metadata: authors: 0: author: Larch,Mario
1: author: Yotov,Yoto Valentinov
count: World
docna: 0: docna: Deep Trade Agreements and FDI in Partial and
            General Equilibrium: A Structural Estimation Framework
docty: Policy Research Working Paper
abstracts: cdata!: This paper quantifies the relationships
            between deep trade liberalization and foreign direct
            investment. To this end, it focuses on the effects of deep
            trade agreements. The analysis relies on a structural
            framework that simultaneously enables (i) estimating the
            direct impact of deep trade agreements on foreign direct
            investment, (ii) translating the partial deep trade
            agreement estimates into general equilibrium effects on
            foreign direct investment; and (iii) obtaining partial deep
            trade agreement effects on trade and quantifying the impact
            of deep trade agreements on foreign direct investment
            through trade. The paper obtains sizeable, positive, and
            statistically significant estimates of the effects of deep
            trade agreements on both trade and foreign direct
            investment. A counterfactual analysis suggests that together
            with direct and indirect channels deep trade agreements have contributed.
display_title: Deep Trade Agreements and FDI in
            Partial and General Equilibrium: A Structural Estimation Framework
listing_relative_url: /research/2023/03/34011336/deep-trade-agreements-fdi-partial-general-equilibrium-structural-estimation-framework
topicv3: Trade Agreements and Negotiations m1327641 779,Trade and FDI m1504264 2556
publishtoextweb_dt: 2023-03-01T00:00:00Z
dois: 10.1596/1813-9450-10338
origu: Off of Sr VP Dev Econ/Chief Econ (DECVP)
author_keys: 0: author_key: 1669054
1: author_key: 1187776
model_extractions: Policy Research Working Paper 10338 Deep Trade Agreements and FDI in Partial and General Equilibrium A Structural Estimation Framework _Mario Larch Yoto V. Yotov_ Development Research Group & Macroeconomics, Trade and Investment Global Practice March 2023
Policy Research Working Paper 10338

# **Abstract**

This paper quantifies the relationships between deep trade liberalization and foreign direct investment. To this end, it focuses on the effects of deep trade agreements. The analysis relies on a structural framework that simultaneously enables (i) estimating the direct impact of deep trade agreements on foreign direct investment, (ii) translating the partial deep trade agreement estimates into general equilibrium effects on foreign direct investment; and (iii) obtaining partial deep trade agreement effects on trade and quantifying the impact of deep trade agreements on foreign direct investment through trade. The paper obtains sizeable, positive, and statistically significant estimates of the effects of deep trade agreements on both trade and foreign direct investment. A counterfactual analysis suggests that together with direct and indirect channels deep trade agreements have contributed.

This paper is a product of the Development Research Group, Development Economics and 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 mario.larch@uni-bayreuth.de and yotov@drexel.edu.

_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 Agreements and FDI in Partial and General Equilibrium: A Structural Estimation Framework Mario Larch Yoto V. Yotov<sup>*</sup> University of Bayreuth Drexel University

# JEL Classication Codes: F10, F43, O40

Keywords: Foreign Direct Investment (FDI), Trade Liberalization, Deep Trade Agreements.

> *We are grateful to Vanessa Alviarez for a very thoughtful and constructive discussion of our paper, and for her excellent suggestions during the World Bank's Deep Trade Agreements Conference: Eects Beyond Trade. We also thank Emily Blanchard, Keith Maskus, Gianluca Orece, Nadia Rocha, and Michele Ruta for very useful comments and suggestions. This paper has beneted from support from the World Bank's Umbrella Facility for Trade trust fund nanced by the governments of the Netherlands, Norway, Sweden, Switzerland and the United Kingdom. All errors are our own.

> Contact: LarchDepartment of Law and Economics, University of Bayreuth, CEPII, CESifo, GEP, ifo, mario.larch@uni-bayreuth.de; YotovSchool of Economics, LeBow College of Business, Drexel University, yotov@drexel.edu.
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vestment provisions.<sup>2</sup> Against this backdrop, we make three contributions to the existing literature on the links between deep trade liberalization and FDI. First, we contribute to the debate on whether deep trade agreements with investment provisions stimulate FDI by estimating the direct/partial equilibrium eects of DTAs and DTAs with investment and other provisions on FDI. Second, we use our partial estimates to obtain novel general equilibrium (GE) estimates of the eects of DTAs on FDI. Third, within the same structural framework, we obtain estimates of the eects of DTAs on trade ows, and we translate those eects into general equilibrium eects of DTAs on FDI through trade liberalization.

Guided by the theoretical model of Anderson et al. (2019),<sup>3</sup> we specify two estimating gravity equationsone for trade and one for FDI, which are (i) consistent with and representative of a large number studies that quantify the impact of various determinants on FDI (e.g., Eicher et al., 2012; Blonigen and Piger, 2014; Kox and Rojas-Romagosa, 2020; Laget et al., 2021), and (ii) capitalize on the latest developments in the trade gravity literature (e.g., Head and Mayer, 2014; Yotov et al., 2016). Specically, we rely on the Poisson PseudoMaximum-Likelihood estimator to account for potential heteroskedasticity in the bilateral trade and FDI data and to take advantage of the information in the zero trade and FDI ows (cf. Santos Silva and Tenreyro, 2006, 2011). In addition, we employ a very rich set of xed eects (including origin-time, destination-time, and directional country-pair xed eects), which control for and absorb all possible country-specic and time-invariant bilateral deter-

> 2For example, Lesher and Miroudot (2006) obtain positive eects of investment provisions on FDI, while, more recently, Kox and Rojas-Romagosa (2020) and Laget et al. (2021) do not nd that investment provisions have signicant additional impact on FDI. Moreover, we are not aware of existing work that quanties the full/general equilibrium impact of DTAs and their investment provisions on FDI.

> 3The theoretical model of Anderson et al. (2019) suits our objectives well because it (i) oers structural foundations for both our trade and FDI estimating gravity models and (ii) enables us to translate our partial estimates of the eects of DTAs on trade and welfare into GE eects on FDI. Our contributions in relation to Anderson et al. (2019) are twofold. First, from a methodological perspective, they calibrate the model in a cross section while we build a panel data set to estimate some of the structural equations in order to test for and establish causality in the relationships of interest to us. Second, from a policy perspective, Anderson et al. (2019) simulate a world without FDI, while our aim is to quantify the impact of deep trade agreements on FDI. In policy work that is not intended for publication, Anderson et al. (2016) rely on the framework of Anderson et al. (2019) to quantify the eects of CETA.

3
minants of trade and FDI. Thus, mitigating omitted variable bias and endogeneity concerns with the key variables of interest to us. In addition to PTAs and DTAs, we control for other policy variables such as WTO membership, economic sanctions, and bilateral investment treaties.

To perform the empirical analysis we build a balanced panel data set for 89 countries covering more than 96 percent of world GDP and more than 94 percent of FDI throughout the sample period, 1990-2011. Our data set covers foreign direct investment, trade agreements, trade ows, gross domestic product (GDP), employment, physical capital, bilateral investment treaties, sanctions, and WTO membership. An important feature of the data set is that we capitalize on the richness of the Database on the Content of Regional Trade Agreements (DCRTA), cf. Hofmann et al. (2019) and Mattoo et al., eds (2020). Specically, the DCRTA enables us to distinguish between several indicator and continuous PTA variables, including a standard dummy variable for PTAs, an indicator variable for DTAs, an indicator for DTAs that include investment provisions, and two continuous variables for the overall depth of DTAs and for the depth of the DTAs with investment provisions.

Three main ndings stand out from our estimates of the eects of DTAs on trade. First, we nd that the average impact of PTAs in our sample is not statistically signicant. However, second, we obtain positive and statistically signicant estimates of the eects of deep trade agreements. Specically, our estimates suggest that the DTAs in our sample have led to a 16.1% (std.err. 3.184) increase in bilateral trade among member countries. Finally, our estimates reveal that deeper trade agreements (as measured by the number of provisions) lead to larger increases in the trade ows among DTA members. Depending on the number of provisions that they include, the DTAs in our sample have led to trade increases between 0.576% (std.err. 0.289) and 23.012% (std.err. 12.744). Overall, our estimates of the DTA effects in trade are consistent with ndings from recent studies that have utilized the database on the Content of Regional Trade Agreements and reinforce the view that `depth' matters 4
for the eectiveness of PTAs.<sup>4</sup> Similar to our results for trade, the estimates of the eects of DTAs on FDI are also heterogeneous. Specically, we do not obtain signicant estimates of the eects of PTAs and DTAs on FDI. However, when we zoom in on the eects of DTAs that include investment provisions, we obtain a positive, sizable, and statistically signicant estimate, which suggests that, on average, the PTAs with investment provisions in our sample have led to a 34.33% (std.err. 14.535) increase in FDI between their members. This result is consistent with earlier ndings from Lesher and Miroudot (2006). We also obtain positive estimates of the eects on FDI of several other DTA provisions including `labor market regulations', `export taxes', `public procurement' and `state owned enterprises'. This analysis reinforces and complements the ndings of Laget et al. (2021) who study the impact of dierent DTA provisions with rm-level data for the period 2003-2015. Finally, our estimates do not reveal a signicant impact of the increase in the depth (number of provisions) on FDI. In fact, our results suggest that an increase in the number/complexity of some investment provisions (e.g., related to `transparency' and `regulations') may actually decrease FDI.

We use the structural model in combination with our estimates of the partial eects of DTAs on trade and FDI in order to quantify the GE impact of DTAs on FDI.<sup>5</sup> We focus the analysis on inward usage of technology FDI per country and outward technology FDI stocks per country used abroad. The main conclusions from this analysis are as follows. DTAs have had large and strongly asymmetric eects on FDI. The DTAs that were in force in 2011 have contributed to about 3% of inward FDI in the world and about 70% of outward FDI. The large average eect of outward FDI is driven by some large outward FDI countries (such as the United States and China), where, consistent with our theoretical model of non-rival

> 4We refer the reader to Fernandes et al., eds (2021), an eBook from the World Bank and CEPR, which is a collection of excellent papers that focus on various aspects of the determinants of DTAs and on the DTA eects on trade and other economic outcomes.

> 5As discussed in more detail in Section 4.2, a caveat with our GE analysis is that the underlying theory is based on the assumption of non-rival technology FDI, while our data includes all/aggregate FDI ows. While this gap, of course, has implications for the quantitative results, our conclusions about the disproportionately large impact of outward FDI will remain qualitatively the same if applied to better suited data.

5
capital, any change in the technology stock of these countries has a multiplying eect due to the usage in many other countries, resulting in a large boost in outward FDI stock usage abroad. We view our result about the disproportionately large impact of outward FDI as novel and potentially important from a policy perspective, both for the negotiations of trade and investment agreements and for properly quantifying their implications.

Finally, we also nd that changes in trade costs due to DTAs have led to additional boosts in FDI through the GE links between trade and FDI in our model. Specically, through their impact on trade costs, the 2011 DTAs in our model have boosted inward FDI by an additional 1 percentage point and outward FDI by 10 additional percentage points, i.e., eects that are about a quarter of the corresponding estimates due to FDI liberalization. By demonstrating that the impact of DTAs on FDI through trade is signicant, we complement some recent work on the GE links between DTAs and trade, cf. Fontagne et al. (2021), and also, from a broader perspective, papers that have studied the GE links between trade liberalization and FDI, cf. Baltagi et al. (2008), Tintelnot (2017), and Anderson et al. (2019).

The rest of the paper is organized as follows. Section 2 presents the methodological foundations of our analysis, including the theoretical foundations (in Subsection 2.1), a discussion of the alternative channels through which DTAs impact FDI (in Subsection 2.2), and the specications of our estimating equations for bilateral trade ows and FDI (in Subsection 2.3). Section 3 describes the main variables and the corresponding data sources that we use to construct them. Section 4 presents and discusses our partial estimates (in Subsection 4.1) and our GE results (in Subsection 4.2). Section 5 concludes and we oer a supplementary Appendix that includes the derivations of the theoretical model.

# 2 Methods

In order to quantify the impact of deep trade agreements on FDI, we rely on the theoretical framework of Anderson et al. (2019). While, our current contribution is purely empirical, 6
we nd it helpful to summarize the model of Anderson et al. (2019). We do this in Section 2.1 for two reasons. First, as demonstrated in Section 2.2, it will enable us to describe and decompose several partial and GE channels through which DTAs impact FDI. In addition, in Section 2.3, we will capitalize on the structural equations for bilateral trade ows and FDI in order to specify the corresponding estimating equations, which in turn will deliver our key estimates of the direct impact of DTAs on trade and FDI.

# 2.1 Theoretical Foundations

To motivate and perform the empirical analysis, we rely on the theoretical framework of Anderson et al. (2019), who derive a multi-country dynamic model of trade, investment in physical capital and FDI under the following assumptions.<sup>6</sup> Each country ( _i, j ∈ N_ ) produces a single tradeable good (dierentiated by place of origin, Armington, 1969), which, subject to iceberg trade frictions ( _tij,t ≥_ 1 ), can be used for consumption ( _Cj,t_ ) and to build countryspecic physical capital ( _Kj,t_ ) in any other country. In addition, each country invests in non-rival technology capital ( _Mj,t_ ),<sup>7</sup> and the technology capital of one country can be used in all other countries subject to investment frictions ( 1 _≥ ωij,t ≥_ 0 ).<sup>8</sup> The decisions on aggregate consumption ( _Cj,t_ ), aggregate investment in physical capital ( Ω _j,t_ ), and aggregate investment in technology capital ( _χj,t_ ) in each country are made by representative agents who maximize the present discounted value of their lifetime utility subject to a sequence of constraints, as captured by the following consumer optimization

> 6We refer the reader to Anderson et al. (2019) for motivation behind some of the assumptions and for details on all derivations. For the convenience of the reader, we enclose the online Appendix from Anderson et al. (2019) along with this paper.

> 7The modeling of FDI in form of non-rival technology capital is in the spirit of Markusen (2002), McGrattan and Prescott (2009, 2010, 2014) and McGrattan and Waddle (2017). One interpretation of technology capital is akin to the notion of knowledge capital, and possible examples include patents, blue-prints, management skills/practices, etc.

> 8If _ωij,t_ = 1 , then country _j_ is totally open to the use of foreign technology of country _i_ capital at time _t_ within its borders. If _ωij,t_ = 0 , no foreign technology from country _i_ can be used in country _j_ at time _t_ .

7
problem: Equation (1) is the representative agent's intertemporal utility function, where aggregate consumption, _Cj,t_ = �� _Ni_ =1<sup>_γ_</sup> _i_ 1 _−σσ cij,tσ−σ_ 1 � _σ−σ_ 1 , comprises of domestic and foreign goods ( _cij,t_ ) from all possible countries. Equation (2) is the transition function for accumulation of physical capital, where _δj,K_ is the depreciation rate and Ω _j,t_ = �� _Ni_ =1<sup>_γ_</sup> _i_ 1 _−σσ_ � _Iij,t_<sup>_K_</sup> �<sup>_σ−_</sup> _σ_<sup>1�</sup> _σ−σ_ 1 denotes the aggregate ow of investment in physical capital in country _j_ at time _t_ as a CES aggregate of investment goods ( _Iij,t_<sup>_K_)fromallcountries.9Similarly,equation(3)isthetransition</sup> function for accumulation of technology capital, where _δj,M_ is the depreciation rate and _χj,t_ = �� _Ni_ =1<sup>_γ_</sup> _i_ 1 _−σσ_ � _Iij,t_<sup>_M_</sup> �<sup>_σ−_</sup> _σ_<sup>1�</sup> _σ−σ_ 1 denotes the CES-aggregated ow of investments of technology capital ( _Iij,t_<sup>_M_)in</sup><sup>_j_attime</sup><sup>_t_fromallcountries,including</sup><sup>_j_itself.</sup> Equation (4) is the production value function. Here, _pj,t_ denotes the factory-gate price of good (country) _j_ at time _t_ , _Aj,t_ is the local, country-specic technology, _Lj,t_ is countryspecic (internationally immobile) labor, and all other variables are dened earlier. Note that the last term _Ni_ =1<sup>(max</sup><sup>_{_1</sup><sup>_, ωij,tMi,t}_)</sup><sup>_ηi_�</sup> is the global technology stock applied locally. �� When _ωij,t_ = 0 , no foreign technology from country _i_ can be used in country _j_ at time _t_ , and when _ωij,t_ = 1 usage of foreign technology is frictionless. With _ωij,t >_ 0 every unit of

> 9The assumption that consumption and investment goods are subject to the same CES aggregation is very convenient analytically. Allowing for heterogeneity in preferences and prices between and within consumption and investment goods requires sectoral treatment and will open additional channels for the interaction between trade liberalization and FDI.

8
( = ( ©) ~~—(—)~~ Y( ~~-) +~~ X( ~~-) -~~ <u>(re</u> ~~<u>)</u>~~ ( ) (0 > YF ~~Cr)~~
Equations (8)-(11) may look familiar, because they represent the structural gravity trade system of Anderson and van Wincoop (2003). Equation (8) is the standard structural gravity equation. Π _i,t_<sup>1</sup><sup>_−σ_</sup> and _Pj,t_<sup>1</sup><sup>_−σ_</sup> are the multilateral resistance terms (MRTs, outward and inward, respectively), which consistently aggregate bilateral trade costs and decompose their incidence on the producers and the consumers in each region. As dened earlier, equations (12) and (13) dene the value of production and the expenditure in country _j_ , respectively.

Equation (14) is the solution for physical capital. Intuitively, the direct relationship between _Kj_ and _Yj_ reects the fact that there will be more investment the higher the value of marginal product of physical capital. The inverse relationship between _Kj_ and _Pj_ can be interpreted through the lens of the law of demand, i.e., if _Pj_ is interpreted as the price of investment goods. Alternatively, if _Pj_ is the price of consumption or technology goods, then the intuition for inverse relationship is that there will be less investment when the opportunity cost of it (i.e., investment in consumption or technology goods) is higher.

Finally, equation (15) is the structural gravity equation for FDI, where _FDIij_ is the value of the stock of FDI from origin _i_ at destination _j_ , Γ _i_ = 1 _−βφβ_ +<sup>2</sup> _βδηi_<sup>2</sup> _i,M_<sup>isacompositecountry-</sup> specic constant term, and all other variables are dened above. Intuitively, and similar to the gravity model of trade, (15) captures the direct relationship between FDI and the sizes of the source and the destination countries. The explanation for the inverse relationship between FDI and _Pj_ is similar to the relationship between physical capital and _Pj_ . The inverse relationship between FDI and _Mi_ is a reection of the law of diminishing marginal productivity, i.e., the larger the stock of technology capital in country _i_ , the smaller the marginal productivity by an additional unit of investment in technology. Finally, _ωij_ denotes the openness measure for foreign technology of country _i_ in country _j_ .<sup>11</sup> The stock of FDI can be dened as:

> 11A notable dierence between the FDI gravity model (15) and the standard trade gravity model, as captured by (8), is that the FDI gravity equation does not include explicitly an outward multilateral resistance. The intuitive explanation for this is the non-rival nature of technology capital.

10
For given parameters and variables that are exogenous in the model, i.e., _α_ , _β_ , _φ_ , _~~ξ~~_ ~~,~~ _ηj_ , _γj_ , _σ_ , _δK_ , _δM_ , _Aj,t_ , _Lj,t_ , _tij,t_ , and _ωij,t_ , we can use system (8)-(15) to simulate the impact of deep trade liberalization on trade and investment in the world. We capitalize on this in Section 4.2. Before that, in Section 2.2, we use (8)-(15) to describe and decompose the partial and GE channels through which DTAs impact FDI. Then, in Section 2.3, we rely on system (8)-(15) to specify the econometric models that will deliver our key estimates of the direct impact of DTAs on trade and FDI.

# 2.2 On the Links between DTAs and FDI: A Discussion

The objective of this section is to describe and decompose the channels through which DTAs aect FDI. To this end, and consistent with the estimation results that we present in Section 4.1, as comparative static shock to system (8)-(15) we consider the formation of a DTA with investment provisions, which is successful in liberalizing both trade and FDI. For clarity and ease of exposition, we consider a specic hypothetical examplea DTA between the US and the EU. Moreover, consistent with the counterfactual analysis that we perform in Section 4.2, we discuss the eects of trade liberalization and investment liberalization sequentially, starting with the eects of investment liberalization, which is captured by an increase of _ωij_ in our model. A decrease in FDI barriers will have a direct eect and several indirect (GE) eects on FDI in system (8)-(15).

-  Direct DTAs impact on FDI. The direct eect of lower bilateral investment costs between EU and US is captured by equation (15), and it would lead to an immediate increase in FDI between the liberalizing partners. In the empirical analysis below, we will be able to identify the direct impact of DTAs on FDI from our estimating FDI gravity model. Then, we will use our partial estimates of these direct eects to simulate the indirect/GE eects, which we describe next.

-  First-order GE eect of DTAs on FDI. The removal of FDI barriers (i.e., an increase

11
of _ωij_ ) between US and the EU will lead to higher income, through (12), and higher expenditure, through (13), in the two regions. In turn, via equation (15), the changes of the sizes of the liberalizing partners will lead to more FDI between them and also, ceteris paribus, between each of them and all other countries in the world. These GE size eects are similar to the familiar size eects from the trade gravity literature.

-  Second-order GE eect of DTAs on FDI. The changes of the sizes of the two regions will lead to changes in the multilateral resistances through system (9)-(10). This relationship is inverse, which means that the MRs will fall. In turn, a lower inward multilateral resistance will stimulate investment via (15). As discussed earlier, the intuition for this result is that the IMR can be interpreted alternatively as the price of investment or the opportunity cost of investment.

-  Third-order GE eects of DTAs on FDI. Finally, we label the eects of changes in FDI barriers through variables that are not explicitly included in equation (15) as `third-order GE eect of DTAs on FDI'. System (8)-(15) captures at least two such eects. The rst one is via the outward multilateral resistance. As noted earlier, the OMR does not appear explicitly in (15). Nevertheless, it is linked to the other endogenous variables in our model via the MR system (9)-(10). The second one is via physical capital accumulation, which, as captured by equation (14), would respond to the changes in size and the IMR and, in turn, will stimulate further increase in size.

Next, we turn to the eects of DTAs on FDI through trade liberalization, e.g., a reduction in the bilateral trade costs ( _tij_ ) between US and the EU countries in our model. Naturally, all such eects would be indirect and, similar to the analysis of FDI liberalization, we discuss three GE channels through which trade liberalization could impact FDI.

-  First-order GE eect of DTAs on FDI. A fall in the barriers between US and the EU will lead to lower inward multilateral resistance, via the direct relationship between

12
bilateral trade frictions and the IMR as captured by equation (9). In turn, a lower inward multilateral resistance will stimulate investment via equation (15).

-  Second-order GE eect of DTAs on FDI. Triggered by trade liberalization the outward multilateral resistances for US and the EU will decrease (via (9)-(10)). In turn, this will lead to higher factory-gate prices (via (11)) and larger sizes (via (12) and (13)) in the US and the EU. As discussed earlier, larger sizes would stimulate FDI (via (15)).

-  Third-order GE eects of DTAs on FDI. Finally, similar to the impact of FDI liberalization, a fall in trade barriers will trigger `third-order GE eect of DTAs on FDI', which are channeled via the OMR, through system (9)-(10), and via physical capital accumulation, as captured by equation (14).

In sum, this section demonstrated how our structural system captures and decomposes a series of channels through which DTAs may aect FDI in member and non-member countries. We capitalize on this analysis in Section 4.2, where we simulate the GE eects of DTAs based on our own partial estimates of the eects of DTAs on trade and investment, which we obtain in Section 4.1 based on the econometric models that we specify next.

# 2.3 From Theory to Empirics

A key objective and contribution of this paper is to test for causal links between DTAs, trade, and FDI. Establishing such links, and obtaining estimates of the corresponding direct/partial eects of DTAs on trade and FDI, would also enable us to translate them into GE eects of trade liberalization on FDI through the structural links that we just described in the previous section. In this section, we rely on system (8)-(15) to specify our estimating equations for trade and FDI. Specically, as noted earlier, equation (8) is the standard structural gravity equation from the trade literature, while (15) is our theoretical gravity equation for FDI. To estimate both equations, we will capitalize on the latest developments in the empirical trade 13
literature.<sup>12</sup> We start with the estimating equation for bilateral trade ows: Here, _Xij,t_ denotes nominal (cf. Baldwin and Taglioni, 2006) exports from _i_ to _j_ at time _t_ . Consistent with theory, _Xij,t_ includes international and domestic trade ows (cf. Yotov, 2022). Estimating equation (17) includes three sets of xed eects. _ψi,t_ and _φj,t_ denote exportertime and importer-time xed eects, respectively, which will account for the country-size and the multilateral resistance terms (cf. Anderson and van Wincoop, 2003) in equation (8), and also for any other observable or unobservable factors that aect trade ows on the exporter or on the importer side. _μij_ denotes a set of pair xed eects, which will control for all time-invariant bilateral trade costs (cf. Egger and Nigai, 2015) and will mitigate endogeneity concerns with respect to the bilateral policy variables in our setting (cf. Baier and Bergstrand, 2007), including DTAs. Our main results will be obtained with directional pair xed eects, which allow for asymmetric time-invariant trade costs depending on the direction of trade ows, i.e., from _i_ to _j_ vs. from _j_ to _i_ .

The vector **GRAV** _ **TRADEij** _,_ **t** includes a set of time-varying bilateral control variables that control for WTO membership ( _WTOij,t_ ), economic sanctions ( _SANCTij,t_ ), and bilateral investment treaties ( _BITij,t_ ). In addition, we also include a full set of time-varying border indicators (<sup>�</sup> _t_<sup>_BRDRij,t_),whichwouldcaptureanycommonglobalizationtrends(e.g.,im-</sup> provements in communication, transportation, communication, etc.). Finally, the vector **DTASij** _,_ **t** includes the variables whose estimates would be of central interest to us. Speci- cally, we will dierentiate between the eects of preferential trade agreements ( _PTAij,t_ ), the eects of deep trade agreements ( _DTAij,t_ ), and we will allow for the eects of DTAs to vary depending on their depth ( _DEPTHij,t_ ), which will be measured by the number of provisions that they include.

We estimate equation (17) with the Poisson Pseudo-Maximum-Likelihood (PPML) esti-

> 12Larch and Yotov (2022) survey the empirical gravity literature and synthesize the best practices for gravity estimations.

14
mator in order to account for the presence of heteroskedasticity in the trade data and to take advantage of the information contained in the zero trade ows, cf. Santos Silva and Tenreyro (2006, 2011). We use three-year interval data, cf. Cheng and Wall (2005) and Egger et al. (2022).<sup>13</sup> Finally, we cluster the standard errors by country pair.

Next, guided by equation (15), we specify our estimating gravity equation for FDI as follows: Here, _FDIij,t_<sup>_value_</sup> is the value of FDI stock from origin _i_ to destination _j_ at time _t_ . Capitalizing on the developments in the bilateral trade and FDI literatures, and for consistency with our estimating equation for trade ows, we specify our FDI econometric model to be as close as possible to our estimating equation for trade ows given in equation (17). Specically, we use the same estimator (i.e., PPML), we include the same set of xed eects (i.e., origintime xed eects ( _ψi,t_ ), destination-time xed eects ( _φj,t_ ), and directional pair xed eects ( _μij_ )), and we employ the same set of time-varying policy covariates (i.e., indicators for WTO membership ( _WTOij,t_ ), for bilateral investment treaties ( _BITij,t_ ), and for sanctions ( _SANCTij,t_ )). Finally, just as in our trade specication, we rely on three-year interval data and we use the same clustering (i.e., by country pair).

Even though, from an econometric perspective, we will use exactly the same set of exporter-time and importer-time xed eects as in our trade equation, the country-time xed eects in the FDI model would proxy and account for dierent variables. Following the existing empirical FDI literature,<sup>14</sup> possible robust determinants of FDI in the country of

> 13Cheng and Wall (2005) note that `[f]ixed-eects estimation is sometimes criticized when applied to data pooled over consecutive years on the grounds that dependent and independent variables cannot fully adjust in a single year's time.' (footnote 8, p. 52). Treer (2004) also criticizes trade estimations pooled over consecutive years. He uses three-year intervals. Baier and Bergstrand (2007) use 5-year intervals. Olivero and Yotov (2012) provide empirical evidence that gravity estimates obtained with 3-year and 5-year lags are very similar. Most recently, Egger et al. (2022) show that gravity models with three-way xed eects deliver similar estimates of the common estimates of FTAs.

> 14The two leading empirical FDI studies are Eicher et al. (2012) and Blonigen and Piger (2014). The objective of both studies is to identify a set of robust FDI determinants. Both papers utilize Bayesian Model Averaging and each of them comes up with a set of covariates which vary across the four dimensions that

15
origin include corporate tax rate, corruption, and bureaucratic red tape, while possible candidates at the destination include level of corruption, internal tensions, corporate tax rate, bureaucratic red tape, quality of institutions, etc. Finally, the pair xed eects in (18) will absorb and account for bilateral distance, common ocial language, colonial relationships, which, similar to the trade literature, have been found to be among the most robust FDI determinants by both Eicher et al. (2012) and Blonigen and Piger (2014).

There are two dierences between equations (17) and (18). First, we cannot include the set of time-varying border eects (<sup>�</sup> _t_<sup>_BRDRij,t_)inequation(18)sinceweonlyusedata</sup> on international transactions. This is why we use dierent notation for the vector of timevarying gravity covariates ( **GRAV** _ **FDIij** _,_ **t** ). We also allow for potential dierences in the estimated impact of the common policy covariates by denoting the vector of their estimates ̃ _α_ . Second, and more important for our purposes, we use a dierent set of variables to capture the impact of DTAs on FDI in vector **DTA** _ **FDIij** _,_ **t** . Specically, in addition to including indicators for PTAs ( _PTAij,t_ ) and DTAs ( _DTAij,t_ ), we add two more covariates. First, motivated by Osnago et al. (n.d.), Crawford and Kotschwar (2020), and Laget et al. (2021), we include a separate indicator variable ( _INVij,t_ ) that takes a value of one for agreements that include investment provisions. Second, we also account for depth of the investment treaties by using a count variable ( _INV_ DEPTHij,t_ ) for the number of investment provisions within the agreements with investment provisions, i.e., similar to the relationship between _PTAij,t_ and _DEPTHij,t_ on the trade side, _INV_ DEPTHij,t_ is a continuous variable that is equal to zero when _INV_ is zero.<sup>15</sup> _ij,t_ Estimating equations (17) and (18) will deliver the estimates of the eects of DTAs on trade and investment that we will describe in Section 4.1 and use to obtain GE results in we propose to capture in our study.

> 15The inclusion of trade agreement variables in our FDI gravity model is consistent with Eicher et al. (2012) and Blonigen and Piger (2014) who nd that regional trade agreements are among the most important timevarying bilateral determinants of FDI ows. Interestingly, however, neither Eicher et al. (2012) nor Blonigen and Piger (2014) distinguish between the average eects of RTAs and the eects of RTAs covering FDI. As demonstrated by Crawford and Kotschwar (2020) and Laget et al. (2021), FDI chapters and provisions are an important part of contemporary integration eorts. We will provide evidence that such provisions are indeed a very important determinant of FDI.

16
Section 4.2. Before that, we describe our data.

# 3 Data and Sources

To perform the empirical analysis we build a balanced panel data set for 89 countries over the period 1990-2011, covering more than 96 percent of world GDP and for more than 94 percent of FDI throughout the sample period.<sup>16</sup> Our data set includes the following variables: foreign direct investment, trade agreements, trade ows, gross domestic product (GDP), employment, physical capital, bilateral investment treaties, sanctions, and WTO membership. We describe the sources to obtain these variables, as well as their construction, in turn next.

 FDI Data. We use two sources to construct the FDI variable, ( _FDIij,t_ ), which takes a central stage in our analysis. The main source for FDI data is the Bilateral FDI Statistics database of the United Nations Conference on Trade and Development (UNCTAD). These data can be accessed at http://unctad.org/en/Pages/DIAE/FDI%20Statistics/ FDI-Statistics-Bilateral.aspx. UNCTAD's FDI data covers inows, outows, inward stock, and outward stock for 206 countries over the period 1990-2011. Data are collected from national sources and international organizations and to ensure maximum coverage the data are mirrored. The second source of FDI data is the International Direct Investment Statistics database, which is constructed and maintained by the Organization for Economic Co-operation and Development (OECD). OECD's data oers detailed statistics for inward and outward foreign direct investment ows and positions (stocks) of the OECD countries, including transactions between the OECD members and non-member countries. We use the OECD data to ensure consistency and maximum coverage. Finally, we note that, given our theory, we focus our analysis on FDI stocks (positions), which is also the FDI category for which most data are available.<sup>17</sup>

> 16The list of countries and their respective alpha ISO3 codes appear in the rst two columns of Table 5.

> 17Anderson et al. (2019) utilize the same sources to construct a cross-section FDI data set. For the

17
-  Trada Agreements Data. To account for the presence and depth of trade agreements, we use the World Bank's database on deep trade agreements (DTA), cf. Hofmann et al. (2019) and Mattoo et al., eds (2020), (https://datatopics.worldbank.org/ dta/about-the-project.html).<sup>18</sup> Capitalizing on the rich dimensionality of the DTA database, we construct and utilize several variables for our analysis. _PTAij,t_ is an indicator variable for the presence of any trade agreement between _i_ and _j_ at time _t_ . _DTAij,t_ is an indicator denoting the presence of a deep agreement between _i_ and _j_ at time _t_ . _DEPTHij,t_ is a count variable for the number of provisions in the corresponding DTA between _i_ and _j_ . _INVij,t_ is an indicator variable that takes a value of one if the DTA between _i_ and _j_ includes investment provisions. Finally, _INV_  DEPTHij,t_ is a count variable for the number of investment provisions in the corresponding DTA between _i_ and _j_ . For further details on the general features of the DTA database we refer the reader to Hofmann et al. (2019) and Mattoo et al., eds (2020). In addition, for analysis with specic focus the investment provisions in the DTAs, we refer the reader to Crawford and Kotschwar (2020).

-  Production Data. Data on GDP, employment, and capital stocks are from the Penn World Tables 8.0, cf. Feenstra et al. (2013) (http://www.rug.nl/research/ggdc/data/ pwt/). For data on GDP, we employ Output-side real GDP at current PPPs ( _CGDP_<sup>_o_</sup> ), which compares relative productive capacity across countries at a single point in time, as the initial level in our counterfactual experiments, and we use Real GDP using national-accounts growth rates ( _CGDP_<sup>_na_</sup> ) for our income-based cross-country growth regressions. We measure employment in eective units by multiplying the Number of persons engaged in the labor force with the Human Capital Index, which is based on average years of schooling. Finally, capital stocks in the Penn World Tables 8.0

> estimation analysis in this paper, we also utilize the time variation in the FDI data. In the counterfactual experiments, we rely on the methods of Anderson et al. (2019) to calibrate some parameters and vectors. See Section 4.2 for further details.

> 18Specically, we used the DTA 2. Database: Information by Trade agreements. Bilateral observations.

18
are constructed based on accumulating and depreciating past investments using the perpetual inventory method.

-  Trade Data. Data on international trade ows come from the United Nations Statistical Division (UNSD) Commodity Trade Statistics Database (COMTRADE). We complement the international trade ows data with data on domestic trade ows from Anderson et al. (2020), which we use both for the estimation and for the counterfactual analysis. Anderson et al. (2020) construct domestic trade ows at the aggregate level in two steps. First, they use the ratio between aggregate manufacturing in gross values and total exports of manufacturing goods to construct a multiplier at the country-time level. (Data on gross manufacturing production, which came from the United Nations' IndStat database.) Then, they use this multiplier along with data on aggregate exports to project the values for domestic sales. Availability of data on domestic trade ows predetermined the time coverage of our estimating sample.

-  Other Data. Finally, in the estimation analysis we employ the following additional covariates as control variables. We control for the presence of bilateral investment treaties with an indicator variable _BITij,t_ , which comes from the UNCTAD 's data on international investment agreements (IIAs), which can be found at http://investmentpolicyhub.unctad.org/IIA. Data on sanctions come from the Global Sanctions Database (GSDB), cf. Felbermayr et al. (2020) and Kirilakha et al. (2021) (http://www.rug.nl/research/ggdc/data/pwt/). We use the GSDB to include and indicator variable ( _SANCTij,t_ ) for the presence of sanctions in our estimations. Finally, data on WTO membership, captured by an indicator variable _WTOij,t_ in our analysis, come from the Dynamic Gravity Dataset (DGD) of the U.S. International Trade Commission, cf. Gurevich and Herman (2018) (http://www.rug.nl/research/ggdc/data/ pwt/).

19
# 4 Empirical Findings and Analysis

Subsection 4.1 presents our partial estimates of the impact of DTAs on trade and FDI. Then, in Subsection 4.2, we translate the partial estimates into corresponding GE eects, and we analyze the total impact of DTAs on FDI within our framework.

## 4.1 Estimation Results

Our estimates of the eects of DTAs on trade are presented in Table 1. As discussed earlier, all estimates are obtained with the PPML estimator with three-way xed eects, including exporter-time, importer-time, and directional country-pair xed eects. In addition, all specications use time-varying border dummy variables to control for the presence of common globalization trends, and indicator variables for WTO membership ( _WTOij,t_ ), bilateral investment treaties ( _BITij,t_ ), and economic sanctions ( _SANCTij,t_ ). In order to highlight the importance of DTAs and their provisions, we develop the estimation analysis in three steps, depending on the denition of the indicator variables designed to capture the impact of trade agreements.

The estimates in column (1) include a single indicator variable, _PTAij,t_ , that reects the presence of a trade agreement of any type (e.g., deep or shallow) between _i_ and _j_ at time _t_ . Several ndings stand out from column (1). Most important for our purposes, we note that, while positive, the estimate on _PTAij,t_ is economically small and it is not statistically signicant. A possible explanation for this result is that we impose a common eect for all trade agreements in our sample, regardless of their type and depth. We demonstrate that this is indeed the case in column (2) of Table 1. Before that, however, we briey discuss the estimates of the other policy covariates in our specication.

First, we note that the estimate of the impact of WTO is positive, large, and statistically signicant. This result is at odds with some of the existing literature, e.g., Rose (2004) and Esteve-Pérez et al. (2020) who nd that WTO membership did not promote international 20
Table 1: Estimates of the Eects of DTAs on Trade

||(1)|(2)|(3)|
|---|---|---|---|
||PTA|DTA|DEPTH|
|_PTAij,t_|0.083|-0.051|-0.069|
||(0.057)|(0.059)|(0.055)|
|_DTAij,t_||0.148|0.047|
|||(0.028)<sup>_∗∗_</sup>|(0.059)|
|_DEPTHij,t_|||0.000<br>(0.000)<sup>_∗_</sup>|
|_WTOij,t_|0.516|0.526|0.538|
||(0.044)<sup>_∗∗_</sup>|(0.044)<sup>_∗∗_</sup>|(0.043)<sup>_∗∗_</sup>|
|_BITij,t_|0.281|0.288|0.289|
||(0.099)<sup>_∗∗_</sup>|(0.098)<sup>_∗∗_</sup>|(0.097)<sup>_∗∗_</sup>|
|_SANCTij,t_|0.031|0.028|0.022|
||(0.024)|(0.023)|(0.022)|
|_N_|58,323|58,323|58,323|

Notes: This table reports estimates of the eects of trade agreements on trade ows over the period 1990-2011. The dependent variable is nominal trade ows. The estimator is PPML. All estimates are obtained with three-year interval data and three-way xed eects, including exporter-time, importer-time, and directional pair xed eects. In addition, all specications include a full set of time-varying border variables. The estimates of the border dummies and all xed eects, including the constant, are omitted for brevity. The standard errors in all specications are clustered by country pair. The dierence between the three columns are in the set of trade agreement variables. Specically, column (1) reports the average PTA eect across all agreements in the sample. Column (2) adds the eects of DTAs. Finally, in addition to PTAs and DTAs, column (3) introduces a continuous variable for DTA depth. The estimate on _DEPTHij,t_ in column (3) is 0 _._ 00047 . See text for further details.

21
trade, however, our estimate on _WTOij,t_ conrms the ndings of Larch et al. (2019) for positive WTO eects when domestic trade ows are used to estimate gravity equations. In robustness analysis, which is available upon request, we conrm that when the model is estimated without domestic trade ows, the estimate of the eects of WTO is smaller and it is not statistically signicant.

Second, we obtain a positive, sizable, and statistically signicant estimate of the impact of bilateral investment treaties (BITs) on trade ows. A possible explanation for this result is multinational production. Third, we do not obtain a signicant estimate of the impact of economic sanctions on trade. This result is consistent with estimates from Felbermayr et al. (2020), who argue that average estimates of the eects of sanctions may mask signicant heterogeneity across the eects of sanctions by type. In addition, Kirilakha et al. (2021) demonstrate that the relative importance of trade sanctions has fallen signicantly over time. In robustness analysis, we allowed for dierential eects of dierent types of sanctions. This did not aect our main ndings and conclusions.

Finally, the estimates of the time-varying border variables from our specications, which we visualize in Figure 3, reveal signicant globalization eects during the period 1990-2011. Due to the use of pair xed eects, we need to drop one border variable, and we selected the border in 1990 as the baseline. Thus, all other border estimates are obtained as deviations from 1990 and should be interpreted accordingly, i.e., the positive and increasing estimates in Figure 3 capture the positive eects of globalization (smaller impact of borders) on international trade. The dip in 2002 is probably a reection of the economic recession during this period, while, due to the use of three-year intervals, the deep global economic recession of 2009 is not captured in our graph.

In column (2) of Table 1 we allow for heterogeneous eects between shallow vs. deep trade agreements. To this end, we capitalize on the data from World Bank's DCRTA, cf. Hofmann et al. (2019) and Mattoo et al., eds (2020), to dene _DTAij,t_ as an indicator that takes a value of one for deep trade agreements (i.e., we use the variable `pta_mapped' 22
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from the DCRTA database), and it is equal to zero otherwise. Thus, by construction, the observations that take a value of one in the _DTA_ variable are a subset of the observations _ij,t_ that are equal to one in the _PTAij,t_ dummy from column (1). The main nding from column (2) of Table 1 is encouraging and expected. Specically, we obtain a positive and statistically signicant estimate on _DTAij,t_ , which suggests that, on average, the deep trade agreements in our sample have led to a 16.1% (std.err. 3.184) increase in bilateral trade among member countries. This result is consistent with and reinforces the general message from Fernandes et al., eds (2021) that DTAs have been eective in stimulating international trade.

Finally, in column (3) of Table 1, we use the DTA database to construct a continuous variable ( _DEPTHij,t_ ), which counts the number of provisions within each of the DTAs in our sample. The number of provisions across the DTAs in our sample varies between 12 and 432. The main result from column (3) is that, on average, the deeper the agreement, the more it would promote trade among its members. Specically, we obtain a positive and statistically signicant estimate on _DEPTHij,t_ ( 0 _._ 00047 , std.err. 0 _._ 00023 ), which is consistent with the ndings from Osnago et al. (n.d.). Our estimate suggests that, depending on the number of provisions that they include, the DTAs in our sample have led to trade increases between 0.576% (std.err. 0.289) and 23.012% (std.err. 12.744). We capitalize on this variation in Section 4.2, where we obtain corresponding GE eects on FDI.

Our estimates of the eects of DTAs on FDI are presented in Table 2. As discussed earlier, and similar to our trade specication, all estimates are obtained with the PPML estimator with three-way xed eects, including origin-time, destination-time, and directional pair xed eects. In addition, all specications include indicator variables for WTO membership ( _WTOij,t_ ), bilateral investment treaties ( _BITij,t_ ), and economic sanctions ( _SANCTij,t_ ). Similar to our approach with trade ows, in order to highlight the importance of DTAs and their provisions for FDI, we develop the estimation analysis sequentially, in four steps.

The estimates in column (1) of Table 2 include a single indicator variable, _PTAij,t_ , that reects the presence of a trade agreement of any type (e.g., deep or shallow) between _i_ and _j_ 24
Table 2: Estimates of the Eects of DTAs on FDI

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
||PTA|DTA|INV|DEPTH|
|_PTAij,t_|-0.029|-0.100|-0.162|-0.134|
||(0.072)|(0.102)|(0.101)|(0.100)|
|_DTAij,t_||0.072|-0.089|-0.120|
|||(0.075)|(0.084)|(0.083)|
|_INVij,t_|||0.295|0.582|
||||(0.108)<sup>_∗∗_</sup>|(0.251)<sup>_∗_</sup>|
|_INV_DEPTHij,t_||||-0.010<br>(0.009)|
|_BITij,t_|0.017|0.017|0.023|0.028|
||(0.104)|(0.104)|(0.107)|(0.108)|
|_SANCTij,t_|-0.019|-0.017|-0.012|-0.012|
||(0.049)|(0.048)|(0.048)|(0.048)|
|_WTOij,t_|0.465|0.476|0.469|0.463|
||(0.356)|(0.353)|(0.359)|(0.363)|
|_N_|18,158|18,158|18,158|18,158|

Notes: This table reports estimates of the eects of trade agreements on FDI over the period 1990-2011. The dependent variable is the value of FDI stock. The estimator is PPML. All estimates are obtained with three-year interval data and three-way xed eects, including origin-time, destination-time, and directional pair xed eects. The estimates of all xed eects, including the constant, are omitted for brevity. The standard errors in all specications are clustered by country pair. The dierence between the three columns are in the set of trade agreement variables. Specically, column (1) reports the average PTA eect across all agreements in the sample. Column (2) adds the eects of DTAs. Column (3) isolates the DTAs with investment provisions. Finally, in addition to PTAs, DTAs, and DTAs with investment provisions, column (4) introduces a continuous variable for investment depth. See text for further details.

25
at time _t_ . The main result from column (1) is that none of the eects of the policy variables in our model, including the impact of trade agreements and BITs, are statistically signicant. A possible explanation for this result is that some of the most signicant determinants of FDI are country-specic variables on the origin and/or on the destination side (e.g., corporate tax rate, corruption, bureaucratic red tape, quality of institutions, etc.). However, such determinants are fully controlled for and absorbed by the origin-time and the destinationtime xed eects in our specication. Moreover, it is also possible (cf. Eicher et al., 2012; Blonigen and Piger, 2014) that a number of time invariant characteristics (e.g., bilateral distance, common ocial language, etc.) are important for FDI. However, similar to the country-specic variables, these eects are also absorbed in our econometric model (by the pair xed eects). Our nding on the insignicant impact of BITs may seem particularly strange, however, this result is common in the related literature, cf. Lesher and Miroudot (2006) and Laget et al. (2021).

Next, in column (2) of Table 2 we allow for heterogeneous eects between shallow vs. deep trade agreements by using the same _DTA_ variable which we constructed for our trade regressions. Even though the estimate on _DTAij,t_ is positive, it is economically small and not statistically signicant. Thus, unlike their signicant impact on trade, our estimates suggest that DTAs per se do not promote FDI.

Motivated by Crawford and Kotschwar (2020) and Laget et al. (2021), in our next specication (in column (3) of Table 2), we isolate the eects of DTAs that include investment provisions. To this end, we again rely on the World Bank's DCRTA, cf. Hofmann et al. (2019) and Mattoo et al., eds (2020), which includes 66 possible investment provisions. Based on this information, we construct a dummy variable, _INVij,t_ , which takes a value of one if an agreement includes at least one investment provision, and it is equal to zero otherwise. Thus, by construction, the observations that take a value of one in the _INVij,t_ indicator are a subset of the observations that are equal to one in the _DTAij,t_ dummy from column (2).

The main nding from column (3) is that we obtain a positive, sizable, and statistically 26
signicant estimate on _INVij,t_ , which suggests that, on average, the PTAs with investment provisions in our sample have led to a 34.33% (std.err 14.535) increase in FDI between their members. This result complements the ndings from Laget et al. (2021), who use rm level data for the period 2003-2015 and obtain positive estimates of the eects of provisions related to `intellectual property rights' and `visa and asylum', which vary between 32 and 50 percent, but do not nd signicant eects of investment provisions on FDI.

Finally, in column (4) of Table 2 we use the DTA database to construct a continuous variable ( _INV_ DEPTHij,t_ ), which counts the number of investment provisions within each of the DTAs in our sample. The number of investment provisions across the DTAs in our sample varies between 7 and 41. The estimates from column (4) do not reveal a signicant impact of the increase in the depth (number of provisions) on FDI. In fact, and pushing inference to the limit, our estimates suggest that the impact of additional provisions is actually negative. A possible interpretation of this result is that more investment provisions make the agreements more dicult to comply with. Despite the fact that our estimate on _INV_ DEPTHij,t_ is insignicant, we use it in combination with the positive estimates on _INVij,t_ to construct a continuous FDI response to the impact of DTAs. The resulting eects are all positive and vary between 16.4% (std.err. 19.06) and 66.41% (std.err. 32.99), depending on the number of investment provisions.<sup>19</sup> We conclude the econometric analysis with two additional sets of experiments that further capitalize on the richness of the DCRTA data set to shed light on the links between DTA provisions and FDI.<sup>20</sup> First, we complement the analysis of Laget et al. (2021), who study the impact of several alternative PTA disciplines/provisions on FDI, by investigating the eects of all provision types from DCRTA on aggregate FDI. To this end, we rely on the dummy-variable specication from column (3) of Table 2 by sequentially replacing the

> 19 ˆ Specically, to obtain these bounds, we used the expression (exp(ˆ _βINVij,t_ + _βINV_  DEP T Hij,t × Nmin,max_ ) _−_ 1) _×_ 100 , where _β_<sup>ˆ</sup> _INVij,t_ and _β_<sup>ˆ</sup> _INV_  DEP T Hij,t_ are the corresponding estimates from column (4) of Table 2), and _Nmin,max_ denotes the minimum (7) and the maximum (42) number of investment provisions in our sample.

> 20 We are very grateful to Nadia Rocha and Vanessa Alviarez for suggesting to investigate the eects of additional provisions and alternative measures of the depth of DTAs.

27
dummy variable for DTAs with investment provisions ( _INVij,t_ ) with corresponding indicator variables for each of the other seventeen types of provisions from DCRTA. For brevity, in Table 3 we just summarize our main ndings with respect to the DTA provisions.

Table 3: Estimates of the Eects of Alternative DTA Provisions on FDI

|(1)|(2)|(3)|
|---|---|---|
|Provision Description|Estimate|Standard Error|
|Investment|0.295|0.108|
|Labor Market Regulations|0.298|0.103|
|Export Taxes|0.273|0.164|
|Public Procurement|0.542|0.135|
|State-Owned Enterprises|0.364|0.134|
|Movement of Capital|0.182|0.117|
|Environmental Laws|-0.221|0.239|
|Intellectual Property Rights|-0.054|0.124|
|Visa and Asylum|0.121|0.113|
|Rules of Origin|0.116|0.147|
|Services|0.084|0.102|
|Technical Barriers to Trade|0.034|0.109|
|Subsidies|-0.278|0.235|
|Sanitary and Phytosanitary|-0.063|0.122|
|Trade Facilitation and Customs|-0.246|0.187|
|Anti-dumping Duties|.|.|
|Countervailing Duties<br>Competition Policy|.<br>.|.<br>.|

Notes: This table reports estimates of the eects of a series of DTA provisions on FDI over the period 1990-2011. Each row corresponds to a separate econometric model based on the specication from column (3) of Table 2 after replacing the dummy variable for DTAs with investment provisions ( _INVij,t_ ) with corresponding indicator variables for each of the other seventeen types of provisions from DCRTA. The dependent variable is the value of FDI stock. The estimator is PPML. All estimates are obtained with three-year interval data and three-way xed eects, including origin-time, destination-time, directional pair xed eects, and all other control variables from column (3) of Table 2. The estimates of all xed eects and controls are omitted for brevity. The standard errors in all specications are clustered by country pair. See text for further details.

The following results stand out from our analysis. First, due to (near) perfect collinearity with the DTAs dummy, we could not identify the eects of agreements with provisions covering `antidumping duties', `countervailing duties', and `competition policy'. Our indicator for agreements including `competition policy' provisions is perfectly collinear with the DTA dummy, while the correlations between the indicators for agreements with provisions 28
covering `antidumping duties' and `countervailing duties' and the DTA dummy were larger than 0.99. This is why these three types of provisions appear in the bottom panel of Table 3 without corresponding estimates.

Second, we obtain positive and statistically signicant estimates for four additional provisions, which, together with our estimate for investment provisions, are reported in the top panel of Table 3. Consistent with the result from Laget et al. (2021) for FDI in servicerelated activities, we nd that `labor market regulations' promote overall FDI. In addition, we obtain a positive eect of provisions related to `export taxes'. We nd this result intuitive as well. Finally, we obtain large, positive, and statistically signicant estimates of the eects of agreements with provisions that cover `public procurement' and `state owned enterprises'. A possible explanation for this result is that such provisions ensure transparency and protection from the host state. We are not aware of existing estimates that link `public procurement' and `state owned enterprises' to FDI and, given the size and signicance of our estimates, we view this as an interesting and important channel that deserves a more detailed analysis and investigation.

Finally, we do not nd evidence that other provisions, some of which potentially closely related to capital and technology movement, have had a signicant impact on FDI. Specifically, our estimates of the eects of provisions related to `intellectual property rights' and `movement of capital' are not statistically signicant. Consistent with Laget et al. (2021), our estimate of the eects of `movement of capital' is sizable and positive, but it is not statistically signicant in our case. Also similar to Laget et al. (2021), we obtain a negative estimate of the eects of provisions related to `environmental laws', but, again, our estimate is not statistically signicant. A possible explanation for the lack of signicance is that the correlation between the indicators for DTAs and agreements with environmental provisions is large, i.e., 0.724. Overall, the estimates in Table 3 indicate that, on average, only a few types of DTA provisions aected FDI directly.

In our next experiment, we zoom in on the eects of alternative sets of provisions within 29
the broad category of `investment' provisions. Specically, the DCRTA distinguishes between six types of investment provisions, which are listed in the rst column of Table 4. As before, we start with a dummy-variables specication, as in column (3) of Table 2, by sequentially replacing the dummy variable for DTAs with investment provisions ( _INVij,t_ ) with corresponding indicator variables for each of the six types of investment provisions from DCRTA. Our results appear in panel A of Table 4, and we see that they are all positive and signicant. In fact, some of the new estimates are identical to our estimate for the overall impact of DTAs with investment provisions. The simple, mechanical explanation for this result is that some of the subcategories of investment provisions (e.g., for `protection' and `liberalization') appear in all DTAs that cover investment. Thus, the indicator variables for such provisions are perfectly collinear with our main dummy variable ( _INVij,t_ ) from column (3) of Table 2. We do, however, obtain positive estimates for some provision types (e.g., covering `transparency') that are not perfectly collinear with _INVij,t_ .

Table 4: Estimates of the Eects of Alternative Investment Provisions on FDI

||A. In|dicator||B.|Depth||
|---|---|---|---|---|---|---|
|Provision Description|Est.|Std.Err.|Est.|Std.Err.|Est.|Std.Err.|
|(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|Protection|0.295|0.108|0.378|0.169|-0.007|0.013|
|Liberalization|0.295|0.108|0.264|0.273|0.007|0.056|
|Transparency|0.185|0.108|0.461|0.164|-0.141|0.064|
|Regulation|0.289|0.108|0.532|0.159|-0.128|0.055|
|Dispute Settlement|0.297|0.108|0.553|0.298|-0.095|0.100|
|Scope and Denitons|0.295|0.108|0.287|0.392|0.002|0.072|

Notes: This table reports estimates of the eects of a series of DTA investment provisions on FDI over the period 1990-2011. Each row in each panel corresponds to a separate econometric model. The results in panel A are based on the specication from column (3) of Table 2 after replacing the dummy variable for DTAs with investment provisions ( _INVij,t_ ) with corresponding indicator variables for each of the six types of investment provisions from DCRTA. The results in panel B are based on the specication from column (4) of Table 2, where we also allow for dierential eects depending on the number of investment provisions (`depth') within each type. The dependent variable is always the value of FDI stock. The estimator is PPML. All estimates are obtained with three-year interval data and three-way xed eects, including origin-time, destination-time, directional pair xed eects, and all other control variables from columns (3) and (4) of Table 2, respectively. The estimates of all xed eects and controls are omitted for brevity. The standard errors in all specications are clustered by country pair. See text for further details.

Since, due to perfect or near-perfect collinearity, we could not obtain informative estimates of the eects of all types of investment provisions with an indicator-variable approach, 30
we also allow for additional and dierential eects of such provisions depending on their depth. Specically, in panel B of Table 4 we rely on the specication from column (4) of Table 2 by sequentially replacing the dummy variables for DTAs with investment provisions ( _INVij,t_ ) and their depth ( _INV_ DEPTHij,t_ ) with the corresponding indicator and continuous variables, respectively, for each of the six types of investment provisions. The main message from panel B of Table 4 is consistent with our previous result for no signicant impact of the increase in the depth (number of investment provisions) on FDI. In fact, our estimates of the eect of `depth' for some provision types (e.g., `transparency' and `regulation') are negative and statistically signicant, suggesting that more investment provisions and additional complexity may make the agreements more dicult to comply with.

In sum, the analysis in this section demonstrated that while trade agreements do not necessarily promote trade and FDI on average, the impact of deep trade agreements on trade and the impact of deep trade agreements that include investment provisions on FDI are positive and statistically signicant. In addition to `investment' provisions, we identied several other types of DTA provisions that have stimulated FDI. We also oered evidence that deeper trade agreements (as measured by the number of provisions) lead to larger trade liberalization eects. However, we do not see evidence that the increase in the number of investment provisions in DTAs has lead to more FDI. In fact, some of our results suggest that additional provisions and added complexity may make DTAs less eective in promoting FDI. Next, in Section 4.2, we rely on the partial estimates from this section to obtain GE eects of DTAs on FDI.

# 4.2 Counterfactual Analysis

This section translates the partial equilibrium estimates from Tables 1 and 2 into GE eects of DTAs on FDI. To this end, we rely on the structural trade and investment system from Section 2.1.

We start the analysis by describing the steps that we took to make system (8)-(15) 31
operational for our purposes. For the counterfactual analysis we use as baseline the latest available year in our data set, which is 2011 and was determined by the availability of capital stock data. In order to perform the counterfactual analysis, we need to set values for the parameters. Some parameters are borrowed from the literature: i) the elasticity of substitution is set equal to _σ_ = 6 , which is standard in the trade literature, ii) the consumer discount factor is set equal to _β_ = 0 _._ 98 (Yao et al., 2012), and iii) the country-specic capital shares of production _αj_ and the country-specic adjustment costs of capital _δj_ are calculated using the Penn World Tables and reported in columns (3) and (4) of Table 5, respectively.

We calibrate other parameters in order to match the observed data. The share of technology capital of a country to all destinations as a share from total world technology capital ( _ηi_ ) is calculated using _FDIij_<sup>_value_</sup> : _φj_ is calculated using the relationship between inward FDI ( _FDIj_<sup>_in_</sup> =<sup>�</sup> _i_<sup>_FDI_</sup> _ij_<sup>_value_</sup> ) and physical capital in the production function along with FDI and physical capital data and data on the capital shares: The exact values for _η_ and _φ_ are given in columns (5) and (6) of Table 5.

Table 5: Calibrated Parameters

|(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|
|ISO3|Country|_α_|_δ_|_η_|_φ_|
|AGO|Angola|0.47|0.0528|0.00078|0.056|
|ARG|Argentina|0.57|0.0394|0.00792|0.024|
|AUS|Australia|0.44|0.0375|0.01375|0.052|
|AUT|Austria|0.43|0.0442|0.00521|0.058|
|AZE|Azerbaijan|0.79|0.0725|0.00058|0.041|
|BEL|Belgium|0.38|0.0452|0.00677|0.235|
|BGD|Bangladesh|0.47|0.0407|0.00322|0.003|
|BGR|Bulgaria|0.51|0.0565|0.00093|0.091|
|BLR|Belarus|0.48|0.0506|0.00152|0.031|
|BRA|Brazil|0.44|0.0475|0.02653|0.042|
|CAN|Canada|0.39|0.0371|0.01658|0.057|

Continued on next page 32
Table 5 Continued <u>from previous page</u>

|(1)<br>ISO3|(2)<br>Countr|(3)<br>_α_|(4)<br>_δ_|(5)<br>|(6)<br>_φ_|
|---|---|---|---|---|---|
|CHE|y<br>Switzerland|0.35|0.0568|_η_<br>0.00683|0.190|
|CHL<br>CHN|Chile<br>China|0.55<br>0.46|0.0427<br>0.0530|0.00305<br>0.18395|0.057<br>0.009|
|COL|Colombia|0.39|0.0411|0.00536|0.008|
|CYP<br>|Cyprus<br>|0.48<br>|0.0357<br>|0.00117<br>|0.186<br>|
|CZE|Czech Republic|0.49|0.0416|0.00359|0.063|
|DEU|Germany|0.39|0.0389|0.04024|0.031|
|DNK|Denmark|0.37|0.0431|0.00320|0.054|
|DOM|Dominican Republic|0.34|0.0307|0.00097|0.009|
|ECU|Ecuador|0.55|0.0466|0.00160|0.007|
|EGY<br>|Egypt, Arab Rep.<br>|0.62<br>|0.0597<br>|0.00351<br>|0.029<br>|
|ESP|Spain|0.39|0.0375|0.02146|0.047|
|EST|Estonia|0.42|0.0461|0.00030|0.086|
|ETH|Ethiopia|0.47|0.0494|0.00073|0.002|
|FIN<br>|Finland<br>|0.39<br>|0.0412|0.00323<br>|0.049|
|FRA|France|0.37|0.0382|0.03254|0.036|
|GBR<br>|United Kingdom<br>|0.39<br>|0.0379<br>|0.02665<br>|0.083<br>|
|GHA|Ghana|0.47|0.0553|0.00057|0.018|
|GRC|Greece|047|00335|000396|0014|
|GTM|Guatemala|.<br>0.58|.<br>0.0454|.<br>0.00037|.<br>0.030|
|HKG|Hong Kong SAR, China|0.48|0.0435|0.00687|0.228|
|HRV|Croatia|0.34|0.0436|0.00106|0.039|
|HUN|Hungary|0.41|0.0436|0.00229|0.065|
|IDN|Indonesia|0.54|0.0370|0.01334|0.019|
|IND|India|0.50|0.0558|0.04216|0.007|
|IRL|Ireland|0.52|0.0496|0.00238|0.292|
|IRN|Iran, Islamic Rep.|0.74|0.0588|0.01147|0.001|
|IRQ|Iraq|0.70|0.0558|0.00099|0.004|
|ISR<br>|Israel<br>|0.45<br>|0.0448<br>|0.00242<br>|0.026<br>|
|ITA|Italy|0.46|0.0380|0.03135|0.021|
|JPN|Japan|0.39|0.0466|0.07416|0.004|
|KAZ|Kazakhstan|0.58|0.0400|0.00270|0.065|
|KEN<br>KOR|Kenya<br>Korea, Rep.|0.57<br>0.50|0.0519<br>0.0501|0.00049<br>0.02197|0.017<br>0.012|
|KWT<br>LBN|Kuwait<br>Lebanon|0.75<br>0.56|0.0557<br>0.0413|0.00204<br>0.00134|0.008<br>0.002|
|LKA|SriLanka|031|00446|000119|0001|
|LTU|<br>Lithuania|.<br>0.53|.<br>0.0418|.<br>0.00050|.<br>0.058|
|LUX|Luxembourg|0.46|0.0463|0.00649|0.634|
|LVA|Latvia|0.45|0.0336|0.00037|0.051|
|MAR|Morocco|0.51|0.0521|0.00176|0.045|
|MEX|Mexico|0.61|0.0362|0.01590|0.050|
|MKD|North Macedonia|0.47|0.0406|0.00026|0.031|
|MLT|Malta|0.46|0.0529|0.00015|0.219|
|MYS<br>NGA|Malaysia<br>Nigeria|0.47<br>0.50|0.0596<br>0.0581|0.00587<br>0.00178|0.034<br>0.050|
|NLD|Nthld|041|00401|001680|0109|
|NOR|eerans<br>Norway|.<br>0.48|.<br>0.0399|.<br>0.00348|.<br>0.094|
|NZL|New Zealand|0.43|0.0408|0.00124|0.081|
|OMN|Oman|0.70|0.0602|0.00110|0.029|
|PAK<br>PER|Pakistan<br>Peru|0.47<br>0.69|0.0551<br>0.0395|0.00468<br>0.00364|0.007<br>0.016|
|PHL|Philippines|0.64|0.0488|0.00485|0.012|
|POL|Poland|0.44|0.0491|0.00746|0.046|
|PRT<br>QAT|Portugal<br>Qatar|0.39<br>0.81|0.0351<br>0.0960|0.00382<br>0.00235|0.043<br>0.034|
|ROM|Romania, Socialist Republic of|0.53|0.0518|0.00298|0.049|
|RUS|Russian Federation|0.26|0.0402|0.03052|0.011|
|SA|SdiAbi|||||
|U|au raa|0.72|0.0530|0.00976|0.009|
|SDN|Sudan|0.41|0.0664|0.00035|0.008|
|SER|Serbia|0.42|0.0402|0.00103|0.035|
|SGP<br>|Singapore<br>|0.56<br>|0.0533<br>|0.00494<br>|0.182<br>|
|SVK<br>SVN|Slovak Republic<br>Slovenia|0.46<br>0.33|0.0520<br>0.0439|0.00125<br>0.00086|0.073<br>0.023|

Continued on next page 33
Table 5 Continued <u>from previous page</u>

|(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|
|ISO3|Country|_α_|_δ_|_η_|_φ_|
|SWE|Sweden|0.45|0.0453|0.00408|0.182|
|SYR|Syrian Arab Republic|0.47|0.0552|0.00122|0.003|
|THA|Thailand|0.61|0.0655|0.00946|0.035|
|TKM|Turkmenistan|0.47|0.0430|0.00115|0.001|
|TUN|Tunisia|0.50|0.0474|0.00108|0.005|
|TUR|Türkiye|0.56|0.0554|0.00729|0.037|
|TZA|Tanzania, United Republic of|0.57|0.0435|0.00055|0.032|
|UKR|Ukraine|0.44|0.0308|0.00631|0.013|
|USA|United States|0.40|0.0475|0.17546|0.023|
|UZB|Uzbekistan|0.47|0.0327|0.00099|0.003|
|VEN|Venezuela, RB|0.63|0.0389|0.00366|0.020|
|VNM|Vietnam|0.47|0.0455|0.00507|0.010|
|ZAF|South Africa|0.46|0.0506|0.00419|0.066|
|ZWE|Zimbabwe|0.44|0.0371|0.00004|0.083|

Notes: This table reports results from our calibration for some parameters. Column (1) gives the iso3-country codes, column (2) the country names. The country-specic capital shares of production _αj_ are reported in column (3), while in column (4) we give the values of the country-specic adjustment costs of capital _δ_ . The values for the _η_ 's, i.e., the share of technology capital of a country to all destinations, is given in columns (5). Column (6) gives the values for the production share of FDI ( _φ_ ). See text for further details.

For the baseline, we calibrate bilateral trade frictions to the power of 1 _− σ_ , i.e., trade openness _t_<sup>1</sup> _ij_<sup>_−σ_</sup> , using data on trade ows, income, and expenditure and solving Equations (9) and (10) for given trade costs and calculating a new matrix _tij_<sup>1</sup><sup>_−σ_</sup> using Equation (8) until convergence, where we normalize all internal trade costs and trade costs for one exporter to one. Given trade costs, we can calculate the inward and outward multilateral resistance indexes using Equations (9) and (10), respectively, where we set the inward MRT for Angola to one.

_Mj_ is calibrated using data on income, FDI, and constructed MRTs and the following theory-consistent equation for technology capital: With this, we can construct FDI openness ( _ωij_ ) using the following equation for FDI ows 34
in values:<sup>21</sup> _Aj/γj_ , the preference-adjusted technology, is calibrated using Equations (11) and (12). As the value of domestic income and expenditure calculated from the trade data do not perfectly match up, we dene _ψj ≡ Ej/_ � _Yj_ + _ηj_ � _i∈_ N _ji,t_<sup>_φiYi−φjYj_</sup> � _i∈_ N _ij,t_<sup>_ηi_</sup> � as an exogenous country-specic parameter that accounts for these trade imbalances. In the spirit of Dekle et al. (2007, 2008), we rst eliminate all exogenous trade imbalances and take the equilibrium without trade imbalances as baseline.

In order to highlight the alternative channels through which DTAs aect FDI, and also to capitalize on the full set of our partial estimates, we perform two sets of experiments. First, we rely on our estimates of the dummy variables for DTAs and DTAs with investment provisions from column (2) of Table 1 and column (3) of Table 2, respectively. Then, we also obtain corresponding eects based on the estimates of the continuous depth variables from column (3) of Table 1 and column (4) of Table 2. Consistent with the discussion in Section 2.2, we perform each of the two experiments in two steps. First, we change the vector of FDI frictions. Then, in addition, we change the vector of trade costs. As the DTAs are already in place, we perform an ex-post evaluation, i.e., we assume that in the baseline the agreement is in place and simulate the eect without DTAs as counterfactual. We then report the change from the baseline to the counterfactual, i.e., baseline value minus counterfactual value relative to the counterfactual value.

> 21 The values of _ωij_ are restricted to be between zero and one. Hence, we normalize each row by the maximum element. Further, all zero FDI ows are leading to zero _ωij_ 's by construction. To avoid this, we set _ωijMi_ = 1 _._ 00001 for those observations.

35
Table 6: Total GE Eects on Inward and Outward FDI

|ISO3|Pan<br>FDI|el A: Dumm<br>ib|y DTA vari<br>FDIand|able<br>tradelib|Pan<br>FD|el <br>l|B: Continu<br>b|ous DTA vari<br>FDIand|able<br>radelib|
|---|---|---|---|---|---|---|---|---|---|
|(1)|<br>inw. FDI<br><br>(2)|.<br>outw. FDI<br>(3)|<br>inw. FDI<br>(4)|.<br>outw. FDI<br>(5)|inw. FDI<br>(6)|<br>o|.<br>utw. FDI<br>(7)|<br>inw. FDI<br>(8)|.<br>outw. FDI<br>(9)|
|AGO|0.12|0.00|0.59|0.00|0.13||0.00|0.42|0.00|
|ARG|1.15|0.53|2.06|1.55|1.37||0.65|1.95|1.08|
|AUS|6.93|4.47|7.90|4.85|6.36||3.72|6.97|4.04|
|AUT|0.99|0.53|1.86|1.75|1.35||0.77|1.91|1.43|
|AZE|0.24|0.00|1.12|0.01|0.25||0.00|0.82|0.01|
|BEL|1.01|1.00|1.93|1.45|1.36||1.39|1.95|1.66|
|BGD|0.24|0.02|1.09|0.17|0.25||0.02|0.80|0.05|
|BGR|0.92|0.10|1.80|0.26|1.24||0.14|1.78|0.23|
|BLR|0.22|0.01|1.01|0.10|0.23||0.01|0.72|0.04|
|BRA|0.60|1.77|1.52|2.58|0.69||2.16|1.28|2.47|
|CAN|6.16|3.25|7.07|6.29|7.13||3.25|7.72|6.75|
|CHE<br>CHL|3.47<br>17.97|1.96<br>2.50|4.35<br>18.93|2.62<br>3.04|3.28<br>21.01||2.28<br>3.29|3.84<br>21.65|2.52<br>3.70|
|CHN|1.43|69.50|2.33|90.59|1.04||51.93|1.61|61.73|
|COL|1.54|0.52|2.24|0.74|1.58||0.59|2.03|0.72|
|CYP|0.82|0.06|1.48|0.18|1.13||0.09|1.54|0.16|
|CZE|1.00|0.14|1.89|0.36|1.35||0.21|1.92|0.34|
|DEU<br>DNK|1.00<br>1.00|5.31<br>0.42|1.88<br>1.92|10.94<br>1.01|1.35<br>1.35||7.39<br>0.58|1.92<br>1.94|10.53<br>0.91|
|DOM|10.17|0.06|10.78|0.12|10.23||0.06|10.65|0.12|
|ECU|0.14|0.00|0.68|0.06|0.15||0.00|0.50|0.02|
|EGY|0.16|0.02|0.79|0.49|0.17||0.02|0.55|0.17|
|ESP|0.96|2.14|1.80|4.31|1.31||2.90|1.85|4.11|
|EST|0.23|0.02|1.09|0.03|0.24||0.02|0.79|0.03|
|ETH|0.11|0.01|0.48|0.04|0.12||0.01|0.35|0.03|
|FIN|0.98|0.32|1.84|0.72|1.33||0.47|1.89|0.70|
|FRA|1.01|3.35|1.95|7.58|1.35||4.89|1.95|7.25|
|GBR|0.99|3.61|1.90|7.01|1.34||5.02|1.92|6.96|
|GHA<br>GRC|0.13<br>0.87|0.02<br>0.17|0.63<br>1.66|0.03<br>0.54|0.14<br>1.18||0.02<br>0.24|0.44<br>1.67|0.02<br>0.45|
|GTM<br>HKG|5.53<br>0.21|0.01<br>0.07|6.02<br>0.93|0.04<br>0.56|5.06<br>0.22||0.01<br>0.07|5.38<br>0.67|0.04<br>0.24|
|HRV|024|007|100|007|028||011|075|012|
|HUN|.<br>0.93|.<br>0.16|.<br>1.81|.<br>0.56|.<br>1.23||.<br>0.23|.<br>1.80|.<br>0.46|
|IDN|1066|212|1147|353|800||165|850|251|
|IND|.<br>4.31|.<br>11.85|.<br>5.25|.<br>15.97|.<br>3.52||.<br>9.43|.<br>4.11|.<br>11.44|
|IRL<br>|0.97<br>|0.30<br>|1.81<br>|0.74<br>|1.32<br>||0.43<br>|1.87<br>|0.68<br>|
|IRN|0.10|0.05|0.51|0.54|0.11||0.06|0.34|0.25|
|IR|011|000|046|003|011||000|034|000|
|Q<br>ISR|.<br>0.19|.<br>0.02|.<br>0.90|.<br>0.30|.<br>0.21||.<br>0.02|.<br>0.67|.<br>0.14|
|ITA|1.04|4.22|1.99|7.80|1.40||5.82|2.00|7.82|
|JPN|3.51|27.05|4.39|33.86|3.25||25.62|3.82|29.94|
|KAZ|0.22|0.01|1.07|0.09|0.23||0.01|0.77|0.04|
|KEN|0.18|0.00|0.87|0.01|0.20||0.00|0.63|0.01|
|KOR|10.07|23.79|11.07|26.18|12.10||28.46|12.75|29.89|
|KWT|011|000|050|001|012||000|036|000|
|LBN|.<br>0.12|.<br>0.03|.<br>0.63|.<br>0.22|.<br>0.13||.<br>0.04|.<br>0.44|.<br>0.13|
|LKA<br>|0.13<br>|0.02<br>|0.58<br>|0.16<br>|0.14<br>||0.03<br>|0.41<br>|0.09<br>|
|LTU|0.89|0.02|1.78|0.05|1.19||0.03|1.76|0.05|
|LUX|096|099|174|152|131||142|180|174|
|LVA|.<br>0.82|.<br>0.00|.<br>1.48|.<br>0.03|.<br>1.12||.<br>0.00|.<br>1.53|.<br>0.02|
|MAR|550|007|621|036|520||007|562|019|
|MEX|.<br>8.72|.<br>3.60|.<br>9.72|.<br>8.56|.<br>9.77||.<br>4.00|.<br>10.41|.<br>8.88|
|MKD|645|010|715|012|984||015|1029|016|
|MLT|.<br>0.90|.<br>0.01|.<br>1.54|.<br>0.04|.<br>1.26||.<br>0.01|.<br>1.66|.<br>0.03|
|MYS|1067|122|1151|201|771||091|822|141|
|NGA|.<br>0.22|.<br>0.00|.<br>0.93|.<br>0.03|.<br>0.23||.<br>0.00|.<br>0.67|.<br>0.01|
|NLD|1.04|1.81|1.99|5.38|1.40||2.64|2.00|4.64|
|NOR<br>|1.51<br>|0.70<br>|2.43<br>|1.04<br>|1.64<br>||0.89<br>|2.22<br>|1.16<br>|
|NZL<br>OMN|7.45<br>5.47|0.29<br>0.05|8.32<br>6.07|0.33<br>0.06|5.33<br>5.16||0.21<br>0.04|5.88<br>5.52|0.25<br>0.05|

Continued on next page 36
Table 6 Continued <u>from previous page</u>

||Pa|nel A: Dumm|y DTA vari|able|Pane|l B: Continu|ous DTA va|riable|
|---|---|---|---|---|---|---|---|---|
|ISO3<br>(1)|FDI <br>inw. FDI<br>(2)|lib.<br>outw. FDI<br>(3)|FDI and<br>inw. FDI<br>(4)|trade lib.<br>outw. FDI<br>(5)|FDI<br>inw. FDI<br>(6)|lib.<br>outw. FDI<br>(7)|FDI and <br>inw. FDI<br>(8)|trade lib.<br>outw. FDI<br>(9)|
|PAK|5.96|0.13|6.63|0.51|7.67|0.14|8.09|0.32|
|PER|13.33|0.42|14.26|0.74|12.90|0.39|13.50|0.65|
|PHL|10.72|0.77|11.59|1.78|7.72|0.59|8.25|1.23|
|POL|1.01|0.52|1.94|1.44|1.36|0.77|1.95|1.29|
|PRT|0.86|0.26|1.67|0.87|1.17|0.38|1.69|0.72|
|QAT|0.16|0.01|0.72|0.02|0.16|0.01|0.51|0.01|
|ROM|0.19|0.01|0.95|-0.01|0.21|0.02|0.67|0.00|
|RUS|0.24|0.29|1.13|1.06|0.26|0.31|0.82|0.70|
|SAU|0.18|0.03|0.87|0.12|0.19|0.04|0.63|0.07|
|SDN|0.06|0.00|0.26|0.00|0.06|0.00|0.17|0.00|
|SER|0.23|0.00|1.04|0.00|0.24|0.00|0.74|0.00|
|SGP|16.96|3.47|17.89|5.02|13.62|2.82|14.18|3.99|
|SVK|0.91|0.01|1.81|0.08|1.21|0.02|1.79|0.06|
|SVN|0.88|0.06|1.69|0.10|1.19|0.08|1.69|0.11|
|SWE|0.96|0.46|1.73|1.23|1.31|0.67|1.79|1.12|
|SYR|0.11|0.01|0.52|0.09|0.12|0.01|0.37|0.03|
|THA|10.72|1.56|11.62|3.22|7.78|1.23|8.32|2.34|
|TKM|0.06|0.01|0.33|0.07|0.07|0.01|0.21|0.04|
|TUN|0.14|0.01|0.67|0.20|0.15|0.01|0.46|0.10|
|TUR|0.23|0.05|1.06|0.70|0.24|0.05|0.75|0.33|
|TZA|0.19|0.00|0.87|0.04|0.20|0.00|0.63|0.01|
|UKR|0.16|0.02|0.81|0.28|0.17|0.03|0.56|0.11|
|USA|2.07|72.87|3.01|87.52|2.18|70.94|2.78|83.34|
|UZB|0.12|0.00|0.55|0.04|0.13|0.00|0.39|0.01|
|VEN|1.26|0.24|2.12|0.32|1.51|0.30|2.06|0.33|
|VNM|10.65|0.11|11.39|0.37|7.99|0.09|8.43|0.26|
|ZAF|0.24|0.02|1.10|0.14|0.25|0.02|0.78|0.08|
|ZWE|0.12|0.00|0.59|0.00|0.13|0.00|0.41|0.00|
|World|2.77|72.87|3.67|87.52|2.79|70.94|3.36|83.34|
|Lib-Countries|3.00|72.87|3.69|87.52|3.01|70.94|3.38|83.34|
|ROW|0.54|0.01|0.98|0.00|0.64|0.01|0.70|0.00|

Notes: This table reports results from our counterfactual analysis. Column (1) gives the iso3-country codes. Panel A reports results based a uniform change in the bilateral FDI frictions between all countries that have signed a DTA with investment provisions that is based on our estimate of 0 _._ 295 . Columns (2) and (3) report the percentage changes in inward and outward FDI, respectively, when DTAs change FDI frictions, while in columns (4) and (5) in addition to the change in FDI frictions DTAs also change trade frictions. Panel B reports results based on the estimates on the continuous depth variables. Columns (6) and (7) report the percentage changes in inward and outward FDI, respectively, when DTAs change FDI frictions, while in columns (8) and (9) in addition to the change in FDI frictions DTAs also change trade frictions.

Given the main purpose of our analysis, and to keep the presentation of our results manageable, we focus the discussion of our counterfactual results on the percentage changes (between the baseline and the counterfactual scenarios) in the stocks of FDI per country. Specically, we construct and report percentage changes in inward and outward FDI stocks, i.e., the percentage changes in technology capital used in total at home and technology 37
capital from one country used abroad: where superscript _b_ denote baseline values with DTAs in place, and superscript _c_ the counterfactual situation without DTAs in place, and, consistent with our theory, inward FDI and outward FDI stocks per country can be calculated as follows: Note that the inward FDI stock can be seen as the global technology stock applied locally, whereas the outward FDI stock is the usage of a country's technology capital abroad. _η_ determines the usage of FDI abroad of one country, i.e., outward FDI stocks per country will change a lot if this share is large (i.e., _η_ is large), even if the change in technology capital _Mi_ is comparably small. This is a result of the non-rival nature of FDI that we capture. On the other hand side, for inward FDI per country the _η_ 's always sum to one and therefore changes in inward FDI are a weighted average of changes in the _ωijMi_ 's.

Before we discuss our ndings, we draw the reader's attention to a caveat with our GE analysis, which is due to the fact that the underlying theory is based on the assumption of non-rival technology FDI, while our data includes all/aggregate FDI ows. This gap, of course, has implications for the quantitative results. Therefore, the specic indexes that we obtain and report in this section should be interpreted accordingly and with caution. Nevertheless, we believe that the main conclusions and policy implications that we will draw in this section about a disproportionately large impact of outward FDI will remain qualitatively the same if applied to appropriate data on technology FDI.

Our ndings are reported in Table 6, where the rst column lists the ISO3 country codes 38
for the countries in our sample, Panel A reports the results from the scenario based on the estimates of the dummy DTA variables, and Panel B reports the eects that are based on the estimates of the continuous depth variables.

The results in columns (2) and (3) of Table 6 are obtained in response to a uniform change in the bilateral FDI frictions between all countries that have signed a DTA with investment provisions that is based on our estimate of 0 _._ 295 (std.err. 0 _._ 108 ) from column (3) of Table 2. There are several things noteworthy. First, both inward and outward FDI increase for most of the countries. For the countries that have signed a DTA, the eect for inward FDI is on average an about 3% increase, while it amounts to 72% for outward FDI. The huge values for outward FDI are driven by the importance of the United States and China as the largest outward FDI countries. Their technology capital as a share of total world technology capital (i.e., their _η_ 's) is about 18% (see Table 5). Hence, their stocks are used substantially in many countries of the world (the exact usage at the bilateral level also depends on the FDI frictions _ω_ ). Even though the United States and China only increase their technology capital stock ( _M_ ) by about 0.7% and 1%, respectively, the eect on their outward FDI stocks is large due to the huge share of their FDI in world FDI and the non-rival nature, allowing to use the technology capital in all countries in the world simultaneously.

On the inward FDI side, we see the largest increase for Chile, Singapore, Peru, Thailand, the Philippines, Malaysia, Indonesia, Vietnam, the Dominican Republic and the Republic of Korea. Those are all countries that have many DTAs and also rely substantially on inward FDI. On the other end of the spectrum are countries that are hardly aected, neither on the inward nor on the export side, such as Sudan, Turkmenistan, the Islamic Republic of Iran, and Iraq. Those countries do not have many (or any) DTAs in place and are also relatively closed in terms of FDI. Overall, we see a wide heterogeneity among countries. This is even more extreme for outward FDI, where the importance of the large outward FDI investors is very dominant.

The estimates in columns (4) and (5) of Table 6 are obtained when, in addition to the 39
change in bilateral FDI frictions, we also change uniformly the vector of bilateral trade frictions based on our estimate from column (2) of Table 1. Relative to the scenario where only the bilateral FDI frictions are changed (i.e., the results presented in columns (2) and (3) of Table 6), we see qualitatively a very similar picture and quantitatively an increase in both, inward and outward FDI. Specically, on average, trade liberalization has contributed to 0.7 percentage points (or about 25%) increase in inward FDI and about 15 percentage points in outward FDI. These estimates reveal that trade liberalization via DTAs is an important channel to stimulate FDI, thus complementing the results from Anderson et al. (2019), who show that FDI liberalization is important for trade.

Panel B of Table 6 reports estimates that are obtained based on the estimates on the continuous depth variables from Section 4.1. The estimates in columns (6) and (7) rely on the estimates from column (4) of Table 2. Allowing for the continuous depth leads overall quantitatively and qualitatively similar results, with heterogeneous changes across countries. Finally, the estimates in columns (8) and (9) of Table 6 are obtained when, in addition to the change in bilateral FDI frictions, we also change the vector of bilateral trade frictions based on our estimate from column (3) of Table 1. Similar to the uniform changes, the additional allowance for changes of bilateral trade frictions leads to larger eects for inward and outward FDI.

To sum up, according to our analysis, the DTAs that were in force in 2011 have contributed to about 3% of inward FDI in the world and about 70% of outward FDI. The latter is heavily driven by the fact that some countries have large stocks of FDI used in many countries in the world, multiplying the eect of any change in outward FDI of those countries due to changes in frictions.

40
# 5 Conclusion

The objective of this paper was to study the links between deep trade liberalization in the form of DTAs and FDI. To this end, we identied and decomposed three channels through which DTAs impact FDI. First, we obtained signicant direct/partial equilibrium eects of DTAs and their investment provisions on FDI from a theory-motivated FDI gravity model. Second, we translated the partial estimates of the DTA eects on FDI into GE eects. This analysis highlighted the importance of the GE links between DTAs and FDI, and uncovered signicant asymmetries in the response of inward vs. outward FDI in our model. Finally, we performed counterfactual analysis of the impact of deep trade liberalization on FDI, which revealed that, through their impact on trade, DTAs promote FDI additionally.

While, as discussed earlier, our counterfactual analysis is subject to criticism on the mismatch between the data used and the underlying theory, we believe that our conclusions about the disproportionately large impact of outward FDI would remain qualitatively the same if applied to appropriate data on technology FDI. We view this nding as novel and potentially important from a policy perspective, both for the negotiations of trade and investment agreements and for properly quantifying their implications. Moreover, we see signicant potential in developing and utilizing data sets on global technology transfers that would generate more precise partial estimates and more informative GE analysis of the links between trade liberalization and FDI and lead to clearer policy recommendations. In addition to the theory on the intensive margin that we utilize here, we expect signicant payos from developing theories that would capture the links between trade liberalization and the extensive margins (both domestic and international) of technology capital and its diusion in the global economy.

41