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EC2333: Topic #5, Urbanization

EC2333: Topic #5, Urbanization. Professor Robert A. Margo Spring 2014. Outline. Background: Michaels et al and Boustan et al Bleakley-Lin on portage Baum-Snow on suburbanization

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EC2333: Topic #5, Urbanization

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  1. EC2333: Topic #5, Urbanization Professor Robert A. Margo Spring 2014

  2. Outline • Background: Michaels et al and Boustan et al • Bleakley-Lin on portage • Baum-Snow on suburbanization • If time: Collins and Margo on 1960s riots. NOTE: please read the Shester paper if you are interested in evolution of US urban policy in C20.

  3. Background • Modern economic growth is associated with shift of labor out of agriculture. Non-agricultural labor is spatially concentrated compared with agriculture – urbanization. • Output is higher if spatially concentrated. Why? “Agglomeration” economies. • “Endogenous growth”: NO diminishing returns to human capital. Complementarities between human capital and urbanization.

  4. Michaels et al: Stylized Facts of Modern Urbanization • Distribution of population density becomes more dispersed over time. Population is more concentrated. • U-shaped relationship between population growth and initial population density. • Initial share of employment in agriculture decreases in range where population growth is positively correlated with initial density

  5. Six Stylized Facts, Continued • Standard deviation of agricultural employment < non-ag employment • Across locations, agricultural employment follows “Gibrat’s” law, or “regression to the mean”. Implication of diminishing returns to land in agriculture. BUT • Non-agricultural employment does NOT follow Gibrat’s law → agglomeration economies. • Their story emphasizes structural change.

  6. A version of Figure 2, 1840-90 • Perlman PhD dissertation dataset has urban information consistently coded for 1840-1890. • At my request she produced a version of Michaels et al Figure 2 for 1840-90. Uses 1840 county definitions. • Regression to the mean is clearly evident up to a threshold population density. This density is to the RIGHT of the level in Figure 2. Also note that rising portion of Figure 2 is NOT yet present in the Perlman graph. • So, there must have been important shifts in the relationship between population growth and population density sometime in the C20. In terms of Michaels et al story, possibly due to declining labor requirements in agriculture + increasing agglomeration economies (relative) in larger cities (OR Boustan et al, amenity value of large cities is rising).

  7. Boustan et al • Overview of urbanization in US economic history • Figure 1 shows overall percent urban and percent metropolitan. Figure 1.1. shows by region, substantial differences. • Figure 2 shows median population density, again substantial differences by region.

  8. Explaining urbanization • Roback-Rosen: examine wage and rent premium. • Rents will be higher in cities as long as dense agglomeration are favored by firms. • Wages will be higher if there are offsetting productivity gains on net. • On net: amenity differences. • Boustan et. al attempt to estimate urban wage and rent premiums through US history.

  9. Urban wage and rent premiums • For wages, estimated back to 1820. Uses manufacturing samples for C19. Iowa census manuscripts for 1915, IPUMS 1940-present. Analysis is restricted to men as far as possible. Deflated by David-Solar price index. • Suggests rising urban wage premium in C19, decline from around WWI to 1980, increase in the last three decades (BUT see below). • Urban rent premium rises to WW2, declines to 1980, rises since 1980. • Interpretation: in C19, technological change raises demand for workers in urban areas. Post WWI urban amenities improve, causing wage premium to decline but not rent premium until later. • Post WW2 technology raises value of suburban locations for both employment and residence, and there is a decline in urban premiums until 1980. Post-1980 may reflect same forces driving wage inequality.

  10. Problems with UWP Estimation • Boustan, et. al. UWP estimates for 1850-80 are based on the Atack-Bateman manufacturing samples. • I discovered an error in their construction (confirmed by email). Probably not much of a trend from 1850-1940. I’m personally skeptical of the 1820 and 1832 estimates. • Better data available for 1850-1880 from manuscript censuses of social statistics and the 1880 Atack-Bateman manufacturing sample (not used by Boustan et al).

  11. Bleakley and Lin • VERY important paper. Seeks to distinguish between two standard explanations of urbanization – “natural advantage” vs. “agglomeration economies” Very difficult to do this because geography is obviously very persistent. • Find a characteristic that was important in the past but no longer because of technological progress. If urbanization persists, must be because of agglomeration economies. • Example: portage. Places where water transportation must be bridged in some manner, so it is natural to stop and engage in trade. • No longer economically relevant, but BL show that such locations in the US still strongly predict urbanization.

  12. Collins and Margo: Economic Effects of 1960s Riots • During the 1960s there were numerous race-related riots in American cities. Did the riots have adverse effects on cities? • Answer: yes, employment and housing values. Effects concentrated on African-Americans.

  13. History • U.S. has long and sorry history of race-related “civil disturbances”, mostly white on black violence • How were the 1960s different? • Hundreds of riots within a few years, widespread geographically • Peak in 1968 (King assassination)

  14. Data on Riots • Spillerman: “spontaneous outburst” of violence or property damage involving at least 30 participants (some African-American), outside a school setting and in cities with at least 25K residents • Carter: extension of Spillerman, reports deaths, injuries, arrests, arsons, and days of rioting • Primary sources: Congressional reports, NY Times, Lemberg Center for the Study of Violence (Brandeis University)

  15. Previous Work • Official Inquiries in the Immediate Aftermath: Local and State Commissions, Federal Investigations (Kerner Commission) • Academic Literature on Causes: Spilerman (1970-71, 1976), Carter (1986), Myers (1997, 2000), DiPasquale and Glaeser (1998) • Academic Literature on Consequences: Aldrich and Reiss (1970), Frey (1979), Kelly and Snyder (1980)

  16. Conceptual Framework • Prior to riots, businesses and residents are located (i) such that Y(i) > Y(j) – C for all possible j, Y = discounted benefits and C is a moving cost • Direct Effects of Riots: Deaths, Injuries, Property Destruction • Indirect Effects: Changes in expected net benefits of current location relative to others • Indirect Effects > Direct Effects if riots effects are observable at city-level • Indirect Effects PROBABLY negative but could be positive under certain conditions

  17. Empirical Strategy • Use variation in riot “severity” to measure economic impact • Assumptions: (a) effect concentrated where riots occurred (b) increasing function of severity • Compare ∆ in riot-afflicted areas for AA versus no-riot: DD specification • DDD: AA/W

  18. Problem: Endogeneity of Riots • DD strategy “works” if riots were unconditionally “random” (or we can condition on enough prior characteristics) • Sociological literature appears to supports random assignment IF we condition on absolute size of black population + region (Spilerman) • Evidence: very difficult to find consistent predictors of probability (and severity) of riots using cross-sectional data at city level from 1950 and/or 1960 censuses • NOT implausible (example of Detroit) • Problem: does not rule out unobservable correlates (or time-varying covariates in early 1960s) • Our Approach: OLS DD with covariates + IV approach

  19. IV approach • Two IVs • IV #1: rainfall in April 1968 • Martin Luther King is assassinated on April 4, 1968 • King assassination is a nationwide “spark” • Rainfall in 1968 is a significant (negative) predictor of cumulative severity • Related evidence: “the riot that didn’t happen” in Detroit 1966, Benton Harbor 2003 • Important to use April 1968 rainfall: not true for rainfall in general or April 1967 rainfall • IV #2: city manager dummy (negatively related): cities with city managers more “professional” (i.e. better run police departments)

  20. Measuring Riot Severity • Construct severity index (Carter data) • Five components: days of rioting, injuries, arrests, deaths, and arsons • Index for city k is Σ (xk/xT) where x is a component of severity • Example: suppose all rioting, injuries, etc. took place in Watts in 1965. Value of S for LA would be 5, 0 for all other cities. • Components highly correlated; weighting has no effect on results • Index is CUMULATIVE • Severity Index (0, 1 = moderate, 2 = severe). 2 = about 90th percentile in distribution of severity

  21. Tables 1 and 2 • Table 1: shows summary statistics of riot data, 1974-1971 • Years of peak activity: 1967 and 1968 (nearly 40 percent of severity) • Table 2 shows summary statistics for change in property values, percent black, and region: negative association between riot severity and change in median value of AA (and overall) home values, city level data • City-level data: published census volumes, 1950-80, roughly 100 cities in sample

  22. Table 1: The Riots of the 1960s, Frequency and Severity

  23. Table 2: Summary Statistics, City-Level Data, by Severity Group

  24. Table 3: Regressions of City-Level Data • Significant negative effect of riot severity on black median housing values especially for high levels of severity • Adding controls has little effect on coefficient • Cannot estimate similar regression for whites but we do find negative effects on overall median • Most of the effect occurs between 1960 to 1970 • NO “mean reversion” in 1970s: 1960-80 effect is ≥ 1960-70 effect

  25. Table 3A: Riots and Property Values, City-Level Data, 1960-1970

  26. Table 3B: Riots and Property Values, City-Level Data, 1960-1980

  27. Table 4 • Additional control variables, contemporaneous changes, eg. Change in population or income (endogenous) • Most of the riot effect remains

  28. Table 4: Riots and Black-Owned Property Values, City-Level Data with Contemporaneous Controls, 1960-70 and 1960-80 (Regional Dummies Included)

  29. Table 5: First-Stage IV • April 1968 rainfall is significantly negative EVEN if we control for average annual rainfall, average annual April rainfall, April 1967 rainfall • City manager effect is negative but not significant • Possible weak instrument problem

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