Access LSEG's Economic database, featuring global data coverage, consumer confidence data, and macro data indicators.

Blue Chip Economic Indicators - Monthly surveys of top analysts at some of America’s largest manufacturers, banks, insurance companies, and brokerage firms about their insights on U.S. economic growth, inflation, interest rates, and a host of other critical indicators of future business activity – including GDP (Gross Domestic Product), Consumer Price Index (CPI), industrial production, income, corporate profits, treasury bill rates, unemployment rates, housing starts and vehicle sales. Blue Chip Economic Indicators provides forecasts for this year and next from each panel member, plus an average or consensus, of their forecasts for each variable—there also are five to nine quarters of quarterly forecasts. DATA AVAILABLE FOR YEARS: 1976-2024

Ireland Life Insurance: Benefits and Claims: Death / Critical Illness Claims data was reported at 868.100 EUR mn in 2016. This records an increase from the previous number of 775.900 EUR mn for 2015. Ireland Life Insurance: Benefits and Claims: Death / Critical Illness Claims data is updated yearly, averaging 745.600 EUR mn from Dec 2002 (Median) to 2016, with 13 observations. The data reached an all-time high of 868.100 EUR mn in 2016 and a record low of 404.100 EUR mn in 2002. Ireland Life Insurance: Benefits and Claims: Death / Critical Illness Claims data remains active status in CEIC and is reported by Insurance Ireland. The data is categorized under Global Database’s Ireland – Table IE.RG004: Life Insurance: Benefits and Claims.

View LSEG's extensive Economic Data, including content that allows the analysis and monitoring of national economies with historical and real-time series.

I want to understand the effect of liberalisation, privatisation and globalisation in Indian lifestyle and economy for last 26 years.

This file contains critical economic indicators (Employment, Unemployment, Labor force etc.) and some social indicators (Population, birth rate, death rate etc.) of India since the inception of liberalisation, privatisation and globalisation in 1991 till 2016.

Raw data is taken from World Bank site and used under their license. Data Cleaning is completely done by me.

Insights into the magnitude and performance of an economy are crucial, with the growth rate of real GDP frequently used as a key indicator of economic health, highlighting the importance of the Gross Domestic Product (GDP). Additionally, remittances have drawn considerable global interest in recent years, particularly in The Gambia. This study introduces an innovative model, a hybrid of recurrent neural network and long-short-term memory (RNN-LSTM), to predict GDP growth based on remittance inflows in The Gambia. The model integrates data sourced both from the World Bank Development Indicators and the Central Bank of The Gambia (1966–2022). Pearson’s correlation was applied to detect and choose the variables that exhibit the strongest relationship with GDP and remittances. Furthermore, a parameter transfer learning technique was employed to enhance the model’s predictive accuracy. The hyperparameters of the model were fine-tuned through a random search process, and its effectiveness was assessed using RMSE, MAE, MAPE, and R2 metrics. The research results show, first, that it has good generalization capacity, with stable applicability in predicting GDP growth based on remittance inflows. Second, as compared to standalone models the suggested model surpassed in term of predicting accuracy attained the highest R2 score of 91.285%. Third, the predicted outcomes further demonstrated a strong and positive relationship between remittances and short-term economic growth. This paper addresses a critical research gap by employing artificial intelligence (AI) techniques to forecast GDP based on remittance inflows.

In 2024, China's gross domestic product amounted to approximately ***** trillion U.S. dollars, which was the highest GDP across the Asia-Pacific region. Japan followed with a GDP of around **** trillion dollars.  China, Asia-Pacific's titan The significance of the Asia-Pacific region to the world is multifaceted, ranging from geopolitical importance to being home to more than half of the world's population. Characterized by emerging countries and dynamic economic activities, the region plays a key role in the global economy. China, the most populous country after India, and the second largest economy on the planet, accounted for about half of the total gross domestic product (GDP) in APAC as of 2023. The GDP growth in China was characterized by high rates for decades. Following the COVID-19 pandemic, the country has struggled to catch up with the previous level of growth rates and was forecast to stay at more modest real GDP growth rates in the coming years.  A new paradigm of development in the Asia-Pacific region Even though the Asia-Pacific region has made significant economic improvements in the last decades, from a developmental perspective, tackling existing socio-economic issues will be critical for future growth. An aspect worth mentioning is the GDP per capita in the region. EU countries, for example, had about ***** times as much GDP per capita compared to East Asia and the Pacific region in 2022. China has been working towards changing its economic focus to high-tech and service sectors while reducing its concentration on agriculture.

In 2024, the real gross domestic product (GDP) in Vietnam grew by approximately **** percent, marking the highest growth rate in Southeast Asia. In comparison, Myanmar's real GDP growth rate dropped by **** percent. Southeast Asia, a tapestry of economic and cultural complexity Historically a critical component of global trade, Southeast Asia is a diverse region with heterogeneous economies. The region comprises ** countries in total. While Singapore is a highly developed country economy and Brunei has a relatively high GDP per capita, the rest of the Southeast Asian countries are characterized by lower GDPs per capita and have yet to overcome the middle-income trap. Malaysia is one of these countries, having reached the middle-income level for many decades but yet to grow incomes proportionally to its economic development. Nevertheless, Southeast Asia’s young population will further drive economic growth across the region’s markets. ASEAN’s economic significance Aiming to promote economic growth, social progress, cultural development, and regional stability, all Southeast Asian countries except for Timor-Leste are part of the political and economic union Association of Southeast Asian Nations (ASEAN). Even though many concerns surround the union, ASEAN has avoided trade conflicts and is one of the largest and most dynamic trade zones globally. Factors such as the growing young population, high GDP growth, a largely positive trade balance, and exemplary regional integration hold great potential for future economic development in Southeast Asia.

Available data for gross domestic product (GDP) and population density are useful for defining divisions in socio-economic gradients across Europe, since economic power and human population pressure are recognised as two of the most critical factors causing ecosystem changes. To overcome both the limitations in data availability and in the distortions caused by using administrative regions, we decided to base the socio-economic dimension on an economic density indicator, defined as the income generated per square kilometre (EUR km-2), which can be mapped at a 1km2 spatial resolution. Economic density forms an integrative indicator that is based on two key drivers that were identified above: economic power and human population pressure. The indicator, which has been used to rank countries by their level of development, can be considered a crude measure for impacts on the environment caused by economic activity. An economic density map (EUR km-2) at 1 km2 spatial resolution was constructed by multiplying economic power (EUR person-1) with population density (person km-2). Subsequent logarithmic divisions resulted in an aggregated map of four economic density zones. Although the map has a fine spatial resolution it has to be realised that they form a spatial disaggregation of coarser census statistics. Importantly, the finer resolution discerns regional gradients in human activity that are required for many environmental studies, whilst broad gradients in economic activity is also treated consistently across Europe. GDP and population density data used were for the year 2001. The dataset consists of GeoTiff files of the economic density map and the four economic density zones.

Dataset Summary

Inflation is a critical economic indicator that reflects the overall increase in prices of goods and services within an economy over a specific period. Understanding inflation trends on a global scale is crucial for economists, policymakers, investors, and businesses. This dataset provides comprehensive insights into the inflation rates of various countries for the year 2022. The data is sourced from reputable international organizations and government reports… See the full description on the dataset page: https://huggingface.co/datasets/aswin1906/countries-inflation.

Mexico UR: ENOE 2010: Critical Employment Conditions: Female data was reported at 13.625 % in Dec 2018. This records a decrease from the previous number of 13.971 % for Sep 2018. Mexico UR: ENOE 2010: Critical Employment Conditions: Female data is updated quarterly, averaging 11.490 % from Mar 2005 (Median) to Dec 2018, with 56 observations. The data reached an all-time high of 14.290 % in Mar 2018 and a record low of 9.938 % in Mar 2008. Mexico UR: ENOE 2010: Critical Employment Conditions: Female data remains active status in CEIC and is reported by National Institute of Statistics and Geography. The data is categorized under Global Database’s Mexico – Table MX.G025: Unemployment: ENOE 2010: Age 15 and Above.

Twenty-five Area Agencies on Aging (AAAs) provide critical services to the 1.9+ million Virginians aged 60+ across the Commonwealth. AAAs depend on timely and relevant demographic data to make informed decisions about how to best serve their populations. The data products listed below aim to assist the AAAs in understanding some of the basic demographic characteristics of older adults in Virginia as a whole, and in each of Virginia's Planning and Service Areas (PSAs).

Data tables include: Poverty status, SNAP/Food stamp usage, labor force participation, and renter/homeowner status

Series Name: Direct economic loss resulting from damaged or destroyed critical infrastructure attributed to disasters (current United States dollars)Series Code: VC_DSR_CILNRelease Version: 2020.Q2.G.03 This dataset is the part of the Global SDG Indicator Database compiled through the UN System in preparation for the Secretary-General's annual report on Progress towards the Sustainable Development Goals.Indicator 1.5.2: Direct economic loss attributed to disasters in relation to global gross domestic product (GDP)Target 1.5: By 2030, build the resilience of the poor and those in vulnerable situations and reduce their exposure and vulnerability to climate-related extreme events and other economic, social and environmental shocks and disastersGoal 1: End poverty in all its forms everywhereFor more information on the compilation methodology of this dataset, see https://unstats.un.org/sdgs/metadata/

Abstract

The Socio-Economic Insights Survey (SEIS) conducted in May and June 2024 in Poland provides essential data to inform the 2025 Refugee Response Plan. This survey captures critical information on the socio-economic conditions of refugees, including demographics, protection, education, livelihoods, health, and accommodation. The findings are pivotal for shaping strategies and funding decisions that address the needs of refugee populations. Data were collected from a representative sample of refugee households and individuals across 16 regions of Poland, helping to identify key challenges and opportunities for enhancing refugee integration and well-being.

Geographic coverage

Poland

Analysis unit

Household and Individual

Universe

All refugee households and individuals residing in metropolitan and rural areas across 16 voivodeships (regions) of Poland.

Kind of data

Sample survey data [ssd]

Sampling procedure

Purposive/Convenience sampling with geographical coverage and different accommodation types. The sample includes 1,290 households (3,093 individuals), 80% of whom live outside collective sites, and 18% live in collective accommodation.

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaire covered socio-economic indicators, including demographics, protection, education, livelihoods, and health.

An economy's financial markets are critical to its overall development. Banking systems and stock markets enhance growth, the main factor in poverty reduction. Strong financial systems provide reliable and accessible information that lowers transaction costs, which in turn bolsters resource allocation and economic growth. Indicators here include the size and liquidity of stock markets; the accessibility, stability, and efficiency of financial systems; and international migration and workers remittances, which affect growth and social welfare in both sending and receiving countries.

Indicator 11.5.2Direct economic loss in relation to global GDP, damage to critical infrastructure, and number of disruptions to basic services, attributed to disasters.Methodology:The original national disaster loss databases usually register physical damage value (housing unit loss, infrastructure loss, etc.), which needs conversion to a monetary value according to the UNISDR methodology*. The converted global value is divided by global GDP (inflation-adjusted, constant USD) calculated from the World Bank Development Indicators.Data Source:Ministry of Interior.

(The data acquisition from the International Monetary Fund (IMF) [56] and World Bank data repositories [57]).

In 2018, the USAID/Jordan Monitoring and Evaluation Support Project (MESP) conducted a nationally representative survey in Jordan (N=11,963). The survey was designed to support USAID/Jordan learning and decision making by providing a better understanding of the broader context in which projects and activities are implemented, explore determinants of indicator performance, and to provide implementing partners data critical to their activity planning and implementation. The survey provides critical data on key international economic and social development indicators, and data relevant to USAID performance indicators and learning agenda questions.

Abstract

The Socio-Economic Insights Survey (SEIS) conducted in May and June 2024 in Poland provides essential data to inform the 2025 Refugee Response Plan. This survey captures critical information on the socio-economic conditions of refugees, including demographics, protection, education, livelihoods, health, and accommodation. The findings are pivotal for shaping strategies and funding decisions that address the needs of refugee populations. Data were collected from a representative sample of refugee households and individuals across 16 regions of Poland, helping to identify key challenges and opportunities for enhancing refugee integration and well-being.

Geographic coverage

Poland

Analysis unit

Household and Individual

Universe

All refugee households and individuals residing in metropolitan and rural areas across 16 voivodeships (regions) of Poland.

Kind of data

Sample survey data [ssd]

Sampling procedure

Purposive/Convenience sampling with geographical coverage and different accommodation types. The sample includes 1,290 households (3,093 individuals), 80% of whom live outside collective sites, and 18% live in collective accommodation.

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaire covered socio-economic indicators, including demographics, protection, education, livelihoods, and health.

This study conducts a comprehensive analysis of gender inequality in Sri Lanka, focusing on the relationship between key socioeconomic factors and the Gender Inequality Index (GII) from 1990 to 2022. By applying machine learning techniques, including Decision Trees and Ensemble methods, the study investigates the influence of economic indicators such as GDP per capita, government expenditure, government revenue, and unemployment rates on gender disparities. The analysis reveals that higher GDP and government revenues are associated with reduced gender inequality, while greater unemployment rates exacerbate disparities. Explainable AI techniques (SHAP) further highlight the critical role of government policies and economic development in shaping gender equality. These findings offer specific insights for policymakers to design targeted interventions aimed at reducing gender gaps in Sri Lanka, particularly by prioritizing economic growth and inclusive public spending.