Stanley K. Smith

Demographic effects of natural disasters: a case study of hurricane andrew

Many studies have considered the economic, social, and psychological effects of hurricanes, earthquakes, floods, tornadoes, and other natural disasters, but few have considered their demographic effects. In this paper we describe and evaluate a method for measuring the effects of Hurricane Andrew on the housing stock and population distribution in Dade County, Florida. Using information collected through sample surveys and from other data sources, we investigate the extent of housing damages, the number of people forced out of their homes, where they went, how long they stayed, and whether they returned to their prehurricane residences. We conclude that more than half the housing units in Dade County were damaged by Hurricane Andrew; that more than 353,000 people were forced to leave their homes, at least temporarily; and that almost 40,000 people left the county permanently as a direct result of the hurricane. We believe that this study will provide methodological guidance to analysts studying the demographic effects of other large-scale natural disasters.

Fleeing The Storm(s): An Examination of Evacuation Behavior During Florida’s 2004 Hurricane Season

The 2004 hurricane season was the worst in Florida’s history, with four hurricanes causing at least 47 deaths and some

Aging and Disability: Implications for the Housing Industry and Housing Policy in the United States

45 billion in damages. To collect information on the demographic impact of those hurricanes, we surveyed households throughout the state and in the local areas that sustained the greatest damage. We estimate that one-quarter of Florida’s population evacuated prior to at least one hurricane; in some areas, well over one-half of the residents evacuated at least once, and many evacuated several times. Most evacuees stayed with family or friends and were away from home for only a few days. Using logistic regression analysis, we found that the strength of the hurricane and the vulnerability of the housing unit had the greatest impact on evacuation behavior; additionally, several demographic variables had significant effects on the probability of evacuating and the choice of evacuation lodging (family/friends, public shelters, or hotels/motels). With continued population growth in coastal areas and the apparent increase in hurricane activity caused by global warming, threats posed by hurricanes are rising in the United States and throughout the world. We believe the present study will help government officials plan more effectively for future hurricane evacuations.

Stability Over Time in the Distribution of Population Forecast Errors

Problem: The elderly population of the United States is large and growing rapidly. Since disability rates increase with age, population aging will bring substantial increases in the number of disabled persons and have a significant impact on the nations housing needs. Purpose: We demonstrate the impact of population growth and aging on the projected number of households with at least one disabled resident and estimate the probability that a newly built single-family detached unit will have at least one disabled resident during its expected lifetime. Methods: We calculate disability rates using two alternative measures of disability and construct projections of the number of households with at least one disabled resident. We develop and apply a technique for estimating the probability that a newly built single-family detached unit will house at least one disabled resident using data on the average lifespan of those units, the average length of residence for households occupying those units, and the projected proportion of households with at least one disabled resident. Results and conclusions: Under our medium assumptions, we project that 21% of households will have at least one disabled resident in 2050 using our first disability measure (physical limitation) and 7% using our second (self-care limitation). We estimate that there is a 60% probability that a newly built single-family detached unit will house at least one disabled resident during its expected lifetime using our first measure, and a 25% probability using our second measure. When disabled visitors are accounted for, the probabilities rise to 91% and 53%, respectively. Given the desire of most people to live independently for as long as possible, these numbers reflect a large and growing need for housing units with features that make them accessible to disabled persons. Takeaway for practice: The lack of accessible housing provides an opportunity for homebuilders to develop and market products that meet the needs of an aging population. In light of concerns about the civil rights of people with disabilities and the high public cost of nursing home care, housing accessibility is a critical issue for planners and policymakers as well. We believe planners should broaden their vision of the built environment to include the accessibility of the housing stock. Research support: None.

An Evaluation of Population Projections by Age

A number of studies in recent years have investigated empirical approaches to the production of confidence intervals for population projections. The critical assumption underlying these approaches is that the distribution of forecast errors remains stable over time. In this article, we evaluate this assumption by making population projections for states for a number of time periods during the 20th century, comparing these projections with census enumerations to determine forecast errors, and analyzing the stability of the resulting error distributions over time. These data are then used to construct and test empirical confidence limits. We find that in this sample the distribution of absolute percentage errors remained relatively stable over time and data on past forecast errors provided very useful predictions of future forecast errors.

Some new techniques for applying the housing unit method of local population estimation

A number of studies have evaluated the accuracy of projections of the size of the total population, but few have considered the accuracy of projections by age group. For many purposes, however, the relevant variable is the population of a particular age group, rather than the population as a whole. We investigated the precision and bias of a variety of age-group projections at the national and state levels in the United States and for counties in Florida. We also compared the accuracy of state and county projections that were derived from full-blown applications of the cohort-component method with the accuracy of projections that were derived from a simpler, less data-intensive version of the method. We found that age-group error patterns are different for national projections than for subnational projections; that errors are substantially larger for some age groups than for others; that differences in errors among age groups decline as the projection horizon becomes longer; and that differences in methodological complexity have no consistent impact on the precision and bias of age-group projections.

A Regression Approach to Estimating the Average Number of Persons per Household

The housing unit method of population estimation is often characterized as being imprecise and having an upward bias. We believe that the method itself cannot properly be characterized by a particular level of precision or direction of bias. Only specific techniques of applying the method can have such characteristics. In this paper we discuss several new techniques we have developed for estimating households and the average number of persons per household. Estimates produced by these techniques are compared to estimates produced by several other techniques. Special census results from Florida provide preliminary evidence that the new techniques produce more precise, less biased estimates than the other techniques.

Toward a Methodology for Estimating Temporary Residents

In the housing unit method, population is calculated as the number of households times the average number of persons per household (PPH), plus the population residing in group quarters facilities. Estimates of households and the group quarters population can be derived directly from concurrent data series, but estimates of PPH have traditionally been based on previous values or estimates for larger areas. In our study, we developed several regression models in which PPH estimates were based on symptomatic indicators of PPH change. We tested these estimates using county-level data in four states and found them to be more precise and less biased than estimates based on more commonly used methods.

An Empirical Analysis of the Effect of Length of Forecast Horizon on Population Forecast Errors

Abstract Most population statistics for states, counties, and cities refer to permanent residents, or persons who spend most of their time in an area. At certain times, however, many states and local areas have large numbers of temporary residents who exert a significant impact on the areas economy, physical environment, and quality of life. Typically, very little is known about the number, timing, and characteristics of these residents. This study discusses the problems of defining and estimating temporary residents, focusing on the strengths and weaknesses of a number of data sources and estimation techniques. It closes with an assessment of the potential usefulness of developing methods to estimate temporary residents.

Evaluating the Housing Unit Method: A Case Study of 1990 Population Estimates in Florida

Many studies have found that population forecast errors generally increase with the length of the forecast horizon, but none have examined this relationship in detail. Do errors grow linearly, exponentially, or in some other manner as the forecast horizon becomes longer? Does the error-horizon relationship differ by forecasting technique, launch year, size of place, or rate of growth? Do alternative measures of error make a difference? In this article we address these questions using two simple forecasting techniques and population data from 1900 to 1980 for states in the United States. We find that in most instances there is a linear or nearly linear relationship between forecast accuracy and the length of the forecast horizon, but no consistent relationship between bias and the length of the horizon. We believe that these results provide useful information regarding the nature of population forecast errors.