What the employer cost calculator is built on

Every number behind the calculator, with its source and how strongly we rate it. 160 data points, 95 sources.

What the confidence marks mean

  • Highpeer-reviewed publication, directly for this value.
  • Mediumpeer-reviewed source, but a different population or some conversion.
  • Lowindirect estimate, indicative only.
  • Not verifiedthe source is real and linked, but we have not yet checked the claim against it line by line.

Baseline values

These apply across every category, and most of the arithmetic rests on them.

Data pointValueConfidenceSource
Working minutes per shiftStandard 8-hour shift in minutes.480High[29]
Working days per yearHungarian working calendar baseline excluding weekends and public holidays. Used as the EU-wide default; differs from the prior 230-day value used during placeholder mode.220Medium[29]
Base break minutes per shiftBaseline paid break minutes per 8-hour shift. Context for the lost-time values, which are already net of this baseline: the engine does not multiply by it. NOTE: the nicotine lost-time note references a 25-minute baseline; reconcile with this 30-minute value in the research workbook.30Medium[26]
Triple overlap correctionSingle multiplicative factor applied after pair-wise comorbidity correction to account for higher-order (triple/quadruple) overlaps. Max triple ≈ 0.91% of population; sum across 35 triplets ≈ 3–5%.1.04Low[28]

Values per category

Seven habit categories, each with its prevalence and its four cost types. Where blue-collar and white-collar values differ, both are shown, because the engine applies them separately.

Nicotine

Data pointValueConfidenceSource
Prevalence, EUDaily smokers among persons aged 15 and over, EU-27 (Eurostat 2021).18.4%High[34]
Prevalence, HungaryWHO Hungary country profile 2023: daily smoker prevalence.22.1%High[92]
Lost working time, minutes/day (general)Average smoker takes ~2 additional breaks/day above the 25-minute paid baseline, ~12.5 min each.25High[4]
Lost working time, minutes/day (blue collar)Upward adjustment for manual labour settings: smoke breaks more tolerated, outdoor access easier.27.5Medium[4]
Lost working time, minutes/day (white collar)Downward adjustment for office settings: stricter break policies and longer indoor-to-designated-area travel.22.5Medium[4]
Extra sick days/year (general)Smoking figure used as conservative upper bound across all nicotine products. Vapers show 34% higher absenteeism, broadly consistent.2.89High[84]
Reduced performance, share (general)6% productivity loss vs. never-smokers (WPAI 24% vs 18%). Captures withdrawal-driven concentration loss between doses + smoking-specific physiology.6%High[3]
Reduced performance, share (blue collar)Physical/procedural work less sensitive to nicotine withdrawal between doses.5%Medium[3]
Reduced performance, share (white collar)Focus-dependent work suffers more from concentration loss between cigarettes.7%Medium[3]
Annual turnover rate (general)Danish cohort N=87,830. Current smokers 1.31× higher work-to-unemployment transition; heavy smokers 1.52×. 3% applied as the attributable elevation above EU baseline.3%Medium[18]

Alcohol

Data pointValueConfidenceSource
Prevalence, EUNumber of people in the EU with alcohol use disorder, per WHO Europe 2024 fact sheet.10.7%High[90]
Prevalence, HungaryWHO 2018 Global Status Report on Alcohol: Hungary country profile.11.8%High[91]
Extra sick days/year (general)NSDUH N=110,701 full-time US workers. Mild AUD 17.7 days, moderate AUD 23.6 vs. 13 for non-AUD. Attributable ≈ 7 extra days/year.7High[70]
Reduced performance, share (general)≈30 hours/year of presenteeism (hangover + chronic effects of harmful drinking). Derived from 8.3 hangover days × 24.9% productivity loss × 8h shift.1.7%Medium[86], [52]
Annual turnover rate (general)French CONSTANCES cohort N=18,879. Dangerous alcohol use OR 1.46 for job loss; problematic up to OR 1.92. Captures only work-to-unemployment moves.3%Medium[2]

Smartphone

Data pointValueConfidenceSource
Prevalence, EUSmartphone addiction, European region subgroup of Meng et al. (18.51%; 95% CI 11.44–28.54; 17 studies, n=19,934). Not the global pool (26.99%) and not the social media construct (17.42%). Screening-scale based and scale-dependent: SAS-SV gives 41.43%, CERM-10 gives 5.21%.18.5%Medium[59]
Prevalence, HungaryNo HU cohort; Hungary sits inside that WHO region, so the European figure applies directly. Same source and caveats as the EU row.18.5%Medium[59]
Lost working time, minutes/day (general)Two independent methods, 0.2 min apart. Dora et al. logged 92s per 20 min of work, i.e. 36.8 min/day, with the lunch break excluded by design (n=83, selected for heavy private phone use at work). Duke & Montag's 1.76 h/week lost on a 23.08 h week is 36.6 min/day at full time. Jeong's logs give 59.4 but include paid breaks. Neither anchor isolates problematic users, so applied to the screened 18.5% this is a floor.36.8Medium[23], [25], [49]
Lost working time, minutes/day (blue collar)A midpoint, not a measurement: no study measures non-work phone minutes for blue collar workers. Whelan & Turel found use stayed "very low" at a European pharma plant after a 20-year phone ban was lifted (n=82); McBride found 78.1% of 825 nurses use a personal phone at work despite policies. This collar spans both, so the value is the midpoint of ~0 and 36.8. Probably an under-count.18.4Low[88], [57], [23]
Lost working time, minutes/day (white collar)The measured value applied unchanged, since every population behind this category is already white collar. No uplift for the affected group: volume and problematic use correlate only moderately (Teo et al., r = 0.41), so charging them above the measured average would be an invented adjustment.36.8Medium[23], [80]
Extra sick days/year (general)No peer-reviewed study isolates absenteeism for compulsive smartphone use. Indirect pathways exist (PSU → poor sleep → fatigue) but are unquantified. 0 days applied to avoid invented estimates.0High[27]
Reduced performance, share (general)Duke & Montag report a correlation (r = 0.436 with self-rated productivity impact, 0.372 with hours lost), never a share of payroll: the 2% is our conversion, hence low confidence. Self-reported, cross-sectional, 262 self-selected German volunteers; the authors note participants underestimate their own use.2%Low[25]
Reduced performance, share (blue collar)Zero applied: blue-collar work is less attention-fragile, and structural restrictions limit presenteeism beyond the lost-time component. Same caveat as the general row, plus the source studied no manual workers.0%Low[25]
Reduced performance, share (white collar)Focus-dependent desk work is the most sensitive to attention fragmentation beyond the explicit time-lost component. Same caveat as the general row: the 2.5% is our conversion of a correlation, not a figure the source reports.2.5%Low[25]
Annual turnover rate (general)No cohort study measures actual quit rates attributable to PSU. Turnover intention surveys exist but are not the same as observed turnover. 0% applied.0%High[27]

The figures above count only the time spent on the phone. They do not include the time it takes to get properly back into work after an interruption. One study puts that at an average of 8 minutes each time, by the respondents' own estimate. Since we found no figure for how often this happens in a day, we left the item out of the calculation. Because of that, the real cost may be considerably higher than the calculator shows. Source: Lim, V. K. G., & Chen, D. J. Q. (2012). Cyberloafing at the workplace: Gain or drain on work? Behaviour & Information Technology, 31(4), 343-353. https://doi.org/10.1080/01449290903353054

Pornography

Data pointValueConfidenceSource
Prevalence, EUInternational Sex Survey 2024 (N≈80k across 42 countries).3.2%High[7]
Prevalence, HungaryHungary subset of the International Sex Survey 2024.3.6%High[7]
Lost working time, minutes/day (general)Nielsen workplace traffic estimates + Ofcom online usage figures + Mecham workplace behaviour studies.10Medium[64], [66], [58]
Lost working time, minutes/day (blue collar)Blue-collar shop-floor environments do not realistically permit at-work porn use; conservative 0.0High[27]
Lost working time, minutes/day (white collar)White-collar private-office / device-on-desk scenario.15Medium[64], [58]
Extra sick days/year (general)No peer-reviewed cohort study isolates the absenteeism effect of PPU at the workplace level. 0 days applied.0High[27]
Reduced performance, share (general)Indirect, derived from documented PPU → anxiety/depression pathways. Limited workplace-specific data.2%Low[13], [43]
Reduced performance, share (blue collar)1.5%Low[13]
Reduced performance, share (white collar)2.5%Low[13]
Annual turnover rate (general)No cohort study isolates PPU-attributable turnover. 0% applied.0%High[27]

Gambling

Data pointValueConfidenceSource
Prevalence, EUSystematic review/meta-analysis of worldwide problem-gambling prevalence by age cohort (Dellosa & Browne 2024).1.3%High[21]
Prevalence, HungaryHungarian validation of the SOGS (SOGS-HU). Best available HU-specific figure pending newer national data.1.4%High[44]
Lost working time, minutes/day (general)NRC 1999 baseline + Rafi et al. 2023 systematic review of gambling/productivity literature.12Medium[63], [74]
Lost working time, minutes/day (blue collar)Lower opportunity than white-collar; conservative downshift from the general 12 min.8Low[63], [14]
Lost working time, minutes/day (white collar)Same as the general baseline: desk access enables in-shift gambling app/web use.12Medium[63], [74]
Extra sick days/year (general)Latvala et al. on stress/sleep-related absence; Binde on debt-stress-related impacts.4Low[50], [5]
Reduced performance, share (general)Preoccupation + debt-stress impact on concentration. Sparse direct evidence; conservative estimate.3%Low[5], [50]
Reduced performance, share (blue collar)3%Low[5]
Reduced performance, share (white collar)3.5%Low[5]
Annual turnover rate (general)NRC + Latvala: limited direct turnover data; conservative estimate above the alcohol turnover floor.4%Low[63], [50]

Medications

Data pointValueConfidenceSource
Prevalence, EUFive-country EU survey. 6% combined past-year prevalence figure derived with overlap correction. Buyers in regulated industries (healthcare, transportation) should note occupation-specific rates may be substantially higher.6%High[65]
Prevalence, HungaryEU proxy applied: no recent HU-specific cohort study on non-medical prescription drug use.6%Medium[65]
Extra sick days/year (general)Saha 2024 MEPS analysis: attributable absenteeism for prescription misuse subgroup.4Medium[75]
Extra sick days/year (blue collar)5Medium[75]
Extra sick days/year (white collar)3Medium[75]
Reduced performance, share (general)MEPS analysis (Saha 2024) + CONSTANCES benzodiazepine cohort (Hoven 2022).6%Medium[75], [47]
Reduced performance, share (blue collar)Sedating-drug misuse hits physical/safety-critical roles harder.7%Medium[75]
Reduced performance, share (white collar)Lower presenteeism impact for white-collar roles relative to safety-critical tasks.5%Medium[75]
Annual turnover rate (general)Maclean 2023 opioid-crisis economic review + Burke 2022 RX labour-market study.4%Medium[56], [9]
Annual turnover rate (blue collar)5%Medium[56]
Annual turnover rate (white collar)3%Medium[56]

Drugs

Data pointValueConfidenceSource
Prevalence, EUEuropean Drug Report 2025: past-year illicit drug use prevalence in the EU.8.4%High[33]
Prevalence, HungaryOLAAP 2015 representative HU adult survey on addictive behaviours (Paksi et al. 2018).2.3%High[68]
Lost working time, minutes/day (general)Frone 2006: best available US distribution-of-use study; HU/EU primary data is thin.15Low[38]
Lost working time, minutes/day (blue collar)20Low[38]
Lost working time, minutes/day (white collar)10Low[38]
Extra sick days/year (general)AJPM 2024 illicit drug × absenteeism US study (Yang) cross-checked against CONSTANCES.6High[94], [2]
Extra sick days/year (blue collar)7Medium[94]
Extra sick days/year (white collar)5Medium[94]
Reduced performance, share (general)Tran 2017 community-based opioid productivity survey + Frone 2006 baseline.8%Medium[82], [38]
Reduced performance, share (blue collar)9%Medium[82]
Reduced performance, share (white collar)7%Medium[82]
Annual turnover rate (general)CONSTANCES cohort: illicit drug use OR-equivalent for job loss.6%Medium[2]
Annual turnover rate (blue collar)7%Medium[2]
Annual turnover rate (white collar)5%Medium[2]

Readiness to change

The recoverable figure comes from how many people are ready to change. The three stages are the transtheoretical model split, per category.

Data pointValueConfidenceSource
Nicotine: not considering yetDaily nicotine users in pre-contemplation (no plan to quit in next 6 months).65%High[31], [69]
Nicotine: considering or preparingConsidering quitting + actively preparing (intent within 6 months / 30 days).20%High[31], [69]
Nicotine: actively changingActive quit attempt currently in progress.15%High[31], [69]
Alcohol: not considering yetHazardous/harmful drinkers in pre-contemplation.69%High[45], [76]
Alcohol: considering or preparingConsidering reducing + actively preparing.19%Medium[45], [76]
Alcohol: actively changingActive reduction attempt currently in progress.12%Medium[45], [76]
Smartphone: not considering yetCompulsive smartphone users in pre-contemplation. Lowest pre-contemplation share of all 7 categories.21%Medium[48], [22], [51]
Smartphone: considering or preparingConsidering reducing + actively preparing.53%High[48], [22]
Smartphone: actively changingActive reduction attempt currently in progress.26%High[48], [51]
Pornography: not considering yetPPU users in pre-contemplation (no plan to reduce).70%Medium[7]
Pornography: considering or preparingConsidering reducing + actively preparing.23%High[7]
Pornography: actively changingActive reduction attempt currently in progress.7%High[7]
Gambling: not considering yetProblem gamblers in pre-contemplation.40%Medium[46]
Gambling: considering or preparingConsidering stopping + actively preparing.40%Medium[46]
Gambling: actively changingActive stop/reduction attempt currently in progress.20%Medium[46]
Medications: not considering yetLong-term prescription users in pre-contemplation about deprescribing.77%Medium[72], [15]
Medications: considering or preparingConsidering deprescribing + preparing.13%Medium[72], [15]
Medications: actively changingActive deprescribing attempt currently in progress.10%Medium[72], [15]
Drugs: not considering yetIllicit drug users in pre-contemplation; highest pre-contemplation share of all 7 categories.80%High[87], [32]
Drugs: considering or preparingConsidering stopping + preparing.15%Low[87]
Drugs: actively changingActive stop attempt currently in progress.5%Medium[87]

Cost of replacing a leaver, by industry

Replacing one departing employee costs this many months of salary. It varies by industry and by role, so all 22 values are listed.

Data pointValueConfidenceSource
Construction & Trades (blue collar)Skilled craft workers (welders, electricians, masons): certified trade qualifications, site safety inductions, and tool provisioning. Conservative end of the SHRM 6–9 month range, adjusted down for EU labour market structure.5×Medium[77]
Construction & Trades (white collar)Site engineers, project managers, superintendents: non-transferable project context. CFMA 2024 confirms construction-specific scarcity. Absorbs executive search fees and FIDIC/NEC/JCT liquidated-damages exposure.9×Medium[89], [19]
Finance & Insurance (blue collar)Structured back-office roles. Linckh et al. 2024 linked-employer-employee data: total hiring + adaptation ≈ 22 weeks of pay (~5.1×); 84% post-match disruption costs.5×High[54]
Finance & Insurance (white collar)Oxford Economics 2014 isolated UK accounting sector: total cost per departure ≈ £40k, driven by 21.3-week output deficit. Equals 13× monthly salary.13×High[67]
Healthcare & Social Care (blue collar)Umbrella review estimates single nurse turnover cost at three times their monthly salary.3×High[93]
Healthcare & Social Care (white collar)General industry benchmark of 6 months of salary. In healthcare felt as agency-rate hires; Moscelli BMJ study links nurse/doctor turnover to higher patient mortality.6×High[62]
Hospitality & Food Service (blue collar)Cornell US line-level turnover ≈ $5,694 (~3–3.5× monthly wage). Lost productivity = 55.2% of total cost. Likely sits at the upper end in the EU due to Working Time Directive.3.5×Medium[81]
Hospitality & Food Service (white collar)Mid-level managers at 50–75% of annual salary. Eurofound 2024: ~80% of EU employers struggle to recruit skilled workers; accommodation/food service faces elevated vacancy pressure.7×Medium[8], [30]
IT & Software (blue collar)Blatter et al. 2016: core recruitment + adaptation peaks at 17 weeks of wage payments = 4× monthly salary, for skilled technical staff.4×High[6]
IT & Software (white collar)IT/Tech logistical turnover £6,455 per vacancy. Ramp-up to optimal productivity 15 weeks insider / 32 weeks outsider, the senior engineer "deciphering legacy code at full salary" cost.12×High[67]
Logistics & Transportation (blue collar)Skilled vocational hire ≈ 4.5-month adaptation + 220+ hours of operational disruption. Specialized logistics (forklift, yard master) ≈ 4× monthly wage.4×High[54]
Logistics & Transportation (white collar)White-collar logistics (Network Planning, Compliance) ≈ 83% of annual salary. Prolonged adaptation compounds systemic scheduling errors and supply chain disruptions.10×High[6]
Manufacturing (blue collar)Replacement cost $20–40k per skilled frontline manufacturing employee. 60–90-day ramp-up, new operators at 40–60% of baseline capacity.4×Medium[85]
Manufacturing (white collar)Specialized technical-management roles (industrial engineers, plant managers) at 50–200% of annual salary. 13× is the conservative baseline.13×Medium[40]
Professional Services (blue collar)Support staff hiring + adaptation ≈ 1/3 of annual salary (~4× monthly). Disruption ≈ half of total: every hour a senior trains a hire = an hour of forgone billable revenue.4×High[1]
Professional Services (white collar)Oxford Economics: UK legal sector cost per departure £39,887, the highest in the study. £35,307 lost economic output during ramp-up. Equals 14× monthly salary.14×High[67]
Public Sector & Education (blue collar)Frontline baseline 40% of annual salary (~5× monthly). In public education sits at upper end due to DBS clearances, safety training, mandatory staffing ratios → premium-rate agency cover.5×Medium[79]
Public Sector & Education (white collar)Administrative professionals + certified teachers. Aepli 2024 baseline 4× plus Gallup 80%-of-annual-salary professional scale; 9× accounts for mid-term curriculum disruption.9×Medium[1]
Retail & Wholesale (blue collar)German firm-level data for skilled retail/trade roles: total replacement ~200% of gross monthly pay. 1/3 recruitment, 2/3 adaptation. 3.8-month ramp-up at 42.2% deficit.2×High[61]
Retail & Wholesale (white collar)UK retail specialist/manager departure ≈ £20,113 = 9.6× monthly salary. 28-week ramp drops store-level top-line revenue. Sector total £673M/year.9×High[67]
Other (blue collar)Frontline baseline 40% of annual salary (12 × 0.40 = 4.8 months) rounded to 5×. Default when no industry-specific source applies.5×High[79]
Other (white collar)Professional/technical baseline 80% of annual salary (12 × 0.80 = 9.6 months) rounded to 10×. Default when no industry-specific source applies.10×High[79]

Overlap between categories

Many people live with more than one habit, and without correcting for that we would count the same person twice. The value is the share of the whole population estimated to have both habits at once. For most of these pairs no high-quality study measures the overlap directly, so this is our best estimate, used only to correct the official prevalence numbers.

Data pointValueConfidenceSource
Nicotine + AlcoholTrue 12-month joint prevalence from NESARC (US, n=43,093): 2.9% of adults had both an alcohol use disorder and nicotine dependence. No EU-wide equivalent exists, so this is a US fallback. Directly measured, not derived.2.9%Medium[35]
Nicotine + SmartphoneModeled estimate, not observed: EU daily nicotine use (19.7%) × EU problematic smartphone use (9.5%) gives an independence baseline of 1.87%, scaled up ×1.52 using a cross-sectional co-occurrence odds ratio. No study measures this pair directly, so both the smartphone base rate and the multiplier are judgment calls. Treat as a rough estimate.2.85%Low[34], [59], [11]
Nicotine + PornographyEstimate built from two separate EU marginals: nicotine prevalence × PPU prevalence (3.2%), independence floor ≈0.59%, scaled up to ~0.90% assuming up to a 3x co-occurrence multiplier. No single study measures this pair jointly, so the multiplier is an assumption, not a measurement. Treat as directional, not precise.0.9%Low[7], [24], [34]
Nicotine + GamblingFrom an English population health survey (n=20,698): smokers were 2.2x more likely to be at-risk gamblers, giving a sex-averaged prevalence of ~0.5% (0.7% men, 0.3% women). UK fallback. No direct EU figure exists. A reasonably solid single-source estimate.0.5%Medium[10]
Nicotine + MedicationsDerived from US NESARC data: 10.5% of daily smokers reported past-year nonmedical opioid use, applied to the ~17.5% adult smoking rate to get ~1.8% joint prevalence. Using stricter clinical-disorder criteria instead gives a lower 1.09%. US-only data, no EU equivalent exists.1.8%Low[95]
Nicotine + DrugsTrue (not derived) NESARC-III finding: among US adults with 12-month tobacco use disorder, 14.1% also had a 12-month drug use disorder, yielding a population joint prevalence of ~2.8%. US fallback since no comparable EU-wide figure exists. Directly measured, not estimated.2.8%Medium[17]
Alcohol + SmartphoneFrom a US university sample (n=3,425): 33.3% of students with problematic smartphone use also drank at harmful levels (AUDIT≥8) vs 22.5% of others (OR 1.72), giving a sample joint prevalence of 6.66%. Student sample, not general population. Likely overstates the true rate for working adults.6.66%Low[42]
Alcohol + PornographyDerived, not measured directly: PPU prevalence (13.0%, meta-analysis) × pan-European alcohol use disorder prevalence (8.8%, WHO Europe) under an independence assumption ≈ 1.1%. No study measures this pair jointly, and the two are weakly positively correlated in the one sample that checked (r≈0.19), so the true figure is likely slightly higher, ~1.3-1.5%. Swapping in the broader "hazardous drinking" definition (~19-26% of adults) instead would raise this to ~2.5-3.4%.1.1%Low[16], [73]
Alcohol + GamblingDerived by multiplying pooled EU/global problem-gambling prevalence (1.29%) by the rate of alcohol use disorder among gamblers (28.1%, a separate meta-analysis), giving ≈0.36-0.40%. Sanity-checked against a similar NESARC-derived estimate (~0.31%). Combines two different studies/populations, so treat as an approximation.0.4%Low[39], [55], [83], [71]
Alcohol + MedicationsDerived from NESARC: 8.46% of US adults have a 12-month alcohol use disorder, cross-multiplied against prescription-drug-disorder rates within that group (opioid, sedative, etc.) to get ~0.3-0.4% combined. Data predates the opioid-crisis peak, so today's true figure is plausibly higher. US-only. No EU data exists for this pair.0.35%Low[78]
Alcohol + DrugsTrue (not derived) NESARC finding: 1.10% of US adults met 12-month criteria for both an alcohol use disorder and a drug use disorder. US fallback, no EU-wide joint figure exists. Directly measured, not estimated.1.1%Medium[78]
Smartphone + PornographyEstimate combining a Spanish smartphone-problem-use rate (~19-20.5%) with EU PPU prevalence (3.2%): independence floor ~0.6%, scaled to ~1.2% using an assumed 2x co-occurrence multiplier. No study measures this pair directly. The multiplier is a judgment call, not a measured association.1.2%Low[20]
Smartphone + GamblingFrom a Swedish sample: 3.1% scored above a mobile-dependence cutoff and 8.1% were lifetime problem gamblers, found statistically independent in that sample (r=0.03). The ~0.25% joint figure is simply the product of the two marginals. Using Sweden's more representative gambling rate (2.5%) instead gives a lower ~0.08%.0.25%Low[37]
Smartphone + MedicationsDerived from the same US student sample as Alcohol+Phone (n=3,425): ~2% had both problematic smartphone use and past-year prescription misuse. Important: this association was NOT statistically significant in the source data, essentially chance-level co-occurrence. Treat this figure as weak evidence.2%Low[42]
Smartphone + DrugsModeled from EU smartphone-addiction prevalence (~18.5%, skews young/student) × EU past-year illicit drug use (~8%): independence ~1.5%, scaled up to ~2-2.5% for a known positive association (OR~1.5-2.3). A more conservative 12% smartphone base rate would lower this to ~1-1.5%.2%Low[59], [33]
Pornography + GamblingModeled by multiplying EU problem-gambling prevalence (0.12-3.4% range) by PPU prevalence (3.2%), then applying a ~3x association multiplier inferred from a clinical (not general-population) sample. No direct study measures this overlap. Strongly male-skewed. The true rate for men alone is likely several times higher.0.1%Low[7], [12], [60]
Pornography + MedicationsDerived by multiplying PPU prevalence (3.2%) by EU past-year prescription misuse (~5.8-8%), independence floor ~0.19-0.26%, adjusted upward to ~0.30% for a likely (but unmeasured) positive correlation. No single study measures both constructs together. This is a product of two separate surveys.0.3%Low[7], [24]
Pornography + DrugsDerived by multiplying EU PPU prevalence (3.2%) by a narrow illicit-drug-USE-DISORDER rate (~1%), not the broader any-past-year-use figure — using that broader definition instead would give ~0.3%, 10x higher. A scoping review found PPU-illicit associations mostly non-significant except for cocaine. Two different surveys combined, not a single cross-tab.0.03%Low[7], [33], [41], [24]
Gambling + MedicationsFrom a Swedish general-population panel (n=2,038): 10 of 116 lifetime problem gamblers also reported past-year illicit-drug-or-Rx-misuse use, giving 0.49%. This is an upper bound for the Rx-only pairing since the survey question lumps illicit drugs and Rx misuse together. Small sample cell (n=10); the association didn't survive multivariable adjustment.0.49%Low[36]
Gambling + DrugsDerived from NESARC: lifetime pathological-gambling prevalence (0.42%) × the share of those gamblers with a lifetime drug use disorder (38.1%) = 0.16%. US, lifetime not past-year data; uses strict pathological-gambling criteria, so a broader "problem gambling" definition would push this higher.0.16%Low[71]
Medications + DrugsTrue (not derived) finding from pooled NSDUH 2015-2019 (large US household survey): 2.5% of adults had BOTH past-year prescription-drug misuse AND illicit drug use, in a genuine 3-way split (5.4% Rx-only, 2.9% illicit-only, 2.5% both). A real measured "both" category, not a derived estimate.2.5%Medium[53]

What this number cannot tell you

This is an estimate, not a measurement. An exact loss for one specific company could only be calculated from that company's own data. The calculator applies population-level averages to your headcount, so read it as a range. Where no Hungarian figure exists we use a European one, and you can see that in the source row.

Sources used in the employer report

The PDF we send a prospect rests on the same numbers, but it also makes a few claims the calculator does not compute. These are the sources behind those. Anything already in the numbered list above is not repeated here.

  • Ashare, R. L., Falcone, M., & Lerman, C. (2014). Cognitive function during nicotine withdrawal: Implications for nicotine dependence treatment. Neuropharmacology, 76, 581–591. https://doi.org/10.1016/j.neuropharm.2013.04.034 doi.org/10.1016/j.neuropharm.2013.04.034Not verified
  • Christakis, N. A., & Fowler, J. H. (2008). The collective dynamics of smoking in a large social network. New England Journal of Medicine, 358(21), 2249–2258. https://doi.org/10.1056/nejmsa0706154 doi.org/10.1056/nejmsa0706154
  • Ebrahim, I. O., Shapiro, C. M., Williams, A. J., & Fenwick, P. B. (2013). Alcohol and sleep I: Effects on normal sleep. Alcoholism: Clinical and Experimental Research, 37(4). https://doi.org/10.1111/acer.12006 doi.org/10.1111/acer.12006Not verified
  • Frone, M. R. (2004). Alcohol, drugs, and workplace safety outcomes: A view from a general model of employee substance use and productivity. In The psychology of workplace safety (pp. 127–156). American Psychological Association. https://doi.org/10.1037/10662-007 doi.org/10.1037/10662-007Not verified
  • Frost, H., Campbell, P., Maxwell, M., O'Carroll, R. E., Dombrowski, S. U., Williams, B., Cheyne, H., Coles, E., & Pollock, A. (2018). Effectiveness of motivational interviewing on adult behaviour change in health and social care settings: A systematic review of reviews. PLOS ONE, 13(10), e0204890. https://doi.org/10.1371/journal.pone.0204890 doi.org/10.1371/journal.pone.0204890
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