Trade, Migration and Regional Income Differences: Evidence from China

Introduction Model Quantitative Analysis Conclusion Appendix Trade, Migration and Regional Income Differences: Evidence from China Trevor Tombe U...
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Introduction

Model

Quantitative Analysis

Conclusion

Appendix

Trade, Migration and Regional Income Differences: Evidence from China Trevor Tombe University of Calgary

Xiaodong Zhu University of Toronto and SAIF

Department of Economics, Yale

October 7, 2014

1 / 36

Introduction

Model

Quantitative Analysis

Conclusion

Appendix

Motivation • Aggregate gains from trade widely studied • What about spatial distribution of gains from trade? • Aggregate and spatial effects of trade liberalization depend on

costs to internal trade and factor movements • How large are internal trade and migration costs? Do they differ across space? ... change through time? .. interact with each other?

• To answer these questions, we develop a model and apply it to

a useful setting (China) • Significant recent liberalizations (internal and external) • Large inter-province worker flows (40M in 2005; 86M in 2010) • Massive internal income differences

1 / 36

Introduction

Model

Quantitative Analysis

Conclusion

Appendix

Related Literature • International trade with multi-region countries: • Henderson (1982), Rauch (1991), Bond (1993), Courant and Deardorff (1993), Krugman and Livas Elizondo (1996), Matsuyama (1999), and Venables and Limao (2002)

• Costly Internal Trade (no labour frictions): • Ramondo et al. (2011); Allen and Arkolakis (2012); Cosar and Fajgelbaum (2012); Caliendo et. al. (2014); Redding (2014); Fajgelbaum and Redding (2014); Tombe and Winter (2014)

• Trade Induced Labour Reallocation (no internal trade): • Kambourov (2009); Artuc et al. (2010); Menezes-Filho and Muendler (2011); Cosar (2013); Dix-Carneiro (2014)

• Commuting Decisions: • Ahlfeldt, Redding, Sturm, and Wolf (2012)

• Occupational Choice: • Cortes and Gallipoli (2014) 2 / 36

Introduction

Model

Quantitative Analysis

Conclusion

Appendix

This Paper • Build unique dataset for China: 2000/02 – 2005/07 • We develop a general equilibrium model of internal and

external trade with goods and factor market frictions • We introduce factor mobility frictions: model migration decisions (Artuc et al., 2008; Ahlfeldt et al., 2012; Redding, 2014; Cortes and Gallipoli, 2014)

• Measure and quantify the effect of (1) international trade

liberalization, (2) internal trade liberalization, (3) factor market liberalization, (4) productivity change on: • Welfare — aggregate and regional • Migration — between provinces • Income Differences — between provinces 3 / 36

Introduction

Model

Quantitative Analysis

Conclusion

Appendix

Data (in brief) • Migration: 2000 and 2005 census data • From 2005 census, we can identify for each province those who have immigrated between 2000 and 2005 • Individual earnings data (2005 only) will prove important

• Trade Flows: Extended regional I/O tables 2002 and 2007 • Information on international trade for each province and bilateral trade for each pair of provinces (2002) or regions (2007) • Province-level gross output and total expenditures

• Real Income: Price and GDP data • Nominal GDP by provinces • Province-specific price levels • 1990 common basket price levels + provincial CPI changes

4 / 36

Introduction

Model

Quantitative Analysis

Conclusion

Appendix

Visualizing Key Features of the Data Figure: The Geography of China

Heilongjiang

Inner Mongol

Jilin Liaoning

Xinjiang

Beijing Tianjin Shanxi Hebei

Ningxia Qinghai

Shandong Gansu

Shaanxi

Tibet

Henan Hubei

Sichuan

Jiangsu Anhui Shanghai Zhejiang

Guizhou

Hunan Jiangxi Fujian

Yunnan Guangxi Guangdong

Hainan

Table

5 / 36

Introduction

Model

Quantitative Analysis

Conclusion

Appendix

Visualizing Key Features of the Data Figure: Output per Capita (90th/10th ∼ 7)

Heilongjiang

Inner Mongol

Jilin Liaoning

Xinjiang

Beijing Tianjin Shanxi Hebei

Ningxia Qinghai

Shandong Gansu

Shaanxi

Tibet

Henan Hubei

Sichuan

Jiangsu Anhui Shanghai Zhejiang

Guizhou

Hunan Jiangxi Fujian

Yunnan Guangxi Guangdong

Hainan

Table

6 / 36

Introduction

Model

Quantitative Analysis

Conclusion

Appendix

Visualizing Key Features of the Data Figure: Home-Share of Spending (90th=0.86, 10th=0.62 )

Heilongjiang

Inner Mongol

Jilin Liaoning

Xinjiang

Beijing Tianjin Shanxi Hebei

Ningxia Qinghai

Shandong Gansu

Shaanxi

Tibet

Henan Hubei

Sichuan

Jiangsu Anhui Shanghai Zhejiang

Guizhou

Hunan Jiangxi Fujian

Yunnan Guangxi Guangdong

Hainan

Table

7 / 36

Introduction

Model

Quantitative Analysis

Conclusion

Appendix

Visualizing Key Features of the Data Figure: Migrant Worker Shares (90th=0.2, 10th=0.006)

Heilongjiang

Inner Mongol

Jilin Liaoning

Xinjiang

Beijing Tianjin Shanxi Hebei

Ningxia Qinghai

Shandong Gansu

Shaanxi

Tibet

Henan Hubei

Sichuan

Jiangsu Anhui Shanghai Zhejiang

Guizhou

Hunan Jiangxi Fujian

Yunnan Guangxi Guangdong

Hainan

Table

8 / 36

Introduction

Model

Quantitative Analysis

Conclusion

Appendix

Table: Migrant Characteristics (from Census Data)

Total Migrants Inter-Provincial Migrants Inter-Provincial Migrant Workers

1990

2000

2005

32.7 M 10.5 M 2M

130.6 M 35.8 M 28 M

165.4 M 53 M 40 M

(a) Migrant Stock All Migrants Number 165.4 M Reason for Migrating Work 45% Family 30% Education 6% Other 18% Other Characteristics With Children 30% Agricultural Hukou 62% Male 50%

Inter-Provincial Migrants

Employed Inter-Provincial Migrants

53 M

40 M

73% 21% 2% 4%

91% 6% 2% 0.3%

28% 83% 53%

27% 86% 57%

(b) Characteristics of Migrant Stock (Census 2005) 9 / 36

Introduction

Model

Quantitative Analysis

Conclusion

Appendix

Migration Costs Wide variety of very large costs to live outside one’s Hukou region: • Lack of employment contracts (no provision of benets or other

legal rights; reform 2007) • Difficult to find housing (couldn’t rent an apartment in Beijing

until 2005) • Unregistered migrants detained/deported (until 2003,

following a death) • Limited health insurance access • Children attend school barred or expensive fees (can be 20% of

income) • Other (more standard) costs: • communication with and travel to home province • language/ethnic dierences

10 / 36

Introduction

Model

Quantitative Analysis

Conclusion

Appendix

Internal Trade Barriers Pre-2001: • Strong local protectionism and high internal trade barriers in

the 1980s and 1990s (Young, 2000; Poncet, 2003) • The degree of local market protection is positively associated

with the size of the state sector in the region Post-2001: • Downsizing the state-owned sector • State council’s directive about eliminating local market

protection in 2001

11 / 36

Introduction

Model

Quantitative Analysis

Conclusion

Appendix

Main Results

• Welfare gains are, by far, largest for domestic reforms

(especially internal trade cost reductions) • Trade flows respond very little to changes in migration costs • Internal migration responds very little to changes in trade costs • Internal (not external) liberalization lowers income differences

12 / 36

Model

Introduction

Model

Quantitative Analysis

Conclusion

Appendix

Regions and Preferences • N + 1 regions: N within China + rest of the world • Endowments: • L0n initial Hukou registrants • Each of whom differ in productivity (more on this later) • Sn fixed land used for housing and production

• Representative H.H. Objective: • Maximize utility per effective-worker

un = cnα su1−α n subject to Pn cn + rn sun ≤ vn

13 / 36

Introduction

Model

Quantitative Analysis

Conclusion

Appendix

Production • Final Good: composite of a continuum of intermediates

ˆ Yn =

1

yn (j)(σ−1)/σ dj

σ/(σ−1)

0

• Elasticity of substitution σ > 1 • Final goods are consumed (C ) and used in production as

inputs (Q); market clearing ⇒ Yn = Cn + Qn • Tradable Intermediates: y produced with CRS technology

using effective-labour (H), land (SY ), and inputs (Q) yn (j) = ϕn (j)Hn (j)β SYn (j)η Qn (j)1−β−η • TFP ϕ differs across firms; as in Eaton and Kortum (2002)

14 / 36

Introduction

Model

Quantitative Analysis

Conclusion

Appendix

Prices and Trade Patterns • Iceberg trade costs τni + perfect competition ⇒

pni (ϕ) = τni MCi (ϕ) ∝ τni wiβ riη Pi1−β−η /ϕ

15 / 36

Introduction

Model

Quantitative Analysis

Conclusion

Appendix

Prices and Trade Patterns • Iceberg trade costs τni + perfect competition ⇒

pni (ϕ) = τni MCi (ϕ) ∝ τni wiβ riη Pi1−β−η /ϕ −θ • With TFP ϕ distributed Frechet: Fi (ϕ) = e −Ti ϕ , fraction of

region n spending allocated to goods produced in region i is  −θ Ti τni wiβ riη Pi1−β−η πni = −θ  PN+1 β η 1−β−η T τ w r P k nk k=1 k k k

15 / 36

Introduction

Model

Quantitative Analysis

Conclusion

Appendix

Prices and Trade Patterns • Iceberg trade costs τni + perfect competition ⇒

pni (ϕ) = τni MCi (ϕ) ∝ τni wiβ riη Pi1−β−η /ϕ −θ • With TFP ϕ distributed Frechet: Fi (ϕ) = e −Ti ϕ , fraction of

region n spending allocated to goods produced in region i is  −θ Ti τni wiβ riη Pi1−β−η πni = −θ  PN+1 β η 1−β−η T τ w r P k nk k=1 k k k • Aggregate price index in region n (for tradables)

Pn ∝

"N+1 X

Tk



τnk wkβ rkη Pk1−β−η

−θ

#−1/θ

k=1

• Finally, market clearing for land ⇒ rn ∝ wn Hn /Sn 15 / 36

Introduction

Model

Quantitative Analysis

Conclusion

Appendix

Regional Income • Nominal Income (Expenditures) per Effective Worker:

w n + ηwn /β + |(1 − α)v{z | {zn } } Labour Income Spending on Land   β+η wn = αβ

vn =

• Total expenditures: Xn = vn Hn

16 / 36

Introduction

Model

Quantitative Analysis

Conclusion

Appendix

Regional Income • Nominal Income (Expenditures) per Effective Worker:

w n + ηwn /β + |(1 − α)v{z | {zn } } Labour Income Spending on Land   β+η wn = αβ

vn =

• Total expenditures: Xn = vn Hn • Real Income per Effective Worker (vn /Pnα rn1−α ): α



α

η+(1−α)β

(S /H ) β+η Tnθ(β+η) π θ(β+η) · · | {z } | nn{z } | n n{z } Vn ∝ Technology Market Access Land / effective worker 16 / 36

Introduction

Model

Quantitative Analysis

Conclusion

Appendix

Inter-Provincial Migration • Real Income per Effective Worker: Vi in region i • Worker Productivity: different across region • Denote the effective labour units by z • Effective labour is i.i.d. across individuals and locations

• Real Cost of Migration: share time/income lost, 1 − µni • Rule: Migrate from n to i iff µni zi Vi = maxk=1,...,N {µnk zk Vk } • If z follows a Frechet distribution Fz (x) = e

 −κ − γx

, then share of region n registered workers that move to region i is (Vi µni )κ

mni = PN

κ k=1 (Vk µnk )

Gravity Evidence 17 / 36

Introduction

Model

Quantitative Analysis

Conclusion

Appendix

Expected Income and Aggregate Welfare Proposition 2: If worker productivities zi are distributed Frechet with mean 1 and variance parameter κ, and agents are able to migrate between regions at cost µij , then the expected real income net of migration costs for workers from region i is −1/κ

Vi0 = Vi mii

,

and aggregate average real income (welfare) is therefore W

=

N X

−1/κ

λ0i Vi mii

,

i=1

where λ0i =

L0 PN i

j=1

L0j

is region i’s registration share.

  −1/κ Proof: See paper. Intuition: max {µnk zk Vk } ∼ Frechet κ, Vn mnn

18 / 36

Introduction

Model

Quantitative Analysis

Conclusion

Appendix

Eq’m Effective Labour in Each Region • Workers in Region n: Ln =

PN

0 i=1 min Li

19 / 36

Introduction

Model

Quantitative Analysis

Conclusion

Appendix

Eq’m Effective Labour in Each Region PN

0 i=1 min Li

• Workers in Region n: Ln =

• Analogous to Eaton-Kortum, where πni is both (1) share of

goods and (2) share of spending, we can show mni is both 1. Share of workers registered in n that work in region i 2. Share of income earned by all workers registered in n from those workers working in region i

• Effective Workers in Region n:

Hn Vn = ⇒ Hn =

N X

−1/κ

min L0i Vi mii

i=1 N  X

−1 µin min κ



min L0i

i=1

19 / 36

Introduction

Model

Quantitative Analysis

Conclusion

Appendix

Eq’m Effective Labour in Each Region PN

0 i=1 min Li

• Workers in Region n: Ln =

• Analogous to Eaton-Kortum, where πni is both (1) share of

goods and (2) share of spending, we can show mni is both 1. Share of workers registered in n that work in region i 2. Share of income earned by all workers registered in n from those workers working in region i

• Effective Workers in Region n:

Hn Vn = ⇒ Hn =

N X

−1/κ

min L0i Vi mii

i=1 N  X

−1 µin min κ



min L0i

i=1

• Useful with data on real GDP (Hi Vi ), migration (mni , mnn ),

and hukou registrations (L0n ) → solves for Vi , then Hi 19 / 36

Quantitative Analysis

Introduction

Model

Quantitative Analysis

Conclusion

Appendix

The Equilibrium Conditions • Trade balance: vn Hn =

PN+1 i=1



πin vi Hi

• Trade flows: πni ∝ Pnθ Ti τni wiβ riη Pi1−β−η

−θ

20 / 36

Introduction

Model

Quantitative Analysis

Conclusion

Appendix

The Equilibrium Conditions • Trade balance: vn Hn =

PN+1 i=1

πin vi Hi



• Trade flows: πni ∝ Pnθ Ti τni wiβ riη Pi1−β−η • Final good price: Pn−θ ∝

PN+1 i=1

−θ

 −θ Ti τni wiβ riη Pi1−β−η

• Land rental price: rn ∝ wn Hn /Sn • Real income: Vn ∝ wn /Pnα rn1−α

20 / 36

Introduction

Model

Quantitative Analysis

Conclusion

Appendix

The Equilibrium Conditions • Trade balance: vn Hn =

PN+1 i=1

πin vi Hi



• Trade flows: πni ∝ Pnθ Ti τni wiβ riη Pi1−β−η • Final good price: Pn−θ ∝

PN+1 i=1

−θ

 −θ Ti τni wiβ riη Pi1−β−η

• Land rental price: rn ∝ wn Hn /Sn • Real income: Vn ∝ wn /Pnα rn1−α κ

• Migration flows: mni = PN(Vi µni )

κ k=1 (Vk µnk )

20 / 36

Introduction

Model

Quantitative Analysis

Conclusion

Appendix

The Equilibrium Conditions • Trade balance: vn Hn =

PN+1 i=1

πin vi Hi



• Trade flows: πni ∝ Pnθ Ti τni wiβ riη Pi1−β−η • Final good price: Pn−θ ∝

PN+1 i=1

−θ

 −θ Ti τni wiβ riη Pi1−β−η

• Land rental price: rn ∝ wn Hn /Sn • Real income: Vn ∝ wn /Pnα rn1−α κ

• Migration flows: mni = PN(Vi µni )

κ k=1 (Vk µnk )

• Migrant real incomes: Hn Vn ∝

−1/κ 0 i=1 min Li Vi mii

PN

20 / 36

Introduction

Model

Quantitative Analysis

Conclusion

Appendix

The Equilibrium Conditions • Trade balance: vn Hn =

PN+1 i=1

πin vi Hi



• Trade flows: πni ∝ Pnθ Ti τni wiβ riη Pi1−β−η • Final good price: Pn−θ =∝

PN+1 i=1

Ti



−θ

τni wiβ riη Pi1−β−η

−θ

• Land price: rn ∝ wn Hn /Sn • Real income: Vn ∝ wn /Pnα rn1−α • Migration flows: mni = PN(Vi µni )

κ

κ k=1 (Vk µnk )

• Migrant real incomes: Hn Vn ∝

−1/κ 0 i=1 min Li Vi mii

PN

20 / 36

Introduction

Model

Quantitative Analysis

Conclusion

Appendix

Exact-Hat Algebra (Dekle et al., 2008) • Solving for relative changes eases the analysis

rn ∝ wn Hn /Sn ⇒ rˆn = w ˆn Hˆn • Another (less trivial) example:

PN+1

Pˆn−θ =

=



0

Ti



0

0β 0η

0 1−β−η

−θ

τni wi ri Pi  −θ PN+1 β η 1−β−η T τ w r P i ni i=1 i i i −θ  −θ PN+1  β η 1−β−η β η ˆ 1−β−η ˆi τˆni w τ w r P ˆ r ˆ P T T i ni i i i i=1 i i i  −θ PN+1 β η 1−β−η i=1 Ti τni wi ri Pi i=1

N+1 X

−θ  Tˆi τˆni w ˆiβ rˆiη Pˆi1−β−η πni

i=1 21 / 36

Introduction

Model

Quantitative Analysis

Conclusion

Exact-Hat Algebra (Dekle et al., 2008) 

Appendix

Data: πni

  0 , π 0 , given (π , L , m , X , L0 ) Model mapping F : τˆni , Tˆn , µ ˆni → Vˆn , mni n n ni ni n ni     0 , π0 Our strategy: infer τˆni , Tˆn , µ ˆni from F −1 Vˆn , mni ni

w ˆn Hˆn Xn



=

N+1 X

πin0 w ˆi Hˆi Xi ,

(1)

i=1

πni0

=

Pˆn−θ

=

 −θ ˆ ˜η Pˆnθ πni Tˆi τˆni w ˆiβ+η Pˆi1−β−η L , i N+1 X

 −θ ˆ ˜η πni Tˆi τˆni w ˆiβ+η Pˆi1−β−η L , i

(2) (3)

i=1

Vˆn

=

0 min

=

Hn0 Vn0

=

w ˆnα w ˆn = , Pˆnα rˆn1−α Pˆnα Hˆn1−α  κ min Vˆn µ ˆin  κ , PN ˆ ˆik k=1 mik Vk µ N X

0 min L0i Vi0 mii0

−1/κ

(4)

(5)

.

(6)

i=1 22 / 36

Introduction

Model

Quantitative Analysis

Conclusion

Appendix

Calibrating the Model Parameter

Value

Target

η

0.1 0.6 0.13 Data Data Model-Implied 4

Land’s share of gross output Intermediate’s share of output Housing’s share of expenditure National Employment Level Bilateral Trade Shares Initial Eq’m GDP Elasticity of Trade

1−β−η 1−α Li πij Xn θ

Details to Follow τˆni κ µ ˆni Tˆn

Pair Specific 2.21 Pair Specific Region Specific

Bilateral Trade Shares Ex-Post Income Dispersion Migration and Real Income Gaps Real Income Data

22 / 36

Introduction

Model

Quantitative Analysis

Conclusion

Appendix

Estimating Trade Costs • Flexible trade costs index (Head and Ries, 2001; Novy, 2013)



• Recall: πni ∝ Pnθ Ti τni wiβ riη Pi1−β−η

• ⇒ ln



πni πnn



= ln

−θ

 −θ ! Pnθ Ti τni wiβ riη Pi1−β−η  −θ Pnθ Tn wnβ rnη Pn1−β−η

≡ Sn −Si −θln(τni )

23 / 36

Introduction

Model

Quantitative Analysis

Conclusion

Appendix

Estimating Trade Costs • Flexible trade costs index (Head and Ries, 2001; Novy, 2013)



• Recall: πni ∝ Pnθ Ti τni wiβ riη Pi1−β−η



πni πnn



= ln

• ⇒ ln ππni nn



+ ln

• ⇒ ln



−θ

 −θ ! Pnθ Ti τni wiβ riη Pi1−β−η  −θ Pnθ Tn wnβ rnη Pn1−β−η



πin πii



≡ Sn −Si −θln(τni )

= −θ [ln (τni ) + ln (τin )]

23 / 36

Introduction

Model

Quantitative Analysis

Conclusion

Appendix

Estimating Trade Costs • Flexible trade costs index (Head and Ries, 2001; Novy, 2013)



• Recall: πni ∝ Pnθ Ti τni wiβ riη Pi1−β−η



πni πnn



= ln

• ⇒ ln ππni nn



+ ln

• ⇒ ln



−θ

 −θ ! Pnθ Ti τni wiβ riη Pi1−β−η  −θ Pnθ Tn wnβ rnη Pn1−β−η



πin πii



≡ Sn −Si −θln(τni )

= −θ [ln (τni ) + ln (τin )]

• The Head-Reis Index: τ¯ni ≡



τni τin =



πnn πii πni πin

1/2θ

• Notice: it’s symmetric (¯ τni = τ¯in ) • We modify the index to incorporate region-specific costs • i.e. an exporter-specific cost ti implies

τni = τ¯ni

p

ti /tn 23 / 36

Introduction

Model

Quantitative Analysis

Conclusion

Appendix

Estimating Trade Cost Asymmetries • If asymmetries are export costs, then τni = dni ti • Waugh (2010): asymmetries are exporter-specific • Tombe and Winter (2014) show this is also true within countries

• Models imply ln (πni /πnn ) = Si − Sn − θln (τni ); so, estimate



 πni ln = ρni + ιn + ηi + ni , πnn where ρni is a directionless pair-effect such that ρni = ρin , and ιn and ηi are importer- and exporter-effects • As the exporter effect is ηi = Si − θln (ti ) and the importer

effect is ιn = −Sn , we infer export costs as tn = e −(ιn +ηn )/θ

Export Cost Estimates 24 / 36

Introduction

Model

Quantitative Analysis

Conclusion

Appendix

Relative Change in Trade Costs

Table: % Change in Trade Costs τni2007 /τni2002

Importer n NE B&T N Cst C Cst S Cst Cntrl NW SW WLD Distance, not ρni

NE -14.2 -5.7 -16.4 -18.4 -6.6 -4 -3.8 -3.8

B&T -11.8

N Cst -16.7 -15

-1 -5.2 -4 -5.2 -10.6 -1.2 -0.2

C Cst -23.5 -15.5 -1

-4.5 -15.1 -15.1 -20 -17.5 -6.5

Source i S Cst -24.7 -13.8 -11.2 -11.2

-12 -6.7 -12 -5.4 -1.6

-11.2 -18.6 -13.8 9.7



Cntrl -23 -23.9 -20.7 -15.9 -20.7

NW -18 -25.7 -22.6 -17.9 -24.7 -19.1

-21.9 -19.1 -21

SW -18.5 -18.5 -20.7 -12.4 -20.8 -16.8 -17.8

-17.2 -29.4

-18.5

WLD -27.7 -26.9 -20.3 -19.1 -10.6 -27.9 -37.8 -27.7

By Sector

25 / 36

Introduction

Model

Quantitative Analysis

Conclusion

Appendix

Calibrating the Model Parameter

Value

Target

η

0.1 0.6 0.13 Data Data Model-Implied 4

Land’s share of gross output Intermediate’s share of output Housing’s share of expenditure National Employment Level Bilateral Trade Shares Initial Eq’m GDP Elasticity of Trade

1−β−η 1−α Li πij Xn θ

Details to Follow τˆni κ µ ˆni Tˆn

Pair Specific 2.21 Pair Specific Region Specific

Bilateral Trade Shares Ex-Post Income Dispersion Migration and Real Income Gaps Real Income Data

25 / 36

Introduction

Model

Quantitative Analysis

Conclusion

Appendix

Heterogeneity of Labour Productivity, κ • Variation in the ex-post wage distribution given by a simple

function of this paramtere (Cortes and Gallipoli, 2014) • Ex-post income is r.v. Z = max {µnk zk Vk }, which is Frechet 

• CDF given by Pr (Z < x) ≡ Fi (x) = e −

x/[

PN

j=1

(cij Vj )κ ]1/κ

−κ

√ 

• Log income is therefore ∼Gumbel, with s.d. π/ κ 6

• Census 2005 has individual earnings data; the average standard

deviation within origin-destination pairs implies κ ≈ 2.21 • Controlling for age, occupation, hukou location, marital status, industry, gender, education, etc... implies κ ≈ 2.85

26 / 36

Introduction

Model

Quantitative Analysis

Conclusion

Appendix

Migration Costs • As with trade, µni = (mni /mnn )1/κ (Vn /Vi ) 140

200

120 160

Frequency

100 Frequency

120

80 60

80

40 40

0 0

20

0.05 0.1 0.15 0.2 Disposable Income Net of Migration Costs, in 2000

(a) Histogram of cni

0.25

0 0

1

2

3 4 5 6 "Disposable" Income Share

7

8

(b) Histogram of cni zi

• Average migration cost: 89.6% of income • Average migration costs for migrants: 9.6% of income • Average change in migration costs, 2000-2005: µ ˆni = 1.14 Example: To Beijing 27 / 36

Introduction

Model

Quantitative Analysis

Conclusion

Appendix

Calibrating the Model Parameter

Value

Target

η

0.1 0.6 0.13 Data Data Model-Implied 4

Land’s share of gross output Intermediate’s share of output Housing’s share of expenditure National Employment Level Bilateral Trade Shares Initial Eq’m GDP Elasticity of Trade

1−β−η 1−α Li πij Xn θ

Details to Follow τˆni κ µ ˆni Tˆn

Pair Specific 2.21 Pair Specific Region Specific

Bilateral Trade Shares Ex-Post Income Dispersion Migration and Real Income Gaps Real Income Data

27 / 36

Introduction

Model

Quantitative Analysis

Conclusion

Appendix

Infer Productivity Changes from Real Income Growth 1.5

% Change in (Adjusted) Productivity Parameter

120

1.3

1.2

1.1

1

0.9

80 60 40 20 0

0.9 1 1.1 1.2 1.3 Relative Real Income Change, Data from 2002−07

1.4

(c) Real Income, when Tˆn = 1

1.5

er

0.8

Mo Sh ngo an lia do He ng Sh nan Sh anx an i Tia nxi Jia njin n J gsu Sh iangx an i gh He ai Hu bei Sic nan Gu huan a Gu ngx izh i ou Zh Jilin eji a He H ng ilo ub ng ei ji Nin ang gx Fu ia G jian Lia ansu on Qin ing g Gu Yun hai an na gd n o Be ng Xin ijing jia n Ch Anh g on ui gq Ha ing ina n

−20

0.8

0.7 0.7

100

Inn

Relative Real Income Change, Model

1.4

α

(d) Required Change in Tˆnθ(β+η)

Notes: Compares the model-implied change in each province’s real income Vˆn when underlying productivity is constant to real income changes measured in data. Both are expressed relative to the mean. In order for the model implies real income changes to match data, we require changes in underlying productivity draws Tˆn . The implied α/θ(β+η) productivity change in display in the right panel, adjusted as Tˆn . 28 / 36

Introduction

Model

Quantitative Analysis

Conclusion

Appendix

Counterfactual Exercises • We run a variety of counterfactuals 1. 2. 3. 4. 5. 6.

Internal Trade: τˆni for i, n 6= N + 1 only External Trade: τˆni for i, n = N + 1 only All Trade: τˆni for all pairs Migration: µ ˆni for all pairs All Domestic: Internal Trade + Migration Everything

• We then repeat all of the above with Tˆn changes • Changes in outcomes of interest • • • •

Internal and external trade Stock of migrants Income differences (variance of log-income) Aggregate welfare 29 / 36

Introduction

Model

Quantitative Analysis

Conclusion

Appendix

Counterfactual Aggregate Outcomes

Measured Cost Reduction of

Change in Trade GDP Ratio (p.p.) Internal External

Migrant Stock

Income Differences

Aggregate Welfare

Internal Trade

38.7

-2.7

-1.8%

-3.6%

7.3%

External Trade

-3.1

17.9

0.8%

2.1%

2.5%

All Trade

34.2

13.7

-0.9%

-1.5%

9.6%

Migration

0.0

0.1

37.1%

-8.9%

0.4%

All Domestic

38.7

-2.6

33.1%

-11.9%

7.7%

Everything

34.1

13.8

34.2%

-10.2%

10.1%

Data (2002-07)

17

12

18.5%

-0.1%



30 / 36

Introduction

Model

Quantitative Analysis

Conclusion

Appendix

Lower Migration Costs: Employment and Real Income 20

25 Beijing 20

15

Shanghai

% Change in Real Income

% Change in Labour Force

15 10

Guangdong

5

0

10

5 Inner Mongolia 0 Guangdong −5 Shanghai

−5 Inner Mongolia

−10

0

0.5

1 1.5 2 Initial Real Income, Relative to Mean

(e) Employment

−10

2.5

3

−15

Beijing

0

0.5

1 1.5 2 Initial Real Income, Relative to Mean

2.5

3

(f) Real Income

ˆn and real income Vˆn by province Notes: Displays the percentage change in employment L in response to lower inter-provincial migration costs.

31 / 36

Introduction

Model

Quantitative Analysis

Conclusion

Appendix

Lower Migration Costs: Trade Volumes 4

4

3 Beijing

Beijing

% Change in Trade Volume (Imports+Exports)

% Change in Trade Volume (Imports+Exports)

3

2

1 Guangdong

Shanghai

0

−1

−2

−3

0.5

1 1.5 2 Initial Real Income, Relative to Mean

(g) International Trade

1

Guangdong

2.5

3

Shanghai

0

−1

Inner Mongolia

−2

Inner Mongolia

0

2

−3

0

0.5

1 1.5 2 Initial Real Income, Relative to Mean

2.5

3

(h) Internal Trade

Notes: Displays the percentage change in trade volumes, both internationally and internally, for each provinces resulting from lower migration costs. Aggregate trade changes little, but there is substantial variation across provinces. Coastal regions trade more more as a result of lower internal migration costs; interior regions trade less. 32 / 36

Introduction

Model

Quantitative Analysis

Conclusion

Appendix

Distributional Effects on Real Income Differences 25

25

20

20

Jilin

15 % Change in Real Income

% Change in Real Income

15

10 Beijing Guangdong

Shanghai

5

10 Tianjin Inner Mongolia 5 Beijing Guangdong

0

0

−5

−5

−10

0

0.5

1 1.5 2 Initial Real Income, Relative to Mean

(i) Internal Trade

2.5

3

Shanghai

−10

0

0.5

1 1.5 2 Initial Real Income, Relative to Mean

2.5

3

(j) International Trade

Notes: Displays the percentage change in real incomes for each provinces resulting from selected counterfactual.

33 / 36

Introduction

Model

Quantitative Analysis

Conclusion

Appendix

Distributional Effects on Real Income Differences 25

25

20

20

Inner Mongolia

15 % Change in Real Income

% Change in Real Income

15 10

5 Inner Mongolia 0 Guangdong

10

Tianjin 5 Guangdong

0

−5

Shanghai Beijing

Shanghai −10

−15

−5

Beijing

0

0.5

1 1.5 2 Initial Real Income, Relative to Mean

(k) Migration Costs

2.5

3

−10

0

0.5

1 1.5 2 Initial Real Income, Relative to Mean

2.5

3

(l) Infernal Reforms

Notes: Displays the percentage change in real incomes for each provinces resulting from selected counterfactual.

34 / 36

Introduction

Model

Quantitative Analysis

Conclusion

Appendix

... with Productivity Changes

Change in Productivity and ...

p.p. Change in Trade/GDP Ratio Internal External

Productivity Only

-1.1

Migrant Stock

Regional Income Variance

Aggregate Welfare

Marginal Welfare Change

Prior Welfare Change

-4.6

-9.3%

11.5%

40.3%





Internal Trade

36.8

-6.9

-12.3%

6.2%

50.2%

7.1%

7.3%

External Trade

-3.8

11.4

-8.3%

13.1%

43.3%

2.2%

2.5%

All Trade

32.9

7.9

-11.2%

8.0%

53.1%

9.2%

9.6%

Migration

-1.1

-4.6

22.4%

3.1%

40.8%

0.4%

0.4%

Internal Reform

36.8

-6.8

17.2%

-1.5%

50.7%

7.5%

7.7%

Everything

32.8

7.9

18.5%

-0.1%

53.7%

9.5%

10.1%

Data (2002-07)

17

12

18.5%

-0.1%







35 / 36

Introduction

Model

Quantitative Analysis

Conclusion

Appendix

Conclusion • We develop a general equilibrium model of internal-external

trade with partial factor mobility • Highly tractable; eas to implement quantitative exercises • Useful for “measure” magnitude of trade and migration costs

• We apply the model to China and quantify the impacts of

trade liberalization and migration on aggregate welfare and regional income differences • Domestic reforms substantially more important than external

liberalization • Lower migration and trade costs are complementary policies

• Opening up is important for China, but not because of goods

trade 36 / 36

Introduction

Model

Quantitative Analysis

Cross-Province Differences

Conclusion

Appendix

Back

Summary Metric Across Province Importer

Mean

Median

p90

p10

90/10 Ratio

Real GDP per Capita Exports per Capita Home Share Migration Share

1 1 0.74 0.06

0.67 0.17 0.76 0.02

2.64 2.08 0.86 0.2

0.37 0.03 0.62 0.006

7.14 69.33 1.39 31.67

37 / 36

Introduction

Model

Migration and Gravity

Quantitative Analysis

Conclusion

Appendix

Back

• Evidence that gravity equations capture commuting decisions (Erlander and Stewart, 1990; Sen and Smith, 1995; Ahlfeldt et al., 2012)

−2 Log(Normalized Migration mij/mii for 2000) −10 −8 −6 −4 −12

−12

Log(Normalized Migration mij/mii for 2000) −10 −8 −6 −4

−2

• Inter-provincial migration also consistent with gravity

9

10 11 12 Log(Driving Time, Between Capitals)

(m) Migration vs. Travel Time

13

−3

−2 −1 0 1 2 Ratio of Real Income per Effective Worker, Log(Vj/Vi)

3

(n) Migration vs. Income Differences 38 / 36

Introduction

Model

Quantitative Analysis

Conclusion

Trade Cost Changes, Using Distance (not ρni )

Appendix

Back

• Capture symmetric effect with bilateral distances dni

 ln

πni πnn

 = δln(dni ) + ιn + ηi + ni

Table: % Change in Trade Costs

Importer NE B&T N Cst C Cst S Cst Cntrl NW SW WLD

NE -16.2 -13.3 -23.9 -22.7 -12.6 -4.9 -10.6 -13.1

B&T -9.7

N Cst -9.4 -9.7

-6.9 -11.6 -6.8 -9.2 -9.3 -6.0 -7.6

C Cst -16.0 -9.4 -0.1

-5.4 -12.4 -13.6 -13.8 -16.6 -8.1

Source S Cst -20.6 -11.2 -13.9 -14.7

-8.3 -4.2 -4.2 -3.5 -2.3

-12.5 -14.9 -15.5 4.6

Cntrl -17.6 -20.5 -22.0 -18.1 -19.6

NW -17.3 -26.7 -28.2 -24.5 -27.9 -23.6

-17.2 -19.6 -23.6

SW -12.3 -14.3 -21.6 -14.2 -19.2 -16.3 -12.3

-22.3 -35.6

-20.7

WLD -19.9 -21.0 -19.0 -18.5 -6.2 -25.4 -31.8 -25.6

39 / 36

Quantitative Analysis

Back

n io

st

eg lR

lC

ea

tra en

N

or

th

an Ti g/

oa

st

jin

2007

st ijin Be

ut

h

C So

N

or

th

th or N

C oa

oa

es t w

hw es t ut

st

2002

So

Appendix

C

Province−Specific Export Cost, Tariff−Equivalent % −40 −20 0 20 40

Provincial Export-Cost Estimates

Conclusion

tra

Model

C en

Introduction

Notes: Displays the tariff-equivalent (in percentage points) region-specific export costs. All expressed relative to the average for the year. A value of 30 for a certain region implies it is 30 percent more costly to export from that region to any other, relative to 40 / 36 the export costs for the average region.

Introduction

Model

Migration Costs

Quantitative Analysis

Conclusion

Appendix

Back

• As with trade, µni = (mni /mnn )1/κ (Vn /Vi ) Figure: Costs of Migrating Into Beijing

Heilongjiang

Inner Mongol

Jilin Liaoning

Xinjiang

Beijing Tianjin Shanxi Hebei

Ningxia Qinghai

Shandong Gansu

Shaanxi

Tibet

Henan Hubei

Sichuan

Jiangsu Anhui Shanghai Zhejiang

Guizhou

Hunan Jiangxi Fujian

Yunnan Guangxi Guangdong

Hainan

41 / 36

Introduction

Model

Quantitative Analysis

Trade Patterns, by Region

Conclusion

Appendix

Back

Table: Expenditure Shares πni , Year 2002

Source Importer

NE

B&T

N Cst

C Cst

S Cst

Cntrl

NW

SW

WLD

NE

0.879

0.007

0.010

0.008

0.013

0.011

0.008

0.009

0.055

B&T

0.039

0.634

0.094

0.030

0.026

0.033

0.014

0.012

0.119

N Cst

0.018

0.033

0.798

0.034

0.018

0.038

0.009

0.008

0.044

C Cst

0.002

0.002

0.006

0.810

0.015

0.024

0.005

0.005

0.133

S Cst

0.005

0.004

0.005

0.026

0.723

0.019

0.004

0.015

0.198

Cntrl

0.006

0.003

0.011

0.048

0.023

0.878

0.007

0.007

0.018

NW

0.020

0.008

0.021

0.033

0.045

0.036

0.774

0.038

0.026

SW

0.009

0.003

0.004

0.018

0.043

0.014

0.009

0.880

0.020

WLD

0.000

0.000

0.000

0.001

0.002

0.000

0.000

0.000

0.996 42 / 36

Introduction

Model

Quantitative Analysis

Conclusion

% Change in Internal Bilateral Costs (2002-07)

Appendix

Back

Summary Metric Across Pairs Importer

Mean

Median

Min

Max

Agriculture Mining Food and Tobacco Textiles Wood & Furniture Paper & Printing Chemicals Non-Metallic Min Metal Products Machinery Transport Equip. Electrical Machi Other Utilities Construction Transportation Other Services

-7.33 -8.92 -9.99 -6.51 -14.00 3.09 -6.64 -17.54 -10.59 -14.99 2.29 -1.74 -3.67 -0.35 -16.78 -11.86 -10.25

-6.96 -8.29 -13.14 -4.53 -14.82 1.88 -6.96 -19.89 -9.89 -17.65 -7.58 -6.59 -0.55 -16.29 -36.75 -17.64 -12.88

-37.63 -36.95 -29.52 -45.49 -48.26 -21.93 -19.56 -36.80 -27.73 -33.71 -34.18 -24.40 -49.32 -52.22 -73.30 -42.91 -30.60

31.07 29.35 7.43 56.12 37.93 29.40 10.16 4.61 5.53 33.64 76.13 60.12 50.53 101.42 111.76 26.58 19.37 43 / 36

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