# **1. Introduction**

The financial development literature and policy fora have been keenly interested in understanding the empirical link between bank ownership and lending over the business cycle (Bertay et al., 2015; Panizza, 2022; Schnabel, 2019) and in relation to economic growth (Levine,1996; Adrianova et al., 2008; Beck et al., 2000). The literature focused on studying the effects of two contrasting types of commercial bank ownership: by the state (Micco et al., 2007; Cull and Xu 2003; Berger et al., 2008) and by the foreign investors/parent foreign banks (Claessens et al., 2008; Ehlers and McGuire, 2017; De Hass, 2014; Giannetti and Ongena, 2012; Beck et al., 2018; Detragiache et al. 2008; Berger et al., 2008). Generally, state banks tend to be less procyclical and less efficient in their lending and may support greater risk-taking of borrowers—we label them "patient capital" in this paper. By contrast, foreign banks tend to be more vulnerable to external or home country shocks but support knowledge transfer and external market connectivity—we label them "knowledge capital" in this paper. There is a gap in the literature regarding the foreign state ownership of local banks—that is, regarding the mix of _patient_ and _knowledge_ capital. To our knowledge, only one paper studies the role of foreign state ownership of banks in the local credit cycle, by Borsuk, Kowalewski, and Pisany (2022). We complement this study by focusing on the effect of foreign state ownership on one driver of productivity and long-run economic growth: innovation.

This paper first revisits the effect of state and foreign bank ownership on firm access to finance and innovation (Mao and Wang, 2022; Lee et al., 2015; Bakhouche, 2022; Agénor and Canuto, 2017; Amore et al., 2013; Brown et al., 2009; Chava et al., 2013; Hsu et al., 2014; Kerr and Nanda, 2015) using a merged cross-country data set of firm-level data from the World Bank Enterprise Survey and bank-level data from FitchConnect covering the period from 2016 to 2022 and about 22,000 firms in 49 countries. In our regressions, we control for firm-level characteristics, bank-level financial soundness, and legal origins of bank ownership. We then zoom specifically on foreign state ownership and its effect on innovation outcomes. We try to address the concerns about possible endogeneity (reverse causality) in the link from access to credit to firm innovation by isolating a positive credit shock based on a scoring model estimated on data for countries with (more) efficient national credit markets. We also conduct several robustness checks, including specifications with alternative dependent variables such as firm research and development (R&D) spending. In addition, we test whether baseline estimation results for firms that introduced a new or improved product or service in the past three years can also hold for a subset of firms that introduced a new/improved product or service that is also new to their main market. We thus contrast the potential difference between less risky innovation ("innovation diffusion or new to the firm") and more risky innovation ("radical innovation or new to the market") to confirm the limit of bank credit, or debt in general, versus equity in financing radical innovation (Cornelli and Yosha, 2003; Hall and Lerner, 2010; Lerner and Nanda, 2020).

We find that firms with credit from state banks tend to innovate more than firms with credit from other types of banks. We do not find a positive effect on firm innovation outcomes when the firm borrows from a foreign-owned bank—this could be related to the studied period that included global crises and the associated capital shocks, such as repatriation of capital to the parent level of international banks. Focusing on foreign state ownership, we estimate a significant effect on firm innovation of about seven times larger than the estimated positive effect of state bank lending alone. This result holds even when we address reverse causality concerns and is robust to using an alternative measure of state ownership. The results weaken, however, when we use specifications with alternative dependent variables. When we 2