UC Santa Cruz

The World as a "Global Village" was first envisaged by Marshall McLuhan, a media and communication theorist, in 1964. In today's world, we live in a global economy inter-connected by trade, capital flows and technology. The unprecedented integration among economies which started since 1990 was blamed for contagion effects of the global financial crisis in 2008 (IMF, 2012). Different countries responded with various policy measures to counter the spillover impacts. While import tariffs were used as policy tool to protect domestic interests (UNCTAD, 2013), capital controls along with other macro prudential measures, were used to safeguard domestic economies from global financial uncertainties (Korinek

amp; Sandri, 2015). My dissertation focuses on the implication of such policy measures on the inter-connectedness of economies, mainly highlighting the impact of tariffs on trade and the effect of capital control measures on international capital flows. The chapters of my dissertation are briefly described in the following section. \newline

The first chapter of my dissertation focuses on the empirical evidence of trade diversion from the recent trade war between the US and China for India. The recent trade dispute between the United States and other trade partners resulted in higher tariffs imposed by the United States Trade Commission on other trade partners. The tariff imposition happened between 2018 and 2020. A majority of tariffs during this trade war targeted imports from China. China retaliated with similar large tariffs on significant imports from the United States. This opened up an opportunity for other trade partners like India. In this chapter, I evaluate the trade diversion effect on India on account of the higher tariffs between US and China. The empirical analysis studies the change in trade intensity between 2019 and 2017 using detailed product level trade flows of India with the United States and China. I estimate the average change in trade intensity to India using a difference-in-difference regression. Due to the short term nature of the trade war tariffs, the average effect of trade intensity can be grossly under-estimated due to differing levels of elasticity of substitution across different product categories. Hence, I have refined the framework by introducing product level heterogeneity in the specification. For that, I have mainly considered three broad categories of product classifications namely (i) final goods vs intermediate goods (ii) homogeneous goods vs differentiated goods and (iii) highly elastic vs low elastic goods. The intermediate goods, used for final goods production, are not easily substitutable compared to final goods. Hence, one can expect that any short run effect of trade diversion is likely to increase trade intensity in final goods products, compared to intermediate goods products. Similarly, differentiated goods are hard to substituted for and are the low elastic goods. The empirical findings suggests that India benefitted from the higher tariffs on China as India's export intensity increased to the US. However, no such effect was observed in India's export to China. This finding suggests that Indian manufacturers benefitted from the higher tariffs on China due to similar or comparable comparative advantages in products targeted under US tariffs on China. However, India does not have similar comparative advantages with the US manufacturers on products targeted by China (like Soybean, agriculture products, electronics etc.). The empirical findings of average impact on imports was not statistically significant. Further, I observe significant product heterogeneity in trade diversion for India. More specifically, India's export intensity to the US increased in final products, homogeneous goods and highly elastic goods. \newline

My second chapter analyzes changes in trade policy uncertainty and its effect on global trade flows using a structural model. The recent literature on the trade war observed that different trade partners experience varying degree of trade diversion on account of higher tariffs between US and China. During the same period of trade war, the trade policy uncertainty index scaled to historical high values due to lack of clarity on the trade war scenarios. Researchers have attributed the heterogeneity in trade diversion to the change in trade policy uncertainty. In this chapter, I assess the impact of trade policy uncertainty on global trade flows by introducing trade policy uncertainty in a multi-country Ricardian trade model. The proposed model uses multi-country multi-sector trade model proposed by Eaton

amp; Kortum (2002) and builds in the uncertainty component. The trade policy uncertainty is drawn from two sources - first, the uncertainty around trade policy changes and second, stochastic uncertainty around the tariff sizes. The trade policy uncertainty affects the price distribution which translates to demand uncertainty. The rationale behind using these two sources of uncertainty is drawn from the experience in global protectionism like Brexit and US trade war. The policies adopted under these episodes increased uncertainty about trade environment as the trade partners were unsure about the possibility of trade policy changes and the effect of the trade policy changes on trade costs. Such uncertainties in trade policy creates challenges for trade partners due to the high adjustment cost in production planning. The trade partners make their production plans when there is lack of clarity about the future trade policy and allocates the factors of production accordingly. However, the trade policies are announced at later stage when it becomes difficult to modify the factor allocations. I introduce uncertainty in the model by adding a distribution of beliefs about future trade policy. Each partner has beliefs about the probability of a trade policy change and the possible change in tariff sizes on account of the policy change. The stochastic nature of tariff sizes and the probability of the policy change translates into the trade partners' assessment of final demand conditions which can be very different from actual tariff scenario (after trade policy is announced). I establish the effect of trade policy uncertainty using analytical derivations and quantitative calibration of the model. The analytical derivations shows that the possible heterogeneity in trade diversion is driven by the stochastic choice of trade partners about future policy. Further, it also provides the boundary conditions of different trade diversion scenarios given trade partners' belief. Later, I extend the analytical model to full scale calibration using two stage approach. The trade policy uncertainty is calibrated under different scenarios of tariff sizes and probability of policy changes. Lastly, I demonstrate that the framework can be generalized to model other scenarios where uncertainty may appear due to other externalities like lockdown imposed by China. \newline

The third chapter looks into the heterogeneous effect of capital controls on the gross capital flows across sectors. Capital controls are macro-prudential policies adopted by different countries to safeguard their domestic interest from the volatility of capital flows. Often times these policies includes taxation on foreign investments, volume restrictions on foreign inflows, legislative steps on foreign investment etc. Generally, advanced economies invest in emerging markets in search for higher yields. However, as the domestic and global investment conditions deteriorate in the destination countries, the direction of capital flows reverses towards advanced economies and other emerging market economies. Such sudden reversal of the foreign capital flows destabilizes the domestic currency, worsens the trade balance, widens the debt burden and de-stabilizes the growth potentials of the emerging market economies. The majority of Latin American economies and South-East Asian economies faced currency crisis on account of the volatile capital flows during 1990's. In response, the International Monetary Fund prescribed capital controls as suitable macro-prudential policy measures to safeguard the emerging market economies from the volatile capital flows from advanced economies. Capital controls are used as macro-prudential policy to safeguard domestic economy from the volatility of external capital flows. The effects of capital controls are studied across many dimensions. Beyond the intended consequence of capital controls, the indirect effects of such policies are often highlighted by the investors. The survey of investors, carried out by Forbes et. al. (2016), observed that the capital control policies send a signal to the global investors about the state of domestic economy. Such signaling effect of capital control interacts with the intended effect and can lead to heterogeneous outcome on gross capital flows across different institutional sectors. The institutional sectors, namely government, banks and private corporates, have different risk profiles and the portfolio allocations across these sectors are driven by the risk profile heterogeneity. Following investors assessments about the domestic economy, one can expect that the signaling effect of capital controls can trigger heterogeneous effects on capital flows across these institutional sectors. I examine such heterogeneity in the direct and spillover effects of capital control on gross capital flows using cross-country international capital flows data across various sectors. The direct effect of capital control captures the effect of capital control on gross capital flows across these sectors. The spillover effect, on the other hand, is mainly driven by the network effect of capital flows restrictions on capital flows among different recipient nations. In this chapter, I provide the theoretical underpinning of the possible signaling effects and then, validate the heterogeneity using sector level global capital flows data. First, I introduce the signaling effect of capital controls in a portfolio choice model with a multi-country set up to demonstrate the possible heterogeneity in the direct effect and the spillover effect on gross capital flows as one country increases capital taxation on capital inflows. I argue that the direct effect and spillover effect of capital control can be heterogeneous on capital inflows due to the signaling effect of capital controls. To validate the heterogeneity, I use quarterly capital flows data to different institutional sectors in a spatial econometric framework. The empirical findings indicate that the domestic direct effect of capital controls moderates portfolio inflows to the public sector whereas the portfolio inflows to banks and the corporate sector does not respond to the domestic capital control measures. The spillover effect of capital controls increases capital inflows to all sectors in other countries.