Table 6 Definition of Variables employed in analysis

From: Husband, sons and the fertility gap: evidence from India

Variable Description
Fertility gap (dependent variable) Measured as difference between ideal number of children and actual number of children (\(Ideal-Actual)\). Actual number of children includes current pregnancies and living children only
Ideal number of children identified from the response to the following question:
Respondents with no children were asked, “If you could choose exactly the number of children to have in your whole life, how many would that be?” Respondents who already had children were asked, “If you could go back to the time you did not have any children and could choose exactly the number of children to have in your whole life, how many would that be?”
Son preference Binary variable, taking value 1 if woman’s ideal number of boys > ideal number of girls,0 otherwise
Proportion of daughters Number of daughters in total living children (in %), calculated as \(\left(\left(\frac{number of daughters}{Living children}\right)\times 100\right)\)
First child male Binary variable, taking value 1 if first child male, 0 if female
Second child male Binary variable, taking value 1 if first child male, 0 if female
Husband son preference Binary variable, taking value 1 if husband’s ideal number of boys > ideal number of girls,0 otherwise
Husband ideal number of children Husband’s ideal family size
Difference in son preference Binary variable, taking value 1 if husband and wife son preference differ, 0 otherwise
Difference in ideal no of children Measured as difference between husband and wife’s ideal number of children (\(husban{d}^{^{\prime}}ideal -wif{e}^{^{\prime}}sideal).\)
Never used contraception Binary variable, taking value 1 if never used contraception, 0 otherwise
Woman: Education (yrs) Education years of woman
Woman: Employed Binary variable, taking value 1 if currently working, 0 otherwise
Woman: Media exposure Combines three modes of media; ‘reading newspaper’ ‘watching TV’ and ‘listening radio’. This usage of media is converted into binary variables, taking value 1 if woman reads/watches/listens to newspaper/TV/radio at least once a week, 0 otherwise. Media exposure is then calculated as sum of these three dummy variables and converted into %. \(\left(\left(reading dummy+watching dummy+listening dummy\right) \times 100/3 \right)\)
Woman: Age (yrs) Age of woman in years
Woman: Age at first birth (yrs) Age of woman at the time of first child’s birth in years
Woman: Married (yrs) Number of marital years for woman
Husband: Age (yrs) Age of husband in years
Husband: Education (yrs) Education years of husband
Husband: Employed Binary variable, taking value 1 if husband is employed, 0 otherwise
Social group Binary variable, taking value 1 if household belongs to SC/ST group, 0 otherwise
Hindu Binary variable, taking value 1 if religion of household head is ‘hindu’,0 otherwise
Household size Number of household members
Economic status Binary variable, taking value 1 if household belongs medium to rich wealth status, 0 otherwise. The underlying variable is wealth index which takes values 1 to 5 with 1 being poorest and 5 being richest
Region: North Binary variable, taking value 1 if household is located in northern region, 0 otherwise. Northern region includes states of Gujarat, Rajasthan, Uttar Pradesh, Madhya Pradesh, Punjab, Haryana, Himachal Pradesh and Delhi
Region: South Binary variable, taking value 1 if household is located in Southern region, 0 otherwise. Southern region includes states of Andhra Pradesh, Karnataka, Kerala, Maharashtra and Tamil Nadu
Reference region is ‘East’. Eastern region includes states of Assam, Bihar, Orissa and West Bengal
Rural Binary variable, taking value 1 if household is located in rural area, 0 if urban area