Table 1

Variable definitions

VariableDefinition
Dependent variables
FERatioThe percentage of females in top management teams including board of directors, supervisory board members and top executives
FERatio_managementThe percentage of females in top executives
Wage_diffThe difference between the average salary of male and female executives in the firm, scaled by the average salary of all executives
Independent variables
ClanNumber of genealogies per 10,000 residents in the city where the firm is incorporated
Clan_SurnameProportion of the top three surnames in the city where the firm is incorporated
Instrumental variable
Pop_densityProvincial-level indicators calculated by aligning the areas from the 26th year of the Hongwu period with current administrative districts. Population density is determined by dividing the population by the area, expressed in persons per square kilometer
Control variables
SizeTotal assets (in logarithms)
LeverageThe ratio of total liabilities to total assets
ROAReturn on total assets
GrowthRevenue growth rate
Largest_share_ratioThe ownership percentage of the largest shareholder
Board_sizeNumber of board members (in logarithms)
Ind_dir_ratioProportion of independent directors relative to the total number of board members
DualWhen the chairman and CEO of the two positions concurrently, take the value of 1, otherwise 0
Ln_GDP_PCGross domestic product per capita of the province where the firm is incorporated (in logarithms)
First_Ind_GDPThe GDP of the first industry sector in the province where the firm is incorporated, relative to the total GDP of the province
MKT_indexMarketization index for the province where the firm is incorporated
EducationThe number of universities in the province where the firm is incorporated (in logarithms)
MarriageThe number of marriage registrations in the province where the firm is incorporated (in logarithms)
ConfucianismThe natural logarithm of the number of schools in each city in the Ming and Qing Dynasties
Social_trustFirm trustworthiness at the provincial level, based on the question in the CGSS (2018) questionnaire: “In general, do you agree that the vast majority of people in the society can be trusted?” Responses are coded as follows: “Strongly agree” and “Agree” are assigned a value of 1, while “Neither agree nor disagree”, “Disagree”, and “Strongly disagree” are assigned a value of 0. The average value is then calculated at the province level and matched to firms based on the province where the firm is incorporated
Gender inequality perception variables
LD_GenderPercep1Gender inequality perception measure from the labor demand side perspective. This measure is derived from the CGSS (2018) questionnaire, based on the question: “Do you agree with the statement: Men are inherently more capable than women?” Responses are coded as follows: “Strongly agree” and “Agree” are assigned a value of 1, while “Neither agree nor disagree”, “Disagree”, and “Strongly disagree” are assigned a value of 0. The average value is then calculated at the province level and matched to firms based on the province where the firm is incorporated
LD_GenderPercep2Another gender inequality perception measure from the labor demand side perspective. This measure is derived from the CGSS (2018) questionnaire, based on the question: “Do you agree with the statement: In times of economic depression, female employees should be fired first?” The construction method is identical to that of LD_GenderPercep1
LS_GenderPercep1Gender inequality perception measure from the labor supply side perspective. The first measure is the female labor force participation, which is defined as the ratio of women's average weekly working hours to men's. Using responses to the CGSS (2018) question: “When you have a job, how many hours do you usually work in a week, including overtime?”, we calculate the average weekly working hours separately for women and men, and use their ratio to measure the female labor force participation
LS_GenderPercep2Gender inequality perception measure from the labor supply side perspective. The second measure is the female educational attainment, which is defined as the proportion of women with higher education in the total female population. Using data from the CGSS (2018) questionnaire, we constructed this measure based on responses to the question: “What is your current highest level of education?” Higher education is defined as a college degree or above, and respondents with such qualifications are coded as 1; otherwise, they are coded as 0
Partitioning variables for cross-sectional tests
SOEA dummy variable that equals 1 if the firm is a stated-owned business, and 0 otherwise
High_GDPA dummy variable that equals 1 if the GDP of the province where the firm is incorporated is higher than the annual median GDP of all provinces, and 0 otherwise
OverseaShockA dummy variable that equals 1 if the province where the firm is incorporated historically had an open treaty port, and 0 otherwise

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