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Table 1 Association between unemployment rate and substance abuse treatment admissions, 1993–2016

From: Unemployment rate, opioids misuse and other substance abuse: quasi-experimental evidence from treatment admissions data

Primary diagnosis

Model 1

Model 2

Model 3

\( \hat{\beta} \)

SE(\( \hat{\beta} \))

p-value

\( \hat{\beta} \)

SE(\( \hat{\beta} \))

p-value

\( \hat{\beta} \)

SE(\( \hat{\beta} \))

p-value

 Opiates

0.087

0.012

<.001

0.002

0.010

0.84

0.005

0.008

0.54

 Cocaine

0.081

0.014

<.001

0.033

0.011

<.01

−0.004

0.009

0.64

 Alcohol

0.050

0.011

<.001

0.039

0.007

<.001

0.026

0.007

<.001

 Marijuana/hashish

0.036

0.012

<.01

0.024

0.009

<.01

0.006

0.009

0.49

 Stimulants

−0.081

0.016

<.001

−0.057

0.012

<.001

− 0.067

0.010

<.001

 Other drugs

0.095

0.013

<.001

0.068

0.011

<.001

0.049

0.010

<.001

Any diagnosis

 Opiates

0.080

0.014

<.001

0.009

0.010

0.34

0.010

0.008

0.22

 Cocaine

0.063

0.012

<.001

0.030

0.009

<.001

−0.002

0.008

0.80

 Alcohol

0.041

0.011

<.001

0.029

0.007

<.001

0.017

0.007

0.01

 Marijuana/hashish

0.029

0.011

<.01

0.025

0.008

<.01

0.010

0.007

0.16

 Stimulants

−0.066

0.014

<.001

−0.033

0.010

<.01

−0.040

0.009

<.001

 Other drugs

0.098

0.014

<.001

0.065

0.011

<.001

0.050

0.010

<.001

  1. p < 0.05 is presented in bold. Primary diagnosis represents primary substances cited for alcohol or drug treatment. Any diagnosis represents that the substance was one of the primary, secondary, or tertiary substances cited for alcohol or drug treatment
  2. Model 1 adjusted all listed state-level characteristics, including log of population, mean age, percentage of state population that is male, percentage of state population that is white, state insurance coverage rate, state median household income (in thousands), medical marijuana laws, census division fixed effects and survey year. State beer taxes were adjusted for alcohol substance use treatment
  3. Model 2 adjusted for state fixed effects to control unobserved confounding influences that are time-invariant and state-specific in addition to Model 1 state-level characteristics
  4. Model 3 added interaction between state and year to Model 2, in order to allow for a state- unique time trend and control for unobserved state-level factors that evolve at a constant smooth function