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I. INTRODUCTION

Asian countries’ participation in global trade has increased substantially during the last two decades. Globalization has altered Asia’s trade patterns and paved the way for the internationalization of production networks linking across borders. The rapid growth of trade is closely related to specialization, where consumers seek “product variety” or indulge in “love for variety.” Trade specialization through export-oriented industrialization leads to international fragmentation of the production process, which has contributed to the increasing importance of Intra-Industry Trade (IIT) in Asian countries (Haddad, 2007). Fragmented trade links the production process located in different countries, thus reaping economies of scale and efficiency. Fragmented trade also enhances the gains from trade through comparative advantages in the production process, as noted in new trade theories such as economies of scale and product differentiation (Krugman, 1981; Falvey and Kierzkowski, 1986).

Hence, IIT is a phenomenon that arises when a country simultaneously exports and imports goods produced by the same industry (Balassa, 1986). IIT is presumed to occur between developed countries with similar factor endowments. However, it is argued that, within Asian economies, IIT also plays a vital role in the production process, trade, and growth (Sawyer et al., 2010). Asian economies are the major players in international fragmentation, as production of goods fragmented into several stages, with each stage produced in the most cost-effective location or country. Therefore, goods cross borders several times before being transformed into final products, further increasing trade interconnectedness. Emerging countries in Asia tend to be downstream in the supply chain, with relatively large shares of imported content in their exports (International Monetary Fund, 2011).

The literature in the context of IIT is mostly focused within major three strands: i) measuring IIT (Grubel and Lloyd, 1975: Brulhart, 1994), ii) theoretical developments within IIT (Linder, 1961; Falvey, 1981; Helpman and Krugman, 1985), and iii) empirical studies examining the determinants of IIT (Bergstrand, 1983; Balassa, 1986; Andresen, 2003; Zhang and Li, 2006; Clark and Denise, 1999). In the third strand, many empirical studies have analyzed the determinants of IIT among Asian economies (Menon, 1996; Sohn and Zhang, 2005; Thorpe and Zhang, 2005; Cortinhas, 2007; Sawyer et al., 2010). However, the bilateral analysis of Asian countries such as India and Indonesia is a less explored area of research. A bilateral trade analysis is more focused than a multilateral one, since it is between two countries. Moreover, a bilateral analysis explores the comparative advantages between the two countries, the scope of economies of scale, and possible areas of specialization that can help in framing policies to reduce trade barriers and in developing regional trade agreements. Hence, the present study analyzes the trade pattern of India with Indonesia by exploring the determinants of IIT between the two countries.

India, one of the emerging economies in the world, has extensively involved in international trade and developed strong multilateral and bilateral trade relations with major countries. Economic reforms initiated in 1991 led to significant changes in the country’s trade patterns and direction. In 1991, India’s commodity trade was primarily dominated by Organization for Economic Co- operation and Development (OECD) countries, with an export share of 56.6% and

Intra-Industry Trade Between India and Indonesia	507

an import share of 57.2%, but, by 2017–2018, these shares had declined to 39.4% and 27.2%, respectively. During the same period, the country’s export share with Asia increased from 14.4% to 32% and its share of imports increased from 14.4% to 35.6% (OECD, 2018). These shifts in the destination of trade indicate India’s growing interconnectedness with Asian economies.

Among Asian economies, India’s trade with Indonesia also increased substantially during this period. Indonesia has emerged as India’s largest trading partner within Asia.

Figure 1. Trends in India’s Trade with Indonesia (in USD Million)

The figure presents the trends in India’s trade (in USD million) with Indonesia. The data come from the Direction of Trade Statistics, Handbook of Economics and Statistics, RBI.

18,000
16,000	Exports	Imports
14,000
12,000
10,000
8,000
6,000
4,000
2,000
0	1991-92 1993-94 1995-96 1997-98 1999-00 2001-02 2003-04 2005-06 2007-08 2009-10 2011-12 2013-14 2015-16 2017-18

Figure 1 shows the increasing trend of India’s exports and imports with Indonesia between 1990–1991 and 2013–2014. It is important to highlight that India’s exports increased from USD 109 million to USD 3,964 million between 1990–1991 and 2017–2018. At the same time, India’s imports from Indonesia have displayed greater dynamism, rising from USD 81 million to USD 16,438 million during the same period. Therefore, Indonesia is an attractive import source for India, compared to India as an export destination for Indonesia. Subsequently, India’s trade balance with Indonesia dropped from a surplus of USD 28 million in 1990–1991 to a trade deficit of USD 12,475 million in 2017–2018.

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Figure 3. IIT Index (Bilateral IIT between India and Indonesia)

The figure presents the Grubel-Lloyd Intra-industry trade index of specialization between India and Indonesia. The value of GL index ranges from 0 to 1 and GL takes the value 0 if there is no IIT, and takes the value 1 when all trade is IIT. The data for calculation of index was taken from Direction of Trade Statistics, Handbook of Economics and Statistics, RBI.

0,8

0,7

0,6

0,5

0,4

0,3

0,2

0,1

0 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016

Table 1.

Commodity Wise Contributions to Aggregate IIT

The table shows the five-year average percentage contribution of each commodity category to the aggregate IIT. Here aggregate IIT is the sum of the weighted average of IIT of each commodity group.

Period	1996-2000 2001-05		2006-10	2011-15	2016-17
Food and beverages (primary)	9.6	8.9	7.6	5.5	5.5
Food and beverages (processed)	6.0	12.4	2.5	3.9	8.0
Industrial supplies (primary)	4.0	5.3	9.3	14.8	5.5
Industrial supplies (processed)	71.7	60.4	60.0	51.8	53.1
Capital goods	2.8	3.2	5.7	8.7	10.7
Parts and accessories of capital goods	2.3	4.4	4.0	5.2	4.0
Fuels and lubricants (processed)	1.1	0.3	6.3	1.7	2.0
Fuels and lubricants (primary)	1.1	0.3	6.3	1.7	2.0
Others	2.1	4.4	3.2	5.3	7.9

Figure 3 depicts the calculated IIT index and shows that the values are above

0.5in most years, indicating IIT between India and Indonesia. However, the trends indicate the IIT is not increasing, implying that the full potential of specialization is not being exploited. This result also highlights the limited reciprocity of the trade between the two countries. Further, Table 1 reports the commodity-wise contributions to total IIT. It shows that the processed industrial supply commodity group accounted for a larger share, more than 70%, during 1996–2000, which

Intra-Industry Trade Between India and Indonesia	513

subsequently declined to 53% during 2016–2017. In the same period, the share of capital goods increased from 2.8% to 10.7%, highlighting the inclination toward trade specialization in the sector. It is also interesting to note that the contribution of food and beverages (primary) declined from 9.6% to 5.5% and the share of food and beverages (processed) increased from 6% to 8% during the same period. These figures reveal that specialization takes place more with processed than with primary products. It is also important to note that, over the years, trade between India and Indonesia has moved away from primary products to more industrial processed commodities. This indicates a significant change in the trade patterns between the two countries in terms of composition, that is, a shift from labor-intensive to more capital-intensive products. Further, the increasing IIT in processed commodities indicates the scope of specialization in these commodities, where rising economies of scale and product differentiation have a significant influence.7

The above discussion highlights the following facts: 1) trade between India and Indonesia has been increasing over the years, 2) IIT between the two countries has not grown during 1995–2017, implying there is room for further specialization, and 3) IIT is exploited in commodity groups such as processed industries and capital goods. Given this scenario, it is crucial to understand the underlying factors that determine the level of IIT between the two countries to exploit the full potential of specialization and reap the gains from trade.

III. EMPIRICAL MODEL OF BILATERAL IIT

We specify the following bilateral IIT equation in log-linear form, in line with Balassa and Bauwens (1987):

(3)

where i stands for the home country, India; j stands for its respective trading partner, Indonesia; β1 , β2 , β3 , β4 , β5 , and β6 , are the parameters to be estimated; β0 is the intercept; t denotes time; and εijt stands for the error term. All variables are expressed in logarithmic form. The variable IIT indicates the weighted Grubel– Lloyd index for India’s trade with Indonesia. Similarly, RDPCI is the relative difference in per capita income between India and Indonesia, an indicator of demand structure and a proxy for inequality in economic development. In other words, the difference in per capita income measures the extent of variation in demand for differentiated products. IIT would tend to be more intense between countries with more similar levels of per capita income.

7Increasing economies of scale have a long-run decreasing trend in average cost and, during that time, most countries concentrate their limited factors of production on smaller numbers of huge firms. This helps in the exploitation of all the advantages and is a common phenomenon in many industrial processes (Maric, 2011).

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The variable TB is the trade balance between India and Indonesia, where the higher the trade imbalance, the lower the IIT (β1 < 0). In other words, lowering trade barriers and opening up a country to trade promotes simultaneous exports and imports in the same industry and reduces trade imbalances and hence increases IIT. Apart from that, since the IIT indexes are generally biased toward trade imbalance, the trade balance is usually included in models of IIT determination to correct for this (Xing, 2007).

The variable FDI stands for inflows of FDI from partner countries to the home country. Higher FDI from partner countries is expected to increase the home country IIT, since it leads to product differentiation and higher economies of scale and hence β3 > 0.8 In other words, FDI promotes IIT, particularly when foreign companies are set up to take advantage of the host country’s factor endowments and their production is subsequently exported back to it.

The variable MSD stands for the market size differential between the home country and the partner country. The greater the Market Size Differential (MSD), the lower the level of IIT between countries (β4 < 0). “As economies become more similar in terms of their market size, the potential for overlapping demand for differentiated products is enhanced” (Sawyer et al., p.487,2010). The variable HKE captures the difference in the level of human capital endowment between the home and partner countries. Large differences in factor endowments can reduce IIT and increase inter-industry trade rather than IIT (Helpman and Krugman, 1985). Finally, Open indicates the level of trade openness in India and the greater the openness, the higher the levels of IIT.

IV. DATA AND METHODOLOGY

The study uses annual data from 1995 to 2017. Disaggregated bilateral export and import data are obtained from the UN’s Comtrade Database and the rest of the data is from the World Bank and UNESCO websites9. The beginning period analysis is attributed to the availability of disaggregated bilateral trade data on India and Indonesia under the BEC classification. The details of the measurement of the variables in Equation (3) are given in the Appendix.

Equation (3) is estimated using the ARDL cointegration procedure developed by Pesaran and Shin (1999) and Pesaran et.al (2001). This test can be performed irrespective of whether variables in the model are purely I(0), purely I(1), or mutually cointegrated. The ARDL cointegration procedure involves two steps. The first step is to examine the existence of a long-run relationship between the variables in the model. If cointegration exists, the second step is to estimate the

8There are two different schools of thought on the relationship between FDI and IIT. One school argues that goods produced in multinational economies are differentiated and involved firms engage in either horizontally or vertically trading the differentiated goods to meet different incomes and tastes. The second school of thought states that most IIT is intra-firm trade between multinational economies and follows a fragmented production process in different countries (Chen, 2000).

9https://data.worldbank.org and http://data.uis.unesco.org

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long- and short-run coefficients using associated ARDL and error correction models. The error correction model of the ARDL model pertaining to the variable is

(4)

where the first part of the right-hand side with parameter λ represents the long- run relationship and the second part, with β, δ, γ, φ, θ, and ω, represent the short- run dynamics of the model.

To examine the existence of a long-run relationship between IIT and its determinants, an F-test procedure is followed for the joint significance of the coefficients of the lagged levels of the variables, that is, H0 : λ1 = λ2 = λ3 = λ4 = λ5 = λ6 = λ7 = 0 against H1 : λ1 = λ2 = λ3 = λ4 = λ5 = λ6 = λ7 = 0. If the null hypothesis is rejected, this indicates the existence of a long-run relationship or cointegration. Pesaran et al. (2001) propose lower and upper critical values for the F-statistic, assuming all variables are I(0) for the lower bound and all variables are I(1) for the upper bound. If the computed F-statistic exceeds the upper critical value, then the null of no cointegration can be rejected, irrespective of the order of integration of the variables. Conversely, if the test statistic falls below the lower critical bound, then the null of no cointegration cannot be rejected.

As a third alternative, if the test statistic falls between the lower and upper critical values, then the result is inconclusive. In the present case, the critical values proposed by Narayan (2005) for a small sample size are used. If cointegration is established, then the long-run coefficients can be estimated by the ARDL model using ordinary least squares. Since the ARDL model assumes no serial correlation in errors, an appropriate lag level (m) should be chosen. We estimate a total of (m+1)k+1 different ARDL models, where k is the number of variables, and choose a model based on information criteria such as Akaike’s information criterion or Schwarz’s Bayesian information criterion. The short-run dynamics are then estimated through the error correction model.

V. EMPIRICAL RESULTS

First, we use the augmented Dickey–Fuller and Phillips–Perron tests to check the stationarity of the variables. The results are reported in Table 2. They indicate that all variables are found to be integrated with order one, except the difference in human capital endowments (HKE). This evidence of the mixed order of integration of the variables, that is, I(0) and I(1), enables us to employ the ARDL approach to cointegration for estimation.

Intra-Industry Trade Between India and Indonesia	517

of Narayan (2005) noted above, implying that the null of no cointegration can be rejected at the 5% level and there exists a long-run relationship, or cointegration, between the IIT index and its determinants. Having established the cointegration relationship, the next step is to estimate the long-run coefficients of the equation by using the ARDL specification. The estimated long-run coefficients of the ARDL models suggested by the Schwarz’s Bayesian information criterion are shown in Table 4.

Table 4.

Long-Run Coefficient Estimates by the ARDL Approach

The table reports the long-run coefficients estimated by Auto Regressive Distributed Lag (ARDL) method to cointegration. Asterisks *, ** and *** denote statistical significance at the 1%, 5 % and 10% levels, respectively and values in parenthesis indicate standard errors. The sample period used is from 1995 to 2017. In the models, IIT, RDPCI, TB and FDI denote Intra-industry trade index, relative difference in per-capita income, trade balance, foreign direct investment, respectively. Similarly, MSD, HKE and Open denote market size differential, human capital differential and trade openness, respectively.

ln is the natural logarithm.

	Variables	Model 1	Model 2	Model 3
		ARDL(1,0,0,0)	ARDL(1,0,0,0)	ARDL(1,0,0,0,0)
	lnRDPCI	-1.154		-0.912
		[0.2089]*		[0.304]**
	lnTB	-0.488
		[0.1072]*
	lnFDI	0.206	0.411	0.529
		[0.058]*	[0.090]*	[0.202]*
	lnMSD		2.908	1.352
			[2.55]	[1.404]
	lnHKE		-1.405
			[0.473]**
	lnOPEN			2.462
				[0.971]**
	Constant	1.524	-11.152	1.945
		[0.507]*	[4.453]**	[0.801]**

In Table 4, the long-run coefficients show that all the regressors in the bilateral trade equation exhibit their theoretically expected signs. Importantly, all the variables are statistically significant at the 5% level in the long run. We now discuss this model in detail. The long-run elasticity of RDPCI is a measure of dissimilarities in income or demand structures between countries and it is statistically significant in the model. The high elasticity of RDPCI, -1.15, indicates that a 1% increase in inequality in per capita income between India and Indonesia leads to 1.15% decrease in IIT. Hence, an increase in dissimilar income has a negative impact on IIT between these countries. This result also suggests that the lower values of IIT between India and Indonesia are partially due to the income gap between the two countries. Differing demand structures thus often create barriers to the extensive exchange of goods in the same categories and hence suppresses IIT. This result

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is similar to the findings of Linder (1961), Moreover, the negative relationship between RDPCI and IIT is well explained by theoretical models ( Falvey,1981; Falvey and Kierzkowski,1987; Helpman and Krugman,1985).

Similarly, the estimated coefficient of the Trade Balance (TB) is -0.48 and statistically significant, implying that the value of the IIT index decreases as trade imbalance increases. Therefore, improving the trade balance between countries can improve their IIT between them. Similarly, the variable FDI is statistically significant in all three models, with a coefficient ranging from 0.2 to 0.5, implying that higher FDI inflows to India enhance the IIT between countries. The evidence of a positive impact of FDI on IIT shows that trade facilitation through FDI leads to greater product differentiation and more intense IIT.

Table 5.

Error Correction Representation for the ARDL Model

The table report the short-run coefficients estimated by Auto Regressive Distributed Lag (ARDL) approach to cointegration. In the models, IIT, RDPCI, TB and FDI denote Intra-industry trade index, relative difference in per- capita income, trade balance, foreign direct investment, respectively. Similarly, MSD, HKE and Open denote market size differential, human capital differential and trade openness, respectively. ln is the natural logarithm. Where D and ecmt-1 denote first difference and error correction term, respectively. Similarly,

χA2C and χA2RCH are LM statistics for serial correlation and ARCH effects (at lag 1), respectively. Likewise, χN2ORMALITY is the LM statistic for normality in residual at

lag 2, respectively. Finally, *. ** and *** denote statistical significance at the 1%, 5% and 10% levels, respectively. Figures in parenthesis are standard errors.

	Variables		Model 1	Model 2	Model 3
			ARDL(1,0,0,0)	ARDL(1,0,0,0)	ARDL(1,0,0,0,0)
	∆lnRDPCI		1.112 [0.204]*		0.878 [0.315]**
	∆lnTB		-0.226 [0.089]**
	∆lnFDI		0.1988 [0.085]**	0.118 [0.042]*	0.096 [0.01]*
	∆lnMSD			0.213 [0.734]	-0.584 [0.721]
	∆lnHKE			-0.348 [0.569]
	∆lnOPEN				1.102 [0.66]
	ECM(-1)		-0.962 [0.149]*	-0.889 [0.230]	-0.964 [0.22]*
	R2		0.55	0.75	0.72
	χ	2	2.22 [0.170]	1.59 [0.309]	1.91 [0.209]
		A C	0.245 [0.62]	0.049 [0.827]	0.022 [0.891]
	χ A2RCH
	χ N2ORMALITY		0.457 [0.79]	0.805 [0.668]	0.705 [0.568]
	CUSUM		Stable	Stable	Stable
	CUSUMQ		Stable	Stable	Stable

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Table 6.

Long-Run Coefficient Estimates by the ARDL Approach

The table reports the long-run coefficients estimated by Auto Regressive Distributed Lag (ARDL) method to cointegration. The sample period used is from 1995 to 2017. In the models, IIT, RDPCI, TB and FDI denote Intra- industry trade index, relative difference in per-capita income, trade balance, foreign direct investment, respectively. Similarly, MSD, HKE and Open denote market size differential, human capital differential and trade openness, respectively. Dum_1998 and Dum_2008 denotes the dummy variables that account for the shift in intercept. ln denotes natural logarithm form. Finally, *, ** and *** denote statistical significance at the 1%, 5% and 10% levels, respectively, and values in parenthesis indicate standard errors.

	Variables	Model 1	Model 2	Model 3
		ARDL(1,1,0,0)	ARDL(1,1,0,0)	ARDL(1,1,0,0,0,0)
	lnRDPCI	-1.029		-0.854
		[0.201]*		[0.412]**
	lnTB	-0.460
		[0.129]*
	lnFDI	0.202	0.396	0.510
		[0.051]*	[0.093]*	[0.131]*
	lnMSD		2.613	1.062
			[2.43]	[1.510]
	lnHKE		-1.316
			[0.481]**
	lnOPEN			2.258
				[0.962]**
	Dum_1998	-0.523	-0.502	-0.543
		[1.322]	[1.132]	[1.322]
	Dum_2008	-0.821	-1.031	-0.881
		[0.722]	[0.926]	[0.817]
	Constant	1.313	-10.164	1.727
		[0.507]*	[4.211]**	[0.907]**

Additionally, the estimated coefficient of the Market Size Differential (MSD) exhibits a positive sign, which is theoretically unexpected but statistically insignificant. This finding indicates that the disparity in market size between the two countries does not prevent the growth of IIT. The difference in Human Capital Endowment (HKE), however, is statistically significant and exhibits a negative sign. The greater the disparity in human capital endowment, the lower the IIT, which suggests that reduction of the disparity in human capital endowments between countries through better skills training and education, for example, can improve IIT. Finally, the trade openness variable (Open) is positive and statistically significant, suggesting that India’s trade liberalization has expanded IIT. This result is consistent with theoretical expectations.

The short-run dynamics of IIT through the error correction model is shown in Table 5. The signs of the short-run coefficients are consistent with theoretical predictions and are statistically significant in all three models. The error correction terms (ECMt-1) are significant at the 1% level in all four models and have the expected sign. The estimated coefficient of the error correction term ranges from -0.44 to -0.84, indicating that more than 50% of the deviation from equilibrium

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is eliminated within one year. The diagnostics statistics reported in Table 6 do not show any serial correlation or autoregressive conditional heteroskedasticity effects in the residuals of the error correction model. Further, the models confirm normality in the residuals and a functional form test does not indicate any model misspecification. Moreover, the cumulative sum (CUSUM) and cumulative sum of squares (CUSUMQ) of the recursive residuals do not show evidence of any instability of the coefficients across sample periods. For a robustness check, first, we include slope dummy variables to capture the effects of the Indonesian financial crisis in 1998 and the global financial crisis in 2008 on IIT. Second, we use the Akaike information criterion as an alternative lag selection criterion. The estimated long- run coefficients are reported in Table 6 and show that the financial crises did not have a significant effect on bilateral IIT. Similarly, the overall empirical findings are consistent with the previous results reported Table 6.

VI. CONCLUSION

India initiated the economic reforms during the early 1990s and its trade patterns have changed substantially. Focusing on India’s growing trade with Indonesia, this paper analyzes the dynamic changes of India’s bilateral IIT in commodity trade. IIT measures the simultaneous export and import of goods with the same product categories and hence an increase in IIT indicates progress in product variety, improved economies of scales, and reduced technology gaps with trading partners.

The study calculates a bilateral IIT index using commodity trade data at the two-digit BEC code level from 1995 to 2017. The estimates demonstrate the presence of IIT between the two countries, since the index is above 0.5 in most years. However, there is no remarkable increase in the intensity of IIT, implying meager reciprocal trade between the two countries. Our findings also show that IIT is mostly taking place in processed industrial supplies and capital goods and the trade pattern between the two countries has significantly changed in terms of composition, that is, shifting from labor-intensive to more capital-intensive products.

This paper also investigates the determinants of IIT and shows that the per capita income gap between the two countries has a significant role in determining IIT, indicating that dissimilar demand structures between countries can create barriers to extensively exchanging goods in the same categories. Similarly, the evidence from the analysis also notes the significant role of FDI in improving IIT, indicating that trade facilitation through FDI leads to greater product differentiation and more intense IIT. Hence efficiency-seeking FDI can play a crucial role in trade facilitation in India. The empirical findings of this paper also shed light on the role of human capital endowment in the two countries in determining IIT. This result suggests room for increasing intensity of IIT by reducing disparities in human capital endowment between countries by implementing various programs to enhance skills and education.

Finally, policies that facilitate more trade and fair trade by reducing barriers can undoubtedly improve economies of scale in trade between countries. Our empirical findings demonstrate that trade imbalance significantly reduces the

Intra-Industry Trade Between India and Indonesia	521

level of India’s IIT with Indonesia. India’s large trade deficit with Indonesia could be a major cause of non-increasing trends in IIT. Therefore, policies are warranted to minimize the trade imbalance between the two countries, such as encouraging more exports to Indonesia, and to facilitate more trade by reducing trade barriers. There is ample scope for improving trade specialization through reciprocity in trading commodities between the two countries. This will reduce trade barriers and technology-led costs and allow supply chains to become both more regional and international. The absence of IIT in many commodity groups could be due to restrictions on them, such as tariffs and quotas. If IIT is to be encouraged between India and Indonesia, trade barriers need to be removed.

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Appendix

Variable Name	Variable Definition		Calculation and Source
RDPCI	Relative Difference in Per Capita	Followed Balassa (1986) to construct the
	Income	index of relative inequality.
		Where				i, j are the
		respective countries, and PC stands for
		per capita. This index takes on values
		between 0 and 1, with relative inequality
		increasing as the index increases.
		Source: World development indicators
					(World Bank)
TB	Trade Balance	Difference between India’s aggregate
		exports and import to Indonesia.
		Source: United Nations’ Comtrade
					database
FDI	Foreign Direct Investment		Net FDI inflows to India
		Source: UNCTAD database
MSD	The Market Size Differential	The absolute difference of total GNP
		between India and Indonesia
		Source: World development indicators
					(World Bank)
HKE	Human Capital Endowment	The difference between two countries
		in the total number of students enrolled in
		tertiary education in a given academic year
		divided by country’s population and
			multiplied by 1,00,000.
		Source: UNESCO database
Open	Trade openness	India’s total trade (exports plus imports)
					to GDP.
		Source: Reserve Bank of India