Dimensions of well-being and the millennium development goals.

Haq, Rashida ; Zia, Uzma

Well-being and happiness, individually and collectively, is a main indicator of a good life. This paper attempts to implement empirically some of the multidimensional concepts of human well-being by using data from the 'Pakistan Living Standards Measurement Survey' 2006-07. Objective well-being index and subjective well-being index are constructed to study regional disparities in the quality of life. The results reveal that most of the top ranked districts are located in the province of Punjab which tends to indicate that Punjab is ahead of other provinces in terms of objective well-being. Sindh and NWFP districts are dominated in the category of lower medium well-being category. At the lower end of the distribution districts of Balochistan emerged in lowest category of well-being. It is observed that Punjab have highest share of population in top category of well-being while population of Balochistan gets major share in lowest category of well-being. It is important to note that those districts which have higher achievements in hard facts of well-being, acquire less subjective well-being in term of satisfaction. Districts of Balochistan, with least developed indicators, perception about the quality of life is evident in their lowest level of satisfaction. Since the underlying premise of the MDGs is still the concept of human development, so priorities is needed to concentrate on least developed districts for achieving the MDGs by 2015.

JEL classification: I31, I32, D19, D78

Keywords: Well-being, Objective, Subjective, Measurement, Quality of Life

1. INTRODUCTION

The concept of well-being has deep roots in philosophy [Cantril (1965)]. Much later in the 19th century modern definitions of well-being emerged. The utilitarian movement defined well-being subjectively and proclaimed individuals' well-being as an important goal of individuals' behaviour and public policy. During the 20th century social scientists started to examine well-being empirically, but a unified concept of well-being was lacking. At the beginning of the 20th century, economists developed elaborate quantitative theories of well-being, but rejected the possibility that individuals' could provide valid reports of their own well-being. In the second half of the 20th century social scientists started to develop subjective measures of well-being, and started to examine how these measures relate to demographic variables or other characteristics of individuals [Andrews and Withey (1976)].

The relationship between GDP and well-being likely depends on how rich a country is. As income increases it contributes little to overall well-being at low levels of GDP in poor country, since only a narrow segment of the population is benefiting directly. Moreover, as noted by Sen (2001) non-monetary benefits such as health and education that improve individual capabilities are often more important than income in poor countries. As the benefits of continued growth trickle down to a burgeoning middle class, social well-being rises dramatically [Torras (2008)]. It is in this context that a number of alternatives to GDP have been introduced. For example, the United Nations Development Programme's (UNDP) human development index (HDI) uses GDP per capita to measure "access to economic resources" in well-being assessments but accords it only one-third weight in determination of the level of human development. Although national income accounting measures may sometimes not agree with popular perceptions of trends in economic well-being, GDP per capita is one of the three main components of the HDI, whose objective is to indicate the capability of people "to lead a long and healthy life, to acquire knowledge and to have access to resources needed for a decent standard of living" [Osberg and Andrew (2005)]. A second approach, multi-criteria analysis, is the Human Well-being Index which measures more realistically socioeconomic conditions than narrowly monetary indicators such as the GDP and covers more aspects of human well-being than HDI. 'Human Well-being is a condition in which all members of society are able to determine and meet their needs and have a large range of choices and opportunities to fulfil their potential' that generates a more comprehensive picture of the state of the world. It is the average of indices of health and population, wealth, knowledge, community and equity [Prescott-Allen (2003)].

The principal thrust of human well-being has been to supplement traditional economic indices of well-being with alternative indicators that capture non-economic or non-material dimensions of human life. In particular, it is now commonly accepted that human well-being should be treated as a multidimensional concept along the lines advocated by Sen (1993). He emphasised on promotion of human well-being and development by adding another dimension of well-being research. He argued that quality of life do not depend merely on opportunities and is determined by human capabilities as well. Classifying various well-being definitions, distinction between objective and subjective definitions of well-being is important which is based on the selection process of the criteria that are used to judge individuals' well-being. Objective definitions assume that the criteria can be defined without reference to the individual's own preferences, interests, ideals, values, and attitudes. The objective indicators of well-being are only proxies; these are indirect measures of true conditions that researchers try to evaluate. It is assumed that the objective circumstances influence satisfaction within specific life domain [Sumner (1996)]. Objective measurement is based on explicit criteria and performed by external observer. Subjective definitions require that individual preferences, interests, ideals, values, and attitudes matter. Well-being indicators can also be subjective which is based on people's perceptions of their happiness and satisfaction with living standards. These indicators are survey based and directly enquire individuals about their satisfaction with life [Hasan (2008)]. Subjective measurement involves self reports based on implicit criteria.

In response to the changing global conditions, new research priorities and improved data resources, social science research on living standards, human well-being and quality of life has altered. In this scenario all United Nations Member States in 2000, adopted the eight Millennium Development Goals (MDGs) as a framework for the development activities of over 190 countries in ten regions; they have been articulated into over 20 targets and over 60 indicators, towards the target date--2015--by which the MGDs are to be achieved. Pakistan has adopted 16. targets and 37 indicators for monitoring the MDGs. Since then the Millennium Development Goals have become a universal framework for development and a means for developing countries and their development partners to work together in pursuit of a shared future for all. The underlying premise of the MDGs is still the concept of human development. It is noted that the MDGs concentrate on the non-monetary variables which are not measured in terms of monetary units; rather the goals focus on the distribution of capabilities-education, health, nutrition, gender relations, and physical environment. They are characterised as qualitative variables or in terms of quantity [United Nation (2002)].

This paper proposes a conception of dimensions of human well-being: objective well-being by concentrating on MDGs, i.e., education, health and environmental sustainability to determine the extent of variation among districts of Pakistan in the level of well-being. It also focuses on softer issues of subjective well-being, i.e., satisfaction with facilities/services used, education, health and security. It also elaborates a basic configuration of objective and subjective well-being across districts of Pakistan.

The paper is divided into five main sections and an appendix. Section 2 gives literature review. Section 3 examines data and methodology. Section 4 presents analyses. Finally Section 5 concludes.

2. LITERATURE REVIEW

The notion of well-being is receiving growing attention, both in academic research and policy-oriented analysis, especially in the context of MDGs. There is expanding literature that provides various measures of well-being which are discussed here.

Schimmack (2008) defined well-being as preference realisation which can be measured with affective and cognitive measures. The paper examined similarities and differences between cognitive measures of well-being and four items (happy, sad, angry, and afraid) as an affective measure of well-being.

Prescott-Allen (2003) prepared a common framework of dimensions consisting of (a) human dimensions, including health and population, national and household wealth, education and culture, community and social capital, and equity; and (b) ecosystem dimensions, including land and forests, water quality and diversity, air quality, species and genetic diversity, and energy and resources use.

Sumner (1996) provided distinction between objective and subjective definitions of well-being. The distinction is based on the selection process of the criteria that are used to judge individuals' well-being. Objective definitions assume that the criteria can be defined without reference to the individual's own preferences, interests, ideals, values, and attitudes while subjective definitions require that individuals' preferences, interests, ideals, values, and attitudes matter.

Hasan (2008) explored the concepts of city ranking as a way to measure the dynamics and complexities of urban quality of life. These ranking had various dimensions and uses. Both the context in which these rankings were organised and their nature had changed considerably over time.

Akhtar and Sarwer (2007) employed two different techniques-Z sum and weighted factor scores and 12 indicators to quantify the intertemporally compared levels of development in the districts of Pakistan. The study highlighted that provincial capital, i.e., Karachi, Lahore and Quetta consistently appear in the top ten ranking under both techniques in 1998 and 2005. In regressive districts, 5 belonged to Balochistan, 3 from Punjab and two districts were found from Sindh province.

Jamal and Amir (2007) highlighted changes in human development status in districts of Pakistan during the period 1998 and 2005. The estimates of a district level Human Development Indices provide an indication of existing trends in regional disparities in terms of economic development as well as education and health status.

Uddin (2007) reviewed social development in Pakistan with focus on the issues of access to and quality of social services and identified areas that should receive greater attention to enhance the public access to quality social services. It was observed that the demand for social services is expanding rapidly, mainly owing to high population growth and rapid urbanisation.

Siddiqui (2006) tested whether direct provision of social services improve capabilities by estimating a basic need model for Pakistan. She viewed that government provision of social services affects human capabilities significantly. She analysed that aggregate statistics at the national or provincial level hides region specific reason of poverty and inequalities. The variations in these indicators across the district within a province and across the provinces are an indicative of regional disparities in terms of income, health, education and the quality of life.

UNDP (2003) estimated that variation in Human Development Indices between provinces and districts are indicative of regional disparities in both the level of economic growth as well as in terms of health, education and quality of life.

Midhet (2004) derived development ranking by applying composite indices of several district-level variables derived from factor analysis, which are then used to predict two important indicators of reproductive health; the child-woman ratio(CWR) and maternal mortality rate (MMR).This study was designed to facilitate selection of districts for implementing operations research in safe motherhood. It is indicated that MMR decreased with accessibility of hospitals and primary health facilities. The study also identified which districts are developing satisfactorily and which are stagnant or deterioration in terms of development.

Pasha and Naeem (1999) examined whether the low level of social indicators in the country is a consequence of poor initial conditions or has there been deterioration due to relatively low rate of improvement over time? The study concluded that Pakistan is a case of a country which not only started with low level of human endowment but the situation has been exacerbated by the low level of improvement in it over time.

Ghaus, et al. (1996) explored regional variation in the development of social infrastructure across districts of Pakistan. The study demonstrated the importance of education indicators in determining the overall level of social development in terms of female literacy and enrolment rates. However the analysis indicated substantial variation among districts within a province in the level of social development. Least developed districts within each province are identified as targets for special development.

Pasha, et al. (1990) demonstrated that there are marked changes in the development ranking of a number of districts from the early 1970's to the early 1980's, especially among districts at the intermediate level of development. The indicators were selected from diverse sectors like industry, agriculture, transport and communications with basic social indicators including education, health, gender equality and housing. Districts of Punjab have generally improved their ranking in the education sector, gender equality and labour force indicators while province of Balochistan continued to fall behind the rest of the country.

Jamal and Salman (1988) concluded that despite the regional development policies pursued in the province of Sindh during the 70s little success has been achieved in narrowing regional disparities among districts. It is indicated that there is need for a fundamental re-evaluation of nature, scope and content of these policies.

Pasha and Tariq (1982) indicated that districts development rankings hide major intraprovincial disparities. The analysis demonstrates that all the provincial capitals and federal capital are included in top quartile of the national population. Provinces that are considered relatively underdeveloped like Balochistan and NWFP to have some highly developed pockets while a significant part of Punjab and Sindh appeared to be relatively underdeveloped.

The above studies discussed various measures of well-being and districts level social development in Pakistan. It is concluded that there is substantial variation among districts within a province in the level of social development and districts of Balochistan are identified as least developed in terms of quality of life.

3. DATA AND METHODOLOGY

3.1. Data

The study employs the 'Pakistan Social and Living Standards Measurement Survey' (PSLM) 2006-07 data which consists of Core Welfare Indicators Questionnaire (CWIQ) approach. It is one of the main mechanisms for monitoring the implementation of the MDGs and Poverty Reduction Strategy Paper (PRSP). It provides a set of representative, population-based estimates of social indicators and their progress under MDGs and PRSP. An important objective of the PSLM Survey is to try to establish what is the distributional impact of different government programs carried out in social sector. Policymakers need to know, for example, whether the poor have benefited from the programme or whether increased government expenditure on the social sectors has been captured by the better off. PSLM Survey consists of data relating education, child health, maternal health, household assets/amenities. It also provides subjective data relating to perception of economic situation of the households and communities where they live and satisfaction of services. The sample size for the four provinces has been fixed at 73953 households comprising 5198 sample villages / enumeration blocks, which is expected to produce reliable results at each district [Pakistan (2008)].

3.2. Methodological Choices Encountered in the Construction of Composite Indices of Well-being

The first choice encountered in index construction is the general form of the index: will it be a single composite, or a complementary composite. A single composite is a single aggregation of variables that are used in an index, whereas a complementary composite is comprised of two separate indices: a conglomerative index and a deprivational index. A conglomerative index measures the overall well-being of a society, in contrast, a deprivational index measures only the welfare of the worst off.

The next choice encountered is which variables to include in the index. This choice can be made by simply choosing data that an index constructor wants to include, or by first determining concepts that the developers seek to measure, such as inequality. After variables have been picked, functional forms must be chosen. The functional form is a functional transformation that is applied to the raw data in order to represent the significance of marginal changes in its level. Once functional forms associated to variables have been established, a uniform method of standardisation should be considered. One choice is to use raw data and not standardise. This choice leads to many problems when an attempt is made to aggregate variables. Standardisation methods allow standardised values to be compared meaningfully. Three techniques to standardise absolute values of variables are reviewed: Linear Scaling Technique which linearly scales variables to a uniform range, ordinal response, where experts assign a score to each variable, and Gaussian normalisation, or Z-score, in which the standardised variable is the number of standard deviations away from its mean.

The final step in forming a composite index is setting the weights within the aggregation scheme. The most widely accepted and used techniques to set explicit weights in aggregation are: expert weighting set by specialist, Principal Component Analysis and explicitly set weights by another mechanism, such as equal weighting [Salzman (2003)].

3.3. Strategies to Study Dimensions of Well-being

The multidimensional view of well-being is receiving growing attention, both in academic research and policy-oriented analysis. The multifaceted nature of well-being is implicit in the set of indicators to monitor the performance of countries. Indicators are commonly recommended as tools for assessing the attainment of development, and the current vogue is for aggregating a number of indicators together into a single index. It is claimed that such indices of development help to facilitate maximum impact in policy terms by appealing to those who may not necessarily have technical expertise in data collection, analysis and interpretation. This paper constructs indices of well-being by focusing on the (UNDP) Human Development Index (HDI). While the HDI offers a composite index that summarises basic choices available to people, it has been criticised on many grounds. For example, it is argued that it does not capture the totality of issues that affect human well-being. Hence, this study is being made to widen the scope of issues covered by the index. The study examines the non-income dimensions of objective well-being that contribute to quality of life, i.e., education, child health, maternal health and housing facilities that affect human well-being while their absence will constitute some form of deprivation. Subjective well-being index is also developed to measure individuals' preferences, interests, ideas, values, and attitudes towards the satisfaction of facilities available, i.e. education, health and security. After selecting the variables 'Linear Scaling Technique' which linearly scales variables to a uniform range is applied before aggregating. However, for ease of comparison, this index is standardised to a scale of 0 to 1.

(a) Linear Scaling Technique (LST)

Let [X.sub.1], [X.sub.2] ..., [X.sub.n] be the indicators. The indicators are standardised to maintain uniformity. Each of the [X.sub.i]'s are observed for each district.

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (1)

X[min.sub.ij] = Minimum value of ith indicaor in jth district

[X.sub.ij] = Value of ith indicator in jth district

X[max.sub.ij] = Maximum value of ith indicaor in jth district

3.4. Dimensions of Objective Well-being Index (OWBI)

Dimensions of well-being are non-hierarchical, irreducible, incommensurable and hence basic kinds of human ends. Objective well-being assumes that the criteria can be defined without reference to the individual's own preferences, interests, ideas, values, and attitudes. Its indicators are based on attributes that can be measured, for example maternal mortality rate, poverty rates and adult literacy rate, etc. In this study three basic components education, health and living conditions with sub components are taken to rank districts on the basis of objective well-being followed by [Akhtar and Sarwer (2007)]. It is assumed that the selected objective indicators of well-being are only proxies, i.e., they are indirect measures of true conditions of well-being that also influence satisfaction with specific life domain. In this study a non monetary well-being index is preferred to explain the group of variables with equal weights for each of its domain.

The formula for the overall index comprises of three main components (education, health and living conditions) each affecting, in one way or another, a human being's life by way of his / her success to 'means' or desires 'ends'. Let [X.sub.1], [X.sub.2] ..., [X.sub.n] be the indicators. The indicators are standardised by 'Linear Scaling Technique' to maintain uniformity. Each of the [X.sub.i]'s are observed for each district.

The three main components of OWBI with equal weights (1) are:

[OWBI.sub.j] = 1/3 [([EDI.sub.ij]) + ([HI.sub.ij]) + ([LCI.sub.ij])] x 100 ... (2)

ith indicator in jth district

Where,

[OWBI.sub.j] = Objective well-being index in jth districts

j = 1,2,3 ..., 100

[[EDI.sub.ij]] = Education index [[HI.sub.ij]] = Health index [[LCI.sub.ij]] = Living conditions index.

[[EDI.sub.ij]] = 1/3 [[LRI.sub.j]] + 1/3[[NPEI.sub.j]] + 1/3[[GEI.sub.j]] ... (3)

[[LRI.sub.j]] = Literacy rate index, [[NPEI.sub.j]] = Net primary enrolment rate index,

[[GEI.sub.j]]=Gender equality in education at primary level or higher.

[[HI.sub.ij]] = 1/2 [[CHI.sub.j]] + 1/2[[MHI.sub.j]] ... (4)

[[CHI.sub.j]] = 1/2 [[IRI.sub.j]] ... (5)

[[IRI.sub.j]] = Immunisation rate index

[[MHI.sub.j]]=1/4[[PCI.sub.j]] + 1/4[[SDI.sub.j]] + 1/4[[PDI.sub.j]] + 1/4[[PNI.sub.j]] ... (6)

[[MHI.sub.j]]=Maternal health index

[[PCI.sub.j]] = Prenatal care index, [[SDI.sub.j]] = Safe delivery index.

[[PDI.sub.j]] = Place of delivery index, [[PNI.sub.j]] = Post natal care index

[[LCI.sub.ij]] = 1/4 [[DWI.sub.j]] + 1/4[[SF.sub.j]] + 1/4 [[SFI.sub.j]] + 1/4 [[SFI.sub.j] ... (7)

[[DWI.sub.j]] = Source of drinking water index, [[SFI.sub.j]]=Sanitation facilities index

[[SFI.sub.j]]=Source of lighting index, [[SFI.sub.j]]=Source of fuel for cooking index.

A summary of objective well-being indicators are given in Table 1 with values of minimum, maximum, mean, coefficient variation and MDGs targets. The variation in these indicators of well-being across the districts of Pakistan is an indicative of regional disparities in the quality of life.

3.5. Choice of Indicators

To measure objective well-being three goals of MDGs are taken, i.e, education, health and environmental sustainability.

(i) Education

Goal 2: Universal Primary Education.

Goal 3: Promote Gender Equality and Empower Women.

MDGs Goal 2 aims at ensuring that by 2015 children everywhere, boys and girls alike would be able to complete a full course of primary schooling. This target is assessed in Pakistan by the trends in gross and net enrolments, the proportion of students who completed their studies from grade one to grade five and adult literacy rates. In this-study two indicators are taken to analyse universal primary education; literacy, net enrolment at primary level. Literacy is taken as the ability to read a newspaper and to write a simple letter. Population aged 10 years and older that is literate expressed as a percentage of the population age 10 years and older. Net enrolment rate at primary level is taken as [number of children age 5-9 years attending primary level (classes 1-5) divided by number of children aged 5-9 years] multiplied by 100; enrolment in katchi is excluded.

MDGs goal 3 aims to eliminate gender disparity in primary and secondary preferably by 2005 and to all levels of education no latter than 2015. To measure progress in this goal the study takes the ratio of girls to boys in completed primary level or higher: number of girls per 100 boys [United Nation (2002)].

(ii) Health

Goal 4: Reduced Child Mortality

This goal targets a reduction in child mortality by two third between 1990 and 2015 (reduction in infant mortality rate to 52 and child mortality rate to 77). Progress in this goal is measured through an indicator: proportion of fully immunised children 12-23 months old. The Pakistan Expanded Programme on Immunisation (EPI) follows the international guidelines recommended by the World Health Organisation (WHO). The guidelines recommended for all children a BCG vaccination against tuberculosis; three doses of DPT vaccine for the prevention of diphtheria, pertussis (whooping cough) and tetanus; three doses of polio vaccine and a vaccination against measles during the first year of the child's life. Progress in child health is measured through recall and record of full immunisation course which means that the children age 12-23 months had received: BCG, DPT1, 2, 3, Poliol, 2, 3 and measles [United Nation (2002)].

Goal 5: Improve Maternal Health

This goal aims to reduce maternal mortality rate by three quarters between the 1990-2015 periods that is 140 per 100,000 lives births. Efforts to reduce maternal mortality need to be tailored to local conditions, since the causes of death vary across developing regions and countries. The over all maternal mortality ratio is at 276 maternal deaths per 100,000 live births and approximately 1 in 89 women in Pakistan will die of maternal causes during her life time taken as lifetime risk [NIPS (2008)]. The success of this goal is measured through these indicators; prenatal consultation measured as woman received at least one Tetanus Toxoid injection, safe delivery is taken as health personals that assisted in delivery (doctor, nurse, midwives), location of delivery is considered as child birth taken place at government or private health units and post natal consultations is measured as received medical check up within six weeks of delivery for women aged 15-49 years who had a birth in the last three years.

(iii) Living Conditions

Goal 7: Ensure Environmental Sustainability

A household's access to civic amenities is determined not only by its location but also by its economic circumstances. Thus access to such services can vary across households from different districts because no district provides universal coverage. In Pakistan for the measurement of environmental sustainability four indicators are adopted; proportion of population with sustainable access to an improved water source (tap water, motor pump and hand pump) and proportion of people with access to improved sanitation ('flush' consists of flush connected to public sewerage/septic tank / open drain) which are included in MDGs indicators [United Nation (2002)]. Two more indicators are also taken to ensure environmental sustainability, i.e. source of lighting measured as percentage of households have electricity connections and percentage of households using gas or kerosene oil as fuel used for cooking.

3.6. Dimensions of Subjective Well-being Index (SWBI)

By dimension mean "any of the component aspects of a particular situation". The key features of dimensions of subjective well-being are based on people's perceptions of their quality of life and satisfaction with living conditions. These indicators are survey based and directly enquire individuals about their satisfaction with the services/facilities available to them. Subjective measurement involves self reports based on implicit criteria.

Subjective Indicators

To estimate human well-being objective indicators be supplemented by subjective ones, as proposed by [Veenhoven (2007) and Hasan (2008)] since both capture different dimensions of well-being. The formula for the overall index of subjective well-being is as follows:

[SWBI]j = {1/3 [[EDI].sub.j] + 1/3[[HI].sub.j] + 1/3 [[SI].sub.j]} x 100 ... (8)

where,

[[EDI].sub.j] = Education index, [[HI].sub.j] = Health index, [[SI].sub.j] = Security index.

To measure subjective well-being, indicators are taken which are based on use and satisfaction with the facilities, expressed as percentage of those households who used these services. (2) This type of information has been collected for the first time in FBS household surveys. Since government is spending lot to improve the economic situation of people and also investing considerable amount in providing different types of facilities and services. Considering as how facilities / services are being passed on to the general public, the respondents are asked to give their perception in their economic as well as community improvement and how effectively services are available to them. To measure subjective well-being education, health and security measured by police services, households are asked to give opinion about their satisfaction of the facilities/services provided by the government.

3.7. Standard Scores for Categorisation of Well-being Index (WBI)

It indicates where the score lies in comparison to mean i.e. if the mean of index is [X.sub.w], then the score can be compared to see if it is above or below this average. Standard deviation (SD) around the mean (both side plus and minus) is taken to categorisation of the distribution of well-being index; where, w = 1, 2 (objective index and subjective index, simultaneously). Following [Li, et al. (1998) and Cummins (2000)], the six categories are classified as:

1. Highest well-being ([X.sub.w] + 1.0 standard deviation) < WBI [less than or equal to] 100

2. High well-being ([X.sub.w] + 0.5 st. deviation) < WBI [less than or equal to] ([X.sub.w] + 1.0 st. deviation)

3. Upper medium well-being ([X.sub.w]) < WBI [less than or equal to] ([X.sub.w] + 0.5 st.deviation)

4. Lower medium well-being ([X.sub.w]-0.5) < WBI [less than or equal to] ([X.sub.w])

5. Low well-being ([X.sub.w] - 1.0 st. deviation) < WBI [less than or equal to] ([X.sub.w] -0.5)

6. Lowest well-being 0 < WBI [less than or equal to] ([X.sub.w] - 1.0 st. deviation)

3.8. The Z Score

This technique is also used to observe the sensitiveness of the results with respect to the choice of technique for deriving the composite indicators. The [Z.sub.sum] is the standardised score, which has zero mean and unit variance. The higher the [Z.sub.-sum] the more developed the district.

4. ANALYSIS

Classifying the districts in terms of categories of objective index value, i.e., highest, higher, upper medium, lower medium, low and lowest provides a useful basis for the analysis. For ease of comparison, absolute values of variables are standardised to a scale of 0 to 1 by using Linear Scaling Technique (LST) which linearly scales variables to a uniform range. It also assigns the lowest implicit weights to variables and deals with the directionality issue and provides a consistent way to aggregate variables. The composite index value gives the achievement in the level of well-being; the higher the value of index the more the level of well-being. The findings of this analysis indicate that average index value of 100 districts is 49.02 percent whereas average achievement is 74.9 percent for 17 districts in highest category while the average value of the lowest well-being index is 21.75 percent. Table 3.a gives information regarding the ranking of districts in term of highest and high well-being. Karachi, Rawalpindi and Lahore etc, are ranked in highest category among 17 districts with average 74.9 percent achievements in its dimensions with overall 37.37 percent share in population (Table 4). Second category is high well-being which includes 14 districts with overall population share is 16.48 percent. Multan, Sahiwal and Nowshera are ranked top approximately with average achievement of 63.65 percent. It is important to note that three out of four provincial capitals, i.e., Karachi, Lahore and Quetta are ranked in highest category while Peshawar comes at 29 in district ranking of well-being. The dominance of Punjab is observed in highest well-being category where thirteen out of seventeen districts belong to this province, like Rawalpindi, Lahore, Gujrat, Gujranwala, Sialkot, Jehlum, Toba Tek Singh, Faisalabad etc. In second category of high well-being only districts of Punjab and NWFP are emerged. This tends to indicate that Punjab is ahead of the other provinces in terms of objective indicators. The relatively high enrolment rates at primary level along with access to maternal health care services are the prime reason for the relatively high ranking of districts in this province [Pakistan (2008)]. Ghaus, et al. (1996) ranked districts in terms of social development using Z_sum and weighted factor scores also come to same conclusion as in the present analysis.

Table 3b classifies districts with upper medium and lower medium level of well-being. The upper medium category has 19 districts with average achievement of 54.51 percent with population share of 22.9 percent. Khanewal, Nowshero Feroz and Mardan are ranked top in this classification. Districts of Punjab again dominates this category where ten out of 19 districts are from this province, Sindh and NWFP have 3 and 5 districts respectively while only one district is from Balochistan. One can draw the conclusion that if a district starts with an advantage in human endowment, it is easier to maintain its relative position [Pasha and Naeem (1999)]. The fourth category of well-being is lower medium with average index value is 43.48 percent which is less than overall average value of well-being index. Sindh and NWFP districts are dominated in this category.

The last two categories which consist of 31 districts are dominated by Balochistan, with 19 districts belonging to this province followed by NWFP and Sindh as presented in Table 3c. By and large, the differences in health and educational outcomes between districts reflect the differences in access to these services. The rank ordering of districts indicates that gender disparity in education and lack of maternal health care services dominates the outcome. Analysis of the magnitude of indicators in the relatively underdeveloped districts indicates that the profile of backwardness is primarily of poor quality of civic immunities with low access to water, sanitation, electricity and gas and also with low standards of provision of health and education facilities.

The ranking exercises help in identifying the districts having the greatest need for intervention to achieve the MDGs targets. It can be used in the process of policy making and planning, decision-making regarding resource allocation and selection of districts for intervention programmes, and monitoring and evaluation at the district level.

[FIGURE 1 OMITTED]

Figure 1 plots the relative position of districts across four provinces of Pakistan where the name of districts are labeled in alternative manner. Karachi ranks at the top while Dera Bugti is placed at the lower end.

A look at Table 4 shows disparities in terms of percentage share of population in objective well-being categories across provinces. It is observed that Punjab has highest share of population in top category of well-being while population of Balochistan gets major share in lowest category.

To estimates the quality of life in Pakistan, [Veenhoven (2007) and [Hasan (2008)] recommended that objective indicators be supplemented by subjective ones, since both capture different dimensions of well-being. Subjective indicators focus on soft matters such as satisfaction with income and measures individual perceptions based on a respondent's judgment rather than that of policy-makers or researchers while objective indicators measures hard facts. The following tables rank districts of Pakistan in three categories which further splits into six classifications. To measure subjective well-being of households, indicators are taken which are based on use and satisfaction with the facilities, expressed as percentage of those households who used these services i.e., education, health and security measured by police services. It is interesting to note that ranking on the bases of subjective well-being is entirely different from objective well-being as highest districts are not appeared at the top ranked in subjective well-being index.

It is important to note here that subjective view of utility recognises that everybody has his or her own ideas about happiness and the quality of life that observed behaviour is an incomplete indicator for individual. People evaluate their level of subjective well-being with regard to circumstances and comparison to other person, past experiences and expectation of the future. Measure of subjective well-being can thus serve as proxies for 'utility' since its item are subject to the law of diminishing utility [Veenhoven (2007)].

Keeping in view of above discussion, subjective well-being in hundred districts of Pakistan is estimated. Out of which 16 districts lie in first category of highest well-being, where Swat, Vehari and Nowshero Feroz ranks at the top while in second category of high well-being Lakki Marwat, Dera Ismail Khan and Layyah comes first as presented in Table 5a, although Ghaus, et al. (1996) indicated that these districts are least developed in terms of social development related to education, health and water supply.

Tables 5b and 5c ranks other two categories of subjective well-being in districts of Pakistan. Three provincial capitals, Quetta, Karachi and Lahore which are classified in top ranking of objective well-being are now ranked in second and third category of subjective well-being. Most of the less developed districts of Balochistan invariably have not changed their position in these two well-being indices i.e., objective and subjective well-being. Here the important role of hard facts of well-being is not denied or minimised, because not only people living in developed regions score higher in the measurement of their satisfaction index but also when poor people receive even a modest increase in their facilities, their satisfaction level grows. Nevertheless, for less developed regions, the modest increase is merely a temporary phenomenon because such a nominal increase might simply fulfil their basic human needs and not their desires.

A look at Table 6 shows disparities in terms of percentage share of population in subjective well-being categories across provinces. It is observed that Sindh has highest share of population in top category of well-being while perception of Punjab population is lowest in this category. This indicates that people of Punjab are least satisfied with exiting facilities available to them in terms of education, health and security while people of Sindh are happier with services available to them. Several authors argue that subjective satisfaction is affected by comparisons between one's own situation and that of his or her peers.

Figure 2 plots index of subjective well-being where the ranking are labeled in alternative districts. District Swat ranks at the top while Qilla Safullaha is placed at the lower end.

[FIGURE 2 OMITTED]

It is argued that social policy still needs subjective indicators and those objective indicators taken alone are inadequate. It is commonly objected that matter of the mind are unstable, incomparable and unintelligible and the subjective appraisals cannot be compared between persons. One assertion is that different people use different criteria, so two persons stating they are very happy can say so for different reasons. Another claim is that people have different scales in mind, and that people who report they are 'very happy' may in fact be equally as happy as someone who characterises his life as 'fairly happy'. Likewise it is argued that subjective appraisals can not be compared across culture as notion of poverty differ greatly between rich and poor nations and within nations between upper and lower classes which means for social policy these kinds of indicators tell policy makers little about relative performance. A related objection is that the criteria used for these subjective appraisals are largely implicit. In spite of these weaknesses, subjective indicators are indispensable in social policy, both for assessing policy success and for selecting policy goals. Achieving some goals or targets of MDGs, different dimensions of well-being should be taken into account as objective measures have limited validity and reliability. Joint use of objective and subjective measures is mostly helpful to get a complete picture, while rigid restriction to objective indicators considerably narrows the perspective [Veenhoven (2007)]. Since the underlying premise of the MDGs is still the concept of human development, so main streaming of subnational or local targets into the national targets and priorities is needed to concentrate on least developed districts for achieving the MDGs by 2015. These can be achieved if immediate steps are taken to implement existing commitments. Reaching the goals for development in each district of Pakistan is vital to building better, healthier and decent lives for millions of people in the country. Least developed districts within each province are identified as targets for special development allocations with Medium Term Development Framework (MTDF).

Table 7 presents a matrix of objective well-being and subjective well-being differences as developed by Veenhoven (2002) which is constructed by taking into account the major three classification of well-being [Tables 3a, b, c and Tables 4a,b,c]. The districts which are placed at diagonal, objective and subjective well-being coincide. It is interesting to note that all the provincial capitals are placed in high objective well-being index but the perception towards satisfaction of available services is low except NWFP provincial capital, Peshawar. Most of the districts of Balochistan with least developed social indicators are in low category in respect of these two well-being indices. Information about perception and satisfactions of households is quite useful in the policy process and the degree to which long and happy life is an important criterion for final policy effectiveness of MDGs. To meet MDGs targets by 2015, Pakistan will have to achieve GDP growth rate of 7-8 percent per annum, ensure continuity and sustainability of reforms, allocate additional resources and ensure their effective use, and above all increasingly involve communities in the development process [Pakistan (2008)].

In Appendix Tables 1 to 4, findings from Z-sum technique are also presented to observe the robustness of the results with respect to the choice of technique for deriving the composite indicators. The analysis shows the validity of well-being measures by indicating convergence in both well-being measures as there are no important discrepancies in districts ranking which generalised that there are no major unobserved variations in well-being indices.

How to Explain Districts Disparities in Well-being?

The real question is how to explain districts disparities in well-being in Pakistan. In other words why is quality of life considerably lower in one area than in other areas? Some explanations in terms of socio-economic development indicators are also given as:

(1) Remittances from overseas migrants, especially from Middle East play an important role in quality of life for Pakistani people. Recent data shows that sixty percent Pakistani in the Middle East migrated from only 20 districts with heavy concentration from Karachi, Rawalpindi, Lahore, Swat, Faisalabad, Gujranwala, etc.

(2) Incidence of poverty is low in high well-being districts while it is quite high in 'low' or 'lowest' objective well-being districts. Per capita expenditure is quite high in 'good' and 'fair' rated objective well-being districts as compared to 'poor' or 'bad' rated quality of life [Cheema, et al. (2008)].

(3) The level of urbanisation is high in 'good' objective well-being district; Karachi, Lahore, Gujranwala, Faisalabad, Multan, Rawalpindi, etc.

(4) High dependency of the rural labour force on the agriculture sector in poor districts is seen.

(5) Districts which have industrial zone i.e., Karachi, Lahore, Faisalabad, Gujranwala, etc are in high well-being.

(6) Large family size, high dependency ratio in poor districts is observed in the Population Census of Pakistan, 1998.

(7) Inequality in ownership of land is observed in Pakistan; only less than half of all rural households own any agriculture land while the top 2.5 percent of all households account for over 40 percent of all land owned. Gini coefficient for land distribution is high in 'poor' or 'bad' rated objective well-being districts. [Amjad, et al. (2008)].

6. CONCLUSIONS

The concern for measuring well-being objectively and subjectively is found in modern political philosophy. This paper attempts to implement empirically some of the multidimensional concepts of human well-being. Using data from the 'Pakistan Social and Living Standards Measurement Survey' 2006-07, objective well-being index and subjective well-being index are constructed. In the objective well-being approach the focus is on measuring 'hard' facts such as living conditions while subjective well-being approach in contrast consider 'soft' matters such as satisfaction with available facilities. Non-monetary human development indicator i.e. education, health and living conditions are taken in the context of Millennium Development Goals to analyse the level of well-being across districts of Pakistan. The indices are classified in three categories, high, medium and low each with two sub categories.

The findings of the study indicate variation in the indicators of well-being across the districts of Pakistan which is an indicative of regional disparities in the quality of life. The composite index value gives the achievement in the level of well-being; the higher the value of index the more the level of well-being. Karachi, Rawalpindi, Lahore, Gujrat, Gujranwala, Sialkot, Jehlum, Chakwal, T.T.Singh and Faisalabad, etc. are ranked in highest objective well-being category among 17 districts which accounts for 37 percent share of country population. Federal and all the provincial capitals are ranked as, Islamabad, Karachi, Lahore, Quetta and Peshawar in high well-being category. It may be noted that most of the top ranked districts are located in the provinces of Punjab which tends to indicate that Punjab is ahead of other provinces in terms of objective well-being. Sindh and NWFP districts are dominated in the category of lower medium well-being. At the lower end of the distribution districts of Balochistan emerged in lowest well-being category. It is observed that Punjab have highest share of population in top category of well-being (67.8 percent) while population of Balochistan gets major share in bottom well-being category (73 percent). It is interesting to note that ranking on the bases of subjective well-being is entirely different from objective well-being as highest objective well-being districts are appeared in medium and low subjective well-being categories. It means the higher the achievements in hard facts of well-being the less satisfaction in terms of services/facilities they used. But most of the districts of Balochistan, with least developed well-being indicators, perception about the quality of life is evident. Since, subjective appraisals can not be compared across culture as concept of well-being differ greatly between rich and poor within nations between upper and lower classes which means for social policy these kinds of indicators tell policy makers little about relative performance. In spite of these weaknesses, subjective indicators are indispensable in social policy, both for assessing policy success and for selecting policy goals. However, the results indicate substantial variation among districts within a province in the level of well-being.

Since the underlying premise of the MDGs is still the concept of human development, so main streaming of sub-national or local targets into the national targets and priorities is needed to concentrate on least developed districts for achieving the MDGs by 2015. These can be achieved if immediate steps are taken to implement existing commitments. Reaching the goals for development in each district of Pakistan is not only vital for building better, healthier and decent lives for millions of people in the country. Least developed districts within each province are identified as targets for special development allocations with MTDF.

Appendix Table A.1
Z-Sum for Provincial Ranking of Well-being

 Objective Well-being

 Districts of Provincial National Z (Sum)
 Punjab Ranks Ranks
 Highest = 1 Highest = 1
 Lowest = 34 Lowest = 100

Rawalpindi 1 2 20.89
Lahore 2 3 19.74
Jhelum 3 4 14.86
Gujranwala 4 5 14.46
Gujrat 5 6 14.13
Sialkot 6 7 12.92
Faisalabad 7 9 11.61
T.T.Singh 8 10 11.49
Chakwal 9 11 10.84
Attock 10 13 10.16
Sheikhupura 11 16 8.85
Multan 12 17 8.67
Sargodha 13 19 8.14
Sahiwal 14 20 7.96
M. Bahudin 15 21 7.59
Narowal 16 22 7.51
Hafizabad 17 23 7.48
Khushab 18 24 6.81
Mianwali 19 27 5.03
Layyeh 20 29 4.07
Kasur 21 30 3.83
Bahawalnagar 22 32 3.43
Khanewal 23 35 3.28
Jhang 24 36 2.62
Vehari 25 38 2.08
Pakpatten 26 39 1.95
Okara 27 42 1.64
Bahawalpur 28 44 1.33
Bhaker 29 48 0.68
R Yar Khan 30 49 0.66
D.G.Khan 31 53 -0.84
Lodheran 32 54 -0.89
MuzafferGarh 33 60 -2.61
Raiinpur 34 85 -9.12

 Subjective Well-being

 Districts of Provincial National Z (Sum)
 Punjab Ranks Ranks
 Highest = 1 Highest = 1
 Lowest = 34 Lowest = 100

D.G.Khan 1 7 3.45
Layyeh 2 9 3.01
Bahawalnagar 3 11 2.78
Okara 4 19 1.71
Faisalabad 5 20 1.66
Chakwal 6 23 1.3
Hafizabad 7 24 1.19
Sheikhupura 8 25 1.16
Lodheran 9 27 0.96
Jhelum 10 29 0.6
Pakpatten 11 35 0.38
Gujranwala 12 38 0.15
Rajinpur 13 39 0.06
M. Bahudin 14 41 0.02
Jhang 15 43 -0.08
Lahore 16 44 -0.08
Rawalpindi 17 46 -0.11
Kasur 18 48 -0.18
Sahiwal 19 51 -0.29
Sialkot 20 53 -0.31
T.T.Singh 21 54 -0.32
Multan 22 55 -0.38
Khanewal 23 59 -0.61
MuzafferGarh 24 60 -0.65
Vehari 25 61 -0.71
Mianwali 26 64 -0.78
Gujrat 27 66 -0.89
Narowal 28 67 -0.9
Bahawalpur 29 74 -1.11
R. Yar Khan 30 77 -1.36
Attock 31 79 -1.46
Bhaker 32 87 -1.92
Khushab 33 88 -1.93
Sargodha 34 89 -2.03

Source: Computations are based on Pakistan
Living Standard Measurement Surveys, 2006-07.

Appendix Table A.2
Z-Sum for Provincial Ranking of Well-being

 Objective Well-being

 Districts of Provincial National Z(sum)
 NWFP Ranks Ranks
 Highest = 1 Highest = 1
 Lowest = 24 Lowest = 100

Abbotabad 1 14 9.26
Swat 2 15 8.95
Nowshera 3 18 8.55
Haripur 4 25 6.56
Peshawer 5 26 5.59
Chitral 6 31 3.52
Mardan 7 37 2.43
Manshera 8 40 1.75
Charsada 9 41 1.64
Malakand 10 43 1.57
Kohat 11 45 1.23
Lower Dir 12 47 0.99
Hangu 13 50 0.21
Bannu 14 51 -0.14
Karak 15 52 -0.45
Swabi 16 56 -1.28
Lalcki Marwat 17 62 -3.44
Bonair 18 66 -4.24
Batagram 19 69 -5.45
Upper Dir 20 70 -5.97
Tank 21 72 -6.91
Sangila 22 78 -7.85
Dera I. Khan 23 81 -8.42
Kohistan 24 95 -13.72

 Subjective Well-being

 Districts of Provincial National Z (Sum)
 NWFP Ranks Ranks
 Highest = 1 Highest = 1
 Lowest = 24 Lowest = 100

Bonair 1 1 4.05
Chitral 2 2 3.93
Malakand 3 3 3.87
Sangila 4 4 3.8
Lower Dir 5 5 3.51
Swat 6 8 3.24
Upper Dir 7 10 2.92
Charsada 8 12 2.74
Swabi 9 13 2.74
Lakki Marwat 10 14 2.3
Karak 11 15 2.27
Peshawer 12 16 1.9
Bannu 13 18 1.74
Dera I. Khan 14 22 1.47
Nowshera 15 28 0.83
Hangu 16 31 0.5
Mardan 17 33 0.43
Tank 18 34 0.39
Batagram 19 47 -0.12
Haripur 20 50 -0.25
Kohat 21 71 -1.02
Kohistan 22 76 -1.34
Manshera 23 81 -1.58
Abbotabad 24 85 -1.77

Source: Computations are based on Pakistan
Living Standard Measurement Surveys, 2006-07.

Appendix Table A3
Z-Sum for Provincial Ranking of Well-being

 Objective Well-being

 Districts of Provincial National Z (sum)
 Sindh Ranks Ranks
 Highest = 1 Highest = 1
 Lowest = 16 Lowest = 100

Karachi 1 1 23.56
Hyderabad 2 8 11.96
Sukker 3 28 4.32
Larkana 4 33 3.35
Noshro Feroz 5 34 3.29
Mirpur khas 6 55 -1.26
Khairpur 7 57 -1.43
Dadu 8 58 -1.88
Nawabshah 9 59 -1.98
Shanger 10 64 -4.08
Shikarpur 11 65 -4.15
Ghotki 12 67 -4.38
Badin 13 68 -5.37
Jaccobabad 14 75 -7.57
Thatta 15 82 -8.9
TharParker 16 98 -15.54

 Subjective Well-being

 Districts of Provincial National Z (sum)
 Sindh Ranks Ranks
 Highest = 1 Highest = 1
 Lowest = 16 Lowest = 100

TharParker 1 6 3.46
Mirpur khas 2 30 0.58
Jaccobabad 3 37 0.35
Noshro Feroz 4 52 -0.3
Ghotki 5 65 -0.84
Sukker 6 68 -1
Khairpur 7 69 -1.01
Karachi 8 72 -1.04
Shikarpur 9 80 -1.51
Hyderabad 10 83 -1.65
Badin 11 91 -2.17
Thatta 12 94 -2.37
Dadu 13 96 -2.58
Shanger 14 97 -2.76
Larkana 15 99 -3.07
Nawabshah 16 100 -3.08

Source: Computations are based on Pakistan
Living Standard Measurement Surveys, 2006-07.

Appendix Table 4
Z-Sum for Provincial Ranking of Well-being

 Objective Well-being

 Districts of Provincial National Z (Sum)
 Balochistan Ranks Ranks
 Highest = 1 Highest = 1
 Lowest = 26 Lowest = 100

Quetta 1 12 10.17
Mastung 2 46 1.17
Kalat 3 61 -3.37
Gwader 4 63 -3.71
Ketch 5 71 -0.1
Kharan 6 73 -7.2
Pishin 7 74 -7.49
Awaran 8 76 -7.61
Sibi 9 77 -7.62
Ziarat 10 79 -8.06
Khuzdar 11 80 -8.2
Chaghi 12 83 -8.95
Jafferabad 13 84 -8.98
Barkhan 14 86 -9.45
Qilla Saifulah 15 87 -9.56
Lasbella 16 88 -9.58
Zhob 17 89 -10.49
Bolan 18 90 -11.4
Qilla Abdulah 19 91 -12.29
Loralai 20 92 -12.68
Musakhel 21 93 -13.27
Panjgur 22 94 -13.36
JhalMagsi 23 96 -14.25
Nasirabad 24 97 -14.99
Dera Bugti 25 99 -16.65
Kolhu 26 100 -19.24

 Subjective Well-being

 Districts of Provincial National Z (Sum)
 Balochistan Ranks Ranks
 Highest = 1 Highest = 1
 Lowest = 26 Lowest = 100

Ziarat 1 17 1.79
Pishin 2 21 1.63
Qilla Abdulah 3 26 1.04
JhalMagsi 4 32 0.45
Sibi 5 36 0.37
Jafferabad 6 40 0.04
Quetta 7 42 0.02
Qilla Saifullah 8 45 -0.1
Kharan 9 49 -0.21
Kolhu 10 56 -0.53
Nasirabad 11 57 -0.56
Gwader 12 58 -0.57
Zhob 13 62 -0.75
Ketch 14 63 -0.77
Barkhan 15 70 -1.01
Khuzdar 16 73 -1.09
Mastung 17 75 -1.23
Musakhel 18 78 -1.37
Dera Bugti 19 82 -I.62
Loralai 20 84 -1.66
Kalat 21 86 -1.9
Bolan 22 90 -2.16
Chaghi 23 92 2.24
Awaran 24 93 -2.28
Lasbella 25 95 -2.47
Panjgur 26 98 -2.93

Source: Computations are based on Pakistan
Living Standard Measurement Surveys, 2006-07.

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Authors' Note: The authors are indebted to Dr Attiya Javid and Lubna Hasan for their comments and suggestions on this paper.

Rashida Haq <rashida_haq@hotmail.com> and Uzma Zia <uzmazia06@gmail.com> are Senior Research Economist and Staff Economist, respectively at the Pakistan Institute of Development Economics, Islamabad.

(1) Equally weighted indices are used frequently in the literature of well-being for example UNDP's Human Development Index and International Development Research Centre's (IDRC) Human Well-being Index.

(2) The non-marketed services such as education, health and sanitation etc., are used as evaluative criteria in subjective well-being [Kingdon and John (2005)].

(3) Population shares are based on 'Pakistan Population and Housing Census (1998)'; although absolute number of districts population has increased during 1998 to 2006-07 but there is less significant change in proportional share of districts population.

Table 1
Summary of Objective Well-being Indicators (%)

Indicators Mean Minimum Maximum

Literacy 10+ 46 20 80
Net Enrolment at Primary 51 20 88
Gender Equality in Education 42 3.2 90.32
Fully Immunisation 70 14 100
Prenatal Care 44 6 86
Safe Delivery 38 2 80
Place of Delivery 22 1 78
Post-natal Care 20 1 63
Safe Drinking Water 69.8 5.74 100
Sanitation Facilities 41.93 0.13 93.48
Source of Lighting 78.72 7.34 99.84
Source of Fuel 15.51 0 92.26

 MGDS
 Coefficient Target
Indicators Variation 2015

Literacy 10+ 0.27 88
Net Enrolment at Primary 0.27 100
Gender Equality in Education 0.50 100
Fully Immunisation 0.30 90
Prenatal Care 0.63 100
Safe Delivery 0.66 90
Place of Delivery 0.51 --
Post-natal Care 0.65 --
Safe Drinking Water 0.42 93
Sanitation Facilities 0.57 90
Source of Lighting 0.28 --
Source of Fuel 1.21 --

Source: Computations are based on 'Pakistan Social and Living Standards
Measurement Survey', 2006-07.

Table 2
Summary of Subjective Indicators of Well-being (%)

Indicators
(Satisfaction with the Coefficient
 Services/Facilities) Mean Minimum Maximum Variation

Education 61.23 21.18 84.32 0.21
Health 35.31 5.88 81.03 0.46
Security (Police Services) 6.61 0 29.2 0.95

Source: Computations are based on 'Pakistan Social and Living
Standards Measurement Survey', 2006-07.

Table 3a
Overall Objective Well-being Rank Orders

 Highest Well-being High Well-being

Districts Overall Index Districts Overall Index
 Rank Value Rank Value
 Orders (%) Orders (%)

Karachi 1 89.59 Multan 18 67.14
Rawalpindi 2 88.42 Sahiwal 19 67.12
Lahore 3 86.40 Nowshera 20 66.91
Gujrat 4 80.20 Sargodha 21 66.34
Gujranwala 5 79.28 Khushab 22 66.18
Sialkot 6 78.76 Hafizabad 23 65.95
Jehlum 7 78.44 Haripur 24 63.35
Chakwal 8 73.37 Swat 25 62.24
T.T.Singh 9 72.30 Mianwali 26 62.20
Faisalabad 10 70.75 Layyah 27 62.02
Attock 11 70.75 Kasur 28 61.19
Mandi 12 70.37 Peshawar 29 60.80
Bahauddin
Quetta 13 69.76 Bahawalnagar 30 60.80
Hyderabad 14 69.51 Chitral 31 59.59
Sheikhupura 15 69.50
Narowal 16 69.30
Abbottabad 17 68.75

Source: Computations are based on the 'Pakistan Social and Living
Standards Measurement Survey', 2006-07.

Note: Standard scores: highest well-being index = 67.47 percent above
with average index value = 74.9 percent high well-being index range =
67.46-58.25 with average index value =63.65 percent. Islamabad is top
ranked with index value 95.11 percent.

Table 3b
Overall Objective Well-being Rank Orders

 Upper Medium Well-being Lower Medium Well-being

Districts Overall Index Districts Overall Index
 Rank Value Rank Value
 Orders (%) Orders (%)

Khanewal 32 57.86 Lower Dir 51 49.01
Nowshero Feroz 33 57.61 Swabi 52 48.17
Mardan 34 57.16 Khairpur 53 47.17
Bhakhar 35 56.66 Karak 54 47.08
Vehari 36 56.63 Muzaffarghar 55 46.79
Sukkur 37 56.46 Dadu 56 46.52
Okara 38 56.43 Bannu 57 45.45
Mastung 39 56.43 Hangu 58 44.13
Jhang 40 55.24 Mir Pur 59 44.20
Pakpatten 41 55.23 Kalat 60 44.13
Larkana 42 55.07 Nawabshah 61 42.97
Bahawalpur 43 54.25 Sanghar 62 41.21
Malakand 44 54.13 Ghotki 63 41.52
Charsada 45 53.66 Gwadar 64 41.14
Mansehra 46 53.22 Bonair 65 41.12
R. Y. Khan 47 52.10 Lakki Marwat 66 40.39
Kohat 48 51.10 Ketch 67 40.22
D.G. Khan 49 50.67 Upper Dir 68 40.01
Lodhran 50 50.34 Shikarpur 69 39.80

Source: Computations are based on the 'Pakistan Social and Living
Standards Measurement Survey', 2006-07.

Note: Standard scores: upper medium index range =58.24-49.03 with
average index value = 54.51 percent, lower medium index range =
49.02-39.81 with average index value = 43.48 percent.

Table 3c
Overall Objective Well-being Rank Orders

 Low Well-being Lowest Well-being

Districts Overall Index Districts Overall Index
 Rank Value Rank Value
 Orders (%) Orders (%)

Khuzdar 70 36.71 Chaghi 85 29.26
Tank 71 36.69 Qilla Saifullah 86 28.50
Awaran 72 36.56 Lasbilla 87 28.47
Badin 73 35.58 Jafarabad 88 27.66
Pashin 74 34.86 Thatta 89 27.48
Batagram 75 34.49 Loralai 90 25.33
D.I. Khan 76 34.13 Bolan 91 23.54
Shangla 77 32.90 Panjgur 92 23.03
Sibbi 78 32.30 Musa Khel 93 21.73
Ziarat 79 31.55 Kohistan 94 21.15
Rajanpur 80 31.45 Jhal Magsi 95 20.92
Barkhan 81 31.34 Qilla Abdullah 96 18.51
Zhob 82 31.15 Tharparkar 97 16.23
Kharan 83 30.92 Nasirabad 98 14.17
Jaccobad 84 30.80 Kohlu 99 10.96
 Dera Bugti 100 10.66

Source: Computations are based on the 'Pakistan Social and Living
Standards Measurement Survey', 2006-07.

Note: Standard scores: low well-being index range =39.80-30.58, lowest
well-being index range = 30.57 below.

Table 4
Percentage Share of Population in Level of Objective Well-being (3)

 Upper Lower
Area Highest High Middle Middle

Punjab 46.10 21.70 27.09 3.58
Sindh 41.87 0 12.88 30.15
NWFP 4.965 29.10 30.26 22.66
Balochistan 11.34 0 2.73 12.74
Total 37.73 16.48 22.93 12.93

Area Low Lowest Total

Punjab 1.49 0 100
Sindh 8.41 6.66 100
NWFP 10.33 2.66 100
Balochistan 25.92 47.23 100
Total 5.62 4.36 100

Table 5a
Overall Subjective Well-being Rank Orders

 Highest Well-being

Districts Overall Rank Index Value
 Orders (%)

Swat 1 82.89
Vehuri 2 82.01
Nowshero Feroz 3 75.78
Sibbi 4 74.21
Chitral 5 73.72
Bannu 6 70.92
Pashin 7 67.56
Nowshera 8 65.89
Sanghar 9 65.88
Karak 10 64.88
Mastung 11 64.41
Mardan 12 63.93
Peshawar 13 63.48
Jhal Magsi 14 59.83
Malakand 15 59.75
Lower Dir 16 59.39

 High Well-being

Districts Overall Rank Index Value
 Orders (%)

Lakki Marwat 17 56.55
D.I.Khan 18 54.62
Layyah 19 53.84
Charsada 20 53.74
Khairpur 21 53.56
Shangla 22 53.38
Hyderabad 23 52.96
Bonair 24 52.33
Tank 25 51.76
Hangu 26 51.40
D.G.Khan 27 51.12
Badin 28 50.63

Source: Computations are based on the 'Pakistan Social and Living
Standards Measurement Survey', 2006-07.

Note: Standard scores: highest well-being index = 57.87 above,
highest index range = 57.86 -50.39

Table 5b
Overall Subjective Well-being Rank Orders

 Upper Medium Well-being

Districts Overall Index
 Rank Value
 Orders (%)

Bahawalpur 29 49.89
Quetta 30 48.99
Chakwal 31 48.80
Larkana 32 47.93
Kohat 33 47.51
Ghotki 34 47.51
Rawalpindi 35 46.72
R Y Khan 36 46.38
Upper Dir 37 45.51
Nawabshah 38 45.50
Bhakhar 39 45.41
Bahawalnagar 40 44.44
Hafizabad 41 44.31
Dadu 42 44.18
Batagram 43 44.07
Panjgur 44 43.85
Jehlum 45 43.47
hang 46 43.06
Gujranwar 47 43.01
Mandi Bahuddin 48 42.92

 Lower Medium Well-being

Districts Overall Index
 Rank value
 Orders (%n)

Sahiwal 49 42.902
Gujrat 50 42.708
Pakpatten 51 42.587
Lodhran 52 41.539
T.T.Sing 53 41.314
Attock 54 40.383
Swabi 55 40.234
Sukkur 56 40.206
Gwadar 57 39.643
Faisalabad 58 39.082
Jafarabad 59 37.984
Bolan 60 37.530
Kharan 61 37.066
Lasbilla 62 36.985
Ketch 63 36.426
Abbottabad 64 36.281
Khuzdar 65 36.051
Okara 66 35.796
Mianwali 67 35.664

Source: Computations are based on the 'Pakistan Social
and Living Standards Measurement Survey', 2006-07.

Note: Standard scores: upper medium index range =50.3 -.42.91,
lower medium index range = 42.90-35.44 Islamabad is ranked in
lower medium with index value 41.43

Table 5c
Overall Subjective Well-being Rank Orders

 Lower Well-being

Districts Overall Rank Index Value
 Orders (%)

Mir Put 68 35.41
Sargodha 69 35.36
Barkhan 70 34.07
Narowal 71 33.73
Khushab 72 33.60
Ziarat 73 33.32
Multan 74 32.33
Muzaffarghar 75 32.17
Karachi 76 32.09
Sialkot 77 31.72
Sheikhupra 78 31.71
Mansehra 79 31.23
Haripur 80 30.72
Chaghi 81 30.63
Kalat 82 30.57
Jaccobabad 83 30.51
Nasirabad 84 30.08
Shikarpur 85 29.25
Musa Khel 86 27.99

 Lowest Well-being

Districts Overall Rank Index Value
 Orders M

Lahore 87 27.92
Khanewal 88 27.58
Tharpark 89 27.54
Zhob 90 26.59
Kasur 91 26.31
Rajanpur 92 25.87
Qilla Abdulah 93 24.80
Loralai 94 22.73
Awaran 95 22.15
Thatta 96 21.75
Dera Bugti 97 20.75
Kohistan 98 16.19
Kohlu 99 8.08
Qilla Safullaha 100 7.48

Source. Computations are based on the 'Pakistan Social
and Living Standards Measurement Survey', 2006-07.

Note: Standard scores: low well-being index range =
35.44-27.97, lowest well-being index range = 27.96 below.

Table 6
Percentage Share of Population in Subjective Well-being

 Upper Lower
Area Highest High Medium Medium

Punjab 2.84 3.75 30.30 24.40
NWFP 8.345 18.31 18.60 2.98
Sindh 46.26 21.75 8.15 10.75
Balochistan 12.8 0 14.911 34.37
Pakistan 10.64 9.50 23.24 18.00

Area Low Lowest Total

Punjab 22.60 16.11 100
NWFP 45.11 6.66 100
Sindh 10.40 2.70 100
Balochistan 14.58 23.36 100
Pakistan 25.83 12.79 100

Table 7
Objective and Subjective Well-being Differences: Basic Configuration

 Subjective Well-being
Objective
Well-being High Medium

High (6.00%) (24.19%)
 Hyderabad, Chitral, Rawalpindi, Gujrat, Gujranwala,
 Nowshera, Peshawar, Swat Jehlum, Chakwal, T.T.Singh,
 Faisalabad, Attock, Mandi
 Bahauddin, Quetta, Sheikhupura,
 Sahiwal Bahawalnagar,
 Hafizabad, Abbottabad, Mianwali

Medium (8.96%) (17.90%)
 Vehari, Nowshero, Bannu, Bahawalpur, Larkana, Ghotki, R
 Sanghar, Karak, Mastung, Y Khan, Upper Dir, Nawabshah,
 Malakand, Lower Dir, Lodhran Pakpatte,Bhakhar, Swabi
 Mardan, Lakki Marwat, Dadu,Jhang, Sukkur, Gwadar,
 Bonair, Hangu, D.G.khan Ketch, Okara

Low (5.40%) (1.72%)
 Sibbi, Pashin, Jhal Mag, Batagram, Panjgur, Jafarabad,
 D.I.Khan, Layyah, Bolan, Kharan, Lasbilla, Khuzdar
 Charsada, Khairpur,
 Shangla, Tank, Badin

 Subjective Well-being
Objective
Well-being Low

High (24.9%)
 Karachi, Lahore,
 Sialkot,Kasur,Narowal,
 Haripur, Khushab, Multan,
 Mansehra, Sargodha,
 Shikarpur

Medium (5.17%)
 Khanewal, Mir Pur, Kalat,
 Muzaffar,

Low (5.67%)
 Tharpark, Zhob, Rajanpur,
 Qilla Abdua, Loralai,
 Awaran, Thatta,
 Derabugti, Kohistan,
 Kohlu, Qilla Saifullaha,
 Barkhan, Chaghi,
 Jaccobad, Musa Khel,
 Nasirahad Ziarat

Source: Computations are based on the 'Pakistan Social
and Living Standards Measurement Survey', 2006-07.

Note: Population shares are in parentheses.