Intersectoral Transfer, Growth, and Inequality
in Rural Ecuador
Vrije Universiteit Amsterdam, The Netherlands
Vrije Universiteit Amsterdam, The Netherlands and The World Bank,
Washington, DC, USA
Summary. ÐIn this paper we study intersectoral transfer and its impact on the distribution of income in Ecuador. We ®nd that income shares between farm and nonfarm activities are roughly equal, on average, although the rich in rural areas typically receive a greater share of income from nonfarm sources. Thus decomposing inequality by income source reveals that a rise in nonfarm incomes increases inequality. Drawing on a new method to estimate local-level distributional outcomes, growth of the high-productivity nonfarm sector is observed to have a strong and positive association with average consumption and inequality. Growth of the low-productivity nonfarm sector is associated with little change in either average income or income inequality. Irrespective of subsector, growth of the nonfarm sector is associated with a substantial fall in poverty. Ó 2001
Elsevier Science Ltd. All rights reserved.
Key words Ðnonfarm employment, poverty, inequality, Lewis model, Ecuador
The interaction between economic growth and the distribution of income has been a central theme of economics since its inception. In the sub®eld of development economics, an early and in¯uential view of the development process was set out by Lewis (1954), in which growth takes place against a background of labor transfer out of traditional subsistence agriculture toward a modern sector, often tacitly assumed to be industrial and urban. Fields (1980, 2000) demonstrates that such a process is able to generate the well-known ``Inverted U-Curve'' of rising and then falling income inequality, ®rst described by Kuznets (1955, 1963).1
This paper revisits some of these classic themes in the context of rural Ecuador and poses two speci®c questions. First, must the process of intersectoral transfer necessarily occur between the rural and urban sectors, or can one view the rural nonfarm sector as an alternative to the modern urban sector
descri-bed by Lewis? Second, what are the distribu-tional consequences in rural areas of a growing
nonfarm sector?2 The paper sheds empirical
light on these questions on the basis of
house-hold survey and census data for Ecuador.3
We illustrate in this paper that the nonfarm sector in rural Ecuador is quite large, and very diverse. Because big dierences in productivity are observed, with many of the poor involved in low-productivity, residual activities, it is not immediately obvious whether the nonfarm sector is inequality-increasing or inequality-re-ducing. We show, however, that income shares from nonfarm activities are in fact highest among the rich. This suggests that the nonfarm sector is, on balance, inequality-increasing. Ó2001 Elsevier Science Ltd. All rights reserved Printed in Great Britain 0305-750X/01/$ - see front matter PII: S0305-750X(00)00110-8
*We are grateful to Hans Hoogeveen, Jenny Lanjouw, Martin Ravallion, Mitch Renkow, and two anonymous referees for useful comments and suggestions. The views in this paper are those of the authors and should not be interpreted as those of the World Bank or any of its aliates. All errors are our own.
This is con®rmed when we decompose income inequality by source, although the elasticity of inequality with respect to nonfarm income in rural areas is low.
We explore further the distributional impli-cations of the notion that the nonfarm sector comprises a low-productivity and a high-pro-ductivity subsector (as described, for example,
by Ranis & Stewart, 1993).4 Building on a
recently developed technique for estimating distributional outcome measures at geographi-cally disaggregated levels, we investigate eco-nometrically the relationship between the low-productivity and high-low-productivity nonfarm
subsectors and economic welfare. Taking
Ecuador's ``villages'' (parroquias) as our unit of analysis, we ®nd that village-level income inequality rates tend to rise with the share of the village labor force employed in high-pro-ductivity nonfarm activities. Employment in low-productivity nonfarm activities has either no, or a negative, correlation with inequality, depending on geographic region. Alongside their association with income inequality, we ®nd that employment shares in the high-pro-ductivity nonfarm sector are signi®cantly (and positively) correlated with village average per capita consumption levels, and negatively correlated with poverty rates. Our data allow us to assess to what extent the high-productivity part of the nonfarm sector in Ecuador acts as an engine-of-growth, contributes to income inequality, and reduces poverty, thus perform-ing a role originally ascribed by Lewis to the modern sector. Similarly, we measure the importance of the low-productivity nonfarm sector in acting as a safety net to protect the poor.
The layout of the paper is as follows. In the next section we provide the empirical evidence to document the size, variety, and importance (in terms of employment shares) of the nonfarm economy in rural Ecuador. Section 3 presents a framework within which to view the relation-ship between the nonfarm sector and the distribution of income. In Section 4, we analyze household survey data to identify the individ-ual, household, and community-level factors which appear to in¯uence whether a person is engaged in the nonfarm sector. We report on our exercise of decomposing inequality in rural Ecuador by income source. In Section 5, we describe the construction of our census-based dataset of village-level income inequality and poverty outcomes, and present empirical evi-dence on the association of the nonfarm sector
with village average per capita consumption, poverty and inequality. Section 6 concludes the paper.
2. THE RURAL NONFARM ECONOMY IN ECUADOR
A nationally representative household survey
®elded in Ecuador in 1995 (the Encuesta de
Condiciones de Vida, ECV) provides consider-able detail on the extent and nature of nonfarm activities undertaken in rural areas. The survey covered a total of 5,760 rural and urban households, and follows a multi-module format based on the World Bank's Living Standards Measurement Surveys (LSMS). We draw on this survey to describe the basic features of the
nonfarm economy in rural Ecuador.5
Table 1 presents a breakdown of nonfarm wage employment shares by subsector of
employment and geographic region.6In all the
three regions of Ecuador, the proportion of the working population employed in nonagricul-tural activities is substantial, ranging from just over a quarter in the Oriente to more than 43%
Table 1. Nonfarm wage employment in rural ecuador (principal and secondary occupations)
Extraction 0.7 (1.6) 0.9 (2.4) 0.3 (1.1) Manufacture 4.4 (10.1) 6.7 (17.9) 2.6 (9.2) Textiles/Garments 0.9 (2.1) 1.4 (3.7) 0.3 (1.1) Wood/Straw/
0.4 (0.9) 2.5 (6.7) 5.8 (20.6)
Utilities 0.2 (0.5) 0.0 (0.0) 0.0 (0.0) Construction 3.2 (7.3) 6.2 (16.6) 2.2 (7.8) Commerce 15.8
7.7 (20.6) 6.6 (23.4)
Restaurant/Hotel 1.6 (3.7) 0.9 (2.4) 0.6 (2.1) Transport 2.1 (4.8) 1.8 (4.8) 2.2 (7.8) Finance 0.1 (0.2) 0.0 (0.0) 0.3 (1.1)
0.7 (1.6) 0.2 (0.5) 0.0 (0.0)
Administration 1.3 (3.0) 1.9 (5.1) 3.0 (10.6) Teaching 1.9 (4.3) 2.4 (6.4) 0.9 (3.2) Social services 0.5 (1.1) 0.6 (1.6) 0.6 (2.1) Community work 0.5 (1.1) 3.1 (8.3) 2.0 (7.1) Domestic service 1.4 (3.2) 1.1 (2.9) 0.8 (2.8)
in the Costa (although this ®gure includes ®shing activities that are signi®cant in the Costa but not elsewhere). Commerce activities are particularly important in the Costa, while in the Sierra manufacturing and construction are also signi®cant.
Table 2 provides a breakdown of home-business activities and their contribution to family employment in rural Ecuador. In total just under half a million small ®rms were esti-mated to be operating in rural Ecuador in 1995,
providing employment to nearly 900,000
persons.7 Most rural businesses are quite
small, with an average of 1.8 workers. More than four-®fths of all persons employed in home businesses are family members. On average, more than two-thirds of all self-owned businesses are home-based. The total range of activities in which home businesses are engaged is quite large, but more than 40% of them are involved in small-scale commerce, such as shops selling basic provisions, restaurants, etc. Other important sectors include agricultural goods and food processing (4% of businesses),
®shing (7%), textiles and garments (9%) wood and straw crafts (4%), transport services (5%) and other services.
3. NONFARM EMPLOYMENT AND INCOME INEQUALITY: A FRAMEWORK
In this section we consider a highly simpli®ed scheme of income generation within which to assess the interaction of the nonfarm sector with income inequality. The scheme is very much in the spirit of the Lewis (1954) model of development. We consider three types of activities in rural areas, summarized in Table 3. The third activity in Table 3 refers to a last-resort activity for those who can ®nd no other employment, or income source. One should think of residual activities, such as very simple trade or irregular services, generating a very
low income wlow, probably well below the
poverty line.8 In a well-functioning labor
marketwlowshould equal marginal productivity
Table 2. Nonfarm rural enterprises in Ecuadora No. of
No. of workers
No. of family workers
Percent home-based (%)
Total employment Agriculture (sales/services) 9,056 2.37 1.44 55 21,477
Forestry 2,152 2.37 1.53 58 4,815
Fishing 34,440 1.89 1.28 4 65,294
Mining/Extraction 4,319 6.61 1.63 92 28,563
Food processing 9,074 2.09 1.80 95 19,027
Textiles and garments 40,537 1.37 1.29 99 55,513
Leather goods 1,529 2.01 2.01 100 3,074
Wood and straw crafts 20,235 1.59 1.33 85 32,367
Paper 633 1.00 1.00 100 633
Sound/Recording 486 1.00 1.00 100 486
Rubber goods 425 3.63 0.12 100 1,544
Metals 6,466 3.06 1.83 100 19,783
Metal products 2,274 2.45 1.09 81 5,570
Machinery and equipment 573 1.00 1.00 100 573
Automotive 727 1.94 1.94 94 1,409
Furniture 14,250 2.11 1.81 94 30,090
Construction 10,547 2.41 1.48 68 25,418
Sales/Repair of vehicles 3,312 1.25 1.00 98 4,132
Wholesale commerce 1,179 2.55 1.83 47 3,008
Petty commerce 194,760 1.72 1.56 75 335,010
Hotel/Restaurant 13,855 2.29 2.14 81 31,727
Transport services 21,482 1.83 1.25 1 39,235
Financial intermediation 340 3.00 2.00 100 1,020
Machinery rental 547 2.32 1.32 32 1,268
Administration/Managerial 3,020 1.27 1.00 59 3,844
Teaching 2,667 1.17 1.07 100 3,129
Other services 71,797 1.45 1.13 69 104,188
Total 470,682 1.79 1.44 69 842,197
of labor in agriculture. 9 Hence one would
expect the wage rate wlow to be close to the
incomes of landless laborers in agriculture. Of course, the incomes of the owners of land and other nonlabor production factors in agricul-ture (the ®rst activity listed in Table 3) can be much higher, saywlowp, wherepis the pro®t
per land owner.
To work in the high-productivity, nonagri-cultural sector (activity 2 in Table 3) one needs to possess special skills or opportunities. These might come from education or, if this segment of the labor market is characterized by infor-mation problems, or is otherwise not perfectly competitive, these might come from other individual-level opportunities to ®nd employ-ment in this sector (such as relatives, friends, or bribed civil servants). Typically the workers in this sector are much better o than the low-productivity workers in and outside agricul-ture, so that wage ratewhighis much higher than
wlow and could well be higher than the income
of land owners.
Suppose that the process of economic devel-opment in a particular setting takes the form of a Lewis-style enlargement of a modern sector (for example, the expansion of an industrial sector with accompanying services sector). But, suppose that instead of occurring in some distant urban setting, and accompanied by migration from rural areas to the cities (as described in the original Lewis story), this process takes the form of an expansion of
high-productivity nonagricultural employment. 10
An example of such a transition is presented in Table 4.
Going from employment Pattern I to Pattern II in Table 4, it is clear that the incidence of
poverty is lower (since fewer people are work-ing for a below-poverty line wage), average income is higher, and inequality could very well have risen. 11
In this simple characterization of the devel-opment process, the expansion of
nonagricul-tural, high-productivity activities raises
incomes, while it may lead to a higher degree of measured inequality. Moreover (also very much following the Lewis model) one would expect to see indirect eects deriving from increased employment in the high-productivity sector. As low-productivity labor becomes relatively more scarce, increasing its opportunity cost, one would expect the low-productivity wage rate
wlow to go up between Patterns I and II. Thus
the growth of high-productivity employment could eventually trickle down to increase labor productivity in the low-productivity sectors as well, thereby helping to reduce poverty.
Below we further examine the rural nonfarm economy in Ecuador, with an eye toward assessing how well the stylized scheme
descri-bed above is re¯ected in the data.12 Note,
however, that the statistical analysis that follows stands on its own and is not meant to be a formal test of the framework of analysis described above.
4. NONFARM INCOMES, ACCESS, AND DISTRIBUTION: EVIDENCE FROM
(a) Income shares
Total income from nonagricultural activities derives from wage employment and home businesses. Table 5 indicates that for the rural population in Ecuador more than 40% of income derives from nonagricultural activities, only marginally less than the farm-income share.13The nonagricultural sector in Ecuador is thus signi®cant not only in terms of employment but also income. Across quintiles
(de®ned in terms of per capita consumption
expenditure), the share of total income from
Table 4. Hypothetical employment patterns
Pattern I (%) Pattern II (%)
Nonagriculture, low-productivity wlow 50 25
Agriculture, landless labor wlow 25 25
Agriculture, land owners wlowp 25 25
Nonagriculture, high-productivity whigh ± 25
Table 3. Types of rural activities
1. Agricultural activities
Required inputs: labor and land
2. Rural nonagriculture, high-productivity activities Required inputs: labor and speci®c opportunities (capital, connections, etc.)
nonagricultural sources rises sharply with living standards. But, even the poorest quintile in rural Ecuador receives about one-®fth of its total income from nonagricultural activities. This rises to 37% for the second and third quintiles, and is as high as 64% for the top quintile.
Between the two types of nonagricultural income sources, home-enterprise income is consistently more important as a fraction of total income than nonagricultural labor income. Again, the correlation between share of income from home enterprises and consumption rank-ings is marked. The nonagricultural wage income share represents only about 9% of total income on average. This source is also less monotonically linked to consumption rankings than home-enterprise income. While the poorest quintile receives 6% of total income from nonagricultural wage labor sources, this rises to 11% for the second quintile, falls back to 9% for the next two quintiles, and then rises back to 12% for the top quintile.
(b) Employment probabilities and determinants of wage-labor earnings
We now turn to the factors which are asso-ciated with employment in nonagricultural activities, and the level of earnings that such activities generate. Table 6 presents three Probit models linking the probability of having primary employment in a nonagricultural wage-labor occupation to a range of
explana-tory variables.14 In the ®rst regression, the
dependent variable takes a value of 1 if the person is primarily employed in nonagricultural wage labor and 0 otherwise. The second and third models split those employed in the nonagricultural wage-labor force into two
groups; those with a low-productivity job and those with a high-productivity job, respectively. The distinction between low- and high-pro-ductivity is based on whether earnings, respec-tively, fall below, or exceed, the average earnings of someone with agricultural wage labor as a primary occupation.
Considering all nonagricultural employment together, women are signi®cantly more heavily represented in the nonagricultural wage-labor force than men. At average values of all other variables, the probability of primary employ-ment in the nonagricultural sector rises from
8% for a man to 21% for a woman.15What is
striking, however, is that after dividing the types of occupations into two groups depend-ing on whether earndepend-ings are on average lower or higher than average earnings from agricul-tural labor, women are signi®cantly lesslikely to be employed in the relatively high-produc-tivity occupations. The likelihood of being employed in a high-productivity job falls from 1.2% for a man to 0.6% for women, at average values of all other variables.
Relative to the uneducated, those with edu-cation are generally more likely to be employed in the nonagricultural sector, particularly in the high-productivity jobs. In low-productivity jobs, the only statistically signi®cant education variable is a dummy variable for secondary education. In the high-productivity jobs the primary, secondary, and university education dummies are all statistically signi®cant. At average values of other variables, having completed primary education raises the prob-ability of employment in a high-productivity job from 0.3% for the uneducated to 1%. Education at the secondary-level increases this probability to 5%. The probability of being employed in a high-productivity job then jumps
Table 5. Sources of income by expenditure quintile in rural Ecuador share of income from the respective sourcesa Farm
Agricultural labor (%)
Poorest quintile 69 6 16 6 22 3
2nd 46 13 26 11 37 4
3rd 46 14 28 9 37 3
4th 41 8 37 9 46 5
5th 27 6 52 12 64 3
Total 46 9 32 9 41 4
to 37% for individuals with education levels at
the university level. It is important to
acknowledge that the exogeneity of education in these models can be questioned, so one must be careful to refrain from concluding that improvements in education would necessarily lead to increased employment in high-produc-tivity nonagricultural occupations. The evi-dence does suggest, however, that this question merits further research.
In all models, age is positively associated
with the probability of nonagricultural
employment up to about 55 years of age in the full model. Beyond that age, the probability of
nonagricultural employment declines. The
corresponding turning points in the low-pro-ductivity and high-prolow-pro-ductivity models are 65 and 50, respectively. Being indigenous plays a role only in the case of Shuar speakers and the high-productivity jobs.
Individuals from households which report some income from cultivation are signi®cantly less likely to be employed in the nonfarm sector in all three modelsÐpresumably because for cultivating households the ®rst call on family labor is on the farm. Per capita land holdings have a signi®cantly negative eect on nonagri-cultural employment and, given nonagrinonagri-cultural
Table 6. Probability of nonagricultural employment as a primary occupation
Probit model All employment in
Employment in low-productivity jobb
Employment in high-productivity jobb
Estimate Prob. Estimate Prob. Estimate Prob. Intercept )1.674 0.0001 )1.551 0.0001 )3.073 0.0001 Household size )0.006 0.5428 )0.020 0.0660 0.021 0.1111
Female 0.642 0.0001 0.852 0.0001 )0.248 0.0012
Age 0.073 0.0001 0.035 0.0001 0.101 0.0001
Age squared )6E)4 0.0001 )2E)4 0.0013 )0.001 0.0001 Quichua speaker 0.102 0.3076 )0.007 0.9473 0.156 0.3296 Shuar speaker 0.419 0.2392 0.061 0.8950 0.694 0.0894 Pre-primary education 0.186 0.2929 0.248 0.1834 0.025 0.9253 Primary school education 0.253 0.0017 0.053 0.5311 0.435 0.0004 Secondary school education 0.604 0.0001 0.307 0.0066 0.669 0.0001 University education 0.777 0.0045 )0.428 0.2268 1.299 0.0001 Other tertiary education 7.344 0.9986 7.493 0.9986 )5.070 0.9994 Post-graduate education 5.592 0.9993 )5.720 0.9993 6.722 0.9995 Vocational training 0.127 0.4244 0.118 0.4896 0.003 0.9894 Land owned per capita )0.018 0.0056 )0.025 0.0030 )0.003 0.7868 Land owned Squared 2.3E)6 0.0394 3.3E)6 0.0171 )2E)5 0.8860 Cultivating household
1.026 0.0001 )0.620 0.0001 )0.939 0.0001
Rural periphery )0.784 0.0001 )0.416 0.0006 )0.812 0.0001 Rural dispersed )0.863 0.0001 )0.646 0.0001 )0.536 0.0001
Costa 0.247 0.0001 0.293 0.0001 )0.002 0.9806
Oriente )0.357 0.0002 )0.323 0.0035 )0.156 0.2160 Migrant during past decade 0.033 0.6695 )0.016 0.8497 0.036 0.7151
Log likelihood (model) )1479 )1248 )815
Log likelihood (constant) )2147 )1618 )1109
Total observations 4523 4523 4523
Observations at 0 3699 4001 4221
Observations>0 824 522 302
LR test (model) 1336 740 588
Degrees of freedom 21 21 21
Criticalv2 32.67 32.67 32.67
aNonagricultural employment here denotes only those individuals with
wage employmentin the nonagricultural sector as aprimaryoccupation.
employment, on employment in low-productivity jobs. The nonsigni®cance of this variable (indeed, the zero estimate of the relevant coef-®cient) in the high-productivity regression, suggests that a positive eect is neutralizing this general negative eect. It is sometimes argued that if o-farm employment opportunities, particularly the more attractive ones, are rationed, then access might be in¯uenced by the household's wealth and in¯uenceÐand this might be correlated with landholding (see Lanjouw & Stern, 1998). This would imply a positive and signi®cant coecient on land in the case of the high-productivity jobs. While the evidence here does not oer strong support to this contention, the lack of a strongnegative
relationship between landholding and high-productivity nonfarm jobs suggests that one cannot exclude a wealth eect.
The ECV95 data disaggregate rural areas into three subregions; ruralperiferia, rural amanza-nado, and ruraldisperso. Ruralperiferia refers to the rural areas immediately surrounding the
larger conurbations. Rural amanzanado
corre-sponds to rural communities with some basic infrastructure but a population of less than
5,000 persons. Rural disperso refers to the
remaining rural areas. In Table 6 it appears that, relative to persons living in the rural
amanzanadoareas, persons from both the urban periphery and outlying areas are less likely to be employed in the nonagricultural sector (both high- and low-productivity jobs). In the case of the outlying areas this is not surprising, as presumably households are more likely to be engaged in cultivation there. But, the lower probability of nonagricultural employment for persons in the urban periphery is puzzling: one would think that wage employment opportuni-ties are relatively common in urban centers. However, poverty in the urban periphery is
much higher than in either the
amanza-nado areas or in urban areas (Lanjouw, 1999).
Several factors might combine to explain this observation. First, periurban areas might func-tion as a temporary stepping stone for migrants from remote rural areas aiming to enter the urban sector. As such, few are likely to be prepared to make the investments necessary to establish substantial nonfarm activities. Second, proximity to large urban markets might prompt intensive agricultural activitiesÐparticularly the cultivation of perishable foodcrops which can be sold on the urban markets.
Relative to those living in the Sierra, the population in the Costa is more likely to be
employed in nonagricultural activities. This is not, however, signi®cant in the high-produc-tivity job model, suggesting that while there may be more nonagricultural activity in the Costa, much of this is relatively low paid. This observation is consistent with the ®nding in World Bank (1995) that in the Costa the poor are more widely engaged in both the agricul-tural and nonagriculagricul-tural labor markets, while the poor in the Sierra are often subsistence cultivators. In the Oriente the probability of nonagricultural employment is lower than in the Sierra, particularly in the low-productivity jobs.
In Table 7 we examine earnings from nonagricultural jobs on the basis of an OLS regression for the subset of persons with
Table 7. Nonagricultural wage labor incomea OLS modelb Estimate Prob.
Intercept 12.40 0.0001
Household size 0.05 0.0028
Female )1.22 0.0001
Age 0.106 0.0001
Age squared )0.001 0.0001 Quichua speaker )0.03 0.8853 Shuar speaker 0.54 0.4498 Pre-primary education 0.03 0.9248 Primary school
University education 1.27 0.0638 Other tertiary
Vocational training )0.23 0.3482 Land owned per capita 0.05 0.1586 Land squared )5E)4 0.6561 Cultivating household
Costa )0.25 0.0163
Oriente )0.15 0.4359
Migrant during past decade
Mills ratio 1.0E)8 0.8892 AdjustedR2: 0.267
Number of observations: 825
aNonfarm incomes are calculated as earnings from individuals' primary wage employment. Household-en-terprise incomes are therefore not included. Incomes are expressed in annual sucres (in 1995 US$1.00 was approximately equal to 3,000).
primary employment in the nonagricultural sector. The speci®cation for this model includes a correction for sample-selection based on the
®rst Probit model in Table 6. 16The
insigni®-cant parameter estimate on the Mills ratio variable suggests that in the present example there is no correlation between unobserved variables which in¯uence the probability of employment in the nonfarm sector and unob-served variables aecting earnings in that sector.
As we would expect, given the dierent
probabilities of employment in low- versus
high-productivity jobs observed in Table 6, women earn less than men from nonagricul-tural jobs. Based on the parameter estimates of Table 7, a woman would expect to earn about 70% less than a man from her nonagricultural
occupation.17 The association between
nonfarm earnings and education is very strong (although once again, direction of causation has not been established here).
In the Probit analysis of Table 6, there was at best a weak suggestion that persons with greater wealth (proxied by per capita land holdings) might be more highly represented in high-productivity nonagricultural occupations. In terms of earnings this conjecture receives
additional support; an additional hectare
owned is associated with higher nonagricul-tural earnings (by about 5%). Once again,
however, if the household is cultivating some
land (as opposed to simply owning land), earnings decline. A person from a cultivating household earns about 37% less than if the household is not cultivating. The probable explanation for this is that a person who belongs to a cultivating household may spend at least some time helping on the farm (for example, for harvesting and at other periods of peak labor demand) and this reduces his
average monthly income from nonfarm
employment, even if the latter is his primary occupation.
As suggested from the Probit models, while those in the Costa were more likely to be employed in the nonagricultural sector than those in the Sierra, they earn signi®cantly less from such occupations. A person with a primary occupation in nonagricultural wage employment in the Costa would earn about 22% less than a person in the Sierra. There is no signi®cant earnings dierential between the Sierra and Oriente, although the point esti-mates also suggest that the dierential would favor the Sierra.
(c) Home-enterprise activities
Table 8 returns to a Probit model examina-tion of the likelihood that a household will possess a home enterprise. This model is at the household rather than individual level, and although roughly similar explanatory variables are applied as in the previous models, a few variables relating to infrastructure access were added.
Education is again strongly correlated. If the most educated family member has a primary school education or a secondary school edu-cation, then the household is more likely to own a business than a household where nobody is educated. Further, those households in which
Table 8. Probability of rural enterprise
Probit model Estimate Prob. value Intercept )0.50 0.0003 Household size 0.05 0.0001 Quichua speaker 0.16 0.1080 Shuar speaker )0.12 0.7295
Education of best-educated household member
Pre-primary schooling )0.05 0.6478 Primary school 0.19 0.0757 Secondary school 0.20 0.0009 University education 0.21 0.1016 Other tertiary education )0.13 0.5721 Post-graduate education 6.53 0.9987
All family members literate 0.17 0.0106
Land owned by household )0.00007 0.7931 Cultivating household
Rural periphery )0.39 0.0014 Rural dispersed )0.62 0.0001
Costa 0.15 0.0136
Oriente 0.15 0.1163
Migrant during past decade
Connection to electricity network
Telephone connection 0.30 0.0744 Water connection 0.05 0.4661
Log likelihood (M):)1487.08 Log likelihood (0):
Total Observations: 2492 Observations at 0: 1504 Observations >0: 988
all family members are literate are more likely to own a business than those where nobody is educated. Unlike in the case of employment, however, higher levels of education appear to be relatively less strongly correlated with household business activity. This ®nding could indicate that those with tertiary levels of edu-cation are more likely to enter into a salaried occupation than set up a family business.
As before, cultivating households are less likely to have a home business, and land holding exercises no signi®cant independent in¯uence. Those residing in the rural periphery and outlying areas are once again less likely to own family businesses. As before, the Costa region has a relatively higher incidence of family businesses than the Sierra.
Whether a household is connected to the public electricity network, and whether it has a telephone connection are strongly related to the likelihood of home-enterprise ownership. These observations add to the perception that infra-structure is an important facilitator of nonag-ricultural activity.
(d) Decomposing inequality by factor source
In this section we decompose income
inequality by factor components to assess the contribution of sources of income to total income inequality. What we can focus on here is theelasticityof overall inequality, the degree to which overall inequality changes with small changes in rural nonagricultural incomes.
Table 9 presents the Gini coecient for income in Ecuador as a whole (including urban
areas), decomposed by income components. 18
Following Shorrocks (1982) the Gini coecient
G, can be obtained as a weighted average of
``pseudo-Gini'' coecientsGi for each compo-nent, where the weights are given by the share
ak of component income in total income: 19
k anG n:
The pseudo-Gini coecient for an income component is similar to the Gini coecient for that component but with the modi®cation that individuals are ranked in terms of their total
income rather than component income.20
In general, the change in the overall income inequality brought about by an increase or a reduction of income from a given source will be smaller the closer the pseudo-Gini coecient for that source is to the overall Gini. To see this, suppose we decompose income into two components:
GaG1 1 aG2:
Consider a change in income from a dierent mix of the two income sources, assuming that the distribution between income sources does not change
G0a0G1 1 a0G2:
This implies that
DGG0 G Da G 2 G
G 2 G
G G 1
1 a ;
the change inGcan be written as
G G 1:
The smaller the dierence between the pseudo-Gini coecient for a given source and the overall Gini coecient, the smaller will be the impact on inequality from a change in income from that source. The elasticity of the Gini coecient with respect to a change in income from component 1 is thus proportional to the percentage dierence between the overall Gini coecient and this pseudo-Gini coecient
Table 9. Income inequality by factor components
Per capita incomes in rural Ecuador Farm income
Agricultural labor income
Rural nonfram income
Total income Pseudo-Gini coecient (G) 0.791 0.665 0.817 0.611 0.785 Share of total per capita income (ak) 0.372 0.089 0.497 0.042 1.000 Gini coecient (Gk) 0.926 0.889 0.895 1.055 0.785 Coecient of rank correlation (RkGk=Gk) 0.854 0.748 0.913 0.579 1.000 Contribution to overall Gini coecienta 38% 8% 52% 3% 100% Elasticity of overall Gini to small increase in
0.005 )0.015 0.040 )0.01
From Table 9 we see that the contribution of nonagricultural income inequality to overall inequality in rural Ecuador is 52%, compared to 38% for farm income. With an elasticity of 0.04 we can see that an increase in rural
nonagricultural income increases overall
inequality in rural areas. An increase in rural
nonagricultural income of 10% wouldraisethe
Gini coecient for rural Ecuador from 0.785 to 0.789.
It thus appears that on the basis of survey data for rural areas, nonagricultural incomes go primarily to the better o, so that higher nonagricultural incomes (as opposed to more nonagricultural income earners) is associated with higher inequality.
5. INTERSECTORAL TRANSFER, GROWTH, POVERTY, AND INEQUALITY
A single-period household survey oers limited scope to investigate directly the rela-tionship between growth of the nonfarm sector and average incomes and their distribution. The decomposition exercise carried out in the preceding section, for example, while sugges-tive, does not capture the indirect eects that rising nonfarm incomes can exert on total incomes and their distribution. In addition, the exercise is at best able to point to the impact of a small change in nonfarm incomes.
The detailed time-series data necessary to investigate these dynamic questions more completely are unfortunately not available for Ecuador (nor for most other developing coun-tries). In this section we describe an alternative approach to investigate the impact of a growing nonfarm sector. We scrutinize the relationship between distributional outcomes and employ-ment shares in the nonfarm sector at the level
of Ecuador's 1,000-odd rural parroquias, the
small administrative-delineated geographic unit in the country. This is possible as a result of recent research in which data from the 1994 ECV survey are combined with the 1990
Ecuadorean census (Elbers, Lanjouw, &
Lanjouw, 2000). The technique is brie¯y described below.
Elbers et al. (2000) use data from the 1994 ECV survey to estimate a model of per capita
consumption expenditure and then use the resulting parameter estimates to weight
census-based characteristics of the entire population of
Ecuador and calculate each household's
expected welfare level. They show that this merging of data sources yields an estimator which can be clearly interpreted, extended in a consistent way to any aggregated welfare measure (poverty rate, measure of inequality, etc.) and which can be assessed for statistical reliability. They show that the method yields estimates of poverty and inequality measures which are quite precise and reliable for popu-lations of 5,000 households and still remark-ably good for populations as small as 500 households.21
Based on the method described in Elberset
al. (2000), a database was constructed
comprising estimates of headcount rates,
aver-age per capita consumption levels, and
consumption inequality (calculated on the basis of the Atkinson measure with an inequality aversion parameter value of two) for each of
the 915 parroquias in rural Ecuador. This
database was combined with census-based
information on parroquia-level demographic
composition and employment shares in dier-ent sectors. The resultingparroquia-level data-set constructed in this way permits one to carry out an analysis within Ecuador along lines similar to what has traditionally been carried out at a cross-country level. 22
We estimate three sets of regression models,
with respectively the parroquia average per
capita consumption level, the headcount rate, and the Atkinson 2 measure of inequality as dependent variables. These dependent variables
are regressed on the same speci®cation
comprising the share of the working population employed in low-productivity nonfarm activi-ties, the share employed in high-productivity nonfarm activities, and a set of variables capturing the demographic composition of each parroquia. 23 The models are estimated separately for the three main agro-climatic regions in Ecuador. The results are reported in Table 10.
Costa region, US$82 higher in the Sierra region and US$59 higher in the Oriente. More low-productivity nonfarm employment is associated with higher average consumption in the Sierra and Oriente (albeit modestly), but with lower average consumption in the Costa (although this is not statistically signi®cant). In this latter region, agricultural wage labor is widespread and relatively well-paid in at least some places (due to the location of many export-oriented plantations in this region), so that low-pro-ductivity nonfarm activities are particularly likely to indicate distress in the rural Costa region. In the Sierra, with its widespread rural manufacturing tradition, the low-productivity category is less clearly a category of last-resort, distress activities. In all regions, the coecients on employment shares are statistically signi®-cant (except for rural Costa with the low-pro-ductivity category). The parameter estimates on the demographic variables indicate that average
consumption levels are highest in parroquias
with large working age populations. The total
population of theparroquiadoes not appear to
markedly in¯uence average consumption levels (only in the Sierra is this parameter estimate signi®cant, and even then it is small). This simple speci®cation explains a remarkably high 78% of total variation of average per capita
consumption acrossparroquiasin rural Sierra,
56% in Costa and 67% in Oriente.
The positive association of nonfarm
employment shares with average income is mirrored by a largely negative correlation with poverty. Once again, the share of the working
population in high-productivity nonfarm
employment has the most marked impact. An additional percentage of the working popula-tion employed in high-productivity activities is associated with a decline in the headcount of 42% points in the Costa, 41 in the Sierra and 47 in the Oriente. While more low-productivity nonfarm activity is associated with lower poverty in the Sierra and Oriente, the opposite is true in the Costa. The response of poverty to changes in the low-productivity employment shares is milder than in the case of high-pro-ductivity nonfarm activities, but the correlation
Table 10. Diversi®cation into the rural nonfarm sector and welfare: explaining parroquia level per capita consumption, poverty and inequality
Average per capita consumption Poverty (headcount)
Sierra Costa Oriente Sierra Costa Oriente Sierra Costa Oriente
Share of labor AdjustedR2 0.778 0.563 0.669 0.750 0.647 0.585 0.537 0.415 0.445 No. of
490 271 145 490 271 154 490 271 145
with the low-productivity shares is far from negligible. In the Sierra and Oriente the eect of an increase in the low-productivity employment share is between a third and half as strong as the eect of a change in the high-productivity employment share (compared to a relative impact on average consumption of less than one-eighth). A 1% increase in the share of the working population employed in low-produc-tivity nonfarm activities is associated with poverty, respectively, 13% and 25% points lower. In the Costa the association is with poverty eight percentage points higher. It is clear that the low-productivity category in the Costa region is most clearly associated with distress, while in the other two regions,
employment in low-productivity activities
remains more attractive than the default activ-ities in agriculture. Once again a fairly high degree of explanatory power is obtained in all
three regions (adjusted R2's ranging between
0.75 and 0.59).
While a bigger nonfarm sector is clearly associated with higher average incomes and lower poverty, it is also associated with greater consumption inequality. This is particularly the case with the high-productivity nonfarm activ-ities that were also associated most strongly with higher consumption and lower poverty. The low-productivity nonfarm activities are either associated with lowerinequality (Sierra) or with no discernable impact on consumption inequality. While the explanatory power of this model is lower than in the other two models, it remains quite high.
Overall, the evidence suggests that
high-productivity nonfarm jobs raise average
incomes and result in higher income inequal-ity. But they do also have a dampening eect on poverty. It is not obvious that an expan-sion of high-productivity nonfarm activities
results directly in improved employment
opportunities for the poor. Yet, the impact we observe on rural poverty is very strong. One
possibility is that the mechanism works
through the agricultural labor market: greater employment in the high-productivity nonfarm sector tightens the agricultural labor market (as those farmers who are relatively well edu-cated are drawn to work in the nonfarm sector), and this results in greater participation and/or higher wage rates by the poor in the agricultural labor market. Other explanations might also apply. It is important to emphasize that the models estimated here have not been able to establish the direction of causation
from the nonfarm sector to the welfare outcomes. It is possible, for example, that
exogenously driven growth in agriculture
raises incomes and income inequality, and reduces poverty, and that these changes in well-being result in increased demand for nonagricultural goods and services (so that employment rates in these subsectors change). The wide variety and strength of ``linkages'' between the nonfarm sector and the agricul-tural sector has been the subject of much theoretical and empirical analysis. 24
Traditional theories of development paid considerable attention to the role of intersec-toral transfer (from agriculture to industry) as a central feature of the development process. There has also been a long-standing interest in understanding how economic development aects the distribution of income.
In this paper we have concentrated on the case of a single developing country, Ecuador, to study both the process of intersectoral transfer and its impact on the distribution of income. Our analysis shows that while traditional theories tended to locate the modern sector in urban areas, the rural nonfarm sector in Ecuador is both large and extremely heteroge-neous. Income shares in rural areas from nonagricultural activities are only slightly lower, on average, than from farming, with on balance, the share of nonfarm income being highest among the top quintile in the distribu-tion of income. When we split nonfarm activi-ties into two categories, low-productivity and high-productivity activities, we show that the pro®le of persons involved in these two subsectors are quite dierent. Women are highly represented in low-productivity activi-ties. The well educated are highly represented in high-productivity occupations. There is evi-dence that nonfarm activities are most common in and around rural townships, and when households have better access to infrastructure services.
We have shown, on the basis of a decom-position of inequality by income source, that a
rise in nonfarm incomes would increase
inequality. We pursue this question further at
the level of Ecuador's parroquias. Drawing on
nonfarm sector has a strong and positive association with average consumption and also on inequality. Growth of the low-pro-ductivity nonfarm sector is associated with little change in either average income or
income inequality. Growth in eithersubsector
of the nonfarm sector, however, is associated with a substantial and negative impact on poverty (with the single exception occurring in the case of low-productivity employment in rural Costa). We suggest the possibility that
expansion of high-productivity nonfarm
activities in¯uences poverty via a tightening of the agricultural labor market that raises participation rates and/or wage rates in the agricultural labor market.
Overall our analysis suggests that the tradi-tional Lewis model of growth through inter-sectoral transfer remains highly relevant once it is recognized that the modern, nonagricultural sector can develop in rural areas as well as in
the cities. Our evidence suggests that this process is likely to be inequality-increasing, but that this should not be interpreted to imply that the poor do not bene®t.
A ®nal word on the limitations of our analysis is warranted. We have been careful in this paper to refrain from attributing a strong causal link to the relationship between the nonfarm sector and welfare outcomes. While we believe that such a link may well apply, our statistical analysis does not establish it. In particular, we cannot exclude the possibility that it is the agricultural sector that acts as the driving force behind both changes in
welfare as well as changes in nonfarm
employment patterns. Further research, in which agricultural performance is separately controlled for, is clearly necessary to build up the story of an expanding nonfarm sector
driving growth and poverty reduction in rural areas.
1. Strictly speaking, as Fields shows in a stylized example, the Lewis process will typically produce crossing Lorenz curves so that dierent summary measures of income inequality will trace out a dierent path over time.
2. We utilize the terms ``nonfarm'' and ``nonagricul-tural'' sectors interchangeably.
3. In a survey of the literature, Reardon, Taylor, Stamoulis, Lanjouw, and Balisacan (2000) suggest that across countries the impact of the nonfarm sector on income inequality is far from uniform.
4. In this paper we use the terms high- or low-productivity, and high- or low-income interchangeably. In Ecuador there is no special reason to expect that large income dierences are not based on productivity dier-ences.
5. For further details, and an analysis of the interac-tion between the nonfarm sector and poverty in rural Ecuador based on this same dataset, see Lanjouw (1998, 1999)
6. Nonfarm wage employment activities included all activities which are not explicitly a home-enterprise or business activity. As such, they would include self-employment activities such as petty trading in the
local market. Ecuador can be divided into three regions: Costa, which is the region bordering the Paci®c Ocean on the west, Oriente, which is the area representing the Amazon region in the east, and Sierra, representing the Andean mountainous regions in the center.
7. The total rural population in Ecuador is around 4.5 million. The expansion factors included with the house-hold survey were used to in¯ate up from the ECV survey to the total population.
8. Ranis and Stewart (1993), building on earlier work by Hymer and Resnick (1969), posit a model of the nonfarm sector in which one part of the sector engages in producing traditional goods and services in house-holds and villages and the other comprises more modern activities. They suggest that this framework describes well certain developing countries, such as colonial Taiwan. For further discussion, see Lanjouw and Lanjouw (forthcoming).
9. It is well known that agricultural productivity varies over the seasons and the wage rate in agriculture might re¯ect peak labor demand in agriculture, leading to wage income abovewlow.
10. We are not addressing here the question of why
alongside growth of urban centres in developing countries. But, what is becoming increasingly evident is that such expansion does occur in many developing countries (see Lanjouw & Lanjouw, forthcoming, for a review of the evidence on growth of the nonfarm sector).
11. Note that not all possible summary measures of income inequality need describe the distribution in Pattern II as more unequal. As an anonymous referee noted, Pattern II ®rst-order dominates Pattern I and hence would be unambiguously preferable to Pattern I, even if the latter has lower variance.
12. Other outcomes might also change as a result of the processes described here. For example, one might expect changes in tenancy arrangements to accompany a shock in the rural nonfarm sector. Or there might be an alteration of rural±urban ¯ows. We do not pursue these questions in the analysis here.
13. See the annex in Lanjouw (1998) for a detailed description of how an income aggregate was constructed from the 1995 ECV.
14. Almost all explanatory variables in Table 6 are household characteristics. Other variables, related to local agrarian structure, have been suggested as deter-minants of the size and importance of nonfarm high-productivity activities. Such variables are, however, not available in our data set or would have shown too little variation to distinguish their eects from the location dummies used in the regression of Table 6. For additional empirical evidence on the determinants of nonfarm employment in rural Ecuador, based on case studies, the interested reader is referred to North (1999) and Larrea (1987).
15. This is calculated by evaluating the predicted values of the regression in turn when the female dummy takes a value of 0 and 1, respectively, and when all variables are taken at their mean values.
16. The identifying variables from the probit model are the dummies which break the rural areas into outlying areas, built-up rural communities, and the urban periphery. To check whether these variables in fact in¯uence the likelihood of o-farm employment, but not the earnings from such jobs, the residuals from the model in Table 7 were regressed on all explanatory variables in that model plus the disperso
and periferia dummies. None of the parameter esti-mates in this regression was signi®cant, and theR2was 0.0024.
17. A coecientcmultiplying a dummy variable can be interpreted as a percentage change in earnings only as long ascis close to zero. For larger values, in absolute terms, the percent change in earnings is given by 100exp c 1.
18. The overall Gini coecient (for per capita income) of 0.785, obtained from the Encuesta de Condiciones de Vida, is remarkably highÐand is certainly higher than what studies conventionally indicate for income inequal-ity in Ecuador or Latin America in general (it compares to a Gini of 0.42 for consumption expenditures). It is, however, fairly robust to exclusion of extreme values, at both ends of the distribution. As is shown in Lanjouw (1998), the de®nition of income taken here is fairly comprehensive (although not perfectly so) in, for exam-ple, the fact that it includes labor earnings from primary and secondary jobs, self-employment and home-enter-prise income, net farm income, and income from a range of additional sources. Note that a little under 4% of households in the survey were observed withnegative
19. Similar techniques for decomposition by factor components have been discussed in Fei, Ranis, and Kuo (1978), and Anand (1983).
20. The pseudo-Gini for a particular component divided by the true Gini for that component can be shown to be equal to the rank correlation coecient between incomes from the component and total incomes. The lower is this ratio, the more uncorrelated are incomes from that component with total incomes. We could consider the Gini as a sum, component by component, of the product of three terms akRkGk, whereRkis the rank correlation andGkis the component Gini. Note also that the pseudo-Gini can take a value less than zero.
21. Elberset al. (2000) show that with population sizes much below 500 standard errors on poverty and income inequality measures begin to increase rapidly.
data-generating process in this approach, while not some-thing we can test, is more attractive in our context than it is in a cross-country framework. In addition, it should be emphasized that our approach does not suer from problems of data comparability that plague empirical analysis at the cross-country level (see, for example, Atkinson & Brandolini, 1999). Elbers et al. (2000) decompose national-level consumption inequal-ity by subgroup and show that nearly 90% of total consumption inequality in Ecuador occurs within parroquias as opposed to being attributable to dier-ences in average consumptionbetween parroquias. This provides some additional surface plausibility to an analysis in which each parroquia is interpreted to represent a stylized rural Ecuador, varying only in terms of diering nonfarm employment and demo-graphic con®gurations. On the other hand, migration within a country is easier than across countries, hence the average expenditure-inequality pro®le might not be
fully independent betweenparroquias. In particular, it might depend on the relative wealth of neighbouring
parroquias. We have not investigated this further.
23. Deaton and Paxson (1994, 1997) and Higgens and Williamson (1999) have commented on the importance of including population demographics in this type of analysis. Note, our de®nition of labor force excludes those who are of working age but unemployed. In addition, our employment de®nition is based on princi-pal occupationand thereby excludes the possibility that a person might be simultaneously employed in more than one sector.
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