Essays on trade, fiscal policy and gender equality

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Essays on Trade, Fiscal Policy and Gender

Equality

Alica Ida Bonk

Thesis submitted for assessment with a view to obtaining the degree of Doctor of Economics of the European University Institute

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European University Institute

Department of Economics

Essays on Trade, Fiscal Policy and Gender Equality

Alica Ida Bonk

Thesis submitted for assessment with a view to obtaining the degree of Doctor of Economics of the European University Institute

Examining Board

Prof. Evi Pappa, University Carlos III of Madrid, Supervisor Prof. Leonardo Melosi, EUI and Federal Reserve Bank of Chicago Prof. Sarantis Kalyvitis, Athens University of Economics and Business Prof. Antonio Navas, University of Sheffield

No part of this thesis may be copied, reproduced or transmitted without prior permission of the author

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Researcher declaration to accompany the submission of written work

I, Alica Ida Bonk, certify that I am the author of the work “Essays on trade, fiscal policy and gender equality” I have presented for examination for the PhD thesis at the European University Institute. I also certify that this is solely my own original work, other than where I have clearly indicated, in this declaration and in the thesis, that it is the work of others. I warrant that I have obtained all the permissions required for using any material from other copyrighted publications.

I certify that this work complies with the Code of Ethics in Academic Research issued by the European University Institute (IUE 332/2/10 (CA 297).

The copyright of this work rests with its author. [quotation from it is permitted, provided that full acknowledgement is made.] This work may not be reproduced without my prior written consent. This authorisation does not, to the best of my knowledge, infringe the rights of any third party.

Statement of inclusion of previous work (if applicable):

I confirm that chapter 2 was jointly co-authored with Ms Chloe Larkou and I contributed 50% of the work. Furthermore, I confirm that chapter 3 was jointly co-authored with Ms Laure Simon and I contributed 50% of the work.

Signature and Date:

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Abstract

Chapter 1 introduces a new data set containing daily trade policy statements issued by govern-ment agencies between 2007 and 2019. Entries are classified along several dimensions: the direction of the policy change, the stage of the reform, and the initiating country. I argue that combining this narrative information with stock market data allows to identify unanticipated trade policy shocks. Stock returns generated around statement releases vary by firms’ trade exposure and pro-vide timely signals on the sign and size of trade policy changes. Furthermore, US exporters’ stocks only respond to official statements but not to trade-related tweets by Donald Trump, demonstrat-ing the ability to filter out particularly noisy signals.

Chapter 2 builds on Chapter 1 and analyzes the short- and medium-term effects of trade pol-icy on the US economy. Estimating impulse response functions by local projections, we uncover interesting asymmetries and non-linearities depending on the sign and size of trade policy shocks. Moreover, firm investment, unemployment and consumption react more strongly to shocks caused by trade partners rather than by the US. In addition, we show that implementations elicit more sig-nificant responses than announcements. Uncertain about whether policymakers will follow through with announced policy changes, firms and households take a “wait and see” approach.

Chapter 3 turns to a different topic. Men typically bear the brunt of recessions due to stronger cyclicality of their employment and wages relative to women’s. We study whether fiscal policy may offset or worsen these asymmetries across genders. Using US micro-level data, we find that men are hurt or benefit less than women from increases in major government spending components.

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This result is largely driven by negative spillovers for men working in the private sector. Our analysis highlights that fiscal expansions cannot reconcile both policy goals: offsetting inequitable business cycle effects and closing gender gaps.

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Acknowledgements

This thesis is the product of the kindness, support and intellectual stimulus received from many different people.

First, I would like to express my sincere gratitude to my advisors Prof. Evi Pappa and Prof. Leonardo Melosi for their guidance throughout my Ph.D. research. Without their invaluable advice, enthusiasm and encouragement, this thesis would not have been written. Evi guided me especially during the early stages of this Ph.D. when I lacked a sense of direction. Her creative energy and inquiring mind paved the way for this research and encouraged me to ”dig deeper”. Moreover, her drive and talent for teaching were truly inspirational. Leonardo took me on as a graduate student at a point when I was torn between different topics and interests. Thank you for helping me organize my thoughts and for ensuring that I always left our meetings feeling motivated and full of drive. I have rarely met someone whose comments and literature advice are so continuously on point.

I would also like to thank the remaining members of the committee, Prof. Sarantis Kalyvitis and Prof. Antonio Navas, for agreeing to read this thesis and for their flexibility in times of big uncertainty. Moreover, I am grateful to Prof. Juan Dolado for all his encouraging words during the first year, which was the most academically challenging period of my life. Thank you for patiently answering all my econometric questions, for uplifting aperitivos, magic tricks and great music advice.

The process of writing a thesis can feel very lonely at times. Luckily, I had the privilege of working with incredibly smart and dedicated co-authors. Laure Simon, Chloe Larkou, Alexandra Fotiou and Georgios Manalis: Thank you for your company, trust and honest feedback - I have

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learned a lot from you.

I am grateful to the German Academic Exchange Service (DAAD), the EUI and the Sandmann Stiftung whose generous funding made this journey possible. Moreover, I feel lucky to have been given the opportunity to participate in the Summer Research Fellowship of the Federal Reserve Bank of Chicago. In particular, I would like to thank Robert Barsky, Stefania D’Amico, Gene Amromin, Anna Paulson, Luca Benzoni, Gadi Barlevy, Jing Zhang and, of course, Leonardo Melosi for their warm welcome and useful comments. Furthermore, I am thankful to the Directorate General International & European Relations of the European Central Bank, in particular Beatrice Scheubel, for having me as trainee.

After all this time, I am still in awe of the EUI’s beauty and the outstanding services we are offered. I would like to especially acknowledge Sarah Simonsen, Lucia Vigna, Julia Valerio and Rossella Corridori for their excellent administrative support as well as economics information specialist Thomas Bourke for pointing me at the right resources and Alessandro Barucci for his IT support. A special thanks goes to the cafeteria team Antonella and Loredana for making Villa la Fonte feel like ”home”.

Furthermore, I would like to thank my Ph.D. cohort without whom I would not have passed the first year and who made the remaining journey a lot more fun. The sense of community and team spirit experienced with you were unprecedented for me. Our trips to the Tuscan country side, coffee breaks, Coppa Pavones, June balls and lasagne dinners will remain my most cheerful memories of the past five years. In addition, I was lucky to have met my salsa dancing companions G¨ozde, Tleu and Leiry as well as many other friends at the EUI and beyond who provided the necessary opportunities to de-stress.

Finally, I owe a big ”thank you” to my family for supporting me throughout this Ph.D. and life in general. In particular, I am deeply grateful to my mum Kristin for providing me with a role model of a strong and independent woman, for sharing her positive outlook on life with me and for reminding me of what truly matters. I am immensely thankful to Ivo who is the most caring, dedicated and supportive dad I could have wished for. Thank you for encouraging me to ”dream big”, for flying across the Atlantic just to hold my hand on the plane and for being my biggest fan

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at every award ceremony. I also want to thank Erti for being there for me in the happy and the scary moments with his unswerving optimism and for teaching me about patience and gratitude for the small things.

This thesis is dedicated to my incredible grandmothers Erika and Edith. Thank you for being my ”gold standard” of selflessness, generosity and tenderness.

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Chapter 1 Combining narrative and stock market

data to identify trade policy shocks:

Evidence from a new database

1.1 Introduction

What is the economic impact of changing barriers to trade? Answering this question is chal-lenging because trade policy reforms are often implemented after several rounds of negotiations or prior threats. This time lag complicates any causal identification since households and firms might anticipate changes. Furthermore, the effects may be asymmetric depending on whether barriers are raised or lowered and whether export or import barriers are affected. This paper offers a po-tential route for overcoming these challenges by introducing a new data set containing daily trade policy statements covering various types of reforms. I argue that this narrative approach can be combined with stock market data to identify unanticipated trade policy shocks.

Previous papers exploring the effect of trade policy typically exploit a single event or episode such as the formation of NAFTA (Rodriguez (2003)), the US granting China permanent normal trade relations (Greenland et al. (2019)) or the election of Donald Trump (Wagner et al. (2018)). As

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a result, liberalizing and protectionist events as well as changes to import versus export barriers are not contrasted within the same empirical framework.1 Furthermore, by looking at single events, it is hard to infer whether the results hold on average and are transferable to similar future trade reforms. In addition, the question of whether trade policy implementations still contain an element of surprise or are fully anticipated has remained unanswered. This paper aims to give future research the opportunity to fill these gaps by presenting a new data set containing trade policy announcements and implementations communicated via government agencies and major newspapers between 2007 and 2018. The data is available daily, and entries are classified along several dimensions. In particular, the direction of the policy change (liberalizing or protectionist), the stage of the reform (pure announcement or implementation), and whether the policy was initiated by the US, its trade partners or jointly are recorded. Additionally, the data set also lists the goods affected and countries involved in each incidence.

The richness of information contained in the data set can serve a range of research purposes. This paper elaborates on one potential route, namely the possibility of combining narrative infor-mation with evidence from the stock market which provides timely hard signals on the sign and size of trade policy changes. Based on this paper’s findings, Bonk and Larkou (2020)2 construct a series of unanticipated shocks to study the macroeconomic impact of trade policy.

To show that stocks contain desirable complementary information, I analyze average market movements in a 2-day window around trade policy statements by means of a simple linear re-gression. The results highlight that the aggregate US stock market – proxied by the S&P 500 – is rarely affected by trade policy statements since it represents the net effect on ”winning” and ”losing” firms. Instead, distinguishing S&P 500 firms by trade exposure, I find that stock returns for ”treated” firms behave as expected. Exporters and importers benefit from liberalizations due to higher expected future sales abroad and lower input costs, respectively.3 Instead, protectionist

1_{An exception to the former is Griffin (2018) who studies the stock market effect of both China being} offered permanent normal trade relations and Trump’s announcement on March 1, 2018, to levy new tariffs on China.

2_{Chapter 2 of the this thesis.}

3_{Profits of exporters may also rise in expectations if they can leverage on economies of scale resulting} from trade creation or enhanced opportunities for off-shoring.

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statements reduce the return on trade-exposed firms’ stock. The opposite holds for companies that only sell domestically since their revenues depend more strongly on weak foreign competi-tion.4 Hence, the results are consistent with the efficient market hypothesis, according to which equity movements reflect ”genuine news that alters rationally grounded forecasts of future earn-ings” (Baker et al. (2019)). The observation that trade exposure determines stocks’ responsiveness to innovations in trade policy has been made previously for single events by Huang et al. (2019), Breinlich (2014), Greenland et al. (2019), Wagner et al. (2018) and Davis and Seminario (2019). In this paper, I document that the result also holds on average using the 105 major trade policy statements between 2007 and 2019 that are part of my data set.

My analysis goes further than merely confirming the relationship between cross-sectional dif-ferences in trade exposure and stock returns. In particular, I find that these returns also reflect the extent to which trade policy statements are anticipated. For example, exporting firms’ stock value falls significantly after the US issues a protectionist statement but remains unresponsive to for-eign retaliations. Hence, investors correctly anticipate that trade partners reciprocate. Moreover, implementations of liberalizing policies – unlike announcements – have no effect on trade-exposed firms’ stock price. The potential reason is that trade agreements are usually implemented after several years of multilateral negotiations. In contrast, protectionist measures often take effect shortly after they were first announced, sometimes with last-minute revisions. Consequently, both protectionist announcements and implementations contain an element of surprise.

Next, I show that exporters’ stock returns respond to official statements but not to trade-related tweets by Donald Trump. Since the latter are usually vague and rarely contain specific trade policy implications, the result illustrates that stocks only respond to trusted information and ignore particularly noisy signals. Finally, I verify that exporters’ stock movements (relative to non-exporters) are exceptionally large following momentous and surprising events and are therefore suitable for measuring the size of unanticipated trade policy shocks.

To summarize, this paper makes two main contributions: First, a new data set of trade policy

4_{Trade-exposed firms may also be adversely affected by foreign competition, but this effect seems to} be offset by the benefits of higher revenues generated abroad as a consequence of trade liberalizations.

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statements made by the US and its trade partners during the last twelve years is presented. Its detailed information allows to contrast the effects of liberalizations and protectionism, export and import barriers as well as announcements and implementations. Second, to the best of my knowledge, this is the first paper to demonstrate the suitability of combining a narrative approach with stock market data to identify trade policy shocks.

The rest of this paper is structured as follows: Section 1.2describes the novel trade policy data set based on official government sources and presents an alternative that relies on Trump’s trade-related tweets. Section 1.3 elaborates on building stock indices based on trade exposure, which are then used in Section 1.4 to analyze average returns around different types of trade policy statements. Section 1.5 outlines those events that elicit particularly large market movements and

Section 1.6 concludes.

1.2 Trade policy data sets based on official statements and

tweets

The first part of this section describes the information contained in the novel data set based on official trade policy statements and presents some descriptive statistics. The second part presents an alternative data set based on Trump’s tweets on trade.5 Information transmitted through these communication channels differ in the timing of their release, degree of concreteness and potentially their credibility. Which of these sources has a larger impact on market participants is an interesting question that will be explored later on in Section 1.4.

1.2.1 Official trade policy statements

To allow for a high-frequency identification of the effects of trade policy, official statements issued by the US and its trade partners are recorded for every day starting on 1 January 2007 and ending on 31 August 2019. The fact that the data series starts eleven years before President Trump

5_{Both data sets will shortly be available for download from my}

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took office provides the opportunity for future research to contrast the potentially asymmetric effects of trade liberalization and protectionism.

The dominant source of information until 2016 are press releases of the Office of the United States Trade Representative (USTR) which is the government agency charged with developing trade policy, advising the president and conducting trade negotiations.6 From 2016 onward, USTR statements are complemented by publications of other US Executive Branches such as the White

House, the Department of Commerce and the International Trade Commission.7 Additionally,

newspaper articles from Bloomberg, the Financial Times and Reuters are consulted in case they precede official statements or provide complementary information.8 Information on trade part-ners’ policy actions towards the US are mostly taken from press releases of the USTR but are supplemented with the respective national sources (e.g. the Chinese Ministry of Commerce).

The dataset contains the following variables:

1. tp dummy: A trade policy dummy which equals 1 on days on which trade policy-relevant statements were issued and 0 otherwise.

2. positive: A categorization into trade liberalizing proposals (positive=1) if they imply lower barriers to trade (e.g. through trade talks, trade agreements or WTO reforms) or protec-tionist statements (positive=0, e.g. the imposition of tariffs). However, on some days the classification is not straightforward (positive=0.5), either because a statement is neutral, contains mixed signals or because liberalizing and protectionist statements were issued on the same day.9 Example statements for each of the three categories can be found in Ap-pendix A.1.

6_{Archived press releases (2007-2017) of the USTR can be found here} _{USTR(2007-2017)}_{whereas more} recent statements are available hereUSTR(2017-Today)An extension of the data set back to the first avail-able statement in 2001 is planned but may lead to less reliavail-able results as statements become increasingly patchy.

7_{Additional sources include the Departments of State and Agriculture, the US Customs and Border} Protection, the Federal Register and the Department of the Treasury

8_Statements _made _during _Trump’s _presidency _are _also _{cross-checked} _with _piie.com _and

shenglufashion.com

9_{An example of a common neutral statement would be filings of WTO dispute complaints against} trade partners. On the one hand, these could lead to the removal of trade barriers but on the other hand, they could prompt foreign retaliation and could turn into the beginning of a trade war.

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3. majortp: An indicator of the importance of the policy statement for the US economy, which can take three values: 1 if the statement has major implications, 0.5 for minor implications, and 0 for close to irrelevant statements. The classification ”major” is assigned if either one of the major US trade partners (Canada, Mexico, China, the EU, Korea, Japan or the UK) or a group of at least five trade partners is involved and if a large amount of goods is explicitly or implicitly affected by a drastic change in trade policy, for example through tariffs or a trade agreement.10 Close to irrelevant statements are for example, those on recurring reports examining trade partners’ currency practices or WTO compliance without mentioning changes to the status quo.

4. To pinpoint why major statements have potentially larger effects, I introduce two additional categories. many tradepartners=1, if more than five trade partners are involved, e.g., at the WTO level or during trade negotiations. Moreover, many goods=1, if a list of traded goods with more than five products is explicitly mentioned.11

5. tpimplem dummy indicates whether the statement is an ”announcement” (=0) notifying the public of potential future policy changes or an ”implementation” (=1) marking the day on which policies are formally approved (e.g., signing of trade agreements) or go into effect.

6. against us=1 if a statement was issued by a trade partner targeting the US (e.g., through tariff increases or decreases). This variable equals 0 not only for days with US statements but also for bilateral trade talks, multilateral WTO meetings or simultaneous protectionist measures by the US and its trade partners.

7. In contrast, us only=1 if the US imposes or removes barriers unilaterally without trade partners reciprocating on the same day.

10_{An alternative approach that I will explore in the future is to classify major statements based on} traded volumes potentially affected and/or based on the importance of targeted goods for domestic and foreign production networks.

11_{Note that some statements classified as ”major” fall in neither of the two categories if many products} are potentially affected but not explicitly mentioned or if less than five major trade partners are involved.

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8. product records which goods are affected by the trade policy measure, in case this information is available.

9. trade partner contains the trade partners involved.

10. china involved takes a value of 1 if China is targeted by the US or vice versa and equals 0 otherwise.

11. event description contains the statement or a description of the policy.

12. Finally, trade policy statements are further categorized following Lu (2020). If the trade pol-icy consists of a trade remedy measure such as an antidumping measure, a countervailing duty or a safeguard measure trade remedy measures equals 1 and 0 otherwise. trade agreement signals whether a statement reflects initial or final steps towards a trade agreement. wto in-dicates the involvement of the World Trade Organisation (WTO). personnel shows whether the statement concerns a personnel decision, e.g., the designation of a new US Trade Repre-sentative.

Out of the 3180 working days in the sample, official trade policy statements have occurred on 861 days - 105 of these were ”major”. Moreover, 17 days contain major statements issued unilaterally by trade partners and 25 days saw releases by the US only, i.e., without any dissemi-nation by trade partners. Furthermore, 30% of the major entries refer to policy implementations and the rest to announcements. Figure 1.1 depicts the number of protectionist and liberalizing statements per month and Figure 1.2 shows the same information but for major statements only. As expected, the frequency of major statements has increased under President Trump, and their tone has become substantially more protectionist. The spike in major liberalizing statements in February 2016 coincides with the signing of the Trans-Pacific Partnership agreement (TPP). The hike in major protectionist statements in the first halves of 2018 and 2019 corresponds to the tit-for-tat tariff war with China. This period was only briefly interrupted by a liberalizing phase at the end of 2018 and in early 2019, when the US-Mexico-Canada Free Trade Agreement was

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signed and trade talks with China were held. Appendix A.2 provides a more in-depth timeline of major trade policy events.

|

{z

}

Bush

|

Obama

{z

}

T rump

z

}|

{

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Bush

z

}|

{

Obama

z

}|

{

T rump

z

}|

{

Figure 1.2: Number of monthly major trade policy statements by sign.

The top 10 most commonly mentioned trade partners in US statements remain the same before and after Trump’s inauguration but their relative importance changes.12 In the first part of the sample, members of the Trans-Pacific Partnership and the EU – due to the planned T-TIP agreement – were most frequently involved. After January 2017, China and Mexico became the most common targets of statements. Another difference is the gap between initial announcements and implementations before and after 2017. This observation is at least partly due to liberalizing actions dominating the Bush and Obama era. These tend to be based on several rounds of bilateral negotiations, whereas unilateral protectionist measures are implemented more swiftly. As a result, the impact of implementing liberalizing policies may have a lower surprise component for investors – a conjecture that will be confirmed in Section 1.4.

The detailed categorization of statements allows for a more nuanced analysis than traditional

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event studies by acknowledging the heterogeneity across different trade policy types. However, this paper’s empirical analysis will only make use of a subset of variables provided in the data set. Future research may exploit the supplementary information. For example, knowledge of the type of goods and trade partners affected may be used to understand the role of elasticities in shaping dead-weight losses from tariffs and track the transmission of policy changes through production networks. Ultimately, these insights should allow policymakers to fine-tune their trade policy strategies and evaluate the true costs and benefits of different approaches.

1.2.2 Tweets on trade policy

With the start of Donald Trump’s presidency the social media platform Twitter has become an alternative channel for communicating policy intentions related to trade. In order to compare the power of tweets over market expectations to the influence of official statements, I create a second data set. First, I download all tweets issued by Donald Trump (@realDonaldTrump) that mention at least one of the words ”trade”, ”tariff”, ”China” or ”NAFTA”.13 I then eliminate those that are off-topic (i.e. unrelated to trade policy) and follow Bianchi et al. (2019) by discarding re-tweets.14 Furthermore, I only keep tweets that were re-tweeted at least 20,000 times. This ensures that the announcement received an above-average amount of public attention, and it may also reflect that the disseminated information had a news component that followers had the urge to share.15 Second, I indicate whether a tweet conveys a protectionist tone (positive=0) or signals trade liberalisation (positive=1), e.g., by mentioning trade negotiations with China or striking the new US-Mexico-Canada (USMCA) trade deal. If tweets are neutral or cannot be classified unequivocally, positive=0.5. An example tweet for each category can be found in Appendix A.1. In the subsequent analysis, I do not use unclassified tweets and I drop observations with several tweets on the same day if they have opposing signs or if one of the multiple tweets cannot be classified.

13_{Tweets were downloaded via} _{http://www.trumptwitterarchive.com/archive}_.

14_{Re-tweets are tweets that were not originally published by Donald Trump but shared by him from} someone else’s account.

15_{On average, Trump’s tweets which contain one of the keywords mentioned above are re-tweeted 10,815} times. After dropping tweets with 20,000 re-tweets or less, the new mean becomes 26,570.

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After discarding unclassified tweets, the data set contains 99 observations, two-thirds are pro-tectionist, and one-third is liberalizing. Interestingly, the share of liberalizing proclamations is higher among Trump’s tweets (33%) than among official statements (24%) after Trump took of-fice. The first tweet in the sample occurred on 26 July 2016 and the last on 19 August 2019. As depicted inFigure 1.3, the frequency of tweets was initially low but picked up at the beginning of 2018, similar to the dynamics of official statements.16

Figure 1.3: Number of monthly trade-related tweets by D. Trump.

On 20 days, both communication channels were used simultaneously and for all of them the categorization into liberalizing and protectionist declarations matches. However, tweets and offi-cial statements overlap thematically, e.g., in terms of trade partners mentioned, in only 13 cases.

Table A.1 compares these observations in order to highlight the differences in tone and commu-nication style. The most striking feature of Trump’s tweets compared to official releases is their unspecificity, simple language and appeal to emotion. As a result of potentially lower credibility

16_{Note that although tweets are plotted until the end of 2019 only those until 31 August 2019 are used} later since my series of stock prices for exporting and non-exporting firms ends then.

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and a larger noise component, tweets might provoke weaker stock market reactions than official statements. However, this may not be true if Trump tweets are more closely followed by the public than USTR press releases. Section 1.4 compares the importance of the two information sources for stock price movements.

1.3 Stock indices by trade exposure

Another building block of my analysis is daily stock market data.17 Specifically, the S&P 500 index helps me gauge the effect of official statements and tweets on the aggregate stock market. In addition, I exploit two stock baskets constructed by Goldman Sachs and provided by Bloomberg. On the one hand, I use their “International Sales basket” which is used to construct a stock market index based on the 50 S&P 500 companies with the highest international sales share (henceforth “exporters”). According to Bloomberg, the international portfolio contains companies from eleven different sectors, and the median firm derives 71% of its revenues from abroad compared with 27% for the median S&P 500 company. In addition to being affected by export barriers set by trade partners, these firms’ stocks may also react to US statements due to anticipation of foreign reciprocation. On the other hand, I rely on the “Domestic sales basket” based on the 50 S&P 500 stocks with the highest domestic revenue exposure. The median firm in this basket generates 100% of its revenues domestically, whereas for the median S&P 500 company, the share is 73%. Even though Goldman Sachs does not publicly disclose the full list of firms that make up each basket, examples can be found in Appendix A.4.

To ensure that the following results are robust to using an alternative definition of trade ex-posure, I build a new importer stock price index. Importing firms’ profits and stock prices may be impacted via changes in the cost of input factors. These changes can occur directly through

17_{I would have liked to use stock market data at a higher frequency, but I did not have access to such} data. Besides, I only know the day but not the exact time at which trade policy statements were issued. Furthermore, Wagner et al. (2018) have documented the time lag with which the stock market responds to trade policy changes. This finding stands in contrast to monetary policy decisions for which the planned time of announcement is roughly known ex-ante, and hence investors may react more quickly to policy changes.

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modified import barriers (e.g., tariffs) or, indirectly, through trade partners’ unilateral actions that are expected to elicit retaliation by the US. The basket of importers is constructed based on the Hoberg-Moon Offshoring data set (Hoberg and Moon (2017)), which records how often firms mention purchasing inputs from abroad in their 10-K financial statements.18 For each year, I pick the top 50 importers.19 These firms are matched to their daily stock prices taken from the Center for Research in Security Prices (CRSP). An index is then built by weighing each firms’ stock price by their import intensity.20 Finally, I calculate the ratio of importers’ and non-importers’ indices where the latter consists of firms that have never imported during 2007-2015.

According to the information contained in the Hoberg-Moon data set, importing firms also tend to be eager exporters. Hence, I expect both types of globally active firms to react to changes in export barriers (imposed by trade partners) as well as import barriers (imposed by the US). Furthermore, trade-exposed (importing or exporting) firms should respond in the opposite way to the respective non-trade-exposed ”control group” or should at least be more affected by trade policy. To test this conjecture, I use the ratio of both types of firms’ stock indices.21

1.4 Average stock market effect of different trade policy

types

In the following, I analyze average market movements in the short window around trade policy statements. Responses across different types of statements are explored to determine whether stock returns can capture qualitative differences as well as anticipation effects. InSection 1.4.1, I consider liberalizing and protectionist statements separately and I also distinguish between the effects of statements issued by the US and its trade partners. Moreover, in Section 1.4.2, heterogeneous

18_{A 10-K financial statement is a detailed financial report that public companies have to submit to the} US Securities and Exchange Commission (SEC).

19_{Given that 2015 is the last year in the Hoberg-Moon data set, I assume that the import intensities} for 2016-2019 are equal to the average of the last two years in the sample.

20_{I also checked that my results are robust to using simple averages and weighing stock prices by firms’} market capitalization.

21_{When attempting to identify trade policy shocks, using this ratio is superior to relying the stock price} of exporters as it may better capture the ”pure” effects of trade purged of other confounding news.

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responses to trade policy announcements and implementation are analyzed. Finally, Section 1.4.3

explores whether investors are only influenced by official sources or also react to Trump’s tweets on trade policy.

Four results stand out: First, only trade liberalizations initiated by the US increase the return on the S&P 500, whereas all other types of statements elicit no systematic response. Second, dis-tinguishing S&P 500 firms by trade exposure, I find that, in line with prior expectations, exporters and importers benefit from liberalizations and are harmed by protectionism. The opposite holds for firms that only operate domestically. Third, besides having the ”correct” sign, stock returns also reflect the extent to which trade policy statements are anticipated. The value of exporting firms falls significantly after the US issues a protectionist statement that should only affect import barriers. Hence, investors seem to expect trade partners to retaliate. Alternatively, the effect may also be driven by the fact that exporting firms are also likely to be importers. In addition, imple-mentations of liberalizing policies seem to be anticipated because lengthy multilateral negotiations often precede free trade agreements. In contrast, protectionist implementations contain an element of surprise. Fourth, trade-exposed firms’ stocks rarely react to tweets, and hence only respond to trusted official information and ignore particularly noisy signals.

1.4.1 Liberalizing and protectionist policy changes

In this subsection, I only use statements made via official channels (e.g. press releases by the Office of the Trade Representative) without distinguishing announcements from implementa-tions. For gauging the aggregate stock market effects of protectionist and liberalizing trade policy statements, I estimate the following simple linear equation by OLS:

t+1

X

x=t

SP 500retx = α + β1T P_t⊕+ β2T Pt+ ψzt+ t, (1.1)

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day of the statement release (t) and the day after (t + 1).22 This variable is regressed on two trade policy dummies, T P_t⊕ and T P_t, which equal 1 on days with major liberalizing or protectionist statements, respectively, and 0 if no statement was issued or if it could not be classified. Finally, zt denotes a vector of controls. In particular, I include dummies for other macroeconomic news,

namely monetary policy decisions by the Fed as well as the publication of CPI, PPI or labor market statistics by the BLS.23 Furthermore, I control for the state of the business cycle by including the spread between 10-Year and 3-Month Treasury bills. In addition, I include the oil price, the effective federal funds rate and the broad US dollar index since movements in these variables may affect stock prices and policymakers’ tendency to enact trade policy changes.24 Standard errors are calculated based on Newey and West (1987) to correct for heteroscedasticity and serial correlation. Results are presented in Table 1.1. As the first column shows, neither protectionist nor lib-eralizing statements affect the aggregate stock market index. The reason is that the S&P 500 represents the net effect on ”winning” and ”losing” firms that offset each other. Distinguishing S&P 500 firms by trade exposure reveals heterogeneous responses and shows that stock returns for ”treated” firms behave as expected. To illustrate this, I calculate the cumulative change in the ratio of exporters’ and non-exporters’ stock indices over t and t + 1 and use this series instead of the S&P 500 in Equation (2.3).25 The S&P 500 index is added as regressor to purge the results

22_{For each day, the cumulative return on the S&P 500 is calculated as}

t+1 X x=t SP 500retx= t+1 X x=t SP 500x− SP 500x−1 SP 500x−1 .

For simplicity, it will be denoted by ∆S&P 500 in subsequent regression tables. To verify robustness, the time window over which returns are cumulated is extended to three days, i.e. [t, t+1, t+2], and shortened to include just the day of the event. Since I only know the day but not the exact time at which trade policy statements were issued, using higher frequency stock market data would not have improved the accuracy of my results. Furthermore, Wagner et al. (2018) have identified a substantial time lag with which the stock market responds to trade policy changes.

23_{This data was downloaded from:} _{fraser.stlouisfed.org} _{and Haver Analytics.}

24_{Changes in these control variables may also affect the stock price of trade-exposed and non-exposed} firms differently, and hence alter their ratio.

25_{The cumulative change in the ratio of stock indices is calculated as}

t+1 X x=t Ratio retx= t+1 X x=t PEX x PDOM x − Px−1EX PDOM x−1 PEX x−1 PDOM x−1 ,

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of pure market movements.26 Column (2) shows that liberalizing trade policy statements benefit exporters relatively more than domestically oriented firms, and vice versa for protectionist state-ments.27 However, the magnitude of responses is small and lies below one standard deviation for both types of policy changes. Thus, there are potentially more important factors than trade policy that influence profits of exporters and domestically oriented firms and that momentous reforms are necessary to generate large movements in returns (see Section 1.5).

Table 1.1: The stock market effect of liberalizing and protectionist official trade policy statements

(1) (2) (3) (4) (5) ∆S&P 500 ∆Pexporter P- non-exporter ∆Pexporter ∆P - non-exporter ∆ Pimporter P- non-importer T P_t (Protectionist statement) -0.162 -0.371∗∗∗ -0.203∗∗ 0.149∗ -0.0683 (-0.72) (-3.60) (-2.96) (2.31) (-0.57) T P_t⊕ (Liberalizing statement) 0.188 0.443∗∗∗ 0.318∗∗∗ -0.115∗ 0.493∗∗ (0.78) (4.32) (3.66) (-2.13) (2.85) Constant 0.902 0.534 0.800∗ 0.358 1.887∗∗ (0.81) (1.17) (2.06) (1.08) (2.71) Observations 3140 3140 3140 3140 3075

Controls Yes Yes Yes Yes Yes

t statistics in parentheses.

∗ _{p < 0.05,}∗∗ _{p < 0.01,}∗∗∗ _{p < 0.001.}

Notes: Trade policy dummies, T P_t⊕and T P_t, equal 1 on days with major liberalizing or protectionist statements, respectively, and 0 if no statement was issued or if it could not be classified. Dependent variables are cumulative for t0 and t1: (1) return on the S&P 500 index, (2) change in the ratio of the stock price indices of exporting and non-exporting firms, (3) excess return on the stock price index of exporters, (4) excess return on the stock price index of non-exporters, (5) cumulative change in the ratio of the stock price indices of importing and non-importing firms. Controls include the oil price, the federal funds rate, the broad USD index (exchange rate), the spread between 10-Year and 3-months Treasury Bills and dummies for other macroeconomic news (FOMC meetings, BLS announcements of inflation and employment statistics). In (2) and (5) the cumulative return on the S&P 500 is included as additional control. Standard errors are calculated based on Newey-West.

where P_xEX and P_xDOM are the stock indices of exporting and non-exporting (domestically oriented) firms, respectively. For simplicicity, the whole term will be denoted by ∆Pexporter

P-

non-exporter in subsequent regression tables.

26_{Trade-exposed firms tend to move more strongly with the aggregate stock market than domestically} oriented firms.

27_{Regressing the cumulative change in the ratio on leads and lags of the trade policy dummies, I verified} that market movements do not already occur before statements are issued.

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To pinpoint whether the changes in the stock price ratio are driven by the numerator or the denominator, I calculate the excess return on exporters’ and importers’ baskets over the return on the S&P 500 index based on a standard market model. More precisely, for each of the two types of firms, using data for 2006 I estimate

rett = α + βSP 500rett+ et. (1.2)

Excess returns for each day in the period 2007-2019 are then obtained from

[

exesst= rett− ˆα − ˆβSP 500rett. (1.3)

This approach ensures that general market movements cause none of the return variation. Columns (3) and (4) show that excess returns on the two stock baskets are pushed in opposite directions. Thus, in case of liberalizations, exporters benefit from easier access to foreign markets and poten-tially cheaper foreign inputs.28 In contrast, non-exporting firms seem to be adversely affected by increased competition from abroad, resulting from lower trade barriers.

A similar conclusion can be drawn when using the cumulative change in the stock price ratio of importing and non-importing firms (Table 1.1, column (5)). The former benefit relatively more from lower trade barriers than the latter. Again, this effect is partly due to the fact that many importers also export. However, entering the two types of stock indices as separate regressands yields no significant results, and the coefficients are therefore not displayed.

Next, I differentiate positive and negative statements further into those released by the US, by its foreign partners as well as joint statements. Thus, changes in import or export barriers can have different stock market effects. I include six separate dummy variables inEquation (2.3), i.e.

t+1

X

x=t

SP 500retx =α + β1T Pt⊕,U S+ β2T Pt⊕,F oreign+ β3T Pt⊕,J oint

+ β4T Pt,U S + β5T Pt,F oreign+ β6T Pt,Joint+ ψzt+ t,

(1.4)

28_{As mentioned earlier, exporting firms are more likely to import than non-exporters (see also Bernard} et al. (2009)).

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where the dependent stock market variables are rotated in, as above. Each of the dummies takes the value 1 if the respective statement type was released and equals 0 otherwise. Table 1.2

summarizes the results. Column (1) shows that, on average, liberalizing statements issued unilat-erally by the US lead to a cumulative return on the S&P 500 over two days of 1.42%, which is less than one standard deviation. It remains unclear which types of firms drive this result. Other types of statements have no significant effect on the S&P 500.

Table 1.2: The stock market effect of liberalizing and protectionist official trade policy statements, distinguishing by initiating party

(1) (2) (3) (4) (5) ∆S&P 500 ∆Pexporter P- non-exporter ∆Pexporter ∆P - non-exporter ∆ Pimporter P- non-importer

T P_t⊕,U S (Liberal., US) 1.419∗∗∗ 0.617 0.113 -0.419∗ -0.419

(3.30) (1.43) (0.38) (-2.45) (-0.56)

T P_t,U S (Protection., US) -0.295 -0.449∗∗ -0.211∗ 0.207 -0.315∗

(-0.89) (-2.74) (-2.24) (1.88) (-2.07)

T P_t⊕,F oreign (Liberal., partner) -0.619 0.6∗ 0.580 -0.133 0.445

(-0.48) (2.33) (1.38) (-0.76) (0.76)

T P_t,F oreign (Protection., partner) 0.0997 -0.353 -0.221 0.128 -0.0879

(0.31) (-1.47) (-1.50) (1.14) (-0.24)

T P_t⊕,J oint (Liberal., joint) 0.217 0.399∗∗∗ 0.307∗∗∗ -0.0788 0.565∗∗

(0.92) (3.45) (3.35) (-1.36) (3.25)

T P_t,Joint (Protection., joint) -0.356 -0.338 -0.0564 0.256∗ 0.241

(-0.46) (-1.94) (-0.34) (1.99) (0.99)

Constant 0.985 0.576 0.816∗ 0.337 1.815∗∗

(0.88) (1.26) (2.11) (1.02) (2.62)

Observations 3140 3140 3140 3140 3075

∗ _{p < 0.05,}∗∗ _{p < 0.01,}∗∗∗ _{p < 0.001.}

Notes: See Table 1.1. Independent variables: Trade policy dummies indicating whether or not a liberalizing or protectionist statement was issued by the US, its trade partners or jointly.

Focusing on columns (2) and (3), we see that only protectionist actions announced or imple-mented by the US significantly reduce exporters’ stock prices. This finding may seem

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counter-intuitive at first since US statements should not directly influence export barriers. However, in light of the fact that exporting firms also tend to import intermediate inputs, which will become more expensive as a consequence of US protectionist policies, the result is plausible. Moreover, since the US has often been the first mover regarding trade policy changes, investors seem to ex-pect trade partners to retaliate such that US exporting firms are harmed already earlier. Indeed, foreign protectionist statements against the US seem to be anticipated as they do not trigger a significant response. In addition, US tariffs may weaken the Chinese economy implying lower expected profits for US exporters. Another interesting result that emerges from column (3) is that only liberalizations that are jointly proclaimed raise exporters’ valuation. Three factors may drive this result. First, joint liberalizations often involve ”deep” trade agreements that lead to lower tariff and non-tariff barriers (import quotas, subsidies, customs delays and technical barri-ers). Moreover, in contrast to unilateral liberalizations, multilateral agreements often lower export and import barriers simultaneously. Since many exporters are importers, these firms benefit dis-proportionately. Second, multilateral liberalizations may be more credible than those announced unilaterally. The third reason is rather mechanical and stems from the fact that there are more data points for joint liberalizations than for unilateral ones or joint protectionist actions.

Column (4) shows that, as expected, the stock price of domestically focused firms declines after US liberalizations due to increased competition from foreign firms entering the US market. Another indirect effect may contribute to the result: lower trade barriers may encourage exporters to hire putting upward pressure on domestic wages.29 As a consequence, non-exporters are harmed without being compensated by the benefits of reduced tariffs.

Finally, the coefficients in column (5) demonstrate that importers react similarly to exporters: US protectionist actions significantly reduce their stock price relative to non-importers due to higher procurement costs. Joint liberalisations have the opposite effect.

To summarize, stock returns distinguishing by firms’ trade exposure correctly reflect the direc-tion of trade policy changes. The above provides some evidence that equity movements indicate

29_{However, this may partially be offset if liberalizations provide exporters with enhanced opportunities} for off-shoring production.

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whether or not trade reforms are anticipated. The next subsection provides additional proof for this claim.

1.4.2 Trade policy announcements versus implementations

Almost all trade policy changes in my sample only take effect after they have been previously announced. In this subsection, I show that stock returns reflect certain anticipatory effects. For that purpose, I split the two dummy variables in Equation (2.3) further into announcements and implementations.30 The results in Table 1.3 offer interesting insights. First, the stock price of importers and exporters increases significantly relative to their non-trade-exposed counterparts only if liberalizations are announced but not when they are implemented (columns (2) and (5)). Hence, there is evidence that investors anticipate trade liberalizations. Second, both protectionist announcements and implementations harm exporters relatively more than non-exporters. While the former is driven by falling stock prices of exporters, the latter is due to non-exporters benefiting from barriers to foreign competition. The observation that implementations of liberalizing policies are anticipated but protectionist actions contain an element of surprise is intuitive. Both types of policies differ in their implementation lag. For example, the Trans-pacific Partnership (TPP) – one of the major liberalizing events in my data set – was implemented more than ten years after launching the initial negotiations. In contrast, most of President Trump’s protectionist policies took effect only a few weeks after their announcement providing the market with less detailed information and time to ”digest” the news. Also, since protectionist actions tend to be taken unilaterally, they are more prone to abrupt revisions upon implementation, which may surprise investors.

These results have important implications for event studies trying to determine the causal macroeconomic effects of trade policy changes. Anticipation effects imply that firms and households will have already changed their behavior before the reform takes effect. Consequently, future studies, e.g. on NAFTA, should fix the event window based on early announcements. However,

30_{A further distinction by issuing party (i.e., US or trade partner) could be made but at the expense} of statistical significance due to fewer observations in each category.)

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one should keep in mind that the previous results hold on average and that certain implementations of liberalizations may still influence the ratio of stock indices as Section 1.5 shows. Furthermore, the fact that relative stock prices of exporting and non-exporting firms seem to reasonably capture surprises in trade policy make them an ideal tool for identifying trade shocks, which is exploited by Bonk and Larkou (2020).

Table 1.3: The stock market effect of liberalizing and protectionist official trade policy statements, differentiating announcements and implementations

(1) (2) (3) (4) (5) ∆S&P 500 ∆Pexporter P- non-exporter ∆Pexporter ∆P - non-exporter ∆ Pimporter P- non-importer T P_t⊕,Announcement 0.183 0.502∗∗∗ 0.370∗∗∗ -0.121 0.563∗∗ (Liberalizing announcement) (0.60) (4.43) (3.74) (-1.91) (2.97) T P_t⊕,Implementation 0.198 0.299 0.191 -0.0999 0.310 (Liberalizing implementation) (0.52) (1.45) (1.21) (-0.96) (0.99) T P_t,Announcement -0.0573 -0.371∗∗ -0.216∗ 0.141 -0.0887 (Protectionist announcement) (-0.25) (-2.87) (-2.51) (1.78) (-0.61) T P_t,Implementation -0.512 -0.371∗∗∗ -0.155 0.177∗∗ -0.00108 (Protectionist implementation) (-0.94) (-3.43) (-1.74) (2.72) (-0.01) Constant 0.886 0.544 0.811∗ 0.358 1.901∗∗ (0.80) (1.19) (2.09) (1.08) (2.73) Observations 3140 3140 3140 3140 3075

∗ _{p < 0.05,}∗∗ _{p < 0.01,}∗∗∗ _{p < 0.001.}

Notes: See Table 1.1. Independent variables: Trade policy dummies indicating whether or not a liberalizing or protectionist statement was issued and whether it was a preliminary announcement or policy implementation.

1.4.3 Official news channels versus tweets

During Donald Trump’s presidency, official trade policy communication channels have become complemented by messages released via the social media platform Twitter. As explained in Sec-tion 1.2.2, comparing the content transmitted via both channels reveals the lack of specificity,

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simple language and larger noise component of tweets. The question that arises is whether stock returns reflect these differences. Answering this question may also provide valuable knowledge about President Trump’s power to single-handedly shape public opinions via social media as well as insights into optimal communication strategies for policymakers in general.

I include official statements and trade-related tweets published by Donald J. Trump simul-taneously in Equation (2.3). At first, I pool protectionist and liberalizing statements. The two variables take the value of 1 if a liberalizing policy change was suggested via the respective commu-nication channel, -1 if the tone was protectionist and 0 otherwise. Hence, the coefficients displayed inTable 1.4should be interpreted as the impact of a liberalization, assuming that the effect across both types of policies is linear. Since some of the President’s tweets simply mention and comment on foreign trade actions, I use all official statements, i.e., those issued by the US and its trade partners, for comparability. Unlike in the previous sections in which I use data from 2007 onward, the sample now starts in 2017 when President Trump took office.31

Estimates of the effect of official policy statements are qualitatively in line with previous sec-tions. Strikingly, once official statements are included as regressors, tweets have no significant effect on any of the dependent stock market variables. This conclusion only changes slightly when differentiating between liberalizing and protectionist policy changes, i.e., official statements and tweets are split, and four separate dummy variables are included in the regression.32 Table 1.5

shows that liberalizing tweets have a positive and significant (at the 10% level) effect on the ra-tio of exporters to non-exporters (column (2)). However, it can not be determined whether this result is driven by the numerator or the denominator of the ratio since excess returns on neither exporters nor non-exporters are significantly affected by tweets (columns (3) and (4)). Moreover, tweets implying lower trade barriers also significantly raise the stock price ratio of importers to non-importers, whereas official statements no longer have a significant effect. As a robustness check, I weight liberalizing and protectionist tweet dummies by the number of re-tweets.33 This

31_{Hence, this subsection also provides information on whether some of the previously uncovered effects} of trade policy on the stock market hold if only Trump’s presidency is considered.

32_{As in the previous sections, these dummies only take the value of 1 or 0 and hence, a negative} coefficient on a protectionist dummy implies falling stock prices.

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should reflect the public attention they received and potentially their degree of novelty. The above results remain unchanged.

Overall, there is some evidence that investors continue to respond to news obtained from government agencies despite the rise of novel communication channels. Exporters’ unresponsive stock returns reflect that tweets do not provide additional information.

Table 1.4: The stock market effect of official statements and tweets

(1) (2) (3) (4) (5) ∆S&P 500 ∆Pexporter P- non-exporter ∆Pexporter ∆P - non-exporter ∆ Pimporter P- non-importer T P_tall (Official) 0.241 0.321∗∗∗ 0.211∗∗∗ -0.105∗ 0.252∗ (1.17) (3.92) (3.62) (-2.00) (2.33) T weetall_t 0.186 0.0697 0.0655 -0.00435 0.182 (1.19) (0.86) (0.93) (-0.09) (1.96) Constant 2.389 0.258 1.013 0.709 3.364 (0.84) (0.14) (0.87) (0.73) (1.75) Observations 646 646 646 646 599

∗ _{p < 0.05,}∗∗ _{p < 0.01,}∗∗∗ _{p < 0.001.}

Notes: SeeTable 1.1. Independent variables: Trade policy and tweet variable indicating, respectively, whether an official statement (T P_tall) or a trade-related tweet by D. Trump (T weetall_t ) was issued. The two variables take the value of 1 if a liberalizing policy change was suggested via the respective communication channel, -1 if the tone was protectionist and 0 otherwise.

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Table 1.5: The stock market effect of protectionist and liberalizing official statements and tweets (1) (2) (3) (4) (5) ∆S&P 500 ∆Pexporter P- non-exporter ∆Pexporter ∆P - non-exporter ∆ Pimporter P- non-importer

T weet_t (Protectionist tweet) 0.248 -0.0417 0.0170 0.0586 0.0513

(1.54) (-0.44) (0.26) (1.05) (0.48)

T weet⊕_t (Liberalizing tweet) 0.136 0.287∗ 0.174 -0.112 0.396∗

(0.63) (2.27) (1.95) (-1.51) (2.47) T P_t (Protectionist statement) -0.161 -0.217∗ -0.173∗ 0.0352 -0.126 (-0.86) (-1.97) (-2.23) (0.54) (-1.35) T P_t⊕ (Liberalizing statement) 0.443 0.605∗∗ 0.309∗ -0.303∗∗ 0.586 (1.37) (3.19) (2.31) (-2.70) (1.83) Constant 2.476 0.498 1.114 0.567 3.605 (1.14) (0.39) (1.24) (0.75) (1.91) Observations 646 646 646 646 599

∗ _{p < 0.05,}∗∗ _{p < 0.01,}∗∗∗ _{p < 0.001.}

Notes: See Table 1.1. Independent variables: Dummies taking the value of 1 or 0 depending on whether a liberalizing (T P_t⊕) or protectionist (T P_t) official trade policy statement was issued and/or a liberalizing (T weet⊕_t) or protectionist (T weet_t ) trade-related tweet was released by D. Trump.

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1.5 Key trade policy events (2007-2019) according to the

stock market

Section 1.4 confirmed that firms’ trade exposure determines the equity impact of trade policy events. In particular, returns on exporting and non-exporting firms’ stocks move in opposite direc-tions such that their ratio increases after liberalizing policy statements and decreases in response to protectionist messages. This section verifies whether changes in the stock price ratio accurately reflect momentous trade policy changes, i.e., those that are particularly surprising, involve a large share of traded goods or substantial tariff changes. If this is the case, we can be more confident that equity data can correctly capture the size of trade policy shocks.

Figure 1.4 depicts the ten most impactful liberalizing and protectionist events as measured by the 2-day change in the ratio of exporters’ and non-exporters’ stocks. The ”most liberalizing” day between 2007 and 2019 is October 21, 2011 which contains two events: First, Mexico announced to suspend its last retaliatory tariffs it had imposed in response to the US blocking Mexican trucks from entering the country for several years. Second, Obama signed trade agreements with Korea and several Latin American countries into law. Both policy steps combined resulted in a 1.77% cumulative increase in the stock price ratio which represents 2.4 standard deviations. The second most beneficial day for US exporters marked the signing of the revised NAFTA (now: US-Mexico-Canada (USMCA) Trade Agreement) in 2018. Since Mexico and US-Mexico-Canada are among the largest trade partners and combined account for more than a quarter of US imports and exports, this result is in line with prior expectations. Table A.4 inAppendix A.5lists the other eight important liberalizing events together with movements in the cumulative stock price ratio and the S&P 500. Strikingly, negotiations towards an updated NAFTA show up a second time when the US and Mexico reached a preliminary agreement without Canada, leading to a slightly weaker response in the stock price ratio than the final signature. Furthermore, negotiations of the Trans-Pacific Partnership (TPP) Agreement (from which President Trump later withdrew) appear three times among the most liberalizing events. This seems reasonable considering that TPP would have been

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the largest new trade agreement of the last decade (member countries account for 40% of global GDP), which was estimated to increase US incomes by 0.5% of GDP and US exports by 9.1% (Peterson Institute, 2016).

The two ”most protectionist” days are those on which Donald Trump was announced to have won the US election and the UK voted to leave the EU implying higher future barriers to trade with the US. The fact that both decisions caught the public by surprise explains at least parts of the large stock price response.34 Furthermore, Trump’s election was the first occasion on which investors realized that the US stance on free trade might change since Trump’s campaign promise had been to withdraw from or re-negotiate existing trade agreements.35

Table A.5 ranks the remaining key protectionist events. These are almost exclusively tariff threats and implementations that were part of the US trade war against China, the EU and Canada, starting in early 2018. Again, the change in the ratio reasonably pinpoints events that one would have chosen to be most crucial based on common sense.36 In line with my findings from

Section 1.4, the S&P 500 shows that protectionist events are not detrimental to all firms. After Trump’s election, for example, the S&P 500 increased as high-tax firms and those operating only domestically were hoping for policy changes in their favor (see Wagner et al. (2018)).

Although not all events are depicted in Figure 1.4, it becomes apparent that other forces beside trade policy (e.g. oil price and exchange rate movements) drive the stock price ratio. This highlights that identifying meaningful trade policy shocks, requires combining stock price data with a narrative approach in the form of trade policy statements (see Bonk and Larkou (2020)).

To summarize, using the stock price ratio of exporting and non-exporting firms seems to be a promising route to identify trade policy shocks. Equity movements help to objectively classify a shock’s sign, accurately capture the relative importance of policy decisions and provide a useful signal for unanticipated policy changes.

34_{After all, the UK only accounts for approx. 3% or US trade.}

35_{However, this result needs to be interpreted with caution since it may capture the impacts of other} policy changes proposed during the election campaign with disproportionate implications for exporting firms.

36_{As the 2020 US presidential election approaches, these results foreshadow what may happen to stock} prices in case the administration changes and the protectionist measures implemented in recent years are reverted.

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Figure 1.4: Stock price ratio of exporting and non-exporting firms together with the ten most impactful liberalizing (green) and protectionist (red) trade policy events

1.6 Conclusion

This paper present a novel daily data set of trade policy events based on official statements spanning the years 2007-2019. Entries are classified along several dimensions, such as the direction of the policy change, the stage of the reform and the initiating country. Using this wealth of information, I demonstrate that returns on trade-exposed firms’ stock capture the sign and size of policy changes and accurately reflect unanticipated information. As a result, I argue that stock

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market data should be used to complement a narrative approach to identify trade policy shocks and study their macroeconomic impact. This method could be applied to a variety of countries for which data on firms’ trade exposure is available.

Even though equity data can be a powerful tool for refining a narrative identification based on policy statements, several caveats should be kept in mind. First, at the time of announcement, stock returns might not correctly anticipate the likelihood of implementation. Some political promises or threats that caused market movements never materialized (e.g. the T-TIP agreement) and hence, did not change economic fundamentals. Hence, future revisions of the data set should focus on flagging policy plans that were later abandoned. Another drawback stems from using Goldman Sachs’ subset of exporting and non-exporting S&P 500 firms, which ignores smaller, less productive firms. As a result, changes in the stock price ratio presented above may not translate one-to-one into macroeconomic effects if unlisted firms with a high share in value-added are also affected by trade policy. Although I cannot capture the effect on non-public companies, the issue can be mitigated by incorporating listed firms that are too small to be part of the S&P 500. The universe of US stock prices could be combined with data provided by Factset on the share of revenues that each firm generates abroad. Furthermore, future research should disentangle what part of stock price changes following trade policy statements is driven by second-moment effects, i.e., increases in uncertainty.37 Compared to first moment effects, these may translate differently into macroeconomic outcomes.

All in all, the data set presented above has a range of potential applications that may help to broaden our understanding of the impact of heterogeneous trade reforms.

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Chapter 2 The macroeconomic impact of trade

policy: A new identification approach

(joint with Chloe Larkou)

Keywords: Trade policy; Tariffs; Protectionist; Trade agreement; Stock market

2.1 Introduction

While there is a consensus among economists regarding the long-term benefits of free trade, the jury is still out on both its short-term implications and the consequences of adopting a more protectionist stance. The scant empirical evidence on both issues is due to the scarcity of post-war protectionist actions and the low frequency of macroeconomic data. Moreover, identifying the unanticipated component of trade policy changes is difficult since implementations are often preceded by several rounds of publicly-advertised negotiations and consultations.

This paper attempts to address these challenges while answering the question: What are the short- and medium-term implications of both liberalizing and protectionist trade policies on macroeconomic variables? Using a novel data set containing daily official trade policy statements

Essays on trade, fiscal policy and gender equality

Essays on Trade, Fiscal Policy and Gender

Equality

Alica Ida Bonk

European University Institute

Department of Economics

Essays on Trade, Fiscal Policy and Gender Equality

Alica Ida Bonk

Abstract

Acknowledgements

Contents

Chapter 1

Combining narrative and stock market

data to identify trade policy shocks:

Evidence from a new database

1.1

Introduction

1.2

Trade policy data sets based on official statements and

tweets

1.2.1

Official trade policy statements

|

{z

}

|

{z

}

z

}|

{

z

}|

{

z

}|

{

z

}|

{

1.2.2

Tweets on trade policy

1.3

Stock indices by trade exposure

1.4

Average stock market effect of different trade policy

types

1.4.1

Liberalizing and protectionist policy changes

X

[

X

1.4.2

Trade policy announcements versus implementations

1.4.3

Official news channels versus tweets

1.5

Key trade policy events (2007-2019) according to the

stock market

1.6

Conclusion

Chapter 2

The macroeconomic impact of trade

policy: A new identification approach

(joint with Chloe Larkou)

2.1

Introduction