Health Policy and Planning Advance Access originally published online on March 29, 2006
Health Policy and Planning 2006 21(3):231-240; doi:10.1093/heapol/czl007
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Do community factors have a differential impact on the health outcomes of boys and girls? Evidence from rural Pakistan
Department of Economics, Middlebury College, VT, USA
Correspondence: Jessica Holmes, Department of Economics, Middlebury College, Middleybury, VT 05753, USA. Tel: + 1 802–443–3439; E-mail: jholmes{at}middlebury.edu
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Abstract |
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 Top Abstract IntroductionGender-specific model of human...Previous workDataPotential determinants of child...ResultsConclusionBiographiesAppendix: Gender interactions...EndnotesReferences |
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In countries with large gender disparities in health status, can investments in local communities mitigate the gender bias observed in intra-household resource allocations? This paper explores the evidence for gender differences in the impact of community prices and infrastructure on child nutritional outcomes. Standardized heights and weights of rural Pakistani children are used as health indicators, while community factors include wheat prices, availability of piped water, accessibility of shops and government health clinics and the quality of the closest health facilities. The results suggest that food subsidies and programmes designed to improve the access and quality of local services may reduce the impact of intra-household gender bias on child nutrition, particularly in the long run. Specifically, by increasing the affordability of staple foods, improving the access to shops and government health centres and enhancing the quality of local care, particularly (gender-neutral) prenatal care, gender gaps in health outcomes are likely to diminish.
Key Words: anthropometrics, child health, community infrastructure, gender bias, Pakistan
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Introduction |
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TopAbstractIntroductionGender-specific model of human...Previous workDataPotential determinants of child...ResultsConclusionBiographiesAppendix: Gender interactions...EndnotesReferences |
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Gender inequality in basic human rights, access to resources and opportunities, and political participation impose substantial costs on a nation's productivity and economic growth as well as on the social welfare of its citizens (King and Mason 2001
). It is not surprising that closing gender gaps in schooling and health outcomes, wages and labour force participation, and access to land and financial resources has become an important challenge for policymakers in regions with the most pronounced male bias, such as South Asia, China, Korea and some countries in Africa and the Middle East.
Alderman and Gertler's (1997) gender-specific model of human capital investment illustrates that when market returns or parental preferences lead households to invest more in the human capital of sons than daughters, the demand for a daughter's human capital will be more responsive to price changes. This theoretical result has broad policy implications; it suggests that public investments that reduce the price of human capital investment have the potential to mitigate gender bias in intra-household resource allocation. Support for this hypothesis exists in the literature for child schooling (e.g. King and Lillard 1987; Schultz 1987; deTray 1988; Gertler and Glewwe 1992; Lavy 1996; Sipahimalani 1999), but surprisingly few studies have examined gender differences in the impact of price on the demand for child health. One might expect, for example, that by increasing the access and/or quality of local medical facilities, the price of obtaining health care falls and girls disproportionately benefit. Similarly, since improvements in access to local shops (that sell food, soap and other health inputs) decrease the price of achieving a given level of child health, such services should disproportionately increase the household's demand for girl's health.
This paper examines the evidence for gender differences in the impact of local infrastructure and prices on the health outcomes of children in Pakistan, a country known for its pervasive gender bias. Using the Pakistan Integrated Household Survey (Government of Pakistan 1991), standardized heights and weights of children under six are used as health indicators, while community factors include wheat price, availability of piped water, accessibility of shops and government health clinics, and the quality of the closest health facilities. The sample is restricted to rural Pakistan since only rural communities were surveyed about the quality characteristics of local health centres.
Consistent with the theoretical model discussed in the next section, it is found that improvements in the access and quality of public services have a larger impact on the nutritional status of girls, particularly in the long run. These results imply that policies designed to improve community services are likely to mitigate the effect of male bias on child health outcomes.
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Gender-specific model of human capital investment |
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TopAbstractIntroductionGender-specific model of human...Previous workDataPotential determinants of child...ResultsConclusionBiographiesAppendix: Gender interactions...EndnotesReferences |
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Alderman and Gertler (1997
) develop a theoretical model of household behaviour that focuses on gender differences in human capital investment. In their two-period model, parents derive utility (or satisfaction) from their own consumption and the wealth of their children. In the first period, parents allocate income between first-period consumption and investment in the human capital of their son and their daughter. In the second period, transfers from their two children finance parents’ consumption. The level of second-period transfers depends on their children's wealth and respective remittance rates. In turn, children's wealth is a function of the human capital investments made by parents in the first period as well as the rate of return to those investments. In patriarchal societies, women are often confined to subordinate, low wage jobs. Alderman and Gertler incorporate this market discrimination in their model by allowing gender-specific rates of return to human capital investment. Furthermore, in deeply ingrained systems of patriarchy, sons are often expected to provide financial security in old age, while daughters provide little if any old age support and instead require a large dowry at marriage. Alderman and Gertler account for this possibility by introducing gender-specific remittance rates. Lastly, by allowing the son's wealth to provide more household satisfaction than daughter's wealth, Alderman and Gertler account for the possibility that parental preferences or cultural norms may lead to son preference.
Alderman and Gertler's model suggests that if a son's wealth matters more to households than daughter's wealth, or if the remittance rate of sons exceeds the remittance rate of daughters, or if the rate of return of human capital investment is larger for sons than for daughters (as one might expect in a patriarchal society), then parent's lifetime utility is maximized when the investment in human capital is higher for sons than daughters. They further show that a reduction in the cost of human capital investment leads to a larger increase in the human capital investment of daughters than sons. In other words, demand for a daughter's human capital is more responsive to price changes than the demand for a son's human capital.
Applying Alderman and Gertler's general model to the demand for child health suggests that policies designed to decrease the price of child health investment will not have a gender-neutral impact on health outcomes. It should be noted that gender asymmetry in the responsiveness of health inputs may also occur if the biological production functions of child health are gender-specific or if initial health stocks differ in a world of diminishing returns to health inputs; however, in patriarchal societies such as Pakistan, the Alderman and Gertler model provides a more compelling explanation of the large gender disparities observed in the region. Regardless, it is clear that public investments that increase the access and quality of community services, or improve the affordability of staple foods, should have a larger impact on the nutritional status of daughters relative to sons. Furthermore, the importance of estimating the determinants of child health separately by sex in areas characterized by strong gender bias cannot be understated.
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Previous work |
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TopAbstractIntroductionGender-specific model of human...Previous workDataPotential determinants of child...ResultsConclusionBiographiesAppendix: Gender interactions...EndnotesReferences |
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Perhaps the region most well known for strong male preference and persistent discrimination against females is South Asia. According to the UNDP's Human Development Report 2000 (UNDP 2000
), the Gender-related Development Index (GDI) for South Asia is among the worst in the world, as are the ratios of female to male adult literacy and schooling enrollment. Not surprisingly, gender disparities in health outcomes in South Asia are also among the largest in the world. Numerous studies have documented ‘missing women’ or ‘excess’ female mortality for the region as a whole, and specifically for India, Bangladesh and Pakistan (e.g. D'Souza and Chen 1980; Rosenzweig and Schultz 1982; Das Gupta 1987; Hill and Upchurch 1995; Filmer et al. 1998; Gangadharan and Maitra 2000; King and Mason 2001).
Since post-neonatal survival rates are nearly equal in regions with less pronounced male bias, many believe that gender differences in South Asia's survival rates are the direct result of greater allocation of food and health care to boys. There is substantial support for this hypothesis in the empirical literature.1 For example, D'Souza and Chen (1980) find that girls in Bangladesh are fed less, suffered more malnutrition and, despite similar rates of illness, are given fewer medications than boys. Chen et al. (1981) and Das Gupta (1987) find significant gender bias in both health care expenditures and the household distribution of nutrients in Bangladesh and India, respectively. In a study of rural India, Rosenzweig and Schultz (1982) find that children with greater earning opportunities receive a larger share of the resources and face a higher probability of survival. Mahmood and Mahmood (1995) find a slight male advantage in treatment for fever, cough and diarrhoea in Pakistan, while Filmer et al. (1998) find that girls in India and Pakistan are about 30% less likely than boys to receive treatment for fever and/or acute respiratory infections. Bhuiya et al. (1995) report that in rural Bangladesh, boys are more likely to be immunized than girls. Hazarika (2000) concludes that boys are more likely than girls to be immunized and to be treated by a health professional for illness in Pakistan. Lastly, Alderman and Gertler (1997) find that low-income households in Pakistan are both more likely to seek health care and to seek better quality care for sick sons than sick daughters. Interestingly, Pokhrel and Sauerborn (2004) suggest that gender bias emerges in the perception of illness (boys are more likely to be reported ill), but not necessarily in subsequent care-seeking in Nepal.
Despite the obvious importance of the topic, very few studies examine the hypothesis that prices have a gender-specific impact on child health. There are a few exceptions. For example, Behrman and Deolalikar (1990) report that the demand for nutrients is significantly more responsive to price changes for girls than for boys in South India. Lavy et al. (1996) find that food prices, availability of child health services, health consultation fees, and sanitation and water supply have a greater impact on female child survival than male child survival in Ghana. Conversely, Haddad and Hoddinott (1994) find that the distance to health facilities has a gender-neutral impact on anthropometric outcomes in Côte d’Ivoire. Bishai et al. (2002) rely on results from a natural experiment to show that the intensive public health outreach services (which lowered the price of health care) significantly reduced gender disparities in immunization coverage in rural Bangladesh. In a similar study, Bishai et al. (2005) find that door-to-door delivery of vitamin A supplements narrowed gender differentials in child mortality in Nepal. Lastly, using data obtained in 1986 from five low-income districts in rural Pakistan, Alderman and Gertler (1997) focus on the demand for health inputs rather than outcomes; they find evidence that a reduction in provider fees has a larger impact on the utilization of such services for daughters than for sons, particularly among the poorest households.2 The present work extends the Alderman and Gertler study and the work of others by looking at the impact of community prices and infrastructure on child nutritional outcomes in rural Pakistan, a country known for its pervasive gender discrimination.
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Data |
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TopAbstractIntroductionGender-specific model of human...Previous workDataPotential determinants of child...ResultsConclusionBiographiesAppendix: Gender interactions...EndnotesReferences |
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The data used in the analysis are from the Pakistan Integrated Household Survey (Government of Pakistan 1991
) or PIHS, a joint project of the World Bank and the Pakistan Federal Bureau of Statistics. Individuals from approximately 4800 households residing in 150 urban and 150 rural communities were surveyed about household composition, education, employment, health, time-use etc. Male and female interviewers surveyed males and females separately. In addition, community surveys were administered directly to groups of local council members and ‘knowledgeable individuals’ in the nearest schools, health facilities and local markets. Shopkeepers were surveyed about the prices of their products, and health care workers and school officials were questioned about the characteristics of their respective facilities. Since only rural communities were surveyed about the quality characteristics of local health facilities, the present study restricts the estimation to rural children.
Height-for-age measures linear growth relative to age and is frequently used as a proxy for longer run health status or chronic malnutrition. Weight-for-age measures body mass relative to age. Since body mass is influenced both by stature and weight of children, deficits in weight-for-age may be an indicator of both retarded growth and weight loss. Evidence suggests that well-nourished children achieve, on average, similar heights and weights as they grow, regardless of ethnicity or country (Martorell and Habicht 1986). Thus, mean standardized z-scores have been computed for each child where a z-score of zero implies that the child's height-for-age (HAZ) or weight-for-age (WAZ) is equal to the median level of a well-nourished child of the same sex and age. Like most previous studies, the National Center for Health Statistics tables of age- and gender-specific height for United States children serve as the reference standard. Children with z-scores less than –2 are generally considered to be malnourished, while children with z-scores below –3 are thought to suffer from severe malnutrition (Kostermans 1993).3
Table 1 contains the summary statistics for rural children aged under six in Pakistan, with information on objectively measured heights and weights. The negative mean values of HAZ and WAZ suggest that both rural Pakistani boys and girls are shorter and lighter than the gender-specific international standard. Despite known gender differences in child health investments, the mean z-scores suggest that girls are closer to the international reference population than boys in rural Pakistan.4 This does not preclude the existence of male bias in child nutritional outcomes but more likely reflects the excess female infant and child mortality that eliminates the most undernourished females from the sample. In theory, if mortality selection exists but is not addressed (due, as in this case, to data limitations and the lack of suitable identifiers), empirical estimates of the demand for child health will understate the effectiveness of health-augmenting interventions (Lee et al. 1997).5 Thus, in the presence of excess female mortality, gender differences in the impact of community infrastructure on child health outcomes will be underestimated and our results will serve as a lower bound on the true gender differential.
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The summary statistics in Table 1 also suggest that for the average rural child, a government health centre is within 3 km and a shop is within 1 km of the centre of their community. There is much variation in access however, as some communities are as far as 50 km away from a government health centre and 30 km away from the nearest shop. Furthermore, only about one-quarter of rural children live in communities with piped water, roughly 60% of rural children live near health facilities that offer prenatal care, and the average child's health centre stocks about 8 out of 11 standard medicines.6
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Potential determinants of child health |
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TopAbstractIntroductionGender-specific model of human...Previous workDataPotential determinants of child...ResultsConclusionBiographiesAppendix: Gender interactions...EndnotesReferences |
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Individual and household characteristics
Many studies have examined the individual and household factors that influence the demand for child health and nutrition [see Behrman and Deolalikar (1988) and Strauss and Thomas (1995) for a review of the theoretical background and empirical results].7 Dummies for age are often included in anthropometric models to account for age-specific differences in the health production of children. During the first few months of life, children are most often breastfed and have limited exposure to contaminated water. There is a critical period of transition from breast milk to supplementary milk and prepared foods which causes stress and trauma in the young child, as it occurs when children still have underdeveloped immune systems and often coincides with increased mobility of the child (Barrera 1990; Olango and Aboud 1990). It is during this weaning period that susceptibility to disease is greatest, after which incidence usually falls. This pattern of decline and subsequent improvement in child health has been observed in other studies (e.g. Barrera 1990; Olango and Aboud 1990).
Birth order may affect anthropometric status in several ways.8 Earlier born children may be more malnourished due to limited household resources early in the life cycle or, alternatively, may receive a greater share of nutritional resources if parents desire earlier returns on their child health investments. Later born children may be at a disadvantage if maternal depletion syndrome deprives them of nutrients in utero or at an advantage if parents have more experience in producing child health. Gender may also play an important role in the effect of birth order on health status. For example, in a pro-male society, early born males may receive preferential treatment while later born girls may face greater neglect.
If families are credit constrained, current income may influence a family's willingness to invest in health. However, since labour supply choices are determined jointly with health investment decisions, current income is endogenous. The value of land and property owned by the household is used to proxy the long run resources available for health outlays. A positive association is expected as families with command over more resources are likely to have well-nourished children and are better able to afford health care.
Mother's and father's education levels are also included in both nutrition models. Higher levels of parental education may increase economic resources through assortive mating with wealthier partners, as well as through the parent's own increased market opportunities. More educated parents, particularly mothers, have access to more and better information which may lead to both technical and allocative efficiency in producing child health and seeking appropriate medical care (Rosenzweig and Schultz 1982; Barrera 1990; Thomas et al. 1990; Glewwe 1999). If, as several researchers argue, mothers prefer daughters and fathers prefer sons, we may expect mother's education to have a larger impact on her daughter's health, and father's education to have a larger impact on his son's health (see Thomas 1994; Sahn and Stifel 2002).9 Mother's age is also included in the analysis as a proxy for maternal experience; older mothers are likely to have acquired more information regarding child nutrition. Mother's age may also control for cohort effects if child nutrition practices have changed over time.
Community-level determinants of child health
Previous work confirms that community prices and infrastructure play an important role in determining child health outcomes (see Horton 1986; Behrman and Wolfe 1987; Barrera 1990; Strauss 1990; Thomas et al. 1991; Cebu Study Team 1992; Thomas and Strauss 1992; Bhuiya and Chowdhury 1995; Lavy et al. 1996; Pebley et al. 1996; Thomas et al. 1996; Sahn and Alderman 1997; Hazarika 2000; Paknawin-Mock et al. 2000). An indicator of a community-level presence of piped water is included to control for exposure to faecal pathogens. If the existence of piped water lowers the price of achieving a given level of child health, parents in such communities may allocate more resources to child health. If so, Alderman and Gertler's model predicts the impact of such public health services to be greater for daughters relative to sons.
The community-level distance to the nearest government health centre is included to proxy access to health services and exposure to health information. The distance to nearest health facility also reflects the time costs of obtaining health care and immunizations. It is hypothesized that children who live in communities whose centres are far from health facilities suffer from greater malnutrition. Furthermore, the impact should be larger for daughters than sons since increased distance to health care centres clearly raises the price of child health investment.
Children with access to high quality health care should be healthier. Furthermore, the presence of a high quality facility in the community should lower the price of achieving a given level of child health. Quality of the health facility is proxied by two covariates: the presence of prenatal services, which captures the quality of local preventative care, and the number of drugs in stock (e.g. antibiotics, aspirin, oral rehydration salts, anti-worm medicine, etc.), which proxies the quality of local curative care. Again, one expects facility quality to have a larger effect on a daughter's health than a son's health.
The distance to the nearest shop from the centre of the community is also included in the analysis. Shops sell food, soap, heating oil and other inputs into the health production process, and thus the distance to the nearest shop is likely to be negatively associated with standardized heights and weights. Again, one expects the impact of shop distance to be greater for girls.
Since wheat is the main staple in Pakistan, the local price of wheat is also included in the empirical model. Higher food prices may reduce the caloric intake of children, thereby lowering their nutritional status. Since food is an input in the production of child health, it is expected that the effect of local wheat prices should be greater for daughters than sons.
Province indicators control for the differences in geography, culture and people of Pakistan, and have been included in both anthropometric models. Provincial health departments are responsible for the supervision and provision of general health services by hospitals and other facilities, and the provincial dummies may also capture some of this variation. Estimates of per capita health expenditures suggest that the Northwest Frontier provincial government allocates the most resources to health with 25.62 rupees per person, followed by Punjab (22.66), Sind (14.55) and Balochistan (12.19).10
One caveat deserves mention; it is possible that the included community-level characteristics may be correlated with unobserved factors that are responsible for the estimated effects. While the potential for omitted variable bias is always a concern, the inclusion of multiple community and family characteristics reduce the likelihood of this bias. Robust standard errors are also corrected for any non-independence of the error terms among children within the same community.
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Results |
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TopAbstractIntroductionGender-specific model of human...Previous workDataPotential determinants of child...ResultsConclusionBiographiesAppendix: Gender interactions...EndnotesReferences |
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Table 2 presents the ordinary least squares estimates of the determinants of the standardized heights (columns 1 and 2) and weights (columns 4 and 5) of rural boys and girls under age six. Columns 3 and 6 indicate which coefficients differ significantly by gender.11 A quick comparison of columns 3 and 6 reveals more gender differences in the height model than in the weight model. To the extent that weight-for-age may reflect more short-term nutritional status, this suggests that when parents are faced with an acutely malnourished child, they are more likely to act in a gender-neutral way to alleviate the short-term health concern, but that gender bias plays an important role in explaining chronic malnutrition among children in rural Pakistan.
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The standardized height results verify that the determinants of long-run nutritional status differ by gender, and more specifically, that community factors have a greater impact on the standardized height of girls than boys.12 Comparing columns 1 and 2, one finds that distance to nearest government health centre, local drug stock and local price of wheat have significant impacts on girls’ standardized height (with the expected signs), but no significant impacts on the height-for-age of boys. Furthermore, column 3 indicates that the impact of both distance to nearest government health centre and local price of wheat on height-for-age is significantly greater for girls than boys. With almost 90% confidence (p = 0.11), we can also reject that the male and female coefficients on local drug stock are the same.
More specifically, a one standard deviation reduction in the distance to the nearest government health facility (approximately 4.5 km) reduces the mean gap in height-for-age between rural Pakistani girls and the reference population by 13%, but has no significant impact on the male gap.13 A reduction in the local price of wheat by 0.67 rupee (or one standard deviation) reduces the average female height-for-age gap by 10% but has no measurable impact on the height gap for boys. Lastly, each additional drug stocked in the local health centre is associated with a 5% reduction in the mean height gap for females but has no significant effect on the male height gap.
Columns 1–3 further reveal that although statistically significant point estimates of the availability of prenatal care cannot be obtained for either gender-specific regression, there is a significant difference in these coefficients across gender (i.e. the difference between –0.05 for the boys and 0.27 for the girls is significant). Given the near significance of the prenatal care coefficient in the girls’ sample (p = 0.17 for girls, p = 0.78 for boys), the large and significant difference in their relative marginal effects and the possibility that mortality selection is more likely to underestimate the coefficient in the girls’ sample, we may reasonably conclude that the effect of prenatal care on long-run nutritional status is greater for girls than boys. Similarly, while the distance to nearest shop is insignificant in both gender-specific regressions, column 3 suggests that there is a differential impact of shop distance by gender; girls benefit more than boys from better access to local shops that stock food, soap and other health inputs.
Columns 4–6 provide evidence that the determinants of weight-for-age differ by gender, and in particular, that access to health inputs has a greater impact on standardized weights of girls than boys.14 Distance to government health centre, distance to nearest shop and availability of prenatal services are significantly associated with the standardized weight of girls, but not boys. Furthermore, the impacts of distance to nearest shop and availability of prenatal services on weight-for-age are significantly greater in the girls’ sample.
A closer look at the results suggests that a one standard deviation decrease in the distance to a local shop (about 2.5 km) closes the mean gap in weight-for-age between Pakistani girls and the international standard by 7%, but has no significant impact on the mean gap for boys. The introduction of prenatal services in the community closes the female weight gap by a remarkable 17%, but again has no measurable impact on the standardized weights of boys. One might also recall the large and significant gender difference in the point estimates of prenatal care in the height-for-age model. Clearly, the existence of prenatal services in the local community lowers the price of achieving a given level of child health and disproportionately benefits girls in rural Pakistan. Why might this be so, if prenatal care is provided before gender is known? It is precisely because such care is delivered gender neutrally that girls reap greater benefits. In other words, if boys receive a larger household share of health dollars postnatally, then prenatal care may be one of many health investments made for boys, while it may be one of few health investments made for girls. Thus the impact of prenatal care on nutritional outcomes should be greater for girls than boys. This has strong policy implications; it suggests that improvements in local prenatal care may mitigate the postnatal gender bias in health investments.
For both standardized height and weight, the other covariates behave as predicted. For example, as expected in a pro-male society, the impact of birth order on long run nutritional status is positive for boys and negative for girls (and this difference is statistically significant). Increasing a child's birth order by one widens the mean gap (with respect to the reference population) in height for age by about 3% for girls, but shrinks it by almost 4% for boys. Birth order does not seem to affect the standardized weights of either gender. The age coefficients in Table 2 suggest that when rural Pakistani children reach the period of weaning and increased exposure to harmful pathogens, they are significantly shorter and leaner than the international healthy standard. Furthermore, mothers with more education are better able to improve the nutritional status of their children in rural Pakistan. Lastly, while father's education has no measurable impact on the standardized heights of either gender, household resources are positively associated with the nutritional status of both sons and daughters and, as columns 3 and 6 indicate, the impact is greater for boys than girls.
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Conclusion |
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TopAbstractIntroductionGender-specific model of human...Previous workDataPotential determinants of child...ResultsConclusionBiographiesAppendix: Gender interactions...EndnotesReferences |
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Several important conclusions emerge from these results. First, empirical estimates of the demand for child health that pool boys and girls together and use a gender dummy to control for gender disparities fail to capture the complex gender differences in child health outcomes.15 In particular, the evidence presented above indicates substantial gender differences in the impact of community factors on the nutritional outcomes of boys and girls in rural Pakistan. This has important implications for policymakers. In patriarchal societies, it is common for the intra-household allocation of resources to strongly favour sons, often to the detriment of daughters’ health and educational status. Policymakers find it challenging to alter this deeply ingrained household behaviour. The results presented above suggest that food subsidies and programmes designed to improve the access and quality of local services may reduce the impact of intra-household gender discrimination on child nutritional outcomes, particularly in the long run. Specifically, by increasing the affordability of staple foods, improving the access to shops and government health centres and enhancing the quality of care (both preventative and curative) at local facilities, gender gaps in nutritional outcomes are likely to diminish. Perhaps most interesting is the finding that the availability of prenatal services disproportionately benefits girls in rural Pakistan. This has strong policy implications; it suggests that improvements in local (gender-neutral) prenatal care may effectively mitigate the postnatal gender bias in health investments.
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Biographies |
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TopAbstractIntroductionGender-specific model of human...Previous workDataPotential determinants of child...ResultsConclusionBiographiesAppendix: Gender interactions...EndnotesReferences |
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Jessica Holmes, PhD, has been Assistant Professor of Economics in the Economics Department at Middlebury College, USA, since 2001. She teaches microeconomics, statistics, public finance, health economics and the economics of social issues. Previously, she taught at Colgate University and worked as a litigation consultant for National Economic Research Associates. Her research fields include health economics and economic development. She received a PhD in Economics in 1998 from Yale University.
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Appendix: Gender interactions from pooled OLS estimation of the determinants of height-for-age (HAZ) and weight-for-age (WAZ) for rural boys and girls, under age six |
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TopAbstractIntroductionGender-specific model of human...Previous workDataPotential determinants of child...ResultsConclusionBiographiesAppendix: Gender interactions...EndnotesReferences |
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Source: PIHS (1991).
Robust standard errors corrected for non-independence within communities.
***denotes significance at 0.01; **denotes significance at 0.05; *denotes significance at 0.10; ^denotes p-value equals 0.11.
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Acknowledgements |
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I would like to thank T Paul Schultz, John Maluccio, David Colander, Paul Sommers, Peter Matthews, Hugo Nopo, Jill Tiefenthaler and two anonymous referees for their comments on earlier drafts. As is customary, I accept full responsibility for any remaining errors.
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Endnotes |
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TopAbstractIntroductionGender-specific model of human...Previous workDataPotential determinants of child...ResultsConclusionBiographiesAppendix: Gender interactions...EndnotesReferences |
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1See Haddad et al. (1999
) for an extensive review that includes other regions. 
2In particular, they report a price elasticity of demand for physicians that is 58% higher for daughters than for sons in the lowest income group.
3Children with z-scores greater than +6 or lower than –6 have been excluded from the analysis since even in a country with a lot of malnutrition, a z-score outside of the –6<Z<+6 is very unlikely and probably indicates an error in measurement (Kostermans 1993).
4Many other studies report no gender differences or even a slight female advantage in anthropometric outcomes in South Asia (see Haddad et al. 1996; Haddad 1999; Hazarika 2000).
5In practice, however, Lee et al. (1997), using data from both Bangladesh and the Philippines, and Pitt (1997), using data from 14 sub-Saharan African nations, reveal that parameter estimates of the determinants of child health change very little when mortality selection is accounted for in the analysis.
6Medicine chests in the clinics were checked for the following medicines: chloroquine, fansidar, aspirin, paracetamol, ponstan, oral rehydration salts, lomotil, flagyl, anti-worm medicine, penicillin or other antibiotics, tetanus vaccination.
7UNICEF has also developed an important structural framework for understanding the determinants of malnutrition (1990). Unfortunately, individual-level data on caloric intake or food expenditure (one component of the UNICEF concept of food security) were not available in the PIHS. A reduced form model of the determinants of malnutrition is estimated in the following section.
8It is possible that birth order is endogenous since households may jointly determine quantity and quality of children, but note that the results presented in the next section do not qualitatively change with the exclusion of birth order.
9The intuition is that women with more schooling have better market opportunities and thus stronger bargaining power within the household. If women prefer daughters, then mother's education should have a larger impact on the health outcomes of daughters relative to sons.
10Budget figures are from Ahmad and Qureshi (1990). To approximate province-specific population levels for 1990–91, data from the last available census (1981) were adjusted by the population growth rate for each province from 1972–1981 (Government of Pakistan 1995; Government of Pakistan 1996).
11By pooling the males and females and then interacting each explanatory variable with a gender dummy, it is possible to test whether there are significant differences between the male and female coefficients. The interaction terms from this pooled model are found in the Appendix. An F-test that the interaction terms are jointly equal to zero is rejected at the 0.01 level for both models.
12An F-test verifies the joint significance of the community-gender interactions in the standardized height model (p<0.00).
13This was calculated as follows: a one standard deviation reduction in the distance to nearest government centre (4.5 km) will close the mean height-for-age gap by 0.23 (i.e. the standard deviation of the distance to nearest government centre multiplied by the female coefficient on distance to nearest government centre (0.05) is 0.23). Since the mean female height-for-age gap is 1.73, this represents a 13% reduction in the mean female gap in height-for-age (0.23/1.73 = 0.13).
14An F-test verifies the joint significance of the community-gender interactions in the standardized weight model (p < 0.00).
15See, for example, Behrman and Wolfe (1987); Barrera (1990); Strauss (1990); Thomas et al. (1991); Cebu Study Team (1992); Thomas and Strauss (1992); Bhuiya et al. (1995); Pebley et al. (1996); Thomas et al. (1996); Hazarika (2000); Paknawin-Mock et al. (2000).
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References |
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TopAbstractIntroductionGender-specific model of human...Previous workDataPotential determinants of child...ResultsConclusionBiographiesAppendix: Gender interactions...EndnotesReferences |
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In Ahmad R and Qureshi K (Eds.). Budgets of Pakistan: 1990–91 1990. Lahore, Pakistan Development Studies Institute.
Alderman H and Gertler P. 1997. Family resources and gender differences in human capital investments: the demand for children's medical care in Pakistan. In Haddad L, Hoddinott J, Alderman H (Eds.). Intrahousehold resource allocation in developing countries: models, methods, and policy Baltimore Johns Hopkins University Press for the International Food Policy Research Institute.
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