Flooding remains a major problem for the United States, causing numerous deaths and damaging countless properties. To reduce the impact of flooding on communities, the U.S. government established the Community Rating System (CRS) in 1990 to reduce flood damages by incentivizing communities to engage in flood risk management initiatives that surpass those required by the National Flood Insurance Program. In return, communities enjoy discounted flood insurance premiums. Despite the fact that the CRS raises concerns about the potential for unevenly distributed impacts across different income groups, no study has examined the equity implications of the CRS. This study thus investigates the possibility of unintended consequences of the CRS by answering the question: What is the effect of the CRS on poverty and income inequality? Understanding the impacts of the CRS on poverty and income inequality is useful in fully assessing the unintended consequences of the CRS. The study estimates four fixed‐effects regression models using a panel data set of neighborhood‐level observations from 1970 to 2010. The results indicate that median incomes are lower in CRS communities, but rise in floodplains. Also, the CRS attracts poor residents, but relocates them away from floodplains. Additionally, the CRS attracts top earners, including in floodplains. Finally, the CRS encourages income inequality, but discourages income inequality in floodplains. A better understanding of these unintended consequences of the CRS on poverty and income inequality can help to improve the design and performance of the CRS and, ultimately, increase community resilience to flood disasters.
Income and Poverty in the COVID-19 Pandemic
This paper addresses the economic impact of the COVID-19
pandemic by providing timely and accurate information on the impact of the
current pandemic on income and poverty to inform the targeting of resources
to those most affected and assess the success of current efforts. We construct
new measures of the income distribution and poverty with a lag of only a few
weeks using high-frequency data from the Basic Monthly Current Population
Survey (CPS), which collects income information for a large, representative
sample of US families. Because the family income data for this project are
rarely used, we validate this timely measure of income by comparing historical
estimates that rely on these data to estimates from data on income and consumption
that have been used much more broadly. Our results indicate that at
the start of the pandemic, government policy effectively countered its effects
on incomes, leading poverty to fall and low percentiles of income to rise across
a range of demographic groups and geographies. Simulations that rely on the
detailed CPS data and that closely match total government payments made
show that the entire decline in poverty that we find can be accounted for by
the rise in government assistance, including unemployment insurance benefits
and the Economic Impact Payments. Our simulations further indicate that of
those losing employment the vast majority received unemployment insurance, though this was less true early on in the pandemic, and receipt was uneven
across the states, with some states not reaching a large share of their out of
work residents. Updated results during the pandemic for a subset of the tables
in this article can be found at povertymeasurement.org.
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- Award ID(s):
- 2033893
- NSF-PAR ID:
- 10230640
- Date Published:
- Journal Name:
- Brookings papers on economic activity
- Volume:
- 2020
- Issue:
- Summer
- ISSN:
- 1057-8641
- Page Range / eLocation ID:
- 85-118
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract -
null (Ed.)We examine the uneven social and spatial distributions of COVID-19 and their relationships with indicators of social vulnerability in the U.S. epicenter, New York City (NYC). As of July 17th, 2020, NYC, despite having only 2.5% of the U.S. population, has [Formula: see text]6% of all confirmed cases, and [Formula: see text]16% of all deaths, making it a key learning ground for the social dynamics of the disease. Our analysis focuses on the multiple potential social, economic, and demographic drivers of disproportionate impacts in COVID-19 cases and deaths, as well as population rates of testing. Findings show that immediate impacts of COVID-19 largely fall along lines of race and class. Indicators of poverty, race, disability, language isolation, rent burden, unemployment, lack of health insurance, and housing crowding all significantly drive spatial patterns in prevalence of COVID-19 testing, confirmed cases, death rates, and severity. Income in particular has a consistent negative relationship with rates of death and disease severity. The largest differences in social vulnerability indicators are also driven by populations of people of color, poverty, housing crowding, and rates of disability. Results highlight the need for targeted responses to address injustice of COVID-19 cases and deaths, importance of recovery strategies that account for differential vulnerability, and provide an analytical approach for advancing research to examine potential similar injustice of COVID-19 in other U.S. cities. Significance Statement Communities around the world have variable success in mitigating the social impacts of COVID-19, with many urban areas being hit particularly hard. Analysis of social vulnerability to COVID-19 in the NYC, the U.S. national epicenter, shows strongly disproportionate impacts of the pandemic on low income populations and communities of color. Results highlight the class and racial inequities of the coronavirus pandemic in NYC, and the need to unpack the drivers of social vulnerability. To that aim, we provide a replicable framework for examining patterns of uneven social vulnerability to COVID-19- using publicly available data which can be readily applied in other study regions, especially within the U.S.A. This study is important to inform public and policy debate over strategies for short- and long-term responses that address the injustice of disproportionate impacts of COVID-19. Although similar studies examining social vulnerability and equity dimensions of the COVID-19 outbreak in cities across the U.S. have been conducted (Cordes and Castro 2020, Kim and Bostwick 2002, Gaynor and Wilson 2020; Wang et al. 2020; Choi and Unwin 2020), this study provides a more comprehensive analysis in NYC that extends previous contributions to use the highest resolution spatial units for data aggregation (ZCTAs). We also include mortality and severity rates as key indicators and provide a replicable framework that draws from the Centers for Disease Control and Prevention’s Social Vulnerability indicators for communities in NYC.more » « less
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null (Ed.)The COVID-19 public health emergency caused widespread economic shutdown and unemployment. The resulting surge in Unemployment Insurance claims threatened to overwhelm the legacy systems state workforce agencies rely on to collect, process, and pay claims. In Rhode Island, we developed a scalable cloud solution to collect Pandemic Unemployment Assistance claims as part of a new program created under the Coronavirus Aid, Relief and Economic Security Act to extend unemployment benefits to independent contractors and gig-economy workers not covered by traditional Unemployment Insurance. Our new system was developed, tested, and deployed within 10 days following the passage of the Coronavirus Aid, Relief and Economic Security Act, making Rhode Island the first state in the nation to collect, validate, and pay Pandemic Unemployment Assistance claims. A cloud-enhanced interactive voice response system was deployed a week later to handle the corresponding surge in weekly certifications for continuing unemployment benefits. Cloud solutions can augment legacy systems by offloading processes that are more efficiently handled in modern scalable systems, reserving the limited resources of legacy systems for what they were originally designed. This agile use of combined technologies allowed Rhode Island to deliver timely Pandemic Unemployment Assistance benefits with an estimated cost savings of $502,000 (representing a 411% return on investment).more » « less
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A new generation of poverty programs around the globe provides cash payments to poor and vulnerable households. Studies show that these social cash transfer programs create income and welfare benefits for poor households and the local economies where they live. However, this may come at the cost of damaging local environments if cash payments stimulate food production that conflicts with natural resource conservation. Evaluations of the economic impacts of poverty programs do not account for the welfare consequences of environmental impacts, which are potentially large for poor communities closely tied to natural resources. We use an ex-ante policy simulation tool, a bioeconomic local computable general equilibrium model parameterized with microsurvey data, to analyze the expected welfare consequences of environmental degradation caused by a cash transfer program. For a Philippine fishing community that is a net importer of fish, we show that a government cash transfer program initially increases real incomes for all households. However, increased demand for fish leads to a decline in the local fish stock that reduces program benefits. Household groups experience declines in real income benefits of 2–63%, with fishing households suffering the largest declines. Impacts on local fish stocks depend on the extent to which markets link fishing communities to outside regions through trade. Greater market integration can mitigate the fish stock decline, but this reduces the local income benefits of cash transfers.more » « less
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Abstract During the first year of the COVID‐19 pandemic, federal spending on government safety net programs in the United States increased dramatically. Despite this unparalleled spending, government safety nets were widely critiqued for failing to fully meet many households' needs. Disaster research suggests that
informal modes of social support often emerge during times of disruption, such as the first year of the pandemic. However, use of formal government programs and informal support are rarely examined relative to each other, resulting in an incomplete picture of how households navigate disaster impacts and financial shocks. This study compares estimates of informal social support to formal government program use in the rural U.S. West, drawing on data from a rapid response survey fielded during the summer of 2020 and the 2021 Annual Social and Economic Supplement of the Current Population Survey (CPS‐ASEC). We find that informal social support systems were, on aggregate, used almost as extensively as long‐standing government programs. Our findings highlight the critical role of person‐to‐person assistance, such as sharing financial resources, among rural households during a disruptive disaster period. Routine and standardized data collection on these informal support behaviors could improve future disaster research and policy responses, especially among rural populations.
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- The DOI is not currently available.
