In 2021, the birth rate in the United States was highest in families that had under 10,000 U.S. dollars in income per year, at 62.75 births per 1,000 women. As the income scale increases, the birth rate decreases, with families making 200,000 U.S. dollars or more per year having the second-lowest birth rate, at 47.57 births per 1,000 women. Income and the birth rate Income and high birth rates are strongly linked, not just in the United States, but around the world. Women in lower income brackets tend to have higher birth rates across the board. There are many factors at play in birth rates, such as the education level of the mother, ethnicity of the mother, and even where someone lives. The fertility rate in the United States The fertility rate in the United States has declined in recent years, and it seems that more and more women are waiting longer to begin having children. Studies have shown that the average age of the mother at the birth of their first child in the United States was 27.4 years old, although this figure varies for different ethnic origins.

About 50.4 percent of the household income of private households in the U.S. were earned by the highest quintile in 2023, which are the upper 20 percent of the workers. In contrast to that, in the same year, only 3.5 percent of the household income was earned by the lowest quintile. This relation between the quintiles is indicative of the level of income inequality in the United States. Income inequalityIncome inequality is a big topic for public discussion in the United States. About 65 percent of U.S. Americans think that the gap between the rich and the poor has gotten larger in the past ten years. This impression is backed up by U.S. census data showing that the Gini-coefficient for income distribution in the United States has been increasing constantly over the past decades for individuals and households. The Gini coefficient for individual earnings of full-time, year round workers has increased between 1990 and 2020 from 0.36 to 0.42, for example. This indicates an increase in concentration of income. In general, the Gini coefficient is calculated by looking at average income rates. A score of zero would reflect perfect income equality and a score of one indicates a society where one person would have all the money and all other people have nothing. Income distribution is also affected by region. The state of New York had the widest gap between rich and poor people in the United States, with a Gini coefficient of 0.51, as of 2019. In global comparison, South Africa led the ranking of the 20 countries with the biggest inequality in income distribution in 2018. South Africa had a score of 63 points, based on the Gini coefficient. On the other hand, the Gini coefficient stood at 16.6 in Azerbaijan, indicating that income is widely spread among the population and not concentrated on a few rich individuals or families. Slovenia led the ranking of the 20 countries with the greatest income distribution equality in 2018.

Graph and download economic data for Median Household Income in the United States (MEHOINUSA646N) from 1984 to 2023 about households, median, income, and USA.

In 2024, the average annual per capita disposable income of households in China amounted to approximately 41,300 yuan. Annual per capita income in Chinese saw a significant rise over the last decades and is still rising at a high pace. During the last ten years, per capita disposable income roughly doubled in China. Income distribution in China As an emerging economy, China faces a large number of development challenges, one of the most pressing issues being income inequality. The income gap between rural and urban areas has been stirring social unrest in China and poses a serious threat to the dogma of a “harmonious society” proclaimed by the communist party. In contrast to the disposable income of urban households, which reached around 54,200 yuan in 2024, that of rural households only amounted to around 23,100 yuan. Coinciding with the urban-rural income gap, income disparities between coastal and western regions in China have become apparent. As of 2023, households in Shanghai and Beijing displayed the highest average annual income of around 84,800 and 81,900 yuan respectively, followed by Zhejiang province with 63,800 yuan. Gansu, a province located in the West of China, had the lowest average annual per capita household income in China with merely 25,000 yuan. Income inequality in China The Gini coefficient is the most commonly used measure of income inequality. For China, the official Gini coefficient also indicates the astonishing inequality of income distribution in the country. Although the Gini coefficient has dropped from its high in 2008 at 49.1 points, it still ranged at a score of 46.5 points in 2023. The United Nations have set an index value of 40 as a warning level for serious inequality in a society.

This statistic shows the median household income in the United States from 1990 to 2023 in 2023 U.S. dollars. The median household income was 80,610 U.S. dollars in 2023, an increase from the previous year. Household incomeThe median household income depicts the income of households, including the income of the householder and all other individuals aged 15 years or over living in the household. Income includes wages and salaries, unemployment insurance, disability payments, child support payments received, regular rental receipts, as well as any personal business, investment, or other kinds of income received routinely. The median household income in the United States varies from state to state. In 2020, the median household income was 86,725 U.S. dollars in Massachusetts, while the median household income in Mississippi was approximately 44,966 U.S. dollars at that time. Household income is also used to determine the poverty line in the United States. In 2021, about 11.6 percent of the U.S. population was living in poverty. The child poverty rate, which represents people under the age of 18 living in poverty, has been growing steadily over the first decade since the turn of the century, from 16.2 percent of the children living below the poverty line in year 2000 to 22 percent in 2010. In 2021, it had lowered to 15.3 percent. The state with the widest gap between the rich and the poor was New York, with a Gini coefficient score of 0.51 in 2019. The Gini coefficient is calculated by looking at average income rates. A score of zero would reflect perfect income equality and a score of one indicates a society where one person would have all the money and all other people have nothing.

Graph and download economic data for Real Median Personal Income in the United States (MEPAINUSA672N) from 1974 to 2023 about personal income, personal, median, income, real, and USA.

Abstract

THE CLEANED AND HARMONIZED VERSION OF THE SURVEY DATA PRODUCED AND PUBLISHED BY THE ECONOMIC RESEARCH FORUM REPRESENTS 25% OF THE ORIGINAL SURVEY DATA COLLECTED BY THE DEPARTMENT OF STATISTICS OF THE HASHEMITE KINGDOM OF JORDAN

Surveys related to the family budget are considered one of the most important surveys types carried out by the Department Of Statistics, since it provides data on household expenditure and income and their relationship with different indicators. Therefore, most of the countries undertake periodic surveys on household income and expenditures. The Department Of Statistics, since established, conducted a series of Expenditure and Income Surveys during the years 1966, 1980, 1986/1987, 1992, 1997, 2002/2003, 2006/2007, and 2008/2009 and because of continuous changes in spending patterns, income levels and prices, as well as in the population internal and external migration, it was necessary to update data for household income and expenditure over time. Hence, the need to implement the Household Expenditure and Income Survey for the year 2010 arises. The survey was then conducted to achieve the following objectives: 1. Provide data on income and expenditure to enable computation of poverty indices and determine the characteristics of the poor and prepare poverty maps. 2. Provide data weights that reflect the relative importance of consumer expenditure items used in the preparation of the consumer price index. 3. Provide the necessary data for the national accounts related to overall consumption and income of the household sector. 4. Provide the data necessary for the formulation, follow-up and evaluation of economic and social development programs, including those addressed to eradicate poverty. 5. Identify consumer spending patterns prevailing in the society, and the impact of demographic, social and economic variables on those patterns. 6. Calculate the average annual income of the household and the individual, and identify the relationship between income and different socio-economic factors, such as profession and educational level of the head of the household and other indicators. 7. Study the distribution of individuals and households by income and expenditure categories and analyze the factors associated with it.

The raw survey data provided by the Statistical Agency were cleaned and harmonized by the Economic Research Forum, in the context of a major project that started in 2009. During which extensive efforts have been exerted to acquire, clean, harmonize, preserve and disseminate micro data of existing household surveys in several Arab countries.

Geographic coverage

The General Census of Population and Housing in 2004 provided a detailed framework for housing and households for different administrative levels in the Kingdom. Where the Kingdom is administratively divided into 12 governorates, each governorate is composed of a number of districts, each district (Liwa) includes one or more sub-district (Qada). In each sub-district, there are a number of communities (cities and villages). Each community was divided into a number of blocks. Where in each block, the number of houses ranged between 60 and 100 houses. Nomads, persons living in collective dwellings such as hotels, hospitals and prison were excluded from the survey framework.

Analysis unit

1- Household/family. 2- Individual/person.

Universe

The survey covered a national sample of households and all individuals permanently residing in surveyed households.

Kind of data

Sample survey data [ssd]

Sampling procedure

THE CLEANED AND HARMONIZED VERSION OF THE SURVEY DATA PRODUCED AND PUBLISHED BY THE ECONOMIC RESEARCH FORUM REPRESENTS 25% OF THE ORIGINAL SURVEY DATA COLLECTED BY THE DEPARTMENT OF STATISTICS OF THE HASHEMITE KINGDOM OF JORDAN

The Household Expenditure and Income survey sample, for the year 2010, was designed to serve the basic objectives of the survey through providing a relatively large sample in each sub-district to enable drawing a poverty map in Jordan. A two stage stratified cluster sampling technique was used. In the first stage, a cluster sample proportional to the size was uniformly selected, where the number of households in each cluster was considered the weight of the cluster. At the second stage, a sample of 8 households was selected from each cluster, in addition to another 4 households selected as a backup for the basic sample, using a systematic sampling technique. Those 4 households were sampled to be used during the first visit to the block in case the visit to the original household selected is not possible for any reason. For the purposes of this survey, each sub-district was considered a separate stratum to ensure the possibility of producing results on the sub-district level. In this respect, the survey framework adopted that provided by the General Census of Population and Housing Census in dividing the sample strata. To estimate the sample size, the coefficient of variation and the design effect of the expenditure variable provided in the Household Expenditure and Income Survey for the year 2008 was calculated for each sub-district. These results were used to estimate the sample size on the sub-district level so that the coefficient of variation for the expenditure variable in each sub-district is less than 10%, at a minimum, of the number of clusters in the same sub-district (6 clusters). This is to ensure adequate presentation of clusters in different administrative areas to enable drawing an indicative poverty map. It should be noted that in addition to the standard non response rate assumed, higher rates were expected in areas where poor households are concentrated in major cities. Therefore, those were taken into consideration during the sampling design phase, and a higher number of households were selected from those areas, aiming at well covering all regions where poverty spreads.

Mode of data collection

Face-to-face [f2f]

Research instrument

To reach the survey objectives, 3 forms have been developed. Those forms were finalized after being tested and reviewed by specialists taking into account making the data entry, and validation, process on the computer as simple as possible.

(1) General Form/Questionnaire This form includes: - Housing characteristics such as geographic location variables, household area, building material predominant for external walls, type of tenure, monthly rent or lease, main source of water, lighting, heating and fuel cooking, sanitation type and water cycle, the number of rooms in the dwelling, in addition to providing ownership status of some home appliances and car. - Characteristics of household members: This form focused on the social characteristics of the family members such as relation to the head of the family, gender, age and educational status and marital status. It also included economic characteristics such as economic activity, and the main occupation, employment status, and the labor sector. to the additions of questions about individual continued to stay with the family, in order to update the information at the beginning of the second, third and fourth rounds. - Income section which included three parts · Family ownership of assets · Productive activities for the family · Current income sources

(2) Expenditure on food commodities form/Questionnaire This form indicates expenditure data on 17 consumption groups. Each group includes a number of food commodities, with the exception of the latter group, which was confined to some of the non-food goods and services because of their frequent spending pattern on daily basis like food commodities. For the purposes of the efficient use of results, expenditure data of the latter group was moved with the non-food commodities expenditure. The form also includes estimated amounts of own-produced food items and those received as gifts or in an in-kind form, as well as servants living with the family spending on themselves from their own wages to buy food.

(3) Expenditure on non-food commodities form/Questionnaire This form indicates expenditure data on 11 groups of non-food items, and 5 sets of spending on services, in addition to a group of consumption expenditure. It also includes an estimate of self-consumption, and non-food gifts or other items in an in-kind form received or sent by the household, as well as servants living with the family spending on themselves from their own wages to buy non-food items.

Cleaning operations

Raw Data

The data collection phase was then followed by the data processing stage accomplished through the following procedures: 1- Organizing forms/questionnaires A compatible archive system, with the nature of the subsequent operations, was used to classify the forms according to different round throughout the year. This is to effectively enable extracting the forms when required for processing. A registry was prepared to indicate different stages of the process of data checking, coding and entry till forms are back to the archive system. 2- Data office checking This phase is achieved concurrently with the data collection phase in the field, where questionnaires completed in the fieldwork are immediately sent to data office checking phase. 3- Data coding A team was trained to work on the data coding phase, which in this survey is only limited to education specialization, profession and economic activity. In this respect, international classifications were use, while for the rest of the questions, all coding were predefined during

Graph and download economic data for Household Debt Service Payments as a Percent of Disposable Personal Income (TDSP) from Q1 1980 to Q4 2024 about disposable, payments, debt, personal income, percent, personal, households, services, income, and USA.

Created by the Tax Reform Act of 1986, the Low-Income Housing Tax Credit program (LIHTC) gives State and local LIHTC-allocating agencies the equivalent of nearly $8 billion in annual budget authority to issue tax credits for the acquisition, rehabilitation, or new construction of rental housing targeted to lower-income households. Although some data about the program have been made available by various sources, HUD's database is the only complete national source of information on the size, unit mix, and location of individual projects. With the continued support of the national LIHTC database, HUD hopes to enable researchers to learn more about the effects of the tax credit program.HUD has no administrative authority over the LIHTC program. IRS has authority at the federal level and it is structured so that the states truly administer the program. The LIHTC property locations depicted in this map service represent the general location of the property. The locations of individual buildings associated with each property are not depicted here. The location of the property is derived from the address of the building with the most units. Location data for HUD-related properties and facilities are derived from HUD's enterprise geocoding service. While not all addresses are able to be geocoded and mapped to 100% accuracy, we are continuously working to improve address data quality and enhance coverage. Please consider this issue when using any datasets provided by HUD. When using this data, take note of the field titled “LVL2KX” which indicates the overall accuracy of the geocoded address using the following return codes:‘R’ - Interpolated rooftop (high degree of accuracy, symbolized as green)‘4’ - ZIP+4 centroid (high degree of accuracy, symbolized as green)‘B’ - Block group centroid (medium degree of accuracy, symbolized as yellow)‘T’ - Census tract centroid (low degree of accuracy, symbolized as red)‘2’ - ZIP+2 centroid (low degree of accuracy, symbolized as red)‘Z’ - ZIP5 centroid (low degree of accuracy, symbolized as red)‘5’ - ZIP5 centroid (same as above, low degree of accuracy, symbolized as red)Null - Could not be geocoded (does not appear on the map)For the purposes of displaying the location of an address on a map only use addresses and their associated lat/long coordinates where the LVL2KX field is coded ‘R’ or ‘4’. These codes ensure that the address is displayed on the correct street segment and in the correct census block.The remaining LVL2KX codes provide a cascading indication of the most granular level geography for which an address can be confirmed. For example, if an address cannot be accurately interpolated to a rooftop (‘R’), or ZIP+4 centroid (‘4’), then the address will be mapped to the centroid of the next nearest confirmed geography: block group, tract, and so on. When performing any point-in polygon analysis it is important to note that points mapped to the centroids of larger geographies will be less likely to map accurately to the smaller geographies of the same area. For instance, a point coded as ‘5’ in the correct ZIP Code will be less likely to map to the correct block group or census tract for that address. To learn more about the Low-Income Housing Tax Credit Program visit: https://www.hud.gov/program_offices/public_indian_housing/programs/ph/, for questions about the spatial attribution of this dataset, please reach out to us at GISHelpdesk@hud.gov. Data Dictionary: DD_Low Income Tax Credit Program

In 2024, the average annual per capita disposable income of rural households in China was approximately 23,119 yuan, roughly 43 percent of the income of urban households. Although living standards in China’s rural areas have improved significantly over the past 20 years, the income gap between rural and urban households is still large. Income increase of China’s households From 2000 to 2020, disposable income per capita in China increased by around 700 percent. The fast-growing economy has inevitably led to the rapid income increase. Furthermore, inflation has been maintained at a lower rate in recent years compared to other countries. While the number of millionaires in China has increased, many of its population are still living in humble conditions. Consequently, the significant wealth gap between China’s rich and poor has become a social problem across the country. However, in recent years rural areas have been catching up and disposable income has been growing faster than in the cities. This development is also reflected in the Gini coefficient for China, which has decreased since 2008. Urbanization in China The urban population in China surpassed its rural population for the first time in 2011. In fact, the share of the population residing in urban areas is continuing to increase. This is not surprising considering remote, rural areas are among the poorest areas in China. Currently, poverty alleviation has been prioritized by the Chinese government. The measures that the government has taken are related to relocation and job placement. With the transformation and expansion of cities to accommodate the influx of city dwellers, neighboring rural areas are required for the development of infrastructure. Accordingly, land acquisition by the government has resulted in monetary gain by some rural households.

Abstract

Hungary Household Budget Survey has the following objectives: - obtain weights for consumer price index; - estimate household expenditure for national accounts; - study income/expenditure patterns of disadvantaged groups, including pensioner households, single parent households, etc.; - study income/expenditure disparities among socio-economic groups; - study consumer behavior among socio-economic groups; - contribute to general poverty and/or income distribution studies; - calculate minimum subsistence level (national poverty line).

Study respondents are chosen randomly from Hungarian citizens living in private households in Hungary. Data is gathered through face-to-face interviews and monthly diaries of household expenditures and incomes.

Geographic coverage

National

Analysis unit

• Households;
• Individuals.

Kind of data

Sample survey data [ssd]

Sampling procedure

The Primary, Secondary and Ultimate Sampling Units are enumeration area/district, none and dwelling respectively.

Stratification: Areas/districts were stratified using the following criteria: geographical regions

Households/Consumption Unit, Income Unit, Family Unit were stratified using the following criteria: - age group of the head - educational level of household head, household size,economic activity of the household head

The sampling frames for the Primary Sampling Unit (PSU) and Ultimate Sampling Unit (USU) were the list of Census enumeration areas and the master sample of households respectively. Primary Sampling Units (PSU) were selected using probability proportional to size. The sample size was 11000 households or other units. The overall response rate for the survey was 62 percent.4 Errors/biases were minimized by using systematic substitution.

Enumeration procedure: Enumeration uses a panel design in which each reporting unit is enumerated more than once. The sample is divided into 3 representative sub-samples, some of which are replaced with new ones during the lifetime of the panel. Each sub-sample remains in the panel for 36 months. The survey uses 3 sub-samples at the same time and drops 1 sub-samples each time. The panel has an expected lifetime of 3 years, and each reporting household/unit is enumerated 3 times in total. If a reporting household/unit drops out from the panel, it is abandonned. If changes occur in composition of the reporting household/unit during the lifetime of the panel, then it continues in the panel. A smaller set of reporting units is selected from which information on specific issues is gathered or more detailed questions are asked.

Mode of data collection

Face-to-face [f2f]

Research instrument

Household questionnaires and the income/expenditure diary collect the following information:

• Main characteristics of households: number of persons; activity of the members; data of housing: type of occupation; type of heating, number of room; stock of consumer durables;
• Monthly diary about incomes and expenditures of the households by categories;

Incomes by main categories: - Income from work: earnings from main activity; supplementary compensations; entrepreneurial income; agricultural income; - Social income: pensions, pension supplements; unemployment benefits; child-care benefits; family allowance; child-care allowance; - Other income: other income in cash and in kind; - Gross income; social security contributions; personal income tax; net disposable income; - Child tax allowance.

Expenditures by main categories: - Meat and meat products; eggs; milk, cheese, other dairy products; fats and oils; bread and rolls; - Cereals; Sugar; Sweet products; Vegetables; Fruits; Other foods; Food consumption outside home; - Coffee, tea; Soft drinks; Wine; Beer; Other alcoholic drinks; Tobacco; Men’s clothing articles; - Women’s clothing articles; Children’s clothing articles; Other clothing articles; Clothing services; - Rent, tax on houses; Maintenance cost of dwelling; Other service of housing or real estate: water charge, sewerage fee, other; Insurance of real estate; Solid fuel and heating oil; District heating; Electricity; Piped gas; Bottled gas; - Repair of dwelling; Furniture; Household durable goods; Household cleaning supplies, and other materials; - Household textiles, Household tools and appliances; Household services; Pharmaceuticals, medical devices; - Health services; Gratuities; Personal care; Passenger car new; - Other vehicles; Spare parts for vehicles; Fuel for vehicles; Insurance fees for vehicles; Maintenance of vehicles; Local transportation; Long-distance transportation; Other purchased transport services; - Telephone, fax, message receiver; Telephone charges; - Postal charges; - Electronic entertainment equipment; Personal computer; Instruments; - Other cultural durable goods; Newspapers, magazines, books; Schoolbooks; - School fee; School supplies, stationery; Other cultural and sport equipment and their repair; - Television subscription; Theatre, concert, cinema, other entertainment tickets, fee; - Recreation domestic; Recreation abroad; Personal related insurances; - Other personal expenditures; - New construction, renovation, purchase of real estate.

Personal and household goods rental and leasing revenue is forecast to contract at a compound annual rate of 3.7% over the five years through 2024 to €23.9 billion, including an estimated drop of 0.3% in 2024. As technology and appliances become more affordable, consumers and businesses increasingly prefer owning rather than renting. The trend against rentals is robust in countries like Poland and Italy, which have the lowest EU prices on home appliances and electronics. However, the rental market remains viable for short-term needs such as those of international students, accounting for a significant portion of rentals in countries like Germany, France and the Netherlands. In response to changing consumer tastes, rental companies now offer rent-to-own schemes that allow consumers to purchase rented equipment at a reduced price. While the profitability of the rental industry has suffered due to lower electronics prices and increased sourcing from low-cost countries, rental companies have sustained their profit through multiple rentals over the lifespan of their equipment. Revenue is forecast to expand at a compound annual rate of 5.7% over the five years through 2029 to €31.6 billion, while the average profit margin is expected to shrink. Major electronic retailers are cutting prices to boost competitiveness, threatening income. Technological advancements reducing the life cycle of electronics, coupled with a solid economic climate in Germany, promise higher disposable incomes and increased consumption. Higher sales of electronic goods will make electronic appliances more accessible to a broader consumer base and impact the growth of the rental sector.

This poll, conducted March 22-25, 2001, is part of a continuing series of monthly surveys that solicit public opinion on the presidency and on a range of other political and social issues. Respondents were asked to give their opinions of President George W. Bush and his handling of the presidency, the economy, international affairs, and environmental issues. They also expressed their opinions on whether President Bush cared more about ordinary people or large business corporations, as well as the most important task for President Bush and the Congress. The poll elicited respondents' views on the economic situation in the country, President Bush's tax cut proposal, the recent drop in the stock market, a plan in which people could invest some of their Social Security contributions in the stock market, and the budget surplus of $5.6 trillion over the next ten years forecast by the federal government. Respondents also answered a set of questions regarding political campaign funding, including whether they supported stricter laws controlling the way political campaigns can raise and spend money, whether politicians do special favors for people and groups who give them campaign contributions, and ways to reduce improper campaign fundraising. Background information on respondents includes age, gender, education, race, party affiliation, political orientation, and household income.

Graph and download economic data for Real Disposable Personal Income: Per Capita (A229RX0) from Jan 1959 to Jan 2025 about disposable, personal income, per capita, personal, income, real, and USA.

In 2022, San Francisco had the highest median household income of cities ranking within the top 25 in terms of population, with a median household income in of 136,692 U.S. dollars. In that year, San Jose in California was ranked second, and Seattle, Washington third.

Following a fall after the great recession, median household income in the United States has been increasing in recent years. As of 2022, median household income by state was highest in Maryland, Washington, D.C., Utah, and Massachusetts. It was lowest in Mississippi, West Virginia, and Arkansas. Families with an annual income of 25,000 and 49,999 U.S. dollars made up the largest income bracket in America, with about 25.26 million households.

Data on median household income can be compared to statistics on personal income in the U.S. released by the Bureau of Economic Analysis. Personal income rose to around 21.8 trillion U.S. dollars in 2022, the highest value recorded. Personal income is a measure of the total income received by persons from all sources, while median household income is “the amount with divides the income distribution into two equal groups,” according to the U.S. Census Bureau. Half of the population in question lives above median income and half lives below. Though total personal income has increased in recent years, this wealth is not distributed throughout the population. In practical terms, income of most households has decreased. One additional statistic illustrates this disparity: for the lowest quintile of workers, mean household income has remained more or less steady for the past decade at about 13 to 16 thousand constant U.S. dollars annually. Meanwhile, income for the top five percent of workers has actually risen from about 285,000 U.S. dollars in 1990 to about 499,900 U.S. dollars in 2020.

United States CSI: Personal: Expected HH Income Change: Next Yr: Don’t Know data was reported at 0.000 % in May 2018. This stayed constant from the previous number of 0.000 % for Apr 2018. United States CSI: Personal: Expected HH Income Change: Next Yr: Don’t Know data is updated monthly, averaging 1.000 % from Feb 1978 (Median) to May 2018, with 467 observations. The data reached an all-time high of 5.000 % in May 1978 and a record low of 0.000 % in May 2018. United States CSI: Personal: Expected HH Income Change: Next Yr: Don’t Know data remains active status in CEIC and is reported by University of Michigan. The data is categorized under Global Database’s USA – Table US.H024: Consumer Sentiment Index: Personal Finance. The questions were: 'During the next 12 months, do you expect your (family) income to be higher or lower than during the past year?' and 'By about what percent do you expect your (family) income to increase during the next 12 months?'

Low income measure (LIM) thresholds by household size for market income, total income and after-tax income, in current and constant dollars, annual.

Distribution of household total income in constant 2020 dollars by household type (couple family, one-parent family, non-census family households) and characteristics of household members (number of earners and number of people in different age groups).

Abstract

Over the past decade, Albania has been seeking to develop the framework for a market economy and more open society. It has faced severe internal and external challenges in the interim – extremely low income levels and a lack of basic infrastructure, the rapid collapse of output and inflation rise after the shift in regime in 1991, the turmoil during the 1997 pyramid crisis, and the social and economic shocks accompanying the 1999 Kosovo crisis. In the face of these challenges, Albania has made notable progress in creating conditions conducive to growth and poverty reduction.

A poverty profile based on 1996 data (the most recent available) showed that some 30 percent of the rural and some 15 percent of the urban population are poor, with many others vulnerable to poverty due to their incomes being close to the poverty threshold. Income related poverty is compounded by the severe lack of access to basic infrastructure, education and health services, clean water, etc., and the ability of the Government to address these issues is complicated by high levels of internal and external migration that are not well understood.

To date, the paucity of household-level information has been a constraining factor in the design, implementation and evaluation of economic and social programs in Albania. Multi-purpose household surveys are one of the main sources of information to determine living conditions and measure the poverty situation of a country, and provide an indispensable tool to assist policymakers in monitoring and targeting social programs.

Two recent surveys carried out by the Albanian Institute of Statistics (INSTAT) – the 1998 Living Conditions Survey (LCS) and the 2000 Household Budget Survey (HBS) – drew attention, once again, to the need for accurately measuring household welfare according to wellaccepted standards, and for monitoring these trends on a regular basis. In spite of their narrow scope and limitations, these two surveys have provided the country with an invaluable training ground towards the development of a permanent household survey system to support the government strategic planning in its fight against poverty.

In the process leading to its first Poverty Reduction Strategy Paper (PRSP; also known in Albania as Growth and Poverty Reduction Strategy, GPRS), the Government of Albania reinforced its commitment to strengthening its own capacity to collect and analyze on a regular basis the information it needs to inform policy-making.

In its first phase (2001-2006), this monitoring system will include the following data collection instruments: (i) Population and Housing Census; (ii) Living Standards Measurement Surveys every 3 years, and (iii) annual panel surveys.

The Population and Housing Census (PHC) conducted in April 2001, provided the country with a much needed updated sampling frame which is one of the building blocks for the household survey structure.

The focus during this first phase of the monitoring system is on a periodic LSMS (in 2002 and 2005), followed by panel surveys on a sub-sample of LSMS households (in 2003, 2004 and 2006), drawing heavily on the 2001 census information. The possibility to include a panel component in the second LSMS will be considered at a later stage, based on the experience accumulated with the first panels.

The 2002 LSMS was in the field between April and early July, with some field activities (the community and price questionnaires) extending into August and September. The survey work was undertaken by the Living Standards unit of INSTAT, with the technical assistance of the World Bank. The present document provides detailed information on this survey. Section II summarizes the content of the survey instruments used. Section III focuses on the details of the sample design. Sections IV describes the pilot test and fieldwork procedures of the survey, as well as the training received by survey staff. Section V reviews data entry and data cleaning issues. Finally, section VI contains a series of annotations that all those interested in using the data should read.

Geographic coverage

National coverage. Domains: Tirana, other urban, rural; Agro-ecological areas (coastal, central, mountain)

Analysis unit

• Households
• Individuals

Kind of data

Sample survey data [ssd]

Sampling procedure

Sampling frame

The Republic of Albania is divided geographically into 12 Prefectures (Prefekturat). The latter are divided into Districts (Rrethet) which are, in turn, divided into Cities (Qyteti) and Communes (Komunat). The Communes contain all the rural villages and the very small cities. For the April 2001 General Census of Population and Housing census purposes, the cities and the villages were divided into Enumeration Areas (EAs). These formed the basis for the LSMS sampling frame.

The EAs in the frame are classified by Prefecture, District, City or Commune. The frame also contains, for every EA, the number of Housing Units (HUs), the number of occupied HUs, the number of unoccupied HUs, and the number of households. Occupied dwellings rather than total number of dwellings were used since many census EAs contain a large number of empty dwellings. The Housing Unit (defined as the space occupied by one household) was taken as the sampling unit, instead of the household, because the HU is more permanent and easier to identify in the field.

A detailed review of the list of census EAs shows that many have zero population. In order to obtain EAs with a minimum of 50 and a maximum of 120 occupied housing units, the EAs with zero population were first removed from the sampling frame. Then, the smallest EAs (with less than 50 HU) were collapsed with geographically adjacent ones and the largest EAs (with more than 120 HU) were split into two or more EAs. Subsequently, maps identifying the boundaries of every split and collapsed EA were prepared

Sample Size and Implementation

Since the 2002 LSMS had been conducted about a year after the April 2001 census, a listing operation to update the sample EAs was not conducted. However, given the rapid speed at which new constructions and demolitions of buildings take place in the city of Tirana and its suburbs, a quick count of the 75 sample EAs was carried out followed by a listing operation. The listing sheets prepared during the listing operation became the sampling frame for the final stage of selection.

The final sample design for the 2002 LSMS included 450 Primary Sampling Units (PSUs) and 8 households in each PSU, for a total of 3600 households. Four reserve units were selected in each sample PSU to act as replacement unit in non-response cases. In a few cases in which the rate of migration was particularly high and more than four of the originally selected households could not be found for the interview, additional households for the same PSU were randomly selected. During the mplementation of the survey there was a problem with the management of the questionnaires for a household that had initially refused, but later accepted, to fill in the food diary. The original household questionnaire was lost in the process and it was not possible to match the diary with a valid household questionnaire. The household had therefore to be dropped from the sample (this happened in Shkoder, PSU 16). The final sample size is therefore of 3599 households.

Stratification

The sampling frame was divided in four regions (strata), Coastal Area, Central Area, and Mountain Area, and Tirana (urban and other urban). These four strata were further divided into major cities, other urban, and other rural. The EAs were selected proportionately to the number of housing units in these areas.

In the city of Tirana and its suburbs, implicit stratification was used to improve the efficiency of the sample design. The implicit stratification was performed by ordering the EAs in the sampling frame in a geographic serpentine fashion within each stratum used for the independent selection of EAs.

The sample is not self-weighted. In order to obtain correct estimates the data need to be weighted. A file with household weights is included in the dataset (filename: weights.dta, variable: weight). When using individual rather than household variables an individual weight should be created by multiplying the household weight by the household size.

The survey is representative for Tirana, other urban and rural areas, as well as for Tirana and the three main agro-ecological/economic areas (Coastal, Central and Mountain).

Selection of households

Twelve valid households (HH's) were selected systematically and with equal probability from the Listing Forms in Tirana and 12 housing units (HU's) from census forms in the other areas. Once the 12 HH's were selected, 4 of them were chosen at random and kept as reserve units. During the fieldwork, the enumerator only received the list of the first eight HH's plus a reserve HH. Each time the enumerator needed an additional reserve HH, she had to ask the supervisor and explain the reason why the reserve unit was needed. This process helped determine the reason why reserve units were used and provided more control on their use.

If a HH was not able to have its enumeration completed, the enumerator used the first reserve unit. Full documentation was required of every non-completed interview. If in one PSU more than 4 HH selected were invalid, other units from that PSU were randomly selected by the Central Office as replacement units to keep the enumerator load constant and maintain a uniform sample size in each PSU. This only occurred in a couple of cases.

For the listing of the 75 selected PSU's in Tirana, the census data and the EA maps were used as a base, and then buildings

Divergent trends in the downstream construction and industrial markets have influenced the performance of the Heavy Machinery and Scaffolding Rental and Hiring industry. Hiring companies have encountered wide cyclical fluctuations in the residential building and household markets corresponding with the injection and withdrawal of Central Government (Te Kāwanatanga o Aotearoa) fiscal stimulus during the pandemic. Recent mortgage interest rate hikes have since curtailed activity in these markets. Unfavourable trends in household discretionary income have also dampened equipment rental by DIY homeowners, a core source of demand for power tool rentals. Weak trends in the Mining division have harmed sales to specialist hire companies. Some larger rental businesses have enjoyed buoyant conditions supplying complex lifting, site preparation and scaffolding equipment for large-scale non-residential building and infrastructure construction projects. Similarly, the economic resurgence from the pandemic has underpinned solid rental demand for forklifts and access equipment in the wholesale and industrial markets. Given the divergent trends in the industry’s core markets, revenue is expected to have fallen at an annualised 2.2% over the five years through 2024-25, to $851.3 million. This trend includes an anticipated 1.3% drop in 2024-25 as conditions continue to deteriorate in the housing market and significant non-residential building and transport infrastructure projects reach completion. The industry’s profitability has narrowed over the five years through 2024-25, and employment has also fallen. Companies that supply complex equipment and scaffold systems for large-scale construction projects and industrial applications have improved their profitability. But profit margins have been squeezed for the many small-scale hire companies chasing a share of the subdued house construction and household markets. Machinery and scaffolding hire will remain buoyant in the non-residential building and infrastructure construction markets, supporting modest growth in industry revenue, which is forecast to inch upwards at an annualised 0.6% over the five years through 2029-30, to $876.9 million. Many smaller suburban hire companies will contend with further deterioration in the residential building market, while the prospects remain grim for specialist mining equipment suppliers. Still, favourable household income trends will support equipment rental by DIY homeowners, and solid merchandise trade will underpin forklift hire.