The E ect of Infrastructure Investment in a Low-Growth Environment

Transcription

1 The E ect of Infrastructure Investment in a Low-Growth Environment Evidence from the Great Depression Miguel Morin and Scott N. Swisher IV, University of Cambridge June 22, 2016 Miguel Morin and Scott N. Swisher IV, University of Cambridge Infrastructure investment 1/26

2 Overview I General question: role of government spending in the economy Miguel Morin and Scott N. Swisher IV, University of Cambridge Infrastructure investment 2/26

3 Overview I General question: role of government spending in the economy I Our question: What is the e ect of infrastructure spending (road construction) on industrial development during a recession? Miguel Morin and Scott N. Swisher IV, University of Cambridge Infrastructure investment 2/26

4 Overview I General question: role of government spending in the economy I Our question: What is the e ect of infrastructure spending (road construction) on industrial development during a recession? I Treatments: Miguel Morin and Scott N. Swisher IV, University of Cambridge Infrastructure investment 2/26

5 Overview I General question: role of government spending in the economy I Our question: What is the e ect of infrastructure spending (road construction) on industrial development during a recession? I Treatments: I Local: Total road mileage within a county Miguel Morin and Scott N. Swisher IV, University of Cambridge Infrastructure investment 2/26

6 Overview I General question: role of government spending in the economy I Our question: What is the e ect of infrastructure spending (road construction) on industrial development during a recession? I Treatments: I I Local: Total road mileage within a county Global: market size of all other counties weighted by inverse distance Miguel Morin and Scott N. Swisher IV, University of Cambridge Infrastructure investment 2/26

7 Overview I General question: role of government spending in the economy I Our question: What is the e ect of infrastructure spending (road construction) on industrial development during a recession? I Treatments: I I Local: Total road mileage within a county Global: market size of all other counties weighted by inverse distance I Identification strategy: county i a ected by change in road network outside of county i Miguel Morin and Scott N. Swisher IV, University of Cambridge Infrastructure investment 2/26

8 Overview I General question: role of government spending in the economy I Our question: What is the e ect of infrastructure spending (road construction) on industrial development during a recession? I Treatments: I I Local: Total road mileage within a county Global: market size of all other counties weighted by inverse distance I Identification strategy: county i a ected by change in road network outside of county i I We find a positive e ect of increased market access on industrial development in manufacturing for tradable industries, but not for the least tradable ones Miguel Morin and Scott N. Swisher IV, University of Cambridge Infrastructure investment 2/26

9 Literature Review I E ect of infrastructure and market access: I Distance: Head and Mayer (2011) I Roads: Banerjee, Duflo, Qian (2012); Baum-Snow, Turner (2012); Michaels, Rauch, Redding (2013); Duranton, Morrow, Turner (2014); I Railroads: Fogel (1964); Perez-Cervantes (2014); Donaldson (2015); Donaldson, Hornbeck (2015); Swisher (2015) I Government spending during Great Depression: Brown (1956), Fishback, Horrace, and Kantor (2015) Miguel Morin and Scott N. Swisher IV, University of Cambridge Infrastructure investment 3/26

10 Literature Review I E ect of infrastructure and market access: I Distance: Head and Mayer (2011) I Roads: Banerjee, Duflo, Qian (2012); Baum-Snow, Turner (2012); Michaels, Rauch, Redding (2013); Duranton, Morrow, Turner (2014); I Railroads: Fogel (1964); Perez-Cervantes (2014); Donaldson (2015); Donaldson, Hornbeck (2015); Swisher (2015) I Government spending during Great Depression: Brown (1956), Fishback, Horrace, and Kantor (2015) I Our contribution Miguel Morin and Scott N. Swisher IV, University of Cambridge Infrastructure investment 3/26

11 Literature Review I E ect of infrastructure and market access: I Distance: Head and Mayer (2011) I Roads: Banerjee, Duflo, Qian (2012); Baum-Snow, Turner (2012); Michaels, Rauch, Redding (2013); Duranton, Morrow, Turner (2014); I Railroads: Fogel (1964); Perez-Cervantes (2014); Donaldson (2015); Donaldson, Hornbeck (2015); Swisher (2015) I Government spending during Great Depression: Brown (1956), Fishback, Horrace, and Kantor (2015) I Our contribution I New data (road networks, manufacturing non-tradables) Miguel Morin and Scott N. Swisher IV, University of Cambridge Infrastructure investment 3/26

12 Literature Review I E ect of infrastructure and market access: I Distance: Head and Mayer (2011) I Roads: Banerjee, Duflo, Qian (2012); Baum-Snow, Turner (2012); Michaels, Rauch, Redding (2013); Duranton, Morrow, Turner (2014); I Railroads: Fogel (1964); Perez-Cervantes (2014); Donaldson (2015); Donaldson, Hornbeck (2015); Swisher (2015) I Government spending during Great Depression: Brown (1956), Fishback, Horrace, and Kantor (2015) I Our contribution I I New data (road networks, manufacturing non-tradables) Heterogeneity of local and global market access Miguel Morin and Scott N. Swisher IV, University of Cambridge Infrastructure investment 3/26

13 Literature Review I E ect of infrastructure and market access: I Distance: Head and Mayer (2011) I Roads: Banerjee, Duflo, Qian (2012); Baum-Snow, Turner (2012); Michaels, Rauch, Redding (2013); Duranton, Morrow, Turner (2014); I Railroads: Fogel (1964); Perez-Cervantes (2014); Donaldson (2015); Donaldson, Hornbeck (2015); Swisher (2015) I Government spending during Great Depression: Brown (1956), Fishback, Horrace, and Kantor (2015) I Our contribution I I I New data (road networks, manufacturing non-tradables) Heterogeneity of local and global market access Heterogeneity by sector: tradables and non-tradables Miguel Morin and Scott N. Swisher IV, University of Cambridge Infrastructure investment 3/26

14 Market Access I Local market access: gains from better road coverage within county i LMA i = log Paved i, Unpaved i,1939 Paved i, Unpaved i,1930 Miguel Morin and Scott N. Swisher IV, University of Cambridge Infrastructure investment 4/26

15 Market Access I Local market access: gains from better road coverage within county i LMA i = log Paved i, Unpaved i,1939 Paved i, Unpaved i,1930 I Global market access: gains from lower distance to all other counties GMA it = X MS jt GMAi,1939, GMA i = log d j6=i ijt GMA i,1930 Miguel Morin and Scott N. Swisher IV, University of Cambridge Infrastructure investment 4/26

16 Market Access I Local market access: gains from better road coverage within county i LMA i = log Paved i, Unpaved i,1939 Paved i, Unpaved i,1930 I Global market access: gains from lower distance to all other counties GMA it = X MS jt GMAi,1939, GMA i = log d j6=i ijt GMA i,1930 I Market size MSjt : population (or retail sales), d ijt time distance of shortest-path over entire network Miguel Morin and Scott N. Swisher IV, University of Cambridge Infrastructure investment 4/26

17 Market Access I Local market access: gains from better road coverage within county i LMA i = log Paved i, Unpaved i,1939 Paved i, Unpaved i,1930 I Global market access: gains from lower distance to all other counties GMA it = X MS jt GMAi,1939, GMA i = log d j6=i ijt GMA i,1930 I I Market size MSjt : population (or retail sales), d ijt time distance of shortest-path over entire network paved roads 2x faster than unpaved Miguel Morin and Scott N. Swisher IV, University of Cambridge Infrastructure investment 4/26

18 Market Access I Local market access: gains from better road coverage within county i LMA i = log Paved i, Unpaved i,1939 Paved i, Unpaved i,1930 I Global market access: gains from lower distance to all other counties GMA it = X MS jt GMAi,1939, GMA i = log d j6=i ijt GMA i,1930 I Market size MSjt : population (or retail sales), d ijt time distance of shortest-path over entire network I paved roads 2x faster than unpaved I = 2, but robust to 2 [1, 4] Miguel Morin and Scott N. Swisher IV, University of Cambridge Infrastructure investment 4/26

19 Market Access I Local market access: gains from better road coverage within county i LMA i = log Paved i, Unpaved i,1939 Paved i, Unpaved i,1930 I Global market access: gains from lower distance to all other counties GMA it = X MS jt GMAi,1939, GMA i = log d j6=i ijt GMA i,1930 I Market size MSjt : population (or retail sales), d ijt time distance of shortest-path over entire network I paved roads 2x faster than unpaved I = 2, but robust to 2 [1, 4] I Estimating equation Y i = c + 1 LMA i + 2 GMA i + X i + " i county i, outcome Y i,localmarketaccesslma i, global Miguel Morin and Scott N. Swisher IV, University of Cambridge Infrastructure investment 4/26

20 Data: Road Network I Source: Rand McNally and Co. road atlases Miguel Morin and Scott N. Swisher IV, University of Cambridge Infrastructure investment 5/26

21 Data: Road Network I Source: Rand McNally and Co. road atlases I Scanned at Library of Congress Miguel Morin and Scott N. Swisher IV, University of Cambridge Infrastructure investment 5/26

22 Data: Road Network I Source: Rand McNally and Co. road atlases I Scanned at Library of Congress I Digitized into GIS, availalbe online soon Miguel Morin and Scott N. Swisher IV, University of Cambridge Infrastructure investment 5/26

23 Data: Road Network I Source: Rand McNally and Co. road atlases I Scanned at Library of Congress I Digitized into GIS, availalbe online soon I High level of detail (paved and unpaved roads) Miguel Morin and Scott N. Swisher IV, University of Cambridge Infrastructure investment 5/26

24 Data: Road Network I Source: Rand McNally and Co. road atlases I Scanned at Library of Congress I Digitized into GIS, availalbe online soon I High level of detail (paved and unpaved roads) I Covers major roads within and between counties Miguel Morin and Scott N. Swisher IV, University of Cambridge Infrastructure investment 5/26

25 Data: Raw Road Map Image in 1930 Miguel Morin and Scott N. Swisher IV, University of Cambridge Infrastructure investment 6/26

26 Data: Digitized image in 1930 Miguel Morin and Scott N. Swisher IV, University of Cambridge Infrastructure investment 7/26

27 Data: Digitized image in 1939 Miguel Morin and Scott N. Swisher IV, University of Cambridge Infrastructure investment 8/26

28 Data: Network of paved roads in 1930 Miguel Morin and Scott N. Swisher IV, University of Cambridge Infrastructure investment 9/26

29 Data: Network of paved roads in 1939 Miguel Morin and Scott N. Swisher IV, University of Cambridge Infrastructure investment 10/26

30 Data: Increase in road mileage Miguel Morin and Scott N. Swisher IV, University of Cambridge Infrastructure investment 11/26

31 Data: Fastest path Miguel Morin and Scott N. Swisher IV, University of Cambridge Infrastructure investment 12/26

32 Data: Increase in global market access Miguel Morin and Scott N. Swisher IV, University of Cambridge Infrastructure investment 13/26

33 Data: Outcome Variables I Employment, sales, value added, labor productivity, wages, number of firms I All of manufacturing: I Haines et. al (ICPSR 2896) I Source: Census Bureau (aggregated from Census of Manufactures) I Least-tradables manufacturing I Beverages, Ice cream, concrete I most closely track distribution of population I Firm-level, I Source: National Archives, digitized by us I All outcomes at county level I outliers dropped (1% of each tail) Miguel Morin and Scott N. Swisher IV, University of Cambridge Infrastructure investment 14/26

34 Results: Market Access on Manufacturing Y i = c + 1 LMA i + 2 GMA i + X i + " i Dependent variable # L VA VA/L GMA ** 0.275*** 0.130** ** (0.0412) (0.0706) (0.0628) (0.0399) LMA (0.0172) (0.0298) (0.0265) (0.0169) county-level controls y y y y Observations 2,269 2,191 1,973 1,973 R-squared county-level controls: proportion of blacks among males, proportion of men, proportion of rural population, proportion of manufacturing in population, initial road Density, population, area Miguel Morin and Scott N. Swisher IV, University of Cambridge Infrastructure investment 15/26

35 Market Access on Manufacturing (tradables, 29-39) num emp wages VP VA prodvp prodva sizevp sizeva sizeemp wage Local Global Segments are 1%, 5%, and 10% confidence levels. Variables: num (number of firms), emp (employment), VP (sales), VA (value added), prodvp (labor productivity of sales), prodva (labor productivity of value added), sizeva (value added per firm). All outcomes in changes. Miguel Morin and Scott N. Swisher IV, University of Cambridge Infrastructure investment 16/26

36 Market Access on Manufacturing (non-tradables, 29-35) num Local Global N=1340 emp N=1308 wages N=1299 VP N=1327 VA N=1319 prodvp N=1307 prodva N=1299 sizevp N=1328 sizeva N=1319 sizeemp N=1308 wage N= Miguel Morin and Scott N. Swisher IV, University of Cambridge Infrastructure investment 17/26

37 Market Access on Agriculture (30-40, no state FE s) Local Global num wages VP sizevp Miguel Morin and Scott N. Swisher IV, University of Cambridge Infrastructure investment 18/26

38 Market Access on Agriculture (30-40, state FE s) Local Global num N=2746 N=2746 wages N=2737 N=2737 VP N=2746 N=2746 sizevp N=2746 N= Miguel Morin and Scott N. Swisher IV, University of Cambridge Infrastructure investment 19/26

39 Robustness: including state fixed e ects num emp wages VP VA prodvp prodva sizevp sizeva sizeemp wage Local Global Miguel Morin and Scott N. Swisher IV, University of Cambridge Infrastructure investment 20/26

40 Robustness: retail sales instead of population num emp wages VP VA prodvp prodva sizevp sizeva sizeemp wage Local Global Miguel Morin and Scott N. Swisher IV, University of Cambridge Infrastructure investment 21/26

41 Robustness: drop 10 largest MSA s num emp wages VP VA prodvp prodva sizevp sizeva sizeemp wage Local Global Miguel Morin and Scott N. Swisher IV, University of Cambridge Infrastructure investment 22/26

42 Balancing test Y i = c + GMA i + " i county characteristics Dep. variable % males % farms % mfg GMA (0.0226) (0.229) (0.0483) Observations 2,778 2,778 2,288 R-squared manufacturing initial values Dep. variable # L wl VP VA GMA (1.287) (2.195) (2.295) (2.253) (2.280) Observations 2,288 2,288 2,288 2,288 2,288 R-squared Miguel Morin and Scott N. Swisher IV, University of Cambridge Infrastructure investment 23/26

43 Robustness and validity I Fewer counties for least-tradables ( 1200) than all of manufacturing ( 1800), but results on number, employment, and productivity still go through restricting to the fewer counties I changes in Global Market Access driven by changes in road network outside of own county I change in GMA orthogonal to initial county characteristics (except for road density, area, population, and blacks included as controls) I usual instruments in the literature are not relevant as expected (Pershing map, ruggedness) I if results were driven by spatial auto-correlation or county characteristics, they should a ect least tradables manufacturing as well, which is not the case Miguel Morin and Scott N. Swisher IV, University of Cambridge Infrastructure investment 24/26

44 Problems I results for wholesale and retail sector (less tradable than manufacturing) similar as manufacturing, while the story would predict that they are similar to the least tradables I we lose the results from Louisiana that we had last year with this more global road network (all counties included in GMA, instead of just Louisiana) and the timing over (instead of ). I usual instruments in the literature are not relevant as expected (Pershing map, ruggedness) I the balanced specification we chose was cherry-picked among thousands I results di erent between manufacturing and agriculture Miguel Morin and Scott N. Swisher IV, University of Cambridge Infrastructure investment 25/26

45 Conclusion I Summary of results Local Global least tradables 0 0 trabables 0 % number, employment, value added & labor productivity Miguel Morin and Scott N. Swisher IV, University of Cambridge Infrastructure investment 26/26

46 Conclusion I Summary of results Local Global least tradables 0 0 trabables 0 % number, employment, value added & labor productivity I interpretation: roads spur the development of manufacturing sector I increased market access increases demand for products I firms hire more labor to satisfy demand I newly hired labor less productive, so average labor productivity falls Miguel Morin and Scott N. Swisher IV, University of Cambridge Infrastructure investment 26/26

47 Conclusion I Summary of results Local Global least tradables 0 0 trabables 0 % number, employment, value added & labor productivity I interpretation: roads spur the development of manufacturing sector I increased market access increases demand for products I firms hire more labor to satisfy demand I newly hired labor less productive, so average labor productivity falls I story for agriculture I firms consolidate, economies of scale, only most e cient firms survive I e ect on total size of the sector ambiguous: more access to foreign trade, but foreign traders also get access to own market Miguel Morin I and Future Scott N. work: Swisher IV, University of Cambridge Infrastructure investment 26/26