Graph and download economic data for Net Worth Held by the Top 0.1% (99.9th to 100th Wealth Percentiles) (WFRBLTP1246) from Q3 1989 to Q1 2025 about net worth, wealth, percentile, Net, and USA.

Between December 2019 and 2021, the top one percent of earners accumulated 63 percent of all new wealth worldwide. This is more than six times more wealth than accumulated by the bottom 90 percent over the same time period.

Global wealth distribution Newly generated wealth landing in the hands of the few is not a new story and has been the focus of international development policy for many years. Looking at a regional level, Latin America was the region with the starkest distribution of wealth. In this region, 77 percent of the wealth was held by the richest 10 percent in 2021, and only 0.5 percent held by the poorest 50 percent. At an individual level, around 2.82 billion adults worldwide had a net worth of less than 10,000 U.S. dollars in 2021.

Billionaires In 2021, the highest concentration of billionaires could be found in North America. However, China had the largest number of billionaires in its population in 2022, with most living in Beijing. Looking at wealth distribution amongst billionaires themselves, 20 people had fortunes of 50 billion U.S. dollars or more, but the majority of billionaires had a personal fortune between two and five billion U.S. dollars.

In December 2022, Elon Musk slipped from the top spot of richest people on Earth. The number one spot was taken by French magnate, Bernard Arnault of Moët Hennessy Louis Vuitton.

This table presents income shares, thresholds, tax shares, and total counts of individual Canadian tax filers, with a focus on high income individuals (95% income threshold, 99% threshold, etc.). Income thresholds are based on national threshold values, regardless of selected geography; for example, the number of Nova Scotians in the top 1% will be calculated as the number of taxfiling Nova Scotians whose total income exceeded the 99% national income threshold. Different definitions of income are available in the table namely market, total, and after-tax income, both with and without capital gains.

Income of individuals by age group, sex and income source, Canada, provinces and selected census metropolitan areas, annual.

Abstract

The purpose of this survey is to obtain information on the income, consumption pattern, incidence of poverty, and saving propensities for different groups of people in the Republic of Palau. 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 consumer price indices. 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 Palau.

Geographic coverage

National

Analysis unit

• Household
• Individual

Universe

All private households.

Households that had not been residing in Palau for the last 12 months and did not intend to stay in Palau for the next 12 months at the time of the survey, were still selected in the survey, but treated as out-of-scope.

Kind of data

Sample survey data [ssd]

Sampling procedure

A sample of 20 per cent was considered more than sufficient for Palau. An additional 10 per cent of sample was selected to allow for sample loss. As a result, a sample size of 1,041 households (20 per cent of 4,684, with a 10 per cent top-up) was considered suitable for the survey.

Six target areas were identified as sub-populations for which estimates would be desirable. These six areas, which also can be considered stratum were: 1) Koror 2) Airai 3) East Babeldaob 4) West Babeldaob 5) Peleliu 6) Kayangel/Angaur

To accommodate this requirement, the sample of 1,041 households needed to be distributed amongst each of these six strata in such a manner that the level of accuracy derived from each stratum would be roughly equal. The manner in which this is achieved is to over-sample (proportion wise) from the smaller strata to ensure they still have sufficient sample.

To make workloads even and manageable in the field for interviewers and supervisors, the final sample size was adjusted such that it was divisible by 15 within each stratum. The number 15 was chosen as it was considered a suitable number of dwellings for an interviewer to enumerate over a three week period.

Another modification to the sample was with Kayangel/ Angaur. Given the required sample for this area was derived to be 60 dwellings, and there are only 73 dwellings in these areas, it was decided to completely enumerate this stratum.

Although it would be desirable to cover all of Palau for this survey, due to cost and time constraints a couple of areas were excluded from the frame before the selections were made. The two areas removed from scope were: 1) Sonsorol 2) Tobi

The impact on final estimates is considered to be very small given the small populations on these two islands; 18 households on Sonsorol, and 10 households on Tobi. This accounts for less than 0.5 per cent of the population of Palau.

The sample of dwellings was selected independently within each stratum. A complete list of all dwellings identified during the recent census was used as a frame. The first task was to sort the dwellings within each stratum by two variables: 1) Hamlet (on Koror) and State (rest of Palau) 2) Household Size (number of persons)

Once the list had been sorted, systematic sampling was used to produce the sample of dwellings. A skip was produced by dividing the population size for each stratum by the required sample size (N/n). Having produced the skip, a random start was then generated between 0 and the skip to determine the starting point for the systematic sample.

For details please refer to the attached document entitled Documentation for Sample Selection.

Mode of data collection

Face-to-face [f2f]

Research instrument

The survey schedules adopted for the HIES included the following: • Household Control Form • Expenditure Questionnaire • Income Questionnaire • Diary (x2)

Information collected in the four schedules covered the following: a) Household Control Form: This form includes the following information: 1. Name 2. Sex 3. Date of Birth 4. Ethnicity 5. Marital Status 6. Educational Attainment 7. Activity Status 8. Literacy Status 9. Internet Usage

b) Income questionnaire: This questionnaire has 8 sections and includes the following information: 1. Working for Wage and / or Salary 2. Agriculture, livestock, fishing and other sales 3. Other Self Employed & Business Operations 4. Previous Jobs held in the last 12 months 5. Services Provided to Other Private Households 6. Receipts from Custom Occasions 7. Welfare Benefits/Allowances 8. Other Income, including Remittances

c) Expenditure Questionnaire: This questionnaire has 16 sections and includes the following information: 1. Dwelling characteristics 2. Dwelling tenure 3. Mortgages and loans for purchase of dwellings 4. Insurance policies 5. Construction of new dwellings 6. Major home improvements 7. Household operation 8. Transportation 9. Travel – Domestic & Overseas 10. Education, recreation, sport and culture 11. Loans 12. Credit Cards/ Charge accounts 13. Contribution to benefit schemes 14. Medical and health services 15. Customs Occasions 16. Miscellaneous payments 17. Agricultural Assets

d) Weekly Diary: This questionnaire has 4 sections and includes the following information: 1. Items Bought 2. Consumption of Items Produced by the Household 3. Gifts 4. Winnings from Betting, Raffles and Lotteries

For the household control form, expenditure questionnaire and income questionnaire, a face-to-face interview was conducted with the household to capture the information. For the two diaries, the first diary was left with the household for the first week, for the household to fill out. After the first week, the diary is picked up and the second week diary is dropped off to be filled out and picked up at the end of second week. Interviewers were required to contact each household every two to three days to make sure households were filling out their diaries appropriately.

Response rate

The overall response rate for Palau was 73%, which was a lower response rate than what was expected. The final response status for the 1,063 households selected in the HIES, 760 households fully responded to the survey, 28 partially responded (of which 16 could be included in the analysis) and 275 didn’t respond at all for various reasons.

For details please refer to section 4.2.1 NON-RESPONSE BIAS in the attached report entitled Republic of Palau Household Income and Expenditure Survey 2006.

Sampling error estimates

To determine the impact of sampling error on the survey results, relative standard errors (RSEs) for key estimates were produced.

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.

Some 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 30.1%), 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.

Relative Standard Errors for key estimates at the region level can be found in Appendix 2 of the survey report.

Data appraisal

Non-response Bias In was seen that 760 households fully responded to the survey, 28 partially responded (of which 16 could be included in the analysis) and 275 didn’t respond at all for various reasons. Despite the table indicating that the vast majority of nonresponses were “vacant/out-of-scope”, this was unlikely as the dwellings were occupied at the time of the census, only one year prior to the HIES. The assumption was therefore made that these households were more than likely mis-coded during the HIES collection, and would more likely have been a refusal or non-contact.

The data contains inequality measures at the municipality-level for 1892 and 1871, as estimated in the PhD thesis "Institutions, Inequality and Societal Transformations" by Sara Moricz. The data also contains the source publications: 1) tabel 1 from “Bidrag till Sverige official statistik R) Valstatistik. XI. Statistiska Centralbyråns underdåniga berättelse rörande kommunala rösträtten år 1892” (biSOS R 1892) 2) tabel 1 from “Bidrag till Sverige official statistik R) Valstatistik. II. Statistiska Centralbyråns underdåniga berättelse rörande kommunala rösträtten år 1871” (biSOS R 1871)

moricz_inequality_agriculture.csv

A UTF-8 encoded .csv-file. Each row is a municipality of the agricultural sample (2222 in total). Each column is a variable.

R71muncipality_id: a unique identifier for the municipalities in the R1871 publication (the municipality name can be obtained from the source data) R92muncipality_id: a unique identifier for the municipalities in the R1892 publication (the municipality name can be obtained from the source data) agriTop1_1871: an ordinal measure (ranking) of the top 1 income share in the agricultural sector for 1871 agriTop1_1892: an ordinal measure (ranking) of the top 1 income share in the agricultural sector for 1892 highestFarm_1871: a cardinal measure of the top 1 person share in the agricultural sector for 1871 highestFarm_1871: a cardinal measure of the top 1 person share in the agricultural sector for 1892

moricz_inequality_industry.csv

A UTF-8 encoded .csv-file. Each row is a municipality of the industrial sample (1328 in total). Each column is a variable.

R71muncipality_id: see above description R92muncipality_id: see above description indTop1_1871: an ordinal measure (ranking) of the top 1 income share in the industrial sector for 1871 indTop1_1892: an ordinal measure (ranking) of the top 1 income share in the industrial sector for 1892

moricz_R1892_source_data.csv

A UTF-8 encoded .csv-file with the source data. The variables are described in the adherent codebook moricz_R1892_source_data_codebook.csv.

Contains table 1 from “Bidrag till Sverige official statistik R) Valstatistik. XI. Statistiska Centralbyråns underdåniga berättelse rörande kommunala rösträtten år 1892” (biSOS R 1892). SCB provides the scanned publication on their website. Dollar Typing Service typed and delivered the data in 2015. All numerical variables but two have been checked. This is easy to do since nearly all columns should sum up to another column. For “Folkmangd” (population) the numbers have been corrected against U1892. The highest estimate of errors in the variables is 0.005 percent (0.5 promille), calculated at cell level. The two numerical variables which have not been checked is “hogsta_fyrk_jo“ and “hogsta_fyrk_ov“, as this cannot much be compared internally in the data. According to my calculations as the worst case scenario, I have measurement errors of 0.0043 percent (0.43 promille) in those variables.

moricz_R1871_source_data.csv

A UTF-8 encoded .csv-file with the source data. The variables are described in the adherent codebook moricz_R1871_source_data_codebook.csv.

Contains table 1 from “Bidrag till Sverige official statistik R) Valstatistik. II. Statistiska Centralbyråns underdåniga berättelse rörande kommunala rösträtten år 1871” (biSOS R 1871). SCB provides the scanned publication on their website. Dollar Typing Service typed and delivered the data in 2015. The variables have been checked for accuracy, which is feasible since columns and rows should sum. The variables that most likely carry mistakes are “hogsta_fyrk_al” and “hogsta_fyrk_jo”.

In 2022, the listed insurance companies in the United Arab Emirates had a collective investment income of around one billion United Arab Emirates Dirham down from around 1.2 billion United Arab Emirates Dirham the year before. This was a drop of approximately 16.7 percent. The top three listed insurance companies had an investment income of around 0.5 billion United Arab Emirates Dirham.

The total operating revenue in the construction industry in Spain increased by over 0.5 percent in 2021. This index reached a peak in 2018 at over 115, while in 2012 it was its lowest at 80.5 index points. The decrease in operating revenue since 2009, responds to the economic crisis that had started two years earlier.