The Rise of the Sunbelt

Abstract

In the last 50 years, population and incomes have increased steadily throughout much of the Sunbelt. This paper assesses the relative contributions of rising productivity, rising demand for Southern amenities and increases in housing supply to the growth of warm areas, using data on income, housing price and population growth. Before 1980, economic productivity increased significantly in warmer areas and drove the population growth in those places. Since 1980, productivity growth has been more modest, but housing supply growth has been enormous. We infer that new construction in warm regions represents a growth in supply, rather than demand, from the fact that prices are generally falling relative to the rest of the country. The relatively slow pace of housing price growth in the Sunbelt, relative to the rest of the country and relative to income growth, also implies that there has been no increase in the willingness to pay for sun-related amenities. As such, it seems that the growth of the Sunbelt has little to do with the sun.

Population Growth in High-Amenity Nonmetropolitan Areas: What's the Prognosis?*

“…Additional evidence was provided by increased in‐migration to lower tiered amenity areas in the Great Plains (Cromartie, 1998) and reduced in‐migration into the most scenic U.S. rural areas (McGranahan, 2008). Glaeser and Tobio (2008) conclude that elastic housing supply in the South was more important for growth in the U.S. South near the end of the previous century than was increased demand for favorable weather.…”

What drags and drives mobility? Explaining Canada's aggregate migration patterns

“…As such, we include the absolute difference for each bilateral pair in the percentage of the population that is French‐speaking ( AbsDiffFrePop ji ). Marginal tax rates: Based on work by Day and Winer (), we control for provincial differences in marginal income tax rates for the lower, middle and upper tax brackets ( DiffLowTax ji , DiffMidTax ji and DiffHiTax ji ). Homeownership rates: To capture costs associated with selling a home that may act as a disincentive to out‐migration (Henley , Oswald ), the home ownership rate in the origin is included ( HomeOwnRate j ). Adjacent regions: A dummy variable that equals one if the origin and destination region are adjacent is included ( Adjacent ji ) to control for short‐distance migration across boundaries separating economic regions (Helliwell , Flowerdew and Amrhein ). Retirement community and weather: To control for the migration of Canadians moving for lifestyle reasons, such as retirement decisions, the differences between regions in the percentage of total income that is other non‐labour or non‐government income ( DiffOthInc ji )—mostly retirement income differences – are included . We also include a variable on the difference in average January temperatures ( DiffJanTemp ji ) as others have suggested that favourable climates may be associated with increased in‐migration (Graves , Renas and Kumar , Glaeser and Tobio ). Aboriginal communities: There are some economic regions that have particularly high concentrations of Aboriginal people. As Aboriginal communities are very heterogeneous and most Aboriginal people belong to particular reserves, few Aboriginal people out‐migrate, and due to cultural differences there is also little in‐migration to regions that are predominantly Aboriginal.…”

Density Zoning and Class Segregation in U.S. Metropolitan Areas*

“…In the full model, the demographic vector includes the percentage black and Latino, the number of recent in‐migrants, and population density; the socioeconomic vector includes the poverty rate, affluence rate, the share of the adult population without a diploma, the adult college attainment rate, the manufacturing share of employment, union membership, union membership interacted with the manufacturing sector, the share of local revenue from local sources, per‐capita state taxes, median household income, the unemployment rate, the ratio of suburban to central city housing, the share of rural housing in the metropolitan area, and the share of commuters with long commute times, as well average January temperature from 1971–2000 (from the National Climatic Data Center), which prior work has shown to predict population growth (Glaeser and Tobio, 2007).…”