Re-examining Multidimensional Poverty in Pakistan: A New Assessment of Regional Variations

Abstract

The objective of this study is to investigate the multidimensional poverty (MDPT) level in the rural and urban areas of Pakistan. This study further adhered three essential factors such as education, health and living standard of people. It is observed that most of the studies have conducted and adhered regarding monetary poverty. The data have been obtained from the ‘Pakistan Social and Living Standard Management’ (PSLM) statistical survey. The finding of this analysis shows that, during all periods, MDPT in Pakistan remained significantly more in rural areas compared to urban regions. This empirical analysis provides an integrating technique adopting MDPT to overcome the socio-economic issues, which rapidly upsurge in Pakistan. Furthermore, it is a fundamental obligation of the state to provide sustainable and millennium development necessities of life such as food, health, water and education to meet the global standards of well-being of their people.

Keywords

Poverty health education deprivation Pakistan

Introduction

Significant literature exists on income poverty and its dynamic and the measurement of MDPT. However, it has taken massive attention in recent years. The Oxford Poverty and Human Development Initiative (OPHI) designed an index for measurement of MDPT. Alkire and Santos (2014) revealed that this index directly focussed on a real deprivation, especially in the third world countries by showing a different angle. In Pakistan, MDPT index (MDPT index) has been used to identify socio-economic factors which improve the development process by minimizing poverty ratio, meeting the requirements of vision (2015) and identifying various social goals. Pakistan’s MDPT index tries to fulfil the requirements of Pakistan’s vision 2025. However, to encourage the growth process, monitoring and readapting programming, providing policy for effective governance as well as designing and targeting integrating policies are the main functions of MDPT index. The key objective of Pakistan’s vision (2025) is to achieve significant and sustainable growth and development to minimize the deprivations from the economy. The purpose of MDPT in Pakistan is to control the money deficiency-based poverty and deficiency of hospitals and healthcare facilities in the country which further adhere to the high rate of illiteracy and various social issues for all population across the country. Moreover, Pakistan’s commitment to eradicate poverty report explores the national MPIN based on the method of Alkire–Foster (AFS); from in this report we take statistical data of PSLM various surveys to estimate MDPT in Pakistan. This survey also estimates the impact of socio-economic indicators and dimensions on MDPT (Government of Pakistan, 2016).

There is a prerequisite of well-being to realize the main indicators of deprivation through economic as well as social aspects and some issues are related to poverty (Bourguignon & Chakravarty, 2003). The famous ‘Chronic Poverty Research Center’ explores that deprived condition of the people cannot be measured through lowness of income. Instead it has numerous manifestations such as hunger and malnutrition; absence of health facilities; financial constraints; inadequate educational facilities and many other basic necessities of life like main household services, deficiencies of houses and mortality from illness, increased morbidity, unsafe and unhealthy environmental conditions, social exclusion and marginalization (CPRC, 2004). The financial requirement of income is a partial proxy, which explains as an only necessary condition but not a sufficient measurement of welfare for people but it only deals with one pillar for overcoming the problem of derivation.

The government of Pakistan is launching anti-poverty policies to reduce poverty, but still these policies are not accurately applied by MDPT regarding measurement of poverty. This study adopted the Alkire and Foster (2008) methodology to address the objective of this study. Moreover, this study is based upon an aggregation of the poor and method of dual cut-off for the identification. In order to resolve the socio-economic issues, this modern concept of MDPT is more beneficial than the conventional approach (Alkire & Santos, 2010; Bourguignon & Chakravarty, 2003; Shabbir, 2018a). The ordinal data even for the subgroups of the population are used in this study, which also include the set of axioms. However, numerous measurements and key indicators are used in this study to understand the issue of poverty in a multidimensional level.

This analysis is a pioneering effort to discuss the rural and urban poverty in the multidimensional continuum across the country. This study also checks the importance of this issue in the perspective of the national and international levels in the recent era. However, various set of indicators with three inside dimensions such as education, health and standard of living are used in this study following the method of Alkire and Santos (2010). The prior studies of Khan, Saboor, Ali, Malik, and Mahmood (2016) have discussed only urban poverty multidimensional, and few studies have been done on MDPT in Pakistan such as Saboor, Khan, Hussain, Ali, and Mahmood (2015). This current study extended the years till 2015 and also checked it with robustness analysis.

The remaining sections describe the details of the article. The Review of Literature section represents the conceptual framework. The Data and Research Methodology section relates to the literature review. The Empirical Results section presents a methodology and an empirical analysis of the study. The findings of this empirical study are mentioned in the Discussion section. The Conclusions section is based on the conclusion and discusses the policy suggestions and implications.

Conceptual Frameworks

The Main Concept of Poverty

Theoretically, this study is based on the approach of Amartya Sen’s Capability which described that deprivation is a multidimensional concept in nature (Sen, 1999). The poverty is not a single aspect as it contains multiple aspects, so measurement of poverty in only monetary term cannot be beneficial (Ravallion, 2011), and various empirical analyses exist. It is not necessary that improvement of welfare is related to the economic growth (Ahluwalia, 2011). Khilji (2014, 2015) stressed on policymakers to design various appropriate economic policies, programmes for poverty reduction, which are essential to understand the central concept of poverty. However, poverty is a multidimensional, diverse and narrow dimensional concept (Misturelli & Heffernan, 2010). The economic deprivation in terms of money and deficiency of financial resources to access the necessities are the main aspects of poverty, and according to prior literature, that minimum level of consumption and necessities are needed for a healthy life of the people (Lipton, 1997).

A strong theoretical foundation is given by the millennium development goals (MDGs) and sustainable development goals (SDGs), which have highly focussed their attention on the human development programme and poverty reduction policies. Due to theoretical development, the availability of data based on micro level and advances in methodology in the past few years have provided research into the measurement of poverty with its all dimensions. Angulo, Diaz, and Pardo (2016) discussed that poverty measurement includes not only money-based poverty, but it also tries to estimate poverty with its all dimensions and aspects. The estimation of MDPT with its new measures are encouraging by Oxford Poverty and Human Development and United Nation Development Programs UNDP (1997) as well as explained by Alkire & Foster, 2011; Alkire & Santos, 2010).

Review of Literature

The consumption or income of the household sector is used to measure poverty and for determination of poverty line (Barrington, 1997). The severity of poverty is measured by the squared poverty gap index, the intensity of poverty (IA) measures by the poverty gap index and poverty prevalence measures by the headcount index, which is used in empirical literature (Chen & Ravallion, 2007). The prior literature (Duclos, David, & Stephen, 2006; Osinubi & Amaghionveodiwe, 2004; Shabbir, 2018b) used monetary approach and (Alkire et al., 2015) applied a multi-dimensional approach. The in-depth analysis of poverty in terms of MDPT includes the human development and capability paradigm which gives an important theoretical foundation in this regard. The main features of MDGs and the SDGs are based on MDPT indices. The empirical and theoretical development with micro-level data availability and advanced methodology can be seen through the research analysis. The estimation of MDPT and finding the solution of policies are the primary purposes of many national governments (Angulo, Diaz, & Pardo, 2016). The OPHI has established the measures of MDPT and encouraging this method measure (Alkire & Santos, 2010; UNDP, 1997, 2010). The empirical studies of Saboor, Khan, Hussain, Ali, and Mahmood (2015) examined the regional variations and temporal shifts of poverty. They measured MDPT for five time periods, such as from 1998 to 2008 in 26 regions of Pakistan, using the MDPT approach which was designed by Alkire and Foster (2008) and accepted by UNDP (2010).

More than 1,129 million people are living in a severe socio-economic deprivation condition, with a certain disparity in magnitude across different economies and regions (UNDP, 2013). The dilemma of deprivation of people has been a notable challenge in the past many decades of third world countries; it is more challengeable because it has a terrible influence on the economic growth and development process. While globally, 557 million people are poor in South Asian countries, and with the passage of time, the rate of deprivation has risen significantly (UNDP, 2013). However, Pakistan is not free from poverty; over time, the proportion of poverty increased significantly (Naseem, 2012). During the period of the 1990s in Pakistan, the poverty had increased because of destabilizing policies, poor quality of governance, inflation (increasing prices of goods and services) and slow growth rate (Haq & Bhatti, 2001; Naseem, 2012).

However, various empirical analyses have employed different indicators and dimensions to quantify MDPT (such as Mohanty, 2011; Sahn & Stifel, 2000; Shabbir, 2018; then several recent studies Batana, 2013; Battiston, Cruces, Lopez-Calva, Lugo, & Santos, 2013; Hanandita & Tampubolon 2015; Santos, 2013; Yu, 2013; and finally Angulo, Diaz, & Pardo, 2016). A systematic review and numerous methods have been developed for estimating MDPT (for instance, Alkire & Foster, 2008; Decancq & Lugo, 2013; Dehury & Mohanty, 2015; Mishra & Ray, 2013). The estimation of MDPT is done by Anand and Sen (1997), Bourguignon and Chakravarty (2003), Alkire and Shen (2017), Coromaldi and Zoli (2012), Jayaraj and Subramanian (2010); Khan, Saboor, Hussain, Karim, and Hussain (2015), Mishra and Shukla (2016) and Rippin (2010). Though, empirical analysis on the application of MDPT findings remains scarce (Mohanty, 2011). Several studies such as (Shabbir, 2015, 2016, 2018; Saleem, Shahzad, Khan, & Khilji, 2019) explained the importance and preference of Pakistan economy and its growth rate using different policy variables. They also highlighted the importance of innovation, combined financial solutions and bilateral trade, which facilitate the people of Pakistan in eradicating the poverty.

Sasmal and Guillen (2015) describes the importance of child labour poverty, lack of education and their impact on Indian economy. This study has collected data of literacy rate and child labour from 27 states of India in different census such as 1971, 1981, 1991, 2001 and 2011. The result indicates that poverty has inversed effect on children schooling and trapping the child labour. Mitra and Das (2018) explained the significance of inclusive economic growth among 16 Asian countries. They have used two kinds of weight schemes, for instance, principle component analysis and ad hoc weighting schemes, to construct the index. The purpose of this indexation is to categorize the countries or region based on the performance of growth level in these countries.

Data and Research Methodology

Description of Data

This study tries to examine the deprivation ratio in Pakistan including its four provinces except for Gilgit Baltistan such as Punjab, Khyber Pakhtunkhwa, Sindh and Baluchistan. The data were collected from the PSLM survey of Pakistan for different periods, from 2004/2005, 2006/2007, 2008/2009, 2010/2011, 2012/2013 and 2014/2015. The data on facilities of health, education and houses are the main indicators of socio-economic aspects of the economy. These main indicators with its main 10 variables are mentioned in Table 1. The reading and writing ability and total numbers of educational years are used as years of education than the availability of clean water (for drinking); immunization and pre- and post-delivery consultation are the proxies of health services. Furthermore, in this analysis, the access to electricity and telephone services, house occupancy status and access to facilities of the gas connection and toilet in houses are used as the proxies of the deficiencies of facilities of the housing.

Finally, MDPT is estimated which is based on estimated dimension levels in all provinces for five time periods.

Table 1.

Details of MDPT Dimensions, Main Indicators and Deprivation Thresholds

Dimensions	Main Indicators	Criteria Used for DEP, CUT-OFF (Deprived if…)
Income (CY)	One-dimensional	If income is less than (<) national poverty line (then D = 1, and 0 otherwise)
Facilities of Education (ED)	Total numbers of educational years	If highest classes ≤ 6 (then D = 1, and 0 otherwise)
	Reading and writing ability	If cannot read and write in any language (then D = 1, and 0 otherwise)
Facilities of Health (HF)	Vaccination	If not vaccinated (then D = 1, and 0 otherwise)
	Clean drinking water facility	If not vaccinated (then D = 1, and 0 otherwise)
	Pre/post-natal consultation	If did not go for any pre-natal consultation (then D = 1, and 0 otherwise)
Facilities of housing (HU)	Own house or not status	If the house is not owned (then D = 1, and 0 otherwise)
	Availability of electricity	If electric connection is not available (then D = 1, and 0 otherwise)
	Availability of gas	If gas connection is not available (then D = 1, and 0 otherwise)
	Availability of telephone services	If telephone connection is not available(then D = 1, and 0 otherwise)
	Toilet/facility of washroom	If flush not related to public sewerage/pit (then D = 1, and 0 otherwise)

Source: Pakistan Social and Living Standard Management Survey, PSLM.

Note: Dep = Deprivation.

Methodology of the Study

It is noted that several methods are discussed to measure MDPT in the prior literature; these are the approach of fuzzy set, efficiency analysis, latent class models, the information theory approach, cluster analysis, the axiomatic approach and ordinal and factor approaches (Kakwani & Silber, 2008). The study used AFS methodology designed by (Alkire & Foster, 2010) because of its instinctive and appropriate properties of policy. The methodology of MDPT study is divided into two different segments, for instance, multidimensional headcount ratio (HD) and identification. Moreover, MDPT can be measured by various available methods; the choice of methods depends upon the data and its type and context of segregation. The AFS approach with its various benefits is now getting popularity, with its various dimensions to decompose poverty; that is why, the AFS method is being used in this article. The aggregation phases (methodology of HD) and identification or dual cut-off method are two main segments of MDPT. The aggregation method is related to the knowledge of the deprived persons and thus poverty line which explains the poverty at the combined platform. The identification system in all dimensions depends upon the difference between the deprived and rich people, secondly, to check about the poor from non-poor people across the domain than the identification is applied (Alkire & Foster, 2008).

The methodology of AFS provides comprehensive information about the three significant indices of MDPT, the HD of MDPT, the IA and finally the MDPT Index (Alkire & Foster, 2015). The equations are as follows:

The percentage of poor persons of the country’s population is denoted by the ratio of headcount (HD), following equation based on total numbers of deprived person.

HD = tq ÷ tn

(1)

Where tq indicates the total number of deprived persons in the economy, and the total population is denoted as tn.

The intensity (IA)of MDPT is described as the ‘average weighted deprivation’ practised by the derived households and mentioned as follows:

I A = \sum c i (k) \div t q

(2)

Where the censored deprivation is symbolized as c_i(k) and the number of deprived people or multidimensionally poor people is symbolized as tq.

The multiplication of HD and IA represents the MDPT index

MDPT = HD × IA

(3)

Where the index of MDPT shows the proportion of the total number of multidimensional.

Different indicators with its significant contribution to the MDPT

w_{i} \times censored headcount rati o_{i} \div MDPTINDE X_{country} \times 100

(4)

Where, w_i indicates the weight of the indicator (ith).

This analysis used the structure of equal weighting among the significant magnitudes of deprivation in terms of educational facilities, services of health and housing and across many aspects (aforementioned domains) due to the non-availability of suitable justification. The problems of basic necessities of life can be discussed multidimensionally with a suitable solution to the weighting methodology (Saboor, Khan, Hussain, Ali, & Mahmood, 2015). Kruijk and Rutten (2007) revealed that the adoption of weights preferably demonstrates the significance of the several structures among the different sets of elements. Thus, the different dimensions of deprivation allow the investigators to use these dimension separately (by controlling weights) (Noble, Barnes, Wright, & Roberts, 2009). It is essential to find the suitable direction to put proper weights to each domain, otherwise putting more weights to one dimension as compared to another dimension will not be rational (Foster, 2007).

Identification Stage

The dual cut-off method is described in this phase, where deprivation threshold identification explains person is poor or not poor in 1^st phase. Table 1 explains the different achievement levels of deprivation are regularized and that for deprived person, with the positive values indicate as ‘1’ and then ‘0’ shows otherwise. Second, before assigning a weight (the number of deprivation) for each deprived household, we designed a vector by calculating each column vertically. The sample data are based on poor and non-poor people. The method of cut-off denotes as ‘kt’ which relates to the deprived person that should be greater than or can be equal to the ‘kt’. To identify the dimension of poverty, it is essential to estimate kt which can be estimated by dividing the number of dimensions by two (Naveed & Islam, 2010).

Robustness of Estimates

By setting the kt point of the weighted deprivation, the MDPT can be measured. The kt ratio of deprivation is fixed at 33.3 per cent (with its three dimensions) globally and for Pakistan also. The level of MDPT declines when the value of kt is increased. If the score of deprivation of individuals is equal or more than the values of 33.3 per cent, those will be considered as a multidimensional deprived person. The person will not be identified multidimensionally poor if his/her score does not exceed 33.3 per cent. Before setting the points of cut-off, this analysis carried out a robustness test (shown in Figure 1) by different values of kt for Pakistan in different years. When the kt value is fixed at 33.3 per cent, the value of the multidimensional index (MDPT index) dropped from 0.292 in 2004–2005 to 0.197 in 2014–2015. Pakistan tackled statistically significant decline in its poverty (MDPT).

However, to find the main significant indicators of MDPT, this study used a logistic regression model (logit model) for Pakistan. In this logistic model, the dependent variable was the grouping of family member constructed on MDPT, that is, is a person is multi-dimensionally deprived or not poor (denoted as ‘0’ if the person is multidimensionally none deprived and 1 if the individual is deprived.). The following model is followed by Mohanty et al. (2018).

The following equation represents the regression model:

Multi_dimentional poori=α + β_{1} {resi}^{i} + β_{2} {sex_hhl}_{i} + β_{3} {age_hhl}_{i} + β_{4} {educ_hhl}_{i} + β_{5} {size_hhl}_{i} + β_{6} {cons}_{i} + β_{7} {deat}_{i} + β_{8} {relig}_{i} + e_{i}

(5)

In equation 5, the intercept is denoted as α, Multi_dimentional poor_i is defined as MDPT. Sex_hhl_i is described as the sex (male/female of the head of household). resi is the residence (population lives in rural or urban regions) The age of the head of the family is denoted by the education of head of the family that is denoted as educ_hhl_i, size_hhl_i related to the size of household, cons_i relates to the consumption of the household, if any person of the family has died in the last one is symbolized as deat_i, year, the religion of the head of the family is defined as relig_i and i subscript is applied for i^th household.

Empirical Results

Pakistan’s Regional MDPT Results

Table 2 reports the provincial disparities in Pakistan; urban areas are less deprived as compared to rural areas. The results at province level indicate that Baluchistan has the highest MDPT, while Punjab has the lowest incidence.

The IA HD, the severity of poverty, Vulnerable to poverty per cent, Inequality (among HD × IA people %) and per cent of the population presents for provinces including regions of urban and rural, and capital of Pakistan in Table 3, Panels A and B. The statistical figures indicate that higher deprivation rates are shown in rural regions as compared to urban regions. Overall poverty in rural areas high and IA is higher because in Pakistan two-thirds or 180 million people live in rural areas; this difference is not strictly as more as the inconsistency in the poverty HD between cities and rural regions.

Table 2.

Results of Headcount MPI Rate (%)

		2004–2005	2006–2007	2008–2009	2010–2011	2012–2013	2014–2015
Overall	Overall	55.2	52.5	49.3	46.5	40.8	38.8
	Urban	24	19.4	17.3	13.9	10.1	9.3
	Rural	70.3	69.5	65.2	62.3	56	54.6
Punjab	Overall	49.7	46.4	43.2	40	34.7	31.5
	Urban	19.7	16.1	13.2	11	8.4	6.3
	Rural	62.7	61	57	53.4	46.9	43.9
Sindh	Overall	57.3	53.7	51.2	49.5	44.6	43.2
	Urban	27.2	19.6	20	14.9	10.9	10.5
	Rural	88.1	87.4	81	79.9	75.5	75.7
Khyber Pakhtunkhwa (KPK)	Overall	65.8	66.1	66.1	57	49.1	49.1
	Urban	30.5	32.9	23.2	19.2	10	10.2
	Rural	72.9	72.8	68	64.8	57.1	57.7
Baluchistan	Overall	83.4	79.8	78.9	76.7	71.9	71
	Urban	49.4	42.6	40.1	37.2	29	37.4
	Rural	91.6	91.9	90.7	89.3	85.8	84.5

Source: Authors’ own calculations based on data collection.

Table 3.

Multidimensional Poverty by Region in Pakistan, 2014–2015

Panel (A)		Provinces			Values
					MDPT		HD (Incidence)		IA (Intensity)
Punjab region		Total			0.152		31.40%		48.40%
		Urban			0.026		6.30%		41.80%
		Rural			0.214		43.70%		48.90%
Sindh region		Total			0.231		43.10%		53.50%
		Urban			0.046		10.60%		43.40%
		Rural			0.415		75.50%		54.90%
KPK region		Total			0.25		49.20%		50.70%
		Urban			0.042		10.20%		41.50%
		Rural			0.295		57.80%		51.10%
Baluchistan		Total			0.394		71.20%		55.30%
		Urban			0.172		37.70%		45.70%
		Rural			0.482		84.60%		57.00%
Panel (B)	MDPT (HD × IA)		HD%	IA%		Vulnerable to poverty %	Severe poverty	Inequality (among HD × IA people %)	% of population
Rural areas	0.29		55.7	53.6		18.8	32.2	–	67.7
Urban areas	0.08		20.0	43.3		7.3	5.9	–	32.3
Overall	0.23		44.2	52.1		15.1	23.7	0.2	100
Capital	0.04		9.2	42.7		7.1	2.5	0.1	0.4
Punjab	0.18		36.6	50.3		17.2	18.4	0.2	55.3
Northern zone	0.23		46.9	49.8		22.8	23.9	0.1	0.8
KPK province	0.24		50.1	49.4		19.1	22.4	0.2	14.1
Sindh	0.29		53.2	55.4		8.3	32.7	0.3	24.5
Baluchistan	0.42		70.6	57.0		12.7	45.2	0.3	4.7

Sources: Panel A: PSLM survey for four provinces (2014–2015); Panel B: Oxford Poverty and Human Development Initiative statistics 2017.

The intensity of derivation of the poor population is depicted in the pie chart in Figure 2; the deprivation intensities range start from more than 33.3 per cent to less than 40 per cent. These groups with different ranges showed the intensity of poor people in percentage (Figure 3). Figure 4 with column indicates the country’s impoverished population in that percentage of indicators with deprived in the category of Y people, if the ratio is 40% plus, it represents that more than 40 per cent of people are deprived. Severe poverty is identified if the proportions of people deprived are 50 per cent or greater than 50 per cent.

Table 4.

Pakistan’s National MDPT Results: 2014–2015

Index	Population (in %)	Value	Level of Confidence Interval (95%)
		Urban
MDPT		0.04	0.035	0.045
HD	33.10%	9.40%	8.20%	10.50%
IA		3.10%	42.50%	43.60%
		Rural
MDPT		0.281	0.273	0.29
HD		54.60%		56%
IA	67.00%	51.60%		52%

Source: Authors’ estimation constructed on data from the PSLM survey (2014/2015).

Table 4 shows a statistical result that is estimated for the IA, MDPT and HD ratios as per cent of the population share (including rural and urban regions) and adds the confidence intervals.

Trends and Overtime Changes in MDPT in Pakistan

The level of poverty (Table 5) rose during the period of 1992/1993 to 2001/2002 except for the year of 1996/1997 and then dropped quickly 10.6 percentage points after the periods of 2001/2002 (34.5%) to 2004/2005 (23.9%).

Table 5.

Overtime Changes in Multidimensional Poverty/Based on the Poverty Line (Official)

Years	Headcount Ratio of Poverty			Poverty Gap			Extreme Level of Poverty
Years	Urban	Rural	Pak	Urban	Rural	Pak	Urban	Rural	Pak
1992–1993	20.00%	27.60%	25.50%	3.40%	4.60%	4.30%	0.90%	1.20%	1.10%
1993–1994	15.90%	33.50%	28.20%	2.70%	6.30%	5.20%	0.70%	1.80%	1.40%
1996–1997	15.80%	30.20%	25.80%	2.40%	5.30%	4.40%	0.60%	1.40%	1.10%
1998–1999	20.90%	34.70%	30.60%	4.30%	7.60%	6.40%	1.30%	2.40%	2.00%
2001–2002	22.70%	39.30%	34.50%	4.60%	8%	7%	1.40%	2.40%	2.10%
2004–2005	14.90%	28.10%	23.90%	2.90%	5.60%	4.80%	0.80%	1.80%	1.50%
2005–2006	13.10%	27.00%	22.30%	2.10%	5.00%	4.00%	0.50%	1.40%	1.10%
2007–2008	10.00%	20.60%	17.20%	–	–	–	–	–	–
2010–2011	7.10%	15.10%	12.40%

Sources: Government of Pakistan (2014); Pakistan Bureau of Statistics; Cheema (2005).

Table 6 shows the incidence of HD, the IA and level of MDPT as the three main three key statistics for the measurement of poverty. Results indicate that these three statistics have changed over periods of 2004 to 2015 which include four provinces. The ratio of MDPT decreased slowly between the periods of 2004 and 2015, and it is also evident in results that the rate of decrease found statistically significant. The results of MDPT are shown as a decline from 0.292 points in the period of 2004–2005 to 0.197 points in 2014–2015, while the ratio of headcount (HD) dropped from 55.2 per cent to 38.8 per cent (16.4% is a difference). However, the average intensity (IA) rate declined strikingly very little, from 52.9 per cent to 50.9 per cent. Nevertheless, the results found a positive and significant change in poverty reduction rate in terms of MDPT ratio or HD × IA between 2004–2005 and 2014–2015.

Table 6.

Overtime Changes in HD, IA and the MDPT, 2004–2015

Kt = 33% (Cut-off)	HD × IA or MDPT (%)	HD (%)	IA (%)
2004–2005(a)	0.292	55.2	52.9
2006–2007	0.281	52.5	53.4
2008–2009	0.26	49.3	52.6
2010–2011	0.228	44.7	51
2012–2013	0.207	40.8	50.7
2014–2015 (b)	0.197	38.8	50.9
∆, 2004 (a) to 2015 (b)	(0.095)***	(0.164)***	(0.020)***
Std. err (aggregate)	0.0052	0.0091	0.0025
Hypothesis testing	18.16	17.99	8.08
Probability (value)	0. 0.	0. 0.	0. 0.

Source: Authors’ calculation based on data from (various issues) of the PSLM statistics.

Note: *** 1 per cent level of significance, ∆ = change and Std.err= Standard error.

Significant Change in Headcount (HD) for Four Provinces

The results of HD and MDPT incidence over time across provinces are reported in Table 7. The changes between different years 2004–2005/2014–2015 and 2014 also compared with 2004 in this analysis. The results of Punjab across all years showed a reduction in terms of HD and MDPT prospective and also found a significant reduction in the whole country (2004 to 2015), a significant drop can be seen in MDPT for the province of Sindh, from 2008 to 2010, and 2006 to 2008. The statistical figures of Khyber Pakhtunkhwa show an increasing and a decreasing trend in both HD and MDPT ratios. The significant drop between the periods of 2006 and 2012 can be observed in HD and MDPT, and a reduction between 2004 and 2014 is also found significant. The poverty in Baluchistan is higher as compared to other provinces; only one significant change in MDPT was found between 2008 and 2010. Though, HD and MDPT have significantly decreased over the years of 2004 to 2014.

Table 7.

Change in Headcount for All Provinces (with Statistical Significance)

Province	Years	Change in HD	Change in MDPT	Province	Years	Change in HD	Change in MDPT
Punjab	2012–2014	−0.03288**	0.01649**	KPK	2012–2014	0.00094	0.00051
	2010–2012	−0.0339**	−0.01984**		2010–2012	−0.05931**	−0.03129**
	2008–2010	−0.0516**	−0.03037**		2008–2010	−0.05431**	−0.04052**
	2006–2008	−0.03221**	−0.02019**		2006–2008	−0.05638**	−0.02955*
	2004–2006	−0.03296**	−0.01519*		2004–2006	0.00271	−0.00007
	2004–2014	−0.18353**	−0.10208**		2004–2014	−0.16635**	−0.10092**
Sindh	2012–2014	−0.01441	−0.00523	Baluchistan	2012–2014	−0.00688	−0.01008
	2010–2012	−0.0344*	−0.01635		2010–2012	−0.04015	−0.01143
	2008–2010	−0.03234*	−0.02714**		2008–2010	−0.03012	−0.04371**
	2006–2008	−0.02448	−0.02282*		2006–2008	−0.00873	−0.01183
	2004–2006	−0.03568*	−0.01452		2004–2006	−0.03584	−0.0075
	2004–2014	−0.14131**	−0.08606**		2004–2014	−0.12172**	−0.08455**

Note: * at 5 per cent and ** at 1 per cent significance levels, respectively.

Source: Authors’ estimation constructed on statistical data from PSLM statistical surveys, Pakistan bureau of statistics, Government of Pakistan.

Discussion

The ‘No Poverty’ in all its dimensions everywhere by 2030 is one of the main goal of SDGs. The vision 2025 of Pakistan is based on poverty reduction in terms of ‘No left behind’, but at the same time, it stances the challenges of eliminating poverty in all its dimensions and from all regions of Pakistan. The statistical survey of PSLM (various waves) and reports on MDPT 2016 revealed shed light on the state of deprivation and dynamics in Pakistan.

The government of Pakistan had launched programmes based on poverty reduction in different regimes such as Rural Works Programme (in 1963–1972), the Integrated Rural Development Programme (in 1972 to 1980), then the Five-Point Program (in 1985 to 1988). In 1999, the Tameere-Watan Programme and scheme of the Khushal Pakistan Programme were launched; after 2001, most development plans were focussed on rural development (as Arif & Farooq, 2012; Saboor, Khan, Hussain, Ali, & Mahmood, 2015). Some countries such as Mexico and Colombia have incorporated social action programme into their poverty reduction programmes (Angulo, Diaz, & Pardo, 2016; Coneval, 2007). In the 1980s, the Government of Pakistan launched a poverty reduction programme, though mainly concentrating on the basic necessities of life, for example, health, education, clean drinking water, etc. The government launched different social safety net programmes for poverty reduction such as cash transfer, the Benazir Income Support Programme, Pakistan Bait ul ul-Mal, Worker’s Welfare Fund, Pakistan Microfinance network and another social welfare programmes for old people s ‘Employees Old-Age Benefits Institution’.

The result of these schemes to overcome the problem of poverty is not beneficial in the entire country. The poverty with its all dimension is prevailing in Pakistan because of corruption and wrong implementation of social action programmes, high rate of terrorist attacks, a significant increase in population, regional disparities, political instability, energy crises and a dearth of active targeting. Reductions of socio-economic deprivations are a prerequisite for the sustainable living standard (explained by Bourguinon & Ckakravarty, 2002). Pakistan as a developing country couldn’t achieve the required targets from these social action programmes. Arif and Farooq (2012) explored that education is an essential indicator which can remove poverty and poverty gap from economy efficiently. World Bank (2002) also supported that poverty gap between urban and rural regions can be reduced by providing more educational facilities to rural regions as well. Higher education and technical education directly affect poverty reduction through the creation of job opportunities, but the health and house facilities are also necessary for poverty reduction. The worse health facilities have been associated with a higher rate of poverty (Arif & Farooq, 2012). Poverty alleviation programmes should be appropriately implemented to improve all three dimensions. The fundamental issues related to agriculture-based livelihood, higher dependency ratio, large household sizes and imperfect non-farm employment prospects should be decreased (Datt & Jolliffe, 1999), housing and health services should be provided; better job opportunities and education should be also provided (Datt & Jolliffe, 1999).

Conclusions

This study is based on poverty with its different shapes in terms of the regional variations including four provinces, rural and urban regions of Pakistan. Applying AFS methodology with the Global Multidimensional Poverty Index standard, the results shows the increasing trend between 2010/2011 and 2012/2013. The results at the provincial level indicate that Baluchistan found a higher rate of poverty with its all dimensions, while Punjab has the lowest incidence. This empirical analysis provides that an integrating technique is adopting for MDPT to overcome the socio-economic issues rapidly in Pakistan because it is an essential requirement of MDGs to provide necessities of life (food, health, water and education) to meet the global standards of well-being. For the policies based on poverty alleviation in Pakistan, there are some steps taken by government, especially in the regions with a higher level of poverty, such as trying to improve the quality education through increasing number of schools and teachers, increasing school enrolment ratio, providing some inducements of scholarships, trying to compensate various poor families with money and sending their children to school. The government may provide high quality technical education such as knowledge and skills which can be contributed to economic growth and development (also supported by Khilji, 2014, 2015). Pakistan is an agriculture-based country, and number of opportunities in terms of providing agriculture credit with an easy process, subsidized pesticides, seeds, cheap availability of gas and electricity, proper infrastructure in these areas may also be used for controlling on poverty. Similarly, health facilities should be increased in rural areas, especially by providing them well-resourced hospitals, including trained doctors and other paramedical staff and by checking their efficiency; there should be a proper system which induces proper monitoring.

Further, poor people may increase their income if the government provides them with free and easy entry to technical schools (Chawanote & Barrett, 2012). The poverty can be removed from remote areas of Pakistan by providing subsidized inputs, easy access to credit for farmers in the agriculture sector and better services. Similarly, health facilities should be increased in rural regions; it can be possible through improved health facilities by increasing the number of hospitals along with proper resources and trained nursing staff. Thus, escalating social protection schemes and pursuing appropriate programmes to the poor and most vulnerable can further decrease poverty. Social protection programmes should be included based on particularly cash transfers programmes, school feeding schemes and improvement in labour market, as well as social and unemployment insurance, an increase in old-age pensions, skills training, disability pensions and wage subsidies, among others. Moreover, applying the suggestion of this study to adopt those policy mechanisms which is beneficial for poverty alleviation, developing countries can also reduce poverty from the economy.

Footnotes

Acknowledgements

The authors are grateful to the anonymous referees and the editorial team of the journal for their extremely useful suggestions to improve the quality of the article.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.

Funding

The authors have not received any financial support for this research, authorship and/or publication of this article.

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