Mire épül a munkáltatói költségkalkulátor

A kalkulátorban szereplő összes szám, forrásával és általunk becsült megbízhatóságával. 160 adatpont, 91 forrás.

A jegyzetek angolul szerepelnek, ahogy a kutatási munkafüzetben.

Mit jelentenek a megbízhatósági jelölések

  • Erőslektorált publikáció, közvetlenül erre az értékre.
  • Közepeslektorált forrás, de más populáción vagy némi átszámítással.
  • Gyengeközvetett becslés, tájékoztató jellegű.
  • Nem ellenőrzötta forrás megvan és hivatkozható, de tételesen még nem vetettük össze vele az állítást.

Alapértékek

Ezek minden kategóriára érvényesek, és a legtöbb számítás ezekre épül.

AdatpontÉrtékMegbízhatóságForrás
Munkaperc egy műszakbanStandard 8-hour shift in minutes.480Erős[24]
Munkanapok éventeHungarian 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.220Közepes[24]
Alap szünet, perc/műszakBaseline 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.30Közepes[21]
Hármas átfedés korrekciójaSingle 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%.1,04Gyenge[23]

Kategóriánkénti értékek

Hét szokáskategória, mindegyiknél az elterjedtség és a négy költségtípus. Ahol fizikai és szellemi dolgozókra külön érték van, ott mindkettőt kiírjuk, mert a motor is külön számol velük.

Nikotin

AdatpontÉrtékMegbízhatóságForrás
Elterjedtség, EUDaily smokers among persons aged 15 and over, EU-27 (Eurostat 2021).18,4%Erős[29]
Elterjedtség, MagyarországWHO Hungary country profile 2023: daily smoker prevalence.22,1%Erős[88]
Kieső munkaidő, perc/nap (általános)Average smoker takes ~2 additional breaks/day above the 25-minute paid baseline, ~12.5 min each.25Erős[5]
Kieső munkaidő, perc/nap (fizikai)Upward adjustment for manual labour settings: smoke breaks more tolerated, outdoor access easier.27,5Közepes[5]
Kieső munkaidő, perc/nap (szellemi)Downward adjustment for office settings: stricter break policies and longer indoor-to-designated-area travel.22,5Közepes[5]
Többlet betegnap/év (általános)Smoking figure used as conservative upper bound across all nicotine products. Vapers show 34% higher absenteeism, broadly consistent.2,89Erős[80]
Csökkent teljesítmény, arány (általános)6% productivity loss vs. never-smokers (WPAI 24% vs 18%). Captures withdrawal-driven concentration loss between doses + smoking-specific physiology.6%Erős[4]
Csökkent teljesítmény, arány (fizikai)Physical/procedural work less sensitive to nicotine withdrawal between doses.5%Közepes[4]
Csökkent teljesítmény, arány (szellemi)Focus-dependent work suffers more from concentration loss between cigarettes.7%Közepes[4]
Éves fluktuációs ráta (általános)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%Közepes[15]

Alkohol

AdatpontÉrtékMegbízhatóságForrás
Elterjedtség, EUNumber of people in the EU with alcohol use disorder, per WHO Europe 2024 fact sheet.10,7%Erős[86]
Elterjedtség, MagyarországWHO 2018 Global Status Report on Alcohol: Hungary country profile.11,8%Erős[87]
Többlet betegnap/év (általános)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.7Erős[65]
Csökkent teljesítmény, arány (általános)≈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%Közepes[82], [47]
Éves fluktuációs ráta (általános)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%Közepes[2]

Okostelefon

AdatpontÉrtékMegbízhatóságForrás
Elterjedtség, EUGlobal systematic review/meta-analysis of digital addiction (Meng et al. 2022).17,4%Erős[54]
Elterjedtség, MagyarországNo HU-specific cohort; EU proxy applied. Same source as EU figure.17,4%Közepes[54]
Kieső munkaidő, perc/nap (általános)Lim & Chen 2012 (51 min/day cyberloafing baseline); Jeong et al. 2020 objective app-switching logs upper-bound. 60 min/day applied to the affected (compulsive use) subset.60Közepes[48], [43]
Kieső munkaidő, perc/nap (fizikai)Downward adjustment: blue-collar environments routinely enforce phone bans / no-pocket policies (manufacturing, warehouses, retail, hospitality, healthcare, construction).30Közepes[48]
Kieső munkaidő, perc/nap (szellemi)Upward adjustment: white-collar desk work has near-zero structural barriers to phone access; non-work browsing visually indistinguishable from work tasks.75Közepes[48], [20]
Többlet betegnap/év (általános)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.0Erős[22]
Csökkent teljesítmény, arány (általános)Smartphone Addiction Scale scores correlate with self-reported work hours lost. Effect strengthens after controlling for ill-health.2%Gyenge[20]
Csökkent teljesítmény, arány (fizikai)Blue-collar work is less attention-fragile; structural restrictions further limit measurable presenteeism beyond lost time.0%Gyenge[20]
Csökkent teljesítmény, arány (szellemi)White-collar focus-dependent work is most sensitive to attention fragmentation beyond the explicit time-lost component.2,5%Gyenge[20]
Éves fluktuációs ráta (általános)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%Erős[22]

Pornográfia

AdatpontÉrtékMegbízhatóságForrás
Elterjedtség, EUInternational Sex Survey 2024 (N≈80k across 42 countries).3,2%Erős[8]
Elterjedtség, MagyarországHungary subset of the International Sex Survey 2024.3,6%Erős[8]
Kieső munkaidő, perc/nap (általános)Nielsen workplace traffic estimates + Ofcom online usage figures + Mecham workplace behaviour studies.10Közepes[59], [61], [53]
Kieső munkaidő, perc/nap (fizikai)Blue-collar shop-floor environments do not realistically permit at-work porn use; conservative 0.0Erős[22]
Kieső munkaidő, perc/nap (szellemi)White-collar private-office / device-on-desk scenario.15Közepes[59], [53]
Többlet betegnap/év (általános)No peer-reviewed cohort study isolates the absenteeism effect of PPU at the workplace level. 0 days applied.0Erős[22]
Csökkent teljesítmény, arány (általános)Indirect, derived from documented PPU → anxiety/depression pathways. Limited workplace-specific data.2%Gyenge[12], [36]
Csökkent teljesítmény, arány (fizikai)1,5%Gyenge[12]
Csökkent teljesítmény, arány (szellemi)2,5%Gyenge[12]
Éves fluktuációs ráta (általános)No cohort study isolates PPU-attributable turnover. 0% applied.0%Erős[22]

Szerencsejáték

AdatpontÉrtékMegbízhatóságForrás
Elterjedtség, EUSystematic review/meta-analysis of worldwide problem-gambling prevalence by age cohort (Dellosa & Browne 2024).1,3%Erős[17]
Elterjedtség, MagyarországHungarian validation of the SOGS (SOGS-HU). Best available HU-specific figure pending newer national data.1,4%Erős[37]
Kieső munkaidő, perc/nap (általános)NRC 1999 baseline + Rafi et al. 2023 systematic review of gambling/productivity literature.12Közepes[58], [69]
Kieső munkaidő, perc/nap (fizikai)Lower opportunity than white-collar; conservative downshift from the general 12 min.8Gyenge[58], [13]
Kieső munkaidő, perc/nap (szellemi)Same as the general baseline: desk access enables in-shift gambling app/web use.12Közepes[58], [69]
Többlet betegnap/év (általános)Latvala et al. on stress/sleep-related absence; Binde on debt-stress-related impacts.4Gyenge[45], [6]
Csökkent teljesítmény, arány (általános)Preoccupation + debt-stress impact on concentration. Sparse direct evidence; conservative estimate.3%Gyenge[6], [45]
Csökkent teljesítmény, arány (fizikai)3%Gyenge[6]
Csökkent teljesítmény, arány (szellemi)3,5%Gyenge[6]
Éves fluktuációs ráta (általános)NRC + Latvala: limited direct turnover data; conservative estimate above the alcohol turnover floor.4%Gyenge[58], [45]

Gyógyszerek

AdatpontÉrtékMegbízhatóságForrás
Elterjedtség, 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%Erős[60]
Elterjedtség, MagyarországEU proxy applied: no recent HU-specific cohort study on non-medical prescription drug use.6%Közepes[60]
Többlet betegnap/év (általános)Saha 2024 MEPS analysis: attributable absenteeism for prescription misuse subgroup.4Közepes[70]
Többlet betegnap/év (fizikai)5Közepes[70]
Többlet betegnap/év (szellemi)3Közepes[70]
Csökkent teljesítmény, arány (általános)MEPS analysis (Saha 2024) + CONSTANCES benzodiazepine cohort (Hoven 2022).6%Közepes[70], [40]
Csökkent teljesítmény, arány (fizikai)Sedating-drug misuse hits physical/safety-critical roles harder.7%Közepes[70]
Csökkent teljesítmény, arány (szellemi)Lower presenteeism impact for white-collar roles relative to safety-critical tasks.5%Közepes[70]
Éves fluktuációs ráta (általános)Maclean 2023 opioid-crisis economic review + Burke 2022 RX labour-market study.4%Közepes[50], [10]
Éves fluktuációs ráta (fizikai)5%Közepes[50]
Éves fluktuációs ráta (szellemi)3%Közepes[50]

Kábítószerek

AdatpontÉrtékMegbízhatóságForrás
Elterjedtség, EUEuropean Drug Report 2025: past-year illicit drug use prevalence in the EU.8,4%Erős[28]
Elterjedtség, MagyarországOLAAP 2015 representative HU adult survey on addictive behaviours (Paksi et al. 2018).2,3%Erős[63]
Kieső munkaidő, perc/nap (általános)Frone 2006: best available US distribution-of-use study; HU/EU primary data is thin.15Gyenge[30]
Kieső munkaidő, perc/nap (fizikai)20Gyenge[30]
Kieső munkaidő, perc/nap (szellemi)10Gyenge[30]
Többlet betegnap/év (általános)AJPM 2024 illicit drug × absenteeism US study (Yang) cross-checked against CONSTANCES.6Erős[90], [2]
Többlet betegnap/év (fizikai)7Közepes[90]
Többlet betegnap/év (szellemi)5Közepes[90]
Csökkent teljesítmény, arány (általános)Tran 2017 community-based opioid productivity survey + Frone 2006 baseline.8%Közepes[79], [30]
Csökkent teljesítmény, arány (fizikai)9%Közepes[79]
Csökkent teljesítmény, arány (szellemi)7%Közepes[79]
Éves fluktuációs ráta (általános)CONSTANCES cohort: illicit drug use OR-equivalent for job loss.6%Közepes[2]
Éves fluktuációs ráta (fizikai)7%Közepes[2]
Éves fluktuációs ráta (szellemi)5%Közepes[2]

Készenlét a változásra

A visszanyerhető összeg abból jön, hogy hányan állnak készen a változásra. A három szakasz a transzteoretikus modell szerinti megoszlás, kategóriánként.

AdatpontÉrtékMegbízhatóságForrás
Nikotin: még nem fontolgatjaDaily nicotine users in pre-contemplation (no plan to quit in next 6 months).65%Erős[26], [64]
Nikotin: fontolgatja vagy készülConsidering quitting + actively preparing (intent within 6 months / 30 days).20%Erős[26], [64]
Nikotin: aktívan változtatActive quit attempt currently in progress.15%Erős[26], [64]
Alkohol: még nem fontolgatjaHazardous/harmful drinkers in pre-contemplation.69%Erős[38], [72]
Alkohol: fontolgatja vagy készülConsidering reducing + actively preparing.19%Közepes[38], [72]
Alkohol: aktívan változtatActive reduction attempt currently in progress.12%Közepes[38], [72]
Okostelefon: még nem fontolgatjaCompulsive smartphone users in pre-contemplation. Lowest pre-contemplation share of all 7 categories.21%Közepes[41], [18], [46]
Okostelefon: fontolgatja vagy készülConsidering reducing + actively preparing.53%Erős[41], [18]
Okostelefon: aktívan változtatActive reduction attempt currently in progress.26%Erős[41], [46]
Pornográfia: még nem fontolgatjaPPU users in pre-contemplation (no plan to reduce).70%Közepes[8]
Pornográfia: fontolgatja vagy készülConsidering reducing + actively preparing.23%Erős[8]
Pornográfia: aktívan változtatActive reduction attempt currently in progress.7%Erős[8]
Szerencsejáték: még nem fontolgatjaProblem gamblers in pre-contemplation.40%Közepes[39]
Szerencsejáték: fontolgatja vagy készülConsidering stopping + actively preparing.40%Közepes[39]
Szerencsejáték: aktívan változtatActive stop/reduction attempt currently in progress.20%Közepes[39]
Gyógyszerek: még nem fontolgatjaLong-term prescription users in pre-contemplation about deprescribing.77%Közepes[68], [14]
Gyógyszerek: fontolgatja vagy készülConsidering deprescribing + preparing.13%Közepes[68], [14]
Gyógyszerek: aktívan változtatActive deprescribing attempt currently in progress.10%Közepes[68], [14]
Kábítószerek: még nem fontolgatjaIllicit drug users in pre-contemplation; highest pre-contemplation share of all 7 categories.80%Erős[84], [27]
Kábítószerek: fontolgatja vagy készülConsidering stopping + preparing.15%Gyenge[84]
Kábítószerek: aktívan változtatActive stop attempt currently in progress.5%Közepes[84]

Kilépő pótlásának költsége, iparáganként

Egy kilépő munkavállaló pótlása ennyi havi bérbe kerül. Iparág és munkakör szerint eltér, ezért mind a 22 értéket kiírjuk.

AdatpontÉrtékMegbízhatóságForrás
Építőipar (fizikai)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×Közepes[75]
Építőipar (szellemi)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×Közepes[85], [16]
Pénzügy (fizikai)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×Erős[49]
Pénzügy (szellemi)Oxford Economics 2014 isolated UK accounting sector: total cost per departure ≈ £40k, driven by 21.3-week output deficit. Equals 13× monthly salary.13×Erős[62]
Egészségügy (fizikai)Umbrella review estimates single nurse turnover cost at three times their monthly salary.3×Erős[89]
Egészségügy (szellemi)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×Erős[57]
Vendéglátás (fizikai)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×Közepes[78]
Vendéglátás (szellemi)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×Közepes[9], [25]
IT és szoftver (fizikai)Blatter et al. 2016: core recruitment + adaptation peaks at 17 weeks of wage payments = 4× monthly salary, for skilled technical staff.4×Erős[7]
IT és szoftver (szellemi)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×Erős[62]
Logisztika (fizikai)Skilled vocational hire ≈ 4.5-month adaptation + 220+ hours of operational disruption. Specialized logistics (forklift, yard master) ≈ 4× monthly wage.4×Erős[49]
Logisztika (szellemi)White-collar logistics (Network Planning, Compliance) ≈ 83% of annual salary. Prolonged adaptation compounds systemic scheduling errors and supply chain disruptions.10×Erős[7]
Gyártás (fizikai)Replacement cost $20–40k per skilled frontline manufacturing employee. 60–90-day ramp-up, new operators at 40–60% of baseline capacity.4×Közepes[81]
Gyártás (szellemi)Specialized technical-management roles (industrial engineers, plant managers) at 50–200% of annual salary. 13× is the conservative baseline.13×Közepes[32]
Üzleti szolgáltatások (fizikai)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×Erős[1]
Üzleti szolgáltatások (szellemi)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×Erős[62]
Közszféra (fizikai)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×Közepes[77]
Közszféra (szellemi)Administrative professionals + certified teachers. Aepli 2024 baseline 4× plus Gallup 80%-of-annual-salary professional scale; 9× accounts for mid-term curriculum disruption.9×Közepes[1]
Kereskedelem (fizikai)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×Erős[56]
Kereskedelem (szellemi)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×Erős[62]
Egyéb (fizikai)Frontline baseline 40% of annual salary (12 × 0.40 = 4.8 months) rounded to 5×. Default when no industry-specific source applies.5×Erős[77]
Egyéb (szellemi)Professional/technical baseline 80% of annual salary (12 × 0.80 = 9.6 months) rounded to 10×. Default when no industry-specific source applies.10×Erős[77]

Átfedés a kategóriák között

Sok ember egyszerre több szokással él, és ha ezt nem korrigálnánk, ugyanazt az embert többször számolnánk. Az érték relatív kockázat: 1,0 azt jelenti, hogy a két szokás egymástól függetlenül fordul elő, az ennél nagyobb szám azt, hogy együtt járnak.

AdatpontÉrtékMegbízhatóságForrás
Nikotin + AlkoholUK-population, Scientific Reports 2025.2,04Gyenge[42]
Nikotin + OkostelefonCross-sectional postgraduate sample; conservative RR.1,6Gyenge[3]
Nikotin + PornográfiaScoping review of substance use among individuals with PPU.3,1Gyenge[19]
Nikotin + SzerencsejátékNCS-R baseline; matches gambling × alcohol/drug dependence ORs.3,9Gyenge[44]
Nikotin + GyógyszerekBlended across opioid (AOR 4.82) and benzodiazepine (AOR 1.8) NSDUH studies: RR 2.5 reflects all-prescription-misuse coverage.2,5Gyenge[91], [83]
Nikotin + KábítószerekUS 2002–2014 illicit drug × smoker trend analysis.4,8Gyenge[33]
Alkohol + OkostelefonGrant 2019 primary; European triangulation via Cabré-Riera 2025.1,7Gyenge[35], [11]
Alkohol + PornográfiaRange across published studies: 1.0–2.25. Mixed literature; midpoint applied.2Gyenge[19], [51]
Alkohol + SzerencsejátékNCS-R alcohol × pathological-gambling co-occurrence.5,8Gyenge[44]
Alkohol + GyógyszerekRange across general-population studies 1.4–3.0; blended across benzo and opioid subclasses.2,1Gyenge[52], [71]
Alkohol + KábítószerekRange across population epidemiological surveys 5.0–7.4. Conservative midpoint applied.6Gyenge[34]
Okostelefon + PornográfiaNetwork-analysis of addictive behaviours with psychiatric comorbidity.1,7Gyenge[74]
Okostelefon + SzerencsejátékNo published OR isolates this pair (Shiferaw 2025 reports OR 1.47 but CI 0.77–2.17 crosses 1.0). RR 1.0 = no correlation; conservative.1Gyenge[22]
Okostelefon + GyógyszerekPéter et al. 2023 (HU young adults) specifically examined this association and found phone-use scores did not follow the elevated pattern seen with other digital addictions. RR 1.0; conservative.1Gyenge[22], [66]
Okostelefon + KábítószerekMeta-analysis of youth populations (mean age ≤25, predominantly university students).1,94Gyenge[73]
Pornográfia + SzerencsejátékCo-occurrence predictors in gambling-disorder + PPU cohorts.3,5Gyenge[55]
Pornográfia + GyógyszerekDerived from observed PPU prevalence 4.5% in 1,272 OUD treatment-seekers vs ~1.9% general-population baseline. Opioid figure extrapolated to broader medications category.2,4Gyenge[76], [8]
Pornográfia + KábítószerekDubois 2025 scoping review (949 articles): only 8 PPU + substance use studies; cocaine showed significant correlation but cannabis, general drug use, and chemsex did not. RR 1.0; conservative.1Gyenge[22], [19]
Szerencsejáték + GyógyszerekGaleazzi 2025 meta-analysis: OR 5–12 for drug use disorder × gambling. RR 5.8 at the conservative end, matching the alcohol × gambling figure from the same NCS-R dataset.5,8Nem ellenőrzött[31], [44]
Szerencsejáték + KábítószerekNESARC: 38.1% of pathological gamblers met lifetime criteria for illicit drug use disorder vs 8.8% of non-gamblers (N=43,093). OR 4.40 confirmed by Galeazzi 2025 meta-analysis.4,4Nem ellenőrzött[67], [31]
Gyógyszerek + KábítószerekNESARC-III (N=36,309): prescription misuse clusters strongly with illicit drug use disorders. RR 6.0 at conservative midpoint of measured AOR range 4.6–8.0, matching alcohol × illegal drugs from the same dataset.6Nem ellenőrzött[71], [34]

Amit ez a szám nem tud

Ez becslés, nem mérés. Egy konkrét cégre vonatkozó pontos veszteséget csak a cég saját adataiból lehetne kiszámolni. A kalkulátor népességszintű átlagokat alkalmaz a te létszámodra, tehát tartományként érdemes olvasni. Ahol nincs magyar adat, ott európai értéket használunk, és ezt a forrás sorában látod.

A munkáltatói riport forrásai

A prospektusban elküldött PDF ugyanezekre a számokra épül, de van néhány állítása, amit a kalkulátor nem számol. Az ezek mögötti forrásokat itt soroljuk fel. Amelyik már szerepel a fenti számozott listában, azt nem ismételjük meg.

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