This dataset provides annual personal income estimates for State of Iowa produced by the U.S. Bureau of Economic Analysis beginning in 1997. Data includes the following estimates: personal income, per capita personal income, wages and salaries, supplements to wages and salaries, private nonfarm earnings, compensation of employees, average compensation per job, and private nonfarm compensation. Personal income is defined as the sum of wages and salaries, supplements to wages and salaries, proprietors’ income, dividends, interest, and rent, and personal current transfer receipts, less contributions for government social insurance. Personal income for Iowa is the income received by, or on behalf of all persons residing in Iowa, regardless of the duration of residence, except for foreign nationals employed by their home governments in Iowa. Per capita personal income is personal income divided by the Census Bureau’s annual midyear (July 1) population estimates. Wages and salaries is defined as the remuneration receivable by employees (including corporate officers) from employers for the provision of labor services. It includes commissions, tips, and bonuses; employee gains from exercising stock options; and pay-in-kind. Judicial fees paid to jurors and witnesses are classified as wages and salaries. Wages and salaries are measured before deductions, such as social security contributions, union dues, and voluntary employee contributions to defined contribution pension plans. Supplements to wages and salaries consists of employer contributions for government social insurance and employer contributions for employee pension and insurance funds. Private nonfarm earnings is the sum of wages and salaries, supplements to wages and salaries, and nonfarm proprietors' income, excluding farm and government. Compensation to employees is the total remuneration, both monetary and in kind, payable by employers to employees in return for their work during the period. It consists of wages and salaries and of supplements to wages and salaries. Compensation is presented on an accrual basis - that is, it reflects compensation liabilities incurred by the employer in a given period regardless of when the compensation is actually received by the employee. Average compensation per job is compensation of employees divided by total full-time and part-time wage and salary employment. Private nonfarm compensation is the sum of wages and salaries and supplements to wages and salaries, excluding farm and government. More terms and definitions are available on https://apps.bea.gov/regional/definitions/.

This dataset is a listing of all active City of Chicago employees, complete with full names, departments, positions, employment status (part-time or full-time), frequency of hourly employee –where applicable—and annual salaries or hourly rate. Please note that "active" has a specific meaning for Human Resources purposes and will sometimes exclude employees on certain types of temporary leave. For hourly employees, the City is providing the hourly rate and frequency of hourly employees (40, 35, 20 and 10) to allow dataset users to estimate annual wages for hourly employees. Please note that annual wages will vary by employee, depending on number of hours worked and seasonal status. For information on the positions and related salaries detailed in the annual budgets, see https://www.cityofchicago.org/city/en/depts/obm.html

Data Disclosure Exemptions: Information disclosed in this dataset is subject to FOIA Exemption Act, 5 ILCS 140/7 (Link:https://www.ilga.gov/legislation/ilcs/documents/000501400K7.htm)

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

This dataset has been published by the Human Resources Department of the City of Virginia Beach and data.virginiabeach.gov. The mission of data.virginiabeach.gov is to provide timely and accurate City information to increase government transparency and access to useful and well organized data by the general public, non-governmental organizations, and City of Virginia Beach employees.Distributed bydata.virginiabeach.gov2405 Courthouse Dr.Virginia Beach, VA 23456EntityEmployee SalariesPoint of ContactHuman ResourcesSherri Arnold, Human Resources Business Partner IIIsharnold@vbgov.com757-385-8804Elda Soriano, HRIS Analystesoriano@vbgov.com757-385-8597AttributesColumn: DepartmentDescription: 3-letter department codeColumn: Department DivisionDescription: This is the City Division that the position is assigned to.Column: PCNDescription: Tracking number used to reference each unique position within the City.Column: Position TitleDescription: This is the title of the position (per the City’s pay plan).Column: FLSA Status Description: Represents the position’s status with regards to the Fair Labor Standards Act (FLSA) “Exempt” - These positions do not qualify for overtime compensation – Generally, a position is classified as FLSA exempt if all three of the following criteria are met: 1) Paid at least $47,476 per year ($913 per week); 2) Paid on a salary basis - generally, salary basis is defined as having a guaranteed minimum amount of pay for any work week in which the employee performs any work; 3) Perform exempt job duties - Job duties are split between three classifications: executive, professional, and administrative. All three have specific job functions which, if present in the employee’s regular work, would exempt the individual from FLSA. Employees may also be exempt from overtime compensation if they are a “highly compensated employee” as defined by the FLSA or the position meets the criteria for other enumerated exemptions in the FLSA.“Non-exempt” – These positions are eligible for overtime compensation - positions classified as FLSA non-exempt if they fail to meet any of exempt categories specified in the FLSA. Column: Initial Hire DateDescription: This is the date that the full-time employee first began employment with the City.Column: Date in TitleDescription: This is the date that the full-time employee first began employment in their current position.Column: SalaryDescription: This is the annual salary of the full-time employee or the hourly rate of the part-time employee.Frequency of dataset updateMonthly

Employees correspond to the ILO definition of 'paid employment'. The relationship of employer to employee exists when there is a contract, which may be formal or informal, between an enterprise and a person, entered into voluntarily by both parties, whereby the person works for the enterprise in return for remuneration in cash or in kind. Persons having both a job as an employee and a job as a self-employed person are classified as an employee if the employee job constitutes their principal activity by income. If income is not available, then hours worked is to be used as a proxy. This also covers persons temporarily not at work, provided they have a formal job attachment. Data are sourced from National accounts data and expressed as percentage change comparing year Y with year Y-1 and in 1000 persons. The ESA 2010 distinguishes two employment concepts depending on the geographical coverage: resident persons in employment (i.e. the national scope of employment) and employment in resident production units irrespective of the place of residence of the employed person (i.e. domestic scope). The table presents employees according to the domestic concept.

This dataset is a listing of all active City of Chicago employees customer_name = Name of employee job_title = Title of employee at the time when the data were updated department = Department where employee worked. Full_or_part_time = Whether the employee was employed full- (F) or part-time (P). salary_or_hourly = Defines whether an employee is paid on an hourly basis or salary basis. Hourly employees are further defined by the number of hours they work in a week typical_hour = Describes the typical amount of work for hourly employees. This data does not apply to salary employees. 40 - Employee paid on an hourly basis; works an 8 hour day; can be either full-time permanent (FT/P) or full-time temporary (FT-T) which is a seasonal employee; 35 - Employee paid on an hourly basis; works a 7 hour day; can be either full-time permanent (FT/P) or full-time temporary (FT-T) which is a seasonal employee; 20 - Employee paid on a part-time, hourly basis; typically works a 4 hour day, 5 days a week; 10 - Employee paid on a part-time, hourly basis; works 10 hours or less in a week. salary = Salary rates

Source : https://data.cityofchicago.org/Administration-Finance/Current-Employee-Names-Salaries-and-Position-Title/xzkq-xp2w

These figures show the median gross annual pay for full-time workers on a workplace basis for the area, who are on adults rates of pay, and whose pay was not affected by absence. Figures are for GB pounds per annum. Full-time workers are defined as those who work more than 30 paid hours per week or those in teaching professions working 25 paid hours or more per week. Figures for earnings come from the Annual Survey of Hours and Earnings (ASHE) which is based on a 1 per cent sample of employees, information on whose earnings and hours is obtained from employers. The survey does not cover people who are self-employed, nor does it cover employees not paid during the reference period. Information relates to a pay period in April. The earnings information collected relates to gross pay before tax, national insurance or other deductions, and excludes payments in kind (i.e. payment made in the form of goods and services rather than cash). It is restricted to earnings relating to the survey pay period and so excludes payments of arrears from another period made during the survey period; any payments due as a result of a pay settlement but not yet paid at the time of the survey will also be excluded. Estimates for 2011 and subsequent years use a weighting scheme based on occupations which have been coded according to Standard Occupational Classification (SOC) 2010 that replaced SOC 2000. Therefore care should be taken when making comparisons with earlier years. Where the estimate is assessed with a coefficient of variation (CV) of over 20 per cent, these figures have been suppressed, as they are considered by the ONS as unreliable.Data is Powered by LG Inform Plus and automatically checked for new data on the 3rd of each month.

This dataset focuses on workforce attrition in the IT industry. It contains demographic and professional information of individuals, along with their job experiences, work conditions, and reasons related to workforce attrition.

Below are the column descriptions:

• What is your age?: The age of the employee (in years).
• What is your gender?: Gender of the employee (e.g., Male, Female).
• Your marital status?: The marital status of the employee (e.g., Married, Single).
• Do\Did you work overtime?: Indicates if the employee worked overtime (Yes/No).
• Have you ever experienced attrition?: Indicates whether the employee experienced attrition, i.e., if they left or considered leaving the company (Yes/No).
• Do you have\had remote work facility?: Whether the employee had the option to work remotely (Yes/No).
• How satisfied are\was you with your workplace culture?: The employee’s satisfaction level with the workplace culture, with responses ranging from 1 to 5.
• Do\Did you get financial compensation from company?: Indicates whether the employee received financial compensation (Yes/No).
• Do\Did you get career development opportunities at your workplace?: Whether the employee received career development opportunities in their workplace (Yes/No).
• Tell your number of years with current\previous company (in digits): Number of years the employee spent at their current or previous company.
• Tell your number of years in current\previous role (in digits): Number of years the employee spent in their current or previous role.
• What is your monthly income? (In digits): Monthly income of the employee (in local currency).
• What is\was your job level?: Job level of the employee (e.g., Entry Level, Junior, Senior).
• What is\was your job role?: The specific job role of the employee (e.g., Web Developer, QA Engineer).
• Are you a part-time Freelancer?: Indicates if the employee is a part-time freelancer in addition to their main job (Yes/No).

Abstract

Survey based Harmonized Indicators (SHIP) files are harmonized data files from household surveys that are conducted by countries in Africa. To ensure the quality and transparency of the data, it is critical to document the procedures of compiling consumption aggregation and other indicators so that the results can be duplicated with ease. This process enables consistency and continuity that make temporal and cross-country comparisons consistent and more reliable.

Four harmonized data files are prepared for each survey to generate a set of harmonized variables that have the same variable names. Invariably, in each survey, questions are asked in a slightly different way, which poses challenges on consistent definition of harmonized variables. The harmonized household survey data present the best available variables with harmonized definitions, but not identical variables. The four harmonized data files are

a) Individual level file (Labor force indicators in a separate file): This file has information on basic characteristics of individuals such as age and sex, literacy, education, health, anthropometry and child survival. b) Labor force file: This file has information on labor force including employment/unemployment, earnings, sectors of employment, etc. c) Household level file: This file has information on household expenditure, household head characteristics (age and sex, level of education, employment), housing amenities, assets, and access to infrastructure and services. d) Household Expenditure file: This file has consumption/expenditure aggregates by consumption groups according to Purpose (COICOP) of Household Consumption of the UN.

Geographic coverage

National

Analysis unit

• Individual level for datasets with suffix _I and _L
• Household level for datasets with suffix _H and _E

Universe

The survey covered all de jure household members (usual residents).

Kind of data

Sample survey data [ssd]

Sampling procedure

Sample Frame The list of households obtained from the 2001/2 Ethiopian Agricultural Sample Enumeration (EASE) was used as a frame to select EAs from the rural part of the country. On the other hand, the list consisting of households by EA, which was obtained from the 2004 Ethiopian Urban Economic Establishment Census, (EUEEC), was used as a frame in order to select sample enumeration areas for the urban HICE survey. A fresh list of households from each urban and rural EA was prepared at the beginning of the survey period. This list was, thus, used as a frame in order to select households from sample EAs.

Sample Design For the purpose of the survey the country was divided into three broad categories. That is; rural, major urban center and other urban center categories.

Category I: Rural: - This category consists of the rural areas of eight regional states and two administrative councils (Addis Ababa and Dire Dawa) of the country, except Gambella region. Each region was considered to be a domain (Reporting Level) for which major findings of the survey are reported. This category comprises 10 reporting levels. A stratified two-stage cluster sample design was used to select samples in which the primary sampling units (PSUs) were EAs. Twelve households per sample EA were selected as a Second Stage Sampling Unit (SSU) to which the survey questionnaire were administered.

Category II:- Major urban centers:- In this category all regional capitals (except Gambella region) and four additional urban centers having higher population sizes as compared to other urban centers were included. Each urban center in this category was considered as a reporting level. However, each sub-city of Addis Ababa was considered to be a domain (reporting levels). Since there is a high variation in the standards of living of the residents of these urban centers (that may have a significant impact on the final results of the survey), each urban center was further stratified into the following three sub-strata. Sub-stratum 1:- Households having a relatively high standards of living Sub-stratum 2:- Households having a relatively medium standards of living and Sub-stratum 3:- Households having a relatively low standards of living. The category has a total of 14 reporting levels. A stratified two-stage cluster sample design was also adopted in this instance. The primary sampling units were EAs of each urban center. Allocation of sample EAs of a reporting level among the above mentioned strata were accomplished in proportion to the number of EAs each stratum consists of. Sixteen households from each sample EA were inally selected as a Secondary Sampling Unit (SSU).

Category III: - Other urban centers: - Urban centers in the country other than those under category II were grouped into this category. Excluding Gambella region a domain of "other urban centers" is formed for each region. Consequently, 7 reporting levels were formed in this category. Harari, Addis Ababa and Dire Dawa do not have urban centers other than that grouped in category II. Hence, no domain was formed for these regions under this category. Unlike the above two categories a stratified three-stage cluster sample design was adopted to select samples from this category. The primary sampling units were urban centers and the second stage sampling units were EAs. Sixteen households from each EA were lastly selected at the third stage and the survey questionnaires administered for all of them.

Mode of data collection

Face-to-face [f2f]

Abstract

This file contains data on Gini coefficients, cumulative quintile shares, explanations regarding the basis on which the Gini coefficient was computed, and the source of the information. There are two data-sets, one containing the "high quality" sample and the other one including all the information (of lower quality) that had been collected.

The database was constructed for the production of the following paper:

Deininger, Klaus and Lyn Squire, "A New Data Set Measuring Income Inequality", The World Bank Economic Review, 10(3): 565-91, 1996.

This article presents a new data set on inequality in the distribution of income. The authors explain the criteria they applied in selecting data on Gini coefficients and on individual quintile groups’ income shares. Comparison of the new data set with existing compilations reveals that the data assembled here represent an improvement in quality and a significant expansion in coverage, although differences in the definition of the underlying data might still affect intertemporal and international comparability. Based on this new data set, the authors do not find a systematic link between growth and changes in aggregate inequality. They do find a strong positive relationship between growth and reduction of poverty.

Geographic coverage

In what follows, we provide brief descriptions of main features for individual countries that are included in the data-base. Without being comprehensive, these notes are intended to indicate some of the considerations underlying our decision to include or exclude certain observations.

Argentina Various permanent household surveys, all covering urban centers only, have been regularly conducted since 1972 and are quoted in a wide variety of sources and years, e.g., for 1980 (World Bank 1992), 1985 (Altimir 1994), and 1989 (World Bank 1992). Estimates for 1963, 1965, 1969/70, 1970/71, 1974, 1975, 1980, and 1981 (Altimir 1987) are based only on Greater Buenos Aires. Estimates for 1961, 1963, 1970 (Jain 1975) and for 1970 (van Ginneken 1984) have only limited geographic coverage and do not satisfy our minimum criteria.

Despite the many urban surveys, there are no income distribution data that are representative of the population as a whole. References to national income distribution for the years 1953, 1959, and 1961(CEPAL 1968 in Altimir 1986 ) are based on extrapolation from national accounts and have therefore not been included. Data for 1953 and 1961 from Weisskoff (1970) , from Lecaillon (1984) , and from Cromwell (1977) are also excluded.

Australia Household surveys, the result of which is reported in the statistical yearbook, have been conducted in 1968/9, 1975/6, 1978/9, 1981, 1985, 1986, 1989, and 1990.

Data for 1962 (Cromwell, 1977) and 1966/67 (Sawyer 1976) were excluded as they covered only tax payers. Jain's data for 1970 was excluded because it covered income recipients only. Data from Podder (1972) for 1967/68, from Jain (1975) for the same year, from UN (1985) for 78/79, from Sunders and Hobbes (1993) for 1986 and for 1989 were excluded given the availability of the primary sources. Data from Bishop (1991) for 1981/82, from Buhman (1988) for 1981/82, from Kakwani (1986) for 1975/76, and from Sunders and Hobbes (1993) for 1986 were utilized to test for the effect of different definitions. The values for 1967 used by Persson and Tabellini and Alesina and Rodrik (based on Paukert and Jain) are close to the ones reported in the Statistical Yearbook for 1969.

Austria: In addition to data referring to the employed population (Guger 1989), national household surveys for 1987 and 1991 are included in the LIS data base. As these data do not include income from self-employment, we do not report them in our high quality data-set.

Bahamas Data for Ginis and shares are available for 1973, 1977, 1979, 1986, 1988, 1989, 1991, 1992, and 1993 in government reports on population censuses and household budget surveys, and for 1973 and 1975 from UN (1981). Estimates for 1970 (Jain 1975), 1973, 1975, 1977, and 1979 (Fields 1989) have been excluded given the availability of primary sources.

Bangladesh Data from household surveys for 1973/74, 1976/77, 1977/78, 1981/82, and 1985/86 are available from the Statistical Yearbook, complemented by household-survey based information from Chen (1995) and the World Development Report. Household surveys with rural coverage for 1959, 1960, 1963/64, 1965, 1966/67 and 1968/69, and with urban coverage for 1963/64, 1965, 1966/67, and 1968/69 are also available from the Statistical yearbook. Data for 1963/64 ,1964 and 1966/67, (Jain 1975) are not included due to limited geographic coverage, We also excluded secondary sources for 1973/74, 1976/77, 1981/82 (Fields 1989), 1977 (UN 1981), 1983 (Milanovic 1994), and 1985/86 due to availability of the primary source.

Barbados National household surveys have been conducted in 1951/52 and 1978/79 (Downs, 1988). Estimates based on personal tax returns, reported consistently for 1951-1981 (Holder and Prescott, 1989), had to be excluded as they exclude the non-wage earning population. Jain's figure (used by Alesina and Rodrik) is based on the same source.

Belgium Household surveys with national coverage are available for 1978/79 (UN 1985), and for 1985, 1988, and 1992 (LIS 1995). Earlier data for 1969, 1973, 1975, 1976 and 1977 (UN 1981) refer to taxable households only and are not included.

Bolivia The only survey with national coverage is the 1990 LSMS (World Development Report). Surveys for 1986 and 1989 cover the main cities only (Psacharopoulos et al. 1992) and are therefore not included. Data for 1968 (Cromwell 1977) do not refer to a clear definition and is therefore excluded.

Botswana The only survey with national coverage was conducted in 1985-1986 (Chen et al 1993); surveys in 74/75 and 85/86 included rural areas only (UN 1981). We excluded Gini estimates for 1971/72 that refer to the economically active population only (Jain 1975), as well as 1974/75 and 1985/86 (Valentine 1993) due to lack of national coverage or consistency in definition.

Brazil Data from 1960, 1970, 1974/75, 1976, 1977, 1978, 1980, 1982, 1983, 1985, 1987 and 1989 are available from the statistical yearbook, in addition to data for 1978 (Fields 1987) and for 1979 (Psacharopoulos et al. 1992). Other sources have been excluded as they were either not of national coverage, based on wage earners only, or because a more consistent source was available.

Bulgaria: Data from household surveys are available for 1963-69 (in two year intervals), for 1970-90 (on an annual basis) from the Statistical yearbook and for 1991 - 93 from household surveys by the World Bank (Milanovic and Ying).

Burkina Faso A priority survey has been undertaken in 1995.

Central African Republic: Except for a household survey conducted in 1992, no information was available.

Cameroon The only data are from a 1983/4 household budget survey (World Bank Poverty Assessment).

Canada Gini- and share data for the 1950-61 (in irregular intervals), 1961-81 (biennially), and 1981-91 (annually) are available from official sources (Statistical Yearbook for years before 1971 and Income Distributions by Size in Canada for years since 1973, various issues). All other references seem to be based on these primary sources.

Chad: An estimate for 1958 is available in the literature, and used by Alesina and Rodrik and Persson and Tabellini but was not included due to lack of primary sources.

Chile The first nation-wide survey that included not only employment income was carried out in 1968 (UN 1981). This is complemented by household survey-based data for 1971 (Fields 1989), 1989, and 1994. Other data that refer either only to part of the population or -as in the case of a long series available from World Bank country operations- are not clearly based on primary sources, are excluded.

China Annual household surveys from 1980 to 1992, conducted separately in rural and urban areas, were consolidated by Ying (1995), based on the statistical yearbook. Data from other secondary sources are excluded due to limited geographic and population coverage and data from Chen et al (1993) for 1985 and 1990 have not been included, to maintain consistency of sources..

Colombia The first household survey with national coverage was conducted in 1970 (DANE 1970). In addition, there are data for 1971, 1972, 1974 CEPAL (1986), and for 1978, 1988/89, and 1991 (World Bank Poverty Assessment 1992 and Chen et al. 1995). Data referring to years before 1970 -including the 1964 estimate used in Persson and Tabellini were excluded, as were estimates for the wage earning population only.

Costa Rica Data on Gini coefficients and quintile shares are available for 1961, 1971 (Cespedes 1973),1977 (OPNPE 1982), 1979 (Fields 1989), 1981 (Chen et al 1993), 1983 (Bourguignon and Morrison 1989), 1986 (Sauma-Fiatt 1990), and 1989 (Chen et al 1993). Gini coefficients for 1971 (Gonzalez-Vega and Cespedes in Rottenberg 1993), 1973 and 1985 (Bourguignon and Morrison 1989) cover urban areas only and were excluded.

Cote d'Ivoire: Data based on national-level household surveys (LSMS) are available for 1985, 1986, 1987, 1988, and 1995. Information for the 1970s (Schneider 1991) is based on national accounting information and therefore excluded

Cuba Official information on income distribution is limited. Data from secondary sources are available for 1953, 1962, 1973, and 1978, relying on personal wage income, i.e. excluding the population that is not economically active (Brundenius 1984).

Czech Republic Household surveys for 1993 and 1994 were obtained from Milanovic and Ying. While it is in principle possible to go back further, splitting national level surveys for the former Czechoslovakia into their independent parts, we decided not to do so as the same argument could be used to

Data Description: This dataset lists all current City of Cincinnati employees, including full names, department, position title, full-time employee status, employee age range, employee race, and annual salary rate.

Data Creation: This data is pulled directly from the City's HR software; which centralizes all department HR actions city wide.

Data Created By: City Human Resource Information System (CHRIS)

Refresh Frequency: Daily

CincyInsights: The City of Cincinnati maintains an interactive dashboard portal, CincyInsights in addition to our Open Data in an effort to increase access and usage of city data. This data set has an associated dashboard available here: https://insights.cincinnati-oh.gov/stories/s/Employee-Profile/wjqv-hgc9/

Data Dictionary: A data dictionary providing definitions of columns and attributes is available as an attachment to this dataset.

Processing: The City of Cincinnati is committed to providing the most granular and accurate data possible. In that pursuit the Office of Performance and Data Analytics facilitates standard processing to most raw data prior to publication. Processing includes but is not limited: address verification, geocoding, decoding attributes, and addition of administrative areas (i.e. Census, neighborhoods, police districts, etc.).

Data Usage: For directions on downloading and using open data please visit our How-to Guide: https://data.cincinnati-oh.gov/dataset/Open-Data-How-To-Guide/gdr9-g3ad

Abstract

Survey based Harmonized Indicators (SHIP) files are harmonized data files from household surveys that are conducted by countries in Africa. To ensure the quality and transparency of the data, it is critical to document the procedures of compiling consumption aggregation and other indicators so that the results can be duplicated with ease. This process enables consistency and continuity that make temporal and cross-country comparisons consistent and more reliable.

Four harmonized data files are prepared for each survey to generate a set of harmonized variables that have the same variable names. Invariably, in each survey, questions are asked in a slightly different way, which poses challenges on consistent definition of harmonized variables. The harmonized household survey data present the best available variables with harmonized definitions, but not identical variables. The four harmonized data files are

a) Individual level file (Labor force indicators in a separate file): This file has information on basic characteristics of individuals such as age and sex, literacy, education, health, anthropometry and child survival. b) Labor force file: This file has information on labor force including employment/unemployment, earnings, sectors of employment, etc. c) Household level file: This file has information on household expenditure, household head characteristics (age and sex, level of education, employment), housing amenities, assets, and access to infrastructure and services. d) Household Expenditure file: This file has consumption/expenditure aggregates by consumption groups according to Purpose (COICOP) of Household Consumption of the UN.

Geographic coverage

National

Analysis unit

• Individual level for datasets with suffix _I and _L
• Household level for datasets with suffix _H and _E

Universe

The survey covered all de jure household members (usual residents).

Kind of data

Sample survey data [ssd]

Sampling procedure

Sample Design The 1999/2000 Household Income, Consurnption, and Expendi.ture Survey covered both the urban and the sedentary rural parts of the country. The survey has not covered six zones in Somalia Region and two zones in Afar Region that are inhabited mainly by nomadic population. For the purpose of the survey, the country was divided into three categories . That is, the rural parts of the country and the urban areas that were divided into two broad categories taking into account sizes of their population. Category I: Rural parts of nine Regional States and two administrative regions were grouped in this category each of which were the survey dornains (reporting levels). These regions are Tigrai,Afar, Amhara, Oromia, Sornalia, Eenishangul-Gunuz, SNNP,Gambela, Flarari, Addis Ababa and Dire Dawa.

Category II: All Regional capitals and five major urban centers of the country were grouped in this category. Each of the urban centers in this category was the survey domain (reporting level) for which separate survey results for rnajor survey characteristics were reported.

Category III: Urban centers in the country other than the urban centers in category II were grouped in this category and formed a single reporting level. Other than the reporting levels defined in category II and category III one additional domain, namely total urban (country level) can be constructed by eombining the basic domains defined in the two categories. All in all 35'basie rural and urban domains (reporting levels) were defined for the survey. In addition to the above urban and rural domains, survey results are to be reported at regional and eountry levels by aggregating the survey results for the conesponding urban and rural areas. Definition of the survey dornains was based on both technical and resource considerations. More specifically, sample size for the domains were determined to enable provision of major indicators with reasonable precision subject to the resources that were available for the survey.

Selection Scheme and Sample Size in Each Category CategoryI : A stratified two-stage sample design was used to select the sample in which the primary sampling units (PSUs) were EAs. Sample enumeration areas( EAs) from each domain were selected using systematic sampling that is probability proportional to the size being number of households obtained from the 1994 population and housing census.A total of 722 EAs were selected from the rural parts of the country. Within each sample EA a fresh list of households was prepared at the beginning of the survey's field work and for the administration of the survey questionnaire 12 households per sample EA for rural areas were systematically selected.

Category II: In this category also,a stratified two-stage sample design was used to select the sample. Here a strata constitutes all the "Regional State Capitals" and the five "Major Urban Centers" in the country and are grouped as a strata in this category. The primary sampling units (PSUs) are the EA's in the Regional State Capitals and the five Major Urban Centers and excludes the special EAs (non-conventional households). Sample enumeration areas( EAs) from each strata were selected using systematic sampling probability proportional to size, size being number of households obtained from the 1994 population and housing census. A total of 373 EAs were selected from this domain of study. Within each sample EAs a fresh list of households was prepared at the beginning of the survey's field work and for the administration of the questionnaire 16 household per sample EA were systematically selected-

Category III: Three-stage stratified sample design was adopted to select the sample from domains in category III. The PSUs were other urban centers selected using systematic sampling that is probability proportional to size; size being number of households obtained from the 1994 population and housing census. The secondary sampling units (SSUs) were EAs which were selected using systematic sampling that is probability proportional to size; size being number of households obtained from the 1994 population and housing census. A total of 169 sample EAs were selected from the sample of other urban centers and was determined by proportional allocation to their size of households from the 1994 census. Ultimately, 16 households within each of the sample EAs were selected systematically from a fresh list of households prepared at the beginning of the survey's fieldwork for the administration of the survey questionnaire.

Mode of data collection

Face-to-face [f2f]

Research instrument

The Household Income, Consumption and Expenditure Survey questionnaire contains the following forms: - Form 1: Area Identification and Household Characteristics - Form 2A: Quantity and value of weekly consumption of food and drinks consumed at home and tobacco/including quantity purchased, own produced, obtained, etc for first and second week. - Form 2B: Quantity and value of weekly consumption of food and drinks consumed at home and tobacco/including quantity purchased, own produced, obtained, etc for third and fourth week . - Form 3A: All transaction (income, expenditure and consumption) for the first and second weeks except what is collected in Forms 2A and 2B - Form 3B: All transaction (income, expenditure and consumption) for the third and fourth weeks except what is collected in Forms 2A and 2B - Form 4: All transaction (expenditure and consumption) for last 6 months for Household expenditure on some selected item groups - Form 5: Cash income and receipts received by household and type of tenure. The survey questionnaire is provided as external resource.

📹Project Video available on YouTube - https://youtu.be/fykrwQD3HR4

🖇️Connect with me on LinkedIn - https://www.linkedin.com/in/rohit-grewal

Human Resource (HR) Data of a Multi-national Corporation (MNC) - 2 Million Records

This dataset contains HR information for employees of a multinational corporation (MNC). It includes 2 Million (20 Lakhs) employee records with details about personal identifiers, job-related attributes, performance, employment status, and salary information. The dataset can be used for HR analytics, including workforce distribution, attrition analysis, salary trends, and performance evaluation.

This data is available as a CSV file. We are going to analyse this data set using the Pandas. This analyse will be helpful for those working in HR domain.

Using this dataset, we answered multiple questions with Python in our Project.

Q.1) What is the distribution of Employee Status (Active, Resigned, Retired, Terminated) ?

Q.2) What is the distribution of work modes (On-site, Remote) ?

Q.3) How many employees are there in each department ?

Q.4) What is the average salary by Department ?

Q.5) Which job title has the highest average salary ?

Q.6) What is the average salary in different Departments based on Job Title ?

Q.7) How many employees Resigned & Terminated in each department ?

Q.8) How does salary vary with years of experience ?

Q.9) What is the average performance rating by department ?

Q.10) Which Country have the highest concentration of employees ?

Q.11) Is there a correlation between performance rating and salary ?

Q.12) How has the number of hires changed over time (per year) ?

Q.13) Compare salaries of Remote vs. On-site employees — is there a significant difference ?

Q.14) Find the top 10 employees with the highest salary in each department.

Q.15) Identify departments with the highest attrition rate (Resigned %).

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These are the main Features/Columns available in the dataset :

1) Unnamed: 0 – Index column (auto-generated, not useful for analysis, will be deleted).

2) Employee_ID – Unique identifier assigned to each employee (e.g., EMP0000001).

3) Full_Name – Full name of the employee.

4) Department – Department in which the employee works (e.g., IT, HR, Marketing, Operations).

5) Job_Title – Designation or role of the employee (e.g., Software Engineer, HR Manager).

6) Hire_Date – The date when the employee was hired by the company.

7) Location – Geographical location of the employee (city, country).

8) Performance_Rating – Performance evaluation score (numeric scale, higher is better).

9) Experience_Years – Number of years of professional experience the employee has.

10) Status – Current employment status (e.g., Active, Resigned).

11) Work_Mode – Mode of working (e.g., On-site, Hybrid, Remote).

12) Salary_INR – Annual salary of the employee in Indian Rupees.

Employment, Commuting, Occupation, Income, Health Insurance, Poverty, and more. This service is updated annually with American Community Survey (ACS) 5-year data. Contact: District of Columbia, Office of Planning. Email: planning@dc.gov. Geography: Census Tracts. Current Vintage: 2019-2023. ACS Table(s): DP03. Data downloaded from: Census Bureau's API for American Community Survey. Date of API call: January 2, 2025. National Figures: data.census.gov. 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 layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. 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 2020 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). Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page. Data processed using R statistical package and ArcGIS Desktop. Margin of Error was not included in this layer but is available from the Census Bureau. Contact the Office of Planning for more information about obtaining Margin of Error values.

Footnotes:1The boundaries and names of census geographies can change from one census to the next. In order to facilitate data comparisons between censuses, previous census data have been adjusted to reflect as closely as possible the 2021 boundaries of these areas. The methodology used for this adjustment involved spatially linking blocks of previous censuses (concordance to the 1996 Census used the 1996 enumeration areas to the 2021 boundaries). A previous census block was linked to the 2021 area within which its representative point fell. A limited number of interactive linkages were completed to further enhance the adjustment in certain areas. For some census geographies, it was not possible to reflect the 2021 boundaries. The 2021 boundaries may not be reflected as there was no previous census block to assign to the 2021 area. As well previous census data for some 2021 areas may not be available due to the fact that the concordance did not produce an accurate representation of the 2021 area.2Age 'Age' refers to the age of a person (or subject) of interest at last birthday (or relative to a specified, well-defined reference date).3Gender Gender refers to an individual's personal and social identity as a man, woman or non-binary person (a person who is not exclusively a man or a woman). Gender includes the following concepts: gender identity, which refers to the gender that a person feels internally and individually; gender expression, which refers to the way a person presents their gender, regardless of their gender identity, through body language, aesthetic choices or accessories (e.g., clothes, hairstyle and makeup), which may have traditionally been associated with a specific gender. A person's gender may differ from their sex at birth, and from what is indicated on their current identification or legal documents such as their birth certificate, passport or driver's licence. A person's gender may change over time. Some people may not identify with a specific gender. Sex 'Sex' refers to whether the person is male or female.4Given that the non-binary population is small, data aggregation to a two-category gender variable is sometimes necessary to protect the confidentiality of responses provided. In these cases, individuals in the category “non-binary persons” are distributed into the other two gender categories and are denoted by the “+” symbol. The sex variable in census years prior to 2021 and the two-category gender variable in the 2021 Census are included together in the [data table]. Although sex and gender refer to two different concepts, the introduction of gender is not expected to have a significant impact on data analysis and historical comparability, given the small size of the transgender and non-binary populations. For additional information on changes of concepts over time, please consult the Age, Sex at Birth and Gender Reference Guide. ¿5The median income of a specified group is the amount that divides the income distribution of that group into two halves, i.e., the incomes of half of the units in that group are below the median, while those of the other half are above the median. Median incomes of individuals are calculated for those with income (positive or negative).6Average income of a specified group is calculated by dividing the aggregate income of that group by the number of units in that group. Average incomes are calculated for those with income (positive or negative).7The median income of a specified group is the amount that divides the income distribution of that group into two halves, i.e., the incomes of half of the units in that group are below the median, while those of the other half are above the median. Median incomes of individuals are calculated for those with income (positive or negative).8Average income of a specified group is calculated by dividing the aggregate income of that group by the number of units in that group. Average incomes are calculated for those with income (positive or negative).9Total income refers to the sum of certain incomes (in cash and, in some circumstances, in kind) of the statistical unit during a specified reference period. The components used to calculate total income vary between: – Statistical units of social statistical programs such as persons, private households, census families and economic families; – Statistical units of business statistical programs such as enterprises, companies, establishments and locations; and – Statistical units of farm statistical programs such as farm operator and farm family. In the context of persons, total income refers to receipts from certain sources, before income taxes and deductions, during a specified reference period. In the context of census families, total income refers to receipts from certain sources of all of its family members, before income taxes and deductions, during a specified reference period. In the context of economic families, total income refers to receipts from certain sources of all of its family members, before income taxes and deductions, during a specified reference period. In the context of households, total income refers to receipts from certain sources of all household members, before income taxes and deductions, during a specified reference period. The monetary receipts included are those that tend to be of a regular and recurring nature. Receipts that are included as income are: * employment income from wages, salaries, tips, commissions and net income from self-employment (for both unincorporated farm and non-farm activities); * income from investment sources, such as dividends and interest on bonds, accounts, guaranteed investment certificates (GICs) and mutual funds; * income from employer and personal pension sources, such as private pensions and payments from annuities and registered retirement income funds (RRIFs); * other regular cash income, such as child support payments received, spousal support payments (alimony) received and scholarships; * income from government sources, such as social assistance, child benefits, Employment Insurance benefits, Old Age Security benefits, COVID-19 benefits and Canada Pension Plan and Québec Pension Plan benefits and disability income. Receipts excluded from this income definition are: * one-time receipts, such as lottery winnings, gambling winnings, cash inheritances, lump-sum insurance settlements and tax-free savings account (TFSA) or registered retirement savings plan (RRSP) withdrawals; * capital gains because they are not by their nature regular and recurring. It is further assumed that they are more relevant to the concept of wealth than the concept of income; * employers' contributions to registered pension plans, Canada Pension Plan, Québec Pension Plan and Employment Insurance; * voluntary inter-household transfers, imputed rent, goods and services produced for barter and goods produced for own consumption.10The sum of employment income (wages, salaries and commissions, net self-employment income from farm or non-farm unincorporated business and/or professional practice), investment income, private retirement income (retirement pensions, superannuation and annuities, including those from registered retirement savings plans [RRSPs] and registered retirement income funds [RRIFs]) and other money income from market sources during the reference period. It is equivalent to total income minus government transfers. It is also referred to as income before transfers and taxes.11All income received as wages, salaries and commissions from paid employment and net self-employment income from farm or non-farm unincorporated business and/or professional practice during the reference period.12Gross wages and salaries before deductions for such items as income taxes, pension plan contributions and employment insurance premiums during the reference period. While other employee remuneration such as security options benefits, board and lodging and other taxable allowances and benefits are included in this source, employer's contributions to pension plans and employment insurance plans are excluded. Other receipts included in this source are military pay and allowances, tips, commissions and cash bonuses associated with paid employment, benefits from wage-loss replacement plans or income-maintenance insurance plans, supplementary unemployment benefits from an employer or union, research grants, royalties from a work or invention with no associated expenses and all types of casual earnings during the reference period.13Net income (gross receipts minus cost of operation and capital cost allowance) received during the reference period from self-employment activities, either on own account or in partnership. In the case of partnerships, only the person's share of income is included. Net partnership income of a limited or non-active partner is excluded. It includes farming income, fishing income and income from unincorporated business or professional practice. Commission income for a self-employed commission salesperson and royalties from a work or invention with expenses associated are also included in this source.14Income received during the reference period in the form of interest from deposits in banks, trust companies, co-operatives, credit unions and caisses populaires, interests on savings certificates, bonds and debentures, dividends from both Canadian and foreign stocks, net rental income from real estate, mortgage and loan interest received, regular income from an estate or trust fund, interest from insurance policies and net partnership income for a limited or non-active partner. This variable does not include net capital gains or losses as they are not part of the standard income definition.15All regular income received during the reference period associated with employer or personal retirement pensions, benefits or savings plans. It includes payments received from all annuities, including payments from employers'

Agricultural real factor income measures the income generated by farming, which is used to remunerate borrowed or rented factors of production (capital, wages and land rents) as well as own production factors (own labour, capital and land). Annual work units (AWUs) are defined as full-time equivalent employment (corresponding to the number of full-time equivalent jobs), which is calculated by dividing total hours worked by the average annual number of hours worked in full-time jobs within the economic territory. This can be interpreted as a measure of labour productivity in agriculture. The data stem from the Economic Accounts for Agriculture (EAA), which provide detailed information on agricultural sector income.