MSc economics 2010 app eco project

CANDIDATE NUMBER - 42335

AGGREGATE INCOME SHOCKS AND INFANT MORTALITY: THE CASE OF INDIA

In this project I investigate the impact of aggregate income on income mortality for India where micro data of within mother is merged with a state level panel data. The results are largely driven by rural households and that rural infant mortality is counter cyclical with an elasticity of -0.92. This is the within mother relationship and results are largely driven by the rural households. The results are also significant for rural households for within state relationship and using sate specific linear trends, the income marginal effect being -0.04. By examining the heterogeneity in the income effect, female infants or child are at a much greater risk than boys and risk of infant deaths are larger when mother has no education in the rural sample however it is slightly significant. Hence, in contrary to the previous findings in developed countries where infant mortality and child health is pro cyclical, the income effect from recession dominates the substitution effect in developing countries like India due to poor credit facilities, markets and state level resources, thus increasing the opportunity cost of maternal time. Also recession especially in the rural households increase distress labour amongst mothers. Hence, demand for pre natal care falls and risk or natal injuries increases.

1.INTRODUCTION

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In poor developing countries about 30% of all deaths occur in childhood (infant

mortality) compared to just 1% in developed countries (Bhalotra et al, 2010). The

proximate cause of excess deaths is infectious disease (associated with poor

sanitation, scanty supply of safe drinking water etc which are intrinsic in poor

countries especially in rural areas) combined with inadequate nutrition (Bhalotra et

al, 2010). Policy interest concerns the extent to which improving income hence

reducing poverty will lower the risk of infant deaths.

In this project we will investigate and study the effect of aggregate income shocks on

infant mortality in India, where the infant mortality is defined as death rate in the first

year of life, by using a state level panel data. There is also a micro level panel data

where the cross sectional unit is the mother; this is the micro or the individual data

on infant mortality for mothers across the 15 major sates of India from 1997 to 1998.

This micro data of child within mother are merged by cohort and state of birth with a

state panel containing information on income (state net domestic product). So every

mother has a sequence of births over time. That is the micro panel. Every birth

occurs in a state and a year and so the macroeconomic events in the region and the

year of birth can be modelled as influencing the health and the survival of the birth.

2.DATA ANALYSIS

2.1 BASIC DATA ANALYSIS

The data on linked siblings, is collected by means of a family survey (National Family

of Health Service of India) carried out in 1998-1999 where data on the time and

incidence of child births and any child deaths are recorded for mothers aged 15-49.

Individual mortality data are thus available for cohorts of children (implicitly) followed

over time from birth. Children in the sample survey are born in 1961-1999.1 These

micro data are then merged by state and year of birth with a state level panel of data

on income, rainfall and other economic indicators2. There is also disaggregation on

the data by rural and urban households. The key variables in the analysis are infant

1 The analysis carried out by this paper only uses a sub sample of the original survey sample. 2 More information on data is provided in the paper by Bhalotra (2010). The original sample analysed consists of 117,088 rural children of 36,068 mothers and 35,783 urban children of 13,414 mothers born during 1970-1997 in one of the 15 major states.

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mortality rate (“infant” in the data file) which is a dummy variable that equals to one if

died in the period on infancy i.e. within twelve months and zero otherwise, and state

income (“income” in the data file) which is the natural logarithm of per capita net

state domestic product deflated by the consumer price index. The mother identifier

in the variable “seqid” and children within mother are identified by their birth order

(“bord”).

We scrutinize the infant mortality rate which was defined as the death rate of the

first year of life and in the data it is categorical or a dummy variable that takes the

value 1 when infant or a child dies in the first year or within the twelve months since

birth. The following tables provide brief descriptive statistics of the variable infant.

TABLE 2.1.1 Percentage of Infant deaths

1 if died in first year inclusive Frequency Percent Cumulative frequency 0 35,826 91.05 91.051 3,522 8.95 100

Total 39,348 100

Table 2.1.1 provide us with the frequency information of the infant mortality rate. In

this sample consisting of 39,348 observations, 8.95% of child died in the period

within twelve months. Therefore the mean infant mortality in the given sample is

8.95%.

TABLE 2.1.2 Summary statistics: Infant

Infant mortality rate in the rural sub sample Observations Mean Std. Dev.

28758 0.1005981 0.300801

Infant mortality rate in urban sub sample Observations Mean Std.Dev.

10590 0.0593957 0.2363749

Table 2.2.2 depicts basic summary statistics for infant mortality by sub samples. The

top table of table 2.1.2 is for the rural sample and the average infant mortality rate in

the rural subsample is around 10% which is about twice as high compared to urban

sub sample. The number of missing observations for infant mortality rate is zero, in

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other words there are no missing observations for infant mortality rate in the sample.

Table 2.1.3 provides more descriptive statistics of the variable3

TABLE 2.1.3: More Summary statistics for Infant

Mean 0.089509Median 0Standard Mean 0.001439Standard Deviation 0.285481Variance 0.081499Coefficient of Variation 3.189407Skewness 2.875825Kurtosis 9.27037Non Missing 39348

Figure 2.1.1, below depicts the scatter plot of mean infant mortality by years from

1970 to 1998, against the year of birth of the child.

FIGURE 2.1.1 Average Infant by years from 1970-1998

1970

1971

1972

1973

1974

1975

19761977

19781979

19801981

19821983

1984

1985

1986

1987

1988

19891990

1991

19921993

1994

19951996

19971998

.05

.1.1

5.2

Mea

n In

fant

1970 1980 1990 2000Year of birth of child

Figure 2.1.1, above shows that over the years from 1970 to 1998, the infant mortality

rate in India was decreasing over time. The figure might indicate that the

observations for 1972 and 1970 might be outliers to the data. However observations

for 1972, 1971, 1970, 73 which might be detected as outliers doesn’t seem to

3 The STATA syntax is provided in the do file.

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suggest that they are large and influential observations. Figure 2.1.2 plots the

leverage against the normalized residual squared from the regression of average

infant rates on average income for the years 1970 to 1998. An observation with a

high value for the predictor or response variable is an observation with a high

leverage. Again, from the previous figure, figure 2.1, our conjecture about the above

mentioned observations are projected in figure 2.1.2.

FIGURE 2.1.2 Detecting outliers

19701971

19721973

1974

1975

197619771978

19791980

1981 1982198319841985198619871988

198919901991

1992

19931994

1995

1996

1997

1998

0.0

5.1

.15

.2Le

vera

ge

0 .1 .2 .3Normalized residual squared

Figure 2.1.2 clearly shows that observations 1972 and 1970 are the outliers in the

data. The observation exerts a higher leverage compared to all other observations.

However, figure 2.1.2 indicates that 1970 and 1972 may be influential points in the

data set. By applying Cooks test for detecting outliers, we see the years 1970, 72, 73

and 74 since the residuals from the regression for these observations exceed the

truncation limit of the test, which is 4/n, where n is the number of observations. The

original STATA output of the test is produced in Appendix A2.1.2, where d1

represents those observations under Cook’s distance test that exceed the truncation

limit mentioned above.

Appendix A2.1.3 shows the original STATA output for the missing values of all the

variables in the data sample. From appendix A2.1.3, we can see that there are four

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variables in the data set or sample, which have some missing observations. Most of

the missing observations are attributed to the three caste dummy variables “sc”, “st”,

“hicaste” and “obc”. They have total of 395 observations missing which is about 1

percent of the entire sample. The original STATA output in appendix A2.1.2 seems

to suggest that there is some pattern in their missing observations. Years of

education of mothers (“edyrs”) and log gini coefficient for urban sample (“lgini2”)

have 4 and 204 missing observations respectively. The key variables in the data are

infant mortality as defined above and income. To control for cross sectional

heterogeneity we have variables that pertain to mother characteristics for instance

educational attainment, number of years of education of mother and of her partner,

age of mother at the birth of the child, religion and caste dummy variables etc. And

control for child characteristics which include birth order, weather female or male etc

2.2 DATA DESCRIPTION

In this section we look at some variables and gain some familiarity with the data.

Muslim, Hindu, Xian, Sikh are four religious dummy variables and used as control for

mother heterogeneity. In addition we have additional variable listed as “reloth” which

is again simply a dummy and it stands for other religion apart from those mentioned

above. The mean of a dummy variable is simply its relative frequency in the sample.

Table 2.2.1 gives the mean values of these variables4.

TABLE 2.2.1 Mean of religion dummy variables

Religion Variable Mean (%) Mean

Muslim 13.39 0.1339331

Hindu 81.49 0.814908

Christian 1.78 0.0178408

The percentage of rural people in the sample or in particular rural mothers is

approximately 73%. The average mortality rate5 in the sample (already provided in

section 2.1) is 8.95%. Table 2.2.2 gives the average infant mortality rate by decades.

4 The STATA syntax to find the mean of religion indicator variables in given in the do file. 5 The average mortality rate was already provided in section 2.1 Data Analysis.

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TABLE 2.2.2 Average Infant mortality by decade

Year Infant Year Infant Year Infant1970 0.1652422 1980 0.096731 1990 0.07361961971 0.1314286 1981 0.099364 1991 0.06836621972 0.1805825 1982 0.087131 1992 0.08603811973 0.0997024 1983 0.085862 1993 0.08099691974 0.1266003 1984 0.103912 1994 0.09477121975 0.1434599 1985 0.091578 1995 0.0592911976 0.1088632 1986 0.080021 1996 0.06590621977 0.1152074 1987 0.105023 1997 0.05706871978 0.1105769 1988 0.087642 1998 0.05681121979 0.1063029 1989 0.079027 Average 1990-1998 0.0714299

Average 1970-79 0.1287966 Average 1979-1989 0.091629

The table 2.2.2 above gives the mean infant rate for each year and the mean infant

rate for each decade however notice that the observation for 1999 is not given, so

the mean infant rate is done only for the years 1990 to 1998. The average rate by

decade is 0.097 or 9.7%. Hence we can infer that the mean infant mortality not only

has been falling by year to year, but also by decades, for nearly three decades from

1970 to 1998. This may be attributed to the economic development due to trade and

domestic economic policies in India over the years, better access to health care

facilities over time, etc.

Figure 2.2.1 plots and compares state trends in income showing upward tendency

over the period 1970 to 1998. Figure 2.2.2, depicts the state specific trends in the

infant mortality. The graph shows, overall for the period between 1970 to 1998 infant

mortality rates showed a declining downward tendency. Also the differences in state

specific trends in infant mortality rates have narrowed over time. This may be due to

advancements in medical technology, reduction in instability or changes to culture

and institutions.

Figure 2.2.3, plots the scatter and linear fit between infant mortality rate and income

by state. As it is evident from figure 2.2.3, the relationship is negative.

Figure 2.2.4 plots the detrended series of Infant mortality rate residuals and log state

income residuals against birth year of the child and suggests that deviations from the

trend is weaker. Detrending is defined as the practice of removing time trend from a

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time series data. One of the advantages of obtaining such a series is that it removes

the time effect and relationships which are simply due to trend. Hence detrending is

can be thought as a process of “filtering” out time trend. Hence we may want to

detrend two variables because they are cointegrated, or we to remove trend effects

and see if there are any economic effects behind fluctuations of some data over

time.

FIGURE 2.2.1 State specific trends in log state income

66

.57

7.5

8

Lo

g r

ea

l p

.c. n

et sta

te d

om

estic p

rod

uct

1970 1980 1990 2000Birth year of child

AP AS

BI GU

HA KA

KE MP

MT OR

PU RA

TN UPWB

Lowess Fit, 1970-98

State-Specific Trends in Log Income

FIGURE 2.2.2 State specific trends in the infant mortality rate

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0.1

.2.3

Infa

nt m

ort

ality

ra

te

1970 1980 1990 2000Birth year of the child

AP ASBI GU

HA KAKE MPMT OR

PU RATN UP

WB

State-Specific Trends in the Infant Mortality Rate

Lowess Fit, 1970-98

FIGURE 2.2.3 Infant mortality against log income by state

0.1

.2.3

.4

Infa

nt

Mo

rta

lity

Ra

te

6 6.5 7 7.5 8Log State Income

AP

BI

HAKEMT

PUTN

WBAS

GUKA

MPOR

RAUP

Scatter and Liner Fit by State in 1970-98Infant Mortality Against Log Income

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FIGURE 2.2.4 Infant mortality and log state income: detrended

-.2-.1

0.1

.2

mor

talit

y, L

og s

tate

Inco

me


Mortality residuals Log income residuals

All India, 1970-1998Mortality and State Income: Detrended Series

FIGURE 2.2.5 Mortality and log state income: trend series 6.

66.

87

7.2

7.4

Log

Sta

te In

com

e

.05

.1.1

5.2

Infa

nt m

orta

lity

rate


Mortality Log State Income

All India, 1970-98Mortality and State Income Time Series

Figure 2.2.5 plots the non detrended series for both Infant mortality rates and income

aggregated at the state level, over the period 1970 to 1998, and the figure clearly

depicts the inverse relationship between infant mortality rates and income. Over the

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years, as income has shown a rising general upward trend whereas infant showed

tendency to decline.

FIGURE 2.2.6 Infant mortality and log state income: the between state variation

Andhra Pradesh

Assam

BiharGujarat

Haryana

Karnataka

Kerala

Madhya Pradesh

Maharashtra

Orissa

Punjab

Rajasthan

Tamil NaduWest Bengal

Uttar Pradesh

.04

.06

.08

.1.1

2In

fant

Mor

talit

y R

ate

6.6 6.8 7 7.2 7.4 7.6Log State Income

minfant Fitted values

State Averages: Between VariationMortality Against Log Income

In figure 2.2.6, we have the between state relationship or variation between infant

mortality against log state income. Here we see that the linear prediction or the fitted

line is negatively sloped i.e. higher income leads to lower infant mortality rates. The

linear predictions provides a reasonably good fit, but also, the data is quite spread

around it especially the states of Kerala which the fitted line over predicts and states

of Uttar Pradesh and Madhya Pradesh for which it under predicts. Hence one can

infer that relationship between infant mortality rate and log state income by state is

inversely related. However some parts some the data are reasonably dispersed

away from the fitted line which slightly weakens the robustness of the linear

relationship. Nevertheless, a policy maker may like information on the between state

relationship of infant mortality and income different income groups or income

percentiles since the policy maker may devise policies that target different income

gropus. Hence figure 2.2.6 lacks in portraying this relationship.

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3.ECONOMETRIC METHODS AND ANALYSIS

Since one of our key variables is a dummy, infant mortality rate which equals one, if

died in the first period of infancy, we use discrete or limited dependent variable

models of the econometrics literature.

3.1 LINEAR PROBABILITY MODEL VERSUS PROBIT6

Infant mortality rate is binary dependent variable which takes only the values of zero

and one. The linear regression model with binary dependent variable is called the

linear probability model (LPM) where we use simple OLS. However we can also use

a limited dependent variable called Probit model or probabilistic model whenever the

dependent variable is binary or categorical. Probit is also known as binary response

model. Table 3.1.1 gives the estimates table of OLS and Probit regression where the

standard errors are provided in the parentheses.

TABLE 3.1.1 OLS and Probit estimates

Variable OLS Probit

Income -0.05263578 -0.34059838(0.00395343) (0.02546517)

Constant 0.46146127 1.0528952(0.02797394) (0.17896805)

N 39348 39348

Since the dependent variable now is binary in nature, the linear probability estimates

tells us that with a one percent increase in income, since income is given in logs,

decreases the probability of infant mortality deaths, or, reduces the risk of infant

mortality by 0.053. The Probit regression coefficients, however, gives the change in z

score or the probit index of a one unit increase in the predictor or the explanatory

variables. Thus a one percent increase in income leads to decrease in z - score by

6 The detailed STATA syntax is provided in the do file.

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0.34. Alternatively an increase in income by one percent leads to decrease in

likelihood of infant deaths by 0.34. From table 3.1.1, both the estimates are

statistically significant at 1 percent level of significance. The Probit model is a non

linear model, thus it requires the use of numerical optimization techniques and

estimates are obtained from maximum likelihood procedures rather than OLS.

However, to compare Probit estimates with OLS we need to compute their marginal

effect, and should not compare the Probit estimates that are obtained from Probit

regressions. Hence the marginal effects of an increase in income by one percent

implies that the likelihood of infant mortality reduces by 0.054 which is very close to

the OLS estimate of approximately 0.053, and it also statistically significant at 1

percent level, whereas the percentage of correctly predicted probabilities is 88% at

the mean income which indicates a good goodness –of-fit measure. This result

posits that a policy of reducing the child or infant mortality rates in India would be

ways on how to increase income through job creation, creating better social safety

net, policies that support agricultural or rural sector, government subsidies etc. The

result of the marginal effect is produced in appendix 3.1.1.

One advantage of linear probability model is that it is simple to use. Nevertheless it

has some short comings. One major drawback of the linear probability model is that

it does not “bound” the predicted values to lie between 0 and 1, since the probability

cannot be less than zero or greater than one. It is likely that linear predictions might

fall outside this range. In such cases, Probit models or Logit models can be used to

overcome this problem. However, we can get around this problem and we can define

a predicted value to be equal to one if is greater than or equal to five, and

conversely, we can assign a value zero when it is less than or equal to five

(Wooldridge, 2006). To see what fraction of OLS predictions that lie outside the

interval [0, 1], we construct a histogram of linear predictions or predicted

“probabilities”. This is given in figure 3.1.1.

FIGURE 3.1.1 Predicted Probabilities

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0.0

2.0

4.0

6.0

8F

ract

ion

.04 .06 .08 .1 .12 .14Predicted "probabilities" of Infant mortality from Linear Probability model

The histogram fitted with a normal density in the figure 3.1.1 suggests that the linear

predictions from the linear probability model do not fall outside the interval [0 1].

Table 3.1.2 presents a table of basic summary statistics of “yhat” i.e. predicted

“probabilities” from the linear probability model, regressing infant mortality on

income. Table 3.1.3 indicates that the predicted probabilities all lie inside the interval

[0 1]. Using linear probability nevertheless, we expect the probabilities to lie outside

the interval [0 1]; we could then have possibly added more controls. However linear

probability model usually works well for values of the independent variable that are

near the averages in the sample (Wooldridge, 2006)

TABLE 3.1.2 Summary Statistics for predicted probabilities

VariableObserva

tions Mean Std. Dev. Min MaxYhat 39348 0.089509 0.019119 0.038352 0.136351

In addition to the above problem of linear probability model, it is heteroskedastic

hence the usual OLS inference using the F and t tests are wrong. One needs to use

White’s robust standard errors or FGLS or WLS to correct for heteroskedasticity.

Nevertheless, because of its simplicity, it is still useful and also found used in many

applications in Economics.

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3.2 PANEL DATA ESTIMATES: STATE FIXED EFFECTS

Table 3.2.1 gives the correlation between infant mortality rates and state income.

The correlation results are also given by rural and urban samples.

Table 3.2.1 Correlation of Infant mortality and Income

Rural Sample Urban Sample Infant Income Infant Income Infant Income

Infant 1 1 1 Income -0.067 1 -0.0527 1 -0.079 1

Table 3.2.1 indicates that the correlation i.e. linear association, between infant rates

and state income are 6.7%, 5.27% and 7.9% respectively for aggregate, rural and

urban sample. Although the correlation is not so high but the sign indicates that they

are negatively or inversely related to each other, implying that in the period when the

income is low or there is recession, infant mortality rates increases. However infant,

the main dependent variable defined here is an indicator or dummy variable which

might plausibly explain low correlations with income.

However figure 2.2.1 suggests that there is a strong negative correlation between

average infant rates and average income over the years. Over the years infant rates

has been on the declining trend and the correlation estimate is -0.82 or 82%7.

The state fixed effects implicit in the mother fixed effects control for state specific

time invariant unobservable for instance political and social institutions Using state

as the cross sectional variable, table 3.2.2 reports8 the within state relationship

between infant and income using state fixed effects, using robust standard errors,

clustered at state level.

Table 3.2.2 State fixed effects: aggregate level

7 The related syntax is provided in the do file. 8 The detailed STATA syntax is provided in the do file and the pertinent detailed regression results using state fixed effects are not reported in the appendix.

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Variable State fixed effects, non robustState fixed effects,

robustincome -0.08283631 -0.08283631

std. error (-0.0060602) (-0.00883248)Constant 0.67487414 0.67487414std. error (-0.04284851) (-0.06241501)

Observations 39348 39348

The estimates are both significant at 1 percent level of significance. An increase in

state income by 1 percent reduces the risk of infant mortality by approximately by 8.3

percent. The third column corrects the standard errors for hetroskedasticity clustered

at state level and are provided within the parentheses. Table 3.2.3 reports the

income effect by rural and urban samples using state fixed effects and robust

standard errors clustered at state level. Columns 1 and 2 give the income marginal

effects for rural and urban households using the state fixed effects. Columns 3 and 4

however add state specific trends to control of omitted trends that vary by state. The

effect brings down both the income estimates of risk of infant mortality of the rural

and urban households where rural is significant at 1%, whereas urban becomes

insignificant. Hence from column 3, an increase in income by 1% reduces the risk of

infant mortality by -0.041. This implies that, associated with pertinent social,

historical or political institutions, states that were not effective in translating high

income growth, are not the ones with risk of high infant mortality. Also, from column

3 and 4, the inclusion of state specific trends implies that failing to control for trended

unobservables would result in over estimation of income effect, consistent with the

mortality-reducing influence of trends in medical and scientific technical progress

(Bhalotra 2010).

Table 3.2.39 State fixed effects by samples and with state specific trends

(1) (2) (3) (4)Variable Rural Urban Rural Urbanincome -0.09195105* -0.06871926* -0.0405998* -0.0253293

9 The detailed STATA syntax is provided in the do file, which shows how I have used and generated state specific trends. The robust standard errors clustered at state are given in parentheses.*Significance at 5%

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std. error (0.00961486) (0.0099839) (0.0133716) (0.0180218)Constant 0.74741206 0.55101108 0.4195718 0.2694891std. error (0.06763406) (0.07142451) (0.0878795) (0.1196008)

Observations 39348 39348 39348 39348

Again the standard errors are given in parentheses. Both estimates from the urban

and rural samples are significant at one percent level, however the rural sample has

a greater income effect than in the urban sample. Thus in period of recession the

income effect dominates the substitution effect of increase in maternal labour supply

in both rural and urban households. Table 3.2.4 reports the within state relationship

using de trended variables by including year dummies from 1970 to 1998 for the

aggregate, rural and urban sample.

Table 3.2.4

Within state relationship using de trended variables10

Variable Aggregate Rural Urbanincome -0.02776946 -0.02759 -0.03869

Std error (0.01559993) (0.020532) (0.027602)p-value 0.0968 0.2005 0.1828

Constant 0.35395344 0.373196 0.376568Std error (0.11619682) (0.153153) (0.17853)p-value 0.0087 0.0288 0.0534

Obs. 39348 28758 10590

As table 3.2.4 indicates that none of the estimates are significant at 5 percent. The

standard errors are adjusted for robustness and clustered at state level. F tests

statistic reject the hypothesis that the state fixed effects implicit in mother fixed

effects at 1 percent level of significance suggesting that pooled OLS would have

produced inconsistent estimates.

3.3 PANEL DATA ESTIMATES: MOTHER FIXED EFFECTS

10 The detailed STATA syntax is provided in the do file and the pertinent detailed regression results using state fixed effects are not reported in the appendix.

17


The basic equation is

Mimst = βyst + αm + ηt + Zimstρ + εimst (1)

M is a dummy variable that indicates weather index child I of mother m born in year t

in state s died by the age of 12 months.

y is the logarithm of per capita net domestic product in state s and year t deflated by

the consumer price index for agricultural workers (henceforth income). β is the

parameter of interest and it measures the change in infant mortality associated with

a 100 % change in income.

αm denotes the mother fixed effects. Since, by construction of the sample, mothers

do not migrate between the states, the mother fixed effects incorporates a state fixed

effect. Mother fixed effects captures the differences across mothers in periods of

frailty, fertility, contraception preferences and awareness of health related

technology.

Heterogeneity in death risk is allowed for by including child specific Zimst, specified as

dummies for gender, birth order, birth month and age of mother at the birth index of

the child.

ηt are year (or cohort) dummies that control for aggregate shocks.

Table 3.3.111 Impact of state aggregate income shocks on infant mortality risk:

mother fixed effects

11 The detailed STATA syntax command is provided in the do file. The dependent variable is an indicator for infant mortality and income is the log of real per capita net state domestic product. Robust standard errors clustered at the state level are given in parentheses. Mother characteristics refer to her education, her partner’s education, her religion and her caste. Child characteristics are gender, birth order of the child and the age of the mother at the birth of the child. Changes are cumulative in moving from columns 1 to 5. In column 6, I drop time dummies. * Significant at 5% level.

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Unconditional Controlling for cross sectional heterogeneity Aggregate shocks1 2 3 4 5

No controls Mother

characteristics Mother fixed effects Child characteristics Time dummies

Rural -.0445402*(.0195) -.0378205*(.0175) -.204454*(.0314) -.0921433*(.040948

) -.0870492(.049272) Urba

n -.0506672*(.0118) -.0447526*(.0116) -.1548182*(.0431) -.0758556 (.066645)

-.0424726(.0806625)

6Baseline model

Rural-.0921433*(.040948

) Urba

n -.0758556 (.0666453)

Table 3.3.1 displays results of infant mortality rates for both urban and rural samples.

Column 1 reports the unconditional marginal effect; the unconditional marginal effect

of aggregate income on infant mortality risk is -0.0445 and -0.0507 for rural and

urban samples and both are significant at 1% level of significance. Introducing

controls for mother level characteristics reduces the income coefficients to -0.0378

and -0.0448 for rural and urban samples respectively. Every step up in both the

parents’ education reduces the risk of infant mortality where the marginal effect of

maternal education is nearly 2.5 times larger than paternal education. Both the

estimates of paternal and maternal education are significant at 5% for both the

samples. All the religious and caste variables are insignificant at 10% level. In

column 3 we introduce mother fixed effects to control for mother level observables,

the income coefficient increases to -0.0245 and -0.1548 for rural and urban samples

respectively. The percentage of the error variance due to mother fixed effects or

mother-level-time invariant-heterogeneity is around 43 for both rural and urban

samples. In column 3, we are comparing children of the same mother who, in their

first year of life were exposed to different economic conditions. This eliminates the

potential concern that the income effect is simply a compositional effect (Bhalotra,

2010). Hence births in recession are low selectively low risk, either because higher

risk women are more likely to consciously defer fertility or because they are more

likely to suffer miscarriage or still birth. In column 4 we introduce controls for child

19


level characteristics, where the rural income coefficient comes down to -0.09214.

The birth order coefficient is significant at 5% level. Hence, higher the birth order or

rank of a child born to a mother, the lower the risk of death in the post natal periods,

compared to children that are born afterwards to the same mother. Conducting a

joint test statistic, all child specific are jointly significant at 1% level.12 However the

income coefficient for urban sample is not significant even at 10% level. In column 5,

time dummies are introduced to control for aggregate shocks. The rural and urban

estimates are not significant at 5%, although rural income is at 10%. In column 6, I

have dropped time dummies since urban estimate is insignificant and rural estimate

doesn’t change by much. Since the average infant mortality rate in the rural sample

is 0.1005981, the elasticity at the mean is -0.92 (to 2 decimal places). In table 3.3.1

the data are largely driven by rural sample which is 73.09% of the total sample13

3.4 PANEL DATA ESTIMATES: VARIOUS

Table 3.4.1 displays the various estimates of the baseline model for rural sample in

column 6 of table 3.3.1, where the dependent variable is infant mortality. All the

robust standard errors clustered at state are given in parentheses.

Table 3.4.114 Various panel data estimates

Variable OLS(robust) Between Fixed Fixed(robust) Random

Random(robust)

income -0.0333 -0.0222*-

0.0921* -0.0921* -0.0333* -0.0333* (0.0158) (0.0054) (0.039) (0.0409) (0.0052) (0.0158)hindu -0.0049 -0.0101 -0.0049 -0.0049 (0.0131) (0.0229) (0.0148) (0.0131)muslim -0.0253 -0.0303 -0.0253 -0.0253 (0.0154) (0.0236) (0.0158) (0.0154)Xian -0.0091 0.0269Sikh -0.0019 -0.0055 -0.0019 -0.0019 (0.0283) (0.0254) (0.0251) (0.0283)reloth 0.0063 0.0063 0.0063 (0.0288) (0.0267) (0.0288)Sc 0.0099 0.0131 0.0099 0.009912 The syntax is provided in the do file. 13 Using the syntax tab rural in STATA.14 Detailed STATA syntax for between fixed beween and random effects estimator is provided in the do file.

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(0.0087) (0.0054) (0.0052) (0.0087)St -0.0073 -0.005 -0.0073 -0.0073 (0.0064) (0.0065) (0.0064) (0.0064)Obc -0.0022 0.0001 -0.0022 -0.0022 (0.0084) (0.0046) (0.0045) (0.0084)educf -0.0078* -0.0082* -0.0078* -0.0078* (0.0018) (0.0017) (0.0017) (0.0018)educm -0.0036* -0.0031* -0.0036* -0.0036* (0.001) (0.0012) (0.0012) (0.001)

bord 0.0151 0.017*-

0.0262* -0.0262* 0.0151* 0.0151* (0.0022)* (0.0016) (0.0058) (0.0066) (0.0014) (0.0022)female 0.0021 0.0019 0.0011 0.0011 0.0021 0.0021 (0.0046) (0.004) (0.0069) (0.0106) (0.0035) (0.0046)agemay -0.0071* -0.0066* 0.0056* 0.0056 -0.0071* -0.0071* (0.0008) (0.0005) (0.0026) (0.0031) (0.0005) (0.0008)Constant 0.4683 0.3702 0.6969 0.6969 0.4683 0.4683 (0.114) (0.0459) (0.2549) (0.2584) (0.0403) (0.114)

N 28430 28430 28430 28430 28430 28430R2 0.0137 0.0162 0.0135 0.0135

R2(Over) 0.0132 0.0001 0.0001 0.0137 0.0137R2(Between

) 0.0162 0.0009 0.0009 0.0157 0.0157R2(With) 0.0003 0.0135 0.0135 0.002 0.002sigma_u 0.2665 0.2665sigma_e 0.308 0.308 0.308 0.308

rho 0.428 0.428

The estimated coefficients vary considerably across the income estimates. The

various measure of R2 (overall, between, within) and variance components also

considerably vary. Using Hausman test, we clearly reject the null that random effects

estimate produces consistent estimates or that individual time invariant effects is not

correlated with error term, as the p value from the chi square test is significant at 1%

level15. This is indicates that OLS and Random effects estimates are inconsistent.

The between estimators uses only the between or the cross sectional variation

hence the coefficients of individual time invariant coefficients cannot be identified. I

provide this estimate just for completeness.

15 STATA syntax for Hausman test has been provided in the do file.

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3.5 EXTENSION: HETEROGENEITY IN THE INCOME EFFECT

Table 3.5.1 produces the heterogeneity in the income effect by using mother fixed

effects, by parent’s education and gender of the infant, where also, elasticity, mean

and percentage of sub groups are also reported. Columns 1 and 2 produce the

baseline estimates from table 3.3.1.The results in table 3.5.1 are restricted to the

rural sample except for column 2. In the rural sample, 63.05% and 33.39% of

mother’s and father’s have no education. The income effect is larger for uneducated

mothers (column 3) compared to father’s with no education but it is not significant at

5%, but rather at 10% level. There are 47.63% and 52.37% girls and boys in the

sample. Comparing by gender, there is a much bigger impact of income shocks on

girls than on boys, which indicates are insured in periods of recession. And this is

also consistent with previous studies that in recession the welfare of females are

overlooked compared to health and welfare of boys.

Table 3.5.116 Heterogeneity in the effect of income shocks on mortality

(1) (2) (3) (4) (5) (6) (7) (8)Sector Mother's education Father's education Child gender

Rural Urban None Some None Some Girls BoysIncome -0.09214* -0.07586 -0.0863917 -0.09853 -0.0638 -0.11347 -0.22836* 0.0208

(0.04094) (0.066645) (0.050501) (0.10799) (0.055225) (0.063244) (0.074338) (0.091992)Elasticity -0.916 -1.277 -0.809 -1.642 -0.569 -1.453 -2.552 0.243

Mean 0.1006 0.0594 0.1068 0.06 0.1122 0.0781 0.0895 0.0856% of group 73.09 26.91 63.05 36.95 33.39 66.61 47.63 52.37

4.ANALYSIS

The essential aim of the papers by Bhalotra (2010) and Dehejia and Lleras-Muney

(2004) (henceforth D-LM) is to investigate infant health in periods of recession and

16 Detailed STATA syntax is provided in the do file. The robust standard errors are clustered at state are given in parentheses. *Significance at 5% level.17 Significant at 10%.

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booms or changes to short term income or business fluctuations, conditional on

various state and non state controls. And also they investigate the relationship

between infant health and income changes, by sub samples to exploit heterogeneity.

For instance Bhalotra (2010) does this by disaggregating the results by urban and

rural samples. D-LM carries it out for US data by looking at race or ethnicity i.e. white

and black people. Simply speaking, the above authors investigate whether there is a

causal relationship from income fluctuations to child health, via parental behavioural

and/or non behavioural characteristics, or selectivity.

However, as we shall see, even though the strong similarity in the objectives of the

above mentioned authors, there are important differences in the strategies that they

have employed to investigate this behaviour. One very important difference, between

these two papers is that, D-LM (2004) uses a US state level panel data, whereas

Bhalotra (2010) uses a mother level panel data or a micro panel nested within a

state level panel, for India.

In particular, D-LM (2004), in their paper, study the relationship between

unemployment rate at the time of the baby’s conception and health outcomes at

birth, and further investigates weather this relationship is due to the effect of

unemployment rate of health behaviour induced by changes in labour supply or

fertility decisions. In their paper, D-LM (2004) has a very simple reduced form

specification, where they estimate the within state relationship, (as mentioned above)

and include unemployment rate, year dummies and state specific trends to control

for aggregate shocks. The dependent variable refers to outcomes (such as mother’s

characteristics, babies’ health, or use of prenatal care) for children conceived at

some time, t. D-LM (2004), as pointed above, further investigates their finding at a

disaggregated level or by samples. That is, they examine heterogeneity in the effect

of unemployment rate on child health or infant mortality by race, i.e. white and black

mothers; to what extent is their level of education; the consumption of health

improving goods and “bad” goods pertinent and also by race, during the ante natal

and neo natal periods, and to what credits are constrained.

The dependent variable used in the paper by Bhalotra (2010) is an indicator variable

that equals 1 if an infant child died during the period infancy. Hence In contrast to

unemployment rate, Bhalotra (2010) uses log of real per capita net of state domestic

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product. As mentioned earlier, Bhalotra (2010) uses a mother panel merged by

cohort and state of birth. The introduction of such form of a data structure overcomes

some of the specification problems in the previous literature. Bhalotra (2010) allows

controls for cross sectional heterogeneity for instance mother characteristics such as

her ethnicity, education etc, mother fixed effects, child characteristics such as

gender, birth order etc, aggregate shocks and state specific trends, and state

specific shocks such as rainfall and population. Bhalotra (2010) disaggregates her

results and findings and examine the heterogeneity of the income effect on infant

mortality rate by rural and urban households.

D-LM (2004) conclude that child born or conceived during the periods of recession

have low and very low birth weight, and lower infant mortality. These health

improvements are attributable both to selection (changes in the type of mothers who

give birth during recessions) and to improvements in health behaviour during

recessions. Hence according to their findings, infant mortality and hence infant

health is pro cyclical. In contrast, Bhalotra (2010) show that average results are

driven by rural households and that rural infant mortality is counter cyclical and this

despite the finding that relatively high risk women avert birth or suffer fetal loss in

recessions.

In richer countries for instance in US, health care related activities (like prenatal care,

visits to clinic, exercises) and consumption of harmful goods increases and

decreases during the period of recessions. Hence mothers substitute away their time

from the labour market and therefore the opportunity cost of maternal labour time

falls or is low. Therefore in the periods of recessions, the substitution effect

dominates the income effect in maternal labour supply. The theoretical framework

that is employed here is that children can be viewed as normal good. The decrease

in women’s or mother’s wages holding other household’s income constant, can be

decomposed into income and substitution effects. Because children are relatively

time intensive, decrease in wages lowers the relative cost of children, and therefore

increases the demand for children. This is the substitution effect. On the other hand,

a decline in wages lowers income thus decreasing the demand for children. In

contrast in developing countries both income and mortality risks are higher in

developing countries like India, especially for the rural households. As Bhalotra

(2010) concludes, rural infant mortality is counter cyclical. Hence, in developing

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countries, substation effect is dominated by the income effect and consequently

infant mortality rates are higher compared to developed countries. In periods of

recession, distress labour amongst mother or women increases giving less maternal

time since opportunity of maternal time increase, and also consumption of health

related activities and pre natal care declines. Moreover state social expenditure is

pro cyclical in poor countries during the periods of recession and markets don’t have

sufficient resources to insure against recession or income fluctuations. Therefore in

developed countries, as D-LM (2004) analyses, child survival is pro cyclical and

maternal labour supply is counter cyclical. Whereas in the case of developing

country as Bhalotra (2010) analyses, child survival is counter cyclical and maternal

labour supply increases for distress labour and the effects are especially large for

rural households.

As mention above, Bhalotra (2010) uses a micro panel. The micro panel is exploited

to control for endogenous heterogeneity in the composition of live births. Also it is

further exploited to investigate heterogeneity in the income effect for example

including controls for child and mother characteristics. We have looked at the

outcome of infant mortality due to behavioural effects in periods of recession. There

is also a selectivity effect that arises that can achieve the outcome. In particular, if

adverse shocks induce women to postpone fertility, and this effect is stronger

amongst women with inherently high risks of infant mortality, the composition of

births in recession will be selectivity low risk. A similar selection effect arises if higher

risk women are disproportionately subject to fetal loss during the periods of low

income activity. And this effect may be true for a developing country like India, where

significant number of women are under nourished and have limited control over birth

spacing (Bhalotra, 2010). Hence if such mechanisms are dominant, then it induces

downward selection bias i.e. expected infant mortality rates are higher that what the

estimator estimates. Such selection biases are taken care of by the use of micro

panel mentioned above. Also mentioned above that micro panel exploits

heterogeneity in the income effect which helps confirm identification since any

omitted variables would have to behave differently for different slices of data in order

to exhibit the sorts of heterogeneous that we find (Bhalotra, 2010).

Having a state level panel data helps to exploit the state level heterogeneity, that is

state specific characteristics of income effect on infant mortality. Having a state level

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panel can control for state specific unobservable heterogeneity for example certain

state laws, practice culture, climate, and political instability. In addition if time series

were used to study the effect of income changes on infant mortality rates then the

problem of the presence trended unobservable would make our estimates

inconsistent and biased since it will amplify the income coefficient. Such problems

are taken care of by using panel data at state level that allows us to study the effects

of deviations from trend, by including time dummies and state specific linear trends.

Time dummies control for aggregate time variation for instance any political events

or events like floods, or secular improvements in health technology. The state

specific trends allow for omitted trends that vary by state.

6. CONCLUSION

In contrast to pro cyclical child health and infant mortality in developed countries,

child health and infant mortality are counter cyclical in response to business cycle

fluctuations. This result is largely driven by the mothers in rural households, where

the behavioural mechanisms dominate the selective mechanisms of fertility in

response sluggish economic activity. Hence the income effects following recession

dominate in poor developing countries like in India thereby increasing the opportunity

cost of maternal care, while the substitution effect is prevalent in developed countries

like US. Therefore policies should be catered towards creating and improving state

run maternal clinics, improving social and economic institutions, and policies to

smooth consumption in the rural households in the developing countries to reduce

the risk of infant deaths.

7. REFERENCES

1. Bhalotra, S., 2010, Fatal fluctuations? Cyclicality in infant mortality in India,

Journal of Development Economics, Volume 93, Issue no.1, pp. 7-19.

2. Dehejia, R., Lleras-Muney, A., 2004, Booms, busts and babies Health, Quarterly

Journal of Economics, Volume 119, Issue no.3, pp. 1091-1130

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APPENDIX A: STATA OUTPUTS

A 2.1.2 DTECTING OUTLIERS USING COOKS’S TEST

A 2.1.3 MISSING VALUES IN THE DATA

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MSc economics 2010 app eco project

Economy & Finance

Transcript of MSc economics 2010 app eco project