Medicion multidimensional de la pobreza en Pakistan: analisis provincial.
Multidimensional measurement of poverty in Pakistan: provincial analysis1. Introduction
Poverty is one of the most familiar phenomena and fact of human societies. It has involved many of the most prominent social thinkers, specifically academia, researchers and policy makers from all over the world in debates about its origin, causes and types. Arouse of all this resist, till now a common man is incapable to answer a simple question: what is poverty, exactly? Even this most simple question is unlikely to produce a universally accepted answer, although most would agree it involves such concerns as hunger, unemployment, illiteracy, malnutrition, ill-being, incompetency, gaps between the different segments of society and combination of all these or something bigger than it. Precisely speaking the term "poverty" encompasses multiple aspects of human life. None is seems to disagree that deprivations exist in multiple domains and are often correlated. In order to understand the threat that the problem of poverty poses, it is necessary to know its dimension and the process through which it seems to be deepened. The measurement of correlated multiple domains with respect to poverty, fabricates the new concept, i.e. multidimensional poverty. Now theoretical and analytical evidence is ample, while remaining insoluble issues in poverty analysis are related directly or indirectly to the multidimensional nature and dynamics of poverty (Thorbecke, 2005: 3-30). Analysis on multidimensional poverty has occupied much attention of economists and policymakers, particularly since the writing (Sen, 1976: 219-231) and the rising of data availability for relevant research purpose. The justification behind this multidimensional measurement of poverty is based on the idea that income indicator is incomplete and its deficit leads to vague estimations of poverty (Diaz, 2003: 674-697). Having said that, alternative dimensions such as health, educational attainment, social exclusion, and insecurity are often weakly correlated with income or expenditure (Appleton and Song, 1999: 1-56). These poor correlations highlight the fact that measuring these additional dimensions enriches and provides additional information to the poverty picture (Calvo and Dercon, 2005: 1-29). However, the strength of measurement lies in the construction of indexes that capture the relative importance of each indicator in the total poverty picture. The weighting of each indicator is meant to reflect the strength of the relationship with "wealth factor" for asset-based measurement as proposed by Sahn and Stifel (2000: 463-489). While the most important component in poverty measures is identification, there are two main approaches in identifying the poor in a multidimensional setting (Alkire and Foster, 2007: 77-89), i.e. "union" and "intersection" approach.
Alkire and Foster (2007: 77-89) proposed a counting approach for measuring the multidimensional poverty. This approach has a number of characteristics that deserve mention. First, the identification method mentioned in this approach is poverty focused, i.e. an increase in the achievement level of a non-poor person leaves its value unchanged. Second, it is deprivation focused, i.e. an increase in any non-deprived achievement leaves the value of the identification unchanged. Third, this approach can be meaningfully used with ordinal data. Fourth, this approach satisfies several desirable properties including decomposability. Fifth, we can also assign different weights to each dimension.
The main objective of the paper is to apply the above mentioned methodology to estimate multidimensional poverty in four provinces of Pakistan, which would complement the income poverty estimates performed by Planning Commission of Pakistan and other government agencies. This study also highlights the importance of each dimension because the beauty of this methodology is that: we find out the effect of each dimension in overall poverty
Rest study is balanced as, part two explains the data and methodology used in this paper; part three discusses the selected dimensions and cut-offs; part four presents the results, and part five concludes the study and also give some policy options to control the problem.
2. Data and methodology
The dataset used in this paper is the 2005-06 Pakistan social and living standard measurement survey (PSLM) conducted by Federal Bureau of Statistics (FBS) Pakistan. This is the second round of PSLM. The Household Integrated Economic Survey (HIES) [Part of PSLM] is the main source of data for poverty estimates in Pakistan (Arif, 2003: 12-47). HIES Questionnaire was revised in 1990 in order to incorporate the requirements of the new system of national accounts. 199091, 1992-93, 1993-94 & 1996-97 surveys were conducted using revised questionnaire. In 1998-99 and 2001-02, the HIES data collection methods and questionnaire were changed to reflect the integration of the HIES with the Pakistan Integrated Household survey (PIHS). The HIES 2004-05 was conducted as part of first round of PSLM survey covering 14 708 household taken as sub-sample of the 77 000 households of PSLM survey. The current round of HIES has been carried out covering 15 453 households [FBS-2005-06].
In this paper we use a methodology for multidimensional poverty measurement proposed by Alkire and Foster's (2007: 77-89). First we define the notations which will be helpful to provide an outline of the measure.
Let M n,d denote the set of all nxd matrices, and y [member of] [M.sup.n,d] represents an achievement matrix of n people in d different dimensions. For every i = 1, 2,..., n and j = 1, 2,..., d, the typical entry [y.sub.ij] of y is individual i's achievement in dimension j. The row vector [y.sub.i] ([y.sub.i1], [y.sub.2j], [y.sub.id]) lists individual i's achievements and the column vector = ([y.sub.i1], [y.sub.2j], [y.sub.id]) gives the distribution of achievements in dimension j across individuals. Let [z.sub.j] represent the cut-off below which a person is considered to be deprived in dimension j, and z represent the row vector of dimension specific cut-offs. Following Alkire and Foster's (2007: 77-89) notations, any vector or matrix [,v.sub.,v] [absolute value of v] denotes the sum of all its elements, whereas [mu](v) is the mean of v.
Alkire and Foster (2007) suggest that it is useful to express the data in terms of deprivations rather than achievements. For any matrix y, it is possible to define a matrix of deprivations [g.sup.o] = [[g.sup.o.sub.ij]], whose typical element [g.sup.o.sub.ij] is defined by [g.sup.o.sub.ij] = 1 when [y.sub.ij] < [z.sub.p] and [g.sup.o.sub.ij] = 0 when [y.sub.ij] > [z.sub.j] [g.sup.o] is an nxd matrix whose ijth entry is equal to 1 when person i is deprived in jth dimension, and 0 when person is not; [g.sup.o.sub.i] is the ith row vector of [g.sup.o] which represent person i's deprivation vector. From [g.sup.o] matrix, define a column vector of deprivation counts, whose ith entry [c.sub.i] = [absolute value of [g.sup.o.sub.ij]] represents the number of deprivations suffered by person i. If the variables in y are only ordinally significant, [g.sup.o] and c are still well defined. If the variables in y are cardinal, then we have to define a matrix of normalized gaps [g.sup.1]. For any y, let [g.sup.l] = [g.sup.1.sub.ij]] be the matrix of normalized gaps, where the typical element is defined by [g.sup.o.sub.ij] = ([z.sub.j] - [y.sub.ij])/ [Z.sub.j] when [y.sub.ij] < z, and [g.sup.o.sub.ij] = 0 otherwise. The entries of this matrix are non-negative numbers less than or equal to 1, with [g.sup.o.sub.ij] being a measure of the extent to which person i is deprived in dimension j. This matrix can be generalized to [g.sup.[alpha]][g.sup.[alpha].sub.ij] with [alpha] > 0, whose element [g.sup.[alpha].sub.ij] is normalized poverty gap raised to the [alpha]-power.
After defining the notation, now we provide an outline of the class of multidimensional poverty measure suggested by Alkire and Foster (2007: 77-89). A reasonable starting point is to identify who is poor and who is not. Most of the identification method suggested in the literature normally follows the union or intersection approach. According to the union approach a person i is said to be multidimensionally poor if there is at least one dimension in which the person is deprived, whereas according to intersection approach a person i is said to be multidimensionally poor if that person is deprived in all dimensions. If dimensions are equally weighted, then the methodology to identify the multidimensionally poor proposed by Alkire and Foster (2007) compares the number of deprivations with a cut-off level k, where k = 1, 2,..., d. Let us define the identification method [[rho].sub.k] such that [[rho].sub.k](y.sub.i],s) = 1 when [c.sub.i] k , and [[rho].sub.k](y.sub.i],s) when [c.sub.i] < k. This means that a person is identified as multidimensionally poor if that person is deprived in at least k dimensions. This is called dual cut-off method of identification because fit is dependent on both the within dimension cut-offs z; j and across dimensions cut-off k. This identification criterion defines the set of the multidimensionally poor people as [Z.sub.k] = {i: [[rho].sub.k] ([y.sub.i]',z) = 1}. A censored matrix [g.sup.o](k) is obtained from [g.sup.o]. by replacing the ith row with a vector of zeros whenever [[rho].sub.k] ([y.sub.i],z) = 0. An analogous matrix g[alpha](k) is obtained for [alpha] > 0, with the ijth element [g.sup.[varies].sub.ij] (k) [g.sup.[varies].sub.ij] if [c.sub.i][greater than or equal to] k & [g.sup.[varies].sub.ij] (k) = 0 if [c.sub.i] < k.
On the basis of this identification method, Alkire and Foster (2007) define the following poverty measures. The first natural measure is the percentage of individuals that are multidimensionally poor: the multidimensional Headcount Ratio H - H(y;z) is defined by H = q/n, where q = q(y,z) is the number of people in set [Z.sub.k]. This is entirely analogous to the income headcount ratio. This measure has the advantage of being easily comprehensible and estimable, and this can be applied using ordinal data. However, it suffers from the disadvantages first noticed by Sen (1976) in the unidimensional context, namely being insensitive to the depth and distribution of poverty, violating monotonicity and the transfer axiom. Where as in the multidimensional context, it also violates dimensional monotonicity (Alkire and Foster, 2007: 77-89). Alkire and Foster (2007) explain this as if a poor person already identified as poor become deprived in an additional dimension (in which this person was not previously deprived), H does not change.
To overcome this problem of multidimensional headcount, Alkire and Foster (2007) propose the dimension adjusted FGT measures, given by [M.sub.[alpha]](y,z) = [mu](g.sup.[alpha](k)) for a [greater than or equal to] 0. When [alpha] = 0, the measure is called Adjusted Headcount Ratio, defined by Mo = [mu]([g.sup.o](k)) = HA. The adjusted headcount ratio is the total number of deprivations experienced by the poor [absolute value of c(k)] = [absolute value of [g.sup.o](k)], divided by the maximum number of deprivations that could possibly be experienced by all people (nd). It can also be expressed as the product between the percentage of multidimensionally poor individuals (H) and the average deprivation share across the poor, which is given by A = [absolute value of c(k)]/(qd\ In words, A provides the fraction of possible dimensions d in which the average multidimensionally poor individual is deprived. In this way, [M.sup.o] summarizes information on both the incidence of poverty and the average extent of a multidimensionally poor person's deprivation. This measure is easy to compute as H, and can be calculated with ordinal data and it is superior to H because it satisfies the dimensional monotonicity property.
The class of dimension adjusted FGT measure also yields the Adjusted Poverty Gap, give by [M.sub.1][mu]([g.sup.1](k)) = HAG, which is the sum of the normalized gaps of the poor ([absolute value of [g.sup.1](k)])) divided by the highest possible sum of the normalized gaps (nd). It can also be expressed as the product between the percentage of multidimensionally poor persons (H), the average deprivation share across the poor (A) and the average poverty gap (G), which is given by ? G|[g.sup.1](k)|/|g.sup.o]. The poverty measure [M.sub.1] ranges in value from 0 to 1. If the dimension of poor person deepens in any dimension, then the respective [g.sub.1](k) will rise and hence so will [M.sub.1]. Consequently [M.sub.1] satisfies monotonicity.
Finally, when [alpha] = 2, the measure is the Adjusted Poverty Gap, and it is represented by [M.sub.2] and[M.sub.2] [mu](g.sup.2](k)) which is the sum of the squared normalized gaps of the poor ([absolute value of [g.sup.2](k)]) divided by the highest possible sum of the normalized gaps (nd). It can also be expressed as the product between the percentage of multidimensionally poor persons (H), the average deprivation share across the poor (A) and the average severity of deprivations (S), which is given by S = [absolute value of [g.sup.2](k)]/|g.sup.(k). [M.sub.2] Summarizes information on the incidence of poverty, the average range and severity of deprivations, and the average depth of deprivations of the poor. If a poor person becomes deprived in a certain dimension, [M.sub.2] will increase more the larger the initial level of deprivation was for this individual in this dimension. This measure satisfies both types of monotonicity and also transfer, being sensitive to the inequality of deprivations among the poor as it emphasizes the deprivations of the poorest.
All members of the [M.sub.[alpha]](y;z) family are decomposable by population subgroups. Given two distributions x and y, corresponding to two population subgroups of size n(x) and n(y) correspondingly, the weighted average of sum of the subgroup poverty levels (weights being the population shares) equals the overall poverty level obtained when the two subgroups are merged:
M (x,y;z)= n(x)/n(x,y) M (x;y) + n(x)/n(x,y) M (x;z)
All members of the [M.sub.[alpha]] (y;z) family can also be broken down into dimension subgroups. To see this, note that the measures can be expressed in the following way: [M.sub.[alpha]] (y,z) = [n.summation over i=1] [mu]([g.sup.[alpha]/sib/*j](k))/d, where [g.sup.[alpha]]j is the jth column of the censored matrix [g.sup.[alpha]]{k). Strictly speaking, this is not decomposability in terms of dimensions, since the information on all dimensions is needed to identify the multidimensional poor. However, once the identification step has been completed, and the nonpoor rows of [g.sub.[alpha]] have been censored to obtain [g.sub.[alpha]](k) the above aggregation formula shows that overall poverty is the average of the d many dimensional values [mu]([g.sub.j](k)) Consequently, [mu]([g.sub.j](k))/d/ [M.sub.[alpha]](y,z) can be interpreted as the contribution of dimension j to overall multidimensional poverty.
The [M.sub.[alpha]] (y,z) family adopts the neutral assumption of considering dimensions as independent. In this way, it satisfies a property, based on Atkinson and Bourguignon (1982: 183-201), called weak rearrangement. The concept is based on a different sort of "averaging" across two poor persons, whereby one person begins with weakly more of each achievement than a second person, but then switches one or more achievement levels with the second person so that this ranking no longer holds. In other words, we can say that a simple rearrangement among the poor reallocates the achievements of two poor persons, but leaves the achievements of everyone else unchanged. This is called an association decreasing rearrangement. Under such rearrangement one would expect multidimensional poverty not to increase. This is postulated by the weak rearrangement axiom and it is precisely satisfied by the [M.sub.[alpha]] (y,z), which will not change under such transformation. Because of its completely additive form, it evaluates each individual's achievements in each dimension independently of the achievements in the other dimensions of other's achievements.
We use same weights for all dimensions but this [M.sub.[alpha]](y,z) family can be extended into a more general form, admitting different weighting structures (Awan, Waqas & Aslam, 2011: 133-144).
3. Selected dimensions and deprivation cut-offs
This section presents the dimensions, indicators and cut-offs for each dimension used in this paper. In the following table, we summarize the question asked in PSLM 2005-06, dimensions and the cut-offs that we want to apply for each indicator in this paper.
4. Results and discussion
Table 2 presents the estimated multidimensionally poor headcount (H), adjusted headcount ([M.sup.o]) and average deprivation (A) for different levels of cut-off, i.e. k = 3, 4, 5 & 6. Suppose k = 3, result shows that more than 89% of households in Balochistan are deprived in at least three dimensions and the Adjusted Headcount Ratio ([M.sup.o]) is 0.6117. Where as in case of Balochistan rural, situation is even worst as Multidimensional Headcount Ratio is almost 96% and on average these households are deprived in 6.5 dimensions, so the Adjusted Headcount Ratio in this case is 0.6974. In case of Balochistan urban, almost 65% households are deprived in at least three dimensions and the value of the Adjusted Headcount Ratio is 0.2917. Almost 67% of household in NWFP overall 71% in rural NWFP and 43.5% in NWFP urban are deprived in at least three dimensions and the Multidimensionally Adjusted Headcount Ratios for these regions are 0.6673, 0.7129 and 0.4355, respectively. More than 38% of households of urban Sindh are deprived in at least three dimensions and the Adjusted Headcount Ratio in this case is 0.1613. More than 91% of rural households of Sindh are deprived in three or more than three deprivations and ([M.sup.o]) in case of rural Sindh is 0.5649. Almost 63% are deprived in at least three dimensions in case of Sindh overall and the corresponding Adjusted Headcount Ratio in this case is 0.3504. More than 57% households of overall Punjab are deprived in at least three dimensions and the Adjusted Headcount Ratio in this case is 0.2952. More than 70% households in case of rural Punjab and 29% in case of urban Punjab's households are deprived in three or more out of nine dimensions and their corresponding Adjusted Headcount Ratios are 0.3760 and 0.1221. Overall Balochistan shows the worst picture, followed by NWFP, Sindh and Punjab. In urban areas of different provinces, Balochistan is more multidimensionally poor followed by NWFP, Sindh and Punjab. As far as the rural area is concerned, Balochistan is multidimensionally poor followed by Sindh, NWFP and Punjab.
Figure 1 expresses the Multidimensional Poverty Index ([M.sup.o]) at different levels of K along with the regional bifurcation. Figure shows that rural Balochistan is the most deprived region of Pakistan, among all eight regions, for all levels of K while urban Sindh is the least deprived one.
Dimensions of land, empowerment and housing are the major contributors to MPI in urban Punjab, while along with the three dimensions the sanitation adds up to 14% to MPI in rural Punjab. Similar is the case of province Sindh; the dimensions of empowerment, land, and housing constitute 72% of overall MPI in urban Sindh, while the same three dimensions contribute 50% to overall MPI in rural Sindh, which shows that intensity of multidimensional poverty is high in urban areas as compared to rural ones. Similar is the case with provinces of Balochistan and KPK. But in the province of KPK, dimension of sanitation is equally contributing to overall MPI.
5. Conclusion
This paper has estimated multidimensional poverty for four provinces of Pakistan using PSLM dataset for years 2005-06 by applying Alkire and Foster (2007) methodology. Nine dimensions were selected for this study: Housing, Electricity, Water, Asset, Sanitation, Education, Expenditure, Empowerment and Land. Results found that overall Balochistan shows the worst picture followed by NWFP, Sindh and Punjab. In urban areas of different provinces, Balochistan is more multidimensionally poor followed by NWFP, Sindh and Punjab. As far as the rural area is concerned, Balochistan is multidimensionally poor followed by Sindh, NWFP and Punjab. Results show that the most pervasive level of poverty exists in rural areas of different provinces. The analysis of contribution of each dimension in multidimensional poverty at different cut-offs showed that the major contributors are Land, Empowerment, Housing, Assets and Sanitation. This study also presents an empirical evidence of significant lack of overlap in the identification by the monetary and multidimensional approach in the case of Pakistan.
Fecha de recepcion: 2 de julio de 2013
Fecha de aceptacion: 16 de marzo de 2014
Annexure
Table 1: Dimension wise deprivation of Punjab province
Urban Punjab
Dimension k = 3 k = 4 k = 5 k = 6
Electricity 0.017 0.025 0.033 0.066
Water 0.010 0.008 0.009 0.013
Sanitation 0.038 0.054 0.076 0.113
Asset 0.097 0.131 0.149 0.140
Housing 0.228 0.193 0.165 0.149
Education 0.081 0.105 0.123 0.131
Expenditure 0.068 0.0950 0.1031 0.104
Empowerment 0.220 0.179 0.159 0.129
Land 0.23 0.206 0.181 0.147
Rural Punjab
Dimension k = 3 k = 4 k = 5 k = 6
Electricity 0.046 0.053 0.064 0.080
Water 0.015 0.014 0.011 0.011
Sanitation 0.144 0.151 0.151 0.143
Asset 0.143 0.150 0.149 0.144
Housing 0.145 0.140 0.136 0.134
Education 0.092 0.100 0.108 0.117
Expenditure 0.060 0.068 0.077 0.089
Empowerment 0.160 0.146 0.139 0.130
Land 0.190 0.174 0.159 0.147
Table 2: Dimension wise deprivation of Sindh province
Urban Sindh
Dimension k = 3 k = 4 k = 5 k = 6
Electricity 0.015 0.023 0.037 0.059
Water 0.031 0.034 0.033 0.028
Sanitation 0.038 0.061 0.090 0.121
Asset 0.085 0.124 0.141 0.139
Housing 0.231 0.192 0.165 0.139
Education 0.069 0.097 0.113 0.125
Expenditure 0.042 0.065 0.075 0.087
Empowerment 0.249 0.201 0.171 0.149
Land 0.235 0.200 0.172 0.151
Rural Sindh
Dimension k = 3 II k = 5 k = 6
Electricity 0.062 0.066 0.074 0.085
Water 0.032 0.034 0.037 0.044
Sanitation 0.154 0.155 0.151 0.144
Asset 0.133 0.140 0.145 0.142
Housing 0.143 0.137 0.131 0.127
Education 0.072 0.076 0.082 0.092
Expenditure 0.061 0.065 0.070 0.078
Empowerment 0.169 0.160 0.151 0.140
Land 0.169 0.164 0.155 0.144
Table 3: Dimension wise deprivation of NWFP province
Urban NWFP
Dimension k = 3 k = 4 k = 5 k = 6
Electricity 0.005 0.007 0.010 0.016
Water 0.041 0.047 0.046 0.061
Sanitation 0.075 0.084 0.095 0.119
Asset 0.108 0.132 0.138 0.128
Housing 0.195 0.169 0.155 0.136
Education 0.083 0.097 0.110 0.121
Expenditure 0.083 0.099 0.112 0.114
Empowerment 0.218 0.189 0.170 0.152
Land 0.188 0.172 0.159 0.149
Rural NWFP
Dimension k = 3 k = 4 k = 5 k = 6
Electricity 0.020 0.022 0.028 0.035
Water 0.079 0.084 0.089 0.098
Sanitation 0.140 0.145 0.144 0.140
Asset 0.139 0.143 0.142 0.137
Housing 0.147 0.140 0.134 0.129
Education 0.072 0.077 0.083 0.092
Expenditure 0.062 0.066 0.073 0.081
Empowerment 0.182 0.166 0.155 0.143
Land 0.156 0.153 0.148 0.141
Table 4: Dimension wise deprivation
of Balochistan province
Urban Balochistan
Dimension k = 3 k = 4 k = 5 k = 6
Electrici 0.013 0.014 0.015 0.024
Water 0.038 0.047 0.057 0.041
Sanitation 0.134 0.142 0.141 0.133
Asset 0.087 0.115 0.135 0.145
Housin 0.166 0.148 0.136 0.137
Education 0.057 0.063 0.082 0.105
Expenditure 0.072 0.090 0.096 0.115
Empowerment 0.224 0.186 0.161 0.146
Land 0.205 0.191 0.173 0.150
Rural Balochistan
Dimension k = 3 k = 4 k = 5 k = 6
Electrici 0.100 0.103 0.107 0.112
Water 0.095 0.096 0.098 0.101
Sanitation 0.145 0.142 0.136 0.130
Asset 0.112 0.114 0.119 0.123
Housin 0.110 0.110 0.109 0.106
Education 0.090 0.093 0.097 0.102
Expenditure 0.061 0.062 0.065 0.068
Empowerment 0.141 0.136 0.129 0.123
Land 0.142 0.139 0.135 0.130
Table 5: List of assets
S. No. Assets
01 Refrigerator
02 Freezer
03 Air conditioner
04 Air cooler
05 Geyser
06 Washing machine
07 Camera movie
08 Cooking range
09 Car/vehicle
10 Motorcycle
11 tv
12 vcr
13 Vacuum cleaner
14 pc
Table 6: List of property items
S. No. Property
01 Agriculture land
02 Non-agriculture land
03 Residential building
04 Commercial building
References
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Awan, M. S., M. Waqas & M. A. Aslam (2011). "Multidimensional Poverty in Pakistan: Case of Punjab". Journal of Economics and Behavioral Studies, 2(8), 133-144.
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Diaz, G. (2003). "Multidimensional Poverty". The Wider Conference on Inequality, Poverty and Human Well-Being. Helsinki.
Federal Bureau of Statistics (FBS) (2006). Pakistan Social and Living standard Measurement Survey 2005-06. Islamabad: government of Pakistan.
Government of Pakistan (2008). "Poverty Reduction Strategy Paper II". Available in: http://www.finance.gov.pk
Sahn, D. E. & D. Stifel (2000). "Exploring Alternative Measures of Welfare in the Absence of Expenditure Data". Review of Income and Wealth, 49, 463-489.
Sen, A. K. (1976). "Poverty: An Ordinal Approach to Measurement". Econometrica, 44, 219-231.
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Masood Sarwar Awan (1)
Muhammad Waqas (2)
Muhammad Amir Aslam (3)
(1) Masood Sarwar Awan. Degree: Ph. D. Economics. Specialization: Development Economics. Nationality: Pakistani. Department of Economics, University of Sargodha, Pakistan. e-mail: awan811@hotmail.com
(2) Muhammad Waqas. Degree: M. Phil. Economics. Specialization: Development Economics. Nationality: Pakistani. Department of Economics, University of Lahore, Sargodha Campus, Pakistan. e-mail: economist147@hotmail.com.
(3) Muhammad Amir Aslam. Degree: M. A. Social Work. Specialization: Social Policy. Nationality: Pakistani. Department of Social Work, University of Sargodha, Pakistan. e-mail: amir_aslam22@yahoo.com.
Note: We are great full to Sabina Alkire, Director of Oxford Poverty & Human Development Initiative (ophi), for her guidance, comments and helpful suggestions. The author was the Visiting Fellow at ophi, Queen Elizabeth House (qeh), Department of International Development, 3 Mansfield Road, Oxford ox41sd; e-mail: masood.awan@qeh.ox.ac.uk Correspondence to: Masood Sarwar Awan, Associate Professor of Economics, University of Sargodha, Pakistan; e-mail: awan811@hotmail.com
Table 1: Different dimensions along with
questions (Over all Pakistan) (1)
Dimension Questions in pslm Poverty line cut-off
(The household is
deprived if)
Housing How many rooms does Three or more than
your household occupy? three persons are
living in one room
Water What is the source of There is no access of
drinking water for the clean drinking water,
household? i.e. piped water, hand
pump, motorized
pumping/tube well,
closed well
Sanitation What type of toilet is Uses dry raised
used by your household? latrine, dry pit
latrine, no toilet in
the household
Electricity Does your household If no access to
have electricity electricity
connection?
Asset Were/Are any of the If does not own any
following items owned of the following
by the household assets: refrigerator,
(List is in appendix)? freezer, air
conditioner, geyser,
washing machine,
camera movie,
car/vehicle,
motorcycle, TV, VCR,
vacuum cleaner, PC
Education What was the highest Maximum year of
class completed/What education completed by
class are ... Currently any member is less
attending? than five years
Land Did any of the If value of property
household members own is less than
or had owned during RS: 300,000
the last one year any
of the following
property (List is
in appendix)?
Expenditure (1) Expenditure of Household per adult
household on equivalent expenditure
non-durables and food < RS: 944.47 per month
items Pakistan's national
poverty line
Empowerment Who in your household If women is not
usually make decision consulted in basic
about the purchase of decision about
the following purchase of some basic
consumption items? consumption item
Food, clothing,
medical treatment,
recreation and travel
(1) A household is considered as expenditure deprived
if per adult equivalent household expenditure of this
household is less than the poverty line of RS: 944.47
per month given by the government of Pakistan, according
to the Economic Survey of Pakistan 2008.
Table 2: Multidimensional Headcount Ratio
(H), Adjusted Headcount Ratio ([M.sup.0]),
and average deprivation (A) in rural and
urban areas of Pakistan at different
K values
K = 3
Province H [M.sup.0] A
Punjab [U] 0.2912 0.1221 0.4192
Punjab [R] 0.7094 0.3760 0.5301
Punjab [O] 0.5763 0.2952 0.5122
Sindh [U] 0.3808 0.1613 0.4236
Sindh [R] 0.9196 0.5649 0.6142
Sindh [O] 0.6332 0.3504 0.5533
NWFP [U] 0.4355 0.2050 0.4707
NWFP [R] 0.7129 0.3932 0.5516
NWFP [O] 0.6673 0.3623 0.5429
Baloch [U] 0.6469 0.2917 0.4509
Baloch [R] 0.9616 0.6974 0.7253
Baloch [O] 0.8950 0.6117 0.6834
K = 4
Province H [M.sup.0] A
Punjab [U] 0.1399 0.0716 0.5121
Punjab [R] 0.5352 0.3179 0.5941
Punjab [O] 0.4093 0.2395 0.5852
Sindh [U] 0.1788 0.0940 0.5255
Sindh [R] 0.8059 0.5270 0.6539
Sindh [O] 0.4726 0.2968 0.6281
NWFP [U] 0.2660 0.1485 0.5583
NWFP [R] 0.5579 0.3416 0.6122
NWFP [O] 0.5099 0.3098 0.6076
Baloch [U] 0.3786 0.2022 0.5343
Baloch [R] 0.9019 0.6776 0.7512
Baloch [O] 0.7913 0.5771 0.7293
K = 5
Province H [M.sup.0] A
Punjab [U] 0.0584 0.0354 0.6064
Punjab [R] 0.3654 0.2425 0.6636
Punjab [O] 0.2677 0.1766 0.6597
Sindh [U] 0.0791 0.0496 0.6278
Sindh [R] 0.6583 0.4614 0.7008
Sindh [O] 0.3505 0.2425 0.6921
NWFP [U] 0.1568 0.1000 0.6376
NWFP [R] 0.4071 0.2746 0.6744
NWFP [O] 0.3659 0.2458 0.6718
Baloch [U] 0.2036 0.1245 0.6115
Baloch [R] 0.7878 0.6268 0.7957
Baloch [O] 0.6643 0.5206 0.7838
K = 6
Province H [M.sup.0] A
Punjab [U] 0.0169 0.0124 0.7313
Punjab [R] 0.2164 0.1597 0.7380
Punjab [O] 0.1529 0.1128 0.7378
Sindh [U] 0.0340 0.0246 0.7234
Sindh [R] 0.4727 0.3582 0.7579
Sindh [O] 0.2395 0.1809 0.7553
NWFP [U] 0.0788 0.0566 0.7187
NWFP [R] 0.2550 0.1900 0.7453
NWFP [O] 0.2260 0.1681 0.7438
Baloch [U] 0.0739 0.0525 0.7096
Baloch [R] 0.6688 0.5607 0.8384
Baloch [O] 0.5430 0.4533 0.8347
Table 3: Percentage of poor in different
dimensions in different provinces
Punjab Sindh
Dimension Frequency Percent Frequency Percent
0 517 7.7 73 1.9
1 1241 18.6 491 13.0
2 1367 20.5 574 15.2
3 1117 16.7 590 15.6
4 873 13.1 520 13.8
5 690 10.3 483 12.8
6 450 6.7 477 12.6
7 297 4.4 354 9.4
8 123 1.8 177 4.7
9 7 .1 33 .9
Total 6682 100.0 3772 100.0
nwfp Balochistan
Dimension Frequency Percent Frequency Percent
0 101 3.4 10 .5
1 472 16.0 107 5.2
2 529 17.9 234 11.4
3 463 15.7 301 14.7
4 426 14.4 311 15.2
5 396 13.4 283 13.8
6 278 9.4 245 12.0
7 204 6.9 249 12.2
8 62 2.1 212 10.4
9 19 .6 96 4.7
Total 2950 100.0 2048 100.0
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| Title Annotation: | Multidisciplinario de Ciencias Sociales; articulo en ingles |
|---|---|
| Author: | Sarwar Awan, Masood; Waqas, Muhammad; Amir Aslam, Muhammad |
| Publication: | Noesis. Revista de Ciencias Sociales Y Humanidades. |
| Date: | Jul 1, 2015 |
| Words: | 5672 |
| Previous Article: | Cooperacion territorial internacional y desarrollo local: el caso de Canelones (Uruguay) con los territorios espanoles. |
| Next Article: | Pensamiento estrategico emergente en la construccion de la realidad sustentable, Sector Cacao; Estado Sucre, Venezuela. |
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