Abstract
China’s evolving monetary policy framework has recently increased its emphasis on quantity-based monetary policy tools relative to the use of traditional interest rate tools. Firm-level investment data from China suggests that these supply-driven policies may however face challenges in reproducing the relatively broad-based demand-side impacts of interest rate policies, especially in reaching smaller and more cyclically vulnerable firms. This suggests that while well-designed and market-based quantity-based policy tools have a role in China’s monetary policy framework, they should not replace interest rate policies as the primary policy instrument.
Monetary Policy and the Role of Credit Policies1
China’s evolving monetary policy framework has recently increased its emphasis on quantity-based monetary policy tools relative to the use of traditional interest rate tools. Firm-level investment data from China suggests that these supply-driven policies may however face challenges in reproducing the relatively broad-based demand-side impacts of interest rate policies, especially in reaching smaller and more cyclically vulnerable firms. This suggests that while well-designed and market-based quantity-based policy tools have a role in China’s monetary policy framework, they should not replace interest rate policies as the primary policy instrument.
A. Introduction
1. China’s continuously evolving monetary policy framework features both price- and quantity-based policy instruments. Up until 2012, the People’s Bank of China (PBC) primarily relied on a quantity-based monetary policy framework that emphasized managing credit aggregates through window guidance and loan growth targets, augmented with strict deposit and loan rate regulations. About a decade ago, the PBC began gradual liberalization of interest rate regulations and implementation of a price-based monetary policy framework which sought to maintain short-term risk-free rates within a defined interest rate corridor.2
2. Quantity-based targets have continued to play an important role in overall monetary policy, however. Credit growth targets have become more tailored to bank-based characteristics like capital and asset quality, with target compliance an important criterion in the Macroprudential Assessment (MPA) framework. In 2018, maintaining stable credit growth that matched the level of nominal GDP growth was elevated to a key objective of monetary policy, reflecting the importance assigned to controlling the growth of debt relative to GDP. In the last five years, quantity-based credit policies (hereafter, “credit policies”) have proliferated at the borrower-segment level, bolstering credit to preferred groups such as micro and small enterprises, privately owned firms, or advanced manufacturing firms.3 Some of these use market-based mechanisms to incentivize such lending, like PBC relending facilities, but others are policy requirements, like the State Council’s financial inclusion lending growth requirements for large banks.
Selected Emerging Markets: Monetary Policy Usage
(In percentage points, three-year rolling sum of changes in policy interest rate)
Citation: IMF Staff Country Reports 2023, 081; 10.5089/9798400233517.002.A003
Sources: Haver Analytics; and IMF staff calculations.Note: Data is calculated using rolling sums of absolute values of policy rate changes.Selected Emerging Markets: Monetary Policy Usage
(In percentage points, three-year rolling sum of changes in policy interest rate)
Citation: IMF Staff Country Reports 2023, 081; 10.5089/9798400233517.002.A003
Sources: Haver Analytics; and IMF staff calculations.Note: Data is calculated using rolling sums of absolute values of policy rate changes.Selected Emerging Markets: Monetary Policy Usage
(In percentage points, three-year rolling sum of changes in policy interest rate)
Citation: IMF Staff Country Reports 2023, 081; 10.5089/9798400233517.002.A003
Sources: Haver Analytics; and IMF staff calculations.Note: Data is calculated using rolling sums of absolute values of policy rate changes.3. The PBC’s usage of price-based monetary policy tools has recently become less frequent. Adjustments to key policy interest rates have become smaller and less frequent in recent years, particularly in contrast to other emerging market economies without currency pegs (see figure). While there are no simple quantitative indicators to summarize the frequency or intensity of credit policies, the growth in PBC relending and rediscount facilities, as well as the proliferation of industry-targeted relending facilities in April 2022, are indicative of how credit policy tools have once again become a more prominent feature of monetary policy. In recent policy communications, the PBC has characterized the benefits of credit policies as providing precisely targeted and direct support to small firms and sectors experiencing difficulties, as well as providing stabilization to the overall macroeconomy.4 More broadly, the use of credit policies has gained traction due to their perceived financial stability benefits, insofar as they enable “targeted” economic support while avoiding the excesses of what policymakers have characterized as the “flood-like” credit stimulus that followed the 2008 crisis.
PBC Rediscount and Relending Facility Credit Outstanding
(In trillions of RMB)
Citation: IMF Staff Country Reports 2023, 081; 10.5089/9798400233517.002.A003
Source: CEIC Data Company Limited. Note: MSE = micro and small enterprisePBC Rediscount and Relending Facility Credit Outstanding
(In trillions of RMB)
Citation: IMF Staff Country Reports 2023, 081; 10.5089/9798400233517.002.A003
Source: CEIC Data Company Limited. Note: MSE = micro and small enterprisePBC Rediscount and Relending Facility Credit Outstanding
(In trillions of RMB)
Citation: IMF Staff Country Reports 2023, 081; 10.5089/9798400233517.002.A003
Source: CEIC Data Company Limited. Note: MSE = micro and small enterprise4. The increased reliance on quantity-based tools could weaken monetary policy’s countercyclical impact and bring other macroeconomic costs. Interest rate adjustments have a variety of powerful and self-reinforcing effects on financial conditions and activity. The analysis laid out below provides evidence of this for China and, at the same time, finds that policy interventions aimed at controlling the supply of aggregate credit appear to fall short in reproducing the same effects. Empirically, credit policy appears to have comparatively more muted capacity to stimulate activity among smaller and younger firms. This may reflect bank risk aversion in allocating additional credit to risky firms, other factors segmenting risky credit markets, or the frequent use of this tool during periods of interest rate-based tightening. As these smaller and younger firms tend to be the most financially constrained and vulnerable to cyclical fluctuations, credit policy therefore may have limited effectiveness for countercyclical demand management. The benefits of credit policy are somewhat clearer for larger, more established firms, which may also exacerbate disparities in credit availability for smaller firms and could drag on authorities’ macro-critical task of raising medium-term productivity growth.
5. This paper uses local projections to directly compare the impact of different monetary policy tools on firm-level investment. Interest rate tools are identified using the monetary policy shock approach used in Kamber and Mohanty (2018) and Das and Song (2022). To assess credit policy interventions, which are much more challenging to measure, this paper introduces a novel identification technique that quantifies the amount of credit growth unexplained by macroeconomic and financial factors, including monetary policy shocks. The relative impact of these shocks— traditional monetary policy shocks and unexplained shocks to total credit—are then jointly estimated across a cross-section of 14,000 firms captured in a proprietary database of firm-level financial statements.
6. The remainder of the paper is organized as follows. The next section reviews some of the literature on monetary policy transmission and the more recent role of quantity-based policy instruments. The subsequent section explains the methodology and data behind the analysis and the fourth section lays out the empirical findings. The fifth section discusses implications for policy and the final section concludes.
B. Monetary Policy Transmission and the Role of Quantity-Based Policies
7. Monetary policy operates through multiple channels to smooth the business cycle.5 The interest rate channel affects output via the impact of interest changes on interest-sensitive components of aggregate demand such as housing and consumer durable goods. The credit channel amplifies the impact of changes in short-term interest rates via larger effects on borrowers’ external financing costs. This occurs partially through what is called the balance sheet channel, as lower interest rates boost the value of borrower’s assets and reduce their interest expenses, easing market frictions that normally drive lenders to charge a credit risk premium. This channel is often included within a broader risk-taking channel of monetary policy, which focuses on how rising asset prices, expanding credit, and reduced financial volatility can create procyclical feedback loops in part by reducing financial institutions’ perception of risk (Adrian and Shin, 2010).6 Finally, the credit channel is also considered to operate through a bank lending channel, by boosting the supply of intermediated credit.
8. Transmission via the credit channel is generally thought to be asymmetric at the firm-level, disproportionately benefiting smaller and more cyclically exposed firms. Theories of financial frictions imply that smaller or younger firms are more financially constrained relative to larger, more established firms, with weaker and more variable access to credit. This reflects lenders’ comparatively higher costs in monitoring their credit risks, higher business failure rates during cyclical downturns, and generally more limited access to credit markets, factors which tend to increase lenders’ demand for collateral. As monetary policy eases the supply of credit and reduces credit risk premiums, these financially constrained firms see the largest improvement in their access to credit. A wide range of empirical studies across a number of countries provide evidence for the higher relative sensitivity of financially constrained firms to business cycle fluctuations and to monetary policy shocks.7
9. Quantity-based tools with resemblance to those used in China have been deployed around the world to stimulate the bank lending channel of monetary policy transmission. Particularly in the years following the Global Financial Crisis, many advanced economy central banks used quantity-based policies in the form of “funding-for-lending” schemes to stimulate bank lending, most notably the European Central Bank’s Targeted Long-Term Refinancing Operations (TLTRO) facilities. These facilities varied but generally introduced mechanisms to lower a bank’s funding cost in exchange for meeting certain quantitative credit supply goals.8 These aimed to boost bank lending during a period when bank balance sheet constraints weighed on credit supply and further policy rate cuts were limited by the zero lower bound.
10. The impact of quantity-based tools in benefiting financially constrained firms however is not well established. Banks can be induced to lend more via market-based incentives like subsidized funding, but it remains ambiguous how banks allocate this extra lending. Traditional theory suggests a higher supply of loans might lead to lower lending costs across the borrower risk spectrum, but balance sheet effects and other factors that might increase bank risk-taking under interest rate-based monetary policy transmission may not be present. If banks’ pre-intervention supply of lending was constrained by capital constraints or underwriting standards, banks may prefer to allocate additional credit to lower-risk borrowers. The empirical literature on the effect of funding-for-lending schemes on bank risk-taking is only emerging, with some early findings suggesting that the ECB’s most recent TLTRO facility induced banks to lend but not to scale up the risk profile of their loan portfolios.9 Other work found banks responded to TLTRO funding via a “flight to quality” response, consistent with weak effects on bank risk-taking.10
11. In China’s case, the suitability of a monetary policy framework centered on quantity-based tools will, in part, depend on its capacity to generate strong credit channel effects for more vulnerable firms. Quantity-based policies have historically played an important role in supporting the countercyclical investment role of state-owned firms, providing credit for large-scale infrastructure investment while minimizing crowding-out effects for private borrowers. Scope for this form of economic support has however narrowed in recent years as economy-wide leverage has risen to risky levels and productive state-owned projects have become scarcer. The remainder of this paper will assess empirically the relative effectiveness of interest rate and credit policy shocks in easing borrowing conditions for more cyclically exposed, financially constrained firms, which will be key for maintaining monetary policy effectiveness. While the following analysis is limited in its capacity to empirically examine the effect of recent credit policies that are designed to directly benefit these segments (via financial inclusion or micro and small enterprise lending), the findings have important implications for the effectiveness of these policies.
C. Methodology and Data
12. The differential impacts of interest rate and credit policy shocks are explored using panel local projections. In line with recent works such as Cloyne (2018) and Durante et al (2021), the impact of policy shocks on firm-level investment are estimated via impulse response functions using Jorda’s (2006) approach. The estimations are performed for selected groupings of firms, which facilitates exploration of the cross-sectional heterogeneity in the sensitivity of investment to monetary policy shocks.
13. Interest rate-based monetary policy shocks are derived from a standard identification strategy in the literature. Unexpected innovations in monetary policy are proxied by using changes in short-term interest rate swaps on days of monetary policy announcements. This is in line with Gertler and Karadi (2015) and later extended to the Chinese context in Mohanty and Kamber (2018) and Das and Song (2022).
14. Credit policy-based monetary policy shocks are proxied by changes in credit growth that are unexplained by macroeconomic fundamentals and interest rate shocks. Using a simple model of equilibrium credit growth based at quarterly frequency, changes in the gap between a broad measure of private credit growth and its trend level are explained by the Taylor rule-based expected interest rate (which proxies credit demand and is derived from activity and inflation variables) and the policy interest rate shock series described above.11 The residual is assumed to be explained by policy interventions to either slow or accelerate total credit growth relative to a counter factual market-driven outcome (hereafter “credit policy”).
15. This identification strategy generates a useful proxy for measuring the impact of quantity-based policy interventions. The use of the interest rate shock variable as a control helps address the endogeneity problem between interest rate shocks and credit growth, resulting in a series that captures the intended properties of quantity-based policies’ additional impact via the bank lending channel. The identified credit policy shock generates periods of both tightening as well as loosening, which is in line with authorities’ periodic interventions to limit credit growth, most notably during the deleveraging campaign in the years before the COVID crisis. As credit growth becomes more stable after 2014, the identified shocks are assumed to capture the policy interventions necessary to maintain stable credit growth amid fluctuations in output and interest rate levels. This is desirable as it links to authorities’ stated policy goal of broadly matching credit growth to nominal GDP growth and allows for investigation of the empirical effects of such interventions. The policy-induced stability of credit growth results in a period where credit shocks move in the opposite direction to interest rate shocks for several years (see chart). This is in contrast to the positive co-movement seen during earlier and later periods, when more concerted efforts at policy stimulus drove interest rate and credit policy coordination.
Identified Monetary Policy Shocks
(In percentage points)
Citation: IMF Staff Country Reports 2023, 081; 10.5089/9798400233517.002.A003
Sources: Haver Analytics; CEIC Data Company Limited; and IMF staff calculations. Note: Data are at semi-annual frequency. Interest rate shocks are unexpected policy-induced changes in 1-year interest rate swaps. Credit shocks are sums of unexplained quarterly deviations of nominal private credit from trend.Identified Monetary Policy Shocks
(In percentage points)
Citation: IMF Staff Country Reports 2023, 081; 10.5089/9798400233517.002.A003
Sources: Haver Analytics; CEIC Data Company Limited; and IMF staff calculations. Note: Data are at semi-annual frequency. Interest rate shocks are unexpected policy-induced changes in 1-year interest rate swaps. Credit shocks are sums of unexplained quarterly deviations of nominal private credit from trend.Identified Monetary Policy Shocks
(In percentage points)
Citation: IMF Staff Country Reports 2023, 081; 10.5089/9798400233517.002.A003
Sources: Haver Analytics; CEIC Data Company Limited; and IMF staff calculations. Note: Data are at semi-annual frequency. Interest rate shocks are unexpected policy-induced changes in 1-year interest rate swaps. Credit shocks are sums of unexplained quarterly deviations of nominal private credit from trend.16. The credit policy shock has its limitations as a proxy for actual policy interventions. Shifts in observed credit growth outcomes are potentially driven by omitted variables, for instance bank or firm leverage, or external shocks, or may reflect structural breaks in demand and supply functions. These factors can bias the residual and thus the identified shock. By design it cannot address sector- and segment-level credit policies which have become an important component of the credit policy toolkit, although the sample period has reasonable coverage of the early years of
those policies. Nevertheless, it provides a reasonable proxy for the direction and magnitude of credit policy shocks.
17. The baseline equation that applies the policy shocks to the firm-level data (see below) is as follows:
ΔYi, t+ h = αi,h + Σ β1,h shockIRt * Dg,i + Σ β2,h shockCPt* Dg,i + Γ'1Xi,t-1 + Γ'1Xi,t-2 + ɛ i,t+h
The left-hand variable is year-on-year growth in net property, plants and equipment for a given firm i at the time h periods after the shock. The variables shockIRt and shockCPt are the interest rate and credit policy shocks, respectively. Dg,i is a dummy variable specifying the firm grouping. Controls include one and two period lagged year-on-year growth of firm-level revenue, investment, liquidity, and leverage, plus one and two period lags on the policy shocks. Firm-level fixed effects and Driscoll-Kraay standard errors are used to control for spatial correlation as well as autocorrelation and heteroscedacity, in line with Cloyne (2018).
China: Descriptive Statistics of the Level Dataset
China: Descriptive Statistics of the Level Dataset
| SOE | POE | Other | Total | |
|---|---|---|---|---|
| Year-on-year growth in net property plants and equipment | ||||
| (percent) | ||||
| median | 5.2 | 4.2 | 4.5 | 4.6 |
| mean | 24.0 | 27.9 | 27.3 | 26.8 |
| sd | 105.0 | 111.3 | 114.4 | 109.5 |
| Assets (RMB mn) | ||||
| median | 13,569 | 661 | 1,955 | 1,767 |
| mean | 37,648 | 6,265 | 6,861 | 15,543 |
| sd | 96,606 | 38,791 | 23,236 | 63,249 |
| Firm age (years) | ||||
| median | 18 | 16 | 19 | 17 |
| mean | 23 | 18 | 22 | 20 |
| sd | 16 | 10 | 13 | 12 |
| Obs. | 40,867 | 94,676 | 2,879 | 138,422 |
| N. firms | 3,613 | 9,288 | 229 | 13,130 |
China: Descriptive Statistics of the Level Dataset
| SOE | POE | Other | Total | |
|---|---|---|---|---|
| Year-on-year growth in net property plants and equipment | ||||
| (percent) | ||||
| median | 5.2 | 4.2 | 4.5 | 4.6 |
| mean | 24.0 | 27.9 | 27.3 | 26.8 |
| sd | 105.0 | 111.3 | 114.4 | 109.5 |
| Assets (RMB mn) | ||||
| median | 13,569 | 661 | 1,955 | 1,767 |
| mean | 37,648 | 6,265 | 6,861 | 15,543 |
| sd | 96,606 | 38,791 | 23,236 | 63,249 |
| Firm age (years) | ||||
| median | 18 | 16 | 19 | 17 |
| mean | 23 | 18 | 22 | 20 |
| sd | 16 | 10 | 13 | 12 |
| Obs. | 40,867 | 94,676 | 2,879 | 138,422 |
| N. firms | 3,613 | 9,288 | 229 | 13,130 |
18. Firm-level data come from a unique 14,000-firm database with strong coverage of private firms. The financial data is sourced from Capital IQ with additional firm-level identification and classification information obtained from WIND and Bloomberg. The data is semi-annual, covering the period from 2011 to 2021, and is primarily drawn from financial statements required by public equity or bond issuance, which strictly limits the coverage of firms that count as micro and small enterprises by official definitions. That said, of the 138,422 firm-period observations with complete data, about 95,000 are private firms, many of which have been listed on specialized small and medium-sized enterprise (SME) bourses.12 The firm coverage is unbalanced. Additional descriptive statistics are provided in Text Table.
19. The firm sample source and the limited time period coverage presents challenges for empirical analysis. The fact that firm data is collected based on SME equity exchanges biases it towards dynamic and relatively well-funded small and medium firms, and skews the observation count larger for years after 2014, when more of these firms’ data becomes available. The persistently high investment growth among some of these firms, as well as the noisiness in some firms’ investment, requires special attention in interpreting the econometric results. The time period is also relatively short for analyses of this kind and does not capture a large economic downturn except the first half of 2020. That said, the investment growth of smaller and younger firms declines notably relative to more established firms in the years leading up to 2020, and in general appear more cyclical, suggesting there is cyclical variation captured within the sample.13 These various challenges are addressed through robustness tests and do not affect the overall findings.
Median Investment Growth by Firm Age
(In percentage points)
Citation: IMF Staff Country Reports 2023, 081; 10.5089/9798400233517.002.A003
Sources: Bloomberg; WIND; S&P Market Intelligence; and IMF staff calculations.Note: Young firms are <14 years old; middle-aged are 14-20; and old firms are >21 years old.Median Investment Growth by Firm Age
(In percentage points)
Citation: IMF Staff Country Reports 2023, 081; 10.5089/9798400233517.002.A003
Sources: Bloomberg; WIND; S&P Market Intelligence; and IMF staff calculations.Note: Young firms are <14 years old; middle-aged are 14-20; and old firms are >21 years old.Median Investment Growth by Firm Age
(In percentage points)
Citation: IMF Staff Country Reports 2023, 081; 10.5089/9798400233517.002.A003
Sources: Bloomberg; WIND; S&P Market Intelligence; and IMF staff calculations.Note: Young firms are <14 years old; middle-aged are 14-20; and old firms are >21 years old.D. Empirical Results
20. The local projections analysis suggests that interest rate-based shocks in China operate in line with theory and evidence from other countries. Interest rate-based shocks have a disproportionately large impact on smaller firms. An unexpected 25 basis point decrease to the short-term interest rate swap—a close approximation to a one standard deviation shock—generates a 9.5 percentage point increase in the annual rate of growth in investment for the smallest third of firms (with assets below RMB 330 million) one and a half years later, controlling both for firm-level variables as well as the credit policy shock.14 This result is statistically different from that of the largest third of firms by size (with assets above RMB 4.5 billion), who increase investment but by four times less, reaching about 2.6 percent one and a half years later. The reaction by midsize firms is smallest and statistically insignificant. As is predicted by the literature, transmission to investment is lagged manifesting most strongly three periods (one and a half years) after the initial shock, and fading thereafter.
21. By contrast, credit policy shocks manifest somewhat earlier and with more muted differences between large and small firms. Unexpected credit policy easing that generates a 12.5 percentage point increase in the level of credit relative to trend—roughly a one standard deviation shock—is estimated to make its peak impact on the growth rate of investment two periods later.15 Small firms’ investment growth increases by 3.6 percentage points one year ahead, which is only about 1.5 times greater than the result for large firms. This differential is both smaller than the comparable difference generated by interest rate shocks and less precisely estimated, as the small firm results are not statistically different from zero or the credit policy effect on the larger firms.
Firm-Level Investment Impulse Response to Monetary Policy Shocks, by Firm Size
Citation: IMF Staff Country Reports 2023, 081; 10.5089/9798400233517.002.A003
Firm-Level Investment Impulse Response to Monetary Policy Shocks, by Firm Size
Citation: IMF Staff Country Reports 2023, 081; 10.5089/9798400233517.002.A003
Firm-Level Investment Impulse Response to Monetary Policy Shocks, by Firm Size
Citation: IMF Staff Country Reports 2023, 081; 10.5089/9798400233517.002.A003
22. Results are broadly similar when grouping firms by age. The relative benefits for small firms are still largest for interest rate shocks when policy shocks are estimated across age-based groupings, another common proxy for financial constraints, although the differences are not statistically significant.16 Interest rate-based shocks generate the largest investment response for young firms (below 14 years old), with incrementally smaller responses for middle-aged (below 20 years old) and older firms. The results are smaller in magnitude than for the size-based groupings and are generally statistically significant at one and one and a half years after the shock, except for older firms. In contrast to size-based groupings, credit policy shocks create the quantitatively largest impact for older and middle-aged firms, although the results are not statistically significant across any segment (see Appendix, Figure 1).
23. In ownership-based groupings, credit policy shocks are also statistically significant only for larger state-backed firms. Central SOEs’ investment growth response has the correct sign and is statistically significant at three periods ahead, while entities classified by the bond market regulator as local government financing vehicles (LGFVs) have large and significant responses to credit policy shocks two periods ahead. This is line with previous IMF staff findings that SOEs and private firms do not face competitive neutrality in credit markets.17
24. Robustness checks support the validity of the findings. In an extension of the baseline equation controlling for the sign of the policy shock, the results for small and young firms one and a half years ahead show the correct sign during periods of both interest rate tightening as well as loosening, although only the results for small firms are statistically significant. This suggests that the results are not capturing noise from the dispersion and skew of the observed investment growth rates for these firms. Similarly, for the credit policy shock results, only the results for large, old, and middle-aged firms are correctly signed in both directions (see Appendix, Tables 1-4). The findings also hold when controlling for different asset thresholds for the size-based groupings and variance in firm count across periods. Similar results obtain when the firm sample is restricted to firms captured in 12 or more periods, and when the COVID-period results are excluded.18
25. Additional groupings provide evidence of the importance of credit constraints in driving the results. The baseline equation is re-run for subgroupings of firms that have higher financial constraints than the median small or young firm. The first grouping are small (assets below RMB 330 million), private firms with average cash flows that are negative over three periods, a group with negative median investment growth throughout the sample period. While the statistical power of this grouping is weaker with a sample size of only 8,950 observations, these firms exhibit large and statistically significant responses to interest rate policy shocks at three periods ahead. The response to credit policy shocks are smaller and statistically insignificant. A second grouping captures firms that are in the top third of observations for the ratio of revenue-to-fixed assets for their sector and year—a reasonable proxy for firm-level productivity—but with relatively modest average investment growth over the sample period (<10%), suggesting some difficulty accessing external finance. Like the loss-making firm group, the impulse response from this group is larger and more statistically significant for the interest rate shock, although the credit policy response is notably larger and closer to being significant. In general, firms with productivity proxy measures in the top third of observations exhibit large and statistically significant responses to interest rate shocks, while those in the bottom tercile see a statistically significant reaction but with the opposite sign. Credit policy shocks are statistically insignificant across all productivity-based firm groupings (see Appendix, Figure 2).
26. Extensions of this analysis suggest that uncoordinated use of interest rate- and credit-policy shocks undermines their impact. The results are re-run using an extension of the baseline equation where the firm grouping dummy variable Dg,i is replaced with a time-based categorical variable Dcoord, t. This variable takes the value of 1 during the half-year periods when policy rate and credit policy shocks have the same sign—that is, both loosening or both tightening—and zero for periods when they appear to counteract, with the result measuring the investment response across the entire firm sample. For uncoordinated shocks (n=88,619), both have the correct sign and a similar magnitude, suggesting these shocks on balance largely offset each other. For coordinated shocks (n=40,042), the net investment response across the two shocks (i.e., the sum of the coefficients) is large and with the correct sign, reflecting a large contribution from the interest rate shock (see Appendix, Figure 3). The statistical strength of the coordinated result is marginally weakened, however, by the fact that policy is coordinated in only five periods. The credit policy shock impact is wrongly signed towards the end of the projection period due to the relatively larger magnitude of credit policy easing in one of the coordinated easing periods where investment later weakened.
E. Policy Implications
27. Credit policies do not appear to generate robust “credit channel” effects. Policies that induce additional supply of credit from banks in theory should create broad-based reductions in the price of credit that help stimulate activity among financially constrained borrowers. The evidence presented above suggests that these effects are not taking place, in line with initial results from similar studies drawn from the euro area. This could be attributed to a variety of non-mutually exclusive potential explanations.
Bank risk aversion. In the absence of borrower-based balance sheet effects, prudential requirements, capital constraints, or risk aversion may lead banks to allocate policy-induced lending to lower risk firms or assets, like mortgages or government bonds.
Limited spillovers to riskier credit markets. Additional credit supply for low-risk borrowers may not spill over to credit markets for high-risk borrowers if these markets are highly segmented or risk-adjusted returns for higher-risk borrowers are insufficient, as appears the case for much of Chinese bank lending.19 Lower-risk firms with weak governance (like an LGFV) may choose to absorb the bulk of the policy-induced credit supply despite limited productive uses for the funding, potentially resulting in leakages into asset market investments.
The impact of a credit policy shock may be limited if it is not implemented in tandem with interest rate-based monetary policies. In theory, inducing an increase in money supply should stimulate demand in part by reducing interest rates and raising prices. If a mixed price- and quantity-based monetary policy framework increases the money supply while constraining the complementary adjustment of interest rates, the broader impact on demand may be limited
Firm-Level Investment Impulse Response to Monetary Policy Shocks, by Selected Groupings
Citation: IMF Staff Country Reports 2023, 081; 10.5089/9798400233517.002.A003
Firm-Level Investment Impulse Response to Monetary Policy Shocks, by Selected Groupings
Citation: IMF Staff Country Reports 2023, 081; 10.5089/9798400233517.002.A003
Firm-Level Investment Impulse Response to Monetary Policy Shocks, by Selected Groupings
Citation: IMF Staff Country Reports 2023, 081; 10.5089/9798400233517.002.A003
28. Credit policies may therefore be relatively less effective in managing cyclical fluctuations compared to interest rate policies. A macroeconomic policy framework that operates via credit supply shocks with weak benefits for more financially constrained firms presents several challenges for countercyclical demand management.
Indirect and uncertain transmission mechanisms to the most cyclical sectors. Larger, low-risk firms are unlikely to use additional credit supply to fund productive investments given that they were not credit-constrained prior to the change in credit policies. The ultimate demand spillovers to the cyclically weakest sectors is more indirect and uncertain compared to interest rate tools which have clear and broad-based demand effects. In part, spillovers will depend on cyclically weak sectors’ proximity to these large firms’ supply chains as well as large firms’ willingness to invest in less useful projects at sufficient scale (creating tradeoffs with productivity).
The “credit channel” impacts may become weaker as cyclical stress worsens. Banks with sound risk management and credit underwriting frameworks would normally increase risk aversion as economic uncertainty increases, reinforcing credit allocation towards the least risky borrowers. Similarly, demand for new credit from market-oriented borrowers would likely decline.
29. The use of credit policies as a primary monetary policy tool may also drive adaptive impacts on creditor lending behavior and financial conditions. Firms that tend to directly benefit from credit policies, or those proximate to such firms, may benefit from structurally easier external financing availability as investors anticipate policy support during downturns. Conversely, creditors would rationally charge higher risk premiums to higher-risk borrowers or others with weaker prospects of benefiting from credit supply interventions. Chinese investors’ sensitivity to patterns of government support is documented in recent Global Financial Stability Reports as well as Zhe and Jun (2019), which shows how investors charged higher risk premiums for non-SOE borrowers and SOEs with weak government support following changes in default outcomes.20
30. Use of credit policies in place of interest rate policies may also negatively impact capital allocation and productivity growth. The older and predominantly state-owned firms that react most to credit supply interventions tend to have relatively weak productivity compared to private sector peers.21 Interest rate-based easing by contrast is particularly effective in boosting investment among financially constrained smaller firms with higher productivity and leads to weakening investment among low productivity firms. Compared with interest-rated based policies, credit policy-based easing is therefore likely to generate a larger share of incremental new investment from relatively low productivity firms. This implies weaker aggregate productivity growth in the short-term and potentially over the longer term.22
31. While this analysis has been focused on “aggregate” credit policies, the findings are relevant for “financial inclusion"-targeted credit policies as well. Recent credit supply interventions aimed at MSEs and other financial inclusion borrowers may be sufficiently narrowly targeted to ensure that increased credit supply create positive spillovers for marginal and constrained firms. Bank risk aversion is however likely to be an even more important factor within the MSE segment, given higher credit risks, forcing banks to make larger tradeoffs with underwriting quality. Lenders may choose to manage their MSE lending risk in the form of a strong preference for firms with collateral, for instance, residential real estate, rather than entrepreneurs. Banks may also have difficulty in monitoring the activities and track records of MSEs, creating an elevated risk of regulatory arbitrage.
32. MSE credit policies appear to have had weak broad-based impacts for credit availability for more financially constrained firms. In the period since MSE credit targets were introduced in 2018, privately owned and lower-rated firms have seen a steady net contraction in their corporate bond issuance outstanding, underscoring how tight financing conditions have persisted for firms not directly benefiting from credit policies.
33. MSE credit policies also appear to fall short of one of the rationales of credit policy, which is to avoid the risks of excessive credit growth. An elevated incidence of regulatory arbitrage may also explain the lack of spillovers along the risk spectrum, in part reflecting the difficulty that banks have in monitoring the track records and activities of MSEs. A National Audit Office report from June 2022 found that 364 of 517 audited financial inclusion loans borrowers were not actually operational business entities, suggesting a high rate of arbitrage. The report found that some of these entities were shell companies used to route such funding to housing market investments or to ineligible large firms. One recent analysis identifies a link between MSE company formation and house price appreciation at the city-level, underscoring the potential arbitrage-driven asset market spillovers (Sun, Wang et al, 2022).
F. Conclusion
34. Interest rate policies’ strong effects suggest they should continue to play a primary role in managing cyclical fluctuations. Interest rates have a clear impact on activity among cyclically more vulnerable business segments, likely reflecting borrower balance sheet effects, underscoring their capacity to support demand. Unlike credit policies, policy transmission does not rely on the risk preferences or balance sheet capacity of financial institutions. While the investment impact of interest rate policy shocks manifest with a slight lag compared to credit policies, the easing of financial conditions may operate rapidly, which can help limit scarring by reducing firm closures and employment losses. Increased financial risk-taking that accompany reductions in interest rates should be addressed via prudential policy tools such as regulation, supervision, and macroprudential policy.
35. Well-designed and market-based quantity-based instruments have a role in China’s monetary policy toolkit, but should not replace interest rates as the primary instrument of policy. Credit policies and other policy interventions in banks’ credit supply function have an important function when there are clear market failures in credit supply, or in amplifying traditional interest rate-based policy shocks. Firm-level evidence suggests that credit policies do not have strong impacts independent of interest rate-based shocks for most firms, particularly among more financially constrained firms. Credit policies should be based on market-based incentives, for instance the PBC’s re-lending facility that provides interest cost subsidies for qualifying loans to MSEs. Administrative requirements for loan growth, loan pricing requirements, or other non-market-based features may create inconsistencies with prudential loan underwriting and risk management, raising the risk of arbitrage and financial risks.
Appendix I. Additional Empirical Results
China: Firm-Level Investment Impulse Response to Monetary Policy Shocks, by Firm Age
Citation: IMF Staff Country Reports 2023, 081; 10.5089/9798400233517.002.A003
China: Firm-Level Investment Impulse Response to Monetary Policy Shocks, by Firm Age
Citation: IMF Staff Country Reports 2023, 081; 10.5089/9798400233517.002.A003
China: Firm-Level Investment Impulse Response to Monetary Policy Shocks, by Firm Age
Citation: IMF Staff Country Reports 2023, 081; 10.5089/9798400233517.002.A003
China: Robustness: Average Effect of Interest Rate Shock: By Firm Size & Shock Direction
China: Robustness: Average Effect of Interest Rate Shock: By Firm Size & Shock Direction
| ΔYi, t | ΔYi, t+1 | ΔYi, t+2 | ΔYi, t+3 | ΔYi, t+4 | |
|---|---|---|---|---|---|
| Small: Easing | -0.09 | -0.07 | -0.41*** | -0.35** | 0.01 |
| (0.17) | (0.27) | (0.14) | (0.14) | (0.11) | |
| Small: Tightening | 0.65** | 0.6 | 0.16 | -0.52* | -0.24 |
| (0.3) | (0.39) | (0.28) | (0.29) | (0.23) | |
| Mid-size: Easing | -0.08 | -0.15** | -0.1* | 0 | -0.02 |
| (0.09) | (0.06) | (0.05) | (0.06) | (0.08) | |
| Mid-size: Tightening | 0.16 | 0.58** | 0.27 | -0.27 | -0.24 |
| (0.26) | (0.24) | (0.17) | (0.34) | (0.32) | |
| Large: Easing | -0.2** | -0.11* | -0.05 | -0.04 | 0.06 |
| (0.09) | (0.06) | (0.05) | (0.05) | (0.06) | |
| Large: Tightening | 0.49** | 0.17 | -0.1 | -0.41* | -0.42* |
| (0.19) | (0.28) | (0.09) | (0.2) | (0.22) | |
| Controls | Y | Y | Y | Y | Y |
| Observations | 95213 | 83461 | 73791 | 63740 | 55183 |
China: Robustness: Average Effect of Interest Rate Shock: By Firm Size & Shock Direction
| ΔYi, t | ΔYi, t+1 | ΔYi, t+2 | ΔYi, t+3 | ΔYi, t+4 | |
|---|---|---|---|---|---|
| Small: Easing | -0.09 | -0.07 | -0.41*** | -0.35** | 0.01 |
| (0.17) | (0.27) | (0.14) | (0.14) | (0.11) | |
| Small: Tightening | 0.65** | 0.6 | 0.16 | -0.52* | -0.24 |
| (0.3) | (0.39) | (0.28) | (0.29) | (0.23) | |
| Mid-size: Easing | -0.08 | -0.15** | -0.1* | 0 | -0.02 |
| (0.09) | (0.06) | (0.05) | (0.06) | (0.08) | |
| Mid-size: Tightening | 0.16 | 0.58** | 0.27 | -0.27 | -0.24 |
| (0.26) | (0.24) | (0.17) | (0.34) | (0.32) | |
| Large: Easing | -0.2** | -0.11* | -0.05 | -0.04 | 0.06 |
| (0.09) | (0.06) | (0.05) | (0.05) | (0.06) | |
| Large: Tightening | 0.49** | 0.17 | -0.1 | -0.41* | -0.42* |
| (0.19) | (0.28) | (0.09) | (0.2) | (0.22) | |
| Controls | Y | Y | Y | Y | Y |
| Observations | 95213 | 83461 | 73791 | 63740 | 55183 |
China: Robustness: Average Effect of Credit Policy Shock: By Firm Size & Shock Direction
China: Robustness: Average Effect of Credit Policy Shock: By Firm Size & Shock Direction
| ΔYi, t | ΔYi, t+1 | ΔYi, t+2 | ΔYi, t+3 | ΔYi, t+4 | |
|---|---|---|---|---|---|
| Small: Easing | -0.15 | -0.05 | -0.25 | -0.18* | 0.41** |
| (0.09) | (0.14) | (0.16) | (0.1) | (0.16) | |
| Small: Tightening | 0.51*** | 0.08 | 0.08 | 0.24 | -0.42 |
| (0.15) | (0.22) | (0.21) | (0.18) | (0.29) | |
| Mid-size: Easing | 0.07 | 0.08 | -0.15 | -0.14 | 0.15 |
| (0.06) | (0.1) | (0.13) | (0.14) | (0.15) | |
| Mid-size: Tightening | 0.02 | -0.06 | 0.06 | 0.11 | -0.07 |
| (0.13) | (0.08) | (0.16) | (0.12) | (0.1) | |
| Large: Easing | -0.09** | 0.06 | -0.02 | 0.01 | 0.37*** |
| (0.04) | (0.06) | (0.1) | (0.11) | (0.05) | |
| Large: Tightening | 0.28** | -0.07 | -0.21** | -0.1 | -0.34*** |
| (0.11) | (0.06) | (0.09) | (0.16) | (0.1) | |
| Controls | Y | Y | Y | Y | Y |
| Observations | 101297 | 89010 | 78898 | 68385 | 59424 |
China: Robustness: Average Effect of Credit Policy Shock: By Firm Size & Shock Direction
| ΔYi, t | ΔYi, t+1 | ΔYi, t+2 | ΔYi, t+3 | ΔYi, t+4 | |
|---|---|---|---|---|---|
| Small: Easing | -0.15 | -0.05 | -0.25 | -0.18* | 0.41** |
| (0.09) | (0.14) | (0.16) | (0.1) | (0.16) | |
| Small: Tightening | 0.51*** | 0.08 | 0.08 | 0.24 | -0.42 |
| (0.15) | (0.22) | (0.21) | (0.18) | (0.29) | |
| Mid-size: Easing | 0.07 | 0.08 | -0.15 | -0.14 | 0.15 |
| (0.06) | (0.1) | (0.13) | (0.14) | (0.15) | |
| Mid-size: Tightening | 0.02 | -0.06 | 0.06 | 0.11 | -0.07 |
| (0.13) | (0.08) | (0.16) | (0.12) | (0.1) | |
| Large: Easing | -0.09** | 0.06 | -0.02 | 0.01 | 0.37*** |
| (0.04) | (0.06) | (0.1) | (0.11) | (0.05) | |
| Large: Tightening | 0.28** | -0.07 | -0.21** | -0.1 | -0.34*** |
| (0.11) | (0.06) | (0.09) | (0.16) | (0.1) | |
| Controls | Y | Y | Y | Y | Y |
| Observations | 101297 | 89010 | 78898 | 68385 | 59424 |
China: Robustness: Average Effect of Interest Rate Shock: By Firm Age & Shock Direction
China: Robustness: Average Effect of Interest Rate Shock: By Firm Age & Shock Direction
| ΔYi, t | ΔYi, t+1 | ΔYi, t+2 | ΔYi, t+3 | ΔYi, t+4 | |
|---|---|---|---|---|---|
| Young: Easing | -0.32** | -0.23** | -0.2*** | -0.07 | -0.04 |
| (0.11) | (0.09) | (0.07) | (0.07) | (0.1) | |
| Young: Tightening | 0.54 | 0.54 | 0.25 | -0.52 | -0.33 |
| (0.31) | (0.33) | (0.25) | (0.35) | (0.29) | |
| Middle-Aged: Easing | -0.08 | -0.14* | -0.11** | -0.04 | 0.02 |
| (0.08) | (0.07) | (0.05) | (0.06) | (0.08) | |
| Middle-Aged: Tightening | 0.49* | 0.45 | -0.02 | -0.4 | -0.37 |
| (0.26) | (0.28) | (0.16) | (0.31) | (0.27) | |
| Old: Easing | -0.09 | -0.1* | -0.09 | -0.02 | 0.03 |
| (0.08) | (0.06) | (0.05) | (0.06) | (0.07) | |
| Old: Tightening | 0.21 | 0.43 | 0.24 | -0.25 | -0.2 |
| (0.15) | (0.31) | (0.14) | (0.23) | (0.21) | |
| Controls | Y | Y | Y | Y | Y |
| Observations | 95213 | 83461 | 73791 | 63740 | 55183 |
China: Robustness: Average Effect of Interest Rate Shock: By Firm Age & Shock Direction
| ΔYi, t | ΔYi, t+1 | ΔYi, t+2 | ΔYi, t+3 | ΔYi, t+4 | |
|---|---|---|---|---|---|
| Young: Easing | -0.32** | -0.23** | -0.2*** | -0.07 | -0.04 |
| (0.11) | (0.09) | (0.07) | (0.07) | (0.1) | |
| Young: Tightening | 0.54 | 0.54 | 0.25 | -0.52 | -0.33 |
| (0.31) | (0.33) | (0.25) | (0.35) | (0.29) | |
| Middle-Aged: Easing | -0.08 | -0.14* | -0.11** | -0.04 | 0.02 |
| (0.08) | (0.07) | (0.05) | (0.06) | (0.08) | |
| Middle-Aged: Tightening | 0.49* | 0.45 | -0.02 | -0.4 | -0.37 |
| (0.26) | (0.28) | (0.16) | (0.31) | (0.27) | |
| Old: Easing | -0.09 | -0.1* | -0.09 | -0.02 | 0.03 |
| (0.08) | (0.06) | (0.05) | (0.06) | (0.07) | |
| Old: Tightening | 0.21 | 0.43 | 0.24 | -0.25 | -0.2 |
| (0.15) | (0.31) | (0.14) | (0.23) | (0.21) | |
| Controls | Y | Y | Y | Y | Y |
| Observations | 95213 | 83461 | 73791 | 63740 | 55183 |
China: Robustness: Average Effect of Credit Policy Shock: By Firm Age & Shock Direction
China: Robustness: Average Effect of Credit Policy Shock: By Firm Age & Shock Direction
| ΔYi, t | ΔYi, t+1 | ΔYi, t+2 | ΔYi, t+3 | ΔYi, t+4 | |
|---|---|---|---|---|---|
| Young: Easing | -0.15** | -0.02 | -0.15 | -0.01 | 0.33* |
| (0.06) | (0.14) | (0.14) | (0.08) | (0.16) | |
| Young: Tightening | 0.36*** | 0.1 | 0.06 | 0.13 | -0.32 |
| (0.11) | (0.18) | (0.19) | (0.13) | (0.22) | |
| Middle-Aged: Easing | -0.01 | 0.04 | -0.14 | -0.09 | 0.32*** |
| (0.05) | (0.09) | (0.12) | (0.13) | (0.11) | |
| Middle-Aged: Tightening | 0.2 | -0.06 | -0.05 | 0.02 | -0.23 |
| (0.12) | (0.1) | (0.14) | (0.14) | (0.15) | |
| Old: Easing | 0.03 | 0.09 | -0.14 | -0.13 | 0.24*** |
| (0.05) | (0.07) | (0.1) | (0.12) | (0.07) | |
| Old: Tightening | 0.12 | -0.11 | -0.05 | 0.06 | -0.18** |
| (0.12) | (0.08) | (0.1) | (0.14) | (0.06) | |
| Controls | Y | Y | Y | Y | Y |
| Observations | 95213 | 83461 | 73791 | 63740 | 55183 |
China: Robustness: Average Effect of Credit Policy Shock: By Firm Age & Shock Direction
| ΔYi, t | ΔYi, t+1 | ΔYi, t+2 | ΔYi, t+3 | ΔYi, t+4 | |
|---|---|---|---|---|---|
| Young: Easing | -0.15** | -0.02 | -0.15 | -0.01 | 0.33* |
| (0.06) | (0.14) | (0.14) | (0.08) | (0.16) | |
| Young: Tightening | 0.36*** | 0.1 | 0.06 | 0.13 | -0.32 |
| (0.11) | (0.18) | (0.19) | (0.13) | (0.22) | |
| Middle-Aged: Easing | -0.01 | 0.04 | -0.14 | -0.09 | 0.32*** |
| (0.05) | (0.09) | (0.12) | (0.13) | (0.11) | |
| Middle-Aged: Tightening | 0.2 | -0.06 | -0.05 | 0.02 | -0.23 |
| (0.12) | (0.1) | (0.14) | (0.14) | (0.15) | |
| Old: Easing | 0.03 | 0.09 | -0.14 | -0.13 | 0.24*** |
| (0.05) | (0.07) | (0.1) | (0.12) | (0.07) | |
| Old: Tightening | 0.12 | -0.11 | -0.05 | 0.06 | -0.18** |
| (0.12) | (0.08) | (0.1) | (0.14) | (0.06) | |
| Controls | Y | Y | Y | Y | Y |
| Observations | 95213 | 83461 | 73791 | 63740 | 55183 |
China: Firm-Level Investment Impulse Response to Monetary Policy Shocks, by Firm Productivity Groupings
Citation: IMF Staff Country Reports 2023, 081; 10.5089/9798400233517.002.A003
China: Firm-Level Investment Impulse Response to Monetary Policy Shocks, by Firm Productivity Groupings
Citation: IMF Staff Country Reports 2023, 081; 10.5089/9798400233517.002.A003
China: Firm-Level Investment Impulse Response to Monetary Policy Shocks, by Firm Productivity Groupings
Citation: IMF Staff Country Reports 2023, 081; 10.5089/9798400233517.002.A003
China: Firm-Level Investment Impulse Response to Monetary Policy Shocks During Periods of Coordinated Credit and Interest Rate Shocks
Citation: IMF Staff Country Reports 2023, 081; 10.5089/9798400233517.002.A003
China: Firm-Level Investment Impulse Response to Monetary Policy Shocks During Periods of Coordinated Credit and Interest Rate Shocks
Citation: IMF Staff Country Reports 2023, 081; 10.5089/9798400233517.002.A003
China: Firm-Level Investment Impulse Response to Monetary Policy Shocks During Periods of Coordinated Credit and Interest Rate Shocks
Citation: IMF Staff Country Reports 2023, 081; 10.5089/9798400233517.002.A003
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Prepared by Henry Hoyle, Phakawa Jeasakul, and Fan Zhang
See Harjes (2016), Harjes (2017), McMahon, Schipke et al (2018), Hoyle (2021), and (Das (2022) for recent IMF staff analysis on China’s evolving monetary policy framework.
Reserve requirement ratio adjustments are considered quantity-based tools but are not credit policies for this purpose of this analysis.
People’s Bank of China Monetary Policy Department. “The Precise Targeting of Structural Monetary Policies to Reduce Difficulties Among Market Entities and Help Real Economic Development.” The People’s Bank of China Policy Research Journal No. 18 (2022).
Cecchetti 1995, Bernanke and Gertler, 1995; Gertler and Karadi, 2015
Gertler and Gilchrist 1994; Ehrmann 2005, Cloyne et al 2018, Durante et al 2021,
The Bank of England’s Funding for Lending Scheme launched in 2012 also linked funding rates to banks’ overall loan portfolio growth. The Federal Reserve’s Main Street Lending facility launched in 2020 provided low-cost funding for bank loans to SMEs but did not incorporate additional quantity-based incentives to boost aggregate loan growth.
https://www.ecb.europa.eu/pub/pdf/scpwps/ecb.wp2682~c88eac6aca.en.pdf https://www.ecb.europa.eu/pub/economic-bulletin/articles/2021/html/ecb.ebart202106_02~35bf40777b.en.html
Matteo Benettona Davide Fantino. Targeted monetary policy and bank lending behavior. Journal of Financial Economics, vol 142 iss. 1, Oct. 2021, pp. 404-429
The credit measure is total social financing excluding government bonds and equity. The activity indicator is a gap between a proprietary current activity indicator and its trend level. The inflation indicator is the National Bureau of Statistics’ Consumer Price Index. The use of the interest rate shock as a control in the credit shock identification equation precludes the use of narrative-based monetary policy shocks or other identification strategies that could capture the impact of credit policies as well as interest rate policies.
The data were cleaned to remove observations without complete data needed to compute firm investment growth, size, age, leverage (debt to assets), liquidity (cash and equivalents to assets), and revenue growth. Observations were further trimmed by discarding observations with investment values below the first and 99th percentiles or outside the bounds of -99 to 2000 percent.
Given lags in reporting and pandemic-related delays, the magnitude of the surge in investment depicted during the COVID pandemic period in part reflects a sharp drop-off in the sample size of middle-aged and particularly young firms.
The magnitude of this result is very close to that reported for young European firms in Durante et al (2020), but larger than the results for young US and UK firms reported in Cloyne et al (2018). The large investment impulse for small Chinese firms may reflect the sample’s firm coverage skew towards more dynamic, well-funded SMEs, which in part explains the elevated mean and standard deviation of investment growth shown in the descriptive statistics.
Standardizing the interest rate and credit policy shocks for comparison purposes is complicated due to the different units of the series (basis points of the one year-ahead swap rate and percentage points of credit above trend growth). In this case, 25 bps and 12.5 percentage points are chosen because they closely approximate one standard deviation of their respective time series (23.3 and 12.7, respectively).
See Cloyne (2018) for a discussion of age as a proxy for financial constraints.
Restricting results to firms with results in all periods excludes over 90 percent of firms with less than RMB 330 mn in assets. The threshold of 12 periods is used as it approximates the median reporting frequency for the smallest and youngest terciles of firms.
See in particular the October 2021 IMF Global Financial Stability Report, pp. 16-19
The cumulative impact on productivity growth would depend on the intensity and frequency of future tightening cycles, as well as the symmetry of the cross-firm effect during tightening episodes. The heterogenous cross-firm impact of monetary policy is in line with an emerging literature on the supply-side effects of monetary policy, which finds that traditional monetary easing tends to improve capital allocation and productivity. See Baqaee, Farhi, and Sangini (2021) and David and Zeke (2022) for representative examples.
