Why do we think that education of women the nearest we have to a magic bullet in development?

1. Why do we think that education of women the nearest we have to a magic bullet in development? Richard Palmer-JonesSchool of International Development, University of East Angliapaper presented at DESTIN, LSE, 26/2/10

2. The argument: • Educating females is the single most effective development intervention – an (almost) undisputed orthodoxy of the past 25 years: • Two prominent examples • Duncan Green, Head of Research, Oxfam • Amartya Sen - female autonomy & empowerment – Development as Freedom; The Idea of Justice • Women’s agency as intrinsic and instrumental to well-beings of all – particularly: • fertility, infant survival, child quality (nutritional status and educational attainments), etc. • Important, convincing and well institutionalised development agenda • But leads to misdirected policy and projects • Despite more than 25 years of advocacy, and intervention, the juvenile sex ratio in India has become more adverse to females – more missing women! • So do we believe these arguments more than reason warrants? • leads to accepting relatively uncritically evidence of causal links between female education and beneficent development outcomes • Wastes opportunity to try to do better and implicitly kills and maims persons • Illustrate with 4 examples • Whatever happened to fertility in Nigeria?- John Caldwell, 1979 • Does gender inequality in education harm growth? - Stephen Klasen (,2002, & 2009) • Are females are better transmitters and receivers of literacy externalities? - Basu & Foster, 1998 & Basu, Narayan & Ravallion, 2001, .. • Does educating females reduce fertility, infant mortality, & gender discrimination in India? Murthi, Guido & Dreze, 1996, and Dreze and Murthi, 2001. • These ill founded ideas lead to the production and academic plainly publication of silly things (and reproduction in the media) • Have heights of adult males have increased faster than those of adult females in recent cohorts in India? - Deaton, 2008 • Lessons of this sorry tale

3. Duncan Green, Head of Research, OXFAM

4. Questioning the orthodoxy • Female education and fertility reduction in Nigeria • Caldwell, 1979 • Rising female education has not greatly improved child well-being • And partner’s education (and ethnicity – not religion) also counts • assortative mating, labour market mechanisms, and unobservables • Gender and Growth • Female education is good for growth (Klasen, 2001;Klasen & Lammana, 2009) • Not when you control for institutions and the “resource curse” (Papyrakis & Palmer-Jones, 2009) • Proximate literacy in Bangladesh (and India) • Basu & Foster, 1998; Basu, Narayan and Ravallion, 2002 - females are better recipients as well as better transmitters of literacy externalities • Selective reporting of results, which do not survive in other data sets, and fail to exclude alternative hypotheses (Iversen and Palmer-Jones, 2008) • It’s the type of man who chooses to marry educated women, stupid! • Sex Ratios in India • Fertility, mortality and female disadvantage reducedby females education (and labour force participation) • Murthi, Guido and Dreze, 1996 & Dreze & Murthi, 2001 • Culture rather than female autonomy drives beneficent outcomes • Heights of adult males increased faster than females’ heights in India • Deaton, 2008 – adult male heights have increased faster than female • Who would use changes in absolute heights? Duh…. • Why question this orthodoxy? • slings

5. Since 1970s this orthodoxy has been almost uncontested • Caldwell in Nigeria (1979) • “[T]he preceding analysis has shown that maternal education is the single most significant determinant of these marked differences in child mortality” (Caldwell, 1979:408) • But UN,1985, “Socio-Economic Differentials in Child Mortality” “The other variable with effects comparable in magnitude to mother’s education is father’s education, …. (Table II.13 & Table III.6 ) * Seems to be an error – Table II.13 & Table III.6 show these figures to be Stage III results; stage III is stage iii run for urban and rural samples separately Stage II regressions include “all other variables” (p10)

6. Many more cases of more educated partner than carer What are the likely implications of there being a higher proportion of educated males than females at each level of education, and what are the implications for the regression coefficients? What if there is a non linear (declining) relationship between outcomes and levels of education (as measured)? (spine plot)

7. Results not significant Or only on years of education not fertility Carers & partners education equally significant partners education changes sign when wealth score included (for DID estimation see Osili & Long, 2008)

8. Partner’s education is just as important as carer’s Fertility has not decreased greatly in South West (area of Caldwell’s research) despite increases in female (and male) education

9. Selective estimation and reporting; • Difficulties of replication with same data sets, and getting same results with other similar datasets • Neglecting assortative mating and “reverse talents” effect – men are more educated than their partners but tend to be less able at each level of education;

10. Education externalities • Females are better transmitters • Effective Literacy (Basu and Foster, 1998) • Literacy rate = L/N (L = literates, N = Population) • Effective literacy = (L+e(N-L))/N, 0<e<1 • Females better transmitters of literacy externalities • ef > em where superscripts reflect gender of literates(e.g. ef = being “female-proximate”, or proximate to a female literate; em similarly) • and recipients of literacy externalities! • Proximate-illiteracy externalities (Basu, Narayan and Ravallion, 2002) • Wages of female proximate-illiterates in non-farm employment greater than those of isolated female illiterates (Bangladesh HIES, 1995/6) • ef > em where subscripts are gender of illiterates (ef = “female proximate-illiterate”, em similarly) Basu and Foster, 1998, Economic Journal; Basu, Narayan, and Ravallion, 2002, Labour Economics

11. Female illiterates are more likely to be proximate to a male than a female literate • Female proximate-illiterates are generally male-proximate • Hence if ef > em then em >= ef • E.g. if females are better recipients then males are at least reasonable transmitters • Selection coefficient on household literacy is negative • I.e. the female labour force participation of illiterate females is lower in literate households • Presumably an adverse to (illiterate) females outcome since they are denied “empowerment” in the wage labour market?

12. Female proximate-illiterates in off-farm employment have negative externalities of child nutritional status • Non-replicability and misleading reporting of proximate-illiteracy findings (B/d HIES 1999/2000; Indian NSS CES, various years) • Replication in econometric studies? • Prominent papers which are framed as supporting female “literacy externalities” have simple flaws: • Gibson, 2001, and Alderman et al., 2003, show greater positive child nutritional externalities of female-proximate illiteracy but use community proportions of female literates with limited controls on community characteristics • details in Iversen and Palmer-Jones, 2008, JDS, 44(6):797-838

13. Gender inequalities in education and economic growth • Gender inequalities in education are bad for growth • Klasen, 2002, World Bank Economic Review, and Klasen and Lammana, 2009, Feminist Economics • Based on cross-country regressions • Are gender-growth relations different in “resource curse” economies? • Resource curse economies have particularly low growth & • low female “empowerment” indicators • Gender-growth orthodoxy neglects “institutions” and political geography • Institutional variables eliminates significance of increase in relative female education • Maybe only when institutions are right is female empowerment effective?

16. Low female education Poor female human capital Low female Low female Poor child human Low female political labour force capital accumulation entrepreneurial participation participation earnings Low economic growth Klasen type explanatory framework

17. Weak institutions Grabbing politics Appropriable resources Resource curse Dutch disease Relative decline of Rise in government Rise in agriculture & tradables rents & transfers male earnings Fall in female Rise in female opportunity cost wage/entrepreneurial (unearned income) earnings Fall in female labour force participation Fall in female political participation/ representation Adapted to resource curse

18. Adapted to resource curse and institutional specificities Weak institutions Anti-female Grabbing politics Appropriable culture/politics resources Resource curse Islamic fundamentalism Dutch disease More rent-seeking politics Relative decline of Rise in government Rise in agriculture & tradables rents & transfers male earnings Fall in female Rise in female opportunity cost wage/entrepreneurial (unearned income) earnings Low/fall in female (formal) labour force participation Low female education/health/ maternal mortality Low/fall in female political participation/ representation High fertility Low child (female) human capital Low (per capita) economic growth Possible connections between Islam and poor performance rejected by Ross Additional gendered resource curse pathways

19. Macro-economic results

20. “it is also the case that the limited role of women’s active agency seriously afflicts the lives of all people” “focusing on women’s agency … precisely [because of] … the role that such agency can play in removing iniquities that depress the well-being of women” …variables such as women’s ability to earn a independent income and find employment outside the home, … to have literacy and be educated participants in decisions … has more voice … employment often has useful ‘educational’ effects .. The diverse variables identified in the literature thus have a unified empowering role .. (Sen, Development as Freedom, 1999:191-2; emphasis in original) • Dreze, Sen & Women’s agency • Employment • Education • Bargaining power • Perception and status • Entitlements • In his analyses of Indian data Sen relies heavily on Muthi, Guido & Dreze, 1996, and Dreze & Murthi, 2001 – e.g. Sen, 1999, p194 fn 14, 16, 17 – also refers to John Caldwell, 1986 .

21. “Sen. 1999: There is considerable evidence that women’s education and literacy tend to reduce the mortality rates of children” (195)… p196relies on Murthi et al oevre)

22. Fertility, Child Mortality and (Juvenile) Sex Ratios in India • Adverse to female sex ratios historically • Female neglect/infanticide/foeticide • “North-south” differences (decreasing) • wheat/rice – economic value of female labour (Bardhan) • Culture & kinship (Dyson & Moore) • Hypergamous & exogamous “Indo-Aryan” north vs endogamous endogamous “Dravidian” south • Sopher, Miller, Kishore, Murthi,, et al., Agnihotri, Croll, …and many others • Basic argument is that less adverse outcomes are driven by female autonomy, by which they mean • education (literacy) and waged employment

23. The problem is that despite increases in female employment and female literacy, the juvenile sex ratio has deteriorated between 1991 & 2001 Censuses • Disregarding fertility decline (intensification effect) & spread of sex-selective abortion/foeticide

24. Female to male ratio 1961, 1981, 2001 Source: Guilemotto, 2007

25. However …. • Need to use juvenile sex ratios, especially 5-9 (excess male migration among youth and adults) • Excess male infant deaths 0-1 means that 0-4 age range confounds excess male infant deaths 0-1 (0-3 months) with excess female deaths 1-4 (actually 4-48 months) • Ethnic differences especially STs & SCs • STs - no/little discrimination apparent against girls but • higher infant deaths -> pro-female sex ratios • SCs – bias against females but high infant mortalities results in • apparenlty lower anti-female juvenile sex ratios • E.g. confounding poverty and gender bias effects • Muslims not showing gender bias • Census has 20-30% missing infants 0-1, 1-2 compared to 2-3 ….. Due to (sex biases?) under-reporting • Is female literacy more effective than male? • Are literacy and employment confounded with “culture” (proxied by language) • Basic issue of causality or correlation • Agnihotri, PJ & Parikh, 2002….

27. “Sen. 1999: There is considerable evidence that women’s education and literacy tend to reduce the mortality rates of children” (195)… p196

28. replication Variable VIF 1/VIF tflit15ppc 6.61 0.151278 tmlit15pc 5.71 0.175242 flpmw15ppc 2.39 0.418229 south 2.19 0.456148 east 2.04 0.489198 scpc 2.00 0.500571 stpc 1.96 0.509003 west 1.94 0.516167 hcr83 1.74 0.573782 med_pc 1.62 0.616277 urbpc 1.40 0.716794 Mean VIF 2.69 collinearity

29. Problems • Numbers of districts in MGD are 296, while we have 335 (or 332 in spatial estimations due to districts with two locations) • 1981 census has 366 Districts • Only use districts in main states • Excluding Jammu & Kashmir, Himachal Pradesh, North East States & Assam (no census in 1981), and Union territories • 326 districts • .Why exclude HP (or J&K) in 1981? • Seemingly HP has missing poverty line data as not in figure 1, but this does not apply to J&K which is? • A further 30 districts are lost • which ones are not reported • Bias?

31. Little difference between effects and significance of female and male literacy

32. Ditto

33. First rule of rhetoric – ignore alternative explanations (Sheila Ryan Johannson, ….) • Is there any variable representing culture? • Spatial dummies • MGD use regional dummies – what do these represent? • South, and West vs North • Agnihotri invents the “kinship variable” representing Indo-Aryan “culture” • “Disadvantageous” hypergamous, exogamous marriage • Based on informal classification of Districts by predominant language • Use predominant language data from 1961, 1971 and 1991 censuses to “explain” the kinship variable • The “trick” is to separate out the Hill Hindus (Berreman, …..) • How does one understand the very similar effects of female and male literacy? • Assortative mating means households with educated females also have educated males • Who decides whether there is an educated female in the household? Husbands? • Alaka Basu, 1999, (in Bledsoe, ed., ) suggests it is the husband, or rather the husband’s natal family which decides whether to marry an educated female into the family, and it is this rather than female education per se which drives the association of female education with beneficial outcomes.

34. The techniques of rhetoric • Ignore dissenting views, positions, perspectives • Ignore problems raised by other scholars • Refuse constructive dialogue or manipulate the context of debate • BURYING THE (BAD) NEWS – RHETORIC OR REALITY? Politician activism • But • Who speaks for the muted? • Does anything go, or is there (an only provisionally, fallibly known) reality? • Construct convincing abstract model piling (dubious) assumption of (dubious) assumption • Employ emotional hooks (frames) • Family, bereavement, injustice, explotiation, tragedy, disaster • Use data selectively • Pick and choose – use carefully selected and misleadingly presented data • Build on extreme or unusual events/actions • Homogenise phenomena • Treat unlike things the same – ceteris is not paribus • Use inappropriate (but “telling”) metaphores or allegories

35. “Figure 1 shows that … Indian men are.. [getting taller] at more than three times the rate of Indian women” (Deaton, 2008, American Economic Review, 98(2):471 • How seriously misled can you be by uncritical acceptance of gender bias?

36. NEW! Deaton's new paper on health, happiness and wealth is highlighted in The Economist In India the starkest divisions are sometimes within the household. Indian women tend to have less clout than their African counterparts. Their claim on a family's resources may be weak, even as the demands made on them are heavy. Many women are consequently underfed or overworked during pregnancy. Their offspring, especially their daughters, are also undernourished during infancy. India may be growing taller as it grows richer. But, Mr Deaton shows, the average height of Indian men is rising three times faster than that of Indian women.

37. Really? Without sample weights With sample weights Absolute heights of males rise about a third faster than females’ Very odd

38. … some more problems, though • Male sample is biased • NFHS report is not explicit how the male sample was chosen • DHS “wealth index” indicates bias • Young males have higher wealth index than younger females and vice versa for older males and females • “opportunistic” or convenience sampling? • Main sample is “ever-married” women • Unemployed male graduates and older labour dependent males? • Including wealth index mitigates but does not eliminate female absolute height change disadvantage

39. Older women have higher z-scores Wealth and height of males and females Older men have lower z-scores Younger men have higher z-scores Younger women have higher z-scores

40. But, really, raw height is not the appropriate metric! • You should use z-scores of height! • How to calculate adult height z-scores? • Needs an nutritionally/health unconstrained population as a standard & appropriate methods – you don’t just pool the data … • You are now entering the weird world of height measurement …. • Cross section & longitudinal studies with height • Initially I used USA’s NHANES3 • Large sample, whites only …. • many problems – white heights declining? (Komlos, et al.) • More recently I have been using England Health Surveys and other NHANES data from USA • Both USA and UK height data vary between surveys & cohorts • USA heights of recent cohorts are unstable across survey years • In the UK heights of males increasing faster than those of females • Use UK longitudinal surveys • NSHD – 1946 cohort; NCDS – 1958 cohort • Unfortunately, some problems • Restricted access – eventually (only last week) received data • Often use self-reported heights • Measured heights at only a few ages • Some errors in measured data ….

41. Computing Z-scores from large cross-section surveys • Heights not normally distributed • NCHS used different standard deviations above and below the median height at each age • Distributions with variations in Location (Mean/median) and Shape (skewness/kurtosis) with age, sex and ethnicity (?) • New WHO standards use LMS/GAMLSS methods • LMSChartmaker (Excel macros) • GAMLSS suite – runs in “R” • To cut a long story short • Use standard population (NHANES3) to estimate “standard” distributions by age (needs more recent data) • Compute z-scores of observed heights from these estimated distributions.

42. Nhanes3 data - bumpy Nevertheless, ploughing on to see what it might be possible to say ….

43. Standardising Indian heights z-score (NFHS3) using distributions from NHANES 3 Female height increase as fast as male Female height does not increase as much as male Female height increases faster than male Column 1: raw height with age estimated by OLS;Column 2: z-score computed with NHANES3 estimated mean and sd for age Columns 3 & 4: z-score computed with LMS or GAMLSS estimates of height by age distributions

44. But, NHANES3 is unreliable Pooled whites born in USA only data from NHANES (to 2004-5) – with covariates education, PIR, region ….

45. Or use England Health Surveys (1991-2005) Female Male Females reach maximum height earlier; males increasing in height over cohorts; problem of male 1940 born cohort

46. UK & USA data compared to Dutch (tallest population in the world) self-reported heights

47. Longitudinal studies by • Cline et al., 1989 • Sorokin et al. 1999 used by • Niewenweg et al., 2003; Webb et al., 2007 • Estimate height shrinkage/age functions • Varies with sex • Females start shrinking earlier and more • Many (Deaton) who use adult height data assume no shrinkage till 50s • but this is cavalier • Height probably starts shrinking around mid ’30s, and is faster in women than men • (maybe earlier in poor populations?)

48. Depends on assumptions of maximum potential height – Waaler, 1984 suggests 185 cms for men and ?170? for women

49. Male and Female Mean Heights of Adults by Sex and Age (NHANES 3)

50. Compute z-scores from stylised height distributions • apply shrinkage equations with standard height distribution parameters • Sorkin 1999a (because the cross country study shows heights increasing to mid 30s) • Max male height at 23 – 183cm • Max female height at 23 – 169 cms • Coefficient of variation of height - 3.8%