Levels Of Geographic Aggregation Research Articles

MEASURING MULTIGENERATIONAL NEIGHBORHOODS

Abstract There are growing concerns that societies are becoming more generationally divided and that this is having negative social consequences, such as increasing loneliness and social isolation. Residential age segregation as a key driver of this generational divide and there is worrying evidence that levels of residential age segregation have increased in the USA, UK and Europe. However, measures of age segregation have a number of methodological limitations: i) they only compare younger and older age groups and therefore overlook the wider generational mix in the neighbourhood; ii) different measures of young/older age groups are used in different studies, iii) different geographical scales, e.g. wards, districts, etc., are used in different studies and iv) measures are often at a high level of geographical aggregation, e.g. local authorities. To address these issues we have developed a new measure of multigenerational neighbourhoods, based on an ecological measure used to calculate biodiversity, for all neighbourhoods in England and Wales from 2011-2020. Our analyses shows that i) there is a low correlation between measures of age segregation and neighbourhood multigenerationality and ii) contrary to some findings on age segregation, the vast majority of people in England and Wales live in multigenerational neighbourhoods. However, some areas have seen a drop in multigenerationality, especially those in inner city and costal areas.

Understanding the association between county-level unemployment and health stratified by education and income in the southwestern United States

Past research on the relationship between unemployment rates and population health has produced mixed findings. The relationship can be influenced by the kinds of health outcomes observed, time frame, level of geographic aggregation, and other factors. Given these mixed findings, there is a need to add to our knowledge about how unemployment rates and population health are related. There is limited research that examines the association of unemployment rates with both physical and mental health, while simultaneously stratifying populations by income and education levels. Using survey-based self-reported data, this first population-based study examined the association between unemployment rates and physically and mentally unhealthy days in the southwestern United States, by county-level stratification of income (high and low) as well as education (high and low), from 2015 to 2019. After controlling for covariates, associations were modelled using negative binomial regression, with autocorrelative residuals, and were reported as rate ratios (RR). Overall, we found that a 1% rise in unemployment rates was significantly associated with an increase in physically unhealthy days [adjusted RR 1.007; 95% CI, 1.004–1.011, P < 0.001] and mentally unhealthy days [RR 1.006; 95% CI, 1.003–1.009, P  < 0.001]. Upon stratification, a significant risk was found among the high education and high income category [RR 1.035; 95% CI, 1.021–1.049, P  < 0.001], as well as for the high education and low income category [RR 1.026; 95% CI, 1.013–1.040, P  < 0.001]. A better understanding of how unemployment is associated with the health of communities with different education and income levels could help reduce the burden on society through tailored interventions and social policies not only in the United States, but also in other developed nations.

Community-level ethnic diversity and community-level socio-economic development: evidence from 20 African countries

ABSTRACT There is evidence for a strong negative association between country-level socio-economic development and country-level ethnic diversity. One explanation for this association is that diversity is associated with less social capital, hampering co-operative economic activity and good governance. However, evidence at lower levels of geographical aggregation is mixed, with some evidence that community-level development is positively associated with community-level diversity. One explanation for this difference is that repeated inter-group contact mitigates the negative consequences of diversity and promotes the adoption of capacity-enhancing innovative practices. This paper uses household survey data from 20 African countries to explore the association of community-level development with both community-level diversity and diversity at a higher level of geographical aggregation. Within this single dataset, we find strong evidence that the first association is positive and the second is negative. We also provide some evidence that the positive association is at least partly explained by an innovation channel.

Carbon tax pass‐through in Canadian retail gasoline markets

AbstractA carbon tax's pass‐through is one factor influencing its effectiveness in internalizing the externality created by greenhouse gas emissions. This paper measures the pass‐through of carbon taxes introduced in retail gasoline markets of four Canadian provinces that did not meet the carbon pollution pricing federal benchmark stringency requirements. Those four provinces are Saskatchewan, Manitoba, Ontario and New Brunswick. Using daily retail gasoline prices for 40 treated cities and nine control cities we find the pass‐through rates are city‐specific and vary from 0% to over 140%. City‐specific pass‐through rates imply that estimations at a higher level of geographical aggregation assume homogeneous effects where heterogeneous effects might be present. Our results also suggest it would be difficult for a government to impose an optimal and nationwide carbon tax on automotive greenhouse gas emissions. Although the degree of competition can explain city‐specific pass‐through rates, it cannot explain over‐shifting. Over‐shifting can be explained, however, by the demand functional form.

SPATIAL CONCENTRATION AND FIRM-LEVEL PRODUCTIVITY IN KAZAKHSTAN

Purpose of the research. This paper studies the effect of spatial agglomeration on firms’ total factor productivity in Kazakhstan using panel data from 2009 to 2017.Methodology. We employ a two-stage estimation strategy and control for endogeneity biases by making use of the GMM approach. The firm-level data is obtained from the Bureau of National Statistics of the Agency for Strategic Planning and Reforms of the Republic of Kazakhstan.Originality / value of the research. This study contributes to an empirical study of spatial concentration and firm-level productivity in developing countries and provides valuable insights for policymakers to consider before implementing government programs.Findings. The results suggest that productivity increases with clustering: a 10 % increase in the number of employees of the neighboring firms inside the same industry increases firm-level productivity by 1.36 %, while a 10 % increase in employment in other industries enhances firm performance by 1.95 %. The productivity gains are higher at the 2-digit regional level rather than at the 9-digit sub-regional level of geographical aggregation, implying that the denser geography increases firms’ performance.

A framework for estimating commute accessibility and adoption of ridehailing services under functional improvements from vehicle automation

This paper develops an analytical framework to estimate commute accessibility and adoption of various ridehailing service concepts across the US by synthesizing individual commute trips using national Longitudinal Employer-Household Dynamics Origin-Destination Employment Statistics (LODES) data. Focusing on potential improvements in cost and time that could be enabled by vehicle automation, we use this modeling framework to simulate a lower-price autonomous service (e.g., 50% or 75% lower) with variable wait times and implementation levels (solo, pooled, and first/last mile transit connections services, alone or in combination) to determine how they might affect adoption rates. These results are compared across metrics of accessibility and trip density, as well as socioeconomic factors such as household income. We find – unsurprisingly – that major cities (e.g. New York, Los Angeles, and Chicago) support the highest adoption rates for ridehailing services. Decreases in price tend to increase market share and accessibility. The effect of a decrease in price is more drastic for lower income groups. The proposed method for synthesizing trips using the LODES contributes to current travel demand forecasting methods and the proposed analytic framework can be flexibly implemented with any other mode choice model, extended to non-commute trips, or applied to different levels of geographic aggregation.

A Monte Carlo analysis of false inference in spatial conflict event studies.

Spatial event data is heavily used in contemporary research on political violence. Such data are oftentimes mapped onto grid-cells or administrative regions to draw inference about the determinants of conflict intensity. This setup can identify geographic determinants of violence, but is also prone to methodological issues. Problems resulting from spatial aggregation and dependence have been raised in methodological studies, but are rarely accounted for in applied research. As a consequence, we know little about the empirical relevance of these general problems and the trustworthiness of a popular research design. We address these questions by simulating conflict events based on spatial covariates from seven high-profile conflicts. We find that standard designs fail to deliver reliable inference even under ideal conditions at alarming rates. We also test a set of statistical remedies which strongly improve the results: Controlling for the geographic area of spatial units eliminates an important source of spurious correlation. In time-series analyses, the same result can be achieved with unit-level fixed effects. Under outcome diffusion, spatial lag models with area controls produce most reliable inference. When those are computationally intractable, geographically larger aggregations lead to similar improvements. Generally, all analyses should be performed at two separate levels of geographic aggregation. To facilitate future research into geographic methods, we release the Simple Conflict Event Generator (SCEG) developed for this analysis.

Depicting socioeconomic disadvantage and social gradient in quality of life in Limassol, Cyprus

Abstract Background The lack of validated and accepted area-level indices of socioeconomic disadvantage at national or regional level in Cyprus poses a barrier to “spatial thinking” among Public Health policy-makers and researchers. Methods A composite index of socioeconomic disadvantage was constructed by exploring the factor structure of 28 commonly used census indicators at city quarter (N = 38, median population 3894, IQR 2570-6169) and postcode level (N = 124, median 1320, IQR 798-2014). Predictive validity was explored in mixed random-effect regression models in term of associations with SF-36 health-related quality of life among a stratified random sample aged 45-64 (N = 450, 45 neighbourhoods). Results There was wide variability across communities in all indicators, for instance, 0.4%-25.9% unemployment, 41.1%-90.7% at most secondary level education, 3.6%-95.9% residences built prior to 1980. Two factors were identified: “socioeconomic disadvantage” (SED), with the same set of 11-12 indicators at the two levels of geographical aggregation, and “built environment”, reflecting proximity to the city centre, with nevertheless no meaningful associations with the outcome of interest. The SED index captured a stepwise decrease in physical (-1.39 95%CI -2.06, -0.72) and mental health (-1.26 95% -1.98, -0.54) scores across quartiles of postcodes with increasing disadvantage, of similar clinically significant effect size by gender. Associations attenuated slightly but remained significant after adjusting for person-based indicators of social position, with some weak evidence of cross-level interactions for physical, but not mental, health. Conclusions The composite index, tapping on educational, economic, material, occupational and social disadvantage, allowed the exploration of socioeconomic disadvantage patterning at a small-area level in the city of Limassol and documented the social gradient in health-related quality of life among a late working-life sample of residents. Key messages First-time depiction of the geographical patterning of socioeconomic disadvantage across the small geographical level of postcodes in the city of Limassol, Cyprus. Social gradient in physical and mental health in men and women across the continuum of socioeconomic disadvantage over and above person-based indicators of social position.

Bayesian approach for sample size determination, illustrated with Soil Health Card data of Andhra Pradesh (India)

A crucial decision in designing a spatial sample for soil survey is the number of sampling locations required to answer, with sufficient accuracy and precision, the questions posed by decision makers at different levels of geographic aggregation. In the Indian Soil Health Card (SHC) scheme, many thousands of locations are sampled per district. In this paper the SHC data are used to estimate the mean of a soil property within a defined study area, e.g., a district, or the areal fraction of the study area where some condition is satisfied, e.g., exceedence of a critical level. The central question is whether this large sample size is needed for this aim. The sample size required for a given maximum length of a confidence interval can be computed with formulas from classical sampling theory, using a prior estimate of the variance of the property of interest within the study area. Similarly, for the areal fraction a prior estimate of this fraction is required. In practice we are uncertain about these prior estimates, and our uncertainty is not accounted for in classical sample size determination (SSD). This deficiency can be overcome with a Bayesian approach, in which the prior estimate of the variance or areal fraction is replaced by a prior distribution. Once new data from the sample are available, this prior distribution is updated to a posterior distribution using Bayes’ rule. The apparent problem with a Bayesian approach prior to a sampling campaign is that the data are not yet available. This dilemma can be solved by computing, for a given sample size, the predictive distribution of the data, given a prior distribution on the population and design parameter. Thus we do not have a single vector with data values, but a finite or infinite set of possible data vectors. As a consequence, we have as many posterior distribution functions as we have data vectors. This leads to a probability distribution of lengths or coverages of Bayesian credible intervals, from which various criteria for SSD can be derived. Besides the fully Bayesian approach, a mixed Bayesian-likelihood approach for SSD is available. This is of interest when, after the data have been collected, we prefer to estimate the mean from these data only, using the frequentist approach, ignoring the prior distribution. The fully Bayesian and mixed Bayesian-likelihood approach are illustrated for estimating the mean of log-transformed Zn and the areal fraction with Zn-deficiency, defined as Zn concentration <0.9 mg kg −1, in the thirteen districts of Andhra Pradesh state. The SHC data from 2015–2017 are used to derive prior distributions. For all districts the Bayesian and mixed Bayesian-likelihood sample sizes are much smaller than the current sample sizes. The hyperparameters of the prior distributions have a strong effect on the sample sizes. We discuss methods to deal with this. Even at the mandal (sub-district) level the sample size can almost always be reduced substantially. Clearly SHC over-sampled, and here we show how to reduce the effort while still providing information required for decision-making. R scripts for SSD are provided as supplementary material.

Too close for comfort? Microgeography of agglomeration economies in the United Kingdom

AbstractThe issue of whether firm productivity is affected by agglomeration externalities is a longstanding area of research. However, the appropriate geographical level to better detect the effects of agglomeration economies and at which level these externalities work is still unclear. Using detailed firm‐level longitudinal data on 4927 manufacturing firms in the United Kingdom over the period 2008–2016, we investigate the relation between the microgeography of external agglomeration economies and firm productivity. We compare different geographical levels: city‐wide and narrowly defined neighborhoods around a firm. Results from a multilevel (mixed‐effect) model show that urbanization externalities play a role at a higher level of geographical aggregation, such as the city, whereas localization externalities operate at a finer level, within the city and in a closer neighborhood to the firm. Failing to control for more granular levels of geography results in confounding the two types of externalities. We also provide novel evidence that these externalities vary across firm (such as age, size, and productivity) and location (such as population density) characteristics.

Transition to agriculture and first state presence: A global analysis

It has often been observed that the emergence of states in a region is typically preceded by an earlier transition to agricultural production. Using new data on the date of first state emergence within contemporary countries, we present a global scale analysis of the chronological relationship between the transition to agriculture and the subsequent emergence of states. We find statistically significant relationships between early reliance on agriculture and state age in all sub-samples and also when we use alternative sources of data at different levels of geographical aggregation. A one millennium earlier transition to agriculture among non-pristine states predicts a 317-430 year earlier state emergence. We uncover differences in cases where states were imposed from outside or when they emerged through internal origination. The agriculture-state lag is on average 3.1 millennia in internally originated (including pristine) states, and 2.7 millennia in externally originated states. We also explore some of the mechanisms through which agriculture is believed to have influenced the emergence of states. Our results indicate that the rise of social classes was often an intermediate step towards the presence of early states.

Studying the social determinants of COVID-19 in a data vacuum.

Race‐based and other demographic information on COVID‐19 patients is not being collected consistently across provinces in Canada. Therefore, whether the burden of COVID‐19 is falling disproportionately on the shoulders of particular demographic groups is relatively unknown. In this article, we first provide an overview of the available geographic and demographic data related to COVID‐19. We then make creative use of these existing data to fill the vacuum and identify key demographic risk factors for COVID‐19 across Canada's health regions. Drawing on COVID‐19 counts and tabular census data, we examine the association between communities’ demographic composition and the number of COVID‐19 infections. COVID‐19 infections are higher in communities with larger shares of Black and low‐income residents. Our approach offers a way for researchers and policymakers to use existing data to identify communities nationwide that are vulnerable to the pandemic in the absence of more detailed demographic and more granular geographic data.

The uneven transition towards universal literacy in Spain, 1860–1930

ABSTRACT This study provides new evidence on the advance of literacy in Spain during the period 1860–1930. A novel dataset, built with historical information from the Spanish population censuses (over 8000 municipalities), enables this process to be described in detail from the end of the Ancien Régime to the Second Republic. The study thus presents stylised facts at a very low level of geographic aggregation, thereby permitting a closer examination of the main patterns. Overall, spatial differences in literacy were sizeable during the whole nineteenth century. Furthermore, these disparities were only significantly reduced between 1900 and 1930 when the growing demand for these basic skills was met by stronger government intervention.

Geographic Dispersion of Economic Shocks: Evidence from the Fracking Revolution: Comment

Feyrer, Mansur, and Sacerdote (2017) estimates the spatial dispersion of the effects of the recent shale-energy boom by unconditionally regressing income and employment on energy production at various levels of geographic aggregation. However, producing counties tend to be located near each other and receive inward spillovers from neighboring production. This inflates the estimated effect of own-county production and spatial aggregation does not address this. We propose an alternative estimation strategy that accounts for these spillovers and identify reduced propagation effects. The proposed estimation strategy can be applied more generally to estimate the dispersion of multiple, simultaneously occurring economic shocks. (JEL E24, E32, J31, Q35, Q43, R11, R23)

Visualizing the Geographic and Demographic Distribution of COVID-19

Whereas African Americans are disproportionately among the coronavirus disease 2019 (COVID-19) pandemic’s sick and dead, less is known about whether some racial/ethnic groups are more likely to be affected in Canada. In this data visualization, the authors address two issues limiting understanding of the spatial and demographic distribution of the COVID-19 pandemic in Canada: (1) COVID-19 infection and death counts are collected at a very high level of geographic aggregation, and (2) these counts are not tallied by sociodemographic group, including race/ethnicity. The authors use a bivariate choropleth map to illustrate the correlation between COVID-19 infections and the percentage of residents who are Black across census subdivisions. Canada is more similar to the United States than expected in this respect: areas with higher shares of Black Canadians also see more infections.

Using spatially explicit data to improve our understanding of land supply responses: An application to the cropland effects of global sustainable irrigation in the Americas

Land supply elasticities determine the rates of land conversion in global policy models. However, they are only available for few countries in the world. Therefore, analysts seeking to improve the spatial resolution of their models are forced to impose regionally homogeneous parameters over highly heterogeneous regions. This article estimates spatially explicit land supply elasticities using gridded data for the American continent. These estimates reasonably reproduce changes in land use observed at different levels of geographical aggregation across the continent. Plugging our estimates in a previous analysis of the land-use effects of eliminating global unsustainable irrigation, reveals higher pressure to convert land in the ecoregions in the south of the continent that have experienced most rapid cropland expansion in the recent past.

Local House Price Paths: Accelerations, Declines, and Recoveries

Mortgage credit risk measurement hinges on the choice of a house price stress path, which is used to project loan losses and determine financial capital requirements. House price paths are commonly constructed at national or state levels and shock scenarios are created to mimic historical adverse market conditions. We provide evidence that this level of geographic aggregation is not granular enough in many cases—collateral risk often varies within cities. Using local house price indices that cover the United States from 1975 to 2016, we focus on house price performance in the years immediately following sustained periods of rapid acceleration. Price accelerations tend to exhibit temporal clustering and occur with greater frequency in large versus small cities. We exploit within-city variation in price dynamics to provide evidence that price initially overshoot sustainable levels but, in some areas, dynamics may reflect positive underlying economic fundamentals and can be sustained. After accelerating, price reach their trough after 4 or 5 years. Small cities show uniform declines whereas large cities exhibit greater price decreases farther away from city centers. These findings suggest differential collateral risk exists in large cities, financial losses can be predictable based on real estate location theory, and localized house price paths could aid credit risk management.

Evaluation of geospatial methods to generate subnational HIV prevalence estimates for local level planning.

There is evidence of substantial subnational variation in the HIV epidemic. However, robust spatial HIV data are often only available at high levels of geographic aggregation and not at the finer resolution needed for decision making. Therefore, spatial analysis methods that leverage available data to provide local estimates of HIV prevalence may be useful. Such methods exist but have not been formally compared when applied to HIV. Six candidate methods - including those used by the Joint United Nations Programme on HIV/AIDS to generate maps and a Bayesian geostatistical approach applied to other diseases - were used to generate maps and subnational estimates of HIV prevalence across three countries using cluster level data from household surveys. Two approaches were used to assess the accuracy of predictions: internal validation, whereby a proportion of input data is held back (test dataset) to challenge predictions; and comparison with location-specific data from household surveys in earlier years. Each of the methods can generate usefully accurate predictions of prevalence at unsampled locations, with the magnitude of the error in predictions similar across approaches. However, the Bayesian geostatistical approach consistently gave marginally the strongest statistical performance across countries and validation procedures. Available methods may be able to furnish estimates of HIV prevalence at finer spatial scales than the data currently allow. The subnational variation revealed can be integrated into planning to ensure responsiveness to the spatial features of the epidemic. The Bayesian geostatistical approach is a promising strategy for integrating HIV data to generate robust local estimates.

Secular Trends in Regional Differences in Biomarkers of Dietary Exposure and Inflammation among American Adults: NHANES III, 1988–1994 to C‐NHANES 1999–2010

Regional disparities in health outcomes, including age‐adjusted mortality rates, are evident at several different levels of geographic aggregation in the US. The regional gradient of health outcomes generally reveal the South and Midwest regions to compare unfavorably with the West and Northeast regions. Moreover, regional gaps in health outcomes have persisted over time. We used data from several NHANES surveys to examine time trends in the association of census region of residence (Northeast, Midwest, West, and South) with serum concentrations of several nutritional biomarkers among American adults aged ≥20 years. The biomarkers available for this period included: serum (and RBC) folate and c‐reactive protein (CRP) (NHANES 1988–1994 to 2009–2010, n&gt;40,000); α‐carotene, β‐carotene, β‐cryptoxanthin, lutein‐zeaxanthin, lycopene, and vitamin D (NHANES 1988–1994 to 2001–2006; n&gt;26,500), vitamin E (NHANES 1988–1994, 1999–2001 and 2005–2006, n&gt; 26,500); vitamin C (NHANES 1988–1994 and 2003–2006, n&gt;21,900); and vitamin B‐6 (NHANES 2003–2008, n&gt;12, 800). In multivariable regression models adjusted for covariates, the interactions of survey cycle and region were not significant, indicating similar secular trends among regions. Main effect of region was significant (P&lt;0.01) for all biomarkers except serum vitamin B‐6. Mean serum concentrations of all examined biomarkers were lower but that of CRP were higher in the South census region of the country. Generally the observed regional gradients in biomarker concentrations were in the same direction as those reported for health outcomes.Support or Funding InformationFunded by National Institute of Aging grant (AG046540) (AKK).

Missing the Mark: House Price Index Accuracy and Mortgage Credit Modeling

We make two contributions to the study of house price index and mortgage credit modeling accuracy. First, we assess the predictive power of house price indices calculated at different levels of geographic aggregation. Lower levels of aggregation offer superior fit when appreciation rates vary substantially across submarkets and the indices are based on a sufficient number of transactions. Second, we estimate a competing options credit model using 15 years of mortgage performance data in the United States. Model accuracy is highest when using indices at a city or lower level of aggregation to construct current loan-to-value ratios. Fit is weaker when using state or national price indices. Overall, this research highlights the benefits of using more localized house price indices when predicting property values and mortgage performance.