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Marginality as a Root Cause of Poverty: A Case of Punjab
Kanwal Zahra PhD Scholar GCU, Lahore Assistant Professor, University of Central Punjab, Lahore, Pakistan
Dr Tasneem Zafar Head of Department, Economics GCU, Lahore
Abstract Historically poverty as a concept considered to be a key factor to design social policy. The social development normally concerned with socio-economic empowerment of poor of concerned society. The treatment of poverty is different from society to society. In advanced countries, an individual who is unable to actively participate in society, has weak social network, environment , health and education etc is considered to be poor, parallel with financial empowerment is also considered to be important in these countries but it takes into account with other dimensions of poverty as well ( Lyberak and Tinios, 2005) while in developing countries, policy focus is on uni dimensional definition of poverty however this multidimensional poverty concept is also going to get acceptance in these countries. This study tries to see the intensity of multidimensional poverty among marginalized class in Punjab. With the help of two waves of Multiple Indicator Cluster Survey (MICS) 2007 and 2011, problem with national level data set is that they do not address issue of marginality, therefore this study used sample of marginalized class from Zahra and Tasneem (2014) and employee Poison Regression technique on the extracted sample. The findings reveals that marginalized class has a great incidence of poverty and poor in multidimensional aspects.
JEL Classification: I31, I
Introduction: Historically poverty as a concept considered to be a key factor to design social policy. The social development normally concerned with socio-economic empowerment of poor of concerned society. It is always been a key issue for developing as well as developed countries, however the nature and treatment of issue is different among nations. The treatment of poverty is different from society to society. In advanced countries, an individual who is unable to actively participate in society, has weak social network, environment, health and education etc. is considered to be poor, parallel with financial empowerment is also considered to be important in these countries but it takes into account with other dimensions of poverty (Lyberak and Tinios, 2005) while in developing countries, policy focus is on uni-dimensional definition of poverty where a single dimension either consumption or income is a strong factor which affect standard of living of an individual (Wagle, 2005). However this multidimensional poverty concept is also going to get acceptance in these countries with a perception that an individual’ status in one dimension cannot represent his status in another dimension. Another important transformation in the literature of poverty is seen in form of marginality and social exclusion (Ruth et al, 2007, Zoran et al, 2006. Whelan & Bartrand, 2005). Marginality and social exclusion is highlighted as policy focuses and treated as an independent phenomenon related with poverty. In developed countries, especially in Britain, different initiatives were taken by government and non-government agency to reduce social exclusion hence poverty. Separate surveys was conducted to see the gross root of the problem, in Canada, Canadian Institute of health sciences introduce marginality index as a policy measure. In developing countries, unfortunately very limited literature is available in the area of marginality and social exclusion. However in India, due to caste inequalities, this issue is getting great attention of the researchers (World Bank, 2011, Thorat & Nidhi, 2010, Thorat et al 2009, Mitra, 2004). Marginality broadly defines as a state situated at the margin, this could lead toward social exclusion hence poverty or a marginal person can be out of poverty. On the other hand the term “social exclusion” is a vibrant, multidimensional process driven by unequal power relationships. This exclusion can affect individual, household, group, community and countries across four dimensions i.e. economic, political, social and cultural and make certain object more vulnerable which leads them to high incidence of poverty (Jennie et al, 2008). In this respect, the study of
rest of the community. This vulnerability to poverty may be same or different within certain socially excluded groups and is strongly dependent upon the clan network of households existing in a marginalized group. Evidence shows a strong impact of social networking on the extent of vulnerability in these marginalized groups, social relationships of reciprocal exchange and mutual help in the struggle for the survival play an important role (Bruce, 2004, Margarida, et al (2007)). Unfortunately these factors leading to poverty have received less attention of the researchers in Pakistan, mostly research on the issue of poverty in Pakistan explores levels, trends and dynamics but not much attention is given to the issue of vulnerability to multidimensional poverty of the marginalized livelihood of this country. A person is normally considered vulnerable due to deficit consumption but there are other factors that contribute significantly to make one feel deprived including the shortfall of living needs. The living standards are highly affected by insecurity and powerlessness of future shortfalls. Calvo (2008) therefore considered this vulnerability to multidimensional poverty as a form of hardship that is defined in both conceptual and empirical way. He extended his own index that he developed in 2005 and used bi-dimensional measures of consumption and leisure. His findings suggest that these two dimensions are negatively correlated in both rural and urban cases. This vulnerability is different from poverty much attention is needed to differentiate between vulnerability and poverty. Vulnerability is related with poverty but it is not necessary that all poor are vulnerable or all vulnerable are poor. Angemi (2011) supported this view in his study with the help of household level analysis within poverty framework. He pointed out that the characteristic of vulnerability is consistent with the characteristics of poor so by this he found that poverty and vulnerability both are related with each other. However, an important point of his analysis was that all poor are not vulnerable while some proportions of non-poor are vulnerable. In the same lines Susan and
Takashi (2002) employed two period panel data set of the North-West Frontier Province, Pakistan and proved that the sample household was subject to a high risk of income poverty. Results also revealed the households are more vulnerable to consumption poverty and are affected by the shock of outside employment as compared to self-employed households. An important outcome from this analysis concludes that the age, having less land and irregular sources of income strongly affect the extent of vulnerability among households. Diego (2011) is of the view that the dynamics of risk and uncertainties are helpful to understand the nature of
poverty. By applying the pooled GLS method on the national data sets of Uganda, he discovered that along with a sharp reduction in poverty, the vulnerability to poverty in Uganda has also declined, however, the issue of marginalization existed due to geographical segregation. The results revealed that the central region experienced reduction in incidence of vulnerability while the rural areas, where 90% of population is living under extreme poverty conditions, the incidence of vulnerability has increased. Supporting the findings of Diego (2011), a worldly accepted truth is that this high incidence of vulnerability to poverty is mostly dominant in socially excluded and marginalized group.
Early research also support the idea of this social exclusion, In industrialized countries, the evolution of one parent family defines a new pattern of poverty and marginalization. This marginalization exists not only in labor market of these countries but also exists in the provision of public housing (HILARY, 1989). On the other hand, David, et al (2000) tried to develop a baseline for understanding the nature of poverty and social exclusion. They used poverty in terms of deprivation from goods, services and social activities. They are of the view that this way of measuring deprivation satisfied both absolute and relative poverty terms. The analysis shows the there is an increase in the multiple deprivation and poverty in Britain during the survey period. By identifying these issues in family-cycle approach, Dewilde (2003) tried to develop a framework of analysis of poverty and social exclusion. As per his views, a life course perspective re-conceptualizes the traditional approaches and combines their best element into the analysis of social exclusion hence poverty. He used three sociological perspectives on the life course i.e. the traditional North-American life course perspective by Elder (1974), the Continental institutional approach and “political economy of the life course”. With the help of these three approaches, he proposed a new framework to analyze poverty and social exclusion relationship over the life course, both theoretically and empirically. These circumstances of poverty are strongly related with level of social exclusion and parental social class. The factors that provide the poverty prospects at childhood age due to parental social class are strongly associated with current lacking of basic infrastructure (Aya, K., 2009). This was also proved by Christopher et al (2013) with the help of a comparative analysis between four important factors i.e. social exclusion, parental status, childhood economic status and state of current poverty. With the help of EU-SILC module, they figured out how the welfare regimes mediate the impact of parental social class and childhood economic circumstances on
4.2. Theoretical Framework Poverty is a long term debate and developing countries are targeting to be free of poverty by 2015, the millennium development goals directly and indirectly target poverty eradication and aims for a good standard of living for the livelihood of the society. To eradicate extreme poverty and to make people out of extreme hunger requires a good educational infrastructure; reduced child mortality, improved maternal health and gender equality and enhanced women empowerment (United Nation, 2007). These goals not only help to reduce poverty among general population but also address the issue of social exclusion in the deprived class. The gender inequality is considered to be one of the important factors of social and economic exclusion, women in developing countries have fewer opportunities to grow in education and professional life, and globally around 25% of senior positions have been occupied by women but are paid 23% less than men on average. Although in paid job women share has been increased by 40% but still there is a large room for improvement (David et al, 2011). In education and health sector, these women are discriminated, and have fewer chances to avail good health and education facilities as maternal mortality in developing world is fifteen times higher than the developed world. The least developed economies face serious challenges in eradication such exclusion in their respective countries. Progress towards reducing poverty is slower which addresses policy gaps in achieving the target. Policies overlook the depth in the issues of poverty and take poverty at general level, but the population who is actually excluded from rest is ignored, that population is living below poverty line and marginalized in participating socio- economic activities with rest of the population of the region. Unfortunately pro-poor growth ignores this important aspect of poverty. The facts shows progress is slower in developing world where globalization is seen in form of higher rural-urban migration but on other side, the economic and social side is still deprived and fails to meet the challenges of this higher rate of rural-urban migration. This causes an increase in the burden of city management and also an increase in the size of the excluded area within the city or periphery of the city. Such population is marginalized while living in the slums and katchi abadies of urban area and face a lack of opportunities to acquire skills and access to labor market. This marginalized population then becomes socially and ethnically excluded from the rest of the society and has less access to educational, health and other urban services.
This marginalization defines boundaries between groups living in a society, some groups are economically excluded and to some extent social inclusion prevail in such group, but on the other hand some are demographically and economically excluded, in a society of developed as well as developing countries, therefore marginalization can be considered as a process in which a community or individual lives at margin and gradually become economically, culturally, socially and politically excluded from rest of population (Zahra and Tasneem, 2014). There are some deprived groups who are excluded in all dimensions of exclusion and spent deprived and vulnerable life even being part of that society. Thus marginality leads to social exclusion in long run and this social exclusion is blamed to be primarily responsible for social conflict due to its inability to transform itself since it is strongly connected to the systems of oppression and domination.
Figure 4.1:
Usually poverty links with material lacks, it has theoretical as well as strategic importance, but the increasing understanding is that poverty is not just a name of material lacks, but also associated with restricted access to resources that can make an individual or household well off. UN has defined poverty through the “capability approach” and “the human rights approach”. These inter-related themes provide an enriched understanding of poverty and we can define poverty as:
Marginalization Societal Geographical
Social Exclusion economic exclusion social exclusion Cultural exclusion
Multidimensional Poverty
ii. Suffers from physical or psychological disabilities and poor access to health facilities iii. Poor infrastructure and have less capacity to improve it iv. Few productive and financial assets and has limited access to credit market v. Poor social networking and excluded from normal lives of society. vi. Poor access to job market opportunity.
Therefore:
A marginalized household is considered to be poor if it has a limited access to the living needs, has limited or restricted access to social, economic and political life of its society due to residential, societal, spatial, environmental deprivations etc. and has poor capacity to ensure good standard of living for its members. 4.3. Methodology
The geographical focus of this paper is Punjab, Pakistan which is an economic hub of the country. The dynamic nature of agriculture and industrial production along with having major population share of the country makes it more important than other areas. Punjab witness major urbanization in past few decades and trade liberalization is not proved to be beneficent for entire population and segments of urban areas remain in extreme poverty. Numbers of studies are available which covers issues of poverty in Punjab as well as Pakistan. The focus of the extent of poverty and inequality among household, however advance level analysis on poverty is rare in literature. In Pakistan, the studies based on the household level determinants of poverty are no exception. Primary data from the combined round of PIHS was used by Siddiqui (2007) whereas Siddiqui, A. (2009) used PSLM 2004-05 survey. Sikander (2009) use the data from Multiple Indicator Cluster Survey (MICS)-2003-04 to analyze the determinants of poverty in Punjab. Malik (1996) used self-collected data on a rural locality called “Wanda” (District Bhakkar, Punjab). His results were based on a sample of size 100 and however were not nationally representative for inference about the determinants of poverty.
The analysis of marginalization and poverty in this study is based on two waves of data from Multiple Indicator Cluster Survey (MICS) conducted in 2007-08 and 2011-2012. In 2007-08, 91,280 households drawn to be participated in data collection process from which 59456 were rural and 31824 were urban. Of this sample 594851 individual from urban and rural areas
covered with a wide range of socio-economic issues on living condition, economic situation, health and education, housing etc. in data collected in 2011-12, 3102048 household was covered, in which 3488 was urban and 3788 are rural, and this data set also covered more than 90 indicators from different socio-economic perspective. The unit of observation for the analysis of this study is individual resides in urban areas of Punjab, Pakistan.
4.3.1. Empirical Model and Estimation Procedure
This paper is primarily concern with the measurement poverty among marginalized people of Punjab with immediate focus on whether these people are living in extreme poverty or out of poverty. The definition behind is that poverty is a relative concern which can be explain with economic and social wellbeing, capability and social inclusion. Whereas marginalized and socially excluded concept is another important dimension of the study which has been extracted from Zahra and Tasneem (2014). The flow of empirical analysis based on marginalized population as this study is concerned about measurement of poverty among marginalized people. Zahra and Tasneem (2014) extract marginalized and socially excluded population in urban areas of Punjab with the help of Index and calculate income inequalities of the socially excluded population of all cities of Punjab.
Multidimensional poverty has been evaluated with different techniques in literature. Ramya et al (2014) and Labar &Bresson (2011) estimated multidimensional poverty index based on alkair foster measure, whereas Mahlberg & Obersteiner (2001), Sikander and Mudassar (2008) and Merz & Rathjen (2011) used logit regression to see multidimensional poverty. Wagle (2005) contributed in literature by introducing index based estimation of multidimensional poverty and used structural equation modeling. Literature support a wide range of methodologies which used structural as well as simultaneous equation modeling. Attention has now been diverting to analyze the impact of different deprivation on extent of multidimensional poverty. The extent of multidimensional poverty can be seen with the help of number of areas in which a specific household or individual is deprived (Alkair & Foster, 2011, Jhon et al. 2013). Dimensions in which household or individual are deprived measured as count data (number of dimension in which each individual is deprived) are assessed with Poisson regression, a useful technique for count data modeling. It is one of the robust model for discrete data modeling with an assumption that the dependent variable (number of dimensions in which individual is poor) is distributed as
The maximum likelihood poison multidimensional poverty equation can be:
X’s in above equation are the set of independent socio-economic variables which describe individual’s characteristics. The full model therefore can be written as:
∑
Where
δi = the expected number of dimension in which individual is poor
e = the base of natural logrithem
β 0 = the intercept
βj’s = coefficient of regression
xj’s = explanatory variables
4.3.2. Explanation of Variables:
The study takes “number of dimension in which an individual is deprived” as dependent variable. To calculate the number of dimension in which an individual can be poor, Alkair foster (2010) methodology helps to measure dimensions of poverty. Furthermore Ataguba et al (2013) also used same technique to find dimensions in which an individual can be poor. Taseer and Zaman (2013) use this technique to show time series breakdown in multidimensional poverty in Pakistan. This methodology uses dual cut-offs to find dimension adjusted measure of poverty and is better than other methodologies as it satisfy assumption of monotonicity and decomposability.
To identify and measure multidimensional poverty, head-counts and dimension adjusted head count rations are used. The dimension adjusted head count M 0 can be calculated as:
where H 0 is the proportion of people who are deprived in certain dimension and A is the mean share of deprivation among the poor, M 0 use as dependent variable in the model. Internationally eleven dimensions has been selected to measure multidimensional poverty among household or
individuals but in case of MICS dataset, it is only useful to calculate seven dimensions. These include economic, housing, air quality, health, education, water & sanitation, assets. Detail composition of these dimensions in given in annexure.
The contribution of human capital to poverty alleviation is proved by previous literature. The development of human capital leads to an increase in standard of living at household level. Communities with more low-skilled workers in general are more likely to experience high rates of poverty. The educational attainment as measure of the quality of human capital is important, High educational attainment may imply a greater set of employment opportunities which cause to decrease poverty (Cameron, 2000; chaudhary et al, 2009). The availability of education facilities serve as main indicator of remains poor. If the household have an accessibility of school then there is a greater chance to get rid from poverty. Theory shows a fundamental impact of health on households, it is considered that the accessibility to health services directly influence the productivity of individual household (McDonough et al, 2009; Zhong, 2009). Another indicator of housing standards is access to electricity. The housing indicators also affect the standard of living of households. Employment is considered as an important factor to affect poverty. The occupational affiliation of the head of household is found to be an important determinant of poverty.. The empirical results suggested that the industry specific employment is necessary for reducing poverty (increased per capita consumption and ultimately per capita food consumption) (Sikander, 2009). The employment trend is defined by participation rate which is the ratio of the number of workers to the number of adults in a household. The participation rate is expected to be negatively correlated to poverty. Household income is an important determinant of household expenditure since it serves as the budget constraints to the amount that can be spent within a period, there is also bound to be correlation between income and poverty level of a household, all other things being equal. The household income is also important to define the poor and non-poor households for further analysis. In economic perspective, to judge, the standard of living of households, the household Property and Assets which contains the land, livestock and other accessories of life is also play role to determine the poverty level among households.
As this paper is more concerned with relative poverty related with socio-economic inclusion, capability etc. Therefore this study also uses some indices based on socio-economic characteristics of individual and household from where s/he belongs developed by Wagle (2005).
Income based poverty line ($1.5 per day)
Food consumption based poverty (Rs. 1668)^3 Extremely Poor > 50% 40.8 94 Ultra Poor 50% 75% 30.0 4. Poor 75% 100%^ 11.0^ 1. vulnerable 100% 125% 5.8 0. Non poor 125% <X 6.8 0. Around 70% of total population live within extremely poor and ultra-poor and only 6.8% of marginal class live out of poverty in 2007 while in 2011 the poverty line is based on expenditure approach, where 94% population live in extreme poverty. 4.3.4. Poisson Regression Analysis and Results The results from poison regression analysis is presented in table 4.2, the study use four models (two for each data set) to prove hypothesis. Theory suggest a chain of marginality, social exclusion and poverty, therefore model 1 of each dataset shows results which includes marginality as explanatory variable, while model 2 contains all other variable of model one and use social exclusion index as independent variable to prove the theoretical link. We found that coefficient has correct signs as defined in theory except some minor contradictions. Table 4.2: Poisson Regression Output (2007-08) Dependent Variable: poverty counts Model 1 Model 2 Coefficient Standard Error Coefficient Standard Error Income - .000261*** 0.000157 - .000259. Poor Health .00362 .01121 .001378. No Education (reference) Pre-primary - .238136 .019691 - .22552. Primary - .254944 .008643 - .23253. Middle - .26673 .0107994 - .237417***.
(^3) Planning Commission of Pakistan, 2011
Matric - .292634*** .022817 - .25584. Higher - .292006 .0278123 - .25359. Madrassa - .316337 .0978124 - .28196. Poor Housing Condition .11037 .00729 .11064. Occupation - .00135 .000102 - .00130. Assets - .21986** .09553 - ..19139** .. Capability - .315201*** ..035955 - .26964. Environment wellbeing .26406 .03086 .26964. Social wellbeing - .71456 .096468 - .724314. Economic wellbeing - .55788 .072224 - ..632038*. Marginality index - .03103 .007036 - - Social exclusion index - - .02941. Log likelihood Pseudo R LR χ2 (12) Prob > χ
Results shows that income has a negative impact on the proportion of dimension in which household can be poor, increase in income level will reduce poverty threats by .02% (e0.0002=1.00), keeping all other variable constant. The coefficient is significant at 1%. This also proves the importance of multidimensional poverty that income has contributory role if defining a person poor but not has a unique role. While occupation of an individual also plays a negative impact on the possibility to be poor and can draw him out from poverty, individual who has good mean of earning than an individual with no or odd job has lesser threat of poverty by 13% (e.0013= ) at 1% level of significance. As well as education of individual is concerned, compared to those individuals who are illiterate, people having incomplete primary education, threat of poverty lower by 23% (e0.2381^ = 1.269), compared to not being literate, people having primary education is found to be at minimal threat of poverty by 25% (e 0.2549^ = 1.290 ) again assuming all other variable constant. for those persons, who have matric and higher education has a lesser threat to be poor by 29% (e 0.2926^ = 1.339). As well as housing conition is concerned, the variable reported those individual who have poor housing condition, the result shows a positive
Capability - .173256** .069413 - .151884. Social wellbeing 3.6445* .12478 3.7985. Economic inclusion - .163873^ .022886^ - .10671^. Marginality index .035296^ .006772^ -^ - Social exclusion index
Log likelihood Pseudo R LR χ2 (12) Prob > χ
Results of Poisson regression of 2011-12 data wave had only expenditure data while income aspect of household has been ignored. Therefore above table has two variables missing due to non-availability of data, one is income of an individual and the other is environmental wellbeing while one variable is additional i.e. expenditures. According to results, expenditure has negatively affect the risk to be in poverty, increase in expenditure will down deprivation by .03% (e0.0003=1.00), keeping all other variable constant. The coefficient is significant at 5%. As well as education of individual is concerned, compared to those individuals who are illiterate, people having incomplete primary education, threat of poverty increase by 24% (e0.2416^ = 1.269), compared to not being literate, people having middle level education is found to be at minimal threat of poverty by 3% (e 0.0333^ = 1.034 ) again assuming all other variable constant. for those persons, who have matric and higher education has a lesser threat to be poor by 5% (e 0.0497^ = 1.051). As well as housing condition is concerned, the variable reported those individual who have poor housing condition, the result shows a positive relationship of both variable, compared to people living in better housing, the threat to be poor for those individual living in poor housing is increased by 5%(e 0.0497^ = 1.051). The coefficient of housing is significant at 5%. Capability to be better off has also strongly affect the status of poverty of an individual, a person with good capabilities has a 17% (e 0.1732^ = 1.189) less chances to be in multidimensional poverty than a person with no capabilities. Economic inclusion also lower the risk of poverty, an individual who
has greater inclusion in economic activities has 16% (e 0.1638^ = 1.178) chances of deprivation in different dimension that a person with no economic inclusion. Similar with marginality index and social exclusion index, person with higher marginality and social exclusion has high threat tobe poor in different dimensions than a person who is not at marginal position and not socially excluded. Model 2 of this wave also shows similar results with same nature of relationship.
The results showing almost significant relationship with relationship with poverty perceived in theory expect some of variable which shows opposite results. Above results shows a picture of poverty in two time period i.e. 2007-08 and 2011-12 respectively, increase in income and expenditure making an individual better off and reduce chances to be in poverty. An individual with high income and good nutrition can access living facilities well and can be more productive than a person with less food consumption (Headey, 2008), result also shows a negative and significant impact of income and expenditure on deprivation and poverty in both waves. Wealth of an individual also includes type and number of assets which an individual have, therefore the state of poverty is strongly depends upon the asset ownership of an individual or household (Moser, 1998, 2006). Results shows a negative, strong and significant relationship of assets ownership on risk of multidimensional poverty, a person with good assets has lower chances to be poor in different dimension than a person with no assets. Same relationship is proved by Meck & Lansley (1985) and Milton (2003), where lack of assets make a person more poor. Liverpool and Alex (2010) shows a positive impact of asset building on consumption expenditure.
Another important determinant of poverty is education which is proved by results from both waves. To make a detail analysis, we split education into different levels and compare risk to be poor with illiterate person. The higher education lower chances of poverty, Haroon (2009) shows a positive impact of education on expenditures of household, Dewilde (2004) proves that with increase in educational attainment, the risk of poverty has been reduced, he tested this theory both on uni-dimensional and multidimensional poverty risk, results also reflect theoretical base, first wave supports the attainment in education lower the chances for household to be poor, all results are significant at 1% level, while second wave (2011-12) shows a positive relation of education attainment till primary level with poverty risk for an individual, while education attainment (above primary and onward) will lower risk of poverty significantly. Jhon et al (2013) also found a positive impact of education attainment till primary on multidimensional poverty
