Trade,MigrationandRegionalIncomeDiﬀerences: EvidencefromChina ·...

Introduction Model Quantitative Analysis Conclusion Appendix

Trade, Migration and Regional Income Differences:Evidence from China

Trevor TombeUniversity of Calgary

Xiaodong ZhuUniversity of Toronto and SAIF

Department of Economics, Yale

October 7, 2014

1 / 36


Motivation

• Aggregate gains from trade widely studied• What about spatial distribution of gains from trade?

• Aggregate and spatial effects of trade liberalization depend oncosts 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 toa 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


Related Literature• International trade with multi-region countries:

• Henderson (1982), Rauch (1991), Bond (1993), Courant andDeardorff (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


This Paper

• Build unique dataset for China: 2000/02 – 2005/07

• We develop a general equilibrium model of internal andexternal 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 andGallipoli, 2014)

• Measure and quantify the effect of (1) international tradeliberalization, (2) internal trade liberalization, (3) factormarket liberalization, (4) productivity change on:• Welfare — aggregate and regional• Migration — between provinces• Income Differences — between provinces

3 / 36


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


Visualizing Key Features of the Data

Figure: The Geography of China

Hainan

Anhui

Zhejiang

Jiangxi

Jiangsu

Jilin

Qinghai

Fujian

Heilongjiang

Henan

Hebei

Hunan

Hubei

Xinjiang

Tibet

Gansu

Guangxi

Guizhou

Liaoning

Inner Mongol

Ningxia

Beijing

Shanghai

Shanxi

Shandong

Shaanxi

Sichuan

Tianjin

Yunnan

Guangdong

Table5 / 36



Figure: Output per Capita (90th/10th ∼ 7)

Hainan

Anhui

Zhejiang

Jiangxi

Jiangsu

Jilin

Qinghai

Fujian

Heilongjiang

Henan

Hebei

Hunan

Hubei

Xinjiang

Tibet

Gansu

Guangxi

Guizhou

Liaoning

Inner Mongol

Ningxia

Beijing

Shanghai

Shanxi

Shandong

Shaanxi

Sichuan

Tianjin

Yunnan

Guangdong

Table 6 / 36



Figure: Home-Share of Spending (90th=0.86, 10th=0.62 )

Hainan

Anhui

Zhejiang

Jiangxi

Jiangsu

Jilin

Qinghai

Fujian

Heilongjiang

Henan

Hebei

Hunan

Hubei

Xinjiang

Tibet

Gansu

Guangxi

Guizhou

Liaoning

Inner Mongol

Ningxia

Beijing

Shanghai

Shanxi

Shandong

Shaanxi

Sichuan

Tianjin

Yunnan

Guangdong

Table 7 / 36



Figure: Migrant Worker Shares (90th=0.2, 10th=0.006)

Hainan

Anhui

Zhejiang

Jiangxi

Jiangsu

Jilin

Qinghai

Fujian

Heilongjiang

Henan

Hebei

Hunan

Hubei

Xinjiang

Tibet

Gansu

Guangxi

Guizhou

Liaoning

Inner Mongol

Ningxia

Beijing

Shanghai

Shanxi

Shandong

Shaanxi

Sichuan

Tianjin

Yunnan

Guangdong

Table 8 / 36


Table: Migrant Characteristics (from Census Data)

1990 2000 2005

Total Migrants 32.7 M 130.6 M 165.4 MInter-Provincial Migrants 10.5 M 35.8 M 53 M

Inter-Provincial Migrant Workers 2 M 28 M 40 M

(a) Migrant Stock

EmployedAll Inter-Provincial Inter-Provincial

Migrants Migrants Migrants

Number 165.4 M 53 M 40 MReason for Migrating

Work 45% 73% 91%Family 30% 21% 6%

Education 6% 2% 2%Other 18% 4% 0.3%

Other CharacteristicsWith Children 30% 28% 27%

Agricultural Hukou 62% 83% 86%Male 50% 53% 57%

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


Migration Costs

Wide variety of very large costs to live outside one’s Hukou region:• Lack of employment contracts (no provision of benets or otherlegal rights; reform 2007)

• Difficult to find housing (couldn’t rent an apartment in Beijinguntil 2005)

• Unregistered migrants detained/deported (until 2003,following a death)

• Limited health insurance access• Children attend school barred or expensive fees (can be 20% ofincome)

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

10 / 36


Internal Trade Barriers

Pre-2001:• Strong local protectionism and high internal trade barriers inthe 1980s and 1990s (Young, 2000; Poncet, 2003)

• The degree of local market protection is positively associatedwith the size of the state sector in the region

Post-2001:• Downsizing the state-owned sector• State council’s directive about eliminating local marketprotection in 2001

11 / 36


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


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 = cαn s1−αun

subject toPncn + rnsun ≤ vn

13 / 36


Production

• Final Good: composite of a continuum of intermediates

Yn =

(ˆ 1

0yn(j)(σ−1)/σdj

)σ/(σ−1)• 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 technologyusing effective-labour (H), land (SY ), and inputs (Q)

yn(j) = ϕn(j)Hn(j)βSYn(j)ηQn(j)1−β−η

• TFP ϕ differs across firms; as in Eaton and Kortum (2002)

14 / 36


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

pni (ϕ) = τniMCi (ϕ) ∝ τniwβi r

ηi P

1−β−ηi /ϕ

• With TFP ϕ distributed Frechet: Fi (ϕ) = e−Tiϕ−θ, fraction of

region n spending allocated to goods produced in region i is

πni =Ti

(τniw

βi r

ηi P

1−β−ηi

)−θ∑N+1

k=1 Tk

(τnkw

βk r

ηkP

1−β−ηk

)−θ• Aggregate price index in region n (for tradables)

Pn ∝

[N+1∑k=1

Tk

(τnkw

βk r

ηkP

1−β−ηk

)−θ]−1/θ• Finally, market clearing for land ⇒ rn ∝ wnHn/Sn

15 / 36




ηi P

1−β−ηi /ϕ



πni =Ti

(τniw

βi r

ηi P

1−β−ηi

)−θ∑N+1

k=1 Tk

(τnkw

βk r

ηkP

1−β−ηk

)−θ

• Aggregate price index in region n (for tradables)

Pn ∝

[N+1∑k=1

Tk

(τnkw

βk r

ηkP

1−β−ηk


15 / 36




ηi P

1−β−ηi /ϕ



πni =Ti

(τniw

βi r

ηi P

1−β−ηi

)−θ∑N+1

k=1 Tk

(τnkw

βk r

ηkP

1−β−ηk

)−θ• Aggregate price index in region n (for tradables)

Pn ∝

[N+1∑k=1

Tk

(τnkw

βk r

ηkP

1−β−ηk


15 / 36


Regional Income

• Nominal Income (Expenditures) per Effective Worker:

vn =wn︸︷︷︸

Labour Income+ (1− α)vn + ηwn/β︸︷︷︸

Spending on Land

=

(β + η

αβ

)wn

• Total expenditures: Xn = vnHn

• Real Income per Effective Worker (vn/Pαn r1−αn ):

Vn ∝T

αθ(β+η)n︸︷︷︸

Technology

· π− αθ(β+η)

nn︸︷︷︸Market Access

· (Sn/Hn)η+(1−α)ββ+η︸︷︷︸

Land / effective worker

16 / 36


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 µniziVi = maxk=1,...,N {µnkzkVk}

• If z follows a Frechet distribution Fz(x) = e−(

xγ

)−κ, then

share of region n registered workers that move to region i is

mni =(Viµni )

κ∑Nk=1 (Vkµnk)κ

Gravity Evidence

17 / 36


Expected Income and Aggregate Welfare

Proposition 2: If worker productivities zi are distributed Frechet withmean 1 and variance parameter κ, and agents are able to migratebetween regions at cost µij , then the expected real income net ofmigration costs for workers from region i is

V 0i = Vim

−1/κii ,

and aggregate average real income (welfare) is therefore

W =N∑i=1

λ0i Vim

−1/κii ,

where λ0i =

L0i∑N

j=1 L0j

is region i ’s registration share.

Proof: See paper. Intuition: max {µnkzkVk} ∼ Frechet(κ,Vnm

−1/κnn

)

18 / 36


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

∑Ni=1minL

0i

• Analogous to Eaton-Kortum, where πni is both (1) share ofgoods and (2) share of spending, we can show mni is both1. Share of workers registered in n that work in region i2. Share of income earned by all workers registered in n from those

workers working in region i

• Effective Workers in Region n:

HnVn =N∑i=1

minL0i Vim

−1/κii

⇒ Hn =N∑i=1

(µinm

− 1κ

in

)minL

0i

• Useful with data on real GDP (HiVi ), migration (mni ,mnn),and hukou registrations (L0n) → solves for Vi , then Hi

19 / 36

Quantitative Analysis


The Equilibrium Conditions• Trade balance: vnHn =

∑N+1i=1 πinviHi

• Trade flows: πni ∝ PθnTi

(τniw

βi r

ηi P

1−β−ηi

)−θ

• Final good price: P−θn ∝∑N+1

i=1 Ti

(τniw

βi r

ηi P

1−β−ηi

)−θ• Land rental price: rn ∝ wnHn/Sn

• Real income: Vn ∝ wn/Pαn r

1−αn

• Migration flows: mni = (Viµni )κ∑N

k=1(Vkµnk )κ

• Migrant real incomes: HnVn ∝∑N

i=1minL0i Vim

−1/κii

20 / 36



∑N+1i=1 πinviHi


(τniw

βi r

ηi P

1−β−ηi

)−θ• Final good price: P−θn ∝

∑N+1i=1 Ti

(τniw

βi r

ηi P

1−β−ηi

)−θ• Land rental price: rn ∝ wnHn/Sn


1−αn


k=1(Vkµnk )κ


i=1minL0i Vim

−1/κii

20 / 36



∑N+1i=1 πinviHi


(τniw

βi r

ηi P

1−β−ηi

)−θ• Final good price: P−θn =∝

∑N+1i=1 Ti

(τniw

βi r

ηi P

1−β−ηi

)−θ• Land price: rn ∝ wnHn/Sn


1−αn


k=1(Vkµnk )κ


i=1minL0i Vim

−1/κii

20 / 36


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

rn ∝ wnHn/Sn

⇒ rn = wnHn

• Another (less trivial) example:

P−θn =

∑N+1i=1 T

′i

(τ′niw

′βi r′ηi P

′1−β−ηi

)−θ∑N+1

i=1 Ti

(τniw

βi r

ηi P

1−β−ηi

)−θ=

∑N+1i=1 Ti

(τni w

βi r

ηi P

1−β−ηi

)−θTi

(τniw

βi r

ηi P

1−β−ηi

)−θ∑N+1

i=1 Ti

(τniw

βi r

ηi P

1−β−ηi

)−θ≡

N+1∑i=1

Ti

(τni w

βi r

ηi P

1−β−ηi

)−θπni

21 / 36


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

Model mapping F :(τni , Tn, µni

)→(Vn,m′ni , π

′ni

), given (πni , Ln,mni ,Xn, L0n)

Our strategy: infer(τni , Tn, µni

)from F−1

(Vn,m′ni , π

′ni

)

wnHnXn =N+1∑i=1

π′inwi HiXi , (1)

π′ni = Pθn πni Ti

(τni w

β+ηi P1−β−η

iˆLηi

)−θ, (2)

P−θn =N+1∑i=1

πni Ti

(τni w

β+ηi P1−β−η

iˆLηi

)−θ, (3)

Vn =wn

Pαn r1−αn

=wα

n

Pαn H1−αn

, (4)

m′in =min

(Vnµin

)κ∑N

k=1 mik

(Vk µik

)κ , (5)

H ′nV′n =

N∑i=1

m′in L0i V ′i m′ii−1/κ

. (6)

22 / 36


Calibrating the Model

Parameter Value Target

η 0.1 Land’s share of gross output1− β − η 0.6 Intermediate’s share of output1− α 0.13 Housing’s share of expenditureLi Data National Employment Levelπij Data Bilateral Trade SharesXn Model-Implied Initial Eq’m GDPθ 4 Elasticity of Trade

Details to Follow

τni Pair Specific Bilateral Trade Sharesκ 2.21 Ex-Post Income Dispersionµni Pair Specific Migration and Real Income GapsTn Region Specific Real Income Data

22 / 36


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

• Recall: πni ∝ PθnTi

(τniw

βi r

ηi P

1−β−ηi

)−θ• ⇒ ln

(πniπnn

)= ln

(PθnTi

(τniw

βi rηi P

1−β−ηi

)−θPθnTn

(wβn rηn P

1−β−ηn

)−θ)≡ Sn−Si−θln(τni )

• ⇒ ln(πniπnn

)+ ln

(πinπii

)= −θ [ln (τni ) + ln (τin)]

• The Head-Reis Index: τni ≡√τniτin =

(πnnπiiπniπin

)1/2θ• Notice: it’s symmetric (τni = τin)• We modify the index to incorporate region-specific costs• i.e. an exporter-specific cost ti implies

τni = τni√

ti/tn

23 / 36


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

• Recall: πni ∝ PθnTi

(τniw

βi r

ηi P

1−β−ηi

)−θ• ⇒ ln

(πniπnn

)= ln

(PθnTi

(τniw

βi rηi P

1−β−ηi

)−θPθnTn

(wβn rηn P

1−β−ηn

)−θ)≡ Sn−Si−θln(τni )

• ⇒ ln(πniπnn

)+ ln

(πinπii

)= −θ [ln (τni ) + ln (τin)]

• The Head-Reis Index: τni ≡√τniτin =

(πnnπiiπniπin

)1/2θ• Notice: it’s symmetric (τni = τin)• We modify the index to incorporate region-specific costs• i.e. an exporter-specific cost ti implies

τni = τni√ti/tn

23 / 36


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

ln

(πniπnn

)= ρni + ιn + ηi + εni ,

where ρni is a directionless pair-effect such that ρni = ρin, and ιnand ηi are importer- and exporter-effects

• As the exporter effect is ηi = Si − θln (ti ) and the importereffect is ιn = −Sn, we infer export costs as

tn = e−(ιn+ηn)/θ

Export Cost Estimates

24 / 36


Relative Change in Trade Costs

Table: % Change in Trade Costs(τ2007ni /τ2002

ni

)Source i

Importer n NE B&T N Cst C Cst S Cst Cntrl NW SW WLDNE -11.8 -16.7 -23.5 -24.7 -23 -18 -18.5 -27.7B&T -14.2 -15 -15.5 -13.8 -23.9 -25.7 -18.5 -26.9N Cst -5.7 -1 -1 -11.2 -20.7 -22.6 -20.7 -20.3C Cst -16.4 -5.2 -4.5 -11.2 -15.9 -17.9 -12.4 -19.1S Cst -18.4 -4 -15.1 -12 -20.7 -24.7 -20.8 -10.6Cntrl -6.6 -5.2 -15.1 -6.7 -11.2 -19.1 -16.8 -27.9NW -4 -10.6 -20 -12 -18.6 -21.9 -17.8 -37.8SW -3.8 -1.2 -17.5 -5.4 -13.8 -19.1 -17.2 -27.7WLD -3.8 -0.2 -6.5 -1.6 9.7 -21 -29.4 -18.5

Distance, not ρni By Sector

25 / 36





Details to Follow


25 / 36


Heterogeneity of Labour Productivity, κ

• Variation in the ex-post wage distribution given by a simplefunction of this paramtere (Cortes and Gallipoli, 2014)

• Ex-post income is r.v. Z = max {µnkzkVk}, which is Frechet

• CDF given by Pr(Z < x) ≡ Fi (x) = e−(x/[∑N

j=1(cijVj)κ]1/κ

)−κ

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

• Census 2005 has individual earnings data; the average standarddeviation 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


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

0 0.05 0.1 0.15 0.2 0.250

40

80

120

160

200

Disposable Income Net of Migration Costs, in 2000

Fre

quen

cy

(a) Histogram of cni

0 1 2 3 4 5 6 7 80

20

40

60

80

100

120

140

"Disposable" Income Share

Fre

qu

ency

(b) Histogram of cnizi

• Average migration cost: 89.6% of income• Average migration costs for migrants: 9.6% of income• Average change in migration costs, 2000-2005: µni = 1.14

Example: To Beijing

27 / 36





Details to Follow


27 / 36


Infer Productivity Changes from Real Income Growth

0.7 0.8 0.9 1 1.1 1.2 1.3 1.4 1.50.7

0.8

0.9

1

1.1

1.2

1.3

1.4

1.5

Relative Real Income Change, Data from 2002−07

Rela

tive R

eal In

com

e C

hange, M

odel

(c) Real Income, when Tn = 1

−20

0

20

40

60

80

100

120

Inner M

ongolia

Shandong

Henan

Shanxi

Shannxi

Tia

njin

Jiangsu

Jiangxi

Shanghai

Hebei

Hunan

Sic

huan

Guangxi

Guiz

hou

Jilin

Zhejia

ng

Hubei

Heilo

ngjia

ng

Nin

gxi

aFujia

nG

ansu

Lia

onin

gQ

inghai

Yunnan

Guangdong

Beiji

ng

Xin

jiang

Anhui

Chongqin

gH

ain

an

% C

ha

ng

e in

(A

dju

ste

d)

Pro

du

ctivity P

ara

me

ter

(d) Required Change in Tα

θ(β+η)n

Notes: Compares the model-implied change in each province’s real income Vn whenunderlying productivity is constant to real income changes measured in data. Both areexpressed relative to the mean. In order for the model implies real income changesto match data, we require changes in underlying productivity draws Tn. The impliedproductivity change in display in the right panel, adjusted as T

α/θ(β+η)n .

28 / 36


Counterfactual Exercises

• We run a variety of counterfactuals

1. Internal Trade: τni for i , n 6= N + 1 only2. External Trade: τni for i , n = N + 1 only3. All Trade: τni for all pairs4. Migration: µni for all pairs5. All Domestic: Internal Trade + Migration6. Everything

• We then repeat all of the above with Tn changes

• Changes in outcomes of interest• Internal and external trade• Stock of migrants• Income differences (variance of log-income)• Aggregate welfare

29 / 36


Counterfactual Aggregate Outcomes

Change in TradeMeasured Cost GDP Ratio (p.p.) Migrant Income AggregateReduction of Internal External Stock Differences 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


Lower Migration Costs: Employment and Real Income

0 0.5 1 1.5 2 2.5 3−10

−5

0

5

10

15

20

Beijing

Shanghai

Guangdong

Inner Mongolia

Initial Real Income, Relative to Mean

% C

hange in L

abour

Forc

e

(e) Employment

0 0.5 1 1.5 2 2.5 3−15

−10

−5

0

5

10

15

20

25

Beijing

Shanghai

Guangdong

Inner Mongolia


% C

hange in R

eal In

com

e

(f) Real Income

Notes: Displays the percentage change in employment Ln and real income Vn by provincein response to lower inter-provincial migration costs.

31 / 36


Lower Migration Costs: Trade Volumes

0 0.5 1 1.5 2 2.5 3−3

−2

−1

0

1

2

3

4

Beijing

ShanghaiGuangdong

Inner Mongolia


% C

ha

ng

e in

Tra

de

Vo

lum

e (

Imp

ort

s+

Exp

ort

s)

(g) International Trade

0 0.5 1 1.5 2 2.5 3−3

−2

−1

0

1

2

3

4

Beijing

ShanghaiGuangdong

Inner Mongolia


% C

ha

ng

e in

Tra

de

Vo

lum

e (

Imp

ort

s+

Exp

ort

s)

(h) Internal Trade

Notes: Displays the percentage change in trade volumes, both internationally and inter-nally, for each provinces resulting from lower migration costs. Aggregate trade changeslittle, but there is substantial variation across provinces. Coastal regions trade moremore as a result of lower internal migration costs; interior regions trade less.

32 / 36


Distributional Effects on Real Income Differences

0 0.5 1 1.5 2 2.5 3−10

−5

0

5

10

15

20

25

Beijing

ShanghaiGuangdong

Jilin


% C

hange in R

eal In

com

e

(i) Internal Trade

0 0.5 1 1.5 2 2.5 3−10

−5

0

5

10

15

20

25

Beijing

Shanghai

Guangdong

Tianjin

Inner Mongolia


% C

hange in R

eal In

com

e

(j) International Trade

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

33 / 36


Distributional Effects on Real Income Differences

0 0.5 1 1.5 2 2.5 3−15

−10

−5

0

5

10

15

20

25

Beijing

Shanghai

Guangdong

Inner Mongolia


% C

hange in R

eal In

com

e

(k) Migration Costs

0 0.5 1 1.5 2 2.5 3−10

−5

0

5

10

15

20

25

Beijing

Shanghai

Guangdong

Tianjin

Inner Mongolia


% C

hange in R

eal In

com

e

(l) Infernal Reforms

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

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... with Productivity Changes

p.p. Change in Regional Marginal PriorChange in Trade/GDP Ratio Migrant Income Aggregate Welfare Welfare

Productivity and ... Internal External Stock Variance Welfare Change Change

Productivity Only -1.1 -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% – – –

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Conclusion

• We develop a general equilibrium model of internal-externaltrade 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 oftrade liberalization and migration on aggregate welfare andregional income differences

• Domestic reforms substantially more important than externalliberalization• Lower migration and trade costs are complementary policies

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

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Cross-Province Differences Back

Summary Metric Across Province

Importer Mean Median p90 p10 90/10 Ratio

Real GDP per Capita 1 0.67 2.64 0.37 7.14Exports per Capita 1 0.17 2.08 0.03 69.33

Home Share 0.74 0.76 0.86 0.62 1.39Migration Share 0.06 0.02 0.2 0.006 31.67

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Migration and Gravity Back

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

• Inter-provincial migration also consistent with gravity

−1

2−

10

−8

−6

−4

−2

Lo

g(N

orm

aliz

ed

Mig

ratio

n m

ij/m

ii fo

r 2

00

0)

9 10 11 12 13Log(Driving Time, Between Capitals)

(m) Migration vs. Travel Time

−1

2−

10

−8

−6

−4

−2

Lo

g(N

orm

aliz

ed

Mig

ratio

n m

ij/m

ii fo

r 2

00

0)

−3 −2 −1 0 1 2 3Ratio of Real Income per Effective Worker, Log(Vj/Vi)

(n) Migration vs. Income Differences

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Trade Cost Changes, Using Distance (not ρni) Back

• Capture symmetric effect with bilateral distances dni

ln

(πniπnn

)= δln(dni ) + ιn + ηi + εni

Table: % Change in Trade Costs

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

NE -9.7 -9.4 -16.0 -20.6 -17.6 -17.3 -12.3 -19.9B&T -16.2 -9.7 -9.4 -11.2 -20.5 -26.7 -14.3 -21.0N Cst -13.3 -6.9 -0.1 -13.9 -22.0 -28.2 -21.6 -19.0C Cst -23.9 -11.6 -5.4 -14.7 -18.1 -24.5 -14.2 -18.5S Cst -22.7 -6.8 -12.4 -8.3 -19.6 -27.9 -19.2 -6.2Cntrl -12.6 -9.2 -13.6 -4.2 -12.5 -23.6 -16.3 -25.4NW -4.9 -9.3 -13.8 -4.2 -14.9 -17.2 -12.3 -31.8SW -10.6 -6.0 -16.6 -3.5 -15.5 -19.6 -22.3 -25.6WLD -13.1 -7.6 -8.1 -2.3 4.6 -23.6 -35.6 -20.7

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Provincial Export-Cost Estimates Back

−4

0−

20

02

04

0P

rovin

ce

−S

pe

cific

Exp

ort

Co

st,

Ta

riff

−E

qu

iva

len

t %

South

wes

t

Nor

thwes

t

Nor

th C

oast

South

Coa

st

Beijin

g/Tia

njin

Nor

thea

st

Cen

tral C

oast

Cen

tral R

egion

2002 2007

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 regionimplies it is 30 percent more costly to export from that region to any other, relative tothe export costs for the average region. 40 / 36


Migration Costs Back

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

Figure: Costs of Migrating Into Beijing

Hainan

Anhui

Zhejiang

Jiangxi

Jiangsu

Jilin

Qinghai

Fujian

Heilongjiang

Henan

Hebei

Hunan

Hubei

Xinjiang

Tibet

Gansu

Guangxi

Guizhou

Liaoning

Inner Mongol

Ningxia

Beijing

Shanghai

Shanxi

Shandong

Shaanxi

Sichuan

Tianjin

Yunnan

Guangdong

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Trade Patterns, by Region 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

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% Change in Internal Bilateral Costs (2002-07) Back

Summary Metric Across Pairs

Importer Mean Median Min Max

Agriculture -7.33 -6.96 -37.63 31.07Mining -8.92 -8.29 -36.95 29.35

Food and Tobacco -9.99 -13.14 -29.52 7.43Textiles -6.51 -4.53 -45.49 56.12

Wood & Furniture -14.00 -14.82 -48.26 37.93Paper & Printing 3.09 1.88 -21.93 29.40

Chemicals -6.64 -6.96 -19.56 10.16Non-Metallic Min -17.54 -19.89 -36.80 4.61Metal Products -10.59 -9.89 -27.73 5.53

Machinery -14.99 -17.65 -33.71 33.64Transport Equip. 2.29 -7.58 -34.18 76.13Electrical Machi -1.74 -6.59 -24.40 60.12

Other -3.67 -0.55 -49.32 50.53Utilities -0.35 -16.29 -52.22 101.42

Construction -16.78 -36.75 -73.30 111.76Transportation -11.86 -17.64 -42.91 26.58Other Services -10.25 -12.88 -30.60 19.37

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