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Intra-Industry Trade and North-North Migration: How TTIP Would Change the Patterns

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Intra-Industry Trade and North-North Migration: How TTIP Would Change the Patterns Model Data Results References Intra-Industry Trade and North-North Migration: How TTIP Would Change the Patterns Mario Larch1 Steffen Sirries1 1University of Bayreuth ETSG 2014 1 / 38 Model Data Results References Funding and Notes on the Project This project is funded by DFG within the Project \Gravity with Unemployment" part of a bigger research agenda: Heid and Larch (2012) for unemployment, Heid (2014) for informality up to now without labor market imperfections work in progress ! input is very welcome! 2 / 38 Model Data Results References Why trade and migration? I Traditional view: H-O-type trade models with migration: Trade substitutes for migration (Mundell (1957)). Signing free trade agreements lowers the pressure of migration. Expected (bilateral) correlation between trade and migration ! negative! Gaston and Nelson (2013) BUT... 3 / 38 Model Data Results References Why trade and migration? II 15 10 5 0 TRADEFLOWS in logs (normalized) −5 −5 0 5 10 15 20 MIGRATION STOCKS in logs (normalized) 4 / 38 Model Data Results References Why trade and migration? III 2 0 −2 Residuals of trade gravity −4 −5 0 5 Residuals of migration gravity corr(^etrade ; e^migr ) = 0:2304 5 / 38 Model Data Results References Why trade and migration? IV There might be more than just a spurious correlation between trade and migration. New trade models with intra-industry trade and north-north migration ! complementarity of trade and migration (e.g OECD). This is not new (Head and Ries (1998)): Migrants also bring along preferential access to the markets of the country of origin. We think simpler: Migrants are not just a factor but consumers and though they bring their demand! Migrants have idiosyncratic shocks which drive the migration decision! 6 / 38 Model Data Results References Why \A Quantitative Framework"?I One of the core issues in empirical international trade is the quantification of the welfare gains from trade liberalization. Workhorse model: gravity equation for trade flows in new quantitative trade models (Eaton and Kortum (2002) (Econometrica), Anderson and van Wincoop (2003) (AER), Santos Silva and Tenreyro (2006) (RevStat), Anderson and Yotov (2010) (AER), Waugh (2010) (AER), Fieler (2011) (Econometrica), Arkolakis, Costinot, and Rodr´ıguez-Clare (2012) (AER)). General equilibrium effects are natural to trade shocks, as are third country effects ! need for structural estimation. All frameworks in the literature so far assume labor to be immobile (to the best of our knowledge). 7 / 38 Model Data Results References Why \A Quantitative Framework"? II To the best of our knowledge, there is no structurally estimable model with trade AND migration. (Redding (2012), di Giovanni, Levchenko, and Ortega (2012), Behrens, Mion, Murata, and S¨udekum(2011)). The interdependence might be interesting especially for (ex-ante) welfare analysis of migration- and trade-policy evaluations. \The exact relationship between trade and migration is potentially important. For example, if the relationship were a complementary one, and if policy makers were to view further reductions in trade barriers as improbable, then in order to facilitate trade, liberalizing migration might make economic sense." (Gaston and Nelson (2013)) 8 / 38 Model Data Results References What we do! I Research Question How does welfare analysis of trade liberalization change when we allow for labor to be mobile? Put differently: 1 What happens when we add a recent migration model (and a production side) to Anderson and van Wincoop (2003)? 2 For policy makers: Should we also negotiate for labor market integration within FTAs? 9 / 38 Model Data Results References What we do! II We develop a multi-country, GE structurally estimable trade model which explicitly accounts for migration. Therefore we use one of the most simple new trade frameworks for intra-industry trade. We add a very recent and simple modeling of the migration decision. So, we propose a framework which enables us to disentangle trade and migration cost reductions in counterfactual analysis (examples: TTIP, EU single market, ...). 10 / 38 Model Data Results References What we don't do! ... for the moment ... unemployment, heterogeneous migrants, assimilation, return migration (dynamics), refugee migration, remittances, heterogeneous firms or sectors, ... 11 / 38 Model Data Results References What we find! I From theory (link between trade and migration): real wage differences influence migration decision ! increased immigration increases size (respectively nominal GDP) of a country ! changes nominal wages and price indices ! equilibrium changes 12 / 38 Model Data Results References What we find! II From empirics: Analysis of a hypothetical Transatlantic Trade and Investment Partnership; once with migration and once without. Take home messages: Signing TTIP would have positive welfare effects for signers. Accounting for migration for such an evaluation matters a lot in terms of welfare! 13 / 38 Model Data Results References Trade I We start with a standard trade model: Multi-country perfect competition model (Armington (1969)) with differentiated goods at the country-level and simplest rationale for trade iceberg type-trade costs. 14 / 38 Model Data Results References Trade II Representative consumer in country j. The quantity of purchased goods from country i is given by cij , leading to the following utility function: σ " N # σ−1 1−σ σ−1 X σ σ Uj = βi cij : (1) i=1 With iceberg trade costs tij > 1, profit maximization implies that pij = pi tij , where pi is the factory gate price of the good in country i. 15 / 38 Model Data Results References Trade III The representative consumer maximizes Equation (1) subject to the PN budget constraint Yj = i=1 pi tij cij . The value of aggregate sales of goods from country i to country j can then be expressed as 1−σ βi pi tij Xij = pi tij cij = Yj ; (2) Pj and Pj is the standard CES price index. In general equilibrium, total sales correspond to nominal income, i.e., PN Yi = j=1 Xij . Assuming labor to be the only factor of production and full employment, GDP is given by total factor income, i.e., Yi = wi Li . ) nothing new here! 16 / 38 Model Data Results References Migration I Migration is a choice from a menu of locations with individual/idiosyncratic utility from migration. bilateral migration costs are equal to all migrants from i to j and we end up with McFadden (1974) like discrete choice equation. ) comparable to Anderson (2011) 17 / 38 Model Data Results References Migration II So we start with the log utility from migration from i to j for a worker h ∗ ∗ Vij = ln(Ui ) − ln(Uj ) + "ijh − ln(δij ); (3) ∗ ∗ where Uj and Ui are the indirect utility functions taken from the trade system, "jih captures idiosyncratic, individual specific utility from migration and δji are migration costs. The migration flow from i to j is given by Mij = G(Vij )Ni ; (4) where Ni is the number of natives in i and G(Vij ) gives the proportion of migrants from i to j. When "ijh is assumed to be i.i.d. this proportion is given by the probability as in McFadden (1974) exp(Vij ) G(Vij ) = P ; (5) k exp Vik 18 / 38 Model Data Results References Migration III w since U∗ = j and U∗ = wi for one worker, we get j Pj i Pi wi wj Vij = ln( ) − ln( ) + "ijh − ln(δij ): (6) Pi Pj Inserting (6) in (4) gives wj Pi 1 wj Pj δij Mij = Ni (7) P ( wk Pi 1 ) k wi Pk δij wj 1 Pj δij = Ni : (8) P ( wk 1 ) k Pk δik 19 / 38 Model Data Results References Linking Trade and Migration The simple channel is: migration is driven by real wage differences ! migration changes demand/size of a country ! changes wages (nominal) and price indices ! again changes real wage differences How we go on now: 1 numerical example 2 estimate structural parameters (estimation stage) 3 plug parameters and observed variables into the model, solve and extract results 4 re-set the trade and/or migration cost vectors (counterfactual) 5 do 3 again and compare the results 20 / 38 Model Data Results References Linking Trade and Migration (numerically) I 3 countries (A, B, C), same size, same GDP, σ = 5, no trade costs, no migration costs (1−σ) (1−σ) Country Yi Ni tij δij wi Li Xii Mii βi Pi wi =Pi A 100 100 1 1 1 100 33,33 33,33 1 0,76 1,3158 B 100 100 1 1 1 100 33,33 33,33 1 0,76 1,3158 C 100 100 1 1 1 100 33,33 33,33 1 0,76 1,3158 21 / 38 Model Data Results References Linking Trade and Migration (numerically) II Introducing trade costs: (1−σ) (1−σ) (1−σ) (1−σ) tAB = tBA = 0; 5; tBC = tCB = 0; 5; (1−σ) (1−σ) tAC = tCA = 0; 4. ! B is less remote. No migration at all (infinite migration costs) (1−σ) (1−σ) Country Yi Ni tij δij wi Li Xii Mii βi Pi wi =Pi A 100 100 0,4 0 1 100 53,106 100 fix 0,758 1,3193 B 100,67 100 0,5 0 1,0067 100 49,563 100 fix 0,75 1,3423 C 100 100 0,4 0 1 100 53,106 100 fix 0,758 1,3193 22 / 38 Model Data Results References Linking Trade and Migration (numerically) III Allowing for free migration: trade costs as before (1−σ) free migration δij = 1 (1−σ) (1−σ) Country Yi Ni tij δij wi Li Xii Mii βi Pi wi =Pi A 100 100 0,4 1 1,006 99,425 52,948 33,142 fix 0,762 1,3202 B 102,32 100 0,5 1 1,012 101,15 50,573 33,717 fix 0,753 1,344 C 100 100 0,4 1 1,006 99,425 52,948 33,142 fix 0,762 1,3202 23 / 38 Model Data Results References Data We just keep the OECD sample of two bilateral data sets CEPII bilateral trade data from Head, Mayer, and Ries (2010) with many geographical information.
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