2.1 Financial versus trade openness

Trade openness is measured as the percent of gross trade, exports plus imports, to GDP². We use the Lane and Milesi-Feretti (2007) index of financial openness measured by the ratio of the sum of external assets and liabilities to GDP.

Table 1 shows select emerging economies listed in order of the degree of trade openness relative to financial openness. Emerging markets show a wide range in this measure. For example, the data shows that Malaysia is 1.06 times more open with regard to trade flows compared with financial flows, while Argentina is only 0.2 times more open trade-wise.

We observe that the south, east and south-east Asian countries, namely Malaysia, Thailand, Korea, Philippines, China and India have more open trade accounts compared to financial accounts. In contrast, Latin American countries, such as Colombia, Chile, Peru, Brazil and Argentina have less relative openness in trade. Mexico, Turkey and Indonesia are similar to India with about 0.6 times more openness in trade than financial flows. While the Czech Republic, Hungary and Poland show higher relative trade openness, Russia and South Africa appear lower in the order. In general, there is significant heterogeneity in the degree of trade openness relative to financial openness among emerging economies.

2.2 Relative volatility of the terms of trade

Figure 1 shows the relation between relative trade openness to financial openness as measured in Table 1 and the relative volatility of TOT to output for the group of emerging economies. Movement along the horizontal axis implies higher trade openness and lower financial openness. The cyclical components of the net barter terms of trade and GDP are used to calculate the volatilities³. We see a negative relation: countries with more closed capital accounts and higher relative trade openness have a lower ratio of TOT volatility to output volatility.

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Terms of trade volatility and openness

Citation: IMF Working Papers 2013, 119; 10.5089/9781484354605.001.A001

The scatter plot shows the relation between the ratio of trade to financial openness and the ratio of TOT volatility to output volatility for emerging economies. The negative correlation implies that when relative financial openness is low, the ratio of TOT volatility to output volatility is low.Source: WDI; Lane and Milesi-Feretti (2007); IFS

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2.3 Current account volatility

To explore the hypothesis that when financial openness is high, the economy is not bound to keep current account volatility low, we plot the relation between financial openness using the Lane and Milesi-Feretti (2007) measure and the volatility of trade balance to output ratio (TBY), relative to output volatility for the sample of emerging economies in Figure 2. Volatility is calculated as the percent standard deviation of the cyclical components of TBY and GDP.

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Financial openness and current account volatility

Citation: IMF Working Papers 2013, 119; 10.5089/9781484354605.001.A001

The graph plots the relation between financial openness and current account volatility relative to output volatility. We see a positive relation, i.e., in countries with low financial integration, current account is stable and in countries where financial openness is high, volatility is high.Source: WDI; Lane and Milesi-Feretti (2007); IFS

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The positive relation shows that when financial openness is low, the volatility of TBY is low. For example, according to the Lane and Milesi-Feretti (2007) measure, countries such as Poland, Mexico, Turkey, Colombia etc. have lower levels of financial integration and low levels of relative current account volatility (less that 0.3 percent). On the other hand, in countries that have more capital account openness, current account volatility is high. Take the case of Malaysia, which has the highest financial openness among the economies considered at 186 percent of GDP. The relative volatility of the current account is also highest at 1.24 percent.

3.1 Limited financial openness

India has a large and complex structure of legal and administrative controls that restrict the flow of capital in the economy (Patnaik and Shah, 2011). Various measures of de-jure capital controls such as Chinn and Ito (2008); Abiad et al. (2008); Quinn and Toyoda (2007) show that India has a relatively closed capital account. As shown in Table 2, this not only implies that India is financially less open, but it is one of the least open among other emerging markets.

Table 2

Measures of financial openness

The table shows indices of capital account liberalisation in emerging markets. A higher score indicates higher integration due to lower capital controls, except in the case of Schindler (2009), where lower values imply higher restrictiveness. This evidence suggests that India has a relatively closed capital account, and one of the least open when compared to peers. Source: Schindler (2009); Chinn and Ito (2008); Lane and Milesi-Feretti (2007)

Table 2

Measures of financial openness

Country	Schindler	Chinn-Ito	LMF
China	0.96	−1.13	0.87
Russia	0.93	−0.52	1.56
India	0.89	−1.13	0.58
Malaysia	0.84	−0.09	1.96
Morocco	0.82	−1.13	1.15
Thailand	0.78	−0.22	1.32
Philippines	0.75	0.14	1.36
South Africa	0.66	−1.13	1.41
Brazil	0.61	0.29	0.91
Chile	0.59	1.74	1.92
Korea	0.52	−0.19	1.03
Mexico	0.52	0.92	0.76
Hungary	0.44	1.66	2.07
Indonesia	0.44	1.18	1.01
Argentina	0.40	−0.26	1.93
Turkey	0.29	−1.13	0.87
Czech	0.20	2.07	1.57
Egypt	0.05	2.29	1.09
Peru	0.00	2.53	1.12
Colombia	−	−0.61	0.89
Poland	−	−0.18	1.02
Average	0.56	0.31	1.26

The table shows indices of capital account liberalisation in emerging markets. A higher score indicates higher integration due to lower capital controls, except in the case of Schindler (2009), where lower values imply higher restrictiveness. This evidence suggests that India has a relatively closed capital account, and one of the least open when compared to peers. Source: Schindler (2009); Chinn and Ito (2008); Lane and Milesi-Feretti (2007)

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Table 2

Measures of financial openness

Country	Schindler	Chinn-Ito	LMF
China	0.96	−1.13	0.87
Russia	0.93	−0.52	1.56
India	0.89	−1.13	0.58
Malaysia	0.84	−0.09	1.96
Morocco	0.82	−1.13	1.15
Thailand	0.78	−0.22	1.32
Philippines	0.75	0.14	1.36
South Africa	0.66	−1.13	1.41
Brazil	0.61	0.29	0.91
Chile	0.59	1.74	1.92
Korea	0.52	−0.19	1.03
Mexico	0.52	0.92	0.76
Hungary	0.44	1.66	2.07
Indonesia	0.44	1.18	1.01
Argentina	0.40	−0.26	1.93
Turkey	0.29	−1.13	0.87
Czech	0.20	2.07	1.57
Egypt	0.05	2.29	1.09
Peru	0.00	2.53	1.12
Colombia	−	−0.61	0.89
Poland	−	−0.18	1.02
Average	0.56	0.31	1.26

The table shows indices of capital account liberalisation in emerging markets. A higher score indicates higher integration due to lower capital controls, except in the case of Schindler (2009), where lower values imply higher restrictiveness. This evidence suggests that India has a relatively closed capital account, and one of the least open when compared to peers. Source: Schindler (2009); Chinn and Ito (2008); Lane and Milesi-Feretti (2007)

A measure of de-jure restrictions at a more disaggregate level of financial transactions is provided by Schindler (2009). The main categories in this dataset, which are further divided into sub-categories are: Shares or other securities of a participating nature; Bonds or other debt securities; Money market instruments; Collective investments; Financial credits; and Direct investment. According to this measure, 0 implies no restrictions, while 1 implies complete restrictions in the categories. India features high in the list among emerging markets, with an average restriction index between 1995 – 2005 of 0.89.

The last column of Table 2 shows a measure of de-facto capital account openness using the Lane and Milesi-Ferreti database. This index is constructed based on a country’s external assets and liabilities. Lane and Milesi-Feretti (2007) describe the methodology for the construction of the index. Accordingly, international holdings and transactions are classified as: Portfolio investment, subdivided into equity securities and debt securities; Foreign direct investment; Other investment, which includes debt instruments such as loans, deposits, and trade credits; Financial derivatives; and Reserve assets. The average index for India from 2000 – 2007 stands at 0.58, which again is one of the lowest among the emerging markets, and shows low level of financial integration.

Cross-border financial flows are small, and India’s financial integration with the world is limited, as indicated by the de-jure and de-facto measures of capital account openness.

3.2 Role of terms of trade

Trade openness is around 30 percent of GDP as shown in Table 1. India has a diversified export basket. However, it is largely a commodity importer, and has a large oil import bill. Like most developing countries, India can be assumed to be a price-taker in the world market, since it cannot influence the prices of the goods that it exports and imports (Broda, 2004; Kose, 2002).

The terms of trade defined as the ratio of the home price at which goods are exported to the foreign price of imported goods in domestic currency, are largely exogenous for India. TOT are usually measured as the ratio of unit price of exports to unit price of imports. Unit value measures are obtained by dividing the current value over the volume, and do not reflect direct price indexes of exports and imports (Mendoza, 1995). In India, we can address this by measuring TOT by the ratio of Consumer Price Index for Industrial Workers (CPI-IW) to Wholesale Price Index (WPI). CPI contains tradeables and non-tradeables prices and can be considered a proxy for the home price. WPI reflects global prices of tradeables and the fluctuations of the rupee, due to a large share of tradeables in it (Patnaik et al., 2011). This price index can be used as a proxy for foreign price.

Figure 3 plots the cyclical components of GDP, TOT and the ratio of trade balance (exports minus imports) to output (TBY) over the period 1992 – 2010. The graph shows that the TOT are highly volatile over the business cycle, with respect to output.

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Cyclical pattern

Citation: IMF Working Papers 2013, 119; 10.5089/9781484354605.001.A001

The graph plots the cyclical components of GDP, TOT – measured as the ratio of CPI-IW to WPI, and the ratio of the trade balance to output. While TOT is more volatile than output, current account is less volatile.Source: National sources

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As reported in Table 3, the standard deviation of TOT relative to output is 1.7 for the period 1992 – 2010. We also take the ratio of CPI to WPI-nonfood and the net barter TOT (unit value of export/unit value of import) as alternate measures of TOT . They all show that TOT are more volatile than output. While the first two measures suggest a low correlation for TOT with GDP, the third measure shows a higher value.

Table 3

Cyclical features of terms of trade

The table presents the key moments in the period 1992 – 2010, namely volatility, relative volatility and correlation with output for the TOT in India. The ratio of CPI-IW to WPI in India represents TOT, and shows that TOT are more volatile than output and mildly procyclical at the business cycle frequency.

Table 3

Cyclical features of terms of trade

	CPI/WPI	CPI/WPI(nonfood)	exp price/imp price
std. dev(TOT)	2.97	4.13	6.97
std. dev(TOT)/std.dev(Y)	1.71	2.38	3.73
corr(TOT, Y)	0.07	0.14	0.74

The table presents the key moments in the period 1992 – 2010, namely volatility, relative volatility and correlation with output for the TOT in India. The ratio of CPI-IW to WPI in India represents TOT, and shows that TOT are more volatile than output and mildly procyclical at the business cycle frequency.

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Table 3

Cyclical features of terms of trade

	CPI/WPI	CPI/WPI(nonfood)	exp price/imp price
std. dev(TOT)	2.97	4.13	6.97
std. dev(TOT)/std.dev(Y)	1.71	2.38	3.73
corr(TOT, Y)	0.07	0.14	0.74

The table presents the key moments in the period 1992 – 2010, namely volatility, relative volatility and correlation with output for the TOT in India. The ratio of CPI-IW to WPI in India represents TOT, and shows that TOT are more volatile than output and mildly procyclical at the business cycle frequency.

While TOT are highly volatile relative to output, the current account (TBY in Figure 3) is seen to be relatively more stable than output (Table 3 shows that the standard deviation relative to output is less than one). Thus in response to exogenous and highly volatile relative price movements, given that the current account is relatively stable, it implies that the quantity of exports and imports are adjusting. The terms of trade shocks may be responsible for fluctuations in the business cycle as seen in the data.

Based on this empirical evidence on the potential role for terms of trade fluctuations in propagating the business cycle of an emerging economy, we use a small open economy model with exogenous terms of trade shocks to study the effect of financial openness in this relation.

Households

The household maximises its expected discounted lifetime utility

subject to the resource constraint:

where Y_t is output, C_t is consumption, I_t is investment, and B_t is the internationally traded bond denominated in terms of foreign goods. R_t is the time t interest rate payable for the debt due in period t + 1. P_Ft and P_Ht denote the prices of foreign and home goods respectively, and P_t is the aggregate price index. $q_t=\frac{P_{Ht}}{P_{FT}}$ is the terms of trade. Γ_t is the non-stationary labour productivity described below.

Access to world financial markets is assumed to be imperfect. Financial integration is determined by a convex adjustment cost, κ, to bond holdings, such that κ = 0 implies full integration and κ → ∞ represents financial autarky. The cost can be interpreted as taxes and other restrictions on capital account transactions that prevent free mobility.

Capital, κ_t, evolves according to the law of motion:

where δ is the depreciation rate and ϕ is the parameter governing the investment adjustment cost.

Aggregate consumption is assumed to be a Cobb-Douglas function of consumption of domestic goods C_Ht and foreign goods C_Ft given by:

where γ is the share of foreign consumption goods in the basket. Similarly, investment is also a Cobb-Douglas aggregate of domestic and foreign investment goods, I_Ht and I_Ft. Then the corresponding consumption-based price index is

Thus the resource constraint can be rewritten as:

to include the terms of trade, q_t.

A shock to the terms of trade evolves according to an AR(1) process given by

Firms

Output is produced using a Cobb-Douglas technology with capital and one unit of labour inelastically supplied by the household. It takes the form:

where α ∈ (0,1) represents the share of labour in output, e^at denotes the level of total factor productivity and Γ_t represents labour productivity. Total factor productivity evolves according to an AR(1) process as follows:

with |ρ_a| < 1. Labour productivity Γ_t is non-stationary and defined as

where g_t is the growth rate of labour productivity.

Interest rate and country premium

Domestic interest rate is assumed to be the sum of the world interest rate R* > 0, exogenous to the small open economy, and a country premium that is increasing in a detrended measure of aggregate debt (Aguiar and Gopinath, 2007; Garcıa-Cicco et al., 2010). The country premium takes the form:

The total debt of the economy ${\tilde{B}}_t$ is exogenously given to the household, which does not internalise the premium payable on the foreign interest rate determined by the indebtedness of the economy. However, in equilibrium, total foreign debt of the economy coincides with the amount of debt acquired by the household. $\overline{b}$ denotes the steady state level of debt, and ψ > 0 governs the elasticity of the premium to changes in the indebtedness of the economy. ψ can be regarded as a reduced form of frictions in the economy.

Equilibrium

For any variable X, its detrended counterpart is defined as $x_t=\frac{X_t}{{\Gamma }_{t-1}}$. The trend growth is represented by μ_g. The households’ optimality conditions in stationary form are:

and

The resource constraint is

where

and

With initial capital stock k₀ and debt b₀, the competitive equilibrium is defined as a set of prices (R_t) and quantities (y_t, c_t, i_t, k_t, b_t), given the sequence of shocks to a_t and q_t, that solve the maximisation problem of the household, and satisfy the resource constraint and interest rate dynamics.

Shock process in the total factor productivity series

In order to obtain the amplitude and persistence of the shock process, we construct the total factor productivity (TFP) for the Indian economy for the period 1980–2009. Aggregate TFP series is computed as the weighted average of sectoral TFP series. Sectoral GDP and the net fixed capital stock data (at 1999 – 2000 constant prices at annual frequency) are available from 1951. From 2005 these series are available at 2004 – 2005 constant prices. The series for the two variables are linked to their 2004 – 2005 base series to obtain a longer time series.

The distribution of labour force (per 1000 households, male/female, rural/urban) is reported for each sector in the NSSO’s quinquennial Employment Unemployment Survey as well as in the annual surveys based on a thin sample. We generate a time series of the distribution of sectoral employment based on these reports. National labour force data published by the World Bank are available from 1980 at annual frequency. Using the sectoral distribution of labour force and the total labour force data, we obtain sectoral employment series. Finally, we measure the sectoral TFP series for India using sectoral real GDP, net fixed capital stock and employment data. Given the availability of employment data, our TFP series span 1980 – 2009.

Using the sectoral shares $\left({\omega }_s^j\right)$ of capital, labour and land in agriculture, industry and services from Verma (2008), shown in Table 5, we measure the sectoral TFP series as

Table 5

Sectoral shares of factors of production

Table 5

Sectoral shares of factors of production

	Land	Physical capital	Labour
Agriculture	0.2	0.24	0.56
Industry		0.3	0.7
Services		0.3	0.7

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Table 5

Sectoral shares of factors of production

	Land	Physical capital	Labour
Agriculture	0.2	0.24	0.56
Industry		0.3	0.7
Services		0.3	0.7

where n_s is the number of inputs used in sector s. Here s denotes major sectors constituting the economy namely, agriculture, industry and services, Y represents real GDP and X^j denotes factors of production in the respective sector.

For example when, s = agriculture, j = land, physical capital, labour. When s = industry, services, j = physical capital, labour.

Aggregate TFP is measured as a weighted average of the sectoral TFPs:

Next, we de-trend the TFP series using HP filter with the value of the smoothing parameter λ = 100. We fit an AR(1) model on the cyclical component of TFP to obtain the persistence parameter (ρ_a) and the standard deviation (σ_a) of the residual as 0.495 and 0.015 respectively.

Shock process in the growth of labour productivity

Using sectoral real GDP and labour force data, we obtain the annual time series of sectoral and aggregate labour productivity for India based on the following formulae:

The gross growth rate of labour productivity $\frac{{\Gamma }_t}{{\Gamma }_{t-1}}$ is

Next, we de-trend the growth of labour productivity using HP filter with value of the smoothing parameter λ = 100. The mean trend net growth rate, μ_g, over the period 1980 – 2009 is estimated as 1.047.

Shock process in the terms of trade

We fit an AR(1) model on the cyclical component of TOT, measured as the ratio of CPI-IW to WPI, to obtain the persistence (ρ_q) and standard deviation (σ_q) of the residual. The values are 0.54 and 0.026 respectively.

6.1 Financial openness and volatility

Figure 4 plots the volatilities of output, consumption, investment and trade balance to output from the model, for a range of values of κ, where κ = 0 implies full financial integration and κ = 100 represents financial autarky. The baseline parametrisation has γ = 1 indicating trade openness (the solid black line).

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Volatility against κ

Citation: IMF Working Papers 2013, 119; 10.5089/9781484354605.001.A001

The graphs plot the volatilities of simulated output, consumption, investment and trade balance to output ratio against a range of values for κ. The relation is obtained for different values of γ. Lower values of financial integration are associated with higher volatility of output, consumption and investment, and lower volatility of the current account.

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The key result is that as κ increases, volatility of output, consumption and investment increases, i.e., with lower financial openness, the volatility of the business cycle increases in response to a terms of trade shock. The volatility of consumption is above that of output. Further, as expected, the trade balance to output volatility declines as κ increases. When the capital account is closed, trade balances each period and the current account volatility is low.

Sensitivity analysis

Figure 4 also plots the volatilities against κ for various values of γ, the parameter that governs the share of foreign consumption goods in total consumption, as well as the share of the foreign price in the aggregate price index.

As κ increases, for all values of γ, output volatility increases and the volatility of TBY decreases. When γ is 0.4 and below, i.e., the trade account is relatively closed, the volatility of consumption falls below that of output. When γ = 0, the volatility of consumption and investment declines as κ increases.

6.2 Business cycle features of an emerging economy

We check whether the model with terms of trade shocks can explain the business cycle features of an emerging economy. We compare the moments from the model with the data for an emerging economy that is characterised by limited financial openness and higher trade openness, namely India.

India: Model and data moments

The key moments of the business cycle, i.e., volatility, relative volatility to output, contemporaneous correlation and autocorrelation are reported in the first row under each category in Table 6. Data on output (GDP), consumption (private consumption expenditure), investment (gross fixed capital formation), TBY or trade-balance-to-GDP (ratio of net export to GDP) and TOT (ratio of CPI-IW to WPI) are logged and detrended using the Hodrick Prescott filter to obtain the cyclical components⁴. The time period considered is 1992 – 2010. This is the period post reforms, when the economy transitioned to a more market driven system characterised by cyclical movements in output, consumption and investment. In this period, the business cycle properties of India are similar to those documented for other emerging economies (Ghate et al., 2013; Aguiar and Gopinath, 2007). Consumption and investment are more volatile than output, while trade-balance-to-output ratio is less volatile than output. Consumption and investment are procyclical, while TBY is strongly countercyclical. The terms of trade are more volatile than output and mildly procyclical.

Table 6

Business cycle moments (India): model and data

The table compares the moments from the data and the model, for the cases of full financial integration and no financial integration. For India, the model with no financial integration produces moments that match the data. Relative consumption volatility is higher than one and TBY is countercyclical. For κ between 0 and 100, the relative volatility of TBY may match the data.

Table 6

Business cycle moments (India): model and data

Statistic	Y	C	I	TBY	TOT
Standard deviation
Data	1.73	1.88	4.95	1.18	2.97
Model (κ = 0)	1.89	2.71	5.00	1.55	2.81
Model (κ = 100)	1.90	2.82	5.74	0.03	2.81
Relative std dev
Data	1.00	1.09	2.85	0.68	1.71
Model (κ = 0)	1.00	1.43	2.64	0.82	1.49
Model (κ = 100)	1.00	1.48	3.02	0.02	1.48
Contemporaneous correlation
Data	1.00	0.88	0.77	−0.66	0.07
Model (κ = 0)	1.00	0.61	0.97	0.16	0.78
Model (κ = 100)	1.00	0.60	0.60	−0.63	0.07
First order auto correlation
Data	0.71	0.53	0.59	0.51	0.43
Model (κ = 0)	0.58	0.68	0.57	0.10	0.40
Model (κ = 100)	0.58	0.52	0.42	0.26	0.44

The table compares the moments from the data and the model, for the cases of full financial integration and no financial integration. For India, the model with no financial integration produces moments that match the data. Relative consumption volatility is higher than one and TBY is countercyclical. For κ between 0 and 100, the relative volatility of TBY may match the data.

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Table 6

Business cycle moments (India): model and data

Statistic	Y	C	I	TBY	TOT
Standard deviation
Data	1.73	1.88	4.95	1.18	2.97
Model (κ = 0)	1.89	2.71	5.00	1.55	2.81
Model (κ = 100)	1.90	2.82	5.74	0.03	2.81
Relative std dev
Data	1.00	1.09	2.85	0.68	1.71
Model (κ = 0)	1.00	1.43	2.64	0.82	1.49
Model (κ = 100)	1.00	1.48	3.02	0.02	1.48
Contemporaneous correlation
Data	1.00	0.88	0.77	−0.66	0.07
Model (κ = 0)	1.00	0.61	0.97	0.16	0.78
Model (κ = 100)	1.00	0.60	0.60	−0.63	0.07
First order auto correlation
Data	0.71	0.53	0.59	0.51	0.43
Model (κ = 0)	0.58	0.68	0.57	0.10	0.40
Model (κ = 100)	0.58	0.52	0.42	0.26	0.44

The table compares the moments from the data and the model, for the cases of full financial integration and no financial integration. For India, the model with no financial integration produces moments that match the data. Relative consumption volatility is higher than one and TBY is countercyclical. For κ between 0 and 100, the relative volatility of TBY may match the data.

The second row reports the moments from the model with full financial integration (κ = 0). The third row shows the moments for the case of high costs to financial integration (κ = 100).

The model with productivity and terms of trade shocks, and low financial integration (κ = 100) does well in matching the features of the data. It is able to reproduce the higher relative consumption volatility, relative investment volatility and relative TOT volatility, as well as the highly countercyclical trade balance to output. The correlation of TOT with output also shows a good match.

For comparison, when κ is small (the case of full financial integration), the relative volatility of trade balance to output ratio is high at 0.82. However, when κ is large (the case of financial autarky), the relative volatility is negligible. Again, in the case of small κ, the TBY is small at 0.16, whereas when κ is large, it is highly countercyclical at −0.63. Depending on the extent of financial integration in India, for a κ between 0 and 100, we can obtain moments for TBY that are close to the data. While the model with high κ produces a high correlation of TOT with output at 0.78, the model with low κ produces a better match at 0.07.

When there is a terms of trade shock, with full financial integration, agents can borrow or lend to continue consuming at the same level. Hence trade balance does not depend on the shock and is not correlated with output movements. When agents cannot borrow or lend, then following a positive terms of trade shock, their purchasing power increases, and they import more leading to a countercyclical trade balance to output ratio. Since trade has to balance each period, the volatility of TBY is low.

An economy with high financial openness

We compare the results of the model to another type of emerging economy that is exposed to similar TOT shocks as India, but has higher relative financial openness. Using annual data (1995 – 2010), we calculate the moments for output, consumption, investment and TOT for Brazil.⁵ The standard deviation of TOT is 4.39 (for India the standard deviation is 6.39 using this data). As seen in Table 1, the ratio of trade openness to financial openness for Brazil is 0.29.

We assume the same deep parameters hold for Brazil and compare the moments from the model when κ = 0 (the case of financial integration), and the data.

As seen in Table 7, the model is able to match the relative volatility of trade balance to output ratio of 0.8. With higher financial openness, the economy is not restricted to balance its current account, and hence its volatility is higher. The correlation of TOT with output is also high as in the data. However, the data for Brazil shows countercyclical trade balance to output at -0.53, whereas the model produces a positive correlation when κ = 0. As κ increases to 100, seen in the previous table, this correlation becomes negative. For an exact parametrisation of κ between 0 and 100, for the Brazilian economy, we may be able to match this moment.

Table 7

Business cycle moments (Brazil): model and data

The table compares the moments from the data and the model, for the case of full financial integration in Brazil. The model reproduces the higher relative volatility of TBY.

Table 7

Business cycle moments (Brazil): model and data

Statistic	Y	C	I	TBY	TOT
Standard deviation
Brazil (1995–2010)	1.63	2.23	6.60	1.32	4.39
Model (κ = 0)	1.89	2.71	5.00	1.55	2.81
Relative std dev
Brazil	1.00	1.38	4.04	0.81	2.69
Model (κ = 0)	1.00	1.43	2.64	0.82	1.49
Contemporaneous correlation
Brazil	1.00	0.75	0.84	−0.53	0.68
Model (κ = 0)	1.00	0.61	0.97	0.16	0.78

The table compares the moments from the data and the model, for the case of full financial integration in Brazil. The model reproduces the higher relative volatility of TBY.

View Table

Table 7

Business cycle moments (Brazil): model and data

Statistic	Y	C	I	TBY	TOT
Standard deviation
Brazil (1995–2010)	1.63	2.23	6.60	1.32	4.39
Model (κ = 0)	1.89	2.71	5.00	1.55	2.81
Relative std dev
Brazil	1.00	1.38	4.04	0.81	2.69
Model (κ = 0)	1.00	1.43	2.64	0.82	1.49
Contemporaneous correlation
Brazil	1.00	0.75	0.84	−0.53	0.68
Model (κ = 0)	1.00	0.61	0.97	0.16	0.78

The table compares the moments from the data and the model, for the case of full financial integration in Brazil. The model reproduces the higher relative volatility of TBY.