Gender, Intra-Household Discrimination and Cash Transfer Schemes: The Case of Indian Punjab
2. Gender Discrimination in Punjab
3. Data Sources
5. Key Findings
5.1. Gender Bias in Breastfeeding Practices
5.2. Gender Bias in Food Allocation
5.3. Efficacy of Cash Transfer Schemes in Bringing about Better Nutritional Outcomes among Girl Children
6. Conclusions and Policy Recommendations
Conflicts of Interest
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Sen’s methodology was later refined by Coale (1991). He argued that Sen had compared the sex ratios in Asian countries with those of the developed nations of Europe and North America. However, Coale considered this unfair as these regions had very high sex ratios as a result of high levels of male mortality in the past wars. Coale refined Sen’s calculations and concluded that the number of “missing women” is 60 million and not 100 million as suggested by Sen (Coale 1991, p. 522).
The GDI measures the gender gaps in human development achievements by accounting for disparities between women and men in three dimensions: health, knowledge and living standards.
UNFPA stands for United Nations Population Fund.
Calculations have been made by the author on the basis of National Sample Survey Organisation Employment/Unemployment Data (1979–1980).
Kynch and Sen (1983) in a study based on admissions data from two large public hospitals in Mumbai, found it very striking that there was clear evidence that the admitted girls were typically more ill than boys, suggesting the inference that a girl has to be more stricken before she is taken to the hospital (see also Sen 2001).
Health status is estimated on the basis of prevalence of anaemia in the last 12 months.
We adopted the standard NFHS classification and categorized households as deprived households, middle-income households and higher income households.
Fertility rate in Punjab (1.6) is below the replacement ratio of 2.1 children born per women (NFHS-4 2015–16).
Culturally, it is considered inauspicious to accept gifts and financial aid from daughters in India.
|Variable||Mean Value||St. Dev||Mean||St Dev|
|Child’s age (months)||25.2||16.24||26.3||17.18|
|Mother’s age (years)||24.9||3.1||23.5||4.09|
|Wealth Index of Household||-||-||-||-|
|Variable||OR||95% CI||OR||95% CI||OR||95% CI|
|Female||1.11||[0.97 to 1.22]||1.04||[0.92 to 1.10]||−0.45 **||[−0.75 to 0.15]|
|Age of the Child||1||[1.00 to 1.01]||1.03 **||[1.03 to 1.03]||0.23 **||[0.23 to 0.25]|
|Mother’s age||1.03||[1.02 to 1.04]||1.01||[0.99 to 1.02]||0.01||[0.06 to 0.03]|
|Mother’s highest Level of Education||1.83 *||[1.56 to 2.15]||1.6||[1.43 to 1.77]||−0.82 **||[0.36 to 1.00]|
|Deprived Household||1.08||[0.98 to 1.09]||1.05||[0.95 to 1.17]||0.98||[1.43 to 0.18]|
|Mother’s ID (fixed effects)||1 ***||[1.00 to 1.00]||1||[1.00 to 1.00]||1 ***||[1.000 to 1.00]|
|Food Item||OR||95% CI|
|Fresh Milk||0.84 ***||[0.75 to 0.92]|
|Chicken, duck and any other poultry products||0.78 **||[0.70 to 0.90]|
|Any other meat||0.99||[0.87 to 1.22]|
|Lentils||1.02||[0.88 to 1.15]|
|Nuts||0.99||[0.64 to 1.25]|
|Eggs||0.86 **||[0.78 to 0.94]|
|Fish||0.87||[0.80 to 1.12]|
|Cheese, yoghurt and other dairy products||0.96||[0.90 to 1.17]|
|Porridge||1.07||[0.98 to 1.24]|
|Roti, bread, noodles, biscuits and other grains||0.86||[0.77 to 1.13]|
|Potato, cassava and other vegetables made from roots||0.98||[0.91 to 1.12]|
|Pumpkin, carrot and squash||1.03||[0.93 to 1.15]|
|Dark green leafy vegetables||0.96||[0.87 to 1.12]|
|Mango, papaya and jackfruit||0.99||[0.89 to 1.10]|
|Other fruits and vegetables||0.96||[0.84 to 1.10]|
|Oil, fat and butter||0.95||[0.85 to 1.11]|
|Other solid/semi-solid foods||1.05||[0.91 to 1.12]|
|Variable||Weight-for-Age z-Score||Weight-for-Height z-Score||Height-for-Age z-Score|
|Beneficiary of cash transfer scheme||0.0132 (0.001)||0.0032||0.053|
|Child Specific Characteristics|
|Age of the Child||0.02||0.039||0.024 (0.029)|
|Health Status of the Child||−0.232 **||−0.181||−0.085|
|Parent Specific Characteristics|
|Highest Years of Schooling (Mother)||0.20 *||0.29 **||0.018 **|
|Highest Year of Schooling (Father)||0.45||0.76||0.21|
|Wealth Index||0.47 **||0.93||0.63 **|
|Number of Children in the household||−0.85||−0.73||−0.13 (0.029)|
|Whether burning biomass at home||−0.42 **||−0.26||−0.35 *|
|Source: Unit Record data of NFHS-4 (2015–16)|
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Singh, N. Gender, Intra-Household Discrimination and Cash Transfer Schemes: The Case of Indian Punjab. Economies 2019, 7, 75. https://doi.org/10.3390/economies7030075
Singh N. Gender, Intra-Household Discrimination and Cash Transfer Schemes: The Case of Indian Punjab. Economies. 2019; 7(3):75. https://doi.org/10.3390/economies7030075Chicago/Turabian Style
Singh, Nadia. 2019. "Gender, Intra-Household Discrimination and Cash Transfer Schemes: The Case of Indian Punjab" Economies 7, no. 3: 75. https://doi.org/10.3390/economies7030075