IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2511.03091.html

The Economics of Spatial Coordination in Critical Infrastructure Investment

Author

Listed:
  • L Kaili Diamond
  • Benjamin Gilbert

Abstract

We develop a hybrid approach to estimate spatial coordination mechanisms in structural dynamic discrete choice models by combining nested fixed-point (NFXP) dynamic programming with method of simulated moments (MSM), achieving computational tractability in spatial settings while preserving structural interpretation. Applying this framework to GPU replacement data from 12,915 equipment locations in Oak Ridge National Laboratory's Titan supercomputer, we identify two distinct coordination mechanisms: sequential replacement cascades (gamma_lag = -0.793) and contemporaneous failure batching (gamma_fail = -0.265). Sequential coordination dominates - approximately three times stronger than failure batching - indicating that operators engage in deliberate strategic behavior rather than purely reactive responses. Spatial interdependencies account for 5.3% of variation unexplained by independent-decision models, with coordination concentrated in high-risk thermal environments exhibiting effects more than 10 times stronger than cool zones. Formal tests decisively reject spatial independence (chi-squared(2) = 685.38, p

Suggested Citation

  • L Kaili Diamond & Benjamin Gilbert, 2025. "The Economics of Spatial Coordination in Critical Infrastructure Investment," Papers 2511.03091, arXiv.org.
    • Handle: RePEc:arx:papers:2511.03091
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2511.03091
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Rust, John, 1987. "Optimal Replacement of GMC Bus Engines: An Empirical Model of Harold Zurcher," Econometrica, Econometric Society, vol. 55(5), pages 999-1033, September.
    2. McFadden, Daniel, 1989. "A Method of Simulated Moments for Estimation of Discrete Response Models without Numerical Integration," Econometrica, Econometric Society, vol. 57(5), pages 995-1026, September.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Heutel, Garth, 2011. "Plant vintages, grandfathering, and environmental policy," Journal of Environmental Economics and Management, Elsevier, vol. 61(1), pages 36-51, January.
    2. Zvi Eckstein & Michael Keane & Osnat Lifshitz, 2019. "Career and Family Decisions: Cohorts Born 1935–1975," Econometrica, Econometric Society, vol. 87(1), pages 217-253, January.
    3. Hamish Low & Costas Meghir, 2017. "The Use of Structural Models in Econometrics," Journal of Economic Perspectives, American Economic Association, vol. 31(2), pages 33-58, Spring.
    4. Kristensen, Dennis & Mogensen, Patrick K. & Moon, Jong Myun & Schjerning, Bertel, 2021. "Solving dynamic discrete choice models using smoothing and sieve methods," Journal of Econometrics, Elsevier, vol. 223(2), pages 328-360.
    5. Chen He & Tobias J. Klein, 2023. "Advertising as a Reminder: Evidence from the Dutch State Lottery," Marketing Science, INFORMS, vol. 42(5), pages 892-909, September.
    6. Victor Aguirregabiria & Allan Collard-Wexler & Stephen P. Ryan, 2021. "Dynamic Games in Empirical Industrial Organization," NBER Working Papers 29291, National Bureau of Economic Research, Inc.
    7. Erhan Artuc & Irene Brambilla & Guido Porto, 2022. "Patterns of Labour Market Adjustment to Trade Shocks with Imperfect Capital Mobility," The Economic Journal, Royal Economic Society, vol. 132(646), pages 2048-2074.
    8. Lee, Lung-Fei, 1997. "Simulated maximum likelihood estimation of dynamic discrete choice statistical models some Monte Carlo results," Journal of Econometrics, Elsevier, vol. 82(1), pages 1-35.
    9. Jean-Jacques Forneron, 2019. "A Sieve-SMM Estimator for Dynamic Models," Papers 1902.01456, arXiv.org, revised Jan 2023.
    10. Daniel Ackerberg, 2009. "A new use of importance sampling to reduce computational burden in simulation estimation," Quantitative Marketing and Economics (QME), Springer, vol. 7(4), pages 343-376, December.
    11. Tomlin, Ben, 2014. "Exchange rate fluctuations, plant turnover and productivity," International Journal of Industrial Organization, Elsevier, vol. 35(C), pages 12-28.
    12. Schündeln, Matthias, 2005. "Modeling Firm Dynamics to Identify the Cost of Financing Constraints in Ghanaian Manufacturing," Proceedings of the German Development Economics Conference, Kiel 2005 29, Verein für Socialpolitik, Research Committee Development Economics.
    13. Keane, Michael P. & Todd, Petra E. & Wolpin, Kenneth I., 2011. "The Structural Estimation of Behavioral Models: Discrete Choice Dynamic Programming Methods and Applications," Handbook of Labor Economics, in: O. Ashenfelter & D. Card (ed.), Handbook of Labor Economics, edition 1, volume 4, chapter 4, pages 331-461, Elsevier.
    14. Richard Blundell & Ran Gu & Søren Leth‐Petersen & Hamish Low & Costas Meghir, 2026. "Durables and lemons: Private information and the market for cars," Quantitative Economics, Econometric Society, vol. 17(1), pages 38-91, January.
    15. Erhan Artuç & Germán Bet & Irene Brambilla & Guido Porto, 2013. "Trade Shocks and Factor Adjustment Frictions: Implications for Investment and Labor," IIE, Working Papers 101, IIE, Universidad Nacional de La Plata.
    16. Daniel A. Ackerberg & Gautam Gowrisankaran, 2006. "Quantifying equilibrium network externalities in the ACH banking industry," RAND Journal of Economics, RAND Corporation, vol. 37(3), pages 738-761, September.
    17. Geweke, John & Houser, Dan & Keane, Michael, 1999. "Simulation Based Inference for Dynamic Multinomial Choice Models," MPRA Paper 54279, University Library of Munich, Germany.
    18. Zvi Eckstein & Michael P. Keane & Osnat Lifshitz, 2016. "Sources of Change in the Life-Cycle Decisions of American Men and Women: 1962-2014," Economics Papers 2016-W07, Economics Group, Nuffield College, University of Oxford.
    19. Jenkins, Mark & Liu, Paul & Matzkin, Rosa L. & McFadden, Daniel L., 2021. "The browser war — Analysis of Markov Perfect Equilibrium in markets with dynamic demand effects," Journal of Econometrics, Elsevier, vol. 222(1), pages 244-260.
    20. Good, D. & Nadiri, M.I. & Sickles, R., 1996. "Index Number and Factor Demand Approaches to the Estimarion of Productivity," Working Papers 96-34, C.V. Starr Center for Applied Economics, New York University.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:arx:papers:2511.03091. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.