First - this is more my area of expertise - it is frankly pretty extreme to claim that theoretical arguments can do anything convincing on this issue. The theoretical channels for government investment to be complementary to private investment are so ample that any argument to the contrary could not possibly stand on its own. More on that at the end. The well-identified empirical evidence lines up against Bryan's view pretty decisively. The strongest quasi‑experimental studies all show that public R&D dollars crowd in private effort. Regression‑discontinuity at NIH paylines finds a US$10 m grant shock yields about three extra corporate patents with no loss of business R&D [1]. Shift‑share instruments linking post‑9/11 defense budgets to industry exposure show every public dollar induces 40‑60 cents of additional private R&D and raises total‑factor productivity [2]. Event‑study evidence from sudden university‑level cuts reverses these gains: publications fall 15 % and VC‑backed start‑ups 30 % [3]. A richer SR&ED tax credit for Canadian SMEs boosts their R&D by 17 % and spills over into patent citations [4]. Scientist‑level NIH lotteries confirm grants lift later publications and citations by roughly a quarter [5]. Across designs and contexts, the sign is always positive and the magnitudes large—directly contradicting claims of systematic crowd‑out. What's wrong with your evidence? Kealey‑type papers err in both identification and theory. Their headline correlations use aggregate time‑series where governments boost subsidies precisely when private investment slumps, creating spurious negative signs [6,7]. Damrich, Kealey & Ricketts merely embed those raw aggregates in a stylised contribution‑good model that assumes knowledge is fully appropriable and ignores spillovers, so any public share mechanically lowers measured productivity [8]. Once exogenous variation is isolated—as in the five studies above—the relationship flips, and the omitted spillovers emerge as large and positive. Descriptive regressions that confound cause and effect cannot overturn the causal evidence that well‑designed public funding unlocks socially valuable, privately under‑supplied innovation. There are SO MANY ways of understanding theoretically why the facts would be like this. Romer’s endogenous‑growth framework treats ideas as non‑rival but only partially excludable, so inventors capture just a slice of their social surplus [10], while Aghion‑Howitt’s creative‑destruction model adds business‑stealing, further depressing private incentives [11]. Jones‑Williams calibrate spillovers, duplication and congestion and still find laissez‑faire R&D at barely a third of the optimum [12]. Grossman‑Helpman’s quality‑ladder model layers cross‑sectoral input‑output spillovers, another benefit absent from Caplan’s crowd‑out narrative [13]. Liang & Mu show that when research lines are complements, decentralized scientists cluster on topics that leave high‑return yet neglected areas that only public funding can unlock [14]. [1] @pierre_azoulay , Graff Zivin, J., Li, D. & Sampat, B. (2019) ‘Public R&D and Private Patenting: Evidence from NIH Funding’, Review of Economic Studies 86(1): 117‑152.| [2] Moretti, E., Steinwender, C. & Van Reenen, J. (2024) ‘The Economic Spillovers of Defence Research’, Review of Economics & Statistics 106(2): 235‑256. [3] Babina, T., He, J., Howell, S. et al. (2023) ‘Cutting the Innovation Engine’, Quarterly Journal of Economics 138(4): 2201‑2260. [4] Agrawal, A., Rosell, C. & Simcoe, T. (2020) ‘Tax Credits and Small‑Firm R&D: Evidence from Canada’, American Economic Journal: Economic Policy 12(3): 1‑30. [5] Jacob, B. & Lefgren, L. (2011) ‘The Impact of Research Funding on Scientific Output’, Journal of Public Economics 95(9‑10): 1168‑1177. [6] Kealey, T. (1996) The Economic Laws of Scientific Research. Springer. [7] Kealey, T. & Ricketts, M. (2014) ‘Modelling Science as a Contribution Good’, Research Policy 43(6): 1014‑1024. [8] Damrich, S., Kealey, T. & Ricketts, M. (2022) ‘Crowding In and Crowding Out within a Contribution‑Good Model of Research’, Research Policy 51(1): 104400. [10] Romer, P. M. (1990) ‘Endogenous Technological Change’, Journal of Political Economy 98(5): S71‑S102. [11] Aghion, P. & Howitt, P. (1992) ‘A Model of Growth through Creative Destruction’, Econometrica 60(2): 323‑351. [12] Jones, C. I. & Williams, J. C. (1998) ‘Measuring the Social Return to R&D’, Quarterly Journal of Economics 113(4): 1119‑1135. [13] Grossman, G. M. & Helpman, E. (1991) ‘Quality Ladders in the Theory of Growth’, Journal of Political Economy 99(3): 433‑449. [14] [9] Liang, J. & Mu, X. (2020) ‘Complementary Information and Learning Traps’, Quarterly Journal of Economics 135(4): 1929‑1984.