Neighborhood Economic Change in an Era of Metropolitan Divergence

2021 ◽  
pp. 107808742110169
Author(s):  
Jared N. Schachner
Keyword(s):  
Fixed Effects ◽  
Multilevel Models ◽  
Economic Change ◽  
Neighborhood Change ◽  
Median Income ◽  
Multiple Sources ◽  
Income Segregation ◽  
Great Divergence ◽  
Political Conditions ◽  
Key Driver

This study foregrounds the metropolitan area as a key driver of contemporary neighborhood economic change. The recent “Great Divergence” in metros’ economic, social, and political conditions suggests metros increasingly stratify neighborhood trajectories. Yet, many studies only consider neighborhood-level predictors of change or implicate metro-level factors more applicable to the twentieth century (e.g., manufacturing exposure) than the twenty first. To clarify the metro's contemporary role, this study synthesizes multiple literatures, deriving novel hypotheses that link metropolitan skill agglomeration and income segregation to neighborhood economic change, and then tests them using multilevel models and data drawn from multiple sources, including the census, Opportunity Insights, and National Transit Database. Analyses, based on all neighborhoods within 325 metros between 2000 and the mid-2010s, suggest over 10% of the variance in neighborhood median income change resides between, rather than within, metros. As predicted, metro skill agglomeration dynamics appear to boost neighborhoods’ median incomes, and metro income segregation depresses them. Results remain intact after accounting for state fixed effects and controls for five plausible alternative explanations of metro effects. Overall, the study provides a theoretical and empirical foundation for future neighborhood change research highlighting the metro in general, and two higher order spatial processes—income segregation and skill agglomeration—in particular.

scholarly journals Understanding, Choosing, and Unifying Multilevel and Fixed Effect Approaches

2020 ◽  
pp. 1-20
Author(s):  
Chad Hazlett ◽  
Leonard Wainstein
Keyword(s):  
Random Effects ◽  
Fixed Effects ◽  
Multilevel Models ◽  
Standard Errors ◽  
Point Estimate ◽  
Fe Model ◽  
Grouped Data ◽  
Coefficient Estimates ◽  
Political Science Education ◽  
Robust Standard Errors

Abstract When working with grouped data, investigators may choose between “fixed effects” models (FE) with specialized (e.g., cluster-robust) standard errors, or “multilevel models” (MLMs) employing “random effects.” We review the claims given in published works regarding this choice, then clarify how these approaches work and compare by showing that: (i) random effects employed in MLMs are simply “regularized” fixed effects; (ii) unmodified MLMs are consequently susceptible to bias—but there is a longstanding remedy; and (iii) the “default” MLM standard errors rely on narrow assumptions that can lead to undercoverage in many settings. Our review of over 100 papers using MLM in political science, education, and sociology show that these “known” concerns have been widely ignored in practice. We describe how to debias MLM’s coefficient estimates, and provide an option to more flexibly estimate their standard errors. Most illuminating, once MLMs are adjusted in these two ways the point estimate and standard error for the target coefficient are exactly equal to those of the analogous FE model with cluster-robust standard errors. For investigators working with observational data and who are interested only in inference on the target coefficient, either approach is equally appropriate and preferable to uncorrected MLM.

scholarly journals Spatial income inequality in Toronto: a longitudinal study

2021 ◽  
Author(s):  
Candace Safonovs
Keyword(s):  
Income Inequality ◽  
Fixed Effects ◽  
Spillover Effects ◽  
Spatial Inequality ◽  
Income Segregation ◽  
Inequality Measures ◽  
Housing Characteristics ◽  
Fixed Effects Panel Regression ◽  
Macro Scale ◽  
Housing Regulation

This paper examines the trends and changes in both spatial and non-spatial income inequality in the Toronto CMA between 1985 and 2015 at various geographic scales, including both within and between neighbourhoods. Fixed effects panel regression models are used to uncover which local demographic and housing characteristics are most significant in explaining changes in inequality within neighbourhoods over time. Findings indicate that macro-scale income segregation among neighbourhoods has declined, while micro-scale intra-neighbourhood income segregation has increased since 1985. Further, compared to overall changes in income inequality in the region, neighbourhoods have become more homogenous in terms of their household income distribution. Thus, neighbourhood sorting by households based on income has increased since 1985. Consistent with extant literature, local housing characteristics have spillover effects on income segregation. Specifically, variables associated with greater housing affluence are negatively correlated with intra-neighbourhood inequality measures, and thus positively correlated with income homogenization. This confirms and adds to the literature that local land use regulations impact income spatial inequality. KEYWORDS Spatial Income Inequality; Segregation; Neighbourhoods; Toronto CMA; Fixed Effects Models; Quantitative Analysis; GIS; Housing Regulation

The Cuban Civic Movement

2006 ◽  
Vol 18 (1) ◽  
pp. 157-170
Author(s):  
Orlando Gutierrez-Boronat ◽  
Keyword(s):  
Civil Society ◽  
Communist Party ◽  
First Generation ◽  
Economic Change ◽  
Current Status ◽  
Social Origins ◽  
Totalitarian Regime ◽  
Popular Participation ◽  
Revolutionary Movement ◽  
Political Conditions

During the 1990s, the dissident movement in Cuba has grown in effectiveness, popular participation, and intemational support. While facing a first-generation totalitarian regime, with a sophisticated repressive apparatus, the civic movement in the Island has persevered and grown in spite of constant persecution, offering hope for political, social, and economic change from within Cuba itself. This essay seeks to provide a brief overview of the civic movement in Cuba covering its social origins and growth, theoretical repercussions of its existence, major leaders and initiatives, its relationship with the Cuban exile community, its ideological history and development, intemational support, and its current status in light of recent events affecting political conditions in the Island. Born initially out of dissident cells within Cuba's revolutionary movement and the Communist Party, the dissident movement in Cuba has transformed itself into a microcosm of a re-emerging civil society through which Cuban citizens are reclaiming their sovereignity and constructing the blueprint for a new Republic. The Varela Project is of particular significance for the development of the civic movement in Cuba.

Transition to Retirement and Use of Private Health Care

2016 ◽  
Vol 47 (2) ◽  
pp. 312-332 ◽  
Author(s):  
Jenni Blomgren
Keyword(s):  
Health Care ◽  
Fixed Effects ◽  
Health Care Services ◽  
Health Care Use ◽  
Median Income ◽  
Private Health Care ◽  
Universal System ◽  
Private Care ◽  
Care Services ◽  
Demographic Covariates

Associations between retirement and changes in health care use have not been shown in a longitudinal setting. In the Finnish universal health care system, transition into retirement from employment entails loss of access to occupational health care that provides easily accessible primary health care services, which may cause changes in utilization of other health care sectors. The aim of this study was to find out whether transition into old-age retirement is associated with change in utilization of private health care. The panel data consist of a 30% random sample of the Finnish population aged 62–75 in 2006–2011. Register data on National Health Insurance compensation were linked to socio-demographic covariates. Fixed-effects logistic and Poisson regression models were used. Adjusted for changes in covariates, retirement from employment was associated especially with private general practitioner visits but also with specialist visits among both women and men. Interaction analyses showed that retirement was associated with an increase in private care use only among those with higher-than-median income. The results may indicate preferences for quick access to care, mistrust toward the universal system, and problems of the public system in delivering needed services.

scholarly journals The effects of misspecifying the random part of multilevel models

Methodology ◽  
2020 ◽  
Vol 16 (3) ◽  
pp. 224-240
Author(s):  
David M. LaHuis ◽  
Daniel R. Jenkins ◽  
Michael J. Hartman ◽  
Shotaro Hakoyama ◽  
Patrick C. Clark
Keyword(s):  
Effect Size ◽  
Fixed Effects ◽  
Multilevel Model ◽  
Multilevel Models ◽  
The Other ◽  
Standard Errors ◽  
Moderate Effect Size ◽  
Moderate Effect ◽  
Level 1 ◽  
Random Part

This paper examined the amount bias in standard errors for fixed effects when the random part of a multilevel model is misspecified. Study 1 examined the effects of misspecification for a model with one Level 1 predictor. Results indicated that misspecifying random slope variance as fixed had a moderate effect size on the standard errors of the fixed effects and had a greater effect than misspecifying fixed slopes as random. In Study 2, a second Level 1 predictor was added and allowed for the examination of the effects of misspecifying the slope variance of one predictor on the standard errors for the fixed effects of the other predictor. Results indicated that only the standard errors of coefficient relevant to that predictor were impacted and that the effect size for the bias could be considered moderate to large. These results suggest that researchers can use a piecemeal approach to testing multilevel models with random effects.

scholarly journals Does a Rising Median Income Lift All Birth Weights? County Median Income Changes and Low Birth Weight Incidence Rates for Black and White Americans

2019 ◽  
Author(s):  
David Stuart Curtis ◽  
Thomas E Fuller-Rowell ◽  
Daniel L. Carlson ◽  
Ming Wen ◽  
Michael R. Kramer
Keyword(s):  
Risk Factors ◽  
Birth Weight ◽  
Low Birth Weight ◽  
Fixed Effects ◽  
Health Resources ◽  
Incidence Rates ◽  
Median Income ◽  
Time Varying ◽  
Smoking During Pregnancy ◽  
Black And White

Differences in low birth weight incidence (LBW) by race and place are long-standing, often embedded in enduring social ecologies where insufficient health resources are paired with an array of risk factors. Local or group-specific economic resources are known to be a fundamental contributor to these social ecologies, yet few studies have investigated how within-area changing economic conditions are linked to birth outcomes. This study examines county-level change in median income and black-white income differences as predictors of LBW incidence and LBW racial disparities. Time-varying county prevalence and black-white differences in maternal sociodemographic and health risk factors (e.g., non-marital childbearing, smoking during pregnancy) are considered as explanations for income estimates. Data come from U.S. birth records for approximately 24.8 million non-Hispanic black and white mothers with a singleton live birth (1992-2014). Records were aggregated in three-year county-period measurements for the 732 counties meeting eligibility requirements. Based on county by period fixed effects models, a $10,000 increase in county median income was associated with a reduction in LBW incidence of 2.7 per 1000 live births, and in the black-white LBW gap by 5.6 per 1000. Time-varying county maternal sociodemographic and health risks attenuated the link between median income and LBW by 72% and 31%, respectively, but not the association between median income and the racial LBW gap. Contrary to our hypothesis, conditioning on median income changes, a widening racial income difference was associated with a smaller black-white LBW gap (a finding explored in post hoc analyses). Our results suggest that, if successful in raising median income, local government efforts to stimulate economic growth and employment opportunities are likely to reduce both population incidence and black-white differences in LBW. [This draft paper is intended for review and comments only. It is not intended for citation, quotation, or other use in any form]

scholarly journals Modeling Error in Quantitative Macro-Comparative Research

2009 ◽  
pp. 86-114 ◽  
Author(s):  
Salvatore Babones
Keyword(s):  
Comparative Research ◽  
Fixed Effects ◽  
Multilevel Models ◽  
Published Data ◽  
Science Literature ◽  
Cross Sectional ◽  
Random Effects Models ◽  
Large Panels ◽  
Differences Of Opinion ◽  
Spurious Results

Much quantitative macro-comparative research (QMCR) relies on a common set of published data sources to answer similar research questions using a limited number of statistical tools. Since all researchers have access to much the same data, one might expect quick convergence of opinion on most topics. In reality, of course, differences of opinion abound and persist. Many of these differences can be traced, implicitly or explicitly, to the different ways researchers choose to model error in their analyses. Much careful attention has been paid in the political science literature to the error structures characteristic of time series cross-sectional (TSCE) data, but much less attention has been paid to the modeling of error in broadly cross-national research involving large panels of countries observed at limited numbers of time points. Here, and especially in the sociology literature, multilevel modeling has become a hegemonic – but often poorly understood – research tool. I argue that widely-used types of multilevel models, commonly known as fixed effects models (FEMs) and random effects models (REMs), can produce wildly spurious results when applied to trended data due to mis-specification of error. I suggest that in most commonly-encountered scenarios, difference models are more appropriate for use in QMC.

scholarly journals Getting the Within Estimator of Cross-Level Interactions in Multilevel Models with Pooled Cross-Sections: Why Country Dummies (Sometimes) Do Not Do the Job

2018 ◽  
Vol 49 (1) ◽  
pp. 190-219 ◽  
Author(s):  
Marco Giesselmann ◽  
Alexander W. Schmidt-Catran
Keyword(s):  
Cross Sections ◽  
Fixed Effects ◽  
Multilevel Models ◽  
Unobserved Heterogeneity ◽  
Level Structure ◽  
Cross Sectional Survey ◽  
Social Scientists ◽  
Cross Sectional ◽  
Country Level ◽  
Fixed Effects Estimator

Multilevel models with persons nested in countries are increasingly popular in cross-country research. Recently, social scientists have started to analyze data with a three-level structure: persons at level 1, nested in year-specific country samples at level 2, nested in countries at level 3. By using a country fixed-effects estimator, or an alternative equivalent specification in a random-effects framework, this structure is increasingly used to estimate within-country effects in order to control for unobserved heterogeneity. For the main effects of country-level characteristics, such estimators have been shown to have desirable statistical properties. However, estimators of cross-level interactions in these models are not exhibiting these attractive properties: as algebraic transformations show, they are not independent of between-country variation and thus carry country-specific heterogeneity. Monte Carlo experiments consistently reveal the standard approaches to within estimation to provide biased estimates of cross-level interactions in the presence of an unobserved correlated moderator at the country level. To obtain an unbiased within-country estimator of a cross-level interaction, effect heterogeneity must be systematically controlled. By replicating a published analysis, we demonstrate the relevance of this extended country fixed-effects estimator in research practice. The intent of this article is to provide advice for multilevel practitioners, who will be increasingly confronted with the availability of pooled cross-sectional survey data.

Uneven Neighborhood Recovery: Hurricane Damage and Neighborhood Change in the Houston–Galveston Region since 1970

2019 ◽  
Vol 18 (2) ◽  
pp. 689-709 ◽  
Author(s):  
Christopher K. Wyczalkowski ◽  
Eric J. van Holm ◽  
Ann–Margaret Esnard ◽  
Betty S. Lai
Keyword(s):  
Socioeconomic Status ◽  
Low Socioeconomic Status ◽  
Low Income ◽  
Fixed Effects ◽  
Natural Hazard ◽  
Census Data ◽  
Neighborhood Change ◽  
Low Ses ◽  
The Impact ◽  
Rent Gap Theory

Despite the growing number of natural disasters around the globe, limited research exists on post–disaster patterns of neighborhood change. In this paper, we test two theories of neighborhood change, the “recovery machine” and “rent gap,” which predict opposing effects for low socioeconomic status (SES) neighborhoods following damage from hurricanes, tropical storms, and other natural hazard events. The recovery machine theory posits that after natural hazard events, local communities experience patterns of recovery based on their pre–disaster SES and access to resources, suggesting that wealthier neighborhoods will recover robustly while lower status neighborhoods languish. In contrast, the rent gap theory suggests that developers will identify a profit opportunity in the depressed values created by damage from natural hazard events, and seek to redevelop low SES areas. We use fixed effects models with census data from 1970 to 2015 to test the impact of damage from natural hazards on neighborhood change. We find substantial recovery and change in low–income neighborhoods, but not in the high–income neighborhoods supporting the rent gap theory. We conclude that natural hazard events resulting in damage produce uneven recovery by socioeconomic status of neighborhoods, potentially leading to displacement of low SES groups.

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