Are Mortality Rates Random Walk? Panel Unit-

root Tests with the Evidences from Micro Data*

University of Notre Dame

April 19, 2013

Abstract

This study investigates the relationship between economic conditions and health. Previous

studies have estimated that they have a significantly negative correlation. However, there is a

concern that mortality rates are non-stationary series, with which a chance of spurious

correlation exhibits. By taking first difference on regressors and conducting unit-root tests for

panel data, it shows some evidences that the unit-root concern is reasonable and the

association between health and economic shocks is exaggerated. An accompanying analysis of

micro data indicates that the influence of risky behaviors on mortality rate in response to

economic conditions cannot match the extent to which the basic fixed-effect model predicted.

1

* I am extremely grateful to Professor William Evans for his constant guidance and support. All errors are mine. ѱ Department of Economics, University of Notre Dame, IN 46556. Email: sgu1@nd.edu

1. Introduction

A recent research shows a procyclical fluctuation of total mortality (Ruhm 2000) by Fixed-effect

(FE) models of panel data regression with health and economic condition proxied by mortality

rates and unemployment rates respectively. By using the same method with the undated data

for the 1984-2010 periods, I draw a similar conclusion with even large coefficient (absolute

value) on the unemployment rate. The negative correlation of total mortality and

unemployment rates can be partially explained by an evident increase in risky behavior,

including smoking and drinking; and a reduction of physical activities and medical care during

the economic expansion era. However, is that statistically enough to say a strengthening

economy is bad for people’s health?

In light of the concern that two independent random walks may have a statistically

significant correlation, I am curious about whether these two series are non-stationary. Many

literatures have already shown that we do not have to worry about a random walk for some

important economic indicators, including unemployment rates. However, few researches focus

on whether the mortality rates are random walk.

This study uses unit-root tests to investigate the reliability of the negative relationship

between people’s health and economic condition. Differences over one period are taken for

both dependent and independent variable to make the potential un-stationary variables

stationary. A Fisher-Type unit root test for panel data is put into use to explore which variable is

randomly dependent from time.

The analysis provides strong evidence that the negative correlation between health and

economic condition is exaggerated. When taking a one-period difference for both state

mortality and unemployment rate respectively, the regression result shows a more than 80%

shrinking of the coefficient, though it is still significant. Consistent with this result, the Fisher-

Type test shows that the state mortality rate fails to pass the unit-root test, despite that the

unemployment rate is not a random walk.

In addition, micro data from the Behavioral Risk Factor Surveillance System (BRFSS) are

used to provide some evidences for potential problem of unit root by showing the negative

coefficient between mortality and unemployment rates cannot be explained by some related

risky behaviors. Ruhm (2000) indicates that the mortality rates are procyclical by decomposing

the variation in mortality into several components, including tobacco use, drinking, physical

activity, diet and medical care. Furthermore, Sparks, Cooper, Fried, and Shirom (1997) argue

that a rising workload has a negative effect on people’s health status by using meta-analysis to

examine the relationship between the length of the working week and health symptoms. White

and Beswick (2003), and Siegrist and Rodel (2006) draw the same conclusion. Nonetheless, I

show none of these can lead to such a sharp increase in mortality rate in the expansion era with

data analysis and intuitive induction.

Fixed-effect models are estimated using panel data for the smoking rate for 1996-2010

period and alcohol-related mortality for 1984-1992 period. The results reveal that both smoking

and drinking related deaths are significantly increasing during the expansion era. However, the

coefficients are far smaller relative to the total mortality rate. Aguiar, Hurst and Karabarbounis

(2011) explore how foregone market work hours are allocated to other activities over the

business cycle of 2003-2010, with special focus on the era of the great recession. They show

that a 4% increase of the unemployment rate during the 2008-2010 leads to 7% reduction in

working hours for average workers. The result is significant but still too small to alter people’s

health status. In addition, a fairly small amount of the reduced working hours are allocated to

activities that improve people’s health (physical practices, medical care and sleep). As a matter

of fact, a very large fraction of time is used to non-market production and housework, which

consume energy as well.

The paper is structured as follows. In section 2 I use the Fixed-effect model to examine the

relationship between health and economic shock with updated data. In section 3 I use some

methods to examine if the result I got in section 2 can pass the unit-root tests. In section 4 I use

micro data to explore to what extent the risk behaviors which is responsive to the economic

shock can alter people’s health status. Finally in section 5 I make conclusions.

2. Fixed-effect model with state data

2.1 Model

I use the subscripts j and t to index the state and year. The basic regression equation is

(1)

for H the natural log of the mortality rate, E the proxies for economic conditions, X a vector of

supplementary regressors, and ε the error term. The fixed-effect controls for time-invariant

state characteristics, accounts for nationwide time effects and γ captures the impact of

within-state deviations in economic conditions. The effect of national business cycle is also

obtained by estimating

(2)

where indicates national economic conditions and the time effects are excluded.

2.2 Descriptive statistics

Summary statistics, weighted by the total resident population in each state by year, are

displayed in Table I and are largely self-explanatory. The death rate refers to all-caused deaths

per 10000 persons. The state and unemployment rates are the weighted average of period

1984-2010 by state. The per capital personal income is the weighted average of nominal dollar

of the same period.

TABLE I VARIABLES USED IN ANALYSIS OF STATE AGGREGATE DATA

Variable Mean Standard deviation

Death rate per 10000 people 84.63 13.26 State unemployment rate in % 5.97 1.89 National unemployment rate in % 5.96 1.38 Per capita personal income (in thousands) 36.86 10.84 All variables are weighed by state populations. The data of mortality rates are from CDC WONDER. The unemployment rates are provided by Bureau of Labor Statistics, Local Area Unemployment Statistics (LAUS) program. Income data are constructed using the data from U.S. Census Bureau, Current Population Survey, Annual Social and Economic Supplements. Population statistics are provided by Population Division, U.S. Census Bureau.

Figure I displays national total mortality and unemployment rates in each year. The

variables normalized by subtracting mean of variable. The result shows some evidences of

inverse relationship between economic conditions and fatality rates. However, unlike Ruhm

(2000), the negative correlation does not hold for each period. From 1984-1992 and 2007-2010,

the inverse relationship is evident. However, during 1993-2000, and positive correlation is

observed. This problem appears if I drop the some observations of the earliest and latest years.

For instance, if the observations of 1984-1987 and 2007-2010 are dropped, the coefficient on

unemployment rate severely shrinks, though the sample is still large enough.

Figure I Total Mortality and Unemployment Rates (Normalized)

2.3 Result

This subsection examines the relationship between economic conditions and total mortality

rates. Unless otherwise stated, the dependent variable is the natural logarithm of the death

rate (per100,000 persons). Table II summarizes the results of a variety of specifications, all of

which control for state fixed-effects, and use state or national unemployment rates to proxy

macroeconomic conditions. Some specifications also include year dummy variables, and some

hold constant personal incomes.

As Table II shows, the negative relationship between unemployment rate and mortality

rate is even larger than previous predictions. One percentage increase of state unemployment

rates is associated with 1.2 percentage decrease of mortality rates. With the control of personal

income, the result holds. If the year dummy is excluded, a little bit weaker relationship is

observed. This contradicts to the result from Ruhm (2000), which states that the negative

relationship is stronger by dropping the year dummy. If the independent estimator is the

national mortality rate, the relationship is even weaker, but still very significant. One

percentage increase of national unemployment rate leads to 0.6 decline of mortality rate.

The middle and bottom panel of Table II presents separate estimates for the 1984-1997

and 1998-2010 periods, the ten highest and lowest income city based on the income in 2010.

Splitting the sample into shorter time period is likely to reduce the influence of within-state

changes in omitted factors that are correlated with unemployment rate. The rationale for

singling the states with highest and lowest income is that if the negative heath effect of

economic expansion is due to migration, a difference may be observed for those to

specifications.

Mortality is procyclical for all variations. However by splitting the period, coefficients

decline for about 30 percent. Both highest income and lowest income have larger negative

health effects. But the two specifications exhibit similar results.

TABLE II FIXED-EFFECT ESTIMATES OF THE DETERMINANTS OF TOTAL MORTALITY RATE

Full sample estimates

Basic specification

(a) (b) (c) (d) (e)

State unemployment rate -0.0120 -0.0124 -0.0083 U.S. unemployment rate

(0.0011) (0.0011) (0.0007) -0.0057 (0.0009)

-0.0066 (0.0009)

Personal income -0.0498 (0.0219)

-0.0289 (0.0046)

Year effect Yes Yes No No No

Split-sample estimates

1984-1997 1998-2010

(a) (b) (a) (b)

State unemployment rate -0.0081 -0.0085 -0.0073 -0.0043 (0.0007) (0.0009) (0.0007) (0.0007) Personal income -0.0068 -0.1262 (0.0068) (0.0112)

Split-sample estimates

10 highest income states 10 lowest income states

(a) (b) (a) (b)

State unemployment rate -0.0192 -0.0189 -0.0189 -0.0212 (0.0034) (0.0033) (0.0036) (0.0037) Personal income -0.1751 -0.1450 (0.05492) (0.0617)

The dependent variable is the natural logarithm of the total mortality rate per 10,000 populations. All specifications also include vectors of state dummy variables. Year dummy variables are also controlled for, except models (c), (d) and (e) in the top panel. Standard errors are in parentheses. The sample in the top panel includes annual observations for the 50 states and the District of Columbia covering the period 1984–2010. The ten largest states refer to rankings in 1991 and include, in descending order of size, California, New York, Texas, Florida, Pennsylvania, Illinois, Ohio, Michigan, New Jersey, and North Carolina.

3. Unit-root test

In light of concern that two independent random walks may have a statistically significant

correlation, I use two methods to check the unit-roots on both dependent and independent

variables. Many literatures (Romero-Ávila and Usabiaga, 2007; Schwert 1987) have already

shown that we do not have to worry about a random walk for some important economic

indicators, including unemployment rates. However, few researches focus on whether the

mortality rates are random walk. The rationale is that for individuals, we can separate the

causes of death to two classifications. One is the internal causes of death and the other is

external causes of death. The latter one is triggered by some risky behaviors associated with

the change of economic condition, which likely to be cyclical. However, independent from

unhealthy behaviors, the time of death is very random. The amount of fatalities possibly is

heterogeneous across years. That gives another potential cyclical of mortality rate which barely

has a correlation with economic shocks. If the internal causes are dominant, we probably can

observe a negative relationship between mortality and unemployment rate. However,

economic expansion and recession cannot be applied to explain the correlation. In turn, the

results I got in Section 2 are exaggerated.

3.1 Model

3.1.1 Difference on difference test

I use the subscripts j and t to index the state and year. The difference on difference regression

equation is

(3)

for is the first difference of natural log of the mortality rate, is the proxies of the first

difference of economic conditions, X a vector of supplementary regressors, and ε the error term,

accounts for nationwide time effects.

By taking the first difference between variable and one period lagged variable, I convert the

non-stationary series into stationary, by which the spurious regression can be avoided.

3.1.2 Fisher-type test

To investigate the causal relationship between mortality and unemployment rate, I further

check for the stationarity of each variable separately. Recently, there has been a heightened

development of panel-based unit root tests (Hadri, 1999; Breitung, 2000; Choi, 2001; Levin, Lin

and Chu, 2002; Im, 2003; Breitung and Das, 2005). These studies have shown that the panel

unit root tests are more powerful than tests based on times series data.

I use the subscripts j and t to index the state and year. The fisher-type ADF specification

regression equation can be written as

(4)

(5)

for is the first difference of natural log of the mortality rate, is the proxies of the

one period lagged economic conditions, X a vector of supplementary regressors, and ε the error

term.

In testing for panel-data unit roots, Fisher-type tests conduct the unit-root tests for each

panel individually and then combine the p-values from these tests to produce an overall test. In

this context, I perform a unit-root test on each of panel units j (state) separately and then I use

their combined p-values to construct a Fisher-type test to investigate whether or not the series

exhibit a unit-root. The null hypothesis is that all the panels have unit roots and the alternative

hypothesis is that at least one panel does not have unit roots or some panels do not have unit

roots. This routine provides 4 different unit-root test methods as proposed by Choi (2001).

3.2 Result

3.2.1 Difference on difference test

The way in which I interpret the Table III is analogous to the Section 2 while the numbers are

significantly different. By difference on difference test, one percentage increase of state

unemployment rate is associated to 0.26% decline of mortality rate. The coefficient is still

significant but has shrunk about 5/6 compared to the basic regression. Adding year dummy or

not, controlling personal income or not and estimating by state or national economic predictors

give similar observations.

TABLE III FIRST DIFFERENCE TEST OF DETERMINANTS OF MORTALITY RATE

Full sample estimates

Basic specification

(a) (b) (c) (d) (e)

State unemployment rate -0.0026 -0.0024 -0.0035 U.S. unemployment rate

(0.0009) (0.0011) (0.0006) -0.0037 (0.0006)

-0.0027 (0.0007)

Personal income -0.0186 (0.0090)

-0.0102 (0.0024)

Year effect Yes Yes No No No

The dependent variable is the natural logarithm of the total mortality rate per 10,000 populations. State dummy here is excluded for avoiding bias. Year dummy variables are controlled for, except models (c), (d) and (e). Standard errors are in parentheses. The sample in the top panel includes annual observations for the 50 states and the District of Columbia covering the period 1985–2010.

The test draws a conclusion that by converting the non-stationary series into stationary,

the updated results show evidences that the basic regression exaggerates the correlation

between mortality rate and unemployment rate and concerns about a spurious regression is

reasonable. Next I want to use the panel date unit-roots test to show which variable is possibly

non-stationary.

3.2.2 Fisher-type test

The Table IV shows the result of fisher-type panel data unit-root test for mortality rate and

unemployment rate for 1984-2010 period. By observing the p-value, I can reject the null

hypothesis that all panels of unemployment rate have unit root while the total mortality rate

does not pass the test.

The result is consistent with the conclusion I draw on previous subsection. Since very likely

the mortality rates have unit roots, its correlation with unemployment rates cannot be

identified by simply run a fixed-effect regression. A process of taking first difference is not long

reasonable, but also required.

TABLE IV FISHER-TYPE UNIT-ROOT TEST ON PANEL DATA WITH ONE PERIOD LAG

All mortality Unemployment Drinking mortality

Test statistic Statistic P-value Statistic P-value Statistic P-value

Inverse chi-squared P 88.75 0.82 302.36 0 134.01 0.02 Inverse normal Z 2.72 0.99 -10.55 0 -0.02 0.49 Inverse logit t L* 2.74 0.99 -11.04 0 -0.17 0.43 Modified inv. chi-squared Pm -0.92 0.82 14.03 0 2.24 0.01

3.3 Discussion

As Figure II shows, the red line represents the unemployment rate; dot line indicates the

external mortality rate, which is responsive to the economic shocks; dashed line shows the

internal mortality rate, which is determined by people’s own health status rather than risky

behavior; and cross line exhibits the aggregate mortality rate, which is an additive combination

of internal and external mortality rate.

Figure II Schematic Diagram of Separation of Mortality Rates

The reason why I cast doubt on the method given by Ruhm (2000) is that he actually

regresses the red line (unemployment rate) on the cross line (aggregate mortality rate).

However, only external mortality is responsive to the economic shocks while the internal one is

probably a random cycle. Therefore, an exaggerated result is inevitable because some non-

economic reason is mistakenly attributed.

My fisher-type test shows the aggregate mortality rate is a random walk, which implies

that the internal death-caused reasons are dominant. The difference on difference test

indicates a significant but small correlation between health and economic condition. That

means people’s health status is still associated with economic condition but is severely

exaggerated due to the random property of internal mortality.

4. Evidences from Micro Data

4.1 Background

In this subsection, I want to introduce where my curiosity of applying unit root test on mortality

rate comes from. This can be identified by the intuition when I was observing the micro data.

By running the fixed-effect estimates of the determinants of total mortality rate, Ruhm

(2000) provides an evidence of a negative correlation between unemployment rate and

mortality rate. In other words, mortality rate is procyclical, which seems contradicts to our

common sense. He thereafter tries to explain why the procyclical phenomenon makes sense by

using the Micro data from Behavioral Risk Factor Surveilance System. Some leading behavioral

responses to the change of economic condition are smoking and drinking, physical activity, diet

and preventable medical care. Mortality rate is likely to be sensitive to these factors.

TABLE V HOW RISKY BEHAVIORS REPOND TO STATE MORTALITY RATE

Smoker Drinker Any physical activity

Daily gram of fat consumes

Medical checkup

State unemployment rate .0064 (.0021) Source: Ruhm. “Are regressions good for your health”, Quarterly Journal of Economics, 2000

As the Table V shown, a one percentage point increase in the state unemployment rate

reduces the predicted number of current smokers by 0.3 percentage point. Physical activities,

diet and medical care are significantly improved during the recession era.

It is true that some behavioral responses to the change of economic condition significantly

influence people’s health status. However, obviously none of these factors leads people’s death

immediately except drunk driving, which is not statistically significant when varying

unemployment rate. For instance, one can drastically increase his/her tobacco use during a

good economic state. But he/she is almost unlike to die during the period of one business state.

Especially when taking the age factor into account, this intuition is more reasonable. Apparently,

Young people’s behaviors are more sensitive to the economic condition. But only after decades

their mortalities can be recorded into data. Their life expectancies were shortened by the

unhealthy behaviors during good states. However to what extent one’s life expectancy is

altered is highly heterogeneous and unable to be measured. Possibly, in a random year, there is

an explosion of mortality rate. However, this is irrelevant to the current economic condition but

related to a collective behavior changes due to good economic condition a few decades ago.

Thus, it reminds me that if a random walk can explain the change of mortality rate because of

the heterogeneous effects of behavioral change on people’s life expectancy.

4.2 Model

Econometric estimates of the determinants of smoking and drinking are summarized in Table

VII. The basic econometric specification is

(6)

where i indicates the individual, H the health input, and other variables are as defined above.

The linear probability model can estimate how state smoking rates respond to the economic

shocks. For drinking-related mortality, I use the same model as Section 2 shows.

4.3 Descriptive Statistics

Summary statistics, weighted by the total resident population in each state by year, are

displayed in Table VI. The drinking-related mortality rate refers to alcohol-caused deaths per

100000 persons. The state and unemployment rates are the weighted average of period (a)

1996-2010, (b) 1984-1992, and (c) 1996-2000. The per capital personal income is the weighted

average of nominal dollar of the same period.

TABLE VI VARIABLES USED IN ANALYSIS OF INDIVIDUAL DATA

Variables Mean Standard deviation

Smoking rate 20.99 3.65 Drinking-related mortality per 10000 people (1) 43.73 8.06 Drinking-related mortality per 10000 people (2) 35.56 8.17 Tobacco use 18.19 1.49 Per capita personal income (in thousands) (a) 43.14 6.44 Per capita personal income (in thousands) (b) 26.32 5.46 Per capita personal income (in thousands) (c) 37.53 6.4 State unemployment rate in % (a) 5.67 1.86 State unemployment rate in % (b) 6.54 1.94 State unemployment rate in % (c) 4.63 1.18 National unemployment rate in % (a) 5.60 1.62 National unemployment rate in % (b) 6.51 0.83 National unemployment rate in % (c) 4.60 0.50 All variables are weighed by state populations. The data of drinking mortality rates are from Stinson, F.S., Nephew, T.M., Dufour, M.C., and Grant, B.F. 1996. U.S. Alcohol Epidemiologic Data Reference Manual. The data of smoking rate and tobacco use are provided by T Behavioral Risk Factor Surveillance System Survey Data (BRFSS) from the period of 1996-2010 and 1996-2000 respectively. The unemployment rates are provided by Bureau of Labor Statistics, Local Area Unemployment Statistics (LAUS) program. Income data are constructed using the data from U.S. Census Bureau, Current Population Survey, Annual Social and Economic Supplements. Population statistics are provided by Population Division, U.S. Census Bureau.

4.4 Reexamination of risky behaviors

A. Smoking and drinking

Smoking rate has a significantly negative relationship with state unemployment rate. One

percentage increases in unemployment rate leads to 0.16 percentage decrease in smoking rate.

However, does it enough to show that the increasing consumption of smoking can largely

explain the deteriorated health condition during economic expansion?

To show this, I run another regression to find the relationship between smoking rate and

mortality rate. The result exhibits that the smoking rate and mortality rate has a significant

positive relationship and one percentage rise in smoking rate is associated with 0.18

percentage increase in mortality rate. Then by using simple algebra I can conclude that if

unemployment rate goes up by 1%, the mortality rate driven solely by rising tobacco

consumption decreases about 0.03% (0.16*0.18). If compared with 1.2% decrease in total

mortality rate, this number is quite small.

What about drinking? By intuition we know that the death caused by drinking during

economic expansion is more noticeable than smoking. This is because of drunk driving. During

1984-1992, the annual alcohol-related driving fatalities are 22500 on average. Drunk driving,

unlike smoking even binge drinking, probably leads to death in next minute. Thus, even though

Ruhm (2000) argues that the drinking rate is not very procyclical, some treatment is still needed.

The regression result shows that one percentage increase in state unemployment rate

leads to 1.7% decrease in drinking death rate. The result is significant and quite big. In addition,

as the Figure III shows, during the period of 1984-1992, the alcohol-related death shares a same

pattern as total mortality rate, both of which is procyclical. Nevertheless I still have to do some

adjustment on the number. By calculation I get among all people who died during 1984-1992, 5%

is related to drinking. This means that if unemployment rate rises by 1%, the mortality rate

driven solely by drinking-rated reason decreases about 0.08% (1.7*0.05). First of all, this

number is consistent with my previous guess that drinking-rated death is more than smoking-

rated death triggered by economic reason. Moreover, 0.08% is still very small relative to the 1.2%

in total. If we add the coefficients on smoking and drinking up, 0.11% (0.03+0.08) can only

explain 10% of decrease in total mortality rate caused by good economic condition. Besides, to

find the relationship between unemployment rate and mortality rate, I use the data from 1984-

1992. However, the annual death rate caused by drunk driving has decreased over 70%. This

means that 0.08% is actually overestimated because about 29% of drinking-related death can

be explained by drunk driving.

TABLE VII FIXED-EFFECT ESTIMATES OF THE DETERMINANTS OF RISKY BEHAVIORS

Smoking rate Mortality rate

(a) (b) (a) (b)

State unemployment rate -0.1604 -0.1767 Smoking rate 0.0018 0.0018 (0.0534) (0.0541) (0.0009) (0.0009) Personal income -1.6125 Personal income -0.0250 (0.9048) (0.0214)

Drinking death rate 1 Drinking death rate 2

(a) (b) (a) (b)

State unemployment rate -0.0169 -0.0169 -0.0158 -0.0157 (0.0018) (0.0018) (0.0020) (-0.0020) Personal income 0.0000

(0.0398) -0.1447

(0.0442)

Cigarettes per day Mortality rate

(a) (b) (a) (b)

State unemployment rate -0.1810 -0.1808 Cigarettes per day 0.0002 0.0002 (0.1407) (0.1410) (0.0016) (0.0016) Personal income 0.2751

(1.1726) Personal income -0.0137

(0.0259)

All specifications include vectors of year and state dummy variables. Robust standard errors, estimated assuming observations are independent across years and states but not within states in a given year, are displayed in parentheses.

Figure III Comparison between Total Mortality Rate and Alcohol-Related Death

B. Workload

If the behavioral change on smoking or drinking patterns cannot explain the large negative

figure, what about stress? An increase of working hours during economic expansion exerts

more responsibilities for workers. This in turn deteriorates people’s health. Conversely, during

recessions, people tend to have less workload, which subsequently reduce their stress. In

practice, Sparks, Cooper, Fried, and Shirom (1997) argue that a rising workload has a negative

effect on people’s health status by using meta-analysis to examine the relationship between

the length of the working week and health symptoms. White and Beswick (2003) and Siegrist

and Rodel (2006) draw the same conclusion.

I want to cast doubt on this argument by three insights. First, intuitively speaking, both

internal and external stress may influence people’s health. Working hours are external stress.

Conceivably this exerts negative effects on people’s health. How about internal stress? When

people are losing job, they meanwhile lose their steady income for living. It apparently makes

people stressful. No one feels happy if they do not know how to fund for the food tomorrow.

People in that case tend to be self-motivated. They start searching for job actively. This process

does take time and energy. However, during bad era, finding a job is considerably difficult. It

not only increases their workload of preparing for processes, but also discourages them. When

they are so discouraged that forgoing the efforts, they will not be seen as job seekers.

Unemployment rate in this case does not count them.

Furthermore, indeed, if people have to work beyond the typical 8 hours per day, extra

workload does make people stressful. However, a modest workload could help people arrange

their daily routine regularly. Especially for young people, unemployment does not mean that

they can have a good rest. In contrast, their schedule might be entirely disorganized, which

threatening their health. Thus, increasing working hours have an effect in combination of extra

responsibility and good schedule. Which one is more influential is mysterious. However,

obviously they may cancel each other out.

Besides, let us consider some related empirical result. Aguiar, Hurst and Karabarbounis

(2011) explore how foregone market work hours are allocated to other activities over the

business cycle of 2003-2010. We focus on the most recent great recession. During the period of

2008-2010, the aggregate unemployment rate was rising from around 5.8% to around 9.6%.

The data shows that the market work hours fell by 7%. This is consistent with the statistics

given by the Bureau of Labor Statistics (BLS). A simple calculation shows that one percentage

increase of unemployment rate is associated with a 1.84% decrease in working hours. This

means that a representative worker who is working about 40 hours per week has extra 44

minutes leisure hours during a recession when the unemployment rate increases by 1 percent. I

do not have empirical result to show how this 44-minute difference per week alters people’s

health condition. However, this is obviously a small number in practice.

Moreover, this number could be shrinking if we take how people allocate the extra hours

during a recession into account. Aguiar, Hurst and Karabarbounis (2011) shows that roughly 30%

of the foregone market working hours are reallocated to non-market production (excluding

child care), about 32% of foregone work hours are allocated to some regular housework,

including cooking, cleaning, shopping, and child care. People cannot feel much more relaxed if a

very large fraction of forgone working time is allocated like this. Comparably speaking, sleeping

only account for 20% and health care and civic activities is even less.

C. Diet, physical activities and medical care

Among all behavioral changes in response to the economic shocks, I choose smoking and

drinking to do further research. This is because a drastic increase in consumption of them may

significantly deteriorate people’s health. Diet, for instance, do help people keep good health

indicators (blood pressure, blood sugar, etc.). However, they are long-term determinants of

people’s life expectancy. In a short-run, they are obviously less important since my dependent

variable is mortality rate. Medical checkup, likewise, do not directly improve or jeopardize our

health.

Furthermore, my research focused on the relationship between mortality rate and

economic condition. Therefore, I want to single out some behaviors driven by economic factors

which are subject to change during business cycle. In other words, if people can find some free

alternatives during a bad state, for example running around your garden instead of going to

gym, the correlation would be weakened even though a significant coefficient was produced.

That’s why I excluded the physical activities because people are easily accessible to free

alternatives.

5. Conclusion

This study casts doubt on the strong relationship between macroeconomic conditions and

health. Previous and my studies show a one percentage point rise in the state unemployment

rate is associated with 0.6-1.2% percent decrease in total mortality by fixed-effect model.

However, when converting the dependent and independent variables into stationary series, the

correlation shrinks about 50-80%. A fisher-type panel unit-root tests reveal that this is probably

due to the mortality rates are essentially random walk.

Micro data of smoking rates and alcohol-related death give some evidences on the unit-

root concern. Even though the linear probability and fixed-effect models prove that the risky

behaviors lead to more fatalities in expansion era, they are too small to explain such a negative

relationship. Stress may be another assumption to interpret the procyclical nature of the

mortality rate. However, some related researched have already shown the average saving

working hours are not influential enough to alter people‘s health status. Even if people have

less working responsibilities in recession era, they tend to allocate a large fraction of the saving

hours to non-market production and housework, which consume energy as well.

Are recessions good for your health? Maybe the answer appears to be yes. However, the

effect is far less than previously predicted. Further research may find more evidences by

focusing more on age distribution and some specific causes of death.

Reference

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