The effect of natural disasters on economic activity in US counties: A century of data

Boustan, Leah Platt; Kahn, Matthew E.; Rhode, Paul W.; Yanguas, Maria Lucia

doi:10.1016/j.jue.2020.103257

Cited by 190 publications

(124 citation statements)

References 62 publications

(63 reference statements)

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“…Figure 3a shows estimated effects of Hurricane Katrina on annual mortality (equation 2, solid black lines) as well as on cumulative mortality (equation 3 Using a value of $100,000 per life-year (Cutler, 2004) and a discount rate of 3 percent (Siegel, 1992) (Strobl, 2011;Deryugina, 2017;Boustan et al, 2017).…”

Section: Annual Mortality and Relocationmentioning

confidence: 99%

Does When You Die Depend on Where You Live? Evidence from Hurricane Katrina

Deryugina

Molitor

2018

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We follow Medicare cohorts over time and space to estimate Hurricane Katrina's long-run mortality effects on elderly and disabled victims initially living in New Orleans. Inclusive of the initial shock, the hurricane improved survival eight years past the storm by 1.74 percentage points. Migration to lower-mortality regions explains most of this survival increase. Migrants to low-versus high-mortality regions look similar at baseline, but migrants' subsequent mortality is 0.83-0.90 percentage points lower for each percentage-point reduction in local mortality, quantifying causal effects of place on mortality among this population. By contrast, migrants' mortality is unrelated to local Medicare spending.

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Section: Annual Mortality and Relocationmentioning

confidence: 99%

Does When You Die Depend on Where You Live? Evidence from Hurricane Katrina

Deryugina

Molitor

2018

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“…In terms of total population, some studies have found that the typical disaster leads to net population gain or an acceleration in total growth (Pais and Elliott 2008;Schultz and Elliott 2013). Other studies have documented a net out-migration (Boustan et al 2017), a reduction in population growth (Logan et al 2016), or even no change at all (Deryugina 2017). Many studies also have highlighted considerable effect heterogeneity (Elliott and Pais 2010;Fussell et al 2017;Logan et al 2016).…”

Section: Introductionmentioning

confidence: 99%

“…Beyond the effects on total population, sociologists and demographers also model changes among various demographic subgroups. Although some have found a net out-migration of disadvantaged groups, such as racial minorities and impoverished individuals (Boustan et al 2017;Elliott 2015), other studies have suggested that wealthy and White people move away and that lower-income and racial minorities are stuck in place (Logan et al 2016). These disparate findings signal a need for further research with rigorous empirical strategies and refined theoretical models.…”

Section: Introductionmentioning

confidence: 99%

Natural Hazards, Disasters, and Demographic Change: The Case of Severe Tornadoes in the United States, 1980–2010

Raker

2020

Demography

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Natural hazards and disasters distress populations and inflict damage on the built environment, but existing studies yielded mixed results regarding their lasting demographic implications. I leverage variation across three decades of block group exposure to an exogenous and acute natural hazard—severe tornadoes—to focus conceptually on social vulnerability and to empirically assess local net demographic change. Using matching techniques and a difference-in-difference estimator, I find that severe tornadoes result in no net change in local population size but lead to compositional changes, whereby affected neighborhoods become more White and socioeconomically advantaged. Moderation models show that the effects are exacerbated for wealthier communities and that a federal disaster declaration does not mitigate the effects. I interpret the empirical findings as evidence of a displacement process by which economically disadvantaged residents are forcibly mobile, and economically advantaged and White locals rebuild rather than relocate. To make sense of demographic change after natural hazards, I advance an unequal replacement of social vulnerability framework that considers hazard attributes, geographic scale, and impacted local context. I conclude that the natural environment is consequential for the sociospatial organization of communities and that a disaster declaration has little impact on mitigating this driver of neighborhood inequality.

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“…Menurut IMF, bencana alam juga memberikan dampak negatif dan signifikan terhadap pertumbuhan ekonomi serta dapat menyebabkan terjadinya pemburukan keseimbangan perdagangan maupun fiscal (Lee et al, 2018). Selain itu, daerah yang mengalami bencana parah akan memiliki migrasi penduduk keluar yang besar, harga rumah di daerah terdampak turun drastis, serta meningkatnya tingkat kemiskinan pada daerah terdampak bencana (Boustan et al, 2017).…”

Section: Pendahuluanunclassified

Ekstraksi Informasi Semantik dan Spatiotemporal pada Artikel Online Terkait Bencana di Indonesia

Tantri

Rakhmawati

2020

J. Sistem Info. Bisnis

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Indonesia is one country that has a high risk of natural disasters. Ranked in 36 out of 172 countries in disaster index and having 2,372 disaster incidents in 2017, actions need to be taken to minimize the impact of natural disasters. One of it is to do a hazard map modeling. In making hazard maps, several approaches can be used, one of which is the semantic approach to extract disaster information. Therefore, this study aims to develop a system that can be used to extract spatiotemporal and semantic information related to natural disasters in Indonesia. This study uses the NLP method in conducting the information extraction process and carried out using the GATE (General Architecture for Text Engineering) application. In processing Indonesian language articles, it is necessary to develop the plugin because the Indonesian information structure is different from the default information structure in GATE application. The plugin development process is done by using ontology as the basis for determining semantic information. Literature study was carried out related to government regulations that further explained the need for semantic and spatiotemporal information about disaster events. system performance developed produces a precision value of 38% and a recall value of 32%. this is because the system experiences some difficulties in carrying out the information inference process. The reason for low precision rate is because the rules used in the inference process to pair the three types of information still cannot accommodate the variation of information positions in different sentences.

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The effect of natural disasters on economic activity in US counties: A century of data

Cited by 190 publications

References 62 publications

Does When You Die Depend on Where You Live? Evidence from Hurricane Katrina

Does When You Die Depend on Where You Live? Evidence from Hurricane Katrina

Natural Hazards, Disasters, and Demographic Change: The Case of Severe Tornadoes in the United States, 1980–2010

Ekstraksi Informasi Semantik dan Spatiotemporal pada Artikel Online Terkait Bencana di Indonesia

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