As of 2022, ******* was the European country which had the greatest share of its household income made up by wages and salaries. At the same time, Denmark had the most heavily taxed household, with taxes being worth over ******* of gross household income. Countries such as *************, and ******, on the other hand, saw much smaller shares of household income being contributed by wages and salaries, with households in these countries receiving greater contributions from operating surplus and mixed income - i.e. income earned from the ownership of a business or through self-employment. In Greece, this is, in fact, larger than the contribution of wages and salaries, reflecting the importance of small businesses and self-employment to the Greek economy.

Survey on Households and the Environment: Percentage of dwellings whose residents place importance on specific factors when purchasing a new product, by net monthly household income and importance placed on it. National.

Abstract

The survey was conducted during December 2006, following an initial mini census listing exercise which was conducted about two months earlier in late September 2006. The objectives of the HIES were as follows: a) Provide information on income and expenditure distribution within the population; b) Provide income estimates of the household sector for the national accounts; c) Provide data for the re-base on the consumer price index; d) Provide data for the analysis of poverty and hardship.

Geographic coverage

National coverage: whole island was covered for the survey.

Analysis unit

• Household;
• Individual.

Universe

The survey covered all private households on the island of Nauru. When the survey was in the field, interviewers were further required to reduce the scope by removing those households which had not been residing in Nauru for the last 12 months and did not intend to stay in Nauru for the next 12 months. Persons living in special dwellings (Hospital, Prison, etc) were not included in the survey.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample size adopted for the survey was 500 households which allowed for expected sample loss, whilst still maintaining a suitable responding sample for the analysis.

Before the sample was selected, the population was stratified by constituency in order to assist with the logistical issues associated with the fieldwork. There were eight constituencies in total, along with "Location" which stretches across the districts of Denigamodu and Aiwo, forming nine strata in total. Although constituency level analysis was not a priority for the survey, sample sizes within each stratum were kept to a minimum of 40 households, to enable some basic forms of analysis at this level if required.

The sample selection procedure within each stratum was then to sort each household on the frame by household size (number of people), and then run a systematic skip through the list in order to achieve the desirable sample size.

Sampling deviation

No deviations from the sample design took place.

Mode of data collection

Face-to-face [f2f]

Research instrument

The survey schedules adopted for the Household Income and Expenditure Survey (HIES) included the following: · Expenditure questionnaire; · Income questionnaire; · Miscellaneous questionnaire; · Diary (x2).

Whilst a Household Control Form collecting basic demographics is also normally included with the survey, this wasn't required for this HIES as this activity took place for all households in the mini census.

Information collected in the four schedules covered the following: -Expenditure questionnaire: Covers basic details about the dwelling structure and its access to things like water and sanitation. It was also used as the vehicle to collect expenditure on major and infrequent expenditures incurred by the household. -Income questionnaire: Covers each of the main types of household income generated by the household such as wages and salaries, business income and income from subsistence activities. -Miscellaneous questionnaire: Covers topics relating to health access, labour force status and education. -Diary: Covers all day to day expenditures incurred by the household, consumption of items produced by the household such as fish and crops, and gifts both received and given by the household.

All questionnaires are provided as External Resources.

Cleaning operations

There were 3 phases to the editing process for the 2006 Household Income and Expenditure Survey (HIES) of Nauru which included: 1. Data Verification operations; 2. Data Editing operations; 3. Data Auditing operations.

The software used for data editting is CSPro 3.0. After each batch is completed the supervisor should check that all person details have been entered from the household listing form (HCF) and should review the income and expenditure questionnaires for each batch ensuring that all items have been entered correctly. Any omitted or incorrect items should be entered into the system. The supervisor is required to perform outlier checks (large or small values) on the batched diary data by calculating unit price (amount/quantity) and comparing prices for each item. This is to be conducted by loading the data into Excel files and sorting data by unit price for each item. Any changes to prices or quantities will be made on the batch file.

For more information on what each phase entailed go the document HIES Processing Instructions attached to this documentation.

Response rate

The survey response rates were a lot lower than expected, especially in some districts. The district of Aiwo, Uaboe and Denigomodu had the lowest response rates with 16.7%, 20.0% and 34.8% respectively. The area of Location was also extremely low with a responses rate of 32.2%. On a more positive note, the districts of Yaren, Ewa, Anabar, Ijuw and Anibare all had response rates at 80.0% or better.

The major contributing factor to the low response rates were households refusing to take part in the survey. The figures for responding above only include fully responding households, and given there were many partial responses, this also brought the values down. The other significant contributing factor to the low response rates was the interviewers not being able to make contact with the household during the survey period.

Unfortunately, not only do low response rates often increase the sampling error of the survey estimates, because the final sample is smaller, it will also introduce response bias into the final estimates. Response bias takes place when the households responding to the survey possess different characteristics to the households not responding, thus generating different results to what would have been achieved if all selected households responded. It is extremely difficult to measure the impact of the non-response bias, as little information is generally known about the non-responding households in the survey. For the Nauru 2006 HIES however, it was noted during the fieldwork that a higher proportion of the Chinese population residing in Nauru were more likely to not respond. Given it is expected their income and expenditure patterns would differ from the rest of the population, this would contribute to the magnitude of the bias.

Below is the list of all response rates by district: -Yaren: 80.5% -Boe: 70% -Aiwo: 16.7% -Buada: 62.5% -Denigomodu: 34.8% -Nibok: 68.4% -Uaboe: 20% -Baitsi: 47.8% -Ewa: 80% -Anetan: 76.5% -Anabar: 81.8% -Ijuw: 85.7% -Anibare: 80% -Meneng: 64.3% -Location: 32.2% -TOTAL: 54.4%

Sampling error estimates

To determine the impact of sampling error on the survey results, relative standard errors (RSEs) for key estimates were produced. When interpreting these results, one must remember that these figures don't include any of the non-sampling errors discussed in other sections of this documentation

To also provide a rough guide on how to interpret the RSEs provided in the main report, the following information can be used:

Category  Description
RSE < 5%  Estimate can be regarded as very reliable
5% < RSE < 10% Estimate can be regarded as good and usable
10% < RSE < 25% Estimate can be considered usable, with caution
RSE > 25%  Estimate should only be used with extreme caution

The actual RSEs for the key estimates can be found in Section 4.1 of the main report

As can be seen from these tables, the estimates for Total Income and Total Expenditure from the Household Income and Expenditure Survey (HIES) can be considered to be very good, from a sampling error perspective. The same can also be said for the Wage and Salary estimate in income and the Food estimate in expenditure, which make up a high proportion of each respective group.

Many of the other estimates should be used with caution, depending on the magnitude of their RSE. Some of these high RSEs are to be expected, due to the expected degree of variability for how households would report for these items. For example, with Business Income (RSE 56.8%), most households would report no business income as no household members undertook this activity, whereas other households would report large business incomes as it's their main source of income.

Data appraisal

Other than the non-response issues discussed in this documentation, other quality issues were identified which included: 1) Reporting errors Some of the different aspects contributing to the reporting errors generated from the survey, with some examples/explanations for each, include the following:

a) Misinterpretation of survey questions: A common mistake which takes place when conducting a survey is that the person responding to the questionnaire may interpret a question differently to the interviewer, who in turn may have interpreted the question differently to the people who designed the questionnaire. Some examples of this for a Household Income and Expenditure Survey (HIES) can include people providing answers in dollars and cents, instead of just dollars, or the reference/recall period for an “income” or “expenditure” is misunderstood. These errors can often see reported amounts out by a factor of 10 or even 100, which can have major impacts on final results.

b) Recall problems for the questionnaire information: The majority of questions in both of the income and expenditure questionnaires require the respondent to recall what took place over a 12 month period. As would be expected, people will often forget what took place up to 12 months ago so some

In 2024, the most crucial factor while buying electronics and household appliances online in Poland was the price. A detailed description of products and a wide range of products were also meaningful for the shoppers. Website design, as well as a familiar arrangement of categories, were the least crucial for shopping online for such services.

IntroductionLow and middle income countries bear the majority burden of self-harm, yet there is a paucity of evidence detailing risk-factors for self-harm in these populations. This study aims to identify environmental, socio-economic and demographic household-level risk factors for self-harm in five impoverished urban communities in Johannesburg, South Africa.MethodsAnnual serial cross-sectional surveys were undertaken in five impoverished urban communities in Johannesburg for the Health, Environment and Development (HEAD) study. Logistic regression analysis using the HEAD study data (2006–2011) was conducted to identify household-level risk factors associated with self-harm (defined as a self-reported case of a fatal or non-fatal suicide attempt) within the household during the preceding year. Stepwise multivariate logistic regression analysis was employed to identify factors associated with self-harm.ResultsA total of 2 795 household interviews were conducted from 2006 to 2011. There was no significant trend in self-harm over time. Results from the final model showed that self-harm was significantly associated with households exposed to a violent crime during the past year (Adjusted Odds Ratio (AOR) 5.72; 95% CI 1.64–19.97); that have a member suffering from a chronic medical condition (AOR 8.95; 95% 2.39–33.56) and households exposed to indoor smoking (AOR 4.39; CI 95% 1.14–16.47).ConclusionThis study provides evidence on household risk factors of self-harm in settings of urban poverty and has highlighted the potential for a more cost-effective approach to identifying those at risk of self-harm based on household level factors.

Abstract

The purpose of the HIES survey is to obtain information on the income, consumption pattern, incidence of poverty, and saving propensities for different groups of people in Nauru. This information will be used to guide policy makers in framing socio-economic developmental policies and in initiating financial measures for improving economic conditions of the people.

Some more specific outputs from the survey are listed below: a) To obtain expenditure weights and other useful data for the revision of the consumer price index; b) To supplement the data available for use in compiling official estimates of household accounts in the systems of national accounts; c) To supply basic data needed for policy making in connection with social and economic planning; d) To provide data for assessing the impact on household living conditions of existing or proposed economic and social measures, particularly changes in the structure of household expenditures and in household consumption; e) To gather information on poverty lines and incidence of poverty throughout Nauru.

Geographic coverage

National

Analysis unit

• Household
• Person
• Expenditure Commodity

Universe

The survey covered all private households on the island of Nauru. When the survey was in the field, interviewers were further required to reduce the scope by removing those households which had not been residing in Nauru for the last 12 months and did not intend to stay in Nauru for the next 12 months.

Persons living in special dwellings (Hospital, Prison, etc) were not included in the survey.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample size adopted for the survey was 500 households which allowed for expected sample loss, whilst still maintaining a suitable responding sample for the analysis.

Before the sample was selected, the population was stratified by constituency in order to assist with the logistical issues associated with the fieldwork. There were eight constituencies in total, along with "Location" which stretches across the districts of Denigamodu and Aiwo, forming nine strata in total. Although constituency level analysis was not a priority for the survey, sample sizes within each stratum were kept to a minimum of 40 households, to enable some basic forms of analysis at this level if required.

The sample selection procedure within each stratum was then to sort each household on the frame by household size (number of people), and then run a systematic skip through the list in order to achieve the desirable sample size.

Sampling deviation

No deviations from the sample design took place.

Mode of data collection

Face-to-face [f2f] for questionnaires, self-enumeration for the diaries

Research instrument

The survey schedules adopted for the HIES included the following: · Expenditure questionnaire · Income questionnaire · Miscellaneous questionnaire · Diary (x2)

Whilst a Household Control Form collecting basic demographics is also normally included with the survey, this wasn't required for this HIES as this activity took place for all households in the mini census.

Information collected in the four schedules covered the following:

Expenditure questionnaire: Covers basic details about the dwelling structure and its access to things like water and sanitation. It was also used as the vehicle to collect expenditure on major and infrequent expenditures incurred by the household.

Income questionnaire: Covers each of the main types of household income generated by the household such as wages and salaries, business income and income from subsistence activities.

Miscellaneous questionnaire: Covers topics relating to health access, labour force status and education.

Diary: Covers all day to day expenditures incurred by the household, consumption of items produced by the household such as fish and crops, and gifts both received and given by the household.

Cleaning operations

There were 3 phases to the editing process for the 2006 Nauru HIES which included: 1. Data Verification operations 2. Data Editing operations 3. Data Auditing operations

For more information on what each phase entailed go the document HIES Processing Instructions attached to this documentation.

Response rate

The survey response rates were a lot lower than expected, especially in some districts. The district of Aiwo, Uaboe and Denigomodu had the lowest response rates with 16.7%, 20.0% and 34.8% respectively. The area of Location was also extremely low with a responses rate of 32.2%. On a more positive note, the districts of Yaren, Ewa, Anabar, Ijuw and Anibare all had response rates at 80.0% or better.

The major contributing factor to the low response rates were households refusing to take part in the survey. The figures for responding above only include fully responding households, and given there were many partial responses, this also brought the values down. The other significant contributing factor to the low response rates was the interviewers not being able to make contact with the household during the survey period.

Unfortunately, not only do low response rates often increase the sampling error of the survey estimates, because the final sample is smaller, it will also introduce response bias into the final estimates. Response bias takes place when the households responding to the survey possess different characteristics to the households not responding, thus generating different results to what would have been achieved if all selected households responded. It is extremely difficult to measure the impact of the non-response bias, as little information is generally known about the non-responding households in the survey. For the Nauru 2006 HIES however, it was noted during the fieldwork that a higher proportion of the Chinese population residing in Nauru were more likely to not respond. Given it is expected their income and expenditure patterns would differ from the rest of the population, this would contribute to the magnitude of the bias.

Sampling error estimates

To determine the impact of sampling error on the survey results, relative standard errors (RSEs) for key estimates were produced. When interpreting these results, one must remember that these figures don't include any of the non-sampling errors discussed in other sections of this documentation

To also provide a rough guide on how to interpret the RSEs provided in the main report, the following information can be used:

Category  Description
RSE < 5%  Estimate can be regarded as very reliable
5% < RSE < 10% Estimate can be regarded as good and usable
10% < RSE < 25% Estimate can be considered usable, with caution
RSE > 25%  Estimate should only be used with extreme caution

The actual RSEs for the key estimates can be found in Section 4.1 of the main report

As can be seen from these tables, the estimates for Total Income and Total Expenditure from the HIES can be considered to be very good, from a sampling error perspective. The same can also be said for the Wage and Salary estimate in income and the Food estimate in expenditure, which make up a high proportion of each respective group.

Many of the other estimates should be used with caution, depending on the magnitude of their RSE. Some of these high RSEs are to be expected, due to the expected degree of variability for how households would report for these items. For example, with Business Income (RSE 56.8%), most households would report no business income as no household members undertook this activity, whereas other households would report large business incomes as it's their main source of income.

Data appraisal

Other than the non-response issues discussed in this documentation, other quality issues were identified which included: 1) Reporting errors Some of the different aspects contributing to the reporting errors generated from the survey, with some examples/explanations for each, include the following:

a) Misinterpretation of survey questions: A common mistake which takes place when conducting a survey is that the person responding to the questionnaire may interpret a question differently to the interviewer, who in turn may have interpreted the question differently to the people who designed the questionnaire. Some examples of this for a HIES can include people providing answers in dollars and cents, instead of just dollars, or the reference/recall period for an “income” or “expenditure” is misunderstood. These errors can often see reported amounts out by a factor of 10 or even 100, which can have major impacts on final results.

b) Recall problems for the questionnaire information: The majority of questions in both of the income and expenditure questionnaires require the respondent to recall what took place over a 12 month period. As would be expected, people will often forget what took place up to 12 months ago so some information will be forgotten.

c) Intentional under-reporting for some items: For whatever reasons, a household may still participate in a survey but not be willing to provide accurate responses for some questions. Examples for a HIES include people not fully disclosing their total income, and intentionally under-reporting expenditures on items such as alcohol and tobacco.

d) Accidental under-reporting in the household diaries: Although the two diaries are left with the household for a period of two weeks, it is easy for the household to forget to enter all expenditures throughout this period - this problem most likely increases as the two

Normalized raster output of Median Household Income by block group in the State of Iowa based on U.S. Census Bureau, 2013-2017 American Community Survey 5-Year Estimates. Used in the Transit Dependency Analysis as part of the 2020 Iowa DOT Public Transit Long Range Plan update. This factor was one of seven utilized in the analysis that was based on MTI Report 12-30 "Investigating the Determining Factors for Transit Travel Demand by Bus Mode in US Metropolitan Statistical Areas" by the Mineta Transportation Institute of San José State University (SJSU) in May 2015. https://transweb.sjsu.edu/research/investigating-determining-factors-transit-travel-demand-bus-mode-us-metropolitan

Cameroon CM: GDP: PPP: Household Final Consumption Expenditure data was reported at 108,034.123 Intl $ mn in 2023. This records an increase from the previous number of 101,913.804 Intl $ mn for 2022. Cameroon CM: GDP: PPP: Household Final Consumption Expenditure data is updated yearly, averaging 33,795.525 Intl $ mn from Dec 1990 (Median) to 2023, with 34 observations. The data reached an all-time high of 108,034.123 Intl $ mn in 2023 and a record low of 13,230.187 Intl $ mn in 1990. Cameroon CM: GDP: PPP: Household Final Consumption Expenditure data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Cameroon – Table CM.World Bank.WDI: Gross Domestic Product: Purchasing Power Parity. This indicator provides values for households and NPISHs final consumption expenditure expressed in current international dollars converted by purchasing power parity (PPP) conversion factor. Household final consumption expenditure (formerly private consumption) is the market value of all goods and services, including durable products (such as cars, washing machines, and home computers), purchased by households. It excludes purchases of dwellings but includes imputed rent for owner-occupied dwellings. It also includes payments and fees to governments to obtain permits and licenses. Here, household consumption expenditure includes the expenditures of nonprofit institutions serving households, even when reported separately by the country. PPP conversion factor is a spatial price deflator and currency converter that eliminates the effects of the differences in price levels between countries. From July 2020, “Households and NPISHs final consumption expenditure: linked series (current LCU)” [NE.CON.PRVT.CN.AD] is used as underlying expenditure in local currency unit so that it’s in line with time series of PPP conversion factor, private consumption (LCU per international $), which are extrapolated with linked CPI.;International Comparison Program, World Bank | World Development Indicators database, World Bank | Eurostat-OECD PPP Programme.;Gap-filled total;

The availability of branches and ATMs in the neighborhood was a factor influencing the selection of financial institutions for the majority of single households, according to a survey conducted in Japan in 2021. Around ** percent of one-person households named the availability of ATMs and branches as a selection criterion, while about ** percent stated that a wide range of online services was a factor influencing their choice.

This statistic shows the share of survey respondents by parental status that listed by importance the factors that they consider most important when considering a new home to rent in the United Kingdom in 2017. Both households with children (65.2 percent) and without children (70 percent) considered the price of a rental property the most important factor when considering a new rental home.

For nearly ********** of surveyed Russians, high product quality was the most important factor determining their choice of electronics and household brands, according to data from May 2022. The availability of discounts and special offers was the second leading driver, named by approximately *** out of ten respondents. By contrast, environmental friendliness of products was cited as the least important factor.

Brazil, 2019.

U.S. citizens with a professional degree had the highest median household income in 2023, at 172,100 U.S. dollars. In comparison, those with less than a 9th grade education made significantly less money, at 35,690 U.S. dollars. Household income The median household income in the United States has fluctuated since 1990, but rose to around 70,000 U.S. dollars in 2021. Maryland had the highest median household income in the United States in 2021. Maryland’s high levels of wealth is due to several reasons, and includes the state's proximity to the nation's capital. Household income and ethnicity The median income of white non-Hispanic households in the United States had been on the rise since 1990, but declining since 2019. While income has also been on the rise, the median income of Hispanic households was much lower than those of white, non-Hispanic private households. However, the median income of Black households is even lower than Hispanic households. Income inequality is a problem without an easy solution in the United States, especially since ethnicity is a contributing factor. Systemic racism contributes to the non-White population suffering from income inequality, which causes the opportunity for growth to stagnate.

Graph and download economic data for Total Factor Productivity for Manufacturing: Household Appliance Manufacturing (NAICS 3352) in the United States (IPUEN3352M001000000) from 1988 to 2021 about appliances, productivity, NAICS, IP, households, manufacturing, and USA.

European Journal of Social Sciences

This statistic shows the most important factors for consumers when purchasing home environment products in the United States as of 2020. The survey revealed that product quality/performance was the most important factor for **** percent of U.S. consumers when purchasing home environment products.

Catastrophic dental health expenditure at different income thresholds among all adults and those who used dental services in the last year, by individual-level factors

NMCDC Copy of Living Atlas map. Source: https://www.arcgis.com/home/item.html?id=23ab8028f1784de4b0810104cd5d1c8fIllustration by Brian BrenemanThis layer shows population broken down by race and Hispanic origin. This is shown by tract, county, and state boundaries. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. This layer is symbolized to show the predominant race living within an area. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2013-2017ACS Table(s): B03002 (Not all lines of this ACS table are available in this feature layer.)Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 7, 2018National Figures: American Fact FinderThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:This dataset is updated automatically when the most current vintage of ACS data is released each year. The service contains the ACS data as of the current vintage listed. Tabular data is updated annually with the Census Bureau's release schedule. This may alter data values, fields, and boundaries. Click here to learn more about ACS data releases.Boundaries come from the US Census TIGER geodatabases. Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines clipped for cartographic purposes. For census tracts, the water cutouts are derived from a subset of the 2010 AWATER (Area Water) boundaries offered by TIGER. For state and county boundaries, the water and coastlines are derived from the coastlines of the 500k TIGER Cartographic Boundary Shapefiles. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters). The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -555555...) have been set to null. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small. NOTE: any calculated percentages or counts that contain estimates that have null margins of error yield null margins of error for the calculated fields.

Colombia CO: GDP: PPP: Household Final Consumption Expenditure data was reported at 769,201.201 Intl $ mn in 2023. This records an increase from the previous number of 691,401.835 Intl $ mn for 2022. Colombia CO: GDP: PPP: Household Final Consumption Expenditure data is updated yearly, averaging 247,326.829 Intl $ mn from Dec 1990 (Median) to 2023, with 34 observations. The data reached an all-time high of 769,201.201 Intl $ mn in 2023 and a record low of 97,597.214 Intl $ mn in 1990. Colombia CO: GDP: PPP: Household Final Consumption Expenditure data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Colombia – Table CO.World Bank.WDI: Gross Domestic Product: Purchasing Power Parity. This indicator provides values for households and NPISHs final consumption expenditure expressed in current international dollars converted by purchasing power parity (PPP) conversion factor. Household final consumption expenditure (formerly private consumption) is the market value of all goods and services, including durable products (such as cars, washing machines, and home computers), purchased by households. It excludes purchases of dwellings but includes imputed rent for owner-occupied dwellings. It also includes payments and fees to governments to obtain permits and licenses. Here, household consumption expenditure includes the expenditures of nonprofit institutions serving households, even when reported separately by the country. PPP conversion factor is a spatial price deflator and currency converter that eliminates the effects of the differences in price levels between countries. From July 2020, “Households and NPISHs final consumption expenditure: linked series (current LCU)” [NE.CON.PRVT.CN.AD] is used as underlying expenditure in local currency unit so that it’s in line with time series of PPP conversion factor, private consumption (LCU per international $), which are extrapolated with linked CPI.;International Comparison Program, World Bank | World Development Indicators database, World Bank | Eurostat-OECD PPP Programme.;Gap-filled total;

Household Coffee Machine Market Outlook

The global household coffee machine market size was valued at approximately $13.5 billion in 2023 and is projected to reach around $20.3 billion by 2032, growing at a compound annual growth rate (CAGR) of 4.5% during the forecast period. This growth is driven by increasing coffee consumption globally, advancements in coffee machine technology, and rising disposable incomes.

The rise in coffee consumption is one of the primary factors propelling the growth of the household coffee machine market. With the increasing consumer inclination towards specialty coffee and the growing coffee culture across various regions, the demand for efficient and versatile coffee machines has surged. Additionally, the convenience offered by these machines, which allows consumers to brew coffee at home with minimal effort and high-quality outcomes, has further fueled their popularity. The trend of working from home, which has increased significantly due to the COVID-19 pandemic, continues to drive the demand for household coffee machines as people seek to replicate their favorite coffee shop experiences at home.

Technological advancements in coffee machines are also significantly contributing to market growth. Manufacturers are continually innovating to introduce smart coffee machines equipped with features like Wi-Fi connectivity, programmable settings, and integration with smart home systems. These advancements not only enhance the user experience but also cater to the growing consumer demand for convenience and customization. The introduction of eco-friendly and energy-efficient machines is another factor promoting market growth, as environmentally conscious consumers increasingly seek sustainable options.

The rise in disposable incomes, particularly in emerging economies, is another crucial factor driving the household coffee machine market. As the middle-class population grows and urbanization increases, more consumers can afford premium coffee machines. This trend is particularly evident in regions like Asia Pacific and Latin America, where a burgeoning middle class is increasingly adopting Western lifestyles and preferences, including the consumption of coffee. Additionally, the increasing number of nuclear families and single-person households is leading to a higher demand for compact and user-friendly coffee machines.

From a regional perspective, North America and Europe currently dominate the household coffee machine market, owing to the well-established coffee culture and high consumer spending power in these regions. However, the Asia Pacific region is expected to witness the highest growth during the forecast period, driven by rising disposable incomes, increasing urbanization, and the growing influence of Western culture. The demand for coffee machines in Latin America and the Middle East & Africa is also projected to grow steadily, supported by similar factors.

Product Type Analysis

The household coffee machine market is segmented by product type into drip coffee machines, espresso machines, capsule/POD coffee machines, French press, and others. Drip coffee machines are among the most popular, especially in North America and Europe, due to their ease of use, affordability, and ability to brew larger quantities of coffee. These machines have evolved to include programmable features and improved brewing technology, making them a staple in many households.

Espresso machines are increasingly gaining traction among coffee aficionados who seek the authentic coffee shop experience at home. These machines, which are available in both manual and automatic versions, offer precise control over the brewing process, allowing users to create their preferred espresso-based drinks. Technological advancements, such as thermoblock heating systems and built-in grinders, have enhanced the functionality and performance of espresso machines, making them a popular choice among discerning coffee drinkers.

Capsule/POD coffee machines represent a significant and growing segment of the market. These machines provide unmatched convenience and consistency, using pre-packaged coffee pods to deliver a variety of coffee flavors and styles with minimal effort. The popularity of capsule/POD machines has been bolstered by partnerships with major coffee brands, which offer a wide range of pod options. However, concerns around the environmental impact of single-use pods have led to the development of recyclable and compostable options, addressing consumer demand for sustainability.