1. on children’s height, fixed effects regression and observations from a rural district of Uttar Pradesh Diane Coffey Office of Population Research, Princeton University & Centre for Development Economics, Delhi University & rice institute (www.riceinstitute.org) photo credit: Kit Shangpliang

2. what we’ll talk about for the next 2 hours some methodology – fixed effects as an identification strategy (with examples from population studies) some new quantitative research – women’s status and children’s height in India: evidence from joint rural households some reflections on fieldwork in Uttar Pradesh – thinking about why Indian children are so short and what can be done about it

3. some methodology: using fixed effects as an identification strategy (with examples from population studies)

4. what is an identification strategy? use the words identification strategy to talk about how we argue that the independent variable has a causal effect on the dependent variable correlation is not causation – example: ice cream and drowning

5. what are some common identification strategies? randomized controlled experiments – time intensive, costly and quite risky instrumental variables – a good instrument is hard to find… natural experiments – again, hard to find well thought through cross sectional analysis panel data – fixed effects is one way of analyzing panel, or group, data

6. fixed effects used with group or panel data allow the researcher to correlate within group changes/differences/deviations in y with changes/differences/deviations in x some examples of panel data: – more than one observation about the same person – more than one observation about the same place – comparison within groups of similar individuals (siblings, cousins)

7. fixed effects number of Cuban cigars smoked per day length of life

8. example 1: African orphans & schooling Case, Anne and Callie Ardington. (2006). The Impact of Parental Death on School Outcomes: Longitudinal Evidence From South Africa. Demography 43(3): 401-420.

9. is a causal effect of parental death on children’s school outcomes? families in which a parent dies may be worse off than families in which a parent does not die – it is important to control for the child’s family background panel data really helps with that example 1: African orphans & schooling

10. (technically a first difference analysis, but same concept as fixed effects) same child was surveyed at two points in time – is she enrolled in school? – what grade has she achieved? regress difference in education outcomes (change in enrollment, change in grade achieved) on whether a mother died between the two surveys children whose mothers die are more likely to drop out of, or fall behind in school example 1: African orphans & schooling

11. example 2: height and early life disease Bozzoli, C., A. Deaton, and C. Quintana-Domeque (2009). Adult height and childhood disease. Demography 46 (4), 647– 669.

12. example 2: height and early life disease does early life disease affect height in the populations of modern, developed countries? – children who spend energy fighting infection cannot grow as tall as they otherwise would use PNM (deaths per thousand live births of children 1-12 months old) as a measure of early life disease but maybe the correlation between a cohort’s height and its PNM is just reflecting things getting better over time?

13. add year fixed effects to rule out the time trend explanation – now the regression is asking: how do differences from the time trend in postneonatal mortality correlate with differences from the time trend in the heights of a cohort? this provides stronger evidence that early life disease was a determinant of adult height in modern developed countries example 2: height and early life disease

14. example 2: sex work & HIV in Mexico Gertler, P., M. Shah, and S. Bertozzi (2005). Risky business: The market for unprotected commercial sex. Journal of political economy 113 (3), 518-550.

15. example 3: sex work & HIV in Mexico why do sex workers offer sex without condoms despite the risk of HIV? authors expected that sex without a condom fetches a higher price – but what if price and condom use are both based on other characteristics of the sex worker?

16. example 3: sex work & HIV in Mexico repeated observations from the same sex worker (four from each sex worker) regress an the price paid on condom use and a fixed effect for each sex worker – controls for her education, bargaining power, looks and other things that might determine price and condom use comparing clients of the same sex worker, those who do not use a condom pay more – on average, it is enough to highly compensate the sex worker for the risk of contracting HIV (transmission rates in Mexico are low)

17. are fixed effects a good idea for my project? do I have appropriate data? – do I have more than one observation for each group? – do I know which group each observation is in? – do the observations within groups have, at least in some cases, different values of the independent variable?

18. are fixed effects a good idea for my project? why does the independent variable deviate or change? – example: imagine regressing adult health in a given country year on income in that country year, with country fixed effects reverse causality? is some third factor, say investment in education, causing both?

19. could fixed effects “concentrate bias?” – example: imagine comparing difference in wages of twins (who share same socioeconomic and health environment) on difference in schooling – you might end up identifying ability bias! are fixed effects a good idea for my project?

20. do you have a good measurement of the independent variable? – fixed effects uses a smaller fraction of the variation, so measurement error is a bigger deal – measurement error may attenuate the coefficient (that is, bias to zero) – (or, maybe you’ve eliminated omitted variable bias?) are fixed effects a good idea for my project?

21. is within variation something that you care about? are fixed effects a good idea for my project?

22. within and across variation by Dean Spears, from How much international variation in height can sanitation explain?

23. programming fixed effects in Stata: sanitation & stunting example Spears, Dean. (2013). How much international variation in height can sanitation explain?

24. research question: can international differences in sanitation coverage explain international differences in children’s heights? programming fixed effects in Stata: sanitation & stunting example mechanisms: where open defecation is practiced, children are more exposed to illness from fecal pathogens calorie loss due to diarrhea chronic sub-clinical enteropathy (Jean Humphrey, Lancet, 2009)

25. observations: 140 collapsed DHS surveys, 1990-2010 DHS survey (country-year) mean height and open defecation country fixed effect year fixed effect other variables from DHS: female literacy, water women’s height, knowledge of oral rehydration, electrification ln(GDP per capita), population from Penn World Tables “polity” and “democracy” from Polity IV programming fixed effects in Stata: sanitation & stunting example

28. questions & discussion

29. some new quantitative research: women’s status and children’s height in India: evidence from joint rural households

30. Diane Coffey, Reetika Khera & Dean Spears women’s status and children’s height in India: evidence from joint rural households photo credit: Kyle Merrit Ludowitz

31. Indian children are short Indian children under 5 years old, are, on average, 2 standard deviations below the heights of children in the international reference population (NFHS 2005) for a 5 year old girl, this is a deficit of about 10 centimeters, or 3.9 inches introduction

32. height, health, and wealth height is a summary measure of early life health height in childhood is correlated with height in adulthood (Waterlow, 2011) height in adulthood is a marker of human capital, economic productivity, and lifespan (Case & Paxson, 2008; Vogl, 2011; Jousilahti et al., 2000) introduction

33. why are Indian children so short? energy going in: quality and quantity of food – poor nutrition of pregnant and lactating women – young children are fed little and late energy coming out: much early life disease – intestinal disease: diarrhea and chronic enteropathy – pneumonia and other infections could women’s status be something that contributes to or aggravates these processes? introduction

34. prior papers: children’s health reflects women’s health Ramalingaswami et al., 1996 36% of Indian women have BMIs below 18.5 (NFHS 2005) almost 60% of pregnant women are anemic (NFHS 2005) weight gain in pregnancy is very low (WHO, 1995; Agarwal et al., 1996) introduction

35. prior papers: women’s autonomy Das Gupta, 1995 in India, women have low status in their child-bearing years, it grows as they age cultural norms around behavior in women’s marital homes mean that they do not seek resources for themselves or their children introduction

36. prior papers: it’s hard to identify an effect of women’s status on kids’ health several papers regress children’s anthropometric indicators on an index of women’s status variables omitted variables women’s status is hard to measure – education?...seems to be different… – some “empowerment” variables may suffer from reporting problems introduction

37. preview: our strategy compare the children of higher and lower ranking daughters-in-law in the same household find that the children of lower ranking daughters- in-law are on average shorter than their cousins born to higher ranking daughters-in-law provide evidence for our interpretation of this finding as an effect of women’s status on children’s height introduction

38. how could mother’s rank within households affect children? in utero – weight gain: a function of consumption, work, and possibly stress during breastfeeding – poor nutrition status may decrease quality of breastfeeding ability to get resources for young children – food: getting the right things to eat, and enough of them – disease: getting treatment introduction

39. outline o background—joint Indian households o empirical strategy o main results—the children of lower ranking daughters-in-law are shorter than their cousins o interpretation—women’s status o confirming lower status o decision making, mobility & nutrition o ruling out pre-marriage sorting o ruling out differences in nuclear family resources introduction

40. background

41. fraction of rural households and children under five in the NFHS living in joint households NFHS 1993NFHS 1999NFHS 2006 households no daughters-in-law0.7710.7880.812 one daughter-in-law0.1710.1600.149 two daughters-in-law0.0450.0400.031 more than two daughters-in-law0.0140.0120.007 children under five no daughters-in-law0.6640.6450.678 one daughter-in-law0.2040.2250.213 two daughters-in-law0.0910.0920.082 more than two daughters-in-law0.0410.0390.027 background

42. where are the joint households? background

43. Indian joint households are characterized by patriarchy and age- hierarchy (Mandelbaum, 1948) older brothers are afforded higher social status than younger brothers (Seymour, 1993) daughters-in-law defer to senior members of their marital families background

44. rank among daughters-in-law a wife inherits her husband’s status in the household, which is determined by his birth order (Singh, 2005) there are more people to whom a second daughter-in-law must defer than a first daughter-in-law (Mandelbaum, 2005) “senior wives tend to dominate young in- marrying wives” (Dyson & Moore, 1983) background

45. ways daughters-in-law defer remaining quiet in the presence of senior men and women veiling lowering her gaze sitting on the floor photo credit: dinodia.com www.nationalgeographic.com our strategybackground

46. empirical strategy

47. our sample: children in joint rural households in NFHS 3 empirical strategy older brother younger brother children in our sample household heads

48. main regression low ranking mother ih indicates that the child's mother is the low ranking daughter-in-law  h is a household fixed effect A ih is a vector of 120 age-in-months X sex dummies empirical strategy

49. D ih is a vector of demographic controls about the child – dummy for first born to her mother, single birth, mother’s age at birth, child’s birth order in joint household M ih is a vector of controls about the mother – height, years of education, age at marriage F ih is a vector of controls about the father – education, age at survey empirical strategy

50. main results

51. in the same household, are children of lower ranking mothers shorter than children of higher ranking mothers? main results

52. nonparametric comparison of children of lower and higher ranking DsIL main results

53. children’s height & mother’s rank main results

54. why control for child’s age? main results

55. children’s height & mother’s rank main results

56. demographic controls: are the results driven by direct effects of household size? main results do grandmothers prefer their earlier born grandchildren (or even the first born), regardless of mothers’ status? could having older cousins increase babies’ exposure to disease?

57. children’s height & mother’s rank main results

58. height difference not due to comparing children of different birth orders main results

59. mother controls: do lower ranking wives differ on pre-marriage characteristics? main results could “inferior” daughters become lower ranking daughters-in-law? could women who are “less fit” to be mothers become lower ranking daughters-in-law?

60. children’s height & mother’s rank main results

61. height difference present for all maternal heights main results

62. father controls: could resource differences between “nuclear families” (within joint families) influence the results? main results

63. children’s height & mother’s rank main results

64. interpretations 1. confirming lower status: decision making, mobility & nutrition 2. ruling out pre-marriage sorting 3. ruling out differences in nuclear family resources

65. interpretations: confirming lower status

66. decision making: say in household decisions v interpretations: confirming lower status in NFHS 3, does the woman have “final say” in decisions related to: own health care? large household purchases? daily purchases? visits to her relatives and friends? what to do with the money her husband earns? regress an indicator for “say” on intrahousehold status using joint household fixed effects

67. v interpretations: confirming lower status decision making: say in household decisions lower ranking daughters-in-law similarly have less “say” in the NFHS 2

68. mobility: time spent outside women‘s mobility, particularly in the public sphere, has been used by other researchers as a measure of status (Rahman & Rao, 2004; Kabeer, 1999) we analyze data from India Time Use Survey, 1999 – all adults in 12,750 rural households in six states – 1.2% of rural households interviewed (n=312) had two daughters-in-law – data time use for the “typical” day before the survey v interpretations: confirming lower status

69. mobility: time spent outside v interpretations: confirming lower status

70. mobility: time spent outside v interpretations: confirming lower status

71. nutrition: body mass index v interpretations: confirming lower status low body mass index scores of women in India are an indicator of their malnourishment – 36% of women in the NFHS 2005 have a body mass index score (BMI) below 18.5 low body mass index scores indicate poor pre- natal nutrition, which has been shown to influence children's height (Kusin et al., 1992; Adair, 2007)

72. nutrition: body mass index v interpretations: confirming lower status h

73. nutrition: body mass index "The person who cooked and the youngest daughter in law, usually the same person, ate last. This acted against her, even if there was no conscious discrimination. Thus after feeding unexpected guests, the person who ate last, the cook, could prefer to do without rather than cook again. In middle peasant households, often there could be no vegetables or lentils left and she made do with a pepper paste and/or raabri. In a situation of deficit she went hungry when other household members did not have to.” from: Palriwala, 1993 pg. 60 v interpretations: confirming lower status

74. interpretations: ruling out pre-marriage sorting

75. no differences on pre-marriage characteristics v interpretations: ruling out pre-marriage sorting are lower ranking daughter-in-laws inferior on pre-marriage characteristics? regress characteristics of mothers fixed before marriage on intrahousehold rank and household fixed effect dependent variables: height, education, literacy, age at marriage (from NFHS 3)

76. no differences on pre-marriage characteristics v interpretations: ruling out pre-marriage sorting

77. interpretations: ruling out differences in nuclear family resources

78. no differences between brothers can older brothers contribute more resources to their children’s early life health than younger brothers? use NFHS 3 men’s survey to look at a representative sample of brothers who live in the same household v interpretations: ruling out differences in nuclear family resources

79. no differences between brothers v interpretations: ruling out differences in nuclear family resources

80. conclusion

81. used a novel identification strategy to show the children of lower ranking daughters-in-law are shorter than the children of higher ranking daughters-in-law interpreted this difference as evidence that women’s status influences children’s health provided evidence that women’s status indeed differs by daughter-in-law’s intrahousehold rank ruled out competing explanations for the result

82. why it matters conclusion little prior well-identified evidence of an effect of women’s status on children’s health potentially broad implications for human capital formulation – other manifestations of low women’s status may also hurt children – other forms of hierarchy may also hurt children

83. comments? questions? photo credit: Gates Foundation conclusion

84. some reflections on fieldwork in Sitapur, Uttar Pradesh: thinking about why Indian children are so short and what can be done about it v photo credit: forbes website

85. Sitapur, Uttar Pradesh

86. Sitapur Photo credit: Dean Spears

87. Sitapur Photo credit: Julian Hill

88. Sitapur Photo credit: Julian Hill

89. Sitapur Photo credit: Julian Hill

90. Sitapur Photo credit: Julian Hill

91. Sitapur Photo credit: Kyle Merritt Ludowitz (right) & Claude Renault (left)

92. why Sitapur? to complicate our understanding of health production and health promotion to gain a deeper understanding of quantitative findings to generate ideas and build intuition

93. complicating our understanding of children’s health: a mini-ethnography from a qualitative perspective, what factors seem to be influencing infant health and can parents and the government do anything about it? an ethnographic project following 20 infants from 3 villages from February, 2012

94. complicating our view of children’s health: death 4 of 20 infants have died cause of death is very hard to identify; in some cases there are likely many contributing factors – neonatal death to a severely anemic teenaged mother – death at 3.5 months old from diarrhea and dehydration related to not breastfeeding – death at 3 months old from diarrhea and probably poor maternal nutrition – death at 1 year old, probably from pneumonia

95. complicating our view of children’s health: stunting like other babies in Uttar Pradesh, and India, these babies were born smaller than healthy babies and get shorter relative to healthy children as they get older for me, watching mothers care for babies has raised many questions about how growth deficits happen

96. complicating our view of children’s health: questions raised to what extent are poor breastfeeding practices contributing to poor growth? (not just the whether breastfed soon after birth or exclusive breastfeeding for 6 months, but harder to measure things, like time spent breastfeeding, milk production, and habits)

97. complicating our view of children’s health: questions raised to what extent is the practice of feeding cow’s milk to infants, particularly among better off families, contributing to poor growth?

98. complicating our view of children’s health: questions raised where parents are uneducated and health providers have almost no incentive to provide proper treatment, what is the role of a health system in producing infant health?

99. complicating our view of children’s health: questions raised how could sanitation and hygiene be improved in a place with extremely low state capacity and where people do not believe in the germ theory of disease?

100. gaining a deeper understanding of quantitative findings: watching bahus

101. generating ideas and building intuition: sanitation & health 828 people per square kilometer in UP 64% of households openly defecating about 485 open defecators per square kilometer!

102. generating ideas and building intuition: sanitation policy

104. questions & comments diane.l.coffey@gmail.com riceinstitute.org diane.l.coffey@gmail.com Photo credit: Kit Shangpliang