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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 di€erences 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 aliates. 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.


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)

Percentage of

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.



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, saywlow‡p, 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 e€ects 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.



(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

Wage Employment

Pattern I (%) Pattern II (%)

Nonagriculture, low-productivity wlow 50 25

Agriculture, landless labor wlow 25 25

Agriculture, land owners wlow‡p 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 (%)

Nonfarm Other

(%) Enterprise


Labor (%)

Total (%)

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 e€ect on nonagri-cultural employment and, given nonagrinonagri-cultural

Table 6. Probability of nonagricultural employment as a primary occupation

Probit model All employment in

nonagricultural sectora

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

(dummy) )

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 e€ect is neutralizing this general negative e€ect. 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 coecient on land in the case of the high-productivity jobs. While the evidence here does not o€er 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 e€ect.

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


)0.47 0.0001

Costa )0.25 0.0163

Oriente )0.15 0.4359

Migrant during past decade

0.10 0.4580

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 a€ecting earnings in that sector.

As we would expect, given the di€erent

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 di€erential between the Sierra and Oriente, although the point esti-mates also suggest that the di€erential 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

(dummy) )

0.30 0.0001

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

)0.11 0.1037

Connection to electricity network

0.26 0.0002

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 coecient for income in Ecuador as a whole (including urban

areas), decomposed by income components. 18

Following Shorrocks (1982) the Gini coecient

G, can be obtained as a weighted average of

``pseudo-Gini'' coecientsGi for each compo-nent, where the weights are given by the share

ak of component income in total income: 19

Gˆa1G1‡ ‡akG

k‡ ‡anG n:

The pseudo-Gini coecient for an income component is similar to the Gini coecient 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 coecient for that source is to the overall Gini. To see this, suppose we decompose income into two components:

GˆaG1‡ …1 a†G2:

Consider a change in income from a di€erent mix of the two income sources, assuming that the distribution between income sources does not change

G0ˆa0G1‡ …1 a0†G2:

This implies that

DGˆG0 Gˆ DG 2 G



G 2 G

…G G 1†

…1 a† ;

the change inGcan be written as

DGˆ Da

1 a

…G G 1†:

The smaller the di€erence between the pseudo-Gini coecient for a given source and the overall Gini coecient, the smaller will be the impact on inequality from a change in income from that source. The elasticity of the Gini coecient with respect to a change in income from component 1 is thus proportional to the percentage di€erence between the overall Gini coecient and this pseudo-Gini coecient

Table 9. Income inequality by factor components

Per capita incomes in rural Ecuador Farm income

Agricultural labor income

Rural nonfram income

Other income

Total income Pseudo-Gini coecient (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 coecient (Gk) 0.926 0.889 0.895 1.055 0.785 Coecient of rank correlation (RkGk=Gk) 0.854 0.748 0.913 0.579 1.000 Contribution to overall Gini coecienta 38% 8% 52% 3% 100% Elasticity of overall Gini to small increase in

component income

0.005 )0.015 0.040 )0.01



eG1 ˆ D

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 coecient 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.


A single-period household survey o€ers 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 e€ects 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 di€er-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 coecients 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

Dependent variable

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 e€ect of an increase in the low-productivity employment share is between a third and half as strong as the e€ect 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 e€ect 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 a€ects 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 di€erent. 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 di€erent summary measures of income inequality will trace out a di€erent 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 di€erences are not based on productivity di€er-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 e€ects 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 coecientcmultiplying 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 100‰exp…c† 1Š.

18. The overall Gini coecient (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

total incomes.

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 coecient 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 su€er 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 di€er-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 di€ering 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.

24. See, for example, Mellor and Lele (1973), and Mellor (1976), for early and in¯uential studies. The survey by Lanjouw and Lanjouw (forthcoming), provides further discussion and references.


Anand, S. (1983). Inequality and poverty in Malaysia: Measurement and decomposition. Oxford: Oxford University Press.

Atkinson, A. B., & Brandolini, A. (1999).Promise and pitfalls in the use of secondary data-sets: Income inequality in OECD countries. Unprocessed, Nueld College, Oxford University.

Deaton, A., & Paxson, C. (1994). Intertemporal choice and inequality.Journal of Political Economy,102(3), 437±467.

Deaton, A., & Paxson, C. (1997). The e€ects of economic and population growth on national saving and inequality.Demography,34(1), 97±114. Elbers, C., Lanjouw, J.O., Lanjouw, P. (2000).Welfare

in villages and towns:Micro-measurement of poverty and inequality. Unprocessed, Free University of Amsterdam.

Fei, J., Ranis, G., & Kuo, S. (1978). Growth and family distribution of income by factor components. Quar-terly Journal of Economics,26, 17±53.

Fields, G. (1980).Poverty, inequality and development. Cambridge University Press: Cambridge.

Fields, G. (2000).Distribution and development: A new look at the developing world. Cambridge, MA: Russel Sage Foundation and MIT Press.

Higgens, M., & Williamson, J. (1999). Explaining inequality the world round: Cohort size, Kuznets curves and openess. National Bureau of Economic Research Working Paper 7224.

Hymer, S., & Resnick, S. (1969). A model of an agrarian economy with nonagricultural activities. American Economic Review,50, 493±506.

Kuznets, S. (1955). Economic growth and income inequality.American Economic Review, March, 1±28.

Kuznets, S. (1963). Quantitative aspects of the economic growth of nations: VIII, Distribution of income by size. Economic Development and Cultural Change, January, Part 2, 1±80.

Lanjouw, P. (1998).Ecuador's rural nonfarm sector as a route out of poverty. Policy Research Working Paper 1904, The World Bank, Washington, DC.

Lanjouw, P. (1999). Rural nonagricultural employment and poverty in Ecuador.Economic Development and Cultural Change,1(48), 91±122.

Lanjouw, J. O., & Lanjouw, P. (forthcoming). The rural nonfarm sector: Issues and evidence from developing countries.Agricultural Economics.

Lanjouw, P., & Stern, N. H. (1998).A kind of growth: Palanpur 1957±1993. Oxford: Oxford University Press.

Larrea, C. (1987). El banano en el Ecuador. Quito, Ecuador: CEN.

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22, 139±191.

Mellor, J. (1976). The new economics of growth: A strategy for India and the developing world. Ithaca, NY: Cornell University Press.

Mellor, J., & Lele, U. (1973). Growth linkages of the new food grain technologies. Indian Journal of Agricultural Economics,18(1), 35±55.

North, L. (1999). El programa de Salinas: Una experiencia de desarollo microregional. In M. Chiriboga,Cambiar se Puede. Quito: FEPPÐAbya Yala Quito.

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