The Spatial Dimension of Vulnerability to Poverty – First ... · The Spatial Dimension of...

RSAI World Congress, Sao Paulo, March 17-19th, 2008

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The Spatial Dimension of Vulnerability to Poverty – First Results from a

Household Survey in Rural Thailand

Ingo Liefner1 and Carsten Lohmann

University of Giessen, Department of Economic Geography, 35390, Giessen, Germany

- Abstract -

Vulnerability to poverty is still a major problem of rural households in developing countries. Micro-economic analyses dominate this current debate on vulnerability. Most of these studies do not take into account the fact that not only household assets but also the location of the household affects its vulnerability. For example, the opportunity of gaining remunerative regional non-agricultural income (RNAI) is unevenly distributed between peri-urban and rural-remote regions. Peri-urban households face lower transportation costs and travel times than their rural-remote counterparts. The aim of clarifying the spatial dimension of vulnerability to poverty is part of a recently completed survey of 2,200 rural households in northeastern Thailand. The survey itself is part a multi-disciplinary research project on vulnerability in Thailand and Vietnam sponsored by the German Research Foundation (DFG). The data obtained in this survey underline the importance of location: proving significant differences between peri-urban and rural-remote households regarding transportation costs, travel times and earnings from regional non-agricultural employment (RNAE).

1 Introduction

Since the “World Development Report 2000/2001 - Attacking Poverty” was published much

attention has been drawn on the issue of vulnerability to poverty. The vulnerability concept

proclaims to dynamise the static concept of poverty by asking not only whether somebody is

poor today but also with what probability will one be poor tomorrow (Worldbank 2000: 135).

Most research on vulnerability enables a livelihood-approach to embed the vulnerability

context in a broader and more comprehensive framework, which pays attention to the

complexity of socio-economic processes in rural areas of developing countries (Bohle 2001;

Chambers & Conway 1991; Chaudhuri et al. 2002; Heitzmann et al. 2002; Hoddinott &

Quisumbing 2003; Kijimaa et al. 2006; Kurosaki 2006; Sen 2003). Another issue which has

risen on the research agenda throughout the past decade has been the exploration of the rural

1 Corresponding author. Tel.: +49 (0)641 99 36 220; fax: +49 (0)641 99 36 229. E-mail addresses: [email protected] (I. Liefner), [email protected] (C. Lohmann).


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nonfarm economy and nonfarm income diversification of rural households as means of

overcoming poverty and reducing vulnerability (Ellis 2000; Haggblade et al. 2007; Islam

2006; Lanjouw & Lanjouw 2001; Otsuka & Yamano 2006; Reardon et al. 2001; Rigg 2006;

Rosegrant & Hazell 2000; Sen 2003; Zhu & Luo 2006). Finally, the last field of research

which will be treated by this paper is the spatial dimension of socio-economic processes. The

importance of spatial issues, e.g. spatial disparities in production, distribution and

consumption patterns and hence the wellbeing of people will be on the agenda of the

upcoming “World Development Report 2009 – Spatial Disparities and Development Policy”

(Dicken 2007; Worldbank 2008).

The objective of this paper is to bring these three research fields together by extracting the

relevant factors from the vulnerability and nonfarm concepts and focus on their spatial

dimensions. In a broad outline this means that vulnerability depends on both household assets

(e.g. education, land) and regional factors (e.g. availability of regional non-agricultural

employment (RNAE), infrastructure) which together determine the level, structure and

volatility of household income. The positive role of RNAE as an accumulation and

diversification strategy of rural households and the importance of regional factors holds true

for lower-middle income countries where the economy already offers regional and non-

regional non-agricultural employment opportunities to rural residents and where infrastructure

is already sufficiently developed (Otsuka & Yamano 2006: 396; Rosegrant & Hazell 2000:

97). The empirical section uses data from Thailand as an example of an emerging Asian

country. It is argued that the opportunity of gaining rural non-agricultural income (RNAI) is

especially unevenly distributed across rural space and that proximity to rural towns (i.e.

centers of non-agricultural employment) is a major determinant of physical access to this. In

most micro-economic analyses this argument is only seldom taken into account or just

recognized briefly, like the notion of Barrett et al. (2001: 326) on physical market access,

where they noted that “… the benefits of such investments [education, communication and


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transportation infrastructure for improved access to nonfarm employment opportunities] not

just come from reducing transactions cost on existing activities but, perhaps more

importantly, from opening up whole new opportunities previously inaccessible to rural

populations”. Due to this notion it is assumed that there are huge disparities in access to

remunerative RNAE opportunities between different rural region types. On the one hand there

must be a type of region equipped with sufficient infrastructure and proximity to centers of

RNAE called peri-urban and on the other hand there must be a rural-remote type of region

characterized by insufficient infrastructure and distance to centers of RNAE (Lanjouw et al.

2001: 386). As of today there is only limited evidence on this topic.

The structure of this paper is as follows: Section 2 discusses theoretical concepts of

vulnerability, non-agricultural income diversification and location opportunity. Section 3

provides a review of the literature on the role of location in gaining RNAI. Section 4 discusses

the data gathering process and sets the working definitions. Section 5 presents empirical

findings of participation in and earnings from RNAE. Finally, section 6 concludes.

2 Vulnerability, non-agricultural income diversification and location opportunity

2.1 Vulnerability

By now it is not possible to give a comprehensive and inter-disciplinary accepted definition of

vulnerability to poverty. While Chambers (1989) is the founder of the concept the recently

updated definition of Heitzmann et al. is used to highlight major elements of the concept.

They define vulnerability to poverty as "the forward-looking state of expected outcomes,

which are in themselves determined by the correlation, frequency and timing of realized risks

and the risk responses. Households are vulnerable if a shock is likely to push them below (or

deeper below) a predefined welfare threshold (e.g., poverty)” (Heitzmann et al. 2002: 6).

Important aspects of the definition are the notions of risk, shock and the strategies that a

household can use to reduce and mitigate risks ex-ante or cope with shocks ex-post. A risk is a


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probability distribution of events, for example the risk of flooding in a certain area will be

20% this year. And if this event occurs and pushes a household below the poverty line it is

labeled a shock. It is useful to distinguish between idiosyncratic risks / shocks which only

concerns single individuals and household (e.g. illness, death, divorce) or covariate risks /

shocks which have impacts on a large number of people in villages, regions, nations, or larger

units (e.g. floods, droughts, earthquakes, economic crises).

Households can use risk management strategies assigned to three broad categories: risk

reduction, risk mitigation and coping with shocks. “Risk reduction aims at reducing the

probability of a shock” (World Bank 2000: 141). That means, for example, using preventive

health practices, digging a well, or building a dam to prevent the occurrence of a shock. In

this category, households have only a limited capability to reduce covariate risks effectively

by themselves. “Risk mitigation aims at reducing the impact of shocks” (World Bank 2000:

141). Common strategies are, on the one side, diversification of income sources and assets

and, on the other side, informal and formal insurance practices. The main feature of these

actions is that they have to be in place ex-ante in order to reduce the impact of a shock ex-

post. Most of these measures can be taken by the households themselves. “Coping strategies

aim to relieve the impact of a shock after it occurs” (World Bank 2000: 142). Ex-post coping

strategies are for example the sale of assets, child labor, seasonal or temporary migration,

taking up low paid off-farm jobs, borrowing from friends and banks or reducing food

consumption in order to survive a shock. These coping activities are always open to

households but they bring often only short term benefits with long term losses, like

incomplete schooling of children or indebtedness.

In total it is important for a household to be aware of all future risks and employ appropriate

ex-ante strategies to manage them in order to reduce its vulnerability. For example the

previously mentioned flooding destroys 100% of the annual income of a farm household


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while a diversified household loses only 50% of its total income due to the fact that the other

50% is gained through non-agricultural activities.

2.2 Non-agricultural income diversification

Income diversification or, more comprehensively, livelihood diversification can be defined as

“the process by which rural households construct an increasingly diverse portfolio of

activities and assets in order to survive and improve their standard of living” (Ellis 2000: 15).

Key issues are the income generating activities mainly differentiated into agricultural and

non-agricultural types and the underlying household assets differentiated in human, physical,

financial, natural and social capital which are generally employed to gain safer and higher

household income (Ellis 1998: 1). From the vulnerability literature non-agricultural income

diversification is seen both as a household level ex-ante risk mitigation or ex-post shock

coping strategy (Heitzmann et al. 2002: 15; Worldbank 2000: 141). Income diversification

used for risk mitigation means to seek employment in the relative high productivity and well

paid demand-pull subsector of the non-agricultural economy while coping with shocks refers

to search for work in the relative low productivity and badly paid distress-push subsector after

a shock did destroy agricultural income sources (Buchenrieder & Möllers 2005: 24). The aim

of risk mitigation through ex-ante income diversification is to achieve an income portfolio

with a “low covariate risk between its components” (Ellis 2000: 60). It is often stated that this

leads to safer but lower total household income, which is associated with distress-push

diversification into low-return activities which are also open to asset poor households

(Dercon 2002: 151f.; Elbers et al. 2003). On the other hand this trade-off disappears in the

case of demand-pull diversification of asset-rich households into high return activities where

the objectives of safer and higher household income can be reached hand in hand (Ellis 1998:

1). It is argued in this article that access to different non-agricultural activities mainly depends

on regional factors like the level of economical and infrastructural development and thus, on


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the location of a household within a country. For example, a well developed regional

economy offers many non-agricultural wage jobs in food processing, construction, garment

fabrication, trade and transport etc. at low entry barriers to local people, which enables

formerly asset poor households to take up remunerative non-agricultural employment.

Income-generating activities of rural households can be classified along three dimensions of

sector, space and function. The sectoral dimension is derived from standard national

accounting classifications where non-agricultural is simply defined as “activity outside

agriculture (own farming plus wage-employment in farming), hence manufactures and

services” (Reardon et al. 2001: 396). In spatial terms it is differentiated between regional (at

home, in village, regional rural, regional urban) and non-regional (domestic rural, domestic

urban, international) employment, the latter normally requiring migration. And functional

non-agricultural employment can be done by self- or by wage-employment (Barrett et al.

2001) (Table 1).

Table 1: Three-way Classification of Household Income Generating Activities: Sectoral, Functional, and Spatial

AGRICULTURE NON‐AGRICULTURE Primary sectors Secondary sectors Tertiary sectors Agriculture, fishing,

hunting Mining, construction,

manufacturing Services

Regional Wage‐employment

Self‐employment

Wage‐employment

Self‐employment

Wage‐employment

Self‐employment

Non‐regional

Wage‐employment

Self‐employment

Wage‐employment

Self‐employment

Wage‐employment

Self‐employment

Source: adapted from Barrett et al. (2001: 319)

2.3 Location Opportunity

Whether the non-agricultural economy offers more regional or non-regional, and wage or self-

employment depends mainly on the level of national economic development and the

organization of the space economy which transmits economic development through space

from the center to the periphery. It is argued that economic change, described as a shift from

agricultural to non-agricultural activities or from labor to capital and knowledge intensive


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industries, trickles down the urban hierarchy from the center to the periphery which leads to

an integration of the space economy after a period of sustained economic growth2 (Fafchamps

& Shilpi 2003; Friedmann 1970: 30f., 38). This process will bring regional non-agricultural

employment opportunities not only to residents in rural towns but also to people in

surrounding peri-urban hinterlands and to residents along the axes between the cities

(Friedmann 1970: 31; Rosegrant & Hazell 2000: 111). The same line of thought is carried on

by Mohapatra et al. (2006) in their recently published work about spatial economic

development in mainland China. They argue that the rural development process can be

explained by the level of economic development and the proximity to urban centers. It is

characterized by a sequence of four developmental stages in time and locations in space

beginning with subsistence agriculture in the most remote areas, followed by labor migration,

small scale self-employment which finally evolves into an economy dominated by medium

and large scale companies (Mohapatra et al. 2006: 1026f.). It is assumed that this

development process is facilitated by proximity to urban centers where rural residents have

the opportunity to profit from the same urbanization advantages as their urban counterparts

do. Urbanization advantages can materialize in lower transaction costs, better market access

and lager market size for inputs and outputs, higher communication density, better access to

technology, and a well developed technical and social infrastructure3 (Mohapatra et al. 2006:

1026; Schätzl 2001: 34f.). It is important to note that the aim of these models is primarily to

explain and describe the regional development process in the periphery by focusing on the

emergence of the regional, often termed rural non-agricultural economy (Tab. 1 upper row),

where out-migration is only seen as a temporary phenomenon. To fully capture the rural non-

2 The long standing and still open discussion about backwash and spread effects in spatial economic development gives no clear answer to which effects prevail, how long it will take, and whether there really is a continuous impulse of economic change from the center to the periphery channeled down the urban hierarchy. But in this paper we assume that in emerging economies some spread effects will definitely be seen after several decades of economic growth. 3 Disadvantages of urbanization like high land and labor costs, traffic jams, pollution, etc. only occur after a certain level of city size is reached are not used for the concept of this paper.


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agricultural sector one has to include “small rural towns, growth centers and their industries”

(Start 2001: 492) in the analysis or one will only tell half side of the story (Rosegrant &

Hazell 2000: 82, 111-113).

These well established concepts can be combined with micro-economic survey data and the

vulnerability concept to gain new insights into the phenomenon of location opportunities in

rural areas. In taking these arguments into account and taking the perspective of a rural

household, it is important to note that it can only take these advantages under certain

circumstances. First, the rural town economy has to provide enough well paid non-agricultural

jobs or entrepreneurial market potential for its hinterland members. Second, the household has

to be located in the hinterland of one town or at an axis between two cities (relative location).

Third, the transportation infrastructure (e.g. road network and connectivity (Douglass 2006))

must allow daily travel from village to town. Fourth, marginal transportation costs have to be

lower than marginal earnings from town based activities. Together these circumstances

determine the ‘Location Opportunities’ of the household to gain regional non-agricultural

income or improve its asset base (Fig. 1).

Figure 1: The Influence of Location Opportunities on Non-Agricultural Employment & Earnings and Household Asset Accumulation

Source: own

Location Opportunities ‐ Level of economic development ‐ Relative location ‐ Infrastructure ‐ Transportation cost

Non‐agricultural Employment &

Earnings ‐ Wage‐employment ‐ Self‐employment

Asset Accumulation

‐ Human capital ‐ Financial capital


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In the case of low location opportunities migration can be seen as an alternative strategy. It is

important to note that location opportunities influence both the physical access to non-

agricultural employment and the access to asset accumulation like education and credit which

are needed to engage in non-agricultural wage- and self- employment respectively. This paper

focuses only on the employment aspect, not on asset accumulation. To use the location

opportunity concept in the case of regional non-agriculture wage employment (RNAwE) three

factors are important: wage rate, transportation cost and distance from village to rural town

(i.e. center of non-agricultural employment).

From the theoretical section the following hypotheses can be drawn:

H1: Households in peri-urban areas have a higher participation rate in RNAwE than rural-

remote households, i.e. opportunities are available.

H2: Remote areas are characterized by lesser RNAwE and hence more labor migration exists,

i.e. opportunities are missing.

H3: Proximity to rural towns is a main determinant of physical access to remunerative

RNAwE due to lower travel times, lower transportation costs, and higher connectivity.

H4: RNAwI is higher than agricultural wage income.

3 Literature Review

In this section findings from empirical studies are presented that support the hypotheses laid

out in the previous section.

Ellis (2000: 200) purposefully selected 3 villages with different degrees of remoteness from

public infrastructure and services in northern Tanzania in 1997 in order to capture the effect

of location on income portfolio. He measured remoteness by distance in km from the main

road which connects two cities. The most remote village is only accessible by a dirt road

which gets impassable in rainy season and is 40 km away from the district HQ. The least


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remote village is located on a tarmac spur from the main road and 25 km away from the

district HQ. He found that the most remote village had only half of the per capita income of

the least remote village. While the share of non-agriculture income was also only 23%

compared to 47% in the least remote village.

A larger survey of 595 households in 50 villages located in the peri-urban areas of Tanzania’s

six largest cities was conducted one year later in 1998. Peri-urban was defined as being within

a 20 km distance from the city perimeter. By dividing the sample into four distance groups of

0-5, 5-10, 10-15 and 15-20 km Lanjouw et al. (2001: 395) found that the most distant group

had only half of per capita income of the nearest group. On the other hand the share of non-

agricultural income was surprisingly low, only around 16% for the nearest and most distant

group and 24% and 36% for the two middle-distant groups. They argue that households

located closest to the city grow perishable but profitable agricultural goods like fruits, which

cannot easily be transported over large distances, to serve the urban food market. A probit

analysis of determinants of non-agricultural employment reveals also only limited evidence

between proximity to city and participation in non-agricultural employment. Main

explanatory variables were gender, education, small and large landholdings and road access

(2001: 398). Another point appears when analyzing the earnings from non-agricultural

employment. Earnings from wage-employment are especially higher in the nearest group than

in the most distant one, suggesting that remunerative employment is located close to urban

areas (2001: 401). It is interesting to note that while the villages of Ellis’s study are not

located in the peri-urban area of Lanjouw’s et al. study it shows higher non-agricultural

income shares and a more pronounced location effect. This can be due to the fact that the 20

km definition of a peri-urban area is too tight in Lanjouw’s et al. survey or, on the other hand,

that the methods of data collection were too different between this two studies to compare

them effectively.


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Another measure to distinguish between rural and peri-urban areas is to define thresholds for

total population size per settlement. By using census data from rural Mexico, Verner (2005: 1)

defines rural as a locality with less than 2,500 and peri-urban as a locality with between 2,500

and less than 15,000 inhabitants. She found that earnings from rural non-agricultural wage-

employment are 12% higher for the median worker in peri-urban areas (2005: 23).

Participation in the high-return sub-sector of the rural economy is more likely for peri-urban

residents, rural-remote dweller tend to work in the low return sub-sector (2005: 26). The

overall statement is that participation in and earnings from RNAwE are significantly

determined by education and location (2005: 27).

Further evidence for the location argument is found by Isgut (2004: 63) in analyzing the 1998

Household Survey of Honduras. The survey contains 2,805 rural households from 16 of the

country’s 18 departments. A rural area is defined by a combination of two criteria: first the

population has to be less than 2,000 inhabitants or, despite a larger population, one of the

following services is missing: piped water; communication by road, railway, or regular

maritime transportation; a primary school; a postal service, or a telephone service. His results

show that rural non-agricultural wage-employment is only open to workers with twice as

many years of education than the typical agricultural worker and it is geographically

concentrated close to urban centers. “Households located in these areas can commute to work

in nearby towns or cities and perhaps have access to good schools which provide the

necessary skills for that type of employment” (Isgut 2004: 70). This commuting argument is

also supported by Wiggins and Proctor (2001: 435) who argue that only areas within a daily

commuting range around a town or city should be termed peri-urban. (RNAsE) shows

different characteristics as requiring lesser years of education, being less profitable than

RNAwE and more geographically dispersed. Development motors for RNAsE are profitable

agriculture, access to important roads and proximity to tourist areas (Isgut 2004: 81). These


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findings show similarities in all ways except the migration issue with the Mohapatra et al.

stages-center-periphery model mentioned earlier in this paper.

On the other hand studies like Sen’s (2003) panel study of 379 households in 21 villages in

rural Bangladesh in 1987-88 and 2000 give only weak support for the location argument. He

found that households escaping from poverty had higher non-agricultural income shares from

local and migratory sources, better schooling, and both higher financial and non-agricultural

productive assets. But they were not concentrated in a specific region measured by agro-

ecological conditions and endowments of community and public assets on village and district

level (Sen 2003: 519f., 522). He probably did not take proximity to rural towns into account

or, on the other hand, the level of spatial economic development may still be too low in

Bangladesh to create peri-urban regions.

To sum up the short discussion of the previous mentioned studies, it can be stated that under

certain circumstances location matters for gaining access to RNAwE. However, empirical

evidence is far from clear. The literature still lacks comprehensive investigations of the

relationship between opportunities of gaining RNAwE, gaining RNAwI, and vulnerability

when comparing regions, different economic systems, and labor market segments with a

consistent method. Most investigations, so far, only deliver evidence on household income but

are unable to give exact insights into the interplay of non-agricultural labor-market

participation, wages, professions, and locations.

4 Data

The information necessary for such a comprehensive and detailed analysis is being acquired

in a multi-disciplinary research project on vulnerability to poverty in rural areas in Thailand

and Vietnam sponsored by the German Research Foundation (DFG). The survey covers both

222 villages in three provinces of northeast Thailand consisting of a representative dataset of


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2,186 households selected through a multistage stratified random sampling method and

corresponding data from Vietnam that is not yet available.

Sampling and setting

Thailand is chosen because it is a middle fast growing emerging market economy, growing at

an average real4 annual GDP growth rate of 6.1% from 1981 to 2004. This growth is mainly

fueled by an expanding BMR- and ESBR5-based manufacturing sector, growing from a share

of 23.2% in 1981 to 38.7% of GDP in 2004. The real GDP per capita itself grew from 20,278

THB in 1981 to 57,193 THB in 2004 (NESDB) which equals in nominal terms to 2,490 US-$

in 2004 (Worldbank 2006: 20).

Secondly, the northeast region is chosen because it is economically lagging behind the other

three regions of Thailand and has the highest poverty incidence (headcount) of 18.8 %

compared to the whole country of 11.2 % in 2004 (Somchai et al. 2004: 3). In the third step

there were randomly selected three bordering provinces of the northeastern region; namely

Buriram, Ubon Ratchathani and Nakhon Phanom (see table 2 for basic information). The term

rural is defined by population size related to a sub-district (tambon) being less than 5,000

persons (Nso 1990: 25).

In the fourth step, the sub-districts (tambons) were selected with probability proportional to

size by a systematic sample from a list ordered by population density. Resulting in a selection

of 41 sub-districts in Buriram, 49 in Ubon Ratchathani and 20 in Nakhon Phanom. In the fifth

step two villages were sampled out of each of the selected sub-districts with probability

proportional to size. In the final step, a fixed size sample of households was selected

systematically from a list of households ordered by household size. In result 819 households

in Buriram, 970 in Ubon Ratchathani and 397 in Nakhon Phanom could be interviewed

respectively. As outlined above the households were randomly selected and by this covering

4 At 1988 constant prices. 5 BMR = Bangkok Metropolitan Region; ESBR = Eastern Seabord Region


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all region types of each province including peri-urban and rural-remote ones. The large

sample size gives the opportunity to derive statistically representative and statistically

significant results while maintaining high accuracy in the data collection process with average

interview duration of approx. 100 minutes.

Table 2: Basic Information About Population and Economy of the Three Sample Provinces Ubon Ratchathani, Buriram and Nakhon Phanom Ubon

Ratchathani Buriram

Nakhon Phanom

Thailand

Population in million persons (2005)

1.783 1.536 0.693 62.418

Level of Urbanization (2005)

14.6 % 13.9 % 12.7 % 29.1 %

Population of provincial capital (2005)

122,782* 28,319 27,710 ‐

Real GDP per capita in THB** and relative level compared with Thailand (2004)

16,235(28.3 %)

13,516(23.6 %)

13,010 (22.7 %)

57,193(100 %)

Share of non‐agricultural sector in % of GDP (2004)

80.9 % 74.8 % 73.7 % 90.7 %

Share of non‐agricultural employment in % (Quarter 4)***

38.1 % (2006)

31.8 % (2006)

35.5 % (2005)

57.9 % (2005)

Distance and road travel time to Bangkok in km and hours

600 km 9 hrs.

400 km 6 hrs.

700 km 10 hrs.

‐

Note: * The cities of Ubon Ratchathani (92,261) and Warin Chamrap (30,521) can be characterized as a twin city and are counted together ** At 1988 constant prices *** In rural areas employment figures are heavily dependent on seasonal fluctuations: generally Q 1 and Q 2 have high non-agricultural shares and Q 3 and Q 4 have low non-agricultural shares Source: Provincial Statistical Yearbooks of Ubon Ratchathani, Buriram and Nakhon Phanom 2006; Thailand Statistical Yearbook 2006

The following data collection process was done with two questionnaires. One two-page

questionnaire for the village headman in order to gain information about location,

infrastructure, main village occupations, main problems and natural resource use practices in

the village. The other 29-pages questionnaire was used for the households to get information

about household demographics, health, education, employment and income in agriculture and

non-agriculture, migration activities, shocks and risks, borrowing and lending activities, as

well as expenditures and household assets.


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Working definitions

For the purpose of the analysis, the following working definitions are used:

1. To measure the proximity to a town the following recommendations are given from the

literature by Douglass for peri-urban areas in Indonesia as being within a 60 km town-

distance (DOUGLASS 2006: 141). Wiggins and Proctor give a general statement that rural

residence and urban workplace “can be seen for any rural area within one to two hours

travel of a substantial city [above 250,000 inhabitants]” (2001: 432). The proximity to a

rural town is measured by travel time in minutes (isochrones) and not travel distance in

km, because road conditions can differ substantially in developing countries (Fafchamps &

Shilpi 2003: 36). Peri-urban is defined as being within a perimeter of 70 minutes travel

time from a rural town. Regions outside this 70 minutes perimeter are labeled rural-remote.

2. Working in the regional labor market means working within the province and working in

the non-regional labor market means working outside the province, normally requiring

migration.

3. Agricultural wage-employment means working in the agricultural sector. Working in all

other sectors means working in the non-agricultural sector.

4. The differentiation between the demand-pull and distress-push subsectors is derived from

wage levels. If the average wage in one occupation is below the average wage of all

occupations, the subsector is designed as low-return or distress-push. And if the average

wage in one occupation is above the average wage of all occupations, the subsector is

designed as high-return or demand-pull (Verner 2005: 24).

5 Empirical findings

5.1 Participation in regional and non-regional wage-employment

The location opportunity concept is broadly laid out for explaining two issues: The first being

gaining remunerative regional non-agricultural wage- and self-employment and the second


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being accumulating household assets. In this paper only the topic of regional non-agricultural

wage-employment (RNAwE) will be analyzed in detail (Table 1), after giving a brief

overview over other wage-employment activities and leaving the other parts of the concept for

forthcoming publications.

Before the employment section starts it has to be stated that 49.6 % of all reported shocks in

the sample can be directly assigned to agricultural activities (e.g. drought, flooding, crop pest,

strong increase of input prices, strong decreases of output prices) while the figure for non-

agriculture shocks (e.g. job loss, migration, collapse of business) is ten times lower and stands

at 4.4%. The other 46% of shocks like illness or death of a household member cannot be

assigned directly to one of the two broad income categories. This gives strong support to the

argument that income diversification is needed to mitigate risks and smooth total household

income.

On the other side it can be argued that shocks are not often reported for the non-agricultural

sector because only a few households have non-agricultural employment. But indeed the

percentage of households engaged in non-agricultural employment is quite high with 68.2%

engaged in wage-employment and 31.1% engaged in self-employment.

For further analysis the sample is divided into two spatial groups, according to the definition

above. By doing this 80% (n=1,737) of the households are located in peri-urban areas and

20% (n=439) are resident in rural-remote ones. While focusing on non-agricultural wage-

employment agricultural wage-employment is used as a control group. Information is given

on the household as well as on the job levels.

To test the first and second hypothesis:

H1: Households in peri-urban areas have a higher participation rate in RNAwE than rural-

remote households, i.e. opportunities are available.

H2: Remote areas are characterized by lesser RNAwE and hence more labor migration exists,

i.e. opportunities are missing.


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the following two cross-tables are drawn. If comparing non-regional to regional-NAwE it can

be seen that overall in both region types non-regional-NAwE is still more important for

households than regional employment with an overall difference of 10.3 percentage-points

(47.8% to 37.5%) for all households divided into a 7.6 percentage-points difference (47.6% to

40%) for peri-urban ones compared to a 20.7 percentage-points (48.7% to 28%) difference for

remote ones (Table 3, columns c, a, b; rows 1, 5). These findings are quite noteworthy

because, on the one hand, it can be seen that the sampled provinces are still on a low

development level where in general non-agricultural employment opportunities are more

available outside the own province; namely, in Bangkok with 55.7% as the major migratory

destination. On the other hand, while the level of out-migration is nearly equal for both region

types the engagement in RNAwE shows a significant spatial difference between peri-urban

and remote households of 12 percentage-points (40% to 28 %; table 3, column d; row 1). This

gives support to the argument that proximity to rural towns, i.e. location matters for gaining

RNAwE.

The participation gap of 12 percentage-points narrows to 9.5 percentage-points when

including regional agriculture wage-employment and further narrows to 5.6 percentage-points

when including regional and non-regional wage employment (columns 2, 4). A quite

interesting turning point can be seen when only non-regional wage-employment is taken in

account with the quite higher shares for the rural-remote households of 1.1 to 2.1 percentage-

points. This leads to the conclusion that agriculture and far more pronounced migrated non-

regional wage-employment is relatively over proportionally present in rural-remote regions

and both have equalizing effects on overall labor market participation.


18

Table 3: Household-level: Differences Between Wage Labor Market Participation Rates of Households by Type of Region (70 minutes threshold) Ordered by Column (d); (n=2,186) Part of labor market (a)

Peri‐urban (b)

Rural‐remote(c)

Total (d) Diff.

between (a)&(b) in

% ‐points

spatial sectoral

region

al

non‐

region

al

agri‐

culture

non‐agri‐

culture

% n % n % n chi2 sig Cramer's V

1 HH engaged in X X 40.0% 694 28.0% 123 37.5% 817 12.0% 21.29 0.000 0.099

2 HH engaged in X X X 46.6% 809 37.1% 163 44.7% 972 9.5% 12.65 0.000 0.076

3 HH engaged in X X X 69.9% 1215 61.3% 269 68.2% 1484 8.6% 12.15 0.000 0.075

4 HH engaged in X X X X 77.8% 1348 72.2% 317 76.7% 1665 5.6% 6.08 0.014 0.014

5 HH engaged in X X 47.6% 826 48.7% 214 47.8% 1040 ‐1.1% 0.20 0.665 0.010

6 HH engaged in X X X 48.5% 843 50.6% 222 48.9% 1065 ‐2.1% 0.58 0.445 0.016

Note: Part of labor market: Regional = within home province, Non-Regional = outside home province; Type of Region: peri-urban = within 70 minutes travel time to rural town, rural-remote = more than 70 minutes travel time The sum of (1)+(5) exceeds the value of (3) because HH engaged in both regional & non-regional non-agricultural wage employment were assigned to both categories; The sum of (2)+(6) exceeds the value of (4) because HH engaged in both regional & non-regional off-farm wage employment were assigned to both categories. Source: own calculation based on DFG-FOR 756, Household Survey Thailand, 2007


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Further insights can be found when looking directly at the 1,475 regional jobs of the 972

households engaged in regional agricultural and non-agricultural wage-employment. The

broad picture shows that one third of all jobs are in the agriculture sector.

Two thirds are in the non-agricultural sector, mainly construction and service industries (table

4, column c). Significant differences are found between the two region types with a 26.1

percentage-point (70.9% to 44.8%) higher share of jobs in the non-agricultural sector of

households in the peri-urban region type (table 4, column d).

Table 4: Job-level: Sectoral Structure of Regional Wage Employment by Type of Region (70); (n=1.475)

(a) Peri‐ urban

(b) Rural‐remote

(c) Total

(d) Diff.

(a)&(b) in %‐points

% n % n % n chi2 sig Cramer's

V

Agriculture 29.1 356 55.2 138 33.5 494 ‐26.1

66.94 0.000 0.213

Construction 20.3 249 16.4 41 19.7 290 3.9

Production 12.2 150 5.2 13 11.1 163 7.0

Private service 22.9 280 12.8 32 21.2 312 10.1

Public service 15.5 190 10.4 26 14.6 216 5.1

Total 100.0 1,225 100.0 250 100.0 1,475 0.0 Source: own calculation based on DFG-FOR 756, Household Survey Thailand, 2007

To test the third hypothesis:

H3: Proximity to rural towns is a main determinant of physical access to remunerative

RNAwE due to lower travel times and lower transportation costs, and higher

connectivity.

mean values of key variables of proximity are compared. Reasons for the better chances of

gaining RNAwE can be found in factors affecting the location of the household. Peri-urban

households benefit from 59% lower travel times and a 21% lower transportation cost, while

having 130% higher connectivity by bus travel frequency to rural towns than do their remote

counterparts (table 5).


20

Table 5: Household-level: Travel Times, Transportation Cost and Connectivity of Households by Type of Region; (n=2,186)

(a) Peri‐ urban

(b) Rural‐remote

(c) Mean diff.

(a)&(b)

(d) Mean diff.

(a)&(b) in % Variable Unit Mean n Mean n t sig

Travel time to next town Minute 40.5 1737 98.6 439 ‐58.1 ‐59% ‐60.32 0.000

Minimum cost of a one‐way ‐trip to the next town

THB* 20.22 1241 25.52 349 ‐5.29 ‐21% ‐8.190 0.000

Frequency of bus trips per day to next town

Number 13.4 1221 5.8 329 7.6 131% 4.322 0.000

* Current exchange rates are 1 US-D = 32 THB; or 1 EUR = 46 THB Source: own calculation based on DFG-FOR 756, Household Survey Thailand, 2007

5.2 Earnings from regional non-agricultural wage-employment

To see if the non-agricultural sector really matters for gaining high return income, the next

question leads to earnings and income in RNAwE compared to RAwE and deals with the

fourth hypothesis:

H4: RNAwI is higher than agricultural wage income.

From table 6 it is obvious that the non-agricultural sector offers significantly more

remunerative jobs than the agricultural one with 62% higher salaries per day. Multiplied with

the double duration of annual working months this leads to higher annual incomes of 280%.

Table 6: Job-level: Mean Salary, Annual Working Duration and Annual Incomes of RNAwE and RAwE (70); (n=1.460)

(a) Agriculture

(b) Non‐

agriculture

(c) Mean Diff.

(a)&(b)

(d) Mean Diff.

(a)&(b) in %

Variable Unit Mean n Mean n t sig

Approx. salary per hour* THB 18.6 490 30.2 940 11.6 63% 8.586 0.000Approx. salary per day* THB 143 491 231 971 88 62% 8.836 0.000Annual working duration Month 4.0 494 8.5 988 4.5 114% 20.481 0.000Annual income THB 14,822 491 56,329 966 41,507 280% 13.192 0.000

Note: * differences due to different working hours per day Source: own calculation based on DFG-FOR 756, Household Survey Thailand, 2007


21

Hence RNAwE is, on average, a high-return activity compared to RAwE which leads to the

empirical sub-hypothesis that RNAwE can rather be seen as a demand-pull driven activity

enabled by an already well developing regional non-agricultural sector.

The next question is whether earnings from RNAwE are also affected by the location of

households. Salaries and income from RNAwE differ only slightly with a benefit of 8% and

16% for peri-urban households but these differences are statistically not significant (Table 7).

Only the duration of annual working months is significantly 20% (1.4 months) longer for peri-

urban households.

Table 7: Job-level: Mean Salary, Annual Working Months and Annual Incomes of RNAwE by Type of Region (70); (n=960)

(a) Peri‐urban

(b) Rural‐remote

(c) Mean Diff.

(a)&(b)

(d) Mean Diff.

(a)&(b) in %




From these findings an empirical generated sub-hypothesis can be drawn that wages in rural

northeast Thailand are relatively inflexible within sectors. Reasons may include fixed wages

in the public sector, domination of single companies in construction and production industries

and the fact that most if not all wage-employment is located in towns or peri-urban areas. That

means that rural dwellers, independent of their home village location, are engaged in the same

companies at the same locations. On the theoretical side these findings fit in the location

opportunity concept because it does not assume that gross wages have to differ spatially. It

assumes that net wages (= gross wage minus transportation cost) differ spatially due to the

fact that transportation cost rises with distance and that this effect reduces the net wage for

workers located farther away. A detailed analysis of this issue has to be done in ongoing


22

exploration of this data set. Another conceptual issue to remember is that different wage and

income-levels are not primarily relevant for the location opportunity concept but rather the

availability of RNAwE. This is expressed by the household participation rate which is

significantly higher for peri-urban households with 40% compared to 28% for remote ones

(table 3).

A last finding shows that when analyzing the influence of agriculture and non-agriculture

wage-employment together on wages, employment duration and incomes the results are

significantly different between jobs of peri-urban and of remote households. Jobs done by

peri-urban households are paid 19% higher, worked 48% longer and yield finally 55% higher

annual incomes from regional wage-employment (table 8). Simple statistical measures like

the Pearson correlation coefficient underline these results with positive correlation

coefficients of 0.100 between regional wage income and travel distance to the next rural town,

0.098 for the minimum cost of a one-way-trip to next town and 0.192 for the frequency of bus

trips per day to the next town, all significant at the 99% level.

Table 8: Job-level: Mean Salary, Annual Working Months and Annual Incomes of RNA & RA Jobs by Type of Region (70); (n=1,460)

(a) Peri‐urban

(b) Rural‐remote

(c) Mean Diff.

(a)&(b)

(d) Mean Diff.

(a)&(b) in %




The fact that rural-remote households predominantly take agricultural jobs with substantially

lower wages, employment duration and of lower incomes leads to large and significant

disparities between the two region types.


23

7 Summary and Conclusions

Peri-urban and rural-remote regions offer different opportunities for households gaining

RNAwE and RNAwI. The most important findings are as follows: Households in peri-urban

areas have a higher RNAwE-participation rate of 40% compared to 28% of rural-remote

households (H1); households in remote areas are relatively more dependent on labor migration

for gaining non-agricultural employment (H2); peri-urban households benefit from 59% lower

travel times and 21% lower transportation cost, while having a 130% higher connectivity by

bus travel frequency to rural towns than do their remote counterparts (H3); RNAwE offers

62% higher salaries per day and twice the duration of annual working months which leads to

higher annual incomes of 280% compared to jobs in agriculture (H4); wages and incomes

between regional non-agricultural jobs done by peri-urban and rural-remote household

members do not differ spatially, rather overall wages and incomes between regional jobs

including agricultural wage-employment done by peri-urban and rural-remote household

members do significantly differ spatially. In addition to the knowledge discussed in section 3

our analysis shows that differences between locations are mainly driven by participation rates,

i.e. opportunity, travel times, transport costs, and connectivity while regional differences in

RNAwE wage levels are less important.

Location matters for gaining RNAwE and RNAwI, which are important factors for mitigating

risks / shocks and reducing household vulnerability. Our data provide a solid basis for an

extension of the vulnerability concept. Thus, the general vulnerability concept can be

extended into a spatial vulnerability concept focusing not only on the spatial variation of

shocks but also on the spatial variation of opportunities. If a location offers high opportunities

to gain RNAwI this reduces household vulnerability; like a location which is only seldom

affected by uncorrelated and predicted shocks. On the other side the location can increase

vulnerability if the opportunity to gain RNAwE is low; comparing to a location which is

frequently affected by correlated and unpredicted shocks.


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This spatial extension of the vulnerability concept needs further validation by empirical

studies. From our data it will be possible to analyze the long term interplay of opportunities

and shocks using panel data. Second, when including the Vietnam data, cross-country and

cross-system comparisons will be possible. Third, a more detailed definition of peri-urban

areas is recommended which also incorporates additional information about locations of non-

town based non-agricultural companies. Fourth, it would be worthwhile to analyze regions

with larger cities in order to discover a more pronounced location opportunity effect.

In general, future research should focus on the spatial variations of non-agricultural

employment opportunities of rural households in order to reduce its vulnerability and

overcome poverty.

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