International Journal of Drug Policy - 2014

Volume 25 Issue 3 May 2014

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Page 52 of 153

D. Rosenblum et al. / International Journal of Drug Policy 25 (2014) 543–555 553 Table 3 OLS: relationship between Puerto Rican segregation and the average price per expected pure gram of heroin. Price per pure gram Price per pure gram Price per pure gram 1990–1992 1993–2000 2001–2008 (1) (2) (3) (4) (5) (6) High P.R. Segregation City −1193 * −479 −512 * −158 −375 −326 (583) (892) (257) (389) (218) (357) Low P.R. Segregation City −509 −101 −411 −209 −385 −357 (710) (806) (312) (352) (265) (310) Hispanic Dis. Index 1990 −3044 −1508 −208 (1161) (1257) (1109) R-Squared 0.1908 0.2406 0.2091 0.0001 0.1876 0.1893 Observations 21 21 21 21 21 21 Notes: Standard errors are reported in parentheses. The dependent variable is the estimated price per expected pure gram in each MSA averaged over the corresponding time period. The Hispanic Dissimilarity Index is for Hispanic/Latino with white non-Hispanic as the reference group. High Puerto Rican Segregation Cities are Boston, Chicago, Newark, and Philadelphia. Low Puerto Rican Segregation Cities are Los Angeles, Miami, and San Diego. The omitted region is West. East = Atlanta, Baltimore, Boston, Miami, New York, Newark, Philadelphia, and Washington, DC. Center = Chicago, Dallas, Detroit, Houston, New Orleans, and St. Louis. West = Denver, Los Angeles, Oakland, Phoenix, San Diego, San Francisco and Seattle. Significance levels: * p < 0.1, ** p < 0.05, *** p < 0.01. Limitations Even with data cleaning and our statistical methodology STRIDE has its limitations. STRIDE does not contain a randomly collected sample of observations and thus there is debate about the useful- ness of the data (Horowitz, 2001; Arkes et al., 2008). In particular, undercover agents may be paying systematically more for drugs or acquiring drugs of low quality relative to typical drug consumers. And informants may be not be truthful about market prices. If prices are consistently inflated in the STRIDE data across geography and time, one can still use it to reliably measure time trends in the heroin market, if not point estimates of prices paid by typical drug buyers. As a way to verify the data, we compared STRIDE obser- vations for Philadelphia with the ethnographically observed street prices of heroin and changes in heroin purity over time. The STRIDE data was accurate in its recent street prices and trends in heroin purity for this city, giving us some confidence in the reliability of STRIDE (data not reported). Hence although STRIDE is not perfect it is the only large-scale dataset on the price, purity, and source of US heroin and we believe STRIDE provides reasonable trend estimates of the US heroin market. In addition our empirical analysis is limited by our small sam- ple of MSAs making it difficult to control for potential confounders. 0 500 1000 1500 2000 2500 3000 3500 Average Price Per Pure Gram (Constant 2008 $) .2 .4 .6 .8 1 Hispanic and Black Dissimilarity Index, 1990 Fig. 6. Lines of best fit for the relationship between heroin price and segregation. Black lines indicate the average price per pure gram, 1990–1992. The dark grey lines indicate the average price per pure gram, 1993–2000. The light grey lines indicate the average price per pure gram, 2001–2008. Solid lines are Hispanic segregation and dashed lines are black segregation. Limited DEA data prior to 1990 has reduced our ability to quan- titatively analyze the composition of the heroin market prior to the entry of Colombian-sourced heroin. However, our quantita- tive analysis shows a clear, if complex, pattern between ethnically distinct patterns of segregation and the US heroin market. Participant-observation data by definition relies on the subjectivity and positionality of the researchers who col- lect it. We collected our qualitative data over a five-year period with 24-h immersion in our research site as a multi- ethnic/generational/class/gender/sexuality research team in order to triangulate our findings across our multiple positionalities, increase time-series robustness and identify potential researcher bias effects. The primary motive for bringing our quantitative and qualitative data into systematic, iterative methodological dialogue was to address the inevitable limitations of each methodological approach when employed in isolation without the benefit of a mul- tidisciplinary perspective. Discussion We have demonstrated that while geographic location matters in understanding illicit drug markets (Cunningham et al., 2010), a multifaceted analysis utilizing social, cultural and political econ- omy perspectives delivers an explanation that is more useful for public health. Complex social processes are difficult to document with aggregate analyses of large statistical samples because they often result in non-linear public health and demographic out- comes changing over time and space (Messac, Ciccarone, Draine, & Bourgois, 2013). A key innovation of our approach is to use mixed methods to test empirically the ethnographically observed rela- tionship between segregation and the heroin market in a broad national sample. By combining techniques from anthropology, eco- nomics and epidemiology we are able to develop a more expansive explanation of the underpinnings of the illicit heroin market. The mixed methods help to overcome the limitations inherent in each discipline. Biological plausibility is a core element for understanding causality in multivariate analysis; without it implausible asso- ciations arise. Ethnography in dialogue with epidemiology can identify meaningful risk variables and aid in determining the "social plausibility" of those variables. This leads to better causal expla- nations and highlights the hidden social and structural logics for unforeseen outcomes, all of which can expand modern epidemi- ology and promote more meaningful and durable interventions (Bourgois et al., 2006; Ciccarone, 2003; Messac et al., 2013). Sim- ilarly, economists working with ethnographers can shed light on

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