A. Measuring Financial Integration

There is not yet a measure of the intensity of financial integration that is widely used by economists. Existing measures of financial integration, including that of Quinn (1997) that looks at capital account regulations, focus on potential financial integration rather than on actual financial integration. Using Quinn’s measure of financial integration, a country that imposed no restrictions on capital flows, but that received or sent little capital flows, would qualify as financially integrated without actually benefiting from financial integration.

Table 1 presents two measures of actual financial integration for 32 emerging and developing economies. The first one (index 1) is the ratio of the sum of all claims and liabilities of a country to its GDP. Its components are also given for G-3 economies (the United-States, Japan, and Germany) so as to provide a benchmark against which actual financial integration of developing and emerging economies can be compared.

Table 1.

Measuring Financial Integration for 32 Developing and Emerging Economies in 1996

(Ratio to GDP)

Unless indicated, data come from Lane and Milesi-Ferreti (2001). (1) is the ratio to GDP of the sum of the estimate of stock of direct investment assets (cumulative flow adjusted for relative price variations), the estimate of stock of portfolio equity assets (cumulative flow adjusted for stock market price variations), and the sum of cumulative flows of portfolio debt assets, other assets and net errors and omissions. (2) is the ratio to GDP of total reserves minus gold. (3) is the ratio to GDP of gross external debt for developing countries, and the ratio to GDP of stock of other investment liabilities for G3 economies, (3b) is the ratio to GDP of public and publicly guaranteed long term debt (World Bank, Global Development Finance), and (3a) = (3) – (3b). (4) is the ratio to GDP of the estimate of stock of direct investment liabilities (cumulative flow adjusted for relative price variations). (5) is the ratio to GDP of the estimate of stock of portfolio equity liabilities (cumulative flow adjusted for stock market price variations).

Table 1.

Measuring Financial Integration for 32 Developing and Emerging Economies in 1996

(Ratio to GDP)

			Whole sample	Level of financial integration		Region				For memo
				Low	High	Africa	Asia	Latin America	Middle East	G-3 economies
# of countries			32	12	20	3	8	15	6	3
- Claim on foreign assets (excluding FX reserves) (1)			0.23	0.06	0.33	0.17	0.11	0.32	0.11	0.45
- Foreign Exchange reserves (2)			0.14	0.17	0.13	0.38	0.12	0.11	0.15	0.03
- Foreign Debt (3) Of which:			0.47	0.37	0.53	0.25	0.38	0.48	0.67	0.26
	- Private foreign debt (3 a)		0.14	0.06	0.18	0.09	0.18	0.13	0.09
		- Government foreign debt (3 b)	0.33	0.31	0.35	0.15	0.20	0.35	0.58
- Stock of FDI (4)			0.17	0.12	0.19	0.07	0.14	0.23	0.11	0.04
- Stock of equities liabilities (5)			0.02	0.01	0.03	0.02	0.03	0.02	0.01	0.06
- Financial Integration
- Index 1 (1)+(2)+(3)+(4)+(5)			0.71	0.42	0.88	0.74	0.58	0.82	0.47	0.86
- Index 2 (1)+(3 a)+(4)+(5)			0.56	0.26	0.75	0.36	0.47	0.71	0.32
Net Credit Position (1)+(2)-(3)			-0.10	-0.14	-0.07	0.30	-0.05	-0.05	-0.41	0.22

Unless indicated, data come from Lane and Milesi-Ferreti (2001). (1) is the ratio to GDP of the sum of the estimate of stock of direct investment assets (cumulative flow adjusted for relative price variations), the estimate of stock of portfolio equity assets (cumulative flow adjusted for stock market price variations), and the sum of cumulative flows of portfolio debt assets, other assets and net errors and omissions. (2) is the ratio to GDP of total reserves minus gold. (3) is the ratio to GDP of gross external debt for developing countries, and the ratio to GDP of stock of other investment liabilities for G3 economies, (3b) is the ratio to GDP of public and publicly guaranteed long term debt (World Bank, Global Development Finance), and (3a) = (3) – (3b). (4) is the ratio to GDP of the estimate of stock of direct investment liabilities (cumulative flow adjusted for relative price variations). (5) is the ratio to GDP of the estimate of stock of portfolio equity liabilities (cumulative flow adjusted for stock market price variations).

View Table

Table 1.

Measuring Financial Integration for 32 Developing and Emerging Economies in 1996

(Ratio to GDP)

			Whole sample	Level of financial integration		Region				For memo
				Low	High	Africa	Asia	Latin America	Middle East	G-3 economies
# of countries			32	12	20	3	8	15	6	3
- Claim on foreign assets (excluding FX reserves) (1)			0.23	0.06	0.33	0.17	0.11	0.32	0.11	0.45
- Foreign Exchange reserves (2)			0.14	0.17	0.13	0.38	0.12	0.11	0.15	0.03
- Foreign Debt (3) Of which:			0.47	0.37	0.53	0.25	0.38	0.48	0.67	0.26
	- Private foreign debt (3 a)		0.14	0.06	0.18	0.09	0.18	0.13	0.09
		- Government foreign debt (3 b)	0.33	0.31	0.35	0.15	0.20	0.35	0.58
- Stock of FDI (4)			0.17	0.12	0.19	0.07	0.14	0.23	0.11	0.04
- Stock of equities liabilities (5)			0.02	0.01	0.03	0.02	0.03	0.02	0.01	0.06
- Financial Integration
- Index 1 (1)+(2)+(3)+(4)+(5)			0.71	0.42	0.88	0.74	0.58	0.82	0.47	0.86
- Index 2 (1)+(3 a)+(4)+(5)			0.56	0.26	0.75	0.36	0.47	0.71	0.32
Net Credit Position (1)+(2)-(3)			-0.10	-0.14	-0.07	0.30	-0.05	-0.05	-0.41	0.22

Unless indicated, data come from Lane and Milesi-Ferreti (2001). (1) is the ratio to GDP of the sum of the estimate of stock of direct investment assets (cumulative flow adjusted for relative price variations), the estimate of stock of portfolio equity assets (cumulative flow adjusted for stock market price variations), and the sum of cumulative flows of portfolio debt assets, other assets and net errors and omissions. (2) is the ratio to GDP of total reserves minus gold. (3) is the ratio to GDP of gross external debt for developing countries, and the ratio to GDP of stock of other investment liabilities for G3 economies, (3b) is the ratio to GDP of public and publicly guaranteed long term debt (World Bank, Global Development Finance), and (3a) = (3) – (3b). (4) is the ratio to GDP of the estimate of stock of direct investment liabilities (cumulative flow adjusted for relative price variations). (5) is the ratio to GDP of the estimate of stock of portfolio equity liabilities (cumulative flow adjusted for stock market price variations).

The second measure (index 2) excludes government and monetary authority claims and liabilities; it is the ratio of claims on foreign assets (excluding foreign exchange reserves) and liabilities (excluding public and publicly-guaranteed debt) of a country to its GDP. Index 2 aims to measure the part of financial integration that is not channeled by the government and the monetary authorities of the country.

Within our set of 32 emerging and developing economies, we distinguish two subsets. The first one includes economies that are more financially integrated than the average country of our sample; the second one includes economies that are less financially integrated than the average country of our sample². In what follows, we will refer to these two groups as LIEs (less integrated economies) and MIEs (more integrated economies).

Figures in Table 1 show that the intensity and nature of financial integration differ a lot from one group to another, and across regions. Index 1 varies from 0.42 for LIEs to 0.88 for MIEs. For both LIEs and MIEs, the main component of index 1 is government debt and government-guaranteed debt (31 percent of GDP for LIEs and 35 percent of GDP for MIEs). For MIEs, the second-largest component is claims on foreign assets (excluding foreign exchange reserves), which amount to 33 percent of GDP. For LIEs, it is foreign exchange reserves. As measured by index 1, financial integration is as high in MIEs as it is in G-3 economies. However, the nature of financial integration differs considerably between these emerging economies and G-3 economies. The typical MIE has a foreign debt of more than 50 percent of GDP and receives FDI, whereas G-3 economies exhibit a high index of financial integration thanks to their holdings of foreign assets

Index 2, which excludes government and monetary authorities, ranges from 0.26 for LIEs to 0.75 for MIEs. The main component of index 2 is the stock of FDI for both LIEs (12 percent of GDP) and MIEs (19 percent of GDP). For MIEs, the stock of private debt (18 percent of GDP) is close to that of FDI, and equity liabilities amount to a mere 3 percent of GDP. For LIEs, private foreign debt is only 6 percent of GDP, and equity liabilities amount to only 1 percent of GDP.

Put together, these figures show that for developing and emerging economies, actual financial integration takes place through FDI and debt. Therefore, gains of financial integration should mainly be related to these two components of financial integration. Moreover, with equity liabilities that are only 3 percent of GDP (at most), stock market liberalization, although a potential source of gains, should not be responsible for a large share of the benefit of actual financial integration. Finally, as shown in the bottom line of Table 1, developing and emerging economies in our sample are net borrowers; at the country level, because foreign assets are more than offset by debt, there is no risk sharing.

B. Empirical Evidence

Research aiming to measure benefits of financial integration usually proceeds by comparing two economies that differ in their level of financial integration. But there are at least three types of comparisons. The comparison can be between an economy with financial autarky and its fully financially integrated counterpart. This is the approach followed by most papers based on the calibration of small theoretical models; it focuses on the potential gain of full financial integration (see, for example, Obtsfeld, 1994, or Athanasoulis and Van Wincoop, 2000). A second approach consists in comparing an actual economy with its fully financially integrated counterpart. This is the approach followed by Gourinchas and Jeanne (2004). Finally, a third approach consists in comparing the economic performance of an actual economy (with its current level of financial integration) with its counterpart under a lower level of financial integration or under financial autarky. This is the approach followed in econometric studies. This paper follows this approach as well.

Evaluating the potential gain of financial integration by calibrating small theoretical models, Obtsfeld (1994) and Athanasoulis and Van Wincoop (2000) found that risk-sharing gains that would come with financial integration are huge. However, in a deterministic growth model in which, by construction, risk sharing does not bring any benefit, Gourinchas and Jeanne (2004) show that the welfare gain from capital account liberalization is rather small (around 1 percent of consumption each year along the growth path of the economy). As far as the effect of capital account liberalization on volatility is concerned, Kose, Prasad, and Terrones (2003) conclude by reviewing the literature on international business cycles, that theoretical models (RBC models, NOEM models) do not provide a clear guide to the effects of financial integration on macroeconomic volatility. A recent paper by Tille (2004) confirms this conclusion. In a two-country NOEM model, he shows that the impact of integration is not universally beneficial (it depends on the degree to which exchange rate fluctuations are passed through to consumer prices).

The comparison of economies at different stages of their financial integration process provides mixed empirical evidence that capital account liberalization promotes long-run economic growth in developing economies (see the survey by Edison, Klein, Ricci, and Sløk, 2002). Performing econometric estimations using a new dataset, Edison, Klein, Ricci, and Sløk (2002b) do not find any significant positive effect of financial integration on economic growth. However, Henry (2003) reports empirical evidence that stock market liberalizations (a component of capital account liberalization and financial integration) are followed by lower cost of capital, higher investment, and higher growth. Finally, Bekaert, Harvey, and Lundblad (2001, 2004a) report that, on average, equity market liberalizations lead to a 1 percent increase in economic growth over a five-year period.

Empirical literature is also mixed regarding the effect of financial openness on macroeconomic volatility. For example, Razin and Rose (1994) find no evidence of a link between trade and financial openness and macroeconomic volatility. A recent paper by Easterly, Islam, and Stiglitz (2000) concludes that neither financial openness nor volatility of capital flows has a significant impact on macroeconomic volatility. They show, however, that a higher level of development of the domestic financial sector (as measured by private credit to GDP) reduces growth volatility. Buch, Dopke, and Pierdzioch (2002) do not find any consistent empirical relationship between financial openness and the volatility of output. Nevertheless, Bekaert, Harvey and Lundblad (2004b) find a significant decrease in both GDP and consumption growth variability after equity market liberalizations.

In this paper, we follow the approach that consists in comparing an actual economy to an economy that is less financially integrated. However, we do not rely on econometric estimations, but rather on the calibration of a theoretical model. Therefore, we provide a theoretical framework to understand the mixed empirical results reviewed above. Moreover, our model considers simultaneously the potential effects of FDI, of the openness of financial markets, and of risk sharing on both growth and volatility.

A. Technology and the Sharing of GDP Between the Representative Agent and Owners of FDI

B. Other Features of the Model

The representative household consumes a domestically produced good (C_D) and an imported one (C_M). Total consumption in terms of domestic currency then depends on both the price of the imported good denominated in foreign currency and the exchange rate.

A. Portfolio Allocation

In the autarky economy, there is no portfolio choice since the only asset available to the representative agent is domestic capital.

Under financial integration, maximizing the utility function subject to the wealth accumulation equation provides expressions for the share of the two assets (as well as for consumption of the imported good and domestically produced good) in the composition of the wealth of the representative agent. These shares are (see Appendix 1):

As in any other portfolio choice, the optimal portfolio composition depends on expected returns and risk. The share of domestic capital in the domestic agent’s portfolio can be larger than 1, meaning that the domestic investor borrows abroad to invest in domestic capital. The fact that the economy benefits from FDI (τ >0) increases the determinist part of the return on domestic capital for the domestic agent and thus his incentive to borrow abroad to invest at home. It increases as well the volatility of this return, thus limiting the incentive to borrow.

In the case where the economy does not benefit from foreign investment (τ = 0), condition (2) implies that the expected return on domestic capital adjusted for risk is the same as the one on claims on foreign assets. Therefore, the domestic agent holds half of her wealth as domestic capital, the other half being invested abroad for diversification purposes.

B. Growth

In the autarky economy, the deterministic part of the GDP growth rate is:

where A^A is the propensity to consume wealth, equal to: A^A=εδ-n + (1- ε)CEq^A, where Ceq^A is the certainty equivalent of the return on wealth. This expression for the marginal propensity to consume is standard. Note that, thanks to the recursive utility specification, it is clear that the effect on A^A of the certainty equivalent Ceq^A depends on the intertemporal elasticity of substitution. When the intertemporal elasticity of substitution is less than 1, a higher Ceq^A increases the propensity to consume, and hence reduces the growth rate of the economy. In turn, risk aversion does affect the relationship between risk and Ceq^A:

When the economy is financially integrated, the deterministic part of the economy growth rate is:

A^L, the constant propensity to consume wealth, is equal to:

where Ceq^L is the certainty equivalent of the return on wealth, which depends on the portfolio allocation. It is equal to:

In the financially integrated economy, part of the certainty equivalent of the return on wealth comes from foreign debt/assets, and part of it comes from domestic capital income (net of the income that goes to foreign investors).

• In a net borrowing country (n_K>1), the stock of physical capital is higher both because domestic agents borrow abroad to invest at home and because there are FDI flows. The component of the certainty equivalent of the return on domestic activity due to deterministic productivity increases, but the component linked to the stochastic part (which reduces Ceq^L) rises as well.

• In the case of net lenders (n_K< 1), it is even more ambiguous since more wealth comes from foreign investors, but domestic agents now devote part of their wealth to invest abroad

The condition for international financial integration to spur growth is that g^L > g^A. This condition is not met for any set of parameters.

In the simplest case where the intertemporal elasticity of substitution is equal to 1, and the country is neither a lender nor a borrower, financial integration spurs growth (one can show that g^L– g^A=τϕ*μ_y> 0) through FDI. Moreover, when the intertemporal elasticity of substitution is equal to 1, one can show that the derivatives of g^L– g^A to n_k are always positive, which means that the more debt the country holds, the higher its growth rate. Let us recall, though, that the optimal amount of debt is not a parameter but is endogenously determined within the model.

When the intertemporal elasticity of substitution is different from 1, the effect of financial integration on growth is ambiguous and cannot be analyzed analytically. Therefore, we rely on calibration experiments in the next section.

C. Volatility

D. Welfare Gain from Financial Integration

Splitting-Up the Welfare Gain From Financial Integration

To appraise whether the welfare gain from financial integration comes from FDI or from the openness to foreign debt/assets, we break down the total welfare gain computed previously.

To split the total welfare gain from financial integration, we consider three different economies. Two of them have already been considered above – the autarky economy and the actual economy. We consider a third economy that receives FDI but is closed to other capital flows. We then proceed in two stages as illustrated in Figure 1. First, we consider the switch from the actual economy to an economy with FDI but no access to global financial markets; this allows us to compute the welfare gain from access to global financial markets. Next, we consider the switch from the latter economy to autarky, and this gives us the welfare gain of FDI.

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Splitting Up the Welfare Gain from Financial Integration

Citation: IMF Working Papers 2005, 067; 10.5089/9781451860863.001.A001

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Let us note V^PL, the optimal value of the program of an economy with FDI but no other capital flows. One can show that it is:

$V^{\mathit{\text{PL}}}={(A^{\mathit{\text{PL}}})}^{\frac{1-\gamma }{1-\sigma }}{\left(\frac{\theta \mathit{\text{EP}}}{1-\theta }\right)}^{-(1-\gamma )(1-\theta )}\frac{{\left[\theta K^d\right]}^{1-\gamma }}{1-\gamma }{e}^{-(1-\gamma )\mathit{\text{nt}}}$

$\begin{array}{l}{A}^{\mathit{\text{PL}}}=\epsilon \delta -n+(1-\epsilon )\mathit{\text{CE}}{q}^{\mathit{\text{PL}}}\\ \mathit{\text{CE}}{q}^{\mathit{\text{PL}}}=(1+\tau \varphi *)\left[{\mu }_Y-\gamma (1+\tau \varphi *){\sigma }_Y^2/2\right]-(1-\theta )\left[{\mu }_P+{\mu }_E-((1-\theta )(1-\gamma )+1){\sigma }_P^2/2\right].\end{array}$

The welfare gain from access to financial markets is then computed as the percentage of wealth that the domestic representative agent should receive to be as well off in the economy where there is FDI but no access to global financial markets as she is in the actual economy. It is:

$\begin{array}{ccc}{k}^{\mathit{\text{AFM}}}={\left[\frac{A^L}{A^{\mathit{\text{PL}}}}\right]}^{\frac{1}{1-\epsilon }}-1.& & (7)\end{array}$

Finally, the welfare gain from FDI is the percentage of wealth that the domestic representative agent should receive to be as well off in the autarky economy as she is in the economy with FDI but no other capital flows. It is then:

$\begin{array}{ccc}{k}^{\mathit{\text{FDI}}}={\left[\frac{A^{\mathit{\text{PL}}}}{A^A}\right]}^{\frac{1}{1-\epsilon }}-1& & (8)\end{array}$

The three welfare gains are such that:

$(1+k)=\left[1+k^{\mathit{\text{FDI}}}\right]\left[1+k^{\mathit{\text{AFM}}}\right]$

A. The Data

To measure the upper bound for the welfare effect of financial integration, we calibrate our model on 32 developing and emerging economies over the period 1990-98. Our aim is to measure the welfare gain of a country representative agent that would result from the observed level of financial integration of that country in the specific (and optimistic) case where no costly side effects of financial integration hurt the country.

Tables 2 and 3 contain common and country-specific parameters used in the calibration. Common parameters (Table 2) are set to standard values with a discount rate of 2 percent; an international interest rate of 4 percent, an intertemporal elasticity of substitution set at 0.5, and a risk aversion of 5. Robustness of our results to preference parameters and the international interest rate is checked for additional values indicated in Table 2.

Table 2.

Common Parameters

Table 2.

Common Parameters

Annual discount rate	δ	0.02
Intertemporal elasticity of substitution	ε	0.5; 0.9;
Risk aversion	γ	2; 5
International nominal interest rate	i*	0.04; 0.05

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Table 2.

Common Parameters

Annual discount rate	δ	0.02
Intertemporal elasticity of substitution	ε	0.5; 0.9;
Risk aversion	γ	2; 5
International nominal interest rate	i*	0.04; 0.05

Table 3.

Country-Specific Parameters

Table 3.

Country-Specific Parameters

		Source (see also Appendix 2)	mean	max	min
Population growth rate (in percent)	n	HS 6.1 mean over period 1990-1998	1.9	3.1	0.9
Ratio of imported goods to absorption	1-θ	HS 6.1 mean over the period 1990-1998	0.31	0.81	0.08
Variance of productivity shocks (in percent)	${\sigma }_y^2$	HS 6.1 variance of the growth rate of the economy over the period 1990-1998	0.08	0.27	0.00
Trend in price of imported goods (foreign currency)	μ P	HS 6.1 annual mean over the period 1990-1998	-0.12	0.06	-0.56
Price of imported good volatility (in percent)	${\sigma }_P^2$	HS 6.1 variance of the annual growth rate in the price of imported goods over the period 1990-1998	1.67	14.46	0.04
Share of domestic capital in the agent’s wealth	n K	HS 6.1 + Lane and Milesi-Ferreti (year1996)See Appendix 3.	1.09	1.42	0.73
Ratio of foreign owned capital to domestically owned capital	ϕ*	HS 6.1 + LM-F (year 1996) See Appendix 3.	0.08	0.22	0.00
GDP growth rate	g L	HS 6.1 mean over period 1990-1998	0.04	0.09	0.01

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Table 3.

Country-Specific Parameters

		Source (see also Appendix 2)	mean	max	min
Population growth rate (in percent)	n	HS 6.1 mean over period 1990-1998	1.9	3.1	0.9
Ratio of imported goods to absorption	1-θ	HS 6.1 mean over the period 1990-1998	0.31	0.81	0.08
Variance of productivity shocks (in percent)	${\sigma }_y^2$	HS 6.1 variance of the growth rate of the economy over the period 1990-1998	0.08	0.27	0.00
Trend in price of imported goods (foreign currency)	μ P	HS 6.1 annual mean over the period 1990-1998	-0.12	0.06	-0.56
Price of imported good volatility (in percent)	${\sigma }_P^2$	HS 6.1 variance of the annual growth rate in the price of imported goods over the period 1990-1998	1.67	14.46	0.04
Share of domestic capital in the agent’s wealth	n K	HS 6.1 + Lane and Milesi-Ferreti (year1996)See Appendix 3.	1.09	1.42	0.73
Ratio of foreign owned capital to domestically owned capital	ϕ*	HS 6.1 + LM-F (year 1996) See Appendix 3.	0.08	0.22	0.00
GDP growth rate	g L	HS 6.1 mean over period 1990-1998	0.04	0.09	0.01

Country-specific parameters (see Table 3) have been computed using the Penn World table (HS 6.1) and the database on stock of international wealth put together by Lane and Milesi-Ferreti (2001). For our evaluation, the two most important parameters are ϕ*, the ratio of foreign-owned capital to domestically owned capital, and n_K, the share of domestic capital in the representative agent’s wealth. The mean value of n_K is 1.09, just above unity and ranges from 0.73 (Botswana) to 1.42 (Pakistan). Among the 32 countries we consider, only 7 of them are net lenders (Botswana, China, Egypt, Malaysia, Panama, South Africa, and Venezuela). The ratio of foreign-owned capital to domestically owned capital varies from 0 (Syria), which means that there is no FDI in this country, to 0.22 (Malaysia), which means that 18 percent of the physical capital installed in Malaysia is owned and managed by foreigners.

B. Calibration Issues

Our theoretical model has been solved under the hypothesis that the representative agent maximizes her intertemporal utility. In doing so, she allocates her wealth between domestic capital and debt/assets abroad according to her tastes, returns, and risk. She also determines her savings and consumption according to her tastes. We had no difficulties calibrating the intertemporal choice of the domestic agents of the countries we studied. The portfolio choices were trickier to reproduce.

Actual data show that the volatility of emerging and developing economies, although higher than that of industrial economies, is low compared with the excess return of domestic investment for the representative agent. This should lead emerging and developing economies to borrow a lot abroad to invest at home, or, in our setting, a high n_k. This is not what we observe in the data. Numbers reported in Table 3 for actual n_k show that it is above but close to unity, much lower than what one would expect.

Our calibration strategy is then to stick to the n_k observed in the data and consider that this low level of indebtedness results from borrowing constraints that prevent emerging countries from holding their optimal portfolio. Consequences of this strategy will be discussed later in the paper. Note, however, that since portfolio choices and consumption-saving decisions are independent, the value function of the programs still holds under portfolio constraint. Thus, the value function of the program can be used to evaluate the welfare gain from financial integration.

Calibration runs as follows. We used equation (2) and the system composed of equations (4.a)-(4.c) to find the parameter τ, which measures the share of production using FDI that goes to domestic agent, the parameter μ_E, the expected change in the nominal exchange rate, and the productivity parameter μ_Y. Note that condition (2) still holds when the domestic agent is constrained in her borrowings. Indeed, this condition is derived from the perspective of foreign agents owning FDI in the country who are not affected by the borrowing constraint. It is the expression of the optimal share n_K (equation (3)) that is no longer valid under the borrowing constraint.

The growth rate of the economy (g^L), the volatility (σ_Y), and the portfolio allocation n_k are taken from the data (see Table 3). We calibrated the model for each country as well as for the average country, which we define as a country with country-specific parameters set to the mean of the sample.

Table 4 contains the outcome of this calibration process. Recall that the parameter r measures the extent to which a country benefits from FDI. Our calibration indicates that for the average country of our sample, 37 percent of the production on foreign-owned plants goes to the domestic agent; in our sample, it ranges from a mere 2 percent to 51 percent, with a mean of 25 percent. The expected annual depreciation of emerging market currency against the dollar ranges from 0 percent to 8 percent, lower than actual between 1990 and 1998.

Table 4.

Calibrated Parameters Under the Borrowing Constraint Hypothesis

Note that parameters reported in this table are those derived with common parameters (Table 2) set at the following values: i*= 0.04, γ=2, ε =0.5

Table 4.

Calibrated Parameters Under the Borrowing Constraint Hypothesis

				Mean	max	min	Average country
Share of production from foreign owned capital that goes to the domestic agent	τ	}	Equations (2), (4.a)-(4.c)	0.25	0.51	0.02	0.37
Expected annual change in the nominal exchange rate	μ E			0.02	0.08	0.00	0.02
Productivity parameter	μ Y			0.09	0.18	0.03	0.10

Note that parameters reported in this table are those derived with common parameters (Table 2) set at the following values: i*= 0.04, γ=2, ε =0.5

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Table 4.

Calibrated Parameters Under the Borrowing Constraint Hypothesis

				Mean	max	min	Average country
Share of production from foreign owned capital that goes to the domestic agent	τ	}	Equations (2), (4.a)-(4.c)	0.25	0.51	0.02	0.37
Expected annual change in the nominal exchange rate	μ E			0.02	0.08	0.00	0.02
Productivity parameter	μ Y			0.09	0.18	0.03	0.10

Note that parameters reported in this table are those derived with common parameters (Table 2) set at the following values: i*= 0.04, γ=2, ε =0.5

C. Evaluating the Welfare Gain From Financial Integration

We now use the calibrated model to compute the welfare gain from financial integration. Recall that we compare actual economies with economies under financial autarky. And the question we answer is the following: by how much should the total wealth of the representative agent be increased for her to accept to switch back to financial autarky? This is what we call the welfare gain from financial integration. It is computed using equation (6). This gain is then split into two components: the gain from FDI (equation (8)) and the gain from access to global financial markets (the ability to borrow and/or to invest abroad, equation (7)). Finally, we compute the difference in the growth rates of the two economies (the actual one and the one in financial autarky) and their relative volatility (equation (5)). Results are shown in Table 5.

Table 5.

Welfare Gain from Financial Integration (average country)

Table 5.

Welfare Gain from Financial Integration (average country)

	Whole sample	Level of financial integration		Region
		Low	High	Asia	Latin America	Middle East
# of countries	32	12	20	8	15	6
K	10.9%	6.0%	14%	11.6%	9.7%	5.2%
k AFM	5.6%	3.9%	6.0%	7.0%	4.8%	4.6%
k FDI	4.9%	1.9%	7.5%	4.2%	4.6%	1.8%
g L - g A	0.31%	0.42%	0.16%	0.31%	0.29%	0.13%
VolL/VolA	1.25	1.26	1.25	1.28	1.29	1.18

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Table 5.

Welfare Gain from Financial Integration (average country)

	Whole sample	Level of financial integration		Region
		Low	High	Asia	Latin America	Middle East
# of countries	32	12	20	8	15	6
K	10.9%	6.0%	14%	11.6%	9.7%	5.2%
k AFM	5.6%	3.9%	6.0%	7.0%	4.8%	4.6%
k FDI	4.9%	1.9%	7.5%	4.2%	4.6%	1.8%
g L - g A	0.31%	0.42%	0.16%	0.31%	0.29%	0.13%
VolL/VolA	1.25	1.26	1.25	1.28	1.29	1.18

For the average country of our sample, the gain from financial integration is around 11 percent of the representative agent’s wealth, and the gain comes equally from FDI (4.9 percent of wealth) and from access to global financial markets (5.6 percent). Compared with autarky economies, we calculate that, on average, actual economies enjoy an additional 0.31 percentage points of annual growth. This comes at the cost of more volatility, which is 25 percent higher in actual economies compared with the autarky situation.

Figure 2 illustrates the dynamics of production, consumption, and capital accumulation in the two economies. The solid line refers to actual economies as described in our model, and the dotted line refers to the autarky economy. Let us consider what happens to the economy at the time the representative agent agrees to switch back to autarky and is compensated so as to keep intertemporal utility the same. At the beginning, the representative agent is richer (her wealth increases by the compensating amount she receives), but there is less productive capital available in the autarky economy since it is closed to FDI. As a result, GDP is lower in the autarky economy, while gross national income (GDP less interest and dividend payments), is almost the same in the two economies (note that in the autarky economy, GNI is equal to GDP). Propensity to consume wealth is higher in the actual economy than in the autarky one, but because wealth is initially higher in the autarky economy, it turns out that initial consumption is higher too in the autarky economy. From this initial situation, all macroeconomic aggregates in each economy grow at the same rate, which is lower in the autarky economy, and eventually the two economies diverge. The divergence process is slow. In our calibration, because the difference in the annual growth rates is small (around 0.3 percent per year), it takes around twenty years for consumption in the integrated economy to catch up with that in the autarky economy. The dashed line in Figure 2 illustrates the dynamics of the autarky economy in which no compensation is provided to the representative agent at the time she leaves the financially integrated economy. This economy exhibits the same growth rate as the autarky economy with compensation paid, but starts at a lower level. The comparison of these two economies gives us a measure of the compensation in terms of permanent consumption. It is straightforward to show that it is equal to the percentage compensation in terms of initial wealth. Thus, the welfare gain of financial integration can also be evaluated at around 11 percent of permanent consumption.

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Dynamics of Financially Integrated and Financial Autarky Economies

Citation: IMF Working Papers 2005, 067; 10.5089/9781451860863.001.A001

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It is of interest to distinguish the welfare gain from financial integration by level of financial integration and by region. It is no surprise that the gain from financial integration is higher for countries that are more financially integrated, since we measure the gain from complete financial autarky to the actual level of financial integration. However, one can notice that the gain from financial integration in terms of annual growth rates is not that high. Our calibration says that more financially integrated economies would grow by an additional 0.42 percentage point compared with autarky, while less financially integrated economies grow by an additional 0.16 percentages point. The difference between these two figures is only 0.26 percentage point per year. This rather small number may explain why econometric studies so far have not provided clear evidence on the effect of financial integration on economic growth.

Turning to the gain from financial integration by region reported in Table 5, we see that the average Asian country benefits more from financial integration than countries in any other region. Financial integration indexes reported in Table 1 indicate that Latin America was the most financially integrated region, yet our results show that it benefits less from financial integration than Asia; the welfare gain from financial integration is equal to 11.6 percent for Asia compared with 9.7 percent for Latin America. This underscores that the degree of financial integration is not the only component of the welfare gain from financial integration; the nature of financial integration (FDI, debt) and the capacity of the country to benefit from FDI (parameter τ) also matter.

D. Checking for Robustness

Sensitivity to Preference Parameters and Interest Rate

To check for the robustness of the above evaluation of the welfare gain from financial integration, we start by modifying preference parameters (risk aversion and intertemporal elasticity of substitution) and international interest rates. Table 6 contains the results. The welfare gain from financial integration is a decreasing function of all these parameters. An international interest rate 1 percent higher reduces the overall evaluation of the welfare gain from financial integration by 1 percent (from 10.9 percent to 9.8 percent). A higher risk aversion translates into a smaller welfare gain. This comes from the fact that financial integration, and the foreign borrowing that accompanies it in most emerging economies, increases the volatility of gross national income. As a consequence, the more risk averse the representative agent, the smaller the gain from financial integration. In our calibration, changing the risk aversion parameter from 2 to 5 reduces the welfare gain from 10.9 percent to 8.7 percent. Finally, a higher intertemporal elasticity of substitution reduces the sensitivity of the marginal propensity to consume with respect to financial integration. Therefore, the larger the intertemporal elasticity of substitution, the smaller the gains from financial integration.

Table 6.

Sensitivity to Preference Parameters and Interest Rate

Table 6.

Sensitivity to Preference Parameters and Interest Rate

	i*=4% γ=2 ε= 0.5	i*=5% γ=2 ε= 0.5	i*=4% γ=5 ε= 0.5	i*=4% γ=2 ε= 0.9
# of countries	32	32	32	32
k	10.9%	9.8%	8.7%	4.5%
k AFM	5.6%	5.1%	4.5%	2.3%
k FDI	4.9%	4.5%	4.0%	2.1%
g L − g A	0.31%	0.28%	0.24%	0.10%
VolL/VolA	1.25	1.24	1.23	1.21

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Table 6.

Sensitivity to Preference Parameters and Interest Rate

	i*=4% γ=2 ε= 0.5	i*=5% γ=2 ε= 0.5	i*=4% γ=5 ε= 0.5	i*=4% γ=2 ε= 0.9
# of countries	32	32	32	32
k	10.9%	9.8%	8.7%	4.5%
k AFM	5.6%	5.1%	4.5%	2.3%
k FDI	4.9%	4.5%	4.0%	2.1%
g L − g A	0.31%	0.28%	0.24%	0.10%
VolL/VolA	1.25	1.24	1.23	1.21

Alternative Calibration Strategy

So far our calibration strategy has been to consider that, because of borrowing constraints, countries were unable to hold their optimal portfolio. As said before, given the model parameters and the relatively low volatility in most emerging countries, our model predicted that emerging countries would want to borrow a lot abroad to invest at home. This is not what is observed in the data, which show that emerging countries are indebted in reasonable proportions (with nk slightly above 1). This discrepancy between observed and predicted portfolios could also come from the existence of a risk premium on foreign borrowing. Indeed, we have assumed so far that investors required a risk premium only on risky FDI, but not on lending that was considered as risk-free for the foreign investor. We now relax this hypothesis and calibrate a risk premium (RP) on emerging country borrowing such that the observed n_K is optimal. Under this risk premium assumption, equation (5) becomes:

$\mathit{\text{dW}}=\left[(1+\tau \varphi *){\mu }_Y{K}^d+\mathit{\text{EB}}(i*+{\mu }_E+\mathit{\text{RP}})-(C_D+{\mathit{\text{EPC}}}_M)\right]\mathit{\text{dt}}+(1+\tau \varphi *){\sigma }_Y{K}^d{\mathit{\text{dz}}}_Y,$

and the optimal portfolio share is now given by:

$\begin{array}{ccc}{n}_K=\frac{1}{2}+\frac{\left[(1+\tau \varphi *){\mu }_Y-\gamma (1+\tau \varphi *){\sigma }_Y^2/2\right]-\left[i*+{\mu }_E+\mathit{\text{RP}}\right]}{\gamma (1+\tau \varphi *){\sigma }_Y^2}.& & (9)\end{array}$

The growth rate of the financially integrated economy is:

$g^L=(1+\tau \varphi *)n_K{\mu }_Y+(1-n_K)(i*+{\mu }_E+\mathit{\text{RP}})-A^L,$

with

$A^L=\epsilon \delta -n+(1-\epsilon )\mathit{\text{CE}}{q}^L,$

and

$\begin{array}{c}{\mathit{\text{CEq}}}^L=n_K(1+\tau \varphi *)\left({\mu }_Y-\gamma (1+\tau \varphi *)\frac{{\sigma }_Y^2}{2}\right)+(1-n_K)(i*+{\mu }_E+\mathit{\text{RP}})\\ -(1-\theta )\left(({\mu }_P+{\mu }_E)-((1-\theta )(1-\gamma )+1)\frac{{\sigma }_P^2}{2}\right).\end{array}$

Note that equation (2), which expresses the required return on FDI, remains the same.

To calibrate this new version of our model, we keep all the other parameters equal to their value in the previous calibration and calculate the RP that exactly solves equation (9). The risk premium that we obtain on foreign borrowing is given in Table 7. For the average country, it is around 3 percent and varies from 8 percent to - 1 percent. It is higher for more financially integrated economies than for less integrated ones. Finally, it is relatively stable across regions.

Table 7.

Calibrated Risk Premium on Foreign Debt

Note that parameters reported in this table are those derived with common parameters (Table 2) set at the following values: i*= 0.04, γ=5, ε =0.5

Table 7.

Calibrated Risk Premium on Foreign Debt

Whole sample				Level of financial integration		Region
Mean	max	min	Average country	Low	High	Asia	Latin America	Middle East
0.03	0.08	-0.01	0.03	0.02	0.05	0.03	0.03	0.02

Note that parameters reported in this table are those derived with common parameters (Table 2) set at the following values: i*= 0.04, γ=5, ε =0.5

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Table 7.

Calibrated Risk Premium on Foreign Debt

Whole sample				Level of financial integration		Region
Mean	max	min	Average country	Low	High	Asia	Latin America	Middle East
0.03	0.08	-0.01	0.03	0.02	0.05	0.03	0.03	0.02

Note that parameters reported in this table are those derived with common parameters (Table 2) set at the following values: i*= 0.04, γ=5, ε =0.5

Table 8 shows that the welfare gain from financial integration is much lower (around 5 percent of initial wealth) when we introduce a risk premium, although small, on foreign borrowing. The decomposition of the welfare gain shows that the gain from FDI is unchanged compared to with previous calibration. On the contrary, the gain from access to global financial markets becomes a lot smaller – almost negligible. This is not surprising. The risk premium emerging countries have to pay on their debt reduces the benefit they get from borrowing. This was not the case in the previous calibration in which they only bear the riskless international interest rate augmented for currency depreciation.

Table 8.

Welfare Gain from Financial Integration – Risk Premium Hypothesis

(average country)

Table 8.

Welfare Gain from Financial Integration – Risk Premium Hypothesis

(average country)

	Whole sample	Level of financial integration		Region
		Low	High	Asia	Latin America	Middle East
# of countries	32	12	20	8	15	6
k	4.98%	1.96%	7.52%	4.24%	4.63%	1.85%
k AFM	0.01%	0.01%	0.00%	0.03%	0.02%	0.01%
k FDI	4.97%	1.96%	7.51%	4.21%	4.61%	1.84%
g L - g A	0.15%	0.16%	0.24%	0.13%	0.15%	0.13%
VolL/VolA	1.25	1.26	1.25	1.28	1.29	1.18

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Table 8.

Welfare Gain from Financial Integration – Risk Premium Hypothesis

(average country)

	Whole sample	Level of financial integration		Region
		Low	High	Asia	Latin America	Middle East
# of countries	32	12	20	8	15	6
k	4.98%	1.96%	7.52%	4.24%	4.63%	1.85%
k AFM	0.01%	0.01%	0.00%	0.03%	0.02%	0.01%
k FDI	4.97%	1.96%	7.51%	4.21%	4.61%	1.84%
g L - g A	0.15%	0.16%	0.24%	0.13%	0.15%	0.13%
VolL/VolA	1.25	1.26	1.25	1.28	1.29	1.18