- Email: [email protected]
Retail margins in illegal drug markets
Economic Analysis and Policy 62 (2019) 187–191
Contents lists available at ScienceDirect
Economic Analysis and Policy journal homepage: www.elsevier.com/locate/eap
Retail margins in illegal drug markets ∗
Alexi Thompson , Chris Jeffords Indiana University of Pennsylvania, United States
article
info
Article history: Received 17 March 2018 Received in revised form 27 February 2019 Accepted 10 March 2019 Available online 13 March 2019
a b s t r a c t This paper analyzes a retail margin for four common street drugs using the US Drug Enforcement Agency’s (DEA) System to Retrieve Information on Drug Enforcement (STRIDE). The retail margin is defined as the difference between the midlevel price and street level price of the drug. We regress various supply and demand side determinants to see which has the greatest effect on retail margin using Zellner’s (1962) Seemingly Unrelated Regression (SUR). Law enforcement agencies can use the results to help squeeze retail margins and decrease profits. The results indicate that an increase in arrests does not decrease margins, and an increase in the DEA budget only has an effect on the retail margin for methamphetamines. We also find a decrease in marijuana prices decreases the methamphetamine retail margin but increases the cocaine retail margin. Published by Elsevier B.V. on behalf of Economic Society of Australia, Queensland.
1. Introduction The War on Drugs is expensive. According to the Office of National Drug Control Policy, the funding request for 2017 was $31.4 billion with $1 trillion spent since President Nixon declared drugs ‘‘public enemy #1’’ in 1971 (Coyne and Hall, 2017). Demand-side interventions, rather than supply-side interventions, may be preferred due to their desired effect on both equilibrium quantity and price (National Research Council, 2010). A reduction in demand reduces equilibrium quantity and price while a reduction in supply reduces equilibrium quantity but leads to an increase in equilibrium price. In practice, the US spends considerable resources addressing both sides of the market thereby making it difficult to pin down the likely effect on the equilibrium price. Fig. 1, for example, shows the total number of arrests for possession of illegal drugs (arrb) and selling/manufacturing of drugs (arrs) from 1986 to 2007. While the number of arrests for possession has been trending upward since the mid 1980s the number of arrests for selling/manufacturing of drugs has remained relatively constant. The majority of empirical drug studies focus on the demand side of the illegal drug market (Saffer and Chaloupka, 1999; Caulkins and Reuter, 1998; Pudney, 2003; Kandel et al., 2006). These studies typically estimate cross-price elasticities across drugs where the results focus on examining the potential effects of legalization of marijuana – which is expected to lower the price of marijuana – on the use of other drugs (Thompson and Yamaura, 2017). Other empirical studies have focused on the supply side of illegal drug markets. Kuziemko and Levitt (2004) find that the increase in imprisonment for drug related offenses leads to 5% to 15% increases in the price of cocaine and slightly fewer property crimes. DiNardo (1993) and Yuan and Caulkins (1998) focus on incarceration at the wholesale level and find no relationship between incarceration rates and drug prices. Pollack and Reuter (2014) provide a summary of studies investigating the relationship between incarceration rates and drug prices. These studies point out that higher drug prices ∗ Corresponding author. E-mail address: [email protected] (A. Thompson). https://doi.org/10.1016/j.eap.2019.03.001 0313-5926/Published by Elsevier B.V. on behalf of Economic Society of Australia, Queensland.
188
A. Thompson and C. Jeffords / Economic Analysis and Policy 62 (2019) 187–191
Fig. 1. Drug arrests in the US from 1986–2007.
may reduce consumption. In their study of gang finances, Levitt and Venkatesh (2000) find that the average gang member earns less than minimum wage but is motivated by higher potential earnings if able to climb gang hierarchy. By focusing purely on supply-side or demand-side interventions to combat illegal drug markets, previous papers have largely ignored the reality that law enforcement targets both sides of the market. To reduce the size of the drug market in the US, law enforcement can try to make the illegal drug business less profitable thus discouraging new dealers. The present paper addresses the above concerns by estimating the determinants of the retail margin for four common street drugs including cocaine, heroin, methamphetamines, and marijuana. Supply and demand determinants are included as regressors. Our results have broader policy implications than previous studies as we focus on the demand and supply side of the market. 2. Data Drug price data are from the U.S. Drug Enforcement Agency’s (DEA) dataset called the System to Retrieve Information on Drug Enforcement (STRIDE). STRIDE reports drug price data at the wholesale price, midlevel price, and street level price. The three price levels are categorized by quantity. For example, the street level price of marijuana includes fewer than 10 g, midlevel prices include 10 to 100 g, and wholesale prices are quantities over 100 g. Retail margins are constructed by calculating the difference between the street level price Pstreet and midlevel price Pmid and dividing by the midlevel price Pmid , ((Pstreet − Pmid )/(Pmid )),
(1)
The retail margin is not a Lerner Index as we do not have full information on total costs which may include the cost of risk, operating costs, and market power. We construct retail margins for cocaine, heroin, marijuana, and methamphetamines which are the dependent variables in our study. STRIDE data are collected by the prices that undercover agents offer to dealers prior to arrest. Horowitz (2001) criticizes the accuracy of the data, but undercover agent lives or identities may be in danger if the prices they offer dealers did not reflect street level prices. This dataset is still commonly used to analyze illegal drug markets (DiNardo, 1993). The dataset unfortunately stops in 2007, limiting our yearly data to 1986–2007. Fig. 2 illustrates the retail margins for the street drugs. The marijuana (‘‘lmari’’) retail margin is the most volatile of the group with an increasing margin the last two years of data. The largest increase in retail margin over the time span appears in the methamphetamine (‘‘lmeth’’) market. The heroin (‘‘lh’’) retail margin has increased steadily since 1990 and the cocaine (‘‘lc’’) retail margin has remained relatively constant over time. We include various demand and supply side determinants in our study. Demand side determinants include income per capita and street level prices of substitute drugs. Supply side determinants include the DEA’s yearly budget. Total arrests for each drug are also included as explanatory variables. Total arrest data does not differentiate between individuals arrested for selling drugs and individuals arrested for drug possession. We expect an increase in income per capita to increase the demand for drugs and have a positive effect on the retail margins assuming drugs are normal goods. Income per capita data are from the World Bank. The effect of the prices of other drugs on retail margins depend on whether or not drugs are complements or substitutes. Other drug prices come from STRIDE data. To avoid issues of endogeneity, own prices are not included as explanatory variables. For example, the street price of cocaine is not used as an explanatory variable in the cocaine retail margin regression. Yearly arrest data for each drug comes from the Federal Bureau of Investigation’s (FBI) Uniform Crime Reports (UCR). We expect an increase in the number of arrests to have a negative effect on the retail margins indices. The data includes
A. Thompson and C. Jeffords / Economic Analysis and Policy 62 (2019) 187–191
189
Fig. 2. Computed retail margins.
arrests for heroin and cocaine together, so we divided the arrest data in half. Therefore arrest data for cocaine and heroin are the same but methamphetamine and marijuana have their own unique series. To calculate the methamphetamine arrest series, we use the arrests listed under the category ‘‘synthetic drugs’’. We hypothesize that an increase in the DEA yearly budget, if used effectively, should decrease retail margins. These data are from the DEA. 3. Method We employ Zellner’s (1962) Seemingly Unrelated Regression (SUR) to estimate the effect of the various supply and demand determinants on retail margins. SUR estimation is useful to estimate a system of equations with different dependent variables, potentially different independent variables, and correlated error terms. Our system of equations meets this criteria as we have retail margins for four street drugs and our independent variables differ slightly with respect to the price of other drugs, as own prices are not included to avoid endogeneity issues. As mentioned previously, arrest data differ across drugs except for heroin and cocaine. In addition, we assume the errors may be correlated as drug gangs may sell various drugs simultaneously. We thus estimate a system of four equations that each take the following form, lit = a0 + a1 pjt + a2 it + a3 ait + a5t bt + εit ,
(2)
where lit is the retail margin of drug i (i = cocaine (c), heroin (h), methamphetamines (m), marijuana (k)) in year t (t = 1986, . . . , 2007); pjt is the street price of drug j in year t where i ̸ = j; it is income per capita in year t; ait is the total number of arrests associated with possession or selling/manufacturing drug i in year t; and bt is the DEA’s yearly budget in year t. Before estimating the system, we check the correlation across variables1 and serial correlation for each of the four equations (that make up the system) separately. Some variables appear highly correlated, and due to the time series nature of the data, we check serial correlation of the error terms via the Durbin Watson (DW) test and Breusch–Godfrey (BG) test. No serial correlation in the following regression, εit = ρεit−1 + uit , implies ρ = 0. The derived DW test statistic takes the form DW ≈ 2(1 - ρ ), and with ρ = 0, DW≈ 2. The DW test reports two critical values: DWUPPER and DWLOWER . With DW > DWUPPER there is no positive serial correlation, whereas DW < DWLOWER implies there is positive serial correlation. If the DW test statistic lies between DWUPPER and DWLOWER , the test is inconclusive. The results from the DW test in Table 1 imply the DW test for serial correlation is inconclusive. With inconclusive results from the DW test, we perform the BG test. According to the BG test results, we cannot reject the null hypothesis of no serial correlation. Based on these test results, we proceed with SUR estimation. 4. Results Results from the SUR estimation are displayed in Table 2. An increase in the number of arrests does not decrease retail margins for any of the street drugs. One explanation is that an arrest of a street dealer is offset by new street dealers entering these markets. Another explanation is that if the retail margin increases, it is because either the street price has increased, the midlevel price has decreased, or both. If the street level price increased, street level dealers may have raised the price to account for the risk of being arrested. If the midlevel price has decreased, midlevel dealers may have lowered their price to the street level dealers to entice them to continue selling given the higher likelihood of being arrested. 1 The correlation matrix is available upon request.
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A. Thompson and C. Jeffords / Economic Analysis and Policy 62 (2019) 187–191 Table 1 DW test results.
lc lh a lk lj
DW test DWUPPER = 2.09 DWLOWER = 0.77
Breusch–Godfrey test Prob > X 2
2.57 2.44 2.09 2.05
0.12 0.22 0.62 0.69
a
In the heroin equation, initially it appeared autocorrelation may be an issue. By lagging income per capita one period we cannot reject the null of no autocorrelation according to the BG test. The lag of income per capita is included in the heroin equation in the SUR estimation and therefore we lose one observation. Each equation of the system has 21 observations despite the original dataset covering 22 years. Table 2 SUR estimation results. Dependent variable
lct
pct
lht
lmt
lkt
−2.41*
−1.49*** (0.47)
1.23 (1.84)
−1.63*** (0.53)
0.32 (2.26)
(1.35) pht
1.12** (0.54)
pmt
−0.14 (0.24)
pkt
−0.76**
−1.34** (0.64)
0.49 (1.08)
(0.37)
−1.08 (0.71)
1.19*** (0.34)
ait
1.05 (1.16)
−1.52 (3.42)
0.67 (0.49)
0.18 (3.28)
it
−0.20 (2.62)
−11.91*
−2.25
−2.31
(6.27)
(3.45)
(8.84)
bt
1.73 (1.22)
4.44 (3.40)
−2.55*** (0.69)
0.96 (2.78)
R-squared Observations
0.23 21
0.51 21
0.76 21
0.18 21
Note: *Significant at the 10% level. **Significant at the 5% level. ***Significant at the 1% level.
An increase in the DEA’s budget appears to negatively affect the methamphetamine retail margin only. A 1% increase in the DEA’s budget decreases the methamphetamine retail margins by 2.55% While income plays an important role in the heroin market our results indicate other margins are unaffected by changes in income. A 1% increase in income decreases the heroin index by nearly 12%, implying heroin is an inferior good. We assume street level dealers act as multiproduct firms, able to purchase various street drugs from wholesalers. The drug(s) the street dealer sells may depend on street prices. With respect to the price of other drugs, a 1% increase in the price of cocaine decreases the heroin and methamphetamine retail margins by 2.41% and 1.49%, respectively. A 1% increase in the price of heroin decreases the methamphetamine retail margin by 1.63% but increases the cocaine retail market by 1.12%. A 1% increase in the price of methamphetamine decreases the heroin retail by 1.34%. As more states legalize marijuana we should expect a decrease in its price. According to our results, a 1% decrease in the price of marijuana increases the cocaine retail margin by 0.76% but decreases the methamphetamine retail margin by 1.19%. 5. Conclusion In this paper, we calculate retail margins for four common street drugs. While previous illegal drug studies typically focus on the demand side of the market, we include various demand and supply determinants in the illegal drug markets. The results serve two purposes: (1) to determine law enforcement’s ability to disrupt the profitability of drug markets; and, (2) to provide some insight on other drug markets with the possible legalization of marijuana in more US states. With respect to the former point, arresting individuals involved in drug markets does not appear to have its desired effect for any of the street drugs. A few possible reasons could explain this non-effect on retail markups. Dealers may
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be easily replaced, as arresting one dealer creates a vacuum that other dealers are eager to fill. Alternatively, arresting dealers may be accompanied with a street price increase or a wholesale price decrease to account for the increased likelihood of being arrested. The increased profitability thus accounts for the increased risk. An increase in the DEA’s budget decreases retail margins in the methamphetamine market only. An increase in the DEA budget, which may include increased funding for drug awareness programs, should target methamphetamine markets. The results do, in general, suggest law enforcement has not been effective in decreasing the profitability of drug markets and should possibly approach combating the drug problem differently. One possible solution may include more of an emphasis of rehabilitation rather than incarceration for drug addicts as an increase in arrests does not decrease retail margins according to our results. Secondly, if the legalization of recreational marijuana spreads to more US states, we may expect a decrease in marijuana prices. Our results suggest that a decrease in marijuana prices decreases methamphetamine’s retail margin so the current methamphetamine epidemic plaguing some US states could diminish as methamphetamine dealers find other occupations. Unfortunately, marijuana legalization increases the cocaine retail margin, so former methamphetamine dealers may turn to dealing cocaine. References Caulkins, Jonathan P., Reuter, Peter, 1998. What price data tell us about drug markets. J. Drug Issues 28 (3), 593–612. Coyne, Christopher J., Hall, Abigail R., 2017. Four Decades and Counting: The Continued Failure of the War on Drugs. DiNardo, John, 1993. Law enforcement, the price of cocaine and cocaine use. Math. Comput. Modelling 17 (2), 53–64. Horowitz, Joel L., 2001. Should the DEA’s STRIDE data be used for economic analyses of markets for illegal drugs? J. Amer. Statist. Assoc. 96 (456), 1254–1271. Kandel, Denise B., Yamaguchi, Kazuo, Klein, Laura Cousino, 2006. Testing the gateway hypothesis. Addiction 101 (4), 470–472. Kuziemko, Ilyana, Levitt, Steven D., 2004. An empirical analysis of imprisoning drug offenders. J. Public Econ. 88 (9), 2043–2066. Levitt, Steven D., Venkatesh, Sudhir Alladi, 2000. An economic analysis of a drug-selling gang’s finances. Q. J. Econ. 115 (3), 755–789. National Research Council, 2010. National Research Council. Understanding the demand for illegal drugs. National Academies Press. Pollack, Harold A., Reuter, Peter, 2014. Does tougher enforcement make drugs more expensive? Addiction 109 (12), 1959–1966. Pudney, Stephen, 2003. The road to ruin? Sequences of initiation to drugs and crime in Britain. Econ. J. 113 (486). Saffer, Henry, Chaloupka, Frank, 1999. The demand for illicit drugs. Econ. Inquiry 37 (3), 401–411. Thompson, Alexi, Yamaura, Koichi, 2017. Does previous marijuana use increase the use of other drugs: An almost ideal demand system approach. BE J. Econ. Anal. Policy. Yuan, Yuehong, Caulkins, Jonathan P., 1998. The effect of variation in high-level domestic drug enforcement on variation in drug prices. Socio-Econ. Plann. Sci. 32 (4), 265–276. Zellner, Arnold, 1962. An efficient method of estimating seemingly unrelated regressions and tests for aggregation bias. J. Amer. Assoc. 57 (298), 348–368.
Contents lists available at ScienceDirect
Economic Analysis and Policy journal homepage: www.elsevier.com/locate/eap
Retail margins in illegal drug markets ∗
Alexi Thompson , Chris Jeffords Indiana University of Pennsylvania, United States
article
info
Article history: Received 17 March 2018 Received in revised form 27 February 2019 Accepted 10 March 2019 Available online 13 March 2019
a b s t r a c t This paper analyzes a retail margin for four common street drugs using the US Drug Enforcement Agency’s (DEA) System to Retrieve Information on Drug Enforcement (STRIDE). The retail margin is defined as the difference between the midlevel price and street level price of the drug. We regress various supply and demand side determinants to see which has the greatest effect on retail margin using Zellner’s (1962) Seemingly Unrelated Regression (SUR). Law enforcement agencies can use the results to help squeeze retail margins and decrease profits. The results indicate that an increase in arrests does not decrease margins, and an increase in the DEA budget only has an effect on the retail margin for methamphetamines. We also find a decrease in marijuana prices decreases the methamphetamine retail margin but increases the cocaine retail margin. Published by Elsevier B.V. on behalf of Economic Society of Australia, Queensland.
1. Introduction The War on Drugs is expensive. According to the Office of National Drug Control Policy, the funding request for 2017 was $31.4 billion with $1 trillion spent since President Nixon declared drugs ‘‘public enemy #1’’ in 1971 (Coyne and Hall, 2017). Demand-side interventions, rather than supply-side interventions, may be preferred due to their desired effect on both equilibrium quantity and price (National Research Council, 2010). A reduction in demand reduces equilibrium quantity and price while a reduction in supply reduces equilibrium quantity but leads to an increase in equilibrium price. In practice, the US spends considerable resources addressing both sides of the market thereby making it difficult to pin down the likely effect on the equilibrium price. Fig. 1, for example, shows the total number of arrests for possession of illegal drugs (arrb) and selling/manufacturing of drugs (arrs) from 1986 to 2007. While the number of arrests for possession has been trending upward since the mid 1980s the number of arrests for selling/manufacturing of drugs has remained relatively constant. The majority of empirical drug studies focus on the demand side of the illegal drug market (Saffer and Chaloupka, 1999; Caulkins and Reuter, 1998; Pudney, 2003; Kandel et al., 2006). These studies typically estimate cross-price elasticities across drugs where the results focus on examining the potential effects of legalization of marijuana – which is expected to lower the price of marijuana – on the use of other drugs (Thompson and Yamaura, 2017). Other empirical studies have focused on the supply side of illegal drug markets. Kuziemko and Levitt (2004) find that the increase in imprisonment for drug related offenses leads to 5% to 15% increases in the price of cocaine and slightly fewer property crimes. DiNardo (1993) and Yuan and Caulkins (1998) focus on incarceration at the wholesale level and find no relationship between incarceration rates and drug prices. Pollack and Reuter (2014) provide a summary of studies investigating the relationship between incarceration rates and drug prices. These studies point out that higher drug prices ∗ Corresponding author. E-mail address: [email protected] (A. Thompson). https://doi.org/10.1016/j.eap.2019.03.001 0313-5926/Published by Elsevier B.V. on behalf of Economic Society of Australia, Queensland.
188
A. Thompson and C. Jeffords / Economic Analysis and Policy 62 (2019) 187–191
Fig. 1. Drug arrests in the US from 1986–2007.
may reduce consumption. In their study of gang finances, Levitt and Venkatesh (2000) find that the average gang member earns less than minimum wage but is motivated by higher potential earnings if able to climb gang hierarchy. By focusing purely on supply-side or demand-side interventions to combat illegal drug markets, previous papers have largely ignored the reality that law enforcement targets both sides of the market. To reduce the size of the drug market in the US, law enforcement can try to make the illegal drug business less profitable thus discouraging new dealers. The present paper addresses the above concerns by estimating the determinants of the retail margin for four common street drugs including cocaine, heroin, methamphetamines, and marijuana. Supply and demand determinants are included as regressors. Our results have broader policy implications than previous studies as we focus on the demand and supply side of the market. 2. Data Drug price data are from the U.S. Drug Enforcement Agency’s (DEA) dataset called the System to Retrieve Information on Drug Enforcement (STRIDE). STRIDE reports drug price data at the wholesale price, midlevel price, and street level price. The three price levels are categorized by quantity. For example, the street level price of marijuana includes fewer than 10 g, midlevel prices include 10 to 100 g, and wholesale prices are quantities over 100 g. Retail margins are constructed by calculating the difference between the street level price Pstreet and midlevel price Pmid and dividing by the midlevel price Pmid , ((Pstreet − Pmid )/(Pmid )),
(1)
The retail margin is not a Lerner Index as we do not have full information on total costs which may include the cost of risk, operating costs, and market power. We construct retail margins for cocaine, heroin, marijuana, and methamphetamines which are the dependent variables in our study. STRIDE data are collected by the prices that undercover agents offer to dealers prior to arrest. Horowitz (2001) criticizes the accuracy of the data, but undercover agent lives or identities may be in danger if the prices they offer dealers did not reflect street level prices. This dataset is still commonly used to analyze illegal drug markets (DiNardo, 1993). The dataset unfortunately stops in 2007, limiting our yearly data to 1986–2007. Fig. 2 illustrates the retail margins for the street drugs. The marijuana (‘‘lmari’’) retail margin is the most volatile of the group with an increasing margin the last two years of data. The largest increase in retail margin over the time span appears in the methamphetamine (‘‘lmeth’’) market. The heroin (‘‘lh’’) retail margin has increased steadily since 1990 and the cocaine (‘‘lc’’) retail margin has remained relatively constant over time. We include various demand and supply side determinants in our study. Demand side determinants include income per capita and street level prices of substitute drugs. Supply side determinants include the DEA’s yearly budget. Total arrests for each drug are also included as explanatory variables. Total arrest data does not differentiate between individuals arrested for selling drugs and individuals arrested for drug possession. We expect an increase in income per capita to increase the demand for drugs and have a positive effect on the retail margins assuming drugs are normal goods. Income per capita data are from the World Bank. The effect of the prices of other drugs on retail margins depend on whether or not drugs are complements or substitutes. Other drug prices come from STRIDE data. To avoid issues of endogeneity, own prices are not included as explanatory variables. For example, the street price of cocaine is not used as an explanatory variable in the cocaine retail margin regression. Yearly arrest data for each drug comes from the Federal Bureau of Investigation’s (FBI) Uniform Crime Reports (UCR). We expect an increase in the number of arrests to have a negative effect on the retail margins indices. The data includes
A. Thompson and C. Jeffords / Economic Analysis and Policy 62 (2019) 187–191
189
Fig. 2. Computed retail margins.
arrests for heroin and cocaine together, so we divided the arrest data in half. Therefore arrest data for cocaine and heroin are the same but methamphetamine and marijuana have their own unique series. To calculate the methamphetamine arrest series, we use the arrests listed under the category ‘‘synthetic drugs’’. We hypothesize that an increase in the DEA yearly budget, if used effectively, should decrease retail margins. These data are from the DEA. 3. Method We employ Zellner’s (1962) Seemingly Unrelated Regression (SUR) to estimate the effect of the various supply and demand determinants on retail margins. SUR estimation is useful to estimate a system of equations with different dependent variables, potentially different independent variables, and correlated error terms. Our system of equations meets this criteria as we have retail margins for four street drugs and our independent variables differ slightly with respect to the price of other drugs, as own prices are not included to avoid endogeneity issues. As mentioned previously, arrest data differ across drugs except for heroin and cocaine. In addition, we assume the errors may be correlated as drug gangs may sell various drugs simultaneously. We thus estimate a system of four equations that each take the following form, lit = a0 + a1 pjt + a2 it + a3 ait + a5t bt + εit ,
(2)
where lit is the retail margin of drug i (i = cocaine (c), heroin (h), methamphetamines (m), marijuana (k)) in year t (t = 1986, . . . , 2007); pjt is the street price of drug j in year t where i ̸ = j; it is income per capita in year t; ait is the total number of arrests associated with possession or selling/manufacturing drug i in year t; and bt is the DEA’s yearly budget in year t. Before estimating the system, we check the correlation across variables1 and serial correlation for each of the four equations (that make up the system) separately. Some variables appear highly correlated, and due to the time series nature of the data, we check serial correlation of the error terms via the Durbin Watson (DW) test and Breusch–Godfrey (BG) test. No serial correlation in the following regression, εit = ρεit−1 + uit , implies ρ = 0. The derived DW test statistic takes the form DW ≈ 2(1 - ρ ), and with ρ = 0, DW≈ 2. The DW test reports two critical values: DWUPPER and DWLOWER . With DW > DWUPPER there is no positive serial correlation, whereas DW < DWLOWER implies there is positive serial correlation. If the DW test statistic lies between DWUPPER and DWLOWER , the test is inconclusive. The results from the DW test in Table 1 imply the DW test for serial correlation is inconclusive. With inconclusive results from the DW test, we perform the BG test. According to the BG test results, we cannot reject the null hypothesis of no serial correlation. Based on these test results, we proceed with SUR estimation. 4. Results Results from the SUR estimation are displayed in Table 2. An increase in the number of arrests does not decrease retail margins for any of the street drugs. One explanation is that an arrest of a street dealer is offset by new street dealers entering these markets. Another explanation is that if the retail margin increases, it is because either the street price has increased, the midlevel price has decreased, or both. If the street level price increased, street level dealers may have raised the price to account for the risk of being arrested. If the midlevel price has decreased, midlevel dealers may have lowered their price to the street level dealers to entice them to continue selling given the higher likelihood of being arrested. 1 The correlation matrix is available upon request.
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A. Thompson and C. Jeffords / Economic Analysis and Policy 62 (2019) 187–191 Table 1 DW test results.
lc lh a lk lj
DW test DWUPPER = 2.09 DWLOWER = 0.77
Breusch–Godfrey test Prob > X 2
2.57 2.44 2.09 2.05
0.12 0.22 0.62 0.69
a
In the heroin equation, initially it appeared autocorrelation may be an issue. By lagging income per capita one period we cannot reject the null of no autocorrelation according to the BG test. The lag of income per capita is included in the heroin equation in the SUR estimation and therefore we lose one observation. Each equation of the system has 21 observations despite the original dataset covering 22 years. Table 2 SUR estimation results. Dependent variable
lct
pct
lht
lmt
lkt
−2.41*
−1.49*** (0.47)
1.23 (1.84)
−1.63*** (0.53)
0.32 (2.26)
(1.35) pht
1.12** (0.54)
pmt
−0.14 (0.24)
pkt
−0.76**
−1.34** (0.64)
0.49 (1.08)
(0.37)
−1.08 (0.71)
1.19*** (0.34)
ait
1.05 (1.16)
−1.52 (3.42)
0.67 (0.49)
0.18 (3.28)
it
−0.20 (2.62)
−11.91*
−2.25
−2.31
(6.27)
(3.45)
(8.84)
bt
1.73 (1.22)
4.44 (3.40)
−2.55*** (0.69)
0.96 (2.78)
R-squared Observations
0.23 21
0.51 21
0.76 21
0.18 21
Note: *Significant at the 10% level. **Significant at the 5% level. ***Significant at the 1% level.
An increase in the DEA’s budget appears to negatively affect the methamphetamine retail margin only. A 1% increase in the DEA’s budget decreases the methamphetamine retail margins by 2.55% While income plays an important role in the heroin market our results indicate other margins are unaffected by changes in income. A 1% increase in income decreases the heroin index by nearly 12%, implying heroin is an inferior good. We assume street level dealers act as multiproduct firms, able to purchase various street drugs from wholesalers. The drug(s) the street dealer sells may depend on street prices. With respect to the price of other drugs, a 1% increase in the price of cocaine decreases the heroin and methamphetamine retail margins by 2.41% and 1.49%, respectively. A 1% increase in the price of heroin decreases the methamphetamine retail margin by 1.63% but increases the cocaine retail market by 1.12%. A 1% increase in the price of methamphetamine decreases the heroin retail by 1.34%. As more states legalize marijuana we should expect a decrease in its price. According to our results, a 1% decrease in the price of marijuana increases the cocaine retail margin by 0.76% but decreases the methamphetamine retail margin by 1.19%. 5. Conclusion In this paper, we calculate retail margins for four common street drugs. While previous illegal drug studies typically focus on the demand side of the market, we include various demand and supply determinants in the illegal drug markets. The results serve two purposes: (1) to determine law enforcement’s ability to disrupt the profitability of drug markets; and, (2) to provide some insight on other drug markets with the possible legalization of marijuana in more US states. With respect to the former point, arresting individuals involved in drug markets does not appear to have its desired effect for any of the street drugs. A few possible reasons could explain this non-effect on retail markups. Dealers may
A. Thompson and C. Jeffords / Economic Analysis and Policy 62 (2019) 187–191
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be easily replaced, as arresting one dealer creates a vacuum that other dealers are eager to fill. Alternatively, arresting dealers may be accompanied with a street price increase or a wholesale price decrease to account for the increased likelihood of being arrested. The increased profitability thus accounts for the increased risk. An increase in the DEA’s budget decreases retail margins in the methamphetamine market only. An increase in the DEA budget, which may include increased funding for drug awareness programs, should target methamphetamine markets. The results do, in general, suggest law enforcement has not been effective in decreasing the profitability of drug markets and should possibly approach combating the drug problem differently. One possible solution may include more of an emphasis of rehabilitation rather than incarceration for drug addicts as an increase in arrests does not decrease retail margins according to our results. Secondly, if the legalization of recreational marijuana spreads to more US states, we may expect a decrease in marijuana prices. Our results suggest that a decrease in marijuana prices decreases methamphetamine’s retail margin so the current methamphetamine epidemic plaguing some US states could diminish as methamphetamine dealers find other occupations. Unfortunately, marijuana legalization increases the cocaine retail margin, so former methamphetamine dealers may turn to dealing cocaine. References Caulkins, Jonathan P., Reuter, Peter, 1998. What price data tell us about drug markets. J. Drug Issues 28 (3), 593–612. Coyne, Christopher J., Hall, Abigail R., 2017. Four Decades and Counting: The Continued Failure of the War on Drugs. DiNardo, John, 1993. Law enforcement, the price of cocaine and cocaine use. Math. Comput. Modelling 17 (2), 53–64. Horowitz, Joel L., 2001. Should the DEA’s STRIDE data be used for economic analyses of markets for illegal drugs? J. Amer. Statist. Assoc. 96 (456), 1254–1271. Kandel, Denise B., Yamaguchi, Kazuo, Klein, Laura Cousino, 2006. Testing the gateway hypothesis. Addiction 101 (4), 470–472. Kuziemko, Ilyana, Levitt, Steven D., 2004. An empirical analysis of imprisoning drug offenders. J. Public Econ. 88 (9), 2043–2066. Levitt, Steven D., Venkatesh, Sudhir Alladi, 2000. An economic analysis of a drug-selling gang’s finances. Q. J. Econ. 115 (3), 755–789. National Research Council, 2010. National Research Council. Understanding the demand for illegal drugs. National Academies Press. Pollack, Harold A., Reuter, Peter, 2014. Does tougher enforcement make drugs more expensive? Addiction 109 (12), 1959–1966. Pudney, Stephen, 2003. The road to ruin? Sequences of initiation to drugs and crime in Britain. Econ. J. 113 (486). Saffer, Henry, Chaloupka, Frank, 1999. The demand for illicit drugs. Econ. Inquiry 37 (3), 401–411. Thompson, Alexi, Yamaura, Koichi, 2017. Does previous marijuana use increase the use of other drugs: An almost ideal demand system approach. BE J. Econ. Anal. Policy. Yuan, Yuehong, Caulkins, Jonathan P., 1998. The effect of variation in high-level domestic drug enforcement on variation in drug prices. Socio-Econ. Plann. Sci. 32 (4), 265–276. Zellner, Arnold, 1962. An efficient method of estimating seemingly unrelated regressions and tests for aggregation bias. J. Amer. Assoc. 57 (298), 348–368.