Baseline Indicators Report v0.7[1]

Baseline Indicators Report for PEOP

DFID TAMA/CERP

Baseline Report April 2010

Draft

Contract Reference No. DFID CNTR 07 7831 Crown Agents Reference No. T25417

Baseline Indicators Report for PEOP Crown Agents

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Crown Agents Table of Contents

List of Acronyms and Abbreviations (ii)

List of Tables (ii)

List of Figures (iii)

Executive Summary (v)

Section Page

Section 1: Introduction 1

1. Introduction 2

1.1 Methodology 2

Section 2: Poverty and Income Indicators 4

2. Poverty and Income Indicators 5

2.1 Poverty Headcount Ratio 5 2.2 Tehsil Level Poverty 6 2.3 Economic Growth 7

Section 3: Individual and Household Attributes of Target Groups 15

3. Individual and Household Attributes of Target Groups 16

3.1 Poverty Profile 16 3.2 Poverty Profile at Tehsil Level 19 3.3 Poverty Band Analysis 23 3.4 Profile of other Potential Target Groups 25

Section 4: Occupations and Skills Indicators 29

4. Occupations and Skills Indicators 30

4.1 Occupational Structure 30 4.2 Supply of Skilled Workers and Trainers 34 4.3 Available Skills Training Programs 36

Section 5: Livestock Indicators 38

5. Livestock Indicators 39

5.1 Livestock Population by District 39 5.2 Number of people owning at least 1 animal for livestock farming 40 5.3 Use of Veterinary Centers and Artificial Insemination Centers 48 5.4 Milk Yields 49 5.5 Access to Veterinary Centers 49

Section 6: Conclusions and Recommendations 54

6. Conclusions and Recommendations 55


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Crown Agents Appendices Page

Appendix A: Baseline Indicators 58

Appendix B: Poverty Estimations 67

Appendix C: Enrolment data of high and low skill training by district 71

Appendix D: Proportional Enrolment of PVTC by District 75

Appendix E: Proportional Employment by Category 81

List of Acronyms and Abbreviations

BISP Benazir Income Support Programme

CPI Consumer Price Index

DAE Diploma of Associate Engineering

GDP Gross Domestic Product

HH Households

LFS Labor Force Survey

MICS Multiple Indicator Cluster Survey

PVTC Punjab Vocational Training Council

SE Standard Error

TEVTA Technical and Vocational Training Authority

List of Tables

Table 1: Poverty Head Count Ratio 5

Table 2: Average Income of all Households at District Level 8

Table 3: Average Income of Poor Households 9

Table 4: Average Income of Employed 11

Table 5: Average Income of Employed Poor 12

Table 6: Difference in average income in Rs. and growth rate by quintile 13

Table 7: Individual Attributes by District 17

Table 8: Attributes of Poor and Non-poor households 19

Table 9: Attributes at Tehsil Level 20

Table 10: Attributes of Households and Individuals at Tehsil Level 21

Table 11: Households and Individual Attributes, Lodhran District 22

Table 12: Households and Individual Attributes at Tehsil Level 23

Table 13: Profile of Poor across poverty bands 25

Table 14: Profile of female-headed households 26

Table 15: Profile of Unemployed and Employed 27


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Crown Agents Table 16: Occupational Categories 30

Table 17: Employment Categories by Groups 31

Table 18: Profile of most common occupations 34

Table 19: Trained Labor 34

Table 20: Supply of Training, TEVTA capacity 35

Table 21: Supply of Training, PVTC Capacity 35

Table 22: Animal wise contribution of PEOP districts to overall livestock population of Punjab 39

Table 23: Households reporting use of veterinary services 48

Table 24: Milk yields in PEOP districts, by animal type 49

Table 25: Number of health facilities and distance from mouzas, Lodhran district 50

Table 26: Number of health facilities and distance from mouzas, Muzaffargarh district 51

Table 27: Number of health facilities and distance from mouzas, Bahawalpur district 52

Table 28: Number of health facilities and distance from mouzas, Bahawalnagar district 52

List of Figures

Figure 1: Poverty Headcount Ratio Bahawalnagar District 6

Figure 2: Poverty Headcount Ratio Bahawalpur District 6

Figure 3: Poverty Headcount Ratio Lodhran District 7

Figure 4: Poverty Headcount Ratio Muzaffargarh District 7

Figure 5: Real Income Growth over all income quintiles 13

Figure 6: Income Density plot by poverty status 14

Figure 7: Growth Rate of Income by Occupation 32

Figure 8: Real Income Growth by Occupation and Poverty Status 33

Figure 9: Livestock Population in Punjab, by district 39

Figure 10: Percentage breakdown of animal types in PEOP districts 40

Figure 11: Distribution of Households and Cattle in Lodhran District 42

Figure 12: Distribution of households and cattle in Muzaffargarh District 43

Figure 13: Distribution of Households and cattle in Bahawalpur District 44

Figure 14: Distribution of Households and cattle in Bahawalnagar District 45


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Crown Agents

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FS 33234


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Crown Agents

Executive Summary


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Crown Agents The Government of Punjab, Pakistan and DFID have launched a collaborative development programme, Punjab Economic Opportunities Programme, for four economically marginalized districts in Southern Punjab: Bahawalpur, Bahawalnagar, Lodhran and Muzaffargarh. The Punjab Economic Opportunities Programme (PEOP) will focus on the provision of marketable skills and interventions related to the livestock and dairy sector of the target areas.

This report offers baseline indicators for the PEOP programme logframe, which may be used to measure progress against targets indicated in the LFA. These indicators have been developed for the two major components of the programme, skill development and livestock. The skills and poverty indicators have been developed by CERP with Technical and Management Agency (TAMA) support, by using data from the 2003-4 and 2007-8 Multiple Indicators Cluster Survey (MICS), Labor Force Survey (LFS), TEVTA and PVTC. The Livestock indicators have been developed by TAMA using data from 2006 Livestock Census, Milk Production Survey and Mouza Census.

Poverty in all target districts increased between 2003/04 and 2007/08 except for Muzaffargarh where it decreased marginally. The data suggests increasing divergence in incomes. The poor in the region witnessed a decrease in their purchasing power as their real income decreased, while overall average income in the region increased. Clearly, the current economic structure of these high-poverty districts is not enabling inclusive growth outcomes to be realized.

The poor have consistently fewer assets across districts; they are less likely to own land or their own homes. The non poor have better housing, are more urban and are more likely to receive remittances from outside their locality. Across districts the poor have similar literacy rates and demographic profiles, but are less literate and younger than the non poor. There are substantial variations within the poor as well. Among the poor literacy correlates with income; it is lowest among the poorest. Unemployment indicators, remittances and nominal income all move in the expected directions as we compare different bands of poverty. Comparing genders, female headed households are less likely to own land, have fewer average numbers of animals and are more urban than non-female headed households.

Interestingly, the unemployed have almost the same mean years of schooling as the employed and a higher proportion of the unemployed have ever attended school compared to the employed. They are, however, less likely to own land and a smaller proportion of the unemployed own livestock relative to the employed. With regard to labour market and skills provision the most important finding is that the occupational structure of the target districts is extremely narrow; within this structure the income growth for poor is always behind and in most cases opposite to the growth for non poor. The most common occupations reported, such as agriculture, agriculture related labor and laborer in construction etc require little formal education. Nearly half of people employed in such jobs have never been to school. Not only is the economic structure in these districts creating low returns for poor households, labour market opportunities are creating divergent returns within broadly similar occupation categories. Clearly, increasing returns to low-income, low-skill and poor households through skill provision and occupational diversification has to be a fundamental pillar of PEOP without which the objective of inclusive growth will not be realized.

The available evidence suggests a severe lack of skilled labour in the region, a very small proportion of labour force received any on- or off-the-job training in last eight years in all four districts. The existing capacity of skills training in public sector may be one of the reasons for such low supply of skilled labour. The PVTC, second largest public sector training provider, courses are oversubscribed and the organization is running at full capacity. The lack of information about TEVTA‟s capacity does not allow us to make the same judgment, however, the situation appears to be similar. If the current enrollment is indicative of the capacity of both organizations, they can together


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Crown Agents train only a minuscule fraction of the unemployed population. The proportion of high end skills is particularly small and the supply is not uniform across districts.

On the other side, the livestock baseline indicators have been developed using data from the Pakistan Livestock Census (Punjab report) (2006), Milk Production Survey (2006) and Punjab Mouza Statistics (2008). All three data sources only report data at the district level, and while the surveys are conducted at a household level, they do not report other household characteristics that would be useful in developing a profile of the poor or small livestock holders.

The data available suggests that the livestock profiles of the four districts are very similar. Muzaffargarh has the largest stock of all livestock other than buffaloes (in which Bahawalnagar leads) while Lodhran has the smallest stock of animals. The report also explores the size of animal holdings by household and the number of animals in each category. Cattle, for instance, are owned overwhelmingly in small numbers by households (close to 50% of households in all four districts report having only 1-2 cattle) but there are also clear distributional inequities as large animal owners (very few in number themselves) hold a disproportionately large percentage of total cattle in each district.

With regards to use of veterinary and artificial insemination services, the data only reveals sparse use of artificial insemination services. The data also indicates that while all districts, and even tehsils, have veterinary centers, access may be a concern on account of considerable distance from individual mouzas (or settlements).

Apart from reporting on available indicators, this section also identifies knowledge gaps that can be filled only by way of detailed surveys at the community level that, for instance, capture information regarding access to fodder and nutrition for animals by households.

This report provides a strong baseline against which progress of PEOP can be measured in four focus areas 1) real income growth for poor; 2) graduation of target groups from unskilled employment to comparatively higher skilled jobs; 3) improvement in the average income of the poorest tehsils; and 4) improvement in livestock holdings across districts as well as improved access to livestock services. However, data limitations will not make it possible to track log-frame indicators according to the schedule given in the LFA and will constrain effective targeting of the programme. The report underscores the importance of conducting independent baseline surveys to obtain a more comprehensive and nuanced understanding of the labor market dynamics, income and employment challenges, and household profile of livestock holders as well as market and livestock service access in the region.

PEOP interventions would greatly benefit from the conduct of such surveys. For instance, the preliminary analysis on the skills side is consistent with a number of possible failures in the labor market, each of which implies a different policy solution. On the livestock side, the specific policy interventions would be better served with in-depth information regarding the livestock ownership patterns of the target groups as well as their access to, or lack thereof, markets and support services.


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Section 1: Introduction


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1. Introduction

The Punjab Economic Opportunities Programme (PEOP) has been jointly launched by DFID and Government of Punjab, to address the chronic poverty prevailing in the southern districts of the province i.e. Bahawalpur, Bahawalnagar, Lodhran, and Muzaffargarh. In order to address the prevailing poverty, PEOP is adopting dual approach of providing skills training to the poor and marginalized communities along with improving the livestock and dairy sector through various interventions. The skills training will be provided through private and public sector organizations. A market driven approach will be established in which the trainers will be offered courses based on the demand. This strategy will be implemented through establishment of a Punjab Skills Development Fund. The livestock interventions will focus on improving milk yield, better farm management and strengthening market linkages. These interventions will be implemented by special project implementation unit in Livestock and Dairy Department.

This report provides baseline estimates for the indicators listed in the PEOP programme log frame1. It is organized into four sections, the first part deals with poverty estimates, second part is about poverty profile, third deals with skills indicators and fourth section reports livestock indicators. Appendix A reports the baseline indicators developed against the LFA.

1.1 Methodology

The report draws on several data sources to make a credible assessment of baseline indicators. For the poverty and skills sections of the analysis, Multiple Indicators Cluster Survey (MICS) 2003-04 and 2007-08 were used along with Labor Force Surveys and data from public sector training organizations, TEVTA and PVTC. The fourth section of the report draws on the Pakistan Livestock Census (Punjab section primarily) as well as Milk Production Survey and Mouza Statistics.

The Multiple Indicator Cluster Survey (MICS) is an instrument originally designed by UNICEF to provide a comparable set of education, health and other social indicators across the globe particularly in developing countries. “MICS findings have been used extensively as a basis for policy decisions and programme interventions, and for the purpose of influencing public opinion on the situation of children and women around

the world”2. In Punjab the MICS instrument was fielded in 2003 and 2007 by the GoPb

to assess the social indicators in the province and help in monitoring and planning of policies pertaining to the social sector. The survey was conducted by Bureau of

1 This report has been jointly produced by TAMA and Center for Economic Research, Pakistan

(CERP). Economists from CERP, with support from IGC (International Growth Centre) Pakistan, have provided support regarding estimation of baseline indicators (other than livestock) and assisted in development of indicators. The poverty estimates, poverty profile and skills indicators have been developed by Yasir Khan, Research Associate TAMA, while preparation of livestock indicators and compilation of report has been done by Sara Qutub, Research Associate TAMA. The final report benfitted from valuable comments and feedback from Mr. Asad Mekan, Coordinator PEOP TAMA, and Mr. Raza Ahmed, Deputy Programme Manager TAMA.

2 MICS, UNICEF, <http://www.unicef.org/statistics/index_24302.html>

http://www.unicef.org/statistics/index_24302.html


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Statistics Punjab with technical assistance from UNICEF and sample design assistance from Federal Bureau of Statistics.

As is evident from the above, the MICS offers two main advantages for undertaking poverty and income analysis: the survey has international reliability, and it provides data disaggregated to the district and tehsil levels for our districts of interest. The latter is critical as it allows for intra-district variations to be observed at the baseline stage, which may be monitored over the duration of the programme.

The main source of information regarding livestock across Pakistan and Punjab is the Livestock Census conducted on a ten year basis by the Agricultural Census Organisation, under the auspices of the Federal Bureau of Statistics. The Livestock Census report provides detailed statistics regarding the number of livestock, as reported by a sample of households. It also collects valuable information regarding the use of veterinary and artificial insemination services by households. A separate but conjoined exercise is also undertaken by the ACO under which a survey of milk production is undertaken. This report also draws on Mouza Census conducted by the ACO which reports data at the tehsil level regarding a variety of socio-economic indicators.

The utility of the Livestock Census for the purposes of this exercise is limited by the fact that the Census only reports statistics at the district level. This is presumably a reasonable level of disaggregation given the country wide scope of the study, but the fact that PEOP already limits the scope of its interventions to four districts implies that the Census is not useful in distilling intra-district variations. These variations are in fact, critical to the design of livestock interventions, beneficiary selection process, and choice of delivery mechanism. A comparison with Section A of the report suggests that tehsil level differences are important to take into account.


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Section 2: Poverty and Income Indicators


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2. Poverty and Income Indicators

2.1 Poverty Headcount Ratio

The four districts selected for PEOP (i.e. Bahawalnagar, Bahawalpur, Lodhran and

Muzaffargarh) are among the districts with highest poverty headcount ratio3. However

those estimates were based on data available until 2003-04. In order to better understand the poverty situation in those districts before the start of PEOP, we need to study the most current trends in poverty. A major reason to study the recent poverty trends before designing the interventions is the high level of growth achieved by Punjab during first seven years of the decade. There has been immense debate whether this growth has altered the poverty situation in the province, and particularly in the southern regions of the province.

Appendix 1 describes the methodology used for estimation of poverty headcount ratio4

in this section; the poverty line is Rs. 807.53 per capita per month for 2003-04 and Rs. 957.3 per capita per month for 2007-08. The following table reports the poverty ratio for 2003-04 and 2007-08 in the four districts. As mentioned above we have used the MICS data sets for measuring poverty. Table 1 reports the district level poverty headcount ratios.

Table 1: Poverty Head Count Ratio

Bahawalnagar Bahawalpur Lodhran Muzaffargarh

2007-08 51.30% 55.07% 50.40% 51.75%

Std Error (1.2) (1.25) (1.7) (1.1)

2003-04 47.80% 54.70% 47.04% 52.34%

Std Error (2.2) (2.3) (3.02) (2.3)

Data Source: Bureau of Statistics, Planning and Development Department, Government of Punjab, Multiple Indicator Cluster Survey, Punjab 2007-08 & 2003-04

Except for the district of Muzaffargarh which showed marginal improvement, the rest of districts have witnessed an increase in poverty. This points to direction that most of the economic growth of the province has left behind the southern region and failed to create economic opportunities. The biggest jump in poverty was witnessed by Bahawalnagar district in the time period 2003-2007, whereas Bahawalpur witnessed the highest poverty rate of 55.07% among four districts in 2007-08.

3 Dr. Ali Cheema and Lyyla Khalid, Poverty in Punjab causes and constraints. Other highest

poverty districts according to this article are D.G. Khan, Rajanpur, Rahimyaar Khan and Pakpathan.

4 Poverty headcount ratio is the proportion of people living below the poverty line.


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2.2 Tehsil Level Poverty

The additional benefit of using MICS data is the availability of credible data to analyze variations in poverty ratios at tehsil level. This analysis is only possible for 2007-08 because 2003-04 MICS does not provide data at tehsil level. Figure 1 provides a glimpse of poverty head count ratio for different tehsils of Bahawalnagar district.

Figure 1: Poverty Headcount Ratio Bahawalnagar District

Data Source: Bureau of Statistics, Planning and Development Department, Government of Punjab, Multiple Indicator Cluster Survey, Punjab 2007-08, * denotes the poverty rate is significantly different from district poverty

Poverty numbers show huge variations within the district of Bahawalnagar, which has the second lowest poverty rate in districts of our interest. The poorest tehsil is that of Minchin Abad which has 63.30% poverty headcount ratio, while Chishtian is the least poor with 43.9% poverty. It is important to note that Bahawalnagar tehsil, which is the district headquarter, has very high poverty as well, just behind Fort Abbass. It would be natural to expect that district headquarters has relatively lower poverty rate however this is not the case in Bahawalnagar.

The district of Bahawalpur has a highest poverty headcount ratio, and the variation at sub district level is even higher. As we can see Bahawalpur City, which is district headquarter as well, has the lowest poverty rate 41%, whereas Ahmadpur East is the tehsil with highest poverty rate of 62.09%. Bahawalpur Sadar has a higher poverty rate compared to the city.

Figure 2: Poverty Headcount Ratio Bahawalpur District


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Data Source: MICS 2007-08, * denotes the poverty rate is significantly different from district poverty rate

Next we calculate the poverty headcount ratio for tehsils of Lodhran district. Lodhran has the least poverty ratio in all the districts as reported in Table 1. Poverty in Lodhran varies from 53% to 47%. Within Lodhran, Kehror Pacca tehsil has the highest poverty rate followed by Lodhran tehsil. Dunya pur has the lowest poverty rate of 47% within the district.

Figure 3: Poverty Headcount Ratio Lodhran District

Muzaffargarh is the only district where poverty situation has improved between 2003 and 2007. Though the improvement is marginal, unfortunately we cannot trace the variation in this improvement at sub-district level for lack of data. In 2007-08, Kot Adu tehsil had the highest poverty rate of 54.36%. Jatoi follows with second highest poverty rate of 53.3%. The District headquarter Muzaffargarh tehsil has poverty rate of 50.8%. The lowest poverty ratio is found in Alipur tehsil, where the poverty rate stood at 47.9%.

Figure 4: Poverty Headcount Ratio Muzaffargarh District

Data Source: Bureau of Statistics, Planning and Development Department, Government of Punjab, Multiple Indicator Cluster Survey, Punjab 2007-08

2.3 Economic Growth

The programme document for PEOP requires an assessment of economic growth at the level of the target districts. However, district-level GDP estimates are not available in Pakistan that could be used to estimate economic growth at the level of the district. Therefore, we use household income data from the two rounds of MICS to analyze differences in income at the district-level and to generate a proxy measure for the growth in income levels for target districts.


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2.3.1 Average Income for all Households

This section provides data on the level of average household income and its rate of growth for the four target districts. Given that PEOP is directly concerned with poor households we provide the same information separately for poor households and for households that have been able to acquire employment through the labor market.

Table 2: Average Income of all Households at District Level5


Year 2003 (000 Rs) 4.01 3.33 6.91 6.64

Year 2007(000 Rs) 10.25 9.63 10.6 10.1

Nominal Growth (Yearly) 26%* 30%* 11% 11%

Real 2007(000 Rs) 7.21 6.78 7.50 7.15

Growth Four year 0.80 1.03 0.08 0.07

Real Growth-yearly 16%* 19%* 2% 2%

Data Source: Multiple Indicator Cluster Survey, Punjab 2007-08 & 2003-04

* means growth rate significantly different from zero

Table 2 reports the household average income per month for all households at district level. From the table we can see that on average the yearly growth rate has been substantial for all districts. However this is nominal growth rate, a household‟s income might be increasing but they may be becoming worse off due to ever increasing inflation. Therefore in order to assess the real increase in income of the households we had to make the numbers comparable. In order to do that we calculated inflation

from 2003 to 2007-08, this came out to be 42%6. We deflated 2007-08 average

5 An alternative to using CPI for making the incomes comparable would be the GDP deflator.

We have reported the results using GDP deflator in table 1 annex 4; however the variation in income growth remained the same.

6 We have used CPI reported in Economic Survey of Pakistan available from Ministry of

Finance‟s website to calculate the inflation figure. The CPI for 2003-04 is 111.63 and for 2007-08 it is 158.9. Base year for both numbers is 2000.


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income figures with the four year inflation to get real income in 2007 comparable to the income of 2003-04. The yearly real growth figures calculated from the real income show a considerably lower growth in income especially for Lodhran and Muzaffargarh. These figures lead us to confirm again that economic growth witnessed by Punjab province had been not uniform especially in south Punjab.

2.3.2 Average Income for Poor Households

In order to assess income situation of the poor, we carried out a comparative analysis of the poor in both the periods. The average household income for those classified as poor households is reported in table 3. The surprising fact coming out of this analysis is the negative income growth in three districts. So even in nominal terms the average household income of poor belonging to Bahawalnagar, Lodhran and Muzaffargarh saw a decline during the time period. Analysis of the real income shows negative growth for all the districts. The average household real income of the poor has decreased considerably in time period between 2003 and 2008. These households have lost up to 14% of their income every year to inflation during this time.

Table 3: Average Income of Poor Households7


Year 2003(000 Rs) 4.51 2.84 5.8 4.42

Year 2007(000 Rs) 4.33 4.11 4.44 4.27

Nominal Growth (Yearly) -1%* 10% -6%* -1%*

Real 2007(000 Rs) 3.05 2.89 3.12 3.01

Growth Four year -0.32 0.01 -0.46 -0.31

Real Growth-yearly -9%* 0% -14%* -9%*



2.3.3 Income of Employed Individuals

The average income analysis at household level gave us a direction in which the economic growth analysis could have gone, given the data was available. The income




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of the employed individuals, not aggregated at household level, will further help us in understanding effects of economic growth.


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Table 4: Average Income of Employed8


Year 2003(000 Rs) 2.72 2.24 2.66 3.24

Year 2007(000 Rs) 4.88 4.6 4.32 5.58

Nominal Growth 16%* 20%* 13% 15%*

Real 2007(000 Rs) 3.44 3.24 3.04 3.93

Growth Four year 0.265 0.445 0.14 0.21

Real Growth-yearly 6%* 10%* 3% 5%*



Table 4 provides the average income for all those who are in labor force and have been employed in a paying job. The average real income for those employed saw a positive upward trend. The real yearly growth rate of income has been in the range of 3% to 10%, the lowest is found in Lodhran while the highest in Bahawalpur. This could mean that those had employment did benefit from the economic growth.

2.3.4 Income of Employed Poor Individuals

We studied the trends in income reported by poor adults employed in a paying job to confirm whether the trend of positive income for employed individuals is also confirmed by poor individuals. Table 5 reports the average income earned by working individual for the two points of reference in the four districts.




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Table 5: Average Income of Employed Poor9


Year 2003 (000 Rs) 1.92 1.49 1.56 2.16

Year 2007 (000 Rs) 2.41 1.99 1.91 2.52

Nominal Growth (Yearly) 6% 8% 5%* 4%*

Real 2007 (000 Rs) 1.69 1.40 1.34 1.79

Growth Four year -0.10 -0.051 -0.14 -0.17

Real Growth-yearly -3% -2% -4%* -5%*



The nominal income showed healthy yearly growth rate for employed poor individuals in all four districts, the highest rate was achieved in Bahawalpur. However this positive growth disappears as we deflate the incomes to get real growth. The real growth has been negative in all four districts. The poor employed have lost on average 2% to 5% of their income every year to inflation. Their counterparts in non poor category have seen a real gain in their income for the same period. We can safely speculate that the economic growth period that Punjab has seen, largely benefited the labor categories that required technical or professional skills, since there is divergent growth in income of two groups of employed individuals. And the lack of those skills is one of the reasons why the poor are below poverty line in the first place. Poor are mostly employed in sectors that did not fare better in the economic growth period, discussed further in section 3 of the report.

The real income growth rate is quite skewed, as we can see from figure 5 which reports growth rates for income quintiles across four districts for household incomes. The bottom quintile is moving in the opposite direction of the top quintile, implying that the richest grew richer and the poorest became poorer. The increase for exact middle quintile is marginally positive; it is the IV and V quintiles driving the overall increase in household incomes we discussed earlier.




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Table 6: Difference in average income in Rs. and growth rate by quintile

I II III IV V

Bahawalnagar -258 [-5%] -418[-1%]

119 [1%] 572 [6%] 3885 [3%]

S.E (47.08)* (31)* (48)* (87)* (4519)

Bahawalpur -325 [-4%] 310 [-1%]

159 [0%] 749 [2%]

3787 [7%]

S.E (38)* (28)* (54)* (91)* (2272)

Lodhran -346[-5%] -150 [-1%] 34[0%] 865 [3%] 1203 [2%]

S.E (63)* (54)* 72 (105)* (2479)

Muzaffargarh -303[-4%] -73 [-1%] 64 [0%] 287 [0%]

5806 [8%]

S.E (50)* (48)* 52 (87)* (2387)*

Note: standard errors are in parentheses. * denotes that changes are significant at 5%, Growth rate in braces

The above table represents standard errors of the income growth rates across quintiles and their significance.

Figure 5: Real Income Growth over all income quintiles10


We have reported the results using GDP deflator in figure 1 annex 4; however the variation in income growth remained the same.


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The analysis in this section highlights the skewed effects of economic growth in south Punjab. While the non poor might have benefited from economic growth, the poor segment of society clearly did not benefit. It is commonly understood that poor lag behind in skills and are almost always employed in low skill jobs. This analysis confirms that the economic growth failed to impact such low skills jobs, which are usually, paid for though daily wages and are mostly agriculture based.

Figure 6: Income Density plot by poverty status

The above graph shows income density of households per adult equivalent per month. The red line signifies poverty line; on the left of this line are the log incomes of the poor and on the right, non poor. The figure has a long right hand tail which indicates a small fraction of households with high income.


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Crown Agents

Section 3: Individual and Household Attributes of Target Groups


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Crown Agents 3. Individual and Household Attributes of Target Groups

This sections reports on a wide dimension of attributes of the poor and non-poor households. It also reports distinctions in attributes within the poor. Lastly, it identifies attributes of specific groups- such as unemployed and females.

3.1 Poverty Profile

In this section we discuss the poverty profile both at individual and household level. The table below compares poor and non poor individuals on certain necessary indicators using the MICS data for 2007-08. The literacy rate for poor individuals is consistently lower across all districts compared to non-poor; the disparity is also visible in school enrollment indicator. Females have relatively greater representation in poor versus non poor category. The poor individuals are younger and less literate. The proportion of widows is same for both groups across all districts, except Bahawalnagar where smaller proportion are poor. The proportion of people working without pay is same as well except for Bahawalnagar. The unemployment rate is clearly higher for the poor group across all districts. Smaller proportions of poor reside in urban areas compared to non poor households.


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Crown Agents Table 7: Individual Attributes by District

Bahawalnagar

Bahawalpur

Lodhran

Muzaffargarh

Non- Poor

Percent Literate (%) 40 38 33 33

Percent Ever Enrolled in School (%) 58 54 52 49

Percent Females (%) 48 48 48 48

Mean Age 26 25 25 24

Percent Widows (%) 4 3 3 2

Percent Unpaid Worker (%) 1 1 1 0

Percent Unemployed (%) 5 4 3 5

Percent Urban (%) 26 35 16 18

Poor

Percent Literate (%) 25 23 22 22

Percent Ever Enrolled in School (%) 44 40 41 40

Percent Females (%) 49 49 48 49

Mean Age 22 22 22 20


Percent Unpaid Worker (%) 2 1 1 0

Percent Unemployed (%) 7 8 8 8


Difference

Percent Literate (%) 15* 15* 11* 11*

Percent Ever Enrolled in School (%) 14* 14* 11* 9*

Percent Females (%) -1* -1* 0 -1

Mean Age 4* 3* 3* 4*


Percent Unpaid Worker (%) -1 0 0 0

Percent Unemployed (%) -2 -4 -5 -3

Percent Urban (%) 10* 15* 3* 6*

* indicates that the difference between the two groups is significant at 5%


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Crown Agents We compare the poor with non-poor on household attributes; the table below presents the indicators for two groups in 2007-08. The proportion of landless poor households is higher across all districts compared to proportion of landless non-poor households. There is not much difference between the two groups in terms of home ownership,

however there is significant difference if compared for housing stock i.e. kacha11

.

Larger proportion of poor live in kacha households compared to non poor families. Interestingly, poor households have higher proportion of livestock ownership across all districts, that is because livestock is the most important and probably only affordable asset of poor families. Larger proportions of non poor households receive remittances from outside their area compared to poor families. The non poor have higher proportions of households living in urban areas compared to the poor. The trends are mostly consistent across all districts

11 This indicators is constructed from two other variables which reported the material used in

walls and roof


19

Crown Agents Table 8: Attributes of Poor and Non-poor households


Non Poor HHs

Percent Landless (%) 53 53 47 46

Percent Home ownership (%) 86 71 77 93

Percent Livestock (%) 67 60 70 71

Average number of livestock 7 5 6 6

Percent Housing Stock-Kacha (%) 43 37 32 45

Percent Remittance receiving HHs (%)

12 13 13 6


Poor HHs


Percent Home ownership (%) 84 62 74 93

Percent Livestock (%) 79 71 76 76

Average number of livestock 7 5 6 5

Percent Housing Stock-Kacha (%) 68 60 45 59


8 10 10 8


Difference

Percent Landless (%) -9* -13* -14* -15*

Percent Home ownership (%) 2 9* 3 0

Percent Livestock (%) -12* -11* -6* -5*

Average number of livestock 0 0 0 1*

Percent Housing Stock-Kacha (%) -25* -23* -13* -14*


4 3 3 -2

Percent Urban (%) 9 4* 3* 5*

* indicates significant difference between two groups of households at 5% level

3.2 Poverty Profile at Tehsil Level

In this section we discuss indicators that usually help in understanding the profile of poor household. In order to better understand the attributes of poor we compare the indicators with attributes of non poor households as well.

3.2.1 Tehsils of Bahawalnagar

Poverty at sub district level shows variation in the district of Bahawalnagar. The headcount ratio is highest for Minchinabad, where poverty stood at 63.3%, whereas it is lowest in Chistian tehsil at 43%. The agriculture land ownership patterns are more or


20

Crown Agents less the same as at district level however Chishtian, which has the lowest poverty, had highest landlessness reported by the poor. The home ownership is consistent across the district. The table reports the unemployment rate for poor individuals. The unemployment rate is calculated by self reporting of individuals who are actively looking for jobs. The highest unemployment rate for the poor is found in Bahawalnagar. All tehsils have higher than 5% unemployment rate for the poor except for Minchinabad, which is the poorest tehsil. It is also important to notice that Minchinabad has the highest livestock ownership in all districts.

Table 9: Attributes at Tehsil Level

Chishtian Fort Abbas

Haroonabad Michinabad Bahawalnagar

Percent Poverty (%)

43 53 48 63 51

Non Poor HHs

Percent Landless (%)

51 41 53 46 56

Percent Home Ownership (%)

84 87 85 77 91

Percent Livestock Ownership (%)

77 84 76 90 76

Percent Urban (%)

24 14 29 20 33

Unemployment Rate

4 2 3 4 5

Poor HHs


70 55 64 54 58


87 88 87 82 89


71 82 73 83 71

Percent Urban (%)

20 12 20 7 22

Unemployment Rate

6 5 6 2 7

Difference


-19* -14* -11* -8 -2


-3 -1 -2 -5 2


6 2 3* 7 5

Percent Urban (%)

4 2 9* 13* 11*

Unemployment Rate

-2* -3* -3* 2* -2*

Data Source: Multiple Indicator Cluster Survey, Punjab 2007-08 * indicates the difference between two groups is significant at 5% level

The proportion of landless households in non poor group is higher compared to the poor households; the highest is reported for Bahawalnagar tehsil. This might be indicating that lack of agriculture land has lead households to find alternative income sources which paid off more than agriculture and hence such households are not poor. Similarly the unemployment figures of the non poor group are lower compared to unemployment rate of the poor.


21

Crown Agents 3.2.2 Tehsils of Bahawalpur

Bahawalpur has the highest poverty headcount ratio of all districts. In this section we will analyze individual and household attributes at tehsil level for poor and non poor segment of society. The tehsil level poverty shows variation in Bahawalpur as discussed in section 1.1.1. The most notable feature of poor households at tehsil level is the variation in landlessness. Bahawalpur city clearly has the lowest agriculture land ownership by poor because it is an urban area mostly. However Ahmadpur East which has the highest poverty headcount ratio in Bahawalpur has the lowest reported landlessness. Bahawalpur city being an urban district has lowest proportion of livestock ownership by the poor, while the rest of tehsils are almost at same level. Overall unemployment rate for poor individuals is highest in the Hasilpur district, while khairpur has lowest rate.

Table 10: Attributes of Households and Individuals at Tehsil Level

* indicates the difference between two groups is significant at 5% level

The landlessness indicator has considerable variation across tehsils for non poor group. In Ahmedpur East half the households are landless, whereas in Bahawalpur city 87% report to have no land. The unemployment rate for non poor individuals is lower than the poor group except for Khairpur Tamewali. The proportion of urban households is comparable to the same proportion in poor group.

3.2.3 Tehsils of Lodhran

Lodhran has the lowest poverty headcount rate and very little variation in tehsil level poverty as discussed earlier. Nearly a third of poor in all tehsils report to be landless. The unemployment rate for poor is almost at same level in all tehsils compared; Lodhran has the highest 9% rate.

The non poor households have lower landless ratio compared to the poor, Dunya Pur has the lowest reported landless households. The unemployment rate for non poor is highest in Lodhran, and along with Dunya pur it is more than the unemployment rate of the poor. Kahror Pacca has lower proportion of non poor households living in urban areas compared to poor families, while the rest of tehsils have a higher non poor urban resident ratio.

Ahmedpur East Bahawalpur City Bahawalpur Sadar Hasil Pur Yazman Khairpur Tamewali

Percent Poverty (%) 62 41 57 58 52 56

Percent Landless (%) 40 75 41 50 41 50

Percent Home Ownership (%) 79 68 62 69 33 48

Percent Livestock Ownership (%) 77 39 80 74 83 79

Percent Urban (%) 16 86 0 22 13 14

Unemployment Rate 4 4 4 3 3 6

Percent landless (%) 52 87 61 70 66 67

Percent Home Ownership (%) 78 67 61 47 68 32

Percent Livestock Ownership (%) 76 38 79 78 74 82

Percent Urban (%) 13 81 0 17 12 8

Unemployment Rate 6 7 5 9 6 2

Percent landless (%) -12* -12 -20* -20* -25* -17*

Percent Home Ownership (%) 1 1 1 22* -35* 16*

Percent Livestock Ownership (%) 1 1 1 -4 9 -3

Percent Urban (%) 3 5 0 5 1 6*

Unemployment Rate -2* -3 -1 -6 -3* 4*

Non Poor

Poor HHs

Difference


22

Crown Agents Table 11: Households and Individual Attributes, Lodhran District

Kahror Pacca Lodhran DunyaPur

Percent Poverty (%) 53 51 47

Non Poor HHs

Percent Landless (%) 56 61 61

Percent Home Ownership (%) 79 71 88

Percent Livestock Ownership (%) 81 72 81

Percent Urban (%) 8 19 14

Unemployment Rate 8 9 8

Poor HHs

Percent Landless (%) 65 71 65


Percent Livestock Ownership (%) 66 67 70


Unemployment Rate 9 8 6

Difference

Percent Landless (%) -9 -10* -4


Percent Livestock Ownership (%) 15* 5 11*

Percent Urban (%) -11* 5 6*

Unemployment Rate -1 1 2

Data Source: Multiple Indicator Cluster Survey, Punjab 2007-08 * indicates

difference between two groups is significant


23

Crown Agents 3.2.4 Tehsils of Muzaffargarh

Muzaffargarh is the only district which showed slight improvement in poverty from 2003 to 2007. There is some marginal variation in tehsil level poverty in this district. The land ownership pattern suggests more than nearly two third of poor have no landownership. The unemployment rate varies within the district, Muzaffargarh has lowest unemployment rate of only 2.9% where as in Kot Adu, which has the highest poverty, and nearly 7% people reported unemployment. The landlessness indicator for non poor does not show clear trend compared to poor households, Muzaffargarh and Kot Adu have lower landless ratio compared to the poor group while Alipur and Jatoi have higher ratios. Ali pur has overall highest landless ratio reported for the non poor group. The unemployment rate for non poor is higher in Muzaffargarh and Alipur while lower in Kot Adu and Jatoi compared to the poor.

Table 12: Households and Individual Attributes at Tehsil Level

Muzaffargarh Alipur Kot Adu

Jatoi

Percent Poverty (%) 51 48 54 53

Non Poor


Percent Home Ownership (%) 94 94 90 97


80 73 87 77


Unemployment rate 6 5 4 5

Poor Households


Percent Home Ownership (%) 91 89 94 97


73 76 69 75


Unemployment rate 3 4 7 6

Difference

Percent Landless (%) -7* 7 -19* 3

Percent Home Ownership (%) 3 5* -4 0


7* -3 18* 2

Percent Urban (%) 6* 5* 12* 5*

Unemployment rate 3* 1 -3* -1

Data Source: Multiple Indicator Cluster Survey, Punjab 2007-08 * indicates difference between two groups is significant at 5% level

3.3 Poverty Band Analysis

In this section we compare individual and household attributes for different bands of poor. We have divided the poor in five bands, the lowest being the most poor and highest band represents comparatively less poor.


24

Crown Agents The table compares profiles of individuals who fall in different bands .Literacy shows a clear correlation with the poverty band; the most poor are the least literate. The literacy rate increases as we move up the bands, the highest literacy is reported for individuals falling in the least poor band. The proportion of individuals who have ever enrolled in school also follows the same pattern and has clear correlation with poverty band. The relatively less poor have higher proportion of people who have ever attended school. This table also reports some demographic indicators for poor in different bands, less than half of the poor individuals are women in all bands except the lowest and the average age is around 22 years. The proportion of widows does not show a clear relationship and the proportion of people working without pay is uniform across all bands. Male unemployment does not show a clear trend. The proportion of most poor are comparatively more rural as the smallest proportion of most poor is found in urban areas.

We have also reported the household attributes for different poverty bands. The land ownership is lowest for second poverty band, and there is no clear relationship with the severity of poverty. The home ownership on the other hand is lowest for the most poor and increases as the poverty situation improves but stays almost same for top three bands. The livestock ownership does not show a clear trend and neither does the average number of livestock. Both of these indicators are lowest for the second band of poverty. The type of housing improves as we move up the bands; the most kacha houses are reported by the poorest households. The poorest band has the highest proportion of female headed households and the difference with other bands is comparatively high. The remittances do not show a clear trend, the second band has lower households receiving remittances compared to the most poor. The second band seems to lag behind the first band of poverty in land ownership, livestock ownership and remittances. This may be because of the geographical location of most of the households in these two bands. The first band, most poor, have smaller proportion residing in urban areas compared to the second band and that may be the reason why they have higher land and livestock ownership along with higher proportion of household receiving remittances.


25

Crown Agents Table 13: Profile of Poor across poverty bands

Bands I II III IV V

Individual Attributes

Percent Literate (%) 17 20 23 26 33

Percent Ever Enrolled (%) 33 38 41 45 51

Female (%) 50 49 49 49 47

Mean Age 22 21 21 22 22

Percent Widows (%) 3 2 3 2 2

Percent Unpaid-Workers (%) 1 1 1 1 1

Nominal Income of the Employed (Rs)

1373 1920 2341 2691 2931

Percent Urban (%) 10 17 13 21 22

Percent Unemployed Men (%) 8 9 7 8 10

Household Attributes

Percent Landless (%) 56 74 54 62 56

Percent Home Ownership (%) 74 78 81 81 81

Percent Livestock Ownership (%) 74 71 80 74 78

Average Livestock 4.8 4.3 6.8 5.9 7.8

Percent Housing Stock-Kaccha (%)

66 65 59 51 51

Percent Female Headed HH (%) 8 1 3 1 1

Percent Remittances receiving HH (%)

10 5 7 10 15

3.4 Profile of other Potential Target Groups

In this section we discuss household attributes of other potential target groups besides poor. We have selected different attributes and compared the profile of these groups across overall population.

3.4.1 Female Headed Households

Female headed households are an important potential target group for PEOP; however the number of such households is comparatively small, therefore their attributes will have large variations across districts. Female headed households reported to have higher landless ratio compared to rest of population. Homeownership is also lower however the difference is comparatively small and insignificant. The proportion of livestock ownership and average number of livestock both are significantly lower for female headed households indicating a lower asset ownership ratio. A higher proportion of female headed households are located in urban regions compared to the overall population. The proportion of kaccha housing is higher as well compared to other households.


26

Crown Agents Table 14: Profile of female-headed households

Female Headed HH All HHs Difference

Percent Landless (%) 68 55 13*

Percent Home Ownership (%) 76 81 -5

Percent Livestock (%) 56 73 -17*

Average Livestock 3.07 6.5 -3.43*


Housing Stock-Kaccha (%) 43 48 -5

* indicates difference between the two is significant at 5% level

3.4.2 Unemployed Individuals

People who are unemployed and actively looking for jobs are one of the important target groups of this programme; the table below reports attributes of this potential target group. The proportion of unemployed who had ever attended a school is highest in Bahawalnagar, while lowest in Bahawalpur. Landless indicator is almost the same except that Muzaffargarh is the only district with less than 60% of landlessness in the unemployed. Livestock ownership does not show large variation either, Muzaffargarh has the highest followed by Lodhran, both Bahawalpur and Bahawalnagar have the same proportion of livestock ownership. Comparing the unemployed to the employed yields some interesting observations. The proportion of unemployed who have ever attended school is higher in all districts compared to the employed. The mean years of schooling is also higher for Muzaffargarh and Lodhran and marginally lower for Bahawalpur and Bahawalnagar.


27

Crown Agents Table 15: Profile of Unemployed and Employed

Bahawalpur Bahawalnagar Muzaffargarh Lodhran

Unemployed

Ever Attended School (%)

60 71 63 63

Mean Schooling 8.01 7.6 8.17 8

Landless (%) 62 65 59 63

Livestock (%) 66 66 71 70

Urban Unemployment (%)

7 9 9 10

Rural Unemployment (%) 6 5 6 5

Employed


42 49 46 43

Mean Schooling 8.14 8.28 8.13 7.6

Landless (%) 59 58 51 55

Livestock (%) 69 73 75 77

Difference


18* 22* 17* 20*

Mean Schooling -0.13 -0.68 0.04 0.4

Landless (%) 3 7* 8* 8

Livestock (%) -3 -7* -4 -7

* indicates difference between two groups is significant at 5% level

The landless indicator is higher for the unemployed; this along with lower schooling and enrollment indicator suggests that most of the employed are working in agriculture or low skills occupations. The unemployment in urban areas is consistently higher in all districts compared to rural unemployment. This might be the case because mostly unemployed people from rural areas move towards the city to find a job, hence increasing the unemployment rate in urban areas.


29

Crown Agents

Section 4: Occupations and Skills Indicators


30

Crown Agents 4. Occupations and Skills Indicators

In this section we discuss the occupational structure, availability of skilled labor and provision of training in the target districts. We have used several data sources from MICS, Labor Force Survey, TEVTA and PVTC to get a clear picture of the above mentioned broad areas.

4.1 Occupational Structure

In this section we discuss the individual employment profiles in the target districts and compare them across groups to study the occupational structure.

Table 16: Occupational Categories

Bahawalpur Bahawalnagar Muzaffargarh Lodhran

Govt employee 6.83% 6.50% 6.67% 4.98%

Pvt employee 11.52% 13.65% 14.59% 11.47%

Self-employed 12.80% 11.51% 11.23% 12.86%

Employer 0.56% 0.22% 0.35% 0.37%

Laborer 34.18% 29.76% 38.48% 31.27%

Rental income 0.13% 0.42% 0.08% 0.17%

Profit from deposits/shares 0.03% 0.44% 0.21% 0.03%

Agriculture 28.04% 31.83% 24.85% 33.84%

Livestock, poultry, fishery 2.89% 3.25% 1.78% 2.13%

Home-based work/cottage 0.02% 0.02% 0.06% 0.07%

Pension 1.61% 1.78% 1.30% 1.64%

Tutor 0.33% 0.11% 0.12% 0.35%

Embroidery/stitching 0.84% 0.48% 0.26% 0.66%

Student laborer 0.23% 0.04% 0.03% 0.14%

Table 16 provides the proportion of employed population in different occupational categories. It is very clear that the current structure of occupations is such that most of the employment is in agriculture sector, labor of various kinds, and self/private employment across all districts. Employment in low end skill oriented occupational groups such as home based work/ cottage and embroidery, makes a small proportion of the employed labor across districts. That can either mean there are no skilled


31

Crown Agents workers to take up employment in these groups or there are no opportunities, but this is suggestive of the possible gaps in the skilled sector.

Table 17 helps in understanding occupational structure of target groups. The Poor individuals of the target district have higher employment in agriculture and labor, which indicates even narrower employment structure. The proportion of employed poor is smaller in all other categories compared to non poor, except for livestock/poultry/ fishery. The proportion of self employment poor is also smaller compared to non-poor, this category is usually an important source of income in case the individual has a particular skill.

There is not much divergence in occupational structure between rural and urban area, except for agriculture and self employment. Larger proportion of urbanites is self employed compared to inhabitants of rural areas. However it gives a clear picture that there is not much difference in availability of opportunities on the basis of location. The structural difference is most visible if we do the analysis by gender, the largest proportion of females is involved in house work (not reported in table). If we look at only income earning activities, largest proportion of women are working as labor in various activities, while the second notable proportion works in embroidery and stitching category. This trend makes it intriguing to explore what percentage of females working as laborers are engaged as skilled workers. Also of interest is the comparatively higher percentage of self employed in the urban areas and a lower percentage in the rural areas because this is contrary to general perception that more people are self employed in rural areas. This trend augurs well for the programme as it appears that even in the face of a weak industrial base in these districts, there could potentially be high utilization of skilled trainees due to this trend of self employment.

Table 17: Employment Categories by Groups

Non Poor

Poor Urban Rural Female Male

Govt employee 9.80% 1.95% 13.25% 4.55% 5.40% 6.42%

Pvt employee 14.52% 10.83% 15.79% 12.15% 3.66% 12.93%

Self-employed 14.99% 8.13% 24.96% 8.49% 1.10% 12.03%

Employer 0.46% 0.28% 0.80% 0.27% 0.12% 0.38%

Laborer 27.61% 41.88% 33.49% 33.84% 69.36% 33.76%

Rental income 0.25% 0.13% 0.27% 0.18% 0.02% 0.20%

Profit from deposits/shares 0.19% 0.19% 0.17% 0.19% 0.12% 0.19%

Agriculture 27.38% 31.24% 6.07% 35.33% 1.15% 29.04%

Livestock, poultry, fishery 1.80% 3.56% 0.74% 3.06% 2.05% 2.56%

Home-based work/cottage 0.04% 0.03% 0.09% 0.02% 0.60% 0.04%

Pension 2.05% 0.94% 3.00% 1.18% 0.81% 1.57%

Tutor 0.26% 0.16% 0.20% 0.22% 0.45% 0.22%

Embroidery/stitching 0.59% 0.51% 1.03% 0.42% 15.02% 0.55%

Student laborer 0.07% 0.17% 0.14% 0.10% 0.15% 0.11%

Source: MICS 2007-08

On the basis of our analysis in this section so far we can summarize that


32

Crown Agents a) The occupation structures that exist in south Punjab are very narrow, indicating that people are concentrated in low skill low return labor work.

b) Smaller proportion of poor work in government and private sector

c) The occupational structure do not differ much across rural and urban divide, except for availability of agriculture sector in rural areas

It is also important to understand the growth in income in each category of occupation. As most of the adult working population is predominantly involved in agriculture and labor category, irrespective of the poverty status, therefore exploring the income changes might indicate the skill level of individuals having different economic standing. The following figure reports that income growth is not uniform across occupations, some even reporting negative growth. The biggest growth is observed for tutors followed by rental income and profit on deposits. As this was a time of easy money and booming real estate the growth in the latter two is not unexpected.

Figure 7: Growth Rate of Income by Occupation12

* indicate growth rates are significant at 5% level

The growth in income for individuals by broad occupational categories show divergent trends based on the poverty status of a household. The following figure show real growth rate per year from 2003-07 to 2007-08 for occupational groups by poverty status.


We have reported the results using GDP deflator in figure 2 Appendix 4; however the variation in income growth remained the same

-0.3-0.2-0.1

00.10.20.30.40.50.60.7

Go

vt emp

loyee*

Pvt em

plo

yee*

Self-emp

loyed

Emp

loyer

Labo

urer*

Ren

tal inco

me*

Pro

fit from

…

Agricu

lture*

Livestock, p

ou

ltry, fishery

Ho

me-b

ased …

Pen

sion

Tuto

r*

Emb

roid

ery/stitchin

g

Stud

ent lab

ou

rer*


33

Crown Agents Figure 8: Real Income Growth by Occupation and Poverty Status13

Individuals from non poor households have their earnings increased in most of the categories, while earnings of poor individuals have consistently decreased for most occupational categories. Even the cases where income increased for both the groups, the increase in income of poor individuals had been lagging behind the non poor, but wherever incomes have decreased the decline has been sharp in case of poor vis a vis the non poor. This analysis highlights that within the narrow structure of employment that exist in southern Punjab the poor are worse off compared to rest of the economic groups. This can be attributed to lack of capacity of the poor in terms of skills that have higher return.

Next we analyze the profile of individuals employed in the most common occupation on the basis of Labor Force Survey. This will give us a fair idea about the background of labor force in the region i.e. education, income, rural/urban and gender. The table below has some very important attributes of people employed in most common occupation. The most visible is the gender distribution in low skill, subsistence agriculture farming. The table suggests that only 22% of those employed in subsistence farming are males. Similarly in agriculture related labor 60% are women.

Majority of the most common occupation pay less than Rs.5000 per month, except for sales service, general management and driver. Trades employing sizeable proportion of women are the least paying jobs. Similarly the proportion of people having no education at all is the highest for female employing jobs. It is also noteworthy that majority of these occupations do not seem to have basic education requirement. More than half of the individuals employed in seven job categories have no education at all. The highest mean years of education is for general manager category while the lowest is for subsistence agriculture/fishery worker.


We have reported the results using GDP deflator in figure 3 Appendix 4; however the variation in income growth remained the same

-30%

-20%

-10%

0%

10%

20%

30%

40%

50%

60%Non Poor

Poor


34

Crown Agents Table 18: Profile of most common occupations

General Manager

Market Oriented Agri/fish

Subsistence Agri/fish

Extraction & building

Metal and Machinery

Percent Male (%) 96 77 22 95.5 97

Percent Urban (%) 39 6.7 .6 22 32

Average Income (000 Rs) 13.6 3.38 2.15 1.04 3.30

No Education (%) 35 57% 87% 54% 30%

Mean Schooling 6 2 .5 2 3

Other Craft Driver/operator Sales/Services

Agri/Fishery related

Labor Laborer in Mining/Const

Percent Male (%) 34 100 81 40 97.6

Percent Urban (%) 14 28 37 1.4 10

Average Income (000 Rs) 3.89 6.35 5.21 2.78 3.35

No Education (%) 66 25 58 71 68

Mean Schooling 1 3 2 1 1

Source : LFS 2007-08

4.2 Supply of Skilled Workers and Trainers

In this section we analyze the supply of skilled labor in the south Punjab market and also study the supply of trainers of skills. The supply of skilled labor is extremely inadequate in the target districts, as visible from the following table. To further the observation that occupational structure of the region is very narrow because of the lack of skills, consider the proportion of trained labor force in the region.

Table 19: Trained Labor

Bahawalnagar Bahawalpur Lodhran Muzaffargarh All

Trained Labor 2% 3% 1% 3% 3%

Source: LFS 2007-08

The labor force survey suggests that in our target districts, only 3% of the labor force have received any kind of on or off the job training in last eight years. This statistic presents a very clear picture of the lack of skills in the region. The proportion of skilled labor force is highest in Bahawalpur and Muzaffargarh followed by Bahawalnagar and Lodhran. This also indicates to the lack of training providers in the region as well.


35

Crown Agents Table 20: Supply of Training, TEVTA capacity

District Population Poverty Rate

Total Enrollment

Percent Commerce

Enrollment without Commerce

Capacity as percent of young poor*

Capacity as percent of young un-employed*

Bahawalnagar 2,340,000 55.07% 2518 59% 1032 0.22% 0.61%

Bahawalpur 2,761,000 51.30% 5483 26% 4057 0.79% 2.26%

Lodhran 1,330,000 50.40% 506 60% 202 0.08% 0.18%

Muzaffargarh 2,992,000 51.75% 1219 62% 463 0.08% 0.26%

Source: TEVTA, www.tevta.gov.pk, Population Estimate 2004, * Young defined as 15-35 years

of age

The above table gives a sense of supply of skills training in the region through aggregate enrollment figures of TEVTA, which is the largest training provider in public sector. The total capacity of TEVTA reflected through the total enrollment numbers is nothing compared to the young poor population of the region. Another problem is the nature of courses offered through TEVTA institutes, more than half of the capacity is dedicated to courses in commerce which is not a skills programme. The capacity issues become even clearer when we look at the enrollment without commerce. In order to understand the capacity constraints we define the target population as poor individuals between age of 15 and 35 years and the second target population is defined as unemployed individuals looking for work in these districts. It is clear that only smaller fractions of target populations can be trained through the available training programs. Such a limited supply of training is one of the major sources of narrow occupational structure.

Table 21: Supply of Training, PVTC Capacity

Population Poverty Rate

Male Female Total

Capacity as percent of young poor population

Capacity as percent of young unemployed

Bahawalpur 2,340,000 55.07% 510 468 978 0.21% 0.58%

Muzaffargarh 2,761,000 51.30% 344 417 761 0.15% 0.42%

Lodhran 1,330,000 50.40% 562 507 1069 0.44% 0.96%

Bahawalnagar 2,992,000 51.75% 604 396 1000 0.18% 0.56%

Source: PVTC Population Estimate 2004, * Young defined as 15-35 years of age

PVTC is the second largest skills training provider in Punjab and in our target region. It caters exclusively to Zakat recipients, who by definition are assumed to poorest of the poor. However the maximum capacity of PVTC, like TEVTA, is not enough to cater to the target population of young poor individuals and the unemployed. PVTC‟s capacity

http://www.tevta.gov.pk/


36

Crown Agents is even smaller compared to TEVTA although they may have greater outreach since they offer mostly vocational skills training courses. Capacity problem in public sector could have been addressed through investments from private sector; however there are only a few training providers from private sector operating in the region.

4.3 Available Skills Training Programs

The current skills provided by public sector though in limited capacity cover a whole band of skills from high to low end. TEVTA has the capacity to offer high end skill courses along with certain low end skills as well, while PVTC focus is more towards low end.

4.3.1 High-end Skills Training

The first enrollment table in Appendix C indicates course enrollment in TEVTA courses in Bahawalpur. The institutes in Bahawalpur are the only centers in the entire target region to offer high end technology courses through three years diploma DAE. These centers offer training in civil, mechanical and electrical engineering fields along dress designing and farm machinery. The mid range skills training is also offered through two years such as B-Tech in Mechanical, Auto and Diesel and Diploma in Vocational training.

High end skill training courses are not offered in Bahawalnagar district, the TEVTA institutes offer medium level two year certificate courses in trades such as electrician, heating ventilation and draftsman. High or medium skill courses, particularly related to engineering, are not offered by any institute in Muzaffargarh and Lodhran.

4.3.2 Low-end Skills Training

TEVTA institutes in our target districts offer low skill courses as well. In Bahawalpur the number of short term, low skill courses is very small, only few courses in computer application, wireman and quantity surveyor are offered. In Bahawalnagar, low skill courses such as computer applications six months certificate course in wood work and electrician are offered, however demand for such courses is extremely low as evident by enrollment numbers in table 2 Appendix C. In Muzaffargarh and Lodhran most of the courses besides commerce are short term and low skill such as plumbing, tailoring and industrial electrician; however like Bahawalnagar enrollment in these courses is comparatively lower.

Most of the courses offered by PVTC are short term and focus on low end skills training. PVTC offers courses exclusively targeted at the poor zakat receiving section of the society who can be regarded as one of the most economically marginalized group. Another important feature of the PVTC programmes is the availability of on job training so that the income sources of the participants remain intact during the course of training.

Annex 3 represents graphically the proportional enrollment in different courses by gender. It gives us an idea about the skill that is high in demand in market. An analysis of the female charts reveals that dress making is the most demanded trade in two out of four districts, while Bahawalpur, the course of beautician is the most demanded and in Muzaffargarh the course on embroidery seems to be most in demand. The female demand for courses is not related only to the business targeting female clients, in Muzaffargarh and Bahawalnagar a sizeable proportion of girls are enrolled in Computer applications course. Lodhran and Bahawalpur have a decent demand of courses training females in trade of clinical assistant.


37

Crown Agents Analysis of male enrollment trends reveals wide variation in course enrollments across districts. There is no one course that can be regarded as the most demanded course by the participants of programmes. In Muzaffargarh course on repair and maintenance of electrical appliances seems to be the most demanded, followed by a course in computer application and database management.

Annex 3 also reports the enrollment numbers for PVTC courses in different Tehsils. It is clear from quick look on the tables that there are certain courses where the enrollment exceeds available capacity, highlighting the skills that are high on demand. In Bahawalpur computer related courses have highest combined enrollment in all tehsils. The clinical assistant course in Bahawalpur tehsil is the most oversubscribed courses as the enrollment exceeds by more than twice of the available capacity. In Yazman and Ahmedpur the same can be said for computer operator/office assistant course.

In Rangpur area of Muzaffargarh, courses offered to females are highly demanded. The enrollment in embroidery and dress making far exceeds the available capacity. The clinical assistant course in Muzaffargarh tehsil, is the most demanded courses as the enrollment exceeds the capacity. Similarly the database management skill is also on top of demanded courses not only by males but females as well. This course has highest enrollment in Jatoi tehsil as well. Bahawalnagar and Chistian tehsils have demand for database management and clinical assistants‟ skills as evident from high enrollment, where as in Fort Abbas the course on clinical assistant is not demanded at all. Dress making and database management are the most demanded skills in Fort Abbass and Minchinabad.

4.3.3 Educational Requirement

The existing skills training offered in the region differ by educational pre requisite depending on the level of skill. The high end engineering related courses offered by TEVTA require matric and intermediate background depending on the level of degree, whereas the certificate courses also require matriculation.

The most demanded PVTC courses as discussed above require comparatively higher education background as well. Overall different computer related courses are in high demand, which require the participant to have at least matric education with science subjects. Same is the case for courses of clinical assistant and electrical related courses. The courses offered exclusively to females such as embroidery and dress making have lower education pre requisite, most of these courses requires the participants to have cleared middle school.


38

Crown Agents

Section 5: Livestock Indicators


39

Crown Agents

5. Livestock Indicators

The portfolio of livestock populations is quite similar across the four districts of interest- Lodhran, Muzaffargarh, Bahawalpur and Bahawalnagar.

5.1 Livestock Population by District

The figure below summarizes the total numbers of livestock populations in the Punjab.

Figure 9: Livestock Population in Punjab, by district

Source: Punjab Livestock Census, 2006, Agricultural Census Organisation, Statistics Division

Figure 1 identifies that the largest livestock populations are those of poultry and goats in the four districts. Within the districts, Lodhran consistently has the lowest stock of all animals. Muzaffargarh has the largest number of cattle, sheep, goats and poultry among the four districts while Bahawalnagar has the largest number of buffaloes among the four districts. Overall, the livestock portfolio of Bahawalpur is similar to that of Bahawalnagar.

Together, the four districts of PEOP hold 15.34% of the entire livestock of the Punjab. Table 1 below lists the percentage wise contribution of the four districts to the entire livestock population of the province, disaggregated by animal type.

Table 22: Animal wise contribution of PEOP districts to overall livestock population of Punjab

Cattle Buffaloes Sheep Goats Camels Horses Mules Asses Poultry

18.26 12.40 11.61 17.59 8.57 5.75 6.48 7.72 15.723


0

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Livestock Population by district

Lodhran Muzaffargarh Bahawalpur Bahawalnagar


40

Crown Agents As per the programme logframe, it is expected that beneficiaries of the livestock interventions would either be individuals or households. For this reason, it is particularly relevant to consider the reporting of livestock numbers, and use of services by the sample of households surveyed under the Census.

Figure 10 provides an in-depth breakdown of the percentage of each animal type within the four districts.

Figure 10: Percentage breakdown of animal types in PEOP districts


5.2 Number of people owning at least 1 animal for livestock farming

One of the main livestock related indicators of PEOP is that the number of individuals having at least one animal for the purposes of livestock farming is increased over the length/duration of the programme. The stipulated target for the end of the programme is that there should be an increase of 15% in livestock ownership over the baseline.

Cattle23%

Buffaloes

15%

Sheep 2%

Goats27%

Camels0%

Horses0%

Mules0%

Asses1%

Poultry32%

Lodhran

Cattle15%

Buffaloes22%

Sheep 6%

Goats27%

Camels0%

Horses0%

Mules0%

Asses2%

Poultry28%

Bahwalnagar

Cattle16%

Buffaloes

18%

Sheep 5%

Goats31%

Camels0%

Horses0%

Mules0%

Asses1%

Poultry29%

Bahawalpur

Cattle24%

Buffaloes13%

Sheep 7%

Goats22%

Camels0%

Horses0%

Mules0%

Asses1%

Poultry33%

Muzaffargarh


41

Crown Agents 5.2.1 Cattle

Overall, in Punjab over 47.7% of households report owning 1 to 2 animals, while 28.32% report owning 3 to 4 animals, and 12.56% report owning 5 to 6 animals. Less than 0.9% of sample households own more than 20 animals. In terms of percentage of animals held by each category of household, the largest number of cattle is owned by families which have 3 to 4 animals i.e. 24.13% of all cattle in Punjab belong to households which report having 3 to 4 animals. 18.96% of all animals in the province are held by households which own 1 to 2 animals. However, there are clear issues of equity as households which own 51 animals or more (and constitute only 0.2% of all households) own 11.1% of all livestock in the province.

These provincial trends reinforce themselves in the four districts of concern.

In Lodhran, the largest percentage of sample households report having 1 to 2 animals (48.07%). 29.11% of households report having 3 to 4 animals while only 0.52% of all sample households report having more than 20 animals. The distribution of animals within these household categories is distinguished by the fat tails already observed in the province wide distribution. In Lodhran, the households possessing 1 to 2 animals hold only 17.91% of all animals in the district whereas households with 3 to 4 animals hold 22.8% of the district wide stock of cattle. On the upper end, 0.27% of households which own 51 or more animals, own 21.5% of all cattle in Lodhran district.


42

Crown Agents Figure 11: Distribution of Households and Cattle in Lodhran District


In Muzaffargarh, the largest percentage of sample households report having 1 to 2 animals (38%). 32.34% of households report having 3 to 4 animals while only 0.31% of all sample households report having more than 20 animals. The distribution of animals within these household categories is distinguished by the fat tails already observed in the province wide distribution. In Muzaffargarh, the households possessing 1 to 2 animals hold only 13.9% of all animals in the district whereas households with 3 to 4 animals hold 24.44% of the district wide stock of cattle. On the upper end, 0.09% of households which own 51 or more animals, own 14.59% of all cattle in Muzaffargarh district.

39.581

23.972

10.7675.232

1.845 0.507 0.114 0.091 0.22705

1015202530354045

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Household Type

Lodhran- Household Distribution

Households

65.561

83.51

57.779

42.535

23.252

8.7022.666 3.419

78.573

0102030405060708090

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Lodhran- Cattle Distribution

Cattle


43

Crown Agents Figure 12: Distribution of households and cattle in Muzaffargarh District


In Bahawalpur, the largest percentage of sample households report having 1 to 2 animals (50.61%). 29.15% of households report having 3 to 4 animals while only 0.91% of all sample households report having more than 20 animals. The distribution of animals within these household categories is distinguished by the fat tails already observed in the province wide distribution. In Bahawalpur, the households possessing 1 to 2 animals hold 21.4% of all animals in the district whereas households with 3 to 4 animals hold 26.16% of the district wide stock of cattle. On the upper end, 0.18% of households which own 51 or more animals, own 11.5% of all cattle in Bahawalpur district.

96.544

82.172

40.119

26.604

6.415 1.38 0.377 0.185 0.2260

20

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100

120N

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Muzaffargarh-Household Distribution

Households

162.994

286.621

217.796 214.368

79.81

23.9468.859 6.885

171.129

0

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150

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250

300

350

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Muzaffargarh-Cattle Distribution

Cattle


44

Crown Agents Figure 13: Distribution of Households and cattle in Bahawalpur District


In Bahawalnagar, the largest percentage of sample households report having 1 to 2 animals (47.95%). 26.05% of households report having 3 to 4 animals while only 2.18% of all sample households report having more than 20 animals. The distribution of animals within these household categories is distinguished by the fat tails already observed in the province wide distribution. In Bahawalnagar, the households possessing 1 to 2 animals hold 15.79 % of all animals in the district whereas households with 3 to 4 animals hold 18.52% of the district wide stock of cattle. On the upper end, 0.43% of households which own 51 or more animals, own 16% of all cattle in Bahawalnagar district.

72.379

41.694

15.9238.597

2.449 0.66 0.81 0.225 0.2680

1020304050607080

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Household Type

Bahawalpur-Household Distribution

Households

117.848

144.04

86.03268.694

30.53211.495

19.6288.827

63.379

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Bahawalpur-Cattle Distribution

Cattle


45

Crown Agents Figure 14: Distribution of Households and cattle in Bahawalnagar District


In summary, with the exception of Bahawalnagar, we find that less than 1% of sample households own more than 20 cattle. With the exception of Muzzafargarh, over 45% of sample households report owning only 1 to 2 cattle. This would imply that the majority of households own animals for domestic use, including small scale subsistence farming. Follow-up tracer studies could add analytical value by probing the economic profile of households which own few numbers of cattle.

5.2.2 Buffaloes

Buffaloes are typically important work animals as well as an important source of milk in the Punjab.

The distribution of buffaloes among household types as well as the number of each household type is quite different from that of cattle. 43.43% of households own 1 to 2 animals, while 28.26% own 3 to 4 animals. Interestingly, 73.5% of all buffaloes in the province are owned by households which have up to 10 animals.

0

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60N

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Household Type

Bahawalnagar- Household Distribution

Households

85.846100.691

69.97175.382

38.6226.034

27.11432.512

87.365

0

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Bahawalnagar-Cattle Distribution

Cattle


46

Crown Agents

In Lodhran, the trends observed at the provincial level exhibit themselves more starkly. 97.7% of all sample households own 10 or less animals. These 97.7% of households also own much of the buffalo stock in the district at 87%. Large stock owners (owning more than 20 animals) together own 5.4% of all buffaloes in Lodhran.

These trends persist in Muzaffargarh and Bahawalnagar. In Muzzafargarh, 97.5% of households own 10 or less animals each but collectively own 86.5% of buffalo stock in the district. Owners of more than 20 animals own only 5.6% of buffalo stock. In Bahawalnagar, 94.5% of households own 10 or less animals and collectively own 74.47% of buffalo stock. Owners of more than 20 animals own only 12.56% of buffalo stock.

In Bahawalpur, 97.6% of households own 10 or less animals but collectively own 75.38% of buffalo stock in Bahawalpur. An interesting anomaly in the case of Bahawalpur is that the upper most percentile of households (those who more than 50 animals) own 15.83% of all buffalo stock.

5.2.3 Milch Cows and Buffaloes

Punjab

Of sample households, 27.84% of households report owning only milch cows, while 42.19% own only milch buffaloes. 29.9% of households report owning both milch cows and buffaloes.

In case of overall ownership of milch cows and buffaloes, 63.05% of households own less than 3 animals, these households constitute 33% of ownership of all milch animals. Another 22.3% of households own 3 to 4 animals, and together these households own 28% of all milch animals. The distribution of household type and animal ownership is extremely right-skewed as there are a negligible number of large owners and their percentage share in total animals also declines as household size increases.

In the case of households which report owning only milch cows, 94.68% of such households own less than 4 animals while these same households own 70.4% of milch cows. About 8.8% of milch cows are owned by households that have more than 50 animals.

In case of milch buffaloes, again 93.7% of sample households report having less than 5 animals. Again, these same households own 74.7% of all milch buffaloes in the province. In case of households which own both milch cows and buffaloes, the distribution is somewhat more normally distributed. Households which own 4 animals or less constitute 65% of all reporting households. 18.27% of households own 5 to 6 animals while 11.25% of households own 7 to 10 animals. Within these households, 75.95% of animals are owned by households with 10 animals or less.

Lodhran district:

In Lodhran district, 38.5% of respondent households report owning only milch cows, while another 27.5% report owning only milch buffaloes. The remaining (34%) of households report owning both milch cows and buffaloes.

An overall assessment of ownership of milch cows and buffaloes reveals that 65% of households own 2 or less animals while another 23.4% own 3 to 4 animals. Households with 2 or less animals own 33.2% of all milch animal stock while those with 3 to 4 animals own another 28.2%. As in the overall Punjab assessment, 95.77% of households report having 4 or less milch cows, and these households own 79% of all milch cows. A miniscule number of large owners (0.1%) own 6% of milch cows in the district. With regards to milch buffaloes, the district is distinct in that no household owns more than 20 animals. 96.7%


47

Crown Agents of households reporting owning 4 animals or less, and these households own 88.5% of milch buffaloes. The distribution of households which own both cows and buffaloes is slightly better distributed. While 73.6% of households report owning 4 or less animals, another 15.5% own 5 to 6 animals. Households with 4 or less animals own over 44.8% of both milch cows and buffaloes while those with 5 to 6 animals own 18.18% of both animals together, while those owning 7 to 10 animals own 14.18% of both milch animals together. This last trend suggests that families with larger stocks of animals treat this as an income generating activity and probably constitute the lower tier of milk producers (gawalas).

Muzaffargarh District:

In this district, 45.5% of households report owning only milch cows while another 16% report owning only milch buffaloes. 38.35% of households report owning both types of animals.

The ownership of milch cows and buffaloes in Muzaffargarh is highly skewed toward small stock holders. Of the overall ownership, 57.6% of households report owning 2 animals or less. Another 25% of households report owning 3 to 4 animals. The former household category (those with 1 to 2 animals) own 27.26% of all milch animals in the district, while the latter category owns 27.8% of milch animals. 79% of households report owning 1 to 2 milch cows only and own 56.8% of such animals. The situation is nearly the same in case of milch buffaloes, with 80% of households owning 1 to 2 animals and 57% of buffalo stock in the district. As before, households which own both types of animals have a less skewed distribution- for instance, households which own 7 to 10 animals (15.4% of households) reporting owning 24.46% of combined milch cow and buffalo stock.

Bahawalpur District:

30.76% of households report owning only milch cows in this district, while another 40.6 own only milch buffaloes. The remaining households, 28.72%, own both milch cows and buffaloes.

The ownership of milch cows and buffaloes in Bahawalpur is skewed toward small stock holders. Almost 70% of households which report owning either one or both type of animals, own 1 to 2 animals. Another 20.66% of households report owning 3 to 4 animals. Households which own 1 to 2 animals own 38.53% of all milch animals while those who own 3 to 4 animals own 27% of all milch cattle stock in the district. These general trends are reinforced by households which own either one of milch cows or buffaloes. Of households which own only milch cows, 85% own 1 to 2 animals only. Of households which own only milch buffaloes, 85.5% own 1 to 2 animals only. As in other districts, households which own both milch cows and buffaloes have a more equitable distribution of stock. 30.4% of households own 1 to 2 animals, while another 43.8% own 3 to 4 animals.

Bahawalnagar District:

18% of households report owning only milch cows in this district, while another 54.38% own only milch buffaloes. The remaining households, 27.6%, own both milch cows and buffaloes.

The overall ownership of milch cows and buffaloes in Bahawalnagar is skewed toward small stock holders. Almost 63% of households which report owning either one or both type of animals, own 1 to 2 animals. Another 22.18% of households report owning 3 to 4 animals. Households which own 1 to 2 animals own 28% of all milch animals while those who own 3 to 4 animals own 23.74% of all milch cattle stock in the district. These general trends are reinforced by households which own either one of milch cows or buffaloes. Of households which own only milch cows, 76.49% own 1 to 2 animals only. Of households which own only milch buffaloes, 77.78% own 1 to 2 animals only. As in other districts, households which own both milch cows and buffaloes have a more equitable distribution of stock. 25.29% of households own 1 to 2 animals, while another 39.5% own 3 to 4 animals.


48

Crown Agents 5.3 Use of Veterinary Centers and Artificial Insemination Centers

The Livestock Census also provides information regarding the number of households using either artificial insemination or veterinary services.

Table 23: Households reporting use of veterinary services


The use of artificial insemination is quite low in the province as a whole- 22.8% of entire population of cows and buffaloes have been artificially inseminated. Two of the PEOP districts- Lodhran and Muzaffargarh exceed the provincial average at 28.6% and 46% respectively. In Bahawalpur, only 20.76% of cows and buffaloes have reportedly been inseminated, while Bahawalnagar lags the furthest behind in this regard as only 12.56% of animals have been artificially inseminated.

With regards to veterinary services, in Lodhran, of the 82337 households reporting ownership of cattle, only 2.79% reported vaccinating their bullocks, cows and youngstock. In Muzaffargarh, of 254022 households reporting ownership of cattle, 10% reported vaccinating their bullocks, cows and youngstock. In Bahawalpur, of 143006 households reporting ownership of cattle, 8.89% reported vaccinating their bullocks, cows and youngstock. In Bahawalnagar, of 112289 households reporting ownership of cattle, 18.62% reported vaccinating their bullocks, cows and youngstock.

With regards to buffaloes, usage rates are much lower. In Lodhran, of 67423 households reporting ownership of buffaloes, 2.47% vaccinated their animals. In Muzaffargarh, of 167037 households reporting ownership of buffaloes, 1.86% vaccinated their animals. In Bahawalpur, of 155843 households reporting ownership of buffaloes, 1.53% vaccinated their animals. In Bahawalnagar, of 167699 households reporting ownership of buffaloes, 2.44% vaccinated their animals.

The Census also reports number of households who have „treated‟ sick animals but it is not clear whether they visited veterinary centers for this treatment.

The Census also reports number of households who have „treated‟ sick animals but it is not clear whether they visited veterinary centers for this treatment.

PUNJAB 1198622 22.81 7376380 1114191 9896181 898762

Lodhran 29275 28.61 190205 26364 127247 18651

Muzaffargarh 131372 46.03 596028 159963 326956 71748

Bahawalpur 42798 20.76 303453 31190 291850 31189

Bahawalnagar 24334 12.56 251000 21373 434029 24715

Number of Buffaloes

Total

Artificially Inseminated

Number

As

percentage

of total

number

5255810

Artificially Inseminated

As % total

number of

cows/buffal

Households

Reporting

cows/buffaloes

(3 years and

above) Number Total Number

As percentage

of total

number

Number of CowsHouseholds reporting

artificial insemination of

cows and buffaloes

15.11 9.08

102343

285386

206132

193714

13.86

26.84

10.28

14.66

21.94

10.69

8.52 5.69


49

Crown Agents

5.4 Milk Yields

The table below lists the average and total milk yields in the districts of interest in 2006.

Table 24: Milk yields in PEOP districts, by animal type

Compared to the provincial average milk yield for cows, both Bahawalpur and Bahawalnagar are performing well. On the other hand, Lodhran lags the furthest behind the provincial average for cows‟ milk. However, with regards to both buffaloes and goats, the average milk yield is close to or above the provincial average in all four districts.

Source: Milk Production Survey, 2006, Agricultural Census Organisation, Statistics Division

5.5 Access to Veterinary Centers

Improved access to animal health facilities has been designated as one of the intended programme outputs under PEOP. An end of programme target for this output is to have a fully functional animal health facility in each of the 4 target districts. A snapshot view of the existing state of veterinary facilities is provided by the Mouza Census which is also conducted by the Agricultural Census Organization. The Mouza Census reports statsitsics at the tehsil level, therefore, we are able to assess rather accurately the distance from a particular tehsil to the nearest vterienary centre or animal dispensary.

Lodhran District:

Lodhran district has 432 populated mouzas, which are served by 177 mobile veterinary dispensaries. The overall mean distance within the district to a government veterinary centre or dispensary is 9 km while that to a private facility is 8 km. The actual distance to a veterinary facility varies among the tehsils considerably.

Punjab

Lodhran

Muzzafargarh

Bahawalpur

Bahawalnagar 1.167

16972

33339

20398

22807

19789.352

50408.568

30066.652

26615.7698.201

6233315

78995

207230

180549

263762

48046392.02

553122.99

1706746.28

1609233.237

2163112.1627.33

4050048

95327

325079

165513

134174

25580103.17

445653.725

1881232.173

1402722.675

983495.42

Average yield of milk

per goat in-milk

Number of goats in-

milk

Production of milk

per day

Goats

Administrative Unit

6.316

4.675

5.787

8.475

7.708

7.002

8.236

8.913

1.356 809762 1098037.272

1.166

1.512

1.474

Average yield of milk per

cow in-milk Number of cows in-milk Production of milk per day

Cows

Average yield of milk per

buffalo in-milk

Number of buffaloes in-

milk

Production of milk per

day

Buffaloes


50

Crown Agents Table 25: Number of health facilities and distance from mouzas, Lodhran district

Source: Mouza Statistics, 2008, Agricultural Census Organisation, Statistics Division

Interestingly, Lodhran tehsil has the greatest mean distance to veterinary centres and dispensaries as well as to private facilities. The shortest mean distance is for Karor Pacca tehsil. Lodhran tehsil has the variation with its mouzas. The majority of mouzas in Lodhran Tehsil have either a government veterinary facility/dispensary or private facility located at a distance of 1 to 10 km. However, there is considerable variation within the tehsil as 15 mouzas have government veterinary centers/dispensaries at a distance of less than 1 km, while 11 mouzas have a private animal health facility within less than 1 km. However, on the other end of the spectrum, around 34 mouzas have a distance of between 11 and 25 km to the nearest government veterinary center/dispensary or private facility.

Dunya Pur tehsil has an overall mean distance of 9 km to a veterinary centre/dispensary and 8 km to private health facility. As with Lodhra tehsil, the largest number of mouzas have a distance of 1 to 10 km to the nearest animal health facility. The next largest number of mouzas have a distance of 11 to 25 km to the nearest health facility. 14 mouzas report a veterinary centre/dispensary within 1 km while 32 mouzas report a private facility in less than 1 km. The same trends persist in Karror Pacca tehsil, but this tehsil has the overall lowest mean distance to animal health center.

Lodhran District 432 177 9 8 42 70 268 268 107 87 15 7

Lodhran Tehsil 143 18 10 10 15 11 87 93 34 32 7 7

Dunya Pur Tehsil 187 94 9 8 14 32 119 123 46 32 8

Karor Pacca Tehsil 102 65 8 8 13 27 62 52 27 23

Overall Mean distance (km)

Veterinary

Centre/Disp

Less than 1

Private

Facility

Veterinary

Centre/Dis

Private

Facility

1 to 10

Mouzas by Distance (in kilometers) from the veterinary facility

Mobile

Veterinary

Dispensary

Veterinary

centre/Dispe

nsary

Private

Facility

Veterinary

Centre/Dis

Private

Facility

11 to 25

Veterinary

Centre/Dis

Private

Facility

26 to 50

Veterinary

Centre/Dis

Private

Facility

51 and above

Administrative Unit

Rural

Populated

Mouzas


51

Crown Agents Muzaffargarh District:

Table 26: Number of health facilities and distance from mouzas, Muzaffargarh district


On average, the distribution of animal health centers in Muzzafargarh follows the same trends as in Lodhran- most mouzas have health facilities located within 1 to 10 km, while a greater proportion of the remaining mouzas have government and private animal health facilities located at a distance of 11 to 25 km. Within the district, Muzaffargarh tehsil has the lowest overall mean distance to an animal health facility. Furthermore, 79% of populated mouzas in Muzaffargarh tehsil are located at a distance of 1 to 10 km from a government veterinary centre/dispensary while 76% are the same distance away from a private facility. Kot Addu tehsil has the largest number of mouzas located at a distance of 25 to 50 km from an animal health facility. The overall trends persist with Alipur and Jatoi tehsils, it is worth noting that Alipur tehsil has the most widely disbursed facilities as the overall mean distance is 10 km.

Muzzafargarh District 926 137 7 8 122 132 670 612 125 158 9 24

Muzaffargarh Tehsil 410 61 6 6 52 65 325 311 33 34

Kot Addu Tehsil 333 27 8 11 36 28 222 184 72 102 3 19

Alipur Tehsil 95 43 10 10 13 18 60 56 16 16 6 5

Jatoi Tehsil 88 6 5 6 21 21 63 61 6 6

Administrative Unit

Rural

Populated

Mouzas

Mobile

Veterinary

Dispensary

Overall Mean distance (km) Mouzas by Distance (in kilometers) from the veterinary facility

Veterinary

centre/Dispe

nsary

Private

Facility

Less than 1 1 to 10 11 to 25 26 to 50 51 and above

Veterinary

Centre/Disp

Private

Facility

Veterinary

Centre/Dis

Private

Facility

Veterinary

Centre/Dis

Private

Facility

Veterinary

Centre/Dis

Private

Facility

Veterinary

Centre/Dis

Private

Facility


52

Crown Agents Bahawalpur District:

Table 27: Number of health facilities and distance from mouzas, Bahawalpur district


Of the four districts, Bahawalpur district has the lowest overall mean distance (7 km) to a government owned veterinary centre/dispensary or private facility. The tehsil also has the best concentration of veterinary and private facilities as none of the mouzas are located at a distance of more than 25 km from a facility. Bahawalpur City has the least number of health facilities, the majority of which are located at a distance of 1 to 10 km, with the remaining located at a distance of less than 1 km. The other three tehsils follow the overall district trend- most mouzas located at a distance of 1 to 10 km from an animal health facility.

Bahawalnagar District:

Table 28: Number of health facilities and distance from mouzas, Bahawalnagar district

Bahawalpur District 687 159 7 7 123 151 452 417 112 119

Bahawalpur City Tehsil 34 9 5 6 12 17 21 15 1 2

Bahawalpur Saddar Tehsil 114 28 7 7 19 25 77 72 18 17

Hasilpur Tehsil 108 25 7 7 23 27 69 64 16 17

Ahmadpur East Tehsil 185 50 6 7 39 47 119 108 27 30

Yazman Tehsil 142 43 7 6 21 21 99 101 22 20

Khairpur Tamewali Tehsil 104 4 9 9 9 14 67 57 28 33

Administrative Unit

Rural

Populated

Mouzas

Mobile

Veterinary

Dispensary


Veterinary

centre/Dispe

nsary

Private

Facility


Veterinary

Centre/Disp

Private

Facility

Veterinary

Centre/Dis

Private

Facility

Veterinary

Centre/Dis

Private

Facility

Veterinary

Centre/Dis

Private

Facility

Veterinary

Centre/Dis

Private

Facility

Bahawalnagar District 1057 282 9 10 123 164 639 542 285 330 10 21

Bahawalnagar Tehsil 233 139 9 10 32 38 147 127 51 65 3 3

Minchinabad Tehsil 259 32 10 10 23 53 150 129 83 75 3 2

Chistian Tehsil 215 72 7 9 24 31 151 128 39 55 1 1

Haroonabad Tehsil 184 17 8 11 25 21 113 90 46 69 4

Fort Abbas Tehsil 166 22 11 13 19 21 78 68 66 66 3 11

Administrative Unit

Rural

Populated

Mouzas

Mobile

Veterinary

Dispensary


Veterinary

centre/Dispe

nsary

Private

Facility


Veterinary

Centre/Disp

Private

Facility

Veterinary

Centre/Dis

Private

Facility

Veterinary

Centre/Dis

Private

Facility

Veterinary

Centre/Dis

Private

Facility

Veterinary

Centre/Dis

Private

Facility


53

Crown Agents Source: Mouza Statistics, 2008, Agricultural Census Organisation, Statistics Division

Bahawalnagar district has the largest number of mobile veterinary dispensaries. Interestingly enough, it is also the only district where health facilities are widely disbursed- very few mouzas have facilities at a distance of less than 1 km. Fort Abbas tehsil has the least number of health facilities overall but Minchanabad has the largest number of mouzas with facilities located a distance of 11 to 25 km.

In conclusion, the following observations may be gleaned from the baseline statistics on veterinary and animal health facilities:

As per the programme logframe, all four districts have more than 1 animal health facility.

The main outstanding concern remains the distance to these facilities (which is considerable in the case of some tehsils) as well as the availability of mobile veterinary centers. Given that the costs of transportation are considerable for most poor families, efforts should be extended toward easing access to these facilities

The available data reveals nothing about the capacity or efficiency of these facilities. An extensive baseline survey would have to take stock of the service delivery capacity of existing centers and health facilities

.

.


54

Crown Agents

Section 6: Conclusions and Recommendations


55

Crown Agents 6. Conclusions and Recommendations

The analysis above has established that poverty incidence is high with rising inequality in the four districts of Southern Punjab included in the Punjab Economic Opportunities Programme, going as high as 55% +/- 3 percentage points. Moreover, the average real income in two of these districts, Lodhran and Muzaffargarh, did not change between 2003 and 2007 despite high overall economic growth in the country. For the poor households in these two districts, this period was especially hard as their average income as a group actually went down in real terms, even without including the poorest (unemployed poor). When the unemployed poor are included, the average real income among the poor households declined in all districts except Bahawalpur, where no significant change was observed.

While a large fraction of the poor households are clustered close to the poverty line, as shown by Figure 6, therefore making head-count ratios very sensitive to where the poverty line is drawn, the above findings show that the economic boom in the recent past may have passed by a sizable fraction of the population in the Programme area. Provided there is a lack of marketable skills or human capital in this population, a targeted government programme would be required for long-term and sustainable improvement in their livelihoods.

LFS 2007-08 indicates that a miniscule fraction of population in the Programme region (3%) received any kind of on- or off-the-job skills training. Even though systematic survey information on skills demand in these areas is missing, anecdotal evidence from talking to PVTC and TEVTA officials suggests that there is more demand for skills training than is currently being met given substantial over-subscription in their training courses and a general lack of vocational training schools in the private sector. An alternative indicator of this under-supply was also presented in this report: the fraction of eligible training-age beneficiary population that can be trained each year by the two main skills training providers, (given their current enrollment rates) is less than 3% in each of the four districts.

The report also looks at the occupational distribution in the four Programme districts and finds interesting patterns. Household surveys indicate the salience of the following occupations in the urban areas: government employment, private employment, self-employment and labour work; whereas in the rural areas agriculture and laborer categories together account for the majority of employed workforce with a smaller fraction employed in the first three categories. Whereas real incomes have increased in all of these occupations except self-employment between 2003 and 2007, the income differentials for the poor households in all of these occupations were worse compared to the non-poor (Figures 7 and 8). This result makes sense when one considers the fact that the poor are also likely to be less educated and less skilled than the non-poor on average and thus likely to serve in a lower income position in each of these occupational categories, e.g., poor are more likely to be peons or sweepers in the “government employee” category, with lower wage growth. The difference in income growth among poor and non-poor in self-employment and laborer categories is particularly relevant to an explanation revolving around different skills.

Hence the analysis suggests that a skills training programme, which circumvents the training supply constraints identified above, and is targeted towards the poor and the unemployed population could be an effective tool in sustained poverty alleviation and economic uplift in this region. That much is clear and is supported by the data. But there are several fundamental economic questions that could not be answered by the data above, such as what the demand for skills and constraints to acquiring them are, especially for the poor, and why the markets have not responded by providing cheap low-end skills training. This, and related questions, are essential to understanding the


56

Crown Agents dynamics of poverty, in informing the PEOP Programme design and in furthering its eventual success.

An attempt has been made to make the best use of available data bringing to fore some important information about poverty trends at the tehsil and district level, the real income growth of different segments of population, livestock population and trends thereof at the district level and skills levels, demands and supply in the four districts. This information will help in determining some benchmarks for reporting on the goals laid out in the log frame; however, it is important to highlight here that the data available is inadequate for tracking the log frame indicators of PEOP. This underscores our concern that the analysis presented in this report is constrained by multiple data limitations.

First, the use of MICS as the main source of household information will be inadequate for PEOP log-frame monitoring for the following reasons: MICS is a large province-level survey that takes place every four to five years and the next round may come too late to provide adequate benchmarks or to measure any intermediate program effects; the methodology of MICS for household welfare measurement, for instance, was not consistent between 2003 and 2007 and any such arbitrary changes can introduce errors in the analysis; MICS does not track households over time and is therefore not helpful for understanding the dynamic impacts of a program like PEOP at the household level as required by the log-frame indicators.

Second, even though this report does important ground work in identifying the necessary ingredients for a strong programme, the maximum impact of the skills training is dependent on precise targeting of the poor and marginalized groups who are most likely to benefit from the training. Such precise targeting can only take place through a purpose-built survey designed to identify potential beneficiary households. If the targeted beneficiary group for the Program consists of, say, all the poor households lacking vocational skills, then a poverty census is required which enumerates all households in the region on their poverty status as well as skills, education and employment etc. Such pre-program data collection to anchor the program roll out is common in large programs and is currently being conducted in the form of a poverty scorecard exercise for BISP.

Lastly, there is no existing data that directly provides information on the potential demand or supply side failures in skill acquisition. Understanding these failures is critical for developing effective solutions that make the best use of DFID and Government of Punjab funds. On the demand side, one needs to know what the constraints are to individuals, especially the poor, in acquiring skills. On the supply side, one needs to understand the capacity and needs of private skills training providers, and employers.. The extent to which skills are provided by such suppliers of training is a function of the demand for those skills in the formal and informal sector occupations, which can have considerable local variation and flavours. All of this information is not only vital for the design of a program like PEOP but also helps us understand more generally why individuals may not be productively engaging with the labor market and being effectively served by the public sector.

We believe that a well-designed program requires a lot of micro-level information and conclude by proposing that PEOP should invest in creating these program-specific data sources, including a comprehensive baseline household census and community/employer survey- depending upon the time frame either the surveys could be phased out functionally or geographically or else the surveys could be scaled down in terms of the sample size- to ensure its own success as well as that of the other programmes that may follow in future. This census would be designed to move beyond a simple enumeration by building in select questions that help us learn why people are


57

Crown Agents disengaging with the labor market and training opportunities in the first place. Knowledge of those factors will help in designing a range of interventions in the future.

On the livestock side, this baseline study is able to provide rudimentary information on the numbers of livestock, use of veterinary and artificial insemination services, and milk yields. The utility of this information in measuring progress against log frame indicators is limited on two accounts: firstly, this data is only available at the district level so no useful information can be gleaned from therein on targeting of livestock interventions. Secondly, the lack of auxiliary indicators on households covered in the Census implies that there is no way of identifying the characteristics of poor vs non-poor livestock owners. The data on ownership that is available broadly suggests that a large number of livestock owners in the PEOP districts are small holders- usually of less than 4 animals. Likewise, there appear to be animal health centers in each tensile of the 4 PEOP districts; however, no information is available on the state of these facilities or their utilization rate. The case for further in-depth studies and surveys may be made as ardently in favour of livestock indicators as it has been in the case of skills indicators above. Without some knowledge of the specific target communities, intra-district variations, and on-ground assessments of available support facilities, both finalisation of livestock side interventions and monitoring and evaluation of their impact would be premature.


58

Crown Agents

Appendix A: Baseline Indicators


59

Crown Agents

Indicator Baseline Summary Explanation

Data Source

Missing Information

Po

vert

y/in

com

e Number of People living below poverty line

Poverty Headcount ratio Bahawalpur= 55.07% Bahawalnagar= 51. 3% Lodhran= 50.4% Muzaffargarh= 51.75%

Proportion of population living below the

MICS 2003-04 & 2007-08

Rate of economic growth in the 4 selected districts

Real Growth in Household Income Bahawalpur = 19% Bahawalnagar= 16% Lodhran= 2% Muzaffargarh= 2%

This is the yearly real growth rate of income, adjusted for inflation

MICS 2003-04 & 2007-08

District level GDP estimates are not available

Incomes of the Targeted Population

Real Growth in Household Income of the Poor Bahawalpur= 0% Bahawalnagar= -9% Lodhran= -14% Muzaffargarh= -9%

This is the yearly real growth rate of income, adjusted for inflation

MICS 2003-04 & 2007-08


60

Crown Agents Indicator Baseline Summary Explanation Data

Source Missing Information

Po

vert

y/in

com

e Literacy rate of individuals Poor:

Bahawalpur=23% Bahawalnagar=25% Lodhran22% Muzaffargarh=22% Non-Poor: Bahawalpur=38% Bahawalnagar=40% Lodhran=33% Muzaffargarh=33%

Mean Age of individuals in targeted population

Poor: Bahawalpur=22 Bahawalnagar=22 Lodhran=22 Muzaffargarh=20 Non-Poor: Bahawalpur=25 Bahawalnagar=26 Lodhran=25 Muzaffargarh=24


61



Po

vert

y/in

com

e Literacy levels of poor Band I= 17%

Ban II=20% Band III=23% Band IV=26% Band V=33%

The numbers in opposite column correspond to poverty bands (poor households divided

according to income levels). Refer to Section 2

Male unemployment among the poor

Band I=8% Band II=9% Band III=7% Band IV=8% Band V=10%

Remittance receiving households among the poor

Band I=10% Band II=5% Band III=7% Band IV=10% Band V=15%


62

Crown Agents

Indicator Baseline Summary Explanation Data Source

Missing Information

Skill

s

Number of Skills trainees commencing Income generating activities

Proportion of skilled labor force Bahawalpur= 3% Bahawalnagar= 2% Lodhran= 1% Muzaffargarh=3%

This proportion of labor force who have received technical or professional training in last 8 years

Labor Force Survey 2007-08

Information on TEVTA graduates

Public Sector Training Capacity

TEVTA: Bahawalnagar=.61% Bahawalpur=2.26% Lodhran= 0.18% Muzaffargarh=0.26% PVTC: Bahawalnagar=0.58% Bahawalpur=0.42% Lodhran=0.96% Muzaffargarh=0.56%

Total enrollment capacity as proportion of unemployed population

Low End Skills Capacity (current enrolment numbers)

Bahawalpur= 1418, Bahawalnagar=1271, Lodhran=920, Muzaffargarh=1446

Low end courses are of duration less than or equal to 12 months


63

Crown Agents

Indicator Baseline Summary Explanation Data Source Missing Information

Skill

s

High End Skills Capacity (current enrolment numbers

Bahawalpur =2633, Bahawalnagar=312, Lodhran=42, Muzaffargarh=79

High end courses are of duration greater than 24 months

Most common Occupation Categories

Bahawalpur: Labor=34%, Agriculture=28%, Self Employed 12.8% Bahawalnagar: Labor=29%, Agriculture=31%, Private Employee=13% Lodhran: Labor=31%, Agriculture 33% Self Employed=12% Muzaffargarh: Labor 38%, Agriculture 24% Private Employee=14%

Percentage of Labor force Employed in these categories


64



Live

sto

ck

Number of people owning at least 1 animal for livestock farming

(Figures in opposite column report number of households having 1 to 2 animals)

Lodhran: Cattle: 48.07% Buffaloes: 52.4% Milch cows/buffaloes: 65.07% Muzaffargarh: Cattle: 38% Buffaloes: 48.9% Milch cows/buffaloes: 57.66% Bahawalpur: Cattle: 50.61% Buffaloes: 49.93% Milch cows/buffaloes: 69.55% Bahawalnagar: Cattle: 47.95% Buffaloes: 39.12% Milch cows/buffaloes: 63.06%

The Livestock Census survey is designed such that it drops observations on households that do not report owning a particular kind of animal.


65



Live

sto

ck

Milk yields amongst dairy farmers (per day production in liters)

Lodhran: Cows: 445653.725 Buffaloes: 553122.99 Goats: 19789.352 Muzaffargarh: Cows: 1881232.173 Buffaloes: 1706746.28 Goats: 50408.568 Bahawalpur: Cows: 1402722.675 Buffaloes: 1609233.237 Goats: 30066.652 Bahawalnagar: Cows: 983495.42 Buffaloes: 2163112.162 Goats: 26615.769


66



Live

sto

ck

Percentage of households reporting use of artificial insemination(cows and buffaloes)

Lodhran: 28.61% Muzaffargarh: 46.03% Bahawalpur: 20.76% Bahawalnagar: 12.56%

Overall mean distance (miles) to animal health facility

Lodhran: Veterinary Centre: 9 Dispensary: 8 Muzaffargarh: Veterinary Centre: 7 Dispensary: 8 Bahawalpur: Veterinary Centre: 7 Dispensary: 7 Bahawalnagar: Veterinary Centre: 9 Dispensary: 10


67

Crown Agents

Appendix B: Poverty Estimations


68

Crown Agents Measurement of poverty is the most important step of any analysis targeting the poor. Poverty can be estimated using either expenditure or income of the family, however most of the literature supports using expenditure to establish a poverty line and measure the poverty. However in this analysis we were faced with a dilemma because MICS 2007-08 did not report expenditure data for the households interviewed. Therefore we took a longer route to calculate poverty rather than simply comparing income with poverty line. In this report we first estimated poverty for 2003-04 using the

expenditure method as favored by Deaton and Zaidi14

. Once we had the poverty

estimates, we used the income of those households who were on the poverty line, adjusted it for inflation and used as poverty line for 2007-08.

We used the national poverty line which was estimated to be Rs. 723.415

per capita

per month for 2000-01. The national line uses the calorific requirement approach and is

based on calorie intake requirement of 2350 calories per adult equivalent per day. We adjusted

the national poverty line for inflation to get Rs. 807.53 per capita per month, in terms of

year 2000 rupees. After measuring the income of households on poverty line and adjusting for inflation the poverty line for 2007-08 came out to be Rs. 957.3 per capita per month.

In estimating the expenditure based poverty line for 2003, we construct consumption aggregate for each household that includes food items, non-food items, expenditure on house maintenance and rents. We excluded payment of taxes, loans and expenditures of marriage etc. As not all households report rents therefore we imputed house rents using the hedonic rents methodology described by Cheema(2008). “...we regress house rent of rented households on a number of house characteristics such as number of rooms, facilities provided in the house (gas, electricity, water, telephone) etc, and then using the parameters developed by our model impute rent for the rest of the population. Aggregating over the above mentioned items gives us an estimate of

the total monthly expenditure for each household”16

.

The aggregate expenditure by each household cannot be used directly in measurement of poverty. Deaton & Zaidi (2002) argue that there are spatial price differences across geographical locations therefore such differences need to be taken into account. They propose two kinds of indices to deflate expenditure in order to make them comparable across regions. In this report we have used Paache price index to measure spatial price differences and deflate the household aggregates. In line with the methodology of Cheema (2008) we have used cluster as the unit of analysis for constructing Paache price indices. “Given this, the real value of total monthly

expenditure of household h is:

14 Deaton, A. & Zaidi, S., 2002, Guidelines for Constructing Consumption Aggregates for

Welfare Analysis, World Bank Publications.

15 World Bank, 2002, “Pakistan Poverty Assessment, Poverty in Pakistan: Vulnerabilities,

Social Gaps, and Rural Dynamics,” Report No. 24296-PAK, South Asia Region. Washington DC.

16 Cheema, A. Khalid, L and Patnam, M.(2008) The Geography of Poverty: Evidence from

Punjab, The Lahore Journal of Economic. pp 163-188.


69

Crown Agents

and the Paasche price index, is given by:

where is the share of cluster c’ s budget devoted to food item k; is the Punjab level

median price for food item k, and is the cluster level median price for food item k. These

cluster level price indices are then normalized by the average price indices”17

.

As members of households have differing needs on the basis of their age and gender, e.g. the needs and requirements of children are different from adults; therefore we construct effective household size for each household by using the following equivalence scale.

Table 1: Equivalence Scale18

Age Bracket Energy Per Person Daily Requirement

Children

< 1 1010 0.4298

1-4 1304 0.5549

5-9 1768 0.7523

Males

10-14 2,816 1.1983

15-19 3,087 1.3136

20-39 2,760 1.1745

40-49 2,640 1.1234

50-59 2,460 1.0468

60 or more 2,146 0.9132

17 Cheema, A. Khalid, L and Patnam, M.(2008) The Geography of Poverty: Evidence from

Punjab, The Lahore Journal of Economic. pp 163-188.

18 Ibid.


70

Crown Agents Females

10-14 2464 1.0485

15-19 2332 0.9881

20-39 2080 0.8851

40-49 1976 0.8409

50-59 1872 0.7966

60 or more 1632 0.6945

We calculate the expenditure per month per adult equivalent using the effective household size and compare it with poverty line to determine the poverty status of a household. The sampling weights are then used to obtain district-level poverty rate. This gives us poverty headcount ratio for 2003-04. Then we use the monthly income (in per capita adult equivalent terms) of those sitting on the poverty line in 2003-04 to obtain an “income poverty line”, inflate it with the CPI inflation rate and compare with the reported monthly income (in per capita adult equivalent terms) to get poverty rate

for 2007-08.19

19 This last step was necessitated by the fact that the latter round of survey, MICS 2007-

08, did not include information on household consumption expenditure.


71

Crown Agents

Appendix C: Enrolment data of high and low skill training by district


72

Crown Agents Low end and High end Skills Training Capacity

TEVTA Bahawalpur

Bahawalpur Bahawalnagar Lodhran Muzaffargarh

High End Skills 2633 312 42 79

Low End Skills 1419 1271 920 1446

Trade Name Duration

(In

Months)

Boys Girls Co-Ed Total

Diploma in Commerce (Diploma 2 Years) 24 0 0 545 545

Civil (DAE) 36 470 0 0 470

B.Com(Degree) 24 0 0 467 467

Mechanical (DAE) 36 462 0 0 462

Electrical (DAE) 36 460 0 0 460


Electronics Application (Radio & TV)(G-II) 36 283 0 0 283

Auto and Farm (DAE) 36 254 0 0 254


Computer Information Technology (DAE) 36 151 0 0 151

Dress Designing & Making (DAE) 36 0 136 0 136

Telecom(DAE) 36 115 0 0 115

M.Com(Master Degree) 24 0 0 98 98

Certificate Vocational Girls (1 Year Certificate 12 0 98 0 98

Mechanical (B.Tech Pass) 24 80 0 0 80

Certificate in Computer Applications 3 0 75 0 75

Mechanical (B.Tech Hons) 24 60 0 0 60

Wireman 6 60 0 0 60

Draftsman Civil(G-II) 24 55 0 0 55

Electronics (B.Tech Pass) 24 55 0 0 55

Auto & Diesel(B.Tech Pass) 24 49 0 0 49

Electrician(G-II) 24 46 0 0 46


Civil (B.Tech Pass) 24 40 0 0 40

Quantity Surveyor 6 40 0 0 40

Diploma in Vocational Girls ( Diploma 2 Years 24 0 37 0 37

Electronics Application (Radio & TV) 24 37 0 0 37

Welder(G-II) 24 36 0 0 36

Draftsman Mechanical(G-II) 24 33 0 0 33

Electrician(G-III) 12 33 0 0 33

Dress Designing & Making (G-III) 12 0 32 0 32

B.Com(Degree) 24 0 31 0 31

Beautician (G-III) 12 0 30 0 30

Machinist(G-II) 24 28 0 0 28



73

Crown Agents

TEVTA Bahawalnagar

C0-

Ed

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

Trade Name Duration (In

Months)

Boys Girls Total

Diploma in Commerce (Diploma 2 Years) 24 1,114 0 1,114

Certificate in Computer Applications 6 461 0 461

B.Com(Degree) 24 262 0 262

Certificate Vocational Girls (1 Year Certificate 12 0 148 148

Diploma in Vocational Girls ( Diploma 2 Years 24 0 97 97

M.Com(Master Degree) 24 50 0 50

Diploma in Commerce (Diploma 2 Years) 24 0 48 48

Electrician(G-II) 24 41 0 41

Auto and Farm(G-II) 24 39 0 39

Fitter General(G-II) 24 36 0 36

Heating Ventilation Air Conditioning (HVACR) 24 31 0 31

Draftsman Civil(G-II) 24 30 0 30

Auto Cad 6 25 0 25

Heating Ventilation & Air Conditioning 6 25 0 25

Wireman 6 21 0 21

Welder(G-II) 18 20 0 20

Beautician 3 0 19 19

Electronics Application (Radio & TV) 24 18 0 18

Welder 6 15 0 15

Auto & Farm Machinery 6 5 0 5

Wood Work 6 3 0 3

Total: 2,206 312 2,518

Auto and Farm 6 5 0 5

Turner 6 5 0 5


74

Crown Agents TEVTA Muzaffargarh

TEVTA Lodhran

Source: For all tables, TEVTA website

Total

422

235

146

79

66

40

32

25

17

16

16

15

15

15

15

15

15

10

10

8

5

2

1,219

Trade Name Duration (In

Months)

Boys Girls Co-Ed



Certificate Vocational Girls (1 Year Certificate 12 0 146 0

Diploma in Vocational Girls ( Diploma 2 Years 24 0 79 0

B.Com(Degree) 24 66 0 0

B.Com(Degree) 24 0 0 40

Welder 6 32 0 0

Beautician 3 0 25 0

Wireman 6 17 0 0

Auto Mechanic(G-III) 12 16 0 0

Electrician(G-III) 12 16 0 0

Machinist(G-III) 12 15 0 0

Certificate in Computer Applications 6 15 0 0

Domestic Tailoring 6 0 15 0

Industrial Electrician 6 15 0 0

Machine Embroidery 6 0 15 0

Hand Embroidery 3 0 15 0

Electronics Application (Radio & TV)(G-III) 12 10 0 0

Auto and Farm 6 10 0 0

Refrigeration & Air Conditioning(G-III) 12 8 0 0

Total: 649 295 275

Turner 6 5 0 0

Carpenter 6 2 0 0

Trade Name Duration

(In

Months)

Boys Girls Co-Ed Total


Diploma in Vocational Girls ( Diploma 2 Year 24 0 42 0 42

Welder 6 24 0 0 24

B.Com(Degree) 24 22 0 0 22

Wireman 6 21 0 0 21



Electrician 6 15 0 0 15

Plumber 6 15 0 0 15

Tailoring 6 0 15 0 15

Auto Mechanic(Petrol) 6 4 0 0 4

Beautician 3 0 4 0 4

Diploma in Vocational Girls (Additional) (Diplo 12 0 3 0 3

Turner 6 1 0 0 1

Domestic Tailoring 3 0 1 0 1

Total: 426 80 0 506


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Crown Agents

Appendix D: Proportional Enrolment of PVTC by District


76

Crown Agents

77

Crown Agents

Enrolment in PVTC courses, Bahawalpur

Tehsil Name of trades/Course Capacity

Male Female Male Female Male Female

Repair & Maintenance of Electrical Appliances 28 26 - - - 26 -

Computer Hardware & Nework Assistant 28 27 - - - 27 -

Computer Application & DataBase Management 56 60 - 50 - 110 -

Mobile Phone Repairing 28 26 - 22 - 48 -

Clinical Assistant 28 46 12 24 - 70 12

Dress Making 28 - 27 - - - 27

Beautician 28 - 28 - 26 - 54

Embroidery 28 - 25 - - - 25

Computer Operator/Office Assistant 28 - 30 - 28 - 58

Computer Application for Businsess 28 - 27 - - - 27

Dress Making 28 - 25 22 - 47

Beautician 28 - 19 - 19 - 38

Repair & Maintenance of Electrical Appliances 28 26 - 23 - 49 -

Embroidery 28 - 24 - 20 - 44

Computer Operator/Office Assistant 28 45 - 27 - 72 -

Dress Making 28 - 27 - - - 27

Beautician 28 - 28 - 26 - 54

Repair & Maintenance of Electrical Appliances 28 27 - - - 27 -

Computer Application for Businsess 28 18 8 - - 18 8

Computer Operator/Office Assistant 28 35 21 28 26 63 47

On-roll strength OJT Total

Bah

aw

alp

ur-

Male

Bah

aw

alp

ur-

Fem

ale

Yazm

an

Ah

med

Pu

r E

ast

79

Crown Agents Enrolment in PVTC courses, Muzaffargarh

Enrolment in PVTC courses, Lodhran


On-roll

strength OJT Total


Dress Making 28 - 40 - - - 40

Repair & Maintenance Electrical Appliances 28 25 - - - 25 -

Embroidery 28 - 39 - - - 39

Computer Application & Data Base Management 28 20 0 - - 20 -

Computer Application & Data Base Management 28 - 20 - - - 20

Repair & Maintenance Electrical Appliances 28 26 0 - - 26 -

Dress Making 28 - 27 - - - 27

Embroidery 28 - 28 - 24 - 52

Clinical Assistant 28 57 - 26 - 83 -

Dress Making 28 - 28 - - - 28

Computer Application & Data Base Management 28 28 27 - - 28 27


Embroidery 28 - 26 - - - 26

Repair & Maintenance Electrical Appliances 28 29 - 25 - 54 -

Motorcycle Mechanic 28 27 - - - 27 -

Dress Making 28 - 27 - - - 27

Embroidery 28 - 27 - - - 27

Dress Making 28 - 23 - - - 23


Computer Application & Data Base Management 28 27 28 - - 27 28

Embroidery 28 - 29 - 24 - 53

Ran

g P

ur

Ko

t A

dd

uM

uza

ffar

Gar

hM

uza

ffar

gar

hJa

toi


On-roll

strength OJT Total


Clinical Assistant 28 41 10 18 4 59 14

Beautician 28 - 22 - 23 - 45

Dress Making 28 - 28 - - - 28

Computer Hardware Repair & Network Assistant 28 26 - - - 26 -


Textile Weaving 28 20 - - - 20 -

Dress Making (Gogran Camp) 28 - 52 - - - 52

Clinical Assistant 28 46 6 18 3 64 9

Dress Making 28 - 52 - - - 52

Computer Application & Databse Management 28 28 - - - 28 -


Computer Operator / Office Assistant 28 16 12 37 16 53 28

Dress Making 28 - 50 - - - 50

Embroidery 28 - 43 - - - 43


Auto Mechanic 28 24 - - - 24 -

Dress Making 28 - 28 - 29 - 57

Computer Application & Databse Management 28 28 28 29 20 57 48


Refrigeration & Air Conditioning 28 25 - - - 25 -

Motorcycle Mechanic 28 27 - - - 27 -

Dress Making 28 - 55 - - - 55

Computer Application & Databse Management 28 28 26 - - 28 26


Refrigeration & Air Conditioning 28 28 - - - 28 -

Lodhran

Dunyapur

Dunyapur

Dunyapur

Kehror

Pakka

80

Crown Agents

Enrolment in PVTC courses, Bahawalnagar

81

Crown Agents

Appendix E: Proportional Employment by Category

82

Crown Agents

Occupation Percent

Market oriented skilled agri or fishery 30.36

Subsistence agri. And fishery workers 18.13

Agri. Fishery related labourer 11.51

Other craft & related trade worker 6.93

General manager 6.25

labourer in mining construction manuf & 6.15

Sale and service elementary occupation 5.99

Metal Machinery and related trade worke 2.24

Driver and mobile plant operator 2.24

Extraction & building trade worker 2.19

Personal or protective services workers (1.3%) 1.35

teaching associate professional (1.3%) 1.3

Precision handicraft printing related w (0.8%) 0.83

Other professional 0.73

Office clerk 0.68

Model or sale person or demonstrators 0.68

Other associate professional 0.63

Life & health sc. Associate prof. 0.57

Machine operator & assemblers 0.36

Physical or engineering or science asso 0.26

Teaching professional 0.21

Corporate manager 0.16

Legislator or senior officer 0.1

Customer service clerk 0.1

Physical or engineering or science pro 0.05

83

Crown Agents Table 1: Average Income of all households at District level


Year 2003 (000 Rs) 4.01 3.33 6.91 6.64

Year 2007 (000 Rs) 10.25 9.63 10.6 10.1

Nominal Growth (Yearly) 26%* 30%* 11% 11%

Real 2007(000 Rs) 7.48 7.03 7.74 7.37

Growth Four year 0.87 1.11 0.12 0.11

Real Growth-yearly 17%* 21%* 3% 3%

Table 2: Average income of poor households at District level


Year 2003(000 Rs) 4.51 2.84 5.8 4.42

Year 2007(000 Rs) 4.33 4.11 4.44 4.27

Nominal Growth -1%* 10% -6%* -1%*

Real 2007(000 Rs) 3.16 3.00 3.24 3.12

Growth Four year -0.30 0.06 -0.44 -0.29

Real Growth-yearly -9%* 1% -14%* -8%*

Table 3: Average Income of the employed

Bahawalnagar Bahawalpur Lodhran Muzaffargar

Year 2003(000 Rs) 2.72 2.24 2.66 3.24

Year 2007(000 Rs) 4.88 4.6 4.32 5.58

Nominal Growth 16%* 20%* 13% 15%*

Real 2007(000 Rs) 3.56 3.36 3.15 4.07

Growth Four year 0.31 0.50 0.19 0.26

Real Growth-yearly 7%* 11%* 4% 6%*

84

Crown Agents Table 4: Average Income of the employed poor

Bahawalnagar Bahawalpur Lodhran Muzaffargar

Year 2003 (000 Rs) 1.92 1.49 1.56 2.16

Year 2007 (000 Rs) 2.41 1.99 1.91 2.52

Nominal Growth (Yearly) 6% 8% 5%* 4%*

Real 2007 (000 Rs) 1.76 1.45 1.39 1.84

Growth Four year -0.08 -0.03 -0.11 -0.15

Real Growth-yearly -2% -1% -3%* -4%*

Figure 1: Growth rates by income quintile

Figure 2: Growth rates of income by job category

Figure 3: Growth rates of income by job category and poverty status

-6%

-4%

-2%

0%

2%

4%

6%

8%

10%

12%

I II III IV V

Bahawalnagar

Bahawalpur

Lodran

Muzafargar

-30%

-20%

-10%

0%

10%

20%

30%

40%

50%

85

Crown Agents

Crown Agents

St Nicholas House

St Nicholas Road

Sutton

Surrey

SM1 1EL

United Kingdom

T: +44 (0)20 8643 3311

F: +44 (0)20 8643 8232

e-mail [email protected]

www.crownagents.com

Documents

Baseline Indicators Report v0.7[1]