Income, Wages and the Impact of Weather: Evidence from half-million American Storms

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Wages, Income and the Impact of Weather:

Evidence from half-million American

Damaging Winds

Candidato: Federico CRIPPA

Relatore: Prof. Andrea ROVENTINI

1

Οἵοισιν νεφέεσσι περιστέφει οὐρανὸν εὐρὺν Ζεύς, ἐτάραξε δὲ πόντον, ἐπισπέρχουσι δ΄ ἄελλαι παντοίων ἀνέμων.

Abstract

Ongoing climate change is likely to bring about an increase in the frequency and size of extreme weather events such as floods, hurricanes, and storms. Understanding the effects of these events on economic performance is essential to policy making and the design of damage functions. In the present dissertation, the impact of damaging winds on wages, employment, and income is investigated, taking advantage of a panel dataset covering more than three hundred thousand events occurred in the U.S. from 1990 to 2016. Evidence of a significant negative effect of storm strength on all macroeconomic variables is found, with no recovery in the long run. Results are robust to a variety of specifications.

Further, I document that areas relatively more exposed suffer a significantly stronger negative im-pact of damaging winds, suggesting that adaptation mechanisms do not intervene in determining the macroeconomic outcome of weather impacts. When breaking down the sample across different eco-nomic sectors, I find evidence of stronger effects in constructions and services-provision to firms. Results contradict the “build-back-better” hypothesis and suggest that also employment tend to fly away from affected counties, possibly deepening the impact to storms.

Contents

2.2 Storms and damaging winds . . . 14

2.3 Impact of natural disasters . . . 15

2.4 The New weather-economy literature . . . 18

2.5 The New weather-economic literature and climate change . . . 21

2.6 Conclusions . . . 23

3 Data sources and exploratory analysis 24 3.1 Introduction . . . 24

3.2 Storm Events Database . . . 25

3.3 Definition of the exposure measure . . . 28

3.4 Economic data . . . 32

3.5 Weather data . . . 34

3.6 Conclusions . . . 37

CONTENTS 4

4 Methodology 38

4.1 Introduction . . . 38

4.2 Estimation using the cross section . . . 39

4.3 Estimating using panel . . . 40

4.4 Hybrid approaches . . . 40

4.4.1 Heterogeneous marginal effects . . . 41

4.4.2 Long differences . . . 42 4.4.3 Partitioning variation . . . 43 4.5 My empirical strategy . . . 44 4.6 Conclusions . . . 47 5 Results 48 5.1 Introduction . . . 48 5.2 Main regression . . . 49 5.3 Adaptation . . . 50 5.4 Spatial spillover . . . 54 5.5 Sectors . . . 55 5.6 Robustness checks . . . 58 5.6.1 Lag length . . . 58 5.6.2 Exposure measures . . . 58

5.6.3 Wages, income and employment . . . 59

5.6.4 Sets of controls . . . 61

5.6.5 Fixed effects . . . 61

5.6.6 Data subsamples . . . 64

5.6.7 Random assignment . . . 65

CONTENTS 5

6 Discussion 67

6.1 A theoretical intuition . . . 67

6.2 Damaging winds impact and climate change . . . 69

6.3 Policy implications . . . 70

6.4 Future works . . . 71

List of Tables

3.1 Frequency of the events in SED . . . 26

3.2 Correlation between exposure measures . . . 31

3.3 Summary statistics . . . 37

5.1 Baseline model estimates . . . 51

5.2 Estimates varying controls . . . 62

5.3 Estimates varying fixed effects . . . 63

5.4 Estimates varying the sample . . . 64

1 Adaptation estimates . . . 79

2 Adaptation estimates . . . 80

3 Spatial spillover estimates . . . 81

4 Sectors estimates . . . 81

5 Ten years estimates . . . 82

6 Estimates varying the exposure measure . . . 83

7 Estimates varying the dependent variable . . . 83

List of Figures

1.1 Hypotheses on long run impacts . . . 10

3.1 Recorded damaging winds per year . . . 28

3.2 Maximum wind speeds . . . 29

3.3 Damaging winds in 2016 . . . 29

3.4 Income and wages time series . . . 33

3.5 Income per capita distributions . . . 33

3.6 Annual average wage distributions . . . 34

3.7 Growth rates distributions . . . 35

3.8 Precipitation in 2016 . . . 36

3.9 Temperature in 2016 . . . 36

5.1 Damaging wind cumulative effects . . . 50

5.2 Adaptation and storm experience . . . 52

5.3 Adaptation and richness . . . 53

5.4 Spatial spillovers . . . 55

5.5 Impacts on different sectors . . . 56

5.6 Impacts on different sectors . . . 57

5.7 Damaging wind cumulative effects . . . 59

5.8 Impacts with different exposure measures . . . 60

LIST OF FIGURES 8

5.9 Impacts on different variables . . . 60

Chapter 1

Introduction

The fact that climate and weather affect economic activities is clear since many (thousands of) years ago. In agricultural-based societies, where a dry season or a very heavy hail could induce significant losses, this was probably the daily concern for peasants and farmers. Moreover, even 2700 years ago Hesiod noticed that the climate was not just affecting crops, but also human labor activity (when the artichoke flowers in the season of wearisome heat, men are feeblest, Works and Days vv. 582-586). Since little doubt remains on the fact that the climate is changing, a good understanding of the possible future impacts is essential for policy making and for preparing our societies and economies to such changes. The New weather-economy literature (Dell et al., 2014) identifies the effects of temperature, precipitation, and extreme weather events on many economic outcomes and provides results about how they affect the economy. This literature is necessary to build the correct damage function in climate change models, and hence to correctly evaluate any interventions or policies.

Even if public debates on climate change usually focus on rising temperatures only, the picture is far more complex. Hsiang and Kopp (2018) points out how - for economic purposes - climate can be defined as the joint probability distribution describing the state of the atmosphere, ocean, and freshwater systems. Climate change involves all these high-dimensional systems, and hence it is appealing to work with summary statistics such as global mean surface temperature, even if changes in place are more complex and go beyond it. In fact, the consequences of climate change also regard frequency and strength of extreme (i.e. severe and unusual) weather events. Any damage function utilized in climate models should include them.

This dissertation aims to examine one of these weather-economy relations: how a specific type of extreme weather event, damaging winds, affects wages, income, and employment in the United States.

CHAPTER 1. INTRODUCTION 10

Figure 1.1: Four hypotheses, proposed in the literature, that describe the long-term evolution of GDP per capita following a natural disaster. Source: Hsiang and Jina (2014).

The literature on catastrophic natural disasters has already converged on four different and contradictory hypotheses (Hsiang and Jina, 2014) describing the impact of a weather extreme, summarized in Figure 1.1. The “creative destruction” hypothesis argues that disasters may temporarily stimulate economies to grow faster. The “build back better” hypothesis claims that growth may suffer initially, but then the gradual replacement of lost assets with modern state-of-the-art units has a positive net effect on long-run growth. The “recovery to trend” hypothesis argues that growth should suffer for a limited period, but that it should eventually rebound to abnormally high levels, causing income levels to converge back to their pre-disaster trend. The “no recovery” hypothesis states that disasters slow growth and no rebound occurs. These hypotheses come from a literature of empirical studies that, considering different extreme events, do not reach common conclusions: it found no effects (Cavallo et al., 2013), negative (Klomp and Valckx, 2014) or positive impacts (Skidmore and Toya, 2002).

This dissertation aims to test the four hypotheses in the case of damaging winds. These events, considered at national level, would not be neither unusual (in the U.S., more than ten thousand events are reported each year) nor particularly severe (to be considered as extreme, a storm must exhibit a wind speed greater than 80 kilometers per hour; it means that larger branches break off trees and some small trees, construction and barricades blow over). Nonetheless, on a county level, their impact may be relevant: they are the most typical extreme event, and every year they cause 45% of all weather-related insured property losses (Kunkel et al., 1999). Further, although trends in the occurrence of storms

CHAPTER 1. INTRODUCTION 11

are subject to considerable uncertainties, current results in climate studies suggest that such events are increasing over time (Diffenbaugh et al., 2013; Vose et al., 2014; Chan and Shi, 1996).

I construct a panel dataset of county-year observations of economic variables and damaging winds activity for the years 1990-2016, resorting to four different primary sources (SED, QCEW, REIS, and nClimDiv) provided by three U.S. scientific and government agencies (NOAA, BLS, and BEA). The panel is used to estimate the impact of damaging winds on wages: a significant negative effect is found, with no recovery in the long run. On average, going from none to one damaging wind of a small magni-tude halves the growth rate of wages in a county of average size.

Heterogeneous impacts appear when different groups of counties are considered, indicating that storm experience and wages level play a role in determining the effect of damaging winds. My findings, robust to a variety of model specifications, imply a substantial rejection of the “creative destruction”, the “build back better” and the “recovery to trend” hypothesis. Similarly, when the focus is restricted on single sectors, persistent negative effects on wages and employment are found, notably in Professional and Business services, Construction, Goods-producing, and Trade, transportation, and utilities.

A crucial topic in new weather-economy literature is adaptation. A storm may be expected to be more disruptive when it affects a county that has never experienced that kind of event. Different effects are found in less and more affected counties, but the negative impact is stronger for counties with more storms experience. Bakkensen and Mendelsohn (2016) finds no evidence of adaptation to damage from hurricanes in the United States; likewise, no decline in effects appears in the case of damaging winds. The literature suggests the existence of an adaptation deficit, which is limits in the ability of poorer regions to adapt (Fankhauser and McDermott, 2014). This deficit seems to exist also between United States counties: where the average wage is lower than the median, storms negative impact is larger.

Several studies have estimated impacts on economic outcomes of wind-related events (hurricanes, cyclones, winter storms), addressing the issue of defining an appropriate metrics for winds (Pielke Jr and Landsea, 1999; M¨unchener R¨uck, 2002; Emanuel, 2005; Nordhaus, 2010; Hsiang and Narita, 2012; Deryugina, 2013; Hsiang and Jina, 2014). These studies propose different metrics, but the negative effect found in this dissertation does not depend on the choice of one metrics rather than another: similar results are obtained whatever the metrics used. It confirms that all these metrics can measure the impact of wind damages.

At least two useful implications can be derived from the findings of this dissertation. First of all, they detect how damaging winds have an impact on wages. This is a new piece added to the understanding of the relationship between weather and the economy: as far as I know, this is the first work that focuses

CHAPTER 1. INTRODUCTION 12

on wages, and that considers such events. On the other hand, the estimates show how adaptation seems not able to neutralize the negative impact of storms. It is an interesting result in the face of climate change, which involves the frequency of severe storms too (Diffenbaugh et al., 2013): if storms activity is increasing, and no adaptation mechanism takes action, a higher cost must be attributed to changes in climate. Hence, damaging winds impact must be taken into account in the cost-benefit evaluation of reducing greenhouse emissions.

The dissertation is organized as follows: Chapter 2 reviews the literature; physics of storms and dam-aging winds is introduced, theoretical hypotheses on the impact of extreme events presented, empirical works reviewed and the link between extremes and climate change explained. Chapter 3 reports data sources used in the construction of the panel, and exploratory analysis to describe the data; the exposure measures, independent variables in the regressions, are defined. In Chapter 4, the methodology used for the empirical analysis is described: first, approaches employed in the literature are reviewed; then, my methodology is developed. Chapter 5 reports results of the analysis, with several robustness checks which confirm the negative and heterogeneous impact of damaging winds. Chapter 6 discuss the results, providing a theoretical framework to interpret them; projections of climate change consequences due to the increase of storm activity are addressed, policy implications descending from the findings suggested and future works outlined.

Chapter 2

Literature Review

2.1

Introduction

In the last years, scientific evidence of climate change has led to taking an interest in the phenomenon many scientists from fields outside the climatology. If changes are in place, concerns about their conse-quences on human activities rise: how much are they costing? And how much will they cost? Economics scholars may be the ones who have the tools to respond. The first step to answer these questions does not directly face climate change: instead, it requires to understand how economic activities are normally affected by weather conditions, whether they be temperatures, precipitation or extreme events.

This is what this dissertation tries to do, and one of the aims of this chapter is to show how this research is not an isolated work. It is part of at least two lines of studies, strictly connected but not wholly overlapping: a literature on the effects of natural disasters and the so-called New weather-economic literature, the name given by Dell et al. (2014) to a range of empirical studies investigating relations between weather and economy. Both these pieces of literature will be reviewed.

A second goal of the chapter is to briefly introduce storms and damaging winds, the weather events under discussion in the rest of the dissertation, and how they may be affected by climate change. It will enter into some more specific climate science studies, to give a summary description of the physics of the phenomena.

The chapter is organized as follows: Section 2.2 introduces storms, damaging winds and extreme events; Section 2.3 reviews the debate on medium and long term impacts of natural disasters and presents the New weather-economic literature, whose main empirical findings are reported in Section 2.4. Section

CHAPTER 2. LITERATURE REVIEW 14

2.5 explains the link between these pieces of literature and climate change and illustrates climatological studies on alterations of storm activity due to it, and Section 2.6 concludes.

2.2

Storms and damaging winds

From the meteorological point of view, storms are created when a center of low pressure develops with the system of high pressure surrounding it, and this combination of opposing forces creates se-vere weather phenomena, like strong winds, tornadoes, hails, thunderstorms and heavy precipitations. In the United States, storms are a fundamental part of the nation’s climate, producing between 15% (West Coast) and 70% (high plains) of the average precipitation across the nation. Storm-related damages oc-cur across all parts of the country and are common each year, causing 45% of all weather-related insured property losses (Kunkel et al., 1999), mainly since storms are relatively well insured, compared to other events (Jahn, 2015).

Damaging winds (also called strong winds or high winds) are one in the wide variety of weather phenomena that might be considered severe produced by convective storms (Johns and Doswell III, 1992). To achieve deep convection which almost all severe local storm events are associated with, there are three necessary ingredients (Doswell III, 1987): a moist layer of sufficient depth in the low or mid-troposphere, a steep enough lapse rate to allow for a substantial positive area, and sufficient lifting of a parcel from the moist layer to allow it to reach its level of free convection. Moisture, conditional instability, and lifting are all necessary, and each affects the convective potential differently.

Damaging straight-line winds associated with deep convection are most of the time generated by outflow that occurs at the base of a downdraft, and most of these winds are a result of outflow generated by a thunderstorm downdraft. Fujita and Byers (1977) designated exceptionally strong downdrafts as downbursts, and the term’s meaning has grown to encompass any damaging (or potentially damaging) winds produced by downdrafts.

Given that deep convection develops, ingredients necessary for damaging winds at the surface are those promoting strong downdrafts. Precipitation loading and negative buoyancy due to evaporative cooling are recognized factors in initiating and sustaining a downdraft. Precipitation loading is the drag effect of liquid water, which enhances parcel descent. Essentially, the greater the quantity of liquid water per unit volume, the greater the precipitation drag. The other factor, negative buoyancy due to evaporative cooling, is created when precipitation falls through a layer of unsaturated air. Once a downdraft is established, continued entrainment of unsaturated air can aid evaporation.

CHAPTER 2. LITERATURE REVIEW 15

Evaporative cooling (and downdraft strength) also is enhanced by high liquid water content per unit volume, small drop size and a steep lapse rate, as noted by Kamburova and Ludlam (1966) and Srivastava (1985). The high liquid water content and drop-size factors relate to the amount of liquid water surface available for evaporation. Further, a steep lapse rate acts to maintain negative buoyancy as a downdraft parcel descends.

A downward transfer of horizontal momentum from strong flow aloft can enhance outflow (Brandes, 1977). Generally, the stronger the environmental winds in the downdraft entrainment region, the higher the potential contribution to outflow strength.

To be considered extreme, a weather event must be unexpected, unusual, unpredictable, severe or unseasonal, an event at the extremes of the historical distribution, of the range that has been seen in the past. Wind events can be classified according to the Beaufort wind force scale, an empirical measure that relates wind speed to observed condition at sea or on land (Barua, 2005). Not all downdrafts end to generate extreme events: outflow winds are considered extreme (and hence called damaging, high or strong winds) when classified by Beaufort scale as severe gales (75 - 88 km/h), whole gales (89 - 102 km/h), violent storms (103 - 117 km/h) or hurricane forces (≥ 118 km/h). Associated impacts go from slight structural damages for severe gales to devastation for hurricane forces: that is the reason why the consequences of storms can be studied in the framework of effects evaluation of natural disasters.

2.3

Impact of natural disasters

To evaluate the impact of weather events on economic outcomes two different approaches can be taken: bottom-up micro-founded estimates, which put together effects on productivity, life expectancy, mor-tality, crime. . . , and top-down macro-level studies, which estimate how overall economic performance directly responds to temperature or extreme events changes, without knowledge of the underlying mecha-nisms generating those losses (Hsiang et al., 2017). The bottom-up method gives a deeper understanding of the weather’s impact but requires highly defined economic data that are seldom available, even in countries filled in data.

To bridge the gap of knowledge in the top-down approach in case of extreme events, several works try to explain ex-post how these events could affect macroeconomic output. On the one hand, it is clear that natural disasters tend to deteriorate or destroy physical and social infrastructure, change the environment, cause loss of property, impact on livelihoods and disrupt family and social relationships (Ibarrar´an et al., 2009). Overall, macroeconomic studies agree that natural disasters may lead to an immediate contraction

CHAPTER 2. LITERATURE REVIEW 16

in economic output, a worsening of a country’s balance of trade, a deterioration of the fiscal balances, and an increase in poverty, usually accompanied by an increase in income disparities (Rasmussen, 2004).

However, moving to medium and long-run effects, direction and magnitude of the effects are still open questions: the literature on catastrophic natural disasters has converged on different, and contradic-tory, hypotheses. Before reporting them as in Hsiang and Jina (2014), two points must be highlighted: these hypotheses have been developed in empirical papers, as answers to explain empirical evidence, without comprehensive theoretical frameworks (only recently Bakkensen and Barrage (2018) tried to develop a specific growth model accounting for natural disasters); and no study has credibly falsified any of the four and the actual behavior of economies is widely disputed. Hypotheses are schematically summarized in Figure 1.1.

• “Creative destruction” hypothesis

The “creative destruction” hypothesis argues that disasters may temporarily stimulate economies to grow faster because the demand for goods and services increase as populations replace lost capital, because inflowing international aid and attention following a disaster may promote growth, or because environmental disruption stimulates innovation (Skidmore and Toya, 2002).

Ahlerup (2013) finds positive effects, in short, medium, and long run for natural disasters. As causes of these positive impacts, he adds the demand for assistance and reconstruction, whereby production in surrounding areas increases and a process of unification or reconciliation, which increases investment and production via reduced uncertainty.

The notion of creative disruption is partially motivated by the observation that construction indus-tries often exhibit short (1-2 year) increases in output after catastrophes (Belasen and Polachek, 2008; Hsiang, 2010; Deryugina, 2011), but it is unknown if this transient sector-specific response has an enduring impact on the broader economy.

• “Build back better” hypothesis

The “build back better” hypothesis argues that growth may suffer initially, since lives may be lost and productive capital destroyed, but then the gradual replacement of lost assets with modern state-of-the-art units has a positive net effect on long-run growth since the capital that is destroyed in a disaster may be older and outdated (Crespo Cuaresma et al., 2008; Hallegatte and Dumas, 2009). This hypothesis might be correct if firms do not upgrade their capital efficiently in the absence of disasters and if the productivity benefits of post-disaster capital upgrading exceed the productivity losses imposed by the disaster in the long run.

CHAPTER 2. LITERATURE REVIEW 17

Sawada et al. (2011) found a negative impact of natural disasters in the short run, while in the long term they have positive impacts on per capita GDP growth. It may depend on the intertemporal elasticity of substitution for consumption: when it is less than one, more destructive disasters or more frequent occurrence of historical disasters foster investment in human capital, which results in a higher economic growth rate (Akao and Sakamoto, 2018).

• “Recovery to trend” hypothesis

The “recovery to trend” hypothesis argues that growth should suffer for a limited period, but that it should eventually rebound to abnormally high levels, causing income levels to converge back to their pre-disaster trend. It means that no effect can be seen in the long run: a conclusion reached by several empirical works (Cavallo et al., 2013). It is argued that the rebound should occur because the marginal product of capital will rise when capital and labor become relatively scarce after a disaster (due to destruction and mortality), causing individuals and wealth to migrate into devastated locations until output recovers to the regional trend (Yang, 2008; Strobl, 2011).

• “No recovery” hypothesis

Finally, the “no recovery” hypothesis argues that disasters slow growth by either destroying pro-ductive capital directly or by destroying durable consumption goods that are replaced using funds that would otherwise be allocated to productive investments but no rebound occurs because the various recovery mechanisms above fail to outweigh the direct negative effect of losing capital (Field et al., 2012).

The latter effect may be particularly significant if, in the wake of a disaster, consumption falls so that the marginal utility of consumption rises enough that post-catastrophe consumption becomes preferable relative to investment (Anttila-Hughes and Hsiang, 2013). According to this hypothesis, the post-disaster output may continue to grow in the long run, even if it remains permanently lower than its pre-disaster trajectory.

Bakkensen and Barrage (2018) proposes a stochastic endogenous growth model that tries to reconcile these different hypotheses. They distinguish between the impact of event risk (average exposition to extreme events) and of extreme event strike (the fact to be hit by the event): this distinction is crucial and shows how the four hypotheses are built considering both the impacts with little distinction, while they are somewhat distinct. The model explains different signs of effects in the short and in the long run. Extreme event risk may affect average growth by three channel: precautionary savings effect (if households are sufficiently risk averse, an increase in storm risk may increase the equilibrium savings

CHAPTER 2. LITERATURE REVIEW 18

rate, increasing average growth), portfolio effect (if physical capital is more susceptible to storms, higher storm risk may induce households to invest relatively more in human capital), and direct depreciation effect (higher storm risk increases average depreciation, decreasing average growth; Hsiang and Jina (2015) gives a more in-depth analysis of this effect). Extreme event strike affects long-run growth just in case of the incompleteness of financial markets, and without compensating rebound the effect on long-run growth is negative.

The theoretical approach adopted by Bakkensen and Barrage (2018) adds to the four hypotheses, whose original formulations have been developed in empirical works, where contradictory results were reached (Cavallo et al., 2011; Klomp and Valckx, 2014). In the last year, these empirical analyses are rising, in a wave of new empirical research to such an extent that Dell et al. (2014) called it the New weather-economy literature.

2.4

The New weather-economy literature

The New weather-economy literature tries to identify the economic effects of temperature, precipitation, and weather extremes, exploiting high-frequency changes in these variables. Since this dissertation is part of this line of New weather-economy research, these empirical works will be reviewed. I will go through analyses of medium and long-run impacts of temperature, precipitation, and weather extremes on aggregate measures since they follow the same top-down approach I adopt in my empirical analysis.

Temperature and precipitation

In a world sample from 1950 to 2003, Dell et al. (2012) examine how seasonal variation in temperature and precipitation affects per capita income. They show that being 1◦C warmer in a given year reduces per capita income by 1.4 percent, but only in developing countries. Moreover, estimating a model with lags of temperature, they find that this large effect is not reversed once the temperature shock is over, suggesting that temperature is affecting growth rates, not just income levels. Growth effects, which compound over time, have potentially first-order consequences for the scale of economic damages over the longer run, greatly exceeding level effects on income. They further find that over 10–15 year time scales, temperature shocks have similar effects to annual shocks, although statistical precision decreases. Variation in mean precipitation levels is not found to affect the path of per capita income. Temperature shocks appear to have little effect in rich countries, although estimates for rich countries are not statistically precise.

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This seeming lack of correlation between temperature and growth in rich countries is explained by Burke et al. (2015): they account for non-linearity in the temperature-growth relationship and show how overall economic productivity is non-linear in temperature for all countries, with productivity peaking at an annual average temperature of 13◦C and declining sharply at higher temperatures. The relationship is globally generalizable, unchanged since 1960, and apparent for agricultural and non-agricultural activity in both rich and developing countries. Moreover, since the distribution of rich-country temperatures is roughly symmetrical about the optimum, linear regression recovers no association. The global non-linear effect of temperature on economic production is a remarkable finding that must be kept in mind.

Other studies have focused on subsamples of developing countries, and their results were consistent with global ones. Hsiang (2010) shows findings similar to Dell et al. using seasonal variation in a sample of twenty-eight Caribbean-basin countries over the 1970–2006 period. National output falls 2.5 percent per 1◦C warming. This study further examines output effects by the time of year and shows that positive temperature shocks have negative effects on income only when they occur during the hottest season.

Barrios et al. (2010) focus on sub-Saharan Africa over the 1960–1990 period, using a subsample of twenty-two African and thirty-eight non-African countries and weather variation occurring across five-year periods. The authors find that higher rainfall is associated with faster growth in these sub-Saharan African countries but not elsewhere. They estimate that worsening rainfall conditions in Africa since the 1960s can explain 15–40 percent of the per capita income gap between sub-Saharan Africa and the rest of the developing world by the year 2000. Unlike the majority of studies, which consider the effect of precipitation and temperature levels, this study uses weather anomalies (deviations from country average, normalized by country standard deviations). Either way, other studies, like Miguel et al. (2004) and Dell et al. (2012) find that anomalies-based analyses tend to provide broadly similar results to levels-based analyses when predicting national income growth, but with weaker statistical precision.

Extreme weather events

A number of studies investigate the impact on growth of extreme weather events, such as tornadoes, typhoons, hurricanes, sand storms, and severe droughts.

Several studies examine windstorms by constructing meteorological databases that track storm paths. For example, Hsiang and Narita (2012) use a detailed global windstorm dataset and investigate the effect of windstorms across 233 countries from 1950 to 2008. They find that higher wind speeds present substantially higher economic losses. Their results are also confirmed by Bakkensen and Mendelsohn

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(2016), which focus on non-linearity and heterogeneity of impacts. Studying twenty-eight Caribbean nations, Hsiang (2010) shows no average effect on income from cyclones, though there are significant negative impacts in some sectors (such as agriculture, tourism, retail, and mining), but positive impacts in construction (presumably due to its role in reconstruction).

Hsiang and Jina (2014) also find evidence for growth effects from cyclones, rather than level effects. Using annual fluctuations in windstorms, they find that the effects of cyclones reduce growth rates, with effects that cumulate over time. On net, they estimate that the annual growth rate of world GDP declined by 1.3 percentage points due to cyclones during the period 1970–2008.

Looking within countries, Deryugina (2011) examines U.S. counties and finds no effect on county earnings ten years after a hurricane, a result supported by massive government transfers into the affected counties after these events (suggesting that there may be a substantial loss in locally produced income, with consumption effects dampened by the transfer). Anttila-Hughes and Hsiang (2013) study a panel of provinces in the Philippines and show that local exposure to a typhoon reduces household incomes in the province on average by 6.7 percent. Elliott et al. (2015), examining the impact of typhoons on local economic activity in coastal China, show that typhoons have a negative and significant, but short-term, impact on local activity: a typhoon that is estimated to destroy 50% of the property reduces local economic activity by 20% for that year. Foreman (2019) considers a slightly different event, dust storms, and how they could be a contributing factor to reduce economic development in West Africa, where economic growth is estimated to be reduced by 3% per standard deviation increase in dust exposure over two years. He concludes that dust storms should be considered an essential part of geographic endowments alongside other climate indicators.

Additional studies examine “economic losses” as the dependent variable, rather than looking at the income path itself. To measure such losses in cross-country studies, authors use the Emergency Events Database (EM-DAT), which includes fatalities and direct economic loss estimates that countries self-report (and it must be noted that self-self-reporting origins of these damages may represent a non-trivial issue, especially when comparing damages reported in different countries or at different times). Yang (2008) finds that stronger storms, as measured from meteorological data from 1970–2002, lead to higher economic losses (damage from the EM-DAT database as a fraction of GDP) and higher deaths and injuries, as well as more massive international aid flows in response.

Studies focused on the United States also find substantially increased economic losses with increasing storm severity (Nordhaus, 2010; Mendelsohn et al., 2011). For example, Nordhaus (2010) estimates the relationship between wind speed and damages, finding that annual hurricane costs in the United States

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from 1950–2008 averaged 0.07 percent of GDP, but with high variability. Hurricane Katrina made 2005 an outlier, with damages nearing 1 percent of GDP, even if a comprehensive analysis of the hurricane’s long-term economic impact on its victims shows a surprisingly transitory effect on employment and income, with a stronger economic performance of Hurricane Katrina victims with respect to controls in similar unaffected cities (Deryugina et al., 2018).

Ordinarily, studies find significant effects of windstorms on local income but generally smaller effects on national income, although damages from windstorms are highly convex in wind speed.

2.5

The New weather-economic literature and climate change

Even if the New weather-economic literature may directly face climate change, the renewed interest in relations between the economy and weather is easily understandable just in this light. In the face of the evidence of climate change, it is worth asking how much does it cost now and how much will it cost. To answer these questions, a damage function that maps climate into economic outcomes is needed, and the weather-economic literature is the starting point trying to estimate it.

Weather and climate are distinct notions, even if strictly related. Auffhammer (2018) recalls that for economic purposes it is possible to consider the weather what someone encounters when she leaves her home in the morning; these weather outcomes are drawn from an underlying distribution, and the moments of this distribution are the climate. Climate change is hence a slow shift in some moments of the weather distribution over time. Note that this adds a further meaning to distinction in Bakkensen and Barrage (2018) model between the impact of event risk and event strike: the first can be thought as the impact of climate, and the second as the impact of weather.

To monitor climate change, it is appealing to work with summary statistics such as global mean sur-face temperature, but it must be clear that such simplifications do not capture the entire picture. Defined climate as the joint probability distribution describing the state of the atmosphere, ocean, and freshwater systems (including ice), to think that climate change would determine only global warming is reductive. Hsiang and Kopp (2018) illustrates all climate changes that are likely to have economic consequences: in addition to changes in temperature, changes in precipitation, humidity, tropical cyclones, sea level, droughts and floods, clouds and ocean acidification.

Henceforth, climate change concerns also weather extremes, even if its consequences on storms are still not completely clear. Both frequency and strength of storms may be affected, and Trenberth et al.

CHAPTER 2. LITERATURE REVIEW 22

(2015) highlight that for global storms, even though natural variability always dominates in such events, human-induced warming plays at least a partial role in explanation for why extremes are being magnified. The effect on storm tracks (the paths that storms take toward land) is uncertain and may offset or enhance the effect of increased storm intensity in some regions, causing a heterogeneous spatial distribution of these changing risks. For example, systematic changes in the spatial distribution of storm tracks within an ocean basin may reallocate cyclone risk between populations, even if the overall frequency of storms does not change (Hsiang and Kopp, 2018).

When the focus is on the United States, trends in the occurrences of storms, ranging from severe thunderstorms to winter storms to tropical storms, are subject to more considerable uncertainties. Con-nections between global warming and the factors that cause these extremes are still being explored. How-ever, current results suggest a positive trend: Diffenbaugh et al. (2013) proposes a projected increase in the frequency of conditions favorable for severe thunderstorms, for the entire Northern Hemisphere there is evidence of an increase in both storm frequency and intensity during the cold season since 1950 (Vose et al., 2014) and in the North Pacific basin a positive trend has been observed both in tropical storm activity and typhoons since the mid-1970s (Chan and Shi, 1996).

Anyhow, even if accurate projections of future storms activity were available, quantifying their costs using parameters estimated on historical data is a critical issue. There have long been concerns that the effect of weather variation (event strike) on outcomes cannot be used to identify the effect of climate changes (event risk) because the response to short-run weather fluctuations may be fundamentally dif-ferent from the response to a permanent change in the distribution of those fluctuations. This difference is due to adaptation: people and firms may respond differently to permanent changes in the expected distribution of weather than to short-term and unanticipated fluctuations in weather. If this adaptation is significant, then the impact of weather fluctuations may not be a good analog for the effect of a permanent change in the climate. The question of how best to estimate the effects of long-run changes in climate has long been one of contention, at a point that Dell et al. (2014) identify as a high priority area for future research. In Chapter 4 some strategies are presented, although they not resolve this issue, which remains essential if recent empirical results are to be used to inform damage functions and ultimately the social cost of carbon used in the cost-benefit analysis of climate policy.

CHAPTER 2. LITERATURE REVIEW 23

2.6

Conclusions

Several works in literature estimate the effects of wind-related events on economic growth. Even if none of these events (hurricanes, typhoons, cyclones) is precisely the same as storms studied in this dissertation, from the comparing review did above at least three suggestions can be taken home: first, a well tested empirical methodology to evaluate the impact of extreme events on growth, fully presented in Chapter 4. It can be used to verify one of the mentioned hypotheses in the case of damaging winds.

Second, the advice that for wind events wind speeds matter; measuring consequences of storms, differences in wind speeds should be accounted since they seem to be the main drivers for different impacts.

Third, the suggestion that the size of storms’ impact is more likely to be limited than national-spread: events considered are not Katrina nor Sandy, but smaller phenomena intended to be on the cover of a local newspaper more than on national press. Measuring their impacts at the county level looks like the more reasonable choice.

Furthermore, the review of the literature has also better explained the aim of this research: to quantify the impacts of storm events on growth is the first necessary step to include them in damage function to carry out a cost assessment of climate change, which affects storms activity too. Nevertheless, how to implement the inclusion, moving from the impact of storm strikes to the impact of storm risk, requires further strategies.

Chapter 3

Data sources and exploratory analysis

3.1

Introduction

For this dissertation, a panel dataset of county-year observations of damaging winds and economic vari-ables for the years 1990-2016 is constructed. As the empirical approach of the work, the construction of the dataset covers a not negligible proportion of what I did, collecting data on storm events, county economic activity, and county weather.

Storms data come from an NOAA (National Oceanic and Atmospheric Administration) dataset, mainly used for official publications. Processing these data in an economic framework is not trivial, and including them in a panel structure requires to define an exposure measure, to move from a collec-tion of damaging winds to a county-year value.

Data on county growth rates are needed to measure the impact of storms on growth. Yearly data on county G.D.P. is not available, while data on total income, income per capita, total wages, and the average pay per worker can be found. It is worth noting that, since the Census (the primary source of many U.S. county data) is decennial, it tends to be useless in this analysis: yearly estimated (often linear) interpolation may not be reliable when effects of shocks are studied, and then these kinds of data are not considered.

This chapter is devoted to illustrate data sources and provide summary statistics: Storm Events Database is presented in Section 3.2, and exposure measures discussed in Section 3.3. Section 3.4 and 3.5 describes respectively data on regional economic activity and weather conditions, while Section 3.6 concludes.

CHAPTER 3. DATA SOURCES AND EXPLORATORY ANALYSIS 25

3.2

Storm Events Database

Data on storm events come from the Storm Events Database (SED). SED contains the records used to create the official NOAA Storm Data publications. It documents:

• The occurrence of storms and other significant weather phenomena having sufficient intensity to cause loss of life, injuries, significant property damage, and disruption to commerce;

• Rare, unusual, weather phenomena that generate media attention, such as snow flurries in South Florida or the San Diego coastal area;

• Other significant meteorological events, such as record maximum or minimum temperatures or precipitation that occur in connection with another event.

A profuse, and sometimes odd, variety of events is included in the records, as can be seen in Table 3.1. Events are reported in SED with features like start and end time and place, caused damages and, in some cases, a magnitude measure. These magnitude measures are crucial since two events with the same label are likely to be very different depending on size and intensity.

SED includes more than 70 different types of events, but magnitude measures are reported only for a few of them. However, event types for which magnitudes are reported are by far the ones which include the majority of single events (more than 60%). SED provides hail size for hails and marine hails; wind speed (maximum wind speed recorded during the event) for thunderstorm winds, high winds, strong winds, marine high winds, marine strong wind, and marine thunderstorm wind; Fujita scale category (based on damages, and not on physical characteristics) for tornadoes. This dissertation is focusing on events labeled as “thunderstorm wind”, “high wind” and “strong wind” (from now on, if I use the generic “storms” I will refer to them). I intended these labels indicating damaging winds, also called straight-line winds to stress the difference from tornados, whirlwinds or other circular wind phenomena.

In the previous chapter, the physics of damaging winds from convective events has been briefly in-troduced. It should be noted that “high wind” and “strong wind” (less than 20% of damaging wind events) could also include non-convective events (Knox et al., 2011). Non-convective high winds are an underrated, yet damaging and deadly weather phenomenon: although less familiar than their convective counterparts, these wind events occur in several American regions (East Coast (Ashley and Black, 2008), the Great Lakes region (Lacke et al., 2007; Niziol and Paone, 2000), the northern Great Plains (Kapela et al., 1995), the Pacific Northwest (Mass and Dotson, 2010)). Non-convective high winds are in many

CHAPTER 3. DATA SOURCES AND EXPLORATORY ANALYSIS 26

Table 3.1: Frequencies of different types of events registered in SED in the period 1990-2016.

Event type Frequency Event type Frequency

Astronomical Low Tide 486 Avalanche 570

Blizzard 12, 052 Coastal Flood 2, 402

Cold/Wind Chill 12, 879 Debris Flow 651

Dense Fog 11, 412 Dense Smoke 21

Drought 50, 968 Dust Devil 205

Dust Storm 936 Excessive Heat 6, 767

Extreme Cold/Wind Chill 10, 111 Flash Flood 72, 999

Flood 43, 521 Freezing Fog 382

Frost/Freeze 10, 282 Funnel Cloud 7, 658

Hail 293, 003 Hail flooding 1

Hail/icy roads 1 Heat 17, 916

Heavy Rain 20, 389 Heavy Snow 55, 405

Heavy Wind 4 High Snow 1

High Surf 7, 767 High Wind 59, 891

Hurricane 191 Hurricane (Typhoon) 1, 620

Ice Storm 10, 439 Lake-Effect Snow 2, 024

Lakeshore Flood 49 Landslide 372

Lightning 15, 572 Marine Dense Fog 8

Marine Hail 613 Marine High Wind 335

Marine Hurricane/Typhoon 23 Marine Lightning 1

Marine Strong Wind 105 Marine Thunderstorm Wind 21, 229

Marine Tropical Depression 1 Marine Tropical Storm 29

Rip Current 1, 103 Seiche 59

Sleet 678 Sneakerwave 10

Storm Surge/Tide 1, 201 Strong Wind 17, 714

Thunderstorm Wind 327, 104 Wind/Tree 1

Wind/Trees 3 Winds funnel clou 2

Winds heavy rain 1 Winds lightning 2

Winds/floods 2 Winds/flash floods 1

Winds/flooding 1 Winds/heavy rain 1

Tornado 35, 350 Tornado/waterspout 1

Tornadoes, TSTM wind, hail 1 Tropical Depression 329

Tropical Storm 4, 328 Tsunami 33

Volcanic Ash 70 Volcanic Ashfall 4

Waterspout 4, 468 Wildfire 6, 185

CHAPTER 3. DATA SOURCES AND EXPLORATORY ANALYSIS 27

ways comparable to convective events in terms of societal impact. These wind events are usually asso-ciated with extratropical cyclones, but there is no one universally accepted explanation for them: several different near-surface and mid-to-upper-tropospheric processes have been proposed, but no one physical mechanism has been identified yet as causing non-convective high winds.

Once selected 404,709 “thunderstorm wind”, “high wind” and “strong wind” events, I keep just the 307,161 ones with wind speed higher than 75 km/h (or 21 m/s, the commonly accepted threshold for winds to be defined extreme, as SED itself reports). The rest of the dissertation is dealing with estimating the economic impact of these 307,161 events.

SED damaging winds

Figure 3.1 reports annual numbers of recorded damaging winds in SED and Figure 3.2 shows maximum wind speed for every year from 1990 to 2016: at least in the first part of the period considered, number of recorded events increases, whereas no pattern emerges for maximum wind speed. Based on their magnitude, damaging winds considered can be classified as follows: 189,548 severe gales (61.7%), 89,937 whole gales (29.3%), 20,625 violent storms (6.7%) and 7,051 hurricane forces (2.3%).

To estimate storms impact at the county level, it is crucial to assign events to the county where they happen correctly. SED includes start and end latitudes and longitudes, although they are not reported for all the events. I assign the 255,103 winds (83%) for which coordinates are reported to the county to which their starting points belong. The remaining events are assigned to the county indicated by SED, or to the county which includes the area indicated (it is to say that the records reported in “Upper Sioux County, Iowa” are assigned to “Sioux County, Iowa”).

Start and end coordinates of the events can be used to calculate their sizes: the average radius is slightly above the kilometer (1.06 km) and even the 95th percentile is not very high (7.17 km). The same small scale can be found on the time dimension: almost every event (97.6%) begins and ends in the same day, as can be seen comparing the starting and final date.

The slightly restricted spatial and temporal extent is consistent with studies on damaging winds in literature (Changnon Jr, 1980; Fujita, 1985; Caracena et al., 1989). As for events in SED, they report that large downburst winds last from 5 to 30 minutes and microbursts are typically short-lived, on the order of 10 min or less, although occasionally they can last five or six times as long. Damaging winds are confined events, that wildly hit just a limited portion of land.

CHAPTER 3. DATA SOURCES AND EXPLORATORY ANALYSIS 28 0 5000 10000 15000 20000 1990 1995 2000 2005 2010 2015 Year

Number of recorded damaging winds

Figure 3.1: Bar-chart shows number of recorded damaging wind events for each year considered in the analysis. All storms affecting at least one county in given year with a reported wind speed larger than 74 km/h are considered. Black line reports 5-years moving average.

Coordinates make possible to locate these events on a map, to gain an idea on their spatial distribu-tion across the U.S. Figure 3.3 clearly shows how they are not evenly distributed: the vast majority of damaging winds hits states East of the Rocky Mountains and particularly states along the East coast.

3.3

Definition of the exposure measure

To estimate the impact of damaging winds on county economic output, a county- and year-based expo-sure meaexpo-sure is needed. It should be a function of the events affecting a county in a year, that is to say

Mi,y = µ(η1,i,y, η2,i,y, . . . , ηn,i,y) (3.1)

where Mi,y is the exposure measure for county i in year y, µ is the metric function and ηj,i,y, j ∈ (1, . . . , n) are the n events reported in SED affecting county i in year y.

The definition of metrics µ is the crucial choice that allows moving from a collection of single events to a county panel of exposure measures. The metrics can account both for frequency and intensity of the