Metropolitan Statistical Areas Research Articles

Unravelling the impact of the Community Reinvestment Act on SBA lending: Insights from a regression discontinuity design analysis

ABSTRACT This study investigates the influence of the Community Reinvestment Act (CRA) on Small Business Administration (SBA) lending in various U.S. urban areas. Focusing on central cities, inner-ring, and outer-ring suburbs across seven major metropolitan statistical areas, it employs a regression discontinuity design to analyze CRA’s effect on neighborhoods with median family incomes at or below 80% of the area’s median. Results show that CRA significantly increases SBA lending in inner-ring suburbs but not in central cities or outer-ring suburbs, indicating a geographically varied impact. This research underscores the spatial differences in CRA’s effectiveness, suggesting a need for more tailored strategies to improve SBA lending in diverse urban settings.

The interaction between smartness and the urban economy

ABSTRACT While many studies have tried to measure the magnitude of smart cities, they have not highlighted the degree of smartness and the interaction between smartness and the urban economy across all regions in the country. This study highlights them by employing a new 5Ic smart city index (Information, communication, and technology, Innovation, Intelligence, Infrastructure, and Inflow) based on all US Metropolitan Statistical Areas (MSAs) by employing the Seemingly Unrelated Regression (SUR) model. This study finds that New York, NY is the highest smart MSA, followed by Los Angeles, CA and San Francisco, CA. Second, the innovation and inflow indices and the GDP positively interact with each other (innovation → GDP: 0.006 and GDP → innovation: 0.018 and inflow → GDP: 0.031 and GDP → inflow: 0.028) when other important variables are controlled. Third, the SUR model is a better model than the OLS model since some smart city indices are associated with the GDP. Therefore, governments and urban planners should develop their smart city strategies based on the magnitude of smartness and the interaction between smartness and the urban economy in their regions. Highlights This study highlights the degree of smartness by employing a new 5Ic smart city index (Information, communication, and technology, Innovation, Intelligence, Infrastructure, and Inflow). This study employs the Seemingly Unrelated Regression model based on all US Metropolitan Statistical Areas. New York is the highest smart Metropolitan Statistical Area, followed by Los Angeles and San Francisco. The innovation and inflow indices and the Gross Domestic Product positively interact with each other.

A Cost Analysis of Phosphodiesterase Type 5 Inhibitors in the United States

ObjectiveTo perform a cost analysis of generic and brand-name PDE5 inhibitors at different dosages and pharmacies across the US. MethodsUsing an all-payer retail pharmacy-claims database, we analyzed prescription drug data for three generic and six brand-name oral PDE5 inhibitors at different dosages across US chain and independent pharmacies in 2019. ResultsWe obtained cash price data from 60,186 pharmacies (35,976 chain and 24,210 independent). The nationwide mean cash price per unit (PPU) ranged from $8.6±5.2 (sildenafil 20mg at chain pharmacies) to $107.1±71 (Adcirca 20mg at independent pharmacies) equal to 1,145.3% difference. Chain pharmacies provided significantly lower average prices for one brand-name and six generic PDE5 inhibitors. Tadalafil PPU was cheaper at higher quantities, however PPU increased with quantity prescribed for sildenafil. Looking at the top 10 metropolitan statistical areas, the highest PPUs were observed for tadalafil (Cialis) 10mg and sildenafil (Viagra) 50mg in Atlanta ($67.4±8.7) and Los Angeles ($50.3±24.0), while New York ($9.7±2.6) and Miami ($27.9±16.4) had the lowest PPUs for tadalafil (Cialis) 5mg and sildenafil (Viagra) 100mg, respectively. ConclusionA substantial variability in PDE5 inhibitor cash prices exists across manufacturer, dosage, quantity, pharmacy type, and location. In addition, the pricing does not necessarily correlate with the regional socioeconomic factors. This highlights the importance of provider awareness and patient counseling on drug price including potentially assisting patients in identifying opportunities for cost savings.

Where the Landlords Are: A Network Approach to Landlord-Rental Locations

The United States is home to more than 100 million renters and approximately 11 million landlords, yet these two sides of the rental market are rarely studied in tandem. This study uses a multiscalar network-based approach to identify landlord market areas. Building on administrative data of the locations of rental properties and landlords, I define a landlord–property network as a spatial bipartite network, where landlords’ addresses are connected to their properties’ addresses, and vice versa. I first examine the location of landlords relative to their properties. I then compare the differences in socioeconomic characteristics in landlord and rental tracts. I simplify this network by extracting its backbone, defining a core component of a landlord market. I compare these networks to Metropolitan Statistical Areas and commuting networks to evaluate the performance of the backbone extraction method. I find that most landlords are local, and, perhaps unsurprisingly, that landlord neighborhoods are richer, Whiter, and more expensive than where their properties are located. Extracting the backbone of the commuting network results in a network that mirrors a regional definition, whereas the landlord market area is much more national in scope. These two networks differ geographically and also with regard to their network statistics. Whereas renters and homeowners search within a region for new housing, landlords and capital can search nationally for locations in which to invest. This article provides a new, robust foundation to understanding rental market investor dynamics and the relationship among owner, renter, and property.

Predicting hurricane evacuation for local neighborhoods across a metropolitan region

To mitigate the devastating impacts of hurricanes on people's lives, communities, and societal infrastructures, disaster management would benefit considerably from a detailed understanding of evacuation, including the socio-demographics of the populations that evacuate, or remain, down to disaggregated geographic levels such as local neighborhoods. A detailed household evacuation prediction model for local neighborhoods requires both a robust household evacuation decision model and individual household data for small geographic units. This paper utilizes a recently published statistical meta-analysis for the first requirement and then conducts a rigorous population synthesis procedure for the second. Our model produces predicted non-evacuation rates for all US Census block groups for the Tampa-St. Petersburg-Clearwater Metropolitan Statistical Area for a Hurricane Irma-like storm along with their socio-demographic and hurricane impact risk profiles. Our model predictions indicate that non-evacuation rates are likely to vary considerably, even across neighboring block groups, driven by the variability in evacuation risk profiles. Our results also demonstrate how different predictors may come to the fore in influencing non-evacuation in different block groups, and that predictors which may have an outsize impact on individual household evacuation decisions, such as Race, are not necessarily associated with the greatest differentials in non-evacuation rates when we aggregate households to block group level and above. Our research is intended to provide a framework for the design of hurricane evacuation prediction tools that could be used in disaster management.

Disparities in access to high-volume parathyroid surgeons in the United States: A call to action

BackgroundParathyroidectomy by a high-volume surgeon is associated with a reduced risk of perioperative complications and of failure to cure primary and secondary hyperparathyroidism. There are limited data on disparities in access to high-volume parathyroid surgeons in the United States. MethodsWe used publicly available 2019 Medicare Provider Utilization and Payment data to identify all surgeons who performed >10 parathyroidectomies for Medicare fee-for-service beneficiaries, anticipating that fee-for-service beneficiaries likely represent only a subset of their high-volume practices. High-volume parathyroid surgeon characteristics and geographic distribution were evaluated. Inequality in the distribution of surgeons was measured by the Gini coefficient. The association between neighborhood disadvantage, based on the Area Deprivation Index, and proximity to high-volume parathyroid surgeons was evaluated using a one-way analysis of variance with Bonferroni-corrected pairwise comparisons. A sensitivity analysis was performed restricting to high-volume parathyroid surgeons within each hospital referral region, evidence-based regional markets for tertiary medical care. ResultsWe identified 445 high-volume parathyroid surgeons who met inclusion criteria with >10 parathyroidectomies for Medicare fee-for-service beneficiaries. High-volume parathyroid surgeons were 71% male sex, and 59.8% were general surgeons. High-volume parathyroid surgeons were more likely to practice in a Metropolitan Statistical Area with a population >1 million than in less populous metropolitan or rural areas. The number of high-volume parathyroid surgeons per 100,000 fee-for-service Medicare beneficiaries in the 53 most populous Metropolitan Statistical Areas ranged from 0 to 4.94, with the highest density identified in Salt Lake City, Utah. In 2019, 50% of parathyroidectomies performed by high-volume parathyroid surgeons were performed by 20% of surgeons in this group, suggesting unequal distribution of surgical care (Gini coefficient 0.41). Patients in disadvantaged neighborhoods were farther from high-volume parathyroid surgeons than those in advantaged neighborhoods (median distance: disadvantaged 27.8 miles, partially disadvantaged 20.7 miles, partially advantaged 12.1 miles, advantaged 8.4 miles; P < .001). This association was also shown in the analysis of distance to high-volume parathyroid surgeons within the hospital referral region (P < .001). ConclusionOlder adults living in disadvantaged neighborhoods have less access to high-volume parathyroid surgeons, which may adversely affect treatment and outcomes for patients with primary and secondary hyperparathyroidism. This disparity highlights the need for actionable strategies to provide equitable access to care, including improved regionalization of high-volume parathyroid surgeon services and easing travel-related burdens for underserved patients.

Association Between Changes in Racial Residential Segregation and Trends in Racial Disparities in Early Mortality in 220 Metropolitan Areas, 2001-2018.

Racial residential segregation has been shown to affect the absolute levels of racial disparities in a wide variety of health outcomes in the USA but it is not known whether changes in segregation also influence these racial health disparities. This study examines the relationship between changes in racial residential segregation over four decades (1980-2020) and trends in racial disparities in early mortality (under age 65) rates among non-Hispanic Black and non-Hispanic White persons across a wide range of health outcomes in 220 metropolitan statistical areas (MSAs) during the period 2001-2018. Using the CDC WONDER Underlying Cause of Death database, we derived annual estimates of race-specific death rates and rate ratios for each MSA. We used latent trajectory analysis to examine the relationship between the level of segregation and changes in segregation over time in an MSA and trends in death rate disparities in that MSA. The trajectory analysis resulted in a linear, three group model in which trajectory Groups 1 and 2 had decreasing trends in the ratios of Black to White death rates over time while in Group 3, the disparity remained almost constant over time. Increases in the level of segregation in an MSA from 1980 to 2000 were significantly associated with the likelihood that the MSA was in Group 3 and experienced no improvement in racial health disparities in mortality over time. This paper provides new evidence that changes in segregation are related to trends in racial health disparities in mortality rates over time.

Complicating ‘Suburbanization’ and Spatial Assimilation: The Complex Residential Patterns of Southeast Asian Americans in the Minneapolis-St. Paul Metropolitan Area from 1990 to 2010

Although spatial assimilation has often been defined as the process whereby a group attains residential propinquity with majority members of a host society, we argue that for certain immigrant groups, substantial suburbanization does not necessarily lead to racial integration. Our analysis using data from the U.S. Census Bureau reveals that between 1990 and 2010, Southeast Asian former refugees in the Minneapolis-St. Paul Metropolitan Statistical Area experienced substantial suburbanization, which is expected given their improved socioeconomic status. However, Southeast Asians’ suburbanization has not led to residential propinquity with non-Hispanic Whites. Despite a small decline in Southeast Asians’ overall segregation at the metropolitan area level during the previous two decades, their segregation levels, as measured by the dissimilarity index, remained unchanged or increased in the central city and the suburbs. Furthermore, our findings reveal different ethnic concentration and segregation patterns among four Southeast Asian subgroups, complicating the meaning of ‘suburbanization’ as simply a process in which people move from the inner city to its less urban outskirts. The finding that substantial suburbanization coexists with high levels of segregation and ethnic concentration raises questions about the assumptions of both the spatial assimilation and place stratification models of immigrant residential processes and outcomes.

Regional innovation networks & high-growth entrepreneurship

We investigate the influence of regional innovation networks (RINs) on high-growth entrepreneurship within the framework of the knowledge spillover theory of entrepreneurship (KSTE). While previous studies have separately examined RINs' role in knowledge diffusion and the geographical characteristics of high-growth firms, the connection between these two areas remains unexplored. To address this gap, we develop a conceptual model that highlights the positive impact of RINs on high-growth entrepreneurship, moderated by regional entrepreneurial capital. Using a unique longitudinal dataset at the metropolitan statistical area (MSA) level, we employ two-way fixed effects and instrumental variables regressions to analyze the data. Our findings support the conceptual model, revealing that robust RINs facilitate high-growth entrepreneurship. Additionally, we conduct post-hoc exploratory analyses to investigate potential moderating factors, including the influence of the public policy environment.

The Effect of Market Asset Returns, Economic Conditions, and Firm Fundamentals on Net Lease Capitalization Rates

This study fills a void in the literature by examining real estate capitalization rates for single-tenant net lease (STNL) properties. First, we examine cap rate variation in relation to market and firm-level fundamentals using individual transaction data in a multistage regression approach. Second, our single-tenant dataset, which allows us to control for characteristics such as industry and tenant credit ratings, gives us unique insight into not only the pricing of cap rates, but also their underlying drivers and their relationship to market fundamentals and returns on alternative assets. Using this unique dataset of more than 8,000 single-tenant net lease retail property transactions, we develop a quarterly cap rate index controlling for Metropolitan Statistical Area (MSA) and industry fixed effects, property and lease characteristics, and localized influences such as population density and household income. Third, we examine the effect of excess corporate bond spreads, excess stock returns, stock market indicators, firm financials, and economic and demographic indicators. Finally, we examine the effect on cap rates of MSA characteristics such as size, wealth, poverty, crime, gross domestic product, and growth. The findings show that, besides the systematic risk from stock and bond returns, national and metropolitan economic forces and firm fundamental factors explain variation in cap rates.

Immigration policy shocks and infant health

This paper evaluates the effect of positive and negative immigration policy shocks on infant health outcomes in the U.S. I examine changes in mean birth weight and the incidence of low birth weight (LBW) at the metropolitan statistical area (MSA) level around two major institutional shocks: The 1986 Immigration Reform Act (IRCA), which favored immigrants, and the increase in Immigration and Customs Enforcement (ICE) agency arrests at the start of 2017 which might have put immigrants at greater risk of apprehension. It uses a triple difference approach, comparing birth outcomes of foreign-born mothers with U.S.-born mothers (relative to mothers living in MSAs with a higher and lower concentration of IRCA applications and an increase in ICE arrests) before and after the two immigration policies. I find that in MSAs that had higher IRCA applications, mean birth weight increased, and the incidence of LBW decreased by 3–6 percent for babies born to foreign-born mothers. By contrast, in MSAs that had higher ICE arrests starting in 2017, mean birth weight decreased, and the incidence of LBW increased by 3–7 percent for babies born to foreign-born mothers. The effect of the increase in ICE arrests was more pronounced among mothers who were born in Latin and Central American countries. Sub-sample analysis shows that the incidence of LBW increased by as much as 12 percent for babies born to foreign-born mothers of Hispanic origin.

The impact of remote work on green space values in regional housing markets

We examine the extent to which the increased prevalence of work from home (WFH) due to the COVID-19 pandemic has made green amenities more desirable.22Data provided by Zillow through the Zillow Transaction and Assessment Dataset (ZTRAX). More information on accessing the data can be found at http://www.zillow.com/ztrax. The results and opinions are those of the authors and do not reflect the position of the Zillow Group. Specifically, we focus on ten metropolitan statistical areas (MSAs) in the United States (Baltimore, Chicago, Cleveland, Los Angeles, Minneapolis/St. Paul, New York City, Philadelphia, Pittsburgh, St. Louis, and Tampa) and use a hedonic pricing approach to identify changes in the implicit prices of yard space and park proximity. We use a combination of data sources, including the Zillow Transaction and Assessment Database (ZTRAX), Open Street Maps, Longitudinal Employer Household Dynamics Origin–Destination Employment Statistics (LODES), National Land Cover Database (NLCD), and the Environmental Protection Agency’s EnviroAtlas to study the interactions between the exposure of a given neighborhood to WFH shock and private yard space, as well as proximity to the nearest park. Our findings suggest that home buyers in all the MSAs, except Minneapolis and Pittsburgh, assigned a greater value to private green amenities during the post-COVID period. However, for the MSAs of Los Angeles, New York, Philadelphia, and Pittsburgh, the marginal willingness to pay (MWTP) for private green spaces decreased in the post-COVID period in the areas with large WFH shocks. No significant change in the MWTP for proximity to public green spaces is noted in the post-COVID period for most MSAs. An event study of yard space reveals that most MSAs experienced an increase in the hedonic price of yard space in the post-COVID period. In the pre-COVID period, for many MSAs, the hedonic price of yard space was decreasing over the years, and this trend reversed in the post-COVID period. These results suggest that the preferred amenity bundles of people living in major cities in the U.S. have shifted as a result of changes in their commutes and work habits.

Disparities in arthroplasty utilization for rotator cuff tear arthropathy.

Rotator cuff tear arthropathy (CTA) carries a significant symptomatic burden for patients. Reverse shoulder arthroplasty (RSA) is an effective treatment intervention for CTA. Disparities in musculoskeletal medicine are well documented; however, there is a paucity of literature on how social determinants of health affect utilization rates. The purpose of this study is to determine how social determinants of health affect the utilization rates of RSA. A single-center retrospective review was conducted for adult patients diagnosed with CTA between 2015 and 2020. Patients were divided by those who underwent RSA and those who were offered RSA but did not undergo surgery. Each patient's zip code was used to determine the most specific median household income in the US Census Bureau database and compared to the multistate metropolitan statistical area median income. Income levels were defined by the US Department of Housing and Urban Development's (HUD's) 2022 Income Limits Documentation System and the Federal Reserve's (FED's) Community Reinvestment Act. Because of numeric restrictions, patients were grouped into racial cohorts of Black, White, and all other races. Patients of other races had significantly lower odds of continuing to surgery compared with White patients in models controlled for median household income (odds ratio [OR] 0.38, 95% confidence interval [CI] 0.18-0.81, P=.01), HUD's 3 income levels (OR 0.36, 95% CI 0.18-0.74, P=.01), and FED's income levels (OR 0.37, 95% CI 0.17-0.79, P=.01). There was no significantly different odds of going on to surgery between FED income levels and median household income levels, but when compared with those with low HUD income, those below median had significantly lower odds of going on to surgery (OR 0.43, 95% CI 0.23-0.80, P=.01). Although contradictory to reported health care utilization for Black patients, our study supports reported disparities in utilization for other ethnic minorities. These findings may suggest that improvements in utilization efforts targeted Black-identifying patients but not necessarily other ethnic minorities. The findings of this study can help providers understand how social determinants of health play a role in the utilization of care for CTA and direct mitigation efforts to reduce disparities in access to adequate orthopedic care.

Characterizing the Impact of Race and Redlining on Receipt of Guideline-Compliant Locoregional Therapy among Older Women with Breast Cancer

Breast cancer (BC) is one of few malignancies in which improved locoregional control has been shown to improve overall survival. Contemporary redlining, or mortgage lending bias on the basis of home location, is a form of structural racism which has been demonstrated to impact BC survival in older patients. Self-reported race has been thought to serve as a surrogate for interpersonal racism and has similarly been shown to have associations with survival, redlining, and access to care. We aim to examine the relationship between race, redlining, and the receipt of guideline compliant locoregional therapy (LRT) in older women with BC. Women aged 66-90 years with an initial Stage I-III BC diagnosis in 2010-2017 and known metropolitan statistical area (MSA) were identified in SEER-Medicare. Redlining was estimated using Home Mortgage Disclosure Act data (2010-2017). Guideline compliant LRT was assessed based on whether patients underwent surgery (lumpectomy or mastectomy), and if surgery was performed, whether they received adjuvant radiation treatment per the National Quality Forum and National Comprehensive Cancer Network guidelines in effect during the study period. A multiple logistic regression model was fitted to estimate ORs for the relationship between redlining and receipt of guideline compliant LRT, accounting for covariates (age, race/ethnicity, comorbidities, dual enrollment, tumor stage, hormone receptor status, census region, and year of diagnosis). Cluster bootstrap at the MSA-level was used to obtain P-values and confidence intervals. The cohort included 64,987 women: 31% aged 66-70, 82% Non-Hispanic (NH) White, 7.4% NH Black, 24% with 2+ comorbidities, 12% with dual Medicaid/Medicare enrollment, 60% stage I, 31% stage II, and 77% HR+/HER2-. Overall, 6.4% did not undergo surgery; 84% received guideline compliant LRT. Women in the highest redlining areas had 81% guideline compliant LRT compared to 84-85% in the least, low and moderate redlined areas (p<0.001). However, model results revealed that contemporary redlining was not a predictor of guideline compliant LRT. NH Black women were less likely than NH White women to receive guideline-compliant LRT (OR 0.77, 95% CI 0.71-0.84, p<0.001). No significant differences were noted between NH White and NH Asian or Hispanic women. In this population-based cohort of older women with breast cancer, NH Black race, even after adjusting for several important clinical and demographic factors, was associated with a lower likelihood of receiving guideline-compliant LRT. This finding demonstrates the profound impact of interpersonal racism on receipt of cancer-directed therapies. Though contemporary redlining did not significantly impact guideline-compliant LRT, further work is needed to identify systematic factors explaining known associations between contemporary redlining and BC survival.

Examining the geographical distribution of air pollution disparities across different racial and ethnic groups: Incorporating workplace addresses

BackgroundMost previous studies on air pollution exposure disparities among racial and ethnic groups in the US have been limited to residence-based exposure and have given little consideration to population mobility and spatial patterns of residences, workplaces, and air pollution. This study aimed to examine air pollution exposure disparities by racial and ethnic groups while explicitly accounting for both the work-related activity of the population and localized spatial patterns of residential segregation, clustering of workplaces, and variability of air pollutant concentration. MethodIn the present study, we assessed population-level exposure to air pollution using tabulated residence and workplace addresses of formally employed workers from LEHD Origin-Destination Employment Statistics (LODES) data at the census tract level across eight Metropolitan Statistical Areas (MSAs). Combined with annual-averaged predictions for three air pollutants (PM2.5, NO2, O3), we investigated racial and ethnic disparities in air pollution exposures at home and workplaces using pooled (i.e., across eight MSAs) and regional (i.e., with each MSA) data. ResultsWe found that non-White groups consistently had the highest levels of exposure to all three air pollutants, at both their residential and workplace locations. Narrower exposure disparities were found at workplaces than residences across all three air pollutants in the pooled estimates, due to substantially lower workplace segregation than residential segregation. We also observed that racial disparities in air pollution exposure and the effect of considering work-related activity in the exposure assessment varied by region, due to both the levels and patterns of segregation in the environments where people spend their time and the local heterogeneity of air pollutants. ConclusionsThe results indicated that accounting for workplace activity illuminates important variation between home- and workplace-based air pollution exposure among racial and ethnic groups, especially in the case of NO2. Our findings suggest that consideration of both activity patterns and place-based exposure is important to improve our understanding of population-level air pollution exposure disparities, and consequently to health disparities that are closely linked to air pollution exposure.

Do Firms Mimic Industry Leaders’ Accounting? Evidence from Financial Statement Comparability

ABSTRACT Following management theory on organizational legitimacy, we predict that managers mimic the accounting of industry-leading companies to gain legitimacy. Such demand for legitimacy is expected to be greater for new managers because stakeholders are more uncertain about the managers’ ability. Using a sample of CEO turnovers, we find that a firm increases financial statement comparability with industry leaders after the new CEO assumes office. This relation is stronger when (1) new managers lack executive experience at larger firms, are younger, or belong to an underrepresented group (i.e., are female or nonwhite); (2) networks that facilitate imitation are more intense, such as when firms and peers are located in the same metropolitan statistical area (MSA) and when they share auditors or blockholders; and (3) firms’ operating environments are more volatile. These findings support the idea that CEOs’ demand for legitimacy leads to more comparable accounting. Data Availability: Data are available from the public sources cited in the text.

Differential contagiousness of respiratory disease across the United States

The initial contagiousness of a communicable disease within a given population is quantified by the basic reproduction number, R0. This number depends on both pathogen and population properties. On the basis of compartmental models that reproduce Coronavirus Disease 2019 (COVID-19) surveillance data, we used Bayesian inference and the next-generation matrix approach to estimate region-specific R0 values for 280 of 384 metropolitan statistical areas (MSAs) in the United States (US), which account for 95% of the US population living in urban areas and 82% of the total population. We focused on MSA populations after finding that these populations were more uniformly impacted by COVID-19 than state populations. Our maximum a posteriori (MAP) estimates for R0 range from 1.9 to 7.7 and quantify the relative susceptibilities of regional populations to spread of respiratory diseases. One-Sentence SummaryInitial contagiousness of Coronavirus Disease 2019 varied over a 4-fold range across urban areas of the United States.

Long-Run Labor Costs of Housing Booms and Busts

Abstract We show large flows of workers into the real estate agent (REA) occupation during the early 2000s from virtually all parts of the skill, wage, and education spectrums. We find those entering REA in Metropolitan Statistical Areas (MSAs) with house price bubbles end up in occupations paying significantly less in the long-run as compared to similar REA entrants in non-bubble areas. Even in 2017, when house prices and employment return to their pre-crisis levels, REA entrants in Bubble MSAs are in occupations earning about 6% less. These results point to lasting effects of labor allocation decisions in response to distorted price signals.

A machine learning approach for predicting hurricane evacuee destination location using smartphone location data

Evacuation destination choice modeling is an integral aspect of evacuation planning. Outputs from such models are required to estimate the clearance times on which evacuation orders are based. The number of evacuees arriving at each destination also informs allocation of resources and shelter planning. Despite its importance, evacuee destination modeling has not received as much attention as identifying who evacuates and when. In this study, we present a new approach to identify evacuees and determine where they go and when using privacy-enhanced smartphone location data. We demonstrate the method using data from four recent U.S. hurricanes affecting multiple geographies (Florence 2018, Michael 2018, Dorian 2019, and Ida 2021). We then build on those results to develop a new machine learning model that predicts the number of evacuees that move between pairs of metropolitan statistical areas. The machine learning model incorporates hurricane characteristics, which have not been thoroughly exploited by existing methods. The model’s predictive power is comprehensively evaluated through a tenfold cross validation, holdout validation using Hurricane Ida (2021), and comparison with the traditional gravity model. Results suggest that the new model substantially outperforms the traditional gravity model across all performance indicators. Analysis of feature importance in the machine learning model indicates that in addition to distance and population, hurricane characteristics are important in evacuee destination choices.

Isolation and survival: The impact of local and MSA isolation on survival among non-Hispanic Black women diagnosed with breast cancer in the United States using a SEER-Medicare cohort.

Residential segregation is an important factor that negatively impacts cancer disparities, yet studies yield mixed results and complicate clear recommendations for policy change and public health intervention. In this study, we examined the relationship between local and Metropolitan Statistical Area (MSA) measures of Black isolation (segregation) and survival among older non-Hispanic (NH) Black women with breast cancer (BC) in the United States. We hypothesized that the influence of local isolation on mortality varies based on MSA isolation-specifically, that high local isolation may be protective in the context of highly segregated MSAs, as ethnic density may offer opportunities for social support and buffer racialized groups from the harmful influences of racism. Local and MSA measures of isolation were linked by Census Tract (CT) with a SEER-Medicare cohort of 5,231 NH Black women aged 66-90 years with an initial diagnosis of stage I-IV BC in 2007-2013 with follow-up through 2018. Proportional and cause-specific hazards models and estimated marginal means were used to examine the relationship between local and MSA isolation and all-cause and BC-specific mortality, accounting for covariates (age, comorbidities, tumor stage, and hormone receptor status). Of 2,599 NH Black women who died, 40.0% died from BC. Women experienced increased risk for all-cause mortality when living in either high local (HR=1.20; CI=1.08-1.33; p<0.001) or high MSA isolation (HR=1.40; CI=1.17-1.67; p<0.001). A similar trend existed for BC-specific mortality. Pairwise comparisons for all-cause mortality models showed that high local isolation was hazardous in less isolated MSAs but was not significant in more isolated MSAs. Both local and MSA isolation are independently associated with poorer overall and BC-specific survival for older NH Black women. However, the impact of local isolation on survival appears to depend on the metropolitan area's level of segregation. Specifically, in highly segregated MSAs, living in an area with high local isolation is not significantly associated with poorer survival. While the reasons for this are not ascertained in this study, it is possible that the protective qualities of ethnic density (e.g., social support and buffering from experiences of racism) may have a greater role in more segregated MSAs, serving as a counterpart to the hazardous qualities of local isolation. More research is needed to fully understand these complex relationships. National Cancer Institute.