## Section 6 — Recent government policies to address voluntary firm exit issues in Japan (wpiea2020182-print-pdf)

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### Introduction and scope
- Business dynamism defined as turnover of firm entry and exit is crucial for efficient allocation of resources, with implications for aggregate employment, growth, and productivity.
- Dataset covers Japanese firms for the period 2007 to 2017 and contains balance-sheet information, exit type (if a firm exited), and inter-firm transactional data enabling identification of supply-chain structure.
- Analysis focuses on why firms exit, which firms are likely to exit, spillover effects through supply chains, and policies to prevent healthy firms from exiting, especially in the context of the COVID-19 pandemic.

### Key empirical findings on exit patterns and CEO characteristics
- Firm exit rates in Japan are still very low relative to other advanced economies.
  - SMEA (2019): firm exit rate in Japan is "4 percent".
  - Firm exit rates are close to "10 percent" in the United States and the United Kingdom in 2011.
  - In the TSR data, the firm exit rate is around "2 percent" in 2017.
- Overall financial health improved: very low bankruptcy rates and a declining ratio of “zombie firms”.
- Majority of firm exits in recent years are voluntary exits (business closures).
- Voluntary exits are strongly positively correlated with the age of the chief executive officer (CEO).
  - Average age of CEO for the firms observed in the data is "61 years old" for the 2017 data (TSR).
  - The fraction of companies having CEOs who are over 65 years of age was as high as "41 percent" (TSR 2017 sample).
  - Peak of the CEO age distribution shifted: in 1995 the largest share was 50 to 54 years; in 2015 it was 65 to 69 years.
  - Fraction of companies having CEOs over 65 years old in 2015 was "37 percent", nearly two times larger than "18 percent" in 1995.
- Voluntary exits reach above "2 percent for CEOs aged between 70-80 years" and "about 2.5 percent for CEOs aged above 80 years old."
- Voluntary exits are relatively more pronounced in rural areas than urban areas:
  - There are more elderly CEOs in rural areas; in 2017 average age of business owners in rural areas is "61.5 years."
  - Firms with fewer inter-firm connections are more likely to choose voluntary exit; voluntary exits are positively correlated with exits of connected firms.
- CEO turnover is very low: "2.2 percent for rural areas, 2.6 percent for urban areas in 2017."
- Regional CEO ages in 2017:
  - Iwate, Kochi and Akita have average CEO age exceeding "63 years".
  - Youngest CEO prefectures in 2017 include Shiga, Osaka and Aichi with average CEO age around "60 years."
- Urban vs rural CEO average in 2017: urban = "60.8 years" (Figure 5 left chart).

### Data description and comparability (TSR dataset)
- Data source: Tokyo Shoko Research, LTD (TSR) firm-level longitudinal data, annual frequency.
- Coverage: about "1.2 million" Japanese firms from 2007 to 2017.
- Firm information includes industry classification (Japanese Standard Industrial Classification 4-digit), firm address, number of employees, total sales amount, year of establishment, CEO’s name and birth date.
- Transactional relationship information includes lists of up to "24" suppliers and customers.
- About "91 percent" of total firms are small and medium-sized enterprises (SMEs) in the 2017 data set.
- TSR contains information on firm exits and exit types:
  - Exit types categorized into three groups: tosan (bankruptcy), gappei (merger), and voluntary exit.
  - TSR distinguishes three types of voluntary exits: kyugyo (temporarily suspension of business), haigyo (business closure), and kaisan (dissolution of company).
  - TSR includes credit rating scores ranging from "1 to 100"; 1 denotes the highest default risk and 100 denotes the lowest default risk.
- Comparability with Census:
  - Geographic distribution: TSR percentage figures nearly equal to the 2016 Economic Census for Business Activity for many prefectures; small differences of "1 to 2 percentage points" for Tokyo, Kanagawa, Osaka, Aichi, and Hokkaido.
  - Firm size distribution (percentage of firms by number of employees in 2016):
    - Census: 0-4: "56.2", 5-9: "17.5", 10-19: "11.8", 20-29: "4.7", 30-49: "3.9", 50-99: "3.0", 100-299: "2.0", 300-999: "0.6", 1000-1999: "0.1", 2000-: "0.1"
    - TSR: 0-4: "49.3", 5-9: "21.6", 10-19: "13.3", 20-29: "4.9", 30-49: "4.2", 50-99: "3.3", 100-299: "2.4", 300-999: "0.8", 1000-1999: "0.1", 2000-: "0.1"
- Summary statistics (Annex 2): total number of firms by year:
  - 2007: "1,034,221" (non-SMEs "32,124", SMEs "1,002,097")
  - 2008: "1,055,557" (non-SMEs "36,854", SMEs "1,018,703")
  - 2009: "1,104,151" (non-SMEs "53,577", SMEs "1,050,574")
  - 2010: "1,158,362" (non-SMEs "70,153", SMEs "1,088,209")
  - 2011: "1,192,175" (non-SMEs "82,027", SMEs "1,110,148")
  - 2012: "1,240,063" (non-SMEs "92,951", SMEs "1,147,112")
  - 2013: "1,271,667" (non-SMEs "90,256", SMEs "1,181,411")
  - 2014: "1,264,155" (non-SMEs "88,684", SMEs "1,175,471")
  - 2015: "1,256,867" (non-SMEs "94,781", SMEs "1,162,086")
  - 2016: "1,281,440" (non-SMEs "100,869", SMEs "1,180,571")
  - 2017: "1,292,421" (non-SMEs "103,800", SMEs "1,188,621")

### Zombie firms: definitions and trends
- Three zombie definitions used:
  - CHK: Caballero, Hoshi, and Kashyap (2008).
  - FN: Fukuda and Nakamura (2011) — preferred methodology.
  - Imai (2016).
- Identification rules (as reported):
  - CHK identifies zombie if I_i,t* > I_i,t (minimum required interest payment proxy).
  - FN requires I_i,t* > EBIT_i,t and (B_i,t > B_i,t-1 and I_i,t < I_i,t*).
  - Imai requires multi-year profitability shortfall: sum_{m=0}^3 (EBIT_{i,t-m} - I^*_{i,t-m}) < 0 and (B_i,t > B_i,t-1 and I_i,t < I_i,t*).
- Trends:
  - Share of zombie firms declined over years regardless of definition.
  - CHK zombie ratio peaked in "2012".
  - FN zombie ratio peaked in "2009".
  - Imai series begins in "2011" and shows continued decline thereafter.
  - Share of zombie firms in 2017 ranges from "5 percent (Imai)" to "25 percent (CHK)."
  - CHK yields nearly five times higher share than FN and Imai because CHK does not filter based on profitability.
- Transition dynamics:
  - Zombie firms that remain zombie in the next period stabilized at around "30 percent in 2017."
  - More than half of zombie firms became non-zombie firms in the following period.
- CEO age and zombie firms:
  - Firms with older CEOs have a higher ratio of zombie firms.
  - Ratio of zombie firms in rural areas has been higher than in urban areas.

### Spillovers and network effects
- Firm exits (bankruptcies and voluntary exits) produce spillover effects through inter-firm supply networks.
- Magnitude of propagation differs across industries and depends on firm location.
- Inter-firm networks can amplify negative shocks and propagate positive shocks that promote growth.
- OLS estimates (Table 2) — key reported coefficients (preserved exactly):
  - lna ge coefficient on dlnsales: "-0.0220***"
  - lna ge_exe coefficient on dlnsales: "-0.0467***"
  - d_change_exe on dlnsales: "-0.0104***"
  - d_change_exe on lnindeg_new: "0.328***"
  - d_change_exe on lnoutdeg_new: "0.227***"
  - Supplier bankruptcy (t-1) on dlnsales: "-0.0297***"
  - Supplier bankruptcy (t-1) on lnindeg_new: "0.917***"
  - Consumer bankruptcy (t-1) on dlnsales: "-0.0278***"
  - Consumer bankruptcy (t-1) on lnoutdeg_new: "0.668***"
  - Supplier voluntary exit (t-1) on dlnsales: "-0.0133**"
  - Supplier voluntary exit (t-1) on lnindeg_new: "0.444***"
  - Consumer voluntary exit (t-1) on dlnsales: "-0.0125**"
  - Consumer voluntary exit (t-1) on lnoutdeg_new: "0.846***"
- Magnitude interpretation examples:
  - A "one-fifth of one percent increase in the variable of exit rate of business connections" (0.2 percentage point increase) associated with:
    - Supplier bankruptcy: sales drop by "0.59 percent (=1/5*-0.0297*100)" the following period.
    - Supplier voluntary exit: sales drop by "0.26 percent (=1/5*-0.0133*100)" the following period.
- Network formation:
  - Exit of a partner is associated with building new connections for all exit types.
  - Exit of a supplier leads to finding new suppliers, not consumers.
  - Sales growth and new network creation are negatively correlated with CEO age and firm age.
  - Change of CEO is positively correlated with creation of new supplier and consumer links.

### Partner exits and own exit probability (Probit results)
- Key reported Probit coefficients (Table 3; preserved exactly):
  - lna ge on exit dummy (all industries): "-0.0602***"
  - lna ge_exe (CEO Age) on Exit (all): "0.600***"
  - Supplier bankruptcy (t-1): "0.362***" increases probability of own exit (all industries).
  - Consumer bankruptcy (t-1): "0.288***" increases probability of own exit (all industries).
  - Supplier voluntary exit (t-1): "0.240***" increases probability of own exit (all industries).
  - Consumer voluntary exit (t-1): "0.0897*" increases probability of own exit (all industries).
  - Merger by partner generally does not increase partner exit probability.
- Industry heterogeneity (summary):
  - Supplier bankruptcy increases exit likelihood in retail, wholesale, construction.
  - Consumer bankruptcy increases exit likelihood for wholesale and construction, but not retail.
  - Supplier voluntary exit increases exit probability for wholesalers and for non-manufacturing overall.
  - Consumer voluntary exit affects retail (consumer channel).
  - Partner mergers increase probability of own merger (supplier and consumer channels vary by sub-sector).

### Determinants of exit types (reported coefficients, Table 11 summary)
- Firm age:
  - Younger firms more likely to go bankrupt or be merged:
    - lna ge (All Exit) = "-0.0602***"
    - (Bankruptcy) = "-0.0558***"
    - (Merger) = "-0.213***"
  - For voluntary exits, older firms more likely to exit voluntarily:
    - (Voluntary Exit) lna ge = "0.0472***"
- CEO age:
  - CEO Age (All Exit) = "0.600***"
  - CEO Age by exit type: Bankruptcy = "0.135***"; Merger = "0.110***"; Voluntary Exit = "1.004***"
- Change of CEO:
  - Change of CEO (Bankruptcy) = "-0.102***" (less likely to go bankrupt)
  - Change of CEO increases probability of merger and voluntary exit: Merger = "0.314***"; Voluntary Exit = "0.154***"
- Network effects:
  - Number of suppliers increases probability of bankruptcy: Number of Suppliers (Bankruptcy) = "0.0777***"
  - Number of consumers reduces voluntary exit probability: Number of Consumers (Voluntary Exit) = "-0.0910***"
- Lagged partner exits (preserved):
  - Supplier Bankruptcy (t-1): All Exit = "0.362***"; Bankruptcy = "0.587***"; Merger = "-0.114"; Voluntary Exit = "0.145*"
  - Supplier Voluntary Exit (t-1): All Exit = "0.240***"; Bankruptcy = "0.244***"; Merger = "0.112"; Voluntary Exit = "0.210***"

### Policy implications, recent measures, and recommended focus
- Empirical diagnosis:
  - Increase in voluntary exits associated with demographic trends — older CEOs nearing or beyond retirement ages and difficulties in identifying business successors.
  - Lower bankruptcy rates and declining share of zombie firms suggest average improvement in health of Japanese SMEs.
  - Voluntary exits have meaningful spillover effects through inter-firm supply chain networks, amplifying economic impact.
- Policy measures enacted (examples cited):
  - Small and Medium Enterprise Agency’s ‘Five-Year Plan for Business Succession’ (2017).
  - FY2018 and FY2019 tax reforms: "100 percent deferral of payment of inheritance tax and gift tax concerning succession of land, buildings, machinery and equipment."
  - Increased support for finding successors:
    - awareness-raising by sending financial institutions and professional experts to business owners;
    - establishment of public centers for supporting business succession;
    - special measures funded by the business succession fund of the Organization for Small and Medium Enterprises and Regional Innovations (FY2019 tax reform);
    - reduction or exempt of registration license tax for M&A and real estate acquisition tax to support succession to non-family members (FY2018 tax reform).
  - METI package (December 2019) to enlarge succession to non-relatives/non-employees; target: "600,000 business owners can turn over their businesses to new owners in the coming decade" (government aim stated).
  - FY2020 first supplementary budget included "10 billion Japanese Yen" for SME business succession support, including subsidizing costs (legal, due diligence) for succession to a third-party.
- Targeting rural areas:
  - Rationale:
    - Population aging advances more rapidly in rural areas; CEOs are aging faster in rural prefectures.
    - Firm density disparities in 2017:
      - average firms per ten square kilometers in urban areas = "1,343"; in rural areas = "205".
      - Tokyo ≈ "close to 6,000 firms" per ten square kilometers; Osaka ≈ "close to 3,000 firms" per ten square kilometers.
      - For about "40 out of 47" prefectures, average firms per ten square kilometers is less than "500."
  - Implication:
    - Lower firm density increases search frictions and makes it harder to find alternative partners or successors in rural areas.
- Recommended policy directions (implied from analysis):
  - Increase targeted support for business succession in rural areas given faster CEO aging and lower firm density.
  - Reduce search frictions and improve matching mechanisms between retiring CEOs and business successors (public centers, cooperation with financial institutions and M&A intermediaries, information access).
  - Continue and expand incentives for non-family succession and support (tax measures, subsidies, awareness-raising, legal/due-diligence cost support).
  - Emphasize early preparation and awareness to increase chances of successful successor matching.
  - Consider policies that leverage inter-firm networks to spread positive shocks and mitigate negative propagation.

*Source: wpiea2020182-print-pdf, https://www.imf.org/-/media/files/publications/wp/2020/english/wpiea2020182-print-pdf.pdf*

### References ________________________________________________________________34

### References

### Introduction and scope
- Business dynamism defined as turnover of firm entry and exit is crucial for efficient allocation of resources, with implications for aggregate employment, growth, and productivity.
- Dataset covers Japanese firms for the period 2007 to 2017 and contains balance-sheet information, exit type (if a firm exited), and inter-firm transactional data enabling identification of supply-chain structure.
- The analysis focuses on why firms exit, which firms are likely to exit, spillover effects through supply chains, and policies to prevent healthy firms from exiting, especially in the context of the COVID-19 pandemic.

### Key empirical findings
- Firm exit rates in Japan are still very low relative to other advanced economies.
- Overall financial health improved in recent years: very low bankruptcy rates and a declining ratio of “zombie firms”.
- Majority of firm exits in recent years are voluntary exits (business closures).
- Voluntary exits are strongly positively correlated with the age of the chief executive officer (CEO).
  - Older CEOs, at or past retirement age, often close businesses voluntarily even when firms are solvent, linked to difficulty securing next-generation family successors.
  - Voluntary exits by older CEOs are broad-based across sectors and regions.
- Voluntary exits are relatively more pronounced in rural areas than in urban areas for two main reasons:
  - There are more elderly CEOs in rural areas, reflecting faster population aging and shrinkage in rural regions.
  - Firms in rural areas face greater difficulty finding business partners because firm density is lower.
- Firms with fewer inter-firm connections are more likely to choose voluntary exit; voluntary exits are positively correlated with exits of connected firms.

### Spillovers and network effects
- Firm exits, whether voluntary or bankruptcies, can produce non-negligible spillover effects through highly interconnected firm supply networks.
- The magnitude of propagation effects from a firm’s exit differs across industries and depends on firm location.
- Inter-firm networks can both amplify negative shocks and propagate positive shocks that promote firm growth.

### Policy implications and recommended focus
- Voluntary exits tied to demographic trends require different policy considerations than traditional bankruptcy resolution mechanisms.
  - Measures designed to facilitate bankruptcies may be less effective for voluntary exits of otherwise healthy firms.
  - Profitability and productivity, which matter for bankruptcy decisions, may play a less important role in CEOs’ voluntary exit decisions.
- Policy priorities should include:
  - Improving mechanisms that facilitate business succession and reduce search frictions for matching retiring CEOs with potential successors.
  - Establishing efficient market mechanisms to enable matching between retiring CEOs and successors, to prevent voluntary exits of potentially healthy firms when search costs exceed benefits.
  - Considering policies that leverage inter-firm networks to spread positive shocks and support macroeconomic growth while mitigating negative propagation.

### Contributions to the literature
- Extends literature on firm growth, business dynamism, inter-firm networks, and “zombie firms” in Japan by:
  - Highlighting a unique phenomenon in Japan where voluntary exits are often unrelated to firms’ financial distress but are instead driven by business succession issues.
  - Emphasizing the role of CEO characteristics (age) and the likelihood of finding successors in exit decisions.
  - Providing evidence on how inter-firm transactional networks influence firm exits and propagation of shocks.

### Contextual statistics and survey evidence (as reported)
- According to the 2017 White Paper on Small and Medium Enterprises in Japan (SMEA), one third of CEOs of Japanese medium-sized firms mentioned “cannot secure successor” as a reason that could make them consider business closure.
- For small firms in the same survey, nearly half of respondent firms checked “cannot secure successor” as the reason for considering business closure.
- The 2018 SMEA survey reported that nearly half of CEOs over 60 years old have not determined who will succeed them.

### Paper organization (overview)
- Section 2: Importance of business succession issues in Japan in the context of an aging and shrinking population.
- Section 3: Description of datasets used.
- Section 4: Changing patterns of firm exits in Japan and driving factors, including aging CEOs and business succession; extensive discussion of voluntary exit patterns.
- Section 5: Empirical results on the impact of a firm’s exit on other firms’ performance and exit patterns when firms are connected in a production network.

*Source: wpiea2020182-print-pdf (References), https://www.imf.org/-/media/files/publications/wp/2020/english/wpiea2020182-print-pdf.pdf*

### Section 6   discusses recent government  policies  adopted  to  address  voluntary  firm  exit  issues  in

### Section 6 — Recent government policies to address voluntary firm exit issues in Japan (excerpt)

### II. Aging society and business succession in Japan
- Peak of the CEO age distribution for Japanese small- and medium-sized enterprises (SMEs) has shifted to the right during the last two decades.
- In 1995, the age group with the largest share of CEOs was 50 to 54 years; in 2015, it was 65 to 69 years.
- The fraction of companies having CEOs over 65 years old in 2015 was 37 percent, which was nearly two times larger than the fraction of 18 percent in 1995.
- SMEA (2017) survey findings:
  - Not being able to find business successors is mentioned as a serious threat to business transition.
  - Nearly a third of medium-sized companies reported ‘cannot secure successor’ as the second important reason for considering exits, followed by ‘poor earnings’.
  - About one in five companies (22.7 percent) reported ‘getting old (physical strength/judgement declining)’ as a reason for considering business closure.
  - For small firms, nearly half of respondent firms checked ‘cannot secure successor’ as the number one reason that would make firms consider business closure.
- TSR (2017 sample) confirmation:
  - Average age of CEO for the firms observed in the data is 61 years old for the 2017 data.
  - The fraction of companies having CEOs who are over 65 years of age was as high as 41 percent.
  - Little difference across industries in average CEO age: construction sector average age 60 years, wholesale sector average age 62 years.

### III. Data description (TSR dataset and comparability)
- Data source: Tokyo Shoko Research, LTD (TSR) firm-level longitudinal data, annual frequency.
- Coverage: about 1.2 million Japanese firms from 2007 to 2017.
- Firm information includes: industry classification (Japanese Standard Industrial Classification 4-digit), firm address, number of employees, total sales amount, year of establishment, CEO’s name and birth date.
- Transactional relationship information includes lists of up to 24 suppliers (upstream firms) and customers (downstream firms).
- About 91 percent of total firms are small and medium-sized enterprises (SMEs) in the 2017 data set.
- TSR contains information on firm exits and exit types:
  - Exit types categorized into three groups: tosan (bankruptcy), gappei (merger), and voluntary exit.
  - TSR distinguishes three types of voluntary exits: kyugyo (temporarily suspension of business), haigyo (business closure), and kaisan (dissolution of company).
  - TSR includes credit rating scores ranging from 1 to 100; 1 denotes the highest default risk and 100 denotes the lowest default risk.
- Comparability with Census:
  - Geographic distribution: percentage figures based on the TSR dataset are nearly equal to those based on the 2016 Economic Census for Business Activity for many prefectures; exceptions with small differences (ranging from 1 to 2 percentage points) include Tokyo, Kanagawa, Osaka, Aichi, and Hokkaido.
  - Firm size distribution (Table 1) — percentage of firms by number of employees in 2016:
    - Census: 0-4: 56.2, 5-9: 17.5, 10-19: 11.8, 20-29: 4.7, 30-49: 3.9, 50-99: 3.0, 100-299: 2.0, 300-999: 0.6, 1000-1999: 0.1, 2000-: 0.1
    - TSR: 0-4: 49.3, 5-9: 21.6, 10-19: 13.3, 20-29: 4.9, 30-49: 4.2, 50-99: 3.3, 100-299: 2.4, 300-999: 0.8, 1000-1999: 0.1, 2000-: 0.1
  - Note: Largest gaps are for micro-enterprises (less than 10 employees); alternative groupings reduce the gap.

### IV. Changing patterns of Japanese firm exits
- General observation: Exit rate of Japanese firms is low compared to other countries.
  - SMEA (2019): firm exit rate in Japan is 4 percent.
  - Firm exit rates are close to 10 percent in the United States and the United Kingdom in 2011.
  - In the TSR data, the firm exit rate is around 2 percent in 2017.
  - This is well below the target of 10 percent under Japan’s Revitalization Strategy.
- Time trends (TSR 2007–2017):
  - Overall firm exit rates have continued to slide since the beginning of the sample, with a mild increase since 2016.
  - Recent upward trend in firm exit rates is driven by an increase in voluntary exits.
  - Exits due to bankruptcy have declined steadily since 2008.
  - Exits due to merger have the lowest ratio and remained constant (and close to zero) throughout the sample period.
- Regional and industry patterns (Figure 4 summary):
  - Bankruptcy:
    - Share of firms that exited due to bankruptcy declined steadily for both rural and urban areas and for all sectors.
    - Bankruptcy rate is slightly higher for urban areas than for rural areas (difference marginal).
  - Voluntary exits and mergers:
    - Share of firms that exited due to voluntary exits and merger have increased in recent years for all regions and sectors.
    - Firms in rural areas have an equally high ratio of exiting voluntarily as those in urban areas.
    - Across sectors, all sectors excluding construction had higher voluntary exits in 2017 compared to 2016.
    - Retail sectors have the highest ratio of voluntary exits throughout the sample period; manufacturing sectors exhibit relatively low ratio.
    - Regional difference for merging: firms in urban areas have higher share of merging compared to those in rural areas.
    - Wholesale and retail sectors exhibited a higher ratio of exiting through merging in 2017 compared to 2016; construction sectors had a very low rate of merger.
    - Despite the recent uptick in merger, the level of firm exits due to merger remains at a very low level.
- CEO age and voluntary exits:
  - Until 2010, average age of CEOs was higher for urban areas compared to rural areas; since then the order reversed.
  - In 2017, the average age of business owners in rural areas is 61.5 years.

*Source: Section 6 excerpt from wpiea2020182-print-pdf (TSR data and related figures and tables).*

### 60.8 years for urban areas (Figure 5, left chart). This reversal is consistent with the fact that the

### wpiea2020182-print-pdf - 60.8 years for urban areas (Figure 5, left chart). This reversal is consistent with the fact that the

### CEO Age, Regional Patterns, and CEO Turnover
- Average CEO age: urban and rural series plotted (Figure 5, left chart); urban areas include prefectures: Tokyo, Kanagawa, Chiba, Saitama, Aichi, Osaka and Kyoto; all other prefectures are ‘rural’.
- Change of CEOs (Figure 5, right chart):
  - Japanese firms show very low rate of change in their CEOs in 2017: "2.2 percent for rural areas, 2.6 percent for urban areas in 2017."
- Correlation implication:
  - “The average age of business owners is likely to be negatively correlated with the ratio of changing business owners.”  

### CEO Age and Voluntary Exits
- Positive and strong correlation between CEO age and voluntary exit rates (Figure 6):
  - Voluntary exits reach above "2 percent for CEOs aged between 70-80 years" and "about 2.5 percent for CEOs aged above 80 years old."
- This positive correlation is observed only for voluntary exit, and not for other exit types (bankruptcy, merger).

### SMEs versus non-SMEs: Age and Voluntary Exit Rates
- Correlation is stronger for SMEs than non-SMEs (Figure 7):
  - No significant difference in voluntary exit rates for SMEs and non-SMEs until below 50 years of age.
  - From age 50, voluntary exit rate rises more rapidly for SMEs compared to non-SMEs.
  - For each age group above 50 years old, voluntary exit rates are higher for SMEs than for non-SMEs.
  - For non-SMEs, voluntary exit rates rise mildly until above 75 years of age and pick up after that.

### Sector-level and Prefecture-level Correlations
- Sector-level correlation between average CEO age and voluntary exit rates: "correlation: 0.52" (Figure 8, left).
- Prefecture-level correlation: "correlation: 0.31" (Figure 8, right).
- Interpretation:
  - Sectors or prefectures with average CEO ages higher than the overall average tend to have higher-than-average voluntary exit rates.

### Zombie Firms: Definitions and Trends
- Three zombie definitions adopted:
  - (1) Caballero, Hoshi, and Kashyap (2008) — CHK.
  - (2) Fukuda and Nakamura (2011) — FN (preferred methodology).
  - (3) Imai (2016).
- CHK identifies zombie if I_i,t* > I_i,t (minimum required interest payment proxy).
- FN requires I_i,t* > EBIT_i,t and (B_i,t > B_i,t-1 and I_i,t < I_i,t*).
- Imai requires multi-year profitability shortfall: sum_{m=0}^3 (EBIT_{i,t-m} - I^*_{i,t-m}) < 0 and (B_i,t > B_i,t-1 and I_i,t < I_i,t*).
- Trends (Figure 9, left):
  - Share of zombie firms declined over years regardless of definition.
  - CHK zombie ratio peaked in 2012.
  - FN zombie ratio peaked in 2009.
  - Imai series begins in 2011 and shows continued decline thereafter.
  - Share of zombie firms in 2017 ranges from "5 percent (Imai)" to "25 percent (CHK)."
  - CHK yields nearly five times higher share than FN and Imai because CHK does not filter based on profitability.
- Transition dynamics (Figure 9, right):
  - Zombie firms that remain zombie in the next period stabilized at around "30 percent in 2017."
  - More than half of zombie firms became non-zombie firms in the following period.

### CEO Age, Region, and Zombie Firms
- Firms with older CEOs have a higher ratio of zombie firms (Figure 10, left).
- Ratio of zombie firms by region (Figure 10, right):
  - The ratio of zombie firms in rural areas has been higher than in urban areas.

### Impact of Partner Firm Exits on Business Activity (OLS results)
- Regression specification examines sales growth, new supplier connections (lnindeg_new), new consumer connections (lnoutdeg_new).
- Key estimated effects (Table 2; coefficients preserved as reported):
  - Firm and CEO ages:
    - lna ge coefficient on dlnsales: "-0.0220***"
    - lna ge_exe coefficient on dlnsales: "-0.0467***"
  - Change of CEO (d_change_exe):
    - On dlnsales: "-0.0104***"
    - On lnindeg_new: "0.328***"
    - On lnoutdeg_new: "0.227***"
  - Supplier bankruptcy (t-1):
    - On dlnsales: "-0.0297***"
    - On lnindeg_new: "0.917***"
  - Consumer bankruptcy (t-1):
    - On dlnsales: "-0.0278***"
    - On lnoutdeg_new: "0.668***"
  - Supplier voluntary exit (t-1):
    - On dlnsales: "-0.0133**"
    - On lnindeg_new: "0.444***"
  - Consumer voluntary exit (t-1):
    - On dlnsales: "-0.0125**"
    - On lnoutdeg_new: "0.846***"
- Magnitude interpretation examples:
  - A "one-fifth of one percent increase in the variable of exit rate of business connections" (i.e., 0.2 percentage point increase) associated with:
    - Supplier bankruptcy: sales drop by "0.59 percent (=1/5*-0.0297*100)" the following period.
    - Supplier voluntary exit: sales drop by "0.26 percent (=1/5*-0.0133*100)" the following period.
- Network formation results:
  - Exit of a partner is associated with building new connections for all exit types.
  - Exit of a supplier leads to finding new suppliers, not consumers.
  - Sales growth and new network creation are negatively correlated with CEO age and firm age.
  - Change of CEO (d_change_exe) is positively correlated with both lnindeg_new and lnoutdeg_new.

### Spillovers: Partner Exits and Own Exit Probability (Probit results)
- General findings (Table 3):
  - CEO age: "lna ge -0.0602***" on exit dummy (all industries) — firms with older CEOs have higher likelihood of exits (negative coefficient due to log-age coding).
  - lna ge_exe (CEO Age) positive: "0.600***" on Exit (all).
  - Supplier bankruptcy (t-1): "0.362***" increases probability of own exit (all industries).
  - Consumer bankruptcy (t-1): "0.288***" increases probability of own exit (all industries).
  - Supplier voluntary exit (t-1): "0.240***" increases probability of own exit (all industries).
  - Consumer voluntary exit (t-1): "0.0897*" (weaker) for all industries.
  - Merger by partner generally does not increase partner exit probability.
- Industry heterogeneity (Tables 4–10):
  - Bankruptcy spillovers:
    - Supplier bankruptcy increases exit likelihood in retail, wholesale, construction.
    - Consumer bankruptcy increases exit likelihood for wholesale and construction, but not retail.
  - Voluntary exit spillovers:
    - Supplier voluntary exit increases exit probability for wholesalers (supplier channel) and for non-manufacturing overall.
    - Consumer voluntary exit affects retail (consumer channel).
  - Merger spillovers:
    - Partner mergers increase probability of own merger (supplier and consumer channels vary by sub-sector).

### Determinants of Exit Types (Table 11 summary)
- Firm age:
  - Younger firms are more likely to go bankrupt or be merged: lna ge (All Exit) = "-0.0602***"; (Bankruptcy) = "-0.0558***"; (Merger) = "-0.213***".
  - For voluntary exits, older firms are more likely to exit voluntarily: (Voluntary Exit) lna ge = "0.0472***".
- CEO age:
  - Positive association with exits in general: CEO Age (All Exit) = "0.600***".
  - CEO Age by exit type: Bankruptcy = "0.135***"; Merger = "0.110***"; Voluntary Exit = "1.004***".
- Change of CEO:
  - Less likely to go bankrupt if firm changed CEO: Change of CEO (Bankruptcy) = "-0.102***".
  - Change of CEO increases probability of merger and voluntary exit: Merger = "0.314***"; Voluntary Exit = "0.154***".
- Network effects:
  - Number of suppliers increases probability of bankruptcy: Number of Suppliers (Bankruptcy) = "0.0777***".
  - Number of consumers reduces voluntary exit probability: Number of Consumers (Voluntary Exit) = "-0.0910***".
- Common determinants across exit types:
  - Larger firms, with higher sales growth and with younger CEOs tend to have lower probability of exits.
- Lagged partner exits:
  - Supplier Bankruptcy (t-1): All Exit = "0.362***"; Bankruptcy = "0.587***"; Merger = "-0.114"; Voluntary Exit = "0.145*".
  - Supplier Voluntary Exit (t-1): All Exit = "0.240***"; Bankruptcy = "0.244***"; Merger = "0.112"; Voluntary Exit = "0.210***".

### Policy Implications and Recommendations
- Key empirical diagnosis:
  - Increase in voluntary exits is associated with demographic trends — older CEOs nearing or beyond retirement ages and difficulties in identifying business successors.
  - Lower bankruptcy rates and declining share of zombie firms suggest average improvement in health of Japanese SMEs.
  - Voluntary exits have meaningful spillover effects through inter-firm supply chain networks, amplifying economic impact.
- Policy measures enacted (examples cited in the text):
  - The Small and Medium Enterprise Agency’s ‘Five-Year Plan for Business Succession’ (2017).
  - FY2018 and FY2019 tax reforms: 100 percent deferral of payment of inheritance tax and gift tax concerning succession of land, buildings, machinery and equipment.
  - Increased support for finding successors: (i) awareness-raising by sending financial institutions and professional experts to business owners; (ii) establishment of public centers for supporting business succession; (iii) special measures funded by the business succession fund of the Organization for Small and Medium Enterprises and Regional Innovations (FY2019 tax reform); (iv) reduction or exempt of registration license tax for M&A and real estate acquisition tax to support succession to non-family members (FY2018 tax reform).
  - METI package (December 2019) to enlarge succession to non-relatives/non-employees; target: "600,000 business owners can turn over their businesses to new owners in the coming decade" (government aim stated).
  - FY2020 first supplementary budget included "10 billion Japanese Yen" for SME business succession support, including subsidizing costs (legal, due diligence) for succession to a third-party.
- Targeting rural areas:
  - Rationale:
    - Population aging advances more rapidly in rural areas; CEOs are aging faster in rural prefectures (Figure 11).
    - CEO ages in 2017: Iwate, Kochi and Akita have average CEO age exceeding "63 years"; youngest CEO prefectures in 2017 include Shiga, Osaka and Aichi with average CEO age around "60 years."
    - Firm density disparities (Figure 12): average firms per ten square kilometers in urban areas = "1,343" in 2017; in rural areas = "205" in 2017.
    - Tokyo ≈ "close to 6,000 firms" per ten square kilometers; Osaka ≈ "close to 3,000 firms" per ten square kilometers.
    - For about 40 out of 47 prefectures, average firms per ten square kilometers is less than "500."
  - Implication:
    - Lower firm density increases search frictions and makes it harder to find alternative partners or successors in rural areas.
- Recommended policy directions (implied from analysis):
  - Increase targeted support for business succession in rural areas given faster CEO aging and lower firm density.
  - Reduce search frictions and improve matching mechanisms between retiring CEOs and business successors (public centers, cooperation with financial institutions and M&A intermediaries, information access).
  - Continue and expand incentives for non-family succession and support (tax measures, subsidies, awareness-raising, legal/due-diligence cost support).
  - Emphasize early preparation and awareness to increase chances of successful successor matching.

*Source: Tokyo Shoko Research, LTD data and analyses as presented in wpiea2020182-print-pdf (excerpts).*

### References

### References

### Key research on zombie firms, firm dynamics, and productivity
- Acemoglu, Daron, 2008, Introduction to Modern Economic Growth. Princeton, MJ: Princeton University Press.
- Acemoglu, Daron, Vasco M. Carvalho, Asuman Ozdaglar, and Alireza Tahbaz-Salehi, 2012, “The Network Origins of Aggregate Fluctuations,” Econometrica, 80 (5), pp.1977-2016.
- Adalet McGowan, Müge., Dan Andrews, and Valentine Millot, 2017, “The Walking Dead? Zombie Firms and Productivity Performance in OECD Countries,” OECD Economics Department Working Papers No.1372.
- Banerjee, Ryan and Boris Hofmann, 2018, “The Rise of Zombie Firms: Causes and Consequences,” BIS Quarterly Review, September 2018.
- Caballero, Ricardo J., Takeo Hoshi, and Anil K. Kashyap, 2008, “Zombie Lending and Depressed Restructuring in Japan,” American Economic Review, 98(5), pp.1943-77.
- Fukuda, Shin-ichi and Jun-ichi Nakamura, 2011, “Why Did ‘Zombie’ Firms Recover in Japan?” The World Economy, 34(7), pp.1124-37.
- Goto, Yasuo and Scott Wilbur, 2019, “Unfinished Business: Zombie Firms among SME in Japan’s Lost Decades,” Japan and the World Economy, 49, pp.105-12.
- Imai, Kentaro. 2016, “A Panel Study of Zombie SMEs in Japan: Identification, Borrowing and Investment Behavior,” Journal of the Japanese and International Economics, 39, pp.91-107.
- Ogura, Yoshiaki, Ryo Okui, and Yukiko U. Saito, 2015, “Network-motivated Lending Decisions,” RIETI Discussion Paper Series 15-E-057.

### Production networks, supply-chain disruptions, and firm performance
- Bernard, Andrew B., Andreas Moxnes, and Yukiko U. Saito, 2019, “Production Networks, Geography, and Firm Performance,” Journal of Political Economy, 127 (2), pp.639-88.
- Carvalho, Vasco M. 2014, “From Micro to Macro via Production Networks,” Journal of Economic Perspectives, 28(4), pp.23-48.
- Carvalho, Vasco M., Makoto Nirei, Yukiko U. Saito, and Alireza Tahbaz-Salehi, 2017. “Supply Chain Disruptions: Evidence from the Great East Japan Earthquake,” Becker Friedman Institute for Research in Economics Working Paper No. 2017-01.
- Fujii, Daisuke, Yukiko U. Saito, and Tatsuro Senga, 2017, “The Dynamics of Inter-firm Networks and Firm Growth,” RIETI Discussion Paper Series 17-E-110.
- Hong, Gee Hee, Yoshiaki Ogura, and Yukiko U. Saito, 2019, “Structural Change in Firm Dynamics: From Inter-Firm Network and Geospatial Perspectives,” RIETI Policy Discussion Paper Series 19-P -031.
- Hong, Gee Hee, Arata Ito, Yukiko Saito and Anh Thi-Ngoc Nguyen, 2020, “Structural Changes in Japanese SMEs: Business Dynamism in Aging Society and Inter-Firm Transaction Network,” RIETI Policy Discussion Paper, 20-P-003.
- Okubo, Toshihiro, Yukako Ono, and Yukiko U. Saito, 2015, “Roles of Wholesalers in Transaction Networks,” RIETI Discussion Paper Series 14-E-059. Revised, February 2015.

### Firm exit, bankruptcy, entrepreneurship, and job creation
- Balcaen, Sofie, Sophie Manigart, Jozefien Buyze, and Hubert Ooghe, 2012, “Firm Exit After Distress: Differentiating between Bankruptcy, Voluntary Liquidation and M&A,” Small Business Economics, 30, pp.949-75.
- Bhattacharjee, Arnab, Chris Higson, Sean Holly, and Paul Kattuman. (2009), “Macroeconomic Instability and Business Exit: Determinants of Failures and Acquisitions of UK Firms,” Economica, 76(301), pp.108-31.
- Davis, Steven J., John Haltiwanger, Ron Jarmin, and Javier Miranda, 2007, “Volatility and Dispersion in Business Growth Rates: Publicly Traded versus Privately Held Firms,” NBER Macroeconomics Annual 2006, Volume 21, pp.107-56.
- Decker, Ryan, John Haltiwanger, Ron Jarmin, and Javier Miranda, 2014, “The Role of Entrepreneurship in US Job Creation and Economic Dynamism,” Journal of Economic Perspectives, 28(3), pp.3-24.
- Doi, Noriyuki, 1999, “The Determinants of Firm Exit in Japanese Manufacturing Industries,” Small Business Economics, 13(4), pp.331-37.
- Haltiwanger, John, Ron Jarmin, and Javier Miranda, 2011, “Historically Lage Decline in Job Creation from Startup and Existing Firms in the 2008-2009 Recession,” Business Dynamics Statistics Briefing 5, Kauffman Foundation.
- Haltiwanger, John, Ron Jarmin, and Javier Miranda, 2012, “Where Have All the Young Firms Gone?” Business Dynamics Statistics Briefing 6, Kauffman Foundation.
- Harhoff, Dietmar, Konrad Stahl, and Michael Woywode (1998), “Legal Form, Growth and Exit of West German Firms – Empirical Results for Manufacturing, Construction, Trade and Service Industries,” Journal of Industrial Economics, 46 (4), pp.453-488.
- Prantl, Susanne, 2003, “Bankruptcy and Voluntary Liquidation: Evidence for New Firms in East and West Germany after Unification,” ZEW Discussion Paper No. 03-72.
- Prescott, Edward C. and Lee E. Ohanian, 2014, “Behind the Productivity Plunge: Fewer Startups” Commentary, Wall Street Journal, June 25. https://www.wsj.com/articles/behind-the-productivity-plunge-fewer-startups-1403737197
- Karahan, Fatih, Benjamin Pugsley, and Ayşegül Şahin, 2019, “Demographic Origins of the Startup Deficit,” NBER Working Paper No. 25874.

### Firm characteristics, management, and R&D
- Barker, Vincent L. and George C. Mueller, 2002, “CEO Characteristics and Firm R&D Spending,” Management Science, 48 (6), pp.782-801.
- Campbell, Robert J., Seung-Hwan Jeong, and Scott D. Graffin, 2019, “Born to Take Risk? The Effect of CEO Birth Order on Strategic Risk Taking,” Academy of Management Journal, 62(4), pp.1278-1306.
- Hsieh, Chang-Tai and Peter J. Klenow, 2014, “The Life Cycle of Plants in India and Mexico,” Quarterly Journal of Economics, 129 (3), pp.1035-84.
- Imai, Kentaro. 2016, “A Panel Study of Zombie SMEs in Japan: Identification, Borrowing and Investment Behavior,” Journal of the Japanese and International Economics, 39, pp.91-107.

### Japan-specific institutional sources and IMF country reports
- International Monetary Fund, 2017, “Financial Sector Assessment Program, Japan,” IMF Country Report No. 17/283, Washington DC.
- International Monetary Fund, 2018, “Japan: 2018 Article IV Consultation Staff Report,” IMF Country Report No. 18/333, Washington DC.
- International Monetary Fund, 2020, “Japan: 2019 Article IV Consultation Staff Report,” IMF Country Report No. 20/39, Washington DC.
- Organization for Economic Cooperation and Development, 2017, OECD Economic Survey of Japan, OECD Publishing, Paris.
- Small and Medium Enterprises Agency, 2017, “Chusho Kigyo Hakusho 2017 nenban [2017 White Paper on Small and Medium Enterprises in Japan],” https://www.chusho.meti.go.jp/pamflet/hakusyo/H29/PDF/2017hakusho_eng.pdf, Tokyo.
- Small and Medium Enterprises Agency, 2018, “Chusho Kigyo Hakusho 2018 nenban [2018 White Paper on Small and Medium Enterprises in Japan],” https://www.chusho.meti.go.jp/pamflet/hakusyo/H30/PDF/2018hakusho_eng.pdf, Tokyo.
- Small and Medium Enterprises Agency, 2019, “Chusho Kigyo Hakusho 2019 nenban [2019 White Paper on Small and Medium Enterprises in Japan].”

### Other empirical and methodological contributions cited
- Bhattacharjee, Arnab, Chris Higson, Sean Holly, and Paul Kattuman. (2009), “Macroeconomic Instability and Business Exit: Determinants of Failures and Acquisitions of UK Firms,” Economica, 76(301), pp.108-31.
- Colacelli, Mariana and Gee Hee Hong, 2019, “Productivity Drag from Small and Medium-Sized Enterprises in Japan,” IMF Working Paper Series No. 19/137.
- Fujii, Daisuke, Yukiko U. Saito, and Tatsuro Senga, 2017, “The Dynamics of Inter-firm Networks and Firm Growth,” RIETI Discussion Paper Series 17-E-110.
- Ogura, Yoshiaki, Ryo Okui, and Yukiko U. Saito, 2015, “Network-motivated Lending Decisions,” RIETI Discussion Paper Series 15-E-057.
- Okubo, Toshihiro, Yukako Ono, and Yukiko U. Saito, 2015, “Roles of Wholesalers in Transaction Networks,” RIETI Discussion Paper Series 14-E-059. Revised, February 2015.

### Annex 1. Average Age of CEOs
Source: Tokyo Shoko Research, LTD.

YearTotalRuralUrbanConstructionManufacturingRetailServicesWhole s a leOther
200759.459.259.658.460.460.059.860.258.1
200859.559.459.758.560.660.460.060.558.3
200959.759.659.758.660.860.560.260.758.1
201059.959.859.958.860.960.760.461.058.2
201160.160.160.059.061.160.960.661.258.4
201260.260.360.159.161.361.160.861.458.5
201360.460.660.259.261.461.461.061.658.6
201460.660.760.359.361.561.561.261.858.9
201560.761.060.459.561.761.661.461.959.0
201660.961.260.659.861.861.861.662.159.1
201761.261.560.860.261.962.161.862.359.3
Region
Industry

### Annex 2. Summary Statistics
Source: Tokyo Shoko Research, LTD.
Note: See footnotes   9 and 10 for the definition of an SME adopted in our analysis.

yearTotal Number of Firmsnon-SMESME
20071,034,221                                32,124    1,002,097
20081,055,557                                36,854    1,018,703
20091,104,151                                53,577    1,050,574
20101,158,362                                70,153    1,088,209
20111,192,175                                82,027    1,110,148
20121,240,063                                92,951    1,147,112
20131,271,667                                90,256    1,181,411
20141,264,155                                88,684    1,175,471
20151,256,867                                94,781    1,162,086
20161,281,440                                100,869  1,180,571
20171,292,421                                103,800  1,188,621

*Source: wpiea2020182-print-pdf - References*

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_Source: https://www.imf.org/-/media/files/publications/wp/2020/english/wpiea2020182-print-pdf.pdf_