チャイナショックのアメリカでの影響 - ESRI · 1990 1995 2000 2005 2010 2015 ... ESRI 経済政策フォーラム平成31年2月5日 6 / 14....

チャイナショックのアメリカでの影響

笹原彰

アイダホ大学商経学部助教授

平成３１年２月５日

第 56回 ESRI-経済政策フォーラム－世界的な対外経済不均衡の趨勢と今後の展望－

笹原彰 (アイダホ大学商経学部助教授) ESRI 経済政策フォーラム平成３１年２月５日 1 / 14

‘China Shock (チャイナショック)’ とは

China Shockとは中国からの安価な製品の輸入の増加によるアメリカ経済へのマイナスの影響のこと

例えば...

製造業の雇用の減少 (Autor, Dorn, and Hanson, 2013)

住宅価格の低下 (Barrot et al., 2016; Feler and Senses, 2017)

婚姻率の低下 (Autor, Dorn, and Hanson, 2017)

イノベーションの低下 (Autor, Dorn, Hanson, Pisano, and Shu, 2016)

政治的二極化 (Autor, Dorn, Hanson, and Majlesi, 2016)


北米自由貿易協定 (NAFTA)

1990-2000年代、貿易自由化は米国の繁栄をもたらすものと期待された。

ビル・クリントン大統領, NAFTA調印, 1993年 12月 8日

「私は NAFTAがより経済成長を促進し、より平等な社会をつくり、環境保全につながり、さらに世界平和の礎になると信じています。」


中国の世界貿易機構 (WTO)への加盟

石廣生 (Shi Guangsheng), WTOへの加盟, 2001年 11月 11日

「アメリカと中国の両国は既に世界貿易システムにおいて非常に影響力のある 2国であり、中国のWTOへの加盟は我々アメリカにも中国にも成長をもたらすでしょう」米通商代表部Robert Zoellick


貿易自由化のアメリカの製造業の雇用への影響

Manufacturing employment

Import penetration from China

0.5

11.5

22.5

Import

penetr

ation fro

m C

hin

a, as a

share

of G

DP

10

12

14

16

18

U.S

. em

plo

ym

ent, m

illio

ns o

f w

ork

ers

1990 1995 2000 2005 2010 2015

中国からの輸入額の GDP比：0.1%から 2.3%まで上昇アメリカの製造業の雇用者数: ピーク時の約 1800万人から 2007年まで 1380万人まで減少 (420万人の減少, cf: 横浜市の人口が約 372万人)

データ出所: 米国労働統計局笹原彰 (アイダホ大学商経学部助教授) ESRI 経済政策フォーラム平成３１年２月５日 5 / 14

420万人の雇用減少のうちどれくらいがチャイナショックのせい？

中国からの輸入増加と製造業の雇用減少が同時進行で起こっただけで、両者の間に因果関係はないかもしれない。

Autor, Dorn, and Hanson (2013)は米国内の地域間の産業構造の違いに着目して、チャイナショックの効果を取り出すことに成功した。

例: ネバダ州のラスベガスは観光業に特化、アラバマ州のハンツビルは繊維産業に特化


Autor, Dorn, and Hanson (2013)は製造業への影響を分析

彼らも経済全体への効果はプラスであることを否定していない。→数値例 (実際の推定結果ではありません)

アメリカのGDP19.4兆ドル 20.4兆ドル

77%

12%

1%

10%

サービス業14.9兆ドル

製造業2.3兆ドル

農業0.2兆ドル

その他

80%

10%

1% 9%

製造業2.0兆ドル

サービス業16.3兆ドル

国際貿易

その他農業

0.3兆ドル


アメリカからの輸出も増加している

産業連関業を用いた雇用分析 (Feenstra and Sasahara, 2018)アメリカから他国への輸出によって 1995年から 2011年の間に 660万人の雇用が創出された（うち 410万の雇用はサービス業）。

回帰分析を用いた雇用分析 (Feenstra, Ma, and Xu, 2017)輸出と輸入の両方を考慮すると、1991年から 2011年の間の貿易による雇用へのプラスの効果とマイナスの効果はほぼ同じ大きさ。

Autor, Dorn, and Hanson (2013, 左) and Feenstra-Sasahara (2018, 右)



0.5

11.5

22.5

Import

penetr

ation fro

m C

hin

a, as a

share

of G

DP

10

12

14

16

18

U.S

. e

mp

loym

en

t, m

illio

ns o

f w

ork

ers

1990 1995 2000 2005 2010 2015


Service employment


0.5

11.5

22.5

Imp

ort

pe

ne

tra

tio

n f

rom

Ch

ina

, a

s a

sh

are

of

GD

P

20

40

60

80

100

U.S

. e

mp

loym

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f w

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1990 1995 2000 2005 2010 2015


Caliendo, Dvorkin, and Parro (2018)サービス業も考慮した一般均衡分析

2000-2007年の間のチャイナショックのアメリカへの影響製造業の雇用が 55万人減少（実際の製造業の雇用減少の 16%）アメリカ全体の厚生は 0.2%増加労働者は製造業からサービス業に再配分

アメリカのコンピューター/家電産業の雇用の減少への各州の貢献（パーセント）

Fig. 7: Regional employment declines in manufacturing industries

1. Contribution to industry employment decline in the U.S. (%) 2. Normalized by regional employment share

a.1: Computer and electronics a.2: Computer and electronics

AL0.05

AK0.01

AZ1.2

AR1.2

CA14.2

CO2.1

CT2

DE0.16

FL2.2

GA1.4

HI0.02

ID0.23

IL6.7 IN

0.38

IA1.05

KS0.64 KY

0.34

LA0.36

ME0.4

MD1.1

MA5.8

MI1.6

MN2.4

MS0.83

MO3.9

MT0.13

NE0.31NV

0.3

NH0.69

NJ2.2

NM0.4

NY8.2

NC7.1

ND0.33

OH4.7

OK1.3

OR2.5

PA2.6

RI0.67

SC1.6

SD0.07

TN0.79

TX7.6

UT0.84

VT0.21

VA3.4

WA1.6

WV0.2

WI1.9

WY0.07

AL0.03

AK0.06

AZ0.72

AR1.4

CA1.2

CO1.2

CT1.5

DE0.53

FL0.41

GA0.48

HI0.04

ID0.55

IL1.5 IN

0.17

IA0.96

KS0.67 KY

0.25

LA0.26

ME0.81

MD0.57

MA2.3

MI0.44

MN1.2

MS0.95

MO1.9

MT0.44

NE0.5NV

0.41

NH1.3

NJ0.69

NM0.74

NY1.2

NC2.4

ND1.5

OH1.1

OK1.1

OR2

PA0.58

RI1.7

SC1.2

SD0.26

TN0.38

TX1.1

UT1.2

VT0.86

VA1.2

WA0.74

WV0.37

WI0.9

WY0.44

b.1: Machinery b.2: Machinery

AL0.03

AK0.01

AZ1.02

AR0.41

CA2.9

CO1.8

CT0.34

DE0.1

FL1.3

GA5.1

HI0

ID0.19

IL5.1 IN

2.1

IA1.7

KS2.8 KY

6.2

LA2

ME0.06

MD2.4

MA1.2

MI4.1

MN5

MS2.8

MO2.8

MT0.13

NE2.4NV

0.28

NH1.4

NJ0.24

NM0.17

NY6.2

NC1.6

ND0.6

OH8.5

OK2.3

OR0.31

PA5.4

RI0.42

SC1.9

SD0.43

TN2.3

TX5

UT0.62

VT0.31

VA2.1

WA2.5

WV0.93

WI2.4

WY0.04

AL0.02

AK0.03

AZ0.6

AR0.47

CA0.25

CO1.1

CT0.25

DE0.33

FL0.25

GA1.7

HI0

ID0.46

IL1.1 IN

0.94

IA1.5

KS2.9 KY

4.6

LA1.4

ME0.12

MD1.2

MA0.47

MI1.1

MN2.5

MS3.2

MO1.4

MT0.44

NE3.9NV

0.38

NH2.6

NJ0.08

NM0.3

NY0.93

NC0.52

ND2.8

OH2

OK2.1

OR0.25

PA1.2

RI1.1

SC1.3

SD1.7

TN1.1

TX0.72

UT0.88

VT1.3

VA0.75

WA1.1

WV1.7

WI1.2

WY0.23

c.1: Textiles c.2: Textiles

AL0.16

AK0.01

AZ0.49

AR0.28

CA11.9

CO0.3

CT1.1

DE0.17

FL1.3

GA1.1

HI0.51

ID0.03

IL8.7 IN

2.8

IA1.02

KS1.2 KY

2.1

LA0.06

ME0.36

MD0.86

MA0.98

MI0.28

MN0.92

MS2.7

MO1.6

MT0.17

NE0.35NV

0.38

NH0.5

NJ0.99

NM0.15

NY4.6

NC11.7

ND0.09

OH4.9

OK0.71

OR2.5

PA3.1

RI0.37

SC5

SD0.01

TN3.2

TX5.9

UT3.6

VT0.03

VA8.7

WA0.69

WV0.1

WI1.4

WY0.001

AL0.11

AK0.07

AZ0.29

AR0.32

CA1.03

CO0.18

CT0.8

DE0.58

FL0.24

GA0.37

HI1.3

ID0.07

IL1.9 IN

1.2

IA0.93

KS1.3 KY

1.6

LA0.04

ME0.72

MD0.44

MA0.39

MI0.08

MN0.46

MS3.1

MO0.82

MT0.56

NE0.57NV

0.52

NH0.95

NJ0.31

NM0.27

NY0.69

NC3.9

ND0.42

OH1.2

OK0.64

OR2

PA0.69

RI0.97

SC3.5

SD0.05

TN1.5

TX0.85

UT5.1

VT0.12

VA3.1

WA0.32

WV0.18

WI0.66

WY0.01

Note: This figure presents the reduction in local employment in manufacturing industries. Column 1 presents the

contribution of each state to the U.S. aggregate reduction in the industry employment due to the China shock.

Column 2 presents the contribution of each state to the U.S. aggregate reduction in the industry employment

normalized by the employment size of each state relative to the U.S. aggregate employment. Panels a present the

results for the computer and electronics industry. Panels b present the results for the machinery industry. Panels

c present the results for the textiles industry.

33


Caliendo, Dvorkin, and Parro (2018)サービス業も考慮した一般均衡分析

2000-2007年の間のチャイナショックのアメリカへの影響製造業の雇用が 55万人減少（実際の製造業の雇用減少の 16%）アメリカ全体の厚生は 0.2%増加労働者は製造業からサービス業に再配分

アメリカのサービス業の雇用の増加への各州の貢献（パーセント）

Fig. 8: Regional employment increase in non-manufacturing industries

1. Contribution to industry employment increase in the U.S. (%) 2. Normalized by regional employment share

a.1: Construction a.2: Construction

AL0.15

AK0.19

AZ2.3

AR1

CA8.9

CO3.6

CT1.6

DE0.42

FL8.1

GA3.9

HI0.4

ID0.96

IL3.2 IN

-0.15

IA0.95

KS1.1 KY

0.53

LA1.9

ME0.58

MD3.8

MA3

MI1.2

MN2.7

MS0.65

MO2.4

MT0.39

NE1.5NV

0.94

NH0.78

NJ2.4

NM1.1

NY5.7

NC3.6

ND0.27

OH1.3

OK2.5

OR1.2

PA4.2

RI0.45

SC1.1

SD0.45

TN3.7

TX4.8

UT0.9

VT0.56

VA4.5

WA2.9

WV0.39

WI0.67

WY0.37

AL0.1

AK0.95

AZ1.3

AR1.1

CA0.77

CO2.2

CT1.2

DE1.4

FL1.5

GA1.3

HI1

ID2.3

IL0.71 IN

-0.07

IA0.87

KS1.1 KY

0.39

LA1.4

ME1.2

MD2

MA1.2

MI0.32

MN1.4

MS0.75

MO1.2

MT1.3

NE2.5NV

1.3

NH1.5

NJ0.76

NM2.1

NY0.84

NC1.2

ND1.3

OH0.3

OK2.2

OR0.93

PA0.95

RI1.2

SC0.75

SD1.7

TN1.8

TX0.68

UT1.3

VT2.3

VA1.6

WA1.4

WV0.71

WI0.32

WY2.3

b.1: Services b.2: Services

AL0.05

AK0.16

AZ2.7

AR0.94

CA12.5

CO2.7

CT1.9

DE0.4

FL6.7

GA3.6

HI0.47

ID0.63

IL3.4 IN

0.39

IA1

KS0.89 KY

0.25

LA0.85

ME0.64

MD3.4

MA4

MI1.5

MN2.1

MS0.83

MO1.8

MT0.39

NE0.93NV

2

NH0.79

NJ3.1

NM0.94

NY8.5

NC2.4

ND0.33

OH2.4

OK1.7

OR1.2

PA5.1

RI0.58

SC0.99

SD0.3

TN2.3

TX0.45

UT0.84

VT0.42

VA6.1

WA2.6

WV0.37

WI1.4

WY0.15

AL0.03

AK0.79

AZ1.6

AR1.1

CA1.1

CO1.6

CT1.4

DE1.4

FL1.2

GA1.2

HI1.2

ID1.5

IL0.75 IN

0.17

IA0.92

KS0.92 KY

0.18

LA0.63

ME1.3

MD1.7

MA1.6

MI0.42

MN1.1

MS0.95

MO0.91

MT1.3

NE1.5NV

2.8

NH1.5

NJ0.98

NM1.7

NY1.3

NC0.79

ND1.5

OH0.57

OK1.5

OR0.92

PA1.1

RI1.5

SC0.7

SD1.2

TN1.1

TX0.06

UT1.2

VT1.7

VA2.2

WA1.2

WV0.68

WI0.66

WY0.94

c.1: Whole. & Retail c.2: Whole. & Retail

AL-0.23

AK0.05

AZ3.1

AR3.7

CA19.5

CO4.2

CT3.5

DE0.48

FL7.5

GA3.9

HI0.2

ID1.6

IL2.1 IN

0.18

IA2.8

KS0.97 KY

-2.17

LA-0.69

ME2.8

MD2.5

MA4.5

MI-10.05

MN3.7

MS1.7

MO-0.12

MT0.39

NE1.9NV

0.67

NH2.2

NJ4.6

NM0.58

NY6.8

NC5.3

ND0.95

OH5.8

OK2.7

OR3.3

PA4.8

RI1.2

SC1.9

SD0.52

TN7.4

TX-29.07

UT2

VT1.04

VA11

WA2.6

WV1.1

WI4.8

WY0.14

AL-0.16

AK0.24

AZ1.8

AR4.1

CA1.7

CO2.5

CT2.6

DE1.6

FL1.4

GA1.3

HI0.5

ID3.7

IL0.45 IN

0.08

IA2.6

KS1.01 KY

-1.61

LA-0.51

ME5.6

MD1.3

MA1.8

MI-2.77

MN1.9

MS1.9

MO-0.06

MT1.3

NE3.1NV

0.92

NH4.2

NJ1.4

NM1.1

NY1.01

NC1.8

ND4.4

OH1.4

OK2.4

OR2.6

PA1.1

RI3.1

SC1.3

SD2

TN3.6

TX-4.16

UT2.8

VT4.2

VA4

WA1.2

WV2

WI2.3

WY0.91

Note: This figure presents the rise in local employment in non-manufacturing industries. Column 1 presents the

contribution of each state to the U.S. aggregate increase in the industry employment due to the China shock.

Column 2 presents the contribution of each state to the U.S. aggregate increase in the industry employment

normalized by the employment size of each state relative to the U.S. aggregate employment. Panels a present the

results for the construction industry. Panels b present the results for all services industry. Panels c present the

results for the wholesale and retail industry.

34


Fort, Pierce, and Schott (2018)製造業の雇用減少の新しい側面（New Perspectives）

製造業の付加価値は増加している製造業の付加価値は 2008年時点で約２兆ドル非製造業の付加価値は 2008年時点で約１３兆ドル製造業の労働生産性は上昇している

アメリカの製造業と非製造業の付加価値

48 Journal of Economic Perspectives

30

50

70

90

110

130

Oth

er non

-farm

12

14

16

18

20

Man

ufac

turi

ng

1948 1958 1968 1978 1988 1998 2008 2018

Manufacturing (left axis)

Other non-farm (right axis)

A: Employment, 1948–2016(millions of workers)

5

10

15

Non-m

anufacturing

1

2

3

Man

ufac

turi

ng

1948 1958 1968 1978 1988 1998 2008 2018

Non-manufacturing/Other GDP (right axis)

B: Real Value Added, 1958–2011(trillions of dollars)

Manufacturing(left axis)

Figure 1 US Employment and Value Added within and outside Manufacturing

Source: Monthly employment data are from the US Bureau of Labor Statistics. Annual manufacturing real value added data are from NBER-CES Manufacturing Industry Database (Becker, Gray, and Marakov 2013). Annual real GDP data are from US Bureau of Economic Analysis. Non-manufacturing value added is real GDP less manufacturing real value added. Note: Shading corresponds to NBER-dated recessions.


Fort, Pierce, and Schott (2018)製造業の雇用減少の新しい側面（New Perspectives）

サンプル期間を通じて存続している製造業の企業の事業所数製造業の事業所を閉鎖して非製造業の事業所を新規に設置している製造業の企業が退出して別の非製造業の企業が事業を拡大しているというよりも、製造業の企業が非製造業の事業に進出している

製造業の企業の事業所数

New Perspectives on the Decline of US Manufacturing Employment 67

activities.14 Further insight into these explanations comes from analysis of the particular activities occurring at non-manufacturing plants of manufacturing firms. Toward that end, we break non-manufacturing industries into three groups based on their two-digit NAICS sectors: retail (NAICS 44 to 45), professional services (NAICS 51 to 56), and all other non-manufacturing industries. Perhaps unsurpris-ingly, given the broad definition of manufacturing firms noted above, we find that about one-third of the overall growth in non-manufacturing employment of manu-facturing firms between 1977 and 2012 is in retail, while another third falls into the “other” category.

However, 32 percent of the increase in non-manufacturing employment at manufacturing firms is driven by professional services, which captures a wide range of often skill-intensive activities: information technology (NAICS 51); finance, insur-ance, real estate and leasing (NAICS 52-3); engineering and other technical services (NAICS 54); headquarters services (NAICS 55); and administrative support and waste management (NAICS 56). The growing use of workers in such industries may reflect

14 While recent research suggests that US manufacturers increasingly outsource ancillary services such as cleaning to domestic contractors (Dey, Houseman, and Polivka 2012; Berlingieri 2014; Katz and Krueger 2016), such activity would not be captured in Figure 7 as it traces non-manufacturing employment within manufacturing firms.

10

15

20

25

30

35

40

Mill

ion

s of

wor

kers

1977 1982 1987 1992 1997 2002 2007 2012

All plants

Manufacturing plants

Non-manufacturing plants

Figure 7 Employment at Manufacturing Firms Decomposed into Employment at Manufacturing versus Non-Manufacturing Establishments, 1977–2012

Source: Longitudinal Business Database and author’s calculations.Note: Manufacturing firms are defined as any firm observed to have a manufacturing establishment during the sample period. The shading corresponds to NBER-dated recessions.


その他のチャイナショックの研究

Bloom, Handley, Kurmann, and Luck (2019)

米国の国勢調査のデータを用いた研究。チャイナショックが製造業から非製造業への雇用者の再配分を促した

Feenstra, Ma, and Xu (2019)

チャイナショックの負の影響が大きかった時期（2000年-2011年）とアメリカの住宅バブルとその崩壊の時期が重なったことで、チャイナショックの影響が大きくなった

Feenstra and Sasahara (2019) 　

中国への輸出機会が東アジア、アセアン諸国で雇用創出している

Amiti, Dai, Feenstra, and Romalis (2018) 　

中国からの輸入によって 2000-2006年の間にアメリカの製造業の消費者価格指数が約７％低下した。


Auer, Bonadio, and Levchenko (2018)NAFTAを撤回した場合の経済効果

アメリカ全体の厚生は 0.22%減少

2016年大統領選挙でトランプへの投票が多かった地域で特に厚生が低下 (オハイオ州の第 4選挙区で-0.41%)

NAFTAによってどれくらい貿易の影響を受けているのか３つの指標を使って数値化

輸入競争指数 (どれくらい輸入競争にさらされているか)輸出機会指数 (どれくらい輸出機会に恵まれているか)中間財輸入指数 (どれくらい中間財の輸入に頼っているか)

3つの指標の間に強い相関→ 輸入競争によって打撃を受けていると思われる地域は、それと同時に、輸出機会にも恵まれており、中間財の輸入による恩恵も受けている



輸入競争指数と 2016年大統領選のトランプ投票シェア

Figure 7: Heuristic measures, real wage changes and 2016 Trump vote

Import exposure Export orientation Imported input intensity

Real wage change

DE

FLFL

FLFL

FLFLFL FLFLFLFL

FLFLFLFLFLFLFLFLFL FLFLFL FLFL FLFL

GA

GAGA

GA

GA

GA

GA

GA

GAGAGA GA

GA

GA

HIHI IDID

ILIL IL ILILIL IL

ILIL

ILILILILIL ILIL ILIL

IN

ININ

IN

IN

IN

ININ

INIA

IA

IA

IA

AL

AL AL

AL

AL

ALAL

KSKS

KS

KSKY

KY

KYKY

KY

KY

LA

LA

LA

LA

LA

LAMEME

MDMDMD MDMD

MDMDMD

MAMAMAMAMA

MAMAMA

MA MI

MI

MI MIMI

MI

MIMI

MI

MI

MIMI

MI

MIMN

MNMN

MNMN

MN

MN

MN

MSMS

MS MSMOMO

MOMOMO

MOMOMO

AK

MT

NENE

NE

NV

NV

NVNV

NHNH

NJNJ

NJNJNJNJNJ

NJNJNJNJNJNM

NM

NM

NYNYNY

NYNY NY

NYNY

NY NYNYNY

NYNY

NYNY

NYNY NYNY

NYNYNY

NYNY NY

NY

NCNC

NCNC

NC NC

NCNC

NCNC

NC

NCNC

ND

OH OHOHOH

OHOH

OH

OH

OHOH

OHOH

OH

OHOH

OH

OKOK

OK

OKOK

OROR

OR

OR

ORPA PA

PA

PAPAPA

PAPAPA

PA

PA

PAPA PAPAPA

PA

PA

RI RISC

SC

SCSC

SC

SCSCSD

TN

TN

TN TN

TN

TN

TNTN

TN

TX

TX

TX

TX

TX

TX

TX

TX TXTX

TX

TXTXTX TX

TX

TXTX

TX

TXTX

TXTX

TX

TXTX TX

TX

TX TX

TXTX

TX

TXTX

TX

UTUTUT

UT

AZAZAZAZ

AZAZAZAZAZVT

VAVA

VA

VAVA VAVA

VA

VAVAVA

WA

WA

WA

WA

WA

WAWA

WAWAWA

WV

WV

WV

WI

WIWIWI

WIWI

WI

WI

WY

AR

AR

ARAR

CA

CA

CACA

CACA CA

CA

CACACACA CA CA

CA

CACA

CACACACACA

CACA

CACACACACA

CA

CA CACACACA

CACA

CACA CACACA CACA

CA

CACACACACACA

CACACO CO

CO

COCOCO CO

CTCT

CTCTCT

Coeff = -3.539Std.Err. = 1.25R2 = .092

-.4-.3

-.2-.1

0.1

Coun

terfa

ctua

l wag

e ch

ange

(with

NTB

)

0 .01 .02 .03NAFTA import exposure

DE

FLFL

FLFL

FLFLFL FLFLFLFL

FLFLFLFLFLFLFLFLFL FLFLFLFLFL FLFL

GAGA

GAGA

GA

GA

GA

GA

GAGAGA GA

GA

GA

HIHI IDID

IL IL IL ILILIL IL

ILIL

ILILILIL IL ILIL ILIL

IN

ININ

IN

IN

IN

ININ

INIA

IA

IA

IA

AL

AL AL

AL

AL

ALAL

KSKS

KS

KSKY

KY

KYKY

KY

KY

LA

LA

LA

LA

LA

LAMEME

MDMDMDMDMD

MDMDMD

MAMA MAMAMA

MAMAMA

MA MI

MI

MI MIMI

MI

MIMI

MI

MI

MIMI

MI

MIMN

MNMN

MNMN

MN

MN

MN

MSMS

MS MSMOMO

MOMOMOMOMO

MO

AK

MT

NENE

NE

NV

NV

NVNV

NHNH

NJNJ

NJNJNJNJNJ

NJNJNJNJNJ

NM

NM

NM

NYNYNY

NYNY NY

NYNY

NYNYNYNY

NYNY

NYNY

NYNY NYNY

NYNY

NY

NYNY NY

NY

NCNC

NCNC

NC NC

NCNC

NCNC

NC

NCNC

ND

OH OHOHOH

OH OH

OH

OH

OHOH

OHOH

OH

OHOH

OH

OKOK

OK

OKOK

OROR

OR

OR

ORPA PA

PA

PAPAPA

PAPAPA

PA

PA

PAPA PAPAPA

PA

PA

RI RISC

SC

SCSC

SC

SCSCSD

TN

TN

TN TN

TN

TN

TNTN

TN

TX

TX

TX

TX

TX

TX

TX

TX TXTX

TX

TXTXTX TX

TX

TXTX

TX

TXTX

TXTX

TX

TXTX TX

TX

TX TX

TXTX

TX

TXTXTX

UTUTUT

UT

AZAZ

AZAZAZAZAZAZAZ

VT

VAVA

VA

VAVA VAVA

VA

VAVAVA

WA

WA

WA

WA

WA

WAWA

WAWAWA

WV

WV

WV

WI

WIWIWI

WIWI

WI

WI

WY

AR

AR

ARAR

CA

CA

CACA

CACA CA

CA

CACACACA CACA

CA

CACA

CACACACACACA

CACACACACACA

CA

CACACACACA

CACA

CACA CACACACACA

CA

CACACACACACA

CACACO CO

CO

COCOCOCO

CTCT

CTCT CT

Coeff = -8.18Std.Err. = 0.40R2 = .4

-.4-.3

-.2-.1

0.1

Coun

terfa

ctua

l wag

e ch

ange

(with

NTB

)

0 .005 .01 .015 .02 .025NAFTA export orientation

DE

FLFL

FLFL

FLFLFL FLFLFLFL


GA

GAGA

GA

GA

GA

GA

GA

GAGAGA GA

GA

GA

HIHI IDID

ILIL IL ILILIL IL

ILIL


IN

ININ

IN

IN

IN

ININ

INIA

IA

IA

IA

AL

AL AL

AL

AL

ALAL

KSKS

KS

KSKY

KY

KYKY

KY

KY

LA

LA

LA

LA

LA

LAMEME

MDMDMD MDMDMDMDMD

MAMAMAMAMA

MAMAMA

MA MI

MI

MI MIMI

MI

MIMI

MI

MI

MIMI

MI

MIMN

MNMN

MNMN

MN

MN

MN

MSMS

MS MSMOMO

MOMOMO

MOMOMO

AK

MT

NENE

NE

NV

NV

NVNV

NHNH

NJNJ

NJNJ NJNJNJ

NJNJNJNJNJ

NM

NM

NM

NYNYNY

NYNY NY

NYNY

NYNYNYNY

NYNY

NYNY

NYNYNYNY

NYNY

NY

NYNY NY

NY

NCNC

NCNCNC NC

NCNC

NCNC

NC

NCNC

ND

OH OHOHOH

OHOH

OH

OH

OHOH

OHOH

OH

OHOH

OH

OKOK

OK

OKOK

OROR

OR

OR

ORPA PA

PA

PAPAPA

PA PAPA

PA

PA

PAPA PAPAPA

PA

PA

RIRISC

SC

SCSC

SC

SCSCSD

TN

TN

TN TN

TN

TN

TNTN

TN

TX

TX

TX

TX

TX

TX

TX

TX TXTX

TX

TXTX TX TX

TX

TXTX

TX

TXTX

TXTX

TX

TXTX TX

TX

TX TX

TXTX

TX

TXTX

TX

UTUTUT

UT

AZAZ

AZAZAZAZAZAZAZ

VT

VAVA

VA

VAVA VAVA

VA

VAVAVA

WA

WA

WA

WA

WA

WAWA

WAWAWA

WV

WV

WV

WI

WIWIWI

WIWI

WI

WI

WY

AR

AR

ARAR

CA

CA

CACA

CACA CA

CA

CACACACACA CA

CA

CACA

CACACACACA

CACA

CACACACA CA

CA

CA CACACACA

CACA

CACA CACACA CACA

CA

CACACACACACA

CACACO CO

CO

COCO CO CO

CTCT

CTCTCT

Coeff = -6.15Std.Err. = 1.86R2 = .088

-.4-.3

-.2-.1

0.1

Coun

terfa

ctua

l wag

e ch

ange

(with

NTB

)

.01 .015 .02 .025NAFTA imported input intensity

Trump vote

DE

FL

FL

FL

FL

FL

FL

FL FL

FL

FL

FL

FL

FL

FLFL

FL

FL

FL

FLFL

FL

FL

FL

FL

FL

FL

FL

GA

GAGAGA

GA

GA

GA

GA

GA

GA

GAGA

GA

GA

HIHI

ID

ID

IL

ILIL

IL

IL IL

IL

IL

IL

IL

IL

IL

IL

IL

IL

IL

IL

IL

IN

IN

ININ

IN

IN

IN

ININ

IAIAIA

IAAL AL AL

AL

AL

AL

AL

KS

KS

KS

KS

KY

KY

KY

KY

KY

KY

LA

LA

LA

LALA LA

ME

ME

MD

MDMD

MD

MD

MD

MD

MD

MAMAMAMA

MA

MA

MA

MA

MA

MI

MI

MI

MI

MIMI

MIMI

MI

MI

MIMI

MI

MI

MN

MN

MN

MN

MN

MNMN

MN

MS

MS

MS

MS

MO

MO

MOMO

MO

MO

MO

MO

AKMT NE

NE

NE

NV

NVNV

NVNH

NH

NJ

NJ

NJ

NJ

NJNJNJ

NJ

NJ

NJ

NJ

NJNM

NM

NM

NY

NY

NY

NY

NY

NY

NY

NY

NY

NY NYNY

NY

NY NY NY

NY

NY NY

NY

NYNY

NY

NY

NYNYNY

NC

NCNC

NC

NC NC

NC

NC

NCNCNCNCNC

ND

OH OH

OH

OH

OH

OHOH OHOH

OH

OHOH

OH

OHOH

OH

OK

OK OK

OK

OK

OR

OR

OR

ORORPA

PA

PA

PA

PA

PA

PA

PA

PA

PA

PA

PA

PAPA

PAPA

PA

PA

RI

RI

SCSC

SC

SCSC

SC

SCSD

TN

TN TNTN

TN

TN

TNTN

TN

TX

TX

TX

TX

TX

TX

TX

TX

TX

TX

TX

TX

TX

TX TX

TXTX

TX

TX TX

TX

TX

TX

TX

TX

TX

TX

TX

TX

TXTX

TX

TX

TX

TX

TX

UTUTUT

UT

AZAZ

AZ

AZ

AZ

AZ

AZ

AZ

AZ

VT

VA

VA

VA

VA

VA

VA

VA

VA

VA

VA

VA

WAWA

WA

WA

WA

WA

WA

WA

WA

WA

WVWV

WV

WI

WI

WI

WI

WI WIWIWI

WY

AR

AR

ARAR

CA

CA

CA

CA CA

CA

CA

CA

CACA CACA CA

CA

CA

CA

CA

CA

CA

CA

CA

CA

CA

CA

CA

CACA

CA

CA

CA

CA

CA

CA

CA

CA

CA

CA

CACA

CA

CACA

CACA

CA

CA

CA

CA

CA

CA

CA

CA

CA

CO

CO

CO

COCO

CO COCT

CT

CTCT

CT

Coeff = 2342.11Std.Err. = 161.50R2 = .335

020

4060

8010

0Vo

te s

hare


DE

FL

FL

FL

FL

FL

FL

FL FL

FL

FL

FL

FL

FL

FLFL

FL

FL

FL

FLFL

FL

FL

FL

FL

FL

FL

FL

GA

GAGAGA

GA

GA

GA

GA

GA

GA

GAGA

GA

GA

HIHI

ID

ID

IL

ILIL

IL

IL IL

IL

IL

IL

IL

IL

IL

IL

IL

IL

IL

IL

IL

IN

IN

ININ

IN

IN

IN

ININ

IAIAIA

IAAL AL AL

AL

AL

AL

AL

KS

KS

KS

KS

KY

KY

KY

KY

KY

KY

LA

LA

LA

LALA LA

ME

ME

MD

MDMD

MD

MD

MD

MD

MD

MAMA MAMA

MA

MA

MA

MA

MA

MI

MI

MI

MI

MIMI

MIMI

MI

MI

MIMI

MI

MI

MN

MN

MN

MN

MN

MNMN

MN

MS

MS

MS

MS

MO

MO

MOMO

MO

MO

MO

MO

AKMT NE

NE

NE

NV

NVNV

NVNH

NH

NJ

NJ

NJ

NJ

NJNJNJ

NJ

NJ

NJ

NJ

NJNM

NM

NM

NY

NY

NY

NY

NY

NY

NY

NY

NY

NY NYNY

NY

NY NY NY

NY

NY NY

NY

NYNY

NY

NY

NYNYNY

NC

NCNC

NC

NC NC

NC

NC

NCNCNCNCNC

ND

OH OH

OH

OH

OH

OHOH OHOH

OH

OHOH

OH

OHOH

OH

OK

OKOK

OK

OK

OR

OR

OR

ORORPA

PA

PA

PA

PA

PA

PA

PA

PA

PA

PA

PA

PAPA

PAPA

PA

PA

RI

RI

SCSC

SC

SCSC

SC

SCSD

TN

TN TNTN

TN

TN

TNTN

TN

TX

TX

TX

TX

TX

TX

TX

TX

TX

TX

TX

TX

TX

TX TX

TXTX

TX

TX TX

TX

TX

TX

TX

TX

TX

TX

TX

TX

TXTX

TX

TX

TX

TX

TX

UTUTUT

UT

AZAZ

AZ

AZ

AZ

AZ

AZ

AZ

AZ

VT

VA

VA

VA

VA

VA

VA

VA

VA

VA

VA

VA

WAWA

WA

WA

WA

WA

WA

WA

WA

WA

WVWV

WV

WI

WI

WI

WI

WI WIWIWI

WY

AR

AR

ARAR

CA

CA

CA

CA CA

CA

CA

CA

CACACACA CA

CA

CA

CA

CA

CA

CA

CA

CA

CA

CA

CA

CA

CACA

CA

CA

CA

CA

CA

CA

CA

CA

CA

CA

CACA

CA

CACA

CACA

CA

CA

CA

CA

CA

CA

CA

CA

CA

CO

CO

CO

COCO

COCOCT

CT

CTCT

CT

Coeff = 2204.057Std.Err. = 172.43R2 = .241

020

4060

8010

0Vo

te s

hare


DE

FL

FL

FL

FL

FL

FL

FL FL

FL

FL

FL

FL

FL

FLFL

FL

FL

FL

FLFL

FL

FL

FL

FL

FL

FL

FL

GA

GAGAGA

GA

GA

GA

GA

GA

GA

GAGA

GA

GA

HIHI

ID

ID

IL

ILIL

IL

ILIL

IL

IL

IL

IL

IL

IL

IL

IL

IL

IL

IL

IL

IN

IN

ININ

IN

IN

IN

ININ

IA IAIA

IAALAL AL

AL

AL

AL

AL

KS

KS

KS

KS

KY

KY

KY

KY

KY

KY

LA

LA

LA

LALA LA

ME

ME

MD

MDMD

MD

MD

MD

MD

MD

MAMAMAMA

MA

MA

MA

MA

MA

MI

MI

MI

MI

MIMI

MIMI

MI

MI

MIMI

MI

MI

MN

MN

MN

MN

MN

MNMN

MN

MS

MS

MS

MS

MO

MO

MOMO

MO

MO

MO

MO

AKMTNE

NE

NE

NV

NVNV

NVNH

NH

NJ

NJ

NJ

NJ

NJNJNJ

NJ

NJ

NJ

NJ

NJNM

NM

NM

NY

NY

NY

NY

NY

NY

NY

NY

NY

NY NYNY

NY

NY NY NY

NY

NYNY

NY

NYNY

NY

NY

NYNYNY

NC

NCNC

NC

NC NC

NC

NC

NCNCNCNCNC

ND

OH OH

OH

OH

OH

OHOH OHOH

OH

OHOH

OH

OHOH

OH

OK

OK OK

OK

OK

OR

OR

OR

ORORPA

PA

PA

PA

PA

PA

PA

PA

PA

PA

PA

PA

PAPA

PAPA

PA

PA

RI

RI

SCSC

SC

SCSC

SC

SCSD

TN

TN TNTN

TN

TN

TNTN

TN

TX

TX

TX

TX

TX

TX

TX

TX

TX

TX

TX

TX

TX

TX TX

TXTX

TX

TX TX

TX

TX

TX

TX

TX

TX

TX

TX

TX

TXTX

TX

TX

TX

TX

TX

UTUTUT

UT

AZAZ

AZ

AZ

AZ

AZ

AZ

AZ

AZ

VT

VA

VA

VA

VA

VA

VA

VA

VA

VA

VA

VA

WAWA

WA

WA

WA

WA

WA

WA

WA

WA

WVWV

WV

WI

WI

WI

WI

WI WIWIWI

WY

AR

AR

ARAR

CA

CA

CA

CA CA

CA

CA

CA

CACA CACACA

CA

CA

CA

CA

CA

CA

CA

CA

CA

CA

CA

CA

CACA

CA

CA

CA

CA

CA

CA

CA

CA

CA

CA

CACA

CA

CACA

CACA

CA

CA

CA

CA

CA

CA

CA

CA

CA

CO

CO

CO

COCO

CO COCT

CT

CTCT

CT

Coeff = 4118.811Std.Err. = 296.88R2 = .326

020

4060

8010

0Vo

te s

hare


Notes: The top row of the Figure depicts the scatterplots of the real wage change at a congressional district level against each of the heuristicmeasures defined in Section 5.2. The bottom row of the Figure depicts the scatterplots of the Trump vote share against each of the heuristic measuresdefined in Section 5.2. The lines through the data are the OLS fit. The boxes report the coefficient, robust standard error, and the R2 of the bivariateregression.

25



輸出機会指数と 2016年大統領選のトランプ投票シェア



Real wage change

DE

FLFL

FLFL

FLFLFL FLFLFLFL


GA

GAGA

GA

GA

GA

GA

GA

GAGAGA GA

GA

GA

HIHI IDID

ILIL IL ILILIL IL

ILIL


IN

ININ

IN

IN

IN

ININ

INIA

IA

IA

IA

AL

AL AL

AL

AL

ALAL

KSKS

KS

KSKY

KY

KYKY

KY

KY

LA

LA

LA

LA

LA

LAMEME

MDMDMD MDMD

MDMDMD

MAMAMAMAMA

MAMAMA

MA MI

MI

MI MIMI

MI

MIMI

MI

MI

MIMI

MI

MIMN

MNMN

MNMN

MN

MN

MN

MSMS

MS MSMOMO

MOMOMO

MOMOMO

AK

MT

NENE

NE

NV

NV

NVNV

NHNH

NJNJ

NJNJNJNJNJ

NJNJNJNJNJNM

NM

NM

NYNYNY

NYNY NY

NYNY

NY NYNYNY

NYNY

NYNY

NYNY NYNY

NYNYNY

NYNY NY

NY

NCNC

NCNC

NC NC

NCNC

NCNC

NC

NCNC

ND

OH OHOHOH

OHOH

OH

OH

OHOH

OHOH

OH

OHOH

OH

OKOK

OK

OKOK

OROR

OR

OR

ORPA PA

PA

PAPAPA

PAPAPA

PA

PA

PAPA PAPAPA

PA

PA

RI RISC

SC

SCSC

SC

SCSCSD

TN

TN

TN TN

TN

TN

TNTN

TN

TX

TX

TX

TX

TX

TX

TX

TX TXTX

TX

TXTXTX TX

TX

TXTX

TX

TXTX

TXTX

TX

TXTX TX

TX

TX TX

TXTX

TX

TXTX

TX

UTUTUT

UT

AZAZAZAZ

AZAZAZAZAZVT

VAVA

VA

VAVA VAVA

VA

VAVAVA

WA

WA

WA

WA

WA

WAWA

WAWAWA

WV

WV

WV

WI

WIWIWI

WIWI

WI

WI

WY

AR

AR

ARAR

CA

CA

CACA

CACA CA

CA

CACACACA CA CA

CA

CACA

CACACACACA

CACA

CACACACACA

CA

CA CACACACA

CACA

CACA CACACA CACA

CA

CACACACACACA

CACACO CO

CO

COCOCO CO

CTCT

CTCTCT

Coeff = -3.539Std.Err. = 1.25R2 = .092

-.4-.3

-.2-.1

0.1

Coun

terfa

ctua

l wag

e ch

ange

(with

NTB

)


DE

FLFL

FLFL

FLFLFL FLFLFLFL


GAGA

GAGA

GA

GA

GA

GA

GAGAGA GA

GA

GA

HIHI IDID

IL IL IL ILILIL IL

ILIL


IN

ININ

IN

IN

IN

ININ

INIA

IA

IA

IA

AL

AL AL

AL

AL

ALAL

KSKS

KS

KSKY

KY

KYKY

KY

KY

LA

LA

LA

LA

LA

LAMEME

MDMDMDMDMD

MDMDMD

MAMA MAMAMA

MAMAMA

MA MI

MI

MI MIMI

MI

MIMI

MI

MI

MIMI

MI

MIMN

MNMN

MNMN

MN

MN

MN

MSMS

MS MSMOMO

MOMOMOMOMO

MO

AK

MT

NENE

NE

NV

NV

NVNV

NHNH

NJNJ

NJNJNJNJNJ

NJNJNJNJNJ

NM

NM

NM

NYNYNY

NYNY NY

NYNY

NYNYNYNY

NYNY

NYNY

NYNY NYNY

NYNY

NY

NYNY NY

NY

NCNC

NCNC

NC NC

NCNC

NCNC

NC

NCNC

ND

OH OHOHOH

OH OH

OH

OH

OHOH

OHOH

OH

OHOH

OH

OKOK

OK

OKOK

OROR

OR

OR

ORPA PA

PA

PAPAPA

PAPAPA

PA

PA

PAPA PAPAPA

PA

PA

RI RISC

SC

SCSC

SC

SCSCSD

TN

TN

TN TN

TN

TN

TNTN

TN

TX

TX

TX

TX

TX

TX

TX

TX TXTX

TX

TXTXTX TX

TX

TXTX

TX

TXTX

TXTX

TX

TXTX TX

TX

TX TX

TXTX

TX

TXTXTX

UTUTUT

UT

AZAZ

AZAZAZAZAZAZAZ

VT

VAVA

VA

VAVA VAVA

VA

VAVAVA

WA

WA

WA

WA

WA

WAWA

WAWAWA

WV

WV

WV

WI

WIWIWI

WIWI

WI

WI

WY

AR

AR

ARAR

CA

CA

CACA

CACA CA

CA

CACACACA CACA

CA

CACA

CACACACACACA

CACACACACACA

CA

CACACACACA

CACA

CACA CACACACACA

CA

CACACACACACA

CACACO CO

CO

COCOCOCO

CTCT

CTCT CT

Coeff = -8.18Std.Err. = 0.40R2 = .4

-.4-.3

-.2-.1

0.1

Coun

terfa

ctua

l wag

e ch

ange

(with

NTB

)


DE

FLFL

FLFL

FLFLFL FLFLFLFL


GA

GAGA

GA

GA

GA

GA

GA

GAGAGA GA

GA

GA

HIHI IDID

ILIL IL ILILIL IL

ILIL


IN

ININ

IN

IN

IN

ININ

INIA

IA

IA

IA

AL

AL AL

AL

AL

ALAL

KSKS

KS

KSKY

KY

KYKY

KY

KY

LA

LA

LA

LA

LA

LAMEME

MDMDMD MDMDMDMDMD

MAMAMAMAMA

MAMAMA

MA MI

MI

MI MIMI

MI

MIMI

MI

MI

MIMI

MI

MIMN

MNMN

MNMN

MN

MN

MN

MSMS

MS MSMOMO

MOMOMO

MOMOMO

AK

MT

NENE

NE

NV

NV

NVNV

NHNH

NJNJ

NJNJ NJNJNJ

NJNJNJNJNJ

NM

NM

NM

NYNYNY

NYNY NY

NYNY

NYNYNYNY

NYNY

NYNY

NYNYNYNY

NYNY

NY

NYNY NY

NY

NCNC

NCNCNC NC

NCNC

NCNC

NC

NCNC

ND

OH OHOHOH

OHOH

OH

OH

OHOH

OHOH

OH

OHOH

OH

OKOK

OK

OKOK

OROR

OR

OR

ORPA PA

PA

PAPAPA

PA PAPA

PA

PA

PAPA PAPAPA

PA

PA

RIRISC

SC

SCSC

SC

SCSCSD

TN

TN

TN TN

TN

TN

TNTN

TN

TX

TX

TX

TX

TX

TX

TX

TX TXTX

TX

TXTX TX TX

TX

TXTX

TX

TXTX

TXTX

TX

TXTX TX

TX

TX TX

TXTX

TX

TXTX

TX

UTUTUT

UT

AZAZ

AZAZAZAZAZAZAZ

VT

VAVA

VA

VAVA VAVA

VA

VAVAVA

WA

WA

WA

WA

WA

WAWA

WAWAWA

WV

WV

WV

WI

WIWIWI

WIWI

WI

WI

WY

AR

AR

ARAR

CA

CA

CACA

CACA CA

CA

CACACACACA CA

CA

CACA

CACACACACA

CACA

CACACACA CA

CA

CA CACACACA

CACA

CACA CACACA CACA

CA

CACACACACACA

CACACO CO

CO

COCO CO CO

CTCT

CTCTCT

Coeff = -6.15Std.Err. = 1.86R2 = .088

-.4-.3

-.2-.1

0.1

Coun

terfa

ctua

l wag

e ch

ange

(with

NTB

)


Trump vote

DE

FL

FL

FL

FL

FL

FL

FL FL

FL

FL

FL

FL

FL

FLFL

FL

FL

FL

FLFL

FL

FL

FL

FL

FL

FL

FL

GA

GAGAGA

GA

GA

GA

GA

GA

GA

GAGA

GA

GA

HIHI

ID

ID

IL

ILIL

IL

IL IL

IL

IL

IL

IL

IL

IL

IL

IL

IL

IL

IL

IL

IN

IN

ININ

IN

IN

IN

ININ

IAIAIA

IAAL AL AL

AL

AL

AL

AL

KS

KS

KS

KS

KY

KY

KY

KY

KY

KY

LA

LA

LA

LALA LA

ME

ME

MD

MDMD

MD

MD

MD

MD

MD

MAMAMAMA

MA

MA

MA

MA

MA

MI

MI

MI

MI

MIMI

MIMI

MI

MI

MIMI

MI

MI

MN

MN

MN

MN

MN

MNMN

MN

MS

MS

MS

MS

MO

MO

MOMO

MO

MO

MO

MO

AKMT NE

NE

NE

NV

NVNV

NVNH

NH

NJ

NJ

NJ

NJ

NJNJNJ

NJ

NJ

NJ

NJ

NJNM

NM

NM

NY

NY

NY

NY

NY

NY

NY

NY

NY

NY NYNY

NY

NY NY NY

NY

NY NY

NY

NYNY

NY

NY

NYNYNY

NC

NCNC

NC

NC NC

NC

NC

NCNCNCNCNC

ND

OH OH

OH

OH

OH

OHOH OHOH

OH

OHOH

OH

OHOH

OH

OK

OK OK

OK

OK

OR

OR

OR

ORORPA

PA

PA

PA

PA

PA

PA

PA

PA

PA

PA

PA

PAPA

PAPA

PA

PA

RI

RI

SCSC

SC

SCSC

SC

SCSD

TN

TN TNTN

TN

TN

TNTN

TN

TX

TX

TX

TX

TX

TX

TX

TX

TX

TX

TX

TX

TX

TX TX

TXTX

TX

TX TX

TX

TX

TX

TX

TX

TX

TX

TX

TX

TXTX

TX

TX

TX

TX

TX

UTUTUT

UT

AZAZ

AZ

AZ

AZ

AZ

AZ

AZ

AZ

VT

VA

VA

VA

VA

VA

VA

VA

VA

VA

VA

VA

WAWA

WA

WA

WA

WA

WA

WA

WA

WA

WVWV

WV

WI

WI

WI

WI

WI WIWIWI

WY

AR

AR

ARAR

CA

CA

CA

CA CA

CA

CA

CA

CACA CACA CA

CA

CA

CA

CA

CA

CA

CA

CA

CA

CA

CA

CA

CACA

CA

CA

CA

CA

CA

CA

CA

CA

CA

CA

CACA

CA

CACA

CACA

CA

CA

CA

CA

CA

CA

CA

CA

CA

CO

CO

CO

COCO

CO COCT

CT

CTCT

CT

Coeff = 2342.11Std.Err. = 161.50R2 = .335

020

4060

8010

0Vo

te s

hare


DE

FL

FL

FL

FL

FL

FL

FL FL

FL

FL

FL

FL

FL

FLFL

FL

FL

FL

FLFL

FL

FL

FL

FL

FL

FL

FL

GA

GAGAGA

GA

GA

GA

GA

GA

GA

GAGA

GA

GA

HIHI

ID

ID

IL

ILIL

IL

IL IL

IL

IL

IL

IL

IL

IL

IL

IL

IL

IL

IL

IL

IN

IN

ININ

IN

IN

IN

ININ

IAIAIA

IAAL AL AL

AL

AL

AL

AL

KS

KS

KS

KS

KY

KY

KY

KY

KY

KY

LA

LA

LA

LALA LA

ME

ME

MD

MDMD

MD

MD

MD

MD

MD

MAMA MAMA

MA

MA

MA

MA

MA

MI

MI

MI

MI

MIMI

MIMI

MI

MI

MIMI

MI

MI

MN

MN

MN

MN

MN

MNMN

MN

MS

MS

MS

MS

MO

MO

MOMO

MO

MO

MO

MO

AKMT NE

NE

NE

NV

NVNV

NVNH

NH

NJ

NJ

NJ

NJ

NJNJNJ

NJ

NJ

NJ

NJ

NJNM

NM

NM

NY

NY

NY

NY

NY

NY

NY

NY

NY

NY NYNY

NY

NY NY NY

NY

NY NY

NY

NYNY

NY

NY

NYNYNY

NC

NCNC

NC

NC NC

NC

NC

NCNCNCNCNC

ND

OH OH

OH

OH

OH

OHOH OHOH

OH

OHOH

OH

OHOH

OH

OK

OKOK

OK

OK

OR

OR

OR

ORORPA

PA

PA

PA

PA

PA

PA

PA

PA

PA

PA

PA

PAPA

PAPA

PA

PA

RI

RI

SCSC

SC

SCSC

SC

SCSD

TN

TN TNTN

TN

TN

TNTN

TN

TX

TX

TX

TX

TX

TX

TX

TX

TX

TX

TX

TX

TX

TX TX

TXTX

TX

TX TX

TX

TX

TX

TX

TX

TX

TX

TX

TX

TXTX

TX

TX

TX

TX

TX

UTUTUT

UT

AZAZ

AZ

AZ

AZ

AZ

AZ

AZ

AZ

VT

VA

VA

VA

VA

VA

VA

VA

VA

VA

VA

VA

WAWA

WA

WA

WA

WA

WA

WA

WA

WA

WVWV

WV

WI

WI

WI

WI

WI WIWIWI

WY

AR

AR

ARAR

CA

CA

CA

CA CA

CA

CA

CA

CACACACA CA

CA

CA

CA

CA

CA

CA

CA

CA

CA

CA

CA

CA

CACA

CA

CA

CA

CA

CA

CA

CA

CA

CA

CA

CACA

CA

CACA

CACA

CA

CA

CA

CA

CA

CA

CA

CA

CA

CO

CO

CO

COCO

COCOCT

CT

CTCT

CT

Coeff = 2204.057Std.Err. = 172.43R2 = .241

020

4060

8010

0Vo

te s

hare


DE

FL

FL

FL

FL

FL

FL

FL FL

FL

FL

FL

FL

FL

FLFL

FL

FL

FL

FLFL

FL

FL

FL

FL

FL

FL

FL

GA

GAGAGA

GA

GA

GA

GA

GA

GA

GAGA

GA

GA

HIHI

ID

ID

IL

ILIL

IL

ILIL

IL

IL

IL

IL

IL

IL

IL

IL

IL

IL

IL

IL

IN

IN

ININ

IN

IN

IN

ININ

IA IAIA

IAALAL AL

AL

AL

AL

AL

KS

KS

KS

KS

KY

KY

KY

KY

KY

KY

LA

LA

LA

LALA LA

ME

ME

MD

MDMD

MD

MD

MD

MD

MD

MAMAMAMA

MA

MA

MA

MA

MA

MI

MI

MI

MI

MIMI

MIMI

MI

MI

MIMI

MI

MI

MN

MN

MN

MN

MN

MNMN

MN

MS

MS

MS

MS

MO

MO

MOMO

MO

MO

MO

MO

AKMTNE

NE

NE

NV

NVNV

NVNH

NH

NJ

NJ

NJ

NJ

NJNJNJ

NJ

NJ

NJ

NJ

NJNM

NM

NM

NY

NY

NY

NY

NY

NY

NY

NY

NY

NY NYNY

NY

NY NY NY

NY

NYNY

NY

NYNY

NY

NY

NYNYNY

NC

NCNC

NC

NC NC

NC

NC

NCNCNCNCNC

ND

OH OH

OH

OH

OH

OHOH OHOH

OH

OHOH

OH

OHOH

OH

OK

OK OK

OK

OK

OR

OR

OR

ORORPA

PA

PA

PA

PA

PA

PA

PA

PA

PA

PA

PA

PAPA

PAPA

PA

PA

RI

RI

SCSC

SC

SCSC

SC

SCSD

TN

TN TNTN

TN

TN

TNTN

TN

TX

TX

TX

TX

TX

TX

TX

TX

TX

TX

TX

TX

TX

TX TX

TXTX

TX

TX TX

TX

TX

TX

TX

TX

TX

TX

TX

TX

TXTX

TX

TX

TX

TX

TX

UTUTUT

UT

AZAZ

AZ

AZ

AZ

AZ

AZ

AZ

AZ

VT

VA

VA

VA

VA

VA

VA

VA

VA

VA

VA

VA

WAWA

WA

WA

WA

WA

WA

WA

WA

WA

WVWV

WV

WI

WI

WI

WI

WI WIWIWI

WY

AR

AR

ARAR

CA

CA

CA

CA CA

CA

CA

CA

CACA CACACA

CA

CA

CA

CA

CA

CA

CA

CA

CA

CA

CA

CA

CACA

CA

CA

CA

CA

CA

CA

CA

CA

CA

CA

CACA

CA

CACA

CACA

CA

CA

CA

CA

CA

CA

CA

CA

CA

CO

CO

CO

COCO

CO COCT

CT

CTCT

CT

Coeff = 4118.811Std.Err. = 296.88R2 = .326

020

4060

8010

0Vo

te s

hare



25



中間財輸入指数と 2016年大統領選のトランプ投票シェア



Real wage change

DE

FLFL

FLFL

FLFLFL FLFLFLFL


GA

GAGA

GA

GA

GA

GA

GA

GAGAGA GA

GA

GA

HIHI IDID

ILIL IL ILILIL IL

ILIL


IN

ININ

IN

IN

IN

ININ

INIA

IA

IA

IA

AL

AL AL

AL

AL

ALAL

KSKS

KS

KSKY

KY

KYKY

KY

KY

LA

LA

LA

LA

LA

LAMEME

MDMDMD MDMD

MDMDMD

MAMAMAMAMA

MAMAMA

MA MI

MI

MI MIMI

MI

MIMI

MI

MI

MIMI

MI

MIMN

MNMN

MNMN

MN

MN

MN

MSMS

MS MSMOMO

MOMOMO

MOMOMO

AK

MT

NENE

NE

NV

NV

NVNV

NHNH

NJNJ

NJNJNJNJNJ

NJNJNJNJNJNM

NM

NM

NYNYNY

NYNY NY

NYNY

NY NYNYNY

NYNY

NYNY

NYNY NYNY

NYNYNY

NYNY NY

NY

NCNC

NCNC

NC NC

NCNC

NCNC

NC

NCNC

ND

OH OHOHOH

OHOH

OH

OH

OHOH

OHOH

OH

OHOH

OH

OKOK

OK

OKOK

OROR

OR

OR

ORPA PA

PA

PAPAPA

PAPAPA

PA

PA

PAPA PAPAPA

PA

PA

RI RISC

SC

SCSC

SC

SCSCSD

TN

TN

TN TN

TN

TN

TNTN

TN

TX

TX

TX

TX

TX

TX

TX

TX TXTX

TX

TXTXTX TX

TX

TXTX

TX

TXTX

TXTX

TX

TXTX TX

TX

TX TX

TXTX

TX

TXTX

TX

UTUTUT

UT

AZAZAZAZ

AZAZAZAZAZVT

VAVA

VA

VAVA VAVA

VA

VAVAVA

WA

WA

WA

WA

WA

WAWA

WAWAWA

WV

WV

WV

WI

WIWIWI

WIWI

WI

WI

WY

AR

AR

ARAR

CA

CA

CACA

CACA CA

CA

CACACACA CA CA

CA

CACA

CACACACACA

CACA

CACACACACA

CA

CA CACACACA

CACA

CACA CACACA CACA

CA

CACACACACACA

CACACO CO

CO

COCOCO CO

CTCT

CTCTCT

Coeff = -3.539Std.Err. = 1.25R2 = .092

-.4-.3

-.2-.1

0.1

Coun

terfa

ctua

l wag

e ch

ange

(with

NTB

)


DE

FLFL

FLFL

FLFLFL FLFLFLFL


GAGA

GAGA

GA

GA

GA

GA

GAGAGA GA

GA

GA

HIHI IDID

IL IL IL ILILIL IL

ILIL


IN

ININ

IN

IN

IN

ININ

INIA

IA

IA

IA

AL

AL AL

AL

AL

ALAL

KSKS

KS

KSKY

KY

KYKY

KY

KY

LA

LA

LA

LA

LA

LAMEME

MDMDMDMDMD

MDMDMD

MAMA MAMAMA

MAMAMA

MA MI

MI

MI MIMI

MI

MIMI

MI

MI

MIMI

MI

MIMN

MNMN

MNMN

MN

MN

MN

MSMS

MS MSMOMO

MOMOMOMOMO

MO

AK

MT

NENE

NE

NV

NV

NVNV

NHNH

NJNJ

NJNJNJNJNJ

NJNJNJNJNJ

NM

NM

NM

NYNYNY

NYNY NY

NYNY

NYNYNYNY

NYNY

NYNY

NYNY NYNY

NYNY

NY

NYNY NY

NY

NCNC

NCNC

NC NC

NCNC

NCNC

NC

NCNC

ND

OH OHOHOH

OH OH

OH

OH

OHOH

OHOH

OH

OHOH

OH

OKOK

OK

OKOK

OROR

OR

OR

ORPA PA

PA

PAPAPA

PAPAPA

PA

PA

PAPA PAPAPA

PA

PA

RI RISC

SC

SCSC

SC

SCSCSD

TN

TN

TN TN

TN

TN

TNTN

TN

TX

TX

TX

TX

TX

TX

TX

TX TXTX

TX

TXTXTX TX

TX

TXTX

TX

TXTX

TXTX

TX

TXTX TX

TX

TX TX

TXTX

TX

TXTXTX

UTUTUT

UT

AZAZ

AZAZAZAZAZAZAZ

VT

VAVA

VA

VAVA VAVA

VA

VAVAVA

WA

WA

WA

WA

WA

WAWA

WAWAWA

WV

WV

WV

WI

WIWIWI

WIWI

WI

WI

WY

AR

AR

ARAR

CA

CA

CACA

CACA CA

CA

CACACACA CACA

CA

CACA

CACACACACACA

CACACACACACA

CA

CACACACACA

CACA

CACA CACACACACA

CA

CACACACACACA

CACACO CO

CO

COCOCOCO

CTCT

CTCT CT

Coeff = -8.18Std.Err. = 0.40R2 = .4

-.4-.3

-.2-.1

0.1

Coun

terfa

ctua

l wag

e ch

ange

(with

NTB

)


DE

FLFL

FLFL

FLFLFL FLFLFLFL


GA

GAGA

GA

GA

GA

GA

GA

GAGAGA GA

GA

GA

HIHI IDID

ILIL IL ILILIL IL

ILIL


IN

ININ

IN

IN

IN

ININ

INIA

IA

IA

IA

AL

AL AL

AL

AL

ALAL

KSKS

KS

KSKY

KY

KYKY

KY

KY

LA

LA

LA

LA

LA

LAMEME

MDMDMD MDMDMDMDMD

MAMAMAMAMA

MAMAMA

MA MI

MI

MI MIMI

MI

MIMI

MI

MI

MIMI

MI

MIMN

MNMN

MNMN

MN

MN

MN

MSMS

MS MSMOMO

MOMOMO

MOMOMO

AK

MT

NENE

NE

NV

NV

NVNV

NHNH

NJNJ

NJNJ NJNJNJ

NJNJNJNJNJ

NM

NM

NM

NYNYNY

NYNY NY

NYNY

NYNYNYNY

NYNY

NYNY

NYNYNYNY

NYNY

NY

NYNY NY

NY

NCNC

NCNCNC NC

NCNC

NCNC

NC

NCNC

ND

OH OHOHOH

OHOH

OH

OH

OHOH

OHOH

OH

OHOH

OH

OKOK

OK

OKOK

OROR

OR

OR

ORPA PA

PA

PAPAPA

PA PAPA

PA

PA

PAPA PAPAPA

PA

PA

RIRISC

SC

SCSC

SC

SCSCSD

TN

TN

TN TN

TN

TN

TNTN

TN

TX

TX

TX

TX

TX

TX

TX

TX TXTX

TX

TXTX TX TX

TX

TXTX

TX

TXTX

TXTX

TX

TXTX TX

TX

TX TX

TXTX

TX

TXTX

TX

UTUTUT

UT

AZAZ

AZAZAZAZAZAZAZ

VT

VAVA

VA

VAVA VAVA

VA

VAVAVA

WA

WA

WA

WA

WA

WAWA

WAWAWA

WV

WV

WV

WI

WIWIWI

WIWI

WI

WI

WY

AR

AR

ARAR

CA

CA

CACA

CACA CA

CA

CACACACACA CA

CA

CACA

CACACACACA

CACA

CACACACA CA

CA

CA CACACACA

CACA

CACA CACACA CACA

CA

CACACACACACA

CACACO CO

CO

COCO CO CO

CTCT

CTCTCT

Coeff = -6.15Std.Err. = 1.86R2 = .088

-.4-.3

-.2-.1

0.1

Coun

terfa

ctua

l wag

e ch

ange

(with

NTB

)


Trump vote

DE

FL

FL

FL

FL

FL

FL

FL FL

FL

FL

FL

FL

FL

FLFL

FL

FL

FL

FLFL

FL

FL

FL

FL

FL

FL

FL

GA

GAGAGA

GA

GA

GA

GA

GA

GA

GAGA

GA

GA

HIHI

ID

ID

IL

ILIL

IL

IL IL

IL

IL

IL

IL

IL

IL

IL

IL

IL

IL

IL

IL

IN

IN

ININ

IN

IN

IN

ININ

IAIAIA

IAAL AL AL

AL

AL

AL

AL

KS

KS

KS

KS

KY

KY

KY

KY

KY

KY

LA

LA

LA

LALA LA

ME

ME

MD

MDMD

MD

MD

MD

MD

MD

MAMAMAMA

MA

MA

MA

MA

MA

MI

MI

MI

MI

MIMI

MIMI

MI

MI

MIMI

MI

MI

MN

MN

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Coeff = 4118.811Std.Err. = 296.88R2 = .326

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25


まとめ

今回の報告では以下の研究の紹介を行いました。

David Autorらによるチャイナショックの負の側面の研究製造業雇用婚姻率イノベーション政治的二極化

Robert Feenstraらによるチャイナショックの正の側面の研究サービス業雇用財価格の低下

その他の第一線の研究者による分析Lorenzo Caliendoらによるチャイナショックの一般均衡分析Teresa Fort, Nicholas Bloomらによるミクロデータを用いた実証研究Auer, Bonadio, Levchenkoによる NAFTA撤回の経済分析


参考文献

Amiti, Mary, Mi Dai, Robert C. Feenstra, and John Romalis (2018) “How DidChina’s WTO Entry Affect U.S. Prices?”, NBER Working Paper No. 23487.

Auer, Raphael A., Barthelemy Bonadio, and Andrei A. Levchenko (2018) “TheEconomics and Politics of Revoking NAFTA”, NBER Working Paper, No. 25379.

Autor, David H., David Dorn, and Gordon H. Hanson (2013) “The ChinaSyndrome: Local Labor Market Effects of Import Competition in the UnitedStates”, American Economic Review, Vol. 103, No. 6, pp. 2121-2168.

Autor, David H., David Dorn, and Gordon H. Hanson (2017) “When WorkDisappears: Manufacturing Decline and the Falling Marriage-Market Value ofYoung Men”, forthcoming in American Economic Review: Insights.

Autor, David H., David Dorn, Gordon H. Hanson, and Kaveh Majlesi (2016)“Importing Political Polarization? The Electoral Consequences of Rising TradeExposure”, Unpublished Manuscript.

Autor, David H., David Dorn, Gordon H. Hanson, Gary Pisano, and Pian Shu(2017) “Foreign Competition and Domestic Innovation: Evidence from U.S.Patents”, NBER Working Paper No. 22879, forthcoming in American EconomicReview: Insights.


参考文献

Bloom, Nicholas, Kyle Handley, Andre Kurmann, Philip Luck (2019) “The Impactof Chinese Trade on U.S. Employment: The Good, The Bad, and TheApocryphal”, Unpublished manuscript.

Barrot, Jean-Noel, Erik Loualiche, Matthew Plosser, and Julien Sauvagnat (2016)“Import Competition and Household Debt”, Unpublished manuscript.

Caliendo, Lorenzo, Maximiliano Dvorkin, and Fernando Parro (2018) “Trade andLabor Market Dynamics: General Equilibrium Analysis of the China Trade Shock”,FRB St. Louis Working Paper No. 2015-9, forthcoming in Econometrica.

Feenstra, Robert C., Hong Ma, and Yuan Xu (2017) “US Exports andEmployment,”NBER Working Paper No. 24056.

Feenstra, Robert C., Hong Ma, and Yuan Xu (2019) “Magnification of the ‘ChinaShock’ through the U.S. Housing Market,”Unpublished manuscript.

Feenstra, Robert C. and Akira Sasahara (2018) “The ’China Shock’, Exports andU.S. Employment: A Global Input-Output Analysis,” Review of InternationalEconomics, Vol. 26, No. 5, pp. 1053-1083.

Feenstra, Robert C. and Akira Sasahara (2019) “The ’China Shock’ in Trade:Consequences for ASEAN and East Asia,” In: World Trade Evolution: Growth,Productivity and Employment, edited by Lili Yan Ing and Miaojie Yu, Routledge.


参考文献

Feler, Leo and Mine Z. Senses (2017) “Trade Shocks and the Provision of LocalPublic Goods”, American Economic Journal: Economic Policy, Vol. 9, No. 4, pp.101-143.

Fort, Teresa C., Justin R. Pierce, and Peter K. Schott (2018) “New Perspectiveson the Decline of US Manufacturing Employment”, Journal of EconomicPerspectives, Vol. 32, No. 2, pp. 47-72.

Institute for Fiscal Studies on YouTube (2017) “What Would the US Look Likewithout the China Shock?”, available athttps://www.youtube.com/watch?v=c07dcInoG0Y&t=2s


https://www.youtube.com/watch?v=c07dcInoG0Y&t=2s

チャイナショックのアメリカでの影響 - ESRI · 1990 1995 2000 2005 2010 2015 ... ESRI 経済政策フォーラム 平成31年2月5日 6 / 14....

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チャイナショックのアメリカでの影響 - ESRI · 1990 1995 2000 2005 2010 2015 ... ESRI 経済政策フォーラム平成31年2月5日 6 / 14....