2.1 Global Financial Cycle

Our starting point is the research on the GFCy: Miranda-Agrippino and Rey (2020b) document co-movements in gross inflows across countries and argues that these flows are not aligned with domestic countries’ domestic conditions. Instead, gross inflows arise from “center” economies’ policies. The very simple premise is that, when center countries monetary policy changes, this affects the leverage of global banks, which in turn has an impact on cross border capital flows and credit growth in domestic economies.

Bruno and Shin (2013) develop a model that formalizes this concept and also perform an empirical analysis that highlights the credit channel of monetary policy: an increase in the spread between the borrowing rates for the local banks (which corresponds to the lending rate for the global banks) and the US Fed Fund Rate (which corresponds to the wholesale funding rate of global banks), expands global banks’ balance sheets and gives rise to greater risk-taking, which in turn translates in further capital inflows and a real exchange rate appreciation in the domestic economy; this, eventually, improves local borrowers’ balance sheets and, in so doing, further lowers the risk on the loan books of global intermediaries, which are then able to extend further credit to local banks. According to Rey (2015), the main implication of a GFCy is the mutation of the trilemma of international finance into a dilemma, in which the insulating properties of a flexible exchange rate become ineffective in maintaining monetary autonomy. Kalemli-Özcan (2019) presents a more nuanced view by arguing that while it is the case that GFCy spillovers affect countries irrespective of the exchange rate regime in place, preventing full exchange rate adjustment using monetary policy can be counterproductive in the face of risk premia shocks, when UIP deviations are endogenous to changes in interest rates. This happens in EMEs because, in a scenario with UIP deviations, an increase in the domestic policy rate, following a U.S. policy rate tightening, has a smaller effect on the exchange rate and a greater impact on the real economy, relative to a scenario without UIP deviations.

2.2 Factor Models

A growing literature employs latent factor models to study co-movements in international capital flows. For example, Cerutti et al. (2019b) use both static and dynamic factor models of gross capital flows and Davis et al. (2019) extract two factors from a static model of gross capital flows and verify that the first factor closely matches the common factor of Miranda-Agrippino and Rey (2020b) (MAR factor). Other works include Sarno et al. (2016) and Barrot and Serven (2018). One common question is then to study how domestic macroeconomic, financial and structural characteristics of the recipient countries, together with explicit policy levers, such as capital controls, affect the estimated sensitivity to the GFCy.

A first approach involves regressing the common factors loadings on a vector of explanatory variables, including measures of trade integration, financial regulation, financial development and financial liabilities. For example Davis et al. (2019) find that countries with larger net foreign liabilities are more exposed to the GFCy. The authors do not find a statistically significant association with measures of capital controls. Using a similar methodology, Cerutti et al. (2019a) use a dynamic factor model which distinguishes between emerging markets and advanced economies and among world regions. The authors’ main finding is that market structure, i.e. the composition of investor’s base, is a better predictor of countries’ sensitivities to the GFCy compared to fundamentals². Compared to Cerutti et al. (2019a), our work emphasizes the role of policies, especially around capital flows surge episodes.

A second approach consists instead in regressing the time series of capital flows over a set of key country-specific variables, controlling for common factors, as done in Cerutti et al. (2019b). This latter approach is more general as it investigates the drivers of capital flows instead of the the factors that influence the sensitivity to the GFCy. Other examples include Fratzscher (2012) who finds that explanatory variables such as differences in quality of domestic institutions, country risk and macroeconomic fundamentals are significant predictors of capital flows.

2.3 Push and Pull literature

Our work is also related to the vast literature on the role of push and pull factors as drivers of capital flows. The seminal works of Calvo et al. (1992) and Fernandez-Arias (1996) brought push factors to the center stage of the debate, by arguing that the pull interpretation is inconsistent with empirical evidence. For example, Fernandez-Arias (1996) shows how domestic economies’ creditworthiness is tied to international interest rates, which affect investors’ discount rate and, as a consequence, the present value of resources available for payment by the domestic economy. However the debate is far from settled, with some studies arguing for a greater role of pull factors (Taylor and Sarno (1997)). In this respect, some research has highlighted how the debate regarding the relative importance of push versus pull factors should differentiate between types of capital flows: for example, while push factors may be more important for portfolio flows (Baek (2006)), pull factors, such as productivity growth, may matter more for FDI flows (De Vita and Kyaw (2008)).

Following the Global Financial Crisis, a new wave of research developed more nuanced views with an emphasis on the circumstances that would make push factors relatively more important. An example is Forbes and Warnock (2012) who argue that global factors are often associated with sudden stops (that is, a fall in gross inflows) and retrenchments (that is, a fall in gross outflows). An interesting finding of the paper is the relatively lack of association between these episodes and measures of capital controls³. Similarly, Carney (2019), while acknowledging the increasing importance of push factors, also emphasizes that EMDEs bear a responsibility in contributing to building a system with less disruptive capital flows, through reforms in the domestic institutional frameworks, and also through the appropriate use of policy tools, including fiscal policy and the development of macroprudential toolkits. For instance, in a related research, Coman and Lloyd (2019) find that tightening of prudential policy in EMEs dampens the spillover from US monetary policy tightening by approximately a quarter.

Recent work analyses the transmission mechanism behind monetary policy spillovers. For example, Giovanni et al. (2017) examine, at a microeconomic level, how fluctuations in the VIX affect domestic credit market conditions, reflecting a “pass-through” effect of domestic banks’ funding costs on firms’ borrowing costs. In Kalemli-Özcan (2019) the GFCy affects the country risk-premium and, in this sense, capital flows to EMEs are driven by both global and country-specific risks.

2.4 Capital Flows Management Measures: Over-hype?

Despite the seemingly lack of support for capital controls in reducing the probability of sharp changes in capital inflows, and in affecting the sensitivity of a country to the GFCy, other empirical evidence supports the view that capital controls reduce the volume of capital flows and the probability of extreme episodes (Nispi Landi and Schiavone (2018)) and are effective in reducing portfolio inflows, following an exogenous U.S. monetary policy shock (Wang and Wu (2019)).

When turning to macroprudential policies (MPPs), the evidence is mixed; for example, while Ostry et al. (2012) find evidence that foreign currency related prudential measures reduce the share of a country’s foreign currency credit to total credit, the same does not hold for the share of debt to total external liabilities. Forbes et al. (2015) examine the effectiveness of capital controls and MPPs using a weekly database for 60 countries, over the period from 2009 to 2011, and conclude that while MPPs is successful in influencing financial fragility there is no significant impact on capital flows. There are however other studies that find strong evidence in support of the effects of MPPs on capital flows measures. An example is Zhang and Zoli (2014), who divide their sample in regions and find that in non-Asian economies MPPs affect equity inflows. Among macroprudential policies Fendoğlu (2017) finds that borrower-based tools, measures with a domestic focus and domestic reserves requirements are particularly effective in containing credit cycles. In Gelos et al. (2019) macroprudential policies are successful in reducing portfolio flows, but only so in the medium term. Other work suggests a role for macroprudential policies in influencing GDP: for instance macroprudential policies significantly lower the sensitivity of GDP growth to VIX movements and capital flows shocks in Bergant et al. (2020), and also reduce downside risks to growth in Brandao-Marques et al. (2020).

3.1 Capital Flow Data

We focus on private gross inflows⁴ from a sample of EMDEs. Since in EMDEs net inflows are largely driven by gross inflows, using gross or net flows to investigate drivers of international transactions in these countries is likely leading to similar results in estimation. Using gross flows, however, has the advantage to better capture the impact of changes in foreign investors’ flows on domestic macroeconomic and financial stability, as emphasized by the theoretical literature on the GFCy (see for example Miranda-Agrippino and Rey (2020b)).

The source of the data is the IMF’s Balance of Payment (BOP) database. Gross flows series describe transactions by non-residents in domestic financial assets (“gross inflows”) and by residents in foreign financial assets (“gross outflows”). Notice that the difference between these two series is equal to net inflows. We analyze a series of aggregate capital inflows, which we compute as the sum of Foreign Direct Investments (FDI) flow, Portfolio Investments (PI) flows and Other Investments (OI) flows. FDI flows involve a controlling stake in a corporation (of at least 10%). PI flows involve both bonds and equity holdings flows that do not lead to controlling rights. OI flows is a residual category, which records transactions between residents and non-residents in a wide variety of instruments including loans, deposits and trade credits, and therefore can be broadly associated with banking flows.

We fit the model with annual data from 1996 to 2017. The sample of countries is as exhaustive as possible, with the exclusion for series in which there are gaps (we require all the three series to be available) or for which capital flows are consistently unavailable through the sample period. All data series are adjusted following Forbes and Warnock (2012)⁵. Capital flows series are normalized by trend GDP (extracted from an Hodrick-Prescott (HP) Filter of annual GDP series) as suggested by Broner et al. (2013), and then standardized before the model estimation, as recommended by Choi (2017), to address non stationarity in factor models⁶. The final sample of countries that we have in our regression exercises is instead also restricted by the availability of the explanatory variables of interest and it is composed of: Argentina, Bulgaria, Brazil, Chile, China, Colombia, Costa Rica, Czech Republic, Hungary, Indonesia, India, Israel, Korea, Sri Lanka, Morocco, Mexico, Pakistan, Peru, Philippines, Poland, Romania, Russia, Thailand, Turkey, Ukraine, Uruguay and South Africa.

3.2 Pull Variables

We consider two groups of macroeconomic and financial variables; the first group focuses on countries’ fundamentals, while the second focuses on domestic policies. In the next paragraphs we proceed with descriptions of each variable together with a summary of arguments that highlight their relevance for this study (Also, in Table 1 we group the associated summary statistics).

3.2.2 Policy Variables

The second group of variables focus on policy actions; we follow existing literature and also build new indexes, which we describe in more detail in the following paragraphs.

Resistance Index: We borrow from the literature on Resistance Indexes (see for example Cardarelli et al. (2010)) and quantify the extent to which a country is “leaning against the wind” of capital flows, by building a novel resistance index. In practice, the greater the Net Foreign Assets (NFA) for a given exchange rate change, the higher the degree of resistance.

We first introduce an Exchange Market Pressure (EMP) Index, which quantifies the total pressure on the exchange rate. This pressure is resisted through foreign exchange intervention or relieved by allowing the exchange rate to change. Specifically, we define:

$$\begin{array}{cc}EM{P}_{it}=\%\Delta F{X}_{it}& \left(1\right)\end{array}$$

where ∆e_it is the quarter-on-quarter percentage change in the exchange rate, expressed so that an increase corresponds to an appreciation of the domestic currency (with respect to the USD), ∆FX_t is the quarter-on-quarter change in foreign reserves, measured in billions of USD (both series are available at a monthly frequency). The parameter ρ_i,t is a conversion factor and it measures the change in the exchange rate associated with a $1 billion USD intervention. The EMP Index falls when the exchange rate is either depreciating or the domestic central bank is reducing international reserves⁸.

The monthly conversion factor ρ_i,t is estimated by Patnaik et al. (2017) and reflects the degree of currency market liquidity: countries with more developed financial markets will have a smaller ρ_i,t, consistently with the assumption that, for a given amount of intervention, the impact on financial markets will be muted, ceteris paribus⁹.

The resistance index that we then use is given by:

$$\begin{array}{cc}R{I}_{it}=1-\frac{\%\Delta e_{it}}{EM{P}_{it}}& \left(2\right)\end{array}$$

Notice that the index can take any values, with larger values being associated with greater resistance. That is, when the resistance index is high, the exchange rate is prevented to react as it would in the absence of a foreign reserves intervention. As done in Cardarelli et al. (2010), we bound the index to be between 0 and 1.

Hypothesis 1: To the extent that capital inflows are associated with a real exchange rate appreciation (see Combes et al. (2012)), we expect that countries that resist nominal exchange rate appreciation should be less sensitive to the GFCy. The argument is formalized in Bruno and Shin (2014), where real exchange appreciation begets capital inflows through the impact that the former has on the balance sheets of local intermediaries. However, in practice allowing for the exchange rate to fluctuate may also reduce the frequency of “one-way” bet positions from financial intermediaries, which could in turn decrease the sensitivity of the domestic economy to the GFCy, as, for example, discussed in IMF (2011).

Sterilization Index: Second, we use a measure of sterilization policy, following the methodology of Aizenman and Glick (2009); specifically, we calculate the change in the degree of sterilization using a regression of the year-on-year monetary authority’s change in holdings of net domestic assets on the change in net foreign assets, using monthly data. We include the year-on-year growth rate of nominal GDP to also control for additional explanatory variables that can influence money demand. The regression is given by:

$$\begin{array}{cc}\Delta D{C}_{it}/M{B}_{i,t-12}=\alpha +{\beta }_i\Delta NF{X}_{it}/M{B}_{it-12}+{\varphi }_i\Delta GD{P}_{it}/GD{P}_{i,t-12}+u_{it}& \left(3\right)\end{array}$$

Where NFX denotes net foreign assets expressed in domestic currency¹⁰ and DC is net domestic credit assets defined as

$$\begin{array}{cc}D{C}_{it}\equiv M{B}_{it}-NF{X}_{it}& \left(4\right)\end{array}$$

The Monetary Base variable is constructed from the balance sheet of the central bank summing currency issued (both held in banks and outside banks) together with deposits of commercial banks with the central bank.The above identity naturally follows from the central bank balance sheet.

It is clear that when β_i = — 1 there is full monetary sterilization (that is the monetary base remains unchanged), while in the case of β_i = 0 there is no sterilization. Because we are controlling for GDP growth, a negative unit coefficient implies that the central bank allows domestic credit to match changes in money demand that arise from GDP growth but does not allow for credit expansion when instead the flows only lead to reserves accumulation. There can be circumstances in which the estimated coefficient lead to β_i —1, which means that the monetary authority is implementing a tightening, maybe to counteract inflationary pressures. Differently, when β_i 0 monetary policy is expansionary. We run the above regression using monthly data (quarterly for GDP growth) in a rolling fashion, by adding one month at a time. We then collect the vector of country-specific estimated β_i coefficients, which we then include as a regressor in our main exercise¹¹.

Hypothesis 2: On one hand, sterilization of capital inflows prevents the domestic interest rate from falling, which begets further inflows and hence can possibly lead to an increase in the sensitivity of the domestic economy to the common factor. On the other hand, sterilization reduces the amount of credit growth, which should limit the extent of real exchange appreciation and therefore reduce the sensitivity of the domestic economy to the common factor (for a description of the impact of capital inflows sterilization see Lee (1997)). It follows that the impact of sterilization on the sensitivity to the GFCy is ambiguous.

Monetary and Fiscal Policy: Third, we include a measure of Monetary Policy and one of Fiscal Policy. The monetary policy variable is the short-term rate. For the case of fiscal policy we compute the deviation of government expenditures to GDP with respect to its backward-looking rolling trend¹². The latter is estimated using an HP filter (with a parameter set equal to 1000), using the first 5 years (when available) to construct a trend. A rolling backward-looking trend exploits only the information available up to the time in which there is an increase in government expenditures. This approach reflects the idea that from a foreign investor perspective it may be difficult to disentangle whether a given deviation from an expenditure trend is due to a temporary shock or whether instead it is paving the way to less fiscal discipline¹³.

Hypothesis 3: Fiscal consolidation limits capital inflows’ absorption in the economy, which in turn limits exchange rate appreciation. In parallel, however, fiscal consolidation also improves a country’s creditworthiness, which can lead to a higher domestic return, potentially increasing the sensitivity of the domestic economy to changes in global financial conditions (similarly, fiscal consolidation may reduce inflation expectations,which in turn would increase the expected return on domestic assets).

The overall effect of fiscal policy on capital inflows is therefore ambiguous and will reflect the relative impact that changes in fiscal policy have on country risk. It is generally suggested that a debt-to-GDP ratio of 40% shall be taken as a prudential limit for EMDEs to maintain fiscal sustainability (Abbas et al. (2019)). In our exercise we will evaluate whether the potential impact of changes in fiscal policies is dependent on the level of gross debt to GDP.

Capital Controls & Macroprudential Policies: Fourth, we include indexes of capital account restrictions (capital controls) from Fernández et al. (2016) and a measure of macroprudential policy index from Cerutti et al. (2017). In the case of capital controls, the index by Fernández et al. (2016) is an indicator variable that takes a value of zero or one depending on whether a restriction on international capital flows, in a given asset category, is in place, at a given time. In the first part of this study we will focus on the aggregate measure, but we will also distinguish between the sub-index on only inflows and the one targeting only outflows. The macroprudential policy index is based on 12 specific instruments including capital buffer, leverage ratio and loan-to-value ratio; each instrument is coded as a (0–1) binary variable, with the value equal to 1 starting when the instrument is in place and until it is discontinued. The final index is simply computed as the sum across categories in a given period.

Notice that neither index embeds an element capturing the intensity of the regulation; while on one hand this might be seen as a drawback, on the other hand it ensures objectivity.

Hypothesis 4: Higher capital account and macroprudential restrictions, if effective, should reduce the exposure to the GFCy. The current literature exposes several challenges when it comes to the impact of CFMs on international capital flows. Aside from the standard endogeneity arguments, which might render the estimated association positive, the Fernández et al. (2016) index, as many of the other indexes developed so far by the literature, cannot capture intensity changes in capital control regulations (i.e a tightening or loosening of restrictions). While there exist a few exceptions of indexes that quantify the intensive margin (for example, see Pasricha et al. (2018)), these are often limited to a restricted sample of countries.

Warranted Macro-Financial Adjustments Index (WaMaFi Index): Fifth, we include an index which reflects the prescriptions of the IMF Institutional View (IV) on capital flows management. The IV is a macroeconomic framework used to provide consistent advice to member countries on policies related to capital flows (see Arora et al. (2013)). The IV recognizes that, when faced with a capital flows disruption, CFMs can be used in certain circumstances even though “they should not substitute for warranted macroeconomic adjustment”. The index that we propose is based on the set of macroeconomic and financial policies adjustments that countries are advised to implement as a first line of defense, and it is therefore suited to capture the extent to which the prescription of the IV help dampen capital flows volatility and insulate the economy from the GFCy.

In order to summarize the policy choices that countries face when managing capital flows surges we consider a Venn Diagram, as shown in Figure 2.

Managing Capital Flow Surges according to the IV

Citation: IMF Working Papers 2021, 034; 10.5089/9781513569451.001.A001

Notes: Source: Arora et al. (2013)

• Download Figure
• Download figure as PowerPoint slide

View Full Size

Managing Capital Flow Surges according to the IV

Citation: IMF Working Papers 2021, 034; 10.5089/9781513569451.001.A001

Notes: Source: Arora et al. (2013)

• Download Figure
• Download figure as PowerPoint slide

Managing Capital Flow Surges according to the IV

Citation: IMF Working Papers 2021, 034; 10.5089/9781513569451.001.A001

Notes: Source: Arora et al. (2013)

• Download Figure
• Download figure as PowerPoint slide

The three major areas in the diagram represent three relevant states: “Economy Overheating”, “Reserves Adequate” and “Exchange Rate Overvalued”. These three states can intersect, hence leading to occurrences that combine two or three of these features. Depending on the state that a given country is experiencing, the IV proposes the use of specific policy tools. Notice that the IV gives rise to 6 possible states,

S = {Adeq.Res.,Overh.,Overv.,Adeq.Res.&Overv.,Overv.&Overh.,Adeq.Res.&Overh.}

together with a residual state that allows using CFMs¹⁴.

As an example, consider an economy which experiences a capital inflow surge and which is characterized by both exchange rate overvaluation and overheating of domestic activity (expressed in our work as deviation of output from its potential level) but does not have an “adequate” level of reserves (as defined by the IMF ARA metric or, alternatively, when the latter is not available by looking at a measure of the import-cover ratio); then the appropriate policy mix that the IV recommends involves a combination of foreign exchange accumulation and sterilization. Foreign exchange accumulation is warranted as it allows to better align the country with its precautionary needs and it also alleviates exchange rate volatility, hence smoothing the impact of a surge on domestic balance sheets, as advocated by Neely (2008); at the same time, sterilization is warranted as it prevents the domestic interest rate from falling, which could further overheat the domestic economy and foster inflows of capital.

To construct our WaMaFi Adjustment index we start with (0–1) binary variables to code a policy response depending on whether it is in accordance with the IMF IV, given the state in which the country is in a given year. For example, if a country is currently overheating, does not have adequate reserves and the exchange rate is not overvalued, then we attribute a value of 1 if we observe an appropriate policy response that can be codified by either looking at the exchange rate appreciation or by looking at foreign exchange interventions (coupled with appropriate sterilization). Otherwise we attribute a value equal to zero. In this case the policy action should be aimed at helping external adjustment and counter domestic inflationary pressures. Each of these binary (0–1) variables is then weighted to reflect the intensity of the undertaken policy action: to continue with our preceding example, the weight would be given by the percentage increase in the exchange rate and foreign reserves, where each value is scaled by the year on year change in the ratio of gross capital inflows to GDP, in order to allow for cross-country and years comparison; for example, in the case of the exchange rate:

$$I_{i,t}^s=1\times \left|\frac{\%\Delta e_{i,t}^s}{\Delta c{f}_{i,t}}\right|$$

where i denotes the country, t the time, s = {Overh.} the state of the world, and cf_i,t denotes capital flows (to GDP).

All indexes are straightforward to compute and follow from the Venn Diagram; the only exception is how we account for sterilization, when there is foreign reserves accumulation; in this latter case we assume that sterilization enters as a multiplicative term:

$$I_{i,t}^s=1\times \left|\frac{\%\Delta F{X}_{i,t}^s}{\Delta c{f}_{i,t}}\right|\times (-{\hat{\beta }}_{i,t}^{sterilization})$$

where $${\beta }_{i,t}^{sterilization}$$ is the coefficient computed above; in the expression, we then attribute a larger weight to those countries that are sterilizing inflows more aggressively.

As a final step, for each one of the six states of the world (plus the residual CFM state), we then evaluate the distribution of the resulting weighted indices across the full sample of countries and years to determine the corresponding sample quantiles. We then map each one of the weighted index into a quantile, which gives a score between 0 and 5. The WaMaFi index, for each country-year pair takes a value between 0 and 5, depending on the country’s stance (the index will take a value of 0 when the country does not follow the IV) and the relative intensity of the taken policy action. We assume that when two different codifications for policy actions are compatible with the IV, for a given state of the world, (for example when, as described above, the country’s policy action can be codified by either looking at the change in the exchange rate or at the change in foreign reserves) we only include the highest of the two computed scores.

Given the novelty of the index, it is instructive to illustrate its evolution over time, also in comparison to changes in gross inflows. In Figure 3 we therefore show the average WaMaFi index together with the average gross inflows¹⁵ (the average is taken over the full sample of countries). Overall, the index tracks the changes in average gross inflows discretely well, especially in the runup that preceded the 2008 global financial crisis. These results parallel the findings of Batini and Durand (2020), who develop a qualitative and more stylized index of “policy appropriateness” so as to show that EMEs pursue an active policy stance, when faced with capital inflows surges¹⁶.

WaMaFi Index and Gross Inflows

Citation: IMF Working Papers 2021, 034; 10.5089/9781513569451.001.A001

Notes: The figure shows the average WaMaFi index (normalized to 1) together with the gross inflows (to GDP) series. Both averages are taken over the full sample of countries used in the regression exercises.

• Download Figure
• Download figure as PowerPoint slide

View Full Size

WaMaFi Index and Gross Inflows

Citation: IMF Working Papers 2021, 034; 10.5089/9781513569451.001.A001

Notes: The figure shows the average WaMaFi index (normalized to 1) together with the gross inflows (to GDP) series. Both averages are taken over the full sample of countries used in the regression exercises.

• Download Figure
• Download figure as PowerPoint slide

WaMaFi Index and Gross Inflows

Citation: IMF Working Papers 2021, 034; 10.5089/9781513569451.001.A001

Notes: The figure shows the average WaMaFi index (normalized to 1) together with the gross inflows (to GDP) series. Both averages are taken over the full sample of countries used in the regression exercises.

• Download Figure
• Download figure as PowerPoint slide

Hypothesis 5: Because a score of zero on the WaMaFi Index reflects no warranted macroe-conomic adjustments aligned with the IV, while a positive score reflects warranted adjustments of monetary and exchange rate policies (with higher values indicating greater relative intensity of adjustments, compared to other countries in the sample), it follows that a higher score on the WaMaFi Index should reduce countries’ sensitivity to the GFCy.

4.1 Econometric Framework

We estimate a dynamic factor model with 1 factor and time-varying loadings¹⁷ following the model of Ritschl et al. (2016). In this section we introduce the main building blocks of the framework, while leaving further technical details to the Appendix. We model capital flows as:

Where CF_t is a N x 1 vector that stacks each country’s capital flows series, cf_i,t); Λ_t is a N x 1 vector that stacks each country’s factor loadings, λ_i,t; F_t is a 1 x 1 common factor and E_t is a N x 1 vector of idiosyncratic elements. The variable F_t describes the systematic source of variation among capital flow series; λ_i,t determines the strength of the association between country i flows with respect to the common component (where a higher value implies a stronger relationship). We assume that the factor evolution is described by an AR(q) process:

where u_t = e^htϕ_t and ϕ_t ~ N(0,1). The log-volatility h_t is described by a random walk without drift:

with $${\eta }_t~N(0,{\sigma }_{\eta }^2)$$.

The error terms are instead described by an AR(p) process:

with Θ_i being a diagonal matrix and χ_t ∼ N(0_N×1,Ω_χ) with

Finally, we introduce time-varying factor loadings. In practice we assume that factor loadings, At are described as a driftless random walk:

where I_N is a N x N identity matrix and ε_t ∼ N(0_N×1, Ω_ε) with

The disturbances χ_t and ε_t are assumed independent of each other.

The model is estimated using Gibbs Sampling with lags p = 1,q = 2 taking 100,000 draws and discarting the first 80,000 as burn-in. We set fairly lose priors, following Del Negro and Otrok (2008) (see Appendix for details). Also, after the estimation, we checked convergence of the Gibbs sampler through visual diagnostics including recursive mean plots of non-time varying parameters (such as, for example, the AR coefficients of the model).

Notice that by fixing A_t over time we can also analyze the case of a fixed loadings factor model, similar to the one developed by Neely and Rapach (2011) and applied, within the context of the GFCy, by Cerutti et al. (2019a). In the Appendix we present a throughout analysis based on a fixed loadings version of the model. Here we report our main findings, which are broadly consistent with the preceding literature, namely: i) there is a high degree of crosscountry heterogeneity in terms of exposure to the estimated global factor; ii) there is a high degree of cross-country heterogeneity in terms of the share of variance that can be accounted for by the global factor; iii) when performing a regression of the estimated factor loadings on the sample averages of the variables of interest there is no strong evidence in favor of a role for domestic policies.

4.2 Estimation Results

In this section we proceed to illustrate and interpret the estimated common factor, together with a description of the estimated factor loadings.

In Figure 4 we first illustrate the evolution of the estimated common factor, which mimics that of “classic” representations of the GFCy, namely the Miranda-Agrippino and Rey (2020b) factor and the VIX. Specifically, the Figure highlights a strong co-movement between the Miranda-Agrippino and Rey (2020b) measure of the GFCy and the factor estimated from the capital flow series. Notably, during the period preceding the global financial crisis, the two series point to a relaxation of financial conditions, which is then abruptly reversed starting in 2008–2009¹⁸. In this work we adopt the interpretation that an increase in the estimated factor corresponds to loosening of global financial conditions and vice-versa.

Estimated Factor, VIX and Miranda-Agrippino Factor

Citation: IMF Working Papers 2021, 034; 10.5089/9781513569451.001.A001

Notes: Comparison between estimated factor, the (negative of the) VIX and the MA factor.

• Download Figure
• Download figure as PowerPoint slide

View Full Size

Estimated Factor, VIX and Miranda-Agrippino Factor

Citation: IMF Working Papers 2021, 034; 10.5089/9781513569451.001.A001

Notes: Comparison between estimated factor, the (negative of the) VIX and the MA factor.

• Download Figure
• Download figure as PowerPoint slide

Estimated Factor, VIX and Miranda-Agrippino Factor

Citation: IMF Working Papers 2021, 034; 10.5089/9781513569451.001.A001

Notes: Comparison between estimated factor, the (negative of the) VIX and the MA factor.

• Download Figure
• Download figure as PowerPoint slide

In Figures 5 and 5a we also show the results for the estimated time-varying factor loadings. Unsurprisingly, the degree of time variation of the loadings tends to differ depending on the country; for example, in some cases, such as Russia, the sensitivity consistently increases through time, while in other countries, such as South Africa, it remains fairly constant. Moreover, while in some countries the estimated loadings are significantly different from zero throughout the sample period, in other countries the loadings are either always insignificant, or only become significant around the 2008 financial crisis period (i.e. ARG). In general, the evolution of the loadings reflects the evolving exposure to the GFCy, while the significance of the loadings depends on the relevance of the GFCy factor in the evolution of total gross capital inflows¹⁹.

Time-Varying Factor Loadings

Citation: IMF Working Papers 2021, 034; 10.5089/9781513569451.001.A001

Notes: Solid lines represent the posterior mean and dashed lines represent the 16th and 84th percentiles.

• Download Figure
• Download figure as PowerPoint slide

View Full Size

Time-Varying Factor Loadings

Citation: IMF Working Papers 2021, 034; 10.5089/9781513569451.001.A001

Notes: Solid lines represent the posterior mean and dashed lines represent the 16th and 84th percentiles.

• Download Figure
• Download figure as PowerPoint slide

Time-Varying Factor Loadings

Citation: IMF Working Papers 2021, 034; 10.5089/9781513569451.001.A001

Notes: Solid lines represent the posterior mean and dashed lines represent the 16th and 84th percentiles.

• Download Figure
• Download figure as PowerPoint slide

Time-Varying Factor Loadings

Citation: IMF Working Papers 2021, 034; 10.5089/9781513569451.001.A001

Notes: Solid lines represent the posterior mean and dashed lines represent the 16th and 84th percentiles.

• Download Figure
• Download figure as PowerPoint slide

View Full Size

Time-Varying Factor Loadings

Citation: IMF Working Papers 2021, 034; 10.5089/9781513569451.001.A001

Notes: Solid lines represent the posterior mean and dashed lines represent the 16th and 84th percentiles.

• Download Figure
• Download figure as PowerPoint slide

Time-Varying Factor Loadings

Citation: IMF Working Papers 2021, 034; 10.5089/9781513569451.001.A001

Notes: Solid lines represent the posterior mean and dashed lines represent the 16th and 84th percentiles.

• Download Figure
• Download figure as PowerPoint slide

5.1 Identification of Capital Flows Episodes

In order to identify specific episodes of surges and retrenchments we first compute a 5 years rolling moving average series for each country’s gross capital flows series, and then we identify an “episodic” surge if the deviation of the series of gross inflows from its moving average in a given year exceeds one standard deviation of the series (calculated over the preceding 5 years). In addition to the above criteria, we also include a minimum threshold, which is set at 2% of GDP. A similar approach is followed by Dell’Erba and Reinhardt (2015) for the case of FDI. Overall, our methodology is in line with other works in the area of capital flows surges, including Forbes and Warnock (2012), Scheubel et al. (2019a) and Cardarelli et al. (2010). In Table 3 and 4 we summarize the whole set of identified surges, by year and country (where a surge is codified with a binary variable equal to 1, and 0 otherwise).

It is useful to illustrate one country case: in Figure 6 we therefore report our results for South Africa. Notice that the methodology captures relevant dates where there is an obvious uptick in capital inflows. Furthermore, notice that while in 1994 the capital flow series exceeds the one standard deviation threshold, the event remains below the minimum threshold, and hence it is not recorded as a surge.

Identification of Capital Flows Episodes: The case of South Africa

Citation: IMF Working Papers 2021, 034; 10.5089/9781513569451.001.A001

Notes: The Figure shows the series of capital flows (Cap. Flows) for South Africa, the rolling moving average of the series (MovAv.), the estimated series of capital flows computed, by adding to the moving average, the standard deviation (MovAv. + 1 S.D.), together with the identified episodes (Episode).

• Download Figure
• Download figure as PowerPoint slide

View Full Size

Identification of Capital Flows Episodes: The case of South Africa

Citation: IMF Working Papers 2021, 034; 10.5089/9781513569451.001.A001

Notes: The Figure shows the series of capital flows (Cap. Flows) for South Africa, the rolling moving average of the series (MovAv.), the estimated series of capital flows computed, by adding to the moving average, the standard deviation (MovAv. + 1 S.D.), together with the identified episodes (Episode).

• Download Figure
• Download figure as PowerPoint slide

Identification of Capital Flows Episodes: The case of South Africa

Citation: IMF Working Papers 2021, 034; 10.5089/9781513569451.001.A001

Notes: The Figure shows the series of capital flows (Cap. Flows) for South Africa, the rolling moving average of the series (MovAv.), the estimated series of capital flows computed, by adding to the moving average, the standard deviation (MovAv. + 1 S.D.), together with the identified episodes (Episode).

• Download Figure
• Download figure as PowerPoint slide

It is interesting to also visualize whether the evolution of the total number of capital flows episodes closely tracks the estimated global factor: as shown in Figure 7 there is a strong co-movement between our estimated measure of the global financial cycle and the number of capital inflows episodes that we identify in each period, across the sample of countries included in the analysis²⁰. This stylized fact supports the idea that changes in global financial conditions might be at the source of at least some of the capital inflow surges observed within emerging market economies.

Global Financial Cycle and Number of Capital Inflows Surges

Citation: IMF Working Papers 2021, 034; 10.5089/9781513569451.001.A001

Notes: The Figure shows the estimated global factor from the time varying factor model (left axis) together with the total number of identified capital inflows surges in the sample per year (right axis).

• Download Figure
• Download figure as PowerPoint slide

View Full Size

Global Financial Cycle and Number of Capital Inflows Surges

Citation: IMF Working Papers 2021, 034; 10.5089/9781513569451.001.A001

Notes: The Figure shows the estimated global factor from the time varying factor model (left axis) together with the total number of identified capital inflows surges in the sample per year (right axis).

• Download Figure
• Download figure as PowerPoint slide

Global Financial Cycle and Number of Capital Inflows Surges

Citation: IMF Working Papers 2021, 034; 10.5089/9781513569451.001.A001

Notes: The Figure shows the estimated global factor from the time varying factor model (left axis) together with the total number of identified capital inflows surges in the sample per year (right axis).

• Download Figure
• Download figure as PowerPoint slide

5.2 Complementary log-log Model

Having identified “episodes” of surges, we select the subset of episodes that can be attributed to positive swings in the GFCy (i.e. relaxation in global financial conditions). The motivation is that the time varying factor loadings (λ_i,t) characterize the relationship of capital flows with respect to the GFCy and therefore it is important to exclude those episodes that are instead more likely attributed to other factors.

We estimate a complementary log-log model, which gives the probability of a surge occurring. The cloglog framework is generally used when the event probability is either small or large and it is therefore adequate for the study of capital flows episodes, as also explained in Calderón and Kubota (2019).

In terms of our application we regress the capital flows episodes identified above on the extracted principal component from the time varying loadings model and a set of explanatory variables. The specification for a given type of capital flows is then given by the following fixed effect cloglog model:

where Episode_i,t = 1 denotes an episode happening in country i, at time t. Episodes are coded as binary variables (0,1), with a 1 denoting a surge episode; X is a vector of control variables, which we further discuss below. We also include country fixed effects (a_i).

In the model, and given the focus on surges, we complement the explanatory variables from the previous exercises with a measure of private sector domestic credit growth; furthermore, we include a variable that tracks commodity prices in order to control for surges not directly related to financial flows.

Following the standard approach in the literature, and in order to somewhat reduce the problems of endogeneity and reverse causality all domestic variables are lagged by one year, unless specified otherwise.

Notice that in the model, the Maximum Likelihood effect of an increase in the global financial cycle on the probability of a capital inflow surge is given by:

5.3 Marginal Effects

The above model estimates the probability of an episode happening. Since the model is nonlinear we cannot detect whether an episode is driven by changes in global conditions by simply referring to the statistical significance of the estimated $\hat{\beta }$. Therefore, and in order to accurately estimate the role of the global factor, we next compute the marginal effect of the GFCy on the probability of a capital flow episode happening, which is given by:

The margin (marg_i,t) is calculated at each realized values of the global factor and other variables and it quantifies the marginal contribution for country i at time t of a change in the GFCy on the probability of a capital flow surge episode. As a final step we then compute:

which is the deviation in the margin; this is calculated using the delta method for standard errors and it is a variance estimate. Using marg_i,t and ξ_i,t we can then select the subset of episodes where there is a statistical significant contribution coming from the GFCy on the probability of the identified capital flow episode.

Overall we find that out of 94 total surges recorded in our sample, 45 episodes can be attributed to changes in the global financial cycle, with a 1% significance level; this number raises to 68 episodes, with a 5% significance level and to 76 at the 10% significance level. In Figure 8 we show the number of global and local episodes; the figure highlights that the highest number of globally driven episodes are clustered around the years preceding the 2008 financial crisis and that in the post-2009 years locally-driven episodes are relatively more frequent compared to the pre-crisis period²¹. There is a growing literature exploring how, in the post-2009 world, changes in the financial system and regulations (including macroprudential policies) might have affected the international transmission of core countries monetary policy shocks (a major driver behind fluctuations in the GFCy) and hence the likelihood of experiencing globally-driven surges. For example, Miranda-Agrippino and Rey (2020b) highlights the increasing relevance of asset managers and nonbank financial intermediaries in transmitting shocks. Forbes and Warnock (2020) underscore that what used to be “global waves” of capital flows have become smaller “idiosyncratic ripples” that are more difficult to explain with standard measures, such as global risk and global growth.

Global and Local Episodes by Year

Citation: IMF Working Papers 2021, 034; 10.5089/9781513569451.001.A001

Notes: Episodes are defined global when the deviation in the margins is significant at the 5% level.

• Download Figure
• Download figure as PowerPoint slide

View Full Size

Global and Local Episodes by Year

Citation: IMF Working Papers 2021, 034; 10.5089/9781513569451.001.A001

Notes: Episodes are defined global when the deviation in the margins is significant at the 5% level.

• Download Figure
• Download figure as PowerPoint slide

Global and Local Episodes by Year

Citation: IMF Working Papers 2021, 034; 10.5089/9781513569451.001.A001

Notes: Episodes are defined global when the deviation in the margins is significant at the 5% level.

• Download Figure
• Download figure as PowerPoint slide

5.4 Clog-log: Regression Results

We show the results from the baseline model in Table 5. For completeness, we also report results for a modified version of the model that accounts for interaction terms between the global factor and the other variables. This is justified on the grounds that domestic variables can amplify or contain the impact of global financial conditions on the probability of a surge (see for example Scheubel et al. (2019b)).

The regression results reported in Table 5 suggest that an easing of global financial conditions significantly increases the likelihood of a capital inflow surge, validating the main motivation behind this exercise. The overall impact of a relaxation of the GFCy on the probability of a surge can also be appreciated by calculating the Average Marginal Effects (AME) by year, as shown in Figure 9. Unsurprisingly, as shown in the Figure, the average marginal effect is steadily increasing throughout the pre-crisis period, before declining sharply. Afterwards, the AME is increasing again, before the kick-in from the taper tantrum in 2013.

A verage Marginal Effect of the GFCy on the prob. of a surge

Citation: IMF Working Papers 2021, 034; 10.5089/9781513569451.001.A001

Notes: The Figure shows the average marginal effect of a relaxation of the GFCy in the model.

• Download Figure
• Download figure as PowerPoint slide

View Full Size

A verage Marginal Effect of the GFCy on the prob. of a surge

Citation: IMF Working Papers 2021, 034; 10.5089/9781513569451.001.A001

Notes: The Figure shows the average marginal effect of a relaxation of the GFCy in the model.

• Download Figure
• Download figure as PowerPoint slide

A verage Marginal Effect of the GFCy on the prob. of a surge

Citation: IMF Working Papers 2021, 034; 10.5089/9781513569451.001.A001

Notes: The Figure shows the average marginal effect of a relaxation of the GFCy in the model.

• Download Figure
• Download figure as PowerPoint slide

Looking at what variables have a significant impact on the probability of a capital inflow surge, the framework suggests that fundamentals matter independently of the GFCy, while some policy variables become significant only in-so-far as they interact with the GFCy. On a general level, these results are consistent with the view that, while fundamentals might be pulling capital, independently of global conditions, domestic policies become consequential following changes in push factors. For instance, and as we have already stated in the introduction, to the extent that capital inflows surges arise from the feedback loop between an initial positive push shock and the resultant relaxation of domestic financial condition, tighter capital controls can help weaken the link.

Specifically, starting with variables that describe the fundamentals, we notice that the CPI index negatively impacts the probability of a surge, while a higher degree of institutional quality, a higher level of NFA and more trade integration, all tend to support the probability of inflow surges. Interestingly, we also find that a peg regime increases the probability of a surge. Turning to statistically significant policy variables, we find that the impact of the GFCy is contained by a higher level of capital controls and expansionary fiscal policy in low debt countries. Other policy variables, despite not being statistically significant, are worthwhile of a discussion: specifically, the model also predicts that the effects of the GFCy are amplified by a higher domestic interest rate and higher capital inflows sterilization, while they are subsumed when a country is enacting more macrorprudential policies measures, resisting exchange rate appreciation and scoring higher on the WaMaFi index.

6.1 Baseline Regression Results

In this paragraph we introduce the baseline econometric framework and discuss the results. In particular, we estimate panel fixed effect regressions of the type:

where Episode_i,t is a dummy variable, which takes the value of 1 in the years when country i experiences globally-driven episodes, and 0 otherwise. We include both country and time fixed effects (a_i,T_t, respectively). The vector X includes all “policy variables” of interest and Z contains the “fundamental” variables. In order to capture the role of policy variables around capital flows surges we also allow for an interaction term. In this sense our primary interest is toward the vector coefficient γ.

We include all episodes which are global (in a statistical significant sense) and we compare the cases for different significance levels of the surge (at the 1%, 5% and 10% levels). The regression includes country fixed effects (α_i) and time fixed effects (t_t). The dependent variable, λ_i,tt, corresponds to the estimated posterior 50^th percentile from the time varying factor loading model discussed in the previous sections. Higher values of the loadings should be interpreted as implying a larger exposure to the estimated global factor. Independent variables are lagged one period (one year) in order to reduce endogeneity, as commonly done in this stream of literature.

Results: Table 7 reports the main results: first, looking at Episode_i,t we find that, indeed, periods of capital inflows surges are associated with a higher average sensitivity to the GFCy, ceteris paribus (albeit the coefficient, absent interactions, is statistically insignificant). Turning to policy variables, our analysis suggests that the use of both capital controls and macroprudential policies are effective at reducing the estimated sensitivity, but only during episodes of capital inflows surges²². This result contrasts with the conclusions of Davis et al. (2019) and suggests that international financial integration matters when it comes to domestic autonomy and a country’s resilience to global forces, giving credit to Rey (2015). Our finding that CFMs emerge as an effective tool directly speaks to the literature arguing against managing the exchange rate and in favor of alternative policy options that can influence risk spillovers related to U.S. monetary policy shocks. This tallies with findings in Kalemli-Özcan (2019) for example, who argues that, in EMDEs, exchange rate management through monetary policy is counterproductive on the grounds that, under risk spillovers, UIP deviations are endogenous to monetary policy. Countries can nevertheless act on the transmission channel of the GFCy by limiting credit growth and leverage during booms. Our results show that indeed, on average, countries can successfully rely on those type of instruments to shield their exposure to capital inflow surges. Notice that, as shown in Figure 10, the association is robust and the estimated significance does not result from the influence of outlier observations.

Correlation between GFCy sensitivity and CFMs

Citation: IMF Working Papers 2021, 034; 10.5089/9781513569451.001.A001

Notes: The Left (Right) Figure shows the correlation between capital controls (Macroprudential Index) and the estimated sensitivity to the GFCy during capital inflows surges (selected at the 1% significance level).

• Download Figure
• Download figure as PowerPoint slide

View Full Size

Correlation between GFCy sensitivity and CFMs

Citation: IMF Working Papers 2021, 034; 10.5089/9781513569451.001.A001

Notes: The Left (Right) Figure shows the correlation between capital controls (Macroprudential Index) and the estimated sensitivity to the GFCy during capital inflows surges (selected at the 1% significance level).

• Download Figure
• Download figure as PowerPoint slide

Correlation between GFCy sensitivity and CFMs

Citation: IMF Working Papers 2021, 034; 10.5089/9781513569451.001.A001

Notes: The Left (Right) Figure shows the correlation between capital controls (Macroprudential Index) and the estimated sensitivity to the GFCy during capital inflows surges (selected at the 1% significance level).

• Download Figure
• Download figure as PowerPoint slide

Importantly, we find that during periods of capital inflows surges, a higher score on the WaMaFi Index is significantly associated with a reduction in the sensitivity to the GFCy. Turning to fiscal policy our results confirm the role of public expenditures as a driver of capital flows, in high debt countries, where we find an economically significant positive effect associated with a positive deviation of public expenditures from trend. Concerning low debt countries we find that higher deviation of public expenditures from trend is associated with a lowered sensitivity to the GFCy during inflows surges.

Resistance to exchange rate appreciation is associated with a lowered sensitivity during non-episodes times, but this effect is muted during capital inflows surges, even though in both cases, coefficients turn out to be insignificant. One interpretation, generally considered in the literature, is that preventing exchange rate appreciation during inflows episodes can encourage one-way bet positions from international financial intermediaries. Moreover, we also find that higher sterilization tends to increase capital inflows sensitivity, even though we do not find statistical significance (and the coefficients with the interaction term flip sign when looking at the 5% and 10% significance classification of surges).

Turning to fundamentals, we find that both trade and financial integration are associated with higher sensitivity. Finally, we find a positive association, when it comes to net foreign assets; as we have already outlined, an interpretation is that, perhaps, foreign investors perceive that a higher level of net foreign assets implies higher net foreign assets return and that the latter represent a major stabilizing force against global income shocks, as documented by Adler and Garcia-Macia (2018).

6.2 Disaggregation of Capital Controls Measures

Until now, we have considered an aggregate measure of capital controls. This is justified both in order to streamline the presentation but also because countries might use capital controls on different flows as substitutes, making more challenging to estimate a significant relationship when looking at finer metrics. However, given the scope of this paper, it is natural to complement our baseline results with more granular measurements; we therefore re-estimate equation (5) by decomposing the capital control measure into i) the sub-indices targeting inflows and outflows ii) the sub-indices targeting inflows and outflows based on residency²³.

On a first pass, capital controls on inflows might be perceived as more likely to be enforceable, as it could be easier to discourage foreign investors than to close all the escape doors; also, the theoretical literature often emphasizes the role of controls on inflows to prevent capital flows surges (see Jeanne and Korinek (2010)). At the same time, capital controls on outflows, while in principle harder to effectively implement could still achieve their purpose, by discouraging international investors to buy assets in a country, if they get worried about their ability to take either the principal or their returns out in times of need (Bartolini and Drazen (1996)). In Table 8 we present the results for the case of total inflows and total outflows controls²⁴. Interestingly, we find that only controls on capital outflows remain statistically significant (and only so when looking at the most stringent classification of globally-driven surges).

Turning to Tables 9 and 10, we further disaggregate the inflows and outflows controls by looking first at the restrictions affecting only non-residents and second at those restrictions that are instead targeted towards residents. The results confirm the statistical significance of capital controls on outflows, but also highlight the relevance of capital controls on non-residents inflows. Interestingly, Gupta and Masetti (2018) find that during surge episodes, on average, countries do not change pre-existing levels of capital controls restrictions, with the exception of loosening controls on resident outflows. The authors then suggest that, perhaps, this happens because countries are trying to mitigate the macroeconomic impact of surges (by reducing impediments on residents’ ability to flight away). While our exercise differs in several ways both in terms of research question, sample and interpretation of the capital control variable²⁵, we believe that our results offer an interesting complementary perspective that is worthy of further scrutiny²⁶.

6.3 WaMaFi Index without CFMs component

In this paragraph we estimate a regression where the WaMaFi index is outstripped of the CFMs component. This modification to the baseline regression allows us to emphasize the more conventional macroeconomic and financial aspects of domestic adjustments and to confirm that the results are not exclusively driven by the use of CFMs (as it already qualitatively emerged from Figure 3). In Table 11 we report the results, which are highly significant across the three models and also remain quantitatively in line with the previous findings.

6.4 Complementarity of CFMs and Warranted Macroeconomic Adjustments

In the previous paragraphs we showed that higher levels of warranted macroeconomic adjustments, controlling for CFMs, lower the estimated sensitivity to the GFCy²⁷. In this paragraph we propose to modify equation (5) to allow for an interaction term between the WaMaFi Index (without CFMs component) and our measures of CFMs, during periods of surges. This extension allows us to condition the estimated impact of macroeconomic adjustments on the level of CFMs. Formally, equation (5) is now given by:

where $$\tilde{X}$$ has the same interpretation as before, but without the CFM and WaMaFi Index components (and the variable CFM is either the level of capital controls or macroprudential regulations); we are interested in the coefficient ζ₁; specifically, ζ₁ < 0 would suggest that, on average, the impact of warranted macroeconomic adjustments on the sensitvity to the GFCy during surge episodes is increasing in the degree of CFMs restrictions in place. We report the results in Tables 12, 13 and 14, which suggest a complementary role between macroeconomic policy levers and CFMs (especially so in the case of macroprudential policies). These results also speak in relation to the concern that CFMs are used as a substitute for warranted macroeconomic adjustment; Batini and Durand (2020) argue that this concern seems largely unfunded. The results from this exercise further support the view that CFMs and other policy levers complement each other in dwarfing the sensitivity to the GFCy.