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

• 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

The argument:

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 )

Average effect of an additional year of mother’s and father’s schooling on child mortality (stage II*)

Schooling of:

Living in:

Urban Rural

Mother -0.039 -0.035

Father -0.035 -0.012

* 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

Partner Carer

none primary secondary higher non-

standard Total

none 6,857 717 584 133 204 8,495

primary 369 2,236 838 185 35 3,663

secondary 160 509 2,252 394 16 3,331

higher 10 31 112 588 2 743

non-standard

24 19 20 13 385 461

Total 7,420 3,512 3,806 1,313 642 16,693

0

.25

.5

.75

1

frac

tion

of c

are

rs

0 .25 .5 .75 1fraction of partners

none

prim

ary

seco

ndar

y

highe

r

non-

stand

ard

curri

culum

non-standard curriculum

higher

secondary

primary

none

source: author's calculations from MICS3

Highest Education Levels of Carer and Partners , MICS3

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

Table 1 Child HAZ, parental education, religion and ethnicity

Dependent variable WHO HAZ _1 _2 _3 _4 _5 Muslim -0.693*** -0.538*** -0.617*** -0.225*** -0.257*** -0.034 -0.04 -0.04 -0.054 -0.054 Other -0.264* -0.139 -0.146 -0.165 -0.113 -0.134 -0.134 -0.135 -0.135 -0.135 Careers’ primary 0.065 0.055 0.008 -0.047 -0.06 -0.061 Carers’ secondary 0.429*** 0.339*** 0.186** 0.046 0.065 0.068 Partners’ primary 0.016 -0.123* -0.158** -0.049 0.06 0.06 Partners’ secondary 0.218*** 0.077 -0.162** 0.047 0.062 -0.062 Partners’ higher 0.418*** 0.107 0.092 0.066 0.08 0.083 Fulani -0.640*** -0.600*** -0.096 -0.095 Izon -0.163 -0.198 -0.134 -0.134 Tiv 0.307** 0.399*** -0.099 -0.099 Yoruba -0.331** -0.364** -0.114 -0.114 Igbo -0.121* -0.223*** -0.059 -0.06 Bassa 0.044 -0.011 -0.063 -0.063 Hausa -0.358 -0.339 -0.228 -0.227 Kanuri -0.658*** -0.660*** -0.068 -0.068 Other 0.389** 0.407*** -0.121 -0.121 wlthscor 0.184*** 0.023 Constant -0.953*** -1.176*** -1.094*** -1.032*** -0.908*** -0.025 -0.04 -0.042 -0.056 -0.058 r2_a 0.027*** 0.034*** 0.032*** 0.050*** 0.054*** N 14483 14483 14143 14143 14143 Source: authors calculations from MICS3 Note: base variables are Christian, Carer’s and partners with no education, and “not known” for ethnic group.

Table 1 Difference in Difference Design

Cohort exposure

States by UPE intensity

Not exposed (born before

1970)

Exposed (born 1970-1975)

Treatment effects

Low (South-western)

X00 X01 X01-X00

High (other) X10 X11 X11-X10

Double difference effect

(X11-X10)/ (X10-X00)

False treatment X010 1950-55

(21-26 in 1976)

X00 1956-61

(15-20 in 1976)

X00-X010

Table 1 Difference in Difference UPE with High Intensity Dummy Variable (NDHS2) Panel A yearsedn childrenbornlt25

Base model

Add state Fixed

Effects

Add state a & year of

birth Fixed Effects

Base model

Add state Fixed

Effects

Add state a & year of

birth Fixed Effects

Ik*C70-75 0.818 (1.884)

0.797 (1.885)

0.770 (1.824)

-0.098 (-0.569)

-0.078 (-0.457)

-0.087 (-0.512)

C70-75 2.015*** (5.178)

2.062*** (5.439)

2.698*** (4.312)

-0.286 (-1.861)

-0.301 (-1.955)

-0.809** (-3.276)

Ik

-1.491*** (-3.597)

1.644* (2.051)

1.725* (2.158)

0.309 (1.891)

0.000 (.)

0.000 (.)

r2 Number of obs

0.357 2603

0.399 2603

0.409 2603

0.078 2608

0.090 2608

0.110 2608

Difference in Difference UPE with UPE Expenditure Variable Panel B yearsedn childrenbornlt25 Ik = Fed. Gov’t UPE FUnds

Add state dummies

Add state & yob

Add state dummies

Add state & yob

Ik*C70-75 0.006* (2.027)

0.006* (2.244)

0.007* (2.482)

-0.002 (-1.562)

-0.002 (-1.651)

-0.002 (-1.695)

C70-75 2.128*** (6.738)

2.126*** (6.922)

2.625*** (4.373)

-0.201 (-1.614)

-0.192 (-1.539)

-0.692** (-2.958)

Ik

-0.001 (-0.418)

-0.011** (-2.734)

-0.012** (-2.905)

0.001 (1.018)

0.004* (2.259)

0.004* (2.455)

r2 Number of obs

0.356 2603

0.400 2603

0.410 2603

0.077 2608

0.091 2608

0.111 2608

Source: authors calculations from NDHS2 * p<0.05, ** p<0.01, *** p<0.001; “t” values in brackets Controls do not include proportion of females in total enrolment in 1970, nor proportion of civil servants who are female when the individual was 6, as used by Osili and Long, due to lack of access to the data. However, we doubt that this causes the difference in results as discussed below. We include coefficients for C70-57 & Ik in specification 2, 3, 4 & 5, and 3 & 6 respectively despite the change in interpretation due to inclusion of state and yob fixed effects.

(for DID estimation see Osili & Long, 2008)

Carers & partners

education equally

significant

partners education

changes sign when wealth

score included

Results not significantOr only on years of

education not fertility

8

Table 1 Child Mortality and Parental Education in Nigeria 1 2 3 4 5 6 7 8 mother_primary -0.368***

(0.0000) -0.368***

(0.0000) -0.202***

(0.0000) -0.166***

(0.0000) -0.082*** (0.0000)

mother_secondary -0.716*** (0.0001)

-0.713*** (0.0001)

-0.379*** (0.0001)

-0.371*** (0.0001)

-0.185*** (0.0001)

mother_higher -1.411*** (0.0003)

-1.413*** (0.0003)

-0.862*** (0.0003)

-1.237*** (0.0003)

-0.931*** (0.0003)

father_primary -0.334*** (0.0000)

-0.334*** (0.0000)

-0.211*** (0.0000)

-0.254*** (0.0000)

-0.181*** (0.0000)

father_secondary -0.687*** (0.0001)

-0.685*** (0.0001)

-0.460*** (0.0001)

-0.523*** (0.0001)

-0.394*** (0.0001)

father_higher -0.821*** (0.0001)

-0.819*** (0.0001)

-0.439*** (0.0001)

-0.482*** (0.0001)

-0.268*** (0.0001)

Female$ -0.089*** (0.0000)

-0.087*** (0.0000)

Wealth index -0.272*** (0.0000)

-0.248*** (0.0000)

-0.230*** (0.0000)

r2_p N

0.001*** 28123

0.001*** 27791

0.001*** 28123

0.001*** 27791

0.002*** 28123

0.002*** 27791

0.001*** 27791

0.002*** 27791

Source: authors from NDHS1 Note $ - child is female Table 1: Total Fertility Rate by Region

period region 1987-

89 1984-

86 1981-

3 1978-

80 1975-7

NDHS1 southeast 4.61 5.48 6.14 6.66 5.12 southwest 4.48 5.26 6.58 6.58 5.80 northwest 4.91 5.24 6.39 5.26 5.39 northeast 4.55 4.44 6.29 5.24 4.90

region 2000-3 1997-9 1994-

6 1991-3 1988-

90 NDHS3 north central 5.65 5.64 6.21 6.23 6.71 north east 7.05 7.61 7.91 6.29 6.40 north west 6.65 6.78 7.58 6.45 6.52 south east 4.12 3.76 4.78 4.66 5.40 south south 4.61 4.72 5.39 5.37 5.71 south west 4.11 3.97 4.62 3.86 4.54 Source: NDHS1&3

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

Total fertility and Parental Education in Nigeria – MICS3 _1 _2 _3 _4 _5 _6 mothers_ed2 0.294*** 0.260*** 0.224*** 0.224*** (0.044) (0.060) (0.045) (0.060) mothers_ed3 0.670*** 0.590*** 0.483*** 0.481*** (0.042) (0.064) (0.051) (0.067) mothers_ed4 -0.284** 0.062 -0.297** 0.037 (0.109) (0.155) (0.109) (0.155) partners_ed2 0.268*** 0.037 0.182*** 0.013 (0.046) (0.061) (0.048) (0.062) partners_ed3 0.467*** 0.069 0.290*** 0.016 (0.045) (0.063) (0.050) (0.064) partners_ed4 0.668*** 0.216** 0.369*** 0.088 (0.068) (0.084) (0.077) (0.087) partners_ed (Quranic)

-0.372*** -0.416** -0.374*** -0.401**

(0.095) (0.135) (0.095) (0.134) wlthscor 0.136*** 0.169*** 0.119*** (0.021) (0.021) (0.022) Constant -1.498*** -1.476*** -1.504*** -1.433*** -1.391*** -1.440*** (0.025) (0.027) (0.027) (0.027) (0.029) (0.030) r2_a 0.019*** 0.014*** 0.020*** 0.021*** 0.019*** 0.022*** N 14483 14143 14143 14483 14143 14143

Source: authors calculations from Nigeria MICS3

• 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 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?

Proportion of proximate-illiterates with non-farm wages Household

Literacy Male Female

Male only .33 .43

Female only .39 .11 Male & female

.28 .46

Female illiterates are more likely to be proximate to a

male than a female literate

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?

14

15

16

Low female education

Low female labour forceparticipation

Low female entrepreneurialearnings

Low economic growth

Poor female human capital

Poor child human capital accumulation

Low femalepolitical participation

Klasen type explanatory framework

17

Weak institutions

Dutch disease

Resource curse

Relative decline of agriculture & tradables

Rise in male earnings

Rise in government rents & transfers

Fall in female wage/entrepreneurial earnings

Rise in female opportunity cost (unearned income)

Fall in female labour force participation

Fall in female political participation/representation

Grabbing politicsAppropriableresources

Adapted to resource curse

18

Weak institutions

Dutch disease

Resource curse

Relative decline of agriculture & tradables

Rise in male earnings

Rise in government rents & transfers

Fall in female wage/entrepreneurial earnings

Rise in female opportunity cost (unearned income)

Low/fall in female (formal) labour force participation

Low/fall in female political participation/representation

Grabbing politicsAppropriableresources

Low female education/health/maternal mortality

Anti-female culture/politics

Low child (female) human capital

Low (per capita) economic growth

High fertility

Islamic fundamentalism

More rent-seeking politics

Possible connections between Islam and poor performance rejected by Ross

Additional gendered resource curse pathways

Adapted to resource curse and institutional specificities

19

Paper Klasen, 2002

Klasen and Lanamma, 2003, 2009 Papyrakis & Palmer-Jones, 2008

RHS depvar growth growth growth growth growth growth Klasen lninc60 -1.13 *** -2.27 *** 1.49 *** 1.23 *** 0.91 *** 1.83 *** popgro -0.55 * -2.8 *** -0.09 ns ns ns lfg 0.62 * 2.33 *** a a a a open 0.007 ** -0.001 0.45 1.29 ** ns ns inv 0.056 ** 0.06 *** 0.04 ns ns ns ed60 0.19 ** 0.01 -0.17 ns ns ns ged 12.61 ** 10.42 *** -0.12 * ns ns ns red60 0.9 * 0.68 2.29 * 2.28 * ns ns rged 0.69 *** 0.7 *** 0.03 ns ns ns Institutions Gov't effective 1.25 *** ethnic fracionalisation -1.53 *** Resources mineral production 3.82 *** Agricultural production 8.78 *** other vars b b b b b b N 109 93 Notes a: data problems; we cannot reproduce the labour force growth convincingly from WISTAT 4 b: regional dummies and constant lninc60: log of incomepc 1960; popgro compound growth rate of population; lfg compound growth rate of labourforce; open & inv openness and average investment in last 4 decades; ed60 male education in 1960; ged growth of male education; red60 female-male education in 1960; gred growth of female-male education. Other variables include dummies for region (SA, SSA, ECA, LAC, MENA, OECD)

Macro-economic results

20

• 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 .

“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)

21

“Sen. 1999: There is considerable evidence that women’s education and literacy tend to reduce the mortality rates of

children” (195)… p196 relies 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

Table 1: Sex Ratios in the Indian Census, 1961-2001 All ages 0-6 % Year Total Rural Urban Total Rural Urban FLP FLIT 1961 941 976 14.4 11.1 1971 930 964 18.5 16.5 1981 934 962 20.3 22.3 1991 923 935 895 945 948 934 22 32 2001 933 949 901 927 934 903 26 45 Sources: 1961-1981; University of Maryland; GOI, 2001; 1991: Census of India CD notes: 1 FLP Female Labour Force Participation (% females 15+ who are main workers) 2 FLIT Female Literacy (% females 15+ who are literate)

24

(1.2,3](1.15,1.2](1.1,1.15](1.05,1.1](1,1.05][0,1]No data

(1.2,3](1.15,1.2](1.1,1.15](1.05,1.1](1,1.05][0,1]No data

Source: Guilemotto, 2007

Female to male ratio 1961, 1981, 2001

(1.2,3](1.15,1.2](1.1,1.15](1.05,1.1](1,1.05][0,1]No data

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….

26

0.0

e+00

5.0

e+06

1.0

e+07

1.5

e+07

2.0

e+07

num

ber

0 10 20 30 40 50 60 70 80 90 100age

male female

source: Indian Census CDs, author's calculations

Numbers of persons by sex and age, India, 2001

27

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

280

2040

6080

(mea

n) tf

lit15

ppc

20 40 60 80 100(mean) tmlit15pc

Notes: Sources are author’s calculation from 1981 Maryland Indian Census data files or published documents unless otherwise stated.

Language class-ification

mean sd

tfr1981 Total fertility rate, S&R, 1990 5.08 0.87 tflit15ppc Female (15+) literacy % 12.88 22.64 mlit15pc Male (15+) literacy % 41.28 23.74 flpmw15ppc Female labour force participation (15+) 13.05 22.23 fd Female disadvantage (see M, G & D, 1996) 6.04 12.37 hcr83 16.00 Headcount ratio 1983 (Nssr, OPL) 48.55 16.00 q1f81 Female infant mortality (0-1), 1981 (S&R, 1990) 111.97 36.29 q5f81 Female child mortality (0-4), 1981 (S&R, 1990) 162.16 47.31 q1m81 Female infant mortality, 1981 (S&R, 1990) 124.83 41.94 q5m81 Female cild mortality (0-4), 1981 (S&R, 1990) 151.67 40.17 tmfr04 Male/female population, 0-4, total population 1.03 0.03 tmfr59 Male/female population, 5-9, total population 1.07 0.07 urbpc Urban population %, total population 40.05 41.29 scpc Schedulted castes % total population 6.50 8.15 stpc Scheduled tribes % total population 5.08 14.00 aglappc Agricultural labourers, main workers, % population 9.36 11.05 cultpc Cultivator main workers, % population 6.45 8.72 ass_1971_pc Assamese mother tongue, % population 1971

census IA(e) 0.00 0.00

ben_1971_pc Bengali IA(e) 4.33 17.95 guj_1971_pc Gujarati IA(w) 4.63 19.84 hin_1971_pc Hindi IA(v) 48.27 42.85 kan_1971_pc Kannada D 4.10 16.05 kas_1971_pc Kashmiri Dardic 0.02 0.18 mal_1971_pc Malayalam D 3.81 17.86 mar_1971_pc Marathi IA(s) 6.85 21.52 ori_1971_pc Oriya IA(e) 3.36 16.11 pun_1971_pc Punjabi IA 3.84 15.66 san_1971_pc Sanscrit IA 0.00 0.00 sin_1971_pc Sindhi IA(nw) 0.39 2.38 tam_1971_pc Tamil D 3.92 16.82 tel_1971_pc Telugu D 6.54 21.12 urd_1971_pc Urdu IA© 4.90 5.99 south AP, TN, KAR, KER 0.21 east Bih, Ori, WB 0.18 west Guj, Mah 0.13 hillhind HP & Uttaranchal (NSSR 251) 0.06 N 335.00

Variable VIF 1/VIF

tflit15ppc 6.61 0.151278tmlit15pc 5.71 0.175242flpmw15ppc 2.39 0.418229south 2.19 0.456148east 2.04 0.489198scpc 2.00 0.500571stpc 1.96 0.509003west 1.94 0.516167hcr83 1.74 0.573782med_pc 1.62 0.616277urbpc 1.40 0.716794

Mean VIF 2.69

replication

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?

30

tfr 1981 q5f / q5m fmr59

flit 15+ mlit 15+ flp(mw) 15+

31

Determinants of fertility, child mortality & female disadvantage

Tfr 1981 Q5 1981 Fmr59 OLS female literacy -0.029***

(0.005) -0.030***

(0.003)

-0.428*** (0.126)

0.001*** (0.000)

tmlit15pc -0.001 (0.005)

-0.025*** (0.003)

-0.474*** (0.124)

0.001*** (0.000)

flpmw15ppc -0.016*** (0.002)

-0.016*** (0.002)

-0.011*** (0.002)

-0.067 (0.110)

0.016 (0.111)

0.001*** (0.000)

0.001*** (0.000)

hcr83 0.004 (0.002)

0.004 (0.002)

0.006** (0.002)

0.795*** (0.107)

0.827*** (0.105)

0.000** (0.000)

0.000 (0.000)

med_pc -0.002 (0.001)

-0.002 (0.001)

-0.003* (0.001)

-0.129* (0.059)

-0.135* (0.058)

-0.001*** (0.000)

-0.000*** (0.000)

urbanisation 0.002 (0.003)

0.002 (0.003)

0.000 (0.003)

-0.412** (0.128)

-0.404** (0.126)

-0.001** (0.000)

-0.000* (0.000)

scheduled castes -0.017** (0.005)

-0.017** (0.005)

-0.015** (0.006)

0.045 (0.266)

0.037 (0.264)

0.001 (0.000)

0.001 (0.000)

scheduled tribes -0.008** (0.003)

-0.008*** (0.002)

-0.012*** (0.002)

0.192 (0.111)

0.112 (0.113)

0.001*** (0.000)

0.001*** (0.000)

south -0.815*** (0.102)

-0.818*** (0.096)

-1.102*** (0.094)

-41.002*** (4.658)

-44.498*** (4.343)

0.052*** (0.006)

0.062*** (0.006)

east -0.780*** (0.104)

-0.786*** (0.103)

-0.830*** (0.108)

-42.692*** (5.024)

-42.517*** (4.990)

0.034*** (0.006)

0.036*** (0.007)

west -0.594*** (0.115)

-0.605*** (0.113)

-0.636*** (0.119)

-25.629*** (5.513)

-25.183*** (5.489)

0.027*** (0.007)

0.029*** (0.007)

Constant 6.727*** (0.235)

6.696*** (0.187)

7.283*** (0.224)

108.816*** (9.120)

121.997*** (10.333)

0.864*** (0.012)

0.850*** (0.014)

r2_a N

0.652*** 332

0.657*** 334

0.621*** 334

0.528*** 334

0.532*** 334

0.692*** 334

0.675*** 334

Source: authors calculations from data described in the text

Little difference between effects and significance of female and male literacy

32

Fertility, Child mortality, and female disadvantage, India 1991

Dep. var. Tfr1981 Q5

Spatial ml

tflit15ppc -0.024*** (0.006)

-0.025*** (0.004)

-0.550** (0.177)

tmlit15pc -0.002 (0.006)

-0.019*** (0.004)

-0.468** (0.160)

flpmw15ppc -0.010*** (0.003)

-0.010*** (0.003)

-0.006* (0.003)

0.029 (0.122)

0.113 (0.120)

urbpc -0.001 (0.002)

-0.002 (0.002)

-0.004 (0.002)

-0.539*** (0.106)

-0.579*** (0.101)

scpc -0.006 (0.006)

-0.006 (0.006)

-0.003 (0.006)

0.183 (0.248)

0.233 (0.247)

stpc -0.006* (0.003)

-0.006** (0.002)

-0.009*** (0.003)

-0.029 (0.109)

-0.095 (0.115)

south -0.770*** (0.177)

-0.741*** (0.176)

-0.879*** (0.191)

-44.767*** (9.020)

-47.265*** (8.761)

east -0.317 (0.166)

-0.408* (0.184)

-0.424* (0.191)

-10.308 (8.956)

-12.217 (8.833)

west -0.589*** (0.166)

-0.599*** (0.166)

-0.610*** (0.174)

-25.304** (7.945)

-24.501** (7.957)

hcr83

0.003 (0.003)

0.006 (0.003)

0.033 (0.159)

0.105 (0.157)

med_pc

-0.001 (0.001)

-0.001 (0.001)

-0.137* (0.055)

-0.150** (0.055)

Constant 6.442*** (0.259)

6.256*** (0.245)

6.556*** (0.300)

143.461*** (11.875)

152.044*** (13.226)

Lambda 0.725*** (0.051)

0.725*** (0.051)

0.752*** (0.048)

0.814*** (0.038)

0.804*** (0.039)

sigma 0.393*** (0.016)

0.392*** (0.016)

0.396*** (0.016)

16.902*** (0.691)

16.988*** (0.693)

ll 183.09 182.55 188.44 1440.68 1441.25

sqCorr 0.629*** 0.631*** 0.588*** 0.429*** 0.441***

N 332 332 332 332 332

Source: authors calculations from data desribed in the text * p<0.05, ** p<0.01, *** p<0.001

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.

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

• How seriously misled can you be by uncritical acceptance of gender bias?

“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

36

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.

NEW! Deaton's new paper on health, happiness and wealth is highlighted in The Economist

37

Really?

With sample weights

female (left axis) slope = -0.027

male (right axis) slope = -0.045

162

163

164

165

166

ma

le h

eigh

ts (

cm)

151

152

153

154

155

156

fem

ale

hei

ghts

(cm

)

20 30 40 50 60age

mean female e_female mean male e_male

regressions of unweighted mean height on age (age >= 25) using population weights

Mean Female and Male heights by Age, India, 2005-6

Without sample weights

female (left axis) slope = -0.026

male (right axis) slope = -0.037

162

163

164

165

166

ma

le h

eigh

ts (

cm)

151

152

153

154

155

156

fem

ale

hei

ghts

(cm

)

20 30 40 50 60age

mean female e_female mean male e_male

regressions of survey weighted mean height on age (age >= 25) using popoulation 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

02

46

8ag

e ra

nge

(yea

rs)

-.3 -.2 -.1 0height z-score

-.3 -.2 -.1 0height z-score

female male 95% CI

Wealth and height of males and females

Older men have lower z-scores

Older women have higher 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

120

140

160

180

200

20 40 60 80 100

female male

epanichev smoother with bandwidth 0.1

Nevertheless, ploughing on to see what it might be possible to say ….

43

Standardising Indian heights z-score (NFHS3) using distributions from NHANES 3

Height - cms z-score LMS GAMLSS (NO) 1 2 3 4 Age -0.047***

(-8.647) -0.006*** (-8.097)

-0.006*** (-6.934)

0.025*** (23.553)

female -13.507*** (-53.405)

-0.047 (-1.252)

0.095* (2.553)

0.711*** (13.554)

Female*age 0.021** (3.174)

0.000 (0.430)

-0.003** (-2.817)

-0.022*** (-15.281)

Constant 166.275*** (769.488)

-1.560*** (-50.185)

-1.611*** (-51.641)

-3.984*** (-99.896)

rsquare df model Number of obs

0.496 3

119800

0.002 3

119800

0.003 3

119800

0.011 3

119776

Column 1: raw height with age estimated by OLS;Column 2: z-score computed with NHANES3 estimated mean and sd for ageColumns 3 & 4: z-score computed with LMS or GAMLSS estimates of height by age distributions

Female height does not

increase as much as male

Female height

increase as fast as male

Female height increases faster

than male

44

17

01

72

17

41

76

17

81

80

pre

dic

ted

he

igh

t

0 20 40 60 80age at last birthday

1980 1970 1960

1950 1940 1930

1920 1910

15

61

58

16

01

62

16

4p

red

icte

d h

eig

ht

0 20 40 60 80age at last birthday

1980 1970 1960

1950 1940 1930

1920 1910

Predicted heights of males and females by age and cohort (USA)

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)

16

51

70

17

51

80

pre

dic

ted

he

igh

t

0 20 40 60 80 100age last birthday

1980 1970 1960

1950 1940 1930

1920 1910

14

51

50

15

51

60

16

5p

red

icte

d h

eig

ht

0 20 40 60 80 100age last birthday

1980 1970 1960

1950 1940 1930

1920 1910

England Health Surveys, 1991, 2000 - 2003 & 2005

estimated using fractional polynomials

Predicted heights of males and females by age and cohort (England)

Male Female

Females reach maximum height earlier; males increasing in height over cohorts; problem of male 1940 born cohort

46

165

170

175

180

185

0 20 40 60 80 100

England heights adjusted to 1980-84 age group

USA heights adjusted to 1980-84 age group Dutch self reported heights

male

150

155

160

165

170

0 20 40 60 80 100

England heights adjusted to 1980-84 age group

USA heights adjusted to 1980-84 age group Dutch self reported heights

female

England health surveys 1991-2005 (except 2004), NHES, NHANES 1 -3 & continuous NHANES

Average male and female heights, pooled date

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

female chc = equn. 1male chc = equn. 1

male chc = equn. 2female chc = equn 2

male_chc 1 = 0.0435*age - 0.00009*age^2 - 0.000015*age^3female_chc 1 = 0.0714*age - 0.00075*age^2 - 0.000016*age^3male_chc 2 = 0.1258*age - 0.0021*age^2female_chc 2 = 0.1727*age - 0.0027*age

-2-1

01

2cu

m m

ale

heig

ht lo

ss (

cms)

20 30 40 50 60

male equ. 1 female equ. 1 equ. 2 equ. 2 x1/x2

equation 1 from Sokin et al., 1999a from Baltimore longitudinal studyequation 2 from Sorkin et al. 1999b, scombined 11 studies from developed coutnries

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)

135

140

145

150

155

160

165

fem

ale

heig

ht (

cms)

150

155

160

165

170

175

180

mal

e he

ight

(cm

s)

20 40 60 80 100age

male female

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%

51

Regressions of Height and Age-standardised Height Z-scores1 with co-variates z-score z-score Age 0.00048

(1.009) 0.00014 (0.307)

Female2 -0.187*** (-9.414)

-0.170*** (-8.809)

Female * age -0.003*** (-5.152)

-0.004*** (-6.486)

Wealth index 0.194*** (35.743)

Wealth * male 0.069*** (9.806)

Constant -2.885*** (-161.255)

-2.831*** (-166.790)

rsquare2 df model Number of obs

0.020 3

188040

0.071 5

188040 Source: authors calculations from NFHS 3 t-values in brackets * p<0.05, ** p<0.01, *** p<0.001 Notes: 1. z-scores computed from shrinkage adjusted height with cv 3.8% and male potential height at 21 = 183cm and female height at 169 cm. 2. If female potential height is 167 cms then “female” coefficient is positive but “female * age” remains negative.

Use shrinkage equations applied to guestimated potential maximum height

Note: coefficient on female dummy sensitive to maximum height (location)

but

coefficients on Female * age are now negative and not sensitive to location

Age shrinkage

Females growing faster

52

Or use longitudinal studies – NSHD (7/3/1946 cohort) & NCDS (1958 cohort)

NCDS measured data

1.2

1.4

1.6

1.8

low

ess

: dvh

t

10 20 30 40 50age

male female

Measured heights of persons age 3 to 45 (NSHD)

010

2030

dens

ity: d

iff_

45_3

3

-1 -.5 0 .5 1diff_45_33

male female

Height differences age 45-33

53

8010

012

014

016

018

0lo

we

ss: h

t

0 10 20 30 40 50age

male female

Measured heights of persons age 3 to 45 (NCDS )

0.1

.2.3

.4he

ight

diff

ere

nce

de

nsity

-10 -5 0 5 10difference in measured height (cms)

male female

author's calculations from NSHD data; age 36 & 53

vertical lines are mean and median height differences: median difference male: -0.700 cmsmedian difference female: -0.90 cms

Difference in measured heightsof males and females between 1982 & 1999

NSHD data (1946 cohort, measured data)

Distribution of height differences, age 53 - 36

54

Differences in Self-reported heights

Height lumping and inaccuracies

0.1

.2.3

.4de

nsity

-10 -5 0 5 10height difference (cms)

male female

author's calcualtions from NSHD data; 1966 & 1972 sweeps

Difference in self-reported heightsof males and females between age 20 & 26

55

• Problems in measuring heights– E.g. self reported height differences (ages 26 & 22)– According to Deaton difference in height rise between Indian

males (0.5) and females (0.2) was about 0.3 cm per decade– Shrinkage estimate is about 0.5-1.0 cm per decade (40s- & 50s)

» Excess female shrinkage in .03 – 0.07 cms/decade

• Difference in measured heights between morning and evening average 0.6 cm

– Eg diurnal variation can be a significant proportion of shrinkage

– Untrained measurers of adult females (USA) (Lipman et al., 2006)

• Up to 18 cm difference from research assistant• Even experienced measurers have significant variation

– Standard deviation of height measurement about 0.09cm » 95% obs within +/- 0.18cm

– 1-5 years experience - 4.3 cms mean difference from RA– >5 years experience - 2.4cms difference form RA

– E.g. even for trained measurers (compared to a research assistant) measurement error can be a significant proportion of height change difference

56

• These points are important because it reflects unreasonable acceptance of the female nutrition/health deprivation hypothesis– Reported in Sen of course – foetal undernourishment and low birth

weight• Cardiovascular diseases among south Asians (Osmani and Sen, 2002)• Foetal undernourishment traced to female deprivation (LSE, 2010)

– But is this deprivation relative to males, or to what they, females, could, ideally, be?

– Partha Dasgupta (an inquiry into . & J of Econometrics, 1997)• too poor not to discriminate (female foeticide among the Inuit)

– But why does income growth not translate into nutritional improvements?

• Some left behind by income growth?• Other priorities for expenditure - adult consumption, tobacco,

alcohol)?• Nutrition/health gender bias within the household?

– But, there is a significant history of ignoring criticism of claims of female nutrition bias

• Harris-White and Watson; Jo Kynch; Gillespie & McNei• Men are malnourished too – perhaps even more so (but anemia, ..)

– After the age of 35 (natality related mortality) male deaths exceed female» Dyson attributes this to fatigue related vulnerability to TB etc.

57

• What to make of this?– Recall the 25 years of excess female child and foetus

mortality in India because education and employment of women has not been working

• If educating and getting women into the labour force is not working, then what to do?

– Recent legislation in India on dowry, domestic violence, sex-selection, midday meals, even NREGA ..

– Don’t base policy on poor analysis• Goldacre’s 5 reasons “[W]hy clever people believe stupid

things” (Ch 12, Bad Science): they– see patterns where there is only randomness– see cause where there is none (only correlation)– overvalue confirmatory information– seek out confirmatory information– assess quality of new evidence based on prior beliefs

– Constructionist talk• creating new languages for interventionist initiatives

– Capabilities, functionings, opportunities, rights– Women’s subordination, double/triple burden, endangered species ..

• Opportunities for experts and adepts -> institutionalisation– For example in the development industry

The development industry

DC CountryDonors (USAID,DFID, CIDA,SIDA, etc\ )

MultilateralsWorld Bank,IMF, UNDP,UNICEF, WTO ....

National Governments

Local Gov’t AgenciesInternational

NGOs, CARE,OXFAM (INGOs)

National NGOs(fieldworkers)

Administration, Projects and activities (lobbying, advocacy, activism)

PEOPLE

Consultants ),contractors,suppliers

Politicians

Media

Brokers, leaders, followers, margninalised, exc ludeddiverse, differentiated, included/excluded

The Development Industry

Academics

59

• To raise doubts about this claim is to challenge the morally unimpeachable…(slings and arrows …)

• To suggest possible reasons why it has become such an orthodoxy– Philosophy of science– Policy sciences– Social constructionism– “Moral selving” and the

authoritarian turn

The pious say that faith can do great things, and, as the gospel tells us, even move mountains. The reason is that faith breeds obstinacy. To have faith means simply to believe firmly - to deem almost a certainty, - things that are not reasonable; or, if they are reasonable, to believe them more firmly than reason warrants. A man of faith is stubborn in his beliefs; he goes his way, undaunted and resolute, disdaining hardship and danger, ready to suffer any extremity.Now, since the affairs of the world are subject to chance and to a thousand and one different accidents, there are many ways in which the passage of time may bring unexpected help to those who persevere in their obstinacy. And since this obstinacy is the product of faith, it is then said that faith can do great things.Francesco Guicciardini, Ricordi, Series C i (translated by Mario Domandi; quoted in John Dunn, 1972(?))