The estimated per capita income across the northern state of Punjab in India stood at around *** thousand Indian rupees in the financial year 2025. There was a consistent increase in the income per capita in the state since the financial year 2012 till 2020. Karnataka recorded the highest per capita income in the country.

NSDP Per Capita: Punjab data was reported at 209,452.302 INR in 2025. This records an increase from the previous number of 195,030.509 INR for 2024. NSDP Per Capita: Punjab data is updated yearly, averaging 144,904.096 INR from Mar 2012 (Median) to 2025, with 14 observations. The data reached an all-time high of 209,452.302 INR in 2025 and a record low of 85,576.648 INR in 2012. NSDP Per Capita: Punjab data remains active status in CEIC and is reported by Ministry of Statistics and Programme Implementation. The data is categorized under Global Database’s India – Table IN.GEI004: Memo Items: State Economy: Net State Domestic Product per Capita.

The estimated per capita income across Sikkim was the highest among Indian states at around *** thousand Indian rupees in the financial year 2024. Meanwhile, it was the lowest in the northern state of Bihar at over ** thousand rupees. India’s youngest state, Telangana stood in the fifth place. The country's average per capita income that year was an estimated *** thousand rupees. What is per capita income? Per capita income is a measure of the average income earned per person in a given area in a certain period. It is calculated by dividing the area's total income by its total population. If absolute numbers are noted, India’s per capita income doubled from the financial year 2015 to 2023. Wealth inequality However, as per economists, the increase in the per capita income of a country does not always reflect an increase in the income of the entire population. Wealth distribution in India remains highly skewed. The average income hides the disbursal and inequality in a society. Especially in a society like India where the top one percent owned over ** percent of the total wealth in 2022.

NAS 1999-2000: Gross District Domestic Product per Capita: Punjab: Hoshiarpur data was reported at 37,872.000 INR in 2006. This records an increase from the previous number of 33,767.000 INR for 2005. NAS 1999-2000: Gross District Domestic Product per Capita: Punjab: Hoshiarpur data is updated yearly, averaging 28,341.000 INR from Mar 2000 (Median) to 2006, with 7 observations. The data reached an all-time high of 37,872.000 INR in 2006 and a record low of 23,919.000 INR in 2000. NAS 1999-2000: Gross District Domestic Product per Capita: Punjab: Hoshiarpur data remains active status in CEIC and is reported by Planning Commission. The data is categorized under India Premium Database’s National Accounts – Table IN.AP003: Gross District Domestic Product per Capita: Current Price.

Climate change remains a major challenge for farmers who rely on nature-based livelihoods such as livestock, which is a crucial aspect of income generation and food security in developing countries. In this study, we examine the determinants of livestock farmers’ adoption of climate-smart agricultural (CSA) practices and the impact of adoption on food security and household income in Punjab, Pakistan. The two CSA practices include livestock management (housing modification, livestock diversification, reducing herd size, and incorporating trees into livestock farming) and health and feed management (animal healthcare measures, feeding practices, enhanced fodder, and manure incorporation). We employ data from 428 livestock farmers in five districts of Punjab, employing a multinomial endogenous switching regression model to address potential selection bias. The results reveal that factors affecting CSA practice adoption include livestock units, landholdings, perception of climate change, climate indicators, veterinary center access, farming experience, and perception of increasing animal diseases. We also demonstrate that livestock farmers who adopt combined CSA practices benefit more than those who do not adopt any or adopt an individual practice, in terms of food security and household income. The findings also reveal that farmers’ perception of climate change and veterinary center access promote the adoption of CSA practices.

Census: Population: Punjab: Batala data was reported at 158,621.000 Person in 03-01-2011. This records an increase from the previous number of 147,872.000 Person for 03-01-2001. Census: Population: Punjab: Batala data is updated decadal, averaging 53,575.000 Person from Mar 1901 (Median) to 03-01-2011, with 12 observations. The data reached an all-time high of 158,621.000 Person in 03-01-2011 and a record low of 26,122.000 Person in 03-01-1921. Census: Population: Punjab: Batala data remains active status in CEIC and is reported by Office of the Registrar General & Census Commissioner, India. The data is categorized under India Premium Database’s Demographic – Table IN.GAC029: Census: Population: By Towns and Urban Agglomerations: Punjab.

Abstract

In order to elucidate the financial lives of smallholder households and build the evidence base on this important client group, Consultative Group to Assist the Poor (CGAP) of the World Bank launched the year-long Financial Diaries with Smallholder Families (the "Smallholder Diaries"). The study captured the financial and in-kind transactions of 270 households in Tanzania, Pakistan and Mozambique, of which 94 households are in the Punjab province, the breadbasket of Pakistan. The sample was drawn from 2 villages in Pakistan. Villages were selected based on their involvement in agriculture, and convenience in reaching them. Between June 2014 and July 2015, enumerators visited sample families every fortnight to conduct comprehensive face-to-face interviews to track all the money flowing into and out of their households.

Geographic coverage

In Pakistan, the Smallholder Diaries were conducted in Bahawalnagar, southern Punjab, within the country's breadbasket. Rice, wheat, and cotton are commonly grown and typically sold through a network of local commission agents (known as arthis) and village traders. Given the dominance of agricultural middlemen in Pakistan, two villages in the district of Bahawalnagar were selected as representative of an area with relatively looser connections to agricultural value chains and middlemen.

Analysis unit

The main unit for data collection for transactions was the household. However, each income source and financial instrument was ascribed to a specific household member during the initial questionnaire. Thus all transactions associated with that instrument or income source are registered under its owner. Similarly, transactions related to expenses were individually attributed to the member who initiated the respective transaction.

There was a small number of cash flows where the interviewer was not able to unambiguously identify the initiating household member. In these cases, the cash flow was recorded as belonging to the entire household (in the dataset the member ID field would be blank).

Analysis can be performed at two different levels of aggregation: a) The household itself b) Individual household members

In our study the household is defined as including those who consistently share financial resources, live together, share the same cooking arrangement, and report to the same household head. This includes babies, children, people who travel for work or school during the week and consider the household to be their main residence. However, the definition does not include people who are currently spending an extended period of time away from the household, including college students, students away at boarding school, military personnel, people in prison, or people who live in the house but maintain completely separate expenses (e.g. roommates, other families).

Universe

Once the villages for the Smallholder Diaries were selected, the research teams used a screening process to help identify a range of families with 5 acres of land or less, diverse income sources, access to agricultural inputs, wealth levels, and crops to participate in the research.

In Pakistan, the sample was selected using a traditional screener survey with questions related to household demographics, crops and livestock, main income sources, and wealth indicators, administered to all households in the selected villages. As a supplement to this process, village leaders and community representatives were consulted to help ensure local participation and eliminate households with large landholdings.

Kind of data

Event/Transaction data [evn]

Sampling procedure

The methodology and sample size of the Smallholder Diaries was designed to generate a rich pool of detailed information and insights on a targeted population. The Smallholder Diaries are not intended to be statistically representative of smallholder families in participating countries.

Total number of households in sample: 93 (Mozambique); 86 (Tanzania); 94 (Pakistan). The sample came was drawn from 3 villages in Mozambique, 2 villages in Tanzania, and 2 villages in Pakistan. Villages were selected based on their involvement in agriculture, and convenience in reaching them.

The research teams used a screening process to help identify a range of families with 5 acres of land or less, diverse income sources, access to agricultural inputs, wealth levels, and crops to participate in the research. In Pakistan, the sample was selected using a traditional screener survey with questions related to household demographics, crops and livestock, main income sources, and wealth indicators, administered to all households in the selected villages. As a supplement to this process, village leaders and community representatives were consulted to help ensure local participation and eliminate households with large landholdings, harvests per year, use of inputs, and integration with local markets and a variety of families were chosen.

In Pakistan, the sample was selected using a traditional screener survey with questions related to household demographics, crops and livestock, main income sources, and wealth indicators. As a supplement to this process, village leaders and community representatives were consulted to help ensure local ownership and eliminate households with large landholdings.

Mode of data collection

Face-to-face [f2f]

Research instrument

Interviewers visited each household and conducted three initial questionnaires. They 1) collected a household roster and demographic information about household members; 2) captured a register of physical assets and income sources for each household member and 3) registered the unique financial instruments used by each household member. This baseline information was then used to generate a custom cash flows questionnaire for each household, built to collect income, expenditure, and financial transactions for each individual. This customized cash flows questionnaire was then used for the collection of cash flows data. During regular visits about every two weeks, interviewers captured a complete set of daily, individual transactions from the preceding two-week period. Households were asked only about transactions using financial instruments and income sources that they actually have, rather than going through a generic list of questions. However, the cash flows questionnaire was continuously updated as new members joined the household, members acquired new financial instruments or income sources, or as the interviewers became aware of previously undisclosed ones.

Cleaning operations

All data editing was done manually.

Response rate

The sample initially included 286 households in all three countries, and the study ended with 273 households in total – an attrition rate similar to what has been observed in the past in similar Financial Diaries exercises. Households left the study due to moving from the study villages, seasonal migration, and occasionally by the prompting of the research team due to concerns about the household’s willingness to be forthcoming about important sources of income.

The gross domestic product (GDP) in current prices in Pakistan stood at 371.41 billion U.S. dollars in 2024. Between 1980 and 2024, the GDP rose by 332.79 billion U.S. dollars, though the increase followed an uneven trajectory rather than a consistent upward trend.This indicator describes the gross domestic product at current prices. The values are based upon the GDP in national currency converted to U.S. dollars using market exchange rates (yearly average). The GDP represents the total value of final goods and services produced during a year.

The Pakistan Rural Household Panel Survey (PRHPS) 2014, Round 3 is the third round of the PRHPS; Round 1 and 2 were conducted in 2012 and 2013 respectively. This survey aims to provide a quantitative basis to identify and address urgent economic development and policy priorities in Pakistan. Many modules and questions in Round 3 are consistent with the prior rounds. PRHPS Round 3 was able to collect complete data from 1,876 households in the rural areas of three provinces namely: (i) Punjab; (ii) Sindh; and (iii) Khyber Pakhtunkhwa (KPK).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. 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, women decision making, economic shocks, transfers in and out, health and nutrition, 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.

Abstract

The Pakistan Integrated Household Survey (PIHS) was conducted jointly by the Federal Bureau of Statistics (FBS), Government of Pakistan, and the World Bank. The survey was part of the Living Standards Measurement Study (LSMS) household surveys that have been conducted in a number of developing countries with the assistance of the World Bank. The purpose of these surveys is to provide policy makers and researchers with individual, household, and community level data needed to analyze the impact of policy initiatives on living standards of households.

The Pakistan Integrated Household Survey was carried out in 1991. This nationwide survey gathered individual and household level data using a multi-purpose household questionnaire. Topics covered included housing conditions, education, health, employment characteristics, selfemployment activities, consumption, migration, fertility, credit and savings, and household energy consumption. Community level and price data were also collected during the course of the survey.

Geographic coverage

National

Analysis unit

• Households
• Individuals
• Communities

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample for the PIHS was drawn using a multi-stage stratified sampling procedure from the Master Sample Frame developed by FBS based on the 1981 Population Census.

SAMPLE FRAME:

This sample frame covers all four provinces (Punjab, Sindh, NWFP, and Balochistan) and both urban and rural areas. Excluded, however, are the Federally Administered Tribal Areas, military restricted areas, the districts of Kohistan, Chitral and Malakand and protected areas of NWFP. According to the FBS, the population of the excluded areas amounts to about 4 percent of the total population of Pakistan. Also excluded are households which depend entirely on charity for their living.

The sample frame consists of three main domains: (a) the self-representing cities; (b) other urban areas; and (c) rural areas. These domains are further split up into a number of smaller strata based on the system used by the Government to divide the country into administrative units. The four provinces of Pakistan mentioned above are divided into 20 divisions altogether; each of these divisions in turn is then further split into several districts. The system used to divide the sample frame into the three domains and the various strata is as follows: (a) Self-representing cities: All cities with a population of 500,000 or more are classified as self-representing cities. These include Karachi, Lahore, Gujranwala, Faisalabad, Rawalpindi, Multan, Hyderabad and Peshawar. In addition to these cities, Islamabad and Quetta are also included in this group as a result of being the national and provincial capitals respectively. Each self-representing city is considered as a separate stratum, and is further sub-stratified into low, medium, and high income groups on the basis of information collected at the time of demarcation or updating of the urban area sample frame. (b) Other urban areas: All settlements with a population of 5,000 or more at the time of the 1981 Population Census are included in this group (excluding the self-representing cities mentioned above). Urban areas in each division of the four provinces are considered to be separate strata. (c) Rural areas: Villages and communities with population less than 5,000 (at the time of the Census) are classified as rural areas. Settlements within each district of the country are considered to be separate strata with the exception of Balochistan province where, as a result of the relatively sparse population of the districts, each division instead is taken to be a stratum.

Main strata of the Master Sample frame

Domain / Punjab / Sindh / NWFP / Balochistan / PAKISTAN Self-representing cities / 6 / 2 / 1 / 1 / 10 Other urban areas / 8 / 3 / 5 / 4 / 20 Rural areas / 30 / 14 / 10 / 4 / 58 Total 44 / 19 / 16 / 9 / 88

As the above table shows, the sample frame consists of 88 strata altogether. Households in each stratum of the sample frame are exclusively and exhaustively divided into PSUs. In urban areas, each city or town is divided into a number of enumeration blocks with welldefined boundaries and maps. Each enumeration block consists of about 200-250 households, and is taken to be a separate PSU. The list of enumeration blocks is updated every five years or so, with the list used for the PIHS having been modified on the basis of the Census of Establishments conducted in 1988. In rural areas, demarcation of PSUs has been done on the basis of the list of villages/mouzas/dehs published by the Population Census Organization based on the 1981 Census. Each of these villages/mouzas/dehs is taken to be a separate PSU. Altogether, the sample frame consists of approximately 18,000 urban and 43,000 rural PSUs.

SAMPLE SELECTION:

The PIHS sample comprised 4,800 households drawn from 300 PSUs throughout the country. Sample PSUs were divided equally between urban and rural areas, with at least two PSUs selected from each of the strata. Selection of PSUs from within each stratum was carried out using the probability proportional to estimated size method. In urban areas, estimates of the size of PSUs were based on the household count as found during the 1988 Census of Establishments. In rural areas, these estimates were based on the population count during the 1981 Census.

Once sample PSUs had been identified, a listing of all households residing in the PSU was made in all those PSUs where such a listing exercise had not been undertaken recently. Using systematic sampling with a random start, a short-list of 24 households was prepared for each PSU. Sixteen households from this list were selected to be interviewed from the PSU; every third household on the list was designated as a replacement household to be interviewed only if it was not possible to interview either of the two households immediately preceding it on the list.

As a result of replacing households that could not be interviewed because of non-responses, temporary absence, and other such reasons, the actual number of households interviewed during the survey - 4,794 - was very close to the planned sample size of 4,800 households. Moreover, following a pre-determined procedure for replacing households had the added advantage of minimizing any biases that may otherwise have arisen had field teams been allowed more discretion in choosing substitute households.

SAMPLE DESIGN EFFECTS:

The three-stage stratified sampling procedure outlined above has several advantages from the point of view of survey organization and implementation. Using this procedure ensures that all regions or strata deemed important are represented in the sample drawn for the survey. Picking clusters of households or PSUs in the various strata rather than directly drawing households randomly from throughout the country greatly reduces travel time and cost. Finally, selecting a fixed number of households in each PSU makes it easier to distribute the workload evenly amongst field teams. However, in using this procedure to select the sample for the survey, two important matters need to be given consideration: (a) sampling weights or raising factors have to be first calculated to get national estimates from the survey data; and (b) the standard errors for estimates obtained from the data need to be adjusted to take account for the use of this procedure.

Mode of data collection

Face-to-face [f2f]

Research instrument

The PIHS used three questionnaires: a household questionnaire, a community questionnaire, and a price questionnaire.

HOUSEHOLD QUESTIONNAIRE:

The PIHS questionnaire comprised 17 sections, each of which covered a separate aspect of household activity. The various sections of the household questionnaire were as follows: 1. HOUSEHOLD INFORMATION 2. HOUSING 3. EDUCATION 4. HEALTH 5. WAGE EMPLOYMENT 6. FAMILY LABOR 7. ENERGY 8. MIGRATION 9. FARMING AND LIVESTOCK 10. NON-FARM ENTERPRISE ACTIVITIES 11. NON-FOOD EXPENDITURES AND INVENTORY OF DURABLE GOODS 12. FOOD EXPENSES AND HOME PRODUCTION 13. MARRIAGE AND MATERNITY HISTORY 14. ANTHROPOMETRICS 15. CREDIT AND SAVINGS 16. TRANSFERS AND REMITTANCES 17. OTHER INCOME

The household questionnaire was designed to be administered in two visits to each sample household. Apart from avoiding the problem of interviewing household members in one long stretch, scheduling two visits also allowed the teams to improve the quality of the data collected.

During the first visit to the household (Round 1), the enumerators covered sections 1 to 8, and fixed a date with the designated respondents of the household for the second visit. During the second visit (Round 2), which was normally held two weeks after the first visit, the enumerators covered the remaining portion of the questionnaire and resolved any omissions or inconsistencies that were detected during data entry of information from the first part of the survey.

Since many of the sections of the questionnaire pertained specifically to female members of the household, female interviewers were included in conducting the survey. The household questionnaire was split into two parts (Male and Female). Sections such as SECTION 3: EDUCATION, which solicited information on all individual members of the household (male as well as female) were included in both parts of the questionnaire. Other sections such as SECTION 2: HOUSING and SECTION 12: FOOD EXPENSES AND HOME PRODUCTION , which collected data at the aggregate household level, were included in either the male questionnaire or the female questionnaire, depending upon which member of the household was more likely

Mixed cropping and livestock production is a widespread farming system in less developed countries. The literature has mainly highlighted the synergistic effects between crop and livestock systems from an agronomic and environmental point of view, but has never investigated the (economic) complementarity that may exist between the two activities. Complementarity exists when mixed farming allows smallholders to earn higher incomes than in specialized systems, i.e., crop-only or livestock-only. Our paper is the first to test for complementarity in mixed farming by deriving empirical predictions from the theory of supermodularity, which are tested econometrically using a database of 360 farming households in the Punjab province of Pakistan. Our estimation results confirm the existence of a significant and positive complementary effect between crop and livestock activities, and also provide a direct measure of this effect. The smallholder can earn an average additional income of 791 rupees (out of an average total income of 12,010 rupees) by choosing mixed farming. This implies that smallholders adopt mixed farming not only for its agronomic and environmental benefits, but also because it can generate higher incomes than specialized farming systems to alleviate smallholder poverty. Apart from the choice of activity, our estimation results show that the other variables that significantly increase smallholder incomes are the education level of the household head, as well as access to urban markets, herd size, and land size. We also find that the positive impact of land expansion does not depend on the property rights regime, i.e., the additional land can be owned or rented (sharecropping). A specific public policy aimed at reducing smallholder poverty must prioritize the improvement of these key factors, especially access to urban markets and sharecropping.