Abstract
This thesis sets out to discuss how the gravity model is used to account for the presence of non-tariff barriers (NTBs) in world trade, and how different applications have consequences for policy analysis. This is discussed through the models use in in two independent studies trying to predict the effects of a trade integration agreement between the EU and US. I also run my own gravity regression using a unique dataset to further supplement the discussion. NTBs are complex measures which impact trade in other ways than standard ad-valorem tariffs. They can be argued to correct market failures (e.g. as sanitary measures or safety regulations), or function as protectionist tools (i.e. as substitutes and/or compliments for tariffs). Furthermore, NTBs are difficult to monitor and measure, much more so than tariffs. Therefore, NTBs pose a serious challenge for economic research, especially since it is a general consensus that the presence of NTBs has become more apparent in recent decades, as shown by e.g. World Bank (2012). I investigate how the gravity model of trade, the most common tool for estimating trade flows, is used to account for the presence of NTBs. In particular, I look at how the model is used differently in two comprehensive studies that both try to predict the effects of the Transatlantic Trade and Investment partnership (TTIP) – a trade agreement between the EU and US currently under negotiation. NTB reduction is an explicit goal of the agreement, which makes this an important part of both studies. The studies are performed by the Leibniz Institute for Economic Research (IFO) and the Centre for Economic Policy Research (CEPR). They reach very different conclusions on the effects of TTIP, both regarding the magnitude of the effects and sometimes also the direction of the outcome. I find that they use the gravity equation in different ways in the two studies, and argue that this is one of the reasons for their divergent results. To further discuss the presence of NTBs, and to provide an alternative to the CEPR and IFO studies, I construct an independent dataset. I use data on tariffs, NTBs and regional trade agreements (RTAs), and run my own regressions based on a thorough discussion on both the theoretical and empirical aspects of the gravity model. My data confirms that NTBs are more important than tariffs (on average) and my regressions show that there are gains to be made from reducing both NTBs and tariffs, but that the success of TTIP, or any trade agreement for that matter, to a large extent will hinge on NTB reductions. In this respect my data confirm similar observations in both the CEPR and IFO study. The results also imply that the effects of trade agreements seem somewhat underestimated in the CEPR study. Furthermore, my results indicate that the method used by IFO is highly sensitive to which trade agreements that are included in their RTA dummy variable, as their method consists of simulating a TTIP scenario based on the average effect of existing trade agreements.
This thesis sets out to discuss how the gravity model is used to account for the presence of non-tariff barriers (NTBs) in world trade, and how different applications have consequences for policy analysis. This is discussed through the models use in in two independent studies trying to predict the effects of a trade integration agreement between the EU and US. I also run my own gravity regression using a unique dataset to further supplement the discussion. NTBs are complex measures which impact trade in other ways than standard ad-valorem tariffs. They can be argued to correct market failures (e.g. as sanitary measures or safety regulations), or function as protectionist tools (i.e. as substitutes and/or compliments for tariffs). Furthermore, NTBs are difficult to monitor and measure, much more so than tariffs. Therefore, NTBs pose a serious challenge for economic research, especially since it is a general consensus that the presence of NTBs has become more apparent in recent decades, as shown by e.g. World Bank (2012). I investigate how the gravity model of trade, the most common tool for estimating trade flows, is used to account for the presence of NTBs. In particular, I look at how the model is used differently in two comprehensive studies that both try to predict the effects of the Transatlantic Trade and Investment partnership (TTIP) – a trade agreement between the EU and US currently under negotiation. NTB reduction is an explicit goal of the agreement, which makes this an important part of both studies. The studies are performed by the Leibniz Institute for Economic Research (IFO) and the Centre for Economic Policy Research (CEPR). They reach very different conclusions on the effects of TTIP, both regarding the magnitude of the effects and sometimes also the direction of the outcome. I find that they use the gravity equation in different ways in the two studies, and argue that this is one of the reasons for their divergent results. To further discuss the presence of NTBs, and to provide an alternative to the CEPR and IFO studies, I construct an independent dataset. I use data on tariffs, NTBs and regional trade agreements (RTAs), and run my own regressions based on a thorough discussion on both the theoretical and empirical aspects of the gravity model. My data confirms that NTBs are more important than tariffs (on average) and my regressions show that there are gains to be made from reducing both NTBs and tariffs, but that the success of TTIP, or any trade agreement for that matter, to a large extent will hinge on NTB reductions. In this respect my data confirm similar observations in both the CEPR and IFO study. The results also imply that the effects of trade agreements seem somewhat underestimated in the CEPR study. Furthermore, my results indicate that the method used by IFO is highly sensitive to which trade agreements that are included in their RTA dummy variable, as their method consists of simulating a TTIP scenario based on the average effect of existing trade agreements.