In this Supplemental Digital Appendix, we provide a detailed explanation of the methodology used to estimate the lost tax revenue caused by opioid-induced reductions in labor supply. We estimate two primary sets of revenue reductions: (1) tax revenues lost due to the effect of opioid misuse on employment; and (2) tax revenue lost due to premature mortality from opioid misuse.
In each case, we estimate the lost income tax and sales tax revenue for each state and the federal government.
Tax Revenue Losses due Opioid Misuse and Changes in Employment
Reduced labor force participation due to opioid misuse
Our analysis uses existing estimates of opioid-induced reductions in prime-age labor force participation (LFP) from Krueger (2017),1 which uses county-level prescribing and employment data to estimate the effect of opioid prescribing on LFP while controlling for a variety of county-level demographic and employment-related factors, as well as a version using county fixed effects. Krueger1 estimates an opioid-attributable decline of 1.1 percentage points for males and 1.4 percentage points for females over the years 2000-2015 in response to a one- unit increase in the natural log of per capita opioid prescriptions, measured in morphine milligram equivalent (MME) units. Men were 53% of the civilian labor force in 2000 and women were 47% (https://www.census.gov/prod/2003pubs/c2kbr-18.pdf). Given the stability in these fractions, the overall effect of opioids on the combined prime-age workforce is -1.24 percentage points, multiplied by the increase in the natural log of per capita opioid scripts over this period. Per capita opioid prescriptions increased by a factor of 3.5 over this period,1 which corresponds to an increase of 1.25 in natural log units. Thus, the national decline in prime-age labor force participation during this period, attributable to opioids, would be -1.55 percentage
points. Relative to a national prime-age labor force of 99,973,000 in 2000, this represents a total of 1,549,582 missing workers nationally due to opioids.
The civilian labor force includes both those who are employed and those who are unemployed but looking for work; individuals who are neither working nor seeking work are deemed to be out of the labor force. Thus, opioid misuse could affect tax receipts either by increasing the number of workers who become unemployed or the number who exit the labor force. However, a recent study found that the effect of opioids was largely on labor force exits rather than unemployment (Harris et al., 2018).2 We therefore assume that the effect of opioid misuse on employment operates entirely through labor force exits. Focusing on the civilian non- institutionalized population, the change in employment attributable to opioids is equal to the change in labor force participation multiplied by the size of the labor force in the base year.
We use Krueger’s estimated opioid-induced decline in the prime-age labor force to determine the number of prime-age workers missing from each state’s labor force in each year between 2000 and 2015, and then extrapolate to 2016 to take advantage of the most recent data available. For each state i and each year t, we calculate the number of missing workers as follows:
MWit=t
(
ΔL15)
Liwhere ΔL is the weighted average of the male and female opioid-attributable declines in the prime-age labor force from 2000-2016 as estimated by Krueger1 (i.e. -1.55 percentage points);
Li is the size of the prime-age labor force in state i in the year 2000 (our base year); and t indexes years, running from 1 to 16. In the absence of information on the timing or duration of labor market exits, we apportion the overall decline in the labor force across years using linear
interpolation. This means the decline in the number of workers in each year is the total
percentage point change ΔL divided by 16, multiplied by the size of the state labor force in the base year. The linear trend assumption requires that the number of missing workers grow by this increment in each year. For example, in the second year (t = 2) the total number of missing
workers would be the number missing in the first year,
(
ΔL15)
Li , plus the number missing inthe second year,
(
ΔL15)
Li , and continuing for all subsequent years.Our estimate of the reduction in labor force participation due to opioids is likely to be conservative for two reasons. First, it applies only to prime-age (25-54) workers because that is the group used in the Krueger estimates. Second, the labor force reductions estimated by Krueger apply to people moving out of the labor force entirely and do not capture unemployment spells.
However, as discussed above, a recent study by Harris et al. (2018)2 finds a comparatively small and not statistically significant effect of opioid prescribing on unemployment rates. Instead they find the effect largely operates through changes in the labor force participation rate.
To estimate the forgone taxes in each year in a given state, we multiply the number of missing workers in that year and state, MWit , by our estimate of the income or sales tax paid by the median opioid-using worker in that state (discussed in more detail below). Summing the forgone taxes across years and inflating into current dollars yields the total inflation-adjusted loss in revenue for each state as well as estimates for the federal government.
Calculation of Income Tax Losses
We implement a multi-step approach to estimate how the decline in employment due to opioid misuse, based on the Krueger1 estimates, translates into a loss in income tax revenue for both state and federal governments. To do this we use annual National Survey on Drug Use and Health (NSDUH) data to estimate wages and family structure for the sample who report
misusing opioids and then enter this into the National Bureau of Economic Research’s (NBER) TAXSIM model3 to calculate the effect on state and federal taxes liabilities that would have been paid by workers who reported misusing opioids.
For each year between 2000 and 2016, we use NSDUH data to estimate median wages, marital status, and number of dependent children, as the key inputs for the TAXSIM model. We impose several sample restrictions in the NSDUH data. First, consistent with the analysis by Krueger,1 we restrict attention to prime-age workers. Given the limited age brackets in the NSDUH, we use ages 26 to 64. Next, to account for the fact that opioid misusers might have had poorer labor market outcomes than a random person, we restrict to individuals who report ever misusing one of 19 opioid medications. We use the same list of medications described in a 2015 study by Cerda et al., 4 omitting Fioricet or Fiorinal, which are barbiturates rather than opioids. 5 As a sensitivity analysis, we further restrict to those who report misusing an opioid within the past 12 months. One limitation of the NSDUH is that respondents’ income is reported
categorically: “<$10,000”, “$10,000 - $19,999”, “$20,000 - $29,999”, “$30,000 - $39,999”,
“$40,000 - 49,000”, “$50,000 - $74,999”, “$75,000 or more”. We use an interpolated median approach to identify the median income.
In summary, forgone state income taxes are for the median opioid-using worker based on the state income tax rules in effect in each year. Plugging this representative worker into the NBER's TAXSIM calculator3 for each state and each year provides an estimate of what those misusing
opioids would have paid in state income taxes in each year in each state had they remained in the labor force. Because those estimates are in contemporaneous dollars, we inflate the amounts into 2017 dollars using the seasonally adjusted nonfarm wage index from the Bureau of Labor
Statistics.6
Calculation of Sale Tax Losses
We calculate forgone sales tax revenue in an analogous fashion as for income taxes, albeit with two additional assumptions. We again begin with the number of missing workers in each state and year, MWit , and the same median income and household structure obtained from the NSDUH. Based on findings from existing research, we assume that individual
consumer spending falls by about 20 percent following a labor market exit.7,8 In addition, states vary not only in their sales tax rates, but also in the fraction of goods that are subject to tax. We rely on a study by the Tax Foundation, which finds that in the median state, 23 percent of personal income is spent on goods and services subject to sales tax.9 We use sales tax rates for each state for each year, obtained from the Urban-Brookings Tax Policy Center.10 The sales tax losses are calculated by multiplying the number of missing workers in that year and state (i.e.
MWit ) by the sales tax liability.
Adjusting for inflation and aggregation over time:
After obtaining the above estimates for each year, we inflate the each year’s revenue loss into 2017 dollars using the seasonally adjusted nonfarm wage index from the Bureau of Labor Statistics.6 Finally, we sum the inflation-adjusted numbers across year to get the total income tax losses for the entire 2000-2016 period.
Tax Revenue Losses due to Premature Mortality due to Opioid Misuse
The study by Krueger uses Current Population Survey (CPS) data, which is limited to the civilian non-institutionalized population, 11 and hence necessarily excludes those who have died due to opioid overdoses. Consequently, we separately estimate tax revenue losses due to opioid- related mortality.
Total deaths due to opioid misuse
We first obtain data on the number of opioid-related deaths from the Centers for Disease Control and Prevention’s (CDC) WONDER database using their Multiple Cause of Death file.12 To identify opioid-related deaths we use the approach described by the Kaiser Family
Foundation 13 in their estimates of annual state-specific opioid mortality rates. We include all deaths in the prime-age working population of those aged 25 to 54, by state and year from 2000 to 2016, identified by the following underlying causes of death: unintentional poisoning,
intentional poisoning or poisoning by and exposure to an opioid (based on ICD-10 codes of X40- 44, X60-64, X85, Y10-14), together with an indication that the cause of death was related to natural and semisynthetic opioids, methadone, or synthetic opioids other than methadone.13 The mortality numbers are suppressed in some states for certain years. In such cases, we evenly apportion the unaccounted-for deaths (i.e. the overall number of opioid deaths in the US less the ones available in each state) to each of the states with suppressed data in a given year. This is less than 1% of total opioid-related deaths in most years. Our estimate of opioid-related deaths is likely conservative because opioid-related deaths may be significantly underreported on death records.14
Calculation of Income Tax Losses
Similar to the approach for estimating lost income tax revenue from an increase in labor force exits, we input estimates from NSDUH data into TAXSIM to estimate lost income tax revenue and multiply it by the number of opioid-attributable deaths. The one difference, though, is that we further multiply this estimate by the counterfactual employment rate. Specifically, we use NSDUH data with similar restrictions (i.e. prime age adults who report either ever misusing opioids or misusing opioids in the past 12 months) to create two sets of employment rate
estimates. We do this to account for the fact that not everyone who dies prematurely would have been employed even absent opioid misuse.
Calculation of Sales Tax Losses
Similar to the calculation of lost sales tax revenue due to opioid-related labor force exits, we obtain estimates of sales tax losses due to opioid-related premature mortality by multiplying the following: cumulative deaths by: (1) the counterfactual wage rate; (2) the counterfactual employment rate; (3) the percent of income spent on goods subject to sales tax; and (4) the state sales tax rate.
Methodological Limitations
Our analysis of opioid misuse and LFP is subject to several caveats. The first is that Krueger1 provides an estimate of opioid misuse on LFP, but to estimate the impact on costs we need to make an assumption about how this translates into changes in employment. While we use results from an unpublished study by Harris et al.2 (2017) to set the impact of opioids on
unemployment to zero, and thereby equate the reduction in LFP with the reduction in overall employment, we must acknowledge that this issue remains unsettled, as does the larger issue of
how opioids affect total employment and its component parts, including the various transitions that individuals make across differing levels of labor market activity.
A second limitation, as Krueger carefully notes, is that controlling for individual-level demographic characteristics and a small number of county level economic factors (or county fixed effects) may not be sufficient to render the remaining geographic variation in prescribing behavior free of all independent determinants of (un)employment.
A third limitation is that Krueger estimates a change in LFP over a 15 year period, which requires us to make an assumption about how the 15-year decline in the labor force is
apportioned over time and, consequently, about how many workers are absent from the labor market in a given year. While we have assumed the most straightforward approach of using a linear decline over the study period, no data exists of the exact rate. In addition, to take advantage of the most recent data we linearly extrapolate this change to 2016 as well.
A fourth limitation is that for the premature mortality estimates, we only include the working age population and acknowledge that opioid deaths may be under-reported,14 which may imply a conservative estimate of the number of opioid-related deaths. In addition, we include three other limitations. First, by using the number of deaths for prime-age workers in any given year we potentially have censoring issues on either end of the age distribution. For the early years (e.g. 2000 and 2001), some of these individuals may have exited the labor force towards the end of our timeframe (e.g. by 2015 or 2016). However, counterbalancing this limitation, individuals who died before age 25 are excluded even though they would have potentially entered the labor market in later years. Unfortunately, the CDC Wonder data only allows
estimates by 10-year age groups, limiting the ability to systematically include or exclude specific one-year age groups. The second, related, limitation is that we do not explicitly account for the
fact that these individuals might have died from other competing causes. However, the one-year mortality rate for a 45-year old is 0.025%,15 so it is unlikely to have a large effect on our
estimates. To the extent that competing risk mortality might have been higher for opioid misusers (even absent opioids) this limitation could be more important.
The final, and potentially most important limitation, is that we are not actually able to estimate the counterfactual labor market outcomes for those who die from an opioid overdose.
While we try to approximate it using the NSDUH data for those who report ever misusing an opioid (or misuse within the past 12 months), this likely includes many individuals who never ultimately die from an opioid overdose. To the extent that those who do die from an overdose have lower wages or are more likely to exit the labor force (e.g. due to disability) then we may be overestimating the mortality costs. However, without additional data we are unable to estimate the extent of this issue.
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