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RSAI World Congress, Sao Paulo, March 17-19th, 2008
1
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).
RSAI World Congress, Sao Paulo, March 17-19th, 2008
2
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
RSAI World Congress, Sao Paulo, March 17-19th, 2008
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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
RSAI World Congress, Sao Paulo, March 17-19th, 2008
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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
RSAI World Congress, Sao Paulo, March 17-19th, 2008
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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
RSAI World Congress, Sao Paulo, March 17-19th, 2008
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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
RSAI World Congress, Sao Paulo, March 17-19th, 2008
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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.
RSAI World Congress, Sao Paulo, March 17-19th, 2008
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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
RSAI World Congress, Sao Paulo, March 17-19th, 2008
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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
RSAI World Congress, Sao Paulo, March 17-19th, 2008
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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.
RSAI World Congress, Sao Paulo, March 17-19th, 2008
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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
RSAI World Congress, Sao Paulo, March 17-19th, 2008
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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
RSAI World Congress, Sao Paulo, March 17-19th, 2008
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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.
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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).
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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
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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 %
Variable Unit Mean n Mean n t sig
Approx. salary per hour* THB 30.3 821 28.1 107 2.1 8% 0.745 0.456Approx. salary per day* THB 230 849 225 109 5 2% 0.243 0.808Annual working duration Month 8.6 863 7.2 112 1.4 20% 3.425 0.001Annual income THB 56,586 844 48,666 109 7,920 16% 1.143 0.253
Note: * differences due to different working hours per day Source: own calculation based on DFG-FOR 756, Household Survey Thailand, 2007
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
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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 %
Variable Unit Mean n Mean n t sig
Approx. salary per hour* THB 26.8 1183 22.6 246 4.2 19% 2.440 0.015Approx. salary per day* THB 203 1212 183 248 21 11% 1.639 0.101Annual working duration Month 7.3 1229 5.0 251 2.4 48% 7.715 0.000Annual income THB 44,485 1207 28,611 248 15,874 55% 3.853 0.000
Note: * differences due to different working hours per day Source: own calculation based on DFG-FOR 756, Household Survey Thailand, 2007
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.
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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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