About this dataset

Context

The dataset tabulates the median household income in Indian Village. It can be utilized to understand the trend in median household income and to analyze the income distribution in Indian Village by household type, size, and across various income brackets.

Content

The dataset will have the following datasets when applicable

Please note: The 2020 1-Year ACS estimates data was not reported by the Census Bureau due to the impact on survey collection and analysis caused by COVID-19. Consequently, median household income data for 2020 is unavailable for large cities (population 65,000 and above).

• Indian Village, IN Median Household Income Trends (2010-2021, in 2022 inflation-adjusted dollars)
• Median Household Income Variation by Family Size in Indian Village, IN: Comparative analysis across 7 household sizes
• Income Distribution by Quintile: Mean Household Income in Indian Village, IN
• Indian Village, IN households by income brackets: family, non-family, and total, in 2022 inflation-adjusted dollars

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Interested in deeper insights and visual analysis?

Explore our comprehensive data analysis and visual representations for a deeper understanding of Indian Village median household income. You can refer the same here

Urbanisation is a form of social transformation from traditional rural societies to modern, industrial and urban communities. It is long term continuous process. It is progressive concentration of population in urban unit. Kingsley Davies has explained urbanisation as process of switch from spread out pattern of human settlements to one of concentration in urban centers. Migration is the key process underlying growth of urbanization.

Challenges in urban development--->;

Institutional challenges

Urban Governance 74th amendment act has been implemented half-heartedly by the states, which has not fully empowered the Urban local bodies (ULBs). ULBs comprise of municipal corporations, municipalities and nagar panchayats, which are to be supported by state governments to manage the urban development. For this , ULBs need clear delegation of functions, financial resources and autonomy. At present urban governance needs improvement for urban development, which can be done by enhancing technology, administrative and managerial capacity of ULBs.

Planning Planning is mainly centralized and till now the state planning boards and commissions have not come out with any specific planning strategies an depend on Planning commission for it. This is expected to change in present government, as planning commission has been abolished and now focus is on empowering the states and strengthening the federal structure.

In fact for big cities the plans have become outdated and do not reflect the concern of urban local dwellers, this needs to be take care by Metropolitan planning committee as per provisions of 74th amendment act. Now the planning needs to be decentralized and participatory to accommodate the needs of the urban dwellers.

Also there is lack of human resource for undertaking planning on full scale. State planning departments and national planning institutions lack qualified planning professional. Need is to expand the scope of planners from physical to integrated planning- Land use, infrastructure, environmental sustainability, social inclusion, risk reduction, economic productivity and financial diversity.

Finances Major challenge is of revenue generation with the ULBs. This problem can be analyzed form two perspectives. First, the states have not given enough autonomy to ULBs to generate revenues and Second in some case the ULBs have failed to utilize even those tax and fee powers that they have been vested with.

There are two sources of municipal revenue i.e. municipal own revenue and assigned revenue. Municipal own revenue are generated by municipal own revenue through taxes and fee levied by them. Assigned revenues are those which are assigned to local governments by higher tier of government.

There is growing trend of declining ratio of own revenue. There is poor collection property taxes. Use of geographical information system to map all the properties in a city can have a huge impact on the assessment rate of properties that are not in tax net.

There is need to broaden the user charge fee for water supply, sewerage and garbage disposal. Since these are the goods which have a private characteristics and no public spill over, so charging user fee will be feasible and will improve the revenue of ULBs , along with periodic revision. Once the own revenue generating capacity of the cities will improve, they can easily get loans from the banks. At present due to lack of revenue generation capabilities, banks don’t give loan to ULBs for further development. For financing urban projects, Municipal bonds are also famous, which work on the concept of pooled financing.

Regulator

There is exponential increase in the real estate, encroaching the agricultural lands. Also the rates are very high, which are not affordable and other irregularities are also in practice. For this, we need regulator, which can make level playing field and will be instrumental for affordable housing and checking corrupt practices in Real estate sector.

Infrastructural challenges

Housing Housing provision for the growing urban population will be the biggest challenge before the government. The growing cost of houses comparison to the income of the urban middle class, has made it impossible for majority of lower income groups and are residing in congested accommodation and many of those are devoid of proper ventilation, lighting, water supply, sewage system, etc. For instance in Delhi, the current estimate is of a shortage of 5,00,000 dwelling units the coming decades. The United Nations Centre for Human Settlements (UNCHS) introduced the concept of “Housing Poverty” which includes “Individuals and households who lack safe, secure and healthy shelter, with basic infrastructure such as piped water and adequate provision for sanitation, drainage and the removal of hou...

Abstract

The Global Consumption Database (GCD) contains information on consumption patterns at the national level, by urban/rural area, and by income level (4 categories: lowest, low, middle, higher with thresholds based on a global income distribution), for 92 low and middle-income countries, as of 2010. The data were extracted from national household surveys. The consumption is presented by category of products and services of the International Comparison Program (ICP) 2005, which mostly corresponds to COICOP. For three countries, sub-national data are also available (Brazil, India, and South Africa). Data on population estimates are also included.

       The data file can be used for the production of the following tables (by urban/rural and income class/consumption segment):
       - Sample Size by Country, Area and Consumption Segment (Number of Households)
       - Population 2010 by Country, Area and Consumption Segment
       - Population 2010 by Country, Area and Consumption Segment, as a Percentage of the National Population
       - Population 2010 by Country, Area and Consumption Segment, as a Percentage of the Area Population
       - Population 2010 by Country, Age Group, Sex and Consumption Segment
       - Household Consumption 2010 by Country, Sector, Area and Consumption Segment in Local Currency (Million)
       - Household Consumption 2010 by Country, Sector, Area and Consumption Segment in $PPP (Million)
       - Household Consumption 2010 by Country, Sector, Area and Consumption Segment in US$ (Million)
       - Household Consumption 2010 by Country, Category of Product/Service, Area and Consumption Segment in Local Currency (Million)
       - Household Consumption 2010 by Country, Category of Product/Service, Area and Consumption Segment in $PPP (Million)
       - Household Consumption 2010 by Country, Category of Product/Service, Area and Consumption Segment in US$ (Million)
       - Household Consumption 2010 by Country, Product/Service, Area and Consumption Segment in Local Currency (Million)
       - Household Consumption 2010 by Country, Product/Service, Area and Consumption Segment in $PPP (Million)
       - Household Consumption 2010 by Country, Product/Service, Area and Consumption Segment in US$ (Million)
       - Per Capita Consumption 2010 by Country, Sector, Area and Consumption Segment in Local Currency
       - Per Capita Consumption 2010 by Country, Sector, Area and Consumption Segment in US$
       - Per Capita Consumption 2010 by Country, Sector, Area and Consumption Segment in $PPP
       - Per Capita Consumption 2010 by Country, Category of Product/Service, Area and Consumption Segment in Local Currency
       - Per Capita Consumption 2010 by Country, Category of Product/Service, Area and Consumption Segment in US$
       - Per Capita Consumption 2010 by Country, Category of Product/Service, Area and Consumption Segment in $PPP
       - Per Capita Consumption 2010 by Country, Product or Service, Area and Consumption Segment in Local Currency
       - Per Capita Consumption 2010 by Country, Product or Service, Area and Consumption Segment in US$
       - Per Capita Consumption 2010 by Country, Product or Service, Area and Consumption Segment in $PPP
       - Consumption Shares 2010 by Country, Sector, Area and Consumption Segment (Percent)
       - Consumption Shares 2010 by Country, Category of Products/Services, Area and Consumption Segment (Percent)
       - Consumption Shares 2010 by Country, Product/Service, Area and Consumption Segment (Percent)
       - Percentage of Households who Reported Having Consumed the Product or Service by Country, Consumption Segment and Area (as of Survey Year)

Geographic coverage notes

For all countries, estimates are provided at the national level and at the urban/rural levels. For Brazil, India, and South Africa, data are also provided at the sub-national level (admin 1): - Brazil: ACR, Alagoas, Amapa, Amazonas, Bahia, Ceara, Distrito Federal, Espirito Santo, Goias, Maranhao, Mato Grosso, Mato Grosso do Sul, Minas Gerais, Para, Paraiba, Parana, Pernambuco, Piaji, Rio de Janeiro, Rio Grande do Norte, Rio Grande do Sul, Rondonia, Roraima, Santa Catarina, Sao Paolo, Sergipe, Tocatins - India: Andaman and Nicobar Islands, Andhra Pradesh, Arinachal Pradesh, Assam, Bihar, Chandigarh, Chattisgarh, Dadra and Nagar Haveli, Daman and Diu, Delhi, Goa, Gujarat, Haryana, Himachal Pradesh, Jammu and Kashmir, Jharkhand, Karnataka, Kerala, Lakshadweep, Madya Pradesh, Maharastra, Manipur, Meghalaya, Mizoram, Nagaland, Orissa, Pondicherry, Punjab, Rajasthan, Sikkim, Tamil Nadu, Tripura, Uttar Pradesh, Uttaranchal, West Bengal - South Africa: Eastern Cape, Free State, Gauteng, Kwazulu Natal, Limpopo, Mpulamanga, Northern Cape, North West, Western Cape

Kind of data

Data derived from survey microdata

Overview

The India Customer Financial Profiles & Transactions Dataset contains 20,000 fully synthetic records that represent the demographic, financial, credit, and transactional behavior of customers across India. This dataset is ideal for machine learning, financial analytics, risk modeling, fintech simulation, and academic research.

All data is algorithmically generated, ensuring:

No real person is represented

No sensitive or identifiable information is included

Full compliance with privacy and research ethics

📂 Dataset File india_customer_financial_profiles_20000_cleaned.csv

A fully cleaned, validated, and standardized dataset containing demographic, financial, and transaction details.

🧾 Data Dictionary 👤 Demographic Information Demographic fields included in the dataset: id — Unique customer ID current_age — Customer age birth_year — Year of birth birth_month — Month of birth gender — Male / Female / Other address — Synthetic Indian address (City, State, PIN)

💰 Financial Attributes Financial attributes included: per_capita_income — Monthly per-person household income yearly_income — Annual income total_debt — Total outstanding debt credit_score — Score between 300–900 num_credit_cards — Number of credit cards]

🧾 Transaction Details Transaction-related fields: transaction_id — Unique transaction ID date — Transaction date (YYYY-MM-DD) client_id — Synthetic ID linked to customer card_id — Card identifier amount — Transaction amount (INR) use_chip — Chip used (Yes/No) merchant_id — Merchant identifier merchant_city — Transaction location (city) merchant_state — State of merchant zip — 6-digit PIN code

🔍 Key Features ✔ 20,000 synthetic customer profiles ✔ Includes demographic + financial + transaction data ✔ Standardized date format (YYYY-MM-DD) ✔ PIN codes extracted and cleaned ✔ No missing values ✔ Consistent and realistic Indian data patterns ✔ High usability for ML and analytics

🧠 Use Cases 🟦 Machine Learning Credit scoring Loan default prediction Fraud detection Customer segmentation Transaction classification

🟩 Data Analytics Financial behavioral trends Income–debt correlation analysis Merchant-level insights Urban vs rural customer profiles

🟧 Fintech Research Synthetic simulation Risk modeling Customer persona creation Spending behavior research

🟪 Education & Learning Data cleaning practice Feature engineering EDA & visualization projects Full ML pipeline demonstrations

🛠️ Data Generation Methodology The dataset was generated using: Synthetic demographic distribution modeling Indian address & PIN pattern simulation Income and credit-score probability distributions Transaction behavior simulation Randomized merchant profiles Rule-based statistical generation No scraping or real-world data sources were used. This dataset is 100% synthetic.

📜 License

This dataset is released under the Creative Commons Attribution 4.0 International (CC BY 4.0) license. You may: Use Modify Redistribute Adapt As long as you provide citation.

✍️ Citation

Use the following format to cite this dataset: Bedmutha, Kundan (2025). India Customer Financial Profiles & Transactions Dataset (20,000 Records).

🙌 Acknowledgements

This dataset was created to support: Students Researchers Machine learning practitioners Fintech analysts Educators Your feedback and suggestions are welcome for future dataset enhancements.

🎉 Thank You!

The dataset contains survey data from a total of 226 low-income migrant workers (100 in Jalandhar and 126 in Guwahati) in India. It contains data on 60 variables, focussing on socio-economic background, migratory experience, ill-treatment and access to justice and access to basic services. Abstract of the study: Indian cities attract a considerable number of low-income migrants from marginal rural households experiencing difficult economic, political and social conditions at home who migrate in search of livelihoods and security. These migrants come from around the country as well as across the border from Nepal, Bangladesh and Myanmar to work in low-income manual occupations in a range of small-scale petty trade, service sector work, transport and construction work. Low-income migrants live and work in precarious conditions and are often denied basic amenities and fundamental rights. Poorly-paid intermittent and insecure jobs make them vulnerable to abuse, extortion or bribery. Many such migrants, both internal and international, lack documentation and proof of identity, whether for basic services such as health care and schooling or electoral voting. Their marginal position entails poorer access to health care provisions and other determinants of health than general (non-migrant) populations, thereby enhancing their vulnerability to ill-health, abuse and ill treatment whilst simultaneously compromising their ability to access protection, legal support or redress, and forms of accountability. Language, appearance and cultural differences exposes many low-income migrants from interior parts of the country or across the border to harassment and political exclusion. Moreover, despite their ubiquitous presence, their precarious livelihoods, informality and invisibility keep them unnoticed in urban planning, in the work of civil society organisations and in social science research. In this context, this collaborative project was designed to generate evidence to advance the rights and protection mechanisms that must be planned and provided for low-income urban migrants. We examined what India's urban transformation means for low-income migrants, their inclusion and social justice by exploring: 1. Low-income migrants' views on transformations in Indian cities, and the opportunities and challenges that confront them; 2. Low-income migrants perceptions of their entitlements, claim-making processes and attempts to protect their own health in a context of poor living and working conditions; 3. The prevalence of violence and extent of exclusion experienced by low-income migrants and how they protect themselves from various forms of violence; 4. The legal, developmental, humanitarian and human rights responses to low-income migrants in Indian cities. Fieldwork based in Guwahati (Assam) and Jalandhar (Punjab), two of India's fastest growing cities, aimed to enrich our understanding of access to health care, the social determinants of health, and experiences of violence, inclusion/exclusion and accessing justice, from the vantage point of diverse low-income migrant workers, from within India as well as cross-border. The project focussed on migrants' perceptions and lived experiences and will generate evidence to advance the rights and protection mechanisms that must be planned and provided for low-income urban migrants. Low-income migrants are mobile, dispersed and invisible, so they present methodological challenges, especially for creating a sampling frame or mapping in a particular locality. A distinctive strength of the project is its innovative methods for accessing these 'hard-to-reach' groups. The proposed research adopted a mixed methods approach. In order to unravel the nuances and complexities of low-income migrants' experiences and situate these within the broader processes of urban transformation in Jalandhar and Guwahati, we combined ethnographic fieldwork with in-depth interviews, a brief survey, and participatory methods such as photovoice.

This file-set contains data on indoor and outdoor temperature measurements in five-study sites in the South Asian region. Two study sites focus on the rural part; Yavatmal and Jalna (Maharashtra, India) while Dhaka (Bangladesh), Delhi (India) and Faisalabad (Pakistan) focus on urban heat exposure. The fileset consists of a total of 15 .csv files. These are: Faisalabad AWS Data.csvJalna AWS Data.csvYavatmal AWS Data.csvDelhi AWS Data.csvDhaka AWS Data.csvYavatmal Indoor Data.csvDelhi Indoor Data.csvDhaka Indoor Data.csvFaisalabad Indoor Data.csvJalna Indoor Data.csvJalna Housing Structure Data.csvYavatmal Housing Structure Data.csvDelhi Housing Structure Data.csvDhaka Housing Structure Data.csvFaisalabad Housing Structure Data.csv Additionally, four.doc files and one PDF file are included. Informed Consent Form – WOTR.doc is the informed consent form. The data file Data Supplement.doc (as a readme file), contains tables explaining all the variables in the .csv files. README FILE 1- Housing Roofing Structure data, README FILE 2- Indoor Data Loggers, README FILE 3 - AWS Urban-Rural Area and README FILE 4 -Davis Installation Manual (PDF file) Outdoor observations (air temperature, humidity, wind speed, and solar radiation) based on the Automated Weather Station (AWS), are contained in the 5 AWS .csv files.Indoor temperature measurements are contained in the 5 Indoor Data .csv files.Data on housing structure are contained in the 5 Housing Structure Data .csv files. Study aims and methodology: Rising temperatures have been causing distress across the world, but for those most vulnerable, it is a silent killer. Information about indoor air temperatures in residential buildings is of interest for a range of reasons, such as the health, indoor comfort of dwellers and coping practices. But to date, there have been only a few long-term studies that measure and characterize indoor air temperatures in a rural area. Here, the authors have created a dataset on indoor and outdoor temperatures across rural and urban locations. The indoor and outdoor temperature and humidity measurements were taken in five study locations. Indoor and outdoor temperature measurements were carried out in Yavatmal and Jalna (Maharashtra, India), Delhi (India), Dhaka (Bangladesh) and Faisalabad (Pakistan). Two study sites focused on the rural areas: Yavatmal and Jalna (Maharashtra, India), while Delhi, Dhaka and Faisalabad focused on urban heat exposure. These measurements were used to determine indoor heat exposure in different types of rural and urban settlements. This helped to understand indoor temperature variations in different types of housings across different geographic locations in urban South Asia and for India specifically, provide a rural-urban context. For details on the methodology, please read the related article.

The Global Consumption Database (GCD) contains information on consumption patterns at the national level, by urban/rural area, and by income level (4 categories: lowest, low, middle, higher with thresholds based on a global income distribution), for 92 low and middle-income countries, as of 2010. The data were extracted from national household surveys. The consumption is presented by category of products and services of the International Comparison Program (ICP) 2005, which mostly corresponds to COICOP. For three countries, sub-national data are also available (Brazil, India, and South Africa). Data on population estimates are also included. The data file can be used for the production of the following tables (by urban/rural and income class/consumption segment): - Sample Size by Country, Area and Consumption Segment (Number of Households) - Population 2010 by Country, Area and Consumption Segment - Population 2010 by Country, Area and Consumption Segment, as a Percentage of the National Population - Population 2010 by Country, Area and Consumption Segment, as a Percentage of the Area Population - Population 2010 by Country, Age Group, Sex and Consumption Segment - Household Consumption 2010 by Country, Sector, Area and Consumption Segment in Local Currency (Million) - Household Consumption 2010 by Country, Sector, Area and Consumption Segment in $PPP (Million) - Household Consumption 2010 by Country, Sector, Area and Consumption Segment in US$ (Million) - Household Consumption 2010 by Country, Category of Product/Service, Area and Consumption Segment in Local Currency (Million) - Household Consumption 2010 by Country, Category of Product/Service, Area and Consumption Segment in $PPP (Million) - Household Consumption 2010 by Country, Category of Product/Service, Area and Consumption Segment in US$ (Million) - Household Consumption 2010 by Country, Product/Service, Area and Consumption Segment in Local Currency (Million) - Household Consumption 2010 by Country, Product/Service, Area and Consumption Segment in $PPP (Million) - Household Consumption 2010 by Country, Product/Service, Area and Consumption Segment in US$ (Million) - Per Capita Consumption 2010 by Country, Sector, Area and Consumption Segment in Local Currency - Per Capita Consumption 2010 by Country, Sector, Area and Consumption Segment in US$ - Per Capita Consumption 2010 by Country, Sector, Area and Consumption Segment in $PPP - Per Capita Consumption 2010 by Country, Category of Product/Service, Area and Consumption Segment in Local Currency - Per Capita Consumption 2010 by Country, Category of Product/Service, Area and Consumption Segment in US$ - Per Capita Consumption 2010 by Country, Category of Product/Service, Area and Consumption Segment in $PPP - Per Capita Consumption 2010 by Country, Product or Service, Area and Consumption Segment in Local Currency - Per Capita Consumption 2010 by Country, Product or Service, Area and Consumption Segment in US$ - Per Capita Consumption 2010 by Country, Product or Service, Area and Consumption Segment in $PPP - Consumption Shares 2010 by Country, Sector, Area and Consumption Segment (Percent) - Consumption Shares 2010 by Country, Category of Products/Services, Area and Consumption Segment (Percent) - Consumption Shares 2010 by Country, Product/Service, Area and Consumption Segment (Percent) - Percentage of Households who Reported Having Consumed the Product or Service by Country, Consumption Segment and Area (as of Survey Year)

This dataset provides detailed budget allocation insights for urban and rural households in India, capturing present living standards. The data includes various spending areas such as housing, food, transportation, healthcare, education, and discretionary expenses. The dataset is designed to help researchers, policymakers, and individuals understand spending habits and optimize budget planning.

Context: The dataset is derived from various government reports, surveys, and market research studies that provide a snapshot of the current economic conditions and living standards in India. It includes average income levels, typical expenses, and common savings patterns for both urban and rural households.

Sources:

National Sample Survey Office (NSSO) Ministry of Statistics and Programme Implementation (MoSPI) Various market research reports and publications Inspiration: The inspiration behind this dataset is to provide a clear and detailed picture of how households in different regions of India allocate their budgets. This can be a valuable resource for economists, social scientists, financial advisors, and anyone interested in understanding the financial behavior of Indian households.

Net-Income Time Series for Jiangsu Zhongchao Cable Co Ltd. Jiangsu Zhongchao Holding Co., Ltd. manufactures and sells wires and cables in China. Its products include power cables, wires and cables for electrical equipment, bare wires, flame-retardant ultra-high voltage cross-linked cables, aluminum alloy overhead lines, and computer cables. The company's products are primarily used in the construction and transformation project of urban and rural power grids. It also exports its products to India, Vietnam, Australia, Oman, Sudan, Tanzania, Nigeria, Kenya, Sri Lanka, Mauritius, South Africa, Brazil, Cyprus, and internationally. The company was formerly known as Jiangsu Zhongchao Cable Corporation and changed its name to Jiangsu Zhongchao Holding Co., Ltd. in September 2015. Jiangsu Zhongchao Holding Co., Ltd. was founded in 1996 and is based in Yixing, China.

Net-Income-Including-Non-Controlling-Interests Time Series for Aavas Financiers Limited. Aavas Financiers Limited provides housing finance services to low- and middle-income customers in semi-urban and rural areas in India. The company offers home loans for flats, houses, and bungalows, as well as resale properties; home construction loans for self-construction of residential house; and home improvement loans, including loans for tiling or flooring, plaster or painting, etc. It also provides loans against property; micro, small, and medium enterprise loans; and home loan balance transfer, as well as cash salaried plus loans and small ticket size loan. The company was formerly known as AU Housing Finance Limited and changed its name to Aavas Financiers Limited in May 2017. Aavas Financiers Limited was incorporated in 2011 and is based in Jaipur, India.

Net-Income-Dividend-Coverage Time Series for Jiangsu Zhongchao Cable Co Ltd. Jiangsu Zhongchao Holding Co., Ltd. manufactures and sells wires and cables in China. Its products include power cables, wires and cables for electrical equipment, bare wires, flame-retardant ultra-high voltage cross-linked cables, aluminum alloy overhead lines, and computer cables. The company's products are primarily used in the construction and transformation project of urban and rural power grids. It also exports its products to India, Vietnam, Australia, Oman, Sudan, Tanzania, Nigeria, Kenya, Sri Lanka, Mauritius, South Africa, Brazil, Cyprus, and internationally. The company was formerly known as Jiangsu Zhongchao Cable Corporation and changed its name to Jiangsu Zhongchao Holding Co., Ltd. in September 2015. Jiangsu Zhongchao Holding Co., Ltd. was founded in 1996 and is based in Yixing, China.

BackgroundEven in low and middle income countries most deaths occur in older adults. In Europe, the effects of better education and home ownership upon mortality seem to persist into old age, but these effects may not generalise to LMICs. Reliable data on causes and determinants of mortality are lacking. Methods and FindingsThe vital status of 12,373 people aged 65 y and over was determined 3–5 y after baseline survey in sites in Latin America, India, and China. We report crude and standardised mortality rates, standardized mortality ratios comparing mortality experience with that in the United States, and estimated associations with socioeconomic factors using Cox's proportional hazards regression. Cause-specific mortality fractions were estimated using the InterVA algorithm. Crude mortality rates varied from 27.3 to 70.0 per 1,000 person-years, a 3-fold variation persisting after standardisation for demographic and economic factors. Compared with the US, mortality was much higher in urban India and rural China, much lower in Peru, Venezuela, and urban Mexico, and similar in other sites. Mortality rates were higher among men, and increased with age. Adjusting for these effects, it was found that education, occupational attainment, assets, and pension receipt were all inversely associated with mortality, and food insecurity positively associated. Mutually adjusted, only education remained protective (pooled hazard ratio 0.93, 95% CI 0.89–0.98). Most deaths occurred at home, but, except in India, most individuals received medical attention during their final illness. Chronic diseases were the main causes of death, together with tuberculosis and liver disease, with stroke the leading cause in nearly all sites. ConclusionsEducation seems to have an important latent effect on mortality into late life. However, compositional differences in socioeconomic position do not explain differences in mortality between sites. Social protection for older people, and the effectiveness of health systems in preventing and treating chronic disease, may be as important as economic and human development. Please see later in the article for the Editors' Summary

This dataset explores marriage trends in India, comparing love marriages and arranged marriages across various demographic, social, and economic factors. I, capturing key aspects such as age at marriage, caste and religion dynamics, parental approval, dowry exchange, marital satisfaction, divorce rates, income levels, and urban-rural differences.

The dataset aims to provide valuable insights into changing marriage patterns, the role of tradition vs. modernity, and their impact on marital outcomes. Researchers, sociologists, and data analysts can use this dataset to study relationship trends, predict marriage success, and analyze social influences on marriage in India.

ID – Unique identifier

Marriage_Type – Love / Arranged

Age_at_Marriage – Age of the person at marriage

Gender – Male / Female

Education_Level – School / Graduate / Postgraduate / PhD

Caste_Match – Same / Different

Religion – Hindu / Muslim / Christian / Sikh / Others

Parental_Approval – Yes / No / Partial

Urban_Rural – Urban / Rural

Dowry_Exchanged – Yes / No / Not Disclosed

Marital_Satisfaction – Low / Medium / High

Divorce_Status – Yes / No

Children_Count – Number of children (0-5)

Income_Level – Low / Middle / High

Years_Since_Marriage – Number of years since marriage

Spouse_Working – Yes / No

Inter-Caste – Yes / No

Inter-Religion – Yes / No

Interest-Income Time Series for Aavas Financiers Limited. Aavas Financiers Limited provides housing finance services to low- and middle-income customers in semi-urban and rural areas in India. The company offers home loans for flats, houses, and bungalows, as well as resale properties; home construction loans for self-construction of residential house; and home improvement loans, including loans for tiling or flooring, plaster or painting, etc. It also provides loans against property; micro, small, and medium enterprise loans; and home loan balance transfer, as well as cash salaried plus loans and small ticket size loan. The company was formerly known as AU Housing Finance Limited and changed its name to Aavas Financiers Limited in May 2017. Aavas Financiers Limited was incorporated in 2011 and is based in Jaipur, India.

Operating-Expenses Time Series for Aptus Value Housing Finance India Limited. Aptus Value Housing Finance India Limited, together with its subsidiary, Aptus Finance India Private Limited, provides housing finance solutions in India. The company offers home and quasi-home loans for the purchase, construction, renovation, and extension of houses; loans against property for construction and purchase of houses; and secured, small business, and refinance loans. It also provides life, credit shield, and property insurance products. The company serves individual homebuyers; low and middle income salaried and self-employed individuals; and first-time homeowners from rural and semi-urban areas. Aptus Value Housing Finance India Limited was incorporated in 2009 and is headquartered in Chennai, India.

A focus group discussion (FGD) was conducted to identify food habits, attitudes towards food purchase and access, and dishes usually consumed by households belonging to a low and middle-income group in urban and rural West Bengal and Odisha, India. Housewives who were actively involved in the decision making and/or cooking for the family were invited to participate in the FGD.

Despite dramatic reductions in global risk exposures to unsafe water sources, lack of access to clean water remains a persistent problem in many rural and last-mile communities. A great deal is known about demand for household water treatment systems; however, similar evidence for fully treated water products is limited. This study evaluates an NGO-based potable water delivery service in rural Bihar, India, meant to stand-in for more robust municipal treated water supply systems that have yet to reach the area. We use a random price auction and discrete choice experiment to examine willingness to pay (WTP) and stated product preferences, respectively, for this service among 162 households in the region. We seek to determine the impact of short-term price subsidies on demand for water delivery and the extent to which participation in the delivery program leads to changes in stated preferences for service characteristics. We find that mean WTP for the first week of service is roughly 51% of market price and represents only 1.7% of median household income, providing evidence of untapped demand for fully treated water. We also find mixed evidence on the effect of small price subsidies for various parts of the delivery service, and that one week of initial participation leads to significant changes in stated preferences for the taste of the treated water as well as the convenience of the delivery service. While more evidence is needed on the effect of subsidies, our findings suggest that marketing on taste and convenience could help increase uptake of clean water delivery services in rural and last-mile communities that have yet to receive piped water. However, we caution that these services should be seen as a stopgap, not a substitute for piped municipal water systems.

Common-Stock-Shares-Outstanding Time Series for Aptus Value Housing Finance India Limited. Aptus Value Housing Finance India Limited, together with its subsidiary, Aptus Finance India Private Limited, provides housing finance solutions in India. The company offers home and quasi-home loans for the purchase, construction, renovation, and extension of houses; loans against property for construction and purchase of houses; and secured, small business, and refinance loans. It also provides life, credit shield, and property insurance products. The company serves individual homebuyers; low and middle income salaried and self-employed individuals; and first-time homeowners from rural and semi-urban areas. Aptus Value Housing Finance India Limited was incorporated in 2009 and is headquartered in Chennai, India.

Ebitda Time Series for Aavas Financiers Limited. Aavas Financiers Limited provides housing finance services to low- and middle-income customers in semi-urban and rural areas in India. The company offers home loans for flats, houses, and bungalows, as well as resale properties; home construction loans for self-construction of residential house; and home improvement loans, including loans for tiling or flooring, plaster or painting, etc. It also provides loans against property; micro, small, and medium enterprise loans; and home loan balance transfer, as well as cash salaried plus loans and small ticket size loan. The company was formerly known as AU Housing Finance Limited and changed its name to Aavas Financiers Limited in May 2017. Aavas Financiers Limited was incorporated in 2011 and is based in Jaipur, India.

Price-To-Tangible-Book-Ratio Time Series for Aptus Value Housing Finance India Limited. Aptus Value Housing Finance India Limited, together with its subsidiary, Aptus Finance India Private Limited, provides housing finance solutions in India. The company offers home and quasi-home loans for the purchase, construction, renovation, and extension of houses; loans against property for construction and purchase of houses; and secured, small business, and refinance loans. It also provides life, credit shield, and property insurance products. The company serves individual homebuyers; low and middle income salaried and self-employed individuals; and first-time homeowners from rural and semi-urban areas. Aptus Value Housing Finance India Limited was incorporated in 2009 and is headquartered in Chennai, India.