This dataset provides a comprehensive collection of key economic indicators for India, encompassing various aspects of the economy. It includes data on Gross Domestic Product (GDP), inflation rates, employment statistics, trade balances, foreign exchange reserves, and more. The dataset is formatted as CSV files, ensuring ease of use for data analysis and visualization.

FocusEconomics' economic data is provided by official state statistical reporting agencies as well as our global network of leading banks, think tanks and consultancies. Our datasets provide not only historical data, but also Consensus Forecasts and individual forecasts from the aformentioned global network of economic analysts. This includes the latest forecasts as well as historical forecasts going back to 2010. Our global network consists of over 1000 world-renowned economic analysts from which we calculate our Consensus Forecasts. In this specific dataset you will find economic data for India.

Monthly and long-term India economic indicators data: historical series and analyst forecasts curated by FocusEconomics.

TThe ERS International Macroeconomic Data Set provides historical and projected data for 181 countries that account for more than 99 percent of the world economy. These data and projections are assembled explicitly to serve as underlying assumptions for the annual USDA agricultural supply and demand projections, which provide a 10-year outlook on U.S. and global agriculture. The macroeconomic projections describe the long-term, 10-year scenario that is used as a benchmark for analyzing the impacts of alternative scenarios and macroeconomic shocks.

Explore the International Macroeconomic Data Set 2015 for annual growth rates, consumer price indices, real GDP per capita, exchange rates, and more. Get detailed projections and forecasts for countries worldwide.

Annual growth rates, Consumer price indices (CPI), Real GDP per capita, Real exchange rates, Population, GDP deflator, Real gross domestic product (GDP), Real GDP shares, GDP, projections, Forecast, Real Estate, Per capita, Deflator, share, Exchange Rates, CPI

Afghanistan, Albania, Algeria, Angola, Antigua and Barbuda, Argentina, Armenia, Australia, Austria, Azerbaijan, Bahamas, Bahrain, Bangladesh, Barbados, Belarus, Belgium, Belize, Benin, Bhutan, Bolivia, Bosnia and Herzegovina, Botswana, Brazil, Brunei, Bulgaria, Burkina Faso, Burundi, Côte d'Ivoire, Cabo Verde, Cambodia, Cameroon, Canada, Central African Republic, Chad, Chile, China, Colombia, Congo, Costa Rica, Croatia, Cuba, Cyprus, Denmark, Djibouti, Dominica, Dominican Republic, Ecuador, Egypt, El Salvador, Equatorial Guinea, Eritrea, Estonia, Eswatini, Ethiopia, Fiji, Finland, France, Gabon, Gambia, Georgia, Germany, Ghana, Greece, Grenada, Guatemala, Guinea, Guinea-Bissau, Guyana, Haiti, Honduras, Hungary, Iceland, India, Indonesia, Iran, Iraq, Ireland, Israel, Italy, Jamaica, Japan, Jordan, Kazakhstan, Kenya, Kuwait, Kyrgyzstan, Laos, Latvia, Lebanon, Lesotho, Liberia, Libya, Lithuania, Luxembourg, Madagascar, Malawi, Malaysia, Maldives, Mali, Malta, Mauritania, Mauritius, Mexico, Moldova, Mongolia, Morocco, Mozambique, Myanmar, Namibia, Nepal, Netherlands, New Zealand, Nicaragua, Niger, Nigeria, Norway, Oman, Pakistan, Panama, Papua New Guinea, Paraguay, Peru, Philippines, Poland, Portugal, Qatar, Romania, Russia, Rwanda, Samoa, Saudi Arabia, Senegal, Serbia, Seychelles, Sierra Leone, Singapore, Slovakia, Slovenia, Solomon Islands, South Africa, Spain, Sri Lanka, Sudan, Suriname, Sweden, Switzerland, Syria, Tajikistan, Tanzania, Thailand, Togo, Tonga, Trinidad and Tobago, Tunisia, Turkey, Turkmenistan, Uganda, Ukraine, United Arab Emirates, United Kingdom, Uruguay, Uzbekistan, Vanuatu, Venezuela, Vietnam, Yemen, Zambia, Zimbabwe, WORLD Follow data.kapsarc.org for timely data to advance energy economics research. Notes:

Developed countries/1 Australia, New Zealand, Japan, Other Western Europe, European Union 27, North America

Developed countries less USA/2 Australia, New Zealand, Japan, Other Western Europe, European Union 27, Canada

Developing countries/3 Africa, Middle East, Other Oceania, Asia less Japan, Latin America;

Low-income developing countries/4 Haiti, Afghanistan, Nepal, Benin, Burkina Faso, Burundi, Central African Republic, Chad, Democratic Republic of Congo, Eritrea, Ethiopia, Gambia, Guinea, Guinea-Bissau, Liberia, Madagascar, Malawi, Mali, Mozambique, Niger, Rwanda, Senegal, Sierra Leone, Somalia, Tanzania, Togo, Uganda, Zimbabwe;

Emerging markets/5 Mexico, Brazil, Chile, Czech Republic, Hungary, Poland, Slovakia, Russia, China, India, Korea, Taiwan, Indonesia, Malaysia, Philippines, Thailand, Vietnam, Singapore

BRIICs/5 Brazil, Russia, India, Indonesia, China; Former Centrally Planned Economies

Former centrally planned economies/7 Cyprus, Malta, Recently acceded countries, Other Central Europe, Former Soviet Union

USMCA/8 Canada, Mexico, United States

Europe and Central Asia/9 Europe, Former Soviet Union

Middle East and North Africa/10 Middle East and North Africa

Other Southeast Asia outlook/11 Malaysia, Philippines, Thailand, Vietnam

Other South America outlook/12 Chile, Colombia, Peru, Bolivia, Paraguay, Uruguay

Indicator Source

Real gross domestic product (GDP) World Bank World Development Indicators, IHS Global Insight, Oxford Economics Forecasting, as well as estimated and projected values developed by the Economic Research Service all converted to a 2015 base year.

Real GDP per capita U.S. Department of Agriculture, Economic Research Service, Macroeconomic Data Set, GDP table and Population table.

GDP deflator World Bank World Development Indicators, IHS Global Insight, Oxford Economics Forecasting, as well as estimated and projected values developed by the Economic Research Service, all converted to a 2015 base year.

Real GDP shares U.S. Department of Agriculture, Economic Research Service, Macroeconomic Data Set, GDP table.

Real exchange rates U.S. Department of Agriculture, Economic Research Service, Macroeconomic Data Set, CPI table, and Nominal XR and Trade Weights tables developed by the Economic Research Service.

Consumer price indices (CPI) International Financial Statistics International Monetary Fund, IHS Global Insight, Oxford Economics Forecasting, as well as estimated and projected values developed by the Economic Research Service, all converted to a 2015 base year.

Population Department of Commerce, Bureau of the Census, U.S. Department of Agriculture, Economic Research Service, International Data Base.

The dataset contains All India Yearly Macroeconomic Aggregates at Constant Price from Handbook of Statistics on Indian Economy.

Note: 1. Data for 2020-21 are Third Revised Estimates for 2021-22 are Second Revised Estimates and for 2022-23 are First Revised Estimates. 2. Data for 2023-24 are Provisional Estimates.

Full Year GDP Growth in India decreased to 6.50 percent in 2025 from 9.20 percent in 2024. This dataset includes a chart with historical data for India Full Year GDP Growth.

This attachment contains data linked to charts in the research article titled "Is it time to recast India's fiscal and monetary policy frameworks?"The data contains trends on fiscal and monetary indicators of the Indian economy, historical and projected debt level relative to GDP for central, state and combined governments, trends in macroeconomic indicators in the Indian economy such as real GDP growth, GDP-deflator based inflation, CPI inflation, central government's gross tax revenues.

Context

The dataset was created to predict market recession as inspired by assignment notebook in an online course, Python and Machine Learning for Asset Management by Edhec Business School, on Coursera. However, I aimed at doing this exercise for Indian economy but due to lack of monthly data for most indicators, I used FRED database similarly used in the course.

Content

The time period chosen is 1996-2020 according to most data available.

Acknowledgements

1. Python and Machine Learning for Asset Management by EDHEC Business School, www.coursera.org for recession period data formed according to NBER. 2.FRED, fred.stloiusfed.org for macro-economic indicator data
2. Business cycle dating, www.nber.org
3. Yahoo Finance for stock market data
4. Macrotrends.net for PE ratio
5. quandl.com for snp dividend yield

Inspiration

This dataset is inspired by the assignment notebook in the online course mentioned to predict market recession for portfolio management.

The influence of macroeconomic indicators makes it important to study the relationship between macroeconomic indicators and stock market return.

This dataset contains** India**’s quarterly **Gross Domestic Product (GDP) **and Gross Value Added (GVA) from 2011–12 to 2022–23 (Q1), based on official releases from the Ministry of Statistics and Programme Implementation (MOSPI), Government of India. All values in this dataset are expressed in** Indian Rupees (₹) crore at constant 2011–12 prices**, ensuring that the figures are inflation-adjusted and comparable across years.

What the dataset includes

1. Sector-wise GVA

The dataset provides quarterly GVA values for major sectors of the Indian economy, including:

• Agriculture, forestry and fishing
• Mining and quarrying
• Manufacturing
• Electricity, gas, water supply and other utility services
• Construction
• Trade, hotels, transport, communication and services related to broadcasting
• Financial, real estate and professional services
• Public administration, defence and other services

These sectors represent the key components of India’s economic structure and their contribution to quarterly growth.

2. GDP Expenditure Components

The dataset also includes the primary expenditure-side components used to compute GDP:

• PFCE (Private Final Consumption Expenditure)
• GFCE (Government Final Consumption Expenditure)
• GFCF (Gross Fixed Capital Formation)
• CIS (Changes in Stocks)
• Valuables
• Exports of goods and services
• Imports of goods and services
• Discrepancies
• Aggregate GDP Each quarter provides a complete macroeconomic snapshot of the Indian economy.

Context

Consumer Price Indices (CPI) measure changes over time in general level of prices of goods and services that households acquire for the purpose of consumption. CPI numbers are widely used as a macroeconomic indicator of inflation, as a tool by governments and central banks for inflation targeting and for monitoring price stability, and as deflators in the national accounts. CPI is also used for indexing dearness allowance to employees for increase in prices. CPI is therefore considered as one of the most important economic indicators. For construction of CPI numbers, two requisite components are weighting diagrams (consumption patterns) and price data collected at regular intervals.

Content

The data refers to group wise all India Consumer Price Index for Rural & Urban with base year 2010.

Acknowledgements

• The dataset is published by Central Statistical Office.
• This data is sourced from India's Goverment's Open Data website: data.gov.in
• Released Under: National Data Sharing and Accessibility Policy (NDSAP)

Inspiration

This can be used for various purposes including tasks such as exploring growth/inflation in India over the time.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

IDX Composite: A Barometer of India's Economic Health?

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

Dataset

This dataset was created by MAHESRAM P S

Contents

Abstract

The National Sample Surveys (NSS) are being conducted by the Government of India since 1950 to collect socio-economic data employing scientific sampling methods. The Household Consumption Expenditure Survey (HCES) is designed to collect information on consumption of goods and services by the households. Information collected in HCES is used for analyzing and understanding the consumption and expenditure pattern, standard of living and well-being of the households. Besides, the data of the survey provides budget shares of different commodity groups that is used for preparation of the weighting diagram for compilation of official Consumer Price Indices (CPIs). The data collected in HCES is also utilized for deriving various other macroeconomic indicators.

Geographic coverage

The survey covers the whole of the Indian Union except the villages in Andaman and Nicobar Islands which are difficult to access. Total 15016 FSUs was surveyed for the central sample at all-India level.

Analysis unit

Households and Individuals

Kind of data

Sample survey data [ssd]

Sampling procedure

A multistage stratified sampling design, considering villages/urban blocks as the first stage units has been used in the survey. The households are the ultimate stage units. Simple Random Sampling Without Replacement (SRSWOR) method is used for selecting the samples.

In order to ensure proper representation of households of different economic categories, all the households of a selected village/urban block are classified into three groups depending on a criterion based on (i) land possessed in rural areas and (ii) possession of car in urban areas as on the date of the survey. A total of 18 households with proportional representation from the three groups have been selected.

Note: The details of survey methodology and estimation procedure are provided in Appendix B of the survey report “Survey on Household Consumption Expenditure: 2022-23”.

Mode of data collection

Face-to-face [f2f]

Research instrument

In the HCES 2022–23, the consumption basket was categorized into three broad groups: (i) Food items, (ii) Consumables and Services, and (iii) Durable Goods. Based on this classification, three separate questionnaires were developed: the Food Questionnaire (FDQ), the Consumables and Services Questionnaire (CSQ), and the Durable Goods Questionnaire (DGQ). These were administered to selected households across three consecutive monthly visits, with each visit focusing on a different category.

Additionally, a separate Household Characteristics Questionnaire (HCQ) was used to collect demographic and other background information about the household members.

To minimize any potential bias from the order of questionnaire administration, the survey employed all six possible sequences of the three main questionnaires:

(FDQ, CSQ, DGQ)

(FDQ, DGQ, CSQ)

(CSQ, FDQ, DGQ)

(CSQ, DGQ, FDQ)

(DGQ, FDQ, CSQ)

(DGQ, CSQ, FDQ)

This approach ensured that no particular sequencing influenced the results.

This paper provides a review of the developments which has been occurring in the labour market in the making of neoliberal India beginning early 1980s.The macroeconomic data shows that the share of wages in the total gross value added has been constantly falling along with increase in the incidence of unemployment and informalisation of the formally employed workforce. Numerous ethnographic studies provide the evidence that there has been no abatement in precarious work in the informal sector of the economy in the post-reforms period compared to the pre-reforms period. Both, the macroeconomic and ethnographic studies, reach to the conclusion that these developments in India's labour market are not the result of the natural market forces but to a great extent due to the occurrence of primitive accumulation in the economy. The capitalist class has been using primitive accumulation as a tool to extract the surplus by embedding it in the growth process in India's economy.

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The dataset under consideration delves into the intricate details of India's financial landscape, encompassing a spectrum of key indicators crucial for a comprehensive understanding. The data, sourced from the National Institution for Transforming India (NITI Aayog)/Planning Commission, Government of India, spans the substantial time frame from 1980-81 to 2015-16, providing an extensive overview of the nation's economic evolution.

One pivotal aspect highlighted in the dataset is the Aggregate Expenditure, serving as a barometer for the overall government spending. This encompasses both Capital Expenditure, directed towards long-term asset creation, and Revenue Expenditure, focusing on day-to-day operational costs. The dataset further dissects the Revenue Expenditure, shedding light on the intricacies of social sector spending, a key driver for societal development.

A critical metric, the Revenue Deficit, surfaces as a key focal point in the dataset, offering insights into the fiscal health by gauging the shortfall in revenue expenditure against revenue receipts. Another imperative parameter is the Gross Fiscal Deficit, a metric indispensable for assessing the government's borrowing requirements.

Delving into the revenue side, the dataset encapsulates Own Tax Revenues, elucidating the proportion of funds generated internally by the government through taxes. This insight is pivotal for understanding the self-sufficiency of the Indian government in funding its operations.

The Nominal Gross State Domestic Product (GSDP) series unfolds as a cornerstone, encapsulating the overall economic output at current prices. This metric is quintessential for comprehending the economic trajectory over the specified period.

In summary, this dataset serves as a treasure trove of information, offering a nuanced perspective on India's financial dynamics. From the intricacies of expenditure to the macroeconomic indicators, each facet contributes to a holistic understanding of the nation's fiscal journey. Researchers, policymakers, and economists can leverage this dataset to unravel patterns, discern trends, and inform strategic decisions for India's economic future.