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The effects of recreational cannabis access on labor markets: evidence from Colorado Cover

The effects of recreational cannabis access on labor markets: evidence from Colorado

IZA Journal of Labor Economics
Volume 10 (2021): Issue 1 (January 2021)
By: ,   and    
Open Access
|Nov 2021
Figures & tables

Figures & Tables

Figure 1

Cannabis sales in Colorado counties, by year.

Notes: (A) Sales of medical and recreational cannabis. Data were obtained from the Colorado Department of Revenue and are in U.S. dollars. (B) The number of new counties selling cannabis, based on sales data obtained from the Colorado Department of Revenue.

Table 1

County-level summary statistics

MeanSDMinMaxN
Panel A: monthly
Unemployment rate (%)5.232.771.117.46,144
Labor force44,68290,397273417,7176,144
Unemployed2,2685,017733,0836,144
All industry employees37,90984,671195524,9196,144
Construction employees2,7205,0991424,1634,992
Manufacturing sector employees2,9295,4271021,4364,512
Natural resource and mining employees8021,821813,1205,088
Service-providing employees26,69262,82481401,9216,144
Amount of recreational sales629,2962,712,311035,343,7726,144
Number of medical patients1,6223,568220,9766,144
Panel B: quarterly
All industry wages749.06200.834102,1022,048
Construction wages881.75227.474152,4891,664
Manufacturing wages844.94339.173102,6501,504
Natural resource and mining wages1,071.30641.533766,4751,696
Service-providing wages684.91216.462942,6192,048

[i] Notes: The data cover the period from 2011 to 2018. Our monthly sample includes 6,144 county–month observations, while our quarterly sample includes 2,048 county–quarter observations. Subindustry measures are based on fewer observations due to BLS suppression of county-level information for confidentiality reasons.

[ii] BLS, U.S. Bureau of Labor Statistics; Min, minimum; Max, maximum; SD, standard deviation.

Figure 2

Effect of recreational cannabis entry on the unemployment rate, Ln(labor force) and Ln(unemployed) – event study.

Notes: The points represent the τk coefficient estimates from the estimation of Eq. (3), omitting τ−6. The bars extending from each point represent a 95% confidence interval calculated from the standard errors that are clustered at the county level. There are no standard error bars for the relative half-year k = −6 as the plot reflects that zero is imposed rather than estimated. The x-axis denotes time with respect to the commencement of the sale. Period 0 is when the sale begins. All regressions include county, month, and year fixed effects. Standard errors are clustered at the county level and are reported in parentheses.

Figure 3

Effect of recreational cannabis entry on Ln(employees), by industry – event study.

Notes: The points represent the τk coefficient estimates from the estimation of Eq. (3), omitting τ−6. The bars extending from each point represent a 95% confidence interval calculated from the standard errors that are clustered at the county level. There are no standard error bars for the relative half-year k = −6 as the plot reflects that zero is imposed rather than estimated. The x-axis denotes time with respect to the commencement of the sale. Period 0 is when the sale begins. All regressions include county, month, and year fixed effects. Standard errors are clustered at the county level and are reported in parentheses.

Figure 4

Effect of recreational cannabis entry on Ln(wage), by industry – event study.

Notes: The points represent the τk coefficient estimates from the estimation of Eq. (3), omitting τ−2. The bars extending from each point represent a 95% confidence interval calculated from the standard errors that are clustered at the county level. There are no standard error bars for the relative half-year k = −2 as the plot reflects that zero is imposed rather than estimated. The x-axis denotes time with respect to the commencement of the sale. Period 0 is when the sale begins. All regressions include county, month, and year fixed effects. Standard errors are clustered at the county level and are reported in parentheses.

Table 2

Effect of preexisting county-level economic conditions on dispensary entry

Dependent variable=Rcmy6-month change1-year change
Pop change0.000* (0.0000)0.000 (0.0000)
Unrate change0.013* (0.0050)0.039* (0.0191)
Ln(labor force change)−0.049 (0.0497)−0.379 (0.3405)
Ln(Unemp change)0.021 (0.0256)−0.005 (0.0554)
Ln(All Emp change)0.007 (0.0345)−0.032 (0.1537)
Ln(Cons Emp change)0.022 (0.0478)0.026 (0.0593)
Ln(Manu Emp change)0.097 (0.0839)0.149 (0.0926)
Ln(NR Emp change)0.028 (0.0381)0.030 (0.0874)
Ln(Service Emp change)−0.002 (0.0186)−0.122 (0.1650)
Ln(All wage change)0.053 (0.0549)0.084 (0.1125)
Ln(Cons wage change)0.022 (0.0473)0.019 (0.080)
Ln(Manu wage change)−0.009 (0.0407)−0.028 (0.0875)
Ln(NR wage change)−0.011 (0.019)−0.100 (0.0697)
Ln(Service wage change)−0.024 (0.0310)−0.001 (0.1016)

Notes: Each cell indicates a separate regression of dispensary entry in county c at month/quarter m and year y on the preexisting trends in the labor market outcome variables used in our study. Columns 1 and 2 show results with 6-month and 1-year changes of each labor market variable, respectively. All regressions include county, month, and year fixed effects. Standard errors are clustered at the county level and are reported in parentheses.

+ p < 0.1,

* p < 0.05,

** p < 0.01.

Cons, construction; Emp, employment; Manu, manufacturing; NR, natural resources; Unemp, unemployment; Unrate, unemployment rate.

Table 3

Effect of recreational dispensary entry and sales on the unemployment rate (Unrate), Ln(labor force), and Ln(unemployed) – regression analysis

UnrateLn(labor force)Ln(unemp)
(1)(2)(3)
Panel A: start of sales
Recreational sale−0.684** (0.2522)0.001 (0.0115)−0.068** (0.0194)
Ln(number of medical patients)−0.621* (0.2692)−0.006 (0.0126)−0.025 (0.0249)
Linear combination
  Coefficient−0.407*0.008−0.068**
  SE(0.2264)(0.0125)(0.0238)
Weighted linear combination
  Coefficient−0.3390.008−0.060*
  SE(0.2433)(0.0133)(0.0243)
R20.8810.999
Observations6,1446,1446,144
Panel B: amount of sales
$0 < sales ≤ $500,000−0.727** (0.2803)0.003 (0.0109)−0.058** (0.0223)
Sales > $500,000−0.630** (0.2641)−0.001 (0.0144)−0.081** (0.0245)
Ln(number of medical patients)−0.613** (0.2724)−0.006 (0.0128)−0.027 (0.0261)
R20.8820.9990.995
Observations6,1446,1446,144

Notes: Panel A uses a {0,1} any sales as the treatment variable, while Panel B compares counties with sales between $0 and $500,000 or sales >$500,000 with counties with zero sales. All regressions include county, month, and year fixed effects. Each column represents a separate regression. Below each column in Panel A, we report the average and weighted average of the τk coefficients from the event study, including only the τk coefficients for the periods post-dispensary entry. Standard errors are clustered at the county level and are reported in parentheses.

+ p < 0.1,

* p < 0.05,

** p < 0.01.

SE, standard error.

Table 4

The effect of recreational dispensary entry and sales on employment – regression analysis

Ln(All)Ln(Cons)Ln(Manu)Ln(NR)Ln(Service)
(1)(2)(3)(4)(5)
Panel A: start of sales
Recreational sale0.044** (0.0151)0.058 (0.0546)0.129** (0.0352)−0.015 (0.0665)0.038* (0.0153)
Ln(number of medical patients)−0.022 (0.0144)−0.175+ (0.0914)−0.187** (0.0597)0.172 (0.1163)−0.026 (0.0183)
Linear combination
  Coefficient0.047*0.0800.134**0.0280.042*
  SE(0.0145)(0.0542)(0.0333)(0.0563)(0.0188)
Weighted linear combination
  Coefficient0.054**0.0860.142**0.0460.044*
  SE(0.0154)(0.0657)(0.0328)(0.0642)(0.0204)
R20.9980.9890.9960.9700.988
Observations6,1444,6083,9364,6086,144
Panel B: amount of sales
$0 < sales ≤ $500,0000.029+ (0.0155)0.054 (0.0486)0.147** (0.0379)−0.031 (0.0659)0.025 (0.0173)
Sales >$500,0000.063** (0.0177)0.062 (0.0662)0.108** (0.0370)0.003 (0.0804)0.056** (0.0169)
Ln(number of medical patients)−0.019 (0.0149)−0.174+ (0.0918)−0.192** (0.0582)0.175 (0.1168)−0.024 (0.0180)
R20.9980.9890.9960.9700.998
Observations6,1444,6083,9364,6086,144

Notes: Panel A uses a {0,1} any sales as the treatment variable, while Panel B compares counties with sales between $0 and $500,000 or sales >$500,000 with counties with zero sales. All regressions include county, month, and year fixed effects. Each column represents a separate regression. The industry subsectors are Construction, Manufacturing, Natural Resources and Mining, and Service. Below each column in Panel A, we report the average and weighted average of the τk coefficients from the event study, including only the τk coefficients for the periods post-dispensary entry. Standard errors are clustered at the county level and are reported in parentheses.

+ p < 0.1,

* p < 0.05,

** p < 0.01.

SE, standard error.

Table 5

The effect of recreational dispensary entry and sales on wages – regression analysis

Ln(All)Ln(Cons)Ln(Manu)Ln(NR)Ln(Service)
(1)(2)(3)(4)(5)
Panel A: start of sales
Recreational sale0.001 (0.0100)0.008 (0.0264)0.010 (0.0192)0.025 (0.0316)0.004 (0.0126)
Ln(number of medical patients)0.003 (0.0112)−0.082+ (0.0466)−0.035 (0.0268)0.063+ (0.0337)0.002 (0.0175)
Linear combination
  Coefficient0.0110.0090.0220.0190.008
  SE(0.0076)(0.0239)(0.0265)(0.0269)(0.0093)
Weighted linear combination
  Coefficient0.0130.0170.0220.0260.004
  SE(0.0092)(0.0314)(0.0298)(0.0323)(0.0105)
R20.9370.7770.9320.9090.925
Observations2,0481,5361,3121,5362,048
Panel B: amount of sales
$0 < sales ≤ $500,0000.006 (0.0106)0.009 (0.0254)0.026 (0.0248)0.029 (0.0293)0.010 (0.0138)
Sales > $500,000−0.006 (0.0116)0.007 (0.0331)−0.010 (0.0190)0.022 (0.0376)−0.003 (0.0132)
Ln(number of medical patients)0.002 (0.0109)−0.082+ (0.0467)−0.039 (0.0276)0.063+ (0.0334)0.001 (0.0172)
R20.9370.7770.9330.9090.925
Observations2,0481,5361,3121,5362,048

Notes: Panel A uses a {0,1} any sales as the treatment variable, while Panel B compares counties with sales between $0 and $500,000 or sales >$500,000 with counties with zero sales. All regressions include county, month, and year fixed effects. Each column represents a separate regression. The industry subsectors are Construction (Cons), Manufacturing (Manu), Natural Resources and Mining (NR), and Service. Below each column in Panel A, we report the average and weighted average of the τk coefficients from the event study, including only the τk coefficients for the periods post-dispensary entry. Standard errors are clustered at the county level and are reported in parentheses.

+ p < 0.1,

* p < 0.05,

** p < 0.01.

Table 6

The effect of recreational dispensary entry and sales on the Unemployment Rate (Unrate), Ln(labor force), and Ln(unemployed) – regression analysis

UnrateLn(labor force)Ln(unemp)
(1)(2)(3)
Panel A: amount of sales
Recreational sale−0.689* (0.2745)−0.002 (0.0131)−0.073** (0.0210)
Recreational sale=1 × < 50km not-sellingmy=10.014 (0.3418)0.010 (0.0132)0.015 (0.0369)
Ln(number of medical patients)−0.622* (0.2704)−0.006 (0.0126)−0.026 (0.0255)
R20.8810.9990.995
Observations6,1446,1446,144
Month FE
Year FE
County FE

Notes: Table reports the τk coefficients for k ≥ 0 from the event study regressions. Each column represents a separate regression. Below the table, we report the average and weighted average of the τk coefficients from the event study, including only the τk coefficients for the periods post-dispensary entry. Standard errors are clustered at the county level and reported in parentheses.

+ p < 0.1,

* p < 0.05,

** p < 0.01.

FE, fixed effect.

Table 7

The effect of recreational dispensary entry and sales on employment – regression analysis

AllConsManuNRService
(1)(2)(3)(4)(5)
Panel A: amount of sales
Recreational sale0.048** (0.0172)0.064 (0.0599)0.139** (0.0407)−0.021 (0.0750)0.043* (0.0167)
Recreational sale=1 × < 50km not–sellingmy=1−0.012 (0.0202)−0.019 (0.0630)−0.037 (0.0487)0.021 (0.0995)−0.013 (0.0228)
Ln(number of medical patients)−0.022 (0.0144)−0.176+ (0.0906)−0.186** (0.0609)0.170 (0.1154)−0.026 (0.0180)
R20.9980.9890.9960.9700.998
Observations6,1444,6083,9364,6086,144
Month FE
Year FE
County FE

Notes: Table reports the τk coefficients for k ≥ 0 from the event study regressions. Each column represents a separate regression. Below the table, we report the average and weighted average of the τk coefficients from the event study, including only the τk coefficients for the periods post-dispensary entry. Standard errors are clustered at the county level and are reported in parentheses. The industry subsectors are Construction (Cons), Manufacturing (Manu), Natural Resources and Mining (NR), and Service.

+ p < 0.1,

* p < 0.05,

** p < 0.01.

FE, fixed effect.

Figure 5

The effect of recreational dispensary entry on the unemployment rate, Ln(labor force), and Ln(unemployed) – GSC.

Notes: This figure shows the estimates for the main GSC results with the same set of controls as in the DID estimations. The x-axis denotes time with respect to the commencement of the sale. Period 0 is when dispensaries start selling. The path of counties where recreational sale started and counterfactuals are on the left column. The right column shows the difference between the two from the column in terms of months relative to the dispensary sale starting. The standard errors are bootstrapped, and in the mean squared prediction error (MSPE), there is an optimal number of unobserved factors (r*) selected from the model. DID, difference-in-differences; GSC, generalized synthetic control.

Figure 6

The effect of recreational dispensary entry on employment – GSC.

Notes: This figure shows the estimates for the main GSC results with the same set of controls as in the DID estimations. The x-axis denotes time with respect to the commencement of the sale. Period 0 is when dispensaries start selling. The path of counties where recreational sale started and counterfactuals are shown on the left column. The right column shows the difference between the two from the column in terms of months relative to the dispensary sale starting. The standard errors are bootstrapped, and in the mean squared prediction error (MSPE), there is an optimal number of unobserved factors (r*) selected from the model. DID, difference-in-differences; GSC, generalized synthetic control.

Figure 7

The effect of recreational dispensary entry on wages – GSC.

Notes: This figure shows the estimates for the main GSC results with the same set of controls as in the DID estimations. The x-axis denotes time with respect to the commencement of the sale. Period 0 is when dispensaries start selling. The path of counties where recreational sale started and counterfactuals are shown on the left column. The right column shows the difference between the two from the column in terms of quarters relative to the dispensary sale starting. The standard errors are bootstrapped, and in the mean squared prediction error (MSPE), there is an optimal number of unobserved factors (r*) selected from the model. DID, difference-in-differences; GSC, generalized synthetic control.

Table 8

The effect of recreational dispensary entry and sales on wages – regression analysis

AllConsManuNRService
(1)(2)(3)(4)(5)
Panel A: amount of sales
Recreational sale0.002 (0.0114)0.018 (0.0269)0.007 (0.0229)0.041 (0.0326)0.002 (0.0136)
Recreational sale=1 × <50km not-sellingmy=1−0.004 (0.0114)−0.035 (0.0363)0.014 (0.0385)−0.051* (0.0227)0.006 (0.0140)
Ln(number of medical patients)0.004 (0.0113)−0.084+ (0.0459)−0.035 (0.0271)0.066+ (0.0340)0.002 (0.0173)
R20.9370.7780.9320.9100.925
Observations2,0481,5361,3121,5362,048
Quarter FE
Year FE
County FE

Notes: Table reports the τk coefficients for k ≥0 from the event study regressions. Each column represents a separate regression. Below the table, we report the average and weighted average of the τk coefficients from the event study, including only the τk coefficients for the periods post-dispensary entry. Standard errors are clustered at the county level and reported in parentheses. The industry subsectors are Construction (Cons), Manufacturing (Manu), Natural Resources and Mining (NR), and Service.

+ p < 0.1,

* p < 0.05,

** p < 0.01.

FE, fixed effect.

Figure A1

State Cannabis Laws in 2018.

Note: Data were obtained from the National Conference of State Legislatures.

Figure A2

Counties with cannabis-selling dispensaries.

Note: The underlying data were obtained from the Colorado Department of Revenue.

Figure A3

Unemployment rate, Ln(labor force), and Ln(unemployed) over time.

Note: The underlying data were obtained from the Local Area Unemployment Statistics.

Figure A4

Ln(employees) over time.

Note: The underlying data were obtained from the Quarterly Census of Employment and Wages.

Figure A5

Ln(wages) over time.

Note: The underlying data were obtained from the Quarterly Census of Employment and Wages.

Figure A6

Effect of recreational cannabis entry on the unemployment rate, Ln(labor force), and Ln(unemployed) – event study.

Notes: The points represent the τk coefficient estimates from the estimation of Eq. (3), omitting τ−6. The bars extending from each point represent a 95% confidence interval calculated from the standard errors that are clustered at the county level. There are no standard error bars for the relative half-year k = −6 as the plot reflects that zero is imposed rather than estimated. The x-axis denotes time with respect to the commencement of the sale. Period 0 is when the sale begins. All regressions include county, month, and year fixed effects. Standard errors are clustered at the county level and reported in parentheses.

Figure A7

Effect of recreational cannabis entry on Ln(employees) by industry – event study.

Notes: The points represent the τk coefficient estimates from the estimation of Eq. (3), omitting τ−6. The bars extending from each point represent a 95% confidence interval calculated from the standard errors that are clustered at the county level. There are no standard error bars for the relative half-year k = −6 as the plot reflects that zero is imposed rather than estimated. The x-axis denotes time with respect to the commencement of the sale. Period 0 is when the sale begins. All regressions include county, month, and year fixed effects. Standard errors are clustered at the county level and reported in parentheses.

Figure A8

Effect of recreational cannabis entry on Ln(wage) by industry – event study.

Notes: The points represent the τk coefficient estimates from the estimation of Eq. (3), omitting τ−2. The bars extending from each point represent a 95% confidence interval calculated from the standard errors that are clustered at the county level. There are no standard error bars for the relative half-year k = −2 as the plot reflects that zero is imposed rather than estimated. The x-axis denotes time with respect to the commencement of the sale. Period 0 is when the sale begins. All regressions include county, month, and year fixed effects. Standard errors are clustered at the county level and reported in parentheses.

Figure A9

Effect of recreational cannabis entry on the unemployment rate, Ln(labor force), and Ln(unemployed) – event study.

Notes: The points represent the τk coefficient estimates from the estimation of Eq. (3), omitting τ−6. The bars extending from each point represent a 95% confidence interval calculated from the standard errors that are clustered at the county level. There are no standard error bars for the relative half-year k = −6 as the plot reflects that zero is imposed rather than estimated. The x-axis denotes time with respect to the commencement of the sale. Period 0 is when the sale begins. All regressions include county, month, and year fixed effects. Standard errors are clustered at the county level and reported in parentheses.

Figure A10

Effect of recreational cannabis entry on Ln(employees) by industry – event study.

Notes: The points represent the τk coefficient estimates from the estimation of Eq. (3), omitting τ−6. The bars extending from each point represent a 95% confidence interval calculated from the standard errors that are clustered at the county level. There are no standard error bars for the relative half-year k = −6 as the plot reflects that zero is imposed rather than estimated. The x-axis denotes time with respect to the commencement of the sale. Period 0 is when the sale begins. All regressions include county, month, and year fixed effects. Standard errors are clustered at the county level and reported in parentheses.

Figure A11

Effect of recreational cannabis entry on Ln(wage) by industry – event study.

Notes: The points represent the τk coefficient estimates from the estimation of Eq. (3), omitting τ−2. The bars extending from each point represent a 95% confidence interval calculated from the standard errors that are clustered at the county level. There are no standard error bars for the relative half-year k = −2 as the plot reflects that zero is imposed rather than estimated. The x-axis denotes time with respect to the commencement of the sale. Period 0 is when the sale begins. All regressions include county, month, and year fixed effects. Standard errors are clustered at the county level and reported in parentheses.

Figure A12

The effect of recreational dispensary entry on the unemployment rate, Ln(labor force), and Ln(unemployed) – GSC.

Notes: This figure shows the estimates for the main GSC results with the same set of controls as in the DID estimations. The x-axis denotes time with respect to the commencement of the sale. Period 0 is 1 year after dispensaries started selling. The path of counties where recreational sale started and counterfactuals are shown on the left column. The right column shows the difference between the two from the column in terms of months relative to the dispensary sale starting. The standard errors are bootstrapped, and in the mean squared prediction error (MSPE), there is an optimal number of unobserved factors (r*) selected from the model. DID, difference-in-differences; GSC, generalized synthetic control.

Figure A13

The effect of recreational dispensary entry on employment – GSC.

Notes: This figure shows the estimates for the main GSC results with the same set of controls as in the DID estimations. The x-axis denotes time with respect to the commencement of the sale. Period 0 is 1 year after dispensaries started selling. The path of counties where recreational sale started and counterfactuals are on the left column. The right column shows the difference between the two from the column in terms of months relative to the dispensary sale starting. The standard errors are bootstrapped, and in the mean squared prediction error s(MSPE), there is an optimal number of unobserved factors (r*) selected from the model. DID, difference-in-differences; GSC, generalized synthetic control.

Figure A14

The effect of recreational dispensary entry on wages – GSC.

Notes: This figure shows the estimates for the main GSC results with the same set of controls as in the DID estimations. The x-axis denotes time with respect to the commencement of the sale. Period 0 is 1 year after dispensaries started selling. The path of counties where recreational sale started and counterfactuals are on the left column. The right column shows the difference between the two from the column in terms of quarter relative to the dispensary sale starting. The standard errors are bootstrapped, and in the mean squared prediction error (MSPE), there is an optimal number of unobserved factors (r*) selected from the model. DID, difference-in-differences; GSC, generalized synthetic control.

Table A1

Sources for our variable of interest

Variable typeSourceChronology
Unemployed, Labor force, and Unemployment rate (2011–2018)Local Area Unemployment Statistics (LAUS)graphic/j_izajole-2021-0005_ingr_001.png
Employees and wages (2011–2018)Quarterly Census of Employment and Wages (QCEW)graphic/j_izajole-2021-0005_ingr_002.png
Recreational cannabis sales (2014–2018)Colorado Department of Revenue (CDOR)graphic/j_izajole-2021-0005_ingr_003.png
Medical cannabis patients (2011–2018)Colorado Department of Public Health and Environment (CDPHE)graphic/j_izajole-2021-0005_ingr_004.png
Population (2011–2018)United States Census Bureaugraphic/j_izajole-2021-0005_ingr_005.png

[i] Notes: All variables are monthly, except average wage, which is quarterly.

Table A2

Descriptive statistics by treatment status

All countiesSellingNot sellingDifferences
BeforeAfterBeforeAfterDiffDiffDiff
(1)(2)(3)(4)(5)(6) = (3)–(2)(7) = (5)–(4)(8) = (6)–(7)
Panel A: monthly
Unemployment rate, %5.237.863.597.093.49−4.268**−3.605**−0.663**
6,1441,6041,9489721,620
Ln(labor force)9.119.489.748.388.430.258**0.0480.21**
6,1441,6041,9489721,620
Ln(unemployed)6.026.896.355.684.99−0.539**−0.691**0.152**
6,1441,6041,9489721,620
Ln(all industry employees)8.769.119.428.038.070.318**0.0350.282**
6,1441,6041,9489721,620
Ln(construction employees)6.516.506.945.946.010.436**0.0720.364**
4,6081,4161,752540900
Ln(manufacturing sector employees)6.316.066.536.326.310.470**−0.0070.477**
3,9361,2811,503432720
Ln(natural resource and mining employees)5.785.865.965.555.590.1050.0460.059**
4,6081,2741,5106841,140
Ln(service-providing employees)8.178.588.937.317.360.349**0.0560.293**
6,1441,6041,9489721,620
Panel B: quarterly
Ln(all industry wages)6.596.556.676.496.590.119**0.097**0.021**
2,048527657324540
Ln(construction wages)6.766.736.846.636.710.116**0.075*0.041**
1,536466590180300
Ln(manufacturing wages)6.706.626.766.656.740.137**0.091+0.046**
1,312421507144240
Ln(natural resource and mining wages)6.876.866.986.746.820.119**0.081+0.038**
1,536419509228380
Ln(service-providing wages)6.496.446.576.406.500.125**0.098**0.027**
2,048527657324540

Notes: The row after each variable represents the number of observations for the respective variable by column. Columns (2) and (3) report means for selling counties, before and after they started selling. Columns (4) and (5) report means for not-selling counties, before and after 2014. Columns (6) and (7) report differences in means from a two-sided t-test. Column (8) tests the statistical significance of the difference in the differences between selling counties, μ1 in Column (6), and not-selling counties, μ2 in Column (7) by using a t-test. For the t-test in Column (8), we obtain μ1 using unadjusted regressions of outcomes on a dummy variable for the start of sale for selling counties and obtain μ2 using unadjusted regressions of outcomes on a dummy variable for post-2014 for not-selling counties. We then calculate a t-statistic for the null hypothesis μ1 − μ2 = 0 by using the pooled variance and number of observations of the two groups.

+ p < 0.1,

* p < 0.05,

** p < 0.01.

Table A3

Event study estimates post-dispensary entry periods

UnrateLn(labor force)Ln(unemp)
(1)(2)(3)
event0:treat−0.0260.0170.024
event1:treat−0.0010.0180.020
event2:treat0.0900.0180.035
event3:treat−0.1730.012−0.007
event4:treat−0.0850.0030.005
event5:treat−0.414*0.004−0.058**
event6:treat−0.373*0.006−0.046*
event7:treat−0.365+0.011−0.046+
event8:treat−0.388+0.010−0.057*
event9:treat−0.466*0.008−0.074*
event10:treat−0.493*0.002−0.079**
event11:treat−0.652**0.015−0.103**
event12:treat−0.3000.020−0.008
event13:treat−0.2850.019−0.011
event14:treat−0.1810.0150.006
event15:treat−0.2990.009−0.025
event16:treat−0.227−0.002−0.015
event17:treat−0.397+0.002−0.055+
event18:treat−0.420+0.001−0.068*
event19:treat−0.429+0.005−0.087*
event20:treat−0.3700.003−0.080*
event21:treat−0.532*0.002−0.114**
event22:treat−0.578+−0.002−0.124**
event23:treat−0.674**0.013−0.140**
event24:treat−0.575*0.011−0.078*
event25:treat−0.592*0.016−0.078*
event26:treat−0.4340.012−0.041
event27:treat−0.489+0.009−0.069+
event28:treat−0.473−0.004−0.080*
event29:treat−0.563*0.003−0.095**
event30:treat−0.511+0.001−0.102**
event31:treat−0.542+0.007−0.132**
event32:treat−0.3930.003−0.108**
event33:treat−0.527+0.003−0.137**
event34:treat−0.612*−0.003−0.165**
event35:treat−0.647*0.010−0.164**
event36:treat−0.510+0.006−0.069*
event37:treat−0.454+0.011−0.059*
event38:treat−0.506+0.009−0.098*
event39:treat−0.544+0.012−0.118**
event40:treat−0.545+−0.000−0.122**
event>40:treat−0.1290.009−0.035
Linear combination
  Coefficient−0.407*0.008−0.068**
  SE0.22640.01250.0238
Weighted linear combination
  Coefficient−0.3390.008−0.060*
  SE0.24330.01330.0243
R20.8870.9990.995
Observations6,1446,1446,144

Notes: Table reports the τk coefficients for k ≥ 0 from the event study regressions. Each column represents a separate regression. Below the table, we report the average and weighted average of the τk coefficients from the event study, including only the τk coefficients for periods post-dispensary entry. All regressions include month, year, and county fixed effects. Standard errors are clustered at the county level and reported in parentheses.

+ p < 0.1,

* p < 0.05,

** p < 0.01.

FE, fixed effect; SE, standard error; Unrate, unemployment rate.

Table A4

Event study estimates post-dispensary entry periods for employees, by industry

AllConsManuNRService
(1)(2)(3)(4)(5)
event0:treat0.0200.0170.0100.0280.024
event1:treat0.0200.0270.0060.0030.028
event2:treat0.0170.0330.0160.0200.013
event3:treat0.0130.0320.0290.0200.006
event4:treat0.0080.052+0.030−0.0100.008
event5:treat0.0150.058+0.0360.0010.018
event6:treat0.019*0.050+0.0300.0060.023**
event7:treat0.028*0.0420.0700.0370.027*
event8:treat0.025+0.057*0.0770.0250.026
event9:treat0.031+0.073*0.093+0.0200.031
event10:treat0.0310.093*0.103*0.0360.028
event11:treat0.048*0.090*0.108*0.0300.050*
event12:treat0.052*0.096+0.123*0.0490.052+
event13:treat0.051*0.0880.123*0.0530.051+
event14:treat0.044+0.0720.117*0.0690.036
event15:treat0.039+0.0560.126*0.0510.036
event16:treat0.030+0.0630.140**0.0250.028
event17:treat0.039**0.0710.152**0.0080.037**
event18:treat0.039**0.0630.152**0.0020.035**
event19:treat0.047**0.0460.154**0.0060.041**
event20:treat0.044**0.0600.160**0.0200.038*
event21:treat0.050**0.0790.161**0.0010.045*
event22:treat0.049*0.0800.163**−0.0100.045+
event23:treat0.066**0.0700.158**0.0020.066*
event24:treat0.070**0.1060.173**0.0290.064*
event25:treat0.072**0.0900.169**0.0130.067*
event26:treat0.063**0.0980.172**0.0290.051+
event27:treat0.062**0.0890.175**0.0480.050*
event28:treat0.048*0.1010.188**0.0340.034
event29:treat0.057**0.1120.197**0.0120.047**
event30:treat0.057**0.1020.188**−0.0030.033+
event31:treat0.063**0.0850.185**0.0090.040+
event32:treat0.059**0.0930.180**0.0190.036
event33:treat0.067**0.1030.189**0.0170.054*
event34:treat0.059*0.1140.197**0.0270.060*
event35:treat0.074**0.1030.189**0.0420.077**
event36:treat0.073**0.1220.179**0.0650.067*
event37:treat0.070**0.1150.180**0.0440.067*
event38:treat0.066**0.1170.184**0.0620.055+
event39:treat0.067**0.1130.193**0.0600.057+
event40:treat0.062**0.1230.195**0.0710.043
event>40:treat0.076**0.1070.171**0.1050.054+
Linear combination
  Coefficient0.047*0.0800.134**0.0280.042*
  SE0.01450.05420.03330.05630.0188
Weighted linear combination
  Coefficient0.054**0.0860.142**0.0460.044*
  SE0.01540.06570.03280.06420.0204
R20.9980.9890.9960.9700.998
Observations6,1444,6083,9364,6086,144

Notes: Table reports the τk coefficients for k ≥ 0 from the event study regressions. Each column represents a separate regression. The industry subsectors are Construction (Cons), Manufacturing (Manu), Natural Resources and Mining (NR), and Service. Below the table, we report the average and weighted average of the τk coefficients from the event study, including only the τk coefficients for the periods post-dispensary entry. All regressions include month, year, and county fixed effects. Standard errors are clustered at the county level and reported in parentheses.

+ p < 0.1,

* p < 0.05,

** p < 0.01.

FE, fixed effect; SE, standard error.

Table A5

Event study estimates post-dispensary entry periods for wages, by industry

AllConsManuNRService
(1)(2)(3)(4)(5)
event0:treat−0.005−0.017−0.0060.0330.000
event1:treat0.0080.041+0.0280.0330.009
event2:treat−0.018*−0.018−0.018−0.020−0.003
event3:treat0.0070.0100.0050.0080.010
event4:treat0.011−0.016−0.0020.0290.015
event5:treat0.0180.0070.0260.0340.014
event6:treat0.004−0.0100.027−0.0110.012
event7:treat0.023*0.0100.059*−0.0270.020
event8:treat0.0060.0120.0420.034−0.005
event9:treat0.030*0.0330.0510.0250.013
event10:treat0.027*0.0300.0260.0450.017
event>10:treat0.0180.0290.0230.039−0.004
Linear combination
  Coefficient0.0110.0090.0220.0190.008
  SE0.00760.02390.02650.02690.0093
Weighted linear combination
  Coefficient0.0130.0170.0220.0260.004
  SE0.00920.03140.02980.03230.0105
R20.9380.7800.9330.9100.925
Observations2,0481,5361,3121,5362,048

Notes: Table reports the τk coefficients for k ≥0 from the event study regressions. Each column represents a separate regression. The industry subsectors are Construction (Cons), Manufacturing (Manu), Natural Resources and Mining (NR), and Service. Below the table, we report the average and weighted average of the τk coefficients from the event study, including only the τk coefficients for the periods post-dispensary entry. All regressions include month, year, and county fixed effects. Standard errors are clustered at the county level and reported in parentheses.

+ p < 0.1,

* p < 0.05,

** p < 0.01.

FE, fixed effect; SE, standard error.

Table A6

The effect of recreational dispensary entry and sales on the unemployment rate, Ln(labor force), and Ln(unemployed) – regression analysis

UnrateLn(labor force)Ln(unemp)
(1)(2)(3)
Panel A: start of sales
Recreational sale−0.718** (0.2428)−0.014 (0.0105)−0.078** (0.0200)
Ln(number of medical patients)−0.607* (0.2714)0.001 (0.0111)−0.021 (0.0250)
Ln(population)1.158 (2.4361)0.531** (0.1261)0.338+ (0.1900)
R20.8820.9990.995
Observations6,1446,1446,144
Panel B: amount of sales
$0 < sales ≤$500,000 (0.2720)−0.752** (0.0099)−0.009 (0.0224)−0.066**
Sales >$500,000 (0.2515)−0.669** (0.0125)−0.021 (0.0251)−0.094**
Ln(number of medical patients)−0.601** (0.2737)0.000 (0.0108)−0.023 (0.0260)
Ln(population)1.069 (2.4483)0.543** (0.1276)0.369+ (0.1872)
R20.8820.9990.995
Observations6,1446,1446,144

Notes: Panel A uses a {0,1} any sales as the treatment variable, while Panel B compares counties with sales between $0 and $500,000 or sales >$500,000 with counties with zero sales. Each column represents a separate regression. All regressions include month, year, and county fixed effects. Standard errors are clustered at the county level and reported in parentheses.

+ p < 0.1,

* p < 0.05,

** p < 0.01.

Unrate, unemployment rate.

Table A7

The effect of recreational dispensary entry and sales on employment – regression analysis

AllConsManuNRService
(1)(2)(3)(4)(5)
Panel A: start of sales
Recreational sale0.016 (0.0120)0.020 (0.0633)0.113** (0.0396)−0.066 (0.0712)0.014 (0.0132)
Ln(number of medical patients)−0.010 (0.0128)−0.130 (0.0904)−0.166** (0.0541)0.205 (0.1224)−0.015 (0.0158)
Ln(population)0.953** (0.1578)1.993+ (1.0110)0.732 (0.5178)1.576 (1.0049)0.834** (0.1491)
R20.9980.9890.9960.9700.998
Observations6,1444,6083,9364,6086,144
Panel B: amount of sales
$0 < sales ≤$500,0000.007 (0.0138)0.022 (0.0550)0.133** (0.0403)−0.074 (0.0693)0.006 (0.0159)
Sales >$500,0000.029** (0.0136)0.017 (0.0757)0.086+ (0.0434)−0.057 (0.0860)0.026+ (0.0142)
Ln(number of medical patients)−0.009 (0.0132)−0.130 (0.0901)−0.171** (0.0522)0.206+ (0.1222)−0.014 (0.0159)
Ln(population)0.930** (0.1612)1.996+ (1.0238)0.777 (0.5181)1.561 (1.0195)0.813** (0.1507)
R20.9980.9890.9960.9700.998
Observations6,1444,6083,9364,6086,144

Notes: Panel A uses a {0,1} any sales as the treatment variable, while Panel B compares counties with sales between $0 and $500,000 or sales >$500,000 with counties with zero sales. Each column represents a separate regression. The industry subsectors are Construction (Cons), Manufacturing (Manu), Natural Resources and Mining (NR), and Service. All regressions include month, year, and county fixed effects. Standard errors are clustered at the county level and reported in parentheses.

+ p < 0.1,

* p < 0.05,

** p < 0.01.

Table A8

The effect of recreational dispensary entry and sales on wages – regression analysis

AllConsManuNRService
(1)(2)(3)(4)(5)
Panel A: start of sales
Recreational sale−0.005 (0.0093)0.001 (0.0290)0.019 (0.0192)0.037 (0.0347)−0.004 (0.0116)
Ln(number of medical patients)0.006 (0.0118)−0.074 (0.0452)−0.046 (0.0282)0.055 (0.0343)0.006 (0.0193)
Ln(population)0.191 (0.1207)0.359 (0.4475)−0.391 (0.2890)−0.368 (0.2931)0.291+ (0.1620)
R20.9370.7780.9330.9100.926
Observations2,0481,5361,3121,5362,048
Panel B: amount of sales
$0 < sales ≤$500,0000.001 (0.0100)0.003 (0.0273)0.032 (0.0245)0.038 (0.0318)0.002 (0.0127)
Sales >$500,000−0.014 (0.0108)−0.001 (0.0364)0.000 (0.0173)0.036 (0.0420)−0.014 (0.0125)
Ln(number of medical patients)0.005 (0.0114)−0.074 (0.0453)−0.049+ (0.0281)0.055 (0.0341)0.005 (0.0189)
Ln(population)0.208+ (0.1213)0.362 (0.4527)−0.358 (0.2822)−0.366 (0.2991)0.310+ (0.1615)
R20.9370.7780.9330.9100.926
Observations2,0481,5361,3121,5362,048

Notes: Panel A uses a {0,1} any sales as the treatment variable, while Panel B compares counties with sales between $0 and $500,000 or sales >$500,000 with counties with zero sales. Each column represents a separate regression. The industry subsectors are Construction (Cons), Manufacturing (Manu), Natural Resources and Mining (NR), and Service. All regressions include quarter, year, and county fixed effects. Standard errors are clustered at the county level and reported in parentheses.

+ p < 0.1,

* p < 0.05,

** p < 0.01.

Table A9

The effect of recreational dispensary entry and sales on the unemployment rate (Unrate), Ln(labor force), and Ln(unemployed) – regression analysis

UnrateLn(labor force)Ln(unemp)
(1)(2)(3)
Panel A: start of sales
Recreational sale−0.693** (0.2522)0.002 (0.0116)−0.070** (0.0195)
Ln(number of medical patients)−0.588* (0.2675)−0.009 (0.0127)−0.021 (0.0252)
R20.9150.9990.996
Observations6,1446,1446,144
Panel B: amount of sales
$0 < sales ≤$500,000−0.807** (0.2798)0.006 (0.0111)−0.070** (0.0214)
Sales >$500,000−0.552** (0.2653)−0.003 (0.0144)−0.070** (0.0244)
Ln(number of medical patients)−0.568** (0.2727)−0.009 (0.0127)−0.021 (0.0259)
R20.9150.9990.996
Observations6,1446,1446,144

Notes: Panel A uses a {0,1} any sales as the treatment variable, while Panel B compares counties with sales between $0 and $500,000 or sales >$500,000 with counties with zero sales. Each column represents a separate regression. All regressions include month, year, county, and month x county fixed effects. Standard errors are clustered at the county level and reported in parentheses.

+ p < 0.1,

* p < 0.05,

** p < 0.01.

Table A10

The effect of recreational dispensary entry and sales on employment – regression analysis

AllConsManuNRService
(1)(2)(3)(4)(5)
Panel A: start of sales
Recreational sale0.045** (0.0152)0.056 (0.0551)0.131** (0.0357)−0.018 (0.0673)0.040* (0.0153)
Ln(number of medical patients)−0.026+ (0.0142)−0.171+ (0.0917)−0.186** (0.0604)0.170 (0.1165)−0.031+ (0.0179)
R20.9990.9890.9960.9730.999
Observations6,1444,6083,9364,6086,144
Panel B: amount of sales
$0 < sales ≤$500,0000.035** (0.0158)0.050 (0.0493)0.156** (0.0394)−0.035 (0.0675)0.034+ (0.0173)
Sales >$500,0000.057** (0.0176)0.062 (0.0670)0.103** (0.0366)0.000 (0.0814)0.046** (0.0166)
Ln(number of medical patients)−0.024 (0.0147)−0.171+ (0.0922)−0.192** (0.0581)0.173 (0.1169)−0.030+ (0.0179)
R20.9990.9890.9960.9730.999
Observations6,1444,6083,9364,6086,144

Notes: Panel A uses a {0,1} any sales as the treatment variable, while Panel B compares counties with sales between $0 and $500,000 or sales >$500,000 with counties with zero sales. Each column represents a separate regression. The industry subsectors are Construction (Cons), Manufacturing (Manu), Natural Resources and Mining (NR), and Service. All regressions include month, year, county, and month x county fixed effects. Standard errors are clustered at the county level and reported in parentheses.

+ p < 0.1,

* p < 0.05,

** p < 0.01.

Table A11

The effect of recreational dispensary entry and sales on wages – regression analysis

AllConsManuNRService
(1)(2)(3)(4)(5)
Panel A: start of sales
Recreational sale−0.000 (0.0100)0.006 (0.0264)0.007 (0.0189)0.023 (0.0319)0.004 (0.0125)
Ln(number of medical patients)0.004 (0.0109)−0.082+ (0.0470)−0.036 (0.0262)0.056+ (0.0320)0.004 (0.0170)
R20.9560.8170.9500.9410.951
Observations2,0481,5361,3121,5362,048
Panel B: amount of sales
$0 < sales ≤ $500,0000.004 (0.0107)0.004 (0.0253)0.021 (0.0247)0.025 (0.0300)0.009 (0.0141)
Sales > $500,000−0.007 (0.0119)0.009 (0.0332)−0.011 (0.0187)0.020 (0.0376)−0.002 (0.0131)
Ln(number of medical patients)0.003 (0.0106)−0.082+ (0.0470)−0.040 (0.0270)0.055+ (0.0317)0.003 (0.0168)
R20.9560.8170.9510.9410.951
Observations2,0481,5361,3121,5362,048

Notes: Panel A uses a {0,1} any sales as the treatment variable, while Panel B compares counties with sales between $0 and $500,000 or sales >$500,000 with counties with zero sales. Each column represents a separate regression. The industry subsectors are Construction (Cons), Manufacturing (Manu), Natural Resources and Mining (NR), and Service. All regressions include quarter, year, county, and quarter x county fixed effects. Standard errors are clustered at the county level and reported in parentheses.

+ p < 0.1,

* p < 0.05,

** p < 0.01.

Table A12

The effect of recreational dispensary entry and sales on the unemployment rate (Unrate), Ln(labor force), and Ln(unemployed) – regression analysis

UnrateLn(labor force)Ln(unemp)
(1)(2)(3)
Panel A: start of sales
Recreational sale−0.230 (0.1384)0.003 (0.0066)−0.038* (0.0174)
Ln(number of medical patients)−0.550** (0.2050)0.005 (0.0071)−0.063* (0.0300)
R20.9230.9990.996
Observations6,1446,1446,144
Panel B: amount of sales
$0 < sales ≤ $500,000−0.207 (0.1517)0.002 (0.0069)−0.026 (0.0203)
Sales > $500,000−0.301** (0.1427)0.007 (0.0077)−0.077** (0.0223)
Ln(number of medical patients)−0.555** (0.2072)0.005 (0.0073)−0.066** (0.0309)
R20.9230.9990.996
Observations6,1446,1446,144

Notes: Panel A uses a {0,1} any sales as the treatment variable, while Panel B compares counties with sales between $0 and $500,000 or sales >$500,000 with counties with zero sales. Each column represents a separate regression. All regressions include month, year, county, fixed effects and a county-level time trend. Standard errors are clustered at the county level and reported in parentheses.

+ p < 0.1,

* p < 0.05,

** p < 0.01.

Table A13

The effect of recreational dispensary entry and sales on employment – regression analysis

AllConsManuNRService
(1)(2)(3)(4)(5)
Panel A: start of sales
Recreational sale−0.001 (0.0097)0.022 (0.0332)0.040 (0.0545)0.031 (0.0441)0.001 (0.0097)
Ln(number of medical patients)0.024* (0.0095)−0.011 (0.0520)−0.028 (0.0504)0.085 (0.0955)0.035** (0.0102)
R20.9990.9930.9970.9810.998
Observations6,1444,6083,9364,6086,144
Panel B: amount of sales
$0 < sales ≤ $500,000−0.007 (0.0106)0.019 (0.0341)0.040 (0.0590)0.037 (0.0453)0.006 (0.0113)
Sales > $500,0000.017+ (0.0099)0.032 (0.0348)0.043 (0.0453)−0.016 (0.0560)−0.025** (0.0120)
Ln(number of medical patients)0.026** (0.0101)−0.010 (0.0521)0.028 (0.0494)0.086 (0.0955)0.037** (0.0106)
R20.9990.9930.9970.9810.998
Observations6,1444,6083,9364,6086,144

Notes: Panel A uses a {0,1} any sales as the treatment variable, while Panel B compares counties with sales between $0 and $500,000 or sales >$500,000 with counties with zero sales. Each column represents a separate regression. The industry subsectors are Construction (Cons), Manufacturing (Manu), Natural Resources and Mining (NR), and Service. All regressions include month, year, county, fixed effects and a county-level time trend. Standard errors are clustered at the county level and reported in parentheses.

+ p < 0.1,

* p < 0.05,

** p < 0.01.

Table A14

The effect of recreational dispensary entry and sales on wages – regression analysis

AllConsManuNRService
(1)(2)(3)(4)(5)
Panel A: start of sales
Recreational sale−0.005 (0.0094)−0.018 (0.0161)−0.006 (0.0189)0.003 (0.0241)0.017* (0.0076)
Ln(number of medical patients)−0.002 (0.0098)0.010 (0.0284)−0.061+ (0.0329)0.010 (0.0511)−0.007 (0.0153)
R20.9460.8440.9500.9260.936
Observations2,0481,5361,3121,5362,048
Panel B: amount of sales
$0 < sales ≤ $500,000−0.002 (0.0096)−0.013 (0.0179)−0.003 (0.0192)0.007 (0.0236)0.019** (0.0081)
Sales > $500,000−0.013 (0.0111)−0.033+ (0.0179)−0.015 (0.0219)−0.015 (0.0337)0.011 (0.0075)
Ln(number of medical patients)−0.002 (0.0097)0.008 (0.0288)−0.062+ (0.0329)0.009 (0.0504)−0.008 (0.0153)
R20.9460.8440.9500.9260.936
Observations2,0481,5361,3121,5362,048

Notes: Panel A uses a {0,1} any sales as the treatment variable, while Panel B compares counties with sales between $0 and $500,000 or sales >$500,000 with counties with zero sales. Each column represents a separate regression. The industry subsectors are Construction (Cons), Manufacturing (Manu), Natural Resources and Mining (NR), and Service. All regressions include quarter, year, county, fixed effects and a county-level time trend. Standard errors are clustered at the county level and reported in parentheses.

+ p < 0.1,

* p < 0.05,

** p < 0.01.

Table A15

The effect of recreational dispensary entry on the unemployment rate (Unrate), Ln(labor force), and Ln(unemployed) – regression analysis

UnrateLn(labor force)Ln(unemp)
(1)(2)(3)
Panel A: start of sales
Recreational sale−0.688** (0.2548)0.000 (0.0116)−0.069** (0.0197)
Ln(number of medical patients)−0.618* (0.2703)−0.006 (0.0127)−0.025 (0.0250)
R20.8810.9990.995
Observations6,0486,0486,048
Panel B: amount of sales
$0 < sales ≤ $500,000−0.729** (0.2809)0.003 (0.0109)−0.058** (0.0223)
Sales > $500,000−0.634** (0.2693)−0.003 (0.0146)−0.083** (0.0253)
Ln(number of medical patients)−0.610** (0.2736)−0.006 (0.0128)−0.027 (0.0263)
R20.8810.9990.995
Observations6,0486,0486,048

Notes: Panel A uses a {0,1} any sales as the treatment variable, while Panel B compares counties with sales between $0 and $500,000 or sales >$500,000 with counties with zero sales. Each column represents a separate regression omitting Denver. All regressions include month, year, and county fixed effects. Standard errors are clustered at the county level and reported in parentheses.

+ p < 0.1,

* p < 0.05,

** p < 0.01.

Table A16

The effect of recreational dispensary entry on employment – regression analysis

AllConsManuNRService
(1)(2)(3)(4)(5)
Panel A: start of sales
Recreational sale0.043** (0.0152)0.054 (0.0552)0.131** (0.0360)−0.019 (0.0674)0.037* (0.0155)
Ln(number of medical patients)−0.022 (0.0143)−0.175+ (0.0918)−0.190** (0.0606)0.170 (0.1178)−0.026 (0.0183)
R20.9980.9880.9950.9650.998
Observations6,0484,5123,8404,5126,048
Panel B: amount of sales
$0 < sales ≤ $500,0000.029+ (0.0155)0.053 (0.0489)0.148** (0.0381)−0.032 (0.0661)0.025 (0.0173)
Sales > $500,0000.062** (0.0181)0.055 (0.0677)0.110** (0.0387)−0.003 (0.0829)0.054** (0.0172)
Ln(number of medical patients)−0.019 (0.0149)−0.175+ (0.0921)−0.195** (0.0593)0.173 (0.1182)−0.024 (0.0181)
R20.9980.9880.9950.9650.998
Observations6,0484,5123,8404,5126,048

Notes: Panel A uses a {0,1} any sales as the treatment variable, while Panel B compares counties with sales between $0 and $500,000 or sales >$500,000 with counties with zero sales. Each column represents a separate regression omitting Denver. The industry subsectors are Construction (Cons), Manufacturing (Manu), Natural Resources and Mining (NR), and Service. All regressions include month, year, and county fixed effects. Standard errors are clustered at the county level and reported in parentheses.

+ p < 0.1,

* p < 0.05,

** p < 0.01.

Table A17

The effect of recreational dispensary entry on wages – regression analysis

AllConsManuNRService
(1)(2)(3)(4)(5)
Panel A: start of sales
Recreational sale0.001 (0.0101)0.007 (0.0265)0.010 (0.0197)0.028 (0.0318)0.004 (0.0127)
Ln(number of medical patients)0.004 (0.0113)−0.081+ (0.0469)−0.036 (0.0271)0.061+ (0.0341)0.002 (0.0176)
R20.9320.7680.9320.9000.919
Observations2,0161,5041,2801,5042,016
Panel B: amount of sales
$0 < sales ≤ $500,0000.006 (0.0106)0.008 (0.0254)0.025 (0.0249)0.029 (0.0294)0.009 (0.0139)
Sales > $500,000−0.007 (0.0119)0.006 (0.0338)−0.012 (0.0196)0.026 (0.0384)−0.004 (0.0134)
Ln(number of medical patients)0.002 (0.0109)−0.081+ (0.0469)−0.041 (0.0280)0.061+ (0.0339)0.001 (0.0173)
R20.9320.7680.9320.9000.919
Observations2,0161,5041,2801,5042,016

Notes: Panel A uses a {0,1} any sales as the treatment variable, while Panel B compares counties with sales between $0 and $500,000 or sales >$500,000 with counties with zero sales. Each column represents a separate regression omitting Denver. The industry subsectors are Construction (Cons), Manufacturing (Manu), Natural Resources and Mining (NR), and Service. All regressions include quarter, year, and county fixed effects. Standard errors are clustered at the county level and reported in parentheses.

+ p < 0.1,

* p < 0.05,

** p < 0.01.

Table A18

The effect of recreational dispensary entry and sales on the unemployment rate (Unrate), Ln(labor force), and Ln(unemployed) – regression analysis

UnrateLn(labor force)Ln(unemp)
(1)(2)(3)
Panel A: start of sales
Recreational sale−0.648* (0.2546)0.005 (0.0110)−0.064** (0.0195)
Ln(number of medical patients)−0.543* (0.2664)0.002 (0.0100)−0.018 (0.0251)
R20.8800.9990.995
Observations6,0486,0486,048
Panel B: amount of sales
$0 < sales ≤ $500,000−0.713** (0.2831)0.004 (0.0108)−0.056** (0.0225)
Sales > $500,000−0.561** (0.2626)0.006 (0.0130)−0.076** (0.0251)
Ln(number of medical patients)−0.526+ (0.2690)0.002 (0.0101)−0.020 (0.0267)
R20.8800.9990.995
Observations6,0486,0486,048

Notes: Panel A uses a {0,1} any sales as the treatment variable, while Panel B compares counties with sales between $0 and $500,000 or sales >$500,000 with counties with zero sales. Each column represents a separate regression omitting Las Animas. All regressions include month, year, and county fixed effects. Standard errors are clustered at the county level and reported in parentheses.

+ p < 0.1,

* p < 0.05,

** p < 0.01.

Table A19

The effect of recreational dispensary entry and sales on employment – regression analysis

AllConsManuNRService
(1)(2)(3)(4)(5)
Panel A: start of sales
Recreational sale0.047** (0.0151)0.077 (0.0532)0.147** (0.0319)0.001 (0.0653)0.039* (0.0156)
Ln(number of medical patients)−0.016 (0.0142)−0.120 (0.0828)−0.139** (0.0446)0.213+ (0.1140)−0.025 (0.0190)
R20.9980.9890.9960.9700.998
Observations6,0484,5123,8404,5126,048
Panel B: amount of sales
$0 < sales ≤ $500,0000.030+ (0.0156)0.063 (0.0485)0.158** (0.0361)−0.026 (0.0655)0.025 (0.0175)
Sales > $500,0000.070** (0.0172)0.091 (0.0629)0.133** (0.0322)0.032 (0.0777)0.058** (0.0173)
Ln(number of medical patients)−0.012 (0.0142)−0.117 (0.0828)−0.144** (0.0437)0.222+ (0.1129)−0.021 (0.0186)
R20.9980.9890.9960.9700.998
Observations6,0484,5123,8404,5126,048

Notes: Panel A uses a {0,1} any sales as the treatment variable, while Panel B compares counties with sales between $0 and $500,000 or sales >$500,000 with counties with zero sales. Each column represents a separate regression omitting Las Animas. The industry subsectors are Construction (Cons), Manufacturing (Manu), Natural Resources and Mining (NR), and Service. All regressions include month, year, and county fixed effects. Standard errors are clustered at the county level and reported in parentheses.

+ p < 0.1,

* p < 0.05,

** p < 0.01.

Table A20

The effect of recreational dispensary entry and sales on wages – regression analysis

AllConsManuNRService
(1)(2)(3)(4)(5)
Panel A: start of sales
Recreational sale0.003 (0.0100)0.009 (0.0261)0.014 (0.0193)0.031 (0.0315)0.005 (0.0128)
Ln(number of medical patients)0.007 (0.0110)−0.078 (0.0504)−0.035 (0.0285)0.077* (0.0334)0.004 (0.0181)
R20.9380.7770.9350.9110.925
Observations2,0161,5041,2801,5042,016
Panel B: amount of sales
$0 < sales ≤ $500,0000.007 (0.0107)0.010 (0.0253)0.030 (0.0247)0.031 (0.0295)0.010 (0.0140)
Sales > $500,000−0.003 (0.0116)0.009 (0.0334)−0.008 (0.0197)0.031 (0.0376)−0.002 (0.0136)
Ln(number of medical patients)0.006 (0.0108)−0.078 (0.0507)−0.042 (0.0299)0.077** (0.0329)0.002 (0.0180)
R20.9380.7770.9350.9110.925
Observations2,0161,5041,2801,5042,016

Notes: Panel A uses a {0,1} any sales as the treatment variable, while Panel B compares counties with sales between $0 and $500,000 or sales >$500,000 with counties with zero sales. Each column represents a separate regression omitting Las Animas. The industry subsectors are Construction (Cons), Manufacturing (Manu), Natural Resources and Mining (NR), and Service. All regressions include quarter, year, and county fixed effects. Standard errors are clustered at the county level and reported in parentheses.

+ p < 0.1,

* p < 0.05,

** p < 0.01.

Table A21

The effect of recreational dispensary entry and sales on the unemployment rate (Unrate), Ln(labor force), and Ln(unemployed) – regression analysis

UnrateLn(labor force)Ln(unemp)
(1)(2)(3)
Amount of sales
$0 < sales ≤ $250,000−0.705* (0.2983)−0.002 (0.0115)−0.045 (0.0291)
$250,000 < sales ≤ $500,000−0.781* (0.3365)0.015 (0.0132)−0.089** (0.0296)
Sales > $500,000−0.634* (0.2675)−0.000 (0.0145)−0.084** (0.0251)
Ln(number of medical patients)−0.622* (0.2787)−0.004 (0.0130)−0.032 (0.0285)
R20.8820.9990.995
Observations6,1446,1446,144

Notes: Each column represents a separate regression. The omitted sales category is sales = $0. Each column represents a separate regression. All regressions include month, year, and county fixed effects. Standard errors are clustered at the county level and reported in parentheses.

+ p < 0.1,

* p < 0.05,

** p < 0.01.

FE, fixed effect.

Table A22

The effect of recreational dispensary entry and sales on employment – regression analysis

AllConsManuNRService
(1)(2)(3)(4)(5)
Amount of sales
$0 < sales ≤ $250,0000.018 (0.0155)0.048 (0.0504)0.141** (0.0383)−0.044 (0.0648)0.013 (0.0175)
$250,000 < sales ≤ $500,0000.055** (0.0199)0.068 (0.0502)0.166** (0.0498)−0.003 (0.0920)0.053* (0.0234)
Sales > $500,0000.065** (0.0178)0.064 (0.0667)0.110** (0.0377)0.005 (0.0806)0.058** (0.0172)
Ln(number of medical patients)−0.015 (0.0146)−0.171+ (0.0918)−0.187** (0.0600)0.180 (0.1154)−0.019 (0.0171)
R20.9980.9890.9960.9700.998
Observations6,1444,6083,9364,6086,144

Notes: The industry subsectors are Construction (Cons), Manufacturing (Manu), Natural Resources and Mining (NR), and Service. Each column represents a separate regression. The omitted sales category is sales = $0. Each column represents a separate regression. All regressions include month, year, and county fixed effects. Standard errors are clustered at the county level and reported in parentheses.

+ p < 0.1,

* p < 0.05,

** p < 0.01.

FE, fixed effect.

Table A23

The effect of recreational dispensary entry and sales on wages – regression analysis

AllConsManuNRService
(1)(2)(3)(4)(5)
Panel A: amount of sales
$0 < sales ≤ $250,0000.006 (0.0108)0.002 (0.0253)0.015 (0.0232)0.029 (0.0311)0.008 (0.0140)
$250,000 < sales ≤ $500,0000.005 (0.0125)0.021 (0.0301)0.050 (0.0341)0.028 (0.0425)0.012 (0.0158)
Sales > $500,000−0.007 (0.0116)0.009 (0.0334)−0.007 (0.0198)0.022 (0.0379)−0.003 (0.0134)
Ln(number of medical patients)0.002 (0.0107)−0.078 (0.0479)−0.031 (0.0258)0.062+ (0.0324)0.001 (0.0176)
R20.9370.7770.9330.9090.925
Observations2,0481,5361,3121,5362,048

Notes: The industry subsectors are Construction (Cons), Manufacturing (Manu), Natural Resources and Mining (NR), and Service. Each column represents a separate regression. The omitted sales category is sales = $0. Each column represents a separate regression. All regressions include quarter, year, and county fixed effects. Standard errors are clustered at the county level and reported in parentheses.

+ p < 0.1,

* p < 0.05,

** p < 0.01.

FE, fixed effect.

Table A24

The effect of recreational dispensary entry and sales on the unemployment rate (Unrate), Ln(labor force), and Ln(unemployed) – regression analysis

UnrateLn(labor force)Ln(unemp)
(1)(2)(3)
Panel A: start of sales
Recreational sale−0.425 (0.2868)0.007 (0.0136)−0.067** (0.0216)
Ln(number of medical patients)−0.505+ (0.2580)0.001 (0.0107)−0.026 (0.0253)
R20.8760.9990.996
Observations4,5124,5124,512
Panel B: amount of sales
$0 < sales ≤ $500,000−0.230 (0.2756)0.008 (0.0126)−0.042 (0.0279)
Sales > $500,000−0.564+ (0.3091)0.007 (0.0156)−0.084** (0.0252)
Ln(number of medical patients)−0.528** (0.2551)0.001 (0.0107)−0.029 (0.0272)
R20.8770.9990.996
Observations451245124512

Notes: Panel A uses a {0,1} any sales as the treatment variable, while Panel B compares counties with sales between $0 and $500,000 or sales >$500,000 with counties with zero sales. Each column represents a separate regression. All regressions include month, year, and county fixed effects. Standard errors are clustered at the county level and reported in parentheses.

+ p < 0.1,

* p < 0.05,

** p < 0.01.

Table A25

The effect of recreational dispensary entry and sales on employment – regression analysis

AllConsManuNRService
(1)(2)(3)(4)(5)
Panel A: start of sales
Recreational sale0.050* (0.0222)0.101+ (0.0579)0.180** (0.0380)0.078 (0.0876)0.037+ (0.0207)
Ln(number of medical patients)−0.016 (0.0160)−0.123 (0.0969)−0.121* (0.0458)0.299* (0.1385)−0.027 (0.0206)
R20.9980.9900.9980.9690.998
Observations4,5123,2642,5923,2644,512
Panel B: amount of sales
$0 < sales ≤ $500,0000.016 (0.0252)0.078 (0.0494)0.203** (0.0525)0.027 (0.1017)0.002 (0.0262)
Sales > $500,0000.074** (0.0220)0.114+ (0.0648)0.170** (0.0362)0.101 (0.0924)0.061** (0.0199)
Ln(number of medical patients)−0.012 (0.0148)−0.124 (0.0965)−0.122** (0.0441)0.301** (0.1376)−0.023 (0.0191)
R20.9980.9900.9980.9690.998
Observations4,5123,2642,5923,2644,512

Notes: Panel A uses a {0,1} any sales as the treatment variable, while Panel B compares counties with sales between $0 and $500,000 or sales >$500,000 with counties with zero sales. Each column represents a separate regression The industry subsectors are Construction (Cons), Manufacturing (Manu), Natural Resources and Mining (NR), and Service. Fixed effects (FEs) pertain to both the panels. Standard errors are clustered at the county level and reported in parentheses.

+ p < 0.1,

* p < 0.05,

** p < 0.01.

Table A26

The effect of recreational dispensary entry and sales on wages – regression analysis

AllConsManuNRService
(1)(2)(3)(4)(5)
Panel A: start of sales
Recreational sale0.004 (0.0136)−0.009 (0.0301)−0.009 (0.0312)0.042 (0.0413)0.021 (0.0158)
Ln(number of medical patients)0.003 (0.0120)−0.120+ (0.0658)−0.040+ (0.0196)0.079+ (0.0436)0.006 (0.0213)
R20.9270.7700.9500.9070.911
Observations1,5041,0888641,0881,504
Panel B: amount of sales
$0 < sales ≤ $500,0000.012 (0.0153)−0.028 (0.0338)0.012 (0.0595)0.036 (0.0412)0.038** (0.0172)
Sales > $500,000−0.001 (0.0150)0.002 (0.0345)−0.019 (0.0234)0.045 (0.0449)0.009 (0.0163)
Ln(number of medical patients)0.002 (0.0119)−0.121+ (0.0651)−0.041** (0.0196)0.079+ (0.0434)0.004 (0.0206)
R20.9270.7710.9510.9070.911
Observations1,5041,0888641,0881,504

Notes: Panel A uses a {0,1} any sales as the treatment variable, while Panel B compares counties with sales between $0 and $500,000 or sales >$500,000 with counties with zero sales. Each column represents a separate regression The industry subsectors are Construction (Cons), Manufacturing (Manu), Natural Resources and Mining (NR), and Service. All regressions include quarter, year, and county fixed effects. Standard errors are clustered at the county level and reported in parentheses.

+ p < 0.1,

* p < 0.05,

** p < 0.01.

Table A27

The effect of recreational dispensary entry and sales on the unemployment rate (Unrate), Ln(labor force), and Ln(unemployed) – regression analysis

UnrateLn(labor force)Ln(unemp)
(1)(2)(3)
Panel A: start of sales
Recreational sale−1.129** (0.3478)−0.013 (0.0159)−0.079* (0.0334)
Ln(number of medical patients)−0.416 (0.3879)−0.012 (0.0179)−0.003 (0.0397)
R20.8860.9990.994
Observations4,2244,2244,224
Panel B: amount of sales
$0 < sales ≤ $500,000−1.245** (0.3743)−0.006 (0.0150)−0.077** (0.0344)
Sales > $500,000−0.895** (0.3789)−0.028 (0.0225)−0.081 (0.0498)
Ln(number of medical patients)−0.419 (0.3888)−0.011 (0.0175)−0.003 (0.0396)
R20.8860.9990.994
Observations4,2244,2244,224

Notes: Panel A uses a {0,1} any sales as the treatment variable, while Panel B compares counties with sales between $0 and $500,000 or sales >$500,000 with counties with zero sales. Each column represents a separate regression. All regressions include month, year, and county fixed effects. Standard errors are clustered at the county level and reported in parentheses.

+ p < 0.1,

* p < 0.05,

** p < 0.01.

Table A28

The effect of recreational dispensary entry and sales on employment – regression analysis

AllConsManuNRService
(1)(2)(3)(4)(5)
Panel A: start of sales
Recreational sale0.042* (0.0173)0.031 (0.0831)0.113* (0.0520)−0.079 (0.0803)0.041* (0.0188)
Ln(number of medical patients)−0.026 (0.0161)−0.282* (0.1329)−0.144 (0.1006)0.124 (0.1086)−0.025 (0.0223)
R20.9980.9790.9920.9530.997
Observations4,2242,7842,4963,1684,224
Panel B: amount of sales
$0 < sales ≤ $500,0000.042** (0.0163)0.056 (0.0734)0.141** (0.0462)−0.055 (0.0787)0.044** (0.0201)
Sales > $500,0000.043+ (0.0244)−0.005 (0.1138)0.053 (0.0626)−0.126 (0.1007)0.037+ (0.0209)
Ln(number of medical patients)−0.026 (0.0161)−0.283** (0.1280)−0.146 (0.0919)0.124 (0.1059)−0.025 (0.0224)
R20.9980.9790.9930.9530.997
Observations4,2242,7842,4963,1684,224

Notes: Panel A uses a {0,1} any sales as the treatment variable, while Panel B compares counties with sales between $0 and $500,000 or sales >$500,000 with counties with zero sales. Each column represents a separate regression. The industry subsectors are Construction (Cons), Manufacturing (Manu), Natural Resources and Mining (NR), and Service. All regressions include month, year, and county fixed effects. Standard errors are clustered at the county level and reported in parentheses.

+ p < 0.1,

* p < 0.05,

** p < 0.01.

Table A29

The effect of recreational dispensary entry and sales on wages – regression analysis

AllConsManuNRService
(1)(2)(3)(4)(5)
Panel A: start of sales
Recreational sale−0.004 (0.0121)0.022 (0.0352)0.032 (0.0231)0.018 (0.0348)−0.014 (0.0154)
Ln(number of medical patients)−0.001 (0.0133)−0.124+ (0.0708)−0.052 (0.0494)0.077+ (0.0393)0.003 (0.0275)
R20.9190.7240.9060.9020.904
Observations1,4089288321,0561,408
Panel B: amount of sales
$0 < sales ≤ $500,0000.001 (0.0127)0.034 (0.0282)0.039 (0.0245)0.031 (0.0327)−0.011 (0.0166)
Sales > $500,000−0.017 (0.0129)0.002 (0.0569)0.015 (0.0292)−0.012 (0.0436)−0.021 (0.0154)
Ln(number of medical patients)−0.001 (0.0129)−0.124+ (0.0706)−0.052 (0.0491)0.078** (0.0375)0.003 (0.0274)
R20.9200.7240.9060.9020.904
Observations1,4089288321,0561,408

Notes: Panel A uses a {0,1} any sales as the treatment variable, while Panel B compares counties with sales between $0 and $500,000 or sales >$500,000 with counties with zero sales. Each column represents a separate regression The industry subsectors are Construction (Cons), Manufacturing (Manu), Natural Resources and Mining (NR), and Service. All regressions include quarter, year, and county fixed effects. Standard errors are clustered at the county level and reported in parentheses.

+ p < 0.1,

* p < 0.05,

** p < 0.01.

Table A30

The effect of recreational dispensary entry and sales on the unemployment rate (Unrate), Ln(labor force), and Ln(unemployed) – regression analysis

UnrateLn(labor force)Ln(unemp)
(1)(2)(3)
Panel A: Amount of sales
Recreational sale−0.428 (0.2914)−0.005 (0.0158)−0.079** (0.0252)
Recreational sale=1 × < 60km not-sellingmy=1−0.403 (0.2790)0.009 (0.0147)0.017 (0.0283)
Ln(number of medical patients)−0.602* (0.2738)−0.006 (0.0126)−0.026 (0.0255)
R20.8820.9990.995
Observations6,1446,1446,144

Notes: Each column represents a separate regression. All regressions include month, year, and county fixed effects. Standard errors are clustered at the county level and reported in parentheses.

+ p < 0.1,

* p < 0.05,

** p < 0.01.

FE, fixed effect.

Table A31

The effect of recreational dispensary entry and sales on employment – regression analysis

AllConsManuNRService
(1)(2)(3)(4)(5)
Panel A: amount of sales
Recreational sale0.056** (0.0204)0.054 (0.0719)0.091+ (0.0486)0.013 (0.0897)0.040* (0.0184)
Recreational sale=1 × < 60 km not-sellingmy=1−0.019 (0.0204)0.007 (0.0635)0.064 (0.0543)−0.047 (0.0915)−0.002 (0.0189)
Ln(number of medical patients)−0.021 (0.0148)−0.175+ (0.0910)−0.191** (0.0581)0.174 (0.1161)−0.026 (0.0183)
R20.9980.9890.9960.9700.998
Observations6,1444,6083,9364,6086,144

Notes: Each column represents a separate regression. All regressions include month, year, and county fixed effects. Standard errors are clustered at the county level and reported in parentheses. The industry subsectors are Construction (Cons), Manufacturing (Manu), Natural Resources and Mining (NR), and Service.

+ p < 0.1,

* p < 0.05,

** p < 0.01.

FE, fixed effect.

Table A32

The effect of recreational dispensary entry and sales on wages – regression analysis

AllConsManuNRService
(1)(2)(3)(4)(5)
Panel A: amount of sales
Recreational sale0.010 (0.0129)0.037 (0.0285)0.000 (0.0208)0.049 (0.0351)0.009 (0.0125)
Recreational sale=1 × < 60 km not-sellingmy=1−0.015 (0.0115)−0.049+ (0.0245)0.017 (0.0344)−0.041+ (0.0231)−0.008 (0.0114)
Ln(number of medical patients)0.004 (0.0117)−0.082+ (0.0471)−0.036 (0.0287)0.065+ (0.0345)0.002 (0.0177)
R20.9370.7790.9320.9090.925
Observations2,0481,5361,3121,5362,048

Notes: Each column represents a separate regression. All regressions include quarter, year, and county fixed effects. Standard errors are clustered at the county level and reported in parentheses. The industry subsectors are Construction (Cons), Manufacturing (Manu), Natural Resources and Mining (NR), and Service.

+ p < 0.1,

* p < 0.05,

** p < 0.01.

FE, fixed effect.

Table A33

The effect of recreational dispensary entry and sales on the unemployment rate (Unrate), Ln(labor force), and Ln(unemployed) – regression analysis

UnrateLn(labor force)Ln(unemp)
(1)(2)(3)
Panel A: amount of sales
Recreational sale−0.365 (0.3154)−0.004 (0.0195)−0.089** (0.0299)
Recreational sale=1 × < 70km not-sellingmy=1−0.430 (0.2864)0.006 (0.0183)0.028 (0.0311)
Ln(number of medical patients)−0.580* (0.2774)−0.006 (0.0132)−0.028 (0.0258)
R20.8820.9990.995
Observations6,1446,1446,144

Notes: Each column represents a separate regression. All regressions include month, year, and county fixed effects. Standard errors are clustered at the county level and reported in parentheses.

+ p < 0.1,

* p < 0.05,

** p < 0.01.

FE, fixed effect.

Table A34

The effect of recreational dispensary entry and sales on employment – regression analysis

AllConsManuNRService
(1)(2)(3)(4)(5)
Panel A: amount of sales
Recreational sale0.054* (0.0223)0.065 (0.0754)0.094 (0.0700)−0.007 (0.1125)0.035+ (0.0205)
Recreational sale=1 × < 70 km not-sellingmy=1−0.014 (0.0221)−0.010 (0.0682)0.048 (0.0726)−0.011 (0.1112)0.005 (0.0198)
Ln(number of medical patients)−0.021 (0.0153)−0.173+ (0.0946)−0.195** (0.0648)0.173 (0.1135)−0.027 (0.0184)
R20.9980.9890.9960.9700.998
Observations6,1444,6083,9364,6086,144

Notes: Each column represents a separate regression. All regressions include month, year, and county fixed effects. Standard errors are clustered at the county level and reported in parentheses. The industry subsectors are Construction (Cons), Manufacturing (Manu), Natural Resources and Mining (NR), and Service.

+ p < 0.1,

* p < 0.05,

** p < 0.01.

FE, fixed effect.

Table A35

The effect of recreational dispensary entry and sales on wages – regression analysis

AllConsManuNRService
(1)(2)(3)(4)(5)
Panel A: amount of sales
Recreational sale0.003 (0.0148)0.036 (0.0288)0.007 (0.0214)0.043 (0.0368)0.006 (0.0135)
Recreational sale=1 × < 70 km not-sellingmy=1−0.004 (0.0135)−0.039 (0.0250)0.004 (0.0341)−0.024 (0.0258)−0.003 (0.0112)
Ln(number of medical patients)0.004 (0.0113)−0.077 (0.0476)−0.036 (0.0306)0.067+ (0.0345)0.002 (0.0178)
R20.9370.7780.9320.9090.925
Observations2,0481,5361,3121,5362,048

Notes: Each column represents a separate regression. All regressions include quarter, year, and county fixed effects. Standard errors are clustered at the county level and reported in parentheses. The industry subsectors are Construction (Cons), Manufacturing (Manu), Natural Resources and Mining (NR), and Service.

+ p < 0.1,

* p < 0.05,

** p < 0.01.

FE, fixed effect.

Table A36

Multiple inference – adjusted p–value

Dependent variablep–valueSharpened q–values
Panel A: monthly
Unemployment rate0.009**0.024*
Ln(labor force)0.9151
Ln(unemployed)0.001**0.007**
Ln(all industry employees)0.005**0.019*
Ln(construction employees)0.2920.638
Ln(manufacturing sector employees)0.001**0.007**
Ln(natural resource and mining employees)0.8231
Ln(service–providing employees)0.015*0.031*
Panel B: quarterly
Ln(All industry wages)0.9521
Ln(Construction wages)0.7611
Ln(Manufacturing wages)0.5941
Ln(Natural resource and mining wages)0.4240.941
Ln(Service–providing wages)0.741

Panel A: p–values are from Tables 3–4.

Panel B: p–values are from Table 5.

+ p < 0.1,

* p < 0.05,

** p < 0.01.

Table A37

The effect of recreational dispensary entry – ATT from GSCM

UnrateLn(labor force)Ln(unemp)
(1)(2)(3)
Start of sales
Recreational sale−0.566* (0.4191)0.024 (0.0334)−0.006 (0.0332)
Ln(number of medical patients)0.018 (0.2547)−0.010* (0.0084)0.043 (0.0713)
Observations6,1446,1446,144

Notes: Each column represents a separate regression with the synthetic control method. The standard errors are bootstrapped, and in the mean squared prediction error (MSPE), there is an optimal number of unobserved factors (r*) selected from the model.

+ p < 0.1,

* p < 0.05,

** p < 0.01.

ATT, average treatment effect on the treated; GSCM, generalized synthetic control method; Unrate, unemployment rate.

Table A38

The effect of recreational dispensary entry on employment – ATT from GSCM

Ln(All)Ln(Cons)Ln(Manu)Ln(NR)Ln(Service)
(1)(2)(3)(4)(5)
Start of sales
Recreational sale−0.035 (0.0203)−0.120 (0.0804)0.137** (0.0562)−0.298 (0.1314)0.042 (0.0587)
Ln(number of medical patients)0.001 (0.0148)−0.502 (0.2495)0.097 (0.1150)−0.010 (0.0894)0.007 (0.0229)
Observations6,1444,6083,9364,6086,144

Notes: Each column represents a separate regression with the synthetic control method. The industry subsectors are Construction (Cons), Manufacturing (Manu), Natural Resource and Mining (NR), and Service. The standard errors are bootstrapped, and in the mean squared prediction error (MSPE), there is an optimal number of unobserved factors (r*) selected from the model.

+ p < 0.1,

* p < 0.05,

** p < 0.01.

ATT, average treatment effect on the treated; GSCM, generalized synthetic control method.

Table A39

The effect of recreational dispensary entry on wage – ATT from GSCM

Ln(All)Ln(Cons)Ln(Manu)Ln(NR)Ln(Service)
(1)(2)(3)(4)(5)
Start of sales
Recreational sale−0.003 (0.0178)0.007 (0.0531)0.011 (0.0321)0.036 (0.0424)−0.004 (0.0152)
Ln(number of medical patients)−0.017 (0.0161)0.006 (0.1258)−0.027 (0.0474)0.114** (0.0698)−0.010 (0.0438)
Observations61444608393646086144

Notes: Each column represents a separate regression with the synthetic control method. The industry subsectors are Construction (Cons), Manufacturing (Manu), Natural Resource and Mining (NR), and Service. The standard errors are bootstrapped, and in the mean squared prediction error (MSPE), there is an optimal number of unobserved factors (r*) selected from the model.

+ p < 0.1,

* p < 0.05,

** p < 0.01.

ATT, average treatment effect on the treated; GSCM, generalized synthetic control method.

Table A40

Event study estimates post-dispensary entry periods

UnrateLn(labor force)Ln(unemp)
(1)(2)(3)
event0:treat0.382*0.041*0.113**
event1:treat0.320+0.043*0.093**
event2:treat0.4400.041*0.112*
event3:treat0.1790.031+0.064+
event4:treat0.192−0.0070.025
event5:treat−0.224−0.003−0.062**
event6:treat−0.283+0.001−0.060**
event7:treat−0.3000.009−0.066*
event8:treat−0.3730.013−0.087**
event9:treat−0.486*0.015−0.103**
event10:treat−0.3930.016−0.085*
event11:treat−0.564*0.036*−0.105**
event12:treat−0.0260.040+0.042
event13:treat−0.1190.038+0.012
event14:treat0.1330.0300.039
event15:treat0.0580.0230.014
event16:treat0.227−0.0140.010
event17:treat−0.092−0.014−0.081*
event18:treat−0.126−0.011−0.099**
event19:treat−0.246−0.004−0.137**
event20:treat−0.163−0.004−0.117**
event21:treat−0.302−0.003−0.132**
event22:treat−0.266−0.002−0.119**
event23:treat−0.4110.024−0.140**
event24:treat−0.2630.021−0.030
event25:treat−0.2490.023−0.028
event26:treat0.0980.0190.036
event27:treat0.1430.0080.029
event28:treat0.195−0.026−0.000
event29:treat0.051−0.017−0.035
event30:treat0.040−0.010−0.046
event31:treat−0.024−0.002−0.069+
event32:treat0.050−0.003−0.058
event33:treat−0.191−0.003−0.109**
event34:treat−0.341−0.003−0.157**
event35:treat−0.524+0.021−0.200**
event36:treat−0.4120.015−0.048
event37:treat−0.499+0.019−0.079**
event38:treat−0.3090.015−0.091
event39:treat−0.2660.011−0.096*
event40:treat−0.023−0.024−0.052
event>40:treat0.037−0.002−0.020
Linear combination
  Combo coefficient−0.1170.010−0.046*
  Combo SE0.22570.01390.0212
Weighted linear combination
  Combo coefficient−0.0730.006−0.038+
  Combo SE0.24330.01480.0218
R20.8800.9990.996
Observations4,5124,5124,512

Notes: Table reports the τk coefficients for k ≥0 from the event study regressions. Each column represents a separate regression. Below the table, we report the average and weighted average of the τk coefficients from the event study, including only the τk coefficients for periods post-dispensary entry. All regressions include month, year, and county fixed effects. Standard errors are clustered at the county level and reported in parentheses.

+ p < 0.1,

* p < 0.05,

** p < 0.01.

FE, fixed effect; SE, standard error; Unrate, unemployment rate.

DOI: https://doi.org/10.2478/izajole-2021-0005 | Journal eISSN: 2193-8997
Journal RSS Feed
Language: English
Accepted on: Apr 26, 2021
Published on: Nov 25, 2021
Published by: Sciendo
In partnership with: Paradigm Publishing Services
Keywords:
cannabis,
marijuana,
labor demand,
manufacturing
JEL:
J21,
R11,
J3,
K00
Related subjects:
Business and economics,
Political economics,
Microeconomics,
Macroeconomics,
Economic policy,
Mathematics and statistics for economists,
Econometrics

© 2021 Avinandan Chakraborty, Jacqueline Doremus, Sarah Stith, published by Sciendo
This work is licensed under the Creative Commons Attribution 4.0 License.

Volume 10 (2021): Issue 1 (January 2021)
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