WORKING POVERTY, SOCIAL EXCLUSION AND DESTITUTION:
AN EMPIRICAL STUDY
Partha Gangopadhyay1, Sriram Shankar and Mustafa A.
Rahman
“The perspective of social exclusion does offer useful insights for diagnostics and policy.”Amartya Sen (2000).
Abstract: In economics destitution is traditionally interpreted as a product of labour market
exclusion. Our work departs from the dominant models of poverty by considering destitution
among a specific category of workers known as the working poor. Low wages and job
insecurity in the informal sector in developing economies can create and perpetuate
destitution among the working poor. Our precise contributions are three-fold: first, in order to
understand causes and consequences of destitution, we develop an index of destitution from
social exclusion of the working poor. Secondly, we test the predictions of this index by using
micro-data collected from a sample survey in Bangladesh. Thirdly, from the micro-data we
not only offer insights into the sources of destitution but also explain the factors that prevent
the working poor to fall into the traps of destitution.
Keywords: Destitution among working poor, Indicators of destitution, MIMIC model of destitution.
JEL: C62, C72, J23, J30, Q12, O12.
1
All three authors are from School of Economics and Finance, University of Western Sydney, Parramatta
Campus, Locked Bag 1797, NSW 1797, Australia. Corresponding author is Sriram Shankar, email:
[email protected], Telephone:+61-02-96859372. The HREC Registration and the NEAF Protocol for the
field survey are respectively H6605 and 6269. The principal investigator for the survey is Partha Gangopadhyay
and the associate investigator is Mustafa A. Rahman.
1
1. Introduction:
The traditional view of destitution is an extreme form of poverty that forces individuals to
rely on social transfers like public and private charity, alms and welfare programs for
survival. According to this view, destitution is created by a lack of ownership of relevant
economic and social assets and skills of a specific group of people in a society. Destitution is
usually related to non-working poverty (Dasgupta, 1993), which causes an “ill-being”, or
extreme and chronic deprivation, to people in terms of “basic physiological needs”. For a
destitute, destitution is nothing short of “personal calamity”. In a rather emotive expression
Drèze (2002) describes destitution as follows:
“There are millions of households in rural India that might be described as “destitute”. These
households typically have no able-bodied adult member and no regular source of income.
They survive by doing a variety of informal activities such as gathering food from the village
commons, making baskets, selling minor forest produce and keeping the odd goat”2.
The main thrust of our work is to argue that destitution has spread from “households with no
able-bodied adult members and no regular source of income” to the working poor who are
regularly employed in the informal sector in various developing nations. Our intuition is
closely related to the powerful observation of Sen (2000) that social exclusion can be caused
by social inclusion - an apparent contradiction in terms – that is based on “unfair terms”. In
other words, in the era of globalisation working poverty has created a new class of workers
many of whom are reduced to destitution in developing nations despite their regular streams
of earnings from the labour market. Destitution of some workers arises due to unfair terms in
the labour markets of the informal sector in many developing nations.
2
Dreze Continues, “We were shocked to find that even in prosperous villages some households lived in
conditions of extreme poverty and hunger. A casual visitor is unlikely to notice them, as destitute households
keep a low profile and are often socially invisible. But if you look for them, you will find them, quietly
struggling to earn their next meal or patiently starving in a dark mud hut. Destitute households are beyond the
pale of most development programmes and welfare schemes”.
2
It is therefore imperative to measure economic destitution among working poor by
developing a suitable index. The purpose of this work is two-fold: firstly, we seek to develop
an index of destitution to measure destitution among the new class of working poor.
Secondly, we have undertaken a field survey to apply the measure to understand the sources
of destitution for the working poor in Bangladesh. The plan of the paper is as follows: in
Section 2 we provide a detailed survey of the relevant literature on destitution and social
exclusion. Section 3 describes the approach, data source and context of the study. Section 4
describes econometric methodology and summary results. In Section 5 we conclude.
2. Related Literature: Destitution, Social Exclusion and Social Inclusion
A starting point for defining a workable measure of destitution comes from a distinction
between the “poor” and the “ultra-poor” (see Lipton, 1988)3. The “ultra-poor” or “absolutely
poor” fail to acquire their minimum subsistence needs, despite spending 80 per cent or more
of their incomes on food (Lipton, 1988). Closely related to this approach is the World Bank‟s
“dollar a day” definition of extreme poverty. The definition of extreme poverty in terms of
kilocalorie food consumption has a similar intuition. The main problem with this approach is
the arbitrary cut-off point below which an individual is considered a destitute. A more
sophisticated definition of destitution is based on the labour market status of an individual as
Dasgupta (1993) came to identify exclusion from the labour market as a cause as well as a
manifestation of destitution. A further characterisation calls forth the inclusion of other
assets, besides human capital and skills, for defining destitution (see Bevan, 2000 and Sen,
1981). The sociological definition of destitution requires social characterisation in addition to
economic characteristics as highlighted before. Harriss-White (2002) explains destitution as
„an economic, social and political phenomenon‟.
3
The poor are those households who are unable to meet their minimum subsistence needs despite spending 60
per cent or more of their incomes on food,
3
From the above discussion it is important to note that destitution is a
multidimensional concept: first and foremost, the failure to acquire the “basic needs” is an
important component of destitution. Secondly, the degree of exclusion from, or failure to
have access to, various markets like credit and labour markets and local public services, e.g.
health services, also signifies the level of destitution of a group of people. Finally, the degree
of discrimination in building human capital - that can perpetuate exclusion in other markets –
will also play an important role in excluding various opportunities to a group of people. It is
hence required to combine various sources of destitution to form a workable index of
destitution. The crux of the matter is that destitution arises due to social exclusion. We will
hence link the measure of destitution to the various sources of social exclusion.
2.1. Understanding Social Exclusion
The main goal of this research is to combine various approaches to social inclusion /exclusion
to form a simple index to measure destitution for making the concept a useful tool in
diagnosing and eradicating persistent poverty and relational deprivation. The source of the
problem is that economic growth is usually characterised by significant doses of divergence,
or non-convergence, at two important levels: first and foremost, economic growth is rarely
balanced within an economy. It is usually accompanied by an unequal, or unbalanced sectoral
growth rates that in turn propel the distribution of benefits from economic growth. Secondly,
economic growth exhibits persistent divergence between nations even in the regional context
– some nations achieve fast growth rates to reach a high level of GNP per capita like
Singapore while others seem to get stuck in low level equilibrium growth traps like Vietnam
and Laos. In this work our focus is on the first type of divergence. The first type of
divergence becomes a serious problem only when a particular group of citizens of a nation
systematically fails to share the benefits of economic growth over a long haul so that they
4
suffer from social exclusion. Social inclusion is the process by which the economic and social
benefits of economic growth are diverted to those who suffer from social exclusion.
2.1.1. Economic Growth and Natural Tendency towards Divergence
In the broader framework of economic development, it is one of the most enduring realities
that economic activities are unevenly distributed in space. The natural advantages of some
regions over others usually lead to clusters of economic activities in more advantageous
centres while others usually experience somewhat lower and less exalted levels of economic
activities. Should it surprise a keen observer of human development that nature is divisive? It
should not since natural advantages are not equal among regions as different areas of our
globe exhibit climatic advantages, relative resource abundance and different degrees of
accessibility. All these features, which are clubbed under the much-celebrated label of first
nature, play an important role in explaining economic concentration in specific locations,
which is usually called agglomeration forces.
In other words, the first nature sets some unequal constraints across the space, which
leads to a spatial distribution of economic activities and unequal spatial (economic)
development. It is also a part of our collective history that human beings have always
responded to the binding constraints of first nature, mainly to pursue their self-interests by
taking advantages of the unequal (spatial) distribution of economic activities. The intended
and unintended consequences of these human responses have acted upon and re-written the
constraints of the first nature. The human responses to the constraints of the first nature form
the core of the second nature that is usually motivated by the human zeal to soften the
constraints of first nature wherefrom human beings always expect an additional return, which
in turn propels the homo-economicus to try to lessen the tyranny of the first nature. Thus,
within every region of every nation, there are forces that promote concentration of economic
5
activities in the region, known as the centripetal force, which is in constant opposition with
the centrifugal forces that tend to disperse economic activities away from the region. The
structure of an economy is influenced by the constant tensions between these twin forces.
By modelling the sources of increasing returns to spatial concentration against the
tendency to disperse, the New Economic Geography (NEG) teaches something extremely
valuable about how and when these returns undergo changes and then examine how the
regional economy‟s behaviour changes with them. There are two important lessons from the
basic findings of the NEG: first, it is widely held that the agriculture sector is rather a misfit
for creating and driving agglomeration forces. Secondly, as the forces of agglomeration
gather momentum the rural-urban divide will steadily rise with the rural sector lagging
behind the urban sector. Both these views are in consonance with early models of economic
development. Hirschman (1958, pp. 183) pithily put forward the argument of unequal spatial
development:
“We may take it for granted that economic progress does not appear everywhere at the same
time and that once it has appeared powerful forces make for a spatial concentration of
economic growth around the initial starting points.”
Myrdal (1957, pp. 26), sounded an early caution:
“The main idea I want to convey is that the play of the forces in the market normally tends to
increase, rather than to decrease, the inequalities between regions.”
A clearer dynamics was etched out by Kaldor (1970, pp 340),
“As communication between different regions becomes more intensified (with improvements
in transport and marketing organization), the region that is initially more developed
industrially may gain from the progressive opening of trade at the expense of the less
developed region whose development will be inhibited by it.”
6
It is only recently some new developments took place that can change the perception of
agriculture and the rural economy. The most important paradigm shift has come about the
role of agglomeration forces for agriculture. In a seminal work, one of the creators of the
NEG, Fujita (2006) forcefully argues that it is possible to introduce product differentiation
and consequent market power in the agricultural sector. By doing so and introducing a new
and more comprehensive model in Fujita and Hamaguchi (2007), Fujita and Hamaguchi
advanced a new direction of research to unravel the role of economics of agglomeration in
explaining rural development, or a lack of it. The second important change took place in the
very context of analysing agglomeration. Traditionally, spatial externalities have been treated
in a “black box” manner that failed to highlight the actual micro-interactions giving rise to
such externalities. It is only recently economists started highlighting micro interaction
behaviour. Despite these advancements, we still know very little about how such behaviour
leads to those aggregate external effects for the agricultural sector and whether agglomeration
plays an important role in rural development and fighting rural poverty.
2.1.2. Destitution from Social Exclusion
Social exclusion is the flip side of social inclusion. The strengths of this twin concept derive
from their focus on the multifaceted nature of relational deprivation and also from their
analysis of the mechanisms and institutions that function to exclude (include) people from
(into) economic and social spheres (de Haan 1998, de Haan and Simon, 1998). In other
words, the concept of social exclusion has encouraged scholars to examine simultaneously
the economic, political and social facets of poverty, inequality and deprivation. Yet both
social exclusion and social inclusion are “loaded terms” with significant economic, political
and social connotations and dimensions. Not only these terms are somewhat vague but this
vagueness has led to the often strategic misinterpretation of these terms to suit the user‟s
7
ulterior motives and disguised goals. This vagueness attracted a severe criticism of the term
of social exclusion by Else Oyen (1997):
“….pick up the concept and are now running all over the place arranging seminars and
conferences to find a researchable content in an umbrella concept for which there is limited
theoretical underpinning” (pp. 63).
A more balanced, and somewhat utilitarian, view has been championed by Sen (2000):
“While the underlying idea behind the concept of social exclusion is not radically new, the
growing literature on the subject has helped to enrich causal understanding and empirical
analysis of certain aspects of poverty and deprivation. To be excluded from common facilities
or benefits that others can certainly be a significant handicap that impoverishes the lives that
individual can enjoy (section 1). No concept of poverty can be satisfactory if it does not take
adequate note of the disadvantages that arise from being excluded from shared opportunities
enjoyed by others.” (p. 44).
The origin of the term social inclusion is traced back to the debate on the French
social policy in the 1970s, especially by Lenoir (1974), in the context of swelling
unemployment triggered by the European stagflation and consequent economic restructuring
(see Silver, 1995). The swelling of unemployment unleashed forces of racial and gender
inequality that would usually lead to the formation of an underclass (Sen, 1997). Since the
mid 1970s in Europe and North America a group of people came to be identified as the
underclass. The underclass is a “new” social category that fails to participate productively in
the modern economy and its society and thereby constitutes the most vulnerable and hapless
section of a society.
8
2.1.3. Dominant Paradigms of Social Exclusion
Silver (1995) highlighted three paradigms of social exclusion, all of which play important
roles in inhibiting social progress for all. The paradigm depends on the precise manner in
which social integration is conceptualized and an ideal society is construed. First and
foremost, from France came the dominant paradigm what is commonly known as the
solidarity paradigm that idealizes a primordial tribal society that thrives on mutual bonds and
is fueled by social norms and reciprocity. In such tribal groupings, heterogeneity is not only
difficult to sustain but it also creates serious social cleavages and social divisions. Social
exclusion is the rupture of the social bond between individuals and the society through
cultural isolation. The poor and unemployed are predominantly drawn from ethnic minorities
who are increasingly labelled and identified as “outsiders”. Secondly, the specialization
paradigm, a focal point of discussion in the US, highlights how market forces unintentionally
isolate a group of people who fall victims to active discrimination. Discrimination denies
these individuals to have full access to the exchange mechanisms. Even when access is
granted, discrimination causes poverty and inequality. Discrimination can be based on real
economic factors or completely spurious4. Finally the monopoly paradigm, with its principal
champions in Britain and many Northern European countries, highlights the social order as
coercive that is enforced by hierarchical power relation. Exclusion is defined as a
consequence of the formation of group monopolies that actively block specific groups in the
society for their self love.
4
Discrimination can be spurious if wages are partly based on factors like caste, ethnicity and
language of a worker.
9
2.2. Causes of Relational Deprivation, Social Exclusion and Inclusion
The above three paradigms are reflected in the various meanings and dimensions of the
concept of social exclusion built upon relational deprivation by Sen (2000). Sen drew
distinction between the situation where some people are excluded (at least left out), and
where some people are being included (forced to be included) on utterly unfavorable terms.
The solidarity paradigm and the monopoly paradigm highlight what Sen called “unfavorable
exclusion”. The discrimination paradigm can be explained by Sen‟s notion of “unfavorable
inclusion.” Exclusion and inclusion on unfair terms can cause relational deprivation for target
groups, which can in turn rupture the bonds between the target groups and society. If working
poverty in the informal sector of developing nations is turned on unfair terms, then working
poverty can result in serious deprivation and even destitution among workers. Sen also
differentiated between “active and passive exclusion”, which partly determine the above
paradigms. Sen convincingly argued that “it is important to distinguish between “active
exclusion” vis-à-vis “passive exclusion”. Active exclusion is crafted through the deliberate
policy interventions by the government, or by powerful agents to exclude some other people
from some opportunity. On the other hand, passive exclusion works through the social
process and the exchange mechanisms in which there are no deliberate attempts to exclude.
Yet some people are excluded from a specific gamut of circumstances. Regardless of the
nature of exclusion, the unfair terms of exchange can cause deprivation among workers in the
informal sector.
2.3. Social Exclusion and Unfavourable Inclusion: The Crux of the Problem
Social exclusion has two main dimensions: first, it represents a complex process that acts on
the mechanism and influences and propels institutions that will in turn - willingly, or
10
unwillingly- cause and perpetuate relational deprivation in the economic, political and social
domains. Secondly, social exclusion is a pure economic and political outcome that directly
disadvantages some people. Following Sen (2000) one can argue that social exclusion arises
either as causes or as outcomes. Exclusion could occur through direct exclusion, violating fair
norms of exclusion (that is unfavorable exclusion), or through inclusion, but under
unfavorable conditions, again violating fair norms of inclusion (that is unfavorable inclusion),
or through deliberate government policies (that is active exclusion), and through unintended
attempts and circumstances (passive exclusion), or exclusion caused through inability of
some persons to relates to other persons (constitutive relevance). The mainstream economists
have further elaborated the concept of discrimination that operates particularly through
markets.
3. A Latent Variable Approach to Destitution of the Working Poor
The fundamental notion of our work is that some workers in the informal sector of
developing nations are employed on „unfair terms” such that they are virtually excluded from
the society. This is a simplification of Sens‟ idea of exclusion from economic relationships
based on unfair terms (see Sen, 2000). In this context, there are a host of indicators of
relational deprivation and destitution. Our goal is to derive an index of destitution from the
multiple indicators and multiple of causes of destitution for the working poor. The model that
we propose is close to the latent variable technique. The model postulates that an aggregate
latent variable is used to capture a host of unobserved variables in a family/society given by
the economic and social conditions, psychological states of people, hidden opportunities for
employment and unobserved business/production climates that influence the indicators of
destitution of subjects. We call the latent variable z that causes actual destitution. We define
yi as the indicator variable. In other words, yi is an indicator variable that is an imperfect
indicator of level of destitution that is observable and not latent. yij is the indicator of
11
destitution on an individual j in terms of the ith indicator. We assume that there are 4 possible
indicators of destitution for each individual j: for i=1, we define the indicator y1j as the per
capita food expenditure of the family that individual j belongs. Thus the variable y1 measures
the fulfilment of basic needs. For i=2, we define the indicator y2j as the frequency of protein
intake in the family that individual j belongs. Thus the variable y2 measures the fulfilment of
non-basic needs. For i=3 we define the indicator y3j in terms of per capita expenditure on
education of individual j‟s family. Thus the variable y3 denotes educational expenses which is
used to measure of access to education. Finally, for i=4 we define the indicator y4j as an
indicator of access to health facilities for an individual j. y4 denotes health expenses and is
used to measure access to health. We assume the latent variable z to be a function of causal
and observed variables given by the vector x with, say k observed variables x1 , x2 ,..., xK . We
define
(1a)
z  f (x,  )
Note that the causal and observed variables x characterize an individual subject and one‟s
family in terms of his socio-economic status and personal data - like wage rate, education,
literacy, family size, different types of asset holding, marital status, type of the household etc.
In (1a) the function f (.) indicates the form of the relationship between observed and latent
variables and ε is the stochastic error in the equation. The model also specifies a set of
equations corresponding to the indicator variables of destitution given by y j :
(1b)
y j  F j ( z, u j )
j {1, 2,3, 4}
where u j is the standard error term. In order to complete the specification of the model, we
must specify the form of the interdependent structural equations (1a)-(1b), and we must also
12
specify the form of the probability distribution of the errors terms. Suppose the latent
variables z is linearly determined by k observable causal variables contained in vector x , we
can re-write (1b) as:
(1c)
y  βz  u
Note we will need to derive some estimates of the latent variables so that we can define
equation (1c). To do this we express z as the following:
(1d)
z  αx  ε
In equations (1c) and (1d) y  ( y1 ,..., ym ) ' is a vector of endogenous indicator variables;
α  (1 ,...,  k ) ' and β  (1 ,..., m ) ' are parameters; and  and μ are the disturbances.
The foundation of the econometric model is explained in Figure 1. Instead of substituting
(1d) into (1c) and converting the model into the MIMIC model, we undertake a sequential
estimation procedure which is discussed in detail in the econometric methodology section.
13
Observed Variables
y1
1. Value of Assets
2. Land Holding
3. P
3. Occupation
4. Unemployment Rate
4. Ownership and Type of
Dwelling
5. Social Status
6. Education/Literacy
y2
7. Gender
Latent Variable
8. Age
8. Resource Rent
9. Rural/Urban Dummy
9. Civil Liberties
10. Work Related
Personal Safety
Problems/Political
Victimisation
y3
y4
11. Access to Water
12. Access to Fuel
13. Working
Poverty/Discrimination
Figure 1: The Latent Variable Approach to Destitution in a Group
14
3.1. Data Source
Data for estimating the model have been obtained from a sample survey conducted in
Bangladesh in 2008-09. The survey was conducted in both rural and urban areas. The rural
area refers to agriculture sector while urban area includes manufacturing, transport,
construction and service sectors. A multi-stage stratified random sampling technique was
used to select sample locations and respondents.
For the rural area, at the first stage, the sample locations were selected from the
profile of districts (second level administrative unit from the top) based on head-count index.
The profile shows that the North Western districts of Bangladesh are most poverty prone.
Among them the poverty situation is the worst in Nilphamari district in the North West. This
district is pre-dominantly rural and agriculture based. Thus, for our purpose we concentrated
on this district, as it gave us the best picture of the worst poverty in rural Bangladesh. At the
second stage, for rural area - one Thana (it is the third level administrative unit of the
government from the top) was picked up from the selected district.
At the third stage, one union was picked up from the selected Thana. Information on
area of land, number of households, total population, landholding and literacy rate was
obtained for all villages under the selected union from the 2005 district census reports
published by the Bangladesh Bureau of Statistics. Given resources and time, one village was
then purposively selected on the basis of this information so that (a) the selected village is
neither too small nor too large - the village with less than 500 households was excluded, (b)
the land-person ratio and the literacy rate of the village are similar to other villages under the
selected union. The village has both poor and non-poor workers. From the village we have
selected only poor workers (respondents) based on an objective assessment i.e., if a worker‟s
income was below $1 a day he was treated as poor.
15
After selecting the village, we prepared a complete list of households which provided
us with the sample frame for drawing sample. Then, we decided to select 248 households as
sample accounting for 23.0 percent of the total households. The selection of sample was
however predetermined. Then, the 248 households were selected using the stratified random
sampling technique, the sample being drawn from each of the three activities (farmers, wage
labourers, fishermen) proportionate to the size as indicated in the population.
For the urban category of working poor, one location - the metropolitan city of Dhaka
located at the central part of Bangladesh - was selected as a sample location. This area
constituted the largest industrial belt of the country where the poor industrial workers are
concentrated. Out of various industry types, five major types (garments, textiles, steel &
engineering, electronics, leather & footwear) covering 60 percent of the total production
workers were selected to identify working poor employed therein. Generally, production
workers are supposed to be low paid workers constituting a major bulk of the working poor.
This perception was supplemented by information provided by local trade union officials.
Then we prepared a list of 804 production workers from the payroll provided by the
industries selected. This list provided us with the sample frame for drawing sample. In order
to draw sample, the five major industries were classified into five different strata. Then, we
drew 200 production workers from five different industries proportionately through stratified
random sampling technique.
Apart from agricultural and manufacturing sectors, there are poor workers in service,
construction and transport sectors. So, we have included these three sectors in our sampling
scheme. From the three sectors we have selected 212 workers as a sample - of which 100
workers were from the service sector, 60 workers from the transport sector and 52 workers
from the construction sector. The workers have been selected at random from a list of
workers (sample frame) collected from relevant organizations and workers‟ associations. A
16
larger sample was taken from the service sector because of its relative large size compared to
transport and construction sectors. Thus, total of 660 households including rural (agriculture)
and urban areas have been selected for the survey. From each household we selected one
worker (respondent) for detailed interview. The sample turned out to be about 25 percent of
the population. The heads of households constituted our sample respondents. The 660
respondents were the subjects of our detailed survey.
3.2. Variables and Indicators
Variables determining destitution
X1: Value of Assets; X2: Land Holding; X3: Age; X4: Gender Dummy; X5: Education Level;
X6: Rural/urban Dummy; X7: Ownership of Dwelling Dummy; X8: Access to Drinking Water
Type; X9: Access to Fuel for Cooking; X10: Index of Social Status; X11: Problem with
Previous jobs; X12: Severity of Unpaid Work/Exploitation X13: Self-Emphasis on Working
Poverty as a Cause of Destitution .
Indicators of (Lack of) Destitution:
Y1: Per Capita Food Expenditure (Monthly)-Basic Needs; Y2: Frequency of Protein (Fish)
Intake (Weekly)-Non-Basic Needs; Y3: Monthly Expenditure on Education (Access to
Education); Y4: Monthly Expenditure on Health Facilities (Access to Health)
3.3. Context of the study
Among others, seasonal unemployment is an important factor contributing to destitution of
the workers. In a developing country like Bangladesh, particularly in the rural area,
employment varies directly with seasonality in agriculture. During peak seasons i.e., cropping
and harvesting period labour force mostly remain employed. But, in lean seasons the extent
of unemployment and underemployment becomes severe in rural areas. A study by Rahman
(2005) shows that during this period, employment opportunities drop by 50.0 percent on
average. The wage rate too drops by 20.0 percent on average. In that study agricultural
workers were found worst sufferers. For example, in 2005, the daily real wage rate for male
17
agricultural workers in the study area was only 46 Taka compared to 64 Taka in the rest of
the economy (Khandker, 2009).
Data indicate that only 9 percent of the agricultural workers worked round the year. The rest
91 percent of the agricultural workers remained unemployed for a particular period extending
from 2-6 months a year (Rahman, 2011). Workers remaining jobless for a period of 3 months
were the highest (27.0 percent) followed by those remaining unemployed for four months
(17.0 percent). Among the workers remaining unemployed for three months a year, the
proportions of male and female workers were 30.0 and 21.0 percent respectively. Only 9.0
percent of the workers remained jobless for a period of two months only which is the lowest
(Table 1).
In the urban area, 65.0 percent of the workers worked round the year. Among them male
workers constituted 72 percent while female workers accounted for 28 percent. The urban
workers have been found to remain jobless for a period of 1- 4 months. The workers
remaining unemployed for the shortest period (one month) account for 1.0 percent only
while those remaining jobless for the longest period (4 months) turned out to be 3.0 percent.
Workers remaining unemployed for three months turned out to be highest (19.42 percent)
followed by those left unemployed for two months accounting for 11.65 percent (Table 1).
18
Table 1: Employment Pattern of the Workers by Sex and Area
Area
Employment pattern
Sex
Male
Female
Total
21
(11.86)
17
(9.60)
12
(6.78)
23
(12.00)
26
(14.69)
53
(29.94)
15
(8.47)
10
(5.60)
177
(100.00)
2
(2.82)
1
(1.40)
2
(2.82)
14
(19.72)
15
(21.13)
15
(21.13)
11
(15.50)
11
(15.50)
71
(100.00)
23
(9.27)
18
(7.30)
14
(5.65)
37
(14.92)
41
(16.53)
68
(27.42)
26
(10.48)
21
(8.50)
248
(100.00)
194
(70.04)
1
(.36)
29
(10.47)
44
(15.88)
9
(3.25)
277
(100.00)
73
(54.07)
3
(2.22)
19
(14.07)
36
(26.67)
4
(2.96)
135
(100.00)
267
(64.81)
4
(.97)
48
(11.65)
80
(19.42)
13
(3.16)
412
(100.00)
Rural:
Works round the year
Jan – Jun
March – April
March – August
May – August
June – August
April – August
June – July
Total
Urban:
Work round the year
1 month
2 months
3 months
4 months
Total
Note: Figures in the parentheses indicate percentage.
Source: Calculated by the authors from data obtained from the sample survey.
In order to examine employment pattern of the rural workforce at occupational level we have
decomposed them into six broad occupational groups i.e., day labourers, marginal farmers,
rickshaw pullers, fishermen, traders and stone pickers. As indicated earlier, the number of
workers remaining unemployed for 3 months (June-August) was the highest. Among this
19
type of workers, the stone pickers constituted the majority (41.18 percent) followed by
rickshaw pullers i.e., 34.38 percent (appendix Table 1). About one fifth of them are marginal
farmers. The stone pickers are unskilled day labourers and mostly illiterate. There is no
employment opportunity for them in the locality. As they are less mobile due to lack of skill
the precedence of unemployment among them is discernible. This is equally true for
labourers and marginal farmers as well.
As indicated above, in the urban area majority of the workers worked round the year
because, urban economic activities are less likely to be affected by seasonality. The
manufacturing, service and transport workers mostly work round the year. These types of
workers work full-time and a on a regular basis. Only construction workers are subject to
seasonality and as such they remain unemployed for a particular period of the year. That is
why the proportion of workers working round the year in the urban area was relatively high
compared to that of in the rural area. Workers remaining jobless for 2-3 months belonged
mainly to construction and manufacturing sectors. During the rainy season there is little
construction work and therefore, they have to sit idle in that period. There are some
manufacturing workers such as footwear workers who do not have any job after festivals (Eid
for Muslims and Durga Puja for Hindus). Therefore, after festivals are over, they remain
unemployed for one or two months a year. Workers remaining jobless for four months
constituted only 3 percent – these types of workers were illiterate and unskilled. They were
unable to furnish jobs other than the ones they were occupied.
Table 1 above shows that in the urban area 65 percent of the workers worked round
the year while this figure is only 9 percent in the rural area. This indicates inadequacy of jobs
and the acuteness of un- and underemployment in the rural area compared to the urban area.
In the urban area, among the workers working round the year, the proportion of male workers
was higher than that of female workers. On the contrary, 35 percent of the workers remained
20
unemployed for a period of 1-4 months a year on average. Interestingly, among the workers
remaining unemployed for the period mentioned above, the proportion of male workers (57
percent) was also higher than that of female workers accounting for 43 percent. Across all the
sectors (i.e. manufacturing, transport, construction and service), the proportion of female
workers remaining unemployed for a period of 1-4 months was relatively high in service
sector compared to other sectors under study (appendix Table 1).
4. Econometric Methodology
The Expectation-Maximization (EM) algorithm is widely applicable to the iterative
computation of maximum likelihood (ML) estimates, in a number of incomplete-data
problems, where algorithms such as the Newton-Raphson method may be more complicated
to implement. There are two steps in every iteration, named the expectation step or the E-step
and the maximization step or the M-step. For this reason Dempster, Laird, and Rubin (1977 )
called it the EM algorithm in their seminal paper. The EM algorithm is particularly useful in
solving incomplete data problems. We must also emphasize that EM algorithm can be applied
not only to incomplete-data problems where there are missing data, truncated distributions,
censored or grouped observations, but they can also be applied to a whole range of problems
where the incompleteness of data in not obvious.
The main idea behind the EM algorithm is to formulate an incomplete-data problem
in the framework of a complete-data problem which is computationally tractable. Even if the
problem is not actually an incomplete-data problem, it is computationally advantageous to
artificially formulate it as an incomplete-data problem. The E-step manufactures data for the
complete data problem, using the observed data of the incomplete-data problem and the
present value of the parameters, in order that the simpler M-step can be applied to the
21
corresponding complete data set. Since it is based on unobservable data, it is replaced by its
conditional expectation given the observed data, and the E-step is incorporated using the
current fit for the unknown parameters. Suitable initial parameter values are used as starting
values, and then E-step and M-step are repeated until convergence.
The specification of MIMIC model is as follows:
(2a)
z  αx  
(2b)
y  βz  μ
Note that in equations (2a) and (2b) the variable z indicates the welfare of an individual;
hence one can use the inverse of z as the measure of destitution. In other words, the positive
(negative) values of α and β will indicate the negative (positive) impact of the factor on
destitution.
We assume 
N (0,1) and μ  N (0, 2 ) where   diag (12 ,...,m2 ) . It is important to note
here that we assume var( )  1 in order to overcome the identification problem. Assuming
that the errors  and μ are independent we arrive at the following likelihood function:
T
(3)
 1

L  Y, z X, α, β, H   H 2 exp   tr ( Y  zβ)( Y  zβ) H  ( z  Xα )( z  Xα )
 2

Where X :T  k and Y :T  m represent a sample of T observations on x and y respectively;
the unobserved variable z captures destitution through x and y ; and H  1 .
E-step:
Given the distribution of  and μ above we have
(4)
E ( z Y)  1  βHβ 
1
 Xα  YHβ 
and variance
(5)
V ( z Y)  1  βHβ 
1
22
Therefore at the ith iteration the following relationships hold good:
 zz (i )   E ( zz Y) (i )
 E ( z  Y)(i ) E ( z Y)(i )  T 1  β( i ) H ( i )β( i ) 
1
 1  β(i ) H (i )β (i )  
2
(6)
 α XXα  2α
T 1  β H β 
(i )
(i )
(i )
XYH ( i )β( i )  β( i ) H ( i ) YYH ( i )β( i )  
1
(i )
(i )
(i )
 zY (i )   E ( zY Y) (i )   E ( z Y) (i ) Y
(7)
 1  β(i ) H (i )β(i ) 
1
 α
(i )
XY  β(i ) H (i ) YY 
 zX (i )   E ( zX Y) (i )   E ( z Y) (i ) X
(7‟)
 1  β(i ) H (i )β(i ) 
1
 α
(i )
XX  β(i ) H (i ) YX 
M-Step
Instead of maximizing the likelihood function L  Y, z X, α, β, H  in the M-step we find the
mode of the posterior distribution5 of
α, β, H given Y.
Bayesian simulation provides stable
estimates particularly when a large number of parameters need to be estimated and the ratio
of sample size to number of parameters is relatively small. We assume a flat prior for our
parameters, that is
(8)
p(α, β, H)  1
5
This approach is not new to Bayesians. Lindley and Smith (1972) and Chen (1981) have
used a similar technique to obtain the modal estimates of the parameters of interest.
23
Therefore the posterior distribution parameters α, β, H , which is the product of prior and
likelihood function is proportional to the likelihood function, that is
T
(9)
 1

p  α, β, H Y, z , X   H 2 exp   tr ( Y  zβ)( Y  zβ) H  ( z  Xα )( z  Xα )
 2

In order to sample from posterior density it is helpful to use a Gibbs sampler. In Gibbs
sampling algorithm, (see Gelfand and Smith; 1990, for details) draws from joint posterior
density are generated by sampling from a series of conditional posteriors. The Gibbs sampler
necessitates drawing sequentially from the following conditional6 posteriors:
(10)
p(α(i ) Y, X, z(i ) , H(i ) , β(i ) )  f N (α(i ) ( XX)1 ( Xz)(i ) ,( XX)-1 )
(11)
p(β(i ) Y, X, z(i ) , α(i ) , H(i ) )  f N (β(i ) (( zz)(i ) )1 ( zY)( i ) , H( i )m (( zz)( i ) ) 1 )
where m is a column vector of one‟s having length m .
(12)
p(h j Y, X, z, α, β)  fG (h j s j 2 , j )
j  1,..., m
where7
j
6
 T  j
The notation f N (α β ,C) indicates that α is a multivariate normal vector with mean β and
covariance matrix C and the notation fG (a b , c) indicates that a has a gamma distribution
with shape parameter b and scale parameter c .
7
In our simulation we assume  j  12 and s j 2  10 for j  1,..., m .
24
s j2
and

Where Y * j and zβ(i )


j
'
 *

*
2
 Y j   zβ(i )  j  Y j   zβ(i )  j    j s j


j


are the jth column of matrix Y and zβ(i ) respectively.
Draws from these conditional posteriors will converge to draws from the posterior
p  α, β, H Y, z , X  . Simulating from the multivariate normal densities in (10) and (11) and
gamma density in (12) is straightforward using random number generators available in most
statistical software packages. We simulated 650,000 observations from the conditional
posteriors (10), (11) and (12), and discarded the first 50,000 draws as burn-in. Figure 4 and 5
in the Appendix presents‟ convergence plots for each of the elements of α, β and H
respectively. The convergence plots clearly indicate that the Markov chain Monte Carlo
(MCMC henceforth) sequence for all the parameters is stationary. We formally checked
convergence for each of parameters using Gelman and Rubin‟s (1990) diagnostic R.
The estimated posterior means, standard deviations and 95% highest posterior density
(HPD) interval limits for the parameters are presented in Table 3. The point estimates are the
means of the MCMC samples and are optimal Bayesian point estimates under quadratic loss.
The standard errors are the standard errors of the MCMC samples and suggest that most of
the parameters have been estimated with good degree of reliability. A more complete picture
of the level of uncertainty surrounding the unknown parameters is presented in Figures 2 and
3.
25
4.1. Results
Our main results are provided in Table 3. Note that the values of β, α indicate the magnitude
and direction of the welfare for an individual and is hence the inverse of destitution, or illfare. Since the index given by (2a) and (2b) measures the welfare, a positive sign implies the
variable to reduce destitution and a negative sign implies the variable to increase destitution.
Table 4 explains the impact of various factors on destitution.
Table 3: Parameter Estimates
2.5%
97.5%
MEAN
STDEV
HPD limit
HPD limit
0
167.665
18.273
132.840
205.478
1
-0.002
0.002
-0.005
0.003
2
55.471
20.575
16.976
90.891
3
-1.504
0.288
-2.006
-0.927
4
29.536
10.382
10.572
51.265
5
23.854
4.157
13.992
30.945
6
-96.395
10.346
-115.311
-75.743
7
11.793
4.789
2.396
22.519
8
17.165
4.107
9.286
24.909
9
29.922
2.892
24.477
35.651
10
-0.663
0.890
-2.269
1.373
11
-0.323
0.460
-1.107
0.654
26
12
-4.240
0.776
-5.707
-2.672
13
3.259
8.645
-15.585
17.615
1
2.640
0.085
2.489
2.810
2
0.010
0.000
0.009
0.010
3
0.682
0.047
0.592
0.776
4
1.067
0.053
0.967
1.174
h1
1.261E-05
9.357E-07
h2
8.597E-01
4.704E-02
h3
1.663E-05
9.127E-07
h4
1.519E-05
8.447E-07
27
Figure 2: Estimated Posterior Pdfs for  ' s
28
Figure 3: Estimated Posterior Pdfs for  ' s and h ' s
Table 4: Summary Results: Nature of Impacts of Exogenous Variables on Destitution
Exogenous Variables
Nature of Impacts on Destitution
Value of Assets (X1)
-
Land Holding (X2)
+
Age (X3)
-
Gender Dummy (X4)
+
Education Level (X5)
+
Rural/Urban Dummy (X6)
-
Ownership of Dwelling Dummy (X7)
+
29
Access to Drinking Water Type (X8)
+
Access to Fuel for Cooking (X9)
+
Index of Social Status (X10)
-
Problem with Previous Jobs (X11)
-
Problem with Previous Jobs (X12)
-
Self-Emphasis on Working Poverty as a
Cause of Destitution
+
Our main idea is that destitution is a manifestation of various latent factors that are, in turn,
influenced by various “exogenous variables”. Some of these exogenous factors help people to
avoid destitution. On the other hand, some of the factors push people towards destitution. The
above table shows how these exogenous variables influence the latent factors that, in turn,
influence the degree of destitution of a working poor. Note that the index of destitution is the
inverse of the measure of welfare. In other words equation (2b) is a measure of individual
welfare as signalled by the per capita expenditure on the basic and non-basic goods and
access to health and education. The inverse of z in (2a) is used as an index of destitution.
Interesting results are six-fold from Table 4: first and foremost, land ownership and
ownership of dwelling are important factors in reducing destitution. Secondly, access to local
public goods reduces destitution. Thirdly, asset mismatches, as reflected in value of asset
variable, promote destitution. Fourthly, there is evidence of gender bias against female, age
bias against the elderly, and strong urban bias in the destitution. Fifthly, workers suffer from
destitution if they have had problems in their past jobs and also because of the “unpaid work”
they have to do for their employers. Finally, a lower social status increases destitution.
Once we look at the impacts of the various indicators (  s ) we note that the signs are
as expected. The welfare of an individual is positively predicated on the per capita
expenditure on food. In a similar vein, the access to education and health also correctly
30
signals the ill-fare, or welfare, of an individual. What is interesting are the magnitudes of
these impacts: for the working poor, the most important indicator of destitution is the access
to health facilities followed by their access to education. The role of basic goods like food, as
expected, is relatively minor for signalling destitution among the working poor.
5. Conclusion
In the usual parlance of economics the destitute are the jobless, homeless and disabled people
who rely on private and public charities for their physical survival. These are the people who
are excluded from the economic, political and social domains and decision-making. In an
early work Lenoir (1974) advanced the view that a group of social pariahs constitutes the
class of destitute in a society:
“...the following constitutes the “excluded” - a tenth of the French population: mentally and
physically handicapped, suicidal people, aged invalids, abused children, substance abusers,
delinquents, single parents, multi-problem households, marginal, asocial persons, and other
social misfits”, p.3
Within a short span of two decades the list became rather laden (see Sen, 2000) as Silver
(1995) extends the list of destitute:
“...the list of “a few of the things the literature says people may be excluded from” must
include the following: a livelihood; secure, permanent employment; earnings; property,
credit, or land; housing; minimal or prevailing consumption levels; education, skills, and
cultural capital; the welfare state; citizenship and legal equality; democratic participation;
public goods; the nation or the dominant race; family and sociability; humanity, respect,
fulfilment and understanding”. (p.60).
31
Our work provides a further bulging of the already “bulging list of socially excluded
people” by bringing the working poor in developing nations within the realm of destitution.
In the traditional literature on destitution the working poor, being working, is usually
excluded from the list of destitute, or social pariahs. From Sen (2000) we know that unfair
and unfavourable terms of exchange can cause social exclusion and abject poverty even if
people are not physically excluded. We apply this powerful intuition of Sen to argue that
working poverty and destitution are not always mutually-exclusive.
Our work develops an index of destitution for the working poor. The foundation of
the index rests on the observation that destitution is not only multidimensional in nature but
also a manifestation of various latent, or underlying, forces that drive people towards, or
away from, destitution. In other words, there are various exogenous variables that cause and
impinge on the latent factors. Our work develops an index of destitution within a suitable
framework to capture the complex and latent mechanisms causing destitution in the economic
and social contexts of working poverty . In terms of this index we explain various economic
and social variables that promote and exacerbate destitution among the working poor. The
purpose of this work is to point to the anomalies in informal markets that can cause serious
economic and social problems for the working poor in the developing economy. The fact that
the working poor work is by no means a guarantee for these people to avoid social exclusion
and consequent destitution that pushes them towards physical extinction. On the contrary, our
work highlights the causes of destitution among the working poor. Our work also highlights
the precise instruments that a responsible society can use to prevent the working poor to fall
into abject poverty and hopelessness associated with destitution. Finally, our work confirms
the dominant theme of the literature that destitution is multidimensional. There are various
indicators of destitution. Our work provides a means to aggregate these various indicators
into a simple index that can help policy makers to understand the efficacy of various pro-poor
32
policies in fighting destitution. We noted the following from our micro study: first,
ownerships of land and dwellings, and local public goods can help working poor escape from
destitution. Secondly, asset mismatch is a causal factor towards destitution for the working
poor. Thirdly, past employment problems can have lasting effects in perpetuating destitution
and working poverty. Finally, destitution falls asymmetrically on females and elderly even
for the working poor.
Acknowledgements:
We acknowledge the full funding support from the University of Western Sydney (UWS).
The preliminary research was presented at the 6th and 7th Australian Development Economics
Workshop (ADEW) and we are grateful to AusAid funding related to the 6th ADEW held at
UWS. We benefited from comments from and discussion with Mike Intriligator, Mark
Rosenzweig, Abu Wahid, Debraj Ray, Hal Hill, Steve Killelea, Jurgen Brauer, Peter
Robertson, Michael Gibson, Ranjan Ray, Binh Tran-Nam, Ashok Kotwal, Nripesh Podder,
Arsenio Balisacan, Pushkar Maitra, Murray Kemp, Pranab Bardhan and T. N. Srinivasan.
33
References:
Bevan, P., 2000. „Poverty in Ethiopia‟, paper prepared for a DFID Ethiopia Seminar, London,
7 November, London: Department for International Development
Chen, C. 1981. The EM approach to the multiple indicators and multiple causes model via
the estimation of the latent variable. Journal of the American Statistical Association. 76 ,
704–708.
de Haan, A., 1998. Social Exclusion: An Alternative Concept for the Study of
Deprivation? IDS Bulletin 29 (1), 10-19.
de Haan, A., Simon, M., 1998. Poverty and Social Exclusion in North and South.University
of Sussex, Poverty Research Unit.
Dasgupta, P., 1993. An Inquiry into Well-Being and Destitution. Clarendon Press, Oxford.
Dempster, A.P., Laird, N.M., Rubin D.B., 1977. Maximum likelihood from incomplete data
via the EM algorithm. Journal of the Royal Statistical Society B. 39, 1–38.
Drèze, J., 2002. Food transfers to the destitute are a good way of using the surplus grain
stocks. The Hindu, 29 July.
Fujita, M., 2006. Economic Development Capitalizing on Brand Agriculture: Turning
Development Strategy on its Head. Paper Presented at the World Bank Annual Conference on
Development Economics in Tokyo, 29-30 May.
Fujita, M., Hamaguchi N., 2007. Brand Agriculture and Economic Geography: A General
Equilibrium Analysis. Discussion Paper 207, Kobe University.
Gelman, A., Rubin, D. B., 1992. Inference from Iterative Simulation Using Multiple
Sequences. Statist. Sci. 7(4), 457-472.
Gelfand, A. E., Smith, A. F. M., 1990. Sampling-based approaches to calculating
34
marginal densities, Journal of the American Statistical Association. 85, 398-409.
Hirschman, A. O., 1958. The Strategy of Development. Yale University Press, New Haven.
Harriss-White. B., 2002.A note on destitution. QEH Working Paper 86, Oxford: Queen
Elizabeth House, University of Oxford.
Kaldor, N., 1970. The Case for Regional Policies. Scottish Journal of Political Economy. 17,
337-348.
Khandker, S.R., 2009. Poverty and Income Seasonality in Bangladesh. Policy Research
Working Paper, WPS 4923, the World Bank, Washington D.C.
Lenoir, René., 1974/1989. Les Exclus: Un Francais sur Dix. Paris: Editions du Seuil.
Lindley, D. V., Smith, A.F.M., 1972. Bayesian Estimates for Linear Model. Journal of the
Royal Statistical Society, Ser. B, 34, 1-41.
Lipton, M., 1988. The poor and the poorest. Discussion Paper 25, Washington, D.C.: World
Bank.
Myrdal, G., 1957. Economic Theory and Underdeveloped Regions, Duckworth, London.
Oyen, Else., 1997. The Contradictory Concepts of Social Exclusion and Social Inclusion. In
Social Exclusion and Anti-Poverty Policy, edited by Charles Gore and Jose B. Figueiredo.
Geneva: International Institute of Labour Studies.
Rahman, H. Z., 2005. Monga Forecast 2005: Field Assessment and Action Plan, PPRC
Poverty Research. Power and Participation Research Centre, Dhaka.
Rahman, H. Z., 2011. Working Poor in Bangladesh: An Attempt at Understanding its Nature
and Causes. Unpublished PhD dissertation, University of Western Sydney, Australia.
35
Sen, Amartya., 1981. Poverty and Famines: An Essay on Entitlement and Deprivation.
Oxford: Clarendon Press.
Sen, Amartya., 1997. Inequality, Unemployment and Contemporary Europe. International
Labour Review 136 (2): 155-72.
Sen, Amartya., 2000. Social Exclusion: Concept, Application, and Scrutiny., Asian
Development Bank Working Paper No 1.
Silver, Hilary., 1995. Reconceptualizing Social Disadvantge: Three Paradigms of Social
Exclusion. In Social Exclusion: Rhetoric, Reality, Responses. edited by Gerry Rodgers,
Charles Gore, and Jose Figueiredo. Geneva: International Institute for Labour Studies.
36
Appendix
Figure 4: Convergence plots for  ' s
37
Figure 5: Convergence plots for  ' s and h ' s
38
Table 1: Employment Pattern of the Rural Workers by Occupation
Extent of Joblessness
Works round the year
Occupation
Day
labour Marginal
er
farmer
-
Jan – Jun
-
-
March – August
26
(70.27)
(29.55)
24
(58.54)
(27.27)
7
(50.00)
(7.95)
12
(17.65)
(13.64)
13
(50.00)
(14.77)
6
(28.57)
(6.82)
88
(35.48)
4
(10.81)
(8.89)
14
(34.15)
(31.11)
3
(21.43)
(6.67)
15
(22.06)
(33.33)
8
(30.77)
(17.78)
1
(4.76)
(2.22)
45
(18.15)
Rickshaw
puller
7
(30.43)
(21.88)
1
(5.56)
(3.13)
2
(5.41)
(6.25)
2
(4.88)
(6.25)
2
(14.29)
(6.25)
11
(16.18)
(34.38)
1
(3.85)
(3.13)
6
(28.57)
(18.75)
32
(12.90)
(100)
(100)
(100)
May – August
March – April
June – August
April – August
June – July
Total
Total
Fisherman
-
17
(94.44)
(70.83)
2
(5.41)
(8.33)
-
Business
16
(69.57)
(76.19)
-
-
-
1
(7.14)
(4.17)
1
(1.47)
(4.17)
-
-
3
(14.29)
(12.50)
24
(9.68)
4
(19.05)
(19.05)
21
(8.47)
(100)
(100)
1
(1.47)
(4.76)
-
Stone
picker
-
-
3
(8.11)
(7.89)
1
(2.44)
(2.63)
1
(7.14)
(2.63)
28
(41.18)
(73.68)
4
(15.38)
(10.53)
1
(4.76)
(2.63)
38
(15.32)
(100)
23
(100.00)
(9.3)
18
(100.0)
(7.3)
37
(100.00)
(14.92)
41
(100.00)
(16.53)
14
(100.00)
(5.65)
68
(100.00)
(27.42)
26
(100.00)
(10.48)
21
(100.00)
(8.47)
248
(100)
(100)
Note: Figures in the parentheses indicate percentage.
Source: As in Table 1.
39