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)