Corresponding author: Natalya Ketenci ( nketenci@yeditepe.edu.tr ) © 2015 Nonprofit partnership “Voprosy Ekonomiki”.
This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BYNCND 4.0), which permits to copy and distribute the article for noncommercial purposes, provided that the article is not altered or modified and the original author and source are credited.
Citation:
Ketenci N (2015) Capital mobility in Russia. Russian Journal of Economics 1(4): 386403. https://doi.org/10.1016/j.ruje.2016.02.003

This paper investigates the level of capital mobility in Russia, testing the Feldstein–Horioka (1980) puzzle (FHP). The study examines relations between saving and investment flows in Russia in the presence of structural breaks. It employs the quarterly data for the period 1995–2013, in which all estimations are made for two periods: the full period 1995–2013 and 2000–2013, the postRussian crisis period. The empirical analysis includes the Kejriwal and Perron (2008, 2010) structural break test to determine the presence of structural breaks in series and estimate the savings retention coefficient under the consideration of structural shifts. To facilitate comparison, the parameters of the model were estimated employing the OLS and FMOLS procedures. To test the cointegration relationships between investment and saving flows in Russia, two different cointegration tests were applied to the data. The first applied was the Maki (2012) cointegration test, which allows for an unknown number of breaks; then, in a case where only one break was detected, the CarrioniSilvestre and Sanso (2006) cointegration test was employed. The results of this study provide evidence of high capital mobility and reject the existence of the FHP in the postRussian crisis period. Evidence of the cointegration presence indicates the solvency of a current account in Russia.
Feldstein–Horioka puzzle, saving–investment association, capital mobility, cointegration, structural breaks, Russia
For the last several decades, economic crises throughout the world have been influenced by the rise of global financial integration. Numerous studies have been carried out to investigate capital mobility issues. The most popular concern in capital mobility studies is to explain and solve the Feldstein–Horioka puzzle (FHP). Related to the seminal work of
For the last several decades, transition and emerging economies have experienced the liberalization process in trade and capital transactions. However, little attention has been given in the literature to transition and emerging economies, which increasingly are becoming important players in the global financial market (Fidrmuc,
With a population of 143.5 million, Russia is one of the ten most populous countries in the world. In 2012, the GDP of Russia was 2.015 trillion USD, which represents 3.25% of the world economy, putting it on the list of the ten largest world economies.
Russia: BRICS — de jure capital flow restrictiveness.
Note: * Maximum index value is normalized at one. Source:
Since the transition began, the capital liberalization policy for capital accounts has been cautious and gradual in transition countries, where nonFDIrelated transactions have been restricted. However, Russia has had a different program for capital liberalization compared to that of the Commonwealth of Independent States (CIS), which started the process of transition at the same time. The liberalization of FDI transactions has been executed under strict limitation with gradual ease. Restrictions on nonresident portfolio investments were gradually removed by early 1998. However, during the crisis, some capital restrictions were returned with further gradual liberalization after 2000. Comparing Russia to the CIS, at the beginning of the transition, most total net capital flows in the CIS involved Russia, with a continuous increase until the August 1998 crisis and gradual recovery after 1999.
In terms of structure, foreign direct investments accounted for a small share of Russian capital inflows, whereas the net shortterm external liabilities significantly increased before the crisis, followed by a decline during the Russian crisis (
Following the gradual liberalization after the crisis, investments grew again. Particularly, capital flows increased sharply after 2004, when the new foreign exchange law came into force, which was directed toward the progressive liberalization of capital movements. The new law still had various restrictive capital control arrangements, but they were phased out in 2006 (
The purpose of this article is to make a contribution to the literature on the capital mobility analysis in Russia. The study examines the FHP, employing the latest econometric techniques that accommodate structural breaks. Quarterly data are taken from the Organisation for Economic Cooperation and Development (OECD), Quarterly National Accounts Dataset, covering the period from 1995 to the third quarter of 2013. Estimates are made for two periods: 1995 to 2013 is the full period; and 2000 to 2013 is the period during which gradual capital mobility liberalization was applied, or the postRussian crisis period. The remainder of the paper consists of the following sections: Section
This study examines the degree of capital mobility in Russia in the presence of structural breaks.
where I is the gross domestic investment, S is the gross domestic savings, and Y is the gross domestic product of considered country i. Coefficient β, which is known as the saving retention coefficient, measures the degree of capital mobility. If a country possesses perfect international capital mobility, the value of β must be close to 0. If β is close to 1, it would indicate capital immobility within the country. The results of
In the long run, macroeconomic series including investment and savings may contain a variety of structural changes within a country or at the international level. For example,
Gross domestic investment and gross domestic savings in Russia.
Source: Author's representation of the employed dataset.
The methodology considers multiple linear regression in the presence of m breaks, which results in m+ 1 regimes.(2)
where t = Tj–1 + 1,…, Tj is the time period with j= 1,…, m+ 1 regimes; yt is the dependent variable of the regression, xt and zt are vectors of covariates with sizes of (p ×1) and (q ×1), respectively; β and δj are vectors of coefficients, where the parameter vector β is not subject to change, whereas δj changes across regimes; and et is the error term of the regression. The purpose of this methodology is to estimate the unknown coefficients of the regression together with the unknown m number of break points. For every partition m (T1,…, Tm), estimates of coefficients β and δj are generated by minimizing the sum of squared residuals, which is represented by the following equation:(3)
By substituting estimates $\stackrel{\u02c6}{\beta}\left(\left\{{T}_{J}\right\}\right)$ and $\stackrel{\u02c6}{\delta}\left(\left\{{T}_{J}\right\}\right)$ into equation (3), the estimators of break locations will be obtained, which are the global minima of the sum of squared residuals objective function and can be expressed by the following equation:(4)
The minimization of the sum of squared residuals is obtained in all partitions (T1,…, Tm), for which Ti– Ti–1 ≥ q. The estimates of regression parameters are leastsquares estimates associated with partition m $\left\{{\stackrel{\u02c6}{T}}_{j}\right\}\text{i.e.,}\stackrel{\u02c6}{\beta}=\stackrel{\u02c6}{\beta}(\{{T}_{j}\})\text{\hspace{0.17em}}\text{and}\text{\hspace{0.17em}}\stackrel{\u02c6}{\delta}=\stackrel{\u02c6}{\delta}(\{{T}_{j}\})$.
The procedure for the specification of the number of breaks proposed by
where FT (λ1,..., λm; q) is the sum of m dependent chisquare random variables, each one divided by m, with q as the degree of freedom;(6)
where c(q, α, m) is the asymptotic critical value of the individual tests with α as the significance level.
Next, Wald type tests must be applied, where the sup F(01) test examines for the hypothesis of no breaks against 1 existing break. If the statistics of this test reject the hypothesis of no breaks, sup F (l+1 l) must be applied to specify the number of breaks in the series. The number of breaks in the series can also be chosen on the basis of the Bayesian Information Criteria (BIC) and the modified version of BIC proposed by
Before proceeding to the cointegration tests, the stationarity of the employed variables must be examined. To test the integration properties of variables, two different unit root tests were applied. The first test is the unit root test proposed by
To test the integration properties of variables in the presence of structural shifts, the
Before testing the stationarity, the presence of structural shifts in series must be investigated. Ignorance of the presence of structural shifts in a series can lead to misspecification errors. The
Finally, to test for cointegration characteristics between variables under the consideration of a structural break presence, the
The
The
To test for the presence of structural breaks in individual variables, the
Perron– Yabu test for structural changes in the deterministic components.
The null hypothesis of the test, no structural shifts, was rejected for both variables, investment and savings for two estimated periods, 1995–2013 and 2000–2013. The break dates detected by the test are first quarters of 1999 and 2000 for the full period for investment and savings, respectively. These years are characterized by the fast recovery of the Russian economy after the 1998 Russian financial crisis. Between 1999 and 2008, Russia was ranked as one of the world's fastestgrowing economies and had the highest per capita income among the BRIC (Brazil, Russia, India and China) countries, which are considered as newly advanced countries (
Unit root tests: Ng and Perron (2001).
Next, the
Unit root tests: CarrioniSilvestre et al. (2009).
The results of the unit root tests demonstrate the nonstationarity of the employed variables in both periods. Having verified the nonstationarity of the series under observation by the
The Kejriwal and Perron (
To determine the rank of cointegration space, two test statistics are presented, the Trace and the MaxEigenvalue (
Standard cointegration test: Johansen.
The results of the Trace likelihood ratio test statistic and the MaxEigenvalue likelihood ratio test statistic were consistent with each other. The results of the tests indicated two cointegration relationships at the 5% significance level between the savings and investment variables for the 1995–2013 period. For the second period, 2000–2013, the estimation results revealed one cointegration equation at the 5% significance level and two cointegration equations at the 10% significance level. Thus, the results of
Having verified the existence of longrun relationships between the variables, the Kejriwal and Perron (
Structural break tests of Kejriwal and Perron (2008, 2010).
Sequential test of l versus l+1 structural changes.
Tables
The Maki (2012) cointegration test with unknown number of breaks.
CarrioniSilvestre and Sanso cointegration test (2006).
The results of the
The test statistics rejected the null hypothesis of no cointegration for the 1995– 2013 period when more than one break is allowed. However, when one break is allowed, it failed to reject the null. The test statistics did not detect cointegration relationships for the 2000–2013 period for any number of structural shifts allowed. Based on the results of the
Therefore, the
Estimated regression parameters under breaks.
The results of the cointegration estimations that allow for structural shifts provide strong evidence for the existence of cointegration relationships in both periods. In the literature, the cointegration between savings and investment is interpreted as the longrun solvency condition, which exists regardless of the level of capital mobility, implying the effective realization of government policies targeting a sustainable current account (Coakley et al.,
Estimates of break locations are given in the last three columns $\left\{{\stackrel{\u02c6}{T}}_{J}\right\}$ of the table, based on a 95% confidential level. Estimates of the savings retention coefficient, $\stackrel{\u02c6}{\beta}\text{,}$ corrected for the presence of structural breaks, are given in the second column. Break locations detected by the Kejriwal and Perron test are consistent with break locations detected by the CarrioniSilvestre and Sanso test (
In the full estimated period, 1995–2013, the saving retention coefficient was found at a low level, close to zero, or –0.01 when three breaks are detected by the BIC and the LWZ procedures and 0.05 when two breaks are detected by the sequential test. However, in both cases, the savings retention coefficient estimates were not found to be significant. Estimations of the postcrisis period 2000–2013 produced significant results for the savings retention coefficient when one structural break was detected by the BIC and LWZ procedures. Thus, the estimate of the savings retention coefficient in the presence of a structural break was found at the level –0.10, which is relatively close to zero.
For comparison, the saving retention coefficient is estimated using the OLS and FMOLS procedures (
Estimated regression parameters OLS and FMOLS.
The problem of capital flight in Russia has been present since the early 1990s. Three different examples of domestic capital flight exist: to transfer assets abroad that are denominated in a foreign currency, to accumulate profits from financial assets that are located abroad and denominated in a foreign currency, and to transfer financial assets in a national currency into financial assets denominated in a foreign currency. Domestic capital flight has existed since the Russian economy moved to the market economy model. However, capital flight from Russia is mainly not connected to the normal decision of profit maximization; rather, it can be explained by motivations driven by general or currency risk that lead to significant reduction in national investments (
Except for the period 2004–2008, when Russia experienced net capital inflow and approximately onequarter of inward FDI was a result of capital inflows from Cyprus accounts owned by Russian nationals (
The results of the saving retention coefficient estimates illustrate a high mobility of capital in Russia in the postcrisis period. Consideration of structural shifts does not significantly affect estimation results in which structural shifts are not allowed. Nevertheless, the allocation of structural breaks in the model may correct estimated parameters for the provision of better capital mobility illustration. Thus, the results of the regression estimates provide rather weak evidence for the presence of the FHP in Russia in the postcrisis period.
The limited literature on the measurement of capital mobility in Russia provides mixed results. For example,
Thus, the results of this study employing OLS and FMOLS estimations provide weak evidence for the presence of the FHP in Russia in the postcrisis period, whereas estimations with accommodation for structural breaks illustrate high capital mobility and no evidence of the FHP.
This paper examined capital mobility in Russia in the presence of structural breaks for two periods: 1995–2013 and the postcrisis period from 2000–2013. Recently developed econometric methods were applied to quarterly series to investigate the cointegrating relationships of investment and savings variables, considering the presence of structural shifts in the model when relevant, and to estimate the savings retention coefficient. The longrun macroeconomic series including investment and saving flows may contain a variety of structural changes within a country or at the international level. Therefore, to examine the regression model (1) in the presence of multiple structural breaks, the approach of Kejriwal and Perron (
To examine the cointegration relationships of the series in the presence of structural breaks, the
The OLS and FMOLS estimates of the savings retention coefficient and the coefficient estimates of the Kejriwal and Perron (
The results of the study indicate the presence of high capital mobility in Russia in the postcrisis period. The negative sign of the savings retention coefficient confirms the high level of domestic capital flight. The consideration of structural shifts does not significantly affect the estimation results where structural shifts are not allowed. Nevertheless, the allocation of structural breaks in the model corrects estimated parameters for the illustration of better capital mobility. Thus, the results of this study employing OLS and FMOLS estimations provide weak evidence of the FHP in Russia in the postcrisis period, whereas estimations with accommodation of structural breaks illustrate high capital mobility and no evidence of the FHP.
World Bank.
BRICS—Brazil, Russia, India, China and South Africa.
UNCTAD, Global Investment Trends Monitor.
A detailed analysis of this period is in the explanation part of Table 8.
Sergei Ignatyev, Chief of the Central Bank of Russia. RIA Novosti, 2013, June 5.
UNCTAD, Global Investment Trends Monitor.