What Do Ancient Cities and an Obscure Mathematical Law Tell Us About Indiana s Regional Cities?
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1 What Do Ancient Cities and an Obscure Mathematical Law Tell Us About Indiana s Regional Cities? September 29, 2015 Michael J. Hicks, Ph.D. George & Frances Ball Distinguished Professor
2 The frequency of a word is inversely proportional to its statistical rank George Kingsley Zipf ( )
3 Iron Age Jerusalem (1,000 BCE) Shiloh, 1980
4 Jerusalem/Palestine, 300s BCE Jerusalem
5 Byzantine Palestine (500 BCE) (Broshi, 1979)
6 Palestine during Roman Occupation (CE 50) Shiloh, 1980
7 Western Europe, (CE 800) (Bosker, Buringh and van Zanden, 2013)
8 Tenochtitlan (Mexico City), Early 1500s
9 Mesoamerica (Mexico, CE 1400) Smith, 2004
10 North African Cities (CE 1500) Clio-Infra Database, 2015
11 Africa, 1300s
12 Western Europe, 1600s
13 Western Europe (CE 1650) Clio-infra Database, 2015
14 Calcutta (India), 1800s
15 South Asia (1750 A.D.) Clio-Infra Database, 2015
16 Afghanistan (CE 1800) Clio-Infra Database, 2015
17 Southern Africa (CE 1900) Clio-Infra Database, 2015
18 African Cities (CE 1950) (United Nations)
19 Metropolitan Expansion, USA, 2010s
20 USA Metropolitan Statistical Areas (CE 2010) (US Census)
21 Why? Cannot be a purely natural (statistical anomaly) Gabaix (1999): Gibrat s Law and random shocks Popula'on Growth Rate and Year of Founding
22 Human behavior? Axtell and Florida (2006): agglomerations and firm/ entrepreneur location decisions Behrens, Duranton and Robert-Nicaud (2014): Agglomerations, clustering of entrepreneurs and talent sorting
23 What are agglomerations? [In a city] the mysteries of the trade become no mysteries; but are as it were in the air, and children learn many of them unconsciously... (Marshall, 1890) GDP/N population
24 ln( Y/N )= ln (A) +γln (pop) Table 1, Total Factor Productivity and Agglomeration Effects in US Cities Location TFP (A) Agglomeration (γ) All US Metropolitan Statistical Areas Pop > 1,000, ,000 > pop > 1,000, Pop < 500, Indiana cities
25 The Talent Premium in US Metropolitan Areas in 2010 BA degree share and population (log-scale)
26 Table 2, Disequilibrium Model Results for Agglomeration Model (Henderson, 1974) Variable USA Indiana Population Density *** ** Industrial HHI (smaller value is a more diversified economy) ** 6.99E-05 Home Price Index ** Square Miles of Water *** Share in the same home as last year * ** Single State Metro * *** Average HH size BA Share ** *
27 Table 3, Agglomeration and Talent Adjusted Agglomeration Effects in US Cities Location Agglomeration (γ) γ controlled for talent Elasticity of talent to city size (γ-γ T ) All US Metropolitan Statistical Areas Pop > 1,000, ,000 > pop > 1,000, Pop < 500, Indiana cities Indiana cities (no Chicago) Indiana Cities (without college towns)
28 Table 4, Relative Performance in Agglomeration Model Difference from predicted GDP/worker Bloomington, IN 1.77% Chicago-Naperville-Elgin, IL-IN-WI -1.02% Cincinnati, OH-KY-IN 0.28% Columbus, IN 18.43% Elkhart-Goshen, IN 11.30% Evansville, IN-KY 8.97% Fort Wayne, IN 1.76% Indianapolis-Carmel-Anderson, IN 7.18% Kokomo, IN 10.29% Lafayette-West Lafayette, IN 3.63% Louisville/Jefferson County, KY-IN 0.72% Michigan City-La Porte, IN -3.56% Muncie, IN -7.79% South Bend-Mishawaka, IN-MI -0.75% Terre Haute, IN -0.66%
29 What Can We Do? Demand side: strong business climate, low taxes, transparent regulatory process Supply side: transportation infrastructure, transportation services, amenities such as arts and culture, quality K-12 outcomes, endogenous amenities such as retail and recreation
30 Questions? Contact Michael J. Hicks at
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