1. Running Head: SUBSTANCE ABUSE AND ECONOMIC INDICATORS 1
Substance Abuse and Economic Indicators
Johnny Wright
Southern New Hampshire University
2. SUBSTANCE ABUSE AND ECONOMIC INDICATORS 2
Abstract
Substance abuse is a problem that has implications for not only every day consumers who are or
are not a part of its solicitation or usage, but also the movement of the economic system
aggregately. The White House estimated that as of 2014, Americans spend approximately $100
billion annually on illicit substance consumption, while tax payers spend almost double that in
the externalities that particular market creates. This analysis serves to evaluate how economic
indicators are impacted by substance abuse rates for the 9 most commonly used substances in
America. By taking a sample of self reported 1 time use additions from treatment centers across
the country, it was found that usage rates for common substances seem to move positively with
production, measured in GDP, as well as per capita income while it slide with unemployment.
This effect suggests that illicit substance most likely have a non linear effect on the economy as a
whole but does go to encourage wanted economic behaviors, such as consumption, and decrease
unwanted behaviors, such as unemployment.
3. SUBSTANCE ABUSE AND ECONOMIC INDICATORS 3
Substance Abuse and Economic Indicators
Substance Abuse, more commonly known as drug addiction, is a problem that takes
different forms and sizes. Similar to alcohol, many drugs take the user to an altered state of mind,
where normal body movements become a struggle and users are more sensitive to external
stimuli. Continued use can cause long-term permanent physical, mental, or financial
consequences on the user as well as society. It forces the necessity of drug treatment programs
and other related services. While it is true that drug addictions are not equal and do not always
imply a natural deviance toward criminal behavior, it is still reasonable to infer that those people
will have trouble gaining or maintaining full time employment. Substance abuse, defined as
using an illegally solicited controlled substance more than once a month, jeopardizes public
safety in that users do not act with full rationality while under the influence, evident with harsher
penalties for operating a motor vehicle in the altered state. Since response teams (police,
firefighters, etc…) are funded through taxpayer dollars, ultimately the cost of putting the public
at risk falls to those who citizens who are taxpayers and decline in productivity. It was estimated
that citizens spend over $100 billion on illicit substances annually and the cost to taxpayers is
almost double that amount in health care, production, and criminal activity costs because of it
(Zobeck, 2014). Research on this topic is especially important to those concerned with the
growing problem of substance abuse as well as those concerned with drug prevention programs
and services for education. It will serve to understand which substances is related to the largest
change in productivity and employment. Empirical understanding of the consequences of
substance abuse on the economy is useful for federal and metropolitan expansionary policies
created to tackle the problem and the most efficient allocation of time and resources to combat
the problem. It could also serve as a way to understand why certain countries such as Portugal,
4. SUBSTANCE ABUSE AND ECONOMIC INDICATORS 4
who decriminalized the personal use of illicit substances, experiences the success it does in
falling usage rates.
LITERATURE REVIEW
To propose a solution to the substance abuse problem, it is prudent to isolate the possible
macroeconomic effects of substance abuse. Harwood, Fountain, & Livermore (2001) proposed 5
different ways substance abuse problems could impact the economy. There were two in
particular that aligned with the goals of this research: the possibility of impact on productivity
and expenditure on the social welfare system. They use a cost of illness (COI) study to evaluate
what adverse effects that drug addiction, treated as an illness, has on other variables, such as
productivity. By identifying and quantifying the immediate impacts of such behavior, an
economic assessment can be made. Once the impacts are known, a proper monetary valuation
can be performed to analyze the scope of the effect.
Koenig et al. (2005) studied the effectiveness of substance abuse intervention programs
in Northeast Ohio. Using individual data, they were able to measure crime and health care costs
associated with each person on entrance to the treatment program. The participants were once
again interviewed at 6, 12, 24, and 30 month intervals. The main result this study produced was a
trend between increased earnings and decreased criminal activity costs. This study loosely
supports a connection between unemployment, production, and drug abuse.
In addition to the costs of treatment itself measured against the societal benefit,
production loss is another problem that drug abuse creates. Those who engage in the activity and
need treatment for their addiction creates downward pressure on GDP, due to earnings.
McDowell Group, Inc (2012) in Alaska estimated the potential lost income by analyzing the
number of days in a treatment of all its residents. By converting the number of days to years and
5. SUBSTANCE ABUSE AND ECONOMIC INDICATORS 5
calculating an average yearly income, they estimated that nearly $11 million in come was lost by
those in treatment centers.
Based on the literature reviewed for this research, the response variables appear to be the
macroeconomic indicators. There exists no apparent impact to productivity or joblessness on a
national scale based on substance abuse; however, considering that substance abuse does have a
role in gainful employment, crime, and earnings, it can be surmised that substance abuse leads to
leakages in the economic system. Since the proceeds from substance transactions are not
typically associated with national production, it must be the case that value from the traditional
economy is being transferred to the underground, or black market economies. In addition to its
overall impact, since each drug does not produce the same level of social cost or benefit for
treatment (Harwood et al., 2005), it can be reasoned that some drugs have a high affinity toward
larger crime and health care costs.
DATA
In order to empirically test a hypothesis on the substance type and impact on the
economy, data was collected from the US HHS Substance Abuse and Mental Illness section. It
collects national data on yearly basis for primary drug abuse treatment entrances. The data has a
range from 1992-2012, a limitation of the data. Based on a yearly number of valid cases, it
weighs out the number of individuals treated for 18 common types of substance abuse items
(“United States Department of Health and Human Services…,” 2014). Next, data on production
(real GDP) was gathered from the Bureau of Economic Analysis and Bureau of Labor Statistics,
respectively. The unemployment data consisted of a percentage of those in the labor force who
identified as unemployed. The preliminary scatterplot analysis showed a weakly positive trend
between people entering treatment for alcoholism and unemployment, while it showed a weakly
6. SUBSTANCE ABUSE AND ECONOMIC INDICATORS 6
negative trend between those who entered with marijuana additions and unemployment. Based
on these results, the original hypothesis can be justified that there exists a substance that exhibits
a stronger impact on economic indicators. In order to make the prediction model more concise, a
truncation was made based on the commonly abuse substance types, based on the most prevalent
drug types found in the United Kingdom as of 2009 (Allan and Roberts, 2009). This truncation
reduced the number of necessary explanatory variables to 10.
In addition to data on fundamental economic indicators such as national production and
unemployment, data was collected on the consumer price index (CPI) in order to estimate the
effect on the real price of what would be a single unit of each drug type. Nesvisky (n.d.) reported
this technique being used in an empirical study conducted on consumption rates of certain illegal
substances, using all items CPI with base year of 1975. The results were that trends in substance
use, such as cigarettes, alcohol, and marijuana, were associated with real price changes. This is
one of the limitations in the data. Real price fluctuations will be difficult to estimate due to unit
price differences in many areas of the country; however, the impact of changes in CPI can be
used estimate a baseline price. With this goal in mind, a hypothesis of whether or not individual
substance consumption rates impact the fundamentals can be formulated. With this in mind, the
inverse of the question will be explored: how do economic fundamentals relate to substance
abuse prices and, thus, abuse rates.
EMPIRICAL APPROACH
Substance Abuse types on the Fundamentals
Given the data, two preliminary models are created. First, in order to explain the effect of
substance abuse on production, the hypothesis is that there exists a relationship between at least
one of the 18 drug types reported and production. In other words, trends in real GDP,
7. SUBSTANCE ABUSE AND ECONOMIC INDICATORS 7
unemployment, and personal income should be statistically correlated with yearly treatment rates
for substance abuse problems. This models are expressed in its functional format as follows:
rGDP = 0 + 1x1+ 2x2+…+9x9+ , for n = 9 representing each drug type. The functional
models for unemployment and personal income are derived the same way, substituting UNP and
PersInc for the left side of the real GDP equation. Symbolically our null hypothesis is given by
assuming that there exists no such n in our model that is correlated with rGDP, UNP, or
PersInc; Ho= 0 = 1 = 2 =…= 9 = 0. For all 3 models, a secondary hypothesis can be used to
test the impact of specifically more “acceptable” illicit drugs such as marijuana. With the
growing push in many states to have its legalization placed up for public decision, a possible
impact of its use on production can be studied as well: H0,marij:marij = -1. This hypothesis is to
say that the impact on society based on the fundamentals is strictly negative.
The Fundamentals and Abuse Rates on the Inferred Real Price Changes. A secondary
regression model can be used to explain what the impact on the inferred real price changes each
substance type has and/or personal income has. With the goal of finding a possible correlation,
our secondary null hypothesis is as follows: H0: 1 = 1 = …= n = 0 for n = 18 representing the
drug types and 1 representing personal income. In functional form, the model is expressed as
follows: CPI1992 = 0 + 1x1 + 2=1x2=1 + 3=2x3=2+…+ ixi=n for n = 18.
RESULTS
Plotting the usage rate changes over time in a scatter gram, alcohol and crack/cocaine
usage declined by approximately 35% and 60%, respectively, since 1992. Marijuana, heroin, and
amphetamine use increased 191%, 50%, and 400% respectively since 1992 as well. These trends
suggest the effects of changes in demand and real price of a single unit of these items (Figure 1).
In addition to having large rises and declines, those changes seem to be similar with changes in
8. SUBSTANCE ABUSE AND ECONOMIC INDICATORS 8
real GDP. The same drugs that rose and declined over time were associated with high values in
GDP (Figure 2). A tend as this suggest that when those aforementioned 5 substances behave
similarly over time, GDP tends to rise. When the drugs were plotted against unemployment,
higher alcohol usage rates were associated with unemployment between the 4% - 7.5% range. A
similar relationship is seen between lower rates of reported usage on other drugs (Figure 3).
These relationships suggest an a priori assumption that can be made: fundamental indicators are
negatively impacted by alcohol and crack/cocaine usage and positively impacted by marijuana,
heroin, and amphetamine usage. An interesting trend forms with the marijuana substance and the
economic fundamentals. It developed the same positive trend with its higher usage rates with
personal income and real GDP. A priori, it leads a researcher to believe that there is a positive
trend in real GDP and Personal Income with higher reported usage of marijuana (Figure 4).
Substance abuse rates on Economic Fundamentals
The following 3 sample regression models were derived form the regression results with
respect to the economic fundamentals as the response variable:
𝑙𝑛𝑟𝐺𝐷𝑃̂ = 6.595 + 2.034𝐴𝐿𝐶̂ + 0.542𝐶𝑂𝐶̂ + 4.014𝑀𝐴𝑅𝐼̂ + 2.424𝐻𝐸𝑅̂ + 90.235𝑀𝐸𝑇𝐻̂
+ 11.838𝐻𝐴𝐿𝐿̂ + 6.462𝐴𝑀𝑃̂ + 38.774𝐵𝐸𝑁𝑍̂ + 70.096𝐵𝐴𝑅𝐵̂
𝑙𝑛𝑃𝑒𝑟𝑠𝐼𝑛𝑐̂ = 5.51 + 3.0574𝐴𝐿𝐶̂ + 1.04𝐶𝑂𝐶̂ + 5.117𝑀𝐴𝑅𝐼̂ + 3.354𝐻𝐸𝑅̂ + 97.167𝑀𝐸𝑇𝐻̂
+ 10.712𝐻𝐴𝐿𝐿̂ + 7.158𝐴𝑀𝑃̂ + 46.971𝐵𝐸𝑁𝑍̂ + 55.391𝐵𝐴𝑅𝐵̂
𝑙𝑛𝑈𝑁𝑃̂ = 1.614 − 1.429𝐴𝐿𝐶̂ − 1.883𝐶𝑂𝐶̂ − 1.781𝑀𝐴𝑅𝐼̂ − 1.54𝐻𝐸𝑅̂ − 13.361𝑀𝐸𝑇𝐻̂
− 11.348𝐻𝐴𝐿𝐿̂ − 1.707𝐴𝑀𝑃̂ − 9.989𝐵𝐸𝑁𝑍̂ + 11.721𝐵𝐴𝑅𝐵̂
These models had an R2 value over 90%, suggesting that the explanatory variables explain the
variation well enough; however, there were some issues with multicollinearity in the real GDP
and personal income models. Unlike the unemployment model where 6 of 9 explanatory
variables were statistically significant at the 95% confidence level and 1 at the 99% level, the
real GDP and personal income models had relatively few statistically significant variables (Table
1). Each model’s overall significance was discovered from its overall critical P value from its F
9. SUBSTANCE ABUSE AND ECONOMIC INDICATORS 9
Distribution. Each p value was less than 0.01, indicating significance at the 95% and 99% levels
of confidence. Since multicollinearity is present and the only violator of OLS in this model,
auxiliary regressions were necessary to get a better model.
After running an auxiliary regression of crack/cocaine, marijuana, heroin, and
Benzodiazepines, the auxiliary regression produced the following ASRF:
𝐴𝐿𝐶̂ = 1.195 − 2.023𝐶𝑂𝐶̂ − 1.959𝑀𝐴𝑅𝐼̂ − 0.679𝐻𝐸𝑅̂ − 19.198𝐵𝐸𝑁𝑍̂
Using this auxiliary regression equation, the values were substituted in as predicted alcohol. A
new regression for real GDP and personal income was run which yielded the following equation
with alcohol accounting for the variation from crack/cocaine, marijuana, heroin, and
benzodiazepines:
𝑙𝑛𝑟𝐺𝐷𝑃̂ = 10.364 − 2.788𝐴𝑈𝑋𝐴𝐿𝐶̂ + 88.66𝑁𝑂𝑁 − 𝑃𝐸𝑅𝑆𝐶̂ − 8.42𝐻𝐴𝐿𝐿̂ + 1.671𝐴𝑀𝑃̂
− 37.683𝐵𝐴𝑅𝐵̂
𝑙𝑛𝑃𝑒𝑟𝑠𝐼𝑛𝑐̂ = 10.38 − 3.09𝐴𝑈𝑋𝐴𝐿𝐶̂ + 86.752𝑁𝑂𝑁 − 𝑃𝑅𝐸𝑆̂ − 11.724𝐻𝐴𝐿𝐿̂ + 0.933𝐴𝑀𝑃̂
− 63.124𝐵𝐴𝑅𝐵̂
These new models revealed a lower R2 value than the previous predecessors and the explanatory
variables of Aux Alc and Non-Prescription Stimulants were statistically significant at the 95%
and 95% levels. Since the p value computed maintains the model’s overall significance, it is
mostly likely that this is these are the best models for research.
Marijuana on Economic Fundamentals. Based on the trends displayed by marijuana’s
relationship to real GDP and income, a second set of regressions were run based on this
substance alone, with the null hypothesis for this drug kept in mind. The models were derived as
follows:
𝑙𝑛𝑟𝐺𝐷𝑃̂ = 8.212 + 7.594𝑀𝐴𝑅𝐼̂
𝑙𝑛𝑃𝑒𝑟𝑠𝐼𝑛𝑐̂ = 7.995 + 7.819𝑀𝐴𝑅𝐼̂
𝑈𝑁𝑃̂ = 0.047 + 0.093𝑀𝐴𝑅𝐼̂
The real GDP and personal income models had an R2 value greater than 90% and the explanatory
variable of marijuana usage was statistically significant at the 95% and 99% confidence levels.
10. SUBSTANCE ABUSE AND ECONOMIC INDICATORS 10
In addition, the two models had an 95% and 99% overall significance indicated by its calculated
p value from its F distribution. The unemployment model had no statistical significance at the
95% or 99% levels, suggesting a null hypothesis rejection failure with respect to the
unemployment response variable. There is no indication that the models violate any OLS
assumptions and no further action is needed.
Substance Abuse Rates and Personal Income on Consumer Price Index. Aforementioned in
the data section, the CPI fundamental is used to evaluate real price movements in a single unit
amount of the listed substance. For this empirical study, the 1992 base year CPI was calculated
for each year then regressed against personal income and the 9 drug types. The following model
was derived:
𝐶𝑃𝐼̂ = −1.871 + 0.539𝑙𝑛𝑃𝑒𝑟𝑠𝐼𝑛𝑐̂ − 2.796𝐴𝐿𝐶̂ − 3.229𝐶𝑂𝐶̂ − 3.549𝑀𝐴𝑅𝐼̂ − 1.87𝐻𝐸𝑅̂
+ 10.095𝑀𝐸𝑇𝐻̂ − 14.076𝐻𝐴𝐿𝐿̂ − 3.02𝐴𝑀𝑃̂ − 19.808𝐵𝐸𝑁𝑍̂ + 19.161𝐵𝐴𝑅𝐵̂
This model had an R2 value over 90 percent, suggesting that the explanatory variables speak to a
large amount of variation in the data. Similar to the unemployment model for all substances,
alcohol, crack/cocaine, and marijuana were significant at the 95% and 99% levels, while heroin
had significance at the 95% level only. There is a small chance the model may be a violator of
the multicollinearity OLS assumption, however, based on having the same number of significant
variables as non-significant variables, that will be difficult measure. Also, the model has an
overall significance indicated by its p value from the F Distribution at the 95% and 99% levels.
This value indicates that the explanatory variables explain real price changes in a single unit of
the substance.
CONCLUSION AND RECOMMENDATIONS
The analysis of the relationship between substance abuse and national production and
labor does have some limitations in the conclusions that can be drawn from it. One major
11. SUBSTANCE ABUSE AND ECONOMIC INDICATORS 11
limitation is lack of attention given to the economic recessions that have occurred since 1992.
From a basic study of recession effects on the population, all items, legal or otherwise,
experience a decline in consumption and, thus, substance abuse cannot be used to accurately
answer what would happen. Another limitation in this study is the data on substance abuse. As
aforementioned, it only measures individuals who have used the substance at least once in the
last 30 days since the self-reporting period and reported for substance abuse treatment. It is not
known if any or all of the participants reported in the data had truly only used the substance once
or if there were any continual addiction problems. The former of the two conditions shows that
one-time use of substances is related to less severe consequences on society (see Table 1 for
regression coefficients) while the latter condition could imply more serious effects on the
economic system, such as job search discouragement or increased criminal activity. Those
questions require further study and is not accounted in this analysis.
One of the more puzzling conclusions of this analysis is why higher abuse rates were
associated with higher amounts of production and personal income and lower amounts of
unemployment. One possible reason for this observation could be due to self exclusion from the
economic cycle. Since transactions for illicit substances are not counted as good that was
produced and sold, according to national income accounting standards, it could be the case that
those using the substances have become discouraged and chose not to enter the labor force.
Further study is required on substance abuse rates and labor force participation. The new a priori
assumption of how unemployment is impacted across various substances can be tested. Its
models suggest that rises in abuse rates relate to a decline in unemployment.
In each model, non-prescription stimulants not only retained its level of significance, but
was also associated with the largest movements across the three indicators tested. This could be
12. SUBSTANCE ABUSE AND ECONOMIC INDICATORS 12
due to its application in society as they can be treated in the same arena as performance
enhancements. These substances tend to help the users to become more productive, in contrast to
the effects of other substances. Based on the statistical analysis, it almost seems to imply that
abuse of non-prescription stimulants are actually a benefit to the country’s increasing production
and declining unemployment. Because of the suggested magnitude of its effect on society, this
analysis could be used to start a discussion on how to create policies lessens each substances’
impact on production and labor.
The empirical analysis of relationships between substance abuse and economic indicators
created additional questions that the derived models could not fully capture. One such detail
involves the true relationship between marijuana usage and the economy. When measured in
conjunction with other commonly abused substances, marijuana predicted a fall in
unemployment and a rise in personal income and GDP when usage rates are increased. When
marijuana is solely regressed against the indicators, the same relationship is observed. In addition
to this observation, this particular explanatory variable was significant in explaining
unemployment variation in comparison to other substances but not by itself, while it was
significant in explaining positive trends in personal income and GDP with the absence of other
substances. A trend such as this suggests a truly nonlinear relationship between marijuana usage
and the traditional market economy. These empirical results should not be the basis for
legalization policies, but as a way to forecast the direction its social impact when laws are
unrestrained.
Finally, rises in real unit prices for illicit substances seem to follow the income effect,
from microeconomic theory. The all-items consumer price index responded negatively to the
alcohol, crack/cocaine, and marijuana variables and positively to personal income. This effect
13. SUBSTANCE ABUSE AND ECONOMIC INDICATORS 13
seems to imply that the only way to quell usage of those substances relative to personal income
is to add a price control in some fashion. One way that it could feasibly be done through
federally mandated added cost on the components used to make the illicit substances. This
solution is a driving force behind the legalization of marijuana in the United States, albeit
through taxing the items sold and having them contribute to the traditional economy similar to
how cigarettes and alcohol are treated in the modern American society. In contrast to how
America evaluates the drug policy issue, the country of Portugal took a different approach by
decriminalizing all illicit substances for personal use as of 2001 (Specter, 2011). Further research
should be done to compare the effects on aggregate production and labor with a decriminalized
environment as a control group and environments that have an atypical structure for dealing with
illicit substance abuse.
14. SUBSTANCE ABUSE AND ECONOMIC INDICATORS 14
References
Allan, A. R., & Roberts, I. S. D. (2009). Post-mortem toxicology of commonly-abused drugs.
Diagnostic Histopathology, 15(1), 33-41. doi:10.1016/j.mpdhp.2008.11.001
Consumer Price Index Data from 1913 to 2015. (2008, July 19). Retrieved August 22, 2015.
Harwood, H., Fountain, D., & Livermore, G. (2001). Analytical Principles in Estimating the
Costs of Alcohol and Drug Abuse. In The economic costs of drug abuse in the United
States, 1992-1998. Washington, D.C.: Executive Office of the President, Office of
National Drug Control Policy.
Koenig, L., M. Siegel, J., Harwood, H., Gilani, J., Chen, Y., Leahy, P., & Stephens, R. (2005).
Economic benefits of substance abuse treatment: Findings from cuyahoga county, ohio.
Journal of Substance Abuse Treatment, 28(2), S41-S50. doi:10.1016/j.jsat.2004.09.004
McDowell Group, Inc. (2012). Economic costs of alcohol and other drug abuse in Alaska: 2005
update. PsycEXTRA Dataset.
National Economic Accounts. (2015, August 7). Retrieved August 22, 2015, from
http://www.bea.gov/national/
Nesvisky, M. (n.d.). Smoking, Drinking, and Drug Use Respond to Price Changes. Retrieved
August 22, 2015.
Specter, M. (2011, October 17). Getting a Fix - The New Yorker. Retrieved September 13, 2015.
Table 2.1. Personal Income and Its Disposition. (2015, July 30). Retrieved August 22, 2015,
from
http://www.bea.gov/iTable/iTable.cfm?ReqID=9&step=1#reqid=9&step=3&isuri=1&90
3=58
15. SUBSTANCE ABUSE AND ECONOMIC INDICATORS 15
United States Department of Health and Human Services. Substance Abuse and Mental Health
Services Administration. Center for Behavioral Health Statistics and Quality. Treatment
Episode Data Set -- Admissions (TEDS-A), 2012. ICPSR35037-v1. Ann Arbor, MI:
Inter-university Consortium for Political and Social Research [distributor], 2014-05-07.
http://doi.org/10.3886/ICPSR35037.v1
Zobeck, T. (2014, March 7). How Much Do Americans Really Spend on Drugs Each Year?
Retrieved September 9, 2015.
