domain: sdv sde shs

text:
Johanna Sigaux
email: johanna.sigaux@aphp.fr

Catherine Cavalin

Alain Lescoat

Sarah El Rharras

Odile Macchi

Pierre-Yves Yves Brillet

Lucile Sesé

Hilario Nunes

Marie-Christophe Boissier

Paul-André Rosental

Luca Semerano

Are cleaning activities a source of exposure to crystalline silica in women with rheumatoid arthritis? A case-control study

niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

INTRODUCTION

Inhalation of crystalline silica (silicon dioxide, SiO 2 ) is associated with a wide range of acute and chronic diseases, [START_REF] Marinaccio | Association between asbestos exposure and Pericardial and Tunica vaginalis Testis malignant Mesothelioma: a case-control study and Epidemiological remarks[END_REF] including rheumatoid arthritis (RA). [START_REF] Klareskog | The importance of differences; on environment and its interactions with genes and immunity in the causation of rheumatoid arthritis[END_REF] In a recent meta-analysis of epidemiological studies, occupational exposure to SiO 2 was associated with a 2.59-fold higher odds of RA. [START_REF] Mehri | The association between occupational exposure to silica and risk of developing rheumatoid arthritis: A meta-analysis[END_REF] Exposure to SiO 2 has been identified as a risk factor for incident RA in the Swedish population, [START_REF] Stolt | Silica exposure is associated with increased risk of developing rheumatoid arthritis: results from the Swedish EIRA study[END_REF] showing a closer association with seropositive RA 5 6 and positive interaction with smoking.

SiO 2 is present in diverse polymorphs like quartz, cristobalite and tridymite; quartz is the most common form found in nature, and the main component of the crystalline silica dust that may be emitted in manufacturing processes. [START_REF] Stolt | Silica exposure among male current Smokers is associated with a high risk of developing ACPApositive rheumatoid arthritis[END_REF] SiO 2 is used in a wide range of WHAT IS ALREADY KNOWN ON THIS TOPIC ⇒ Inhalation of crystalline silica (silicon dioxide, SiO 2 ) is associated with rheumatoid arthritis (RA). ⇒ Research on the role of SiO 2 in human disease has been traditionally focused on occupational exposures, with male workers as the main target population. ⇒ Substantial exposure from both occupational and non-occupational sources may occur in both men and women over the life course.

WHAT THIS STUDY ADDS

⇒ The Dust Exposure Life-Course Questionnaire (DELCQ), is a novel tool that allows to quantify retrospectively both occupational and non-occupational lifetime exposure to SiO 2 . ⇒ Women with RA have high lifetime exposure to SiO 2 from sources that are currently neglected in research: cleaning activities and dusty clothes laundry. ⇒ High occupational DELCQ scores were associated with mediastinal lymphadenopathy, a proxy of crystalline silica exposure.

HOW THIS STUDY MIGHT AFFECT RESEARCH, PRACTICE OR POLICY

industrial activities. High exposure to crystalline silica dust may occur in blast furnace, electronics glass, cement and ceramic manufacturing. It may also occur in other sectors such as mining (historically the main sector of exposure to SiO 2 and subsequent silicosis), farming and construction. [START_REF] Yassin | Occupational exposure to crystalline silica dust in the United States, 1988-2003[END_REF] Since these activities may involve intense exposure for workers, research on SiO 2 in human diseases has historically focused on occupational exposure and on male workers as the main target population. [START_REF] Ilar | Occupational exposure to organic dusts and risk of developing rheumatoid arthritis: findings from a Swedish population-based case-control study[END_REF] However, SiO 2 is ubiquitous in the environment. Over a lifetime, both men and women may experience substantial exposure across many professional activities and even beyond occupational contexts.

SiO 2 is present in a wide range of products (scouring powders, cat litter, rubber tires, etc) that may emit airborne particles. Thus, an accurate measure of actual lifetime exposure to crystalline silica remains a challenging task. [START_REF] Epa | Health Effects Of Inhaled Crystalline And Amorphous Silica[END_REF] The Dust Exposure Life-Course Questionnaire (DELCQ) is a novel tool that enables retrospective quantification of both occupational and non-occupational lifetime exposure to SiO 2 . [START_REF] Cavalin | Crystalline silica exposure in patients with rheumatoid arthritis and systemic sclerosis: a nationwide cross-sectional survey[END_REF] A multidisciplinary team of epidemiologists, social scientists, occupational health and medicine collaborators developed the questionnaire to identify situations likely to expose people to SiO 2 . The exposures were based on an extensive list of products and activities summarised by the International Agency for Research on Cancer 11 and complemented by medical and statistical surveys of the general population [START_REF] Matinet | Les exposition aux Risques Professionnels. LES Produits chimiques[END_REF][START_REF] Couraud | Biocast/IFCT-1002: Epidemiological and molecular features of lung cancer in never-Smokers[END_REF][START_REF] Parks | Occupational exposures and risk of systemic lupus erythematosus: a review of the evidence and exposure assessment methods in Population-and clinic-based studies[END_REF] as well as data from the literature about exposure to SiO 2 and inorganic particles in occupational or environmental settings. 9 15 DELCQ was administered in 2016 to a representative sample of the French general population (ELIPSS cohort, ELIPSSilice2 survey) selected from the French national rolling census by the French National Institute of Statistics and Economic Studies (INSEE). This population serves as a control for case-control studies of lifetime exposure to SiO 2 in patients with specific diseases such as RA and systemic sclerosis. [START_REF] Cavalin | Crystalline silica exposure in patients with rheumatoid arthritis and systemic sclerosis: a nationwide cross-sectional survey[END_REF] Using DELCQ, we recently found that both male and female RA patients had higher occupational exposure to SiO 2 than their respective controls, and that women with RA also had higher non-occupational exposure than controls. [START_REF] Cavalin | Crystalline silica exposure in patients with rheumatoid arthritis and systemic sclerosis: a nationwide cross-sectional survey[END_REF] In this case-control study, we sought to identify the actual sources of SiO 2 exposure in patients with RA, and to explore the association between the level of SiO 2 exposure and major disease features and outcomes.

PATIENTS AND METHODS Patients

The case and control populations and SiO 2 exposure assessment are detailed elsewhere. [START_REF] Cavalin | Crystalline silica exposure in patients with rheumatoid arthritis and systemic sclerosis: a nationwide cross-sectional survey[END_REF] In short, 97 patients who were diagnosed with RA according to 2010 ACR/ EULAR classification criteria and followed by the Department of Rheumatology of Avicenne Teaching Hospital (GHUPSSD, APHP, Bobigny, France) were consecutively recruited in a retrospective observational study between June and December 2016.

A specially trained interviewer administered DELCQ to all patients. The interviews typically lasted 45-60 min and were conducted in person for 56 patients and by phone for 41.

Clinical, laboratory and radiological data were retrieved from patient files.

For a subgroup of patients, high-resolution CT (HRCT) images of the chest were available. HRCT was not performed on purpose for this study, and had been carried out in the setting of patient usual care or prior initiating a targeted treatment. Two investigators evaluated HRCT images of the chest while blinded to the results of the SiO 2 exposure evaluations (LSesé, P-YB). The investigators determined the presence and extent of interstitial lung disease (ILD), and the presence of emphysema, mediastinal lymphadenopathy (LA), nodules and micronodules (number and shape). They assessed the presence and extent of ILD according to the Fleischner Society criteria. [START_REF] Hansell | CT staging and monitoring of Fibrotic interstitial lung diseases in clinical practice and treatment trials: a position paper from the Fleischner society[END_REF] The most relevant CT signs of ILD are reticular patterns, traction bronchiectasis, ground-glass opacity and honeycombing. We also included ILAs (interstitial lung abnormalities) in our definition of ILD. ILAs are described as non-dependent abnormalities affecting more than 5% of any lung zone. [START_REF] Hatabu | Interstitial lung abnormalities detected incidentally on CT: a position paper from the Fleischner society[END_REF] Mediastinal LAs were measured along their short axis and considered significant if >10 mm.

Controls

For each patient, up to four gender, age and smokingstatus matched controls were randomly sampled from the ELIPSSilice2 cohort (online supplemental figure 1). Briefly, ELIPSSilice2 is composed of subjects sampled by INSEE, based on national rolling census data and representative of the French general population (aged 18-76 at the time of the census), that is, independent of any health considerations. Subjects (n=2 739) completed the DELCQ questionnaire in 2016. The questionnaire was self-administered on tablets.

Patient and public involvement

Patients and the public were neither involved in the design and conduct of the study, nor the choice of outcome measures, nor the recruitment.

Assessment of silica exposure

The design and the content of DELCQ are thoroughly described elsewhere. [START_REF] Cavalin | Crystalline silica exposure in patients with rheumatoid arthritis and systemic sclerosis: a nationwide cross-sectional survey[END_REF] Briefly, DELCQ addresses both occupational and nonoccupational exposure over a lifetime through more than 130 questions (online supplemental data 1 and 2).

The questionnaire was conducted by phone or in-person interview by a trained interviewer (OM or SER) who was unaware of the results of clinical, immunological and pulmonary assessments. A score from 0 to 5 (based on the cumulative exposure duration and level of protection) was given for each specific exposure. Both the occupational (OES) and non-occupational exposure scores (NOES) were calculated by adding up the scores for each specific exposure. The two scores were analysed separately.

Statistical analysis

The occupational and non-occupational DELCQ scores related to each single source of exposure were compared between women with RA and controls using the Wilcoxon test. The Bonferroni correction was applied to avoid alpha-risk inflation due to multiple comparisons. The associations between DELCQ score quartiles and patient variables (for both male and female patients) were analysed with the Chi-square test or Fisher's exact test and expressed with OR and 95% CIs. A logistic regression model including age, gender, disease duration, anticitrullinated protein antibody (ACPA)/rheumatoid factor (RF) positivity and smoking status (defined as never, current or former smoker) was used to assess the association between SiO 2 exposure and HRCT abnormalities at multivariable analysis. Patients with an exposure score in the fourth quartile were considered to be highly exposed, and were compared with patients in the first to third quartiles. For all tests, the significance level was set at p<0.05. All statistical analyses were performed with R and RStudio V.2022.07.0 software.

Role of the funding source

The SILICOSIS project was funded with an Advanced Grant from the European Research Council. As such, its principal investigator Paul-André Rosental of the Centre for European Studies and Comparative Politics (Sciences Po, Paris) and his team are solely responsible for the design of the study; the data collection, analysis and interpretation; the writing of publications; and decisions to submit written outputs for publication in journals or books.

RESULTS

A total of 97 patients with RA (mean age: 59.4±13.2 years; 76 women and 21 men) completed the DELCQ questionnaire (table 1).

Mean RA duration was 13.1±11 years, most patients were RF and/or ACPA positive, and 61.9% were never smokers (19% of male vs 73% of female patients, p<0.05). At least one HRCT scan of the chest was available for 63.9% of patients.

As previously reported, [START_REF] Cavalin | Crystalline silica exposure in patients with rheumatoid arthritis and systemic sclerosis: a nationwide cross-sectional survey[END_REF] RA patients had significantly higher OES than their matched controls, whereas no difference appeared for non-occupational exposures. However, women with RA had both higher occupational and non-occupational exposures to SiO 2 than their female matched controls. We therefore sought to uncover the most relevant sources of SiO 2 exposure in female patients by studying and comparing the distribution of occupational and non-occupational sources of SiO 2 exposure in women with RA and their controls.

Sources and levels of occupational and non-occupational silica exposure in women with RA and controls Cleaning activities are the most important source of occupational exposure, accounting for 31% of women's total occupational exposure (15.7% from 'proper' cleaning activities+15.3% from laundry for dusty work clothes). Then came farming (9.1%), steel welding (4.2%) and construction (3.0%) activities (data not shown). The sources of occupational SiO 2 exposures in controls were also primarily cleaning activities (15.2% from 'proper' cleaning activities+6.2% from laundry for dusty work clothes=21.4%) and farming activities (19.6%) (data not shown).

We then compared the mean score allocated to each main occupational and non-occupational source for women with RA versus controls (table 2).

Within occupational activities, women with RA had higher exposure scores for cleaning activities (1.03±1.71 vs 0.46±1.32, p=0.02) and laundry for dusty work clothes (1.00±1.86 vs 0.19±0.86, p=0.01).

Among non-occupational SiO 2 exposure sources, women with RA had higher scores than controls for cleaning activities (8.03±4.29 vs 3.92±4.48, p<0.0001).

Association between silica exposure and patient variables

Ever-smoker RA patients (active or former) were more likely to be in the highest quartile of SiO 2 OES than never-smoker patients (p=0.002), but no difference was observed for NOES (p=0.18) (table 3).

Being in the highest quartile of SiO 2 exposure (occupational or non-occupational) showed no association with ACPA or RF status, radiographic erosion, or the need for biologic disease-modifying antirheumatic drugs (DMARDs). Conversely, high occupational exposure to SiO 2 was associated with ILD, emphysema and mediastinal LA (all p<0.05, table 3).

The association between high occupational SiO 2 exposure, ILD and mediastinal LA at HRCT persisted after adjustment for smoking status (ie, ever-smoker or neversmoker), with a sixfold increased risk for patients in the highest OES quartile (table 4). Further adjusting for ACPA and/or RF status, gender and disease duration had no bearing on the associations.

DISCUSSION

This study shows that women with RA, not selected on the basis of their professional activity, have high lifetime exposure to SiO 2 from sources that are currently neglected in research: cleaning activities and dusty clothes laundry. The use of an accurate questionnaire derived from social sciences enabled identification of these unconventional sources of SiO 2 exposure.

Cleaning activities in particular were a relevant source of exposure to SiO 2 in both occupational and nonoccupational contexts. Given that around 60% of the women in the series were retired or officially unemployed, our study's retrospectivity allowed us to identify substantial exposure to SiO 2 in a population that would not usually be considered at risk.

Like cigarette smoke, SiO 2 is an airborne agent that may initiate an inflammatory response in lung tissue. In predisposed individuals, local inflammation is conducive to the activation of adaptive immunity, production of autoantibodies and progression towards systemic disease. 5 6 The association between high occupational exposure to SiO 2 and incident RA is supported by several observational studies. 3 18 A Swedish nationwide study also found an association between the risk of incident RA and a jobmatrix score based on occupations with established high exposure to SiO 2 . [START_REF] Ilar | Occupational exposure to organic dusts and risk of developing rheumatoid arthritis: findings from a Swedish population-based case-control study[END_REF] However, all these studies focused on specific jobs known to expose workers to high levels of SiO 2 .

Conversely, the novelty of this study was the assessment and quantification of SiO 2 exposure in an unselected sample of consulting RA patients. In a previous study, we showed that patients (men and women with RA) primarily differ from their (respective) matched controls on the level of lifetime occupational exposure. [START_REF] Cavalin | Crystalline silica exposure in patients with rheumatoid arthritis and systemic sclerosis: a nationwide cross-sectional survey[END_REF] Consistent with the literature, men and women with RA had higher occupational exposure to SiO 2 than both (respective) gender-matched controls. Occupational exposure was mainly attributable to male-dominated activities such as construction, glass, mining, agriculture, ceramics and electronics. [START_REF] Yassin | Occupational exposure to crystalline silica dust in the United States, 1988-2003[END_REF] The novelty of this work lies in its measurement of non-occupational exposure, and of occupational exposure from occupational activities that are not necessarily identified as at-risk. In the case of women, we had previously shown that our sample of female RA patients were more exposed to SiO 2 from both occupational and non-occupational than their matched controls randomly sampled from the general population. [START_REF] Cavalin | Crystalline silica exposure in patients with rheumatoid arthritis and systemic sclerosis: a nationwide cross-sectional survey[END_REF] Moreover, while tobacco plays a role in the pathogenesis of RA, since the vast majority of these female patients were non-smokers, SiO 2 may be a major contributor to the pathogenesis of the disease in this subgroup.

The thoroughness of DELCQ enables the identification of specific sources of SiO 2 exposure in women with RA. The main sources are related to cleaning activities. Contact with SiO 2 comes from household cleaning products [START_REF] Dumontet | Silicosis due to inhalation of domestic cleaning powder[END_REF] and from the manipulation of clothes belonging to workers directly exposed to high levels of SiO 2 (dusty work clothes laundry). As those activities (mainly performed by women) are not considered to be associated with SiO 2 hazards, specific protections are rarely (if ever) implemented. Even fewer protections are considered when these activities are practiced at home. Hence, the absence of protection during house chores impacted the DELCQ score and led us to identify hitherto unrecognised sources of non-occupational exposure to SiO 2 .

According to DELCQ, people who perform housekeeping activities are exposed to SiO 2 , which many detergents contain. However, the use of cleansing agents has not been demonstrated to result in substantial SiO 2 exposure, and the association with RA may be due to other compounds contained in the detergents. In the Swedish Epidemiological Investigation of Rheumatoid Arthritis cohort, a strong association between occupational exposure to detergents and the risk of both ACPA-positive and ACPA-negative RA was found. Detergents were the most prevalent occupational inhalant for men (percentage exposed: 31%) and women (51%) with RA. [START_REF] Tang | Occupational Inhalable agents constitute major risk factors for rheumatoid arthritis, particularly in the context of genetic predisposition and smoking[END_REF] In a Swedish cross-sectional study of 40 000 workers, the prevalence of RA was higher in women working as cleaners; doctors and nurses; nurse assistants; textile workers; shop assistants; bank, postal and telegraph personnel; and housewives. [START_REF] Hellgren | The prevalence of rheumatoid arthritis in occupational groups[END_REF] The study hypothesised the action of an environmental factor, but SiO 2 was not mentioned as a possible one. Another epidemiological study found a looser association with RA in women working as hairdressers or beauticians, especially those exposed to hair products. No information was provided about the composition of the products, including any possible SiO 2 content. [START_REF] Tang | Occupational Inhalable agents constitute major risk factors for rheumatoid arthritis, particularly in the context of genetic predisposition and smoking[END_REF] Interestingly, Colinet's patients (whose description was used for the Caplan-Colinet syndrome [START_REF] Caplan | Certain unusual radiological appearances in the chest of coal-miners suffering from rheumatoid arthritis[END_REF] ) were employees of a soap factory, involved in silica-scouring powder packing. [START_REF] Ronsmans | Colinet-Caplan syndrome: history of an outbreak of autoimmune disease in scouring powder workers[END_REF] Our female patient sample includes a vast majority of housewives from low-income households, partially reflecting Avicenne hospital's location in a poor urban area. Their cumulative exposure may be compared with an occupational one, even though housecleaning products may differ from those used in occupational contexts. A documented association between low socioeconomic status and RA exists, 24 25 but no study has ever considered the potential role of SiO 2 exposure in this association.

The association of high occupational DELCQ scores with HRCT abnormalities, notably mediastinal LA, provides external validity to the questionnaire. Since mediastinal LA is a common characteristic of silicosis, it can be considered a proxy for high SiO 2 exposure. [START_REF] Satija | Spectrum of high-resolution computed tomography imaging in occupational lung disease[END_REF] [START_REF] Antao V Dos | High-resolution CT in Silicosis: correlation with radiographic findings and functional impairment[END_REF] A similar association with high occupational DELCQ scores was demonstrated in a series of scleroderma patients. [START_REF] Ballerie | Association of silica exposure with chest HRCT and clinical characteristics in systemic sclerosis[END_REF] High occupational SiO 2 exposure was also independently associated with ILD after adjusting for potential confounders. This association is intriguing as it links SiO 2 exposure with a type of rheumatoid lung involvement. However, this association may only be due to chance and needs to be confirmed in larger series. In addition, we may have overestimated the proportion of ILD and the strength of the association. As chest HRCT had been performed only for some patients in the setting of their usual care or prior initiating a targeted treatment, a selection bias based on the presence of pulmonary involvement or on the severity of RA is likely present.

No association was found between the DELCQ and other markers of disease severity like radiographic erosion, ACPA/RF positivity and level, or the need for biologic DMARDs therapy.

The main limitation of the study is the limited sample size and the recruitment of patients from a single centre located in a poor urban area, which may result in under or overestimating relevant sources of exposure among RA patients. This may reduce the generalisability of the results and larger studies in more heterogeneous populations are needed to confirm this finding. However, the patients were recruited from a 2 million inhabitants territory that stretches over two different administrative areas, one of which displays a sociodemographic profile corresponding to that of the French society. In fact, the main sources of exposure that we have identified did not greatly differ from those of controls extracted from a representative sample of the French general population. Another limitation is that the questionnaire administration method differed between the controls (self-administered questionnaire on digital tablets) and the cases (administered by a trained interviewer by phone or in person), potentially introducing an evaluation bias. We estimated and discussed the impact of that potential bias elsewhere, 10 29 as we compared the DELCQ scores of our hospital cohort of patients with RA to those of selfreported RA patients inside the general population panel from which the healthy controls were selected (ELLIP-SILICE 1 and 2). Although occupational (but not nonoccupational) scores were lower in self-declared patients, the difference was not major and may be partly explained by higher diagnostic accuracy in a hospital setting. More importantly, inside the general population sample, the DELCQ score was able to discriminate between healthy subjects and self-reported patients both on occupational and non-occupational scores. Thus, although the different mode of administration might have led to overestimate the size of the difference, this is unlikely to affect the validity of the analysis. The study's main strength lies in the tool we used to assess SiO 2 exposure. DELCQ is a detailed questionnaire that is not excessively time-consuming but enables quantification of lifetime SiO 2 exposure, accounting for the source of SiO 2 , context of exposure (occupational vs nonoccupational), cumulative duration of exposure and use of protection.

In conclusion, this study identifies underestimated sources of potential SiO 2 exposure in women with RA. Both mechanistic and larger epidemiological studies are warranted to explore the relevance of SiO 2 exposure from those sources and the possible need for preventive strategies.

Table 1

 1 RA patient characteristics

		Overall RA population	Female RA	Male RA
	General characteristics	(n=97)	(n=76)	(n=21)	P value
	Age (years)	59.4±13.2	57.4±13.0	66.4±12.2	<0.01
	RA duration (years)	13.1±11.0	13.4±10.9	12.1±11.6	NS
	ACPA positive	78 (83.0)	57 (75.0)	21 (100)	<0.05
	RF positive	76 (84.4)	58 (76.3)	18 (85.7)	NS
	Erosive RA	80 (84.2)	61 (80.3)	19 (90.5)	NS
	Tobacco users
	Never a smoker	60 (61.9)	56 (73.7)	4 (19.0)	<0.0001
	Current smoker	14 (14.4)	9 (11.8)	5 (23.8)	NS
	Former smoker	23 (23.7)	11 (14.5)	12 (57.1)	<0.001
	Occupation
	Never worked or student	6 (6.4)	6 (7.9)	0 (0)	NS
	Active worker	29 (30.8)	23 (30.3)	6 (28.6)	NS
	Non-active worker (retired or unemployed)	59 (62.8)	48 (63.2)	11 (52.4)	NS
	RA treatments
	MTX	53 (54.6)	42 (55.3)	11 (52.5)	NS
	bDMARDs	54 (55.7)	44 (57.9)	10 (47.6)	NS
	Never bDMARDs user	30 (30.9)	23 (30.3)	7 (33.3)	NS
	bDMARD 1st line	37 (38.1)	31 (40.8)	6 (28.6)	NS
	bDMARD 2nd line	18 (18.6)	13 (17.1)	5 (23.8)	NS
	bDMARD 3rd line	6 (6.2)	5 (6.6)	1 (4.8)	NS
	bDMARD >3rd line	6 (6.2)	4 (5.3)	2 (9.5)	NS
	Chest HRCT scan	62 (63.9)	50 (65.8)	12 (57.1)	NS
	Normal HRCT chest scan	12 (19.3)	12 (24.5)	0 (0)	NS
	ILD	11 (18.0)	7 (14.3)	4 (16.7)	NS
	Emphysema	12 (19.7)	5 (10.2)	7 (58.3)	<0.001
	Mediastinal lymph nodes	17 (27.4)	12 (24.5)	5 (41.7)	NS
	Pulmonary nodules *	18 (29.0)	13 (26.5)	5 (41.7)	NS

Data are expressed as absolute number and percentage (%) or mean (SD). *Nodules or micronodules. ACPA, anticitrullinated protein antibody; bDMARD, biologic disease-modifying antirheumatic drug; HRCT, high-resolution CT; ILD, interstitial lung disease; MTX, methotrexate; NS, not significant; RA, rheumatoid arthritis; RF, rheumatoid factor.

Table 2

 2 Main sources of occupational and non-occupational silica exposure in women with RA versus controls (expressed as the mean and SD of exposure score)

				Control women
		RA women n=76	n=308
	Activities	Mean	SD	Mean	SD	P value* Bonferroni correction †
	Main occupational activities (OES)
	Construction	0.19	1.71 0.12	1.22	0.99	NS
	Farming	0.60	2.64 0.60	2.52	0.72	NS
	Stone	0.09	0.57 0.03	0.32	0.13	NS
	Wood	0.00	0.00 0.10	1.05	0.39	NS
	Cleaning	1.03	1.71 0.46	1.32	<0.001	†
	Metallurgy	0.05	0.46 0.03	0.51	0.29	NS
	Painting	0.06	0.41 0.06	0.40	0.99	NS
	Plumbing	0.00	0.00 0.00	0.00	-	NS
	Steel welding	0.27	1.48 0.03	0.33	0.02	NS
	Dusty work clothes laundry	1.00	1.86 0.19	0.86	<0.001	†
	Main non-occupational activities (NOES)
	Farming	2.30	4.07 1.06	1.88	0.13	NS
	DIY	1.05	1.61 0.80	1.40	0.19	NS
	Wood	0.32	0.97 0.29	0.96	0.73	NS
	Cleaning	8.03	4.29 3.92	4.48	<0.001	†
	Dusty work clothes laundry	2.55	3.71 1.76	3.30	0.064	NS
	Cat litter	0.47	0.79 0.82	0.95	0.003	†
	Mud bath	0.49	0.64 0.81	0.94	0.03	NS

*Wilcoxon test, level of significance p<0.05. †Bonferroni correction, level of significance p<0.004 for occupational activities, p<0.006 for non-occupational activities. NOES, non-occupational exposure score; OES, occupational exposure score; RA, rheumatoid arthritis.

Table 3

 3 Association between SiO 2 exposure (occupational and non-occupational, by quartile) and patient characteristics

		Occupational exposure score		Non-occupational exposure score
	RA patient variable	Q1-Q3	Q4	P value*	Q1-Q3	Q4	P value*
	Ever-smoking, n (%)	21 (28.8)	16 (66.7)	0.002	25 (33.8)	12 (55.2)	0.18
	ACPA-positivity, n (%)	56 (78.9)	22 (95.6)	0.16	61 (85.9)	17 (73.9)	0.31
	Erosive RA patients, n (%)	61 (84.7)	19 (82.6)	0.93	62 (84.9)	18 (81.8)	0.98
	bDMARD treatment	44 (60.3)	10 (41.7)	0.17	38 (51.3)	16 (69.6)	0.19
	n (%)
	ILD, n (%)	5 (10.9)	6 (37.5)	0.04 *	9 (19.1)	2 (13.3)	0.9
	Emphysema,	5 (10.9)	7 (43.7)	0.004 *	8 (17.4)	4 (25.0)	0.51
	n (%)
	Mediastinal LA, n (%)	9 (19.6)	8 (50.0)	0.04 *	14 (28.0)	3 (25.0)	0.88
	Lung micronodules,	4 (8.7)	3 (18.7)	0.27	5 (10.6)	2 (13.3)	0.77
	n (%)

*χ 2 test, level of significance p<0.05. ACPA, anticitrullinated protein antibody; bDMARD, biologic disease-modifying antirheumatic drug; ILD, interstitial lung disease; LA, lymphadenopathy; RA, rheumatoid arthritis.

Table 4

 4 Association between occupational silica exposure (by quartile) and HRCT lung abnormalities ORs adjusted for smoking status, gender, disease duration, anticitrullinated protein antibody and rheumatoid factor status. HRCT, high-resolution CT; OES, occupational exposure score.

		OES	Unadjusted	Adjusted
	HRCT abnormality	(quartiles)	OR (95% CI)	OR (95% CI)
	Mediastinal lymphadenopathy	Q1-Q3	(ref)	(ref)
		Q4	4.1 (1.2 to 13.9)	6.3 (1.4 to 27.7)
	Interstitial lung disease	Q1-Q3	(ref)	(ref)
		Q4	6.3 (1.5 to 26.6)	6.5 (1.3 to 32.6)
	Emphysema	Q1-Q3	(ref)	(ref)
		Q4	6.4 (1.6 to 24.7)	2.9 (0.6 to 13.7)

⇒ This study highlights the importance of daily-life sources of exposure to crystalline silica, particularly in household products, and their association with RA in women.

Funding SILICOSIS Project, led by Paul-André Rosental, Centre for European Studies and Comparative Politics, Sciences Po (Paris, France), sponsored by the European Research Council (ERC); (grant number ERC-2011-ADG_20110406, project ID 295817).

Author affiliations

Contributors JS, CC, P-AR and LSemerano conceptualised the study. JS, CC and LSemerano created the methodology for the study. CC, SER, OM, P-YB, LSesé and HN carried out the data collection. JS and LSemerano did the data analysis and verified the data. LSemerano and AL supervised the data collection. JS and CC wrote the original draft. JS is the guarantor. All authors reviewed and edited the manuscript. All authors had full access to all of the data and the final responsibility to submit for publication.

Competing interests None declared.

Patient consent for publication Not applicable.

Ethics approval This study involves human participants and was approved by CLEA (Comité local éthique Avicenne), N° 2016-256. Participants gave informed consent to participate in the study before taking part.

Provenance and peer review Not commissioned; externally peer reviewed.

Data availability statement Data are available upon reasonable request.

Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.


Eric Kulanthaivelu
email: eric-sivatharsan.kulanthaivelu@univ-reunion.fr

The growth effects of tropical cyclones in the U.S.: new evidence from state to county level

Keywords: Natural disasters, Cyclones, Growth, Economic impacts, Environmental issues JEL classification: O44, Q54, R11

Tropical cyclones have always been a concern for public authorities in the U.S., with a season lasting nearly half of the year. Using longitudinal data on economic growth and exposure to tropical cyclones, we provide new comprehensive analyses of these disasters' growth impact in U.S. states and counties. While we find that results remain insignificant with country-wide samples, this study shows that the effects can be, instead, significant at regional level. In fact, a specific analysis of Florida State stresses significant short-term negative effects, both at county and state levels. At this stage, this study calls for an in-depth investigation of the underlying mechanisms leading to such growth depletion but already pins down the important heterogeneity in the effects of a catastrophe on growth patterns within a country.

Introduction

Natural disasters commonly designate catastrophic events caused by natural processes of the Earth. These phenomena are often distinguished between those of geological, climatic or even biological nature. Throughout history, their consequences on human systems and societies have always been felt, and the ability to predict or prepare for them has accordingly represented a perennial challenge. In the context of assessing the impacts of natural disasters, the U.S. turns out to be an interesting case study as it is exposed to a wide range of disasters such as volcanic eruptions, earthquakes, winter storms, tornadoes, wildfires, droughts etc. This paper particularly focuses on tropical cyclones, which are arguably the costliest and the most threatening for material assets.

Among other countries, the U.S. regularly vied for the attention of researchers in the empirical literature. Some single-event studies such as those on the 2005 Hurricane Katrina examine the impact on jobs or population displacement, housing market, education or health outcomes (Vidgor, 2008;Groen & Polivka, 2008;Sacerdote, 2012;Deryugina & al., 2018;Deryugina & Molitor, 2020). More closely related to the present study, [START_REF] Strobl | The economic growth impact of hurricanes: Evidence from US coastal counties[END_REF] evaluates the impact of hurricanes on U.S. coastal areas' economic growth from 1970 to 2005. On the year the hurricane occurs, a detrimental effect is estimated at county level, while this negative impact is only detected on the quarter of occurrence and is offset by the end of the year at state level. Finally, no significant effect is perceived at national level.

Using annual and quarterly panel data sets of U.S. counties' or states' economic growth respectively from 1970 to 2020, this paper provides a reappraisal of the latter research question in the context of tropical cyclones for different levels of exposure. To achieve this, we exploit a geophysical data set to build a physical intensity measurement for all tropical cyclones that affect the U.S. at some point in their trajectory. Then, as tropical cyclones not only affect directly impacted areas but also neighboring territories, we resort to spatial panel models as in [START_REF] Strobl | The economic growth impact of hurricanes: Evidence from US coastal counties[END_REF] to take into consideration the influence of contiguous counties' or states' growth on each area under study. Overall, across our different panels of states or counties, we find that tropical cyclones do not disrupt economic activity. In a second phase of the study, we focus on one particular state, namely Florida. The interest in this State is not new in the empirical literature. [START_REF] Belasen | How disasters affect local labor markets the effects of hurricanes in Florida[END_REF] stress the negative impact on local employment in the wake of hurricanes. This detrimental effect leads, in counterpart, to a positive relationship between hurricane occurrence and earnings in the short-run. [START_REF] Brown | The economic impacts of tropical cyclones on a mature destination, Florida, USA[END_REF] evaluate revenue losses within the tourism sector due to tropical cyclones and highlight the increased effect on waterfront counties compared to those located deeper in Floridian lands. More recently, Pollack & Kauffmann (2022) demonstrate how cyclonic risk is imperfectly captured in Florida Keys' housing prices. Unlike other states in the U.S., Florida combines several characteristics that might amplify tropical cyclones' impacts. First, it is the most frequently exposed state in the U.S., while it is one of the richest states and one of the most populated. The latter combination of factors is even more concerning than Florida's largest metropolitan areas such as Miami are all located close to the shoreline where tropical cyclones make landfall with the greatest intensity. Finally, as pointed out in the above literature review, Florida has an increased vulnerability due to the importance of tourism for its economy. This single-state assessment is carried out by means of time series analysis which constitutes one of the novelties in this paper. After having a built stationary measurement for quarterly economic growth, our modeling concludes with a continuous negative effect over a year, which is merely significant on the quarter the cyclone occurs and two quarters later. These conclusions at state level are emphasised by complementary results on Floridian counties, which suggest that growth penalties accumulate over three years on a smaller scale. At last, the main contributions of this paper originate from its complete replicability, the use of updated data up to 2020, a twofold econometric strategy and a specific focus on Florida.

The paper is structured as follows. Section 2 introduces the cyclone and economic data, and describes construction of a stationary measure of economic growth at state level. Section 3 presents the methodology as well as the main results on the effects of tropical cyclones on U.S. economic growth at different levels while Section 4 examines the specific case of Florida. Finally, Section 5 concludes.

Data

This section introduces the data. First, we present those exploited in order to build our tropical cyclone intensity measurement. Then, the set of economic data at the state and county levels is presented. As the analysis of personal income per capita data at state level discloses the existence of seasonality, all the transformations made to address this issue are described step-by-step.

Cyclone data

Our primary cyclone data come from from Tropical Cyclone Exposure Database (TCE-DAT; [START_REF] Geiger | A global historical data set of tropical cyclone exposure (TCE-DAT)[END_REF], with an extension up to 2020 kindly provided by the research team upon request. TCE-DAT is a longitudinal data set on each landfalling tropical cyclone event worldwide from 1950 to 2020. As this paper focuses on the U.S., we only extract data related to this country, i.e. the events that have affected the U.S. at some point of their trajectory. TCE-DAT reports wind speed and location data for each cyclone event that has reached at least 34 knots (≈ 63 km/h) wind speed for at least one minute. It actually relies on the set of events reported by the widely used International Best Track Archive for Climate Stewardship (IBTrACS; [START_REF] Knapp | The international best track archive for climate stewardship (IBTrACS) unifying tropical cyclone data[END_REF], maintained by the National Climatic Data Center of the National Oceanic and Atmospheric Administration (NOAA). Using the 6-hourly observations on latitude and longitude position, wind speed, surface air pressure and trajectory of cyclone centres (called the "eye") provided by the IBTrACS, TCE-DAT builds overall cyclone trajectories and intensities at 0.1°latitude x 0.1°longitude grid cell level for each of the aforementioned event by applying Holland's (2008) wind field model. Hence, we consider here all the events during which at least one grid cell of the U.S. is hit according to the simulated wind field. Due to economic data limitations, our panel starts in 1970 and ends in 2020. Florida is the most frequently hit State during this time period with 83 events recorded, followed by North Carolina, Louisiana and Texas, with respectively 65, 54 and 53 cyclone events (Figure 1). Apart from being the most exposed State in the U.S., Florida is also characterized by a wide range of factors of vulnerability regarding cyclonic risk. These factors are detailed, in-depth, in section 4 and constitute the reason why we choose to focus on this specific state in a second part of the study. Among others, the exposure to cyclonic risk is homogeneous in Florida, as it is shown in Figure 2. The yearly average wind speed over Florida calculated from 1970 to 2020 shows small dispersion as this State is often entirely affected when it is hit by a tropical cyclone, unlike Texas, for instance, where all areas are not exposed to cyclonic risk.

Economic data

Data on personal income are taken from the Bureau of Economic Analysis's (BEA) regional economic accounts. More specifically, we extract data on Quarterly Personal Income by State as well as Local Area Personal per Capita Income, which provides annual county-level estimates from 1969 to 2020. The BEA defines personal income as the income received by people from labor, properties, financial income such as interests or dividends, rents, and government benefits. As personal income data are released in current US$ values, these have been converted to constant 2020 US$ using the consumer price index reported by the U.S. Bureau of Labor Statistics. State level personal income per capita data are obtained after including state population data from the U.S. Bureau Census (2021) to our data set.

Having said that, as we are in the context of panel data, it is necessary to assess whether these personal income per capita series are stationary. To do this, we first run panel and individual unit root tests for each series (logged) in levels and in first-difference for State level quarterly personal income per capita data. Table 1 outlines the results. According to the individual unit root tests, i.e. the Augmented Dickey-Fuller (ADF) and Philips-Perron (PP) test, and one of the panel unit root tests, i.e. the Im-Pesaran-Shin (IPS) test, first differencing the series seems to remove the unit root. However, the Levin-Lin-Chu (LLC) panel unit root test suggests that there is a problem as the null hypothesis of having at least one non stationary series among the panel is accepted with a P-value of 1. To deal with this issue, we compute the auto-correlation function (ACF) of one of our series and analyse the results. Figure 3 plots the ACF obtained for Florida, and the latter highlights the presence of seasonality, which explains the results presented in Table 1. 2 now shows that first-differences of deseasonalized series are all stationary.

Regarding our cyclone data, it is found to be I(0) across all unit root tests in levels, and further examination of its ACF shows that this series is arguably exogenous. As for county level annual personal income per capita, first differencing the series is enough to render them stationary. Notes: Augmented Dickey-Fuller tests with 5 lags and trend. Philips-Perron tests with 5 lags and trend.

Levin-Lin-Chu tests with 3 lags and trend, H 0 = panels contain unit roots Im-Pesaran-Shin tests with 3 lags and trend, H 0 = all panels contain unit roots 3 Measuring the impact of tropical cyclones on economic growth from state to county level

This third section outlines our methodology and main results. After having described the construction of our cyclone intensity predictor, this section presents the econometric model selected to measure the impact of tropical cyclones on economic activity and the obtained results. In a last subsection, we check the robustness of our main results for a variety of changes in the empirical framework.

Cyclone intensity measurement

This paper relies on a physical measure of tropical cyclones that is measured quarterly to fit with our state level economic data time interval, and annually for our analyses at county level. It takes the form of a population-weighted average of the maximum sustained wind speed over all pixels in each state -respectively, county -in a given sample. Mathematically, one can write the indicator at state level as:

Cyc i,q = P i p=1 max s∈{1,...,S p,i,q }
{W ind p,s,i,q }Exposed P opulation p,q T otal P opulation i,q

(1)

where W ind p,s,i,q is the wind speed recorded in pixel p, for storm s that struck state i in quarter q. Exposed P opulation p,q corresponds to the estimated population in pixel p and reported in TCE-DAT, while T otal P opulation i,q is the number of inhabitants in state i during quarter q as estimated in the U.S. Bureau Census (2021) data set. All pixel values are then averaged across the total number of pixels per state P i .

As for the yearly indicator, it includes a temporal weight (12 -m p,s )/12 as in [START_REF] Noy | The macroeconomic consequences of disasters[END_REF], this additional parameter is incorporated to capture the contemporaneous impact with more accuracy as a cyclone that strikes in the early months of a given year has arguably a bigger impact in the same year than another one -with same intensity -occurring some months later. This augmented version of equation ( 1) can be written as follows:

Cyc c,t = Pc p=1 max s∈{1,...,Sp,c,t} {W ind p,s,c,t } 12 -m p,s 12
Exposed P opulation p,t

T otal P opulation c,t (2)
with m p,s the month in which the storm occurred, and with county and year indices corresponding respectively to c and t. This monthly weighting is a core characteristic in our model as tropical cyclone season in the U.S. runs from June to November. Whether the indicator is measured at state or county level, we select the maximum wind speed value felt in each pixel each period, irrespective of the number of cyclone events S identified within each pixel for the given year as in [START_REF] Felbermayr | Naturally negative: The growth effects of natural disasters[END_REF]. This methodological choice is challenged in robustness estimations with an analysis of the impact with respect to the frequency of events.

In each case, the indicator weights wind speed levels over all pixels by the share of exposed population. Such weighting process follows recommendations to normalize natural disaster predictors in [START_REF] Nordhaus | The economics of hurricanes in the United States[END_REF]. Ultimately, the aim is to determine the average effect of storm events on an average pixel for a given area. This measurement can also be interpreted as an estimate of a cyclone's average intensity in a randomly selected unit of land for each county, or the value of a given cyclone's intensity if this one had struck the county homogeneously across all its locations [START_REF] Hsiang | The causal effect of environmental catastrophe on long-run economic growth: Evidence from 6,700 cyclones[END_REF].

Empirical strategy

The empirical framework follows essentially [START_REF] Strobl | The economic growth impact of hurricanes: Evidence from US coastal counties[END_REF] and resort to analyses with spatial panel models including a lagged value for the log of personal income per capita, so that spatial correlations between states -or likewise, counties -and initial growth conditions are properly considered. More specifically, among the wide range of spatial dependence models, our specification tests for states, which are presented in Appendix A, call for the use of spatial error model (SEM), which can be expressed as follows: g i,q = αln(P ers.Inc.pc) i,q-1 + βCyc i,q + µ i + δ q + u i,q

(3)

u i,q = λ i̸ =j w i,j u j,q + ε i,q
with, g i,q = ln(P ers.Inc.pc) i,q -ln(P ers.Inc.pc) i,q-1 for a state i and quarter q. w i,j corresponds to an element of the spatial weighting matrix W N ∈ R N ×N , which defines neighboring relationships between each of the N states. µ i and δ q represent respectively unobserved state-specific and timespecific effects. λ denotes the scalar spatial autoregressive coefficient, and assumed to verify |λ| < 1.

Finally, we assume that ε i,q are all independent and identically distributed with mean 0 and finite variance σ2 ε . As for county level estimations, specification tests leading to the choice of a SEM are rejected. Instead, results rather show that the appropriate model corresponds to a spatial autoregressive model with autoregressive disturbances (SARAR), also called spatial autocorrelated model (SAC) which is a more general class of spatial models than SEM. 2 Thus, the equation of interest for counties corresponds to:

g c,t = ρ c̸ =j w c,j g j,t + αln(P ers.Inc.pc) c,t-1 + βCyc c,t + µ c + δ t + u c,t (4)
u c,t = λ c̸ =j w c,j u j,t + ε c,t
where indices c and t are related to a given county and year, respectively. The dependent variable, all regressors and disturbances are defined as in the previous equation, with the exception that, now, the equation of interest includes a spatial lag W g = c̸ =j w c,j g j,t , and as λ its regression coefficient is also assumed to be bounded in absolute value: |ρ| < 1.

As already stated in subsection 3.1, the availability of quarterly data at state level allows for greater accuracy in the impact evaluation. More importantly, the latter point also offers the possibility to withdraw the monthly weighing that is included to Cyc c,t in county-year analyses.

In what follows, we split up our sample into three categories for states, and five categories for counties. First, we consider all 48 contiguous U.S. states (respectively all counties). Then we run regressions on the subsample of exposed states (respectively exposed counties). Third, we focus on 19 coastal states as in [START_REF] Strobl | The economic growth impact of hurricanes: Evidence from US coastal counties[END_REF] and all their counties that are exposed to tropical cyclones. In addition, we estimate the impact of tropical cyclones on coastal counties and on shoreline counties as defined by the NOAA. NOAA's list of coastal counties is based on NOAA coastal watersheds and U.S. Geological Survey's (USGS) coastal cataloging units. It corresponds to counties within which water flows into the ocean or Great Lakes. A coastal watershed county has at least 15 percent of its area in a coastal watershed or has a land area that accounts for at least 15 percent of a coastal cataloging unit as defined by the USGS. Shoreline counties are a subgroup of coastal counties. These counties are defined as those that are directly adjacent to the open ocean, major estuaries, and the Great Lakes.3 Both of these categories are highlighted in Figure 5. Table 3

Results at state level

Table 4 outlines the results at state level. Across all our estimations on different samples, results remain statistically insignificant. Notwithstanding the absence of impact of tropical cyclones on states' economic growth, these results confirm the presence of spatial autocorrelation within our data set as the spatial error coefficient λ is always significant at the 1% level. Additional estimations that include lagged values of cyclone predictor are presented in Appendix B. Indeed, one can rightly think that the impact of tropical cyclones might be visible only some quarters later. As such, we include up to three lags so that the impact is now estimated over a year. This change in specification does not alter the above conclusions, and the impact is consistently insignificant in the three quarters that follow the one when the cyclone makes landfall.

Results at county level

Table 5 presents the results from estimating equation (4), i.e. at county level. Once again, the null hypothesis that tropical cyclones do not affect growth is accepted across all our estimations. Columns 1 to 4 show a negative but statistically insignificant relationship between cyclone intensity and economic activity on average across all U.S. counties, exposed counties, coastal states' counties, or coastal counties as defined by the NOAA. In contrast, point estimate is positive and non-significant on average across our last sample of shoreline counties. As in our estimations at state level, results confirm the presence of spatial autocorrelation as coefficients on the spatially lagged dependent variable and spatial error term are both significant at the 1% level in all estimations, except for the sample of shoreline counties, where point estimate for ρ is significant at the 5% level. Additionally, to interpret the sign of the coefficient associated with ρ, as this one is negative, this means that neighboring counties have a detrimental effect on a given county's growth.

Robustness checks

This section considers a variety of robustness checks. Analyses are of two kinds. First, we keep the empirical strategy used above and consider robustness to alternative cyclone predictors. More specifically, on the one hand, we investigate an alternative formulation of our cyclone indicator that only keeps the share of exposed population as in [START_REF] Krichene | Long-term impacts of tropical cyclones and fluvial floods on economic growth -Empirical evidence on transmission channels at different levels of development[END_REF]. On the other hand, we check whether baseline results are altered when examining the effects of the frequency of cyclone events instead of our wind speed intensity measurement. Second, we reconsider the main specification and report the results when using a fixed effects estimator with [START_REF] Conley | GMM estimation with cross sectional dependence[END_REF] standard errors. These standard errors are robust to cross-sectional spatial dependence and serial correlation below some fixed cutoff values which are here set to 1000km and 3 time lags respectively. Hence, this alternative estimator provides a strong complement to our baseline spatial panel models estimations in which neighboring relationship is defined by sharing common borders. Results for each different state or county sample are summarized in Table 6 andTable 7.

State level

Overall, we find that using alternative indicators or changing the estimator does not change our conclusions. Removing the wind speed parameter from our baseline indicator does not change the sign of the relationship but increases the magnitude of coefficients. The quarterly number of cyclone events has a positive contemporaneous impact on average across all different samples, i.e. across all states, exposed states and coastal states. Finally, estimating equation (3) by applying a fixed effects estimator with [START_REF] Conley | GMM estimation with cross sectional dependence[END_REF] standard errors consistently displays negative immediate effects. However, in no case are these results statistically significant, meaning that state growth is arguably unaffected by tropical cyclone events in the quarter they occur. but not reported in the table. Bottom section presents OLS regression results with standard errors corrected for cross-sectional spatial dependence and panel-specific serial correlation as in [START_REF] Conley | GMM estimation with cross sectional dependence[END_REF].

The spatial correlation cutoff is 1000km, and the serial correlation one is 3 lags. Significance levels : *** 1% ; ** 5% ; * 10%.

County level

Table 7 reports the results for counties. Point estimates for the share of exposed population are positive across all estimations. The annual number of cyclone events has always a negative effect, except for the estimation on NOAA coastal counties. Estimations with [START_REF] Conley | GMM estimation with cross sectional dependence[END_REF] standard errors show a positive effect in the year the cyclone occurs across all samples. However, as none of the coefficients associated with our cyclone indicators are significant, the absence of impact is confirmed once again by these estimations. (1999). The spatial correlation cutoff is 1000km, and the serial correlation one is 3 lags. Significance levels : *** 1% ; ** 5% ; * 10%.

Focusing on Florida

So far, our results show that tropical cyclones do not impact economic activity in the quarter they occur in U.S. states, but also in the year they occur in U.S. counties. Having said that, in a second phase, we wonder if our main results imply that there is no effect either if we address our research question at a smaller scale.

In particular, we now focus on Florida, which is the most frequently exposed state in the U.S. from 1970 to 2020 with 83 cyclones (Figure 1). This feature is all the more important, given that the following most frequently hit states are North Carolina with 65 events recorded during the sample period, then Louisiana with 54 tropical cyclones and Texas with 53. Hence, there is quite a large gap in the exposure to these catastrophes between Florida and its U.S. counterparts. Several other reasons might be put forward to justify a focus on this specific state when studying cyclonic risk.

Florida was the fourth richest state in the U.S. at the end of 2020, behind the States of New York, Texas and California according to our personal income data. It is also the third most populated State of the U.S. in 2020 behind Texas and California. In addition, one striking feature in Florida is the concentration of most of its largest metropolitan areas along the shoreline, or at least, considered as coastal according to the NOAA. Indeed, Miami, which is the 8th largest metropolitan area in the U.S. in 2020 with 6.1 million inhabitants, Tampa (18th largest), Orlando (22nd largest) and Jacksonville (39th largest) are all located near U.S. coasts, and arguably more likely to face higher cyclone intensities compared to other areas that would be located deeper in lands.4 Notably, no other state exposed to tropical cyclones shows such geographic or demographic characteristics in our sample. Third, Florida's increased vulnerability to tropical cyclones due to this high concentration of population along the coasts is emphasised by a strong dependence on tourism activity. In fact, tourism represents the first source of revenue of the State. Agricultural activity, which is also at risk in the context of tropical cyclones, represented 1,2% of Floridian GDP in 2020 (United States Department of Agriculture, 2021). Hence, agriculture also brings a non-negligible contribution to Florida State's economy. Finally, as Florida is at the edge of the Caribbean, its local climate is more prone to higher cyclone exposure and intensities as it is surrounded by warmer water temperatures compared to other states due to its greater proximity to the equator. Florida also contains main harbours that export towards Latin America or the Caribbean. For all these reasons, several studies on the economic impact of tropical cyclones have chosen to focus on Florida [START_REF] Belasen | How disasters affect local labor markets the effects of hurricanes in Florida[END_REF][START_REF] Brown | The economic impacts of tropical cyclones on a mature destination, Florida, USA[END_REF]Pollack & Kauffmann, 2022).

State level analysis

To estimate the impact of tropical cyclones on Floridian economic growth, we opt for an Auto-Regressive Integrated Moving Average with exogenous factors (ARIMAX) model and follow the [START_REF] Box | Time Series Analysis: Forecasting and Control[END_REF] time series modeling approach. This method consists in a three-stage procedure: model identification, parameter estimation, and diagnostic checking. In the present case, the equation of interest is expressed as:

g t = A(L)g t-1 + B(L)Cyc t + C(L)ε t (5)
with A(L), B(L) and C(L) polynomials in the lag operator L, g (deseasonalized) personal income per capita growth, Cyc t the usual population-weighted average of the maximum sustained wind speed over all pixels in Florida each quarter t, and ε the white noise error term, independent and identically distributed. The identification step relies on an examination of the time path and the autocorrelation function (ACF) of Floridian series in order to get information about stationarity, structural breaks, trends, or seasonality. This step corresponds to the analyses presented at the end of section 2, and led to the choice of g t . Floridian growth's ACF and PACF suggest that the model can be either autoregressive (AR) as point estimates are alternating positive and negative, or a mixed autoregressive and moving average (ARMA) model as the decay is starting after a couple of lags, and more specifically, here, five lags (Figure 6). Then, the second step is to estimate the model. To do this, the optimal number of lags to be included in the model can be determined using the ACF plot of g t . The latter plot recommends a number of lags comprised between 3 and 5. Another alternative is to perform optimal lag tests. In this paper, four of them are selected, namely, the Final Prediction Error (FPE), Akaike information criterion (AIC), Hannan-Quinn criterion (HQIC) and Schwarz information criterion (SIC). The estimated optimal lag length corresponds to the one minimizing the criterion. The advantage of these optimal lag tests is to include the exogenous variable in the estimation. Table 8 presents the results of the tests. FPE, HQIC and SIC all suggest 5 lags, which somehow joins the interpretations made with the ACF plot, while SIC suggests that no lag should be included. Hence, considering altogether the results of the ACF, the PACF and those of the optimal lag tests, we choose to estimate the impact with 5 lags. Notes: * denotes the optimal number of lags to be included according to a given criterion. Estimations with a maximum number of lags fixed at 30. Cyc i,q exogenous variable.

Finally, Table 9 outlines the results. Across all estimations, Floridian economic activity is found to be adversely affected one quarter after a cyclone strike, and this negative impact is muted afterwards with most of the specifications. According to the AIC and BIC criteria, the ARIMAX (5, 0, 2) model seems to provide the best fit. A one km/h increase in the cyclone intensity measurement is responsible for 0.005 percentage points of growth loss in the quarter the tropical cyclone strikes Florida (significant at the 10% level). The point estimate two quarters later is also found to be negative and significant at the 10% level (-0.006 percentage points). For all the regressions, the invertibility condition is verified, meaning that all the roots of polynomials in lag operators lie inside the unit circle.

In order to prove that there is a differential impact across states, and that high exposure to tropical cyclones is not the only criterion that is responsible for a local negative impact, in Appendix C, we examine the results using the same methodology for North Carolina, Louisiana and Texas. The preferred specification according to AIC and BIC criteria is ARIMAX(4,0,0) for North Carolina, ARIMAX(1,0,2) for Louisiana, and ARIMAX(1,0,0) for Texas. In no case a significant impact is found for these three states. (5, 0, 0) (5, 0, 1) (5, 0, 2) (5, 0, 3) (5, 0, 4) Cyc q -0.00004 -0.00006* -0.00005* -0.00005* -0.00005* (0.00003) (0.00003) (0.00003) (0.00003) (0.00003) Cyc q-1 0.00002 -5.02e-06 -7.58e-07 3.29e-06 -1.45e-06 (0.00003) (0.00003) (0.00003) (0.00004) (0.00004) Cyc q-2 -0.00004 -0.00007* -0.00006* -0.00006 -0.00006 (0.00004) (0.00004) (0.00004) (0.00004) (0.00004) Cyc q-3 -0.00002 -0.00003 -0.00003 -0.00005 (0.00004) (0.00003) (0.00004) (0.00004) (0.00004) Cyc q-4 -0.00001 -0.00003 -0.00003 -0.00003 -0.00003 (0.00003) (0.00006) (0.00003) (0.00003) (0.00003) Cyc q-5 7.16e-06 -9.35e-06 -7.03e-06 -1.02e-06 2.10e-06 (0.00003) (0.00004) (0.00003) (0.00003) (0.00003) AIC -1201.304 -1203.568 -1205.651 -1204.697 -1201.396 BIC -1158.169 -1157.114 -1159.198 -1154.925 -1144.988 Notes: robust standard errors in parentheses. * significant at 10 %; ** significant at 5 %; *** significant at 1 %

County level analysis

Results obtained at county level using a spatial autocorrelated model are shown in Table 10. We decide to include 3 lags of the cyclone indicator in order to provide insights about longer-term effects. When using our cyclone intensity measurement Cyc c,t , the impact is consistently negative and significant over three years. The estimated immediate effect is -0.005 percentage points of growth for each km/h increase in storm intensity, -0.002 one year later, and then -0.003 two years later. The impact after three years is insignificant. If tropical cyclones are proxied by the share of the exposed population over each county, the immediate impact is comparable in magnitude but loses its statistical significance. However, the impact is larger in magnitude with better statistical significance one and two years after the occurrence of the catastrophe. Finally, the frequency of events in Floridian counties has no impact, whether in the short-or long run according to our model. As in the last subsection, we replicate the estimations for North Carolinian, Louisianian, and Texan exposed counties with our main cyclone intensity measurement. Point estimates are still mostly insignificant, except one and two years later in North Carolina, for which a positive impact is found (+0.002 and +0.001 percentage points, respectively) and the second cyclone lag's point estimate for Texas (+0.004 percentage points). These positive and significant delayed impacts suggest the efficiency of recovery processes after a cyclone strike, even though the immediate impact is, in each case, insignificant. This might also mean that tropical cyclones actually stimulate local economic activity one and two years later.

Conclusion

Tropical cyclones are fast onset and hardly predictable disasters, which have a tremendous potential to disrupt societies. This paper evaluates their impact on U.S. economic growth at county and state levels. Using 0.1 • latitude × 0.1 • longitude pixel level cyclone data, we build a measurement of disaster intensity which include both wind speed and share of exposed population. Data on personal income per capita come from the Bureau of Economic Analysis, which releases annual data at county level and quarterly data at state level. As the economic activity of a given area is undoubtedly influenced by those of its neighbours, our panel data estimations consider spatial correlations across groups, which are incorporated either by using spatial error models or spatial autocorrelated models.

Our first strand of results suggests that tropical cyclones do not influence economic activity in the U.S. when counties or states are sampled all over the country. However, in its second part, this study focuses on Florida State and its counties. Florida presents several factors of vulnerability to tropical cyclones. In particular, it is the most frequently exposed state in the U.S. Analyses are conducted using time series modeling for the single-state investigation, while the econometric strategy remains unchanged for the estimations within counties. This last series of estimations unveils significant results of growth depletion. On the quarter the cyclone occurs, the effect of a one km/h increase in intensity is to decrease contemporaneous growth rate by 0.005 percentage points, and by 0.006 more two quarters later. At county level, we estimate that such an increase in cyclone intensity is responsible for 0.005 percentage points in growth reduction on the year the cyclone occurs, and this negative effect remains significant up to two years after the strike.

Hence, our findings add to the recent body of literature that claims for regional or local investigations in environmental economics as we demonstrate the heterogeneity of causal effects within the same country in the context of tropical cyclones. Further research on economic channels such as sectoral growth is required to better understand the observed growth reduction in Florida and might bring more external validity to the present results.

B Additional estimations

Figure 1 :

 1 Figure 1: Total number of cyclone events from 1970 to 2020 in the contiguous U.S., by state.

Figure 2 :

 2 Figure 2: Yearly average wind speed at pixel level in Florida from 1970 to 2020.

  formula for MA(q) 95% confidence bands

Figure 3 :

 3 Figure 3: ACF for Floridian quarterly personal income per capita growth rate

  1

Figure 4 :

 4 Figure 4: Plots for Floridian quarterly personal income through time. Deseasonalized series on right hand-side.

Figure 5 :

 5 Figure 5: Categories of counties that were exposed to tropical cyclones from 1970 to 2020, given their geographical position in the U.S. and based on NOAA classification.

  bands [se = 1/sqrt(n)]

Figure 6 :

 6 Figure 6: Autocorrelation function (left) and partial autocorrelation function (right) of Floridian deseasonalized personal income par capita growth.

Table 1 :

 1 Unit root rests for raw series

	Individual unit root tests	Fraction of states for which test Panel unit root tests	P-value of the test
		is rejected at the 10% level
	1) Personal income per capita (logged) in levels		1) Personal income per capita (logged) in levels
	Augmented Dickey-Fuller test	0/37	Levin-Lin-Chu test	0.4705
	Philips-Perron test	2/37	Im-Pesaran-Shin test	1.0000
	2) Personal income per capita (logged) in first-difference		2) Personal income per capita (logged) in first-difference
	Augmented Dickey-Fuller test	37/37	Levin-Lin-Chu test	1.0000
	Philips-Perron test	37/37	Im-Pesaran-Shin test	0.0000
	Notes: Augmented Dickey-Fuller tests with 5 lags and trend. Philips-Perron tests with 5 lags and trend.
	Levin-Lin-Chu tests with 3 lags and trend, H 0 = panels contain unit roots
	Im-Pesaran-Shin tests with 3 lags and trend, H 0 = all panels contain unit roots

Table 2 :

 2 Unit root rests for deseasonalized seriesIndividual unit root testsFraction of states for which test Panel unit root tests P-value of the test is rejected at the 10% level 1) Personal income per capita (logged) in levels 1) Personal income per capita (logged) in levels

	Augmented Dickey-Fuller test	0/37	Levin-Lin-Chu test	0.0001
	Philips-Perron test	0/37	Im-Pesaran-Shin test	0.9972
	2) Personal income per capita (logged) in first-difference		2) Personal income per capita (logged) in first-difference
	Augmented Dickey-Fuller test	37/37	Levin-Lin-Chu test	0.0000
	Philips-Perron test	37/37	Im-Pesaran-Shin test	0.0000

  summarizes economic growth and cyclone intensity measurement data for each different sample. At state level, the highest cyclone measurement value is recorded in New Jersey in 2012 (Q4) when the State was affected by Hurricane Sandy (178.8 km/h). As for counties, according to our indicator, the most powerful cyclonic year is recorded in Baker County in Georgia in 2018 (196.2 km/h), followed by Cameron County in Louisiana in 2005 (186.4 km/h).

	50°N
	45°N
	40°N
	35°N
	30°N
	25°N
	120°W	110°W	100°W	90°W	80°W	70°W
		Coastal county	Other exposed county	Shoreline county

Table 3 :

 3 Summary Statistics

			Average values from 1970 to 2020
	Sample	Obs.	Mean	Std. Dev.	Min.	Max.

Table 4 :

 4 Results for regressing quarterly state growth on cyclone intensity

		All states Exposed states Coastal states
		(1)	(2)	(3)
	ln(P ers.Inc.pc) i,q-1 -0.034***	-0.021***	-0.020***
		(0.003)	(0.002)	(0.003)
	Cyc i,q	-8.54e-07	6.61e-06	0.00001
		(0.00001)	(7.63e-06)	(8.22e-06)
	λ	0.40***	0.25***	0.17***
		(0.01)	(0.02)	(0.02)
	Overall R 2	0.00	0.00	0.00
	Observations	9792	7548	3876
	Number of states	48	37	19

Notes: Spatial error model estimator with two-way fixed effects. Robust standard errors are in parentheses.

Time and state fixed effects are included, but not reported in the table.

Significance levels : *** 1% ; ** 5% ; * 10%.

Table 5 :

 5 Results for regressing annual county growth on cyclone intensity Spatial autocorrelated model estimator with two-way fixed effects. Robust standard errors are in parentheses. Time and county fixed effects are included, but not reported in the table.

		All counties Exposed counties Coastal states'	Coastal counties	Shoreline counties
				counties	(NOAA definition) (NOAA definition)
		(1)	(2)	(3)	(4)	(5)
	ln(P ers.Inc.pc) c,t-1	-0.182***	-0.130***	-0.137***	-0.108***	-0.122***
		(0.001)	(0.002)	(0.002)	(0.003)	(0.004)
	Cyc c,t	-6.38e-06	-0.00001	-0.00001	-0.00002	5.48e-07
		(0.00001)	(0.00001)	(0.00002)	(0.00002)	(0.00002)
	ρ	-0.093***	-0.073***	-0.066***	-0.084***	-0.025**
		(0.001)	(0.002)	(0.002)	(0.004)	(0.011)
	λ	0.138***	0.127***	0.121***	0.131***	0.103***
		(0.0003)	(0.0007)	(0.0008)	(0.002)	(0.008)
	Overall R 2	0.00	0.00	0.00	0.00	0.01
	Observations	154938	104652	55335	21573	11832
	Number of counties	3038	2052	1085	423	232
	Notes:

Significance levels : *** 1% ; ** 5% ; * 10%.

Table 6 :

 6 Robustness checks results: regressions on quarterly state growth The two first sections of the table report regressions that use spatial error model estimator with two-way fixed effects. Robust standard errors are in parentheses. Time and state fixed effects are included,

	All states Exposed states Coastal states
	(1)	(2)	(3)

Table 7 :

 7 Robustness checks results: regressions on annual county growth

		All counties Exposed counties Coastal states'	Coastal counties	Shoreline counties
				counties	(NOAA definition) (NOAA definition)
		(1)	(2)	(3)	(4)	(5)
	Alternative cyclone indicator: share of exposed population
	ln(P ers.Inc.pc) c,t-1	-0.182***	-0.130***	-0.136***	-0.108***	-0.122***
		(0.001)	(0.002)	(0.002)	(0.003)	(0.004)
	P op. exposed c,t	0.0003	0.0001	0.0002	0.0002	0.0003
		(0.0003)	(0.0002)	(0.0003)	(0.0004)	(0.0005)
	ρ	-0.093***	-0.073***	-0.066***	-0.083***	-0.025**
		(0.001)	(0.002)	(0.002)	(0.004)	(0.011)
	λ	0.138***	0.127***	0.121***	0.131***	0.103***
		(0.0003)	(0.0007)	(0.001)	(0.002)	(0.008)
	Overall R 2	0.00	0.00	0.00	0.00	0.01
	Observations	154938	104652	55335	21573	11832
	Number of counties	3038	2052	1085	423	232
	Alternative cyclone indicator: yearly number of cyclone events
	ln(P ers.Inc.pc) c,t-1	-0.182***	-0.130***	-0.136***	-0.108***	-0.122***
		(0.001)	(0.002)	(0.002)	(0.003)	(0.004)
	N b. Cyc c,t	-0.0008	-0.0007	-0.0003	0.0003	-0.0002
		(0.0008)	(0.0006)	(0.0006)	(0.0008)	(0.0009)
	ρ	-0.093***	-0.072***	-0.066***	-0.083***	-0.025**
		(0.001)	(0.0006)	(0.002)	(0.004)	(0.011)
	λ	0.138***	0.127***	0.121***	0.131***	0.103***
		(0.0003)	(0.0007)	(0.001)	(0.002)	(0.008)
	Overall R 2	0.02	0.00	0.00	0.00	0.01
	Observations	154938	104652	55335	21573	11832
	Number of counties	3038	2052	1085	423	232
	Fixed effects estimator using Conley 1999 standard errors
	ln(P ers.Inc.pc) c,t-1	0.002***	0.002***	0.002***	0.001***	0.001***
		(0.0002)	(0.0002)	(0.0002)	(0.0002)	(0.0002)
	Cyc c,t	0.00003	0.00003	0.00003	0.00005	0.00005
		(0.00005)	(0.00004)	(0.00004)	(0.00004)	(0.00004)
	R 2	0.05	0.11	0.11	0.14	0.13
	Observations	154938	104652	55335	28203	15246
	Number of counties	3038	2052	1085	423	232

Notes: The two first sections of the table report regressions that use spatial autocorrelated model estimator with two-way fixed effects. Robust standard errors are in parentheses. Time and county fixed effects are included, but not reported in the table. Bottom section presents OLS regression results with standard errors corrected for cross-sectional spatial dependence and panel-specific serial correlation as in Conley

Table 8 :

 8 Optimal lag tests for personal income per capita growth

		Final Prediction Error Akaike information criterion Hannan-Quinn criterion Schwarz information criterion
		(FPE)	(AIC)	(HQIC)	(SIC)
	Nb lags
	0	.000148	-5.97863	-5.96739	-5.9507*
	1	.000148	-5.97778	-5.96092	-5.93588
	2	.000148	-5.98358	-5.9611	-5.92771
	3	.000147	-5.98817	-5.96008	-5.91834
	4	.000145	-6.0039	-5.97019	-5.92011
	5	.000143*	-6.01646*	-5.97712*	-5.91869
	6	.000144	-6.00882	-5.96387	-5.8971
	7	.000145	-6.00165	-5.95108	-5.87596
	8	.000145	-6.00022	-5.94403	-5.86056
	9	.000146	-5.99685	-5.93504	-5.84322

Table 9 :

 9 Results for regressing quarterly growth on cyclone indicatorARIMAX ARIMAX ARIMAX ARIMAX ARIMAX

Table 10 :

 10 Results for regressing annual Floridian counties' growth on cyclone variables

		Independent variable Independent variable Independent variable
		x = Cyc c,t	x = P op. Exposed c,t	x = N b. Cyc c,t
		(1)	(2)	(3)
	ln(P ers.Inc.pc) i,t-1	-0.047***	-0.047***	-0.046***
		(0.005)	(0.005)	(0.005)
	x c,t	-0.00005*	-0.00005	-0.0007
		(0.00004)	(0.00007)	(0.0007)
	x c,t-1	-0.00002*	-0.002***	0.0002
		(0.00001)	(0.0008)	(0.0008)
	x c,t-2	-0.00003***	-0.003***	-0.002
		(9.85e-06)	(0.0008)	(0.0008)
	x c,t-3	6.09e-06	-0.0003	-0.0009
		(9.27e-06)	(0.0008)	(0.0008)
	ρ	0.106***	0.105***	0.106***
		(0.007)	(0.007)	(0.007)
	λ	-0.088***	-0.086***	-0.089***
		(0.012)	(0.013)	(0.011)
	Overall R 2	0.00	0.00	0.00
	Observations	3283	3283	3283
	Number of counties	67	67	67
	Notes: Spatial autocorrelated model estimator with two-way fixed effects. Robust standard errors are in
	parentheses. Time and county fixed effects are included, but not reported in the table.
		Significance levels : *** 1% ; ** 5% ; * 10%.

Table A .

 A 1: Results for regressing quarterly State growth on cyclone intensity using a Spatial Durbin Model Notes: Spatial Durbin Model estimator with two-way fixed effects. Robust standard errors are in parentheses. Time and state fixed effects, as well as lagged per capita income spatial lags are included, but not reported in the table. Significance levels : *** 1% ; ** 5% ; * 10%.

		All states Exposed states Coastal states
		(1)	(2)	(3)
	W g	0.398***	0.252***	0.146***
		(0.012)	(0.015)	(0.022)
	ln(P ers.Inc.pc) i,t-1	-0.038***	-0.025***	-0.019***
		(0.003)	(0.003)	(0.003)
	Cyc i,t	5.93e-07	2.69e-06	9.49e-06
		(0.00001)	(9.86e-06)	(0.00001)
	W Cyc	-1.00e-06	4.68e-06	6.90e-06
		(0.00002)	(0.00001)	(0.00002)
	Observations	9792	7548	3876
	Number of states	48	37	19
	Overall R 2	0.00	0.00	0.00
	Wald-type test P > χ 2	0.958	0.263	0.851
	θ = -ρβ

Table B .

 B 1: Results for regressing quarterly state growth on cyclone intensity using a specification that includes lags Spatial error model estimator with two-way fixed effects. Robust standard errors are in parentheses.Time and state fixed effects are included, but not reported in the table.

		All states Exposed states Coastal states
		(1)	(2)	(3)
	ln(P ers.Inc.pc) i,q-1 -0.034***	-0.021***	-0.020***
		(0.003)	(0.002)	(0.003)
	Cyc i,q	1.12e-06	6.61e-06	0.00001
		(0.00001)	(7.64e-06)	(8.21e-06)
	Cyc i,q-1	-2.35e-06	3.12e-06	9.17e-06
		(0.00001)	(7.73e-06)	(8.31e-06)
	Cyc i,q-2	0.00001	5.32e-06	7.85e-06
		(0.00001)	(7.64e-06)	(8.23e-06)
	Cyc i,q-3	2.97e-06	5.82e-07	-2.03e-06
		(0.00001)	(7.69e-06)	(8.23e-06)
	λ	0.40***	0.25***	0.17***
		(0.01)	(0.02)	(0.02)
	Overall R 2	0.00	0.00	0.00
	Observations	9792	7548	3876
	Number of states	48	37	19
	Notes:

Significance levels : *** 1% ; ** 5% ; * 10%.

All the statistical tests related to both of these variables are available upon request.

See Baltagi (2021) for a detailed presentation of spatial panel models.

For further information on these definitions, see https://coast.noaa.gov/digitalcoast/training/ defining-coastal-counties.html.

Population size rankings come from U.S. Census Bureau data. For further information, see https://www.census.gov/data/tables/time-series/demo/popest/ 2020s-total-metro-and-micro-statistical-areas.html.

APPENDIX A Specification test

Spatial dependence models are relevant models in the presence of cross-section of countries, regions, states, or more generally, any geographic units. In fact, introducing spatial effects in panel models leads models to take into consideration the interdependence between groups. It also allows to estimate spillover effects or externalities. These models are particularly attractive when dealing with regional science or urban economics issues. One can write a general equation for spatial panel data models as:

with y i,t dependent variable, x i,t explanatory variable, and w i,j element of the spatial weighting matrix W N ∈ R N ×N that defines the neighboring links between each of the N groups.

Based on this general equation, we can define the following models:

• Spatial Autoregressive Model (SAR), if λ = θ = 0.

• Spatial Durbin Model (SDM), if λ = 0.

• Spatial Autoregressive Model with Auto Regressive disturbances (SARAR), which are also called spatial autocorrelated model (SAC), if θ = 0.

• Spatial Error Model (SEM), if ρ = θ = 0.

Apart from these four basic models, many other specifications can be defined such as those with µ i random and depending on spatial lags, dynamic models, etc.

In order to find the most appropriate model, some specification tests can be run using parametric statistical measures such as Wald tests. For instance:

• After estimating a SDM, one can check whether θ = 0. If the null hypothesis is accepted, then it means that in fact, the appropriate model is a SAR.

• After estimating a SDM, one can check whether θ = -βρ. In this case, not rejecting the null hypothesis calls for a SEM model.

Table A.1 examines the results obtained for states with a SDM. In each case, the coefficients associated with the spatial lags of regressors are always insignificant, and the equality θ = -βρ is verified. Altogether, this means that the correct model for state level analyses is the SEM. However, as for county level estimations, all θ = -βρ Wald-type tests are rejected (p-value < 0.10), and spatially weighted independent variables' coefficients are found to be zero. Hence, we exploit a generalization of the SAR model, namely the SAC model.

C Analysing the effects on other frequently exposed states