Here we present empirical evidence supporting our expectation that the link between CFV and outflow controls is contingent on peer markets’ capital account policy choices.
Data, models, and methods
The outcome of interest
We focus on a government’s decision to tighten restrictions on capital outflows as our outcome of interest. In our analyses, we rely on new measures of capital account policy introduced by Fernández, Klein, Rebucci, Schindler, and Uribe (FKRSU, 2016). Similar to the widely used KAOPEN measure (Chinn and Ito, 2006), the measures reflect de jure capital account policy based on the IMF’s Annual Report on Exchange Arrangements and Exchange Restrictions. However, the new measures improve on KAOPEN in two key ways. First, they more accurately reflect annual changes in capital account policies.Footnote 11 Second, they distinguish between controls on inflows and outflows, which allows for a disaggregated analysis essential for testing our argument. We rely on the KAO index from the dataset which accounts for the level of outflow controls. For our placebo tests, we use the KAI index which measures the level of inflow controls. The measures range from zero (open) to one (closed) and are available from 1995 through 2015.Footnote 12
Key predictors
Capital flow volatility (CFV) is the first of our key predictors in the analysis. To account for country-level CFV, we use quarterly ARIMA estimates of the standard deviation of net portfolio inflows provided by Pagliari and Hannan (2017). This measure improves upon existing measures of volatility in two main ways. First, by using finer-grained quarterly data, it provides a more accurate picture of volatility compared to existing measures that rely on annual data (Pagliari & Hannan, 2017, 12–13). Second, recent analyses have shown that ARIMA estimates are superior to estimates from alternative approaches such as those based on a rolling-window (Broto et al., 2011; Pagliari & Hannan, 2017). Specifically, measures under a rolling-window approach lose observations at the beginning of the sample, depending on the size of the window. Additionally, such measures can generate problems of endogeneity, serial correlation, and overly smoothed estimates. Volatility estimates based on an ARIMA model have been shown to mitigate these problems. The ARIMA measure is available from the first quarter of 1970 through the first quarter of 2016 for 37 emerging markets and developing economies. To approximate annual volatility, we calculate the average ARIMA estimate across quarters within the same year. The ARIMA measure is highly skewed to the right, and thus we log-transform the measure in our analysis to reduce the influence of extreme values. Figure 1 shows that the average CFV among the twenty-five emerging markets in our sample has generally increased over time, with particular spikes consistent with economic crises.Footnote 13
To account for peer markets’ capital account policies, we construct three additional key predictors. We follow Brooks et al. (2015) in identifying the proper classifications for capital market peers. First, since investors often categorize countries into geographic groupings, we construct a Geographic Peers time-lagged spatial lag. Specifically, we calculate the weighted average of capital account restrictions in the previous year among members of a country’s geographic reference group, with greater weights assigned to countries closer in their geographic distance. To measure geographic proximity, we rely on CEPII (2011)’s GeoDist dataset for dyadic distance data and calculate the inverse-square distance.Footnote 14 Since geography is an immutable national characteristic, it is reasonable to assume that governments are aware of which countries qualify as their geographic peers.
Next, to account for a country’s equity market peers, we create an MSCI Peers time-lagged spatial lag using the Morgan Stanley Capital International (MSCI) market classification index. This index accounts for the development and performance of countries’ equity markets. MSCI divides countries into three categories: “Developed Markets,” “Emerging Markets,” and “Frontier Markets.” MSCI also updates the countries included in each category annually and publishes this for public consumption.Footnote 15 These decisions have significant implications for an economy’s ability to attract foreign capital investment (Brooks et al., 2015). Thus, governments are highly likely to be aware of where MSCI places them and other countries on this list. To create this spatial lag, we calculate for each country and year the mean capital account restriction in the previous year of other countries that belong in the same MSCI category. Note that the list of countries in our sample includes those classified by MSCI as “Emerging Markets” and “Frontier Markets” but excludes countries classified as “Developed Markets.” Thus, MSCI Peers varies across observations depending on the relevant reference category.Footnote 16
Lastly, to account for a country’s bond market peers, we construct a Ratings Peers time-lagged spatial lag based on sovereign credit ratings, specifically Fitch’s Risk Rating index. Like other major credit rating agencies, Fitch’s ratings (e.g., AAA, AA+, etc.) reflect the agency’s assessment of the probability that a government will default on its debts. Fitch determines the probability of default relying on a range of economic, political, and social factors in each rated country. Fitch’s ratings are regularly updated and, like MSCI, the information is publicly available.Footnote 17 Once again, it is safe to assume that governments are not only aware of their own sovereign rating but also know which countries are rated similarly. Following Brooks et al. (2015), we create this spatial lag by computing for each country and year the weighted average of capital account restrictions in the previous year among other countries in the same Fitch rating group.Footnote 18
Figure A.1 in the Online Appendix shows, on average, positive but small within-country correlations between the spatial lags. Such correlations suggest that our measures are capturing a similar latent concept. However, they also indicate that variation still exists across the measures depending on how we define market peers. Results that are consistent across the measures should thus increase our confidence in our findings. To guard against excessive model dependency and the potential lack of common support in the data, we bin each spatial lag by terciles (Hainmueller et al., 2019). Specifically, we set “high” levels of capital account restrictions among peers as the omitted baseline category and create two separate dummy variables that indicate “low” or “medium” levels.Footnote 19 We include the interaction between CFV and these dummy variables in our empirical models to estimate the heterogeneous effect of volatility conditional on peers’ capital restrictions.
While our study focuses on the three peer categories explicitly identified in the literature (Brooks et al., 2015), we also explore partisanship (right, center, left-government) as an additional peer category.Footnote 20 That is, investors may compare the capital account policies of countries with the same government partisanship leaning. This implies that the level of partisanship peers’ outflow restrictions may matter too. For example, while right-leaning governments tend to maintain lower levels of capital controls (Kastner and Rector, 2003; Brooks & Kurtz, 2007), it may be easier for them to implement capital controls when other right governments are doing the same. It is less clear, however, whether investors use government partisanship as a group-level heuristic to guide investment decisions in the same way they use the three common categories discussed above. Partisanship may be a weaker heuristic as information on parties and party platforms may be more limited in an emerging country context. To test the potential implications of partisanship peers, we construct a partisanship spatial lag by computing the weighted average of capital outflow restrictions in the previous year among members of an emerging markets partisanship reference group (right, center, left-government) using the “EXECRLC” variable in the Database of Political Institutions (Cruz et al., 2017). Overall, we do not find conclusive evidence in support of investors relying on this peer categorization, and we present the results and further discussions in Table B.5 in the Online Appendix.
Control covariates
In addition to the key predictors, our models include a standard set of economic and political covariates that can affect CFV and also influence capital outflow controls. For economic covariates, we control for the size of the economy (the natural log of Gross Domestic Product, GDP) because larger emerging economies may attract more substantial capital flows, and such flows may also be more prone to sudden stops and reversals. Since developed economies are less likely to employ capital controls, we also account for the level of economic development (log of GDP per capita). Governments with liberal trade policies may also be less likely to restrict cross-border capital flows, so we control for trade openness (natural log of total trade as a % of GDP). Because capital flows are highly sensitive to interest rate differentials and monetary policy represents an alternative policy option to address cross-border capital flows, we include Real Interest Rate in our models (Ahmed & Zlate, 2014). We also account for the annual rate of Inflation in consumer prices. We rely on the World Bank’s World Development Indicators database for data on the variables above. Lastly, because capital mobility complicates the maintenance of a fixed exchange rate, governments using an Exchange Rate Peg are more likely to employ capital controls. We thus account for countries that peg their currency using updated data from Shambaugh (2004).
For political covariates, we include a measure of Democracy based on “Polity” scores (Marshall et al., 2013) since existing studies have shown that regime type influences economic openness (Milner and Mukherjee, 2009). To capture institutional constraints on capital account policy-making, we follow the literature (Kastner & Rector, 2003; Mukherjee & Singer, 2010; Brooks & Kurtz, 2007) and control for Veto Players, which we measure using the “Checks” variable from the Database of Political Institutions (Cruz et al., 2017) as a proxy.
In a battery of robustness tests, we also control for Economic Crises and Partisanship. First, crises in a peer market may increase both volatility and outflow restrictions among countries in the same peer group. As a result, economic challenges that originated from peer countries might confound our findings. To account for this, we measured economic crises in three ways: whether a peer, in a given year, (1) experienced a speculative attack on its currency (Leblang, 2003), (2) participated in an IMF lending program (McDowell, 2017), or (3) experienced a banking crisis (Laeven & Valencia, 2018). We then created three time-lagged spatial lags of each measure based on the corresponding peer group (geographic, MSCI, or ratings) and included them as additional controls in each respective model. Furthermore, we also include controls for economic crises in each emerging market country of focus. The inclusion of all these controls does not affect our main results.Footnote 21
Second, extant research suggests that right-leaning governments are less likely to maintain high levels of capital controls (Quinn & Inclán, 1997; Kastner & Rector, 2003; 2005; Brooks & Kurtz, 2007; Quinn & Toyoda, 2007; Mukherjee & Singer, 2010). Following this literature, we fit models that include a dummy variable that controls for the partisanship of the executive branch in emerging markets (Right/Center vs. left-government), which is drawn from the “EXECRLC” variable in DPI. Our focus on the partisanship of the executive branch instead of the legislature is preferable because capital account policy-making is mostly driven by executives (Brooks & Kurtz, 2007, 710–711). It is important to note that when controlling for partisanship in emerging markets, a considerable number of substantively informative emerging markets (e.g., Indonesia and Malaysia) and emerging market country-years (e.g., Thailand, 2002-2015) drop from our sample due to missing data in DPI.Footnote 22 Nevertheless, the inclusion of this partisanship control variable does not affect our main results even in the smaller sample.Footnote 23
The universe of analysis
Overall, we focus on twenty-five emerging markets that data exist for both capital outflow restrictions and CFV from 1995 through 2015. It is important to note that our empirical analysis starts in 1996 instead of 1995 as the time-lagged spatial lags require data on capital account restrictions in the previous year. In Online Appendix A.1, we provide a list of the emerging markets included in the analysis.
Models and methods
To test our argument, we fit a set of linear mixed-effects models:
$$ \begin{array}{@{}rcl@{}} & y_{it} \ = \ \alpha_{i} + \lambda_{t} + \beta x_{it-1} + \gamma x_{it-1} \psi_{it}^{low} + \kappa x_{it} \psi_{it}^{med} + \boldsymbol{Z}_{it} + \epsilon_{it} \\ & \alpha_{i} \sim \text{N}(\mu_{\alpha}, \sigma_{\alpha}^{2}), \quad \lambda_{t} \sim \text{N}(\mu_{\lambda}, {\sigma_{t}^{2}}), \quad \epsilon_{it} \sim \text{N}(0, {\sigma_{y}^{2}}) \end{array} $$
(1)
where yit represents the outcome of interest capital outflow restrictions (KAO). The variable xit− 1 indicates the log-transformed ARIMA measure of capital flow volatility while ψit represents time-lagged spatial lags (e.g., Geographic Peers). The key quantity of interest is β which represents the baseline effect of volatility when peers’ capital account restrictions are high (i.e., when both \(\psi _{it}^{low} = 0\) and \(\psi _{it}^{med} = 0\)). The variables Zit− 1 include potential confounding covariates discussed above. We lag CFV and control variables by one year to account for the possibility of reverse causality. The variables αi and λt denote varying intercepts for countries and years, respectively. Note that, under a mixed-effects model, one assumes that these intercepts follow some distribution with their mean (μα and μλ) and standard deviation (\(\sigma _{\alpha }^{2}\) and \({\sigma _{t}^{2}}\)) estimated from the data. This approach enables the estimation of β with smaller variance by partially pooling information across units or time (Gelman and Hill, 2007). In contrast, fixed effects models assume independent intercepts with \(\sigma _{\alpha }^{2} \rightarrow \infty \) and \( {\sigma _{t}^{2}} \rightarrow \infty \) which disregards such group-level information and yields estimates with higher variance. Overall, we show in Appendix B that results from fixed-effects models with clustered standard errors (Table B.2) are substantively similar to that of our main mixed-effects model (Table B.1).
We fit four different models based on the model specification above. In our baseline model, we omit the interaction terms. In other words, we examine the relationship between volatility and capital outflow restrictions regardless of the level of restrictions among peers. In the next three models, we augment the baseline model with the interaction terms for Geographic Peers, MSCI Peers, and Ratings Peers, respectively. These models allow us to separate the effect of volatility at different levels of restrictions among peers.
Main results
Consistent with our argument, results from the baseline model show a positive correlation between CFV and capital outflow restrictions in emerging markets. Specifically, column (5) of Table B.1 in the Online Appendix indicates that a one-unit increase in log volatility increases capital outflow restrictions in emerging markets by around 0.03 units. These results are consistent with studies that find that increasing CFV to be associated with outflow controls (Aizenman & Pasricha, 2013).Footnote 24 However, results from our interaction models show that the positive correlation between CFV and capital outflow restrictions stems mainly from periods when peers’ capital outflow restrictions are high. Figure 2 summarizes the estimated results when conditioning on different levels of outflow restrictions among Geographic Peers (left), MSCI Peers (middle), and Ratings Peers (right), respectively.Footnote 25 The results in the left panel show that the relationship between CFV and outflow restrictions is mixed and statistically indistinguishable from zero when outflow restrictions among geographic peers are at low or medium levels. Yet when outflow restrictions among geographic peers are high, a one-unit increase in log volatility increases capital outflow restrictions by around 0.06, which is approximately double the size of the effect in the baseline model. Similarly, the results in the middle and right panel both show null effects when outflow restrictions among MSCI or Ratings peers are at low or medium levels. Conversely, when outflow restrictions among MSCI or Ratings peers are high, a one-unit increase in log volatility increases capital outflow restrictions by around 0.04 or 0.07 units, respectively.
To help gauge the substantive magnitude of these effects, Online Appendix C shows how predicted levels of outflow restrictions change as volatility increases under the scenario of high outflow restrictions among market peers. Overall, the results suggest that increasing volatility from one standard deviation below the mean to one standard deviation above increases outflow restrictions by around 0.07 to 0.12 units. How big is this effect in substantive terms? For a typical emerging market with a mean outflow restriction of 0.53 (e.g., Chile), it represents a 13 to 23% increase in restrictions. Given that one standard deviation in outflow restrictions in our sample is 0.39, it also represents an effect size of 0.18 to 0.3 standard deviations of the outcome. In comparison, the effect of increasing an emerging market’s GDP per capita—traditionally one of the strongest predictors of capital controls—from one standard deviation below the mean to above reduces outflow restrictions by around 0.61 units. Hence, when peer outflow restrictions are high, the effect of volatility is approximately 11 to 20% of the effect of GDP per capita.
Lastly, we find even stronger results when fitting the above models to an outcome measure that excludes outflow restrictions on long-term capital investments less relevant to our argument. Our argument about outflow controls relates primarily to short-term portfolio investments rather than longer-term capital flows like foreign direct investment (FDI). Additionally, research indicates that peer country categories, while important for portfolio capital flows, may not matter as much for long-term investment categories (Linsi and Schaffner, 2019). Consequently, we excluded from the construction of the KAO index any restrictions on direct investment outflows (DIO) and real estate outflows (REO). We then used this measure of short-term capital outflow restrictions (KAO short) as the outcome in the models. We find that the effect of volatility when peers’ restrictions are high is statistically significant and larger in magnitude (see the first row of Table B.3 in the Online Appendix). Meanwhile, the effects of volatility were, again, null when peers’ restrictions were at low or medium levels.
Together, these results support our expectation that as CFV increases, emerging market governments are more likely to tighten restrictions on capital outflows, but only when market peers have already employed heterodox measures. Conversely, capital account policy appears constrained when market peers maintain liberal, orthodox policies. In this environment, governments refrain from using outflow controls when facing CFV. The results are consistent with our argument that reputational considerations play a meaningful role in dictating whether emerging markets impose restrictions on capital outflows in response to destabilizing cross-border capital flows.
Robustness tests
To further probe the robustness of our findings, we first conduct two sets of placebo tests and then investigate empirical support for alternative explanations.
Placebo moderating variables
One of our central claims is that capital outflow policies of peer markets are the key reference point for governments weighing the reputational costs of using outflow controls. Relative to non-peer markets, investors are more likely to compare a government’s reputation for openness against peer economies when deciding where to allocate assets. If we are correct, then non-peer reference points should have little moderating effects on the relationship between CFV and outflow controls.
To test this, we employ two alternative reference points. First, we consider the average global level of outflow controls. Our findings based on a peer market variable may be merely picking up on broader global patterns in the use of outflow controls. In this case, the moderating effect of peer policies would be epiphenomenal to general international trends. Second, we consider the use of outflow controls among the world’s fastest-growing economies since existing analyses show that the use of controls among this group can influence foreign capital account policy choices (Simmons & Elkins, 2004). Movement in either of these non-peer reference points may have reputational effects on the use of outflow controls. For example, when outflow controls are more common globally or among the world’s best performing economies, emerging markets may adjust their expectations about reputational costs and become more inclined to use outflow controls in the face of CFV. However, if peer market policy is the more relevant benchmark for generating reputational costs, and investors tend to draw comparisons within such narrower categories (Brooks et al., 2015), then the potential reputational effects of these non-peer reference points should be less pronounced. In particular, the reputational costs of emerging markets deviating from the global average or the use among successful economies should be less severe compared to deviating from the use among their direct peers. Meanwhile, the use of outflow controls among direct peers should provide stronger protection against reputational damage than the use among non-peer groups. As a result, observing a moderating effect by these non-peer reference points should be less likely. To conduct the tests, we fit the same set of models in equation (1) but use the mean global level of KAO in the previous year and the mean level of KAO in the previous year among the fastest-growing economies (top decile) as placebo moderators in place of our peer market spatial lags.Footnote 26
We find no evidence that the capital account policies of non-peers moderate the link between CFV and restrictions on outflows in ways consistent with our reputational argument. Figure 3 shows that the association between CFV and outflow controls is relatively small and statistically indistinguishable from zero when the global use of controls is high. Similarly, the association between CFV and KAO is also small and imprecisely estimated even when fast-growing countries have high levels of outflow controls. These findings contrast with our main results. They suggest that our findings based on peer market policies are not simply picking up on the use of outflow controls more generally or among fast-growing economies. Rather, there is something uniquely important about the moderating effect of peer market policies on how governments calculate the reputational costs of outflow controls in different contexts.
Placebo outcome variable
Like outflow controls, restrictions on capital inflow can temporarily diminish an economy’s attractiveness as they reduce expected returns from onshore investment. Yet, unlike outflow controls, measures regulating inflows do not generate reputational costs. Because they are imposed before investment, rather than ex post, inflow restrictions do not violate implicit or explicit contracts with investors the way outflow restrictions do (Ghosh et al., 2020). Studies on investor attitudes toward capital controls echo this point. While the use of outflow controls was consistently met with opprobrium in the 1990s and 2000s, inflow controls were viewed with greater nuance, understood as an occasionally useful policy tool (Chwieroth 2010; Forbes 2007, 172). Moreover, since the 2008 financial crisis, inflow controls have been further legitimized as valuable, if temporary, measures for countries facing destabilizing cross-border capital flows (Chwieroth, 2014). As a result, employing inflow controls should not send a “heterodox” signal that harms a government’s market reputation. If our argument is correct, then peer market policy contexts should not affect government decisions to impose inflow controls the way they do for outflow controls since the reputational effects are present only in the latter case. To assess this, we again fit the same set of models in equation (1), this time substituting KAI—a measure of inflow controls—as a placebo outcome for KAO.
We find no evidence that peer market policies influence emerging market government decisions to employ capital inflow restrictions in the face of CFV. In contrast to our main findings, Figure 4 shows that, even when the level of peers’ inflow restrictions are high, the point estimates associated with the effect of CFV on inflow controls are generally small and statistically indistinguishable from zero. Furthermore, these findings persist across all three types of market peers. These differences between the placebo outcome results and our main analysis are notable given that government inflow and outflow restrictions tend to be highly correlated with one another (Pond, 2018). In addition, the results suggest that our findings on outflow restrictions are unlikely due to chance alone or an artifact of how CFV affects capital restrictions in general. Instead, the placebo tests show that the effects of CFV are endemic to when peers’ outflow restrictions are high, which is uniquely linked to concerns about market reputation.
Alternative mechanisms
One alternative explanation for our main results is that they may simply be driven by short-term competition for capital instead of longer-term reputational concerns. That is, governments refrain from using outflow controls when peers are open not because they worry about sending heterodox signals to investors that can damage their market reputation but because they do not want to lose out on investments while the policies are in place. In other words, a simple competition-based mechanism may be able to explain our results without invoking reputation.
To assess empirical support for the different explanations, we explore whether the effect of CFV, when peers are open, varies by the amount of liquidity in global capital markets. When interest rates on safe assets are low, global capital tends to be plentiful, and research has shown that investors are less critical of domestic policy environments during such periods (Ballard-Rosa et al., 2021). The logic is straightforward: When the return on safe assets is negligible, investors become less discerning and more risk-tolerant. Thus, short-term competitive pressures will be diminished in times of abundant global capital as investors are willing to overlook otherwise distasteful policy measures, like outflow controls.
A pure competition explanation void of long-term reputational concerns would predict that during periods of plentiful liquidity, government policy space is freed up as investors become more risk-tolerant. As competitive pressures decline, peer market policies should become less of a constraint on governments’ choices. Thus, we should observe a consistent positive effect of CFV on outflow controls—even when peer markets are open. Alternatively, if our reputational mechanism is correct, in times of abundant global capital, liberal peers should still constrain governments facing CFV from using outflow controls. Because a tarnished market reputation is sticky, using outflow controls presents lasting effects on investor perceptions of an economy’s attractiveness as an investment destination. While outflow controls may do little to reduce investment in the current period of abundant capital, governments should still worry that a heterodox reputation will have deleterious effects on investment flows as global liquidity dries up in future periods.
We test these different predictions by fitting a set of models similar to those in equation (1) but further interacting CFV and peer policies with a measure of global capital liquidity. Following Ballard-Rosa et al. (2021) and Oatley et al. (2013), we adopt U.S. Interest Rates as a proxy for global capital abundance.Footnote 27 Again, we bin the variable into “low” versus “high” levels to guard against excessive model dependency and the potential lack of common support (Hainmueller et al., 2019). Low levels indicate minimal returns on safe assets, suggesting abundant global capital, while high levels indicate higher returns corresponding to periods of scarce global capital.Footnote 28
Overall, we find little empirical support for a pure competition explanation void of reputational concerns. In particular, when global capital is abundant and peers’ markets are open, the point estimates associated with the effect of CFV on outflow controls are generally small and statistically indistinguishable from zero across models.Footnote 29 This indicates that even when global capital markets are flush, peer market policies still have a moderating effect on the link between CFV and outflow controls, constraining government policy choices. Furthermore, holding constant peer openness, we find no statistically distinguishable difference in the effect of CFV when global capital is abundant or scarce.Footnote 30 Given (1) null effects of CFV even when global capital is abundant, and (2) the absence of a notable difference in the effect of CFV between global liquidity contexts, the results provide further empirical evidence that our main findings are more consistent with an explanation based on lasting reputational concerns than short-term competitive pressures alone.Footnote 31