DP2024-30

Effects of Capital Flow Management
Measures on Wealth Inequality: New
Evidence from Counterfactual Estimators
Ya n g Z H O U
S h i g e t o K I TA N O

September 2, 2024

Eﬀects of Capital Flow Management Measures on WealthInequality: New Evidence from Counterfactual Estimators
Yang Zhou1,2
1

IDE, JETRO

Shigeto Kitano2
2

RIEB, Kobe University

Abstract
We provide cross-country evidence that variations in capital ﬂow management measures (CFMs) result in diﬀerences in wealth inequality and distribution by using counterfactual estimators for causal inference. The imposition of aggregate CFMs increases
wealth inequality in advanced economies, and the imposition of aggregate CFMs on
outﬂows increases wealth inequality in emerging economies signiﬁcantly. Diverging
from previous studies, we analyze the impacts of ten distinct asset-speciﬁc CFMs.
In particular, the imposition of the related CFMs to money market and derivatives
reduces wealth inequality signiﬁcantly. The decrease in wealth inequality is due to adecrease in the wealth shares of the top 1% and 10% groups along with an increase in
the wealth shares of the middle 40% and bottom 50%. Overall, the eﬀects of CFMs
on wealth inequality and distribution are quite heterogeneous; they depend on income
levels, capital ﬂow directions, and asset categories.
Keywords: Capital ﬂow management measures (CFMs), Wealth inequality, Ginicoeﬃcient, Wealth distribution, Counterfactual estimator
JEL classiﬁcation: D63, E21, F38, G15, G28, O16

1Introduction

Although capital ﬂow management measures (CFMs) (or capital controls) are not a newpolicy instrument, it is not until the global ﬁnancial crisis that the potential eﬀects of
CFMs have been rigorously examined as one of the most important topics in international
economics.1 During the global ﬁnancial crisis and its aftermath, policymakers in emerging
economies have struggled with unprecedented magnitude in international capital ﬂows. In
fact, some emerging economies such as such as Brazil, Indonesia, South Korea, Taiwan,
and Thailand have responded to instability by imposing CFMs. As well known, even
the International Monetary Fund (IMF), a former critic of CFMs, has been forced toreconsider such measures as an important policy response to volatile capital ﬂows undercertain circumstances. Against this background, an increasing number of researchers haveextended studies on CFMs in a variety of new directions. A strand of the literature
focuses on pecuniary externalities associated with ﬁnancial crises and provides a rationale
for CFMs to prevent excessive borrowing (e.g., Jeanne and Korinek, 2010; Jeanne et al.,2012; Brunnermeier and Sannikov, 2015). More eﬀects of CFMs as a policy tool havebeen rigorously examined from a broader perspective (e.g., Chang et al., 2015; Kitano and
Takaku, 2018; Agénor and Jia, 2020; Kitano and Takaku, 2020; Nispi Landi and Schiavone,2021). Overall, the theoretical studies tend to emphasize that CFMs are a good policy
tool for managing the risk from volatile capital ﬂows, and then CFMs enhance welfare in
the capital recipient economies.
However, as argued above, the IMF used to emphasize the desirability of free capital
movements among countries. Free international capital mobility beneﬁts both lenders andborrowers owing to the traditional gains from trade (Obstfeld and Rogoﬀ, 2009). Lending
countries can earn higher returns to their capital and achieve greater portfolio diversiﬁcation. Borrowing countries can expect that capital inﬂows are invested in productive
investments, or allow their residents to smooth their consumption. Borrowing countries
can also beneﬁt from foreign investment such as foreign direct investment (FDI) as it may
lead to technology transfer to the recipient countries. In fact, many empirical studies show
1
The term “capital ﬂow management measures (CFMs)” refers to policy measures that are speciﬁcallydesigned to limit capital ﬂows (IMF, 2012). Although some prefer the term “CFMs” to “capital controls”,
the latter is also used by the IMF Article of Agreement for such interventions and they are generally usedinterchangeably in the related literature (Erten et al., 2021).

1

that capital ﬂows beneﬁt source and recipient economies (e.g., Desai et al., 2009; Reinhardt
et al., 2013).
In examining the advantage and disadvantage of CFMs as a policy tool, it is necessary
to take into account how CFMs aﬀect income (or wealth) inequality and distribution. The
topic’s importance leads to the proliferation of the related literature on the relationshipbetween CFMs (liberalization) and inequality. A priori reasoning for the relationshipseems quite diﬃcult as CFMs aﬀect income and wealth distributions through multiple
channels. Obviously, CFMs in various asset markets such as FDI, international banklending, portfolio debt, and equity aﬀect the corresponding capital ﬂows separately, andtheir eﬀects are not necessarily the same across the various asset markets. In addition, it
is not straightforward to understand how the diﬀerent channels of capital ﬂows inﬂuence
inequality and distribution. For example, Jaumotte et al. (2013) argue that “[i]f ﬁnancial
ﬂows make resources available to a broader cross-section of the work-force, they would
serve to reduce inequality by allowing investment in skills and human capital. However,if they make more ﬁnancial resources available to those who already have capital andcollateral, this would likely exacerbate inequality” (line 23-28, page 285). The aggregate
indices of CFMs consist of the various asset-speciﬁc CFMs on diﬀerent capital ﬂows, andthe asset speciﬁc CFMs aﬀect diﬀerent groups of income and wealth distribution throughdiﬀerent channels. Therefore, it is not an easy task to grasp how CFMs aﬀect inequality
in total. However, as we argue in the literature review, most previous studies examine
the relationship between CFMs and inequality by using the aggregate (not asset speciﬁc)indices of CFMs.
In order to ﬁll the above mentioned gap in the literature, in this paper, we explore theeﬀects of both aggregate and disaggregated CFMs on wealth (not income) inequality using
counterfactual estimators for causal inference with a dataset encompassing 100 economies
from 1995 to 2019. Using the full sample, we ﬁrst examine the eﬀects of aggregate CFMs
on wealth equality. Our result suggests that tightening aggregate CFMs reduces wealthinequality, which is consistent with the literature on income inequality, but the result
is not statistically signiﬁcant. Separating the full sample into advanced and emerging
economies, we obtain a statistically signiﬁcant result for advanced economies, but theresult indicates that tightening aggregate CFMs increases (not reduces) wealth inequality.
2The result for advanced economies is consistent with the some previous studies arguing that
capital account liberalization decreases income inequality when the economy’s ﬁnancial andinstitutional level is high. Diﬀerentiating further between capital inﬂows and outﬂows, we
obtain a statistically signiﬁcant result for emerging economies indicating that tightening
aggregate CFMs on outﬂow increases wealth inequality. The result for aggregate CFMs on
outﬂow in emerging economies seems opposite to the previous literature.
Using the disaggregated indices of CFMs by Fernández et al. (2016b), we next examine
the eﬀects of asset-speciﬁc CFMs on wealth inequality. We obtain statistically signiﬁcantresults showing that tightening CFMs in the asset markets of money market and derivative decreases wealth inequality, which is consistent with the previous literature. Further
diﬀerentiation between inﬂow and outﬂow yields the statistically signiﬁcant results showing that tightening asset-speciﬁc inﬂow CFMs reduces wealth inequality in the assets ofequity, money market, and direct investment, whereas it increases wealth inequality in thebond market. The result on inﬂow for the former group (equity, money market, and direct
investment) is consistent with the previous literature, while that for the latter (bond) isnot. As for outﬂows, our statistical signiﬁcant results indicate that tightening CFMs in thederivative market reduces wealth inequality, but tightening CFMs in the asset markets ofﬁnancial credit, direct investment, and real estate increases wealth inequality. The resulton outﬂow for the former (derivative) is consistent with the previous literature, while theresult on outﬂow for the latter group (ﬁnancial credit, direct investment, and real estate)
is not.
We also explore how CFMs change wealth distribution in order to understand thelinkage between CFMs and the wealth inequality (i.e., Gini coeﬃcients). We ﬁnd that theincrease in wealth inequality due to tightening aggregate CFMs in advance economies canbe explained by a fall in the share of the bottom 50% group. We also ﬁnd that the increase
in wealth inequality due to tightening aggregate outﬂow CFMs in emerging economies canbe explained by a rise in the wealth share of the top 10% group. As for speciﬁc asset
markets, we ﬁnd that tightening CFMs in the money market reduces the wealth shares of
the top 1% and 10% groups, but increases those of middle 40% and bottom 50% groups,
both of which explain the decrease in wealth inequality due to tightening CFMs in themoney market. We also ﬁnd that tightening CFMs in the derivative market reduces the3

wealth shares of the top 10% group, but increases that of the bottom 50% group, both ofwhich explain the decrease in wealth inequality due to tightening CFMs in the derivative
market.
In summary, diﬀerentiating among income levels, capital ﬂow directions, and asset categories, we ﬁnd that the eﬀects of CFMs on wealth equality and distribution vary considerably. Some of our results are consistent with the previous studies that employ aggregate
data, but others are not. Our results suggest that using only aggregate data might not beenough to fully understand how CFMs aﬀect wealth inequality and distribution.
We contributes to the literature in threefold. To the best of our knowledge, this paper
is the ﬁrst to provide empirical evidence on the eﬀects of CFMs on wealth inequality (notincome inequality). As we argue in the next section, the previous literature suggests that
wealth inequality is a much more serious issue than income inequality, and demonstrates
that income distribution cannot perfectly substitute for wealth distribution. However, mostof the previous literature focus on income inequality, and only a very few studies analyze
wealth inequality. Furthermore, we examine the eﬀects of CFMs on wealth distribution.
We show how various CFMs aﬀect the wealth shares of diﬀerent distribution groups in linewith the studies by Li and Su (2021) and Teixeira (2023).
In addition, our study is the ﬁrst to use the up-to-date counterfactual estimators forcausal inference with time-series cross-sectional data proposed by Liu et al. (2024) for
establishing causality from CFMs to wealth inequality. Most of the previous literature
use the generalized method of moments (GMM) and system GMM estimator for dynamic
panels to mitigate the endogeneity of the capital account liberalization variables. In thisstudy, we use the counterfactual estimators rather than GMM. As we explain in detail in
the next section, the method of counterfactual estimators diﬀer from traditional ones andmore appropriate for establishing causality in our case.
Our third contribution is that we consider not only aggregate CFMs indicators but also
those across various speciﬁc capital markets. This point is important because policymakers regulate various capital accounts individually rather than the entire capital accountcollectively. We also make a distinction between capital inﬂow and outﬂow managementsin the asset speciﬁc market because the direction of capital movements is of concern topolicy makers, especially in emerging economies.
4

The remainder of this paper is organized as follows. Section 2 reviews the empiricalliterature related to income and wealth inequality. Section 3 describes the data on wealthinequality, CFMs, and control variables used in our estimation. Section 4 presents themethodology of the counterfactual estimator to establish causality from CFMs to wealthinequality. Section 5 presents the empirical results on the eﬀects of various types of CFMs
on wealth distribution. Section 6 presents a series of robustness exercises. Finally, conclusions are presented in Section 7.

2

Literature review

This paper studies the eﬀects of CFMs on wealth inequality. There is a vast literature on
the eﬀects of capital account liberalization on income inequality (not wealth inequality).
Previous early studies tend to suggest that capital account liberalization increases income
inequality. In most early studies, the relationship between capital account liberalizationand income inequality is unconditional. Jaumotte et al. (2013) examine the relationshipbetween the rapid pace of ﬁnancial globalization and the rise in income inequality in 51
developed and developing countries, and report that ﬁnancial globalization, particularly
in FDI, is associated with an increase in inequality. Asteriou et al. (2014) also investigate
the relationship between income inequality and globalization, and suggest that ﬁnancial
globalization through FDI and capital account openness have been the driving force of inequality in the EU27 countries. Batuo and Asongu (2015) apply two methods, the dynamic
panel econometric method and the “before and after” approach for African countries, andshow that in their ﬁrst method, ﬁnancial liberalization tends to escalate income inequality,
whereas in their second method, ﬁnancial liberalization has made considerable progress
towards decreasing income inequality. Using a sample of 143 countries, Zhang and Naceur(2019) suggest that ﬁnancial sector liberalization (both external and domestic) tend to
widen the inequality and poverty gap.
Other related studies show that the relationship between capital account liberalization and income inequality is conditional. Furceri and Loungani (2018) investigate threechannels through which capital account liberalization increases income inequality. Theyshow that the eﬀect of liberalization on income inequality intensiﬁes when ﬁnancial inclu-

5

sion is low, when liberalization precedes a ﬁnancial crisis, and when there is a shift in therelative bargaining power between ﬁrms and workers. Furceri et al. (2017) are similar to
Furceri and Loungani (2018), but they focus on low-income countries and ﬁnd that theeﬀects of capital account liberalization on inequality are more pronounced in countries
with underdeveloped credit markets and limited ﬁnancial inclusion. De Haan and Sturm(2017) and Gallagher et al. (2019) both show that institutional quality may condition
the relationship between capital account liberalization and income inequality. Assumingthat economic institutional strength is associated with GDP per capita, Gallagher et al.
(2019) ﬁnd that in most developing countries with weak institutions, the lack of preemptive
policies means that capital account liberalization is likely to exacerbate income inequality
during economic downturns. Utilizing the indicator of the quality of political institutions
as a proxy for economic institutional strength, De Haan and Sturm (2017) ﬁnd that withhigher levels of democratic accountability, the positive impact of ﬁnancial liberalization on
inequality intensiﬁes. Bumann and Lensink (2016) suggest that capital account liberalization is likely to reduce income inequality when the ratio of private credit to GDP, whichindicates ﬁnancial depth, exceeds 25 percent.
As we argue above, income inequality has been extensively studied.2 However, few
studies focus on wealth (not income) inequality. As argued by Hasan et al. (2020), thescarcity of empirical research on wealth inequality is mainly due to limitations in wealthinequality data. Most wealth inequality data are based on household surveys. For instance, Carpantier et al. (2018) utilize data from the Eurosystem Household Finance andConsumption Survey (HFCS) to demonstrate the relationship between loan-to-value (LTV)
ratios and wealth inequality.3 Similarly, Hasan et al. (2020) assess wealth inequality using the wealth Gini coeﬃcient derived from the Credit Suisse Wealth Databook (CSWD),

2
Income inequality has been studied from many other diﬀerent perspectives. For example, Jatmiko
et al. (2023) examine the relationship between Sukūk development and income inequality.
3
According to Albacete et al. (2012), the Eurosystem Household Finance and Consumption Survey(HFCS) is carried out in a decentralized manner by the central bank or statistical institute of each country.
The fourth wave of the HFCS was primarily conducted in 2021, the third wave in 2017, the second wave
in 2014, and the ﬁrst wave in 2010. This survey gathers comprehensive data on households’ real assets,ﬁnancial assets, debt, and expenditures, enabling detailed scientiﬁc analyses of household balance sheetsin accordance with international standards.

6which is based on Household Balance Sheet (HBS) data.4 5 However, household surveys,
which are traditionally used to observe inequality dynamics, fail to accurately capture theevolution of wealth and income, especially at the highest levels. The World Inequality
Database (WID) addresses this shortcoming by integrating various data sources, including
national accounts, survey data, ﬁscal data, and wealth rankings. This approach allows fora more precise tracking of income and wealth evolution across all levels, from the bottom
to the top (Alvaredo et al., 2016). Therefore, we use the WID data as it is most suitablefor our analysis.
CFMs inﬂuence wealth inequality through their impact on cross-border capital ﬂows.
Macroprudential policies, another policy related to CFMs, aﬀect inequality by directly
inﬂuencing domestic credit. The literature on the impact of macroprudential policies oninequality is also closely related to this paper. Delis et al. (2014) examine the impactof banking regulations on income distribution across various countries. They ﬁnd thatthe general liberalization of banking systems signiﬁcantly reduces income inequality. Yet,this impact fades and becomes negligible in countries with low levels of economic and institutional development. Frost and Van Stralen (2018) diﬀerentiate between the eﬀects ofmacroprudential policies on market income inequality (income inequality before redistributive policies) and net inequality (inequality after redistribution). In contrast to Delis et al.
(2014), their ﬁnding reveals that macroprudential policies such as concentration limits,
macroprudential reserve requirements, and interbank exposure limits are positively correlated with market income inequality, while LTV limits are positively associated with net
inequality. Teixeira (2023) ﬁrst examines the eﬀects of macroprudential policy on wealthinequality and also analyses the wealth share of the top 1%, top 10%, and bottom 50%of the distribution. In line with Frost and Van Stralen (2018), Teixeira (2023) ﬁnds that
implementing macroprudential policies causes a 3.4% point increase in wealth concentration within the aﬀected countries over ten years. This outcome can be attributed to an
4

The Credit Suisse Wealth Databook (CSWD) oﬀers estimates on the level and distribution of wealthfor over 200 countries, spanning from the year 2000, using the methodology developed by Davies et al.
(2011). It examines the patterns and trends of wealth among individuals aged 20 and above at bothregional and national levels. However, as noted by Hasan et al. (2020), the CSWD has been available on
an annual basis only from 2010 onwards.
5
The primary source for the CSWD is the Household Balance Sheet (HBS) data, which are currently
provided by 51 countries. However, data on nonﬁnancial wealth are missing for many of these countries,
necessitating supplementation through econometric estimations (Hasan et al., 2020). Of these 51 countries,
only 24 provide information on both ﬁnancial assets and debts.

7

increase in the wealth share of the top 1% combined with a signiﬁcant reduction in thewealth share of the bottom 50%.
Unlike monetary policies that can be succinctly described by using quantiﬁable indicators such as policy interest rates, CFMs are considerably more complex and depend
signiﬁcantly on institutional details. Although CFMs can be categorized into various categories, most previous studies on the impact of CFMs use aggregate (synthetic) indicators.
Gallagher et al. (2019), Furceri and Loungani (2018), Furceri et al. (2017), Batuo andAsongu (2015), Bumann and Lensink (2016), and Asteriou et al. (2014) use the aggregate de jure indicator of ﬁnancial openness KAOPEN developed by Chinn and Ito (2006,2008). Although the earlier literature frequently employ them, aggregate indicators suchas KAOPEN can only describe the overall level of capital account openness of a country as
a whole, and are not adequate for precisely describing CFMs in individual capital markets
and diﬀerent capital ﬂows. Researches on income inequality using disaggregated indicesof CFMs are scarce. Few exceptions are Li and Su (2021) and Jaumotte et al. (2013).
However, they only cover equities, bonds, and FDI, and the other asset categories appear
in aggregate forms and are not further classiﬁed. According to Fernández et al. (2016a)’s
categorization, there exist up to ten distinct CFMs. As the eﬀects of such policies employing speciﬁc asset categories can diﬀer from those employing aggregate indicators, further
analysis utilizing the disaggregated indices is necessary.
The contribution of this paper to the literature is in threefold. Firstly, to the bestof our knowledge, this paper is the ﬁrst to provide empirical evidence on the eﬀects of
CFMs on wealth inequality rather than income inequality. Highlighting its higher levels,
more pronounced concentration, and much thicker tail compared to income inequality, Saez
(2017) and Osakwe and Solleder (2023) argue that wealth inequality is a much more serious
issue than income inequality.6 Compared to wages or earned income, capital income is more
susceptible to the eﬀects of CFMs because these policies are implemented to prevent large
ﬂuctuations of capital ﬂows. A signiﬁcant portion of these ﬂows moves into the stock (orbond) market in the form of hot money for portfolio investment, while another portion
6
Zucman (2019)’s study indicates that the recent history of global wealth is more complex than that ofthe pre-1980s era, given that global wealth growth rates vary signiﬁcantly across the distribution. Davies
et al. (2011) note that “in all countries with the requisite data, wealth distribution is more unequal thanincome.” Hasan et al. (2020) and Bagchi and Svejnar (2015) demonstrate that income distribution cannot
perfectly substitute for wealth distribution.

8

accelerates the ﬁnancial cycle through real estate investment. Teixeira (2023) argues that
if policies make it diﬃcult for people to obtain a loan or proﬁt from increases in asset
prices, wealth inequality is better suited for capturing the true eﬀects of such policies
compared to income inequality. However, most of the empirical research focus on income
inequality, and only a very few studies analyze wealth inequality. Hasan et al. (2020)
investigate the determinants of wealth inequality, capturing various economic, ﬁnancial,political, institutional, and geographical indicators. However, they do not consider the
impact of economic policies on wealth inequality. Teixeira (2023) examines the eﬀects ofmacroprudential policy on wealth inequality. We are the ﬁrst to study the eﬀects of CFMs
on wealth inequality.7 Furthermore, we examine not only the eﬀects of CFMs on the Ginicoeﬃcient but also the eﬀects of CFMs on diﬀerent wealth shares of the distribution. We
show how these eﬀects vary among these groups in line with Li and Su (2021) and Teixeira(2023).
Secondly, this study is the ﬁrst to use the up-to-date counterfactual estimators forcausal inference with time-series cross-sectional data proposed by Liu et al. (2024) for
establishing causality from CFMs to wealth inequality. Most of the previous literature
use the generalized method of moments (GMM) and system GMM estimator for dynamic
panels developed by Arellano and Bond (1991) and Blundell and Bond (1998) to mitigate the endogeneity of the capital account liberalization variables (Furceri and Loungani,
2018; Bumann and Lensink, 2016; Batuo and Asongu, 2015; Asteriou et al., 2014; Deliset al., 2014; Li and Yu, 2014; Hamori and Hashiguchi, 2012). Although the GMM method
can address ﬁxed eﬀects and the endogeneity of regressors by using the lagged dependent variable as instrumental variables to avoid dynamic panel bias, there is a commonly
encountered issue in the application of GMM and system GMM known as instrument proliferation (Roodman, 2009).8 In this study, we use the counterfactual estimators rather
than GMM. Our methods of counterfactual estimators diﬀer from traditional ones in the
7

Although both types of policy measures are likely to make the economy more stable and reduce theincidence and severity of crises, capital controls and macroprudential policies are not the same. Korinek
and Sandri (2016) detail the diﬀerence. In simple terms, macroprudential policies restrict borrowing by
domestic agents, regardless of whether it is ﬁnanced by domestic or foreign lenders; capital controls restrictﬁnancial transactions between residents and non-residents.
8
If T equals 3, diﬀerence GMM produces merely one instrument per variable for instrumenting, andsystem GMM produces only two. However, as T increases, the number of instruments can quickly expand
relative to the sample size, potentially leading to misleading asymptotic results concerning the estimators
and associated speciﬁcation tests.

9following respects. Compared to the traditional two-way ﬁxed eﬀects model, the counterfactual estimators that we utilize in this study oﬀer more reliable causal inferences insituations where the treatment eﬀect is heterogeneous or when there are unobserved timevarying confounders. Speciﬁcally, the two-way ﬁxed eﬀects approach is predicated on theassumption of “absence of time-varying confounders”, which is often not easily satisﬁed. To
address violations of this assumption, Liu et al. (2024) employ the interactive ﬁxed-eﬀectcounterfactual (IFEct) and matrix completion (MC) estimators, attempting to accountfor unobserved, time-varying confounders using a latent factor approach. Mathematically,both estimators aim to construct a lower-rank approximation of the outcome data matrix,
utilizing information from only non-treated observations. The equivalence test also indicates that the two-way ﬁxed eﬀects estimator cannot pass the test, meaning that there isa very high probability of the presence of potential time-varying confounders. Moreover,compared to the generalized synthetic control method (GSCM) proposed by Xu (2017), thecounterfactual estimators are capable of handling more intricate time-series cross-section
designs, including staggering adoption (Athey et al., 2021) and treatment reversal, which
are characteristics of data pattern in this paper. Besides, we consider not only the shortto medium-term eﬀects occurring within 5 years but also the long-term eﬀects extending
beyond 5 years (up to 10 years) following the implementation of CFMs.
Thirdly, this paper considers policy indicators across various speciﬁc capital markets aswell as aggregate indicators of CFMs. It matters because policymakers typically regulate
various capital accounts individually rather than the entire capital account collectively.
Depending exclusively on aggregate indicators, we cannot capture the heterogeneous eﬀects
of these policy interventions (Zhou, 2024). Furthermore, as stated in Asteriou et al. (2014)
and Li and Su (2021), the composition of ﬁnancial ﬂows is signiﬁcant for the net eﬀect of
globalization on inequality.9 We also make a distinction between capital inﬂow and outﬂow
managements because the direction of capital movements is of concern to policy makers,
especially in emerging economies. While capital inﬂows like FDI can boost technology
9Performing an extensive review of the literature, Li and Su (2021) show that the composition of capitalﬂows matters and even an identical type of capital ﬂows such FDI can have diverse impacts. For example,
FDI, which often targets high-skilled sectors, typically leads to increased inequality (Choi, 2006; Acharyya,
2011; Wu and Hsu, 2012; Jaumotte et al., 2013). Herzer and Nunnenkamp (2013) ﬁnd that FDI lowers
income inequality over the long-term, while it may have a positive eﬀect in the short-term. IMF (2007)shows that the liberalization of foreign bank lending is likely to be linked to better ﬁnancial access for thepoor, thereby reducing inequality.

10and growth, they may also cause economies to overheat and housing prices to surge. On
the other hand, capital outﬂows or sudden stops can lead to currency devaluation andeconomic declines. The eﬀects of inﬂows and outﬂows on ﬁnancial cycles are obviously
distinct. Moreover, there are variations in the combination of policymakers’ inﬂow andoutﬂow controls. Fernández et al. (2016b) show that controls on direct investment and realestate have a lower correlation between their inﬂow and outﬂow controls, which implies
that policymakers’ decisions on inﬂow and outﬂow controls are signiﬁcantly diﬀerent.

3

Data

This section presents the data used for our estimation. We collect the data from 100
economies between 1995 to 2019 on annual basis.10 The core variables are the measuresof wealth inequality and CFMs. As for wealth inequality, we use the Gini coeﬃcients andthe wealth share groups of wealth distribution. As for CFMs, we use the capital control
restrictions data, in which economies implementing capital controls can be categorized as
the treatment group while those not implementing them can be categorized as the control
group. Table 3.1 reports the data sources and descriptive statistics of the variables usedin this paper.

3.1

Wealth inequality

The data used for the Gini coeﬃcients are from the World Inequality Database (WID).
The choice of indicators from this database is based on two main reasons. (i) In contrast
to the previous literature, this paper focuses on the eﬀects on wealth inequality rather
than income inequality. The most suitable indicators come from the WID. This databaseprovides the aggregate wealth-income ratios and the changing structure of national wealth,allowing for the production of reliable estimates of wealth inequality. However, the traditional World Income Inequality Database (WIID) and the Standardized World Income
Inequality Database (SWIID) do not provide corresponding indicators. (ii) The Gini coeﬃcient provides an overall distribution structure but does not indicate which part of the
distribution is aﬀected by CFMs. Therefore, the Gini coeﬃcient is not suﬃcient enough to
10

The samples (treatment and control group) used for estimation are actually less than 100 economies
due to the data missing, no pretreatment period, and few control units.

11

Table 3.1: Descriptive statistics and data sources

Gini coeﬃcient, wealth
Wealth share, top 1%
Wealth share, top 10%
Wealth share, middle 40%
Wealth share, bottom 50%
Capital control episode “ka”
Capital control episode (inﬂow) “kai”
Capital control episode (outﬂow) “kao”
Equity control “eq”
Equity control (inﬂow) “eqi”
Equity control (outﬂow) “eqo”
Equity control: purchase locally by nonresident
Equity control: sale or issue abroad by resident
Equity control: purchase abroad by resident
Equity control: sale or issue locally by nonresident
Bond control “bo”
Bond control (inﬂow) “boi”
Bond control (outﬂow) “boo”
Bond control: purchase locally by nonresident
Bond control: sale or issue abroad by resident
Bond control: purchase abroad by resident
Bond control: sale or issue locally by nonresident
Money market control “mm”
Money market (inﬂow) “mmi”
Money market (outﬂow) “mmo”
Money market: purchase locally by nonresident
Money market: sale or issue abroad by resident
Money market: purchase abroad by resident
Money market: sale or issue locally by nonresident
Collective inv. control “ci”
Collective inv. (inﬂow) “cii”
Collective inv. (outﬂow) “cio”
Collective inv.: purchase locally by nonresident
Collective inv.: sale or issue abroad by resident
Collective inv.: purchase abroad by resident
Collective inv.: sale or issue locally by nonresident
Derivatives control “de”
Derivatives (inﬂow) “dei”
Derivatives (outﬂow) “deo”
Derivatives: purchase locally by nonresident
Derivatives: sale or issue abroad by resident
Derivatives: purchase abroad by resident
Derivatives: sale or issue locally by nonresident
Commercial credit control “cc”
Commercial credit (inﬂow) “cci”
Commercial credit (outﬂow) “cco”
Financial credit control “fc”
Financial credit (inﬂow) “fci”
Financial credit (outﬂow) “fco”
Guarantees control “gs”
Guarantees (inﬂow) “gsi”
Guarantees (outﬂow) “gso”
Direct inv. control “di”
Direct inv. (inﬂow) “dii”
Direct inv. (outﬂow) “dio”
Real estate control “re”
Real estate: purchase locally by nonresident “rei”
Real estate: purchase abroad by resident “re_pabr”
Real estate: sale locally by nonresident “re_slbn”
Financial development
Inﬂation
Total population
Gov. expenditure on education
Real GDP per capita
Average education
Population growth
Government subsidies
Money supply

Obs

Mean

Std. Dev

Min

Max

Source

2500
2500
2500
2500
2500
2500
2500
2500
2500
2500
2500
2473
2463
2480
2469
2500
2500
2500
2225
2222
2222
2207
2500
2500
2500
2452
2434
2441
2431
2500
2500
2500
2419
2418
2457
2414
2500
2500
2500
2299
2311
2357
2300
2500
2483
2463
2500
2485
2459
2500
2455
2458
2500
2499
2464
2500
2456
2445
2387
2500
2397
2500
1908
2467
1918
2500
1718
2034

0.773
0.300
0.632
0.331
0.037
0.062
0.055
0.088
0.596
0.455
0.550
0.309
0.366
0.412
0.469
0.540
0.393
0.514
0.246
0.394
0.423
0.489
0.553
0.391
0.534
0.254
0.347
0.423
0.445
0.557
0.358
0.544
0.234
0.300
0.405
0.468
0.504
0.366
0.498
0.306
0.342
0.412
0.433
0.397
0.270
0.335
0.507
0.373
0.436
0.376
0.251
0.343
0.560
0.447
0.356
0.675
0.566
0.349
0.270
0.393
10.57360.6054.493
16559.640
87.7951.334
43.08362.155

0.065
0.085
0.080
0.061
0.025
0.240
0.228
0.284
0.491
0.498
0.498
0.462
0.482
0.492
0.499
0.498
0.489
0.500
0.431
0.489
0.494
0.500
0.497
0.488
0.499
0.436
0.476
0.494
0.497
0.497
0.480
0.498
0.424
0.458
0.491
0.499
0.500
0.482
0.500
0.461
0.474
0.492
0.496
0.489
0.444
0.472
0.500
0.484
0.496
0.485
0.433
0.475
0.496
0.497
0.479
0.468
0.496
0.477
0.444
0.239
104.284
177.980
1.518
18538.626
26.9501.557
18.79248.315

0.536
0.121
0.408
0.157
-0.081
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.038
-8.4840.268
0.000
231.468
5.283-3.758
0.234
6.823

1.061
0.582
0.909
0.478
0.160
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
4145.106
1400.939
9.897
87123.660
163.935
19.36084.610403.314World inequality database (WID)
World inequality database (WID)
World inequality database (WID)
World inequality database (WID)
World inequality database (WID)
Fernandez et al. (2016)
Fernandez et al. (2016)
Fernandez et al. (2016)
Fernandez et al. (2016)
Fernandez et al. (2016)
Fernandez et al. (2016)
Fernandez et al. (2016)
Fernandez et al. (2016)
Fernandez et al. (2016)
Fernandez et al. (2016)
Fernandez et al. (2016)
Fernandez et al. (2016)
Fernandez et al. (2016)
Fernandez et al. (2016)
Fernandez et al. (2016)
Fernandez et al. (2016)
Fernandez et al. (2016)
Fernandez et al. (2016)
Fernandez et al. (2016)
Fernandez et al. (2016)
Fernandez et al. (2016)
Fernandez et al. (2016)
Fernandez et al. (2016)
Fernandez et al. (2016)
Fernandez et al. (2016)
Fernandez et al. (2016)
Fernandez et al. (2016)
Fernandez et al. (2016)
Fernandez et al. (2016)
Fernandez et al. (2016)
Fernandez et al. (2016)
Fernandez et al. (2016)
Fernandez et al. (2016)
Fernandez et al. (2016)
Fernandez et al. (2016)
Fernandez et al. (2016)
Fernandez et al. (2016)
Fernandez et al. (2016)
Fernandez et al. (2016)
Fernandez et al. (2016)
Fernandez et al. (2016)
Fernandez et al. (2016)
Fernandez et al. (2016)
Fernandez et al. (2016)
Fernandez et al. (2016)
Fernandez et al. (2016)
Fernandez et al. (2016)
Fernandez et al. (2016)
Fernandez et al. (2016)
Fernandez et al. (2016)
Fernandez et al. (2016)
Fernandez et al. (2016)
Fernandez et al. (2016)
Fernandez et al. (2016)
Svirydzenka (2016)
World development indicators (WDI)
World inequality database (WID)
World development indicators (WDI)
World development indicators (WDI)
World development indicators (WDI)
World development indicators (WDI)
World development indicators (WDI)
World development indicators (WDI)

12

provide an adequate picture of wealth inequality (Piketty and Zucman, 2014). The WID
perfectly oﬀers series on the entire distribution of wealth from the bottom to the top.
The Gini coeﬃcient ranges from 0 to 1. The value of 0 represents perfect equality,
and that of 1 represents that a single individual has all the wealth while all others havenone. All wealth series are based on the concept of “net personal wealth for equal-split
adults,” which means that the wealth is distributed to adults and distributed equally within
couples or households (Alvaredo et al., 2016).11 Following Teixeira (2023) and Alvaredo
et al. (2016), we choose the wealth share of the top 1%, top 10%, middle 40% and bottom
50% of the distribution. These four groups map relatively well to the idea of top, upper,
middle and lower classes. They summarize changes happening to the overall distribution
fairly well. Although wealth inequality shows slow annual variation over time, it becomesa suitable candidate for quasi-synthetic controls. This is so because if the outcome ofinterest experiences signiﬁcant volatility, minor interventions such as adjustments in the
disaggregated level of CFMs might be indistinguishable from other shocks (Abadie, 2021;
Teixeira, 2023).

3.2

Capital ﬂow management measures

Capital ﬂow management measures (CFMs) are our explanatory variables of interest in
this paper. We use the de jure indicator of capital controls from Fernández et al. (2016b)(FKRSU hereafter). The previous literature has predominantly focused on studying theaggregate level of capital account liberalization. However, the ease or tightness of aggregate
level of restrictions depends on the strength of various asset speciﬁc restrictions such as
restriction on equity inﬂow. Policymakers take into account the eﬀects of particular policies
when formulating their strategies. Therefore, this paper places a greater emphasis on theimpact of granular level of asset-speciﬁc CFMs on wealth inequality.
The FKRSU dataset provides such granular indices by distinguishing the directions andcategories of capital ﬂows, enabling us to analyze these eﬀects more comprehensively. Thisdataset ranges from 1995 to 2019 for 100 economies. Furceri and Loungani (2018) argue
11
Alvaredo et al. (2016) use “equal-split adults” as their benchmark variables. On the one hand, as theprimary goal is to study the wealth of diﬀerent individuals, it is logical to allocate wealth predominantly
to working adults. On the other hand, although the concept of equal distribution of income and wealthamong partners in couples might seem simplistic, it is also unrealistic to expect zero resource sharing andthis approach tends to underestimate the resources accessible to nonworking spouses.

13

that many capital account liberalization episodes have occurred in the 1990s and 2010s,
particularly in the 1990s. Binici and Das (2021) ﬁnd that between 2008 and 2019, over 40
countries adjusted their CFMs including both liberalization and tightening. These adjustments involved either relaxing or strengthening existing measures, as well as introducing
new measures or removing existing ones. In other words, the coverage of time period is
suﬃcient for identifying the policy eﬀects of CFMs.
The granular indices of FKRSU dataset are 0-1 dummy, for 1 representing the presenceof a restriction and 0 representing no restriction for a given country at a given time.
According to Klein (2012)’s classiﬁcation, we can categorize the countries in our sample into
three groups: “open”, “gate”, and “wall”. An “open” country has virtually no CFMs on any
asset category throughout the sample period. A “wall” country has extensive restrictions
on all or almost all categories of assets, and a “gate” country applies CFMs episodically. Inline with these classiﬁcations, “open” countries are suitable for using as the control group.
The counterfactual estimator that we employ in this paper utilizes data from the control
group to construct models and estimate counterfactuals for treated observations based on
the models (Liu et al., 2024). “Gate” countries can be classiﬁed as the treatment group.
However, “wall” countries, especially those implementing controls continuously from theoutset of our sample in 1995, cannot provide a suﬃcient pretreatment period. In suchcases, the presence of “incidental parameters” can lead to biased estimates of treatmenteﬀects, and these samples are subsequently excluded (Xu, 2017). This data pattern aligns
with the counterfactual estimation approach developed by Liu et al. (2024) for time-series
cross-sectional (TSCS) data with dichotomous treatments that switch back and forth.
The FKRSU dataset contains CFMs for 10 types of speciﬁc asset categories and we use
the two-letter abbreviations in Fernández et al. (2016b) for simplicity: equity (“eq” hereafter), bonds or other debt securities (“bo” hereafter), money market instruments (“mm”
hereafter), collective investment (“ci” hereafter), derivatives (“de” hereafter), commercial
credits (“cc” hereafter), ﬁnancial credit (“fc” hereafter), guarantees, sureties and ﬁnancial
back-up facilities (“gs” hereafter), direct investment (“di” hereafter), and real estate (“re”hereafter). Fernández et al. (2016b) diﬀerentiate among transaction types based on theresidency of either the buyer or the seller, and the nature of the transaction, whether it
involves a purchase, sale, or issuance. Using “eq” as an illustration, there are four trans14

action control categories: two concerning inﬂows, which encompass purchase locally by
non-residents (“eq_plbn”) and sale or issue abroad by residents (“eq_siar”), and two related to outﬂows, purchase abroad by residents (“eq_pabr”) and sale or issue locally by
non-residents (“eq_siln”). As we have mentioned above, all the granular indices of FKRSU
dataset are 0-1 dummy. Regarding aggregate indices, rather than employing Fernández
et al. (2016b)’s arithmetic mean of each granular index, we opt for an alternative approachto maintain the aggregate indices in binary form. Taking equity “eq” as an example, theconstruction process of the equity inﬂow/outﬂow management indicator is as follows:
1 If either “eq_plbn” or “eq_siar” equals 1,
equity inﬂow management: “eqi” =0 otherwise.

1 If either “eq_pabr” or “eq_siln” equals 1,

equity outﬂow management: “eqo” =0 otherwise.

1 If either “eqi” or “eqo” equals 1,

equity ﬂow management: “eq” =0 otherwise.

As for aggregated indicator for all categories, we can also compile it in the same way:
1 if one of the asset speciﬁc index equals 1,
aggregate all categories: “ka” =
0 otherwise.

Figure 3.1a presents the visualization results of the treatment and control groups for aggregated “ka”. The control group and the treatment group are marked in light and dark
blue, respectively. This indicates that upwards of 74% of the observations, classiﬁed astreated economies (dark blue) from the year 1995, are excluded from the sample intended
for estimation. Furthermore, only three countries, the Netherlands, Panama, and Zambia,
have remained in a state of no capital ﬂow management since 1995, resulting in a control
group sample size such that it is too limited to allow for precise estimation of factors andthe derivation of estimated counterfactuals (Xu, 2017). Therefore, the aggregated “ka”
indicator is not suitable for the counterfactual estimator, and the use of this indicator may
15result in a signiﬁcant estimation bias.
ka episode

Economies

Economies

Aggregate ka
AGO
ARE
ARG
AUS
AUT
BFA
BGD
BGR
BOL
BRA
BRN
CAN
CHE
CHL
CHN
CIV
COL
CYP
CZE
DNK
DOM
DZA
ECU
EGY
ETH
FIN
GHA
IDN
IND
IRN
ISL
ITA
JAM
KAZ
KEN
KGZ
KOR
KWT
LBN
LKA
LVA
MAR
MDA
MEX
MLT
MMR
MUS
MYS
NGA
NZL
OMN
PAK
PHL
POL
QAT
ROM
RUS
SAU
SGP
SVN
SWZ
TGO
THA
TUN
TUR
TZA
UGA
UKR
USA
UZB
VEN
VNM
YEM
ZAF
SWE
PRT
FRA
HUN
ISR
DEU
BEL
GTM
ESP
IRL
BHR
GEO
PRY
JPN
SLV
NOR
GRC
CRI
HKG
PER
NIC
URY
GBR
NLD
PAN
ZMB
1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019

BGD
SAU
SVN
CYP
KWT
UZB
LBN
BGR
BHR
DZA
VNM
LVA
POL
TZA
ETH
MDA
VEN
ARG
JAM
COL
DEU
CZE
AUT
FIN
CHE
BEL
HUN
PRT
TUR
SWE
KAZ
PRY
BOL
ECU
MMR
EGY
KGZ
BRA
ISL
RUS
GRC
ESP
AGO
ARE
AUS
BFA
BRN
CAN
CHL
CHN
CIV
CRI
DNK
DOM
FRA
GBR
GEO
GHA
GTM
HKG
IDN
IND
IRL
IRN
ISR
ITA
JPN
KEN
KOR
LKA
MAR
MEX
MLT
MUS
MYS
NGA
NIC
NLD
NOR
NZL
OMN
PAK
PAN
PER
PHL
QAT
ROM
SGP
SLV
SWZ
TGO
THA
TUN
UGA
UKR
URY
USA
YEM
ZAF
ZMB
1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019Year
Control Economies

Year
Treated Economies

Control Economies(a) Aggregate all categories “ka”

Treated Economies(b) “ka” episode using Furceri & Loungani (2018)

Figure 3.1: Visualization results of the treatment and control groups
Following Furceri and Loungani (2018), we reconstruct the aggregated indicator to
identify the capital ﬂow management episodes. Fernández et al. (2016b) provide the aggregated “ka” as the arithmetic mean of each granular index. We identify these episodes
by assuming that an episode takes place when, for a speciﬁc country at a speciﬁc time,
the annual ﬂuctuation in the arithmetic mean “ka” indicator surpasses the average annualchange across all observations by two standard deviations. Figure 3.1b presents the visualization results of the treatment and control groups for these episodes. Based on thesecriteria, the identiﬁcation of episodes classiﬁes more than half of the economies as having
no CFMs. These criteria selectively identify economies that have implemented substantial regulatory measures on their capital accounts compared to previous periods from anaverage perspective (without depending on the overall degree of the restriction of sucheconomies’ capital account). Unlike in the previous case of Figure 3.1a, this identiﬁcation
method places greater emphasis on the extent of changes in CFMs rather than the speciﬁclevels of tightness or looseness of their implementation. As a substitute measure for the16

aggregated “ka”, we later explore the impacts of the identiﬁed CFMs episodes on wealthinequality.
As we employ diﬀerent CFMs indicators, the countries composing the treatment andcontrol groups vary for each speciﬁc indicator. Additionally, as there are economies where
policy indicators have been implemented before the beginning of our sample period in 1995,
the corresponding samples are removed. Consequently, the actual number of samples usedin each model speciﬁcation diﬀers. We will annotate the number of economies in thetreatment and control groups in each regression result and specify the economies includedin the treatment group.

3.3

Control variables

We choose control variables based on three reasons: (i) we follow the existing research on
policy and inequality (Teixeira, 2023; Hasan et al., 2020; Frost et al., 2020; Delis et al.,2014); (ii) the control variables should also be closely related to wealth inequality; (iii) the
selection should guarantee that the synthetic controls can precisely replicate the characteristics of countries using CFMs. From these reasons, we choose ﬁnancial development,
inﬂation growth, population, real GDP per capita, and government expenditure on education (%GDP) as the control variables. In Section 6, for robustness checks, we employ theaverage of education and population growth as alternative variables of the government expenditure on education (%GDP) and population. We also consider other policy indicatorsthat might aﬀect wealth inequality, such as the government subsidies as a proxy for ﬁscal
policy and broad money as a proxy for conventional monetary policy.12

4Methodology

In this paper, we utilize the up-to-date counterfactual estimator proposed by Liu et al.
(2024) to establish causality from CFMs to wealth inequality. The proposed counterfactual
estimator is adopted from the following reasons. Firstly, our data pattern is a time-series
cross-sectional setting with a dichotomous treatment. Secondly, the treatment eﬀects of
CFMs are heterogeneous both across units and over time. For example, although several
12

Xu (2023) argues that the estimator permitting user discretion such as cherry-picking covariates toimprove the appearance of pre-treatment ﬁt results in spurious ﬁndings.

17

economies implement such policies, they have heterogeneous impacts on diﬀerent economies
depending on the economy’s demographics, level of economic development, and ﬁnancial
market structure, etc. The counterfactual estimators used in this paper can capture such
diﬀerential trends, and the no-time-varying-confounder assumption will be relaxed to a certain extent.13 Thirdly, our data pattern involves reversal and staggering adoption, making
the conventional generalized synthetic control method (GSCM) invalid.14 Additionally, as
we do not focus on the speciﬁc policies of a speciﬁc country, the synthetic control method(SCM) itself is not applicable. Lastly, technically, the counterfactual estimator method
is embedded in a cross-validation scheme that can choose the number of potential factors(or tuning parameter) based on model performance measured by mean-squared prediction
error (MSPE).

4.1

Identiﬁcation

The outcome of interest, wealth inequality Giniit , is given by a linear factor model:

Giniit = δit CF M sit + X ′it β + λ′i f t + αi + ξt + εit ,(1)

where CF M sit is the aggregated or asset speciﬁc CFMs that equal 1 when an economy i istreated at time t and equal 0 otherwise. Diﬀering from a conventional two-way ﬁxed eﬀects
model in which δit is constant, we allow δit , which is our interest treatment eﬀect, to beheterogeneous across economy i over time t. X is a (p × 1) vector of observed covariates.
β = [β1 , · · · , βp ]′ is a (p × 1) vector of unknown parameters. f t = [f1t , · · · , frt ]′ is an(r × 1) vector of unobserved common factors and λi = [λ1r , · · · , λir ] is an (r × 1) vectorof unknown factor loadings. εit represents unobserved idiosyncratic shocks for economy iat time t and has zero mean. αi and ξt are the two-way ﬁxed eﬀects. For identiﬁcation,
we need to impose two constrains (Liu et al., 2024):
Λ′ Λ = diagonal;
13

F ′F
= I,
T

Fixed-eﬀect counterfactual estimator (FEct) can result in biased estimates in the presence of unobserved time-varying confounders. Bai (2009), and Bai and Ng (2021) suggest that the use of factoraugmented models resolves this issue when the confounders can be broken down into time-speciﬁc factors
interacting with unit-speciﬁc factor loadings (Liu et al., 2024).
14
In Xu (2017), he does not consider the cases in which the treatment switches on and oﬀ (or “multipletreatment-time”).

18where Λ = [λ1 , · · · , λN ]′ captures the heterogeneous impacts caused by each economy’s
various unobserved characteristics and F = [f1 , · · · , fT ]′ can be understood as time-varying
trends that aﬀect each economy diﬀerently. The causal parameter of interest is the averagetreatment eﬀect on the treated economies (ATT):[]\it (0) |CF M sit = 1, ∀i ∈ T , ∀t ,AT T = E [δit |CF M sit = 1, ∀i ∈ T , ∀t] = E Giniit (1) − Gini
where the potential outcomes of Giniit are deﬁned as Giniit (CF M sit ). T is deﬁned
as units whose treatment status has changed during the observed time window. The
counterfactual estimators treat data under the treatment group as missing and use data
under the control group to build a model for non-treated potential outcome Giniit (0).
Then, we predict Giniit (0) for the observations under the treatment conditions. ATT is\it (0) for theobtained by taking an average of the diﬀerences between Giniit (1) and Gini
treated observations.

4.2

Estimators\it (0):We use two counterfactual estimators constructing the treated counterfactuals Giniinteractive ﬁxed-eﬀect counterfactual (IFEct) estimator and matrix completion (MC) estimator. As argued by Liu et al. (2024), both IFEct and MC use lower-rank matrix
approximation techniques to enhance predictions for treated counterfactuals. Their distinction lies in the regularization approach: IFEct chooses the number of factors, whereas
MC depends on a tuning parameter θ.
In the absence of covariates, the non-treated potential outcome matrix Gini (0) =[Giniit (0)]i=1,2,··· ,N ;t=1,2,···T can be approximated by L = [Lit ]i=1,2,··· ,N ;t=1,2,···T :Gini (0) = L + ε,

E [ε|L] = 0,

where εit can be thought of as measurement error. The goal is to estimate the matrix
L. As for IFEct, L can be expressed as the product of two matrices: L = ΛF . Using an Expectation-Maximization (EM) algorithm, the factors and factor loadings can be19estimated by minimizing the least squares objective function:
(
F̂

(h+1)

, Λ̂

(h+1)

[(

)
= arg min tr
s.t.

where
W(h+1) :=W(h+1) − F̃ Λ̃

) (
′ ′

W(h+1) − F̃ Λ̃

′)]F˜ ′ F̃′= Ir , Λ̃ Λ̃ = diagonal,TGiniit − X ′it β̂ (h+1) ,CF M sit = 0

′

(h)

α̂i(h) + ξˆt(h) + λ̂(h)i f̂ t , CF M sit = 1,,

and h means the h round in the EM algorithm.15
Diﬀerent from IFEct, the MC estimator directly estimates L by solving the following
minimization problem:
∑ (Giniit − Lit )2+ θ ∥L∥ ,L̂ = arg min |O|L(i,t)∈O

where O = {(i, t) |CF M sit = 0} and |O| is the number of element in O. ∥L∥ is the matrix
norm of L and θ is the tuning parameter. Athey et al. (2021) develop an iterative algorithm
to obtain L̂ and we provide an abridged description as follows: (i) Given a tuning parameterθ and start with the initial value

L0 (θ) = PO (Gini) =Giniit , (i, t) ∈ O


0,

;

/O
(i, t) ∈(ii) For h = 0, 1, 2, ..., calculate[]L(h+1) (θ) = shrinkθ PO (Gini) + PO⊥ (Lh (θ)) = SΣ̃RT ,()
where Σ̃ equals to Σ with the i-th singular value σi PO (Gini) + PO⊥ (Lh (θ)) replaced by( ())
max σi PO (Gini) + PO⊥ (Lh (θ)) − θ, 0 16 ; (iii) Repeat until the sequence {Lh (θ)}h≥0
15

The interested reader is referred to the original paper by Liu et al. (2024) for additional details.
impute and there is another form called hard impute, where( This form is ⊥ called soft)σi PO (Gini) + PO (Lh (θ)) can be replaced by
(
) { (
)
}
⊥
⊥
σi PO (Gini) + PO
(Lh (θ)) 1 σi PO (Gini) + PO
(Lh (θ)) ≥ θ .
1620

converges.
In both the IFEct and MC estimators, we use a k-fold cross-validation scheme to selectthe optimal number of factors r and the tuning parameter θ that minimizes mean-squared
prediction error (MSPE) of the wealth outcome. For the IFEct estimator, we restrict theanalysis to countries with at least 7 pre-treatment periods (Xu, 2017; Teixeira, 2023). The
uncertainty estimates are obtained through a block bootstrap procedure for 1000 times.

4.3

Statistical Tests

Following Liu et al. (2024), we conduct two diagnostic tests to examine the validity of
the identifying assumptions. The ﬁrst assumption is the function form of equation (1)of a linear (interactive) ﬁxed eﬀects model with a dichotomous treatment. The second
assumption is a strict exogeneity for interactive ﬁxed eﬀects as follows: for any i, j =1, 2, · · · , N and t, s = 1, 2, · · · , T ,CF M sjs , X js , αj , ξs , λj , f t .

⊨εitWe ﬁrst use the equivalence test proposed by Hartman and Hidalgo (2018). The basic
idea is to jointly test a set of null hypotheses that the average of residuals for any pretreatment period fall within a pre-speciﬁed narrow range:
AT Ts < −θ2 or AT Ts > θ1 ,∀s ≤ 0.

Rejecting the null hypothesis suggests that for any s ≤ 0, the range of −θ2 ≤ AT Ts ≤ θ1 ismaintained. In other words, if we collect suﬃcient data to demonstrate that the averages
of pre-treatment residuals lie within a narrowly deﬁned range, it serves as evidence backing
the assumption that there are no time-varying confounders. The range [−θ2 , θ1 ] is thus
referred to as the equivalence bound. Following the suggestion in Liu et al. (2024) andHartman and Hidalgo (2018), we use the two one-side test to check the equivalence of AT Ts
to zero for each s < 0. We use a conservative standard that the null hypothesis is deemed
rejected only if the tests across all pre-treatment periods yield signiﬁcant outcomes. The
Liu et al. (2024) show that hard impute is equivalent to IFEct since both algorithms impose penalties
based on the number of factors, and the only diﬀerence between MC with hard impute and IFEct lies intheir initial values.

21equivalence bound is set θ1 = θ2 = 0.36σε where σε is the standard deviation of residualized
non-treated outcome. The equivalence test is criticized on two fronts: on one hand, theselection of the equivalence bound is seen as overly arbitrary, and using 0.36σε might be
too forgiving when the eﬀect size is small compared to the outcome’s variance. On theother hand, this test suﬀers from an over-ﬁtting issue, making it very easy to pass this test.
The second test is the (out-of-sample) placebo test. The fundamental concept involves
presuming that the treatment begins S periods prior to its actual start for each economy
in the treatment group, and subsequently utilizing the identical counterfactual estimator
to compute AT Ts for s = − (S − 1) , · · · , −1, 0. If the assumption of no time-varying
confounders is valid, then this ATT estimate should be statistically equivalent to zero,with an insigniﬁcant outcome supporting the assumption’s credibility. S should not be
excessively large since a larger S means there will be fewer pre-treatment periods available
for estimation. Conversely, if both S and the number of treatment observations are too
small, the test might lack suﬃcient power. In this and subsequent examples, we havechosen S = 3. Unlike the in-sample equivalence test, the placebo test involves out-ofsample predictions of Gini (0) during the placebo periods, making it more robust tomodel mis-speciﬁcation and immune to over-ﬁtting.
Table 4.1 shows the results of the equivalence test and placebo test explained above
for diﬀerent CFMs indicators (equity “eq”, bond “bo”, money market “mm”, collective investment “ci”, derivative “de”, commercial credit “cc”, ﬁnancial credit “fc”, guarantee andsureties “gs”, direct investment “di”, real estate “re”, and aggregated index “ka”) basedon three diﬀerent estimators: FEct, IFEct, and MC. For FEct, all asset speciﬁc indicesindicate a trend towards the treatment onset that exceeds the equivalence bound. Consequently, we cannot dismiss the null hypothesis that pre-treatment residual averages are
not zero, which means that we cannot assert with high probability that equivalence ismaintained. From this perspective, even if they all pass the placebo test, this paper will
not employ the ﬁxed-eﬀect counterfactual estimator (FEct) to estimate the ATT of CFMs.
As for IFEct, apart from restrictions on commercial credit “cc” and guarantees and sureties“gs”, all other asset speciﬁc indicators pass the equivalence test. This also means that the
conﬁdence intervals of pre-treatment residual averages fall within the equivalence bounds.
As for MC, we ﬁnd that for all the CFMs indices, both tests passed the examination.
22

Regarding the choice between IFEct and MC, Liu et al. (2024) suggest that researchers
may choose between the two models either based on two diagnostic tests aforementioned
and/or their relative predictive power (e.g., as measured by the Mean Squared Prediction
Error (MSPE)). We utilize the toolbox from Liu et al. (2024) to determine which estimator
has a smaller MSPE. When both IFEct and MC pass the equivalence and placebo tests,
we make a choice for each indicator basically depending on the MSPE.
Table 4.1: Equivalence tests and placebo tests
Estimators
FEct
IFEct
MC

Tests

“eq”

“bo”

“mm”

“ci”

“de”

“cc”

“fc”

“gs”

“di”

“re”

“ka”

Equivalence tests
Placebo tests
Equivalence tests
Placebo tests
Equivalence tests
Placebo tests

×






×






×






×






×






×

×




×






×

×




×






×






×






Note: The above table shows the results of the equivalence tests and placebo tests based on three
diﬀerent estimators (FEct, IFEct, and MC) for diﬀerent asset speciﬁc CFMs indicators (equity“eq”, bond “bo”, money market “mm”, collective investment “ci”, derivative “de”, commercial credit“cc”, ﬁnancial credit “fc”, guarantee and sureties “gs”, direct investment “di”, real estate “re”, andaggregate index “ka”). Checked mark “” means it passes the test, and “×” mark means not.
Each equivalence holds when the bootstrapped one-side conﬁdence interval of ATTs falls within[−0.36σ̂ε , 0.36σ̂ε ], the equivalence bound. Three pre-treatment periods (s = −2, −1, 0, · · · ) serveas the placebo. Standard errors are based on 1000 parametric bootstraps at the country level.

5

Empirical Results

This section presents the eﬀects of CFMs on wealth inequality. We ﬁrst provide the results
using the aggregated indicators. We examine the eﬀects for aggregated index “ka”, andaggregated inﬂow “kai”, outﬂow index “kao”. We also investigate these eﬀects under diﬀerent
income levels (i.e., advanced and emerging economies). As one of the contributions of thispaper, we investigate the eﬀects of asset speciﬁc indicators on wealth inequality, and we also
consider both inﬂow and outﬂow asset-speciﬁc indicators. Lastly, since the Gini coeﬃcients
do not fully capture all segments of the wealth distribution, we also examine the eﬀects of
CFMs on diﬀerent wealth shares groups.

23

5.1

Aggregate CFMs and wealth inequality

Figure 5.1 shows that implementing aggregate CFMs reduces wealth inequality. The resultaligns with previous research on income inequality, where Li and Su (2021), Gallagher et al.
(2019), Furceri et al. (2017), Furceri and Loungani (2018), De Haan and Sturm (2017), andBumann and Lensink (2016) all found that capital account liberalization could increase
income inequality. However, unlike their ﬁndings, the eﬀect of CFMs on wealth inequality
is not statistically signiﬁcant in our analysis. This insigniﬁcant result may be related to two
reasons. On one hand, as we have already mentioned earlier, the eﬀects of CFMs on wealthinequality are not uniform across economies; in fact, this policy encompasses a high degreeof heterogeneity. Erten et al. (2021) show that the practical implementation of CFMs ishighly complex and heavily reliant on institutional speciﬁcs. On the other hand, previous
research has shown that the impact of capital account liberalization on income inequality is
conditional. This conditionality depends on many factors, such as ﬁnancial depth, ﬁnancial
development, quality of institutions, and crises, most of which are closely related to theincome level of the country. Gallagher et al. (2019) argue that there is a consensus among
researchers regarding the importance of institutions, noting that countries with robust
institutions typically experience greater penetration of ﬁnancial services. Therefore, we
further categorize our sample into advanced and emerging economies based on the WorldBank Income Group and investigate the eﬀects of CFMs for diﬀerent income levels of
economies.

24

Aggregate ka
0.04

Coefficient

0.02

0.00

−0.02

−0.04

−5

0

5

10

Time relative to Treatment

Figure 5.1: The dynamic cumulative average treatment eﬀects (ATT) of aggregate CFMs“ka” on wealth inequality
Note: The horizontal axis represents the relative time before and after implementing CFMs at T0 ;the vertical axis represents the estimated coeﬃcient of the average eﬀect ATT. The solid black line
represents the average treatment eﬀect; the gray area shows the 90% conﬁdence interval. Standard
errors are based on 1000 parametric bootstraps at the country level. The coeﬃcient of the average
eﬀect ATT is estimated using IFEct.

Table 5.1 show the results for aggregated index “ka” for the full sample, advanced
economies, and emerging economies. As we explain above (Figure 5.1), in the full sample
case, although CFMs can reduce wealth inequality, their results are not statistical significant. However, Figure 5.2 shows that the use of CFMs in advanced economies actually
increases wealth inequality, and this result is signiﬁcant in the T0 + 8 period. After 8
years of CFMs, the Gini coeﬃcient of wealth concentration in the treated economies is
estimated to be 3.8 percentage points higher than that in the control economies. Ourﬁnding is in line with those obtained by Gallagher et al. (2019), Furceri and Loungani(2018), and Bumann and Lensink (2016) for income inequality. Gallagher et al. (2019)show that capital openness correlates with reduced inequality as a country moves into ahigher income group, typically characterized by stronger institutional quality. Furceri andLoungani (2018) show that the eﬀects between capital account openness and income inequality are more precisely estimated for high-income economies. Bumann and Lensink

25

Table 5.1: The eﬀects of aggregate CFMs “ka” on wealth inequality

Indices
Income level
Number of treatment economies
Number of control economies
Gini coeﬃcient (↓ or ↑)

All
18
64
↓

“ka”
AEs EMs
12
6
26
38
↑*
↓

Note: The above table presents the cumulative average treatment eﬀects of aggregate CFMs (“ka”)
on the treated economies (ATT) for diﬀerent income levels (full sample, advanced economies (AEs),and emerging economies (EMs)) after aggregate CFMs adoption. The result with ↑ mark meansthat CFMs increase wealth inequality, and ↓ mark means CFMs decrease wealth inequality. We
also show the number of treatment and control economies used in the sample in the third and forthrow. The arrow mark with asterisk means the result is statistically signiﬁcant at the 10% level.

(2016) ﬁnd that capital account liberalization generally reduces income inequality only
when ﬁnancial depth, indicated by the ratio of private credit to GDP, is above 25 percent.
Conversely, their ﬁndings suggest that in most emerging economies with limited ﬁnancial
depth, capital account liberalization is likely to exacerbate income inequality. We obtaina consistent result on emerging economies to them (“ka” EMs in Table 5.1). Our result
regarding emerging economies (“ka” EMs) indicates that CFMs reduce wealth inequality,
but it is not statistically signiﬁcant as well as in the full sample case (“ka” All).

26

Aggregate ka: advanced economies
0.075

Coefficient

0.050

0.025

0.000

−5

0

5

10

Time relative to Treatment

Figure 5.2: The dynamic cumulative average treatment eﬀects (ATT) of aggregate CFMs“ka” on wealth inequality in advanced economies
Note: The horizontal axis represents the relative time before and after implementing CFMs at T0 ;the vertical axis represents the estimated coeﬃcient of the average eﬀect ATT. The solid black line
represents the average treatment eﬀect; the gray area shows the 90% conﬁdence interval. Standard
errors are based on 1000 parametric bootstraps at the country level. The coeﬃcient of the average
eﬀect ATT is estimated using IFEct.

Overall, CFMs signiﬁcantly exacerbate wealth inequality in advanced economies, whilein emerging economies and full sample, CFMs reduce wealth inequality, but the two results
are not statistically signiﬁcant. We will show how the Gini coeﬃcient changes from theperspective of the wealth distribution in Section 5.6. More speciﬁcally, we will show how
the CFMs aﬀect the wealth share of the top 1%, top 10%, middle 40% and bottom 50%
in each case.

5.2

Aggregate inﬂow/outﬂow CFMs and wealth inequality

The aggregate indices examined in Section 5.1 only reﬂect the overall level of CFMs.
However, in practice, the direction of capital movements is of concern to policy makers
especially in emerging economies. Capital ﬂows like FDI can bring technology and promoteeconomic growth in emerging economies, but they can also lead to an overheated economy
and excessive increases in housing prices (Hernandez-Vega, 2023; Cesa-Bianchi et al., 2018;
Kim and Yang, 2011). Conversely, capital outﬂows (or capital ﬂight) or sudden stops
27

can cause currency devaluations in emerging economies, leading to economic downturns(Bianchi, 2011; Mendoza, 2010). The impacts of capital inﬂows and outﬂows on economic(or ﬁnancial) cycles are clearly diﬀerent. Moreover, the policymakers’ combination ofcontrols on inﬂow and outﬂow varies across countries (Fernández et al., 2016b). In thissection, we examine the eﬀects of inﬂow and outﬂow controls on wealth inequality, andalso explore those in advanced and emerging economies individually.
Table 5.2 summarizes the results for the aggregate inﬂow “kai” and outﬂow “kao” management indices. The full sample result of “kai” (All) indicates that inﬂow controls increase
wealth inequality, although the result is not statistically signiﬁcant. Even when the sample is divided into advanced and emerging economies, the results remain not statisticallysigniﬁcant. The full sample result of “kao” (All), while consistent with the aggregate index“ka” (All), is also not statistically signiﬁcant. However, dividing the full sample into advanced and emerging economies, we ﬁnd that outﬂow controls signiﬁcantly increase wealthinequality in emerging economies (“kao” EMs). We show this outcome in Figure 5.3, where,
in the T0 + 3 period, the adoption of outﬂow controls “kao” in emerging economies raises
the Gini coeﬃcient of wealth concentration by 6.25 percentage points. Although the result
does not seem consistent to the related literature, we will explain how the increase in Ginicoeﬃcient in emerging economies is caused by the wealth distribution change in Section
5.5. Regarding advanced economies, “kao” also tends to increase wealth inequality, but thisestimated result is not statistically signiﬁcant.

28

Table 5.2: The eﬀects of aggregate inﬂow CFMs “kai” and outﬂow CFMs “kao” on wealthinequality

Indices
Income level
Number of treatment economies
Number of control economies
Gini coeﬃcient (↓ or ↑)

All
12
71
↑

“kai”
AEs EMs
5
7
36
35
↓
↑

All
21
61
↓

“kao”
AEs EMs
14
7
25
36
↑
↑*

Note: The above table presents the cumulative average treatment eﬀects of “kai” and “kao” on
the treated economies (ATT) for diﬀerent income levels (full sample, advanced economies (AEs),and emerging economies (EMs)) after the adoption of CFMs. The result with ↑ mark means that
CFMs increase wealth inequality, and ↓ mark means CFMs decrease wealth inequality. We also
show the number of treatment and control economies used in the sample in the third and forthrow, respectively. The arrow mark with asterisk means the result is statistically signiﬁcant at the
10% level.

Aggregate kao: emerging economies
0.20

0.15

Coefficient

0.10

0.05

0.00

−0.05
−5

0

5

10

Time relative to Treatment

Figure 5.3: The dynamic cumulative average treatment eﬀects (ATT) of aggregate outﬂow
controls “kao” on wealth inequality in emerging economies
Note: The horizontal axis represents the relative time before and after implementing CFMs at T0 ;the vertical axis represents the estimated coeﬃcient of the average eﬀect ATT. The solid black line
represents the average treatment eﬀect; the gray area shows the 90% conﬁdence interval. Standard
errors are based on 1000 parametric bootstraps at the country level. The coeﬃcient of the average
eﬀect ATT is estimated using IFEct.

In summary, by examining capital inﬂow/outﬂow managements separately, we obtaindiﬀerent results from the full sample case. We ﬁnd that in emerging economies, the implementation of capital outﬂow management “kao” increases wealth inequality, and the result
is statistically signiﬁcant.
29

5.3

Asset speciﬁc CFMs and wealth inequality

Compared with other economic policies, CFMs are considerably more complex and heavily reliant on institutional speciﬁcs. In practice, CFMs can be classiﬁed across various
dimensions (Erten et al., 2021). One of the most critical dimensions is that CFMs involve diﬀerent types of capital account transactions. The Annual Report on Exchange
Arrangements and Exchange Restrictions (AREAER) reports on the rules and regulations
for international transactions across 10 asset categories covering a signiﬁcant portion of
global cross-national asset holdings. Policymakers rarely regulate the entire capital account as a whole, but instead impose controls on separate capital accounts.17 Relying
solely on aggregate indices, we cannot capture the heterogeneity in the policy eﬀects inseparate capital accounts (Kitano and Zhou, 2022; Zhou, 2024). In this section, therefore,
we study the eﬀects of diﬀerent asset-speciﬁc CFMs on wealth inequality.
Table 5.3 presents the results of the relationship between CFMs and wealth inequality
in terms of the diﬀerent transaction (assets) categories. Diﬀerent transaction types havediﬀerent eﬀects on wealth inequality. Overall, the eﬀects of asset speciﬁc CFMs exhibit
signiﬁcant heterogeneity. Speciﬁcally, CFMs in the asset categories of equity “eq”, bond“bo”, collective investment “ci”, ﬁnancial credit “fc”, direct investment “di”, and real estate“re” seem to increase the Gini coeﬃcient, but the results are not statistically signiﬁcant. In
contrast, CFMs in the asset categories of money market “mm”, derivatives “de”, commercial
credit “cc”, and guarantees and sureties “gs” seem to reduce the Gini coeﬃcient. Amongthem, the eﬀects of CFMs in the money market “mm” and derivatives “de” are statisticallysigniﬁcant as shown in Figure 5.4. From Figure 5.4a, it is evident that the CFMs in“mm” reduces wealth inequality immediately after policy implementation, with long-lastingstatistical signiﬁcance from the T0 + 6 period onwards. The largest decline in the Ginicoeﬃcient occurs at T0 + 9, about a 4% point drop. Figure 5.4b shows that the CFMs
in “de” also reduces wealth inequality, with statistical signiﬁcance starting from T0 + 6
period. The largest decline in the Gini coeﬃcient occurs at T0 + 10, about a 2.5% point
drop. The decline in the Gini coeﬃcient due to CFMs in “mm” and “de” is consistent with

17

For example, in January 2008, Brazil imposed a tax on inﬂows related to external loans. In October
2009, Brazil implemented the IOF tax, which covered, at varying rates, diﬀerent types of capital ﬂows,
such as ﬁxed-income securities, stocks, margin deposits, derivative contracts, and FDI (IMF, 2019).

30

the related literature implying that capital account liberalization leads to the increase in
the Gini coeﬃcient. As mentioned by Furceri and Loungani (2018) and Teixeira (2023),
the Gini coeﬃcient changes relatively slowly over time. Taking it into account, the eﬀects
of implementing CFMs in “mm” and “de” on wealth concentration are quite large. We willshow how the speciﬁc CFMs aﬀect the wealth share of the top 1%, top 10%, middle 40%
and bottom 50% of the wealth distribution in Section 5.6.
Table 5.3: The eﬀects of asset-speciﬁc CFMs on wealth inequality
IndicesNumber of treatment economies
Number of control economies
Gini coeﬃcient (↓ or ↑)

“eq”
12
18
↑

“bo”
10
20
↑

“mm”
13
22
↓*

“ci”
14
24
↑

“de”
15
22
↓*

“cc”
4
44
↓

“fc”
14
29
↑

“gs”
4
47
↓

“di”
7
25
↑

“re”
9
16
↑

Note: The above table presents the cumulative average treatment eﬀects of asset-speciﬁc CFMs
indices (equity “eq”, bond “bo”, money market “mm”, collective investment “ci”, derivative “de”,
commercial credit “cc”, ﬁnancial credit “fc”, guarantees and sureties “gs”, direct investment “di”,
real estate “re”) on the treated economies (ATT) after the adoption of CFMs. The mark ↑ (↓)
means that tightening CFMs increases (decreases) Gini coeﬃcient. We also show the number oftreatment and control economies used in the sample in the third and forth row. The arrow markwith asterisk means this result is statistically signiﬁcant at the 10% level.

31Money market mm

Coefficient

0.00

−0.02

−0.04

−0.06

−5

0

5

10

Time relative to Treatment(a) money market “mm”

Derivative de
0.01

0.00

Coefficient

−0.01

−0.02

−0.03

−0.04

−5

0

5

10

Time relative to Treatment(b) derivative “de”

Figure 5.4: The dynamic cumulative average treatment eﬀects (ATT) of asset-speciﬁc
CFMs on wealth inequality
Note: We show the statistically signiﬁcant results for CFMs on (a) money market “mm” and (b)derivatives “de”. The horizontal axis represents the relative time before and after implementing
CFMs at T0 ; the vertical axis represents the estimated coeﬃcient of the average eﬀect ATT. The
solid black line represents the average treatment eﬀect; the gray area shows the 90% conﬁdence
interval. Standard errors are based on 1000 parametric bootstraps at the country level. The
coeﬃcients of the average eﬀect ATT are estimated using MC.

32

Table 5.4: The eﬀects of asset-speciﬁc inﬂow CFMs on wealth inequality
IndicesNumber of treatment economies
Number of control economies
Results (↓ or ↑)

“eqi”
5
36
↓*

“boi”
8
38
↑*

“mmi”
9
35
↓*

“cii”
9
39
↑

“dei”
9
38
↓

“cci”
3
54
↓

“fci”
10
43
↑

“gsi”
6
56
↓

“dii”
10
32
↓*

Note: The above table presents the cumulative average treatment eﬀects of asset-speciﬁc inﬂow
CFMs indices (quity “eqi”, bond “boi”, money market “mmi”, collective investment “cii”, derivative“dei”, commercial credit “cci”, ﬁnancial credit “fci”, guarantees and sureties “gsi”, direct investment“dii”, real estate “rei”) on the treated economies (ATT) after the adoption of inﬂow CFMs. The
result with ↑ (↓) mark means that tightening CFMs increases (decreases) wealth inequality. We
also show the number of treatment and control economies used in the sample in the third andforth row. The arrow mark with asterisk means this result is statistically signiﬁcant at the 10%
level.

5.4

Asset speciﬁc inﬂow/outﬂow CFMs and wealth inequality

We also examine the eﬀects of asset speciﬁc CFMs by breaking down into inﬂow andoutﬂow indices. Table 5.4 shows the results for asset-speciﬁc inﬂow indices. Compared toTable 5.3, the asset-speciﬁc inﬂow CFMs exhibit a general consistency with the results inTable 5.3, but they diﬀer in some important points. As shown in Figure 5.5a, inﬂow CFMs
on equity “eqi” signiﬁcantly reduces wealth inequality. The eﬀect peaks in four years after
policy implementation, and wealth inequality decreases by 2 percentage points. Figure
5.5d shows that inﬂow CFMs on direct investment “dii” also reduces wealth inequality
by 6% points after four years, but the eﬀect becomes non-signiﬁcant after the T0 + 5
period in the long-run. The eﬀects of inﬂow CFMs on bonds “boi” and money market“mmi” align with the results in Table 5.3. The inﬂow CFMs on “boi” signiﬁcantly increases
wealth inequality after the T0 + 7 period, reaching a 10% points peak at the 8th period
as shown in Figure 5.5b. Figure 5.5c shows that the inﬂow CFMs on “mmi” signiﬁcantly
reduces wealth inequality after the T0 + 5 period, reducing the Gini coeﬃcient of wealthconcentration by 4% points at the 7th period. Both exhibit long-term sustainability intheir eﬀects.

33

“rei”
11
22
↑

Equity inflow eqi

Bond inflow boi

0.02

0.15

0.00

Coefficient

Coefficient

0.10

−0.02
0.05

0.00

−0.04

−5

0

5

10

−10

−5

Time relative to Treatment

0

5

10

Time relative to Treatment(a) Equity inﬂow “eqi”(b) Bond inﬂow “boi”

Money market inflow mmi

Direct investment inflow dii

0.000

Coefficient

Coefficient

0.1−0.0250.0−0.050−0.1−0.075
−5

0

5

10

−5

Time relative to Treatment

0

5

10

Time relative to Treatment(c) Money market inﬂow “mmi”(d) Direct investment inﬂow “dii”

Figure 5.5: The dynamic cumulative average treatment eﬀects (ATT) of asset-speciﬁc
inﬂow CFMs on wealth inequality
Note: We show the statistically signiﬁcant results for CFMs on (a) equity inﬂow “eqi”, (b) bondinﬂow “boi”, (c) money market inﬂow “mmi”, and (d) direct investment inﬂow “dii”. The horizontal
axis represents the relative time before and after implementing CFMs at T0 ; the vertical axis
represents the estimated coeﬃcient of the average eﬀect ATT. The solid black line represents theaverage treatment eﬀect; the gray area shows the 90% conﬁdence interval. Standard errors are
based on 1000 parametric bootstraps at the country level. The coeﬃcients of the average eﬀect
ATT in sub-ﬁgure 5.5a and 5.5c are estimated using MC, and the coeﬃcients of the average eﬀect
ATT in sub-ﬁgure 5.5b and 5.5d are estimated using IFEct.

Table 5.5 shows the results for asset-speciﬁc outﬂow CFMs indices. The directionsof their impacts on wealth inequality (i.e., increase or decrease) are consistent with theresults in Table 5.3. Among them, outﬂow CFMs on derivatives “deo” signiﬁcantly reduces
wealth inequality as shown in Figure 5.6a. This eﬀect becomes statistical signiﬁcant in thelong-term (after T0 + 6) and reaches its peak at T0 + 8 period, reducing the Gini coeﬃcientof wealth concentration by 2.5 % points. Outﬂow CFMs on ﬁnancial credit “fco”, direct
investment “dio”, and real estate “reo” signiﬁcantly increase wealth inequality, as shown in
Figures 5.6b, 5.6c, and 5.6d, respectively. Unlike “deo” in Figure 5.6a, the eﬀects of these34

Table 5.5: The eﬀects of asset-speciﬁc outﬂow CFMs on wealth inequality
IndicesNumber of treatment economies
Number of control economies
Results (↓ or ↑)

“eqo”
11
21
↑

“boo”
10
22
↑

“mmo”
9
27
↓

“cio”
14
25
↑

“deo”
16
23
↓*

“cco”
3
51
↓

“fco”
14
33
↑*

“gso”
5
48
↓

“dio”
5
44
↑*

Note: The above table presents the cumulative average treatment eﬀects of asset-speciﬁc outﬂow
CFMs indices (quity “eqo”, bond “boo”, money market “mmo”, collective investment “cio”,
derivative “deo”, commercial credit “cco”, ﬁnancial credit “fco”, guarantees and sureties “gso”, direct
investment “dio”, real estate “reo”) on the treated economies (ATT) after the adoption of outﬂow
CFMs. The result with ↑ (↓) mark indicates that tightening the CFMs increases (decreases)
wealth inequality. We also show the number of treatment and control economies used in thesample in the third and forth row. The arrow mark with asterisk means this result is statisticallysigniﬁcant at the 10% level.

three policies (“fco”, “dio”, and “reo”) materialize in the short term (within T0 + 5) andhave a shorter duration of signiﬁcance. The adoption of “fco” increases the Gini coeﬃcientof wealth concentration by 2.2 % points at T0 + 6 (Figure 5.6b). The implementation of“dio” becomes signiﬁcant from T0 + 1 and reaches its maximum at T0 + 5, with a 7.5%
points increase (Figure 5.6c). The adoption of “reo” signiﬁcantly raises wealth inequality
at T0 + 1, but the eﬀect is relatively smaller, at 1.1% points (Figure 5.6d).

35

“reo”
15
39
↑*

Derivative outflow deo

Financial credit outflow fco

0.01

0.00
0.03

Coefficient

Coefficient

−0.01

−0.02

0.00

−0.03

−0.03
−0.04

−0.05
−5

0

5

10

−5

Time relative to Treatment

0

5

10

Time relative to Treatment(a) Derivative outﬂow “deo”(b) Financial credit outﬂow “fco”

Direct investment outflow dio

Real estate outflow reo

0.10

Coefficient

Coefficient

0.04

0.05

0.02

0.00
0.00

−0.02

−5

0

5

10

−5

Time relative to Treatment

0

5

10

Time relative to Treatment(c) Direct investment outﬂow “dio”(d) Real estate outﬂow “reo”

Figure 5.6: The dynamic cumulative average treatment eﬀects (ATT) of asset-speciﬁc
outﬂow CFMs on wealth inequality
Note: We show the statistically signiﬁcant results on CFMs on (a) derivative outﬂow “deo”, (b),ﬁnancial credit outﬂow “fco”, (c) direct investment outﬂow “dio”, and (d) real estate outﬂow “reo”.
The horizontal axis represents the relative time before and after implementing CFMs at T0 ; thevertical axis represents the estimated coeﬃcient of the average eﬀect ATT. The solid black line
represents the average treatment eﬀect; the gray area shows the 90% conﬁdence interval. Standard
errors are based on 1000 parametric bootstraps at the country level. The coeﬃcient of the average
eﬀect ATT in sub-ﬁgure 5.6a is estimated using MC, and the coeﬃcients of the average eﬀect ATT
in sub-ﬁgure 5.6b, 5.6c and 5.6d are estimated using IFEct.

On the whole, these results indicate that the granular CFMs exhibit high heterogeneity.
Among the asset-speciﬁc CFMs cases, only the results of CFMs on “mm” and “de” arestatistically signiﬁcant and their implementation reduces wealth inequality (Figure 5.4).
The outcomes for asset-speciﬁc inﬂow CFMs on “eqi” and “dii” indicate that these two
inﬂow CFMs signiﬁcantly reduce wealth inequality (Figure 5.5a and 5.5d), which contrasts
with “eq” and “di” (Table 5.4). The results for asset-speciﬁc outﬂow CFMs shown in
Table 5.5 are basically consistent with those for asset-speciﬁc CFMs shown in Table 5.3.
“deo” signiﬁcantly reduces wealth inequality (Figure 5.6a), while “fco”, “dio”, and “reo”
36

signiﬁcantly increase wealth inequality (Figure 5.6b, 5.6c, and 5.6d). A notable point isthat the inﬂow CFMs on “dii” signiﬁcantly reduces wealth inequality, whereas the outﬂow
CFMs on “dio” signiﬁcantly increases wealth inequality. This implies that even though
they are related to the same asset market, the inﬂow and outﬂow CFMs may have theopposite eﬀects on wealth inequality.

5.5

Aggregate CFMs and wealth shares

The Gini coeﬃcient provides an overall distribution structure, but does not indicate whichpart of the distribution is aﬀected by CFMs, and is not suﬃcient to provide an adequate
picture of wealth inequality (Piketty and Zucman, 2014). Besides, Piketty and Saez (2003)
argue that an important trend in income inequality is that income is concentrated in very
high income earners. In this section, we study the eﬀects of CFMs on the wealth shares of
the top 1%, top 10%, middle 40% and bottom 50% of the distribution.
Table 5.6 summarizes the results for aggregate index “ka” and inﬂow/outﬂow management indices “kai” and “kao”. For the aggregate index “ka” for the full sample and emerging
economies (All and EMs), the implementation of this policy leads to an increase in the shareof the top wealth level combined with a decrease in the proportion of the middle/bottom
levels, but all the results are statistically insigniﬁcant. As shown in Figure 5.7a, however,in advanced economies (AEs), the implementation of “ka” causes a signiﬁcant decline (by1.5% points in 8 years after the adoption of “ka”) in the share of the bottom 50% group.
This decline in the share of the bottom 50% group explains the signiﬁcant increase in theGini coeﬃcient in advanced economies shown in Figure 5.2. Similarly, in the “kai” (All)case, the policy’s implementation signiﬁcantly reduces the share of the bottom 50% group(Figure 5.7b). The decline in the share of the bottom 50% group accounts for the increasein the Gini coeﬃcient in the full sample case (in Table 5.2). Moreover, the implementationof “kai” (AEs) signiﬁcantly reduces the share of the top 1% in advanced economies by 7.5%
points after 5 years as shown in Figure 5.7c. The decline in the share of the top 1% due to“kai” (AEs) explains the decrease in the Gini coeﬃcient in advanced economies (in Table
5.2). Figure 5.7d shows that the implementation of “kao” (AEs) signiﬁcantly increases theshare of the middle 40% in advanced economies by 1.25% points after T0 + 4. In spiteof the increase in the share of the middle 40% due to “kao” (AEs), the Gini coeﬃcient37

Table 5.6: The eﬀects of aggregate “ka”, inﬂow “kai”, outﬂow “kao” on diﬀerent wealthsharesIndices
Income level
Number of treatment economies
Number of control economies
Average Top 1%
Average Top 10%
Average Middle 40%
Average Bottom 50%
Gini coeﬃcient (↓ or ↑)

All
18
64
↑
↑
↓
↓
↓

“ka”
AEs EMs
12
6
26
38
↑
↑
↑
↑
↑
↓
↓*
↓
↑*
↓

All
12
71
↑
↑
↓
↓*
↑

“kai”
AEs EMs
5
7
36
35
↓*
↑
↓
↑
↓
↓
↓
↓
↓
↑

All
21
61
↓
↑
↓
↓
↓

“kao”
AEs EMs
14
7
25
36
↓
↑
↓
↑*
↑*
↓
↓
↓
↑
↑*

Note: The above table presents the cumulative average treatment eﬀects of aggregate CFMs indices(aggregate “ka”, inﬂow “kai”, and outﬂow “kao”) on the treated economies (ATT) for the full sample(All), advanced economies (AEs), and emerging markets (EMs) after the adoption of CFMs. The
mark ↑ (↓) in the Gini coeﬃcient indicates that tightening CFMs increases (decreases) wealthinequality (Tables 5.1 and 5.2). We also show the number of treatment and control economies usedin the sample in the third and forth row. Row 4-7 show the wealth share for top 1%, 10%, middle
40%, and bottom 50%, respectively. The arrow mark with asterisk means this result is statisticallysigniﬁcant at the 10% level.

increases in Table 5.2, but the corresponding result is not statistically signiﬁcant. Figure
5.7e shows that the implementation of “kao” (EMs) increases the share of the top 10% in
emerging economies by 12.5% points in T0 + 7. This increase in top 10% level share due to“kao” (EMs) accounts for the increase of the Gini coeﬃcient in emerging economies shownin Figure 5.3.

38

Aggregate kai: wealth bottom 50%

Aggregate ka: advanced economies, wealth bottom 50%

0.010
0.00

0.005

Coefficient

Coefficient

−0.01

0.000

−0.02

−0.005−0.03−0.010

−5

0

5

10

−5

0

Time relative to Treatment

5

10Time relative to Treatment(a) Aggregate “ka” for advanced economies,
wealth bottom 50%(b) Aggregate “kai”, wealth bottom 50%

Aggregate kai: advanced economies, wealth top 1%

Aggregate kao: advanced economies, wealth middle 40%

0.1

0.03

0.02

Coefficient

Coefficient

0.0

−0.1

0.01

0.00

−0.2
−0.01

−10

−5

0

5

10

−5

Time relative to Treatment

0

5

10

Time relative to Treatment(c) Aggregate “kai” for advanced economies,
wealth top 1%(d) Aggregate “kao” for advanced economies,
wealth middle 40%

Aggregate kao: emerging economies, wealth top 10%
0.25

0.20

Coefficient

0.15

0.10

0.05

0.00

−0.05
−5

0

5

10

Time relative to Treatment(e) Aggregate “kao”
foreconomies, wealth top 10%

emerging

Figure 5.7: The dynamic cumulative average treatment eﬀects (ATT) of aggregate “ka”,
inﬂow“kai”, outﬂow “kao” on diﬀerent wealth shares
Note: We show the statistically signiﬁcant results for (a) aggregate “ka” for advanced economies,(b) inﬂow “kai” for advanced economies, (c) outﬂow “kao” for advanced economies, (d) outﬂow “kao”
for emerging economies, and (e) inﬂow “kai” for the full sample. The horizontal axis represents
the relative time before and after implementing capital control at T0 ; the vertical axis represents
the estimated coeﬃcient of the average eﬀect ATT. The solid black line represents the averagetreatment eﬀect; the gray area shows the 90% conﬁdence interval. Standard errors are based on
1000 parametric bootstraps at the country level. The coeﬃcients of the average eﬀect ATT are
estimated using IFEct.

39

5.6

Asset speciﬁc CFMs and wealth shares

Table 5.7 summarizes the results on the eﬀects of asset speciﬁc CFMs on diﬀerent wealthshares. Overall, the rise (fall) in the Gini coeﬃcient is due to the rise (fall) in the wealthshare of top 1% and (or) 10%, and the fall (rise) in the wealth share of middle 40% and(or) bottom 50%.
The typical case is that of CFMs in money market “mm”. Figure 5.8 (row 1) shows thatthe wealth shares of top 1% and 10% fall and those of middle 40% and bottom 50% rise,
which leads to the fall in the Gini coeﬃcient shown in Figure 5.4a. The eﬀect of CFMs in“mm” on wealth shares is persistently statistically signiﬁcant. Similarly, Figure 5.8 (row 2)
shows that CFMs in derivative “de” signiﬁcantly reduce the wealth share of the top 10%
and increases that of the bottom 50%, although the fall in the top 1% and the rise in thethe middle 40% are not statistically signiﬁcant. The fall in the top 10% and the rise in
the bottom 50% due to “de” shown in the second row of Figure 5.8 are likely to cause thefall in the Gini coeﬃcient due to “de” shown in Figure 5.4b. Figure 5.8 (row 3) shows that
CFMs in guarantees and sureties “gs” signiﬁcantly reduce the wealth share of the top 1%
and increase those of the middle 40% and bottom 50%, although the rise in the top 10%
is not statistically signiﬁcant. The fall in the top 1% and the rise in the middle 40% andbottom 50% due to “gs” shown in the third row of Figure 5.8 are likely to cause the fallin the Gini coeﬃcient due to “gs” (Table 5.3), although it is not statistically signiﬁcant.
These results on “mm”, “de”, and “gs” are consistent with those obtained by Li and Su(2021) and Teixeira (2023). Li and Su (2021) ﬁnd that capital account liberalization isassociated with a decrease in the income share of the poor and an increase in the income
share of the rich. Teixeira (2023) shows that the increase in wealth inequality is explainedby a rise in the wealth share of the top 1% combined with a sharp decline in the wealthshare of the bottom 50%. Unlike those in “mm”, “de”, and “gs”, CFMs in “di” signiﬁcantly
reduce the share of the top 10% and decrease the share of the bottom 50% at the sametime, which plausibly explains the statistically insigniﬁcant result on the Gini coeﬃcientdue to “di” (Table 5.3).

40

Table 5.7: The eﬀects of asset-speciﬁc CFMs on diﬀerent wealth shares
IndicesNumber of treatment economies
Number of control economies
Average Top 1%
Average Top 10%
Average Middle 40%
Average Bottom 50%
Gini coeﬃcient (↓ or ↑)

“eq”
12
18
↑
↑
↓
↓
↑

“bo”
10
20
↑
↑
↓
↓
↑

“mm”
13
22
↓*
↓*
↑*
↑*
↓*

“ci”
14
24
↑
↑
↓
↓
↑

“de”
15
22
↓
↓*
↑
↑*
↓*

“cc”
4
44
↓*
↓
↑
↑
↓

“fc”
14
29
↑
↑
↓
↓
↑

“gs”
4
47
↓*
↓
↑*
↑*
↓

“di”
7
25
↓
↓*
↑
↓*
↑

“re”
9
16
↓
↑
↑
↓
↑

Note: The above table presents the cumulative average treatment eﬀects of asset-speciﬁc CFMs
(quity “eq”, bond “bo”, money market “mm”, collective investment “ci”, derivative “de”, commercial
credit “cc”, ﬁnancial credit “fc”, guarantees and sureties “gs”, direct investment “di”, real estate “re”)
on the treated economies (ATT) after the adoption of CFMs. The mark ↑ (↓) in Gini coeﬃcient
presents that CFMs increase (decrease) wealth inequality. We also show the number of treatment
and control economies used in the sample in the third and forth row. Row 4-7 show the wealthshares of top 1%, 10%, middle 40%, and bottom 50%, respectively. The arrow mark with asteriskmeans this result is statistically signiﬁcant at the 10% level.

41

Wealth top 1%

Wealth top 10%

Money market mm, wealth top 1%

Money market mm, wealth top 10%

Wealth middle 40%

Wealth bottom 50%

Money market mm, wealth middle 40%

Money market mm, wealth bottom 50%

0.02

0.02

0.03
0.00

Coefficient

Coefficient−0.02

Coefficient

0.02

−0.02

Coefficient

Money market “mm”

0.04

0.00

0.01

0.01
−0.04

−0.04

0.00
0.00
−0.06
−0.01
−5

0

5

10

−5

0

5

10

−5

0

5

10

−5

0

5

10

Time relative to Treatment

Time relative to Treatment

Time relative to Treatment

Time relative to Treatment

Derivative de, wealth top 1%

Derivative de, wealth top 10%

Derivative de, wealth middle 40%

Derivative de, wealth bottom 50%
0.020

0.015

0.00

0.02

−0.02

Coefficient

Coefficient

Coefficient

0.010

Coefficient

Derivatives “de”

0.03

0.00

0.01

0.005

−0.02

0.00

0.000

−0.04
−0.04

−0.005

−0.01
0

5

10

−5

0

5

10

−5

0

5

10

−5

5

10

Time relative to Treatment

Time relative to Treatment

Time relative to Treatment

Guarantees and Sureties, wealth top 10%

Guarantees and Sureties, wealth middle 40%

Guarantees and Sureties, wealth bottom 50%

0.06

0.04

0.04

0.00
0.04

0.00
−0.05

0.02

−0.10

Coefficient

Coefficient

Coefficient

0.02

0.00

−0.04

0.00

−0.02

−0.15

−0.08
−10

−5

0

5

10

−10

−5

0

5

10

−10

−5

0

5

10

−10

−5

0

5

Time relative to Treatment

Time relative to Treatment

Time relative to Treatment

Time relative to Treatment

Direct investment, wealth top 1%

Direct investment, wealth top 10%

Direct investment, wealth middle 40%

Direct investment, wealth bottom 50%

0.05

10

0.000

0.00

0.10
−0.025−0.05

Coefficient−0.05

Coefficient

Coefficient

0.00

Coefficient

Direct investment “di”

0Time relative to Treatment

Guarantees and Sureties, wealth top 1%

Coefficient

Guarantees and Sureties “gs”

−5

0.05

−0.050−0.075
−0.10
−0.10
0.00
−0.100

−5

0

5

10

−5

Time relative to Treatment

0

5

10

Time relative to Treatment−50Time relative to Treatment

5

10

−5

0

5

10

Time relative to Treatment

Figure 5.8: The dynamic cumulative average treatment eﬀects (ATT) of asset-speciﬁc
CFMs on diﬀerent wealth shares
Note: We show the results for CFMs on money market “mm”, derivatives “de”, guarantees andsureties “gs”, and direct investment “di”. The four column shows the wealth shares of top 1%, 10%,
middle 40%, and bottom 50%, respectively. The horizontal axis represents the relative time before
and after the CFMs’ implementation at T0 ; the vertical axis represents the estimated coeﬃcient of
the average eﬀect ATT. The solid black line represents the average treatment eﬀect; the gray areashows the 90% conﬁdence interval. Standard errors are based on 1000 parametric bootstraps at
the country level. The coeﬃcients of the average eﬀect ATT for money market “mm”, derivatives“de” are estimated using MC, and the coeﬃcients of the average eﬀect ATT for guarantees andsureties “gs”, and direct investment “di” are estimated using IFEct.

6

Robustness Checks

In this section, we perform various robustness checks on the relationship between CFMs
and wealth inequality. We provide a series of robustness exercises and we ﬁnd that ouranalysis results are basically robust. We test if the results are robust when we use thealternative measures of CFMs, alternative control variables, and alternative pre-treatment
periods. We also test the robustness in the case where we include the other possible factors

42such as ﬁscal and monetary policies.

6.1

Diﬀerent measures of capital ﬂow managements

We ﬁrst test if the results about the eﬀects of aggregate CFMs on wealth inequality arerobust to the use of alternative measures of CFMs. We re-estimate Eq.(1) using the Chinn
and Ito (2006, 2008) “KAOPEN” indicator of synthetic capital account liberalization. Since
the index is available for an unbalanced panel of 182 countries from 1970 to 2020, ranging from -1.9 (more restricted capital account) to 2.3 (less restricted), we need to deﬁneaggregate CFMs episodes in a way that allows the index to be classiﬁed as a 0-1 dummy.
Following the method of Furceri and Loungani (2018), we identify CFMs episodes as follows. We assume that a CFM occurs when, for a given country at a given time, the annualchange in the “KAOPEN” indicator is below the average annual change observed acrossall observations by more than two standard deviations.18 Using the alternative measure,
we obtain the consistent results to Section 5.1. Similarly, as in the main analysis, therobustness check also yields the similar result for aggregate CFMs in the full sample case.
However, the robustness check for advanced economies does not yield a statistically signiﬁcant result. The diﬀerent outcome for advanced economies may be due to the construction
method of CFMs episodes in the robustness check, in which rare outcomes are classiﬁed ashaving implemented CFMs. Additionally, whereas in Fernández et al. (2016b)’s database,twelve advanced economies are selected as treatment economies, the CFMs episodes here
turn out to include only two advanced economies as treatment economies. This could
explain the diﬀerent result from the main analysis in the robustness check.

6.2

Diﬀerent conditioning factors

Following Teixeira (2023), we re-estimate Eq.(1) using the alternative measures of education and population. We use “average of education” instead of “Government expenditure
on education (%GDP),” and “ population growth” instead of “population.” As Figures 6.1aare broadly consistent with Figure 5.2 in Section 5.1, we conﬁrm that the implementationof aggregate CFMs increases wealth inequality in advanced economies. However, unlike in
Figure 5.3, the eﬀects on aggregate outﬂow CFMs in emerging economies are not statisti18

Unlike FKRSU, the “KAOPEN” index indicates that a higher value means a more open capital account.

43

cally signiﬁcant, which implies that we should be cautious in interpreting our result (Figure6.1b). Figures 6.2a and 6.2b also provide robust evidences showing that the imposition ofasset-speciﬁc CFMs on money market “mm” and derivative “de” are signiﬁcantly associatedwith a decrease in wealth inequality, which is similar to Figure 5.4 in Section 5.3 in themain analysis.

Aggregate ka: advanced economies robustness_edu

Aggregate ka: advanced economies robustness_pop
0.08

0.04

0.04

0.02

Coefficient

Coefficient

0.06

0.02

0.00

0.00

−0.02
−5

0

5

10

−5

Time relative to Treatment

0

5

10

Time relative to Treatment(a) Aggregate “ka” for advanced economies
Aggregate kao: emerging economies robustness_edu

Aggregate kao: emerging economies robustness_pop
0.2

0.10

0.1

Coefficient

Coefficient

0.05

0.00
0.0

−0.05

−0.1

−5

0

5

10

−5

Time relative to Treatment

0

5

10

Time relative to Treatment(b) Aggregate “kao” for emerging economies

Figure 6.1: Robustness check: the dynamic cumulative average treatment eﬀects (ATT) ofaggregate CFMs on wealth inequality; diﬀerent conditioning factors
Note: We show the results on (a) aggregate CFMs “ka” for advanced economies using average
education (left), and population growth (right), (b) aggregate outﬂow CFM “kao” for emerging
economies using average education (left), and population growth (right). The horizontal axis
represents the relative time before and after implementing CFMs at T0 ; the vertical axis represents
the estimated coeﬃcient of the average eﬀect ATT. The solid black line represents the averagetreatment eﬀect; the gray area shows the 90% conﬁdence interval. Standard errors are based on
1000 parametric bootstraps at the country level. The coeﬃcients of the average eﬀect ATT are
estimated using IFEct.

44

Money market mm robustness_edu

Money market mm robustness_pop

0.00

Coefficient

Coefficient

0.00

−0.02

−0.02

−0.04
−0.04

−0.06
−5

0

5

10

−5

0

Time relative to Treatment

5

10Time relative to Treatment(a) Money market “mm”
Derivative de robustness_edu

Derivative de robustness_pop

0.00

0.00

Coefficient

Coefficient

−0.01

−0.02

−0.02

−0.03

−0.04

−0.04
−5

0

5

10

−5

Time relative to Treatment

0

5

10

Time relative to Treatment(b) Derivative “de”

Figure 6.2: Robustness check: the dynamic cumulative average treatment eﬀects (ATT) ofasset-speciﬁc CFMs on wealth inequality; diﬀerent conditioning factors
Note: We show the results of CFMs on (a) money market “mm” using average education (left), andpopulation growth (right), and (b) derivatives “de” using average education (left), and population
growth (right). The horizontal axis represents the relative time before and after implementing
CFMs at T0 ; the vertical axis represents the estimated coeﬃcient of the average eﬀect ATT. The
solid black line represents the average treatment eﬀect; the gray area shows the 90% conﬁdence
interval. Standard errors are based on 1000 parametric bootstraps at the country level. The
coeﬃcients of the average eﬀect ATT are estimated using MC.

6.3

Other possible factors driving wealth inequality

Teixeira (2023) and Furceri et al. (2018) include ﬁscal policy and monetary policy in theiranalyses. We do not include the two additional variables in our main analysis because it
drastically reduces our sample size. In this section, however, we use government subsidies
as a proxy for ﬁscal policy following Teixeira (2023) and broad money as a proxy forconventional monetary policy based on McPhail (2000).19
19

Broad money may not be an ideal indicator for describing monetary policy. However, considering thediﬃculty of obtaining the policy rate for 100 economies, we still choose broad money as our policy variable.
Moreover, McPhail (2000) contends that broad money is a useful indicator of inﬂation over long horizons

45

Aggregate kao: emerging economies robustness_bm

0.3

Coefficient

0.2

0.1

0.0

−5

0

5

10

Time relative to Treatment

Figure 6.3: Robustness check: The dynamic cumulative average treatment eﬀects (ATT)of outﬂow CFMs “kao” in emerging economies; the additional conditioning variable (broadmoney) caseNote: We show the statistically signiﬁcant results for outﬂow CFMs “kao” in emerging economies
using additional conditioning variable (broad money). The horizontal axis represents the relative
time before and after implementing a CFM at T0 ; the vertical axis represents the estimated coeﬃcient of the average eﬀect ATT. The solid black line represents the average treatment eﬀect;
the gray area shows the 90% conﬁdence interval. Standard errors are based on 1000 parametricbootstraps at the country level. The coeﬃcient of the average eﬀect ATT is estimated using IFEct.

Although we include the broad money, as shown in Figure 6.3, the outﬂow CFMs “kao”
in emerging economies exhibit strong and signiﬁcant consistency with the results shown in
Figure 5.3 in Section 5.1. Similarly, as shown in Figure 6.4a and 6.4b, the asset-speciﬁc
CFMs on money market “mm” and derivative “de” also exhibit strong consistency with
Figures in 5.4a and 5.4b in Section 5.3.

and thus serves as a practical monetary policy tool.

46

Money market mm robustness_sub

Money market mm robustness_bm

0.02

0.00

Coefficient

Coefficient

0.00

−0.02

−0.02

−0.04
−0.04

−0.06
−5

0

5

10

−5

Time relative to Treatment

0

5

10

Time relative to Treatment(a) Money market “mm”
Derivative de robustness_sub

Derivative de robustness_bm

0.00

Coefficient

Coefficient

0.00

−0.02

−0.02

−0.04
−0.04

−5

0

5

10

−5

Time relative to Treatment

0

5

10

Time relative to Treatment(b) Derivative “de”

Figure 6.4: Robustness check: the dynamic cumulative average treatment eﬀects (ATT) ofasset-speciﬁc CFMs; the additional conditioning variables
Note: We show the results on CFMs on (a) money market “mm” using additional government
subsidies (left) and broad money (right) and (b) derivatives “de” using additional government
subsidies (left) and broad money (right). The horizontal axis represents the relative time before
and after implementing capital control at T0 ; the vertical axis represents the estimated coeﬃcientof the average eﬀect ATT. The solid black line represents the average treatment eﬀect; the grayarea shows the 90% conﬁdence interval. Standard errors are based on 1000 parametric bootstraps
at the country level. The coeﬃcients of the average eﬀect ATT are estimated using MC.

6.4

Diﬀerent pre-treatment periods

As mentioned in Section 4.3, the pre-treatment periods are crucial for estimating thecounterfactual eﬀects. Too few pre-treatment periods might prevent us from determining
whether the speciﬁc estimator can pass the equivalence test, and if it is the case, it is not
appropriate to use them for estimation. On the other hand, setting too long pre-treatment
periods may limit the number of treated economies in the sample, because the treatedunits with fewer pre-treatment periods than speciﬁed are excluded in estimations. Therefore, selecting an appropriate number of pre-treatment periods is essential for accurate
47

estimations. As a robustness check, we choose pre-treatment periods of 6 or 8 close to thesetting of 7 in the main analysis. We conﬁrm that asset-speciﬁc CFMs on the money market“mm” and derivatives “de” tend to decrease wealth inequality across diﬀerent pre-treatment
periods as shown in Figure 6.5a and 6.5b.

Money market mm robustness_t0_6

Money market mm robustness_t0_8
0.025

0.00

Coefficient

Coefficient

0.000

−0.02

−0.025−0.04−0.050

−0.06
−5

0

5

10

−5

Time relative to Treatment

0

5

10

Time relative to Treatment(a) Money market “mm”
Derivative de robustness_t0_6

Derivative de robustness_t0_8

0.02

0.00

Coefficient

Coefficient

0.00

−0.02

−0.02

−0.04

−5

0

5

10

−5

Time relative to Treatment

0

5

10

Time relative to Treatment(b) Derivative “de”

Figure 6.5: Robustness check: the dynamic cumulative average treatment eﬀects (ATT) ofasset-speciﬁc CFMs; diﬀerent pre-treatment periods
Note: We show the results on (a) money market “mm” using pre-treatment period 6 (left) and 8(right) years and (b) derivatives “de” using pre-treatment period 6 (left) and 8 (right) years. The
horizontal axis represents the relative time before and after implementing CFMs at T0 ; the verticalaxis represents the estimated coeﬃcient of the average eﬀect ATT. The solid black line represents
the average treatment eﬀect; the gray area shows the 90% conﬁdence interval. Standard errors are
based on 1000 parametric bootstraps at the country level. The coeﬃcients of the average eﬀect
ATT are estimated using MC.

7

Concluding Remarks

In this study, we have examined the eﬀects of various CFMs on wealth equality. Firstly, we
examine the eﬀects of aggregate CFMs on wealth equality using the full sample. Although48

the result suggests that tightening CFMs reduces wealth inequality, we can not obtain astatistically signiﬁcant result in this case (Figure 5.1). We next separate the full sample
into advanced and emerging economies, and then examine the eﬀects of aggregate CFMs
on wealth equality in each case. In this case, as for advanced economies, we obtain astatistically signiﬁcant result indicating that tightening CFMs increases wealth inequality(Figure 5.2). We further diﬀerentiate between capital inﬂows and outﬂows, and examine
the eﬀects of inﬂow and outﬂow CFMs on wealth inequality in each case. In this case, as for
emerging economies, we obtain a statistically signiﬁcant result indicating that tightening
outﬂow CFMs increases wealth inequality (Figure 5.3). Using the more granular indicesof CFMs by Fernández et al. (2016b), we examine the eﬀects of asset-speciﬁc CFMs on
wealth inequality and ﬁnd that tightening CFMs in the asset markets of money market
and derivative reduce wealth inequality, which are statistically signiﬁcant (Figure 5.4).
Diﬀerentiating between inﬂow and outﬂow further, we ﬁnd that tightening asset-speciﬁc
inﬂow CFMs signiﬁcantly reduce wealth inequality in the assets of equity, money market,
and direct investment, while they signiﬁcantly increase that in the bond market (Figure5.5). As for outﬂows, we obtain statistical signiﬁcant results indicating that tightening
CFMs in the asset markets of ﬁnancial credit, direct investment, and real estate increase
wealth inequality, whereas that in the derivative market reduces wealth inequality (Figure
5.6).
We next examine how CFMs change the wealth distribution in order to fully comprehend the linkage between CFMs and the wealth inequality (i.e., Gini coeﬃcients). We
obtain a statistically signiﬁcant result showing that tightening CFMs reduces the share of
the bottom 50% group in advance economies (Figure 5.7a), which explains the rise in thewealth inequality in advance economies shown in Figure 5.2. We also ﬁnd a statisticallysigniﬁcant result indicating that tightening outﬂow CFMs increases the wealth share of
the top 10% group in emerging economies (Figure 5.7e), which explains the rise in thewealth inequality in emerging economies (Figure 5.3). Furthermore, we ﬁnd a statisticallysigniﬁcant result showing that tightening CFMs in the money market reduces the wealthshares of the top 1% and 10% groups, but increases those of middle 40% and bottom 50%
groups (the ﬁrst row of Figure 5.8), which explains the fall in the wealth inequality shownin Figure 5.4a. We also ﬁnd a statistically signiﬁcant result showing that tightening CFMs49

in the derivative market reduces the wealth shares of the top 10% group, but increases
that of the bottom 50% group (the second row of Figure 5.8), which explains the fall in
the wealth inequality shown in Figure 5.4b.
Overall, our results imply that the eﬀects of CFMs on wealth equality and distribution
are quite heterogeneous: they depend on income levels, capital ﬂow directions, and asset
categories. Our heterogeneous results are consistent with Kitano and Zhou (2022) andZhou (2024)’s ﬁnding that the estimated eﬀects of CFMs are distinct for diﬀerent capitalﬂow types and ﬂow directions.
Despite the robust analysis and signiﬁcant ﬁndings, our study has a certain limitation.
Although we show that the eﬀects of CFMs on wealth equality and distribution depend on
income levels, capital ﬂow directions, and asset categories, we do not have fully convincing
explanations for the heterogeneous results. Future research should aim to address thetransmission mechanism in each speciﬁc case.

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