The “richness index” represents the level of economical wellbeing a country certain area in 2010. Regions with higher income per capita and low poverty rate and more access to market are wealthier and are therefore better able to prepare for and respond to adversity. The index results from the second cluster of the Principal Component Analysis preformed among 9 potential variables. The analysis identifies four dominant variables, namely “GDPppp per capita”, “agriculture share GDP per agriculture sector worker”, “poverty rate” and “market accessibility”, assigning weights of 0.33, 0.26, 0.25 and 0.16, respectively. Before to perform the analysis all variables were log transformed (except the “agriculture share GDP per agriculture sector worker”) to shorten the extreme variation and then were score-standardized (converted to distribution with average of 0 and standard deviation of 1; inverse method was applied for the “poverty rate” and “market accessibility”) in order to be comparable. The 0.5 arc-minute grid total GDPppp is based on the night time light satellite imagery of NOAA (see Ghosh, T., Powell, R., Elvidge, C. D., Baugh, K. E., Sutton, P. C., & Anderson, S. (2010).Shedding light on the global distribution of economic activity. The Open Geography Journal (3), 148-161) and adjusted to national total as recorded by International Monetary Fund for 2010. The “GDPppp per capita” was calculated dividing the total GDPppp by the population in each pixel. Further, a focal statistic ran to determine mean values within 10 km. This had a smoothing effect and represents some of the extended influence of intense economic activity for the local people. Country based data for “agriculture share GDP per agriculture sector worker” were calculated from GDPppp (data from International Monetary Fund) fraction from agriculture activity (measured by World Bank) divided by the number of worker in the agriculture sector (data from World Bank). The tabular data represents the average of the period 2008-2012 and were linked by country unit to the national boundaries shapefile (FAO/GAUL) and then converted into raster format (resolution 0.5 arc-minute). The first administrative level data for the “poverty rate” were estimated by NOAA for 2003 using nighttime lights satellite imagery. Tabular data were linked by first administrative unit to the first administrative boundaries shapefile (FAO/GAUL) and then converted into raster format (resolution 0.5 arc-minute). The 0.5 arc-minute grid “market accessibility” measures the travel distance in minutes to large cities (with population greater than 50,000 people). This dataset was developed by the European Commission and the World Bank to represent access to markets, schools, hospitals, etc.. The dataset capture the connectivity and the concentration of economic activity (in 2000). Markets may be important for a variety of reasons, including their abilities to spread risk and increase incomes. Markets are a means of linking people both spatially and over time. That is, they allow shocks (and risks) to be spread over wider areas. In particular, markets should make households less vulnerable to (localized) covariate shocks. This dataset has been produced in the framework of the “Climate change predictions in Sub-Saharan Africa: impacts and adaptations (ClimAfrica)” project, Work Package 4 (WP4). More information on ClimAfrica project is provided in the Supplemental Information section of this metadata.

Data publication: 2014-05-15

Supplemental Information:

ClimAfrica was an international project funded by European Commission under the 7th Framework Programme (FP7) for the period 2010-2014. The ClimAfrica consortium was formed by 18 institutions, 9 from Europe, 8 from Africa, and the Food and Agriculture Organization of United Nations (FAO).

ClimAfrica was conceived to respond to the urgent international need for the most appropriate and up-to-date tools and methodologies to better understand and predict climate change, assess its impact on African ecosystems and population, and develop the correct adaptation strategies. Africa is probably the most vulnerable continent to climate change and climate variability and shows diverse range of agro-ecological and geographical features. Thus the impacts of climate change can be very high and can greatly differ across the continent, and even within countries.

The project focused on the following specific objectives:

1. Develop improved climate predictions on seasonal to decadal climatic scales, especially relevant to SSA;

2. Assess climate impacts in key sectors of SSA livelihood and economy, especially water resources and agriculture;

3. Evaluate the vulnerability of ecosystems and civil population to inter-annual variations and longer trends (10 years) in climate;

4. Suggest and analyse new suited adaptation strategies, focused on local needs;

5. Develop a new concept of 10 years monitoring and forecasting warning system, useful for food security, risk management and civil protection in SSA;

6. Analyse the economic impacts of climate change on agriculture and water resources in SSA and the cost-effectiveness of potential adaptation measures.

The work of ClimAfrica project was broken down into the following work packages (WPs) closely connected. All the activities described in WP1, WP2, WP3, WP4, WP5 consider the domain of the entire South Sahara Africa region. Only WP6 has a country specific (watershed) spatial scale where models validation and detailed processes analysis are carried out.

Contact points:

Metadata Contact: FAO-Data

Resource Contact: Selvaraju Ramasamy

Resource constraints:

copyright

Online resources:

Richness index (2010)

Project deliverable D4.1 - Scenarios of major production systems in Africa

Climafrica Website - Climate Change Predictions In Sub-Saharan Africa: Impacts And Adaptations

The Global 15x15 Minute Grids of the Downscaled GDP Based on the Special Report on Emissions Scenarios (SRES) B2 Scenario, 1990 and 2025, are geospatial distributions of Gross Domestic Product (GDP) per Unit area (GDP densities). These global grids were generated using the Country-level GDP and Downscaled Projections Based on the SRES B2 Scenario, 1990-2100 data set, and CIESIN's Gridded Population of World, Version 2 (GPWv2) data set as the base map. First, the GDP per capita was developed at a country-level for 1990 and 2025. Then the gridded GDP was developed within each country by applying the GDP per capita to each grid cell of the GPW, under the assumption that the GDP per capita was uniform within a country. This data set is produced and distributed by the Columbia University Center for International Earth Science Information Network (CIESIN).

Explore gender statistics data focusing on academic staff, employment, fertility rates, GDP, poverty, and more in the GCC region. Access comprehensive information on key indicators for Bahrain, China, India, Kuwait, Oman, Qatar, and Saudi Arabia.

academic staff, Access to anti-retroviral drugs, Adjusted net enrollment rate, Administration and Law programmes, Age at first marriage, Age dependency ratio, Cause of death, Children out of school, Completeness of birth registration, consumer prices, Cost of business start-up procedures, Employers, Employment in agriculture, Employment in industry, Employment in services, employment or training, Engineering and Mathematics programmes, Female headed households, Female migrants, Fertility planning status: mistimed pregnancy, Fertility planning status: planned pregnancy, Fertility rate, Firms with female participation in ownership, Fisheries and Veterinary programmes, Forestry, GDP, GDP growth, GDP per capita, gender parity index, Gini index, GNI, GNI per capita, Government expenditure on education, Government expenditure per student, Gross graduation ratio, Households with water on the premises, Inflation, Informal employment, Labor force, Labor force with advanced education, Labor force with basic education, Labor force with intermediate education, Learning poverty, Length of paid maternity leave, Life expectancy at birth, Mandatory retirement age, Manufacturing and Construction programmes, Mathematics and Statistics programmes, Number of under-five deaths, Part time employment, Population, Poverty headcount ratio at national poverty lines, PPP, Primary completion rate, Retirement age with full benefits, Retirement age with partial benefits, Rural population, Sex ratio at birth, Unemployment, Unemployment with advanced education, Urban population

Bahrain, China, India, Kuwait, Oman, Qatar, Saudi Arabia

Follow data.kapsarc.org for timely data to advance energy economics research.

The Country-Level GDP and Downscaled Projections Based on the Special Report on Emissions Scenarios (SRES) A1, A2, B1, and B2 marker scenarios, 1990-2100, were developed using the 1990 base year GDP (Gross Domestic Product) from national accounts database available from the UN Statistics Division. SRES regional GDP growth rates were calculated from 1990 to 2100 based on the SRES marker model regional data and applied uniformly to each country that fell within the SRES-defined regions. This data set is produced and distributed by the Columbia University Center for International Earth Science Information Network (CIESIN).

This dataset measures food availability and access for 76 low- and middle-income countries. The dataset includes annual country-level data on area, yield, production, nonfood use, trade, and consumption for grains and root and tuber crops (combined as R&T in the documentation tables), food aid, total value of imports and exports, gross domestic product, and population compiled from a variety of sources. This dataset is the basis for the International Food Security Assessment 2015-2025 released in June 2015. This annual ERS report projects food availability and access for 76 low- and middle-income countries over a 10-year period. Countries (Spatial Description, continued): Democratic Republic of the Congo, Ecuador, Egypt, El Salvador, Eritrea, Ethiopia, Gambia, Georgia, Ghana, Guatemala, Guinea, Guinea-Bissau, Haiti, Honduras, India, Indonesia, Jamaica, Kenya, Kyrgyzstan, Laos, Lesotho, Liberia, Madagascar, Malawi, Mali, Mauritania, Moldova, Mongolia, Morocco, Mozambique, Namibia, Nepal, Nicaragua, Niger, Nigeria, North Korea, Pakistan, Peru, Philippines, Rwanda, Senegal, Sierra Leone, Somalia, Sri Lanka, Sudan, Swaziland, Tajikistan, Tanzania, Togo, Tunisia, Turkmenistan, Uganda, Uzbekistan, Vietnam, Yemen, Zambia, and Zimbabwe. Resources in this dataset:Resource Title: CSV File for all years and all countries. File Name: gfa25.csvResource Title: International Food Security country data. File Name: GrainDemandProduction.xlsxResource Description: Excel files of individual country data. Please note that these files provide the data in a different layout from the CSV file. This version of the data files was updated 9-2-2021

More up-to-date files may be found at: https://www.ers.usda.gov/data-products/international-food-security.aspx

Linear relationship between per capita GDP (dependent variable) and the explanatory variables under different poverty incidence.

The Gross Domestic Product per capita in Ivory Coast was last recorded at 2390.75 US dollars in 2024. The GDP per Capita in Ivory Coast is equivalent to 19 percent of the world's average. This dataset provides the latest reported value for - Ivory Coast GDP per capita - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

The study examines how the recent economic crisis and the related unfavourable economic features affect poverty. As economic crisis is usually associated with many economic and social problems, it tries to determine to what extent it influences poverty. The paper attempts to prove that economic recession contributes not only to the impoverishment of a significant section of society, but also increases the depth of poverty significantly. If the research supports this hypothesis, it is worth examining to what extent one percent economic growth or economic decline can decrease or increase the rate of the poor and the depth of poverty. Besides the effect of economic growth on the given area, the paper also analyses the effect of the economic growth of the neighbouring areas. The initial hypothesis states that the economic growth of the neighbouring regions can also alleviate poverty. As for spatial effects, spatial autocorrelation is examined in the average income level to reveal how the economic growth of the neighbouring areas affects a given region. The study examines Northern Hungary, one of the most backward regions in Hungary (based on GDP per capita). Eurostat (2010) reports this region is among the poorest twenty regions within the European Union (based on GDP per capita PPP, Northern Hungary is the 259th among the 271 regions of the European Union).

After the return to democracy, Ghana achieved significant economic growth and poverty reduction. However, in recent years, the rate of poverty reduction has slowed, becoming insignificant after 2012. The largest reduction in poverty, 2 percent per year, was reached from 1991–1998. Subsequently, the rate of decline fell to 1.4 percent in 1998–2005, 1.1 percent in 2005–2012, and dropped to 0.2 percent per year between 2012 and 2016. The slowdown in poverty reduction was not due to a reduction in GDP per capita growth, which peaked between 2005 and 2012 and remained high between 2012 and 2016. Rather, it was due to a drop in the rate to which economic growth translated into poverty reduction. The growth elasticity of poverty (percentage reduction in poverty associated for every one percentage change in GDP per capita) was 1.2 between 1991 and 1998 but declined to less than 0.1 between 2012 and 2016, indicating a 1 percent increase in GDP per capita led to less than 0.1 percent reduction in poverty.

The dataset contains the following features: Year, Industry Type, Contribution to GDP, Growth by GDP, Employment Types, and Total Employment of Kenya. This dataset was extracted from Statistical reports published by Kenya National Bureau of Statistics reports from 2011 to 2023. Researchers utilised advanced statistical techniques, machine and deep learning algorithms to predict the current extent of working poverty in Kenya, and assist policy makers in making informed decisions for future policy formulations.

Economic Fitness (EF) is both a measure of a country’s diversification and ability to produce complex goods on a globally competitive basis. Countries with the highest levels of EF have capabilities to produce a diverse portfolio of products, ability to upgrade into ever-increasing complex goods, tend to have more predictable long-term growth, and to attain good competitive position relative to other countries. Countries with low EF levels tend to suffer from poverty, low capabilities, less predictable growth, low value-addition, and trouble upgrading and diversifying faster than other countries. The comparison of the Fitness to the GDP reveals hidden information for the development and the growth of the countries.

Explore comprehensive data on various indicators such as self-employment, female employment, average tariffs, net ODA provided, AIDS estimated deaths, fertility rate, school enrollment, GNI, gender parity index, agricultural support, poverty, and much more from the World Bank Millennium Development Goals dataset.

Self-employed, female (% of female employment), Average tariffs imposed by developed countries on agricultural products from developing countries (%), Net ODA provided to the least developed countries (% of donor GNI), AIDS estimated deaths (UNAIDS estimates), Fertility rate, total (births per woman), School enrollment, primary (% net), GNI, Atlas method (current US$), Average tariffs imposed by developed countries on clothing products from developing countries (%), School enrollment, primary (gross), gender parity index (GPI), Self-employed, total (% of total employment), Agricultural support estimate (% of GDP), Share of women in wage employment in the nonagricultural sector (% of total nonagricultural employment), Linear mixed-effect model estimates, Net ODA provided, total (current US$), School enrollment, secondary (gross), gender parity index (GPI), India, Bilateral, sector-allocable ODA to basic social services (% of bilateral ODA commitments), Average tariffs imposed by developed countries on clothing products from least developed countries (%), Bilateral ODA commitments that is untied (current US$), Qatar, Rural poverty gap at national poverty lines (%), GNI per capita, Atlas method (current US$), Urban poverty headcount ratio at national poverty lines (% of urban population), PPP conversion factor, private consumption (LCU per international $), Forest area (% of land area), Terrestrial protected areas (% of total land area), Poverty gap at national poverty lines (%), Annual, Proportion of seats held by women in national parliaments (%), Vulnerable employment, female (% of female employment), Contributing family workers, total (% of total employment), Net ODA provided, total (% of GNI), Total debt service (% of exports of goods, services and primary income), Total bilateral sector allocable ODA commitments (current US$), Average tariffs imposed by developed countries on textile products from least developed countries (%), Weighted Average, Net official development assistance received (current US$), Average tariffs imposed by developed countries on textile products from developing countries (%), Tuberculosis case detection rate (%, all forms), Oman, School enrollment, primary and secondary (gross), gender parity index (GPI), Prevalence of undernourishment (% of population), Population living in slums (% of urban population), Vulnerable employment, male (% of male employment), Debt service (PPG and IMF only, % of exports of goods, services and primary income), Ratio of school attendance rate of orphans to school attendance rate of non orphans, Weighted average, Net ODA received per capita (current US$), Population, total, Contributing family workers, male (% of male employment), Trade (% of GDP), Goods (excluding arms) admitted free of tariffs from least developed countries (% total merchandise imports excluding arms), Self-employed, male (% of male employment), PPP conversion factor, GDP (LCU per international $), Marine protected areas (% of territorial waters), Average tariffs imposed by developed countries on agricultural products from least developed countries (%), Pregnant women receiving prenatal care of at least four visits (% of pregnant women), Forest area (sq. km), Persistence to last grade of primary, total (% of cohort), Persistence to last grade of primary, female (% of cohort), Tuberculosis treatment success rate (% of new cases), Primary completion rate, total (% of relevant age group), School enrollment, tertiary (gross), gender parity index (GPI), Improved sanitation facilities (% of population with access), Poverty headcount ratio at national poverty lines (% of population), Net official development assistance and official aid received (current US$), Gross capital formation (% of GDP), Births attended by skilled health staff (% of total), Rural poverty headcount ratio at national poverty lines (% of rural population), Status under enhanced HIPC initiative, Children orphaned by HIV/AIDS, Vulnerable employment, total (% of total employment), Kuwait, Life expectancy at birth, total (years), Bahrain, Bilateral ODA commitments that is untied (% of bilateral ODA commitments), Persistence to last grade of primary, male (% of cohort), Bilateral, sector-allocable ODA to basic social services (current US$), Renewable internal freshwater resources per capita (cubic meters), Antiretroviral therapy coverage (% of people living with HIV), Pregnant women receiving prenatal care (%), Contributing family workers, female (% of female employment), Improved water source (% of population with access), Goods (excluding arms) admitted free of tariffs from developing countries (% total merchandise imports excluding arms), China, Total bilateral ODA commitments (current US$), Gap-filled total, Saudi Arabia, Adjusted net enrollment rate, primary (% of primary school age children), Reported cases of malaria, Annual freshwater withdrawals, total (% of internal resources), Net ODA received (% of GNI), Urban poverty gap at national poverty lines (%), Sum, Net ODA provided to the least developed countries (current US$), %

India, Qatar, Oman, Kuwait, Bahrain, China, Saudi Arabia

Follow data.kapsarc.org for timely data to advance energy economics research.

The Organisation for Economic Co-operation and Development (OECD) Social and Welfare Statistics (previously Social Expenditure Database) available via the UK Data Service includes the following databases:

The OECD Social Expenditure Database (SOCX) has been developed in order to serve a growing need for indicators of social policy. It includes reliable and internationally comparable statistics on public and mandatory and voluntary private social expenditure at programme level. SOCX provides a unique tool for monitoring trends in aggregate social expenditure and analysing changes in its composition. The main social policy areas are as follows: old age, survivors, incapacity-related benefits, health, family, active labour market programmes, unemployment, housing, and other social policy areas.

The Income Distribution database contains comparable data on the distribution of household income, providing both a point of reference for judging the performance of any country and an opportunity to assess the role of common drivers as well as drivers that are country-specific. They also allow governments to draw on the experience of different countries in order to learn "what works best" in narrowing income disparities and poverty. But achieving comparability in this field is also difficult, as national practices differ widely in terms of concepts, measures, and statistical sources.

The Child Wellbeing dataset compare 21 policy-focussed measures of child well-being in six areas, chosen to cover the major aspects of children’s lives: material well being; housing and environment; education; health and safety; risk behaviours; and quality of school life.

The Better Life Index: There is more to life than the cold numbers of GDP and economic statistics. This Index allows you to compare well-being across countries, based on 11 topics the OECD has identified as essential, in the areas of material living conditions and quality of life.

The Social Expenditure data were first provided by the UK Data Service in March 2004.

This study employs a multilevel model, nesting firm observations within industry and province groups, to investigate the influences on corporate contributions to poverty alleviation while considering the industrial and provincial contexts. Using a sample of Chinese firms listed in Shanghai and Shenzhen Stock Exchanges between 2016 and 2019, we find that Herfindah-Hirschman Index (HHI) does not affect corporate contribution. The results show a significantly negative relationship between industry dynamism and a firm’s substantial poverty contributions, as well as a significantly positive relationship between number of state-owned enterprises (SOEs) in industry and the likelihood and extent of a firm’s contributions. Moreover, a firm’s likelihood to participate in anti-poverty activities and make substantial contributions is affected by more intense government intervention and lower per capita GDP. A province’s poverty rate is positively associated with the extent of corporate investments in poverty alleviation. Additional analyses note that firms competitive in an industry that is less dynamic environment are more likely to invest funds into poverty alleviation instead of material contribution. Moreover, for firms headquartered in an industry with more SOEs and in provinces with a stronger government, a higher poverty rate and lower per capita GDP mean it is more likely for them to make both monetary and material contributions for anti-poverty campaigns.

World Development Indicators (WDI) is the primary World Bank collection of development indicators, compiled from officially recognized international sources. It presents the most current and accurate global development data available, and includes national, regional and global estimates. [Note: Even though Global Development Finance (GDF) is no longer listed in the WDI database name, all external debt and financial flows data continue to be included in WDI. The GDF publication has been renamed International Debt Statistics (IDS), and has its own separate database, as well.

Last Updated:01/28/2025

Data contains Following 20 Countries 'Argentina', 'Australia', 'Brazil', 'China', 'France', 'Germany', 'India', 'Indonesia', 'Italy', 'Japan', 'Korea, Rep.', 'Mexico', 'Netherlands', 'Russian Federation', 'Saudi Arabia', 'Spain', 'Switzerland', 'Turkiye', 'United Kingdom', 'United States'

Dataset contains below Development Indicators 'Adolescent fertility rate (births per 1,000 women ages 15-19)', 'Agriculture, forestry, and fishing, value added (% of GDP)', 'Annual freshwater withdrawals, total (% of internal resources)', 'Births attended by skilled health staff (% of total)', 'Contraceptive prevalence, any method (% of married women ages 15-49)', 'Domestic credit provided by financial sector (% of GDP)', 'Electric power consumption (kWh per capita)', 'Energy use (kg of oil equivalent per capita)', 'Exports of goods and services (% of GDP)', 'External debt stocks, total (DOD, current US$)', 'Fertility rate, total (births per woman)', 'Foreign direct investment, net inflows (BoP, current US$)', 'Forest area (sq. km)', 'GDP (current US$)', 'GDP growth (annual %)', 'GNI per capita, Atlas method (current US$)', 'GNI per capita, PPP (current international $)', 'GNI, Atlas method (current US$)', 'GNI, PPP (current international $)', 'Gross capital formation (% of GDP)', 'High-technology exports (% of manufactured exports)', 'Immunization, measles (% of children ages 12-23 months)', 'Imports of goods and services (% of GDP)', 'Income share held by lowest 20%', 'Industry (including construction), value added (% of GDP)', 'Inflation, GDP deflator (annual %)', 'Life expectancy at birth, total (years)', 'Merchandise trade (% of GDP)', 'Military expenditure (% of GDP)', 'Mobile cellular subscriptions (per 100 people)', 'Mortality rate, under-5 (per 1,000 live births)', 'Net barter terms of trade index (2015 = 100)', 'Net migration', 'Net official development assistance and official aid received (current US$)', 'Personal remittances, received (current US$)', 'Population density (people per sq. km of land area)', 'Population growth (annual %)', 'Population, total', 'Poverty headcount ratio at $2.15 a day (2017 PPP) (% of population)', 'Poverty headcount ratio at national poverty lines (% of population)', 'Prevalence of HIV, total (% of population ages 15-49)', 'Prevalence of underweight, weight for age (% of children under 5)', 'Primary completion rate, total (% of relevant age group)', 'Revenue, excluding grants (% of GDP)', 'School enrollment, primary (% gross)', 'School enrollment, primary and secondary (gross), gender parity index (GPI)', 'School enrollment, secondary (% gross)', 'Surface area (sq. km)', 'Tax revenue (% of GDP)', 'Terrestrial and marine protected areas (% of total territorial area)', 'Time required to start a business (days)', 'Total debt service (% of exports of goods, services and primary income)', 'Urban population growth (annual %)

Over the past decade, Albania has been undergoing a transition toward a market economy and a more open society. It has faced severe internal and external challenges, such as lack of basic infrastructure, rapid collapse of output and inflation rise after the collapse of the communist regime, turmoil during the 1997 pyramid crisis, and social and economic instability because of the 1999 Kosovo crisis. Despite these shocks, Albanian economy has recovered from a very low income level through a sustained growth during the past few years, even though it remains one of the poorest countries in Europe, with GDP per capita at around 1,300$. Based on the Living Standard Measurement Study (LSMS) 2002 survey data (wave 1, henceforth), for the first time in Albania INSTAT has computed an absolute poverty line on a nationally representative poverty survey at household level. Based on this welfare measure, one quarter (25.4 percent) of the Albanian population, or close to 790,000 individuals, were defined as poor in 2002. The distribution of poverty is also disproportionately rural, as 68 percent of the poor are in rural areas, against 32 percent in urban areas (as compared to a total urban population well over 40 percent). These estimates are quite sensitive to the choice of the poverty line, as there are a large number of households clustered around the poverty line. Income related poverty is compounded by the severe lack of access to basic infrastructure, education and health services, clean water, etc., and the ability of the Government to address these issues is complicated by high levels of internal and external migration that are not well understood. The availability of a nationally representative survey is crucial as the paucity of household-level information has been a constraining factor in the design, implementation and evaluation of economic and social programs in Albania. Two recent surveys carried out by the Albanian Institute of Statistics (INSTAT) –the 1998 Living Conditions Survey (LCS) and the 2000 Household Budget Survey (HBS)– drew attention, once again, to the need for accurately measuring household welfare according to well-accepted standards, and for monitoring these trends on a regular basis. This target is well-achieved by drawing information over time on a panel component of LSMS 2002 households, namely the Albanian Panel Survey (APS), conducted in 2003 and 2004. An increasing attention to the policies aimed at achieving the Millennium Development Goals (MDGs) is paid by the National Parliament of Albania, recently witnessed by the resolution approved in July 2003, where it pushes “[...] the total commitment of both state structures and civil society to achieve the MDGs in Albania by 2015”. The path towards a sustained growth is constantly monitored through the National Reports on Progress toward Achieving the MDGs, which involves a close collaboration of the UN with the national institutions, led by the National Strategy for Social and Economic Development (NSSED) Department of the Ministry of Finance. Also, in the process leading to the Poverty Reduction Strategy Paper (PRSP; also known in Albania as Growth and Poverty Reduction Strategy, GPRS), the Government of Albania reinforced its commitment to strengthening its own capacity to collect and analyze on a regular basis information it needs to inform policy-makers. In its first phase (2001-2006), this monitoring system will include the following data collection instruments: (i) Population and Housing Census; (ii) Living Standards Measurement Surveys every 3 years, and (iii) annual panel surveys. The focus during this first phase of the monitoring system is on a periodic LSMS (in 2002 and 2005), followed by panel surveys on a sub-sample of LSMS households (APS 2003, 2004 and 2006), drawing heavily on the 2001 census information. Here our target is to illustrate the main characteristics of the APS 2004 data with reference to the LSMS. The survey work was undertaken by the Living Standards Unit of INSTAT, with the technical assistance of the World Bank.

GDP dissociation inhibitor (GDI) regulates the GDP/GTP exchange reaction of most Rab proteins by inhibiting GDP dissociation. This study evaluated the potential prognostic and predictive value of GDI1 in colorectal cancer (CRC). To address the prognostic power of GDI1, we performed individual and pooled survival analyses on six independent CRC microarray gene expression datasets. GDI1-enriched signatures were also analyzed. Kaplan–Meier and Cox proportional analyses were employed for survival analysis. An immunohistochemistry (IHC) analysis was performed to validate the clinical relevance and prognostic significance of the GDI1 protein level in CRC tissue samples. The results revealed that GDI1 mRNA level was significantly linked with the aggressiveness of CRC, which is compatible with gene set enrichment analysis. A meta-analysis and pooled analysis demonstrated that a higher mRNA GDI1 expression was dramatically correlated with a worse survival in a dose-dependent manner in CRC patients. Further IHC analysis validated that the protein expression of GDI1 in both cytoplasm and membrane also significantly impacted the outcome of CRC patients. In CRC patients with stage III, chemotherapy significantly reduced the relative risk of death in low-GDI1 subgroup (hazard ratio (HR) = 0.22; 95% confidence interval (95% CI) 0.09–0.56, p = 0.0003), but not in high-GDI1 subgroup (HR = 0.63; 95% CI 0.35–1.14, p = 0.1137). Therefore, both high mRNA and protein levels of GDI1 were significantly related to poor outcomes in CRC patients. GD11 may serve as a prognostic biomarker for CRC.

This dataset (dt_elecstds.dta) contains panel data by municipality on infrastructure investment by the Ministry of Public Works, electoral information and socioeconomic variables in Chile for the period 1989-2018. Electoral data includes: three dummies for municipalities' political alignment (a dummy coded one if the mayor belongs to one of the political parties of the central government coalition (m); a dummy coded one if the government coalition parties won presidential elections in the municipality (p); and a dummy coded one if the mayor belongs to one of the coalition parties and the coalition won presidential elections in the municipality (mp)); and two dummies coded one for local ballot years (ym0), and for national ballot years (yp0). The sample comprises seven local (1992, 1996, 2000, 2004, 2008, 2012 and 2016) and seven presidential elections (1989, 1993, 1999, 2005, 2009, 2013 and 2017). Socioeconomic data includes: population (log); regional GDP per capita; regional GDP growth rate in t and t-1; the percentage of new-borns measuring less than 50 cm; the percentage of new-borns weighting less than 3000 gr; and the percentage of mothers with more than three children. In the absence of poverty indicators for the whole sample series, the latter accounted for proxies since they are correlated with municipalities’ socioeconomic conditions (Mardones & Acuña, 2020). Population data is from the National Statistics Institute, GDP data from the Central Bank and poverty proxies from Mardones & Acuña (2020). This dataset has been used to test whether infraestructure investment has been distributed on electoral criteria or not. In particular, it allows to evaluate if the municipalities lined up with the central government coalition parties (m, p and mp dummies) have been systematically benefited, and if allocations increased during local and presidential ballot years (ym0 and yp0 dummies). Since the data goes from the first democratic elections after Pinochet's dictatorship (1989) to 2018, it provides the opportunity to analyse if electoral criteria have changed as democracy entrenched. In particular, it can be tested whether distributions favoured aligned municipalities and whether a political budget cycle persisted during the early years and later. We also include a code file (do_elecstds_050420.do) with several estimations that show how these two electoral distortions evolved during the early years (1989-2005) and once democracy took root (2006-2018). These estimations are part of the paper entitled "Electoral incentives and distributive politics in young democracies: evidence from Chile".

Quality of life is a measure of comfort, health, and happiness by a person or a group of people. Quality of life is determined by both material factors, such as income and housing, and broader considerations like health, education, and freedom. Each year, US & World News releases its “Best States to Live in” report, which ranks states on the quality of life each state provides its residents. In order to determine rankings, U.S. News & World Report considers a wide range of factors, including healthcare, education, economy, infrastructure, opportunity, fiscal stability, crime and corrections, and the natural environment. More information on these categories and what is measured in each can be found below:

Healthcare includes access, quality, and affordability of healthcare, as well as health measurements, such as obesity rates and rates of smoking. Education measures how well public schools perform in terms of testing and graduation rates, as well as tuition costs associated with higher education and college debt load. Economy looks at GDP growth, migration to the state, and new business. Infrastructure includes transportation availability, road quality, communications, and internet access. Opportunity includes poverty rates, cost of living, housing costs and gender and racial equality. Fiscal Stability considers the health of the government's finances, including how well the state balances its budget. Crime and Corrections ranks a state’s public safety and measures prison systems and their populations. Natural Environment looks at the quality of air and water and exposure to pollution.

The Gross Domestic Product (GDP) in Philippines expanded 5.40 percent in the first quarter of 2025 over the same quarter of the previous year. This dataset provides - Philippines GDP Annual Growth Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.