Policy diffusion in federal systems during a state of emergency: diffusion of COVID- 19 statewide lockdown policies across the United States

• Autorzy: Sharon Elhadad , Udi Sommer

Warianty tytułu

Dyfuzja polityki publicznej w systemach federalnych podczas stanu wyjątkowego. Upowszechnianie polityki lockdownu podczas pandemii COVID-19 w Stanach Zjednoczonych

• Języki publikacji: EN

Abstrakty

Niniejszy artykuł przedstawia ujednolicony model dyfuzji polityki publicznej w celu analizy szybkości przyjmowania stanowych polityk lockdownu w systemie federalnym podczas pandemii COVID-19. Został tu zbudowany zmodyfikowany ujednolicony model w celu lepszego zrozumienia dyfuzji polityki publicznej w kontekstach, w których istniejące modele nie spełniają oczekiwań. Wyróżniono trzy główne kanały dyfuzji polityki publicznej: regionalny, wertykalny i wewnętrzny. Artykuł zawiera empiryczny test modelu na przykładzie Stanów Zjednoczonych i stwierdza, że efekty wertykalne, takie jak wyższe oceny poparcia dla prezydenta Donalda Trumpa, a także stosunkowo wysoki udział federalnego wsparcia finansowego na walkę z COVID-19, mają silny pozytywny związek z szybkością przyjmowania lockdownu. Ponadto ważne są również pewne efekty wewnętrzne - wyższe oceny akceptacji gubernatorów są pozytywnie powiązane z szybkością polityki przyjmowania lockdownu w całym stanie, podobnie jak wydatki stanowe i lokalne, demokratyczne rządy i świadomość ludności na temat wirusa. Jednak inne czynniki wewnętrzne, takie jak rygorystyczne stanowe polityki lockdownu i względny odsetek zgonów z powodu COVID- 19 na poziomie stanowym, były minimalnie związane z szybkością przyjmowania polityki lockdownów. Co zaskakujące, w przeciwieństwie do wcześniejszych badań, horyzontalne efekty regionalne nie odegrały znaczącej roli w analizie - szybkość przyjmowania polityki lockdownu przez sąsiednie państwa nie ma związku z tempem przyjmowania lockdownu w całym kraju. Ogólnie rzecz biorąc, wyniki sugerują silny wpływ czynników politycznych na szybkość wdrażania polityki lockdownu w Stanach Zjednoczonych na poziomie stanowym.(abstrakt oryginalny)

EN

This paper develops a unified model of policy diffusion to analyze the speed of adoption of statewide lockdown policies within a federal system during the COVID-19 pandemic. The modified unified model was built to improve our understanding of policy diffusion in contexts where existing models fall short. The authors highlight three main policy diffusion channels: regional, vertical, and internal. The paper shows the empirical test of the model across US states and finds that vertical effects, such as higher approval ratings for President Donald Trump, as well as a comparatively high proportion of COVID-19 federal funding support, bear a strong positive association with the speed of statewide lock-down adoption policies. In addition, certain internal effects are also important - higher governor approval ratings are positively associated with the speed of statewide lockdown adoption policies, as are state and local spending, democratic state governments, and population awareness of the virus. However, other internal factors, such as the stringency of statewide lockdown policies and the relative proportion of COVID-19 deaths in a state, were minimally associated with the speed of lockdown policy adoption. Surprisingly, unlike past studies, horizontal regional effects did not play a significant role in the presented analysis - the speed of adoption of lockdown policies by neighboring states bears no as-sociation with the speed of policy adoption of statewide lockdowns. Overall, the results suggest a strong influence of political factors on the speed of statewide lockdown adoption policies in the US.(original abstract)

Słowa kluczowe

PL

COVID-19; Dyfuzja; Polityka publiczna; Zarządzanie kryzysowe

EN

COVID-19; Diffusion; Public policy; Crisis management

• Czasopismo: Studia z Polityki Publicznej / Szkoła Główna Handlowa
• Rocznik: 2022
• Tom: 9
• Numer: nr 1
• Strony: 55-82

Twórcy

autor

Sharon Elhadad

• University of Tel Aviv, Tel Aviv, Izrael

autor

Udi Sommer

• University of Tel Aviv, Tel Aviv, Izrael

Bibliografia

• Adolph, C., Amano, K., Bang-Jensen, B., Fullman, N., Wilkerson, J. (2021). Pandemic politics: Timing state-level social distancing responses to COVID-19. "Journal of Health Politics, Policy and Law", 46 (2): 211-233.
• Barak, N., Sommer, U., Mualam, N. (2021). Urban attributes and the spread of COVID-19: The effects of density, compliance and socio-political factors in Israel. Science of the Total Environment, 148626.
• Berry, F. S., Berry, W. D. (1990). State lottery adoptions as policy innovations: An event history analysis. "American Political Science Review", 84 (2): 395-415.
• Berry, W. D., Baybeck, B. (2005). Using geographic information systems to study interstate competition. "American Political Science Review", 99 (4): 505-519.
• Blake, A. (2020). 49 of 50 Governors Have Better Coronavirus Poll Numbers than Trump. The Washington Post. https://www.washingtonpost.com/politics/2020/05/19/49-50-governors-have-better-coronavirus-numbers-than-trump/ (accessed: 6.02.2021).
• Bowling, C. J., Pickerill, J. M. (2013). Fragmented federalism: The state of American federalism 2012-13. "Publius: The Journal of Federalism", 43 (3): 315-346.
• Capano, G., Howlett, M., Jarvis, D. S., Ramesh, M., Goyal, N. (2020). Mobilizing policy (in) capacity to fight COVID-19: Understanding variations in state responses. "Policy and Society", 39 (3): 285-308.
• CDC (Centers for Disease Control and Prevention) (2019). Coronavirus Disease (COVID-19) - Cleaning and Disinfection for Households. https://www.cdc.gov/coronavirus/2019-ncov/prevent-getting-sick/cleaning-disinfection.html (accessed: 5.02.2021).
• CDC (Centers for Disease Control and Prevention) (2020). About Global Health Security, https://www.cdc.gov/globalhealth/healthprotection/ghs/about.html (accessed: 1.02.2021).
• Chantrill, C. (2020). Monthly Federal Spending/Revenue/Deficit Charts Federal Coronavirus/COVID-19 Response. United States Government Spending. https://www.usgovern-mentspending.com/gdp_by_state (accessed: 5.03.2021).
• Cheng, C., Barceló, J., Hartnett, A.S., Kubinec, R., Messerschmidt, L. (2020). COVID- 19 government response event dataset (CoronaNet v. 1.0). "Nature Human Behaviour", 4 (7): 756-768.
• Cillizza, C. (2020). 7 Governors Still Haven't Issued Stay-At-Home Orders. Here's Why. CNN. https://www.cnn.com/2020/04/13/politics/asa-hutchison-arkansas-coronavirus/index.html (accessed: 9.02.2021).
• CIVIQS (2020). Donald Trump: Job Approval Registered Voters. https://civiqs.com/results/approve_president_trump?annotations=true&uncertainty=true&zoomIn=true (accessed: 7.02.2021).
• Cyert, R. M., March, J. G. (1963). A Behavioral Theory of the Firm. Englewood Cliffs, NJ: Prentice-Hall.
• Eaton, L. J. (2013). Policy adoption by state governments: An event history analysis of factors influencing states to enact inpatient health care transparency laws (Doctoral dissertation, The Florida State University).
• Elazar, D. J. (1972). American federalism: A view from the states. 3 rd ed. New York, NY: Harper & Row.
• Ellerbeck, A. (2021). The Health 202: Here's How the U. S. Compares to Other Countries on the Coronavirus Pandemic. The Washington Post. https://www.washingtonpost.com/ politics/2021/04/12/health-202-here-how-us-compares-other-countries-coronavirus- pandemic/ (accessed: 4.02.2021).
• Google Trend Search (2020). Coronavirus Search Trend. https://trends.google.com/trends/story/US_cu_4Rjdh3ABAABMHM_en (accessed: 15.02.2021).
• Gostin, L. O., Friedman, E. A., Wetter, S. A. (2020). Responding to COVID-19: how to navigate a public health emergency legally and ethically. Hastings Center Report, 50 (2): 8-12.
• Gray, V. (1973). Innovation in the states: A diffusion study. "American Political Science Review", 67 (4): 1174-1185.
• Grossback, L. J., Nicholson-Crotty, S., Peterson, D. A. (2004). Ideology and learning in policy diffusion. "American Politics Research", 32 (5): 521-545.
• Hart, P., Heyse, L., Boin, A. (2001). New trends in crisis management practice and crisis management research: Setting the agenda. "Journal of Contingencies and Crisis Management", 9 (4): 181-188.
• Holzinger, K., Knill, C. (2005). Causes and conditions of cross-national policy convergence. "Journal of European Public Policy", 12 (5): 775-796.
• Jones, J. (2016). Illinois Residents Least Confident in Their State Government. Gallup. https://news.gallup.com/poll/189281/illinois-residents-least-confident-state-government.aspx (accessed: 20.02.2021).
• Kamisar, B., Holzberg, M. (2020). Americans Trust Governors More Than Trump on Coronavirus. NBC News. https://www.nbcnews.com/politics/2020-election/americans-trust-governors-more-trump-coronavirus-new-poll-finds-n1233031 (accessed: 6.03.2021).
• Koerth, M. (2020). Americans Don't Trust the People in Charge of the Coronavirus Fight. Five Thirty-Eight. https://fivethirtyeight.com/features/politicians-are-the-last-people-americans-want-fighting-coronavirus/ (accessed: 1.02.2021).
• Levi-Faur, D. (Ed.) (2011). Handbook on the Politics of Regulation. Cheltenham, UK: Edward Elgar Publishing.
• Lundgren, M., Klamberg, M., Sundström, K., Dahlqvist, J. (2020). Emergency powers in response to COVID-19: Policy Diffusion, democracy, and preparedness. "Nordic Journal of Human Rights", 38 (4): 305-318.
• Mallinson, D. J. (2016). Building a better speed trap: Measuring policy adoption speed in the American states. "State Politics & Policy Quarterly", 16 (1): 98-120.
• Mallinson, D. J. (2020). Cooperation and conflict in state and local innovation during COVID-19. "The American Review of Public Administration", 50 (6-7): 543-550.
• McCannon B. C. (2021). Do Governors Lead or Follow? Timing of Stay-at-Home Orders. "Eastern Economic Journal", 47: 506-518. DOI: 10.1057/s41302-021-00204-9.
• Mei, C. (2020). Policy style, consistency and the effectiveness of the policy mix in China's fight against COVID-19. "Policy and Society", 39 (3): 309-325.
• Mervosh, S., Swales, D. (2020). See Which States and Cities Have Told Residents to Stay at Home. "The New York Times". https://www.nytimes.com/interactive/2020/us/coronavirus-stay-at-home-order.html (accessed: 17.03.2021).
• Mohr, L. B. (1969). Determinants of innovation in organizations. "American Political Science Review", 63 (1): 111-126.
• Monaghan, H. P. (1993). The Protective Power of the Presidency. "Columbia Law Review", 93 (1): 1-74.
• Mooney, C. Z., Lee, M. H. (1995). Legislative morality in the American states: The case of pre-Roe abortion regulation reform. "American Journal of Political Science": 599-627.
• Moore, S., Laffer, A., Griffith, J. (2015, May). 1,000 People a Day: Why Red States Are Getting Richer and Blue States Poorer. The Heritage Foundation: 1-19.
• National League of Cities. (2020). What's the Difference Between Shelter in Place, Safer at Home, and Stay Home Orders? https://citiesspeak.org/2020/03/30/whats-the-difference-between-shelter-in-place-safer-at-home-and-stay-home-orders/

Identyfikatory

DOI

https://doi.org/10.33119/KSzPP/2022.1.3