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### Matching and sample construction
- Propensity score matching (PSM) used to obtain comparable subsamples of connected and unconnected firms based on equity and debt financing, size, cash flow volatility, and investment ex-ante.
- R&D excluded from PSM because only about 54 percent of firms have non-missing R&D data.
- Analysis focuses on Seasoned Equity Offerings (SEOs) rather than Initial Public Offerings (IPOs) because most equity issuances after 2013 were SEOs and SEOs allow comparison of firm performance before and after capital raising activity.
- Largest observable differences between connected and unconnected firms are related to firm size (Table 2, Panel B); unreported results using only size-related variables produce results very similar to baseline.

### Equity issuance patterns (aggregate and firm-level)
- By 2016, the aggregate amount of equity raised by connected firms had quadrupled compared to 2013, while issuance activities of unconnected firms and foreign listed firms remained relatively stable.
- Connected and unconnected firms had very similar equity issuances-to-asset ratios up to 2014; divergence is apparent since then.
- For connected firms, the average equity raised as a fraction of total assets in 2012 more than tripled between 2014 and 2016.
- By 2020:
  - The average ratio of cumulative equity raised over assets for connected firms was about 1.5 times the ratio for unconnected firms.
  - The average ratio for connected firms was twice that for foreign firms.

### 4.1 Firm Financing — Baseline results (difference-in-differences)
- Dependent variables: amount of equity raised per firm-year and cumulative amount of equity raised per firm up to each year; both normalized by total assets in 2012.
- Difference-in-differences comparing domestic listed (treated) vs foreign listed (control):
  - No statistically significant difference in equity raised over assets between domestic and foreign listed firms up to 2014.
  - 2015: domestic listed firms increased equity-to-asset ratio by 4 percentage points (p.p.) more than foreign listed firms.
  - 2016: the difference increased to more than 10 p.p.
  - By 2020: cumulative amount of equity raised over assets was 21 p.p. higher for domestic listed firms relative to foreign listed firms.
- Results persist when excluding dual listed firms and Hong-Kong listed firms from foreign listed sample.

### Connected vs unconnected firms (domestic listed firms)
- Difference-in-differences comparing connected and unconnected firms (full sample and PSM sample):
  - Both groups show similar equity issuance patterns before Stock Connect programs; since then connected firms raised substantially more equity.
  - Full sample:
    - 2015: ratio of equity raised over assets ~4 p.p. higher for connected vs unconnected firms.
    - 2016: difference ~6 p.p.
    - By 2020: cumulative amount ~18 p.p. higher for connected firms.
  - PSM sample:
    - Differential effect significant already in 2014.
    - 2015: ratio of equity raised over assets ~8 p.p. higher for connected firms.
    - 2016: difference rose to 18 p.p.
    - Baseline year 2012: both types had low equity issuance (~1 percent of equity raised over assets).
    - By 2020: cumulative amount of equity raised over assets was 51 p.p. higher for connected firms than unconnected firms.

### Size heterogeneity and decomposition
- Size is the most important difference between full samples of connected and unconnected firms; connected firms are, on average, significantly larger.
- PSM sample balances size distributions; PSM sample average firm size is smaller because it excludes very large firms.
- Disaggregating connected firms in the PSM sample by size (total assets in 2010-12):
  - Smallest firms (lowest quartile) in the PSM sample raised the most equity (as fraction of total assets in 2012) following the reform; impact magnitude decreases monotonically with firm size.
  - Within connected firms, relatively smaller ones (below median) increased equity issuances more than larger ones (above median):
    - Difference rose from 4 p.p. in 2014 to 23 p.p. in 2016.
    - Cumulative difference rose above 73 p.p. by 2020.
- Size-reaction correlation persists when excluding firms with strong political connections or state-owned enterprises (SOEs).
- Interpretation: smaller firms, more financially constrained, react more to internationalization events; consistent with internationalization helping constrained firms overcome financial frictions.

### Robustness and extensions
- Four robustness tests for full and PSM samples: (1) use log of equity raised as dependent variable; (2) add additional controls; (3) exclude financial firms; (4) exclude margin trading firms.
- Main takeaway: difference in issuance activity between connected and unconnected firms is robustly significant post-2012.
- Logarithmic specification (log(1 + equity raised)) in PSM sample:
  - Connected firms increased equity raising activity approximately 169 percent more in 2014 than unconnected firms (differential change zero in 2012).
  - Differences rose to 307 percent in 2015 and to 223 percent in 2016.
- Additional controls (lagged total assets, lagged sales growth) leave estimates significant but slightly smaller.
- Financial firms constitute around 3.4 percent of sample; excluding them barely changes results.
- Margin trading eligibility is a potential confounder:
  - Margin trading began in 2010 and expanded in 2013; around 39 percent of domestic listed sample are margin trading firms.
  - Most stocks that became eligible for margin trading were issued by connected firms.
  - Excluding margin trading firms increases estimated impact of internationalization; margin trading firms are about 36 percent larger than the rest of domestic listed firms.

### 4.2 Event studies (Shanghai Connect 2014; Shenzhen Connect 2016)
- Event-specific samples restrict firms to the specific stock market; treatment dummy becomes event specific.
- To reduce endogeneity, remove margin trading eligible firms and include only the first group of connected firms in Shanghai (2014) and Shenzhen (2016).
- PSM regressions run for each event using total assets in 2010-2012 to predict connected probability within each exchange.
- Findings:
  - Shanghai Connect:
    - 2015: connected firms increased ratio of equity raised over assets by about 7 p.p. more than unconnected firms.
    - 2020 cumulative difference: 17 p.p.
  - Shenzhen Connect:
    - 2016: increase in ratio of equity raised over assets was 22 p.p. higher for connected firms than unconnected firms.
    - 2020 cumulative difference: 42 p.p.
- Larger reactions in Shenzhen are consistent with size-related results because firms listed in Shanghai are on average larger than firms listed in Shenzhen.

### 4.3 Investment Activity — measures and main difference-in-differences findings
- Investment activity examined: capex, spending on acquisitions, R&D, and cash and short-term investments.
- Cash and short-term investments measured as stock values in each year; capex, acquisitions, and R&D are flows.
- Dependent variables in baseline DiD: capex over total assets, acquisitions over assets, R&D over assets, and cash and short-term investments over assets, where denominator is measured as of 2012.
- Difference-in-differences findings (connected vs. unconnected firms, PSM sample), by 2016 the difference was approximately:
  - 8 p.p. for the growth in capex-to-asset,
  - 6 p.p. for the growth in acquisitions-to-asset,
  - 2 p.p. for R&D-to-asset,
  - 28 p.p. for the growth in cash-to-asset.
- Differential effects are sizeable relative to reform policy impacts: taking 2016 as an example, the differential impact accounts for approximately 60 percent, 50 percent, and 70 percent of the predicted effect on connected firms’ capex, cash, and acquisitions, respectively.

### Financing of investment — linking equity issuances to uses of proceeds (methodology)
- Methodology follows Kim and Weisbach (2008), controlling for other sources of financing.
- Panel dataset constructed keeping observations in each year t ∈ (2013, 2020) with positive equity issuances and the pre-issuance (t-1) and post-issuance (t+1) years.
- Regression estimated for k = 0 (issuance year) and k = 1 (post-issuance year) with industry fixed effects (αj) and year fixed effects (γi).
- Coefficient β1 measures the proportion of proceeds raised per issuance allocated to each type of investment.
- Dependent variable specifications:
  - For V = cash: Y_i,t+k = ln[(V_i,t − V_i,t−1)/A_i,t−1 + 1]
  - For V = capex, acquisitions, R&D: Y_i,t+k = ln[Σ_{j=i+1}^{i+k} V_i,j / A_i,t−1 + 1]
- Dollar effects computed by predicting dependent variable with observed issuance value, then re-computing with issuance + $1 and taking marginal change; average difference per firm reported.

### Estimated use of equity proceeds (issuance year and post-issuance)
- Issuance year (t=1), median connected firm: for every dollar raised in equity:
  - 15 cents invested in capex,
  - 28 cents in acquisitions,
  - 3 cents in R&D,
  - 58 cents in cash and short-term investments.
- Post-issuance year (t=2):
  - Capex increased to 27 cents per dollar raised.
  - Cash and short-term investments remained the largest use of proceeds.

### Aggregate impact — approach and back-of-the-envelope magnitudes
- Aggregation approach: use DiD coefficients β̂i estimated in levels and multiply average impact by number of connected firms N_C to obtain aggregate dollar impact for each year: Y_t^T − Y_t^CF = N_C β̂i.
- Cumulative aggregate effect between 2013 and 2020 computed as a percentage of actual aggregate outcomes using three denominators: connected firms; all domestic listed firms; all publicly listed firms.
- Back-of-the-envelope aggregate results (full sample estimates):
  - Around 33 percent of all equity raised by connected firms between 2013 and 2020 is associated with the internationalization events.
  - 28 percent of all equity raised by domestic listed firms associated with the events.
  - 20 percent of all equity raised in China associated with the events.
  - Effects on market capitalization by 2020 are of similar magnitudes.
  - Internationalization can plausibly explain about:
    - a quarter of all cash and short-term investments,
    - 24 percent of all R&D expenditures,
    - 12 percent of acquisitions,
    - 11 percent of all capex by all domestic listed firms between 2013 and 2020.
- PSM sample: coefficient estimates are larger, implying larger aggregate impacts in percentage terms; approximately 35 to 40 percent of investment activities (all types, including cash and short-term investments) by all connected firms in the PSM sample can be attributed to the internationalization events.

### Limitations, caveats, and interpretation of aggregation
- Difficulty in fully disentangling internationalization impacts from other concurrent aggregate shocks in domestic financial markets.
- Definition of connected firms: those exposed to internationalization for the first time since the Stock Connect Program; dual-listed firms with A shares participating in the program imply estimates do not include impacts on their equity issuances and investment activities.
- Partial equilibrium aggregation: regression estimates measure direct impacts on connected firms and do not capture potential spillover effects from connected to unconnected firms, or general equilibrium effects on prices and wages.
- Unclear whether general equilibrium effects would dampen or amplify firm-level responses without a structural model.
- Despite limitations, approach provides a simple and transparent first step to quantify potential aggregate impacts of internationalization events in China.

### Data, evolution of foreign participation, and broader context
- Firm-level data on domestic and foreign ownership structure from Refinitiv. Aggregate country-level data on foreign equity inflows from the IMF (balance of payments) and asset holdings from the Coordinated Portfolio Investment Survey (CPIS).
- Evolution of aggregate foreign equity inflows:
  - 2010: total foreign equity inflows about $30 billion.
  - 2020: total foreign equity inflows more than $80 billion.
  - Notable increases around the Shanghai Connect (2014) and the announcement and incorporation of Chinese stocks to MSCI indexes (2017-2019).
  - Foreign equity inflows were much lower than equity issuance in China during this period, implying domestic investors purchased most new shares issued.
- Firm-level foreign ownership patterns:
  - Before 2016: foreign ownership remained more or less stable; on average domestic investors own over 98.5 percent of each domestic listed firm in China.
  - 2016: average ratio of foreign owned shares per firm 1.3 percent.
  - 2020: average ratio of foreign owned shares per firm 3.8 percent (almost tripled from 2016).
- MSCI and CPIS evidence:
  - China’s weight in MSCI Emerging Markets Index increased gradually between 2010 and 2017, and accelerated since 2018 after inclusion of A shares.
  - By 2020: China constituted approximately 40 percent of the MSCI Emerging Markets Index, but the share of China in emerging market equity portfolios was less than 30 percent.
  - CPIS-weight diverged from MSCI weight since 2013, suggesting scope for further foreign investor inflows if inclusion ratios increase.
- Aggregate and firm-level impacts summary:
  - Targeted firms in post-2012 internationalization events raised significantly more external financing, increased their cash position, and invested more than other domestic firms.
  - Until the late 2010s, domestic investors primarily supported early equity issuances by connected firms (bridge financing) until foreign investors accelerated entry after MSCI A-share inclusion.

### Implications and considerations for policy and generalizability
- Continued integration (for example, fuller reflection of domestic market size in benchmark indexes) could lead to expanded international investor exposure to China and increased financing of domestic firms.
- Generalizability caveats:
  - China’s exceptionally high savings rate may have allowed domestic investors to finance firms pre-internationalization, so results could overstate benefits for other emerging economies without a strong domestic investor base.
  - Conversely, if China remains underrepresented in international portfolios, results could understate potential benefits for countries with higher foreign participation.

*IMF Working Paper — The Internationalization of China’s Equity Markets (excerpted material from wpiea2023026-print-pdf)*

### 2. These estimations allow us to obtain comparable subsamples of connected and unconnected firms based on their equity a

### 2. These estimations allow us to obtain comparable subsamples of connected and unconnected firms based on their equity and 

### Matching and sample construction
- Propensity score matching (PSM) is used to obtain comparable subsamples of connected and unconnected firms based on equity and debt financing, size, cash flow volatility, and investment ex-ante.
- R&D is not used in the PSM because only about 54 percent of firms have non-missing R&D data.
- Analysis focuses on Seasoned Equity Offerings (SEOs) rather than Initial Public Offerings (IPOs) because most equity issuances after 2013 were SEOs and SEOs allow comparison of firm performance before and after capital raising activity.
- Largest observable differences between connected and unconnected firms are related to firm size (Table 2, Panel B); unreported results using only size-related variables produce results very similar to baseline.

### Equity issuance patterns (aggregate and firm-level)
- By 2016, the aggregate amount of equity raised by connected firms had quadrupled compared to 2013, while issuance activities of unconnected firms and foreign listed firms remained relatively stable.
- Connected and unconnected firms had very similar equity issuances-to-asset ratios up to 2014; divergence is apparent since then.
- For connected firms, the average equity raised as a fraction of total assets in 2012 more than tripled between 2014 and 2016.
- By 2020:
  - The average ratio of cumulative equity raised over assets for connected firms was about 1.5 times the ratio for unconnected firms.
  - The average ratio for connected firms was twice that for foreign firms.

### 4.1 Firm Financing — Baseline results (difference-in-differences)
- Dependent variables: amount of equity raised per firm-year and cumulative amount of equity raised per firm up to each year; both normalized by total assets in 2012.
- Difference-in-differences comparing domestic listed (treated) vs foreign listed (control):
  - No statistically significant difference in equity raised over assets between domestic and foreign listed firms up to 2014.
  - In 2015, domestic listed firms increased equity-to-asset ratio by 4 percentage points (p.p.) more than foreign listed firms.
  - In 2016, the difference increased to more than 10 p.p.
  - By 2020, cumulative amount of equity raised over assets was 21 p.p. higher for domestic listed firms relative to foreign listed firms.
- Results persist when excluding dual listed firms and Hong-Kong listed firms from foreign listed sample.

### Connected vs unconnected firms (domestic listed firms)
- Difference-in-differences comparing connected and unconnected firms (full sample and PSM sample):
  - Both groups show similar equity issuance patterns before Stock Connect programs; since then connected firms raised substantially more equity.
  - Full sample:
    - 2015: ratio of equity raised over assets ~4 p.p. higher for connected vs unconnected firms.
    - 2016: difference ~6 p.p.
    - By 2020: cumulative amount ~18 p.p. higher for connected firms.
  - PSM sample (cleaner estimates addressing sample selection):
    - Differential effect significant already in 2014.
    - 2015: ratio of equity raised over assets ~8 p.p. higher for connected firms.
    - 2016: difference rose to 18 p.p.
    - Baseline year 2012: both types had low equity issuance (~1 percent of equity raised over assets).
    - By 2020: cumulative amount of equity raised over assets was 51 p.p. higher for connected firms than unconnected firms.

### Size heterogeneity and decomposition
- Size is the most important difference between full samples of connected and unconnected firms; connected firms are, on average, significantly larger.
- PSM sample balances size distributions; PSM sample average firm size is smaller because it excludes very large firms.
- Disaggregating connected firms in the PSM sample by size (total assets in 2010-12):
  - Smallest firms (lowest quartile) in the PSM sample raised the most equity (as fraction of total assets in 2012) following the reform; impact magnitude decreases monotonically with firm size.
  - Within connected firms, relatively smaller ones (below median) increased equity issuances more than larger ones (above median):
    - Difference rose from 4 p.p. in 2014 to 23 p.p. in 2016.
    - Cumulative difference rose above 73 p.p. by 2020.
- Size-reaction correlation persists when excluding firms with strong political connections or state-owned enterprises (SOEs).
- Interpretation: smaller firms, more financially constrained, react more to internationalization events; consistent with internationalization helping constrained firms overcome financial frictions.

### Robustness and extensions
- Four robustness tests for full and PSM samples: (1) use log of equity raised as dependent variable; (2) add additional controls; (3) exclude financial firms; (4) exclude margin trading firms (Table 7).
- Main takeaway: difference in issuance activity between connected and unconnected firms is robustly significant post-2012.
- Logarithmic specification (log(1 + equity raised)) in PSM sample:
  - Connected firms increased equity raising activity approximately 169 percent more in 2014 than unconnected firms (differential change zero in 2012).
  - Differences rose to 307 percent in 2015 and to 223 percent in 2016.
- Additional controls (lagged total assets, lagged sales growth) leave estimates significant but slightly smaller.
- Financial firms constitute around 3.4 percent of sample; excluding them barely changes results.
- Margin trading eligibility is a potential confounder:
  - Margin trading began in 2010 and expanded in 2013; around 39 percent of domestic listed sample are margin trading firms.
  - Most stocks that became eligible for margin trading were issued by connected firms.
  - Excluding margin trading firms increases estimated impact of internationalization (Table 7); margin trading firms are about 36 percent larger than the rest of domestic listed firms.

### 4.2 Event studies (Shanghai Connect 2014; Shenzhen Connect 2016)
- Event-specific samples restrict firms to the specific stock market; treatment dummy becomes event specific.
- To reduce endogeneity, remove margin trading eligible firms and include only the first group of connected firms in Shanghai (2014) and Shenzhen (2016).
- PSM regressions run for each event using total assets in 2010-2012 to predict connected probability within each exchange.
- Findings:
  - Shanghai Connect:
    - 2015: connected firms increased ratio of equity raised over assets by about 7 p.p. more than unconnected firms.
    - 2020 cumulative difference: 17 p.p.
  - Shenzhen Connect:
    - 2016: increase in ratio of equity raised over assets was 22 p.p. higher for connected firms than unconnected firms.
    - 2020 cumulative difference: 42 p.p.
- Larger reactions in Shenzhen are consistent with size-related results because firms listed in Shanghai are on average larger than firms listed in Shenzhen.

*IMF Working Papers — The Internationalization of China’s Equity Markets*

### 4.3 Investment Activity

### 4.3 Investment Activity

### Investment measures and data construction
- Investment activity examined: capex, spending on acquisitions, R&D, and cash and short-term investments.
- Cash and short-term investments are measured as stock values in each year; capex, acquisitions, and R&D are flows.
- Dependent variables in the baseline difference-in-differences specification (Equation 1): capex over total assets, acquisitions over assets, R&D over assets, and cash and short-term investments over assets, where the denominator is measured as of 2012.
- Worldscope definitions used for each variable (Appendix Table 1).

### Difference-in-differences findings (connected vs. unconnected firms)
- Pre-2013: connected and unconnected firms followed similar trends in investment activity.
- Post-2012 divergence: connected firms invested significantly more than unconnected firms across all investment types.
- From the PSM sample, by 2016 the difference between connected and unconnected firms was approximately:
  - 8 p.p. for the growth in capex-to-asset,
  - 6 p.p. for the growth in acquisitions-to-asset,
  - 2 p.p. for R&D-to-asset,
  - 28 p.p. for the growth in cash-to-asset (Figure 7).
- Differential effects are sizeable relative to reform policy impacts: taking 2016 as an example, the differential impact accounts for approximately 60 percent, 50 percent, and 70 percent of the predicted effect on connected firms’ capex, cash, and acquisitions, respectively (Appendix Figure 5).

### Financing of investment — methodology for linking equity issuances to uses of proceeds
- To associate increases in investment measures with equity issuances, the paper follows the methodology pioneered by Kim and Weisbach (2008), which controls for other sources of financing.
- Construction: panel dataset for each firm i keeping observations in each year t ∈ (2013, 2020) with positive equity issuances and the pre-issuance (t-1) and post-issuance (t+1) years.
- Regression estimated (Equation (2)) for k = 0 (issuance year) and k = 1 (post-issuance year); industry fixed effects (αj) and year fixed effects (γi) included.
- The coefficient of interest is β1, which measures the proportion of proceeds raised per issuance allocated to each type of investment (capex, acquisitions, R&D, and cash).
- For the dependent variable:
  - For V = cash: Y_i,t+k = ln[(V_i,t − V_i,t−1)/A_i,t−1 + 1]
  - For V = capex, acquisitions, R&D: Y_i,t+k = ln[Σ_{j=i+1}^{i+k} V_i,j / A_i,t−1 + 1]
- Dollar effects computed by predicting the dependent variable with the observed issuance value, then re-computing with issuance + $1 and taking the marginal change; average difference per firm reported.

### Estimated use of equity proceeds (issuance year and post-issuance)
- Issuance year (t=1), median connected firm: for every dollar raised in equity:
  - 15 cents invested in capex,
  - 28 cents in acquisitions,
  - 3 cents in R&D,
  - 58 cents in cash and short-term investments (Table 10).
- Post-issuance year (t=2):
  - Capex increased to 27 cents per dollar raised.
  - Cash and short-term investments remained the largest use of proceeds.

### Aggregate impact (summary of calculations and magnitudes)
- Aggregation approach: use difference-in-differences coefficients β̂i estimated in levels (Equation (1) with dependent variable in levels) and multiply average impact by the number of connected firms N_C to obtain aggregate dollar impact for each year: Y_t^T − Y_t^CF = N_C β̂i.
- Cumulative aggregate effect between 2013 and 2020 computed as a percentage of actual aggregate outcomes:
  - For equity raised, capex, acquisitions, and R&D: ratio of cumulative aggregate impact to cumulative aggregate outcomes (summing 2013–2020).
  - Considered three denominators for Y_i: actual aggregate outcome among all connected firms; all domestic listed firms (connected and unconnected); and all publicly listed firms (domestic and foreign listed).
  - For market capitalization and cash (already cumulative): ratio of aggregate impact in 2020 to aggregate outcome in 2020.
- Back-of-the-envelope aggregate results (full sample estimates):
  - Around 33 percent of all equity raised by connected firms between 2013 and 2020 is associated with the internationalization events (Table 11, column 5).
  - 28 percent of all equity raised by domestic listed firms associated with the events (Table 11, column 6).
  - 20 percent of all equity raised in China associated with the events (Table 11, column 7).
  - Effects on market capitalization by 2020 are of similar magnitudes.
  - Internationalization can plausibly explain about:
    - a quarter of all cash and short-term investments,
    - 24 percent of all R&D expenditures,
    - 12 percent of acquisitions,
    - 11 percent of all capex by all domestic listed firms between 2013 and 2020.
- PSM sample: coefficient estimates are larger, implying larger aggregate impacts in percentage terms; approximately 35 to 40 percent of investment activities (all types, including cash and short-term investments) by all connected firms in the PSM sample can be attributed to the internationalization events.

### Limitations and caveats in aggregation and interpretation
- Difficulty in fully disentangling internationalization impacts from other concurrent aggregate shocks in domestic financial markets.
- Definition of connected firms: those exposed to internationalization for the first time since the Stock Connect Program; dual-listed firms with A shares participating in the program imply the estimates do not include impacts on their equity issuances and investment activities.
- Partial equilibrium aggregation: regression estimates measure direct impacts on connected firms and do not capture potential spillover effects from connected to unconnected firms, or general equilibrium effects on prices and wages.
- Without a structural model incorporating these channels, it is unclear whether general equilibrium effects would dampen or amplify firm-level responses.
- Despite limitations, the approach provides a simple and transparent first step to quantify potential aggregate impacts of internationalization events in China.

*IMF Working Paper — excerpt: 4.3 Investment Activity (includes methodological details, firm-level and aggregate results, and limitations).*

### Appendix Table 4 explores a range of plausible values for the aggregate effect on each variable of interest.

### wpiea2023026-print-pdf - Appendix Table 4 explores a range of plausible values for the aggregate effect on each variable of interest.

### Data and approach
- Firm-level data on domestic and foreign ownership structure from Refinitiv.
- Aggregate country-level data on foreign equity inflows from the IMF (balance of payments) and asset holdings from the Coordinated Portfolio Investment Survey (CPIS).
- Equity inflows estimated as the net variation in foreign equity positions in China.

### Evolution of aggregate foreign equity inflows
- 2010: total foreign equity inflows about $30 billion.
- 2020: total foreign equity inflows more than $80 billion.
- Notable increases around the Shanghai Connect (2014) and the announcement and incorporation of Chinese stocks to MSCI indexes (2017-2019).
- Comparison with equity issuance: foreign equity inflows were much lower than equity issuance in China during this period, implying domestic investors purchased most new shares issued.
- Foreign inflows increased steadily closer to MSCI incorporation dates, consistent with domestic investors providing “bridge financing” between early internationalization stages and arrival of international investors.

### Firm-level foreign ownership patterns
- Before 2016: foreign ownership remained more or less stable; on average domestic investors own over 98.5 percent of each domestic listed firm in China.
- Change after 2016: notable increase in foreign ownership ratio.
- 2016: average ratio of foreign owned shares per firm 1.3 percent.
- 2020: average ratio of foreign owned shares per firm 3.8 percent (almost tripled from 2016).

### MSCI weights, CPIS holdings, and international investor behavior
- China’s weight in MSCI Emerging Markets Index increased gradually between 2010 and 2017, and accelerated since 2018 after inclusion of A shares.
- Two simulated scenarios: (1) if all A shares were included in the index in 2018; (2) if no A shares were included. The actual weight lies between these counterfactuals, implying potential for further rise if inclusion ratio increases.
- CPIS-based weight of China within MSCI Emerging Markets countries:
  - Before 2013: CPIS-weight and MSCI weight were very similar.
  - Since 2013: CPIS-weight started to diverge and lag MSCI weight.
  - By 2020: China constituted approximately 40 percent of the MSCI Emerging Markets Index, but the share of China in emerging market equity portfolios was less than 30 percent.
- Cross-country CPIS comparison (excluding China): investments in China have surpassed investments in other emerging markets and increased significantly since 2006, with pace changing drastically since 2016 (especially since 2018).
- Three economies following China in size and growth of positions: India, South Korea, and Taiwan Province of China.
- Hong Kong received a sizeable share of investments in early periods and may have acted as a conduit, but positions there remained stable over the past decade as investors invested directly in mainland China.

### Aggregate and firm-level impacts of internationalization (post-2012 focus)
- Targeted firms in post-2012 internationalization events:
  - Raised significantly more external financing.
  - Increased their cash position.
  - Invested more than other domestic firms.
- At the aggregate level, internationalization associated with a significant fraction of equity raised and investment activities by all domestic listed firms in China between 2013 and 2020.
- Until the late 2010s, foreign participation lagged behind reforms; domestic investors primarily supported early equity issuances by connected firms.
- Domestic investors provided bridge financing until foreign investors accelerated entry after MSCI A-share inclusion.

### Implications and considerations
- Continued integration (for example, fuller reflection of domestic market size in benchmark indexes) could lead to expanded international investor exposure to China and increased financing of domestic firms.
- Uncertainty about generalizability:
  - China’s exceptionally high savings rate may have allowed domestic investors to finance firms pre-internationalization, so results could overstate benefits for other emerging economies without a strong domestic investor base.
  - Conversely, if China remains underrepresented in international portfolios, the paper’s results could understate potential benefits for countries with higher foreign participation.

*Source: https://www.imf.org/-/media/files/publications/wp/2023/english/wpiea2023026-print-pdf.pdf*

### References

### wpiea2023026-print-pdf - References

### Major cited works
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- Acharya, Viral V., Jun Qian, Yang Su, and Zhishu Yang, 2020b, “In the Shadow of Banks: Wealth Management Products and Issuing Banks’ Risks in China,” CEPR Discussion Papers 14957.
- Antonelli, Stefano, Flavia Corneli, Fabrizio Ferriani, and Andrea Gazzani, 2022, “Benchmark Effects from the Inclusion of Chinese A-shares in the MSCI EM Index,” Economics Letters, Vol. 216, July 2022, 110600.
- Bai, Ye, and Darien Yan Pang Chow, 2017, “Shanghai-Hong Kong Stock Connect: An analysis of Chinese Partial Stock Market Liberalization Impact on the Local and Foreign Markets,” Journal of International Financial Markets, Vol. 50, pp. 182-203.
- Bekaert, Geert, Campbell R. Harvey, and Christian Lundblad, 2001, “Emerging Equity Markets and Economic Development,” Journal of Development Economics, Vol. 66(2), pp. 465-504.
- Bekaert, Geert, Campbell R. Harvey, and Christian Lundblad, 2005, “Does Financial Liberalization Spur Growth?” Journal of Financial Economics, Vol. 77(1), pp. 3-55.
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### Figures — captions and key data notes
- Figure 1. Aggregate Equity Market Indicators
  - Panel A: total equity market capitalization of domestic listed firms (Mainland China, Hong Kong SAR, China, Singapore).
  - Panel B: domestic equity price indexes of domestic listed firms (2012 = 1); mainland China index is average of the Shanghai and Shenzhen composite equity indexes; Hong Kong index is the Hang Seng index; Singapore index is the STI index.
  - Panel C: aggregate equity issuance activity (excluding initial public offerings). Values are expressed in billions of 2011 U.S. dollars (USD). Sources: World Bank and Refinitiv.
- Figure 2. Equity Issuance Activity of Chinese Firms
  - Panel A: aggregate amount of equity raised per type of firm (Domestic Listed Firms, Connected; Domestic Listed Firms, Unconnected; Foreign Listed Firms).
  - Panel B: average amount of equity raised per type of firm and year over 2012 assets.
  - Panel C: average cumulative equity raised per type of firm and year over 2012 assets.
- Figure 3. Differences in Equity Issuance Behavior: Domestic vs Foreign Listed Firms
  - DiD coefficients (and 90% confidence interval) from Equation (1). Panel A uses amount of equity raised over 2012 assets; Panel B cumulative amount. 2012 coefficient shows 2012 baseline difference.
- Figure 4. Differences in Equity Issuance Behavior: Connected vs Unconnected Domestic Listed Firms
  - DiD coefficients (90% CI) from Equation (1). Left panels: full sample; Right panels: propensity-score-matched (PSM) sample. Grey bars mark formal announcement and implementation of Shanghai-Hong-Kong Stock Connect (SS Connect), Shenzhen-Hong-Kong Stock Connect (SZ Connect), and MSCI incorporation.
- Figure 5. Differences in Equity Issuance Behavior: Connected Firms of Different Sizes
  - Connected firms divided by assets percentiles (below P25; P25-P50; P50-P75; above P75). Panels show DiD coefficients (90% CI) comparing these groups to unconnected firms; regressions use PSM sample. Grey bars mark SS Connect, SZ Connect, MSCI incorporation.
- Figure 6. Differences in Equity Issuance Behavior: Shanghai and Shenzhen Events
  - DiD coefficients (90% CI) for PSM samples of Shanghai-listed and Shenzhen-listed firms. Grey bars capture SS Connect and SZ Connect.
- Figure 7. Differences in Investment Behavior: Connected vs Unconnected Domestic Listed Firms
  - DiD coefficients (90% CI) for investment variables (Capex over 2012 assets; Acquisitions over 2012 assets; R&D over 2012 assets; Cash and Short-term Investments over 2012 assets). Regressions use PSM sample; grey bars capture SS Connect, SZ Connect, MSCI incorporation.
- Figure 8. Foreign Equity Ownership
  - Panel A: annual foreign equity inflows into China vs aggregate value of equity issuances by Chinese listed companies (excluding IPOs). Values in billions of 2011 U.S. dollars (USD).
  - Panel B: evolution in ratio of foreign ownership in total market capitalization of domestic listed firms in China. Sources: Authors' calculations based on Balance of Payments data from the IMF and Refinitiv.
- Figure 9. Importance of China in International Equity Portfolios
  - Panel A: Chinese weight in the MSCI Emerging Markets Index and counterfactuals with no inclusion/full inclusion of A shares for 2018-2020, and China weight in equity positions of all countries relative to emerging economies.
  - Panel B: evolution of foreign equity positions in China and other emerging economies in billions of 2011 U.S. dollars (USD). Sources: Authors' calculations based on CPIS and MSCI.

### Tables — highlights, sample sizes, and specific numeric results
- Table 1. Number of Firms and Issuance Activity
  - Panel A: Total Number of Firms and Equity Raised (2000-2020) — sample counts shown: Foreign Listed No. of Firms 438; Domestic Listed Unconnected 728; Domestic Listed Connected 1,289 (these appear in table fragments).
  - Panel B: Equity Raised over Time — aggregate amounts for periods 2000-2005, 2006-2012, 2013-2020 presented in $ Million shares columns (fragmented).
- Table 2. Differences in Firm Characteristics
  - Shows average firm characteristics during 2010-12 and tests for mean differences across firm types. Examples of reported means and differences:
    - Equity Raised over Assets: Foreign 0.07; Domestic 0.03; Difference -0.04***.
    - Assets (Logs): Foreign 20.42; Domestic 19.70; Difference -0.72***.
    - Cash over Assets: Foreign 2.89; Domestic 0.22; Difference -2.67**.
  - Panel comparisons for Unconnected vs Connected and PSM sample also reported with exact numeric entries (e.g., Unconnected Equity Raised over Assets 0.03; Connected 0.04; Difference 0.01**).
- Table 3. DiD Equity Issuance Estimates: Domestic Listed vs Foreign Listed Firms
  - DiD coefficients by year for dependent variables Equity over 2012 Assets and Cumulative Equity over 2012 Assets.
  - Notable coefficients (exact values preserved):
    - Y_2015 x Treated 0.043*** (Equity over 2012 Assets) and 0.063*** (Cum. Equity).
    - Y_2016 x Treated 0.108*** and 0.176***.
    - Y_2018 x Treated -0.005 and 0.202*** in different columns.
  - No. of observations and clusters: examples include No. of observations 38,496; No. of clusters 68.
- Table 4. DiD Equity Issuance Estimates: Connected vs Unconnected Domestic Listed Firms
  - Full Sample and PSM Sample columns. Selected coefficients:
    - Treated (2012 Diff.) -0.001 and 0.027*** in different columns.
    - Y_2016 x Treated 0.035 and 0.036 (Equity over 2012 Assets) in one specification and larger positive coefficients in others (e.g., 0.195***, 0.457***).
  - No. of observations examples: 10,624; 10,608; No. of clusters 55.
- Table 5. DiD Equity Issuance Estimates: Connected Firms of Different Sizes vs Unconnected Firms
  - PSM sample with groups by size percentiles. Selected results:
    - Y_2015 x Treated coefficients for small groups: 0.127*** and 0.215*** in two dependent-variable specifications.
    - Y_2016 x Treated 0.230** and 0.470*** for small-connected vs unconnected.
  - No. of observations examples: 8,448; 5,360; No. of clusters 53, 44.
- Table 6. DiD Small vs Large Connected Firms
  - Compares connected firms below median size vs above median. Selected coefficients (exact):
    - Y_2015 x Small 0.127*** and 0.215*** in two specifications.
    - Y_2016 x Small 0.230** and 0.470***.
  - Sample: PSM Sample and PSM Sample excluding SOEs.
- Table 7. DiD Equity Issuance Estimates: Alternative Specifications
  - Robustness checks including Ln(1 + Equity Raised) dependent variable; controlling for lagged assets and sales growth; excluding financial firms; excluding margin trading firms.
  - Selected coefficients:
    - Y_2015 x Treated 0.074***; Y_2016 x Treated 0.042 and 0.138** in different specs.
  - No. of observations examples: 31,952; 27,968; clusters vary (e.g., 66, 60).
- Table 8. DiD Equity Issuance Estimates: Shanghai and Shenzhen Events
  - PSM sample separate for Shanghai and Shenzhen. Selected coefficients:
    - Y_2015 x Treated 0.022*** (Shanghai) and 0.049*** (Shenzhen) in certain dependent-variable columns.
    - Y_2020 x Treated 0.044*** (Shanghai) and 0.081*** (Shenzhen) in specified columns.
  - No. of observations examples: 28,857; 19,342; No. of clusters 67, 63.
- Table 9. DiD Investment Estimates
  - Dependent variables: Capex over 2012 Assets; Acquisitions over 2012 Assets; R&D over 2012 Assets; Cash and ST. Investments over 2012 Assets.
  - Reported regression setups, clustering at industry (two-digit SIC) level. Example columns and numeric excerpts included in table fragments (e.g., N and Dollar effect entries like 18680.15***; 26840.27***).
- Table 10. Equity Issuances and Use of Funds by Connected Firms
  - Regressions estimate how connected firms used proceeds raised with equity issuances during 2013-2020, following Kim and Weisbach (2008), specification follows Equation (2).
  - Dollar effects report change in dependent variable from one dollar increase in equity issuance; sample panels include Full Sample and PSM Sample. Example dollar-effect metrics preserved as given in fragments.
- Table 11. Aggregate Impact of Internationalization Events
  - Aggregate implications of 2013-2020 foreign internationalization events for firm equity financing and investment activity of publicly listed firms in China.
  - Aggregate Values reported in Trillion of 2011 USD and Share Attributed to Internationalization shown as Percentage of Aggregate Values. Table fragments list example aggregate metrics (e.g., Equity Raised (2013-20 cum.) 0.41; Market Cap. (2020) 4.42; Capex (2013-20 cum.) 1.17) alongside percent shares such as % of Connected 33.1, % of Domestic Listed 28.4, % of All Listed 59.3 in fragments.

### Notation and estimation details (repeated across tables and figures)
- Difference-in-differences (DiD) approach estimating year-by-year DiD coefficients from Equation (1) with year and industry fixed effects; standard errors clustered at the industry (two-digit SIC) level.
- Regressions use alternative samples: full sample; propensity-score-matched (PSM) sample; exclusions (dual listed firms; firms listed in Hong Kong SAR, China; financial firms; margin trading firms); firm subsamples (Shanghai-listed, Shenzhen-listed; small vs large connected; excluding SOEs).
- Significance notation preserved: ∗, ∗∗, and ∗∗∗ indicate statistical significance at the 10%, 5%, and 1% levels, respectively.
- Dependent variables frequently used: amount of equity raised over 2012 assets; cumulative amount of equity raised over 2012 assets; Ln(1 + equity raised); capex, acquisitions, R&D, cash and short-term investments over 2012 assets.
- Key event markers used in figures: QFII and RQFII expansions, MSCI Review/Incorporation, Shanghai Connect, Shenzhen Connect.

*Italic: Content drawn from "wpiea2023026-print-pdf - References" and accompanying figure/table captions and fragments as provided.*

### Appendix Figure 1. Aggregate Trends in Equity Raised: IPOs vs SEOs

### Appendix Figure 1. Aggregate Trends in Equity Raised: IPOs vs SEOs

### Aggregate equity issuance trends
- The figure shows the aggregate value raised through equity issuances per year by Chinese listed companies, distinguishing Initial Public Offerings (IPOs) from Secondary Equity Offerings (SEOs).
- Values are expressed in billions of 2011 U.S. dollars (USD).
- Time series covered (as labeled on the figure): 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020.
- Y-axis label in the figure: Billion of USD.
- Legend entries displayed: IPOs; SEOs.

### Firm size distributions (Appendix Figure 2)
- The figures show the firm size distribution of domestic listed firms in China, distinguishing between connected and unconnected firms.
- Size is measured as average assets in 2010-12 (in logs).
- Panel A: Full Sample (firm size distributions using the full sample of connected and unconnected firms).
- Panel B: PSM Sample (firm size distributions using the propensity-score-matched sample of connected and unconnected firms).
- Caption element: "B. Cumulative Equity Raised over 2012 Assets" (label present in the figure text).

### Predicted equity raised for connected and unconnected firms (Appendix Figure 3)
- The figure plots predicted values, for each year, of yearly amounts of equity issuances for connected and unconnected domestic listed firms; predicted value for the average firm is obtained by estimating Equation (1).
- Panel A uses the amount of equity raised over 2012 assets as dependent variable.
- Panel B uses the cumulative amount of equity raised over 2012 assets as dependent variable.
- Left-side figures: results using the full sample of firms. Right-side figures: results using the propensity-score-matched (PSM) sample of firms.
- Time series on plots: 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020.
- Predicted Value axis ticks (examples shown on figures): -0.05 0.00 0.05 0.10 0.15 0.20 0.25 0.30; and another set: -0.10 0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90.
- Series plotted: Connected; Unconnected.
- Note: "For more information about these estimations see Table 4."

### Firm size distributions by listing market (Appendix Figure 4)
- Shows firm size distribution of domestic listed firms in China, distinguishing between connected and unconnected firms and listing markets (Shanghai and Shenzhen).
- Size measured as average assets in 2010-12 (in logs).
- Panel A: Full Sample. Panel B: PSM Sample.
- Caption: "Panel A shows the firm size distributions using the full sample of connected and unconnected firms. Panel B shows the firm size distributions using the propensity-score-matched sample of connected and unconnected firms."

### Predicted investment for connected and unconnected firms (Appendix Figure 5)
- Figures show predicted investment behavior of connected and unconnected domestic listed firms; plotted, for each year, is the predicted investment value for the average firm obtained by estimating Equation (1).
- Results shown for the propensity-score-matched (PSM) sample of connected and unconnected firms.
- Dependent variable labels and panels referenced:
  - Research and Development over 2012 Assets
  - Cash and Short-term Investments over 2012 Assets
  - Capex over 2012 Assets
  - Acquisitions over 2012 Assets
- Time series on plots: 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020.
- Example Predicted Value axis ticks:
  - Capex/Acquisitions examples: 0.00 0.02 0.04 0.06 0.08 0.10 0.12 0.14 0.16 0.18.
  - Larger series example: 0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80.
  - Small series example: 0.00 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08.
  - Another example including negative tick: -0.01 0.00 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08.
- Series plotted: Connected; Unconnected.
- Note: "For more information about these estimations see Table 9."

### Appendix Table 1: Variable Definitions (selected entries)
- Acquisitions: Assets acquired through pooling of interests or mergers. It does not include capital expenditures of acquired companies. It includes net assets of acquired companies, additions to fixed assets from acquisitions, and working capital of companies acquired. Unit: Constant 2011 U.S. dollars. Source: Wordscope.
- Capital Expenditure: Funds used to acquire fixed assets other than those associated with acquisitions. It includes additions to property and investments in plants, machinery, and equipment. Unit: Constant 2011 U.S. dollars. Source: Wordscope.
- Cash Flow: Operating income over total assets. Operating income represents the difference between revenue and operating expenses. Source: Wordscope.
- Cash Flow Volatility: Standard deviation of cash flow 1991-2012.
- Cash and Short-term Investments: Sum of cash and short-term investments. It includes cash on hand, cash in banks, checks in transit, money orders, demand deposits (non-interest bearing), short-term obligations of the U.S. Government, stocks, bonds, other marketable securities listed as short-term investments, time deposits, and U.S. Government treasury bills. Unit: Constant 2011 U.S. dollars. Source: Wordscope.
- Equity Raised: Total amount of equity raised per year. Unit: Constant 2011 U.S. dollars. Source: Refinitiv's SDC Platinum.
- Financial Firms: Firms with a Standard Industrial Classification (SIC) code between 60 and 67. Source: Worldscope.
- Leverage: Total debt over total assets.
- Margin Trading Firms: Firms whose stocks became available for margin trading during 2010-2017. Source: Hong Kong Stock Exchange webpage.
- Market Capitalization: Product of equity market price (fiscal period end) x common shares outstanding. For companies with more than one type of common/ordinary share, market capitalization represent the total market value of the company. Unit: Constant 2011 U.S. dollars. Source: Wordscope.
- Research and Development: Direct and indirect costs related to the creation and development of new processes, techniques, applications and products with commercial possibilities. It includes software expense design and development expense. Unit: Constant 2011 U.S. dollars. Source: Wordscope.
- State Owned: Firms whose main (top 1) shareholder is a government connected entity. Source: Wind.
- Total Assets: Sum of total current assets, long term receivables, investment in unconsolidated subsidiaries, other investments, net property plant and equipment and other assets. Unit: Constant 2011 U.S. dollars. Source: Wordscope.
- Total Debt: Sum of long and short term debt. Unit: Constant 2011 U.S. dollars. Source: Wordscope.
- Total Sources of Funds: Total funds generated by the company internally and externally during the fiscal period. Unit: Constant 2011 U.S. dollars. Source: Wordscope.

### Appendix Table 2 and Table 3: Difference-in-Differences Estimates (high-level)
- Table purpose: Difference-in-differences (DiD) regressions comparing equity issuance and investment behavior for connected and unconnected domestic listed firms.
- Dependent variables used across regressions: amount of equity raised, capital expenditures (capex), cash and short-term investments, spending on acquisitions, market capitalization, and research & development.
- Treated variable: equals one for connected firms listed in domestic markets and zero otherwise (unconnected firms listed in domestic markets).
- The tables show DiD coefficients for each year, estimating averaged differences between connected and unconnected firms relative to the 2012 difference; the 2012 coefficient shows the differences in 2012.
- Regressions include year and industry fixed effects. Standard errors clustered at the industry (two-digit SIC) level.
- Statistical significance markers used: ∗, ∗∗, and ∗∗∗ indicate significance at the 10%, 5%, and 1% levels, respectively.
- Units are in billions of 2011 U.S. dollars (USD).
- Examples of reported DiD coefficient patterns (selected cells as in the table):
  - From Table with many dependent variables: Treated (2012 Diff.) entries include 0.007** 0.037*** 0.005*** 0.134*** 0.624*** 0.007*** with bracketed standard-error lines shown as [0.00][0.01][0.00][0.02][0.08][0.00].
  - Year interactions include values such as Y_2013 x Treated 0.005 0.002 0.000 0.008** 0.140** 0.002*** with brackets like [0.00][0.00][0.00][0.00][0.05][0.00].
  - PSM-sample table examples: Y_2016 x Treated 0.040*** 0.026*** 0.011*** 0.085*** 0.738*** 0.005*** with bracketed entries [0.01][0.01][0.00][0.01][0.08][0.00].
- Observations and clusters reported (examples):
  - No. of observations 31,952 28,862 19,287 28,797 27,138 15,331.
  - No. of clusters 66 67 67 66 67 63. (as shown in the table text)
  - PSM-sample No. of observations 16,928 15,100 10,202 15,110 14,101 8,541.
  - PSM-sample No. of clusters 58 59 58 58 58 58.

### Appendix Table 4: Aggregate Impact of Internationalization Events: Robustness (selected figures)
- Purpose: Additional results on aggregate implications of the 2013-2020 foreign internationalization events for firm equity financing and investment activity of publicly listed firms in China.
- Method: compute the (2013-2020) aggregate impact for each variable estimated by βN where β is the DiD coefficient in the full sample, and N is the number of connected firms — as a fraction of aggregated data (for connected firms, domestic listed firms, and all listed firms, respectively). Top 1 percent outliers are removed for cleaner identification in specified columns.
- Share Attributed to Internationalization reported as "Percentage of Aggregate Values" with comparisons: % of Connected; % of Domestic Listed; % of All Listed.
- Selected aggregate numbers reported in the appendix table (columns/rows as printed):
  - Equity Raised (2013-20 cum.) 32.7 33.1 41.7 28.0 28.4 35.3 20.1 20.4 25.2
  - Market Cap (2020) 32.4 32.9 45.5 29.4 29.9 39.8 17.6 17.8 27.0
  - Capex (2013-20 cum.) 12.2 12.4 18.1 10.6 10.7 15.4 5.2 5.3 8.8
  - Acquisitions (2013-20 cum.) 13.8 14.0 21.8 12.1 12.3 18.8 7.1 7.2 11.0
  - Cash (2020) 27.5 27.9 44.6 24.8 25.2 38.5 15.4 15.6 24.0
  - R&D (2013-20 cum.) 27.3 27.7 35.3 23.6 23.9 31.6 15.7 16.0 23.9

*The Internationalization of China’s Equity Markets Working Paper No. WP/2023/026*

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