in: IMF Working Papers Volume 2014 Issue 145 (2014)

Publication Date:: 08 Aug 2014

eISBN:: 9781498383202

Language:: English

Download PDF (426.3 KB)

5 Appendix I: Proofs

5.1 Proof of Proposition 1

Maximizing (4) subject to (5), and taking both market frictions into account -domestic externality and market power-, we obtain the following Euler equation:

\begin{matrix} u^{'} (c_{1}^{i}) - β^{i} {R u}^{'} (c_{2}^{i}) - x^{'} (c_{1}^{i} - y_{1}^{i}) - β^{i} u^{'} (c_{2}^{i}) (c_{1}^{i} - y_{1}^{i}) \frac{d R}{d [m^{i} (c_{1}^{i} - y_{1}^{i})]} = 0. & (15) \end{matrix}

Let us investigate separately the two distinct motives for policy intervention, starting with the prudential motive. If country i does not affect the world market equilibrium (mⁱ = 0), then it is $d R / d [m^{i} (c_{1}^{i} - y_{1}^{i})] = 0$ . Hence, for a borrowing country $(c_{1}^{i} - y_{1}^{i} > 0)$ , the Euler equation above simplifies to

\begin{matrix} u^{'} (c_{1}^{i}) = β^{i} R u^{'} (c_{2}^{i}) + x^{'} (c_{1}^{i} - y_{1}^{i}) . & (16) \end{matrix}

In order for (3) to be equal to (16), it must be

\frac{u^{'} (c_{1}^{i})}{β_{i} R (1 + τ^{i})} = \frac{u^{'} (c_{1}^{i}) - x^{'} (c_{1}^{i} - y_{1}^{i})}{β^{i} R},

from which we obtain the formula for the optimal prudential capital controls as $\hat{τ^{i}} = x^{'} (c_{1}^{i} - y_{1}^{i}) / [u^{'} (c_{1}^{i}) - x^{'} (c_{1}^{i} - y_{1}^{i})]$ .

For a lending country $(c_{1}^{i} - y_{1}^{i} \leq 0)$ instead, it is $e (c_{1}^{i} - y_{1}^{i}) = 0$ , and thus all frictions disappear. As a result, the solution to the welfare maximization for a rational forward-looking national planner coincides with the utility maximization of the representative consumer, and the optimal prudential policy becomes $\hat{τ_{1}^{i}} = 0$ . Hence, depending on whether country i is lender or borrower, its optimal prudential capital controls can be described by the step function defined in (9).

Let us now introduce the terms-of-trade motive. If country i is able to affect the world market equilibrium (mⁱ > 0), then it is $d R / d [m^{i} (c_{1}^{i} - y_{1}^{i})] \neq 0$ , and its unilateral optimal policy is found by equalizing Euler equation (6) with the one solved under the decentralized optimization problem (3). After a few algebraic steps, we obtain the optimal policy as

1 + τ^{i *} = (1 + \hat{τ^{i}}) (1 + \tilde{τ^{i}}) .

The expression for $\hat{τ^{i}}$ is given in (9), while the one for $\tilde{τ^{i}}$ is given by $\tilde{τ^{i}} = m^{i} \cdot ɛ^{- i}$ , where ε⁻ⁱ represents the inverse elasticity of global savings faced by country i, that is (and after defining $Y_{1}^{- i} \equiv Σ_{j \neq 1} m^{j} y_{1}^{j}$ and $C_{1}^{- i} \equiv Σ_{j \neq 1} m^{j} c_{1}^{j}$ ):⁴³

\begin{matrix} ɛ^{- i} = \frac{d R}{d (Y_{1}^{- i} - C_{1}^{- i})} \frac{Y_{1}^{- i} - C_{1}^{- i}}{R} . & (17) \end{matrix}

5.2 Proof of Lemma 1

(i) Define

F (R^{*}, τ^{S^{- i}}) \equiv m^{S^{- i}} [y_{1}^{S^{- i}} - c_{1}^{S^{- i}} (R^{*}, τ^{S^{- i}})] + \underset{j \neq S^{- i}}{Σ} m^{j} [y_{1}^{j} - c_{1}^{j} (R^{*}, τ_{1}^{j})] = 0.

as the implicit function of R* w.r.t τ^S⁻ⁱ. From the implicit function theorem, we obtain

\frac{{d R}^{*}}{{d τ}^{S^{- i}}} = - \frac{\partial F / \partial τ^{S^{- i}}}{\partial F / \partial R *},

which is strictly negative, as both ∂F/∂τ^S⁻ⁱ and ∂F/∂R^* are strictly positive.

(ii) World savings are defined as the sum of each individual lending country’s savings, $Σ_{ω} m^{ω} [y_{1}^{ω} - c_{1}^{ω} (R, τ^{ω})]$ , with ω indexing all countries for which $y_{1}^{ω} - c_{1}^{ω} > 0$ . The statement is true given that ${d c}_{1}^{ω} (R, τ^{ω}) / d R < 0$ for any ω.

5.3 Proof of Proposition 2

The proof follows immediately from point (i) of Lemma 1 together with the fact that ${d c}_{1}^{i} (R, τ^{i}) / d R < 0$ . Formally, ${d c}_{1}^{i} (R, τ^{i}) / d τ^{S^{- i}} = ({d c}_{1}^{i} (R, τ^{i}) / d R) \cdot (d R^{*} / d τ^{S^{- i}}) > 0$ .

5.4 Proof of Proposition 3

Deriving expression (8) with respect to τ^S⁻ⁱ we obtain

\frac{d τ^{i *}}{d τ^{S^{- i}}} = \frac{d \hat{τ^{i}}}{d τ^{S^{- i}}} (1 + \tilde{τ^{i}}) + \frac{d \tilde{τ^{i}}}{d τ^{S^{- i}}} (1 + \hat{τ^{i}}),

where $\hat{τ^{i}}, \tilde{τ^{i}} > 0$ for a large borrowing country.

(i) We now prove that $d \hat{τ^{i}} / d τ^{S^{- i}}$ is always strictly positive. It is

\frac{d \hat{τ^{i}}}{d τ^{S^{- i}}} = \frac{d \hat{τ^{i}}}{d R} \frac{d R^{*}}{d τ^{S^{- i}}} .

Lemma 1 has proven that dR^*/dτ^S⁻ⁱ < 0. We only need to show that $d \hat{τ^{i}} / d R < 0$ . Define

G (R, \hat{τ^{i}}) \equiv \frac{x^{'} (c_{1}^{i} (R, \hat{τ^{i}}) - y_{1}^{i})}{u^{'} (c_{1}^{i} (R, \hat{τ^{i}})) - x^{'} (c_{1}^{i} (R, \hat{τ^{i}}) - y_{1}^{i})} - \hat{τ^{i}} = 0

as the implicit function of $\hat{τ^{i}}$ w.r.t. R when country i is a borrower. By the implicit function theorem, it is

\frac{d \hat{τ^{i}}}{d R} = - \frac{\partial G / \partial R}{\partial G / \partial \hat{τ^{i}}} .

The numerator writes as

\frac{\partial G}{\partial R} = \frac{u^{'} (c_{1}^{i}) \cdot x^{''} (c_{1}^{i} - y_{1}^{i}) - u^{''} (c_{1}^{i}) \cdot x^{'} (c_{1}^{i} - y_{1}^{i})}{[u^{'} (c_{1}^{i}) - x^{'} (c_{1}^{i} - y_{1}^{i})]^{2}} \frac{\partial c_{1}^{i}}{\partial R},

which is strictly negative, given that $u^{'}, x^{'}, x^{''} > 0, u^{''} < 0$ , and $\partial c_{1}^{i} / \partial R < 0$ .

On the other hand, the denominator can be calculated as

\frac{\partial G}{\partial \hat{τ^{i}}} = \frac{u^{'} (c_{1}^{i}) \cdot x^{''} (c_{1}^{i} - y_{1}^{i}) - u^{''} (c_{1}^{i}) \cdot x^{'} (c_{1}^{i} - y_{1}^{i})}{[u^{'} (c_{1}^{i}) - x^{'} (c_{1}^{i} - y_{1}^{i})]^{2}} \frac{\partial c_{1}^{i}}{\partial \hat{τ^{i}}} - 1,

which is also strictly negative. It then follows $d \hat{τ^{i}} / d R < 0$ for all borrowing countries, which completes the proof of part (i).

(ii) We are now going to prove that $d \tilde{τ^{i}} / d τ^{S^{- i}}$ may be positive or negative. Knowing that $\tilde{τ^{i}} = m^{i} \cdot ɛ^{- i}$ , this derivative can be calculated from the implicit function defined by

H [\tilde{τ^{i}}, τ^{S^{- i}}] \equiv m^{i} ɛ^{- i} (\tilde{τ^{i}}, τ^{S^{- i}}) - \tilde{τ^{i}} = 0.

Applying the implicit function theorem to function H, we obtain

\begin{matrix} \frac{d \tilde{τ^{i}}}{d τ^{S^{- i}}} = - \frac{\frac{d H}{d τ^{S^{- i}}}}{\frac{d H}{d \tilde{τ^{i}}}} = - \frac{m^{i} \frac{d ɛ^{- i}}{d τ^{S^{- i}}}}{m^{i} \frac{d ɛ^{- i}}{d \tilde{τ^{i}}} - 1}, & (18) \end{matrix}

which can be positive or negative. In fact, exploiting the expression for ε⁻ⁱ given in (17), we find that

\frac{d ɛ^{- i}}{d τ^{S^{- i}}} = \frac{1}{R} [\frac{d (Y_{1}^{- i} - C_{1}^{- i})}{\underset{> 0}{d τ^{S^{- i}}}} (\frac{d^{2} R}{\underset{≶ 0}{d {(Y_{1}^{- i} - C_{1}^{- i})}^{2}}} \frac{Y_{1}^{- i} - C_{1}^{- i}}{\underset{> 0}{R}} + \frac{d R}{d \underset{> 0}{(Y_{1}^{- i} - C_{1}^{- i})}}) - \frac{d R}{\underset{< 0}{d τ^{S^{- i}}}} \underset{> 0}{ɛ^{- i}}] ≶ 0,

and

\frac{d ɛ^{- i}}{d \tilde{τ^{i}}} = \frac{d^{2} R}{\underset{≶ 0}{d (Y_{1}^{- i} - C_{1}^{- i}) d \tilde{τ^{i}}}} - \frac{1}{R} \underset{> 0}{ɛ^{- i}} \frac{d R}{d \underset{< 0}{\tilde{τ^{i}}}} ≶ 0.

For ease of reference, the sign of each term is reported in the expression above. The sign of expression (18) is then ambiguous as both the numerator and the denominator can be either positive or negative.

5.5 Proof of Corollary

The proof of this statement is immediate given that, as proven in Proposition 3, $d \hat{τ^{i}} / d τ^{S^{- i}} = (d \hat{τ^{i}} / d R) (d R^{*} / d τ^{S^{- i}}) > 0$ .

6 Appendix II: Schindler’s Index

Schindler (2009) compiles de jure indices of inflow and outflow controls using publicly available information from the IMF’s Annual Report on Exchange Arrangements and Exchange Restriction (AREAER). Schindler (2009) fully exploits the IMF’s post 1996 disaggregated reporting of different categories of capital transaction. The categories covered in his index are as follows:

Restrictions on transactions in equities (eq), bonds (bo), money market instruments (mm), and collective investments (ci). Transactions are divided into four categories:
- – Purchase locally by nonresidents (plbn)
- – Sale or issue abroad by residents (siar)
- – Purchase abroad by residents (pabr)
- – Sale or issue locally by nonresidents (siln)
Restrictions on financial credits (fc) are divided into two categories:
- – By residents to nonresidents (fco)
- – By nonresidents to residents (fci)
Restrictions on direct investment (di) are divided into three categories:
- – Outward investment (dio)
- – Inward direct investment (dii)
- – Liquidation of direct investment (ldi)

The information contained in the AREAER is coded in binary form, taking a value of 0 (unrestricted) or 1 (restricted). The data can be aggregated in different ways, allowing the construction of capital control sub-indices by asset category, by residency, and by the direction of flows. Sub-indices are aggregated by taking unweighted averages of the subcategories of interest. Indices for outflow controls are constructed for each individual asset category. For example:

inf low controls on asset category i ({k a i}_{i}) = \frac{i_{p l b n} + i_{s i a r}}{2}

where i stands for equities, money market instruments, bonds, or collective investment instruments. The aggregate inflow control (kai) is

k a i = \frac{{k a i}_{e q} + {k a i}_{m m} + {k a i}_{b o} + {k a i}_{c i} + f c i + d i i}{6}

7 Appendix III: Additional Tables

Table 10.

Pairwise Correlation of Different Proxies of Capital Controls

Table 10.

Pairwise Correlation of Different Proxies of Capital Controls

	Schindler Inflow	Schindler General	CAPITAL	CAPIN	FTNCONT2
Schindler Inflow	1.000
Schindler General	0.936	1.000
CAPITAL	0.734	0.802	1.000
CAPIN	0.728	0.782	0.934	1.000
FTNCONT2	0.519	0.534	0.497	0.464	1.000

Table 10.

Pairwise Correlation of Different Proxies of Capital Controls

	Schindler Inflow	Schindler General	CAPITAL	CAPIN	FTNCONT2
Schindler Inflow	1.000
Schindler General	0.936	1.000
CAPITAL	0.734	0.802	1.000
CAPIN	0.728	0.782	0.934	1.000
FTNCONT2	0.519	0.534	0.497	0.464	1.000

Table 11.

Within-group Policy Response with Borrowing Constraint

Note: The sample consists of 64 countries. GDP per capita and composite risk index are insignificant in all specifications and hence not reported in the table to save space. All regressions control for year fixed effects, group contagionvariables, and country fixed effects. Robust standard errors clustered at country level in parentheses. *** p < 0.01, ** p < 0.05, * p < 0.1

Table 11.

Within-group Policy Response with Borrowing Constraint

							Expected
	(1)	(2)	(3)	(4)	(5)	(6)	Sign
Domestic Variables (Lagged)
Real GDP growth rate	−0.00171	−0.000935	−0.00201	−0.00134	−0.00125	−0.00203	+
	(0.00799)	(0.00803)	(0.00783)	(0.00768)	(0.00771)	(0.00810)
Real GDP growth rate shock	0.0101	0.00907	0.0100	0.00901	0.00930	0.0101	−
	(0.00946)	(0.00945)	(0.00944)	(0.00911)	(0.00932)	(0.00969)
REER overvaluation	−0.0345	−0.0248	−0.0288	−0.0282	−0.0348	−0.0370	−
	(0.140)	(0.147)	(0.140)	(0.143)	(0.140)	(0.142)
Inflation	0.0508	0.0523	0.0502	0.0504	0.0497	0.0510	+
	(0.0403)	(0.0393)	(0.0412)	(0.0405)	(0.0420)	(0.0416)
Flexible Exchange Rate	−0.250***	−0.211***	−0.229***	−0.226***	−0.213***	−0.221***	−
	(0.0404)	(0.0338)	(0.0303)	(0.0282)	(0.0302)	(0.0360)
Real GDP (logged)	0.00209	−0.0455	−0.0862	−0.0759	−0.0861	−0.0678	+
	(0.367)	(0.366)	(0.376)	(0.369)	(0.370)	(0.369)
External Debt	−0.0226	−0.0125	−0.0169	−0.0160	−0.0175	−0.0187	+
	(0.0359)	(0.0381)	(0.0372)	(0.0372)	(0.0369)	(0.0371)
Change of Weighted Inflow Controls in the ROG
Group by geographic location	−0.278						+
	(0.236)
Group by export specialization		0.0747					+
		(0.171)
Time-invariant group by growth rate			−0.0610				+
			(0.230)
Time-variant group by growth rate				−0.0244			+
				(0.0560)
Time-invariant group by composite risk					−0.207		+
					(0.259)
Time-variant group by composite risk						0.0189	+
						(0.0766)
Observations	841	841	841	841	841	841
R-squared	0.168	0.167	0.165	0.166	0.168	0.166

Table 11.

Within-group Policy Response with Borrowing Constraint

							Expected
	(1)	(2)	(3)	(4)	(5)	(6)	Sign
Domestic Variables (Lagged)
Real GDP growth rate	−0.00171	−0.000935	−0.00201	−0.00134	−0.00125	−0.00203	+
	(0.00799)	(0.00803)	(0.00783)	(0.00768)	(0.00771)	(0.00810)
Real GDP growth rate shock	0.0101	0.00907	0.0100	0.00901	0.00930	0.0101	−
	(0.00946)	(0.00945)	(0.00944)	(0.00911)	(0.00932)	(0.00969)
REER overvaluation	−0.0345	−0.0248	−0.0288	−0.0282	−0.0348	−0.0370	−
	(0.140)	(0.147)	(0.140)	(0.143)	(0.140)	(0.142)
Inflation	0.0508	0.0523	0.0502	0.0504	0.0497	0.0510	+
	(0.0403)	(0.0393)	(0.0412)	(0.0405)	(0.0420)	(0.0416)
Flexible Exchange Rate	−0.250***	−0.211***	−0.229***	−0.226***	−0.213***	−0.221***	−
	(0.0404)	(0.0338)	(0.0303)	(0.0282)	(0.0302)	(0.0360)
Real GDP (logged)	0.00209	−0.0455	−0.0862	−0.0759	−0.0861	−0.0678	+
	(0.367)	(0.366)	(0.376)	(0.369)	(0.370)	(0.369)
External Debt	−0.0226	−0.0125	−0.0169	−0.0160	−0.0175	−0.0187	+
	(0.0359)	(0.0381)	(0.0372)	(0.0372)	(0.0369)	(0.0371)
Change of Weighted Inflow Controls in the ROG
Group by geographic location	−0.278						+
	(0.236)
Group by export specialization		0.0747					+
		(0.171)
Time-invariant group by growth rate			−0.0610				+
			(0.230)
Time-variant group by growth rate				−0.0244			+
				(0.0560)
Time-invariant group by composite risk					−0.207		+
					(0.259)
Time-variant group by composite risk						0.0189	+
						(0.0766)
Observations	841	841	841	841	841	841
R-squared	0.168	0.167	0.165	0.166	0.168	0.166

References

Bagwell, Kyle and Robert W. Staiger (1999). “An Economic Theory of GATT.” The American Economic Review, 89(1), pp. 215–248.
- Search Google Scholar
- Export Citation
Bagwell, Kyle and Robert W. Staiger (2002). “The Economics of the World Trading System”, Boston, MA: MIT Press.
- Search Google Scholar
- Export Citation
Bartolini, Leonardo, and Allan Drazen (1997). “Capital- Account Liberalization as a Signal.” American Economic Review, Vol. 87 (March), pp. 138–54.
- Search Google Scholar
- Export Citation
Bown, Chad P. and Meredith A. Crowley (2006). “Policy Externalities: How U.S. Antidumping Affects Japanese Exports to the E.U.” European Journal of Political Economy v22, n3 (September 2006): 696–714. [Special issue]
- Search Google Scholar
- Export Citation
Bown, Chad P. and Meredith A. Crowley (2007). “Trade Deflection and Trade Depression.” Journal of International Economics v72, n1 (May 2007): 176–201.
- Search Google Scholar
- Export Citation
Chinn, Menzie and Hiro Ito (2008). “A New Measure of Financial Openness.” Journal of Comparative Policy Analysis 10(3): 309–22.
- Search Google Scholar
- Export Citation
Costinot, Arnaud, Guido Lorenzoni, and Ivan Werning (2013). “A Theory of Capital Controls as Dynamic Terms-of-Trade Manipulation.” Journal of Political Economy, Forthcoming.
- Search Google Scholar
- Export Citation
Farhi, Emmanuel, and Ivan Werning (2013). “Dilemma not Trilemma? Capital Controls and Exchange Rates with Volatile Capital Flows”
- Search Google Scholar
- Export Citation
Fernandez, Andres, Alessandro Rebucci, and Martin Uribe (2013) “Are Capital Controls Prudential? An Empirical Investigation”. NBER Working Paper 19671, November 2013.
- Search Google Scholar
- Export Citation
Forbes, Kristin, Marcel Fratzscher, Thomas Kostka, and Roland Straub (2012). “Bubble Thy Neighbor: Direct and Spillover Effects of Capital Controls.“ NBER Working Paper 18052.
- Search Google Scholar
- Export Citation
Forbes, Kristin and Francis Warnock (2012). “Capital Flow Waves: Surges, Stops, Flight and Retrenchment.” Journal of International Economics, Volume 88, Issue 2, November 2012, Pages 235–25.
- Search Google Scholar
- Export Citation
Forbes, Kristin, Marcel Fratzscher, and Roland Straub (2013). “Capital Controls and Macroprudential Measures: What are They Good For?” MIT-Sloan Working Paper 5061-13.
- Search Google Scholar
- Export Citation
Fratzscher, Marcel (2012). “Capital Controls and Foreign Exchange Policy.” CEPR Discussion Paper 8788, January 2012.
- Search Google Scholar
- Export Citation
Ghosh, Atish R., Jun Kim, Mahvash S. Qureshi, and Juan Zalduendo (2012). “Surges.” IMF Working Paper 1222.
- Search Google Scholar
- Export Citation
Ghosh, Atish R., Mahvash S. Qureshi, and Naotaka Sugawara (2014). “Regulating Capital Flows at Both Ends: Does it Work?.” mimeo, IMF.
- Search Google Scholar
- Export Citation
Grilli, Vittorio and Gian Maria Milesi-Ferretti (1995). “Economic Effects and Structural Determinants of Capital Controls.” IMF Staff Papers, 42(3): 517–551.
- Search Google Scholar
- Export Citation
Ilzetzki, Ethan, Carmen M. Reinhart, and Kenneth S. Rogoff (2010). “Exchange Rate Arrangements Entering the 21st Century: Which Anchor Will Hold?” Data updated at http://personal.lse.ac.uk/ilzetzki/IRRBack.htm
- Search Google Scholar
- Export Citation
IMF (2012). “The Liberalization and Management of Capital Flows: An Institutional View.” International Monetary Fund Position Paper.
- Search Google Scholar
- Export Citation
IMF (2013). “Guidance Note on the Liberalization and Management of Capital Flows.” International Monetary Fund Position Paper.
- Search Google Scholar
- Export Citation
Korinek, Anton (2014). “Capital Controls and Currency Wars.” University of Maryland, Mimeo.
- Search Google Scholar
- Export Citation
Lambert, Frederic, Julio Ramos-Tallada, and Cyril Rebillard (2011). “Capital controls and spillover effects: evidence from Latin-American countries.” Banque de France Working Papers 357.
- Search Google Scholar
- Export Citation
Magud, Nicolas, Carmen Reinhart, and Kenneth Rogoff (2011). “Capital Controls: Myth and Reality – A Portfolio Balance Approach.” Peterson Institute of International Economics. WP 11-7.
- Search Google Scholar
- Export Citation
Orefice, Gianluca and Nadia Rocha (2014). “Deep integration and production networks: an empirical analysis.” The World Economy, vol.37(1), pp.106–136.
- Search Google Scholar
- Export Citation
Ostry, Jonathan D., Atish R. Ghosh, Marcos Chamon, and Mahvash S. Qureshi (2012). “Tools for managing financial-stability risks from capital inflows.” Journal of International Economics, Elsevier, vol. 88(2), pages 407–421.
- Search Google Scholar
- Export Citation
Quinn, Dennis P., Martin Schindler, and A. Maria Toyoda (2011). “Assessing Measures of Financial Openness and Integration.” IMF Economic Review 59.3 (2011): 488–522.
- Search Google Scholar
- Export Citation
Quinn, Dennis P. and A. Maria Toyoda (2008). “Does Capital Account Liberalization Lead to Economic Growth?” Review of Financial Studies 21(3):1403–1449.
- Search Google Scholar
- Export Citation
Rey, Helene (2013). “Dilemma not Trilemma: The global financial cycle and monetary policy independence”. Paper presented at the Jackson Hole Symposium, August 2013.
- Search Google Scholar
- Export Citation
Shindler, Martin (2009). “Measuring Financial Integration: A New Data Set.” IMF Staff Papers, Vol. 56, No. 1.
- Search Google Scholar
- Export Citation
Viner, Jacob (1950). The Customs Union Issue. Carnegie Endowment for International Peace: New York.
- Search Google Scholar
- Export Citation
WTO (2011). The WTO and Preferential Trade Agreements. World Trade Report, 2011. WTO, Geneva.
- Search Google Scholar
- Export Citation

Corresponding Author: Michele Ruta (e-mail: mruta@imf.org). The authors would like to thank Tam Bayoumi, Chad Bown, Arnaud Costinot, Martin Kaufman, Lance Kent, Anton Korinek, Kamal Saggi, Sarah Sanya, Robert Staiger and seminar participants at University of Maryland, University of Groningen, George Mason University, Vanderbilt University, Stanford University, and International Monetary Fund for helpful comments and suggestions. The views expressed in this paper are those of the authors and do not necessarily reflect those of IMF.

Capital controls encompass a variety of measures, such as taxes, quantitative restrictions and regulations, that affect cross-border financial activities by discriminating on the basis of residency (IMF, 2013).

The term “trade deflection” was introduced in Bown and Crowley (2006) to indicate a situation where an increase in a trade barrier in one market determines a change in destination in exports. This is different from the concept of “trade diversion”(Viner, 1950), where the reduction of a tariff granted to a trading partner increases imports from the latter and reduces imports from other (potentially more efficient) exporters. An example of trade deflection discussed in Bown and Crowley (2007) is the steel safeguard, a set of tariffs and quotas, imposed by the US on Chinese exports in 2002. Shortly afterwards, the EU reacted with similar measures, claiming that the change in US policy had deflected Chinese steel exports to its market.

In 2010, Peru increased the fee on non-resident purchase of central bank paper to 400 basis points (from 10 basis points), while Thailand imposed a 15 percent withholding tax on non-residents’ interest earnings and capital gains on state bonds. In 2011, Indonesia introduced a limit on short-term foreign borrowing by banks to 30 percent of capital and Korea restored a 14 percent withholding tax on interest income on non-resident purchases of treasury bonds (IMF, 2013).

In the words of Keynes (1943), “the whole management of the domestic economy depends upon being free to have the appropriate rate of interest without reference to the rates prevailing elsewhere in the world. Capital control is a corollary to this.”

Similar arguments have been informally made by others. In particular, Ostry et al. (2012) and Korinek (2014) talk of the possibility of a “capital control arms race”.

Some may see this argument as contradicting the notion that capital controls can be a legitimate (even if second-best) instrument to address a domestic distortion. This idea, however, has only recently become widely accepted (for instance, the institutional view of the IMF on capital flow management was published in 2012), while our dataset covers the period 1995-2009. An interesting question is, therefore, whether in the future a policy response to capital flow deflection will become a more permanent feature of the international financial system.

The convexity of the externality function is a common assumption in the literature. In the context of financial fragility, convexity is justified by the fact that the risk to a country’s financial stability is likely to increase at a faster rate as foreign borrowing rises.

As the above discussion highlights, in this standard macro framework, we could define national welfare as Wⁱ (Rⁱ, R); that is, directly in terms of the local and world interest rates that capital control selections imply. This general formulation is equivalent to the one used in the trade policy coordination literature (Bagwell and Staiger, 1999 and 2002), where the governments’ objectives are a function of the local and world prices implied by tariff selection.

We allow S ⊂ Ω as in practice financial markets may be (partially) segmented, for instance because different subgroups of borrowing countries may be considered imperfect substitutes by international investors. While we here prefer to remain general and avoid unnecessarily complicated formalization, in the empirical analysis S ⊂ Ω will in fact denote a subgroup of possibly “similar” borrowing countries from the perspective of investors (say, in terms of geographic location or country risk). See subsection 3.2.3 for details.

This is not surprising: by definition, optimal terms-of-trade driven controls for country i are stronger, the greater the sensitivity of the world interest rate to the supply of capital faced by country $i (d R / d (y_{1}^{- i} - c_{1}^{- i}))$ . As a result, if an increase in τ^S⁻ⁱ implies an ever greater impact of capital inflows on the world interest rate $(d^{2} R / d {(y_{1}^{- i} - c_{1}^{- i})}^{2} \cdot d (y_{1}^{- i} - c_{1}^{- i}) / d τ^{S^{- i}} > 0)$ , then, other things equal, country i is more likely to respond to such increase by raising its own policy, thus implying that the terms-of-trade driven capital controls are more likely to be complementary $(d \tilde{τ^{i}} / d τ^{S^{- i}} > 0)$ .

Indeed, it can be proven that aggregate welfare is maximized under the unilaterally optimal policy defined in expression (9). In this respect, the multiplier effect identified in Corollary 1 describes the chain of optimal prudential policy responses to a common shock hitting a borrowing region.

Obviously, capital controls can have more than one spillover effect. Gross outflows are directly affected by inflow controls in recipient countries as one’s outflow is another’s inflow. Following the terminology in the trade literature, we can refer to this spillover effect as capital flow depression, which differs from the capital flow deflection analyzed here.

There are 443 instances of changes in inflow controls from 1995-2009 among developing countries, while the number is 69 among more advanced economies during the same period. The average inflow restriction index for the period is 0.5 among developing countries, while it is only 0.2 among more advanced countries.

Financial assets are shares or other securities of a participating nature, bonds or other debt securities, other market instruments. A more detailed description of the index and its methodology are available in the appendix.

Restrictions on capital outflows are the average of the restriction dummies on purchase of financial assets abroad by residents, sale or issue of financial assets locally by nonresidents, collective investments by residents to nonresidents, financial credits by residents to nonresidents, and outward direct investment.

We follow the IMF World Economic Outlook (WEO) and divide countries into six regional groups: Latin America, Middle East and North Africa, Sub-Sahara Africa, Former Soviet Bloc, and Central and East Europe.

For export specialization we continue to follow the WEO classification and divide countries into five groups: fuel exporters, primary goods exporters, manufacturing exporters, service exporters, and diversified exporters.

Some components of gross inflows have value 0 in some countries, but our data source does not specify whether these are really zero or missing information. In our regressions, we try both interpretations and they lead to the same findings. Below, we report the results where gross capital flows are computed by treating a 0 observation as no capital inflows.

See Magud et al. (2012) for a survey of the literature.

The real GDP growth rate shock is the log difference between the real GDP growth rate and its H-P filtered trend.

In the regressions, we also introduce additional controls used in the literature of push and pull factors. They include: domestic inflation, real effective exchange rate overvaluation, de facto exchange rate regime. The findings discussed below are robust to the inclusion of these additional variables. However, we do not include them in our baseline regressions because their coefficients are never significant.

A priori, it is not obvious what sign the coefficient on real GDP per capita should have. On the one hand, higher real GDP per capita is associated with higher level of financial development, which should lead to more inflows. On the other hand, lower real GDP per capital is also associated with weak economic infrastructure and financial capacity, which implies more need for foreign investment and capital inflows. Previous studies find mixed results. Ghosh et al. (2012) find real GDP per capita to reduce the probability of experiencing a net flow surge, while Forbes and Warnock (2012) find no evidence that real GDP per capita is associated with gross inflow surge.

There are 124 countries in the list, 78 of which are in bold. All the countries listed have both GDP and capital inflow restriction data available throughout the sample period, which allows us to compute the rest of the group’s capital inflow restrictions. The countries in bold are the ones with at least ten years of observations for the rest of the explanatory variables, and hence are used for regressions.

The only major difference is the coefficient for a country’s own capital controls, which is positive but insignificant in the specification with country fixed effects. This finding is consistent with the empirical literature (see Magud et al., 2011) and results from the well-known endogeneity problem between a country’s own capital controls and inflows (i.e. higher flows induce a country to adjust prudential restrictions). Given the persistence of capital controls, it is not surprising that simply lagging inflow restrictions does not entirely solve this problem. Forbes et al. (2013) use a propensity score matching method to deal with this econometric challenge. They find that, while broad measures of inflow controls may not be systematically effective, specific measures (e.g. those targeting equity or bond flows) and “major” policy changes lead to a significant reduction in capital inflows.

The group contagion variable is computed as $\frac{Σ_{j ∊ S_{- i}} y_{t}^{j} {S u r g e}_{t}^{j}}{Σ_{j ∊ S_{- i}} y_{t}^{j}}$ where ${S u r g e}_{t}^{j}$ is defined in 3.2.4.

In subsection 3.2.5, we use an IV approach to further address endogeneity concerns.

Coefficients for the spillover variable are very similar to the ones shown in Table 5 and, hence, are not reported in this paper.

Ghosh et al. (2012) use the threshold of 70% and look at net flows.

The data for the CAPITAL index cover restrictions imposed on capital flows by residents and by nonresidents. FINCONT2 is computed as an average of three components: (i) differential treatment of accounts held by nonresidents; (ii) limits on borrowing from abroad; and (iii) restrictions on maintenance of accounts abroad. The correlation between the various indexes is reported in Appendix. We rescale all proxies along the interval [0,1] with 0 indicating full capital account liberalization, so they are comparable to the Schindler’s index of inflow controls. The pairwise correlations of these proxies are provided in Table 10 in the Appendix.

Another commonly used proxy for capital controls comes from Chinn and Ito (2009). However this measure is less refined than the Schindler index and the other measures used here. Specifically, the Chinn-Ito index is compiled from a broader set of policies than capital controls: they include exchange rate policy, trade policy, and current account policy. As this measure is less appropriate to test our theory we are not surprised to find no robust capital flow deflection when we proxy capital controls with the Chinn-Ito index. For more discussions on and detailed comparisons of various proxies of capital controls refer to Quinn et al. (2011).

The findings of these exercises are not reported here, but are available upon request. All the regressions in this subsection are robust when we deduct FDI from gross inflows.

The full list of the different ways we grouped countries by return is: (i) Return on assets from bank-level data; (ii) Return on equity from bank-level data; (iii) Government bond yield; (iv) Stock market return. In addition, we also ran regressions dividing countries by GDP per capita (a proxy for the level of development) and GDP (a proxy for the country size). As discussed in the main text, we found no significant capital flow deflection using these variables.

Data on deep FTAs are from the WTO’s database on the content of trade agreements. See WTO (2011) for a detailed description. A possible concern with this instrument is that deep FTAs are correlated with FDI flows to member countries. In particular, Orefice and Rocha (2013) document the two way relationship between deep agreements and production networks. While to the best of our knowledge there is no evidence that deep FTAs affect FDIs to third countries (a sort of investment diversion), one may still worry that the exclusion restriction is not satisfied with respect to FDIs. For this reason, we run our regressions both including and excluding FDIs from the definition of gross inflows. All results discussed below do not change.

The data on election year and the government’s political orientation are from World Bank’s Database of Political Institutions.

Due to the data restriction, we have smaller samples for IV regressions. Hence we divide countries into three groups instead. The results in Table 5 continue to hold when countries are divided into three groups.

We also run the IV regression with the combination of ${r i g h t}_{t}^{S^{- i}}$ and ${F T A}_{t}^{S_{- i}}$ as instruments. However, we find this combination to be significant only at 11 percent and, therefore, we do not report the results in Table 8.

We tried using the specifications in Fratzcher (2011) to study capital controls in developing countries, but the model yields little explanatory power.

As countries are more likely to increase controls on destabilizing/short-term inflows such as bond inflows rather than FDIs, we run the same regressions in this subsection excluding FDI restrictions from the dependent variable, and the results are the same.

All the results from the previous subsection on spillover effect are robust to the smaller sample.

We also run instrumental variable regressions replacing ${Δ τ}_{t}^{S^{- i}}$ with the sets of instruments introduced in Section 3.2.5, and these regressions yield the same results.

The results of robustness checks are available upon request.

See the Appendix for further details.

In deriving the expression for ε⁻ⁱ, we exploit the fact that $m^{i} (c_{1}^{i} - y_{1}^{i}) = Σ_{j \neq 1} m^{j} (y_{1}^{i} - c_{1}^{i})$ .

Save
Cite
Email this content

Share Link

Copy this link, or click below to email it to a friend
Email this content
or copy the link directly:

https://www.elibrary.imf.org/view/journals/001/2014/145/article-2014-145-A999-en.xml-en.xml
The link was not copied. Your current browser may not support copying via this button.

Link copied successfully

Collapse
Expand

Capital Flow Deflection

Author:

Paolo Giordani

Michèle Ruta

Hans Weisfeld

, and

Ling Zhu

Volume/Issue:: Volume 2014: Issue 145

Publisher:: International Monetary Fund

ISBN:: 9781498383202

ISSN:: 1018-5941

Pages:: 47

DOI:: https://doi.org/10.5089/9781498383202.001

Keywords
References

Table 11.

Within-group Policy Response with Borrowing Constraint

							Expected
	(1)	(2)	(3)	(4)	(5)	(6)	Sign
Domestic Variables (Lagged)
Real GDP growth rate	−0.00171	−0.000935	−0.00201	−0.00134	−0.00125	−0.00203	+
	(0.00799)	(0.00803)	(0.00783)	(0.00768)	(0.00771)	(0.00810)
Real GDP growth rate shock	0.0101	0.00907	0.0100	0.00901	0.00930	0.0101	−
	(0.00946)	(0.00945)	(0.00944)	(0.00911)	(0.00932)	(0.00969)
REER overvaluation	−0.0345	−0.0248	−0.0288	−0.0282	−0.0348	−0.0370	−
	(0.140)	(0.147)	(0.140)	(0.143)	(0.140)	(0.142)
Inflation	0.0508	0.0523	0.0502	0.0504	0.0497	0.0510	+
	(0.0403)	(0.0393)	(0.0412)	(0.0405)	(0.0420)	(0.0416)
Flexible Exchange Rate	−0.250***	−0.211***	−0.229***	−0.226***	−0.213***	−0.221***	−
	(0.0404)	(0.0338)	(0.0303)	(0.0282)	(0.0302)	(0.0360)
Real GDP (logged)	0.00209	−0.0455	−0.0862	−0.0759	−0.0861	−0.0678	+
	(0.367)	(0.366)	(0.376)	(0.369)	(0.370)	(0.369)
External Debt	−0.0226	−0.0125	−0.0169	−0.0160	−0.0175	−0.0187	+
	(0.0359)	(0.0381)	(0.0372)	(0.0372)	(0.0369)	(0.0371)
Change of Weighted Inflow Controls in the ROG
Group by geographic location	−0.278						+
	(0.236)
Group by export specialization		0.0747					+
		(0.171)
Time-invariant group by growth rate			−0.0610				+
			(0.230)
Time-variant group by growth rate				−0.0244			+
				(0.0560)
Time-invariant group by composite risk					−0.207		+
					(0.259)
Time-variant group by composite risk						0.0189	+
						(0.0766)
Observations	841	841	841	841	841	841
R-squared	0.168	0.167	0.165	0.166	0.168	0.166

Topics

Countries

Series

About

Resources

5 Appendix I: Proofs

5.1 Proof of Proposition 1

5.2 Proof of Lemma 1

5.3 Proof of Proposition 2

5.4 Proof of Proposition 3

5.5 Proof of Corollary

6 Appendix II: Schindler’s Index

7 Appendix III: Additional Tables

References

Share Link

Table of Contents

Headings

Metrics