Shweta Gaonkar, Angelo Mele
arXiv 2 May 2021 · Econometrics · publishedJournal of Economic Behavior & Organization (2023) · 1 citations (OpenAlex)
arXiv:2105.00458 · PDF · DOI · OpenAlex · Extracted main text
How do inter-organizational networks emerge? Accounting for interdependence among ties while studying tie formation is one of the key challenges in this area of research. We address this challenge using an equilibrium framework where firms' decisions to form links with other firms are modeled as a strategic game. In this game, firms weigh the costs and benefits of establishing a relationship with other firms and form ties if their net payoffs are positive. We characterize the equilibrium networks as exponential random graphs (ERGM), and we estimate the firms' payoffs using a Bayesian approach. To demonstrate the usefulness of our approach, we apply the framework to a co-investment network of venture capital firms in the medical device industry. The equilibrium framework allows researchers to draw economic interpretation from parameter estimates of the ERGM Model. We learn that firms rely on their joint partners (transitivity) and prefer to form ties with firms similar to themselves (homophily). These results hold after controlling for the interdependence among ties. Another, critical advantage of a structural approach is that it allows us to simulate the effects of economic shocks or policy counterfactuals. We test two such policy shocks, namely, firm entry and regulatory change. We show how new firms' entry or a regulatory shock of minimum capital requirements increase the co-investment network's density and clustering.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Mele A (2017) a) A structural model of dense network formation | 1.000 | 23 | 5 | 100% |
| 2 | Caimo A, Friel N (2011) Bayesian inference for exponential random graph models | 1.000 | 10 | 4 | 100% |
| 3 | Mele A, Zhu L (2020) Approximate variational estimation for a model of network formation, johns Hopkins University | 1.000 | 10 | 3 | 100% |
| 4 | Kim JY, Howard M, Pahnke EC, Boeker W (2015) Understanding network formation in strategy research: Exponential random graph models | 1.000 | 9 | 4 | 100% |
| 5 | Ahuja G, Soda G, Zaheer A (2012) The genesis and dynamics of organizational networks | 1.000 | 8 | 3 | 100% |
| 6 | Snijders TA (2002) Markov chain monte carlo estimation of exponential random graph models | 1.000 | 7 | 4 | 100% |
| 7 | Jackson MO (2008) Social and Economics Networks | 1.000 | 5 | 3 | 100% |
| 8 | Chung S, Singh H, Lee K (2000) Complementarity, status similarity and social capital as drivers of alliance formation | 1.000 | 5 | 3 | 100% |
| 9 | Gulati R, Gargiulo M (1999) Where do interorganizational networks come from? | 0.928 | 4 | 3 | 100% |
| 10 | Rothaermel FT, Boeker W (2008) Old technology meets new technology: Complementarities, similarities, and alliance formation | 0.928 | 4 | 3 | 100% |
Showing the top 10 of 98 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Homophily in preferences or meetings? Identifying and estimating an iterative network formation model | 0.405 | 1 | 1 |