HAZ had risen for boys from ⳮ1.9 in 1992 to ⳮ1.5 in 2005. Girls experienced similar improvements in health outcomes between 1992 and 2005.
The distributions of births by calendar months by gender and for urban and rural samples are presented in Figure 1.
5
The proportions of boys and girls born in each month are similar. The highest birth rates are registered in August, September, and
October—these children were conceived in winter. The fewest children were born during the winter months of December, January and February—these children were
conceived in spring. The wedding season in India, which falls in the months from November to February could partially explain this seasonality of birth Medora
2003. Figure 2 shows the proportion of children, among all children born in a particular month, who died before the age of three years. The incidence of mortality
appears to be unrelated to the month of birth of boys and girls.
In addition to the data from NFHS, we use historical rainfall data from TS2.1 database supported by the Tyndall Centre for Climate Change Research at University
of East Anglia in Norwich, U. K. This database contains monthly meteorological data for the period from 1901 to 2002 at the nodes of a global grid spaced at 0.25
degree latitude and longitude width Mitchell and Jones 2005. We assigned the monthly rainfall data to the districts of NFHS covered in 1992 and 1998 rounds.
The effect of the rainfall on children’s health is identified through the variation in the rainfall over the three years for which we observe children in each round of
NFHS.
III. Variations of child health outcomes by month of birth
Figure 3 shows the changes in height-for-age Zⳮscores by the month of birth for boys and girls in the pooled sample of three rounds of NFHS.
6
Anthro- pometric measures appeared to be the lowest for children born in summer and im-
proving for children born in fall and early winter. This relationship held for both girls and boys. For example, if the average HAZ for boys born in urban areas of
India in June was about ⳮ1.61 standard error of 0.05, HAZ for boys born in December was ⳮ1.17 SE 0.06. The average HAZ increased from ⳮ1.78 for girls
born in urban areas in June to ⳮ1.21 for girls born in December.
Figure 4 shows the average HAZ by months-of-birth normalized to the beginning of the monsoon season that starts in late Mayearly June in the southern states of
India and in late July in Northern India. According to this normalization, children born in June in the southern states of India would have a normalized month-of-birth
5. The results shown in Figure 1 are calculated based on the sample of children younger than 36 months of age ever born in the household. The distribution of birth by month constructed based on the sample of
survived children is similar to one shown in Figure 1. 6. A relatively large gap between the anthropometric measures of children born in December and January
persists in all rounds of NFHS. We observe similar December-January differences in health outcomes in DHS surveys from other countries. We were not able to find an explanation for that phenomenon. We have
also repeated our analyses on a sample of children with the HAZ bounded in the range from ⳮ3 to Ⳮ3 SD and found our results to be robust to this restriction.
The Journal
of Human
Resources
Table 1 Descriptive Statistics for the Main Explanatory Variables.
Urban Rural
Boys Girls
Boys Girls
Mean Standard
Error Mean
Standard Error
Mean Standard
Error Mean
Standard Error
Survey year dummies 1992
0.282 0.004
0.274 0.004
0.312 0.003
0.300 0.003
1998 0.317
0.005 0.321
0.004 0.379
0.003 0.389
0.003 2005
0.401 0.005
0.405 0.005
0.310 0.003
0.311 0.003
Child’s current age in months 17.84
0.100 18.02
0.096 17.26
0.067 17.44
0.065 Birth order
First-born 0.360
0.005 0.344
0.005 0.279
0.003 0.275
0.003 Second-born
0.302 0.005
0.311 0.004
0.252 0.003
0.249 0.003
Third-born 0.157
0.004 0.162
0.004 0.176
0.003 0.179
0.002 Fourth-born
0.083 0.003
0.086 0.003
0.112 0.002
0.113 0.002
Fifth-born 0.045
0.002 0.044
0.002 0.073
0.002 0.075
0.002 Sixth-born
0.026 0.002
0.024 0.001
0.047 0.001
0.048 0.001
Seventh-born 0.013
0.001 0.014
0.001 0.026
0.001 0.029
0.001 Eighth-born
0.015 0.001
0.016 0.001
0.034 0.001
0.032 0.001
Mother’s current age in years months 26.35
0.049 26.52
0.047 26.08
0.037 26.12
0.035 Education of the mother years in years
6.693 0.053
6.892 0.050
3.196 0.028
3.280 0.027
Education of the mother categories No education
0.283 0.004
0.274 0.004
0.559 0.003
0.553 0.003
Incomplete primary 0.095
0.003 0.088
0.003 0.113
0.002 0.110
0.002 Complete primary
0.067 0.002
0.059 0.002
0.065 0.002
0.062 0.002
Lokshin and
Radyakin
179 Incomplete secondary
0.307 0.005
0.318 0.004
0.193 0.003
0.201 0.003
Complete secondary 0.085
0.003 0.094
0.003 0.038
0.001 0.041
0.001 Higher
0.163 0.004
0.167 0.004
0.032 0.001
0.033 0.001
Scheduled caste 0.143
0.003 0.150
0.003 0.174
0.003 0.180
0.002 Scheduled tribe
0.087 0.003
0.083 0.003
0.175 0.003
0.165 0.002
Religion Hindu religion
0.684 0.005
0.696 0.004
0.752 0.003
0.751 0.003
Muslim religion 0.196
0.004 0.178
0.004 0.114
0.002 0.119
0.002 Christian religion
0.082 0.003
0.080 0.003
0.083 0.002
0.076 0.002
Sikh religion 0.017
0.001 0.022
0.001 0.029
0.001 0.030
0.001 Other or no religion
0.021 0.001
0.024 0.001
0.023 0.001
0.023 0.001
Wealth index score 0.605
0.009 0.642
0.009 0.488
0.005 0.462
0.005 Urban
7.005 0.035
6.980 0.033
7.598 0.025
7.614 0.024
Household size 0.326
0.001 0.324
0.001 0.331
0.001 0.329
0.001 Share of children 0–6 years
0.094 0.001
0.095 0.001
0.123 0.001
0.124 0.001
Share of children 7–14 years 0.266
0.001 0.267
0.001 0.238
0.001 0.238
0.001 Share of adult males
0.266 0.001
0.267 0.001
0.238 0.001
0.238 0.001
Share of adult females 0.029
0.001 0.029
0.001 0.035
0.000 0.037
0.000 Share of elderly
0.657 0.005
0.667 0.004
0.127 0.002
0.133 0.002
Type of toilet Flush toilet
0.139 0.003
0.139 0.003
0.128 0.002
0.123 0.002
Latrine 0.204
0.004 0.194
0.004 0.745
0.003 0.744
0.003 Other or none
0.685 0.005
0.685 0.004
0.246 0.003
0.249 0.003
Source of drinking water Piped water
0.270 0.004
0.265 0.004
0.654 0.003
0.658 0.003
Well or hand pump 0.028
0.002 0.029
0.002 0.087
0.002 0.079
0.002 Surface, river, rain, etc
0.016 0.001
0.020 0.001
0.013 0.001
0.013 0.001
Number of observations 10,099
11,038 22,756
24,358
Sample of children younger than 36 months with nonmissing HAZ.
Figure 1 Percent of Total Number of Births by Month of Birth
of 0, and children born in November would have a normalized month-of-birth equal to six. The trends in health outcomes depicted in Figure 4 are similar to those of
Figure 3: Children born during the monsoon months are more likely to be stunted than the children born during the six months after the start of the monsoon.
7
Would the effect of the month of birth on a child’s health be significant after controlling for the characteristics of a household, a mother, and a child? To find the
answer to this question we rely on a standard theoretical framework of household utility maximization that incorporates the production function determining child’s
health Behrman and Deolalikar 1988. According to that theory, a household utility is a function of consumption and leisure of household members, as well as the
quality health and quantity of their children. A household maximizes its utility subject to standard budget constraints and the restrictions imposed by the health
production function. The household demand for child i’s health Z
i
depends on a set of exogenous characteristics of a child X
i
, household characteristics X
h
, character- istics of its mother X
m
, community characteristics X
c
, and some unobserved factors
7. States in Southern India are exposed to two monsoons, the South-West Monsoon that starts at early June and North-East monsoon that starts in October and lasts till mid-February. Kerala and Tamil Nadu
are the two states that affected by the North-East monsoon the most. Interestingly, the height-by-age z- scores of children living in these states exhibit a U-shape pattern very similar to the patterns found for the
India as a whole.
Figure 2 Proportion of Children that Died Before the Age of 36 Months Among All
Children Born in a Particular Month
captured by random error Thomas, Strauss, and Henriques 1991. This relation
ε
i
can be expressed as: Z ⳱Z
X ,X ,X ,X ,ε . 1
i i
i m
h c
i
The child’s characteristics include, among others, its month of birth. In the linearized form, the production function of child’s health Equation 1 can be expressed as:
11
¯ Z ⳱X Ⳮ
␣ M Ⳮε ,
2
i i
兺
k ik
i k⳱
1
where vector combines the child’s, mother’s, household’s and community’s char-
¯ X
i
acteristics, M
ik
is a dummy variable equal to 1 if child i is born in month k, and
k
and ␣ are parameters. The top panel in Table 2 shows the coefficients of the linear regressions of HAZ for children younger than 36 months. The “month-of-birth”
effect persists when we control for a wide set of exogenous characteristics in Equa- tion 2. Relative to December, children born in other months have worse health
outcomes, with the largest negative difference in May, June, and July. The middle panel in Table 2 shows the regression coefficients for the specifications with dummy
variables corresponding to months-of-birth normalized to the beginning of the mon- soon season. The estimates based on this specification demonstrate that children born
in the months close to the start of the monsoon season have worse health outcomes compared to children born during winter months.
Figure 3 Health Outcomes HAZ by Month of Birth and Gender
The coefficients on the control variables in the regressions used in Table 2 reveal the expected relationship between child health outcomes and the characteristics of a
household, a mother and a child. A higher birth order has a negative impact on the health outcomes for both boys and girls for example, Horton 1988; Angrist and
Evans 1998. Consistent with the findings in the literature for example, Holmes 2006 health outcomes deteriorate with the age of a child. Children living in wealth-
ier households and with better-educated mothers are less likely to be stunted. The relationship between a child’s health and hisher mother’s age has an inverted U-
shape: HAZ improves with the mother’s age till the age of about 40 and then declines for children of older mothers.
The potential endogeneity of the month-of-birth could have strong implications for the findings of this paper. Suppose parents believe that children born during
certain months of the year are more likely to get sick. Then parents would try to plan their pregnancies to give birth in months most favorable for their children’s
health. Or, parents might try to compensate the perceived negative effects by pro- viding better care for children born in “bad” months. In the presence of an unob-
served heterogeneity in the parental inputs to the production function of a child’s health, the variation in health outcomes by the month of birth could be partially
attributed to differences in the parental behavior for example, Rosenzweig and Shultz 1982.
Assume the error terms in Equation 2 can be decomposed into the child-specific IID error term
ij
and the household-specific unobserved factor
j,
capturing the
Figure 4 Health outcomes HAZ by gender and the month of birth normalized relative to
the beginning of monsoon season
heterogeneity in parental inputs to the production function of children’s health.
j
can be correlated with the month in which a child is born. Then, Equation 2 will have a following form:
11
¯ Z ⳱X Ⳮ
␣ M Ⳮ Ⳮ
s.t. CorrM , ⳱0; CorrM , ⬆ 0.
3
ij ij
k ijk
ij j
ijk j
ijk j
兺
k⳱ 1
Under the assumption that the unobserved heterogeneity in parental inputs for ex- ample, far-sightedness of the parents is constant over time siblings, we can account
for the endogeneity of the month-of-birth by estimating the fixed-effect FE re- gression on the sample of siblings, thus removing the household-specific component
j
. The household FE would also absorb a host of other time-invariant geographical differences that might affect child health outcomes. This regression includes explan-
atory variables that differ among the siblings living in the same household: dummies for the month of birth, age of a child and the child’s birth order. The bottom panel
of Table 2 shows that most of the FE coefficients on the 11 dummies for the months of birth are statistically significant.
8
The seasonal patterns in child health are similar to patterns revealed by OLS estimations: children born in fall-winter months are
healthier compared to children born in summer.
8. The F-tests on the FE in all regressions in Table 2 reject the pooled-OLS specification in favor of FE specification.
The Journal
of Human
Resources
Urban Rural
Boys Girls
Boys Girls
Coefficient Standard
Error Coefficient
Standard Error
Coefficient Standard
Error Coefficient
Standard Error
Calendar month January
ⳮ0.281 0.069
ⳮ0.102 0.072
ⳮ0.315 0.049
ⳮ0.317 0.052
February ⳮ0.328
0.072 ⳮ0.363
0.074 ⳮ0.388
0.052 ⳮ0.429
0.054 March
ⳮ0.339 0.070
ⳮ0.399 0.071
ⳮ0.486 0.049
ⳮ0.448 0.051
April ⳮ0.535
0.069 ⳮ0.338
0.075 ⳮ0.426
0.050 ⳮ0.564
0.053 May
ⳮ0.507 0.070
ⳮ0.312 0.071
ⳮ0.528 0.050
ⳮ0.536 0.054
June ⳮ0.490
0.068 ⳮ0.523
0.070 ⳮ0.494
0.049 ⳮ0.531
0.051 July
ⳮ0.383 0.067
ⳮ0.245 0.070
ⳮ0.422 0.047
ⳮ0.527 0.050
August ⳮ0.455
0.063 ⳮ0.270
0.066 ⳮ0.313
0.045 ⳮ0.374
0.047 September
ⳮ0.357 0.065
ⳮ0.242 0.068
ⳮ0.233 0.047
ⳮ0.270 0.050
October ⳮ0.111
0.065 ⳮ0.088
0.066 ⳮ0.182
0.045 ⳮ0.151
0.047 November
ⳮ0.117 0.065
ⳮ0.027 0.067
ⳮ0.030 0.046
⽤
ⳮ0.170 0.048
December Reference month
Number of observations 11,027
10,098 24,357
22,751 R
2
0.167 0.190
0.155 0.176
Month relative to monsoon 5 months prior to monsoon
ⳮ0.172 0.071
ⳮ0.254 0.073
ⳮ0.291 0.051
ⳮ0.229 0.053
ⳮ4 ⳮ0.194
0.073
⽤
ⳮ0.440 0.075
ⳮ0.366 0.052
ⳮ0.320 0.054
ⳮ3 ⳮ0.269
0.071 ⳮ0.369
0.074 ⳮ0.328
0.050 ⳮ0.294
0.053
Lokshin and
Radyakin
185 ⳮ2
ⳮ0.328 0.071
ⳮ0.423 0.075
ⳮ0.380 0.050
ⳮ0.424 0.054
ⳮ1 ⳮ0.372
0.070 ⳮ0.323
0.072 ⳮ0.372
0.049 ⳮ0.332
0.052 Monsoon starts
ⳮ0.264 0.068
ⳮ0.401 0.070
ⳮ0.402 0.048
ⳮ0.350 0.051
Ⳮ1 ⳮ0.206
0.067 ⳮ0.301
0.069 ⳮ0.270
0.047 ⳮ0.301
0.050 Ⳮ2
ⳮ0.331 0.065
ⳮ0.330 0.068
ⳮ0.154 0.047
ⳮ0.237 0.049
Ⳮ3 ⳮ0.178
0.066 ⳮ0.228
0.068 ⳮ0.099
0.047 0.002
0.049 Ⳮ4
0.091 0.066
ⳮ0.024 0.068
ⳮ0.101 0.046
ⳮ0.021 0.048
Ⳮ5 0.140
0.067
⽤
ⳮ0.138 0.068
0.055 0.047
ⳮ0.000 0.050
6 months after monsoon Reference month
Number of observations 11,027
10,098 24,357
22,751 R
2
0.166 0.189
0.153 0.174
Month relative to monsoon: FE 5 months prior to monsoon
ⳮ0.758 0.295
ⳮ0.836 0.287
ⳮ0.346 0.211
ⳮ0.069 0.223
ⳮ4 ⳮ0.474
0.325 ⳮ1.271
0.332 ⳮ0.637
0.213 ⳮ0.309
0.219 ⳮ3
ⳮ0.340 0.310
ⳮ1.382 0.291
ⳮ0.854 0.215
0.187 0.218
ⳮ2 ⳮ0.620
0.298 ⳮ1.008
0.282 ⳮ0.665
0.219 ⳮ0.290
0.221 ⳮ1
ⳮ0.376 0.279
ⳮ0.729 0.285
ⳮ0.440 0.221
ⳮ0.713 0.212
Monsoon starts ⳮ0.512
0.264 ⳮ0.874
0.275 ⳮ0.788
0.195 ⳮ0.619
0.216 Ⳮ1
ⳮ0.200 0.275
ⳮ0.881 0.285
ⳮ0.478 0.205
ⳮ0.270 0.215
Ⳮ2 ⳮ0.402
0.262 ⳮ1.458
0.248 ⳮ0.395
0.204 ⳮ0.231
0.203 Ⳮ3
ⳮ0.381 0.265
ⳮ1.279 0.285
ⳮ0.496 0.206
ⳮ0.141 0.203
Ⳮ4 ⳮ0.209
0.260 ⳮ0.370
0.258 ⳮ0.514
0.196 ⳮ0.348
0.195 Ⳮ5
ⳮ0.317 0.279
ⳮ0.397 0.267
ⳮ0.018 0.204
0.256 0.205
6 months after monsoon Reference month
Number of observations 994
1,013 2,224
2,414 R
2
0.161 0.277
0.228 0.270
Note: is significant at 10 percent level; is significant at 5 percent level; is significant at 1 percent level. Fixed effects estimation is on the sample of households with two or more children younger than three years of age.
⽤
indicates at least 10 percent level significance of Chow test on the equality of corresponding coefficients in the boys “and girls” regressions.
The FE estimates could be criticized on the basis that the subsample of women who have two births in the three-year period prior the survey might not be repre-
sentative for all women. Their births are more likely to be small and to experience growth shortfalls. However, the comparison of estimates with and without fixed
effects for the sample of children used in the fixed effect regression—that is, the sample that excludes children who have no siblings in the relevant age range, also
show that the FE coefficients on the month-of-birth dummies are larger than the OLS coefficients. The OLS coefficients estimated on the restricted sample are close
in magnitude to the OLS coefficients estimated on the general sample these results are available from the authors. This makes us believe that the selection bias might
be not too strong and the FE results can be extrapolated for the whole population.
The coefficients on month-of-birth dummies exhibit similar patterns in regressions for boys and girls in all three specifications shown in Table 2. The Chow test Chow
1960 of the equality of these coefficients confirms this observation—the month-of- birth coefficients for boys are statistically different from the coefficients for girls
only in three out of 42 pair comparisons. Boys and girls might be subjected to different patterns of selection and post-natal treatment and the similar effects of
month of birth on children health indicates that seasonality in outcomes cannot be explained by gender differences in selection and treatments. The further results in
the paper are based on the pooled sample of boys and girls.
The average HAZ of children born during the monsoon season is about 0.5 stan- dard deviations SD lower than the average HAZ of children born in the fall-winter
months Figure 4. After controlling for the characteristics of the child, the mother and the household they live in, the “month-of-birth” effects ranged from 0.4 to 0.8
SD Table 2.
9
The magnitude of this effect is similar to the differences in Z-scores between children of illiterate mothers and mothers with incomplete secondary edu-
cation, as observed in our data. Alderman, Hoogeveen, and Rossi 2005 report that the anthropometric measures of children born during the “lean” season in Tanzania
are 0.2 to 0.4 SD lower than the measures of children born in other months of the year. These effects are comparable with the effects of nutrition programs and the
estimated elasticities of the changes in household’s and mother’s characteristics. For example, in Bangladesh the average HAZ of children with illiterate mothers is about
0.4 SD lower than the HAZ of children whose mothers hold a university degree Moestue and Huttly 2008. Similar effects of maternal education on children’s an-
thropometrics are found by Alderman and Garcia 1994 for Pakistan and Kassouf and Senauer 1996 for Brazil. The provision of the micronutrient supplements re-
sulted in a 0.2 SD increase in HAZ for children in Vietnam and Mexico Thu et al. 1999; Rivera et al. 2001. In Pakistan, a vaccination against upper respiratory ill-
nesses improves HAZ by more than 1 SD according to Alderman and Garcia 1994.
The above estimations demonstrate the consistently strong correlations between the month-of-birth and children’s health outcomes for boys and girls, across different
9. Several studies point to the problems with the quality of age data in NFHS for example, Bhat 1995; Narasimhan et al. 1997; Mishra, Lahiri, and Luther 1999. This measurement error in reported age would
attenuate our results toward zero, so that the estimated effects of the month of birth on children’s health would be lower bounds in absolute terms of the true effects.
regression specifications, years, and for different samples. In the next section we try to establish the causality of this correlation.
IV. Explaining the Correlation between the Month of Birth and Health Outcomes
