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Adler: Toulouse School of Economics. Ahn: Seoul National University. Dao: International Monetary Fund. We thank Giovanni Dell’Ariccia, Carol Corrado, Luis Cubeddu, Aaron Flaaen, Gino Gancia, Kalina Manova, Marti Mestieri, Brent Neiman, and seminar participants at the IMF, Georgetown University, Federal Reserve Bank of Cleveland, the Bank of England, the IMF/WB/WTO Joint Trade Workshop, the Mid-Atlantic Trade Workshop (UVA), Banque de France investment conference and T2M conference (Paris) for helpful comments and discussions.
Corporate cash holdings has been extensively analyzed for the U.S. by a growing literature; see Bates, Kahle and Stulz (2009), Pinkowitz, Stulz and Williamson (2016), Graham and Leary (2015) to just mention a few. Studies on this topic for non-US corporates are still few: Iskandar-Datta and Jia (2012), Dao and Maggi (2017) are among the few.
For example, Korean government implemented a tax on corporate cash stocks in 2015 (e.g., The Economist, September 27th 2014)
Other motives for cash holdings and corporate saving, less related to our paper’s channel, have also found support in the literature: e.g. Foley, Hatzell, Titman and Twite (2007) and Armenter and Hnatkovska (2017) for tax motive, Azar, Kagy and Schmalz (2016) for cost of carry, and Dittmar, Mahrt-Smith and Servaes (2003) for corporate governance motives.
Of course export and import shares are highly correlated at the country level, so the positive correlation could be also driven by enhanced import competition. However, when controlling for both export and import shares, we see only a weak and statistically insignificant positive correlation between cash ratio and import shares, while the coefficient on export shares remain significant and of similar magnitude as in the bivariate regression.
Later, in the Appendix, we relax this simplifying assumption and endogenize the firm’s financing decision as a solution of the optimal contract in the presence of information asymmetry between the firm and outside investors that gives rise to moral hazard. In short, firms will always choose to hold cash in advance to insure against liquidity shocks. This is because moral hazard will prevent the firm from being able to commit the full net present value of the innovation to investors in certain instances, under which the firm would then forego positive investment opportunities were it to rely only on borrowing at the time of the liquidity shock.
In the Appendix, we relax the financing constraint in t = 1 and allow the firm to borrow/raise equity at the time of the liquidity shock, but subject to well-known informational asymmetry that gives rise to moral hazard. We show that all 3 main results still go through, in particular, firms still choose to hold the cash in advance as they cannot commit to the full NPV of the innovation project due to moral hazard.
In other words, in our model, the less productive/typically smaller a firm is, the lower are its expected returns from innovation and the less cash it will be willing to hold. In contrast, in alternative models, the more constrained a firm is by lack of external financing capacity, the more cash it must hold to self-insure against productivity shocks.
Note that this prediction applies to spending on innovation, not necessarily its outcome or quality.
This is a well-known limitation of firm-level R&D obtained from balance sheet and financial statements data, see e.g. Lev and Radhakrishnan (2005).
While we have almost 200,000 firm-year observations over which these summary statistics are computed, in the following, the sample is greatly reduced when we seek to compute a firm-level or sector-level measure of exposure to export and import.
Modern heterogeneous-firms trade models center on the productivity sorting of exporter status: that is, the most productive firms become exporters, as has been strongly supported in the empirical trade literature. Moreover, it has been well established that larger firms are more likely to be exporters (Bernard, Jensen, Redding, and Schott, 2007; Bernard, Jensen, and Schott, 2009). Given the likely noisiness in TFP estimates, researchers have also relied on various proxies for the productivity level, ranging from size, labor productivity, export intensity, to profitability (e.g., Verhoogen, 2008).
In essence, our import shock measure and its instrumental variable exactly follow Autor et al. (2013) and Autor et al. (2016). We apply their idea similarly to construct export shock measures and instrumental variables as in Ahn and Duval (2018), which is in turn comparable to the export demand shock measure developed in Mayer, Melitz, and Ottaviano (2016).
In principle, we could include sector-year fixed effects in the baseline specification. However, our instrumental variable approach aims to extract sector-year-level common exogenous trade shocks induced by China, which would leave little variation across countries once sector-year fixed effects are included.
The baseline measure is defined at two-digit country-sector level because total output data from World Input Output Database (WIOD) is available at two-digit sector level. Since most firms in our sample report two distinct primary and secondary four-digit sectors that belong to a single two-digit sector, it is not feasible to construct firm-level trade shock variables using two-digit country-sector-level measures.
The weight used in calculating export MFN tariff rate is based on the share of exports by destination countries in each four-digit sector in the initial year.
Similar differences in slopes are also found using the other proxies for firm productivity (average labor productivity, foreign sales share, and profitability).
Our main findings broadly hold whether we set missing R&D spending to zero or not.
Unfortunately, our dataset is not well suited to estimate firm-level total factor productivity (TFP) due to incomplete data coverage on intermediate inputs.
This is admittedly only a crude measure of the ex-post outcome of tax strategies: they include combined measure of domestic and foreign taxes, as well as current and deferred taxes that are reported in financial accounts. Changes in ETR can be also driven by changes in statutory tax rates over time and/or reflect the progressivity of the tax regime.
Moral hazard is essential to understand credit rationing and liquidity demand. In the absence of moral hazard, if the NPV of the project exceeds the liquidity shock and firms can issue claims up to the full value of the NPV, there will be no need for liquidity hoarding as the firm can borrow instantaneously when the shock arrives or issue shares to obtain the funding.
Letting payoff to the firm only depend on profit from domestic sales comes with algebraic tractability without loss of generality. Intuitively, if the return to the firm was a fraction of total profits (domestic and foreign), then more export opportunities will lower the ηmin necessary to ensure the firm’s incentive compatibility and therefore raise pledgable income, which will in turn raise the optimal level of cash holdings ρ* even more.
Indeed, because the maximum pledgeable income P(ρ1) ≥ I is evaluated at η = ηmin, and the pledgeable income function P(ρ1, η) is decreasing in η, reducing the pledgeable income from above to equal I – A implies raising η above ηmin, introducing slack into IC constraint (16).
In particular, all firms will be able to hold the first-best cash level ρ1 if I ≤ P1(ϕmin) and no firm will be able to innovate if I > P0(ϕmax) if ϕmin,ϕmax are the min-max boundaries of the initial productivity distribution.
Note that this minimum threshold