Trade Disruptions from Natural Disasters: Evidence from Monthly Data

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1 Trade Disruptions from Natural Disasters: Evidence from Monthly Data Gabriel Felbermayr Jasmin Gröschl Benedikt Heid University of Munich, Ifo, CESifo & GEP Ifo & CESifo University of Adelaide & CESifo European Trade Study Group September 14-16, 2017 G. Felbermayr, J. Gröschl, B. Heid 1 of 30

2 Motivation Understand the economic impacts of natural disasters is important. Climate change implies potential increase in disaster scale and/or frequency (IPCC, 2012; World Bank, 2012; Stern, 2006). Human and material costs of catastrophic events have long-lasting consequences on welfare, and on human and economic development. Direct shock on human and physical capital. Transitory and indirect shocks on production, consumption, and investment. Developing countries are particularly vulnerable. Industrialized countries affected through openness to trade, people flows etc. Relation between natural disasters and disruption of trade networks has not yet been fully understood. G. Felbermayr, J. Gröschl, B. Heid 2 of 30

3 Potential Channels Destruction and damage of capital and equipment moves production possibility frontier down. Immediate contraction in output. Reduces exports in the short-run. G. Felbermayr, J. Gröschl, B. Heid 3 of 30

4 Potential Channels Destruction and damage of capital and equipment moves production possibility frontier down. Immediate contraction in output. Reduces exports in the short-run. Worsening of fiscal balances, dependence of imports on GDP, and anticipation of future disaster event increase precautionary savings. Reduces imports in the short-run. G. Felbermayr, J. Gröschl, B. Heid 3 of 30

5 Potential Channels Destruction and damage of capital and equipment moves production possibility frontier down. Immediate contraction in output. Reduces exports in the short-run. Worsening of fiscal balances, dependence of imports on GDP, and anticipation of future disaster event increase precautionary savings. Reduces imports in the short-run. Reconstruction, insurance, investment, or anticipation of short-run event. Increases imports immediately. G. Felbermayr, J. Gröschl, B. Heid 3 of 30

6 Potential Channels Destruction and damage of capital and equipment moves production possibility frontier down. Immediate contraction in output. Reduces exports in the short-run. Worsening of fiscal balances, dependence of imports on GDP, and anticipation of future disaster event increase precautionary savings. Reduces imports in the short-run. Reconstruction, insurance, investment, or anticipation of short-run event. Increases imports immediately. Destruction of infrastructure (e.g., ports, roads, rail) affect trade costs. Reduces exports and imports in the short-run. G. Felbermayr, J. Gröschl, B. Heid 3 of 30

7 Related Literature Structural Gravity Models General gravity models in international trade (Anderson & van Wincoop, 2003; Head & Mayer, 2014) Two-step approach to identify country specific determinants (Eaton & Kortum, 2002; Redding & Venables, 2004; Head & Ries, 2008; Anderson & Yotov, 2012; Egger & Nigai, 2015). G. Felbermayr, J. Gröschl, B. Heid 4 of 30

8 Related Literature Structural Gravity Models General gravity models in international trade (Anderson & van Wincoop, 2003; Head & Mayer, 2014) Two-step approach to identify country specific determinants (Eaton & Kortum, 2002; Redding & Venables, 2004; Head & Ries, 2008; Anderson & Yotov, 2012; Egger & Nigai, 2015). International Trade and Natural Disasters International trade allows countries to smooth out the effects of temporary output shocks (Gassebner et al., 2010; Oh & Reuveny, 2010). Disasters increase (decrease) imports (exports); stronger (less pronounced) effects when financially integrated (Felbermayr & Gröschl, 2013). Impact of climate change on the spatial distribution of economic activity, trade, migration, growth, and welfare (Desmet & Rossi-Hansberg, 2015). Macro-level consequences of climate shocks on agricultural crops with adjustments through trade and production patterns (Costinot et al., 2016). G. Felbermayr, J. Gröschl, B. Heid 4 of 30

9 This Paper Research Question What are the very short-run effects of natural disasters on trade and can we net out potential transmission channels (output, consumption, and trade costs)? Are these shocks transitory or permanent, and does access to finance play a mitigating role? G. Felbermayr, J. Gröschl, B. Heid 5 of 30

10 This Paper Research Question What are the very short-run effects of natural disasters on trade and can we net out potential transmission channels (output, consumption, and trade costs)? Are these shocks transitory or permanent, and does access to finance play a mitigating role? Approach We put particular emphasis on the timeliness using higher-frequency (monthly) data. We apply a two-step fixed effects gravity approach. To causally identify trade disruptions, we utilize exogenous measures of physical disaster intensity weighted by (affected) population. G. Felbermayr, J. Gröschl, B. Heid 5 of 30

11 Gravity Model with Country Specific Variables Variables of interest (natural hazards) are country-time specific; would not be identified in a theory-consistent structural gravity equation with time-varying importer and exporter fixed effects. X ij,t = exp [α i,t + γ j,t + β 1NH i,t + β 2NH j,t + δd ij,t] + ε ij,t, NH i,t (NH j,t) is the monadic variable of interest - natural hazards in the exporting (importing) country. D ij,t are dyadic controls (the log of distance, adjacency, colonial heritage, same country, RTAs). α i,t and γ j,t represent all other time-varying exporter and importer determinants. ε ij,t is an additive error term. G. Felbermayr, J. Gröschl, B. Heid 6 of 30

12 Two-Step Approach: First Step Follow Head and Mayer (2014) and choose a two-step approach; we first estimate 420 cross-sectional gravities using PPML X ij = exp [ln S i + ln M j + δd ij] + ε ij, Exporter fixed effect ln S i includes natural hazards and other exporter-specific determinants, Importer fixed effect ln M j includes natural hazards and other importer-specific determinants, ε ij is a random disturbance. G. Felbermayr, J. Gröschl, B. Heid 7 of 30

13 Two-Step Approach: Second Step Use the panel dimension of our data (stack the estimated exporter and importer fixed effects) and regress ln S i,t on NH i,t and ln M j,t on NH j,t. and ln S i,t = α 0 + α 2NH i,t + ν t + ν i,m + ω i,t, ln M j,t = α 0 + α 2NH j,t + ν t + ν j,m + ω j,t, ν t are time (month-year) fixed effects. ν i,m and ν j,m are country-month fixed effects. G. Felbermayr, J. Gröschl, B. Heid 8 of 30

14 Gravity Data Monthly bilateral trade flows from IMF s Direction of Trade Statistics (DoTS). Unbalanced panel with 420 month-year combinations from Jan 1980 to Dec Controls Geographical and historical variables stem from CEPII. RTA from WTO RTA-Gateway (in force and formerly in force). Summary Statistics G. Felbermayr, J. Gröschl, B. Heid 9 of 30

15 Natural Disasters Natural disasters extended and improved from the Geological and Meteorological Events (Ifo GAME) database by Felbermayr & Gröschl (2014). Combine pure physical intensities for various kinds of disasters (earthquakes, volcanic eruptions, storms, droughts, precipitation, and temperature anomalies). Collected from primary sources. 0.5 resolution gridded data set (259,200 cells globally, 69,895 on land) Covers 232 countries on a monthly basis from 1979 to G. Felbermayr, J. Gröschl, B. Heid 10 of 30

Earthquakes Incorporated Research Institutions for Seismology (IRIS) Earthquake magnitudes [0,10). Exact locations of earthquake epicenters (latitude, longitude).

16 Earthquakes Incorporated Research Institutions for Seismology (IRIS) Earthquake magnitudes [0,10). Exact locations of earthquake epicenters (latitude, longitude). Treat cells within radial buffer to also consider earthquakes off shore. Spatial distribution of Earthquakes with magnitude 5 or higher in 2014 G. Felbermayr, J. Gröschl, B. Heid 11 of 30

Introduction Empirical Strategy Data Results Outlook Hurricanes and Storms International Best Track Archive for Climate Stewardship (IBTrACS) I Wind speeds in knots.

17 Introduction Empirical Strategy Data Results Outlook Hurricanes and Storms International Best Track Archive for Climate Stewardship (IBTrACS) I Wind speeds in knots. I Exact locations / paths of hurricane centers (latitude, longitude). I Hurricanes are mapped using a windfield model (by courtesy of PIK). Spatial distribution of Hurricanes from 1979 to 2014 Example G. Felbermayr, J. Gröschl, B. Heid 12 of 30

Predicted Windspeed (in kt) 4 to 6 6 to 8 8 to 10 10 to 12 12 to 14 14 to 16 16 to 18 18 to 20 20 to 22 22 to 24 24 to 26 26 to 28 28 to 30 30 to 32 32 to 34 34 to 36 36 to 38 38 to 40 40 to 42 42 to

18 Predicted Windspeed (in kt) 4 to 6 6 to 8 8 to to to to to to to to to to to to to to to to to to to to to to to to to to to to to to 68 Global Summary of the Day (GSOD) Used as a complement to IBTrACS to identify storms and gusts. Exact location of 23,749 weather stations; unevenly distributed across space and time. Image Krige wind speed data for 0.5 x 0.5 grid-cells. Kriging Predicted Maximum Windspeeds G. Felbermayr, J. Gröschl, B. Heid 13 of 30

Introduction Empirical Strategy Data Results Outlook Temperature GHCN_CAMS Gridded 2m Temperature I Interpolated combination of two weather station datasets (Global Historical Climatology Network and

19 Introduction Empirical Strategy Data Results Outlook Temperature GHCN_CAMS Gridded 2m Temperature I Interpolated combination of two weather station datasets (Global Historical Climatology Network and Climate Anomaly Monitoring System). I Monthly means of global land surface temperatures in degrees Celsius. I Extreme temperatures temp i,m,y = temp temp xi,m,y x i,m temp σi,m. 0.5 x 0.5 gridded data G. Felbermayr, J. Gröschl, B. Heid 14 of 30

20 Precipitation University of East Anglia Precipitation Analysis CRU TS v3.23 Monthly precipitation in mm; for 0.5 x 0.5 grid-cells on all land areas. Excessive precipitation: prec i,m,y i,m,y = x prec x prec i,m σ prec i,m. G. Felbermayr, J. Gröschl, B. Heid 15 of 30

21 Precipitation University of East Anglia Precipitation Analysis CRU TS v3.23 Monthly precipitation in mm; for 0.5 x 0.5 grid-cells on all land areas. Excessive precipitation: prec i,m,y i,m,y = x prec x prec i,m σ prec i,m. SPEI Calculation based on CRU TS v3.23 Standardized Precipitation-Evapotranspiration Index (SPEI) is specifically designed to identify droughts (Vicente-Serrano et al., 2010). For each month, calculate climatic water balance in each cell. Standardize for each cell with a log-logistic distribution function (unbiased Probability Weighted Moments method). Standardized measures calculated on a range of monthly timescales is the reference period for obtaining the distribution parameter. G. Felbermayr, J. Gröschl, B. Heid 15 of 30

22 Kernel Densities of Hazard Intensities 0.4 Earthquakes 0.3 Volcanic Explosions 0.08 Storms non-zero max magnitude non-zero max VEI max windspeed (in kt) 0.3 Temperature 0.8 Precipitation 0.4 Drought difference in mean temperature from long-run monthly mean over long-run monthly sd (in C) difference in mean precipitation from long-run monthly mean over long-run monthly sd (in mm) Standardized Precipitation-Evapotranspiration Index (SPEI1) G. Felbermayr, J. Gröschl, B. Heid 16 of 30

23 Aggregation and Disaster Index Aggregation Weight each disaster type by the population affected in each grid-cell. Aggregate mean of population weighted intensities over all grid-cells of a country. Disaster Index Combine all types of disaster intensity measures into an index variable Disaster Index i,t = Quake i,t + Storm i,t + Precipitation i,t + Drought i,t+ Temperature i,t. Weight by the inverse of the standard deviation of each disaster type within a country. No one hazard component dominates the movement of the index. G. Felbermayr, J. Gröschl, B. Heid 17 of 30

24 Second-Step Results First Step Impact of Natural Hazards on Trade, Monthly ( ) Magnitude Dep. var.: Exporter FE ( ln S) Importer FE ( ln M) (1) (2) (3) (4) (5) (6) Disaster Index 0.005*** 0.008*** (0.00) (0.00) (0.00) (0.00) Earthquakes *** (0.00) (0.00) Storms 0.085*** 0.057*** (0.02) (0.01) SPEI 0.003* (0.00) (0.00) pos. Prec *** 0.003** (0.00) (0.00) abs. Temp (0.01) (0.01) Fixed Effects Time yes yes yes yes yes yes Country yes yes yes yes yes yes Country-Month yes yes yes yes R Observations Note: ***, **, * denote significance at the 1%, 5%, 10% levels, respectively. All models estimated use OLS with bootstrapped standard errors in parentheses. G. Felbermayr, J. Gröschl, B. Heid 18 of 30

25 Heterogeneity PANEL A Dep. var.: Exporter FE ( ln S) Sample: OECD Non-OECD Low Income Middle Income High Income (1) (2) (3) (4) (5) Disaster Index *** *** 0.007*** (0.00) (0.00) (0.01) (0.00) (0.00) R Observations PANEL B Dep. var.: Importer FE ( ln M) Sample: OECD Non-OECD Low Income Middle Income High Income (1) (2) (3) (4) (5) Disaster Index 0.005*** ** *** (0.00) (0.00) (0.00) (0.00) (0.00) R Observations Note: ***, **, * denote significance at the 1%, 5%, 10% levels, respectively. All models estimated use OLS with bootstrapped standard errors in parentheses. Country, time, and country-month fixed effects included but not reported. G. Felbermayr, J. Gröschl, B. Heid 19 of 30

26 Financial Openness (Chinn-Ito Index) Dep. var.: Exporter FE ( ln S) Importer FE ( ln M) Sample: Full OECD Non-OECD Full OECD Non-OECD (1) (2) (3) (4) (5) (6) Disaster Index 0.012*** 0.029*** 0.009*** 0.014*** 0.013*** 0.014*** (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) Fin. Open. x Dis *** 0.039*** *** 0.024*** 0.026*** (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) R Observations Note: ***, **, * denote significance at the 1%, 5%, 10% levels, respectively. All models estimated use OLS with bootstrapped standard errors in parentheses. Country, time, and country-month fixed effects included but not reported. G. Felbermayr, J. Gröschl, B. Heid 20 of 30

27 Financial Openness (Chinn-Ito Index) Dep. var.: Exporter FE ( ln S) Importer FE ( ln M) Sample: Full OECD Non-OECD Full OECD Non-OECD (1) (2) (3) (4) (5) (6) Disaster Index 0.012*** 0.029*** 0.009*** 0.014*** 0.013*** 0.014*** (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) Fin. Open. x Dis *** 0.039*** *** 0.024*** 0.026*** (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) R Observations Note: ***, **, * denote significance at the 1%, 5%, 10% levels, respectively. All models estimated use OLS with bootstrapped standard errors in parentheses. Country, time, and country-month fixed effects included but not reported. For the full sample: Mean hazard effect is -8.28% (-9.66%) for the exporter (importer). Increase with mean hazard and high fin. openness is 7.59% (17.94%). Increase with mean hazard and mean fin. openness is 3.64% (8.62%). Increase with mean hazard and low fin. openness is 0.76% (1.79%). G. Felbermayr, J. Gröschl, B. Heid 20 of 30

28 Conclusion and Next Steps Natural hazards reduce exports by 5.52% at the mean, but imports do not show immediate significant effects. Heterogeneity exists across hazard types and income groups. Financial openness mitigates effects on exports and imports. G. Felbermayr, J. Gröschl, B. Heid 21 of 30

29 Conclusion and Next Steps Natural hazards reduce exports by 5.52% at the mean, but imports do not show immediate significant effects. Heterogeneity exists across hazard types and income groups. Financial openness mitigates effects on exports and imports. Next Steps Explore trade cost channel estimate gravity with full matrix of fixed effects (see Egger & Nigai, 2015). Temporary versus permanent shock. Explore the role of access to finance and the banking sector in more detail (e.g., credit to GDP ratio). G. Felbermayr, J. Gröschl, B. Heid 21 of 30

30 Backup Summary Statistics First-Step Variable N mean sd max min Exports 10,766, DIST 10,766, CONTIG 10,766, COL 10,766, SMCTRY 10,766, RTA 10,766, G. Felbermayr, J. Gröschl, B. Heid 22 of 30

31 Backup Summary Statistics Second-Step Variable N mean sd max min ln S i,t 60, ln M j,t 60, Disaster Index 60, Earthquakes 60, Storms 60, SPEI 60, pos. Prec. 60, abs. Temp. 60, Financial Openness 55, OECD 60, Low Income 60, Middle Income 60, High Income 60, Back to top G. Felbermayr, J. Gröschl, B. Heid 23 of 30

32 Backup Hurricane Katrina Satellite Image G. Felbermayr, J. Gröschl, B. Heid (NASA) 24 of 30

33 Backup Hurricane Katrina Satellite Image G. Felbermayr, J. Gröschl, B. Heid (NASA) IBTrACS (Own Representation) 24 of 30

34 Backup Hurricane Katrina Predicted Windfield G. Felbermayr, J. Gröschl, B. Heid (Own Representation) 25 of 30

35 Backup Hurricane Katrina Predicted Windfield Back to (Own Representation) Officially Reported Extent (NOAA) more G. Felbermayr, J. Gröschl, B. Heid 25 of 30

36 Backup Locations of weatherstations Back G. Felbermayr, J. Gröschl, B. Heid 26 of 30

37 Backup From inputs to the grid Example: Storms The Kriging Algorithm 1 Bin data by breaking up distances d between all points, using a lag size 2 For each bin, calculate semivariance: ˆγ( d) = 1 n( d) 2 1 (z(x i + d) z(x i)) 2 (1) n( d) 3 Let algorithm fit a range of models for a range of parameters 4 Pick the best fit: Matérn Model (Matérn, 1960, 1986) ( ) ν ( ) C ν(d) = σ ν 2ν d 2ν d K ν Γ(ν) ρ ρ (includes exponential (special case) and Gaussian (ν lim inf) model) i=1 5 Use fitted function for spatial interpolation 6 Repeat for each month (2) G. Felbermayr, J. Gröschl, B. Heid 27 of 30

38 Backup From inputs to the grid Example: Storms The Semi-Variogram Experimental variogram and fitted variogram model Semi variance Model: Ste Nugget: 0 Sill: 65 Range: Kappa: e e e e e+07 Distance Back G. Felbermayr, J. Gröschl, B. Heid 28 of 30

39 Backup Magnitude of Results (in %) Evaluated at... Dep. var.: Exports Imports Mean SD Mean SD Disaster Index 5.52*** 1.61*** Earthquakes *** 2.30*** Storms 8.33*** 2.13*** 5.59*** 1.43*** SPEI 0.33* 0.46* pos. Prec. 0.59*** 1.03*** 0.30** 0.51** abs. Temp Back to results G. Felbermayr, J. Gröschl, B. Heid 29 of 30

40 Backup First-Step Results Monthly Cross-Sectional Gravity Variables ( ) Estimates: Median Mean s.d. # DIST CNTG COL SMCTR RTA Note: All models are estimated using Poisson Pseudo Maximum Likelihood. Country-specific fixed effects included but not reported. Back to results G. Felbermayr, J. Gröschl, B. Heid 30 of 30

Understanding Weather and Climate Risk. Matthew Perry Sharing an Uncertain World Conference The Geological Society, 13 July 2017

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