L Long Long- -Term Recovery After a Disaster: T T Term Recovery - PowerPoint PPT Presentation

L Long Long- -Term Recovery After a Disaster: T T Term Recovery After a Disaster: R R Aft Aft Di Di t t International Comparisons International Comparisons Ilan Ilan Noy Noy EQC EQC- -MPI Chair in the Economics of Disasters MPI Chair in the Economics of Disasters Professor of Economics Professor of Economics Victoria Victoria Business Business School School

The build The build back The build The build-back back better tale back-better tale better tale better tale

The Vision The Vision The Vision The Vision “At “At CDC we remain fully focused on CDC we remain fully focused on our vision…that our vision…that in in 2031 Christchurch is recognised 2031 Christchurch is 2031 Christchurch is recognised 2031 Christchurch is recognised as recognised as as the best place for as the best place for the best place for the best place for business, work business, work, study , study and living in and living in Australasia.” Australasia.” Tom Hooper, CEO, Canterbury Development Corporation Tom Hooper, CEO, Canterbury Development Corporation (from the Canterbury Report, Autumn 2014, p. 3) (from the Canterbury Report, Autumn 2014, p. 3)

Good comparisons? Good comparisons? Good comparisons? Good comparisons? Galveston, TX Galveston, TX – – 1900 1900 San Francisco San Francisco – 1906 1906 Messina Messina – – 1908 1908 Tokyo Tokyo – – 1923 1923 Kobe Kobe – – 1995 1995 New Orleans New Orleans – 2005 2005 Smaller places? (Napier?) Smaller places? (Napier?)

Kobe: What Happened? Kobe: What Happened? Kobe: What Happened? Kobe: What Happened?

Kobe’s decline Kobe’s decline (per capita income) (per capita income) 4.5 4 4 3.5 3 Hyogo y g 2.5 Synthetic Hyogo 2 1.5 1 1976 1981 1986 1991 1996 2001 2006

Looking at Kobe’s wards and towns Looking at Kobe s wards and towns Looking at Kobe s wards and towns Looking at Kobe’s wards and towns Example results: Population for Nishinomiya Example results: Population for Nishinomiya 1.2 1.15 1 15 1.1 1.05 1 05 1 0 95 0.95 Synthetic Nishinomiya 0.9 0.85 0.8 1980 1985 1990 1995 2000 2005 2010

K b K b Kobe: Population Kobe: Population P P l ti l ti (% deviations from synthetic counterfactual) (% deviations from synthetic counterfactual)

K b K b Kobe: Taxable Income Kobe: Taxable Income T T bl bl I I (% deviations from synthetic counterfactual) (% deviations from synthetic counterfactual)

K b K b Kobe: Unemployed Kobe: Unemployed U U l l d d (% deviations from synthetic counterfactual) (% deviations from synthetic counterfactual)

Conclusions about Kobe’s EQ impact Conclusions about Kobe s EQ impact Conclusions about Kobe s EQ impact Conclusions about Kobe’s EQ impact Long Long ong-run negative impact on Kobe s economy ong run negative impact on Kobe’s economy run negative impact on Kobe’s economy run negative impact on Kobe s economy Population and Population and i p income are all below the ncome are all below the counterfactual, while the number of unemployed is counterfactual, while the number of unemployed is above. above. This varies by Wards: This varies by Wards: The central and most devastated wards were negatively The central and most devastated wards were negatively The central and most devastated wards were negatively The central and most devastated wards were negatively affected. affected. Those less devastated, or near Osaka were not hose less devastated, or near Osaka were not affected, or even benefited. affected, or even benefited. This despite of a massive government investment and This despite of a massive government investment and This despite of a massive government investment and This despite of a massive government investment and a quick reconstruction period. a quick reconstruction period.

Other cases? Other cases? Other cases? Other cases? Dustbowl Dustbowl Katrina Katrina Hilo tsunami Hilo tsunami Man made events?

What’s happening in Canterbury? What s happening in Canterbury?

Any warning signs? Any warning signs?

The cost of rebuilding The cost of rebuilding The cost of rebuilding The cost of rebuilding % GDP % GDP % GDP % GDP 2.0 2.0 June 2011 1.8 1.8 Dec 2011 June 2012 1.6 1.6 Dec 2012 1.4 1.4 June 2013 1 2 1.2 1 2 1.2 1.0 1.0 0.8 0.8 0.6 0.6 0.4 0.4 0.2 0.2 0.2 0.2 0.0 0.0 10 11 12 13 14 15 16

Insurance Insurance Insurance Insurance

Legal Complications Legal Complications Legal Complications Legal Complications

Population Population - Canterbury Population Population Canterbury Canterbury Canterbury Females Males -4,000 -3,000 -2,000 -1,000 0 1,000 2,000 3,000 4,000 5,000

Business migration Business migration - - Canterbury Canterbury

More recent warning signs? More recent warning signs? More recent warning signs? More recent warning signs? The commercial rebuilding in the CBD area The commercial rebuilding in the CBD area has slowed down recently has slowed down recently has slowed down recently. has slowed down recently. Volume of building consents is increasing, but Volume of building consents is increasing, but g g g g fairly slowly. fairly slowly. R R Residential housing pressures are mounting. Residential housing pressures are mounting. id id ti l h ti l h i i ti ti Very low unemployment rate without Very low unemployment rate without Very low unemployment rate without Very low unemployment rate without corresponding increase in migration. corresponding increase in migration. University in trouble. University in trouble.

Why hurry? Why hurry? Why hurry? Why hurry? What can prevent a bad outcome? What can prevent a bad outcome? S S Speed Speed d d Post- Post -reconstruction employment reconstruction employment A functioning CBD A functioning CBD What can speed up the rebuild? What can speed up the rebuild? What can speed up the rebuild? What can speed up the rebuild? Insurance Insurance The courts he courts

Central vs Local Gov’t Central vs Local Gov’t Central vs. Local Gov t Central vs. Local Gov t

The The The bigger picture? The bigger picture? igger picture? igger picture? Cavallo et al. (2013): No long Cavallo et al. (2013): No long- -run adverse run adverse impact of catastrophic natural disasters on impact of catastrophic natural disasters on impact of catastrophic natural disasters on impact of catastrophic natural disasters on national GDP. national GDP. D D Do we care about Christchurch? Do we care about Christchurch? b b t Ch i t h t Ch i t h h? h?

Two more observations Two more observations Two more observations Two more observations

MY BIBLIOGRAPHY MY BIBLIOGRAPHY MY BIBLIOGRAPHY MY BIBLIOGRAPHY Cavallo & Noy (2011) Natural disasters and the economy Cavallo & Noy (2011). Natural disasters and the economy – A Survey. A Survey International Review of Environmental and Resource Economics . Cavallo, Galiani, Noy & Pantano (2013). Catastrophic Natural Disasters and y ( ) p Economic Growth. Review of Economics and Statistics . Coffman & Noy (2012). Hurricane Iniki: Measuring the Long-Term Economic Impact of a Natural Disaster Using Synthetic Control Environment and Impact of a Natural Disaster Using Synthetic Control. Environment and Development Economics . duPont, Yokohama, Noy, & Sawada (2014). The (Non) Recovery of Kobe. , , y, ( ) ( ) y Working paper. duPont & Noy (2014). What happened to Kobe? A reassessment of the impact of the 1995 earthquake. Economic Development and Cultural Change. i t f th 1995 th k E i D l t d C lt l Ch Lynham, Noy & Page (2013). The 1960 Tsunami in Hawaii: Long Term Consequences of a Coastal Disaster. Working paper. q g p p

THANK YOU THANK YOU THANK YOU THANK YOU

The synthetic counterfactual The synthetic counterfactual The synthetic counterfactual The synthetic counterfactual Model: Model: ( ˆ w 2 ,..., ˆ ( w 2 ,..., w J  1 ) 1 ) w Suppose there is a set of optimal weights Suppose there is a set of optimal weights Suppose there is a set of optimal weights Suppose there is a set of optimal weights such that such that   J   1 1 J    2 ˆ , {1,2,..., } w Y Y t T  1 0 j jt t j and and 1 ˆ    J  w Z Z Z Z  1 j j 2 j

The synthetic counterfactual The synthetic counterfactual The synthetic counterfactual The synthetic counterfactual Model: Model: Then (as shown by Abadie et al. (2010) ): Then (as shown by Abadie et al. (2010) ): ( ( y y ( ( ) ) ) )    J 1  N ˆ Y Y 1 t j jt  2 j This suggests using: This suggests using:     1   J ˆ ˆ Y w Y 1 1 1 1 j  t t t t j j jt jt j 2 2 as an estimator for as an estimator for  1 t