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Regional Dynamics Under Adverse Physical and Behavioral Shocks: The Economic Consequences of a Chlorine Terrorist Attack in the Los Angeles Financial District


Emergency management decision makers must make contingency plans for a wide range of threat scenarios. In undertaking ex-ante cost/benefit evaluations of contingency plans, they must understand the economic benefits of threat deterrence and reduction. Appropriate emergency response and recovery activities ex-post can attenuate business interruption (BI) impacts. Regional economic modeling can provide quantitative input to these evaluations. In this paper, we use a large-scale dynamic regional computable general equilibrium (CGE) model of the Los Angeles economy to perform an economic consequence analysis of a terrorist attack with chlorine gas. We divide the event’s direct effects into resource losses (injuries, BI) and behavioral reactions stemming from fear. We provide a decomposition of aggregate economic effects in terms of these various loss components, allowing us to elucidate the relative sizes of potential loss channels. We also discuss the effect of geographic shifts of economic activity within the affected region and in neighboring regions in estimating the losses. Our analysis can assist risk managers in developing plans for pre-event mitigation and post-event resilience.


  • Risk Premium
  • Computable General Equilibrium
  • Wage Premium
  • Computable General Equilibrium Model
  • Regional Wage

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The authors are, respectively, Research Professor, Centre of Policy Studies, Victoria University, Melbourne, Australia (CoPS); Research Scientist, Decision Research, Eugene, Oregon, and Faculty Affiliate, Center for Risk and Economic Analysis of Terrorism Events (CREATE); Research Professor, Price School of Public Policy, and Coordinator for Economics, CREATE, University of Southern California, Los Angeles; Senior Risk Analyst, ABS Consulting, Arlington, Virginia, and Director of Research, Global Catastrophic Risk Institute; and Research Fellow, CoPS.

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Fig. 16.1
Fig. 16.2
Fig. 16.3
Fig. 16.4
Fig. 16.5
Fig. 16.6
Fig. 16.7
Fig. 16.8
Fig. 16.9
Fig. 16.10


  1. 1.

    Recent reviews of regional CGE model applications to the analysis of public policy and regional development are provided by Partridge and Rickman (2010) and Giesecke and Madden (2013).

  2. 2.

    Calculations are available from the authors on request.

  3. 3.

    The model is solved using GEMPACK (Harrison and Pearson 1996).

  4. 4.

    IMPLAN is a widely-used and accepted resource for small region U.S. input-output tables. A balanced input-output table is required as an initial solution to the LA-DYN system of equations. USAGE is a detailed, dynamic CGE model of the U.S. It has been developed at the Centre of Policy Studies in collaboration with the U.S. International Trade Commission. Prominent applications of USAGE by the U.S. International Trade Commission include USITC (2004 and 2007).

  5. 5.

    The starting point for the LA-DYN model is a comparative static LA County model, implemented with IMPLAN data at the finest level of disaggregation—440 sectors. While computationally uncomplicated for comparative statics, 440 sectors is not practical for dynamic modeling. At 72 sectors, our aggregation is based on the standard IMPLAN 64 sector aggregation scheme, but with expanded detail for trade margins, consistent with a model with a business district focus.

  6. 6.

    Namely, truck transport, air transport and other transport.

  7. 7.

    This specification allows us to use a broad range of substitution possibilities among inputs (Hanoch 1971).

  8. 8.

    In this paper we are concerned with reporting the impact of a chlorine gas attack, not with the baseline forecast for the LA economy. As such, we report all results in terms of deviations in the values of variables in the attack scenario away from their baseline (no attack) forecast values. While details of the baseline are unimportant for the present application, this need not be the case for all simulations. Dixon and Rimmer (2013) note that baseline details can be important when: (1) the aim is to supply CGE forecasts to business or government; (2) the counterfactual shocks are heavily weighted towards very fast- or slow-growing sectors; (3) the focus is an evaluation of the adjustment costs of policy change. These are not relevant considerations in the present application.

  9. 9.

    We set α = 0.6.

  10. 10.

    This follows closely the closure described in Giesecke and Madden (2013, pp. 443–445), establishing an environment in which short-run capital stocks, population and the real wage are sticky (and rates of return, regional income relativities, and the employment rate are flexible), transitioning to a long-run environment in which rates of return, regional income relativities and employment rates are sticky.

  11. 11.

    Giesecke et al. (2012) estimate the value of gross output in 90071 at $16.8 billion. We assume the event causes a three day shutdown of activity in 90071. This corresponds to $140 m. of lost output.

  12. 12.

    Notes 12 and 13 of Giesecke et al. (2012) estimate sector-specific values for output and payments to labor and capital for 90071.

  13. 13.

    The format for describing the survey results here conforms very closely with Giesecke et al. (2012) to facilitate behavioral comparison between the chlorine and RDD attacks.

  14. 14.

    Available at

  15. 15.

    LA-DYN models demand for LA County commodities by agents in three regions: LA County, the rest of the U.S., and the rest of the world. Demands by agents located outside LA County are modeled via constant elasticity demand schedules. Following Giesecke et al. (2012), we model declines in willingness to pay by these agents as vertical shifts in these schedules. For agents located within LA County, we follow Giesecke et al. (2012) in modeling the product incentive shifts described in Fig. 16.2 as fear-induced wedges driven between willingness to pay for LA County goods and willingness to pay for the competing product sourced from the rest of the U.S. or the rest of the world.

  16. 16.

    For example, the 2013 deviation in willingness to pay for professional services is −15 %. Hence the 2014 deviation is assumed to be −2.4 % (= −15 % × 9/55).

  17. 17.

    For example, the 2015 deviation in willingness to pay for professional services is assumed to be −1.8 % (= −2.4 % × (5/9)^0.5).

  18. 18.

    The sum, for any variable, of results from the eight individual simulations is close to, but not exactly equal to, the results from the full simulation. This is because the model is non-linear, and interactions between the individual shocks that are captured by the full simulation are missed when the shocks are implemented individually. The difference between the sum of the eight individual simulations and the full simulation is reported as “Residual”. The value for this is small for all variables in all years.

  19. 19.

    Dixon and Rimmer (2013) describe eight ways in which CGE model results can be benchmarked or validated. Not all the methods they outline are required for every application. Rather, they advocate tailoring the validation procedure to the purpose at hand. In our discussion of results, we use the third of Dixon and Rimmer’s procedures: qualitative validation via a narrative relying on economic mechanisms within the model (pp. 1297–1298). At the same time, while not reported in this paper, we have also relied on the first two of their validation methods (test simulations for which the results are known a-priori, and within-simulation cross-checks of national accounts identities). Their remaining methods (particularly vi–viii, p. 1272) are well beyond the scope of the present paper, representing independent CGE validation modeling exercises in their own right. For example, Dixon and Rimmer discuss validation of CGE results through out-of-sample forecasting. Examining the question with a 500 sector model of the U.S. economy, they find their CGE model forecasts over a seven year period are more accurate than trend extrapolation. They go on to argue that CGE forecasts can be improved further with better forecasts for macro and trade variables, and greater use of publicly available information on plausible future paths for commodity-specific and industry-specific variables relating to tastes, technologies and policy (Dixon and Rimmer 2013, pp. 1314–1324).

  20. 20.

    With the size of the shock scaled to reflect the share of the wage bill in zip code 90071 in the total LA County wage bill.

  21. 21.

    This accounts for the temporary dip in the real wage deviation in 2014 (See Fig. 16.5). A decomposition diagram of the real wage deviation is available from the authors on request.

  22. 22.

    Figure 16.10 is constructed from 14 simulations: (1) a 2013 resource loss simulation, in which all resource loss shocks are applied in 2013, and the simulation is run out to 2024; (2) 12 behavioral simulations, in which the values for shocked behavioral variables in year t are moved to their year t deviation values, returned to their baseline values in year t + 1, and the remainder of the simulation run to 2024; and (3) the full simulation. The sum of the results of simulations (1)–(2) are very close to, but not exactly equal to, the results of simulation (3). This is due to interactions between the shocks, captured by simulation (3), but missed when the shocks are modeled as a sequence of unconnected simulations. We report the difference as “residual”.

  23. 23.

    See Fig. 3 in Giesecke et al. (2012).

  24. 24.

    Giesecke et al. (2012) report an annual long-run behavioral loss of $2,628 m. and an event year loss of $889 m., a 3:1 ratio.


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Giesecke, J., Burns, W., Rose, A., Barrett, T., Griffith, M. (2015). Regional Dynamics Under Adverse Physical and Behavioral Shocks: The Economic Consequences of a Chlorine Terrorist Attack in the Los Angeles Financial District. In: Nijkamp, P., Rose, A., Kourtit, K. (eds) Regional Science Matters. Springer, Cham.

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