## ANNEX I. SIMULATIONS OF SHOCKS IN EUROMOD ## Source details **Canonical URL:** [ANNEX I. SIMULATIONS OF SHOCKS IN EUROMOD](https://www.imf.org/-/media/files/publications/wp/2023/english/wpiea2023003-print-pdf.pdf) ## Other formats - [Markdown version](/-/media/files/publications/wp/2023/english/wpiea2023003-print-pdf.pdf.md) - [Structured JSON version](/-/media/files/publications/wp/2023/english/wpiea2023003-print-pdf.pdf.json) --- ### Overview and research purpose - COVID-19 generated widespread economic disruptions and a sharp deterioration in labor markets across Europe. - Employment in the EU remained muted: employment rate remained at "0.7 percentage points" below pre-crisis levels. - Most labor market adjustment occurred via reduction in working hours per worker: working hours per worker fell by "12 percent" year-on-year in Q2 2020. - The paper uses a microsimulation approach (EUROMOD) with household data to assess the effectiveness of job-retention schemes and other fiscal support in stabilizing household income across EU countries and across socio-economic groups. ### Key empirical contrasts with the United States - United States: unemployment rate surged by "11 percentage points" in the first two months of the pandemic; employment plunged by "12 percent" at onset while working hours per worker remained steady. - EU: employment fell by "just under 3 percent" in Q2 2020 while hours worked per worker dropped by "almost 12 percent"—adjustment concentrated on the intensive margin (hours) rather than the extensive margin (jobs). ### Role, coverage, and fiscal cost of job-retention schemes in the EU - Job-retention schemes subsidize wages for workers whose firms reduced hours but preserved jobs; many countries simplified access, relaxed eligibility, and raised benefit levels during the pandemic. - At the beginning of the pandemic, an average of "14 percent" of working-age population were under some job-retention schemes in the four largest EU economies (compared to "2 percent" during the global financial crisis). - More than half of EU countries had take-up rates higher than "12 percent" of working-age population. - Rise in unemployment income support was modest—about "2 percentage points"—reflecting muted employment losses. - Average fiscal cost: "2 percent of GDP" in advanced EU economies and "1.4 percent in emerging market economies in EU" (about one-third of total fiscal support during the pandemic, per Ando and others 2022). ### Selected international/comparative figures - United States Paycheck Protection Program (PPP) estimates: saved about "3.6 million" jobs, equivalent to "2.2 percent" of total employment. - U.S. federal unemployment support scale-up: about "3 percent of GDP" (weekly supplements, expanded eligibility, extended duration). ### Heterogeneous impacts across worker groups - Young workers experienced the largest decline in employment and the largest rise in unemployment between 2019 and 2020. - Workers with low-level education saw an average employment rate decline of "5 percentage points" at the EU level. - Elderly and high-skilled workers were less affected, with employment rates rising by "1.7" and "2.4 percent", respectively. - Pre-pandemic Okun’s Law estimates indicate young and low-skilled workers’ unemployment rates are more responsive to output fluctuations; actual 2020 unemployment rose less than predicted by Okun’s Law (consistent with buffering role of job-retention schemes and labor force non-participation effects). ### Features and take-up of job-retention schemes (selected country highlights) - Country take-up rates (Maximum and Average take-up during March–December 2020; percent of working-age population): - Austria: Maximum "7.7"; Average "7.9". Remarks: Longer duration, more flexible rules; up to "100 precent" working time reduction in hospitality sector. - Belgium: Maximum "6.9"; Average "7.6". - Czech Republic: Maximum "8.6"; Average "4.5". - Denmark: Maximum "7.3"; Average "3.5". Remarks: Introduced temporarily with no membership in unemployment scheme required. - Estonia: Maximum "14.4"; Average "3.3". - France: Maximum "20.6"; Average "8.8". Remarks: No condition on contract type; maximum duration extended from "6 to 12 months"; subsidy is "70 percent" of gross wage; most employers do not bear any cost of hours not worked. - Germany: Maximum "11.2"; Average "6.4". Remarks: Firms can apply if "10 percent" of workforce subject to reduction (down from "30 percent"); replacement rate raised to "70 and 80 percent" (from 4th month and 7th month). - Ireland: Maximum "14.8"; Average "11.8". Remarks: Existing short-time work replaced by temporary wage subsidy. - Luxembourg: Maximum "22.2"; Average "6.4". Remarks: Up to "100 percent" working time reduction; temporary workers and apprentices eligible. - Netherlands: Maximum "23.8"; Average "14.2". Remarks: Existing short-time work replaced by temporary wage subsidy; employees received "100 percent" of their wage. - United Kingdom: Maximum "21.2"; Average "12.8". Remarks: Replacement rate for unworked hours is "80 percent" of gross salary; cost of unworked hours faced by firms set at zero; all workers on payroll on March 19 eligible. ### Research approach, data, and microsimulation design - Model and data: - Static microsimulation model: EUROMOD (version I4.0+). - Microdata: 2019 European Statistics on Income and Living Conditions (EU-SILC) for "26 EU countries". - Analytical scope: - Microsimulation quantifies how tax and benefit systems (existing and pandemic-related measures) buffer shocks to household market income. - Allows detailed, household-level analysis by socioeconomic characteristics but does not capture aggregate feedback effects from behavioral responses. - Simulations: - Two main sets of simulations described below. ### Simulation design and methodology - Two main sets: - First set: baseline (2019 tax-benefit system; 2019 EU-SILC microdata) versus income shock scenario (2019 tax-benefit system with a uniform 5-percent decline in market incomes of all individuals in the 2019 EU-SILC microdata). - Second set: “COVID-19” scenario (2020 tax-benefit system; 2019 EU-SILC microdata adjusted to match 2020 labor market conditions using the Labor Market Adjustment (LMA) Add-on) versus counterfactual “no COVID-19” scenario (no pandemic-related labor market transitions; 2020 tax-benefit system; 2019 EU-SILC microdata). - Income stabilization coefficient (IIS) defined as: - IIS = [1 − (Σ ΔY_h / N) / (Σ ΔM_h / N)] × 100 = [ (Σ ΔT_h / N) / (Σ ΔM_h / N) + (Σ ΔI_h / N) / (Σ ΔM_h / N) − (Σ ΔB_h / N) / (Σ ΔM_h / N) ] × 100 - Where ∆M_h (∆Y_h) is the change in market (disposable) income of household h; ∆T_h, ∆I_h, and ∆B_h refer to changes in personal income taxes, social insurance contributions, and social benefits, respectively. - Social benefits include unemployment benefits, social assistance and housing benefits, family and education benefits, health and disability benefits; in COVID-19 simulations social benefits also include monetary compensation from short-time work programs, wage subsidies, and similar schemes for self-employed. - Interpretation: coefficient = 100 percent when market income shock fully absorbed; zero means no absorption. - Conventions: EUROMOD uprating adjustments applied when reference years differ between microdata and policy rules. ### Income stabilization: pre-pandemic (uniform 5-percent shock) - Aggregate findings: - The tax and benefit system in the EU countries, on average, can absorb 37 percent of an adverse income shock (5 percent decline in market income). - Component contributions (average, EU): - Personal income tax absorbs 24 percent of the adverse income shock on average. - Social insurance contributions and social benefits together absorb a total of 12 percent of the income shock. - Cross-country variation: - Personal income tax stabilization ranges from 14 percent in Bulgaria to 57 percent in Belgium. - Countries with more progressive personal income taxes (Belgium, Denmark, Ireland) tend to have higher stabilization from taxes (personal income taxes absorbing at least 40 percent). - In countries with limited tax progressivity (Bulgaria, Romania), social protection systems contribute over 70 percent of income stabilization. - Distributional patterns (EU averages): - Stabilization coefficients range from 32 percent for the poorest income quintile to 39 percent for the top income quintile. - For the lowest income quintile, social insurance and benefits contribute about two-thirds of overall stabilization (20 percent out of 32 percent). - For the top income quintile, social insurance and benefits contribute about one-quarter of overall stabilization (10 percent out of 39 percent). - Social insurance contributions stabilize income by about 11 percent, broadly similar across the distribution. - Sensitivity: uniform 5-percent decline mutes stabilization from unemployment insurance; alternative scenarios with higher unemployment show unemployment income support playing a larger role. ### Income stabilization during the pandemic (2020) - Aggregate changes and fiscal impact: - Market incomes for households fell by 5.3 percent on average in 2020. - Largest market income drops (>10 percent): Malta, Ireland, Italy, and Slovakia. - Smaller declines (<2 percent): Denmark, Luxembourg, the Netherlands. - Fiscal support mitigated part of the income shock, producing a milder drop of disposable income by 1.6 percent in 2020. - In some countries disposable income rose slightly because of large fiscal support measures at the onset of the pandemic (Croatia, Denmark, Luxembourg). - Distributional outcomes: - Before fiscal support, market incomes fell by a median of 4.6 percent for households in the lowest income quintile and by 3.7 percent for households in the top income quintile. - After tax-and-benefit stabilization and pandemic fiscal support, disposable income remained broadly unchanged for lower-income households, while it declined by about 2 percent for households at the top income quintile. - Conclusion: fiscal policy during the pandemic was impactful and progressive, particularly mitigating shocks for low-income households. ### Role and contribution of fiscal support and job-retention schemes - Overall stabilization with fiscal support: - Together with pre-existing automatic stabilizers, fiscal support absorbed 78 percent of the decline in market incomes across countries during the pandemic—almost double the stabilization observed before the pandemic. - Country variation: stabilization ranged from 55 percent in Malta and Poland to almost 100 percent in Belgium and Denmark. - Component contributions during the pandemic (average, EU): - Job-retention schemes stabilized 47 percent of the income shock on average. - This represents about 60 percent of the overall income stabilization (47 out of 78 percent). - Unemployment income support compensated for 9 percent of adverse shocks (muted given modest rise in unemployment rates). - Job-retention schemes were more impactful in countries where workers receive higher compensation rates for hours not worked (examples: Czech Republic, Denmark, Slovak Republic). - Distributional contribution: - Tax and benefit systems together with pandemic-related support absorbed 88 percent of the income shock for lower-income households during the pandemic, versus 72 percent for households in the top quintile. - Job-retention schemes account for the bulk of the overall stabilization across income groups. ### Additional quantitative counterfactuals and inequality effects - Job-retention schemes compensated 64 percent of income shocks for lower-income households (across the board for all income groups, particularly for lower-income households). - Job-retention schemes absorbed nearly 80 percent of market income shocks—almost doubling the extent of the automatic stabilization of the pre-pandemic tax and benefit systems. - In the absence of job-retention schemes: - Unemployment rates in the EU could have risen by additional 3 percentage points. - Disposable income inequality could have deteriorated further; simulations suggest the pandemic could have led to a rise in market income inequality by 0.65 percentage points. - Without job-retention schemes, disposable income inequality could have risen by 0.38 percentage points compared to the “no COVID-19” scenario. - After accounting for pandemic-related fiscal support, inequality in disposable income during the pandemic decreased by 0.24 percentage points on average. - In a few countries, job-retention schemes stabilized more than 100 percent of the income shocks for the lowest income quintile (because some schemes provided a lump sum support and may have more than compensated for the income losses for households earning very little income). ### Distributional patterns by group and sectoral heterogeneity - Income quintiles calculated at the country level based on household’s market income in the “no COVID-19” scenario. - Job-retention schemes include compensation received by employees on short-time work schemes, wage subsidies, as well as similar schemes for self-employed. - Average stabilization across demographic groups: - Tax and benefit systems, including job-retention schemes, absorbed about 75 percent of the income losses for young workers (aged 18-24) during the pandemic, compared to 70 percent for those aged 25 and older. - Income stabilization absorbed over 72 percent of the income shock for workers with lower education and 72 percent for females. - Job-retention schemes absorbed almost half of the income shock for young workers and about 40 percent for other groups. - Sectoral patterns: - Trade, transport, food & accommodation sectors absorbed 75 percent of income losses on average. - Stabilization lower for less contact-intensive sectors such as finance and agriculture. - Substantial country variation across groups and sectors. ### Regression analysis: specification and selected results - Regression specification: - IStab_i,c = α + X_i,c β + Y_c γ + C_c δ + ε_i,c - IStab_i = (1 − ΔY_i / ΔM_i) × 100 - X_i,c includes age, gender, education level, contact intensity of sector, market income quintile. - Y_c includes: allowance of the job-retention scheme (percent of lost income that a worker receives for hours not worked), change in cyclically adjusted primary deficit between 2019 and 2020 in percent of potential GDP, net replacement rate in unemployment, percentage change in average number of hours worked per worker between 2019 and 2020. - Includes country dummy vector C_c. - Estimation evidence: - Stronger stabilization for female workers, workers in contact-intensive industries, and those with lower education (coefficients positive and statistically significant). - Households in lower-income quintiles tend to have higher stabilization coefficients. - Countries with higher allowance rates of job-retention schemes or more generous unemployment benefits show stronger income stabilization. - Income stabilization effects weaker in countries that experienced a larger decline in working hours. - Countries with stronger counter-cyclical fiscal responses (change in cyclically adjusted primary balance) have greater income stabilization. - Selected pooled OLS coefficient highlights (examples as reported): - Female: 4.447***, 4.027***, 4.087***, 1.740***, 1.918***. - Education level, low: 4.937***, 4.441***, 5.064***, 0.906, 1.308. - Contact-intensive: 3.876***, 5.593***, 2.896***, 2.607***. - Job retention scheme allowance, percent of lost income: 0.495***. - Change in cyclically adjusted primary deficit 2020: 2.378***. - Net replacement rate in unemployment, percent of previous income: 0.407***. - Hours per worker, percentage change in 2020: 0.372***. - Observations: 48,945 in most specifications; 41,365 in last column shown. - Number of countries: 26 in most specifications; 21 in last column shown. ### Policy implications and conclusions - Job-retention schemes during the pandemic were timely, effective, and well-targeted; they provided significant income stabilization, particularly for vulnerable workers (lower-income families, youth, low-skilled workers). - Job-retention schemes complement unemployment income support because they operate on different margins (working hours versus unemployment) and insure different types of workers. - Policy guidance: - Job-retention schemes can be expanded or broadened depending on severity of shocks, particularly to cover workers not qualified for regular unemployment income support. - Link generosity of job-retention schemes to economic activity and incentivize return to normal working hours. - Job-retention schemes are most appropriate for deep but short-lived disruptions to labor markets. - If adverse shocks are persistent, policies should transition from preserving jobs to supporting workers and facilitating job-to-job transitions to avoid hindering necessary reallocation. ### Annex I — methodology and definitions - Simulations based on EUROMOD I4.0+ and microdata from the 2019 EU-SILC (excluding Germany and United Kingdom for broader EUROMOD scope). - Two scenarios for pre-pandemic assessment: - Baseline: 2019 EU-SILC household-level microdata and 2019 tax and benefit policies. - Uniform market income shock: negative 5-percent reduction of market income across all households with nonnegative income. - Definitions: - M = market income. - T(M) = direct income taxes and social insurance contributions payable by households. - B(M) = benefits accrued to households. - Y = disposable income: Y = M - (T(M) - B(M)). - Changes: ΔM = M' - M; ΔY = Y' - Y. - LMA Add-on: EUROMOD LMA Add-on used to adjust microdata and simulate labor market transitions between employment, unemployment and job-retention schemes. *IMF Working Paper — ANNEX I. SIMULATIONS OF SHOCKS IN EUROMOD* ### ANNEX I. SIMULATIONS OF SHOCKS IN EUROMOD ______________________________________________ 19 ### ANNEX I. SIMULATIONS OF SHOCKS IN EUROMOD ### Overview and research purpose - The COVID-19 pandemic generated widespread economic disruptions and a sharp deterioration in labor markets across Europe. - Despite a dramatic economic contraction, employment in the EU remained muted: employment rate remained at "0.7 percentage points" below pre-crisis levels. - Most labor market adjustment occurred via reduction in working hours per worker: working hours per worker fell by "12 percent" year-on-year in Q2 2020. - The paper uses a microsimulation approach (EUROMOD) with household data to assess the effectiveness of job-retention schemes and other fiscal support in stabilizing household income across EU countries and across socio-economic groups. ### Key empirical contrasts with the United States - United States: unemployment rate surged by "11 percentage points" in the first two months of the pandemic; employment plunged by "12 percent" at onset while working hours per worker remained steady. - EU: employment fell by "just under 3 percent" in Q2 2020 while hours worked per worker dropped by "almost 12 percent"—i.e., adjustment concentrated on the intensive margin (hours) rather than on the extensive margin (jobs). ### Role, coverage, and fiscal cost of job-retention schemes in the EU - Job-retention schemes (short-time work, wage subsidies) subsidize wages for workers whose firms reduced hours but preserved jobs. Many countries simplified access, relaxed eligibility, and raised benefit levels during the pandemic. - At the beginning of the pandemic, an average of "14 percent" of working-age population were under some job-retention schemes in the four largest EU economies (compared to "2 percent" during the global financial crisis). - More than half of EU countries had take-up rates higher than "12 percent" of working-age population. - Rise in unemployment income support was modest—about "2 percentage points"—reflecting muted employment losses. - Average fiscal cost: "2 percent of GDP" in advanced EU economies and "1.4 percent in emerging market economies in EU" (about one-third of total fiscal support during the pandemic, per Ando and others 2022). ### Selected international/comparative figures - United States Paycheck Protection Program (PPP) estimates: saved about "3.6 million" jobs, equivalent to "2.2 percent" of total employment. - U.S. federal unemployment support scale-up: about "3 percent of GDP" (weekly supplements, expanded eligibility, extended duration). ### Heterogeneous impacts across worker groups - Young workers experienced the largest decline in employment and the largest rise in unemployment between 2019 and 2020. - Workers with low-level education saw an average employment rate decline of "5 percentage points" at the EU level. - Elderly and high-skilled workers were less affected, with employment rates rising by "1.7" and "2.4 percent", respectively. - Pre-pandemic Okun’s Law estimates indicate young and low-skilled workers’ unemployment rates are more responsive to output fluctuations; actual 2020 unemployment rose less than predicted by Okun’s Law (consistent with the buffering role of job-retention schemes and labor force non-participation effects). ### Features and take-up of job-retention schemes (selected country highlights and take-up rates) - Table overview (take-up rates expressed as percent of working-age population; Maximum and Average take-up during March–December 2020): - Austria: Maximum "7.7"; Average "7.9". Remarks: Longer duration, more flexible rules; up to "100 precent" working time reduction in hospitality sector. - Belgium: Maximum "6.9"; Average "7.6". - Czech Republic: Maximum "8.6"; Average "4.5". - Denmark: Maximum "7.3"; Average "3.5". Remarks: Introduced temporarily with no membership in unemployment scheme required. - Estonia: Maximum "14.4"; Average "3.3". - France: Maximum "20.6"; Average "8.8". Remarks: No condition on contract type; maximum duration extended from "6 to 12 months"; subsidy is "70 percent" of gross wage; most employers do not bear any cost of hours not worked. - Germany: Maximum "11.2"; Average "6.4". Remarks: Firms can apply if "10 percent" of workforce subject to reduction (down from "30 percent"); replacement rate raised to "70 and 80 percent" (from 4th month and 7th month). - Ireland: Maximum "14.8"; Average "11.8". Remarks: Existing short-time work replaced by temporary wage subsidy. - Luxembourg: Maximum "22.2"; Average "6.4". Remarks: Up to "100 percent" working time reduction; temporary workers and apprentices eligible. - Netherlands: Maximum "23.8"; Average "14.2". Remarks: Existing short-time work replaced by temporary wage subsidy; employees received "100 percent" of their wage. - United Kingdom: Maximum "21.2"; Average "12.8". Remarks: Replacement rate for unworked hours is "80 percent" of gross salary; cost of unworked hours faced by firms set at zero; all workers on payroll on March 19 eligible. - Many country schemes were either pre-existing (e.g., Kurzarbeit in Germany, Activité Partielle in France) or introduced/expanded during the pandemic; some schemes provided sector-targeted generosity (e.g., Austria and Luxembourg for contact-intensive sectors). ### Research approach, data, and microsimulation design - Model and data: - Static microsimulation model: EUROMOD (version I4.0+). - Microdata: 2019 European Statistics on Income and Living Conditions (EU-SILC) for "26 EU countries". - Analytical scope: - The microsimulation quantifies how tax and benefit systems (existing and pandemic-related measures) buffer shocks to household market income (income before taxes and transfers). - Approach allows detailed, household-level analysis by socioeconomic characteristics but does not capture aggregate feedback effects from behavioral responses. - Simulations: - The paper conducts two sets of simulations to analyze how changes in taxes and benefits stabilize income during an adverse shock (details of the two sets are described in the full analysis). ### Main analytical objectives and contributions - Quantify aggregate and distributional effectiveness of pandemic-related fiscal support, including job-retention schemes, in stabilizing household income across EU countries. - Assess targeting of schemes across age, gender, occupation, and education groups. - Update micro-based estimates of the degree of income stabilization (automatic stabilizers and discretionary pandemic measures) relative to pre-pandemic literature. *IMF Working Paper — ANNEX I. SIMULATIONS OF SHOCKS IN EUROMOD* ### 1. The first set of simulations aims to assess to what extent income was stabilized in an adverse shock before ### 1. The first set of simulations aims to assess to what extent income was stabilized in an adverse shock before ### Simulation design and methodology - Two main sets of simulations: - First set: baseline (2019 tax-benefit system; 2019 EU-SILC microdata) versus income shock scenario (2019 tax-benefit system with a uniform 5-percent decline in market incomes of all individuals in the 2019 EU-SILC microdata). - Second set: “COVID-19” scenario (2020 tax-benefit system; 2019 EU-SILC microdata adjusted to match 2020 labor market conditions using the Labor Market Adjustment (LMA) Add-on) versus counterfactual “no COVID-19” scenario (no pandemic-related labor market transitions; 2020 tax-benefit system; 2019 EU-SILC microdata). - EUROMOD conventions and uprating adjustments applied when reference years differ between microdata and tax-benefit system (example: align household income reference year 2018 with 2019 tax and benefit system). - Income stabilization measured by an income stabilization coefficient, defined as: - IIS = [1 − (Σ ΔY_h / N) / (Σ ΔM_h / N)] × 100 = [ (Σ ΔT_h / N) / (Σ ΔM_h / N) + (Σ ΔI_h / N) / (Σ ΔM_h / N) − (Σ ΔB_h / N) / (Σ ΔM_h / N) ] × 100 - Where ∆M_h (∆Y_h) is the change in market (disposable) income of household h; ∆T_h, ∆I_h, and ∆B_h refer to changes in personal income taxes, social insurance contributions, and social benefits, respectively. - Social benefits include unemployment benefits, social assistance and housing benefits, family and education benefits, health and disability benefits; in COVID-19 simulations social benefits also include monetary compensation from short-time work programs, wage subsidies, and similar schemes for self-employed (job-retention schemes). - Interpretation: coefficient = 100 percent when market income shock fully absorbed; zero means no absorption. ### Income stabilization before the pandemic (adverse 5-percent uniform shock) - Aggregate findings: - The tax and benefit system in the EU countries, on average, can absorb 37 percent of an adverse income shock (5 percent decline in market income). - Estimates align with other studies (examples cited: Dolls and others 2012; Mohl, Mourre, and Stovicek 2019; Coady and others 2023). - Component contributions (average, EU): - Personal income tax absorbs 24 percent of the adverse income shock on average, accounting for more than half of total income stabilization in most countries. - Social insurance contributions and social benefits together absorb a total of 12 percent of the income shock. - Cross-country and tax progressivity variation: - Personal income tax stabilization ranges from 14 percent in Bulgaria to 57 percent in Belgium. - Countries with more progressive personal income taxes (Belgium, Denmark, Ireland) tend to have higher stabilization from taxes (personal income taxes absorbing at least 40 percent). - In countries with limited progressivity of income taxes (Bulgaria, Romania), social protection systems contribute over 70 percent of income stabilization during adverse shocks. - Distributional patterns across household income quintiles (EU averages): - Stabilization coefficients range from 32 percent for the poorest income quintile to 39 percent for the top income quintile. - For the lowest income quintile, social insurance and benefits contribute about two-thirds of overall stabilization (20 percent out of 32 percent). - For the top income quintile, social insurance and benefits contribute about one-quarter of overall stabilization (10 percent out of 39 percent). - Social insurance contributions stabilize income by about 11 percent, broadly similar across the household income distribution. - Sensitivity notes: - The scenario considers a uniform 5-percent decline without a change in unemployment, muting stabilization from unemployment insurance and assistance. - Alternative scenarios with higher unemployment likelihood show similar overall stabilization coefficients, but with unemployment income support playing a larger role. ### Income stabilization during the pandemic (2020) - Aggregate income changes and fiscal impact: - Simulations show market incomes for households fell by 5.3 percent on average in 2020. - Largest market income drops of more than 10 percent observed in Malta, Ireland, Italy, and Slovakia. - Some economies faced smaller declines in market income, less than 2 percent (Denmark, Luxembourg, the Netherlands). - Fiscal support measures mitigated part of the income shock, producing a milder drop of disposable income by 1.6 percent in 2020. - In some countries disposable income rose slightly because of large fiscal support measures at the onset of the pandemic (Croatia, Denmark, Luxembourg). - Distributional outcomes: - Before fiscal support, market incomes fell by a median of 4.6 percent for households in the lowest income quintile and by 3.7 percent for households in the top income quintile. - After accounting for tax-and-benefit stabilization and pandemic fiscal support, disposable income remained broadly unchanged for lower-income households, while it declined by about 2 percent for households at the top income quintile. - Conclusion: fiscal policy during the pandemic was impactful and progressive, particularly mitigating shocks for low-income households. ### Role and contribution of fiscal support and job-retention schemes - Overall stabilization with fiscal support: - Together with pre-existing automatic stabilizers, fiscal support absorbed 78 percent of the decline in market incomes across countries during the pandemic — almost double the stabilization observed before the pandemic. - Country variation: stabilization ranged from 55 percent in Malta and Poland to almost 100 percent in Belgium and Denmark. - Component contributions during the pandemic (average, EU): - Job-retention schemes stabilized 47 percent of the income shock on average. - This represents about 60 percent of the overall income stabilization (47 out of 78 percent). - Job-retention schemes include compensation received by employees on short-time work schemes, wage subsidies, and similar schemes for self-employed. - Unemployment income support compensated for 9 percent of adverse shocks (muted given modest rise in unemployment rates). - Job-retention schemes were more impactful in countries where workers receive higher compensation rates for hours not worked (examples: Czech Republic, Denmark, Slovak Republic). - Distributional contribution of job-retention and other components: - Tax and benefit systems together with pandemic-related support absorbed 88 percent of the income shock for lower-income households during the pandemic, versus 72 percent for households in the top quintile. - Job-retention schemes account for the bulk of the overall stabilization across income groups. ### Correlation with aggregate outcomes and interpretation - Strong correlation observed between estimated income stabilization coefficients and actual aggregate data: - Countries with stronger income stabilization tended to experience smaller declines in per-capita real disposable income and real per-capita private consumption expenditure. - The paper notes evidence of correlation, not necessarily causation. *IMF WORKING PAPER: How Effective were Job-Retention Schemes during the COVID-19 Pandemic? A Microsimulation Approach for European Countries.* ### 1. Income stabilizatio n co efficien t 2. Contribution of job-reten tion sch emes ### wpiea2023003-print-pdf - 1. Income stabilizatio n co efficien t 2. Contribution of job-reten tion sch emes ### Key findings on income stabilization and job-retention schemes - Job-retention schemes compensated 64 percent of income shocks for lower-income households (across the board for all income groups, particularly for lower-income households compensating 64 percent of their income shocks). - Job-retention schemes absorbed nearly 80 percent of market income shocks—almost doubling the extent of the automatic stabilization of the pre-pandemic tax and benefit systems. - In the absence of job-retention schemes: - Unemployment rates in the EU could have risen by additional 3 percentage points. - Disposable income inequality could have deteriorated further; simulations suggest the pandemic could have led to a rise in market income inequality by 0.65 percentage points. - Without job-retention schemes, disposable income inequality could have risen by 0.38 percentage points compared to the “no COVID-19” scenario. - After accounting for pandemic-related fiscal support, inequality in disposable income during the pandemic decreased by 0.24 percentage points on average. - In a few countries, job-retention schemes stabilized more than 100 percent of the income shocks for the lowest income quintile (because some schemes provided a lump sum support and may have more than compensated for the income losses for households earning very little income). ### Distributional patterns by income quintile and consumption correlation - Income quintiles are calculated at the country level based on household’s market income in the “no COVID-19” scenario. - Job-retention schemes include compensation received by employees on short-time work schemes, wage subsidies, as well as similar schemes for self-employed. - Figure analysis: Change in income stabilization coefficient (difference between coefficients simulated during the pandemic and pre-pandemic levels) is positively correlated with real per capita consumption % change (2019-20), with fitted line y = 0.1163x -11.605 and R² = 0.1687 (scatter includes AUT, BEL, CYP, CZE, DNK, EST, GRC, ESP, FIN, FRA, HUN, IRL, ITA, LTU, LUX, LVA, MLT, NLD, POL, PRT, SVK, ROU, SWE, SVN). ### Worker groups and sectoral heterogeneity - Income stabilization coefficients calculated for working-age individuals (aged 15-64) and for groups by age, gender, and education using: - IStab_j = (1 − (Σ ΔY_i / N_j) / (Σ ΔM_i / N_j)) × 100 (Equation (2) as presented) - Average stabilization across groups: - Tax and benefit systems, including job-retention schemes, absorbed about 75 percent of the income losses for young workers (aged 18-24) during the pandemic, compared to 70 percent for those aged 25 and older. - Income stabilization absorbed over 72 percent of the income shock for workers with lower education and 72 percent for females. - Job-retention schemes absorbed almost half of the income shock for young workers and about 40 percent for other groups. - Workers in contact-intensive sectors experienced stronger stabilization from job-retention schemes: - Trade, transport, food & accommodation sectors absorbed 75 percent of income losses on average. - Stabilization is lower for less contact-intensive sectors such as finance and agriculture. - Country variation is substantial across worker groups and sectors; boxes show interquartile ranges with medians and 5th/95th percentiles (as in Figures 10 panels 2 and 4). ### Regression analysis (specification and empirical results) - Regression specification (3): - IStab_i,c = α + X_i,c β + Y_c γ + C_c δ + ε_i,c - IStab_i = (1 − ΔY_i / ΔM_i) × 100 - X_i,c includes age, gender, education level, contact intensity of sector, market income quintile. - Y_c includes: allowance of the job-retention scheme (percent of lost income that a worker receives for hours not worked), change in cyclically adjusted primary deficit between 2019 and 2020 in percent of potential GDP, net replacement rate in unemployment, percentage change in average number of hours worked per worker between 2019 and 2020. - Includes country dummy vector C_c. - Estimation results provide evidence that: - Tax and benefit systems and pandemic-related support stabilized income more strongly for female workers, workers in contact-intensive industries, and those with lower education (corresponding coefficients are positive and statistically significant). - Households in lower-income quintiles tend to have higher stabilization coefficients. - Countries with higher allowance rates of job-retention schemes or more generous unemployment benefits show stronger income stabilization. - Income stabilization effects are weaker in countries that experienced a larger decline in working hours for workers. - Countries with stronger counter-cyclical fiscal responses (change in cyclically adjusted primary balance) have greater income stabilization. - Selected coefficient highlights from pooled OLS (Table 2): - Age between 15-24: coefficients vary; e.g., 1.953, 1.602, 1.594 in columns shown. - Age between 55-64: -2.307***, -2.164**, -2.164**, -1.651**, -1.769** (across columns). - Female: 4.447***, 4.027***, 4.087***, 1.740***, 1.918***. - Education level, low: 4.937***, 4.441***, 5.064***, 0.906, 1.308. - Contact-intensive: 3.876***, 5.593***, 2.896***, 2.607***. - Contact-intensive × Education level, low: -2.383**, -0.597, -1.462. - Market income, the lowest quintile: 3.713, 3.939 (columns shown). - Market income, 20th to 40th percentile: 3.813**, 3.867*. - Market income, 60th to 80th percentile: -3.605**, -3.253*. - Market income, the top quintile: -11.40***, -11.13***. - Job retention scheme allowance, percent of lost income: 0.495***. - Change in cyclically adjusted primary deficit 2020: 2.378***. - Net replacement rate in unemployment, percent of previous income: 0.407***. - Hours per worker, percentage change in 2020: 0.372***. - Observations: 48,945 in most specifications; 41,365 in last column shown. - Number of countries: 26 in most specifications; 21 in last column shown. - Constant terms reported (e.g., 80.70***, 80.17***, 79.76***, 85.26***, 14.67***). ### Policy implications and conclusions - Job-retention schemes during the pandemic were timely, effective, and well-targeted; they provided significant income stabilization, particularly for vulnerable workers (lower-income families, youth, low-skilled workers). - Job-retention schemes are complementary to unemployment income support because they operate on different margins (working hours versus unemployment) and insure different types of workers. - Policy guidance: - Job-retention schemes can be expanded or broadened depending on the severity of shocks, particularly to cover workers not qualified for regular unemployment income support. - It is crucial to link the generosity of job-retention schemes to economic activity and incentivize return to normal working hours. - Job-retention schemes are most appropriate for deep but short-lived disruptions to labor markets. - If adverse shocks are persistent, policies should transition from preserving jobs to supporting workers and facilitating job-to-job transitions to avoid hindering necessary reallocation. ### Annex I — Simulations in EUROMOD (methodology) - Simulations based on EUROMOD I4.0+ and microdata from the 2019 EU-SILC (excluding Germany and United Kingdom for broader EUROMOD scope). - EUROMOD simulates individual and household tax liabilities and benefit entitlements according to 2019 policy rules; some instruments (contributory benefits and pensions) are taken directly from data due to lack of employment history in surveys. - Two scenarios for pre-pandemic income stabilization assessment: - Baseline: 2019 EU-SILC household-level microdata and 2019 tax and benefit policies. - Uniform market income shock: negative 5-percent reduction of market income across all households with nonnegative income. - Income stabilization coefficients computed as changes in market and disposable incomes between baseline and shock scenarios for each household, and aggregated by country and income quintile. *Source: Authors’ calculations and estimates in IMF Working Paper “How Effective were Job-Retention Schemes during the COVID-19 Pandemic? A Microsimulation Approach for European Countries.”* ### Annex Table I.1. Uniform Market Income Shock ### Annex Table I.1. Uniform Market Income Shock ### Definitions and notation - M stands for market income. - T(M) includes direct income taxes and social insurance contributions payable by households. - B(M) is the benefits accrued to households. - Y is the disposable income: Y = M - (T(M) - B(M)). - Variables with an apostrophe denote the shock scenario (e.g., M'). ### Simulation scenarios and purpose - Objective: Gauge the size of income stabilization during the pandemic by comparing two hypothetical scenarios. - Scenarios: - "No COVID-19": - Input microdata: 2019 EU-SILC household-level microdata. - Policy rules: 2020 tax-benefit policies. - Adjustment: none (baseline). - "COVID-19": - Input microdata: 2019 EU-SILC microdata adjusted to match the 2020 labor market conditions. - Policy rules: 2020 tax-benefit policies. - Adjustment: Labor Market Adjustment (LMA) Add-on used to simulate transitions between employment, unemployment and job-retention schemes based on the European Labor Force Survey and other administrative data. - Income shock variant illustrated: - Baseline input microdata: 2019 SILC and 2019 tax-benefit policy rules. - Income shock input microdata: 2019 SILC & 5% negative market income shock with 2019 tax-benefit policy rules. ### Measurement and metrics - Changes computed: - Changes in market incomes (ΔM = M' - M). - Changes in disposable incomes (ΔY = Y' - Y), using Y = M - (T(M) - B(M)). - Income stabilization coefficients: - Calculated for each country by comparing "no COVID-19" and "COVID-19" scenarios. - Also calculated for every income quintile and for worker groups within each country to estimate stabilization across the income distribution and labor-force subgroups. ### Data and tools referenced (as described in text) - Input microdata: 2019 SILC. - Tax-benefit policy rules: 2019 and 2020 tax-benefit policy rules are used across scenarios as specified. - LMA Add-on: EUROMOD LMA Add-on used to adjust microdata and simulate labor market transitions. *How Effective were Job-Retention Schemes during the COVID-19 Pandemic? A Microsimulation Approach for European Countries — Working Paper No. WP/2023/3* --- _Source: https://www.imf.org/-/media/files/publications/wp/2023/english/wpiea2023003-print-pdf.pdf_