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International Journal of Epidemiology, 2015, Vol. 44, No. 5
International Journal of Epidemiology, 2015, 1484–1490
Commentary: William
doi: 10.1093/ije/dyv288
Advance Access Publication Date: 27 November 2015
Ogburn, Dorothy Thomas
and the influence of recessions
and expansions on mortality
José A Tapia Granados
Department of Politics, Drexel University, 3141 Chestnut St., 3021E MacAlister Hall, Philadelphia, PA
19104, USA. E-mail: [email protected]
Introduction
In 1922 William F. Ogburn, a renowned sociologist at
Columbia University, and Dorothy S. Thomas, a 23-yearold graduate student, authored a paper on the influence of
macroeconomic fluctuations on social conditions.1 In the
paper, the authors noted that changes in the economic
system
are accompanied by profound social changes. Thus, the
industrial revolution of the past century brought
changes in political organization, in the family, the position of women ( … ). Such effects are the materials
back of the theory of the economic interpretation of history. There is, however, another type of economic
change which also occasions social modifications.
These changes are ( … ) oscillatory changes of short duration, ( … ) brief swings in economic conditions through
prosperity and depression ( … ). These fluctuations in
business conditions occur over short intervals with
some regularity and are usually referred to as business
cycles. Do these fluctuations in business produce fluctuations in social conditions? Do we find relatively more
births, deaths, marriages and divorces in periods of
business depression?
To answer these questions, Ogburn and Thomas constructed an index of economic conditions in the USA for
1870–1920 and correlated it with the rates of marriage, divorce, death and birth. To avoid spurious correlations, they
de-trended the correlated series by a variety of ad hoc methods described in the paper. Considering the value and statistical significance of the correlations, they concluded that
during periods of business prosperity—that is, during economic expansions—there are relatively more births, marriages and divorces, but also more deaths than in periods of
business depression—that is, recessions. The relative increase
in births, marriages and divorces during years of prosperity
and their decrease in recessions had been reported by other
investigators, and Ogburn and Thomas thought it consistent
with the fact that couples tend to postpone their marriages,
their break-up and their reproduction when economic circumstances are harsh. But to the authors’ surprise, de-trended
rates of total mortality, infant mortality and pulmonary tuberculosis mortality correlated positively with the index of
business conditions (with correlation coefficients of 0.57,
0.42 and 0.32, respectively); so that these three mortality
rates showed, Ogburn and Thomas said, ‘the strange result of
increasing in prosperity and decreasing in depression’.
Suicides correlated negatively (-0.74) with the business cycle,
oscillating as Ogburn and Thomas expected they should, that
is increasing during recession and decreasing during prosperity. Table 1 shows that Ogburn and Thomas’s results for the
years 1870–1920 are robust to variation in method of detrending and economic indicator used.
Puzzled by their results showing that economic prosperity was associated with rising mortality, Ogburn and
Thomas tried to ascertain whether other investigators had
found the same or if they could replicate the finding with a
different country. They were unable to find ‘any published
results of correlations by other investigators of death rates
and business conditions’. But they examined the correlations of an index of economic conditions—a not very
good one, foreign trade—with mortality in England and
Wales. Foreign trade correlated 0.02 with general mortality rate and 0.09 with infant mortality, so they concluded
that these null correlations supported a sceptical view regarding ‘the existence of a significant correlation between
death rates and the business cycle’.
Dorothy Thomas continued her academic work on the
same issues and, in her PhD dissertation at the London
School of Economics, she re-examined the US data
and reanalysed British data on mortality with better indicators of the business cycle. Now she found a connection
between periods of prosperity and increasing deaths in both
countries. She conjectured again that this connection
C The Author 2015; all rights reserved. Published by Oxford University Press on behalf of the International Epidemiological Association
V
International Journal of Epidemiology, 2015, Vol. 44, No. 5
1485
Table 1. Correlations of two business cycle indicators—the unemployment rate and the business cycle index built by Ogburn
and Thomas1—with the general mortality rate, infant mortality, tuberculosis mortality and suicides
Death rate Infant mortality rate TB death rate Suicide rate
Panel A
Unemployment rate
0.32†
Business cycle indicator
0.31*
0.22
0.15
0.25
0.19
0.63**
0.55*
Mortality series and the unemployment rate are in
first differences, correlated with the business cycle
index which is already de-trended
Panel B
Unemployment rate
0.46**
Business cycle indicator
0.50***
0.37*
0.37**
0.12
0.23
0.72***
0.79***
All variables except the business cycle index in deviation from a Hodrick–Prescott trend (c ¼ 100)
0.74
Ogburn and Thomas’s results:a
Series de-trended as indicated by Ogburn and Thomas
Series de-trended with a 9-year moving average
Panel C
Business cycle indicator
Business cycle indicator
0.57
0.63
0.42
0.37
0.32
Unemployment rates taken from Romer78 (Series UA, Table 9, 1890–900) and from Carter79 (1901–20).
a
Ogburn and Thomas provided standard errors (not P-values) for the correlations they computed. Considering the standard errors of the Pearson correlation r
pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
[which are easily computable with the formula ð1 r2 Þ=ðn 2Þ], the six correlations of panel C are statistically significant at the standard levels of confidence.
†
P < 0.1; *P < 0.05; **P < 0.01; ***P < 0.001.
Data for the years 1870–920 (n ¼ 51) in selected states of the USA, except for suicides which are only for 1900–20 (n ¼ 21) for 100 US cities. In panels A and
B, modern methods for de-trending are applied to the series used by Ogburn and Thomas or the unemployment rate as business cycle indicator. Results are very
similar (obviously with opposite sign when using the unemployment rate) to those in Panel C obtained by Ogburn and Thomas.
is contrary to expectation. Deaths, both in the USA and
England, show a strong tendency to increase with prosperity and diminish with depression ( … ) so, although it
is difficult to find a satisfactory explanation of the cause
of this phenomenon, the conclusion must be drawn
that, in both England and the USA, a high death rate is
associated with periods of prosperity and a low death
rate with periods of depression.2
Thomas became a prestigious scholar—a theorem and
an esteemed award in demography now bear her name—
but her 1927 findings on the relation between mortality
and the business cycle were ignored for half a century. In
1929 the Great Depression started and mortality did not
increase, it dropped. Economic turbulence and war followed for almost two decades, then for a quarter-century
the Western world experienced rapid economic growth
that was supposedly overcoming all social ailments.
Mortality rates were showing a long-term decline (the pattern discovered by Thomas referred to short-term oscillations), so any suggestion of harmful consequences for
health of economic expansions would have gone much
against the grain of the times. Even Dorothy Thomas seems
to have been happy with her discovery being forgotten, as
she apparently never mentioned it again.
Old Controversies
In 1977, the year in which Dorothy Tomas died, a biologist
at the University of Pennsylvania, Joe Eyer, examined the
relation between mortality rates and conditions of the US
economy for the years 1875–1975. Eyer’s analysis was
simple and straightforward: he noticed that peaks in the
unemployment rate coincided with troughs of mortality,
and troughs of unemployment coincided with peaks of
mortality.3 Eyer named this ‘Thomas effect,’ as unemployment is at its lowest levels in expansions, and at its highest
level when the economy is depressed, thus he had found
the same pattern that Dorothy Thomas had described in
1927. But Eyer went beyond just the replication of results
and proposed explanations: he thought that increased mortality in expansions might result from higher levels of stress
at work, declines in networks of social support, and
increasing overwork, overtime, migration and consumption of harmful substances associated with business prosperity. That was indeed quite at odds with the prevalent
view that social ills result mostly if not exclusively from
low income, economic stagnation and lack of work, rather
than from overwork or overconsumption. On the other
hand, researchers had started to notice that unemployed
individuals often have bad health. If that is the case, why it
should be that mortality would increase when less people
are unemployed?
Looking at similar data, Harvey Brenner proposed different explanations. For Brenner, recessions raise mortality by
increasing joblessness, reducing incomes and raising social
stress.4–7 Brenner argued that the correlation between expansions and higher mortality results from the lagged effects
of earlier recessions. Unemployment and business failures
‘are typically associated with increases in mortality two to
three years following the lowest point in the business cycle
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and extending for at least the next 10 to 15 years’.8
Furthermore, because on average business cycles
tend to be 4–5 years in length ( … ), this classic 2–3-year
lag in mortality after the peak in the unemployment rate
means that the first peak in mortality following recession
approximately coincides with the subsequent peak in the
business cycle—that is, the peak of ‘recovery’ or ‘expansion’. Given this observation, one should also note that
the zero lag relationship between unemployment and
mortality rates is actually inverse. The first peak in the
lagged mortality rate at 2–3 years after recession is not
accidental: the delayed impact of economic distress interacts with chronic disease mortality in such a way that the
first wave of increased mortality peaks at about the same
time as growth in the economic cycle.8
Brenner also claimed that recession ‘as measured by
the unemployment rate, and/or by the business failure
rate ( . . . ) is positively related to mortality over 0–10
and 1–7 years, respectively’, though during the recession
itself ‘overall mortality declines, followed by a sharp increase (with no lag) during the initial year of recovery.’8
In summary, what Brenner claimed—in a not very transparent way—is that peaks in mortality actually coincide
with expansions, not with recessions, because recessions
cause an increase in mortality with a lag. But how long
is that lag? Brenner gave a variety of answers to this
question, as the cited passages show. It is well established in economics, though, that the business ‘cycle’ is
actually irregular; some ‘cycles’ of expansion and recession last 1 or 2 years, whereas other cycles last several
years, or even a decade. The mechanism suggested by
Brenner to explain why peaks of mortality coincide with
troughs in unemployment, i.e. with expansions, is problematic for this reason.
Brenner’s studies were criticized for a number of reasons, including the methods used and the lack of sufficient
information about both data and methods.9–15 Authors
trying to replicate Brenner’s results were unable to reproduce them. Specific attempts to find a lagged effect of recessions on population mortality were unsuccessful.16–18
Two epidemiologists summarized the criticisms received
by Brenner’s investigations by stating that they had led to
considerable scepticism about the data and the conclusions.19 An economist who meticulously reviewed the literature concluded that a number of investigators had
pointed out ‘serious technical flaws in Brenner’s methods’.20 Unfortunately, the controversies on Eyer’s and
Brenner’s work generated such a confusion that, during the
last two decades of the past century, for many social researchers it was difficult even to understand the different
views that were at play.
International Journal of Epidemiology, 2015, Vol. 44, No. 5
New Methods
Things started to change in 2000, when a leading journal in
economics published an article in which Christopher Ruhm
showed that mortality for major causes of death increased
in expansions and declined in recessions.16 Ruhm used an
innovative but straightforward technique—panel regression
with fixed effects—applied to 1972–91 data of the 50 states
of the USA. Ruhm did not know at the time of the old publications by Ogburn, Thomas and Eyer, so he did not cite
them. But he found, like Ogburn, Thomas and Eyer before
him, that suicides oscillate contrary to total mortality, i.e.
rising in recessions and falling in expansions.
Economists use the term ‘procyclical’ to describe variables like employment or use of energy which increase in
expansions and decrease in recessions, and the term ‘countercyclical’ for variables like unemployment or business
failures that rise in recessions and fall in expansions. Thus
in economic terminology what Ogburn and Thomas had
discovered in the 1920s was a procyclical oscillation of
mortality rates and a countercyclical oscillation of suicide
rates in US and British data. The same pattern was rediscovered by Joe Eyer for the period 1875–1975 in the USA.
Ruhm found it again in data for the 50 US states in
1972–91.
Ruhm’s paper was considered a ground-breaking investigation, and in the following years other papers replicated
Ruhm’s findings with a variety of data and countries in
Europe, Latin America and Asia.17,21–36 In 2005, I submitted to this journal a paper showing that, during the 20th
century in the USA, the long-term decline of total mortality
and mortality for specific population groups, ages and
causes of death accelerated during recessions and was
reduced or even reversed during periods of economic expansion (the exception being suicides, which increased in
recessions). It was a further replication of the Thomas effect. The editor decided to publish the paper with some
critical commentaries and my response.37–44
Almost a century has now passed since the publication
of the seminal contributions by Ogburn and Thomas1,2
and many investigations17,21–36,45–48 have replicated the
basic findings of these authors. In my view the Thomas effect, the procyclical oscillation of mortality, is one of a
relatively small set of regularities solidly demonstrated in
social science. Christopher Ruhm has been a key author in
the demonstration of that regularity,16,20,49–54 discovered
almost a century ago by Ogburn and Thomas.
New Controversies
Any regularity observed in the past allows us to form expectations about the future. If the Thomas effect is correct,
International Journal of Epidemiology, 2015, Vol. 44, No. 5
then we can expect mortality to fall in recessions and rise in
expansions, just as we expect high blood pressure in obese
individuals, cancer and early death in smokers, or rising
wages when the economy booms. But, of course, that wages
are procyclical has been disputed for decades in economics;
and the Thomas effect, the procyclical oscillation of mortality, has been also disputed by a number of authors.
Ralph Catalano is one author who has disagreed with
the idea that we should expect above-trend mortality during expansions and below-trend mortality during recessions. In a recent review of the health effects of economic
decline—which did not even mention the contributions of
Ogburn and Thomas and ignored many other peerreviewed publications on these issues—Catalano et al.
concluded55 that no definite association of total mortality,
cardiovascular disease mortality or infant mortality with
macroeconomic fluctuations has been proved. The proposition that during the expansions of the US economy
increasing mortality is observed37 was also disputed by
Catalano40 as representing an ecological fallacy.56
Catalano theorized a long time ago on the use of time
series in epidemiology (his views57 can be compared for instance with those of Zeger et al.58). In a practical application of his methodology, Catalano included Danish
economic data as covariates in regressions to model US
mortality.59 With this methodology, Catalano concluded
that whereas recessions have no effect on death rates, expansions are associated with drops in mortality 1 year
later. He argued that this association was attributable to
the fact that extra governmental resources during the upturn would allow for the expansion of health care to persons ‘not thought to be at sufficient risk to warrant
attention at an earlier budget period’.59
David Stuckler and Sanjay Basu have also disagreed
with the idea that general mortality has a procyclical oscillation. Stuckler, Basu and coauthors argue that economic
crises increase suicides and decrease traffic injuries, but
they may or may not raise mortality due to other causes of
death depending on the policies that governments apply to
deal with the crisis.60–63 Using data on ‘economic crises’ in
1970–2007 and mortality in 26 European countries,
Stuckler et al. found that increases in unemployment were
associated with an increase in suicides and a decrease in
traffic mortality, but they did not find any evidence that
all-cause mortality was affected by recessions.60 This is in
contrast to other investigations using data from European
countries, showing that recessions are associated to declines in mortality.21–23,29,50 The discrepancy may be perhaps explained by the fact that Stuckler et al.60 included as
economic crises in the investigated sample not only recessions in market economies of Western Europe, but also the
transition crisis of the early 1990s in Eastern Europe, when
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the sudden transformation of centrally planned economies
into a market economy was associated with major upturns
in mortality rates. The combination of increases in mortality associated with rising unemployment during the transition in the East and decreases in mortality associated with
business-cycle recessions in the West, may well result in no
association overall.
Stuckler has also claimed that banking crises significantly increase heart disease mortality.64 But banking crises and recessions are systematically linked, so Stuckler’s
results are at odds with research by Ruhm and others who
have shown reduced rates of cardiovascular mortality and
ischaemic heart disease mortality during recessions in a
variety of countries.23,24,33,52
A case that illustrates the disputes on the Thomas effect
was a paper by Tapia and Diez Roux, in which we showed
significant declines in mortality due to major causes of
death during the recessions of the period 1920–40, including the Great Depression of the early 1930s.26 Stuckler
et al.65 disputed our major conclusions and a controversy
followed.66–68 Catalano et al. also disputed the view that
the Great Depression had been associated with significant
drops in mortality.69 In both cases, critics argued that there
is no evidence that oscillations of general mortality in the
early 1930s had anything to do with the Great Depression.
The health consequences of the recent Great Recession
have also triggered controversy. Given the deterioration of
living standards and social services during the crisis, many
were expecting an increase of mortality rates. However,
excepting a moderate increase in suicides, no significant increase in general mortality has been detected anywhere.
Indeed in the European Union, evidence shows that—
consistent with the Thomas effect—national mortality
rates for all deaths and major causes of death have evolved
better during the recession than in previous years.33,70–72
Research has abundantly demonstrated that in the long
run economic growth is linked to declines in mortality.
However, all evidence suggests that this link has been weakened with the passage of time. Thus cross-country data from
recent decades ‘show almost no relation between changes in
life expectancy and economic growth over 10, 20, or 40year time periods between 1960 and 2000. Many countries
have shown remarkable improvements in health with little
or no economic growth’;73 but the controversial Thomas effect is quite different: it is a regularity in the relation between mortality and economic growth in the short run.
In my view—which of course is the view of an interested
party in the controversies—there is strong evidence for the
Thomas effect in recent business cycles in a variety of countries,25,31,33,34,36,70 though data also show that the link between business cycles and mortality probably has weakened
in recent decades.18,24,54 Ruhm54 has asserted that total
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mortality in the USA has shifted since the 1970s from being
strongly procyclical to being essentially unrelated to the condition of the economy, but the relationship is likely to be
poorly measured when using less than 15- or 20-year periods. Ruhm claims that some countercyclical patterns have
emerged, for instance deaths caused by overdose of prescription drugs, which rose dramatically in recent recessions.
Based on an analysis of US data for smaller areas (counties
rather than states), Lindo36 has disagreed with Ruhm’s view
that death rates have become less procyclical in recent years.
Some support for Lindo’s view is given by the evolution of
mortality for all causes in the USA in the period 1990–2013.
Using age-adjusted mortality (computed with the 2000
population as standard) to eliminate the effect of ageing,
total mortality in the period decreased on average by 1.1%
per year, but in these 24 years there were only three in
which age-adjusted mortality increased. They were 1993,
1999 and 2005, when age-adjusted mortality grew by
2.3%, 0.6% and 0.2%, respectively. But alas! these three
were years of expansion, not recession.
Final Remarks
Results of investigations on social issues are often disputed
by investigators who disagree about results on the ground
of appropriateness of data or methods.29,52,67,74–76 When
confronting results showing the Thomas effect, one major
concern of many social researchers is that results showing
that recessions are good for health seem to be inconsistent
with studies at the level of individuals, which strongly
suggest that being unemployed is bad for health.77 A recent
paper using individual data from the Panel Study of
Income Dynamics32 has provided evidence that joblessness strongly raises the risk of death among those suffering it, but also that recessions are associated with a
moderate but significant reduction in the risk of death
among the entire population. Both effects are not
inconsistent.
Clarity of the analysis is a major value of compelling
social research. The 1922 article, in which Ogburn and
Thomas discovered the procyclical oscillation of mortality,
is an excellent example of simplicity and transparency.
Ogburn and Thomas were seminal authors in what today
it could be very well called economic epidemiology. The
decision of the International Journal of Epidemiology to
reprint Ogburn and Thomas’s article is to be commended.
In addition to its historical value, the paper will be an
excellent introduction to the topic of the links between
economic recessions and health, an area of enquiry in
which confusion and obscurity—two characteristics that
good science always aims to eliminate—are quite
abundant.
International Journal of Epidemiology, 2015, Vol. 44, No. 5
Conflict of interest: None declared.
References
1. Ogburn WF, Thomas DS. The influence of the business cycle on
certain social conditions. J Am Stat Assoc 1922;18:324–40, pp.
150–59.
2. Thomas DS. Social Aspects of the Business Cycle. New York,
NY: Knopf, 1927, pp. 150–59.
3. Eyer J. Prosperity as a cause of death. Int J Health Serv
1977;7:125–50.
4. Brenner MH. Mortality and economic instability: detailed analyses for Britain and comparative analyses for selected industrialized countries. Int J Health Serv 1983;13:563–620.
5. Brenner MH. Economic change and sex-specific cardiovascular
mortality in Britain 1955-1976. Soc Sci Med 1982;16:431–42.
6. Brenner MH. Mortality and the national economy. A review,
and the experience of England and Wales, 1936-76. Lancet
1979;2:568–73.
7. Brenner MH. Fetal, infant, and maternal mortality during periods
of economic instability. Int J Health Serv 1973;3:145–59.
8. Brenner MH. Political economy and health. In: Amick BC III,
Levine S, Tarlov AR, Walsh DC (eds). Society and Health.
New York, NY: Oxford University Press, 1995 pp. 232, 237–38.
9. Eyer J. Review of [Brenner’s] Mental-Illness and the Economy.
Int J Health Serv 1976;6:139–48.
10. Eyer J. Review of [Brenner’s] Mental-Illness and Economy Rejoinder. Int J Health Serv 1976;6:157–68.
11. Cohen LE, Felson M. On estimating the social costs of national
economic policy: A critical examination of the Brenner study.
Soc Indicators Res 1979;6:251–59.
12. Gravelle HS, Hutchinson G, Stern J. Mortality and unemployment: A critique of Brenner’s time-series analysis. Lancet
1981;2:675–79.
13. Wagstaff A. Time series analysis of the relationship between unemployment and mortality: a survey of econometric critiques and
replications of Brenner’s studies. Soc Sci Med 1985;21:985–96.
14. Cook PJ, Zarkin GA. Homicide and economic conditions: A replication and critique of M. Harvey Brenner’s new report to the
U.S. Congress. J Quant Criminol 1986;2:69–80.
15. Kasl S. Mortality and the business cycle: some questions about
research strategies when utilizing macro-social and ecological
data. Am J Public Health 1979;69:784–89.
16. Ruhm CJ. Are recessions good for your health? Q J Econ.
2000;115:617–50.
17. Tapia Granados JA, Ionides EL. The reversal of the relation between economic growth and health progress: Sweden in the 19th
and 20th centuries. J Health Econ 2008;27:544–63.
18. Tapia Granados JA. Economic growth and health progress in
England and Wales: 160 years of a changing relation. Soc Sci
Med 2012;74:688–95.
19. Kasl SV, Jones BA. The impact of job loss and retirement on
health. In: Berkman LF, Kawachi I (eds). Social Epidemiology.
Oxford; Oxford University Press, 2000.
20. Ruhm CJ. Macroeconomic conditions, health, and government
policy. In: Schoeni RF, House JS, Kaplan G, Pollack H (eds).
Making Americans Healthier: Social and Economic Policy as
Health Policy. New York, NY: Russell Sage, 2008.
International Journal of Epidemiology, 2015, Vol. 44, No. 5
21. Neumayer E. Recessions lower (some) mortality rates: evidence
from Germany. Soc Sci Med 2004;58:1037–47 [erratum corrigendum in Soc Sci Med 2004;59:1993].
22. Tapia Granados JA. Recessions and mortality in Spain,
1980-1997. Eur J Popul 2005;21:393–422.
23. Buchmueller T, Grignon M, Jusot F. Unemployment and mortality in France, 1982-2002. Department of Economics, McMaster
University, 2007.
24. Tapia Granados JA. Macroeconomic fluctuations and mortality
in postwar Japan. Demography 2008;45:323–43.
25. Lin S. Economic fluctuations and health outcome: A panel analysis of Asian-Pacific countries. Appl Econ 2009;41:519–30.
26. Tapia Granados JA, Diez Roux AV. Life and death during the
Great Depression. Proc Natl Acad Sci U S A 2009;106:17290–95.
27. Gonzalez F, Quast T. Macroeconomic Changes and Mortality in
Mexico. Empir Econ 2010;40:305–19.
28. Gonzalez F, Quast T. Mortality and business cycles by level of
development: Evidence from Mexico. Soc Sci Med 2010;71:
2066–73.
29. Tapia Granados JA, Ionides EL. Mortality and macroeconomic
fluctuations in contemporary Sweden. Eur J Popul 2011;27:
157–84.
30. Ionides E, Wang Z, Tapia Granados JA. Macroeconomic effects
on mortality revealed by panel analysis with nonlinear trends.
Ann Appl Stat 2013;7:1362–85.
31. French MT, Gumus G. Macroeconomic fluctuations and motorcycle fatalities in the U.S. Soc Sci Med 2014;104:187–93.
32. Tapia Granados JA, House JS, Ionides EL, Burgard SA, Schoeni
RF. Individual joblessness, contextual unemployment, and mortality risk. Am J Epidemiol 2014;180:280–87.
33. Toffolutti V, Suhrcke M. Assessing the short term health impact
of the Great Recession in the European Union: A cross-country
panel analysis. Prev Med 2014;64:54–62.
34. Rolden HJA, van Bodegom D, van den Hout WB, Westendorp
RGJ. Old age mortality and macroeconomic cycles. J Epidemiol
Community Health 2014;68:44–50.
35. Haaland VF, Telle K. Pro-cyclical mortality across socioeconomic groups and health status. J Health Econ
2015;39:248–58.
36. Lindo JM. Aggregation and the estimated effects of economic
conditions on health. J Health Econ 2015;40:83–96.
37. Tapia Granados JA. Increasing mortality during the expansions
of the US economy, 1900-1996. Int J Epidemiol 2005;34:
1194–202.
38. Neumayer E. Commentary: The economic business cycle and
mortality. Int J Epidemiol 2005;34:1221_22.
39. Ruhm CJ. Commentary: Mortality increases during economic
upturns. Int J Epidemiol 2005;34:1206–11.
40. Catalano R, Bellows B. Commentary: If economic expansion
threatens public health, should epidemiologists recommend recession? Int J Epidemiol 2005;34:1212–13.
41. Edwards RD. Commentary: Work, well-being, and a new calling
for countercyclical policy. Int J Epidemiol 2005;34:1222–25.
42. Brenner MH. Commentary: Economic growth is the basis of
mortality rate decline in the 20th century—Experience of the
United States 1901–2000. Int J Epidemiol 2005;34:1214–21.
43. McKee M, Suhrcke M. Commentary: Health and economic transition. Int J Epidemiol 2005;34:1203–06.
1489
44. Tapia Granados JA. Response: on economic growth, business
fluctuations, and health progress. Int J Epidemiol 2005;
34:1226–33.
45. Mackenbach JP, Kunst AE. Mortality and causes of death during
two major economic recessions: The Netherlands 1915–1984.
In: Veenhoven R, Hagenaars A (eds). Did the Crisis Really Hurt?
Effects of the 1980–1982 Economic Recession on Satisfaction,
Mental Health and Mortality. Rotterdam, The Netherlands:
Universitaire Pers, 1989.
46. Graham JD, Chang B, Evans JS. Poorer is riskier. Risk Analysis
1992;12:333–37.
47. Abdala F, Geldstein RN, Mychaszula SM. Economic restructuring and mortality changes in Argentina – Is there any connection? In: Cornia GA, Paniccià R (eds). The Mortality Crisis in
Transitional Economies. New York, NY: Oxford University
Press, 2000.
48. Khang YH, Lynch JW, Kaplan GA. Impact of economic crisis
on cause-specific mortality in South Korea. Int J Epidemiol
2005;34:1291–301.
49. Ruhm CJ. Healthy living in hard times. J Health Econ
2005;24:341–63.
50. Gerdtham UG, Ruhm CJ. Deaths rise in good economic times:
Evidence from the OECD. Econ Hum Biol 2006;4:298–316.
51. Ruhm CJ. Macroeconomic conditions, health, and mortality. In:
Jones AM (ed). The Elgar Companion to Health Economics.
Cheltenham, UK: Edward Elgar, 2006.
52. Ruhm CJ. A healthy economy can break your heart.
Demography 2007;44:829–48.
53. Heutel G, Ruhm CJ. Air Pollution and Procyclical Mortality.
NBER Working Paper 18958. Cambridge, MA: NBER, 2013
54. Ruhm CJ. Recessions, healthy no more? J Health Econ
2015;42:17–28.
55. Catalano R, Goldman-Mellor S, Saxton K et al. The health effects
of economic decline. Annu Rev Public Health 2011;32:431–50.
56. Jacobs IA, Podobny MT, Bilusich D. One mechanism underlying contrasting health-economy findings. Int J Epidemiol
2007;36:929–31.
57. Catalano R. Time series designs of potential interest to epidemiologists. Am J Epidemiol 1987;126:724–31.
58. Zeger SL, Irizarry R, Peng RD. On time series analysis of public
health and biomedical data. Annu Rev Public Health
2006;27:57–79.
59. Catalano R. The effect of deviations from trends in national income on mortality: the Danish and USA data revisited. Eur J
Epidemiol 1997;13:737–43.
60. Stuckler D, Basu S, Suhrcke M, Coutts A, McKee M. The public
health effect of economic crises and alternative policy responses in
Europe: an empirical analysis. Lancet 2009;374:315–23.
61. Kentikelenis A, Karanikolos M, Papanicolas I et al. Health effects of financial crisis: omens of a Greek tragedy. Lancet
2011;378:1457–58.
62. Stuckler D, Basu S. The Body Economic: Why Austerity Kills.
New York, NY: Basic Books, 2013.
63. Karanikolos M, Mladovsky P, Cylus J et al. Financial crisis, austerity, and health in Europe. Lancet 2013;381:1323–31.
64. Stuckler D, Meissner CM, King LP. Can a bank crisis break your
heart? Globalization and Health 2008;4:1.
1490
65. Stuckler D, Meissner C, Fishback P, Basu S, McKee M. Banking
crises and mortality during the Great Depression: evidence from
US urban populations, 1929–1937. J Epidemiol Community
Health 2012;66:410–19.
66. Tapia Granados JA. Bank failures, mortality and the Great
Depression. J Epidemiol Community Health 2012;66:477–78.
67. Stuckler D, Meissner C, Fishback P, Basu S, McKee M. Was the
Great Depression a cause or correlate of significant mortality declines? An epidemiological response to [Tapia] Granados.
J Epidemiol Community Health 2013;67:467.
68. Tapia Granados JA. Health and the Great Depression: a reply to
Stuckler et al. J Epidemiol Community Health 2013;67:1073–74.
69. Bruckner TA, Noymer A, Catalano RA. Life Expectancy during
the Great Depression in Eleven European Countries. Popul Dev
Rev 2013;39:57–74.
70. De la Fuente VS, López MAC, González IF, Alcántara OJG,
Ritzel DO. The impact of the economic crisis on occupational
injuries. J Saf Res 2014;48:77–85.
71. Regidor E, Barrio G, Bravo MJ, de la Fuente L. Has health in
Spain been declining since the economic crisis? J Epidemiol
Community Health 2014;68:280–82.
International Journal of Epidemiology, 2015, Vol. 44, No. 5
72. Tapia Granados JA, Rodriguez JM. Health, economic crisis, and
austerity: A comparison of Greece, Finland and Iceland. Health
Policy 2015;119:941–53.
73. Cutler DM, Deaton A, Lleras-Muney A. The Determinants of
Mortality. J Econ Perspect 2006;20:97–120.
74. Svensson M. Do not go breaking your heart: Do economic upturns really increase heart attack mortality? Soc Sci Med
2007;65:833–41.
75. Svensson M. Economic upturns are good for your health but
watch out for accidents: a study on Swedish regional data
1976–2005. Appl Econ 2010;42:615–25.
76. Tapia Granados JA. A flawed diagnosis. [Book review of The
Body Economic: Why Austerity Kills]. Science 2013;341:1176–77.
77. Sullivan D, Wachter TV. Job Displacement and Mortality: An
Analysis Using Administrative Data. Q J Econ 2009;124:
1265–306.
78. Romer C. Spurious volatility in historical unemployment data. J
Polit Econ 1986;94:36.
79. Carter SB, Gartner SS, Haines MR, Olmstead AL, Sutch R, Wright
G. Historical Statistics of the United States: Millennial Edition
Online. New York, NY: Cambridge University Press, 2006.
International Journal of Epidemiology, 2015, 1490–1492
Commentary:
doi: 10.1093/ije/dyv289
Advance Access Publication Date: 27 November 2015
Macroeconomic conditions
and social outcomes through a 90-year lens
Christopher J Ruhm
Frank Batten School of Leadership and Public Policy, University of Virginia, Charlottesville, VA 229044893, USA. E-mail: [email protected]
Accepted 1 October 2015
Published over 90 years ago, ‘The Influence of the Business
Cycle on Certain Social Conditions’ by William Ogburn
and Dorothy Thomas1 (hereafter ‘OT’) stands the test of
time with results that are sometimes expected and occasionally surprising. OT use US data from 1870–1920 to
examine the relationship between business cycle fluctuations and marriage, divorce, fertility, crime, total mortality, infant deaths, tuberculosis fatalities and suicides. My
first impression, upon rereading this paper, was to be reminded of the great effort involved to create the dependent
and explanatory variables.
Modern researchers can easily ascertain dates and other
aspects of business cycle conditions from the National
Bureau of Economic Research [www.nber.org/cycles.
html], information on mortality, fertility, marriage and divorce from the National Center for Health Statistics
[http://www.cdc.gov/nchs/nvss.htm] and crime data from
the Federal Bureau of Investigation [https://www.fbi.gov/
stats-services/crimestats] or Bureau of Justice Statistics
[http://www.bjs.gov].
By contrast, OT had to painstakingly construct each
statistic from original sources, often requiring difficult design decisions amid imperfect and incomplete information.
For instance, their main business cycle measure was an
arithmetic mean of nine components capturing aspects of
economic performance related to prices, business failures,
production, railroad mileage, employment and international trade. Presaging modern macroeconomic techniques,
each component was transformed into standard deviations
around the trend-adjusted mean, and the final overall business cycle variable was then compared with plausible alternatives. Similar effort went into measuring the outcomes,
often with information available for only a portion of the
full analysis period.
C The Author 2015; all rights reserved. Published by Oxford University Press on behalf of the International Epidemiological Association
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