The Aggregate and Distributional Effects of Financial Globalization: Evidence from Macro and Sectoral Data1

Davide Furceri; Prakash Loungani; Jonathan David Ostry

doi:10.5089/9781484350898.001.A001

The Aggregate and Distributional Effects of Financial Globalization: Evidence from Macro and Sectoral Data¹

Author:

Davide Furceri

Davide Furceri https://isni.org/isni/0000000404811396 International Monetary Fund

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Mr. Prakash Loungani

Mr. Prakash Loungani
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Mr. Jonathan David Ostry

Mr. Jonathan David Ostry
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Contributor Notes

Author’s E-Mail Address: DFurceri@imf.org, PLoungani@imf.org, JOstry@imf.org

Publication Date:: 06 Apr 2018

eISBN:: 9781484350898

Language:: English

Keywords:: WP; confidence interval; liberalization episode

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We take a fresh look at the aggregate and distributional effects of policies to liberalize international capital flows—financial globalization. Both country- and industry-level results suggest that such policies have led on average to limited output gains while contributing to significant increases in inequality—that is, they pose an equity–efficiency trade-off. Behind this average lies considerable heterogeneity in effects depending on country characteristics. Liberalization increases output in countries with high financial depth and those that avoid financial crises, while distributional effects are more pronounced in countries with low financial depth and inclusion and where liberalization is followed by a crisis. Difference-indifference estimates using sectoral data suggest that liberalization episodes reduce the share of labor income, particularly for industries with higher external financial dependence, those with a higher natural propensity to use layoffs to adjust to idiosyncratic shocks, and those with a higher elasticity of substitution between capital and labor. The sectoral results underpin a causal interpretation of the findings using macro data.

Abstract

I. Introduction

This paper takes a fresh look at the aggregate and distributional effects of policies to liberalize international capital flows—financial globalization.¹ The motivation is twofold. First, the efficiency (or output) benefits claimed for capital account liberalization reforms have often proven elusive, that is, difficult to identify in empirical studies. Second, while the fact that trade generates winners and losers is well recognized, the distributional impacts of financial globalization have received less scrutiny. Identifying these distributional effects is all the more critical if the aggregate benefits are weak or mixed.

As Obstfeld (1998) notes, “economic theory leaves no doubt about the potential advantages” of capital account liberalization. In the neoclassical model, liberalizing the capital account facilitates more efficient allocation of international capital. Resources flow from countries where the return of capital is low to countries where the return is high.² This flow of resources reduces the cost of capital in the recipient economies, triggering a temporary increase in investment and growth and a permanent effect on the level of output. However, several empirical studies find that the growth benefits of capital account liberalization are uncertain (Eichengreen 2001; Prasad et al. 2003; Edison et al. 2004; Kose et al. 2009). Eichengreen (2001) finds at best ambiguous evidence on the effect of capital account liberalization on growth. Edison et al. (2004) survey 10 studies of liberalization and conclude that only three of these provide evidence of positive effects of capital account liberalization. Prasad et al. (2003) extend the coverage to 14 studies and find that in only three of these is financial integration positively associated with economic growth. Kose et al. (2009) further extend the coverage to 26 studies and find that in only three is there robust evidence of positive effects.

Obstfeld also points to the “genuine hazards” of openness to foreign financial flows and concludes that “this duality of benefits and risks is inescapable in the real world.” This indeed turns out to be the case. Since 1980 about 150 episodes of surges in capital inflows have taken place in more than 50 emerging market economies; about 20% of these episodes ended in a financial crisis, and many of them are associated with large output declines (Ghosh, Ostry, and Qureshi 2016). The uncertain aggregate benefits related to capital account liberalization and the pervasiveness of booms and busts have led some observers (e.g., Rodrik and Subramanian 2009) to conclude that the benefits of an open capital account are unclear.

As discussed by Henry (2007), the previous studies examining the macroeconomic effects of capital account liberalization suffer from two main limitations. First, many studies look for permanent growth effects rather than examining the evolution of output in the aftermath of a discrete change in the capital account policy. Second, identifying the causal effect of capital account liberalization is empirically challenging using aggregate macro data, particularly since liberalization episodes often take place alongside other reforms.

This paper takes a fresh look at the macro (output) and distributional (using the Gini coefficient and the labor share of income as proxies) impacts of capital account liberalization to address a number of limitations inherent in previous empirical work. In particular, our contributions are: (i) to identify large and discrete changes in capital account policy that may give rise to aggregate and distributional effects; (ii) to trace the response of the level of output and inequality in the aftermath of these changes; (iii) to identify some of the factors that may shape the macro and distributional impacts of capital account liberalization; and (iv) to provide causal evidence on the effect of capital account liberalization using an IV approach and industry-level data.

Specifically, we apply a difference-in-difference identification strategy à la Rajan and Zingales (1998)—using three alternative identification strategies from theory—to examine the effect of liberalization episodes on industry output and labor share. First, following Gupta and Yuan (1999), among others, we exploit the cross-sectoral heterogeneity in external financial dependence. According to theory, the output and distributional effects of capital account liberalization should be larger in industries with higher external financial dependence. Second, insofar as capital account liberalization reduces the bargaining power of workers (by presenting a credible threat to relocate production abroad), we should expect liberalization episodes to have larger effects on the labor share in industries in which the propensity to use layoffs to adjust to economic conditions is higher. Finally, by reducing the cost of capital, capital account liberalization would lead to a reduction in the labor share in those industries in which the elasticity of substitution between capital and labor is larger than one.

The advantage of using industry-level data—that is, having a three-dimensional (j industries, i countries, and t time periods) data set—and using a difference-in-difference strategy is twofold:

First, it allows us to control for aggregate and country sector shocks by including country-time, country-industry, and industry-time fixed effects. The inclusion of the country-time fixed effect is particularly important, as it absorbs any unobserved cross-country heterogeneity in the macroeconomic shocks that affect countries’ output and income distribution. In a pure cross-country analysis, this would not be possible, leaving open the possibility that the impact attributed to capital account liberalization may be due to other unobserved macroeconomic shocks.
Second, it mitigates concerns about reverse causality. While it is typically difficult to identify the causal effects using macro panel data, it is much more likely that capital account liberalization will affect the cross-industry differences in output (or labor share) than the other way around; since we control for country-time fixed effects—and therefore for aggregate output (or labor share)—reverse causality in our set-up would imply that differences in the output (or labor share) across sectors influence the probability of reforms at the aggregate level—which seems to be implausible. Moreover, our main independent variable is the interaction between capital account liberalization reforms and industry-specific factors, and this makes it even less plausible that causality runs from the industry-level output (labor share) to this composite variable.

Our main findings based on country-level data suggest that capital account liberalization on average has had a limited effect on output but has led to an (economically and statistically) significant increase in inequality. These aggregate and distributional effects vary across countries, depending on the strength of financial institutions, and over time, depending on whether liberalization episodes are followed by financial crises. In particular, while liberalization episodes increase output in countries with high financial depth, the effects on inequality are magnified in countries with lower levels of financial depth and inclusion. Similarly, capital account liberalization episodes lead to significant output contractions and increases in inequality when followed by financial crises, while these adverse effects are greatly reduced when they are not followed by crises.

The evidence obtained using industry-level data corroborates these findings and underpins a causal interpretation of them. We find that, while the output gains associated with capital account liberalization are small and short-lived, the distributional (labor share) effects are economically and statistically significant and long-lasting. In particular, the results suggest that liberalization episodes reduce the share of labor income, particularly for industries with higher levels of external financial dependence, those with a higher natural propensity to use layoffs to adjust to idiosyncratic shocks, and those with a higher elasticity of substitution between capital and labor. ³

Our paper relates to three streams of the literature. The first is related to the macroeconomic effects of capital account liberalization (Eichengreen 2001; Prasad et al. 2003; Edison et al. 2004; Kose et al. 2009) and the way in which these effects depend on the strength of financial institutions (Ostry, Prati, and Spilimbergo 2009; Kose, Prasad, and Taylor 2011; IMF 2012). The second relates to the effect of capital account and stock market liberalization on income distribution (Das and Mohapatra 2003; Jayadev 2007; Larrain 2015; De Haan and Sturm forthcoming; Furceri and Loungani 2018). The third is on the drivers of the labor share, which mostly focused on the roles of technological progress in equipment goods and implied substitution of capital for routine labor tasks (Karabarbounis and Neiman, 2013; Alvarez-Cuadrado et al., 2015; Eden and Gaggl, 2015; Acemoglu and Restrepo, 2016; Dao et al., 2017); rising concentration and pricing power across markets (Autor et al., 2017; Barkai, 2017); globalization of trade and production (Elsby et al. 2013; Boehm et al., 2017; Dao et al., 2017; measurement issues (Rognlie, 2015; Koh et al., 2016; Bridgman, 2017); and labor market institutions (Bentolila and Saint-Paul, 2003; Ciminelli et al. 2018); but much less so on the role of capital account liberalization.

The remainder of the paper is structured as follows. Section 2 presents the data. Section 3 discusses the empirical methodology. Section 4 describes and discusses the results. Section 5 concludes and provides policy implications.

II. Data

A. Country-Level Data

1. Capital Account Liberalization

The measure of capital account liberalization used in this paper is based on a de jure indicator of capital account restrictions. While de jure measures are noisy indicators of the true degree of openness of the capital account, they have the advantage of being less sensitive to reverse causality issues in panel regressions (Collins 2007). The data for capital account openness are taken from the Chinn and Ito (2008) database. While the literature proposes alternative de jure measures of capital account openness (e.g. Quinn 1997; Quinn and Toyoda 2008), the Chinn and Ito index (Kaopen) provides the largest country and time period coverage. The index measures a country’s degree of capital account openness based on binary dummy variables that codify the restrictions on cross-border transactions that the IMF’s Annual Report on Exchange Arrangements and Exchange Restrictions (AREAER) database reports.⁴ The index is available for an unbalanced panel of 182 countries from 1970 to 2010 and ranges from −1.9 (a more restricted capital account) to 2.5 (a less restricted one) (Table A1). The capital account openness index varies greatly across income groups, with stronger restrictions typically recorded in low-income and lower-middle-income countries (Table A2).

Examining the behavior of inequality before and after the removal of restrictions on the capital account requires information about the date on which the country lifted the restrictions. This information is difficult to obtain for a large set of countries, as ideally the study would require information on the dates of policy decrees or legislative changes. To infer the timing of major policy changes, we identify capital account liberalization episodes by large changes in the Kaopen index, specifically changes that exceed by two standard deviations the average annual change over all the observations (i.e. 0.76).⁵ This criterion identifies 224 episodes, the majority of which occurred in the last two decades.

2. Output and Inequality

We obtain data on real GDP growth from the IMF’s WEO database. We use data for Gini coefficients from the Standardized World Income Inequality Database (SWIID), which combines information from the United Nations World Income Database (UNWIDER) and the Luxembourg Income Study (LIS). The SWIID provides comparable estimates of market income inequality for 173 countries for as many years as possible from 1970 to 2010.⁶ The Gini coefficients theoretically fall between 0 (each reference unit receives an equal share of the income) and 100 (a single unit receives all the income). In our sample they range from 18 to 78.

B. Industry-Level Data

We compute the industry-level labor shares using the harmonized data on value added and labor compensation contained in the 2012 release of the EUKLEMS database (for details see O’Mahony and Timmer 2009).⁷

To identify the effects of capital account reforms on sectoral output, we use data on external financial dependence. We analyze the effect of liberalization on the industry labor share through data on external financial dependence, “natural” layoff rates, and the elasticity of substitution between capital and labor. Importantly, these industry-specific factors are weakly correlated among each other (Table A4), suggesting that the channels through which we identify the effect of capital account liberalization on the labor share can be considered as independent.

1. External Financial Dependence

The measure of external financial dependence—constructed for each industry as the median across all the firms in a given industry—is the ratio of total capital expenditures minus current cash flow to total capital expenditures.⁸ Figure A1 presents the industry-specific measures of external financial dependence.

2. Natural Layoff Rate

As a proxy for job destruction rates, we use layoff rates computed by Bassanini, Nunziata and Venn (2009) using US layoff rates data from the 2004 CPS Displaced Workers Supplement. US layoff rate data are available for 22 industries categorized according to the ISIC Rev. 3 classification. The latest vintage of the EU KLEMS database instead follows the ISIC Rev. 4 classification. Hence, we match the US layoff rates of Bassanini, Nunziata, and Venn (2009) from the ISIC Rev. 3 to the ISIC Rev. 4 classification using the many-to-one method that O’Mahony and Timmer (2009) use to backcast value-added data. Additionally, since post and telecommunications were categorized as a single industry under the ISIC Rev. 3 classification, whereas they account for two industries under the ISIC Rev. 4 classification, we impose the same lay-off rate for the postal and courier industry and the telecommunications industry. After matching, we have layoff data for 21 of the 31 industries in our sample. Figure A2 shows the US layoff rates by industry.

3. Elasticity of Substitution between Capital and Labor

To estimate the industry-specific elasticities of substitution, we assume that production in each industry follows a general multiplicative production function featuring capital, labor, and labor- and capital-augmenting technical progress. In this setting it can be shown that the labor share responds to changes in the capital/output ratio in a way that depends on the elasticity of substitution between labor and capital (Bentolila and Saint-Paul 2003):

\begin{matrix} \frac{d s_{i}}{d k_{i}} = - \frac{(1 + σ_{K L, i})}{(k_{i} μ_{i})} & (1) \end{matrix}

where the subscript i denotes industry, s is the share of value added accruing to labor, σ_KL is the elasticity of substitution between labor and capital, holding the price of inputs constant, k is the capital to value added ratio, and μ is the elasticity of the labor demand with respect to wages, holding capital and the real price of inputs constant. Our approach to derive industry-specific elasticities of substitution consists of estimating the $\frac{d s_{i}}{d k_{i}}$ relationship while using external estimates for the elasticity of the labor demand.

We define the labor share according to the following multiplicative function (for details, see Bentolila and Saint-Paul 2003):

\begin{matrix} s_{i j t} = A_{i j t}^{β_{o}} {(k_{i j t})}^{β_{1 i}} {((q_{i j t} / p_{i j t}))}^{β_{2 i}} υ_{i j t}^{β_{3}} & (2) \end{matrix}

where subscripts i, j, and t refer, respectively, to industry, country, and time; s is the share of value added accruing to labor; A is capital-augmenting technical change; q/p is the real price of inputs; k is the capital to value added ratio; ν is a residual term (so that β₃ is 1); and the β_0–2 are technological parameters. Taking the logs, Equation (2) becomes:

\begin{matrix} In s_{i j t} = β_{0} In A_{i j t} + β_{1 i} d_{i} In k_{i j t} + β_{2 i} d_{i} In (q_{i j t} / p_{i j t}) + υ_{i j t} & (3) \end{matrix}

where we allow the β s to vary across industries.

In estimating Equation (3), we treat the right-hand side as potentially endogenous. We characterize this potential endogeneity by assuming the following specification for the error term:

\begin{matrix} υ_{i j t} = γ_{i t} + δ_{j t} + θ_{i t} + ɛ_{i j t} & (4) \end{matrix}

where γ, δ, and θ are, respectively, industry–time, country–time, and country–industry effects that are potentially correlated with the explanatory variables (for instance, they may relate to the economic cycle and time-invariant institutional factors). Further, ε is an industry–country–time shock that we assume to be uncorrelated with the regressors.

Hence, the equation that we estimate becomes:

\begin{matrix} In s_{i j t} = β_{0} \overset{̌}{A_{i j t}} + β_{1 i} d_{i} k_{i j t} + β_{2 i} d_{i} (q_{i j t} / p_{i j t}) + γ_{i t} + δ_{j t} + θ_{i t} + ɛ_{i j t} & (5) \end{matrix}

where the accent ̌ denotes our proxy for capital-augmenting technical change, that is, TFP.

We compute (i) the labor share using labor compensation and value-added data from the EUKLEMS database; (ii) the capital–output ratio using gross capital stock and value added data (both in volumes) from the OECD STAN database; and (iii) the real price of inputs using data for the price deflators of intermediate inputs and gross output, again from the OECD STAN database. We obtain the data for TFP from the EUKLEMS database.

After estimating Equation (5), we derive elasticities of substitution by rearranging Equation (1) in the following form:

\begin{matrix} - σ_{K L, i} = 1 + \frac{d s_{i}}{d k_{i}} k_{i} μ_{i} & (6) \end{matrix}

To proceed, however, we need estimates of the elasticity of the labor demand with respect to wages, holding capital and the price of inputs constant. Following Bentolila and Saint-Paul (2003), we make the further assumption that this does not vary across industries and rely on the average estimate from the 70 studies that Hamermesh (1993) reviews, that is, −0.39. Figure A3 presents industry-specific estimates of the elasticities of substitution between capital and labor.

III. Empirical Methodology

This section describes the empirical methodologies used to examine the aggregate and distributional effects of capital account liberalization at the country and the industry level.

A. Country-Level Approach

1. Baseline

To assess the impact of capital account liberalization, we estimate the following specification:

\begin{matrix} g_{i t} = a_{i} + γ_{t} + Σ_{j = 0}^{l} δ_{k} D_{i, t - k} + Σ_{k = 0}^{l} υ_{k} X_{i, t - k} + ɛ_{i t} & (7) \end{matrix}

where g is the annual change in the log of output (or Gini)⁹; D is a dummy variable that is equal to one at the start of a capital account liberalization episode and zero otherwise; a_i are country fixed effects included to control for unobserved cross-country heterogeneity¹⁰; γ_t are time fixed effects to control for global shocks; and X is a vector of control variables including reforms in trade, the current account, the product market and the labor market (employment protection legislation).¹¹

As a baseline, we estimate Equation (7) using OLS on an unbalanced panel of annual observations from 1970 to 2010 for 149 advanced and developing economies.¹² The number of lags that we choose to capture the medium effect of reforms is 5. In addition to the regression results, we present the impulse response functions (IRFs) to describe the response of output (inequality) following a capital account liberalization episode. We obtain the confidence bands associated with the estimated impulse response functions using the standard errors of the estimated coefficients based on clustered (at the country level) robust standard errors.

2. Accounting for Differential and Threshold Effects

It is commonly argued that the benefits of financial globalization depend on the quality of financial institutions (Ostry, Prati, and Spilimbergo 2009; Kose, Prasad, and Taylor 2011; IMF 2012). Kose, Prasad, and Taylor (2011), for example, identify certain threshold levels of financial development (proxied by the depth of the credit market) that an economy needs to attain before it can benefit from, and reduce the risks associated with, financial globalization. Capital account liberalization may facilitate consumption smoothing and reduce volatility for countries with strong financial institutions, but where institutions are weak and access to credit is not inclusive, it may result in limited output gains.

We examine this hypothesis by assessing whether the effect of capital account liberalization depends on the strength of financial institutions and whether liberalization episodes are followed by crises. Specifically, we estimate the following equation:

\begin{matrix} g_{i t} = a_{i} + γ_{t} + Σ_{j = 0}^{l} υ_{j} M_{i, t - j} + Σ_{j = 0}^{l} δ_{j}^{-} D_{i, t - j} G (z_{i t}) + Σ_{j = 0}^{l} δ_{j}^{+} D_{i, t - j} (1 - G (z_{i t})) + ɛ_{i t} & (8) \end{matrix}

with $G (z_{i t}) = \frac{\exp (- γ z_{i t})}{1 + \exp (- γ z_{i t})}, γ > 0$ ,

in which z is an indicator of financial development, normalized to have zero mean and unit variance, and G(z_it) is the corresponding smooth transition function of the degree of financial development (financial liberalization and inclusion), which takes the value 1 (0) when z goes to minus (plus) infinity. We also check whether the aggregate and distributional effects depend on whether capital account liberalization episodes are followed by crises. In this case we replace G(z) with a dummy variable. M_it is the same set of control variables used in the baseline specification but now also including G(z_it)

The use of the smooth transition function is equivalent to the smooth transition autoregressive (STAR) model developed by Granger and Teravirta (1993) to assess nonlinear effects above/below a given threshold or regime. The main advantage of this approach relative to estimating SVARs for each regime is that it uses a larger number of observations to compute the impulse response functions, improving the stability and precision of the estimates. In addition, this estimation strategy can also handle the potential correlation of the standard errors within countries more easily by clustering at the country level.

B. Industry-Level Approach

We extend the specification applied to the aggregate data using a three-way (industry–country–time) panel. The identification strategy relies on a specific channel through which capital account liberalization may affect sectoral outcomes: (i) dependence on external finance—for output and the labor share; (ii) the rate of job turnover (natural layoff rate)—for the labor share; and (iii) the elasticity of substitution between capital and labor—for the labor share. In particular, we estimate the following specification:

\begin{matrix} g_{j i t} = a_{i j} + γ_{i t} + ρ_{j t} + Σ_{k = 0}^{l} δ_{k} S_{j} D_{i, t - k} + ɛ_{j i t} & (9) \end{matrix}

where a_ij are country–industry fixed effects, which allow us to control for industry-specific factors, including for instance cross-country differences in the growth of certain sectors that could arise from differences in comparative advantages; γ_i are country–year fixed effects, included to control for any variation that is common to all the sectors of a country’s economy, including reforms as well as macroeconomic shocks; ρ_jt are industry–time fixed effects, which allow us to control for common factors affecting specific industries—such as factors that are common across countries driving sectoral reallocation; and S denotes the sector-specific channels discussed in the previous section.

The specification is estimated for an unbalanced panel of 23 advanced economies and 25 industries over the period 1975–2010.¹³

IV. Results

A. Country-Level Analysis

Figure 1 shows the estimated dynamic response of output and inequality to major capital account liberalization episodes over the five-year period following reform implementation, together with the 90% confidence interval around the point estimate. The results suggest that these episodes have not had a significant impact on output but have led to a sizeable and statistically significant increase in inequality of about 4 percentage points five years after the liberalization (see also Table 1). This effect is economically significant, as it corresponds to about one standard deviation of the average increase in the Gini coefficient in our sample.

Figure 1.

The Aggregate and Distributional Effects of Capital Account Liberalization

(%)

Citation: IMF Working Papers 2018, 083; 10.5089/9781484350898.001.A001

Note: The solid lines indicate the response of output (inequality) to a capital account liberalization episode; the dotted lines correspond to 90% confidence bands. The x-axis denotes time. t=0 is the year of the reform. The estimates are based on equation (7).

Table 1.

The Aggregate and Distributional Effects of Capital Account Liberalization

Note: T-statistics based on robust clustered standard errors at the country level are in parentheses. ***, **, and * denote significance at 1%, 5%, and 10%, respectively. We identify capital account reforms as episodes when, for a given country at a given time, the annual change in the Kaopen indicator exceeds by two standard deviations the average annual change over all the observations. The medium-term differential effect denotes the effect of capital account liberalization episodes five years after the reform. The estimates are based on equation (7).

Table 1.

The Aggregate and Distributional Effects of Capital Account Liberalization

	(I)	(II)
	Output	Gini
Capital account reform (t)	−0.223	1 452***
	(−0.62)	(4.45)
Capital account reform (t-1)	0.434	0.944**
	(1.28)	(2.28)
Capital account reform (t-2)	−0.149	0.699*
	(−0.30)	(1.79)
Capital account reform (t-3)	0.312	−0.020
	(0.75)	(−0.05)
Capital account reform (t-4)	0.603	0.414
	(1.51)	(1.01)
Capital account reform (t-5)	0.371	0.529
	(0.988)	(1.13)
Medium-term effect	0.665	4.018***
F-test medium-term effect	0.95	9.78
N	2,001	1,789
R2	0.38	0.13

Table 1.

The Aggregate and Distributional Effects of Capital Account Liberalization

	(I)	(II)
	Output	Gini
Capital account reform (t)	−0.223	1 452***
	(−0.62)	(4.45)
Capital account reform (t-1)	0.434	0.944**
	(1.28)	(2.28)
Capital account reform (t-2)	−0.149	0.699*
	(−0.30)	(1.79)
Capital account reform (t-3)	0.312	−0.020
	(0.75)	(−0.05)
Capital account reform (t-4)	0.603	0.414
	(1.51)	(1.01)
Capital account reform (t-5)	0.371	0.529
	(0.988)	(1.13)
Medium-term effect	0.665	4.018***
F-test medium-term effect	0.95	9.78
N	2,001	1,789
R2	0.38	0.13

1. Addressing Endogeneity

A potential limitation of our approach is that capital account liberalization episodes are not “pure” shocks and therefore could be correlated with unobserved factors (omitted bias). While including reforms in other areas mitigates this issue, it could still be the case that unobserved factors influence the probability of financial liberalization and our outcomes of interest. For example, governments that choose to liberalize the capital account may be more right-wing and less likely to implement redistributive policies. Similarly, capital account liberalization can be associated with more prudent fiscal policies or with the process of development (Obstfeld 1998). To check the robustness of our results, we expand our set of controls to include: (i) a discrete variable for left-, center-, and right-wing governments; (ii) changes in the share of redistributive policies—using changes in the difference between gross and net Gini coefficients as a proxy; (iii) the level and the square of the log of GDP per capita; (iv) changes in the share of government expenditures in GDP; and (v) changes in the share of industry and agriculture in value added. Figure 2 suggests that the inclusion of these additional controls does not affect the results.

Figure 2.

The Aggregate and Distributional Effects of Capital Account Liberalization—Additional Controls

(%)

Citation: IMF Working Papers 2018, 083; 10.5089/9781484350898.001.A001

Note: The solid lines indicate the response of output (inequality) to a capital account liberalization episode; the dotted lines correspond to 90% confidence bands. The solid black lines denote the baseline effect reported in Figure 1. The x-axis denotes time. t=0 is the year of the reform. The estimates are based on equation (7).

Another possible concern is that countries implement capital account liberalization reforms because of concerns regarding future weak economic growth. To address this issue, we also estimate a specification that controls for past growth as well as for the expected growth at t-1 of the future GDP growth rates (using the IMF WEO forecasts) over periods t to t+5—that is, the time horizon over which we compute the impulse response functions. The results reported in Figure 3 are very close to and not statistically different from those that we obtained for the baseline, suggesting that this issue is not empirically important.

Figure 3.

The Aggregate and Distributional Effects of Capital Account Liberalization—Controlling for Expected Future Growth

(%)

Citation: IMF Working Papers 2018, 083; 10.5089/9781484350898.001.A001

Another potential concern is that inequality (as well as GDP growth) may start to increase before the occurrence of major liberalization episodes. While inspection of the data suggests that, on average, this is not the case, we check the robustness of the results to the inclusion of (two) lags of the dependent variables.¹⁴ The results that we obtain following this approach (Figure 4) are similar to and not statistically different from the baseline.

Figure 4.

The Aggregate and Distributional Effects of Capital Account Liberalization—Controlling for Two Lags of the Dependent Variables

(%)

Citation: IMF Working Papers 2018, 083; 10.5089/9781484350898.001.A001

Finally, to address the endogeneity concerns further, we implement an instrumental variable (IV) approach using two instruments that capture the scope of reforms and the peer pressure to reform. We capture the scope of capital account liberalization reform using the initial stance of capital account regulation—with the four-year lagged value of the capital account openness indicator as a proxy (see also Larrain 2015 for an application of this instrument). The idea is that the lower is the indicator of capital account openness, the more scope there is to reform. The second instrument, peer pressure, is proxied by a weighted-average of current and lagged capital account liberalization episodes in other countries, where the weights are determined by the strength of trade linkages between other countries and the country undertaking capital account liberalization. The conjecture is that a country is more likely to implement capital account liberalization when its main trading partners are undertaking or have undertaken capital account liberalization. We use bilateral trade weights given the limited data availability to construct bilateral capital flow weights for most of the observations in the sample. For the country–time observations for which bilateral capital flows are available, the correlation between bilateral trade and capital flow linkages is high (about 0.7) and statistically significant. Specifically, the instrument is computed as follows:

\begin{matrix} I_{i, t} = Σ_{j = 1, . n (j \neq i)} D_{j, t} w_{i, j, t} & (10) \end{matrix}

where I_i,t is the instrument of capital account liberalization reform for country i at time t (D_i,t); D_j,t is the capital account liberalization reform for country j at time t; and w_i,j,t is the share of total exports and imports between country i and country j in the total exports and imports for country $i : \frac{E x p o r t_{i, j, t} + I m p o r t_{i, j, t}}{E x p o r t_{i, t} + I m p o r t_{i, t}}$ .

The first-stage estimates of capital account liberalization reforms using these instruments suggest that these are statistically significant and exhibit the expected sign.¹⁵ In addition, we can plausibly consider both instruments to be exogenous. For example, reforms in other countries do not drive the reforms in the country considered and do not have any effect on the latter other than through pressure on domestic authorities to undertake reforms.¹⁶

The results that we obtain following this approach are also similar to and not statistically different from the baseline (Figure 5).

Figure 5.

The Aggregate and Distributional Effects of Capital Account Liberalization—IV

(%)

Citation: IMF Working Papers 2018, 083; 10.5089/9781484350898.001.A001

2. Liberalization vs Flows

We have focused on de jure measures of capital account liberalization to isolate policy changes that are likely to have led to an increase in capital flows. Interesting questions are whether these policy changes are associated with a sizeable increase in capital flows and whether the output and distributional effects of liberalization depend on the magnitude of capital flows. To address these issues, we re-estimate Equation (8) by interacting our measures of liberalization episodes with the extent of the change in capital flows that occurred in the five years after liberalization—the same horizon as our IRFs. The results suggest that the size of the flows shapes the distributional effects of liberalization (Figure 6). In particular, while the output effects are not statistically significant in both cases, the impact of liberalization on inequality is much stronger and statistically significant in cases of higher flows.

Figure 6.

The Medium-Term Aggregate and Distributional Effects of Capital Account Liberalization—High vs Low Flows

(%)

Citation: IMF Working Papers 2018, 083; 10.5089/9781484350898.001.A001

Note: Medium-term effects (that is, five years after the reform) are estimated as described in equation (8). ***, **, and * denote significance at 1%, 5% and 10%, respectively. Blue (red) bars denote the medium-term response (that is, five years after the reform) of output (inequality). We define flows as the cumulative five-year change in total asset and liabilities as a percentage of GDP after the reform.

3. Threshold Effects

As previously discussed, the existing literature suggests that the output gains from financial globalization depend on the strength of financial institutions (Ostry, Prati, and Spilimbergo 2009; Kose, Prasad, and Taylor 2011; IMF 2012). The results that we present in Figure 7 and Table 2 corroborate these findings but also show that the strength of financial institutions influences the distributional effects of capital account liberalization.

Figure 7.

The Medium-Term Aggregate and Distributional Effects of Capital Account Liberalization—The Role of Institutions and Crises

(%)

Citation: IMF Working Papers 2018, 083; 10.5089/9781484350898.001.A001

Note: We estimate medium-term effects (that is, five years after the reform) as described in equation (8). ***, **, and * denote significance at 1%, 5%, and 10%, respectively.

Table 2.

The Aggregate and Distributional Effects of Capital Account Liberalization—The Role of Financial Institutions and Crises

Note: F-statistics based on robust clustered standard errors at the country level are in parentheses. ***, **, and * denote significance at 1%, 5%, and 10%, respectively. We identify capital account reforms as episodes when, for a given country at a given time, the annual change in the Kaopen indicator exceeds by two standard deviations the average annual change over all the observations. The medium-term differential effect denotes the effect of capital account liberalization episodes five years after the reform. The estimates are based on equation (8). G(Z)=1 (0) for low (high) levels of financial liberalization and financial inclusion and when reforms are (not) followed by crises. The estimates are based on equation (8).

Table 2.

The Aggregate and Distributional Effects of Capital Account Liberalization—The Role of Financial Institutions and Crises

	(I)	(II)	(III)	(IV)	(V)	(VI)
	Output			Gini
	Financial Liberalization	Financial Inclusion	Crises	Financial Liberalization	Financial Inclusion	Crises
Medium-term effect*G(Z)	−2.558	0.666	−3.790*	4.341**	3.959**	4.288**
	(1.95)	(0.09)	(3.42)	(4.47)	(4.34)	*
						(10.21)
Medium-term effect*[1- G(Z)]	3.924*	0.378	2.190	2.769	3.050	1.793
	(3.03)	(0.04)	(2.28)	(1.97)	(2.30)	(0.46)
F-test difference medium-term effect	3.73*	0.01	6.96***	0.25	0.09	0.83
N	2,001	2,001	2,001	1,789	1,789	1,789
R2	0.38	0.38	0.38	0.13	0.13	0.13

Table 2.

The Aggregate and Distributional Effects of Capital Account Liberalization—The Role of Financial Institutions and Crises

	(I)	(II)	(III)	(IV)	(V)	(VI)
	Output			Gini
	Financial Liberalization	Financial Inclusion	Crises	Financial Liberalization	Financial Inclusion	Crises
Medium-term effect*G(Z)	−2.558	0.666	−3.790*	4.341**	3.959**	4.288**
	(1.95)	(0.09)	(3.42)	(4.47)	(4.34)	*
						(10.21)
Medium-term effect*[1- G(Z)]	3.924*	0.378	2.190	2.769	3.050	1.793
	(3.03)	(0.04)	(2.28)	(1.97)	(2.30)	(0.46)
F-test difference medium-term effect	3.73*	0.01	6.96***	0.25	0.09	0.83
N	2,001	2,001	2,001	1,789	1,789	1,789
R2	0.38	0.38	0.38	0.13	0.13	0.13

The figure presents the medium-term response of output and inequality for the following cases: (i) high versus low domestic financial liberalization—based on the structural reform indicator of Ostry, Prati, and Spilimbergo (2009); (ii) high versus low financial inclusion—identified as the ratio of adults in the population who have borrowed from a formal financial institution in past years (Demirguc-Kunt et al. 2015); and (iii) episodes that financial crises have followed in the five years after liberalization—the same time horizon considered in the analysis—versus episodes that have not led to crises.

We find positive output effects in cases in which the domestic financial market is highly liberalized and negative (but not significant) effects when it remains largely restricted. This suggests that the small overall effects might reflect offsetting effects that depend on the extent of domestic financial liberalization. The output effects are also positive (but not statistically significant) in cases where liberalization is not followed by a crisis, but these are outweighed by the sharply negative output effects in cases in which a crisis occurs.

Similarly, we find that the effect of capital account linearization on inequality is magnified in countries with largely restricted domestic financial markets and limited financial inclusion and when a crisis follows liberalization.

B. Industry-Level Analysis

The industry-level analysis corroborates the evidence that we obtained using country-level data. In particular, while we find that the output gains associated with capital account liberalization are small and short-lived, the distributional effects (that is, the effects on the labor share of income) are economically and statistically significant and long-lasting.

Figure 8 and Table 3 present the differential output effect resulting from the estimation of Equation (9). They show that the short-term output effects of capital account liberalization reforms vary across sectors depending on the degree of dependence on external finance. In particular, the results suggest that the differential medium-term output gain associated with liberalizing the capital account for an industry with relatively low dependence (at the 25^th percentile of the distribution of external financial dependence—such as transport equipment) compared with an industry with relatively high external financial dependence (at the 75^th percentile of the distribution—such as construction) is about 1%. The effect is statistically significant—at the 10% confidence level—for up to 2 years after the capital account liberalization reform, but it vanishes in the medium term. The estimates of the effects on labor productivity and employment are quite imprecise, but the point estimates suggest a positive (differential) effect for productivity and a negative one for employment.

Figure 8.

The Differential Effect of Capital Account Liberalization Episodes on Sectoral Output and its Components

(%)

Citation: IMF Working Papers 2018, 083; 10.5089/9781484350898.001.A001

Table 3.

The Effect of Capital Account Liberalization on Sectoral Output and its Components

Note: T-statistics based on robust clustered standard errors at the country*sector level are in parentheses. ***, **, and * denote significance at 1%, 5%, and 10%, respectively. We identify capital account reforms as episodes when, for a given country at a given time, the annual change in the Kaopen indicator exceeds by two standard deviations the average annual change over all the observations. The medium-term differential effect denotes the effect of capital account liberalization episodes between a sector with high external financial dependence (at the 75^th percentile of the distribution) and a sector with low external financial dependence (at the 25^th percentile of the distribution). The estimates are based on equation (9).

Table 3.

The Effect of Capital Account Liberalization on Sectoral Output and its Components

	(II)	(III)	(IV)
	Output	Labor Productivity	Employment
Capital account reform_it*S_j	1.802**	5.990	−5.694
	(1.95)	(0.92)	(−0.88)
Capital account reform_it-1*S_j	0.652	1.331	−1.127
	(0.80)	(0.22)	(−0.18)
Capital account reform_it-2*S_j	−0.006	2.516	−2.020
	(−0.01)	(0.44)	(−0.35)
Capital account reform_it-3*S_j	−1.399	0.461	−0.224
	(−1.55)	(0.08)	(−0.04)
Capital account reform_it-4*S_j	−0.128	−1.004	1.029
	(−0.17)	(−0.20)	(0.20)
Capital account reform_it-5*S_j	0.886	−0.080	0.213
	(1.18)	(−0.02)	(0.05)
Medium-term differential effect	0.520	5.251	−4.641
F-test medium-term effect	0.21	0.14	0.10
N	16,616	16,616	16,616

Table 3.

The Effect of Capital Account Liberalization on Sectoral Output and its Components

	(II)	(III)	(IV)
	Output	Labor Productivity	Employment
Capital account reform_it*S_j	1.802**	5.990	−5.694
	(1.95)	(0.92)	(−0.88)
Capital account reform_it-1*S_j	0.652	1.331	−1.127
	(0.80)	(0.22)	(−0.18)
Capital account reform_it-2*S_j	−0.006	2.516	−2.020
	(−0.01)	(0.44)	(−0.35)
Capital account reform_it-3*S_j	−1.399	0.461	−0.224
	(−1.55)	(0.08)	(−0.04)
Capital account reform_it-4*S_j	−0.128	−1.004	1.029
	(−0.17)	(−0.20)	(0.20)
Capital account reform_it-5*S_j	0.886	−0.080	0.213
	(1.18)	(−0.02)	(0.05)
Medium-term differential effect	0.520	5.251	−4.641
F-test medium-term effect	0.21	0.14	0.10
N	16,616	16,616	16,616

Figure 9 and Table 4 present the differential effects of capital account liberalization on the industry-level labor share that we obtained for the three identification strategies, which rely respectively on industry heterogeneity in: (i) external financial dependence; (ii) the natural layoff rate; and (iii) the elasticity of substitution between capital and labor.

Figure 9.

The Differential Effect of Capital Account Liberalization Episodes on the Labor Share

(Percentage Points)

Citation: IMF Working Papers 2018, 083; 10.5089/9781484350898.001.A001

Note: The estimates are based on equation (9). The solid line denotes the differential effect of capital account liberalization episodes between a sector with high external financial dependence (at the 75^th percentile of the distribution) and a sector with low external financial dependence (at the 25^th percentile of the distribution). The dotted lines indicate the 90% confidence interval based on standard errors clustered at the country–sector level.Note: The estimates are based on equation (9). The solid line denotes the differential effect of capital account liberalization episodes between a sector with high external financial dependence/layoff rate/elasticity of substitution (at the 75^th percentile of the distribution) and a sector with low external financial dependence/layoff rate/elasticity of substitution (at the 25^th percentile of the distribution). Dotted lines indicate the 90% confidence interval based on standard errors clustered at the country–sector level.

Table 4.

The Effect of Capital Account Liberalization on Sectoral Labor Share

Table 4.

The Effect of Capital Account Liberalization on Sectoral Labor Share

	(II)	(III)	(IV)
	External Financial Dependence	Layoff Rate	Elasticity of Substitution
Capital account reform_it*S_j	−1.835***	−0.023	−0.208***
	(−2.82)	(−0.15)	(−3.59)
Capital account reform_it-1*S_j	−0.223	−0.009	0.034
	(−0.32)	(−0.06)	(0.55)
Capital account reform_it-2*S_j	0.176	−0.270	−0.103
	(0.22)	(−1.42)	(−1.56)
Capital account reform_it-3*S_j	−0.266	−0.202	0.117
	(−0.41)	(−1.32)	(0.78)
Capital account reform_it-4*S_j	−0.788	−0.175	−0.057
	(−1.53)	(−1.32)	(−0.83)
Capital account reform_it-5*S_j	−1.013*	−0.087	−0.116**
	(−1.89)	(−0.62)	(−1.94)
Medium-term differential effect	−2.230***	−2.078*	−2.580*
F-test medium-term effect	7.31	3.21	2.85
N	16,616	16,616	16,616

Table 4.

The Effect of Capital Account Liberalization on Sectoral Labor Share

	(II)	(III)	(IV)
	External Financial Dependence	Layoff Rate	Elasticity of Substitution
Capital account reform_it*S_j	−1.835***	−0.023	−0.208***
	(−2.82)	(−0.15)	(−3.59)
Capital account reform_it-1*S_j	−0.223	−0.009	0.034
	(−0.32)	(−0.06)	(0.55)
Capital account reform_it-2*S_j	0.176	−0.270	−0.103
	(0.22)	(−1.42)	(−1.56)
Capital account reform_it-3*S_j	−0.266	−0.202	0.117
	(−0.41)	(−1.32)	(0.78)
Capital account reform_it-4*S_j	−0.788	−0.175	−0.057
	(−1.53)	(−1.32)	(−0.83)
Capital account reform_it-5*S_j	−1.013*	−0.087	−0.116**
	(−1.89)	(−0.62)	(−1.94)
Medium-term differential effect	−2.230***	−2.078*	−2.580*
F-test medium-term effect	7.31	3.21	2.85
N	16,616	16,616	16,616

Panel A shows that, over the medium term—that is, five years after the reform takes place—capital account liberalization episodes tend to reduce the labor share in industries with higher external financial dependence. The results suggest that the differential medium-term reduction in the labor share between an industry with relatively high external financial dependence (at the 75^th percentile of the cross-sector distribution) and one with relatively low external financial dependence (at the 25^th percentile) is about 2 percentage points. This effect is not only statistically but also economically significant. In particular, under the conservative assumption that capital account liberalization episodes did not have any impact on the labor share in sectors with external financial dependence in the 25^th percentile of the distribution, the results suggest that capital account liberalization episodes have on average reduced through the external dependence channel the labor share in a reform country by about 1¾ percentage points.¹⁷

The results presented in Panel B suggest that capital account liberalization episodes also tend to reduce the labor share of income in those sectors with a higher natural layoff rate. In particular, the differential medium-term reduction in the labor share associated with liberalizing the capital account for an industry with a relatively high natural layoff rate (at the 75^th percentile of the distribution of the natural layoff rate—such as textiles) compared with an industry with a relatively low layoff rate (at the 25^th percentile—such as chemicals) is about 2 percentage points. Under similar assumptions made for the external financial dependence, we find that the medium-term reduction in the labor share in a reform country through the natural layoff rate is about 1½ percentage points.

Finally, we also find that the effect of capital account liberalization on the labor share is higher (in absolute values) in industries with higher elasticity of substitution between capital and labor (Panel C). The differential medium-term reduction in the labor share between an industry with relatively high elasticity of substitution (at the 75th percentile of distribution—such as machinery and equipment) and one with relatively low elasticity of substitution (at the 25th percentile—such as accommodation) is about 2.5 percentage points. Interestingly, and consistent with theory, we find that this effect is only significant in industries with elasticity of substitution between capital and labor greater than 1 (Panels C1 and C2). Using the medium-term estimates of the differential effect in these figures—that is, −3.3 (0) for industries with elasticity of substitution above (below) 1, the results suggest that capital account liberalization episodes have on average reduced the labor share in a reform country by about 2½ percentage points.

1. Robustness Checks

A possible concern in estimating Equation (9) is that the results are biased due to the omission of other macroeconomic variables affecting output and the labor share of income through the dependence on external finance (or natural layoff rates or elasticity of substitution) that are at the same time correlated with capital account liberalization episodes.

The first obvious candidate is trade liberalization. However, even though trade costs have continued to decrease in recent decades, in many countries the big push for trade liberalization occurred in the 1970s, well before capital account liberalization (in the 1990s). Moreover, even allowing for the possibility of simultaneous external trade and financial liberalization, this will lead to a bias in the estimates only if trade opening engenders larger output (or labor share) effects in industries with greater dependence on external finance (natural layoff propensity, elasticity of substitution between capital and labor). To check the robustness of our results, we re-estimate Equation (9) adding the interaction between the index of external finance (natural layoff, elasticity of substitutions) and trade reforms. The results of this exercise are similar to those that we obtained in the baseline specification (Figure 10).

Figure 10.

The Differential Effect of Capital Account Liberalization Episodes on Sectoral Output (%) and Labor Share (Percentage Points)—Controlling for Trade Reforms

Citation: IMF Working Papers 2018, 083; 10.5089/9781484350898.001.A001

Another possible omitted variable is current account liberalization. As Quinn and Toyoda (2008) discuss, current and capital account liberalization have proceeded in parallel. However, the results are robust to the inclusion of this variable interacted with the industry-specific channels (Figure 11).

Figure 11.

The Differential Effect of Capital Account Liberalization Episodes on Sectoral Output (%) and Labor Share (Percentage Points)—Controlling for Current Account Reforms

Citation: IMF Working Papers 2018, 083; 10.5089/9781484350898.001.A001

Domestic financial liberalization tends to increase financial depth and therefore may affect output through sectoral external financial dependence. To check whether the inclusion of this variable alters our results, we augment Equation (9) by interacting domestic financial liberalization with the degree of dependence on external finance. The results are again similar to those in the baseline (Figure 12).

Figure 12.

The Differential Effect of Capital Account Liberalization Episodes on Sectoral Output (%) and Labor Share (Percentage Points)—Controlling for Domestic Financial Liberalization Reforms

Citation: IMF Working Papers 2018, 083; 10.5089/9781484350898.001.A001

Product market deregulation can also affect output and the labor share of income, particularly for regulations directed at the privatization of large public network monopolies (Ciminelli et al. forthcoming). Deregulations in product markets would bias our results only if they have larger effects on the output and the labor share in industries with greater dependence on external finance (natural layoff propensity, elasticities of substitution). Estimating Equation (9) by adding the interaction between the index of external finance (natural layoff propensity, elasticity of substitution) and the index of product market regulation does not change the results (Figure 13).

Figure 13.

The Differential Effect of Capital Account Liberalization Episodes on Sectoral Output (%) and Labor Share (Percentage Points)—Controlling for Product Market Reforms

Citation: IMF Working Papers 2018, 083; 10.5089/9781484350898.001.A001

Another potential variable that may affect the industrial labor share through the layoff rate and the elasticity of substitution is employment protection legislation, EPL (Ciminelli et al. forthcoming). The results accounting for the differential effect of EPL on the labor share are again similar to those in the baseline (Figure 14).

Figure 14.

The Differential Effect of Capital Account Liberalization Episodes on Sectoral Output (%) and Labor Share (Percentage Points)—Controlling for Labor Market (EPL) Reforms

Citation: IMF Working Papers 2018, 083; 10.5089/9781484350898.001.A001

Union density can affect the labor share by influencing the bargaining power of workers and be correlated with capital account liberalization (see Scruggs and Lange 2002, for a review). ¹⁸ Estimating Equation (9) by adding the interaction between the index of external finance (natural layoff propensity, elasticity of substitution) and the union density variable does not change the results (Figure 15). Interestingly, we find that union density has a positive effect and statistically significant effect on the labor share through the natural layoff rate.

Figure 15.

The Differential Effect of Capital Account Liberalization Episodes on Sectoral Output (%) and Labor Share (Percentage Points)—Controlling for Union Density

Citation: IMF Working Papers 2018, 083; 10.5089/9781484350898.001.A001

Technology (using the relative price of investment as a proxy), by reducing the cost of capital, can increase output and reduce the labor share (IMF WEO 2017) in industries with elasticity of substitution between capital and labor greater than one. Estimating Equation (9) by adding the interaction between the index of external financial dependence (natural layoff propensity, elasticity of substitution) and the relative price of investment again does not change the results (Figure 16).

Figure 16.

The Differential Effect of Capital Account Liberalization Episodes on Sectoral Output (%) and Labor Share (Percentage Points)—Controlling for Technology (Relative Price of Investment)

Citation: IMF Working Papers 2018, 083; 10.5089/9781484350898.001.A001

Finally, since the labor share is typically countercyclical (Kehrig and Vincent 2017), we check whether the effect on the labor share is robust when controlling for sectoral growth. While the results based on this analysis should not be interpreted as the overall effect of capital account liberalization on the labor share, since part of this effect may materialize through growth effects, they are still statistically significant and close to those obtained in the baseline (Figure 17).

Figure 17.

The Differential Effect of Capital Account Liberalization Episodes on the Labor Share—Controlling for Sectoral Growth (Percentage Points) Panel A. Based on External Financial Dependence

Citation: IMF Working Papers 2018, 083; 10.5089/9781484350898.001.A001

V. Conclusions

This paper takes a fresh look at the economic effects of policies to liberalize international capital flows. It uses aggregate and sector-level data to re-examine the effects of capital account liberalization policies on output and inequality and to determine how these effects depend on the strength of financial institutions.

The results suggest that capital account liberalization reforms on average have led to limited output gains but contributed to significant increases in inequality. These average estimates, however, mask differences across countries and over time. In particular, while liberalization episodes have tended to increase the output in countries with well-liberalized domestic financial sectors, the macroeconomic effects on output have been adverse in cases in which domestic financial markets remain largely restricted or when liberalization episodes have been followed by a crisis. With respect to inequality, our results suggest salient adverse effects on average, particularly when domestic financial liberalization is low and not-inclusive or when a crisis follows liberalization.¹⁹ The results also suggest that capital account liberalization episodes reduce the share of labor income, especially for industries that have a high level of external financial dependence or that are characterized by a higher natural propensity to adjust their workforce in response to idiosyncratic shocks or when the elasticity of substitution between capital and labor is relatively high (and greater than unity).

These findings do not imply that countries should not undertake (or have undertaken) capital account liberalization, but the results regarding distribution do suggest an additional reason for caution (particularly when set against the weak efficiency gains). In particular, our findings suggest that countries where a reduction in inequality is an important policy goal may need to design liberalization in a manner that balances the equity impact against the other effects. This might require specifically the restriction of certain types of flows that generate adverse equity–efficiency trade-offs (such as carry-trade flows or flows that give rise to unhealthy asset price or credit booms) and the encouragement of other types of flows (particularly those that give rise to durable increases in investment and growth, such as greenfield investments)—Ostry (et al., 2012). Beyond this, our paper highlights a number of areas requiring attention in trying to mitigate the undesirable consequences of capital account liberalization. Steps to develop domestic financial institutions as well as depth and inclusion are clearly important in this connection. Fiscal redistribution can also help to mitigate the adverse distributional consequences of financial globalization without exerting much of an effect on economic efficiency unless such redistribution is extreme (Ostry et al. 2014). Finally, in addition to redistribution, policies could be designed to mitigate some of the anticipated effects in advance—for instance, through increased spending on education and training (so-called pre-distribution policies) to foster greater equality of opportunity.