## wpiea2019137

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---

### A. Introduction and key context
- Japan’s productivity growth has moderated since the GFC of 2007-08.
- Pre-crisis average productivity growth (Japan):
  - Multi-factor productivity: 0.8 percent per year
  - Labor productivity: 1.6 percent per year
- Post-GFC: productivity growth decelerated for most countries (IMF, 2017a); Japan’s slowdown has moderated but average productivity growth has declined.
- Importance of SMEs:
  - SMEs employ close to 70 percent of the total work force.
  - SMEs account for more than 50 percent of Japan’s GDP in value-added terms in the manufacturing sector.
- Research questions examined:
  - Stylized facts on productivity across and within sectors.
  - Association between SME characteristics and low productivity growth.
  - Explanations for low SME productivity growth (public credit guarantees, financing constraints, demographics, intangible investment).

### B. Data description and sample
- Main dataset: ORBIS (Bureau van Dijk).
- Sample coverage and structure:
  - Unbalanced panel of almost 2 million observations (2001–13).
  - Covers over 400,000 unique firms, sampled for an average of 5 years per firm.
  - About 2,000 firms labeled as listed; most firms are unlisted.
  - Firms categorized into 81 different 2-digit NACE (revision 2) industry codes.
  - Observations in public administration and financial sectors are excluded.
- SME definition and sample share:
  - SMEs identified using METI’s “2018 White Paper on SMEs in Japan” (number of employees criterion used).
  - 93 percent of ORBIS Japan observations are identified as SMEs.
- Key firm-level variables:
  - Labor productivity: total revenue per number of employees in real terms.
  - Labor productivity growth: annual growth rate of labor productivity.
  - Age: firm age in number of years.
  - Size: log of total assets.
  - Investment: annual change in stock of physical capital (tangible fixed assets) as a share of total assets.
  - Intangible investment: annual change in stock of intangible capital (as a share of total investment).
- Summary firm statistics (medians/averages reported in text):
  - Average age: SMEs 30 years vs. non-SMEs 42 years
  - Average employees: SMEs 21 vs. non-SMEs 250
  - SMEs invest less in tangible fixed assets and have a smaller share of intangible investment in total investment.
  - One quarter of SMEs do not invest in intangible capital.

### C. Productivity growth patterns (SMEs vs non-SMEs; manufacturing vs non-manufacturing)
- Aggregate and distributional findings:
  - Average annual labor productivity growth of non-SMEs is -1.0 percent over the sample period.
  - Average SME labor productivity growth is lower than non-SMEs by about 1 percentage point (Appendix I).
  - SMEs exhibit more dispersion in productivity growth: standard deviation for SMEs is about 1.7 times that of non-SMEs.
  - Simple OLS regression: an average SME firm has significantly lower annual productivity growth than non-SMEs by 1.2 percentage points (Table 1, column (1)).
  - SME labor productivity level is also significantly lower (by about 0.5 percentage points in reported regressions).
- Manufacturing vs non-manufacturing:
  - Manufacturing firms have higher productivity growth and productivity levels than non-manufacturing firms in the full sample.
  - Restricting to SMEs:
    - Manufacturing SMEs have 0.8 percentage point higher annual productivity growth than non-manufacturing SMEs.
    - Manufacturing SMEs have 0.1 percentage point higher productivity level than non-manufacturing SMEs.
- Selected regression coefficients (Table 1, full sample):
  - SME dummy (Labor Productivity Growth, column (1)): -1.187***
  - SME dummy (Labor Productivity, column (2)): -0.465***
  - Manufacturing dummy (Labor Productivity Growth, column (3)): 0.821***
  - Manufacturing dummy (Labor Productivity, column (4)): 0.161***

### D. SME characteristics associated with productivity growth and "laggards"
- Age and size effects (panel regressions with sector and year fixed-effects; Table 2):
  - Age coefficient (Labor Productivity Growth, column (1)): -0.056***
  - Size coefficient (Labor Productivity Growth, column (1)): 0.671***
  - Non-linearities (column (2)):
    - Age^2: 0.003***
    - Size^2: -0.060***
  - SME interactions (column (3)):
    - Age*SME dummy: -0.064***
    - Size*SME dummy: 0.240***
- Interpretation examples (textual calculations based on coefficients):
  - As firms age by ten years, annual labor productivity growth is lower by 0.6 percentage points (from Age coefficient -0.056).
  - A decrease in size (total assets) by 50 percent leads to 0.3 percentage points lower annual labor productivity growth (textual calculation).
- Definition and prevalence of "laggards":
  - Laggard firm: productivity growth in the bottom 10th percentile for two consecutive years.
  - Almost 11,000 unique firms meet this definition; 99 percent of them are SMEs.
- Probability of being a laggard (Table 2, probit specifications):
  - Smaller SMEs have a higher likelihood of becoming a laggard (size coefficients: negative in probit specifications).
  - Age effects on laggard probability are non-linear: older SMEs are more likely to be laggards, but the marginal impact weakens with further age (Age^2 negative coefficient).
- Potential aggregate impact of improving laggard SME performance:
  - Unweighted average annual firm-level labor productivity growth in sample ("All"): -1.98 percent.
  - Weighted average (weighted by firm revenue): 0.3 percent.
  - Under the extreme assumption that labor productivity growth of all laggard SMEs reaches that of the average firm in the rest of the sample, the weighted average of annual firm-level labor productivity growth increases by 1.8 percent (from 0.3 to 2.2 percent).
  - Using unweighted averages, the analogous calculation would improve the average annual firm-level labor productivity growth by 6 percentage points (from -1.98 percent ... [source text truncated]).

### E. Explaining low productivity growth among Japanese SMEs (hypotheses and evidence)
- Main hypotheses:
  - Overly generous SME credit guarantee system:
    - Program introduced in 1950; central pillar of SME support; provides government-backed guarantees in case SMEs go into bankruptcy.
    - Generous guarantees reduce incentives for non-viable SMEs to exit, contributing to survival of unproductive, small and old SMEs.
    - Safety Net Program No. 5: coverage was historically 100 percent; reforms in April 2018 reduced coverage from 100 percent to 80 percent for non-emergency related loans.
  - Financing constraints:
    - SME financing constraints prevent investment, including in productivity-enhancing and often high-risk projects such as intangible investment.
  - Demographics and succession:
    - Shrinking and ageing population hampers SME business manager succession and lowers incentives to invest in long-term productivity gains.
  - Intangible investment:
    - SMEs invest less in intangible capital; one quarter of SMEs do not invest in intangible capital.
    - Intangible investment defined as annual change in stock of intangible capital measured by intangible fixed assets (formation expenses, research expenses, goodwill, development expenses, and other long-term expenses).
- Evidence linking factors to SME performance:
  - Small and older SMEs are concentrated among laggards (99 percent of laggards are SMEs).
  - Regression and descriptive evidence show age and small size are associated with lower productivity growth and higher probability of persistent low performance.
- Intangible capital and productivity (Table 3, panel regressions, 2001–13):
  - Intangibles share (lag): 0.009*** (column 1), 0.013*** (column 2), 0.007*** (column 3), 0.011*** (column 4).
  - Age: -0.021*** (column 1); -0.051*** (column 2); -0.052*** (column 3).
  - Size: 0.485*** (column 2); 0.676*** (column 3); 0.683*** (column 4).
  - Investment (lag): 0.006*** (column 4).
  - Observations: 1,121,618 (all columns).
  - R-squared: 0.016 (columns 1–2), 0.017 (columns 3–4).
  - Conclusion: intangible investment is a positive and significant driver of SMEs’ labor productivity growth.

### F. Credit guarantee scheme, firm dynamism, and resource misallocation
- Generous SME guarantees likely contribute to Japan’s low firm exit rates and misallocation of resources.
- Firm dynamism (entry/exit) is key to productivity growth (Haltiwanger et al. 2013; IMF, 2018a).
- Annual firms’ exit rates were below 4 percent in 2016 in Japan (well below the target of 10 percent under Japan’s Revitalization Strategy).
- Coverage and scale of guarantees:
  - Out of approximately ¥250 trillion in loans outstanding to SMEs, those covered by credit guarantees account for more than 10 percent (IMF, 2017b).
  - Japan’s public credit guarantee program: about 10 percent of total outstanding loans to SMEs (¥29 trillion out of ¥258 trillion) covered under the credit guarantee scheme (Japan Finance Corporation, 2017).
  - Emergency Credit Guarantee Program (ECGP, 2008) planned guarantees amounting to ¥36 trillion (about 7 percent of GDP) and actual guarantees amounting to ¥27 trillion.
  - Under ECGP, guarantee fees were made uniform and fixed at 0.75-0.80 percent.
- Forbearance lending in the 1990s contributed to zombie firms and resource misallocation, dragging productivity growth (Caballero et al. 2008).

### G. Financing constraints, alternative finance, and demographics
- About a third of SMEs might face financing constraints that discourage investment, particularly in intangible capital (IMF, Financial Sector Assessment Program, 2017b).
- Lending practices remain largely based on fixed-asset collateral and personal guarantees despite a credit bureau; this disadvantages SMEs with growth potential and riskier intangible investments.
- Alternative financing forms (asset-based lending and venture capital) have grown from very low levels in Japan but still face important challenges.
- Demographics and succession:
  - Median age of Japanese SME owners increased from 47 to 66 years old (figure referenced).
  - Over the next decade, about 2.45 million SME owners are projected to be over 70 years old.
  - METI survey: about 30 percent of incumbent managers of Japanese SMEs have not decided on a successor.
  - About 42 percent of Japanese SMEs have decided on a business successor; among those, nearly two-thirds of successors are within the family.
  - Exit of enterprises with sound financial health reached nearly 30,000 in 2016 (METI, 2017), attributed to retiring managers unable to find suitable heirs.
- Demographic pressures reduce incentives for long-gestation productivity investments such as R&D and IT-related intangible investments.

### H. Policy discussion and recommendations
- Facilitate exit of non-viable firms to improve resource allocation and productivity; continue reforms to reduce moral hazard from credit guarantees:
  - Overly generous coverage of credit guarantees should be further decreased to levels more in line with other OECD countries.
- Alleviate SME financing constraints:
  - Encourage banks to upgrade credit risk-assessment capacities through greater use of IT and enhanced staff skills.
  - Reduce SME reliance on collateral and personal guarantees.
  - Promote alternative financing for SMEs and start-ups, including asset-backed lending and venture capital.
- Address demographic succession challenges:
  - Target efforts to guarantee smooth succession of business managers to firms with greater potential.
  - The Five-Year Plan for Business Succession (SMEA, 2017) encourages awareness raising, matching owners with successors, human resource management improvements, and promoting sales and M&A; pro-succession efforts should focus on healthy firms with greater growth prospects.
  - Consider tax reforms (under consideration by METI, potentially in fiscal year 2019): expand tax deferral rates, lower conditions to benefit from deferrals, reduce/exempt registration tax and real estate acquisition tax for M&A, expand deferral of inheritance/gift tax on non-listed firms from 53 percent to 100 percent (possible expansion under consideration), and expand scope of companies eligible to use the system to promote M&A and non-family successions.
  - Enhance financial support such as providing low-interest loans to support succession (e.g., for M&A or capital investment).
- Foster entrepreneurship, innovation, and human capital:
  - Promote closer collaboration between universities and SMEs on R&D activities.
  - Improve education and training to enhance human capital formation and productivity.

### I. Appendix I — Key descriptive statistics (Japan: Orbis dataset, 2001–13)
- All firms (N, Mean, Median, p25, p75, SD)
  - labor productivity: 1,919,104; 12.22; 12.19; 11.53; 12.85; 1.05
  - labor productivity growth: 1,919,104; -1.98; -0.02; -0.17; 0.12; 0.30
  - Age: 1,273,859; 31.17; 30.00; 19.00; 42.00; 15.91
  - log (Total Assets): 1,919,093; 14.65; 14.58; 13.28; 15.91; 2.01
  - Investment/Total Assets(%): 1,918,170; 26.74; 21.74; 7.80; 40.83; 23.79
  - Intangibles Investment/Total Investment (%): 1,484,486; 5.25; 0.5; 0.0; 22.58; 14.78
  - Number of Employees: 1,919,104; 33.33; 11; 5.0; 29.0; 69.21
  - Number of Unique Firms: 492,017
- SMEs (N, Mean, Median, p25, p75, SD)
  - labor productivity: 1,795,836; 12.19; 12.16; 11.52; 12.82; 1.02
  - labor productivity growth: 1,795,836; -2.05; -0.02; -0.18; 0.13; 0.30
  - Age: 1,185,501; 30.40; 29.00; 18.00; 41.00; 15.50
  - log (Total Assets): 1,795,825; 14.40; 14.42; 13.18; 15.63; 1.77
  - Investment/Total Assets(%): 1,794,925; 26.74; 21.62; 7.68; 40.90; 23.99
  - Intangibles Investment/Total Investment (%): 1,393,153; 5.06; 0.46; 0.0; 2.38; 14.65
  - Number of Employees: 1,795,836; 20.80; 10.00; 5.00; 24.00; 30.70
  - Number of Unique Firms: 444,413
- non-SMEs (N, Mean, Median, p25, p75, SD)
  - labor productivity: 123,268; 12.71; 12.69; 11.92; 13.31; 1.35
  - labor productivity growth: 123,268; -1.03; -0.01; -0.09; 0.07; 0.18
  - Age: 88,088; 41.54; 43.00; 29.00; 56.00; 17.71
  - log (Total Assets): 123,268; 18.27; 18.09; 17.20; 19.05; 1.78
  - Investment/Total Assets(%): 123,245; 26.82; 23.16; 9.66; 39.85; 20.68
  - Intangibles Investment/Total Investment (%): 91,333; 8.15; 1.61; 0.48; 6.32; 16.36
  - Number of Employees: 104,084; 249.53; 194; 131; 335; 147.43
  - Number of Unique Firms: 47,604

*Source: IMF staff compilation from wpiea2019137 (References section and main chapter text as supplied).*

### References ________________________________________________________________17

### References — wpiea2019137

### A. Introduction and key context
- Japan’s productivity growth has moderated since the GFC of 2007-08.
- Pre-crisis average productivity growth (Japan):
  - Multi-factor productivity: 0.8 percent per year
  - Labor productivity: 1.6 percent per year
- Comparison and trends:
  - Post-GFC productivity growth decelerated for most countries (IMF, 2017a).
  - Japan’s slowdown has not been as pronounced as in some peers (e.g., United Kingdom, United States), but average productivity growth has moderated.
- Importance of SMEs:
  - SMEs employ close to 70 percent of the total work force.
  - SMEs account for more than 50 percent of Japan’s GDP in value-added terms in the manufacturing sector.
- Research questions examined:
  - Stylized facts on productivity across and within sectors.
  - Association between SME characteristics and low productivity growth.
  - Explanations for low SME productivity growth (including public credit guarantees, financing constraints, demographics, intangible investment).

### B. Data description and sample
- Main dataset: ORBIS (Bureau van Dijk).
- Sample coverage and structure:
  - Unbalanced panel of almost 2 million observations (2001–13).
  - Covers over 400,000 unique firms, sampled for an average of 5 years per firm.
  - About 2,000 firms labeled as listed; most firms are unlisted.
  - Firms categorized into 81 different 2-digit NACE (revision 2) industry codes.
  - Observations in public administration and financial sectors are excluded.
- SME definition and sample share:
  - SMEs identified using METI’s “2018 White Paper on SMEs in Japan” (number of employees criterion used).
  - 93 percent of ORBIS Japan observations are identified as SMEs.
- Key firm-level variables:
  - Labor productivity: total revenue per number of employees in real terms.
  - Labor productivity growth: annual growth rate of labor productivity.
  - Age: firm age in number of years.
  - Size: log of total assets.
  - Investment: annual change in stock of physical capital (tangible fixed assets) as a share of total assets.
  - Intangible investment: annual change in stock of intangible capital (as a share of total investment).
- Summary firm statistics (medians/averages reported in text):
  - Average age: SMEs 30 years vs. non-SMEs 42 years
  - Average employees: SMEs 21 vs. non-SMEs 250
  - SMEs invest less in tangible fixed assets and have a smaller share of intangible investment in total investment.
  - One quarter of SMEs do not invest in intangible capital.

### C. Productivity growth patterns (SMEs vs non-SMEs; manufacturing vs non-manufacturing)
- Aggregate and distributional findings:
  - Average annual labor productivity growth of non-SMEs is -1.0 percent over the sample period.
  - Average SME labor productivity growth is lower than non-SMEs by about 1 percentage point (Appendix I).
  - SMEs exhibit more dispersion in productivity growth: standard deviation for SMEs is about 1.7 times that of non-SMEs.
  - Simple OLS regression result: an average SME firm has significantly lower annual productivity growth than non-SMEs by 1.2 percentage points (Table 1, column (1)).
  - SME labor productivity level is also significantly lower (by about 0.5 percentage points in reported regressions).
- Manufacturing vs non-manufacturing:
  - Positive productivity gap: manufacturing firms have higher productivity growth and productivity levels than non-manufacturing firms in the full sample.
  - Restricting to SMEs:
    - Manufacturing SMEs have 0.8 percentage point higher annual productivity growth than non-manufacturing SMEs.
    - Manufacturing SMEs have 0.1 percentage point higher productivity level than non-manufacturing SMEs.
- Selected regression coefficients from Table 1 (full sample; standard errors omitted here but preserved in the source):
  - SME dummy (Labor Productivity Growth, column (1)): -1.187***
  - SME dummy (Labor Productivity, column (2)): -0.465***
  - Manufacturing dummy (Labor Productivity Growth, column (3)): 0.821***
  - Manufacturing dummy (Labor Productivity, column (4)): 0.161***

### D. SME characteristics associated with productivity growth and "laggards"
- Empirical relationships (panel regressions with sector and year fixed-effects):
  - Age and size effects (full sample, Table 2):
    - Age coefficient (Labor Productivity Growth, column (1)): -0.056***
    - Size coefficient (Labor Productivity Growth, column (1)): 0.671***
    - Interpretation: as firms age by ten years, annual labor productivity growth is lower by 0.6 percentage points (textual calculation based on coefficient). A decrease in size (total assets) by 50 percent leads to 0.3 percentage points lower annual labor productivity growth (textual calculation).
  - Non-linearities (column (2)):
    - Age^2: 0.003*** (negative effect of age on growth decreases for older firms)
    - Size^2: -0.060*** (positive impact of size on growth decreases for larger firms)
  - SME interactions (column (3)):
    - Age*SME dummy: -0.064*** (negative age effect on productivity applies to SMEs)
    - Size*SME dummy: 0.240*** (size correlated with productivity growth for both SMEs and non-SMEs, larger effect for SMEs)
- Definition and prevalence of "laggards":
  - Laggard firm: productivity growth in the bottom 10th percentile for two consecutive years.
  - Almost 11,000 unique firms in the sample meet this definition; 99 percent of them are SMEs.
- Probability of being a laggard (Table 2, probit specifications):
  - Smaller SMEs have a higher likelihood of becoming a laggard (size coefficients: negative in probit specifications for probability).
  - Age effects on laggard probability are non-linear: older SMEs are more likely to be laggards, but the marginal impact weakens with further age (Age^2 negative coefficient).
- Potential aggregate impact of improving laggard SME performance:
  - Unweighted average annual firm-level labor productivity growth in sample ("All"): -1.98 percent.
  - Weighted average (weighted by firm revenue): 0.3 percent.
  - Under the extreme assumption that labor productivity growth of all laggard SMEs reaches that of the average firm in the rest of the sample, the weighted average of annual firm-level labor productivity growth increases by 1.8 percent (from 0.3 to 2.2 percent).
  - Using unweighted averages, the analogous calculation would improve the average annual firm-level labor productivity growth by 6 percentage points (from -1.98 percent ... [source text truncated here]).

### E. Explaining low productivity growth among Japanese SMEs (hypotheses and evidence)
- Main hypotheses discussed:
  - Overly generous SME credit guarantee system:
    - History and role: program introduced in 1950; central pillar of SME support; provides government-backed guarantees in case SMEs go into bankruptcy.
    - Implication: generous guarantees reduce incentives for non-viable SMEs to exit, contributing to survival of unproductive, small and old SMEs.
    - Specific program mention: largest SME credit guarantee scheme (Safety Net Program No. 5) — coverage was reduced from full... [text truncates here].
  - Financing constraints:
    - SME financing constraints prevent investment, including in productivity-enhancing and often high-risk projects such as intangible investment.
  - Demographics and succession:
    - Shrinking and ageing population hampers SME business manager succession.
    - Dim demand prospects lower SME incentives to invest in long-term productivity gains.
  - Intangible investment:
    - SMEs invest less in intangible capital; one quarter of SMEs do not invest in intangible capital.
    - Intangible investment defined as annual change in stock of intangible capital measured by intangible fixed assets (formation expenses, research expenses, goodwill, development expenses, and other long-term expenses).
- Evidence linking these factors to SME performance:
  - Small and older SMEs are concentrated among laggards (99 percent of laggards are SMEs).
  - Regression and descriptive evidence show age and small size are associated with lower productivity growth and higher probability of persistent low performance.

*Source: IMF staff compilation from wpiea2019137 (References section and main chapter text as supplied).*

### Appendix I, “All” sample) to 4.04 percent.

### Appendix I, “All” sample) to 4.04 percent.

### Key empirical findings on SME productivity
- SMEs in Japan exhibit lower productivity growth than non-SMEs, with SMEs on average exhibiting low productivity growth compared to non-SMEs (by about 1 percentage point per year).  
- Smaller and older SMEs show particularly weak productivity performance.  
- Firm-level productivity growth displays significant dispersion across sectors and within sectors.  
- A panel regression (Table 3, 2001–13) shows a positive relationship between intangible capital investment and SMEs’ labor productivity growth:
  - Intangibles share (lag): 0.009*** (column 1), 0.013*** (column 2), 0.007*** (column 3), 0.011*** (column 4).
  - Age: -0.021*** (column 1); -0.051*** (column 2); -0.052*** (column 3).
  - Size: 0.485*** (column 2); 0.676*** (column 3); 0.683*** (column 4).
  - Investment (lag): 0.006*** (column 4).
  - Observations: 1,121,618 (all columns).
  - R-squared: 0.016 (columns 1–2), 0.017 (columns 3–4).
- Intangible investment is identified as a driver of productivity growth, consistent with broader literature (e.g., Corrado et al. 2013 and 2016; Duval et al. 2017).

### Credit guarantee scheme, firm dynamism, and resource misallocation
- Generous SME guarantees are likely contributors to Japan’s low firm exit rates and misallocation of resources.  
- Firms’ exit and entry (‘firm dynamism’) are key to productivity growth (Haltiwanger et al. 2013, IMF 2018a).  
- Annual firms’ exit rates were below 4 percent in 2016 in Japan (well below the target of 10 percent under Japan’s Revitalization Strategy).  
- Historically, 100 percent coverage under Japan’s public credit guarantee system was used; reforms in April 2018 reduced coverage from 100 percent to 80 percent for non-emergency related loans (Safety Net Program No. 5).  
- Credit guarantee coverage statistics and scale:
  - Today, out of approximately ¥250 trillion in loans outstanding to SMEs, those covered by credit guarantees account for more than 10 percent (IMF, 2017b).
  - Japan’s public credit guarantee program: about 10 percent of total outstanding loans to SMEs (¥29 trillion out of ¥258 trillion) covered under the credit guarantee scheme (Japan Finance Corporation, 2017).
  - Emergency Credit Guarantee Program (ECGP, 2008) planned guarantees amounting to ¥36 trillion (about 7 percent of GDP) and actual guarantees amounting to ¥27 trillion.
  - Under ECGP, guarantee fees were made uniform and fixed at 0.75-0.80 percent.
- Forbearance lending in the 1990s contributed to zombie firms and resource misallocation, dragging productivity growth (Caballero et al. 2008).

### Financing constraints and alternative finance
- About a third of SMEs might face financing constraints that discourage investment, particularly investment in intangible capital (IMF, Financial Sector Assessment Program, 2017b).
- Lending practices remain largely based on fixed-asset collateral and personal guarantees despite a credit bureau; this disadvantages SMEs with growth potential and riskier intangible investments.
- Alternative financing forms (asset-based lending and venture capital) have grown from very low levels in Japan but still face important challenges.

### Demographic headwinds and business succession
- Demographics and succession issues undermine managers’ incentives to invest in productivity-enhancing projects:
  - Median age of Japanese SME owners increased from 47 to 66 years old (figure referenced).
  - Over the next decade, about 2.45 million SME owners are projected to be over 70 years old.
  - According to METI survey: about 30 percent of incumbent managers of Japanese SMEs have not decided on a successor.
  - About 42 percent of Japanese SMEs have decided on a business successor; among those, nearly two-thirds of successors are within the family.
  - Exit of enterprises with sound financial health reached nearly 30,000 in 2016 (METI, 2017), attributed to retiring managers unable to find suitable heirs.
- Demographic pressures reduce incentives for long-gestation productivity investments such as R&D and IT-related intangible investments.

### Policy discussion and recommendations
- Strengthen policies to facilitate exit of non-viable firms to promote efficient resource allocation, productivity growth, and to prevent moral hazard (IMF, 2018b). Recent reforms of the SME credit guarantee system are a step in the right direction, but:
  - Overly generous coverage of credit guarantees should be further decreased to levels more in line with other OECD countries.
- Alleviate SME financing constraints:
  - Encourage banks to upgrade credit risk-assessment capacities through greater use of IT and enhanced staff skills.
  - Reduce SME reliance on collateral and personal guarantees.
  - Promote alternative financing for SMEs and start-ups, including asset-backed lending and venture capital.
- Address demographic succession challenges:
  - Advance and target efforts to guarantee smooth succession of business managers to firms with greater potential.
  - The Five-Year Plan for Business Succession (SMEA, 2017) encourages awareness raising, matching owners with successors, human resource management improvements, and promoting sales and M&A; pro-succession efforts should focus on healthy firms with greater growth prospects.
  - Consider tax reforms (under consideration by METI, potentially in fiscal year 2019) to expand tax deferral rates, lower conditions to benefit from deferrals, reduce/exempt registration tax and real estate acquisition tax for M&A, expand deferral of inheritance/gift tax on non-listed firms from 53 percent to 100 percent (possible expansion under consideration), and expand scope of companies eligible to use the system to promote M&A and non-family successions.
  - Enhance financial support such as providing low-interest loans to support succession (e.g., for M&A or capital investment).
- Foster entrepreneurship, innovation, and human capital:
  - Promote closer collaboration between universities and SMEs on R&D activities.
  - Improve education and training to enhance human capital formation and productivity.

*Source: wpiea2019137 - Appendix I, “All” sample) to 4.04 percent.*

### References

### wpiea2019137 - References

### Key references cited
- Bank of Japan, 2018. Regional Economic Report, Tokyo (June).
- Caballero, R., T. Hoshi and A. Kashyap, 2008. “Zombie Lending and Depressed Restructuring in Japan,” American Economic Review, 98 (5), pp. 1943–77.
- Calvo, José, 2006. “Testing Gibrat’s Law for Small, Young and Innovating Firms,” Small Business Economics, 26 (2), pp. 117–23.
- Colacelli, Mariana and Emilio Fernandez-Corugedo, 2018. “Macroeconomic Effects of Japan’s Demographics: Can Structural Reforms Reverse Them?” IMF Working Paper 18/248. International Monetary Fund, Washington DC.
- Corrado, C. J. Haskel, C. Jona-Lasinio and M. Iommi, 2013. “Innovation and Intangible Investment in Europe, Japan, and the United States,” Oxford Review of Economic Policy, 29 (2), pp. 261–86.
- Corrado, C., J. Haskel, C. Jona-Lasinio and M. Iommi, 2016. “Intangible Investment in the EU and US Before and Since the Great Recession and its Contribution to Productivity Growth,” EIB Working Papers, 2016/08, European Investment Bank.
- Criscuolo, C., P. Gal, and C. Menon, 2014. “The Dynamics of Employment Growth: New Evidence from 18 Countries,” CEP Discussion Paper, 1274, Center for Economic Performance.
- Duval, R, G. Hong and Y. Timmer, 2017. “Financial Frictions and the Great Productivity Slowdown,” IMF Working Paper 17/129. International Monetary Fund, Washington DC.
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### Appendix 1 — Descriptive statistics (Japan: Orbis dataset, 2001–13)
Source: ORBIS. Notes: N denotes the number of observations; p25 denotes the 25th percentile; p75 denotes the 75th percentile; SD denotes standard deviation.

- All firms (N, Mean, Median, p25, p75, SD)
  - labor productivity: 1,919,104; 12.22; 12.19; 11.53; 12.85; 1.05
  - labor productivity growth: 1,919,104; -1.98; -0.02; -0.17; 0.12; 0.30
  - Age: 1,273,859; 31.17; 30.00; 19.00; 42.00; 15.91
  - log (Total Assets): 1,919,093; 14.65; 14.58; 13.28; 15.91; 2.01
  - Investment/Total Assets(%): 1,918,170; 26.74; 21.74; 7.80; 40.83; 23.79
  - Intangibles Investment/Total Investment (%): 1,484,486; 5.25; 0.5; 0.0; 22.58; 14.78
  - Number of Employees: 1,919,104; 33.33; 11; 5.0; 29.0; 69.21
  - Number of Unique Firms: 492,017

- SMEs (N, Mean, Median, p25, p75, SD)
  - labor productivity: 1,795,836; 12.19; 12.16; 11.52; 12.82; 1.02
  - labor productivity growth: 1,795,836; -2.05; -0.02; -0.18; 0.13; 0.30
  - Age: 1,185,501; 30.40; 29.00; 18.00; 41.00; 15.50
  - log (Total Assets): 1,795,825; 14.40; 14.42; 13.18; 15.63; 1.77
  - Investment/Total Assets(%): 1,794,925; 26.74; 21.62; 7.68; 40.90; 23.99
  - Intangibles Investment/Total Investment (%): 1,393,153; 5.06; 0.46; 0.0; 2.38; 14.65
  - Number of Employees: 1,795,836; 20.80; 10.00; 5.00; 24.00; 30.70
  - Number of Unique Firms: 444,413

- non-SMEs (N, Mean, Median, p25, p75, SD)
  - labor productivity: 123,268; 12.71; 12.69; 11.92; 13.31; 1.35
  - labor productivity growth: 123,268; -1.03; -0.01; -0.09; 0.07; 0.18
  - Age: 88,088; 41.54; 43.00; 29.00; 56.00; 17.71
  - log (Total Assets): 123,268; 18.27; 18.09; 17.20; 19.05; 1.78
  - Investment/Total Assets(%): 123,245; 26.82; 23.16; 9.66; 39.85; 20.68
  - Intangibles Investment/Total Investment (%): 91,333; 8.15; 1.61; 0.48; 6.32; 16.36
  - Number of Employees: 104,084; 249.53; 194; 131; 335; 147.43
  - Number of Unique Firms: 47,604

### Appendix 2 — Official SME definition by sector (Japan)
Source: White Paper on Small and Medium Enterprises in Japan, Ministry of Economy, Trade and Industry, 2018.

- Manufacturing Industry and Others
  - Stated capital or number of employee: 300 million yen or less; 300 or fewer
- Wholesale Trade Industry
  - Stated capital or number of employee: 100 million yen or less; 100 or fewer
- Service Industry
  - Stated capital or number of employee: 50 million yen or less; 100 or fewer
- Retail Trade Industry
  - Stated capital or number of employee: 50 million yen or less; 50 or fewer

*Content from wpiea2019137 - References (source PDF: wpiea2019137 - References).*

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_Source: https://www.imf.org/-/media/files/publications/wp/2019/wpiea2019137.pdf_