Incorporating Factor Productivity Gains in the Relationship between Export Growth and GDP Growth: Empirical Evidences from India

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An annual time-series data of 30 years (1992 to 2022) is taken to conduct an in-depth analysis of how export growth has impacted GDP growth after India’s New Economic Policy of 1991. The methodology of this paper leans towards a model that encapsulates the endogenous growth theory by incorporating the “growth rate of labour force” and the “growth rate of capital stock” as the independent variables in addition to the growth rate of exports. Accordingly, a multiple regression model, adjusted to make its time-series components stationary, is used. Rigorous diagnostic tests to rule out the presence of spurious regression, autocorrelation, heteroskedasticity and multicollinearity are then conducted to ensure that the estimates of the model are best linear unbiased estimators (BLUE). The resulting analysis confirms that export growth has a “positive significant impact” on GDP growth for India. This paper also finds that capital stock growth positively impacts GDP growth, but only at the 10% significance level. This paper also explains the trend of jobless growth in India during the time period, 1992 to 2022. JEL: C32, F14, F43, O40, O47 export growth GDP growth endogenous growth theory multiple regression model jobless growth Introduction In a rapidly globalising world of today, the effects of the “growth of international trade on economic growth” is a hotly contested area of research in both the theoretical and empirical fronts. This lack of consensus is clearly evidenced by the fact that “the neoclassical trade theory” and “the new growth theory” supports the positive impact of “international trade on income and economic growth” while “the neoclassical growth theory” does not recognise this impact. Further, the “new trade theory” remains dubious while “mixed empirical evidences” generate mixed support for the same (Singh, 2010 ). This study takes the growth of exports as a “proxy” for the growth of trade which is justified in many similar studies (Berg & Schmidt, 1994 ; Krueger, 1995 ; Levine & Renelt, 1992 ; Ram, 1985 ). Since the a priori relationship between “exports and output” is generally known, this study is, instead, intended to find the empirical evidence for the existence of a relationship between “export growth and economic growth” for India for the 1992 to 2022 time period. In theory, the “positive externalities” associated with exports would result in economies growing rapidly hand-in-hand with export growth (Dollar, 1992 ). The neoclassical “export-led growth hypothesis”, which is supported by an extensive survey conducted by Giles and Williams ( 2000 ), is particularly relevant here. Accordingly, growth of exports lead to economic growth. In this context, it is crucial to examine whether “exports growth” after the 1991 New Economic Policy of India yielded a “statistically significant” impact on the economic growth of the Indian economy. In one sense, export growth is an “endogenous” variable in that it is influenced by existing policies and policy changes that is “internal” to the economy. This is even more applicable in India’s case with the dramatic policy shift post-1991. In this context, the linkage between “policy and growth” is one of the arguments given by the “endogenous growth theory” (Krueger, 1985). Particularly, as cited in Feenstra, Liang, Madani & Yang (1998), “enhancement in productivity” brought about by increases in product variety from exports is central in the endogenous growth model given by Romer ( 1990 ). As such, the study of the relationship between “export growth and GDP growth” for India also encapsulates the spirit of the endogenous growth theory by attempting to explain India’s GDP growth through the lens of increased “total factor productivity” brought upon by exports growth. Review of Literature Exports and Economic Growth Balassa ( 1978 ) examines the “effects of exports on economic growth” for eleven developing countries that have established an “industrial base”. He found that export growth “favourably effects the rate of economic growth” which is over and above the contributions made by the domestic and foreign labour and capital. Cottani et. el. (1990) collected data for 24 less developed countries (LDCs) and conducted a cross-sectional regression analysis. They found that “export growth rate” and “per capita growth rate” moved in the same direction. Dollar ( 1992 ) examined 95 LDCs to analyse whether outward-oriented developing economies grow more rapidly. He reported in his findings that exports growth due to factors like trade liberalisation, maintenance of a “stable real exchange rate” and devaluation of the real exchange rate improves economic growth performance in these economies. Gupta ( 1985 ) argued that for “developing countries”, export-oriented policies enabled these countries to attain higher levels of growth of GNP (Gross National Product). Further, his study found that economies with export-expansion policies fared better in terms of their GNP growth as compared to those economies focussing on import substitution. Feder ( 1983 ) examines the sources of growth for a group of semi-industrialised LDCs during the time period, 1964 to 1973. His methodology focussed on finding the difference in marginal factor productivities of various sectors of the economy. From the results, he concluded that marginal factor productivity of the export sector is significantly higher. As such, he found that economic growth in these LDCs is generated not only by the aggregate “levels of labour and capital” but also significantly influenced by the “reallocation” of existing resources to the higher productivity export sector from the less efficient non-exports sector. Tyler ( 1981 ) employed data for 55 middle income developing countries for the time period 1960 to 1977 to analyse the relationship between “export expansion and growth” in these developing countries. He used a “Cobb-Douglas” production function which adds exports as an additional input in determining output. His reasoning for doing so was the existence of “scale effects” and “externalities associated with export production and sales”. His results provided additional evidence of a “strong association” between “export performance and GNP growth”. Moreover, he also reported that “lower rates of economic growth” are a direct result of countries neglecting their export sectors through discriminatory economic policies. Michaely ( 1977 ) tried to find an “unbiased relationship” between “exports and economic growth” by removing the autocorrelation component since exports themselves are a “part of the national product”. By analysing data collected from 41 countries for twenty-four years (1950 to 1973), he found a positive association of export expansion with growth in general. Further, he also reported that this positive association is “particularly strong” for “more developed” countries as compared to the “least developed” countries where “this positive association” does not exist at all. Krueger (1980) argued that the advantages of export promotion is directly linked to factor proportions. In this regard, trade represents a means of shifting the labour demand outwards “more rapidly” than what the import-substitution strategy permits. Ram ( 1985 ) uses a production function that incorporates exports as a “production input” along with labour and capital. By analysing data for 73 LDCs for two distinctly separate time periods (1960 to 1970 and 1970 to 1977), he concluded that the importance of “exports in influencing economic growth” had increased during the 1970s. He further found that although “the impact of export performance on growth” was smaller in the LDCs during the 1960 to 1970 time period, the impact differential “almost disappears” in the 1970 to 1977 time period, during which the “large positive impact of exports on growth” was the norm for all the countries studied. Export-led Growth Hypothesis Dreger and Herzer ( 2013 ) examined the “export-led growth” hypothesis by conducting a panel data analysis consisting of 45 developing countries. One of their main findings was the “bidirectional causality” between “exports and non-export GDP” in the short run. They further reported that the “impact of exports on non-export GDP” in the long run is negative on average but there exist “large differences” in this impact across countries. Parida and Sahoo ( 2007 ) also examined the export-led growth hypothesis for four South-Asian economies – India, Pakistan, Bangladesh and Sri Lanka – for the time period, 1980 to 2002. Their panel data study finds a “long-run equilibrium relationship” between exports and GDP for the four countries. Most importantly, they reported in their findings that exports have “statistically significant” impact on GDP growth. Jun ( 2007 ) also conducted a panel data analysis incorporating 81 countries and found a “two-way causal relationship” between “exports and output”. Further, she found that the exports of high-income countries and high investment countries have larger impacts on output (GDP) than that of low-income countries and low investment countries respectively. Export Growth and Economic Growth: Causal Relationship Mehrara and Firouzjaee ( 2011 ) examined the “causal relationship” between “growth of exports and growth of GDP” in 73 developing economies. Their methodology involved categorizing these developing economies into two separate groups consisting of oil-dependent economies and non-oil developing economies. They found that the existence of a “long run bidirectional causal relationship between export growth and GDP growth” for both groups of developing economies. Henriques and Sadorsky ( 1996 ) conducted a detailed study on the nature of relationship between “export growth and GDP growth” for Canada. They report in their findings that there is a “one-way causal relationship” running from export growth to GDP growth. Divya and Ronit ( 2014 ) used the “Vector Autoregressive (VAR) model” and the “Granger Causality test” to examine whether GDP growth causes export growth in India. Their results support the theory of growth led exports in India. Exports and Factor Productivity Growth Numerous papers suggest that the “positive impact” of international trade shows up in the form of factor productivity gains. Krueger (1980) found in his paper that free trade brings “productivity gains” as a direct result of minimum efficient scale of plant, increasing returns to scale, “indivisibilities” and the effect of size of market on competition. He also pointed out the wasteful processes involved due to import substitution policies which included heavy government inclusion in economic decisions, costly paperwork, “unproductive rent seeking” and bureaucratic bottlenecks among others. His work implied that trade restriction hampers total factor productivity. Dollar ( 1992 ), in his paper focussing on LDCs, reported on the positive externalities of the export sector. He further reported in his findings that a country’s export earnings enable it to “use external (foreign) capital without running into difficulties servicing foreign debt”. Cottani et. al. ( 1990 ) found that growth of exports stimulates the growth of the economy as productivity improvements tends to be, more or less, concentrated in the export and import competing industries. Moreover, Ram ( 1985 ) explicitly includes exports as an additional input in his production function. These works suggest the impact and effects of export growth to show up in the form of increases in productivity of the factors of production. Rationale of the Study In an ever-increasingly globalised world, economists are getting increasingly interested in the extent to which exports and imports policies affect economic growth. In this context, Singh ( 2010 ) surveyed the literature on the relationship between “international trade and economic growth” and found that most studies on this theme supported the gains from international trade on economic growth. With India liberalising its economy in 1991, it is pertinent to examine whether the growth of exports (proxy for trade) actually has an influence in shaping the growth of the Indian economy. In doing so, it is relevant to bring in a crucial aspect of trade that is often overlooked in such studies. “Several studies” have found that trade expands productivity and growth by providing a wider range of “intermediate inputs” (Grossman & Helpman, 1991 ; Rivera-Batis & Romer, 1991 ) and also by facilitating an “international” diffusion of technology (Benhabib & Spiegel, 1994 ; Coe & Helpman, 1995; Parente & Prescott, 1994 ). Therefore, examining the impact of “export growth on GDP growth” for India not only achieves the objective of determining the relationship between “export growth and economic growth” but also explains the more nuanced impact of factor productivity gains on economic growth. Research Objectives The research objectives of this study are as under – Examining whether “export growth has a significant impact on GDP growth” for India for the 1992 to 2022 time period. Incorporating the “endogenous growth theory” to determine the “determinants of India’s GDP growth” for the 1992 to 2022 time period. Methodology This section discusses the methodology used in this study in depth and the rationale behind using the specific tools of analysis. Data Collection Annual time-series data on “growth rate of real GDP, labour force, real gross fixed capital formation and growth rate of real exports” for India is collected from the “World Bank Open Data” (World Bank, 2024). This data is collected for the time period of 1992 to 2022 (30 observations). Labour force annual growth rate and the “ratio” between real fixed capital formation and real GDP is calculated from the data. The Relationship between Export Growth and GDP Growth: The Multiple Regression Model adjusted for Stationarity Several of the studies mentioned in the previous section including Balassa (1978), Cottani et. al (1990), Dollar (1992), Feder (1982), Krueger (1980), Michaely (1977), Ram (1985) and Tyler (1981) specify a linear relationship given by the following equation- GGDP = a 0 + a 1 GLF + a 2 GCAP + a 3 TRADE (1) Here, GGDP – growth rate of real domestic product, GLF – growth rate of labour force, GCAP – growth rate of capital stock, TRADE – measure of international trade. Equation (1) resembles the growth equation which is given by – G Y = A + b G K + (1 - b) G L (2) Here, G Y – growth rate of total output, G K – growth rate of capital, G L – growth rate of labour, A – growth rate of total factor productivity. In this context, Berg and Schmidt (1994) argued that lack of capital stock data can be overcome by using the “ratio” of investment to gross domestic product or I / Y in place of GCAP. In this case, the coefficient a 2 should be interpreted as the marginal product of capital. They further argued that the influence of international trade in the economy (GTRADE) can be represented by the “growth rate of real exports” or GEX. Further, Levine and Renelt (1992) also found that any growth rate regression that uses exports share in GDP as an explanatory variable could yield almost “identical results” to that of using imports share in GDP or trade (imports plus exports) share as the explanatory variable. Incorporating these definitions in equation (1) gives – GGDP = a 0 + a 1 GLF + a 2 (I / Y) + a 3 GEX (3) With regards to equation (3), Berg and Schmidt (1994) stated that the “growth rate” of exports (GEX) represents a portion of the leading entry of equation (2), the total factor productivity growth. Equation (3) is adopted in this study as several studies, including that of Cottani et. al. (1990), Dollar (1992), Krueger (1980) and Ram (1985), have found that the “positive impact” of international trade results in the form of productivity gains. Equation (3), by including “growth rate of exports” as one of its dependent variables, assumes productivity growth as a result of specific policy choices that “expands” trade. In other words, the incorporation of GEX in equation (3) suggests total factor productivity growth to be a result of internal or “endogenous” factors. This is in accordance with the spirit of the “endogenous growth theory” as envisaged by Romer (1994). Further, Balassa (1978), Berg and Schmidt (1994), Feder (1982), Ram (1982) and Tyler (1985) have acknowledged the existence of a “predetermined positive relationship” between exports and GDP as exports is a component of GDP. However, they justified this by pointing out that equation (3) contains the “growth rates” of exports and GDP in a production relation and not the “levels” of export and GDP. As Berg and Schmidt (1994) stated, there is “no a priori reason” why the coefficient a 3 is equation (3) must be (strictly) positive. The Multiple Regression Model adjusted for Stationarity Finally, given the results of the Augmented Dickey-Fuller test for stationarity test, equation (3) is specified as a “multiple regression model” with each of the variables adjusted to be stationary. The multiple regression model is estimated using “Ordinary Least Squares” (OLS) method. Test for Stationarity A stationary test is necessary for any analysis involving time-series variables. The Augmented Dickey-Fuller (ADF) Test is used to test the “stationarity” of the variables defined in equation (3) – GGDP, GLF, I / Y and GEX. If the ADF test statistic is significant at “0.05 significance level”, the conclusion is that the variable is stationary (Gujarati & Porter, 2009). Further, the Swartz Information Criterion (SIC) is used to select the most appropriate “number of lags” for the ADF test. Diagnostic Tests Test for Autocorrelation In order to check whether the multiple regression model satisfy the OLS assumption of no autocorrelation, the “Breusch-Godfrey Serial Correlation LM test” is used. If the “Chi-square statistic is not significant at 0.05% level”, then the conclusion is that the assumption of “no autocorrelation” is satisfied (Gujarati & Porter, 2009). Test for Heteroskedasticity The White Heteroskedasticity test is used to assess whether the OLS assumption of homoskedasticity is satisfied. If the Chi-square statistic is “not significant at 0.05% level”, then the conclusion is that the assumption of “homoskedasticity” is satisfied (Gujarati & Porter, 2009). Test for Multicollinearity In order to confirm that the regressors do not have a perfectly linear relationship or multicollinearity, the “Variance Inflation Factor” (VIF) is used. If the VIF value is “less than 10”, then the conclusion of “no multicollinearity” is satisfied (Gujarati & Porter, 2009). Test for Spurious Regression In order to ensure that the regression is not spurious in nature, the residuals of the multiple regression model is tested for stationarity using the ADF test. If the “ADF test statistic is significant at 0.05 level”, then the conclusion is that the “regression is not spurious” (Gujarati & Porter, 2009). Analysis and Results This section includes the discussion of the results and interpretations of the analytical tools and models described in the previous section. Augmented Dickey-Fuller (ADF) Test for Stationarity The following table summarises the results of the ADF test for stationarity for the dependent and independent variables. Table 1: Results of the Augmented Dickey-Fuller Test for Stationary for the dependent and independent variables. Null Hypothesis – “ The variable is not stationary”. Variable ADF test-statistic p-value GGDP -5.14279*** (0) 0.0013 GLF -5.199468***(1) 0.0012 I / Y -8.143186***(1) 0.0000 GEX -4.103737**(0) 0.0156 Note: (i) ***, **, and * indicates significance at 1%, 5%, and 10% levels. (ii) Optimal lags for Augmented Dickey-Fuller Test are determined based on SIC. (iii) The values inside brackets represent the levels of integration. Source: Author’s estimation on EViews. The results of Table 1 show that the independent variables GGDP and GEX are “stationary at levels”, I(0), while GLF and I / Y are “stationary at the first difference”, I(1). As such, while estimating the model using the multiple regression model, these variables are “transformed” according to their respective levels of integration. The Multiple Regression Model Adjusted for Stationarity Equation (3) as defined earlier (see Methodology section) now is specified in accordance with the results of the ADF test. This gives – GGDP [0] = a 0 + a 1 GLF [1] + a 2 (I / Y) [1] + a 3 GEX [0] (4) Equation (4) is the multiple regression equation that is adjusted for stationary of the respective variables. The superscripts inside the square brackets following each variable represent the level of integration to which it is transformed. That is, GGDP [0] and GEX [0] are kept as it is since both variables are stationary at levels and GLF [1] and (I / Y) [1] are “transformed” into their “first difference forms” so as to adjust them for stationarity. The following table shows the OLS estimation results of the multiple regression model as given by equation (4). Table 2: Results of the Multiple Regression Model adjusted for stationarity. Dependent Variable - GGDP [0] Variable Coefficient t-statistic P-value Constant term 4.317240** 2.630107 0.0142 GLF [1] 0.500817 0.314703 0.7555 I / Y [1] 77.57081* 1.948242 0.0623 GEX [0] 0.348172*** 3.448363 0.0019 Note: (i) ***, **, and * indicates significance at 1%, 5%, and 10% levels (ii) The values inside the square brackets represent the levels of integration. Source: Author’s estimation on EViews In Table 2, the coefficient of GLF is insignificant implying that the “growth rate of labour force” has had a statistically insignificant impact on the “growth of GDP” for India. The coefficient of I / Y is “significant at 10% level” but not at 1% and 5% levels. Following standard procedure and taking 5% level as the threshold level, the “growth of capital stock” also has a statistically insignificant impact on the “growth of GDP” for India. Finally, the coefficient of GEX is “significant at 1% level” and its value is positive. Thus, growth of exports has a statistically positive impact on the growth of GDP for India. Diagnostic Tests Breusch-Godfrey Serial Correlation LM Test for Autocorrelation The following table shows the result of the Breusch-Godfrey Serial Correlation LM Test for Autocorrelation. Table 3: Result of the Breusch-Godfrey Serial Correlation Test for Autocorrelation. Null Hypothesis – No Autocorrelation Chi-Square Statistic P-value 0.2406 Source: Author’s estimation on EViews. The p-value for the Chi-Square statistic for the Breusch-Godfrey Serial Correlation LM test is statistically insignificant in table 3. As such, the conclusion of this test is the “acceptance of the null hypothesis” of no autocorrelation. White Test for Heteroskedasticity The following table shows the result of the White Test for Heteroskedasticity. Table 4: Result of the White Test for Heteroskedasticity. Null Hypothesis – Homoskedasticity Chi-Square Statistic P-value 0.9104 Source: Author’s estimation on EViews. The p-value for the Chi-Square statistic for the White test is statistically insignificant in table 4. As such, the conclusion of this test is the “acceptance of the null hypothesis” of homoskedasticity in the model. Test for Multicollinearity: The Variance Inflation Factor (VIF) The following table shows the result of the test for multicollinearity using the VIF. Table 5: Result of the test for multicollinearity using VIF. Variable Centred VIF GLF 1.222117 I / Y 1.238386 GEX 1.466616 Source: Author’s estimation on EViews. The values of the Centred VIF of the individual independent variables are each less than 10. As per the rule of thumb given in Gujarati & Porter (2009), this result confirms the inexistence of multicollinearity among the explanatory variables of the model. Test for Spurious Regression The following table shows the result of the test for spurious regression by using the ADF test for the residuals of the model. Table 6: Result of the test for spurious regression using the ADF test. Null Hypothesis – “ The variable is not stationary”. Variable ADF test-statistic p-value Residuals -6.534448*** (0) 0.0000 Note: (i) ***, **, and * indicates significance at 1%, 5%, and 10% levels. (ii) Optimal lags for Augmented Dickey-Fuller Test are determined based on SIC. (iii) The value inside the bracket represents the level of integration. Source: Author’s estimation on EViews. The “ADF test statistic” is statistically significant at 1% level. This implies that the residuals of the model are stationary at levels. As such, this test confirms that the multiple regression model is not spurious in nature. Findings and Recommendations The “major finding” of this paper is that “export growth has a positively significant impact on GDP growth” for India for the 1992 to 2022 time-period. The estimators of the multiple regression model are also the Best Linear Unbiased Estimators (BLUE) as given by the Gauss-Markov theorem (Gujarati & Porter, 2009). This is confirmed by the results of the diagnostic tests for autocorrelation, heteroskedasticity, multicollinearity and spurious regression. Further, the model is also adjusted to make each variable stationary. The coefficient of growth of exports being significant also implies that, in terms of the endogenous growth theory, “factor productivity gains resulting from trade” has had a “positive impact” on India’s GDP growth for the time-period taken. In this context, a deeper study on the “factor productivity gains” in the economy resulting from exports growth is encouraged. With regards to the remaining independent variables, “growth of labour force” has not had any significant impact on India’s GDP growth. This implies the existence of the phenomenon of jobless growth wherein the economy has grown without growth of the labour force and employment. Indeed, India has transformed into a high-productivity regime without a significant expansion of labour-intensive production (Tejani, 2016). The growth of capital stock, however, has a “significant impact” on GDP growth at the “10% level” but not at the 1% and 5% level. In this regard, the “growth of capital stock” variable in this study does not take account of human capital. This is likely to be one of the main reasons for the insignificant coefficient of I / Y. A study by Haldar and Mallik (2010) found a similar result for India. They found that physical capital investment has no long run or short run effect on India’s per capita GNP but human capital investment had a long run significant impact on per capita GNP of India. Conclusion Despite the existence of a “rich literature” on the “relationship between exports growth and GDP growth”, there has been relatively few studies aimed at recognising exports as a determinant of factor productivity gains. This study approaches the empirical analysis of the relationship between exports growth and GDP growth for India from the lens of the endogenous growth theory. Accordingly, growth of exports is viewed as a direct impact of “internal” policy changes, which in India’s case was the 1991 New Economic Policy. The results of this paper present additional empirical evidences on the positive impact of export growth on economic growth and also provides a thorough examination of how exports growth can be treated as an endogenous variable which influences economic growth by increasing factor productivity. Scope for Future Research Research works solely focused on the nature of factor productivity gains realised from international trade would shed more light on the determinants of factor productivity gains. The incorporation of “human capital” in the growth equation and explaining its “effects on economic growth”, especially for developing economies, should also be explored by future researchers. Note The MS Excel file containing the data for the analysis can be accessed here. References Balassa, B. (1978). Exports and economic growth: Further evidence. Journal of Development Economics, 5 (2), 181-189. DOI: https://doi.org/10.1016/0304-3878(78)90006-8 Benhabib, J. & Spiegel, M.M. (1994). 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Retrieved from https://ideas.repec.org/a/tei/journl/v3y2010i1p7-25.html Henriques, I. & Sadorsky, P. (1996). Export-Led Growth or Growth-Driven Exports? The Canadian Case. Canadian Journal of Economics, 29 (3), 540-55. DOI: http://dx.doi.org/10.2307/136249 Jun, S. (2007). Bidirectional Relationships between Exports and Growth: A Panel Cointegration Analysis. Journal of Economic Research, 12 (2), 38-49. DOI: http://dx.doi.org/10.17256/jer.2007.12.2.002 Krueger, A.O. (1995). East Asian Experience and Endogenous Growth Theory. NBER Chapters in : Growth Theories in Light of the East Asian Experience, pages 9-36, National Bureau of Economic Research, Inc. Retrieved from https://ideas.repec.org/h/nbr/nberch/8543.html Kreuger, A.O. (2000). Trade Policy as an Input to Development. NBER Working Paper No. W0466. Retrieved from https://papers.ssrn.com/sol3/papers.cfm?abstract_id=228069 Levine, R. & Renelt, D. (1992). A Sensitivity Analysis of Cross-Country Growth Regressions. American Economic Review, 82 (4), 942-63. Retrieved from https://ideas.repec.org/a/aea/aecrev/v82y1992i4p942-63.html Michaely, M. (1977). Exports and growth: An empirical investigation. Journal of Development Economics, 4 (1), 49-53. DOI: https://doi.org/10.1016/0304-3878(77)90006-2 Parente, S. & Prescott, E. (1994). Barriers to Technology Adoption and Development. Journal of Political Economy, 102 (2), 298-321. DOI: http://dx.doi.org/10.1086/261933 Parida, P.C. & Sahoo, P. (2007). Export-led Growth in South Asia: A Panel Cointegration Analysis. International Economic Journal, 21 (2), 155-175. DOI: http://dx.doi.org/10.1080/10168730701345414 Ram, R. (1985). Exports and Economic Growth: Some Additional Evidence. Economic Development and Cultural Change, 33 (2), 415-425. Retrieved from https://www.jstor.org/stable/1153235 Romer, P.M. (1990). Endogenous Technological Change. Journal of Political Economy, 98 (5), 71-102. Retrieved from https://ideas.repec.org/a/ucp/jpolec/v98y1990i5ps71-102.html Romer, P.M. (1994). The Origins of Endogenous Growth. Journal of Economic Perspectives, 8 (1), 3-22. Retrieved from https://www.aeaweb.org/articles?id=10.1257/jep.8.1.3 Romer, P.M. & Rivera-Batiz, L.A. (1991). International trade with endogenous technological change. European Economic Review, 35 (4), 971-1001. Retrieved from https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Fwww.sciencedirect.com%2Fscience%2Farticle%2Fpii%2F0014-2921%2891%2990048-N;h=repec:eee:eecrev:v:35:y:1991:i:4:p:971-1001 Singh, T. (2010). Does International Trade Cause Economic Growth? A Survey. World Economy, 33 (11), 77-84. DOI: http://dx.doi.org/10.1111/j.1467-9701.2010.01243.x Tejani, S. (2015). Jobless growth in India: An investigation. Cambridge Journal of Economics, 40 (3), 843-870. DOI: http://dx.doi.org/10.1093/cje/bev025 Tyler, W.G. (1981). Growth and export expansion in developing countries: Some empirical evidence. Journal of Development Economics, 9 (1), 121-130. DOI: https://doi.org/10.1016/0304-3878(81)90007-9 World Bank Open Data. (2024). GDP growth (annual %) – India. The World Bank . Retrieved from https://data.worldbank.org/indicator/NY.GDP.MKTP.KD.ZG?locations=TW-IN World Bank Open Data. (2024). Labour force, total (India). The World Bank . Retrieved from https://data.worldbank.org/indicator/SL.TLF.TOTL.IN?locations=IN World Bank Open Data. (2024). Gross Fixed Capital Formation (Constant 2015 US$) – India. The World Bank . Retrieved from https://data.worldbank.org/indicator/NE.GDI.TOTL.KD?locations=IN World Bank Open Data. (2024). Exports of Goods and Servies (Annual % growth) – India. The World Bank . Retrieved from https://data.worldbank.org/indicator/NE.EXP.GNFS.KD.ZG?locations=IN Additional Declarations The authors declare no competing interests. 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Pangambam","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABBklEQVRIie2PsUrEMBzGrwq95f8APYr4ClcLwaG0D+LSELhbqk9wQ5w6uVfwFW7NHAh0ipc1cg49BKcOvc1JTDyHLm0dBfMbEj74fnz8ZzOH48/CuX295viZ2P+ej5Whp5xHlb+yCv214ofgi1MeU7L5Q/0G8hVvy+d6UYFKt6UwK5vkZnAFdusY9Dtm8o5cd8GeMImNUq9u6ZASFCiETmDGi+ilWu4J4kbxqBhWLtsfRbXLEPIdQeowoQRgFG0UXVyFwHmK9NSKLNDiSYqY6TaOHinJkTYr+cgt81KioK3FBVNF1BxpmiG1PjTdJhlULGfQC/i7mY/ULd5HL2QTZYfD4fiHfAF9HG1t3J6WNgAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0009-0004-5314-8778","institution":"Manipur University","correspondingAuthor":true,"prefix":"","firstName":"Tennyson","middleName":"","lastName":"Pangambam","suffix":""}],"badges":[],"createdAt":"2025-11-20 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16:16:16","extension":"html","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":91076,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8167110/v1/34486b60dbc82fd18869ac4c.html"},{"id":96922160,"identity":"b5a05201-7166-4855-a2b7-e700ed1bea90","added_by":"auto","created_at":"2025-11-27 14:18:19","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":931740,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8167110/v1/9bbbf76b-faeb-4a07-a13b-e46ffa5075ca.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eIncorporating Factor Productivity Gains in the Relationship between Export Growth and GDP Growth: Empirical Evidences from India\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eIn a rapidly globalising world of today, the effects of the \u0026ldquo;growth of international trade on economic growth\u0026rdquo; is a hotly contested area of research in both the theoretical and empirical fronts. This lack of consensus is clearly evidenced by the fact that \u0026ldquo;the neoclassical trade theory\u0026rdquo; and \u0026ldquo;the new growth theory\u0026rdquo; supports the positive impact of \u0026ldquo;international trade on income and economic growth\u0026rdquo; while \u0026ldquo;the neoclassical growth theory\u0026rdquo; does not recognise this impact. Further, the \u0026ldquo;new trade theory\u0026rdquo; remains dubious while \u0026ldquo;mixed empirical evidences\u0026rdquo; generate mixed support for the same (Singh, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). This study takes the growth of exports as a \u0026ldquo;proxy\u0026rdquo; for the growth of trade which is justified in many similar studies (Berg \u0026amp; Schmidt, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e1994\u003c/span\u003e; Krueger, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e1995\u003c/span\u003e; Levine \u0026amp; Renelt, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e1992\u003c/span\u003e; Ram, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e1985\u003c/span\u003e). Since the a priori relationship between \u0026ldquo;exports and output\u0026rdquo; is generally known, this study is, instead, intended to find the empirical evidence for the existence of a relationship between \u0026ldquo;export growth and economic growth\u0026rdquo; for India for the 1992 to 2022 time period.\u003c/p\u003e\u003cp\u003eIn theory, the \u0026ldquo;positive externalities\u0026rdquo; associated with exports would result in economies growing rapidly hand-in-hand with export growth (Dollar, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e1992\u003c/span\u003e). The neoclassical \u0026ldquo;export-led growth hypothesis\u0026rdquo;, which is supported by an extensive survey conducted by Giles and Williams (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2000\u003c/span\u003e), is particularly relevant here. Accordingly, growth of exports lead to economic growth. In this context, it is crucial to examine whether \u0026ldquo;exports growth\u0026rdquo; after the 1991 New Economic Policy of India yielded a \u0026ldquo;statistically significant\u0026rdquo; impact on the economic growth of the Indian economy.\u003c/p\u003e\u003cp\u003eIn one sense, export growth is an \u0026ldquo;endogenous\u0026rdquo; variable in that it is influenced by existing policies and policy changes that is \u0026ldquo;internal\u0026rdquo; to the economy. This is even more applicable in India\u0026rsquo;s case with the dramatic policy shift post-1991. In this context, the linkage between \u0026ldquo;policy and growth\u0026rdquo; is one of the arguments given by the \u0026ldquo;endogenous growth theory\u0026rdquo; (Krueger, 1985). Particularly, as cited in Feenstra, Liang, Madani \u0026amp; Yang (1998), \u0026ldquo;enhancement in productivity\u0026rdquo; brought about by increases in product variety from exports is central in the endogenous growth model given by Romer (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e1990\u003c/span\u003e). As such, the study of the relationship between \u0026ldquo;export growth and GDP growth\u0026rdquo; for India also encapsulates the spirit of the endogenous growth theory by attempting to explain India\u0026rsquo;s GDP growth through the lens of increased \u0026ldquo;total factor productivity\u0026rdquo; brought upon by exports growth.\u003c/p\u003e"},{"header":"Review of Literature","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eExports and Economic Growth\u003c/h2\u003e\u003cp\u003eBalassa (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1978\u003c/span\u003e) examines the \u0026ldquo;effects of exports on economic growth\u0026rdquo; for eleven developing countries that have established an \u0026ldquo;industrial base\u0026rdquo;. He found that export growth \u0026ldquo;favourably effects the rate of economic growth\u0026rdquo; which is over and above the contributions made by the domestic and foreign labour and capital. Cottani et. el. (1990) collected data for 24 less developed countries (LDCs) and conducted a cross-sectional regression analysis. They found that \u0026ldquo;export growth rate\u0026rdquo; and \u0026ldquo;per capita growth rate\u0026rdquo; moved in the same direction. Dollar (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e1992\u003c/span\u003e) examined 95 LDCs to analyse whether outward-oriented developing economies grow more rapidly. He reported in his findings that exports growth due to factors like trade liberalisation, maintenance of a \u0026ldquo;stable real exchange rate\u0026rdquo; and devaluation of the real exchange rate improves economic growth performance in these economies. Gupta (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e1985\u003c/span\u003e) argued that for \u0026ldquo;developing countries\u0026rdquo;, export-oriented policies enabled these countries to attain higher levels of growth of GNP (Gross National Product). Further, his study found that economies with export-expansion policies fared better in terms of their GNP growth as compared to those economies focussing on import substitution.\u003c/p\u003e\u003cp\u003eFeder (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e1983\u003c/span\u003e) examines the sources of growth for a group of semi-industrialised LDCs during the time period, 1964 to 1973. His methodology focussed on finding the difference in marginal factor productivities of various sectors of the economy. From the results, he concluded that marginal factor productivity of the export sector is significantly higher. As such, he found that economic growth in these LDCs is generated not only by the aggregate \u0026ldquo;levels of labour and capital\u0026rdquo; but also significantly influenced by the \u0026ldquo;reallocation\u0026rdquo; of existing resources to the higher productivity export sector from the less efficient non-exports sector. Tyler (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e1981\u003c/span\u003e) employed data for 55 middle income developing countries for the time period 1960 to 1977 to analyse the relationship between \u0026ldquo;export expansion and growth\u0026rdquo; in these developing countries. He used a \u0026ldquo;Cobb-Douglas\u0026rdquo; production function which adds exports as an additional input in determining output. His reasoning for doing so was the existence of \u0026ldquo;scale effects\u0026rdquo; and \u0026ldquo;externalities associated with export production and sales\u0026rdquo;. His results provided additional evidence of a \u0026ldquo;strong association\u0026rdquo; between \u0026ldquo;export performance and GNP growth\u0026rdquo;. Moreover, he also reported that \u0026ldquo;lower rates of economic growth\u0026rdquo; are a direct result of countries neglecting their export sectors through discriminatory economic policies.\u003c/p\u003e\u003cp\u003eMichaely (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e1977\u003c/span\u003e) tried to find an \u0026ldquo;unbiased relationship\u0026rdquo; between \u0026ldquo;exports and economic growth\u0026rdquo; by removing the autocorrelation component since exports themselves are a \u0026ldquo;part of the national product\u0026rdquo;. By analysing data collected from 41 countries for twenty-four years (1950 to 1973), he found a positive association of export expansion with growth in general. Further, he also reported that this positive association is \u0026ldquo;particularly strong\u0026rdquo; for \u0026ldquo;more developed\u0026rdquo; countries as compared to the \u0026ldquo;least developed\u0026rdquo; countries where \u0026ldquo;this positive association\u0026rdquo; does not exist at all. Krueger (1980) argued that the advantages of export promotion is directly linked to factor proportions. In this regard, trade represents a means of shifting the labour demand outwards \u0026ldquo;more rapidly\u0026rdquo; than what the import-substitution strategy permits. Ram (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e1985\u003c/span\u003e) uses a production function that incorporates exports as a \u0026ldquo;production input\u0026rdquo; along with labour and capital. By analysing data for 73 LDCs for two distinctly separate time periods (1960 to 1970 and 1970 to 1977), he concluded that the importance of \u0026ldquo;exports in influencing economic growth\u0026rdquo; had increased during the 1970s. He further found that although \u0026ldquo;the impact of export performance on growth\u0026rdquo; was smaller in the LDCs during the 1960 to 1970 time period, the impact differential \u0026ldquo;almost disappears\u0026rdquo; in the 1970 to 1977 time period, during which the \u0026ldquo;large positive impact of exports on growth\u0026rdquo; was the norm for all the countries studied.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eExport-led Growth Hypothesis\u003c/h3\u003e\n\u003cp\u003eDreger and Herzer (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) examined the \u0026ldquo;export-led growth\u0026rdquo; hypothesis by conducting a panel data analysis consisting of 45 developing countries. One of their main findings was the \u0026ldquo;bidirectional causality\u0026rdquo; between \u0026ldquo;exports and non-export GDP\u0026rdquo; in the short run. They further reported that the \u0026ldquo;impact of exports on non-export GDP\u0026rdquo; in the long run is negative on average but there exist \u0026ldquo;large differences\u0026rdquo; in this impact across countries. Parida and Sahoo (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) also examined the export-led growth hypothesis for four South-Asian economies \u0026ndash; India, Pakistan, Bangladesh and Sri Lanka \u0026ndash; for the time period, 1980 to 2002. Their panel data study finds a \u0026ldquo;long-run equilibrium relationship\u0026rdquo; between exports and GDP for the four countries. Most importantly, they reported in their findings that exports have \u0026ldquo;statistically significant\u0026rdquo; impact on GDP growth. Jun (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) also conducted a panel data analysis incorporating 81 countries and found a \u0026ldquo;two-way causal relationship\u0026rdquo; between \u0026ldquo;exports and output\u0026rdquo;. Further, she found that the exports of high-income countries and high investment countries have larger impacts on output (GDP) than that of low-income countries and low investment countries respectively.\u003c/p\u003e\n\u003ch3\u003eExport Growth and Economic Growth: Causal Relationship\u003c/h3\u003e\n\u003cp\u003eMehrara and Firouzjaee (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) examined the \u0026ldquo;causal relationship\u0026rdquo; between \u0026ldquo;growth of exports and growth of GDP\u0026rdquo; in 73 developing economies. Their methodology involved categorizing these developing economies into two separate groups consisting of oil-dependent economies and non-oil developing economies. They found that the existence of a \u0026ldquo;long run bidirectional causal relationship between export growth and GDP growth\u0026rdquo; for both groups of developing economies. Henriques and Sadorsky (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e1996\u003c/span\u003e) conducted a detailed study on the nature of relationship between \u0026ldquo;export growth and GDP growth\u0026rdquo; for Canada. They report in their findings that there is a \u0026ldquo;one-way causal relationship\u0026rdquo; running from export growth to GDP growth. Divya and Ronit (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) used the \u0026ldquo;Vector Autoregressive (VAR) model\u0026rdquo; and the \u0026ldquo;Granger Causality test\u0026rdquo; to examine whether GDP growth causes export growth in India. Their results support the theory of growth led exports in India.\u003c/p\u003e\n\u003ch3\u003eExports and Factor Productivity Growth\u003c/h3\u003e\n\u003cp\u003eNumerous papers suggest that the \u0026ldquo;positive impact\u0026rdquo; of international trade shows up in the form of factor productivity gains. Krueger (1980) found in his paper that free trade brings \u0026ldquo;productivity gains\u0026rdquo; as a direct result of minimum efficient scale of plant, increasing returns to scale, \u0026ldquo;indivisibilities\u0026rdquo; and the effect of size of market on competition. He also pointed out the wasteful processes involved due to import substitution policies which included heavy government inclusion in economic decisions, costly paperwork, \u0026ldquo;unproductive rent seeking\u0026rdquo; and bureaucratic bottlenecks among others. His work implied that trade restriction hampers total factor productivity. Dollar (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e1992\u003c/span\u003e), in his paper focussing on LDCs, reported on the positive externalities of the export sector. He further reported in his findings that a country\u0026rsquo;s export earnings enable it to \u0026ldquo;use external (foreign) capital without running into difficulties servicing foreign debt\u0026rdquo;. Cottani et. al. (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e1990\u003c/span\u003e) found that growth of exports stimulates the growth of the economy as productivity improvements tends to be, more or less, concentrated in the export and import competing industries. Moreover, Ram (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e1985\u003c/span\u003e) explicitly includes exports as an additional input in his production function. These works suggest the impact and effects of export growth to show up in the form of increases in productivity of the factors of production.\u003c/p\u003e\n\u003ch3\u003eRationale of the Study\u003c/h3\u003e\n\u003cp\u003eIn an ever-increasingly globalised world, economists are getting increasingly interested in the extent to which exports and imports policies affect economic growth. In this context, Singh (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) surveyed the literature on the relationship between \u0026ldquo;international trade and economic growth\u0026rdquo; and found that most studies on this theme supported the gains from international trade on economic growth. With India liberalising its economy in 1991, it is pertinent to examine whether the growth of exports (proxy for trade) actually has an influence in shaping the growth of the Indian economy. In doing so, it is relevant to bring in a crucial aspect of trade that is often overlooked in such studies. \u0026ldquo;Several studies\u0026rdquo; have found that trade expands productivity and growth by providing a wider range of \u0026ldquo;intermediate inputs\u0026rdquo; (Grossman \u0026amp; Helpman, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e1991\u003c/span\u003e; Rivera-Batis \u0026amp; Romer, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e1991\u003c/span\u003e) and also by facilitating an \u0026ldquo;international\u0026rdquo; diffusion of technology (Benhabib \u0026amp; Spiegel, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e1994\u003c/span\u003e; Coe \u0026amp; Helpman, 1995; Parente \u0026amp; Prescott, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e1994\u003c/span\u003e). Therefore, examining the impact of \u0026ldquo;export growth on GDP growth\u0026rdquo; for India not only achieves the objective of determining the relationship between \u0026ldquo;export growth and economic growth\u0026rdquo; but also explains the more nuanced impact of factor productivity gains on economic growth.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eResearch Objectives\u003c/h2\u003e\u003cp\u003eThe research objectives of this study are as under \u0026ndash;\u003c/p\u003e\u003cp\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eExamining whether \u0026ldquo;export growth has a significant impact on GDP growth\u0026rdquo; for India for the 1992 to 2022 time period.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eIncorporating the \u0026ldquo;endogenous growth theory\u0026rdquo; to determine the \u0026ldquo;determinants of India\u0026rsquo;s GDP growth\u0026rdquo; for the 1992 to 2022 time period.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Methodology","content":"\u003cp\u003eThis section discusses the methodology used in this study in depth and the rationale behind using the specific tools of analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Collection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAnnual time-series data on \u0026ldquo;growth rate of real GDP, labour force, real gross fixed capital formation and growth rate of real exports\u0026rdquo; for India is collected from the \u0026ldquo;World Bank Open Data\u0026rdquo; (World Bank, 2024). This data is collected for the time period of 1992 to 2022 (30 observations). Labour force annual growth rate and the \u0026ldquo;ratio\u0026rdquo; between real fixed capital formation and real GDP is calculated from the data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe Relationship between Export Growth and GDP Growth: The Multiple Regression Model adjusted for Stationarity\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSeveral of the studies mentioned in the previous section including Balassa (1978), Cottani et. al (1990), Dollar (1992), Feder (1982), Krueger (1980), Michaely (1977), Ram (1985) and Tyler (1981) specify a linear relationship given by the following equation-\u003c/p\u003e\n\u003cp\u003eGGDP = a\u003csub\u003e0\u0026nbsp;\u003c/sub\u003e+ a\u003csub\u003e1\u0026nbsp;\u003c/sub\u003eGLF + a\u003csub\u003e2\u0026nbsp;\u003c/sub\u003eGCAP + a\u003csub\u003e3\u0026nbsp;\u003c/sub\u003eTRADE \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;(1)\u003c/p\u003e\n\u003cp\u003eHere, GGDP \u0026ndash; growth rate of real domestic product,\u003c/p\u003e\n\u003cp\u003eGLF \u0026ndash; growth rate of labour force,\u003c/p\u003e\n\u003cp\u003eGCAP \u0026ndash; growth rate of capital stock,\u003c/p\u003e\n\u003cp\u003eTRADE \u0026ndash; measure of international trade.\u003c/p\u003e\n\u003cp\u003eEquation (1) resembles the growth equation which is given by \u0026ndash;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eG\u003csub\u003eY\u003c/sub\u003e = A + b G\u003csub\u003eK\u003c/sub\u003e + (1 - b) G\u003csub\u003eL\u003c/sub\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; (2)\u003c/p\u003e\n\u003cp\u003eHere, G\u003csub\u003eY\u003c/sub\u003e \u0026ndash; growth rate of total output,\u003c/p\u003e\n\u003cp\u003eG\u003csub\u003eK\u003c/sub\u003e \u0026ndash; growth rate of capital,\u003c/p\u003e\n\u003cp\u003eG\u003csub\u003eL\u003c/sub\u003e \u0026ndash; growth rate of labour,\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;A \u0026ndash; growth rate of total factor productivity.\u003c/p\u003e\n\u003cp\u003eIn this context, Berg and Schmidt (1994) argued that lack of capital stock data can be overcome by using the \u0026ldquo;ratio\u0026rdquo; of investment to gross domestic product or I / Y in place of GCAP. In this case, the coefficient a\u003csub\u003e2\u003c/sub\u003e should be interpreted as the marginal product of capital. They further argued that the influence of international trade in the economy (GTRADE) can be represented by the \u0026ldquo;growth rate of real exports\u0026rdquo; or GEX. Further, Levine and Renelt (1992) also found that any growth rate regression that uses exports share in GDP as an explanatory variable could yield almost \u0026ldquo;identical results\u0026rdquo; to that of using imports share in GDP or trade (imports plus exports) share as the explanatory variable. Incorporating these definitions in equation (1) gives \u0026ndash;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eGGDP = a\u003csub\u003e0\u0026nbsp;\u003c/sub\u003e+ a\u003csub\u003e1\u0026nbsp;\u003c/sub\u003eGLF + a\u003csub\u003e2\u0026nbsp;\u003c/sub\u003e(I / Y) + a\u003csub\u003e3\u0026nbsp;\u003c/sub\u003eGEX \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;(3)\u003c/p\u003e\n\u003cp\u003eWith regards to equation (3), Berg and Schmidt (1994) stated that the \u0026ldquo;growth rate\u0026rdquo; of exports (GEX) represents a portion of the leading entry of equation (2), the total factor productivity growth. Equation (3) is adopted in this study as several studies, including that of Cottani et. al. (1990), Dollar (1992), Krueger (1980) and Ram (1985), have found that the \u0026ldquo;positive impact\u0026rdquo; of international trade results in the form of productivity gains.\u003c/p\u003e\n\u003cp\u003eEquation (3), by including \u0026ldquo;growth rate of exports\u0026rdquo; as one of its dependent variables, assumes productivity growth as a result of specific policy choices that \u0026ldquo;expands\u0026rdquo; trade. In other words, the incorporation of GEX in equation (3) suggests total factor productivity growth to be a result of internal or \u0026ldquo;endogenous\u0026rdquo; factors. This is in accordance with the spirit of the \u0026ldquo;endogenous growth theory\u0026rdquo; as envisaged by Romer (1994). Further, Balassa (1978), Berg and Schmidt (1994), Feder (1982), Ram (1982) and Tyler (1985) have acknowledged the existence of a \u0026ldquo;predetermined positive relationship\u0026rdquo; between exports and GDP as exports is a component of GDP. However, they justified this by pointing out that equation (3) contains the \u0026ldquo;growth rates\u0026rdquo; of exports and GDP in a production relation and not the \u0026ldquo;levels\u0026rdquo; of export and GDP. As Berg and Schmidt (1994) stated, there is \u0026ldquo;no a priori reason\u0026rdquo; why the coefficient a\u003csub\u003e3\u003c/sub\u003e is equation (3) must be (strictly) positive.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eThe Multiple Regression Model adjusted for Stationarity\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFinally, given the results of the Augmented Dickey-Fuller test for stationarity test, equation (3) is specified as a \u0026ldquo;multiple regression model\u0026rdquo; with each of the variables adjusted to be stationary. The multiple regression model is estimated using \u0026ldquo;Ordinary Least Squares\u0026rdquo; (OLS) method.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTest for Stationarity\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA stationary test is necessary for any analysis involving time-series variables. The Augmented Dickey-Fuller (ADF) Test is used to test the \u0026ldquo;stationarity\u0026rdquo; of the variables defined in equation (3) \u0026ndash; GGDP, GLF, I / Y and GEX. If the ADF test statistic is significant at \u0026ldquo;0.05 significance level\u0026rdquo;, the conclusion is that the variable is stationary (Gujarati \u0026amp; Porter, 2009). Further, the Swartz Information Criterion (SIC) is used to select the most appropriate \u0026ldquo;number of lags\u0026rdquo; for the ADF test.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDiagnostic Tests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eTest for Autocorrelation\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn order to check whether the multiple regression model satisfy the OLS assumption of no autocorrelation, the \u0026ldquo;Breusch-Godfrey Serial Correlation LM test\u0026rdquo; is used. If the \u0026ldquo;Chi-square statistic is not significant at 0.05% level\u0026rdquo;, then the conclusion is that the assumption of \u0026ldquo;no autocorrelation\u0026rdquo; is satisfied (Gujarati \u0026amp; Porter, 2009).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eTest for Heteroskedasticity\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe White Heteroskedasticity test is used to assess whether the OLS assumption of homoskedasticity is satisfied. If the Chi-square statistic is \u0026ldquo;not significant at 0.05% level\u0026rdquo;, then the conclusion is that the assumption of \u0026ldquo;homoskedasticity\u0026rdquo; is satisfied (Gujarati \u0026amp; Porter, 2009).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eTest for Multicollinearity\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn order to confirm that the regressors do not have a perfectly linear relationship or multicollinearity, the \u0026ldquo;Variance Inflation Factor\u0026rdquo; (VIF) is used. If the VIF value is \u0026ldquo;less than 10\u0026rdquo;, then the conclusion of \u0026ldquo;no multicollinearity\u0026rdquo; is satisfied (Gujarati \u0026amp; Porter, 2009).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eTest for Spurious Regression\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn order to ensure that the regression is not spurious in nature, the residuals of the multiple regression model is tested for stationarity using the ADF test. If the \u0026ldquo;ADF test statistic is significant at 0.05 level\u0026rdquo;, then the conclusion is that the \u0026ldquo;regression is not spurious\u0026rdquo; (Gujarati \u0026amp; Porter, 2009).\u003c/p\u003e"},{"header":"Analysis and Results","content":"\u003cp\u003eThis section includes the discussion of the results and interpretations of the analytical tools and models described in the previous section.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAugmented Dickey-Fuller (ADF) Test for Stationarity\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe following table summarises the results of the ADF test for stationarity for the dependent and independent variables.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eTable 1:\u0026nbsp;\u003c/em\u003e\u003c/strong\u003eResults of the Augmented Dickey-Fuller Test for Stationary for the dependent and independent variables.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNull Hypothesis \u0026ndash; \u0026ldquo;\u003c/strong\u003eThe variable is not stationary\u0026rdquo;.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eADF test-statistic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGGDP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35px;\"\u003e\n \u003cp\u003e-5.14279*** (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e0.0013\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGLF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35px;\"\u003e\n \u003cp\u003e-5.199468***(1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e0.0012\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eI / Y\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35px;\"\u003e\n \u003cp\u003e-8.143186***(1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGEX\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35px;\"\u003e\n \u003cp\u003e-4.103737**(0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e0.0156\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote: (i) ***, **, and * indicates significance at 1%, 5%, and 10% levels. (ii) Optimal lags for Augmented Dickey-Fuller Test are determined based on SIC. (iii) The values inside brackets represent the levels of integration.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eSource:\u003c/em\u003e\u003c/strong\u003e Author\u0026rsquo;s estimation on EViews.\u003c/p\u003e\n\u003cp\u003eThe results of Table 1 show that the independent variables GGDP and GEX are \u0026ldquo;stationary at levels\u0026rdquo;, I(0), while GLF and I / Y are \u0026ldquo;stationary at the first difference\u0026rdquo;, I(1). As such, while estimating the model using the multiple regression model, these variables are \u0026ldquo;transformed\u0026rdquo; according to their respective levels of integration.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe Multiple Regression Model Adjusted for Stationarity\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEquation (3) as defined earlier (see Methodology section) now is specified in accordance with the results of the ADF test. This gives \u0026ndash;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eGGDP\u003csup\u003e[0]\u003c/sup\u003e = a\u003csub\u003e0\u0026nbsp;\u003c/sub\u003e+ a\u003csub\u003e1\u0026nbsp;\u003c/sub\u003eGLF\u003csup\u003e[1]\u003c/sup\u003e + a\u003csub\u003e2\u0026nbsp;\u003c/sub\u003e(I / Y)\u003csup\u003e[1]\u003c/sup\u003e + a\u003csub\u003e3\u0026nbsp;\u003c/sub\u003eGEX\u003csup\u003e[0]\u003c/sup\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;(4)\u003c/p\u003e\n\u003cp\u003eEquation (4) is the multiple regression equation that is adjusted for stationary of the respective variables. The superscripts inside the square brackets following each variable represent the level of integration to which it is transformed. That is, GGDP\u003csup\u003e[0]\u003c/sup\u003e and GEX\u003csup\u003e[0]\u003c/sup\u003e are kept as it is since both variables are stationary at levels and GLF\u003csup\u003e[1]\u003c/sup\u003e and (I / Y)\u003csup\u003e[1]\u003c/sup\u003e are \u0026ldquo;transformed\u0026rdquo; into their \u0026ldquo;first difference forms\u0026rdquo; so as to adjust them for stationarity.\u003c/p\u003e\n\u003cp\u003eThe following table shows the OLS estimation results of the multiple regression model as given by equation (4).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eTable 2:\u0026nbsp;\u003c/em\u003e\u003c/strong\u003eResults of the Multiple Regression Model adjusted for stationarity.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 616px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDependent Variable - GGDP\u003csup\u003e[0]\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCoefficient\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e\u003cstrong\u003et-statistic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eConstant term\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e4.317240**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e2.630107\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e0.0142\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGLF\u003csup\u003e[1]\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e0.500817\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e0.314703\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e0.7555\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eI / Y\u003csup\u003e[1]\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e77.57081*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e1.948242\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e0.0623\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGEX\u003csup\u003e[0]\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e0.348172***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e3.448363\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e0.0019\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote: (i) ***, **, and * indicates significance at 1%, 5%, and 10% levels (ii) The values inside the square brackets represent the levels of integration.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eSource:\u003c/em\u003e\u003c/strong\u003e Author\u0026rsquo;s estimation on EViews\u003c/p\u003e\n\u003cp\u003eIn Table 2, the coefficient of GLF is insignificant implying that the \u0026ldquo;growth rate of labour force\u0026rdquo; has had a statistically insignificant impact on the \u0026ldquo;growth of GDP\u0026rdquo; for India. The coefficient of I / Y is \u0026ldquo;significant at 10% level\u0026rdquo; but not at 1% and 5% levels. Following standard procedure and taking 5% level as the threshold level, the \u0026ldquo;growth of capital stock\u0026rdquo; also has a statistically insignificant impact on the \u0026ldquo;growth of GDP\u0026rdquo; for India. Finally, the coefficient of GEX is \u0026ldquo;significant at 1% level\u0026rdquo; and its value is positive. Thus, growth of exports has a statistically positive impact on the growth of GDP for India.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDiagnostic Tests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eBreusch-Godfrey Serial Correlation LM Test for Autocorrelation \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe following table shows the result of the Breusch-Godfrey Serial Correlation LM Test for Autocorrelation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eTable 3:\u0026nbsp;\u003c/em\u003e\u003c/strong\u003eResult of the Breusch-Godfrey Serial Correlation Test for Autocorrelation.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 616px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNull Hypothesis \u0026ndash;\u0026nbsp;\u003c/strong\u003eNo Autocorrelation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 308px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eChi-Square Statistic P-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 308px;\"\u003e\n \u003cp\u003e0.2406\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eSource:\u003c/em\u003e\u003c/strong\u003e Author\u0026rsquo;s estimation on EViews.\u003c/p\u003e\n\u003cp\u003eThe p-value for the Chi-Square statistic for the Breusch-Godfrey Serial Correlation LM test is statistically insignificant in table 3. As such, the conclusion of this test is the \u0026ldquo;acceptance of the null hypothesis\u0026rdquo; of no autocorrelation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eWhite Test for Heteroskedasticity\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe following table shows the result of the White Test for Heteroskedasticity.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eTable 4:\u0026nbsp;\u003c/em\u003e\u003c/strong\u003eResult of the White Test for Heteroskedasticity.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 616px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNull Hypothesis \u0026ndash;\u0026nbsp;\u003c/strong\u003eHomoskedasticity\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 308px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eChi-Square Statistic P-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 308px;\"\u003e\n \u003cp\u003e0.9104\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eSource:\u003c/em\u003e\u003c/strong\u003e Author\u0026rsquo;s estimation on EViews.\u003c/p\u003e\n\u003cp\u003eThe p-value for the Chi-Square statistic for the White test is statistically insignificant in table 4. As such, the conclusion of this test is the \u0026ldquo;acceptance of the null hypothesis\u0026rdquo; of homoskedasticity in the model.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eTest for Multicollinearity: The Variance Inflation Factor (VIF)\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe following table shows the result of the test for multicollinearity using the VIF.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eTable 5:\u0026nbsp;\u003c/em\u003e\u003c/strong\u003eResult of the test for multicollinearity using VIF.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 308px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 308px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCentred VIF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 308px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGLF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 308px;\"\u003e\n \u003cp\u003e1.222117\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 308px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eI / Y\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 308px;\"\u003e\n \u003cp\u003e1.238386\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 308px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGEX\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 308px;\"\u003e\n \u003cp\u003e1.466616\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eSource:\u003c/em\u003e\u003c/strong\u003e Author\u0026rsquo;s estimation on EViews.\u003c/p\u003e\n\u003cp\u003eThe values of the Centred VIF of the individual independent variables are each less than 10. As per the rule of thumb given in Gujarati \u0026amp; Porter (2009), this result confirms the inexistence of multicollinearity among the explanatory variables of the model.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eTest for Spurious Regression\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe following table shows the result of the test for spurious regression by using the ADF test for the residuals of the model.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eTable 6:\u0026nbsp;\u003c/em\u003e\u003c/strong\u003eResult of the test for spurious regression using the ADF test.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNull Hypothesis \u0026ndash; \u0026ldquo;\u003c/strong\u003eThe variable is not stationary\u0026rdquo;.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eADF test-statistic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eResiduals\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35px;\"\u003e\n \u003cp\u003e-6.534448*** (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote: (i) ***, **, and * indicates significance at 1%, 5%, and 10% levels. (ii) Optimal lags for Augmented Dickey-Fuller Test are determined based on SIC. (iii) The value inside the bracket represents the level of integration.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eSource:\u003c/em\u003e\u003c/strong\u003e Author\u0026rsquo;s estimation on EViews.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe \u0026ldquo;ADF test statistic\u0026rdquo; is statistically significant at 1% level. This implies that the residuals of the model are stationary at levels. As such, this test confirms that the multiple regression model is not spurious in nature.\u003c/p\u003e"},{"header":"Findings and Recommendations","content":"\u003cp\u003eThe \u0026ldquo;major finding\u0026rdquo; of this paper is that \u0026ldquo;export growth has a positively significant impact on GDP growth\u0026rdquo; for India for the 1992 to 2022 time-period. The estimators of the multiple regression model are also the Best Linear Unbiased Estimators (BLUE) as given by the Gauss-Markov theorem (Gujarati \u0026amp; Porter, 2009). This is confirmed by the results of the diagnostic tests for autocorrelation, heteroskedasticity, multicollinearity and spurious regression. Further, the model is also adjusted to make each variable stationary. The coefficient of growth of exports being significant also implies that, in terms of the endogenous growth theory, \u0026ldquo;factor productivity gains resulting from trade\u0026rdquo; has had a \u0026ldquo;positive impact\u0026rdquo; on India\u0026rsquo;s GDP growth for the time-period taken. In this context, a deeper study on the \u0026ldquo;factor productivity gains\u0026rdquo; in the economy resulting from exports growth is encouraged.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWith regards to the remaining independent variables, \u0026ldquo;growth of labour force\u0026rdquo; has not had any significant impact on India\u0026rsquo;s GDP growth. This implies the existence of the phenomenon of jobless growth wherein the economy has grown without growth of the labour force and employment. Indeed, India has transformed into a high-productivity regime without a significant expansion of labour-intensive production (Tejani, 2016). The growth of capital stock, however, has a \u0026ldquo;significant impact\u0026rdquo; on GDP growth at the \u0026ldquo;10% level\u0026rdquo; but not at the 1% and 5% level. In this regard, the \u0026ldquo;growth of capital stock\u0026rdquo; variable in this study does not take account of human capital. This is likely to be one of the main reasons for the insignificant coefficient of I / Y. A study by Haldar and Mallik (2010) found a similar result for India. They found that physical capital investment has no long run or short run effect on India\u0026rsquo;s per capita GNP but human capital investment had a long run significant impact on per capita GNP of India.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eDespite the existence of a \u0026ldquo;rich literature\u0026rdquo; on the \u0026ldquo;relationship between exports growth and GDP growth\u0026rdquo;, there has been relatively few studies aimed at recognising exports as a determinant of factor productivity gains. This study approaches the empirical analysis of the relationship between exports growth and GDP growth for India from the lens of the endogenous growth theory. Accordingly, growth of exports is viewed as a direct impact of \u0026ldquo;internal\u0026rdquo; policy changes, which in India\u0026rsquo;s case was the 1991 New Economic Policy. The results of this paper present additional empirical evidences on the positive impact of export growth on economic growth and also provides a thorough examination of how exports growth can be treated as an endogenous variable which influences economic growth by increasing factor productivity.\u003c/p\u003e\u003cdiv id=\"Sec29\" class=\"Section2\"\u003e\u003ch2\u003eScope for Future Research\u003c/h2\u003e\u003cp\u003eResearch works solely focused on the nature of factor productivity gains realised from international trade would shed more light on the determinants of factor productivity gains. The incorporation of \u0026ldquo;human capital\u0026rdquo; in the growth equation and explaining its \u0026ldquo;effects on economic growth\u0026rdquo;, especially for developing economies, should also be explored by future researchers.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eNote\u003c/strong\u003e\u003cp\u003eThe MS Excel file containing the data for the analysis can be accessed here.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBalassa, B. (1978). Exports and economic growth: Further evidence. \u003cem\u003eJournal of Development Economics, 5\u003c/em\u003e(2), 181-189. DOI: https://doi.org/10.1016/0304-3878(78)90006-8\u003c/li\u003e\n\u003cli\u003eBenhabib, J. \u0026amp; Spiegel, M.M. (1994). \u003cem\u003eJournal of Monetary Economics, 34\u003c/em\u003e(2), 143-173. DOI: http://dx.doi.org/10.1016/0304-3932(94)90047-7\u003c/li\u003e\n\u003cli\u003eBerg, H. \u0026amp; Schmidt, J.R. (1994). Foreign trade and economic growth: time series evidence from Latin America. \u003cem\u003eThe Journal of International Trade and Economic Development, 3\u003c/em\u003e(3), 249-268. DOI: https://doi.org/10.1080/09638199400000016\u003c/li\u003e\n\u003cli\u003eCoe, D.T. \u0026amp; Helpman, E. International R\u0026amp;D spillovers. \u003cem\u003eEuropean Economic Review, 39\u003c/em\u003e(5), 859-887. DOI: https://doi.org/10.1016/0014-2921(94)00100-E\u003c/li\u003e\n\u003cli\u003eCottani, J. A., Cavallo, D.F. \u0026amp; Khan, M.S. (1990). Real Exchange Rate Behavior and Economic Performance in LDCs. \u003cem\u003eEconomic Development and Cultural Change, 39\u003c/em\u003e(1), 69-76. DOI: http://dx.doi.org/10.1086/451853\u003c/li\u003e\n\u003cli\u003eDollar, D. (1992). Outward-Oriented Developing Economies Really Do Grow More Rapidly: Evidence from 95 LDCs, 1976-1985. \u003cem\u003eEconomic Development and Cultural Change, 40\u003c/em\u003e(3), 523-544. 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DOI: https://doi.org/10.1016/0304-3878(81)90007-9\u003c/li\u003e\n\u003cli\u003eWorld Bank Open Data. (2024). GDP growth (annual %) \u0026ndash; India. \u003cem\u003eThe World Bank\u003c/em\u003e. Retrieved from https://data.worldbank.org/indicator/NY.GDP.MKTP.KD.ZG?locations=TW-IN\u003c/li\u003e\n\u003cli\u003eWorld Bank Open Data. (2024). Labour force, total (India). \u003cem\u003eThe World Bank\u003c/em\u003e. Retrieved from https://data.worldbank.org/indicator/SL.TLF.TOTL.IN?locations=IN\u003c/li\u003e\n\u003cli\u003eWorld Bank Open Data. (2024). Gross Fixed Capital Formation (Constant 2015 US$) \u0026ndash; India. \u003cem\u003eThe World Bank\u003c/em\u003e. Retrieved from https://data.worldbank.org/indicator/NE.GDI.TOTL.KD?locations=IN\u003c/li\u003e\n\u003cli\u003eWorld Bank Open Data. (2024). Exports of Goods and Servies (Annual % growth) \u0026ndash; India. \u003cem\u003eThe World Bank\u003c/em\u003e. Retrieved from https://data.worldbank.org/indicator/NE.EXP.GNFS.KD.ZG?locations=IN\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"export growth, GDP growth, endogenous growth theory, multiple regression model, jobless growth","lastPublishedDoi":"10.21203/rs.3.rs-8167110/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8167110/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eTaking cognizance of a rapidly globalising world, this paper attempts to determine the relationship between the “growth rate of exports” and the “growth rate of Gross Domestic Product (GDP)” by taking empirical evidences from India. An annual time-series data of 30 years (1992 to 2022) is taken to conduct an in-depth analysis of how export growth has impacted GDP growth after India’s New Economic Policy of 1991. The methodology of this paper leans towards a model that encapsulates the endogenous growth theory by incorporating the “growth rate of labour force” and the “growth rate of capital stock” as the independent variables in addition to the growth rate of exports. Accordingly, a multiple regression model, adjusted to make its time-series components stationary, is used. Rigorous diagnostic tests to rule out the presence of spurious regression, autocorrelation, heteroskedasticity and multicollinearity are then conducted to ensure that the estimates of the model are best linear unbiased estimators (BLUE). The resulting analysis confirms that export growth has a “positive significant impact” on GDP growth for India. This paper also finds that capital stock growth positively impacts GDP growth, but only at the 10% significance level. This paper also explains the trend of jobless growth in India during the time period, 1992 to 2022.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eJEL:\u003c/strong\u003eC32, F14, F43, O40, O47\u003c/p\u003e","manuscriptTitle":"Incorporating Factor Productivity Gains in the Relationship between Export Growth and GDP Growth: Empirical Evidences from India","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-25 16:16:12","doi":"10.21203/rs.3.rs-8167110/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"bb599c36-66b7-40b4-9dc2-0860c45e82c6","owner":[],"postedDate":"November 25th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-11-25T16:16:12+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-25 16:16:12","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8167110","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8167110","identity":"rs-8167110","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}