Responding to Natural Hazards: The Effects of Disaster on - PowerPoint PPT Presentation

Responding to Natural Hazards: The Effects of Disaster on Residential Location Decisions and Health Outcomes James Price Department of Economics University of New Mexico April 6 th , 2012 1

Introduction Analyses Overview Residential Sorting and the Value of Hazard 1. Risk Reduction County Migration Patterns and the Risk of 2. Natural Hazards Determinants of Mental Health & Displacement 3. Following Hurricanes Katrina and Rita 2

Residential Sorting Residential Sorting  Current Efforts:  Improve Disaster Risk Management (DRM) investments  Attempts to quantify the benefits and costs of DRM interventions  Objective: Estimate WTP for reductions in hazard risk within the United States 3

Residential Sorting Previous Literature  Theoretical Framework  Ehrlich and Becker (1972)  Berger et al. (1987)  Hedonic Housing Analyses  Residential Sorting  Timmins (2007)  Bayer et al. (2009) 4

Residential Sorting Theoretical Model  Two-Stage Optimization Problem 1. Determine the optimal allocation of income between consumption goods 2. Select the location that maximizes utility, taking into account location-specific attributes  Assumptions  Knowledge of markets and amenities at each location  Labor and housing market equilibrium  Costly migration 5

Residential Sorting Theoretical Model (cont.)  Expected Utility Function and Budget Constraint E ( U ij ) = (1 � � ) U ND ( C i , H i ; X j , M ij ) + � U D ( C i , H i ; X j , M ij ) I ij = C + � j H C = Composite Numeraire Good H = Housing Services X = Location -Specific Attributes M = Migration Cost � = Probability of Hazard Occurance I = Household Income � = The Price of Housing Services 6

Residential Sorting Theoretical Model (cont.)  Indirect Expected Utility Function ND , � j ND ; X j ND , M ij ) + � V D ( I ij D , � j D ; X j D , M ij ) E ( V ij ) = (1 � � ) V ND ( I ij  Select Optimal Location E ( V ij ) > E ( V ik ) � j � k , k = 1,2,..., j 7

Residential Sorting Empirical Model  Utility Function and Budget Constraint � X + M ij + � j + � ij � C H i � H e X j U ij = C i I ij = C + � j H  Demand Functions � � � � I ij � C � H C i = I ij H i = � � � � � C + � H � C + � H � j � � � � 8

Residential Sorting Empirical Model (cont.)  Indirect Utility Function ( ) = � + � I ln I ij ( ) � � H ln � j ( ) + � X X j + M ij + � j + � ij ln V ij � � � � where � = � c ln � c � + � H ln � H � � � � I � I � � � � and � I = � C + � H  Marginal Willingness-to-Pay i = MU X = � X MWTP I ij MU I � I 9

Residential Sorting Empirical Model (cont.)  Indirect Utility Function ( ) = � + � I ln I ij ( ) � � H ln � j ( ) + � X X j + M ij + � j + � ij ln V ij � � � � where � = � c ln � c � + � H ln � H � � � � I � I � � � � and � I = � C + � H ( ) = ln Î ij ( ) + � ij ln I ij * � j = � j S + � MD M ij D + � MR M ij R M ij = � MS M ij 10

Residential Sorting Empirical Model (cont.)  Indirect Utility Function ( ) + � X X j ( ) = � + � I ln Î ij ( ) � � H ln � * ln V ij j S + � MD M ij D + � MR M ij R + � j + � I � ij + � ij + � MS M ij  Indirect Utility Function S + � MD M ij D + � MR M ij R + � j + � ij ( ) = � I ln Î ij ( ) + � MS M ij ln V ij ( ) + � X X j + � j * where � j = � � � H ln � j and � ij = � I � ij + � ij 11

Residential Sorting Empirical Model (cont.)  Conditional Logit Model S + � MD M ij D + � MR M ij R + � j ( ) + � MS M ij � I ln º ij e P [ln( V ij ) � ln( V ik )] = j S + � MD M ij D + � MR M ij R + � j ( ) + � MS M ij � I ln º ij � e k = 1 12

Residential Sorting Empirical Model (cont.)  Quality-of-Life Decomposition ( ) + � X X j + � j * � j = � � � H ln � j � � I ij � j H i � H H i = � � H = � I � � I ij � C + � H � j � � ( ) = � + � X X j + � j * � j + � H ln � j 13

Residential Sorting Data  2005-2009 American Community Survey  Housing services regression  Wage regressions  Conditional logit model 14

Residential Sorting Data (cont.) 15

Residential Sorting Data (cont.) • Expected Number of Disaster Events 16

Residential Sorting Data (cont.) • Expected Number of Disaster Events by MSA 17

Residential Sorting Results: Conditional Logit * p<0.1 ** p<0.05 *** p<0.01 N=50,000 18

Residential Sorting Results: Quality-of-Life Index 19

Residential Sorting Results: Quality-of-Life Decomposition 20 * p<0.1 ** p<0.05 *** p<0.01 N=296

Residential Sorting Results: WTP Estimates MWTP Variable (Median Income) TEMP (°F) $759 PRECIP (Inches) $383 EMISSIONS (Lb/Per) ($154) NPLSITES (Sites) ($213) HRISK (Events/1000 Years) ($275) 21

Residential Sorting Conclusions  Residential location decisions are partially determined by high-consequence low- probability events  Households are WTP $275 annually for a marginal reduction in the number of expected hazard events per 1000 years. 22

Net Migration Net Migration  The spatial equilibrium model suggests household select their residential location so as to maximize utility--taking into account economic conditions and amenities  Objectives:  Quantify the relationship between county- level migration rates and natural hazard risk  Identify possible spatial heterogeneity in the migration-risk relationship 23

Net Migration U.S. Migration (cont.) 24

Net Migration Empirical Model: SAC  Spatial Simultaneous Autoregressive M = � WM + E � E + D � D + A � A + u u = � Wu + e M = Net In - Migration Rate E = Economic Characteristics D = Demographic Characteristics A = Environmental Amenities W = Spatial Weight Matrix 25

Net Migration Empirical Model: SAC (cont.)  Net Inmigration Rate � 2009 � � Net Domestic Migration it � � � � t = 2001 Net Inmigration Rate i = *100 2009 � � � � 1 � � � * Population it � � � 9 � � � t = 2001  Spatial Weight Matrix � where d ij = 1 if counties i and j are neighbors d ij W ij = � n 0 otherwise . � � d ij i = 1 26

Net Migration Results: SAC N=3107 27 * p<0.1 ** p<0.05 *** p<0.01

Net Migration Empirical Model: GWR  Geographically Weighted Regression M i = � iE E i + � iD D i + � iA A i + � i � ~ i.i.d. N(0, � 2 )  Estimate Parameter Values ) i W i X i ) � 1 � i = ( � X X i W i Y � i  Weight Matrix � � W ik = exp � d ik � � b 2 � � 28

Net Migration Results: GWR (Environmental Variables) 29

Net Migration Results: GWR (Hazard Risk) 30

Net Migration Conclusions  County migration patterns are negatively correlated with hazard risk  Hurricane and flood risk have a substantially greater affect on migration than earthquake risk  There is significant spatial heterogeneity in the relationship between migration and hazard risk  This migration-risk relationship is greatest along the Gulf Coast 31

Mental Health and Displacement Determinants of Mental Health and Displacement  Hurricanes Katrina and Rita devastated parts of the Gulf Coast in 2005  Mass displacement  $191 billion in property damage  Extreme physical and psychological stress  Objectives:  Evaluate the effects of post-disaster stress on long term mental health status  Identify determinants of displacement and displacement duration 32

Mental Health and Displacement Data  Panel Survey of Income Dynamics  2005 and 2007  Supplemental questionnaire for residents of hurricane-affected areas  Federal Emergency Management Agency  Geospatial data regarding hurricane damage 33

Mental Health and Displacement Empirical Model: Mental Health  Simultaneous Equations Model MH i = f ( E i , B i , SS i , PDVI i ) PDVI i = f ( E i , B i , SS i , DS i ) MH = Mental Health Indicator E = Socioeconomic Characteristics B = Behavioral and Health Characteristics SS = Social Support Index PDVI = Post Disaster Vulnerability Index DS = Disaster Severity 34

Mental Health and Displacement Empirical Model: Mental Health  Post-Disaster Vulnerability Index  Displacement Duration  Property Damage  Food Shortages  Water Shortages  Unsanitary Conditions  Loss of Electricity 35

Mental Health and Displacement Data (cont.) 36

Mental Health and Displacement Results and Conclusions: Mental Health  Several socioeconomic variables are correlated with adverse mental health outcomes  The SSI is negatively correlated with adverse mental health outcomes  The PDVI is positively correlated with adverse mental health outcomes 37

Mental Health and Displacement Empirical Model: Displacement  Probit-Weibull Hurdel Model h , B j h , SS j ) x 1 j = f ( H j , DS j , E j h , B j h , SS j ) x 2 j = f ( H j , DS j , E j H = Housing Damage DS = Disaster Severity E = Socioeconomic Characteristics B = Behavioral and Health Characteristics SS = Social Support Index 38

Mental Health and Displacement Displacement Duration • Plot of Kaplan-Meier Estimator 39