The statistic shows the average number of residents living in India's households in 2012, by state and union territory. In that year, Punjab and Arunachal Pradesh had an average of five people living in a household, while the lowest average size was in Tamil Nadu with 3.9 people per household.

Most households in India between 2019 and 2021 had between ***** and **** people. The number of ****-people households accounted for over ** percent during the survey period. Interestingly, about **** percent reported **** or more people in one household. Rural areas had a higher share of households with **** or more members.

In 2019, the average household size of Hindus in Asia-Pacific was *** people per household. In comparison, Hindus in sub-Saharan Africa had the smallest average household size, at *** people per household. The majority of Hindus live in India.

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

Abstract

The National Family Health Survey (NFHS) was carried out as the principal activity of a collaborative project to strengthen the research capabilities of the Population Reasearch Centres (PRCs) in India, initiated by the Ministry of Health and Family Welfare (MOHFW), Government of India, and coordinated by the International Institute for Population Sciences (IIPS), Bombay. Interviews were conducted with a nationally representative sample of 89,777 ever-married women in the age group 13-49, from 24 states and the National Capital Territoty of Delhi. The main objective of the survey was to collect reliable and up-to-date information on fertility, family planning, mortality, and maternal and child health. Data collection was carried out in three phases from April 1992 to September 1993. THe NFHS is one of the most complete surveys of its kind ever conducted in India.

The households covered in the survey included 500,492 residents. The young age structure of the population highlights the momentum of the future population growth of the country; 38 percent of household residents are under age 15, with their reproductive years still in the future. Persons age 60 or older constitute 8 percent of the population. The population sex ratio of the de jure residents is 944 females per 1,000 males, which is slightly higher than sex ratio of 927 observed in the 1991 Census.

The primary objective of the NFHS is to provide national-level and state-level data on fertility, nuptiality, family size preferences, knowledge and practice of family planning, the potentiel demand for contraception, the level of unwanted fertility, utilization of antenatal services, breastfeeding and food supplemation practises, child nutrition and health, immunizations, and infant and child mortality. The NFHS is also designed to explore the demographic and socioeconomic determinants of fertility, family planning, and maternal and child health. This information is intended to assist policymakers, adminitrators and researchers in assessing and evaluating population and family welfare programmes and strategies. The NFHS used uniform questionnaires and uniform methods of sampling, data collection and analysis with the primary objective of providing a source of demographic and health data for interstate comparisons. The data collected in the NFHS are also comparable with those of the Demographic and Health Surveys (DHS) conducted in many other countries.

Geographic coverage

National

Analysis unit

• Household
• Data collected for women 13-49, indicators calculated for women 15-49

Universe

The population covered by the 1992-93 DHS is defined as the universe of all women age 13-49 who were either permanent residents of the households in the NDHS sample or visitors present in the households on the night before the survey were eligible to be interviewed.

Kind of data

Sample survey data

Sampling procedure

SAMPLE DESIGN

The sample design for the NFHS was discussed during a Sample Design Workshop held in Madurai in Octber, 1991. The workshop was attended by representative from the PRCs; the COs; the Office of the Registrar General, India; IIPS and the East-West Center/Macro International. A uniform sample design was adopted in all the NFHS states. The Sample design adopted in each state is a systematic, stratified sample of households, with two stages in rural areas and three stages in urban areas.

SAMPLE SIZE AND ALLOCATION

The sample size for each state was specified in terms of a target number of completed interviews with eligible women. The target sample size was set considering the size of the state, the time and ressources available for the survey and the need for separate estimates for urban and rural areas of the stat. The initial target sample size was 3,000 completed interviews with eligible women for states having a population of 25 million or less in 1991; 4,000 completed interviews for large states with more than 25 million population; 8,000 for Uttar Pradesh, the largest state; and 1,000 each for the six small northeastern states. In States with a substantial number of backward districts, the initial target samples were increased so as to allow separate estimates to be made for groups of backward districts.

The urban and rural samples within states were drawn separetly and , to the extent possible, sample allocation was proportional to the size of the urban-rural populations (to facilitate the selection of a self-weighting sample for each state). In states where the urban population was not sufficiently large to provide a sample of at least 1,000 completed interviews with eligible women, the urban areas were appropriately oversampled (except in the six small northeastern states).

THE RURAL SAMPLE: THE FRAME, STRATIFICATION AND SELECTION

A two-stage stratified sampling was adopted for the rural areas: selection of villages followed by selection of households. Because the 1991 Census data were not available at the time of sample selection in most states, the 1981 Census list of villages served as the sampling frame in all the states with the exception of Assam, Delhi and Punjab. In these three states the 1991 Census data were used as the sampling frame.

Villages were stratified prior to selection on the basis of a number of variables. The firts level of stratification in all the states was geographic, with districts subdivided into regions according to their geophysical characteristics. Within each of these regions, villages were further stratified using some of the following variables : village size, distance from the nearest town, proportion of nonagricultural workers, proportion of the population belonging to scheduled castes/scheduled tribes, and female literacy. However, not all variables were used in every state. Each state was examined individually and two or three variables were selected for stratification, with the aim of creating not more than 12 strata for small states and not more than 15 strata for large states. Females literacy was often used for implicit stratification (i.e., the villages were ordered prior to selection according to the proportion of females who were literate). Primary sampling Units (PSUs) were selected systematically, with probaility proportional to size (PPS). In some cases, adjacent villages with small population sizes were combined into a single PSU for the purpose of sample selection. On average, 30 households were selected for interviewing in each selected PSU.

In every state, all the households in the selected PSUs were listed about two weeks prior to the survey. This listing provided the necessary frame for selecting households at the second sampling stage. The household listing operation consisted of preparing up-to-date notional and layout sketch maps of each selected PSU, assigning numbers to structures, recording addresses (or locations) of these structures, identifying the residential structures, and listing the names of the heads of all the households in the residentiak structures in the selected PSU. Each household listing team consisted of a lister and a mapper. The listing operation was supervised by the senior field staff of the concerned CO and the PRC in each state. Special efforts were made not to miss any household in the selected PSU during the listing operation. In PSUs with fewer than 500 households, a complete household listing was done. In PSUs with 500 or more households, segmentation of the PSU was done on the basis of existing wards in the PSU, and two segments were selected using either systematic sampling or PPS sampling. The household listing in such PSUs was carried out in the selected segments. The households to be interviewed were selected from provided with the original household listing, layout sketch map and the household sample selected for each PSU. All the selected households were approached during the data collection, and no substitution of a household was allowed under any circumstances.

THE RURAL URBAN SAMPLE: THE FRAME, STRATIFICATION AND SELECTION

A three-stage sample design was adopted for the urban areas in each state: selection of cities/towns, followed by urban blocks, and finally households. Cities and towns were selected using the 1991 population figures while urban blocks were selected using the 1991 list of census enumeration blocks in all the states with the exception of the firts phase states. For the first phase states, the list of urban blocks provided by the National Sample Survey Organization (NSSSO) served as the sampling frame.

All cities and towns were subdivided into three strata: (1) self-selecting cities (i.e., cities with a population large enough to be selected with certainty), (2) towns that are district headquaters, and (3) other towns. Within each stratum, the cities/towns were arranged according to the same kind of geographic stratification used in the rural areas. In self-selecting cities, the sample was selected according to a two-stage sample design: selection of the required number of urban blocks, followed by selection of households in each of selected blocks. For district headquarters and other towns, a three stage sample design was used: selection of towns with PPS, followed by selection of two census blocks per selected town, followed by selection of households from each selected block. As in rural areas, a household listing was carried out in the selected blocks, and an average of 20 households per block was selected systematically.

Mode of data collection

Face-to-face

Research instrument

Three types of questionnaires were used in the NFHS: the Household Questionnaire, the Women's Questionnaire, and the Village Questionnaire. The overall content

In 2011, the average slum household in India had *** people. This number was a decrease from 2001. The government defines slums as residential areas with poorly built households. These are most often unhygienic and unfit for human habitation because of dilapidation, overcrowding, lack of sanitation, water and infrastructure.

4,89 (Number) in 2011. Average household size = population/number of households

The catalog contains data related to normal households by household size, census 2001 - India and states. It includes data on Household, Normal Household, Households, Households by Size, Normal Households by Size, Household Size, Normal Households by Household Size, Mean Household Size, Rural Households, Urban Households, Census 2001, Scheduled Caste (SC) and Scheduled Tribe (ST).

The dataset contains state-, region- and gender-wise NSS 78th round compiled data on Average Size and Total Number of Households, and Total Number of Persons (by Gender) in India

In the financial year 2021, the number of super-rich households earning more than ** million Indian rupees went up to **** million from **** million in the financial year 2016. This was an annual growth of **** percent. The number is expected to grow to over **** million in the financial year 2031 and ** million households in the financial year 2047. This will be the fastest growth across all income categories. On the other hand, destitute classified Indian households with earnings of less than *** thousand annually decreased only marginally to ***** million in financial year 2021 from **** million in 2016. However, it is estimated that the number of destitute households will fall to just *** million by the financial year 2047.

5.93 (Number) in 2011. Average household size = population/number of households

In the financial year 2021, a majority of Indian households fell under the aspirers category, earning between ******* and ******* Indian rupees a year. On the other hand, about ***** percent of households that same year, accounted for the rich, earning over * million rupees annually. The middle class more than doubled that year compared to ** percent in financial year 2005. Middle-class income group and the COVID-19 pandemic During the COVID-19 pandemic specifically during the lockdown in March 2020, loss of incomes hit the entire household income spectrum. However, research showed the severest affected groups were the upper middle- and middle-class income brackets. In addition, unemployment rates were rampant nationwide that further lead to a dismally low GDP. Despite job recoveries over the last few months, improvement in incomes were insignificant. Economic inequality While India maybe one of the fastest growing economies in the world, it is also one of the most vulnerable and severely afflicted economies in terms of economic inequality. The vast discrepancy between the rich and poor has been prominent since the last ***** decades. The rich continue to grow richer at a faster pace while the impoverished struggle more than ever before to earn a minimum wage. The widening gaps in the economic structure affect women and children the most. This is a call for reinforcement in in the country’s social structure that emphasizes access to quality education and universal healthcare services.

Key information about India Household Debt

• India Household Debt reached 617.3 USD bn in Mar 2024, compared with the reported number of 486.7 USD bn in the previous year
• India Household Debt: USD mn data is updated yearly, available from Mar 1998 to Mar 2024
• The data reached an all-time high of 617.3 USD bn in Mar 2024 and a record low of 8.8 USD bn in Mar 1998

CEIC converts annual Household Debt into USD. The Reserve Bank of India provides Household Debt in local currency. The Federal Reserve Board period end market exchange rate is used for currency conversions. Loans are used due to the lack of Flow of Funds statistics. Household Debt includes Consumer Durables, Housing and Rest of Personal Loans. Household Debt is in annual frequency, ending in March of each year.


Further information about India Household Debt

• In the latest reports, India Household Debt accounted for 17.4 % of the country's Nominal GDP in Mar 2024
• Money Supply M2 in India increased 760.6 USD bn YoY in Apr 2024
• India Foreign Exchange Reserves was measured at 572.6 USD bn in May 2024
• The Foreign Exchange Reserves equaled 9.2 Months of Import in May 2023
• India Domestic Credit reached 3,045.9 USD bn in Apr 2024, representing an increased of 11.1 % YoY

Abstract

The National Sample Survey Organisation (NSSO) has been carrying out All-India surveys quinquennially on consumer expenditure and employment - unemployment. The 50th round (July 1993 - June 1994) was the Fifth quinquennial survey on Consumer Expenditure and Employment - Unemployment. The previous four quinquennial surveys were the 27th (Oct. 1972 - Sept. 1973), the 32nd (Jul.1977 - Jun. 1978), the 38th ( Jan. - Dec. 1983) and 43rd (Jul. 1987 - Jun. 1988) rounds. In other rounds of NSS, also, a consumer expenditure inquiry on a limited scale was being carried out from the 42nd round (1986-87) onwards. From the 45th round onwards the subject coverage of this schedule has been expanded to include some important questions on employment so that an annual series of consumer expenditure and employment data is now available. While some of these smaller-scale surveys are spread over a full year and others over six months only, the quinquennial (full-scale) surveys have all been of a full year's duration. Household consumer expenditure is measured as the expenditure incurred by a household on domestic account during a specified period, called reference period. It includes the imputed values of goods and services, which are not purchased but procured otherwise for consumption. In other words, it is the sum total of monetary values of all the items (i.e. goods and services) consumed by the household on domestic account during the reference period. The imputed rent of owner-occupied houses is excluded from consumption expenditure. Any expenditure incurred towards the productive enterprises of the households is also excluded from household consumer expenditure. The household consumer expenditure schedule used for the survey collected information on quantity and value of household consumption with a reference period of "last 30 days" for some items of consumption and "last 365 days" for some less frequently purchased items. To minimise recall errors, a very detailed item classification was, as usual, adopted to collect information, including 148 items of food, 13 items of fuel, 28 items of clothing, bedding and footwear, 18 items of educational and medical expenses, 52 items of durable goods, and about 85 other items. The schedule also collected some other household particulars including age, sex and educational level etc. of each household member.

The schedule design for the survey was more or less similar to that adopted in the previous quinquennial round. The field work for the survey was conducted, as usual, by the Field Operations Division of the Organisation. The collected data were processed by the Data Processing Division of NSSO and tabulated by the Computer Centre of Department of Statistics. The reports have been prepared by Survey Design & Research Division (SDRD) of NSSO under the guidance of the Governing Council, NSSO.

Geographic coverage

The survey period of the 50th round was from July 1993 to June 1994. The geographical coverage of the survey was to be the whole of the Indian Union except Ladakh and Kargil districts of Jammu & Kashmir, 768 interior villages of Nagaland and 172 villages in Andaman & Nicobar Islands which remain inaccessible throughout the year. However, certain districts of Jammu & Kashmir viz., Doda, Anantnag, Pulwama, Srinagar, Badgam, Baramula and Kupwara, and Punjab's Amritsar district, had to be excluded from the survey due to unfavourable field conditions.

Analysis unit

Randomly selected households based on sampling procedure and members of the household

Universe

The survey used the interview method of data collection from a sample of randomly selected households and members of the household.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample design adopted for this round of survey was similar to that followed in the past surveys in its general aspects. The general scheme was a two stage stratified design with the first stage units being villages in the rural areas and urban frame survey blocks (UFS) in the urban areas. The second stage units were the households.

Sampling frame for first stage units: The latest available lists of census villages (which are mostly the 1981 census lists) constitute the sampling frame for the rural sector. For Nagaland, the villages located within 5kms of a bus route constitute the sampling frame whereas, for Andaman & Nicobar Islands, the list of accessible villages constituted the sampling frame. For the urban sector, the lists of NSSO Urban Frame Survey (UFS) blocks have been considered as the sampling frame. However, for some of the newly declared towns of 1991 census for which UFS frame has not been received, the lists of 1991 census EBs have been considered as the sampling frame.

Region formation and stratification: States were divided into regions by grouping contiguous districts similar in respect of population density and cropping pattern. In rural sector each district was treated a separate stratum if the population was below 2 million and where it exceeded 2 million, it was split into two or more strata. This cut off point of population was taken as 1.8 million ( in place of 2 million ) for the purpose of stratification for districts for which the 1981 census frame was used. In the urban sector, strata were formed, within each NSS region on the basis of population size class of towns. However, for towns with population of 4 lakhs or more the urban blocks were divided into two classes viz. one consisting of blocks inhabited by affluent section of the population and the other consisting of the remaining blocks.

Selection of first stage units : Selection of sample villages was done circular systematically with probability proportional to population and sample blocks circular systematically with equal probability. Both the sample villages and the sample blocks were selected in the form of two or more independent sub-samples. In Arunachal Pradesh the procedure of cluster sampling has been followed. Further large villages/blocks having present population of 1200 or more were divided into a suitable number of hamlet- groups/ sub-blocks having equal population content. Two hamlet- groups were selected from the larger villages while one sub-block was selected in urban sector for larger blocks.

Selection of households : While listing the households in the selected villages, certain relatively affluent households were identified and considered as second stage stratum 1 and the rest as second stage stratum 2.

A total of 10 households were surveyed from the selected village/hamlet-groups, 2 from the first category and remaining from the second.Further in the second stage stratum-2, the households were arranged according to the means of livelihood. The means of livelihood were identified on the basis of the major source of income as i) self-employed in non-agriculture, ii) rural labour and iii) others. The land possessed by the households was also ascertained and the frame for selection was arranged on the basis of this information. The households were selected circular systematically from both the second stage strata.

In the urban blocks a different method was used for arranging the households for selection. This involved the identification means of livelihood of households as any one of a) self-employed, b) regular salaried/wage earnings, c) casual labour, d) others. Further the average household monthly per capita consumer expenditure (mpce) was also ascertained. All households with MPCE of (i) Rs. 1200/- or more (in towns with population less than 10 lakhs or (ii) Rs. 1500/- or more (in towns with population 10 lakh or more) formed second-stage stratum 1 and the rest, second-stage stratum 2.The households of second-stage stratum 2 were arranged according to means of livelihood class and MPCE ranges before selection of sample households. A total of 10 households were selected from each sample block as follows (i) For affluent strata/classes : 4 households from second- stage stratum 1 and 6 households from second-stage stratum 2 (ii) For other strata/classes : 2 households from second-stage stratum 1 and 8 from second-stage stratum 2. Households were then selected circular systematically with a random start.

Shortfall in the required number of household in any second-stage stratum was made up by increasing the quota for the other second stage stratum.

A total of 7284 sample villages (Rural) and 4792 sample blocks (Urban) were allotted in central sample. 6983 sample villages and 470 sample blocks were successfully surveyed covering 356351 persons in sample villages and 208389 persons in sample blocks.

Sampling deviation

There was no deviation from the original sampling design.

Mode of data collection

Face-to-face [f2f]

India Proportion of People Living Below 50 Percent Of Median Income: % data was reported at 9.800 % in 2021. This records a decrease from the previous number of 10.000 % for 2020. India Proportion of People Living Below 50 Percent Of Median Income: % data is updated yearly, averaging 6.200 % from Dec 1977 (Median) to 2021, with 14 observations. The data reached an all-time high of 10.300 % in 2019 and a record low of 5.100 % in 2004. India Proportion of People Living Below 50 Percent Of Median Income: % data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s India – Table IN.World Bank.WDI: Social: Poverty and Inequality. The percentage of people in the population who live in households whose per capita income or consumption is below half of the median income or consumption per capita. The median is measured at 2017 Purchasing Power Parity (PPP) using the Poverty and Inequality Platform (http://www.pip.worldbank.org). For some countries, medians are not reported due to grouped and/or confidential data. The reference year is the year in which the underlying household survey data was collected. In cases for which the data collection period bridged two calendar years, the first year in which data were collected is reported.;World Bank, Poverty and Inequality Platform. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. For more information and methodology, please see http://pip.worldbank.org.;;The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than 2000 household surveys across 169 countries. See the Poverty and Inequality Platform (PIP) for details (www.pip.worldbank.org).

In the 3 years to March 2021, black households were most likely out of all ethnic groups to have a weekly income of under £600.

Key information about India Household Debt: % of GDP

• India household debt accounted for 17.1 % of the country's Nominal GDP in Mar 2024, compared with the ratio of 14.9 % in the previous year.
• India household debt to GDP ratio is updated yearly, available from Mar 1998 to Mar 2024.
• The data reached an all-time high of 17.1 % in Mar 2024 and a record low of 2.2 % in Mar 1999.

CEIC calculates annual Household Debt as % of Nominal GDP from annual Household Debt and annual Nominal GDP. The Reserve Bank of India provides Household Debt in local currency. Ministry of Statistics and Programme Implementation provides Nominal GDP in local currency based on SNA 2008, at 2011-2012 prices. Loans are used due to the lack of Flow of Funds statistics. Household Debt includes Consumer Durables, Housing and Rest of Personal Loans. Household Debt as % of Nominal GDP is in annual frequency, ending in March of each year.


Related information about India Household Debt: % of GDP

• In the latest reports, India Household Debt reached 617.3 USD bn in Mar 2024.
• Money Supply M2 in India increased 7.9 % YoY in Nov 2024.
• India Foreign Exchange Reserves was measured at 547.5 USD bn in Dec 2024.
• The Foreign Exchange Reserves equaled 9.2 Months of Import in May 2023.
• India Domestic Credit reached 3,168.1 USD bn in Dec 2024, representing an increased of 11.1 % YoY.
• The country's Non Performing Loans Ratio stood at 2.8 % in Mar 2024, compared with the ratio of 3.9 % in the previous year.

According to Cognitive Market Research, the Global Household Refrigerators and Freezers Market Size will be USD XX Billion in 2025 and is set to achieve a market size of USD XX Billion by the end of 2033 growing at a CAGR of XX% from 2025 to 2033.

North America held largest share of XX % in the year 2024 
Europe held share of XX % in the year 2024 
Asia-Pacific held significant share of XX % in the year 2024 
South America held significant share of XX % in the year 2024
Middle East and Africa held significant share of XX % in the year 2024

Market Dynamics

Household Refrigerators and Freezers

Key Drivers of Household Refrigerators and Freezers

The rise in the nuclear families is fueling the refrigerator market-

Growing nuclear families, along with rising disposable incomes and changing lifestyles, are fueling demand for domestic refrigerators and freezers, as the appliances are needed for food storage and preservation in small households. For instance, the joint family was the nucleus of Indian society — an institution that not only maintained cultural practices but also operated as an economic unit. Joint families shared labour and capital for farming work, family businesses, and child care. NFHS-5 data show the proportion of nuclear families in India increased to 58.2% during 2019–21 from 56% in 2016, while the size of the average household declined to 4.4 members from 4.6. In southern states, nuclear families already constitute almost 69% of all households. (Source- https://www.policycircle.org/) Further, For instance, The nuclear family, as per Taeube~5 accounts for 92.4% of the white and 83.5% of the non-white families in the United States of America. based on sample surveys, that approximately 6) % of families amongst Chinese farmers are of the nuclear type. Thus, the number of nuclear families are growing. The demand for the refrigerator and household freezers also increases. (Source- https://iris.who.int/bitstream/handle/) With the growing popularity of nuclear families, the demand for convenient and efficient means to store food rises, which raises demand for freezers and refrigerators. With hectic lifestyles and a love for convenience, individuals are more and more dependent on freezers and refrigerators to hold prepared foods and groceries. The market is witnessing advancements in refrigeration technology, with more energy-efficient versions, smart features, and higher capacities gaining greater popularity. The worldwide household freezers and refrigerators market is expected to continue expanding, based on factors such as increasing disposable incomes, urbanization, and shifting consumer behaviors. Rise in the number of nuclear families. Because Increased household income and educational levels tend to be linked with a higher probability of nuclear family forms, particularly in urban settings where there is a growing desire for economic independence and individual space. Mostly in united states, Canada, Germany. Thus, the increasing popularity of nuclear families, combined with increasing disposable incomes and changing lifestyles, is fueling high demand for household refrigerators and freezers. These products have become a necessity for food storage and preservation. As urbanisation and changing consumer trends reshape the world, the demand for innovative and adaptable refrigeration solutions will continue to increase.

Key Restraints of Household Refrigerators and Freezers

Higher initial outlay and issues with affordability as a hindrance to the household refrigerators and freezers market

Although innovation in smart technology, sustainability, and energy efficiency has benefited the refrigeration and freezing business significantly, one of the major constraints of advanced models is their high cost of purchase. Although consumers' interest in greener and smarter appliances is increasing, the price of these highly advanced refrigerators and freezers may prove a stumbling block to many prospective purchasers. For instance, July 2021, Smart Home Program - Technology Assessment Study and Pilot Design- models with cutting-edge technology such as smart features (touchscreens, voice controls, and Wi-Fi connectivity) or energy-saving compressors will cost more than basic, conventional models. Such high-tech refrigerators are usually several hundred or even a thousand dollars more expensive, out of budge...

In 2022, the majority of Indian adults had a wealth of 10,000 U.S. dollars or less. On the other hand, about *** percent were worth more than *********** dollars that year. India The Republic of India is one of the world’s largest and most economically powerful states. India gained independence from Great Britain on August 15, 1947, after having been under their power for 200 years. With a population of about *** billion people, it was the second most populous country in the world. Of that *** billion, about **** million lived in New Delhi, the capital. Wealth inequality India suffers from extreme income inequality. It is estimated that the top 10 percent of the population holds ** percent of the national wealth. Billionaire fortune has increase sporadically in the last years whereas minimum wages have remain stunted.

Data and insights on Wealth Distribution in India - share of wealth, average wealth, HNIs, wealth inequality GINI, and comparison with global peers.