The nexus of peer-to-peer lending and monetary policy transmission: Evidence from the People's Republic of China

Renzhi, Nuobu; Beirne, John Working Paper The nexus of peer-to-peer lending and monetary policy transmission: Evidence from the People's Republic of China ADB Economics Working Paper Series, No. 749 Provided in Cooperation with: Asian Development Bank (ADB), Manila Suggested Citation: Renzhi, Nuobu; Beirne, John (2024) : The nexus of peer-to-peer lending and monetary policy transmission: Evidence from the People's Republic of China, ADB Economics Working Paper Series, No. 749, Asian Development Bank (ADB), Manila, https://doi.org/10.22617/WPS240494-2 This Version is available at: https://hdl.handle.net/10419/310381 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/3.0/igo/ ASIAN DEVELOPMENT BANK ASIAN DEVELOPMENT BANK 6 ADB Avenue, Mandaluyong City 1550 Metro Manila, Philippines www.adb.org ADB ECONOMICS WORKING PAPER SERIES NO. 749 November 2024 The Nexus of Peer-to-Peer Lending and Monetary Policy Transmission Evidence from the People’s Republic of China This paper examines how booms and busts in peer-to-peer (P2P) lending in the People’s Republic of China (PRC) affect monetary policy transmission to inflation and output. Using state-dependent local projection methods, the results of the paper indicate a weaker transmission during boom phases. Stricter regulation on P2P lending since 2017 in the PRC and the substantial scaling back of P2P lending could positively impact the monetary management of the economy. About the Asian Development Bank ADB is committed to achieving a prosperous, inclusive, resilient, and sustainable Asia and the Pacific, while sustaining its efforts to eradicate extreme poverty. Established in 1966, it is owned by 69 members —49 from the region. Its main instruments for helping its developing member countries are policy dialogue, loans, equity investments, guarantees, grants, and technical assistance. THE NEXUS OF PEER-TO-PEER LENDING AND MONETARY POLICY TRANSMISSION EVIDENCE FROM THE PEOPLE’S REPUBLIC OF CHINA Nuobu Renzhi and John Beirne ASIAN DEVELOPMENT BANK The ADB Economics Working Paper Series presents research in progress to elicit comments and encourage debate on development issues in Asia and the Pacific. The views expressed are those of the authors and do not necessarily reflect the views and policies of ADB or its Board of Governors or the governments they represent. ADB Economics Working Paper Series Nuobu Renzhi and John Beirne No. 749 | November 2024 Nuobu Renzhi ([email protected]) is an assistant professor at the School of Economics, Capital University of Economics and Business. John Beirne ([email protected]) is a principal economist at the Economic Research and Development Impact Department, Asian Development Bank. The Nexus of Peer-to-Peer Lending and Monetary Policy Transmission: Evidence from the People’s Republic of China Creative Commons Attribution 3.0 IGO license (CC BY 3.0 IGO) © 2024 Asian Development Bank 6 ADB Avenue, Mandaluyong City, 1550 Metro Manila, Philippines Tel +63 2 8632 4444; Fax +63 2 8636 2444 www.adb.org Some rights reserved. Published in 2024. ISSN 2313-6537 (print), 2313-6545 (PDF) Publication Stock No. WPS240494-2 DOI: http://dx.doi.org/10.22617/WPS240494-2 The views expressed in this publication are those of the authors and do not necessarily reflect the views and policies ofthe Asian Development Bank (ADB) or its Board of Governors or the governments they represent. ADB does not guarantee the accuracy of the data included in this publication and accepts no responsibility for any consequence of their use. The mention of specific companies or products of manufacturers does not imply that they are endorsed or recommended by ADB in preference to others of a similar nature that are not mentioned. By making any designation of or reference to a particular territory or geographic area inthis document, ADB does not intend to make any judgments as to the legal or other status of any territory or area. This publication is available under the Creative Commons Attribution 3.0 IGO license (CC BY 3.0 IGO) https://creativecommons.org/licenses/by/3.0/igo/. By using the content of this publication, you agree to be bound bytheterms of this license. For attribution, translations, adaptations, and permissions, please read the provisions andterms of use at https://www.adb.org/terms-use#openaccess. This CC license does not apply to non-ADB copyright materials in this publication. If the material is attributed toanother source, please contact the copyright owner or publisher of that source for permission to reproduce it. ADB cannot be held liable for any claims that arise as a result of your use of the material. Please contact [email protected] if you have questions or comments with respect to content, or if you wish toobtain copyright permission for your intended use that does not fall within these terms, or for permission to use theADB logo. Corrigenda to ADB publications may be found at http://www.adb.org/publications/corrigenda. Notes: In this publication, “CNY” refers to yuan. ADB recognizes “China” as the People’s Republic of China and “Hong Kong” as Hong Kong, China. ABSTRACT This paper empirically investigates how the level of peer-to-peer (P2P) lending affects monetary policy transmission in the People’s Republic of China (PRC). Using statedependent local projection methods, we find that the macroeconomic effects of unanticipated changes in monetary policy are dampened during the boom phase of the P2P lending market. The impulse responses of industrial production and inflation are significantly negative in the non-boom state. In contrast, the responses of industrial production and inflation are muted in the boom state. Set against the context of stricter regulation on P2P lending since 2017, our results indicate that the significant scaling back of P2P lending activity and its gradual decline in the PRC could enhance the effectiveness of monetary policy transmission. Our paper also suggests that further work is needed to study the interaction between financial innovation and monetary policy. Keywords: peer-to-peer lending, monetary policy transmission, fintech JEL Codes: E44, E52, F33, F42 1. Introduction The financial system plays a central role in transmitting monetary policy to the real economy. In recent years, the rapid development in the financial technology (Fintech) industry has greatly influenced the financial system. Taking advantage of digitization and big data techniques, Fintech has played an important role in making progress toward financial inclusion and better access to credit for consumers, entrepreneurs, start-ups, and small and medium-sized enterprises (SMEs) at a lower cost (Philippon, 2016). On the other hand, the Fintech industry may amplify the recent trend that credit intermediation is shifting away from traditional banks to nonbank finance, leading to a more diverse financial system (Bernoth et al., 2017). In this sense, Fintech may also bring new risks to the financial system, which could pose challenges to central banks in achieving their mandates. Among Fintech businesses, peer-to-peer (P2P) lending, allowing individuals and small businesses to borrow and lend on an online platform without the presence of traditional financial institutions, has been a leading alternative finance format. Benefiting from being a market leader in digital technologies and a lax regulatory environment, the P2P lending industry in the People’s Republic of China (PRC) experienced rapid growth and served as a dominant driver of the global nonbank finance market in earlier years. The PRC’s P2P lending industry soared from a volume of CNY252 billion in 2014 to CNY2,804 billion by 2017, peaking at around 30% as a share of total new bank lending. This boom in the P2P lending market turned into a considerable decrease at the end of 2017, when regulators imposed a set of policy measures on the sector. While the regulation was implemented gradually, this was aimed at dramatically reducing risk across the PRC’s financial system (Hsu et al., 2021). As noted by Huang et al. (2021), a 2 further strict regulatory policy for the P2P lending market, jointly announced by the People’s Bank of China (PBoC) and the China Banking and Insurance Regulatory Commission at the end of 2017, aimed to regulate cash loans, prohibit illegal financing, and abolish the practice of using funds for student loans, investment speculation, and down payments on real estate. Such restrictions have affected the P2P lending market significantly. As regulators repeatedly seek to remedy P2P platforms through platform exit and transformation, the number of normal operating platforms continues to decline and soon disappears (Hsu et al., 2021). In 2019, P2P platforms were converted into small loan creditors or completely shut down, essentially eliminating the P2P lending market as it once existed (Figure 1). Figure 1: Trends in P2P Lending Market Share in the People’s Republic of China P2P = peer-to-peer. Notes: The figure shows the dynamics of the ratio of new P2P lending to total new bank lending at monthly frequencies. The sample period is from the first month (M1) of 2014 to M12 2019. Sources: WDZJ and CEIC. 3 However, P2P lending remains an active industry in the United States (US) and other developed economies. P2P lending has also been growing in many developing economies, including in Asia, notably India, Indonesia, Malaysia, the Republic of Korea, the Philippines, and Viet Nam (see Appendix Figure). The findings in this paper are therefore also relevant for other economies with active P2P lending markets. This is particularly the case for economies where P2P lending and fintech industry is likely to continue to grow in the future. Through a systematic investigation of the impact of P2P lending on the transmission of monetary policy in the PRC, the paper provides insights on the implications for central banks. Specifically, we employ state-dependent local projections as in Jordà (2005) and Ramey and Zubairy (2018) to estimate impulse responses of key macroeconomic variables to an unanticipated contractionary monetary policy change in the PRC, conditioning on the boom and non-boom states of the P2P lending market. A key issue is to estimate the PRC’s monetary policy change series. To reflect the coexistence of quantity and price targeting in the PRC’s monetary policy, this paper follows the concept of “shadow policy rate” (Wu and Xia, 2016), using the monetary supply and real short-term rate to construct the shadow policy rate (Xu and Jia, 2019). To overcome monetary policy endogeneity, we derive a series of identified shadow policy changes, following the approach of Romer and Romer (2004). Using this approach, we orthogonalize shadow policy rate changes against the central bank’s responses to current, lagged, and forecastable macroeconomic conditions by assuming a Taylor-type rule to extract the exogenous component. The estimated residuals therefore can be regarded as exogenous monetary policy changes and the basis for the impulse response function 4 analysis. We estimate the responses of key macroeconomic variables to the estimated unanticipated monetary policy changes and find that industrial production and inflation decline, and the exchange rate increases steadily after a monetary policy tightening. These textbook results suggest the validity of our identification. To investigate whether the boom of P2P lending can have a moderating effect on the transmission of monetary policy, we use the ratio of new P2P lending to total new bank lending as our state variable and estimate impulse responses for the boom and non-boom states of the P2P lending market. The estimation results indicate clear evidence of heterogeneous effects of unexpected contractionary changes in monetary policy across the two states. The impulse responses of industrial production and inflation are significantly negative in the non-boom state. In contrast, in the boom state of the P2P lending market, the responses of industrial production and inflation are muted and not significantly different from zero for most horizons. These results are robust to a set of sensitivity checks that include alternative monetary policy measures, alternative state definitions, and concerns about additional factors that may affect results. Overall, the estimated state-dependent effects of unanticipated contractionary monetary policy changes suggest that the ongoing development of P2P finance is negatively associated with the effectiveness of monetary policy transmission. As P2P lending functions as an alternative source of external financing, agents are less constrained by the rising cost of bank credits, dampening the overall impact of contractionary monetary policy on the economy. The paper contributes to various strands of literature. First, our paper complements the recent empirical studies that examine the effectiveness of monetary policy transmission 11 calculate impulse responses to exogenous monetary policy changes. LPs have several advantages over the traditional structural vector autoregressive (SVAR) approach. First, LPs are easier to estimate since they simply require the estimation of a series of regressions for each horizon and for each variable of interest. Second, LPs can easily conduct the point or joint-wise inference. Third, using LPs to estimate impulse responses is more robust when a VAR model is misspecified. Last, LPs can be easily extended to a non-linear, state-dependent model by allowing the parameters to change according to the state of the economy.5 Our baseline linear model can be given as follows: 𝑦𝑦 𝑡𝑡+ℎ =𝛼𝛼ℎ+ Φℎ(𝐿𝐿)𝑧𝑧𝑡𝑡−1 + 𝛽𝛽ℎ 𝑠𝑠ℎ𝑜𝑜𝑜𝑜𝑘𝑘𝑡𝑡+𝜀𝜀𝑡𝑡+ℎ ℎ= 0,1,2, ⋯,𝑛𝑛(3) where 𝑦𝑦 is the variable of interest, Φℎ(𝐿𝐿) is a polynomial in the lag operator, 𝑠𝑠ℎ𝑜𝑜𝑜𝑜𝑘𝑘𝑡𝑡 is the series of identified unanticipated monetary policy changes, and 𝑧𝑧 is a vector of control variables including contemporaneous and lagged values for 𝑦𝑦 and 𝑠𝑠ℎ𝑜𝑜𝑜𝑜𝑘𝑘𝑡𝑡 . Specifically, we let 𝑦𝑦 refer to each of our selected P2P lending market variables (i.e., the log of P2P lending volume, the P2P lending rate, the density of P2P borrowers, and the density of P2P lenders) and the indicators of real sector including the log of industrial production and inflation. Our specification includes 3 months of lagged values of the monetary policy change. The coefficient 𝛽𝛽ℎ gives the response of 𝑦𝑦 at time 𝑡𝑡+ℎ to the change at time 𝑡𝑡. Thus, one constructs the impulse responses as a sequence of the 𝛽𝛽ℎ estimated in a series of separate regressions for each horizon ℎ. 5 Unlike regime-switching VARs, state-dependent LPs do not require one to take a stand on the duration of a given state or on the mechanism triggering the transition between regimes. 12 We can further adapt the LP framework to estimate a state-dependent model given as follows: 𝑦𝑦 𝑡𝑡+ℎ =𝐼𝐼𝑡𝑡−1�𝛼𝛼𝐴𝐴,ℎ+ Φ𝐴𝐴,ℎ(𝐿𝐿)𝑧𝑧𝑡𝑡−1 + 𝛽𝛽𝐴𝐴,ℎ 𝑠𝑠ℎ𝑜𝑜𝑜𝑜𝑘𝑘𝑡𝑡� +(1 − 𝐼𝐼𝑡𝑡−1)�𝛼𝛼𝐵𝐵,ℎ+ Φ𝐵𝐵,ℎ(𝐿𝐿)𝑧𝑧𝑡𝑡−1 + 𝛽𝛽𝐵𝐵,ℎ 𝑠𝑠ℎ𝑜𝑜𝑜𝑜𝑘𝑘𝑡𝑡�+𝜀𝜀𝑡𝑡+ℎ (4) where 𝐼𝐼𝑡𝑡−1 ∈{0,1} is a dummy variable that indicates the state of the economy in terms of before the unanticipated change in monetary policy hits. In particular, 𝐼𝐼𝑡𝑡−1 takes a value of 1 in the boom state of the P2P lending market and 0 otherwise. We discuss the construction of this dummy variable in more detail in the next subsection. We allow all of the coefficients of the model to vary according to the state of the economy. One particular complication associated with the LP method is the serial correlation in the error terms induced by the successive leading of the dependent variable. Thus, we use the NeweyWest correction for our standard errors (Newey and West, 1987). 2.4 Definition of the P2P lending market states In order to test whether the transmission of monetary policy is affected by the amount of P2P finance activities, we first need to define which periods constitute the boom state of the P2P lending market. We use the ratio of new P2P lending to total new bank lending as our state variable, since this not only reflects the level of P2P lending but also accounts for the relative importance of P2P finance in the credit market. In order to define the boom and non-boom states of the P2P lending market, following Ramey and Zubairy (2018) and Alpanda and Zubairy (2019), we build a gap measure by taking the deviation of the ratio of new P2P lending to total new bank lending from a smooth trend. We construct this trend by running a Hodrick and Prescott (HP) filter with a smoothing parameter, 𝜆𝜆 = 13 14,400. We define boom states as periods in which the ratio of new P2P lending to total new bank lending is above the time-varying trend.6 3. Empirical results 3.1 Macroeconomic effects of unanticipated changes in monetary policy To reassure the validity of our identification strategy, we present the responses of key macroeconomic variables to the estimated exogenous monetary policy changes. Figure 3 shows the estimated impulse responses based on the linear model of Eq. (3). The solid line in each graph represents the estimated impulse responses in percentage over the following 10 months to an unexpected contractionary monetary policy change. We normalized the scale of the monetary policy change such that it increases the shadow policy rate by 100 basis points (bps). The dotted lines represent 90% confidence bands based on robust standard errors by Newey and West (1987). Figure 3: Impulse Responses of Macroeconomic Variables to an Unexpected Contractionary Monetary Policy Change 6 For our sample period, the boom phase corresponds to two distinct intervals: September 2015 to December 2015, and February 2016 to June 2018. The non-boom phase spans the periods from January 2014 to August 2015, January 2016, and from July 2018 to December 2019. Continued on the next page 14 Notes: The figure plots the impulse responses of industrial production, inflation, and real effective exchange rate to a 100-bps unexpected contractionary monetary policy change. 90% confidence bands in dashed lines are reported. The increase in the exchange rate refers to an appreciation of CNY. The horizontal axis represents months. Source: Authors’ calculations. The impulse responses of macroeconomic variables are consistent with the prediction of standard macroeconomic theory, indicating the soundness of our monetary policy series. Following an unexpected contractionary monetary policy change, industrial production decreases persistently with a maximum impact of around 1.8 bps. The inflation rate also shows a dampening and statistically significant effect after the unanticipated tightening. A 100-bps hike is associated with a 0.56% decline in inflation after 10 months. The real effective exchange rate yields similar responses: a monetary policy tightening leads to a persistent increase in the exchange rate (i.e. an appreciation of CNY). Our results are consistent with the findings of other empirical studies dealing specifically with monetary policy transmission in the PRC (Chen et al., 2017; Kamber and Mohanty, 2018). 3.2 Responses of P2P lending markets to unanticipated monetary policy changes Turning to P2P lending market variables, the impulse responses shown in Figure 4 are strongly in line with the related literature, particularly, the theoretical framework of Wong and Eng (2020). With an unexpected monetary policy tightening that temporarily increases the policy rate by 100 bps, agents’ access to traditional bank finance is constrained, incentivizing agents to seek financing toward P2P lending markets (Hsu et al., 2021). As more agents flow into P2P lending markets, the borrowers’ density increases persistently, with a maximum of around 0.2%. Moreover, with a denser pool of borrowers in the P2P lending market, implying there are more investment options, the density of P2P lenders increases steadily with a peak effect of 0.17%. Given the 15 processing fee (Hsu et al., 2021), P2P lenders are willing to accept a lower lending rate as long as the pool of borrowers is denser. We can observe that there is a short-term drop in the P2P lending rate right after the unanticipated tightening in monetary policy, peaking at –0.3%. In addition, the P2P lending volume rises steadily and significantly with a peak effect of around 0.15%. While it may be expected that P2P lending rates should rise following a tightening in monetary policy, given the rise in demand for P2P lending, a lowering in P2P rates can also occur for several reasons. For example, searching for yield by investors in a higher interest rate environment could lead to portfolio reallocation towards fixed income investment, thereby lowering the supply of funds available for P2P lending and potentially leading to lower P2P lending rates. In addition, in a higher interest rate environment where the risk of default is higher, P2P lenders may lower rates to attract high-quality borrowers. This can also help to lower the risk exposure of the P2P lenders’ portfolio. It can also be the case that a tightening in monetary policy triggers a contraction in economic activity and overall lending, such that P2P lenders may offer lower rates to incentivize borrowing (e.g. Wong and Eng, 2020). 16 Figure 4: Impulse Responses of P2P Lending Market Variables to an Unexpected Contractionary Monetary Policy Change P2P = peer-to-peer. Notes: The figure plots the impulse responses of P2P lending market variables to a 100-bps unexpected contractionary monetary policy change. 90% confidence bands in dashed lines are reported. The horizontal axis represents months. Source: Authors’ calculations. Hence, we find that contractionary exogenous monetary policy changes may trigger an increase in P2P finance activities, which could potentially become a source of financial distress and have implications for monetary policy transmission. 17 3.3 State-dependent effects of unanticipated monetary policy changes In this part of the analysis, we allow the responses of industrial production and inflation to unanticipated monetary policy changes to vary across states of the P2P lending market. Figure 5 shows the impulse responses to a contractionary monetary policy change for the two states of the P2P lending market, namely the non-boom (left panel) and the boom (right panel). A comparison of these two panels reveals the impact of P2P finance on monetary policy transmission. Note that the responses of industrial production and inflation are significantly negative in the non-boom state. They are also shaped similarly to the baseline linear case and are typically of a larger magnitude relative to the baseline linear model. In particular, the inflation rate response peaks at 0.75% in response to an unanticipated 100 bps monetary policy tightening in the non-boom state, relative to 0.56% in the baseline model. Industrial production also significantly declines in the non-boom state relative to the baseline case, especially for the initial periods. In contrast, in the boom of the P2P lending market, we find that the negative response of inflation becomes statistically significant only after 10 months, while the responses of industrial production are also muted and are not significantly different from zero for most horizons. 18 Figure 5: State-Dependent Impulse Responses to an Unexpected Contractionary Monetary Policy Change (a) Industrial Production (b) Inflation Rate Notes: The figure plots the impulse responses of industrial production and inflation to an unexpected 100bps contractionary monetary policy change for the boom and non-boom states of the P2P lending market. We define boom states as periods in which the ratio of new P2P lending to total new bank lending is above the time-varying trend constructed by running a HP filter with a smoothing parameter, λ= 14,400. 90% confidence bands in dashed lines are reported. The horizontal axis represents months. Source: Authors’ calculations. The estimated state-dependent effects of exogenous monetary policy tightening suggest that the ongoing development of P2P finance is negatively associated with the effectiveness of monetary policy transmission. As P2P lending functions as an alternative 19 source of external financing, agents are less constrained by the rising cost of bank credits, dampening the overall impact of contractionary monetary policy on the economy. 3.4 Robustness 3.4.1 Alternative measure of unanticipated monetary policy changes Thus far, we have followed the standard approach by assuming a Taylor-type rule to extract the exogenous components of policy rate variations as a measure of unexpected monetary policy changes. Next, we conduct the same analysis as in the previous subsection but use a narrative series as an alternative measure to capture the PBoC’s policy stance. In this paper, we rely on the approach by Sun (2018), using a narrative monetary policy stance index to measure the PBoC’s policy stance based on the information from the PBoC’s policy announcements.7 We convert the series of monetary policy stance from a meeting frequency into a monthly time series by assigning indexes to the month in which it occurred. If there are multiple meetings within a period, then we aggregate the associated index by summing up indexes within that time period. If there are no policy meetings, the corresponding index is set to zero. Figure 6 reports state-dependent impulse responses based on the monetary policy stance index. We also normalized the scale of the monetary policy stance unanticipated change such that it increases the shadow policy rate by 100 bps. The results are very similar when using alternative measurements of unexpected changes in monetary policy. 7 However, using policy announcements as the policy instrument may also call into question. When the central bank makes announcements, it does not only present pure monetary policy news, but also private information on the economy, causing the private sector to switch its outlook on macroeconomic developments (Bu et al., 2021). Thus, monetary policy news may still reflect changes in economic fundamentals not related to monetary policy. For these concerns, we only use narrative monetary policy index as a robustness check. 20 Our key findings remain robust as responses of both industrial production and inflation in the non-boom state of the P2P lending market tend to be stronger than the responses in the boom state. Figure 6: State-Dependent Impulse Responses to an Unexpected Contractionary Monetary Policy Change: Narrative-Based Monetary Policy Indexes (a) Industrial Production (b) Inflation Rate Notes: The figure plots the impulse responses of industrial production and inflation to an unexpected 100bps contractionary monetary policy change for the boom and non-boom states of the P2P lending market, using the monetary policy stance index. 90% confidence bands in dashed lines are reported. The horizontal axis represents months. Source: Authors’ calculations. 27 APPENDIX A.1 Data definitions and sources Industrial Production: Monthly industrial production index with seasonally-adjusted, IMF Statistics. Variation from the inflation changes is adjusted. Inflation Rate: Monthly consumer price index in the form of year-on-year change, National Bureau of Statistics of the People’s Republic of China. P2P Lending Volume: Monthly P2P lending transaction volume, Wang Dai Zhi Jia. P2P Lending Rate: Monthly integrated rate of P2P lending, Wang Dai Zhi Jia. Density of P2P Borrowers: Share of the number of borrowers to the number of platforms in a monthly frequency, Wang Dai Zhi Jia. Density of P2P Lenders: Share of the number of lenders to the number of platforms in a monthly frequency, Wang Dai Zhi Jia. Shadow policy rate: Monthly shadow short rate series constructed by using monetary supply and real short-term interest rate, Xu and Jia (2019). Economic policy uncertainty: Monthly PRC economic policy uncertainty index series constructed by Huang and Luk (2020). 28 A.2 P2P lending in selected Asian economies Appendix Figure: Development of P2P Lending Market in Emerging Asia, 2013-2020 IND = India, INO = Indonesia, KOR = Republic of Korea, MAL = Malaysia, P2P = peer-to-peer, PAK = Pakistan, PHI = Philippines, SIN = Singapore, VIE = Viet Nam. Notes: The figure shows the dynamics of P2P lending volumes (in logarithm) in emerging Asian economies. 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The Review of Financial Studies, 33(6), 2379–2420. Xu, Z., and Jia, Y. (2019). Natural interest rate and choice of the macro policies in China. Economic Research Journal, 6. ASIAN DEVELOPMENT BANK ASIAN DEVELOPMENT BANK 6 ADB Avenue, Mandaluyong City 1550 Metro Manila, Philippines www.adb.org ADB ECONOMICS WORKING PAPER SERIES NO. 749 November 2024 The Nexus of Peer-to-Peer Lending and Monetary Policy Transmission Evidence from the People’s Republic of China This paper examines how booms and busts in peer-to-peer (P2P) lending in the People’s Republic of China (PRC) affect monetary policy transmission to inflation and output. Using state-dependent local projection methods, the results of the paper indicate a weaker transmission during boom phases. Stricter regulation on P2P lending since 2017 in the PRC and the substantial scaling back of P2P lending could positively impact the monetary management of the economy. About the Asian Development Bank ADB is committed to achieving a prosperous, inclusive, resilient, and sustainable Asia and the Pacific, while sustaining its efforts to eradicate extreme poverty. Established in 1966, it is owned by 69 members —49 from the region. Its main instruments for helping its developing member countries are policy dialogue, loans, equity investments, guarantees, grants, and technical assistance. THE NEXUS OF PEER-TO-PEER LENDING AND MONETARY POLICY TRANSMISSION EVIDENCE FROM THE PEOPLE’S REPUBLIC OF CHINA Nuobu Renzhi and John Beirne