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An Analysis of The Great Recession’s Effect on Health Care Expenditure Growth Rates in the United States
By: Garrett M. Gilmore
Georgetown University
Washington, District of Columbia
Abstract: Health Care Expenditure in the United States has grown at decreasing
rates for the past decade; ultimately hitting a record low of 4% at the height of the Great Recession in 2009. Considering the decreasing trend began way before the onset of the recession, I argue that the recession had limited effects on the growth rate of health care and that its record low growth rate was primarily caused by factors unrelated to the recession. In my analysis, I isolate the effects of private and public health care spending on the health care growth rate in order to estimate the effects of the recession health care growth rates. I proceed to model a recession-less economy in order to compare estimate values from observed and simulated data in order to isolate the effects of the recession on health care growth rates.
I. Introduction
During the most recent global recession commonly referred to as the Great Recession,
health care expenditure in the United States grew at a record low near only 4 percent in 2009.
This steep decline was accompanied by a shift of expenditure from the private sector to the
public sector due to the effects of the recession on individuals across the states. While recessions
are accompanied by decreased demand for goods and services, this recession was accompanied
by a sharp increase in the share of health expenditure as a percentage of government spending.
Government spending on health care services soared to historic levels severely outpacing those
of other industrialized countries. It is, however, important to note that the declining trend in
health care expenditure is observable starting five years before the recession officially began. For
this reason, it is unfair to assume that this recession is fully to blame for the historically low
growth rate. My econometric model is aimed at determining how much the recession has
affected health care spending in the U.S. using data that represents the effects of the recession on
individuals and government agencies.
Health care expenditure in the U.S. has been a huge topic of debate for decades as its
percentage of GDP and growth rate consistently dwarf those of other industrialized counties.
The shift of expenditure from the private to the public sector during the Great Recession is only
an exacerbation of the already crippling hold that health care expenditure has on the
government budget. In the midst of health care reform, it is important to understand where
health care costs are heading and what factors are the driving forces in any observable trends. In
addition, with health care spending constituting 18.7 percent of government spending in 2008
and 54.2 percent of total health care expenditure in 2009 (Martin 2011), it is necessary to
accurately forecast the recession’s effect on individual and government spending to determine
effective public policy aimed at decreasing the governments share of spending and keeping
health care costs affordable for the general public. Health cares share of GDP in the U.S. is so
large that any change can cause huge ripples across the economy. With the government sector
supplying over half of the total expenditure on healthcare services, public policy on health care
can cause severe consequences for the public.
In addition to effective public policy, understanding the growth rate trend in healthcare
can give us insight into how the private healthcare sector is evolving over time. For instance,
changes in healthcare expenditure may be attributable to technological progress, scientific
discovery and/or changes in demand for healthcare services. This could represent changes in
the efficiency of healthcare services or changes in the general health of the population.
Determining the extent of the recession’s effect on the health care spending curve is instrumental
in isolating the strongest driving factors affecting health care in the U.S.
II. Literature Review
As noted, the topic of health care expenditure is closely studied. The decrease in
spending on health care services ranges from 11 percent in 1990 to its historic low of 4 percent
in 2011. Roehrig runs a regression to fit the observable bend in the health care cost cure from
1990 to 2011 (2011). This regression shows a negative trend in health care expenditure that
persisted well before the recent Great Recession. The goal of my research is to determine the
degree to which the Great Recession is to blame for the trend in health care spending from 2007
to 2009 during the brunt of the recession.
Previous studies vary in how much blame is placed on the recession with some
researchers blaming it largely and others marginalizing its effects. It is difficult and perhaps
arrogant to entirely ignore the recession so most literature aims to isolate its effect rather than
completely dismiss it. For instance, by relating annual real per capita overall health care
spending changes from 1970-2012 to the average growth rate of the economy then comparing
results to a similar model assuming real GDP growth remained constant in the absence of a
recession, researchers concluded that the recession could only account for 37 percent of the
overall slowdown (Cutler 2013).
Other studies pinpoint specific factors for health care expenditures movement.
Recessions are usually characterized by decreases in income, employment, and demand that
cause contractions of consumption across the economy. McInerney uses panel data on the 50
U.S. states to conclude that a 1% rise in unemployment corresponded with a .7% increase in
Medicare spending during the Great Recession (2012.) This is important because it shows that
health care spending is cushioned to a degree by the shift of private health care expenditure to
public expenditure while also showing an effect of the recession on spending overall. While
demand for other goods and services may contract tightly, health care seems to be dampened by
government intervention.
Martin isolates increased consumer out-of-pocket costs and decreased provider capital
investment as causes for decreased expenditure in health care during the recession (2011).
Another study agrees and concludes that rising out-of-pocket payments accounts for 20 percent
of the slowdown during the recession (Ryu 2013). These studies are in line with a study by
researchers at the Center for Medicaid & Medicare Services who claim that the lagging growth
rate of wages is to blame for the decrease in expenditure on health care services (CMS 2011.)
On the other-hand, some literature focuses on the resistance of the health care sector to
the recession. Previously observed recessions have been found to have a lagged effect on the
health care sector due to previously negotiated insurance contracts and the ability for individuals
to remain insured even after becoming unemployed (Martin 2012). In addition, due to
legislation that has prevented cuts to Medicare and Medicaid, there seems to be a cushion for
health care expenditure in the midst of a recession (Sisko 2009). Specific to health care
expenditure during the Great Recession, there is growing support that health care cost decreases
are to blame for the decrease in expenditure. This would indicate that the consumption of
health care services is not decrease but the price for these services is. For example, during the
Great Recession, several patents for block-buster drugs expired allowing the sale of generic
drugs which decreased the cost of some common prescription drugs immensely (Hartman 2009).
These studies shed light on the resistance of the health care sector to changes in the
economy. Firstly, health care is considered a necessity and regardless of the recession, people will
continue to develop illnesses and require health care services. In fact, literature even supports
the theory that the recession can even increase the need for health care services, with one study
finding a 50% increase in mortality rates for senior men displaced in the work force (Sullivan
2009). However, it is also important to take into account unnecessary spending in the U.S.
health care spending and how it may be over inflated during expansion period and appear to be
strangled during recessions. For example, wastefulness defined as unnecessary overtreatment has
been estimated at $158-$226 (Berwick 2012).
III. Data
The data on health care expenditure is compiled from several government databases as
well as health care agencies in the United States. There are 500 observations – one for each state
in the U.S. excluding the District of Columbia – and one for each of the years 2000-2009. To
control for differences in population, I have opted to use per capita variables and percentages
instead of absolute numbers. States were chosen as observations because each state has specific
characteristics and were affected differently by the recession. Looking at all the states will allow
me to develop a model that represents the entire country.
DEPENDENT VARIABLE:
The dependent variable I will be using is healthcare expenditure. Health care
expenditures per capita were collected from the Center of Medicaid and Medicare Services.
Growth rates for expenditures were obtained by creating a growth variable with the existing
expenditure data. Means and standard deviations are given in Table 1 in aggregated averages of
state totals for each year. Reviewing the growth rate from 2001-2009, there is an obvious
decreasing trend. The growth rate of health care expenditure decreases by over 4% in just 9
years to it’s historic low in 2009. The mean growth rate from 2001-2009 is about 6% with a
standard deviation of about 2%. The average growth rate represents the early years of my data
set much more than the later years when growth rates dropped significantly. In addition, the
average spending on health care is $5605.00 over the time period with a standard deviation of
about $1,160.90. My model is more focused on the growth rate however so this is not significant
to me, but may be significant when studying the amount spent on healthcare as a measure of
income share.
EXPLANATORY VARIABLES:
Category 1: Public Sector Assistance Measurements
From the Center for Medicaid and Medicare and Medicaid Services, I compiled data on
the percentage of residents enrolled in Medicare and Medicaid programs for each observed state
from 2000-2009. These are two separate variables as Medicare and Medicaid differ greatly.
Medicare is an insurance program that serves residents 65 and older as well as disabled
individuals under the age of 65. Like private insurance, premiums are paid directly to the
program and claims. Medicaid, on the other hand, is an assistance program serving low-income
residents that usually requires no payment by recipients. I chose these variables because I believe
they will explain changes in health care spending by the U.S. government and can show both
recessional effects and non-recessional effects. Further interpretations of usefulness of these
variables are in section
Means and standard deviations are given in Table 2 of the appendix for Medicare and
Medicaid percentage. Due to the size of the data set and number of variables, I have opted to
present the aggregated average of Medicare and Medicaid rates across the 50 U.S. states for
each of the 10 years observed. For the 10-year period the average Medicare enrollment across
the U.S. is 14.56%, ranging from 14.08% in 2000 to 15.41% in 2009. During the time period,
enrollment rates were on an increasing trend. Similarly, rates increased for Medicaid enrollment
as well. However, the average enrollment was lower at 13.69% while the range over the time
period is much greater from 11.36% in 2001 to 15.27% in 2009.
The average increase in Medicare growth was .15-percentage point with a relatively high
standard deviation of .12. This is likely due to the variance in recession severity across different
states. Growth rates also have a relatively smooth increasing trend with the only two significant
jumps being in 2003 and 2008 during the Great Recession. Medicaid’s mean growth rate is
substantially more volatile than Medicare’s across the years. There is no overall trend but
significant jumps up and down with the second highest jump during the recession in 2009. The
mean over the years is .43 with a huge relatively huge standard deviation of .82 indicating the
data does not follow any specific trend.
Category 2: Demographic / Income Measurements
From the Center of Medicare and Medicaid Services, I collected data on the rates of
uninsured individuals. Using the uninsured rates I created a growth variable for the years 2001-
2009. This growth variable is in percentage points and represents new addition/subtractions of
the percentage of the population uninsured from year to year. Table 3 presents summary
statistics. The uninsured rate has a mean of 13.72% with a standard deviation of 3.87% for the
entire time period. There is an upward trend in the uninsured rate with relatively minor
fluctuations over the years. The growth rate in percentage points has a mean of .27 with an
enormous standard deviation of 1.43 again implying that the recession’s effects varied greatly
throughout the observations.
Income per capita and unemployment rate means and standard deviations are given in
Table 3 as well. Data for these variables was collected from the Bureau of Economic Analysis.
Income rates have a strong positive trend up until the last year of my data set when the effects of
the recession start to take effect and incomes decrease drastically. The mean income is $33,920
with a standard deviation of $6,090. At this level, health care expenditure per capita makes up
about 1/6th of income per capita. However, due to insurance and government assistance it is
likely that this is a severe overestimate of how out-of-pocket costs by individuals. I have included
a growth rate for income in percentages. The mean of this growth rate is 3.11% per year with a
large standard deviation of 3.64 showing a scattered data set. Income growth is positive up until
the year 2009 when it drops by a relatively large amount due to the recession.
The unemployment rate increases slightly in the beginning of the time period, decreases
slightly in the middle before almost doubling from 2007-2009. The mean unemployment rate is
5.16% with a standard deviation of about 1.66%. This is about a 3.5% difference from the peak
unemployment level in 2009 showing that the jump in unemployment in 8.45% was significant
as it is so far away from the mean value. The growth rate in percentage points for
unemployment is also shown in Table 3. The mean growth rate is .51% percentage points with
a huge standard deviation of 1.19%. This again implies that the data is largely scattered. Lastly,
income follows the same trend increasing at the beginning before decreasing in 2009.
IV. Empirical Approach
4.1 Variable Selection:
Medicare And Medicaid Rates:
Public sector assistance measurement variables are directly related to healthcare costs
and include government healthcare subsidy and insurance programs that accounted for 54.2%
of healthcare expenditure in 2009 (Martin 2011.) Medicare enrollment rates measure both the
senior citizen population and the percentage of residents relying on the government as an
insurance provider. Considering the fact that the government’s share of total health care
expenditure is over 50% and senior citizens generally require more health care services, it is a
great explanatory variable for changes in health care expenditure. Medicaid enrollment rates
measure the percentage of low-income residents receiving government health care benefits. This
variable should be able represent the recession and could explain changes in health care
spending.
Interpreting these variables effects on health care spending is difficult because on one
hand increases in enrollment may cause increases in health care spending assuming the
government is more able to spend on health care services than individuals. In addition, it is
realistic to assume that those who enroll in these benefits do so because of a pre-existing need for
health care services that they cannot meet themselves. On the other hand, Medicare and
Medicaid services are limited in coverage and benefit payouts more so than most private health
insurance. Recipients are limited in which doctors they can choose as well and many expensive
procedures are not covered with these benefits. For this reason, increased enrollment may
negatively affect health care spending. I believe that Medicare growth will negatively affect
health care spending, while Medicaid growth will negatively affect health care spending as
individuals shift from superior private-insurance coverage to inferior public-assistance.
Income, Unemployment, and Uninsured Rates:
In order to model health care spending, I have opted to include several variables that I
believe will explain changes in the ability individuals to purchase health care services during the
recession. Recessions are characterized by decreased growth rates for income as well as increases
in unemployment. In addition, as incomes decrease and individuals find themselves out of work,
there is less ability to stay covered by health insurance that leads to an increase in uninsured
rates. I have collected data on each of these variables with the idea that their characteristics will
explain changes in health care spending.
Estimating coefficients signs and magnitude for my variables seems intuitive. As
uninsured rates increase, I would expect a decline in the growth rate for healthcare expenditure
as health care services would become less affordable under these conditions. Insurance
companies would not pay and individuals may not have the means to pay under these
circumstances. Decreases in income should affect health care spending and the growth rate
negatively as health care services would become a greater share of personal income. Out-of-
pocket costs are already a small portion of individuals spending on health care costs so any
decrease in income would push health care services further out of reach. Lastly, like uninsured
rates, increases in unemployment should have a negative effect on health care spending as
individuals lose wages and employee sponsored health insurance plans. Even if those who find
themselves out of work or uninsured enroll in programs like Medicare and Medicaid, I argue
that there will be a smaller amount spent on their behalf whether they are financed by the
government or pay out-of-pocket.
4.2: Variable Interaction:
Reviewing these statistics, the problem of multi-collinearity arises. With my variables
having observable trends, there is concern that one or more of these variables may be redundant
and cause inflated standard errors that would invalidate confidence intervals and hypothesis
testing. In order to test the collinearity of these variables, I ran a correlation matrix available in
Table 4 of the appendix. These results showed a large collinearity between unemployment and
income, prompting me to consider dropping one of the two variables. In the end, I decided to
drop income growth from my model due to its correlation with unemployment as well as its
insignificance to my model that I will discuss in further in this section.
4.3: Model Objective
Before discussing the type of model to use for my panel data, I will discuss my approach
to isolating the effects of the recession with my model. Like Cutler, I strive to form a model using
observed data and then hold my explanatory variables constant in order to isolate just how
much the recession was to blame for any decrease in health care expenditure (2013). In order to
do this, I need to manipulate more data to create a test variable that represents the time period
as if there were no recessional effects in 2008 and 2009. The manipulation process was fairly
simply. I took the 5 year average growth rate for all my explanatory variables starting with year
2007. Using this average I calculated new observations for each variable for the years 2008 and
2009. This method was used to model a recession-less time period so that I could isolate the
effect of the downturn on health care spending. Table 5 shows a comparison of the two different
variable observations I will be using in my model for each of my explanatory variables. I do not
list generated non-recession variables for income growth since I ultimately dropped the variable
in the first stage of my model.
4.4: Choosing a Model
Using 50 states as observations, it is obvious that I cannot assume each state had the
same characteristics and thus, I cannot assume they would have the same constant. However,
the point of my model is to look at aggregate spending changes across the U.S. by observing
different trends in spending in each state and the effects that the recession had on those states. It
is arguable, considering the aggregate nature of my study that, I could use one intercept for my
model. In light of this and since there are appropriate tests to determine whether a fix effects
model or pooled model is sufficient, I will run a pooled least squares regression and test my
regression to see if it is possible that my constants are all the same. Results are listed in Table 6.
Least Pooled Squares Equations:
LPS1
𝐻𝐶𝐺𝑅𝑂𝑊𝑇𝐻 = 𝛽1 + 𝛽2 𝐼𝑁𝐺𝑅𝑂𝑊𝑇𝐻 + 𝛽3𝑈𝑁𝐸𝑀𝐺𝑅𝑂𝑊𝑇𝐻 + 𝛽4𝑈𝑁𝐼𝑁𝐺𝑅𝑂𝑊𝑇𝐻 + 𝛽5𝐶𝐴𝑅𝐸𝐺𝑅𝑂𝑊𝑇𝐻
+ 𝛽6𝐶𝐴𝐼𝐷𝐺𝑅𝑂𝑊𝑇𝐻 + 𝜖
LPS2
𝐻𝐶𝐺𝑅𝑂𝑊𝑇𝐻 = 𝛽1 + 𝛽2𝑈𝑁𝐸𝑀𝐺𝑅𝑂𝑊𝑇𝐻 + 𝛽3𝑈𝑁𝐼𝑁𝐺𝑅𝑂𝑊𝑇𝐻 + 𝛽4𝐶𝐴𝑅𝐸𝐺𝑅𝑂𝑊𝑇𝐻 + 𝛽5𝐶𝐴𝐼𝐷𝐺𝑅𝑂𝑊𝑇𝐻 + 𝜖
Each state has specific characteristics that should be taken into account while trying to
model their health care spending habits. These characteristics are not directly observed in my
data and using a dummy variable for each state can be arduous. Characteristics such as the
general health of the population, demographic variables such as race composure, and weather
are not accounted for and can cause differences in the intercept coefficient for each state. In
addition, some starts were hit harder by the recession than others, some handled it well and
others did not. After testing the constants I was assured that the constants were unique to each
individual. For these reasons, I ultimately decided to use a fixed effects model to incorporate
fixed characteristics for each of the states.
Fixed-Effects Model:
𝐻𝐶𝐺𝑅𝑂𝑊𝑇𝐻 = 𝛽𝑖1 + 𝛽𝑖2𝑈𝑁𝐸𝑀𝐺𝑅𝑂𝑊𝑇𝐻 + 𝛽𝑖3𝑈𝑁𝐼𝑁𝐺𝑅𝑂𝑊𝑇𝐻 + 𝛽𝑖4𝐶𝐴𝑅𝐸𝐺𝑅𝑂𝑊𝑇𝐻 + 𝛽𝑖5𝐶𝐴𝐼𝐷𝐺𝑅𝑂𝑊𝑇𝐻
+ 𝜖
* The subtraction of INGROWTH will be discussed in section V.
4.5: Problems
A major problem with this approach is that it suffers from a great deal of assumptions.
First, we have to assume that this is the best model possible and that is accurately forecasts
spending habits based on variables that reflect the recession’s effect on the economy. Second, we
have to assume that the five-year moving average is a good tool to use for creating the recession-
less data to compare the results to. Using two sets of fitted data to compare the recession’s effects
relies on the good-ness of fit for the model. If the model lacks forecasting ability, then predicting
and comparing fitted values will do little to isolate the effects of the recession.
The model may also suffer from an omitted variable bias. In the case of my model,
general health of the population could be an omitted variable. Unfortunately data measuring the
overall health of each state in each year is difficult to find. It follows economic intuition that the
health of the population would drive how much the population spends on health care services.
This health variable is absent from my data but may be correlated with the explanatory
variables. For example, my Medicare and Medicaid variables should be affected by the health of
the population and would likely have a positive correlation with it. Higher percentages of
enrollees would be correlated with a higher percentage of unhealthy individuals in the
population.
The model also suffers from heteroskedasticity. Each of the U.S. states was affected
differently by the recession and each have different and unique characteristics that affected how
they responded to the effects of the recession. For small changes in the explanatory variables
there is most likely smaller reactions in health care spending. As the changes become larger
there it is much more likely that larger reactions will take place and cause heteroskedasticity in
the model. In light of this, I opted use robust standard errors to keep my confidence intervals as
useful as possible.
The problem of endogeneity arises, again because of my Medicare and Medicaid
variables. With the government comprising over half of the total spending on healthcare,
increases in healthcare spending would likely feedback to the percentages of enrollees and the
percentages of enrollees would likely feedback to the amount spent on healthcare. Finding a
useful instrument for Medicaid and Medicare would be the most optimal.
Lastly, the model does not account for the absolute rate of any of my variables. The
variables I am using a growth rates from the previous year, but there is no variable showing the
level at which my variables are at. For example, my model indicates changes in the
unemployment rate in percentage points, but it does not indicate the unemployment rate at any
point in time. This means that the model will assume that a one-percentage point increase in
unemployment will have the same effect whether the unemployment rate is low (3%) or high
(10%). Economic theory would suggest that the absolute rates of my variables would have an
effect on spending. Considering the short time period of my data, I opted to continue with only
growth rates. If there were a greater time range, adding percentage rates would be optimal.
V. Results
The results from my pooled model are listed in Table 6. The income growth variable
was highly insignificant and even had a negative coefficient that indicated the variable did not
have a strong effect on healthcare spending. The coefficient was also extremely small indicating
that even large changes in the growth rate of income had minimal effect on health care
spending. At first this was alarming because one would assume income would play a direct role
in how much people spent on any service. However, in the U.S., the majority of healthcare
spending comes from the government, then private insurance companies with out-of-pocket
costs making up a small portion of total expenditure. In addition, if you recall, this variable was
highly collinear with unemployment growth (Table 4). Due to the insignificance and
inconsistency of the coefficient’s sign and its correlation with another significant explanatory
variable, I opted to remove this variable from my regression. Upon removal, there were
extremely small changes in the results of the other estimated coefficients and my R^2 remained
at .24.
The coefficients of this estimation were half expected and half not. Unemployment
increases and Medicare enrollment percentage increases both affected health care spending’s
growth rate negatively. The magnitude of Medicare was surprising, but considering the fact that
the mean percentage-increase was near .25 it seems less effective. However, the model predicts
that a one percentage-point increase in Medicare enrollment from the year before would
decrease health care growth by 3.37% in the current year. This is over half of the mean value
for health care’s growth rate over the time period! This is in line with my theory that when
people enroll in Medicare, they will generally receive less monetary benefits than if they were
privately insured. Unemployment percentage increases from the previous year have a small
negative effect on growth rate, decreasing it by .62% in the current year. This is also in line with
economic intuition.
While significant, the uninsured variable had a positive estimated coefficient that does
not make sense to the model. When people become uninsured, economic intuition dictates that
the last thing they would do is spend more on health care. The coefficient is the smallest in
magnitude and tiny compared to the expected mean value of the dependent variable so its effect
is negligible. My theory is that the variable may be correlated with the error term due to an
omitted variable. This may be causing the least squares estimator to be inconsistent causing the
unexpected coefficient sign.
Lastly, Medicaid enrollment percentage-point increases from the previous year have a
positive effect on health care growth rates using in this model. This was unexpected; however, it
may not be a problem. Medicaid supports low-income individuals so it makes sense that when a
person is low-income they would spend very little on health care services out-of-pocket. When
they are enrolled into Medicaid, the government begins to spend money on their behalf that
may mean that Medicaid does have a positive effect on health care growth rates. While not as
large the Medicare coefficient, it is a decent size to have a considerable effect on the health care
growth rates.
5.2: Fixed Effects Model
Results from testing the intercepts confirmed my hypothesis that the intercepts of each
State are unique (>p=0000) which allowed me to proceed with a fixed effects model. I
proceeded to use a fixed effects model given in Table 8. The overall goodness-of-fit increased
substantially to .33 indicating the transition to fixed effects helping in fitting the data. All my
variables remained significant and kept their signs. Magnitudes were also quite similar except for
the constant term and the Medicare variables. Both were about double their values from the
pooled model.
For this model, it appears that Medicare growth is an extremely strong explanatory
variable of health care growth rates. Changes in Medicare have a much larger effect on health
care growth rates than any of my other variables. Medicare’s large effect on health care growth
rates may be due to the aging population of the United State. With baby boomers passing the
age of 65 and enrolling in Medicare in large numbers, the shift of individuals from employed
sponsored superior insurance coverage to public insurance coverage my be the cause of the
negative effect Medicare enrollment growth rates have on health care growth rates. Medicare
enrollees may have had greater coverage on previous health care insurance plans and spent
more using them than they do their Medicare benefits which would explain the negative effect of
increases in Medicare enrollment. It can also be argued that the strain placed on the
government budget by the increase in Medicare is directly related to lower growth rates of
spending as funds are spread out over a great percentage of individuals.
5.3: Isolating the Recessions Effects
In order to isolate the effects of the recession on health care spending growth rates, I now
use the “non-recession” variables I described in section IV. Fitting the non-recession variable
observations into the Fixed Effects model previously estimated using observed data; I can
estimate health care growth rates in the absence of the recession. Recall that this non-recession
data simple simulates that the recession never happened by using the 5-year moving averages of
the variables before the recession to replace the observations during 2008 and 2009. Predicted
values from the observed data and the non-recessional data are presented in Table 9. As should
be the case, there is no difference between these values until the year 2008. At this point the
non-recession values data predict different health care growth rates for the years 2008 and 2009.
The difference between these two fitted values is thus the estimated effect that the recession had
on health care spending growth rates. This model predicts that the recession had an overall
effect of -1.36% on health care growth rates with a confidence interval of -2.09% to -.62% at the
95% confidence rate.
VI. Conclusion
The growth rate of health care spending in the United States has been on a downward
trend in the recent past. Even while uninsured rates and unemployment were low, health care
spending was increasing and a decreasing rate. During the recession, growth rates continued to
fall, ultimately to a historic low in 2009 leading to the question of whether the recession was to
blame for the decrease or if changes in non-recession factors were driving the decrease.
Interpreting the causes of the decrease are important for forecasting the future growth rates and
health care expenditure as a while; especially considering the fact that a large share of health
care spending comes from the government.
Through my research and econometric model, I was able to estimate the effects of
unemployment, insured, Medicare, and Medicaid rates on the growth rate of health care
expenditure in the U.S.. Medicare was the most significant with a large magnitude implying that
the increase in enrollment puts a strain on the government. Since Medicare is primarily used by
the individuals over the age of 65, it is arguable that the decrease in the growth rate was in fact
caused by an aging population and perhaps was exacerbated by the recession. By comparing the
observed fitted values of my model to the non-recession fitted values, I can predict the rate at the
recession affected health care spending growth rates. While the fitted values differ from the
observed data, it is a useful comparison technique that helps isolate the effects of the recession.
My main finding is that the recession had an observable effect on the growth rate on
health care spending in 2008 and 2009. I have estimated that the effects of the recession on my
explanatory variables lead to a decrease of about 1.3% points in the health care growth rate.
However, the overall effect of the recession on growth rates is dependent on several other factors
that have most likely been omitted from my model due to unavailable data or difficult to observe
data. These include the health of the population in general and consumer sentiment towards
health care during the time period.
Table 1: Summary Statistics
Table 2: Summary Statistics
Table 3: Summary Statistics
Table 4: Correlation Matrix
Table 5: Generated Non-Recession Variables
Table 6: Pooled Least Squares 1
Table 7: Pooled Least Squares 2
Table 8: Fixed Effects Model
Table 9: Isolating Recessions Effects
References:
Literature
Berwick, Donald M. and Andrew D. Hackbarth. ‘Eliminating Waste in U.S. Health Care.’ Journal of American Medical Association, 307, no.14 (2012)
Center For Medicaid & Medicare Services . National Health Expenditure Projections 2011-2021 [Data file].
Cutler, David M. & Nikhil R Sahni. ‘ If Slow Rate Of Health Care Spending Growth Persists, Projections May Be Off By $770 Billion’ Health Affairs, 32, no.5 (2013): 841-850
Hartman Micha, Anne Martin, Patricia McDonnel & Aaron Catlin. ‘National Health Spending In 2007: Slower Drug Spending Contributes To Lowest Rate Of Overall Growth Since 1998.’ Health Affairs, 28, no.1 (2009):246-261
Martin, Anne, David Lassman, Lekha Whittle, Aaron Catlin, and the National Health Expenditure Accounts Team. ‘Recession Contributes To Slowest Annual Rate Of Increase In Health Spending In Five Decades’ Health Affairs, 30, no. (2011): 11-22
Martin, Anne B., David Lassman, Benjamin Washington, Aaron Catlin and the National Health Expenditure Accounts Team. ‘Growth In US Health Spending Remained Slow In 2010; Health Share Of Gross Domestic Product Was Unchanged From 2009.’ Health Affairs, 31, no.1 (2012):208-219
McInerney, Melissa P. & Mellor, Jennifer. State Unemployment In Recessions During 1991-2009 Was Linked To Faster Growth In Medicare Spending.’ Health Affairs, 31, no.11 (2012):2464-2473
Roehrig, Charles S. & Rousseau, David M. The Growth In Cost Per Case Explains Far More Of US Health Spending Increases Than Rising Disease Prevalence Health Affairs, 30, no.9 (2011):1657-1663
Ryu, Alexander J.,Teresa B. Gibson, M. Richard McKellar and Michael E. Chernew.‘The Slowdown In Health Care Spending In 2009-11 Reflected Factors Other Than The Weak Economy And Thus May Persist’ Health Affairs, 32, no.5 (2013):835-840
Sisko, Andrea, Christopher Truffer, Sheila Smith, Sean Keehan, Jonathan Cylus, John A.Poisal, M. Kent Clemens and Joseph Lizonitz ‘Health Spending Projections Through 2018: Recession Effects Add Uncertainty To The Outlook’ Health Affairs, 28, no.2 (2009):w346-w357
Sullivan, Daniel . ‘Job Displacement and Mortality: An Analysis using Administrative Data.’ Federal Reserve Bank of Chicago The Quarterly Journal of Economics (2009) 124 (3): 1265-1306
Data
U.S. Centers for Medicare and Medicaid Services, "Medicaid Program Statistics, Medicaid Statistical Information System." Health Expenditures by State of Residence. Retrieved (May 2012) at http://www.cms.gov/NationalHealthExpendData/downloads/resident-state-estimates.zip
U.S. Census Bureau, Current Population Survey, 2008 to 2011 Annual Social Unemployment Rates by State. Bureau of Labor Statistics. www.bls.gov/lau/lausmsa.htm
Do File 1. xtset state_id year
2. by state_id: gen incomegrowth = (((income/income[_n-1]) -1) *100)
3. by state_id: gen incomegrowth1 = incomegrowth
4. by state_id: replace incomegrowth1 = ((incomegrowth[_n-1] + incomegrowth[_n-2]
+incomegrowth[_n-3] +incomegrowth[_n-4] +incomegrowth[_n-5]) / (5)) if year
>=2008
5. by state_id: gen unemgrowth = (unem - unem[_n-1])
6. by state_id: gen unemgrowth1 = unemgrowth
7. by state_id: replace unemgrowth1 = ((unemgrowth[_n-1] + unemgrowth[_n-2]
+unemgrowth[_n-3] +unemgrowth[_n-4] +unemgrowth[_n-5]) / (5)) if year >=2008
8. by state_id: gen uningrowth = (unin - unin[_n-1])
9. by state_id: gen uningrowth1 = uningrowth
10. by state_id: replace uningrowth1 = ((uningrowth[_n-1] + uningrowth[_n-2]
+uningrowth[_n-3] +uningrowth[_n-4] +uningrowth[_n-5]) / (5)) if year >=2008
11. by state_id: gen caregrowth = (percare- percare[_n-1])
12. by state_id: gen caregrowth1 = caregrowth
13. by state_id: replace caregrowth1 = ((caregrowth[_n-1]+ caregrowth[_n-2] +
caregrowth[_n-3] +caregrowth[_n-4] + caregrowth[_n-5]) / (5)) if year >=2008
14. by state_id: gen caidgrowth = (percaid- percaid[_n-1])
15. by state_id: gen caidgrowth1 = caidgrowth
16. by state_id: replace caidgrowth1 = ((caidgrowth[_n-1]+ caidgrowth[_n-2] +
caidgrowth[_n-3] +caidgrowth[_n-4] + caidgrowth[_n-5]) / (5)) if year >=2008
17. by state_id: gen hcgrowth = ((hcspending/hcspending[_n-1]) - 1) *100
18. tabstat incomegrowth incomegrowth1 unemgrowth unemgrowth1 uningrowth
uningrowth1 caregrowth caregrowth1 caidgrowth caidgrowth1 hcgrowth hcgrowth1,
by(year)
19. ///Forming my growth variables (Lines: 2,5,8,11,14,17), Coping Variables (Lines
3,6,9,12,15), forming the "Non-Recession" Variables from copied variables
(4,7,10,13,16)
20. reg hcgrowth incomegrowth unemgrowth uningrowth caregrowth caidgrowth
21. ///Pooled Least Squares Model
22. reg hcgrowth ibn.state_id unemgrowth uningrowth caregrowth caidgrowth, noconstant
23. scalar sse_u = e(rss)
24. scalar df_u = e(df_r)
25. scalar sig2u = sse_u/df_u
26. reg hcgrowth incomegrowth unemgrowth uningrowth caregrowth caidgrowth
27. scalar sse_r = e(rss)
28. scalar f = (sse_r - sse_u)/(9*sig2u)
29. scalar fc = invFtail(9,df_u,.05)
30. scalar pval = Ftail(9,df_u,f)
31. di "Ftest of equal intercepts = " f
32. di "F(9,df_u,.95) " = fc
33. di "p value = " pval
34. ///Testing for equal constants (22-33)
35. xtreg hcgrowth unemgrowth uningrowth caregrowth caidgrowth, fe vce(cluster state_id)
36. ///Fixed Effects Model
37. clonevar Unemgrowth = unemgrowth
38. clonevar Uningrowth = uningrowth
39. clonevar Caregrowth = caregrowth
40. clonevar Caidgrowth = caidgrowth
41. ///Copying Variables So I Can Replace Them When Predicting Fitted Values With
"Non-Recession Data" (37-40)
42. tabstat unemgrowth unemgrowth1 uningrowth uningrowth1 caregrowth caregrowth1
caidgrowth caidgrowth1 hcgrowth hcgrowth1 if year >=2008, by(year)
43. xtreg hcgrowth unemgrowth uningrowth caregrowth caidgrowth, fe vce(cluster state_id)
44. predict Observed, xb
45. ///Predicting Fitted Values With Observed Data
46. replace unemgrowth = unemgrowth1
47. replace uningrowth = uningrowth1
48. replace caregrowth = caregrowth1
49. replace caidgrowth = caidgrowth1
50. ///Replacing Variables with "Non-Recession" variables
51. predict Nonrecession, xb
52. ///Predicting Fitted Values with "Non-Recession" variables
53. replace unemgrowth = Unemgrowth
54. replace uningrowth = Uningrowth
55. replace caregrowth = Caregrowth
56. replace caidgrowth = Caidgrowth
57. ///Replacing Observed Variables
58. by state_id: gen Recessioneffect = (Nonrecession - Observed)
59. ///Forming Differencial Fitted Values
60. tabstat Observed Nonrecession Recessioneffect, by(year)
61. tabstat unemgrowth unemgrowth1 uningrowth uningrowth1 caregrowth caregrowth1
caidgrowth caidgrowth1 hcgrowth hcgrowth1 if year >=2008, by(year)