Month of birth and child height in 40 countries

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Month of birth and child height in 40 countries

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Article:

Agarwal, N, Aiyar, A, Bhattacharjee, A orcid.org/0000-0002-0157-1476 et al. (5 more

authors) (2017) Month of birth and child height in 40 countries. Economics Letters, 157.

pp. 10-13. ISSN 0165-1765

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Month of Birth and Child Height in 40 Countries

Neha Agarwal, Anaka Aiyar, Arpita Bhattacharjee, Joseph Cummins

∗

_,

Christian Gunadi, Deepak Singhania, Matthew Taylor and Evan Wigton-Jones

March 1, 2017

Abstract

Lokshin and Radyakin(2012) present evidence that month of birth affects child physical growth in India. We replicate these correlations using the same data and demonstrate that they are the result of spurious correlations between month of birth, age-at-measurement and child growth patterns in developing countries. We repeat the analysis on 39 additional countries and show that there is no evidence of seasonal birth effects in child height-for-age z-score in any country. Furthermore, we demonstrate that the Demographic and Health Survey data used to estimate the correlation is not suitable for the task due to a previously unrecognized source of measurement error in child month of birth. We document results from several papers that should be re-interpreted in light of this issue. Keywords: child health; month of birth; anthropometrics; Demographic & Health Survey

JEL Codes: I15; J13; O15

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1 Introduction

It is an odd fact about the world that month of birth correlates strongly with an incredible number of

human health and development measures. A recent paper in the Journal of Human Resources (Lokshin

and Radyakin,2012, henceforth L&R) argues that month of birth affects child physical growth in India.

Children born at different times of the year are exposed at different points in their developmental

tra-jectory to the deteriorated health environment brought on each year by the monsoon rains. Thus, they

argue, the appearance of correlations between month of birth and height-for-age z-score (HAZ) can be

interpreted as evidence of the importance of the timing of inputs in the health production function.

While there is a long tradition across the social and biological sciences of finding robust effects of

birth season on human development, there is also a history of data artifacts or selection effects being

misinterpreted as effects of birth season (Lewis,1989;Strand et al.,2011;Buckles and Hungerman,2008;

Cummins, 2013). We argue that the apparent seasonal effect documented by L&R is less likely due

to exposure to any monsoon-related disease environment, and more likely a lack of exposure to survey

enumerators in the period after the monsoon rains. The uneven measurement timing across the survey

year, typical for Demographic and Health Survey (DHS) data collection, induces differential mean age

at measurement across birth month in the sampled children. This systematic age difference is then

translated into a difference in HAZ due to the shape of the HAZ-age profile. After correcting for the

spurious correlation between month of birth and age at measurement, our analysis produces no evidence

of month of birth effects on child growth using the Indian DHS data.

The problem of survey timing above can be corrected econometrically. However, we provide new

evidence from 39 additional countries that the DHS data are not suitable for the task of estimating the

effects of birth month on child health. In DHS data from almost every country, children born in December

have abnormally high or the highest relative HAZ and children born in January have abnormally low

or the lowest relative HAZ. The effect is visible prior to correction for the survey-timing problem, but

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midnight on December 31 averages out to between a 0.2 and 0.3 standard deviations (sd). Either the

Gregorian calendar is more intimately based in human biology than has been previously documented (or

even considered plausible), or there is significant measurement error in child month of birth across the

broad family of DHS datasets. The DHS itself has reported such a problem (Assaf et al., 2015), but no

previous work has noted the connection to HAZ and month of birth.

We identify 5 additional papers that report correlations between measures of child health and seasonal

exposures based on analysis of data from the DHS (Brainerd and Menon, 2014; Dorlien, 2015; Mulmi

et al., 2016;Darrouzet-Nardi and Masters, 2015; Tiwari and Jacoby, 2013). We discuss the state of the

literature in light of our findings in section 6.

2 Background

Following L&R, we use data from the 1992/1993, 1998/1999, and 2005/2006 waves of the National

Family Health Survey (the Indian DHS). Sample summary statistics from the first round can be found

inTable A1. While there are measurements from most rounds in most months, there are decidedly fewer

measurements taken in late summer as compared to other months (seeFigure 2). This coincides roughly

with the period after the monsoon rains. The effect on the sample is that, on average, children born

in summer before and during the monsoons were measured at older ages than children born in the dry,

monsoon-less winter. This imbalance in age at measure across birth month induces the appearance of

seasonality in HAZ via the HAZ-age profile.

The HAZ-age profile from our sample in India is shown in the bottom panel of Figure 1, graphing

mean HAZ across age-at-measure (in months). Similar to children in many developing countries, Indian

children grow more slowly than the healthy, well-nourished children in the reference population. The

cumulative effects of insufficient nutrition and poor health environment lead to children falling further

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While L&R estimate birth season effects on a number of anthropometric measures, we focus exclusively

on HAZ. HAZ is a cumulative measure of early life health and nutrition, making it possible for the measure

to reflect month of birth effects that occurred months or years in the past. Weight-for-age and

weight-for-height z-scores fluxuate in the short term and thus contain information on both recent and past health

inputs.

The effect of survey timing by birth month is shown in the bottom panel ofFigure 2, which isolates

survey measurements taken in March and September separately. The bar graphs reflect the effects of

survey timing directly, showing mean age-at-measure and mean-HAZ (Y-axis) for children born in each

month (X-axis). For the measurements taken in March, we see a minimum child age in January and

February, which increases through April, and the opposite pattern for September measurements. The

line graphs then show this age-at-measure effect being translated into an HAZ effect via the HAZ-age

profile. The two measure months generate opposite seasonal patterns in HAZ because the entirety of the

sinusoidal effect is caused by differential age at measurement across birth month. Both line graphs also

hint at an unexpected upward trend in HAZ across birth month, and we return to this in section 5.

3 Econometric Problem

Consider the following regression equation, similar to that employed by L&R:

HAZiam=αi+δm_∗M OBm+Xiam′ βX+f(age) +ǫiam (1)

WhereHAZiam is the HAZ score of childi, ageda, and born in month m. X is a vector of controls

that account for maternal, household, and community characteristics. M OBm is an indicator variable

for a child’s month of birth. f(age) is an unknown function of age representing the “natural” HAZ-age

profile for the country. Sincef(age)is unknown, we assume the researcher specifies a parametric function

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their regressions. Following the modeling suggestions inCummins(2013), we show that the appearance

of seasonality vanishes as we employ increasingly flexible polynomial and non-parametric specifications

of g(age;γ) and thus minimize the bias.

4 Replication

Figure 3 displays the coefficient estimates for month-of-birth effects for five specifications of EQ. 1,

each of which differs only in how age is modeled. The na¨ıve estimates (bottom line, light blue) do not

control for age in any way, and there appears to be a sinusoidal, seasonal relationship in the

month-of-birth coefficients. Children born in summer have the lowest HAZ, and then HAZ improves for each month-of-birth

month from July through December. This is the underlying effect that L&R interpret as evidence of the

negative effects of being born during or after the monsoon rains.

Controlling for age linearly (as do L&R) greatly reduces the sinusoidal nature of the estimates, but

the relationship remains indicative of potential birth season effects. The three final specifications are

increasingly flexible in age, allowing a quadratic, cubic, and finally a completely flexible form employing

age-in-month dummy variables. The apparent seasonality is continually reduced by adding flexibility,

with the polynomial and dummy variable specifications effectively eliminating any trace of a sinusoidal

pattern across the year.

We find no evidence of month-of-birth effects in India in the sample examined by L&R. Instead, we

see what appears to be an upward “trend” from January to December. To call it a “trend”, though,

overlooks the sharp discontinuity that occurs when December again gives way to January, implying (if

interpreted literally) that the change in the Gregorian calendar date affects child HAZ by an amount

greater than the difference in India between having an illiterate mother and a mother with 6 years of

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5 Extensions

We now repeat the preceding analysis on a set of 39 additional countries with similar DHS data, and

the results are similar to those from India. A number of countries (though not all) exhibit a sinusoidal

pattern in HAZ across month of birth, but this is largely driven by differences in the distribution of child

age at measurement across birth months. This can be seen in the left panel of each of the country specific

figures presented inFigure A2that show age at measurement and mean HAZ by birth month (similar to

the bottom panel ofFigure 2).

A more surprising empirical regularity uncovered in our analysis is presented inFigure 4. We aggregate

all 40 countries into three geographic regions – Africa, Asia, and Latin America – and estimate

month-of-birth effects for each region. The results are striking: a near perfect linear increase in HAZ across month-of-birth

month, followed by (one would assume) a large fall between December and the following January. This

same upward “trend” from January to December is also visible in many of the individual countries (see

the right panel ofFigure A2).

It is implausible that this upward trend reflects real month of birth effects. First, within a given country

and age group, December and January born children experience quite similar in utero environments,

making this discontinuity questionable from a social-environmental-biological standpoint. Second, the

makeup of our sample spans a broad range of cultures, geographies, climates, and economies, making the

persistence of this similar trend, were it some real effect in the world, an effect of an arbitrary cut-off from

year to year set by the Gregorian calendar 500 years ago. Instead, the effect must be due to measurement

error in child birth.

6 Affected Literature

The seasonal effects papers we discuss in this section employ one of two analytic frameworks: 1)

estimating the effects of month of birth directly; 2) estimating group differences in seasonal exposures

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Category 1 analyses were the main focus of the discussion above. These analyses suffer from both

the timing artifact bias described in Cummins (2013) and from systematic measurement error of age

across birth month discussed in the preceding section. The month of birth estimates in L&R should

be considered proven spurious. Similarly, the discussion of seasonal stunting patterns inDorlien (2015)

appears to misinterpret its results .

The analyses in category 2 rely on comparing changes in seasonal patterns across groups and/or time

to identify the effect of interest, which is not month of birth directly but instead some exposure in the

world that is changing with month of birth. In other parts of l&R and inTiwari and Jacoby(2013), the

timing of monsoon onset lets the authors claim to differentiate abstract month of birth effects from the

health environment changes induced by the rains and/or income effects from the agricultural production

cycle. InDarrouzet-Nardi and Masters(2015), seasonal differences across the equator and between rural

and urban households identify the effect of market access on consumption smoothing. InBrainerd and

Menon (2014), the authors rely on differential intensity in seasonal rain across cohorts and regions to

generate within-birth-month exposures to toxic runoff in India. A key, but often unmentioned, identifying

assumption in these models is the equality of measurement error in child birth date across treatment

groups.

7 Conclusion

We have demonstrated two problems in the analysis of seasonal birth patterns using DHS HAZ data.

First, we have shown that survey timing is the main determinant of the appearance of month of birth

effects in DHS data across 40 countries. Second, we have shown that any attempt to directly estimate true

season of birth effects on HAZ using DHS data is inherently impossible; the child birthdate data is not of

sufficient quality to make such an analysis possible. We hope that our efforts will focus attention on the

need for improved measurement in child birth date when conducting large-scale health and demographic

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References

Assaf, S., Kothari, M., and Pullum, T. (2015). Dhs methodological reports 16.

Brainerd, E. and Menon, N. (2014). Seasonal effects of water quality: The hidden costs of the Green

Revolution to infant and child health in India. Journal of Development Economics, 107:49–64.

Buckles, K. S. and Hungerman, D. M. (2008). Season of birth and later outcomes: Old questions, new

answers. Review of Economics and Statistics, (0).

Cummins, J. (2013). On the use and misuse of child height-for-age Z-score in the Demographic and

Health Surveys.

Darrouzet-Nardi, A. and Masters, W. (2015). Does Market Access Protect Children against Poor Health

Conditions at Birth? The FASEB Journal, 29(1 Supplement):898.22.

Dorlien, A. M. (2015). Effects of Birth Month on Child Health and Survival in Sub-Saharan Africa.

Biodemography and social biology, 61(2):209–230.

Lewis, M. S. (1989). Age incidence and schizophrenia: Part i. the season of birth controversy.

Schizophre-nia Bulletin, 15(1):59–73.

Lokshin, M. and Radyakin, S. (2012). Month of Birth and Childrens Health in India. Journal of Human

Resources, 47(1):174–203.

Mulmi, P., Block, S. A., Shively, G. E., and Masters, W. A. (2016). Climatic conditions and child height:

Sex-specific vulnerability and the protective effects of sanitation and food markets in Nepal.Economics

& Human Biology, 23:63–75.

Strand, L. B., Barnett, A. G., and Tong, S. (2011). Methodological challenges when estimating the effects

of season and seasonal exposures on birth outcomes. BMC medical research methodology, 11(1):1.

Tiwari, S. and Jacoby, H. G. (2013). Monsoon babies: rainfall shocks and child nutrition in nepal. World

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Results

−3

−2.5

−2

−1.5

−1

−.5

0

HAZ

0 20 40 60

Age in Months

IDHS−1 IDHS−2 IDHS−3

India

−3

−2.5

−2

−1.5

−1

−.5

0

HAZ

0 20 40 60

Age in Months

Africa Asia

[image:10.612.102.513.109.710.2]

S. America

Regions

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0

2000

4000

6000

8000

Count

J F M A M J J A S O N D

month of interview

DHS−1 DHS−2

DHS−3

Number of Measurements by Survey Month

−2.5

−2

−1.5

−1

Mean HAZ

0

5

10

15

20

25

30

35

Mean Age

J F M A M J J A S O N D

month of birth

Age (March Interview) Age (September Interview) HAZ (March Interview) HAZ (September Interview)

[image:11.612.100.513.77.694.2]

Mean HAZ and Age at Measure

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−.4

−.3

−.2

−.1

0 .1

Estimated Coefficient

J

F

M

A

M

J

A

S

O

N

D

Month of Birth

No age Linear age

Quadratic age Cubic age

[image:12.612.82.536.76.410.2]

Age Dummies

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−.5

−.4

−.3

−.2

−.1

0 .1

Estimated Coefficient

J

F

M

A

M

J

A

S

O

N

D

Month of Birth

Africa Asia

[image:13.612.80.538.79.406.2]

S.America

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[image:14.612.190.424.134.331.2]

8 Appendix

Table A1: Summary statistics (India DHS)

Mean Std.Deviation Household size 7.26 3.41

Mother age in years 26.32 5.45

Mother education in years 4.48 5.77

Working Mother 0.28 0.45

Male child 0.52 0.50

Child age in months 24.72 15.36

Birth Order 2.74 1.86

Rural 0.68 0.47

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−.4

−.3

−.2

−.1

0 .1

Estimated Coefficient

J

F

M

A

M

J

A

S

O

N

D

Month of Birth

No age Linear age

Quadratic age Cubic age

[image:15.612.79.537.78.410.2]

Age Dummies

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−.9 −.8 −.7 −.6 Mean HAZ 26 28 30 32 34 Mean Age

J F M A M J J A S O N D month of birth

Age HAZ Armenia −.5 −.4 −.3 −.2 −.1 0 .1 Estimated Coefficient

J F M A M J J A S O N D Month of Birth

No age Age Dummies Armenia −2 −1.95 −1.9 −1.85 −1.8 Mean HAZ 28 29 30 31 Mean Age

Age HAZ Bangladesh −.5 −.4 −.3 −.2 −.1 0 .1 Estimated Coefficient

No age Age Dummies Bangladesh −1.8 −1.7 −1.6 −1.5 −1.4 Mean HAZ 25 26 27 28 29 Mean Age

Age HAZ Benin −.5 −.4 −.3 −.2 −.1 0 .1 Estimated Coefficient

No age Age Dummies Benin −1.45 −1.4 −1.35 −1.3 Mean HAZ 27 27.5 28 28.5 29 Mean Age

Age HAZ Bolivia −.5 −.4 −.3 −.2 −.1 0 .1 Estimated Coefficient

No age Age Dummies

[image:16.612.121.491.72.610.2]

Bolivia

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−1 −.9 −.8 −.7 −.6 Mean HAZ 24 26 28 30 32 34 Mean Age

Age HAZ Brazil −.5 −.4 −.3 −.2 −.1 0 .1 Estimated Coefficient

No age Age Dummies Brazil −1.7 −1.6 −1.5 −1.4 −1.3 Mean HAZ 26 27 28 29 30 Mean Age

Age HAZ BurkinaFaso −.5 −.4 −.3 −.2 −.1 0 .1 Estimated Coefficient

No age Age Dummies BurkinaFaso −2.3 −2.2 −2.1 −2 −1.9 −1.8 Mean HAZ 23 24 25 26 27 Mean Age

Age HAZ Burundi −.5 −.4 −.3 −.2 −.1 0 .1 Estimated Coefficient

No age Age Dummies Burundi −2 −1.9 −1.8 −1.7 −1.6 Mean HAZ 28 29 30 31 32 Mean Age

Age HAZ Cambodia −.5 −.4 −.3 −.2 −.1 0 .1 Estimated Coefficient

No age Age Dummies

Cambodia

[image:17.612.124.494.73.609.2]

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−1.4 −1.3 −1.2 −1.1 −1 Mean HAZ 22 24 26 28 30 Mean Age

Age HAZ Cameroon −.5 −.4 −.3 −.2 −.1 0 .1 Estimated Coefficient

No age Age Dummies Cameroon −.95 −.9 −.85 −.8 Mean HAZ 28.5 29 29.5 30 30.5 31 Mean Age

Age HAZ Colombia −.5 −.4 −.3 −.2 −.1 0 .1 Estimated Coefficient

No age Age Dummies Colombia −1.6 −1.4 −1.2 −1 −.8 Mean HAZ 24 26 28 30 32 Mean Age

Age HAZ Congo −.5 −.4 −.3 −.2 −.1 0 .1 Estimated Coefficient

No age Age Dummies Congo −1.4 −1.3 −1.2 −1.1 −1 Mean HAZ 21 22 23 24 25 Mean Age

Age HAZ CoteDIvoire −.5 −.4 −.3 −.2 −.1 0 .1 Estimated Coefficient

No age Age Dummies

CoteDIvoire

[image:18.612.123.493.74.614.2]

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−.9 −.85 −.8 −.75 −.7 Mean HAZ 24 26 28 30 32 Mean Age

Age HAZ DR −.5 −.4 −.3 −.2 −.1 0 .1 Estimated Coefficient

No age Age Dummies DR −1.2 −1.1 −1 −.9 −.8 Mean HAZ 28 29 30 31 Mean Age

Age HAZ Egypt −.5 −.4 −.3 −.2 −.1 0 .1 Estimated Coefficient

No age Age Dummies Egypt −2 −1.9 −1.8 −1.7 −1.6 Mean HAZ 28 29 30 31 32 Mean Age

Age HAZ Ethiopia −.5 −.4 −.3 −.2 −.1 0 .1 Estimated Coefficient

No age Age Dummies Ethiopia −1.5 −1.4 −1.3 −1.2 Mean HAZ 23 24 25 26 27 Mean Age

Age HAZ Ghana −.5 −.4 −.3 −.2 −.1 0 .1 Estimated Coefficient

No age Age Dummies

Ghana

[image:19.612.123.493.75.612.2]

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−2.45 −2.4 −2.35 −2.3 −2.25 −2.2 Mean HAZ 25 26 27 28 29 Mean Age

Age HAZ Guatemala −.5 −.4 −.3 −.2 −.1 0 .1 Estimated Coefficient

No age Age Dummies Guatemala −1.4 −1.2 −1 −.8 Mean HAZ 24 26 28 30 32 Mean Age

Age HAZ Guinea −.5 −.4 −.3 −.2 −.1 0 .1 Estimated Coefficient

No age Age Dummies Guinea −1.4 −1.3 −1.2 −1.1 −1 Mean HAZ 27 28 29 30 31 Mean Age

Age HAZ Haiti −.5 −.4 −.3 −.2 −.1 0 .1 Estimated Coefficient

No age Age Dummies Haiti −2 −1.9 −1.8 −1.7 −1.6 Mean HAZ 23 24 25 26 27 Mean Age

Age HAZ India −.5 −.4 −.3 −.2 −.1 0 .1 Estimated Coefficient

No age Age Dummies

India

[image:20.612.123.493.75.613.2]

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−.8 −.75 −.7 −.65 −.6 −.55 Mean HAZ 28 29 30 31 32 Mean Age

Age HAZ Jordan −.5 −.4 −.3 −.2 −.1 0 .1 Estimated Coefficient

No age Age Dummies Jordan −1.6 −1.5 −1.4 −1.3 −1.2 Mean HAZ 25 26 27 28 29 Mean Age

Age HAZ Kenya −.5 −.4 −.3 −.2 −.1 0 .1 Estimated Coefficient

No age Age Dummies Kenya −2.2 −2.1 −2 −1.9 −1.8 −1.7 Mean HAZ 25 26 27 28 29 Mean Age

Age HAZ Madagascar −.5 −.4 −.3 −.2 −.1 0 .1 Estimated Coefficient

No age Age Dummies Madagascar −2.2 −2.1 −2 −1.9 −1.8 Mean HAZ 24 26 28 30 32 Mean Age

Age HAZ Malawi −.5 −.4 −.3 −.2 −.1 0 .1 Estimated Coefficient

No age Age Dummies

Malawi

[image:21.612.123.493.75.612.2]

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−1.7 −1.6 −1.5 −1.4 −1.3 −1.2 Mean HAZ 23 24 25 26 27 28 Mean Age

Age HAZ Mali −.5 −.4 −.3 −.2 −.1 0 .1 Estimated Coefficient

No age Age Dummies Mali −1.3 −1.2 −1.1 −1 Mean HAZ 28 28.5 29 29.5 30 30.5 Mean Age

Age HAZ Morocco −.5 −.4 −.3 −.2 −.1 0 .1 Estimated Coefficient

No age Age Dummies Morocco −1.9 −1.8 −1.7 −1.6 −1.5 Mean HAZ 25 25.5 26 26.5 27 27.5 Mean Age

Age HAZ Mozambique −.5 −.4 −.3 −.2 −.1 0 .1 Estimated Coefficient

No age Age Dummies Mozambique −1.4 −1.3 −1.2 −1.1 −1 Mean HAZ 25 26 27 28 29 Mean Age

Age HAZ Namibia −.5 −.4 −.3 −.2 −.1 0 .1 Estimated Coefficient

No age Age Dummies

Namibia

[image:22.612.123.493.75.615.2]

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−2.2 −2.15 −2.1 −2.05 −2 −1.95 Mean HAZ 25 26 27 28 29 30 Mean Age

Age HAZ Nepal −.5 −.4 −.3 −.2 −.1 0 .1 Estimated Coefficient

No age Age Dummies Nepal −2.2 −2 −1.8 −1.6 −1.4 Mean HAZ 22 24 26 28 30 Mean Age

Age HAZ Niger −.5 −.4 −.3 −.2 −.1 0 .1 Estimated Coefficient

No age Age Dummies Niger −1.8 −1.6 −1.4 −1.2 −1 Mean HAZ 26 27 28 29 30 Mean Age

Age HAZ Nigeria −.5 −.4 −.3 −.2 −.1 0 .1 Estimated Coefficient

No age Age Dummies Nigeria −2.4 −2.2 −2 −1.8 Mean HAZ 26 28 30 32 34 Mean Age

Age HAZ Pakistan −.5 −.4 −.3 −.2 −.1 0 .1 Estimated Coefficient

No age Age Dummies

Pakistan

[image:23.612.124.493.75.611.2]

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−1.65 −1.6 −1.55 −1.5 −1.45 −1.4 Mean HAZ 26 28 30 32 34 Mean Age

Age HAZ Peru −.5 −.4 −.3 −.2 −.1 0 .1 Estimated Coefficient

No age Age Dummies Peru −1.95 −1.9 −1.85 −1.8 −1.75 Mean HAZ 27 28 29 30 Mean Age

Age HAZ Rwanda −.5 −.4 −.3 −.2 −.1 0 .1 Estimated Coefficient

No age Age Dummies Rwanda −1.5 −1.4 −1.3 −1.2 −1.1 −1 Mean HAZ 24 26 28 30 Mean Age

Age HAZ Senegal −.5 −.4 −.3 −.2 −.1 0 .1 Estimated Coefficient

No age Age Dummies Senegal −2 −1.9 −1.8 −1.7 Mean HAZ 26 27 28 29 30 Mean Age

Age HAZ Tanzania −.5 −.4 −.3 −.2 −.1 0 .1 Estimated Coefficient

No age Age Dummies

Tanzania

[image:24.612.123.493.75.614.2]

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−1 −.9 −.8 −.7 −.6 Mean HAZ 27 28 29 30 31 32 Mean Age

Age HAZ Turkey −.5 −.4 −.3 −.2 −.1 0 .1 Estimated Coefficient

No age Age Dummies Turkey −1.9 −1.8 −1.7 −1.6 −1.5 Mean HAZ 25 25.5 26 26.5 27 Mean Age

Age HAZ Uganda −.5 −.4 −.3 −.2 −.1 0 .1 Estimated Coefficient

No age Age Dummies Uganda −2.1 −2 −1.9 −1.8 −1.7 Mean HAZ 26.5 27 27.5 28 28.5 29 Mean Age

Age HAZ Zambia −.5 −.4 −.3 −.2 −.1 0 .1 Estimated Coefficient

No age Age Dummies Zambia −1.5 −1.4 −1.3 −1.2 Mean HAZ 22 24 26 28 30 Mean Age

Age HAZ Zimbabwe −.5 −.4 −.3 −.2 −.1 0 .1 Estimated Coefficient

No age Age Dummies

Zimbabwe

[image:25.612.123.493.75.615.2]