Dataset for meta-analysis "The motherhood penalty's size and factors"

<p><strong>PLEASE, CITE AS Kalabikhina IE, Kuznetsova PO, Zhuravleva SA (2024) Size and factors of the motherhood penalty in the labour market: A meta-analysis. Population and Economics 8(2): 178-205.&nbsp;<a href="https://doi.org/10.3897/popecon.8.e121438" target="_blank" rel="noopener">https://doi.org/10.3897/popecon.8.e121438</a></strong></p> <p>&nbsp;</p> <p><strong>Explanatory note 1: List of papers used in the meta-analysis - see the file "Meta_regression_analysis_papers".</strong></p> <p><strong>The data is presented in WORD format.</strong></p> <p>&nbsp;</p> <p><strong>Explanatory note 2: Set of data used in the meta-analysis - see the file "Meta_regression_analysis_table".</strong></p> <p><strong>The data is presented in EXCEL format.&nbsp;</strong></p> <p><strong>Description of table headers:</strong></p> <p>estimate_number - Number of the estimate</p> <p>paper_number - Number of the paper</p> <p>paper_name - Paper (year and first author)</p> <p>paper_excluded - Paper was excluded from the final sample</p> <p>survey - Data source</p> <p>table_in_paper - Number of the table with the regression results in the paper</p> <p>coeff - Regression coefficient for parenthood variable (estimate)</p> <p>se - SE of the estimate</p> <p>t - t-value of the estimate</p> <p>ols - Estimate is obtained using the OLS method</p> <p>fixed_effects &shy;- Estimate is obtained using the fixed effects method</p> <p>panel - Model considers panel data (for several years)</p> <p>quintile - Estimate is obtained using the quintile regression method</p> <p>other - Estimate is obtained using other methods</p> <p>selection_into_motherhood - Estimate is obtained allowing for selection into motherhood</p> <p>hackman - Estimate is obtained allowing for selection into employment (Heckman procedure)</p> <p>annual_earnings - Annual earnings are considered in the model</p> <p>monthly_wage - Monthly wage is considered in the model</p> <p>daily_wage - Daily wage is considered in the model</p> <p>hourly_wage - Hourly wage is considered in the model</p> <p>min_age_kid - Child's age (minimum)</p> <p>max_age_kid - Child's age (maximum)</p> <p>motherhood - Model uses a dummy variable of the presence of children</p> <p>num_kids - Model uses a variable of the number of children</p> <p>kid1 - Model uses a variable of the presence of one child</p> <p>kid2p - Model uses a variable of the presence of two or more children</p> <p>kid2 - Model uses a variable of the presence of two children</p> <p>kid3p - Model uses a variable of the presence of three or more children</p> <p>kid3 - Model uses a variable of the presence of three children</p> <p>kid4p - Model uses a variable of the presence of three or more children</p> <p>race/nationality - Model includes a race/ethnicity variable</p> <p>age - Model includes the age variable</p> <p>marstat - Model includes the marital status variable</p> <p>oth_char_hh - Model includes any other variables of other household characteristics</p> <p>settl_type - Model includes a variable of the type of settlement (urban, rural)</p> <p>region - Model includes a variable of the region of the country</p> <p>education - Model includes information on the level of education</p> <p>experience - Model includes a variable of work experience</p> <p>pot_experience - Model includes a variable of potential work experience, to be calculated from the data on age and number of years of education</p> <p>tenure - Model includes a variable of the duration of employment at the current job</p> <p>interruptions - Model includes a variable of employment interruptions (related to motherhood)</p> <p>occupation - Model includes an occupation variable</p> <p>industry - Model includes a variable of the industry of employment</p> <p>union - Model includes a variable of trade union membership</p> <p>friendly_conditions - Model includes a variable of the favourable working conditions for mothers (flexible schedule, possibility to work from home, etc.).</p> <p>hours - Model includes a variable of the number of hours worked</p> <p>sector - Model includes a variable of the type of employer ownership (public or private)</p> <p>informal - Model includes a variable of informal employment</p> <p>size_ent - Model includes a variable of the employer size</p> <p>min_age_woman - Woman's age (minimum)</p> <p>max_age_woman - Woman's age (maximum)</p> <p>mean_age_woman - Woman's age (mean)</p> <p>restricted - Sample is limited</p> <p>private - Model considers only private sector employees</p> <p>state - Model considers only public sector employees</p> <p>full_time - Model considers only full-time workers</p> <p>part_time - Model considers only part-time workers</p> <p>better_educated - Model considers only women with a high level of education</p> <p>lower_educated - Model considers only women with a low level of education</p> <p>married - Model includes only married women</p> <p>single - Model includes only single women</p> <p>natives - Model includes only native women (born in the country)</p> <p>immigrants - Model includes only immigrant women (born abroad)</p> <p>race - Model includes only women of a particular race</p> <p>min_year - Time period (minimum year)</p> <p>max_year - Time period (maximum year)</p> <p>journal - Type of publication</p> <p>usa - Sample includes women from the USA</p> <p>western_europe - Sample includes women from Western Europe (Belgium, France, Germany, Luxembourg, the Netherlands, Switzerland)</p> <p>north_europe - Sample includes women from Northern Europe (Denmark, Finland, Norway, Sweden)</p> <p>south_europe - Sample includes women from Southern Europe (Greece, Italy, Portugal, Spain)</p> <p>east_centre_europe - Sample includes women from Central or Eastern Europe (Czechia, Hungary, Poland, Russia, Serbia, Ukraine)</p> <p>china - Sample includes women from China</p> <p>Russia - Sample includes women from Russia</p> <p>others - Sample includes women from other countries</p> <p>country - Country name</p>