Financial Sector Volatility, Banking Market Structure and Exports Pei-Chien Lin Department of Industrial Economics Tamkang University Ho-Chuan (River)

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Financial Sector Volatility, Banking Market Structure and Exports Pei-Chien Lin Department of Industrial Economics Tamkang University Ho-Chuan (River) Huang Department of Banking and Finance Tamkang University April 21, 2014

1. Introduction : Research background  Investigation on the finance-growth nexus has been one of the most prolific areas in the field of development, finance, and international economics.  Recently, a ramification extended to the literature of international trade suggests that financial development could also be a source of a country’s comparative advantage.

1. Introduction : Research background  Theoretical literature: Kletzer and Bardhan (1987) and Baldwin (1989) augmented the Hecksher-Ohlin model by incorporating a financial sector and shows that financial development gives countries a comparative advantage in industries relying more on external finance.  Several empirical studies, such as Beck(2002,2003), Svaleryd and Vlachos (2005), Hur et al.(2006) and Manova (2008) have provided evidence to support the theoretical prediction.

1. Introduction : Research background  More recently, some studies have been extended to consider the impact of other financial features on real economic activities, such as considering the impact of financial volatility on economic growth, e.g., Loayza and Ranciere(2006) and Kim, Lin and Suen (2010), Lin and Huang (2012) and the impact of banking market structure on economic growth, e.g., Cetorelli and Gambera (2001) and Claessens and Laeven (2005).  Nevertheless, the literature has not paid much attention to examining the impacts of financial volatility and banking market structure on trade patterns so far. And studying this issue may shed light on the legitimacy of financial supervision and the reform of financial sector for those more open economies.

1. Introduction : Rationale between financial volatility and real activity  According to Mishkin (1999), asymmetric information between lender and borrower leads to two basic problems in the financial system: adverse selection and moral hazard.  Financial instability (or volatility) generally will worsen the problems of adverse selection and moral hazard and thus drive up the possibility of lending bad credits. Consequently, lenders will be reluctant to make more loans, thereby possibly leading to a steep decline in lending and that will result in substantially lower investment and real economic activity.

1. Introduction : Theoretical prediction between banking market structure and real activity  On the positive side, less competitive system may lead to easier access to external financing because banks, with more market power, are more inclined to invest in information acquisition and customer relationship, e.g., Mayer (1988,1990) and Petersen and Rajan (1995).  Oppositely, the less competitive system may make the financial service more costly or with low quality, thereby reducing the effective demand of external financing and hence discouraging real activity, e.g., Pagano (1993).  Accordingly, linkage between banking market structure and real activity remains as an empirical issue.

1. Introduction : Purpose of this paper  To complement the extant literature, this paper plans to further empirically assess the impacts of other financial features, such as financial sector volatility and banking market structure, on industrial export.

2. Methodology : Empirical concerns (1)  Concern 1: reverse causality -> the performance of exports can be the source of financial sector volatility.  Solution: Following Rajan and Zingales (thereafter RZ, 1998), we describe a channel through which the industries might grow differentially in countries where the channel is likely to be more operative.

2. Methodology : Empirical concerns (1) Banking industry with higher volatility Banking industry with lower volatility Country 1 Country 2 Industry 1 (high) Industry 2 (low) Industry 1 (high) Industry 2 (low) External financing Export

2. Methodology: Empirical specification-(1) Our benchmark regression is augmented from RZ’s specification as Export i,k = Σ k α k Country k + Σ i β i Indust ry i + δ 1 (External finance i × Financial development k ) +ε i,k + δ 2 (External finance i × Financial sector volatility k ) + δ 3 (External finance i × Banking market structure k ) + Σ m θ m Other Controls m,i,k. Where subscripts i and k denote the ith industry and kth country respectively.

2. Methodology: Empirical specification-(1) Other controls added in specifications (1) include interaction terms suggested by the trade theory or empirical studies, which include :  Human capital Human Capital Intensity i × Human Capital Level k,1980  Physical capital Capital Intensity i × Capital Output Ratio k,1980  Resource endowment Resource intensity i × Natural resource stock k,1984

2. Methodology: Empirical specification-(2)  In addition, firm’s asset may closely relate to financial development. For instance, Braun (2003) shows that in countries with lower financial development, industries with more tangible assets are relatively larger and grow faster.  Furthermore, this hypothesis has been extended to test the impact of the interaction between industrial asset tangibility and financial development of a country on trade by Hur et al. (2006) and Manova (2008).

2. Methodology: Empirical specification-(2) Export i,k = Σ k α k Country k + Σ i β i Indust ry i + δ 1 (External finance i × Financial development k ) + ε i,k + δ 2 (External finance i × Financial sector volatility k ) + δ 3 (External finance i × Banking market structure k ) + Σ m θ m Other Controls m,i,k + γ 1 (Tangibility i × Financial development k ) + γ 2 (Tangibility i × Financial sector volatility k ) + γ 3 (Tangibility i × Banking market structure k )

2. Methodology: Empirical concerns (2)  Concern 2: endogeneity ->the shocks that trigger financial sector volatility can also affect export performance.  Solution: we apply instrumental variable (IV) technique, using the legal origins of the country as instruments for financial development or financial volatility, to estimate the models.

3. Data The data used in this study mainly obtained from Manova (2008), Braun (2003) and Beck et al. (2000, 2010). The full sample contains 63 countries and 27 industries, for the period. The variables are categorized into country-industry- specific, industry-specific and country-specific variables. (1) Country-Industry-Level variable ( i,k ) Export share i,k : share of industry i’s export in country k in country k’s GDP.

3. Data (2) Industry-Level variable ( i ) External finance i : the fraction of investment not financed by internal funds. Tangibility i : the ratio of net properties, plant and equipment to book value of asset. (3) Country-Level variable ( k ) Private credit k : the ratio of domestic private credit to GDP in Volatility of private credit k : the standard deviation of the growth of the private credit over GDP during the Bank concentration k : the share of the three largest banks’ assets over all commercial banks averaged over the period

4. Results: Benchmark results

4. Results: summary for the benchmark results 1. The volatility and concentration of banking industry respectively exert negative and positive impacts on industrial exports. Nevertheless, the positive impact of financial development disappeared when these two financial features considered. The result is robust to additional controls considered. 2. The interaction of banking industry volatility with asset tangibility has statistically positive impact on the relative exports of industries with more tangible asset, showing that tangible assets can serve as a collateral to ease access of finance when confidence in the economy in low.

4. Results: Robustness check (1) -alternative measures of comparative advantage in trade

4. Results: Robustness check (2)- alternative measures of volatility in banking industry

4. Results: Robustness check (3)- alternative measures of market structure

4. Results: Robustness check (4)- developed countries vs. developing countries

5. Conclusion  The positive effect of financial development on industrial export via the channel of sectoral external financing is attenuated when other features of financial sector are considered.  Financial volatility and banking market structure respectively exert significantly negative and positive impacts on the export of industries that are more external financially dependent.  Our finding is robust to a variety of kinds of sensitivity tests.  To sum up, the findings from this study lend support to the notion that a more stable and concentrated banking system is important to the exports of those industries that rely more on external finance, especially so for those developing countries.

~Thank you for your attention~