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Context

The gender pay gap or gender wage gap is the average difference between the remuneration for men and women who are working. Women are generally considered to be paid less than men. There are two distinct numbers regarding the pay gap: non-adjusted versus adjusted pay gap. The latter typically takes into account differences in hours worked, occupations were chosen, education, and job experience. In the United States, for example, the non-adjusted average female's annual salary is 79% of the average male salary, compared to 95% for the adjusted average salary.

The reasons link to legal, social, and economic factors, and extend beyond "equal pay for equal work".

The gender pay gap can be a problem from a public policy perspective because it reduces economic output and means that women are more likely to be dependent upon welfare payments, especially in old age.

This dataset aims to replicate the data used in the famous paper "The Gender Wage Gap: Extent, Trends, and Explanations", which provides new empirical evidence on the extent of and trends in the gender wage gap, which declined considerably during the 1980–2010 period.

Citation

fedesoriano. (January 2022). Gender Pay Gap Dataset. Retrieved [Date Retrieved] from https://www.kaggle.com/fedesoriano/gender-pay-gap-dataset.

Content

There are 2 files in this dataset: a) the Panel Study of Income Dynamics (PSID) microdata over the 1980-2010 period, and b) the Current Population Survey (CPS) to provide some additional US national data on the gender pay gap.

PSID variables:

NOTES: THE VARIABLES WITH fz ADDED TO THEIR NAME REFER TO EXPERIENCE WHERE WE HAVE FILLED IN SOME ZEROS IN THE MISSING PSID YEARS WITH DATA FROM THE RESPONDENTS’ ANSWERS TO QUESTIONS ABOUT JOBS WORKED ON DURING THESE MISSING YEARS. THE fz variables WERE USED IN THE REGRESSION ANALYSES THE VARIABLES WITH A predict PREFIX REFER TO THE COMPUTATION OF ACTUAL EXPERIENCE ACCUMULATED DURING THE YEARS IN WHICH THE PSID DID NOT SURVEY THE RESPONDENTS. THERE ARE MORE PREDICTED EXPERIENCE LEVELS THAT ARE NEEDED TO IMPUTE EXPERIENCE IN THE MISSING YEARS IN SOME CASES. NOTE THAT THE VARIABLES yrsexpf, yrsexpfsz, etc., INCLUDE THESE COMPUTATIONS, SO THAT IF YOU WANT TO USE FULL TIME OR PART TIME EXPERIENCE, YOU DON’T NEED TO ADD THESE PREDICT VARIABLES IN. THEY ARE INCLUDED IN THE DATA SET TO ILLUSTRATE THE RESULTS OF THE COMPUTATION PROCESS. THE VARIABLES WITH AN orig PREFIX ARE THE ORIGINAL PSID VARIABLES. THESE HAVE BEEN PROCESSED AND IN SOME CASES RENAMED FOR CONVENIENCE. THE hd SUFFIX MEANS THAT THE VARIABLE REFERS TO THE HEAD OF THE FAMILY, AND THE wf SUFFIX MEANS THAT IT REFERS TO THE WIFE OR FEMALE COHABITOR IF THERE IS ONE. AS SHOWN IN THE ACCOMPANYING REGRESSION PROGRAM, THESE orig VARIABLES AREN’T USED DIRECTLY IN THE REGRESSIONS. THERE ARE MORE OF THE ORIGINAL PSID VARIABLES, WHICH WERE USED TO CONSTRUCT THE VARIABLES USED IN THE REGRESSIONS. HD MEANS HEAD AND WF MEANS WIFE OR FEMALE COHABITOR.

1. intnum68: 1968 INTERVIEW NUMBER
2. pernum68: PERSON NUMBER 68
3. wave: Current Wave of the PSID
4. sex: gender SEX OF INDIVIDUAL (1=male, 2=female)
5. intnum: Wave-specific Interview Number
6. farminc: Farm Income
7. region: regLab Region of Current Interview
8. famwgt: this is the PSID’s family weight, which is used in all analyses
9. relhead: ER34103L this is the relation to the head of household (10=head; 20=legally married wife; 22=cohabiting partner)
10. age: Age
11. employed: ER34116L Whether or not employed or on temp leave (everyone gets a 1 for this variable, since our wage analyses use only the currently employed)
12. sch: schLbl Highest Year of Schooling
13. annhrs: Annual Hours Worked
14. annlabinc: Annual Labor Income
15. occ: 3 Digit Occupation 2000 codes
16. ind: 3 Digit Industry 2000 codes
17. white: White, nonhispanic dummy variable
18. black: Black, nonhispanic dummy variable
19. hisp: Hispanic dummy variable
20. othrace: Other Race dummy variable
21. degree: degreeLbl Agent's Degree Status (0=no college degree; 1=bachelor’s without advanced degree; 2=advanced degree)
22. degupd: degreeLbl Agent's Degree Status (Updated with 2009 values)
23. schupd: schLbl Schooling (updated years of schooling)
24. annwks: Annual Weeks Worked
25. unjob: unJobLbl Union Coverage dummy variable
26. usualhrwk: Usual Hrs Worked Per Week
27. labincbus: Labor Income from...

This dataset provides detailed budget allocation insights for urban and rural households in India, capturing present living standards. The data includes various spending areas such as housing, food, transportation, healthcare, education, and discretionary expenses. The dataset is designed to help researchers, policymakers, and individuals understand spending habits and optimize budget planning.

Context: The dataset is derived from various government reports, surveys, and market research studies that provide a snapshot of the current economic conditions and living standards in India. It includes average income levels, typical expenses, and common savings patterns for both urban and rural households.

Sources:

National Sample Survey Office (NSSO) Ministry of Statistics and Programme Implementation (MoSPI) Various market research reports and publications Inspiration: The inspiration behind this dataset is to provide a clear and detailed picture of how households in different regions of India allocate their budgets. This can be a valuable resource for economists, social scientists, financial advisors, and anyone interested in understanding the financial behavior of Indian households.

Aggregate indicators at the level of the country for 7 countries of the East Bloc from the areas of economy, defense, population and society.

Topics: 1. Population and society: population density; population growth from 1970 to 1978; infant mortality and life expectancy; degree of urbanization; rate of provision with running water and sanitary facilities; residential furnishings and housing conditions; hospital beds and doctors per capita; proportion of children in kindergartens; proportion of women in various branchs of the economy; religious affiliation; divorce rate; training level of the population; education expenditures; employees in technology and science; scientific book production; social mobility.

1. Economy: growth rate of the gross national product; GNP per capita; public investments; merchandise import and export; proportion of employees and proportion of production in the individual sectors of the economy; average income; meat consumption and supply of calories; trade with Comecon countries, capitalist and under-developed countries; trade deficit and foreign debt; growth of import and export as well as of income; work productivity; working hours needed for selected goods; capital intensity; provision of households with telephone, television, cars and other durable economic goods; energy import and energy use; employee-worker relationship; development of real income as well as prices; private savings; income concentration; retail trade index; hectare yields and proportion of private agriculture.

2. Military: defense expenditures; export of weapons; strength of military forces; proportion of defense expenditures in gross national product; number of disturbances and protest demonstrations; armed attacks and persons killed; sanctions of the government; internal security forces.

3. Miscellaneous: content analysis of newspapers regarding reports about human rights, disarmament, economic as well as technical cooperation and conflicts after adoption of the final agreement of Helsinki and Belgrad.