metadata: authors: 0: author: Fafchamps, Marcel
1: author: Shilpi, Forhad
count: Nepal
docna: 0: docna: The spatial division of labor in Nepal
docty: Policy Research Working Paper
subtopic: Climate Change and Agriculture,Crops and Crop Management Systems,Livestock and Animal Husbandry,Employment and Unemployment,Transport Services
teratopic: Urban Development
abstracts: cdata!: the authors examine how economic
            activity and market participation are distributed across
            space. Applying a nonparametric von Thunen model to Nepalese
            data, the authors uncover a strong spatial division of
            labor. Nonfarm employment is concentrated in and around
            cities, while agricultural wage employment dominates
            villages located further away. Vegetables are produced near
            urban centers. Paddy and commercial crops are more important
            at intermediate distances. Isolated villages revert to
            self-subsistence. The findings of the study are consistent
            with the von Thunen model of concentric specialization,
            corrected to account for city size. Spatial division of
            labor is closely related to factor endowments and household
            characteristics, especially at the local level.
geo_reg_key: 517198,517212,517191
repnme: repnme: The spatial division of labor in Nepal
display_title: The spatial division of labor in Nepal
listing_relative_url: /research/2002/05/1798586/spatial-division-labor-nepal
topicv3: Crop Production m1326627 174,Transport m1326661 135,Self-Employment m1504000 2698,Wage Employment m1504005 2703,Livestock m1504490 4024
publishtoextweb_dt: 2010-07-01T00:00:00Z
historic_topic: Urban Development
origu: Development Research Group (DECRG)
no_of_pages: 68
model_extractions: POLICY RESEARCH WORKING PAIPER 2845

# Abstract

Fafchamps and Shilpi examine how economic activity and market participation are distributed across space. Applying a nonparanmetric von Thinen model to Nepalese data, the authors uncover a strong spatial division of labor. Nonfarm employment is concentrated in and around cities, while agricultural wage employmenit dominates villages located furtlher away. Vegetables are produced near urban centers. Paddy and commercial crops are more important at intermediate distances. Isolated villages revert to self-subsistence. The findings of the study are consistent with the von Thunen model of concentric specialization, corrected to account for city size. Spatial division of labor is closely related to factor endowments and household characteristics, especially at the local level.

This paper-a product of Rural Development, Developi-nent Research Group-is part of a larger effort **in** the group to understand the importance of spatial factors **in** rural development. Copies of the paper are available free from the World Bank, 1818 H Street NW, Washington, DC 20433. Please contact Forhad Shilpi, room MC3-536, telephone 202-4587476, fax 202-522-1151, email address fshilpti@worldbank.org. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at marcel.fafcliamps@)economics.ox.ac.uk or fshilpi@?worldbank.org. May 2002. (62 pages)

> _The l'olicy Research Working Paper Series dissenimates the findings of wvork in progress to encourage the exchange of ideas aboiut developmzent issues. An objective of the semes is to get the findings out quzickly, even if the presentations are less than fully polished. The papers carry the names of the authors and shotuld be cited aicordingly. Th2e findings, interpretations, and conclusiois expressed in this paper are entirely those of the authors. They do iiot necessarily represent tl7e viewo of the World Bank, its Lxecitive Directors, or the countries they represent._

Produccd by the Research Advisory Staff
# The Spatial Division of Labor in Nepal'

Marcel Fafchamps University of Oxford Forhad Shilpi The World Bank

## Abstract

JEL classification: RI 1, 018 Keywords: economic geography, South Asia, specialization, roads _The Policy Research Working Paper Series disseminates the findings of work_<sup>_in_</sup> _progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less_<sup>_than_</sup> _fully polished. The papers carry the names of the authors and should be_<sup>_cited_</sup> _accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors, or the countries they represent._ We would like to thank Gershon Feder for his constant moral and intellectual support.<sup>Weacknowledge</sup> generous support from the former Chief Economist of South Asia Region John Williamson.<sup>Roaddistances</sup> and population densities were calculated with the help of Uwe Deichmann,<sup>JyotsnaPuriandAndrew</sup> Nelson at the GIS Lab of the Devetopment Economics Research Group (DECRG)<sup>of theWorldBank.We</sup> would like to akowledge Giovania Prennushi and Salman Zaidi for NLSS data and complementary documentation and Lawrence Zhangj and Misuzu Otsuka for excellent research assistance. We also<sup>received</sup> useful comments on an earlier draft from seminar participants at World Bank. Financial support from the Development Economics Research Grout (DECRG) is also gratefully acknowledged.
# 1. **Introduction**

After decades of neglect, the economics profession has rekindled its interest in geographical phenomena. Much recent work has been devoted to agglomeration effects across regions and countries (e.g. Krugman 1991b, Fujita, Krugman and Venables 1999a, Ciccone and Hall 1996). Apart from research focusing on cities (e.g. Henderson 1974, Lamnpard 1968, Abdel-Rahman 1994, Rauch 1993, Fujita, Krugman and Mori 1999b, Arthur 1988, Glaeser, Kallal, Sheinkman and Shleifer 1992, Ellison and Glacser 1997, Glaeser, Scheinkman and Shleifer 1995), most empirical work has remained at a fairly aggregate geographical level (e.g., Ciccone and Hall (1996), Radelet and Sachs (1998), Hummels (1995), Hall and Jones (1996) on countries; Desmet (1998) on European regions; Barro and Sala-I-Martin (1991) on U.S. states). Little attention has been devoted to more disaggregatel geographical units that have a predominantly rural character, such as villages or counties (see, however, Ciccone and Hall (1996), Ciccone (1997), Fafchamps and Helms (1996), Desmet and Fafchamps (2000)). This is somewhat surprising given that much of traditional economic geography focuses on spatially disaggregated phenomena such as the distribution of cities over space and their relationship with surrounding rural areas (e.g. von Thunen 1966, Isard 1956, Christaller 1966, Losch 1954, Dicken and LLoyd 1990, Jacobs 1969).

There is also little recent work by economists on the spatial distribution of economic activity across space in rural and urban areas of the Third World, in spite of a long tradition of research on regional issues among development economists. This is not to say that there has been no work on spatial issues. The contrast between cities and countryside has long attracted the attention of development economists, to the point that this contrast has become a fundamental organizing concept of all development theory (e.g. Lewis 1954, Harris and Todaro 1970). There has also been a lot of work on work migration, non-farm production in rural areas (e.g., survey by Reardon (1997); for Nepal, see Seddon, Adhikari and Gurung (1999)), and on the spatial 2
integration of agricultural markets (e.g. Takayamna and Judge 1971, Ravallion 1986, Dercon 1995, Timmer 1986, Baulch 1997, Fafchamps 1996). But this work remains fragmentary in the sense that it does not examine, in a comprehensive manner, how rural areas interact with each other and with cities as a function of their geographical proximity to cities.

The purpose of this paper is to begin filling this lacuna by examining how Nepalese households fit into the local economy as a function of their proximity to urban centers. Nepal is a particularly suitable place to study spatial specialization given the extreme diversity of the country in terms of accessibility. At one end of the spectrum, Terai villages are a few hours away by truck from many towns and cities, while Nepalese villages tucked in the Himalaya are among the most difficult to access anywhere on earth. As already argued by Jacoby (2000), Nepal is the perfect place to examine the effect of geographical isolation on economic activity.

Using household data from the Living Standard Measurement Survey (LSMS) of 1995/6, we investigate geographical patterns of agricultural production, agricultural sales and purchases, and non-farm work. To control for differences in road quality, travel time is used as a measure of distance instead of mileage. City population and travel time are instrumented to control for possible endogeneity.

Our contribution is threefold. First, by using several different measures of economic activity and market participation, we provide a detailed and compresensive picture of the spatial division of labor in a poor country. Second, thanks to non-parametric methods, we obtain precise estimates of the distance at which various activities dominate. As a result, we can characterize the various forms that market specialization takes depending on location. Third, we show that spatial effects are large and that city size matters.

Regarding spatial specialization, our results largely confirm the von Thilnen hypothesis of concentric circles around cities that vary according to transport costs (e.g. Henderson 1988, 3
Dicken and LLoyd 1990, Abdel-Rahman 1988, Fujita 1988). Nepalese households<sup>living near</sup> markets and cities are more likely to engage in non-farm (wage) work rather than<sup>prodluceagri-</sup> cultural goods for sale. Fears that cities 'steal' non-farm jobs from the surrounding countryside do not appear justified in the case of Nepal. If anything, it is proximity to markets and towns that fosters non-farm activity, especially wage non-farm employment.

As one moves away from markets and towns, the emphasis shifts progressively<sup>fromnon-farm</sup> activity to agricultural production for sale. As anticipated, vegetables tend to be produced<sup>close</sup> to cities because of their perishability while commercial production of storable<sup>cropssuchas rice</sup> and pulses takes place furlher away. Minten and Kyle (1999) obtain a similar result in Zaire. The emphasis on commercial farming also raises hired labor use. Households located<sup>closeto</sup> towns and markets rely primarily on the market for their consumption needs. Labor migrations are also more frequent in these villages but the effect is not strong.

As one moves further away from towns and markets, households revert to self-subsistence for crops but shift to livestoch production as a source of cash income. This is because animals<sup>and</sup> animal products (e.g., ghEe) are more easily transported to distant markets. Isolated villages do not interact with the market at all. These results confirm earlier work in Nepal by Jacoby (2000) and Seddon et al. (1999).

Regarding the spatial range over which certain activities dominate, we find that non-farm wage employment takes place primarily within four hours travel time from large towns. Within villages, non-farm employrment is concentrated within one hour of the nearest market. Vegetable production is important in the 1-3 hours range, while the production of paddy for sale dominates the 3-5 hours range. Beyond 5 hours travel time from cities, households no longer sell crops and rely on production to satisfy consumption needs. They also stop purchasing fertilizer. For those located more than 5 hours from a market or city, cash income can still be obtained from livestock 4
but households located more than 10 hours from the market essentially revert to self-subsistence. To our knowledge, this is the first time that a comprehensive quantification of these distance ranges has been presented for a poor country.

Spatial effects are very strong and robust determinants of activity choice and market participation. For instance, a household located right next to a rural market would allocate 40% of its time to non-farm self-employment and 30% to non-farm wage employment compared to 0% for a household located one hour away from the market. Ignoring such spatial effects would undoubtedly bias poverty estimates.

Spatial effects are also important for policy. Our results, for example, show that households located more than 5 hours away from towns and market do not sell crops and do not consume fertilizer. Any effort to promote agricultural innovation would need to take these effects into account to be successful.

The paper is organized as follows. Concepts and theory are discussed in Section 1, together with the econometric approach. The data and its main characteristics are presented in section 2. We also describe how our various measures of geographical proximity are constructed. Section 3 tests the effect of distance to markets and cities. Section 4 investigates the role of factor endowments and preferences in activity choice and market participation. Conclusions are summarized at the end.

# **2.** Theory **and** Concepts

German economist von ThtOnen was the first to hypothesize, as early as 1842, that economic activity need not be spread evenly across space even if land is undifferentiated between locations. His basic idea was that rural areas surrounding cities specialize in different agricultural products. He further argued that the product they specialize in depends on the cost of trans5
porting output to the marl:et. Locations close to the city specialize in high transportation cost goods, such as milk and vegetables, while locations further away specialize in less perishable, lower transport cost commodities such as cereals and pulses. Rural communities klcated too far from cities to trade with them must turn to self-subsistence in both agricultural and nonagricultural commodities Erugman (1991b), the latter being produced using small-scale, artisan technology. This theory is usually represented graphically as concentric circles of specialization, beyond which lies undifferentiated self-subsistence areas. Adding to this theory, Dore (1987) and Jacobs (1984) argue that peri-urban agriculture also benefits from spillover effects in technology and marketing, thereby suggesting something like concentric circles of technology usage as well. Another addition is the realization that isolated communities can interact with the global economy by sending workers away for extended periods of time, thereby triggering patterns of long-term work migrations to cities or plantations.

Von Thilnen's theory was further refined by Losch (1954), Isard (1956) and Christaller (1966) who studied the relationship between cities of various sizes and between them and surrounding rural areas. They hypothesize the existence of hierarchies of cities performing different roles, each with its own rural hinterland (see Lee (1993) and Fafchamps and Helms (1996) for illustrations in Mexico and Guatemala). These refinements do not qualitatively affect the spatial specialization idea, although they may alter the shape it takes (e.g., honeycomb instead of circle). They also bring out the role that city size has on the width of concentric circles of rural specialization, larger cities requiring more vegetable and food products than small cities. More recently, it has also been shown that, with two or more immobile factors instead of one (e.g., land), more complex patterns can be generated that include incomplete or partial specialization (e.g. ?, Venables and Limao 1999).

The recent economic geography literature has revisited many of these themes (e.g. Henderson 6
1988, Krugman 1991a, Fujita et al. 1999a). Much attention has also been devoted to the inner organization of cities themselves (e.g. Fujita 1988, Abdel-Rahman 1993, Abdel-Rahman 1994, Henderson 1974). In this respect, the literature has emphasized one important organizing principle, namely the tension between, on the one hand, agglomeration effects that incites firms to cluster, and, on the other, housing prices and commuting costs that raise workers' wages. This has, for instance, led to models of cities whereby activities that benefit from strong agglomeration effects, such as financial services, take over city centers while workers live at the periphery where housing costs are lower. Depending on travel costs for consumers, shopping districts locate in city center or in residential areas (e.g. Abdel-Rahman 1988, Fujita 1988). In these models, the size of cities depends critically on their ability to attract workers. This in turn depends on the strength of agglomeration effects, which affects return to labor, and on commuting costs and housing prices, which affect wages. Further refinements, such as pollution, congestion, and the provision of intermediate goods can be added to generate different structures, e.g., industrial basins surrounded by residential towns some distance away (Desmet and Fafchamps 2000).

All these theoretical predictions can be summarized as follows. In its simplest form, the concentric circle theory predicts that what a community produces depends on distance from the nearest town. Villages located nearby town centers are expected to produce perishable products with a high transport cost, such as vegetables, while villages located further away are expected to produce low transport cost commodities such as cereals and pulses. A variant suggested by the urban economics literature has workers reside away from their work place. In this variant, villages and neighborhoods located close enough to cities count a number of commuters, that is, individuals who work in the city but return to the village (the suburb) at night. The income they bring to their place of residence in turn generates suburban jobs in consumer services and retail trade - what could be called the 'shopping mall syndrome'.

7
Theories of hierarchies of cities predict more complex concentric zones whose width and spatial structure depend on their interaction with various cities of different sizes. In particular, they predict that larger cities have a larger hinterland, and that rural dwellers may buy and sell from different cities sirmultaneously. The presence of more than one immobile factor leads to incomplete specialization, whereby villages produce a multiplicity of goods at the same time. Finally, all theories predict that isolated locations must be self-sufficient in both agricultural and non-agricultural products, except for long-term work migrations.

These predictions can be empirically investigated as follows. Consider a vector of measures of economic specialization and market participation for individual households i. Let this vector be denoted _{1y }_ for z = 1, ..., Z, where yil is, say, vegetable production, y2 is non-farm selfemployment, y3 is rice purchases, etc. Further suppose that we have information on the distance to the market center nearest to household i, denoted _di._ The von Thilnen hypothesis, in its simplest form, predicts a relationship between _y1_ and d,. As is clear from the economic geography literature, this relationship is expected be non-linear, with unknown inflection points. The relationship can be written: where _ui_ is an error term and f(di) is an unknown smooth function of distance to the nearest market d,. A similar approach is adopted by Chomitz and Gray (1996) in their analysis of land use in Belize. Estimating function _f (.)_ non-parametrically provides a simple way of testing various hypotheses about the effect of location on economic activity. In addition, inflection points and zeros of the fitted _f (.)_ function provide estimates of the width of various concentric circles, hence providing useful information for policy makers.

Equation (2.1) presen1s a number of drawbacks, however. First, it does not control for town 8
size. Yet refinements of the von Thflnen hypothesis predict that the width of concentric circles and the strength of the spatial division of labor depends critically on the magnitude of urban demand and thus on town size. Second, the equation fails to account for the possible effect of more distant cities and towns. As Isard (1956) has shown, the organization of economic activity over space reverts around a hierarchy of cities and partially overlapping concentric 'circles' (or rather, hexagons). Recent contributions also emphasize a possible overlap between the effect of multiple cities (Fujita et al. 1999b). A better test of the theory should thus allow for town size and the possible effect of multiple towns. Such a test can be constructed provided we have data on urban population residing at various distances _h_ from household i. Let this information be denoted as _{pi(h)}._ An alternative, more general test of spatial division of labor can then be constructed by estimating equations of the form: The presence of two unknown functions in equation (2.2), the fact that function g(.) is multiplied by population _pi(h),_ and the presence of censoring in the dependent variable make estimation by conventional non-parametric techniques very difficult. To turn the above equation into an estimable regression model, we choose instead to discretize both functions _f (.)_ and g(.): where _£i_ is a dummy variable taking the value 1 if j-1 _< di <_ j, and 0 otherwise. **_pFh_** is the urban population residing within, say, _h_ and _h -_ 1 hours of travel from household i, i.e., **_pih == fh__** _pi(s)ds._ Parameter _H_ is chosen large enough that proxmity effects die out, that 9
is, such that _g(H) -_ 0. Estimnation efficiency can be improved by requiring that the estimated **_rj_** and _'Yh_ parameters generate smooth approximations for functions _f(.)_ and 9(.). One such method is the so-called roughness penalty method suggested by Good and Gaskins<sup>(1971)</sup> and Silverman (1982). In the case of ordinary least squares, the estimator is obtained<sup>byminimizing:</sup> where T is sample size and ), and A, are penalty parameters.' All reported standard errors are robust (White) standard eriors corrected for clustering.<sup>2</sup> The purpose of the rest of the paper is to estimate the above model using data on Nepal.

In case yil is dichotomous so that OLS is inappropriate, the sum of squared residuals in equation (??) is replaced by the required likelihood function. Penalty parameters<sup>_Ar_</sup> and A.. are adjusted accordingly. When the estimating function is a likelihood function (and provided some other conditions are satisfied), Silverman (1984) has shown that the roughness penalty approach yields a kernel estimator of _f(.)._ When the dependent variable is censored,<sup>weuse</sup> OLS instead of tobit for two reasons. First, we are interested in how distance affects the unconditional expectation cf yil, not how it affects yi' conditional on being uncensored. Second, in the presence of heteroskedasticity and clustering, tobit is inconsistent and standard errors cannot be corrected.

1 These estimates of the r's and -y's can easily be obtained using the regular OLS command by adding J _+ H -4_ artificial observations at the end of the sample such that dependent variable and regressors are zero, except<sup>for</sup> _Dn_T-_ A Dn_T_ **=-2A,.,** and **D.T+l** = A, for n = _T_ + 1 to T _+ J_ -2; and _Pn -T-1-_ A **_pn-T_** = and **npT+** -A.for _n_ = _T +J_ - 1 to _T + J + H -4._ 2 As is often the case in survey data, correcting for hetereskedasticity and clustering makes a big difference. To check the possible impact of the roughness penalty correction on standard errors, we compared reported standard errors with standard errors obtained by bootstrapping with 200 replications (with replacement).<sup>Giventhelarge</sup> sample size and low level of smoothing, bootstrapped confidence intervals are virtually indistinguishable<sup>from</sup> OLS estimates.

10
# 3. **The Data**

Home to the Everest, Nepal is located nearly entirely at the foot of the Himalaya mountains. It is composed of essentially four regions: the Himalayas themselves, which run along the entire Northern boundary of the country and are very sparsely populated; the central valley around Kathmandu; mountains (locally called 'hills' to contrast them with the Himalayas) that run East-West parallel to the Himalayas; and the plain of Terai that borders India to the South and has the best agricultural land (Government of Nepal 1995).

Nepal is largely rural, with 86% of its 21 million inhabitants living in villages or towns of less than 10,000 people. Kathmandu, the capital city and largest urban center, has a population of around half a million people.<sup>3</sup> . There are only 34 cities and towns of 10,000 inhabitants or more, most of which can be found either in the central valley or in Terai. Given the mountainous terrain, communications are generally more difficult within Northern Nepal itself than within Terai. In fact, people living in the remote Northern part of Nepal must trek many hours by foot or bullock cart before reaching the nearest road. Nepal thus offers a perfect testing ground to examine the effect of isolation on spatial specialization and market participation.

The data we use comes from the Living Standard Measurement Survey (LSMS) of 1995/96. The survey covers some 3373 urban and rural households spread among 274 villages or 'wards' distributed over all regions of the country (Figure 1 - Map of Nepal with location of surveyed villages). 28 of these wards are located in Kathmandu alone. As with other LSMS surveys, data coverage is quite comprehensive. For our purpose, information is available on agricultural production, cropping patterns, self-employment in non-farm activities, wage employment by sector, sales and purchases of crops, and migrations. Unlike other LSMS surveys, the Nepal survey also contains detailed information about distance and travel time to markets and towns.

**3421,000** inhabitants in 1991.
Jacoby (2000) has used this information to show that land prices fall with distance from markets.

Table 1 summarizes the main characteristics of surveyed households. We see that households are mostly nuclear, with a large age gap between the household head and his spouse. Households own a couple heads of livesto(k and cultivate one hectare of land on average, a third of which is irrigated. Educations levels are very low. Given that settlement is dispersed in much of Nepal, the location of individual hovuseholds within the same ward varies a lot. The average household is located two hours away from the nearest market. The median is lower, at just above one hour. In the econometric analysis, the 20 distance dummies D: are constructed by dividing the sample into five percent percentiles.

Table 2 summarizes the measures of economic activity and market participation that we were able to construct on the basis of the available data. We use seven categories of variables. Data on labor form the basis for our first set of specialization and market participation measures. The share of farm work in total employment is taken as rough measure of specialization in farming. Presumably non-farm work is highest either in isolated areas - where everything has to be produced locally - ard at the proximity of urban centers - due to the joint effects of commuting and the shopping mall syndrome mentioned in Section 1. In isolated areas where the size of the market is small, we would expect that most non-farm production takes the form of small enterprises - and hence of self-employment in non-farm activities. Where the market is larger, e.g., in urban centers, larger enterprises could take advantage of returns to scale, hence more emphasis on wage non-farm employment. By the same token, we expect administrative, clerical, and professional jobs to be concentrated in towns where large firms and government offices are concentrated.

Other patterns are also possible. For instance, it is conceivable that in isolated areas, most non-farm production takes place within the household itself and is not counted as work (e.g., 12
food preparation, fuel and water collection, child care, personal services). In this case, nonfarm work would not be recorded as such in self-subsistence regions (see Fafchamps and Helms (1996) for a similar observation in Guatemala). Prosperous agricultural areas may also foster the emergence of an active non-farm sector, especially in the production of non-tradables.

Table 2 shows the share of total household labor going to farm and non-farm work, both for self-employment and wage work. Self-employment in farming is the dominant form of employment, a reflection of the predominantly rural nature of the sample. Wage employment outside agriculture is the second most important category. Agricultural wage work and nonfarm self-employment each represent about 12 percent of all labor. The breakdown of non-farm employment by sub-sector shows that trade and manufacturing (crafts) are the most important self-employment sectors. Production dominates in wage employment, together with various clerical, administrative, and professional jobs.

A measure of work-related short-term migrations is also given in the Table. It is constructed as the number of household members who work outside the survey ward. This is a reasonable approximation given that, to be counted as household member, a person must reside with the household. We expect migration to be most prevalent among households who reside at the vicinity of urban centers, although workers may also commute to neighboring agricultural employment areas.

Other measures of specialization focus on farming for which the survey contains a lot of information. Cultivated area and sale of livestock products capture the relative emphasis on agriculture or livestock. Crop choices are measured by cropping patterns, that is, by the share of land planted to different crops. Given the dependence of actual crop output on the weather, cropping patterns are a better measure of intended output than output itself. By definition, they are only available for farming households. Paddy, pulses, and vegetables are the main crops in 13
the wet season. During the dr.y season, rice is typically replaced by other cereals because they are less dependent on water. Vfegetables are the second most important crop, foLLowed by pulses and oiLseeds. Data on fertilizer use is meant to capture the relationship between location and technology adoption.

Market participation for agricultural products is presented in the second half of the table. We measure the extent to which surveyed households rely on the market to dispose of their excess agricultural output or to meet their food needs. We see that crop commercialization remains low and that most surveyed households consume most if not aLL of their output. Sales are somewhat higher for crop;3 other than paddy and cereals. The situation is reversed in terms of purchases: the average survey household purchases half of the rice and one third of cereals it consumes. The bottom of the table further confirms that the median Nepalese household has a large food deficit, particularly in rice and vegetables. Three quarters of them (two third of all farmers) spend more on rice than the value of their paddy output; two third of them (55% of aLL farmers) spend more on agricultural products than the value of their agricultural output. Marketed surplus is producecL by a small group of farmers. Barrett and Dorosh (1996) describes a similar situation in Madagascar. The geographical location of this marketed surplus is of obvious interest to policy makers.

We complement these L'SMS data with information about urban population in Nepal (34 towns and cities).4 For our purpose, a town is defined as a settlement of more than 10,000 inhabitants. We first compate the distance between each surveyed ward and each of these towns. Distances are normaLly taken along existing roads, except when roads do not exist, in which case we calculate the shortest arc distance to the nearest road, and then the distance

> 41n earlier versions of this paper, we also included population in Indian towns located within 100 km of the Nepalese border. Including them does not affect qualitative results, but their effect on spatial specialization in Nepal is small. They are omitted Irom the paper because instrumenting urban population and distance for Indian towns raises data and computation requirements beyond what we could haridle. This issue is left for further research.

14
to various cities along the road.<sup>5</sup> Distances are then converted into travel time using available information about trucking and walking speeds along various types of roads in Nepal.<sup>6</sup> Off the road travel is assumed to take place by foot - a reasonable assumption for Nepal given the nature of the terrain.

Available information on distance to towns is summarized in Table 3. The average distance from surveyed wards to the nearest town is just under 4 hours, with large differences across wards. Around 30% of surveyed wards are located either within towns or very close to towns. Close to half the surveyed households live at most two hours travel away from a town or city; the median distance is 2 hours and 12 minutes. The other tail of the distribution is very long, reflecting the mountainous and isolated nature of much of Nepal. Twenty percent of surveyed wards are located more than 7 hours travel from the nearest town; ten percent are more than 10 hours away. One surveyed household in 30 is 15 to 30 hours travel away from the nearest town. This much variation should make it easier to identify the effect of distance on economic activity.

Combining information on distance to towns with data on population in these towns, we construct a measure of urban population at various time distances from each ward. These measures, denoted _pih_ for h _=_ 1, ..., H hours, only vary from ward to ward. They are organized as follows. Suppose that a ward i is 3 hours away from the nearest town, which has a population

> 5 This is a very time consuming process that requires a combination of various techniques. e.g., visual inspection of maps, statistical information on road grades, calculation of arc distances, comparisons across various measurements to identify shortest distances, etc. The assistance of Uwe Deichman and Jyotsna Puri (GIS lab, Department of Research of the World Bank) was essential to the success of this operation.

> 6Travel speeds are calculated for various terrains and types of road. Assumed travel times are as follows, in km/hour:

||Highway|Provincialroad|Secondaryroad|Offroad|
|---|---|---|---|---|
|Terai|60|35|10|5|
|Siwalik|51|29.75|8.5|4.25|
|Middlemountain|42|24.5|7|3.5|
|Highmountain|36|21|6|3|
|HighHimalayas|30|17.5|5|2.5|

These figures were obtained through discussion with various transportation experts and South Asia operations staff at the World Bank. Travel on highways and provincial roads is assumed to take place by truck; travel on secondary roads is assumed to be by cart.

15
of 30,000. The next nearest town is 7 hours away and has a population of 100,000. In this case we have, for each household in the ward, **_{Pil;_** ...; **_PFl'}_** _=_ 0;0; 30,000;0 0;0; 100,000;0,0,0. Table 4 summarizes our constructed _pih_ variables. The average surveyed ward has an urban population of 75,000 inhabitants located within an hour travel time. The median, however, is zero. Urban population first goes down with distance, reflecting the fact that some surveyed wards are urban or peri-urban. It then increases steadily, as more and more towns fall within a given travel time radius from the ward. We confine our analysis of urban population effects to a 10 hour radius; beyond 10 hours travel time, urban proximity effects taper off. Population is measured in millions.

Before proceeding with the analysis, we must correct for the possible endogeneity of urban population and travel time. It is indeed conceivable that towns are larger whenever the surrounding countryside prod aces a food surplus. Observing that wards located to large towns sell more food would then be the result of reverse causation. The same reasoning applies to road construction: public authorities might be more inclined to build roads to areas that produce more agricultural surplus. Since travel is faster on roads, this would result in surplus areas being closer to towns in terms of travel time. Similar concerns about reverse causation apply to non-farm production, vegetable production, etc.

> To address these conce.rns, we instrument city population and travel time as follows. Predicted urban population is obtained by regressing the log of actual population on physical characteristics of the district in which the town is located: the log of its size in square kilometers; the log of its arable land area; the log of the distance to the nearest river; its mean elevation; the standard deviation of the elevation within the district; and a dummy if the district is the mountainous part of the country. By limiting our instruments to physical features, we minimize

16
the risk of that instruments are themselves endogenous. 7 Size is included because larger district can hold more people. Arable land area proxies for food production potential. Distance to the nearest river proxies for accessibility, as roads often follow river valleys. Elevation controls for climate - towns are less likely at higher elevations. The standard deviation of elevation is a measure of roughness in the terrain; towns are more likely in flat districts. The mountain dummy is included for the same reason. Regression results are presented in Appendix 1. The _R_<sup>_2_</sup> of the regression is 0.27. Regressors in general have the expected sign but are multicollinear (mean VIF of 8.7), which explains why regressors are not individually significant. Since we are only interested in predicting population, multicollinearity does not matter.

Travel time between a ward and a town is instrumented using foot travel time as well as physical characteristics of the ward and town: size of the district; arable land; mean elevation; distance from the nearest river (available only for the town); standard deviation of elevation; and regional dummies (East-West). Foot travel time is computed using iso-elevation curves to account for the mountainous nature of the terrain. The regression is estimated in log form. Results are presented in Appendix 2. As expected foot distance is the major determinant of travel time. The R<sup>2</sup> of the regression is 0.84. Population variables **_ph_** used in the subsequent analysis are constructed using predicted urban population and travel time instead of actual values.<sup>8</sup> As it turns out, results are virtually identical to those obtained with actual values, suggesting that the potential endogeneity of town population and road construction are not a source of bias in these data.

'We also experimented with a longer list of instruments, including area of irrigated land and the like. This results in a better fit for town population but the rest of the analysis is unaffected. Additional regressors are omitted in the analysis presented here to minimize the risk of endogeneity and overfitting.

8Standard errors are not corrected for the use of predicted regressors. Doing so would be extremely difficult given the way in which regressors are constructed. The fact that instrumented and unininstrumented results are very similar suggest that inference is very unlikely to be affected.

17
# 4. Econometric Results

We now turn to the econometric analysis. We only present selected Figures and Tables. We begin with employment patterns. The dependent variable is the share of household labor going in non-farm and farm activities (self-employment or wage work); it is our measure of economic emphasis on non-farm and farm production. Since respondents are interviewed at their place of residence and most cormnute to work by foot, we expect reported employment patterns to reflect working conditions in the vicinity of the place of residence. We experiment with various values of the penalty parameter A. Our best results (i.e., neither too smooth nor too rugged) are those presented. Qualitative results are not very sensitive to the value of the penalty parameter. Results are presented in graphical form to facilitate interpretation. All Figures report coefficient estimates as well as the bootstrapped 95% confidence interval around them.<sup>9</sup> The shape of function Jf(.) is shown in Figure 2 for each employment variable; the shape of function g(.) is shown in Figure 3. Distance to towns varies across wards only; the distance to the nearest local market varies across households within the same ward. Function f(.) thus controls for within ward spatial variation. Figure 2 (and all subsequent figures relative to within-ward spatial variation) uses distance percentiles, not actual distance, on the x axis.<sup>10</sup> The effect of actual distance is much steeper than shown. With this caveat in mind, the resemblance in the shape of both functions is cunning. Non-farm wage employment decreases sharply with distance from market and urban centers. Within a ward, it tapers off within one hour walking time from the nearest market (Figure 2). Non-farm self-employment also decreases with distance from the local market but it is not significantly affected by proximity to urban population (Figure 3).

9Surveyed wards represent only a small proportion of all wards and are located far apart. It is therefore extremely unlikely that standard errors suffer from bias due to spatial autocorrelation. Correcting standard errors using, for instance, the method proposed by Conley (1999) is not necessary. Reported standard errors do, however, correct for cluster effects, that is, correlation in household residuals within wards.

> '<sup>0</sup> Dummies used in estimation - and thus confidence intervals - are constructed using percentile intervals.

18
These results indicate that proximity to urban population raises the probability<sup>that a house-</sup> hold is involved in non-farm wage employment. Wage employment outside agriculture<sup>thusap-</sup> pears to be primarily an urban phenomenon. Within a ward, households<sup>involvedinnon-farm</sup> employment are located in or very near market centers, except that wage earners<sup>tend tolive</sup> some distance from the market itself - probably to avoid congestion and pay<sup>lowerrent.Self-</sup> employed non-farm workers, in contrast, reside near market centers where business<sup>is likelyto</sup> be better. These findings are consistent with road construction evaluation reports that<sup>doc-</sup> ument the rapid increase in non-farm activities and the relocation of villagers<sup>alonganewly</sup> created road and rural markets (e.g. Rapp 1994, NECMAC 1998, Bajracharya,<sup>Aryal,Sharma,</sup> Manandhar and Pyakuryal 1990).

These results are in line with the conclusions of the Government of Nepal<sup>(1999).They</sup> also indicates that city size matters: the larger the urban population, the stronger the effect on non-farm wage employment, indicating a relationship between firm size and city/market size ((Fafchamps and Shilpi 2001)). Also of interest is the finding that non-farm employment remains significantly higher in peri-urban areas than in more strictly rural areas. The<sup>effect of</sup> town proximity extends for up to four or five hours of travel time away from the town itself - much further away than normal commuting time.<sup>11</sup> This suggests that the development<sup>of non-</sup> farm production in rural areas is intimately related to their proximity from large urban<sup>centers.</sup> In Nepal, proximity to towns and market centers increases non-farm output, not isolation.

Figures 2 and 3 also present results for self-employment and wage employment in agriculture. The dependent variable is the share of household labor in these two categories. As anticipated, the time households devote to farming increases sharply as one moves away from markets<sup>and</sup> urban centers. But the pattern varies between self-employment and wage labor: self-employment

> "This result is not an artifact of the instrumentation of travel time: virtually<sup>identicalresults areobtained</sup> with actual travel time corrected for road transport.

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in farming rises as soon as one gets out of the town and remains high. In contrast, wage employment in agriculture peaks in households residing 1 hour from local markets and in wards located 5 to 8 hours away from urban centers. This is consistent with the idea that, close to cities, household with little or no land can find profitable employment opportunities in the non-farm sector. In more rural wards, the dominant income generation activity for asset poor households is agricultural wage work. This interpretation is consistent with Jacoby's (2000) finding that wages fall slightly with distance from the nearest market.'<sup>2</sup> An immediate implication is that land inequality would have a. stronger effect on income inequality and poverty in rural areas situated far from markets and cities. These areas are also those for which a proper understanding of agrarian institutions is essential to welfare analysis. When farm and non-farm wage work are combined, we see a sharp decrease in the share of wage work as one move away from urban centers. Capitalist modes of production are predominantly an urban phenomenon.

Results regarding migration are less contrasted (Figure 4). The dependent variable is the share of household members who work outside their ward of residence. We find that work-related migrations are most prevalent in areas situated 1 to 5 hours away from urban centers, thereby suggesting that at least part of the non-farm employment encountered in peri-urban areas takes the form of commuting. The offect, however, is not highly significant. Within a ward, migrations are concentrated among those households further away from the nearest market, but the effect is not significant. Taken together, these results suggest that temporary work migration is slightly more prevalent among households located neither too far nor too close from city influences. These conclusions are similar to those reached by Seddon et al. (1999) in their work on Nepalese migration movements.

Turning to farming in more detail, we see that operated land mirrors agricultural employment

> 1<sup>2</sup> Jacoby, however, does not control for nearby urban population.

20
- it increases with distance from markets and towns (Figures 5 and 6). Farmers within<sup>four</sup> hours of a large city have smaller farms than those located in more remote wards. The picture<sup>is</sup> slightly different for livestock production. The sale of livestock and livestock products initially rises with distance from market centers, albeit at a slightly slower pace than cultivated land. But it eventually tapers off for households located very far from a market center. This suggests<sup>that</sup> livestock production as a market-oriented activity is preferred mainly by households<sup>residing</sup> between 1 to 4 hours from the local center and more than 4 hours from towns.

Agricultural input use is also influenced by distance. Figures 5 and 6 report a<sup>sharpdecrease</sup> in fertilizer use as one moves away from cities and market centers - presumably because<sup>of</sup> transportation costs.<sup>13</sup> Similar results are obtained by Jacoby (2000). But our estimation method suggests that the effect is not linear. Fertilizer use is highest among households<sup>residing</sup> within 2 hours of a market center, after which usage drops sharply.<sup>1 4</sup> This is also the zone within which crops are grown for the market. These condusions are in close agreement with Jacoby's (2000) findings that the value of land falls with distance from the market.

Next we investigate the von Thtlnen hypothesis, namely, that what farmers grow varies systematically with distance to towns and markets. For that purpose, the share of land planted to various categories of crops is used as dependent variable. Two cropping seasons are distinguished - the wet season when paddy is produced, and the dry season when the focus is on other<sup>cereals.</sup> The regressions are _conditional_ on the household producing crops, an activity that, as we have seen, takes place outside towns and cities. Some of the results are presented graphically in Figures 7 and 8 for f(.) and g(.), respectively. We find that vegetable production, as predicted, is highest in the vicinity of cities and local markets, both during the dry and wet season. The

> 13A similar regression conducted on fertilizer price indeed shows a sharp price increase with distance from cities. Households located within the same ward all pay the same price for fertilizer, but have to haul it<sup>overdifferent</sup> distances.

> '<sup>4</sup> Virtualy identical results obtain if one uses fertilizer per cultivated area.

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magnitude of the effect is large but it is seldom significant. Vegetable production also increases in wards located far from towns, something we had not anticipated. Contrary to results repcorted so far, spatial cropping patterns are quite different when actual population and travel time figures are used. In that case, we fincl a stronger and clearer relationship between vegetable production and proximity to towns. The difference suggest a tendency for Nepalese roads to be built to serve vegetable producing areas.

Land devoted to paddy initially increases with distance from the local market. This effects, however, tapers off in more reraote parts of the ward. There, more emphasis is put on subsistence crops. Areas located near wrban centers produce slightly more paddy, but the effect is not significant. Here too, the difl9erence with uninstrumented regressions is large, suggesting that roads are built to paddy producing areas. During the dry season, pulses, oilseeds, and other (minor) crops follow a pattern similar to that of paddy, albeit less pronounced. These results are in line with the concentric circles hypothesis. Perishable crops such as vegetables are produced near markets and cities. Less perishable commercial crops appear in areas further away from the city, but not so far that transport becomes problematic. In areas more than 8 hours away from cities, we observed a decreased emphasis on commercial crops and a switch from paddy to other cereals. These changes reflect the self-subsistence focus of isolated areas.

> To confirm our results, we examine whether the commercialization of crops varies systemat-

> ically with distance. To this elfect, we regress the proportion of total agricultural output that is

> sold to the market (Figure **9).1)** By construction, the regression is limited to farming households. Results show a lot of variation in crop commercialization initially among households located near

> markets and towns. As Jacoby (2000), we find that commercialization initially increase with the distance between households and local markets, but the effect is not significant. Up to 6

> 1 Regressions for individual crops yield similar results.

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hours of travel time, distance from towns has the expected monotonic effect on crop sales: more distant wards sell less. Crop sales are zero on average for households residing 6 hours or more from a large town, an indication of the self-subsistence focus of agricultural production.

Next we turn to consumption. We first regress consumption shares in major food products on distance to markets and cities. Taken together, these products account for 86% of measured consumption. Results are sununarized in Figure 10. Rice consumption tends to fall with distance from markets, but proximity to towns has no significant effect. Among households residing more than two hours away from the nearest market spend roughly 25 percentage points less on rice, the difference going to cereal consumption. This pattern is probably related to the need to process paddy into rice before consumption. Indeed, rice milling facilities are typically located in and around markets. In contrast, consumption of other crops increases with distance from markets. Livestock expenditures, in contrast, show no clear pattern. Proximity to urban centers is shown to raise the consumption of livestock products (nearly significant) and reduce the consumption of cereals other than rice. Contrary to expectations, consumption patterns in isolated wards resembles more that of peri-urban areas than that of intermediate wards. We revisit this issue in the next section.

The next step is to investigate whether differences in consumption are related to crop monetization. We regress the share of cash purchases in total consumption for fouu agricultural products for which we have data: rice; other cereals; vegetables; and fruits. Results for all crops combined are summarized in Figure 11. They indicate a systematic decline of the share of purchased consumption with distance: households residing near a town or market are more likely to purchase the food they consume. This hardly comes as a surprise, given that urban households are less likely to farm and thus to produce their own food.<sup>1 6</sup> The effect is large in magnitude

> "The question is whether this is the only reason. We revisit this issue in the next section.

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both within wards and between wards and towns; it is very significant in all cases. Resi,lents of wards located less than 3 hours away from a town rely massively on the market to fulfill their consumption needs. In contrast, households living more than four hours from a city do not at all rely on the market for their consumption of agricultural products. Taken together with earlier findings about the sale of crol) output, these results confirm that crop commercialization - both in terms of sales and purchasE s - is linked to proximity to markets and urban centers.

Before we close this section, we examine how sale and consumption pattern combine to generate a spatial distribution of marketed surplus. We use two definitions of marketed surplus: first, as the ratio of crop production over consumption;<sup>1 7</sup> second, as the difference between production and consumption. The first definition emphasizes self-sufficiency, that is, the degree to which a household is in surplus or deficit, irrespective of the size of its output. The second definition emphasizes the absolute size of the marketed surplus. Results, presented in Figure 12, show that food self-sufficiencyr increases rapidly with distance from towns and markets. Given that all households are included in the regression, this outcome largely reflects the fact that households located near cities and market centers spend less time producing food and more time producing non-farm products and services.

In terms of absolute magnitude, marketed surplus is largest at intermediate distances. Marketed surplus is largest one hour or more from a market center and four hours from towns; the latter effect, however, is not significant. Given the nature of the terrain in Nepal, transporting crop surpluses to the market is a non-negligible task. It is therefore not surprising that the value of the marketed surplus drops among households residing more than two hours from a market. Noticeable for all crops, this outcome stands in contrast with our previous result for livestock, which showed that the sale of livestock products only begins to drop among households located

> 17Because the resulting ratio has a highly skewed distribution, we use the log(l+ ratio) instead as dependent variable.

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more than four hours from a local market. Taken together, these results suggest that in the 2-4 hour range, livestock progressively supplants crops as the main cash generating activity for farmers.

To summarize, we have seen that non-farm activities are concentrated in and around cities and markets. We were able to show that the range of distance over which non-farm wage employment is stimulated by cities is much larger than what is often believed. Contrary to claims occasionally made, we find no evidence that cities eliminate non-farm employment in their hinterland - albeit they may eliminate certain activities.<sup>18</sup> In terms of agriculture, several forces are at work. The dominant force is that which affects commercialization. Proximity to towns and markets has a strong positive effect on the sale and purchase of agricultural products. The commercialization of agriculture is thus predominantly a peri-urban phenomenon. This finding is consistent with the fact that non-farm production is also higher in peri-urban areas: villagers who do not farm rely on the market for their consumption, and they can afford to rely on the market because traded quantities are higher. Fertilizer purchases follow the same pattern, suggesting that technology adoption is related to crop commercialization.

The effect of proximity is felt over a four hour radius around cities. Beyond this distance, crops are rarely sold and purchased. Livestock products, however, continue to be marketed well beyond this limit, suggesting a geographical pattern of specialization with crops close to cities and livestock further away. This is consistent with the fact that livestock products either transport themselves (live animals) or have a high value to weight ratio (ghee). Within the zone of influence of cities, agricultural specialization also varies with distance, with vegetables being produced closest to markets and paddy, pulses, and oilseeds produced further away. The effect, however, is not as strong as anticipated. Concentric circles of specialization in Nepal nevertheless l<sup>8</sup> This issue is the object of future work.

25
seem to fit the von Thunen hypothesis. Our results also demonstrate the usefulness of the nonparametric approach: with few exceptions, the effect of distance is highly non-linear, with urban areas and isolated villages sometimes presenting similar features. The non-parametric approach also proved useful in identify.rng the precise spatial range of particular phenomena.

# 5. Controlling for Household Characteristics

We have seen that what Nepalese households do varies systematically with proximity to towns and market, but we do not know why. Our preferred interpretation is that what people do is influenced by market access, but other explanations are possible as well. In particular, preferences or talent may vary systematically with proximity to markets in such a way as to generate the relationships described in the previous section. Wage work, for instance, might be more frequent in the vicinity of tovns simply because people there are better educated and edlucated workers are more productive in a salaried workplace.

To show this formally, consider a producer in location j having to choose among various activities indexed by i. Let the output price be denoted pij.<sup>19</sup> There is a single production factor, labor. Output _Qij_ of activity i depends on employment _Lj_ in activity i, with _Q' <_ 0. Profit maximization dictates that marginal value product of labor equal the wage rate _wj_ in location j, i.e., that:

# _PijQ'(Lij) = wj_

from which we see that the optimal choice of Lij increases with pij: if output prices _pij_ for certain activities are higher closer to cities and markets, so is _Lij._ More labor is used in remunerative activities.

> l<sup>9</sup> What matter for activity choice are only relative prices. Prices are normalized by setting one price to one.

26
Now consider an alternative model in which output depends on two factors of production, labor Lij and human capital Hij. Further assume that relative output prices are the same across locations. Factor prices are written _wj_ for labor and rj for human capital. Relative factor prices depend on their relative scarcities: locations with a lower _HjlLj_ have a higher rj/wj. Comparative advantage dictates that locations specialize in the activities using more intensively the factor they have in abundance. So, for example, if activity k uses more human capital and human capital is abundant in location j, labor used in activity k will be higher in locations with a higher human capital, i.e., _Lij_ is an increasing function of _Hj._ Further assume that _Hj_ varies systematically with proximity to town and market, say, because schools are located near towns and market centers. Regressing _Lij_ on proximity to town and market, as we have done so far, would show a relationship between Lij and distance even in the absence of differences in relative prices, i.e., in the absence of market effects.

The above discussion suggests an indirect way of testing whether patterns of production vary because of differences in factor endowments or because of differences in marginal returns possibly driven by proximity to markets. Say we have data on factor endowment _Hj._ First regress _Hj_ to ascertain whether it varies systematically with distance. If it does, differences in factor endowment might be responsible for the measured effect of distance on activity. To test this possibility, regress Lij on _Hj_ as well as distance: if including _Hj_ drives the effect of distance to zero, factor endowment differences might be responsible for the observed spatial division of labor. If distance remains significant, distance affects marginal returns through some other process, possibly relative prices, possibly other unobserved endowment. A similar reasoning can be extended to choices other than production. Consumption, for instance, may vary systematically across locations not because relative prices are different but because incomes vary with location.

27
Implementing the above approach is the object of this section. Before presenting the results, however, it is important to understand its limitations. First of all, we can only test the effect of observed endowment diffarences. In the absence of panel data, unobserved differences such as activity specific skills or preferences cannot be controlled for.<sup>2 0</sup> Second, the validity of the test rests on _Hj_ being exogenous. Suppose that this is not the case, i.e., that factors are either perfectly mobile across space or that they can be accumulated instantaneously, so that factor prices are equalized across space. Further assume that prices vary systematically with location. Profit maximization dictates that: <!-- Start of picture text --> Pij 9Q(Lj,") = r<br>Pi Hi,<br><!-- End of picture text --> which implies that _Lij, Hij,_ and _Hj = Hij_ vary systematically with _Pij_ and thus with distance. The endogeneity of _Hj_ generates multicollinearity with distance since _Hj_ depends on relative prices and by assumption _Pij_ = EJ T1kDk + _EhH= 'Yhph_ +gij where eij represents factors other than distance that affect relative prices - and thus Lij _and Hj._ When Eij is large, Hj is much more correlated with Lij than distance itself. Including _Hj_ in the regression thus biases the distance coefficients toward zero.

Since we do not have instruments for endogenous placement of households, we cannot control for the possible endogerLeity of _Hj._ We nevertheless propose to do two things: (1) regress household characteristics _Hj_ on distance and see whether _Hj_ vary systematically with distance; and (2) add _Hj_ to all regressions presented in section 4. Given that _Hj_ is potentially endogenous, it is important to understand the limits of this procedure. If distance coefficients become 20Plans are underway to collect a second round of NLFS panel data in Nepal but the data will not be available for several years.

28
non-significant when we include household characteristics, it could be because the location of endowments drives economic activity, or because households locate endogenously. We cannot tell. If, however, distance is still significant after we include household characteristics, then we can reject the hypothesis that differences in observed factor endowments are responsible for spatial division of labor. If, in addition, observed factor endowments or preference shifters do not vary with distance, then they can safely be ruled out as the driving force behind spatial division of labor.

The household characteristics used in the analysis are measures of human and physical capital for which we have data - age of household head, household size, household composition by sex and age, average education of adult males and females, and inherited wealth. The age of household head controls for experience and life cycle effects. Household size and composition control for labor supply and food preference effects.<sup>2</sup> ' Average years of schooling for adult males and females our measure of human capital. Children education is ignored because it is less likely to affect activity choices but more likely to be influenced by it through income. Inherited wealth is our proxy for wealth. Measures of current wealth such as land and livestock cannot be used because they depend on current activity choice (farming). To control for other geographical effects, we also include regional dummies (east, west, mid-west, far-west, and central - the omitted category) and elevation dummies (mountains, hills, and Terai - the omitted category). To control for income effects, regressions relative to consumption (expenditures on certain food categories; food purchases; and marketed surplus) also include total expenditures as additional regressor.

> We begin by examining whether household characteristics vary systematically with distance **21** Household composition variables are the share of adult males, the share of adult females, the share of children, and the share of youths (the omitted category).

29
to market and towns. Results are summarized in Figures 13 and 14.22 Households residing close to markets have fewer children and more adult males. Household heads residing close to towns tend to be younger. As expected, schooling levels of male and female adults drop with distance from towns and markets. This could be due either to supply effects - school are located closer to market center - or demand effect. The latter would arise if higher returns to schooling near the market center incites eclucated residents and migrants to locate closer to the market and encourages parents to invest in their children's education.

We also examine whether inherited wealth and total consumption vary systematically with distance. Results, presented in Figure 15, show that inherited wealth increases dramatically with distance from towns and markets. This is consistent with the emphasis on agriculture and the role of land and livestock in agricultural production. The lower half of Figure 15 also shows that consumption drops dramatically with distance, albeit it is unclear how much of this drop simply reflects lower food prices in rural areas.

Observed household characteristics vary with distance. Are results reported in the previous section ultimately driven bv differences in endowments and preferences? To investigate this possibility, we estimate regressions of the form: where Hi stands for household characteristics and regional dummies. The model is estimated using the roughness penalty correction as before. To save space, we omit the Figures themselves and simply discuss the diffeiences with Figures 2 to 12.

Our first set of results relate to non-farm work. They can be summarized as follows. The

> 22Some household characteristics are omitted from the figures because non-significant but included in the subsequent analysis.

30
effect of distance from urban population is reduced by about 30% by adding Hi but distance coefficients remain significant if they already were. We can therefore rule out the possibility that the effect of proximity towns is due to differences in observed household characteristics. Within wards, however, distance is much less significant. One possibility is that rural markets locate where educated households reside. A more likely interpretation is that residential mobility and reallocation of productive resources within wards are sufficient to match workers with locationspecific occupations. This would be the case, for instance, if households interested in trade or crafts move to the market center. This would be in line with field observations made by Rapp (1994) and Bajracharya et al. (1990). Without more data, however, we cannot be more conclusive on this issue. The effect of proximity to towns on the propensity to migrate is unchanged.

Next we turn to farming. Results concerning operated land, livestock, fertilizer use, cropping patterns, and crop sales (Figures 5 to 9) are by and large unaffected as far as distance to towns is concerned. But distance to the local market is no longer significant in nearly all cases. The key determinants are education and inherited wealth (which determines the start-up capital available for farming): these regressors account for most of the within-ward variation in farming as more educated households farm less, while those with more inherited wealth farm more. These results are consistent with Fafchamps and Quisumbing (1999) and Yang (1997).

We also reestimate our consumption, crop purchases, and marketed surplus regressions (Figures 10 to 12). Since consumption choices depend on income, consumption shares and crop purchases are likely to be affected by income effects which, in turn, are affected by activity choices. As a result, market participation results presented in the previous section may be spurious because driven by income differentials.

To control for income effects, we add total consumption expenditures (in log) and its square to the list of household characteristics. Results indicate that distance to local market is no 31
longer significant in all three sets of regressions. Household characteristics, especially consumption expenditures, account for observed within-ward differences. The situation is quite different for distance to towns: results here are stronger than before the introduction of household characteristics. As before, crcp purchases decrease with distance from towns and, as a result, self-sufficiency increases. The effect remains strong and significant. The consumption of farm products depends on distance to towns, but in ways that become clearer after income effects are controlled for. This is shcwn in Figure 16. In particular, rice consumption is much higher near towns, while the consumption of other crop and livestock products increases with distance to towns. This is in line WithL studies that have brought to light the role of rice as an urban food, possibly because of ease of preparation (e.g. Savadogo and Brandt 1988, Nagy, Sanders and Ohm 1988).

# 6. Conclusion

Combining household level data with information on distance to markets and towns, we have examined the spatial division of labor in Nepal. A semi-parametric model was used to avoid restricting the shape of the relationship between spatial specialization and distance to markets and towns. Distance is measured in hours of travel time. To control for possible endogeneity, city population and travel time are instrumented using physical characteristics of surveyed wards and districts. Coefficient estimates with and without instrumentation are very similar, thereby suggesting that endogeneity of city size and road construction is not a serious concern.

Result indicate a strong spatial division of labor. Non-farm employment - our indicator of non-farm production - is heavily concentrated in markets and in and around towns. The effect is strong for towns of all sizes. Agricultural wage employment, in contrast, is concentrated in rural areas sufficiently close to cities that they can specialize in commercial crops but neither so 32
> close that non-farm employment takes over, nor so far that they revert to self-subsistence.

Crop choices vary with distance to town and with their size. Vegetable production in both seasons is somewhat concentrated at the vicinity of markets and urban centers while, paddy, pulses, oilseeds, and other commercial crops are more important at intermediate distances. Market participation varies with distance as well. Households near markets and cities buy most of the rice and agricultural products they consume, even when we control for land and other farm assets. These households also sell a larger proportion of their crop production. An examination of the spatial distribution of marketed surplus indicates that food for urban consumption is mainly produced in an intermediate zone located 30 minutes to 2 hours away from the nearest market and 3 to 7 hours from toWIIs. Beyond this zone, households derive some cash income from the sale of livestock products, but this phenomenon itself tapers off beyond 8 hours of travel time from the nearest market.

> Surnmarizing our results, the proximity to markets and size of towns are strongly associated with different patterns of production among Nepalese households. Towns themselves specialize

> in the production of non-farm products. They buy vegetables from immediately surrounding

> areas and cereals from villages located a little further away. Agricultural marketed surplus is produced in an intermediate zone where much agricultural wage work is also found. Surprisingly, this is not the zone where fertilizer consumption is highest.

> Isolated households and villages essentiaLly rely on self-subsistence, both in terms of non-farm production and food self-sufficiency. Broadly consistent with earlier findings by Jacoby (2000),

> all these findings fit the von Thurnen model of concentric specialization, except that our results also show the importance of town size. Less anticipated is our finding that villages located near towns also participate in non-farm production and that the proportion of agricultural wage

> work only rises slowly with distance from cities. These suggest that proximity to cities is not

33
detrimental to non-farm production, contrary to what is sometimes assumed. This appears to be linked not so much to income effects but rather to reliance on the market for the satisfaction of consumption needs.

These results are partly associated with differences in factor endowments and preferences. Distance between surveyed households and the nearest market is no longer significant in most regressions once we control for household characteristics. Without household panel data, we are unable to tell whether i!t is proximity to market that drives household factor endowments (through internal migration or accumulation) or whether it is the opposite. Distance to towns, however, remains strongly significant even after household characteristics are included. This suggests that distance to towns has an effect on economic activity and market participation beyond that of differences in (observed) factor endowments. These issues deserve further research.

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40
Table 1. Characteristics of Surveyed Households

|1.Householdcomposition|Unit|Mean|Std.dev.|Median|N.obs.|
|---|---|---|---|---|---|
|Household size,ofwhich:|Number|5.6|2.8|5|3344|
|Adultmales|Number|1.2|0.8|1|3344|
|Adultfemales|Number|1.3|0.8|1|3344|
|Teenagers|Number|1.9|1.6|2|3344|
|Children|Number|0.9|1.0|1|3344|
|Elderly|Number|0.2|0.5|0|3344|
|2.Humancapital||||||
|Ageofhousehold head|Years|44|15|43|3344|
|Ageofspouse|Years|39|12|37|3344|
|Yearsofschoolingofadultmales|Years|0.9|1.5|0|3344|
|Yearsofschoolingofadultfemales|Years|0.4|0.9|0|3344|
|3.Land||||||
|Totaloperatedarea,ofwhich:|Ha|0.8|1.7|0.4|2653|
|Irrigated land|Ha|0.3|0.8|0|2653|
|Totalowned area|Ha|0.8|1.7|0.3|2653|
|4.Assets||||||
|Cowsandbuffaloes|Number|2.6|3.1|2|3345|
|Farmequipment|Rupees|1549|20948|120|2220|
|5.Location||||||
|Distancefromhhtonearestmarket|Hours|2.2|3.4|1.1|3344|
|Distancefromwardto nearesttown|Hours|4.0|4.7|2.2|3344|

41
Table 2. Production and Consumption

|1.Labor<br>Shareoftotal household employmenlIn:|Unit|Mean|Std.dev.|Median|N.obs.|
|---|---|---|---|---|---|
|.<br>Non-farm employment|Share|30.2%|37.6%|10.5%|3248|
|<br>Non-farmself-employment<br>|Share|12.1%|27.5%|0.0%|3248|
|Non-farmwageemployment<br>|Share<br>|18.1%<br>|30.3%<br>|0.0%<br>|3248<br>|
|Farmemployment<br>|Share<br>|69.8%<br>|37.6%<br>|89.5%<br>|3248<br>|
|Farmself-employment|Share|56.7%|38.1%|65.0%|3248|
|Farmwageemployment|Share|13.1%|24.3%|0.0%|3248|
|Wage employment|Share|31.3%|34.6%|19.2%|3248|
|<br>Self-employment<br>|Share<br>|68.7%|34.6%|80.8%|3248|
|Shareofhouseholdmemberswhoworkoutsidevillage|Share|4.2%|11.1%|0.0%|3337|
|2.Agricultureandlivestock||||||
|Operatedarea|Ha|0.81|1.67|0.37|3337|
|<br>Valueofannual livestocksales<br>|Rupees<br>|2937|7529|0|3337|
|Valueofannual saleoflivestockproducts|Rupees|1202|4207|0|3337|
|3.Croppingpattern(farmersonly)<br>||||||
|Shareofplanted acreageIn:||||||
|<br>Wetseason:<br>||||||
|Paddy|Share|38.2%|35.0%|31.0%|2377|
|OtherCereals|Share|33.7%|30.9%|29.6%|2377|
|Pulses|Share|11.2%|15.7%|0.0%|2377|
|Vegetables<br>|Share<br>|9.6%<br>|16.4%<br>|0.0%<br>|2377<br>|
|Othercrops<br>|Share|7.3%|15.6%|0.0%|2377|
|Dryseason:||||||
|Cereals|Share|53.2%|34.4%|55.3%|2204|
|Pulses<br>|Share<br>|11.3%<br>|19.1%<br>|0.0%<br>|2204<br>|
|Oilseeds|Share|11.5%|18.7%|0.0%|2204|
|Vegetables|Share|16.8%|25.0%|4.2%|2204|
|Othercrops|Share|7.2%|15.8%|0.0%|2204|
|4.AgriculturalInputuse(farmersonly)<br>||||||
|FerUlizerquantity|Kg|70|144|20|2329|
|Fertilizer perarea<br>|Kg/ha|125|440|33|2322|
|5.Saleofagriculturalproducts(producersonly)||||||
|<br>Salesofcropas%ofvalueofcrap output:<br>||||||
|Paddy|Share|9.3%|18.4%|0.0%|1989|
|Othercereals|Share|9.4%|19.5%|0.0%|2358|
|Othercrops<br>|Share<br>|14.4%|25.0%|0.0%|2252|
|Allcropscombined<br>|Share|12.5%|18.9%|0.0%|2505|
|S.Consumptionofagriculturalproducts||||||
|<br>Shareintotalconsumption:||||||
|Rice<br>|Share<br>|29.8%<br>|10.5%<br>|30.9%<br>|3337<br>|
|Other cereals|Share|47.3%|11.9%|47.5%|3337|
|Vegetables|<br>Share|<br>7.2%|<br>5.6%|<br>5.8%|3337|
|Fruits|Share|1.9%|3.5%|1.1%|3337|
|Shareofcashpurchasesinconsumptlonof:<br>||||||
|Rice|Share|52.4%|41.8%|51.7%|3326|
|Othercereals<br>|Share<br>|36.1%<br>|41.1%<br>|15.9%<br>|3206<br>|
|Vegetables|Share|43.1%|40.2%|31.4%|3317|
|<br>Fruits|<br>Share|<br>58.3%|<br>43.5%|<br>75.6%|3058|
|All combined|<br>Share|<br>44.2%|<br>36.4%|<br>37.5%|3337|
|7.Marketedsurplus<br>||||||
|Valueofoutputminusvalueofconsumption||||||
|<br>Paddy/rice|Rupees|-1683|13615|-2660|3337|
|Othercereals|Rupees|545|27248|-240|3337|
|Vegetables<br>|Rupees<br>|-1356<br>|3032<br>|-950<br>|3337<br>|
|Fruits|Rupees|-257|4626|-192|3337|
|<br>Allagriculturalproductscombincid|<br>Rupees|<br>-650|<br>34704|<br>-3538|3337|

42
Table 3. Distance to the Nearest Town

||Numberof|Numberof||
|---|---|---|---|
|7.NearestCitywlthin<br>|Wards<br>Percent|Households||
|0-1 houroftraveltime|80<br>29.2%|951|28.4%|
|1- 2houroftraveltime<br>|52<br>19.0%|634|19.0%|
|2- 3houroftravel time<br>|40<br>14.6%|485|14.5%|
|3-4houroftravel time|16<br>5.8%|199|6.0%|
|4- 5houroftraveltime<br>|10<br>3.6%|117|3.5%|
|5- 6houroftraveltime<br>|12<br>4.4%|152|4.5%|
|6-7houroftraveltime|9<br>3.3%|108|3.2%|
|7- 8houroftraveltime|6<br>2.2%|72|2.2%|
|8-9houroftraveltime<br>|9<br>3.3%|115|3.4%|
|9- 10houroftraveltime|7<br>2.6%|84|2.5%|
|10- 11houroftravel time|6<br>2.2%|75|2.2%|
|11- 12houroftraveltime<br>|6<br>2.2%|80|2.4%|
|12- 13houroftraveltime|7<br>2.6%|98|2.9%|
|13- 14houroftravel time|1<br>0.4%|12|0.4%|
|14- 15houroftravel time|4<br>1.5%|49|1.5%|
|15- 30houroftraveltime|9<br>3.3%|113|3.4%|
||274|3344||

**43**
Table 4. Proximity to Urban Population

||Mean|Std.dev.|Median|
|---|---|---|---|
|Citypopulation within:||||
|<br>0-1houroftraveltime|74804|<br>180351|0<br>|
|1 -2houroftraveltime|54514|<br>130480|<br>0|
|2-3houroftraveltime|97443|<br>167273|<br>28778<br>|
|3-4houroftraveltime|151752|<br>223457|<br>69968|
|4- 5houroftraveltime|164544|<br>225559|<br>103689|
|5-6houroftraveltime|214631|268668|<br>132280|
|6-7houroftraveltime|342046|<br>320305|<br>245629|
|7-8houroftraveltime|338632|<br>320061|296365|
|8-9houroftraveltime|288493|<br>299774|<br>188042|
|9- 10houroftraveltime|341247|<br>309812|<br>284257|

44
# **Appendix 1: Intrumenting equation for town population**

(dependent variable is the log of town population)

||**unit**|**coef.**|**t-stat**|
|---|---|---|---|
|Areaofdistrictinwhichtownislocated|log(squarekm)|-0.472|-1.38|
|Totalarableland area|log(ha)|-0.592|-1.18|
|Distancetonearestnavigablepartofriver|log(km)|-0.206|-1.04|
|Meanelevationofthedistrict|meters|-0.000|-0.04|
|Standarddeviation of districtelevation|meters|0.001|0.97|
|Mountainous terrain|yes=1|-1.218|-1.85|
|Intercept||21.034|3.63|
|Numberofobservations||34||
|R-squared||0.2705||

Source: distance to river, elevation, and area of district come the Nepal GIS data base. Arable land comes from the Statistical Yearbook of Nepal.

45
|Appendix2:Instrumentingequationfortrave<br>(dependent variableisthelogoftravel time betw|ltime<br>een<sup>wardandtown)</sup>|||
|---|---|---|---|
||unit|coef.|t-stat|
|Foottraveltimebetweenwardandtown|log|0.677|174.12|
|Wardcharacteristics||||
|Areaofdistrictinwhichwardislocated|log(squarekm)|0.202|21.65|
|Totalarablelandareaofdistrict|log(ha)|-0.145|-12.82|
|Meanelevation|meters|0.000|14.02|
|Standard deviationofdistrictelevation|meters|-0.000|-6.71|
|Centralregion|yes=1|-0.229|-24.06|
|West region|yes=1|-0.281|-26.65|
|Mid-westregion|yes=1|-0.219|-14.90|
|<br>Far-westregion|yes=1|-0.199|-11.98|
|Towncharacteristics||||
|Areaofdistrictinwhichwardislocated|log(squarekm)|0.067|5.34|
|Distancetonearestnavigablepartofriver|log(km)|0.048|7.28|
|Totalarablelandareaofdistrict|log(ha)|-0.005|-0.30|
|Meanelevation|meters|0.000|8.18|
|Standard deviationofdistrictelevation|meters|-0.000|-6.35|
|Centralregion|yes=1|-0.150|-11.40<br>|
|Westregion|yes=1|-0.186|-15.86|
|Mid-westregion|yes=1|-0.172|-9.55|
|<br>Far-westregion|yes=1|-0.082|-4.20|
|Intercept||-1.780|-9.37|
|Numberofobservations||9187||
|R-squared||0.8443||

Source: foot travel time computed from GIS data correcting<sup>for elevation.</sup> Other variables as in Appendix 1. Eastem region<sup>is the omitted region.</sup> 46
# Figure 1. Map of Nepal and Location of Surveyed Villages

<!-- Start of picture text --> CI CJ<br>,4 'V' /' Urban centers<br>,- 1-29C5O<br>A ,t ; < -~- ), * U)3 123$9L8'5<br>. , t_* .-- , > 4-, > 1533-<br>tLf S12 A : i V <~~~~12W9t2"2%<br>p. P 1 + 4 U. +-*' -4-. *<br><!-- End of picture text -->
<!-- Start of picture text --> 0.38 -0.47-<br>0 2O0<br>0,0<br>a,<br>- 0.03- 0 0.00j_<br>.t2 35stance from local market in hours (log scaleT .stance frbm local market in hours (log scale)<br>Non-farm wage employment Non-farm self-employment<br>0Fg0.23 - 0.84 -<br>00<br>0 ~~~~~~~~~~~~~~~0<br>a,~~~~~~~~~~~~~~~~~~~~~~~~~~~~~I<br>U, U<br>-0.03 0.00<br>.12 2 ~ 12sae<br>cpistance frbm local mlarket in hours (logm lcal mrketsnchous (lo<br>Agricultural wage employment Agricultural self-employment<br>Figure 2. Employment and distance from local market<br><!-- End of picture text -->
<!-- Start of picture text -->
1.93  - 0.63<br>o  0<br>o<br>-0.35  _____________________________  -0.12  ____________________________<br>distance fiom  town  In hours  distance t4om  town  in hours<br>Non-farm  wage  employment  Non-farm  self-employment<br>0.35  - 0.33  -<br>0  0<br>o<br>o  ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~o<br>-0.60  __________________________  -1.93  __________________________<br>distance frm  town in hours  dsac4rntown  in holurs<br>Agricultural wage  employment  Agricultural self-employment<br>Figure  3. Employment  and  distance  from  towns<br><!-- End of picture text -->
<!-- Start of picture text --> 0.07 0.14<br>D0 0<br>0 E E 0<br>*0<br>0 0<br>o 0<br>DiFgstancefrom loal ma re t distancef wnmnhons<br><!-- End of picture text --> _k.n_

**Figure** 4. **W'or'Kmigration** and distance
<!-- Start of picture text --> 0.74 4.27<br>o c<br>0<br>o~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~c<br>0.00 0.00<br>.12 2 0stance frm local market in hours 2 (log scale) b2stance fm locl makti1hus(og2aemakt in hours (logwale$<br>Size of operated land Livestock sales<br>2.96<br>0,<br>0.00<br>1i2 .5l1 lb<br>.t2 lrstance om local market In hours (log scale)<br>Fertilizer per hectare<br>Figure 5. Farming and distance from local market<br><!-- End of picture text -->
<!-- Start of picture text --> 0.13- 4.37 -<br>0)~~~~~~~~~~~~~~~<br>-1 .71 -/-1 2.34-/<br>1 ~~~~distance fEom town in hours 1b1distance ftom town in hoturs<br>0~~~~~~~~~~~~~~<br>.G Size of operated land Livestock sales<br>w<br>14.59-<br>-0.79 -_<br>-1.71 Size distance IAom town in hours<br>Fertilizer per hectare<br>Figure 6. Farming and distance from sowns<br><!-- End of picture text -->
<!-- Start of picture text --> 0.55 0.24<br>co I-<br>-~~~~~~~ Cu~~~~~~~0<br>o -~~~~~~~~~~~~~~~~~~~~~~~~~~~t<br>0 t 0 .3<br>0.00 0.00 _________________________ 0.00 0.00 ________________________<br>.t 3stance Arm local market in frixn~~~~~~~~~~~~~~~~~pstne4 hours (1log scalei 1b12 local market hours. (log scale) 4111tanoe from local market akt in in hours uso (log scaele<br>Wet Wet Season Season - -pulses paddys Wet Wet Season Season-vegetables-- vegetables<br>C F 7i<br>C-fFgrl.Crpigpten n itnefrmlclm re<br><!-- End of picture text -->
<!-- Start of picture text --> 0.86 -0.63-<br>0~~~~~~~~~~~~~~~~<br>0 -~~~~~~~~~~~~i<br>- ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~CD<br>distance ~iA j~ t distance fLontown in0~r<br>Wet season - paddy Wet season - vegetables<br>5>- ~0.22 -0.71-<br>06 0<br>122<br>Cu ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~U)<br>C~~~~~~~~~~~~~~~~~~~r C 3r oni or<br>4- eso use 4-~r r eao egtbe<br>0 Copn atenaddsanefo 0iue8 o n<br><!-- End of picture text -->
<!-- Start of picture text --> 0.17 - 0.68 -<br>o 0<br>o 0<br>0.00 -0.17<br>.12 *t oa ~moni or<br>.t2 stance fom local market in hours (log scale1 distance 4om town in hours<br>Distance from local market Distance from towns<br><!-- End of picture text --> Ln LF Figure 9. Crop Sales and distance
<!-- Start of picture text --> share of consumpbon share of consumption<br>share of consumption share of consumpUon<br>3~~~~~~~~~~~~~~~~~~~~~~~<br>0~~~~~~~~~~~<br>C~~~~~~~~~~~~~~C<br>share of consurpo h fxw n) pbon<br>!X ~~~~~~~~rt<br><!-- End of picture text --> <!-- Start of picture text -->
-n<br>CD<br>-.<br><!-- End of picture text -->

**0**

_cnsar_
<!-- Start of picture text --> 0.80 -1.82-<br>o ~~~~~~~~~~~~~~0<br>E E<br>o 0<br>0.22 ___________________________ -0.23 - ________________________<br>C11stnc fbmloalmarket In hours (log scale, distance &Mtowni or<br>Distance from local market Distance from towns<br><!-- End of picture text -->

> Figure _1 1._ Crop purchases and distance
<!-- Start of picture text --> 0.85 0.16 -<br>-a _<br>00<br>(A(<br>2L C,<br>.0 0<br>.12 '35stance om local ma rket in hors (log s.al 1 distance r town in hours<br>Distance from local market Distance from towns<br>LnF22035.23 - 6029.36 -<br>00<br>IDI<br>-11235.06 -- 47954.89_________________________<br>.12 oa 2tner cl~ 01twihus1<br>(41tnc fbmloalm'arket in hours (log sc ,e ldistance towriamur<br>Distance from local market Distance from towns<br>Figure 12. Market surplus and distance<br><!-- End of picture text -->
<!-- Start of picture text --> 3 81 - ¶76 0.29-<br>E<br>b E= E * 0<X ;0 0<br>3 2 6 11.tanm d16mle mm 121 ocl olm I 1$ 5cm(kog scale) 8 lm 1 48 2.25 5t dm000 m local MI,.1kt1. h0cm I (log scl)allna 8b in 8 loa n 1 . hOInoM 2 (logscle 82 lb,<br>Age Household size Adult males<br>0 19 102 ¶ -j 063<br>° °° f2 Idaltn 8 (mm loal mdLt 1I tlm (log scale) lb ld3ionce 0 00 2 .26 f local mrout in5h I6 loom (log ecab) i lboanCce b -0.02 .12 Isk hor local 8 maak M1.0 In I 2 po (log saee) 8$e lblb<br>Children Education of adult males Education of adult females<br><!-- End of picture text --> Figure 13. Household characteristics and distance<sup>fromlocalmarket</sup>
09 <!-- Start of picture text --> share of members number (in log)<br>i, 0 r 0 tDI<br>I' a<br>CD D<br>CL<br>CD<br>ci)<br>0 C<br>En~~~~~~~~~~~~~~~~~~~a<br>(D~~~~~~~~~~~~~~~D<br>0~~~~~~~~<br>(0<br><!-- End of picture text --> <!-- Start of picture text --> cin<br><!-- End of picture text -->
<!-- Start of picture text --> 0.68 0.67<br>0.00 0.02-/<br>t2 gistance from local market in hours (log scalel .12 3lstance from local mlarkeet in hours (log scalel<br>C Inherited wealth C Inherited wealth<br>o 0<br>.12<br>9.40 - \-0.38 -\<br>.12 istance frbm local market in hours (log scale1 .rFo distance town in ho2urs<br>Distance from local market Distance from towns<br>Figure 15. Inherited wealth, food expenditures, and distance<br><!-- End of picture text -->
<!-- Start of picture text -->
-B  - 294  - 004B<br>E  E1<br>-1324  - _AM  .1.5  It?  - . _  _ In_<br>.2  S1t  jbij  l  2  L  h 1b dl hL0  6 e dh12  zb  Sb  b)  lb  d_  6g,  , 009 ni ) 1<br>Rice  Other crops  Livestock products<br>s  ~~~~~~~~~~~~~~~~~~~~02  e  03<br>dhMonin  oums  d09  A.  in  04m  nowl g<br><!-- End of picture text -->

Figure 16. Consumption and distance with household characteristics
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