributed at most 2% of
overall household incomes in 1996, which is not
sucient to aect our main conclusions regarding multiactivity.
This ®nding diers from those in other
countries of Latin America. The dierences are
probably due to the fact that Chilean rural
households are relatively small (4.2 members
on average) and to the fact that rural Chile had
nearly full employment during 1990±96 which
in principle would facilitate year-long work in
the sector and activity to which a given worker
is best suited.
(d) Multiactivity of rural households
4. COMUNA (MUNICIPAL) LEVEL
RESULTS
Based on how many rural households had
members working in the various categories of
farm and nonfarm employment, we calculated
that in 1996: (i) 5% of the households had
members working in dierent categories of
principal employment within agriculture,
hunting and ®shing (for example, households
with one self-employed agriculturalist and one
farm wage-laborer); (ii) 9% of the households
had members working in dierent categories of
principal employment within the nonfarm sector; (iii) 6% of households with one or more
members working in the farm sector and one or
more working in the nonfarm sector. Thus,
overall, 20% of the households can be considered to be ``multiactive'' in 1996 by the above
de®nitions. The rate in 1990 was 17%.
This suggests that, with respect to the principal occupation of members of rural Chilean
The patterns discussed above at the aggregate level can now be explored in more detail
using the municipal level surveys in Molina
(illustrative of zones with a dynamic agriculture
and relatively low levels of poverty) and Portezuelo (illustrative of zones with traditional
agriculture and relatively high levels of rural
poverty).
The comuna of Molina is in the province of
Curic
o, and is part of the irrigated valley
of Region VII ``del Maule.'' Sixty-eight percent
of the population is rural according to the
Demographic Census of 1992 (INE, 1992).
The city of Molina (17,301 inhabitants) is on
the main highway of Chile, and is near a relatively large city (Curic
o). In the comuna of
Molina, there are four other urban centers.
According to the Agricultural Census of 1997,
Table 3. Evolution of nonfarm employment and income, by employment category
Nonfarm employment
categories of rural
households
a
1990
1996
Number
of
households
Total
monthly
income
Ch$a
% Of total
rural (farm and
nonfarm)
Number
of
households
Total
monthly
income
Ch$a
% Of total
rural (farm and
nonfarm)
Nonfarm self-employed
Nonfarm wage worker
Nonfarm owner or employer
Armed forces and police
Domestic services
Nonfarm unclassi®ed
48,301
104,778
3,149
7,690
17,341
5,122
7
17
5
44,168
121,240
4,901
9,058
20,948
4,511
10.2
23.5
5.1
1,050
13,490
67
228
647
14
0
1
0
2,137
21,766
±
398
1,597
±
0.4
1.8
±
Rural nonfarm total
161,072
31,042
30
177,332
36,511
41.0
6
Chilean Pesos of March 1999 10 . US$1 483.3 Chilean Pesos of March 1999.
416
WORLD DEVELOPMENT
in the cropping patterns in Molina, substantial
shares of cropland are under wine grapes
(18.1%), fruit trees (18.9%) and vegetables
(6.5%). Farmland is extremely concentrated: of
the 868 farms in Molina, 20% of them ®gure
among the smallest and occupy 0.1% of farmland, while the 5% largest farms occupy 88% of
the farmland; the Gini coecient for landholdings in Molina is 0.76. This is more concentrated than the average for Region VII. This
concentration is the result of a process of
transfer of land distributed by the Agrarian
Reform to producer associations and commercial producers. According to our data, 8%
of rural households in Molina are extremely
poor, 15% are poor, 77% are not poor, a situation which places this comuna at a socioeconomic development level above the national
rural average, even though the rate of extreme
poverty is actually the same as the national
rural average.
The comuna of Portezuelo is located in the
~
province of Nuble,
in Region VIII ``del BõÂo
BõÂo,'' in the agro-ecological zone known as the
``Interior Rainfed Zone,'' the agricultural potential of which is very inferior to that of the
irrigated Central Valley where Molina is located. Seventy-®ve percent of the population is
rural, and there is only one urban center, that
of the town of Portezuelo (1,464 inhabitants).
The closest city is Chill
an, distant 35 km. Of
cultivated land, 30.6% is under rainfed vineyards of traditional varieties that have lost
much of their markets due to an increase in
vineyards to the north; only 0.5% of the farmland is under vegetables and 1.3% is under fruit
trees. The Gini coecient of landholding is
0.61. According to our surveys, 38% of the
rural households in Portezuelo are extremely
poor, 31% are poor, and only 31% are not
poor, which places the comuna well below the
national rural average.
At ®rst glance, the dierence between Molina
and Portezuelo in terms of population and
proximity to urban centers, can be considered
an essential dierence for our purposes. Nevertheless, Table 4 shows that the dierences
between them are not so important that they
negate the fact that half of the RNF jobs of
rural households take place in rural areas, and
the other half in urban centers. In addition,
households in Molina and in Portezuelo are
relatively homogeneous in terms of family size
and gender, age, and schooling characteristics.
Almost all the rural households in Portezuelo
have access to land, with an average of 6.0 ha/
Table 4. Location of nonfarm activities of rural households
Activity takes
place in
Urban locality
Rural locality
Total
Percentage of rural households
Molina
Portezuelo
50
50
47
53
100
100
household, of rainfed cropland, much of it
hillside. Less than half of the households in
Molina have access to land, with an average of
2.0 ha, though with irrigation and under intensive cropping.
The landed households of Molina are mainly
engaged in production of vegetables and ¯owers, while those in Portezuelo concentrate on
rainfed vineyards and staples (mainly wheat).
Note that the great majority of landed rural
households in Molina do not grow wine grapes
of high quality, nor do they grow fruit, which,
while being principal crops of the comuna, have
high entry barriers for small farmers.
There are also signi®cant dierences between
Molina and Portezuelo in terms of physical
capital of the households. The households of
Molina tend to have more buildings, but there
are no satistically signi®cant dierences between
them and those of Portezuelo in terms of agricultural machinery and equipment. This is possible because half of the households in Molina
are landless, and by extension would not have
farm machinery. Moreover, relatively more
households in Portezuelo have their own home,
which surely re¯ects the fact that many families
are recent immigrants to Molina, as was the case
in many other comunas enjoying the Chilean
fruit production boom (Rivera & Cruz, 1984).
Finally, Molina residents have access to
better roads than those of Portezuelo, and in
the latter there is absolutely no paved road in
all its breadth and width.
(a) The income composition of rural households
in Molina y Potezuelo
Table 5 shows that the landed households of
rural Molina have higher incomes than the
Molina landless, and both higher than the
households of Portezuelo. The landless of rural
Molina rely the most on nonfarm incomes,
followed by the households of Portezuelo, and
least reliant on nonfarm incomes are the landed
households of Molina. Thus, the degree of reliance on nonfarm incomes, measured by the
CHILE
417
Table 5. Income composition of rural households in Molina and Portezuelo
Income
Ch$a
% Of
earned
% Of
total
Farm, selfemployed
Farm, wage
worker
1,860,709
55
49
591,900
18
Farm, total
Nonfarm,
self
employed
Nonfarm,
wage worker
2,452,609
507,760
Nonfarm,
total
Total earned
Total
non-earned
Total
household
a
Molina landed households
Molina landless
Ch$a
Portezuelo
% Of
earned
% Of
total
Ch$a
% Of
earned
% Of
total
70,450
5
4
275,875
36
24
16
780,395
51
40
175,043
23
15
73
15
65
13
850,945
208,521
56
14
44
11
450,918
139,213
59
18
39
12
396,600
12
10
467,513
31
24
168,367
23
14
904,360
27
23
676,034
44
35
307,580
41
26
3,356,969
100
88
1,526,969
100
78
758,498
100
65
462,285
12
425,066
22
414,061
35
3,819,254
100
1,952,035
100
1,172,559
100
Chilean Pesos of March 1999 106 . US$1 483.3 Chilean Pesos of March 1999.
share of nonfarm income in overall household
income, is determined broadly by access to land
and local labor market opportunities.
Within Portezuelo, the degree of reliance of
households on nonfarm incomes is conditioned
by access to nonearned income (with ``earning''
de®ned as receiving income from work of the
household members in the period of observation, the year); an example of nonearned income is a transfer from the government. As a
share of earned income only, nonfarm income
constitutes 41%, while it is only 26% of overall
income (earned plus nonearned), that is, a 15%
spread. Contrast this with the case of the
landed households of Molina, where there is
only a 4% spread, and only a 9% spread for the
landless of Molina. Hence in the richer Molina,
earned income is a higher share of total income
than in the poorer Portezuelo.
The better paid nonfarm jobs are concentrated in the more dynamic zones and undertaken by the richer households, in particular
the landed. This supports what Reardon, Cruz,
and Berdegue (1998) call the ``meso paradox''
of RNFE: the least dynamic zones have the
greatest need for and rely more on RNFE, but
have fewer opportunities to generate such incomes. The data also support their ``micro
paradox'' whereby the poorest rural households, in a given zone, are most need of RNFE
opportunities but have less capacity to have
access to them. These results are important for
rural development policies and combating
poverty: it is not clear that RNFE is a strong
lever for development in poor zones and
households. Policies promoting RNFE will
confront the same sorts of challenges among
poor zones and households that confront agricultural development policies.
The main reason for this is that in the dynamic zones such as Molina there are greater
employment opportunities than in the poorer
zones such as Portezuelo. Note that the members of the average sample household in Molina
work 367 days/year while in Portezuelo the
®gure is only 157. Yet it is important to note
the contribution of RNF incomes to family
incomes in Portezuelo: without nonfarm incomes, the average rural household income
would be below the ocial poverty line and just
18% above the extreme poverty line. Among
the landless of Molina, without nonfarm incomes their average household income would
be slightly below the ocial poverty line.
In sum, RNF employment and incomes are
indispensable to reduce poverty levels, especially in the case of the poorer zones and
households and the landless, but the greatest
potential as a development instrument is in the
richer zones and households.
418
WORLD DEVELOPMENT
(b) Multiactivity in the rural households of
Molina and Portezuelo
As an indicator of the level of multiactivity of
households we calculated the percentage of
households whose members had two or more
distinct jobs that together generated at least
80% of their earned income. A household is
``specialized in employment'' if 80% or more of
its earned income comes from a single type of
employment.
By these criteria, 37% of the rural households
in Portezuelo and 30% in Molina are multiactive. These percentages are greater than the
17% calculated using the 1996 CASEN data at
the countrywide level. We do not know if the
dierence is due to their having been an increase in multiactivity during 1996±99 (as there
had been during 1990±96), and/or whether
these comunas have greater levels of multiactivity than others in the country, or whether the
dierence is simply due to methodological differences between the two surveys.
Given the comuna, multiactivity increases
with household income. In Molina there is no
extremely poor household that is multiactive,
while 18% of poor households and 33% of
nonpoor households are multiactive. In Portezuelo, 32% of the extremely poor households,
52% of the poor households, and 56% of the
nonpoor households are multiactive. Hence as
in Nicaragua (see Corral & Readon, 2001),
multiactivity is a ``superior good.''
Table 6 shows that poverty conditions access
to nonfarm jobs, with nonfarm incomes rising
with household income. The main policy implication is that, as nonfarm income is as unequally distributed as farm income, one cannot
be an alternative to the other for the poor. In
other words, the poor who lack farm incomes
cannot easily compensate for that lack with
nonfarm income. Our hypothesis is that multiactivity at the household level requires previous access to physical, human, ®nancial, social,
or natural capital. The lower the endowment of
these capital assets, the fewer options are
available to households to undertake nonfarm
employment.
(c) Types of RNFE in Molina and Portezuelo
Linkages between farm and rural nonfarm
activities are greater in the poorer zone, Portezuelo. In Molina, only 22.1% of RNF income
is linked directly (in production linkages) to
agriculture, such as agro-processing, 5 versus
56.5% in Portezuelo. It appears that the economy of the richer comuna oers greater
opportunity for wage employment and selfemployment in nonfarm activities not directly
linked to agriculture; of course, such nonfarm
activities might be indirectly linked via consumption linkages, hence, spurred by eective
demand from incomes arising in the commercial farm sector. In the poorer zone, the weight
of agriculture is much greater and there are few
activities that can develop independent of it.
Moreover, neither in Molina nor in Portezuelo do more than half of rural households
undertake their nonfarm activity in the rural
area per se (and in the rural area, mostly in
their own residences). The other half are undertaken in urban areas by the rural residents,
as shown in Table 4. This is an important
®nding, as it contradicts the conventional wisdom that having a job in urban areas requires
that a rural resident leave the countryside and
move to the town or city. This point can be
added to the other complication discussed
above, that there are also many urban households who work in agriculture in rural areas.
These two complications blur the boundary
between urban and rural and leads to the concept of the emergence of ``rur-urban space.''
In both comunas, returns to labor increase
the further is the nonfarm activity from being
production-linked with agriculture: nonfarm
jobs production-linked with agriculture such as
agro-processing pay only 33±43% of the returns
to nonfarm activities not thus linked. More-
Table 6. Rural poverty and multiactivity (% of households in each poverty class)
Household condition
according to per capita
income of its members
Extremely poor
Poor
Not poor
Molina (dynamic municipality)
Specialized households
Farm
Nonfarm
67
82
32
33
0
35
Multiactive
households
0
18
33
Portezuelo (poor municipality)
Specialized households
Farm
Nonfarm
62
38
36
6
10
8
Multiactive
households
32
52
56
CHILE
over, the rural nonfarm activities undertake in
rural areas pay only 64±70% of the returns to
employment of rural household members in
nonfarm activities in urban centers; the latter
jobs provide substantial income ¯ows to rural
households.
There are no big dierences between the
comunas in rankings of nonfarm wage-employment activities in terms of structure of
employment. Most of the rural individuals who
have nonfarm wage employment work in the
private services sector outside of construction.
Construction is the second source of nonfarm
wage income. Between these two is concentrated 63±70% of persons undertaking nonfarm
wage employment. In third place, but well below the others, is employment in the public
sector.
As for nonfarm self-employment, this time
there are large dierences between the comunas.
In Molina, commerce in farm outputs and inputs is by far the most important enterprise
activity. In second place is machinery repair. In
Portezuelo, 70% of nonfarm self-employment
income is concentrated in small-scale manufactures using agricultural raw material, in
particular agro-processing (winemaking).
(d) The relation between household and
individual characteristics and RNF employment
and incomes
We examined participation in RNFE as a
function of gender, education, and total income
position relative to the poverty line. Women's
participation in the farm and nonfarm wagelabor markets is roughly in the same proportions. The type of RNFE undertaken by men
diers from that undertaken by women.
Women dominate commerce and other services, while men dominate manufactures. The
gender dierence with respect to nonfarm employment production-linked to the farm sector
and/or taking place in the rural area depends
on local labor market conditions and prevailing
agricultural systems. Returns to labor are also
in¯uenced by the worker's gender: women earn
more than men in nonfarm wage employment
(in Molina, $11.3/day versus $10.3/day; in
Portezuelo, $11.0/day versus $8.9/day), but
women earn less than men in farm wage employment (in Molina, $7.3/day versus $8.8/day;
in Portezuelo, $5.7/day versus $6.4/day) and in
nonfarm self-employment (in Molina, $5.6/day
versus $10.6/day; in Portezuelo, $9.0/day versus
$21.1/day).
419
In both comunas, women work fewer days (in
both sectors) per year than men (107 days/year
for women in Molina, versus 245 days/year for
men. In Portezuelo, the ®gures are 44 days/year
for women and 82 for men). Note that in both
comunas almost half of adult women are not in
the labor market. Nevertheless, Molina women
work 143% more days per year than do those of
Portezuelo, while Molina men work 200% more
days per year than do their peers in Portezuelo.
Nonfarm jobs production-linked with the
farm sector are dominated (63%) by women in
Molina but by men (65%) in Portezuelo.
In Molina, nonfarm employment taking place
in urban centers is dominated by men (64%),
but in Portezuelo, women dominate these jobs
(65%) while men stay at home and work on the
farm and in small-scale manufactures enterprises using farm products as inputs (71% of
those jobs are undertaken by men). In both
comunas, manufacture sector jobs are dominated by men (76% in Molina and 79% in
Portezuelo), while women dominate services
(60% in Molina and 59% in Portezuelo).
The upshot of these ®ndings for policies to
improve women's access to nonfarm jobs is
two-fold: (i) measures that eliminate barriers to
participation of women to labor markets in
general will also be useful in improving women's access to RNFE; (ii) that many programs
striving to increase nonfarm self-employment
of rural women (such as in small-scale manufactures enterprises) might be driving women to
enter precisely the types of nonfarm jobs that
have lower pay relative to men; by contrast,
women appear to have advantages when they
undertake wage employment in commerce or
other services or manufactures.
Education has a clear impact on access to
nonfarm jobs, but one should note that the
impact is greater in the richer comuna (Molina)
than in the poorer one (Portezuelo). Between
nonfarm self-employment and wage employment, the persons in the latter have higher education. Farm wage employment is the domain
of the least educated, wherein half to two-thirds
of the workers do not even have a primary
school education.
It is interesting to note that the more-educated workers in Portezuelo tend to be undertaking the tasks done by the less-educated in
Molina. This suggests that the return to a year
of education is not the same in a poor zone as
in a rich one, and that in Molina there are
better opportunities for those with more education.
420
WORLD DEVELOPMENT
5. DETERMINANTS OF RNF INCOMES
Table 7 shows the results of Probit and ordinary least squares (OLS) regressions, linked
using the two-stage Heckman procedure to
control for selectivity bias. The regressions estimate the determinants of the probability of
access to and the level of nonfarm income. The
types of nonfarm income are treated: the total
nonfarm income of the household, nonfarm
self-employment income, and nonfarm wageemployment income. As noted above, the results are speci®c to the case study areas, but
illustrative.
The underlying conceptual model is that the
above dependent variables are functions of the
incentives oered by the economic context
(proxied in our regressions by variables indicating the comuna itself as well as the road
network); and the capacity of households to
respond to those incentives, which in turn
depends on the households assets, including
human capital (age, gender, and education),
physical capital (farmland, access to irrigation,
vehicles, and equipment), social capital (participation in rural economic organizations),
and access to external ®nancial capital (access
to credits and government transfers). Households living in a more favorable economic
context and with more assets will have greater
access to nonfarm jobs and will earn more in
them than households in the opposite situation.
(a) Determinants of participation
The results concerning the probability of
participation in some sort of nonfarm income
generation, abstracting from the levels of nonfarm income earned, are shown in Table 7.
Human capital (gender of the household head,
average age of the married couple, and average
education of the household members older than
15) are statistically signi®cant determinants for
all three dependent variable categories (total
nonfarm, nonfarm wage-income, and nonfarm
self-employment income).
The negative sign of the coecient on the
gender of household head variable indicates
that households headed by women have a
greater probability of earning nonfarm incomes. Households of older couples and those
households with more education also have a
greater probability of earning nonfarm incomes
of both types.
Holdings of vehicles, equipment and machines have a positive eect on the probability
of earning nonfarm income in general, and
from self-employment in particular. The coef®cient is, however, negative for nonfarm wage
employment.
Access to farm credit has a positive eect on
the household's undertaking nonfarm self-employment. Farm households that have access to
more funds use them (or other funds freed by
having the farm credit) at least partly to diversify their incomes.
After controlling for comuna, neither roads,
economic organization participation, nor
landholdings, irrigation, or government transfers, drive households' participation in nonfarm
income generation.
Whether the household is located in the
richer comuna does not signi®cantly aect the
probability of earning nonfarm income in
general. But, as expected from the descriptions
of patterns above, the eect of Portezuelo on
earning nonfarm self-employment income was
signi®cant. Recall that many Portezuelo
households are winemakers.
(b) Determinants of levels
In contrast with the results concerning the
determinants of participation where the zone
and location variables did not have much effect, here, in the determinants of nonfarm income levels, those variables are important. The
most important variable determining total
nonfarm earnings is the location of the
household. Households in Molina earn more
nonfarm income than those in the poorer
Portezuelo.
The regressions show that households near
very poor roads earn more nonfarm income
from self-employment. This ®nding is driven by
the fact that many households in Portezuelo
live near poor roads and produce cheap wine.
The very inferiority of their roads ``protects''
(in a trade sense) the traditional agro-industry
activities of these hinterland households, as
well as increases transaction costs to households to participate in better-paying work further a®eld or to invest in higher quality wine
the pro®table production and sale of which
requires easier contact with the market.
Only in the case of farm wage-income levels
do household assets ®gure signi®cantly; in
particular, those with more farm land work less
on others' farms.
Table 7. Determinants of access to and levels of rural nonfarm incomea
Independent
variables
Probit models: dependent variable access to RNF income
(No RNF income 0; Yes RNF income 1)
Total RNF income
Self-employment
RNF income
Salaried RNF
income
Total RNF income
Self-employment
RNF income
Salaried RNF
income
McFadden's
R2 0.10
McFadden's
R2 0.10
McFadden's
R2 0.13
R2 0.39
R2 0.37
R2 0.42
Est. b
)0.68
0.02
0.01
0.11
)0.38
)0.01
0.00
0.10
0.28
)0.30
)0.20
0.92
)0.12
0.22
)0.14
)0.00
)0.90
t
)2.72
2.91
0.23
2.91
)1.01
)0.43
1.96
1.38
1.57
)1.35
)0.74
1.70
)0.41
0.81
)0.51
)0.86
)1.97
Est. b
)0.54
0.01
)0.08
0.06
0.21
0.00
0.00
0.07
0.43
)0.01
)0.43
0.40
)0.25
0.16
)0.22
)0.00
)0.86
t
)2.14
2.07
)1.23
1.85
0.55
0.05
2.44
1.20
2.41
)0.45
)1.81
0.79
)0.81
0.59
)0.77
)0.57
)1.87
Est. b
)0.58
)0.02
)0.10
0.15
)0.82
0.00
)0.00
)0.01
)0.08
)0.15
0.35
0.38
)0.36
0.07
)0.16
0.00
)1.91
t
)2.13
2.08
1.56
4.02
)1.88
0.17
)0.68
)0.24
)0.45
)0.68
1.45
0.79
)1.16
0.24
)0.53
0.99
)3.79
b
)2.40
0.54
)0.02
0.12
0.06
0.12
)0.00
0.00
)0.13
)0.46
0.03
1.09
)0.38
0.18
0.17
0.84
0.00
13.58
t
)0.94
0.49
)0.57
1.03
0.38
0.14
)0.09
0.36
)1.10
)0.87
0.05
2.13
)0.28
0.31
0.29
1.42
0.58
3.97
b
)0.54
)0.62
)0.00
)0.01
0.12
0.55
0.00
0.00
)0.06
)0.23
)0.41
0.88
1.21
1.19
1.00
1.66
)0.00
11.78
t
)0.16
)0.49
)0.22
)0.44
0.83
0.78
0.21
0.56
)0.43
)0.23
)0.97
0.84
1.01
1.44
1.65
2.19
)0.85
2.63
b
0.84
)0.61
0.04
0.35
0.23
)0.06
)0.03
0.00
)0.04
)0.06
)0.07
0.59
)0.11
)0.64
)0.26
)0.48
0.00
8.16
t
0.25
)0.45
1.13
1.38
0.65
)0.03
)1.85
1.09
)0.39
)0.20
)0.18
0.72
)0.11
)0.66
)0.65
)0.77
0.04
1.13
CHILE
1. Lambda
2. Sex
3. Age
4. Number
5. Schooling
6. Irrigation
7. Land
8. Equipment
9. Distance
10. Credit
11. Organization
12. Municipality
13. Paved road
14. Gravel road
15. Good dirt road
16. Bad dirt road
17. Subsidies
18. Constant
OLS models: dependent variable logn of RNF income
a
Independent variables: 1 Lambda, 2 Sex of head of household (Female 0, Male 1), 3 Age of heads of household, 4 Number of economically active members
of household, 5 Average schooling of members of household 15 years of age or older, 6 Percentage of total farm land with irrigation, 7 Total farm area (hectares),
8 Total value of vehicles, tools, and machinery, 9 Distance to nearest town (km), 10 Access to credit (0 no, 1 yes), 11 Membership in farmers' economic
organization (0 no, 1 yes), 12 Municipality (0 Portezuelo, 1 Molina), 13 Paved road (1 yes, 0 other), 14 Gravel road (1 yes, 0 other), 15 Dirt road,
can be used throughout the year (1 yes, 0 other), 16 Dirt road in bad condition (1 yes, 0 other), 17 Income from public subsidies, 18 Constant.
*
Statistically signi®cant at 10% level.
**
Statistically signi®cant at 5% level.
***
Statistically signi®cant at 10% level.
421
422
WORLD DEVELOPMENT
6. CONCLUSIONS AND
RECOMMENDATIONS
It was once held that rural±urban migrants
are among the poorest, and that by their
migration they, thus, abandon farm wage labor; but our nationwide results ¯y in the face
of these theories. In today's Chile, at least in
the intermediate cities and small towns and in
zones with good roads, many small farmers,
farmworkers and commercial farm owners/
managers have migrated to urban centers, but
they are neither the poorest nor have they
abandoned the farm sector. The urbanization
of the residence of persons who remain employed in agriculture has transferred to rural
areas a phenomenon noted for some time in
the great cities: the spatial segregation of rich
and poor. Nevertheless, seeking to reverse the
urbanization of agriculturalists' residence
would be counterproductive as it implies the
improvement of living conditions for thousands of farm wage-workers and small farmers.
Moreover, we have shown that many rural
households are working outside the farm sector, in nonfarm wage and self-employment. In
fact, these nonfarm sources contribute 41% of
the total income of rural households in Chile. It
is critical to design and reinforce policies that
facilitate that development of these kinds of
employment. In particular, investments in rural
education and policies that ease households'
access to credit and equipment/machinery
would improve rural households capacity to
under nonfarm activities.
An important implication of our ®ndings is
that RNF employment promotion should be
designed with special consideration for femaleheaded households, as they tend to depend
more on such employment. Such programs
should be primarily geared to preparing women
for wage employment in the services or manufactures subsectors, with only secondary attention to what has been the traditional focus
of nonfarm development programs, self-employment in microenterprises. This is because
our results have shown that women have access
to and earn more than men in wage employment as compared to farm labor or in selfemployment.
Policies and programs promoting nonfarm
employment should dier by zone and socioeconomic group, because the motives and situations of households in undertaking such
employment vary greatly. On the one hand,
the nonfarm income share in total income
might be high because nonfarm earnings are
relatively high. An example such as is Molina,
an agricultural boom area, where there is dynamic growth in the nonfarm economy. On
the other hand, the share might be high not
because the nonfarm economy is particularly
successful but merely because farm incomes
are weak and stagnant, such as in Portezuelo,
our case study in a hinterland zone with poor
traditional agriculture and weak infrastructure. Clearly, dierent policies are required to
promote equitable growth in the nonfarm
sector in rural Molina versus in rural Portezuelo.
In zones such as Molina, the growth of
RNFE derives from the dynamic growth of
the overall zone economy. Public sector actions can and should accompany, regulate, and
facilitate this development, but the fundamental dynamic arises from the market itself.
We found that in these situations, much
RNFE is not closely tied (``productionlinked'') to agriculture, such as cinemas and
restaurants, home construction, clothing
stores, banks, pharmacies, public oces, etc.
Of course, the original impetus for this growth
in Molina was dynamic commercial agriculture
and agro-industry, but in other zones with
dynamic economies one can ®nd other original
motors of growth such as tourism, mining,
proximity to a large city, etc. The origin of the
dynamism of RNFE is frequently outside the
countryside itself, although it is incontrovertible that a modern, competitive, and dynamic
agriculture requires and foments links between
itself and services and manufactures, and
generates incomes that are spent in those
subsectors of the nonfarm economy. Thus,
farm and nonfarm development are not exclusive alternatives, but rather can be mutually
reinforcing.
By contrast, in zones such as Portezuelo, one
cannot hope that the market will, by itself,
``endogenously'' create job opportunities in the
nonfarm sector, whether in wage or self-employment. Without strong action by governments there will not be rapid or equitable
growth in RNFE through development of its
component subsectorsÐmanufactures, commerce, other services. At variance with what
one might expect, we have seen that in zones
such as Portezuelo, nonfarm self-employment is
closely linked (in the production-linkages sense)
to the farm sector, and in particular, to small
and medium-scale farming. Promotion of
CHILE
nonfarm self-employment in small enterprises
needs then to be basedÐat least in an initial
phase in which capital is accumulated by
households that in turn diversify their incomes
outside of the farm sectorÐon the development
of small- and medium-scale farming. This requires government action, in particular, measures to develop the land market to help the
poor to obtain land through purchase or rental,
and to intensify production and increase productivity through irrigation, technical assistance, and credit.
Our results also contradicted the conventional wisdom that the poor and the landless
earn the lion's share of nonfarm incomes. It is
true that they tend to rely more on RNF incomes because of lack of farm incomes as in
Portezuelo in general, but they do not necessarily earn higher nonfarm incomes than richer
and landed households. In fact, the latter earn
more nonfarm incomes, either because they
can capitalize their enterprises with farm
pro®ts, or because they earn service sector
income by renting out or using as physical
capital their tractors and trucks, or because
farm incomes fed their family education investments that allowed their sons and daughters to obtain salaried employment in a local
business or to start their own businesses. In
zones such as Portezuelo, households with
more land earn more nonfarm income partly
because the latter is often linked to processing
farm products or selling farmers services and
other inputs.
For similar reasons, multiactivity (where a
household earns substantial income from more
than one source) is more prevalent in nonpoor
households. With more assets (physical, ®nancial, and human capital), a household has more
and better job opportunities in the nonfarm
sector. In either type of zone, however, multiactivity is less common than one wo
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)00102-9
Rural Nonfarm Employment and Incomes in Chile
JULIO A. BERDEGUEÂ
Red Internacional de Metodologõa de Investigaci
on de Sistemas de Producci
on
(RIMISP), Santiago, Chile
EDUARDO RAMIÂREZ
Ministerio de Plani®caci
on y Cooperaci
on, Santiago, Chile
THOMAS REARDON
Michigan State University, East Lansing, USA
and
ESCOBAR *
GERMAN
Red Internacional de Metodologõa de Investigaci
on de Sistemas de Producci
on
(RIMISP), Santiago, Chile
Summary. Ð This article analyzes the evolution of rural nonfarm employment (RNFE) and income
in Chile during 1990±96. The data used come from the National Socioeconomic Survey (CASEN),
and from a household survey undertaken by the authors in two municipalities in 1999. The latter
contrasted two zones, very dierent in terms of economic dynamism and rural poverty. We show
that during the period, RNFE and incomes increased 10% and 18%, respectively, in 1996, reaching
39% of rural employment and 41% of rural incomes. The rate of multiactivity (the share of
households participating in more than one sector) was only 20%, lower than expected, indicating a
tendency toward economic specialization in rural income strategies. The determinants of such
employment are mainly household characteristics, in particular variables related to human capital,
such as the age and gender of the household head, and the schooling of the household members,
although also important are access to credit and physical capital. The level of nonfarm income of
rural households is determined mainly by the economic context, in particular the economic level
and dynamism of the overall zone and the quality of the roads. It is proposed that policies to
develop RNFE should be geared to zone characteristics, and should in general favor investments in
education, in roads, and in access to credit. Moreover, households headed by women should be the
object of special attention. To promote such policies, it will be necessary to address important gaps
and weaknesses in the public institutional structure. Ó 2001 Elsevier Science Ltd. All rights
reserved.
Key words Ð Latin America, Chile, rural nonfarm employment, incomes, rural poverty,
development
* This
research is based on generous grants from the
Inter-American Development Bank (IADB) and the
United Nations Food and Agriculture Organization
(FAO). The authors thank Drs. Ruben EcheverrõÂa
(IADB), Gustavo Gordillo De Anda, Kostas Stamoulis, and Alexander Schejtman (FAO) for valuable
support and comments, and three anonymous peer
411
reviewers for useful comments. The authors also
acknowledge the support of the Planning and Cooperation Ministry of Chile (MIDEPLAN), that facilitated
access to the CASEN survey data. The authors
acknowledge the work of Ms. Ximena Milicevic in
the organization and analysis of the municipal-level
surveys.
412
WORLD DEVELOPMENT
1. INTRODUCTION
There is growing evidence that rural nonfarm
employment (RNFE) is an important source of
income for rural households in Latin American
and the Caribbean (LAC), including for the
landless and other poor rural groups (Berdegue, Reardon, & Escobar, 2000; Reardon,
Berdegue, & Escobar, 2001). Nevertheless, rural development policies, in particular those
aiming at rural poverty alleviation, generally
concentrate on agricultural development. After
many decades of rural development policies
based on the agricultural sector, it is now clear
that many rural zones and households are
®nding few opportunities in agriculture for
sustainable increase in incomes, in sucient
degree to substantially alleviate poverty (Berdegue, 2000).
Although the principal instruments of agricultural development in Chile directed at small
farmers have been successful in raising incomes, the impact of these interventions has
not been signi®cant in the poorest strata, and
indeed there has been little impact on incomes
of rural households not participating in ownfarming (Comite Interministerial de Desarrollo
Productivo, 1998). Thus, to reduce the poverty
that aects a large share of rural households in
Chile, the focus should be not only on smallscale agricultural production, but also on
employment and incomes in the nonfarm
sector.
RNFE can contribute to agricultural development by providing peasants with cash incomes that can be invested in improvements in
agricultural productivity. A substantial share of
rural nonfarm activity is concentrated in the
broad agrifood system (commerce in agricultural inputs and outputs, equipment service
provision, and so on). By this means it can increase the pro®tability of agriculture via the
better linking of agriculture to other sectors
and markets. In turn, the development of agriculture stimulates growth in commerce, industry, and other rural services. These farm±
nonfarm links are crucial for rural regional
development to be balanced, dynamic, and
sustainable (Banco Interamericano de Desarrollo (BID), 1998).
2. APPROACH
The research is based on two sources of information: (a) for the countrywide analysis, we
used data from the National Socioeconomic
Survey (CASEN) of the Ministry of Planning
and Cooperation (MIDEPLAN) for the years
1990 and 1996; (b) For the zone (``comuna'' or
municipality) level analysis, RIMISP (International Farming Systems Research Network in
Santiago, Chile) undertook a survey in two
comunas in March 1999. The comunas were
Portezuelo, to represent zones with extensive
rural poverty and a dearth of agricultural
modernization, and Molina, to represent situations of little rural poverty and rapid economic growth and agricultural modernization,
which in the case of Molina is in fresh fruit and,
in particular, vineyards and wineries of high
quality, oriented toward export markets.
The CASEN surveys provided data on the
socioeconomic conditions of the various socioeconomic groups in the country, problems in
their living and economic situations, the degree
and nature of their poverty, the distribution of
incomes over households, and the geographic
and socioeconomic strata coverage of social
programs and their contributions to monetary
and nonmonetary incomes of households (MIDEPLAN, 1996). The sampling and survey unit
is the residence, while the unit of analysis is the
household, whether a single person or several,
with or without family links among themselves,
who live in the same residence and have a
common food budget. Members of a household
are only the permanent residents of the residence, de®ned as not being absent more than
two months of the year (MIDEPLAN, 1990).
We used the CASEN surveys of 1990 and 1996.
We did not use the 1987 CASEN because of
various changes in methods and de®nitions
between that survey and the later ones, which
restricted comparison, nor did we use the 1998
survey because disaggregated data from that
survey were not available at the time of the
writing of this paper.
The 1990 sample comprised 25,793 households, of which 18,549 urban and 7,244 rural.
In 1996 the sample comprised 35,730 households of which 25,640 urban and 10,090 rural.
The sample in each case is nationally and regionally representative both for urban and rural areas, and the sampling error is 5% with a
con®dence interval of 95% (MIDEPLAN,
1990, 1996).
In 1990, CASEN de®ned rural as population
concentrations of less than 2,000 inhabitants.
In 1996, the cuto changed to 1,000 inhabitants
or 1,001±2,000 inhabitants involved mainly in
primary sector activities. In practice, only 85 of
CHILE
the 37,618 rural localities were aected by this
change in the classi®cation system. The survey
focuses on the location of the household to
determine whether it is rural, and does not
furnish data on the location of the households'
economic activities or whether they migrate or
commute to jobs in urban areas. The employment data indicate sector but not location, and
thus, RNFE refers to nonfarm jobs undertaken
in either urban or rural areas by rural households. Data limitations, therefore, restrict us
from useful analyses of job location and thus,
whether rural households commute or migrate
to urban jobs, or nonfarm jobs in rural areas
undertaken by urban households, or incomes of
households which are today urban but were
recently in rural areas. Moreover, the CASEN
survey generates employment information for
one month of the year, and thus, it is not possible to know whether the employment pro®le
changes over the year, which is of course important to ascertaining accurately the degree of
multiactivity of the household.
The study of RNFE in the comunas of
Portezuelo and Molina is not meant to be
representative in a statistical sense of the situation in all Chile. Rather, these are case studies
that are meant to be illustrative of dierent
situations of rural poverty, economic dynamism, and agricultural modernization, in order
to examine several themes and issues that cannot be studied using the national CASEN data.
The determination of the sample size for
Portezuelo was calculated using the two-step
method of Stein, using the variance and mean
of the incomes of rural households for the
rainfed agriculture zone of Region VIII based
on observations from a survey of 2,900 households in Chile of which 188 households in that
zone. The sample size for our survey in Portezuelo was 200 households. For Molina, the size
of the sample was limited for budgetary reasons
to 75 households, and thus, the sampling error
is higher for that comuna. In Portezuelo, the
200 households were distributed over 22 rural
localities (e.g., villages, small rural towns), in
proportion to the number of residences in the
localities. In Molina, we selected at random 18
of the 47 rural localities, and the number of
households per district was selected in proportion to the number of residences in the districts.
In each district, households were chosen at
random based on geographic sampling. It is
important to note that the observations on incomes and employment covered all households
and their members over the whole year.
413
3. COUNTRY-LEVEL RESULTS
(a) Agricultural incomes
Table 1 shows that during 1990±96 the
number of households whose principal income
was from agriculture, hunting, and ®shing 1
did not change signi®cantly. Nevertheless, urban households principally engaged in agriculture increased 37%, while rural households
thus, engaged fell 15%. 2 This change of residence of households thus, engaged 3 involved
all occupational categories in agriculture: employers/owners, wage-earners, and self-employed farmers, 4 althoughÐas expectedÐthe
change was greatest among employers/owners.
The upshot is that in 1996, 41% of households
depending on agriculture had their residences
in urban areas, a share much greater than the
31% reported in 1990. Our hypothesis is that,
this change is due to improvements in rural
roads.
Table 1 also shows that agricultural income
stayed at about the same level over 1990±96,
but that this is a result of a reduction in agricultural income among rural households and
an increase among urban households. This
occurred because of the change of residence
discussed above, but also, more fundamentally, because the households that shifted to
the towns and cities were households with
greater incomes, in all occupational categories. The average monthly income of households whose principal income comes from
agriculture did not vary signi®cantly over
1990±96. This average, however, masks a
sharp drop in the monthly income of those
who maintained their rural residence (in particular owners and employers with rural residence, whose incomes fell nearly 7% per year
over the period), and an increase in the average monthly incomes of those who migrated
to urban centers, especially in the category of
small producers (the incomes of whom increased at nearly 9.5% per year) (MIDEPLAN, 1998).
The reduction in the number of rural
households with members employed in agriculture, hunting, and ®shing, occurred in all
regions of the country, with the exception of
the Metropolitan Region (around the capital,
Santiago) and the region of Bõo Bõo. That is to
say, it would appear that the process of urbanization of the households of agricultural
workers is a generalized phenomenon occurring
in most of the country.
414
WORLD DEVELOPMENT
Table 1. Farm employment and income
a
Total monthly income Ch$a
Households employed
in agriculture
Households
1990
1996
1996/1990
1990
1996
1996/1990
Rural
Self-employed
Wage workers
Owners and employers
131,110
259,399
17,194
113,569
222,512
11,454
0.87
0.86
0.66
24,128
28,440
19,601
19,735
24,556
8,153
0.82
0.86
0.42
Total
387,037
331,000
0.85
72,169
52,444
0.73
Urban
Self-employed
Wage workers
Owners and employers
31,451
132,527
8,519
46,201
178,623
12,099
1.47
1.35
1.42
6,845
18,600
13,771
15,806
30,689
13,108
2.31
1.65
0.95
Total
169,974
233,194
1.37
39,216
59,602
1.52
Total national
Self-employed
Wage workers
Owners and employers
162,561
391,926
25,713
159,770
401,135
23,553
0.98
1.02
0.92
30,973
47,040
33,372
35,541
52,245
21,261
1.14
1.17
0.64
Total
557,011
564,194
1.02
111,385
112,046
1.01
Chilean Pesos of March 1999106 . US$1 483.3 Chilean Pesos of March 1999.
(b) Rural employment and incomes
These trends oset the decline in agricultural
employment and incomes of rural households
during the period, implying an increase in the
weight of RNFE and RNF incomes in the total
income of rural households, with the result that
in 1996 nonfarm sources constituted 41% of
incomes and 39% of the employment of rural
households, ®gures that are in the range of
those estimated by Reardon et al. (1998) and
Berdegue et al. (2000) as averages for Latin
America.
During 1990±96, RNF income in Chile grew
due to an increase in the number of rural inhabitants working in manufactures and services, as well as to an increase in the average
monthly income of those employed in those
sectors. The number of rural households with
members whose principal income comes from
RNFE increased 10% over 1990±96, coming to
account for nearly 40% of rural households in
1996 (see Table 2). Moreover, the average
monthly income generated by RNFE increased
7% during the same period. These two trends
combined to produce an increase of 18% in
RNF income during that period (MIDEPLAN,
1999a).
(c) Evolution of rural nonfarm incomes by
subsector and occupation category
In 1996, commerce was the main subsector of
the RNF economy, constituting 24% of RNF
Table 2. Nonfarm employment and income
Households
Rural households
1990
Main
ment
Main
ment
Total
a
employ farm
employ nonfarm
Household average monthly
income Ch$a
1996
1996/
1990
1990
1996
1996/
1990
Number
Percentage
Number
Percentage
387,037
78
331,000
74
0.86
186,466
158,438
0.85
161,072
32
177,332
39
1.10
192,719
205,891
1.07
496,616
100
449,075
100
0.91
208,247
198,084
0.9578
Chilean Pesos of March 1999106 . US$1 483.3 Chilean Pesos of March 1999.
CHILE
415
incomes. Manufactures represented 17% of
RNF incomes, although this was below its
contribution to RNF incomes of 23% in 1990.
By contrast, construction increased substantially its share of RNF incomes from 8% in
1990 to 12% in 1996.
Table 3 shows that during 1990±96, there was
an increase in the number of households in all
the occupational categories of RNFE, with the
exception of the self-employed. Nevertheless,
the latter, along with domestic service workers,
experienced a substantial increase in their average monthly incomes, as did the other occupational categories with the exception of
owners and employers, who suered monthly
income declines (MIDEPLAN, 1999b).
households, there is relative specialization. Incomes from secondary occupations of household members contributed at most 2% of
overall household incomes in 1996, which is not
sucient to aect our main conclusions regarding multiactivity.
This ®nding diers from those in other
countries of Latin America. The dierences are
probably due to the fact that Chilean rural
households are relatively small (4.2 members
on average) and to the fact that rural Chile had
nearly full employment during 1990±96 which
in principle would facilitate year-long work in
the sector and activity to which a given worker
is best suited.
(d) Multiactivity of rural households
4. COMUNA (MUNICIPAL) LEVEL
RESULTS
Based on how many rural households had
members working in the various categories of
farm and nonfarm employment, we calculated
that in 1996: (i) 5% of the households had
members working in dierent categories of
principal employment within agriculture,
hunting and ®shing (for example, households
with one self-employed agriculturalist and one
farm wage-laborer); (ii) 9% of the households
had members working in dierent categories of
principal employment within the nonfarm sector; (iii) 6% of households with one or more
members working in the farm sector and one or
more working in the nonfarm sector. Thus,
overall, 20% of the households can be considered to be ``multiactive'' in 1996 by the above
de®nitions. The rate in 1990 was 17%.
This suggests that, with respect to the principal occupation of members of rural Chilean
The patterns discussed above at the aggregate level can now be explored in more detail
using the municipal level surveys in Molina
(illustrative of zones with a dynamic agriculture
and relatively low levels of poverty) and Portezuelo (illustrative of zones with traditional
agriculture and relatively high levels of rural
poverty).
The comuna of Molina is in the province of
Curic
o, and is part of the irrigated valley
of Region VII ``del Maule.'' Sixty-eight percent
of the population is rural according to the
Demographic Census of 1992 (INE, 1992).
The city of Molina (17,301 inhabitants) is on
the main highway of Chile, and is near a relatively large city (Curic
o). In the comuna of
Molina, there are four other urban centers.
According to the Agricultural Census of 1997,
Table 3. Evolution of nonfarm employment and income, by employment category
Nonfarm employment
categories of rural
households
a
1990
1996
Number
of
households
Total
monthly
income
Ch$a
% Of total
rural (farm and
nonfarm)
Number
of
households
Total
monthly
income
Ch$a
% Of total
rural (farm and
nonfarm)
Nonfarm self-employed
Nonfarm wage worker
Nonfarm owner or employer
Armed forces and police
Domestic services
Nonfarm unclassi®ed
48,301
104,778
3,149
7,690
17,341
5,122
7
17
5
44,168
121,240
4,901
9,058
20,948
4,511
10.2
23.5
5.1
1,050
13,490
67
228
647
14
0
1
0
2,137
21,766
±
398
1,597
±
0.4
1.8
±
Rural nonfarm total
161,072
31,042
30
177,332
36,511
41.0
6
Chilean Pesos of March 1999 10 . US$1 483.3 Chilean Pesos of March 1999.
416
WORLD DEVELOPMENT
in the cropping patterns in Molina, substantial
shares of cropland are under wine grapes
(18.1%), fruit trees (18.9%) and vegetables
(6.5%). Farmland is extremely concentrated: of
the 868 farms in Molina, 20% of them ®gure
among the smallest and occupy 0.1% of farmland, while the 5% largest farms occupy 88% of
the farmland; the Gini coecient for landholdings in Molina is 0.76. This is more concentrated than the average for Region VII. This
concentration is the result of a process of
transfer of land distributed by the Agrarian
Reform to producer associations and commercial producers. According to our data, 8%
of rural households in Molina are extremely
poor, 15% are poor, 77% are not poor, a situation which places this comuna at a socioeconomic development level above the national
rural average, even though the rate of extreme
poverty is actually the same as the national
rural average.
The comuna of Portezuelo is located in the
~
province of Nuble,
in Region VIII ``del BõÂo
BõÂo,'' in the agro-ecological zone known as the
``Interior Rainfed Zone,'' the agricultural potential of which is very inferior to that of the
irrigated Central Valley where Molina is located. Seventy-®ve percent of the population is
rural, and there is only one urban center, that
of the town of Portezuelo (1,464 inhabitants).
The closest city is Chill
an, distant 35 km. Of
cultivated land, 30.6% is under rainfed vineyards of traditional varieties that have lost
much of their markets due to an increase in
vineyards to the north; only 0.5% of the farmland is under vegetables and 1.3% is under fruit
trees. The Gini coecient of landholding is
0.61. According to our surveys, 38% of the
rural households in Portezuelo are extremely
poor, 31% are poor, and only 31% are not
poor, which places the comuna well below the
national rural average.
At ®rst glance, the dierence between Molina
and Portezuelo in terms of population and
proximity to urban centers, can be considered
an essential dierence for our purposes. Nevertheless, Table 4 shows that the dierences
between them are not so important that they
negate the fact that half of the RNF jobs of
rural households take place in rural areas, and
the other half in urban centers. In addition,
households in Molina and in Portezuelo are
relatively homogeneous in terms of family size
and gender, age, and schooling characteristics.
Almost all the rural households in Portezuelo
have access to land, with an average of 6.0 ha/
Table 4. Location of nonfarm activities of rural households
Activity takes
place in
Urban locality
Rural locality
Total
Percentage of rural households
Molina
Portezuelo
50
50
47
53
100
100
household, of rainfed cropland, much of it
hillside. Less than half of the households in
Molina have access to land, with an average of
2.0 ha, though with irrigation and under intensive cropping.
The landed households of Molina are mainly
engaged in production of vegetables and ¯owers, while those in Portezuelo concentrate on
rainfed vineyards and staples (mainly wheat).
Note that the great majority of landed rural
households in Molina do not grow wine grapes
of high quality, nor do they grow fruit, which,
while being principal crops of the comuna, have
high entry barriers for small farmers.
There are also signi®cant dierences between
Molina and Portezuelo in terms of physical
capital of the households. The households of
Molina tend to have more buildings, but there
are no satistically signi®cant dierences between
them and those of Portezuelo in terms of agricultural machinery and equipment. This is possible because half of the households in Molina
are landless, and by extension would not have
farm machinery. Moreover, relatively more
households in Portezuelo have their own home,
which surely re¯ects the fact that many families
are recent immigrants to Molina, as was the case
in many other comunas enjoying the Chilean
fruit production boom (Rivera & Cruz, 1984).
Finally, Molina residents have access to
better roads than those of Portezuelo, and in
the latter there is absolutely no paved road in
all its breadth and width.
(a) The income composition of rural households
in Molina y Potezuelo
Table 5 shows that the landed households of
rural Molina have higher incomes than the
Molina landless, and both higher than the
households of Portezuelo. The landless of rural
Molina rely the most on nonfarm incomes,
followed by the households of Portezuelo, and
least reliant on nonfarm incomes are the landed
households of Molina. Thus, the degree of reliance on nonfarm incomes, measured by the
CHILE
417
Table 5. Income composition of rural households in Molina and Portezuelo
Income
Ch$a
% Of
earned
% Of
total
Farm, selfemployed
Farm, wage
worker
1,860,709
55
49
591,900
18
Farm, total
Nonfarm,
self
employed
Nonfarm,
wage worker
2,452,609
507,760
Nonfarm,
total
Total earned
Total
non-earned
Total
household
a
Molina landed households
Molina landless
Ch$a
Portezuelo
% Of
earned
% Of
total
Ch$a
% Of
earned
% Of
total
70,450
5
4
275,875
36
24
16
780,395
51
40
175,043
23
15
73
15
65
13
850,945
208,521
56
14
44
11
450,918
139,213
59
18
39
12
396,600
12
10
467,513
31
24
168,367
23
14
904,360
27
23
676,034
44
35
307,580
41
26
3,356,969
100
88
1,526,969
100
78
758,498
100
65
462,285
12
425,066
22
414,061
35
3,819,254
100
1,952,035
100
1,172,559
100
Chilean Pesos of March 1999 106 . US$1 483.3 Chilean Pesos of March 1999.
share of nonfarm income in overall household
income, is determined broadly by access to land
and local labor market opportunities.
Within Portezuelo, the degree of reliance of
households on nonfarm incomes is conditioned
by access to nonearned income (with ``earning''
de®ned as receiving income from work of the
household members in the period of observation, the year); an example of nonearned income is a transfer from the government. As a
share of earned income only, nonfarm income
constitutes 41%, while it is only 26% of overall
income (earned plus nonearned), that is, a 15%
spread. Contrast this with the case of the
landed households of Molina, where there is
only a 4% spread, and only a 9% spread for the
landless of Molina. Hence in the richer Molina,
earned income is a higher share of total income
than in the poorer Portezuelo.
The better paid nonfarm jobs are concentrated in the more dynamic zones and undertaken by the richer households, in particular
the landed. This supports what Reardon, Cruz,
and Berdegue (1998) call the ``meso paradox''
of RNFE: the least dynamic zones have the
greatest need for and rely more on RNFE, but
have fewer opportunities to generate such incomes. The data also support their ``micro
paradox'' whereby the poorest rural households, in a given zone, are most need of RNFE
opportunities but have less capacity to have
access to them. These results are important for
rural development policies and combating
poverty: it is not clear that RNFE is a strong
lever for development in poor zones and
households. Policies promoting RNFE will
confront the same sorts of challenges among
poor zones and households that confront agricultural development policies.
The main reason for this is that in the dynamic zones such as Molina there are greater
employment opportunities than in the poorer
zones such as Portezuelo. Note that the members of the average sample household in Molina
work 367 days/year while in Portezuelo the
®gure is only 157. Yet it is important to note
the contribution of RNF incomes to family
incomes in Portezuelo: without nonfarm incomes, the average rural household income
would be below the ocial poverty line and just
18% above the extreme poverty line. Among
the landless of Molina, without nonfarm incomes their average household income would
be slightly below the ocial poverty line.
In sum, RNF employment and incomes are
indispensable to reduce poverty levels, especially in the case of the poorer zones and
households and the landless, but the greatest
potential as a development instrument is in the
richer zones and households.
418
WORLD DEVELOPMENT
(b) Multiactivity in the rural households of
Molina and Portezuelo
As an indicator of the level of multiactivity of
households we calculated the percentage of
households whose members had two or more
distinct jobs that together generated at least
80% of their earned income. A household is
``specialized in employment'' if 80% or more of
its earned income comes from a single type of
employment.
By these criteria, 37% of the rural households
in Portezuelo and 30% in Molina are multiactive. These percentages are greater than the
17% calculated using the 1996 CASEN data at
the countrywide level. We do not know if the
dierence is due to their having been an increase in multiactivity during 1996±99 (as there
had been during 1990±96), and/or whether
these comunas have greater levels of multiactivity than others in the country, or whether the
dierence is simply due to methodological differences between the two surveys.
Given the comuna, multiactivity increases
with household income. In Molina there is no
extremely poor household that is multiactive,
while 18% of poor households and 33% of
nonpoor households are multiactive. In Portezuelo, 32% of the extremely poor households,
52% of the poor households, and 56% of the
nonpoor households are multiactive. Hence as
in Nicaragua (see Corral & Readon, 2001),
multiactivity is a ``superior good.''
Table 6 shows that poverty conditions access
to nonfarm jobs, with nonfarm incomes rising
with household income. The main policy implication is that, as nonfarm income is as unequally distributed as farm income, one cannot
be an alternative to the other for the poor. In
other words, the poor who lack farm incomes
cannot easily compensate for that lack with
nonfarm income. Our hypothesis is that multiactivity at the household level requires previous access to physical, human, ®nancial, social,
or natural capital. The lower the endowment of
these capital assets, the fewer options are
available to households to undertake nonfarm
employment.
(c) Types of RNFE in Molina and Portezuelo
Linkages between farm and rural nonfarm
activities are greater in the poorer zone, Portezuelo. In Molina, only 22.1% of RNF income
is linked directly (in production linkages) to
agriculture, such as agro-processing, 5 versus
56.5% in Portezuelo. It appears that the economy of the richer comuna oers greater
opportunity for wage employment and selfemployment in nonfarm activities not directly
linked to agriculture; of course, such nonfarm
activities might be indirectly linked via consumption linkages, hence, spurred by eective
demand from incomes arising in the commercial farm sector. In the poorer zone, the weight
of agriculture is much greater and there are few
activities that can develop independent of it.
Moreover, neither in Molina nor in Portezuelo do more than half of rural households
undertake their nonfarm activity in the rural
area per se (and in the rural area, mostly in
their own residences). The other half are undertaken in urban areas by the rural residents,
as shown in Table 4. This is an important
®nding, as it contradicts the conventional wisdom that having a job in urban areas requires
that a rural resident leave the countryside and
move to the town or city. This point can be
added to the other complication discussed
above, that there are also many urban households who work in agriculture in rural areas.
These two complications blur the boundary
between urban and rural and leads to the concept of the emergence of ``rur-urban space.''
In both comunas, returns to labor increase
the further is the nonfarm activity from being
production-linked with agriculture: nonfarm
jobs production-linked with agriculture such as
agro-processing pay only 33±43% of the returns
to nonfarm activities not thus linked. More-
Table 6. Rural poverty and multiactivity (% of households in each poverty class)
Household condition
according to per capita
income of its members
Extremely poor
Poor
Not poor
Molina (dynamic municipality)
Specialized households
Farm
Nonfarm
67
82
32
33
0
35
Multiactive
households
0
18
33
Portezuelo (poor municipality)
Specialized households
Farm
Nonfarm
62
38
36
6
10
8
Multiactive
households
32
52
56
CHILE
over, the rural nonfarm activities undertake in
rural areas pay only 64±70% of the returns to
employment of rural household members in
nonfarm activities in urban centers; the latter
jobs provide substantial income ¯ows to rural
households.
There are no big dierences between the
comunas in rankings of nonfarm wage-employment activities in terms of structure of
employment. Most of the rural individuals who
have nonfarm wage employment work in the
private services sector outside of construction.
Construction is the second source of nonfarm
wage income. Between these two is concentrated 63±70% of persons undertaking nonfarm
wage employment. In third place, but well below the others, is employment in the public
sector.
As for nonfarm self-employment, this time
there are large dierences between the comunas.
In Molina, commerce in farm outputs and inputs is by far the most important enterprise
activity. In second place is machinery repair. In
Portezuelo, 70% of nonfarm self-employment
income is concentrated in small-scale manufactures using agricultural raw material, in
particular agro-processing (winemaking).
(d) The relation between household and
individual characteristics and RNF employment
and incomes
We examined participation in RNFE as a
function of gender, education, and total income
position relative to the poverty line. Women's
participation in the farm and nonfarm wagelabor markets is roughly in the same proportions. The type of RNFE undertaken by men
diers from that undertaken by women.
Women dominate commerce and other services, while men dominate manufactures. The
gender dierence with respect to nonfarm employment production-linked to the farm sector
and/or taking place in the rural area depends
on local labor market conditions and prevailing
agricultural systems. Returns to labor are also
in¯uenced by the worker's gender: women earn
more than men in nonfarm wage employment
(in Molina, $11.3/day versus $10.3/day; in
Portezuelo, $11.0/day versus $8.9/day), but
women earn less than men in farm wage employment (in Molina, $7.3/day versus $8.8/day;
in Portezuelo, $5.7/day versus $6.4/day) and in
nonfarm self-employment (in Molina, $5.6/day
versus $10.6/day; in Portezuelo, $9.0/day versus
$21.1/day).
419
In both comunas, women work fewer days (in
both sectors) per year than men (107 days/year
for women in Molina, versus 245 days/year for
men. In Portezuelo, the ®gures are 44 days/year
for women and 82 for men). Note that in both
comunas almost half of adult women are not in
the labor market. Nevertheless, Molina women
work 143% more days per year than do those of
Portezuelo, while Molina men work 200% more
days per year than do their peers in Portezuelo.
Nonfarm jobs production-linked with the
farm sector are dominated (63%) by women in
Molina but by men (65%) in Portezuelo.
In Molina, nonfarm employment taking place
in urban centers is dominated by men (64%),
but in Portezuelo, women dominate these jobs
(65%) while men stay at home and work on the
farm and in small-scale manufactures enterprises using farm products as inputs (71% of
those jobs are undertaken by men). In both
comunas, manufacture sector jobs are dominated by men (76% in Molina and 79% in
Portezuelo), while women dominate services
(60% in Molina and 59% in Portezuelo).
The upshot of these ®ndings for policies to
improve women's access to nonfarm jobs is
two-fold: (i) measures that eliminate barriers to
participation of women to labor markets in
general will also be useful in improving women's access to RNFE; (ii) that many programs
striving to increase nonfarm self-employment
of rural women (such as in small-scale manufactures enterprises) might be driving women to
enter precisely the types of nonfarm jobs that
have lower pay relative to men; by contrast,
women appear to have advantages when they
undertake wage employment in commerce or
other services or manufactures.
Education has a clear impact on access to
nonfarm jobs, but one should note that the
impact is greater in the richer comuna (Molina)
than in the poorer one (Portezuelo). Between
nonfarm self-employment and wage employment, the persons in the latter have higher education. Farm wage employment is the domain
of the least educated, wherein half to two-thirds
of the workers do not even have a primary
school education.
It is interesting to note that the more-educated workers in Portezuelo tend to be undertaking the tasks done by the less-educated in
Molina. This suggests that the return to a year
of education is not the same in a poor zone as
in a rich one, and that in Molina there are
better opportunities for those with more education.
420
WORLD DEVELOPMENT
5. DETERMINANTS OF RNF INCOMES
Table 7 shows the results of Probit and ordinary least squares (OLS) regressions, linked
using the two-stage Heckman procedure to
control for selectivity bias. The regressions estimate the determinants of the probability of
access to and the level of nonfarm income. The
types of nonfarm income are treated: the total
nonfarm income of the household, nonfarm
self-employment income, and nonfarm wageemployment income. As noted above, the results are speci®c to the case study areas, but
illustrative.
The underlying conceptual model is that the
above dependent variables are functions of the
incentives oered by the economic context
(proxied in our regressions by variables indicating the comuna itself as well as the road
network); and the capacity of households to
respond to those incentives, which in turn
depends on the households assets, including
human capital (age, gender, and education),
physical capital (farmland, access to irrigation,
vehicles, and equipment), social capital (participation in rural economic organizations),
and access to external ®nancial capital (access
to credits and government transfers). Households living in a more favorable economic
context and with more assets will have greater
access to nonfarm jobs and will earn more in
them than households in the opposite situation.
(a) Determinants of participation
The results concerning the probability of
participation in some sort of nonfarm income
generation, abstracting from the levels of nonfarm income earned, are shown in Table 7.
Human capital (gender of the household head,
average age of the married couple, and average
education of the household members older than
15) are statistically signi®cant determinants for
all three dependent variable categories (total
nonfarm, nonfarm wage-income, and nonfarm
self-employment income).
The negative sign of the coecient on the
gender of household head variable indicates
that households headed by women have a
greater probability of earning nonfarm incomes. Households of older couples and those
households with more education also have a
greater probability of earning nonfarm incomes
of both types.
Holdings of vehicles, equipment and machines have a positive eect on the probability
of earning nonfarm income in general, and
from self-employment in particular. The coef®cient is, however, negative for nonfarm wage
employment.
Access to farm credit has a positive eect on
the household's undertaking nonfarm self-employment. Farm households that have access to
more funds use them (or other funds freed by
having the farm credit) at least partly to diversify their incomes.
After controlling for comuna, neither roads,
economic organization participation, nor
landholdings, irrigation, or government transfers, drive households' participation in nonfarm
income generation.
Whether the household is located in the
richer comuna does not signi®cantly aect the
probability of earning nonfarm income in
general. But, as expected from the descriptions
of patterns above, the eect of Portezuelo on
earning nonfarm self-employment income was
signi®cant. Recall that many Portezuelo
households are winemakers.
(b) Determinants of levels
In contrast with the results concerning the
determinants of participation where the zone
and location variables did not have much effect, here, in the determinants of nonfarm income levels, those variables are important. The
most important variable determining total
nonfarm earnings is the location of the
household. Households in Molina earn more
nonfarm income than those in the poorer
Portezuelo.
The regressions show that households near
very poor roads earn more nonfarm income
from self-employment. This ®nding is driven by
the fact that many households in Portezuelo
live near poor roads and produce cheap wine.
The very inferiority of their roads ``protects''
(in a trade sense) the traditional agro-industry
activities of these hinterland households, as
well as increases transaction costs to households to participate in better-paying work further a®eld or to invest in higher quality wine
the pro®table production and sale of which
requires easier contact with the market.
Only in the case of farm wage-income levels
do household assets ®gure signi®cantly; in
particular, those with more farm land work less
on others' farms.
Table 7. Determinants of access to and levels of rural nonfarm incomea
Independent
variables
Probit models: dependent variable access to RNF income
(No RNF income 0; Yes RNF income 1)
Total RNF income
Self-employment
RNF income
Salaried RNF
income
Total RNF income
Self-employment
RNF income
Salaried RNF
income
McFadden's
R2 0.10
McFadden's
R2 0.10
McFadden's
R2 0.13
R2 0.39
R2 0.37
R2 0.42
Est. b
)0.68
0.02
0.01
0.11
)0.38
)0.01
0.00
0.10
0.28
)0.30
)0.20
0.92
)0.12
0.22
)0.14
)0.00
)0.90
t
)2.72
2.91
0.23
2.91
)1.01
)0.43
1.96
1.38
1.57
)1.35
)0.74
1.70
)0.41
0.81
)0.51
)0.86
)1.97
Est. b
)0.54
0.01
)0.08
0.06
0.21
0.00
0.00
0.07
0.43
)0.01
)0.43
0.40
)0.25
0.16
)0.22
)0.00
)0.86
t
)2.14
2.07
)1.23
1.85
0.55
0.05
2.44
1.20
2.41
)0.45
)1.81
0.79
)0.81
0.59
)0.77
)0.57
)1.87
Est. b
)0.58
)0.02
)0.10
0.15
)0.82
0.00
)0.00
)0.01
)0.08
)0.15
0.35
0.38
)0.36
0.07
)0.16
0.00
)1.91
t
)2.13
2.08
1.56
4.02
)1.88
0.17
)0.68
)0.24
)0.45
)0.68
1.45
0.79
)1.16
0.24
)0.53
0.99
)3.79
b
)2.40
0.54
)0.02
0.12
0.06
0.12
)0.00
0.00
)0.13
)0.46
0.03
1.09
)0.38
0.18
0.17
0.84
0.00
13.58
t
)0.94
0.49
)0.57
1.03
0.38
0.14
)0.09
0.36
)1.10
)0.87
0.05
2.13
)0.28
0.31
0.29
1.42
0.58
3.97
b
)0.54
)0.62
)0.00
)0.01
0.12
0.55
0.00
0.00
)0.06
)0.23
)0.41
0.88
1.21
1.19
1.00
1.66
)0.00
11.78
t
)0.16
)0.49
)0.22
)0.44
0.83
0.78
0.21
0.56
)0.43
)0.23
)0.97
0.84
1.01
1.44
1.65
2.19
)0.85
2.63
b
0.84
)0.61
0.04
0.35
0.23
)0.06
)0.03
0.00
)0.04
)0.06
)0.07
0.59
)0.11
)0.64
)0.26
)0.48
0.00
8.16
t
0.25
)0.45
1.13
1.38
0.65
)0.03
)1.85
1.09
)0.39
)0.20
)0.18
0.72
)0.11
)0.66
)0.65
)0.77
0.04
1.13
CHILE
1. Lambda
2. Sex
3. Age
4. Number
5. Schooling
6. Irrigation
7. Land
8. Equipment
9. Distance
10. Credit
11. Organization
12. Municipality
13. Paved road
14. Gravel road
15. Good dirt road
16. Bad dirt road
17. Subsidies
18. Constant
OLS models: dependent variable logn of RNF income
a
Independent variables: 1 Lambda, 2 Sex of head of household (Female 0, Male 1), 3 Age of heads of household, 4 Number of economically active members
of household, 5 Average schooling of members of household 15 years of age or older, 6 Percentage of total farm land with irrigation, 7 Total farm area (hectares),
8 Total value of vehicles, tools, and machinery, 9 Distance to nearest town (km), 10 Access to credit (0 no, 1 yes), 11 Membership in farmers' economic
organization (0 no, 1 yes), 12 Municipality (0 Portezuelo, 1 Molina), 13 Paved road (1 yes, 0 other), 14 Gravel road (1 yes, 0 other), 15 Dirt road,
can be used throughout the year (1 yes, 0 other), 16 Dirt road in bad condition (1 yes, 0 other), 17 Income from public subsidies, 18 Constant.
*
Statistically signi®cant at 10% level.
**
Statistically signi®cant at 5% level.
***
Statistically signi®cant at 10% level.
421
422
WORLD DEVELOPMENT
6. CONCLUSIONS AND
RECOMMENDATIONS
It was once held that rural±urban migrants
are among the poorest, and that by their
migration they, thus, abandon farm wage labor; but our nationwide results ¯y in the face
of these theories. In today's Chile, at least in
the intermediate cities and small towns and in
zones with good roads, many small farmers,
farmworkers and commercial farm owners/
managers have migrated to urban centers, but
they are neither the poorest nor have they
abandoned the farm sector. The urbanization
of the residence of persons who remain employed in agriculture has transferred to rural
areas a phenomenon noted for some time in
the great cities: the spatial segregation of rich
and poor. Nevertheless, seeking to reverse the
urbanization of agriculturalists' residence
would be counterproductive as it implies the
improvement of living conditions for thousands of farm wage-workers and small farmers.
Moreover, we have shown that many rural
households are working outside the farm sector, in nonfarm wage and self-employment. In
fact, these nonfarm sources contribute 41% of
the total income of rural households in Chile. It
is critical to design and reinforce policies that
facilitate that development of these kinds of
employment. In particular, investments in rural
education and policies that ease households'
access to credit and equipment/machinery
would improve rural households capacity to
under nonfarm activities.
An important implication of our ®ndings is
that RNF employment promotion should be
designed with special consideration for femaleheaded households, as they tend to depend
more on such employment. Such programs
should be primarily geared to preparing women
for wage employment in the services or manufactures subsectors, with only secondary attention to what has been the traditional focus
of nonfarm development programs, self-employment in microenterprises. This is because
our results have shown that women have access
to and earn more than men in wage employment as compared to farm labor or in selfemployment.
Policies and programs promoting nonfarm
employment should dier by zone and socioeconomic group, because the motives and situations of households in undertaking such
employment vary greatly. On the one hand,
the nonfarm income share in total income
might be high because nonfarm earnings are
relatively high. An example such as is Molina,
an agricultural boom area, where there is dynamic growth in the nonfarm economy. On
the other hand, the share might be high not
because the nonfarm economy is particularly
successful but merely because farm incomes
are weak and stagnant, such as in Portezuelo,
our case study in a hinterland zone with poor
traditional agriculture and weak infrastructure. Clearly, dierent policies are required to
promote equitable growth in the nonfarm
sector in rural Molina versus in rural Portezuelo.
In zones such as Molina, the growth of
RNFE derives from the dynamic growth of
the overall zone economy. Public sector actions can and should accompany, regulate, and
facilitate this development, but the fundamental dynamic arises from the market itself.
We found that in these situations, much
RNFE is not closely tied (``productionlinked'') to agriculture, such as cinemas and
restaurants, home construction, clothing
stores, banks, pharmacies, public oces, etc.
Of course, the original impetus for this growth
in Molina was dynamic commercial agriculture
and agro-industry, but in other zones with
dynamic economies one can ®nd other original
motors of growth such as tourism, mining,
proximity to a large city, etc. The origin of the
dynamism of RNFE is frequently outside the
countryside itself, although it is incontrovertible that a modern, competitive, and dynamic
agriculture requires and foments links between
itself and services and manufactures, and
generates incomes that are spent in those
subsectors of the nonfarm economy. Thus,
farm and nonfarm development are not exclusive alternatives, but rather can be mutually
reinforcing.
By contrast, in zones such as Portezuelo, one
cannot hope that the market will, by itself,
``endogenously'' create job opportunities in the
nonfarm sector, whether in wage or self-employment. Without strong action by governments there will not be rapid or equitable
growth in RNFE through development of its
component subsectorsÐmanufactures, commerce, other services. At variance with what
one might expect, we have seen that in zones
such as Portezuelo, nonfarm self-employment is
closely linked (in the production-linkages sense)
to the farm sector, and in particular, to small
and medium-scale farming. Promotion of
CHILE
nonfarm self-employment in small enterprises
needs then to be basedÐat least in an initial
phase in which capital is accumulated by
households that in turn diversify their incomes
outside of the farm sectorÐon the development
of small- and medium-scale farming. This requires government action, in particular, measures to develop the land market to help the
poor to obtain land through purchase or rental,
and to intensify production and increase productivity through irrigation, technical assistance, and credit.
Our results also contradicted the conventional wisdom that the poor and the landless
earn the lion's share of nonfarm incomes. It is
true that they tend to rely more on RNF incomes because of lack of farm incomes as in
Portezuelo in general, but they do not necessarily earn higher nonfarm incomes than richer
and landed households. In fact, the latter earn
more nonfarm incomes, either because they
can capitalize their enterprises with farm
pro®ts, or because they earn service sector
income by renting out or using as physical
capital their tractors and trucks, or because
farm incomes fed their family education investments that allowed their sons and daughters to obtain salaried employment in a local
business or to start their own businesses. In
zones such as Portezuelo, households with
more land earn more nonfarm income partly
because the latter is often linked to processing
farm products or selling farmers services and
other inputs.
For similar reasons, multiactivity (where a
household earns substantial income from more
than one source) is more prevalent in nonpoor
households. With more assets (physical, ®nancial, and human capital), a household has more
and better job opportunities in the nonfarm
sector. In either type of zone, however, multiactivity is less common than one wo