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Economic Impacts of Spillover Effects of Terrorism Countermeasures at Public Assembly Sites

  • Adam Z. Rose ORCID logo EMAIL logo , Dan Wei ORCID logo , Katie Byrd and Richard John
Published/Copyright: November 4, 2021
Journal of Homeland Security and Emergency Management
From the journal Journal of Homeland Security and Emergency Management Volume 19 Issue 1

Abstract

In recent years, there have been many high-profile attacks on large, relatively unprotected venues, including entertainment and shopping complexes in the U.S. and around the world. Public and private decision-makers can choose from a wide array of terrorism countermeasures. A question arises as to whether patrons’ complaints about delays, inconvenience and invasion of privacy actually translate into decisions to attend fewer such events. This paper presents the bottom-line economic impacts of terrorism countermeasures on business revenue at three public assembly venues and on their surrounding regional economic activity. These venues include an MLB Stadium, an NBA/NHL Arena, and a Convention Center. The analysis is based primarily on survey responses relating to changes in attendance that stem from public perception of the implementation of four major types of countermeasures. The surveys indicated that the majority of patrons were not affected either way by the presence of the countermeasures, but nearly all of the remainder felt more secure in the presence of the countermeasures to both terrorism and ordinary crime, which resulted in an increased likelihood of attendance. The economic impact estimates yield a small but notable positive impact on business revenues, though this outcome varies significantly across venue types.

Keywords: terrorism countermeasures; economic impacts; spillover effects; public assembly sites; consumer behavior
Corresponding author: Adam Z. Rose, National Center for Risk and Economic Analysis of Threats and Emergencies, University of Southern California, Los Angeles, CA, USA, E-mail:

The authors are, respectively, Director and Senior Research Fellow, Center for Risk and Economic Analysis of Threats and Emergencies (CREATE), and Research Professor, Sol Price School of Public Policy, University of Southern California (USC); Research Fellow, CREATE, and Research Associate Professor, Price School, USC; PhD student, Department of Psychology, USC; Senior Research Fellow, CREATE, and Professor, Department of Psychology, USC. This research was supported by the United States Department of Homeland Security through CREATE under Task Order HSHQDC-17-J-00316 of Basic Ordering Agreement HSHQDC-17-A-B004. The authors would like to thank Tony Cheesebrough for his support and advice over the course of this project. We are also grateful to other DHS staff, including Mark Coffey, Ernie Lorson, Glenn Sanders, and Mark Kreyer. We thank Molly Stasko for the fine work on supervising preparation for the OMB evaluation of our customer surveys and management interview materials. We also thank Tony Cheesebrough and Steve Rushen for helpful comments on earlier draft of the report. Valuable research assistance was provided by Juan Machado, Konstantinos Papaefthymiou, and Chris Covino of USC as well. We also appreciate the help of CREATE staff members Jeffrey Countryman for handling contracting aspects of the project and Jen Sosenko for her help in editing and formatting the final report. Of course, any remaining errors and omissions are solely those of the authors. Moreover, the views expressed in this paper represent those of the authors and not necessarily those of any of the institutions with which they are affiliated nor the United States Department of Homeland Security that funded the research.

Funding source: United States Department of Homeland Security

Award Identifier / Grant number: CREATE under Task Order HSHQDC-17-J-00316 of Basic Ordering Agreement HSHQDC-17-A-B004

  1. Research Funding: This research was supported by the United States Department of Homeland Security through CREATE under Task Order HSHQDC-17-J-00316 of Basic Ordering Agreement HSHQDC-17-A-B004.

Appendix A: Mathematical Formulations of Input–Output Analysis

The mathematical expression of the technological relationships of production captured in the I–O model can be written as Equation 1. This is based on all market purchases and sales between producing sectors in an economy (see Miller and Blair 2009; Rose and Miernyk 1989).

(1) X i = X i 1 + X i 2 + … + X in + Y i ( i = 1 … n )

where

  1. X i  = total gross output of sector i,

  2. Y i  = final demand for the products of sector i,

  3. X ij  = the sales by sector i to each of the other sector j,

  4. n = total number of producing sectors in the economy.

Three assumptions enable equation 1 to be converted into a model capable of analysis and prediction. They are that: a) each commodity or service is provided by a single production sector, and that there are no joint products; b) each sector’s inputs bear a direct proportional relationship to that sector’s output; and c) there are no external economies or diseconomies.

Assumption (b) may be written as:

(2) X ij = a ij X j

where

  1. a ij  = amount of input from sector i required to generate each dollar of output of sector j; they represent model’s ‘technical coefficients’.

Substituting (2) in equation (1) yields the basic I–O model:

(3) X i = ∑ j = 1 n a ij X j + Y i ( i = 1 … n )

Equation (3) can also be written compactly in matrix notation as:

(4) X = AX + Y

where

  1. X = Vector of industry total gross output,

  2. A = Technical coefficient matrix,

  3. Y = Vector of industry final demand.

Solving for annual gross output needed to deliver the exogenously given set of final demands yields:

(5) X = ( I − A ) − 1 Y

This can also be interpreted as:

(6) Δ X = ( I − A ) − 1 Δ Y

where

  1. Δ X = Vector of changes in industry total gross output,

  2. Δ Y = Vector of changes industry final demand.

In Section 6.4, we first estimate the vectors of direct spending changes stemming from the changes of attendance in each of the four venue/event types because of the deployment of the countermeasures ( Δ Y ) . These vectors are then used as the inputs to the I–O model to calculate the economic impacts of changes in attendance.

Appendix B: Sectoral Economic Impacts

Appendix Table B1:

Sectoral economic impacts of increased attendance at the MLB Stadium on the MSA Region – lower-bound estimate.

Sector Direct spending (106 $) Gross output (106 $) GDP (106 $) Personal income (106 $) Employment (jobs)
1 111 Crop Farming 0.00 0.01 0.01 0.00 0
2 112 Livestock 0.00 0.01 0.00 0.00 0
3 113 Forestry & Logging 0.00 0.00 0.00 0.00 0
4 114 Fishing, Hunting & Trapping 0.00 0.01 0.00 0.00 0
5 115 Ag & Forestry Svcs 0.00 0.00 0.00 0.00 0
6 211 Oil & Gas Extraction 0.00 0.33 0.27 0.29 1
7 212 Mining 0.00 0.03 0.01 0.00 0
8 213 Mining Services 0.00 0.03 0.02 0.02 0
9 221 Utilities 0.00 1.38 0.59 0.24 1
10 230 Construction 0.00 0.79 0.41 0.28 5
11 311 Food products 0.00 0.55 0.13 0.08 1
12 312 Beverage & Tobacco 0.00 0.42 0.12 0.05 1
13 313 Textile Mills 0.00 0.00 0.00 0.00 0
14 314 Textile Products 0.00 0.01 0.00 0.00 0
15 315 Apparel 0.00 0.00 0.00 0.00 0
16 316 Leather & Allied 0.00 0.00 0.00 0.00 0
17 321 Wood Products 0.00 0.04 0.01 0.01 0
18 322 Paper Manufacturing 0.00 0.09 0.02 0.02 0
19 323 Printing & Related 0.00 0.12 0.05 0.04 1
20 324 Petroleum & Coal Products 0.00 0.66 0.26 0.04 0
21 325 Chemical Manufacturing 0.00 0.16 0.05 0.03 0
22 326 Plastics & Rubber Products 0.00 0.06 0.02 0.01 0
23 327 Nonmetal Mineral Products 0.00 0.09 0.04 0.02 0
24 331 Primary Metal Mfg 0.00 0.00 0.00 0.00 0
25 332 Fabricated Metal Products 0.00 0.09 0.03 0.02 0
26 333 Machinery Mfg 0.00 0.01 0.00 0.00 0
27 334 Computer & Electronic Products 0.00 0.03 0.01 0.01 0
28 335 Electrical Eqpt & Appliances 0.00 0.01 0.00 0.00 0
29 336 Transportation Eqpmt 0.00 0.05 0.02 0.02 0
30 337 Furniture & Related Products 0.00 0.04 0.01 0.01 0
31 339 Miscellaneous Mfg 0.00 0.05 0.02 0.01 0
32 42 Wholesale Trade 0.00 2.47 1.65 0.97 10
33 441 Motor Veh & Parts Dealers 0.00 0.41 0.32 0.21 3
34 442 Furniture & Home Furnishings 0.00 0.15 0.10 0.06 1
35 443 Electronics & Appliances Stores 0.00 0.08 0.05 0.06 1
36 444 Bldg Materials & Garden Dealers 0.00 0.29 0.18 0.11 3
37 445 Food & Beverage Stores 1.67 2.14 1.47 0.99 29
38 446 Health & Personal Care Stores 0.84 1.05 0.66 0.46 11
39 447 Gasoline Stations 0.39 0.49 0.31 0.25 7
40 448 Clothing & Accessories Stores 1.07 1.35 0.82 0.39 16
41 451 Sporting Goods, Hobby, Book, & Music Stores 0.49 0.60 0.39 0.28 11
42 452 General Merchandise Stores 1.77 2.27 1.47 0.92 31
43 453 Miscellaneous Store Retailers 0.62 0.78 0.48 0.42 20
44 454 Non-Store Retailers 0.00 0.50 0.26 0.07 4
45 481 Air Transportation 0.00 0.42 0.21 0.11 1
46 482 Rail Transportation 0.00 0.07 0.04 0.02 0
47 483 Water Transportation 0.00 0.00 0.00 0.00 0
48 484 Truck Transportation 0.00 0.55 0.26 0.22 3
49 485 Transit & Ground Passengers 3.62 4.07 1.72 2.23 67
50 486 Pipeline Transportation 0.00 0.04 0.04 0.07 0
51 487 Sightseeing Transportation 0.00 0.23 0.12 0.10 1
52 492 Postal service, Couriers & Messengers 0.00 0.49 0.33 0.27 4
53 493 Warehousing & Storage 0.00 0.25 0.15 0.13 3
54 511 Publishing Industries 0.00 0.42 0.29 0.14 1
55 512 Motion Picture & Sound Recording 0.00 0.14 0.09 0.05 1
56 515 Broadcasting 0.00 0.54 0.16 0.07 1
57 517 Telecommunications 0.00 1.98 0.98 0.31 3
58 518 Internet & Data Process Svcs 0.00 0.32 0.13 0.12 1
59 519 Other Information Services 0.00 0.22 0.07 0.05 0
60 521 Monetary Authorities 0.00 0.90 0.58 0.31 3
61 522 Credit Intermediation & Related 0.00 0.85 0.47 0.45 5
62 523 Securities & Other Financial 0.00 1.50 0.53 0.51 9
63 524 Insurance Carriers & Related 0.00 3.06 1.49 0.93 11
64 525 Funds, Trusts, & Other Financial Vehicles 0.00 0.49 0.20 0.04 2
65 531 Real Estate 0.00 10.16 7.01 0.57 28
66 532 Rental & Leasing Svcs 0.00 0.38 0.22 0.10 2
67 533 Lessor of Nonfinance Intangible Assets 0.00 0.49 0.29 0.01 0
68 541 Professional, Scientific & Tech Svcs 0.00 5.74 3.74 3.23 36
69 551 Management of Companies 0.00 1.76 1.13 0.96 7
70 561 Admin Support Svcs 0.00 2.69 1.86 1.50 36
71 562 Waste Mgmt & Remediation Svcs 0.00 0.37 0.18 0.12 2
72 611 Educational Svcs 0.00 0.69 0.44 0.42 12
73 621 Ambulatory Health Care 0.00 2.29 1.52 1.39 19
74 622 Hospitals 0.00 1.39 0.79 0.71 9
75 623 Nursing & Residential Care 0.00 0.39 0.25 0.24 6
76 624 Social Assistance 0.00 0.43 0.28 0.26 10
77 711 Performing Arts & Spectator Sports 24.51 27.89 17.44 14.55 318
78 712 Museums & Similar 0.28 0.31 0.15 0.16 4
79 713 Amusement, Gambling & Recreation 3.35 3.82 2.47 1.05 39
80 721 Accommodations 5.78 5.81 3.49 1.75 56
81 722 Food Svcs & Drinking Places 9.03 11.27 6.46 4.33 179
82 811 Repair & Maintenance 0.00 0.94 0.65 0.53 9
83 812 Personal & laundry Svcs 0.00 0.48 0.30 0.36 12
84 813 Religious, Grantmaking, & Similar Orgs 0.00 0.54 0.41 0.22 4
85 814 Private Households 0.00 0.05 0.05 0.05 4
86 92 Government 0.00 0.65 0.64 0.53 7
Total 53.41 112.71 67.94 45.55 1075

  1. The bold values presented in the last row represent the total impacts (in terms of Direct Spending, Gross Output, GDP, Personal income, and Employment, respectively) of increased attendance at the MLB Stadium on the MSA Region.

Appendix C: Comparison of lower-bound and upper-bound results

Appendix Table C1:

Lower-bound and upper-bound estimates of changes in attendance.

Venue Attendees from the MSA Attendees from outside of the MSA
Lower-bound estimate Upper-bound estimate Lower-bound estimate Upper-bound estimate
MLB 23.8% 63.5% 20.8% 45.9%
NBA 41.2% 123.1% 45.8% 70.1%
NHL 11.7% 42.6% 8.4% 17.5%
MACC 42.8% 186.9% 60.0% 175.4%

  1. Sample sizes for attendees inside and outside the Metro Area are the same as in Table 1.

Appendix Table C2:

Comparison of the economic impacts lower-bound and upper-bound estimates.

  Lower-bound Upper-bound
GDP (106 $) Employment (# of jobs) GDP (106 $) Employment (# of jobs)
MLB Stadium 68 1,075 150 2,378
NBA Arena 13 202 20 310
NHL Arena 3 44 6 91
MACC 463 6,166 1,352 18,019

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Received: 2020-08-05
Accepted: 2021-10-12
Published Online: 2021-11-04

© 2021 Walter de Gruyter GmbH, Berlin/Boston

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https://doi.org/10.1515/jhsem-2020-0048
Keywords for this article
terrorism countermeasures; economic impacts; spillover effects; public assembly sites; consumer behavior

Articles in the same Issue

  1. Frontmatter
  2. Research Articles
  3. The Hazard Consequence Prediction System: A Participatory Action Research Approach to Enhance Emergency Management
  4. Strengthening Disaster Response Through Analysis of Crime During Hurricane Ike
  5. Examining the Potential Impacts of Social Vulnerability on Damage Levels in Areas Affected by Hurricane Harvey
  6. Incorporating Ethics in Disaster Communication Strategy: The Case of the U.S. Government in Deepwater Horizon
  7. Economic Impacts of Spillover Effects of Terrorism Countermeasures at Public Assembly Sites
  8. Opinions
  9. Agricultural Security: Critical National Infrastructure We Cannot Ignore
  10. The Greening of Homeland Security
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