Merchandise imports from low- and middle-income economies within region (% of total merchandise imports) in Pakistan was reported at 2.1746 % in 2023, according to the World Bank collection of development indicators, compiled from officially recognized sources. Pakistan - Merchandise imports from developing economies within region (% of total merchandise imports) - actual values, historical data, forecasts and projections were sourced from the World Bank on July of 2025.

Pakistan PK: Income Share Held by Highest 10% data was reported at 28.900 % in 2015. This records an increase from the previous number of 26.000 % for 2013. Pakistan PK: Income Share Held by Highest 10% data is updated yearly, averaging 27.100 % from Dec 1987 (Median) to 2015, with 12 observations. The data reached an all-time high of 28.900 % in 2015 and a record low of 25.200 % in 1996. Pakistan PK: Income Share Held by Highest 10% data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Pakistan – Table PK.World Bank.WDI: Poverty. Percentage share of income or consumption is the share that accrues to subgroups of population indicated by deciles or quintiles.; ; World Bank, Development Research Group. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are from the Luxembourg Income Study database. For more information and methodology, please see PovcalNet (http://iresearch.worldbank.org/PovcalNet/index.htm).; ; The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than one thousand six hundred household surveys across 164 countries in six regions and 25 other high income countries (industrialized economies). While income distribution data are published for all countries with data available, poverty data are published for low- and middle-income countries and countries eligible to receive loans from the World Bank (such as Chile) and recently graduated countries (such as Estonia) only. See PovcalNet (http://iresearch.worldbank.org/PovcalNet/WhatIsNew.aspx) for definitions of geographical regions and industrialized countries.

Pakistan PK: Exports: Low- and Middle-Income Economies: % of Total Goods Exports: Outside Region data was reported at 22.784 % in 2016. This records a decrease from the previous number of 24.504 % for 2015. Pakistan PK: Exports: Low- and Middle-Income Economies: % of Total Goods Exports: Outside Region data is updated yearly, averaging 18.319 % from Dec 1960 (Median) to 2016, with 57 observations. The data reached an all-time high of 30.586 % in 1981 and a record low of 8.901 % in 1994. Pakistan PK: Exports: Low- and Middle-Income Economies: % of Total Goods Exports: Outside Region data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Pakistan – Table PK.World Bank: Exports. Merchandise exports to low- and middle-income economies outside region are the sum of merchandise exports from the reporting economy to other low- and middle-income economies in other World Bank regions according to the World Bank classification of economies. Data are expressed as a percentage of total merchandise exports by the economy. Data are computed only if at least half of the economies in the partner country group had non-missing data.; ; World Bank staff estimates based data from International Monetary Fund's Direction of Trade database.; Weighted average;

Pakistan PK: Imports: Low- and Middle-Income Economies: % of Total Goods Imports: Within Region data was reported at 4.568 % in 2016. This records a decrease from the previous number of 4.997 % for 2015. Pakistan PK: Imports: Low- and Middle-Income Economies: % of Total Goods Imports: Within Region data is updated yearly, averaging 3.159 % from Dec 1960 (Median) to 2016, with 57 observations. The data reached an all-time high of 7.426 % in 1977 and a record low of 1.492 % in 1991. Pakistan PK: Imports: Low- and Middle-Income Economies: % of Total Goods Imports: Within Region data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Pakistan – Table PK.World Bank: Imports. Merchandise imports from low- and middle-income economies within region are the sum of merchandise imports by the reporting economy from other low- and middle-income economies in the same World Bank region according to the World Bank classification of economies. Data are as a percentage of total merchandise imports by the economy. Data are computed only if at least half of the economies in the partner country group had non-missing data. No figures are shown for high-income economies, because they are a separate category in the World Bank classification of economies.; ; World Bank staff estimates based data from International Monetary Fund's Direction of Trade database.; Weighted average;

Merchandise exports to low- and middle-income economies within region (% of total merchandise exports) in Pakistan was reported at 6.9446 % in 2023, according to the World Bank collection of development indicators, compiled from officially recognized sources. Pakistan - Merchandise exports to developing economies within region (% of total merchandise exports) - actual values, historical data, forecasts and projections were sourced from the World Bank on July of 2025.

This contains data and scripts used in the paper entitled "High resolution mapping of rural poverty in Pakistan with ensemble deep learning". The "README.me" file provides additional information about the scripts and underlying data.

Policy debates on strategies to end extremist violence frequently cite poverty as a root cause of support for the perpetrating groups. There is little evidence to support this contention, particularly in the Pakistani case. Pakistan’s urban poor are more exposed to the negative externalities of militant violence, and may in fact be less supportive of the groups. To test these hypotheses we conducted a 6000-person, nationally representative survey of Pakistanis that measured affect towards four militant organizations. By applying a novel measurement strategy, we mitigate the item non-response and social desirability biases that plagued previous studies due to the sensitive nature of militancy. Contrary to expectations, poor Pakistanis dislike militants more than middle-class citizens. This dislike is strongest among the urban poor, particularly those in violent districts, suggesting that exposure to terrorist attacks reduces support for militants. Longstanding arguments tying support for violent organizations to income may require substantial revision.

Merchandise imports from low- and middle-income economies in Latin America & the Caribbean (% of total merchandise imports) in Pakistan was reported at 1.5517 % in 2023, according to the World Bank collection of development indicators, compiled from officially recognized sources. Pakistan - Merchandise imports from developing economies in Latin America & the Caribbean (% of total merchandise imports) - actual values, historical data, forecasts and projections were sourced from the World Bank on August of 2025.

IntroductionPakistan has the highest national prevalence of type 2 diabetes mellitus (T2DM) in the world. Most high-quality T2DM clinical practice guidelines (CPGs) used internationally originate from high-income countries in the West. Local T2DM CPGs in Pakistan are not backed by transparent methodologies. We aimed to produce comprehensive, high-quality CPGs for the management of adult DM in Pakistan.MethodsWe employed the GRADE-ADOLOPMENT approach utilizing the T2DM CPG of the American Diabetes Association (ADA) Standards of Medical Care in Diabetes – 2021 as the source CPG. Recommendations from the source guideline were either adopted as is, excluded, or adapted according to our local context.ResultsThe source document contained 243 recommendations, 219 of which were adopted without change, 5 with minor changes, and 18 of which were excluded in the newly created Pakistani guidelines. One recommendation was adapted: the recommended age to begin screening all individuals for T2DM/pre-diabetes was lowered from 45 to 30 years, due to the higher prevalence of T2DM in younger Pakistanis. Exclusion of recommendations were primarily due to differences in the healthcare systems of Pakistan and the US, or the unavailability of certain drugs in Pakistan.ConclusionA CPG for the management of T2DM in Pakistan was created. Our newly developed guideline recommends earlier screening for T2DM in Pakistan, primarily due to the higher prevalence of T2DM amongst younger individuals in Pakistan. Moreover, the systematic methodology used is a significant improvement on pre-existing T2DM CPGs in Pakistan. Once these evidence based CGPs are officially published, their nationwide uptake should be top priority. Our findings also highlight the need for rigorous expanded research exploring the effectiveness of earlier screening for T2DM in Pakistan.

In 2018, there were almost ***** patent applications filed in Thailand. Contrastingly, there were just *** patent applications filed in Pakistan in 2018.

PK: Imports: Low- and Middle-Income Economies: % of Total Goods Imports: Europe & Central Asia data was reported at 1.810 % in 2016. This records an increase from the previous number of 1.521 % for 2015. PK: Imports: Low- and Middle-Income Economies: % of Total Goods Imports: Europe & Central Asia data is updated yearly, averaging 1.415 % from Dec 1962 (Median) to 2016, with 55 observations. The data reached an all-time high of 3.777 % in 2008 and a record low of 0.132 % in 1964. PK: Imports: Low- and Middle-Income Economies: % of Total Goods Imports: Europe & Central Asia data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Pakistan – Table PK.World Bank: Imports. Merchandise imports from low- and middle-income economies in Europe and Central Asia are the sum of merchandise imports by the reporting economy from low- and middle-income economies in the Europe and Central Asia region according to the World Bank classification of economies. Data are expressed as a percentage of total merchandise imports by the economy. Data are computed only if at least half of the economies in the partner country group had non-missing data.; ; World Bank staff estimates based data from International Monetary Fund's Direction of Trade database.; Weighted average;

In 1800, the population of the area of modern-day Pakistan was estimated to be just over 13 million. Population growth in the 19th century would be gradual in the region, rising to just 19 million at the turn of the century. In the early 1800s, the British Empire slowly consolidated power in the region, eventually controlling the region of Pakistan from the mid-19th century onwards, as part of the British Raj. From the 1930s on, the population's growth rate would increase as improvements in healthcare (particularly vaccination) and sanitation would lead to lower infant mortality rates and higher life expectancy. Independence In 1947, the Muslim-majority country of Pakistan gained independence from Britain, and split from the Hindu-majority country of India. In the next few years, upwards of ten million people migrated between the two nations, during a period that was blemished by widespread atrocities on both sides. Throughout this time, the region of Bangladesh was also a part Pakistan (as it also had a Muslim majority), known as East Pakistan; internal disputes between the two regions were persistent for over two decades, until 1971, when a short but bloody civil war resulted in Bangladesh's independence. Political disputes between Pakistan and India also created tension in the first few decades of independence, even boiling over into some relatively small-scale conflicts, although there was some economic progress and improvements in quality of life for Pakistan's citizens. The late 20th century was also characterized by several attempts to become democratic, but with intermittent periods of military rule. Between independence and the end of the century, Pakistan's population had grown more than four times in total. Pakistan today Since 2008, Pakistan has been a functioning democracy, with an emerging economy and increasing international prominence. Despite the emergence of a successful middle-class, this is prosperity is not reflected in all areas of the population as almost a quarter still live in poverty, and Pakistan ranks in the bottom 20% of countries according to the Human Development Index. In 2020, Pakistan is thought to have a total population of over 220 million people, making it the fifth-most populous country in the world.

BackgroundGlobally, a treatment gap exists for individuals with severe mental illness, with 75% of people with psychosis failing to receive appropriate care. This is most pronounced in low and middle-income countries, where there are neither the financial nor human resources to provide high-quality community-based care. Low-cost, evidence-based interventions are urgently needed to address this treatment gap.AimTo conduct a situation analysis to (i) describe the provision of psychosocial interventions within the context of existing care in two LMICs-India and Pakistan, and (ii) understand the barriers and facilitators of delivering a new psychosocial intervention.MethodA situation analysis including a quantitative survey and individual interviews with clinicians, patients and caregivers was conducted. Quantitative survey data was collected from staff members at 11 sites (private and government run hospitals) to assess organizational readiness to implement a new psychosocial intervention. To obtain in-depth information, 24 stakeholders including clinicians and service managers were interviewed about the typical care they provide and/or receive, and their experience of either accessing or delivering psychosocial interventions. This was triangulated by six interviews with carer and patient representatives.Results and discussionThe results highlight the positive views toward psychosocial interventions within routine care and the enthusiasm for multidisciplinary working. However, barriers to implementation such as clinician time, individual attitudes toward psychosocial interventions and organizational concerns including the lack of space within the facility were highlighted. Such barriers need to be taken into consideration when designing how best to implement and sustain new psychosocial interventions for the community treatment of psychosis within LMICs.

The study examines export impact of Pakistan’s integration into Shanghai Cooperation Organization (SCO) on its export’s performance. We apply Poisson Pseudo-Maximum Likelihood (PPML) on augmented gravity model to estimate trade data from the period before and after permanent membership with Shanghai Cooperation Organization in 2017. The study aims to explore changes in exports volume and analyze the key mechanism through which Shanghai Cooperation Organization promotes exports. The study assesses that after integration which key exports sector such as agriculture or manufacturing sectors are affected more significantly. The initial findings suggest that SCO integration positively affect and provide access to Central Asian markets, leading to modest but noticeable promotion in exports promotion. In heterogeneity analysis we find that exports of Pakistan are more significant with low and middle-income level countries compared to higher-income level countries. Additionally, exports in the manufacturing sector benefited more than in the agriculture sector. The significant and positive findings of mechanism analysis indicate that the belt and road (B&R) initiative and bilateral trade agreements are the key factors to enhanced exports. The overall impact remains moderated by structural changes in Pakistan economy, such as poor infrastructure, deficiency in energy sector and limited trade relations with its neighbors India and Iran. The study concludes that although the SCO integration has positively promoted exports of Pakistan however, it requires to address domestic economic constraints and capitalize more effectively the benefits of SCO membership through regional cooperation mechanism. For more potential benefits in the region SCO needs to expand B&R connectivity, encourage more trade agreements, and adopt favorable environment to attract high income countries in the organization. The study provides the base for future research in depth analysis of long-term impact of SCO integration on Pakistan exports.

Merchandise exports to low- and middle-income economies outside region (% of total merchandise exports) in Pakistan was reported at 25.86 % in 2023, according to the World Bank collection of development indicators, compiled from officially recognized sources. Pakistan - Merchandise exports to developing economies outside region (% of total merchandise exports) - actual values, historical data, forecasts and projections were sourced from the World Bank on September of 2025.

Table 3. Concentration of particulate matter (PM2.5 and PM10) during smog from 2019-2023 (IQ Air 2021; EPD 2021) Average monthly analysis Avg. PM2.5 and PM10 (µg/m3) in 2019 Avg. PM2.5 and PM10 (µg/m3) in 2020 Avg. PM2.5 and PM10 (µg/m3) in 2021 Avg. PM2.5 and PM10 (µg/m3) in 2022 Avg. PM2.5 and PM10 (µg/m3) in 2023 September 60 and 67 44and 48 87.5 and 101 64.5 and 95 77and 100 October 97 and 70 60 and 48 112 and 104 123.2 and 130 150 and 165 November 99 and 73 115 and 100 122 and 107 190.5 and 133 157 and 174 December 112 and 100 131 and 112 135 and 125 192.9 and 141 208 and 217 This is the first statistical research based on of five years of smog data from 2019 to 2023 in Lahore, using machine learning techniques. This research used machine learning techniques, namely using Python programming for data analysis. Python-based analyses can effectively assess regions with poor air quality and high pollution levels. This allows targeted interventions and restrictions to safeguard community health and avert respiratory disorders. Table 1. Demographic composition of sample Variable Frequency Percentage (%) Gender · Female · Male 12 18 40 60 Age · 16-35 · 36-40 · 40-50 · >50 10 7 6 7 33.3 23.3 20 11.6 Education level · Illiterate · Pre-school · High school · College · University and other 3 5 5 6 11 10 16.6 16.6 20 36.6 Socioeconomic status · Lower class · Middle class · Upper class 3 22 5 10 73.3 16.6 Table 4. Statistical trend of PM2.5 pollutant from 2019 to 2023 Index Yearly report 2019 2020 2021 2022 2023 Standard deviation 22.3 42.0 20.0 61.3 53.9 Min 60 44 87.5 64.5 77 Mean 92 87.5 114.1 142.7 148 Max 112 131 135 192.9 208 75% 102.2 119 125.2 191.1 169.7 50% 98.0 87.5 117 156.6 154 25% 87.7 56 105.8 108.5 132 Table 5. Statistical trend of PM10 pollutant (2019-2023) Index Yearly report 2019 2020 2021 2022 2023 Standard deviation 15.1 33.8 10.7 20.3 48.3 Min 67 48 101 95 100 Mean 77.5 77 109.2 124.7 164 Max 100 112 125 141 217 75% 79.7 103 111.5 135 184.7 50% 71.5 74 105.5 131 169.5 25% 69.2 48 103.2 121 148.7 Table 2. Health impacts of smog on population Symptoms Yes No Respiratory problem 26 (86%) 4 (13.3%) Sore throat, eye problem 12 (40%) 18 (60%) Fever or chills 5 (17%) 25 (83%) Cough 27 (90%) 3 (10%) Congestion or runny nose 7 (23%) 23 (76%) Chronic obstructive pulmonary disease (COPD) 0 25 (83%)

Abstract

This round of the HIES was conducted covering 15807 households. It provides important information on household income, savings, liabilities, and consumption expenditure and consumption patterns at national and provincial level with urban/rural breakdown. It also includes the requisite data on consumption for the Planning & Development Division for estimation of poverty.

The data generated though HIES Survey will be used to assist the government in formulating the poverty reduction strategy in the overall context of MDGs. The indicators will be developed at National/Provincial level in the following sectors. 1. Education 2. Health 3. Water Supply & Sanitation. 4. Population Welfare 5. Income & Expenditure

Universe

The universe of this survey consists of all urban and rural areas of all four provinces. Military restricted and protected areas have been excluded from the scope of the survey.

Sampling procedure

Sampling Frame:

Urban area: PBS has developed its own urban area frame. All urban areas comprising cities/ towns have been divided into small compact areas known as enumeration blocks (E.Bs) identifiable through map. Each enumeration block comprises about 200-250 households and categorized into low, middle and high-income group, keeping in view the socioeconomic status of the majority of households. Urban area sampling frame consists of 26698 enumeration blocks has been updated in 2003. Rural area: With regard to the rural areas, the lists of villages/mouzas/dehs according to Population Census, 1998 have been used as sampling frame. In this frame, each village/mouza/deh is identifiable by its Name, Had Bast Number, Cadastral map etc. This frame is comprised of 50590 villages/mouzas.

Stratification Plan

Urban Areas: Large sized cities having population five laces and above have been treated as independent stratum. Each of these cities has further been sub-stratified into low, middle and high income groups. The remaining cities/towns within each defunct administrative division have been grouped together to constitute an independent stratum. Rural Areas: The entire rural domain of a district for Punjab, Sindh and KPK provinces has been considered as independent stratum, whereas in Balochistan province defunct administrative division has been treated as stratum.

Sample Size and its Allocation:

To determine optimum sample size for this survey, analytical studies based on the results of Pakistan Demographic Survey, Labour Force and Pakistan Integrated Households Sample Survey were undertaken. Keeping in view the variability that exists within the population for the characteristics for which estimates are to be prepared, as well as population distribution, reliability of estimates and field resources available a sample of size 17,056 households distributed over 1217 PSUs (604 urban and 613 rural) has been considered sufficient to produce reliable estimates in respect of all provinces. Out of these 1217 PSUs, 59 PSUs (19 urban and 40 rural PSUs) were dropped and the remaining 1158 PSUs (585 urban and 573 rural) comprising 15807 households were covered.

Sample Design: A two-stage stratified sample design has been adopted for this survey.

Selection of primary sampling Units (PSUs): Enumeration blocks in the urban domain and mouzas/dehs/villages in rural domain have been taken as PSUs. In urban domain sample PSUs from each stratum have been selected by PPS method of sampling scheme; using households in each block as MOS. Similarly in rural areas, the population of each village has been taken as MOS for the selection of sample villages using again the PPS method.

Selection of Secondary Sampling Units (SSUs): Households within PSU have been considered as SSUs. 16 and 12 households have been selected from each sample village and enumeration block respectively by systematic sampling scheme with a random start.

Mode of data collection

Face-to-face [f2f]

Research instrument

The household income and consumption part of the HIES questionnaire is the same which has been used for the previous rounds since 2001-02 however, some minor improvements have been made for the reference year.

The main structure of the HIES questionnaire used for the survey 2011-12 is as shown:-

Section A: Survey Information Section1: Part-A: Household Information Part-B: Employment & Income Section 2: Education Section 3: Part-A: Darrhoea Part-B: Immunisation Part-C: Malaria & Tuberculosis Section 4: Part-A: Pregnancy History Part-B: Maternity History Part-C Family Planning Part-D: Pre & Post Natal Care Part-E: Women in Decision Making Section 5: Housing Consumption Module Section 6: Household Consumption Expenditure Section 7: Selected Durable Consumption Items Owned/Sold by the Household (During Last One Year) Section 8: Transfers Received and Paid Out (During Last One Year) Section 9: Part- A: Buildings and Land Owned by Members of This Household..... Part- B: Financial Assets And Liabilities, Loans And Credit Section 10: Part A: Agricultural Sheet Part B: Livestock,Poultry,Fish,Forestry,Honey Bee Section 11 :Non-Agricultural Establishment Section 12: Balance Sheet for Income and Expenditure

Data appraisal

Data quality in the HIES Survey has been ensured through a built in system of checking of field work by the supervisors in the field as well as teams from the headquarters. Regional/ Field offices ensured the data quality through preliminary editing at their office level. The entire data entry was carried at the PBS headquarter in Islamabad and the data entry programme used had a number of in built consistency checks

The Pakistan tourism and hotel industry presents a dynamic landscape with significant growth potential. While precise figures for market size and CAGR are unavailable, based on global tourism trends and regional economic indicators, a reasonable estimate for the 2025 market size could be around $5 billion USD. Considering Pakistan's diverse geography, rich cultural heritage, and burgeoning infrastructure development, a conservative Compound Annual Growth Rate (CAGR) of 7% for the forecast period (2025-2033) appears plausible. This growth is fueled by several key drivers, including increased government investment in tourism infrastructure, improved domestic and international connectivity, and a growing focus on promoting adventure tourism and cultural experiences. The rising middle class within Pakistan, coupled with increased disposable income, further contributes to this growth. However, challenges remain, including security concerns which need to be addressed through sustained effort and effective strategies. Seasonal fluctuations in tourism also present a considerable challenge. Segmenting the market reveals a strong presence of both international and domestic players, with a blend of established international hotel chains and local operators catering to diverse budget levels. The industry is expected to see notable trends over the next decade. The increasing adoption of technology for bookings, travel planning, and customer engagement will be critical. Sustainability initiatives, emphasizing responsible tourism practices and ecological preservation, will gain momentum. Furthermore, the industry will likely see increased diversification, with a greater focus on niche markets like eco-tourism, religious tourism, and medical tourism. To fully unlock the potential of the Pakistan tourism and hotel industry, strategic investments in infrastructure, enhanced security measures, and targeted marketing campaigns promoting the country's unique offerings are essential. This integrated approach will be crucial for realizing the sector's substantial growth projections.

This research is part of the PhD project that focuses on development aid effectiveness and fungibility. The methodology used for the thesis is mixed methods. The focus of the research is on individual case studies of Rwanda and Pakistan and an agregate study of 31 low and lower middle income countries. The datasets have been marked based on the chapters of the PhD thesis.

The study examines export impact of Pakistan’s integration into Shanghai Cooperation Organization (SCO) on its export’s performance. We apply Poisson Pseudo-Maximum Likelihood (PPML) on augmented gravity model to estimate trade data from the period before and after permanent membership with Shanghai Cooperation Organization in 2017. The study aims to explore changes in exports volume and analyze the key mechanism through which Shanghai Cooperation Organization promotes exports. The study assesses that after integration which key exports sector such as agriculture or manufacturing sectors are affected more significantly. The initial findings suggest that SCO integration positively affect and provide access to Central Asian markets, leading to modest but noticeable promotion in exports promotion. In heterogeneity analysis we find that exports of Pakistan are more significant with low and middle-income level countries compared to higher-income level countries. Additionally, exports in the manufacturing sector benefited more than in the agriculture sector. The significant and positive findings of mechanism analysis indicate that the belt and road (B&R) initiative and bilateral trade agreements are the key factors to enhanced exports. The overall impact remains moderated by structural changes in Pakistan economy, such as poor infrastructure, deficiency in energy sector and limited trade relations with its neighbors India and Iran. The study concludes that although the SCO integration has positively promoted exports of Pakistan however, it requires to address domestic economic constraints and capitalize more effectively the benefits of SCO membership through regional cooperation mechanism. For more potential benefits in the region SCO needs to expand B&R connectivity, encourage more trade agreements, and adopt favorable environment to attract high income countries in the organization. The study provides the base for future research in depth analysis of long-term impact of SCO integration on Pakistan exports.