Do Regional Integration Agreements with Environmental Provisions Help to Reduce CO2 Emissions? Inmaculada Martínez-Zarzoso Department of Economics, University of Göttingen and Universitat Jaume I Leila Baghdadi and Habib Zitouna LIM, MES cluster, Tunisia Polytechnic School, University Carthage, Celestino Suárez-Burguet Universitat Jaume I

Motivation and main aims Theoretical predictions Related literature Empirical strategy – Data – Stylized facts – Main results Conclusions Outline

Motivation –The impact of trade liberalization on environment is a highly controversial debate –Environmentalists fear that poor open economies with low environment standards may act as pollution havens –Scarce attention has been devoted to the effects of regional trade agreements other than NAFTA –Should trade policy address environmental concerns? What about RTA?

Main aims –This paper: –Explores the impact of regional trade agreements on carbon dioxide emissions of pairs of countries over the period –Using matching and difference-in-difference techniques

Trade and Environment: Theoretical Predictions –Grossman and Krueger (1993) and Copeland and Taylor (1994): –Trade affects the environment through 3 channels: –scale effect (-) –technique effect (+) –composition effect (?)

Trade and Environment: Theoretical Predictions –The composition effect (?): –Two opposite mechanisms are at work: –Factor Endowments Hypothesis (FEH) : countries that are relatively abundant in factors used intensively in polluting industries will on average get dirtier as trade liberalizes –The Pollution Haven Hypothesis (PHH): countries with relatively weak environmental policy will specialize in dirty industries

Trade and Environment: Theoretical Predictions –PHH: –Trade liberalization will cause pollution intensive industries to migrate from countries with strict environmental regulations to countries with lax environmental regulations –Trade liberalization could lead to a convergence in emissions of the trading partners

Trade and Environment: Empirical Evidence –Trade openness is good for the environment –Levinson and Taylor (2008), Antweiler et al. (1991), Dean (2002), Frankel and Rose (2005) –Trade openness has ambiguous effects on environment –Cole and Elliott (2003), Managi et al (2009), Korves, Voicu and Martinez-Zarzoso (2011)

Trade and Environment: Empirical Evidence –Scarce evidence on the effects of post RTAs on environment –Stern (2007): NAFTA leads to a convergence in emissions –This paper aims to fill this gap by investigating the effects of RTAs on the emissions gap of pairs of countries during

Empirical Strategy –A simplified version of the determinants of emissions: –Population –Land per capita –Per-capita GDP –Openness ratio multilateral and bilateral –RTAs with and without environmental provisions Scale, technique and composition effects

Empirical Strategy (1) i, j: countries; t for time Y ijt : Pollution emissions (Em) gap between a pair of countries i, j in year t Pop: population, Landcap: land area in squared kilometres per-capita GDP: Gross Domestic Product, Openness: sum of exports and imports divided by the gross domestic product Biltrade: bilateral exports+imports between i and j RTA: equal to 1 if countries are involved in a regional trade agreement

Empirical Strategy (2) i, j: countries; t for time Y ijt : Pollution emissions (Em) of a pair of countries i, j in year t Pop: population, Landcap: land area in squared kilometres per-capita GDP: Gross Domestic Product, Openness: sum of exports and imports divided by the gross domestic product Biltrade: bilateral exports+imports between i and j RTA: equal to 1 if countries are involved in a regional trade agreement

Empirical Strategy RTA: equal to 1 if countries are involved in a regional trade agreement RTAs with EP: strongest proponents are US, Canada, New Zealand and EU. Contain binding and non-binding principles, include pre-PTA impact assessment, fairly recent dates, implementation not jet studied in detail – Examples: US with Chile, Australia, Bahrain and Morocco; EU with Mexico, Chile and Mediterranean countries Canada with Chile and Costa Rica

Empirical Strategy –Endogeneity of RTAs is an issue –Bergstrand and Baier (2004, 2007): Country pairs involved in RTAs tend to share common characteristics inducing a selection –One possible solution is to use matching techniques (Bergstrand and Baier 2009, Egger et al. 2008) –Endogeneity of income and openness measures –Addressed using instrumental variables –Income: growth equation (lag-income, pop, invest, human c.) –Multilateral and Bilateral trade: gravity with geographical controls

Empirical Strategy –Matching techniques for RTAs with and without EPs –The key to successful matching estimates is to generate a credible counterfactual trade flows for an “untreated” matched country pair –A control group of country pairs that are virtually identical to a pair with an RTA (in all other aspects as trade partners) Validity: – Conditional independence (unobserved factors do not affect participation) – Sizeable common support (overlap in propensity scores across the participant and non participant sample)

Empirical Strategy –Matching techniques –The following determinants of RTAs are used to construct the untreated matched pairs: –Incomes –Distances –Contiguity –Common language

Empirical Strategy –Difference-in-difference techniques –The effect of an RTA (2 periods) –T periods: –Combined with matching techniques –the DID method does not provide valid estimations when the comparison group differs greatly from the treated pairs –Propensity score matching techniques identify a control group without marked differences in characteristics compared to treated pairs of countries

Data –RTA –Jose De Sousa’s website and based on WTO publication –Distance, Common Language and Contiguity –CEPII –Income, Trade and Emissions –World Development Indicators (World Bank)

Stylized Facts Figure 1: Kernel density of log of bilateral distance for pair wise countries without and with RTAs

Stylized Facts Figure 2. Kernel density of the sum of the log of GDPs pair wise countries without and with an RTA

Results Model 1Model 2 AllMatched Sum of the ln of GDPs0.205***0.127*** [0.0034][0.0059] Ln distance-0.955***-0.212*** [ ][0.011] Contiguity0.0620**0.297*** [0.0262][0.0383] Common language0.102***0.110*** [0.0151][0.0239] Pseudo R Observations201,55825,629 Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1 Table 2. Determinants of RTAs

Results –CO2 emissions converge only for RTAs with environmental provisions –Population, land and GDP per capita gaps have positive effects on the emissions gap –A convergence in the scale of the economy as well as in the technology will lead to a convergence in CO2 emissions for a given pair of countries –Openness ratio has a negative effect, but bilateral trade has a positive effect on the emissions gap

Results (1) Effect on Emissions’ GapAll RTAs With EP Without EP VARIABLESMatchedAllMatchedAllMatchedAll RTA Effect ***-0.202***-0.247*** *** [0.0473][0.0420][0.0561][0.0454][0.0464][0.0468] Abs Ln population ratio0.837***0.694***0.841***0.680***0.847***0.682*** [ ][ ][ ][ ][ ][ ] Abs Ln land per capita ratio0.0666***0.0542***0.0604***0.0434***0.0623***0.0556*** [ ][ ][ ][ ][ ][ ] Abs Ln GDP per capita0.124***0.381***0.207***0.414***0.0990***0.376*** predicted ratio[0.0116][ ][0.0116][ ][0.0106][ ] Abs Ln Openness ***-0.138*** ***-0.102***-0.153*** predicted ratio[0.0189][0.0110][0.0204][0.0123][0.0165][0.0113] Abs Ln bilateral trade0.160***0.126***0.133***0.129***0.124***0.133*** predicted (ij)[ ][ ][ ][ ][ ][ ] Observations13,44961,94512,71552,96915,87356,898 R-squared Robust standard errors in brackets *** p<0.01, ** p<0.05, * p<0.1

Results (2) Effect on Total EmissionsALL RTAs RTAs with Environmental Prov RTAs without Environmental Prov MatchedAllMatchedAllMatchedAll D.effect_rta **-.0185***-.0331***-.054*** (.00388)(.00354)(.00809)(.00554)(.00452)(.0042) D.lpopt *** *** *** (.493)(.241)(1.4)(.245)(.559)(.244) D.llandcapt -1.19**-3.44***-2.94**-3.48***-1.85***-3.46*** (.491)(.241)(1.41)(.245)(.561)(.243) D.lgdpcapt.392***.418***.3***.418***.398***.417*** (.0242)(.0112)(.0389)(.0122)(.028)(.0118) D.lopenpredt.046***.0454***.1***.0541*** *** (.0152)(.00652)(.0304)(.0072)(.0174)(.00609) D.lcommerce_pr ed.142***.141*** ***.161***.157*** (.0405)(.0168)(.037)(.0184)(.053)(.0151) R-squared N ll rmse

Results –Taking into account endogeneity by using matching techniques, estimates differ from traditional OLS estimates: –The effect of RTA seems to be overestimated for all RTA –Countries involved in regional trade agreements with environmental provisions converge in terms of pollution emissions

Results for specific agreements EUROMEDEU27NAFTA Effect on Emission gapMatchedAllMatchedAllMatchedAll RTA Effect-0.204***-0.520***-0.318***-0.239*** [0.0649][0.0536][0.0808][0.0446][0.188][0.256] EUROMEDEU27NAFTA Effect on total emMatchedAllMatchedAllMatchedAll D.effect_rta **-.0551***-.0692***-.0468*-.0497** [0.0061][0.0059][0.011][0.0057][0.0234][0.0213] R-squared N

Results –The effect of EU27 appear to be negative and significant –Pollutions emissions of EU27 countries converge at a higher rate than EUROMED and NAFTA –Maybe due to a deeper level of economic integration

Conclusion –CO2 emissions of countries involved in RTAs with environmental provisions seem to converge –Reasons: RTAs with environmental harmonization policies embedded affect relative pollution levels Composition effects Dynamic pro-competitive effects

F uture developments –Differences to be also considered: –Levels of economic integration (customs unions, currency unions etc.) –Levels of harmonization in environmental regulations within RTAs –Other pollutants –Two countries’ “Polity” measures

Thanks for your attention

Results all FTAs with a reduced model VARIABLES Model 3Model 4 AllMatched FTA ij ***-0.179*** [0.0334][0.0315] After t 0.382***0.194*** [0.0294][0.0343] FTA ij *After t *** * [0.0347][0.0379] Abs Ln population ratio0.734***0.823*** [ ][ ] Abs Ln GDP per capita ratio0.391***0.110*** [ ][0.0148] Abs Ln openness ratio-0.414***-0.137*** [ ][0.0222] Time fixed effectsYes Number of observations176,04522,423 R-squared Robust standard errors in parentheses. Abs denotes absolute value and Ln natural logarithms. *** p<0.01, ** p<0.05, * p<0.1

Specific FTAs with a reduced model EUROMED EU-27 NAFTA VARIABLESAllMatchedAllMatchedAllMatched FTA ij ***-0.158***-0.310***-0.155*** [0.0521][0.0439](0.0327)(0.0406)[0.272][0.147] After t 0.108**0.162***0.379***0.546***0.374***1.284*** [0.0523][0.0489](0.0295)(0.0379)[0.0296][0.367] FTA ij *After t ***-0.211***-0.260***-0.409*** *** [0.0591][0.0507](0.0340)(0.0424)[0.307][0.265] Abs Ln population ratio 0.771***0.817***0.733***0.778***0.728*** [0.0125][ ]( )( )[0.0028][0.0853] Abs Ln GDP per capita ratio 0.197***0.118***0.397***0.507***0.407***0.562*** [0.0221][0.018]( )( )[ ][0.191] Abs Ln openness ratio ***-0.156***-0.432***-0.694***-0.439***1.240*** [0.0387][0.0278]( )(0.0153)[ ][0.196] Time fixed effectsYes Observations159,71214,867168,88263,269155, R-squared