How do hurricanes impact the US gulf states economy

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The topic is "how do hurricanes impact the US gulf states economy "and my stance is that hurricanes have a negative affect on the economy. This essay should be 1,000-1,200-words and include a thesis, all major points, evidence to support these points (including in-text citations), counterclaims, and a Works Cited page (six sources can be found attached below and in form of attached links. you will need the username : halsowaidi, and password: Humaid12 in order to access the link. you need to include all six sources in the paper) .The Project consists of a 1000-1200 word academic essay that supports an arguable claim through background/context on the topic, evidence, counterarguments, and conclusion that offers the reader something for further thought. The essay is complemented by a presentation that retains the same purpose and claim as the essay, but presents the evidence-based argument in a digital medium. Part I: The Essay

You will generate an arguable claim and write a 1,000-1,200 word academic essay to support that claim, based on the following requirements:

    1. present your arguable claim in your thesis
    2. provide background on the topic
    3. use evidence to support your claim
    4. explain counterarguments and refute them to support your claim
    5. offer a conclusion that underscores why your argument matters within a larger context

This essay prepares you to compose the type of research-based academic writing that you will be asked to do throughout your academic career. the outline should be something like this

-intro
Body
. Support point + source
. Support point +source
. Against point ( hurricanes do have positive affects on the economy+ source) with a support point that counters it
. Conclusion

Part II: The Presentation

After writing your academic essay, you will translate your argument into a presentation that retains the same purpose/claim as the essay. This presentation should follow the following requirements:

  1. Make a verbal argument that follows the thesis and claims presented in your written essay;
  2. Include a 4-6 minute spoken presentation to an audience of your peers during your class time; and
  3. Use Google Slides as a supplement to your in-class presentation. Your Google Slides presentation should (1) compliment the spoken presentation and (2) follow proper design principles reviewed during class.
  4. Evidence and outside materials (images, etc.) used in the digital remediation must be cited appropriately (document evidence through parenthetical citations in the text as well as on a Works Cited slide). PS there is a presentation attached for further information on how to write the paper please view it. under the name " project three presentation"


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Project Three: Joining the Conversation Free Write Should smoking be allowed anywhere on campus? Project 3: Support An Arguable Claim 1000 -1200 words Expectations For Project 3 Final THE BIG PAPER PEER REVIEW Background/ context on topic, evidence, counterarguments, and conclusion that offers the reader something for further thought. You will do this next week Complemented by a presentation. Due last week of Class PRESENTATION 4-6 minutes long, presented in small groups FINAL DRAFT 1,000 - 1,200 words, and it will include a thesis and use in-text citations -- Must include AT LEAST your six sources Early Draft Due this Sunday, Nov. 12 Early Draft The early draft should be a 700 word draft that argues in support of a particular thesis, uses at least three sources, and includes both an introduction and conclusion. Instructors will provide extensive feedback on this Early Draft. Introduction will do these things: 1. Introduce the topic 2. State why the topic is important 3. State that there is a difference of opinion about this topic 4. Clearly articulated thesis Should include: 1.Summary of works being discussed 2.Definition of key terms 3.Explanation of key theories “ A good argumentative thesis will do the following: Assertive language Demonstrate authority on the subject Address the opposing view Act as a “roadmap” for the argument by listing supporting points BODY GRAPHS: Topic Sentence: The topic sentence should identify the main idea and point of the paragraph. To choose an appropriate topic sentence, read the paragraph and think about its main idea and point. Context: The context (1-2 sentences) should mention the author(s) and his/her/their article or book title. Quote: aim for 1 - 2 lines Analysis: The analysis should accomplish 3 tasks: 1.Summarize the quote and article author’s point of view 2.Relate the quote back to the overall conversation 3.Support the student’s opinion about the topic Concluding Sentence: 1 sentence suggesting how the source contributes to the student’s argument The P3 essay must include a fair examination of the counterargument. How do I include counterargument? What do we mean by “fair”? The other side must be examined without unfair judgement (ie, “All religious people are irrational”) At least one credible source must be used to justify the counterargument’s viewpoint The author must be able to logically point to weaknesses in the counterargument without using logical fallacies Pitfalls: Avoid fallacies Effective presentation design (this presentation is an EXAMPLE OF WHAT NOT TO DO Homework ⊙ Homework: Write Intro Graph and Prepare to Read Aloud to Class ⊙ Bring Laptop Wednesday (In Class): Workshop, Fallacy, Conference Sign-Up, Presentation Hurricane Damage to the Ocean Economy Hurricane Damage to the Ocean Economy Hurricane damage to the ocean economy in the U.S. gulf region in 2005 Counties and parishes of the gulf coast ocean economy affected by Hurricanes Katrina and Rita saw the greatest insured dollar losses in 1 year from suchlike catastrophes in U.S. history; the region has yet to recover fully a year after the maelstrom Charles S. Colgan and Jefferey Adkins Charles S. Colgan is a professor in the National Ocean Economics Program, Muskie School of Public Service, University of Southern Maine, Portland, Maine, and chief economist for Market Data for the National Ocean Edonomics Program; Jefferey Adkins is an economist and program manager with the National Oceanic and Atmospheric Administration’s Coastal Services, Charleston, SC. 76 I n 2005, insured losses from hurricanes and other catastrophes were greater than in any other year in U.S. history. NOAA’s National Hurricane Center estimates that $85 billion of total damages resulted from Hurricanes Katrina and Rita alone. One year later, the region affected by these two hurricanes still struggles to recover, both as a place to live and as a viable economy. Using data from the BLS Quarterly Census of Employment and Wages, the National Ocean Economics Program has developed a data series that allows the economic damage to coastal regions to be seen in a new light: what happens to the economic value derived from the ocean when the ocean turns from resource and respite to a massive engine of destruction?1 According to Federal disaster declarations, Hurricane Katrina affected the entire States of Mississippi and Louisiana, plus 22 counties in Alabama and 9 in Florida. Rita affected all of Louisiana, plus 26 counties in Texas. The greatest effects were in the counties (parishes in Louisiana) closest to the coast, where the storm’s effects were at their maximum intensity. Coastal counties and parishes include those designated as such by each State under the Federal Coastal Zone Management Program, as well as those designated as coastal watershed counties or parishes by the U.S. Geological Survey. Virtually all of the coastal zone and watershed counties2 or parishes of Alabama, Mississippi, and Louisiana, plus the coastal counties in Texas from Houston eastward, were affected by the two hurricanes. The coastal zone counties or parishes of the four States account for nearly a Monthly Labor Review Augustl 2006 quarter of employment and wages in those States. In Louisiana, the coastal parishes are more than half of the State’s economy. The combined coastal zone and watershed counties and parishes on the Gulf of Mexico constituted 14 percent of employment in Alabama, 4 percent in Mississippi, 6 percent in Florida, a considerably greater 33 percent in Texas, and fully 80 percent in Louisiana. The ocean economy is defined as industries in marine construction, living resources (seafood processing and marketing, plus aquaculture), shipbuilding and boatbuilding, minerals (primarily oil and gas exploration and production), marine transportation and related goods and services, and, finally, tourism and recreation industries whose establishments are located close to the shore of the ocean or the Great Lakes. In 2004, the ocean economy of the region encompassing Florida, Alabama, Mississippi, Louisiana, and Texas, stretching from Franklin County, Florida, to Brazoria County, Texas, employed 291,830 people in wage and salary jobs paying nearly $7.7 billion in wages. (See table 1.) The affected States accounted for 13 percent of employment and wages in the U.S. ocean economy. However, these gross figures mask a key fact about the region: it is the industrial heartland of the U.S. ocean economy. As the following tabulation shows, the region accounts for more than a third of U.S. employment in marine construction, more than a fifth of employment in fisheries (living resources), shipbuilding, and boatbuilding, and more than half of employment Table 1. Employment and wages in ocean economy industries in the Gulf of Mexico region, 2004 2004 Sector and industry Employment 2004 Wages (millions of dollars) Ocean economy, total ............... Construction ........................................... Living resources .................................... Fish hatcheries and aquaculture ........ Fishing ................................................ Seafood markets ................................ Seafood processing ............................ Minerals .................................................. Shipbuilding and boatbuilding ................ Boatbuilding and repair ...................... Shipbuilding and repair ....................... 291,830 12,094 12,552 1,653 571 1,686 8,642 15,105 l35,839 3,567 32,272 $7,694.8 548.1 251.9 37.7 10.8 32.0 171.4 1,077.0 1,443.2 125.5 1,317.7 Tourism and recreation ............................ Amusement and recreation services .. Boat dealers ........................................ 178,404 4,150 2,078 2,716.7 74.9 73.5 Chart 1. Sector and industry Employment Wages (millions of dollars) Eating and drinking places ..................... Hotels and lodging places ...................... Marinas ................................................... Recreational vehicles in parks and campsites ...................................... Scenic water tours .................................. Sporting goods ........................................ Zoos and aquariums .............................. 131,985 34,870 1,447 1,718.6 725.5 38.2 470 1,676 106 1,622 8.9 43.0 2.9 31.1 Transportation ......................................... Deep-sea freight transportation .......... Marine passenger transportation ........ Marine transportation services ........... Search and navigation equipment ...... Warehousing ........................................ 37,836 3,109 375 28,485 2,251 3,616 1,657.9 226.0 20.6 1,163.8 107.1 140.4 Loss of employment in the ocean economy industries as a result of Hurricanes Katrina and Rita in 2005 Percent of employment lost 10–27 5–9 3–4 2 0–1 Monthly Labor Review August 2006 77 Hurricane Damage to the Ocean Economy in the ocean-related component of oil and gas exploration and production: Percent of U.S. ocean economy Employment Construction ............................. Living resources ........................ Offshore oil and gas ................. Shipbuilding and boatbuilding .. Tourism and recreation ............. Transportation ......................... 38.8 20.2 51.1 22.0 10.6 13.3 Wages 34.8 13.7 49.3 19.1 8.9 9.1 The region also accounts for a disproportionate share of marine transportation-related employment. Chart 1 shows the counties and parishes bordering the Gulf of Mexico that were declared disaster areas as a result of Hurricanes Katrina and Rita in 2005. The shading represents the portion of employment in each county that was accounted for by construction, living resources, minerals, shipbuilding and boatbuilding, and transportation. The heaviest concentration of these industries extends from Jackson County, Mississippi, to Cameron Parish, Louisiana. In these counties, the portions of the ocean industry other than tourism and recreation range from 3 percent to 27 percent of county employment. The economic effects of these hurricanes have focused on discussions of the losses in New Orleans, perhaps the largest disaster effects on a major American city since the San Francisco earthquake a century ago. But the economy affected was significant to the nation not only because of the loss of the unique charms of the Crescent City. The affected region was the heart of the industrial sectors of the American ocean economy, and the recovery of these industries will be critical to both the region and the Nation. Note 1 For information on the definitions of the ocean economy, visit www.oceaneconomics.org, the Web site of the National Ocean Economics Program. 2 Coastal zone counties are those within a State’s defined coastal zone management program. Watershed counties are defined by the U.S. Geological Survey. LABSTAT available via World Wide Web LABSTAT, the Bureau of Labor Statistics public database, provides current and historical data for many BLS surveys, as well as numerous news releases. Data can be accessed by using the data retrieval tools available at http://www.bls.gov/data If you have questions or comments regarding the LABSTAT system on the Internet, address e-mail to labstat.helpdesk@bls.gov 78 Monthly Labor Review Augustl 2006 Ann Reg Sci (2014) 52:325–342 DOI 10.1007/s00168-013-0587-8 ORIGINAL PAPER When oceans attack: assessing the impact of hurricanes on localized taxable sales Ariel R. Belasen · Chifeng Dai Received: 16 April 2012 / Accepted: 6 December 2013 / Published online: 21 December 2013 © Springer-Verlag Berlin Heidelberg 2013 Abstract We examine the impact of hurricanes in Florida on county-level taxable sales revenues. Conditional on the strength of the hurricane, within 6 months after a hurricane strikes a county, revenues decline as much as 17 %, whereas revenues in neighboring counties increase by upward of 17 % over that same time frame. This decline in revenue is found to be dependent on the commercial makeup of a hurricanestricken county. Particular focus is given to tourism-related subsectors within the local economy. Finally, we show that along the pathways of hurricanes, initially hit counties face a more severe burden, ranging as high as a 33 % immediate decline in taxable revenues in 1 month for coastal counties. As the hurricane weakens, the direct impact is lessened; however, there is evidence of spillover damage in neighboring areas. JEL Classification H71 · R11 1 Introduction Hurricanes regularly make landfall in the USA. The destructive power of hurricanes can wipe out thousands of lives and cause billions of dollars worth of infrastructure and private property losses, as seen in the aftermath of Hurricane Katrina and more recently Hurricane Sandy. The forced migration prompted by hurricanes also creates A. R. Belasen (B) Department of Economics and Finance, Southern Illinois University Edwardsville, Edwardsville, IL 62026, USA e-mail: abelase@siue.edu C. Dai Department of Economics, Southern Illinois University Carbondale, Carbondale, IL 62901, USA e-mail: daic@siu.edu 123 326 A. R. Belasen, C. Dai the potential for an area to lose its human capital. This loss of physical and human capital in a region can have significant short-term and possibly long-term effects on regional economic growth. In addition to their damage to physical and human capital, hurricanes also disrupt regional economic activity due to temporary lack of utility service, employee absenteeism, supply chain interruption, etc. Understanding the effects of hurricanes on a regional economy is critical to mitigate and recover the cost caused by hurricane damage. Policymakers and local businesses could make projections of the potential impacts before actual disasters occur for mitigation strategies and develop better recovery plans (Ewing et al. 2009). Most existing studies of hurricanes and other natural disasters have been conducted in the framework of a singular event study. For example, Guimaraes et al. (1993) study the deviation that Hurricane Hugo caused South Carolina to take from its general trend of economic growth using a with-and-without comparison of the economy. West and Lenze (1994) present a general framework to estimate the regional economic impact of natural disasters using primary and secondary data and apply their research to the problem of estimating the impact of Hurricane Andrew on the economy of Florida. Using time-series econometric models, Ewing and Kruse (2001) study the effect of Hurricane Bertha on unemployment in Wilmington, NC; Ewing et al. (2005) analyze the effect of Hurricane Bret on unemployment in Corpus Christi, Texas, as well as a subsequent study on Tornadoes in Oklahoma City (Ewing et al. 2009). Xiao (2011) and Xiao and Feser (2013) use time-series analysis under a quasi-experimental pairwise matching design to examine local economic impacts of the 1993 Midwest flood. Ewing and Kruse (2002) were among the first to examine a series of hurricanes within a singular framework, namely five hurricanes that struck Wilmington, NC, during the late 1990s. They made use of a time series analysis to look at the effects of each of five hurricanes, both individually and collectively, on the unemployment rate of Wilmington, North Carolina. However, studies of multiple disaster events over large geographic areas are rare. Rose et al. (1997) estimate both the direct and indirect regional economic losses from earthquake-damaged electric utility lifelines using specially designed input– output and linear programming models. Extending the previous study, Rose and Liao (2005) examine how damages from disasters are muted by the inherent and adaptive resilience of individual businesses and of regional markets. They advance the computable general equilibrium (CGE) analysis to reflect resilience and to provide a more accurate estimate of impacts by disasters. On the other hand, using survey methodology, Kapucu (2008) and Kim and Kang (2010) show that it can be difficult at times for governments to convey the seriousness of the impending disaster and that household preparedness depends on the individual household’s access to communication resources. Furthermore, Cho et al. (2001) integrate transportation network and regional economic models to estimate the economic loss due to earthquake impacts on transportation and industrial capacity. These studies provide important insights on how a particular hurricane or a series of hurricanes affect a particular local economy. However, in general, the effects of hurricanes vary depending on the severity and the timing of the storm and the characteristics of the local economy. Consequently, the insights obtained through an analysis of one hurricane or multiple hurricanes on one local economy may not be useful in 123 Assessing the impact of hurricanes on localized taxable sales 327 generalizing to a different hurricane or a different local economy. Moreover, some of these studies fail to account for other macroeconomic changes that coincide with the dates of their natural disasters and therefore could end up with biased estimates of the actual impact of the disasters. In addition, many of these studies (e.g., Rose et al. 1997; Rose and Liao 2005; and Cho et al. 2001) use hypothetical, stylized natural disasters and therefore consider only limited aspects of the complex set of events that actually occur during a natural disaster. This research contributes to the literature by examining changes in localized taxable sales revenues caused by the devastating effects of a series of hurricanes that actually struck Florida between 1992 and 2006. We pay particular attention to specific timing effects in terms of both the paths taken by these hurricanes and the duration of the economic impacts brought on by them, while controlling for external macroeconomic events to isolate the average impact of hurricanes on revenues. We also specifically address local geography to assess the hurricanes as they weaken while moving inland. The current study is closely related to Belasen and Polachek (2008, 2009) who use the generalized difference in difference (GDD) technique to examine a series of hurricanes that hit the state of Florida to identify potential exogenous shocks in the labor market using quarterly data. Belasen and Polachek (2009) found that across a two-year duration, wages tended to rise while employment tended to fall in hurricanestricken counties relative to unaffected counties. Most hurricanes that strike the USA occur in the Gulf States and the southeastern states. Florida is a member of both subsets of states, and more hurricanes hit Florida than any other US state. In the 15-year period between 1992 and 2006, a total of 18 hurricanes struck Florida, and none of Florida’s 67 counties have escaped the effects of these hurricanes. Five of the six most damaging Atlantic hurricanes of all time have struck Florida over the course of this time period along with a number of relatively weak hurricanes as well as providing a fairly comprehensive sampling of various strength hurricanes. Since each of these 18 hurricanes in our study varies in the exact timing, strength, path, and the degree of damage and therefore can be analyzed independently or collectively, they fit into multiple random experimental groups (counties that have been hit by a hurricane in a given month) as well as multiple control groups (counties that have not been hit by a hurricane). By comparing those counties that have been hit to the other counties that were not across multiple hurricanes and time periods, we are able to investigate how the impact of a hurricane differs by both the characteristics of the hurricane and the characteristics of the local economy. Moreover, the estimation techniques address the concern of any macroeconomic changes that coincide with the timelines of the hurricanes by accounting for statewide trends in the data. In this way, we can better isolate the outcome of a hurricane as well as quantify its economic impact over time. Furthermore, hurricanes that strike a specific county directly can also have an impact on the economies of neighboring counties. Thus, similar to Belasen and Polachek (2008, 2009) we study the spillover effects of hurricanes on neighboring counties as well. In contrast to the studies based on hypothetical natural disasters (e.g., Rose et al. 1997; Rose and Liao 2005; and Cho et al. 2001), our analysis is based on the total effect of actual hurricanes on county-level taxable sales. While our analysis does 123 328 A. R. Belasen, C. Dai not explicitly address the effect of a specific hurricane-caused event (e.g., electricity interruption, transportation network damage, or recovery activities), it captures the total effect of the set of complex events that occur during and after hurricanes. Finally, while we are unable to discern a pure hurricane effect separate from recovery efforts, by identifying impacts at multiple subsequent time periods, we can identify the scope of the recovery relative to the initial impact. Our analysis focuses on the impact of hurricanes on county-level taxable sales revenue, ranging from the sales of groceries, automobiles, hotel rooms, utilities telephone services, to banking services. Taxable sales are not only key indicators of economic conditions but also important tax bases for state and local governments. Since most states and local governments base their budgets on their revenue forecasts, strong and viable tax revenue estimates are vital to regional economies recovering from disasters. Understanding the effect of hurricanes on taxable sales helps local policymakers and fiscal analysts make better revenue projection and better expenditure decisions to facilitate the recovery process. And for Florida, in particular, as a state without an income tax, the stability of taxable sales revenue is of upmost importance for long-term budget plans. In addition, knowledge about hurricane-related costs also helps local businesses create better economic plans before and after hurricanes. Hurricanes can have very different effects on different sectors of a local economy. In view of the fact that tourism is a mainstay of Florida’s economy, it is also interesting to study how hurricanes affect Florida’s tourism industries. Therefore, in addition to the overall sales of all categories subject to Florida sales and use taxes, we examine the taxable sales of two tourism-related industries, i.e., the taxable sales of restaurants and the taxable sales of bars and taverns.1 The current study complements Belasen and Polachek (2008, 2009) by analyzing the impact of the same group of hurricanes on taxable sales revenues across a similar period of time. For states that do not collect state and local income tax, such as Florida, sales and usage taxes are the main sources of revenues for state and local governments. Therefore, taxable sales data are better indicators of state and local tax revenues for such states than employment and wages. Furthermore, we make use of monthly county-level sales data in our analysis, which allows us to better distinguish short-term and long-term effects of hurricanes. For example, we find that when a weaker hurricane directly hits a county, taxable sales revenues relative to unaffected counties will immediately decline by more than 7 % on average in that month; however, revenues may begin to rebound in the month that follows. On the other hand, revenues will show no significant change if that quarter is examined as a whole. Meanwhile, stronger hurricanes will have persistent destructive effects that may lead to a 20 % or greater decline in taxable revenues over that same time frame. Finally, in contrast to Belasen and Polachek (2009), by tracking the paths of hurricanes, we find that the impact of hurricanes on local economies depends on the physical locations of the local economies, particularly in relation to the entry and exit points of the hurricanes. 1 Unfortunately, data limitations prevent us from being able to examine the tourism sector as a whole. 123 Assessing the impact of hurricanes on localized taxable sales 329 2 Econometric model In estimating the impact of hurricanes on Florida’s labor market, Belasen and Polachek (2008, 2009) utilized the GDD approach to examine changes in the growth rates of employment and earnings in the average county hit by a hurricane relative to the average county that was not hit. For the purpose of our analysis, it will be more instructive to examine the changes in revenue levels relative to the average as opposed to the changes in growth rates so that the interpretation of the estimated coefficients will have a more practical purpose for policymakers. With that in mind, the model we use in this study will comprise similar controls to the model used in Belasen and Polachek (2009); hence, we will need to use an econometric approach which will control for seasonality, physical and demographic characteristics of each county, as well as the relative wealth of each county. To facilitate this, we will employ a fixed effects (FE) model to estimate the following:  + HitD δit + H N εit + u it ln Rit = β0 + αi + β1 T + Mγ it (1) Here, R captures the level of taxable revenue collected by private firms in Florida county i in time t. We use the natural logarithm of R for ease of interpretation. αi captures any time-invariant county-specific factors such as geographical location and physical size. Furthermore, seasonal trends are captured by the vector M which is a collection of individual monthly dummy variables to control for seasonality, and a time control vector, T , is included to capture the overall time trend. Finally, the factors of interest are the vectors H D and H N , which examine the impact of hurricanes on counties inside and outside the locus of destruction. The D identifies directly hit counties (i.e., counties within the locus of destruction), while the N identifies counties that neighbor the directly hit counties (i.e., counties within the outer band of the hurricane). Each of these hurricane vectors is comprised of a split classification of weaker and stronger hurricanes, separated by the maximum wind speeds. Category 1–3 hurricanes that have a wind speed under 100 mph are classified as weaker hurricanes, and category 4–5 hurricanes with wind speeds in excess of 100 mph are classified as stronger hurricanes. Furthermore, following the findings in Ewing and Kruse (2002) and Belasen and Polachek (2009),2 we supplement these hurricane variables with lags to examine the specific timing of the economic shock following the hurricane strike. To estimate the model with fixed effects, we use the mean-differenced county-level value for revenue regressed on the time and month trends as well as the hurricane effects, such that Eq. (1) becomes:    ln Rit − ln R̄i = (β0 − β0 ) + (αi − αi ) + β1 t + Mγ + HitD δit + H N εit + (u it − ū i ) it (2) 2 Prior studies on hurricanes primarily used quarterly data and found significant results one quarter after the strike (i.e., between 4 and 6 months following the hurricane). With monthly data, we will be examining each of the 6 months after the strike. Xiao (2011) shows that local economies are relatively resilient and will bounce back shortly after a disaster strikes. 123 330 A. R. Belasen, C. Dai and the estimation of Eq. (2) reduces to the following form:  γ̂ + HitD δ̂it + H N ε̂it + Δû it Δ ln R̂it = β̂1 t + M it (3) For counties on the path of hurricanes, the demand for food, water, and other disaster supplies often increases ahead of hurricanes, as households and businesses prepare themselves and their properties for the potential damage of hurricanes. In the aftermath of hurricanes, as the hurricane causes damages to infrastructure and business properties and interrupts utility service and supply chains, supply often decreases immediately in directly hit counties. Consumers in directly hit counties often increase their spending on disaster supplies and are forced to eat out at restaurants or stay in hotels due to loss of utility service or damage to their properties. However, they often are forced out to neighboring areas to find stores, restaurants, and hotels that are unaffected by hurricanes. Over time, as recovery efforts restore utility service and replace damaged infrastructure and properties, we expect supply to steadily recover in affected counties. Moreover, as businesses and households start to repair the damages to their properties financed by their saving accounts and relief funds, we expect the demand for building supplies and construction services to increase significantly in directly hit counties. We expect this increase in demand to have a positive spillover effect on neighboring areas until the supply in directly hit counties completely recovers. Therefore, our hypothesis is that revenues will decrease immediately in the month when hurricanes strike a county. In addition, we expect to see the impact become muted or even positive as we examine counties further out from the locus. Over time, we expect to see revenue gradually recover for both directly hit counties and neighboring counties. We also believe that the impact will vary across different business sectors for the affected counties. Moreover, following Belasen and Polachek (2009), we anticipate that increases in the relative strength of the hurricanes will exacerbate this impact. Among the controls, the monthly dummies should capture the seasonality in revenues by showing somewhat of a downturn in summer months when tourism declines and a jump in the winter months when it picks backup. This is particularly important to control for since the hurricane season coincides with the seasonal decrease in tourism. Finally, the time trend should come out significantly and positively, reflecting the overall growth in sales revenues over the sample period. 3 Data The hurricane data used in this study come from hurricane reports from the National Hurricane Center of the National Oceanic and Atmospheric Administration (NOAA). NOAA is a federal agency within the Department of Commerce that examines the conditions of the oceans and the atmosphere. In particular, the NOAA evaluates ecosystems, climatic changes, weather and water cycles, and commerce and transportation. NOAA reports that most hurricanes that strike the USA strike the Gulf States and the southeastern states. In the 15-year period between 1992 and 2006, a total of 18 hurricanes struck Florida, and none of Florida’s 67 counties have escaped the effects 123 Assessing the impact of hurricanes on localized taxable sales 331 of these hurricanes.3 Details regarding these 18 hurricanes can be found in Table 1 in the “Appendix.” Hurricanes are classified by the Saffir–Simpson scale according to their maximum wind speed, with weaker category 1 hurricanes having wind speeds between 74 and 95 mph and the strongest category 5 hurricanes having speeds greater than 155 mph. Beyond that, the paths of the hurricanes were traced following NOAA coordinates, and following Belasen and Polachek (2008), any county within a 50 km radius (or 100 km diameter) of the path of the hurricane was considered to be directly hit by the hurricane.4 Those counties outside of that band but within a wider 500-km band were considered to be counties neighboring the hurricane and would have faced a relatively weaker impact of the hurricane than those within the locus of destruction. The gross sales data are collected from the Gross and Taxable Sales Reports by the Bureau of Economic and Business Research at the University of Florida (BEBR), which are prepared based upon data provided by the Florida Department of Revenue. The gross sales data include all sales reported monthly by businesses for sales and use taxes as required by the state law. They range from the sales of retail, wholesale, hotels, transportation, utility services, to insurance and banking services as classified by the Florida Department of Revenue. The taxable sales data used in our analysis are county-level data, spanning the time period starting from January 1992 and continuing through December 2006. The average county’s taxable sales revenue is nearly $264 million; however, there is an extremely wide disparity between the different counties. A summary of the sales data can be found in Table 2.5 4 Estimation results Our findings reveal that after controlling for time and seasonality, hurricanes do in fact decrease taxable revenues at several points in the 6 months following a hurricane strike. Table 3 summarizes the results for the coefficients of interest from Eq. (3). The results indicate that stronger hurricanes cause a much more pronounced and enduring downturn in taxable revenues among directly hit counties. This difference can also be seen when examining the neighboring counties as well. Weaker hurricanes actually have a positive net cumulative impact on sales revenues in neighboring counties, presumably due to an increase in demand for goods and services as consumers in directly hit counties may be forced to shop outside their home county due to hurricane-related closures. However, stronger hurricanes seem to cause a downturn in neighboring counties that is similar in scope to the downturn caused by weaker hurricanes in directly hit counties. This indicates that even outside of the locus, stronger 3 Note that five of the six most damaging Atlantic Hurricanes of all time struck Florida during this time period, as did some of the least damaging hurricanes. Therefore, this time period serves to provide a good mix of the potential economic disruption of hurricanes. 4 Note that when expanding the radius to 100 or 150 km the results in our analysis did not have a statistically significant change. 5 Note that in some months, the tax revenues were negative, reflecting months in which the State of Florida received a net tax loss because of adjustments due to overpayments in previous time periods. While the negative values are a relatively rare phenomenon, tax adjustments occur consistently throughout the data. 123 332 A. R. Belasen, C. Dai hurricanes can still yield heavy destructive capabilities. This is consistent with the findings in Hallegatte et al. (2008) that examined the 2004 and 2005 hurricanes used in this study. By aggregating the statistically significant results, we find that when a weaker hurricane directly hits a county, taxable sales revenues relative to unaffected counties will immediately decline by 7.18 % on average in that month; however, revenues will increase by 0.40 % across the next 6 months following the strike. Relative to the mean level of revenues, this implies an immediate decrease of $18.92 million followed by a gain of $1.05 million across the next 6 months for an aggregate change of −$17.87 million in revenues for the average county directly hit by a hurricane relative to unaffected counties. Revenues in neighboring counties do not experience an immediate change; however, they will eventually rise by 7.9 % on average relative to unaffected counties. This reflects a revenue increase of $20.82 million. While the changes in tax dollars collected only amount to a few million dollars at the state level from weaker hurricanes, stronger hurricanes, on the other hand, have a much more significant impact on tax revenues. Among directly hit counties, the average impact is an immediate 9.81 % decline, which corresponds to a $25.85 million decrease in taxable revenues. And in the following 6 months, the average aggregate downturn increases by an additional 7.36 %. This reflects a total aggregate decrease across the 7 months of $45.25 million in taxable revenues or a state-level decrease of $2.7 million in tax dollars collected per directly affected county.6 Larger hurricanes like Ivan which impact over a dozen counties throughout the duration of the hurricane can therefore result in close to $33 million lost tax dollars for the state. Finally, as stated earlier, the impact of stronger hurricanes on neighboring counties is similar to the impact of weaker hurricanes on directly hit counties. There is no significant effect from the hurricane immediately after impact, but the overall aggregate result is a 17.56 % increase in revenues for the average neighboring county relative to unaffected counties. Overall, we see evidence that indicates that a faster recovery is all the more important for the stabilization of not only the local economy affected by the hurricane, but also the state-level economy. Clukey (2010) points out that despite the best efforts of recovery initiatives, there are drawbacks, especially to the individuals who are immersed in the recovery efforts. 5 Expanding the analysis into subsectors of the local economy To check for the specific severity of hurricane damage on tourism, we apply the same model to economic subsectors that we believe will be particularly hard hit by a hurricane, namely the restaurant sector and the bar & tavern sector.7 Currently, only one in three Americans consumes one or more meals at home per day (Berry 2002). 6 While overall sales tax rates differ by locality in Florida, the state government collects a flat 6 % rate across the entire state. 7 We do not include the hotel sector in our study of tourism-related subsectors. First, taxable sales by the hotel sector are combined with those by apartments and rooming houses, etc., in the data provided by the Florida Department of Revenue; second, the classification of this category was not consistent for the duration of our analysis. 123 Assessing the impact of hurricanes on localized taxable sales 333 Therefore, we expect to see that major reductions in supply should heavily impact taxable sales revenues among local restaurants and taverns even if the reductions are only short-lived.8 The framework for the model remains the same except that now we are looking specifically at those two subsectors. Theoretically, unlike other sectors, local residents’ demand for food services could increase immediately for directly hit counties as they are forced to eat out at restaurants due to loss of utility service or damage to their properties; however, consumer demand from tourists will decrease immediately for those counties due to a decrease in the number of tourists. Meanwhile, supply could also be diminished for these counties. Thus, the overall effect could depend on the severity of the damages. For neighboring counties, consumer demand will likely increase, but supply will not be affected as much. So we should expect a more positive effect relative to directly hit counties. Once recovery efforts restore electricity and replace damaged infrastructure, both demand and supply will start to pick up in directly hit counties, but not as much in neighboring counties. Therefore, the results of these subsectors could be different from of the overall economy, especially immediately after hurricanes. The results of this specification are shown in Tables 4 and 5.9 The findings reveal that the subsectors do in fact differ quite a bit from the overall retail sector. In fact, the results in Table 4 suggest that there are opposing impacts on the supply and demand side such that the net impact of a hurricane is mitigated. In the restaurant subsector, in particular, strong hurricanes do not significantly alter total revenue at all, and weaker hurricanes create an immediate 8.5 % drop in revenues. This means that the average impact of a weak hurricane is a $2 million decrease in countylevel taxable revenues relative to unaffected counties and a decrease of $120,000 in the average taxes collected of per affected county. While this may appear surprising, one must remember that strong hurricanes likely create a positive demand shock (as people lose the ability to eat at home) and a negative supply shock (as restaurants are forced to shut down due to damage and electricity loss) which may offset one another. Meanwhile, among neighboring counties, the net cumulative impact of weak hurricanes is actually a 13.8 % increase in revenues (leading to a $3.24 million increase) over the 6 months following the hurricane, whereas stronger hurricanes appear to have a net cumulative 91.5 % decrease in revenues (note that there is a lot of noise in the results here because some of the counties had very low numbers reported for restaurants—hence, one or two closures may have in fact constituted the majority of the market in those rural counties). 8 Individuals in hurricane-stricken regions often report that the loss of electricity and other infrastructure damage often forces them to actually increase restaurant consumption relative to meals at home, but that they often are forced out to neighboring areas to find restaurants that were unaffected by the hurricane. 9 Note that the number of units per county diminishes greatly when examining the subsectors, and some subsectors are completely unreported by the BEBR for certain counties due to the sample size being so low that it would compromise privacy. Hence, the significance level is not nearly as high in these as in the overall examination. So, while the stronger hurricanes came up insignificantly, we attribute that to the smaller number of firms surveyed rather than to the actual impact of the hurricanes. Thus, the analysis in this section will focus only on the weaker hurricanes. 123 334 A. R. Belasen, C. Dai Next, looking specifically at the bar and tavern subsector, taxable revenues will decrease by an average of $221,000 in counties directly hit by a weaker hurricane (7.5 %), which leads to an average $13,000 decline in taxes collected in those counties. Furthermore, we see no significant impact among stronger hurricanes, suggesting once again that the supply and demand shocks are offsetting each other. Among neighboring counties, the aggregate change in taxable revenues in bar and tavern subsector, there is a net cumulative increase of 25.7 % in taxable revenues over the six months following a weak hurricane, which means that tax dollars would increase by roughly $45,000 on average. Among stronger hurricanes, there is a negligible net cumulative −3.4 % decrease, resulting in a $6,000 average decline in tax revenues across the 6-month period. 6 Expanding the analysis into local geographical factors Belasen and Polachek (2009) showed that within labor markets, geographical characteristics of counties were not useful predictors in determining the impact of hurricanes on the labor market of a county. By tracking the paths of hurricanes across Florida and then comparing coastal to inland counties, they found an average impact differential of 0.01 % on wages. A major limitation of this particular study was that Belasen and Polachek (2009) did not examine the origination of the hurricanes relative to the affected counties. A hurricane will be strongest when it first hits land and then weaken as it moves across the land such that its weakest point while it is in the State of Florida will be at its exit point, which means a hurricane that strikes Florida from the Atlantic will have very different effects on the Atlantic coastline than it will on the Gulf coastline. Theoretically, one should expect to see that the entry point will be hit harder than the exit point since the hurricane will have weakened over the few days of travel. Furthermore, as hurricanes weaken, the storm cell tends to move slower, and therefore, while the direct damage will be lessened, the indirect impact of heavy rain will likely be felt even more in neighboring counties. To address this disparity, we introduce a further breakdown of the hurricane data by examining entry and exit points. Hurricanes are broken up into two groups: those that originate in the Atlantic Ocean and those that originate in the Gulf of Mexico. Counties on the Atlantic coastline are “entry counties” if they are hit with a hurricane originating the Atlantic and “exit counties” if they are hit with a hurricane originating in the Gulf. Likewise, Gulf counties are “entry” and “exit” based on the same criteria. Equation (4) contains this breakdown:  + β̂3 H D + β̂4 H N + β̂5 H Δ ln R̂it = β̂1 t + β̂2 M it DEntry N Entry + β̂6 Hit + β̂7 HitDExit + β̂8 HitN Exit + Δû it (4) The individual coefficients reflect marginal impacts of the average hurricane on a specific geographic characteristic. This regression structure lets us examine the impact of hurricanes on six distinct sets of counties relative to baseline counties in which all of the hurricane coefficients take the value of zero. For example, when a hurricane 123 Assessing the impact of hurricanes on localized taxable sales 335 hits a county in the entry group, the coefficients of interest are the combination of β3 and β5 . Likewise, inland counties that are hit next will take a value of zero for the entry coefficient (β5 ). Finally, exit counties will face the combined impact of β3 and β7 . Neighboring counties can also be split into entry, inland, and exit groups. As before, these results will be further split between weak and strong hurricanes to better assess the average impact. The regression results for this analysis are shown in Table 6. If we only account for the statistically significant results, then among the weaker hurricanes, there are no statistical differences between any of the three groups of counties. Stronger hurricanes, on the other hand, have a much greater impact initially but appear to dissipate as the storm moves away from the entry point. Entry counties that are directly hit by the hurricane face an average of 33.42 % decline in taxable revenues relative to unaffected counties. Inland counties face 18.80 % decline, and exit counties face 9.54 % decline. Statistically speaking, the remaining groups of counties do not appear to be any different from one another.10 As we stated previously, a likely explanation for these swings in revenues stems from both demand- and supplyside market shocks as buyers are forced to leave devastated areas for nearby areas to purchase goods. 7 Conclusion Florida finds itself in a unique position among other southeastern US states in that it faces a threat from hurricanes both from the Gulf of Mexico and from the North Atlantic Ocean. This study identified the average impact of a hurricane on Florida’s taxable sales revenue. Since the bulk of Florida’s state revenues come from sales and usage taxes, a disruption of this income stream can be particularly damaging to state-level services. Our findings show that the impact of hurricanes on localized taxable sales depends critically on the strength and path of hurricanes and the economic makeup of the local economies. Furthermore, we find that the coastal counties that are hit by hurricanes as they first make landfall will be harder hit than the counties that are hit as the hurricane exits the state. Additionally, by aggregating the statistically significant results over a six-month period, we find that when a weaker hurricane directly hits a county, taxable sales revenues relative to unaffected counties will immediately decline by 7.2 % on average in that month; however, revenues will increase by 0.4 % within 6 months of the strike. Revenues in neighboring counties do not experience an immediate change; however, they will eventually rise by 7.9 % on average relative to unaffected counties. Stronger hurricanes, on the other hand, have a much more significant impact on tax revenues. While revenues do appear to recover over time, there is evidence that the level of taxable revenues in a county hit by a strong hurricane will be substantially 10 Note that these results may be swayed by Hurricane Andrew which only directly impacted MiamiDade and Monroe counties before passing from the Atlantic Ocean into the Gulf of Mexico. The extreme devastation of Andrew may serve as an outlier for this particular form of analysis. 123 336 A. R. Belasen, C. Dai lower for at least 6 months as they would have been had the county avoided being hit by the hurricane. Among directly hit counties, the average impact is an immediate 9.8 % decline. The average aggregate downturn increases roughly 7.4 % after 6 months; however, the evidence points to a resurgence in the latter half of that period. Stronger hurricanes also cause a downturn in neighboring counties that is similar in scope to the downturn caused by weaker hurricanes in directly hit counties. Moreover, the impact of hurricanes is greatest when it first hits land. Among stronger hurricanes, the first-hit counties face immediate decline in taxable revenues of 33.4 % on average relative to unaffected counties. Inland counties that are directly hit by the storm experience immediate decreases in taxable revenues of 18.8 % on average. Finally, among exit counties, the effect diminishes down to just a 9.5 % average decline. Among weaker hurricanes, there was no statistically significant difference between the three groups. Furthermore, we identified that the effect of hurricanes on the overall local economy is different from that on specific subsectors, which indicates that the effect of hurricanes on a particular county could depend on the economic makeup of the county, such that this decline may be due to a difference in the economic makeup of the commercial sector in hurricane-stricken counties. This indicates that the optimal recovery policy and effort should differ across sectors and therefore, across counties as well, with particular emphasis on recovering potential revenues from tourismbased subsectors since for many counties in Florida, tourism is the largest source of revenues. We acknowledge that this study and its findings could also potentially be improved by surveying firms independently of identifying the behavioral choices made by each firm during the post-hurricane recovery period. Perhaps, this would reveal more effective techniques for policymakers to follow when pursuing recovery efforts. A potential extension would be to examine further subsectors to identify whether this pattern holds across non-tourist-based subsectors as well. A second extension to the study would be to examine property damage as property taxes are another key revenue source for local governments. Finally, a spatial model could be applied to these data to estimate the decaying impact of the average hurricane as it passes across Florida. Such a study would be a natural extension to Sect. 6 and could follow the methodology laid out in Eric Strobl’s (2008) study. Acknowledgments The authors would like to thank Solomon Polachek, Sajal Lahiri, and seminar participants at SIU Carbondale, along with Jamie Kruse and seminar participants at the 77th Annual Southern Economic Conference for their comments and suggestions. Appendix See Tables 1, 2, 3, 4, 5, and 6. 123 Assessing the impact of hurricanes on localized taxable sales 337 Table 1 Hurricane descriptive statistics Name Synoptic lifecycle Monetary damage to FL (in millions of dollars) Deaths in FL Wind speed (mph) Average rainfall Saffir– Simpson scale Andrew Aug-92 40,500 44 175 5 –7 5 Allison Jun-95 1.10 0 75 4 –6 1 Erin Aug-95 0.50 6 87 5 –12 1 Opal Sep-95 4,600 1 115 5 –10 3 Danny Jul-97 0 80 2 –7 1 Earl Sep-98 134 total to US 104 2 92 6 –16 1 Georges Sep-98 406.80 0 103 8 –25 2 Irene Oct-99 1,000 8 75 10 –20 1 Gordon Sep-00 11.90 1 75 3 –5 1 4 Charley Aug-04 14,800 29 150 5 –8 Frances Sep-04 10,200 37 105 10 –20 2 Ivan Sep-04 9,200 19 130 7 –15 3 Jeanne Sep-04 7,900 3 121 8 –13 3 Dennis Jul-05 2,400 14 120 10 –15 3 Katrina Aug-05 1,700 14 81 5 –15 1 Ophelia Sep-05 1 80 3 –5 1 Rita Sep-05 2 115 2 –4 2 Wilma Oct-05 77.2 total to US 12,500 total to US 22,700 31 120 7 –12 3 This table is an updated version of the table found in Belasen and Polachek (2009) using 2010 dollar values. Some statistics were also taken from Thacker et al. (2008) Table 2 County-level taxable sales revenue and business density summary statistics n*t Overall retail Number of business units Number of business units Revenues (in millions of dollars) Number of business units Min Max 263.52 495.64 6,573.43 10,790.39 −106.18 83 3,950.00 78,415 12,759 Revenues (in millions of dollars) Bars and taverns SD 12,795 Revenues (in millions of dollars) Restaurants Mean 23.45 42.83 −82.64 344.00 475.53 816.40 4 5,595 11,181 2.95 4.60 84.54 111.37 −27.33 4 32.05 716 The number of observations is measured in terms of counties (n) and months (t). The negative tax revenues reflect months in which the State of Florida received a net tax loss because of adjustments due to overpayments in previous time periods. While the negative values are a relatively rare phenomenon, the adjustments occur consistently throughout the data 123 338 A. R. Belasen, C. Dai Table 3 Fixed effects estimation results of the impact of hurricanes on the natural logarithm of taxable sales revenue in the average county in Florida Direct impact Neighboring impact Coefficient Robust SE Coefficient Weak hurricane t = 0 −0.0718** (0.0194) −0.0264 Weak hurricane t = 1 0.0400* (0.0207) Robust SE (0.0228) 0.0786*** (0.0289) (0.0227) Weak hurricane t = 2 −0.0283 (0.0187) 0.0104 Weak hurricane t = 3 −0.0384 (0.0328) −0.0038 (0.0344) Weak hurricane t = 4 −0.0582* (0.0317) −0.0362 (0.0289) Weak hurricane t = 5 −0.0521 (0.0327) −0.0557 (0.0364) Weak hurricane t = 6 0.0219* (0.0145) 0.0442 (0.0284) Strong hurricane t = 0 −0.0981*** (0.0274) −0.0281 (0.0285) Strong hurricane t = 1 −0.1095*** (0.0322) −0.0372 Strong hurricane t = 2 −0.0623 (0.0414) Strong hurricane t = 3 −0.1056** (0.0452) Strong hurricane t = 4 0.0771 (0.0478) 0.0896* 0.0720* −0.0174 (0.0280) (0.0369) (0.0263) (0.0471) Strong hurricane t = 5 −0.0319 (0.0553) −0.0561* (0.0344) Strong hurricane t = 6 Month dummies February 0.1415*** (0.0453) 0.0701* (0.0399) 0.0390** (0.0183) March −0.0581*** (0.0097) April 0.0002 (0.0103) May 0.0997*** (0.0071) June 0.0347*** (0.0076) July 0.0074 (0.0103) August 0.1005*** (0.0132) September 0.0408*** (0.0123) October −0.0439*** (0.0096) November 0.0582*** (0.0073) December 0.0941*** (0.0203) R2 0.6288 F 342.09 n, groups 12,784, 67 Weak hurricanes fall into categories 1, 2, and 3, and strong hurricanes fall into categories 4 and 5 *** Significant at the 1 % level; ** significant at the 5 % level; * significant at the 10 % level 123 Assessing the impact of hurricanes on localized taxable sales 339 Table 4 Fixed effects estimation results of the impact of hurricanes on the natural logarithm of taxable restaurant sales revenue in the average county in Florida Direct impact Neighboring impact Coefficient Robust SE Coefficient Robust SE Weak hurricane t = 0 −0.0851** (0.0401) 0.0049 Weak hurricane t = 1 0.0281 (0.0388) 0.1293 (0.0801) Weak hurricane t = 2 −0.0611 (0.0393) 0.0288 (0.0740) (0.0739) (0.0823) Weak hurricane t = 3 −0.0168 (0.0782) 0.1383* Weak hurricane t = 4 −0.1001 (0.0646) 0.0366 (0.0771) Weak hurricane t = 5 0.0548 (0.0812) 0.0141 (0.0814) Weak hurricane t = 6 0.0570 (0.0836) 0.0656 (0.0922) Strong hurricane t = 0 −0.0513 (0.0726) −0.0342 (0.0639) Strong hurricane t = 1 0.0505 (0.0896) −0.0299 (0.0605) Strong hurricane t = 2 0.0498 (0.0785) 0.0664 (0.0779) Strong hurricane t = 3 0.0221 (0.0916) −0.0216 (0.0736) Strong hurricane t = 4 0.1843 (0.3040) −0.3847** (0.1725) Strong hurricane t = 5 0.0642 (0.0960) Strong hurricane t = 6 0.2358 (0.2692) Month dummies February 0.0586** (0.0255) March −0.0131 (0.0222) April 0.0953*** (0.0205) May 0.1746*** (0.0165) June 0.1144*** (0.0166) July 0.0453** (0.0206) August 0.1661*** (0.0220) September 0.0566*** (0.0203) October −0.0127 (0.0190) November 0.0828*** (0.0179) December 0.1101*** (0.0293) R2 0.2057 F 950.22 n, groups 12,752, 67 0.0071 −0.5315*** (0.0941) (0.1855) Weak hurricanes fall into categories 1, 2, and 3, and strong hurricanes fall into categories 4 and 5 *** Significant at the 1 % level; ** significant at the 5 % level; * significant at the 10 % level 123 340 A. R. Belasen, C. Dai Table 5 Fixed effects estimation results of the impact of hurricanes on the natural logarithm of taxable bar and tavern sales revenue in the average county in Florida Direct impact Coefficient Neighboring impact Robust SE Coefficient Robust SE Weak hurricane t = 0 −0.0342 (0.0427) 0.0857 (0.0541) Weak hurricane t = 1 0.0189 (0.0369) 0.1074** (0.0510) Weak hurricane t = 2 −0.0750* (0.0394) 0.0731 (0.0508) Weak hurricane t = 3 0.0704 (0.0611) 0.1494* (0.0778) Weak hurricane t = 4 −0.0911 (0.0645) 0.0040 (0.0665) Weak hurricane t = 5 0.0308 (0.0743) 0.0855 (0.0778) Weak hurricane t = 6 0.0483 (0.0577) 0.0822 (0.0590) Strong hurricane t = 0 0.1281 (0.0781) 0.0795 (0.0582) Strong hurricane t = 1 0.0663 (0.0637) 0.0844 (0.0636) Strong hurricane t = 2 0.1590 (0.1342) 0.1969*** (0.0723) (0.0690) Strong hurricane t = 3 −0.0753 (0.1097) 0.0124 Strong hurricane t = 4 0.0007 (0.1451) −0.2565 (0.1656) Strong hurricane t = 5 −0.1101 (0.1265) −0.1129 (0.1044) Strong hurricane t = 6 Month dummies February 0.0528 (0.1275) −0.2307* (0.1355) 0.0971* (0.0255) March 0.0293 (0.0222) April 0.0688*** (0.0205) May 0.0816*** (0.0165) June 0.0655*** (0.0166) July −0.0282 (0.0206) August 0.0584*** (0.0220) September −0.0506*** (0.0203) October −0.0735*** (0.0190) November 0.0155 (0.0179) December 0.1251** (0.0293) R2 0.2896 F n, groups 130.98 11,180, 67 Weak hurricanes fall into categories 1, 2, and 3, and strong hurricanes fall into categories 4 and 5 *** Significant at the 1 % level; ** significant at the 5 % level; * significant at the 10 % level 123 Assessing the impact of hurricanes on localized taxable sales 341 Table 6 Fixed effects estimation results of the impact of hurricanes on the natural logarithm of taxable sales revenue in the average county in Florida sorted by geographic region Direct impact Coefficient Neighboring impact Robust SE Coefficient Robust SE Weak hurricanes H −0.0455 (0.0351) −0.0488 (0.0302) H Entry −0.1854 (0.0466) −0.0199 (0.0723) H Exit −0.0633 (0.0422) 0.0864 (0.0536) Strong hurricanes H −0.1880*** (0.0367) −0.0062 (0.0438) H Entry −0.1462*** (0.0496) −0.0804 (0.0743) H Exit 0.0926* (0.0515) −0.0347 (0.0473) Month dummies February 0.0460** (0.0181) March −0.0478*** (0.0092) April 0.0084 (0.0101) May 0.1074*** (0.0073) June 0.0425*** (0.0074) July 0.0156 (0.0102) August 0.1089*** (0.0132) September 0.0476*** (0.0121) October −0.0296*** (0.0092) November 0.0629*** (0.0077) December 0.1024*** (0.0206) R2 0.6273 F 298.87 n, groups 12,790, 67 Weak hurricanes fall into categories 1, 2, and 3, and strong hurricanes fall into categories 4 and 5 *** Significant at the 1 % level; ** significant at the 5 % level; * significant at the 10 % level References Belasen AR, Polachek SW (2008) How hurricanes affect wages and employment in local labor markets. 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Environ Hazards. doi:10.1080/17477891.2013.777892 123 Copyright of Annals of Regional Science is the property of Springer Science & Business Media B.V. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. How Disasters Affect Local Labor Markets: The Effects of Hurricanes in Florida Author(s): Ariel R. Belasen and Solomon W. Polachek Source: The Journal of Human Resources, Vol. 44, No. 1 (Winter, 2009), pp. 251-276 Published by: University of Wisconsin Press Stable URL: http://www.jstor.org/stable/20648894 Accessed: 08-10-2017 03:01 UTC JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range of content in a trusted digital archive. We use information technology and tools to increase productivity and facilitate new forms of scholarship. For more information about JSTOR, please contact support@jstor.org. Your use of the JSTOR archive indicates your acceptance of the Terms & Conditions of Use, available at http://about.jstor.org/terms University of Wisconsin Press is collaborating with JSTOR to digitize, preserve and extend access to The Journal of Human Resources This content downloaded from 131.247.112.3 on Sun, 08 Oct 2017 03:01:33 UTC All use subject to http://about.jstor.org/terms How Disasters Affect Local Labor Markets The Effects of Hurricanes in Florida Ariel R. Belasen Solomon W. Polachek ABSTRACT This study improves upon the Difference in Difference approach by examining exogenous shocks using a Generalized Difference in Differenc (GDD) technique that identifies economic effects of hurricanes. Based on t Quarterly Census of Employment and Wages data, worker earnings in Florida counties hit by a hurricane increase up to 4 percent, whereas earnings in neighboring counties decrease. Over time, workers experienc faster earnings and slower employment growth than workers in unaffect counties. Hurricanes have a greater impact in coastal and Panhandle counties, and powerful hurricanes have greater economic effects than weake ones. Further, the GDD technique is applicable to analyze a wider range exogenous shocks than hurricanes. I. Introduction An exogenous shock is an unexpected event that impacts a given market. Such shocks can take many forms, ranging from unexpected new legislation, to sudden population shifts, to domestic weather-related events, and even to terrorist attacks. A number of studies utilize Difference-in-Difference (DD) estimation to ex amine the effects of exogenous shocks. For example, Card (1990) in a well-cited ar ticle used DD to examine migration and found relatively small effects on wages. Such studies look at changes across time periods between the region of interest and a comparable region which was unaffected by the shock to find long-run effects. Angrist and Krueger (1999) call these results into question for failing to identify an Ariel R. Belasen is an assistant professor of economics and finance at Southern Illinois University in Edwardsville, III. Solomon W. Polachek is a distinguished professor of economics at the State University of New York at Binghamton. All data contained in this paper will be made available upon request. The authors would like to thank Joel Elvery, Christopher Hanes, Kajal Lahiri, and Stan Masters along with the seminar participants at: SUNY Albany; SUNY Binghamton; the Third Migrant ITA Conference, Washington, D.C. March 2007; and the Twelfth Society of Labor Economists Meetings, Chicago, May 2007for their comments. [Submitted July 2006; accepted September 2007] ISSN 022 166X E ISSN 1548 8004 ? 2009 by the Board of Regents of the University of Wisconsin System THE JOURNAL OF HUMAN RESOURCES ? 44 ? 1 This content downloaded from 131.247.112.3 on Sun, 08 Oct 2017 03:01:33 UTC All use subject to http://about.jstor.org/terms 252 The Journal of Human Resources appropriate control group. Perhaps, as a result, there is now a literature on appropri ately choosing control groups, for example Bertrand, Duflo, and Mullainathan (2002), Kubik and Moran (2003), and Abadie, Diamond, and Hainvellen (2007). An other problem is the experimental group. Most papers examining exogenous shocks rely on one experimental group; in Card's (1990) case, this experimental group is Miami, the site of the Mariel Boatlift. However it is not obvious that one experimen tal group suffices. In the Card example, the Miami labor market might not be typical of other potential experimental sites. Perhaps in his study, Miami's unemployment did not rise because Miami's economy was growing more rapidly than other simi larly sized cities. This paper finds that by properly addressing these two issues, one can better isolate the direct impact of exogenous shocks. We find that counties hit by hurricanes experience a positive net effect on earnings and a negative net effect on employment, but that these effects dissipate over time. One innovation of this paper is to have many random experimental groups as well as many random control sites. To achieve this, we use a different natural experiment, hurricanes, to examine the effect of an exogenous shock on a local labor market. Hurricanes, in particular, are a good choice for this study because they can affect sev eral counties at a time, and can occur more than once in the time period under study. By having many experiment sites, we are able to test how the impact of exogenous shocks differs by both characteristics of the shock and characteristics of the experi ment group. Other papers have used weather-related events such as rainfall (includ ing Miguel 2005; Waldman, Nicholson, and Adilov 2006; and Connolly 2007) to obtain a purely exogenous variable as an instrument to predict other independent var iables such as how much television children watch (in the case of Waldman et al. 2006), which in turn is used to predict autism using a simultaneous equation ap proach. We use weather (that is hurricanes) directly as the exogenous shock we want to evaluate. To do this, we develop a Generalized Difference-in-Difference (GDD) technique in which we compare affected regions to unaffected regions across multiple exoge nous events and time periods. In addition, exogenous shocks that are felt positively by one specific labor market can also have an effect on nearby labor markets. Thus we can examine multiple exogenous shocks affecting more than one locality at a time. Further, to address the issue of the appropriate definition of treatment and con trol groups, we compare a given hurricane-stricken county to all other unaffected counties within that state. In addition, by using quarterly time-series data, this ap proach has the advantage of distinguishing short-term and long-term effects that pre viously had been neglected. In this way we can better identify the effect of an exogenous shock as well as quantify its effect over time. The destructive power of hurricanes worldwide can wipe out thousands of lives and cause billions of dollars worth of infrastructure and private property losses an nually. Hurricane season runs from June 1st through November 30th each year over warm water, defined as oceanic temperatures exceeding 80 degrees Fahrenheit. How ever, the exact timing and path of the hurricanes cannot be determined in advance. Due to the high temperatures required, most hurricanes that strike the United States strike the Gulf States and the Southeastern States. Because Florida is a member of both subsets of states, it is instructive to look at the county-level Florida labor market to examine the exogenous shocks of hurricanes. This content downloaded from 131.247.112.3 on Sun, 08 Oct 2017 03:01:33 UTC All use subject to http://about.jstor.org/terms Belasen and Polachek Over the course of an average year, the state of Florida will generally see one to two hurricanes during the six-month hurricane season, but there are years when Flor ida is not hit, even once. Over the last two years of the sample (2004 and 2005), how ever, the hurricanes that struck Florida were more frequent and more powerful than ever before.1 Although hurricanes are not completely unexpected shocks to the state of Florida, each hurricane event is exogenous to the specific counties that are hit as well as to the degree of damage unleashed. Therefore, the events we have identified can be used as an independent variable by comparing those counties that have been hit to the other counties that avoided devastation. Florida is comprised of 67 counties and, over the past 18 years, none of them have escaped the effects of hurricanes. Five of the six most damaging Atlantic hurricanes of all time have struck Florida over the course of this time period. Damages to prop erty can be estimated in direct monetary costs, for example, 1992's Hurricane Andrew wound up costing Southern Florida roughly $25.5 billion ($43 billion in 2005 USD) in property losses (Rappaport 1993). However, a county, business or per son's wealth is made up of more than just the stock of assets owned by that person. A major portion of the flow of one's wealth comes from earned income. Thus the ques tion is raised, how can the income-specific and employment-specific effects of a hur ricane be measured? In addition, when looking at the effects of a hurricane on a specific county, are there any spillovers that need to be accounted for in neighboring counties? In addition, do more destructive hurricanes impact labor markets more intensely? And finally, how long are the effects of a hurricane felt in earnings and employment? II. Background on Florida and the Hurricanes Over the course of the last 18 years, the state of Florida has been rav aged by 19 hurricanes. A summary table containing descriptive statistics for each of the hurricanes can be seen in Table 1, which lists magnitude, monetary costs, and death statistics for each storm. Each hurricane is given a standard name by the World Meteorological Organization assigned to the storm in alphabetical order each year based on the timing of the storm. The lists of names for hurricanes change each year, with the gender of the initial storm also alternating each year. There are six lists in total and any time a particularly devastating hurricane occurs, the name of that hur ricane is "retired" from the list (Padgett, Beven, and Free 2004). After the sixth list is used, the first is then cycled back with any retired hurricane names replaced with new names beginning with the same letter as the retired ones. 1. The National Oceanic and Atmospheric Administration retires the names of particularly devastating hur ricanes. Nine of the nineteen hurricanes in the sample occurred in the 2004 and 2005 hurricane seasons. Eight of those storms have had their names retired (as opposed to just three retirees throughout the remain der of the sample), including Hurricane Wilma which set records for intensity. Note, however, that in this past 2006 season, Florida was only hit by one minor hurricane: Ernesto, so this is not necessarily a trend moving forward. This content downloaded from 131.247.112.3 on Sun, 08 Oct 2017 03:01:33 UTC All use subject to http://about.jstor.org/terms 254 The Journal of Human Resources U This content downloaded from 131.247.112.3 on Sun, 08 Oct 2017 03:01:33 UTC All use subject to http://about.jstor.org/terms Belasen and Polachek Hurricanes are categorized according to the Saffir-Simpson Scale based on their wind speed. Hurricanes Florence, Allison, Erin, Danny, Earl, Irene, Gordon, Ophelia, and even the Floridian part of Katrina were Category 1 hurricanes at landfall, mean ing they had wind speeds ranging between 74 and 95 miles per hour. Hurricanes Georges, Frances, and Rita were Category 2 hurricanes and had wind speeds ranging between 96 and 110 miles per hour. With wind speeds ranging between 111 and 130 miles per hour, Hurricanes Opal, Ivan, Jeanne, and Dennis were classified as Cate gory 3 hurricanes. Hurricane Charley reached 150 miles per hour and became Cat egory 4 as it hit the mainland. Hurricanes Andrew and Wilma were Category 5 hurricanes and had winds well above 180 miles per hour. III. Economic Model of Hurricanes According to Lucas and Rapping (1969), when people perceive a shock as having a temporary effect on the economy, they will not alter their long term perception of the economic variables that are affected by the shock. Hurricanes generally last for, at most, two or three days once they strike land. Historically speak ing, even the damages from the most destructive hurricanes are typically repaired within two years of the hurricane. Therefore, one would expect to see perceptions of the future remain largely unchanged in the long run as the variables return to their steady state levels of growth. Guimaraes, Hefner, and Woodward (1993) state that while hurricanes create an economic disturbance in the short run, oftentimes they can lead to economic gains in the long run. More specifically, within labor demand and labor supply, hurricanes will lead to negative shocks on labor supply in the stricken region, along with undetermined shocks to the region's labor demand as some firms attempt to fill vacancies in their work force while others leave town with the outflow of workers. If a hurricane strikes a region and causes people to flee, the work force in that region will decrease. There fore, labor supply would shift downward. At the same time, if that hurricane destroys a lot of private property and physical capital, labor demand could also decrease as employers have to close their shops. However, Skidmore and Toya (2002) point out that the risk of a natural disaster can reduce the expected return to physical cap ital (which may be destroyed during the storm) and, in turn, there is a substitution effect toward human capital as a replacement. Of course, as the demand for human capital rises, the price of human capital will also rise. This leads to an income effect that runs counter to the substitution effect. On the other hand, if the hurricane only destroys residential areas, labor demand also could increase as employers attempt to fill vacant jobs. Thus, the shock on labor demand from a hurricane most likely will be positive leading to changes in earnings and employment. Using the standard labor market framework, with labor supply shocked negatively and labor demand shocked positively, earnings will increase, and employment will have an ambiguous effect depending on whether or not the demand shock outweighs the supply shock. The set of earnings and employment that we are examining in this study are county-level average quarterly earnings per worker in the state of Florida. In order to measure the actual earnings effects of hurricanes on earnings, we will control for other factors that have an effect on earnings and employment. Florida's This content downloaded from 131.247.112.3 on Sun, 08 Oct 2017 03:01:33 UTC All use subject to http://about.jstor.org/terms The Journal of Human Resources economy has been growing rapidly over the last half-century and every county in Florida has benefited from this growth. Card (1990) found that immigrants in Miami had no long-term effects on wages despite increasing the labor force by 7 percent. He deduces that the Florida labor market in the 1980s was able to simply absorb a group of 45,000 immigrants into the labor market without a change in wages because of the rapid growth of Florida's economy. E wing and Kruse (2005) isolated the specific county-level fluctuations from the overall general growth by controlling for the trend of earnings movement across the entire state. In a subsequent paper, Ewing, Kruse, and Thompson (2007) explained that local economies may be influenced by state business cycles. Following their method, we control for the state trends of Florida. Furthermore, Florida's labor market is greatly influenced by seasonal shifts. During the summer months, earnings and employment decrease in several sectors of the la bor market. Thus, one must also control for seasonality. In the end, we have two equations, one for employment (Qit) and one for earnings (yit) which sets the dependent variable equal to a function of state (Qt, yt), county-spe cific time-invariant effects (Z,), seasonal trends (St) as well as hurricane effects (Hit): (1) Qit=f{Qt,ZhSt,Hit) + uit (2) yit=f(ytlZhShHit) + vit As stated earlier, an important question to consider when examining hurricanes and other exogenous shocks is what kind of neighboring effects, if any, will affect the model. If a hurricane forces workers to flee one county for a second county, then labor supply in the original county will be negatively affected while labor supply in the second county will be positively affected. Thus, the model is set up to include a series of hurricane dummy variables that capture direct effects and neighboring effects. This allows us to compare three distinct sets of counties: those that were di rectly hit and faced heavy destruction, those that were close by, and thus affected by heavy rainfall, and those that were farther out, and generally unaffected by the hur ricane. Assuming that counties i and j border one another, the subscript i under HD indicates that the locus of destruction2 from the hurricane is directly passing over county i while subscript ij under HN indicates that the locus of destruction of a hur ricane is passing through county j which borders county /. In other words, HD takes a value of one when the hurricane strikes county /; and HN takes a value of one when the hurricane strikes county j but not county /. More specifically, (3) Qit = %uQt + 2/ ; + 3 ?3 + 4 + 5/ ? + %iHl + uit (4) yu = a, 5 This content downloaded from 131.247.112.3 on Sun, 08 Oct 2017 03:01:33 UTC All use subject to http://about.jstor.org/terms 260 The Journal of Human Resources o S i r o .O a 3 ?2 1I 'C ? a : 5 "O U o. ifi a. This content downloaded from 131.247.112.3 on Sun, 08 Oct 2017 03:01:33 UTC All use subject to http://about.jstor.org/terms ? ? .s Belasen and Polachek 261 m os m o ?s O O O S '
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Explanation & Answer

Attached.

HURRICANES
NEGATIVE EFFECT ON THE ECONOMY AND COUNTRY

INTRODUCTION


A hurricane is an exogenous event

Hurricanes affect livelihoods, infrastructure and
the economy as a whole
The negative attributes outweigh the positive
attributes if any

IMPACT OF HURRICANES ON THE LABOR
MARKET
Hurricanes lead to destruction of property forcing
people to move creating labor shifts
Labor demand and supply is also affected
Layoffs tend to occur as a result of destruction of
businesses

IMPACT OF HURRICANES ON THE OCEAN
ECONOMY
Ocean economy normally includes marine
constructions, making of boats, fishing equipment
and much more
According to the National Hurricane Center,
$85nillion was the amount of money lost as a result
of hurricane Katrina and Rita

IMPACT OF HURRICANES ON TOURISM
Tourism is part of the ocean economy
Decline in tourism as a result of hurricanes
Tourists find hurricane areas unsafe

CONT…
 Events like Mardi Gras declined to have attendees
as compared to previous events
Destruction of property making it hard for tourists
to explore the regions

IMPACT OF HURRICANE ON TAXABLE
SALES
At first, there is a surge in sale before the
hurricane

Retail spending both online and the physical
stores normally experience a decline
Businesses experiences a dip in sales and the
government too in terms of taxes

IMPACT OF HURRICANES ON THE FISH
MARKET
Hurricanes affect the ecosystem adversely
Dip in the income earned by fishermen and also fish
life and other creatures
Hurricanes normally have a negative effect on the
fish market

POSITIVE EFFECTS OF HURRICANES
 New and Improved infrastructure
Increase in the insurance business

Brings a country closer together

CONCLUSION
Life is not replaceable
Hurricanes are simply negative and they have a
negative multiplier effect

REFERENCES
Belasen, Ariel R., & Polachek, Solomon W.
(2007). How disasters affect local labor markets:
the effects of hurricanes in Florida. (Series: IZA
Discussion Papers ; No.2976.) Bonn: Institute for
the Study of Labor (IZA.)
Belasen, A. R., & Dai, C. (March 21, 2014). When
oceans attack: assessing the impact of hurricanes on
localized taxable sales. The Annals of Regional
Science, 52, 2, 325-342.

CONT…
• Colgan, C. S., & Adkins, J. (January 01, 2006).
Hurricane damage to the ocean economy in the U.S.
gulf region in 2005. Monthly Labor Review.
• Kim, H., & Marcouiller, D. W. (May 01, 2015).
Considering disaster vulnerability and resiliency:
the case of hurricane effects on tourism-based
economies. The Annals of Regional Science : an
International Journal of Urban, Regional and
Environmental Research and Policy, 54, 3, 945-971.

CONT…
Solís, D., Perruso, L., del, C. J., Stoffle, B., &
Letson, D. (March 01, 2013). Measuring the
initial economic effects of hurricanes on
commercial fish production: the US Gulf of
Mexico grouper fishery. Natural Hazards :
Journal of the International Society for the
Prevention and Mitigation of Natural
Hazards, 66, 2, 271-289.


Running Head: HURRICANES

1

Hurricanes
Name
University Name
Date

Running Head: HURRICANES

2
Hurricanes
Introduction

The paper will delve into the impact of hurricanes on the economy touching on very
facets of the economy be it infrastructure, trade and also taxes. In recent years, storms have
become a common phenomenon the world over not just limited to the United States of America.
With it, come to a lot of adverse effects that at times leads to economies come to a standstill. The
paper shall focus on this in detail focusing primarily on the US Gulf States. The paper, however,
shall focus keenly on the negative effects of hurricanes on the economy.
Thesis
The impact of hurricanes on economies is more often than not, from a negative context.
Negative Effects of Hurricanes
1. Impact of Hurricanes on the Labor Market
A hurricane is termed as an exogenous shock. This is a shock that occurs as a result of an
unexpected event. Hurricanes usually are known to occur a week or so in advance, but one
cannot prepare for it. Hurricanes as a result of being an exogenous shock have had effects on the
labor market regarding unexpected legislation, population shifts and much more (Belasen, Ariel
& Polachek, et.al., 2007). The most noticeable impact of hurricanes is in population shifts. An
area where a hurricane usually occurs leads to the destruction of property when that takes place,
the population in that area moves to another for shelter, and even those with shelter might shift as
a result of fear the same will occur in the area again. An example of such a scenario was when
hurricane Katrina happened, the death toll to start with was immense, followed by the extensive
destruction of property that leads to people moving from that area to other parts of the United
States.

Running Head: HURRICANES

3

After hurricane Katrina that occurred in August 2005, employment in Louisiana fell
drastically and still to date is well below the August 2005 level. In June 2006, there was a decline
of almost 30% compared to the previous year in non-farm payroll employment in the New
Orleans metro area (Belasen, Ariel & Polachek, et.al., 2007).. In Mississippi, there was a decline
in employment to about 19% compared to the previous year. In the two months after the
occurrence of hurricane Katrina, non-farm payroll declined by 241,000 translating to a 12%
decline. In the New Orleans Metro area, the same scenario occurred with a decrease in
employment by 215,000 or 35% (Belasen, Ariel & Polachek, et.al., 2007). In Mississippi,
nonfarm payroll fell by 14,000 roughly a 1% decline. Businesses in the Louisiana area suffered
as a result of flooding while Mississippi was more of hurricane damage (Belasen, Ariel &
Polachek, et.al., 2007).
Hurricanes affect the labor market regarding migration, in other words, demand, and
supply. For example, in the New Orleans area where hurricane Katrina occurred, the labor
market changed expansively. There was a lot of labor supply but lack of demand for it due to
damaged infrastructure. When this occurs, we normally find labor migrating from one place to
another also affecting the dynamics of the labor market in those areas.
2. Impact of Hurricanes on the Ocean Economy
In the year 2005, the amount that was attached to losses resulting from hurricanes
reached an estimated $85 billion according to the National Hurricane Center. The main
Hurricanes that occurred during this period were hurricane Katrina mentioned above and Rita.
Hurricane Katrina affected Mississippi and Louisiana greatly mainly the areas closest to the
Coast of Louisiana. The Ocean economy mainly touches on industries in those areas such as

Running Head: HURRICANES

4

marine construction, sea food marketing and processing, boatbuilding and much more. It is
estimated that hurricane Katrina affected the employment of individuals around the Coastal
region adversely. As a result of the hurricane, there was a 13% decline in employment and wages
in the United States Ocean economy (Colgan, & Adkins, 2006). It is worth noting that the areas
affected greatly by hurricane Katrina account for most of the income of the ocean economy in
the United States.
3. Impact of Hurricanes on Tourism
In any situation that threatens human life, tourism will automatically be affected. No
tourist will want to risk his or her life to enjoy the pleasures that come with tourism (Kim, &
Marcouiller, 2015). Over the years, the effect of hurricanes on tourism has been adverse. New
Orleans is the heart and soul of Jazz in the United States. The Mardi Gras is one such event that
attracts a lot of tourists to New Orleans. A lot of revenue is made during this period, with local
businesses benefiting immensely from the event.
The aftermath o hurricane Katrina was characterized by extensive reporting of violence,
looting and shooting against rescuers, murder, and rape too. On most occasions, it was not
necessarily looting but people scavenging for basics such as food and water for survival. Mardi
Gras was held but on fewer days with the same route being used all through. Mardi Gras is
notably a well-known festival in New Orleans and gunners a huge crowd attending the events.
As a result of the hurricane, fewer tourists flocked the event, to be precise, the turnout was 50%
lower than it was experienced in prior events before hurricane Katrina.
4. Impact of Hurricanes on Taxable Sales
Hurricane Harvey and Irma had a significant impact on retail sales more so shopping
online than in the physical stores. When the two hurricanes hit, retail spending declined to 58.7%

Running Head: HURRICANES

5

weekly in Houston and its environs. Consumer spending also followed the same trend. Spending
was high the week before the hurricanes with most people upping their stocks with basics such as
food, water, and gasoline. After hurricane Harvey hit, online spending dropped by 41.4%, and ecommerce in the entire United States dropped by 4.3% during the same week. With hurricane
Irma, online activity in Miami dropped the week before Irma by 39.3%. (Belasen, & Dai, 2014).
When the resultant effect of the decline in sales is the decline in taxes such as VAT which have a
huge impact on the national and also state budgets.
5. Impact of Hurricanes on the Fish Market
In the aftermath of both hurricanes Harvey & Irma, a lot of people had lost both their
homes and also businesses. Economically, the impacts are normally seen and felt after the
hurricane. Environmentally, hurricanes destroy ecosystems (Solís, Perruso, et.al., 2013).
They reduce the level of oxygen and instead create bacteria detrimental to fish life and other
creatures in the sea. As a result of hurricane Harvey and Irma, fishermen were affected greatly.
Starting with having to rebuild their homes fisheries and also have to grapple with the reduced
amount of fish life owing to the disruption in the ecosystems.
Positive Impact of Hurricanes
Hurricanes are an opportunity for States affected to start afresh. The...


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