Introduction

Floods are afflicting human settlements with growing frequency and severity. Due to rising sea levels and more frequent and intense precipitation events linked to climate change, as well as settlement patterns and impervious infrastructures that concentrate floodwaters, ever more people find themselves in places that flood (Qiang Citation2019; Wing et al. Citation2018). Up to the middle of the 20^th century, in the U.S. and Europe, governments addressed flood hazards primarily through structural measures like building dams and levees as well as with ad hoc post-disaster aid (Collier Citation2014; Knowles and Kunreuther Citation2014). Since the 1960s, individualized protective measures have become central to flood risk management toolkits. In the United States, flood insurance policies required of homeowners with federally backed mortgages in high-risk areas are the lynchpin of federal flood management policy (Kousky Citation2018). Federal, state, and local governments also urge residents to take measures to prepare for flooding, from watching for flood warnings to obtaining sandbags to making major home modifications to reduce damage from inundation.

Using data from a household survey in two small cities, we examine how social contexts influence household adoption of flood protection measures, focusing on homeownership, race, and community group participation. Homeownership confers resources and rights to make changes in a dwelling that may support adoption of protective measures (Poussin, Botzen, and Aerts Citation2014; Thistlethwaite et al. Citation2018). Racial disparities in access to information and resources may translate into unequal flood preparedness (Semien and Nance Citation2022). Involvement in community groups brings access to information and resources that can help one learn about hazards and undertake protective efforts (Aldrich and Meyer Citation2015; Lee Citation2022). Each of these relationships varies depending on the kinds of protective measures involved: some, like attending to flood warnings, require fewer material resources than costly actions like relocating a furnace.

This study advances research on preparedness for floods and other hazards in four ways. First, we extend scholarship specifying how decisions to undertake different sorts of protective measures present different kinds of processes. Second, we demonstrate how homeownership status conditions the effect of flood experience on adoption of protective measures. Third, we show that participation in voluntary community organizations promotes taking low-cost protective measures, especially for households in areas with greater flood exposure, but does not boost adoption of more costly measures. Finally, we find that households of white-identifying respondents take up low-cost protective measures at higher levels than nonwhite respondents, while in high flood-risk locations, households of nonwhite respondents take more high-cost protective measures.

Background and Hypotheses

Decisions about adopting protective measures against hazards reflect both social and psychological processes. Much research evaluates cognitive processes through which individuals appraise information about hazard probability and consequences as well as the availability, cost, and efficacy of various protective measures (e.g. Bubeck, Botzen, and Aerts Citation2012; Lindell and Perry Citation2004; Poussin et al. Citation2014). This work increasingly incorporates affective evaluation of the emotional valences of hazards and responses (Siegrist and Gutscher Citation2008; Terpstra Citation2011). People make such assessments within social contexts: social networks that convey norms, information, and resources (Aldrich and Meyer Citation2015; Lee Citation2022; Lo Citation2013); cultural settings that shape views of hazards, protective measures, and responsible authorities (Burningham, Fielding, and Thrush Citation2008; Elliott Citation2017); and political-economic contexts that shape available resources and power in decision-making (Howell and Elliott Citation2019; Liévanos Citation2020). We examine how social contexts condition decisions to undertake more and less costly flood protection measures.

There are many protective measures one might adopt to prepare for flooding, from staying tuned for flood alerts to purchasing insurance to buying sandbags. Protective measures differ in their demands of money, time, and skill; their visibility to neighbors; the ways and extents to which they protect against loss from flooding; and many other attributes. Looking at any given protective measure, people may differ in how they perceive and act on these costs and benefits. In particular, protection motivation theory (Poussin et al. Citation2014) and the protective action decision model (Lindell and Perry Citation2004, Citation2012) focus on how a person’s perceptions of a given measure converge with concern about hazards.

Researchers have counted household preparedness measures to assess predictors of taking more or fewer measures (Koerth et al. Citation2013; Thistlethwaite et al. Citation2018); evaluated particular measures individually (Brody, Lee, and Highfield Citation2017; Koerth et al. Citation2013; Terpstra and Lindell Citation2013); and grouped measures into categories, examining, for example, how decisions to make changes to a dwelling structure, such as elevating a home, differ from nonstructural measures like storing values in flood-safe places (Grover et al. Citation2022). Taking a categorizing approach in coastal municipalities in the southern United States, Brody, Lee, and Highfield (Citation2017) find differentiated responses across costly structural measures and less costly nonstructural adjustments. Resource-constrained households tended to adopt low-cost or legally mandated measures, while households with higher home values or longer tenure were more likely to take costly measures. Greater cost may make decisions more responsive to socioeconomic status and less responsive to demographic attributes and participation in voluntary organizations.

Our first line of inquiry addresses interactions between flood experience and homeownership. Past experience of a hazard is a strong predictor of adopting protective measures (Koerth et al. Citation2013; Lee Citation2022; Lindell and Hwang Citation2008; Meyer et al. Citation2018; Thistlethwaite et al. Citation2018). Researchers often argue that past experience generates concern about a hazard that motivates people to adopt protective measures, though studies commonly find that concern does not fully mediate the effect of past experience (Lindell and Hwang Citation2008; Thistlethwaite et al. Citation2018). A recent study comparing eight specific flood protection measures found past experience of flooding significantly correlated only with seeking information from government agencies (Brody, Lee, and Highfield Citation2017). The authors argue that ‘experience has to be layered with length of contact, emotional attachment, and other factors to stimulate flood risk adjustments’ (ibid.:583). The effect of experience may hinge on how it intersects with other aspects of encounters with risk.

Homeownership affects flood preparedness decisions in ways that may moderate the effect of experience. Relative to renters, homeowners tend to have greater resources to invest in flood protection. They also have more latitude. Renters are generally barred from undertaking structural measures like elevating a home or moving a furnace. Homeowners have a greater financial stake in their homes and, often, expectation of long-term residence, both of which may motivate putting time and money into flood protection. All of these reasons may contribute to positive effects of homeownership on flood protection measures (Bubeck, Botzen, and Aerts Citation2012; Poussin et al. Citation2014; Thistlethwaite et al. Citation2018; but see Grover et al. Citation2022).

In the United States, the NFIP further conditions the relationship of homeownership to flood preparedness. The NFIP requires insurance purchase for floodplain homeowners with federally backed mortgages. It offers optional flood insurance for other homeowners and optional contents coverage for renters. Homeowners who have mortgages and live in an NFIP-defined Special Flood Hazard Area (SFHA) often learn about flooding – and measures to mitigate flood risk – through the NFIP mandatory purchase requirement. Recent research finds positive correlations between flood insurance acquisition and other protective measures (Atreya, Ferreira, and Michel-Kerjan Citation2015; Botzen, Kunreuther, and Michel-Kerjan Citation2019; Laird et al. Citation2021). Homeowners who buy without a mortgage may not encounter this requirement and the attendant information. Those who have paid off their mortgages may become distanced from this source of information about flooding, increasingly as time passes. Renters, meanwhile, typically receive little information about flood risk (Zinda et al. Citation2021). In multiunit dwellings, upper-floor renters may have reduced exposure to flooding. Renters may also be more likely to relocate rather than remain in a flood-damaged rental dwelling.

We may thus expect flood experience to matter most for those who own without a mortgage. For homeowners with mortgages, especially within the SFHA, access to NFIP-linked information about flood preparedness may reduce the importance of flood experience. For renters, with limited options to take protective measures, the effect of experience may be attenuated. These differences are likely to be more pronounced with costly structural protective measures than with less costly measures.

Along with income, wealth, and education measures, relationships between homeownership and hazard preparedness can make manifest the impacts of class differentiation. Class and homeownership have complicated relationships. For example, while renters are more likely than owners to be working-class, the population of outright homeowners is bifurcated between affluent people who have paid down mortgages or bought with cash and low-income households living in inherited houses. The latter comprise a large portion of flood-exposed households that do not purchase flood insurance (FEMA Citation2018).

Our second line of inquiry concerns racial inequity. In many locales, histories of flooding and redlining have concentrated Black, Latinx, and other communities of color in flood-prone areas (Bullard and Wright Citation2009; Liévanos Citation2020). In others, particularly in coastal areas, premiums on waterfront property bring white residents to predominate in flood zones (Grineski et al. Citation2015; Qiang Citation2019). In some areas, projects aimed at building resilience to flood hazards have resulted in ‘green gentrification,’ displacing working-class residents of color from waterfront communities (Gould and Lewis Citation2017).

Regardless, where communities of color face flood hazards, racialized social processes contribute to conditions of vulnerability, in which people facing hazards are constrained in preventing and recovering from impacts (Bullard and Wright Citation2009; Semien and Nance Citation2022). In the United States, discrimination in housing markets and mortgage lending has blocked racially marginalized households from accumulating the household capital homeowners often rely on to finance protective measures and cope after disasters (Liévanos Citation2020; Paganini Citation2019). Post-disaster aid processes have been shown aggravate racial disparities, in part through their focus on restoring property and arduous procedures that require substantial time and resources (Howell and Elliott Citation2019). Communities of color also face disproportionate exposure to multifarious health risks (Brailsford et al. Citation2018; Grineski et al. Citation2015; Pastor, Sadd, and Hipp Citation2001). Hence, racially marginalized residents of flood-prone areas, constrained in access to resources and information while burdened with multiple exposures, may be less likely than white residents to take protective measures.

Research on adoption of protective measures for natural hazards shows mixed effects of demographic measures, including race (Grover et al. Citation2022; Lindell and Perry Citation2004). A study of residents in hurricane-affected Texas counties finds higher self-reported hurricane preparedness among white respondents (Reininger et al. Citation2013). Maldonado and colleagues (Citation2016) find greater perceived flood risk and reduced protective measure adoption among Hispanic immigrants relative to people who identify as non-Hispanic white or U.S.-born Hispanic. In contrast, Meyer and colleagues (Citation2018) find a positive effect of nonwhite identification on Louisiana residents’ intent to evacuate in the event of a hurricane. A nationwide survey finds that households with a Black household head are more likely than those with a household head identifying as white to take action-based protective measures but less likely to take costly resource-based measures (Zamboni and Martin Citation2020). Given these observations, we anticipate that identifying as white will have a positive effect on high-cost protective measures.

Participation in community groups grounds our third line of inquiry. For individuals and communities, participation in voluntary organizations can confer access to resources and information that can be useful in disaster recovery as well as in preparing for hazards (Aldrich and Meyer Citation2015). Community groups may gather residents who face common flood risks, conveying information and enabling collective action (Zemaitis et al. Citation2020). Surveying households in Taiwan, Lee (Citation2022) finds a positive effect of participation in voluntary organizations on adoption of flood protection measures, mediating the effect of perceived controllability of flood hazards. Researchers often examine organizational participation as a component of bridging social capital (Aldrich and Meyer Citation2015; Harrison, Montgomery, and Bliss Citation2016; Reininger et al. Citation2013). Recognizing that bridging social capital is a multidimensional construct that group participation cannot fully encompass, we treat group participation in its particulars rather than as an indicator of social capital. Specifically, because community group participation disproportionately confers nonmaterial resources like information and social support, we expect it to promote low-cost measures more than high-cost measures. Furthermore, community organizations are most likely to provide resources relevant to flood preparation if members live in areas where flooding is a major concern. Therefore, we expect this effect to be stronger among households in neighborhoods with greater flood exposure.

We examine these issues in a context that enables us to expand the reach of flood preparedness research. First, this study focuses on small cities with populations of less than 100,000. Studies of flood risk response in the United States often focus on large metropolitan areas like New Orleans, Houston, New York, and Miami (e.g., Buchanan, Oppenheimer, and Parris Citation2019; Gotham, Lauve-Moon, and Powers Citation2017; Grineski et al. Citation2015; Loughran, Elliott, and Kennedy Citation2019).Footnote¹ The concentrations of people and property in large cities makes this focus understandable. Nonetheless, significant risk of flooding exists in smaller municipalities, which typically have weaker resource bases for assessing, communicating, and responding to flood risk (Zemaitis et al. Citation2020). These locales thus need to be represented in research.

Second, the study takes place within an estuary. The Hudson River is hydrologically linked with the Atlantic Ocean such that the river level rises and falls with the tides. As climate change proceeds, flooding in these communities responds not only to riverine flooding and tributary flash flooding with increasingly severe precipitation events, but also to overall rising water levels in the river (Orton et al. Citation2020). Residents may not be broadly aware of these hydrological features. Researchers have noted that inland communities tend to take less active approaches to flooding than coastal ones (Qiang Citation2019). In inland estuarine communities, such inaction could be particularly dangerous. Thus, understanding flood preparation in such places is especially important.

Finally, in each of the study sites, the most recent major flood was 9 years prior to our survey. Immediately after a disaster, people in affected areas tend to show heightened awareness of a hazard. However, awareness and protective action tend to drop off over time (Bubeck, Botzen, and Aerts Citation2012). Even in the places that experience major disasters most people, most of the time, will not have recently experienced major flooding (Meyer and Kunreuther Citation2017). Therefore, it is especially important to understand flood preparations where people may not be expecting a flood.

To sum up, consistent with existing research, we anticipate that people adopt flood preparedness measures in ways that are differentiated by homeownership, race, and participation in community groups. Additionally, we expect that, as each factor differently involves material and informational resources, the effects of each of these factors will differ across more and less costly protective measures.

Sites and Methods

This study draws on a household survey conducted in 2020 in Troy and Kingston, two cities on the Hudson River in the state of New York, United States of America. We selected these cities with consideration of their histories of periodic flooding, expected changes in flood risk, and involvement in current flood risk mitigation efforts. According to the U.S. Census Bureau, in 2020 Troy had a population of 51,401 and Kingston 24,069. After a long period of disinvestment, Troy, once a major industrial center, has recently seen commercial and residential revival in its downtown. The city’s historically working-class and racially diverse north and south sides have been on the periphery of this expansion and are most affected by flooding. Kingston has recently experienced residential and commercial booms linked in part to people relocating from the New York City area. In different ways, each reflects trends of uneven renewal in postindustrial cities across the region. We present basic demographic information from the U.S. Census Bureau in Table 1.

Table 1. Demographics for Kingston and Troy. Source: American Community Survey 5-year estimates, 2019.

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Both cities are located in the Hudson River Estuary and face changing flood regimes due to both increased precipitation and the effects of sea level rise on water levels (U.S. Global Change Research Program Citation2018). In each city, the most recent major flood preceding the survey had come with Hurricane Irene in August, 2011, during which both locations were included in a federal disaster declaration (Lumia, Firda, and Smith Citation2014). Flooding affected substantial areas of each city, but was not catastrophic.

In each city, a panoply of community organizations works on various issues, including flooding. For example, in Troy the Sanctuary for Independent Media has worked with North Side community members to stimulate awareness and action on flooding and water quality, while the Osgood Neighborhood Association on the South Side has mobilized to reclaim vacant lots. In Kingston, citizen groups and advocates in city government have worked to build flood preparedness among homeowners and to establish numerous flood protection measures through the city’s participation in New York State’s Climate Smart Communities program and the Hudson River Flood Resilience Network. While organizations that specifically promote flood preparedness are likely to have the strongest impact on preparedness measure adoption, we anticipate a general correlation of community group participation with adoption of flood protection measures.

Working with the Cornell University Survey Research Institute, we designed a questionnaire and mailed it to 3,750 household addresses, divided equally between the two cities. Four focus group discussions with a racially and socioeconomically diverse mix of homeowners and renters in Troy informed questionnaire design. The questionnaire addressed community perceptions and place attachment; parallel question sets on exposure, risk perception, and protective measures regarding flooding and COVID-19; and individual attributes and demographics. Within each city, the sample was stratified by proximity to Special Flood Hazard Areas designated by the U.S. Federal Emergency Management Agency, oversampling households in census blocks overlapping areas with 1% and 0.2% annual flood probability based on digitized Flood Insurance Rate Maps. Questionnaires were mailed in May 2020. After one postcard reminder, in July an additional questionnaire was sent to households that had not yet responded. A third questionnaire was mailed in August. No incentive was provided. In total 499 questionnaires were returned. The total response rate was 14.7%. We exclude two questionnaires with ‘don’t know’ responses on key variables.

We performed this research in accordance with a protocol approved by the Cornell University Institutional Review Board. The only personally identifiable information obtained were addresses. Once addresses had been used to generate variables based on geographic location, they were removed from datasets used for analysis. Since in combination other attributes could potentially be used to identify individuals, data were maintained in a password-protected cloud directory accessible only to the research team, all of whom had undergone research ethics training.

Outcomes of interest

Our core outcomes of interests are measures of high-cost and lower-cost flood measures undertaken by a household. Similar to Koerth and colleagues (Citation2013) and Grover and colleagues (Citation2022), we measure reported past actions. The questionnaire presented the prompt, ‘Please tell us whether or not you do the following to reduce flood risk in your household,’ followed by a list of protective measures adapted from Koerth and colleagues (Citation2013) (Figure 1). For each, we coded a ‘yes’ response as 1 and ‘no’, ‘don’t know,’ or nonresponse as 0.

Figure 1. Protective Measures

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We took constructed indices of protective measures inductively using polychoric principal components analysis (PPCA). PPCA is a variant of principal components analysis that uses polychoric correlations among binary or categorical indicators (Kolenikov and Angeles Citation2009). Our expectation was that, based on the ways the adoption of different measures are correlated, this approach would differentiate measures that differ quantitatively and qualitatively in the costs, benefits, and challenges they pose for households.

Explanatory Variables

Our core research questions concern effects of homeownership, flood experience, civic group participation, and race on protective measure adoption. Homeownership is a categorical variable identifying whether the respondent indicated that they own their home with a mortgage, own outright with no mortgage, or rent. Flood experience is a binary variable coded 1 if the respondent indicated that flooding had caused any of the following impacts to their current home: flooded basement, contaminated drinking water, private property damage, loss of life or injury to a person, and sewage back-up. We constructed a six-item categorical interaction variable from the combination of homeownership and flood experience. Group participation is the respondent’s evaluation of the statement ‘I regularly attend meetings of community groups or organizations,’ on a Likert scale coded from 1 (Strongly Disagree) to 5 (Strongly Agree). We measured racial identification with a set of checkboxes, asking respondents to check all that describe their racial or ethnic identity. We constructed a binary variable coded 1 if the respondent checked only ‘White or Caucasian,’ and 0 if they checked any other item.

Control Variables

We included several control variables. We measure exposure to flooding using data from the Flood Factor tool hosted by First Street Foundation (Citation2020). We manually entered each household’s street address and recorded its Flood Factor rating. Values range from 1 (minimal flood risk) to 10 (very high flood risk). We calculated the respondent’s age based on their reported year of birth. Woman is 1 if the respondent identified as female or woman, 0 otherwise. Bachelor is coded 1 if the respondent reported having attained a bachelor’s degree or higher level of education. Income is an ordinal variable reporting annual household income the previous year (1: <$10,000; 2: $10,000–29,999; 3: $30,000–49,999; 4: $50,000–69,999; 5: $70,000–89,999; 6: $90,000–109,999; 7: $110,000–129,999; 8: $130,000-$149,999; 9: $150,000–169,999; 10: ≥170,000). Table 2 presents descriptive statistics.

Table 2. Descriptive Statistics.

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We conducted statistical analyses using Stata 17. First, to make full use of information in the dataset, we conducted multiple imputation with data augmentation. Missingness was modest; income (8.0%) and age (5.4%) had the most missing values. However, 150 respondents had missing responses on at least one variable, and using listwise deletion would result in substantial loss of information. Moreover, missingness analyses suggest that missing values are not completely random. Multiple imputation is more robust to nonrandom missingness than listwise deletion (Allison Citation2001).

We imputed 20 datasets with 497 observations. We used execution commands for multiply imputed datasets to evaluate ordinary least squares regression models predicting low-cost and high-cost protective measures respectively. Each set of models builds from demographic predictors including race, age, gender, and education of the respondent as well as household income, adding flood exposure and flood experience, then participation in community organizations, then homeownership, then the interaction of homeownership and flood experience. To evaluate whether patterns differ when assessed over only the population living in homes at moderate to great risk of flooding, regression models were repeated using a subsample of respondents with Flood Factor scores of 2 or greater (n = 148). Using the initial un-imputed dataset, we conducted diagnostic tests for multicollinearity and heteroskedasticity. For each model, tests showed that relationships were at acceptable levels. We set a 95% confidence level (p < 0.05) for assessing statistical significance of correlations; we also note effects identified at a 90% confidence level.

Results

Protective Measures

Respondents undertook flood protection measures at varying rates (Figure 2). Over half of respondents reported attending to flood warnings and keeping documents in a flood-safe location. Less than 10% reported obtaining sandbags, obtaining flood barriers, attending information sessions related to flooding, moving to a less flood-prone area, and making a home modification to reduce flood risk. For nearly every measure, the proportion undertaking it is higher for those who had experienced damage from flooding than for those who had not. Among respondents reporting past flood damage, homeowners show higher rates of adoption than others on several actions, particularly installing a sump pump, keeping valuables in a flood-safe location, locating one’s furnace in a flood-safe location, and making a home modification to reduce flood risk.

Figure 2. Protective Measures by Flood Experience.

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Polychoric principal components analysis confirmed our expectation that protective measures would group together based on costs of money and effort. Together, the first two components account for over 56% of variation in protective measures adoption (Table 3). For the first component, the largest effects are for having a plan for flooding and keeping documents in a safe place – relatively low-cost actions (Table 4). Higher cost options have smaller effects. For the second component, having a sump pump, making home modifications, and placing one’s furnace in a flood-safe location are the most influential items. Low-cost actions like keeping documents safe and preparing a flood kit correlate negatively. Given these relationships, in regression analyses we use the first coefficient as a measure of low-cost measures and the second coefficient as a measure of high-cost measures.

Table 3. Polychoric Principal Components Analysis Outputs.

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Table 4. Polychoric Principal Components Analysis Coefficients.

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The first set of models evaluates predictors of the index of low-cost preparedness measures (Table 5). Controlling for other variables, white respondents take more low-cost flood preparedness measures than respondents of color. When we include flood experience in the model, this effect attenuates. With homeownership, it attenuates further. Participation in community organizations shows a significant, positive correlation with low-cost protective measures. However, when we control for homeownership, this effect is no longer statistically significant. As we anticipated, renters on average take fewer flood preparedness measures than homeowners with mortgages. Homeowners without mortgages do not show a significant difference from those with mortgages.

Table 5. Regressions Predicting Low-Cost Preparedness Measures.

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When we include an interaction between homeownership and flood experience, a more differentiated pattern emerges. Relative to homeowners with mortgages who report no past flood impacts, those with flood experience (with or without mortgages) show higher levels of flood preparedness measures. Those without mortgages and without flood experience also take more low-cost flood preparedness measures than homeowners with mortgages. When we treat renters with no flood experience as base category, renters with flood experience are revealed to take significantly more flood protection measures. Note that these models control for externally evaluated flood exposure.

Among control variables, age has negative effect, significant in models 3, 4, and 5: younger respondents are more likely to report more low-cost protective measures. Flood experience has a consistent and large positive effect. Effects of gender, education, and income are not significant at a 95% confidence level.

In Table 6, we report results from the same set of models with the subsample households with Flood Factor scores of 2 or greater. With a sample reduced by two-thirds, statistical power is weaker, and we note effects at a 90% confidence level. The effect of participation in community organizations remains positive and marginally significant, and coefficients are 50% larger than in the full sample, providing weak confirmation of the hypothesis of a stronger effect of community organization membership on low-cost flood preparation measures within flood-affected neighborhoods. The effect of race is smaller in magnitude and nonsignificant. Even among households with higher flood exposure, flood experience remains a strong positive predictor, as it does in the variable combining experience and homeownership. In models treating owning outright without flood experience as base category, the coefficient for owning outright with flood experience is 1.5262, significant at p < 0.01 and nearly twice as large as the coefficient for homeowners with mortgage with flood experience relative to mortgage holders without flood experience. In other words, the effect of flood experience is larger for homeowners without mortgages than for mortgage-holding homeowners. The other effects of homeownership observed above are no longer significant.

Table 6. Regressions Predicting Low-Cost Preparedness Measures, Subsample of Households with Higher Flood Risk.

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In regressions predicting adoption of high-cost flood preparedness measures, the effect of race is not significant (Table 7). This is contrary to our hypothesis that race would have a stronger effect for more costly measures. It appears that in this case economic resources are more influential, as evidenced by the effects of income and homeownership. In model 1, income shows a significant positive effect on costly flood preparedness measures. Flood experience partially mediates this effect, and when homeownership is introduced, the income coefficient diminishes considerably. Renters take significantly fewer high-cost measures than mortgage-holding homeowners. In the last model, flood experience again correlates with higher adoption of high-cost measures among mortgage holders; those with flood experience also take more costly measures than those without. However, mortgage-free homeowners with flood experience do not differ significantly from mortgage-holders without it. Effects of community group participation are negative and not significant.

Table 7. Regressions Predicting High-Cost Preparedness Measures.

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While flood experience has a consistent positive effect on high-cost flood preparation measures, flood exposure has a consistently negative and significant effect. This result is surprising and reveals that actual flooding experience is a stronger motivator to take protective measures than exposure. This may be because residents are often unaware of their levels of exposure. It might also be that the model of flood risk underlying Flood Factor ratings is not well calibrated in these localities. Among other control variables, age has a positive effect on adoption of high-cost measures – opposite its effect on low-cost measures. Effects of gender and education are not significant.

When we evaluate correlates of costly flood protection measures among households with higher flood exposure (Table 8), most associations (flood risk, exposure, homeownership, age, gender, and education) are not significant. The effect of identifying as white is negative and significant at either the 90% or the 95% level across models, however, contrary to our expectation. Likewise contrary to expectations, participation in community groups shows significant negative correlations.

Table 8. Regressions Predicting High-Cost Preparedness Measures.

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Discussion

Flood experience and homeownership interact to influence protective measure adoption in ways that give partial support to our hypotheses. We anticipated that flood experience would have a positive effect on protective measures and that this effect would be weaker for homeowners in areas with high flood risk and for renters. For low-cost protective measures across the whole sample, mortgage holders with flood experience and outright owners with flood experience take significantly more measures than mortgage holders without flood experience. Outright owners also take significantly more low-cost protective measures. In the sample restricted to respondents with higher flood risk, outright owners and mortgage holders with flood experience again show significantly higher levels of low-cost protective measures. In light of information effects of the NFIP mandatory purchase requirement for mortgage holders in SFHAs, we hypothesized that in high flood-risk areas there would be a significant difference in protective measure adoption between outright owners with flood experience and outright owners without flood experience, but not between mortgage holders with and without flood experience. Regressing low-cost measures in the high-risk subsample, both comparisons showed positive effects of flood experience, though the difference for outright owners was larger, a partial confirmation of the hypothesis. Overall, results suggest that homeowners with flood experience take low-cost protective measures at the highest rates, regardless of mortgage status, while flood experience also leads renters to take more low-cost actions, though at lower rates than homeowners.

For high-cost protective measures, across the full sample mortgage holders with flood experience have the highest rates of protective measures, outright owners with flood experience do not differ from mortgage holders without flood experience, and outright owners without flood experience as well as both groups of renters take fewer high-cost protective measures. For more costly protective measures, the effect of experience is stronger for outright owners than with low-cost measures. Meanwhile, for renters, who are most constrained in making changes to appliances or dwelling structures, flood experience does not differentiate adoption of high-cost protective measures. No significant patterns show within the restricted sample: it does not appear that the mandatory purchase requirement for flood insurance promotes additional protective measures among mortgage holders in high-risk locations.

We expected that racial privilege would result in households of white respondents showing higher levels of costly flood protection measures. This hypothesis was not supported. White respondents show higher levels of low-cost protective measures. For high-cost protective measures in the full sample, white racial identification shows no significant correlations. Among households with substantial flood risk, white respondents took fewer high-cost measures than respondents of color, with correlations significant when controls for group participation and homeownership are included. Studies often find that Black and Latinx individuals report greater flood risk perception than white individuals (Gotham, Lauve-Moon, and Powers Citation2017; Maldonado, Collins, and Grineski Citation2016; Zinda et al. Citation2022); it may be that this greater concern motivates people of color in flood-affected areas to invest in costly protective measures at higher rates.

Our finding that participation in community organizations promotes low-cost protective measures, especially within higher-risk areas, specifies a pattern identified in earlier studies (Aldrich and Meyer Citation2015; Lee Citation2022). In the full sample, community group participation is positively associated with low-cost protected measures. In the higher-risk subsample, the positive coefficient of community group participation is over 50% larger than in the full sample, though with reduced statistical power these correlations are significant only at a 90% confidence level. For costly protective measures, correlations are insignificant for the whole sample and negative for the higher-risk subsample. This last result is difficult to interpret, though it could be that people who are especially distanced from community organizations would be more inclined to focus on individualized protective measures over other approaches to flood risk.

Overall, these results support the propositions that participation in community groups brings information and social support in preparing for flooding, and that people who live in areas with high flood risk are most likely to draw on such groups to take flood protection measures. Still, we acknowledge that our measure of community group participation is a coarse approximation of the social connections likely to support flood preparedness. Organizations respondents are likely to identify as ‘community groups’ are only one of several sources of such support. In particular, religious congregations are often a key resource (Peek and Fothergill Citation2008). Moreover, asking specifically whether and what kind of information and support around flooding respondents obtain from these groups would better establish this mechanism (cf. Brody, Lee, and Highfield Citation2017; Grover et al. Citation2022).

The patterns we observe reflect specificities of the places where we conducted this study. By studying small cities, we shift away from the focus of much research on larger cities. While our results may reflect broader patterns in the Hudson Valley, we cannot statistically generalize beyond the two cities studied. Similarly, timing matters for flood risk response, and the fact that these cities had not seen a flood for nearly a decade likely affects the amount and distribution of flood protection measures.

Conclusion

Households take action to prepare for floods in systematically uneven ways. Experience of flood impacts often brings a tangible sense of flooding’s dangers that spurs protective measures, but this effect is larger for homeowners. Participating in community groups can help people take basic steps to protect from flooding. Racial privilege also contributes to disparities in flood preparedness. Groups interested in reducing vulnerability to flooding must take into account both the factors that enable some people to be more prepared for flooding and tough questions about how to enable groups with lower flood preparedness to reduce their vulnerability.

Homeowners take more measures to protect against flooding than renters do, but homeowners without flood experience still lag. One way to reach this population is to implement disclosure policies that mandate communication about not just flood risk but actions to mitigate it. In the United States, 21 states, including New York, do not require disclosure of flood risk when a house is purchased (Natural Resources Defense Council n.d.). There are many models for local, state, and federal governments to provide incentives to adopt protective measures. These efforts will need to contend with the ways resource constraints and psychological tendencies to downplay flood hazards hamper flood preparedness efforts.

Providing support and information for flood protection is all the more important for renters, who tend to have more limited resources than homeowners as well as less latitude to make changes to their dwellings. The New York State legislature recently passed a law that mandates that lessors inform tenants of flood risk in rental dwellings – but does not specify penalties for not disclosing this information (Feldman Citation2022). Another major question concerns the conditions under which rental property owners undertake flood measures that would protect tenants. Research into flood protection measures by landlords can illuminate the extent to which renters are adequately protected from flood risks and provide a basis for policies aimed at reducing vulnerability (see also Maldonado, Collins, and Grineski Citation2016). Similarly, we need a stronger understanding of how unhoused people and mobile home dwellers cope with flooding, to build on recent work with these populations (Rumbach, Sullivan, and Makarewicz Citation2020; Vickery Citation2018).

Our finding that community group participation promotes low-cost flood protection measures could suggest that strengthening community organizations is a key to broadening flood preparedness. There are likely many contexts in which this approach can make a difference – particularly in affluent and close-knit communities (Zemaitis et al. Citation2020). However, efforts at building social capital often run up against the structural constraints that may be the reasons collective efforts are weak or absent in the first place (Warner Citation1999). Hence, it is important at the same time to explore alternative ways of supporting flood protection in communities and households where community organizations have weak prospects.

The racial patterns we identified merit attention. Given known racial disparities in income and information access, we did not anticipate that among residents in high-risk locations, people who did not identify as white would take high-cost protective measures at greater levels. Meanwhile, white respondents reported more low-cost protective measures than other respondents. This suggests a heightened need for support for racially marginalized residents. The sample of respondents of color in this survey was too small to enable disaggregation by specific racial and ethnic categories, a major shortcoming. There is much room to expand work on racial disparities in disaster preparedness, in conjunction with concerted efforts to reduce disparities in exposure and redress planning and policy actions that make people disproportionately vulnerable.

This study does not center class as an analytical frame, but our findings suggest directions for further inquiry. Social class clearly infuses the patterns we identify, particularly as it manifests in how homeownership and income correlate with flood preparedness measures. In models predicting low-cost preparedness measures, introducing homeownership diminishes the effect of community group participation, suggesting class-differentiated patterns. Finally, race and class combine to influence disaster vulnerability in cross-cutting ways.

When households adopt flood protection measures at different rates, neighborhood inequalities emerge, grow, and persist. Communities with different racial composition, housing conditions, and levels of collective action may assemble people with similar predispositions to adopt protective measures. In contexts of unequal housing policies, labor markets, and urban disaster planning, these variations concatenate into broader vulnerabilities (Fu Citation2016; Liévanos Citation2020; Semien and Nance Citation2022). Understanding patterns of household flood protection can illuminate how these processes unfold and identify leverage points for action.