Youth and Employment Participation

Population of Pakistan: An Analysis of NSER 2010-11

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

Researcher: Dr. Shujaat Farooq

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.

Youth and Employment Participation

Table of Contents

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

2 Methods to Measure Labour Force Participation in Pakistan…………………………... 07

3 The Measurement of Labour Force Indicator in Pakistan: A Comparison of NSER with Other National Data Sources………………………………………………….. 09

4 Employment Participation Rates in Pakistan……………………………………………… 11

5 Youth Employment Participation by Sector……………………………………………….. 15

6 Youth Employment Participation and Socio-Demographic Profile……………………... 17

7 Youth Unemployment in Pakistan………………………………………………………….. 23

8 Conclusions and Policy Recommendations……………………………………………... 27

Appendix figures and tables………………………………………………………………. 29

References…………………………………………………………………………………... 55

Youth and Employment Participation

List of Tables

1 Comparison of Employment Questions of Census 1998, LFS 2010, PSLM 2010 and NSER 2010-2011…………………………………………………………. 09 2 Share of Youth in Population by Gender and Province/Region: BISP, LFS and PSLM Datasets……………………………………………………………………. 11 3 Proportion of Working Youth and Adult by Gender and Province: BISP and LFS Datasets…………………………………………………………………...… 12 4 Age-Specific Employment Rate by Gender: BISP and LFS Datasets………...………..14 5 Proportion of Youth and adult in Various Employment Categories by Gender—BISP Dataset………………………………………………………..………… 15 6 Changes in Employment Status- A Comparison of BISP and Census 1998………..… 16 7 Attained Education and Type of Employment of Youth-NSER 2010-2011………...….. 17 8 Employment Type of Youth by Education of Head of Household-NSER 2010-2011… 19 9 Employment Type of Youth by Employment Type of Head of Household-NSER 2010-2011…………………………………………………………………………………….. 20 10 Employment Type of Youth by Dependency Burden-NSER 2010-2011………………. 20 11 Unemployment Rate by Gender: BISP, LFS and Census 1998 Datasets………..…… 24 12 Estimated Youth Unemployment Rate by Gender and Province: NSER 2010-2011.... 24 13 Age-wise Estimated Unemployment Rate by Gender in Pakistan: BISP and Census 1998…………………………………………………………………………………………… 25

List of Figures

1 Proportion of Working Youth and Adult by Gender in Various Regions-NSER 2010- 2011…………………………………………………………………………………..……….. 12 2 Gender-Wise Age Specific Employment Rate in Pakistan--NSER 2010-2011……...… 13 3 Age-Specific Employment Rate: BISP, PSLM and LFS Datasets ……………………… 13 4 Male Employment Rate and Enrolment Rate for Youth………………………………….. 18 5 Female Employment Rate and Enrolment Rate for Youth………………………………. 18 6 Paid Employment and Poverty rates for Youth…………………………………………… 21 7 Paid Employment and Mean PMT for Youth……………………………………………… 22 8 Mean PMT and Unemployment Rate for Youth………………………………………….. 25 9 Poverty (%) and Unemployment Rate for Youth…………………………….…………… 26

Youth and Employment Participation

1. Introduction

The volume and age-structure of population are important factors in determining the significance of labour force and economic growth of a country. Like other countries, population composition overtime has changed in Pakistan with rising share of youth and working age population. The on-going demographic transition in Pakistan has resulted in a significant rise in youth population. Pakistani youth constitutes about one-fifth of total population and more than one-third of the working-age population. It is an immense economic and human resource if it can be utilised properly as Pakistan has been experiencing once-in-a-lifetime demographic dividend in which share of working age population particularly youth is rising [Nayab (2008)].

Youth is a valuable human resource of every country and considered as an important asset for nation`s development. There are about 1.2 billion young people of age 15 to 24 and comprise around 24.7 percent of the world’s working-age population. About 87 percent of them live in developing countries (ILO, 2009). In Pakistan youth constitutes a major share of working age population as around more than one-fourth of the employed labour force comprises of youth (ages 15-24 years). It represents an important segment of the population both for economic development as well as from the perspective of labour force. This significant proportion of young population is human resource which can be utilised efficiently if proper economic opportunities are given to them.

However, youth labour force participation is still low in Pakistan compared to the other South Asian countries both for men and women having higher gender gap as compared to the other countries of the region [ILO (2008)]. Youth in Pakistan face number of challenges in their way to work, early start to work, failing to enter the labour market and difficulties in moving across the jobs. Besides, lack of education, skill and experience are some of major issues of youth in the labour market. In comparison to other age-groups, Pakistani youth is facing more challenges to perform in labour market including; high unemployment, rising job search periods, high risk of vulnerability, wage penalties and lower level of job satisfaction [Arif and Chaudhry (2008); Pakistan (2011); Farooq (2011, 2012)].

Some research evidences suggest that Pakistani youth is facing strong structural obstacles and economic distress for searching decent work. Their share of paid employment in total employment in Pakistan is low, and a high share of youth particularly young females are likely to work as unpaid family workers. Moreover, youth also face challenges in paid employment and their significant proportion is absorbed in low skilled jobs in Pakistan [Spareboom and Shahnaz (2007)].

The aim of present study is to analyse the youth (age 18-29) employment participation at district level by using 2010 NSER 2010-2011 and make a comparison with other national datasets; PSLM, LFS and Census 1998. There being limited number of labour force related questions in NSER 2010-2011, it is beyond the scope to observe economically active population, industrial structure, occupational distribution, earnings and quality of work. The study has focused on the following three dimensions;

Youth and Employment Participation

 Gender-wise analysis of youth and adult employment participation in Pakistan by comparison of NSER 2010-2011 with other national datasets;  Gender-wise analysis of employment participation by category of employment and linking it with youth socio-demographic profile; and  Gender-wise estimation of potential unemployment of youth on the basis of NSER 2010-2011.

The rest of the study is organised as follows. Section 2 presents a historic review of census and labour force surveys about the measurement of labour force participation followed by a comparison of labour indicators given in 2010 NSER 2010-2011 and in other national datasets in Section 3. The results about the employment participation rates and by sector have been discussed in Sections 4 and 5 while the results about their linkages with the youth socio-demographic characteristics are presented in Section 6. The penultimate section discusses the unemployment rates. Conclusions and policy considerations are given in the final section.

Youth and Employment Participation

2. Methods to Measure Labour Force Participation in Pakistan

The economically active population consists of persons available for work of either sex who provide labour services for the production of goods and services as defined by the United Nation System of National Accounts, during the reference period. Those who are not looking for jobs i.e. women involved in domestic activities, retired persons, and students are considered the inactive part of population. It reflects that the economically active population is the sub-group of working age population who is either employed or seeking employment. The potential size of labour force participation depends on both the quantity and quality of labour; however, latter is usually not considered in developing countries. The accurate estimation of labour force participation of a country depends on various indicators including; working hours, size of labour force, seasonal employment, education/skill level and health status of workers [Standing (1978); Cameron and Irfan (1991)].

In Pakistan, two ways have been adopted to measure economically active population which is the current status and usual status. The former follows the short reference period i.e. availability of work during last one week or one month while the later follows the long reference period i.e. one year. Historically, the census has followed the usual status approach while the LFS and PSLM follow current status: in LFS, reference period is last one week while in PSLM it is one month. Usual status face the difficulty that irregular, seasonal and unpaid family workers can be excluded from the economically active population while the current status might ignore the discouraged workers, the workers who are not looking for work with the belief that they will not find work in their area/field [Nasir (2003)].

Labour Force Survey (LFS) also captures the usual status by asking the respondents to report the principal activities during last twelve months. The estimates of labour force or employment participation measured through both the current and usual status might vary due to reference period and potential inclusion and exclusion of some listed labour categories, as mentioned above. For example, it is likely (as found from LFS) that usual status will report the low level of unemployment rate as compared to the current status due to the narration of probing question, the findings of LFS 2010 also reveal high level of employment and labour force participation rates by current status than the usual status.

It is not easy to accurately measure economically active population in developing countries due to major share of employment in agricultural and informal activities including the unpaid family helpers. Various censuses in Pakistan have not followed the consistent pattern of measurement as well. For example, the 1951 census has followed the usual status to measure economically active population of age 12 and above by excluding the unpaid family helpers. In 1961 and subsequent censuses 1973, 1981, the minimum age limit of the active population was reduced from 12 to 10 years and unpaid family helpers were included. The 1998 census has reduced the age limit from 10 to 5 years further. Only the 1973 census has followed the current status to measure economically active population. In 1951 and 1961 censuses, the economically active population was calculated on the basis of whole data, while a representative sample survey of 3.4 percent, 10 percent and 7.8

Youth and Employment Participation

percent was generated in 1973, 1981 and 1998 censuses, respectively to calculate the labour force participation [Nasir (2003)]. No census was conducted after 1998; however, BISP survey was conducted in all the regions of Pakistan by covering all the households, thus a close declaration of data source like census. The survey has followed the usual status; however it has used the above 18 age to measure employment participation by ignoring the labour force participation related questions and excluded the unpaid family helpers as well. This makes inter-temporal comparisons difficult.

Labour Force Surveys fill the vacuum of census by providing labour related information on annual bases as census are usually conducted after a long period. It is easier said than done to compare both type of datasets due to differences in sample coverage enumeration methods and adopted definitions to measure the various labour indicators. Another potential source of difference is the quality of enumeration as the census data is usually collected by the school teachers and local administrative staff which lack the necessary experience of data collection as compared to the regular staff of Pakistan Bureau of Statistics (PBS).

Youth and Employment Participation

3. The Measurement of Labour Force Indicators in Pakistan: A Comparison of BISP with other National Data Sources

In this section, a comparison about the measurement of labour force indicators of four national datasets has been carried out which are census-1998, PSLM-2010, LFS-2010 and BISP-2010. The 1998 census has used five questions to collect information on the usual economic activity of population of age 5 and above. The details of first question are given in Table 1 while questions 2 to 5 have measured occupation, sector of employment, status of employment (self-employed, govt., semi-govt., private, employer and paid employee) and reason of unemployment, respectively.

The NSER 2010-2011 survey has not been designed to measure the economic activity of population and it has used only one question to measure the employment participation rather than economic activity of population of age 18 above as can be seen in Table 1. The BISP question is close to the fourth question of census 1998 but six sets of key limitations can be found in BISP question. First, economic activity cannot be calculated from the NSER 2010-2011 as inactive part of labour force has not been questioned i.e. student, domestic work, landlord etc., therefore one can only partially measure the employment participation instead of economic activity. Second, NSER 2010-2011 will underestimate the employment participation due to exclusion of unpaid family helpers. Third, NSER 2010-2011 has ignored the employment participation of youth of below 19 age as 19 and above age threshold has been used in question. Fourth, unpaid family helpers, discouraged workers and disabled workers were excluded and they were mainly entered in unemployment category. Fifth, employer category was excluded. Sixth, it is also difficult to calculate unemployment as inactive labour force was not questioned and the enumerators have largely entered the inactive part in unemployment category. In addition, NSER 2010-2011 has not covered the occupation and sector of employment.

Table 1 Comparison of Employment Questions of Census 1998, LFS 2010, PSLM 2010 and NSER 2010-2011 Census 1998 NSER 2010-2011 LFS 2010 PSLM 2010 Q. Kind of activity in Q. Kind of activity in Q. Kind of activity in last one year (age 5 and last one year (age 18 last one year (age 10 Q1. Did …, do any work for pay, profit or above) above) and above) family gain during the last month at least 1. Work for one hour on any day? 2. Job search [1] Yes [2] No 3. Laid off Q2. Even if did not work last month, did 1. Government 4. Unpaid family …., have a job or enterprise such as shop, 2. Semi-government helper 1. Employed business, farm or service establishment 3. Private 5. Student 2. Unemployed (fixed/mobile) during the last month? 4. Pensioner 6. Domestic work 3. Not in labour force [1] Yes 5. Self employed 7. Landlord [2] No, but seeking Work 6. Unemployed 8. Retired [3] No, not seeking Work 9. Handicapped 10. Others

Youth and Employment Participation

The LFS has used both the usual and current status to measure economic activity of age 10 and above. Table 1 shows the question related to usual status. The current approach has used a wide range of questions by capturing all the dimensions of economic activities, occupation, sector of employment, type of employment, reason of unemployment, wages, secondary occupations and sector but has ignored the discouraged workers. The PSLM 2010 has also used the current approach with one month reference period of reporting to measure labour force participation along with the occupation, sector of employment, type of employment, wages, and secondary occupations but has ignored the discouraged workers.

Overall the discussion reflects that NSER 2010-2011 cannot give a good comparison with the other datasets including census 1998, PSLM and LFS due to variation in labour force related asked questions. Majority of the existing research has taken the age of 15 to 24 for youth, however this study has focused on age 19 to 29 as NSER 2010-2011 has probed the employment information only from 19 and above. Due to these limitations, the ongoing study has confined the analysis to NSER 2010-2011 by focusing on the youth of age 19-29 by examining their employment participation rates and linking them with their socio-demographic characteristics including education, parent’s education and occupation and poverty. Though it is difficult to measure unemployment rate; however, an attempt has been made to measure unemployment rate on the basis of NSER 2010-2011. A necessary comparison with other datasets has also been made.

Youth and Employment Participation

4. Employment Participation Rates in Pakistan

As discussed in earlier sections due to limited available information in NSER 2010-2011, the ongoing study has not estimated the labour force participation rates and thus only estimated the employment participation rates. Table 2 shows a comparison of share of youth in total population of three national datasets collected in 2010 i.e. NSER 2010-2011, PSLM and LFS. The numbers reflect that all the three datasets are close; however, both the sampled survey datasets PSLM and LFS are closer than the NSER 2010-2011 with more youth share reflected in NSER 2010-2011 than the other two datasets at overall and at provincial levels especially in Balochistan. It is worth mentioning here that this study has defined the youths of age 18-29 while adults are above 29 years of age.

Table 2 Share of Youth in Population by Gender and Province/Region: BISP, LFS and PSLM Datasets BISP-2010 PSLM-2010 LFS-2010 Province/ Both Both Both Region Male Female Male Female Male Female Sexes Sexes Sexes Overall 40.3 40.1 40.5 38.7 38.8 38.7 39.7 38.9 40.59 Punjab 39.5 39.2 39.8 38.6 38.1 39.1 39.7 38.6 40.8 Sindh 40.4 40.4 40.5 38.6 40.0 37.1 39.6 39.8 39.3 KP 42.2 42.0 42.5 40.8 40.9 40.7 42.0 40.6 43.3 Balochistan 41.5 40.9 42.0 35.6 34.4 36.1 33.5 32.4 34.6 AJK 39.2 39.3 39.0 ------GB 41.3 41.1 41.6 ------Islamabad 40.5 40.0 41.1 ------FATA 44.8 45.3 44.2 ------Source: Author’s estimation from the micro-data of BISP-2010, PSLM-2010 and LFS-2010

It is noteworthy again that NSER 2010-2011 covers only single employment question in which nature of activities have been measured for age 19 and above persons. The question has 6 options which are: government, semi-government, private, pensioner, self-employed and unemployed. After detailed examination of BISP question, the study has estimated the employment participation by merging the four employment categories in NSER 2010-2011 which are employee in govt., semi-govt., private and self-employed while the three categories null/missing, unemployed and pensioner were added in the total population by assuming that the null/missing population is the part of inactive/unemployed population. The results of employment participation for both the youth and adult at national and provincial level have been displayed in Table 3 with a comparison of NSER 2010-2011 to the LFS dataset as well. It is worth mentioning here that for sound comparison, the LFS usual status question was used to estimate the employment participation because the NSER 2010-2011 has also followed the usual status module. The numbers reveal that as expected youth and females have lower employment participation rates than the adult and males respectively, with regional variations as well. However, across the provinces and gender, lower employment participation rate has been reported in NSER 2010-2011 than the LFS survey.

Youth and Employment Participation

Table 3 Proportion of Working Youth and Adult by Gender and Province: BISP and LFS Datasets BISP-2010 LFS-2010 Province/ Region Both sexes Male Female Overall Male Female National - Youth 23.2 39.7 6.1 45.0 73.8 16.7 - Adult 38.8 66.7 9.4 53.7 86.9 19.0 Punjab - Youth 24.2 41.8 6.1 46.5 74.5 20.3 - Adult 39.9 68.4 9.6 54.2 84.1 23.9 Sindh - Youth 26.1 43.5 8.0 48.1 74.9 18.1 - Adult 43.7 73.8 12.1 56.3 90.2 19.0 KP - Youth 19.1 35.4 2.0 37.1 68.2 10.1 - Adult 32.7 60.4 3.2 47.6 82.8 14.1 Balochistan - Youth 17.8 22.3 13.0 44.3 76.5 8.7 - Adult 31.9 43.9 18.7 54.1 94.6 7.7 Source: Author’s estimation from the micro-data of BISP-2010 and LFS-2010

Fig. 1. Proportion of Working Youth and Adult by Gender in Various Regions-BISP Survey

0 Youth Adult Youth Adult Youth Adult Youth Adult Islamabad GB AJK FATA

Both sexes Male Female

Figure 2 shows the gender-wise age specific employment rates which are inverted U-shape for males and overall, reflecting that people participate less in the labour market in their early ages and the later ages as compared to the medium age. Figure 3 provides a comparison of three datasets and the results are in line with those of Table 3 that in all the age brackets, NSER 2010-2011 has reported less employment participation rates as compared to the PSLM and LFS datasets. Two other interesting differences are noteworthy in figure 2 and 3. First, the female employment participation remains almost flatter at various

Youth and Employment Participation

age intervals, and second, BISP dataset shows that employment participation declines soon after 45-49 age but this decline rate is more than the LFS dataset. Age specific employment rates by gender are given in Table 4 while the results at provincial level are given in appendix Table 2.

Fig. 2. Gender-Wise Age Specific Employment Rate

0 18-24 25-29 30-34 35-39 40-44 45-49 50-54 55-59 60-64 65 and above

overall Male Female

in Pakistan--BISP Survey

Fig. 3. Age-Specific Employment Rate : BISP, PSLM and LFS Datasets

0 18-24 25-29 30-34 35-39 40-44 45-49 50-54 55-59 60-64 65 and above

BISP LFS PSLM

After observing the lower reported employment participation as given in Tables 3, 4 and above figures, the question arises whether NSER 2010-2011 has correctly estimated the employment participation? Several reasons can be drawn for lower reporting in NSER 2010-

Youth and Employment Participation

2011 than the other national datasets. First, the mode and motive of collecting employment related information differ in the NSER 2010-2011 than the PSLM and LFS. As mentioned in section 3 that NSER 2010-2011 has missed a number of key employment related indicators i.e. unpaid family helper and inactive labour force. With fewer options given in NSER 2010- 2011, the unpaid family helpers and inactive population were added in unemployment category which lowered the employment participation and raised the unemployment rates. Second is the definitional issue of NSER 2010-2011 as definition followed in NSER 2010- 2011 is not rationally comparable with the LFS and PSLM definitions due to usual and current format. Third, it seems from Table 2 that youth have been over-reported in NSER 2010-2011 thus leaving their lower participation rates. Fourth, it might be possible that NSER 2010-2011 have some more representation from the lower class/region and rural areas by excluding the well-off population/area and urban regions of Pakistan, thus leaving lower participation rates at end. Fifth, since the aim of the BISP was to identify the vulnerable segments of the society by granting them cash, people might have under-reported their employment activities to reap potential benefits from this program. Sixth, the previous study on Census 1998 also reflects the same dilemma with lower participation especially for females in census 1998 than the LFS and concluded that as significant proportion of unpaid family helpers was excluded from the census 1998 which pulled down the participation rates. The study has also concluded that high reported unemployment rates have also pulled down the participation rates [see Nasir (2003), pp. 291-292]. Gender-wise youth employment participation rates at district level are given in appendix Table 1 while age- wise are given in appendix Table 2 and 3.

Table 4 Age-Specific Employment Rate by Gender: BISP and LFS Datasets BISP-2010 LFS-2010 Age (in Category) Male Female Male Female 18-24 28.1 5.0 65.5 15.7 25-29 61.2 8.5 91.2 18.4 30-34 70.8 9.5 96.0 19.8 35-39 74.4 10.0 97.1 20.8 40-44 74.8 10.3 97.4 22.8 45-49 74.3 10.1 96.1 20.7 50-54 71.4 9.8 93.6 20.5 55-59 66.7 9.7 88.0 19.0 60-64 56.3 9.5 69.2 13.2 65 and above 39.5 8.6 35.9 5.9 Source: Author’s estimation from the micro-data of BISP-2010 and LFS-2010.

Youth and Employment Participation

5. Youth Employment Participation by Sector

In this section, the employment participation of youth and adult by sector and type of employment has been discussed. Due to limited information and variation in different datasets, it was not an easy task to make the comparison; however an attempt has been made. Table 5 shows that close to two-third of the youth is working as self-employed, followed by one-third in private jobs and a minor share of 4 percent and 0.4 percent in government and semi-government jobs. Across the gender, greater share of male in government and private jobs can be seen than females while share of adult is greater in government jobs than the youth. Across the provinces, there is higher share of KP in government jobs and least share of Balochistan in private jobs, thus Balochistan is rated at highest in self-employed jobs. Due to different methodology adopted in NSER 2010- 2011, there is no rationale to compare these results with the other national datasets; however, there is under-reporting of paid employment (govt., semi-govt. and private) and over-reporting of self-employment in NSER 2010-2011 than the LFS and PSLM, particularly due to exclusion of unpaid family helpers and several other factors as reported in section 4. One can see the high share of paid employment and lower share of self-employment in LFS and PSLM datasets as given in appendix Table 5. As in Table 5, the district level results of youth in various employment categories by gender have been given in Appendix Table 4. Table 5 Proportion of Youth and adult in Various Employment Categories by Gender—BISP Dataset Youth Adult Employment Category Both Sexes Male Female Both Sexes Male Female Government 4.4 4.7 2.7 7.8 8.1 5.6 - Punjab 4.5 4.7 3.1 7.1 7.2 6.1 - Sindh 2.2 2.4 1.0 6.6 7.2 3.2 - KP 8.7 8.6 9.6 12.3 11.9 19.8 - Balochistan 5.3 7.1 2.0 14.0 18.0 3.9 Semi government 0.4 0.4 0.6 0.6 0.6 0.8 - Punjab 0.5 0.4 0.7 0.5 0.5 0.6 - Sindh 0.4 0.3 0.4 0.6 0.6 1.1 - KP 0.4 0.4 1.2 0.5 0.5 1.2 - Balochistan 0.8 0.9 0.5 1.1 1.3 0.5 Private 32.2 34.2 18.7 26.6 28.3 14.0 - Punjab 33.8 35.5 21.2 26.8 28.4 14.7 - Sindh 32.5 34.9 18.9 28.6 30.6 15.9 - KP 31.1 31.7 21.1 27.3 27.9 15.3 - Balochistan 8.6 11.9 2.6 7.1 9.2 1.9 Self employed 62.9 60.7 78.1 65.0 63.0 79.6 - Punjab 61.3 59.3 75.0 65.6 63.8 78.6 - Sindh 64.9 62.3 79.7 64.2 61.7 79.8 - KP 59.8 59.3 68.1 59.9 59.7 63.6 - Balochistan 85.4 80.2 94.9 77.7 71.6 93.7 Total 100 100 100 100 100 100 Source: Author’s estimation from the micro-data of BISP-2010.

Youth and Employment Participation

Due to non-availability of youth information in census 1998, an overall comparison of working population has been made between the NSER 2010-2011NSER 2010-2011 and Census 1998. Though the results are not comparable as both the surveys had adopted the different definitions to measure the working population (see Table 1), especially the NSER 2010-2011 has excluded the unpaid family helpers. Table 6 shows the wide variation of results with less share of govt./semi-govt. and more share of private jobs in NSER 2010- 2011. The results are close on self-employment but widely different about female self- employment as can be seen between both datasets which indicate the reporting errors in BISP dataset.

Table 6 Changes in Employment Status- A Comparison of BISP and Census 1998 Data source Govt./Semi-Govt. Private Self employed NSER 2010-2011NSER 2010-2011 Both sexes 4.8 32.2 62.9 Male 5.1 34.2 60.7 Female 3.3 18.7 78.1 Census 1998 Both Sexes 13.9 19.8 58.7 Male 13.3 20.0 59.9 Female 30.2 14.6 28.6 Difference (2010-1998) Both Sexes -9.1 12.4 4.2 Male -8.2 14.2 0.8 Female -26.9 4.1 49.5 Source: Author’s estimation from the micro-data of BISP-2010; for census estimates See Nasir (2003, table 14.21 and 14.22).

Youth and Employment Participation

6. Youth Employment Participation and Socio- Demographic Profile

In this section, youth employment participation has been linked with various socio- demographic characteristics including youth education, education completion status, education and employment of head of household, poverty status and PMT score of household. Before further analysis it is worth mentioning here that NSER 2010-2011 lacks the information on quality of employment which can either be judged through the occupational affiliation or the employment status i.e. whether a worker is vulnerable employed or not. Due to this limitation, the bi-variate analysis becomes weak because it is the quality of employment which is influenced either by the own education or the parent’s characteristics including education and employment which ultimately effect the household wellbeing [Mortimer, et al. (1992); Shehzadi, et al. (2012); Javed and Irfan (2012)]. However, still it is a fact that paid employment i.e. working in govt. semi-govt. and private is considered better than the self-employment as the latter is known as the part of vulnerable employment. Table 7 shows the gender-wise youth share of employment in various employment categories by attained education of youth. Overall, the results for both male and female reflect a positive trend between the paid employment and attained education especially the government employment while the share of self-employment decreases as the education increases. A similar picture can be seen at provincial level in Appendix Table 6.

Table 7 Attained Education and Type of Employment of Youth—NSER 2010-2011 Employment by Attained Education (in Categories) Type Upto 5 6-9 10-11 12 and above

Both Sexes Government 1.2 2.4 12.9 19.7 Semi government 0.3 0.3 0.6 1.5 Private 30.3 39.6 38.7 40.9 Self employed 68.2 57.7 47.8 37.8 Total 100 100 100 100 Male Government 1.2 2.4 12.9 19.7 Semi government 0.3 0.3 0.6 1.5 Private 30.3 39.6 38.7 40.9 Self employed 68.2 57.7 47.8 37.8 Total 100 100 100 100 Female Government 0.4 0.7 3.1 11.2 Semi government 0.3 0.4 1.1 3.0 Private 8.5 11.2 22.5 40.2 Self employed 90.8 87.7 73.3 45.7 Total 100 100 100 100 Source: Author’s estimation from the micro-data of BISP-2010.

Youth and Employment Participation

It was evident that the share of current enrolment of youth in educational institutes might negatively influence the labour employment participation rates as the higher preferences to accumulate education and skills for better future reduce the current participation. However, a week relation was observed between the two for both male and female in figure 4 and 5 where district level employment rate has been plotted with the current enrolment rate of youth. The slip-up seems to be on the measurement side of employment rate due to poor coverage.

Fig. 4. Male Employment Rate and Enrolment Rate for Youth

20 Enrolment rate Enrolment

0 0 10 20 30 40 50 60

Employment rate

Fig. 5. Female Employment Rate and Enrolment Rate for Youth

20 Enrolment Rate Enrolment

0 0 5 10 15 20 25 30 35 40 45

Employment rate

Youth and Employment Participation

A significant theoretic and empiric text in Labour Economics stresses the concept of intergenerational mobility especially in developing countries where the parents transfer their potential economic characteristics to the offspring i.e. education, occupation and overall well-being. The study of Zaeema (2013) and Javed and Irfan (2012) also found similar results in Pakistan on the basis of cross-sectional datasets. In NSER 2010-2011, it was not possible to link youth’s employment with parental characteristics i.e. education and employment due to data identification problems, therefore, the study has analysed it with the household head education and employment. Table 8 shows that as the education of head of household improves, the quality of employment also improves with an increase in the share of govt./semi-govt. and private jobs and decline in share of self-employed jobs. The provincial and regional level results as given in appendix Table 7 which also portray the same scenario.

Table 8 Employment Type of Youth by Education of Head of Household— NSER 2010-2011 Employment by 10 and Illiterate 1-9 11 and above Type above Both Sexes Govt./ Semi govt. 4 5 11 17 Private 32 37 40 41 Self employed 65 58 49 43 Total 100 100 100 100 Male Govt./ Semi govt. 4 5 11 17 Private 32 37 40 41 Self employed 65 58 49 43 Total 100 100 100 100 Female Govt./ Semi govt. 2 3 8 14 Private 15 20 29 33 Self employed 83 76 63 53 Total 100 100 100 100

Table 8 displays the relationship between youth employment type and employment type of head of household. While moving on principal diagonal highlighted as grey, it is evident that employment type of head of household significantly affects the employment type of their offspring including male and female. If the head of household is self-employment, there are 90 percent youths who are also in self-employed jobs. Similarly, households whose heads are working in private jobs have about 92 percent youth in private jobs while the households whose heads are working in govt./semi-government jobs have about 48 percent their youth in similar jobs. Females have more intergenerational occupational mobility than the males where their type of employment is highly linked with the employment type of head of household.

Youth and Employment Participation

Table 9 Employment Type of Youth by Employment Type of Head of Household— NSER 2010-2011 Employment of Head of Household Employment by Type Govt./ Semi govt. Private Self Both Sexes Govt./ Semi govt. 44.05 1.94 2.13 Private 23.71 88.57 7.41 Self employed 32.24 9.49 90.47 Total 100 100 100 Male Govt./ Semi govt. 20.9 2.8 1.5 Private 21.1 57.5 5.5 Self employed 58.1 39.8 92.9 Total 100 100 100 Female Govt./ Semi govt. 48.4 1.9 2.2 Private 24.2 91.8 7.7 Self employed 27.4 6.3 90.1 Total 100 100 100 Source: Author’s estimation from the micro-data of BISP-2010.

Table 10 shows the potential association of dependency burden of household with the type of employment.1 Again the results reveal that the paid category of employment including govt., semi-govt. and private have lower dependency burden than the households whose youth is employed in self-category, thus paid employment improve the household wellbeing.

Table 10 Employment Type of Youth by Dependency Burden—NSER 2010-2011 Dependency Level of Household Employment by Type Low Medium High Both Sexes Govt./ Semi govt. 5.8 4.3 3.1 Private 34.9 27.6 22.9 Self employed 59.3 68.1 74.0 Total 100 100 100 Female Govt./ Semi govt. 4.3 2.8 1.8 Private 23.2 13.8 10.8 Self employed 72.6 83.5 87.4 Total 100 100 100 Source: Author’s estimation from the micro-data of BISP-2010

It is believed that poverty is the problem among the people who cannot work or afford work due to any reason, thus employment is considered as best shield against poverty [Kim

1Household size was categorised into two categories dependent (below 15 age and above 64 age) and independent (15-64 year age). Dependency ratio is number of dependents divide by number of independents. Low dependency mean if ratio is 0-0.5, medium mean 0.51-1 and high mean >1.

Youth and Employment Participation

(1998); Kenway (2008); and Bell and Newitt (2010)]. But now a growing body of literature has shown that people living below the poverty line are engaged in the labour market, mostly in vulnerable jobs including self-employment and unpaid family workers [Majid (2001)]. Despite their engagement in the labour market they could not move out of poverty, this clearly shows that having a job has no authenticity of that of reduction in poverty [Eardley (1998), Bell and Newitt (2010)].

In Figures 6 and 7, a district level analysis has been carried out by scatter plot of share of paid employment (govt., semi-govt. and private) with the district level poverty rates and mean PMT scores, respectively. The estimates reveal the positive trend between the paid employment and PMT score and negative trend between the paid employment and poverty rates, thus concluding that as the share of paid employment increases, it improves the welfare level of district by improving the average PMT score of district and reducing the poverty rates. It is worth mentioning here that a reverse relationship can be traced out with the PMT score and self-employment and positive relation between poverty rates and self- employment in the same way. Gender-wise analysis has been given in appendix figure 1 to 4 with a clearer trend for male youth as compared to female youth while provincial and district level PMT score and poverty rates are given in appendix Tables 8 and 9, respectively. It is worth mentioning here that NSER 2010-2011 has calculated the household PMT score on the basis of various household socio-demographic and economic characteristics and used 16.17 threshold to identify the poor and eligible households for cash assistance. The same threshold has been used by the current study as poverty line to calculate the poverty status of household.

Fig. 6. Paid Employment and Poverty Rates for Youth

100

40 Paid employment (%) employment Paid 20

0 0 10 20 30 40 50 60 70 80 90

Poverty (%)

Youth and Employment Participation

Fig. 7. Paid Employment and Mean PMT for Youth

100

Paid employment (%) employment Paid 20

0 0 5 10 15 20 25 30 35 40

Mean PMT

Youth and Employment Participation

7. Youth Unemployment in Pakistan

The hardest task was to estimate the unemployment rate from the BISP dataset as four major drawbacks are evident from the BISP question while estimating unemployment. First is the exclusion of inactive labour force from the NSER 2010-2011 as 60 percent of the youth is part of inactive labour force in Pakistan, this percentage is 40 for males and 80 for females [Pakistan (2011)]. Due to exclusion of inactive labour force category from employment question, a significant percentage of inactive labour force has reported their status of employment as ‘unemployment’, thus raising the unemployment number to around 60 percent for Punjab, 55 percent in Sindh, 63 percent for KP, 64 percent for Balochistan and AJK, 68 percent for GB, 58 percent for Islamabad, and 83 percent for FATA. Though there were some missing cases in the data which can be assumed as inactive labour force; however, this fraction was negligible and almost in single digit at national and provincial level as given in appendix Table 10 in various age categories. Second, the quality of survey also matters as usually census data is not collected by the experienced staff, therefore, the unusual unemployment numbers suggest that the BISP enumerators had not good understanding of employment question and they did not inquire well to measure unemployment. Third, unpaid family helper is the part of employment category but being skipped in question, they were also added in unemployment, thus tolling the number. It is worth mentioning here that two-third of the employed females and near to one-fifth of the employed males are the part of unpaid family helpers in Pakistan. Fourth, as discussed in section 3 that usual status method might over-estimate the unemployment due to exclusion of seasonal employment. Other factors of high unemployment could be the data problems including exclusion of well-off regions and data cleaning.

To avoid this potential bias, the study has used the following methodology to estimate unemployment. The unemployment and null/missing population of NSER 2010-2011 was added and it was subtracted from the inactive given in labour force survey to estimate unemployment rate in BISP by assuming that the inactive labour force is constant in BISP and LFS survey. In this regard the usual status module of LFS was followed.

Unemployment rate BISP = Inactive LFS- (Unemployment BISP + Null BISP) It is also worth mentioning here that these estimated numbers are not necessarily comparable with other datasets and the exercise has been carried out just to observe the potential trends of unemployment. Table 11 shows that the estimated unemployment is around 9 percent overall with high unemployment rates for youth than adults. Overall, these unemployment rates are lower than the census 1998 and higher than the LFS survey. The trend looks satisfactory as census usually estimates high unemployment rates than the LFS [see Nasir (2003) for census (1998)].

Youth and Employment Participation

Table 11 Unemployment Rate by Gender: BISP, LFS and Census 1998 Datasets BISP* LFS 2010 Census 1998 Province/Region Both Both Male Female Male Female Both sexes Male Female sexes sexes Overall 9.4 9.8 13.2 3.0 2.5 3.5 19.7 20.2 5.1 Youth 28.0 39.9 17.8 4.5 4.1 4.7 - - - Adult 17.5 20.1 15.7 2.0 1.0 3.0 - - - Source: Author’s estimation from the micro-data of BISP and LFS-2010; for census estimates See Nasir (2003, table 14.10) * Estimated.

Table 12 shows the provincial level estimated youth and adult unemployment rates across the gender in which the provincial level youth and adult inactive labour force was used as threshold to estimate unemployment rate from NSER 2010-2011. The results reveal that youth is facing more unemployment rates than the adults with higher youths unemployment rates in province Balochistan while the lowest in KP.

Table 12 Estimated Youth Unemployment Rate by Gender and Province: NSER 2010-2011 Youth Adult Province/Region Both sexes Male Female Both sexes Male Female Punjab 29.0 38.6 22.1 18.6 17.6 20.8 Sindh 26.6 38.2 11.9 12.8 16.9 6.6 KP 23.8 39.3 14.0 18.6 25.2 16.0 Balochistan 31.6 62.6 0.8 24.3 51.6 7.9 Source: Author’s estimation from the micro-data of NSER 2010-2011NSER 2010-2011.

Table 13 shows age and gender-wise unemployment rates with a comparison of NSER 2010-2011 with 1998 census. Though the results are not directly comparable due to variation in definitions followed in both the datasets; however, two common trends can be found in both censuses. First, age-wise unemployment rates are inverted-U shape as expected and second, males are facing more unemployment rates than the females. The same trend was also observed in Tables 11 and 12. In NSER 2010-2011, the lower unemployment rates for female might be the estimation problem but in census the figures of female unemployment are unusual at the early age brackets. The district-level estimated unemployment rates are given in appendix Table 11 while age-specific unemployment rates at provincial level are given in appendix Table 12. It is worth mentioning that PSLM 2010 dataset has been used to estimate the inactive labour force at district level while estimating the unemployment rates from the NSER 2010-2011.

Youth and Employment Participation

Table 13 Age-wise Estimated Unemployment Rate by Gender in Pakistan: BISP and Census 1998 BISP* Census 1998 Age (in Category) Both Sexes Male Female Both Sexes Male Female 18-24 31.7 45.4 18.3 27.3 39.6 6.3 25-29 22.8 33.4 17.0 17.4 28.1 3.2 30-34 18.5 26.4 16.9 11.5 18.0 2.6 35-39 19.7 23.3 18.6 9.0 9.2 2.1 40-44 21.0 23.0 19.9 8.0 11.8 2.2 45-49 19.8 22.2 18.3 8.0 8.2 2.3 50-54 20.3 23.2 17.9 9.4 9.5 3.7 55-59 20.2 22.5 15.0 11.3 11.4 4.8 60 and above 14.0 16.8 7.3 21.3 21.4 16.3 Source: Author’s estimation from the micro-data of BISP; for census estimates See Nasir (2003, table 14. 11). * Estimated

Figures 8 and 9 have plotted the district level average mean PMT and poverty rates with the district level unemployment rates for youth. The trend reveals that the districts with higher level of unemployment rates are facing the lower welfare level i.e. lower PMT score and high poverty and vice versa.

Fig. 8. Mean PMT and Unemployment Rate for Youth

Mean PMTMean 20

0 0 10 20 30 40 50 60 70 Unemployment Rate

Youth and Employment Participation

Fig. 9. Poverty (%) and Unemployment Rate for Youth

40 Mean PMT Mean 30

0 0 10 20 30 40 50 60 70

Unemployment Rate

Youth and Employment Participation

8. Conclusions and Policy Recommendations

The paper has attempted to evaluate the youth employment participation by using the NSER 2010-2011 and compare it with the other national datasets including PSLM 2010, LFS 2010 and 1998 Census. Due to limited labour force related questions included in NSER 2010- 2011, the study has not estimated the economically active population, industrial structure, occupational distribution, earnings and quality of work. The analysis has focused only on three dimensions which are; youth employment participation, a bi-variate analysis of youth employment participation by linking it with various individual and household socio- demographic characteristics of youth, and measurement of unemployment rate of youth. An attempt was made to extend the analysis to district level.

A theoretic review of previous censuses and Labour Force Survey reveals that LFS and censuses are not comparable about various labour force indicators due to variation in measurement. It was also noted that censuses are not even comparable overtime due to different modules used in these censuses i.e. one can find variation in 1981 and 1998 censuses. Using a single question, the NSER 2010-2011 has attempted to measure labour force participation but has skipped some critical basics by excluding the inactive labour force and unpaid family helpers. As a result, a significant inactive labour force and unpaid family helpers were added in unemployment category, which over-estimated unemployment and under-estimated the employment participation. A significant youth was also excluded as the employment question was applicable to those who have age 18 and above. Due to non-common approach adopted in NSER 2010-2011, it is not advisable to compare the NSER 2010-2011 with the other datasets.

The analysis reveals that youth have lower employment participation rates than adults; however, lower employment participation rate has been reported in NSER 2010-2011 rather than the LFS survey across the provinces and gender. Close to two-third of the youth is working as self-employed with more females in self-employed category and more males in government and private jobs. Though NSER 2010-2011 lacks the information on quality of employment; however, the current study has found that attained education of youth and head of the household raise the chances of both male youth and female youth to work in paid jobs (govt., semi-govt., and private). The evidences of intergenerational mobility were also found where a strong association of employment type of head of household and employment type of youth has been observed. The study found that as the share of paid employment increases at district level, it improves the welfare level of district by improving the average PMT score of district and reducing the poverty rates.

Due to weak module adopted and poor understanding of enumerators to employment question, both the inactive labour force and unpaid family helpers were added in the unemployment category, thus raising it to 55 percent to 83 percent in various provinces/regions. The fraction 0f missing cases, a potential indicator of inactive labour force, was only in single digit. The study has estimated the unemployment rates by adding the reported unemployment and null/missing population of NSER 2010-2011 and it was subtracted from the inactive category given in labour force survey. It is also worth

Youth and Employment Participation

mentioning here that these estimated numbers are not necessarily comparable with other datasets and the exercise has been carried out just to observe the potential trends of unemployment. Overall, these unemployment rates are lower than the census 1998 and higher than the LFS survey. The trend looks satisfactory as census usually confers high unemployment rates than the LFS. It was also found that district level unemployment rates have a negative association with the welfare level of district.

In future, BISP should measure the labour related information either by following the Census 1998 practice or LFS and PSLM modules can be used to capture labour related information. In addition, the questions related to labour should capture the labour information from age 10 and above population. Second, female self-employed categories show a remarked increase while comparing the NSER 2010-2011 estimates with 1998 Census estimates. BISP can intervention the can be increased more through Waseela-e-Rozgar program to boost up the skills of its beneficiaries which will ultimately help to graduate these beneficiaries by helping them to move out of poverty. Third, within the youth, self-employed youth are facing high dependency burdens, both among males and females. High dependency itself increases the chances of more vulnerability and poverty whereas self- employed might be among the vulnerable employed youth. It is suggested that BISP should have more focus on this category for poverty alleviation by imparting them technical and vocational skills.

Youth and Employment Participation

Appendix Figures and Tables

Appendix Fig. 1. Paid Employment and Mean PMT for Male Youth

0 0 10 20 30 40 50 60 70 80 90 100

Appendix Fig. 2. Paid Employment and Poverty for Male Youth

0 0 10 20 30 40 50 60 70 80 90 100

Youth and Employment Participation

Appendix Fig. 3. Paid Employment and Mean PMT for Female Youth

100

0 0 5 10 15 20 25 30 35 40

Appendix Fig. 4. Paid Employment and Poverty for Female Youth

100

0 0 10 20 30 40 50 60 70 80 90

Youth and Employment Participation

Appendix Table 1 Proportion of Working Youth (18-29) at District Level: BISP Survey Districts Both Sexes Male Female Districts Both Sexes Male Female Punjab Sindh Attock 26.3 49.3 3.1 Badin 28.1 46.0 9.4 Bahawalnagar 25.2 45.6 4.6 Dadu 29.9 50.3 8.5 Bahawalpur 27.8 48.9 6.4 Ghotki 26.3 37.4 14.5 Bhakkar 25.1 44.0 5.6 Hyderabad 19.8 35.0 4.1 Chakwal 22.6 42.3 2.5 Jacobabad 34.0 59.1 9.0 Chiniot 19.7 35.1 3.6 Jamshoro 24.1 43.7 4.0 Dera Ghazi Khan 27.4 43.2 10.0 Kambar Shahdad Kot 33.7 49.6 16.2 Faisalabad 23.1 41.0 4.7 Karachi Central 24.2 42.7 5.2 Gujranwala 28.6 54.0 3.0 Karachi East 24.6 43.2 5.6 Gujrat 24.5 47.3 2.5 Karachi Malir 20.8 36.6 4.4 Hafizabad 18.1 30.4 5.5 Karachi South 21.7 39.3 4.2 Jhang 16.8 26.9 5.9 Karachi West 23.4 43.7 2.9 Jhelum 23.6 44.3 2.6 Kashmore 38.3 51.8 24.5 Kasur 30.9 55.8 4.9 Khairpur 28.2 46.0 9.2 Khanewal 14.3 23.3 4.8 Larkana 36.1 49.7 22.2 Khushab 17.1 30.6 3.6 Matiari 16.2 26.3 5.6 Lahore 27.1 49.1 4.5 Mirpur Khas 44.5 75.7 13.5 Leiah 34.5 59.8 7.7 Naushahro Feroze 15.4 25.3 4.8 Lodhran 19.7 33.5 5.2 Sanghar 40.7 66.6 13.5 Mandi Bahauddin 14.7 27.0 2.3 Shaheed Benazir Abad 18.4 30.3 5.1 Mianwali 40.5 71.1 8.3 Shikarpur 32.2 51.9 10.7 Multan 30.0 42.2 17.6 Sukkur 22.8 41.5 3.4 Muzaffargarh 33.7 42.9 24.1 Tando Allahyar 40.1 62.0 17.0 Nankana Sahib 16.7 28.5 4.3 Tando M. Khan 22.6 41.0 2.8 Narowal 13.4 23.8 2.4 Tharparkar 38.5 64.2 13.0 Okara 21.7 36.1 6.6 Thatta 16.8 26.7 6.3 Pakpattan 30.1 49.5 10.6 Umer Kot 15.2 26.9 1.9 Rahim Yar Khan 11.3 19.1 3.2 Rajanpur 26.3 43.8 8.4 Rawalpindi 23.3 39.4 6.9 Sahiwal 22.3 37.6 6.4 Sargodha 16.3 29.1 3.0 Sheikhupura 25.9 46.6 4.5 Sialkot 27.1 52.0 3.2

Youth and Employment Participation

Toba Tek Singh 26.7 41.9 10.2 Vehari 19.0 29.8 7.3 Districts Both Sexes Male Female Districts Both Sexes Male Female KPK Balochsitan Abbottabad 15.8 38.8 1.3 Awaran 3.8 6.3 1.4 Bannu 6.8 40.7 9.1 Barkhan 15.0 16.0 13.9 Batagram 3.6 26.4 3.6 Chagai 5.8 6.9 4.7 Buner 5.0 46.7 3.9 Dera Bugti 15.4 11.9 19.9 Charsadda 6.7 44.4 1.4 Gwadar 23.2 25.2 21.2 Chitral 7.0 22.0 1.6 Harnai 11.1 15.3 6.9 D. I. Khan 5.4 43.2 3.1 Jaffarabad 39.0 36.7 41.3 Hangu 5.0 24.3 0.4 Jhal Magsi 19.4 21.8 16.7 Haripur 11.0 38.0 1.9 Kachhi 13.8 14.0 13.4 Karak 12.4 30.5 1.0 Kalat 15.4 18.5 12.1 Kohat 8.1 37.4 1.3 Kech 25.5 39.5 12.5 Kohistan 0.5 15.6 6.5 Kharan 6.9 8.7 5.1 Lakki Marwat 6.8 26.9 4.4 Khuzdar 16.0 22.3 10.1 Lower Dir 3.6 26.9 1.5 Killa Abdullah 13.7 20.1 6.6 Malakand P A 18.0 47.4 1.3 Killa Saifullah 40.0 79.2 4.3 Mansehra 9.1 38.6 1.7 Kohlu 14.6 19.1 9.4 Mardan 8.8 46.1 1.2 Lasbela 27.1 32.7 21.2 Null 6.4 30.7 3.8 Loralai 15.7 19.4 11.5 Peshawar 10.1 43.1 2.4 Mastung 10.6 13.1 8.3 Shangla 2.1 11.5 2.5 Musakhel 23.2 41.0 7.9 Swabi 9.2 35.4 1.4 Nasirabad 32.6 31.1 34.3 Swat 4.0 17.7 0.5 Naushahro Feroz 17.8 23.8 12.4 Tank 4.7 40.6 0.6 Nushki 5.7 9.6 1.4 Upper Dir 2.8 13.7 1.1 Panjgur 3.9 3.6 4.2 AJK Pishin 13.0 17.3 9.1 Bagh 17.7 34.0 1.5 Quetta 12.7 20.6 3.6 Bhimbe 18.0 34.8 1.7 Sherani 8.0 9.6 6.2 Hattian Bala 21.8 40.9 2.3 Sibbi 13.5 15.8 10.8 Haveli 18.8 36.6 0.7 Washuk 4.1 5.5 2.7 Kotli 21.2 42.2 1.4 Zhob 10.5 13.4 7.1 Mirpur 18.3 33.9 2.3 Ziarat 13.8 15.5 12.2 Muzaffarabad 17.2 32.9 1.6 FATA Neelum 9.7 18.4 0.7 Bajaur Agency 8.0 14.5 0.8 Poonch 16.2 29.8 2.8 Khyber Agency 9.5 17.9 0.5

Youth and Employment Participation

Sudhno 20.9 39.7 2.2 Kurram Agency 17.4 30.6 3.5 Sudhnoti 21.3 40.9 1.7 Mohmand Agency 5.6 10.0 0.7 GB Orakzai Agency 8.2 15.2 2.5 Astore 17.5 32.0 3.7 S Waziristan Agency 25.0 44.4 0.0 Baltistan 9.3 15.9 2.2 T A Adj Bannu 16.7 16.7 16.7 Diamir 27.4 46.2 10.6 T A Adj D.I.Khan 5.8 10.1 0.9 Ghanche 29.2 30.7 27.8 T A Adj Kohat 15.2 27.4 1.1 Ghizer 18.8 31.8 4.3 T A Adj Peshawar 9.1 17.4 0.6 Gilgit 7.2 12.7 1.6 T.A.Adj.Lakki Marwat 11.3 21.5 1.4 Hunza Nagar 6.7 12.0 1.7 Islamabad Islamabad 19.9 35.6 3.8

Youth and Employment Participation

Appendix Table 2 Age-Specific Employment Rate by Gender and Province: BISP and LFS Datasets BISP-2010 LFS-2010 Age (in Category) Punjab Sindh KP Balochistan Punjab Sindh KP Balochistan Males 18-24 29.3 30.6 23.9 15.5 66.1 67.3 58.8 68.3 25-29 63.8 64.9 55.2 33.3 92.5 91.1 87.5 92.5 30-34 73.1 74.6 65.3 40.5 94.8 97.0 94.5 98.8 35-39 76.1 78.7 68.9 46.0 96.0 97.8 95.5 99.4 40-44 76.0 79.8 69.3 48.0 96.6 98.2 95.3 99.9 45-49 75.0 80.1 68.6 50.7 94.6 98.1 94.3 98.5 50-54 72.3 76.6 66.0 48.1 92.3 95.6 90.0 98.9 55-59 67.6 72.6 60.0 47.0 85.3 91.6 85.9 93.2 60-64 57.0 63.6 47.0 40.2 71.9 68.9 63.8 65.9 65 and above 39.6 49.6 28.0 32.0 39.0 33.4 33.9 16.1 Females 18-24 4.8 6.6 1.6 10.8 19.4 16.2 9.0 7.9 25-29 8.5 10.2 2.9 16.3 22.0 21.5 12.2 9.4 30-34 9.4 11.0 3.4 18.9 23.6 22.8 18.0 7.8 35-39 9.9 11.9 3.7 19.5 28.5 19.9 16.8 6.4 40-44 10.1 12.5 3.4 19.5 29.6 21.0 15.7 9.6 45-49 10.1 12.8 3.3 19.1 28.3 17.9 13.6 5.8 50-54 9.6 12.6 3.1 19.4 27.6 19.0 12.1 7.3 55-59 9.7 12.7 2.8 17.8 23.2 17.1 11.9 16.1 60-64 9.2 12.9 2.7 17.2 13.9 16.5 10.0 5.9 65 and above 8.3 12.3 2.4 15.9 6.5 5.7 5.3 1.5

Youth and Employment Participation

Appendix Table 3 Age-Specific Employment Rate by Gender and Region: BISP Survey Age (in category) Islamabad GB AJK FATA Males 18-24 21.2 12.5 21.9 13.4 25-29 60.3 39.8 57.7 26.4 30-34 76.9 54.4 71.5 30.9 35-39 81.6 57.8 75.7 32.8 40-44 82.9 53.2 73.6 31.8 45-49 82.0 49.3 70.4 29.7 50-54 77.6 45.8 66.4 26.8 55-59 71.0 39.8 57.0 23.3 60-64 49.1 30.1 39.0 17.0 65 and above 22.5 18.3 17.0 11.2 Females 18-24 2.0 3.4 1.0 1.2 25-29 6.7 7.9 2.9 1.5 30-34 7.4 10.0 3.5 1.7 35-39 7.7 10.0 3.7 1.5 40-44 7.8 8.8 3.8 1.4 45-49 7.7 8.1 3.8 1.3 50-54 7.4 8.0 2.8 1.3 55-59 6.5 7.9 1.6 1.2 60-64 4.6 6.6 1.0 1.2 65 and above 2.5 5.7 0.6 0.9

Youth and Employment Participation

Appendix Table 4 Proportion of Youth in Various Employment Categories by Gender at District Level—BISP Survey Both Sexes Male Female

Districts Govt. Govt. Govt. - - - Govt. Govt. Govt. Private Private Private Semi Semi Semi Self employed Self employed Self employed

Punjab Attock 14.2 0.5 16.0 69.4 14.3 0.4 15.2 70.1 11.1 1.3 29.9 57.8 Bahawalnagar 3.3 0.4 11.8 84.5 3.2 0.3 11.8 84.6 4.7 0.6 11.8 82.9 Bahawalpur 2.8 0.3 14.9 82.0 2.7 0.3 15.3 81.7 3.3 0.7 11.9 84.2 Bhakkar 6.5 0.5 16.8 76.2 6.8 0.4 18.0 74.7 3.9 1.2 6.7 88.1 Chakwal 24.4 0.8 27.5 47.3 24.7 0.8 27.0 47.6 18.0 2.2 37.9 41.9 Chiniot 3.8 0.4 23.7 72.1 3.9 0.4 24.2 71.5 3.2 0.4 18.0 78.3 Dera Ghazi Khan 3.4 0.4 13.9 82.3 3.8 0.4 15.5 80.2 1.3 0.4 6.2 92.1 Faisalabad 3.3 0.6 55.2 40.9 3.4 0.5 58.3 37.8 3.3 0.7 27.6 68.4 Gujranwala 2.7 0.2 21.4 75.7 2.5 0.2 20.8 76.5 6.3 1.0 32.8 59.9 Gujrat 5.2 0.3 51.6 42.9 4.8 0.3 51.3 43.6 11.5 1.5 56.9 30.1 Hafizabad 3.2 0.6 30.2 65.9 3.3 0.6 32.7 63.3 2.8 0.8 16.2 80.3 Jhang 4.5 0.5 25.9 69.0 5.1 0.5 28.4 66.0 1.9 0.5 13.6 84.0 Jhelum 11.9 0.6 41.1 46.4 11.8 0.4 40.4 47.4 13.6 3.1 54.4 29.0 Kasur 2.9 0.2 36.3 60.6 2.8 0.2 36.2 60.9 4.7 0.5 37.8 57.0 Khanewal 3.6 0.5 43.4 52.4 3.9 0.5 47.8 47.8 2.0 0.7 21.4 75.9 Khushab 12.7 0.9 20.8 65.6 13.5 0.9 21.5 64.1 5.3 0.9 15.2 78.5 Lahore 3.6 0.5 62.8 33.1 3.4 0.4 62.5 33.6 5.5 1.0 66.2 27.3 Leiah 4.0 0.3 19.8 75.8 4.1 0.3 21.2 74.4 3.0 0.8 8.3 87.9 Lodhran 2.0 0.3 23.1 74.5 2.0 0.3 23.9 73.8 1.9 0.5 18.2 79.4 Mandi Bahauddin 5.7 0.6 15.2 78.4 5.4 0.5 14.9 79.1 9.0 1.8 18.9 70.3 Mianwali 11.3 0.8 17.8 70.1 12.2 0.8 18.9 68.1 2.8 0.8 8.3 88.2 Multan 2.9 0.5 23.0 73.6 3.7 0.6 28.8 66.9 0.8 0.4 8.8 89.9 Muzaffargarh 1.7 0.4 25.5 72.4 2.3 0.5 36.2 60.9 0.5 0.2 5.8 93.5 Nankana Sahib 4.1 0.4 33.8 61.7 4.2 0.4 36.0 59.5 3.4 0.4 18.8 77.4 Narowal 12.7 0.7 21.9 64.7 12.8 0.6 21.7 64.8 11.5 1.7 24.0 62.7 Okara 2.4 0.4 23.8 73.4 2.6 0.4 25.3 71.7 1.2 0.5 15.3 83.0 Pakpattan 2.6 0.5 10.3 86.5 2.9 0.5 11.5 85.1 1.3 0.7 4.6 93.4 Rahim Yar Khan 2.0 0.5 26.0 71.4 2.1 0.5 28.4 69.1 1.7 0.8 11.6 85.9 Rajanpur 2.0 0.1 8.7 89.2 2.1 0.1 9.4 88.4 1.0 0.3 4.9 93.8

Youth and Employment Participation

Rawalpindi 10.8 0.9 48.2 40.1 11.7 0.9 51.6 35.8 5.1 0.9 28.7 65.3 Sahiwal 1.9 0.5 23.8 73.9 2.0 0.4 25.2 72.4 1.3 0.7 15.2 82.8 Sargodha 6.8 0.8 27.3 65.2 7.0 0.8 28.2 64.0 4.2 1.1 17.6 77.1 Sheikhupura 1.8 0.4 43.1 54.7 1.7 0.3 43.4 54.6 2.3 0.8 40.2 56.7 Sialkot 3.0 0.4 48.9 47.7 2.7 0.4 48.0 48.9 7.5 1.4 61.9 29.3 Toba Tek Singh 3.1 0.8 24.2 72.0 3.2 0.8 26.9 69.1 2.5 0.8 11.7 85.0 Vehari 1.8 0.3 10.2 87.6 2.0 0.3 10.7 87.0 1.1 0.4 8.0 90.5 Islamabad 12.0 1.1 47.9 39.0 11.5 1.0 47.1 40.4 16.1 2.5 55.7 25.7 Minimum 1.7 0.1 8.7 33.1 1.7 0.1 9.4 33.6 0.5 0.2 4.6 25.7 Maximum 24.4 0.9 62.8 89.2 24.7 0.9 62.5 88.4 18.0 3.1 66.2 93.8 Sindh Badin 1.7 0.2 3.8 94.2 1.9 0.2 4.3 93.6 0.7 0.2 1.5 97.5 Dadu 2.4 0.1 8.5 88.9 2.6 0.1 9.4 87.9 1.0 0.3 3.5 95.3 Ghotki 3.1 0.2 8.4 88.3 4.0 0.3 10.7 85.0 0.5 0.1 1.9 97.5 Hyderabad 2.6 0.7 19.8 76.9 2.5 0.7 19.5 77.2 2.8 1.1 22.0 74.1 Jacobabad 1.8 0.2 58.0 40.0 2.0 0.2 61.3 36.6 0.7 0.3 36.8 62.2 Jamshoro 2.5 0.3 24.9 72.4 2.5 0.2 26.1 71.2 2.0 0.5 11.4 86.1 Kambar Shahdad Kot 0.8 0.1 9.2 90.0 0.9 0.1 11.2 87.8 0.2 0.0 2.4 97.4 Karachi Central 1.2 0.5 85.8 12.5 1.1 0.4 86.0 12.4 1.4 0.8 84.5 13.3 Karachi East 1.7 0.7 85.5 12.1 1.7 0.6 86.3 11.4 1.6 1.7 79.3 17.5 Karachi Malir 2.0 0.9 69.4 27.7 2.0 0.8 70.2 26.9 1.9 1.2 62.2 34.7 Karachi South 3.1 0.6 71.9 24.4 3.2 0.6 72.3 23.9 2.0 1.1 68.6 28.3 Karachi West 1.4 0.7 86.0 11.9 1.4 0.7 86.3 11.6 1.7 1.2 81.4 15.7 Kashmore 1.0 0.1 4.8 94.1 1.4 0.1 6.6 91.8 0.1 0.0 1.0 98.8 Khairpur 4.5 0.2 5.8 89.6 5.1 0.2 6.4 88.4 1.1 0.2 2.7 96.1 Larkana 2.1 0.3 22.8 74.7 2.9 0.4 30.4 66.3 0.4 0.1 5.6 93.9 Matiari 4.9 0.2 6.4 88.5 5.5 0.2 6.6 87.7 1.6 0.5 5.0 92.8 Mirpur Khas 1.4 0.2 17.5 80.9 1.5 0.2 18.2 80.1 0.5 0.3 13.8 85.5 Naushahro Feroze 6.8 0.3 8.2 84.7 7.6 0.3 9.0 83.1 2.1 0.4 3.8 93.7 Sanghar 1.7 0.2 13.3 84.7 2.0 0.2 14.4 83.4 0.6 0.2 7.9 91.3 Shaheed Benazir Abad 3.4 0.3 11.6 84.7 3.6 0.2 12.5 83.6 2.0 0.5 5.6 91.8 Shikarpur 1.2 0.1 2.6 96.1 1.4 0.1 3.0 95.6 0.3 0.1 0.8 98.8 Sukkur 3.3 0.2 10.9 85.6 3.5 0.2 10.9 85.5 1.7 0.6 10.3 87.5 Tando Allahyar 1.7 0.1 3.3 94.9 2.0 0.1 3.4 94.4 0.5 0.1 2.8 96.5 Tando Muhammad Khan 1.5 0.2 2.9 95.4 1.4 0.1 2.9 95.6 2.9 0.8 3.1 93.3 Tharparkar 1.9 0.3 4.9 92.9 2.1 0.3 5.4 92.2 1.0 0.5 2.5 96.0 Thatta 1.4 0.3 4.1 94.2 1.6 0.2 4.5 93.7 0.6 0.3 2.5 96.6 Umer Kot 4.0 0.5 11.8 83.7 4.0 0.4 11.9 83.7 4.6 1.7 10.7 83.0

Youth and Employment Participation

Minimum 0.8 0.1 2.6 11.9 0.9 0.1 2.9 11.4 0.1 0.0 0.8 0.8 Maximum 6.8 0.9 86.0 96.1 7.6 0.8 86.3 95.6 4.6 1.7 84.5 6.8 KPK Abbottabad 10.6 0.5 65.8 23.1 10.4 0.5 65.8 23.4 16.6 2.1 67.4 13.9 Bannu 5.6 0.4 20.6 73.4 6.3 0.4 22.9 70.5 2.5 0.5 9.2 87.9 Batagram 4.4 0.4 19.6 75.6 4.2 0.5 21.7 73.6 5.7 0.4 4.7 89.2 Buner 3.4 0.2 16.3 80.1 3.2 0.2 17.4 79.1 5.6 0.4 2.5 91.5 Charsadda 6.5 0.2 21.6 71.6 6.3 0.2 21.5 72.0 13.7 1.5 24.0 60.8 Chitral 34.3 1.0 23.2 41.5 34.7 0.6 21.1 43.5 28.1 6.8 51.9 13.2 D. I. Khan 5.9 0.1 17.0 77.0 6.0 0.1 17.4 76.5 3.7 0.5 10.7 85.1 Hangu 7.3 0.4 32.3 60.0 7.1 0.4 32.5 60.1 21.0 1.0 21.0 56.9 Haripur 15.7 0.6 38.2 45.4 15.9 0.5 37.7 46.0 13.0 2.3 49.9 34.8 Karak 40.6 1.7 30.9 26.9 41.1 1.6 30.9 26.4 21.8 4.1 31.4 42.8 Kohat 16.1 0.9 23.3 59.8 16.0 0.8 23.2 60.0 17.5 2.3 27.1 53.1 Kohistan 1.9 0.2 2.0 95.8 2.5 0.2 2.4 94.8 0.5 0.3 1.0 98.2 Lakki Marwat 24.4 1.3 16.8 57.5 27.4 1.4 18.6 52.6 5.3 0.7 5.5 88.5 Lower Dir 7.9 0.3 16.3 75.4 7.3 0.3 16.8 75.7 20.3 1.2 7.4 71.2 Malakand P A 11.3 0.3 61.5 26.8 10.9 0.2 62.1 26.8 28.0 4.9 39.9 27.2 Mansehra 6.1 0.3 38.1 55.4 6.1 0.3 38.5 55.1 8.1 1.4 29.5 61.0 Mardan 7.0 0.2 28.6 64.2 6.7 0.2 28.6 64.4 20.1 1.6 25.5 52.8 Null 6.8 1.0 29.3 62.9 7.1 0.5 31.7 60.7 4.5 4.5 9.1 81.8 Peshawar 4.7 0.3 38.4 56.6 4.7 0.2 38.4 56.7 5.7 0.8 38.6 54.9 Shangla 7.6 1.0 21.9 69.6 8.5 1.0 26.1 64.3 3.1 0.8 2.9 93.2 Swabi 10.7 0.3 37.2 51.7 10.4 0.3 37.8 51.5 19.0 2.3 22.4 56.4 Swat 7.4 0.8 36.9 54.9 6.7 0.7 37.1 55.5 29.3 2.6 30.8 37.3 Tank 14.0 0.2 7.7 78.1 13.7 0.2 7.6 78.5 33.5 1.3 16.7 48.5 Upper Dir 15.9 0.9 21.2 61.9 15.7 0.8 22.1 61.5 19.5 3.2 9.6 67.7 Minimum 1.9 0.1 2.0 23.1 2.5 0.1 2.4 23.4 0.5 0.3 1.0 1.9 Maximum 40.6 1.7 65.8 95.8 41.1 1.6 65.8 94.8 33.5 6.8 67.4 40.6 AJK Bagh 12.6 0.5 64.1 22.8 12.0 0.4 63.9 23.6 24.5 2.4 67.4 5.8 Bhimbe 18.4 0.4 17.8 63.5 18.3 0.3 15.5 66.0 20.7 1.7 63.5 14.2 Hattian Bala 8.4 0.4 18.0 73.3 8.2 0.3 17.9 73.7 13.2 2.0 20.0 64.8 Haveli 18.1 0.5 8.4 73.0 17.8 0.4 8.0 73.8 36.2 6.4 27.7 29.8 Kotli 6.4 0.2 11.4 82.0 5.7 0.2 10.3 83.7 24.6 1.2 39.8 34.3 Mirpur 7.0 0.6 48.4 44.0 6.0 0.5 48.1 45.4 21.4 1.9 52.7 24.1 Muzaffarabad 13.5 1.0 31.1 54.4 13.1 0.9 30.5 55.5 21.7 2.6 44.0 31.7 Neelum 26.2 1.1 4.8 68.0 25.4 0.8 4.7 69.1 51.0 8.8 5.9 34.3

Youth and Employment Participation

Poonch 13.8 0.8 46.6 38.9 14.3 0.7 46.9 38.1 8.2 1.8 43.2 46.8 Sudhno 25.5 0.5 57.4 16.5 25.9 0.4 57.2 16.5 19.0 2.4 60.7 17.9 Sudhnoti 23.1 0.4 62.8 13.8 23.0 0.4 62.8 13.9 23.7 0.4 63.2 12.6 Minimum 6.4 0.2 4.8 13.8 5.7 0.2 4.7 13.9 8.2 0.4 5.9 6.4 Maximum 26.2 1.1 64.1 82.0 25.9 0.9 63.9 83.7 51.0 8.8 67.4 26.2 Balochistan Awaran 18.9 1.6 11.1 68.4 21.1 0.0 13.1 65.8 8.9 8.9 2.2 80.0 Barkhan 6.3 0.5 0.8 92.4 9.1 0.6 1.2 89.1 2.7 0.3 0.3 96.6 Chagai 12.4 1.8 9.9 75.9 15.6 2.1 13.2 69.1 7.5 1.3 4.8 86.4 Dera Bugti 7.4 1.5 1.9 89.2 15.7 3.5 3.6 77.2 1.1 0.1 0.6 98.2 Gwadar 4.3 0.6 8.1 87.0 6.3 0.7 12.3 80.8 1.9 0.5 3.1 94.4 Harnai 10.7 0.6 5.8 82.8 12.1 0.5 7.5 79.9 7.7 0.9 2.0 89.3 Jaffarabad 2.5 0.2 2.8 94.6 4.7 0.3 4.3 90.7 0.4 0.1 1.3 98.2 Jhal Magsi 7.2 0.4 1.7 90.6 11.4 0.6 2.2 85.8 1.2 0.2 0.9 97.7 Kachhi 4.2 0.3 3.5 92.0 6.2 0.4 4.7 88.7 1.3 0.2 1.7 96.7 Kalat 8.6 0.7 3.8 86.9 10.9 0.8 5.5 82.8 5.0 0.4 1.0 93.6 Kech 4.8 0.8 6.6 87.8 5.4 0.9 7.1 86.7 3.1 0.7 5.0 91.2 Kharan 21.8 2.0 4.2 72.0 30.6 1.9 5.6 61.8 7.1 2.1 2.0 88.8 Khuzdar 4.9 0.7 6.8 87.6 6.2 0.7 8.4 84.6 2.0 0.7 3.4 93.9 Killa Abdullah 1.8 0.6 3.9 93.8 1.9 0.5 4.2 93.3 1.3 0.7 2.8 95.3 Killa Saifullah 2.3 0.7 23.3 73.7 2.1 0.5 24.0 73.3 5.7 2.7 11.4 80.2 Kohlu 19.6 5.0 3.1 72.3 25.8 6.8 3.9 63.5 5.0 0.7 1.2 93.1 Lasbela 4.5 0.7 19.2 75.7 6.3 0.8 28.8 64.1 1.5 0.5 3.7 94.3 Loralai 4.5 1.2 5.9 88.4 5.7 1.4 7.6 85.3 2.2 0.8 2.6 94.4 Mastung 7.4 0.9 3.2 88.6 11.0 0.9 4.5 83.5 1.9 0.8 1.2 96.0 Musakhel 7.8 1.0 8.3 82.9 9.0 1.0 9.4 80.6 2.6 0.6 3.5 93.2 Nasirabad 2.6 0.3 1.3 95.8 4.7 0.3 2.1 92.9 0.5 0.2 0.6 98.7 Naushahro Feroz 1.2 0.0 14.9 83.9 1.9 0.0 22.9 75.2 0.0 0.0 1.1 98.9 Nushki 14.8 2.2 14.0 69.0 14.3 2.1 14.1 69.5 18.2 2.7 13.6 65.5 Panjgur 9.9 2.2 3.9 84.0 14.4 2.4 6.7 76.6 6.7 2.1 1.8 89.4 Pishin 5.2 0.8 9.0 84.9 6.5 1.0 13.0 79.5 3.1 0.6 2.4 93.9 Quetta 10.0 1.6 24.3 64.1 10.5 1.5 25.8 62.2 6.8 2.3 14.0 76.9 Sherani 3.5 1.3 5.8 89.3 4.5 1.5 7.9 86.1 1.9 0.9 2.1 95.1 Sibbi 17.9 0.5 4.7 76.9 25.5 0.6 5.9 68.1 5.1 0.5 2.7 91.8 Washuk 9.9 1.0 5.7 83.3 11.5 1.3 7.9 79.3 6.7 0.5 1.5 91.3 Zhob 7.8 1.8 8.2 82.2 9.9 1.6 10.5 78.0 3.5 2.1 3.4 91.0 Ziarat 5.5 0.6 1.8 92.1 7.5 0.8 2.8 88.9 3.3 0.3 0.7 95.8 Minimum 1.2 0.0 0.8 64.1 1.9 0.0 1.2 61.8 0.0 0.0 0.3 1.2

Youth and Employment Participation

Maximum 21.8 5.0 24.3 95.8 30.6 6.8 28.8 93.3 18.2 8.9 14.0 21.8 GB Astore 25.3 1.2 14.0 59.5 27.2 1.3 14.7 56.7 9.5 0.6 7.8 82.2 Baltistan 38.3 4.2 17.0 40.4 39.9 3.9 14.3 41.9 25.6 6.9 38.9 28.5 Diamir 6.8 1.3 2.0 89.9 8.3 1.5 2.1 88.1 1.2 0.2 1.7 96.9 Ghanche 20.3 2.0 15.1 62.6 37.2 2.6 26.2 34.0 2.0 1.4 3.2 93.5 Ghizer 52.8 1.5 10.9 34.8 58.0 0.9 8.1 33.0 10.6 6.4 34.0 49.1 Gilgit 48.3 2.9 16.9 31.9 50.7 2.6 12.7 34.0 29.0 5.2 51.1 14.7 Hunza Nagar 33.4 3.0 33.3 30.4 36.2 2.4 28.1 33.3 14.7 6.6 67.2 11.6 Minimum 6.8 1.2 2.0 30.4 8.3 0.9 2.1 33.0 1.2 0.2 1.7 6.8 Maximum 52.8 4.2 33.3 89.9 58.0 3.9 28.1 88.1 29.0 6.9 67.2 52.8 FATA Bajaur Agency 3.5 0.6 1.9 94.0 3.4 0.6 2.0 94.0 4.8 1.2 1.2 92.8 Khyber Agency 10.3 2.9 60.4 26.4 10.2 2.9 60.9 26.0 13.9 3.4 40.7 42.0 Kurram Agency 2.6 1.2 68.9 27.3 2.4 1.1 70.1 26.4 4.3 2.4 58.5 34.8 Mohmand Agency 9.1 1.4 11.3 78.2 8.9 1.4 11.7 78.0 13.0 2.1 3.3 81.6 Orakzai Agency 24.4 1.6 10.0 64.0 27.1 1.4 11.6 59.9 10.3 2.6 1.9 85.2 S Waziristan Agency 8.3 0.0 91.7 0.0 8.3 0.0 91.7 0.0 0.0 0.0 0.0 0.0 T A Adj Bannu 0.0 0.0 25.0 75.0 0.0 0.0 50.0 50.0 0.0 0.0 0.0 100.0 T A Adj D.I.Khan 12.9 0.3 14.1 72.7 13.0 0.1 12.4 74.5 11.8 2.0 37.3 49.0 T A Adj Kohat 7.8 0.7 53.4 38.0 7.9 0.8 54.3 37.0 3.4 0.0 27.1 69.5 T A Adj Peshawar 18.1 7.0 45.4 29.5 17.7 7.2 45.4 29.6 30.6 0.0 44.4 25.0 T.A.Adj.Lakki Marwat 2.1 2.1 66.7 29.2 0.0 2.2 68.9 28.9 33.3 0.0 33.3 33.3 Minimum 0.0 0.0 1.9 0.0 0.0 0.0 2.0 0.0 0.0 0.0 0.0 0.0 Maximum 24.4 7.0 91.7 94.0 27.1 7.2 91.7 94.0 33.3 3.4 58.5 24.4

Youth and Employment Participation

Appendix Table 5 Share of Employment Category in Total Employment of Youth by Province–BISP, PSLM and LFS Datasets BISP-2010 PSLM-2010 LFS-2010 Age in category Both Both Both Male Female Male Female Male Female sexes sexes sexes Punjab Paid 38.7 40.7 25.0 49.6 52.3 38.7 44.2 48.5 30.3 Self employed 61.3 59.3 75.0 20.2 22.5 10.8 24.1 27.0 14.7 Sindh Paid 35.1 37.7 20.3 55.2 56.8 41.7 39.6 44.3 19.1 Self employed 64.9 62.3 79.7 15.2 16.4 5.1 19.5 23.2 3.4 KP Paid 40.2 40.7 31.9 57.3 60.1 35.7 50.0 54.0 25.9 Self employed 59.8 59.3 68.1 19.7 20.1 16.5 27.7 28.2 24.8 Balochsitan Paid 14.6 19.8 5.1 42.8 45.2 17.8 33.0 34.6 16.6 Self employed 85.4 80.2 94.9 13.5 13.8 9.5 19.7 20.7 10.1

Appendix Table 6 Attained Education and Type of Employment of Youth by Province— BISP Survey Employment by Attained Education (in Categories) Type Upto 5 6-9 10-11 12 and above Punjab Government 1.0 1.8 11.4 19.8 Semi government 0.2 0.3 0.7 1.9 Private 28.9 38.4 38.3 45.3 Self employed 69.8 59.4 49.6 33.1 Total 100 100 100 100 Sindh Government 0.5 1.2 5.9 10.4 Semi government 0.2 0.3 0.5 1.1 Private 25.9 48.5 48.1 45.4 Self employed 73.4 50.0 45.5 43.1 Total 100 100 100 100 KP Government 2.5 3.5 19.3 27.3 Semi government 0.2 0.3 0.5 1.4 Private 29.0 34.7 31.4 33.7

Youth and Employment Participation

Self employed 68.4 61.5 48.7 37.5 Total 100 100 100 100 Balochistan Government 1.7 6.8 24.6 39.7 Semi government 0.4 1.2 2.0 4.1 Private 7.4 15.1 12.6 13.3 Self employed 90.6 76.8 60.8 43.0 Total 100 100 100 100 GB Government 11.8 35.4 69.7 45.2 Semi government 1.5 2.3 1.9 5.0 Private 8.1 13.0 9.2 35.7 Self employed 78.6 49.3 19.1 14.1 Total 100 100 100 100 AJK Government 1.0 1.4 8.6 7.5 Semi government 0.3 0.2 0.5 3.6 Private 6.6 12.0 8.7 7.2 Self employed 92.0 86.5 82.2 81.7 Total 100 100 100 100 ISB Government 2.1 6.1 17.1 29.0 Semi government 0.4 0.6 1.0 3.0 Private 44.2 47.0 47.5 54.6 Self employed 53.4 46.3 34.4 13.5 Total 100 100 100 100 FATA Government 2.1 9.0 15.8 18.9 Semi government 1.0 1.9 2.7 4.0 Private 42.7 41.6 43.0 46.1 Self employed 54.2 47.6 38.5 31.0 Total 100 100 100 100

Youth and Employment Participation

Appendix Table 7 Employment Type of Youth by Education of Head of Household— BISP Survey Education of Head Employment by Type Illiterate 1-9 10 and above 11 and above Punjab Govt./ Semi govt. 3 5 10 17 Private 31 36 39 43 Self employed 66 59 51 40 Total 100 100 100 100 Sindh Govt./ Semi govt. 1 3 6 9 Private 29 34 45 39 Self employed 69 63 49 51 Total 100 100 100 100 KP Govt./ Semi govt. 7 8 17 24 Private 30 33 32 31 Self employed 63 59 51 45 Total 100 100 100 100 Balochistan Govt./ Semi govt. 4 7 17 26 Private 8 10 8 8 Self employed 88 83 76 66 Total 100 100 100 100 GB Govt./ Semi govt. 29 45 53 49 Private 10 16 19 31 Self employed 61 39 27 20 Total 100 100 100 100 AJK Govt./ Semi govt. 11 12 23 33 Private 31 34 32 35 Self employed 57 54 45 32 Total 100 100 100 100 Islamabad Govt./ Semi govt. 6 10 20 30 Private 45 46 50 55

Youth and Employment Participation

Self employed 49 43 29 15 Total 100 100 100 100 FATA Govt./ Semi govt. 5 8 20 21 Private 46 35 38 40 Self employed 49 56 42 39 Total 100 100 100 100

Appendix Table 8

Mean PMT and Poverty of Youth and Adult by Employment Status and Gender in Pakistan—BISP Survey PMT Province/ Region Poverty (%) Youth and adult Youth only Male Female Adult Only Punjab Govt. 41.2 40.7 40.0 47.8 41.3 2.7 Semi- govt. 39.6 39.8 39.3 42.3 39.4 4.8 Private 32.0 33.3 33.0 36.2 31.4 8.7 Self 30.1 31.3 31.7 29.5 29.7 11.7 unemployed 30.2 31.9 31.5 32.1 29.7 10.6 Null 27.3 27.3 27.3 27.2 29.3 20.4 Sindh Govt. 31.7 31.9 31.5 37.0 31.7 12.4 Semi- govt. 34.0 33.6 33.5 33.9 34.1 10.1 Private 29.9 31.2 31.0 33.1 29.4 11.3 Self 19.8 21.9 21.9 21.9 19.0 32.8 unemployed 23.3 24.8 23.9 25.2 22.2 27.0 Null 20.3 20.1 19.5 20.6 24.1 44.8 KP Govt. 32.0 30.4 30.0 37.4 32.5 13.7 Semi- govt. 32.7 32.7 32.2 35.4 32.7 12.2 Private 26.2 27.3 27.0 33.8 25.6 19.5 Self 24.1 25.5 25.5 25.7 23.5 23.6 unemployed 24.4 25.6 25.2 25.9 23.5 24.2 Null 22.2 22.0 22.0 22.0 24.6 36.7 Balochistan Govt. 29.4 30.7 30.2 33.7 29.2 17.3 Semi- govt. 28.8 31.5 31.3 32.2 28.0 18.6 Private 25.4 26.2 26.2 26.5 25.0 26.3

Youth and Employment Participation

Self 22.6 24.2 25.0 23.0 21.9 32.2 unemployed 22.9 24.3 23.3 25.1 22.0 31.0 Null 19.0 19.7 19.3 20.3 21.3 47.1 AJK Govt. 37.5 35.4 34.5 46.4 38.0 5.9 Semi- govt. 37.9 39.1 38.8 40.7 37.6 5.9 Private 31.8 32.5 31.8 41.8 31.5 8.9 Self 30.5 31.5 31.5 32.2 30.1 11.5 unemployed 31.5 32.7 32.9 32.6 30.8 10.3 Null 28.2 28.1 28.2 28.1 31.5 21.5 GB Govt. 33.2 31.3 30.8 42.3 33.8 8.9 Semi- govt. 34.6 33.4 31.3 39.9 35.0 11.4 Private 35.8 35.2 33.0 42.1 36.1 8.3 Self 25.1 26.3 26.6 25.4 24.7 21.3 unemployed 26.4 27.6 27.6 27.6 25.6 18.3 Null 28.1 28.1 28.2 28.0 28.6 15.9 Islamabad Govt. 49.6 49.5 48.0 59.7 49.6 0.3 Semi- govt. 52.8 52.9 50.4 62.6 52.8 1.6 Private 40.4 40.8 39.9 48.4 40.2 4.0 Self 34.8 34.5 34.5 35.0 34.9 6.7 unemployed 39.7 41.0 42.8 40.0 38.7 4.2 Null 36.1 36.1 36.5 35.6 39.0 9.3 FATA Govt. 26.8 26.1 25.4 35.3 27.0 26.7 Semi- govt. 24.2 24.7 24.1 30.5 23.9 29.3 Private 21.8 23.2 23.4 20.6 20.8 33.0 Self 21.9 24.1 23.9 27.2 20.6 30.8 unemployed 19.0 20.5 20.0 20.9 17.8 42.0 Null 17.4 17.2 17.2 17.3 18.7 53.9

Youth and Employment Participation

Appendix Table 9 Poverty Rate by Employment Category of Youth at District Level- BISP Survey Districts Govt. Semi Govt. Private Self Employed Unemployed Punjab Attock 0.9 2.2 3.4 4.3 3.1 Bahawalnagar 1.4 3.3 5.5 6.9 7.3 Bahawalpur 2.4 3.7 11.7 14.2 15.1 Bhakkar 4.0 3.8 9.9 10.6 10.7 Chakwal 2.7 3.4 5.1 8.1 5.0 Chiniot 2.0 3.2 7.9 14.3 10.2 Dera Ghazi Khan 14.7 18.6 24.3 35.3 26.7 Faisalabad 1.5 3.3 4.2 7.4 5.9 Gujranwala 2.0 1.7 7.2 7.9 6.3 Gujrat 1.0 2.9 6.2 3.3 4.1 Hafizabad 0.8 1.1 5.5 8.8 8.0 Jhang 3.2 3.7 5.9 12.9 10.3 Jhelum 1.2 0.9 4.1 1.9 2.7 Kasur 4.1 6.6 16.6 27.8 16.3 Khanewal 2.0 2.0 7.3 6.1 9.2 Khushab 1.0 0.0 3.2 0.0 4.1 Lahore 0.9 2.5 5.5 5.3 4.7 Leiah 8.5 11.0 17.0 27.3 17.4 Lodhran 2.4 4.7 11.0 3.8 14.7 Mandi Bahauddin 0.9 1.9 4.6 - 5.4 Mianwali 10.0 14.7 16.2 7.3 11.6 Multan 8.6 13.2 22.6 22.0 26.2 Muzaffargarh 8.9 17.0 24.2 20.3 26.7 Nankana Sahib 1.0 6.1 6.7 7.7 11.0 Narowal 1.0 0.0 2.8 2.3 5.3 Okara 2.8 4.0 8.2 9.7 10.8 Pakpattan 6.5 19.3 16.6 35.9 17.7 Rahim Yar Khan 6.9 10.9 13.3 12.7 26.9 Rajanpur 10.8 10.8 27.9 11.1 37.4 Rawalpindi 1.1 1.0 3.5 0.5 3.4 Sahiwal 1.2 2.3 5.9 1.0 9.3 Sargodha 1.3 3.1 5.0 4.3 7.2 Sheikhupura 1.2 3.7 6.2 2.4 7.3

Youth and Employment Participation

Sialkot 0.5 1.4 2.7 3.3 2.3 Toba Tek Singh 0.7 0.3 3.4 7.8 5.2 Vehari 2.3 3.0 6.9 9.7 10.6 Sindh Badin 19.9 23.2 35.2 33.3 45.3 Dadu 11.0 12.2 24.3 25.0 28.1 Ghotki 16.3 15.4 24.2 28.1 33.3 Hyderabad 6.6 12.4 10.7 10.0 22.8 Jacobabad 13.9 19.0 35.3 32.3 36.5 Jamshoro 8.3 8.5 25.0 12.0 29.1 Kambar Shahdad Kot 14.2 22.4 36.9 25.6 39.5 Karachi Central 1.7 3.4 3.7 6.4 5.3 Karachi East 1.0 2.7 3.9 6.9 5.5 Karachi Malir 2.1 3.6 6.8 8.2 9.9 Karachi South 0.5 5.6 3.7 5.7 5.6 Karachi West 1.7 5.8 6.7 7.5 9.1 Kashmore 3.8 13.0 13.5 33.3 20.8 Khairpur 18.6 11.9 27.3 28.2 36.2 Larkana 16.9 25.0 36.7 29.7 34.3 Matiari 20.3 16.4 30.5 16.7 44.4 Mirpur Khas 9.1 15.7 24.9 33.3 23.6 Naushahro Feroze 23.7 21.1 27.5 20.0 39.9 Sanghar 10.3 14.1 26.8 25.0 28.1 Shaheed Benazir Abad 20.3 23.4 30.4 31.3 49.0 Shikarpur 17.8 24.4 31.7 - 41.1 Sukkur 9.4 3.6 19.0 21.7 28.6 Tando Allahyar 17.1 21.1 24.0 - 37.6 Tando Muhammad Khan 14.7 14.3 37.5 40.0 52.5 Tharparkar 15.0 28.7 24.1 23.3 31.7 Thatta 19.1 20.3 35.3 50.0 53.9 Umer Kot 17.4 16.0 31.9 50.0 44.5 KP Abbottabad 1.7 2.3 3.4 - 3.6 Bannu 14.7 14.8 21.6 31.6 25.2 Batagram 3.3 2.8 9.4 - 10.0 Buner 11.3 20.5 27.1 22.2 27.5 Charsadda 10.5 4.9 22.0 16.7 23.3 Chitral 14.4 12.4 20.2 16.7 20.5

Youth and Employment Participation

D. I. Khan 14.4 6.4 22.6 10.7 29.4 Hangu 14.9 25.6 19.3 - 19.6 Haripur 2.1 3.7 5.3 4.5 5.0 Karak 17.9 7.8 11.0 26.1 16.3 Kohat 16.6 11.2 18.5 10.7 21.0 Kohistan 13.8 6.3 16.0 - 29.0 Lakki Marwat 31.6 41.4 33.7 52.6 35.2 Lower Dir 18.4 15.9 34.5 18.2 32.9 Malakand P A 14.2 3.1 27.0 - 20.8 Mansehra 7.7 6.1 18.1 31.6 18.9 Mardan 13.2 14.7 25.4 12.5 22.6 Peshawar 8.6 6.7 17.1 13.0 18.9 Shangla 18.2 20.3 29.1 18.8 34.8 Swabi 18.3 6.8 28.6 36.8 29.3 Swat 13.9 18.2 24.5 20.0 29.9 Tank 32.0 25.0 29.8 66.7 39.1 Upper Dir 25.6 25.0 42.3 50.0 43.7 AJK Bagh 5.3 2.6 9.1 - 8.1 Bhimbe 2.6 2.0 3.3 - 4.5 Hattian Bala 10.8 15.2 13.0 - 18.2 Haveli 15.0 10.7 30.2 26.0 23.9 Kotli 2.6 6.5 7.5 - 7.2 Mirpur 2.5 1.2 10.9 - 8.1 Muzaffarabad 6.2 6.2 12.7 - 13.5 Neelum 26.1 25.0 21.5 - 40.6 Poonch 3.1 3.9 3.6 - 3.6 Sudhno 2.3 12.5 7.0 - 4.7 Sudhnoti 4.5 2.4 8.0 9.1 7.3 Balochistan Awaran 14.3 - 30.4 - 25.9 Barkhan 25.7 12.5 28.0 20.0 29.4 Chagai 27.7 43.5 32.2 - 42.7 Dera Bugti 15.6 7.7 44.7 25.0 38.7 Gwadar 21.8 18.0 34.4 66.7 37.3 Harnai 14.5 11.1 35.2 0.0 31.2 Jaffarabad 22.2 25.0 38.3 33.3 43.6 Jhal Magsi 34.4 7.7 28.6 39.0 42.6

Youth and Employment Participation

Kachhi 21.4 41.9 42.7 33.3 46.8 Kalat 16.5 18.8 21.3 14.3 30.7 Kech 17.7 41.7 27.0 9.5 32.9 Kharan 17.1 10.3 17.3 21.6 26.6 Khuzdar 13.0 17.5 26.6 26.7 31.9 Killa Abdullah 11.7 9.4 18.8 22.2 30.6 Killa Saifullah 2.1 6.6 9.4 33.3 6.9 Kohlu 23.4 27.0 14.5 50.0 35.4 Lasbela 33.4 33.8 44.7 44.4 47.8 Loralai 12.8 15.1 19.7 23.1 27.4 Mastung 6.4 - 6.2 50.0 12.9 Musakhel 15.3 13.0 26.3 - 26.1 Nasirabad 33.0 35.3 39.8 33.3 48.0 Naushahro Feroz 0.0 - 29.7 4.8 16.6 Nushki 18.5 31.3 35.5 - 37.8 Panjgur 18.6 - 35.7 21.7 22.6 Pishin 12.9 17.1 25.0 50.0 24.9 Quetta 5.5 4.4 14.6 6.9 13.8 Sherani 19.2 22.2 36.4 - 43.7 Sibbi 20.4 6.3 21.6 - 34.3 Washuk 21.3 0.0 20.7 33.3 43.6 Zhob 17.9 27.3 28.2 10.0 32.0 Ziarat 9.9 26.3 18.3 50.0 26.9 GB Astore 13.7 16.1 17.0 50.0 18.7 Baltistan 13.3 10.6 6.9 14.3 19.5 Diamir 37.2 38.2 49.5 33.3 45.2 Ghanche 16.2 7.9 15.9 12.5 14.7 Ghizer 3.5 2.2 2.8 - 7.7 Gilgit 5.7 4.9 3.4 12.5 15.8 Hunza Nagar 3.3 10.7 2.1 0.0 10.7 Islamabad 0.3 1.6 4.0 3.3 4.2 FATA Bajaur Agency 24.5 22.8 34.9 66.7 47.3 Khyber Agency 35.1 37.4 34.0 53.6 48.5 Kurram Agency 8.8 16.3 34.2 6.8 20.5 Mohmand Agency 26.1 29.4 23.8 0.0 51.2 Orakzai Agency 23.8 11.1 16.4 20.0 25.7

Youth and Employment Participation

S Waziristan Agency 100.0 - 66.7 - 60.9 T A Adj Bannu - - - - - T A Adj D.I.Khan 43.3 - 59.8 42.3 61.3 T A Adj Kohat 3.9 - 11.6 50.0 13.6 T A Adj Peshawar 36.4 40.8 36.8 28.6 41.0 T.A.Adj.Lakki Marwat - - 18.8 - 19.6

Youth and Employment Participation

Appendix Table 10 Summary Statistics of Null/Inactive and Unemployment Rate in Various Age Categories (18-84) by Province/Region—BISP Survey Overall Female Province/ Region Mean Std. Deviation Min. Max. Mean Std. Deviation Min. Max. Overall Null/Inactive 5.0 8.1 0.7 28.3 4.1 8.0 0.8 27.4 Unemployment 59.7 4.6 55.0 70.5 87.2 6.6 67.8 90.2 Punjab Null/Inactive 4.8 7.8 0.5 27.3 3.8 7.9 0.5 26.9 Unemployment 58.8 4.5 54.4 69.6 87.3 6.5 68.4 90.5 Sindh Null/Inactive 4.5 8.2 0.8 28.4 4.3 7.8 1.1 27.0 Unemployment 55.8 4.4 49.94 65.2 84.3 6.2 66.4 88.0 KP Null/Inactive 6.0 9.1 1.0 32.4 5.0 8.9 1.2 30.8 Unemployment 64.4 6.0 54.8 77.7 92.1 8.5 67.6 96.0 Balochistan Null/Inactive 5.7 8.8 1.5 31.2 5.6 8.1 2.0 29.2 Unemployment 65.3 4.5 55.6 72.2 77.0 5.9 59.9 80.6 AJK Null/Inactive 6.8 8.3 0.3 29.4 4.0 8.6 0.3 29.0 Unemployment 65.1 6.7 58.7 80.2 93.6 8.1 70.0 97.8 GB Null/Inactive 7.7 7.9 1.1 29.9 4.4 8.4 0.6 28.7 Unemployment 68.5 5.0 62.1 79.5 88.0 7.1 67.9 92.9 Islamabad Null/Inactive 7.6 9.6 0.5 29.9 4.7 8.7 0.5 29.9 Unemployment 57.5 4.8 52.6 66.7 89.3 7.3 68.1 93.2 FATA Null/Inactive 3.3 4.2 0.8 15.3 2.5 3.6 0.8 13.0 Unemployment 83.8 3.4 77.0 89.8 96.2 3.6 85.7 98.0

Youth and Employment Participation

Appendix Table 11 Estimated Unemployment Rate by Gender at District Level— BISP Survey District Male Female Total District Male Female Total Punjab Sindh Attock 23.0 2.3 11.3 Badin 38.7 0.7 24.4 Bahawalnagar 34.7 25.3 28.5 Dadu 24.3 0.1 17.6 Bahawalpur 34.7 35.6 34.3 Ghotki 48.1 4.1 28.3 Bhakkar 28.6 14.9 19.5 Hyderabad 40.7 9.3 25.9 Chakwal 18.7 6.2 9.7 Jacobabad 28.8 2.4 15.3 Chiniot 45.2 9.2 27.0 Jamshoro 39.1 2.6 24.8 Dera Ghazi Khan 40.2 24.5 33.0 Karachi Central 28.1 1.9 17.4 Faisalabad 34.4 7.4 21.0 Kashmore 30.3 4.7 13.8 Gujranwala 22.2 4.5 14.0 Khairpur 34.6 5.6 22.7 Gujrat 27.2 1.1 11.5 Larkana 29.8 14.4 8.5 Hafizabad 48.3 5.4 26.2 Matiari 52.9 3.8 30.9 Jhang 55.7 18.1 24.0 Mirpur Khas 2.8 11.4 7.5 Shaheed Benazir Jhelum 26.7 7.4 13.8 50.4 13.7 33.7 Abad Kasur 22.7 11.6 18.9 Naushahro Feroze 52.8 3.1 30.4 Khanewal 58.6 32.6 45.2 Sanghar 22.1 8.4 18.4 Khushab 34.3 5.2 17.9 Shikarpur 29.9 11.6 23.4 Lahore 23.7 5.1 14.8 Sukkur 39.4 3.0 24.2 Leiah 19.1 36.3 27.1 Tando Allahyar 16.5 9.2 3.5 Lodhran 47.2 39.9 43.3 Tando M.Khan 44.3 8.6 26.0 Mandi Bahauddin 46.2 1.0 21.4 Tharparkar 22.0 0.6 9.2 Mianwali 3.2 12.1 5.1 Thatta 57.8 2.4 33.4 Multan 36.4 6.3 20.7 Umer Kot 43.4 14.2 30.2 Muzaffargarh 40.7 11.0 25.8 Nankana Sahib 53.5 4.8 28.5 Narowal 51.5 7.5 21.1 Okara 48.1 23.2 33.7 Pakpattan 40.6 34.1 35.9 Rahim Yar Khan 65.0 35.1 49.8 Rajanpur 40.7 35.8 39.0 Rawalpindi 22.3 4.0 12.3 Sahiwal 46.5 25.6 34.2 Sargodha 46.0 6.4 24.5 Sheikhupura 33.0 13.9 21.8

Youth and Employment Participation

Sialkot 27.1 11.0 16.6 Toba Tek Singh 34.3 3.6 16.6 Vehari 51.0 18.1 33.1 KP Balochistan Abbottabad 29.9 8.4 15.0 Awaran 88.8 32.5 59.5 Bannu 35.9 0.3 21.4 Barkhan 37.9 10.1 12.3 Batagram 33.9 10.9 22.7 Kachhi 78.5 5.1 42.9 Buner 26.4 1.0 6.1 Chagai 79.3 2.0 37.4 Charsadda 29.6 3.4 13.5 Dera Bugti 82.7 0.0 40.6 Chitral 41.6 28.1 34.0 Gwadar 71.0 14.0 24.9 D. I. Khan 37.6 0.3 19.4 Harnai 73.0 1.7 52.1 Hangu 35.1 4.6 17.4 Jaffarabad 52.1 31.8 11.3 Haripur 27.3 11.7 18.0 Jhal Magsi 71.4 13.7 30.2 Karak 30.7 3.1 8.6 Kalat 76.1 23.3 52.4 Kohat 33.8 4.1 13.2 Kech 51.6 4.7 20.3 Kohistan 66.1 0.1 35.1 Kharan 87.3 0.2 54.0 Lakki Marwat 49.9 19.9 34.6 Khuzdar 65.2 16.7 41.8 Lower Dir 37.4 4.5 21.4 Kohlu 73.6 3.3 17.7 Malakand P A 18.0 3.0 11.6 Lasbela 49.0 18.8 15.8 Mansehra 27.1 16.4 17.8 Loralai 56.7 10.0 26.4 Mardan 27.3 2.3 11.1 Mastung 68.6 7.4 39.0 Peshawar 27.6 2.0 16.1 Musakhel 44.0 6.8 21.7 Shangla 61.4 4.8 29.8 Nasirabad 61.5 30.2 18.4 Swabi 21.9 1.9 9.3 Nushki 68.3 5.8 43.2 Swat 48.3 22.2 35.2 Panjgur 91.0 16.3 42.1 Tank 35.7 2.2 21.0 Killa Abdullah 55.1 25.4 39.8 Upper Dir 58.0 26.0 41.6 Killa Saifullah 1.8 16.8 12.3 Pishin 66.2 2.9 42.6 Quetta 38.4 17.7 29.1 Sherani 80.0 12.8 56.3 Sibbi 53.1 0.1 32.1 Washuk 88.6 13.6 45.4 Zhob 56.7 12.2 40.0 Ziarat 60.9 24.5 43.2

Youth and Employment Participation

Appendix Table 12 Age-Specific Unemployment Rate by Gender and Province: BISP Survey Age (in category) Punjab Sindh KP Balochistan Both Sexes 18-24 5.3 30.8 10.3 25.5 25-29 23.6 20.8 16.2 26.4 30-34 19.2 15.6 12.9 25.9 35-39 21.8 14.7 13.3 21.3 40-44 22.9 13.8 14.2 23.2 45-49 23.0 12.3 14.0 18.5 50-54 22.5 14.7 10.4 19.8 55-59 19.6 18.3 14.9 21.5 60-64 16.0 2.5 12.3 17.9 65 and above 3.7 -12.6 5.2 0.4 Male 18-24 44.3 45.3 38.3 50.4 25-29 31.0 28.8 31.6 60.0 30-34 23.3 23.4 26.9 57.2 35-39 20.8 20.0 25.7 51.4 40-44 21.2 18.9 25.1 48.6 45-49 20.4 18.7 25.0 44.9 50-54 21.2 20.2 21.1 44.8 55-59 20.2 19.1 19.9 40.2 60-64 19.5 0.1 17.0 31.9 65 to 85 4.1 20.0 10.1 5.1 Female 18-24 22.8 11.6 7.1 4.5 25-29 21.1 12.6 8.3 1.2 30-34 21.0 11.5 9.4 5.2 35-39 25.9 8.4 10.3 1.3 40-44 26.9 8.1 11.1 2.1 45-49 26.1 4.3 8.0 0.4 50-54 25.2 6.3 8.3 0.7 55-59 19.8 4.6 8.7 1.4 60-64 9.7 0.7 4.5 2.5 65 and above 0.9 -7.1 0.0 9.1

Youth and Employment Participation

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