## _cr16220 ## Source details **Canonical URL:** [_cr16220](https://www.imf.org/-/media/websites/imf/imported-full-text-pdf/external/pubs/ft/scr/2016/_cr16220.pdf) ## Other formats - [Markdown version](/-/media/websites/imf/imported-full-text-pdf/external/pubs/ft/scr/2016/_cr16220.pdf.md) - [Structured JSON version](/-/media/websites/imf/imported-full-text-pdf/external/pubs/ft/scr/2016/_cr16220.pdf.json) --- ### Demographic developments and headline findings - Workforce aging reduces growth in labor productivity, mainly through a negative effect on TFP growth. - Projected workforce aging will reduce TFP growth by an average of 0.2 percentage points every year over the next two decades. - Historical effect: workforce aging in the euro area has lowered TFP growth by about 0.1 percentage points each year over the past two decades. - Magnitude estimates: - A 1 percentage point increase in the ratio of workers aged 55+ to the total workforce is associated with a reduction in the growth rate of labor productivity of between 0.2 and 0.6 percentage points per annum. - A 5 percentage point increase in the share of workers aged 55–64 leads to a decline in the growth of output per worker of between 1.1 and 3.2 percentage points. - Demographic drivers and patterns: - Declining fertility rates and increased life expectancy; immigration offsets partially but not fully. - Sharp increase in the share of seniors (workers aged 55+) in the labor force, especially in Spain, Italy, Portugal, Greece and Ireland. - Old-age dependency ratio projected to rise considerably across the euro area. - Contextual benchmark: EC forecasts that most countries are expected to post average TFP growth rates less than 1 percent every year over the projection horizon referenced. ### Mechanisms: how workforce aging affects productivity - Age–productivity profiles: - Productivity typically rises until workers’ 40s and declines toward end of working life due to knowledge depreciation, lower innovation, and adaptation difficulties. - Innovation and adoption channels: - Innovators’ median age ~48; adopters median ~40. - Negative impact of the 50–59 age group on patent applications noted in empirical patterns. - Sectoral composition channel: - Aging associated with shrinking manufacturing share and relative growth of services; aggregate effects may exceed firm- or sector-level effects because of externalities. - Empirical decomposition: - Bulk of adverse effect operates through TFP growth rather than factor accumulation. ### Empirical design and identification - Baseline panel: euro area countries over 1950 to 2014. - Baseline estimating equation (growth of real output per worker): - Δlog YW_it = θ1 w55_it + θ2 YADR_it + θ3 OADR_it + u_i + η_t + ε_it - Definitions: YW = real output per worker; w55 = share of workforce aged 55–64; YADR and OADR = youth and old dependency ratios. - Production decomposition (Cobb–Douglas approach) per Wong (2007): - y_it = (K/Y)_it^(α/(1−α)) A_it h_it (log decomposition into capital intensity, human capital, and TFP) - Endogeneity strategy: - Instrument w55 by the population share aged 45–54 ten years prior. - Instrument youth and old dependency ratios with population share under age 4 and population share aged 55 and 59 ten years prior. - Robustness: instrument with lagged birth rates 40, 30, and 10 years ago (reduced sample size). - Additional checks: - Normalize output and TFP by hours worked (OECD hours data) — results persist for D.lnYH and D.lnAH. - Control for full age-distribution (shares for 30–39, 40–49, 50–54, 55–64 with 15–29 excluded) — coefficient on 55–64 remains negative and significant. - Instrument diagnostics: - First-stage diagnostics indicate instruments are strong (F-stat, Shea R2 exceed conventional thresholds). ### Quantitative results, historical decomposition, and projections - Empirical magnitudes (preserved exactly as estimated): - 1 percentage point increase in 55+ ratio → reduction in labor productivity growth between 0.2 and 0.6 percentage points per annum. - 5 percentage point increase in share aged 55–64 → decline in output-per-worker growth between 1.1 and 3.2 percentage points. - Past two decades: workforce aging lowered TFP growth by about 0.1 percentage points each year on average across the euro area. - Projected 2014–2035: workforce aging could lower TFP growth by about 0.2 percentage points each year. - Interpretation: absent adverse impact of aging, TFP growth could be higher by about a quarter over the next two decades (relative to projections with aging). - Cross-country heterogeneity: - Latvia, Lithuania, Finland, Netherlands and Germany saw workforce aging shave off 0.2 percentage points of TFP growth every year during 1984–2014 (decompositions based on Table 2 column 4 point estimates). - Countries expected to be worst affected by workforce aging through 2035: Greece, Spain, Portugal, Italy, Slovenia, Slovakia and Ireland — average increase in the share of old workers in total workforce about 10 percentage points between 2020 and 2035. - Table summary highlights (selected exact coefficients from source): - Table 1 (OLS) — workforce share 55-64: D.lnA coefficient -0.160*; D.lnA PWT -0.142***. - Table 2 (IV, births 10,30,40 years) — workforce 55-64: D.lnA -0.317**; D.lnA PWT -0.220***. - Table 3 (purging hours, IV) — workforce 55-64: D.lnYH -0.603***; D.lnAH -0.759***. - Table 4 (full age distribution) — workforce 55-64: D.lnA -0.380*; D.lnAH -0.515***. - Table 5 (Policy interactions, dependent variable D.lnA) — W55-64 main effects: -1.252** (col 1), -0.801** (col 2), -0.360 (col 3), -0.00542 (col 4), -0.551** (col 5), -0.571*** (col 6). - Table 5 interactions (W5564 * policy): - W5564*Lagged Physician density: 0.351* (t-stat 2.083). - W5564*ALMP reform dummy (increase in ALMP on training): 0.422** (t-stat 2.385). - W5564*Tax wedge reform dummy: 0.444** (t-stat 2.249). - W5564*Labor market reform dummy (reduction in EPLR): 0.469 (t-stat 1.502). - W5564*Lagged Public sector spending on R&D (in GDP): 2.691*** (t-stat 3.038). - W5564*Lagged Private sector spending on R&D (in GDP): 0.160 (t-stat 0.938). - Additional policy main effects in Table 5: - Lagged Physician density to population: -0.0805** (t-stat -2.816). - Tax wedge reform dummy: -0.046** (t-stat -2.324). - Lagged Public sector spending on R&D (in GDP): -0.229** (t-stat -2.537). - Appendix I population and labor-force pressure tables (selected exact series): - Population share age 65+ (Japan): 2015 26.8 / 2020 29.1 / 2025 30.3 / 2030 31.6 / 2035 33.4. - Labor force share 55–64 (Spain): 2014 13.1 / 2020 19.0 / 2025 22.2 / 2030 25.4 / 2035 27.5. ### Policy implications and recommendations on aging - Policy priority: measures that support TFP growth because most aging-related productivity drag operates through the TFP channel. - Specific policy measures (empirically tested in interaction terms): - Broaden access to health services (increase physician density) — interaction reduces the adverse effect of aging (Table 5 col 1). - Strengthen Active Labor Market Policies (ALMP) focused on training and re-training — interaction positive and significant (Table 5 col 2). - Reduce the tax wedge to increase labor mobility and incentives to delay retirement — interaction positive and significant (Table 5 col 3). - Promote public R&D spending — W5564*Public R&D 2.691*** (t-stat 3.038) (Table 5 col 5). - Increase labor market flexibility (EPLR reforms) — expected sign but weaker statistical significance (Table 5 col 4). - Policy focus should prioritize health access, workforce training, lowering tax wedges, and public R&D to ameliorate aging’s productivity drag. --- ### Corporate leverage, firm size, and investment (executive summary and key results) - Main finding: negative relationship between firm debt and investment, stronger for SMEs than large firms. - Crisis and investment facts: - Gross real investment by NFCs in the euro area slumped almost 15 percent during the global financial crisis, equal to over 2 percentage points of GDP. - For Greece, Ireland, Italy, Portugal, and Spain the fall was about 20 percent. - SMEs account for over half of gross corporate investment in the euro area; SMEs account for over 65 percent of value-added in the euro area in recent years. - Sample and scope: - Over six million observations, about 1.5 million firms in eight euro area countries (Austria, Belgium, Germany, France, Italy, Finland, Spain, and Portugal) over 2001–2013. - Key quantitative results (preserved exactly): - A 10 percentage point rise in a large firm’s leverage → a 3 percentage point fall in the investment-to-capital ratio on average. - For SMEs, a 10 percentage point rise in leverage → about -3.5 percentage points lower investment (about 20 percent larger than large firms). - A 10 percentage point rise in real sales growth → 5 percentage points rise in investment-to-capital for large firms; about 2.5 percentage points rise for SMEs. - Leverage reduces the effect of demand on investment: moving from the 10th percentile to the 90th percentile of leverage (about 5 percent to 40 percent) → about a further 0.5 percentage points lower investment response to a 10 percentage point rise in sales. - Descriptive sample statistics (selected exact figures from Table 1): - Large firms: Net investment ratio N 139,077; mean 13.6; sd 43.1; p25 -6.7; p50 3.4; p75 18.2; min -99.9; max 200.0. - SMEs (All): Net investment ratio N 10,000,000; mean 14.5; sd 57.1; p25 -14.9; p50 -1.9; p75 17.7; min -100.0; max 200.0. - All firms: Debt-to-Assets percent N 9,062,834; mean 17.6; sd 21.2; p25 0.0; p50 9.6; p75 28.5; min 0.0; max 100.0. - Regression evidence (Table A1 summary): - Leverage, lagged: Column (1) -0.362*** (t = -17.07); Column (5) -0.278*** (t = -16.91). - Leverage * SME dummy: Column (3) -0.0681*** (t = -2.813); Column (5) -0.0756*** (t = -3.242). - Sales growth: Column (1) 0.260*** (t = 12.78); Column (5) 0.571*** (t = 20.35). - Long-term debt: Column (1) -0.0539*** (t = -8.471); Column (5) -0.0544*** (t = -8.531). - Assets, log: Column (1) 8.929*** (t = 25.71); Column (5) 8.896*** (t = 25.71). - Country heterogeneity: - Debt overhang effects particularly strong in Austria, Finland, France, and Spain. - Spain and Italy exhibit particularly strong negative effects of leverage on investment. - Policy implications for corporate sector: - Reduce high firm leverage and boost firm size to spur investment and improve monetary/fiscal transmission. - Develop alternatives to bank-based financing to reduce SME vulnerability. - Address non-performing loans to improve bank profitability and lending. --- ### Negative Interest Rate Policy (NIRP), bank profitability, and TLTRO II (selected findings) - Context and use: - Central banks adopted negative policy rates in mid-2014 to early 2016 (examples: DN, ECB, SR, SNB, BoJ, MNB). - Many central banks introduced tiered deposit rates to reduce banks’ cost of holding excess reserves while allowing pass-through to money markets. - Advantages of NIRP: - Removes ZLB, helps avoid deflation equilibrium, flattens the yield curve, encourages portfolio rebalancing, lowers risk premia, supports credit creation and asset prices. - ECB staff estimate negative rates contributed about one percent to corporate lending growth since July 2014. - Disadvantages and risks: - Sticky retail deposit rates compress net interest margins (NIMs). - Retail deposit stickiness reasons include small excess liquidity for households/small businesses and psychological threshold at zero percent. - Prolonged negative rates could raise financial stability concerns (substitution to wholesale funding, distorted long-term debt affordability, delayed exit of nonviable firms). - Empirical impacts and mitigation: - Estimated small effect on banks’ NIMs: 7 basis points for a 50-basis point reduction in the policy rate. - Monthly ECB asset purchases increased from €60 billion to €80 billion; deposit rate reduced to -0.4 percent (down from -0.3 percent). - Estimated average NIM reduction from recent measures: 11 basis points (Germany 5 bps; France 4 bps; Italy 11 bps; Spain 13 bps). - Required aggregate lending growth to offset NIM decline: 2.3 percent annually (up from 1.8 percent at end-January) to maintain profitability. - TLTRO II mechanics and role: - From June, banks can borrow up to 30 percent of eligible non-mortgage private loans over a four-year period at the prevailing MRO rate. - Conditional liquidity at the marginal policy rate if banks exceed a net new lending benchmark of at least 2.5 percent by January 2018; unconditional liquidity at MRO rate if benchmark not met. - TLTRO II could mitigate adverse impact of NIRP by realigning refinancing costs to marginal policy rate, incentivizing new lending, and supporting pass-through. - Tiered reserve regimes and direct cost arithmetic: - Example benchmark: peak charge in Switzerland 0.03 percent of total banking sector assets. - Eurosystem balances: banks’ overnight deposits €310 billion and current account balances €613 billion → about €923 billion; minimum reserve requirement €115 billion remunerated at 0 percent → excess reserves €808 billion subject to -0.4 percent marginal rate. - If tiering excludes 75 percent of excess reserves from negative rate, Eurosystem could theoretically tolerate negative deposit rate up to -1.6 percent without increasing direct cost (tiering arithmetic in source). - Country examples and Appendix II summaries: - Denmark: deposit rate history and use to defend peg; introduced record -0.75 percent in Jan-Feb 2015 sequence. - Sweden: Riksbank reserve repo at -0.50 percent (Feb 2016); QE SEK40 billion. - Japan: three-tier reserve system with marginal tier at -0.1 percent (Feb 16 effective). - Switzerland: deposit rate -0.75 percent (Jan 22, 2015) with exemption threshold. - Hungary: MNB deposit rate -0.05 percent effective March 23, 2016; targeted measures to boost SME lending. --- ### Stagnation scenario, policy package, and macro outcomes - Stagnation scenario assumptions (2017–21): - Investment ¼ percentage point lower each year. - Sovereign and corporate risk premia in high-debt countries (Greece, Ireland, Italy, Portugal, Spain) 75 basis points higher. - TFP growth rate 0.1 percentage point lower each year. - Stagnation impacts: - Investment level about 6 percent lower than baseline by 2021. - GDP growth about 1.1 percent in the medium term, about 0.4 percentage point lower than baseline. - Inflation averaging just 1 percent for 2017–21. - Government deficits ¼ percentage point higher on average during 2017–21. - Euro area public debt-to-GDP rises by 3 percentage points to 86 percent by 2021; larger rises in Portugal (+7 pp) and Italy (+5 pp). - Global output 0.4 percentage point lower than baseline by 2021. - Policy response package (assumed): a) One-year extension of APP until March 2018 at €80 billion/month. b) Targeted centralized investment: EFSI-induced 0.2 percent of euro area GDP each year 2017–21 plus additional 0.1 percent in 2017–18 targeted to countries with projected negative output gaps >1 percent. - Outcomes of policy package: - Growth would exceed WEO baseline in 2017 and 2018: 2.0 percent in 2017 and 1.8 percent in 2018, returning to ~1½ percent thereafter. - Output gap closed by 2018 (earlier than WEO baseline closure in 2021). - Inflation to reach 1.5 percent in 2017, 1.8 percent in 2018, and 2 percent in 2019. - Euro area public debt in 2021 would be 2 percent of GDP lower at 81 percent of GDP relative to baseline. - Numerical table comparisons (selected exact entries): - April 2016 WEO: Real Growth 2017 = 1.6; 2018 = 1.6; 2021 = 1.5. Inflation (HICP) 2017 = 1.1; 2018 = 1.3; 2021 = 1.7. - Stagnation scenario plus policies: Real Growth 2017 = 2.0; 2018 = 1.8; 2021 = 1.6. Inflation 2017 = 1.5; 2018 = 1.8; 2021 = 2.2. - Policy conclusion: - A comprehensive strategy combining continued monetary accommodation, fiscal support and SGP flexibility, centralized investment, balance-sheet cleanup, and structural reforms can significantly raise growth and inflation relative to the stagnation outcome and produce positive spillovers to the rest of the world. --- *Source: _cr16220 - 1. Demographic Developments (page references as listed in the content unit).* ### 1. Demographic Developments _________________________________________________________ 4 ### 1. Demographic Developments ### Major sections and themes - Section 1. Demographic Developments — page 4 - Section 2. Aging and the Macroeconomy — page 7 - Section 3. Aging and Aggregate Productivity — page 8 - Section 4. Demographic Impact — page 13 - Section 5. Effects of Policies — page 20 - References — page 21 - Appendix I. Aging and Productivity — page 23 ### Empirical tables (titles and page locations) - Table 1. OLS Estimates of the Effects of Aging on Output per Worker and TFP Growth — page 16 - Table 2. Controlling for the Endogeneity Bias. Instrumental Variable Estimates — page 17 - Table 3. Purging the Effect of Hours Worked — page 18 - Table 4. Controlling for the Entire Age Distribution — page 19 - Table 5. Effects of Policies — page 20 ### Figures and supporting materials (as indicated in the unit) - Headline figures and graphical material accompanying sections on aging, productivity, demographic impact, and policy effects (figure locations embedded in the unit; specific figure numbers not listed in this content unit) ### Structure implied for substantive analysis (by section headings) - Demographic Developments: framing of population trends and demographic metrics (Section 1) - Macro links: channels through which aging affects macroeconomic aggregates (Section 2) - Productivity focus: relationships between aging and aggregate productivity, including estimation strategies (Section 3) - Quantification: demographic impacts on output and productivity measures (Section 4) - Policy evaluation: assessment of policy options to mitigate demographic effects (Section 5) - Empirical support: multiple econometric approaches including OLS, instrumental variables, controls for hours worked, and full age-distribution controls (Tables 1–4) - Policy simulation/assessment: dedicated tabulation of policy effects (Table 5) - Appendix: additional methodological detail on aging and productivity (Appendix I) *Source: _cr16220 - 1. Demographic Developments (page references as listed in the content unit).* ### References ____________________________________________________________________________ 94 ### THE IMPACT OF WORKFORCE AGING ON EURO AREA PRODUCTIVITY ### Overview and key findings - Workforce aging reduces growth in labor productivity, mainly through a negative effect on TFP growth. - Projected workforce aging will reduce TFP growth by an average of 0.2 percentage points every year over the next two decades. - Historical effect: workforce aging in the euro area has lowered TFP growth by about 0.1 percentage points each year over the past two decades. - Magnitude estimates: - An increase in the ratio of workers aged 55+ to the total workforce of 1 percentage point is associated with a reduction in the growth rate of labor productivity of between 0.2 and 0.6 percentage points per annum. - An increase in the share of workers aged 55–64 by 5 percentage points leads to a decline in the growth of output per worker of between 1.1 and 3.2 percentage points. - Contextual benchmark: EC forecasts that most countries are expected to post average TFP growth rates less than 1 percent every year over the projection horizon referenced. ### Aging headwinds and demographic projections - Drivers of aging: declining fertility rates and increased life expectancy; immigration offsets partially but not fully. - Demographic impacts highlighted: - Sharp increase in the share of seniors (workers aged 55+) in the labor force, especially in Spain, Italy, Portugal, Greece and Ireland. - Old-age dependency ratio projected to rise considerably across the euro area. - Macroeconomic implications of aging (distinct channels): - Higher dependency ratio: mechanically fewer workers per population → lower per capita GDP; lifecycle effects on savings and public finances. - Workforce aging: shift in workforce composition from younger to older cohorts affects average output per worker through age-specific productivity profiles and innovation/adoption dynamics. ### Mechanisms through which workforce aging affects productivity - Age–productivity profiles: productivity typically rises until workers’ 40s and declines toward end of working life due to depreciation of knowledge, lower innovation, and adaptation difficulties. - Innovation channel: older cohorts associated with lower patenting and innovation activity (cited empirical patterns: innovators’ median age ~48; adopters median ~40; negative impact of 50–59 age group on patent applications). - Sectoral composition channel: aging associated with shrinking manufacturing share and relative growth of services; aggregate effects may exceed firm- or sector-level effects due to externalities. - Empirical decomposition finds the bulk of the adverse effect operates through TFP growth rather than factor accumulation. ### Empirical design and econometric approach - Baseline panel sample: euro area countries over 1950 to 2014. - Baseline estimating equation (growth of real output per worker): - Δlog YW_it = θ1 w55_it + θ2 YADR_it + θ3 OADR_it + u_i + η_t + ε_it - Definitions: YW = real output per worker; w55 = share of workforce aged 55–64; YADR and OADR = youth and old dependency ratios. - Transmission channels estimated via methodology of Wong (2007) using Cobb–Douglas production framework: - y_it = (K/Y)_it^(α/(1−α)) A_it h_it (log decomposition into capital intensity, human capital, and TFP) - Endogeneity addressed through instrumentation strategies: - Instrument w55 by the population share aged 45–54 ten years prior. - Instrument youth and old dependency ratios with population share under age 4 and population share aged 55 and 59 ten years prior. - Robustness: instrument workforce aging and dependency ratios with lagged birth rates 40, 30, and 10 years ago (trade-off: much reduced sample size). - Additional robustness checks: - Normalizing output and TFP by hours worked (using OECD hours data) — results persist for output per hour (D.lnYH) and modified TFP (D.lnAH). - Controlling for the full age-distribution vector (shares for 30–39, 40–49, 50–54, 55–64 with 15–29 as excluded category) — coefficient on 55–64 remains negative and significant. - Instrument validity: first-stage diagnostics indicate instruments are strong (F-stat, Shea R2 exceed conventional thresholds). ### Quantitative results, historical decomposition, and projections - Empirical magnitudes (preserved exactly as estimated): - 1 percentage point increase in 55+ ratio → reduction in labor productivity growth between 0.2 and 0.6 percentage points per annum. - 5 percentage point increase in share aged 55–64 → decline in output-per-worker growth between 1.1 and 3.2 percentage points. - Past two decades: workforce aging lowered TFP growth by about 0.1 percentage points each year on average across the euro area. - Projected 2014–2035 horizon: workforce aging could lower TFP growth by about 0.2 percentage points each year. - Interpretation: absent the adverse impact of aging, TFP growth could be higher by about a quarter over the next two decades (relative to projections with aging). - Cross-country heterogeneity: - Some countries experienced larger past impacts (e.g., Latvia, Lithuania, Finland, Netherlands and Germany saw workforce aging shave off 0.2 percentage points of TFP growth every year during 1984–2014 in decompositions based on Table 2 column 4 point estimates). - Hours-worked adjustments and alternative age-distribution specifications do not alter core conclusions that the dominant channel is TFP. ### Policy implications and recommendations - Effective policies can mitigate adverse productivity effects of workforce aging; econometric analysis highlights the role of: - Improving health outcomes. - Boosting productivity of workers through Active Labor Market Policies (ALMP) reforms. - Reducing the tax wedge to increase labor mobility. - Spurring innovation through R&D spending. - Policy focus should prioritize measures that support TFP growth given that most of the aging-related productivity drag operates through the TFP channel. *Prepared by Shekhar Aiyar, Christian Ebeke, and Xiaobo Shao (all EUR); figures and calculations use OECD, Eurostat, European Commission, Penn World Table, AMECO, BLS, and IMF staff calculations as described in the source text.* ### 2035. We then use our econometric estimates of ### _cr16220 - 2035. We then use our econometric estimates of ### Demographic projections and TFP impact - On average aging will shave off about 0.2 percentage points of TFP growth every year until 2035. - Projected average annual TFP growth in the currency bloc is estimated at only about 0.8 percentage points per annum. - In the absence of workforce aging, euro area TFP growth through 2035 could be about one quarter higher than the current forecast. - Countries expected to be worst affected by workforce aging: Greece, Spain, Portugal, Italy, Slovenia, Slovakia and Ireland — where the average increase in the share of old workers in total workforce is about 10 percentage points between 2020 and 2035. - Largest negative impact concentrated in countries with rapid workforce aging and high debt burdens, e.g., Spain, Italy, Portugal, Greece and Ireland. - Euro Area: Share of Working Age Population by Age Group (Percent) — comparative shares shown for 2014 and 2035 in the source figures (age groups 15-24, 25-54, 55-64). ### Empirical estimates of aging effects - Estimated relationship: a 1 percentage point increase in the 55–64 age cohort of the labor force is associated with a reduction in total factor productivity of about 34 ⁄ of a percentage point. - Extrapolation: projected aging will reduce TFP growth by an average of 0.2 percentage points every year over the next twenty years. - Historical and forecasted demographic impacts visualized in figures for periods 1984–2007 and 2014–2035, with country-level variation. - Tables (selected key coefficients and significance): - Table 1 (OLS): Workforce share, aged 55-64 coefficients include -0.118* (D.lnYW), 0.0689*** (D.lnKY), -0.0292*** (D.lnHC), -0.160* (D.lnA), -0.142*** (D.lnA PWT). - Table 2 (IV, births 10, 30 and 40 years ago): Workforce share aged 55-64 coefficients include -0.221*** (D.lnYW), 0.0991** (D.lnKY), -0.0160 (D.lnHC), -0.317** (D.lnA), -0.220*** (D.lnA PWT). - Table 3 (Purging hours worked, IV): Workforce aged 55-64 -0.603*** (D.lnYH) and -0.759*** (D.lnAH) under IV. - Table 4 (Controlling for entire age distribution): Workforce aged 55-64 share — examples: -0.259* (D.lnYW), 0.141*** (D.lnKY), -0.0393 (D.lnHC), -0.380* (D.lnA), -0.515*** (D.lnAH). - Table 5 (Effects of Policies, dependent variable D.lnA): - Workforce aged 55-64 (W55-64) share coefficients: -1.252** (col 1), -0.801** (col 2), -0.360 (col 3), -0.00542 (col 4), -0.551** (col 5), -0.571*** (col 6). - Interaction terms (W5564*...): - W5564*Lagged Physician density to population: 0.351* (t-statistic 2.083) — column 1. - W5564*ALMP reform dummy (Increase in ALMP on training): 0.422** (t-statistic 2.385) — column 2. - W5564*Tax wedge reform dummy (Reduction in tax wedge): 0.444** (t-statistic 2.249) — column 3. - W5564*Labor market reform dummy (Reduction in EPLR): 0.469 (t-statistic 1.502) — column 4. - W5564*Lagged Public sector spending on R&D (in GDP): 2.691*** (t-statistic 3.038) — column 5. - W5564*Lagged Private sector spending on R&D (in GDP): 0.160 (t-statistic 0.938) — column 6. - Additional policy main effects (examples): Lagged Physician density to population -0.0805** (t-statistic -2.816); Tax wedge reform dummy -0.046** (t-statistic -2.324); Lagged Public sector spending on R&D (in GDP) -0.229** (t-statistic -2.537). - Instrumental variable strategy notes: - External instruments used include 10-year lagged population proportions and births 10, 30 and 40 years ago. - Authors prefer specifications using lagged births as instruments given stronger orthogonality to dependent variables. ### Policy analysis — conditional factors tested - Conditioning variables (P) interacted with workforce aging (w55) to test dampening effects: - Health conditions and human capital accumulation: physician density per population and active labor market policies (ALMPs) focused on training (reform dummy when ALMP spending per unemployed on training increases by more than one standard deviation). - Labor market flexibility: reforms to employment protection framework proxied by OECD indicator declines (reform dummy when indicator declines by at least 1 standard deviation). - Tax wedge: reform dummy when OECD indicator of tax wedge declines by at least 1 standard deviation. - Innovation: lagged public and private sector spending on R&D as percentage of GDP. - Estimated dampening effects (Table 5 summary): - Greater access to health services (physician density) and ALMPs focusing on training dampen the TFP growth–reducing effects of aging (columns 1 and 2). - Fiscal reforms lowering the tax wedge are critical in dampening demographic effects (column 3). - Labor market reforms increasing flexibility have the expected sign but are not statistically significant (column 4). - Government (public) R&D spending robustly associated with reduced effect of aging on TFP growth (column 5); private R&D effect remains statistically unclear (column 6). ### Living with aging — policy recommendations - Policy reforms that mitigate the impact of aging on productivity growth: - Broaden access to health services (improve physician density and health inputs relevant for older cohorts). - Strengthen active labor market policies focused on training and re-training, especially for senior workers. - Reduce the tax wedge to increase incentives to delay retirement and raise labor supply among older cohorts. - Promote innovation via higher public R&D spending to adapt technologies that disproportionately benefit older workers. - Increase labor market flexibility (noting empirical significance is weaker for EPLR reforms in these estimates). - Summary conclusion: - Workforce aging is likely to be a significant drag on European productivity growth over the next few decades. - Good policies—improving health access, workforce training, lowering tax wedges, and promoting R&D—can ameliorate the negative productivity impact of an aging workforce and may have disproportionately large benefits in rapidly aging societies such as Europe. *Sources: OECD; WEO and IMF staff calculations.* ### Appendix I. Aging and Productivity ### Appendix I. Aging and Productivity ### Population Pressure (Percent, share of age 65+ to the total population) - 2015 / 2020 / 2025 / 2030 / 2035 - Japan: 26.8 / 29.1 / 30.3 / 31.6 / 33.4 - Austria: 18.7 / 19.7 / 21.5 / 24.0 / 26.1 - Belgium: 18.2 / 19.2 / 20.7 / 22.3 / 23.5 - Denmark: 18.8 / 20.4 / 21.7 / 23.3 / 24.7 - Estonia: 18.8 / 20.4 / 22.4 / 24.2 / 25.4 - Finland: 20.2 / 22.4 / 24.1 / 25.5 / 26.3 - France: 18.8 / 20.6 / 22.1 / 23.6 / 24.8 - Germany: 21.2 / 22.4 / 24.3 / 27.2 / 29.7 - Greece: 20.1 / 21.3 / 22.9 / 24.8 / 27.2 - Ireland: 13.3 / 14.9 / 16.6 / 18.5 / 20.3 - Italy: 21.5 / 22.5 / 23.9 / 26.1 / 28.7 - Latvia: 18.6 / 18.9 / 19.9 / 20.8 / 21.2 - Lithuania: 17.1 / 17.9 / 19.7 / 21.8 / 23.2 - Luxembourg: 15.5 / 16.6 / 18.1 / 20.0 / 21.6 - Netherlands: 17.9 / 19.9 / 22.0 / 24.3 / 26.2 - Norway: 16.7 / 18.0 / 19.3 / 20.6 / 22.0 - Portugal: 20.5 / 22.6 / 24.9 / 27.5 / 29.8 - Slovakia: 13.9 / 16.6 / 19.3 / 21.4 / 23.0 - Slovenia: 17.9 / 20.4 / 22.7 / 24.8 / 26.6 - Spain: 18.6 / 20.4 / 22.8 / 25.9 / 29.3 - Sweden: 19.7 / 20.3 / 21.1 / 22.1 / 23.2 - Switzerland: 19.1 / 20.5 / 22.4 / 24.7 / 26.5 - United Kingdom: 18.0 / 19.0 / 20.2 / 21.9 / 23.2 - Canada: 15.9 / 18.0 / 20.3 / 22.6 / 23.5 - United States: 14.8 / 16.8 / 18.8 / 20.3 / 20.9 - Australia: 14.9 / 16.1 / 17.4 / 18.6 / 19.3 - New Zealand: 14.7 / 16.6 / 18.8 / 21.0 / 22.6 - Categorical labels (as presented): - Super-aged >20 - Aged 14-20 - Aging 7-14 - Not-aging <7 - Sources: OECD and IMF staff calculations. ### Labor Force Pressure (Percent, share of worker 55-64 to the labor force 15-64) - 2014 / 2020 / 2025 / 2030 / 2035 - Austria: 11.3 / 15.6 / 16.9 / 16.3 / 15.5 - Belgium: 12.9 / 16.6 / 16.4 / 15.9 / 15.7 - Denmark: 16.3 / 19.2 / 20.3 / 20.3 / 19.2 - Estonia: 17.8 / 18.9 / 19.1 / 20.8 / 21.7 - Finland: 18.2 / 18.9 / 18.3 / 16.7 / 16.8 - France: 14.3 / 15.8 / 17.4 / 17.6 / 16.6 - Germany: 18.2 / 22.0 / 23.7 / 21.7 / 20.1 - Greece: 11.5 / 18.5 / 21.3 / 23.7 / 25.4 - Ireland: 13.4 / 15.5 / 16.9 / 19.2 / 21.5 - Italy: 14.8 / 19.9 / 23.6 / 25.8 / 25.8 - Latvia: 16.8 / 18.1 / 18.5 / 19.3 / 19.0 - Lithuania: 16.3 / 18.0 / 19.1 / 19.8 / 19.2 - Luxembourg: 10.3 / 12.7 / 13.4 / 12.9 / 12.2 - Netherlands: 16.2 / 18.5 / 20.2 / 19.6 / 17.8 - Norway: 16.6 / 17.2 / 17.9 / 17.9 / 16.8 - Portugal: 14.9 / 18.4 / 20.4 / 22.5 / 24.1 - Slovakia: 13.6 / 14.9 / 15.2 / 17.2 / 20.6 - Slovenia: 11.4 / 17.2 / 18.9 / 19.3 / 20.4 - Spain: 13.1 / 19.0 / 22.2 / 25.4 / 27.5 - Sweden: 18.0 / 17.9 / 18.6 / 18.8 / 18.2 - Categorical labels (as presented): - Super-aged >15 - Aged 10-15 - Aging 5-10 - Not-aging <5 - Sources: OECD; European Comission; and IMF staff calculations. ### Investment, Firm Size, and the Corporate Debt Burden: Executive summary - Corporate investment in the euro area fell markedly with the crisis and has remained weak. - The analysis uses a large, cross-country panel dataset of firms’ balance sheets and income statements. - Main finding: a negative relationship between a firm’s debt and investment, stronger for SMEs than large firms. - Highly indebted firms are less responsive to demand. - Policy implication suggested by findings: weak euro area investment recovery may be partly due to corporate debt burdens, particularly at SMEs, which account for a large share of value-added in the euro area. ### Key background facts and data observations - Gross real investment as a share of GDP by non-financial corporations (NFCs) in the euro area slumped almost 15 percent during the global financial crisis, equal to over 2 percentage points of GDP. - For selected euro area countries (Greece, Ireland, Italy, Portugal, and Spain), the fall was about 20 percent. - Post-crisis, corporate investment has remained at the depressed level; by contrast the U.S. recovered much faster, close to levels seen pre-crisis. - SMEs account for over half of gross corporate investment in the euro area in recent years, with more highly indebted firms contributing most of this share. - The drop in 2009 investment appears largely driven by a severe fall in investment by SMEs. - Corporate leverage: - Overall corporate leverage has remained high for SMEs. - Debt growth slowed significantly after the crisis but did not result in significant deleveraging as lower debt accumulation was partially offset by declining asset values. - The pattern of leverage is similar for large firms but less pronounced. - Corporate financing structure: - Corporate financing in the euro area relies more on loans, possibly reflecting the region’s high share of SMEs in value-added. - SMEs account for over 65 percent of value-added in the euro area. - In the U.S., small businesses account for around half of GDP. - Corporate indebtedness varies significantly across the euro area; debt as a share of corporate financial assets tends to be higher in selected economies than in the core. ### Empirical strategy and main quantitative results - Sample: over six million observations, about one and a half million firms in eight euro area countries (Austria, Belgium, Germany, France, Italy, Finland, Spain, and Portugal) over 2001–2013. - Baseline model controls: firm-specific and sector-country-time fixed effects and selected firm characteristics. - Estimated effects: - A 10 percentage point rise in a large firm’s leverage is associated with a 3 percentage point fall in the investment-to-capital ratio (physical capital growth) on average. - For SMEs, a 10 percentage point rise in leverage would lower investment by about -3.5 percentage points, about 20 percent larger in magnitude than that for a large firm. - A 10 percentage point rise in a firm’s real sales growth (demand) is associated with a 5 percentage point rise in the investment-to-capital ratio of a large firm on average. - This effect is smaller by about half for SMEs. - Leverage reduces the effect of demand on investment — for an SME, moving from the 10th percentile of leverage to the 90th percentile, investment falls by about (text truncated in source). _Italic: Sources: OECD; IMF staff calculations; Banque de France, BACH database; Haver Analytics; OECD stat.; Eurostat; ECB; Board of Governors of the Federal Reserve System; Orbis; KfW SME Panel (as cited in the source content)._ ### 0.5 percentage points, to 2 percentage points. ### _cr16220 - 0.5 percentage points, to 2 percentage points. ### Key findings on leverage, firm size, and investment - Higher leverage is associated with lower investment-to-capital ratios. - For a large firm, a 10 percentage point rise in leverage is associated with physical capital growth (net investment) under 3 percentage points lower. - For an SME, a 10 percentage point rise in leverage is associated with about 3.5 percentage points lower net investment (about 25 percent larger reduction than for large firms). - Demand (real sales growth) raises investment-to-capital, but the effect is: - About 5 percentage points increase in the real investment ratio for a large firm for a 10 percentage point rise in sales. - About 2.5 percentage points increase for an SME for a 10 percentage point rise in sales (about half the large firm response). - Leverage weakens the investment response to demand: - Going from the 10th percentile to the 90th percentile of firm leverage (from about 5 percent to 40 percent leverage) is associated with about a further 0.5 percentage points lower investment response to a 10 percentage point rise in sales growth. - The negative effects of leverage and the dampening of demand responses are generally stronger for SMEs than for large firms. - These effects have increased post-crisis; firms’ investment sensitivities to leverage and demand declined since the crisis (pre-crisis 2001-2007 vs post-crisis 2008-2013). - Country heterogeneity: - Debt overhang effects are particularly strong in Austria, Finland, France, and Spain. - Spain and Italy exhibit particularly strong negative effects of leverage on investment. - Countries with larger leverage differentials between SMEs and large firms show stronger marginal effects of leverage. ### Data and sample - Data source: Orbis database (processed and cleaned by Duval and others (2016)); country-industry purchasing power parity deflators from OECD’s STAN database. - Sample coverage: - Eight euro area countries: Austria, Belgium, Finland, France, Germany, Italy, Portugal, and Spain. - Time dimension: 2001-2013. - Firms: non-financial, private firms excluding mining and resource extraction. - Sample filtered: firms with nonmissing values, positive revenue, at least three employees, and at least three consecutive observations. - Main firm-level variables used (expressed in percentage points where ratios/growth rates): - Net investment-to-capital ratio (annual change in real capital stock divided by lagged capital stock). - Debt-to-assets ratio (leverage). - SME indicator (equals one if less than 250 employees). - Real sales growth (annual percentage change in real operating revenue). - Long-term debt-to-assets ratio. - Natural logarithm of total assets. - Dataset size and descriptive statistics (selected exact figures from Table 1): - Large firms: N for net investment ratio 139,077; mean 13.6; sd 43.1; p25 -6.7; p50 3.4; p75 18.2; min -99.9; max 200.0. - Large firms: Debt-to-Assets percent N 134,989; mean 15.0; sd 17.6; p25 0.3; p50 8.2; p75 25.1; min 0.0; max 100.0. - Large firms: Real Sales growth percent N 139,057; mean 7.1; sd 25.2; p25 -4.2; p50 3.7; p75 13.2; min -100.0; max 100.0. - SMEs (All): Net investment ratio percent N 10,000,000; mean 14.5; sd 57.1; p25 -14.9; p50 -1.9; p75 17.7; min -100.0; max 200.0. - SMEs (All): Debt-to-Assets percent N 8,927,845; mean 17.7; sd 21.3; p25 0.0; p50 9.6; p75 28.5; min 0.0; max 100.0. - All firms: Net investment ratio percent N 10,200,000; mean 14.5; sd 57.0; p25 -14.8; p50 -1.8; p75 17.7; min -100.0; max 200.0. - All firms: Debt-to-Assets percent N 9,062,834; mean 17.6; sd 21.2; p25 0.0; p50 9.6; p75 28.5; min 0.0; max 100.0. - Sectoral representation: Manufacturing and wholesale, retail, and accommodation sectors highly represented; construction and professional services also prominent. - SMEs generate a large share of value-added in the euro area and account for almost 60 percent of gross corporate investment since 2010 (stylized fact reported in conclusion). ### Research design and econometric model - Baseline specification: augmented accelerator model estimated via single-equation linear panel regression relating investment-to-capital ratio to: - lagged DEBT/ASSETS, current SALESGRO, SIZE (SME indicator), interactions between DEBT/ASSETS, SALESGRO, and SIZE (including three-way interaction), controls X (share of long-term debt in debt and ln(total assets)), firm fixed effects (αi), and sector-country-year fixed effects (αsc,t). - All ratios and growth rates are expressed in percentage points. - Marginal effects derived: - ∂(I/K)/∂(DEBT/ASSETS) = β1 + γ1(SALESGRO) + γ2(SIZE) + δ(SALESGRO∙SIZE) - ∂(I/K)/∂(SALESGRO) = β2 + γ1(DEBT/ASSETS) + γ3(SIZE) + δ(DEBT/ASSETS∙SIZE) - Specification allows for heterogeneity across firms, sectors, countries, and over time; estimates are pooled and also unpacked by country and pre-/post-crisis. ### Empirical results (selected quantitative effects and patterns) - Predicted effects: - Large firm: 10 percentage point rise in leverage → < 3 percentage points lower net investment. - SME: 10 percentage point rise in leverage → about 3.5 percentage points lower net investment. - Large firm: 10 percentage point rise in sales → 5 percentage points higher real investment ratio. - SME: 10 percentage point rise in sales → 2.5 percentage points higher real investment ratio. - Leverage reduces marginal effect of sales growth on investment; moving from ~5 percent to 40 percent leverage (10th to 90th percentile) → ~0.5 percentage points lower investment response to a 10 percentage point sales rise. - Post-crisis (2008-2013) average marginal effect of demand fell to about three-quarters of the pre-crisis (2001-2007) value. - Country heterogeneity: average marginal impact of leverage on investment plotted for FR, FI, ES, AT, IT, DE, BE, PT with negative average marginal effects; debt overhang notably strong in AT, FI, FR, ES. ### Policy implications and recommendations - Policies to reduce high levels of firm leverage and boost firm size could help spur corporate investment and enhance transmission of monetary and fiscal policies. - Lower leverage may alleviate financing constraints and strengthen investment incentives for controlling shareholders. - Firms with lower leverage raise investment more in response to higher demand, improving transmission of accommodative monetary and fiscal policies that boost firm sales. - Developing alternatives to bank-based financing could reduce SME vulnerability given their reliance on banks. - Addressing high levels of non-performing loans (some corporate debt) could improve banks’ profitability and propensity to lend, thereby improving monetary policy transmission. - Structural policies suggested (longer-term): - Encourage growth of firms to shift size distribution toward larger firms. - Enable speedier restructuring of corporate balance sheets where leverage is high. - Expand corporate financing options for SMEs. - These are not quick fixes; they require structural changes but could increase corporate investment responsiveness to demand and policy stimuli. *Source: Orbis database and IMF staff calculations (excerpts from IMF staff chapter covering firm-level analysis for eight euro area countries, 2001–2013).* ### References ### References ### Cited studies and reports - Aivazian, Varouj A., Ying Ge, and Jiaping Qiu. 2005. “Debt Maturity Structure and Firm Investment.” Financial Management, v. 34, n. 4: 107—119. - Aiyar, Shekhar, Wolfgang Bergthaler, Jose M. Garrido, Anna Ilyina, Andreas Jobst, Kenneth Kang, Dmitriy Kovtun, Yan Liu, Dermot Monaghan, and Marina Moretti. 2015. “A Strategy for Resolving Europe’s Problem Loans.” IMF Staff Discussion Note, 15/19. - Berger, Allen N. and Gregory F. Udell. 1998. “The Economics of Small Business Finance: The Roles of Private Equity and Debt Markets in the Financial Growth Cycle.” Journal of Banking and Finance, v. 22, n. 6-8: 613-673. - Duval, Romain, Davide Furceri, Alexander Hijzen, João Jalles, and Sinem Kılıç Çelik. 2016. “Time for a Supply-Side Boost? Macroeconomic Effects of Labor and Product Market Reforms in Advanced Economies.” Chapter 3, World Economic Outlook, International Monetary Fund, April. - Kalemli-Özcan, Şebnem, Luc Laeven, and David Moreno. 2015. “Debt Overhang in Europe: Evidence from Firm-Bank-Sovereign Linkages.” Manuscript. - Kashyap, Anil K., Owen A. Lamont, and Jeremy C. Stein. 1994. “Credit Conditions and the Cyclical Behavior of Inventories.” Quarterly Journal of Economics, v. 109, n. 3: 565—592. - Kobe, Kathryn. 2012. “Small Business GDP: Update 2002-2010.” Report for U.S. Small Business Administration. Available at: https://www.sba.gov/sites/default/files/rs390tot_1.pdf, accessed May 14, 2016. - Lang, Larry, Eli Ofek, and René M. Stulz. 1996. “Leverage, Investment, and Firm Growth.” Journal of Financial Economics, v. 40, n. 1: 3—29. - Myers, Stewart C. 1977. “Determinants of Corporate Borrowing.” Journal of Financial Economics, v. 5, n. 2: 147—175. - Oliner, Stephen, Glenn Rudebusch, and Daniel Sichel. 1995. “New and Old Models of Business Investment: A Comparison of Forecasting Performance,” Journal of Money, Credit, and Banking, v. 27, n. 3: 806—826. ### Table A1 — Effects of firm leverage on firm investment ratio (summary) - Estimation method: OLS with fixed effects. Full sample. - Sample exclusions: firms from the resource extraction sectors, financial and public administration sectors in the Orbis database. - Sample period and coverage: spans the years 2003-2013 and includes 8 euro area countries (Austria, Belgium, Germany, France, Finland, Italy, Portugal, and Spain). - Dependent variable: Firm investment measured by the annual increase in real capital stock over lagged real capital stock. - Key independent variables and definitions: - Leverage: ratio of total debt to total assets. - Debt maturity: percentage of long-term debt in total debt. - Data treatment: Observations winsorized following Cleary (1999) with cutoff values: - 200 and -200 for investment/net fixed asset, - 100 and -100 for real sales growth, - 100 and 0 for debt maturity, - 100 and 0 for leverage. ### Regression estimates (Dependent variable: Investment ratio) - Columns reported: (1), (2), (3), (4), (5). - Leverage, lagged: - Column (1): -0.362*** (t = -17.07) - Column (2): -0.294*** (t = -16.67) - Column (3): -0.353*** (t = -16.69) - Column (4): -0.284*** (t = -15.99) - Column (5): -0.278*** (t = -16.91) - Leverage * SME dummy: - Column (3): -0.0681*** (t = -2.813) - Column (4): -0.0697*** (t = -2.886) - Column (5): -0.0756*** (t = -3.242) - Leverage * Sales growth: - Column (3): -0.00104*** (t = -6.432) - Column (4): -0.00104*** (t = -6.433) - Column (5): -0.00151*** (t = -2.605) - Sales growth * SME dummy: - Column (5): -0.286*** (t = -10.45) - Leverage * Sales growth * SME: - Column (5): 0.000507 (t = 0.896) - Sales growth: - Column (1): 0.260*** (t = 12.78) - Column (2): 0.260*** (t = 12.78) - Column (3): 0.289*** (t = 15.12) - Column (4): 0.289*** (t = 15.12) - Column (5): 0.571*** (t = 20.35) - SME dummy: - Column (1): 0.518 (t = 0.375) - Column (2): 0.600 (t = 0.438) - Column (5): 3.857*** (t = 3.552) - Long-term debt: - Column (1): -0.0539*** (t = -8.471) - Column (2): -0.0540*** (t = -8.466) - Column (3): -0.0545*** (t = -8.532) - Column (4): -0.0545*** (t = -8.527) - Column (5): -0.0544*** (t = -8.531) - Assets, log: - Column (1): 8.929*** (t = 25.71) - Column (2): 8.927*** (t = 25.72) - Column (3): 8.906*** (t = 25.70) - Column (4): 8.903*** (t = 25.70) - Column (5): 8.896*** (t = 25.71) ### Model controls and fit - Firm fixed effects: Yes (all columns). - Sector*Country*Year effects: Yes (all columns). - Observations: 6,605,325 (all columns). - R-squared: - Column (1): 0.350 - Column (2): 0.350 - Column (3): 0.350 - Column (4): 0.350 - Column (5): 0.350 - Statistical notation: Robust t-statistics in parentheses. - Significance codes: *** p<0.01, ** p<0.05, * p<0.1 *EURO AREA POLICIES* ### Appendix I. Additional Results Tables ### Appendix I. Additional Results Tables ### Pre-crisis sample: Effects of firm leverage on firm investment ratio (Table A2) - Sample: excludes resource extraction, financial, and public administration sectors; years 2003-2007; countries: Austria, Belgium, Germany, France, Finland, Italy, Portugal, Spain. - Dependent variable: firm investment measured by annual increase in real capital stock over lagged real capital stock. - Key variable definitions: Leverage = total debt / total assets; Debt maturity = percentage of long-term debt in total debt. - Winsorization cutoffs (following Cleary (1999)): - investment/net fixed asset: 200 and -200 - real sales growth: 100 and -100 - debt maturity: 100 and 0 - leverage: 100 and 0 - Regression details: OLS with firm fixed effects and Sector*Country*Year effects; robust t-statistics reported. - Main coefficient estimates (columns (1)–(5)): - Leverage, lagged: -0.564*** (-20.69); -0.404*** (-13.03); -0.562*** (-18.06); -0.402*** (-12.76); -0.366*** (-12.85) - Leverage * SME dummy: -0.162*** (-5.156); -0.162*** (-5.111); -0.199*** (-6.246) [appears in selected columns] - Leverage * Sales growth: -0.000131 (-0.314); -0.000130 (-0.310); -0.00221*** (-2.976) - Sales growth * SME dummy: -0.360*** (-10.83) - Leverage * Sales growth * SME: 0.00213*** (2.709) - Sales growth: 0.397*** (9.216); 0.397*** (9.216); 0.400*** (11.16); 0.400*** (11.15); 0.756*** (25.20) - SME dummy: 1.139 (0.969); 1.148 (0.980); 7.360*** (7.174) - Long-term debt: -0.0369** (-2.470); -0.0370** (-2.476); -0.0370** (-2.462); -0.0371** (-2.468); -0.0369** (-2.454) - Assets, log: 9.884*** (33.58); 9.881*** (33.60); 9.880*** (33.88); 9.877*** (33.91); 9.862*** (34.11) - Sample size and fit: - Observations: 2,581,970 (for all columns) - R-squared: 0.430 (for all columns) - Significance notation: *** p<0.01, ** p<0.05, * p<0.1 ### Post-crisis sample: Effects of firm leverage on firm investment ratio (Table A3) - Sample: excludes resource extraction, financial, and public administration sectors; years 2008-2013; countries: Austria, Belgium, Germany, France, Finland, Italy, Portugal, Spain. - Variable definitions and winsorization identical to pre-crisis sample. - Regression details: OLS with firm fixed effects and Sector*Country*Year effects; robust t-statistics reported. - Main coefficient estimates (columns (1)–(5)): - Leverage, lagged: -0.324*** (-9.098); -0.252*** (-9.350); -0.321*** (-9.168); -0.245*** (-9.119); -0.244*** (-9.465) - Leverage * SME dummy: -0.0726* (-1.936); -0.0774** (-2.052); -0.0784** (-2.166) - Leverage * Sales growth: -0.00117*** (-7.391); -0.00117*** (-7.385); -0.00123 (-1.603) - Sales growth * SME dummy: -0.237*** (-5.937) - Leverage * Sales growth * SME: 9.93e-05 (0.126) - Sales growth: 0.0993*** (8.364); 0.0992*** (8.364); 0.133*** (9.023); 0.133*** (9.022); 0.367*** (9.448) - SME dummy: -0.934 (-0.344); -0.784 (-0.292); 1.330 (0.614) - Long-term debt: -0.0679*** (-10.03); -0.0679*** (-10.02); -0.0678*** (-9.980); -0.0678*** (-9.976); -0.0679*** (-9.992) - Assets, log: 21.65*** (17.51); 21.63*** (17.47); 21.60*** (17.54); 21.58*** (17.51); 21.56*** (17.48) - Sample size and fit: - Observations: 3,688,661 (for all columns) - R-squared: 0.370 (for all columns) - Significance notation: *** p<0.01, ** p<0.05, * p<0.1 ### Country-specific estimations: 2003–2013 (Table A4) - Sample: excludes resource extraction, financial, and public administration sectors; years 2003-2013; countries: AT, BE, DE, ES, FI, FR, IT, PT. - Variable definitions and winsorization identical to previous tables. - Robust standard errors reported below coefficients. Firm fixed effects and Sector*Year fixed effects included for each country. - Coefficients by country (with robust standard-errors below): - Leverage, lagged: - AT: -0.357*** (0.0977) - BE: -0.124*** (0.0346) - DE: -0.200*** (0.0347) - ES: -0.435*** (0.0315) - FI: -0.284*** (0.0922) - FR: -0.197*** (0.0363) - IT: -0.267*** (0.0353) - PT: -0.195*** (0.0598) - Leverage * SME dummy: - AT: 0.0132 (0.116) - BE: -0.125*** (0.0369) - DE: -0.0739* (0.0391) - ES: 0.0697** (0.0331) - FI: -0.229** (0.0906) - FR: -0.381*** (0.0579) - IT: -0.0152 (0.0391) - PT: 0.0405 (0.0695) - Leverage * Sales growth: - AT: 0.00395 (0.00319) - BE: 0.00479** (0.00207) - DE: -0.000459 (0.00121) - ES: -0.000123 (0.000817) - FI: 0.00227 (0.00444) - FR: 0.00584* (0.00306) - IT: -0.00616*** (0.00105) - PT: 0.00129 (0.00226) - Leverage * Sales growth * SME: - AT: -0.00517 (0.00327) - BE: -0.00479** (0.00214) - DE: -0.000476 (0.00145) - ES: -0.0000497 (0.000818) - FI: -0.00268 (0.00442) - FR: -0.00633** (0.00302) - IT: 0.00364*** (0.00106) - PT: -0.00254 (0.00223) - Sales growth * SME dummy: - AT: 0.0583 (0.0652) - BE: 0.286*** (0.066) - DE: 0.373*** (0.0416) - ES: 0.466*** (0.0493) - FI: 0.499*** (0.111) - FR: 0.624*** (0.0587) - IT: 0.754*** (0.0426) - PT: 0.330*** (0.0593) - Sales growth: - AT: 5.231 (4.244) - BE: 5.971*** (1.814) - DE: 2.969 (1.955) - ES: 2.594** (1.172) - FI: 7.323** (3.045) - FR: 9.375*** (3.415) - IT: 4.547*** (1.378) - PT: 4.401 (2.853) - SME dummy: - AT: 0.0145 (0.0686) - BE: -0.0586 (0.071) - DE: -0.141*** (0.043) - ES: -0.230*** (0.0398) - FI: -0.00564 (0.111) - FR: -0.345*** (0.0563) - IT: -0.358*** (0.0393) - PT: -0.127** (0.058) - Long-term debt ratio: - AT: 0.0364 (0.0476) - BE: -0.0736*** (0.00711) - DE: -0.0215*** (0.00703) - ES: -0.0936*** (0.0059) - FI: -0.0283*** (0.0104) - FR: -0.00727 (0.00572) - IT: -0.121*** (0.00414) - PT: -0.00871*** (0.00236) - Assets, log: - AT: 18.74*** (3.423) - BE: 11.03*** (0.621) - DE: 16.65*** (1.159) - ES: 7.851*** (0.245) - FI: 7.156*** (0.226) - FR: 25.10*** (2.41) - IT: 7.466*** (0.272) - PT: 15.47*** (1.291) - Observations by country: - AT: 8,020 - BE: 86,554 - DE: 129,141 - ES: 2,595,429 - FI: 159,874 - FR: 1,670,491 - IT: 1,254,877 - PT: 700,939 - R-squared by country: - AT: 0.423 - BE: 0.282 - DE: 0.416 - ES: 0.352 - FI: 0.392 - FR: 0.317 - IT: 0.403 - PT: 0.329 - Significance notation: *** p<0.01, ** p<0.05, * p<0.1 *Source: _cr16220 - Appendix I. Additional Results Tables* ### References ### References ### Bibliographic entries cited - Allard, Celine; Petya Koeva Brooks; John Bluedorn; Fabian Bornhorst; Katharine Christopherson; Franziska Ohnsorge; Tigran Poghosyan; and IMF Staff Team. 2013. “Toward a Fiscal Union for the Euro Area.” IMF Staff Discussion Note, 13/09. September. - Allard, Celine; John Bluedorn; Fabian Bornhorst; and Davide Furceri. 2015. “Lessons from the Crisis: Minimal Elements for a Fiscal Union in the Euro Area.” In Designing a European Fiscal Union, edited by Carlo Cottarelli and Martine Guerguil (London and New York: Routlege, Taylor & Francis Group). - Andrle, Michal; John Bluedorn; Luc Eyraud; Tidiane Kinda; Petya Koeva Brooks; Gerd Schwartz; and Anke Weber. 2013. “Reforming Fiscal Governance in the European Union.” IMF Staff Discussion Note, 15/09. May. - Asdrubali, Pierfederico; Bent Sorensen; and Oved Yosha. 1996. “Channels of Interstate Risk Sharing: United States 1963–1990.” Quarterly Journal of Economics, 111 (4): 1081–110. - Carnot, Nicolas; Phil Evans; Serena Fatica; and Gilles Mourre. 2015. “Income Insurance: A Theoretical Exercise with Empirical Application for the Euro Area.” EC Economic Papers 546. - Cottarelli, Carlo. 2013. “European Fiscal Union: A Vision for the Long Run.” Swiss Journal of Economics and Statistics, 149 (2): 167–174. - Dolls, Mathias; Clemens Fuest; Dirk Neumann; and Andreas Peichl. 2014. “An Unemployment Insurance Scheme for the Euro Area? A Comparison of Different Alternatives using Micro Data.” IZA Discussion Paper No. 8598. - Escolano, Julio; Dora Benedek; Hui Lin; Carlos Mulas Granados; Masahiro Nozaki; Joana Pereira; Gregoire Rota Graziosi; Laura Sinn; and Jose Torres. 2015. “Distribution of Fiscal Responsibilities in Federations.” In Designing a European Fiscal Union, edited by Carlo Cottarelli and Martine Guerguil (London and New York: Routlege, Taylor & Francis Group). - European Commission. 1977. “Report of the Study Group on the Role of Public Finance in European Integration.” (The MacDougall Report). (Brussels: European Commission). - IMF. 2010. “Navigating the Fiscal Challenges Ahead.” Fiscal Monitor, May (Washington, D.C.: International Monetary Fund). - IMF. 2016. “Time for a Supply-Side Boost? Macroeconomic Effects of Labor and Product Market Reforms in Advanced Economies.” Chapter 3, World Economic Outlook, April (Washington, D.C.: International Monetary Fund). - Juncker, Jean-Claude; Donald Tusk; Jeroen Dijsselbloem; Mario Draghi; and Martin Schulz. 2015. “Completing Europe’s Economic and Monetary Union.” Also known as The Five Presidents Report. June. - Poghosyan, Tigran; Abdelhak Senhadji; and Carlo Cottarelli. 2015. “The Role of Fiscal Transfers in Smoothing Regional Shocks.” In Designing a European Fiscal Union, edited by Carlo Cottarelli and Martine Guerguil (London and New York: Routlege, Taylor & Francis Group). ### NEGATIVE INTEREST RATE POLICY (NIRP): Implications for monetary transmission and bank profitability in the euro area — key findings and analysis - Context and adoption - Central banks cutting key policy rates below zero over mid-2014 to early 2015: Danmarks Nationalbank (DN), European Central Bank (ECB), Sveriges Riksbank (SR), Swiss National Bank (SNB); Bank of Japan (BoJ) in February (year implied in text); Magyar Nemzeti Bank (MNB) adopted a negative deposit rate in March 2016. - Some central banks used NIRP primarily to counter subdued inflation (ECB, BoJ, SR), others to mitigate spillovers from unconventional monetary policy and ward off appreciation pressures (DN, SNB). In Hungary, NIRP was also used to promote new lending and reduce vulnerabilities, in particular regarding public debt. - Most central banks introduced a tiered deposit rate to reduce banks’ cost of holding excess reserves while allowing pass-through to money markets (see Appendix I, Table A1 and Appendix II). - Advantages of NIRP - Restores signaling capacity of central banks by effectively removing the ZLB, helping avoid a deflation equilibrium and contributing to a significant flattening of the yield curve. - Cuts to the central bank deposit rate lower the money market rate and other interest rates if banks hold excess reserves, encouraging greater risk-taking and strengthening the portfolio rebalancing channel (cited: Heider and others, 2016). - Portfolio rebalancing reduces risk premia, eases financial conditions, supports credit creation and economic activity, increases asset prices, generates capital gains on banks’ appreciating asset holdings, and can reduce bank non-performing loans (NPLs). - Portfolio rebalancing helps lower firms’ general cost of capital via lower term premia, putting downward pressure on corporate bond yields and making more investment projects profitable, thereby raising investment and credit demand and reinforcing TLTRO II effects on bank profitability (Appendix I, Box A2). - ECB staff estimate negative rates contributed about one percent to corporate lending growth since July 2014 (Rostagno and others, 2016). - Disadvantages and risks of NIRP - With sticky deposit rates, NIRP weakens bank profitability through lower net interest income; net interest margins (NIMs—net interest income relative to average interest-earning assets) compress as lending rates for new loans decline and existing variable-rate loans re-price while deposit rates remain sticky. - Retail deposit rate stickiness reasons: (i) households and small businesses face relatively small excess liquidity and actual costs of storing cash differ from banks and corporations; (ii) a zero percent interest rate could be a psychological threshold. Footnote references: 3 and 4. - If banks lower retail deposit rates below zero, leakages to cash may increase (see Agarwal and Kimball (2015) for analysis). - Prolonged negative rates could raise financial stability concerns: substitution of less stable wholesale funding for deposits, distortion of long-term debt affordability if lending rates become negative in real terms, delayed exit of nonviable firms, added excess capacity, and delayed efficient allocation of capital and labor. More assertive supervision and regulatory pressure may be needed to address large amounts of NPLs and debt overhang problems (cited: Syed and others, 2009). - The “true” limit on negative deposit rates is the level at which households would hoard cash; given costs of moving and storing cash, this rate can be well below zero. (Footnote 6) - Mitigating factors and compensating channels - Stronger credit growth and/or higher non-interest income (fees and commissions) can offset reduced interest margins, although offset is harder when credit demand is low and competition is high. - Higher asset prices, improved asset quality, and lower funding costs can generate capital gains and reduce NPLs. - Stronger aggregate demand through portfolio rebalancing (wealth effects, lower corporate cost of capital) can raise credit demand and investment. - Banks have supplemented declining interest revenues with higher lending volumes, lower interest expenses, lower risk provisioning and capital gains (cited: Cœuré, 2016). - Empirical impact and transmission - Negative rates have been fairly effective in reducing money market and lending rates (Elliott and others, 2016; Viñals and others, 2016). - Retail and corporate deposit rates also declined in many cases, allowing banks to maintain lending margins and support credit growth given the bank lending channel’s importance. - Money market rates have closely followed the marginal policy rate in all countries (Appendix III, Figure A1). Tiered central bank deposit rates have in some cases facilitated smooth transmission of the marginal policy rate to money markets. - Several design factors of tiered reserve systems can keep the money market rate away from the deposit rate: (i) amount of excess liquidity and the fraction exempted from the marginal policy rate; (ii) spread between the marginal and average policy rate for excess reserves; (iii) banks’ willingness/ability to lend excess liquidity to each other (fragmentation). - So far, limited evidence that negative rates have damaged bank profitability: aggregate NIM has remained broadly stable (Appendix III, Figure A2). Euro area banks reportedly reduced lending rates to households and firms while offsetting negative impacts on lending margins with modest increases in fees and commissions and cost cutting. ECB Bank Lending Survey (ECB, 2016a) indicates negative rates seem to have led to an increase in household lending in the euro area and the impact is expected to continue (Appendix III, Figure A3). - The direct cost imposed on excess bank reserves by NIRP has been found small compared to the size of the overall balance sheet. Implementation implications depend on the structure of reserves and their remuneration (Appendix I, Box A4) and transmission of the marginal policy rate to money markets (Appendix I, Box A5). - Recent work by Claessens and others (2016) suggests banks’ NIMs are negatively impacted by interest rate cuts, and this effect increases the lower the policy rate. - Policy implication highlighted for the euro area - Further monetary accommodation should rely more on credit easing measures than on further lowering negative interest rates, because further rate cuts when deposit rates remain sticky will lower bank profitability and may offset benefits from higher asset prices and lower funding costs in a bank-dominated financial system. *Italic line: Source: _cr16220 - References (IMF PDF chapter content as provided).* ### 0.03 percent of total banking sector assets. In the euro area, and in countries with an even more ### _cr16220 - 0.03 percent of total banking sector assets. In the euro area, and in countries with an even more ### Cash holdings and currency in circulation - Cash hoarding has not been observed in the euro area, or in countries with an even more negative deposit rate (Denmark, Sweden and Switzerland). - Most recent increases in currency in circulation can be explained by the normal relation between currency in circulation and movements in the short-term interest rate, with the latter representing the opportunity cost of holding cash rather than deposits. - Irrespective of whether interest rates are positive or negative, the amount of currency in circulation increases when interest rates decline. - Bank cash balances represent only a fraction of banks’ deposit base; hence bank profitability is far less sensitive to declines in negative rates on excess reserves (even under a tiered system). - Time references and observations in charts: comparisons include periods such as since July 2014 and until June 2014, and monthly intervals around t-3 to t+9 months. ### Short-term effects on lending and expected near-term effects - Negative interest rates had little impact on corporate lending volumes over the past six months, but some positive impact is expected for the coming months. - Within the euro area, lending rates are likely to decline more than deposit rates in the near term, further reducing interest earnings. - Banks might be less inclined to reduce lending rates unless they can offset lower interest margins by substituting wholesale funding for more expensive deposit funding. ### Impact on bank profitability, NIMs, equity and credit supply - Estimates suggest a small effect on banks’ NIMs: 7 basis points for a 50-basis point reduction in the policy rate. - Early evidence suggests the impact on NIMs may increase non-linearly as the policy rate falls below the ZLB (while deposit rates remain non-negative). - Since Q3 2015 equity risk premia have risen and price-to-book ratios have declined, with the average cost of equity now exceeding the return on equity. - Lower expected bank earnings (from further reductions in already negative rates) would weigh on equity prices and raise the cost of equity, encouraging capital-constrained banks to reduce credit and reducing the effectiveness of negative rates. - Banks with weaker profitability experienced larger declines in equity prices since 2015; low equity valuations raise the cost of equity and make banks less willing to lend. - Current loan growth in selected economies is insufficient to offset compressed lending margins; required loan growth to maintain NIMs was compared with current loan growth (y/y, March 2016) in the analysis. ### Heterogeneity across euro area countries and distributional effects - The direct cost of negative deposit rates would be disproportionately greater for banks in surplus countries due to large amounts of excess liquidity generated by Target 2 settlement of capital flows (example surplus countries: Germany and the Netherlands). - Eurosystem asset purchases have generated additional liquidity in some core economies in excess of national shares (example: France), leading to uneven distribution of excess liquidity across the euro area. - Tiering of the deposit rate could mitigate the direct cost of NIRP and ensure effective transmission of the marginal policy rate, but heterogeneity of national banking systems might complicate effective implementation of a tiered reserve regime. - Several countries with NIRP (Bulgaria, Denmark, Japan, and Switzerland) have installed tiered reserve systems, which have facilitated pass-through of the marginal policy rate to money markets and reduced the direct cost of NIRP. ### Policy implications, mitigating measures, and recommended actions - Monetary transmission may become less effective in economies most in need of stimulus if deposit rates are sticky and lending rates do not adjust sufficiently. - TLTRO II could facilitate transmission to lending rates by mitigating the potentially adverse impact of negative rates on banks’ lending margins; note that the extent to which TLTRO II boosts usage of ECB liquidity (and not just facilitates rolling over existing liquidity) will affect Target 2 imbalances. - Further reductions in the deposit rate are likely to entail diminishing returns because the lending channel is crucially influenced by banks’ expected profitability. - Looking forward, further monetary accommodation should rely more on credit easing measures and expanding the ECB’s balance sheet to raise asset valuations and aggregate demand while supporting the bank lending channel. - In principle, tiering of reserve remuneration could mitigate direct costs of NIRP, but operational and cross-country heterogeneity pose implementation challenges. ### Key numerical points and exact figures cited - 0.03 percent of total banking sector assets (context: cash balances as fraction of deposit base). - 7 basis points for a 50-basis point reduction in the policy rate (estimated impact on NIMs). - Time markers and data points referenced include: June 2014, July 2014, March 2016, Q3 2015, Jan. 2016, March 10 (ECB decision), end-2015, end-June 2015, June 2014 to March 2016, and periods such as Jan. 2010 to Feb. 2016 for historical elasticities. - Country examples and groupings explicitly cited: Euro area, Denmark, Sweden, Switzerland, Japan, Hungary, Bulgaria, Italy, Portugal, Spain, Germany, the Netherlands, France, Bulgaria, Denmark, Japan, Switzerland. *International Monetary Fund — EURO AREA POLICIES (excerpts from the source content).* ### References ### References ### Bibliographic references - Agarwal, Ruchir and Miles Kimball, 2015, “Breaking the Zero Lower Bound,” IMF Working Paper No. 15/224 (Washington, D.C.: International Monetary Fund). - Albertazzi, Ugo and Leonardo Gambacorta, 2009, “Bank Profitability and the Business Cycle,” Journal of Financial Stability, Vol. 5, NO. 4, pp. 393-409. - Alessandri, Piergiorgio and Benjamin D. Nelson, 2015, “Simple Banking: Profitability and the Yield Curve,” Journal of Money, Credit and Banking, Vol. 47, No. 1, pp. 143-75. - Alsterlind, Jan; Armelius, Hanna; Forsman, David; Joensson, Bjoern; and Anna-Lena Wretman, 2015, “How Far Can the Repo Rate Be Cut?,” Economic Commentaries, No. 11 (Stockholm: Sveriges Riksbank). - Angelini, Paulo; Neri, Stefano; and Fabio Panetta, 2014, “The Interaction between Capital Requirements and Monetary Policy,” Journal of Money, Credit and Banking, Vol. 46, No. 6, pp. 1073-112. - Barr, Malcolm; Kasman, Bruce; and David Mackie, 2016, “Negative Policy Rates: The Bound Is Lower Than You Think,” Special Report, Economic Research, February 9 (London: J.P. Morgan). - Berkmen, S. Pelin and Andreas A. Jobst, 2015, “An Early Assessment of Quantitative Easing,” in Koeva Brooks, Petya and Mahmood Pradhan (eds.) The Mechanics of a Strong Euro Area—IMF Policy Analysis, pp. 107-38. - Bolt, Wilo; de Haan, Leo; Hoeberichts, Marco; van Oorst, Maarten; and Job Swank, 2012, “Bank Profitability During Recessions,” Journal of Banking and Finance, Vol. 36, No. 9, pp. 2552-64. - Borio, Claudio; Gambacorta, Leonardo; and Boris Hofmann, 2015, “The Influence of Monetary Policy on Bank Profitability” BIS Working Paper No. 514. - Busch, Ramona and Christoph Memmel, 2015, “Bank’s Net Interest Margin and the Level of Interest Rates,” Deutsche Bundesbank Discussion Paper No. 16/2015. - Caballero, Ricardo J.; Hoshi, Takeo; and Anil K. Kashyap, 2008, “Zombie Lending and Depressed Restructuring in Japan,” American Economic Review, Vol. 98, No. 5, pp. 1943-77. - Claessens, Stijn; Coleman, Nicholas; and Michael Donnelly, 2016, “’Low-for-long’ Interest Rates and Net Interest Margins of Banks in Advanced Foreign Economies,” IFDP Notes, April 11 (U.S. Federal Reserve Board of Governors). - Cœuré, Benoît, 2016, “From Challenges to Opportunities: Rebooting the European Financial Sector,” Speech at Süddeutsche Zeitung Finance Day, March 2. - Demirguc-Kunt, Asli and Harry Huizinga, 1999, “Determinants of Commercial Bank Interest Margins and Profitability: Some International Evidence,” The World Bank Economic Review, Vol. 13, No. 2, pp. 379-408. - Danmarks Nationalbank, 2015. Financial Stability Report. 2nd Half 2015. - Elliott, Jennifer; Hoyle, Henry; and Andreas A. Jobst, 2016, “Impact of Low and Negative Rates on Banks [Box 1.3],” in “Chapter I: Potent Policies for A Successful Normalization,” Global Financial Stability Report, April, pp. 44-6. - European Central Bank (ECB), 2016a. The Euro Area Bank Lending Survey (First Quarter of 2016), April. - ECB, 2016b, “The Second Series of Target Longer-term Refinancing Operations (TLTRO II) [Box 3],” ECB Economic Bulletin, Issue 3/2016, May, pp. 24-8. - Genay, Hesna and Rich Podjasek, 2014, “What is the Impact of a Low Interest Rate Environment on Bank Profitability?,” Chicago Fed Letter. - Gerali, Andrea; Neri, Stefano; Sessa, Luca; and Federico M. Signoretti, 2010, “Credit and Banking in a DSGE Model of the Euro Area,” Journal of Money, Credit and Banking, Vol. 42, No. 1, pp. 107-41. - Heider, Florian; Farzad Saidi; and Glenn Schepens, 2016, “Life Below Zero: Negative Policy Rates and Bank Risk Taking.” - Jensen, Carina Moselund and Morten Spange, 2015, “Interest Rate Passthrough and the Demand for Cash at Negative Interest Rates,” Danmarks Nationalbank Monetary Review, 2nd Quarter, pp. 1-12. - Kuelpmann, Mathias; Rohr, Michael; Henskjold, Aleksander; and Oscar Heemskerk, 2016, “Swedish, Swiss Banks' Profitability Resilient to Low Interest Rates, but Margin Compression and Downside Risks Intensify,” Moody’s Investor Service (April 18). - Kwon, Hyeog Ug; Narita, Futoshi; and Machiko Narita, 2015, “Resource Reallocation and Zombie Lending in Japan in the 1990s,” Review of Economic Dynamics, Vol. 18, No. 4, pp. 709-32. - McAndrews, James, 2015, “Negative Nominal Central Bank Policy Rates: Where is the Lower Bound?,” Remarks at the University of Wisconsin, May 8 (Federal Reserve Bank of New York). - Magyar Nemzeti Bank, 2015, “The Monetary Policy Instruments of the Magyar Nemzeti Bank,” September 25. - Mircheva, Borislava; Thegeya, Aaron; Turk, Rima; and Sophia Zhang, 2016, “Adapting to Spillovers from Monetary Policies of Major Advanced Economies,” Cross-Country Report on Spillovers, Selected Issues, European Department [forthcoming]. - Rostagno, Massimo; Bindseil, Ulrich; Kamps, Annette; Lemke, Wolfgang; Sugo, Tomohiro; and Thomas Vlassopoulos, 2016, “Breaking through the Zero Line: The ECB’s Negative Interest Rate Policy,” Presentation at Brookings Institution, June 6. - Sveriges Riksbank, 2015. Financial Stability Report. 1st Half 2015. - Syed, Murtaza; Kenneth Kang; and Kiichi Tokuoka, 2009, “’Lost Decade’ in Transition: What Japan’s Crisis could Portend about Recovery from the Great Recession,” IMF Working Paper No. 09/282. - Turk, Rima A., forthcoming, “Low and Negative Interest Rates: How Big a Challenge for Large Danish and Swedish Banks?”, IMF Working Paper. - Viñals, Jose Gray, Simon and Kelly Eckhold, 2016, “The Broader View: The Positive Effects of Negative Nominal Interest Rates,” iMFdirect (April 10). ### Appendix I — Implementation and Impact of Negative Interest Rates (overview) - Appendix I contains analysis prepared by Jiaqian (Jack) Chen and Andreas (Andy) Jobst. - Focus: assessment of the impact of negative rates on bank profitability and monetary transmission when deposit rates are sticky, adapting the Gerali and others (2010) DSGE model estimated on euro area data. - Banking sector model structure: - Two retail branches for lending and deposit-taking. - A wholesale unit managing capital subject to a simple solvency constraint, providing wholesale loans and raising wholesale funding. - Banks face different adjustment costs when changing rates; higher adjustment cost => more “sticky” rates. ### Box A1. Monetary Transmission under NIRP — key scenarios and findings - Method: full equilibrium DSGE specification with monopoly power in banks and sticky deposit rates. - Three scenarios examined where deposit rates are bounded at zero percent and banks respond to a policy rate cut: - Scenario 1 (blue line): pass-through from the policy rate to deposit rate remains unchanged. - Banks reduce both deposit and lending rates. - Bank profitability increases over time as output and inflation outturns improve. - Scenario 2 (green dotted line): higher adjustment costs in setting deposit rates (deposits are “sticky”). - Banks optimally lower lending rates to increase lending volume. - Banks deviate temporarily from the minimum capital requirement. - Bank profitability declines significantly as lending volumes are initially insufficient to offset lending margin compression due to sticky deposit rates. - Scenario 3 (red line): solvency constraint strictly enforced for the sticky-deposits scenario. - Monetary transmission breaks down as banks increase lending but cannot relax solvency constraints. - Impact on output remains positive but is smaller over the short term due to wealth and substitution effects raising loan demand, consumption and investment. - Caveat: banks can substitute cheaper wholesale funding for deposit funding, but potentially offsetting components of net operating income (e.g., capital gains from higher asset prices and lower provisioning cost) are ignored. ### Box A2. The Impact of NIRP on Bank Profitability and the Mitigating Impact of TLTRO II — key findings and figures - Context: euro area banks face pressure to maintain profitability amid continued monetary easing; decline in term premia and lower marginal policy rate reduce net interest margin (NIM). - Quantified estimated NIM impact from recent ECB measures: - Monthly asset purchases increased from €60 billion to €80 billion. - Deposit rate reduced to -0.4 percent, down from -0.3 percent. - Estimated average reduction in NIM across euro area banks: 11 basis points. - Germany: 5 bps. - France: 4 bps. - Italy: 11 bps. - Spain: 13 bps. - Cross-country vulnerability: - Countries with a higher proportion of variable rate loans and a higher cost of risk (e.g., Italy and Spain) face greater NIM declines. - Required lending growth to offset projected decline in net interest income: - Aggregate euro area lending growth would need to increase to 2.3 percent annually (up from 1.8 percent at end-January) to maintain current profitability over the amortization period of the current loan book. - Role and design of TLTRO II (ECB, 2016b): - From June, banks can borrow up to 30 percent of eligible non-mortgage private loans over a four-year period at the prevailing MRO rate. - Two components incentivize new lending: - (i) Conditional liquidity at the marginal policy rate (equivalent to the rate on the deposit facility prevailing at allotment) if banks exceed a benchmark for net new lending of at least 2.5 percent by January 2018 (red line in text figures). - (ii) Unconditional liquidity at either the MRO rate of currently zero percent if banks do not satisfy the lending benchmark or at a discount to the MRO rate if banks exceed the lower benchmark (blue line in text figures). - The decrease in the interest rate for conditional liquidity is graduated linearly depending on the percentage by which the bank exceeds the lower benchmark. - Modeling assumptions and notes: - Historical pass-through of policy rates and elasticity of net interest margins to changes in term premia between Jan. 2010 and Feb. 2016 used in calculations. - Loan stock sensitivity estimates control for continued amortization, bad debt write-offs, and re-statements of asset recoveries. - Calculations do not include the effects of capital gains from higher asset prices due to negative interest rates and a flattening yield curve. - Note: */ assumes that new lending is fully funded using TLTRO I funds at a weighted average borrowing rate of -20bps. - Scenario assumes an increase of monthly asset purchases (until Sept. 2017) by the ECB and a deposit rate cut of 10bps (as per ECB decision on March 10). - Policy implication: - TLTRO II could mitigate the adverse impact of NIRP on bank profitability by realigning refinancing costs to the marginal policy rate if banks meet minimum net lending growth, thereby encouraging more lending and supporting pass-through to the real economy. - TLTRO II particularly helps banks in countries with high cost of risk or those that would otherwise refrain from lowering lending rates to preserve profit margins. - Operational detail on benchmark treatment: - For banks with positive lending growth over the 12 months prior to January 2016, the benchmark is zero net lending. - The benchmark is lowered by the decline in eligible net lending in the same period for banks that have seen negative net lending. *Italic: References and Appendix I material as contained in _cr16220 - References.* ### 1.2 percent annual lending growth over a two-year period for banks with positive net lending in 2015 but a ### _cr16220 - 1.2 percent annual lending growth over a two-year period for banks with positive net lending in 2015 but a ### TLTRO II Benchmarking and Mechanics - Benchmark: "1.2 percent annual lending growth over a two-year period for banks with positive net lending in 2015" (text heading). - For banks with positive net lending in 2015: - Benchmark timeline shown from Jan-15 through Jul-18 with a target date (Jan-18). - Benchmark lending and "Benchmark +2.5%" are explicit reference levels in the illustrative charts. - Payment rates referenced: "Pay depo rate (-0.4%)" and "Pay MRO (0%)". - A "Graduated linear adjustment of MRO rate" is indicated up to "+2.5%". - For banks with negative credit growth: - "Deleveraging of the past 12 months... is used to lower the benchmark." - Illustration covers Jan-15 through Jul-18 with the same target date (Jan-18) and the same payment rates: "Pay depo rate (-0.4%)" and "Pay MRO (0%)". - "Graduated linear adjustment of MRO rate" applies symmetrically. - Reporting and performance: - Banks are required to report "how much they had lent during the 12 months ending January 31, 2016" to determine eligibility and to "ascertain the lending performance against the benchmark by end of January 2018." - "As opposed to TLTRO I, failure to meet the benchmark for net lending does not result in an early repayment of funds after two years." ### Net Lending Thresholds and Qualification - Chart concept: "Change in Net Lending — Current net lending (Jan. 2016, y/y)" and "Required threshold for cheapest ECB funding 1/". - Note: "Net lending growth required to qualify for TLTRO II borrowing at the ECB deposit rate (-0.4%)." - Country labels shown in chart: "FRA DEU ESP ITA AUT" (implied series across axis). - Exact percent axis markers shown in source figures (preserved as text in figures): e.g., axes annotated "-6 -4 -2 0 2 4" and percentage markers in time series charts. ### Use of TLTRO Funds (Survey Evidence) - Source: "ECB Bank Lending Survey and IMF staff calculations." - Use categories and survey periods (illustrated): - TLTRO (2014) / TLTRO (2015) / TLTRO (2016) - Categories: "Granting loans", "Refinancing", "Purchasing assets" - Survey note: "for survey responses in 2015, the percentage share of responses was re-scaled to unity due to multiple responses or missing responses/responses with no stated preference." - Visual indicator: dashed = expectation (in charts). ### Low and Negative Interest Rates in Denmark and Sweden (Box A3) - Rationale and timing: - Denmark: NIRP introduced in July 2012 to deter speculative pressures on the peg to the euro; further deposit rate cuts totaling "70 bps over the course of four weeks" in 2015 after SNB action and ECB announcements. - Sweden: Riksbank adopted NIRP to raise inflation toward the "two percent target" and prevent de-anchoring of inflation expectations. - Pass-through and margins: - "Money market rates turned negative and banks’ assets re-priced downwards quickly" due to high share of variable rate loans. - "Retail deposit rates did not drop below the ZLB." - Result: "the interest spread between lending and deposit rates narrowed" beginning in Sweden as rates reached low levels in 2014, before turning negative in 2015. - Funding structure: "a relative narrow deposit base (with a high reliance on non-deposit funding at 52 percent of total funding at end-2015)" mitigated NIRP effects. - Aggregate outcome: "NIMs... remained positive in aggregate." Footnote: "During the first quarter of 2016, however, NIMs for Swedish banks have declined (Kuelpmann and others, 2016)." - Compensating factors: - Denmark: "fee income rose as the volume of mortgage refinancing increased" and "provisions declined with improved loan portfolio quality (DN, 2015)." - Sweden: "fee income also increased with rising inflows to banks’ investment funds and an expansion of their corporate advisory services (Asterlind and others, 2015)." - Lending volumes: "lending growth remains subdued in Denmark, higher loan volumes in Sweden have also helped compensate for lower rates." - Policy emphasis: - "The compensatory effect of credit growth in an environment of NIRP... raises the importance of prudent lending, especially to households." - Prepared by: "Rima A. Turk and Andreas (Andy) Jobst." ### Tiered Reserve Systems (Box A4) - Reserve structures and remuneration: - ECB and SNB: excess reserves held as overnight deposits. - Danmarks Nationalbank (DN) and Sveriges Riksbank (SR): use combination of overnight fine-tuning operations and one-week term deposits for liquidity surplus. - Tiering purpose and types: - Objectives: mitigate burdens on bank earnings, facilitate market transactions, discourage higher holdings of physical currency. - Two broad categories of existing tiered regimes: - Constant allocation (example: Switzerland): "exemption threshold for deposits is specific to each bank (as a fixed multiple of a bank’s required reserves)." - Dynamic allocation (examples: Denmark, Sweden, Japan): "fine-tuning operations determine the share of excess reserves to be placed with the central bank as more costly overnight deposits" or exemption designed to increase over time. - Policy calibration: - "The exemption threshold should be as high as possible to minimize the banks’ average cost of holding excess reserves while being sufficiently low to transmit the marginal policy rate to money markets." - Central banks adjust tiering over time to keep money market rates aligned with the marginal policy rate. - Observations: - "A tiered reserve regime enhances central banks’ capacity to lower the effective policy rate by reducing the direct cost of negative rates on excess reserves." - Example cost context: "the peak charge in Switzerland has been 0.03 percent of total banking sector assets." - Notes: - SR administers a "de facto tiered reserve regime" via reserve repo operations and alternative instruments with different rate offsets. - ECB remunerates overnight deposits in the current account at the MRO rate of 0 percent, "effectively exempting about one-seventh of current reserves from the marginal policy rate." ### Reducing the Direct Cost of NIRP and Role of Tiering (Box A5) - Eurosystem balances and rates (figures preserved from text): - "Banks’ overnight deposits (€310 billion) and current account balances (€613 billion) amount to about €923 billion." - "The minimum reserve requirement of €115 billion is remunerated at the MRO rate of 0 percent, which leaves excess reserves of €808 billion subject to the negative deposit rate of -0.4 percent as the marginal policy rate." - Cost tolerance and tiering arithmetic: - Assumption: "the direct cost of NIRP does not exceed 0.03 percent of total assets of the euro area banking sector (which reflects the recent experience in Switzerland as a theoretical benchmark)." - Under current deposit rate, "the current deposit rate has exhausted the theoretical tolerance of euro area banks." - If a tiered reserve regime excludes "75 percent" of excess reserves from the negative deposit rate (in line with Switzerland), "the Eurosystem could theoretically tolerate a negative deposit rate of up to -1.6 percent (as the direct cost of NIRP remains unchanged)." - Transmission and operational considerations: - The money market rate is pushed toward the lowest marginal policy rate "because banks individually will try to lend their surplus liquidity to other banks... but only as long as the lending rate exceeds the deposit rate." - Transmission depends on dispersion of excess liquidity and banks’ willingness/ability to lend to other banks. - Risk: if the exempted portion of excess reserves is too large, "the money market rate could be higher than the lowest marginal policy rate" due to reduced interbank lending incentives. - Market liquidity context: - "Given that the average daily quoted turnover underpinning EONIA fixings has only been about €12.6 billion (or 1.5 percent of excess liquidity) since January 2016, the impact of the marginal policy rate on money market rates is quite sensitive to changes in bank behavior and rate setting." ### Central Banks with Negative Interest Rate Policy (Table A1 — Selected entries) - Bulgaria (two tiers) - Objective: "Transmission of the ECB’s monetary policy stand; also aimed at avoiding potential losses to the central bank" - Instrument: "Deposit rate" - Rate: "-0.30% (Jan. 4, 2016); -0.40% (March 16, 2016); Note: the same interest rate as the ECB’s deposit facility" - Denmark (two tiers) - Objective: "Countering safe haven inflows and exchange rate pressures (continued FX interventions)" - Instrument: "Certificates of deposit (CD)" - Rates: "-0.20 % (July 2012-early 2014); -0.05% (Sept. 2014); -0.20% (Jan. 19, 2015); -0.35% (Jan. 22, 2015); -0.50% (Jan. 29, 2015); -0.75 % (Feb. 5, 2015)" - Euro Area (no tiers) - Objective: "Price stability and anchoring inflation expectations (in conjunction with asset purchase program)" - Instrument: "Deposit rate" - Rates: "-0.10% (June 11, 2014); -0.20% (Sept. 10, 2014); -0.30% (Dec. 9, 2015); -0.40% (March 16, 2016); asset purchase program increased by €20 billion/month (until March 2017)" - Japan (three tiers) - Objective: "Price stability and anchoring inflation expectations (in conjunction with QE)" - Instrument: "Deposit rate" - Rate: "-0.10% (Feb. 16, 2016)" - Switzerland (two tiers) - Objective: "Reducing appreciation and deflationary pressures" - Instrument: "Sight deposits at the SNB (with an exemption threshold)" - Rate: "-0.75% (Jan. 15, 2015)" - Sweden (no tiers) - Objective: "Price stability and anchoring inflation expectations (in conjunction with QE)" - Instrument: "Reverse repo rate" - Rates: "-0.10% (Feb. 12, 2015); -0.25% (March 18, 2015); -0.35% (July 2, 2015); -0.50% (Feb. 11, 2016)" - Note: "A loosely defined tiered reserve system also applies to the ECB, which remunerates overnight deposits in the current account at the MRO rate of 0 percent (as of March 16, 2016), effectively exempting about one-seventh of current reserves from the marginal policy rate." *Source: IMF staff compilation from the provided content unit.* ### Appendix II. Overview of Other Countries with NIRP ### Appendix II. Overview of Other Countries with NIRP ### Denmark - Motivation: To counter large capital inflows speculating against the long-standing Danish peg to the euro following the announcement of the ECB’s QE program and the Swiss National Bank abandoning its exchange rate floor in mid-January 2015. - Policy actions and chronology: - Danmarks Nationalbank (DN) cut its key deposit rate four times between January and February 2015 to a record low -0.75 percent (from -0.05 percent). - In March 2015, DN increased the current account limit to DKK145 billion from DKK37 billion, increasing the amount of deposit that banks could keep at the central bank without being charged the deposit rate. - Transmission and institutional features: - Ability to pass on negative interest rate to depositors was limited to large corporate customers. - Importance of activity-based fees (e.g., mortgage application fees) and a long-term strategy to encourage a shift from deposits into wealth management products to cope with reduced lending margins under NIRP. ### Sweden - Motivation: Rate cuts in 2014 and earlier driven by persistently low inflation; a notable decline in inflation expectations preceded the shift to negative rates and domestic QE in February 2015. The move followed the announcement of the ECB’s QE program in mid-January. - Policy actions and parameters: - In February 2016, the Swedish Riksbank (SR) reduced the reserve repo rate by another 0.25 percentage points to the current level of -0.50 percent. - SR combined the rate cut with its own asset purchase program of government debt securities in the amount of SEK40 billion. - Relative scale: - SEK40 billion amounts to more than 35 percent of the market and more than twice the relative size of the ECB’s QE covering 17 percent of the euro area government bond market. ### Japan - Policy design and implementation: - On January 29, 2016, the Bank of Japan introduced a three-tiered reserve deposit system (effective on February 16) with a negative interest rate on marginal excess reserves. - Three-tier system details: - First tier: remunerated at 0.1 percent; applies to the average outstanding balance of current accounts accumulated under Quantitative and Qualitative Monetary Easing (QQE) up until January 2015 (approx. ¥210 trillion). - Second tier: remunerated at 0 percent; is the macro add-on balance, including required reserves and the reserves equivalent to the amount of the various lending programs (¥40 trillion). An additional portion will be added to this second tier over time in line with the monetary base target. - Third tier: remunerated at -0.1 percent; is the policy rate balance (the residual reserve deposit) where additional reserves created by QE will initially go until the second tier is adjusted (currently ¥80 trillion/year). - The amount in the third tier is expected to remain in the range of ¥10-30 trillion (Barr and others, 2016). - Cash holding rule: - To prevent financial institutions from increasing cash holding significantly, any increase in cash holding are deducted from the zero interest rate tiers of current account balance. ### Switzerland - Policy actions and chronology: - On December 18, 2014, the Swiss National Bank (SNB) announced negative interest rates on Swiss franc-denominated sight deposits above a pre-defined threshold, effective January 22, 2015. - SNB announced a cut in the central bank deposit rate from -0.25 to -0.75 percent (effective January 22, 2015) less than a month after it announced the cut in the policy rate from 0 to -0.25 percent. - Threshold and distributional detail: - For domestic banks, the threshold was set to 20 times a bank’s required reserves as of the reporting period ending November 19, 2014 minus (plus) any increase (decrease) in cash held. - SNB does not charge banks with negative interest rates on their cash deposits below the threshold. - Implications for banks: - Some Swiss banks were able to obtain market funding at negative rates and place the funds raised with the SNB at zero percent, realizing additional net interest income. - Following the announcement, Swiss banks raised significant amounts of interbank and/or customer deposits, which helped improve their NIMs. ### Hungary - Macroeconomic context and objectives: - Subdued inflation pressures and a structural liquidity surplus motivated gradual easing and introduction of unconventional instruments to strengthen interest, credit, and expectation channels and lessen vulnerabilities. - Policy actions and details: - Effective March 23, 2016, the Magyar Nemzeti Bank (MNB) reduced the policy rate and reduced the overnight deposit rate from 0.10 to -0.05 percent. - Conventional measures: gradual reduction of the policy rate, lowering and narrowing of the interest rate corridor, effective reduction of reserve requirements, and changes to collateral requirements for MNB lending facilities. - Unconventional measures include: - Supporting SME lending by providing cheap MNB funding for banks to on-lend to SMEs and offering incentives to banks (through interest rate swaps and a special deposit facility) to increase lending to SMEs. - Incentivizing banks to substitute government securities (especially long-term and local currency-denominated) for excess reserves with the MNB. *Source: Appendix II. Overview of Other Countries with NIRP (extracted content).* ### 2.2 percent. This would have spillover benefits for ### _cr16220 - 2.2 percent. This would have spillover benefits for ### Spillovers and Fiscal Stimulus - A fiscal stimulus raising euro area growth by 2.2 percent would have spillover benefits for the rest of the world: - Higher real exports of 0.4 percent in 2021. - Higher real output of 0.1 percent in 2021. - Spillovers to other EU countries are especially large due to close trade links. - Fiscal stimulus in Germany during 2017–18 reduces its current account relative to the baseline by 0.5 percent of GDP versus 0.2 percent of GDP for France and 0.1 percent of GDP for Italy (chart reference). - Aggregate impacts from a 0.2 percent of euro area GDP fiscal expansion in 2017 by supportive countries: - Raises the average GDP level of countries undertaking supportive fiscal policies by a little more than 0.1 percent in 2017. - Raises the GDP level of other countries by an average of 0.04 percent in 2017. ### Centralized Investment, Fiscal Policy, and Structural Reforms - Centralized investment is the largest growth driver in the first two years: - Estimated multiplier of around one. - Benefits countries with larger output gaps and no fiscal space. - Main driver for closing the euro area output gap and higher inflation. - Fiscal policy support is strongest in 2017–2018 when stimulus is implemented. - Fiscal multipliers are smaller than for centralized investment due in part to spending on lower-multiplier transfers. - Structural reforms: - Lift economic activity by more than 0.3 percentage point on average over 2019–21. - A 1 percent improvement in potential output in 2021 is almost entirely due to productivity and labor market participation increases, with 90 percent of the gain from structural reforms and the remainder mainly from higher investment. - Cleanup of bank and corporate balance sheets: - Moderate positive impact on growth. - Larger role in raising inflation by reducing corporate borrowing rates and encouraging borrowing and investment. - Combined policy synergies in low-inflation/low-interest environment: - Stronger demand raises inflation, lowering real interest rates at/near the zero lower bound (ZLB), encouraging investment and temporarily improving competitiveness. - Balance sheet cleanup reduces real interest rates via lower credit risk premia. - Structural reforms spur near-term growth by improving confidence and bringing forward investment and support long-term fiscal sustainability. ### Stagnation Scenario: Shock, Impacts, and Spillovers - Assumptions for the stagnation scenario (2017–21): - Investment ¼ percentage point lower each year. - Sovereign and corporate risk premia in high-debt countries (Greece, Ireland, Italy, Portugal, and Spain) 75 basis points higher. - Total factor productivity growth rate 0.1 percentage point lower each year. - Investment and growth impacts: - Baseline investment growth of 2½–3 percent vs. stagnation scenario capital expansion falling to ½ percent by 2021. - Investment level about 6 percent lower than baseline by 2021. - GDP growth about 1.1 percent in the medium term, about 0.4 percentage point lower than the baseline. - Inflation averaging just 1 percent for 2017–21. - Output gap widens initially by 0.2 percentage point and is negative 0.7 percent in 2021. - Unemployment up by 0.4 percentage point in 2021. - Fiscal impacts: - Government deficits ¼ percentage point higher on average during 2017–21. - Euro area public debt-to-GDP rises by 3 percentage points to 86 percent of GDP by 2021. - Larger debt increases for high-debt countries (+7 percentage points for Portugal, +5 percentage points for Italy). - Global spillovers by 2021: - Global output 0.4 percentage point lower than baseline. - Euro area current account surplus 0.5 percent of GDP higher with real imports almost 3 percent lower. - Real exports for the rest of the world 0.4 percent lower. - Negative feedback loop in downturn: - Weaker demand lowers inflation, raising real interest rates and worsening competitiveness, which further discourages investment. ### Stagnation Policy Response and Effects - Additional policy measures assumed to counter stagnation: a) One-year extension of the Expanded Asset Purchase Program (APP) until March 2018 at €80 billion each month. b) Slightly larger and targeted centralized investment: - EFSI-induced centralized investment of 0.2 percent of euro area GDP each year for 2017–21 plus additional centralized investment of 0.1 percent of euro area GDP each year for 2017 and 2018. - Additional centralized investment targeted only to member states with projected negative output gaps greater than one percent in 2017, proportionate to their share in the aggregate negative output gap. - Policy package outcomes: - Growth would exceed WEO baseline in 2017 and 2018, reaching 2.0 percent in 2017 and 1.8 percent in 2018, then returning to a path a little over 1½ percent. - Output gap closed by 2018 (earlier than WEO baseline closure in 2021). - Inflation would reach 1.5 percent in 2017, 1.8 percent in 2018, and 2 percent in 2019. - Relative to WEO baseline, euro area public debt in 2021 would be 2 percent of GDP lower at 81 percent of GDP. - Current account surplus would be the same at 2.6 percent of GDP. - Table excerpts (selected scenario comparisons): - April 2016 WEO: Real Growth 2017 = 1.6, 2018 = 1.6, 2021 = 1.5; Inflation (HICP) 2017 = 1.1, 2018 = 1.3, 2021 = 1.7. - Upside: Real Growth 2017 = 1.9, 2018 = 2.0, 2021 = 2.1; Inflation 2017 = 1.3, 2018 = 1.7, 2021 = 2.5. - Stagnation scenario: Real Growth 2017 = 1.3, 2018 = 1.1, 2021 = 1.1; Inflation 2017 = 1.0, 2018 = 1.0, 2021 = 1.0. - Stagnation scenario plus policies: Real Growth 2017 = 2.0, 2018 = 1.8, 2021 = 1.6; Inflation 2017 = 1.5, 2018 = 1.8, 2021 = 2.2. - Fiscal Deficit and Debt comparisons in table (selected): - April 2016 WEO fiscal deficit 2017 = -1.4, 2018 = -0.9, 2021 = -0.3; Government Debt 2017 = 91.3, 2018 = 89.7, 2021 = 83.2. - Stagnation scenario plus policies fiscal deficit 2017 = -1.2, 2018 = -0.7, 2021 = -0.1; Government Debt 2017 = 90.9, 2018 = 88.7, 2021 = 80.9. - Targeting centralized investment toward larger output gaps: - Additional centralized investment of a total of 0.2 percent of euro area GDP over 2017–18, when targeted, raises the cumulative output level by another 0.22 percentage point in 2018. - Implied multiplier around 16 percent higher than for untargeted centralized investment. ### Conclusions and Policy Recommendations - A balanced, comprehensive policy strategy combining: - Continued monetary accommodation, - Use of fiscal space and SGP flexibility, - Centralized investment, - Cleanup of bank and corporate balance sheets, - Structural reforms, can significantly raise growth and inflation versus the baseline and effectively counter a prolonged downturn. - Key outcomes of pursuing the comprehensive strategy: - Significant positive synergies in the current low-inflation, low-interest rate environment. - Reduction in public debt levels and rebuilding of fiscal buffers. - Positive spillovers for the rest of the world through stronger import demand and higher inflation in the euro area. - Targeting support to weaker countries enhances the impact; use of fiscal space by stronger countries still produces positive growth spillovers and can help narrow internal euro area imbalances. *Source: IMF staff calculations and World Economic Outlook data as presented in the provided document content.* --- _Source: https://www.imf.org/-/media/websites/imf/imported-full-text-pdf/external/pubs/ft/scr/2016/_cr16220.pdf_