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.

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.

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.

Abstract

During the second half of 2008, the Government of Pakistan established the Benazir Income Support Program (BISP) as the country's main safety net mechanism. The short term objective of the program is to cushion the adverse impact of the food, fuel and financial crises on the poor, but its broader objective is to meet the redistributive goals of the country by providing a minimum income support package to the chronic poor and those affected by future shocks. The Program provides cash transfers of Rs.1000 [$12] per month to eligible families. In order to quickly launch the program, parliamentarians (Members of the National Assembly and Senators) were asked to identify 8,000 beneficiary households each on a prescribed form, which collected information on names, national ID card, and household income. BISP is a national program that covers all provinces of the country, Azad Jamnu and Kashmir (AJK) and Northern Areas.

In December 2008, the Government of Pakistan (GoP) decided to improve the existing targeting mechanism of BISP through the adoption of a poverty scorecard (“proxy means test” based targeting) that would be used for the selection of beneficiaries. The scorecard roll-out started during April 2009 in 16 districts. The GoP intended to complete the national roll-out by the end of 2010.

Expecting there would be a transition from the Parliamentarian selected beneficiaries to poverty scorecard identified beneficiaries after the poverty scorecard information was collected and cut-off scores were identified, the World Bank team and the GoP decided to launch a BISP rapid assessment survey with the primary objective to examine the overlap between the poverty scorecard identified beneficiaries and Parliamentarian identified beneficiaries in order to inform the policy makers on the potential transition mechanisms. In addition, the rapid assessment survey would also inform the BISP application process and challenges in delivery mechanisms as well as the how BISP beneficiaries tend to spend the cash transfer received.

Geographic coverage

The main survey was conducted in 15 randomly selected districts of Pakistan

Analysis unit

Households Individuals

Kind of data

Sample survey data [ssd]

Sampling procedure

The sampling Survey for the Rapid Assessment of Parliamentarian selected beneficiaries was conducted in the two stages: a test phase stages and the main survey.

1.Test Phase.A test of the planned survey was conducted by IDS in May 2009. The test was based on a random sample of 101 beneficiaries in two districts Nowshera (NWFP) and Rajanpur (Punjab). The 101 beneficiaries came from 101 households composed of 613 household members. The results and lessons from the test-phase were used to refine the final survey questionnaires.

2.Main Survey. The main survey was conducted in 15 randomly selected districts of Pakistan, during the months of August and September before Ramadan.

The sample beneficiaries were selected from the list of the Parliamentarian identified beneficiaries provided by the National Database and Registration Authority's (NADRA). The sample was drawn from the NADRA computer records in April, 2009. There were 1,660,584 existing beneficiaries in that record, composed of 46.23 percent from Punjab, 26.04 from Sindh, 18.35 percent from NWFP, 6.08 percent from Baluchistan, 2.38 percent from FATA, 0.73 percent from Islamabad and 0.14 percent from Northern areas.

The study used the probability to Proportion to Size method to obtain a sample as representative as possible with the sample size of 2,500 households. For this purpose a randomly selected sample of 5680 beneficiaries' names, addresses, NIC numbers and names of spouses/fathers was provided by NADRA for the 15 selected Tehsils in the 13 districts of the four Provinces, and Islamabad in the Federal Capital and Muzafarabad in the Azad Jammu and Kashmir.

The total number of Districts/Tehsils was purposively decided on the basis of an eventually representative sample size to capture the large diversity in levels of development across districts and the resources available. The 13 districts from the four Provinces were then allocated to roughly represent the relative share of the beneficiaries from each Province in the NADRA records on that day. As such 7 districts on Punjab, 3 in Sindh, 2 in NWFP and 1 in Baluchistan were selected.

The literature on Poverty in Pakistan divides provinces into distinct poverty or deprivation bands. For example, Punjab, is looked upon as three regional disparity bands i.e. Northern Punjab (best off), central Punjab (moderately well off) and southern Punjab (worst off). The seven districts of Punjab represent this classification with the number of districts selected in each poverty band being selected on the basis of the share of each region in the population of Punjab. As such, Attack was chosen from the North Punjab, Rajanpur was chosen from the South, and the remaining was chosen from the Center to represent the region as moving from North to South i.e from Sargodha to Faisalabad to Vehari to Multan to Bahawalpur. The same rationale applies to other provinces. For example, in Sindh province, the selected districts ranged from Karachi (the most developed) to Larkana (deprived but politically privileged) to Sanghar (most deprived). In NWFP province, Nowshera is amongst the least deprived in NWFP and Karak is one of the most deprived. Similarly, Muzafarabad was chosen to represent AJK and Islamabad to represent the Federal Area.

In Baluchistan, the unrest and law and order situation made survey almost impossible except in Quetta where a small sample was selected.

The 13 districts in the four provinces for which this list of beneficiaries was selected were chosen to represent 1) the total number of districts in each province, 2) the regional level of deprivation in each province (based on existing literature, districts in each province in Pakistan are grouped by levels of poverty - for example Punjab has three groups namely North (low poverty), central (higher poverty) and Southern (highest poverty) and 3) the share of beneficiaries in each district. One district was selected in Azad Jammu and Kashmir and Islamabad Capital territory was selected as a stand alone.The selected districts represent 70 Members of the National Assembly and 143 members of the Provincial Assembly. There are 2 designated Senate seats for Islamabad while the other Senate seats are allocated on no regional criteria. Having selected the districts, one tehsil in each district was drawn randomly. However, the list of beneficiaries provided by NADRA did not include any coding of addresses by Union Council. The Unions Council names were therefore manually coded into the information on the beneficiaries. From this list sample UCs were designated in each tehsil in such a way that at least a minimum of 10 beneficiaries would be covered in a particular sample UC.

Sample UCs were selected using Probability proportionate to Size (PPS) method of selection. Beneficiaries in each UC will be treated as measure of size. Due to nature of the Survey and to avoid unnecessary field problems as already stated UCs with 10+ beneficiaries were selected in the sample. However, in case if in any Tehsil where the number of UCs/Beneficiaries is small, the UCs with less than 10 beneficiaries were also selected. Sample beneficiaries to be covered from each sample UC in a Tehsil will be in proportion to the size of the sample UCs. Beneficiaries in a sample UC were selected by Simple Random Sample method of selection. In case if a sample beneficiary is not traceable then subsequent beneficiary in the list of beneficiaries of that UC will be selected as replacement.

The issue of replacement HHs in case a HH could not be traced due to incorrect address required clarification. After exhaustive deliberations with the World Bank team, it was agreed that the sample size would be increased to 2595. Out of the sample of 2,595 households (HHs), a total of 2,540 HHs were initially successfully enumerated before the Eid break. The enumeration teams could not reach 55 HHs. These 55 HHs were later verified / enumerated after the break due to Ramazan and Eid holidays. Post Eid all 2595 HHs were successfully enumerated. Out of the 2,595 HHs enumerated, the enumeration teams, in consultation with their field supervisors and the designated IDS supervisors, had to drop 347 HHs. The reasons for dropping these HHS are discussed in detail subsequently. In addition to these 347 dropped households (for whom both female and male questionnaires were not filled), there are 78 households for whom only female questionnaires were filled as either there were no male member in the HH or male members, even after three visits to the HH (refer to table 7 of the BISP Rapid Assessment Report for sample summary).

Sampling deviation

Out of the sample of 2,595 households (HHs), a total of 2,540 HHs were initially successfully enumerated before the eid break. The enumeration teams could not reach 55 HHs. These 55 HHs were later verified / enumerated after the break due to Ramadan and Eid holidays. Post Eid all 2595 HHs were successfully enumerated. Out of the 2,595 HHs enumerated, the enumeration teams, in consultation with their field supervisors and the designated Innovative Development Strategies (Pvt.) Ltd.(IDS) supervisors, had to drop 347 HHs. The reasons for dropping these HHS are discussed in detail subsequently. In addition to these 347 dropped households (for whom both female and male questionnaires were not filled), there are 78 households for whom only female questionnaires were filled as either there were no male member in the HH or male members, even after three visits to the

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.