Rural Nonfarm Employment and Income
Diversi®cation in Colombia
KLAUS DEININGER and PEDRO OLINTO
*The World Bank, Washington, DC, USA
Summary. ÐUsing data for rural households from Colombia we ®nd that o-farm employment contributes a signi®cant share (45% on average) to household income but that the importance of o-farm income and returns to household labor vary over the income distribution. Analysis reveals signi®cant gains from specialization butÐfor households able to specializeÐno systematic dierences in returns to labor between farm and nonfarm sources. We conclude that, in Colombia, there is no con¯ict between development of the farm and the nonfarm sector but that, to maximize gains from nonfarm development and reduce the scope for undesirable distributional consequences, policies enabling households to specialize might be called for. Ó 2001 Elsevier Science Ltd. All rights reserved.
Key words ÐColombia, o-farm employment, income distribution
1. INTRODUCTION
There is little doubt that the importance of rural nonfarm employment, which in many countries already constitutes an important sector of the rural economy, will greatly increase as agriculture becomes more and more integrated into global markets and as the links between urban and rural areas intensify. What is less clear, however, is how these forces for diversi®cation can best be harnessed for nonfarm employment to act as a catalyst for a broader and inclusive pattern of development. From a policy perspective, it is of particular interest to ®nd out whether the rural poor are able to make optimum use of the opportunities provided by nonfarm employment or whether speci®c policy measures to assist them might be needed.
In this paper, we use data from Colombia to address this question. Descriptive statistics illustrate both the importance of nonfarm employment and broad patterns of participa-tion in nonfarm opportunities across dierent groups of the population. Nonfarm income (wages from agricultural and nonagricultural
employment, pro®ts from nonagricultural
enterprises, nonearned income, and remit-tances) contribute on average 45% to house-hold income. There is also a nonlinear (U-shaped) relationship between the importance of o-farm work, asset endowments, and total
household income. By comparison,
specializa-tion (in either farm or nonfarm activities)
increases linearly with income and assets. The strong positive association between total income and specialization suggests that even though nonfarm employment contributes to diversi®cation of income generating opportu-nities at the regional level, individual house-holds may still be better o by relying only on one main income source. More importantly, to the extent that market failures and lack of endowments prevent them from specializing, government policies that improve the func-tioning of factor markets and that help house-holds increase their endowments could play an important role to maximize the gains associated with the emergence of nonfarm employment opportunities.
To explore this in more detail, we examine the impact of specialization and household labor supply as well as the determinants of specialization. We ®nd that, indeed, special-ization allows households to increase their level
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*We are deeply indebted to Elsa Albarracin, Juliana Bottia, Diana Grudzynski, Absalon Machado, Manuel Rojas, Hernando Urbina and Guilermo Otanez without whose enthusiasm and support the data underlying this analysis would never have been collected. We are also grateful for useful comments from three anonymous reviewers.
of welfare (as measured by expenditures) by between 10% and 36%, everything else equal. Why, then, do not all households choose to specialize in one activity? We ®nd that imper-fections in markets for credit and land, lack of education, and inequalities in asset ownership constitute important barriers to increased specialization.
From a policy perspective this implies that in situations such as Colombia, where education and assets are distributed in an unequal manner, the impact of increased nonfarm employment opportunities will not be inde-pendent from households' and communities initial endowments. Households with little human or physical capital may be forced to rely on nonfarm employment as a low return ``ref-uge'', comparable to semi-subsistence, with little prospect for economic advancement. Only if they own suciently high levels of assets or are able to access to credit and land rental markets will households be able to make full use of the opportunities for specialization and increased returns to labor provided by a more
diversi®ed rural nonfarm economy. The
welfare-enhancing impact of nonfarm employ-ment opportunities will thus be maximized if policies aimed at promoting the rural nonfarm sector are complemented with measures to improve the functioning of factor markets and to increase households' opportunities to accu-mulate human and physical capital.
The paper is structured as follows. Section 2 provides descriptive evidence on the impor-tance and incidence of nonfarm employment across the income distribution and the coun-try's dierent regions as well as a description of the data sources underlying the study. Section 3 discusses the main econometric results, in particular the impact of specialization on household welfare and the determinants of households' decision to specialize. Section 4 links the results to the broader discussion of nonfarm employment in the literature and, on this basis, derives a number of conclusions for policy as well as research.
2. INCIDENCE AND CHARACTER OF NONFARM EMPLOYMENT
In this section we describe the data underly-ing the analysis and discuss descriptive statistics about the incidence and nature of nonfarm employment in rural Colombia. We ®nd an U-shaped relationship between the share of
o-farm income and household assets or total income level. This is consistent with the evi-dence from a number of other countries where presence of entry barriers to high-return jobs in the o-farm sector, together with a relatively
unequal distribution of farm assets and
malfunctioning land rental markets, force poor people with scant asset endowments into low-paying o-farm jobs and prevent them from taking maximum advantage from the oppor-tunities oered by nonfarm employment.
(a) Background and data sources
Thanks to a large amount of studies on the nonfarm sector across all continents, the importance of rural nonfarm employment is now widely recognized. Country case studies illustrate that the share of nonfarm income in total household income ranges between about 30% and 40%Ðwith the highest shares (45%) reported from Africa and the lowest ones (29%)
from South Asia (Reardon et al., 1998).
Although household-level evidence on the evolution of nonfarm employment is limited, 1 the contribution of nonfarm income sources and o-farm employment to the rural economy has grown substantially during the last two decades and is likely to continue doing so in view of globalization, progressive insertion of rural areas into the larger economy, and increased access to public services (Berdegue, Reardon, & Escobar, 2000).
While our understanding of the magnitude of the rural nonfarm sector has greatly improved, the contribution of this sector to household welfare, and the distribution of the bene®ts from o-farm employment across the popula-tion, are still imperfectly understood. Evidence on whether nonfarm employment contributes to a more equal distribution of income is decidedly mixed (Reardon, Taylor, Stamoulis, Lanjouw, & Balisacan, 2000). To advance on this issue, it is necessary to explore not only what determines participation in the rural nonfarm economy, but also how such partici-pation aects households' welfare. This is crucial not only for academic reasons but, more importantly, to allow governments to take measures that will enable the poor to take advantage of the opportunities inherent in the growing importance of the nonfarm sector, thus turning it into a catalytic force for rural growth and sustainable reduction of poverty.
the ups and downs of other Latin American countries, the country had, until the recent
upsurge in violence and macroeconomic
problems, been characterized by stable
economic growth. At the same time, it shares with other Latin American economies a highly unequal distribution of assets.2
Maldistribu-tion of assets is particularly acute in rural areasÐdespite more than three decades of land reform, land access is highly unequal with the Gini coecient of land ownership in 1990 estimated to be 0.81 (World Bank, 1996). Other assets are distributed in a slightly less unequal fashion, with a Gini of 0.77. This is relevant for the rural population as agriculture is still the single most important sector in the economy, generating a ®fth of total value added, over a third of foreign exchange, and more than 30% of total employment in the economy.
Starting in the early 1990s, the country
implemented a far-reaching program of
adjustment (apertura) which, by turning away from a paradigm of import-substituting indus-trialization, opened up the agricultural sector to the forces of international competitiveness. This led to large gains for producers who were well connected to markets and able to adjust quickly to the changed system of incentives. At the same time, it tended to reinforce old dichotomies in the distribution of assets as small producers who were not able to shift out of traditional commodities suered consider-able losses. Migration, together with rapid growth of the rural nonfarm sector enabled rural dwellers to improve or at least stabilize their income in the face of these external shocks (Jaramillo, 1998).
To identify whether nonfarm employment can, in addition to constituting a safety net, also act as a catalyst for an inclusive pattern of economic development in the rural sector, we use data from a survey of about 1,000 rural
households that was undertaken by the
Departamento Nacional de Planeacion(DNP) in collaboration with IICA and the World Bank. The main purpose of the survey was to examine factors aecting technical eciency of dierent farm sizes, the functioning of rural factor markets, and sources of income and employ-ment of the rural population. It contains
comprehensive information on labor use,
general household characteristics, asset
endowments, migration, and access to govern-ment services which can provide a better understanding of the rural nonfarm economy. 3
(b) Descriptive evidence
The survey data reveal that Colombia's rural inhabitants draw incomes from a wide variety of sources. As illustrated in column 1 of Table 1, farm pro®ts made up 56% of total income, complemented by wage income from farm and nonfarm sources (30%), nonfarm
enterprise pro®ts and nonearned income
(12.5%), and migration remittances (2.5%). The average household in the survey had slightly less than ®ve members with the head having completed 2.9 years of schooling, compared to a mean of 3.9 for all household members over the age of 15 years. Households' mean asset endowment amounted to about 25 ha of land and business assets (including machinery, live-stock, vehicles, and nonfarm enterprise assets) worth about US$4,500. In line with what is known from other sources of information, our data point toward an unequal distribution of assets. About 13% of the sample have relatives who migrated out and may have provided remittance income.
Individuals' wage rates and thus opportunities in the farm and nonfarm sector vary depending on their level of education, physical location, and type of work performed. To account for this, we complement information on income by source with data on the amount of hours worked in dierent economic activities. This indicates that 62% of the 70 weeks per year which the typical households spent working was used in agricultural activities, 25% in wage labor, and 10% in independent nonfarm enterprises.
The information on credit and savings provided by the survey points toward limited access to ®nancial infrastructure and a reluc-tance to use credit, rather than household savings, to ®nance investment. One-quarter of households had pre-existing savings, but only 15% used credit. Of these, 10% was from the formal and about 5% from the informal sectorÐwhich comprised traders (3.5%) and informal lenders (2.6%). Half of the households did not use credit because of high rates or complicated documentation, while another 14% reported not to need credit and 7% did not have collateral. Access to free technical assistance was, with 33%, fairly widespread.4
(c) Nonfarm employment, asset ownership, and specialization
an inducement for households to turn to nonfarm employment. Households are thought
of being pushed to engage in nonfarm
employment because of imperfections in inter-temporal and factor markets and/or entry barriers to high return activities. Pull factors that would attract households to nonfarm employment include: (i) higher income gener-ated in nonfarm activities (wage and nonwage
employment); (ii) potentially lower risk; and (iii) greater social status attributed to nonfarm activities. Push factors are commonly thought to include (i) lack of access to productive resources (e.g., land) to expand farm output because of unequal distribution and malfunc-tioning land rental markets; (ii) the need to rely on costly mechanisms of diversi®cation and self-insurance to ex ante mitigate risks in an
Table 1. Descriptive statistics by quintile of the per capita expenditure distribution Total Quintiles of per capita expenditure
1 2 3 4 5
Income and expenditure structure
Per capita expenditure 533.22 163.01 284.44 405.21 587.26 1226.19 Total income 2527.78 1254.01 1979.03 2264.34 2974.33 4167.17 of which farm pro®ts 55.96% 39.90% 49.28% 59.09% 65.33% 55.73% of which wage income 29.49% 40.59% 36.16% 27.06% 24.83% 27.51% enterprise pro®ts/non-earned income 12.53% 15.42% 11.74% 11.54% 8.11% 15.73% of which remittances 2.02% 4.09% 2.82% 2.30% 1.73% 1.03%
Household characteristics
Number of household members 4.71 6.20 5.36 4.52 4.20 3.28 Head's education 2.89 2.12 2.80 2.96 3.09 3.47 Mean education (members>15) 3.96 3.27 3.78 4.04 4.13 4.56 Have migrants in the household 12.47% 16.74% 15.35% 11.16% 11.16% 7.91%
Asset ownership and labor supply
Area of land owned (ha) 24.55 6.84 17.49 20.17 31.70 46.53 Business assets (in US$) 4447.97 748.73 3657.34 3240.90 5947.15 8645.74 Household assets 234.28 117.74 178.65 199.78 275.99 399.25 Level of specialization 51.53% 38.60% 48.37% 49.77% 56.74% 64.19% Notional wage rate (US$ per week) 36.51 17.21 26.32 32.21 44.33 68.65 Total weeks worked 69.23 72.87 75.19 70.31 67.10 60.70
Weeks self-employed in agriculture
43.75 39.95 40.29 46.54 46.89 45.08
Weeks spent on wage labor 17.86 25.46 24.31 16.39 14.17 8.97 Of which nonfarm 8.40 6.62 8.04 8.41 9.22 9.70 Weeks spent in nonfarm
enterprises
7.62 7.47 10.59 7.38 6.04 6.65
Weeks searching for employment 0.88 0.89 0.73 1.07 0.93 0.79
Credit and savings
Have savings account 26.79% 11.16% 17.21% 25.12% 33.02% 47.44% Had used credit 15.12% 8.84% 11.63% 11.16% 23.02% 20.93%
Through formal ®nancial institutions
10.60% 6.98% 10.23% 6.98% 16.28% 12.56%
Through traders 3.53% 0.93% 2.79% 1.40% 6.51% 6.05% Through informal lenders 2.60% 2.79% 0.47% 3.72% 2.79% 3.26% Reasons for non-use of credit
Not needed 14.05% 10.23% 8.84% 14.88% 18.60% 17.67% Documentation too dicult 29.30% 39.07% 31.16% 33.95% 23.72% 18.60% Rates too high 21.21% 15.81% 24.65% 19.53% 17.21% 28.84% Do not have collateral 7.35% 10.70% 9.30% 9.77% 4.19% 2.79% Other reasons 4.74% 6.05% 5.58% 5.12% 2.33% 4.65%
Infrastructure and services
Received technical assistance for free 33.21% 31.63% 28.84% 28.84% 42.33% 34.42% Distance to infrastructure 13.701 12.912 13.367 12.145 13.797 16.282 Municipio severely aected by
violence
environment where intertemporal markets for credit and insurance do not function well; and (iii) entry barriers such as minimum require-ments of human or physical capital that prevent the poor from entering high-return activities.
The way in which push and pull factors
interact with a region's agro-ecological
endowment has given rise to a number of speci®c patterns that relate the amount of nonfarm income to overall household wealth or total income. Many African countries with a relatively egalitarian distribution of land assets, an underdeveloped farm labor market, and a predominance of a traditional production technology that relies on inputs of family labor, display a strong positive relationship between the share of nonfarm income and total wealth levels (Reardon, 1997). Similar phenomena are reported from many agricultural regions of China where an egalitarian distribution of land translates into great equality of opportunity in the sense of ensuring a basic level of income and nutrition. Households with higher levels of human capital tend to augment this with employment in local Township and Village
Enterprises and income from temporary
migration (Zhao, 1999; Rozelle, Taylor, & DeBrauw, 1999; Hare, 1999).
By contrast, many case studies from Latin American countries, and from other parts of Asia, ®nd a U-shaped relationship whereby low-income households are often the ones who obtain the highest share of their income from (low-paying) nonfarm employment (see Rear-don et al., 2000; Feldman & Leones, 1998; Garcia & Alderman, 1993 and Adams, 1994, for example). This phenomenon, under which low-income and high-income households both engage in nonfarm employment but house in quite dierent types of occupations will, in
addition to the contemporaneous income
distribution, also aect the longer-term evolu-tion of the rural economy. The reason is that nonfarm income generally provides an impor-tant source for agricultural investment (Ilahi, 1999; Taylor & Yunez-Naude, 1999, De Janvry, Gordillo de Anda, & Sadoulet, 1997). In such a situation, poor households who do not have a suciently large agricultural resource base and have limited access to credit markets, and who lack skills, access to social networks, and ``mi-gration capital'' may well be caught in poverty traps from which there is little escape. As a
consequence, the emergence of nonfarm
employment may give rise to increased
concentration of wealth and dierentiation of
the rural society with associated social tensions, con¯ict, and violence (Andre & Platteau, 1998; Francis & Hoddinott, 1993).
To examine the relevance of these factors for the case of Colombia, we disaggregate the statistics presented previously by quintile of the per capita expenditure distribution (Table 1, columns 2±6). In addition to con®rming that income varies considerably across household groups, doing so points to a strong positive association between the level of income and the extent of specialization in either the farm or the nonfarm sector. Table 1 illustrates the U-shaped relationship between the share of nonfarm income and asset endowments or total income: The poorest quintile obtains 60% of their income from nonfarm sources, a share that declines to 35% for the fourth quintile and then increases again to 45% for the top quin-tile. 5
In addition, and contrary to what one might expect, there are no huge dierences in the relative importance of enterprise pro®ts and non-earned income between the top and the bottom quintileÐin fact both groups obtain about 15% of their income from these sources (Table 1). The contribution of migrant earnings to total household income decreases linearly over the income distribution, from about 4% for the bottom to about 1% for the top quintile. This suggests that, contrary to situations where (international) migration functions as a source of funds for investment and a means of capital accumulation, the amount of return ¯ows in most of rural Colombia is of minor importance. Moving from the composition of income to household assets points toward a strong posi-tive relationship between the amount of assets owned and the level of specialization, de®ned as the share of households in the group who spend all their time in only one activity (i.e. either farming, running a nonfarm enterprise, or wage work). The share of specialized households increases from 39% in the lowest quintile to 64% in the top quintile (Table 1). In terms of the earlier discussion, this suggests that there are either considerable entry barriers to higher paying jobs or that imperfect insurance markets prevent poor households from engaging (and specializing) in high-return activities.6
The potential quantitative importance of
these constraints is demonstrated by a
comparison of total labor supply and wages received over the income distribution. House-holds in the top quintile work almost 20% less
implying that their higher level of income is based on higher returns to labor and other assets. Computation of a notional ``wage rate'' by dividing total income by the number of weeks worked illustrates these dierencesÐ while the poor receive on average US$17 per week worked, the rich receive four times as much, i.e. more than US$68. Descriptive anal-ysis cannot distinguish between returns to labor and other assets but given the magnitude of the dierence, it would be of considerable interest to ®nd out whether it can be explained solely in terms of asset endowments or whether there are additional gains from specialization and/or from work in the nonfarm sector. Examining this in more detail is the topic of the next section.
3. THE IMPACT OF NONFARM EMPLOYMENT
In this section, we aim to assess the impact of nonfarm activity on household welfare. Based on the descriptive statistics presented earlier, we test two hypotheses. First, we surmise that specialization, rather than the choice of sector (farm or o-farm) has a major impact on the returns households are able to obtain for their labor. Second, we believe that, due to pervasive imperfections in the functioning of land, labor, and credit markets, households' endowments have a strong impact on whether or not they are able to specialize. Con®rmation of this hypothesis would imply that, in addition to
augmenting households' endowments of
human capital and other assets, policies to improve the functioning of rural factor markets can go a long way to help harness the bene®cial potential of growing specialization, either in farm or nonfarm activities.
(a) Does nonfarm employment increase returns to labor?
To explore returns to labor as well as other household assets and factors of production, we regress total household expenditure (as a proxy for permanent income) on the household's total labor supply to the market. 7 To identify the impact of specialization on returns to labor, we interact labor supply with a dummy variable that equals one if the household specializes (i.e., supply labor to only one type of activity) and zero otherwise.8We also include ownership of
productive assets (self-reported land values, the
value of business assets and farm machinery, and the value of livestock). Coecients on these variables measure returns to labor and other household assets. Furthermore, access to formal savings and the number of relatives living abroad are included as two characteris-tics that are likely to increase households' ability to draw on resources that would allow to smooth consumption and overcome entry barriers to or the high risk associated with entry into pro®table nonfarm opportunities.
Labor supply and the specialization dummy are clearly endogenous, i.e. correlated to unobserved household characteristics such as entrepreneurial drive etc. which, even though they also have a direct impact on household income, are omitted from the regression. As a consequence, ordinary least squares (OLS) would yield biased estimates of the relevant coecients and it is necessary to use instru-mental variable methods to identify the rela-tionship in question. Given the panel structure of the data, we use household-level changes for the variables of interest (changes in family labor supplied, changes in the livestock herd, changes in the age structure of the household, changes in the value of machinery stock, changes from specialization to multiple activi-ties) over 1997±99 as instruments for labor supplied and the dummy for specialization in 1999.9 Main results of instrumental variables estimation of annual household expenditure equation are summarized in Table 2. We discuss these ®ndings below.
Specialization signi®cantly increases returns to labor:According to the regression estimates, households that adopt multiple income-gener-ating strategies obtain a relatively low return to their labor. By contrast, adopting a specialized strategy more than doubles returns to labor. This large and statistically signi®cant dierence suggests that there are indeed formidable barriers preventing low-income households from adopting ``pure'' strategies. These barriers are likely to include lumpiness of assets, imperfect credit markets, and limited options for diversi®cation and self-insurance. Rural households who, for one of these reasons, are unable to specialize, use nonfarm employment very much as a ``refuge of poverty'' (Berdegue
et al., 2000), similar to low return subsistence agriculture. Note that, from a quantitative point of view, these dierences are quite signi®cantÐthe regression estimates indicate that, depending on the region, shift from
everything else constant, will increase house-holds' welfare (as measured by expenditure) by between 10% and 36%.
Education enhances returns to specialization:
Although specialization alone can be shown to
have signi®cant bene®ts, the returns to
specializing may depend on the households' educational attainment. To test for this possi-bility, we repeat the above regression but interact specialized labor supply with the level of education. Results (column 2 of Table 2) indicate that higher levels of education lead to a signi®cant increase in returns to specialization. According to the regression estimates, an additional year of education increases income for specialized households by between 3.4% and 12%. For a household with seven (rather than the median three) years of education, specialization could thus lead to an increase in
expenditure of between 25% and 70%,
depending on the region. This provides strong support for the notion that bene®ts from expansion of nonfarm employment opportuni-ties will be highest if this is combined with
policies to increase the formation of human capital.
Returns to specialized labor are equalized between farm and nonfarm employment: A second question of interest is whether gains from specialization are sector-speci®c, i.e., whether returns to labor for households who are specialized dier signi®cantly depending on whether or not they work in the farm or the nonfarm sector. To test for this, we run a similar instrumental variable regression that includes an interaction between labor supplied and a dummy variable for specialization in agricultural activities. The estimated coecient for this variable is not signi®cantly dierent from zero at conventional levels, allowing us to reject the notion that returns to specialization are higher in nonfarm activities than they are in farming. In other words, while household
endowments aect the expected returns,
households who specialize decide rationally whether to allocate their labor to farming or nonfarm activity. The policy conclusion is that there are few barriers to entry into the nonfarm
Table 2. Instrumental variable estimation of annual household expenditure equationa;b
Explanatory variables (1) (2) (3)
Labor supplied by the household 2.786 3.167 2.847 (1.526) (1.516) (1.621) Specialization dummylabor supplied 6.141 6.371
(1.308) (2.456)
Specialization dummyeducationlabor
supplied
1.601 (0.352) Agricultural specialization dummy
labor supplied )
2.461 (22.222) Value of non-agricultural business assets
($US)
0.049 0.050 0.051
(0.012) (0.012) (0.019) Value of agricultural machinery/
equipment ($US)
0.063 0.059 0.060
(0.024) (0.024) (0.032) Value of land and livestock owned by
household ($US)
0.011 0.008 0.012
(0.002) (0.001) (0.002) Land owned, squared ($US) )4.34e)9 )4.23e)90 )4.38e)9
()6.89e)10) ()7.55e)10) ()8.02 e)10)
Dummy for positive savings at the beginning of year
530.813 456.629 529.161 (108.872) (108.381) (109.955) Dummy for relatives in other states or
abroad
29.178 54.309 26.266
(98.970) (97.555) (102.464)
Constant 1128.428 1111.452 1143.667
(174.092) (173.831) (221.996)
Number of observations 808 808 808
Adj.R2 0.38 0.39 0.38
aRobust standard errors in parentheses.
bNote: Regional dummies included but not reported. *
Signi®cant at 10% level.
sector other than those that aect specialization in more general terms.
Returns to assets vary by type:The regression also provides an estimate of the returns to the various types of assets held by households in the sample. We ®nd that returns to non-land assets are quite high, ranging between 6.3% in the case of farm machinery and 5% for nonagricultural enterprise assets. Compared to these assets, land and livestock (which are highly correlated) seem to be highly over-valued; the coecient on the value of land assets (self-reported, and including improve-ments) plus livestock indicates that US$1 invested in these two yields a return of only 1.15%.10The negative coecient on the square of this variable indicates that, in addition, these returns decrease with farm size.
There are three possible explanations for such a low return to land and livestock. First, there is likely to be some measurement error. The stream of bene®ts normally derived from land includes housing. No value for housing is imputed, however, on the income/expenditure side of the survey, implying that the regression coecient will suer from downward bias. Second, land may be held for speculative purposes, implying that landowners would be willing to accept a relatively low concurrent yield on their investment in return for expected appreciation of the land in the future. Finally, violence, external shocks, and the threat of losing property rights, may prevent landowners from making economically optimal use of their land. Indeed, there is evidence from the survey that land is left uncultivated. In addition, it is quite likely that the threat of losing property rights or provoking invasion if land is rented out prevents owners from supplying land to the rental market. This is the case even though renting out land could be bene®cial to land-owners and the rural landless because renting could yield higher returns than what is obtained through self-cultivation and at the same time allow poor households with a precarious resource base to increase the returns to their labor. Measures that would help activate land rental markets may thus bene®t all parts of the rural population.
Access to ®nancial infrastructure carries large bene®ts: Access to low-cost means of saving increases households' ability to self-insure and diversify risks. Given the high costs of rural
®nancial intermediation, self-®nancing of
investments is generally also less costly than use of formal credit. 11 Thus, in an environment
characterized by imperfections in the markets for credit and insurance, one would expect access to savings to perform an important function. Indeed, the regressions show that, other things equal, households who had savings at the beginning of the year had income levels signi®cantly higher than that of those who did not have access to savings. It would be of interest to ®nd out whether, as has been observed in the literature, possession of savings is related to previous exposure to the nonfarm sector. Unfortunately no information on this is available from our survey.
Migrant remittances do not perform an important function: Contrary to what has been observed in other countries where migrant remittances provide an important safety net and a source of funds for agricultural invest-ment that allows migrants to increase their agricultural productivity (Mochebelele & Winter-Nelson, 2000), having relatives in other departments or abroad does not have a perceptible impact on household welfare in ColombiaÐthe coecient is positive but not signi®cantly dierent from zero. One possible explanation is that migrants cut the social ties with their communities of origin. Alternatively, and similar to households who pursue diversi-®ed strategies of pluriactivitylocally, migrants' inability to enter the market for higher-paying jobs in the location of destination may force them to pursue low-return activities even in other localities which makes it dicult to generate large surpluses that can be re-invested in the local economy.
(b) Determinants of specialization
Our analysis thus far indicates that, even though returns to labor do not vary signi®-cantly between households engaging in
farm-and nonfarm employment, specialization
greatly increases household welfare. Adoption of diversi®ed strategies due to market imper-fections would not only reduce household welfare but also total production. Any move that could help make markets function better (and thereby increase the level of specialization) would thus be Pareto-improving (Newbery & Stiglitz, 1981). Identi®cation of factors that prevent specialization at the household level and of measures to help households overcome obstacles to specialization would thus be of great interest and policy relevance.
supply decisions, we run a Probit equation for specialization at the household level. As has been stated repeatedly in the literature, if all markets were perfect, household characteristics and endowments should not have any impact on labor supply decisions (e.g., Udry, 1997). The ®nding that households' composition, asset endowments, and educational status have a signi®cant impact on their patterns of factor use, including whether they will specialize, thus con®rms that rural factor markets in Colombia
suer from considerable imperfections.
Combining measures to promote nonfarm employment with those aimed at improving the functioning of markets could be doubly bene-®cial. Key results are displayed in Table 3 and discussed below.
Asset ownership promotes specialization: The coecient for ownership of land and livestock (which, as noted earlier, are highly correlated) is highly signi®cant and positive, suggesting thatÐeither by increasing the scope for self-insurance or by allowing to overcome entry barriersÐhigher levels of land and livestock
ownership signi®cantly reduce households'
propensity to engage in and draw income from a multitude of employment sources.12Finding
mechanisms, such as provision of ®nancial infrastructure that would allow small-scale savings could, by reducing the need for socially inecient diversi®cation, be associated with an increase in overall welfare in rural areas.
Large households are more likely to adopt diversi®ed strategies: The fact that, for any given level of asset endowment, households with a larger number of adults are also more likely to adopt multiple income-generation strategies (Table 3) suggests that, in addition to markets for credit, markets for land and labor also suer from considerable imperfections. Instead of specializing in one main activity and adjusting to variations in household size (which may be life-cycle related) through the land rental market, large households appear to be forced to adopt multiple income-generating strategies, even if this is not in line with the specialized skills they possess. On the other hand, contrary to a prioriexpectations, we are unable to ascertain dierences in the coecients on the number of household members below and above the age of 35 and therefore only report the total number of adults in the household.
Education is an important determinant of specialization: More educated households are less likely to adopt multiple strategies of income generation. This is likely to re¯ect the co-existence of low-paying ``menial'' jobs with little human capital requirements side-by-side with activities that are characterized by high entry barriers (such as possession of a mini-mum level of human capital). Overcoming these entry barriers is a sunk investment. Unless they are forced to do so, households who successfully managed to overcome these barriers will not diversify into areas that have lower returns, thus explaining the positive and highly signi®cant coecient on this variable.
4. CONCLUSION AND POLICY IMPLICATIONS
In addition to con®rming the importance of nonfarm activities as a source of income and
employment, our data also support the
hypothesis that, in view of the relatively unequal distribution of assets and land, o-farm employment in Colombia falls into two quite distinct categories. A signi®cant share of poor households engages in a combination of wage labor in jobs with low entry requirements plus self-employment in ``marginal'' on-farm or
Table 3. Probit regression for households' specializa-tiona;b
Number of adults (16 years and older) in the household )
0.08125 (0.02601) Number of children (15 and
younger) in household
0.04496 (0.02390) Head's years of education (years) 0.03315 (0.01674) Value of agricultural machinery
(1000 US $)
0.03366 (0.01757) Value of land and livestock (1000
US $)
0.00401 (0.00159) Value of land and livestock squared
(1,000,000 US $)
0.01090 (0.00000) Value of non-ag business assets
(1000 US $)
0.00000 (0.00001) Household members living abroad
for more than 2 years
0.06468 (0.10123)
Constant )0.07875
(0.15825) No. of observations 1075
Pseudo-R2 0.0781
Log likelihood )686.451
aRobust standard errors in parentheses. b
Note: Regional dummies included but note reported.
*
Signi®cant at 5%.
informal sector activities, neither of which provide the returns required to sustain signi®-cant investment and oer prospect for longer-term accumulation. At the same time, nonfarm employment oers increased opportunities for enhanced specialization which increase the welfare and the capacity to invest of households who are able to overcome the associated entry barriers, thereby providing the basis for longer-term development of the rural sector.
Our analysis suggests that, in addition to
creating the pre-conditions for vigorous
growth of the nonfarm sector, government can help to maximize the private and social bene-®ts from such growth through three steps, namely by (a) improving the functioning of
land, insurance, and credit markets; (b)
investing in human capital; and (c) taking steps to help improve the asset endowments of the poor. By enabling households to specialize and make full use of the opportunities inher-ent in the developminher-ent of a nonfarm sector, doing so will increase individual as well as social welfare. The Asian example where, in an environment with relatively egalitarian distri-bution of income, well-functioning factor markets, and a strong emphasis on educa-tional expansion, rural nonfarm employment has led to a spurt of broad-based development and rapid income increases for all rural inhabitants (Hayami, 1995) suggests that such a strategy could provide large bene®ts not only to rural dwellers but to the economy as a whole.
NOTES
1. In India, an initial increase in easy entry o-farm jobs which are relatively low-paying gives way to the expansion of better-paying o-farm opportunities which are created in response to the demand for nonfarm products and services (Lanjouw & Stern, 1993). In the Philippines it is found that the expansion of employment opportunities outside of the farming sector precipitates an increase in the returns to human capital through migration which gives rise to a successive shift away from farming toward nonfarm employment (Estudillo & Otsuka, 1998). Household censuses in Latin America also show a secular increase in the importance of rural nonfarm employment (Klein, 1992). Of course, global-ization can, in certain cases, also reduce the extent of rural nonfarm employment.
2. Lack of access to assets has been identi®ed as a major cause of poverty in Colombia (Leibovich & Nunez, 1999).
3. The sample was strati®ed into 11 agro-ecological zones. In each of the zones, 10 municipalities and within these municipalities 10 households were selected randomly. All households were surveyed two times, once in 1997 and then again in 1999. Due to attrition and the inability to visit a number of localities because of violence, the sample in the second round was reduced from 1,075 to 808.
4. A very limited number of households (2%) use paid technical assistance.
5. The presence of a U-shaped relationship is con®rmed by regression analysis (not reported).
6. Households appear to be willing to accept a lower return on labor as a ``risk premium'' in return for the risk diversi®cation advantages associated with the adoption of reliance on a multiplicity of income sources.
7. Per capita expenditure is usually thought of being a better proxy to permanent per capita income since it captures household's ability to smooth consumption.
8. In the second regression reported in Table 2, we include a further interaction between specialization and the amount of labor supplied to the nonfarm sector to test whether there is a statistically signi®cant dierence between returns to labor obtained by house-holds who specialize in on-farm and on-farm activities, respectively.
9. Details for this approach of using ®rst dierenced variables as instruments for endogenous level variables are given in Hausmann and Taylor Edward (1981).
10. Since the high correlation of land and livestock (with a correlation coecient of more than 0.6) resulted in instability of the coecients, we added them together.
12. The negative sign of the squared term points to the presence of decreasing marginal impact of such asset-ownership on the propensity to specialize. By
comparison, the coecient on machinery is signi®cant at the 10% level and enterprise assets is not signi®-cant.
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