International trade, CO2 emissions and heterogeneous rms

Transcription

1 International trade, CO2 emissions and heterogeneous rms Rikard Forslid, Toshihiro Okubo y, and Karen Helene Ulltveit-Moe z July 20, Work in progress Abstract This paper develops a model of trade with heterogenous rms, where rms invest in abatement technology and thereby impact on their level of emissions. The model shows how rm productivity and rm exports are both positively related to investments in abatement technology. Emission intensity is, however, negatively related to rms productivity and exports. The basic reason for these results is that larger production scale supports more xed investments in abatement technology, and in turn lower emissions per ouput. In contrast to the standard models of heterogeneous rms, rms productivity, and thus export performance, is not exogenous, but endogeneously determined by rms investment in abatement We derive closed form solutions for rm level abatement investments and emissions per output, and test the empirical implications of the model using detailed Swedish rm level data. The empirical results are overall strongly supportive of the model. JEL Classi cation: D2, F2, F5 Keywords:heterogeneous rms, CO2-emissions, international trade Introduction There is no consensus on the e ect of international trade on the environment, and in particular on the e ect of trade on global emissions. Neither the theoretical nor the empirical literature provide a clean cut answer on the link between trade and CO2 emissions. We do not know if international trade increases or decreases the emissions of greenhouse gases thereby contributing to global warming. This paper sets out to shed some further light at the forces at work, focusing on inter- rm productivity di erentials. In theoretical neoclassical models, there are opposing e ects. On the one hand, trade increases income, which will tend to increase demand for clean environment and therefore increase investments in clean technology and abatement. However, trade liberalisation may also imply Stockholm University, CEPR; rf@ne.su.se y Keio university, okubo@rieb.kobe-u.ac.jp z University of Oslo and CEPR, k.h.ulltveit-moe@econ.uio.no

2 an overall expansion of dirty production, because trade allows countries with low emission standards to become "pollution havens". Copeland and Taylor (995) show how trade liberalisation increases global emissions if income di erences between the liberalising countries are large, as dirty industry strongly expands in the poor country with low environmental standards. The empirical literature on link between trade in goods and emissions is also inconclusive. Using macro data Antweiler et. al. (200) and Frankel and Rose (2005) nd that trade decreases emissions. Using sector level data for the U.S., Ederington et.al. (2004) do not nd evidence that pollution intensive industries have been disproportionately a ected by tari changes. On the other hand, also using sector level trade data, Levinson and Taylor (2008) nd evidence that higher environmental standards in the US have increased imports from Mexico in dirty industries. Holladay (2009) analyse rm level data for the US, and nd that exporters pollute less per output. Finally, Rodrigue and Soumonni (20) employ Indonesian rm level data to investigate the impact of environmental investment on productivity dynamics and exports. While productivity dynamics do not appear to be a ected, growth in exports does. It has been pointed out (see e.g. Levinson, 2009) that trade may have reduce pollution as trade liberalization may encourage technological upgrading. Or paper is closely related to this idea. We present a model of trade with di erentiated goods and heterogenous rms a la Melitz (2003), where rms invest in abatement technology and thereby impact on their level of emissions. We show that more productive rms nd it pro table to invest in abatement technology as their bigger volumes allow them to spread the xed costs of abatement investment to be spread across more units. Exporting increases investment in abatement technology, since exports sales boost the scale of production. Hence, rm productivity and rm exports are both positively related to investments in abatement technology, while emission intensity is negatively related to rms productivity and exports. In contrast to the standard models of heterogeneous rms, rms productivity, and thus export performance, is not exogenous, but endogeneously determined by rms investment in abatement We derive closed form solutions for rms level abatement investments and for rm level emission intensities (emissions per produced unit). These equations are tested on detailed Swedish rm level data. Our dataset contains rm level emissions and rm level abatement expenditures as well as rm exports. We focus on CO2 emissions, which constitute about 80 percent of the greenhouse gases emitted by Swedish rms (Ministry of the Environment 2009). The empirical results are overall strongly supportive of the results derived in the theoretical model; more productive rms abate more and emit less, and exports are associated with more abatement and lower emission intensity. 2 The Model In order to analyse the impact of trade on global CO2 emission we use an augmented version of Melitz seminal model on monopolistic competition, heterogeneous rms and trade (see Melitz, 2

3 2003). More speci cally we introduce emissions, CO2 taxes and abatement technology in the standard model. We assume there to be two countries with production in two sectors, agriculture (A) and manufacturing (M). Each country j has a single primary factor of production, labour, L j, used in both the A and the M sector. The A sector produces homogenous goods subject to constant returns to scale. The M sector is characterized by increasing returns, monopolistic competition and heterogeneous rms. 2. Demand Consumers preferences are given by a two-tier utility function with the upper tier (Cobb- Douglas) determining the representative consumer s division of expenditure between agricultural and manufactured goods (sector A and M), and the second tier (CES), giving the consumer s preferences over the continuum of di erentiated varieties produced within the manufacturing sector. Hence, all individuals in country j have the utility function U j = C Mj C Aj ; () where 2 (0; ), and C Aj is consumption of the homogenous good. Agricultural (A) goods can be costlessly traded internationally and are produced under constant returns to scale and perfect competition. The A-good is chosen as numeraire, so that the world market price of the agricultural good, p A, is equal to unity. By choice of scale, unit labour requirement in the A-sector is one, which gives p A = w h = w f = ; (2) and thus wages normalized to one across both countries and sectors. This holds provided < 0:5; which implies that demand for agricultural goods is large enough to guarantee that the agricultural sector is active in both countries irrespective of the location of manufacturing. The consumption of nal goods from the manufacturing sector is de ned as an aggregate C Mj ; 2 Z C Mj = 4 i2i c (i) ( )= di 3 5 =( ) ; (3) where c(i) represents consumption of each variety with elasticity of substitution between any pair of di erentiated goods being > : The measure of the set I represents the mass of varieties consumed in country j. Each consumer spends a share of his income on manufactures, and demand for each single variety produced in country k and consumed in country j is therefore given by x j = p jk Y j ; (4) Pj where p jk is the consumer price, Y j is income; and P j manufacturing goods in country j. 3 NR j 0 p (i) di! the price index of

4 2.2 Entry, exit and production in the manufacturing sector To enter the manufacturing sector in country j, a rm bears the xed costs of entry f E measured in labor units. After having sunk f E ;an entrant then draws a labor-per-unit-output coe cient a from a cumulative distribution G(a): We follow Helpman et al (2004) in assuming the probalibility distribution to be a Pareto distribution, i.e.g(a) = ak ; where we normalise the scale a k 0 parameter to unity, a 0 : Upon observing this draw, a rm may decide to exit and not produce. If it chooses to stay, it bears the additional xed overhead labor costs f D. If the rm not only wants to serve the domestic market but also wants to export, it has to bear the additional xed labor costs f : Hence, rm technology is represented by a cost function that exhibits variable cost and a xed overhead cost. In the absence of emissions and abatement investment, labor is used as a linear function of output according to l = f + xa (5) with f = f D for rms only serving the domestic market and f = f D + f for exporters. Each producer operates under increasing returns to scale at the level of the plant, and in line with Dixit and Stiglitz (977), we assume that there is large group monopolistic competition between manufacturers. Thus, the perceived elasticity of demand equals the elasticity of substitution between any pair of di erentiated goods and is equal to. Regardless of its productivity, each rm then chooses the same pro t maximizing markup over marginal costs (MC) equal to =( ): This yields a pricing rule p jk = jkmc (6) for each producer in country j, and re ects that shipping manufactured goods involves a frictional trade cost of the iceberg form. For each unit of a good from country j to arrive in country k, jk > units must be shipped. It is assumed that trade costs are equal in both directions and that jj = : Let us now also account for the fact that manufacturing activity entails pollution in terms of emission of CO2. We follow Copeland and Taylor (2003) and assume that the each rm produces two outputs: a manufactured good (x) and emissions (e). Abatement is possible, but this require diverting the a fraction of the primary factor, labor, away from the production of x. Firms pay the xed overhead costs, and thereafter the joint production is given by x = ( ) l a (7) e = '()x (8) with 0. Emission intensity (e=x) is determined by the abatement function )= '() = ( (f A ) (9) This assumption is consistent with the empirical ndings by e.g Axtell (200). 4

5 which is characterised by '(0) = ; '() = 0; ' 0 (:) < 0; and 0 < < : We depart from the standard formulation of the abatement function by assuming that rms can impact on the e ciency of abatement by investing in abatement technology (f A ). The higher the investment in abatement technology the lesser the emission intensity, i.e. 0 (f A ) > 0: We will below use the functional form: (f A ) = f A : We use (8) and (9) to substitute for in (7), which gives us x = ( (f A ) e) l a (0) from which we can derive the variable costs function. Disregarding the sunk entry cost (f E ) while adding xed overhead costs related to domestic and possibly export activity as well as abatement costs, we get the total costs function t C = f + f A + a ( (f A ) ) x () with ( ) ; and where f = f D for rms only serving the domestic market, and f = f D +f for exporter, i.e. rms serving both the domestic and the foreign market. Emissions are not for free, rather they incur a tax t. By investing in more e cient abatement technology, emissions can be reduced and so the tax bill. Note that while rms are heterogeneous with respect to labor productivity, a; they are identical in all other respect, i.e. they share the same cost function and face the same tax rate. Our analysis exclusively focuses on steady-state equilibria and intertemporal discounting is ignored. The present value of rms is kept nite by assuming that rms face a constant Poisson hazard rate of death independently of productivity. An entering rm with productivity a will immediately exit if its pro t level (a) is negative, or will produce and earn (a) 0 in every period until it is hit by a bad shock and forced to exit. 2.3 Investing in abatement Firms maximize pro ts with respect to volume and investment in abatement. investment in abatement e ciency in order to maximize pro ts They choose = px f f A ; (2) which may be written as = t ( ) a ( )( ) ( ) fa B f f A ; (3) using (4), (), and where B L : Equation (3) reveals that an internal solution P to the pro tmaximising choice of f A requires that ( ) < : We will assume that this condition holds in the remainder of the paper. The condition implies a decreasing marginal e ect of abatement on rm pro t. The optimal xed cost investment in abatement technology will depend on rm type: non-exporter or exporter. As we argue below, abatement investments 5

6 impact on rms marginal costs, and on the pro tability of being a domestic versus and exporting rm. Exporting status is thus, not only determined by rms labor productivity, but also by their abatement investments. Maximizing domestic rms pro ts w.r.t. f A using (6) and ()gives fa D = B a t ( )( ) = D a ; (4) ( ) n o where B L j 8 j is exogenous to the rm and P D B (t ) ( ), j while the optimal xed cost investment in abatement for exporters is fa x = (B + B ) a t ( )( ) = a ; (5) ( ) n o with B L k 8 k 6= j and = and where (B+B ) (t ) ( ) : P k Proposition More productive rms invest more in abatement given that ( ) <. Proof : Investment in abatement rises when rm prodctivity (=a) D A =@a < 0 =@a < 0: These inequalities will always hold as long as the second order condition for pro t maximization is satis ed, as this requires 2 D =@fa 2 < 0, which in turn require that ( ) < (see the Appendix). The logic behind this result is that more productive rms have higher sales. Hence, the exploiting of scale economies makes it pro table for them to make a higher xed cost investment in order to reduce marginal costs. Proposition 2 For any given level of productivity, exporters invest more in abatement than non-exporters, and trade liberalization increases exporting rms investments in abatement given that ( ) <. Proof : Since B < (B + B ), then > D ; from which follows that fa x > f A D for any given productivity level (=a). If trade is liberalized (a higher ), then (B + B ) rises, and so does ; and thus f x A : The intuition for this result is that the production volume increases with exports, and the exploiting of scale economies warrants a higher xed cost investment in abatement. Proposition 3 A higher tax rate on emission leads to lower investments in abatement technology given that ( ) = Proof : It follows from (4) and (5) that n ( ) (B+B ) a( ) ( ) o ( )( )+ t < 0. This result may seem counter intuitive. But the logic behind it is well known from the theory of production: say that that a rm is considering investing in a new machine. Employing the 6

7 machine requires labor. If the price of labor rises, this therefore decreases the incentive to invest in the ne machine. In our case a higher tax rates implies that the price on emission, i.e. cost of emission, rises. As a consequence, rms want to emit less. As rms substitute away from emissions, it becomes less pro table to invest in abatement. 2.4 Equilibrium conditions Equilibrium in the model is determined by the zero pro t conditions for rm only serving the domestic market and exporters respectively D = a t B fd f A = 0; (6) = a t (B + B ) f D f f A = 0: (7) Since a is unit labor requirement, =a depicts labor productivity, and with > ; it follows that a ( )( ) increases along with the productivity, and can thus be used as a productivity index. From (6) and (7) we see that pro ts are increasing in rms productivity. The least productive rms expect negative operating pro ts and therefore exit the industry. This applies to all rms with productivity below a ( )( ) D equal zero, and is determined by f D = a D which is the cuto at which pro ts from domestic sales t B fa D (a D ); (8) (f A ) In order to break even as exporter, rms have to cover a higher xed cost, and the cuto productivity level at which producers serving the domestic as well as the foreign market just break even is determined by f + f D = where a 2 [0; ] depicts freeness of trade. The model is closed by the free entry condition Z f E = n a 0 Z ad +n t (B + B) fa (a ); (9) (f A ) f (a) f f D f A (a)g dg(a) (20) a f D (a) f D f A (a)g dg(a): Together, the zero cuto pro t conditions and the free entry condition ensure the existence and uniqueness of the equilibrium productivity and pro t levels. Based on these conditions we are also able to derive some results on the relative productivity and and relative abatement investment of domestic versus exporting rms. First, the relative cut-o productivities of exporters and non-exporters can be calculated using the cuto conditions (8) and (9), and expressions for optimal abatement investment (4) and (5): 7

8 a a D ( )( ) D = + f = f D + B B + f f D (2) Endogenous exporter status We see that exporters are more productive than non-exporters for high trade costs ( close to zero). This follows from the assumption that the entry cost in foreign markets f is larger than the entry cost in the domestic market f D : From (2) it can also be seen, that higher foreign market entry costs f increases the relative productivity of exporters. A well known feature of the models with heterogeneous rms is that cut-o productivities of exporters and non-exporters converge as trade is liberalised ( increased). While our model share this feature, it di ers from the standard models as we may get a D = a before trade is completely liberalized ( = ). 2 The reason for this is that in our model rms marginal costs are endogenous and depend on investment in abatement technology. As a consequence, and unlike what we nd in the standard models of heterogeneous rms and trade, rms export status is not purely given by their randomly drawn productivity, but also by their deliberate choice of abatement investment. If the foreign market is su ciently large, all rms will nd it optimal to invest in abatement to lower their marginal cost, and thereby to become exporters. Finally, we note that the tax on emissions a ect exporters and non-exporters in the same fashion, and it will therefore not a ect the relative cut-o productivities. However, since the tax on emissions impact on abatement investment, everything else being equal, the tax rate will impact on the pro tability of domestic versus exporting rms, and thus on the choice og becoming exporter, and then in turn on the number of exporters in the economy. 2.5 Emissions Taking abatement investment as given, rms decide on use of labor as well as on emissions. We focus on emissions, and use Shepards lemma on the cost function () to derive optimal emissions and emission intensity (emissions per output). After having substituted for optimal f A using (4) and (5) respectively, we state that the emission intensity non-exporters is given by e D i = a t B ; (22) x i where ( ) > 0 and (( )) ( ) 2 : We assume that is small enough so that > 0. This is limits the e ciency of the abatement technology, which prevents rms from substituting away completely from the use of emissions. For exporters the expression is given by: ( 2 The critical trade cost is determined by = B fd B f +f D ). 8

9 e i = a t (B + B ) : (23) x i Several results emerge directly from equations (22) and (23). Proposition 4 More productive rms have a lower emission intensity. Proof: Since pro t maximization with respect to abatement investment requires that ( ) < (see the Appendix), e D i xi =@a > 0 Proposition 5 For any given level of productivity, exporters have a lower emission intensity than non-exporters, and trade liberalisation (higher ) reduces the emission intensity of exporters. Proof: Since B < (B + B ), then ed i xi > e i xi must be true for any given level of a exporters will have a lower emission intensity than non-exporters. If trade is liberalized (a higher ), then e D i xi =@ < 0: (B + B ) rises, while e i xi declines, These two propositions are closely related to those on abatement investments. Firms with higher sales and thus production volume, will invest more in abatement technology, and they will have lower emission intensities. Proposition 6 Higher emission taxes leads to lower emission intensity when >. Proof: This can be seen directly from the equations (22) and (23). Note that aggregate emissions may decrease even in the case when the rms emission intensity increases in the emission tax (when < ); since a higher tax rate weeds out some of the least productive rms. 3 3 Testable implications We focuc our empirical work on the models predictions on the relationship between (i) productivity and abatement investments, (ii) productivity and emission intensity, (iii) exporting and abatement investments and (iv) exporting and emission intensity. The reduced form solutions for abatement investments, see equations (4) and (5), and for emission intensity, see equations (22) and (23), suggest log linear speci cations of the following type: log emission intensity i = 0 + log productivity i + 2 exportdummy i + " i ; (24) where theory predicts ; 2 < 0 and log abatement investments i = 0 + log productivity i + 2 exportdummy i + " i ; (25) 3 The second order condition for optimal investments in abatatement is that ( ) < : The condition that = ( ) > therefore implies a restriction on ; which must be small enough. 9

10 where the model predicts that ; 2 > 0: The tax rate on emissions (t in the model) does not vary between rms, and it is therefore controlled for by time xed e ects. 4 Empirical implementation 4. Data We use manufacturing census data from Statistics Sweden. 4 The dataset contains information on a large number of variables such as export, employment, capital stock, use of raw materials, value added, production value, at the rm level from We construct CO2 emissions using data on all types of fuel use at the plant level together with the relevant fuel coe cients provided by Statistics Sweden. Data on fuel use is collected for all manufacturing plants with more than 0 employees from Plant emissions are aggregated to the rm level. This gives CO2 emission data for about rms. We also use rm level data on abatement investments per rm over the whole period The abatment data is a semi-random sample of manufacturing rms (all rms with more than 250 employees, 50 percent of the rms with employees, and 20 percent of the rms with employees). Thus, the abatement data is collected for fewer and larger rms; about.500 rms. 4.2 Abatement and export We want to test if exporters and high productive rms have higher investments in abatement. However, our story also implies that rms exporting status depends on their investments in abatement technology. This implies that we would expect to see particularly high investments in abatement technology for rms that are switching from being non-exporters to exporters. Table shows the average abatement investments for non-exporters, exporters, and switchers over the whole period. Productivity is measured as TFP (estimated using the Levinsohn Petrin method), and we use an export dummy to indentify exporters. 5 We also employ the capital labour ratio, and the rm size (employment) as control variables. All variables are in logs. Table Average by type ( in logs) abatement costs productivity Size K/L Switchers Exporters Non exporters Table shows how exporters, which are the largest, most productive, and most capital intensive rms, have much higher abatement investments than non-exporters. The highest abatement investments are found among the switching rms, i.e. the rms that switch from being non-exporters to exporter during over period of observation. 4 The sector classi cation is shown in appendix. 5 Exporters are de ned as rms exporting more than 0.000SEK (about 000 euros) per year. 0

11 We investigate further the relationship between abatement and export status by running a probit panel regression with the zero to one switch from non-exporter to exporter as dependent variable. We only consider pure switches while dropping cases where rms alternate back and forth between beeing non-exporters and exporters. Table 2 shows the results. Table exportdummy logabatinv (0.06)** (0.03) (0.07)** (0.03) (0.02) (0.03) logabatinv(l) (0.06)***(0.027)* (0.07)***(0.030)* (0.07)** (0.03) logabatinv(l2) (0.025)* (0.027)* (0.03) logprod (0.035)***(0.055)***(0.036)***(0.058)*** logkl (0.057)***(0.084)** Constant (0.078)***(0.20)***(0.079)***(0.28)*** (0.66) (0.95) Observations Number of firms Standard errors in parentheses * significant at 0%; ** significant at 5%; *** significant at % Switching to export: the role of abatement investment In particular abatement investments lagged one time period are signi cantly correlated with a switch to exporting. 6 This is consisten with rms investing in abatement to become exporters. A weakness with our data is that almost all rms are exporters (over 90 percent) since abatement data is sampled primarily from larger rms. 7 This means that there are very few switchers; somewhat more than ten per year on average. This leaves us with little variation for identi - cation. Adding lags further diminishes the sample and in the last regression with all variables included we do not get signi cant coe cients for abatement, even if the estimated coe cients are of the expected sign and very similar to the signi ant ones in terms of magnitude. Finally we test equation (25). Table 3 shows OLS regressions for average abatement investment against explanatory variables from Productivity and export status is is signi cantly correlated with rm level babatement investments as predicted by theory. This 6 We have also run the regressions with productivity and capital labour ratio lagged one period with almost identical results. 7 The abatment data is a random sample of all manufacturing rms with more than 250 employees, 50 percent of the rms with employees, and 20 percent of the rms with employees. 8 The choice of 2004 is not important. We have also run a panel, with very similar results. However, we chose to present regressions for average abatement investments because of the lumpiness of abatement investments.

12 holds also when controlling for capital intensity. 9 Table 3 (OLS) MeanAbatinv 2 3 export dummy (0.3425)** (0.3385)** (0.326)** logproductivity (0.36)** (0.248)** logk/l 0.54 (0.0606)*** Constant (0.335)*** (0.3632)*** (.46) Observations R sq Standard errors in parentheses. * significant at 0%; ** significant at 5%;*** significant at %. Sector dummies included. Average abatement costs from 2000 to Abatement investments 4.3 CO2-emission intensity Next we empirically test equation (24). The model predicts that the rm level emission intensity should be negatively correlated with productivity and export status. Emission intensity is measured as rm level CO2 emissions per output. We have emission data for the year Emission taxes do not vary between rms. 0 We control for emission tax changes by including time xed e ects. However, the exclusion of year dummies does not a ect the estimates in any signi cant way. We report regression results where errors are clustered at the rm level. Clustering at sector level gives very similar results. Table 4 shows the OLS regression results. 9 We do not control for size (outside the productivity variable) since we have the absolute level of investments on the left hand side. Below, when the dependent variable is relative, we control for size. 0 Manufacturing rms enjoy a rebate on CO2-taxes in Sweden. The same rebate (and tax rate) applies to all rms in our sample. 2

13 Table 4 (OLS) CO2intensity exportdumm (0.050)*** (0.050)***(0.050)***(0.052)***(0.047)*** (0.047)***(0.048)***(0.049)** lnprd (0.023)** (0.024)** (0.022)** (0.023)** (0.072)***(0.073)***(0.073)***(0.07)*** lnkl (0.023)***(0.023)*** (0.02)* (0.02)*** lnemployees (0.029)*** (0.028)*** Constant (0.043)***(0.072)***(0.079)***(0.257)***(0.260)***(0.043)***(0.90)***(0.87)***(0.290)***(0.278)*** Year dummy Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes SectordummyNo No No No No Yes Yes Yes Yes Yes Observations R squared Robust standard errors in parentheses * significant at 0%; ** significant at 5%; *** significant at % Emission intensity The coe cients for productivity and the exportdummy are negative and strongly signi cant in all regressions. The capital labour ratio is positively signi cant ant the one percent level in all regressions as is rm size (employment). In terms of magnitudes export has a relatively small e ect on emissions, maybe some percent decrease in the emission intensity, after controlling for productivity. Productivty is more important. In the within sector estimates we nd a negative emission intensity elasticity w.r.t. productivity of about. 5 Conclusion This paper introduces emissions and endogenous investments in abatement technology in a heterogeneous rms and trade model. The model shows how rms productivity and rm exports are both positively related to investments in abatement technology. Emissions per output, in turn, are negatively related to rm level productivity and to rm exports. The basic reason for these results is that larger production scale will support more xed investments in abatement technology. We derive closed form solutions for rms level abatement expenditures and for rm level emissions per output, and test these using Swedish rm level data that contain rm level investments in abatement technology and rm level emissions of CO2. The empirical results show that abatement investment are positively related to export status and productivity. A probit regression for switchers from non-export to export status show that lagged abatement investments are a signi cant predictor of a switch to becoming exporter. The empirical results also strongly supports the notion that the rm level CO2 emisssion intensity (CO2-emissions per output) is negatively related to rm productivity and to being an exporter. Overall the estimations provide strong evidence in favour of our model. 3

14 The paper provides evidence of one mechanism whereby international trade can be bene cial for the environment, since trade promotes investements in cleaner technology. This e ect stands in stark contrast to e.g. the pollution haven hypothesis, which suggests that international trade will decrease the e ects of environmental regulations by making it easier for rms to expand polluting activities in countries with less stringent environmental standards. Our results therefore suggests one explanation for the mixed empirical evidence on the e ects of globalisation on emissions. 4

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17 A Appendix A. Optimal abatement To maximise pro t, rms choose optimal level of abatement investments We rst derive optimal level of abatement for rms only supplying the domestic D A = ( ) a t f D A ( ) B = 0 (26) The optimal level of abatement investments is given by f D A = n o with D B (t ) ( ). B a t = D a ( ) ( )( ) D = ( )( ) Da ( )( ) < 0 (28) i.e. abatement investments are increasing in rms productivity level, provided that < 0 (29) However, the latter condition will always hold, as it is a necessary condition for pro t 2 fa D 2 = ()( ) a t f D ( ) 2 A B < 0 r < 0 (30) We proceed in the same way to derive optimal abatement investments A = ( ) a t f A ( ) (B + B ) = 0 (3) The optimal level for exporter is n where fa = (B + B ) a t ( ) o ( )( ) = a ; (32) (B+B ) (t ) ( ) > D. We note that the relationship between abatement investments and rms productivity level is the same as for rms only serving the domestic = ( )( ) a ( )( ) < 0 (33) i.e. abatement investments are increasing in rms productivity level, provided that < 0; (34) 7

18 and that the latter condition will always hold, as it is a necessary condition for pro t fa 2 = ()( ) a t f ( ) A 2 (B + B ) < 0 (35) Comparing with D- rms and - rms, since > D, a D > a and < 0, exporters will invet more in abatement. A.2 Relative cut-o s From the cut-o conditions we have a D fa D a fa B B = f D A + f D f A + f (36) while from the conditions for optimal abatement D A = ( ) a D t f D A ( ) B = A = ( ) a t f A ( ) B = 0 (38) a D a (f D A ) ( ) B = (39) (f A ) ( ) B ( )( ) a, D (fa D) ( ) B ( )( ) a (fa ) = f D A ( ) B fa We can derive one relationship: fa D + f D fa + f = f D A f A (40) Thus f A f D = f D A f (4) fa fa D, = f f D > 8

19 ( )( ) a D a a a D ( )( ) D ( )( ) = f f D (42), = D f f D The cut-o level ratio is given by d(a =a D ) df < 0 and d(a =a D ) lead to more selection mechanism. d = d(a =a D ) d ( )( ) a ad d d = D f fd. This is the standard property. > 0. Trade liberalisation and TBT liberalisation Related to environmental policy, tax has no impact on cut-o ratio. The cuto ratio is not ( )( ) a a function of tax, i.e. ad = B B+B + f fd. A.3 Sector classi cation A.4 Basic statistics 9

20 Data abatement regressions Variable Obs Mean Std.Dev. Min Max Abatement investments ( Export dummy TFP Capital labour ratio E+07 Employment Data CO2 regressions Variable Obs Mean Std.Dev. Min Max CO2 emissions per output E+07 Export dummy TFP E Capital labour ratio E+07 Employment