**The possible noise in the "perceived" innovation measure.** Perception survey data are often criticized for noisy data that can arise because some firms may not fully understand the question or may project their wishes into the response. As another robustness check, we estimate our baseline regression model using an alternative and less noisy dependent variable: the firm's R&D spending. It is a binary variable measuring whether the establishment spent on in-house research and development activities. **Error! Reference source not found.** 6 in the Annex reports the estimation results and shows the positive effect of _patient capital_ on R&D spending disappears when we use R&D spending (input) instead of a measure of (perceived) innovation outcome. There are potential explanations for this result. R&D spending constitutes only one input among various other inputs related to innovation, such as the use of technology licenses, the existing human capital, the purchase of complementary equipment, or the company culture (de Nicola and Chen, 2022; Crespi et al., 2016; Bogliacino and Pianta, 2013). Likewise, Cirera and Maloney (2017) show that even after controlling for the usual innovation inputs such as R&D, firm capabilities— including managerial and organizational practices—are important predictors of innovation at the firm level. The lack of these capabilities helps explain the lower returns to R&D found in lower-income countries because capabilities facilitate R&D and enhance its effectiveness. This suggests that other factors besides R&D spending might co-determine innovation outcomes, such as our baseline measure. Our findings also reveal that large and medium-sized firms, as well as foreign-owned firms, allocate more resources toward research and development (R&D). However, this may be attributed to better financial management and reporting standards within these firms—that separately manage R&D spending—rather than solely reflecting their innovation efforts and outcomes relative to smaller firms. Finally, the 10% significance of the bank count variable could reflect that many firms may invest in R&D using cash-flow liquidity (rather than credit) which they manage through bank accounts.

**Firm introducing new product or service to the market (** **_radical_ innovation)** . Next, we test if the positive effect of _patient capital_ (state bank ownership) and combined _patient and knowledge capital_ (foreign state bank ownership) can carry over to a subset of our firm innovation measure focused on radical innovation. In other words, we explore the bank ownership effects on a firm's likelihood to innovate related only to products and services new to the firm—that is, _innovation diffusion_ —versus a more radical (and risky) innovation when a firm introduces a new product or service to the market. When replacing the baseline dependent variable with the _radical innovation_ one, the number of observations in the sample drops from about 23,000 to about 6,500, which worsens the power of our estimation for inference. Furthermore, to account for potential bias arising from missing responses on "radical innovation" that are not random, we employ the Heckman selection model (Heckman, 1979). For identification purposes, we use the following selection variable(s) in the first stage: whether the firm is located in a city with a population of over 1 million, the number of permanent full-time workers, the number of full-time highly skilled production workers, the share of the firm owned by the largest owners, whether access to finance is at least a major obstacle to business, and whether the firm has invested in R&D. A larger shareholder can exert more control over the direction of the firm, investing in R&D can be a prerequisite of disruptive innovation, being in a metropolitan city the firm can benefit from an agglomeration effect, and facing favorable access to finance or having a skilled workforce could also predetermine if the firm understands and responds to the survey question on radical innovation. <mark>Table 7 in the Annex s</mark> hows that the nonselection hazard is significant for our subsample estimation and that medium-sized and state-owned firms _radically_ innovate less than other firms. Although significant only at the 10% level, the economically large positive effect of foreign state ownership—and the hybrid patient and knowledge capital can also be extended to radical innovation even with the much smaller statistical power of our subsample. This result

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