A Rising Tide? The Local Incidence of the Second Wave of Globalization. Rowena Gray & Greg Wright. UC Merced

Transcription

1 A Rising Tide? The Local Incidence of the Second Wave of Globalization Rowena Gray & Greg Wright UC Merced 1 / 17

2 Overview Have trade shocks been generally welfare-enhancing? Was China special? 2 / 17

3 Overview Have trade shocks been generally welfare-enhancing? Was China special? The period 1970 to 1980 saw a doubling of US trade as a share of GDP 2 / 17

4 Overview Have trade shocks been generally welfare-enhancing? Was China special? The period 1970 to 1980 saw a doubling of US trade as a share of GDP roughly balanced between exports and imports 2 / 17

5 Overview Have trade shocks been generally welfare-enhancing? Was China special? The period 1970 to 1980 saw a doubling of US trade as a share of GDP roughly balanced between exports and imports in large part due to containerization (Bernhofen, et al, 2012) 2 / 17

6 Overview Have trade shocks been generally welfare-enhancing? Was China special? The period 1970 to 1980 saw a doubling of US trade as a share of GDP roughly balanced between exports and imports in large part due to containerization (Bernhofen, et al, 2012) Today: what were the short- and long-run consequences for US local labor markets? 2 / 17

7 Overview Have trade shocks been generally welfare-enhancing? Was China special? The period 1970 to 1980 saw a doubling of US trade as a share of GDP roughly balanced between exports and imports in large part due to containerization (Bernhofen, et al, 2012) Today: what were the short- and long-run consequences for US local labor markets? Can summarize impact by looking at local land values 2 / 17

8 Overview Have trade shocks been generally welfare-enhancing? Was China special? The period 1970 to 1980 saw a doubling of US trade as a share of GDP roughly balanced between exports and imports in large part due to containerization (Bernhofen, et al, 2012) Today: what were the short- and long-run consequences for US local labor markets? Can summarize impact by looking at local land values Also look at median income and home prices to proxy changes in standard of living 2 / 17

9 Overview Have trade shocks been generally welfare-enhancing? Was China special? The period 1970 to 1980 saw a doubling of US trade as a share of GDP roughly balanced between exports and imports in large part due to containerization (Bernhofen, et al, 2012) Today: what were the short- and long-run consequences for US local labor markets? Can summarize impact by looking at local land values Also look at median income and home prices to proxy changes in standard of living Gains to workers vs property owners: explore heterogeneity due to different local housing and labor supply elasticities 2 / 17

10 Trade as Share of US GDP 3 / 17

11 Literature Local labor market literature is large and growing by the minute 4 / 17

12 Literature Local labor market literature is large and growing by the minute China shock: Autor, Dorn and Hanson (2013); Pierce and Schott (2016); Acemoglu, et al (2016) 4 / 17

13 Literature Local labor market literature is large and growing by the minute China shock: Autor, Dorn and Hanson (2013); Pierce and Schott (2016); Acemoglu, et al (2016) Long-run impact of trade: Bernard, Jensen and Schott (2006); Dix-Carneiro & Kovak (2017) 4 / 17

14 Literature Local labor market literature is large and growing by the minute China shock: Autor, Dorn and Hanson (2013); Pierce and Schott (2016); Acemoglu, et al (2016) Long-run impact of trade: Bernard, Jensen and Schott (2006); Dix-Carneiro & Kovak (2017) Exports & Imports jointly: Feenstra, Ma & Xu (2017) 4 / 17

15 Literature Local labor market literature is large and growing by the minute China shock: Autor, Dorn and Hanson (2013); Pierce and Schott (2016); Acemoglu, et al (2016) Long-run impact of trade: Bernard, Jensen and Schott (2006); Dix-Carneiro & Kovak (2017) Exports & Imports jointly: Feenstra, Ma & Xu (2017) Container impact: Bernhofen, et al (2012) 4 / 17

16 Research Design Estimate for 722 CZ c and year t {1980, 1990, 2000}: y ct y c,1970 = α + β 1 XE c, β 2 ME c, Z c, ϵ ct 5 / 17

17 Research Design Estimate for 722 CZ c and year t {1980, 1990, 2000}: y ct y c,1970 = α + β 1 XE c, β 2 ME c, Z c, ϵ ct where y log(y ) and Y {Land Prices, Housing Prices, or Income} (source: Census) 5 / 17

18 Research Design Estimate for 722 CZ c and year t {1980, 1990, 2000}: y ct y c,1970 = α + β 1 XE c, β 2 ME c, Z c, ϵ ct where y log(y ) and Y {Land Prices, Housing Prices, or Income} (source: Census) XE and ME are local export and import exposure, / 17

19 Research Design Estimate for 722 CZ c and year t {1980, 1990, 2000}: y ct y c,1970 = α + β 1 XE c, β 2 ME c, Z c, ϵ ct where y log(y ) and Y {Land Prices, Housing Prices, or Income} (source: Census) XE and ME are local export and import exposure, Autor, Dorn and Hanson (2013), Acemoglu, et al (2016) 5 / 17

20 Research Design Estimate for 722 CZ c and year t {1980, 1990, 2000}: y ct y c,1970 = α + β 1 XE c, β 2 ME c, Z c, ϵ ct where y log(y ) and Y {Land Prices, Housing Prices, or Income} (source: Census) XE and ME are local export and import exposure, Autor, Dorn and Hanson (2013), Acemoglu, et al (2016) will instrument for these regressors 5 / 17

21 Research Design Estimate for 722 CZ c and year t {1980, 1990, 2000}: y ct y c,1970 = α + β 1 XE c, β 2 ME c, Z c, ϵ ct where y log(y ) and Y {Land Prices, Housing Prices, or Income} (source: Census) XE and ME are local export and import exposure, Autor, Dorn and Hanson (2013), Acemoglu, et al (2016) will instrument for these regressors Z is pre-period Mfg Share and other controls 5 / 17

22 Research Design: Labor Market Exposure XE c,66 80 = j L cj,1959 L c,1959 X j,66 80 Y j,1959 where c denotes local labor market and j denotes 4-digit SIC industry 6 / 17

23 Research Design: Labor Market Exposure XE c,66 80 = j L cj,1959 L c,1959 X j,66 80 Y j,1959 where c denotes local labor market and j denotes 4-digit SIC industry ME c,66 80 same but using industry absorption 6 / 17

24 Research Design: Labor Market Exposure XE c,66 80 = j L cj,1959 L c,1959 X j,66 80 Y j,1959 where c denotes local labor market and j denotes 4-digit SIC industry ME c,66 80 same but using industry absorption L cj,1959 : from digitized County Business Patterns, / 17

25 Research Design: Labor Market Exposure XE c,66 80 = j L cj,1959 L c,1959 X j,66 80 Y j,1959 where c denotes local labor market and j denotes 4-digit SIC industry ME c,66 80 same but using industry absorption L cj,1959 : from digitized County Business Patterns, 1959 Y j,1959 : from NBER-CES dataset 6 / 17

26 Research Design: Labor Market Exposure XE c,66 80 = j L cj,1959 L c,1959 X j,66 80 Y j,1959 where c denotes local labor market and j denotes 4-digit SIC industry ME c,66 80 same but using industry absorption L cj,1959 : from digitized County Business Patterns, 1959 Y j,1959 : from NBER-CES dataset Trade flows: Schott (2008), Feenstra (1996, 97) 6 / 17

27 Research Design: Labor Market Exposure XE c,66 80 = j L cj,1959 L c,1959 X j,66 80 Y j,1959 where c denotes local labor market and j denotes 4-digit SIC industry ME c,66 80 same but using industry absorption L cj,1959 : from digitized County Business Patterns, 1959 Y j,1959 : from NBER-CES dataset Trade flows: Schott (2008), Feenstra (1996, 97) Want to instrument for X j, / 17

28 Research Design: Labor Market Exposure XE c,66 80 = j L cj,1959 L c,1959 X j,66 80 Y j,1959 where c denotes local labor market and j denotes 4-digit SIC industry ME c,66 80 same but using industry absorption L cj,1959 : from digitized County Business Patterns, 1959 Y j,1959 : from NBER-CES dataset Trade flows: Schott (2008), Feenstra (1996, 97) Want to instrument for X j,66 80 Exploit the container-driven rise in trade, / 17

29 Containerization Sequence 1966 India Netherlands UK USA West Germany 1968 Australia Austria Belgium Canada Denmark East Germany France Hungary Ireland Italy Spain Sweden Switzerland Taiwan 1969 Finland Yugoslavia Japan Norway Portugal 1970 Hong Kong USSR Greece Israel Romania Singapore 1971 Cote D Ivoire New Zealand Philippines Poland Trinidad 1972 Bulgaria Czechoslovakia 1973 Bahamas Brazil Iceland Jamaica Malaysia 1974 Cameroon Chile Colombia Nigeria Panama South Africa 1975 Barbados Honduras Indonesia Korea Rep Peru Thailand 1976 Argentina Benin Kenya Mexico N Caledonia Saudi Arabia UAE 1977 Bahrain Cyprus Ghana Iran Jordan Kuwait Lebanon Morocco 1978 Ecuador Egypt Gibraltar Haiti Iraq Mozambique Oman Papua N Guinea Samoa Sierra Leone St Kitts Nevis Tanzania 1979 Algeria Angola China Congo Djibouti El Salvador Mauritius NethAntilles Nicaragua Pakistan Qatar Sri Lanka Syria 1980 Guatemala Liberia Libya Madagascar Sudan Uruguay 1981 Brunei/Bhutan Bangladesh Belize Costa Rica DemRepCongo Dominican Rep Fiji Guadeloupe Seychelles Togo Tunisia Turkey Venezuela 1982 Gambia Kiribati Mauritania StHelena 1983 Bermuda Ethiopia Guinea Malta Myanmar 7 / 17

30 Research Design: Instrumental Variable IV Strategy: 8 / 17

31 Research Design: Instrumental Variable IV Strategy: Note: Xj,66 80 = d 80 t=66 X jd,t:t 1, where d is destination 8 / 17

32 Research Design: Instrumental Variable IV Strategy: Note: Xj,66 80 = d 80 t=66 X jd,t:t 1, where d is destination predict ˆX jd,t:t 1 with foreign containerization and lags 8 / 17

33 Research Design: Instrumental Variable IV Strategy: Note: Xj,66 80 = d 80 t=66 X jd,t:t 1, where d is destination predict ˆX jd,t:t 1 with foreign containerization and lags and all combinations of interactions with: 8 / 17

34 Research Design: Instrumental Variable IV Strategy: Note: Xj,66 80 = d 80 t=66 X jd,t:t 1, where d is destination predict ˆX jd,t:t 1 with foreign containerization and lags and all combinations of interactions with: product containerizability (source: German Engineering Society, 1968), distance to foreign port, initial foreign market size, intra-us distance to domestic port, oil price 8 / 17

35 Research Design: Instrumental Variable IV Strategy: Note: Xj,66 80 = d 80 t=66 X jd,t:t 1, where d is destination predict ˆX jd,t:t 1 with foreign containerization and lags and all combinations of interactions with: product containerizability (source: German Engineering Society, 1968), distance to foreign port, initial foreign market size, intra-us distance to domestic port, oil price Two approaches to selecting regressors: 8 / 17

36 Research Design: Instrumental Variable IV Strategy: Note: Xj,66 80 = d 80 t=66 X jd,t:t 1, where d is destination predict ˆX jd,t:t 1 with foreign containerization and lags and all combinations of interactions with: product containerizability (source: German Engineering Society, 1968), distance to foreign port, initial foreign market size, intra-us distance to domestic port, oil price Two approaches to selecting regressors: 1 use foreign container dummy and lags (K-P F-stat = 7) 8 / 17

37 Research Design: Instrumental Variable IV Strategy: Note: Xj,66 80 = d 80 t=66 X jd,t:t 1, where d is destination predict ˆX jd,t:t 1 with foreign containerization and lags and all combinations of interactions with: product containerizability (source: German Engineering Society, 1968), distance to foreign port, initial foreign market size, intra-us distance to domestic port, oil price Two approaches to selecting regressors: 1 use foreign container dummy and lags (K-P F-stat = 7) 2 use LASSO to select best predictors (K-P F-stat = 44) (Chernozhukov & Hanson, 2013) 8 / 17

38 Research Design: Instrumental Variable IV Strategy: Note: Xj,66 80 = d 80 t=66 X jd,t:t 1, where d is destination predict ˆX jd,t:t 1 with foreign containerization and lags and all combinations of interactions with: product containerizability (source: German Engineering Society, 1968), distance to foreign port, initial foreign market size, intra-us distance to domestic port, oil price Two approaches to selecting regressors: 1 use foreign container dummy and lags (K-P F-stat = 7) 2 use LASSO to select best predictors (K-P F-stat = 44) (Chernozhukov & Hanson, 2013) 1 LATE is effect of container technology (but low power) 8 / 17

39 Research Design: Instrumental Variable IV Strategy: Note: Xj,66 80 = d 80 t=66 X jd,t:t 1, where d is destination predict ˆX jd,t:t 1 with foreign containerization and lags and all combinations of interactions with: product containerizability (source: German Engineering Society, 1968), distance to foreign port, initial foreign market size, intra-us distance to domestic port, oil price Two approaches to selecting regressors: 1 use foreign container dummy and lags (K-P F-stat = 7) 2 use LASSO to select best predictors (K-P F-stat = 44) (Chernozhukov & Hanson, 2013) 1 LATE is effect of container technology (but low power) 2 LATE is transport costs loosely defined (but high power) 8 / 17

40 First Stage XE c,66 80 ME c,66 80 IV: XE c, *** (009) 024*** (011) IV: ME c, *** (003) 037*** (013) MFG Share 009*** (002) 019*** (004) K-P Wald F-Statistic / 17

41 Results: Land Price, IV 10 / 17

42 Economic Magnitude: Land Price (LP) Implied net percentage change in Land Price: = ˆβ 1 XE ˆβ 2 ME / 17

43 Economic Magnitude: Land Price (LP) Implied net percentage change in Land Price: = ˆβ 1 XE ˆβ 2 ME : log(lp) = 4% 11 / 17

44 Economic Magnitude: Land Price (LP) Implied net percentage change in Land Price: = ˆβ 1 XE ˆβ 2 ME : log(lp) = 4% 80-90: log(lp) =54% 11 / 17

45 Economic Magnitude: Land Price (LP) Implied net percentage change in Land Price: = ˆβ 1 XE ˆβ 2 ME : log(lp) = 4% 80-90: log(lp) =54% 90-00: log(lp) =52% 11 / 17

46 Results: Home Values, IV 12 / 17

47 Results: Median Income, IV 13 / 17

48 Economic Magnitude Implied net percentage change in INC - HP: 14 / 17

49 Economic Magnitude Implied net percentage change in INC - HP: 70-80: log(inc ) log(hp) = 18% 14 / 17

50 Economic Magnitude Implied net percentage change in INC - HP: 70-80: log(inc ) log(hp) = 18% 80-90: log(inc ) log(hp) = 15% 14 / 17

51 Economic Magnitude Implied net percentage change in INC - HP: 70-80: log(inc ) log(hp) = 18% 80-90: log(inc ) log(hp) = 15% 90-00: log(inc ) log(hp) = 22% 14 / 17

52 Economic Magnitude Implied net percentage change in INC - HP: 70-80: log(inc ) log(hp) = 18% 80-90: log(inc ) log(hp) = 15% 90-00: log(inc ) log(hp) = 22% Upper bound on gains if non-housing component of the price index fell on average due to the shock 14 / 17

53 Role for Labor and Housing Supply Elasticities Estimate local labor supply elasticities at the county level, create emp-weighted CZ mean values 15 / 17

54 Role for Labor and Housing Supply Elasticities Estimate local labor supply elasticities at the county level, create emp-weighted CZ mean values Estimate housing supply elasticities at CZ level 15 / 17

55 Role for Labor and Housing Supply Elasticities Estimate local labor supply elasticities at the county level, create emp-weighted CZ mean values Estimate housing supply elasticities at CZ level For pre-period, (10-year differences), estimate: log HS c,50 70 = α + β HS c [ γc log Prod c,50 70 ] + ϵc, / 17

56 Role for Labor and Housing Supply Elasticities Estimate local labor supply elasticities at the county level, create emp-weighted CZ mean values Estimate housing supply elasticities at CZ level For pre-period, (10-year differences), estimate: log HS c,50 70 = α + β HS c [ γc log Prod c,50 70 ] + ϵc,50 70 where HS is housing supply; γ c are CZ FE; Prod is VA per Worker 15 / 17

57 Role for Labor and Housing Supply Elasticities Estimate local labor supply elasticities at the county level, create emp-weighted CZ mean values Estimate housing supply elasticities at CZ level For pre-period, (10-year differences), estimate: log HS c,50 70 = α + β HS [ ] c γc log Prod c, ϵc,50 70 where HS is housing supply; γ c are CZ FE; Prod is VA per Worker and Prod ct = j L cj,1959 L c,1959 Prod jt 15 / 17

58 Role for Labor and Housing Supply Elasticities Estimate local labor supply elasticities at the county level, create emp-weighted CZ mean values Estimate housing supply elasticities at CZ level For pre-period, (10-year differences), estimate: log HS c,50 70 = α + β HS [ ] c γc log Prod c, ϵc,50 70 where HS is housing supply; γ c are CZ FE; Prod is VA per Worker and Prod ct = j L cj,1959 L c,1959 Prod jt the vector β HS c are our HS elasticities 15 / 17

59 Role for Labor and Housing Supply Elasticities Estimate local labor supply elasticities at the county level, create emp-weighted CZ mean values Estimate housing supply elasticities at CZ level For pre-period, (10-year differences), estimate: log HS c,50 70 = α + β HS [ ] c γc log Prod c, ϵc,50 70 where HS is housing supply; γ c are CZ FE; Prod is VA per Worker and Prod ct = j L cj,1959 L c,1959 Prod jt the vector β HS c are our HS elasticities Repeat for Labor Supply to obtain vector β LS c 15 / 17

60 Preliminary result Preliminary result: effects seem to go in the predicted direction 16 / 17

61 Preliminary result Preliminary result: effects seem to go in the predicted direction CZs with low HS elasticities have a larger response to the shock 16 / 17

62 Preliminary result Preliminary result: effects seem to go in the predicted direction CZs with low HS elasticities have a larger response to the shock those with low LS elasticities also more responsive 16 / 17

63 Summary Post-WWII trade seems to have raised real living standards 17 / 17

64 Summary Post-WWII trade seems to have raised real living standards Winners and losers not just at sector level, but geographic level 17 / 17

65 Summary Post-WWII trade seems to have raised real living standards Winners and losers not just at sector level, but geographic level To Do: 17 / 17

66 Summary Post-WWII trade seems to have raised real living standards Winners and losers not just at sector level, but geographic level To Do: Many things: 17 / 17

67 Summary Post-WWII trade seems to have raised real living standards Winners and losers not just at sector level, but geographic level To Do: Many things: better/more measures of HS and LS elasticities 17 / 17

68 Summary Post-WWII trade seems to have raised real living standards Winners and losers not just at sector level, but geographic level To Do: Many things: better/more measures of HS and LS elasticities look at movement of fixed factors to explain SR vs LR 17 / 17

69 Summary Post-WWII trade seems to have raised real living standards Winners and losers not just at sector level, but geographic level To Do: Many things: better/more measures of HS and LS elasticities look at movement of fixed factors to explain SR vs LR industry-level analysis? Direct v Indirect effects 17 / 17

70 Summary Post-WWII trade seems to have raised real living standards Winners and losers not just at sector level, but geographic level To Do: Many things: better/more measures of HS and LS elasticities look at movement of fixed factors to explain SR vs LR industry-level analysis? Direct v Indirect effects 2nd Paper: Female LFP 17 / 17

71 Summary Post-WWII trade seems to have raised real living standards Winners and losers not just at sector level, but geographic level To Do: Many things: better/more measures of HS and LS elasticities look at movement of fixed factors to explain SR vs LR industry-level analysis? Direct v Indirect effects 2nd Paper: Female LFP any other ideas? 17 / 17