Liberalised capital accounts and volatility of capital flows and foreign exchange rates.
Date: 2014-10-15
Institutions: Directorate-General for Economic and Financial Affairs
Subjects: capital market, capital movement, econometrics, economic model, exchange rate, free movement of capital, international finance

ISSN 1725-3187 (online)
ISSN 1016-8060 (print)

EUROPEAN 
ECONOMY

Economic Papers 521 | July 2014

Liberalised Capital Accounts and Volatility 
of Capital Flows and Foreign Exchange Rates  

                                                              Bogdan Bogdanov

Economic and 
Financial Affairs

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European Commission 

Directorate-General for Economic and Financial Affairs 

Liberalised Capital Accounts and 
Volatility of Capital Flows and Foreign 
Exchange Rates 

Bogdan Bogdanov 

Abstract  

Whether  free  movement  of  international  capital  induces  greater  risk  of  foreign  exchange  rate 
and  balance-of-payments  volatility,  or  not,  is  an  important  question  in international  finance  and 
economic  policy  making.  The  paper  employs  propensity  score  matching  methodologies  to 
estimate the impact of maintaining open capital accounts on the volatility of international capital 
flows and foreign exchange rates using data for 69 countries, in the sample period 1980-2011. The 
findings of the study suggest that maintaining an open capital account could contribute to lower 
foreign  exchange  rate  volatility.  It  also  finds  that  capital  flow  management  measures  may  not 
have an effect on the volatility of short- and long-term capital flows. 

JEL Classification: C21, F30. 

Keywords:  liberalised  capital  accounts,  capital  account  openness,  capital  controls,  volatility  of 
international capital flows, volatility of foreign exchange rates, propensity score matching. 

Contact  information:  European  Commission,  Rue  de  la  Loi  170,  1049  Brussels,  Belgium;  e-mail: 
Bogdan.Bogdanov@ec.europa.eu 

EUROPEAN ECONOMY                                                                                     Economic Papers 521 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
ACKNOWLEDGEMENTS 

I  am  grateful  for  the  valuable  comments  received  from  Peter  Bekx,  Heinz  Scherrer,  Moreno  Bertoldi  and 
Lucinda  Trigo  (European  Commission,  Directorate-General  for  Economic  and  Financial  Affairs).  Excellent 
research assistance from Afrola Plaku (Bocconi University) is gratefully acknowledged. The views expressed in 
this  paper  are  the  sole  responsibility  of  the  author  and  do  not  necessarily  represent  those  of  the  European 
Commission or DG ECFIN. 

2 

 
 
 
 
Content 

1. Introduction 

2. Data Construction 

2.1. Defining Liberalised Capital Accounts 

2.2. Measuring Market Volatility 

3. Estimating Propensity Score Equations 

4. Estimating the Impact of Liberalised Capital Accounts on Volatility of 
Capital Flows and Exchange Rates 

5. Conclusions and Policy Implications 

References 

Appendix A – Literature Review 

A.1. Liberalised capital accounts and volatility of capital flows 

A.2. Liberalised capital accounts and currency volatility 

A.3. Determinants of market volatility 

A.4. Determinants of capital account openness 

A.5. Effectiveness of capital controls 

Appendix B – Matching Methods 

Appendix C – Matching with Panel Data 

Appendix D – Country Sample 

Appendix E – Country-Years with Fully Liberalised Capital Accounts 

Appendix F – Data Sources 

5 

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7 

9 

11 

14 

15 

17 

17 

17 

17 

18 

18 

19 

20 

25 

26 

27 

3 

 
 
 
 
4 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1. Introduction 

During the last two decades, the volatility of both short- and long-term international capital flows has increased 
globally  (see  graph  1.1).  In  this  period,  emerging  market  economies  (EMEs)  have  been  growing  faster  and 
catching  up  with advanced economies (AEs),  while often facing  sudden surges of capital flows and exchange 
rate  pressures.  The  1990s  Asian  crisis  and  the  2007  global  financial  crisis  have  fully  shown  the  potentially 
disruptive  effects  of  highly  volatile  capital  flows.  Since  the  financial  crisis  of  2007,  there  has  been  a 
considerable discussion about the role of capital flows in the formation of asset bubbles, and about strategies for 
managing the movements of "hot money" from country to country in search of yield and/or safety. Recently, the 
uncertainty about the pace of US monetary tapering has been singled out as an important factor for the outflow 
of  capital  from  some  emerging  markets,  raising  again  the  question  of  whether  capital  controls  could  reduce 
market volatility and prevent currency collapses.  

Over time,  many national authorities have considered capital controls as part of the appropriate policy  mix to 
address  the  aforementioned  economic  challenges.  There  is  a  wide  spread  notion  that  capital  account  and 
financial liberalisation has been a global trend since the breakdown of the Bretton Woods System in the early 
1970s. However, today the global movement of international capital seems to be as restricted as in 1984, with a 
strong policy direction towards restrictions since the mid-2000s (see graph 1.2). There is a clear shift towards a 
more  restricted  global  economy.  Fratzscher  (2012)  suggests  that  over  this  past  decade,  the  foreign  exchange 
(FX) rate and the concerns about an overheating of the domestic economy have been the two main motives for 
the (re-) introduction and persistence of capital controls. However, it is fair to say that there is no consensus on 
this issue. 

Graph 1.1: Global capital flow volatility. 

Graph 1.2: Global capital account openness. 

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1980

1985

1990

1995

2000

2005

2010

1980

1985

1990

1995

2000

2005

2010

Short-term capital flows
Long-term capital flows

Global capital account openness

Source: Commission services. 

Source: Commission services. 
Note:  The  chart  is  constructed  using  the  Chinn  Ito  index  of 
Capital Account Openness 1 (KAOPEN). 

For many years, the benefits and costs of liberalising the control on the flow of international capital have been a 
hot subject for discussion for the policy makers around the globe. On the one hand, liberalising such types of 
restrictions could contribute to lower volatility of capital flows and the domestic exchange rate by allowing for 
more  efficient  allocation  of  capital  and  therefore  lowering  its  cost,  promoting  better  investment  quality  and 
diversification as well as risk allocation. Over time, this should encourage financial development, create deeper 
and  more  liquid  capital  and  FX  markets,  which  should  be  relatively  resilient  to  domestic  and  external 
vulnerabilities  and  shocks.  On  the  other  hand,  it  has  been  argued  that  capital  openness  could  lead  to  high 

1 KAOPEN is based on binary dummy variables that codify the tabulation of restrictions on cross-border financial transactions reported in 
the IMF's Annual Report on Exchange Arrangements and Exchange Restrictions (AREAER). 

5 

 
 
 
 
 
                                                           
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
balance-of-payments and currency volatility by facilitating unlimited in-and-out transactions, including frequent 
surges of capital flows. 

Despite  the  large  volume  of  empirical  literature,  including  numerous  country-specific  studies,  measuring  the 
impact  of  liberalised  capital  accounts  on  particular  economic  variables,  in  particular  on  output,  volume  and 
composition  of  capital  flows,  interest  rate  differentials  and  inflation,  little  systematic  work  has  tested  the 
relationship  between  capital  account  openness  and  currency  and  balance-of-payments  volatility.  Overall,  the 
recent work on the matter is largely inconclusive 2. 

In  this  context,  whether  free  movement  of  international  capital  induces  greater  risks  of  foreign  exchange  rate 
and  balance-of-payments  volatility,  or  not,  is  an  important  research  question  in  international  finance  and 
economic policy making. However, a robust empirical analysis on the issue faces the challenge to disentangle 
the  effect  of  maintaining  liberalised  capital  accounts  from  other  macroeconomic,  financial  and  institutional 
factors that could also impact the odds of experiencing high volatility of capital flows and FX rates3. In order to 
address  this  problem,  the  paper  employs  a  propensity  score  matching  methodology  that  results  in  a  matched 
sample  in  which  economies  with  liberalised  and  restricted  capital  accounts  are  comparable,  and  therefore  the 
problem of the estimation bias is solved. The impact of maintaining open capital accounts on the volatility of 
capital  flows  and  FX  rates  is  estimated  using  data  for  69  countries 4,  in  the  sample  period  1980-2011,  using 
several  recently  developed  matching  methods  –  nearest-neighbour,  radius,  and  kernel5.  The  derived  results 
suggest  that  in  the  medium-term  liberalised  capital  accounts  could  contribute  to  lower  domestic  currency 
volatility. The study also finds that capital account policies may not have an effect on the volatility of short- and 
long-term capital flows. 

The outline of the  paper is as follows:  Chapter 2 presents  the construction of the  measures of capital account 
openness  and  volatility  of  capital  flows  and  exchange  rates;  Chapter  3  outlines  the  employed  matching 
methodologies  in  detail  as  well  as  presents  the  identification  of  the  matched  samples;  Chapter  4  presents  the 
main results of the paper, the impact of capital account openness on currency and capital flow volatility; Chapter 
5 concludes the paper. 

2 See Appendix A, sections A.1 and A.2, for a review of the existing recent literature. 
3 See Appendix A, sections A.3 and A.4, for the existing empirical evidence on this issue. 
4 See Appendix D for the full list of countries. 
5 See Appendix B for a detailed description of the applied matching methods. 

6 

 
 
 
                                                           
2. Data Construction 

2.1. Defining Liberalised Capital Accounts 

In  order  to  identify  a  fully  liberalised  capital  account,  a  measure  based  on  the  Chinn  Ito  index  of  Capital 
Account Openness (KAOPEN) is constructed. The KAOPEN index does not differentiate between controls on 
capital  inflows  and  outflows.  Discretion  is  used  to  identify  when  a  country-year  maintains  liberalised  capital 
accounts. For each country-year in the sample, a binary measure of capital account openness is constructed. For 
every  observation,  if  in  the  preceding  three  years  and  the  following  four  years  the  capital  account  was  fully 
liberalised6,  the  country-year  is  considered  treated  (binary  measure  of  capital  account  openness=1,  treated 
group).  All  other  country-years,  with  KAOPEN  index  values  less  than  the  75th  percentile  of  the  index 
distribution,  are  treated  as  observations  with  restricted  capital  accounts  (binary  measure  of  capital  account 
openness=0,  control  group).  With  this  methodology  the  study  identifies  407  country-years  with  open  capital 
account. Appendix E lists the countries included in the sample and the corresponding years with an open capital 
account. 

Table 2.1: Correlations of the Chinn-Ito (KAOPEN) index with other de jure indexes of capital account openness. 

Index 

Chinn-Ito 
(KAOPEN) 
(2007) 

Correlations with 
KAOPEN index 

Coverage 

Notes 

- 

1970-2011 for 181 countries 

Based on AREAER codification, covering as many 
countries and years as those available in the AREAER and 
incorporating the extent and intensity of capital controls. 

Quinn (1997) 

83.9 

1958 – 1997 for 
21 industrialized countries; 1958, 
1973, 1982, and 1988 for 40 less 
developed countries 

Based on AREAER's codification before the 1996 
revision, augmented by information on the existence of 
international agreements with international organizations 
or other countries 

Miniane (2004) 

82.71 

1983 – 2004 for 34 countries 

80.17 

1995-2005 for 91 countries 

Based on the post 1996 disaggregated enumeration of 13 
capital account transactions, and extrapolated back to 1983 

Based on the post-1996 disaggregated enumeration of 19 
capital account transactions, allows for the construction of 
various sub-indices 

82.0 

1995 – 2005, for 181 countries 

The average of the 13 capital account transaction 
categories 

Source: Commission services. 

2.2. Measuring Market Volatility 

Throughout  the  paper,  foreign  direct  investment  flows  (FDI)  are  regarded  as  long-term  capital  flows  and  the 
sum  of  portfolio  and  other  investment  flows  as  short-term  capital  flows.  The  impact  of  liberalised  capital 
accounts  is  estimated  on  the  volatility  of  net  short-  and  long-term  capital  flows,  i.e.  the  sum  of  inflows  and 
outflows for each type of capital flow7. With regard to the exchange rates, the bilateral exchanges rates vis-à-vis 
the US dollar and the nominal effective exchange rates are used. 

6 A capital account is considered to be fully liberalised if the KAOPEN index value of the country-year observation is greater than the value 
of the 75th percentile of the KAOPEN index distribution. 
7 Since the KAOPEN index does not differentiate between controls on capital inflows and outflows, the study finds as most appropriate to 
conduct the analysis using the volatility of net flows. Analysing only net flows represents a shortcoming of the study as gross flows are 
much larger and much more volatile than net flows, and their size and volatility have been growing substantially faster (Broner et al., 2013). 

7 

Schindler 
(2009) 

IMF’s post-
1996 AREAER 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
                                                           
The  volatility  of  capital  flows  and  exchange  rates  in  the  medium-term  is  measured  as  the  forward  looking 
standard  deviation  of  8  and  16  quarters.  The  volatility  of  capital  flows  is  standardised  by  the  country's  gross 
domestic product (GDP). On the other hand, the currency volatility is normalised by the average exchange rate 
in the respective year. Appendix F lists all variables and data sources used in the empirical work. 

8 

 
 
 
3. Estimating Propensity Score Equations 

A  benchmark  logit  equation  explaining  the  likelihood  of  having  liberalised  capital  accounts  is  calculated  in 
order  to  derive  the  propensity  scores  for  the  matching  methods.  In  essence,  the  propensity  score  is  the 
probability of a country maintaining a liberalised capital account conditional on a set of observed characteristics. 
Guided by the vast literature in this area8, a number of structural, political, and economic determinants of capital 
account openness are taken into account. 

In the benchmark logit equation one political, one development and three macroeconomic variables are included 
– a variable for political rights, GDP per capita, openness to trade (calculated as the sum of exports and imports 
as share of GDP), monetary policy independence and exchange rate regime (for a more detailed description of 
the  variables,  see  Appendix  F).  In  the  benchmark  equation,  GDP  per  capita  is  used  as  a  proxy  for  economic, 
financial and institutional development. 

In a complementary exercise, an augmented logit equation with additional variables is also estimated. The added 
variables  measure  financial  sector  development  and  resilience,  namely  financial  system  deposits  as  share  of 
GDP,  foreign  exchange  reserves  as  share  of  imports,  and  inflation  rate.  It  is  worth  noting  that  increasing  the 
number of explanatory variables results in a reduced sample size.  

Table  3.1,  column  (1)  presents  the  benchmark  logit  equation  used  to  estimate  the  likelihood  of  open  capital 
accounts. In this specification, greater political rights, more trade openness and higher GDP per capita as well as 
a  floating  exchange  rate  regime  are  associated  with  a  higher  likelihood  that  a  liberalised  capital  account  is  in 
place. All coefficient signs are statistically significant as well as consistent with the literature on the matter 9. 

Column (2) of table 3.1 reports the augmented specification of the logit equation. Evidently, the added proxies 
for financial sector development and resilience enhance the explanatory power of the model, while confirming 
the  results  obtained  with  the  benchmark  specification.  Larger  ratios  of  financial  system  deposits  to  GDP  and 
lower  levels  of  foreign  exchange  reserves,  indicating  a  developed  and  resilient  financial  sector  with  a  well-
established access to international financial markets, are naturally associated with a higher likelihood of capital 
account openness. In the augmented specification, again, all coefficient signs are statistically significant. 

The augmented logit equation predicts better than the benchmark one as the pseudo R-squared of the augmented 
model is somewhat higher, 0.559 compared to 0.424 for the benchmark model. 

8 See Appendix A, section A.4 for a review of the existing recent literature. 
9 See Appendix A, section A.4. 

9 

 
 
 
                                                           
Table 3.1: Benchmark and Augmented Logit Equations for Estimating the Likelihood of Liberalised Capital Accounts. 

Explanatory Variables 

𝑀𝑜𝑛𝑒𝑡𝑎𝑟𝑦 𝐼𝑛𝑑𝑒𝑝𝑒𝑛𝑑𝑒𝑛𝑐𝑒𝑡−1

𝐸𝑥𝑐ℎ𝑎𝑛𝑔𝑒 𝑅𝑎𝑡𝑒 𝑅𝑒𝑔𝑖𝑚𝑒𝑡−1

𝑃𝑜𝑙𝑖𝑡𝑖𝑐𝑎𝑙 𝑅𝑖𝑔ℎ𝑡𝑠𝑡−1

𝐺𝐷𝑃 𝑝𝑒𝑟 𝐶𝑎𝑝𝑖𝑡𝑎𝑡−1

𝑂𝑝𝑒𝑛𝑛𝑒𝑠𝑠 𝑡𝑜 𝑇𝑟𝑎𝑑𝑒𝑡−1

𝐼𝑛𝑓𝑙𝑎𝑡𝑖𝑜𝑛 𝑅𝑎𝑡𝑒𝑡−1

(𝐹𝑖𝑛𝑎𝑛𝑐𝑖𝑎𝑙 𝑆𝑦𝑠𝑡𝑒𝑚 𝐷𝑒𝑝𝑜𝑠𝑖𝑡𝑠/𝐺𝐷𝑃)𝑡−1

(𝐹𝑋 𝑅𝑒𝑠𝑒𝑟𝑣𝑒𝑠/𝐼𝑚𝑝𝑜𝑟𝑡𝑠)𝑡−1

𝐶𝑜𝑛𝑠𝑡𝑎𝑛𝑡

Observations 

Log likelihood 

Pseudo R-squared 

(1) 
Benchmark Selection 
(odds ratios) 

0.0552*** 

(0.0233) 

2.162*** 

(0.422) 

0.865*** 

(0.0448) 

1.144*** 

(0.0171) 

1.014*** 

(0.00172) 

0.129*** 

(0.0431) 

1,453 

-508.726 

0.424 

(2) 
Augmented Selection 
(odds ratios) 

0.0503*** 

(0.0273) 

2.084*** 

(0.576) 

0.679*** 

(0.0536) 

1.068*** 

(0.0121) 

1.011*** 

(0.00168) 

0.772*** 

(0.0293) 

1.009*** 

(0.00337) 

0.987** 

(0.00547) 

1.520 

(0.641) 

1,309 

-335.480 

0.559 

Source: Commission services. 
Note:  Robust  standard  errors  reported  in  parenthesis.  Results  significant  at  1%,  5%,  and  10%  levels  are  indicated  by  ***,  **,  and  *, 
respectively. 

Due to the limitations of the employed binomial analysis, it is hard to draw a conclusion about the threshold of 
economic,  political  and  institutional  development  at  which  countries  are  most  likely  to  maintain  liberalised 
capital accounts. This is not the aim of the paper. Nevertheless, the analysis does signal out that maintaining a 
liberalised capital account is a commitment to good economic policies, strong institutions and political stability. 
When allowing for free movement of international capital flows, a potential weakening in the domestic policy 
framework  conditions  could  threaten  the  financial  stability  of  the  country  –  the  international  investors  could 
suddenly  withdraw  capital  out  of  the  economy.  This  serves  as  a  strong  incentive  for  legislators  to  maintain 
sound  macroeconomic  policies.  Alternatively,  policy  makers  facing  financial  and  institutional  weaknesses 
and/or political instability may decide to retain capital controls in order to avoid or postpone hard or unpopular 
reforms. 

Appendix C outlines in detail the panel data application of the benchmark and augmented specifications with the 
chosen  matching  methods.  It  also  reports  the  summary  statistics  of  the  panel  data  benchmark  and  augmented 
logit equations for the treatment group, the unmatched control group, and three alternative control groups – the 
observations matched by the nearest-neighbour, radius or kernel methods. It is important to highlight that we do 
not  impose  any  restrictions  that  prevent  matches  between  different  year  observations  for  the  same  country. 
Overall, the bias reduction across all matched control groups is large. The conducted matching achieves a good 
balance of characteristics between the treated and matched observations, except for the case of kernel matching 
in  the  benchmark  specification.  For  all  other  samples,  it  is  likely  that  observations  with  the  same  propensity 
scores, independently of their treatment status, have the same distribution of observable characteristics. 

10 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
4. Estimating the Impact of Liberalised Capital Accounts on 
Volatility of Capital Flows and Exchange Rates 

The  study  first  estimates  propensity  scores  from  the  benchmark  logit  equation  and  then  employs  nearest-
neighbour,  radius,  and  kernel  matching  methods  to  evaluate  the  impact  of  capital  account  openness  on  the 
balance-of-payments and currency volatility.  

Tables 4.1 – 4.4 show that there is no significant difference in the volatility of short- and long-term capital flows 
between  countries  maintaining  liberalised  capital  accounts  and  those  retaining  capital  controls.  This  result  is 
valid when the 8 and 16 quarter forward-looking windows of volatility are applied. The findings are invariant to 
the matching method employed. 

On  the  other  hand,  countries  maintaining  an  open  capital  account  are  likely  to  experience  less  exchange  rate 
volatility  (see  Tables  4.5  –  4.8).  Specifically,  the  volatility  of  the  bilateral  exchange  rate  with  the  USD  in 
countries with liberalised capital accounts ranges from 1.5% to 3.0% lower than in countries with restrictions on 
capital transactions, while the volatility of the nominal effective exchange rate is estimated to be 1.0% to 2.0% 
lower.  These  results  are  economically  and  statistically  significant  as  well  as  consistent  through  the  different 
matching methodologies. 

Tables 4.9 – 4.16 report the matching results with the estimated propensity scores from the augmented selection 
equation.  The  reported  findings  largely  confirm  the  results  derived  from  the  benchmark  specification,  except 
that  the  negative  difference  in  the  volatility  levels  of  the  nominal  effective  exchange  rate  does  not  appear 
significant  in  the  nearest-neighbour  method  (8  quarter  forward-looking  window  of  volatility)  and  the  radius 
method (16 quarter forward-looking window of volatility). 

First, the derived results suggest that in the medium-term liberalised capital accounts could contribute to lower 
domestic currency volatility. These findings confirm the hypothesis developed in the introduction of the paper 
that  maintaining an open capital account contributes to lower currency  volatility by creating deeper and  more 
liquid capital and FX markets. 

Second, the estimated effects on the balance-of-payments volatility are close to zero as well as statistically not 
significant. How should these results be interpreted? One explanation could be that capital account policies, i.e. 
capital  controls,  are  ineffective  and/or  very  short-lived  in  managing  the  volume  of  cross-border  financial 
transactions. Such an argument would be in line with the extensive literature on the subject of the effectiveness 
of  capital  controls.  The  majority  of  recent  studies  on  the  issue 10  conclude  that  capital  account  management 
measures have mainly short-term impact on the volume of flows. 

Another reason for the obtained insignificant results could be that the conducted analysis is constrained to the 
use of volatility of net flows. Analysing only net flows represents a shortcoming of the study as gross flows are 
much larger and much more volatile than net flows, especially valid for portfolio and other investment flows. 
Undoubtedly, further research is needed to address this analytical challenge.  

10 See Appendix A, section A.5. 

11 

 
 
 
                                                           
Tables 4.1 – 4.8: Benchmark Matching Results 

Table 4.1: Volatility of Net Short-Term Capital Flows (forward-
looking standard deviation of 8 quarters) 

Table 4.2: Volatility of Net Short-Term Capital Flows (forward-
looking standard deviation of 16 quarters) 

Procedure 

Estimated Effect of 
Capital Account  
Openness (%) 

No. of Observations in 

Procedure 

Estimated Effect of 
Capital Account  
Openness (%) 

No. of Observations in 

Treatment Group 

Control Group 

Treatment Group 

Control Group 

Nearest Neighbour (2) 

Radius (<0.005) 

Kernel (gaussian) 

-.18 

(.26) 

-.05 

(.21) 

.005 

(.15) 

26 

22 

26 

342 

Nearest Neighbour (2) 

342 

Radius (<0.005) 

342 

Kernel (gaussian) 

-.04 

(.28) 

-.015 

(.27) 

.16 

(.17) 

24 

22 

24 

331 

331 

331 

Table 4.3: Volatility of Net Long-Term Capital Flows (forward-
looking standard deviation of 8 quarters) 

Table 4.4: Volatility of Net Long-Term Capital Flows (forward-
looking standard deviation of 16 quarters) 

Procedure 

Estimated Effect of 
Capital Account  
Openness (%) 

No. of Observations in 

Procedure 

Estimated Effect of 
Capital Account  
Openness (%) 

No. of Observations in 

Treatment Group 

Control Group 

Treatment Group 

Control Group 

Nearest Neighbour (2) 

Radius (<0.005) 

Kernel (gaussian) 

.01 

(.14) 

.04 

(.10) 

.03 

(.12) 

333 

645 

Nearest Neighbour (2) 

207 

645 

Radius (<0.005) 

333 

645 

Kernel (gaussian) 

.08 

(.14) 

.09 

(.11) 

.02 

(.12) 

320 

626 

185 

626 

320 

626 

Table 4.5: Volatility of Bilateral Exchange Rate with the USD 
(forward-looking standard deviation of 8 quarters) 
Estimated Effect of 
Capital Account  
Openness (%) 

No. of Observations in 

Procedure 

Table 4.6: Volatility of Bilateral Exchange Rate with the USD 
(forward-looking standard deviation of 16 quarters) 
Estimated Effect of 
Capital Account  
Openness (%) 

No. of Observations in 

Procedure 

Nearest Neighbour (2) 

-3.02* 

401 

932 

Nearest Neighbour (2) 

-2.60* 

375 

870 

Treatment Group 

Control Group 

Treatment Group 

Control Group 

Radius (<0.005) 

(1.71) 

-1.51** 

(.67) 

294 

932 

Radius (<0.005) 

(1.42) 

-2.22** 

(.99) 

261 

870 

Kernel (gaussian) 

-1.96*** 

401 

932 

Kernel (gaussian) 

-2.41*** 

375 

870 

(.61) 

(.77) 

Table 4.7: Volatility of Nominal Effective Exchange Rate 
(forward-looking standard deviation of 8 quarters) 
Estimated Effect of 
Capital Account  
Openness (%) 

No. of Observations in 

Procedure 

Table 4.8: Volatility of Nominal Effective Exchange Rate 
(forward-looking standard deviation of 16 quarters) 
Estimated Effect of 
Capital Account  
Openness (%) 

No. of Observations in 

Procedure 

Treatment Group 

Control Group 

Treatment Group 

Control Group 

Nearest Neighbour (2) 

-1.73*** 

346 

590 

Nearest Neighbour (2) 

-2.13*** 

328 

557 

Radius (<0.005) 

(.47) 

-1.14** 

(.56) 

211 

590 

Radius (<0.005) 

-1.74*** 

186 

557 

(.65) 

(.64) 

Kernel (gaussian) 

-1.36*** 

346 

590 

Kernel (gaussian) 

-1.97*** 

328 

557 

(.42) 

(.58) 

Source: Commission services. 
Note:  the  tables  report  difference  in  volatility  of  capital  flows  and  exchange  rates  for  matched  observations  with  and  without 
liberalised capital accounts. Matching is based on the propensity scores estimated by the benchmark logit equation reported in table 
C.1 (Appendix C). Reported z-statistic (for hypothesis of no effect) based on bootstrapped standard errors in parenthesis below. Results 
significant at 1%, 5%, and 10% levels are indicated by ***, **, and *, respectively. 

12 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Tables 4.9 – 4.16: Augmented Matching Results 

Table 4.9: Volatility of Net Short-Term Capital Flows (forward-
looking standard deviation of 8 quarters) 

Table 4.10: Volatility of Net Short-Term Capital Flows (forward-
looking standard deviation of 16 quarters) 

Procedure 

Estimated Effect of 
Capital Account 
Openness (%) 

No. of Observations in 

Procedure 

Estimated Effect of 
Capital Account 
Openness (%) 

No. of Observations in 

Treatment Group 

Control Group 

Treatment Group 

Control Group 

Nearest Neighbour (2) 

Radius (<0.005) 

Kernel (gaussian) 

.22 

(.21) 

.26 

(.21) 

.17 

(.17) 

24 

19 

24 

308 

Nearest Neighbour (2) 

308 

Radius (<0.005) 

308 

Kernel (gaussian) 

.39* 

(.23) 

.38 

(.26) 

.26 

(.16) 

24 

19 

24 

296 

296 

296 

Table 4.11: Volatility of Net Long-Term Capital Flows (forward-
looking standard deviation of 8 quarters) 

Table 4.12: Volatility of Net Long-Term Capital Flows (forward-
looking standard deviation of 16 quarters) 

Procedure 

Estimated Effect of 
Capital Account 
Openness (%) 

No. of Observations in 

Procedure 

Estimated Effect of 
Capital Account 
Openness (%) 

No. of Observations in 

Treatment Group 

Control Group 

Treatment Group 

Control Group 

Nearest Neighbour (2) 

Radius (<0.005) 

Kernel (gaussian) 

-.04 

(.18) 

.03 

(.13) 

-.05 

(.14) 

261 

607 

Nearest Neighbour (2) 

126 

587 

Radius (<0.005) 

261 

607 

Kernel (gaussian) 

-.07 

(.23) 

.17 

(.18) 

-.01 

(.17) 

242 

587 

126 

587 

242 

587 

Table 4.13: Volatility of Bilateral Exchange Rate with the USD 
(forward-looking standard deviation of 8 quarters) 

Table 4.14: Volatility of Bilateral Exchange Rate with the USD 
(forward-looking standard deviation of 16 quarters) 

Procedure 

Estimated Effect of 
Capital Account 
Openness (%) 

No. of Observations in 

Procedure 

Estimated Effect of 
Capital Account 
Openness (%) 

No. of Observations in 

Treatment Group 

Control Group 

Treatment Group 

Control Group 

Nearest Neighbour (2) 

-1.88** 

297 

859 

Nearest Neighbour (2) 

-2.51*** 

274 

799 

(.78) 

(1.11) 

Radius (<0.005) 

-2.34*** 

193 

859 

Radius (<0.005) 

-3.35*** 

156 

799 

(.66) 

(.81) 

Kernel (gaussian) 

-2.33*** 

297 

859 

Kernel (gaussian) 

-3.48*** 

274 

799 

(.58) 

(.80) 

Table 4.15: Volatility of Nominal Effective Exchange Rate (forward-
looking standard deviation of 8 quarters) 

Table 4.16: Volatility of Nominal Effective Exchange Rate 
(forward-looking standard deviation of 16 quarters) 

Procedure 

Estimated Effect of 
Capital Account 
Openness (%) 

No. of Observations in 

Procedure 

Estimated Effect of 
Capital Account 
Openness (%) 

No. of Observations in 

Nearest Neighbour (2) 

Radius (<0.005) 

-1.46 

(.62) 

-1.10* 

(.63) 

Treatment Group 

Control Group 

Treatment Group 

Control Group 

252 

521 

Nearest Neighbour (2) 

-1.52* 

229 

490 

153 

521 

Radius (<0.005) 

(.87) 

-.91 

(.92) 

119 

490 

Kernel (gaussian) 

-1.50*** 

252 

521 

Kernel (gaussian) 

-1.89*** 

229 

490 

(.44) 

(.71) 

Source: Commission services. 
Note:  the  tables  report  difference  in  volatility  of  capital  flows  and  exchange  rates  for  matched  observations  with  and  without 
liberalised capital accounts. Matching is based on the propensity scores estimated by the augmented logit equation reported in table 
C.2 (Appendix C). Reported z-statistic (for hypothesis of no effect) based on bootstrapped standard errors in parenthesis below. Results 
significant at 1%, 5%, and 10% levels are indicated by ***, **, and *, respectively. 

13 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
5. Conclusions and Policy Implications 

The  study  asks  how  liberalised  capital  accounts  influence  balance-of-payments  and  currency  volatility.  The 
obtained  results  suggest  that  open  capital  accounts  could  contribute  to  lower  foreign  exchange  rate  volatility. 
The analysis also finds that capital account policies may not have an effect on the volatility of short- and long-
term capital flows. 

Based on the derived results it could be concluded that allowing for free movement of international capital is a 
commitment to good economic policies, strong institutions and political stability. Maintaining liberalised capital 
accounts fosters  more efficient allocation of capital and therefore leads to lower cost of capital, better quality 
investment,  portfolio  diversification  and  risk  distribution.  This  provides  a  strong  incentive  for  legislators  to 
maintain  sound  macroeconomic  policies,  which  over  time  results  in  higher  financial  development,  stronger 
banking sector and deep and liquid FX markets that are more resilient to domestic and external vulnerabilities. 

Based on these findings several policy implications could be drawn. Capital flow management measures should 
not  be  seen  as  part  of  the  policy  toolkit  for  financial  stability.  Policy  makers  should  foster  structural  reforms 
that,  for  instance,  increase  the  capacity  of  domestic  capital  markets,  and  implement  prudential  measures  that 
enhance  the  resilience  of  the  financial  system.  In  addition  to  promoting  overall  cross-border  transactions, 
building strong institutions as well as improving the general regulatory quality and product market regulations 
that promote competition should be on the forefront of the policy framework to reduce vulnerabilities. 

Even though the findings of the paper are statistically significant and robust to changes in the propensity score 
matching methodologies, they should be interpreted with caution. The inferred estimations are based on average 
effects derived from a large sample of countries and time episodes. Country studies and detailed examinations of 
specific capital flow management measures should remain on the agenda for future research. 

14 

 
 
 
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Analysis, Volume 10, Issue 3 September 2008, p. 309 - 322. 

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Country Evidence", NBER Working Paper Series, No. 8967. 

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experience of Colombia”, IMF Working paper 09/30. 

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Papers 9: 369–379. 

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Heinrich, C., A. Maffioli and G. Vazquez (2010), “A Primer for Applying Propensity-Score Matching”, 

Technical notes No. IDB-TN-161, Inter-American Development Bank. 

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framework”. 

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Monetary Fund, Washington DC 

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Global Economy Series 15. 

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Economics, 16(3) 

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NBER Working Paper Series, No.7384 

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Group. 

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score matching, common support graphing, and covariate imbalance testing”, 
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16 

 
 
Appendix A – Literature Review 

A.1. Liberalised capital accounts and volatility of capital flows 

A few authors have scrutinised at the effects of capital account openness decisions on the volatility of capital 
flows. Neumann et al. (2006) analyse the effect of financial openness on a set of 26 countries from 1973-2000, 
and  conclude  that  capital  account  openness  leads  to  a  significant  increase  in  foreign  direct  investment  (FDI) 
volatility  for  emerging  market  economies,  whereas  portfolio  capital  flows,  which  are  often  argued  to  be  a 
volatile  form  of  capital,  tend  to  show  insignificant  responses  for  both  the  mature  and  the  emerging  markets. 
Furthermore, Kray (1998) and Becker and Noone (2008) find that the degree of substitutability determines the 
level of capital volatility. In advanced economies, where the level of substitutability among the different forms 
of capital is high, openness to capital flows may be positively related to stability in the capital account. On the 
other hand, Park and Han (2008) show that in countries,  which cannot use their domestic currency to borrow 
abroad ("original sin" countries), capital account openness increases balance-of-payments volatility for all type 
of flows. 

A.2. Liberalised capital accounts and currency volatility 

Economists have argued for a long time that capital account openness may lead to great currency instability and 
subsequent  crises.  However,  recent  empirical  literature  appears  to  partially  refuse  this  hypothesis,  showing 
instead that capital controls, which are often imposed with the aim of stabilising a country's currency, are not 
beneficial, if not detrimental. Glick and Hutchinson (2004) find that capital account restrictions are associated 
with higher probability of an exchange rate crisis, while countries without capital controls appear to have greater 
exchange rate stability. In another panel study, Rodriguez and Wu (2013) also find that tightening controls on 
non-residents  creates  an  elusive  stability  gain  –  it  reduces  daily  exchange  rate  fluctuations  at  the  cost  of 
increasing  the  probability  of  a  currency  crisis.  Frenkel  et  al.  (2001)  also  attempt  to  provide  a  conceptual 
framework  to  the  capital  control-exchange  rate  volatility  relationship.  They  report  as  examples  the  cases  of 
Malaysia  and  Thailand,  which  both  experienced  a  tightening  of  capital  controls  after  the  1990s  Asian  crises. 
While in Malaysia capital constraints seem to have eased the recovery by increased exchange rate stability, this 
was  not  the  case  for  Thailand.  On  the  other  hand,  Edwards  and  Rigobon  (2005)  analyse  the  1990s  Chilean 
experience  and  conclude  that  capital  account  restrictions  reduce  the  vulnerability  of  the  exchange  rate  to 
external shocks. 

A.3. Determinants of market volatility 

A number of authors provide a theoretical framework to better understand capital flow and FX volatility. Broner 
and  Rigobon  (2005)  address  the  reason  for  higher  volatility  in  emerging  market  economies  compared  to 
advanced countries, and find that fundamentals in the form of domestic and foreign macroeconomic variables 
explain  very  little  of  the  dynamics  of  capital  flows,  while  fundamentals  as  financial  development,  good 
institutions, and high per capita income are all associated with lower volatility. Alfaro et al. (2007) focus on the 
role of institutions in stabilising capital flows and suggest that institutional quality and macroeconomic policy 
play important roles in determining volatility. Wei (2006) also emphasises the role of institutions, showing that 
corruption  and  financial  underdevelopment  can  explain  the  higher  volatility  of  loans  compared  to  FDI  in 
emerging economies.  

17 

 
 
 
 
 
A.4. Determinants of capital account openness 

A few authors provide an in-depth analysis of the determinants of capital account openness. Brune et al. (2001) 
conclude  that  one  of  those  determinants  is  the  exchange  rate  regime,  consistent  with  the  Mundell-Fleming 
model, Mundell (1963) and Fleming (1962), countries with a fixed exchange rate tend to have less open capital 
account,  presumably  to  avoid  the  loss  of  monetary  policy  autonomy.  They  also  conclude  that  countries  with 
higher  GDP  levels  are  more  prone  to  openness,  since  they  have  more  developed  institutional  and  financial 
settings which allow to fully capturing the benefits of such policies. Nevertheless, Brune et al. (2001) also find 
indications that countries with lower GDP levels could tend to liberalise their capital accounts as well in order to 
compensate the lack of domestic capital with foreign financial resources. Brune and Guisinger (2003) find that 
the  international  economic  position  (imports  and  exports)  is  also  likely  to  play  a  role.  Large  presence  of 
internationally oriented activities could lead to higher pressure and influence of the actors interested in capital 
account openness in order to better compete on global markets. In addition, Joyce and Noy (2008) find that the 
presence of currency or banking crisis in previous years as well as short term debt and interest rate differentials 
are also significant factors in explaining the openness policies. As for institutional factors, Brune et al. (2001) 
conclude variables concerning the development of the financial system and of the institutional setting are also 
very likely to determine capital account openness. 

A.5. Effectiveness of capital controls 

Jittrapanun and Prasartset (2009) examine recent measures imposed in Thailand, in the period 2006-2009, and 
find that direct capital controls on portfolio inflows, do cause portfolio inflows to decline. However, their study 
identifies  that  the  restrictions’  effectiveness  rapidly  diminishes  within  six  months  of  the  implementation, 
reflecting  market  adjustment  to  circumvent  the  controls.  Furthermore,  the  impact  of  control  measures  is 
particularly pronounced for equity portfolio investments.  

Emphasis  has  also  been  given  to  the  effectiveness  of  capital  management  techniques  in  changing  the 
composition of capital flows. In the case of Brazil, IMF (2011) concludes that the imposed IOF tax may have 
had an impact on the composition of inflows (by encouraging inflows into the futures market). In China, Prasad 
and  Wei  (2005)  suggest  that  the  unusually  high  share  of  FDI  inflows  reflects  a  policy  mix  of  simultaneously 
discouraging foreign debt and foreign portfolio inflows,.  

In the recent literature, interest has also been paid to the impact of the capital openness on the volumes of capital 
outflows as a tool to reduce net capital flows. In the case of Thailand, Jittrapanun and Prasartset (2009) and IMF 
(2011)  identify  a  rapid  increase  in  portfolio  investment  abroad  because  of  the  wave  of  hasty  relaxation  of 
restrictions over resident outflows during 2005-07 for mutual funds. Nevertheless, these measures have failed to 
ease appreciation pressures on the baht.  

A focus of attention in the recent econometric studies has also been the behaviour of EMEs’ capital flows during 
the recent global financial crisis. Analysing the experiences of Indonesia and Malaysia from 1980 to 2008, Ohta 
(2010) finds that during the recent global financial crisis, the introduction of capital controls and supervision on 
the  capital  account  markets  had  been  effective  crisis  management  tools  in  Indonesia.  Furthermore,  both  for 
Indonesia  and  Malaysia,  capital  and  foreign  exchange  transaction  controls  introduced  in  the  countries  explain 
the minimised economic stagnation.  

On the other hand, Clements and Kamil (2009) and IMF (2011) identify the very recent capital control measures 
imposed in Colombia and Thailand as being relatively ineffective. Clements and Kamil (2009) suggests that in 
Colombia  capital  controls  did  not  result  in  a  reduction  of  aggregate  non-FDI  inflows,  particularly  aggregate 
portfolio inflows and bank account transfers of residents. In the case of Thailand, IMF (2011) concludes that the 
impact  of  removing  a  withholding  tax  exemption  for  non-resident  investors,  on  their  earnings  (interest  and 
capital  gains)  from  state  bonds,  was  not  long-lived  as  inflows  to  the  bond  market  resumed  shortly  after,  and 
have been growing strongly since. 

18 

 
 
 
Appendix B – Matching Methods11 

The greatest challenge in estimating the impact open capital account on the volatility of capital flows and the 
exchange rate is to obtain a credible counterfactual: what would have happened in economies  with liberalised 
capital  accounts,  if  they  had  been  instead  restricted?  One  feasible  solution  to  this  problem  is  to  estimate  the 
counterfactual outcome  based on another group of economies (those that have restricted or partially restricted 
capital  accounts)  and  estimate  the  impact  of  liberalised  capital  accounts  as  the  difference  in  mean  outcomes 
between the two groups. Solving the problem of non-random sample selection and avoiding strong assumptions 
about functional form12, matching methods address the aforementioned problem13. 

To estimate the effect of an environment with liberalised capital account on the volatility of capital flows and 
the exchange rate, three matching algorithms are employed – nearest-neighbour, radius and kernel. All of these 
different  approaches  identify  similar  characteristics  among  observations  (a  propensity  score  capturing  the 
estimated  likelihood  of  a  country  maintaining  liberalised  capital  accounts)  and  build  a  counterfactual  to  the 
observations  when  economies  have  open  capital  account  (the  treatment  group)  from  the  observations  when 
economies  have  restricted  or  partially  restricted  capital  accounts  (the  control  group).  Following  the  matching 
methods, the average treatment effect on the treated is estimated by measuring the post treatment difference in 
an array of outcome variables between the two groups. 

Nearest-neighbour matching is one of the most straightforward matching procedures. An observation from the 
control group is chosen as a match for a treated observation in terms of the closest propensity score (or the case 
most similar in terms of observed characteristics). The treatment effect is computed as a simple average of the 
differences in outcomes across the paired matches 14. The radius approach matches each treated observation to 
the average of all the control observations with propensity scores falling within a prespecified radius from the 
propensity  score  of  the  participation  observation15.  In  this  case,  the  treatment  effect  is  again  computed  as  an 
average  of  the  difference  in  outcomes,  but  with  weighting  according  to  the  number  of  nonparticipation 
observations  used  in  the  construction  of  each  matched  pair.  Kernel  is  a  nonparametric  matching  estimator16. 
Kernel matching uses weighted averages of all observations in the control group to construct the counterfactual 
outcome. Weights depend on the distance between each observation from the control group and the participant 
observation  for  which  the  counterfactual  is  estimated.  The  kernel  function17  assigns  higher  weight  to 
observations  close  in  terms  of  propensity  score  to  a  treated  observation  and  lower  weight  on  more  distant 
observations. One major advantage of this approach is the lower variance which is achieved as more information 
is used. A drawback is the possibility of "bad" matches being also included in the estimations. 

11 Matching methods are applied by using the Stata module PSMATCH2, developed by Leuven and Sianesi (2003). 
12 See Heinrich, Maffioli and Vazquez (2010) for an excellent review of propensity score matching methods. 
13 See Persson (2001) and Glick, Guo and Hutchison (2006) for an application of matching methods with macroeconomic data. 
14 The study matches the two nearest neighbours to each treated observation. 
15 For a radius of magnitude r, each treated observation with an estimated propensity score ũ is matched with all the control observations 
whose propensity scores q satisfy the condition ũ – r < q < ũ + r. The study uses r = 0.005 as the benchmark value. 
16 See Heckman, Ichimura and Todd (1997, 1998) for a detailed description of the kernel matching methods. 
17 When applying kernel matching, gaussian kernel function is employed with a bandwidth parameter of 0.06. 

19 

 
 
 
                                                           
Appendix C – Matching with Panel Data 

The  origins  of  propensity  score  matching  are  associated  with  typical  medical  studies,  where  the  panel  data 
usually  have  small  time-series  (T  =  2)  and  large  cross-sectional  components.  This  data  structure  may  make 
sense theoretically, but it is chosen for practical reasons. In many applications, it is relatively cheap to sample 
additional  units  but  relatively  expensive  to  measure  the  same  units  repeatedly  over  long  time  periods.  In  the 
typical medical study, following up with patients is costly – patients grow tired of study participation, get new 
phone numbers, move to new cities, etc., – so sampling additional patients is a relatively cheap way of gaining 
additional data. As a result, datasets in traditional matching settings are characterised by a large number of units 
with covariates measured in a single time-period prior to treatment. 

In contrast, the typical panel  dataset in  macroeconomics  has a relatively  small cross-section and a large time-
series. Practically speaking, this is because the number of units (countries) is small and relatively fixed, so it is 
cheaper to gain additional information about the causal effect by learning more about the units. Thus, a typical 
economic  study  is  analogous  to  a  medical  study  in  which  few  patients  are  sampled  and  the  life  histories  of 
sample patients are carefully recorded. 

Although  the  mechanics  are  slightly  different,  matching  with  “macroeconomic”  data  is  not  fundamentally 
different from matching with “medical” datasets. Economic data simply provides a richer amount of information 
about each  unit and this additional information can be used  when  matching  units to each other.  For example, 
rather than simply matching two countries to each other if they had similar levels of inflation in the preceding 
year,  a  match  might  be  identified  only  if  they  had  similar  levels  of  inflation  in  each  of  the  preceding  three 
years18.  Each  of  these  additional  years  of  inflation  measurements  is  simply  additional  covariate  on  which  to 
match units. Similarly to the curse of dimensionality (Rosenbaum and Rubin, 1983), by observing more of the 
life  history  of  each  unit,  some  previously  unobserved  covariates  is  moved  into  the  category  of  observed 
covariates, which is the same as increasing the number of covariates. This suggests that if close matches on all k 
x T covariates could be found, then the estimated causal effect is more reliable than if only using the matched k 
covariates  measured  in  time  t  –  1.  In  the  observational  setting  of  this  paper,  the  discretionary  judgement 
considers  three  years  of  the  past  history  of  each  country  as  important  and  sufficient  for  predicting  liberalised 
capital  accounts.  After  estimating  the  propensity  scores,  the  common  support  condition  is  verified.  Treatment 
observations whose propensity score is higher than the maximum or less than the minimum propensity score of 
the controls are dropped. The proper imposition of this condition is of major importance for the kernel matching 
method. Essentially, the off-support treated observations are not matched with any of the controls because they 
do not share similar observed characteristics.  

Tables C.1 and C.2 present the balancing of the covariate variables before and after the matching, respectively 
for the benchmark and the augmented logit equations. There are significant differences between the treatment 
and  unmatched  control  groups  for  all  variables  in  both  specifications.  It  is  important  to  highlight  that  no 
restrictions that prevent matches between different year observations for the same country are imposed. 

Evidently,  the  bias  reduction  across  all  matched  control  groups  is  large.  However,  in  the  benchmark 
specification, when comparing the treatment group with the matching control groups, the mean difference of the 
characteristic variables and the balance across the samples are significantly reduced only in the radius matched 
control  group  and  somewhat  in  the  nearest-neighbour  one.  On  the  other  hand,  the  differences  between  the 
treatment  and  the  matching  control  groups  in  the  augmented  specification  are  substantially  reduced  for  all 
samples. This implies that the added proxies for financial sector development and resilience do play a key role 
in  solving  the  problem  of  non-random  sample  selection.  Overall,  the  conducted  matching  achieves  a  good 
balance of characteristics between the treated and matched observations, except for the case of kernel matching 
in  the  benchmark  specification.  For  all  other  samples,  it  is  likely  that  observations  with  the  same  propensity 
scores, independently of their treatment status, have the same distribution of observable characteristics. 

18 See Nielsen and Shefield (2009) for an application of matching methods with panel data. 

20 

 
                                                           
Graphs C.1 to C.6 visually represent the distribution of the treated and control observations on the propensity 
score axis as well the imposition of the off-support condition.  

21 

 
Table C.1: Sample Characteristics of Treatment and Control Groups (Benchmark Selection Equation). 

Treatment 
Group 

Unmatched 
Control 
Group 

𝜇𝛵
.2809 

.29222 

.3044 

.2973 

.29484 

.28747 

1.7776 

1.7998 

1.8243 

21.054 

19.948 

18.982 

90.184 

88.855 

88.084 

𝜇𝐶
.46698 

.46509 

.46236 

.14485 

.12554 

.10944 

3.426 

3.4571 

3.4946 

5.0002 

4.6579 

4.3757 

66.751 

66.396 

66.106 

t-Statistic 

𝛨0 ∶   𝜇𝛵 =   𝜇𝐶
-17.06*** 

-15.97*** 

-14.68*** 

6.63*** 

7.62*** 

8.30*** 

-14.52*** 

-14.63*** 

-14.74*** 

30.24*** 

31.04*** 

31.60*** 

8.34*** 

8.01*** 

7.82*** 

Matched 
Control 
Group 
(Nearest Neighbour) 

t-Statistic 

Bias 
reduction (%) 

Matched 
Control 
Group 
(Radius<0.005) 

t-Statistic 

Bias 
reduction (%) 

𝜇𝐶𝑁
.3506 

.35162 

.37425 

.41646 

.43017 

.39027 

2.1172 

2.096 

2.1484 

23.283 

22.064 

20.533 

84.234 

84.473 

85.036 

𝛨0 ∶   𝜇𝛵 =   𝜇𝐶𝑁
-5.44*** 

-4.47*** 

-4.99*** 

-3.72*** 

-4.20*** 

-3.27*** 

-2.89*** 

-2.52*** 

-2.70*** 

-2.87*** 

-2.88*** 

-2.39*** 

1.30 

0.95 

0.65 

63.6 

66.7 

56.9 

18.2 

16.8 

39.1 

80.1 

82.8 

81.3 

82.7 

82.9 

86.3 

75.0 

80.8 

86.2 

𝜇𝐶𝑅
.35013 

.35824 

.36664 

.31974 

.3292 

.3133 

2.3352 

2.2952 

2.3701 

21.778 

20.293 

18.924 

92.018 

92.367 

92.758 

𝛨0 ∶   𝜇𝛵 =   𝜇𝐶𝑅
-3.09*** 

-3.07*** 

-2.85*** 

-0.90 

-1.23 

-1.19 

-3.07*** 

-2.61*** 

-2.90*** 

-1.82* 

-1.57 

-1.16 

-0.74 

-1.11 

-1.36 

75.8 

74.3 

73.0 

77.7 

72.3 

75.0 

73.8 

78.2 

75.3 

86.1 

88.4 

91.5 

83.0 

73.4 

66.6 

Matched 
Control 
Group 
(Kernel) 

𝜇𝐶𝐾
.35109 

.35701 

.37414 

.41304 

.42006 

.39304 

2.1415 

2.1342 

2.1756 

23.147 

21.919 

20.753 

78.513 

78.413 

78.964 

t-Statistic 

Bias 
reduction (%) 

𝛨0 ∶   𝜇𝛵 =   𝜇𝐶𝐾
-5.35*** 

-4.80*** 

-5.07*** 

-3.62*** 

-3.90*** 

-3.35*** 

-3.05*** 

-2.80*** 

-2.89*** 

-2.69*** 

-2.68*** 

-2.54** 

2.70*** 

2.40** 

2.08** 

63.3 

63.6 

57.0 

20.5 

22.8 

37.5 

78.6 

80.5 

79.7 

83.5 

83.8 

84.8 

50.6 

53.8 

58.6 

Variable 

𝑀𝑜𝑛𝑒𝑡𝑎𝑟𝑦 𝐼𝑛𝑑𝑒𝑝𝑒𝑛𝑑𝑒𝑛𝑐𝑒𝑡−1

𝑀𝑜𝑛𝑒𝑡𝑎𝑟𝑦 𝐼𝑛𝑑𝑒𝑝𝑒𝑛𝑑𝑒𝑛𝑐𝑒𝑡−2

𝑀𝑜𝑛𝑒𝑡𝑎𝑟𝑦 𝐼𝑛𝑑𝑒𝑝𝑒𝑛𝑑𝑒𝑛𝑐𝑒𝑡−3

𝐸𝑥𝑐ℎ𝑎𝑛𝑔𝑒 𝑅𝑎𝑡𝑒 𝑅𝑒𝑔𝑖𝑚𝑒𝑡−1

𝐸𝑥𝑐ℎ𝑎𝑛𝑔𝑒 𝑅𝑎𝑡𝑒 𝑅𝑒𝑔𝑖𝑚𝑒𝑡−2

𝐸𝑥𝑐ℎ𝑎𝑛𝑔𝑒 𝑅𝑎𝑡𝑒 𝑅𝑒𝑔𝑖𝑚𝑒𝑡−3

𝑃𝑜𝑙𝑖𝑡𝑖𝑐𝑎𝑙 𝑅𝑖𝑔ℎ𝑡𝑠𝑡−1

𝑃𝑜𝑙𝑖𝑡𝑖𝑐𝑎𝑙 𝑅𝑖𝑔ℎ𝑡𝑠𝑡−2

𝑃𝑜𝑙𝑖𝑡𝑖𝑐𝑎𝑙 𝑅𝑖𝑔ℎ𝑡𝑠𝑡−3

𝐺𝐷𝑃 𝑝𝑒𝑟 𝐶𝑎𝑝𝑖𝑡𝑎𝑡−1

𝐺𝐷𝑃 𝑝𝑒𝑟 𝐶𝑎𝑝𝑖𝑡𝑎𝑡−2

𝐺𝐷𝑃 𝑝𝑒𝑟 𝐶𝑎𝑝𝑖𝑡𝑎𝑡−3

𝑂𝑝𝑒𝑛𝑛𝑒𝑠𝑠 𝑡𝑜 𝑇𝑟𝑎𝑑𝑒𝑡−1

𝑂𝑝𝑒𝑛𝑛𝑒𝑠𝑠 𝑡𝑜 𝑇𝑟𝑎𝑑𝑒𝑡−2

𝑂𝑝𝑒𝑛𝑛𝑒𝑠𝑠 𝑡𝑜 𝑇𝑟𝑎𝑑𝑒𝑡−3

Source: Commission services. 
Note: Table reports the sample mean of variables for the treatment group (country-years with liberalised capital accounts), for the matched control group (country-years with restricted capital accounts), 
and for matched groups based on the propensity scores estimated by the benchmark specification reported in  table 3.1 column (1), using  the nearest-neighbour, radius and kernel matching  methods. t-
statistic  for  difference  of  means  between  the  treatment  group  and  the  control  group  are  reported  in  adjacent  column.  Results  significant  at  1%,  5%,  and  10%  levels  are  indicated  by  ***,  **,  and  *, 
respectively. 

22 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Table C.2: Sample Characteristics of Treatment and Control Groups (Augmented Selection Equation). 

Variable 

𝑀𝑜𝑛𝑒𝑡𝑎𝑟𝑦 𝐼𝑛𝑑𝑒𝑝𝑒𝑛𝑑𝑒𝑛𝑐𝑒𝑡−1

𝑀𝑜𝑛𝑒𝑡𝑎𝑟𝑦 𝐼𝑛𝑑𝑒𝑝𝑒𝑛𝑑𝑒𝑛𝑐𝑒𝑡−2

𝑀𝑜𝑛𝑒𝑡𝑎𝑟𝑦 𝐼𝑛𝑑𝑒𝑝𝑒𝑛𝑑𝑒𝑛𝑐𝑒𝑡−3

𝐸𝑥𝑐ℎ𝑎𝑛𝑔𝑒 𝑅𝑎𝑡𝑒 𝑅𝑒𝑔𝑖𝑚𝑒𝑡−1

𝐸𝑥𝑐ℎ𝑎𝑛𝑔𝑒 𝑅𝑎𝑡𝑒 𝑅𝑒𝑔𝑖𝑚𝑒𝑡−2

𝐸𝑥𝑐ℎ𝑎𝑛𝑔𝑒 𝑅𝑎𝑡𝑒 𝑅𝑒𝑔𝑖𝑚𝑒𝑡−3

𝑃𝑜𝑙𝑖𝑡𝑖𝑐𝑎𝑙 𝑅𝑖𝑔ℎ𝑡𝑠𝑡−1

𝑃𝑜𝑙𝑖𝑡𝑖𝑐𝑎𝑙 𝑅𝑖𝑔ℎ𝑡𝑠𝑡−2

𝑃𝑜𝑙𝑖𝑡𝑖𝑐𝑎𝑙 𝑅𝑖𝑔ℎ𝑡𝑠𝑡−3

𝐼𝑛𝑓𝑙𝑎𝑡𝑖𝑜𝑛 𝑅𝑎𝑡𝑒𝑡−1

𝐼𝑛𝑓𝑙𝑎𝑡𝑖𝑜𝑛 𝑅𝑎𝑡𝑒𝑡−2

𝐼𝑛𝑓𝑙𝑎𝑡𝑖𝑜𝑛 𝑅𝑎𝑡𝑒𝑡−3

𝐺𝐷𝑃 𝑝𝑒𝑟 𝐶𝑎𝑝𝑖𝑡𝑎𝑡−1

𝐺𝐷𝑃 𝑝𝑒𝑟 𝐶𝑎𝑝𝑖𝑡𝑎𝑡−2

𝐺𝐷𝑃 𝑝𝑒𝑟 𝐶𝑎𝑝𝑖𝑡𝑎𝑡−3

𝑂𝑝𝑒𝑛𝑛𝑒𝑠𝑠 𝑡𝑜 𝑇𝑟𝑎𝑑𝑒𝑡−1

𝑂𝑝𝑒𝑛𝑛𝑒𝑠𝑠 𝑡𝑜 𝑇𝑟𝑎𝑑𝑒𝑡−2

𝑂𝑝𝑒𝑛𝑛𝑒𝑠𝑠 𝑡𝑜 𝑇𝑟𝑎𝑑𝑒𝑡−3

(𝐹𝑖𝑛𝑎𝑛𝑐𝑖𝑎𝑙 𝑆𝑦𝑠𝑡𝑒𝑚 𝐷𝑒𝑝𝑜𝑠𝑖𝑡𝑠/𝐺𝐷𝑃)𝑡−1

(𝐹𝑖𝑛𝑎𝑛𝑐𝑖𝑎𝑙 𝑆𝑦𝑠𝑡𝑒𝑚 𝐷𝑒𝑝𝑜𝑠𝑖𝑡𝑠/𝐺𝐷𝑃)𝑡−2

(𝐹𝑖𝑛𝑎𝑛𝑐𝑖𝑎𝑙 𝑆𝑦𝑠𝑡𝑒𝑚 𝐷𝑒𝑝𝑜𝑠𝑖𝑡𝑠/𝐺𝐷𝑃)𝑡−3

(𝐹𝑋 𝑅𝑒𝑠𝑒𝑟𝑣𝑒𝑠/𝐼𝑚𝑝𝑜𝑟𝑡𝑠)𝑡−1

(𝐹𝑋 𝑅𝑒𝑠𝑒𝑟𝑣𝑒𝑠/𝐼𝑚𝑝𝑜𝑟𝑡𝑠)𝑡−2

(𝐹𝑋 𝑅𝑒𝑠𝑒𝑟𝑣𝑒𝑠/𝐼𝑚𝑝𝑜𝑟𝑡𝑠)𝑡−3

Treatment 
 Group 

Unmatched 
 Control 
 Group 

𝜇𝛵
.26472 

.27731 

.28838 

.30887 

.30887 

.30275 

1.5168 

1.5321 

1.5566 

2.5113 

2.7805 

3.1372 

21.465 

20.239 

19.096 

94.175 

93.032 

92.237 

73.146 

71.391 

69.734 

26.102 

26.05 

26.103 

𝜇𝐶
.46385 

.46133 

.45739 

.156 

.13504 

.11758 

3.3504 

3.3749 

3.4075 

51.054 

52.999 

51.366 

5.1589 

4.8001 

4.5072 

67.421 

67.003 

66.696 

38.299 

37.713 

37.007 

36.655 

34.699 

32.732 

t-Statistic 

𝛨0 ∶   𝜇𝛵 =   𝜇𝐶
-16.75*** 

-15.55*** 

-14.39*** 

5.99*** 

7.06*** 

7.80*** 

-15.28*** 

-15.39*** 

-15.43*** 

-2.53** 

-2.61*** 

-3.18*** 

27.34*** 

27.91*** 

28.21*** 

8.41*** 

8.17*** 

8.00*** 

19.64*** 

19.10*** 

18.73*** 

-4.73*** 

-4.11*** 

-3.39*** 

Matched 
Control 
Group 
(Nearest Neighbour) 

t-Statistic 

Bias  
reduction (%) 

Matched 
 Control 
 Group 
 (Radius<0.005) 

t-Statistic 

Bias  
reduction (%) 

𝜇𝐶𝑁
.33732 

.35713 

.34428 

.58418 

.58586 

.58586 

1.9175 

1.8704 

1.9411 

2.9553 

3.5033 

3.8011 

21.211 

19.687 

19.01 

93.092 

93.909 

96.054 

69.61 

66.116 

63.849 

31.077 

30.593 

30.784 

𝛨0 ∶   𝜇𝛵 =   𝜇𝐶𝑁
-4.04*** 

-4.73*** 

-2.98*** 

-7.57*** 

-7.61*** 

-7.81*** 

-3.15*** 

-2.68*** 

-2.90*** 

-1.87* 

-2.72*** 

-1.84* 

-1.51 

-1.27 

-1.66* 

-0.08 

-0.36 

-0.80 

-0.04 

0.87 

1.25 

-2.45** 

-2.40** 

-2.63*** 

70.0 

63.5 

73.7 

-92.7 

-70.5 

-63.6 

80.3 

83.8 

81.5 

99.3 

98.8 

99.0 

89.4 

91.5 

88.6 

98.2 

91.4 

80.1 

99.7 

93.6 

90.6 

57.9 

53.0 

34.7 

𝜇𝐶𝑅
.34458 

.36674 

.35292 

.54606 

.54471 

.54116 

1.8559 

1.8446 

1.892 

3.2415 

3.8558 

4.0661 

19.574 

18.191 

17.206 

83.003 

83.84 

85.303 

67.281 

63.931 

62.081 

31.726 

30.875 

30.961 

𝛨0 ∶   𝜇𝛵 =   𝜇𝐶𝑅
-2.06** 

-2.50** 

-1.77*** 

-5.38*** 

-5.12*** 

-5.16*** 

-0.95 

-0.71 

-0.75 

-1.03 

-1.87* 

-0.93 

-0.96 

-0.93 

-1.05 

1.31 

1.06 

0.79 

-0.03 

0.63 

0.74 

-1.55 

-1.63 

-1.74* 

80.9 

74.9 

80.0 

-70.8 

-43.5 

-35.5 

92.3 

94.4 

93.8 

99.4 

98.9 

99.3 

91.6 

92.2 

91.2 

66.7 

71.8 

78.0 

99.7 

94.2 

92.9 

65.7 

59.0 

44.1 

Matched 
 Control 
 Group 
 (Kernel) 

𝜇𝐶𝐾
.33103 

.35093 

.36239 

.55095 

.55387 

.54509 

1.8675 

1.8382 

1.885 

6.479 

7.0307 

7.1032 

21.684 

20.684 

19.864 

82.337 

82.215 

82.967 

66.463 

63.711 

61.165 

30.452 

30.059 

30.073 

t-Statistic 

Bias  
reduction (%) 

𝛨0 ∶   𝜇𝛵 =   𝜇𝐶𝐾
-3.56*** 

-4.07*** 

-4.18*** 

-6.68*** 

-6.76*** 

-6.72*** 

-2.73*** 

-2.37** 

-2.45** 

-0.70 

-0.74 

-0.86 

-1.92* 

-2.13** 

-2.39** 

1.86* 

1.68* 

1.39 

1.23 

1.86* 

2.37** 

-2.08** 

-2.03** 

-2.16** 

73.1 

66.8 

63.0 

-71.0 

-52.1 

-41.6 

83.0 

85.6 

84.6 

92.0 

91.8 

92.1 

86.5 

85.0 

82.8 

61.6 

63.7 

68.7 

91.2 

86.5 

82.4 

63.9 

59.1 

45.4 

Source: Commission services. 
Note: Table reports the sample mean of variables for the treatment group (country-years with liberalised capital accounts), for the matched control group (country-years with restricted capital accounts), 
and for matched groups based on the propensity scores estimated by the augmented specification reported in table 3.1 column (2), using the nearest-neighbour, radius and kernel matching methods. t-
statistic  for  difference  of  means  between  the  treatment  group  and  the  control  group  are  reported  in  adjacent  column.  Results  significant  at  1%,  5%,  and  10%  levels  are  indicated  by  ***,  **,  and  *, 
respectively. 

23 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Graphs C1 – C6: Propensity Score Histograms by Treatment Status across the Different Matching Methods for the Benchmark and the 
Augmented Selection Equations. 

Benchmark Selection Equation 

Augmented Selection Equation 

Graph C.1: Nearest Neighbour (2) 

Graph C.2: Nearest Neighbour (2) 

0

.2

.4
.6
Propensity Score

.8

1

0

.2

.4
.6
Propensity Score

.8

1

Untreated
Treated: Off support

Treated: On support

Untreated
Treated: Off support

Treated: On support

Graph C.3: Radius (<0.005) 

Graph C.4: Radius (<0.005) 

0

.2

.6
.4
Propensity Score

.8

1

0

.2

.6
.4
Propensity Score

.8

1

Untreated
Treated: Off support

Treated: On support

Untreated
Treated: Off support

Treated: On support

Graph C.5: Kernel (Gaussian) 

Graph C.6: Kernel (Gaussian) 

0

.2

.4
.6
Propensity Score

.8

1

0

.2

.4
.6
Propensity Score

.8

1

Untreated
Treated: Off support

Treated: On support

Untreated
Treated: Off support

Treated: On support

Source: Commission services. 

24 

 
 
 
 
 
 
 
 
Appendix D – Country Sample 

Algeria 

Angola 

Argentina 

Australia 

Austria 

Azerbaijan 

Bangladesh 

Belarus 

Belgium 

Brazil 

Bulgaria 

Canada 

Chile 

China 

Colombia 

Costa Rica 

Croatia 

Czech Republic 

Denmark 

Dominican Republic 

Ecuador 

Finland 

France 

Germany 

Greece 

Guatemala 

Hungary 

India 

Indonesia 

Ireland 

Israel 

Italy 

Japan 

Kazakhstan 

Kenya 

Kuwait 

Lebanon 

Lithuania 

Malaysia 

Mexico 

Morocco 

Netherlands 

New Zealand 

Nigeria 

Norway 

Oman 

Pakistan 

Panama 

Peru 

Philippines 

Poland 

Portugal 

Qatar 

Romania 

Saudi Arabia 

Singapore 

Slovenia 

South Africa 

Spain 

Sri Lanka 

Sudan 

Sweden 

Switzerland 

Thailand 

Tunisia 

Ukraine 

United Kingdom 

Uruguay 

Vietnam 

25 

 
 
 
 
 
Appendix E – Country-Years with Fully Liberalised Capital Accounts 

Period with liberalised capital accounts 

1988-1992 

1994-2007 

2002-2007 

1983-2007 

2004 

2004-2007 

1991-2007 

1994-2007 

1996-2007 

1983-2007 

2005-2007 

2004-2007 

2004-2007 

1996-2007 

2002-2007 

1996-2007 

1983-1991, 1999-2007 

1983-1992 

1992-1993 

1999-2004 

1985-1989 

1984-2007 

1987-2007 

1998-2007 

2006-2007 

1989-2007 

2000-2007 

1996-2007 

1983-2001, 2005-2007 

2007 

1983-1993, 2002-2007 

1997-2007 

1996-2007 

1999-2007 

1983-2007 

1999-2007 

Country 

Australia 

Austria 

Belgium 

Canada 

Chile 

Czech Republic 

Denmark 

Finland 

France 

Germany 

Greece 

Guatemala 

Hungary 

Ireland 

Israel 

Italy 

Japan 

Kuwait 

Lebanon 

Lithuania 

Malaysia 

Netherlands 

New Zealand 

Norway 

Oman 

Panama 

Peru 

Portugal 

Qatar 

Romania 

Singapore 

Spain 

Sweden 

Switzerland 

United Kingdom 

Uruguay 

26 

 
 
 
 
 
 
 
 
 
 
Appendix F – Data Sources 

Variable  

Notes 

Monetary Policy 
Independence 

Continuous variable (0-1), monetary 
policy independence = 1 

Source 
Aizenman, J., Chinn, M., and Ito, H., "Assessing the emerging global 
financial architecture: measuring the Trilemma Configuration over 
Time", NBER Working Paper 14533, 2008 

De Facto Floating 
Exchange Rate Regime 

Binary variable (0-1), de facto 
floating exchange rate regime = 1 

Atish R. et al. " Words vs. Deeds: What Really Matters?", IMF 
Working Paper WP\11\112 (2011). Authors’ estimates based on IMF’s 
AREAER and Anderson (2008). 

Political Rights 

GDP per Capita 

Imports and Exports as 
share of GDP 

Inflation Rate 

Financial System 
Deposits 

FX Reserves 

Capital flows (FDI, 
Portfolio Flows, Other 
Flows) 

Bilateral Exchange Rate 
with the US dollar 

Nominal Effective 
Exchange Rate 

Categorical variable (1-7), full 
political rights = 1. 

Freedom House, Freedom in the World Country Ratings Database 

IHS Global Insight  

IHS Global Insight 

IMF, International Financial Statistics (IFS), from the database 
platform IHS Global Insight 

World Bank, Global Development Finance Indicators 

IMF, International Financial Statistics (IFS), from the database 
platform IHS Global Insight 

IMF, International Financial Statistics (IFS), from the database 
platform IHS Global Insight 

IHS Global Insight 

IMF, International Financial Statistics (IFS), from the database 
platform IHS Global Insight 

27 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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