Economic Resilience and Regional Covid-19
Description: Economic Resilience and Regional Covid-19 Policies: Exploring mitigation strategies in the Netherlands. Mark Thissen Frank van Oort Anet Weterings 12-07-2023 IMF Policy Tracker Countries with regional Covid19 policy responses Australia,
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slide1. Economic Resilience and Regional Covid-19 Policies: Exploring mitigation strategies in the Netherlands. Mark Thissen
Frank van Oort
Anet Weterings
12-07-2023<br>
slide2. IMF Policy Tracker Countries with regional Covid19 policy responses
Australia, China, France, Germany
Guyana, Guinea, Iran, Italy
Jordan, Kazachstan, Maldives
Myanmar, Namibia, Peru
Polen, Russia, Spain, Togo
Turkey, Ukraine, US<br>
slide3. Regional-economic Covid19 Policies Impact studies:
Regional differences, institutions and policies (Bourdin et al. 2021, Rodriguez-Pose et al. 2021, Porsse et al. 2021, McCann et al 2021, 2022).
Regional Employment consequences (Bartik et al. 2020, Doerr et al. 2020, Gardiner et al, 2021, Capello et al. 2022, Kim et al 2023).
Sector composition and Trade (Meinen et al 2021, Brakman et al. 2020).
Mitigation Studies:
Targetting specific groups (Bricongne and Meunier 2021, Baldwin en Di Maura 2020).
Targetting regions (Crucinni & O’Flaherty 2020, Bonardi et al. 2021).
Interregional market mechanims:
Self-organising capacities of regional economies (Polenske & Hewings 2005).<br>
slide4. Place-based Policies Domestic version: “Industrial Transfer Policies" (ITP).
Policy instruments: encourage both the movement of workers (migration) and manufacturing jobs from coastal areas to inland areas.
Aims: promote urbanization and create manufacturing hubs in targeted inland cities. Example of so-called “big-push” interventions (Duranton & Venables 2021, Grover et al. 2022).
What are the demographic and economic impacts of ITP on targeted cities?
First, we match ITP targeted cities with cities that were similarly likely to receive the treatment from central government.
Second, we estimate how migration and economic outcomes differ between ITP cities and their closest matches.<br>
slide5. A Wicked Problem How to determine optimal regional economic mitigation policies when the policy objective is non-economic?
No easy optimization rules (goal conflict) resulting in
Too many regional combinations of policy options (complexity)
6 policy options in 12 Dutch regions gives more than 2 billion possible combinations
Where results are highly uncertain (informational uncertainty)
Wicked problems are generally characterized by:
System complexity, goal conflict and informational uncertainty (Irschke et. al., 2019)
where wickedness is a matter of degree (Head and Alford, 2015).<br>
slide6. A Wicked Problem<br>
slide7. Model sector and region-specific model of Supply
Based on productivity effects of policy measures via the labor market.
Model sector and region-specific model of Demand
Interregional Input-Output based, focused on the demand in producing region.
Determine region specific excess demand\supply
Determine potential benefit from interregional economic interaction.
The degree of coexisting sector specific excess demand and excess supply over different regions.
We use mixed integer programming to find an optimal policy solution for the wicked problem, abstracting from informational uncertainty.
The solution to the wicked problem gives an indication for potential mitigation effects due to synergies in the regional economy. Analysis<br>
slide8. Productivity reducing Production constraining Consumption reducing
and changing Policies: Calibration
Measures have been calibrated to reproduce the effect of the first wave of the Covid pandemic in the Netherlands (March-April 2020) according to monthly and quarterly data of Statistics Netherlands (CBS) Supply related policy measures Demand related policy measures Scenario’s of Policies<br>
slide9. We followed Leibovici et al. (2020) and Dingel and Neiman (2020) to determine for all professions whether they can be exercised with social distancing or potentially be done from home using the Occupational Information Network (O*NET) database.
We added information on having young children (school and day-care restrictions), distance to work (transport restrictions) and vital professions (exceptions).
The occupational data has been matched to sector data based on professions in every sector.
Impacts of different policies on the labor force have been recalibrated to match the sector specific productivity effects that occurred during the first wave of the pandemic. Supply<br>
slide10. Supply Side effects of Covid19 related policies: Example Utrecht Province Effects of Covid19 related policies are sector specific.
Especially production constraints in the airline sector, tourism and accommodation & food services are large in scenario 3 (the calibration scenario).
But also: ICT-services, private services, transport services, food<br>
slide11. We used a standard Leontief interregional Input-Output model based on the EUREGIO database to determine demand in the location of production given:
The effect in final demand because of a change in consumption patterns and a reduction in consumption demand due to the different policy measures scaled to the size of the changes during the first wave of the pandemic.
Excess demand E is determined by subtracting sector specific supply from demand at the location of production.
The potential benefit M from interregional economic interaction: Demand and Excess demand<br>
slide12. Interregional demand in the Netherlands (linkages between provinces) 50 percent of the economic effect of national Covid19 related policies are spillover effects from other regions.
The economic effect of policies in a province are equal to the effect on the other 11 provinces together.<br>
slide13. Notice: No regional synergy effect in almost all sectors in the case of nationally implemented policies. Potential synergy/substitution effects: mismatch in regional supply and demand Only small synergy effect in agriculture<br>
slide14. Potential synergy/complementarity/substitution effects due to region specific policies Region specific policies mitigate the effects of Covid19 related policies:
Average regional Excess Demand: This is the average absolute excess demand and supply over all scenarios.
National policies (the same policies in all regions): Small potential synergy effects (0.02 percent of GDP).
Random regional policies: Potential synergy effect is 0.5 percent of total GDP. This is calculated as the average of 200 million randomly chosen policy combinations.
Region specific policies: Potential synergy effect is 1.4 percent of total GDP (which is 25% of GDP-loss).
Region and sector specific policies: Larger sector specific synergies at the cost of total GDP (last column of the table).<br>
slide15. Conclusions: Regional and sector specific variation in the effects of Covid19 related policies may mitigate the economic impact of Covid19 related policies
Regional synergy effects that mitigate the economic impact depend on the interaction of all regional policies and are not determined by policies in only one region (cf foreign regions).
Regional mitigation policies targeted at specific sectors have a price: total GDP will be lower.
Variation in effects of policies determines the degree of mitigation: The largest mitigation is achieved when sector specific supply and demand effects vary strong over the regions, but they are not necessarily related to the size of the measures taken.
How to find the optimal policy mix to achieve the maximum variation in regional effects, is the core of the wicked Covid19 regional policy problem. Interaction with policy in learning evaluation process (Edelenbos et al., 2023)<br>
slide16. Discussion EU-wide analysis of regional policies (dif-in-dif)
Learning evaluation set-up foreseen but not completed
Other shocks than Pandemic: mitigation strategies (sea-level rise, Ukraine War, critical inputs like raw materials, chips, medicine, etc., previously: Brexit)
Wickedness and a-symmetry is exponentially complex.<br>
slide17. Thank you for your attention<br>
Frank van Oort
Anet Weterings
12-07-2023<br>
slide2. IMF Policy Tracker Countries with regional Covid19 policy responses
Australia, China, France, Germany
Guyana, Guinea, Iran, Italy
Jordan, Kazachstan, Maldives
Myanmar, Namibia, Peru
Polen, Russia, Spain, Togo
Turkey, Ukraine, US<br>
slide3. Regional-economic Covid19 Policies Impact studies:
Regional differences, institutions and policies (Bourdin et al. 2021, Rodriguez-Pose et al. 2021, Porsse et al. 2021, McCann et al 2021, 2022).
Regional Employment consequences (Bartik et al. 2020, Doerr et al. 2020, Gardiner et al, 2021, Capello et al. 2022, Kim et al 2023).
Sector composition and Trade (Meinen et al 2021, Brakman et al. 2020).
Mitigation Studies:
Targetting specific groups (Bricongne and Meunier 2021, Baldwin en Di Maura 2020).
Targetting regions (Crucinni & O’Flaherty 2020, Bonardi et al. 2021).
Interregional market mechanims:
Self-organising capacities of regional economies (Polenske & Hewings 2005).<br>
slide4. Place-based Policies Domestic version: “Industrial Transfer Policies" (ITP).
Policy instruments: encourage both the movement of workers (migration) and manufacturing jobs from coastal areas to inland areas.
Aims: promote urbanization and create manufacturing hubs in targeted inland cities. Example of so-called “big-push” interventions (Duranton & Venables 2021, Grover et al. 2022).
What are the demographic and economic impacts of ITP on targeted cities?
First, we match ITP targeted cities with cities that were similarly likely to receive the treatment from central government.
Second, we estimate how migration and economic outcomes differ between ITP cities and their closest matches.<br>
slide5. A Wicked Problem How to determine optimal regional economic mitigation policies when the policy objective is non-economic?
No easy optimization rules (goal conflict) resulting in
Too many regional combinations of policy options (complexity)
6 policy options in 12 Dutch regions gives more than 2 billion possible combinations
Where results are highly uncertain (informational uncertainty)
Wicked problems are generally characterized by:
System complexity, goal conflict and informational uncertainty (Irschke et. al., 2019)
where wickedness is a matter of degree (Head and Alford, 2015).<br>
slide6. A Wicked Problem<br>
slide7. Model sector and region-specific model of Supply
Based on productivity effects of policy measures via the labor market.
Model sector and region-specific model of Demand
Interregional Input-Output based, focused on the demand in producing region.
Determine region specific excess demand\supply
Determine potential benefit from interregional economic interaction.
The degree of coexisting sector specific excess demand and excess supply over different regions.
We use mixed integer programming to find an optimal policy solution for the wicked problem, abstracting from informational uncertainty.
The solution to the wicked problem gives an indication for potential mitigation effects due to synergies in the regional economy. Analysis<br>
slide8. Productivity reducing Production constraining Consumption reducing
and changing Policies: Calibration
Measures have been calibrated to reproduce the effect of the first wave of the Covid pandemic in the Netherlands (March-April 2020) according to monthly and quarterly data of Statistics Netherlands (CBS) Supply related policy measures Demand related policy measures Scenario’s of Policies<br>
slide9. We followed Leibovici et al. (2020) and Dingel and Neiman (2020) to determine for all professions whether they can be exercised with social distancing or potentially be done from home using the Occupational Information Network (O*NET) database.
We added information on having young children (school and day-care restrictions), distance to work (transport restrictions) and vital professions (exceptions).
The occupational data has been matched to sector data based on professions in every sector.
Impacts of different policies on the labor force have been recalibrated to match the sector specific productivity effects that occurred during the first wave of the pandemic. Supply<br>
slide10. Supply Side effects of Covid19 related policies: Example Utrecht Province Effects of Covid19 related policies are sector specific.
Especially production constraints in the airline sector, tourism and accommodation & food services are large in scenario 3 (the calibration scenario).
But also: ICT-services, private services, transport services, food<br>
slide11. We used a standard Leontief interregional Input-Output model based on the EUREGIO database to determine demand in the location of production given:
The effect in final demand because of a change in consumption patterns and a reduction in consumption demand due to the different policy measures scaled to the size of the changes during the first wave of the pandemic.
Excess demand E is determined by subtracting sector specific supply from demand at the location of production.
The potential benefit M from interregional economic interaction: Demand and Excess demand<br>
slide12. Interregional demand in the Netherlands (linkages between provinces) 50 percent of the economic effect of national Covid19 related policies are spillover effects from other regions.
The economic effect of policies in a province are equal to the effect on the other 11 provinces together.<br>
slide13. Notice: No regional synergy effect in almost all sectors in the case of nationally implemented policies. Potential synergy/substitution effects: mismatch in regional supply and demand Only small synergy effect in agriculture<br>
slide14. Potential synergy/complementarity/substitution effects due to region specific policies Region specific policies mitigate the effects of Covid19 related policies:
Average regional Excess Demand: This is the average absolute excess demand and supply over all scenarios.
National policies (the same policies in all regions): Small potential synergy effects (0.02 percent of GDP).
Random regional policies: Potential synergy effect is 0.5 percent of total GDP. This is calculated as the average of 200 million randomly chosen policy combinations.
Region specific policies: Potential synergy effect is 1.4 percent of total GDP (which is 25% of GDP-loss).
Region and sector specific policies: Larger sector specific synergies at the cost of total GDP (last column of the table).<br>
slide15. Conclusions: Regional and sector specific variation in the effects of Covid19 related policies may mitigate the economic impact of Covid19 related policies
Regional synergy effects that mitigate the economic impact depend on the interaction of all regional policies and are not determined by policies in only one region (cf foreign regions).
Regional mitigation policies targeted at specific sectors have a price: total GDP will be lower.
Variation in effects of policies determines the degree of mitigation: The largest mitigation is achieved when sector specific supply and demand effects vary strong over the regions, but they are not necessarily related to the size of the measures taken.
How to find the optimal policy mix to achieve the maximum variation in regional effects, is the core of the wicked Covid19 regional policy problem. Interaction with policy in learning evaluation process (Edelenbos et al., 2023)<br>
slide16. Discussion EU-wide analysis of regional policies (dif-in-dif)
Learning evaluation set-up foreseen but not completed
Other shocks than Pandemic: mitigation strategies (sea-level rise, Ukraine War, critical inputs like raw materials, chips, medicine, etc., previously: Brexit)
Wickedness and a-symmetry is exponentially complex.<br>
slide17. Thank you for your attention<br>