Are Firms That Received R&D Subsidies More Innovative?

This paper looks at the effectiveness of R&D grants for Canadian plants that already benefit from R&D tax credits. Using a non-parametric matching estimator, we find that firms that benefited from both policy measures introduced more new products than their counterparts that only benefited from R&D tax incentives. They also made more world-first product innovations and were more successful in commercializing their innovations.Innovations, R&D, Matching Estimators, Mahalanobis, Innovation Survey, Tax Credits, Grants

Higher rank lamplighter groups are graph automatic

We show that the higher rank lamplighter groups, or Diestel-Leader groups $\Gamma_d(q)$ for $d \geq 3$, are graph automatic. This introduces a new family of graph automatic groups which are not automatic

Data-driven modeling of time-domain induced polarization

We present a novel approach for data-driven modeling of the time-domain induced polarization (IP) phenomenon using variational autoencoders (VAE). VAEs are Bayesian neural networks that aim to learn a latent statistical distribution to encode extensive data sets as lower dimension representations. We collected 1 600 319 IP decay curves in various regions of Canada, the United States and Kazakhstan, and compiled them to train a deep VAE. The proposed deep learning approach is strictly unsupervised and data-driven: it does not require manual processing or ground truth labeling of IP data. Moreover, our VAE approach avoids the pitfalls of IP parametrization with the empirical Cole-Cole and Debye decomposition models, simple power-law models, or other sophisticated mechanistic models. We demonstrate four applications of VAEs to model and process IP data: (1) representative synthetic data generation, (2) unsupervised Bayesian denoising and data uncertainty estimation, (3) quantitative evaluation of the signal-to-noise ratio, and (4) automated outlier detection. We also interpret the IP compilation's latent representation and reveal a strong correlation between its first dimension and the average chargeability of IP decays. Finally, we experiment with varying VAE latent space dimensions and demonstrate that a single real-valued scalar parameter contains sufficient information to encode our extensive IP data compilation. This new finding suggests that modeling time-domain IP data using mathematical models governed by more than one free parameter is ambiguous, whereas modeling only the average chargeability is justified. A pre-trained implementation of our model -- readily applicable to new IP data from any geolocation -- is available as open-source Python code for the applied geophysics community.Comment: 38 pages, 11 figures, 3 tables, 1 appendix. Manuscript submitted to SEG Geophysics for review. Original manuscript uploaded to arxiv.org in accordance with SEG's Preprint Policy ( https://library.seg.org/page/policies/preprints ). For associated code, see https://doi.org/10.5281/zenodo.514853

On the Potential of Foreign Aid as Insurance

In this paper, we argue that it would be fruitful to revisit foreign aid's potential as an insurance mechanism against macroeconomic shocks. In a simple model of aid flows between two endowment economies, we show that at least three fourths of the large welfare costs of macroeconomic fluctuations in poor countries could be alleviated by a simple reallocation of aid flows across time.Foreign aid, Consumption smoothing, Macroeconomic fluctuations, Welfare