OSIPP Discussion Paper : DP-2012-E-003

Influence of Parents’ Unemployment on the Health ofNewborn Babies
March 30, 2012
Miki Kohara†
Osaka School of International Public Policy, Osaka University

Fumio Ohtake
Institute of Social and Economic Research, Osaka University【 Abstract 】 Recent research has shown that low‐income households have a higher
probability of babies being born underweight, which is an indicator of poor health. The
causes and effects of the weights of newborn babies need to be analyzed in Japan, where
the proportion of low‐weight babies is extremely high relative to the other OECD countries.
In this paper, we use panel data by prefecture to analyze the influence of market labor
conditions on the weights of newborn babies in Japan. Controlling for heterogeneity
among prefectures and years as random effects, we first show that high unemployment
rates among parents do reduce the weights of newborn babies. However, our analysis
does not confirm the influence of poverty and low income on the weights of newborn
babies. Unemployment of parents may hamper the health of a baby for reasons other thanfinancial difficulties.
【Key Words】unemployment, low birth‐weight, panel data, Japan【JEL Classification Number】I1, J1, J2
Address: 1‐31 Machikaneyama, Toyonaka, Osaka 560‐0043, E‐mail: kohara@osipp.osaka‐u.ac.jp†

The authors thank Yoshio Higuchi (Keio University), Minoru Ito (The Japan Institute for Labour
Policy and Training), Toshihiro Kodama (Japan Finance Corporation), and participants in the
JIRRA Labor Policy Research Group Meeting for providing valuable comments in compilingthis paper, as well as Hsiufen Hsu for helping with data collection/input. This research received
funding from the Osaka University Global COE Program, Grant‐in‐aid for Scientific Research(Scientific Research (B) 18330049, Young Scientists (B) 20730156), and Special Coordination
Funds for Promoting Science and Technology ‐ Model Program to Support Female Researchers(Osaka University).

1. Introduction

Genetic factors are not the only elements that decide health conditions when a child isborn. As Grossman (2000, 2006) point out, the individual’s health is formed through an
accumulation of income status or budget restriction, the ability to develop good health, andpreference for healthy behavior. Because newborn babies cannot accumulate health on
their own, the parents’ behavior and their surroundings determine the health of newborn
babies.
This paper examines the determinants of the health of newborn babies in Japan.
Specifically, we examine the effects on the weight of newborn babies of parents’ economic
conditions due to unemployment. We use regional panel data, because of the lack ofsufficient microdata on infant birthweight and parents’ economic conditions in Japan. The
use of regional panel data has several advantages, although it does not allow us to examine a
direct relationship between parents’ unemployment and their babies’ health condition.
First, individual microdata usually have short time spans, and only a few parents are
unemployed at any one point in time. It is hard to examine the effect of changes inunemployment status, and the results may be seriously affected by outliers. Prefecturaldata, in contrast, are available over longer time periods, so we can follow the changes intendencies of unemployment and babies’ health condition. This is important in Japan
where these two tendencies have changed a lot during the past 30 years. Further, theproblem raised by the presence of outliers could be smaller since the situation is averaged1

over individuals in a prefecture.1
Note that using regional (prefectural) data may raise the possibility of a spurious
correlation between unemployment and weight.

As is often pointed out in empiricalresearches using microdata, unobserved heterogeneity can cause a problem of endogeneity.
For example, cultural factors, customs, parents’ tastes, preferences, and/or hereditary factors
may be related to both low‐weight babies and high unemployment rates. In these cases, it
is not unemployment but unobserved heterogeneity that causes low birthweight.

The

endogeneity problem is often solved using the instrumental variable method. However,even when using microdata, it is difficult to find sufficient instruments strongly related tothe parents’ employment status but not related to the weight of the fetus or health conditions
during pregnancy. 2

We use panel analysis, removing prefectural and time‐designatedcharacteristics as two random components in an error term.
The objective of this paper is to supplement the existing literature on the determinants ofhealth condition at birth, using regional data. The second objective is to add Japanesefindings to the existing literature.

No economic investigation on this topic has been

conducted in Japan so far, although an increasing volume of economic research has been
accumulated in many other developed countries. According to OECD Health Data 2003,
the percentage of low‐weight babies among newborn babies has been increased dramaticallyin Japan since 1977. The figures since 1990 show a higher rate of increase, and by 2002 the

1

Averaging the conditions, however, could raise an aggregation bias. An analysis using individual

microdata would be required in the future.
2

Unobserved heterogeneity can be solved by utilizing samples of twins or siblings, althoughcollecting these samples is more difficult than collecting general samples.

2

percentage of low‐weight babies is exceptionally high in Japan compared to the otherdeveloped countries. In industrialized countries, generally, 2500g is a threshold of poor
health at birth, and newborn babies weighing 2500g or less are called low‐weight babies.
The determinants of infant birthweight must therefore be clarified, especially for Japan.3
Birthweight has an important implication for future productivity. 4

Almond andMazumder (2005) use influenza infection of the mother during pregnancy as an exogenous
shock that causes a deterioration of the nutritional status of the fetus, and show that low
weight has adverse effects on health in adulthood. Examining U.K. microdata, Currie andHyson (1999) show that babies under 2500g at birth scored lower in math tests at school, andhad lower wage rates among men and lower full‐time employment rates among women.
Case, Fertig, and Paxson (2005) also use data from the U.K. and show that having parents
who smoked during the prenatal period or being born underweight adversely affect healthstatus at the age of 42 and lower social class at that point in time. Black, Devereux, andSalvanes (2007) use data of individuals born in Norway between the late 1960s and the
mid‐1980s, and show that birthweight had a positive impact on height, IQ level, and wage
income at 18 to 20 years of age. Behrman and Rosenzweig (2004) use a sample of twinsborn in the U.S. between 1936 and 1955, and indicate a positive correlation between
birthweight and years of education or wage rate. Oreopoulos, Stabile, Walld, and Roos
(2008) use a different sample of twins, mainly of those born between the late 1970s and the3

The Science Council of Japan (2008) introduced a study on the relationship between low birthweightbabies in Japan and their health in later years.
4

See Currie and Madrian (1999), Grossman (2006), Cutler and Lleras‐Muney (2006), Royer (2009),

Currie (2009), and Almond and Currie (2010) for a conventional survey on this topic.

3

mid‐1980s, and show that those born healthier have higher high school graduation rates andhigher rates of not receiving income support after graduation. Currie, Stabile, Manivong,and Roos (2008) use a follow‐up survey for more than 20 years on the health status of sample
babies born in Canada between 1979 and 1987. They indicate that low‐birthweight babies
suffer from poor health also when they are grown up, which led to lower educational
outcomes such as mathematical competence and reading comprehension ability at schoolage, and a higher possibility of receiving income support during adulthood.5
Note that these findings are not for developing countries suffering from food insecurity
and malnutrition, but for industrialized countries, including Japan.

Examining theJapanese case to find how an adverse economic condition in a society affects health at birthfor the next generation might have an important policy implication for Japan.
In Section 2, we summarize specific literature that examine the relationship between
parents’ employment or income and newborn babies’ birthweight. We then explain ourestimation model. Section 3 introduces data by prefecture used in the study and reviews
the trend of birthweight in Japan through descriptive statistics. Section 4 reports the resultsof the analysis, and Section 5 summarizes the findings of this study.

5

However, studies by Behrman and Rosenzweig (2004) and Almond, Chay, and Lee (2005) using

twins and siblings indicate that negative effects of health on growth can be greatly reduced bycontrolling family components such as heredity. Royer (2009) uses a large‐scale sample of twins in
the U.S. and shows that low birthweight has a minor negative influence on educational outcomes inadulthood. Although the sample size is small, Miller, Mulvey, and Martin (2005) use Australiantwins data and show that if the family fixed effect is removed, birthweight would not affect academic
record or annual income. The cause‐and‐effect relationship needs to be further verified.

4

2. The Empirical Model

Case, Lubotsky, and Paxson (2002) point out that higher income lead to children with
better health. The study uses U.S. data collected between the late 1980s and the mid‐1990s
to show that the proportion of children with chronic diseases is higher and their recovery isslower among lower income households. Currie and Lin (2007) show that U.S. children
from poor households have relatively poor health conditions, including mental diseases.
Almond, Hoynes, and Schanzenbach (2008) indicate that a baby’s birthweight would
increase among low‐income households if they were given income support during theprenatal period. Currie and Moretti (2007) show that the rate of low‐weight babies is lower
among wealthy households, and that its gap between the wealthy and the poor increased in
the 1990s.
We regress birthweight on parents’ employment status, using prefectural panel data
between 1975 and 2000, but for a five‐year interval. In order to control for heterogeneities
among prefectures, we first take the first difference from the previous time period foreach variable (5‐year difference because data are taken every 5 years).

The estimatedmodel can be written as follows:
Δy
where
(t

i

u ,

βΔX

denotesaprefecture

(1)
( i

1, … ,47 ),

and

t

is

a

point

of

time

1980, 1985, 1990, 1995, 2000). In order to take the non‐linear effect into account, thelogarithm of birthweight and percentage of low‐birth babies are used for y in the estimation.
We focus on the effect of labor indicators in X.

We include unemployment rate,5employment rate, income level, and poverty indicators. The poverty indicator is calculatedon the basis of income, as described in detail in the next section. Due to high correlation
between income level and unemployment rate (non‐employment rate), including both ofthem in the explanatory variable at the same time would cause multi‐colinearity. Thus, weestimate them separately as different specifications and compare the results.
If unemployment or non‐employment status hampered the health of a newborn baby, theunemployment coefficient would be expected to be negative in the estimate for weight andpositive in the estimate for the percentage of low‐weight babies. The opposite sign would
be expected if looking at employment. If financial difficulty hampered the health of anewborn baby, the poverty coefficient would be expected to be negative in the estimate forweight and positive in the estimate for the percentage of low‐weight babies. The opposite
sign would be expected if looking at income level. Note that the coefficients on labor
indicator and poverty indicator would not necessarily have the same sign.

Althoughnon‐employment would be expected to have a strong correlation with low income, the twoelements may have different meanings. Rather, if the two coefficients have different signs,
it suggests that the influence of financial problems due to unemployment should be different
from the influences of the other shocks resulting from unemployment.
In equation (1), other factors such as mother’s age when she gave her first baby and theratio of three‐generation households are included as control variables in X.6 The error termis expressed as u

6

µ

λ

ν . The effects by prefecture are expressed as µ , and the

Although mother’s education may also be an important control variable, we cannot include that

because data for educational level are available for each prefecture only every other decade.

6effects by year as λ ,; νX.

is the random variable: ν ~iid 0, σν that is orthogonal to µ ，λ ，By taking the 5‐year difference, the effects, by prefecture, that are captured as

non‐stochastic variables are removed. However, there may be effects by prefecture that arecaptured as random variables. Further, in the case of data by prefecture that are collected
over a long period, it will be important to consider the influence of effects by year, in
addition to effects by prefecture7. In the following analysis, the estimation is first donewith a mixed model in which year effects are captured as a non‐stochastic variable andprefecture effects are captured as a random variable: µ ~iid 0, σµ .

In addition, an

estimation is done with a model that captures year effects also as a random variable—a
two‐way random effect model, assuming that µ ~iid 0, σµand λ ~iid 0, σλare notcorrelated to X.

3. The Data

This paper uses prefectural data on “average weight of newborn babies” and “percentageof newborn babies weighing 2500g or less,” both of which are reported annually in the
National Vital Statistics (Ministry of Health, Labour and Welfare). We take their logarithms
for the estimation.
7

By taking the first difference, we can remove the problem of a spurious correlation that appears onlong‐term time‐series data of birthweight and unemployment if they are both integrated of order 1.
Auto‐correlation may not be a serious problem since our data has 5‐year intervals. The analysis
covers a long‐term trend of 20 years, but the time periods used for the estimation are limited to 6
sequential years due to the few time points of only every 5 years. Therefore, we do not consider
time‐series components in error terms.

7

The main explanatory variables are the unemployment rate and the employment rate of
25‐ to 39‐year‐old males as indicators of the parents’ employment status. The employment
rate is calculated by dividing the number in employment by the population of those aged 25
to 39 years. Both data are from the National Census compiled by the Statistics Bureau,
Ministry of Internal Affairs and Communications. The reason for limiting the age to 25 to
39 years is to capture the probable age group of parents with newborn babies.
Instead of these employment variables, we use the variables reflecting parents’ poverty
due to unemployment for comparison.
income data.

The following two variables are created fromFirst, we calculate the income level of the bottom 10% group for eachprefecture, utilizing the data from the Employment Status Survey (Statistics Bureau,
Ministry of Internal Affairs and Communications).

Since the timing of the survey isdifferent from that of the National Census, we pick up the 1982, 1987, 1992, 1997, and 2002
waves of the survey, which are closest to the National Census years (1980, 1985, 1990, 1995,
and 2000). The survey reports the number of households by income class in each prefecture,and the minimum and maximum points of each income class. Using this, we calculate thelevel of income at the lowest 10% for each prefecture.8 For the estimation, we deflate theincome level by consumer price level in each prefecture.
Second, we calculate the percentage of poor households. Using the same income classdata as described above, we calculate the number of households belonging to the income
8

Suppose that F is the number of households, and x is a middle point of income class j where thebottom 10% households belong to. The bottom 10% income point is x
.

/

/

/

.

x

x

x

Likewise, the median is calculated by changing 0.1 to 0.5 for j‐th class where thehousehold from the bottom 50% points belong to.

8

classes with less than half the national median.

We calculate the percentage of thisʺhalf‐the‐medianʺ group to the total number of the households for each prefecture.
The explanatory variables are as follows. First, the average age at which a mother gave
the first birth in each prefecture. The National Vital Statistics (Ministry of Health, Labourand Welfare) report the number of women who give birth by age group. The data arebroken down into increments of 5 years between 15 and 50 years of age. The weighted
average is calculated using each class mark and frequency for each prefecture.9 The rate ofliving together with parents, which is a unique characteristic of Japanese households, iscontrolled for.

Specifically, the percentage of three‐generation households in eachprefecture is used. Whether or not a family lives with parents has various influences onhousehold behavior. It may change the mother’s employment status and the affluence ofthe household. The attitude of the couple’s parents to childbearing may influence lifestyleor nutritional intake during the prenatal period (during pregnancy), which may affect theweights of the newborn babies.
Because the National Census is taken every five years, the sample size for our estimation
is 235 (47 times 5 years). Table 1 shows the definitions of the variables and their descriptive
statistics. We take the first difference, which is the 5‐year difference, for the estimation.

9This paper does not use the education level of the age group to which the mother belongs. This isbecause the education level by age and by prefecture reported by the National Census is taken only
every 10 years. The other data on female educational level in each prefecture are not available inJapan. In addition, academic background is strongly correlated with labor index and child‐birth age,
and including these variables as explanatory variables in the same equation anyway raises a problem
of multi‐colinearity making the estimates unstable.

9

<Table 1>

4. The Results

Table 2 shows the results of the effect of unemployment or employment on the average
weight of newborn babies (Panel A) and on the ratio of low‐weight babies (Panel B).
Column (1a) shows the result of the mixed model where the prefectural effects are taken as
a random variable and year effects as a non‐stochastic variable. Column (1b) shows theresult of the two‐way random effect model where both prefecture and years are taken as
random variables. As for a model specification, the LM test indicates the necessity ofcapturing individual and time effects at the same time. The result of the Hausman testshows that the null hypothesis that assumptions of the two‐way random effect model areapplicable is not rejected even at the 10% level of significance, and thus supports the
specifications of (1b). The same applies to (2a), (2b), and Panel B.10
The results in column (1) show that an increase in unemployment decreases the weight ofnewborn babies. The coefficients of the unemployment rate of 25‐ to 39‐year‐old males are
all negative, and the coefficient in (1b) is significant at the 5% level of significance, while thecoefficient in (1a) is not significant at the 10% level.

In column (2), an increase inemployment raises the weight of newborn babies: the coefficients of the employment rate arepositive and significant at the 5% and 1% levels of significance in (2a) and (2b), respectively.

10

The estimates would be biased if we used the mixed model when the two‐way random effect model

is correct (Baltagi, 2001).

10

At least with (1b) and (2b), in which specifications are statistically supported, we can say thathigher unemployment rate or lower employment rate disturbs the health of newborn babies
in terms of their weights.
The same implication is shown in Panel B where we use the percentage of low‐weightbabies, instead of average weight. If the unemployment rate (employment rate) for the
male aged between 25 and 39 are higher (lower), the percentage of low‐weight babies
becomes lower. The positive (negative) coefficients of the unemployment (employment)
rate are significant in both (1b) and (2b).

<Table 2 Panel A><Table 2 Panel B>

As for the other variables, Panel A shows that birthweight increases as the mother’s age atfirst birth increases.

Older mothers may be more conscious of their health during

pregnancy. In addition, smoking rates are higher among younger women in Japan.11 The
higher rate of smokers among young women may impair fetal growth. Panel B shows thathigh three‐generation household rates reduce the percentage of low‐weight babies. Living
together with the parent generation (grandparent generation) may lead to better care forhealth such as higher nutritional intake during pregnancy for the mother, which results in

11

According to the National Health and Nutrition Survey Report (Project for National Health andNutrition Survey, Ministry of Health, Labour and Welfare), the female smoking rate as of 2000 was
20.9% in the 20s, 18.8% in the 30s, 13.6% in the 40s, 10.4% in the 50s, 6.6% in the 60s, and 4.0% in the
70s or older.

11the lower number of low‐weight babies.
As described above, parents’ unemployment or non‐employment leads to lower healthstatus among newborn babies. Is this caused by lowered income due to unemployment?
Table 3 summarizes the results of the influence of low income or poverty on birthweight.
As for the specification, Panels A and B both accept the assumptions of the two‐way random
effect model, supporting the specifications of (1b) and (2b).
Panel A in Table 3 shows the positive sign of the coefficients of income level for thebottom 10% group. However, they are not significant at the 10% level of significance. The
coefficients of the percentage of poor households show a negative sign, although they are notsignificant at the 10% level. There are no statistically significant effects of low income orincome‐measured poverty on reducing the average weight of newborn babies.
Panel B in Table 3 shows that the coefficients of the income level for the bottom 10%
group basically are negative, and the coefficients of the percentage of poor households are
positive. However, these coefficients are not significant at the 10% level of significance.

<Table 3 Panel A><Table 3 Panel B>

In sum, the influence of parents’ non‐employment on reducing birthweight is statisticallysignificant, while the influence of income‐measured poverty on birthweight is not significant
at least at the 10% level. Parents’ non‐employment may reflect a low‐income state for a long
period, while low annual income may reflect temporary income loss. Alternatively, parents’12

non‐employment affects the health of newborn babies negatively not because of the lowered
income from being jobless. For a male in his late 20s through 30s, the burden of not having
a job is not limited to financial difficulties. A mental burden such as uncertainty about the
future may damage his health and that of his family. The true burden of non‐employment
may not come from low income. This might be true especially in a society where people
have a stereotyped perception that the husband as the head of the household has to support
the household finances even if the other household members have income.

5. Conclusion

This paper examines whether parents’ unemployment deteriorates the health of newborn
babies in Japan, focusing on their weight at birth. Because sufficient individual data are notavailable to control for unobserved heterogeneity, causing a problem of endogeneity, we use
regional data instead. Using prefectural data for 20 years, we examine a longer trend
between unemployment and birthweight. We conduct panel estimation dealing with bothprefecture and year components in error terms as random variables, since both regional andtime effects cannot be fixed in an error term.
We found that high unemployment rate or low employment rate leads to lower average
weight of newborn babies, and raises the proportion of low‐weight babies among newborns.
In contrast, the influences of low income or income‐measured poverty on reducing theweights of newborn babies were not confirmed at least at the conventional level of statisticalsignificance. Parents’ non‐employment may hinder the health of newborn babies due to13reasons other than financial difficulties.
Previous studies conducted in other countries show that low birthweight depresses
various post‐growth outcomes.

Low birthweight negatively impacts not only physicalhealth but also mental health, as well as cognitive performance such as test scores at schooland non‐cognitive performance. It may also lower productivity and health status during
working years.

Non‐employment of parents creates an income disparity within thatgeneration. At the same time, it leads to deteriorating health of the newborn child, whichthen may result in a health disparity and thus an economic disparity in the next generation.
Measures against unemployment probably need to be discussed with a much longer‐termview than previously thought.

14

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Quarterly Journal of Economics, Vol. 120, No. 3, pp. 1031‐1083.
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on Poverty: The Impact of Food Stamps on Birth Outcomes,” NBER Working Papers, No. 14306.
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pp.258–262.
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115‐128.
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Market? The Effect of Birth Weight on Adult Outcomes,” Quarterly Journal of Economics, Vol.
122, No. 1, pp. 409‐439.
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Socioeconomic Status? The Case of Low Birthweight,” The American Economic Review, Vol. 89,
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Currie, Janet and Wanchuan Lin (2007) “Chipping Away at Health: More on the Relationshipbetween Income and Child Health,” Health Affairs, Vol. 26, No. 2, pp. 1‐13.
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16

Table 1. Definition of Variables and Descriptive Statistics
Variable Name
Birthweight

Definition, etc.
Average weight of newborn babies. Logarithmic values used for the analysis.
(log of gram)

Percentage of low‐weight

Percentage of babies among newborn babies who are 2500 gram or under.

babiesLogarithmic values used for the analysis. (log of %)Unemployment rate of 25‐ Average unemployment rate of males aged 25‐29 years, 30‐34 years old, and39 year‐old males

35‐39 years. (%)

Employment rate of 25‐39 Average percentage of employed males aged 25‐29 years, 30‐34 years old, andyear‐old males

Income level of the bottom
10% group

Proportion of poor
households

Motherʹs age of giving thefirst baby

Percentage of three‐generation households

35‐39 years. (%)

Mean

Standard Dev Minimum

Maximum

1.13720.0191

1.0986

1.1756

1.89120.18001.52612.33213.1336

1.39851.20009.93330.87350.04310.73220.94572439.646

505.2831225.966

3756.979

0.15840.07930.04000.450028.49510.629425.5505

30.3438

15.53176.69002.867533.5724

The annual income level of the bottom 10% group is calculated from income
class data for general households having an employed head of a household.
This income level is deflated by consumer price level for each year. (1000yen)
Using the same data as used in Income level of the bottom 10% group, the ratioof the households with income of less than half the national median value tototal households in each prefecture in each year. (%)
Weighted average of the motherʹs age at the time of giving the first birth iscalculated from classified data for each 5 years of age weighted by the numberof persons in each age group. (years old)

The percentage of three‐generation households among all households. (%)

Notes.
1. All the data are taken as prefectural average.
2. Birthweight, Percentage of low‐birth weight babies, and Motherʹs age of giving the first baby are from 1975, 80, 85, 90, 95, 2000 National Vital Statistics (Ministryof Health, Labour and Welfare), Unemployment rate, Employment rate, and Pecentage of three generation households are from 1975, 80, 85, 90, 95, 2000 National
Census (Statistics Bureau, Ministry of Internal Affairs and Communications), and Income level of the bottom 10% group and Proportion of poor households arecalculated from the data reported by 1977, 82, 87, 92, 97, 2002 Employment Status Survey (Statistics Bureau, Ministry of Internal Affairs and Communications).
3. All the variables are taken as the first difference (5‐year differences) for estimations.

17

Table 2. Unemployment and Birthw eight
Panel A. Explained Variable: Logarithmic values of average weight reflecting 5‐year difference

Unemployment rate of 25‐
39 year‐old male

(1a)

(1b)

‐0.0005(0.0005)‐0.0009 **(0.0004)

Employment rate of 25‐39
year‐old male
Motherʹs age of giving her
first child
Rate of three‐generation
households

Constant term
Effects by prefecture
Effects by year
F test: λt=0
LM statistics
2
for a test:    0
for a test:  2  0
Wald Statistics
Hausman Test: (a) vs (b)

(2a)

(2b)

0.0640 **(0.0278)

0.0739 ***(0.0259)0.0011(0.0008)0.0040 ***(0.0009)

0.0014 *(0.0008)0.0017 **(0.0008)

0.0003(0.0004)

0.0000(0.0003)0.0005(0.0004)

0.0000(0.0003)‐0.0110 ***(0.0011)
randomfixed‐0.0141 ***(0.0010)
randomrandom

‐0.0092 ***(0.0014)
randomfixed‐0.0070 ***(0.0007)
randomrandom313.16‐‐‐326.69‐‐‐12.14
1254.0312.35
1229.790.00000.0000Note s
1. The numbe r of the obse rvations is 235 （47 pre fe cture s×5 pe riods ）．
2. (a) re pre se nts a mixe d mode l of fixe d e ffe ct and random e ffe ct that caputure s an e ffe ct by pre fe cture as a randomvariable , and an e ffe ct by ye ar as a non‐stochastic variable .
(b) re pre se nts random e ffe ct mode l that capture s e ffe cts by pre fe cture and by ye ar as random variable s.
3. As for the de finition of the variable s, se e Table 2 and the de scription in the pape r.
4. *, **, *** re pre se nt the e stimate s at 10%, 5%, and 1% le ve l of significance , re spe ctive ly.

18

Table 2. Continued
Panel B. Explained Variable: Logarithmic values of percentage of low‐weight babies reflecting 5‐year
difference(1a)Unemployment rate of 25‐
39 year‐old male

0.0112(0.0089)(1b)Rate of three‐generation
households

Constant term
Effects by prefecture
Effects by year
F test: λt=0
LM statistics
2
for a test:    0
for a test:  2  0
Wald Statistics
Hausman Test: (a) vs (b)(2b)

0.0201 ***(0.0074)

Employment rate of 25‐39
year‐old male
Motherʹs age of giving her
first child(2a)‐0.9479 *(0.5072)‐1.1204 ***(0.4477)‐0.0088(0.0138)‐0.0169(0.0131)‐0.0118(0.0139)‐0.0145(0.0132)‐0.0138 **(0.0065)‐0.0094 **(0.0047)‐0.0157 **(0.0066)‐0.0094 *(0.0052)

0.1048 ***(0.0199)
randomfixed

0.0956 ***(0.0153)
randomrandom0.0809 ***(0.0258)
randomfixed

0.0526 ***(0.0132)
randomrandom121.85‐‐‐

119.58‐‐‐7.68
390.818.47
396.860.00000.001719

Table 3. Poverty and Birthw eight
Panel A. Explained Variable: Logarithmic values of average weight reflecting 5‐year difference

Income level of the bottom
10% class

(1a)

(1b)

0.00006(0.00014)

0.00006(0.00013)

Proportion of poor
households
Motherʹs age of giving her
first child
Rate of 3‐generation
households

Constant term
Effects by prefecture
Effects by year
F test: λt=0
LM statistics
2
for a test:    0
for a test:  2  0
Wald Statistics
Hausman Test: (a) vs (b)

(2a)

(2b)

‐0.0068(0.0078)‐0.0033(0.0076)0.0011(0.0008)0.0011(0.0008)0.0011(0.0008)0.0012(0.0008)

0.0003(0.0004)‐0.0001(0.0003)0.0003(0.0004)‐0.0001(0.0003)‐0.0110 ***(0.0017)
randomfixed‐0.0051 ***(0.0004)
randomrandom‐0.0107 ***(0.0013)
randomfixed‐0.0055 ***(0.0004)
randomrandom315.84‐‐‐246.18‐‐‐11.38
1134.2710.39
661.310.00000.0000Note
1. Se e footnote s in Table 2.

20

Table 3. Continued
Panel B. Explained Variable: Logarithmic values of percentage of low‐birthweight babies reflecting 5‐
year difference
Income level of the bottom
10% class

(1a)

(1b)

‐0.0013(0.0025)‐0.0026(0.0022)

Percentage of poor
households
Motherʹs age of giving her
first child
Rate of 3‐generation
households

Constant term
Effects by prefecture
Effects by year
F test: λt=0
LM statistics
2
for a test:    0
for a test:  2  0
Wald Statistics
Hausman Test: (a) vs (b)

(2a)

(2b)

0.1153(0.1414)0.0737(0.1293)‐0.0086(0.0139)‐0.0104(0.0133)‐0.0083(0.0138)‐0.0095(0.0133)‐0.0135 **(0.0065)‐0.0066(0.0051)‐0.0134 **(0.0065)‐0.0063(0.0051)

0.1028 ***(0.0301)
randomfixed

0.0657 ***(0.0100)
randomrandom0.1022 ***(0.0243)
randomfixed

0.0646 ***(0.0102)
randomrandom118.72‐‐‐

122.45‐‐‐7.72
340.62

7.61
329.440.00000.000021