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Federal Reserve Bank of Chicago

How Did Schooling Laws
Improve Long-Term Health
and Lower Mortality?
Bhashkar Mazumder

REVISED January 24, 2007
WP 2006-23

How Did Schooling Laws Improve Long-Term Health
and Lower Mortality?
Bhashkar Mazumder
Federal Reserve Bank of Chicago
January 24, 2007

Abstract
Recent evidence using compulsory schooling laws as instruments for education suggests that education has a causal e¤ect on mortality (Lleras-Muney, 2005). However,
little is known about how exactly education a¤ects health. This paper uses compulsory schooling laws to try to identify how education impacts health and to indirectly
assess the merit of using these laws to infer the causal e¤ect of education on health.
I …nd that previous Census mortality results are not robust to the inclusion of statespeci…c time trends but that robust e¤ects of education on general health status can
be identi…ed using individual level data in the SIPP. However, the pattern of e¤ects
for speci…c health conditions in the SIPP appears to depart markedly from prominent
theories of how education should a¤ect health. I also …nd that vaccination against
smallpox for school age children may account for some of the improvement in health
and its association with education.

These results raise concerns about using early

century compulsory schooling laws to identify the causal e¤ects of education on health.
I thank Douglas Almond for many helpful discussions and for contributing to some of the ideas in this
paper. I also thank participants at the 2006 NBER Spring Health meetings for many helpful comments. I
also thank Adriana Lleras-Muney for sharing her computer code, data and for helpful discussions. Similarly
I thank Claudia Goldin for sharing her data. The views expressed here do not necessarily re‡ect those of
the Federal Reserve system.

1

1

Introduction

Social scientists have long been aware that there is a strong association between education
levels and health (Kitagawa and Hauser 1973) but much less is known about how these
factors are connected, and whether the relationship is in fact, causal. As Richard Suzman
of the National Institute on Aging recently stated, "Education ...is a particularly powerful
factor in both life expectancy and health expectancy, though truthfully, we’ not quite
re
sure why." (Lyman 2006). A recent study by Lleras-Muney (2005) provides perhaps the
strongest evidence that education has a causal e¤ect on health. Utilizing a more compelling
research design than most previous work, Lleras-Muney uses state compulsory school and
child labor laws as instruments and …nds that increased schooling for those born in the …rst
quarter of the twentieth century, led to dramatic reductions in mortality rates during the
1960s and 1970s. Her IV point estimates imply that an additional year of schooling reduces
mortality risk by between 30 and 60 percent.1 These results were prominently featured in a
front page story in the New York Times entitled "A Surprising Secret to a Long Life: Stay
in School" (Kolata 2007)
The mechanism by which schooling improves health, however, remains illusive. A variety
of theories have been proposed to explain how schooling might improve health. These
theories emphasize the role of eduction in a¤ecting various proximate determinants of health.
Health determinants leveraged by education include: (i) …nancial resources, (ii) decision
making ability, (iii) time preference. Lleras-Muney (2005) found that adjustment for income
or occupation did not alter her IV estimates, and therefore discounted the role of resources
behind her …ndings. Instead, her results appeared more consistent with a role of “critical
thinking skills." Such skills may allow one to utilize advances in medical technology (e.g.,
Glied and Lleras-Muney (2003)) or manage chronic conditions better (e.g. Goldman and
1 Lleras-Muney

uses the Census to estimate ten year mortality rates for synthetic cohorts using state of

birth, year of birth and sex. The mean death rate in Lleras-Muney’ sample is about 10 percent and her IV
s
estimates are as high as -0.06 implying that a 1 year increase in schooling would lower the death rate by as
much as 6 percentage points.

2

Smith (2002)). These hypothesized mechanisms both reference the ability to obtain and
process information, which may be improved through education.2
In this paper I examine whether compulsory schooling laws can provide insights into
exactly how education may improve health. As part of the analysis I also reassess whether
compulsory schooling laws can be used to draw inferences about the causal e¤ects of education on health. As is well known, there is no test of instrument exogeneity in the exactly
identi…ed case.3 I therefore conduct some (necessarily indirect) exercises to explore the
validity of compulsory schooling law instrument.
I begin by revisiting the mortality results in Lleras-Muney (2005) by adding signi…cantly
more data (e.g. doubling the 1970 Census sample and quintupling the 1980 sample) and
employing several robustness checks. The key …nding is that the Census mortality results
are not robust to the inclusion of state-speci…c time trends. This raises the concern that
the instruments might be picking up smooth cohort trends in educational attainment rather
than discrete increases induced by more stringent compulsory schooling laws.4 I also …nd
that the e¤ects of education on mortality appear to be driven primarily by the earliest
cohorts (born 1901-1912) during the 1960 to 1970 period.
In a second exercise I use a new microdataset: the Survey of Income and Program
Participation (SIPP). The SIPP directly queries the health of each respondent, while the
Census must be aggregated by synthetic cohort (by state of birth, sex and year of birth) in
order to impute mortality by using estimates of "missing" individuals. The latter estimates
arguably could be due in part, to a selection e¤ect if less educated individuals are also less
likely to be captured by the Census over time. In contrast to the synthetic cohort approach,
with the SIPP we can be sure that those who were “treated" by the compulsory school
laws are indeed the same individuals registering the change in health. Moreover, as will be
2 In

addition Lleras-Muney could not rule out possibility that education lowered individual discount rates,

and thereby lead to healthier behaviors. See (Grossman 2005).
3 And
4I

the IV estimator is inconsistent when the instrument is endogenous.

also discovered a coding error that generated some erroneous OLS and IV estimates in Lleras-Muney

(2005) that are quantitatively meaningful. I discuss this in more detail in section 4

3

shown later, the SIPP microdata provides relatively strong statistical power in assessing the
relationship between education and health.
Using the SIPP with the same IV strategy, I …nd large and statistically signi…cant e¤ects
of education on health that are robust to the inclusion of state-speci…c time trends.

In

particular, I document that the summary health measure from the SIPP – self-reported
health – also improves with changes in schooling (as induced by compulsory school laws).
So while there may be some uncertainty about the robustness of the Census mortality results
based on group level data, the evidence is quite strong with the individual level data using
health status as an outcome. This addresses the concern about whether the health e¤ects
were due to idiosyncratic changes in the laws.
I then use the SIPP to examine a broad range of health measures in order to isolate which
speci…c health conditions responded to education improvements induced by compulsory
schooling laws. This is potentially useful for understanding whether or not the use of
compulsory schooling laws as instruments produce sensible results that accord with the
leading hypothesized mechanisms for how education a¤ects health. If for example, all of
the health e¤ects are concentrated in only one or two health conditions that are unrelated
to improvements in medical technology or decision making ability, it might cast some doubt
on the validity of the instruments.

If on the other hand, we were to assume that the

instruments were valid, the results ought to be informative about the critical question of
how exactly higher education levels lead to better health.
In fact, I …nd that among the nineteen health conditions examined, only four show
signi…cant declines in incidence due to education. What is striking is the absence of e¤ects
among the many health conditions where decision-making ability is believed pivotal. For
example, no e¤ect is found for chronic diseases such as arthritis, cancer, heart disease, lung
disease, or stroke incidence. The sole exception is diabetes, where the ability to maintain
a treatment regime is especially important. Moreover, education is found to increase the
likelihood of hypertension and kidney problems: conditions for which self-management and

4

recent technological advance appear to be important. The lack of any e¤ects across most
outcomes also suggests that channel underlying the connection between education and health
is probably not due to …nancial resources or unobserved time preferences which would tend
to improve health across the board.
What then accounts for the positive relationship between schooling and self-reported
health using these instruments? Surprisingly, health conditions where decision-making appears comparatively unimportant underly the relationship. Sensory functions –in particular
hearing and vision –exhibit large and signi…cant impacts when using compulsory school laws
as instruments. I also …nd that education reduces sti¤ness or deformity of the limbs, back
problems, senility, and improves the ability to speak in the IV speci…cations. This pattern
of e¤ects suggests that either: (i) the mechanisms by which schooling impacts health depart
markedly from those hypothesized, or (ii) the use of compulsory school laws as an instrument
may be suspect.
An important caveat is that that with the SIPP I am using a sample of individuals
who have survived into their later years (between the ages of 59 and 83) where presumably
there has already been considerable positive selection on education and health. Of course
among this more selected sample we might expect there to be a bias against detecting any
health e¤ects, so the …nding of a strong e¤ect on overall health status might be considered
surprising. Nonetheless the age of the sample raises questions about the extent to which
these results generalize to the broader population.
Finally, I hypothesize that schooling law changes may be correlated with other contemporaneous policies either inside or outside of schools that improved long-term health.
During the early period of the twentieth century there were fairly dramatic improvements
in public health measures and large declines in concurrent mortality.5 There was also a
recognition that compulsory schooling was useless if students were mentally or phsycially
un…t to attend school.
5 Cutler

This led to other reforms in the schools that were designed to

and Miller (2004), for example argue that the introduction of clean water technologies early in

the century can account for half of the reduction in mortality in large cities during that time.

5

improve children’ wellbeing. In a third exercise I examine one potential factor that might
s
account for the observed relationship between schooling laws and improved health, smallpox vaccination. The vaccination of children against smallpox as a requirement for school
entry is likely to be correlated with years of education and plausibly exerts e¤ects on adult
health outcomes. Data on smallpox incidence and vaccination rates are thin, preventing
de…nitive conclusions. Nevertheless, I …nd that states that appeared to have more stringent
vaccination requirements for school entry experienced most of the gains in long-term health
generated by compulsory schooling laws. The fact that survivors of smallpox are known to
su¤er from compromised vision, hearing and speaking provides some additional suggestive
evidence of a possible link between vaccination requirements and the estimated long-term
health e¤ects of compulsory schooling laws.
The remainder of the paper is organized as follows. In section 2 I review the relevant
literature, in section 3 the Census and SIPP data are described the econometric models are
shown, in section 4 the baseline results are presented, in section 5 I consider the possible
role of smallpox and vaccination in schools and in section 6 I conclude.

2

Literature Review

It has been over thirty years since Grossman published his seminal economic model of
health determination (Grossman 1972). This model includes the assumption that education
increases the e¢ ciency of health production. And while Grossman’ conceptual framework
s
has served as the “work horse" model for applied work in health economics, little is understood about how or what kinds of education enable the production of health.6 For example,
in 2003, the National Institutes of Health solicited (quite general) research proposals on the
“Pathways Linking Education to Health."
This RFA sought “validation of speci…c measures of abilities crucial to educational attain6 Grossman

(2005) noted that “extensive reviews of the literature [concluded that] that years of formal

schooling completed is the most important correlate of good health.

6

ment, such as level of cognitive or language skills" that improved health, and even cautioned
that: “The association or pathway between formal education and either important health
behaviors or diseases may not be causal. Instead it may re‡ the in‡
ect
uence of confounding
or co-existing determinants or may be bi-directional."(NIH 2003)
Recent years have witnessed an upsurge of interest in education’ role in determining
s
health. In one widely-cited paper, Goldman and Smith (2002) noted that more educated
patients may manage chronic conditions better. Those with more schooling adhere more
closely to treatment regimens for HIV infection and diabetes, which can be fairly complex.
For such conditions, the ability to form independent judgements and comprehend treatments
is important, and apparently is fostered by schooling. Accordingly, “self-maintenance is an
important reason for the very steep SES gradient in health outcomes" (Goldman and Smith
(2002):10934).
Glied and Lleras-Muney (2003) looked at health conditions experiencing more rapid
technological change, …nding that more educated respondents faired better. They argued
that “the most educated make the best initial use of new information about di¤erent aspects
of health" permitting them to respond more adeptly to evolving medical technologies. They
noted that no consensus measure existed for assessing the pace of innovation in health.
They therefore consider several measures, including the change in mortality rates for speci…c
conditions from 1986 to 1995 and the number of patents issued for particular conditions.
They found that education gradients were steeper for diseases that were more innovative by
these measures.
A growing literature has also tried to examine whether the education gradient in health
is causal by using instrumental variables. Reviews of these studies may be found in (LlerasMuney 2005) and (Grossman 2005). While these studies typically …nd an e¤ect of more
education leading to better health, in most cases it is questionable whether the instruments
are truly exogenous.7 In contrast, the use of changes in compulsory schooling laws appears
7 For

example Leigh and Dhir (1997) use parent schooling, parent income and state of residence as

instruments, all of which could plausibly a¤ect long-term health independently of their e¤ects through

7

to be a more compelling instrument choice since it is more plausibly exogenous than instruments used in prior work. Previous studies also typically have looked at just one or two
health outcomes and have not systematically compared the e¤ects across a range of health
outcomes to distinguish between competing theories of how education a¤ects health.

3

Data and Methodology

3.1

Mortality Data and Econometric Speci…cation

I begin by describing the procedure used to estimate the e¤ects of education on mortality
in Lleras-Muney (2005). This will provide the basic framework for extending the analysis
to examining other health outcomes in the SIPP and for expanding the analysis along
other dimensions. I brie‡ describe the approach here, for a more detailed discussion that
y
includes alternative estimation strategies see Lleras-Muney (2005).8 The key idea is that in
the absence of a large sample tracking individuals over their entire lifetime, synthetic cohorts
are constructed using Census data. With the Census, we know age, completed education
and state of birth which allows us to infer the compulsory schooling laws that a¤ects each
cohort in each state of birth. Mortality can be measured by tracking population counts of
particular groups across Census years. The mortality rate at time t for cohort c, of gender,
g, born in state s, (Mcgst ) is simply measured as the percentage decline in the population
count (Ncgst ) within these cells over the subsequent ten years:

Mcgst =

Ncgst

Ncgst+1
Ncgst

(1)

schooling.
8 Lleras-Muney

also uses several other approaches. She estimates the model at the individual level using

data from the National Health and Nutrition Examination Survey (NHNES). This is largely for comparative
purposes since the sample is too small to estimate statistically signi…cant e¤ects using IV. She also considers
Wald estimators and introduces a a "mixed" two stage least squares approach using individual data in the
…rst stage but aggregate data in the second satge.

The latter two approaches produce roughly similar

estimates to the aggregate IV estimates which are modeled here.

8

The mortality rate for each cell is then modeled as follows:

Mcgst = a + Ecgst + Wcs +

c

+

s

+

cr

+

t

+ d + "cgst

(2)

where Ecgst is the average education level for that cell at time t, Wcs , measures a set of
cohort and state speci…c controls measured at age 14 intended to capture di¤erences in other
potential early life determinants of mortality (e.g. manufacturing share of employment,
doctors per capita). The model also includes a set of cohort dummies
dummies

s,

interactions between cohort and region of birth,

year dummies,

cr ;a

c,

state of birth

female dummy d and

t.

I construct two datasets for the analysis. The …rst attempts to replicate Lleras-Muney
point estimates and uses the same 1 percent ipums samples drawn from the 1960, 1970
and 1980 Censuses that are produced by the Minnesota Population Center.9 The second
estimation sample replaces the 1970 1 percent sample with a 2 percent sample that combines
the form1 and form 2 "state" 1 percent samples.10 For 1980 I use a 5 percent sample. In
addition, I also add the 5 percent samples for 1990 and 2000. All of the Censuses are scaled
appropriately to produce population estimates that correspond to Ncgst 11 :
Following Lleras-Muney I restrict the anlaysis to cohorts born between 1901 and 1925.
I also follow her sample restrictions to exclude immigrants, blacks, and to topcode years of
education at 18 starting in 1980. For the expanded samples I also exclude cases where age,
state of birth and education are imputed by the Census Bureau. The descriptive statistics
for both samples are shown in Table 1. It is worth noting that the death rate for the 1970 to
9 Ruggles

et al. [2004].

1 0 Unfortunately,

combining any of the other four 1 percent samples that are available for 1970 would lead

to geographically unrepresentative samples.
1 1 The

1960, 1970 and 1980 samples are self-weighting samples so the raw population counts can simply

be scaled up by multiplying by 100, 50, and 20.
representative estimates of the population.

The 1990 and 2000 samples require weights to produce

We found that using the self-weighting 1 percent samples for

1990 and 2000 instead of the 5 percent samples had little e¤ect on the point estimates but increased the
standard errors.

9

1980 period is quite a bit larger with the expanded sample but that the standard deviation
is about 20 percent lower. There are now also 5 additional cells that had missing data when
using just the 1 percent samples. The death rates for the 1980-90 and 1990-2000 periods
are much higher due to the fact that I follow these same cohorts when they are much older.
Figure 1 plots the death rates by age for each Census year. This highlights the importance
of controlling for age in the speci…cations which is done by adding polynomials in age to the
models.
One straightforward way to estimate

in (2) would be through weighted least squares

(WLS), with the weights corresponding to the population represented by each cell. However,
that this would produce a biased estimate due to omitted variables. Any number of factors
could plausibly be associated with both higher education and lower mortality even at the
group level. Therefore, two stage least squares is used where in the …rst stage education is
instrumented with the set of compulsory schooling laws, CLcs , in place for each cohort and
state of birth:

Ecgst = b + CLcs + Xcgst

iv

+ Wcs

iv

+

iv;c

+

iv;s

+

iv;cr

+

t

+ ucgst

(3)

The instruments for the compulsory schooling laws are constructed in the following way.
The variable childcom measures the minimum required age for work minus the maximum
age required for a child to enter school, by state of birth and by the year the cohort is
age 14. This variable takes on one of eight values (that range from 0 to 10). A set of
indicator variables (excluding the 0 category) are used as instruments. In addition there is
an indicator for whether school continuation laws were in place in that state. These laws
required workers of school age to continue school part-time. For comparability, I use the
same dataset as Lleras-Muney (2005).12 In addition to a more detailed description of these
variables in Lleras-Muney (2005), there is also an extended discussion of these measures and
their appropriateness as instruments in Lleras-Muney (2001). The variables contained in
1 2 Downloaded

from Lleras-Muney’ website.
s

10

Wcs are the corrected versions of those used by Lleras-Muney (2005).
I also experiment with a second set of data independently collected by Goldin and Katz
(Goldin and Katz 2003). Goldin and Katz carefully compared their series with the LlerasMuney data and the data collected by Acemoglu and Angrist (2000) and recti…ed di¤erences
wherever possible. Since the Goldin and Katz data go back further in time it is possible to
match all of the cohorts to the school entry age laws in e¤ect when the cohorts were younger
than 14. I use this data to measure the required age for school entry when the cohorts were
likely to be at age 8 instead of age 14. In principle, incorporating this data should provide
a better measure of the total years of compulsory schooling.

3.2

Health Microdata and Speci…cations

The second sample is constructed by pooling individuals across various panels of the Survey
of Income and Program Participation (SIPP) during the 1980s and 1990s.13 Because participation in many programs is closely related to an individual’ health and disability status,
s
the SIPP routinely collects information on health and medical conditions. The SIPP is also
ideally suited for this analysis because it contains the state of birth of all sample members allowing us to implement the IV strategy of using compulsory schooling laws during
childhood.
One useful outcome is self-reported health (SRH). SRH is on a 1 to 5 scale where 1
is “excellent” 2 is "very good", 3 is "good", 4 is "fair" and 5 is “poor” SRH has been
,
.
found to be an excellent predictor of mortality and changes in functional abilities among
the elderly.(Case, Lubotsky, and Paxson 2002). I experiment with this measure in a few
ways. First it is simply used as a continuous variable. Second, I use indicators for being
in poor health or, fair or poor health. Finally I use the health utility scale that scales
the di¤erences between the categories based on a health model using the National Health
Interview Survey.14
1 3 This
1 4 See

includes the 1984, 1986-1988, 1990-1993 and 1996 panels.

(Johnson and Schoeni 2005) and the citations there for a discussion of this approach.

11

A few other general outcomes are also examined. These include whether the individual
was hospitalized during the past year, the number of times she was hospitalized, the total
number of nights spent in the hospital and the number of days spent in bed in the last four
months.
There are also a set of questions dealing with functional activities, activities of daily living
and instrumental activities of daily living.15 I assembled a common set of questions that were
consistently asked across surveys. These include whether the individual has “di¢ culty" with
seeing, hearing, speech, lifting, walking, climbing stairs and, whether the person can perform
any of these activities "at all". In addition there is information on whether individuals have
di¢ culty getting around inside the house, going outside of the house or getting in or out of
bed, and whether they need the assistance of others for these activities.
For a subset of individuals who report limited abilities in certain tasks or who have been
classi…ed as having a work disability, detailed information is collected on a number of very
speci…c health conditions including: arthritis or rheumatism; back or spine problems; blindness or vision problems; cancer; deafness or serious trouble hearing; diabetes; heart trouble;
hernia; high blood pressure (hypertension); kidney stones or chronic kidney trouble; mental illness; missing limbs; lung problems; paralysis; senility/dementia/Alzheimer’ disease;
s
sti¤ness or deformity of limbs; stomach trouble; stroke; thyroid trouble or goiter; tumors
(cyst or growth); or other.16

Since the speci…c health ailments are only asked of speci…c

subsamples, they probably only pick up on the most severe cases. Even though, many of
1 5 These

measures are derived from speci…c codes of the International Classi…cation of Impairments, Dis-

abilities and Handicaps (ICIDH).
16 I

pool responses from the 1984, 1990-93 and 1996 SIPPs in order to maximize sample size. Unfortunately

di¤erent criteria were used across the SIPP survey years to select the subsamples for which speci…c health
conditions were asked. For example, in 1996 the health conditions were asked of those who reported being
in fair or poor health. I found that it was important to combine all of the subsamples in all of the years in
order to have enough power to identify e¤ects. There are also an additional set of 10 outcomes that are not
used because they were not available in the 1984 SIPP. Experimentation with a smaller sample suggests
that the conclusions are not altered by dropping these other outcomes

12

our sample individuals are not actually asked about these speci…c health conditions we still
include them in the estimation sample so that our sample is not a selected sample of only
those in poor health. The summary statistics for this data are shown in Table 2.
Since most of the outcomes in the SIPP are indicator variables I now use Two Stage
Conditional Maximum Likelihood or 2SCML (Rivers and Vuong 1988) rather than IV.17
Rivers and Vuong show that 2SCML has desirable statistical properties, is easy to implement
and produces a simple test for exogeneity. I continue to use IV for the few continuous
dependent variables. Also all of the analysis is now done using individual level data. The
statistical model is similar to (2) only now I use the latent variable framework:

yit = a + Ei + Xi + Wcs +

c

+

s

+ trends +

t

+ "it

yit = 1 if yit > 0; yit = 0 if yit < 0

(4)

(5)

In the …rst stage, I run a similar regression as before:

Ei = b + CLcs + Xi + Wcs +

c

+

s

+ trends +

t

+ "it

(6)

To implement 2SCML, I use the predicted residuals from (6), bit ; and include it as an
"

additional right hand side variable (along with the actual value of Ei ) when running the
second stage probit. For comparability I use the same sample restrictions and covariates
as Lleras-Muney with only a few exceptions. Unlike Lleras-Muney I include a quadratic
in age. In addition state-speci…c cohort trends are used to address concerns that region of
birth interacted with cohort may not adequately control for state-speci…c factors that are
smoothly changing over time.18
17 I

thank Jay Bhattacharya for this suggestion. In a previous version of the paper I found very similar

results using two stage least squares for the dichtomous outcomes.
18 I

generally found that the IV results were larger and more signi…cant when using the state trends than

when using region of birth interacted with cohort.

The OLS results were virtually identical under either

13

4

Baseline Results

4.1

Mortality

This section begins by describing the replication of Lleras-Muney (2005) and the discovery
of a coding error concerning the appropriate base period. Using the correct base period
increases the point estimates: the new WLS estimate is approximately doubled and the IV
estimate is 50% higher than reported in Lleras-Muney (2005). I then expand the Census
data by: (i) expanding beyond the 1% sample to the 2% and 5% IPUMS samples available
for 1970 and 1980, respectively, and (ii) incorporate the 1990 and 2000 Census data in the
mortality analysis.

4.1.1

Replication and Correction

For the mortality analysis I start with the same sample as Lleras-Muney (2005) I have an
identical number of individuals (814,805) drawn from the 1960 and 1970 Census and match
nearly all the summary statistics in her Table 1 Nevertheless I …nd some large di¤erences
when implementing WLS or IV at the cell level. This shown in Table 3. The …rst two
columns show the WLS and IV estimates from Lleras-Muney while columns 3 and 4 show
my estimates. Compared to her WLS estimate of -.017, I obtain a coe¢ cient of -.036. The
di¤erence in the estimates is statistically signi…cant. For IV, once again I obtain a much
larger estimate (-.072) than her estimate of (-.051).
After some experimentation I speculated that Lleras-Muney did not use education calculated in the base period, t, but instead calculated education in period t+1. If I use education
calculated in t + 1, then my estimates are much closer to hers. These are shown in columns
5 and 6. After graciously providing her computer code I con…rmed with Lleras-Muney that
this was indeed the case and she has since written an errata (Lleras-Muney 2006) providing
corrected estimates.19 The problem with using education in the later period is that the
speci…cation.
1 9 The

errata (Lleras-Muney 2006) reports an IV coe¢ cient of -0.063 (0.024) compared to the estimate of

14

sample has already experienced selective mortality based on education.
The discrepancy between the results are not only statistically meaningful but are quantitatively important. Taken at face value, the "corrected" IV result implies that an extra
year of schooling reduces the likelihood of dying over the next ten years by more than 7
percentage points. In the sample, the mean death rate is only about 10.6 percent. This
suggests that one more year of schooling lowers mortality risk by nearly 70 percent – result
a
that is perhaps implausibly large.

4.1.2

Expansion of the Census Sample

In Table 4, I show how the results change as the sample is enlarged and the speci…cations
are modi…ed. I begin by just focusing attention on the …rst two columns showing the WLS
and IV estimates. In panel A I isolate the e¤ects of using larger samples for 1970 and 1980.
Row 1 repeats the results from Table 3. In row 2 I …nd that the WLS estimate rises to -0.045
and that the IV estimates drop considerably to -0.043. The greater precision is evident in
the standard error for the IV estimate which declines by about 25 percent. In rows 3 and 4
I control for age and …nd that this lowers the WLS estimates a little and increases the IV
estimates a little. In row 5 I drop the region of birth interactions with cohort and instead
use state speci…c linear (cohort) trends. This raises the WLS estimate but I now …nd that
IV coe¢ cient is signi…cantly lower and is no longer statistically signi…cant at conventional
levels.
In panel B I add data from the 5 percent samples of the 1990 and 2000 Censuses. With
this larger dataset I construct death rates over four ten year periods and therefore follow
cohorts over a longer period of time with a considerably larger sample. Given that the
sample also tracks the cohorts later in life when mortality rates are much higher, the age
controls are essential. I use a cubic in age although I …nd that the results are not very
sensitive to the choice of the polynomial. Since medical technology and other health-related
factors might change over time, I have also interacted the cubic in age with the Census
-0.072 reported here. I was unable to resolve this di¤erence.

15

year. In my preferred speci…cation (row 6) I now …nd that both the WLS and IV estimates
are about -0.035 which appear to be much more plausible.

With this larger sample the

inclusion of state speci…c cohort trends again results in a point estimate that is much smaller
in magnitude (-0.02) and not statistically distinguishable from zero (row 7).
The third column shows the e¤ects of using the Goldin and Katz data for constructing
the instruments. For most speci…cations in panels A and B they produce similar estimates
as the baseline IV results although the standard errors are a bit higher. It is worth noting
that with the Goldin and Katz data the inclusion of state speci…c cohort trends lowers the
size of the point estimates even more dramatically and also yields the same conclusion, that
the estimates are not statistically di¤erent from zero.

4.1.3

E¤ects by Subgroups

In the remaining panels of Table 4 I examine how the e¤ects vary by year, age and cohort.
In panel C I separately estimate the education coe¢ cient for each Census year. Since the
speci…cation includes a full set of cohort dummies these are equivalent to age controls when
using a single Census year. Although the WLS estimates are signi…cant in all years they
peak in 1970 at -0.061 and drop to only -0.015 by 1990. The IV estimates have large standard
errors so they are likely to be imprecisely estimated. Nonetheless the estimates are large
only for 1960 and are essentially zero for 1980 and 1990. This is true whether I use the
Lleras-Muney data or the Goldin Katz data. In panel D I stratify the sample by three age
ranges: 35-55, 55-65 and 65-89. Here I observe di¤erent patterns across the three columns
making it di¢ cult to interpret the estimates. The WLS and IV estimates from column 2
suggest that the largest e¤ect may be for those aged 55 to 64, while the IV estimates with
the Goldin Katz data suggest the opposite. Given the imprecision of the estimates I cannot
draw any meaningful inferences regarding the age pattern
Panel E however, provides a striking result that appears to be consistent across the two
IV speci…cations. It appears that the entire e¤ect of education on mortality arising from

16

compulsory schooling laws is due to cohorts born from 1901 to 1912 who constitute just
over 40 percent of the sample. In fact for those born from 1913 to 1925, the point estimate
is actually positive in both column 2 and column 3.

Using the Lleras-Muney data, the

estimate is 0.035 and is signi…cant at the 7 percent level. These results taken as a whole
suggest that upon closer inspection, the results from Lleras-Muney are driven by cohorts
born very early in the century and their mortality experience during the 1960-1970 period.
One possible explanation could be that the e¤ect of education stayed roughly constant but
that compulsory schooling laws had its biggest bite for those born earlier in the century.
However, I have run the …rst stage regressions by these cohort groupings and found that the
partial F-statistics on the instruments are similar for both cohorts when using the LlerasMuney data and are actually higher for the 1913 to 1925 cohorts when using the Goldin
Katz data. This suggests that the schooling laws may actually have been more binding for
the later cohorts casting doubt on this alternate explanation. In other estimates that are
not shown in the table I found no statistically signi…cant di¤erence between men and women
although the point estimates were larger (in absolute value) for men using the Lleras-Muney
data and very imprecisely estimated using the Goldin Katz data.

4.2

Health Outcomes from the SIPP

In Table 5 the results using the microdata on health outcomes using the SIPP are presented.
The …rst column shows the e¤ects of education using a simple probit (or OLS) which does
not account for endogeneity. The second column presents the 2SCML (or IV) estimates
using the compulsory schooling laws as instruments. Given the possible e¤ects of education
on mortality and the fact that outcomes in the SIPP are not observed until at least 1984,
one might not expect any remaining health e¤ects to be apparent. As it turns out I do …nd
signi…cant e¤ects using the instruments for several broad health outcomes. The …rst row
shows that self reported health measured as a continuous variable is a¤ected by education.
The IV estimate of -0.23 is more than twice the OLS estimate (-0.09). In column 4 using

17

a Hausman test one can reject that the OLS and IV coe¢ cients are the same at the 7
percent level. Translating SRH into a health index on a 1 to 100 scale following Johnson
and Schoeni’ (2005) approach, the IV estimate implies that an increase in schooling by
s
one year improves the health index by 4.5 points or about 7 percent evaluated at the mean
(column 3). I also estimate that the probability of being in fair or poor health is reduced
by 8.2 percentage points with an additional year of schooling – fairly large e¤ect that is
a
statistically di¤erent from the naive probit at the 18 percent sign…cance level. I do not …nd,
however, that any of the measures of hospitalization or days spent in bed are signi…cant
when accounting for endogeneity.
Looking across a variety of measures of physical functional abilities, I …nd that while
all of the naive probit estimates are sign…ciant and of the expected sign, the two stage
estimates are typically not signi…cant. Given the large health e¤ects discussed above it is
striking that those who have an additional year of schooling due to compulsory schooling
laws are no more likely to have trouble lifting, walking, climbing stairs, getting around the
house, getting around inside the house or getting into or out of bed. In fact for many of
these outcomes the coe¢ cients are actually positive! On the other hand, those with greater
schooling associated with compulsory schooling laws are dramatically less likely to experience
problems with vision, hearing or speaking. In almost all of these cases the di¤erences between
the simple probit and the 2SCML estimates are very large and statistically di¤erent at about
the 10 percent level. For example, the 2SCML estimates imply that an additional year of
schooling reduces the probability of having trouble "seeing" by 5.6 percentage points. In this
sample the mean rate of this health outcome is 13.6 percent. These results might suggest
that the channel by which general health is compromised for those with less schooling, may
be related to sensory functions.
The next set of results estimate the incidence of speci…c health conditions. Recall that
these conditions are only identi…ed for subsets of individuals and that the screening criteria
has changed across SIPP survey years. Also recall that all individuals are included regard-

18

less of whether they were screened for this question so as to avoid using a selected sample
of only those in poor health. Generally, the underlying health conditions were only asked
of individuals who reported particular kinds of activity limitations, reported having a work
disability or reported being in fair or poor health. This is captured by the variable "di¢ culty" which, not surprisingly, is sign…ciant under both probit and 2SCML. When I turn
to the estimated likelihood of having one of the underlying health conditions, the probit
estimates once again are signi…cant in every case. The 2SCML estimates, however, are only
negative and sign…cant for four outcomes: back or spine problems; sti¤ness or deformity of a
limb; diabetes and senility/dementia/Alzheimer’ disease. It is important to point out that
s
"trouble seeing", "trouble hearing" and "trouble speaking" were never used as a screening
criteria for asking about an underlying health condition. This likely explains why blindness
and deafness are not signi…cant with the subsamples.
Another interesting result is that both kidney problems and hypertension appear to
positively associated with more schooling. This is especially notable because these are
two outcomes for which self-management and recent technological advance appear to be
especially important. According to Appendix Table B of Glied and Lleras-Muney (2003),
treatment of kidney infections experienced substantial innovation. Among the 56 causes
of death, it experienced the fastest decline in age-adjusted mortality from 1986 to 1995 falling at more than nine percent per year (Glied and Lleras-Muney (2003)):8, Appendix
Table B). Accordingly, a steep (negative) gradient between education and kidney disease
would presumably be expected. It is therefore of note that the 2SCML speci…cation …nds an
increase in the incidence of kidney problems among those with high education. Treatment of
diabetes is “often considered the prototype for chronic disease management." Goldman and
Smith (2002). Our …ndings, which analyze a broad range of health conditions and chronic
diseases, would suggest that insofar as the formal schooling is concerned, diabetes appears
to be an exception. In the SIPP data, only diabetes enters enters in the expected direction
–i.e. increases in schooling appear to reduce diabetes incidence. An alternate explanation

19

for the diabetes result could be that states that had higher compulsory schooling levels also
promoted nutritional policies that might have reduced adult onset of diabetes. Overall,
however, one conclusion that may be drawn from this table is that there is little support for
the "decision-making" hypothesis.
It is also worth noting that explanations for the link between education and health that
focus on resources (e.g. income, occupation) or unobserved time preferences do not appear
to be consistent with these results. These explanations would likely imply that all outcomes
ought to be a¤ected not just a few.
The major caveat to this analysis is that we observe individuals only if they have survived
into the 1980s and 1990s when they are anywhere between the ages of 59 and 83 This
sample is almost certainly positively selected on education and health, making it unclear
how generalizable these results are.

I suspect that due to this selection the results are

biased against …nding any e¤ects of education on improving health, making it still surprising
why there are very large negative coe¢ cients on the incidence of several negative health
outcomes.

5
5.1

Smallpox Vaccination
Alternative Explanations

The results thus far suggest present something of a puzzle as to exactly how compulsory
schooling laws early in the twentieth century led to improved long-term health status. While
the results cast doubt on the traditional explanations o¤ered in the literature of how education improves health the results do not appear to point to any obvious alternative explanation. One general hypothesis worth considering is that schools served as an important
place for implementing a variety of policies that may have impacted both education and
health directly. It could be that states and cities during this period were introducing many
reforms contemporaneously and schools were one obvious target for these reforms.

20

In fact it was noted at the time that it was pointless to force kids to attend schools if they
were unable to learn. In 1904, Robert Hunter wrote in the book Poverty: "There must be
thousands -very likely sixty or seventy thousand children-in New York City alone who often
arrive at school hungry and un…tted to do well the work required. It is utter folly, from the
point of view of learning, to have a compulsory school law which compels children, in that
weak physical and mental state which results from poverty, to drag themselves to school
and to sit at their desks, day in and day out, for several years, learning little or nothing."
In fact in response to this situation Philadelphia, Boston, Milwaukee, New York, Cleveland,
Cincinnati and St. Louis all began large scale programs to provide food in public schools
during the 1900s and 1910’ (Gunderson 1971).
s
It seems plausible, then that coincident with the enactment of compulsory schooling laws
there were likely many e¤orts (legislative or otherwise) to improve the general condition of
children.

In this section I pursue one speci…c alternative hypothesis that might explain

some of the …ndings. Speci…cally, I examine whether the association between compulsory
schooling laws and health may have been due in part, to early century school requirements
concerning vaccination against smallpox.

5.2

Background on Smallpox

Before Edward Jenner invented the …rst vaccine in 1797, smallpox was a widespread and
brutal disease killing about 400,000 Europeans a year with survivors accounting for about
one-third of all cases of blindness (Henderson and Moss 1999). Smallpox was especially
concentrated among children, in the early 19th century smallpox accounted for one-third
of the deaths of all children(George Palmer and Ingen 1930). More than a century after
the development of the vaccine, smallpox remained a deadly threat in the United States. A
report in the New England Journal of Medicine in 1930 showed that between 1919 and 1928
there were more than half a million cases of smallpox in the US and argued that ". . . the
United States remains now . . . the most smallpox ridden country in the world bar possibly

21

China, India and (doubtfully) Russia.”
In addition to blindness, survivors of smallpox are also known to have a higher rate of
encephalitis20 (in‡
ammation of the brain). Although encephalitis is relatively rare, milder
forms of the condition are likely to go unreported.21 Symptoms of encephalitis include
problems with speech, hearing and double vision.21 This suggests that vaccination against
smallpox in schools may have reduced the incidence of compromised sensory functions as
we …nd in our SIPP sample.

5.3

Vaccination in schools

States began to require vaccination against smallpox in schools beginning in the late 19th
century (Hanlon 1969). I have been able to compile information concerning state laws
regarding school vaccination for the years 1915, 1921, 1926 and 1941. In the …rst snapshot
in 1915, fourteen states had requirements for vaccination. In the other three years I found
no cases of any additional states requiring vaccination for schools. Similarly I found no
cases of any states repealing these laws. Therefore, I am unable to construct an analogous
panel design as employed by Lleras-Muney for compulsory schooling laws since there is no
variation over time.
I also assembled data on states who had laws authorizing the use of vaccination in the
case of outbreaks but found that these laws relied critically on enforcement. There were also
a few cases where states changed laws regarding the prohibition of vaccination in schools but
it is doubtful that these law changes have enough power to identify e¤ects in the samples
used in thsi study.
Fortunately, a 1930 White House sponsored report on the state of young children’
s
health does contain detailed data on young children’ vaccination rates by age for 156 of the
s
largest cities (George Palmer and Ingen 1930). For the time period this was an impressive
data collection e¤ort where information was collected from around 3000 doctors and other
2 0 See

AMA (1999)

2 1 See

http://www.ninds.nih.gov/disorders/encephalitis_meningitis/detail_encephalitis_meningitis.htm

22

health providers on the frequency of health exams and dental exams in addition to rates of
vaccination against smallpox and immunization against diptheria. The data on smallpox
vaccination rates was aggregated to the state level. This is displayed in Table 6. What is
striking is how the vaccination rate jumps sharply from age four to age …ve in many states
as children prepare for school entry. Although some of the richer states in the Northeast
like New York have sizable jumps, there is a great deal of variation even within regions. For
example, Colorado, Georgia and Kentucky are among the states with largest increases in
vaccination between age four and age …ve. These data also illustrate the potential pitfall of
using actual state laws since some of the states that ostensibly required vaccination did not
exhibit big increases (e.g. Arkansas, South Carolina) while other states that did not legally
require vaccination, in practice, exhibited large increases in vaccination by school age.
Although there is only vaccination rate data for one year, 1930, I use this data to test
the extent to which the health e¤ects of education may be operating through di¤erences
in vaccination policy. Speci…cally, I consider how di¤erent the health e¤ects of education
are for states that have stringent vaccination laws versus those that don’ I assume that
t
the stringency of school vaccination requirements can be proxied by the change in the
vaccination rate from age four to age …ve. Obviously, this approach is only ideal for the
youngest cohort (born in 1925), who would have turned …ve in 1930. I analyze this …rst
for the baseline mortality sample (row 6 in Table 4).

The sample is split by states with

a change in vaccination rate of more than 10 percentage points (the median change) and
those with a change that is 10 points or fewer.
The results are shown in Table 7. I …nd that the IV estimates of the e¤ects of schooling on
mortality are statistically signi…cant only in the states with large increases in vaccination by
school age and that the IV coe¢ cients are of the wrong sign in the states with less stringent
vaccination requirements. I experimented with randomly splitting the sample 100 times and
found that the odds of …nding an equivalent result by chance are only about 15 percent.
I then performed the same exercise with the SIPP sample looking at the outcomes that

23

I found to be signi…cant for the full sample. In this sample the results are more mixed. I
show a representative set of results from the SIPP in Table 7 For self reported health, the
estimates are actually larger and more statistically signi…cant in the states with relatively
less stringent vaccination requirements. However, the e¤ect on being in poor health is only
apparent in the high vaccination states. Since poor health is a strong predictor of mortality
this appears to be consistent with ther mortality …nding. Most of the other estimates by
subsample are too noisy to say much of anything but it does appear that hearing is strongly
a¤ected in the states with more stringent school vaccination requirements. Since I do not
have a time series on vaccination rates and given the relative bluntness of the approach I
only claim that these results are suggestive of a possible mechanism relating compulsory
schooling laws and long-term health that operates through school vaccination.

6

Conclusion

This paper expands upon the growing literature that attempts to identify whether there is
a causal e¤ect of education on health by also considering how education might a¤ect health.
I closely examine the e¤ects of education induced by compulsory schooling laws early in
the twentieth century on long-term health using several approaches.

First I revisit the

results in Lleras-Muney (2005) by expanding the Census sample and employing a variety of
robustness checks. The main …nding is that the e¤ects of education on mortality induced
by changes in compulsory school laws are not robust to including state speci…c time trends.
I also …nd that all of the e¤ects are for cohorts born between 1901 and 1912 and their
mortality experience during the 1960s.
Second, I use the SIPP to identify not only general health e¤ects but also speci…c health
outcomes that were induced by changes in state compulsory schooling laws to see if these
outcomes correspond to our existing theories of how education a¤ects schooling. The results suggest that there is a large e¤ect of education on general health status arising from
compulsory schooling laws that is robust to state time trends. However, I …nd that with
24

the sole exception of diabetes none of the other speci…c health conditions that are associated
with education (e.g. vision, hearing, speaking ability, back problems, deformities, senility)
correspond to the leading theories of how education improves health (e.g. technological improvements, better decision-making, higher income). This suggests that either our theories
are incorrect or that the compulsory schooling laws are suspect instruments.

An impor-

tant caveat, however, is that the SIPP analysis uses a sample of older individuals who are
almost surely positively selected on education and health. While this likely makes it more
di¢ cult to detect e¤ects of education on improved health it also raises questions as to how
generalizable these results are.
Third, I look at one speci…c alternate hypothesis of how state-level compulsory schooling laws might have in‡
uenced long-term health, namely through requirements for smallpox
vaccination as a condition for school entry. I stratify the samples by states with stringent
versus nonstringent vaccination requirements and …nd that all of the e¤ects of education
on mortality and poor health status were registered in states with stringent vaccination
requirements. This provides some suggestive evidence that smallpox vaccination may account for some of the link between education and health induced by compulsory schooling
laws. It is also worth noting that survivors of smallpox are known to su¤er from compromised vision, speaking and hearing which are among the few e¤ects that we detected in our
IV results with the SIPP. I conclude from these exercises there is reason to be concerned
about whether compulsory schooling laws can be used as instruments to draw meaningful
inferences about the causal e¤ects of education on long-term health. Instead it could well
be that either other school-based reforms directly impacted long-term health or that other
reforms with long term impacts took place at the same time that compulsory schooling
requirements became more stringent. In any event, the results suggest that even if there is
a causal e¤ect of education on health there is still a great deal of uncertainty about how
education improves health that should remain an important topic for further research.

25

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27

Table 1: Summary Statistics for IPUMs samples
1960-2000
1%, 2% or 5%

1960-1980 1% only

Variables

Mean
Ten Year death rates
overall
1960-70
1970-80
1980-90
1990-00

Std. dev.

N

Mean

Std. dev.

N

0.108
0.110
0.105
---

0.136
0.119
0.152
---

4792
2395
2397
---

0.213
0.113
0.154
0.287
0.433

0.173
0.105
0.125
0.170
0.122

8636
2397
2400
2399
1440

10.548
0.471
--0.517
50.366
0.031
0.038
0.044
0.048
0.050

0.990
0.499
--0.500
8.482
0.174
0.191
0.205
0.213
0.217

4795
4795
--4795
4795
4795
4795
4795
4795
4795

10.729
0.325
0.289
0.142
0.532
56.811
0.025
0.031
0.047
0.052
0.057

1.002
0.469
0.453
0.349
0.499
11.287
0.157
0.174
0.211
0.222
0.232

8636
8636
8636
8636
8636
8636
8636
8636
8636
8636
8636

53.523
11.737
8.983
0.067
7171.387
540.048
0.001
97.006
0.174

21.279
8.523
11.901
0.038
1343.089
276.353
0.000
42.054
0.090

4795
4795
4795
4795
4795
4795
4795
4795
4795

53.778
11.562
8.945
0.066
7206.147
535.182
0.001
99.779
0.172

21.153
8.430
11.787
0.037
1353.573
272.569
0.000
41.706
0.090

8636
8636
8636
8636
8636
8636
8636
8636
8636

Individual Characteristics
Education
1960 Dummy
1970 Dummy
1990 Dummy
Female
Age
Born in 1905
Born in 1910
Born in 1915
Born in 1920
Born in 1925
State of Birth Characteristics
% Urban
% Foreign
% Black
% Emp.in Mfg.
Ann. Mfg. Wage
Val. of Farm per Acre
P.C. # of Doctors
P.C Educ. Expenditures
# Schl Bldgs/ Sq. Mile

Notes : Summary statistics are for state of birth, cohort and gender cells. All means and standard
deviations use sample weights where the weights are the population estimates for the cell in the
base period.

Table 2: Summary Statistics for SIPP sample
Variables
Outcomes
Self Reported Health
Poor Health
Fair or Poor Health
Health Index
Hospitalized in Last Year
Days in Bed, last 4 months
Number of Times Hospitalized
Number of Nights in Hospital
Trouble Seeing
Trouble Hearing
Trouble Speaking
Trouble Lifting
Trouble Walking
Trouble with Stairs
Trouble Getting Around Outside the Home
Trouble Getting Around Inside the Home
Trouble Getting In/Out of Bed
Trouble Seeing at all
Trouble Hearing at all
Trouble Speaking at all
Trouble Lifting at all
Trouble Walking at all
Trouble with Stairs at all
Needs Help Getting Around Outside
Needs Help Getting Around Inside
Needs Help Getting In/Out of Bed
Work limitation due to health conditions
Arthritis
Back
Blind
Cancer
Deaf
Deformity
Diabetes
Heart
Hernia
Hypertension
Kidney
Lung
Mental Illness
Missing Limb
Paralysis
Senility
Stomach
Stroke
Thyroid
Other

Mean

Std. dev.

N

3.084
0.119
0.357
67.992
0.180
3.937
0.282
1.908
0.136
0.152
0.021
0.237
0.289
0.276
0.129
0.059
0.079
0.023
0.013
0.003
0.115
0.154
0.116
0.088
0.024
0.025
0.423
0.129
0.062
0.026
0.016
0.023
0.027
0.030
0.090
0.006
0.036
0.005
0.043
0.005
0.003
0.006
0.007
0.010
0.021
0.003
0.066

1.138
0.324
0.479
24.842
0.384
17.030
1.029
7.898
0.342
0.359
0.144
0.425
0.453
0.447
0.335
0.235
0.270
0.149
0.114
0.052
0.319
0.361
0.321
0.283
0.154
0.156
0.494
0.335
0.242
0.159
0.125
0.149
0.162
0.170
0.287
0.080
0.185
0.067
0.203
0.067
0.056
0.075
0.084
0.099
0.144
0.056
0.247

26030
26030
26030
26030
26484
25223
22229
26274
20853
20845
20834
20837
20799
20820
17401
17643
17636
20811
20819
15138
20789
20723
20775
13610
13893
13868
19073
19073
19073
19073
19073
19073
19073
19073
19073
19073
19073
19073
19073
19073
19073
19073
19073
19073
19073
19073
19073

Table 2: Summary Statistics for SIPP sample
Variables
Individual Characteristics
Education
Female
Age

Notes :

Mean

Std. dev.

N

11.432
0.580
72.079

3.208
0.494
5.606

26030
4795
4795

Table 3: Replicating Lleras-Muney’s Estimates of Effects of Education on Mortality
Dependent variable is ten year mortality rate
Lleras-Muney (2005)

Replication

Replication with
Wrong Base Year

WLS

IV

WLS

IV

WLS

IV

Education

-0.017
(0.004)

-0.051
(0.026)

-0.036
(0.004)

-0.072
(0.025)

-0.016
(0.004)

-0.059
(0.026)

Female

-0.074
(0.003)

-0.071
(0.004)

-0.072
(0.003)

-0.067
(0.004)

-0.074
(0.003)

-0.070
(0.004)

% Urban

-1.0E-04
-(9.4E-04)

0.001
(0.001)

9.3E-04
(9.9E-04)

0.002
(0.001)

4.4E-04
(1.0E-03)

0.002
(0.001)

% Foreign

-5.6E-04
(0.002)

-0.0001
(0.002)

-0.001
(0.002)

-0.0005
(0.002)

-0.002
(0.002)

-0.0011
(0.002)

% Black

-0.002
(0.002)

-0.0009
(0.002)

-8.1E-04
(0.002)

-5.9E-05
(0.002)

-0.001
(0.002)

-5.2E-04
(0.002)

% employed in mfg.

-0.071
(0.101)

-0.11
(0.108)

-9.3E-04
(0.236)

-0.039
(0.246)

0.010
(0.234)

-0.066
(0.246)

Annual mfg. wage

7.4E-07
(3.1E-06)

0.000
(0.000)

3.4E-07
(4.1E-06)

4.7E-07
(4.3E-06)

3.0E-07
(4.0E-06)

5.6E-07
(4.3E-06)

Val. of farm per acre

2.7E-06
(1.7E-05)

0.000
(0.000)

-1.2E-06
(1.9E-05)

-8.9E-06
(2.0E-05)

3.6E-06
(1.8E-05)

-4.9E-06
(2.0E-05)

Per capita # of doctors

0.242
(13.891)

7.926
(15.059)

16.394
(20.993)

42.372
(26.445)

-0.511
(20.897)

26.405
(25.656)

Per capita education exp.

1.9E-05
(7.9E-05)

0.000
(0.000)

5.3E-05
(8.3E-05)

6.2E-05
(9.4E-05)

4.4E-05
(7.6E-05)

4.5E-05
(8.5E-05)

# school bldgs/sq. mile

-0.008
(0.062)

-0.005
(0.065)

-0.0135001
(0.063)

-0.012
(0.067)

-0.015
(0.062)

-0.013
(0.065)

N
R squared

4792
0.3575

4792
--

4792
0.3606

4792
0.3536

4792
0.3549

4792
0.3425

Individual Characteristics

State of Birth Characteristics

Notes : All specifications include a dummy for 1970, 24 cohort dummies, 47 state of birth dummies, region of
birth interacted with cohort and an intercept. Estimates are weighted using the number of observations in
the cell in the base year. Standard errors, shown in parentheses, are clustered at the state of birth and
cohort level.

Table 4: New Estimates of Effects of Education on Mortality
Dependent variable is ten year mortality rate, Table entries are the Coefficient on Education
Goldin/Katz
Sample and specification
WLS
IV
N
Instruments
Panel A (1960-80)
(1) 1% 1960-1980
-0.036
-0.072
4792
-(0.004)
(0.025)
(2) 1% 1960, 3% 1970, 5% 1980
drop allocated age, education, birthplace

-0.045
(0.004)

-0.043
(0.020)

4797

-0.045
(0.024)

(3) Sample (2) with age cubic

-0.039
(0.004)

-0.046
(0.020)

4797

-0.047
(0.024)

(4) Sample (2) with age cubic*yr

-0.040
(0.004)

-0.046
(0.020)

4797

-0.047
(0.024)

(5) Sample (2) with state*cohort trend

-0.048
(0.004)

-0.032
(0.021)

4797

-0.016
(0.024)

-0.034
(0.003)

-0.035
(0.014)

8636

-0.026
(0.015)

-0.036
(0.003)

-0.020
(0.015)

8636

-0.012
(0.016)

-0.025
(0.006)

-0.085
(0.045)

2397

-0.081
(0.052)

(9) Sample (6) 1970 only

-0.061
(0.005)

-0.022
(0.032)

2400

-0.023
(0.033)

(10) Sample (6) 1980 only

-0.043
(0.004)

-0.006
(0.025)

2399

0.023
(0.029)

(11) Sample (6) 1990 only

-0.012
(0.005)

0.021
(0.040)

1440

0.027
(0.039)

-0.017
(0.005)

-0.059
(0.040)

2879

-0.067
(0.036)

(13) Sample (6) 55-64 year olds

-0.039
(0.005)

-0.066
(0.041)

2398

0.063
(0.053)

(14) Sample (6) 65-89 year olds

-0.030
(0.003)

-0.005
(0.019)

3071

-0.047
(0.023)

-0.019
(0.004)

-0.098
(0.037)

3644

-0.203
(0.125)

-0.017
(0.004)

0.039
(0.022)

4992

0.025
(0.023)

Panel B (1960-2000)
(6) 1% 1960, 2% 1970, 5% 1980-00
age cubic*year
(7) Sample (6) with state*cohort trend
Panel C (1960-2000 by year)
(8) Sample (6) 1960 only

Panel D (1960-2000 by age)
(12) Sample (6) 35-54 year olds

Panel E (1960-2000 by cohort)
(15) Sample (6) cohorts 1901-1912
(16) Sample (6) cohorts 1913-1925

Notes : All specifications include year dummies, cohort dummies, state of birth dummies, region of
birth interacted with cohort and an intercept (except for rows 5 and 7). Estimates are weighted using
the number of observations in the cell in the base year. Standard errors, shown in parentheses, are
clustered at the state of birth and cohort level.

Table 5: Estimates of Effects of Education on Health Outcomes in the SIPP

IV /2SCML exogeneity test
Dependent Variable

OLS /Probit

IV /2SCML

% effect

p-value

N

Panel A: General Health Outcomes

Self Reported Health
( 1 is excellent, 5 is poor)

-0.0941
(0.0023)

-0.2289
(0.0745)

-0.074

0.074

26030

Health Index (1 to 100 scale)

1.9674
(0.0511)

4.5345
(1.6738)

0.067

0.131

26030

Fair or Poor Health

-0.0359
(0.0010)

-0.0824
(0.0343)

-0.230

0.176

26030

Poor Health

-0.0141
(0.0006)

-0.0269
(0.0206)

-0.226

0.533

26030

Hospitalized in Last Year

-0.0049
(0.0008)

-0.0268
(0.0241)

-0.149

0.364

26484

Days in Bed, last 4 months

-0.3310
(0.0364)

2.1526
(1.4848)

0.547

0.074

25223

Number of Times Hospitalized

-0.0101
(0.0024)

-0.0944
(0.0884)

-0.335

0.329

22229

Number of Nights in Hospital

-0.0730
(0.0186)

-1.0828
(0.7668)

-0.567

0.185

26289

Panel B: Functional Limitations/ADL/IADL
Trouble Seeing

-0.0122
(0.0007)

-0.0559
(0.0254)

-0.412

0.085

20853

Trouble Hearing

-0.0103
(0.0007)

-0.0499
(0.0247)

-0.329

0.109

20845

Trouble Speaking

-0.0019
(0.0002)

-0.0192
(0.0079)

-0.909

0.039

20573

Trouble Lifting

-0.0198
(0.0009)

-0.0055
(0.0330)

-0.023

0.667

20837

Trouble Walking

-0.0251
(0.0011)

0.0130
(0.0325)

0.045

0.242

20797

Trouble with Stairs

-0.0250
(0.0010)

-0.0066
(0.0324)

-0.024

0.993

20820

Trouble Getting Around
Outside the Home

-0.0120
(0.0008)

-0.0146
(0.0257)

-0.114

0.918

17401

Trouble Getting Around
Inside the Home

-0.0048
(0.0005)

0.0051
(0.0208)

0.087

0.635

17463

Trouble Getting In/
Out of Bed

-0.0056
(0.0006)

0.0013
(0.0230)

0.016

0.764

17621

Trouble Seeing at all

-0.0020

-0.0078

-0.343

0.490

20589

Table 5: Estimates of Effects of Education on Health Outcomes in the SIPP

IV /2SCML exogeneity test
OLS /Probit

IV /2SCML

(0.0002)

(0.0084)

Trouble Hearing at all

-0.0008
(0.0001)

-0.0100
(0.0045)

Trouble Speaking at all

0.0000
(0.0001)

-0.0008
**

Trouble Lifting at all

-0.0100
(0.0007)

-0.0029
(0.0250)

Trouble Walking at all

-0.0148
(0.0008)

Trouble with Stairs at all

Dependent Variable

% effect

p-value

N

-0.758

0.060

20256

0.000

7516

-0.025

0.775

20789

0.0107
(0.0260)

0.069

0.328

20723

-0.0114
(0.0006)

0.0071
(0.0202)

0.061

0.359

20775

Needs Help
Getting Around Outside

-0.0066
(0.0007)

0.0044
(0.0153)

0.050

0.470

13598

Needs Help
Getting Around Inside

-0.0010
(0.0002)

0.0108
(0.0078)

0.446

0.125

13757

Needs Help
Getting In/Out of Bed

-0.0011
(0.0003)

0.0092
(0.0080)

0.372

0.191

13794

Panel C: Specific Health Conditions
Difficulty

-0.0250
(0.0013)

-0.0743
(0.0348)

-0.175

0.157

19073

Arthritis

-0.0088
(0.0008)

-0.0043
(0.0217)

-0.034

0.836

19012

Back

-0.0028
(0.0005)

-0.0349
(0.0167)

-0.561

0.061

18924

Blind

-0.0014
(0.0003)

0.0145
(0.0084)

0.557

0.060

18454

Cancer

-0.0007
(0.0002)

0.0025
(0.0078)

0.161

0.677

18569

Deaf

-0.0003
(0.0002)

-0.0041
(0.0064)

-0.179

0.568

18422

Deformity

-0.0006
(0.0002)

-0.0159
(0.0066)

-0.591

0.018

18821

Diabetes

-0.0023
(0.0003)

-0.0258
(0.0082)

-0.868

0.007

18688

Heart

-0.0062
(0.0006)

-0.0014
(0.0194)

-0.016

0.804

19025

Hernia

-0.0003
(0.0001)

0.0023
(0.0037)

0.362

0.454

17179

Table 5: Estimates of Effects of Education on Health Outcomes in the SIPP

IV /2SCML exogeneity test
OLS /Probit

IV /2SCML

-0.0031
(0.0004)

0.0376
(0.0124)

Kidney

-0.0001
(0.0001)

Lung

Dependent Variable
Hypertension

% effect
1.053

p-value
0.000

N
18683

0.0042
(0.0027)

0.938

0.072

16593

-0.0037
(0.0005)

0.0203
(0.0152)

0.472

0.106

19060

Mental Illness

-0.00009
(0.00008)

-0.0002
(0.0424)

-0.045

0.932

15794

Missing Limb

-0.00007
(0.00005)

-0.0019
(0.0016)

-0.580

0.155

14565

Paralysis

-0.00011
(0.00006)

0.0016
(0.0020)

0.287

0.348

17301

Senility

-0.00005
(0.00002)

-0.0015
(0.0006)

-0.214

0.070

17993

Stomach

-0.0006
(0.0002)

0.0069
(0.0060)

0.695

0.195

17701

Stroke

-0.0008
(0.0003)

0.0084
(0.0090)

0.397

0.295

18918

-0.0000001
(0.000000)

0.000001
**

0.000

0.000

14559

-0.0023
(0.0005)

-0.0013
(0.0152)

-0.019

0.947

19060

Thyroid
Other

Table 6: Small Pox Vaccination Rates of Young Children in 1930

state
Alabama
Alaska
Arizona.
Arkansas.
California.
Colorado.
Connecticut
Delaware.
DC
Florida
Georgia
Hawaii
Idaho
Illinois.
Indiana
Iowa.
Kansas
Kentucky.
Louisiana
Maine.
Maryland.
Massachusetts
Michigan.
Minnesota
Mississippi.
Missouri
Montana
Nebraska
Nevada.
NH
NJ
NM
NY
NC
ND
Ohio.
Oklahoma
Oregon
Pennsylvania
RI
SC
SD
Tennessee.
Texas
Utah.
Vermont
Virginia
Washington
WV
Wisconsin
Wyoming.
Median

at age 4
9
NA
22
5
23
13
35
8
14
NA
30.5
33
11
16
13
18
14
20.5
23
28
34
25
17
10
21.5
21
9
16
28
28
25
NA
23
3.5
33
15
19
13
9
51
11
25
10
13
13
NA
10
14.5
NA
18
NA
17.0

at age 5
15
NA
25
23
33
53
65
4
35
NA
56.5
38
18
26
14
26
26
50
46
50
60
62
25
15
31.5
37
8
15
28
76
53
NA
63
10
37
34
27
15
29
86
17
40
23
27
13
NA
16
24.5
NA
27
NA
27.0

change, age 4 to 5 % change, age 4 to 5
6
NA
3
18
10
40
30
-4
21
NA
26
5
7
10
1
8
12
29.5
23
22
26
37
8
5
10
16
-1
-1
0
48
28
NA
40
6.5
4
19
8
2
20
35
6
15
13
14
0
NA
6
10
NA
9
NA
10.0

66.7
NA
13.6
360.0
43.5
307.7
85.7
-50.0
150.0
NA
85.2
15.2
63.6
62.5
7.7
44.4
85.7
143.9
100.0
78.6
76.5
148.0
47.1
50.0
46.5
76.2
-11.1
-6.3
0.0
171.4
112.0
NA
173.9
185.7
12.1
126.7
42.1
15.4
222.2
68.6
54.5
60.0
130.0
107.7
0.0
NA
60.0
69.0
NA
50.0
NA
66.7

Table 7: IV Estimates of Mortality and Health by Stringency of Compulsory Vaccination Laws

Mortality
-0.028
(0.014)
8636

Health
Index
4.535
(1.674)
26030

Poor
Health
-0.035
(0.024)
26030

Trouble
Seeing
-0.057
(0.028)
20853

Trouble
Hearing
-0.053
(0.027)
20845

Trouble
Speaking
-0.022
(0.012)
20834

Back
-0.035
(0.019)
19073

Sample
with Vacc data

-0.025
(0.015)
7736

4.854
(1.590)
24958

-0.046
(0.023)
24958

-0.050
(0.027)
20045

-0.052
(0.026)
20036

-0.020
(0.011)
20027

-0.032
(0.019)
18371

-0.020
(0.012)
18371

-0.012
(0.007)
18371

Stringent States

-0.023
(0.012)
3600

3.348
(1.804)
13841

-0.059
(0.027)
13841

-0.029
(0.030)
11225

-0.038
(0.024)
11219

-0.002
(0.007)
11207

-0.018
(0.020)
10417

-0.014
(0.012)
10417

-0.001
(0.006)
10417

Non-Stringent
States

0.020
(0.027)
4136

3.774
(1.760)
11117

0.001
(0.029)
11117

-0.023
(0.033)
8820

0.026
(0.032)
8817

-0.009
(0.012)
8820

-0.030
(0.029)
7954

-0.016
(0.018)
7954

-0.022
(0.014)
7954

Baseline
Sample

Stiffness or
deformity
-0.021
(0.012)
19073

Senility
-0.013
(0.007)
19073

Notes: All IV regressions include female dummy, cohort dummies, state of birth dummies, and 7 time varying state of birth
characteristics (at age 14) from Lleras-Muney (2005). These are % urban, % foreign born, %black, %mfg, mfg wage, doctors per-capita,
education expenditures per-capita, and schools per sq. mile. The mortality results also use region of birth interacted with cohort while the
SIPP results use state-specific cohort trends. Instruments are categories of required years of schooling in state of residence at age 14.
Standard errors are clustered on state of birth and cohort

Figure 1: Ten Year Mortality Rates by Age Across Census Years
0.8

0.7

0.6

. ·.

0.4

I~

0.3

0.2

0.1

Age

79

77

75

73

71

69

67

65

63

61

59

57

55

53

51

49

47

45

43

41

39

37

0
35

Death Rate

0.5

1960
1970
1980
1990

I

Figure 2: Vaccination Rates for 4 and 5 year olds by state
70
age 4
age 5

60

50

40

30

20

10

0
Massachusetts

Minnesota

Utah.

Kentucky.

Median State

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Ellen R. Rissman

WP-03-34

Where the Headquarters are – Evidence from Large Public Companies 1990-2000
Tyler Diacon and Thomas H. Klier

WP-03-35

Standing Facilities and Interbank Borrowing: Evidence from the Federal Reserve’s
New Discount Window
Craig Furfine

WP-04-01

Netting, Financial Contracts, and Banks: The Economic Implications
William J. Bergman, Robert R. Bliss, Christian A. Johnson and George G. Kaufman

WP-04-02

Real Effects of Bank Competition
Nicola Cetorelli

WP-04-03

Finance as a Barrier To Entry: Bank Competition and Industry Structure in
Local U.S. Markets?
Nicola Cetorelli and Philip E. Strahan

WP-04-04

The Dynamics of Work and Debt
Jeffrey R. Campbell and Zvi Hercowitz

WP-04-05

Fiscal Policy in the Aftermath of 9/11
Jonas Fisher and Martin Eichenbaum

WP-04-06

Merger Momentum and Investor Sentiment: The Stock Market Reaction
To Merger Announcements
Richard J. Rosen

WP-04-07

Earnings Inequality and the Business Cycle
Gadi Barlevy and Daniel Tsiddon

WP-04-08

Platform Competition in Two-Sided Markets: The Case of Payment Networks
Sujit Chakravorti and Roberto Roson

WP-04-09

Nominal Debt as a Burden on Monetary Policy
Javier Díaz-Giménez, Giorgia Giovannetti, Ramon Marimon, and Pedro Teles

WP-04-10

On the Timing of Innovation in Stochastic Schumpeterian Growth Models
Gadi Barlevy

WP-04-11

Policy Externalities: How US Antidumping Affects Japanese Exports to the EU
Chad P. Bown and Meredith A. Crowley

WP-04-12

Sibling Similarities, Differences and Economic Inequality
Bhashkar Mazumder

WP-04-13

3

Working Paper Series (continued)
Determinants of Business Cycle Comovement: A Robust Analysis
Marianne Baxter and Michael A. Kouparitsas

WP-04-14

The Occupational Assimilation of Hispanics in the U.S.: Evidence from Panel Data
Maude Toussaint-Comeau

WP-04-15

Reading, Writing, and Raisinets1: Are School Finances Contributing to Children’s Obesity?
Patricia M. Anderson and Kristin F. Butcher

WP-04-16

Learning by Observing: Information Spillovers in the Execution and Valuation
of Commercial Bank M&As
Gayle DeLong and Robert DeYoung

WP-04-17

Prospects for Immigrant-Native Wealth Assimilation:
Evidence from Financial Market Participation
Una Okonkwo Osili and Anna Paulson

WP-04-18

Individuals and Institutions: Evidence from International Migrants in the U.S.
Una Okonkwo Osili and Anna Paulson

WP-04-19

Are Technology Improvements Contractionary?
Susanto Basu, John Fernald and Miles Kimball

WP-04-20

The Minimum Wage, Restaurant Prices and Labor Market Structure
Daniel Aaronson, Eric French and James MacDonald

WP-04-21

Betcha can’t acquire just one: merger programs and compensation
Richard J. Rosen

WP-04-22

Not Working: Demographic Changes, Policy Changes,
and the Distribution of Weeks (Not) Worked
Lisa Barrow and Kristin F. Butcher

WP-04-23

The Role of Collateralized Household Debt in Macroeconomic Stabilization
Jeffrey R. Campbell and Zvi Hercowitz

WP-04-24

Advertising and Pricing at Multiple-Output Firms: Evidence from U.S. Thrift Institutions
Robert DeYoung and Evren Örs

WP-04-25

Monetary Policy with State Contingent Interest Rates
Bernardino Adão, Isabel Correia and Pedro Teles

WP-04-26

Comparing location decisions of domestic and foreign auto supplier plants
Thomas Klier, Paul Ma and Daniel P. McMillen

WP-04-27

China’s export growth and US trade policy
Chad P. Bown and Meredith A. Crowley

WP-04-28

Where do manufacturing firms locate their Headquarters?
J. Vernon Henderson and Yukako Ono

WP-04-29

Monetary Policy with Single Instrument Feedback Rules
Bernardino Adão, Isabel Correia and Pedro Teles

WP-04-30

4

Working Paper Series (continued)
Firm-Specific Capital, Nominal Rigidities and the Business Cycle
David Altig, Lawrence J. Christiano, Martin Eichenbaum and Jesper Linde

WP-05-01

Do Returns to Schooling Differ by Race and Ethnicity?
Lisa Barrow and Cecilia Elena Rouse

WP-05-02

Derivatives and Systemic Risk: Netting, Collateral, and Closeout
Robert R. Bliss and George G. Kaufman

WP-05-03

Risk Overhang and Loan Portfolio Decisions
Robert DeYoung, Anne Gron and Andrew Winton

WP-05-04

Characterizations in a random record model with a non-identically distributed initial record
Gadi Barlevy and H. N. Nagaraja

WP-05-05

Price discovery in a market under stress: the U.S. Treasury market in fall 1998
Craig H. Furfine and Eli M. Remolona

WP-05-06

Politics and Efficiency of Separating Capital and Ordinary Government Budgets
Marco Bassetto with Thomas J. Sargent

WP-05-07

Rigid Prices: Evidence from U.S. Scanner Data
Jeffrey R. Campbell and Benjamin Eden

WP-05-08

Entrepreneurship, Frictions, and Wealth
Marco Cagetti and Mariacristina De Nardi

WP-05-09

Wealth inequality: data and models
Marco Cagetti and Mariacristina De Nardi

WP-05-10

What Determines Bilateral Trade Flows?
Marianne Baxter and Michael A. Kouparitsas

WP-05-11

Intergenerational Economic Mobility in the U.S., 1940 to 2000
Daniel Aaronson and Bhashkar Mazumder

WP-05-12

Differential Mortality, Uncertain Medical Expenses, and the Saving of Elderly Singles
Mariacristina De Nardi, Eric French, and John Bailey Jones

WP-05-13

Fixed Term Employment Contracts in an Equilibrium Search Model
Fernando Alvarez and Marcelo Veracierto

WP-05-14

Causality, Causality, Causality: The View of Education Inputs and Outputs from Economics
Lisa Barrow and Cecilia Elena Rouse

WP-05-15

5

Working Paper Series (continued)
Competition in Large Markets
Jeffrey R. Campbell

WP-05-16

Why Do Firms Go Public? Evidence from the Banking Industry
Richard J. Rosen, Scott B. Smart and Chad J. Zutter

WP-05-17

Clustering of Auto Supplier Plants in the U.S.: GMM Spatial Logit for Large Samples
Thomas Klier and Daniel P. McMillen

WP-05-18

Why are Immigrants’ Incarceration Rates So Low?
Evidence on Selective Immigration, Deterrence, and Deportation
Kristin F. Butcher and Anne Morrison Piehl

WP-05-19

Constructing the Chicago Fed Income Based Economic Index – Consumer Price Index:
Inflation Experiences by Demographic Group: 1983-2005
Leslie McGranahan and Anna Paulson

WP-05-20

Universal Access, Cost Recovery, and Payment Services
Sujit Chakravorti, Jeffery W. Gunther, and Robert R. Moore

WP-05-21

Supplier Switching and Outsourcing
Yukako Ono and Victor Stango

WP-05-22

Do Enclaves Matter in Immigrants’ Self-Employment Decision?
Maude Toussaint-Comeau

WP-05-23

The Changing Pattern of Wage Growth for Low Skilled Workers
Eric French, Bhashkar Mazumder and Christopher Taber

WP-05-24

U.S. Corporate and Bank Insolvency Regimes: An Economic Comparison and Evaluation
Robert R. Bliss and George G. Kaufman

WP-06-01

Redistribution, Taxes, and the Median Voter
Marco Bassetto and Jess Benhabib

WP-06-02

Identification of Search Models with Initial Condition Problems
Gadi Barlevy and H. N. Nagaraja

WP-06-03

Tax Riots
Marco Bassetto and Christopher Phelan

WP-06-04

The Tradeoff between Mortgage Prepayments and Tax-Deferred Retirement Savings
Gene Amromin, Jennifer Huang,and Clemens Sialm

WP-06-05

Why are safeguards needed in a trade agreement?
Meredith A. Crowley

WP-06-06

6

Working Paper Series (continued)
Taxation, Entrepreneurship, and Wealth
Marco Cagetti and Mariacristina De Nardi

WP-06-07

A New Social Compact: How University Engagement Can Fuel Innovation
Laura Melle, Larry Isaak, and Richard Mattoon

WP-06-08

Mergers and Risk
Craig H. Furfine and Richard J. Rosen

WP-06-09

Two Flaws in Business Cycle Accounting
Lawrence J. Christiano and Joshua M. Davis

WP-06-10

Do Consumers Choose the Right Credit Contracts?
Sumit Agarwal, Souphala Chomsisengphet, Chunlin Liu, and Nicholas S. Souleles

WP-06-11

Chronicles of a Deflation Unforetold
François R. Velde

WP-06-12

Female Offenders Use of Social Welfare Programs Before and After Jail and Prison:
Does Prison Cause Welfare Dependency?
Kristin F. Butcher and Robert J. LaLonde
Eat or Be Eaten: A Theory of Mergers and Firm Size
Gary Gorton, Matthias Kahl, and Richard Rosen
Do Bonds Span Volatility Risk in the U.S. Treasury Market?
A Specification Test for Affine Term Structure Models
Torben G. Andersen and Luca Benzoni

WP-06-13

WP-06-14

WP-06-15

Transforming Payment Choices by Doubling Fees on the Illinois Tollway
Gene Amromin, Carrie Jankowski, and Richard D. Porter

WP-06-16

How Did the 2003 Dividend Tax Cut Affect Stock Prices?
Gene Amromin, Paul Harrison, and Steven Sharpe

WP-06-17

Will Writing and Bequest Motives: Early 20th Century Irish Evidence
Leslie McGranahan

WP-06-18

How Professional Forecasters View Shocks to GDP
Spencer D. Krane

WP-06-19

Evolving Agglomeration in the U.S. auto supplier industry
Thomas Klier and Daniel P. McMillen

WP-06-20

Mortality, Mass-Layoffs, and Career Outcomes: An Analysis using Administrative Data
Daniel Sullivan and Till von Wachter

WP-06-21

7

Working Paper Series (continued)
The Agreement on Subsidies and Countervailing Measures:
Tying One’s Hand through the WTO.
Meredith A. Crowley
How Did Schooling Laws Improve Long-Term Health and Lower Mortality?
Bhashkar Mazumder

WP-06-22

WP-06-23

8