Policy Research Working Paper 9829 Deep Trade Agreement and Foreign Direct Investments Edith Laget Nadia Rocha Gonzalo Varela Macroeconomics, Trade and Investment Global Practice November 2021 Policy Research Working Paper 9829 Abstract Preferential trade agreements are growing in number and on announcements of bilateral greenfield investment at the deepening in content by incorporating disciplines that go activity level. The findings show that deep trade agreements beyond market access. They increasingly encompass non- matter for investment: every additional discipline in a trade-related disciplines as diverse as intellectual property preferential trade agreement increases foreign direct invest- rights, environment laws, or labor market regulations. ment by 1.4 percent, on average. Deep agreements do not Moreover, because investment is complementary to trade, impact foreign direct investment in natural resources and preferential trade agreements provide relevant institutional extractive activities and have heterogeneous effects across frameworks to partner countries that wish to regulate their manufacturing- and services-related activities. The results foreign investments. This paper studies the impact of deep also reveal that disciplines that go beyond the mandate the trade agreements on foreign direct investment and examines World Trade Organization matter more for foreign direct three sub-questions. First, is the impact of trade agreements investment. Disciplines related to investment liberalization on foreign direct investment heterogeneous across types and protection, intellectual property rights, or migration of business activity? Second, is this impact heterogeneous increase foreign direct investment, whereas disciplines on across disciplines covered in the agreements? Third, does labor market regulations reduce investment. The results the level of development of home and host countries matter are mostly driven by investment between developed and for this impact? The analysis exploits the World Bank’s data developing countries. set on the content of preferential trade agreement and data This paper is a product of the Macroeconomics, Trade and Investment Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at nrocha@worldbank.org. The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent. Produced by the Research Support Team Deep Trade Agreement and Foreign Direct Investments Edith Laget, Nadia Rocha and Gonzalo Varela1 Keywords: Trade Agreements; Foreign Direct Investment; Deep Integration; Regionalism. JEL Codes: F13; F15; F21 1 Contact author: Edith Laget, Email: elaget@gmail.com. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the World Bank and its affiliated organizations, or those of its Executive Directors or the governments they represent. 1. Introduction Preferential trade agreements (PTAs) are a prominent feature of current globalization. Over the last decades, country participation in PTAs has become widespread, with all members of the World Trade Organization (WTO) having signed an average of 10 PTAs, up from 3 PTAs in 1990. Most importantly, the proliferation of PTAs was accompanied by a significant deepening of their scope. Their content spans diverse non-trade related disciplines such as investment, intellectual property rights, visa and asylum, labor market regulations and environmental laws. Meanwhile, the expansion of the fragmentation of production has shifted the composition of trade towards more flows of differentiated intermediate products and less of homogenous goods. This resulted into a rise in global value chains (GVC), reinforcing the need to concurrently regulate the exchange of goods and services with foreign direct investments (FDI) under a common framework (Antràs & Staiger, 2012). Moreover, bilateral investment treaties (BITs) that have traditionally regulated foreign investments are losing ground as more investment related disciplines are incorporated in PTAs (Figure 1). By removing economic uncertainty regarding market access rules and behind-the-border business conditions, PTAs are increasingly relevant in the context of cross-border trade and investment. Their prevalence has revived interest in the literature on regionalism, as researchers examine the role of this new generation of deep trade agreements in shaping the exchange of goods and services as well as investment. Understanding the economic effects of PTAs is essential to design and implement them efficiently. This paper contributes to the debate on the economic impact of deep trade agreements by empirically investigating the key disciplines that promote FDI. We use a new data set on the content of PTAs developed by the World Bank and bilateral cross-border data on greenfield investments run by the Financial Times. The latter provides information on the sector and business activity associated with an FDI 2 operation (i.e. the actual function of a project and can be described as a task along the supply process to end-users: from upstream research and development, passing by manufacturing (i.e. production), to downstream sale and retail). We argue that these activities provide a better understanding of which parts of the supply chain are most impacted by PTAs than sectors defined by standard industrial classification. 2 Using a gravity model of investment, we first quantify the average effect of deep trade agreements on FDI. Our key finding is that deep PTAs increase FDI between member countries. Their positive impact persists even after accounting for the presence of BITs or shallow PTAs, which confirms that the disciplines covered in deep PTAs go beyond the commitments of traditional agreements. Adding a discipline to a PTA increases FDI by 1.4 percent, on average. 3 We then explore three potential sources of heterogeneity: i) effect by activity - we first distinguish FDI by its broad business activity (differentiating between extractives, manufacturing and services related activities); ii) effect by type of discipline - we isolate the effect of specific investment related disciplines included in PTAs; and iii) effect by level of development of the signatory countries. Our results for the first source of heterogeneity by broad business activity are in line with the literature. We find that our baseline result is driven by manufacturing and services related activities, and that deep PTAs do not affect investments in resource extraction activities. Our main contribution lies in the second source of heterogeneity. To the best of our knowledge, we are the first to disentangle the impact of separate disciplines related to investments. Among the 2 Indeed, because a sector characterizes the end-use of goods or services provided by an FDI recipient, it generally entails a succession of activities and does not allow to accurately locate which part of the supply chain is involved on the FDI operation. 3 The effect of deep PTAs on FDI with non-member countries is not the scope of the current paper but could be addressed in further research by identifying FDI announcements with countries that do not share trade agreements. 3 core disciplines 4 of PTAs - i.e. those disciplines that have a clear economic content, as opposed to other provisions that do not (e.g. cultural cooperation, anti-terrorism), those that matter for FDI are investment, intellectual property rights (IPR), visa and asylum, environmental laws, labor market regulations, movement of capital and competition policy. We find that all have a large and positive impact and increase FDI in service-related activities between 32 and 50 percent. However, labor and environmental disciplines decrease FDI in manufacturing-related activities by 48 and 110 percent, respectively, while the remaining disciplines have no significant impact. A plausible explanation is that manufacturing activities tend to employ relatively more low-skill workers and are more polluting than services activities. We believe these two characteristics translate stringent labor market regulations and environmental laws into higher compliance costs, and therefore make these disciplines more binding for manufacturing activities. 5 While we do not test this hypothesis, doing so would require estimates of compliance costs associated with different disciplines in PTAs across different business activities. Finally concerning our third source of heterogeneity, we find that our discipline level results are driven by businesses between developing and developed economies for which the prior regulatory gap is the widest. The importance of services-related activities and the development gap in driving the positive effect of deep PTAs on FDI is consistent with what the GVC literature has found. 6 One of the reasons for these mirroring responses to deep PTAs is the catalyst 4 There are 18 core provisions, which include tariff liberalization for industrial and agricultural goods, technical barriers to trade (TBT) and sanitary and phytosanitary (SPS) measures, export taxes and anti-dumping and countervailing measures, trade related intellectual property (TRIPs) and trade related investment measures (TRIMs), movement of capital, state owned enterprises, state aid, competition policies, intellectual property rights (IPR), investment, public procurement and services. 5 Service activities tend not to be adversely affected by labor and environmental disciplines, likely because of the high-skill intensity and intangibility of such activities. 6 Using sectoral data on value added trade, (Laget, Osnago, Rocha, & Ruta, 2018) find that deep PTAs matter more for GVC trade in services and have a larger impact for trade in intermediates between developed and developing countries. 4 role that FDI plays on GVC integration by providing foreign capital and technical know-how. Research has demonstrated that this high degree of complementarity between GVCs and FDI is enhanced by policies, such as those included in deep PTAs, providing support to the entry and upgrading in GVCs. 7 2. Related literature The economic effects of PTAs have been thoroughly studied in the literature. Researchers have looked at their impact on trade flows, investment, growth and welfare (see (World Trade Organization, 2011) and (Limão, 2016) for recent surveys of the literature on PTAs). Our paper relates to the strand that studies the role of trade agreements in increasing cross-border investments through different channels. One potential channel is driven by the complementarity between trade and investment that is based on the production structure of multinational enterprises. 8 A core argument is that by reducing trade barriers PTAs facilitate the exchange of inputs (tangible or not) and therefore stimulate efficiency-seeking investments along the value chain. An important implication is that the trade creation and trade diversion effects of PTAs may translate into FDI relocation, (Baltagi , Egger, & Pfaffermayr, 2008) and (Tintelnot, 2017) provide empirical and counterfactual evidence for this mechanism. Another channel linking PTAs with FDI comes from the liberalization of services, investment and other behind-the-border disciplines. (Dee & Gali, 2005) show that PTAs in general have a significant impact on investment flows through their non-trade disciplines. (Büthe & Milner, 2008) offer a political economy perspective and argue 7 See the studies on FDI spillovers in the context of GVCs (Amendolagine, et al. 2017) and (Farole and Winkler 2014) . 8 The relationship between trade and FDI is complex and both “tariff jumping” substitutability and “value chain” complementarity motives are supported by the theoretical analysis. (Markusen & Maskus, A Unified Approach to Intra-Industry Trade and Foreign Direct Investment, 2002) provide a comprehensive framework to analyze and understand these mechanisms. While (Fontagné 1999) brings empirical evidence for the dominance of the complementarity relationship. 5 that PTAs serve as commitment mechanisms to foreign investors regarding the treatment of their assets that is more credible than domestic regulations. (Osnago, Rocha, & Ruta, 2015) find that deep trade agreements facilitate vertical FDI because they reduce the contractual uncertainty associated with the difference between PTA members’ institutions. In terms of methodology, recent works have grasped the importance of accounting for the variation in content to distinguish the effects of deep versus shallow PTAs. Advances in data availability regarding the content of PTAs have enabled researchers to depart from the use of restrictive dummy variables and incorporate more sophisticated measures of PTA depth. Equipped with these new empirical tools, some papers have focused on the impact of PTA depth on trade flows, while others have addressed the same question for FDI. 9 For example, (Lesher & Miroudot, 2006) construct an index of investment disciplines in PTAs but limit their coverage to 24 North-South agreements. Several papers have attempted to build similar types of indexes based on careful analysis of the information contained in agreements’ treaties. However, none conducted this exercise on the universe of PTAs as well as their overall content, up until the work conducted by the World Bank for which experts accomplished a thorough mapping of all disciplines included in all PTAs notified to the WTO (see (Hofmann, Osnago, & Ruta, 2018) for a presentation of the content of PTAs data set). (Osnago, Rocha, & Ruta, 2015) were the first to use this data set to construct a comprehensive measure of PTA depth and use it to explore the impact of deep integration on vertical investments. Several papers analyze the impact of specific policy areas on foreign investments 9 (Mattoo, Mulabdic and Ruta 2017) find that deep trade agreements increase gross trade and can have a positive spillover effect on trade with third countries if their design and implementation are non-discriminatory. (Orefice and Rocha 2014) study trade in parts and components, and (Laget, et al. 2018) for trade in value added, both finding that deep PTAs increase countries’ participation in GVCs by enhancing regulatory frameworks and easing cross-border operations. 6 (whether applied domestically or multilaterally). Examples are: (Helpman, 1992), (Ferrantino, 1993), and (Lee & Mansfield, 1996) for the impact of intellectual property rights; (Javorcik & Spatareanu, 2005) for the labor market regulations; (Hanna, 2010) for the environmental laws, and (Gómez-Mera, Kenyon, Margalit, Reis, & Varela, 2014) for the impact of bilateral investment treaties. However, by approaching each of these disciplines separately, this body of research omits the important role provided by a broad PTA framework in shaping the exchange of goods, services, and investments. We fill this gap by exploring the role of relevant disciplines for FDI within the framework of deep PTAs. Similarly to our results, (Medvedev, 2012) finds that the positive relationship between FDI and PTA is driven by North-South investment. This paper is the first to analyze the role of specific disciplines on foreign investment within the context of a PTA. The long time-span, the vast country coverage, and the activity-level information enable us to unveil important heterogeneities underlying the average effect of PTAs on investment already found in the literature. While deep PTAs have an overall positive impact on cross-border economic flows (whether gross trade, GVC-trade, or investments) on average, there exist important variations in the average impact of separate disciplines that matter for FDI (investment, intellectual property rights (IPR), visa and asylum, environmental laws, labor market regulations, movement of capital and competition policy), and variations across business activities (with investment in manufacturing-related activities negatively impacted by labor and environment disciplines). The rest of the paper is organized as follows; Section 3 introduces the data used to identify the content of PTAs and the data used to measure bilateral investments. Section 4 describes the methodology used for our empirical analysis, discusses our findings, and presents robustness checks and addresses endogeneity concerns. Section 5 concludes. 7 3. Data In this section, we discuss the data used to measure the depth of a trade agreement and describe in detail our source for bilateral investments at the sector and activity levels. a. Preferential trade agreements We first estimate overall the effects of deep trade agreements by using a novel measure of depth. Then we move to the estimation of the effect of the specific disciplines included in PTAs. Our variables of depth and disciplines come from the World Bank database on the content of deep agreements. It covers all 279 PTAs that are in force and notified to the WTO as of 2015. (Hofmann, Osnago, & Ruta, 2018) give a thorough description of the database and the way the mapping of PTAs has been implemented. The methodology is based on the work of (Horn, Mavroidis, & Sapir, 2010), which was also used in the World Trade Report 2011 (World Trade Organization, 2011). This comprehensive data informs us on the specific disciplines, or policy areas, that are covered in each agreement. In total, 52 disciplines are identified across the 279 PTAs (see Table A.1 for the list of disciplines). On top of the area coverage, the data provide information on the legal enforcement of each discipline within an agreement. The enforcement coding is based on the analysis of the legal language of the treaty text and the possibility of recourse to dispute settlement. For our baseline measure of depth, we follow previous works that use the content of this data set or other similar sources, by counting the number of disciplines included in a PTA. We focus on the number of legally enforceable disciplines as they are expected to be more impactful than disciplines that are just mentioned in treaties but without any sign of substantial commitment from the parties. We define the variable ℎ = ∑52 =1 - i.e. the simple count of legally enforceable disciplines ( ) included in the agreement between country and at time . Once an 8 agreement is ratified, its content is not expected to change unless there is an enlargement in the future. The variable takes a value of zero before an agreement is signed and then turns on to the above sum after the signature and remains the same until the end of the sample period. Therefore, the identification of the impact of depth comes from time variation within each pair of countries. Another way to measure depth is to separate the disciplines between those that fall under the current mandate of the WTO (such as tariffs, customs and anti-dumping) and those that go beyond this mandate and are not subject to any kind of WTO agreements (examples are agreements on investment, competition policy, labor market laws, or environmental regulations ). Following (Horn, Mavroidis, & Sapir, 2010) we call the former measure of depth WTO-plus and the latter WTO-X and define 38 them as: = ∑14 =1 and = ∑=1 , where are 14 WTO+ disciplines and are 38 WTO-X disciplines included in an agreement between countries and in year . In the second stage of our analysis, we use dummy variables to identify the commitment towards individual disciplines in PTAs. While including these dummies sequentially, we also make sure that we control for the rest of the PTA content by including a variable of the “remaining” depth as the sum of disciplines except the one singled out in the specification. We sequentially tested all core disciplines mapped in the database with clear economic content, to eventually restrict ourselves only to those having a significant impact on FDI. As shown in Figure 2, the selected disciplines are also the most frequent WTO-X areas included in PTAs. b. Investments Reliable investment data are not as readily available as trade data. When available the data usually come from surveys conducted by Central Banks or Statistical Offices and are used to comply with reporting obligations such as balance of payments or international investment position statistics. The data reporting often lacks uniformity 9 across countries, which undermines the quality of investment data. 10 Also investment data are rarely available at the bilateral sectoral level. To circumvent these limitations, we use investments from the fDi Markets database collected by fDi Intelligence, a division of Financial Times Ltd. The data is available at the firm-project level and corresponds to announcements of “Greenfield FDI in a new physical project or expansion of an existing investment, which creates new jobs, and capital investment.” The announcements are collected from publicly available sources, such as media sources, industry and investment promotion agencies, or market search and publication companies. Each project identifies is cross-referenced against multiple sources. One concern is that recorded announcements differ in their advancement. The data set includes (but does not clearly distinguish) FDI projects that have been announced or opened by a company, which means that firms have made their final decision and projects are moving towards implementation in the former case or are already fully operational in the latter. 11 Yet, to the extent possible, project status is updated if a company makes further announcements. Large, announced projects above $1 billion, are researched on a quarterly basis to verify their progress. If the project is cancelled, it is removed from the database. Working with announcements represents an important advantage for our identification strategy in that it reduces the scope for endogeneity caused by simultaneity. 12 fDi Intelligence mentions that it takes on average two years for a project to materialize after an announcement is made in the media. Hence, regressing announcements on PTA variables at time t is 10 Despite the international reporting practices set by the IMF, countries tend to i) deviate from the 10 percent ownership threshold, ii) not use uniform industrial classification, or iii) not report all types of investment properly (short-term intra-company loans, re-invested earnings are often missing). 11 The data does not cover merge and acquisition or other equity and non-equity investments. Typically, projects are captured at the announcement stage for capital-intensive projects and at the opening stage of services operations with limited capital investment required. Capital-intensive projects take on average 2 years to become operational. 12 Recent academic research using fDi Markets data includes: (Crescenzi, Pietrobelli and Rabellotti 2013), Paniagua and Sapena 2014), (Amoroso, Dosso and Moncada-Paternò-Castello 2015), and (Antonietti, Bronzini and Cainelli 2015). 10 equivalent to regressing investment disbursement on lagged PTA variables, which is one of the common techniques used when there is concern about endogeneity. Another advantage of using the fDi Markets database is the availability of company level investment data. This allows us to address endogeneity concerns related to the fact that large investment decisions may determine the adherence to certain disciplines in PTAs by host countries, which would imply that it is large investments that determine PTAs rather than the converse. 13 To tackle that endogeneity concern, in the final part of our analysis we remove the largest investments from the sample, as well as the sectors that spend the most resources on lobbying for specific contents in PTAs, to check the robustness of results to that potential source of endogeneity. Overall, the data covers more than 60,000 companies from 177 source countries investing in 162 destination countries from 2003 to 2015. Each company’s project is classified according to 39 industries and 18 business activities. Table A2 shows how the different sectors and activities overlap in the data. Business activities are defined as the actual function of a project, whereas sectors are based on the FDI recipient company’s core business area. Examples of project announcements are “Wiseway Group (Australia – a transportation services provider) is investing in China in the Transportation sector in a project related to Sales, Marketing and Support activity”; “ProLogis (United States – a leasing and property management company) is investing in Sweden in the Real Estate sector in a construction project”. This means that an industry traditionally classified as manufacturing may receive investments to develop a service-related activity and vice versa. In the data for example, 65 percent of the investment received by the “consumer electronics” sector is dedicated to manufacturing activities but 16 percent falls in retail, 5 percent in research and development, 4 percent in marketing, 2 percent in logistics, distribution and transportation, and so on (all service-related activities). We think that these activities 13 For example, the bilateral investment treaty signed between Finland and Uruguay in 2002, which entered into force in 2004, was largely driven by the undergoing negotiations for a large Finnish investment in the cellulose paste production sector in Uruguay. 11 best describe operations along the supply chain that one could place on a “smile- curve” as illustrated in Figure 3. In Figure 4, we compare our source for bilateral FDI announcements data with bilateral FDI data from UNCTAD. Even though the correlation fluctuates over time, on average our chosen measure of FDI tracks official data on cross-border investments relatively well. We explain the discrepancy by the fact that our data is not based on declarations of firms’ balance sheets and therefore does not record the full set of balance of payment flows, which implies that our constructed measure of inward (or outward) FDI cannot perfectly match official measures. First, inward and outward notions require a reporting country, which is not the case for the fDi Markets data as its compilation is performed by experts from the Financial Times. Second, outward (inward) flows are computed by netting out any transactions that decrease the stake of resident (foreign) investors in foreign (resident) enterprises from transactions that increase it. A crucial stage of the empirical work resides in dealing with the absence of announcements. As acknowledged in the trade literature, zero trade (or investment) flows do not occur randomly, which means that samples restricted on positive values may yield biased estimates. It is particularly important to account for zero flows when studying the effects of deep integration. The nature of our investment data requires assumptions concerning the presence of zero investment flows. Contrary to officially reported trade or investment data, we cannot apply the mirror method to complete missing observations, and lack of announcements in the news can be left to interpretation. Nevertheless, we assume that if a significant investment ever materializes, it will be covered in the news or announced in some other way that fDi Markets will be able to identify. We generate zero investment flows by doing the following: starting from the sample of countries included in the fDi Markets database, we generate all possible source/destination pairs across the sample period 12 and replace missing observations by zeros. We then keep only the pairs (for the whole period) that ever invested at some point during the period. 14 Finally, we do not make any assumption regarding the motives of investments. The data and its relatively broad industry/activity classification do not us allow to distinguish between vertical and horizontal FDI. Even though the theoretical literature has long distinguished market seeking (i.e. horizontal) FDI and efficiency seeking (i.e.vertical) FDI (Markusen 1984, Helpman 1984), recent works have shed light on the complex mixed-strategy of multinationals. In this paper we study the impact of deep integration on the intention to invest in Greenfield projects in general. c. Control variables We control for existence of expired trade agreements by including a corresponding dummy variable. Past agreements are taken from Mario Larch's Regional Trade Agreements Database from Egger and Larch (2008). We also control for bilateral investment treaties to isolate the effect of investment disciplines included in PTAs only. Finally, we use fixed effects to minimize the scope for endogeneity. We initially include country pair, source-time and destination-time fixed effects. When we move to the business activity level, we control additionally for pair-industry, source- industry-time and destination-industry-time fixed effects. 4. Effect of PTAs on FDI a. Empirical strategy The gravity model is the workhorse estimation technique for applied international trade analysis. This model has both empirical and theoretical advantages; it successfully predicts trade flows and can be derived from a large class of structural general equilibrium trade models. Theoretical studies of multinationals and trade 14This way, at the aggregate activity level our estimation sample is composed of 35 percent (38,300) non-zero against 65 percent (71,033) zero observations for FDI announcements. 13 (Markusen & Venables, 1998) and (Egger & Pfaffermayr, 2000)) have found that both types of activity are determined by the same exogenous factors (distance, market size, trade/investment barriers). Despite the lack of micro-founded theory to fit FDI patterns from gravity determinants (as (Anderson & van Wincoop, 2003) does for trade flows), the gravity model is a successful tool for predicting FDI flows and has frequently been used in the literature. In our estimation strategy, we follow the best practices for estimating a structural gravity model. We use directional country-time fixed effects to account for the multilateral resistance terms. These fixed effects absorb any country-specific characteristics that may vary with time such as national policies, institutions or exchange rates. Because we use panel data, we can incorporate country-pair fixed effects to account for time invariant differences between origin and destination countries, such as factor endowments, that can predict FDI flows. By including this extensive set of fixed effects, we alleviate the risk of endogeneity caused by omitted variables. Finally, we rely on the Pseudo Poisson Maximum Likelihood (PPML) estimator, which has been commonly adopted to estimate gravity equations. (Santos Silva & Tenreyro, 2006) show that this non-linear estimator produces consistent estimates in the presence of heteroskedasticity while accounting for the large number of zero flows. Initially, we estimate our baseline specification using both OLS and PPML methods to show that both estimators lead to similar results. OLS: log ( ) = 1 ℎ + 2 + 3 + 4 + + + + PPML: = exp {1 ℎ + 2 + 3 + 4 + + + } + where measures of greenfield investments country and at time , ℎ is a measure of PTA’s depth, and are dummy variables accounting 14 for present and past agreements, respectively, and is a dummy for the existence of bilateral investment treaty; , , and , represent respectively country-pair industry, reporter industry time and partner industry time fixed effects. b. Baseline results Table 1 reports the coefficients of total depth, WTO-plus and WTO-extra variables for both OLS and PPML estimators. The first three columns are estimated with OLS and show that the total depth of PTAs matters for investment, whereas a shallow measure of PTAs using only a dummy variable does not have a significant effect. The bilateral investment treaty dummy is not significant, which is also the case in the literature when the effects of BITs and PTAs are estimated concurrently. Moving to the best practices for the gravity estimation, which recommend using PPML, we find that the total depth variable is also significant and positive when using the PPML estimator. One additional discipline increases FDI announcements by 1.4 percent. Splitting the depth of the PTAs between WTO-plus and WTO-extra disciplines as done in columns (4) and (6) of table 1, we find that the disciplines that go beyond the WTO’s mandate drive the positive effects. In table 2, we test whether deep PTAs matter for FDI in extractive activities. The interaction with a dummy for extraction shows that deeper agreements do not promote FDI in extraction activities as opposed to services and manufacturing activities. This is consistent with the work of (Laget, Osnago, Rocha, & Ruta, 2018) who show that deep PTAs do not matter for value added trade in resource sectors. For the rest of our analysis we focus on manufacturing- and service-related activities only. In the next section we investigate the effects of specific disciplines. 15 c. Impact of single disciplines The data on the content of PTAs allows us to isolate the effect of single disciplines on investment while controlling for the overall depth. The following specification estimates the effect of investment related disciplines while accounting for the rest of a PTA’s depth: = 1 ℎ( ) + + 2 + 3 + 4 + + + + where is a dummy variable that indicates whether the discipline d is included in an agreement. The variable ℎ( ) controls for the rest of the agreement content summing across all other disciplines except d. Table 3 reports the results of the PPML estimation at the discipline level. Commitments relative to intellectual property rights (IPR) increase FDI announcements by 36 percent. When included in PTAs, IPR protect investors’ main assets (brand or innovation efforts) and guarantee secured returns on those assets. Visa and asylum increases FDI announcements by 43 percent shedding light on the importance of facilitating the movement of persons to promote business activities. On the other hand, labor market regulations are negatively correlated with FDI and decreased announcements by 80 percent. The main objective of labor disciplines is not to increase market access but rather to improve social welfare by fostering workers’ bargaining power, which does not liberalize the business environment. The comparison of the magnitudes of the coefficients obtained for the discipline level estimations (ranging between -80 to 43 percent) with the coefficient obtained from the estimation the overall impact of PTA depth (0.14 percent). Some of the disciplines have a significant and positive impact, while others have a negative impact or no impact at all on FDI. For this reason, the aggregated effect of a PTA obtained from the sum of positive and negative discipline level impacts is much smaller in absolute term than the disciplines’ effects. 16 The next estimation studies the potential heterogeneity in the effect of disciplines across business activities. We create a dummy variable that identifies services activities. Services refers to any activity that is neither manufacturing nor extractive that we interact with the discipline dummy. Since we have excluded extractive activities from the rest of the analysis, the control group represents the manufacturing activities. Table A2 summarizes the distribution of the FDI announcements across business activities and shows that our sample is slightly biased towards services (with 53 percent of the outstanding amount of announcements for services projects, 37 percent for manufacturing, and 9 percent for extractive). The new specification is written: = 1 ℎ( ) + ′ + × + 2 + 3 + 4 + + + + Table 4 reports the results of the interactions with the service related activities. Our eight disciplines of interest turn out to be positively correlated with project’s announcements related to service activities. Service activities that are skill- and knowledge-intensive, benefit the most from the reduction of uncertainty conferred by PTAs. As for the environment laws, when production and service activities are combined this discipline is not significant, but separating the effects on each activity reveals that such discipline is negatively correlated with the intention to invest in production activities. This might be because environment policy areas cover commitments on Good Manufacturing Practices that “constraint” mostly production processes. We would need measurement on the vertical depth to test this hypothesis. The opposite effects of the labor market regulations found for services and manufacturing can be explained by the difference in workers’ skill employed in those two types of activities. On top of ILO labor standards (prohibition of child labor, respect of human right, etc.), labor disciplines in PTAs cover (and are not limited to) right to collective bargaining, freedom of association, minimum wages, unemployment benefit, cost of firing. Such stringent market regulations tend to bind 17 for low-skilled workers and hence refrain investors from investing in production-like activities, which tend use more unskilled workers than services. Business activities can be viewed as “tasks” performed along the supply chain, and are different from standards industrial classifications. Therefore, significant effects on services-related activities but none on manufacturing-related activities should not be interpreted as an absence of impact on “goods”. Indeed as reported in Table A2, among all investment announcements directed to companies classified in the textile sector (and therefore producing goods) 18 percent were dedicated to t manufacturing-related activities and 77 percent to retail services activities. d. Level of development The content of trade agreements varies greatly with the level of development of signatories and disciplines may matter differently for developed or developing countries. We first investigate the interaction between the overall PTA depth and the level of development of partner countries. We then use the same type of interaction to understand how the effects of disciplines varies with the development level. The regression specification is as follows: = 1 ℎ + 2 ℎ × + + ′ × + 3 + 4 + 5 + + + + where is a vector of three dummies of the possible North-North, North-South and South-South country pairs. North is defined as the group of high-income countries while South comprises low- and middle-income and LDC countries. Results reported in Table 5 show that North-South agreements are driving our results on overall depth. Without interaction we found in the baseline specification that an additional discipline increase FDI by 0.14 percent regardless of the development level of the member countries. This effect rises to 0.24 percent for FDI between developed and developing countries. Moreover, investment, IPR, labor market regulations and movement of capital have a bigger positive and significant impact on FDI between 18 North-South than pairs of similar income. This sheds light on the role that such disciplines can play to enhance the investment climate and provide stability, two factors that are critical when prior institutional gap is the widest as in between developed and developing countries. The absence of significant effect of South-South agreements (at both overall and discipline levels) is due to the fact that such PTAs rarely included WTO-X disciplines and hence do not significantly commit on investment related issues. On the contrary, North-North agreements often include investment-related disciplines, but institution levels being already similar across high-income countries, commitments established in PTAs do not influence the pattern of FDI between these countries. e. Robustness checks i. Endogeneity concerns Our first concerns relate to the endogeneity of trade agreements. As already mentioned in the data description section, the use of announcements (instead of actual disbursements) reduces the scope for endogeneity. The delay between the announcement of a project and its actual disbursements (that lasts for two years on average) implies that regressing FDI announcements on PTAs at time t provides the same mechanism as regressing actual FDI on PTAs at time t-2, which is a standard technique used to reduce the scope for simultaneity issues. Nevertheless, the two- year lag is an average and is not directly reported by fDi Markets, still leaving room for some endogeneity concerns. We approach the remaining risk for endogeneity by looking at the potential influence that investors might have on the content of PTAs by lobbying for the inclusion of specific disciplines. We can test whether the presence of “big players” drives our main results. There are two possibilities to consider: i) firms might not be organized and only the largest will engage in lobbying for trade policies, and ii) firms are 19 organized and are represented by lobbyists at the industry level. We test these two hypotheses by using FDI data at their project-level. For the first scenario, we proxy the big firms by those having the largest aggregated announcements across the period at the country pair level. We argue that removing the top 1 percent of those firms should leave the effect of PTAs exogenous to the remaining smaller firms. Table 6 confirms that our baseline and discipline level results are robust to removing the top 1 percent of announcements. Then we move to the industry level lobbying hypothesis. To determine which industries lobby most for trade related issues, we rely on the US Lobbying Disclosure Act of 1995, which requires any lobbying activities engaged within the US to be disclosed. 15 The Center for Responsive Politics publishes the full reports of the disclosed lobbying activities indicating the client name (including foreign entities), lobbyist name, contribution level and year, and more interestingly industry classification and the general issue that is lobbied for. Among the possible lobbied issues we focus on the ones which description might relate to the content of PTAs, they are tariffs, trade, labor antitrust and workplace, environment, copyright patent and trademark. Table 7 gives the level of contributions of each lobbied issue by sector and relative to the sector-level total contribution in 2003. The sectors that are relatively more engaged in “PTA-related” lobbying turn out to be Health, Construction and Defense. Table 8 shows that our results are robust to removing these three lobbying intensive sectors from our estimation sample. ii. Composition concerns There is evidence supporting the existence of templates for the content of trade agreements, which are championed by the US and the EU the two major economies 15The US Lobbying Disclosure Act of 1995 contributes to the “public awareness of the efforts of paid lobbyist to influence the public decision making process in both the legislative and executive branches of the Federal Government.” 20 leading active regional integration agendas (Horn, Mavroidis, & Sapir, 2010). While disciplines such as IPR ,investment , and movement of capital are systematically (or almost systematically) included in PTAs negotiated with either the US or the EU, labor market regulations and environmental laws seem to be specificities of US-PTAs, whereas competition policies and visa and asylum disciplines are only found in EU- PTAs. Moreover, the US and the EU are preponderant in our data sample, their PTAs represent 20 percent of the agreements mapped and are source of more than 55 percent of the investment announcements (see figure A1). This weight raises concerns about the validity of our results for other countries and questions the existence of a composition effect. In particular, the negative effect of labor market regulations and environmental laws on FDI announcements could be driven by investments between the US and partner countries that are part of a same PTA including those two disciplines. To test whether our findings are driven by the presence of PTA templates, which would not be captured by our set of country-time fixed effects, we run our specifications by sequentially removing countries from the estimation samples. We find that both baseline and discipline level estimations remain the same, ruling out any role played by a particular economy in our results. 16 As similar concern might be raised with respect to China and its weight in North-South relationships. Table 9 shows that the results we obtained by interacting disciplines with development levels remain the same after we single out China from the North- South interacted terms. 5. Conclusion This paper contributes to the existing literature on the relationship between preferential trade agreements and foreign direct investments. Our baseline results 16 Results available upon request. 21 show that on average adding a discipline to a deep PTA increases FDI by 1.4 percent. In general, WTO-extra disciplines matter more for FDI than WTO-plus. All our disciplines of interest (investment, IPR, visa and asylum, movement of capital, competition policy, labor market regulations and environmental laws) promote FDI in service-related activities. Concerning the production-related activities, only visa and asylum has a positive and significant impact on FDI, while the inclusion of labor market regulations and environmental laws reduces FDI. We also find that discipline level results are mostly driven by investments between developed and developing countries where the institutional gap is the widest. Finally, we address causality concerns by ruling out the influence of potential users of lobbying activities at both the firm and industry levels. 22 Bibliography Amendolagine, V., Presbitero, A., Rabellotti, R., Sanfilippo, M., & Seric, A. (2017). FDI, Global Value Chains, and Local Sourcing in Developing Countries. IMF Working Papers. Amoroso, S., Dosso, M., & Moncada-Paternò-Castello, P. (2015). The Impact of Skill Endowments and Collective Bargaining on Knowledge-Intensive Greenfield FDI. SSRN. Anderson, J. E., & van Wincoop, E. (2003). Gravity with Gravitas: A Solution to the Border Puzzle. American Economic Review. Antonietti, R., Bronzini, R., & Cainelli, G. (2015). Inward greenfield FDI and innovation. Economia e Politica Industriale. Antràs, P., & Staiger, R. W. (2012). Offshoring and the Role of Trade Agreements. American Economic Review, 102(7), 3140-3183. Baltagi , B. H., Egger, P., & Pfaffermayr, M. (2008). Estimating Regional Trade Agreement Effects on FDI in an Interdependent World. Journal of Econometrics, 194-208. Büthe, T., & Milner, H. (2008). The Politics of Foreign Direct Investment into Developing Countries: Increasing FDI through International Trade Agreements? American Journal of Political Science, 741-62. Crescenzi, R., Pietrobelli, C., & Rabellotti, R. (2013). Innovation drivers, value chains and the geography of multinational corporations in Europe. Journal of Economic Geography2013. Dee, P., & Gali, J. (2005). The Trade and Investment Effects of Preferential Trading Arrangements. In T. I. Rose, International Trade in East Asia, NBER-East Asia Seminar on Economics. University of Chicago Press. Egger, P., & Larch, M. (2008). Interdependent Preferential Trade Agreement Memberships: An Empirical Analysis. Journal of International Economics, 76(2), 384-399. Egger, P., & Pfaffermayr, M. (2000). Trade, multinational sales, and FDI in a three-factors model. Johannes Kepler University of Linz, Department of Economics. Farole, T., & Winkler, D. (2014). Making Foreign Direct Investment Work for Sub-Saharan Africa:Local Spillovers and Competitiveness in Global Value Chains. The World Bank. Ferrantino, M. J. (1993). The effect of intellectual property rights on international trade and investment. Weltwirtschaftliches Archiv. Fontagné, L. (1999). Foreign Direct Investment and International Trade: Complements or Subsitutes? OECD Science, Technology and Industry Working Papers. 23 Gómez-Mera, L., Kenyon, T., Margalit, Y., Reis, J. G., & Varela, G. (2014). New Voices in Investment: A Survey of Investors from Emerging Countries. The World Bank. Hanna, R. (2010). US Environmental Regulation and FDI: Evidence from a Panel of US-Based Multinational Firms. American Economic Journal: Applied Economics, 158-89. Helpman, E. (1992). Innovation, Imitation, and Intellectual Property Rights. Working Paper, National Bureau of Economic Research. Helpman, E., Melitz, M., & Rubinstein, Y. (2008). Estimating Trade Flows: Trading Partners and Trading Volumes. Quarterly Journal of Economics, 441-487. Hofmann, C., Osnago, A., & Ruta Michele. (2018). The Content of Preferential Trade Agreements. World Trade Review. Horn, H., Mavroidis, P. C., & Sapir, A. (2010). Beyond the WTO? An Anatomy of EU and US Preferential Trade Agreements. The World Economy. Javorcik, B. S., & Spatareanu, M. (2005). Do Foreign Investors Care about Labor Market Regulations? Review of World Economics, 375-403. Laget, E., Osnago, A., Rocha, N., & Ruta, M. (2018). Deep Trade Agreements and Global Value Chains. Policy Research Working Paper, 8491. Lee, J.-Y., & Mansfield, E. (1996). The modern university: contributor to industrial innovation and recipient of industrial R\&D support. Research Policy. Lesher, M., & Miroudot, S. (2006). Analysis of the Economic Impact of Investment Porvisions in Regional Trade Agreements. OECD Trade Policy Working Papers. Limão, N. (2016). Preferential Trade Agreements. In K. Bagwell, & R. W. Staiger (Eds.), Handbook of Commercial policy. Markusen, J. R., & Maskus, K. E. (2002). A Unified Approach to Intra-Industry Trade and Foreign Direct Investment. Frontiers of Research in Intra-Industry Trade. Markusen, J. R., & Venables, A. J. (1998). Multinational firms and the new trade theory. Journal of International Economics. Mattoo, A., Mulabdic, A., & Ruta, M. (2017). Trade Creation and Trade Diversion in Deep Agreements. Word Bank Group. Medvedev, D. (2012). Beyond Trade: The Impact of Preferential Trade Agreements on FDI Inflows. World Development, 40(1). Orefice, G., & Rocha, N. (2014). Deep Integration and Production Networks: an Empirical Analysis. The World Economy, 37(1), 106-136. Osnago, A., Rocha, N., & Ruta, M. (2015). Deep Trade Agreements and Vertical FDI: The Devil is in the Details. Forthcoming, Canadian Journal of Economics. 24 Paniagua, J., & Sapena, J. (2014). Is FDI doing good? A golden rule for FDI ethics. Journal of Business Research. Santos Silva, J., & Tenreyro, S. (2006). The Log of Gravity. The Review of Economics and Statistics, 88(4), 641-658. Tintelnot, F. (2017). Global Production with Export Platforms. The Quarterly Journal of Economics. World Trade Organization. (2011). World Trade Report 2011: The WTO and Preferential Trade Agreements: From Co-Existence to Coherence. Geneva. 25 Figures Figure 1: BITS and investment related disciplines in PTAs 26 Figure 2: Inclusion frequency of investment related disciplines in PTAs Movement of Capital Competition Policy Investment IPR Visa and Asylum Labor Market Regulation Environmental Laws Statistics Energy Agriculture Social Matters Research and Technology Industrial Cooperation Approximation of Legislation Taxation Illegal Immigration Anti-Corruption Financial Assistance Education and Training Consumer Protection Economic Policy Dialogue Cultural Cooperation Data Protection Public Administration Mining Regional Cooperation Nuclear Safety SME Terrorism Information Society Illicit Drugs Health Audio Visual Money Laundering Human Rights Innovation Policies Civil Protection Political Dialogue 0.00 0.10 0.20 0.30 0.40 0.50 27 Figure 3: Repartition of activities along the production chain 28 Figure 4: Correlation between fDi Markets and UNCTAD bilateral investment data 29 Tables Table 1: Impact of total depth on investment OLS PPML Dependent Log(FDI) FDI variables (1) (2) (3) (4) (5) (6) PTA dummy 0.0345 0.0664 -0.0823 0.173 -0.205 -0.174 (0.0699) (0.0877) (0.126) (0.264) (0.130) (0.243) Expired PTA 0.100 0.146 0.192 0.174 0.179 (0.125) (0.128) (0.137) (0.175) (0.181) BIT -0.00306 0.00985 0.00132 -0.109 -0.110 (0.0661) (0.0668) (0.0670) (0.0684) (0.0689) Total Depth 0.00868* 0.0143** (0.00524) (0.00578) WTO-X 0.0163* 0.0155* (0.00857) (0.00865) WTO-plus -0.0190 0.0107 (0.0254) (0.0233) Observations 33,622 33,622 33,622 33,622 109,383 109,383 R-squared 0.619 0.619 0.619 0.619 0.800 0.800 Country-pair FE Yes Yes Yes Yes Yes Yes Country-time FE Yes Yes Yes Yes Yes Yes Business activities All All All All All All clustered standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 30 Table 2: FDI in extractive activities are not driven by PTAs (1) (2) PPML PPML dependent variables FDI announcements FDI announcements Total depth 0.0142** (0.00577) Total depth * extraction 0.00873 (0.0128) WTO-X 0.0169* (0.00878) WTO-plus 0.00765 (0.0235) WTO-X * extraction -0.0145 (0.0568) WTO-plus * extraction 0.0229 (0.0397) BIT -0.109 -0.111 (0.0684) (0.0689) PTA -0.217* -0.167 (0.132) (0.243) expired PTA 0.167 0.174 (0.175) (0.180) Observations 109,383 109,383 R-squared 0.800 0.800 Country-pair FE Yes Yes Country-time FE Yes Yes Business activities All All clustered standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 31 Table 3: Disciplines level estimations Dependent variable: FDI announcements, PPML estimator (1) (2) (3) (5) (6) (7) (8) Labor Visa and Environment Movement Competition Discipline (d): Investment IPR market asylum laws of capital policy regulations Total depth (d 0.0116* 0.00690 0.00111 0.0272*** 0.0366*** 0.0136* 0.0192*** excluded) (0.00691) (0.00648) (0.00743) (0.00827) (0.00653) (0.00727) (0.00708) Discipline (d) 0.224 0.361*** 0.430*** -0.302 -0.818*** 0.0665 -0.142 (0.210) (0.130) (0.143) (0.188) (0.145) (0.181) (0.221) Observations 103,953 103,953 103,953 103,953 103,953 103,953 103,953 R-squared 0.799 0.800 0.800 0.799 0.801 0.799 0.799 Country-pair FE Yes Yes Yes Yes Yes Yes Yes Country-time FE Yes Yes Yes Yes Yes Yes Yes Business activities P&S P&S P&S P&S P&S P&S P&S clustered standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 Table 4: Discipline level estimations with business activities interactions Dependent variable: FDI announcements, PPML estimator (1) (2) (3) (5) (6) (7) (8) Labor Visa and Environment Movement of Competition Discipline(d): investment IPR market asylum laws capital policy regulations Total depth (d excluded) 0.0115* 0.00723 0.000498 0.0270*** 0.0362*** 0.0139* 0.0192*** (0.00690) (0.00646) (0.00748) (0.00821) (0.00654) (0.00731) (0.00718) Discipline(d) 0.0402 0.178 0.260 -0.485** -1.100*** -0.119 -0.307 (0.226) (0.152) (0.167) (0.195) (0.194) (0.196) (0.242) Discipline(d) * Service 0.340** 0.345** 0.321* 0.372** 0.511** 0.367** 0.334** (0.158) (0.171) (0.177) (0.185) (0.256) (0.174) (0.162) Observations 103,953 103,953 103,953 103,953 103,953 103,953 103,953 R-squared 0.800 0.801 0.800 0.800 0.801 0.800 0.800 Country-pair FE Yes Yes Yes Yes Yes Yes Yes Country-time FE Yes Yes Yes Yes Yes Yes Yes Business activities P&S P&S P&S P&S P&S P&S P&S clustered standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 32 Table 5: Disciplines effects by level of development (1) (2) (3) (4) (5) (6) (7) (8) Labor Total Visa and Environment Movement Competition Discipline (d): IPR market Investment depth asylum laws of capital policy regulations Total depth (without d) 0.00534 0.00256 0.0248*** 0.0371*** 0.0125* 0.0145** 0.0235*** (0.00680) (0.00797) (0.00840) (0.00659) (0.00729) (0.00583) (0.00744) discipline*South-South -0.00837 -0.108 -0.229 0.683 -0.784 -0.353 0.178 0.333 (0.0219) (0.380) (0.311) (0.525) (0.539) (0.451) (0.272) (0.284) discipline*North-North -0.00184 0.0947 0.0914 -0.561*** -0.793*** -0.415** -0.0515 -0.699*** (0.00693) (0.132) (0.299) (0.214) (0.166) (0.205) (0.101) (0.253) discipline*South/North 0.0244*** 0.786*** 0.489*** -0.0235 -0.884*** 0.388** 0.705*** 0.0433 (0.00663) (0.179) (0.143) (0.194) (0.172) (0.186) (0.152) (0.227) Observations 103,953 103,953 103,953 103,953 103,953 103,953 103,953 103,953 R-squared 0.800 0.801 0.800 0.800 0.801 0.801 0.801 0.800 Country-pair FE Yes Yes Yes Yes Yes Yes Yes Yes Country-time FE Yes Yes Yes Yes Yes Yes Yes Yes Business activities P&S P&S P&S P&S P&S P&S P&S P&S Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 33 Table 6: Robustness check: removing top 1 percent of projects announcements Dependent variable: FDI announcements, PPML estimator (1) (2) (3) (5) (6) (7) (8) (9) Labour Total Visa and Environment Movement Competition Discipline(d): investment IPR market depth asylum laws of capital policy regulations Total depth (d 0.00790 0.00289 -0.00438 0.0221*** 0.0256*** 0.00734 0.0120* excluded) (0.00609) (0.00665) (0.00751) (0.00850) (0.00667) (0.00746) (0.00717) Discipline(d) 0.000947 -0.0527 0.0652 0.206 -0.605*** -0.966*** -0.124 -0.328 (0.00708) (0.130) (0.147) (0.162) (0.190) (0.207) (0.211) (0.225) Discipline(d)* 0.0135** 0.283** 0.370** 0.333** 0.502*** 0.549** 0.370** 0.416*** services (0.00605) (0.136) (0.165) (0.156) (0.172) (0.237) (0.161) (0.149) Observations 103,941 103,941 103,941 103,941 103,941 103,941 103,941 103,941 R-squared 0.805 0.805 0.806 0.805 0.806 0.806 0.806 0.806 Country-pair FE Yes Yes Yes Yes Yes Yes Yes Yes Country-time FE Yes Yes Yes Yes Yes Yes Yes Yes Business activities P&S P&S P&S P&S P&S P&S P&S P&S Clustered standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 34 Table 7: Lobbying contributions by issues relative to sectoral General issue Sector Contribution contribution Copyright, Patent & Trademark Health $39,800,000 16% Copyright, Patent & Trademark Transportation $8,730,704 6% Copyright, Patent & Trademark Construction $1,835,212 5% Environment & Superfund Defense $9,905,470 4% Environment & Superfund Energy/Nat Resource $7,355,459 4% Environment & Superfund Construction $1,139,960 3% Labor, Antitrust & Workplace Defense $6,871,012 3% Labor, Antitrust & Workplace Construction $1,202,697 3% Labor, Antitrust & Workplace Labor $1,068,717 2% Tariffs Defense $6,130,000 3% Tariffs Health $5,488,536 2% Tariffs Transportation $2,516,683 2% Trade Health $28,900,000 12% Trade Defense $17,400,000 8% Trade Transportation $7,459,139 5% Table 8: robustness check: removing lobbying intensive sectors Dependent variable: FDI announcements, PPML estimator (1) (2) (3) (5) (6) (7) (8) (9) Labour Total Visa and Environment Movement Competiti Discipline(d): Investment IPR market depth asylum laws of capital on policy regulations Total depth (d excluded) 0.0138** 0.00698 -0.000181 0.0257*** 0.0360*** 0.0134* 0.0193*** (0.00603) (0.00657) (0.00770) (0.00841) (0.00667) (0.00741) (0.00729) Discipline(d) 0.00777 -0.0254 0.180 0.278 -0.454** -1.112*** -0.106 -0.317 (0.00764) (0.140) (0.159) (0.174) (0.206) (0.213) (0.201) (0.248) Discipline(d) * services 0.0128* 0.332** 0.358** 0.312* 0.371* 0.529* 0.372** 0.344** (0.00675) (0.154) (0.178) (0.183) (0.195) (0.272) (0.182) (0.169) Observations 101,526 101,526 101,526 101,526 101,526 101,526 101,526 101,526 R-squared 0.799 0.799 0.800 0.799 0.799 0.801 0.799 0.799 Country-pair FE Yes Yes Yes Yes Yes Yes Yes Yes Country-time FE Yes Yes Yes Yes Yes Yes Yes Yes Business activities P&S P&S P&S P&S P&S P&S P&S P&S Clustered standard errors in parentheses +++" p<1/12 , ** p<1/16 , * p<1/2 35 Table 9: Interaction with China in North-South agreements (1) (2) (3) (4) (5) (6) (7) (8) Movemen Visa and Environme Competiti total depth IPR Labor t of Investment Asylum nt on policy VARIABLES capital Total depth without discipline 0.00521 0.00194 0.0250*** 0.0374*** 0.0277*** 0.0125* 0.0234*** (0.00685) (0.00801) (0.00854) (0.00662) (0.00880) (0.00729) (0.00746) Discipline*HH -0.00154 0.0992 0.112 -0.563*** -0.794*** -0.838*** -0.391 -0.700*** (0.00693) (0.133) (0.303) (0.215) (0.166) (0.203) (0.467) (0.253) Discipline*LL(with China) 0.0514 0.586 0.252 - - 0.0445 -0.413** - (0.0431) (0.514) (1.247) - - (0.504) (0.205) Discipline*LL(without China) -0.0153 -0.271 -0.264 0.676 -0.809 0.585 -0.148 0.334 (0.0239) (0.483) (0.305) (0.528) (0.538) (1.346) (0.410) (0.284) Discipline*LH(with China) -0.00254 0.553*** 0.0434 0.0609 0.341 0.391** -0.195 -0.0567 (0.0146) (0.187) (0.183) (0.189) (0.412) (0.186) (0.269) (0.243) Discipline*LH(withou t China) 0.0416* 1.068** 0.851*** -0.707 -0.109 -3.002*** 0.919** -0.286 (0.0224) (0.486) (0.272) (0.495) (0.526) (0.574) (0.421) (0.271) Observations 103,953 103,953 103,953 103,953 103,953 103,953 103,953 103,953 R-squared 0.801 0.801 0.800 0.800 0.801 0.801 0.802 0.800 Country-pair FE Yes Yes Yes Yes Yes Yes Yes Yes Country-time FE Yes Yes Yes Yes Yes Yes Yes Yes Business activity P&S P&S P&S P&S P&S P&S P&S P&S Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 36 Appendix Table A1: Description of the 52 disciplines in the Content of Deep Trade Agreements database WTO-plus areas Retention of antidumping rights and obligations under the WTO Agreement (Art. VI GATT). Unfair trade AD practices. Discipline of information; publication on the internet of new laws and regulations; training. Incl. Customs disciplines on trade facilitation. CVM Retention of countervailing measures rights and obligations under the WTO Agreement (Art VI GATT). Elimination of export taxes. Examples: Elimination of customs duties on exports, elimination of duties, Export Taxes taxes or other charges on exports. FTA Agriculture Tariff liberalization with regard to agriculture goods; elimination of non-tariff measures. FTA Industrial or Customs Tariff liberalization with regard to industrial goods; elimination of non-tariff measures. GATS Liberalization of trade in services. Progressive liberalization; national treatment and/or non-discrimination principle; publication of laws Public Procurement and regulations on the internet; specification on public procurement regime. SPS Affirmation of rights and obligations under the WTO Agreement on SPS; harmonization of SPS measures. Assessment of anticompetitive behavior; annual reporting on the value and distribution of state aid State Aid given; discipline of information. Incl. export subsidies on products. GATT Art. XVII. Establishment or maintenance of a state enterprise in accordance with and affirming STE disciplines of GATT. Non-discrimination regarding production and marketing condition; discipline of information. Incl. disciplines on public undertakings. Affirmation of rights and obligations under WTO Agreement on TBT; discipline of information; TBT harmonization of regulations; mutual recognition agreements. Disciplines concerning requirements for local content and export performance on FDI. Applies only to TRIMs measures that affect trade in goods. Harmonization of standards; enforcement; national treatment; most-favored nation treatment and any TRIPs other policy covered by TRIPs. International treaties referenced in TRIPS: Paris Convention, Berne Convention, Rome Convention, IPIC Treaty. WTO-X areas Agriculture Policies and technical assistance to conduct modernization projects; exchange of information. Regulations concerning criminal offence measures in matters affecting international trade and Anti-Corruption investment. Approximation of Application of international legislation in national legislation. Any form of legislation that provides for Legislation approximation of laws. [Appears mainly in customs unions.] Audio Visual Promotion of the industry; encouragement of co-production. Civil Protection Implementation of harmonized rules and policies. Chapter/discipline on competition policy in general, could include prescriptions as regards Competition Policy anticompetitive business conduct; harmonization of competition laws; establishment or maintenance of an independent competition authority, among others. Consumer Protection Harmonization of consumer protection laws and policies; exchange of information and experts; training. Cultural Cooperation Promotion of joint initiatives and local culture. 37 Data Protection Exchange of information and experts; joint projects. Economic Policy Dialogue Exchange of ideas and opinions; joint studies. Education and Training Measures to improve the general level of education. Energy Exchange of information; technology transfer; joint studies. Development of environmental standards or policies; enforcement of national and international Environmental Laws environmental laws; establishment of sanctions for violation of environmental laws; publications of laws and regulation. Financial Assistance Policies and rules guiding the granting and administration of financial assistance. Health Monitoring of diseases; development of health information systems; exchange of information. Human Rights Respect for human rights; policies. Illegal Immigration Conclusion of re-admission agreements; prevention and control of illegal immigration. Treatment and rehabilitation of drug addicts; joint projects on prevention of consumption; reduction of Illicit Drugs drug supply; information exchange. Industrial Cooperation Assistance in conducting modernization projects; facilitation and access to credit to finance. Exchange of information; dissemination of new technologies; training. Cooperation and exchange of Information Society information (often in the context of other policies). Innovation Policies Participation in framework programs; promotion of technology transfers. Information exchange; Development of legal frameworks; Harmonization and simplification of procedures; National treatment; Establishment of mechanism for the settlement of disputes. Incl. Investment investment policies not covered by TRIMs (e.g. promotion, protection, liberalization of investment measures, among other). Accession to international treaties not referenced in the TRIPs Agreement. Incl. intellectual property IPR policies and/or the regulation of different types of IPRs not covered by TRIPs. Regulation of the national labor market; affirmation of International Labor Organization (ILO) Labor Market Regulation commitments and standards; enforcement. Mining Exchange of information and experience; development of joint initiatives. Money Laundering Harmonization of standards; technical and administrative assistance. Movement of Capital Liberalization of capital movement; prohibition of new restrictions. Nuclear Safety Development of laws and regulations; supervision of the transportation of radioactive materials. Convergence of the parties’ positions on international issues; encouragement for increased political Political Dialogue dialogue. Public Administration Technical assistance; exchange of information; joint projects; training. Regional Cooperation Promotion of regional cooperation; technical assistance programs. Research and Technology Joint research projects; exchange of researchers; development of public-private partnership. SMEs Technical assistance; facilitation of access to finance. Social Matters Coordination of social security systems; non-discrimination regarding working conditions. Statistics Harmonization and/or development and/or exchange of statistical methods and statistics; training. Taxation Policies and/or assistance in conducting fiscal system reforms. Terrorism Exchange of information and experience; joint research and studies. Visa and Asylum Exchange of information; drafting legislation; training. Incl. international movement of persons. 38 Table A2: FDI-markets activities and industries classification 39 Activities Design, Development & Logistics, Distribution & Shared Services Center Education & Training Sales, Marketing & Customer Contact Technical Support Business Services Maintenance & Transportation Manufacturing ICT & Internet Infrastructure Headquarters Development Construction Outstanding, Research & Extraction Electricity Recycling Servicing Support Testing Center Center % of row total, Retail % of column total - 46,677 157 - 58 9,034 2,313 - 4,265 - 1,820 10,097 2 567 - 2,866 22 1 Aerospace - 59.9% 0.2% - 0.1% 11.6% 3.0% - 5.5% - 2.3% 13.0% 0.0% 0.7% - 3.7% 0.0% 0.0% - 1.2% 0.0% - 0.3% 4.2% 8.4% - 2.6% - 0.3% 37.9% 0.0% 0.5% - 0.6% 0.1% 0.0% Alternative - 52,281 48 - 12 478 48 513,332 1,239 - 508 107 204 1,059 519 56,056 - 2 Renewable - 8.4% 0.0% - 0.0% 0.1% 0.0% 82.0% 0.2% - 0.1% 0.0% 0.0% 0.2% 0.1% 9.0% - 0.0% energy - 1.3% 0.0% - 0.1% 0.2% 0.2% 53.4% 0.8% - 0.1% 0.4% 0.6% 1.0% 0.1% 12.3% - 0.0% - 232,371 - - 43 8,329 459 - 3,934 - 5,119 739 50 3,054 771 1,236 110 107 Automotive Components - 90.7% - - 0.0% 3.2% 0.2% - 1.5% - 2.0% 0.3% 0.0% 1.2% 0.3% 0.5% 0.0% 0.0% - 5.8% - - 0.3% 3.9% 1.7% - 2.4% - 0.8% 2.8% 0.2% 2.9% 0.1% 0.3% 0.7% 0.7% Automotive - 504,999 103 - 60 18,667 2,120 - 6,413 435 2,429 1,882 157 7,166 8,289 4,375 73 118 OEM (Original Equipment - 90.6% 0.0% - 0.0% 3.3% 0.4% - 1.2% 0.1% 0.4% 0.3% 0.0% 1.3% 1.5% 0.8% 0.0% 0.0% Manufacturer) Sectors - 12.6% 0.0% - 0.4% 8.7% 7.7% - 3.9% 0.1% 0.4% 7.1% 0.5% 6.9% 1.3% 1.0% 0.5% 0.8% 137 54,786 110 - 13 176 16 - 1,186 - 2,443 - 4 349 1,270 4,577 91 - Beverages 0.2% 84.1% 0.2% - 0.0% 0.3% 0.0% - 1.8% - 3.7% - 0.0% 0.5% 1.9% 7.0% 0.1% - 0.0% 1.4% 0.0% - 0.1% 0.1% 0.1% - 0.7% - 0.4% - 0.0% 0.3% 0.2% 1.0% 0.6% - - 19,054 76 - 18 801 69 - 1,385 - 205 10 - 9,345 - 1,112 4 12 Biotechnology - 59.4% 0.2% - 0.1% 2.5% 0.2% - 4.3% - 0.6% 0.0% - 29.1% - 3.5% 0.0% 0.0% - 0.5% 0.0% - 0.1% 0.4% 0.3% - 0.8% - 0.0% 0.0% - 9.0% - 0.2% 0.0% 0.1% Building & - 128,699 - - - 264 25 - 533 63 998 - 8 43 973 68 6 - Construction - 97.7% - - - 0.2% 0.0% - 0.4% 0.0% 0.8% - 0.0% 0.0% 0.7% 0.1% 0.0% - Materials - 3.2% - - - 0.1% 0.1% - 0.3% 0.0% 0.2% - 0.0% 0.0% 0.2% 0.0% 0.0% - Business - 40,457 254 - 511 3,947 331 - 4,391 446 1,105 879 784 1,583 529 1,492 2,013 1,781 Machines & - 66.9% 0.4% - 0.8% 6.5% 0.5% - 7.3% 0.7% 1.8% 1.5% 1.3% 2.6% 0.9% 2.5% 3.3% 2.9% Equipment - 1.0% 0.0% - 3.0% 1.8% 1.2% - 2.7% 0.1% 0.2% 3.3% 2.4% 1.5% 0.1% 0.3% 12.7% 11.5% Activities 40 Education & Training Sales, Marketing & Customer Contact Technical Support Business Services Development & Shared Services Maintenance & Transportation Manufacturing ICT & Internet Distribution & Infrastructure Headquarters Development Construction Outstanding, Research & Extraction Electricity Recycling Logistics, Servicing Support Design, Testing Center Center Center % of row Retail total, % of column total 41, 108,1 43 5,30 7,66 - 275 57 4 0 4,098 11,939 - 5 2,306 443 142 23,981 1,657 5,097 42 3,627 1,544 Business 49.7 19. Services - 0.1% % 0% 2.4% 1.9% 5.5% - 3.5% 1.1% 0.2% 0.1% 11.0% 0.8% 2.3% 0.0% 1.7% 0.7% 18.5 2.9 31.4 - 0.0% % % % 1.9% 43.3% - 4.7% 0.5% 0.1% 0.5% 72.1% 1.6% 0.8% 0.0% 23.0% 9.9% - 45,892 - - 47 72 2 - 477 - 542 93 34 25 2,555 139 21 - Ceramics & Glass - 92.0% - - 0.1% 0.1% 0.0% - 1.0% - 1.1% 0.2% 0.1% 0.1% 5.1% 0.3% 0.0% - - 1.1% - - 0.3% 0.0% 0.0% - 0.3% - 0.1% 0.3% 0.1% 0.0% 0.4% 0.0% 0.1% - 2,14 4,17 2 480,193 63 - 24 4,959 646 - 9 - 6,470 272 430 4,904 1,058 5,063 269 193 Chemicals 0.4% 94.0% 0.0% - 0.0% 1.0% 0.1% - 0.8% - 1.3% 0.1% 0.1% 1.0% 0.2% 1.0% 0.1% 0.0% 0.2% 12.0% 0.0% - 0.1% 2.3% 2.3% - 2.5% - 1.1% 1.0% 1.3% 4.7% 0.2% 1.1% 1.7% 1.2% 448 661, ,84 2,65 085 596,779 1,452 - 207 1,541 487 5 5 672 51,942 384 343 3,418 10,929 16,224 1,211 1 Coal, Oil and 36.8 25. Natural Gas % 33.2% 0.1% - 0.0% 0.1% 0.0% 0% 0.1% 0.0% 2.9% 0.0% 0.0% 0.2% 0.6% 0.9% 0.1% 0.0% Sectors 66.5 46. % 14.9% 0.2% - 1.2% 0.7% 1.8% 6% 1.6% 0.2% 8.6% 1.4% 1.0% 3.3% 1.7% 3.6% 7.7% 0.0% 3,13 15,9 - 35,025 922 - 0 46,282 1,092 - 28 363,192 4,090 833 154 8,624 4,153 20,848 1,275 1,859 Communicati ons - 6.9% 0.2% - 0.6% 9.1% 0.2% - 3.1% 71.6% 0.8% 0.2% 0.0% 1.7% 0.8% 4.1% 0.3% 0.4% 18.6 - 0.9% 0.2% - % 21.6% 4.0% - 9.7% 83.2% 0.7% 3.1% 0.5% 8.3% 0.6% 4.6% 8.1% 12.0% 2,92 - 45,436 18 - 161 1,905 562 - 0 - 1,571 390 541 3,070 10,966 2,467 68 142 Consumer Electronics - 64.7% 0.0% - 0.2% 2.7% 0.8% - 4.2% - 2.2% 0.6% 0.8% 4.4% 15.6% 3.5% 0.1% 0.2% - 1.1% 0.0% - 1.0% 0.9% 2.0% - 1.8% - 0.3% 1.5% 1.6% 3.0% 1.7% 0.5% 0.4% 0.9% 4,42 - 32,816 28 - 454 1,454 129 - 2 - 23,298 111 18 1,088 211,751 5,021 151 48 Consumer Products - 11.7% 0.0% - 0.2% 0.5% 0.0% - 1.6% - 8.3% 0.0% 0.0% 0.4% 75.4% 1.8% 0.1% 0.0% - 0.8% 0.0% - 2.7% 0.7% 0.5% - 2.7% - 3.8% 0.4% 0.1% 1.1% 32.7% 1.1% 1.0% 0.3% 12,9 - 199,482 57 - 38 7,379 440 - 39 120 1,975 486 496 2,442 883 4,121 86 209 Electronic Components - 86.3% 0.0% - 0.0% 3.2% 0.2% - 5.6% 0.1% 0.9% 0.2% 0.2% 1.1% 0.4% 1.8% 0.0% 0.1% - 5.0% 0.0% - 0.2% 3.4% 1.6% - 7.9% 0.0% 0.3% 1.8% 1.5% 2.4% 0.1% 0.9% 0.5% 1.3% 41 Activities ICT & Internet Infrastructure Sales, Marketing & Support Research & Development Customer Contact Center Technical Support Center Maintenance & Servicing Design, Development & Logistics, Distribution & Shared Services Center Education & Training Business Services Transportation Manufacturing Headquarters Construction Outstanding, Extraction Electricity Recycling Testing % of row total, Retail % of column total - 33,516 21 - 18 2,307 80 - 838 - 437 422 - 664 - 1,275 125 12 Engines & Turbines - 84.4% 0.1% - 0.0% 5.8% 0.2% - 2.1% - 1.1% 1.1% - 1.7% - 3.2% 0.3% 0.0% - 0.8% 0.0% - 0.1% 1.1% 0.3% - 0.5% - 0.1% 1.6% - 0.6% - 0.3% 0.8% 0.1% - - 415,908 - 2,239 1,170 712 - 19,204 4,043 713 - - 141 8 69,453 1,895 728 Financial Services - - 80.6% - 0.4% 0.2% 0.1% - 3.7% 0.8% 0.1% - - 0.0% 0.0% 13.5% 0.4% 0.1% - - 71.1% - 13.3% 0.5% 2.6% - 11.7% 0.9% 0.1% - - 0.1% 0.0% 15.3% 12.0% 4.7% - 169,263 238 - 132 1,450 222 - 5,389 - 27,106 38 17 2,011 171,624 6,284 336 21 Food & Tobacco - 44.1% 0.1% - 0.0% 0.4% 0.1% - 1.4% - 7.1% 0.0% 0.0% 0.5% 44.7% 1.6% 0.1% 0.0% - 4.2% 0.0% - 0.8% 0.7% 0.8% - 3.3% - 4.5% 0.1% 0.1% 1.9% 26.5% 1.4% 2.1% 0.1% - 195 1,378 16,501 14 434 130 - 276 - - - - 939 17 686 12 - Healthcare - 0.9% 6.7% 80.2% 0.1% 2.1% 0.6% - 1.3% - - - - 4.6% 0.1% 3.3% 0.1% - Sectors - 0.0% 0.2% 1.1% 0.1% 0.2% 0.5% - 0.2% - - - - 0.9% 0.0% 0.2% 0.1% - - - 4 346,737 202 109 143 - 2,031 - - - - - 603 1,215 84 - Hotels & Tourism - - 0.0% 98.7% 0.1% 0.0% 0.0% - 0.6% - - - - - 0.2% 0.3% 0.0% - - - 0.0% 24.0% 1.2% 0.1% 0.5% - 1.2% - - - - - 0.1% 0.3% 0.5% - Industrial - 111,920 232 - 101 8,880 1,605 6 10,647 204 5,318 2,827 316 2,152 305 9,024 597 383 Machinery, Equipment & - 72.4% 0.2% - 0.1% 5.7% 1.0% 0.0% 6.9% 0.1% 3.4% 1.8% 0.2% 1.4% 0.2% 5.8% 0.4% 0.2% Tools - 2.8% 0.0% - 0.6% 4.2% 5.8% 0.0% 6.5% 0.0% 0.9% 10.6% 0.9% 2.1% 0.0% 2.0% 3.8% 2.5% - 41 141 52,939 3 66 198 - 417 320 - - - - 30,799 1,272 - - Leisure & Entertainment - 0.0% 0.2% 61.4% 0.0% 0.1% 0.2% - 0.5% 0.4% - - - - 35.7% 1.5% - - - 0.0% 0.0% 3.7% 0.0% 0.0% 0.7% - 0.3% 0.1% - - - - 4.8% 0.3% - - - 19,987 314 - 21 2,507 762 - 2,613 - 1,680 170 - 3,975 168 1,641 192 45 Medical Devices - 58.7% 0.9% - 0.1% 7.4% 2.2% - 7.7% - 4.9% 0.5% - 11.7% 0.5% 4.8% 0.6% 0.1% - 0.5% 0.1% - 0.1% 1.2% 2.8% - 1.6% - 0.3% 0.6% - 3.8% 0.0% 0.4% 1.2% 0.3% 42 Activities ICT & Internet Infrastructure Sales, Marketing & Support Research & Development Customer Contact Center Technical Support Center Maintenance & Servicing Design, Development & Logistics, Distribution & Shared Services Center Education & Training Business Services Transportation Manufacturing Headquarters Construction Outstanding, Extraction Electricity Recycling Testing % of row total, Retail % of column total 307,344 489,236 94 - 7 744 124 - 2,051 73 7,082 1,258 3,043 1,154 536 2,456 2 47 Metals 37.7% 60.0% 0.0% - 0.0% 0.1% 0.0% - 0.3% 0.0% 0.9% 0.2% 0.4% 0.1% 0.1% 0.3% 0.0% 0.0% 30.9% 12.2% 0.0% - 0.0% 0.3% 0.5% - 1.2% 0.0% 1.2% 4.7% 9.1% 1.1% 0.1% 0.5% 0.0% 0.3% 22,762 7,365 17 - - 814 314 - 244 - 138 - 40 20 313 194 - - Minerals 70.6% 22.9% 0.1% - - 2.5% 1.0% - 0.8% - 0.4% - 0.1% 0.1% 1.0% 0.6% - - 2.3% 0.2% 0.0% - - 0.4% 1.1% - 0.1% - 0.0% - 0.1% 0.0% 0.0% 0.0% - - Non- - 35,218 167 - - 504 204 - 217 - 2,400 3,732 209 284 1,607 858 18 - Automotive - 77.5% 0.4% - - 1.1% 0.4% - 0.5% - 5.3% 8.2% 0.5% 0.6% 3.5% 1.9% 0.0% - Transport OEM - 0.9% 0.0% - - 0.2% 0.7% - 0.1% - 0.4% 14.0% 0.6% 0.3% 0.2% 0.2% 0.1% - - 110,310 53 - 18 570 26 - 187 - 2,004 100 1,196 126 29 476 46 - Paper, Printing & Packaging - 95.8% 0.0% - 0.0% 0.5% 0.0% - 0.2% - 1.7% 0.1% 1.0% 0.1% 0.0% 0.4% 0.0% - Sectors - 2.7% 0.0% - 0.1% 0.3% 0.1% - 0.1% - 0.3% 0.4% 3.6% 0.1% 0.0% 0.1% 0.3% - - 76,207 425 - 1 5,109 195 - 6,750 383 2,497 - - 22,613 898 5,312 876 19 Pharmaceuticals - 62.8% 0.4% - 0.0% 4.2% 0.2% - 5.6% 0.3% 2.1% - - 18.6% 0.7% 4.4% 0.7% 0.0% - 1.9% 0.1% - 0.0% 2.4% 0.7% - 4.1% 0.1% 0.4% - - 21.8% 0.1% 1.2% 5.5% 0.1% - 95,523 21 - 3 2,653 132 - 2,369 - 787 91 721 550 201 744 14 30 Plastics - 92.0% 0.0% - 0.0% 2.6% 0.1% - 2.3% - 0.8% 0.1% 0.7% 0.5% 0.2% 0.7% 0.0% 0.0% - 2.4% 0.0% - 0.0% 1.2% 0.5% - 1.4% - 0.1% 0.3% 2.2% 0.5% 0.0% 0.2% 0.1% 0.2% - 719 32,129 987,122 - 160 11 - 621 - 3,642 1 - - 238 33,917 70 - Real Estate - 0.1% 3.0% 93.2% - 0.0% 0.0% - 0.1% - 0.3% 0.0% - - 0.0% 3.2% 0.0% - - 0.0% 5.5% 68.3% - 0.1% 0.0% - 0.4% - 0.6% 0.0% - - 0.0% 7.5% 0.4% - - 87,490 - - 8 1,387 2 - 712 - 1,167 353 471 424 1,123 514 194 - Rubber - 93.2% - - 0.0% 1.5% 0.0% - 0.8% - 1.2% 0.4% 0.5% 0.5% 1.2% 0.5% 0.2% - - 2.2% - - 0.0% 0.6% 0.0% - 0.4% - 0.2% 1.3% 1.4% 0.4% 0.2% 0.1% 1.2% - 43 Activities ICT & Internet Infrastructure Sales, Marketing & Support Research & Development Customer Contact Center Technical Support Center Maintenance & Servicing Design, Development & Logistics, Distribution & Shared Services Center Education & Training Business Services Transportation Manufacturing Headquarters Construction Outstanding, Extraction Electricity Recycling Testing % of row total, Retail % of column total - 174,354 41 - 14 16,238 127 - 6,365 142 773 112 - 9,424 27 2,048 46 142 Semiconductors - 83.1% 0.0% - 0.0% 7.7% 0.1% - 3.0% 0.1% 0.4% 0.1% - 4.5% 0.0% 1.0% 0.0% 0.1% - 4.3% 0.0% - 0.1% 7.6% 0.5% - 3.9% 0.0% 0.1% 0.4% - 9.1% 0.0% 0.5% 0.3% 0.9% - 1,964 21,920 - 3,697 57,763 1,049 - 21,773 63,523 1,575 290 - 10,218 291 81,499 2,076 8,086 Software & IT services - 0.7% 7.9% - 1.3% 20.9% 0.4% - 7.9% 23.0% 0.6% 0.1% - 3.7% 0.1% 29.6% 0.8% 2.9% - 0.0% 3.7% - 21.9% 27.0% 3.8% - 13.2% 14.6% 0.3% 1.1% - 9.9% 0.0% 17.9% 13.1% 52.0% - 2,237 37 - - 1,041 83 - 975 - 53 134 - 129 - 727 22 - Space & Defence - 41.1% 0.7% - - 19.1% 1.5% - 17.9% - 1.0% 2.5% - 2.4% - 13.4% 0.4% - - 0.1% 0.0% - - 0.5% 0.3% - 0.6% - 0.0% 0.5% - 0.1% - 0.2% 0.1% - - 40,342 2 120 53 220 70 - 2,096 - 7,779 305 - 241 175,844 930 21 21 Sectors Textiles - 17.7% 0.0% 0.1% 0.0% 0.1% 0.0% - 0.9% - 3.4% 0.1% - 0.1% 77.1% 0.4% 0.0% 0.0% - 1.0% 0.0% 0.0% 0.3% 0.1% 0.3% - 1.3% - 1.3% 1.1% - 0.2% 27.2% 0.2% 0.1% 0.1% - 169 584 150 259 419 649 - 4,043 637 257,228 385 - 46 891 107,181 141 - Transportation - 0.0% 0.2% 0.0% 0.1% 0.1% 0.2% - 1.1% 0.2% 69.0% 0.1% - 0.0% 0.2% 28.8% 0.0% - - 0.0% 0.1% 0.0% 1.5% 0.2% 2.4% - 2.5% 0.1% 42.5% 1.4% - 0.0% 0.1% 23.6% 0.9% - - 9,204 - 270 - - 5 - 235 - 177,685 - - - 29 556 1 - Warehousing & Storage - 4.9% - 0.1% - - 0.0% - 0.1% - 94.5% - - - 0.0% 0.3% 0.0% - - 0.2% - 0.0% - - 0.0% - 0.1% - 29.3% - - - 0.0% 0.1% 0.0% - - 32,895 - - - 44 31 - 67 - 623 6 52 20 1,539 747 - - Wood Products - 91.3% - - - 0.1% 0.1% - 0.2% - 1.7% 0.0% 0.1% 0.1% 4.3% 2.1% - - - 0.8% - - - 0.0% 0.1% - 0.0% - 0.1% 0.0% 0.2% 0.0% 0.2% 0.2% - - 44 Figure A1: Shares of investment announcements by recipient countries by source countries Taiwan 2% RoW Russia EU 11% Singapore 2% 19% 2% Australia RoW 2% EU Hong 36% Kong India 37% 2% 2% Switzerland China 2% 12% United Arab Emirates 3% South Korea 3% USA 7% Canada Saudi Arabia 3% India China 2% Japan 5% 3% 8% USA Canada 2% Indonesia 18% 2% Brazil Mexico Viet Nam Australia Russia 3% 3% 3% 3% 3% 45