Context

This data set contains global economic income indicators per country. The data has been prepared for ease of use.

The data is divided into: Male, female, dimestic credit, gross domestic product, gross national income, fixed capital formation, labour share. The individual files are briefly described below:

Income index:

Dimension: Income/composition of resources Definition: GNI per capita (2011 PPP International $, using natural logarithm) expressed as an index using a minimum value of $100 and a maximum value $75,000.

Domestic credit provided by financial sector (% of GDP)

Dimension: Income/composition of resources Definition: Credit to various sectors on a gross basis (except credit to the central government, which is net), expressed as a percentage of GDP.

Estimated gross national income per capita, female (2011 PPP $)

Full and productive employment and decent work for all women and men,including for young people and persons with disabilities, and equal pay for work of equal value Dimension: Income/composition of resources Definition: Derived from the ratio of female to male wages, female and male shares of economically active population and gross national income (in 2011 purchasing power parity terms).

Estimated gross national income per capita, male (2011 PPP $)

Full and productive employment and decent work for all women and men,including for young people and persons with disabilities, and equal pay for work of equal value Dimension: Income/composition of resources Definition: Derived from the ratio of female to male wages, female and male shares of economically active population and gross national income (in 2011 purchasing power parity terms).

GDP per capita (2011 PPP $)

Dimension: Income/composition of resources Definition: GDP in a particular period divided by the total population in the same period.

Gross domestic product (GDP), total (2011 PPP $ billions)

Dimension: Income/composition of resources Definition: Sum of gross value added by all resident producers in the economy plus any product taxes and minus any subsidies not included in the value of the products, expressed in 2011 international dollars using purchasing power parity (PPP) rates.

Gross fixed capital formation (% of GDP)

Dimension: Income/composition of resources Definition: Value of acquisitions of new or existing fixed assets by the business sector, governments and households (excluding their unincorporated enterprises) less disposals of fixed assets, expressed as a percentage of GDP. No adjustment is made for depreciation of fixed assets.

Gross national income (GNI) per capita (2011 PPP $)

Full and productive employment and decent work for all women and men,including for young people and persons with disabilities, and equal pay for work of equal value Dimension: Income/composition of resources Definition: Aggregate income of an economy generated by its production and its ownership of factors of production, less the incomes paid for the use of factors of production owned by the rest of the world, converted to international dollars using PPP rates, divided by midyear population.

Labour share of GDP, comprising wages and social protection transfers (%)

Adopt policies, especially fiscal, wage and social protection policies, and progressively achieve greater equality Dimension: Income/composition of resources Definition: Total compensation of employees given as a percent of GDP, which is a measure of total output. Total compensation refers to the total remuneration, in cash or in kind, payable by an enterprise to an employee in return for work done by the latter during the accounting period.

Additional Information

For more information see : http://hdr.undp.org/sites/default/files/hdr2019_technical_notes.pdf

The title picture is from https://searchengineland.com/international-ppc-deal-currency-fluctuations-245601

About the Project Natural Resource-led Development in New Producing Countries Our project seeks to understand how natural resource extraction can drive inclusive economic growth in new producing countries. We are engaged in a multiyear, multidisciplinary study with four objectives:

Understand the human geography of new producing countries. Assess the magnitude of new discoveries and estimate direct fiscal impact. Understand how industry can be localized to create economic growth. Estimate spillovers and welfare impacts to society.

We recognize that policymaking in new producing countries is a complex process, and our project also seeks to understand the interactions of actors’ interests that drive energy sector policies. Our initial focus is on four countries – Kenya, Mozambique, Tanzania and Uganda – that expect to develop significant oil and gas reserves in the next 5-7 years. Through natural resource development, these countries hope to achieve middle-income economic status by 2030-2040. This project is conducted through close collaboration with leading think tanks and NGOs in Africa. Key Points The term ‘Local Content Policy’ is a catch-all for ensuring that resource owners capture more value from developments than the fiscal revenues alone. KAPSARC has explained the benefits of a dynamic perspective when evaluating firms’ capabilities – their entrepreneurial capacity – and proposed a tool for assessing firms in this paradigm. Here, we present an analysis based on the descriptive statistics gathered from applying the framework outlined in the previous two papers Uganda is on the cusp of developing its oil industry following major discoveries around Lake Albert in the northwest region of the country. It has provided a useful case study for developing insights that can be more generally applied in resource rich economies seeking to maximize the value extracted from their endowments. KAPSARC conducted a study of Ugandan firms, their capabilities and potential to serve the oil and gas industry as suppliers. The specific findings were: Ugandan firms demonstrate relatively good performance in some important dimensions, including absorptive capacity and innovation. However, this is curbed by low levels of linkages with the academic and industrial sectors, limited exports and poor interaction with the financial sector The firms surveyed show an entrepreneurial behavior, which is encouraging for public policies promoting the private sector. Moreover, almost all the firms are privately owned. International standards, important in oil and gas operations, are not widely used. Plugging this gap is an opportunity that can be addressed by local content policies. The oil and gas industry is new in Uganda, yet 29 percent of the firms surveyed are already suppliers to the sector. The contribution to their sales represents only a small percentage. This low level of sales among a relatively high number of suppliers indicates a potentially positive impact on the local supply chain if appropriate local content policies are designed.

Effect of suicide rates on life expectancy dataset

Abstract In 2015, approximately 55 million people died worldwide, of which 8 million committed suicide. In the USA, one of the main causes of death is the aforementioned suicide, therefore, this experiment is dealing with the question of how much suicide rates affects the statistics of average life expectancy. The experiment takes two datasets, one with the number of suicides and life expectancy in the second one and combine data into one dataset. Subsequently, I try to find any patterns and correlations among the variables and perform statistical test using simple regression to confirm my assumptions.

Data

The experiment uses two datasets - WHO Suicide Statistics[1] and WHO Life Expectancy[2], which were firstly appropriately preprocessed. The final merged dataset to the experiment has 13 variables, where country and year are used as index: Country, Year, Suicides number, Life expectancy, Adult Mortality, which is probability of dying between 15 and 60 years per 1000 population, Infant deaths, which is number of Infant Deaths per 1000 population, Alcohol, which is alcohol, recorded per capita (15+) consumption, Under-five deaths, which is number of under-five deaths per 1000 population, HIV/AIDS, which is deaths per 1 000 live births HIV/AIDS, GDP, which is Gross Domestic Product per capita, Population, Income composition of resources, which is Human Development Index in terms of income composition of resources, and Schooling, which is number of years of schooling.

LICENSE

THE EXPERIMENT USES TWO DATASET - WHO SUICIDE STATISTICS AND WHO LIFE EXPECTANCY, WHICH WERE COLLEECTED FROM WHO AND UNITED NATIONS WEBSITE. THEREFORE, ALL DATASETS ARE UNDER THE LICENSE ATTRIBUTION-NONCOMMERCIAL-SHAREALIKE 3.0 IGO (https://creativecommons.org/licenses/by-nc-sa/3.0/igo/).

[1] https://www.kaggle.com/szamil/who-suicide-statistics

[2] https://www.kaggle.com/kumarajarshi/life-expectancy-who

Context

Income inequality is a global issue reflecting the uneven distribution of wealth within and between countries. Developed nations exhibit varying income levels due to economic policies and labor dynamics, resulting in Gini coefficients of around 0.3 to 0.4. Conversely, developing nations often experience higher income disparities due to limited access to education, healthcare, and jobs, leading to Gini coefficients exceeding 0.4, exacerbating poverty cycles and social tensions. This inequality hampers economic growth, social cohesion, and upward mobility. Addressing it requires comprehensive policies, including progressive taxation and equitable resource distribution, to promote a more just and inclusive society.

Content

This dataset comprises historical information encompassing various indicators concerning Inequality in Income on a global scale. The dataset prominently features: ISO3, Country, Continent, Hemisphere, Human Development Groups, UNDP Developing Regions, HDI Rank (2021), and Inequality in Income from 2010 to 2021.

Dataset Glossary (Column-wise)

• ISO3 - ISO3 for the Country/Territory
• Country - Name of the Country/Territory
• Continent - Name of the Continent
• Hemisphere - Name of the Hemisphere
• Human Development Groups - Human Development Groups
• UNDP Developing Regions - UNDP Developing Regions
• HDI Rank (2021) - Human Development Index Rank for 2021
• Inequality in Income from 2010 to 2021 - Inequality in Income from year 2010 to 2021

Data Dictionary

• UNDP Developing Regions:
  • SSA - Sub-Saharan Africa
  • LAC - Latin America and the Caribbean
  • EAP - East Asia and the Pacific
  • AS - Arab States
  • ECA - Europe and Central Asia
  • SA - South Asia

Structure of the Dataset

https://i.imgur.com/LIrXWPP.png" alt="">

Acknowledgement

This Dataset is created from Human Development Reports. This Dataset falls under the Creative Commons Attribution 3.0 IGO License. You can check the Terms of Use of this Data. If you want to learn more, visit the Website.

Cover Photo by: Image by Image by pch.vector on Freepik

Thumbnail by: Image by Salary icons created by Freepik - Flaticon

Description:

This dataset explores the factors influencing life expectancy across various countries and years, aiming to uncover patterns and disparities in health outcomes based on geographic locations. By examining key features such as adult mortality, alcohol consumption, healthcare expenditures, and socioeconomic indicators, this dataset provides insights into the complex interplay of factors shaping life expectancy worldwide.

Feature Description:

Source:

World Health Organization (WHO), United Nations (UN), World Bank, etc.

This map is made using content created and owned by the federal Department of Housing and Urban Development (Esri user HUD.Official.Content). The map uses their Low to Moderate Income Population by Tract layer, filtered for only census tracts in Monroe County, NY where at least 51% of households earn less than 80 percent of the Area Median Income (AMI). The map is centered on Rochester, NY, with the City of Rochester, NY border added for context. Users can zoom out to see the Revitalization Areas for the broader county region.The Community Development Block Grant (CDBG) program requires that each CDBG funded activity must either principally benefit low- and moderate-income persons, aid in the prevention or elimination of slums or blight, or meet a community development need having a particular urgency because existing conditions pose a serious and immediate threat to the health or welfare of the community and other financial resources are not available to meet that need. With respect to activities that principally benefit low- and moderate-income persons, at least 51 percent of the activity's beneficiaries must be low and moderate income. For CDBG, a person is considered to be of low income only if he or she is a member of a household whose income would qualify as "very low income" under the Section 8 Housing Assistance Payments program. Generally, these Section 8 limits are based on 50% of area median. Similarly, CDBG moderate income relies on Section 8 "lower income" limits, which are generally tied to 80% of area median. These data are derived from the 2011-2015 American Community Survey (ACS) and based on Census 2010 geography.Please refer to the Feature Layer for date of last update.Data Dictionary: DD_Low to Moderate Income Populations by Tract

This supplementary file provides comprehensive support for the findings and methodology presented in the study. It includes detailed outputs from the Principal Component Analysis (PCA), such as factor loadings, eigenvalues, and the percentage of variance explained, along with a full classification of the 37 Global Health Security Index (GHSI) indicators across the nine identified principal components. Additionally, it contains visualizations and datasets for all three clustering scenarios: one based on countries’ average scores across the nine extracted components, another using the 13 high-loading indicators from the first principal component, and a third based on aggregated scores from the six original GHSI categories. The file also presents the resulting cluster centroids, validation comparisons, and identified performance patterns. Together, these materials strengthen the credibility of the analytical approach and ensure transparency for replication, deeper analysis, and peer validation. All data are integrated into a single Excel-based tool that includes the underlying values used to generate the study’s tables and figures. This supplementary resource serves as a detailed and practical reference to replicate the study’s procedures and validate its results.

Country: The country to which the data belongs. Year: The year in which the data was collected. Status: Whether the country is classified as "Developing" or "Developed". Life expectancy (men): The average life expectancy of men in that country for that year. Life expectancy (women): The average life expectancy of women in that country for that year. Adult Mortality (men): The mortality rate amongst adult men in that country for that year. Adult Mortality (women): The mortality rate amongst adult women in that country for that year. Infant deaths: The number of infant deaths in that country for that year. Alcohol: Per capita alcohol consumption (in litres of pure alcohol) in that country for that year. Percentage expenditure: Expenditure on health as a percentage of Gross Domestic Product per capita(%). Hepatitis B (men): Hepatitis B vaccination coverage in men (%). Hepatitis B (women): Hepatitis B vaccination coverage in women (%). Measles: Number of reported cases of measles in that country for that year. BMI: Average Body Mass Index of the country's population. Under-five deaths: Number of deaths under five years old. Polio: Polio (Pol3) immunization coverage among 1-year-olds (%). Total expenditure: General government expenditure on health as a percentage of total government expenditure (%). Diphtheria: Diphtheria tetanus toxoid and pertussis (DTP3) immunization coverage among 1-year-olds (%). HIV/AIDS: Deaths per 1 000 live births HIV/AIDS (0-4 years). GDP: Gross Domestic Product per capita (in USD). Population: Population of the country. thinness 1-19 years: Prevalence of thinness among children and adolescents for Age 10 to 19 (%). thinness 5-9 years: Prevalence of thinness among children for Age 5 to 9(%). Income composition of resources: Human Development Index in terms of income composition of resources (index ranging from 0 to 1). Schooling: Number of years of Schooling(years).

Don't forget to upvote when find this useful

Unveiling the Health and Demographics Dataset: Illuminating Life Expectancy

Description: Step into the world of global health and demographics with our rich and comprehensive dataset. It's your passport to unraveling the secrets of life expectancy and understanding the pulse of population health. Dive into a treasure trove of valuable information for public health research and epidemiology, where each column tells a unique story about a nation's health journey.

Discover the Gems in Our Dataset:

• Country: Explore the global tapestry with data from diverse nations.
• Year: Unlock the passage of time and its impact on health trends.
• Status: Understand the development status, whether "Developed" or "Developing," that shapes the course of health.
• Life Expectancy: Peer into the crystal ball of population health, revealing how long people can expect to live.
• Adult Mortality: Gauge the probabilities of survival between ages 15 and 60 per 1,000 population.
• Infant Deaths: Delve into infant health with the number of infant deaths per 1,000 live births.
• Alcohol: Raise a glass to insights on average alcohol consumption in liters per capita.
• Percentage Expenditure: Unearth health expenditure as a percentage of a country's GDP.
• Hepatitis B: Measure immunization coverage for Hepatitis B.
• Measles: Examine the impact of this preventable disease with the number of reported cases per 1,000 population.
• BMI: Step onto the scales of national health with the average Body Mass Index.
• Under-Five Deaths: Shine a spotlight on child mortality with the number of deaths under age five per 1,000 live births.
• Polio: Inspect immunization coverage for Polio.
• Total Expenditure: Track the total health expenditure as a percentage of GDP.
• Diphtheria: Assess immunization coverage for Diphtheria.
• HIV/AIDS: Witness the prevalence of HIV/AIDS as a percentage of the population.
• GDP: Follow the financial pulse of a nation with Gross Domestic Product data.
• Population: Witness the ebb and flow of a nation's populace.
• Thinness 1-19 Years: Explore the prevalence of thinness among children and adolescents aged 1-19.
• Thinness 5-9 Years: Zoom in on thinness among children aged 5-9.
• Income Composition of Resources: Decode the composite index reflecting income distribution and resource access.
• Schooling: Measure the gift of knowledge with data on average years of schooling.

Predictive Targets: - The "Life Expectancy" column is your North Star, guiding the way to predictive insights. Harness the power of data to predict life expectancy using the mosaic of health and demographic indicators at your disposal.

Journey with the Data: 1. Predicting Life Expectancy: Embark on the quest to build regression models that forecast life expectancy for diverse countries and years based on this wealth of features. 2. Identifying Influential Factors: Uncover the gems within the dataset that influence life expectancy the most, providing valuable insights for public health interventions. 3. Health Policy Analysis: Assess the impact of health expenditure, immunization coverage, and disease prevalence on life expectancy and shape policies that safeguard population health.

This dataset is your window into the intricate world of global health. Join us on a journey of discovery as we explore the factors shaping life expectancy and navigate the waters of public health, epidemiology, and population health.

The Bureau of Health Professions Area Resource File is a county-based data file summarizing secondary data from a wide variety of sources into a single file to facilitate health analysis. The file contains over 6,000 data elements for all counties in the United States with the exception of Alaska, for which there is a state total, and certain independent cities that have been combined into their appropriate counties. The data elements include: (1) County descriptor codes (name, FIPS, HSA, PSRO, SMSA, SEA, BEA, city size, P/MSA, Census Contiguous County, shortage area designation, etc.), (2) Health professions data (number of professionals registered as M.D., D.O., DDS, R.N., L.P.N., veterinarian, pharmacist, optometrist, podiatrist, and dental hygienist), (3) Health facility data (hospital size, type, utilization, staffing and services, and nursing home data), (4) Population data (size, composition, employment, housing, morbidity, natality, mortality by cause, by sex and race, and by age, and crime data), (5) Health Professions Training data (training programs, enrollments, and graduates by type), (6) Expenditure data (hospital expenditures, Medicare enrollments and reimbursements, and Medicare prevailing charge data), (7) Economic data (total, per capita, and median income, income distribution, and AFDC recipients), and (8) Environment data (land area, large animal population, elevation, latitude and longitude of population centroid, water hardness index, and climate data).

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F16731800%2F8e1630ccacc7fec2f1851ad4ef7c8368%2FSem%20ttulo-1.png?generation=1707857613704062&alt=media" alt="">

OECD Revenue Statistics: Comparative Tables Introduction

The OECD Revenue Statistics database provides detailed and internationally comparable data on the taxes and social contributions paid by businesses and individuals in OECD countries. The data is collected annually from national governments and covers a wide range of taxes, including personal income tax, corporate income tax, social security contributions, and value-added tax.

Data

The database is divided into two main parts:

Part 1: Revenue by Level of Government This part of the database provides data on the total revenue collected by each level of government (central, state, and local) in each OECD country. The data is broken down by type of tax and by source of revenue (e.g., taxes on income, profits, and capital gains; taxes on goods and services; social security contributions).

Part 2: Revenue by Tax Type This part of the database provides data on the revenue collected from each type of tax in each OECD country. The data is broken down by level of government and by source of revenue.

Uses

The OECD Revenue Statistics database can be used for a variety of purposes, including:

Cross-country comparisons of tax levels and structures The database can be used to compare the tax levels and structures of different OECD countries. This information can be used by policymakers to assess the effectiveness of their tax systems and to identify potential areas for reform.

Analysis of the impact of tax policies The database can be used to analyze the impact of tax policies on economic growth, income distribution, and other outcomes. This information can be used by policymakers to design tax policies that are more effective and efficient.

Research on tax policy The database can be used by researchers to study the effects of tax policy on a variety of economic outcomes. This research can help to inform the design of tax policy and to improve our understanding of the economic effects of taxation.

Conclusion

The OECD Revenue Statistics database is a valuable resource for policymakers, researchers, and anyone interested in the taxation of businesses and individuals in OECD countries. The database provides detailed and internationally comparable data on a wide range of taxes, making it an essential tool for understanding the tax systems of OECD countries.

Data Access

The OECD Revenue Statistics database is available online to subscribers. Subscribers can access the data through the OECD's website.

Overview of the country characteristics in relation to healthcare system features: structure and process strength.

Abstract

Timor-Leste experienced a fundamental social and economic upheaval after its people voted for independence from Indonesia in a referendum in August 1999. Population was displaced, and public and private infrastructure was destroyed or rendered inoperable. Soon after the violence ceased, the country began rebuilding itself with the support from UN agencies, the international donor community and NGOs. The government laid out a National Development Plan (NDP) with two central goals: to promote rapid, equitable and sustainable economic growth and to reduce poverty.

Formulating a national plan and poverty reduction strategy required data on poverty and living standards, and given the profound changes experienced, new data collection had to be undertaken to accurately assess the living conditions in the country. The Planning Commission of the Timor-Leste Transitional Authority undertook a Poverty Assessment Project along with the World Bank, the Asian Development Bank, the United Nations Development Programme and the Japanese International Cooperation Agency (JICA).

This project comprised three data collection activities on different aspects of living standards, which taken together, provide a comprehensive picture of well-being in Timor-Leste. The first component was the Suco Survey, which is a census of all 498 sucos (villages) in the country. It provides an inventory of existing social and physical infrastructure and of the economic characteristics of each suco, in addition to aldeia (hamlet) level population figures. It was carried out between February and April 2001.

A second element was the Timor-Leste Living Standards Measurement Survey (TLSS). This is a household survey with a nationally representative sample of 1,800 families from 100 sucos. It was designed to diagnose the extent, nature and causes of poverty, and to analyze policy options facing the country. It assembles comprehensive information on household demographics, housing and assets, household expenditures and some components of income, agriculture, labor market data, basic health and education, subjective perceptions of poverty and social capital.

Data collection was undertaken between end August and November 2001.

The final component was the Participatory Potential Assessment (PPA), which is a qualitative community survey in 48 aldeias in the 13 districts of the country to take stock of their assets, skills and strengths, identify the main challenges and priorities, and formulate strategies for tackling these within their communities. It was completed between November 2001 and January 2002.

Geographic coverage

National coverage. Domains: Urban/rural; Agro-ecological zones (Highlands, Lowlands, Western Region, Eastern Region, Central Region)

Kind of data

Sample survey data [ssd]

Sampling procedure

SAMPLE SIZE AND ANALYTIC DOMAINS

A survey relies on identifying a subgroup of a population that is representative both for the underlying population and for specific analytical domains of interest. The main objective of the TLSS is to derive a poverty profile for the country and salient population groups. The fundamental analytic domains identified are the Major Urban Centers (Dili and Baucau), the Other Urban Centers and the Rural Areas. The survey represents certain important sub-divisions of the Rural Areas, namely two major agro-ecologic zones (Lowlands and Highlands) and three broad geographic regions (West, Center and East). In addition to these domains, we can separate landlocked sucos (Inland) from those with sea access (Coast), and generate categories merging rural and urban strata along the geographic, altitude, and sea access dimensions. However, the TLSS does not provide detailed indicators for narrow geographic areas, such as postos or even districts. [Note: Timor-Leste is divided into 13 major units called districts. These are further subdivided into 67 postos (subdistricts), 498 sucos (villages) and 2,336 aldeias (sub-villages). The administrative structure is uniform throughout the country, including rural and urban areas.]

The survey has a sample size of 1,800 households, or about one percent of the total number of households in Timor-Leste. The experience of Living Standards Measurement Surveys in many countries - most of them substantially larger than Timor-Leste - has shown that samples of that size are sufficient for the requirements of a poverty assessment.

The survey domains were defined as follows. The Urban Area is divided into the Major Urban Centers (the 31 sucos in Dili and the 6 sucos in Baucau) and the Other Urban Centers (the remaining 34 urban sucos outside Dili and Baucau). The rest of the country (427 sucos in total) comprises the Rural Area. The grouping of sucos into urban and rural areas is based on the Indonesian classification. In addition, we separated rural sucos both by agro-ecological zones and geographic areas. With the help of the Geographic Information System developed at the Department of Agriculture, sucos were subsequently qualified as belonging to the Highlands or the Lowlands depending on the share of their surface above and below the 500 m level curve. The three westernmost districts (Oecussi, Bobonaro and Cova Lima) constitute the Western Region, the three easternmost districts (Baucau, Lautem and Viqueque) the Eastern Region, and the remaining seven districts (Aileu, Ainaro, Dili, Ermera, Liquica, Manufahi and Manatuto) belong to the Central Region.

SAMPLING STRATA AND SAMPLE ALLOCATION

Our next step was to ensure that each analytical domain contained a sufficient number of households. Assuming a uniform sampling fraction of approximately 1/100, a non-stratified 1,800-household sample would contain around 240 Major Urban households and 170 Other Urban households -too few to sustain representative and significant analyses. We therefore stratified the sample to separate the two urban areas from the rural areas. The rural strata were large enough so that its implicit stratification along agro-ecological and geographical dimensions was sufficient to ensure that these dimensions were represented proportionally to their share of the population. The final sample design by strata was as follows: 450 households in the Major Urban Centers (378 in Dili and 72 in Baucau), 252 households in the Other Urban Centers and 1,098 households in the Rural Areas.

SAMPLING STRATEGY

The sampling of households in each stratum, with the exception of Urban Dili, followed a 3-stage procedure. In the first stage, a certain number of sucos were selected with probability proportional to size (PPS). Hence 4 sucos were selected in Urban Baucau, 14 in Other Urban Centers and 61 in the Rural Areas. In the second stage, 3 aldeias in each suco were selected, again with probability proportional to size (PPS). In the third stage, 6 households were selected in each aldeia with equal probability (EP). This implies that the sample is approximately selfweighted within the stratum: all households in the stratum had the same chance of being visited by the survey.

A simpler and more efficient 2-stage process was used for Urban Dili. In the first stage, 63 aldeias were selected with PPS and in the second stage 6 households with equal probability in each aldeia (for a total sample of 378 households). This procedure reduces sampling errors since the sample will be spread more than with the standard 3-stage process, but it can only be applied to Urban Dili as only there it was possible to sort the selected aldeias into groups of 3 aldeias located in close proximity of each other.

HOUSEHOLD LISTING

The final sampling stage requires choosing a certain number of households at random with equal probability in each of the aldeias selected by the previous sampling stages. This requires establishing the complete inventory of all households in these aldeias - a field task known as the household listing operation. The household listing operation also acquires importance as a benchmark for assessing the quality of the population data collected by the Suco Survey, which was conducted in February-March 2001. At that time, the number of households currently living in each aldeia was asked from the suco and aldeia chiefs, but there are reasons to suspect that these figures are biased. Specifically, certain suco and aldeia chiefs may have answered about households belonging, rather than currently living, in the aldeias, whereas others may have faced perverse incentives to report figures different from the actual ones. These biases are believed to be more serious in Dili than in the rest of the country.

Two operational approaches were considered for the household listing. One is the classical doorto-door (DTD) method that is generally used in most countries for this kind of operations. The second approach - which is specific of Timor-Leste - depends on the lists of families that are kept by most suco and aldeia chiefs in their offices. The prior-list-dependent (PLD) method is much faster, since it can be completed by a single enumerator in each aldeia, working most of the time in the premises of the suco or aldeia chief; however, it can be prone to biases depending on the accuracy and timeliness of the family lists.

After extensive empirical testing of the weaknesses and strengths of the two alternatives, we decided to use the DTD method in Dili and an improved version of the PLD method elsewhere. The improvements introduced to the PLD consisted in clarifying the concept of a household "currently living in the aldeia", both by intensive training and supervision of the enumerators and by making its meaning explicit in the form's wording (it means that the household members are regularly eating and sleeping in the aldeia at the time of the operation). In addition,

Eigenvalues and variance explained by principal components.

Internationally comparable tax revenue data from countries included in Global Revenue Statistics, presented as a percentage of GDP and as a share of total tax revenue, as well as in national currency and US dollars. Tax revenues are harmonised according to the OECD classification of taxes.

Related topics: Tax-to-GDP, Taxation, Tax structure, Tax mix, Regional average – change for each region, Domestic resource mobilisation, Public finance, Income tax, Social security contributions, Goods and services, Value added tax, VAT, Excise, Customs, Property tax.

Time series data for the data Current Account and Its Components - Current USD, TTM for the country United States. The Current Account and Its Components The current account is a component of a country's balance of payments that records the transactions of goods, services, income, and current transfers between residents of the country and the rest of the world. It consists of four main components:

a. Trade in Goods Balance

b. Trade in Services Balance

c. Primary Income Balance

d. Secondary Income Balance

1. Trade in Goods Balance Definition: This includes the export and import of physical items such as machinery, food, clothing, etc.

Credit Example: A German car manufacturer exports cars to the United States (value of exported cars).

Debit Example: A German electronics retailer imports smartphones from South Korea (value of imported smartphones).

1. Trade in Services Balance Definition: This includes the export and import of services such as tourism, financial services, consulting, transportation, etc.

Credit Example: A German IT company provides software development services to a client in Japan (value of exported services).

Debit Example: A German tourist books a hotel room in France (value of imported tourism services).

1. Primary Income Balance Definition: This includes earnings from the provision of factors of production such as labor, financial assets, land, and natural resources. It covers income from interest, profits, and dividends.

Credit Example: A German investor receives dividends from shares held in a U.S. company (value of received dividends).

Debit Example: Foreign investors receive interest payments on bonds issued by a German company (value of interest payments).

1. Secondary Income Balance Definition: This includes current transfers such as foreign aid, remittances, and other one-way payments that do not involve an exchange of goods or services.

Credit Example: Remittances sent by German residents working abroad to their families in Germany (value of received remittances).

Debit Example: Germany sends humanitarian aid to a developing country (value of sent aid). Current Account Balance (USD)The indicator "Current Account Balance (USD)" stands at -1.37 Trillion United States Dollars as of 3/31/2025, the lowest value at least since 6/30/2001, the period currently displayed. Regarding the One-Year-Change of the series, the current value constitutes an decrease of -0.4138 Trillion United States Dollars compared to the value the year prior.The 1 year change is -0.4138 Trillion United States Dollars.The 3 year change is -0.4223 Trillion United States Dollars.The 5 year change is -0.9494 Trillion United States Dollars.The 10 year change is -0.9961 Trillion United States Dollars.The Serie's long term average value is -0.579 Trillion United States Dollars. It's latest available value, on 3/31/2025, is -0.795 Trillion United States Dollars lower, compared to it's long term average value.The Serie's change in United States Dollars from it's minimum value, on 3/31/2025, to it's latest available value, on 3/31/2025, is +0.0 Trillion.The Serie's change in United States Dollars from it's maximum value, on 6/30/2014, to it's latest available value, on 3/31/2025, is -1.04 Trillion.Trade in Services Balance (USD)The indicator "Trade in Services Balance (USD)" stands at 0.3089 Trillion United States Dollars as of 3/31/2025. Regarding the One-Year-Change of the series, the current value constitutes an increase of 0.0158 Trillion United States Dollars compared to the value the year prior.The 1 year change is 0.0158 Trillion United States Dollars.The 3 year change is 0.0674 Trillion United States Dollars.The 5 year change is 0.012 Trillion United States Dollars.The 10 year change is 0.0373 Trillion United States Dollars.The Serie's long term average value is 0.187 Trillion United States Dollars. It's latest available value, on 3/31/2025, is 0.122 Trillion United States Dollars higher, compared to it's long term average value.The Serie's change in United States Dollars from it's minimum value, on 12/31/2003, to it's latest available value, on 3/31/2025, is +0.2635 Trillion.The Serie's change in United States Dollars from it's maximum value, on 12/31/2024, to it's latest available value, on 3/31/2025, is -0.003 Trillion.Trade in Goods Balance (USD)The indicator "Trade in Goods Balance (USD)" stands at -1.40 Trillion United States Dollars as of 3/31/2025, the lowest value at least since 6/30/2001, the period currently displayed. Regarding the One-Year-Change of the series, the current value constitutes an decrease of -0.3327 Trillion United States Dollars compared to the value the year prior.The 1 year change is -0.3327 Trillion United States Dollars.The 3 year change is -0.2509 Trillion United States Dollars.The 5 year change is -0.5657 Trillion United States Dollars.The 10 year change is -0.6467 Trillion United States Dollars.The ...

Abstract

THE CLEANED AND HARMONIZED VERSION OF THE SURVEY DATA PRODUCED AND PUBLISHED BY THE ECONOMIC RESEARCH FORUM REPRESENTS 100% OF THE ORIGINAL SURVEY DATA COLLECTED BY THE NATIONAL INSTITUTE OF STATISTICS - TUNISIA (INS)

The National Survey on Household Budget, Consumption, and Standard of Living, 2005 is a quinquennial survey. It is the eighth survey of its kind that was carried out by the National Institute of Statistics (INS) in Tunisia. The seven previous surveys were conducted in 1968, 1975, 1980, 1985, 1990, 1995 and 2000, concurrently with the preparatory work for the Tunisian development plans. The 2005 survey was conducted as part of the preparation work for the Tenth Development Plan (2007-2011). Its expected findings would allow assessing the progress made in the improvements of the living level & conditions of the population.

The survey aims at providing detailed information on the procurement of goods and services for consumption (food consumption as well as household access to community services of health and education). And its data was collected from direct observation of household consumption to allow for having the necessary elements to assess the situation & changes in the living standards & conditions of the households.

Thus, the 2005 survey tackles three major areas of study: 1 - Household expenditure and acquisitions during the survey period 2 - Food consumption and nutritional status of households. 3 - Household access to community services of health and education.

The objectives of the survey are: a- Identifying levels of expenditure on the household level: The survey aims to assess the levels of household expenditure .The total expenditure of the household, is not only an indicator of income, but it is also a quantitative assessment of the standard of living index.

b- Income distribution: Due to the absence of data on income distribution, the mass distribution of expenditure between the different categories of the population constitutes a first outline for the income distribution in the country.

c- Investigating the structure of expenditure: Detailed information collected on expenditures per product used to establish the structures of the household expenditure as well as the budget coefficients according to different levels of classifications of goods in the nomenclature of goods and services. These factors coefficients are particularly useful for revision and development of the weights of the Consumer Prices Index (CPI). It should also be noted that the change in expenditure structure is an indicator of the evolution of living standards.

d- Analysis of household demand: Household behavior in terms of product demand is synthesized by the coefficients of income elasticity which, according to the model of consumption retained and under the assumptions of the growth of income and population, allows predicting future household demand.

e- Resources-use balance in the national accounts: The results related to the consumption by product are necessary elements for the development of balanced resource-use of products in the frame of national accounts.

The raw survey data provided by the Statistical Agency were cleaned and harmonized by the Economic Research Forum, in the context of a major project that started in 2009. During which extensive efforts have been exerted to acquire, clean, harmonize, preserve and disseminate micro data of existing household surveys in several Arab countries.

Geographic coverage

Covering a sample of all urban, small and medium towns and rural areas.

Analysis unit

1- Household/family. 2- Individual/person.

Universe

The survey covered a national sample of households and all individuals permanently residing in surveyed households.

Kind of data

Sample survey data [ssd]

Sampling procedure

THE CLEANED AND HARMONIZED VERSION OF THE SURVEY DATA PRODUCED AND PUBLISHED BY THE ECONOMIC RESEARCH FORUM REPRESENTS 100% OF THE ORIGINAL SURVEY DATA COLLECTED BY THE NATIONAL INSTITUTE OF STATISTICS - TUNISIA (INS)

Sampling method

The National Survey on Household Budget, Consumption and Standard of Living, 2005 has focused initially on a sample of 13,392 households representing 0.61% of total households in the country (61 surveyed household for every 10,000 household) . This sample is distributed across 1116 districts covering all the country governorates, cities, urban and rural areas. The sample was also equally divided on the months of the survey year to take the seasonal changes in household expenditure into account.

These households were drawn using a two stages stratified random sampling in each governorate. The sampling frame follows that of the general Census of Population and Housing in 2004.

Stratification criteria: The sampling frame is stratified by two geographical criteria: namely the governorate and the living area. The latter is stratified as follows: large municipalities, medium and small towns, major cities and the rest of the non-municipal areas. These stratification criteria (governorate, habitat and size of municipalities) represent the differentiation variable of lifestyles households. Strata used are as follows:

Stratum of large cities (stratum 1): the municipalities of the city of Tunis and its suburbs, the city of Bizerte and its suburbs, the city of Sousse and its suburbs, the city of Kairouan and its suburbs, the city Sfax and its suburbs, and the general Gabes. Thus, this stratum is formed of large urban centers corresponding to municipalities with more than 100.000 inhabitants and neighboring municipalities.

Stratum of other cities (stratum 2): This is all small and medium sized cities other than those classified in the stratum of large cities.

Stratum of the main cities (stratum 3): These are non-municipal urban areas classified as major cities during the general census of population and housing 2004 (with a population of more than 70 households).a city is considered a main city if the number of its inhabitants exceeds 400 during the census of 2004.

Stratum dispersed outside communes (stratum 4): These are areas of land located outside the main towns and cities. Households in these areas live in houses scattered or grouped in small towns.

This strata classification is closely related to the levels of household income and lifestyle.

Survey type:

The sampling frame is divided on the level of each governorate according to strata previously defined. It was set, at the level of each stratum, to make a two-stage random sampling for the selection of the household survey sample. This drawing process allows to breakdown the sample into clusters of 12 households relatively little distant from each other, thereby facilitating the conduct of the survey at the time of the information collection in the field

In the first stage: a sample of primary units is drawn in proportion to their size in number of households as they were identified. Taking into consideration that the primary units correspond to the districts that have been defined in the census of the population and these geographic areas contain on average 70 households.

In the second stage: in each sampled district, 12 households are selected according to the following method: The households in each sampled district are classified primarily according to the number of employed persons in the household. Within each category of classified households, households are also classified according to the number of persons in each household. A systematic sampling is then performed to select 12 sampled households per primary unit (sampled district). For each sampled district, another 12 households are drawn according to the same previously illustrated criteria. These households serve as a substitutive sample so that in case the interviewer failed to get in contact with the originally selected household (due to long absence- change of place of residence) , after coordinating with the supervisor, this household can be replaced by one from the substitutive sample. For this purpose, two lists of the names of head of households were developed (original list, substitutive list) that the survey is supposed to cover.

Distribution of districts and households sampled by governorates

Abstract

The purpose of the Household Income and Expenditure Survey (HIES) survey is to obtain information on the income, consumption pattern, incidence of poverty, and saving propensities for different groups of people in Palau. This information will be used to guide policy makers in framing socio-economic developmental policies and in initiating financial measures for improving economic conditions of the people.

Some more specific outputs from the survey are listed below:

a) To obtain expenditure weights and other useful data for the revision of the consumer price index; b) To supplement the data available for use in compiling official estimates of household accounts in the systems of national accounts; c) To supply basic data needed for policy making in connection with social and economic planning, including producing as many of Palau's National Minimum Development Indicators (NMDI's) as possible; d) To provide data for assessing the impact on household living conditions of existing or proposed economic and social measures, particularly changes in the structure of household expenditures and in household consumption; e) To gather information on poverty lines and incidence of poverty throughout Palau.

Geographic coverage

National Coverage, excluding Sonsorol and Hatohobei. Urban and Rural.

Analysis unit

• Households;
• Individuals.

Universe

All private households and group quarters (people living in Work dormitories, as it is an important aspect of the subject matter focused on in this survey, and not addressed elsewhere).

Kind of data

Sample survey data [ssd]

Sampling procedure

The sampling frame used was the 2012 Palau census, which provided population figures for everyone living in both private households and group quarters (e.g. worker barracks, school dormitories, prison). The sampling selection was done separately in private dwellings and group quarters.

It is an accepted practice for the Household Income and Expenditure Survey (HIES) to cover all living quarters regarded as private dwellings, and the Palau 2013/14 HIES will follow this recommendation.

For group quarters it is also recommended to exclude the prison, as it is not considered appropriate to include such institutions in a survey such as HIES.

A decision as to whether the remaining group quarters should be included is based on the following criteria:

1) Ease in accessing and covering them in a survey such as HIES 2) Relevance to the subject matter of the survey 3) Whether their impact on the subject matter is mostly covered already

Under these criteria, the following recommendations are made: -School/college dormitories: Will exclude from HIES as these individuals will be covered in the households from which they came (if selected) -Work dormitories: Aim to include in the HIES as they are an important aspect of the subject matter focused on in this survey, and not addressed elsewhere -Live aboard: Will exclude due to the movement of such vehicles, and the minimal impact they may have on such a survey -Convents/religious quarters: Will exclude based on their expected minimum impact on the survey subject matter

NB: Given students in dorms are expected to have a high portion of their income and expenses covered in their original household of origin, and there were no religious group quarters identified during the census, only persons in the prison and living aboard are expected to be excluded from the survey. These people account for 81 out of 2,322 group quarters residents (only 3.6%).

Although the response rates were down in the 2006 HIES, with a smaller more experienced team working over 12 months, it is expected there will be improvements in this area. However, the expected sample loss of 10 per cent was probably too ambitious, and given the actual rate ended up at 287/1,063 = 27 per cent, it is more realistic to assume a sample loss of around 15 per cent with improvements for the 2013/14 HIES. Based on the RSEs presented in 2.3.2, it also appears that the 20 per cent desirable sample produced sound results for the survey, and with higher response rates anticipated, these results from a sample error perspective should improve. It is therefore proposed for the 2013/14 Palau HIES that a sample size of 20 per cent be adopted, which also allows for sample loss of 15 per cent.

In the 2006 Palau HIES, effort was made to design a sample which could produce results for the six domains (stratum). Whilst reasonable results were generated for each of these domains, it was felt that post survey, there was no great use of these results at that level. For the 2013 HIES it is proposed to focus on generating reliable results at the national level, with focus also being place on producing results for the urban/rural split. In the case of Palau, the urban population is considered to consist of the states of Koror and Airai.

The last phase to finalizing the sample numbers was to adjust the desirable sample numbers, so that they could be easily applied by the HIES team in a practical manner over the course of the 12 month fieldwork. This was achieved by modifying the sample counts (not too much) to enable sample sizes each round would be of a similar size, and workloads for each enumerator were the same size each round. The desirable workload for an enumerator covering the PD population was 10 households, whereas this figure was increased to 14 persons for GQs as it was envisaged the amount of time required to cover a person in a GQ would be significantly less. With this in mind, we wanted to ideally have the PD sample to be divisible by 160 so this would enable an even number of households each round, whilst maintaining a workload of 10 households for interviewers covering these areas. For the GQ sample, given the desirable number of GQs was already 225, and 16x14=224, then a simple reduction of 1 in the GQ sample would result in a nice even workload of 14 persons per round for 1 interviewer. This logic was also applied to the split between urban and rural resulting in 14 workloads in urban and 2 workloads in rural.

Mode of data collection

Face-to-face [f2f]

Research instrument

Developped in English, a questionnaire consisting of four Modules and a Weekly Diary covering 2 weeks was used for the Republic of Palau Household Income and Expenditure Survey (HIES) 2013. Each Module covers distinct but connected portion of the Household.

The Modules are as follows: -Module 1 - Demographic Information: · Demographic Profile · Labor Force Status · Health Status · Communication Status -Module 2 - Household Expenditure: · Housing Characteristics · Housing Tenure Expenditure · Utilities & Communication Details · Utilities & Communication Expenditure · Land & Home Details · Land & Home Expenditure · Household Goods & Assets Details · Household Goods & Assets Expenditures · Vehicles & Accessories Details · Vehicles & Accessories Expenditures · Private Travel Details · Private Travel Expenditures · Household Services Expenditure · Contributions to Special Occasions · Provisions of Financial Support · Loans · Household Assets Insurance & Taxes · Personal Insurance -Module 3 - Individual Expenditures: · Education grants and scholarships · Education Identifications · Education Expenditures · Health Identifications · Health Expenditures · Clothing Identification · Clothing Expenditure · Communication Identification · Communication Expenditures · Luxury Items Identification · Luxury Items Expenditures -Module 4 - Income: · Wages & Salary: In country (current) · Wages & Salary: Overseas (last 12 months) · Wages & Salary: In country (last 12 months) · Income from Non Subsistence Business · Description of Agriculture & Forestry Activities · Income from Agriculture & Forestry Activities · Description of Handicraft & Home Processed Food Activities · Income from Handicraft & Home Processed Food Activities · Description of Livestock & Aquaculture Activities · Income from Livestock & Aquaculture Activities · Description of Fishing & Hunting Activities · Income from Fishing & Hunting Activities · Property Income, Transfer Income & Other Receipts · Remittances & Other Cash Gifts -Weekly Diary - Covering 14 Days (2 weeks): · Daily expenditure of food and non-food items · Payments of service made · Gambling winning and losses · Items received for free · Home produced food and non-food items.

All questionnaires are provided as external resources in this documentation.

Cleaning operations

Program: CSPro 5.1x

Data editing took place at a number of stages throughout the processing, including:

a) Office editing and coding b) During data entry; Error report correction; Secondary editing by Quality Control Officer (QCO) c) Structure checking and completeness

Detailed documentation of the editing of data can be found in the "Data processing guidelines" document provided as an external resource.

Response rate

Some 1,145 households were selected (in private dwellings and workers quarters) to participate in the survey, and the response rate was 75.8% (i.e. 869 households responded). This response rate allows for statistically significant analysis at the national, urban and rural level.

Response rates for private households by State: -Koror: 355 households responded out of 480 selected => 73.9%; -Airai: 119 households responded out of 160 selected => 74.4%; -URBAN: 474 households responded out of 640 selected => 74.1%; -Kayangel: 0 households responded out of 10 selected => 0%; -Ngarchelong: 27 households responded out of 30 selected => 90%; -Ngaraard: 22 households responded

Abstract

The Integrated Living Conditions Survey (ILCS), conducted annually by the NSS National Statistical Service of the Republic of Armenia, formed the basis for monitoring living conditions in Armenia. The ILCS is a universally recognized best-practice survey for collecting data to inform about the living standards of households. The ILCS comprises comprehensive and valuable data on the welfare of households and separate individuals which gives the NSS an opportunity to provide the public with up to date information on the population’s income, expenditures, the level of poverty and the other changes in living standards on an annual basis.

Geographic coverage

Urban and rural communities

Analysis unit

• Households;
• Individuals.

Kind of data

Sample survey data [ssd]

Sampling procedure

During the 2001-2003 surveys two-stage random sample was used; the first stage covered the selection of settlements - cities and villages, while the second stage was focused on the selection of households in these settlements. The surveys were conducted on the principle of monthly rotation of households by clusters (sample units). In 2002 and 2003 the number of households was 387 with the sample covering 14 cities and 30 villages in 2002 and 17 cities and 20 villages in 2003.

During the 2004-2006 surveys the sampling frame for the ILCS was built using the database of addresses for the 2001 Population Census; the database was developed with the World Bank technical assistance. The database of addresses of all households in Armenia was divided into 48 strata including 12 communities of Yerevan city. The households from other regions (marzes) were grouped according to the following three categories: big towns with 15,000 and more population; villages, and other towns. Big towns formed 16 strata (the only exception was the Vayots Dzor marz where there are no big towns). The villages and other towns formed 10 strata each. According to this division, a random, two-step sample stratified at marz level was developed. All marzes, as well as all urban and rural settlements were included in the sample population according to the share of population residing in those settlements as percent to the total population in the country. In the first step, the settlements, i.e. primary sample units, were selected: 43 towns out of 48 or 90 percent of all towns in Armenia were surveyed during the year; also 216 villages out of 951 or 23 percent of all villages in the country were covered by the survey. In the second step, the respondent households were selected: 6,816 households (5,088 from urban and 1,728 from rural settlements). As a result, for the first time since 1996 survey data were representative at the marz level.

During the 2007-2012 surveys the sampling frame for ILCS was designed according to the database of addresses for the 2001 Population Census, which was developed with the World Bank technical assistance. The sample consisted of two parts: core sample and oversample.

1) For the creation of core sample, the sample frame (database of addresses of all households in Armenia) was divided into 48 strata including 12 communities of Yerevan city. The households from other regions (marzes) were grouped according to three categories: large towns (with population of 15000 and higher), villages and other towns. Large towns formed by 16 groups (strata), while the villages and towns formed by 10 strata each. According to that division, a random, two-step sample stratified at the marz level was developed. All marzes, as well as all urban and rural settlements were included in the sample population according to the share of households residing in those settlements as percent to the total households in the country. In the first step, using the PPS method the enumeration units (i.e., primary sample units to be surveyed during the year) were selected. 2007 sample includes 48 urban and 18 rural enumeration areas per month. 2) The oversample was drawn from the list of villages included in MCA-Armenia Rural Roads Rehabilitation Project. The enumeration areas of villages that were already in the core sample were excluded from that list. From the remaining enumeration areas 18 enumeration areas were selected per month. Thus, the rural sample size was doubled. 3) After merging the core sample and oversample, the survey households were selected in the second step. 656 households were surveyed per month, from which 368 from urban and 288 from rural settlements. Each month 82 interviewers had conducted field work, and their workload included 8 households per month. In 2007 number of surveyed households was 7,872 (4,416 from urban and 3,456 from rural areas).

For the survey 2013 the sample frame for ILCS was designed in accordance with the database of addresses of all private households in the country developed on basis of the 2001 Population Census results, with the technical assistance of the World Bank. The method of systematic representative probability sampling was used to frame the sample. For the purpose of drawing the sample, the sample frame was divided into 32 strata including 12 communities of Yerevan City (currently, the administrative districts). According to this division, a two-tier sample was drawn stratified by regions and by Yerevan. All regions and Yerevan, as well as all urban and rural communities were included in the sample in accordance to the shares of their resident households within the total number of households in the country. In the first round, enumeration areas - that is primary sample units to be surveyed during the year - were selected. The ILCS 2013 sample included 32 enumeration areas in urban and 16 enumeration areas in rural communities per month. The households to be surveyed were selected in the second round. A total of 432 households were surveyed per month, of which 279 and 153 households from urban and rural communities, respectively. Every month 48 interviewers went on field work with a workload of 9 households per month.

The sample frame for 2014-2016 was designed in accordance with the database of addresses of all private households in the country developed on basis of the 2011 Population Census results, with the technical assistance of the World Bank. The method of systematic representative probability sampling was used to frame the sample.
For drawing the sample, the sample frame was divided into 32 strata including 12 communities of Yerevan City (currently, the administrative districts). According to this division, a two-tier sample was drawn stratified by regions and by Yerevan. All regions and Yerevan, as well as all urban and rural communities were included in the sample in accordance to the shares of their resident households within the total number of households in the country. In the first round, enumeration areas - that is primary sample units to be surveyed during the year - were selected. The ILCS 2014 sample included 30 enumeration areas in urban and 18 enumeration areas in rural communities per month. The method of representative probability sampling was used to frame the sample. At regional level, all communities were grouped into two categories - towns and villages. According to this division, a two-tier sample was drawn stratified by regions and by Yerevan. All regions and Yerevan, as well as all rural and urban communities were included in the sample in accordance to the shares of their resident households within the total number of households in the country. In the first round, enumeration districts - that is primary sample units to be surveyed during the year - were selected. The ILCS 2015 sample included 30 enumeration districts in urban and 18 enumeration districts in rural communities per month.

Mode of data collection

Face-to-face [f2f]

Research instrument

The Questionnaire is filled in by the interviewer during the least five visits to households per month. During face-to-face interviews with the household head or another knowledgeable adult member, the interviewer collects information on the composition and housing conditions of the household, the employment status, educational level and health condition of the members, availability and use of land, livestock, and agricultural machinery, monetary and commodity flows between households, and other information.

The 2015 survey questionnaire had the following sections: (1) "List of Household Members", (2) "Migration", (3) "Housing and Dwelling Conditions", (4) "Employment", (5) "Education", (6) "Agriculture", (7) "Food Production", (8) "Monetary and Commodity Flows between Households", (9) "Health (General) and Healthcare", (10) "Debts", (11) "Subjective Assessment of Living Conditions", (12) "Provision of Services", (13) "Social Assistance", (14) "Households as Employers for Service Personnel", and (15) "Household Monthly Consumption of Energy Resources".

The Diary is completed directly by the household for one month. Every day the household would record all its expenditures on food, non-food products and services, also giving a detailed description of such purchases; e.g. for food products the name, quantity, cost, and place of purchase of the product is recorded. Besides, the household records its consumption of food products received and used from its own land and livestock, as well as from other sources (e.g. gifts, humanitarian aid). Non-food products and services purchased or received for free are also recorded in the diary. Then, the household records its income received during the month. At the end of the month, information on rarely used food products, durable goods and ceremonies is recorded, as well. The records in the diary are verified by the interviewer in the course of 5

About the Project Natural Resource-led Development in New Producing Countries Our project seeks to understand how natural resource extraction can drive inclusive economic growth in new producing countries. We are engaged in a multiyear multidisciplinary study with four objectives:

Understand the human geography of new producing countries. Assess the magnitude of new discoveries and estimate direct fiscal impact. Understand how industry can be localized to create economic growth. Estimate spillovers and welfare impacts to society.

We recognize that policymaking in new producing countries is a complex process, and our project also seeks to understand the interactions of actors' interests that drive energy sector policies. Our initial focus is on four countries – Kenya, Mozambique, Tanzania and Uganda – that expect to develop significant oil and gas reserves in the next 5-7 years. Through natural resource development, these countries hope to achieve middle-income economic status by 2030-2040. This project is conducted through close collaboration with leading think tanks and NGOs in Africa Summary I n this paper we present an overview of KAPSARC’s local content project, its methodological approach and objective. We describe a measurement device (questionnaire) used to quantify different features of local firms surveyed in Uganda. The data collected in these questionnaires provides essential information to help analyze the current state of local firms in Uganda as they gear up to join the oil and gas supply chain. We also comment on each section of the form explaining its purpose. The questionnaire is attached as part of this document in the Appendix. The design of the questionnaire allows for a combined approach to examining local firms. On the one hand, it is based on selected basic requirements of the oil and gas industry, which companies must usually fulfill if they aspire to be a supplier. On the other, it also considers the local firms from a dynamic perspective, focusing on their potential to adapt and carve a role in the oil and gas supply chain. This perspective is centered on the concepts of innovation and absorptive capacity, which are increasingly considered when evaluating small and medium enterprises (SMEs) at the firm level. The questionnaire was designed to be used in faceto-face interviews, which is usually recommended for surveys carried out in Africa. The document is relatively lengthy and is intended to be utilized in developing nations, although a few questions would have to be adapted to the characteristics of each country based on the level of development and exposure to the oil and gas industry. The questionnaire includes five sections to make it more comprehensible. The data collected is extensive, allowing for a variety of analytical approaches.