Determinants of Material Footprint in BRICS Countries: An Empirical Analysis

2021 · doi:10.21203/rs.3.rs-225820/v1
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This study found that economic growth, natural resources, renewable energy, and urbanization increased BRICS countries' material footprint, while foreign trade and human capital improved environmental quality.

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This preprint studied how renewable energy consumption, urbanization, human capital, foreign trade, and natural resources relate to “material footprint” (an environmental degradation measure) across BRICS countries from 1990 to 2016, using cross-sectional dependence tests, panel unit-root tests (CADF/CIPS), and panel cointegration tests (Pedroni and Westerlund), followed by long-run elasticity estimation. The authors report evidence of cointegration and find long-run associations in which economic growth, natural resources, renewable energy consumption, and urbanization are linked to reduced environmental quality when measured via material footprint, while foreign trade and human capital are linked to improved environmental quality. A stated limitation is that the work is a preprint not peer reviewed by a journal. Relevance to endometriosis: the paper does not explicitly discuss endometriosis or adenomyosis; it was included in the corpus via a keyword match in the upstream search index.

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Abstract

Abstract This paper explores the relationship between renewable energy consumption, urbanization, human capital, trade, natural resources, and material footprint for BRICS countries from 1990 to 2016. We apply the cross-sectional dependency test to check the correlation among the cross-section. Then, we use the second-generation panel test like CADF and CIPS to check the stationary in the series. After that, we go for the panel cointegration test, i.e., Pedroni and Westerlund panel cointegration, to know the long-run relationship of the variable. The test results reject the null hypothesis of no cointegration among the variables and accept cointegration. The long-run results indicate that economic growth, natural resources, renewable energy, and urbanization have reduced the environmental quality for BRICS countries in case of material footprint employed to measure environmental degradation. However, foreign trade, human capital improves environmental quality. Based on the empirical results, the study recommended some important policy suggestions to achieve sustainable development in BRICS countries.
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Determinants of Material Footprint in BRICS Countries: An Empirical Analysis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Determinants of Material Footprint in BRICS Countries: An Empirical Analysis Malayaranjan Sahoo, Seema Saini, Muhammed Ashiq Villanthenkodath This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-225820/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 15 Mar, 2021 Read the published version in Environmental Science and Pollution Research → Version 1 posted 5 You are reading this latest preprint version Abstract This paper explores the relationship between renewable energy consumption, urbanization, human capital, trade, natural resources, and material footprint for BRICS countries from 1990 to 2016. We apply the cross-sectional dependency test to check the correlation among the cross-section. Then, we use the second-generation panel test like CADF and CIPS to check the stationary in the series. After that, we go for the panel cointegration test, i.e., Pedroni and Westerlund panel cointegration, to know the long-run relationship of the variable. The test results reject the null hypothesis of no cointegration among the variables and accept cointegration. The long-run results indicate that economic growth, natural resources, renewable energy, and urbanization have reduced the environmental quality for BRICS countries in case of material footprint employed to measure environmental degradation. However, foreign trade, human capital improves environmental quality. Based on the empirical results, the study recommended some important policy suggestions to achieve sustainable development in BRICS countries. Environmental Engineering Environmental Policy Renewable energy urbanization material footprint BRICS Figures Figure 1 Figure 2 Full Text Due to technical limitations, full-text HTML conversion of this manuscript could not be completed. However, the latest manuscript can be downloaded and accessed as a PDF. Tables Table 1 Description of variables Variables Symbol Measurement Sources Material Footprint MF Sum of biomass, fossil fuels, metal ores, and non-metallic minerals Wiedmann et al. (2017) (MFN-PNAS) Gross Domestic Product GDP GDP per capita (constant 2010 US$) WDI (2019) Renewable Energy consumption REN Renewable energy (% of total final energy) IEA(2018) Education HC School enrollment, (% gross) WDI(2019) Natural resources NR Total natural resources rent (% of GDP) WDI (2019) Trade TR Trade (% of GDP) WDI(2019) Urbanization URB Urban population (% of the total population) WDI (2019) Notes: MFN-PNAS is Material Footprint of Nation- Proceeding of the National Academy of Sciences of the USA; WDI is World Development Indicator; IEA is International Energy Agency. Source: Authors estimation Table 2 Panel summary statistics LNMF LNGDP LNHC LNNR LNTR LNREN LNURB Mean 21.496 8.324 4.316 1.487 3.645 0.992 3.960 Median 21.380 8.709 4.446 1.412 3.775 0.962 4.068 Maximum 24.071 9.392 4.741 3.078 4.706 2.873 4.455 Minimum 19.163 6.355 3.492 0.190 2.719 -0.010 3.241 Std. Dev. 1.200 0.955 0.311 0.712 0.425 0.679 0.408 Observations 135 135 135 135 135 135 135 Note: Results are obtained from using Eviews 10. Source: Authors' estimation. Table 3 Country wise mean LNMF LNGDP LNHC LNNR LNTR LNREN LNURB Brazil 21.498 9.163 4.546 1.049 3.103 1.491 4.397 India 22.033 6.877 3.977 1.076 3.466 0.771 3.360 Russia 20.724 9.039 4.516 2.495 3.970 1.088 4.298 China 23.292 7.765 4.196 1.225 3.729 1.590 3.675 South Africa 19.931 8.777 4.345 1.589 3.955 0.018 4.068 Average 21.496 8.324 4.316 1.487 3.645 0.992 3.960 Note: Results are obtained from using Stata 14.2. Source: Authors' estimation. Table 4 Correlations LNMF LNGDP LNHC LNNR LNTR LNREN LNURB LNMF 1 ----- LNGDP -0.448 (0.000) 1 ----- LNHC -0.207 (0.016) 0.827 (0.000) 1 ----- LNNR -0.346 (0.000) 0.384 (0.000) 0.302 (0.000) 1 ----- LNTR -0.157 (0.069) 0.196 (0.023) 0.243 (0.005) 0.606 (0.000) 1 ----- LNREN 0.643 (0.000) 0.167 (0.052) 0.333 (0.000) -0.109 (0.209) -0.243 (0.005) 1 ----- LNURB -0.458 (0.000) 0.976 (0.000) 0.818 (0.000) 0.357 (0.000) 0.091 (0.292) 0.195 (0.024) 1 ----- Note: Value inside the parenthesis is p-value. Results are obtained from using Eviews 10. Source: Authors' estimation. Table 5 Panel unit root test Level First difference Inference LLC Variable Statistic P- Value Statistic P- Value LNMF -0.646 0.258 -20.84* 0.00 I(1) LNGDP -0.981 0.163 -2.220* 0.01 I(1) LNHC 0.396 0.645 -4.190* 0.00 I(1) LNNR 0.042 0.516 -5.622* 0.00 I(1) LNTR 1.602 0.945 -7.018* 0.00 I(1) LNREN 3.974 1.000 -28.93* 0.00 I(1) LNURB -1.211 0.113 -1.625* 0.05 I(1) Note: * indicates the 1%level of statistical significance. Results are obtained from using Eviews 10 Source: Authors' estimation. Table 6 CD test statistics Test LNMF LNGDP LNHC LNNR LNTR LNREN LNURB CD-test 9.836* 14.739* 4.893* 10.942* 8.067* 4.873* 15.082* p- value 0.00 0.000 0.000 0.000 0.000 0.000 0.000 Note: * indicates the 1%level of statistical significance. Results are obtained from using Stata 14.2 Source: Authors' estimation. Table 7 CIPS and CADF panel unit root tests CIPS CADF Levels Difference Levels Difference LNMF -1.725 -4.590* -2.158 -4.331* LNGDP -1.768 -2.683* -1.085 -3.130** LNHC -0.595 -4.124 * -1.294 -2.985** LNNR -1.690 -5.244* -2.193 -3.918* LNTR -2.488 -4.840* -2.514 -4.044* LNREN -1.533 -4.539* -1.533 -3.742* LNURB 0.071 -5.791* 0.732 4.201* Note: * and ** indicates the statistical significance at 1% and 5%, respectively. Results are obtained from using Stata 14.2. Source: Authors' estimation. Table 8 Pedroni panel co-integration test result Statistic p-value Weighted p-value Alternative hypothesis: Common AR coefs. (within-dimension) Panel v-statistic 0.297 0.382 0.108 0.457 Panel rho-statistic 0.520 0.698 0.667 0.748 Panel PP-statistic -3.467* 0.000 -2.896* 0.002 Panel ADF-statistic -3.463* 0.000 -2.875* 0.002 Alternative hypothesis: Individual AR coefficients (between-dimension) Group rho-statistic 1.320 0.907 Group PP-statistic -3.260* 0.001 Group ADF-statistic -3.103* 0.001 Note: * indicates significance at 1 and 10% level. Results are obtained from using Eviews 10. Source: Authors' estimation. Table 9 Results of Westerlund (2007) co-integration test Statistic Value Z - value P-value Gt -3.897** 1.751 0.040 Ga -0.782 4.720 1.000 Pt -9.682* 3.093 0.001 Pa -1.025 3.892 1.000 Note: *and ** indicates the statistical significance at 1% and 5% level. Results are obtained from using Stata 14.2. Source: Authors' estimation. Table 10 Panel data analysis of long-run material footprint elasticity Variable Dependent variable: LNMF Coefficient SE t-statistics p-value LNGDP 0.609 0.017 35.507 0.000* LNHC -0.392 0.043 -9.202 0.000* LNNR 0.310 0.063 4.902 0.000* LNTR -0.455 0.030 -15.140 0.000* LNREN 0.184 0.029 6.361 0.000* LNURB 1.630 0.002 1066.014 0.000* R 2 0.97 Adjusted R-squared 0.97 Note: * indicate the significance level at 1%. Results are obtained from using Eviews 10. Source: Authors' estimation. Table 11 Heterogeneous panel causality test Null hypothesis: Z bar-stat p-value LNGDP does not homogeneously cause LNMF LNMF does not homogeneously cause LNGDP 23.625* 1.558 0.000 0.119 LNHC does not homogeneously cause LNMF LNMF does not homogeneously cause LNHC -0.223 3.048* 0.823 0.002 LNNR does not homogeneously cause LNMF LNMF does not homogeneously cause LNNR 3.083* 1.207 0.002 0.228 LNREN does not homogeneously cause LNMF LNMF does not homogeneously cause LNREN 7.450* 0.325 0.000 0.745 LNTR does not homogeneously cause LNMF LNMF does not homogeneously cause LNTR 4.298* 31.913* 0.000 0.000 LNURB does not homogeneously cause LNMF LNMF does not homogeneously cause LNURB 15.794* 3.751* 0.000 0.000 Note: * indicate the significance level at 1%. Results are obtained from using Eviews 10. Source: Authors' estimation. Cite Share Download PDF Status: Published Journal Publication published 15 Mar, 2021 Read the published version in Environmental Science and Pollution Research → Version 1 posted Editorial decision: Accept 01 Mar, 2021 Reviewers invited by journal 10 Feb, 2021 Reviews received at journal 10 Feb, 2021 Editor assigned by journal 09 Feb, 2021 First submitted to journal 08 Feb, 2021 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-225820","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":11468107,"identity":"632a1e26-a501-469f-9676-003fa9ec4335","order_by":0,"name":"Malayaranjan Sahoo","email":"","orcid":"","institution":"National Institute of Technology Rourkela","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Malayaranjan","middleName":"","lastName":"Sahoo","suffix":""},{"id":11468108,"identity":"40271ade-2034-4416-9e5c-c69a5f9cdcc3","order_by":1,"name":"Seema Saini","email":"","orcid":"","institution":"Indian Institute of Technology Kanpur","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Seema","middleName":"","lastName":"Saini","suffix":""},{"id":11468109,"identity":"7b18a2f5-d0e6-4c3b-a42b-f4e4bf5bad5c","order_by":2,"name":"Muhammed Ashiq Villanthenkodath","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABEElEQVRIiWNgGAWjYFACHjDJ2ABkHGBg+FfPD+ImFBCv5UCCZANIiwGRWhhAWgwOgGg8WnRn5B78dDPHRra//+zBA2/b7uQZn1+d+OGBAYM8v9gBrFrMbuQlS+duSzOecSMv4eDctmfFZjfebpYAOsxw5uwEHFpyDIBaDic23OAxOMxzhplx242zG0BaEgxu49Ri/Dt32//E+efPQLRsnnF28w8CWsyAthxI3HAgB6il4nDiBv7ebfhtOfPGzDp3W7LxRqALD86pSDOWuMG7zSLBQAK3X47nGN/O3WYnO+/8GeMPbwxs5Pj7z26++aPCRp5fGrsWVACOGgmwSgkilMO18B8gUvUoGAWjYBSMFAAA9+ht0ndisgEAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0002-6617-6071","institution":"Indian Institute of Technology Kharagpur","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Muhammed","middleName":"Ashiq","lastName":"Villanthenkodath","suffix":""}],"badges":[],"createdAt":"2021-02-09 07:55:08","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-225820/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-225820/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s11356-021-13309-7","type":"published","date":"2021-03-15T19:09:18+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":6284466,"identity":"175b0c95-d23a-4bc4-a99e-7c495f72f1e6","added_by":"auto","created_at":"2021-02-24 00:09:28","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":116493,"visible":true,"origin":"","legend":"The pattern of variables during 1990-2016.","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-225820/v1/7b62e5590f2fab97e0fd6e9d.png"},{"id":6284465,"identity":"03396266-b424-4798-ae47-be3e1400cf66","added_by":"auto","created_at":"2021-02-24 00:09:28","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":27266,"visible":true,"origin":"","legend":"Actual, fitted, and residual graph of the FMOLS model.","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-225820/v1/b1e492c049d0972b9939df79.png"},{"id":13592271,"identity":"2dcde316-be5f-4a8c-945f-6d45684a37ec","added_by":"auto","created_at":"2021-09-17 05:12:14","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":535302,"visible":true,"origin":"","legend":"","description":"","filename":"R1manuscriptforpeerreview9.2.2021.pdf","url":"https://assets-eu.researchsquare.com/files/rs-225820/v1_covered.pdf"},{"id":6285053,"identity":"82e0053a-3eca-4c2c-a018-d24bb8293266","added_by":"auto","created_at":"2021-02-24 00:12:31","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":709164,"visible":true,"origin":"","legend":"","description":"","filename":"R1manuscriptforpeerreview9.2.2021.pdf","url":"https://assets-eu.researchsquare.com/files/rs-225820/v1_stamped.pdf"}],"financialInterests":"","formattedTitle":"\u003cp\u003eDeterminants of Material Footprint in BRICS Countries: An Empirical Analysis\u003c/p\u003e","fulltext":[{"header":"Full Text","content":"Due to technical limitations, full-text HTML conversion of this manuscript could not be completed. However, the latest manuscript can be downloaded and \u003ca href='/article/rs-225820/latest.pdf' target='_blank'\u003e accessed as a PDF.\u003c/a\u003e"},{"header":"Tables","content":"\u003cp style=\"text-align: center;\"\u003eTable 1\u003c/p\u003e\n\u003cp style=\"text-align: center;\"\u003eDescription of variables\u003c/p\u003e\n\u003ctable style=\"margin-left: auto; margin-right: auto;\" border=\"1\" width=\"106%\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd width=\"27%\"\u003e\n\u003cp\u003eVariables\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"11%\"\u003e\n\u003cp\u003eSymbol\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"35%\"\u003e\n\u003cp\u003eMeasurement\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"25%\"\u003e\n\u003cp\u003eSources\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"27%\"\u003e\n\u003cp\u003eMaterial Footprint\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"11%\"\u003e\n\u003cp\u003eMF\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"35%\"\u003e\n\u003cp\u003eSum of biomass, fossil fuels, metal ores, and non-metallic minerals\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"25%\"\u003e\n\u003cp\u003eWiedmann et al. (2017) (MFN-PNAS)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"27%\"\u003e\n\u003cp\u003eGross Domestic Product\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"11%\"\u003e\n\u003cp\u003eGDP\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"35%\"\u003e\n\u003cp\u003eGDP per capita (constant 2010 US$)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"25%\"\u003e\n\u003cp\u003eWDI (2019)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"27%\"\u003e\n\u003cp\u003eRenewable Energy consumption\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"11%\"\u003e\n\u003cp\u003eREN\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"35%\"\u003e\n\u003cp\u003eRenewable energy (% of total final energy)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"25%\"\u003e\n\u003cp\u003eIEA(2018)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"27%\"\u003e\n\u003cp\u003eEducation\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"11%\"\u003e\n\u003cp\u003eHC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"35%\"\u003e\n\u003cp\u003eSchool enrollment, (% gross)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"25%\"\u003e\n\u003cp\u003eWDI(2019)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"27%\"\u003e\n\u003cp\u003eNatural resources\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"11%\"\u003e\n\u003cp\u003eNR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"35%\"\u003e\n\u003cp\u003eTotal natural resources rent (% of GDP)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"25%\"\u003e\n\u003cp\u003eWDI (2019)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"27%\"\u003e\n\u003cp\u003eTrade\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"11%\"\u003e\n\u003cp\u003eTR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"35%\"\u003e\n\u003cp\u003eTrade (% of GDP)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"25%\"\u003e\n\u003cp\u003eWDI(2019)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"27%\"\u003e\n\u003cp\u003eUrbanization\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"11%\"\u003e\n\u003cp\u003eURB\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"35%\"\u003e\n\u003cp\u003eUrban population (% of the total population)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"25%\"\u003e\n\u003cp\u003eWDI (2019)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"4\" width=\"27%\"\u003e\n\u003cp\u003eNotes: \u003cem\u003eMFN-PNAS is Material Footprint of Nation- Proceeding of the National Academy of Sciences of the USA; WDI is World Development Indicator; IEA is International Energy Agency. \u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eSource: Authors estimation\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp style=\"text-align: center;\"\u003eTable 2\u003c/p\u003e\n\u003cp style=\"text-align: center;\"\u003ePanel summary statistics\u003c/p\u003e\n\u003ctable style=\"margin-left: auto; margin-right: auto;\" border=\"1\" width=\"0\"\u003e\n\u003ctbody\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" width=\"99\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"59\"\u003e\n\u003cp\u003eLNMF\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"87\"\u003e\n\u003cp\u003eLNGDP\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"89\"\u003e\n\u003cp\u003eLNHC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"69\"\u003e\n\u003cp\u003eLNNR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"89\"\u003e\n\u003cp\u003eLNTR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"78\"\u003e\n\u003cp\u003eLNREN\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"92\"\u003e\n\u003cp\u003eLNURB\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" width=\"99\"\u003e\n\u003cp\u003e\u0026nbsp;Mean\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"59\"\u003e\n\u003cp\u003e21.496\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"87\"\u003e\n\u003cp\u003e8.324\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"89\"\u003e\n\u003cp\u003e4.316\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"69\"\u003e\n\u003cp\u003e1.487\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"89\"\u003e\n\u003cp\u003e3.645\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"78\"\u003e\n\u003cp\u003e0.992\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"92\"\u003e\n\u003cp\u003e3.960\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" width=\"99\"\u003e\n\u003cp\u003e\u0026nbsp;Median\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"59\"\u003e\n\u003cp\u003e21.380\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"87\"\u003e\n\u003cp\u003e8.709\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"89\"\u003e\n\u003cp\u003e4.446\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"69\"\u003e\n\u003cp\u003e1.412\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"89\"\u003e\n\u003cp\u003e3.775\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"78\"\u003e\n\u003cp\u003e0.962\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"92\"\u003e\n\u003cp\u003e4.068\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" width=\"99\"\u003e\n\u003cp\u003e\u0026nbsp;Maximum\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"59\"\u003e\n\u003cp\u003e24.071\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"87\"\u003e\n\u003cp\u003e9.392\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"89\"\u003e\n\u003cp\u003e4.741\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"69\"\u003e\n\u003cp\u003e3.078\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"89\"\u003e\n\u003cp\u003e4.706\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"78\"\u003e\n\u003cp\u003e2.873\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"92\"\u003e\n\u003cp\u003e4.455\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" width=\"99\"\u003e\n\u003cp\u003e\u0026nbsp;Minimum\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"59\"\u003e\n\u003cp\u003e19.163\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"87\"\u003e\n\u003cp\u003e6.355\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"89\"\u003e\n\u003cp\u003e3.492\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"69\"\u003e\n\u003cp\u003e0.190\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"89\"\u003e\n\u003cp\u003e2.719\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"78\"\u003e\n\u003cp\u003e-0.010\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"92\"\u003e\n\u003cp\u003e3.241\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" width=\"99\"\u003e\n\u003cp\u003e\u0026nbsp;Std. Dev.\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"59\"\u003e\n\u003cp\u003e1.200\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"87\"\u003e\n\u003cp\u003e0.955\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"89\"\u003e\n\u003cp\u003e0.311\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"69\"\u003e\n\u003cp\u003e0.712\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"89\"\u003e\n\u003cp\u003e0.425\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"78\"\u003e\n\u003cp\u003e0.679\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"92\"\u003e\n\u003cp\u003e0.408\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35.3324px;\"\u003e\n\u003ctd style=\"height: 35.3324px;\" width=\"99\"\u003e\n\u003cp\u003eObservations\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35.3324px;\" width=\"59\"\u003e\n\u003cp\u003e135\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35.3324px;\" width=\"87\"\u003e\n\u003cp\u003e135\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35.3324px;\" width=\"89\"\u003e\n\u003cp\u003e135\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35.3324px;\" width=\"69\"\u003e\n\u003cp\u003e135\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35.3324px;\" width=\"89\"\u003e\n\u003cp\u003e135\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35.3324px;\" width=\"78\"\u003e\n\u003cp\u003e135\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35.3324px;\" width=\"92\"\u003e\n\u003cp\u003e135\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 13px;\"\u003e\n\u003ctd style=\"height: 13px;\" colspan=\"8\" width=\"99\"\u003e\n\u003cp\u003eNote: Results are obtained from using Eviews 10.\u003c/p\u003e\n\u003cp\u003eSource: Authors' estimation.\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp style=\"text-align: center;\"\u003eTable 3\u003c/p\u003e\n\u003cp style=\"text-align: center;\"\u003eCountry wise mean\u003c/p\u003e\n\u003ctable style=\"margin-left: auto; margin-right: auto;\" border=\"1\" width=\"0\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd width=\"102\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"72\"\u003e\n\u003cp\u003eLNMF\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"87\"\u003e\n\u003cp\u003eLNGDP\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003eLNHC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"69\"\u003e\n\u003cp\u003eLNNR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003eLNTR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"78\"\u003e\n\u003cp\u003eLNREN\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"92\"\u003e\n\u003cp\u003eLNURB\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"102\"\u003e\n\u003cp\u003eBrazil\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"72\"\u003e\n\u003cp\u003e21.498\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"87\"\u003e\n\u003cp\u003e9.163\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003e4.546\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"69\"\u003e\n\u003cp\u003e1.049\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003e3.103\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"78\"\u003e\n\u003cp\u003e1.491\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"92\"\u003e\n\u003cp\u003e4.397\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"102\"\u003e\n\u003cp\u003eIndia\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"72\"\u003e\n\u003cp\u003e22.033\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"87\"\u003e\n\u003cp\u003e6.877\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003e3.977\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"69\"\u003e\n\u003cp\u003e1.076\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003e3.466\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"78\"\u003e\n\u003cp\u003e0.771\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"92\"\u003e\n\u003cp\u003e3.360\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"102\"\u003e\n\u003cp\u003eRussia\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"72\"\u003e\n\u003cp\u003e20.724\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"87\"\u003e\n\u003cp\u003e9.039\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003e4.516\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"69\"\u003e\n\u003cp\u003e2.495\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003e3.970\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"78\"\u003e\n\u003cp\u003e1.088\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"92\"\u003e\n\u003cp\u003e4.298\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"102\"\u003e\n\u003cp\u003eChina\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"72\"\u003e\n\u003cp\u003e23.292\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"87\"\u003e\n\u003cp\u003e7.765\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003e4.196\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"69\"\u003e\n\u003cp\u003e1.225\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003e3.729\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"78\"\u003e\n\u003cp\u003e1.590\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"92\"\u003e\n\u003cp\u003e3.675\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"102\"\u003e\n\u003cp\u003eSouth Africa\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"72\"\u003e\n\u003cp\u003e19.931\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"87\"\u003e\n\u003cp\u003e8.777\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003e4.345\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"69\"\u003e\n\u003cp\u003e1.589\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003e3.955\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"78\"\u003e\n\u003cp\u003e0.018\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"92\"\u003e\n\u003cp\u003e4.068\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"102\"\u003e\n\u003cp\u003eAverage\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"72\"\u003e\n\u003cp\u003e21.496\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"87\"\u003e\n\u003cp\u003e8.324\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003e4.316\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"69\"\u003e\n\u003cp\u003e1.487\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003e3.645\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"78\"\u003e\n\u003cp\u003e0.992\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"92\"\u003e\n\u003cp\u003e3.960\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"8\" width=\"102\"\u003e\n\u003cp\u003eNote: Results are obtained from using Stata 14.2.\u003c/p\u003e\n\u003cp\u003eSource: Authors' estimation.\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp style=\"text-align: center;\"\u003e\u003cbr /\u003eTable 4\u003c/p\u003e\n\u003cp style=\"text-align: center;\"\u003eCorrelations\u003c/p\u003e\n\u003ctable style=\"margin-left: auto; margin-right: auto;\" border=\"1\" width=\"0\"\u003e\n\u003ctbody\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" width=\"97\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"61\"\u003e\n\u003cp\u003eLNMF\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"88\"\u003e\n\u003cp\u003eLNGDP\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"90\"\u003e\n\u003cp\u003eLNHC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"69\"\u003e\n\u003cp\u003eLNNR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"90\"\u003e\n\u003cp\u003eLNTR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"78\"\u003e\n\u003cp\u003eLNREN\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"92\"\u003e\n\u003cp\u003eLNURB\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 27px;\"\u003e\n\u003ctd style=\"height: 54px;\" rowspan=\"2\" width=\"97\"\u003e\n\u003cp\u003eLNMF\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 54px;\" rowspan=\"2\" width=\"61\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003cp\u003e-----\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 27px;\" width=\"88\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 27px;\" width=\"90\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 27px;\" width=\"69\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 27px;\" width=\"90\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 27px;\" width=\"78\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 27px;\" width=\"92\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 27px;\"\u003e\n\u003ctd style=\"height: 27px;\" width=\"88\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 27px;\" width=\"90\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 27px;\" width=\"69\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 27px;\" width=\"90\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 27px;\" width=\"78\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 27px;\" width=\"92\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 27px;\"\u003e\n\u003ctd style=\"height: 54px;\" rowspan=\"2\" width=\"97\"\u003e\n\u003cp\u003eLNGDP\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 54px;\" rowspan=\"2\" width=\"61\"\u003e\n\u003cp\u003e-0.448\u003c/p\u003e\n\u003cp\u003e(0.000)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 54px;\" rowspan=\"2\" width=\"88\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003cp\u003e-----\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 27px;\" width=\"90\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 27px;\" width=\"69\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 27px;\" width=\"90\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 27px;\" width=\"78\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 27px;\" width=\"92\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 27px;\"\u003e\n\u003ctd style=\"height: 27px;\" width=\"90\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 27px;\" width=\"69\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 27px;\" width=\"90\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 27px;\" width=\"78\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 27px;\" width=\"92\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 27px;\"\u003e\n\u003ctd style=\"height: 54px;\" rowspan=\"2\" width=\"97\"\u003e\n\u003cp\u003eLNHC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 54px;\" rowspan=\"2\" width=\"61\"\u003e\n\u003cp\u003e-0.207\u003c/p\u003e\n\u003cp\u003e(0.016)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 54px;\" rowspan=\"2\" width=\"88\"\u003e\n\u003cp\u003e0.827\u003c/p\u003e\n\u003cp\u003e(0.000)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 54px;\" rowspan=\"2\" width=\"90\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003cp\u003e-----\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 27px;\" width=\"69\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 27px;\" width=\"90\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 27px;\" width=\"78\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 27px;\" width=\"92\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 27px;\"\u003e\n\u003ctd style=\"height: 27px;\" width=\"69\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 27px;\" width=\"90\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 27px;\" width=\"78\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 27px;\" width=\"92\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 27px;\"\u003e\n\u003ctd style=\"height: 54px;\" rowspan=\"2\" width=\"97\"\u003e\n\u003cp\u003eLNNR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 54px;\" rowspan=\"2\" width=\"61\"\u003e\n\u003cp\u003e-0.346\u003c/p\u003e\n\u003cp\u003e(0.000)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 54px;\" rowspan=\"2\" width=\"88\"\u003e\n\u003cp\u003e0.384\u003c/p\u003e\n\u003cp\u003e(0.000)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 54px;\" rowspan=\"2\" width=\"90\"\u003e\n\u003cp\u003e0.302\u003c/p\u003e\n\u003cp\u003e(0.000)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 54px;\" rowspan=\"2\" width=\"69\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003cp\u003e-----\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 27px;\" width=\"90\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 27px;\" width=\"78\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 27px;\" width=\"92\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 27px;\"\u003e\n\u003ctd style=\"height: 27px;\" width=\"90\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 27px;\" width=\"78\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 27px;\" width=\"92\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 27px;\"\u003e\n\u003ctd style=\"height: 54px;\" rowspan=\"2\" width=\"97\"\u003e\n\u003cp\u003eLNTR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 54px;\" rowspan=\"2\" width=\"61\"\u003e\n\u003cp\u003e-0.157\u003c/p\u003e\n\u003cp\u003e(0.069)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 54px;\" rowspan=\"2\" width=\"88\"\u003e\n\u003cp\u003e0.196\u003c/p\u003e\n\u003cp\u003e(0.023)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 54px;\" rowspan=\"2\" width=\"90\"\u003e\n\u003cp\u003e0.243\u003c/p\u003e\n\u003cp\u003e(0.005)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 54px;\" rowspan=\"2\" width=\"69\"\u003e\n\u003cp\u003e0.606\u003c/p\u003e\n\u003cp\u003e(0.000)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 54px;\" rowspan=\"2\" width=\"90\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003cp\u003e-----\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 27px;\" width=\"78\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 27px;\" width=\"92\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 27px;\"\u003e\n\u003ctd style=\"height: 27px;\" width=\"78\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 27px;\" width=\"92\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 27px;\"\u003e\n\u003ctd style=\"height: 54px;\" rowspan=\"2\" width=\"97\"\u003e\n\u003cp\u003eLNREN\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 54px;\" rowspan=\"2\" width=\"61\"\u003e\n\u003cp\u003e0.643\u003c/p\u003e\n\u003cp\u003e(0.000)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 54px;\" rowspan=\"2\" width=\"88\"\u003e\n\u003cp\u003e0.167\u003c/p\u003e\n\u003cp\u003e(0.052)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 54px;\" rowspan=\"2\" width=\"90\"\u003e\n\u003cp\u003e0.333\u003c/p\u003e\n\u003cp\u003e(0.000)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 54px;\" rowspan=\"2\" width=\"69\"\u003e\n\u003cp\u003e-0.109\u003c/p\u003e\n\u003cp\u003e(0.209)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 54px;\" rowspan=\"2\" width=\"90\"\u003e\n\u003cp\u003e-0.243\u003c/p\u003e\n\u003cp\u003e(0.005)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 54px;\" rowspan=\"2\" width=\"78\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003cp\u003e-----\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 27px;\" width=\"92\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 27px;\"\u003e\n\u003ctd style=\"height: 27px;\" width=\"92\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 60.7472px;\"\u003e\n\u003ctd style=\"height: 60.7472px;\" width=\"97\"\u003e\n\u003cp\u003eLNURB\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 60.7472px;\" width=\"61\"\u003e\n\u003cp\u003e-0.458\u003c/p\u003e\n\u003cp\u003e(0.000)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 60.7472px;\" width=\"88\"\u003e\n\u003cp\u003e0.976\u003c/p\u003e\n\u003cp\u003e(0.000)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 60.7472px;\" width=\"90\"\u003e\n\u003cp\u003e0.818\u003c/p\u003e\n\u003cp\u003e(0.000)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 60.7472px;\" width=\"69\"\u003e\n\u003cp\u003e0.357\u003c/p\u003e\n\u003cp\u003e(0.000)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 60.7472px;\" width=\"90\"\u003e\n\u003cp\u003e0.091\u003c/p\u003e\n\u003cp\u003e(0.292)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 60.7472px;\" width=\"78\"\u003e\n\u003cp\u003e0.195\u003c/p\u003e\n\u003cp\u003e(0.024)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 60.7472px;\" width=\"92\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003cp\u003e-----\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 13px;\"\u003e\n\u003ctd style=\"height: 13px;\" colspan=\"8\" width=\"97\"\u003e\n\u003cp\u003eNote: Value inside the parenthesis is p-value. Results are obtained from using Eviews 10.\u003c/p\u003e\n\u003cp\u003eSource: Authors' estimation.\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp style=\"text-align: center;\"\u003eTable 5\u003c/p\u003e\n\u003cp style=\"text-align: center;\"\u003ePanel unit root test\u003c/p\u003e\n\u003ctable style=\"margin-left: auto; margin-right: auto;\" border=\"1\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd width=\"100\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"200\"\u003e\n\u003cp\u003eLevel\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"200\"\u003e\n\u003cp\u003eFirst difference\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"100\"\u003e\n\u003cp\u003eInference\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"100\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"4\" width=\"401\"\u003e\n\u003cp\u003eLLC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"100\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"100\"\u003e\n\u003cp\u003eVariable\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"100\"\u003e\n\u003cp\u003eStatistic\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"100\"\u003e\n\u003cp\u003eP- Value\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"100\"\u003e\n\u003cp\u003eStatistic\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"100\"\u003e\n\u003cp\u003eP- Value\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"100\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"100\"\u003e\n\u003cp\u003eLNMF\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"100\"\u003e\n\u003cp\u003e-0.646\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"100\"\u003e\n\u003cp\u003e0.258\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"100\"\u003e\n\u003cp\u003e-20.84*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"100\"\u003e\n\u003cp\u003e0.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"100\"\u003e\n\u003cp\u003eI(1)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"100\"\u003e\n\u003cp\u003eLNGDP\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"100\"\u003e\n\u003cp\u003e-0.981\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"100\"\u003e\n\u003cp\u003e0.163\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"100\"\u003e\n\u003cp\u003e-2.220*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"100\"\u003e\n\u003cp\u003e0.01\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"100\"\u003e\n\u003cp\u003eI(1)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"100\"\u003e\n\u003cp\u003eLNHC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"100\"\u003e\n\u003cp\u003e0.396\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"100\"\u003e\n\u003cp\u003e0.645\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"100\"\u003e\n\u003cp\u003e-4.190*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"100\"\u003e\n\u003cp\u003e0.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"100\"\u003e\n\u003cp\u003eI(1)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"100\"\u003e\n\u003cp\u003eLNNR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"100\"\u003e\n\u003cp\u003e0.042\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"100\"\u003e\n\u003cp\u003e0.516\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"100\"\u003e\n\u003cp\u003e-5.622*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"100\"\u003e\n\u003cp\u003e0.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"100\"\u003e\n\u003cp\u003eI(1)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"100\"\u003e\n\u003cp\u003eLNTR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"100\"\u003e\n\u003cp\u003e1.602\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"100\"\u003e\n\u003cp\u003e0.945\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"100\"\u003e\n\u003cp\u003e-7.018*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"100\"\u003e\n\u003cp\u003e0.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"100\"\u003e\n\u003cp\u003eI(1)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"100\"\u003e\n\u003cp\u003eLNREN\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"100\"\u003e\n\u003cp\u003e3.974\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"100\"\u003e\n\u003cp\u003e1.000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"100\"\u003e\n\u003cp\u003e-28.93*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"100\"\u003e\n\u003cp\u003e0.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"100\"\u003e\n\u003cp\u003eI(1)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"100\"\u003e\n\u003cp\u003eLNURB\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"100\"\u003e\n\u003cp\u003e-1.211\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"100\"\u003e\n\u003cp\u003e0.113\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"100\"\u003e\n\u003cp\u003e-1.625*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"100\"\u003e\n\u003cp\u003e0.05\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"100\"\u003e\n\u003cp\u003eI(1)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"6\" width=\"100\"\u003e\n\u003cp\u003eNote: * indicates the 1%level of statistical significance. Results are obtained from using Eviews 10\u003c/p\u003e\n\u003cp\u003eSource: Authors' estimation.\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp style=\"text-align: center;\"\u003eTable 6\u003c/p\u003e\n\u003cp style=\"text-align: center;\"\u003eCD test statistics\u003c/p\u003e\n\u003ctable style=\"margin-left: auto; margin-right: auto;\" border=\"1\" width=\"0\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd width=\"72\"\u003e\n\u003cp\u003eTest\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"63\"\u003e\n\u003cp\u003eLNMF\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"87\"\u003e\n\u003cp\u003eLNGDP\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003eLNHC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"71\"\u003e\n\u003cp\u003eLNNR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003eLNTR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"78\"\u003e\n\u003cp\u003eLNREN\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"92\"\u003e\n\u003cp\u003eLNURB\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"72\"\u003e\n\u003cp\u003eCD-test\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"63\"\u003e\n\u003cp\u003e9.836*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"87\"\u003e\n\u003cp\u003e14.739*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003e4.893*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"71\"\u003e\n\u003cp\u003e10.942*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003e8.067*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"78\"\u003e\n\u003cp\u003e4.873*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"92\"\u003e\n\u003cp\u003e15.082*\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"72\"\u003e\n\u003cp\u003ep- value\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"63\"\u003e\n\u003cp\u003e0.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"87\"\u003e\n\u003cp\u003e0.000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003e0.000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"71\"\u003e\n\u003cp\u003e0.000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003e0.000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"78\"\u003e\n\u003cp\u003e0.000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"92\"\u003e\n\u003cp\u003e0.000\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"8\" width=\"72\"\u003e\n\u003cp\u003eNote: * indicates the 1%level of statistical significance. Results are obtained from using Stata 14.2\u003c/p\u003e\n\u003cp\u003eSource: Authors' estimation.\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp style=\"text-align: center;\"\u003e\u0026nbsp;Table 7\u003c/p\u003e\n\u003cp style=\"text-align: center;\"\u003eCIPS and CADF panel unit root tests\u003c/p\u003e\n\u003ctable style=\"margin-left: auto; margin-right: auto;\" border=\"1\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd width=\"123\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"247\"\u003e\n\u003cp\u003eCIPS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"253\"\u003e\n\u003cp\u003eCADF\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"123\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"123\"\u003e\n\u003cp\u003eLevels\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"123\"\u003e\n\u003cp\u003eDifference\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"127\"\u003e\n\u003cp\u003eLevels\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"126\"\u003e\n\u003cp\u003eDifference\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"123\"\u003e\n\u003cp\u003eLNMF\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"123\"\u003e\n\u003cp\u003e-1.725\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"123\"\u003e\n\u003cp\u003e-4.590*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"127\"\u003e\n\u003cp\u003e-2.158\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"126\"\u003e\n\u003cp\u003e-4.331*\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"123\"\u003e\n\u003cp\u003eLNGDP\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"123\"\u003e\n\u003cp\u003e-1.768\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"123\"\u003e\n\u003cp\u003e-2.683*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"127\"\u003e\n\u003cp\u003e-1.085\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"126\"\u003e\n\u003cp\u003e-3.130**\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"123\"\u003e\n\u003cp\u003eLNHC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"123\"\u003e\n\u003cp\u003e-0.595\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"123\"\u003e\n\u003cp\u003e-4.124 *\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"127\"\u003e\n\u003cp\u003e-1.294\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"126\"\u003e\n\u003cp\u003e-2.985**\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"123\"\u003e\n\u003cp\u003eLNNR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"123\"\u003e\n\u003cp\u003e-1.690\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"123\"\u003e\n\u003cp\u003e-5.244*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"127\"\u003e\n\u003cp\u003e-2.193\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"126\"\u003e\n\u003cp\u003e-3.918*\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"123\"\u003e\n\u003cp\u003eLNTR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"123\"\u003e\n\u003cp\u003e-2.488\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"123\"\u003e\n\u003cp\u003e-4.840*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"127\"\u003e\n\u003cp\u003e-2.514\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"126\"\u003e\n\u003cp\u003e-4.044*\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"123\"\u003e\n\u003cp\u003eLNREN\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"123\"\u003e\n\u003cp\u003e-1.533\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"123\"\u003e\n\u003cp\u003e-4.539*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"127\"\u003e\n\u003cp\u003e-1.533\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"126\"\u003e\n\u003cp\u003e-3.742*\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"123\"\u003e\n\u003cp\u003eLNURB\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"123\"\u003e\n\u003cp\u003e0.071\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"123\"\u003e\n\u003cp\u003e-5.791*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"127\"\u003e\n\u003cp\u003e0.732\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"126\"\u003e\n\u003cp\u003e4.201*\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"5\" width=\"123\"\u003e\n\u003cp\u003eNote: * and ** indicates the statistical significance at 1% and 5%, respectively. Results are obtained from using Stata 14.2.\u003c/p\u003e\n\u003cp\u003eSource: Authors' estimation.\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp style=\"text-align: center;\"\u003eTable 8\u003c/p\u003e\n\u003cp style=\"text-align: center;\"\u003ePedroni panel co-integration test result\u003c/p\u003e\n\u003ctable style=\"margin-left: auto; margin-right: auto;\" border=\"1\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"107\"\u003e\n\u003cp\u003eStatistic\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"103\"\u003e\n\u003cp\u003ep-value\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"109\"\u003e\n\u003cp\u003eWeighted\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003ep-value\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"6\" width=\"601\"\u003e\n\u003cp\u003eAlternative hypothesis: Common AR coefs. (within-dimension)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" width=\"188\"\u003e\n\u003cp\u003ePanel v-statistic\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"107\"\u003e\n\u003cp\u003e0.297\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"103\"\u003e\n\u003cp\u003e0.382\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"109\"\u003e\n\u003cp\u003e0.108\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e0.457\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" width=\"188\"\u003e\n\u003cp\u003ePanel rho-statistic\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"107\"\u003e\n\u003cp\u003e0.520\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"103\"\u003e\n\u003cp\u003e0.698\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"109\"\u003e\n\u003cp\u003e0.667\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e0.748\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" width=\"188\"\u003e\n\u003cp\u003ePanel PP-statistic\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"107\"\u003e\n\u003cp\u003e-3.467*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"103\"\u003e\n\u003cp\u003e0.000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"109\"\u003e\n\u003cp\u003e-2.896*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e0.002\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" width=\"188\"\u003e\n\u003cp\u003ePanel ADF-statistic\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"107\"\u003e\n\u003cp\u003e-3.463*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"103\"\u003e\n\u003cp\u003e0.000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"109\"\u003e\n\u003cp\u003e-2.875*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e0.002\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"6\" width=\"601\"\u003e\n\u003cp\u003eAlternative hypothesis: Individual AR coefficients (between-dimension)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" width=\"188\"\u003e\n\u003cp\u003eGroup rho-statistic\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"107\"\u003e\n\u003cp\u003e1.320\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"103\"\u003e\n\u003cp\u003e0.907\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"109\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" width=\"188\"\u003e\n\u003cp\u003eGroup PP-statistic\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"107\"\u003e\n\u003cp\u003e-3.260*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"103\"\u003e\n\u003cp\u003e0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"109\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" width=\"188\"\u003e\n\u003cp\u003eGroup ADF-statistic\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"107\"\u003e\n\u003cp\u003e-3.103*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"103\"\u003e\n\u003cp\u003e0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"109\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"6\" width=\"188\"\u003e\n\u003cp\u003eNote: * indicates significance at 1 and 10% level. Results are obtained from using Eviews 10.\u003c/p\u003e\n\u003cp\u003eSource: Authors' estimation.\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp style=\"text-align: center;\"\u003eTable 9\u003c/p\u003e\n\u003cp style=\"text-align: center;\"\u003eResults of Westerlund (2007) co-integration test\u003c/p\u003e\n\u003ctable style=\"margin-left: auto; margin-right: auto;\" border=\"1\" width=\"0\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd width=\"155\"\u003e\n\u003cp\u003eStatistic\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"156\"\u003e\n\u003cp\u003eValue\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"155\"\u003e\n\u003cp\u003eZ - value\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"155\"\u003e\n\u003cp\u003eP-value\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"155\"\u003e\n\u003cp\u003eGt\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"156\"\u003e\n\u003cp\u003e-3.897**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"155\"\u003e\n\u003cp\u003e1.751\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"155\"\u003e\n\u003cp\u003e0.040\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"155\"\u003e\n\u003cp\u003eGa\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"156\"\u003e\n\u003cp\u003e-0.782\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"155\"\u003e\n\u003cp\u003e4.720\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"155\"\u003e\n\u003cp\u003e1.000\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"155\"\u003e\n\u003cp\u003ePt\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"156\"\u003e\n\u003cp\u003e-9.682*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"155\"\u003e\n\u003cp\u003e3.093\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"155\"\u003e\n\u003cp\u003e0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"155\"\u003e\n\u003cp\u003ePa\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"156\"\u003e\n\u003cp\u003e-1.025\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"155\"\u003e\n\u003cp\u003e3.892\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"155\"\u003e\n\u003cp\u003e1.000\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"4\" width=\"155\"\u003e\n\u003cp\u003eNote: *and ** indicates the statistical significance at 1% and 5% level. Results are obtained from using Stata 14.2.\u003c/p\u003e\n\u003cp\u003eSource: Authors' estimation.\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp style=\"text-align: center;\"\u003eTable 10\u003c/p\u003e\n\u003cp style=\"text-align: center;\"\u003ePanel data analysis of long-run material footprint elasticity\u003c/p\u003e\n\u003ctable style=\"margin-left: auto; margin-right: auto;\" border=\"1\" width=\"0\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd width=\"160\"\u003e\n\u003cp\u003eVariable Dependent variable: LNMF\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"106\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCoefficient\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"107\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSE\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"124\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003et-statistics\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"124\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ep-value\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"160\"\u003e\n\u003cp\u003eLNGDP\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"106\"\u003e\n\u003cp\u003e0.609\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"107\"\u003e\n\u003cp\u003e0.017\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"124\"\u003e\n\u003cp\u003e35.507\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"124\"\u003e\n\u003cp\u003e0.000*\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"160\"\u003e\n\u003cp\u003eLNHC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"106\"\u003e\n\u003cp\u003e-0.392\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"107\"\u003e\n\u003cp\u003e0.043\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"124\"\u003e\n\u003cp\u003e-9.202\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"124\"\u003e\n\u003cp\u003e0.000*\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"160\"\u003e\n\u003cp\u003eLNNR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"106\"\u003e\n\u003cp\u003e0.310\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"107\"\u003e\n\u003cp\u003e0.063\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"124\"\u003e\n\u003cp\u003e4.902\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"124\"\u003e\n\u003cp\u003e0.000*\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"160\"\u003e\n\u003cp\u003eLNTR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"106\"\u003e\n\u003cp\u003e-0.455\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"107\"\u003e\n\u003cp\u003e0.030\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"124\"\u003e\n\u003cp\u003e-15.140\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"124\"\u003e\n\u003cp\u003e0.000*\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"160\"\u003e\n\u003cp\u003eLNREN\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"106\"\u003e\n\u003cp\u003e0.184\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"107\"\u003e\n\u003cp\u003e0.029\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"124\"\u003e\n\u003cp\u003e6.361\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"124\"\u003e\n\u003cp\u003e0.000*\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"160\"\u003e\n\u003cp\u003eLNURB\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"106\"\u003e\n\u003cp\u003e1.630\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"107\"\u003e\n\u003cp\u003e0.002\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"124\"\u003e\n\u003cp\u003e1066.014\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"124\"\u003e\n\u003cp\u003e0.000*\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"160\"\u003e\n\u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"106\"\u003e\n\u003cp\u003e0.97\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"107\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"124\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"124\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"160\"\u003e\n\u003cp\u003eAdjusted R-squared\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"106\"\u003e\n\u003cp\u003e0.97\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"107\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"124\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"124\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"5\" width=\"160\"\u003e\n\u003cp\u003eNote: * indicate the significance level at 1%. Results are obtained from using Eviews 10.\u003c/p\u003e\n\u003cp\u003eSource: Authors' estimation.\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp style=\"text-align: center;\"\u003e\u0026nbsp;Table 11\u003c/p\u003e\n\u003cp style=\"text-align: center;\"\u003eHeterogeneous panel causality test\u003c/p\u003e\n\u003ctable style=\"margin-left: auto; margin-right: auto;\" border=\"1\" width=\"106%\"\u003e\n\u003ctbody\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" width=\"71%\"\u003e\n\u003cp\u003eNull hypothesis:\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"15%\"\u003e\n\u003cp\u003eZ bar-stat\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"13%\"\u003e\n\u003cp\u003ep-value\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 60px;\"\u003e\n\u003ctd style=\"height: 60px;\" width=\"71%\"\u003e\n\u003cp\u003eLNGDP does not homogeneously cause LNMF\u003c/p\u003e\n\u003cp\u003eLNMF does not homogeneously cause LNGDP\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 60px;\" width=\"15%\"\u003e\n\u003cp\u003e23.625*\u003c/p\u003e\n\u003cp\u003e1.558\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 60px;\" width=\"13%\"\u003e\n\u003cp\u003e0.000\u003c/p\u003e\n\u003cp\u003e0.119\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 60px;\"\u003e\n\u003ctd style=\"height: 60px;\" width=\"71%\"\u003e\n\u003cp\u003eLNHC does not homogeneously cause LNMF\u003c/p\u003e\n\u003cp\u003eLNMF does not homogeneously cause LNHC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 60px;\" width=\"15%\"\u003e\n\u003cp\u003e-0.223\u003c/p\u003e\n\u003cp\u003e3.048*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 60px;\" width=\"13%\"\u003e\n\u003cp\u003e0.823\u003c/p\u003e\n\u003cp\u003e0.002\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 60px;\"\u003e\n\u003ctd style=\"height: 60px;\" width=\"71%\"\u003e\n\u003cp\u003eLNNR does not homogeneously cause LNMF\u003c/p\u003e\n\u003cp\u003eLNMF does not homogeneously cause LNNR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 60px;\" width=\"15%\"\u003e\n\u003cp\u003e3.083*\u003c/p\u003e\n\u003cp\u003e1.207\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 60px;\" width=\"13%\"\u003e\n\u003cp\u003e0.002\u003c/p\u003e\n\u003cp\u003e0.228\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 60px;\"\u003e\n\u003ctd style=\"height: 60px;\" width=\"71%\"\u003e\n\u003cp\u003eLNREN does not homogeneously cause LNMF\u003c/p\u003e\n\u003cp\u003eLNMF does not homogeneously cause LNREN\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 60px;\" width=\"15%\"\u003e\n\u003cp\u003e7.450*\u003c/p\u003e\n\u003cp\u003e0.325\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 60px;\" width=\"13%\"\u003e\n\u003cp\u003e0.000\u003c/p\u003e\n\u003cp\u003e0.745\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 60px;\"\u003e\n\u003ctd style=\"height: 60px;\" width=\"71%\"\u003e\n\u003cp\u003eLNTR does not homogeneously cause LNMF\u003c/p\u003e\n\u003cp\u003eLNMF does not homogeneously cause LNTR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 60px;\" width=\"15%\"\u003e\n\u003cp\u003e4.298*\u003c/p\u003e\n\u003cp\u003e31.913*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 60px;\" width=\"13%\"\u003e\n\u003cp\u003e0.000\u003c/p\u003e\n\u003cp\u003e0.000\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 60px;\"\u003e\n\u003ctd style=\"height: 60px;\" width=\"71%\"\u003e\n\u003cp\u003eLNURB does not homogeneously cause LNMF\u003c/p\u003e\n\u003cp\u003eLNMF does not homogeneously cause LNURB\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 60px;\" width=\"15%\"\u003e\n\u003cp\u003e15.794*\u003c/p\u003e\n\u003cp\u003e3.751*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 60px;\" width=\"13%\"\u003e\n\u003cp\u003e0.000\u003c/p\u003e\n\u003cp\u003e0.000\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 13.5907px;\"\u003e\n\u003ctd style=\"height: 13.5907px;\" colspan=\"3\" width=\"71%\"\u003e\n\u003cp\u003eNote: * indicate the significance level at 1%. Results are obtained from using Eviews 10.\u003c/p\u003e\n\u003cp\u003eSource: Authors' estimation.\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"environmental-science-and-pollution-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"espr","sideBox":"Learn more about [Environmental Science and Pollution Research](https://www.springer.com/journal/11356)","snPcode":"11356","submissionUrl":"https://submission.nature.com/new-submission/11356/3","title":"Environmental Science and Pollution Research","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Renewable energy, urbanization, material footprint, BRICS","lastPublishedDoi":"10.21203/rs.3.rs-225820/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-225820/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis paper explores the relationship between renewable energy consumption, urbanization, human capital, trade, natural resources, and material footprint for BRICS countries from 1990 to 2016. We apply the cross-sectional dependency test to check the correlation among the cross-section. Then, we use the second-generation panel test like CADF and CIPS to check the stationary in the series. After that, we go for the panel cointegration test, i.e., Pedroni and Westerlund panel cointegration, to know the long-run relationship of the variable. The test results reject the null hypothesis of no cointegration among the variables and accept cointegration. The long-run results indicate that economic growth, natural resources, renewable energy, and urbanization have reduced the environmental quality for BRICS countries in case of material footprint employed to measure environmental degradation. However, foreign trade, human capital improves environmental quality. Based on the empirical results, the study recommended some important policy suggestions to achieve sustainable development in BRICS countries.\u003c/p\u003e","manuscriptTitle":"Determinants of Material Footprint in BRICS Countries: An Empirical Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2021-02-24 00:09:26","doi":"10.21203/rs.3.rs-225820/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Accept","date":"2021-03-01T09:46:45+00:00","index":"","fulltext":""},{"type":"reviewersInvited","content":"","date":"2021-02-11T00:00:00+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2021-02-11T00:00:00+00:00","index":0,"fulltext":""},{"type":"editorAssigned","content":"","date":"2021-02-10T00:00:00+00:00","index":"","fulltext":""},{"type":"submitted","content":"Environmental Science and Pollution Research","date":"2021-02-09T02:54:00+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"environmental-science-and-pollution-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"espr","sideBox":"Learn more about [Environmental Science and Pollution Research](https://www.springer.com/journal/11356)","snPcode":"11356","submissionUrl":"https://submission.nature.com/new-submission/11356/3","title":"Environmental Science and Pollution Research","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"975c17e3-373a-449f-bc8a-aee460a943ea","owner":[],"postedDate":"February 24th, 2021","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":2577742,"name":"Environmental Engineering"},{"id":2577743,"name":"Environmental Policy"}],"tags":[],"updatedAt":"2021-08-18T19:37:07+00:00","versionOfRecord":{"articleIdentity":"rs-225820","link":"https://doi.org/10.1007/s11356-021-13309-7","journal":{"identity":"environmental-science-and-pollution-research","isVorOnly":false,"title":"Environmental Science and Pollution Research"},"publishedOn":"2021-03-15 19:09:18","publishedOnDateReadable":"March 15th, 2021"},"versionCreatedAt":"2021-02-24 00:09:26","video":"","vorDoi":"10.1007/s11356-021-13309-7","vorDoiUrl":"https://doi.org/10.1007/s11356-021-13309-7","workflowStages":[]},"version":"v1","identity":"rs-225820","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-225820","identity":"rs-225820","version":["v1"]},"buildId":"FbvkV6FR0MCFSLy54lSbu","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}

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