Population Dynamics

Population of Pakistan: An Analysis of NSER 2010-11

Population Dynamics

Government of Pakistan -BISP- -

Dignity, Empowerment, Meaning of Life to the most vulnerable through the most scientific poverty database, targeted products and seamless service delivery nationwide.

Material in this publication may be freely quoted or re-printed, but acknowledgement is requested, together with a copy of the publication containing the quotation or reprint Research Wing, BISP

Researcher: Dr. Durr-e-Nayab

Disclaimer: The views expressed in this publication are those of the author and do not necessarily represent the views of Benazir Income Support Programme (BISP) and UNICEF.

Population Dynamics

Table of Contents

1. Introduction………………………………………………………………………………… 05

2. Importance of Understanding Population Dynamics in Literature………………...... 07

3. Methodology……………………………………………………………………………….. 09

4. Dynamics in Population: Findings from the NSER 2010-2011……………………….. 13 i. Population distribution…………………………………………………………...... 13 ii. Age-sex structure………………………………………………………………...... 14 iii. Sex ratios………………………………………………………..………………...... 19 iv. Mean and median age of the population……………………………………...... 21 v. Dependency rate………………………………………………………………...... 22 vi. Household size, type and headship…………………………………………...... 22 vii. Marital status……………………………………………………………………...... 23

5. Conclusions and Implications for BISP

Annexure

List of Tables

Table 1: Population distribution by province/territory and sex…………………………….. 13 Table 2: Changes in population distribution by province from census 1998 to NSER 2010-2011 2010………………………………………………………………………. 14 Table 3: Whipple’s index………………………………………………………………………. 16 Table 4: Myer’s blended index values by province/territory in NSER 2010-2011…….... 18 Table 5: NSER 2010-2011: Age ratio score by province/territory………………………… 18 Table 6: NSER 2010-2011 Mean age of the population by province/territory…………… 21 Table 7: NSER 2010-2011: Household size, type and headship…………………………. 23 Table 8: NSER 2010-2011: Proportion ever married by age, sex and province/territory. 23

List of Figures

Figure 1: Age-sex structure of the population by province/territory in NSER 2010-2011 2010……………………………………………………………... 15 Figure 2: Pakistan: Preference for digits in NSER 2010-2011 according to Myers’ Blended Index by sex………………………………………………………………. 17 Figure 3: NSER 2010-2011: Sex ratios………………………………………………………. 20 Figure 4: NSER 2010-2011: Dependency rates and mean poverty score by province/territories………………………………………………………………...… 22

Population Dynamics

1. Introduction

A changing population structure has serious implications for a country’s socio-economic milieu. The best way to gauge these changes is through the periodically conducted national censuses which give a precise image of the state of population in a country. In Pakistan, however, conduction of censuses has been irregular and infrequent with the last being held some 16 years back in 1998. The National Socio-Economic Registry (NSER), based on the Benazir Income Support Programme’s (BISP) Poverty Score Survey (PSS) conducted in 2010, being a census covering over 27 million households, gives us an ideal opportunity to know and understand the population dynamics of Pakistan.

Population dynamics include many factors including changes in the: size of the population in absolute number and rate of growth; age-sex structure of the population; average size of household; movement of people from one place to another; occupation distribution of the employed labour force; size of urban and rural population; wealth status of the inhabitants; and family structure. The NSER 2010-2011 is helpful in measuring many of these dynamics and the present paper aims to look at these, and some other related factors, using the survey data.

The BISP survey data would be used in the present paper to understand population dynamics in Pakistan at the national, provincial and district level and wherever possible compare these with the trends in the past. The census of 1998 and other credible surveys would be used for the sake of getting some historical sense of the trends found in the present study using the NSER 2010-2011. Based on the data that could be extracted from the BISP survey, the present paper aims to look into the: population composition by region; age structure of the population and gauge the quality of the age reporting in the NSER 2010-2011; sex structure of the population; size of population of children and women in reproductive ages as they have specialised needs to be taken care of; family structure and pattern of household headship prevalent in the country; and marital status of the population.

After this introductory section the paper has four more sections. Section two deals with a brief description of why it is imperative to understand population dynamics and the way they are linked to other factors of life. Section three details the methodology employed in this paper to achieve the objectives set forth in this paper. Section four talks about the population dynamics in the areas demarcated in the objectives of the study, including various aspects of age-sex composition, marital status of the population and family and household structure. The last section presents the conclusions derived from the study, and their implications for BISP.

Population Dynamics

2. Importance of Understanding Population Dynamics in Literature

Pakistan’s population has grown rapidly. Besides the sheer numbers that are hard to manage on their own, the evolving demographic scenario in the country with changing composition of young and old, and differential regional trends of fertility, mortality, morbidity, growth and migration ask for clear understanding of the population dynamics. The fact that population dynamics form the subject area of so many cross-cutting disciplines, including demography, anthropology, human geography, economics, genetics, sociology, ecology and biology, underscores the importance linked to its understanding [Macbeth and Collinson (2002)].

Population dynamics play a pivotal role in determining the potential for economic development. The notion of ‘demographic dividend1’ stems from this very idea of population dynamics, where the stress is not just on the population size but on its composition. While population dynamics carry many opportunities it is not without challenges either. If the dynamics through urbanisation create economies of scale and increase in the number of youths create more labour it is at the same time a challenge to make use of these opportunities. Understanding population dynamics is, therefore, essential for all kinds of policymaking, be it related to health, education or labour. As the UNFPA (2012) very rightly claims that, “…. none of the greatest challenges of our time can be resolved without attention to population dynamics”.

Population dynamics are particularly important in the context of comprehending inequalities. While fertility and mortality rates are declining but they are not doing so equally for everyone. Poor continue to have high fertility, morbidity and mortality rates which in most cases become a source of trapping them in the vicious cycle of poverty [Edwards (2002); Maloney (2009); Rodgers (1984)]. There is geo-demography to be taken into account too as these demographic trends also show geographic patterns as they are spatially concentrated. It is for this reason that the present study looks into the demographic trends at the district level as well.

While there is concern about addressing the need of the young population vis-à-vis education and employment but ageing is another major consequence of demographic transition. With increasing longevity, the number of elderly is increasing in the world and Pakistan is no exception. The ageing population has vast implications for social security, geriatric healthcare and protection of their rights as a citizen of state. With changing family structures and reduced family support the care of the elderly is a major task, especially in

1 “The demographic dividend can be defined as the potential economic benefit offered by changes in the age structure of the population, during the demographic transition, when there is an increase in working age population and an associated decline in the dependent age population………. This relation is summarised in the lifecycle income and consumption model. As a result of declining population growth and consequent changes in age structure, the proportion of working age population is increasing in most developing countries, offering a window of opportunity to these countries, referred to as the ‘demographic dividend’ [Nayab (2008)].

Population Dynamics

countries like Pakistan where the public social protection system is far from universal if not almost non-existent.

More than half of the world population already lives in urban areas and with time urbanisation is bound to increase [ESCAP (2011)]. Pakistan is already the most urbanised country in the South Asian region. With increased urbanisation comes the need to plan for improved access to education, health, housing, transport, communication and most importantly employment. Linked to this trend of urbanisation is the impact of population pressures on ecology (Rogers 2010). Population dynamics are also associated with environmental degradation and it is the poorest segments of the population who suffer the most as they are most dependent on the natural resources.

The age-sex structure of the population has especially important repercussion vis-à-vis the potential demographic dividend. This essentiates the need to focus on the 0-14, 15-64 and 65 and above age categories even more. Also, in order to address our concern for the infant, child and maternal health the 0-1, 0-5, and 15-49 age categories should also be studied.

Evolving population dynamics also need to be taken care of with regard to reproductive health. With demographic transition, along with increasing number of population in the working ages, there is a boom in women in reproductive ages (aged 15-49 years) as well. Their health needs, including ante and postnatal care and provision of family planning services need to be planned far in advance. Women’s reproductive health status is affected by society’s norms linked to marriage and childbearing along with her educational and economic status. State of reproductive health is also influenced by the ability of the health system to deliver comprehensive and quality reproductive health information and services. To achieve all this there is, however, a need to understand the population dynamics.

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3. Methodology

The NSER 2010-2011 would be used to understand the population dynamics in the country. Along with the basic two-way and three-way cross tabulation the study intended to conduct multivariate analysis wherever possible but this could not be done due to the way survey data was managed at its source. Likewise, the original plan to decipher rural-urban patterns was dropped as the NSER 2010-2011 data does not provide a way to identify the two regions. For the same reason the original plan to apply the Zipf’s Law/Rank-Size rule to determine the city structure of the country could also be not implemented.

For ascertaining the age-sex structure of the population the paper along with using some of the basic methods, like age proportions, mean and median ages, and sex ratios, employs certain specialised indices to evaluate the quality of age reporting in the NSER. These include the Whipple’s and Myers’ indices, and Age Ratio Score (ARS) used to validate the age reporting in the survey data. Along with being the most commonly used and understood indices, they are preferred also because they deal with ages in single years, as opposed to dealing them in five year age-groups, like some other available indices do.

The Whipple’s and Myer’s indices and Age Ratio Score (ARS) would be calculated at both the national and provincial level. Whipple’s index detects a preference for ages ending in 0, 5, or both. Whipple’s index is constructed for the age group of 23–62 years using the following formula [Shryock and Seigel (2008)]:

Whipple’s Index for the 5-year range:

∑(푃 +푃 +푃 +⋯….푃 )∗ 100 = 25 30 35 60 1/5 ∑(푃23+푃24+푃25+⋯….+푃62)

Whipple’s Index for the 10-year range:

∑(푃 +푃 +푃 +푃 )∗ 100 = 30 40 50 60 1/10 ∑(푃23+푃24+푃25+⋯….+푃62)

Whipple’s index varies from 0 to 500. A value of 0 indicates that digits ‘0’ and ‘5’ are not reported, 100 means there is no preference for ‘0’ or ‘5’, and a maximum of 500 is seen when only the digits ‘0’ and ‘5’ are reported in the age data. The inference about age distribution based on this index is as follows: <105 = highly accurate; 105–109.9 = fairly accurate; 110–124.9 = approximate; 125–174.9 = rough; ≥175 = very rough.

The ARS are calculated for age up to 74 years and are defined here as the ratio of the population in a given age group to one-third the sum of the population in that age group and in the preceding and following groups, multiplied by 100. The age ratio is expressed for a 5-year age group as follows:

Population Dynamics

ARS for 5Pa:

5푃푎 × 100 5푃푎 = 1⁄3 [ 5푃푎−5 + 5푃푎 + 5푃푎+5]

where 5Pa is the population in the given age group, 5Pa-5 is the population in the preceding

age group, and 5Pa+5is the population in the following age group.

The third measure, the Myers blended index is calculated for the age above 10 years and shows the excess or deficit of people in ages ending in any of the 10 digits expressed as percentages. It is based on the assumption that the population is equally distributed among the different ages. The steps in the calculation of Myers’ blended index are as follows [Shryock and Seigel (2008)]:

(1) Sum of populations ending in each digit over the whole range starting with the lower limit of the range (e.g., 10, 20, 30, 40,….; 11, 21, 31,….)

(2) Ascertain sum excluding the first population combined in step 1 (e.g., 20, 30, 40,….; 21, 31, 41,….)

(3) Weight the sums in steps 1 and 2 and add the results to obtain a blended population (e.g., weights 1 and 9 for 0 digit, weights 2 and 8 for 1, etc.)

(4) Convert distribution in step 3 into percentages.

(5) Take the deviation of each percentage in step 4 from 10.0, which is the expected value for each percentage.

(6) A summary index of preference for all terminal digits is derived as one half of the sum of the deviations from 10.0 percent, each without regard to signs.

Family structure of the household has significant role to play in society likes ours. Families having nuclear, joint/extended structure, and those headed by male or female heads, can act differently in the socioeconomic spheres. Data would be analysed to look into the family structure of the households in general and also by the age and sex of the head of the household. Similarly, in recent literature one of the most important determinants of a household’s economic status comes out to be the size of the household. The NSER 2010-2011 data would be analysed to look at the size of the household at the national, provincial and district level and how it relates to its poverty score.

Marital status of the population is important for its relationship with fertility and hence with population growth. In a high fertility country like Pakistan age at marriage has special significance. Early age at marriage means longer exposure to risk of childbearing. The BISP data do not ask about the age at marriage but the information on the current marital status of the population would be used to estimate the overall marriage situation of the males and females in the country by age.

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It may be emphasised here that all analysis would be done at the national, provincial and district level, using mean poverty score at the relevant level based on the BISP poverty score card measure.

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4. Dynamics in Population: Findings from The NSER 2010-2011

This section would look into those aspects of population dynamics that can be gauged through the NSER 2010-2011 at the national, provincial and district levels, using the mean and median poverty score at the relevant level.

(i) Population Distribution Comparing the distribution of population across the four provinces and the four territories, namely Azad Jammu and Kashmir (AJK), FATA (Federally Administered Tribal Areas (FATA), Islamabad Capital Territory (ICT) and GB (Gilgit Baltistan), in the NSER 2010-2011 with that found in the Census 1998 we see that except for the downward shift in Punjab (from 55.6 percent to 51.6 percent) population proportion of all other areas remain close to what it was sixteen years back (see Table 1). Sindh, however, does show an increase of 1.3 percent in its population share from where it was in Census 1998.2 On the contrary, FATA and Balochistan both show a slight lowering in their population shares. There are obvious geo-political and social reasons for it but presence of GB and AJK in the NSER 2010-2011 figures are also affecting the overall distribution of the population.

Table 1 Population Distribution by Province/Territory and Sex NSER 2010-20111 Census 19982 Province/Territory Male Female Total Total Punjab 51.3 51.8 51.6 55.6 Sindh 24.5 24.2 24.3 23.0 KP 14.0 13.9 14.0 13.4 Balochistan 4.7 4.4 4.6 5.0 GB 0.7 0.7 0.7 - AJK 2.2 2.4 2.3 - FATA 1.9 1.8 1.9 2.4 ICT 0.7 0.7 0.7 0.6 100.0 100.0 100.0 100.0 Source: 1. Author’s computation using NSER 2010-2011 micro-data. 2. Census 1998 published reports.

To nullify the effect of additional areas in the population composition of NSER 2010-2011, namely that of GB and AJK, we subtract these two populations from the total to make the resulting proportions comparable with the Census 1998. As can be seen from Table 2, Punjab (–2.47 percent), Balochistan (–0.27 percent) and FATA (-0.49 percent) show a declining proportion of population in the NSER 2010-2011. Punjab’s loss appears to be Sindh’s gain as it increases its share of population by 2.08 percent (Table 2). Table 2

2Comparing these figures with the results from the same year’s PSLM-HIES (2010-11) we find the share of Punjab to be lower in the NSER 2010-2011, with a corresponding increase in the proportion of population in the other three provinces. This, however, is a crude estimation as the PSLM-HIES dataset does not disaggregate/account for the population in GB, AJK, FATA and ICT which are part of the NSER 2010-2011 dataset. Same trend is reflected when the proportions are disaggregated at the gender-level.

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Change in Population Distribution by Province from Census 1998 to NSER 2010-2011 Province/Territory BISP 1998 Census % Change Punjab 53.16 55.63 -2.47 Sindh 25.08 23.00 2.08 KP 14.44 13.41 1.03 Balochistan 4.69 4.96 -0.27 FATA 1.91 2.40 -0.49 ICT 0.72 0.61 0.11 Total 100.00 100.00 100.00 Source: 1. Author’s computation using NSER 2010-2011 micro-data. 2. Census 1998 published reports.

(ii) Age-Sex Structure As discussed above, age-sex structure is the most fundamental characteristic of population composition and hence its dynamics. Past variation in in the basic components of the demographic processes, that is fertility, mortality and migration, are reflected in the age- sex structure of a population. The effect, however, works in the reverse direction as well. The age-sex composition affects any population’s fertility, mortality, migratory and employment patterns in return making it a cyclic relationship.

The age-sex pyramids are the handiest way to understand structure of a population. Figure 1 presents the age-sex pyramids of Pakistan and its provinces and territories as found in the NSER 2010-2011. Among the most striking features of the pyramids is the small base in all the pyramids and an almost classical shape (see Figure 1). The shrunken base shows poor age reporting for the 0-4 age group which has led to its drastic under-enumeration for all the provinces and territories. One possible explanation for this can be the very objective of the NSER 2010-2011 had adults, specifically adult females, as its focus.

The other prominent feature of Figure 1 is the near to classic shape of the pyramids. It means that the fertility levels in the country continue to be high and reflected in the heavy base of all the pyramids. It is not surprising as the recent Pakistan Demographic and Health Survey (PDHS) 2012-2013 also shows a total fertility rate of 3.8 children per woman in reproductive ages [NIPS (2014)]. The NSER 2010-2011 findings conform to those of the PDHS 2012-2013, as can be seen from Figure 1.

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Fig. 1. Age-Sex Structure of the Population by Provinces/ Territories in BISP-RSS 2010 Pakistan

Punjab Sindh

KP Balochistan

GB AJK

FATA ICT

Source: Author’s computation using NSER 2010-2011 micro-data.

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Evidence suggests that age reporting in developing countries is subject to errors particularly in settings where literacy levels are low [Yazdanparast, Pourhoseingholi and Abadi (2012); Bello (2012); Gunasekera (2009)]. The most common error in age reporting is the tendency to round off ages to the nearest figure ending in ‘0’ or ‘5’ or in fewer cases to the nearest even number. Because of this digit preference age heaping occurs at certain ages in a sample population [Kirkegaard (2015)].

To judge the accuracy of the reported age presented in the pyramids (Figure 1) certain indices are applied to the NSER 2010-2011. These include the Whipple’s and Myers’ indices and the Age Ratio Score. Table 3 presents the summary measures of the Whipple’s Index of the BISP dataset by provinces and territories. The under-100 value of the Whipple’s Index for all the administrative units shows that contrary to the general trend of over- reporting of 0 and/or 5 ages reporting in NSER 2010-2011 under-enumerates ages ending at these two digits. So instead of a digit preference for 0 and/or 5 there is a slight avoidance to report them (Table 3). For Pakistan it can be called a new trend as the Whipple’s Index calculated for the Census 1998 as well we see a clear preference for digits 0 and 5 with the Index having a value of 186.03 [Ali and Sultan (2003)].

Table 3 Whipple’s Index Province/Territory For 0 For 0 & 5 Pakistan 78.2 90.3 Punjab 78.4 88.6 Sindh 78.1 90.4 KP 76.8 93.7 Balochistan 77.9 93.5 GB 75.2 90.9 AJK 86.0 96.3 FATA 71.5 97.4 Source: Author’s computation using NSER 2010-2011 micro-data.

If in the NSER 2010-2011 there is no age-heaping found for ages ending at 0 and 5 there is a need to ascertain if there are any other digits that are reported more often than expected. For this we apply the Myers’ Blended Index which can gauge age heaping for all the digits (from 0 to 9). Figure 2 presents the results of the Myers’ Blended Index applied to the NSER 2010-2011 data. We see that there is age-heaping at ages ending at digit 2, 3, 7 and 8 (i.e. ages like 22, 23, 27, 28, 33, 47, 58). This pattern is contrary to what is generally found in surveys conducted in areas with poor age reporting as in Pakistan. In some recent surveys in Iran, Sri Lanka, Liberia and India there was a visible preference for ages ending at 0 and 5 [Yazdanparast, Pourhoseingholi and Abadi (2012); Pardeshi (2010); Gunasekera (2009); and UN (2011)].

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Fig. 2. Pakistan: Preference for Digits in NSER 2010-2011 According to Myers’ Blended Index by Sex

Total Males Females

13 12 11 10 9 8 7 6 5 4 3 2 1 0 0 1 2 3 4 5 6 7 8 9

Source: Author’s computation using NSER 2010-2011 micro-data.

Figure 2 presented the trends shown for age reporting in Pakistan and if we look at the trends at the provincial/territories’ level, as shown in Table 4, we find a similar trend of age heaping at digits 2, 3, 7 and 8 in most administrative units. The overall Myers’ Blended Index values (given in the last row of Table 4) show that Islamabad Capital Territory (ICT) and Balochistan have stronger digit preferences than other regions (having a cumulative value of 9.1 and 8.8, respectively) of the country. While FATA and AJK, with a cumulative Index value of 4.7 and 4.8, respectively, show minimum age heaping among all the regions (Table 4).

Regionally disaggregated Myers Blended Index values from the Census 1998 are not available, nor can be calculated by the author as it needs data in single years, but if we compare the values on the national level we see a mixed pattern. Census 1998 showed age heaping for digit 0, 5 and 8 (Ali and Sultan 2003), with 8 being the only digit that corroborates with the estimates of NSER 2010-2011. The 1998 Census results showed that the preference for 0 and 5 were much stronger for rural areas than for urban areas, something that could not be calculated for NSER 2010-2011 because of the absence of urban-rural information.

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Table 4 Myers’ Blended Index Values by Province/Territories in NSER 2010-2011 Pakistan Punjab Sindh KP Balochistan GB AJK FATA ICT 0 9.6 9.6 9.8 9.4 9.4 9.2 10.2 8.1 10.7 1 8.8 8.9 8.5 8.5 8.9 8.6 8.6 12.1 7.7 2 12.5 12.7 13.3 11.6 11.5 11.7 12.1 7.4 14.9 3 11.5 11.1 11.6 12.0 14.7 11.4 9.4 15.7 8.1 4 8.9 8.9 8.9 9.2 8.1 9.5 9.6 7.7 10.1 5 9.3 9.0 9.3 10.0 10.2 9.6 9.6 11.2 8.6 6 8.4 8.5 7.8 8.7 7.6 9.1 9.3 9.9 8.4 7 12.0 12.2 12.1 11.3 10.6 11.6 12.3 8.3 13.7 8 11.0 10.8 11.0 11.3 11.7 11.0 10.2 11.4 9.4 9 8.0 8.1 7.6 8.0 7.2 8.4 8.7 8.3 8.3 Total 7.0 6.9 7.9 6.2 8.8 5.6 4.8 4.7 9.1 Source: Author’s computation using NSER 2010-2011 micro-data. Note: Shaded boxes show age-heaping/digit-preference for that particular digit.

So far we have evaluated age reporting in the NSER 2010-2011 data for age-heaping and digit preference, but a quick analysis of cohort comparison is also warranted. For this we use the Age Ratio Scores (ARS) that estimate the ratios between two subsequent age cohorts. Age ratios for 5-year age groups are estimated as indices for detecting possible age misreporting. Normally age ratios are expected to be smooth throughout the age distribution and all of them should be close to a value of 100. Table 5 presents the ARS estimated for the NSER 2010-2011. If we look at the trends at the national level we find jumps in age reporting for cohorts 5-9, 40-44 and 60-64 (Table 5). The biggest jump is for the 5-9 age group which is understandable in the backdrop of poor age reporting for 0-4 year age group in the NSER 2010-2011, as we saw in the section above and Figure 1 as well.

Table 5 NSER 2010-2011: Age Ratio Score by Province/Territory Pakistan Punjab Sindh KP Balochistan GB AJK FATA ICT 5-9 129.1 126.8 133.4 129.1 132.4 130.8 125.74 125.43 125.13 10-14 102.5 101.8 103.3 103.3 105.9 101.3 100.50 99.43 100.10 15-19 96.1 98.5 92.0 95.9 89.5 100.6 101.07 91.33 97.07 20-24 102.0 103.0 100.5 101.2 101.9 100.7 101.25 97.47 106.59 25-29 96.2 94.9 96.0 98.8 98.9 98.5 99.45 104.96 98.33 30-34 104.6 104.7 108.4 99.4 107.8 91.9 98.73 92.94 101.18 35-39 90.3 91.1 88.9 90.7 84.1 95.8 91.09 95.56 91.04 40-44 104.6 102.9 107.4 105.0 109.1 107.5 103.60 101.62 103.38 45-49 100.4 102.1 96.7 100.9 95.8 94.9 104.57 97.51 100.46 50-54 96.3 97.9 94.8 91.5 98.5 94.4 89.11 103.40 101.35 55-59 96.9 94.9 97.4 103.6 92.3 101.9 106.75 93.13 94.87 60-64 106.6 105.9 109.8 103.9 113.2 106.3 95.83 111.02 106.82 65-69 94.2 94.8 90.1 98.7 90.0 96.3 105.63 87.15 90.89 70-74 102.6 104.7 102.7 94.2 104.7 102.1 99.72 101.06 100.73 Source: Author’s computation using NSER 2010-2011 micro-data. Note: Shaded boxes show age-heaping/digit-preference for that particular digit.

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(iii) Sex Ratios After the age structure the second most important structural aspect of populations is the relative numbers of males and females who compose it. Sex ratios are a convenient way of comparing the numbers of males and females in a population, and Figure 3 presents the sex ratios, as found in the NSER 2010-2011, at the national and disaggregated levels. If we look at the sex ratio nationally (Figure 3: A) we see that it fluctuates with age groups. For some it goes above the 100-parity (like for 10-14, 15-19, 60-64, 65-69 and 70-74 age groups) while for others it remains below it (for 20-24, 25-29, 30-34, 35-39 and 40-44 age groups). The above 100 sex ratio conforms to the trend found in Pakistan in all the surveys/censuses (including PDHS 2006-2007, PDHS 2012-2013 and the 1971, 1981 and 1998 censuses as can be seen from Annex I) but the below 100 sex ratio during the reproductive ages for women is rather peculiar. Are females being over-reported keeping eligibility criteria of BISP cash transfer in mind? It is a question worth probing but beyond the scope of the present study.

If we look at sex ratios at sub-national levels (Figure 3 B and C) we see a very erratic pattern across ages and regions. Taking the provincial differences first we see wide variations in sex ratios for various ages across the four provinces (Figure 3: B). For instance, the sex ratio for the 70-74 age group for Sindh is near to 90 while it is approximately 140 for the province of Balochistan. Coming to the territories we see the same erratic pattern (Figure 3: C) but it is worth noting that the sex ratios, especially during the 25 to 44-year age groups are much lower than those found in the province (Figure 3: B).

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Fig. 3. NSER 2010-2011: Sex Ratios A: Pakistan

B: Provinces

C: Territories

Source: Author’s computation using NSER 2010-2011 micro-data. Note: Straight line in 3A indicates the national sex ratio for the sake of reference.

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(iv) Mean and Median Age of the Population The mean and median age of a population is a good summary indicator of its age structure. Looking at Table 6 we see that Pakistan, as one would expect, is a very young country with half the population aged less than 20 years in age. ICT, Punjab and AJK had a slightly older age structure, with a median age of 22 and 21 years, respectively, but most striking is the median age of 14 in the FATA (Table 6). A look at the age-sex structure of the pyramid in Figure 1 also corroborates with this trend. With the very young population in FATA it would not be wrong to infer that the area is still characterised with high fertility levels. The mean age of the population is close to 25 years, with the trend being similar in all provinces and territories as was found for the median ages (see Table 6). If we compare these mean and median ages with the PSLM-HIES 2010-2011 we see that the age profile of the population, at both national and provincial level is slightly older in the NSER 2010-2011 data, especially in the province of Balochistan. This is an expected trend, because as we saw above, the 0- 4 age-group is very small in the NSER 2010-2011 data, pushing up the mean and median age of the population.

Table 6 BISP-PS: Mean Age of the Population by Province/Territory Mean Median Total Male Female Total Male Female Pakistan 24.6 24.5 24.8 20 20 20 Punjab 26.2 26.3 26.2 21 21 21 Sindh 23.9 23.6 24.1 19 18 19 KP 24.3 24.3 24.4 19 18 19 Balochistan 24.1 24.0 24.4 19 18 20 GB 24.6 24.7 24.6 19 18 19 AJK 26.0 26.1 26.0 21 21 21 FATA 22.5 22.3 22.8 14 14 15 ICT 26.4 26.7 26.2 22 22 22 Source: Author’s computation using NSER 2010-2011 micro-data.

The mean age of a population is believed to have a strong link with economic standing of a population. It is also the basis of the notion of demographic dividend that increasing age, till a certain level, means lowering of the young dependency rate. The NSER 2010-2011 data also proves this to be valid for the districts in Pakistan as we see a strong positive relationship between increasing mean age of the population and a district’s improving mean poverty score. Annex III presents the mean age of the population of all the districts of Pakistan along with their mean poverty score. It is evident that as the mean age goes up so does the mean poverty score (see Annex I).

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(v) Dependency Rate Lowering of dependency rates is the basis of the very idea of ‘demographic dividend’ and the NSER 2010-2011 data validates this notion. Figure 4 shows that as the dependency rate of a province/territory decreases its mean poverty score increases. Punjab, ICT and AJK with their higher mean poverty scores have lower dependency rates, while FATA with its high dependency rate has very low mean poverty score (Figure 4). Both have a statistically significant strong negative correlation. The same pattern is visible at the district level as well. As we can see from Annex III, districts with lower dependency rates have higher mean poverty scores. For instance, Muzzafargarh with a dependency rate of 85.7 percent has a mean poverty score of 18.9, while Rawalpindi appears to be on the other end of the spectrum with much lower dependency rate of 54.3 percent with a mean poverty score of 34.1 (Annex III).

Fig. 4. NSER 2010-2011: Dependency Rates and Mean Poverty Score by Province/Territories Mean Poverty Score Poverty Mean

Dependency Rate

Source: Author’s computation using NSER 2010-2011 micro-data.

(vi) Household Size, Type and Headship Population dynamics along with being affected by, and affecting in return, individual factors are linked to certain household factors as well. Among these household factors are household size, type of family structure and household headship. In the NSER 2010- 2011 data, as can be seen in Table 7, the mean household size varies from 6.2 (as in ICT) to 8.3 (as in GB). The size is not far from what was found in the 1998 census, for which the mean household size for Pakistan was found to be 6.8 members per household [Zahir

Population Dynamics

(2003)]. The Pakistan Social and Living Measurement Survey (PSLM) conducted in the same year as the NSER 2010-2011 found the mean household size to be 6.4 members per household [PBS (2011)]. Regarding the type of household, as can be seen from Table 7, a big majority in all provinces had nuclear families. The proportion of female headed households as found in the BISP survey is much higher than what is found in any other survey in Pakistan. Highest estimates put female headed households in Pakistan at around 12 percent [ICF (2011)] while in the NSER 2010-2011 it ranges from 14 to 28 percent in different areas of the country (see Table 7). The FATA having the highest proportion of female headed households need further probing as being the most traditional society in the country it seems a little unlikely even if we consider the migration of working age males. The mean age of the head of the household, however, does not show any vast variations among different regions.

Table 7 NSER 2010-2011: Household Size, Type and Headship Mean Poverty Nuclear Families Mean Female Headed Mean Age of the

Score (%) HH Size households (%) HH Head Punjab 27.73 82.5 6.5 22.3 47.1 Sindh 20.26 84.0 6.6 17.9 44.0 KP 21.99 76.5 7.2 22.0 46.4 Balochistan 20.58 79.0 7.9 14.4 44.5 GB 24.92 63.4 8.3 19.9 48.8 AJK 29.27 67.2 7.1 22.8 49.0 FATA 16.82 75.2 7.9 27.9 43.4 ICT 36.96 80.7 6.2 26.1 45.7 Source: Author’s computation using NSER 2010-2011 micro-data.

Literature shows that all the factors presented in Table 7 are linked to a household’s economic status as well. Analysing NSER 2010-2011 for these factors we see that the size of the household has strong negative relationship with the poverty score. As can be seen from Table 7, provinces/territories with smaller mean household size have comparatively higher mean poverty score as well (implying they are economically better off). Same pattern can be observed if we look at the districts’ information given in Annex III. For instance, district Abbottabad with a mean household size of 6.1 has a mean poverty score of 30.1 while district Buner with a mean household size of 8.3 has a mean poverty score of only 20.7 (Annex III).

(vii) Marital Status As discussed earlier, age at marriage has strong implications for population dynamics as it has repercussions for childbearing/fertility and thus for population growth. The NSER 2010- 2011 does not ask for age at marriage but the current marital status of the population can give us some approximate idea of marital trends in the country. Table 8 shows the current age-sex specific marital status of the population by area. Marriage remains to be almost universal in Pakistan for both males and females with more than 90 percent getting married in their lives. If we, however, compare these trends with those found in the 1998 census

Population Dynamics

[Soomro (2003)] we see fewer younger males and females being married in NSER 2010- 2011 at early ages (see Table 8 and Annex II). If we can rightly infer some delay in getting married for females from the NSER 2010-2011 then it has consequences for population growth in the country. Delay in marriage means fewer years having risk of childbearing in countries like Pakistan where fertility equates marital fertility.

The pattern exhibited by the provinces/territories is mirrored by the districts as well (see Annex IV). Worth noting in Table 8 and Annex IV is the negative relationship between the proportions of females getting married at early (15-19 years) and the mean poverty score. Districts with higher scores generally have lower proportions getting married early, as can be seen from Annex IV. For example, Abbotabad with a mean poverty score of 32.1 has only 1.5 percent of the females getting married in the 15-19 year age group while in Kohistan, having a poverty score of 18.1, 14.9 of their contemporaries have already married (Annex IV).

Table 8 NSER 2010-2011: Proportion Ever Married by Age, Sex and Province/Territory MPS1 15-19 20-24 25-29 30 ≤ Total Male Punjab 27.73 0.7 13.0 44.5 92.4 58.0 Sindh 20.26 0.9 21.1 56.8 93.0 59.8 KP 21.99 0.9 17.2 51.4 93.7 57.2 Balochistan 20.58 2.0 20.1 52.8 90.8 57.9 GB 24.92 1.0 15.4 49.4 92.3 54.6 AJK 29.27 0.5 9.9 41.5 92.4 56.1 FATA 16.82 2.4 39.3 71.5 96.2 64.7 ICT 36.96 0.7 8.9 39.4 93.0 57.1 Females Punjab 27.73 2.5 35.7 71.7 96.4 67.4 Sindh 20.26 2.8 47.9 78.4 96.3 70.6 KP 21.99 5.0 51.4 79.6 95.7 69.3 Balochistan 20.58 5.7 46.1 73.2 94.2 69.3 GB 24.92 3.7 39.5 70.7 95.2 63.8 AJK 29.27 2.0 33.6 70.2 96.0 65.9 FATA 16.82 9.0 60.6 87.6 97.7 74.9 ICT 36.96 2.3 31.6 66.3 95.6 65.5 Source: Author’s computation using NSER 2010-2011 micro-data. Note: 1. Mean Poverty Score.

Population Dynamics

5. Conclusions and Implications for BISP Targeting

Given the fact that population dynamics act as a mediating factor among many facets of life it is important to understand them well. Making use of the NSER 2010-2011, which was a census of the population, the present paper looks into different aspects of population dynamics including: age-sex structure, family type, household size and marital status. The age reporting, which is significant in more ways than one can think, is evaluated using various techniques. Pattern of age reporting in the NSER 2010-2011 is found to be different from what we find in other surveys conducted in the country, having peculiar trends of age- heaping/digit preference. Whether this could be ascribed to the pre-conditions associated with the BISP cash transfer is debatable and is beyond the scope of this paper.

The BISP data proves useful to understand many factors about which data was lacking at the national level as the last census took place eighteen years back. These especially include the marital status of the 15 plus population that using BISP data gave hints of delayed marriage among females and the big proportion of nuclear families in the country. The first has serious consequences for child bearing and the second for the care of the ever increasing numbers of elderly in the country. In the absence of any formal social security system weakening of the family structure has far reaching repercussions.

All its advantages notwithstanding, the age-reporting in the NSER 2010-2011 data has serious implications vis-à-vis the targeting of its beneficiaries. As we saw in the section entailing the findings of the study, the mean age of the population in NSER is higher than what was found in the PSLM-HIES data for the same year. This trend was more evident in the provinces having higher beneficiary rates, and more for the female population than for male. Since females, aged 18 years and above, are the recipients of the BISP cash transfer the probability of reporting increased ages for younger females can be inferred. The issue is magnified by the small proportion of population in the 0-4 age group for which no conclusive reason is assigned. Since it is the females aged over 18 years that are of prime concern as far as BISP targeting matters, it is imperative that the issues linked to age reporting are looked into.

Population Dynamics

Annexure

Annex I: Sex Ratios in Population Census of Pakistan in 1971, 1981 and 1998

Annex II: 1998 Census: Current Marital Status by Sex

Source: Soomro 2003.

Population Dynamics

ANNEX III BISP-PSS: Districts’ Dependency Rate and Mean Age, Household Size and Poverty Score Mean Poverty Dependency Mean Total Mean Male Mean Female Mean HH Score Rate Age Age Age Size Punjab Attock 33.33 52.5 28.9 28.8 29.1 6.5 Bahawalnagar 28.99 67.4 25.8 26.1 25.7 6.4 Bahawalpur 25.23 69.8 25.2 25.2 25.2 6.7 Bhakkar 26.76 68.2 25.7 25.9 25.6 6.4 Chakwal 31.62 50.8 30.0 29.9 30.2 6.0 Chiniot 26.38 66.1 26.6 26.5 26.7 7.3 Dera ghazi khan 19.81 89.3 22.9 22.6 23.2 5.8 Faisalabad 29.59 56.7 26.7 27.1 26.6 7.3 Gujranwala 30.54 60.5 25.8 26.1 25.4 6.9 Gujrat 33.22 57.9 27.8 27.8 27.8 6.4 Hafizabad 27.89 65.0 25.7 26.2 26.0 6.1 Jhang 25.47 67.9 26.0 26.0 26.0 6.8 Jhelum 33.84 53.4 28.7 28.7 28.7 7.2 Kasur 23.95 70.3 25.1 25.3 24.9 6.7 Khanewal 26.29 64.8 25.7 25.8 25.8 6.2 Khushab 31.21 57.6 27.8 27.8 28.0 5.8 Lahore 33.49 54.9 26.1 26.4 25.7 6.9 Leiah 22.81 62.7 25.6 25.8 25.4 6.3 Lodhran 23.85 73.6 24.6 24.5 24.8 6.3 Mandi bahauddin 31.58 58.8 27.1 27.6 27.1 6.6 Mianwali 27.12 56.4 28.1 27.9 28.3 7.0 Multan 21.83 58.2 26.6 26.7 26.6 7.1 Muzaffargarh 18.85 85.7 23.3 23.1 23.5 6.4 Nankana sahib 27.12 62.7 26.2 26.5 26.0 6.0 Narowal 29.84 65.8 25.7 26.0 25.6 6.8 Okara 25.61 68.0 25.7 25.9 25.8 6.2 Pakpattan 24.35 60.4 27.4 27.6 27.1 6.8 Rahim yar khan 20.13 82.6 23.6 23.4 23.8 6.9 Rajanpur 15.89 94.2 22.9 22.7 23.2 7.0 Rawalpindi 34.09 54.3 27.2 27.2 27.2 6.3 Sahiwal 27.5 59.0 26.6 26.8 26.6 6.1 Sargodha 28.59 57.7 27.0 27.3 27.3 6.0 Sheikhupura 28.97 65.1 24.8 25.1 24.8 6.1

Population Dynamics

Sialkot 34.24 60.9 26.2 26.4 26.1 7.3 Toba tek singh 30.72 58.5 27.1 27.2 27.1 5.8 Vehari 26.88 58.7 26.7 26.7 26.8 6.1 Sindh Badin 13.7 86.3 23.2 23.0 23.5 6.5 Dadu 18.09 85.9 23.3 22.9 23.8 6.4 Ghotki 17.37 89.2 22.8 22.6 23.1 6.8 Hyderabad 21.9 62.8 24.8 24.6 25.0 6.7 Jacobabad 15.77 98.8 22.0 21.5 22.5 6.4 Jamshoro 18.45 76.8 24.0 23.8 24.3 6.3 Kambar shahdad kot 15.74 99.2 21.6 21.1 22.2 6.8 Karachi central 33.18 55.6 25.5 25.7 25.5 5.7 Karachi east 31.09 58.4 24.7 24.9 24.7 5.7 Karachi malir 27.1 61.8 24.6 24.7 24.6 5.8 Karachi south 29.96 51.3 26.7 26.8 26.6 6.2 Karachi west 26.47 61.9 24.6 24.9 24.3 6.2 Kashmore 19.3 106.1 21.5 21.1 22.0 6.3 Khairpur 17.26 83.7 23.0 22.7 23.4 7.3 Larkana 16.76 80.1 23.4 23.2 23.7 6.8 Matiari 15.26 78.8 23.5 23.2 23.8 7.1 Mirpur khas 19.86 68.1 25.1 25.0 25.3 6.7 Naushahro feroz 15.97 82.8 23.8 23.4 24.2 7.0 Sanghar 18.61 68.0 25.3 25.0 25.5 6.5 Shaheed benazir 14.43 89.9 22.3 21.9 22.9 7.1 abad Shikarpur 14.13 94.7 22.1 21.5 22.7 6.4 Sukkur 20.22 82.0 23.1 22.9 23.3 8.1 Tando allahyar 15.88 82.6 23.0 22.8 23.3 7.0 Tando muhammad 13.05 84.0 23.3 23.1 23.5 7.1 khan Tharparkar 17.27 84.3 24.4 24.0 24.9 6.5 Thatta 12.33 87.5 23.2 22.9 23.7 6.7 Umer kot 14.14 86.8 23.3 23.0 23.7 6.6 KP Abbottabad 32.06 56.1 27.4 27.4 27.5 6.1 Bannu 21.31 80.9 23.4 23.4 23.5 7.3 Batagram 24.02 79.0 24.7 25.0 24.4 6.5 Buner 20.69 82.4 23.5 23.5 23.4 8.3 Charsadda 21.26 70.1 24.6 24.9 24.3 7.2

Population Dynamics

Chitral 24.34 61.8 26.4 26.6 26.2 8.4 DI. khan 20.13 80.7 23.7 23.8 23.7 7.0 Hangu 22.98 74.5 23.8 23.4 24.4 6.8 Haripur 29.94 55.3 28.0 28.0 28.0 6.3 Karak 23.1 60.9 26.3 26.1 26.6 6.9 Kohat 22.67 66.8 25.2 25.0 25.4 7.2 Kohistan 18.06 108.9 21.0 20.3 22.1 6.1 Lakki marwat 17.53 68.4 25.8 25.6 26.0 7.4 Lower dir 20.1 85.4 22.9 23.0 22.8 8.2 Malakand p a 22.96 72.8 24.1 24.2 24.0 7.5 Mansehra 22.88 71.5 25.4 25.7 25.2 6.3 Mardan 21.81 70.7 24.5 24.6 24.4 7.7 Peshawar 23.53 73.0 23.4 23.6 23.3 7.1 Shangla 17.81 104.7 21.7 21.5 21.9 6.8 Swabi 19.45 68.8 25.3 25.4 25.3 7.0 Swat 20.66 79.1 23.4 23.6 23.2 7.3 Tank 18.19 85.6 23.2 23.0 23.4 8.1 Upper Dir 16.83 93.9 22.0 22.0 22.1 8.2 Balochistan Awaran 19.59 75.2 25.1 25.0 25.4 5.5 Barkhan 19.66 73.0 24.9 24.8 25.0 7.7 Chagai 16.13 75.5 23.8 24.1 23.6 7.8 Dera bugti 16.81 87.7 22.9 22.5 23.4 8.0 Gwadar 18.16 63.1 25.8 25.9 25.8 7.1 Harnai 20.02 67.6 24.5 24.5 24.5 8.1 Jaffarabad 16.27 78.9 23.9 23.5 24.4 8.7 Jhal magsi 16.41 82.9 23.9 23.8 24.1 7.6 Kachhi 16.84 51.1 27.3 27.1 27.6 10.6 Kalat 21.12 80.0 23.5 23.5 23.5 8.5 Kech 18.93 60.3 25.6 25.2 25.9 6.6 Kharan 21.37 73.3 24.3 24.3 24.4 7.8 Khuzdar 19.07 98.8 22.0 22.0 22.2 7.2 Killa abdullah 22.79 84.2 22.0 22.0 22.2 9.7 Killa saifullah 30.25 89.5 22.9 23.2 22.5 7.3 Kohlu 19.69 77.5 23.7 23.6 23.8 9.1 Lasbela 15.35 66.8 25.8 26.0 25.8 8.2 Loralai 21.51 68.2 24.2 24.2 24.3 9.7 Mastung 24.84 69.7 24.9 25.0 25.0 6.1

Population Dynamics

Musakhel 18.03 87.3 23.4 22.8 24.1 6.6 Nasirabad 15.74 75.7 25.2 25.1 25.3 8.6 Nushki 17.38 64.5 24.9 24.0 26.6 7.4 Panjgur 20.77 71.1 20.9 20.7 21.2 5.4 Pishin 22.33 92.1 24.6 24.7 24.6 8.5 Quetta 28.62 64.3 21.1 20.5 21.8 8.5 Sherani 14.74 113.2 26.4 26.3 26.6 8.8 Sibbi 19.32 60.4 25.8 25.5 26.1 8.0 Washuk 16 68.2 23.0 22.9 23.3 7.2 Zhob 19.7 87.5 24.1 23.9 24.3 8.7 Ziarat 22.64 74.9 23.4 23.5 23.4 8.4 GB Astore 22.25 83.7 24.3 24.5 24.0 8.8 Baltistan 26.02 77.1 22.7 22.6 22.8 8.4 Diamir 16.25 90.9 27.2 27.3 27.1 8.9 Ghanche 26.19 62.7 25.9 25.8 25.9 7.9 Ghizer 27.41 65.7 23.9 24.0 23.9 8.6 Gilgit 27.03 71.1 25.1 24.8 25.4 7.6 Hunza nagar 27.67 69.6 26.6 26.5 26.7 7.7 AJK Bagh 29.16 59.4 27.3 27.1 27.5 7.3 Bhimbe 34.48 63.0 24.0 24.3 23.7 7.3 Hattian bala 22.69 79.4 24.8 25.3 24.3 6.6 Haveli 20.05 77.0 26.4 26.2 26.7 7.5 Kotli 31.78 63.5 27.7 27.6 27.8 7.6 Mirpur 33.88 58.1 25.4 25.8 25.0 7.0 Muzaffarabad 26.08 66.5 24.0 23.6 24.5 6.7 Neelum 16.94 89.4 23.3 23.8 22.9 7.5 Poonch 32.96 47.9 28.9 28.8 29.1 6.0 Sudhnoti 29.21 57.7 26.7 26.3 27.0 7.7 FATA Bajaur agency 15.01 121.9 19.2 19.0 19.4 7.8 Khyber agency 15.46 96.7 20.7 20.4 21.3 8.0 Kurram agency 22.79 82.7 21.9 21.5 22.7 7.5 Mohmand agency 14.63 115.5 19.9 20.0 19.8 8.1 Orakzai agency 22.11 104.4 20.9 20.0 22.0 7.0 S Waziristan Agency 10.5 106.7 23.6 22.7 24.6 11.6 T Aadj Bannu 28.26 43.9 27.8 29.4 26.2 8.2

Population Dynamics

T A adj D I Khan 10.82 104.1 21.0 20.8 21.3 7.5 T A adj Kohat 25.83 62.1 25.1 25.0 25.2 6.9 T A adj Peshawar 18.32 79.9 23.2 22.6 23.9 7.4 T A adj Lakki Marwat 23.85 71.1 23.9 23.8 23.9 6.9 ICT Islamabad 36.96 54.8 26.4 26.7 26.2 6.2 Source: Author’s estimation using the BISP-PSS dataset.

Population Dynamics

ANNEX IV BIPS-PSS: Districts’ Current Marital Status by Sex Mean Male Females Poverty Score 15-19 20-24 25-29 30 ≤ Total 15-19 20-24 25-29 30 ≤ Total Punjab Attock 33.3 0.3 6.6 33.2 90.9 57.2 1.8 30.2 65.2 93.8 66.9 Bahawalnagar 29.0 0.9 14.7 48.1 93.6 60.4 3.2 38.5 73.2 97.0 68.5 Bahawalpur 25.2 0.9 17.9 53.2 94.4 61.4 4.2 43.9 77.4 97.5 70.9 Bhakkar 26.8 0.7 15.1 44.9 91.6 58.8 3.2 40.7 70.8 94.9 67.2 Chakwal 31.6 0.5 6.5 32.2 90.9 57.8 1.8 26.2 62.2 94.5 67.3 Chiniot 26.4 0.8 18.5 50.1 92.2 60.1 2.6 44.3 75.6 95.8 70.8 Dera ghazi khan 19.8 0.9 23.1 62.3 95.7 60.0 3.5 54.4 85.0 98.1 72.1 Faisalabad 29.6 0.4 8.7 36.4 91.9 56.6 1.4 28.0 66.3 96.5 65.1 Gujranwala 30.5 0.4 8.5 39.4 93.3 55.6 1.0 26.9 69.3 97.6 63.2 Gujrat 33.2 0.4 7.7 36.3 92.7 56.2 1.2 26.4 66.9 96.7 65.7 Hafizabad 27.9 1.1 13.9 48.1 93.3 59.7 3.5 35.3 71.7 97.0 67.7 Jhang 25.5 1.1 18.3 54.4 94.1 60.8 4.9 45.5 77.9 97.0 71.2 Jhelum 33.8 0.3 6.7 32.4 90.9 56.2 1.4 26.7 63.6 95.3 66.1 Kasur 24.0 0.5 11.3 45.9 93.8 57.4 1.6 34.4 74.5 97.6 65.7 Khanewal 26.3 0.7 14.1 48.3 93.6 59.3 3.6 38.4 73.8 97.0 68.8 Khushab 31.2 0.8 13.3 43.5 91.7 59.4 3.1 34.4 66.7 94.7 68.0 Lahore 33.5 0.5 8.7 37.4 91.9 55.4 1.4 29.2 68.2 96.6 64.2 Leiah 22.8 0.2 9.5 34.4 86.6 50.6 0.7 35.3 71.9 95.6 63.9 Lodhran 23.9 1.0 19.0 58.3 95.4 61.9 4.8 47.4 80.9 97.9 72.2 Mandi bahauddin 31.6 0.6 10.7 40.9 92.5 58.0 1.9 30.4 68.9 97.0 66.2 Mianwali 27.1 0.4 6.1 23.6 84.1 49.6 0.8 26.6 59.3 91.0 61.4 Multan 21.8 0.8 9.0 29.6 85.0 52.1 1.6 31.0 63.7 94.2 64.4 Muzaffargarh 18.9 1.2 27.4 67.8 96.6 64.1 5.5 58.5 86.9 98.3 74.4 Nankana sahib 27.1 0.8 13.7 47.0 94.0 59.3 3.0 36.5 73.8 97.2 67.8 Narowal 29.8 0.7 10.9 47.6 95.6 57.7 1.9 30.2 74.5 98.3 64.8 Okara 25.6 0.9 14.1 48.1 93.7 59.3 3.7 38.4 74.0 96.8 68.4 Pakpattan 24.4 0.8 9.6 30.0 83.8 52.8 1.4 31.9 63.1 92.8 64.5 Rahim yar khan 20.1 1.0 20.1 58.6 95.0 60.3 4.0 45.3 80.0 97.8 70.2 Rajanpur 15.9 0.8 26.9 65.3 95.8 64.2 3.1 57.4 86.9 98.4 75.3 Rawalpindi 34.1 0.4 8.9 36.1 91.3 56.2 1.7 29.2 64.5 94.9 65.6 Sahiwal 27.5 0.8 11.7 41.4 91.8 57.3 2.4 30.9 66.6 95.8 65.9 Sargodha 28.6 0.7 12.3 42.9 92.1 58.0 2.4 32.7 68.8 96.4 67.1 Sheikhupura 29.0 0.6 15.4 55.4 96.0 59.8 2.7 37.3 79.3 98.4 67.3

Population Dynamics

Sialkot 34.2 0.3 7.0 36.9 93.6 55.5 0.8 23.4 67.8 97.4 63.3 Toba tek singh 30.7 0.6 11.9 45.6 94.1 59.1 1.6 29.8 71.0 97.2 67.3 Vehari 26.9 0.8 12.0 46.6 92.9 58.3 2.3 30.6 70.2 96.4 67.0 Sindh Badin 13.7 1.1 27.6 66.8 95.0 65.0 3.6 57.3 85.2 97.6 76.3 Dadu 18.1 0.5 26.7 65.3 94.8 63.1 2.1 54.1 83.4 97.4 73.6 Ghotki 17.4 1.7 29.3 66.5 95.6 64.4 5.3 59.7 87.6 98.4 76.1 Hyderabad 21.9 0.5 10.7 40.4 89.0 52.9 1.4 28.0 62.5 93.2 61.5 Jacobabad 15.8 0.8 36.3 75.8 96.7 66.5 2.8 62.1 89.4 98.2 76.4 Jamshoro 18.4 0.5 20.4 55.4 92.5 60.9 2.2 48.9 76.8 95.9 71.5 Kambar shahdad 15.7 0.7 23.6 68.3 95.8 58.9 1.8 49.9 84.8 97.8 70.9 kot Karachi central 33.2 0.9 9.2 38.1 91.1 55.6 2.6 30.1 67.8 95.1 64.5 Karachi east 31.1 0.8 11.8 45.5 92.6 56.9 2.7 36.6 72.8 95.8 66.0 Karachi malir 27.1 0.8 13.9 47.5 92.0 57.0 3.0 39.9 72.4 95.3 66.7 Karachi south 30.0 0.8 7.1 32.2 88.4 53.9 2.0 27.8 61.4 92.4 62.2 Karachi west 26.5 0.8 10.6 41.2 91.4 55.0 3.2 37.7 70.2 95.2 64.1 Kashmore 19.3 1.0 43.6 82.7 97.8 69.0 4.8 76.4 94.9 99.2 82.4 Khairpur 17.3 1.4 26.4 61.7 93.7 60.6 4.6 54.5 81.1 96.5 71.9 Larkana 16.8 1.2 21.1 58.4 93.6 61.0 4.2 46.6 76.7 96.3 70.1 Matiari 15.3 0.9 20.1 53.8 91.2 58.4 3.3 45.5 74.2 94.2 68.7 Mirpur khas 19.9 0.5 17.8 48.3 89.5 56.7 1.5 47.8 76.2 95.5 69.5 Naushahro feroz 16.0 0.8 19.1 56.6 92.9 57.8 3.0 44.3 76.2 95.2 68.4 Sanghar 18.6 0.5 13.4 40.5 87.6 54.4 1.5 39.7 69.7 94.7 68.0 Shaheed benazir 14.4 0.7 17.2 56.3 93.0 57.0 2.3 42.2 75.7 96.1 69.1 abad Shikarpur 14.1 0.4 24.4 68.6 95.8 60.4 1.6 53.7 85.1 97.8 72.6 Sukkur 20.2 0.9 22.3 57.3 93.2 59.4 3.1 50.2 79.1 96.6 70.1 Tando allahyar 15.9 0.9 25.6 63.9 94.5 62.8 3.2 53.5 81.2 96.8 73.0 Tando 13.0 0.6 23.8 62.7 93.3 62.8 2.2 50.8 81.1 96.3 73.3 muhammad khan Tharparkar 17.3 1.1 19.6 51.3 90.6 58.6 1.9 50.2 84.9 97.3 71.4 Thatta 12.3 0.5 17.2 56.2 92.5 59.7 1.6 43.1 78.1 96.3 71.1 Umer kot 14.1 1.8 31.3 72.4 96.2 66.3 4.7 61.8 89.0 98.4 77.6 KP Abbottabad 32.1 0.2 5.9 33.0 92.3 48.2 1.5 30.7 68.0 95.9 66.7 Bannu 21.3 1.4 23.2 58.0 94.4 59.0 8.8 55.6 79.9 94.3 70.1 Batagram 24.0 0.8 11.2 44.9 90.1 56.3 1.9 58.2 91.2 98.2 72.2 Buner 20.7 0.9 24.7 67.6 97.3 62.6 5.6 62.8 88.5 96.8 72.9 Charsadda 21.3 0.3 9.2 39.8 92.1 53.6 3.7 44.7 73.5 92.9 64.2

Population Dynamics

Chitral 24.3 0.9 12.5 42.1 90.6 53.9 7.5 43.0 70.2 93.7 64.8 D I. khan 20.1 0.5 18.6 54.3 93.6 59.0 2.6 47.9 78.0 96.6 69.4 Hangu 23.0 1.1 28.4 63.8 95.3 60.1 6.0 58.3 83.0 96.6 71.4 Haripur 29.9 0.5 6.6 30.9 90.3 55.7 2.9 33.2 66.0 94.3 66.4 Karak 23.1 0.4 4.9 26.2 88.0 47.6 1.2 31.4 65.6 93.1 61.5 Kohat 22.7 0.8 18.3 52.9 93.8 57.8 4.8 49.3 77.5 95.3 69.1 Kohistan 18.1 3.6 50.6 85.3 97.7 67.0 14.9 80.8 95.6 98.8 83.6 Lakki marwat 17.5 0.5 9.0 30.5 87.2 49.4 2.3 38.2 64.6 92.6 63.2 Lower dir 20.1 0.8 15.0 53.7 95.8 58.3 3.7 52.1 84.1 97.1 69.4 Malakand p a 23.0 0.5 11.0 45.9 94.3 55.5 6.8 52.9 79.9 95.0 68.1 Mansehra 22.9 0.8 13.0 51.2 94.5 60.0 4.2 48.9 80.4 96.8 70.4 Mardan 21.8 0.5 10.3 45.3 93.9 54.7 5.2 48.8 75.9 93.8 66.2 Peshawar 23.5 0.5 13.6 50.5 94.0 55.2 3.7 45.4 75.1 95.0 65.2 Shangla 17.8 2.4 34.6 78.7 98.2 66.1 8.7 75.4 94.0 98.6 77.9 Swabi 19.5 0.4 9.3 44.5 94.1 55.8 4.7 50.0 76.5 93.0 67.4 Swat 20.7 0.6 16.9 56.1 95.9 58.7 5.5 58.1 86.6 96.6 70.4 Tank 18.2 0.8 27.0 64.4 95.6 61.1 3.8 59.7 87.2 97.4 72.6 Upper Dir 16.8 1.1 20.9 62.5 96.4 59.0 5.1 57.8 88.6 98.1 71.2 Balochistan Awaran 19.6 2.9 28.3 67.0 94.8 69.4 12.8 54.8 81.7 96.7 79.7 Barkhan 19.7 2.2 22.7 60.2 92.1 61.3 4.1 47.7 77.4 96.1 72.5 Chagai 16.1 0.9 11.3 43.2 91.5 54.5 4.5 44.4 72.9 93.9 65.4 Dera bugti 16.8 3.1 34.5 75.5 93.0 64.5 10.1 71.1 88.3 97.1 80.6 Gwadar 18.2 1.7 11.2 41.8 89.9 55.9 3.2 31.9 64.3 92.8 63.9 Harnai 20.0 3.3 22.0 52.7 91.0 58.6 5.2 40.3 73.7 95.2 67.9 Jaffarabad 16.3 3.0 31.2 66.1 93.7 65.7 7.1 57.0 81.6 96.0 76.1 Jhal magsi 16.4 2.3 29.3 64.3 91.7 65.3 7.7 60.8 84.9 96.2 77.9 Kachhi 16.8 2.7 17.0 43.4 80.9 53.4 5.3 35.3 64.3 88.2 65.9 Kalat 21.1 1.5 20.9 59.3 93.1 60.7 5.5 51.4 79.3 96.2 72.3 Kech 18.9 1.1 10.1 35.7 86.6 49.8 2.6 37.6 62.3 92.3 62.8 Kharan 21.4 1.1 12.8 46.8 91.1 55.8 3.5 37.5 66.1 92.0 64.2 Khuzdar 19.1 3.4 33.9 69.2 94.8 66.6 15.2 61.5 85.0 97.1 77.9 Killa abdullah 22.8 1.9 17.6 53.6 89.2 52.6 4.8 38.0 66.5 91.6 60.7 Killa saifullah 30.3 0.8 10.1 38.2 88.3 51.2 1.8 47.2 77.8 95.3 67.8 Kohlu 19.7 3.4 29.5 61.5 90.9 62.0 11.1 61.1 81.8 94.7 76.4 Lasbela 15.4 1.8 14.3 44.5 89.0 58.5 3.6 36.4 67.9 94.0 67.3 Loralai 21.5 2.0 16.4 44.7 86.5 51.5 4.2 33.5 61.6 91.0 60.8 Mastung 24.8 1.4 21.3 53.1 92.7 59.6 6.3 42.5 70.2 95.0 68.3

Population Dynamics

Musakhel 18.0 0.3 13.2 40.4 85.3 51.3 2.0 65.1 90.0 98.0 78.9 Nasirabad 15.7 3.0 28.2 64.0 93.0 64.2 7.4 56.1 83.6 95.9 76.1 Nushki 17.4 0.9 9.7 35.2 89.7 50.3 1.2 24.7 56.0 92.5 58.6 Panjgur 20.8 2.4 22.4 54.5 91.6 58.0 6.1 50.8 75.6 94.8 73.1 Pishin 22.3 1.6 26.1 64.1 94.2 57.1 5.0 44.1 67.8 93.1 60.2 Quetta 28.6 1.8 13.8 44.5 90.1 53.2 4.0 34.0 63.4 93.4 62.1 Sherani 14.7 1.1 26.1 65.9 94.5 55.7 6.2 60.3 83.0 96.7 73.1 Sibbi 19.3 2.0 16.4 43.7 90.2 58.4 4.2 42.7 71.6 94.7 70.1 Washuk 16.0 3.3 16.8 49.9 91.2 59.5 5.6 36.3 65.1 90.1 65.9 Zhob 19.7 1.7 16.6 50.8 91.2 54.9 4.5 42.5 71.8 94.2 67.0 Ziarat 22.6 1.3 18.7 49.8 90.8 58.7 5.0 35.5 61.6 92.5 64.7 GB Astore 22.2 0.5 11.1 45.8 92.9 53.3 1.7 24.8 59.0 94.1 58.2 Baltistan 26.0 1.7 24.3 70.9 96.3 60.6 6.3 50.2 86.6 98.4 70.1 Diamir 16.2 2.7 30.9 58.6 88.8 56.2 6.1 62.6 83.4 96.2 71.4 Ghanche 26.2 0.9 13.5 50.4 91.6 55.2 2.5 42.1 75.6 94.7 64.5 Ghizer 27.4 0.6 10.5 42.3 93.8 54.2 4.2 37.4 66.8 95.4 64.2 Gilgit 27.0 0.4 10.6 43.2 92.9 52.1 3.1 36.0 68.1 95.5 61.8 Hunza nagar 27.7 0.3 7.0 34.9 89.5 50.4 2.3 23.3 55.5 92.4 56.5 AJK Bagh 29.2 0.4 6.9 36.0 92.5 55.6 1.9 28.0 65.5 95.6 64.8 Bhimbe 34.5 0.4 7.7 39.3 94.2 55.6 1.3 28.4 70.0 97.0 66.1 Hattian bala 22.7 0.6 14.4 53.9 95.1 60.1 3.3 47.9 81.5 97.5 69.8 Haveli 20.0 0.5 14.4 49.4 94.1 59.8 1.5 41.4 76.4 96.4 67.1 Kotli 31.8 0.4 7.2 36.6 92.5 52.7 1.0 26.0 66.3 96.1 63.6 Mirpur 33.9 0.4 8.6 38.0 92.8 56.1 1.2 27.5 64.7 95.3 64.7 Muzaffarabad 26.1 0.3 9.0 45.0 93.1 58.4 2.2 39.0 74.7 96.4 67.7 Neelum 16.9 1.4 22.1 66.0 96.7 62.2 5.5 56.3 87.4 98.6 71.4 Poonch 33.0 0.3 1.3 10.7 80.1 45.7 0.5 12.4 43.3 90.4 57.9 Sudhnoti 29.2 0.3 7.3 39.7 93.2 54.6 1.1 29.1 71.7 96.9 65.6 FATA Bajaur agency 15.0 1.7 48.5 86.7 99.0 67.9 9.4 75.9 94.2 99.0 77.2 Khyber agency 15.5 3.7 42.7 79.8 97.6 63.3 14.5 70.5 92.5 98.9 77.7 Kurram agency 22.8 1.7 36.5 65.3 95.4 59.9 6.5 58.0 82.9 97.0 72.5 Mohmand agency 14.6 1.7 32.5 69.9 97.3 61.6 8.9 64.8 89.4 98.1 73.4 Orakzai agency 22.1 6.9 62.5 89.4 98.4 74.6 17.8 75.3 92.7 98.3 84.4 S Waziristan 10.5 0.0 36.4 50.0 93.1 62.4 0.0 50.0 100.0 100.0 77.3 Agency

Population Dynamics

T A adj Bannu 28.3 2.8 58.3 75.0 94.4 80.9 8.9 33.3 66.7 92.3 70.4 T A adj D I Khan 10.8 1.6 37.1 78.1 96.3 61.5 5.8 74.1 92.4 98.6 79.1 T A adj Kohat 25.8 1.8 23.8 60.3 95.4 59.5 7.8 58.3 80.9 97.2 71.4 T A adj Peshawar 18.3 1.3 24.0 69.1 97.5 59.3 7.0 56.9 87.6 98.4 71.7 T A adj Lakki 23.8 3.4 29.5 62.8 93.8 60.8 12.9 50.0 84.0 97.0 68.8 Marwat ICT Islamabad 36.9 0.7 8.9 39.4 93.0 57.1 2.3 31.6 66.3 95.6 65.5 Source: Author’s estimation using the BISP-PSS dataset.

Population Dynamics

References

Ali, S. M. and M. Sultan (2003) Age and Sex Distribution of 1998 Census: An Evaluation. In A. R. Kemal, M. Irfan, and N. Mahmood (eds.) Population of Pakistan: An Analysis of 1998 Population and Housing Census. Islamabad: Pakistan Institute of Development Economics and UNFPA.

Edwards, R. L. (2002) Breaking the Poverty Cycle. New York: Information Corporation.

ESCAP (2011) Statistical Yearbook for Asia and Pacific 2011. Bangkok: ESCAP.

Gaeta, S. and S. G. Pardeshi, (2010) Age Heaping and Accuracy of Age Data Collected During a Community Survey in the Yavatmal District, Maharashtra. Indian Journal of Community Medicine 35:3, 391–395.

Gunasekera, H. R. (2009) An Evaluation of Age-Sex Data of Census of Population and Housing 2001- Sri Lanka. Paper presented to the 24th Population Census Conference Hong Kong, 25–27 March.

ICF (2011) Demographic and Health Surveys by ICF International. New York.

Kirkegaard, E. O. W. (2015) What Exactly is Age Heaping and What Use Is It? Paper presented at the VIII Human Ecology Conference, Brisbane, 18-20 June, 2015.

Macbeth, H. and P. Collinson (2002) Human Population Dynamics: Cross-Disciplinary Perspectives. London: Oxford University Press.

Malaney, P. (2009) Demographic Change and Poverty Reduction. Presented at the Population Dynamics and Poverty, Dhaka, November 20-21.

Nayab, D. (2008) Demographic Dividend or Demographic Threat in Pakistan? The Pakistan Development Review 47:1, 1–26.

Rodgers, G. (1984) Poverty and Population Approaches. Geneva: ILO.

Rogers, A. R. (2010) “Resources and Population Dynamics. In E. Smith, B. Winterhalder, and A. Gruyter (eds.) Evolutionary Ecology and Human Behaviour. Utah: Palgrave Press.

Soomro, G. Y. (2003) Levels and Trends of Nuptiality in Pakistan. In A. R. Kemal, M. Irfan, and N. Mahmood (eds.) Population of Pakistan: An Analysis of 1998 Population and Housing Census. Islamabad: Pakistan Institute of Development Economics and UNFPA.

UN (2011) Evaluation of Census Data. A Case of Analysing the Housing and Population Census of Liberia. New York.

UNFPA (2012) UN System Task Team for Post 2015 Development Agenda: Population Dynamics. New York.

Population Dynamics

Yazdanparast, A. M. Pourhoseingholi, and A. Abadi (2012) Digit Preference in Iranian Age Data. Italian Journal of Public Health 9:1.

Zahir, Z. (2003) Housing and Household Characteristics. In A. R. Kemal, M. Irfan, and N. Mahmood (eds.) Population of Pakistan: An Analysis of 1998 Population and Housing Census. Islamabad: Pakistan Institute of Development Economics and UNFPA.

Benazir Income Support Programme (BISP) F-Block, Pak Secretariat, Islamabad, Pakistan Ph: 051 - 9246313, 9246316 Fax: 051 - 9246314 web: www.bisp.gov.pk