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TRADE OPENNESS, FACTOR PRODUCTIVITY, AND ECONOMIC GROWTH: RECENT EVIDENCE FROM OECD COUNTRIES (2000-2015) Sal AMIRKHALKHALI*
Atul DAR Abstract. This paper attempts to contribute to existing empirical studies on the implications of the degree of trade openness for total and individual factor productivity growth in 27 OECD countries over the 2000-2015 period. The period of our analysis covers the recent financial crisis, which was followed by the deepest post-war recession. We divide our sample into two sub-periods of 2000-2007 and 2008-2015, in order to shed some light on the impact of the recent financial/economic crisis. We also classify these 27 economies into three groups according to their trade openness, measured as the ratio of trade (total exports plus imports) to GDP. The growth model employed is based on the concept of an aggregate production function in which the rate of economic growth is a function of capital and labor accumulation and total factor productivity. We explicitly assume that total factor productivity depends, in turn, upon the rate of export expansion and public consumption. We then estimate the model using the random varying coefficients approach. Our group-wise empirical results support the view that higher degree of trade openness would result in higher export expansion and could contribute positively to total factor productivity and economic growth. Our period-wise results indicate that the positive impact of export expansion on economic growth became much stronger over the 2008-2015 sub-period.
JEL: F13, F43, O47.
Keywords: Trade openness; Factor Productivity; Economic Growth 1. Introduction
Recent discussion surrounding international trade policy show just how difficult it is proving for countries to open up their economies. Rich countries urge poorer countries to follow more open trade policies, but continue to protect their own markets on a selective basis. Within the developed world itself, the rise of economic nationalism is strengthening protectionist sentiment there. Witness, for example, the resurgence of the softwood lumber US-Canada dispute, with the US recently imposing countervailing duties on imports of steel and aluminum, and the punitive duty on Bombardier exports of commercial aircraft to the US. This has taken place against a backdrop of broader developments such as the unilateral US withdrawal from Trans Pacific Participation (TPP), and the contentious negotiations following the US demand for a complete overhaul of the North American Free Trade (NAFTA), under the new “America First”
approach to trade issues. These developments make it likely that other countries would follow in kind, thereby posing a threat to world trade, and most certainly leading to a fragmentation of trade flows. They also indicate that how trade orientation impacts on growth remains a highly relevant one. This is especially true since it appears that the industrial world has entered a new environment in which prolonged periods of slow growth appears to be the new norm. Is it possible, that the impact of trade orientation on growth is perhaps no longer the same in the post 2007 period compared to what it was found to be in the pre-2007 period?
* Sal AmirKhalkhali* and Atul Dar, Department of Economics, Saint Mary's University, Halifax, Canada
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There is a considerable empirical literature on the relation between trade openness and economic growth with mixed results. See, for instance, Balassa (1985), Ram (1986), Evans (1989), Alam (1991), Krugman and Smith (1994), AmirKhalkhali and Dar (1995), Dar and AmirKhalkhali (2003), Dollar and Kraay (2004), Freund and Bolaky (2008), Chang, Kaltani, and Loayza (2009), Vlastou (2010), Musila and Yiheyis (2015), Polat et.al (2015), Ulaşan (2015), and Zanohogo(2016). However, in many studies, export expansion is argued to raise the rate of economic growth via its impact on total factor productivity. According to the World Bank, the importance of this effect for any country is predicated on the economic policy framework, with the beneficial effect of exports being greater for relatively more outward-oriented economies.
However, as some studies show, the impact of export growth on economic growth is weakest for the most strongly inward-oriented countries, but that the differences between moderately inward-oriented and outward-oriented countries and strongly outward countries appear to be small. The World Bank classification of countries is based on an assessment of the economic policy framework within each country. A major difficulty with this type of classification is that shifts in economic policies and institutions cause problems if one were to examine countries over long periods of time. Secondly, trade is not entirely a matter of policy, but also of size and geography [see, for instance, Frankel and Romer (1996)]. That is, some countries trade more not necessarily because of an outward policy orientation, but because of their geographical location.
The purpose of this paper is to contribute to existing empirical studies by considering an alternative approach to classifying countries according to openness, and to employ a more general empirical methodology to examine the implications of openness for the impact of export expansion (amongst other things) on economic growth for a group of 27 OECD countries. To this end, we utilize long term data on exports and imports relative to GDP to rank countries in terms of openness. If export expansion fosters economic growth via its favourable impact on productivity, it is likely that trade openness would be an important determinant of the size of that impact. A priori one would expect the beneficial effects on factor productivity to be strongest for countries that are most dependent on trade. Our study combines both time series and cross- sectional data, and applies the more general random coefficients approach. This approach is able to accommodate both endogeneity as well as non-measurable differences between countries/groups to assess the role of trade openness in determining the impact of export expansion on economic growth.
The rest of the paper is organized as follows. Section II discusses the data, the model, and the estimation strategy. Section III presents and analyses the results. Section IV concludes with a summary of the major findings and their implications.
2. The Data, The Model and The Estimation Strategy
The sample used in this study consists of data for 27 countries: Australia, Austria, Belgium, Canada, Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Iceland, Ireland, Israel, Italy, Japan, Korea, Luxembourg, Netherlands, New Zealand, Norway, Portugal, Spain, Sweden, Switzerland, the United Kingdom (UK), and the United States (USA), covering the 2000-2015 period. The data were obtained from various issues of Economic Outlook published by the Organization for Economic Cooperation and Development (OECD), World Development Indicators,
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and International Financial Statistics published by International Monetary Fund (IMF).
Table 1 presents country-specific growth rates of real GDP and trade openness, measured as the ratio of trade (total exports plus imports) to GDP over in 2000, 2009, 2015 as well as over the 2000-2015 period. The selected annual data would provide some country-wise visibility for data on the two key variables and in particular the 2009 data on economic growth show how serious was the recent financial/economic.
Table 1. Country-specific Openness and Growth Rates, Selected Years 2000-2015
Countries Openness Growth Rates
2000 2009 2015 2000-2015 2000 2009 2015 2000-2015 Australia 41.0 45.9 41.5 41.1 0.2 1.8 2.5 2.6 Austria 85.4 87.1 102.1 96.0 3.0 -3.6 0.8 1.5 Belgium 141.1 136.4 160.2 149.4 1.7 -2.3 1.4 1.4 Canada 82.9 58.3 65.5 67.7 2.7 -2.9 1.2 2.0 Czech 98.2 113.5 156.1 125.0 -1.5 -4.7 4.3 2.0 Denmark 83.0 89.8 104.1 93.9 3.6 -5.1 1.2 1.0 Estonia 126.5 116.6 153.2 141.3 0.0 -14.3 1.2 2.8 Finland 75.0 70.5 73.5 76.0 2.1 -8.3 0.5 1.1 France 55.9 50.5 61.8 55.3 2.3 -2.9 1.2 1.2 Germany 61.4 70.7 85.8 74.7 0.6 -5.6 1.4 1.0 Greece 58.4 47.7 63.4 56.2 -1.4 -4.3 -0.3 -0.5 Iceland 71.9 90.0 99.0 85.9 1.7 -4.7 4.0 2.6 Ireland 175.1 173.0 216.2 174.0 10.7 -5.7 7.8 3.9 Israel 71.2 64.0 59.5 71.0 2.2 1.2 2.5 3.1 Italy 50.5 45.6 56.9 52.1 2.5 -5.5 0.6 1.9 Japan 19.8 24.5 35.6 28.2 -2.2 -5.5 0.6 0.4 Korea 67.9 90.4 83.7 83.6 6.4 0.7 2.6 4.2 Luxembourg 272.0 296.0 410.2 316.2 5.7 -5.4 4.9 3.1 Netherlands 126.5 119.0 156.3 133.2 2.7 -3.8 2.0 1.2 New Zealand 68.5 55.2 54.4 59.5 0.1 0.3 3.4 2.3 Norway 74.6 67.0 69.8 70.1 1.4 -1.6 1.6 1.5 Portugal 67.4 61.1 80.2 69.1 4.6 -3.0 1.5 0.6 Spain 60.2 46.5 63.6 56.8 3.0 -3.6 3.2 1.5 Sweden 82.3 83.1 86.2 84.7 1.4 -5.1 3.8 1.9 Switzerland 98.1 106.9 112.8 108.7 1.4 -2.1 0.9 1.7
UK 51.5 54.4 56.5 55.4 0.8 -4.2 2.3 1.5
USA 25.0 24.8 27.9 26.9 3.1 -2.8 2.4 1.9
Average 84.9 84.6 101.2 90.8 2.1 -3.8 2.2 1.8
Although there are differences across countries in the extent to which growth rates decelerated, the significant slowing of growth was experienced by virtually all countries. For the 2000-2015 period, average annual growth rates varied between -0.5 and 4.2% with an overall average of 1.8%, and the degrees of trade openness show considerable variability between 26.9% and 316.2% with an overall average of about 91%. The inter-temporal and inter-country pattern of growth rates show considerable variability. We classified these countries into three groups based on their degree of trade openness over the 2000-2015 period. Group I countries (USA, Japan, Australia, Italy, France, UK, Greece, Spain, New Zealand, Canada, Portugal, Norway, Israel, Germany,
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Finland) display the smallest size of group-average of 57.3% for trade openness while Group III countries (Czech, Netherland, Estonia, Belgium, Ireland, Luxembourg) represents the most open economies with an overall average of 173.2%. Group II countries (Korea, Sweden, Iceland, Denmark, Austria, Switzerland) has a group-average of 92%, which is very close to the overall average of 91%.
Table 2 presents period-wise country-specific trade openness and growth rates of real GDP over the two sub-periods of 2000-2007 and 2008-2015 for these three groups.
Table 2. Period-Wise Group-specific Openness and Growth Rates, 2000-2015
Groups Countries Openness Growth Rates
2000-2007 2008-2015 2000-2007 2008-2015 I
(57%)
United States 24.6 29.1 2.8 1.0
Japan 24.5 32.0 1.5 0.1
Australia 40.5 41.8 3.6 2.5
Italy 50.1 54.1 1.2 -1.3
France 53.4 57.2 1.9 0.3
UK 51.8 59.0 2.8 0.7
Greece 53.0 59.3 3.5 -4.1
Spain 56.0 57.6 3.4 -0.9
New Zealand 61.4 57.5 3.8 1.6
Canada 72.6 62.7 2.7 1.6
Portugal 64.8 73.4 1.3 -1.0
Norway 71.0 69.3 2.5 0.9
Israel 74.6 67.3 3.2 3.4
Germany 67.3 82.2 1.4 0.8
Finland 74.5 77.6 3.3 -0.7
Average 56.0 58.7 2.6 0.3
II
(92%) Korea 68.8 98.5 5.2 3.2
Sweden 82.5 86.9 3.2 0.9
Iceland 73.2 98.5 4.9 0.3
Denmark 87.7 100.2 1.9 -0.4
Austria 91.3 100.8 2.4 0.6
Switzerland 98.4 119.0 2.2 1.4
Average 83.6 100.6 3.3 1.0
III (173%) Czech Rep. 110.0 140.0 4.1 0.4
Netherlands 122.5 143.9 2.2 0.1
Estonia 130.1 152.5 6.7 0.0
Belgium 140.7 158.2 2.3 0.6
Ireland 157.3 190.7 5.7 0.3
Luxembourg 286.0 346.4 4.4 1.4
Average 157.8 188.6 4.2 0.5
Overall Average 85.7 98.4 3.1 0.5
It can be seen that despite an increase in trade openness from an overall average of 85.7% over the 2000-2007 period to 98.4% over the 2008-2015 period, the overall average of 3.1% growth rate during the former period, plummeted to an average of just 0.5% during the latter period. This might somewhat reflect the so-called “secular stagnation” [see Summers (2013a) and (2013b)] during the 2008-2015 period. Table 2 also shows that the most open group (Group III) with 4.2% average growth rate outperforms the other two groups in the 2000-2007 period while Group II with 1%
average growth rate did relatively better in the 2008-2015 period. The least open group
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(Group I) did below average in terms of growth rate in both periods
Table 3 and Table 4 provide more information about the performance of these three groups of countries over the two subperiods 2000-2007 and 2008-2015, respectively. Table 3 shows that Group III outperformed the other two groups in terms of average growth rates of real GDP (GY), investment (GI), employment (GE), exports (GX), and public consumption (GC) while enjoying the smallest size of government, measured as a ratio of government outlays to GDP (GS). Group II outperformed the other two groups in terms of enjoying the lowest unemployment rate (UN) and the highest competitiveness index (CMP). Table 4 shows that Group III still enjoyed the highest growth of exports, however, Group II outperformed the other two groups in terms of other performance indicators. This table also shows a significant slowdown in all growth rates (GY, GI, GE, GX, and GC) but an increase in unemployment rates and government outlays over the 2008-2015 period compared with those of the 2000-2007 period.
Table 3. Group-Wise Average Growth Rates of Real GDP (GY), Investment (GI), Employment (GE), Exports (GX), Public Consumption (GC), Government Outlays as % of GDP (GS), and Unemployment Rate (UN), 2000-2007
Groups 2000-2007
GY GI GE GX GC GS UN I 2.6 3.3 0.2 5.0 2.3 42.7 7.2 II 3.3 4.1 0.1 6.9 2.3 43.4 4.4 III 4.2 5.6 0.3 8.2 3.2 40.7 6.3 All 3.1 3.9 0.2 6.1 2.5 42.4 6.3
Table 4. Group-Wise Average Growth Rates of Real GDP (GY), Investment (GI), Employment (GE), Exports (GX), Public Consumption (GC), Government Outlays as % of GDP (GS), and Unemployment Rate (UN), 2008-2015
Groups 2008-2015
GY GI GE GX GC GS UN I 0.3 -1.1 0.4 1.8 0.9 46.0 8.9 II 1.0 0.1 0.2 3.0 1.4 45.3 5.4 III 0.5 0.0 0.2 4.0 1.2 45.2 7.9 All 0.5 -0.6 -0.3 2.6 1.1 45.6 7.9
The model employed in this study is a commonly-used growth-accounting model based on the concept of the aggregate production function in growth form. In this model, the rate of growth of real gross domestic product (GY) depends on the rate of capital accumulation and the rate of growth of labour. However, since capital stock data are usually not available, most studies use a proxy variable for the rate of capital accumulation [see, for instance, Ram (1985) and Alam (1991)]. In this paper, we use the growth rate of investment (GI) which more closely captures the wide fluctuations in investment activity. We also use the growth rate of employment (GE) rather than the rate of growth of labour because, given the existence of persistent episodes of unemployment, employment more accurately captures the extent of labour utilization.
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GYit = 2 (GI)it + 3 (GE)it + TFit (1)
where GY is the rate of growth of real GDP, GI is the growth rate of investment and GE is the growth rate of employment, and TFit measures the rate of total factor productivity growth. The subscripts i (i=1,2,...,n) and t (t=1,2,...,T) index the cross-sectional units (countries or groups) and time periods in the sample, respectively. In this approach, we assume that export growth raises total factor productivity growth and, by implication, economic growth. This would be realized via its favourable impact on the efficiency of resource use, innovative activity and the rate of technical progress, and the realisation of economies of scale. We also assume that government policy action may have far- reaching implications for a country’s long-run growth performance if such policy action permanently alters the levels of variables which affects the country’s rate of technological advance. Within this context, we assume that
TFit = + (GC)it + (GX) it + uit (2) where GC gives the rate of growth of public consumption, GX is the rate of export growth, and u is the random term. Substituting (2) in (1) yields
GYit = 1 + 2 (GI)it + 3 (GE)it + 4 (GC)it + 5 (GX) it + uit (3)
A major difficulty with the above model is that it is a fixed-coefficients version of the production function. However, this is a major weakness of the model in that it assumes away inter-country differences by virtue of the assumption that all coefficients are the same across countries/groups. In this paper, we overcome this problem by adopting the more general random coefficients model which permits us to treat the fixed-coefficients assumption as a testable proposition. In addition, the random coefficients model can be seen as a refinement of the stochastic law relating economic growth to its main determinants [see Pratt and Schlaifer (1988)]. In dealing with these problems, we first postulate the following benchmark model:
(GY)it = 1 + 2 (GI)it + 3 (GE)it + 4 (GC)it + 5 (GX) it + Wit' (4) where W is the set of excluded variables that along with those that are included are sufficient to determine GY. However, in the linear, deterministic law stated by (4), neither the slope coefficients nor W are unique in that they are sensitive to the parameterization chosen. To ensure uniqueness, we assume
Wit = 1i + 2i (GI)it + 3i (GE)it + 4i (GC)it + 5i (GX) it + vit (5) Substituting (5) into (4) yields
GYit = 1i + 2i (GI)it + 3i (GE)it + 4i (GC)it + 5i (GX) it + uit (6) where 1i =1 + 1i', 2i =2 + 2i', 3i =3 + 3i', 4i =4 + 4i', 5i =5 + 5i', and uit
=vit'.
Note that (6) is a random coefficients model, and that the disturbance is not the joint effect of excluded variables; instead, it is the joint effect of the remainder of the excluded variables after the effect of included variables has been factored out. Note also that whereas the included variables cannot be uncorrelated with every variable that affects
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GY, they can be uncorrelated with the remainder of every such variable [see Pratt and Schlaifer (1988)]. Thus, each of our explanatory variables can be uncorrelated with u, and (6) can be taken to represent the law relating GY to its determinants. Further, all variables included in this model (GY, GI, GE, GC, and GX) are measured as rates of growth. We only use trade openness, which is measured as a share of GDP ratio, to classify the 27 OECD countries into three groups, over the two sub-periods of 2000- 2007 and 2008-2015. Therefore, we avoid any potential analytical problems that might arise by explicitly including a ratio (trade openness) and growth variables in the regression. Guisan (2015) provides a comprehensive survey of potential problems in applied econometric studies, particularly those arising from the mixing of variables measured as rates of growth with variables measured as shares of GDP.
The random coefficients model represented by (6) is an extension of AmirKhalkhali and Dar (1995), and is more general than models employed in other studies, not only because it describes more correctly the law relating GY to its determinants, but also because it accommodates heterogeneity. We use the random coefficients GLS (RGLS) methods to estimate varying parameters s. The validity of the random coefficients model can be tested using the Swamy’s g-statistic, which follows a distribution. For more details of the RGLS estimation methods, see Swamy (1970), Swamy and Mehta (1975), AmirKhalkhali and Dar (1993), and Swamy and Tavlas (1995, 2002).
3. The Empirical Results
Table 5 reports the RGLS results for the pooled as well as each of the three groups over the 2000-2015 period. According to the these empical results, investment, employment and government consumption growth rates have a positive and statistically significant impact on economic growth. The export expansion coefficient is also positive and highly significant. However, Swamy’s g-statistic indicates significant differences among these three groups. With regard to export expansion, we find it has the weakest impact in the least open group of economies (Group I), while its impact in the intermediate group (Group II) is about the same as in the most open group (Group III). Our group-specific results also show that the investment impact on economic growth is stronger for the least open group of economies (Group I), while the intermediate group (Group II) performs better in terms of the impacts of employment growth, and public consumption growth. However, when we examine the relative impact of each of these variables on economic growth, we find no monotonic relationship between the degree of trade openness and the size of these impacts.
Table 5: Pooled RGLS Results, 2000-2015
GYit = I + β2i GIit + β3i GEit + β4i GCit + β5i GXit + uit
Groups β1i β2i β3i β4i β5i
I 0.372* 0.212* 0.169** 0.218* 0.120*
II 0.178 0.111* 0.275* 0.360* 0.217*
III 0.029 0.158* 0.245* 0.317* 0.216*
Pooled 0.273 0.160* 0.247* 0.305* 0.184*
R2 = 0.74 G-STAT (10)= 44.2*
* denotes statistically significant at 5% level.
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In order to assess whether these results mask differences between the pre and post financial crisis periods, we present in Table 6 and Table 7 the RGLS period-wise estimates for the two subperiods 2000-2007 and 2008-2015, respectively.
Table 6: Group-wise RGLS Results, sub-period 2000-2007 GYit = β1i + β2i GIit + β3i GEit + β4i GCit + β5i GXit + uit
Groups β1i β2i β3i β4i β5i
I 0.969* 0.192* 0.310 0.101 0.059*
II 0.613 0.093* 0.369 0.373* 0.182*
III 0.326 0.256* 0.201 0.392* 0.214*
Pooled 0.387 0.180* 0.184 0.293* 0.152*
R2 = 0.49 G-STAT (10)= 47.9*
Table 7: Group-wise RGLS Results, sub-period 2008-2015 GYit = β1i + β2i GIit + β3i GEit + β4i GCit + β5i GXit + uit
Groups β1i β2i β3i β4i β5i
I 0.096 0.199* 0.091 0.338* 0.177*
II 0.059 0.122* 0.207 0.328* 0.222*
III 0.025 0.072 0.601* 0.083 0.237*
Pooled 0.040 0.131* 0.301 0.253* 0.211*
R2 = 0.83 G-STAT (10)= 21.7*
From Table 6 we see that the pooled estimation results support positive and statistically significant impacts of investment, government consumption, and export growth rates on economic growth over the 2000-2007 period. The impact of employment growth rate is also positive but not significant. Swamy’s g-statistic indicates significant difference among these three groups. The group-wise results show that the most open economies (Group III) outperform the other two groups in terms of the impact of GI, GC, and GX, while the least open economies (Group I) would fare almost the worst. In the post-crisis period (2008-2015), the pooled results show that investment, government consumption growth rates and export expansion are all positive and have statistically significant impacts on economic growth. The group-wise results show that Group I fares well in the cases of the positive impacts of GI and GC but Group III outperforms the other two groups in terms of positive impacts of GE and GX, In particular, it is interesting that the impact of export expansion on economic growth rises monotonically with the degree of trade openness.
4. Summary and Concluding Remarks
In this paper, we investigate the impact of the degree of trade openness on total factor productivity and its implications for economic growth rates over the 2000-2015 period in advanced economies. To this end, we apply a random coefficients growth-accounting model to data from 27 OECD countries. We develop the estimating growth model so that it more accurately related to its determinants as emphasized by the political economy literature and a more reliable way of quantifying institutional factors. To assess the sensitivity of our results to the relative degree of openness, we classified these countries into three groups according to their averages of 2000-2015 trade-GDP ratios. Our pooled as well as group-wise results indicate investment, employment, government consumption, and export growth rates have a positive and statistically significant impact
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on economic growth. However, our group-wise results also show that export growth has the weakest impact on economic growth among the least open group of economies (Group I). The same is found for employment and government consumption growth.
We also extended our analysis by disaggregating the 2000-2015 intervals into two subperiods of 2000-2007 and 2008-2015 to allow for period-wise varying coefficients and to shed some light on the impact of the recent financial/economic crisis which plagued many advanced economies in the beginning of the second subperiod. Our pooled period-wise results again indicate that the export expansion and public consumption have unambiguously positive impacts on total factor productivity and economic growth in both subperiods. The 2000-2007 period group-wise results show that Group III outperforms the other two groups, in terms of positive and significant impact of openness on total factor productivity. Within this context, Group II performs next to Group III. In other words, we find a monotonic relationship between the size of these impacts and the degree of trade openness. This monotonic relationship continues to hold only for export expansion over the 2008-2015 period. The 2008-2015 period group-wise results show that Group III outperforms the other two groups in terms of positive impact of export expansion. While Group I and Group II economies outperform Group III in terms of impacts of public consumption and capital productivity on growth, Group III outperforms the other two groups in terms of positive impact of employment.
However, our results also point to the fact that positive impact of export expansion and more openness may not be sufficient to support a sustainable rate of a mostly demand- oriented economic growth when aggregate demand is insufficient because of sluggish private investment and/or consumption. Within this context, an increase in public consumption and in particular, more spending on infrastructure would seem essential to stimulate the economy and also to encourage more private investment and employment growth rates.
Finally, it is noteworthy that our definition of openness does not necessarily imply that the more open an economy, the more outward-oriented its economic policy framework. In fact, the degree of openness, measured by trade-GDP ratio, would reflect not only the economic policy framework but also country size and geographic characteristics. Within this context, our empirical results generally support a positive impact of higher degree of openness in a rules-based trade system on total factor productivity and economic growth, in both sub-periods, suggesting that changes in the economic environment notwithstanding, greater trade openness continues to be an important source of growth in industrial countries.
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