metadata: authors: 0: author: Ha,Jongrim
1: author: Liu,Haiqin
2: author: Rogers,John
count: World,United States
docna: 0: docna: Capital Controls in Emerging and Developing
            Economies and the Transmission of U.S. Monetary Policy
docty: Policy Research Working Paper
abstracts: cdata!: Emerging markets and developing
            economies (EMDEs) exhibit significantly greater volatility
            in asset returns than advanced economies. The commonalities
            in these returns (and flows) across countries are
            particularly strong for EMDEs. If these occur independently
            of the exchange rate regime and if these global financial
            cycle effects are furthermore independent of
            countries&#x2019; financial openness, the result is
            Obstfeld (2022)&#x2019;s &#x201c;Lemma&#x201d;:
            countries can do nothing to decouple from the global
            financial cycle. Under the prevalent view that U.S. monetary
            policy is the key driver of the global financial cycle,
            countries then inherit U.S. monetary policy no matter what
            they do on exchange rates or capital control policies. Using
            structural vector autoregression models for 78 countries
            over 1995&#x2013;2019, as well as different methods of
            identifying U.S. monetary policy shocks from the literature,
            this paper tests the proposition that countries with less
            open capital accounts exhibit systematically smaller
            responses to U.S. monetary policy shocks than low capital
            control countries. This paper also considers the role of
            other institutional features such as exchange rate regimes
            and foreign exchange interventions in explaining
            cross-country differences in the responses to the shocks.
            The empirical results suggest that more stringent capital
            controls exhibit smaller responses of interest rates and
            exchange rates to U.S. monetary policy shocks and that this
            result holds more firmly for EMDEs than advanced economies.
            In contrast, the analysis finds only weak evidence that the
            degree of exchange rate flexibility affects U.S. spillovers
            to foreign interest rates and exchange rates.
display_title: Capital Controls in Emerging and
            Developing Economies and the Transmission of U.S. Monetary Policy
listing_relative_url: /research/2023/10/34174885/capital-controls-emerging-developing-economies-transmission-monetary-policy
topicv3: Macroeconomic Monitoring m1326902 440,Capital Markets m1326967 332
publishtoextweb_dt: 2023-10-04T00:00:00Z
dois: 10.1596/1813-9450-10582
origu: Off of Sr VP Dev Econ/Chief Econ (DECVP)
author_keys: 0: author_key: 1485696
1: author_key: NA
2: author_key: NA
model_extractions: Policy Research Working Paper 10582 Capital Controls in Emerging and Developing Economies and the Transmission of U.S. Monetary Policy _Jongrim Ha Haiqin Liu John Rogers_ Development Economics Prospects Group October 2023
Policy Research Working Paper 10582

# **Abstract**

Emerging markets and developing economies (EMDEs) exhibit significantly greater volatility in asset returns than advanced economies. The commonalities in these returns (and flows) across countries are particularly strong for EMDEs. If these occur independently of the exchange rate regime and if these global financial cycle effects are furthermore independent of countries’ financial openness, the result is Obstfeld (2022)’s “Lemma”: countries can do nothing to decouple from the global financial cycle. Under the prevalent view that U.S. monetary policy is the key driver of the global financial cycle, countries then inherit U.S. monetary policy no matter what they do on exchange rates or capital control policies. Using structural vector autoregression models for 78 countries over 1995–2019, as well as different methods of identifying U.S. monetary policy shocks from the literature, this paper tests the proposition that countries with less open capital accounts exhibit systematically smaller responses to U.S. monetary policy shocks than low capital control countries. This paper also considers the role of other institutional features such as exchange rate regimes and foreign exchange interventions in explaining cross-country differences in the responses to the shocks. The empirical results suggest that more stringent capital controls exhibit smaller responses of interest rates and exchange rates to U.S. monetary policy shocks and that this result holds more firmly for EMDEs than advanced economies. In contrast, the analysis finds only weak evidence that the degree of exchange rate flexibility affects U.S. spillovers to foreign interest rates and exchange rates.

This paper is a product of the Prospects Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at jongrimha@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
Capital Controls in Emerging and Developing Economies and the Transmission of U.S. Monetary Policy Jongrim Ha Haiqin Liu John Rogers World Bank Fudan University Fudan University jongrimha@worldbank.org liuhq21@m.fudan.edu.cn johnrogers@fudan.edu.cn<sup>∗</sup> Keywords: Federal Reserve; Spillovers; Capital Flow Management JEL codes: E44, E52, F38

> ∗We thank Carlos Arteta, Steve Kamin, Michael Klein, M. Ayhan Kose, Franziska Ohnsorge, Hayley Pallan, Franz Ulrich Ruch, and many other participants in the World Bank seminars for useful comments. The findings, interpretations and conclusions expressed in this paper are those of the authors and should not be attributed to the World Bank, Fudan University, or the institutions the authors are affiliated with.
# **1 Introduction**

The current period of unprecedented monetary policy tightening in the U.S. and other advanced economies has given rise to renewed urgency to understanding the extent of international monetary policy spillovers as well as ascertaining the policy options that may be on the table for countries to mitigate potentially harmful effects from these spillovers. According to the classic Trilemma, countries can choose only two of: free international capital mobility, a fixed exchange rate, and sovereign monetary policy. Capital controls allow monetary policy to be set independently of foreign monetary policies while also being used to limit volatility in the exchange rate. Floating exchange rates allow a country to have open international capital markets while its central bank sets monetary policy independently of foreign monetary policies if it so chooses.<sup>1</sup> The literature on spillovers and the Trilemma was given significant impetus by work on the Global Financial Cycle (GFC). Rey (2015) and Miranda-Agrippino & Rey (2020) document persuasive evidence that significant commonalities exist between the movements of credit aggregates, credit flows, and asset prices across countries, correlations that are particularly high during crises. Emerging-market economies are particularly subject to the GFC (Kalemli-Özcan 2019). It has been argued that U.S. monetary policy is the key driver of the global financial cycle, a proposition that has key implications for understanding what policy options may or may not be “on the table”.<sup>2</sup> In his discussion of Miranda-Agrippino & Rey (2020), Obstfeld (2022) cleverly enunciated the implications of this literature: “If being relatively more closed financially does not insulate one from the Global Financial Cycle, then perhaps we have:

**Lemma.** No matter what policies countries follow, they cannot decouple from the Global Financial Cycle.”

In this paper, we estimate the spillover effects of identified U.S. monetary policy shocks to

> 1Note that the Trilemma does not posit that with floating exchange rates, a country’s domestic financial markets and asset prices are fully insulated from foreign monetary policies and financial developments. Even with a floating exchange rate and independently-set policy interest rates, a country’s stock prices, longer-term bond yields, and other asset values will be influenced by global developments, and the Trilemma does not contradict that.

> 2When the Fed eases, the reasoning goes, global intermediaries and other investors are reassured about the economic outlook. Consequently, volatility falls and risk appetite increases, leading in turn to higher leverage and rapid expansion of credit. When the Fed tightens, the process is reversed. See Miranda-Agrippino & Rey (forthcoming) for an exhaustive review of the literature on the GFC.

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foreign interest rates and exchange rates in a panel of up to 78 countries. Aligning those spillover effects with measures of capital control stringency, we examine if countries with less open capital accounts exhibit systematically smaller responses. We also consider the role of other country factors such as the exchange rate regime, economic region and foreign exchange intervention in explaining cross-country differences in responses to U.S. monetary policy shocks.

We find some evidence that countries with more stringent capital controls exhibit smaller responses of interest rates and exchange rates to U.S. monetary policy shocks. This result is more evident for samples of emerging markets and developing economies (EMDEs) and advanced economies (AEs) viewed separately. In contrast, we find only weak evidence that the degree of exchange rate regime flexibility affects U.S. spillovers to foreign interest rates and exchange rates. Our findings indicate that (i) the global financial system lies between the classic Trilemma and Obstfeld’s “Lemma”, and (ii) policy tools like capital controls are still powerful under this backdrop, for EMDEs in particular.

**Related Literature** A vast academic literature since the late 1990s examined a wide range of experiences with capital controls without consensus on their “effectiveness”. Rodrik (1998) found no evidence of a positive correlation between capital account openness and growth or investment/GDP ratios, and argued against capital account convertibility. Edwards (2001) reached the opposite conclusion, using a different measure of capital controls, as did in Grilli & Milesi-Ferretti (1995). Edison et al. (2004) considered a host of international financial openness measures and were unable to find robust evidence that openness accelerates economic growth. Still, Chinn & Ito (2002) found that financial development, measured by private credit creation and stock market capitalization, was negatively correlated with the extent of capital controls, a correlation that was stronger in developed countries with solid institutional frameworks. Dooley (1996), Eichengreen (2002), and Edison et al. (2004) surveyed this earlier literature. Building on this to shed light on the effectiveness of capital controls, Miniane & Rogers (2007) tested the proposition that countries with more stringent capital controls experienced smaller financial market spillovers from U.S. monetary policy shocks. They found no evidence to support this notion of capital control effectiveness.

More recently, Georgiadis (2016) assesses spillovers from identified U.S. monetary policy shocks in a global VAR (GVAR) model. He finds that U.S. monetary policy generates sizable output spillovers to the rest of the world, the magnitude of which depends on the receiving country’s trade and financial integration, financial openness, exchange rate regime, financial market development, labor market rigidities, industry structure, and participation in global value chains. Ahmed et al. (2021) find that 2
vulnerabilities in EMDEs matter for the transmission of U.S. monetary policy changes to EMDEs; Camara (2021) addresses the role of information effects; Landi & Schiavone (2021) examine capital controls; and Brandao-Marques et al. (2020) find that having a modern monetary policy framework matters more for EMDEs in monetary transmission than financial development. This work speaks to a broader literature on the role of institutional frameworks. Our analysis using a medium-sized panel of countries complements case studies on the effectiveness of capital controls. For example, Chamon & Garcia (2016) analyze the impact of restrictions on capital inflows that Brazil adopted after 2009. These measures had some success in segmenting the Brazilian and global financial markets but did not translate into significant changes in the exchange rate. They argue that capital controls may have contributed to the sizable depreciation of the real in 2012.

In work contemporaneous to our own, Arteta, Kamin & Ruch (2022) examine the importance of spillovers by classifying them according to their underlying cause. They distinguish between the effects of three different types of shocks that can boost U.S. interest rates: (1) inflation shocks, which are prompted by rising expectations of U.S. inflation; (2) reaction shocks, which are prompted by investors’ assessments that the Federal Reserve has shifted toward a more hawkish stance; and (3) real shocks, which are prompted by anticipation of strengthening economic activity. The authors note that although sources (1) and (2) should lead to deleterious spillovers, the third one should not.

Finally, although the empirical characterization of the global financial cycle described above (co-movements of risky asset prices) is widely accepted, there are critiques of the literature. For example, Cerutti et al. (2019) report OLS regression R<sup>2</sup> values indicating that U.S. fundamentals explain at most 25% of the variation in capital flows. Applying a robust variance decomposition method, Habib & Venditti (2019) use a combination of zero and sign restrictions to identify four shocks: U.S. monetary policy; U.S. aggregate demand; a global financial shock; and a geopolitical risk shock. They find that changes in global risk caused by pure financial shocks have the largest impact on capital flows, larger than U.S. monetary policy shocks. Boehm & Kroner (2020) emphasize the importance of U.S. macroeconomic news on global risky asset prices. They show that U.S. macro news can explain as much as 15% of the quarterly variation in foreign stock markets and take issue with the conclusion that U.S. monetary policy drives global financial conditions. Rogers, Sun & Wu (2022) document new evidence that U.S. corporate bond spreads play the predominant role in driving the global financial cycle. According to their findings, the hegemony of the U.S. in the global financial system may be rooted in its powerful financial intermediaries rather than the Fed itself.

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# **2 Data and Methodology**

## **2.1 Data Description and Variable Construction**

The variables we use for the U.S. economy include first a measure of U.S. monetary policy shocks (MP), as explained in the next section. We also control for domestic credit conditions and financial market liquidity by including the excess bond premium (EBP) from Gilchrist et al. (2021). Other U.S. variables include commodity prices (CP), consumer price index (P), industrial production index (IP), and the 10-year government bond yield (R10).<sup>3</sup> As our main focus is to evaluate the efficacy of capital controls, we begin considering which non-US (“foreign” hereafter) countries to include in the study. We do this based on the availability of data on capital controls. We use the overall capital restriction index (KC) by Fernández, Klein, Rebucci, Schindler & Uribe (2016) (FKRSU hereafter) in our main analysis. This measure covers 99 foreign countries over 1995-2019, ranging from 0-1, with 1 indicating the greatest restriction.<sup>4</sup> For each of these 99 countries, we collect exchange rate per USD (in logs and denoted as S) from the IMF’s International Financial Statistics (IFS) database. We obtain foreign interest rate (R<sup>_∗_</sup> ) and industrial production (IP<sup>_∗_</sup> ) from three data resources: IFS; Federal Reserve Economic Data (FRED); and Haver Analytics. We supplement interest rate data with the BIS policy rates, and also statistics published by countries’ central banks if possible. When multiple interest rate series are<sup>6</sup> available, we choose the one closest to the U.S. 3-month Treasury bill rate.<sup>5</sup> We select data series—and hence countries—based on the criterion that the series has no more than five missing values.<sup>7</sup> Among the 99 countries, we find 44 with sufficient data for all foreign variables, hence are allowed to be included in a “Full” VAR analysis described later. There are 34

> 3The specific series we use are: “Producer Price Index by Commodity: All Commodities”(PPIACO), “Consumer Price Index: Total All Items for the United States”(CPALTT01USM661S), “Industrial Production: Total Index”(INDPRO) and “Long-Term Government Bond Yields: 10-year: Main (Including Benchmark) for the United States”(IRLTLT01USM156N). They are all downloaded from FRED St. Louis and adjusted for seasonality by Fed X-11 routine whenever needed. In computing CP, P and IP, we take the log of each raw series and multiply by 100.

> 4The measure integrates 0/1 indicators of controls in 32 transaction categories. Each of these indicators belongs to one of the 10 _asset_ categories, and represents restrictions either on inflows or outflows. The subset of indicators under each _asset_ category are then averaged to get two indicators - one for outflow, another for inflow. FKRSU then define the overall inflow (outflow) restrictions index - KCI (KCO) - as the average of the 10 inflow (outflow) indices. Our main KC measure is the average of KCI and KCO. We check robustness to use KCI or KCO, or on selected asset categories.

> 5We refer to the IFS World Notes to determine the exact maturities of each interest rate variable for each country. For instance, when there are no series explicitly defined as “3M Tbills”, we search for descriptions like “91-day” or “3-month”, if these do not exist either, we further try “6M”, “9M” etc. We examine robustness to a sub-sample of countries for which only short-term rates with maturities under 1-year are available.

> 6To enlarge sample size, we use interest rate series with the most sufficient data. In subsection 6.3, we also look at specific “policy rate” following the definition of De Leo et al. (2022).

> 7Relaxing this criterion up to 12 enlarges the sample only by retaining small islands or countries with annual GDP less than 20 bil.$; while further restricting the cutoff to below 5 would drop some Latin American countries like Argentina and Chile with interesting capital control histories.

4
additional countries for which S and R* are available (but not IP*). For the other 21 countries, they are insufficient in either S or R*. Since we wish to analyze the responses of exchange rates and interest rates, we keep the former 34 countries in a “Partial” analysis which investigates S and R* responses in models without IP*. We drop the other 21 countries. This selection produces a broader sample of 78 countries, 34 of which we are unable to control for output. See Table A2 for details.

# **2.2 Monetary Policy Shocks and Econometric Methodology**

As our baseline measure of U.S. monetary policy shocks, we use the policy news shock (NS shock) of Nakamura & Steinsson (2018).<sup>8</sup> As highlighted by Nakamura & Steinsson (2018), the NS shock is similar to the “path factor” in Gurkayanak et al. (2005). It contains an information component that alters private-sector beliefs in non-monetary fundamentals, in contrast with the implications of standard New Keynesian model featured by price rigidity. To check robustness, we consider two alternative measures: the “unified measure” of Bu, Rogers & Wu (2021) (BRW shock) and the forward guidance shock (SS-FG shock) of Swanson (2021). The BRW shock is derived from a two-step, partial-least squares estimation using daily interest rate data across a wide spectrum of maturities. It has three appealing features, which together distinguish it from other shock series in the literature. First, by using the full maturity spectrum of interest rates, this series is able to stably bridge periods of conventional and unconventional monetary policy. Second, the shock is largely devoid of the central bank information effect, the notion that monetary policy announcements also reveal information regarding the central bank’s future macroeconomic outlook (Nakamura & Steinsson 2018). Finally, the Bu, Rogers & Wu (2021) monetary policy shock series is largely unpredictable from available information, including Blue Chip forecasts, “big data” measures of economic activity, news releases and consumer sentiment.<sup>9</sup> These considerations make it more plausible to assume that the shock series is exogenous and thus appropriately to be placed first in the ordering of a Sims (1980) style VAR. Finally, we use Swanson (2021)’s forward guidance shock (SS-FG), the extended “path factor” of Gurkayanak et al. (2005) that covers the post-2005 period.

We plot all three shock series in Figure 1. In subsection B.3, we also apply the sign restriction identification in Jarociński & Karadi (2020) to disentangle the pure monetary policy shock (JKMP)

> 8It is the first principal component of unexpected changes over 30-minute windows around FOMC announcements in five underlying variables: the Fed funds rate following the FOMC meeting, the expected Fed funds rate following the next meeting, and three-month eurodollar future rates with two, three, and four tenors. The original NS series spans 1995-2014, and was extended to 2019 by Acosta & Saia (2020). We drop unscheduled FOMC meetings to avoid potential noise and set shocks in no-meeting months to zero as the standard practice.

> 9See Ramey (2016), Miranda-Agrippino (2016), and Bauer & Swanson (2020) for critiques of earlier monetary policy shock series that exhibited predictability.

5
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significantly different for, e.g., “strict” versus “loose” capital controls regimes or based on different degrees of exchange regime flexibility. This follows Miniane & Rogers (2007). Of the different ways to report results from the panel of countries, we choose two. In the first, we estimate the first principal component (PC1) from the vector of time series in the group of countries within relevant percentiles across the capital controls distribution.<sup>12</sup> We then estimate the 9-variable VAR with the U.S. variables plus the within-group PC1 of IP*, R*, and S. In the second, we estimate the VARs country-by-country and report median impulse responses within country groups. Since the results are largely robust, we report “PC1 IRFs” in the main text and “Median IRFs” in the Appendix.<sup>13</sup>

# **2.3 Country Factors**

We display our main capital control measures for the broad sample of 78 countries in Figure 2. The two panels depict our KC measures across two time ranges: 1995 vs. 2007 and 2007 vs. 2019. From the top panel, we see that many countries liberalized their capital markets during the early period of our sample. However, these “Early Liberalizers” kept their KC stances largely unchanged after 2007, as implied by the narrow domain of countries around the 45-degree line in the bottom panel. In subsection 6.2, we provide evidence that capital account restrictiveness typically persists, in that countries switch their KC stances very infrequently.

That said, capital controls episodically imposed on selected sets of assets can differ from controls that are longstanding and broad. As highlighted by Klein (2012) and Klein & Shambaugh (2015), the duration and scope of capital controls matter in evaluating their efficacy. Thus, we follow Klein (2012) to classify countries as {“Open”, “Gate” or “Wall”} and compare their responses to U.S. monetary policy shocks.<sup>14</sup> We show in Figure A1 how our KC index maps into the episodic vs. longstanding distinction over time. The much more frequent flips in KC stances are evident among the “Gates”, while in sharp contrast the “Walls” (“Open”) stayed in high (low) level of capital controls over the whole sample. We display the Open-Gate-Wall classification directly in Table A1 for each country together with average KC index in ascending order. We see that (i) countries with high (low) capital controls are typically emerging (advanced) economies, (ii) there are many (50%) “Gate” countries crowded in the middle of the overall KC distribution, and (iii) countries with high average

> 12We standardize the PC1 to have zero mean and unit standard deviations.

> 13An alternative approach is to include three PC1 (or median) in one VAR and estimate a 13-variable model. This to some extent controls for interactive impacts among countries. We find essentially the same as results as with the 9-variable model, so we stick to the smaller system to maintain parsimony.

> 14“Open” (“Wall”) countries are those with capital controls on less than 10% (more than 90%) of transaction subcategories over the sample, and never have any year with controls on above 20% (below 60%) of subcategories. Those neither “Wall” or “Open” are classified as “Gate”. As expected, a “Wall” country imposes capital controls persistently and pervasively, while a “Gate” imposes capital controls from time to time over a limited set of assets.

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capital restrictions, more frequent foreign exchange interventions, and greater trade exposure to the United States. Moreover, countries with more restrictive capital control policies have significantly lower degree of trade integration with the US. On the other hand, FXI tends to be a substitute for KC, while there is little statistical significance, unless one investigates the de facto capital controls. We find no strong relationship between capital flow management and the degree of dollar utilization.

Table 1: **Cross-correlation Among Country Factors**

|Variables|KC|KC<br>Infow|KC<br>Outfow|FOpen<br>Chinn-Ito|FOpen<br>(Ha)|EA|PEG|KS<br>PEG|FXI|US<br>TR|DOL|
|---|---|---|---|---|---|---|---|---|---|---|---|
|KC|1.00|||||||||||
|KC<br>Infow|0.98*|1.00||||||||||
|KC<br>Outfow|0.99*|0.93*|1.00|||||||||
|FOpen<br>Chinn-Ito|-0.93*|-0.91*|-0.92*|1.00||||||||
|FOpen<br>(Ha)|-0.67*|-0.69*|-0.64*|0.75*|1.00|||||||
|EA|-0.59*|-0.58*|-0.59*|0.58*|0.53*|1.00||||||
|PEG|0.40*|0.34*|0.44*|-0.39*|-0.16|-0.54*|1.00|||||
|KSPEG|0.42*|0.37*|0.46*|-0.37*|-0.16|-0.66*|0.93*|1.00||||
|FXI|-0.05|-0.11|0.01|0.14|0.30*|-0.36*|0.23|0.41*|1.00|||
|USTR|-0.29*|-0.28*|-0.28*|0.35*|0.35*|0.48*|-0.19|-0.18|0.01|1.00||
|DOL|0.03|0.05|0.01|-0.13|-0.39*|-0.41*|0.19|0.32*|0.07|-0.36*|1.00|

Notes: (1) **KC** is the average capital control index, a larger value indicates greater stringency. **KC Inflow (Outflow)** is the average index for restrictions on capital inflow (outflow). (2) **FOpen (Chinn-Ito)** is the Chinn-Ito index on capial account openness proposed by Chinn & Ito (2006); **FOpen (Ha)** is a de-facto financial openness measure defined as the sum of international assets and liabilities in percent of GDP (Ha et al. 2019), winsorized at 99% and standardized to 0-1. (4) **EA** is the average value of monthly classification from Ilzetzki et al. (2021), a higher value indicates greater exchange rate flexibility; **PEG** and **KSPEG** are the average of the peg indicator from Shambaugh (2004) and Klein & Shambaugh (2008) respectively, a higher value indicates greater fixity. (5) **FXI** is the standard deviation of a broad foreign exchange interventions proxy defined by Adler et al. (2021) divided by the sum of standard deviations of this proxy and log change in the exchange rates against USD. (6) **USTR** is the updated trade integration measure in Miniane & Rogers (2007). It is total imports from and exports to the U.S. divided by annual GDP denominated in USD. (7) **DOL** is the deposit dollarization measure provided by Christiano et al. (2021), and supplemented by the dollar asset share measure in Adrian & Xie (2020). (8) * indicates significance at 90%.

Our large sample of countries allows us to conduct separate analysis of AEs and EMDEs, a crucial distinction emphasized by Kalemli-Özcan (2019). Table 2 highlights the much greater volatility of EMDE exchange rates and asset returns, and also more unstable inflation and output growth compared to AEs. It also shows the much more volatile interest rates, exchange rates and prices for countries with higher KC, more floating EA regimes or fewer foreign exchange interventions.

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Table 2: **Macroeconomic Volatility By Country Groups**

||||**By Ec**|**onomic **|**Region**|**By **|**KC**|**By **|**EA**|**By **|**FXI**|
|---|---|---|---|---|---|---|---|---|---|---|---|
||**Partial**|**Full**|**U.S.**|**AE**|**EMDE**|**Low**|**High**|**Fixers**|**Floaters**|**Low**|**High**|
|_σ_∆S|7.96|2.82|/|2.36|3.41|2.19|3.33|2.40|3.19|3.47|2.35|
||[3.68]|[2.61]|/|[2.33]|[3.05]|[2.16]|[3.06]|[2.29]|[2.93]|[3.20]|[2.21]|
|_σ_R|15.65|10.41|1.56|3.12|14.46|3.49|13.39|6.01|13.1|13.6|8.32|
||[7.34]|[5.21]|[1.56]|[2.42]|[9.63]|[2.62]|[7.80]|[3.63]|[6.79]|[8.04]|[4.24]|
|_σ_∆IP|2.96|2.96|0.65|2.79|3.21|3.07|2.84|3.09|2.82|2.58|3.33|
||[2.57]|[2.57]|[0.65]|[2.32]|[2.98]|[2.58]|[2.56]|[2.75]|[2.39]|[2.28]|[3.01]|
|_σ_∆P|10.17|0.85|0.27|0.29|1.26|0.33|1.13|0.48|1.10|1.09|0.78|
||[1.99]|[0.53]|[0.27]|[0.27]|[0.95]|[0.28]|[0.78]|[0.37]|[0.69]|[0.72]|[0.53]|
|_N_|78|44|1|27|17|22|22|22|22|17|17|

Notes: (1) The table reports the standard deviations among all countries in each group indicated by each column. The bracket below each cell is the average standard deviation for each country’s time series. (2) **Partial** stands for all 78 “Partial analysis” countries and **Full** stands for the 44 “Full analysis” countries. All the other columns except **US** report statistics for some subsets of the “Full analysis” sample. (3) **Low (High) KC** refers to countries with average KC index below (above) the median. **Fixers (Floaters)** refers to countries with average EA below (above) the median. **Low (High) FXI** refers to countries with FXI below (above) the median. (4) ∆S is monthly change in the log of exchange rate per USD, and ∆IP (P) is monthly change in log of IP (CPI). _N_ counts the number of countries in each group.

# **3 Baseline SVAR Results**

## **3.1 U.S. Model with the Global Factor**

We first estimate an “aggregate open economy” VAR including U.S. variables and the global factor constructed by Miranda-Agrippino & Rey (2020). The global factor is a sort of summary statistic of the fortitude of global asset prices. Movements in the factor thus provide an aggregate view of U.S. monetary policy transmission. As shown in Figure 3, the impulse responses of U.S. variables following a U.S. monetary policy shock are conventional, and they suggest an information component in both the NS and forward guidance shocks as expected. Furthermore, we observe an immediate drop in the global factor following a U.S. monetary tightening. This is in line with Miranda-Agrippino & Rey (2020)’s finding that U.S. monetary policy is an important driver of the Global Financial Cycle. Hereafter, we suppress domestic responses to save space.

10
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groups, respectively, sorted by their average capital control index over 1995-2019. In Figure B2, we present results for the broader sample of 78 countries, and in Figure B3, we present median impulse responses for countries within each group.

As seen from the first three rows of Figure 4, the impact of U.S. monetary policy shocks on foreign interest rates is generally larger for countries with lower capital controls. For the group of low KC countries, there is a small yet pronounced rise in interest rates in the near term, while for the middle and high KC groups, interest rate movements are largely independent of U.S. rates, with high KC countries even moving in the opposite direction on impact.

These findings provide some evidence supporting the prediction that for a particular foreign country, greater capital account stringency can insulate domestic financial conditions from U.S. monetary policy shocks. However, this additional buffering effect of capital controls is relatively small in economic magnitude, and is not firmly robust to different shock measures (especially for the forward guidance shock). This is reconfirmed by Figure B2 and Figure B3 showing that the responses in the high KC group are noticeably smaller than those in the low and middle KC ones, but again, the differences are not large and appear to be somewhat sensitive to the measures of U.S. MP shocks. We show in section 5 that the somewhat mixed relationships between R* responses and capital controls can be partly explained by the distinctive nature of advanced economies and emerging market economies in their exposure to the U.S. and macroeconomic and financial market volatility.

12
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<!-- Start of picture text --> NS Shock BRW Shock SS-FG<br>6.0 PE copL 4.0 3.01 pp ID<br>4.0 oH<br>R* . 3.0 we co 2.0 ; cL PL<br>2.0} Ti KR DZ 2.0 sI 1.0) tttneeeeee<br>-2.00.0 uc 'p 0.01.0 i wDcy PE BDPZ -1.00.075 um AU BD IN<br>00 02 04 06 08 1.0 0.0 0.2 04 06 08 1.0 00 02 04 06 08 1.0<br>0.4 0.2 HU KR 0.2 ID<br>03 SE st. CH , 0.1 HU<br>Ofk ee 0.0 ppP 0.1 WAT gp Pepa ---<br>. SG gy ID IN "PE MY IN<br>0.0 BD -0.1 se 0.01_UG<br>00 02 04 06 08 1.0 0.0 02 04 06 08 1.0 00 02 04 06 08 1.0<br>Average KC 1995-2019<br><!-- End of picture text -->
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<!-- Start of picture text --> NS Shock BRW Shock SS-FG<br>6.0 an) 40 3.0 1<br>4.0 WU gy 3.0 UG 2.0 CPL<br>R* 20) Pog ke iT 2.0 Sty 1.0 pg ber<br>0.0 IBD Dig IP.fe 1.0 DZ’ adB--"" ae CL.---- HPU BI 0.0) MYJ GB>-----ERtau<br>-2.0 0.0182 fey -1.0<br>15 20 25 30 35 40 15 20 25 3.0 35 40 15 2.0 25 30 3.5 4.0<br>0.4 0.2 HU xR 0.2 ID<br>0.3 SE sf co 0.1 HU<br>NO py 0-4 CL ~~--~~-ppFt , KU ER<br>Oe ee oopp PPL 01, Rey<br>IND SGoy Mik<br>0.01__BD -0.1 0.0 UG<br>15 20 25 30 35 4.0 15 20 25 30 35 40 15 20 25 3.0 35 4.0<br>Average EA 1995-2019<br>NS Shock BRW Shock SS-FG<br>6.0 co PL PE 4.0 3.0 ID oe<br>4.0 cL HU 3.0 co UG ; 2.0 CLPL<br>2.0 ww AE 2.0) SI 1.0} oop.<br>0.0; PAT “AU ~~~ ~~ yy AUS E32-- co NO KR DG<br>15PUG a 1.0, °° wk *=22>--B% 0.08. ay,HO He----~-<br>IN aoe Dig<br>-2.0 0.01 gO -1.0<br>0.0 02 04 06 08 00 02 04 06 08 0.0 02 04 06 08<br>0.4 0.2 KRHU 0.2 ID<br>x:<br>S 0.3).0.2 “NOLL 0.14. ---0GE..CL My~ 0.1 “s+. gKR woCH<br>co yg Day "A pp IN---7777 DZ 0.1 CON<br>0.17 pes ei 0.0 BR, PE LP pe<br>iba so 0.1 ae<br>0.0 BD -0.1 ; 0.0 UG<br>0.0 02 04 O06 O08 0.0 02 04 06 08 00 O02 04 O06 O08<br>Average FXI 1995-2019<br><!-- End of picture text -->
some negative consequences compared to when a single instrument is used. This issue of integrated policy framework has recently been discussed widely Das, Gopinath & Kalemli-Özcan (2022), and formally modeled by Basu, Boz, Gopinath, Roch & Unsal (2020). We discuss these issues further in section 5.

# **5 Regional Divergence and KC-EA Interaction**

So far, we have found mixed results on the relationship between country characteristics such as capital controls and U.S. monetary spillovers. Motivated by existing work on the divergence between responses of emerging markets and advanced economies to external shocks such as Kalemli-Özcan (2019), Hoek et al. (2021), Ahmed et al. (2021) and De Leo et al. (2022), we examine the relationship between KC and U.S. monetary policy shocks separately for EMDEs and AEs.

Table 3: U.S. Monetary Policy Transmission Across KC and Income Groups

||**Max**|**. R* Respo**|**nses**|**Ma**|**x. S Respon**|**ses**|
|---|---|---|---|---|---|---|
|**Group**|**NS**|**BRW**|**SS-FG**|**NS**|**BRW**|**SS-FG**|
||||_Panel A: F_|_ull Sample_|||
|All|1.37|1.07|0.49|0.23|0.06|0.09|
||[-0.47, 4.79]|[-0.70, 4.12]|[-0.03, 0.14]|[0.02, 0.45]|[-0.03, 0.19]|[0.01, 0.17]|
|Low KC|1.22|1.20|0.21|0.23|0.07|0.09|
||[0.13, 2.25]|[0.44, 2.31]|[0.00, 0.06]|[0.11, 0.36]|[0.00, 0.14]|[0.05, 0.14]|
|High KC|1.52|0.87|0.75|0.23|0.05|0.06|
||[-0.08, 4.12]|[-0.03, 3.14]|[0.00, 0.19]|[0.08, 0.39]|[0.00, 0.13]|[0.02, 0.12]|
||||_Panel B: AE _|_Sub-sample_|||
|AE All|0.88|1.03|0.17|0.25|0.08|0.10|
||[0.14, 1.70]|[0.47, 1.68]|[-0.04, 0.44]|[0.13, 0.37]|[0.01, 0.14]|[0.06, 0.15]|
|Low KC|1.15|1.06|0.18|0.25|0.08|0.09|
||[0.22, 1.70]|[0.55, 1.68]|[-0.04, 0.48]|[0.14, 0.37]|[0.01, 0.15]|[0.05, 0.15]|
|High KC|0.58<br>[-0.01, 1.69]|1.02<br>[0.36, 1.58]|0.15<br>[-0.04, 0.42]|0.24<br>[0.12, 0.37]|0.08<br>[0.00, 0.14]|0.10<br>[0.06, 0.15]|
||||_Panel C: EMD_|_E Sub-sample_|||
|EMDE All|3.04|1.17|0.94|0.20|0.04|0.05|
||[-0.09, 6.45]|[-0.34, 4.37]|[0.03, 2.56]|[0.05, 0.34]|[-0.01, 0.11]|[0.02, 0.10]|
|Low KC|4.31|1.37|1.55|0.14|0.03|0.05|
||[1.19, 9.64]|[-1.24, 6.23]|[0.18, 3.20]|[0.03, 0.28]|[-0.02, 0.11]|[0.02, 0.11]|
|High KC|1.36|1.01|0.52|0.23|0.04|0.06|
||[-0.33, 3.86]|[-0.01, 2.89]|[-0.07, 1.81]|[0.07, 0.37]|[0.00, 0.11]|[0.02, 0.09]|

Notes: (1) This table reports the median of maximum IRFs over 1-30 months for countries in each sub-group as indicated by the **Group** column; (2) Statistics in the brackets show the lower and upper bound for 68% confidence band; (3) “High (Low) KC” indicates countries with average KC above (below) median.

As indicated by Panel B and C of Table 3, we find that the negative relationship between impulse responses and capital controls becomes stronger and robust once we split the sample into AE 18
and EMDE. _Within_ AE or _within_ EMDE, the magnitude of U.S. spillovers to foreign interest rates systematically decreases in capital account stringency. This decline is much larger for EMDEs. In the meantime, we find that EMDEs exhibit greater R* responses to U.S. monetary policy shocks. Since EMDEs typically apply more restrictive capital controls (Table A1), this implies that full-sample results (section 3, Panel A of Table 3) can understate the efficacy of capital controls.

On the other hand, we find in the last three columns in Table 3 that the overall negative correlations between S responses and capital controls disappear once we consider the AE-EMDE division. The reason seems to be that countries with higher capital controls (mostly EMDEs) maintain more fixity in their exchange rates. Thus, for one thing the negative relationship between KC and S responses we see in Panel A could be due to the fact that EMDE countries have higher KC while at the same time have smaller responses in S since they are fixed. In addition, it is unsurprising to see this relationship vanish once we control for the AE-EMDE distinction. What confounds our analysis of S responses here is likely to be the exchange rate regimes.

Table 4 justifies this argument, by investigating impulse responses within EA groupings. As evident in this table, the negative relationship between S responses and capital controls becomes rather robust once we control for the degree of exchange rate flexibility. Meanwhile, it is unsurprising to see that the negative relationship between R* responses and KC does not persist when looking only at “Fixers” or “Floaters”, since we show in Table 3 that what really matters in the case of R* responses is the AE-EMDE dimension. Simply controlling for EA here does not disentangle the true relationship between R* responses and KC.

19
Table 4: U.S. Monetary Transmission Across EA and KC Groups

||**Max**|**. R* Respo**|**nses**|**Ma**|**x. S Respon**|**ses**|
|---|---|---|---|---|---|---|
|**Group**|**NS**|**BRW**|**SS-FG**|**NS**|**BRW**|**SS-FG**|
||||_Panel A: Fixer _<br>|_Sub-sample_<br>|||
|Fixer|1.34|1.07|0.23|0.23|0.05|0.08|
||[0.04, 2.74]|[0.36, 2.64]|[-0.09, 0.79]|[0.09, 0.35]|[0.00, 0.13]|[0.02, 0.12]|
|Low KC|1.15|1.09|0.12|0.23|0.07|0.09|
||[0.02, 1.92]|[0.54, 2.44]|[-0.12, 0.48]|[0.10, 0.35]|[0.00, 0.14]|[0.05, 0.13]|
|High KC|1.52|1.07|0.51|0.17|0.04|0.04|
||[0.13, 4.04]|[0.05, 3.14]<br>|[-0.06, 1.54]<br>_Panel B: Floater_|[0.04, 0.27]<br> _Sub-sample_|[-0.02, 0.10]|[0.01, 0.09]|
|Floater|1.43|1.00|0.80|0.24|0.07|0.10|
||[0.12, 3.41]|[-0.11, 2.73]|[0.17, 1.81]|[0.09, 0.43]|[0.01, 0.14]|[0.04, 0.15]|
|Low KC|1.28|1.15|0.35|0.22|0.09|0.10|
||[0.22, 2.48]|[0.18, 2.43]|[0.02, 0.79]|[0.11, 0.38]|[0.02, 0.14]|[0.04, 0.15]|
|High KC|1.59<br>[-0.34, 4.80]|0.71<br>[-0.20, 2.97]|1.25<br>[0.25, 2.65]|0.27<br>[0.09, 0.49]|0.05<br>[0.00, 0.14]|0.09<br>[0.04, 0.16]|

Notes: (1) This table reports the median of maximum IRFs over 1-30 months for countries in each sub-group as indicated by the **Group** column; (2) Statistics in the brackets show the lower and upper bound for 68% confidence band; (3) “Floater (Fixer)” indicates countries with average EA above (below) median.

The above analyses suggest that the divergence between AEs and EMDEs is central to our analysis. In Table 5, we look more closely into countries within the EMDE, exploiting the rich regional diversity in our sample. In this way, we provide a more granular view on the evaluation of capital control effectiveness.

First, we point out that there exist some salient outliers whose deviations from the general picture are not explained by our country factors in a systematic way and might be due to unobserved country heterogeneity (institutional background, historical reasons, etc.). As suggested by the relative R* responses, among countries in the 25 lowest degree of KC, the interest rate responses are smaller and less significant for Netherlands, Belgium, France, and Zambia. Furthermore, within the group of 25 countries with the highest degree of KC, we find countries—Brazil, Mexico, Angola, the Russian Federation, and Kazakhstan in particular—that exhibit relatively strong R* responses. On the other hand, the S responses of these countries are still in line with our previous results, even though they behave counter-intuitively in the case of R* responses.

Second, we confirm in Table 5 that our analyses still apply when regional economic features are considered. For example, among the seven largest EMDEs (EM7)–i.e., Brazil, China, India, Malaysia, Mexico, Türkiye, and Russia; the seven countries considered to be substantial in economic scales compared to other EMDEs–for those that use capital controls more pervasively like China, India, 20
and Malaysia, the responses of interest rates are weaker than the median. Meanwhile in Türkiye, Russia, Mexico, and Brazil, where capital control stringency is relatively lower (KC below median), the sizes of the R* responses are all higher than those in the median EMDE. These arguments also apply to the exchange rate responses.

In the East Asia and Pacific (EAP) area, where all the countries have above-median capital restrictions, the responses of interest rates and exchange rates are all below the median. A similar pattern is observed in the economic regions of Middle East and North Africa (MNA) and South Asia (SAR), where the responses of interest rates and exchange rates were mostly below median. On the other hand, in the Latin America and Caribbean (LAC) region, where most of the countries have below-median capital controls, the responses of interest rates are relatively sizeable in most cases. Likewise in the Europe and Central Asia (ECA) region, the same pattern exists in particular with respect to the responses of exchange rates.

Table 5: Relative KC and Impulse Responses By EMDE Regions

|Region|Country|�<br>KC|�R<sup>_∗_</sup>|�S|Region|Country|�<br>KC|�R<sup>_∗_</sup>|�S|
|---|---|---|---|---|---|---|---|---|---|
|EAP|Indonesia|0.13|-0.03|0.20|ECA|Bulgaria|-0.34|1.17|0.46|
||Thailand|0.22|0.01|0.06||Hungary|-0.28|0.04|0.11|
||Myanmar|0.27|-0.17|-0.32||Kyrgyz Rep.|-0.25|0.33|0.45|
||Malaysia|0.30|-0.18|0.01||Romania|-0.20|1.08|0.65|
||Philippines|0.34|-0.07|0.14||Türkiye|-0.06|1.03|1.73|
||Vietnam|0.37|-0.18|-0.42||Kazakhstan|-0.03|0.32|0.04|
||China|0.44|-0.16|-0.43||Russian Federation|0.10|1.51|2.00|
|LAC|Uruguay|-0.51|0.72|0.36||Poland|0.21|0.07|-0.07|
||Peru|-0.51|0.17|-0.00||Ukraine|0.28|0.26|0.34|
||Costa Rica|-0.47|-0.07|-0.83|MNA|Egypt, Arab Rep.|-0.34|-0.10|-0.33|
||Nicaragua|-0.47|0.08|-0.67||Kuwait|-0.16|-0.18|-0.29|
||Paraguay|-0.41|0.27|-0.13||Morocco|0.25|-0.10|0.14|
||Bolivia|-0.34|-0.03|-0.32||Algeria|0.34|-0.03|0.02|
||Ecuador|-0.12|0.01|3.16|SSA|Zambia|-0.51|2.00|0.82|
||Chile|-0.12|0.01|-0.00||Uganda|-0.41|-0.15|-0.32|
||Jamaica|-0.04|-0.05|-0.81||Mauritius|-0.40|0.02|0.51|
||Argentina|-0.01|2.71|-0.22||Nigeria|-0.29|0.12|1.19|
||Mexico|0.09|0.83|0.09||Kenya|-0.19|-0.02|-0.37|
||Brazil|0.11|0.09|0.53||Ghana|0.01|0.12|-0.08|
||Colombia|0.12|-0.04|0.36||South Africa|0.13|-0.04|1.76|
|SAR|Pakistan|0.21|0.05|-0.29||Eswatini|0.32|-0.11|1.81|
||Bangladesh|0.31|-0.16|-0.56||Angola|0.35|5.13|2.76|
||India|0.45|-0.11|-0.00||Tanzania|0.41|-0.12|-0.44|

Notes: (1) KC<sup>�</sup> is the relative KC, i.e. KC minus the median KC among EMDE countries; R<sup>�</sup><sup>_∗_</sup> (S<sup>�</sup> ) stands for standardized R* (S) impulse responses, i.e. subtracted by the median and divided by standard deviation. Hence it’s straightforward to evaluate the pattern by the “ _−_ ” signs, since negative (positive) numbers suggest values below (above) the median. (2) We take the median over the three MP shocks in reporting IRFs; (3) EAP: East Asia and Pacific, ECA: European and central Asia, LAC: Latin America and Caribbean, MNA: Middle East and North Africa, SAR: South Asia, and SSA: Sub-Saharan Africa.

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# **6 Robustness**

## **6.1 Different Types of Capital Controls?**

In this section we check the robustness of our key results using alternative measures of capital controls or financial openness. In Figure 10, we regress interest rate responses (both point estimates, and the 68% confidence band) at each horizon, on different capital control measures, controlling for all the other country factors including EA, FXI, an indicator for EM, the deposit dollarization (DOL) measure constructed by Christiano et al. (2021), and the U.S. trade share (USTR) as defined in Miniane & Rogers (2007).<sup>15</sup> As seen from the graph, a higher capital restriction (or lower financial openness) suggests a lower response to U.S. monetary policy shocks, and this conclusion holds for a variety of capital control measures.

## **6.2 Endogenous Capital Controls?**

Conclusions concerning the relationship between capital controls and U.S. spillovers rely on the assumption that capital controls are exogenous. If countries enact more stringent controls in response to undesirable external shocks, the statistical findings will be biased (downward for high KC groups in particular). Table 6 reports countries’ transition probabilities among KC quintiles. Clearly, there is a strong tendency that KC stances maintain rather than jumping around from time to time. This evidence of hysteresis in capital controls provides some support for the exogeneity assumption.

Table 6: Transition Probabilities Among KC Quintiles

||_Nqt_ = 1|_Nqt_ = 2|_Nqt_ = 3|_Nqt_ = 4|_Nqt_ = 5|
|---|---|---|---|---|---|
|_Nqt_+1 = 1|0.9375|0.0740|0.0105|0.0041|0.0022|
|_Nqt_+1 = 2|0.0473|0.8603|0.1477|0.0062|0.0043|
|_Nqt_+1 = 3|0.0152|0.0603|0.7996|0.0372|0.0043|
|_Nqt_+1 = 4|0.0000|0.0055|0.0380|0.8822|0.1041|
|_Nqt_+1 = 5|0.0000|0.0000|0.0042|0.0702|0.8850|

Note: Markov transition matrix of countries moving from the _i_ th capital control quintile at time _t_ to the _j_ th quintile at time _t_ + 1, estimated by maximum likelihood from annual data of 99 countries.

To address the potential endogeneity in capital controls, we estimate state-dependent impulse

> 15Data resource: the Direction of Trade Statistics (DOTS).

22
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Table 7: Maximum Share of Variance Explained By U.S. MP Shocks (in %)

||For|eign interest ra|tes||Exchange rates||
|---|---|---|---|---|---|---|
||Low 20%|40-60%|Top 20%|Low 20%|40-60%|Top 20%|
|NS|2.20|2.08|2.02|16.60|9.30|4.36|
||[0.40, 12.45]|[0.25, 10.14]|[0.41, 8.16]|[3.35, 37.89]|[0.90, 27.55]|[1.13, 14.95]|
|BRW|5.56|8.02|4.96|3.32|5.05|8.73|
||[1.00, 18.09]|[0.86, 21.49]|[0.87, 15.18]|[0.45, 16.25]|[0.54, 18.72]|[0.75, 28.90]|
|SS-FG|2.06|1.49|3.84|11.53|7.50|4.95|
||[0.51, 7.72]|[0.25, 7.33]|[0.82, 11.61]|[1.40, 25.18]|[0.47, 20.22]|[0.71, 19.17]|

Notes: (1) The table shows the maximum share of U.S. monetary policy shocks in a forecast error variance decomposition over 30-month horizons. (2) The lower (upper) bound of 68% confidence bands are shown in the brackets.

# **7 Conclusion**

Emerging markets and developing economies (EMDE) exhibit significantly greater volatility in asset returns and output growth than advanced economies. Across countries, there are significant commonalities in these risky asset returns and flows, as documented by the influential literature on the Global Financial Cycle (GFC). The effects on EMDEs are particularly strong and occur independently of the exchange rate regime (Rey 2015, Miranda-Agrippino & Rey 2020, KalemliÖzcan 2019, Miranda-Agrippino & Rey forthcoming). If these effects are furthermore independent of countries’ financial openness, the classic Trilemma becomes Obstfeld (2022)’s “Lemma”: countries can do nothing to decouple from the GFC. Under the view that U.S. monetary policy is the key driver of the GFC, countries may inherit U.S. monetary policy no matter what they do on exchange rates or capital controls policy.

Empirical results in this paper suggest that countries with more stringent capital controls exhibit smaller responses of interest rates and exchange rates to U.S. monetary policy shocks and that this result holds more firmly for EMDEs than advanced economies. In contrast, we find only weak evidence that the degree of exchange rate regime flexibility affects U.S. spillovers to foreign interest rates and exchange rates. Our findings indicate that (i) the global financial system lies between the classic Trilemma and Obstfeld’s “Lemma”, and (ii) policy tools like capital controls are still powerful under this backdrop, for EMDEs in particular.

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