birth weight; economics of health; perinatal health; nutrition

Tayatat Kanjanapipatkul

English

Research Papers in Economics

1
Comparison of New York and San Fransisco Hospital Record
by  Tayatat Kanjanapipatkul
The following is a comparison of the analysis based on the San Francisco hospital
record, to be referred to as SF, and the New York Lying-in hospital, to be referred to as
NY. The objective of this analysis is to show similarity and difference between results
obtained from the two data sets.
There are several variables in common in the two data sets. Descriptive statistics
of these variables are shown in table 1 and 2.  Mean of child characteristics variables are
very close to each other. The maximum difference in mean of diameter measurement is
0.57 cm for bipareiral diameter. Difference in mean of child length is very low, at 0.2 cm.
Birth weight show slight difference in mean of 171.54 pound, which is strikingly less
than the standard deviation in both samples.
Comparison of standard deviation reveals that in general, SF have a lower
variation than NY. This can easily be seen from graphs on page 7, which plot occipito-
frontal, occipito-mental, bipareital diameter, and child length. The upper right graph on
page 8 shows the plot for birth weight. Virtually, SF data are more clustered then NY,
though they reach peak at about the same value.
Quartile analysis also confirm that both data sets have very similar quartile
distribution. However, NY contains more extreme value. In almost every case, NY have
higher maximum value and smaller minimum value than SF. Besides, range of NY
variables are consistently larger than SF.
There exists some problem in comparison of head circumference. SF provides a
so called SOB circumference, while NY containg 3 differents notions of circumference
measurment. However, it should be noted that SOB value have closer mean and quartile
value compared to Occipito-mental measurement than other measurements. In addition, a
comparison of SOB with any NY measurement consistently give a wider variation in NY
than SF, in range, maximum and minimum value. Nevertherless, the data provides some
evidence to support a high correlation between the two data sets in child characteristic
variables.2
 Birth characteristics provide weaker support to the above results. Gestational weeks
seems to be correlated in both data sets.  However, there exists a large variation in the
distribution of gestational week in NY which does not exist the SF. This is obvious in the
graph on page 8 which contrast a spike pattern of SF with a flat pattern in NY. 
Distribution of parity is clearly not the same. There are more women giving birth
in SF who have parity of 1 and 2 than parity 0, which is the highest in NY. This suggests
that mothers in SF are more experienced in giving birth compared to NY. However, this
might results from the absence of  fatal birth in SF which is probably accrued to mother
with 0 parity.
Mothers’ characteristics compare relatively well. Height and age have a very
close mean, standard deviation and quartile value.  For weight, there are two weight
reported in SF. The comparison shows that weight at physical exam has a similar
distribution to NY than weight at most recent prenatal. Nevertheless, the lower right
graph on page 8 shows that there are a significant different between birth place of mother
in the two data sets. Most mothers in NY were born in Europe while most were born in
the US.
Economics variable are also available for comparison. However, it is not clear to
what extent they can be compared. Fathers’ annual income and monthly rent are higher in
SF. The variation in income is much higher in SF, while the variation in rent is lower.
A small table below the first table shows a comparison of non-numerical
characteristics. These variables compare very well. Data in both sets contain slightly
above 50% male baby born to more than 90% of women whose occupation is housewife,
and about 70-80% of men who were currently employed.
The third table shows a comparison of the estimate of a linear regression of birth
weight on several socioeconomic variables. The estimates are compared with the estimate
based on the New York sample came from the paper by Dora Costa. “Unequal at Birth: A
long-term Comparison of Income and Birth Weight."  Journal of Economic History 58
(December 1998): 987-1009. The sample had been limited to white babies with at least
37 weeks of gestation.
The left hand side of the table gives the SF estimates, and the right hand side of
the table gives the NY estimates. The first column of each part gives the mean of each3
variable in the regression. The second and third column of each part give a coefficient
estimate of a linear regression model fitted to the data excluding and including the
variable mother’s height, respectively.
The mean of each variable compares relatively well with the exception of the
dummy of birthplace of the mother, which indicates that a relatively smaller proportion of
mother is foreign born in the SF regression. Comparison of regressions using both data
sets reveals both similarity and differences in the estimates. This suggests that the
analysis had been conducted in the right direction though differences in the estimates
implies that there is need for further investigation on this issue.
The coefficient estimate of log of father’s wage is, in contrast to the finding based
on NY, significant in the model excluding mother’s height. In fact, the estimate for the
model with mother’s height is almost significant at 10% level. This suggests some
evidence of the income effect on birth weight. However, there is a number of missing
observation on income.
The estimate of the log of gestational period is significant in all regression within
and between the sample. However, the estimate for SF changes considerably by including
the mother’s height variable in the regression. This might suggest that some relationship
between mother’s height and gestational weeks.
Parity is significant, but the degree of significance is a lot less in the SF
regression. In fact, parity squared is not significant at all, though it is in the NY data. It
should be noted that the inclusion of mother’s height makes parity statistically
insignificant. This also could suggest a relation between the two variables.
Mother’s age, year born and dummy if mother is foreign born is not significant in
both models and both data sets. The variable mother’s height is significant in both data
sets. In addition, the magnitude of the estimates is approximately the same. This is a
strong evidence suggesting that the variable is an important determinant of birth weight.
Dummy for child gender is significant. This agrees with the fact that male baby is
heavier. It should be noted that the estimate is significant in both models and in both data
sets. The magnitude of the estimates in the SF regressions is approximately the same.
There are several possibilities to improve on this estimation. It should be noted
that as the number of observation increase father’s wage becomes more and more4
significant. This effect is not limited to the two regressions using the SF data set. It can
be generalized to estimates obtained from almost any regression specification. The most
important one is the inclusion of dummy variable on the father’s wage. This suggests that
an inclusion of dummy variable for missing observation can improve the result. The use
of family income, which is available in the data set, instead of father’s wage can also
reduce the problem.
Selection could be an important issue in the regression analysis. Usually,
relatively poor families are asked to report their income. If this hypothesis is correct, a
more elaborate regression in which selection bias is controlled must be implemented.
Parity and log of gestation period exhibit some sensitivity when mother’s height is
included or excluded from the regression. This suggests a relationship among the three
variables, and hence is also a subject of further research.
Another possible way to improve the result is to replace the inclusion of mother’s
age and year the baby was born by the year mother was born. This method had been
advocated on the argument that there had been an evidence that rapid physical change
had occur to women born in the early part of the twentieth century.
The third table shows comparisons of various characteristics of the sample in the
regression of the two data sets. It is obvious that, in general, the number is about the
same, with the exception of the percentage of babies with birth weight less than 2500
pound.
Five graphs which follows the third table give a distribution of several variables
which describe the data used in the SF regression. The distribution of gestational weeks,
mothers’ age, and  birth year are compared to the distribution of those variables in total
data set. These comparisons serve to confirm that there is no selection bias in the way the
sample is chosen. The distribution of sample is highly correlated to the distribution in the
entire data set.5
Table 1: Comparison of Variables in New York and San Francisco Data Set
Variables Hospital Mean SD Max Q3 Median Q1 Min Range Missing N
Child Characteristics
NY 11.18 1.28 14.5 12 11.5 10.5 5 9.5 33 622 Occipito Frontal Diameter
SF 11.68 0.73 14.5 12 11.75 11.25 8 6.5 32 512
NY 12.99 1.54 19 14 13 12.5 6.5 12.5 33 622 Occipito Mental Diameter
SF 13.49 1.01 16.5 14 13.5 13 8 8.5 41 503
NY 8.77 1.03 13 9.5 9 8.5 4.5 8.5 33 622 Biparietal Diameter
SF 9.34 0.62 14 9.7 9.5 9 7.5 6.5 26 518
NY 50.05 5.4 77 53 51 48 20 57 23 632 Child Length
SF 50.22 2.81 60 52 50 49 34 26 13 531
NY 3234.06 871.41 5443 3800 3360 2840 400 5043.08 34 621 Birth Weight
SF 3405.60 538.46 4920 3740 3400 3080 1620 3300 6 538
Biracromial Circumference 33.77 3.9 45 36 34 32 10 35 36 619
Frontal Circumference 33.89 3.15 45.5 35.5 34 33 10 35.5 36 619
Occipito Mental Circumference
NY
31.42 3.24 38 33 32 31 3 35 35 620
SOB Circumference SF 31.87 1.7 38 33 32 31 20.5 17.5 33 511
Birth Characteristics
NY 38.89 4.34 54.3 41.4 39.75 37.2 19.1 35.2 75 580 Gestation Weeks
SF 38.05 2.78 48 39 38 37 24 24 50 494
NY 1.54 2.04 22 2 1 0 0 22 13 642 Parity
SF 2.06 2.13 15 3 2 1 0 15 7 537
Mothers' Characteristics
NY 157.34 7.49 183 162 157.48 153 106 77 120 535 Mothers' Height
SF 158.806 8.04 189 164 160 154.5 116 73 179 365
NY 26.67 6.05 48 30 26 22 15 33 29 626 Mothers' Age
SF 25.85 6.09 45 30 25 21 15 30 2 542
Mothers' Weight NY 65.5 15.54 175 70 63 56 28 147 124 531
     at Most Recent Prenatal 69.48 14.88 189.4 74.3 67.7 61.7 38.7 150.7 179 365
     at physical Exam
SF
64.49 12.34 147 70.4 63 56.7 36.5 110.5 77 4676
Economics Variables
NY 1056.89 482.75 3900 1300 1040 676 208 3692 70 585 Husband Annual Wage
SF 1171.98 507.06 5184 1440 1200 960 0 5184 261 283
NY 19.98 15.38 220 25 16 12 0 220 61 594 Monthly Rent
SF 24 10.67 72 30 24 17.5 0 72 233 311
Variables New York San Fransisco
Child Gender 54.7% male 53.1% male
Birth Date Jan. 1910 – Nov. 1931 Dec. 1916 – Nov. 1936
Mothers’ Occupation 91.8% housewife 94.3% housewife
Husbands’ Employment 78.1% employed 71.5% employed7
Comparison of Biparietal Diameter at Birth
0.00%
10.00%
20.00%
30.00%
40.00%
50.00%
60.00%
70.00%
4-5 5-6 6-7 7-8 8-9 9-10 10-11 11-12 12-13 13-14
Length (cm)
F
r
e
q
u
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n
c
y
New York San Fransisco
Comparison of Occipito-frontal diameter
0.00%
5.00%
10.00%
15.00%
20.00%
25.00%
30.00%
35.00%
40.00%
45.00%
50.00%
5-6 6-7 7-8 8-9 9-10 10-11 11-12 12-13 13-14 14-15
Length (cm)
F
r
e
q
u
e
n
c
y
New York San Fransisco
Comparison of Occipito-mental Diameter
0.00%
5.00%
10.00%
15.00%
20.00%
25.00%
30.00%
35.00%
40.00%
45.00%
6-7 7-8 8-9 9-10 10-
11
11-
12
12-
13
13-
14
14-
15
15-
16
16-
17
17-
18
18-
19
Length (cm)
F
r
e
q
u
e
n
c
y
New York San Fransisco
Comparison of Child Length
0.00%
10.00%
20.00%
30.00%
40.00%
50.00%
60.00%
30- 30-35 35-40 40-45 45-50 50-55 55-60 60-65 65+
Length
F
r
e
q
u
e
n
c
y
New York San Fransisco8
Comparison of Birght Weight 
0.00%
5.00%
10.00%
15.00%
20.00%
25.00%
30.00%
35.00%
40.00%
400-
1000
1000-
1500
1500-
2000
2000-
2500
2500-
3000
3000-
3500
3500-
4000
4000-
4500
4500-
5000
5000-
5500
Weight (pounds)
F
r
e
q
u
e
n
c
y
New York San Fransisco
Comparison of Gestation Weeks
0.00%
10.00%
20.00%
30.00%
40.00%
50.00%
60.00%
70.00%
24- 24-28 28-32 32-36 36-40 40-44 44-48 48+
Weeks
F
r
e
q
u
e
n
c
y
New York San Fransisco
Comparison of Parity
0.00%
5.00%
10.00%
15.00%
20.00%
25.00%
30.00%
35.00%
40.00%
45.00%
0 1 2 3 4 5 6 7 8 9 10 11+
Parity
F
r
e
q
u
e
n
c
y
New York San Fransisco
Comparison of Mothers' Birthplace
0.00%
10.00%
20.00%
30.00%
40.00%
50.00%
60.00%
70.00%
usa america Europe others missing
Region
F
r
e
q
u
e
n
c
y
New York San Fransisco9
Table 2: Comparison of Regression of Birth Weight for White Babies with at least 37 Weeks of
Gestation Age between San Fransisco and New York Hospital Record
San Fransisco New York Variables
Mean Coefficient Coefficient Mean Coefficient Coefficient
Birth Weight 3498.43 3,532.00
Intercept 8196.73
(14309.43)
11905
(17095.37)
-9288.9
(10672.61)
-12798.21
(11236.62)
Log of Father’s
Wage
4.44 120.71**
(58.00)
114.93
(73.00)
3.74 110.99
(104.55)
59.03
(108.89)
Log of Gestational
Weeks
3.66 1410.86*
(837.20)
2306.88**
(944.42)
3.70 1966.45***
(546.46)
1596.58***
(565.75)
Parity 1.74 139.58**
(55.12)
94.38
(59.66)
1.68 125.57***
(29.48)
127.31***
(31.14)
Parity Squared 5.53 -13.67
(8.82)
-4.93
(9.39)
6.51 -7.84***
(2.96)
-6.30*
(2.96)
Mother’s Age 24.89 -1.35
(7.72)
-4.74
(8.72)
26.48 2.77
(6.33)
-0.96
(6.98)
Year Born 1928 -5.49
(7.09)
-9.96
(8.37)
1921 2.51
(5.43)
4.43
(5.70)
Dummy if mother is
foreign born
0.30 -48.13
(79.40)
37.96
(87.91)
0.69 -28.56
(65.60)
14.12
(71.26)
Mother’s Height 158.95 10.90**
(5.40)
157.54 9.13**
(3.84)
Dummy if child is
male
0.54 140.49**
(70.32)
148.41*
(76.83)
0.55 164.82***
(54.63)
200.33***
(56.39)
Number of
Observation
212 166 380 384
R Squared 0.11 0.14 0.17 0.17
The New York estimates are obtained from Costa, Dora, "Unequal At Birth: A long-term Comparison of Income and Birth Weight," Journal of Economic
History, 1998.10
Table 3: Comparison of San Francisco Data Set and New York Sample
San Francisco Data New York Sample
Mean 3459.57 3463 Birth Weight (White
Babies) Median 3450 3467
% Weight less
 than 2500
3.43 5.5
Female 3380 3430 50
th  Percentile Birth
Weight Male 3500 3500
Female 50 51 50
th  Percentile Length
Male 51 51
Female 34.56 34.5 50
th  Percentile Head
Circumference Male 32 35
90
th-10
th 1210 1375
90
th-50
th 640 745
50
th-10
th 570 630
Birth Weight
Percentile Difference
75
th-25
th 645 880
90
th-10
th 137 118
90
th-50
th 45 51
50
th-10
th 92 67
Predicted Birth Weight
Difference
75
th-25
th 59 67
Frequency Distribution of Father's 
Income
0
10
20
30
40
0
12
39
55
75
85
100
112
130
148
220
Income
F
r
e
q
u
e
n
c
y11
Frequency Distribution of Gestation 
Weeks
0
20
40
60
80
100
120
24
27
29
31
33
35
37
39
41
43
45
47
Total Records Sample
Frequency Distribution of Mother's Age
0
10
20
30
40
50
15
18
21
24
27
30
33
36
39
42
Total Records Sample12
Frequency Distribution of Mother's Height
0
2
4
6
8
10
12
1
1
6
1
4
1
1
4
6
1
5
0
1
5
1
1
5
4
1
5
6
1
5
8
1
6
0
1
6
1
1
6
3
1
6
5
1
6
7
1
7
0
1
7
3
1
8
9
Height
F
r
e
q
u
e
n
c
y
Frequency Distribution of Year Born
0
10
20
30
40
50
1917
1919
1921
1923
1925
1927
1929
1931
1933
1935
Total Records Sample

Comparison of New York and San Francisco Hospital Records

http://www.cpe.uchicago.edu/publication/lib/sfny.pdf

Comparison of New York and San Francisco Hospital Records

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Research Papers in Economics