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Modern Economy, 2010, 1, 100-111 doi:10.4236/me.2010.12010 Published Online August 2010 (http://www. SciRP.org/journal/me) Copyright © 2010 SciRes. ME Environmental Standards and Trade Volume Nicholas Mangee*, Bruce Elmslie University of New Hampshire, Department of Economics, Whittemore School of Business and Economics, Durham USA E-mail: njf7@unh.edu, bte@cisunix.unh.edu Received June 10, 2010; revised July 20, 2010; accepted July 24, 2010 Abstract This paper presents a theoretical and empirical analysis of the effects of environmental regulation on bilateral trade volume. We use a gravity model of trade flows and find weak evidence that differences in regulation are a source of comparative advantage. We also find evidence against the race-to-the-bottom hypothesis in that increases in standards in both high and low standard countries increase bilateral trade volume. We use 1999 data on GDP, population, and environmental stringency for 39 countries. Keywords: Environmental Standards, Trade Volume, Gravity Model, Comparative Advantage 1. Introduction The link between environmental regulation and interna- tional trade has warranted considerable attention by economists, environmentalists and policy makers alike. A substantial amount of theoretical and empirical re- search has addressed the question of how environmental stringency affects international trade. Conventional trade theory suggests that country characteristics, such as land and capital define comparative advantage. Environ- mental quality, endowments, capacities and policies have also been considered as determinants of each country’s comparative advantage1. This paper develops a model of comparative advan- tage based on differences in environmental regulation between countries. We then use a gravity model to test the hypothesis that as the differences in environmental regulation across countries increase, bilateral trade flows also increase as predicted by the theory. Differences in regulation are found to have a positive but weak effect on bilateral trade volume. This result could also be consistent with a race-to-the- bottom in terms of environmental standards for low stan- dard countries. Therefore, we develop a second test bet- ween overall standards and trade volume. We find strong evidence that increased standards by both countries pro- motes bilateral trade volume. Thus, even if trade volume is positively associated with differences in regulation, no incentive appears to exist for low standard countries to increase trade flows by lowering standards. The existing literature has yet to demonstrate the ef- fect of differentiated regulation on gross bilateral trade flows across a large set of countries. Previous studies have examined the effect that regulation has on the com- position of trade regarding one country or a small set of countries. Other studies have demonstrated regulation in isolation, failing to examine the collective differences between stringency as a source of comparative advantage. Given the ubiquitous nature of comparative advantage in the theoretical determination of trade patterns between countries, it is surprising that many of the existing stud- ies have eschewed this approach. This paper represents the first study of the effects of environmental regulation on trade volume that fully incorporates comparative ad- vantages created by differentiated environmental regula- tion between trading partners. Within the last three decades, two popular positions have emerged regarding the interaction of stringency and international trade in environmentally intensive goods rather than in overall trade volume. The first hypothesis suggests that, as a country imposes higher environmental regulations, the cost of production increases. This may result in a decrease in exports of pollution intensive goods and an increase in imports. Thus, higher strin- gency may lead to a decrease in comparative advantage in pollution intensive goods for a country [3,4]. Fur- thermore, to maintain international competitiveness, a country may purposefully set lax environmental stan- dards. This potential global degradation in stringency is consistent with the race-to-the-bottom hypothesis. These theories hypothesize that in order to not lose the business *We want to thank Robert Mohr, Michael Goldberg, Edinaldo Tebaldi, Torsten Schmidt and Sinthy Kounlasa for useful discussion and assis- tance. 1For analysis on the environment and comparative advantage see Pethig [1] and Siebert [2]. N. MANGEE ET AL. Copyright © 2010 SciRes. ME 101 of pollution intensive goods whose production may be shipped abroad, countries deliberately race-to-the-bot- tom in environmental regulations. Similarly, these costs incurred by increasing a firm’s total cost of production coupled with the “pollution haven hypothesis” suggest that pollution intensive production will migrate to re- gions of lax regulations [5,6]. With low environmental demand, developing countries may attempt to increase their share of the global market by setting lax regulations creating a comparative advantage in pollution intensive goods [7]. The empirical performance of these hypothe- ses has been poor [3,8,9]. Our results represent the first clear empirical case that comparative advantage is not associated with the development of pollution havens, because higher standards of low standard countries in- creases rather than decreases bilateral trade volume. The contrasting hypothesis concerning the effect of environmental stringency on foreign trade is the Porter hypothesis [10,11]. This hypothesis predicts that an in- crease in environmental regulation will stimulate ad- vancements in environmentally friendly technology. This tightening of standards will encourage firms to seek new technology for the long run and possibly differentiate their products by producing environmentally friendly ones. Porter, however, makes no distinction between high and low standard countries in terms of the relation- ship between inventiveness and trade volume. However, a loose interpretation of Porter is that countries should not fear that increased regulation will necessarily de- crease trade volume. It may even increase trade volume as increased standards increase the incentive to innovate. The test that we develop supports this loose interpreta- tion of Porter. 2. Empirical Framework and Literature Review There is a vast literature on the relationship between sta- ndards and international trade.2 Typically, the methodo- logical approach for determining the effect of regulation on trade involves a supply side model incorporating the determinants of international trade. The conventional su- pply side approach has followed a Heckscher-Ohlin-Sa- muelson (HOS) theoretical framework and often the He- ckscher-Ohlin-Vanek (HOV) model in empirical work. The HOS model predicts that a country will export the good that intensively uses its relatively abundant factor. The environment is incorporated in HOS as a factor of production predicting that a country with a greater envi- ronmental capacity for pollutants is relatively better en- dowed and will export pollution intensive goods. The HOV model allows for a direct empirical test by predict- ing the factor composition of trade for a country based on factor abundance. This model has been widely applied within the environmental trade literature as it allows for trade to be decomposed by pollution intensity and indus- try. Tobey [9] uses a cross section HOV model to investi- gate whether increased domestic regulation during the 1960’s and 1970’s affected trade patterns in pollution intensive industries. Using 23 countries and 1975 data, he regresses trade in pollution intensive commodities across countries on resource endowment characteristics such as land area, literate workers, and capital. The re- sults suggest that there is no significant effect of in- creased environmental stringency on net exports of pol- lution intensive goods. Tobey [9] does not investigate the role of regulatory differences specifically in explaining trade flows between countries. Ratnayake [3] takes an inter industry trade approach consisting of 109 industries over a 13 year period (1980- 1993) in New Zealand. Following the HOS/HOV models, New Zealand, as a developed capital abundant country is expected to possess a comparative advantage in the pro- duction of goods that intensively use capital in produc- tion. Ratnayake uses a revealed comparative advantage (RCA) index to determine if increased environmental standards lead to a decrease in competitiveness in the manufacturing sector. By comparing trade behavior of New Zealand’s to four other country groups (world, OECD, ASEAN, and DC’s) this study finds that, in spite of high environmental stringency in New Zealand, it’s exports in pollution intensive, or environmentally sensi- tive goods was not decreased. Unlike Ratnayake, a number of other studies have sho- wn an increase in environmental standards to have a ne- gative impact on the exports of pollution intensive indus- tries. Wilson and colleagues [14] takes a developing country perspective of stringency on trade by examining the export behavior of 24 countries (6 OECD and 18 non-OECD) between 1994 and 1998 for 5 pollution in- tensive industries. Utilizing the HOV model, this study finds that more stringent environmental standards result in lower exports of pollution intensive goods. Moreover, this study suggests that increased environmental standa- rds have a significantly greater impact on exports of de- veloping countries than developed ones. It is well known that the HOV equation is inconsistent with trade data [15]. Given these weak general founda- tions, it is not surprising that the results of HOV models applied to the relationship between environmental stan- dards and trade volume have been inconsistent at best. Alternative approaches, therefore, need to be considered. Given the empirical problems with the HOV model, we utilize a gravity framework that incorporates differ- entiated environmental regulation as a source of bilateral trade volume. Such a model of international trade was first developed by Tinbergen [16] to model trade volume 2For an overview of the literature and methodologies see Alpay [12] and Van Beers and Van Den Bergh [13]. N. MANGEE ET AL. Copyright © 2010 SciRes. ME 102 between two countries in terms of their GDP’s and the geographical distance between them.3 The gravity model, after being log linearized, takes the following form: ijijjiij DISTGDPGDPX 3210 lnlnln (1) Where: ln denotes the natural logarithm; ij X denotes gross bilateral trade flows between coun- try i and j; i GDP , j GDP denote the GDP of country i and j re- spectively; ij DIST denotes the distance between country i and j; ij denotes the error term. 3210 ,,, are parameters. The expected relationship between these variables is as follows: since the dependent variable is gross bilateral trade volume, the signs of 1 and 2 are expected to be positive since larger countries trade more with each other. The parameter 3 is expected to be negative due to transportation costs. However, the variable ij DIST may represent any factor that impedes trade. Such factors that have been considered include language, historical, and cultural differences. These characteristics are ex- pected to have a negative relationship with the volume of trade between two countries. In addition, a dummy vari- able controlling for a border effect is often included in empirical work and is normally found to be significant and positive for countries sharing a border. The gravity model is used in the present study because it incorporates gross bilateral trade volume between countries as the dependant variable and it allows for a large number of countries to be analyzed. Furthermore, in contrast to the HOS/HOV performance, the gravity model performs much better empirically. The gravity model has not yet been utilized as a theoretical tool revealing comparative advantage arising from differences in environmental reg- ulation. The empirical literature incorporating regulation into the gravity model include Jug and Mirza [18]; Grether and Melo [5]; Van Beers and Van Den Bergh [19]; and Harris and colleagues [20]. Some studies applying the gravity model decompose total trade into imports and exports and further into (non) pollution intensive imports and/or exports, in an attempt to capture the effect of in- creased stringency on total trade volume. Jug and Mirza [18]4 show that increases in environmental expenditures result in a decrease in net exports. These results are con- sistent with the pollution havens hypothesis and run counter to our so-called “loose” interpretation of the Porter hypothesis. In testing the effects of increased stringency on bilat- eral exports, Van Beers and Van Den Bergh [19] find a strong negative relationship between total exports and total imports of 21 OECD countries. Part of this study gives evidence to support the pollution havens hypothe- sis while the negative effect of standards on a developed country’s imports presents a surprising result. This result may suggest the presence of import barriers when regu- lation is increased within a country. Another study that decomposes bilateral trade volume into imports and ex- ports is Grether and Melo [5]. They test total imports against pollution intensive imports as well as non-res- ource based (footloose) imports against resource based (non-footloose) imports. This study finds support for the pollution haven hypothesis for the footloose industries due to the increase in imports for developed countries when firms are allowed to migrate across countries. The gravity studies have shown no robust conclusive evidence of the effect of regulation on trade flows [18]. The lack of consensus within this literature may stem from the lack of a comprehensive measure of the enviro- nment or regulatory stringency. Measures of stringency should incorporate various indicators of a country’s stan- dards, enforcement and policy implementation. Most da- ta sets used in previous studies are either not comprehen- sive or they cover a small sample of countries. The pre- sent study utilizes a recent 2001 comprehensive measure of environmental stringency over a large sample of coun- tries. In addition to the data problem, the literature to date has been limited by the Solomon-like choice betw- een theory and empirics. Researchers choosing the gen- eral HOS-HOV approach are able to base their study on a solid theoretical foundation, but must pay in terms of weak empirical support. The well-known empirical shor- tcomings of the HOV model with regard to the factor co- ntent of trade in factors such as capital and various quali- ties of labor also plague environmental endowment me- asures. On the other hand, researchers choosing the grav- ity approach are able to tap into a solid empirical frame- work but must pay in terms of theoretical underpinnings. As a result, this literature has developed on an ad hoc basis. We argue that such a choice is not necessary. We aug- ment the work of Helpman and Krugman [21] and Hel- pman [22] on the theoretical foundation of a gravity equ- ation to make theoretical predictions regarding the effect of differential standards on trade volume. In so doing, we are able to develop an empirical gravity model with the strong theoretical underpinnings of an HOS approach. 3. Theory The relationship between environmental standards and 3For an overview of the gravity model see Head [17]. 4For previous studies applying the gravity model to trade and the envi- ronment see Jug and Mirza [18] and references therein. N. MANGEE ET AL. Copyright © 2010 SciRes. ME 103 bilateral trade volume can be developed in a straight for- ward manner using a 2 × 2 × 2 factor proportions model. However, as a basis for empirical work using a gravity equation, the factor proportions model has little to con- tribute. In this section we first develop the factor propor- tions model using the environment as one factor of pro- duction. Then we extend the analysis to allow for one sector to produce differentiated products within a mono- polistically competitive setting. This approach allows for the gravity model and comparative advantage based on differential environmental standards to jointly determine trade volume. 3.1. The Factor Proportions Model Consider a 2 × 2 × 2 economy producing goods manu- facture (M) and food (F) with factors labor (L) and the environment (E). The manufactured good is E intensive. Consumers in each country maximize identical homo- thetic utility functions. Foreign variables will be desig- nated by *. Figure 1 shows the relevant factor-price equalization set for such an economy. O is the Home origin. The world output of M and F are given by *( OOA )'A and )*(' AOOA respectively. First, allow the relative endowments to be equal be- tween countries so that the endowment point falls on the diagonal such as at point C. Home production and con- sumption of the manufactured good and food are at OCM and OCF respectively. Thus with equal relative endow- ments, autarky prevails. Next allow the relative endowment to move along line segment B’B from C to G. Relative consumption remains at C, but production of M increases for Home to OQM, while foreign production of M decreases from CMA to QMA. Similarly for food, Home production falls to OQF while Foreign production increases to F QA'. Home exports QMCM and imports CFQF. However, the move need not be along a given relative GDP line. Under factor-proportions theory trade volume (VT) is a function of relative endowment differences. Thus country size is not an independent determinant of trade volume. As shown in another form by Helpman and Krugman [21], assuming Home is E abundant: LEVT LE (2) where 0 E and 0 L above the diagonal OO*. Therefore isotrade lines within the factor-price equaliza- tion set '* AOAO are linear with slopes OO* and with VT increasing monotonically from zero along OO* to a maximum when relative endowments are at )'(AA . This can be expressed as Proposition 1. H E ' E *'O O* A M Q M C C F F Q G C O L 'A B 'A B' Figure 1. Factor-price equalization set for economy. N. MANGEE ET AL. Copyright © 2010 SciRes. ME 104 Proposition 1 Trade volume within the factor-price equalization set is maximized when relative endowment differenc- es are maximized. Within the factor-price equalization set the world en- dowment of E and L are fixed at OE and OL respectively. Utilizing such a framework the effect of differential en- vironmental standards takes on a very particular inter- pretation. Beginning with the endowment at C, a move from C involves a switching of either or both factors bet- ween countries. For example, a move from C to H imp- lies that Foreign gives CH of its environmental endow- ment to Home. While such an exercise is useful to see how environ- mental policy can influence comparative advantage, it is not a realistic interpretation of operating standards. A more realistic interpretation of an increase in environm- ental standards is the decreasing of a country’s effective environmental endowment while holding the other coun- try’s endowment fixed. Using this interpretation neces- sarily involves moving from one factor-price equaliza- tion set to another. However, the analysis is surprisingly simple. Consider the original endowment point at C. This po- int is on the VT = 0 isotrade line. If the foreign country adopts environmental standards that decrease its endow- ment, the world endowment of environment is decreased from OE to 'OE. The new origin for Foreign is *'O. The new diagonal is shown as OO*′. Now C is above the new *'OO VT = 0 isotrade line. Thus, the change in standards for Foreign away from those of Home incre- ases trade volume. Given linear isotrade lines this is a general result and can be expressed as Proposition 2. Proposition 2 Within the factor proportions model, a change in environmental regulation that increases relative end- owment differences between countries increases trade volume. b) Monopolistic Competition Proposition 2 generates an empirically testable link be- tween relative regulatory environmental differences and trade volume. However, it is inconsistent with an emp- irical model that includes country size as an independent determinant of trade volume. Therefore, we move to a monopolistically competitive setting that links relative standards and trade volume within a gravity framework. Allow M to be monopolistically competitive where each variety m is equally priced, equally produced and earns zero profit. OA is now interpreted as many varie- ties of M. If n is the number of firms in industry mM, nM /. Returning to Figure 1, allow the initial endowment to be at G. Given that m = m*, Home is a net exporter of manufacturing goods. The value of Home exports is For- eign’s share of world GDP (s*) times the value of Home production of nmpsMM *, . Let the Home production of varieties be given by mnM H. Given that trade must be balanced in the absence of international borrow- ing overall trade volume is: HMM MpsnmpsVT *2*2 (3) Assume each country is of equal size in terms of world GDP. The situation is depicted in Figure 2 where OC = O*C and the endowment point is G. Using the standard ^ notation to refer to relative chan- ge s ds sge ˆ .,. log differentiation of 3 generates: H Ms VT ˆ * ˆ (4) Given that GDP + GDP* = world GDP which is con- stant: GDP s s GDP s * ** ˆ (5) Along H M GDP OZ ˆ , so ,GDP GDP s s VT or ^ 1* s VT GDP s (6) Using Equation (6), at 1 * , s s G implying that 0 VT . Moving from O to G along ssOZ*, im- plying that 0 VT . Moving from G to Z, s > s*, thus 0 VT . Trade volume increases from O to G and de- creases from G to Z leading to Proposition 3. Proposition 3 Trade volume is maximized when countries are of equal size (gravity equation). Now, allow the endowment point to move from C along 'BB to 'G so that holding relative size constant, Home is getting more E abundant. Along 0* ˆ ' s BB, thus: H M VT ˆ (7) From C to 'G, OMH ˆ, trade volume is increasing resulting in Proposition 4. Proposition 4 Within a factor-price equalization set, given coun- try size is constant, trade volume increases as endow- ments become less similar. Taking Propositions 3 and 4 together trade volume is maximized at 'G. Holding country size constant, as environmental regulations effectively makes the relative N. MANGEE ET AL. Copyright © 2010 SciRes. ME 105 E L ' B O ' A C G Z 'G A B O* Figure 2. Factor-price equalization set when countries are of equal size. endowments less similar between countries, bilateral tra- de volume increases. However, interpreting an increase in regulation as decreasing the effective size of the envi- ronmental endowment has a negative effect on GDP. Given the non linearity of isotrade lines within the mo- nopolistic competitive framework [21], no general proposition can be stated beyond Proportion 4. Our empirical framework is developed using Proposi- tion 4. In that framework we regress bilateral trade vol- ume against, size, distance and environmental standards. The coefficient on environmental standards is interpreted holding relative size constant, thus moving along 'BB toward 'G is a change in relative environmental stan- dards. From Propositions 2 and 4, the models based on the factor proportions model of interindustry trade pre- dict that increases in the differences in stringency of en- vironmental standards between countries increases bilat- eral trade volume. 4. The Environmental Index The environmental index utilized in this study comes fr- om the Global Competitiveness Report 2001-2002 [23]. The Environmental Regulatory Regime Index (ERRI) ca- ptures the effects of several variables in constructing one absolute ranking for 71 countries to measure environ- mental stringency5. This number of countries far exceeds other indices such as Walter and Ugelow [24] and Das- gupta and colleaugues [25] which include 23 and 31 countries respectively. The absolute ranking in the ERRI for each country ranges from –1.743 for Paraguay to 2.303 for Finland. The statistical methodology follows bilateral regressions and collects all significant variables to construct the ERRI. For the purposes of the present paper, the variables that directly influence the ERRI are presented in detail below. For an explanation of the dependent variables and other indirect explanatory variables within the index see the Appendix. The ERRI is divided into two groups of independent variables. The first group is comprised of six categories: 1) stringency and environmental pollution standards, 2) sophistication of regulatory structure, 3) quality of the environmental information available, 4) extent of subsidization of natural resources, 5) strictness of government, and 6) quality of environmental institu- tions [23]. The six indicators of environmental stringency listed above give a very inclusive measure for regulation. In- sight into a country’s regulatory regime can be collected from these indicators because a variety of data collection processes are utilized in their construction.6 The data 5For a list of all 71 countries see Etsy and Porter [23]. 6Data sources include the Global Competitiveness Report (GCR), En- vironmental Sensitivity Index (ESI), World Bank, and the World Eco- nomic Forum [23,26]. N. MANGEE ET AL. Copyright © 2010 SciRes. ME 106 sources are mentioned below with a detailed description of the stringency indicator. The variables mentioned be- low represent the best available and comprise one of the most extensive, inclusive and accurate measurements of a country’s environmental stringency. The stringency and pollution standards category uses the Global Competitiveness Report (GCR) survey to me- asure air, water, toxic waste, and chemical regulation for a country. It is expected that this regulatory measure has an inverse relationship with all three dependent variables defined in the Appendix. Higher regulation results in lower urban particulates, lower SO2 concentrations and greater energy efficiency. The sophistication of regulatory structure category measures the characteristics of the regime. This concerns the clarity in which the regulations are defined, the pro- gressive nature of the regime, the structure of the regime to promote competitiveness, and the relationship between business and government. This category is also expected to have an inverse relationship with the environmental performance (dependent) variables. The category regarding the quality of environmental information available rests on data from the World Eco- nomic Forum and the Environmental Sustainability In- dex (ESI). This measures the extent to which environ- mental and economic data are available for policy mak- ing and regulatory enforcement. This study relies on four proxy variables for this measure: 1) the extent to which data is collected, 2) the extent to which sustainable de- velopment data is available coupled with plans support- ing environmental policy, 3) structure assessing envi- ronmental decisions, and 4) the extent to which a country has plans for environmental action. Again, there should be a negative relationship between this category and the dependent variables. The extent of the subsidization of natural resources category recovers data from the GCR survey. This capt- ures the extent to which a country subsidizes energy and natural resources. However, there is expected to be a po- sitive relationship between the natural resource subsidies and the three environmental performance variables. The strictness of enforcement category measures two factors within a country. The enforcement of environme- ntal regulations are measured coupled with the extent to which a country follows through with international ag- reements regarding environmental policy. There should be an inverse relationship between this measure and the dependent variables as countries that have high environ- mental enforcement should expect to have lower level of pollution concentration and higher energy efficiency. The final category involves the quality of environ- mental institutions of a country. This measure captures the effect that nongovernmental organizations (NGOs) have on enforcing the environmental decisions and ac- tions of the government. This may include environmental organizations that further research to aid government endeavors or even institutions that become substitutes for the governmental sector. These NGOs may increase a country’s ability to control for pollution by increasing awareness and information on environmental issues. Data is gathered from the ESI and this measure is ex- pected to have a negative relationship with the dependant variables. 5. Empirical Analysis and Data The data on bilateral trade volume was recovered from the World Bank for 1999. The aggregate of bilateral trade volume from country i to country j was collected by summing the total exports from i to j with the imports from j to i. Since, duplicate pairings within the data set would cause statistical problems the country that is alp- habetically first was considered i and no duplicate pair- ings were included. Table 1 expresses the pairings of bilateral trade volume and ERRI Index value for each country. The data for the remaining variables was collected as follows. The real 1999 GDP data is in billions of 2000 U.S. dollars while the per capita 1999 data was in 2000 U.S. dollars and found at the World Bank development indicators. The distance measure is in kilometers.7 This model is measured across 39 countries for the year 1999.8 The gravity model augmented to include differences in environmental regulation is given in Equation (8). Equa- tions (9) to (11) represent logical extensions of the model that are explained in more detail below. Each country’s ERRI number is represented as . The well-known effect of sharing a border is represented with a dummy variable ij with a value of 1 representing countries i and j sharing a border. 01 2 34 5 lnln ln ln ln iji j ij ij ijij X GDP GDP DIST (8) 01 2 345 2 6 lnln ln ln iji j ijijij ij ij XGDP GDP DIST (9) 7For distance and border data see www.macalester.edu/research/eco- nomics/page/Haveman/Trade.Resources/Data/Gravity/dist.txt. 8The countries are Argentina, Australia, Austria, Bulgaria, Bolivia, Canada, Chile, China, Colombia, Costa Rica, Denmark, Ecuador, Egypt, Spain, Finland, France, United Kingdom, Greece, Hungary, Indonesia, India, Ireland, Italy, Jordan, Japan, Korea, Mexico, Malaysia N etherlands, Norway, New Zealand, Peru, Philippines, Poland, Por t u- gal, Sweden, Thailand, United States and Venezuela. N. MANGEE ET AL. Copyright © 2010 SciRes. ME 107 Table 1. Pairings of bilateral trade volume and ERRI index across countries. Country Bilateral Trade Volume (1999: Billions of US$)* ERRI (1999) Argentina 23.98 –0.732 Australia 69.11 1.083 Austria 49.67 1.641 Bulgaria 3.30 –0.584 Bolivia 2.22 –0.743 Canada 370.96 1.297 Chile 17.77 0.177 China 220.04 –0.348 Colombia 11.88 –0.416 Costa Rica 7.63 –0.078 Denmark 52.82 1.384 Ecuador 3.29 –1.616 Egypt 1.51 –0.224 Finland 45.11 2.303 France 312.75 1.464 Greece 21.68 –0.619 Hungary 12.45 0.283 Indonesia 46.19 –0.758 India 34.47 –0.759 Ireland 82.67 0.546 Italy 228.14 0.498 Jordan 2.23 0.002 Japan 473.60 1.057 Korea 168.83 –0.121 Mexico 240.43 –0.602 Malaysia 85.43 –0.127 Netherlands 177.71 1.747 Norway 34.67 1.045 New Zealand 18.47 1.299 Peru 6.74 –0.722 Philippines 31.96 –1.014 Poland 24.08 0.005 Portugal 38.48 –0.028 Spain 158.27 0.437 Sweden 75.25 1.772 Thailand 54.68 –0.389 UK 345.73 1.185 United States 815.80 1.184 Venezuela 17.79 –1.079 *Bilateral trade volume based on sample of 39 countries 01 2 34 5 78 lnln ln ln ln min ,max , iji j ijiji j ijij ij XGDP GDP DIST (10) 01 2 345 2 67 8 lnln ln ln min , max , iji j ijijij ij ij ij ij XGDP GDP DIST (11) The results from Equations (8) through (11) are pre- sented in Table 2. Equation (8) represents the log lin- earized gravity model with a regulatory distance variable, ij , included as the absolute value of environme- ntal differences. The absolute value is used to express the effective differences without regard to sign. Gravity the- ory suggests that the larger the masses the greater the force. The significance of both GDPi and GDPj is no surprise as larger countries trade more with each other. As we further expect, there is a significant positive bor- der effect captured by ij and a significant negative distance effect captured by DISTij. These four measures appear strongly significant across the four equations above. The regulatory distance, ij , displays a positive but weak effect on bilateral trade volume. Equation (9) includes a quadratic regulatory distance term, 2 ij , measuring any diminishing affect of differences in standards. We would expect that this mea- surement be negative. The natural logarithm is dropped in this regression to avoid perfect multicollinearity among the exogenous variables. The absolute distance is still weakly positive but the point estimate is indicating a much stronger effect. The quadratic measure expresses a very weak negative relationship. Equations (10) and (11) add the log of the minimum and maximum regulations per country.9 The results from Equations (8) and (9) can be interpreted as giving weak support to the pollution havens hypothesis and the race-to-the-bottom for low standard countries since a decrease in standards for the low standard country in- creases trade volume. To better control for this effect, we add the high and low value of standards. A negative value on θ7 , which is the coefficient for the low standard country, would indicate that a decrease in standards of the low standard country increases trade volume. Both regressions produce positive and generally significant effects of increased standards on trade volume for each country. When the regulatory distance measure is drop- ped, Equation (11) shows a positive and significant relat- ionship between an increase in the standards of the higher regulated country and trade volume. Both equa- tions yield results refuting the race-to-the-bottom hy- pothesis. No country has an incentive to lower environ- mental standards in order to increase bilateral trade vol- ume given that the coefficient for the low standards country is positive and significant at the 10% level in both equations.10 9The original ERRI values ran from –1.743 to 2.303. In order to take the natural log of the max and min we added 2 to each value insuring that all values are positive. 10We also ran regressions without taking the natural log of R max and R min. The results were consistent though less significant. N. MANGEE ET AL. Copyright © 2010 SciRes. ME 108 Table 2. Results from OLS regression of the natural log of bilateral trade volume between country i and j on explanatory var- iables in column one. (8) (9) (10) (11) ln Xij ln Xij ln Xij ln Xij ln GDPi 1.061*** (35.75) 1.059*** (35.57) 1.025*** (32.52) 1.024*** (32.46) ln GDPj 0.941*** (31.41) 0.939*** (31.29) 0.909*** (28.99) 0.911*** (28.90) δij 1.268*** (4.20) 1.268*** (4.20) 1.344*** (4.47) 1.346*** (4.48) ln DISTij –0.897*** (–16.35) –0.897*** (–16.29) –0.848*** (–14.98) –0.852*** (–14.98) ij 0.217 (1.15) 0.287 (1.11) ln ij 0.038 (0.96) 0.034 (0.53) 2 ij –0.059 (–0.88) –0.041 (–0.062) ln min , ij 0.269* (1.68) 0.446 (1.57) ln max , ij 0.235 (0.94) –0.076 (–0.16) *,**,*** denotes significance levels of 90%, 95%, and 99% respectively. Test statistics are in parentheses R2 = 0.785 # of obs. = 741 R2 = 0.785 # of obs. = 741 R2 = 0.788 # of obs. = 741 R2 = 0.788 # of obs. = 741 Where: ln denotes the natural logarithm; Xij denotes gross bilateral trade flows between country i and j; GDPi, GDPj denote the GDP of country i and j respectively; DISTij denotes the distance between country i and j; , ij denote the environmental regulation index of country i and j respectively; δij denotes a dummy variable equal to 1 if country i and j share a border, 0 otherwise; ij denote the error terms. To control for country size, per capita GDP is substi- tuted into Equations (8), (9), (10), and (11) to generate Equations (12) through (15). A useful economic inter- pretation of the gravity model is that size is a measure of the purchasing power of each country. Therefore an al- ternative measure of size is income per capita. Countries closer in terms of per capita income will be expected to trade more. 01 2 34 5 ln lnln ln ln j i ij ij ijijij ij GDP GDP XNN DIST (12) 01 2 34 5 2 6 ln lnln ln j i ij ij ijiji j ij ij GDP GDP XNN DIST (13) 01 2 34 5 78 ln lnln ln ln min ,max, j i ij ij ijijij ijij ij GDP GDP XNN DIST (14) 01 2 34 5 2 67 8 ln lnln ln min , max , j i ij ij ijijij ij ij ij ij GDP GDP XNN DIST + (15) The results from Equations 12 through 15 are prese- nted in Table 3. Surprisingly, Equation (12) shows a negative but insignificant relationship between regulat- ory differences and trade volume. In controlling for di- minishing returns by including the quadratic term, Equa- tion (13) shows a positive marginally significant effect. As found in the previous results, Equations (14) and (15) present strong evidence against the race-to-the-bottom hypothesis. Holding income per capita, distance, borders, and regulatory differences constant, increased standards of the low standard country increases trade volume. The point estimates indicate that a 1% increase in the stan- dards of the low standard country results in an increase of trade volume of 0.8% and 0.9%. Using the ERRI these results indicate that, for example, countries with index values of 2 and 1.5 trade more with each other than countries with index values with 1 and 0.5 even after controlling for the effect of income per capita. N. MANGEE ET AL. Copyright © 2010 SciRes. ME 109 Table 3. Results from OLS regression of the natural log of bilateral trade volume between country i and j on explanatory variables in column one. (12) (13) (14) (15) ln Xij ln Xij ln Xij ln Xij ln i i GDP N 0.742*** (11.58) 0.737*** (11.49) 0.572*** (6.59) 0.601*** (6.64) ln j j GDP N 0.65*** (9.88) 0.641*** (9.74) 0.483*** (5.52) 0.516*** (5.55) δij 1.139** (2.14) 1.151** (2.17) 1.284** (2.43) 1.29** (2.45) ln DISTij –0.677*** (–6.81) –0.663*** (–6.65) –0.626*** (–6.28) –0.629*** (–6.33) ij 0.461 (1.38) 0.947** (2.01) ln ij –0.071 (–1.00) 0.057 (0.49) 2 ij –0.217* (–1.84) –0.185 (–1.59) ln min , ij 0.931*** (3.25) 1.38*** (2.74) ln max , ij 0.109 (0.21) –0.874 (–0.88) *,**,*** denotes significance levels of 90%, 95%, and 99% respectively. Test statistics are in parentheses R2 = 0.332 # of obs. = 741 R2 = 0.335 # of obs. = 741 R2 = 0.338 # of obs. = 741 R2 = 0.347 # of obs. = 741 6. Conclusions This paper makes four contributions. First, we develop a theoretical model of the relationship between environm- ental standards and bilateral trade volume within a factor proportions framework. We further extend this model to consider monopolistic competition that generates a grav- ity equation. This allows us to produce an empirical equation of the relationship between standards and trade volume with strong theoretical foundations. Second, we utilize a new more comprehensive index of environmental stringency in our empirical analysis of the stringency-trade volume relationship than has been utilized in previous studies. This index represents a com- prehensive measure of overall environmental stringency by country and allows for a relatively large sample of co- untries. We conduct our empirical analysis over 39 de- veloped and less developed countries. Third, we fully utilize the concept of comparative adv- antage in a gravity equation framework to address the question of the relationship between environmental stan- dards and bilateral trade volume. We find that different- ces in environmental standards have a weak but positive effect on trade volume. At the very least, our results in- dicate that differential standards between countries do not hinder trade volume. A country that is considering a unilateral move to increase standards will not appear to pay in terms of decreased trade volume. Finally, we develop a test of whether low standard co- untries have an incentive to decrease standards in order to increase trade volume. Controlling for environmental differences, and measures of country size (GDP and GDP per capita) we find that trade volume increases as the standards of the low standard country increase. Cou- ntries higher on the ERRI trade more with each other than similarly differenced countries lower on the index. This evidence refutes the race-to-the-bottom and pollu- tion havens hypotheses of trade, because neither high nor lower standard countries increase trade volume by re- ducing standards. All of our evidence runs in the oppo- site direction. 7. References [1] R. 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Ugelow, “Environmental policies in de- veloping Countries,” Ambio, Vol. 8, No. 23, pp. 102-109. [25] S. Dasgupta, A. Mody, S. Roy and D.Wheeler, “Envi- ronmental Regulation and Development: A Cross Coun- try Empirical Analysis,” Oxford Development Studies, Vol. 29, 2001, pp. 173-187. [26] World Bank,“Trade Database and Developmental Indi- cators,”1999. URL:http://web.worldbank.org. N. MANGEE ET AL. Copyright © 2010 SciRes. ME 111 Appendix There are three dependent variables used in the ERRI. They are 1) the level of urban particulate matter, 2) av- erage SO2 concentration which is normalized by urban population, 3) energy efficiency. Particulate matter is collected from the World Bank and the World Health organization (WHO). This measures the concentration of air-born dust and is therefore a measure of air quality. A higher particulate concentration corresponds to a higher pollution level. Similarly, the SO2 concentration also measures the quality of air and serves as a gauge for lev- els of pollution. The energy efficiency measure utilizes U.S. Department of Energy data and captures the aggre- gate amount of energy consumption per unit of GDP for each country. The higher the level of energy efficiency the lower the amount of energy consumed per unit of GDP and therefore the more efficient a country’s energy consumption. The ERRI is divided into two groups of independent variables. The first group is comprised of six categories: 1) stringency and environmental pollution standards, 2) sophistication of regulatory structure, 3) quality of the environmental information available, 4) extent of sub- sidization of natural resources, 5) strictness of govern- ment, and 6) quality of environmental institutions [23]. The second group of independent variables is concerned with a country’s economic and legal context. This group can be further divided into two categories: 1) administra- tive infrastructure which includes but is not limited to measures of civil/political rights, private property protec- tion, corruption, and judiciary independence and 2) a country’s technical capacity measuring scientific and technological advancement. A number of proxies are used in this category such as the number of scientists and engineers, intellectual property protection, strength of scientific community, government commitment to tech- nological research and advancements, and the adoption of foreign technologies. |