Pakistan Rural Household Panel Survey (PRHPS) 2013

Data Paper: A User’s Guide to Data from Round 2 of the Rural Household Panel Survey

March, 2016

International Food Policy Research Institute (IFPRI) Innovative Development Strategies (IDS)

Preferred citation: Data paper: IFPRI/IDS (International Food Policy Research Institute/Innovative Development Strategies). 2016. Pakistan Rural Household Panel Survey (PRHPS) 2013, Round 2. Washington, DC/, Pakistan: IFPRI/IDS. http://ebrary.ifpri.org/utils/getfile/collection/p15738coll2/id/130264/filename/130475.pdf Dataset: International Food Policy Research Institute (IFPRI); Innovative Development Strategies (IDS). 2016. Pakistan Rural Household Panel Survey (PRHPS) 2013, Round 2. Washington, DC: International Food Policy Research Institute (IFPRI) [datasets]. http://dx.doi.org/10.7910/DVN/LT631P

INTERNATIONAL FOOD POLICY RESEARCH INSTITUTE The International Food Policy Research Institute (IFPRI), established in 1975, provides evidence-based policy solutions to sustainably end hunger and malnutrition and reduce poverty. The Institute conducts research, communicates results, optimizes partnerships, and builds capacity to ensure sustainable food production, promote healthy food systems, improve markets and trade, transform agriculture, build resilience, and strengthen institutions and governance. Gender is considered in all of the Institute’s work. IFPRI collaborates with partners around the world, including development implementers, public institutions, the private sector, and farmers’ organizations, to ensure that local, national, regional, and global food policies are based on evidence. IFPRI is a member of the CGIAR Consortium.

INNOVATIVE DEVELOPMENT STRATEGIES Established in 2002, Innovative Development Strategies (IDS) is a private sector consulting organization which carries out diagnostic and evaluative exercises. Its research and outreach is devoted to the identification and analysis of economic, social, cultural, political, business, and institutional problems connected most particularly to economic development and poverty alleviation. IDS services reflect the spectrum of support in all areas of economic development, poverty reduction, social sector to public sector reforms and governance, and from agriculture and rural development to industrial development and trade. In all these areas IDS offers support in conducting research, sector capacity building and development of Monitoring and Evaluation capability at all levels. IDS has vast experience in developing and delivering large and complex projects with a proven track record of success. IDS supports donors, NGOs, and government bodies working towards economic development and poverty reduction in Pakistan.

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1. Introduction

Responding to a request from the , the Pakistan Strategy Support Program (PSSP) was launched in July 2011. This program is a flexible country-led and country-wide policy analysis and capacity strengthening program, which provides analytical support on a range of economic policies affecting agricultural growth and food security in the country. The core purpose of the program is to contribute to pro-poor economic growth and enhanced food security through strengthened national capacity for designing and implementing evidence-based policy reforms.

PSSP’s four primary research priorities are as follows: a. Agricultural production and productivity b. Water management and irrigation c. Macroeconomics, markets and trade d. Poverty reduction (Income dynamics) and job creation (social safety nets)

The Pakistan Rural Household Panel Survey (PRHPS) 2013, Round 2 is the second round of the PRHPS; Round 1 was conducted in 2012. This survey aims to provide a quantitative basis to identify and address urgent economic development and policy priorities in Pakistan. Many modules and questions are consistent with the prior round. PRHPS Round 2 covers 2002 of the 2090 households surveyed during Round 1 in 76 primary sampling units in the rural areas of three provinces namely: (i) Punjab; (ii) ; and (iii) (KPK). Information on 88 households could not be collected because they refused to respond, migrated or were not available. The sample is representative of the rural areas of Punjab and Sindh provinces, and of the rural areas in 11 of the districts in KPK province (13 districts of KPK were excluded from the sampling frame due to safety concerns). The survey collected information on a large number of topics including sources of income, nature of employment, consumption patterns, time use, assets and savings, loans and credit, education, migration, economic shocks, governance, and participation in social safety nets. Four survey instruments were developed to collect this information. These included two household questionnaires (designed to collect individual- and household-level information from a main male and a main female respondent who were interviewed separately), a community questionnaire, and a price questionnaire.

2. Sampling Methodology

The second round of the Pakistan Rural Household Panel Survey (PRHPS) was conducted in 76 villages in Punjab, Sindh and Khyber-Pakhtunkhwa (KPK) in April-May 2013. The sampling frame was based on the . Household and population data were available for 1998 at the national, provincial, district, tehsil, union council and mouza1 (revenue village) level. The population and number of households were projected to 2012 for each of these levels using district and tehsil-level population growth rates.

All enumeration blocks classified as urban in the 1998 Census were removed from the sampling frame, as this is a rural household survey. All enumeration blocks with projected population greater than 25,000 in 2013 were also considered urban and removed from the sampling frame. The sample excludes rural areas in Balochistan and the Federally Administered Tribal Areas because they were considered unsafe for

1 A mouza is an administrative unit based on land revenue records and may correspond to a specific land with one or more settlements.

enumeration. Additionally, 13 districts of KPK were excluded from the sampling frame due to safety concerns. The remaining 11 districts of KPK were part of the sampling frame.

The multistage stratified sampling technique was used to select the sample. We first used the proportion of rural households in each province to determine the number of districts that would be chosen from that province. A total of 19 districts were selected from within the three provinces; 12 from Punjab, 5 from Sindh and 2 from KPK. We then used Probability Proportionate to Size (PPS) to select districts from each province. PPS ensures that within a province, districts with more rural households have a greater probability of being selected in the sample.

For each province, the total number of households was calculated, and then the districts were arranged in a random order. The sampling interval was calculated by dividing the total number of households in the province by the number of districts that were chosen from the province. For example, for Punjab the sampling interval is 10,143,181/ 12 = 845,265.

The next step was to generate a random start ‘r’, which was a random number between zero and the sampling interval. The cumulative number of households was calculated in each province across districts. The district that contained ‘r’ within the range of its cumulative number of households was selected. The district that contained the sampling interval plus ‘r’ within the range of its cumulative number of households was selected as the second district. The nth district selected contained the sampling interval plus n-1 multiplied by ‘r’. The process was repeated until the required number of districts was chosen from within the province.

Within each district, 4 mouzas were chosen using an equal probability systematic selection. In other words, mouzas with smaller populations had the same probability of being selected as highly populated ones. Using PPS at this stage would have meant that each household had the same probability of being in the sample. However, that would bias our sample towards more populous mouzas, and possibly ignore the smallest mouzas. Since our survey aims to understand the dynamics of different kinds of villages in rural Pakistan, it is imperative to include mouzas of different sizes.

The mouzas were arranged in a random order, and were assigned a serial number. The sampling interval was calculated by dividing the total mouzas in the district by 4 (the number of mouzas chosen from each district). A random start ‘r’ was generated, which was a random number between zero and the sampling interval. The mouza with the serial number ‘r’ was selected as the first mouza. The mouza with serial number sampling interval plus ‘r’ was selected as the second mouza. The third mouza had serial number sampling interval plus two ‘r’, while the fourth mouza had serial number sampling interval plus three ‘r’.

For each mouza in the sample, the enumeration team conducted reconnaissance and created a map. All mouzas were divided into enumeration blocks. Each enumeration blocks were of the same size, containing 200 or fewer households. If there were fewer than 200 households in a mouza, the entire mouza was considered a single enumeration block. In each mouza, one enumeration block was randomly selected for enumeration.

A complete household listing was conducted of the enumeration block that was selected. Twenty-eight households were then randomly selected from this list using an equal probability systematic selection. The listing form gave every household a serial number. The sampling interval was calculated by dividing the total number of households in the enumeration block by 28 (the number of households chosen in each mouza). A random start ‘r’ was generated, which was a random number between zero and the sampling interval. The household with serial number ‘r’ was the first household selected for enumeration. The household with the serial number sampling interval plus ‘r’ was the next household selected for enumeration. The nth household selected for enumeration had a serial number of the sampling interval

plus n-1 multiplied by ‘r’. The process was continued until 28 households were chosen in each mouza. There was no replacement for households that refused to participate in the survey.

Table 1: Pakistan Rural Household Panel Survey Sample Province Number of Number of Mouza Number of Households Total Number of Districts per District per Mouza Households in Province Punjab 12 4 28 1309 Sindh 5 4 28 557 KPK 2 4 28 224 Total 19 76 2090 2090

The twelve districts surveyed in Punjab were Kasur, Bhakkar, Khanewal, Attock, Vehari, Jhang, Dera Ghazi Khan, Bahawalnagar, Rahim Yaar Khan, , and Sargodha. The five districts surveyed in Sindh were Thatta, Dadu, Sanghar, Jaccobabad and Hyderabad, while the two districts surveyed in KPK were Mansehra and Nowshera.

The 2nd round of PRHPS covers 2002 of the 2090 households in Round 1. Information on 2002 households was either collected from males or females or both. Information on 88 households could not be collected because they either refused to respond, the households migrated or there was no respondent available to respond.

3. Overview of Analysis

The following is an overview of analysis completed using the PRHPS Round 2 data. The household survey collected information on a large number of topics, such as education, nature of employment, sources of income, time use, consumption patterns, economic shocks, women position in decision making and participation in social safety nets.

Pakistan’s population is relatively young. Data indicates that the rural population of Pakistan is relatively young. The average age of the rural population is 24. Over one quarter of the population is aged 9 years or younger, and 61 percent is under the age of 25. The current demographics, if combined with a sustained decline in fertility rates, present a substantial window of opportunity for reaping demographic dividends in the coming decades. However, this will require investments in quality schooling and human capital. Job creation will also be necessary to ensure that the young rural population has economic opportunities.

The majority of Pakistan’s population is illiterate. 50 percent of population, 10 years and older can read and write and the literacy rate2 is 60 percent for males and 35 percent for females. There is considerable geographic variation in literacy rates too. While the literacy rate in KPK is about 59 percent, it is 51 percent in Punjab and only 33 percent in Sindh. The data also show that literacy is considerably higher in younger people. Only 55 percent individuals have ever attended school. About 33 percent individuals were able to complete primary. A little over 11 percent individuals completed matric (10th grade). The proportion of individuals who have completed more than 10 years of education including professional education is only 5 percent.

School enrollment rate significantly differs across gender and provinces. The data show that the enrollment rate among school going children aged 5 to 14 years is 62 percent. However, this aggregate number masks significant variation across gender and province. While school enrollment is 70 percent for boys, it is only 54 percent for girls. It also varies from 86 percent in KPK to 71 percent in Punjab and only 38 percent in Sindh. We also observe that girls have a higher dropout rate than boys. While poverty

2 Literacy rate is defined here using the standard definition, i.e. a person can read and write

is cited as the main reason for non-enrollment, many respondents expressed security concerns about sending their daughters to school. Pakistan’s young rural population, which makes up a significant proportion of total population, presents opportunities as well as challenges for the policymakers. In the coming years, it will be crucial for policymakers to invest in education and provide people with necessary skills and appropriate employment opportunities.

Rural households in Pakistan earn their livelihood from a wide range of activities. A little over 41 percent of our adult sample, above the age of 16, participated in agricultural activities either on their own farms or for paid farming. About 17 percent of women and 24 percent of men, aged 16 and above, participated in one or more aspects of land cultivation. Similarly, 42 percent of adults, aged 16 and above, were engaged in livestock activities. Interestingly, participation rate among men and women is very close in livestock activities. A little under 21 percent of women and a little over 21 percent men, above the age of 16, engaged in own or paid livestock activities in the past year. Overall, almost 69 percent households in the sample were engaged in some form of agricultural activity and the almost 70 percent were engaged in activities relating to livestock, while almost 19 percent households engaged in neither agricultural production nor livestock activities. About 5 percent of the adult population own a business and 12 percent of the adult population earns its livelihood from non-agricultural waged employment. However, in terms of households, 17 percent of the households relied on non-agriculture enterprise and 49 percent households had at least one member who works in non-agriculture wage employment. Land ownership is considerably greater in higher per capita expenditure quintiles, while households in lower expenditure quintiles rely heavily on agricultural labor for earning their livelihood. Among men, retail shops are the most common enterprise across the three provinces, while tailoring and handicraft production is a popular enterprise among women.

Labor force participation rate among females income generating activities is low. Overall participation rate among men and women aged 16 and above in income-generating activities such as business enterprise, non-agriculture labor, paid farming, and pair livestock maintenance is 33 percent. This number is relatively higher for males (46 percent) compared to females (19 percent). Female participation in agricultural labor (paid or unpaid) is high in Punjab and Sindh; 37 percent of women in Punjab, 35 percent in Sindh. And only 10 percent in KPK, are engaged in agricultural production. In livestock activities, female labor force participation rate is comparable across the three provinces. 44 percent women in Punjab, 36 percent in Sindh, and 37 percent women in KPK are engaged in paid or unpaid livestock activities. However, we find extremely low levels of female participation in non-agricultural activities. Less than 5 percent of females aged 16 and above had worked as non-agricultural employee (including business enterprise) in the past year.

Households have extremely low access to sanitation facilities. About 35 percent of households do not have access to any kind of toilet while 54 percent of the households use a toilet facility with plumbing. The remaining 11 percent use dry pit latrines. We also find that 34 percent of the houses do not have a drainage system, while only 8 percent of the houses have a covered drainage system in place. Rural households in three provinces have inadequate garbage disposal facilities; 40 percent of households report that they do not throw garbage in a fixed location but wherever convenient.

An increase in food prices is the most common negative shock for rural households. 67 percent households experienced an increase in food prices in the past five years, whereas, the most recurrent shock between 2012 and 2013, after an increase in prices, is medical expenses due to illnesses or injury. We also find that the greatest value of loss to rural households, in the past year, has been due to floods.

Households used various coping strategies to deal with negative shocks, such as cutting back on non- essential items, working more, and substituting to cheaper goods. However, 37 percent of the households who faced shocks in the past five years did not have any coping strategies to deal with one or more

negative shocks they experienced. We also find that cutting back on non-essential items is the most common strategy to deal with shocks due to price hikes; 57 percent of the households that experienced a price hike cut back in non-essential items while 13 percent did not have any coping strategy. A greater percentage of households (20 percent) did not resort to any coping strategy in case of higher medical expenses due to illnesses.

Most of the loans were taken from friends and relatives. Rural household borrow from diverse sources, such as commercial banks, public sector financial institutions, as well as from informal sources such as, family members and friends. Over 60 percent of the rural households who took loans in the last year borrowed from friends and relatives, while a little over 15 percent borrowed from financial institutions. However, we find that formal credit is even less common in KPK and Sindh; only about 5 percent of the loans were taken from financial institutions. For the households who paid an interest rate for loans, an average rate of 19 percent was paid for loans taken from banks while an average interest rate of 22 percent was paid to informal lenders, such as, money lenders, shopkeepers and aarthi. Our findings also suggest that high interest rate is the second most common hindrance to obtaining loans from financial institutions after the requirement of adequate collateral.

4. Contributors and Acknowledgments

The collaborative partner in Pakistan was Innovative Development Strategies (IDS) in Islamabad, Pakistan. The funding for the survey came from the U.S. Agency for International Development (USAID). IDS served as the data collector and handled all of the survey logistics, from enumerator training to the processing of the completed questionnaires. The units of analysis varied by module and included Individual, Household, and Community.

The survey was designed and supervised by International Food Policy Research Institute (IFPRI) and was administered by Innovative Development Strategies, Islamabad, Pakistan. The funding for the survey was provided by United States Agency for International Development (USAID).

Gratitude is owed to the 150 enumerators who worked diligently in extremely difficult circumstances. Despite several security issues, they put out exemplary efforts to collect the data. Additionally, the 2090 households, 228 key informants, 117 school teachers, and 228 shopkeepers who participated in this survey and provided their valuable time and useful information also deserve commendation.

This work would not have been possible without the guidance and support provided by Dr. Sohail Jehangir Malik, Chairman of IDS, and Dr. Paul Dorosh, Director of the Development Strategy and Governance Division at IFPRI and Dr. Stephen Davies, Program Leader of Pakistan Strategy Support Program and Senior Research Fellow at IFPRI. Their invaluable input and advice at each stage of this process, from survey design to the production of these discussion papers, is gratefully acknowledged.

Additionally, the following individuals contributed to the production of the dataset: Dr. Katrina Kosec, Dr. Valerie Mueller, Fatima Zaidi, Dr. Claudia Ringler, Dr. David Spielman, Madeeha Hameed, Syed Hamza Haider, Stephanie Hausladen, Dr. Nuzhat Ahmad, Brian Holtemeyer, Huma Khan, and Edward Whitney of IFPRI; and and Nishat Malik, Arshad Khurshid, Azhar Amir, Danish Javaid Satti, Muhammad Awais, Anees Majeed, Mubashir Ijaz, Muhammad Imran, Zahid Masood, and Munazza Saboohi of IDS. Special thanks to Eduardo Magalhaes for cleaning up the dataset. The support provided by the PSSP team members, Dr. Hina Nazli, Asjad Tariq, Hassan Shafiq, Saqib Shahzad, Amina Mehmood, Asma Shahzad, Muhammad Ishfaq, Sara Rafi, Sheheryar Rashid, Wajiha Saeed, Saira Malik, Faryal Ahmed, and Tahir Ahmad—is gratefully acknowledged. Further gratitude is owed to Col. Imran Afzal Malik and his team, Haji Afsar Khan, Afzaal Ahmed, and Asjad Iqbal, for providing logistic and administrative support.

Finally, the United States Agency for International Development (USAID) is recognized for their generous funding, without which this survey would not have been possible.

5. Disclaimer

The International Food Policy Research Institute (IFPRI) requests that users of the data acknowledge the source of the Pakistan Rural Household Panel Survey 2012 dataset in all publications, conference papers, and manuscripts, as described under preferred citation.

IFPRI adheres to the principle of unrestricted public access to its own final research outputs and will make such outputs freely available. The Institute encourages the use of the Pakistan Rural Household Panel Survey 2012 dataset; for detailed information on its use, please refer to IFPRI’s Intellectual Property Policy. The data files in this dataset are unit record or ‘raw’ data files. Information that would allow survey respondents to be identified has been deleted from the files, but all other information remains. IFPRI’s decision not to alter the contents of the data files means that the user of these files will need to take care in handling missing observations, outliers, and violations of logical consistency.

The data are provided ‘as is’ and in no event shall IFPRI be liable for any damages resulting from use of the data. While great effort was taken to obtain high-quality data, the accuracy or reliability of the data is not guaranteed or warranted in any way.

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