GCOE Discussion Paper Series
Global COE Program

Human Behavior and Socioeconomic Dynamics
Discussion Paper No.139

Trade And Inequality With Limited Labor Mobility:
Theory And Evidence From China

Muqun Li and Ian Coxhead

June 2010

GCOE Secretariat
Graduate School of Economics

OSAKA UNIVERSITY

1-7 Machikaneyama, Toyonaka, Osaka, 560-0043, Japan

Trade And Inequality With Limited Labor Mobility: Theory And Evidence From China*
Muqun Li and Ian Coxhead

Abstract
Does globalization increase inequality in developing countries, and if so, how? In a theoreticalmodel of a regionally heterogeneous economy, we show how different regional rates of technicalprogress due to trade and FDI interact with constraints to unskilled labor mobility. As favoredregions benefit more from trade, their growing demand for skills drains skilled workers fromdisadvantaged areas, and average incomes in the former grow faster than in the latter. Moreover,this unbalanced regional growth may also raise inequality within each region. It could evenreduce absolute income per capita in the less favored region. We test these predictions with
Chinese data from the Open Door era. Results confirm that different regional growth rates haveincreased both interregional and intraregional inequality. Moreover, growth of skills-based
export industries in coastal regions is associated, other things equal, with lower incomes for thepoor in inland provinces.

JEL codes: F16, O15, R1.
Key words: trade, investment, wage premium, migration, inequality, China*

Li: Samsung Economic Research Institute, 25/F China Merchants Tower, No.118, Jian Guo Lu, Chao Yang District, Beijing,
China 100022. Email: muqunli@gmail.com. Coxhead: University of Wisconsin-Madison, 413 Taylor Hall, 427 Lorch St.,
Madison, WI 53706. Email: coxhead@wisc.edu. We thank seminar participants at the University of Wisconsin-Madison
and Waseda University for comments on earlier drafts. Coxhead thanks the Institute of Social and Economics Research at
Osaka University for hosting a sabbatical research leave during which this paper was extensively revised. A mathematical
appendix to this paper (PDF format) can be accessed at http://tinyurl.com/LiCoxheadAppendix.

1
1 Introduction
Does trade increase inequality in developing countries? According to standard theory, for low-income
countries with comparative advantage in less skill-intensive products, trade lowers the relative prices ofskill-intensive imported goods, and along with them the skill premium (the skilled-unskilled wage ratio).
Thus trade, by raising the relative demand for low-skill workers, should reduce both poverty andinequality. However, many empirical studies show that expanded trade is associated with higher wage
inequality (Hanson and Harrison 1995; Robbins 1996; Wood 1997; Goldberg and Pavcnik 2004). These
challenges to standard theory have generated a number of theoretical responses, discussed below.
At the same time, inequality is rarely unidimensional, so a single nationwide skill premium may
not suffice as a measure of inequality. The standard model assumes domestic factor mobility, but
empirical studies show clearly that the benefits of trade openness don't always spread uniformly within
an economy. Studies in Mexico and China, for example, show that isolated regions lag behind areas
better positioned for trade (Hanson 2004; Hale and Long 2008; Candelaria et al. 2009).
It follows that domestic labor markets are central to both growth and inequality outcomes fromtrade. Industries are never uniformly distributed in space, so structural changes induced by trade andspecialization require physical movement of factors, especially labor. Barriers to labor mobility thus
make it difficult for the poor (who own mainly low-skill labor) to exploit opportunities created by trade.
Understanding how trade affects inequality helps us not only to understand inequality itself, but also
how to design policy to mitigate adjustment costs borne by the poor.
No country exemplifies the interactions of trade, demand for skills, and geographic labor market
rigidities better than China. With rapidly growing foreign trade, China has had rapid growth and poverty
alleviation, but also large increases in inequality. From 1988-95 the Gini coefficient of earnings rose byeight percentage points (Knight and Song 2003; Wang and Shi 2006). Regional incomes have also
diverged, and in particular, the gap between coastal and interior provinces (Jian et al. 1996; Zhang and2Zhang, 2003). China is now on a dual track, with a prosperous and fast-growing coastal region, a poorinterior growing much more slowly, and poverty is more severe in inland than coastal China (Wan andZhang 2008).
Why do spatial disparities arise? As China integrates with global markets, the comparativeadvantage of its regions needs to be evaluated in a global context. Coastal regions enjoy comparativeadvantage in proximity to the world market and have access to a large pool of well-educated labor. As aresult, they have attracted far more FDI and generated more trade volume than have inland provinces.
Why do spatial disparities persist? Partly, because domestic factors are not equally mobile.
Internal barriers to labor mobility are especially significant, and apply differentially to labor of differingskills. In China these include legal impediments based on the hukou (household registration) system aswell as less formal discrimination against migrants from inland provinces. Faster growth in richer
regions may have reduced growth in poorer regions by attracting capital and skilled workers, leaving
behind unskilled workers with fewer complementary resources. Differential labor mobility may haveexacerbated differences in regional comparative advantage despite a uniform national trade policy.
So-called New Economic Geography models explain the location of production within an
economy by showing how internal trade costs lead to regional divergence (Fujita et al 1999; Krugman
and Venables 1995; Monfort and Ypersele 2003). But these models assume that each region has accessto the same technology and labor endowments. This may be good for explaining integration among
similar economies such as EU states, but is less helpful for more heterogeneous developing economies.
Other models have explained rising wage inequality associated with North-South trade (Feenstra andHanson 1996; Zhu and Trefler 2005), but these models do not allow for regional heterogeneity withineither North or South. To explain both wage inequality and interregional disparities requires more detail.
In this paper we address this by supposing that South has two regions, Coast and Inland, eachwith different labor and skills endowments and production technologies. There is a continuum of goods

3ranked by skill-intensity; each good is produced in the region that is most efficient at producing it, andthe rest of the world imports it from that region. By assumption, North produces the most skill-intensive
goods; Coast has intermediate skill-intensity, Inland supplies unskilled-intensive goods. Trade and FDI
offer opportunities for increased production and productivity growth. Coast’s catch-up in technology
with North attracts its less skill-intensive production, so firms in Coast offer a higher wage premium.
Within South, if skilled labor can move freely but unskilled labor cannot, faster growth in Coast may
attract both production and skilled labor from Inland. Inland now has fewer skilled workers, lower
growth, and a higher wage premium. Thus trade may raise inequality both within and between the tworegions. The empirical part of our paper uses Chinese data to test these predictions.
Our model differs from earlier work in that it allows for spatial heterogeneity within South, andfor differential mobility of skilled and unskilled workers. In other departures, we assume neither thatcountries share the same technology nor that regions within a country have equal skill endowments.
Because of this, we can resolve wage inequality and income inequality simultaneously. This paper is thefirst, to our knowledge, to study the relationship between trade and multidimensional inequality in a
low-income country.
Our work builds on the seminal contribution of Zhu and Trefler 2005 (hereafter ZT). In ZT,
initial technology and endowment differences determine that North produces and exports skill-intensive
goods, while South produces and exports low-skill goods. Southern technology catch-up causes
production of North’s least skill-intensive goods to move to South. This raises relative demand for skills,
and thus wage inequality, in both economies. As noted, however, this framework does not admit
different sub-national responses of the skill premium and of economic structure to trade; therefore, it
makes no prediction about regional inequality within the South; nor does it capture general equilibriummechanisms and labor market operations within the South. Neither of these are trivial problems.

4
Our goal is to capture mechanisms linking trade and technology to wages and economic structure
when South consists of two (or potentially more) heterogeneous regions. Countries and regions in ourmodel have different factor endowments and technology; this admits regional growth differences within
South. We are also able to study the influence of factor market segmentation on this process. In section 2of this paper we develop an analytical model and link theoretical predictions to empirical work. In
section 3 we describe data, model specification and empirical results. In section 4, we draw conclusions.
2 ModelFor ease of comparison we use ZT’s notation wherever feasible. There are two factors of production,
unskilled labor (L) and skilled labor (H). These are used to produce a continuum of goods z, ranked inincreasing order of skill intensity. Production functions are neoclassical, with strict quasi-concavity,
constant returns to scale, and continuous derivatives. There are no factor intensity reversals. Goods
markets are perfectly competitive, and thus in equilibrium profit is zero.
Production occurs in (relatively) skill-abundant North and skill-scarce South. South has tworegions, skill-scarce Inland and skill-abundant Coast. In China, barriers to labor mobility persist despite
trade reforms; we stylize this by initially assuming no labor movement between Inland and Coast.
Let Lk ( z ) and H k ( z ) be the unskilled and skilled labor requirements for producing one unit ofgood z in region k (k=N ,C, I). Let w jk be the wage of factor j (j = L, H) in region k . Let

ωk = wHk / wLk > 1 be the skilled-unskilled wage ratio in region k . North is the most skill-abundant
country, so ω N < ωC < ωI . Furthermore, we assume

wHI ≥ wHC > wHN > wLN > wLC ≥ wLI
Given prices and wages, a firm producing variety z in region j maximizes:

max P ( z ) f ( L j ( z ) , H j ( z ) ) − ( wLj L j ( z ) + wHj H j ( z ) )L j ( z ), H j ( z )(1)5where P ( z ) is the price of variety z. With constant returns to scale and free entry, firms bid for workers
until no firm earns a strictly positive profit. Therefore, C j ( wHj , wLj , z , t ) , the unit cost function for z inregion j , must equal P ( z ) . Finally, n stands for total varieties available, and t denotes time andcaptures all factors other than wage that affect unit costs—for instance technology level or infrastructure
in each region. Costs are thus written

C j ( wHj , wLj , z , t ) = wLj L j ( z ) + wHj H j ( z ) .
Here, Lj(z) and Hj(z) represent the optimal quantities of unskilled and skilled labor to produce one unit ofvariety z, given the endowments and technology available in each region.
We assume for simplicity that goods can be costlessly shipped across countries and regions.
Perfect competition exists for good z produced in this region, so the zero profit condition implies
Pj ( z ) = wLj L j ( z ) + wHj H j ( z )(2)As with other technology-oriented theories of trade (Dornbusch et al. 1977; Krugman 1982), our modelallows for international technology differences (for succinctness, in what follows we include t only
when necessary). We assume a Ricardian pattern of comparative advantage, i.e.

∂CN (.,., z ) / CS (.,., z )
≤0
∂z

and

∂CC (.,., z ) / CI (.,., z )
≤0
∂z

(3)

for all z , where CS (.,., z ) = min ⎡⎣CC (.,., z ) , CI (.,., z ) ⎤⎦ . According to (3), North is the technology leader
in all production, especially in more skill-intensive goods. Inland lags in every field, but hascomparative advantage in least skill-intensive goods; Coast is intermediate.
Lemma 1: Endowment-based comparative advantage (inequality 1) and Ricardian comparativeadvantage (inequality 4) together imply∂CN ( wHN , wLN , z ) / CS ( wHS , wLS , z )
<0
∂z

and

∂CC (w HC , w LC , z)/∂CI (w HI , w LI , z)
<0
∂z

6
for all ( wHj , wLj ), that is, the North has a comparative advantage in skill-intensive goods, and Coast isgood at intermediate skill-intensive production.

Inequality 3, Lemma 1 and the zero profit conditions together imply that North produces andexports the most skill-intensive goods, Coast produces and exports intermediate skill-intensive goods,
and the least skill-intensive goods are all produced in Inland. We define the marginal goods, zi , i = 1, 2
by PN ( z2 ) = PS ( z2 ) ,PC ( z1 ) = PI ( z1 ) . We can think of North as producing newly developed goods

that require high skill input. The high cost of skills, lower level of technical know-how, and poor
infrastructure in South all inhibit skill-intensive production. But as products mature, the production
process becomes standardized and routine and less-skilled labor can play a larger role. With technology
diffusion, some production can then be outsourced to South to take advantage of lower costs.
Equilibrium

Consumers supply labor inelastically and have identical preferences over a continuum of differentiatedconsumption goods. The utility function is assumed to be of the form:
U i = ∫ α ( z ) ln xi ( z ) dzz0

where x(z) stands for the amount of variety z demanded by the representative consumer. Consumer imaximizes utility subject to a budget constraint Ei, equal to their labor income, which depends on skill
status and on country or region of origin. The budget constraint of an unskilled worker in the North, forexample, is ∫ P ( z ) x ( z ) dz = wLN , and analogous constraints exist for other types of worker.
z0

The utility function implies that consumers have a strong preference for variety. Individual
demand for variety z , x ( z ) , obtained by maximizing utility with respect to the quantity of z consumed
subject to the budget constraint xi(z) = α(z)[E/P(z)], so α(z) is a budget share. Let Yi be aggregate
income in country I; then worldwide demand is7

Y +Y +Yx( z) = α ( z) N C I .
P(z)

(4)

We assume competitive markets and zero shipping costs, so P ( z ) is the price faced by bothconsumers and producers, and is denoted as follows. For goods produced in North, i.e. z ≥ z2 ,P ( z ) = PN ( z ) ; for goods produced in Coast ( z2 > z ≥ z1 ), P ( z ) = PC ( z ) ; and for goods produced inInland ( z < z1 ), P ( z ) = PI ( z ) . This gives goods market equilibrium.
In the labor market, Hj and Lj, j=N, C, I, are skilled and unskilled labor endowments, and eachregion’s relative skills endowment is hj = Hj/Lj. Labor supply is fixed, so the resource constraints forskilled and unskilled labor in the North are given by∫ x ( z ) H ( z ) dz = H
z

z2

N

and ∫ x ( z ) LN ( z ) dz = LN ,
z

N

z2

where H N ( z ) and LN ( z ) are skilled labor and unskilled labor used in production of good z in North.
We define North's excess demand for skilled labor relative to unskilled labor, N ( z2 ) , byN ( z2 ) ≡ ∫ ⎡⎣ x ( z ) H N ( z ) / H N ⎤⎦ dz − ∫ ⎡⎣ x ( z ) LN ( z ) / LN ⎤⎦ dz
z

z

z2

z2

(5.a)

In the same fashion, SI(z1) and SC(z2) are excess demands for Inland and Coast:
SI (z1 ) = ∫ 0 [x(z)H I (z) /H I ]dz − ∫ 0 [x(z)LI (z) /LI ]dz(5.b)

SC (z2 ) = ∫ z [x(z)HC (z) /HC ]dz − ∫ z [x(z)LC (z) /LI ]dz.

(5.c)

z1

z1

z2

z2

1

1

Relative labor demand can then be rewritten as follows (see appendix for details):

N ( z2 ) =h ( z ) − hN
YN + YC + YI z
dz = 0α ( z) N∫
z2
wLN LN
1 + ωN hN ( z )(6.a)

S C ( z1 ) =h ( z ) − hC
YN + YC + YI z2
α ( z) C
dz = 0∫z
1
1 + ωC hC ( z )wLC LC(6.b)

8
S I ( z1 ) =h ( z ) − hI
YN + YC + YI z1
α ( z) I
dz = 0∫0
1 + ωI hI ( z )wLI LI(6.c).

The final equilibrium condition concerns trade. We assume balanced trade between each area
and the rest of the world. Thus for North:
TBN = (YC + YI ) ∫ α ( z )dzzz2(Y ∫ α ( z)dz ) = 1 ,
z2

N 0

(7.a)

in which the numerator is world demand for goods produced in North and the denominator is North's
import demand. Similarly, for Inland,
TBI = (YN + YC ) ∫ α ( z )dzz10(Y ∫ α ( z)dz ) = 1 ,
z

I

z2

(7.b)

and when both these equalities hold, Coast’s trade is also balanced by Walras’ Law. In contrast with ZT,
each Southern region has two trading partners. In the absence of transfers or factor flows, each region
must have balanced trade. This allows us to trace out the otherwise unobservable interaction between
Southern regions. The exact expressions are provided in the appendix; these show that when labormarkets clear and trade is balanced, equilibrium can be fully represented by the vector of relative wages
and marginal goods (ω N , ωC , ωI , z1 , z2 ) (Dornbusch et al. 1980; Zhu and Trefler 2005).
Technical change
The technology level is a determinant of each region’s economic structure. Restructuring—for example
through trade and FDI— may be associated with different speeds of technical change, a subject with along tradition in the analysis of trade and welfare (e.g., Dixit and Norman 1980). However, thisliterature seldom discusses effects on relative factor prices, which are the main focus of this paper. We
define Southern technology catch-up either as technology spillover from North or as cost-reducing
process innovations. We assume that technical change is exogenous and uses no real resources. As in
Vernon (1966), new products are first produced in North, but once the manufacturing method isstandardized, South can easily imitate and take advantage of its cheaper labor to begin production.

9
In the age of globalization, however, barriers to the flow of technical know-how fromtechnological leaders to followers have been greatly reduced. Trade provides new sources of technicalprogress (Grossman and Helpman 1991; Rivera-Batiz and Romer 1991). Major obstacles, such as
information asymmetries and North-South factor proportion differences, make technology acquisition acomplex process, and the speed of technology diffusion varies across countries and regions. Among thefactors that facilitate technology adoption, human capital availability is well documented. Southern costreducing innovation occurs as Southern labor masters technological know-how.
We define productivity gain in region j by φ j ( z, t ) ≡∂ ln C j ( wHj , wLj , z, t )∂t. From the definitionof efficiency improvement, we know φ j (.,.) is always non-positive. So by definition, technologicalcatch-up in Coast is defined by φN ( z , t ) − φC ( z , t ) > 0 ; otherwise, we say that Coast lags behind North.
Similarly, φC ( z , t ) − φI ( z , t ) < 0 implies that technical progress in Coast outpaces that in Inland.
Lemma 2: As production range increases in one region, both skilled and unskilled wages in that region
increase, for a given technology.
Proposition 1: When Coast catches up with North, and when Inland has a slower rate of technicalprogress than Coast, some skill-intensive goods production migrates into Coast from North, and Inland
loses its most skill-intensive goods to Coast. Wage inequality in North increases; that in Inland
decreases. Wage inequality in Coast can go either way.
Proposition 2: When Coast catches up with North, and Inland has slower technical progress comparedto that of Coast, returns for both skilled and unskilled labor decrease in Inland, while both unskilledand skilled-labor wages increase in Coast. Regional inequality also increases.
Proofs: see appendix.
When there is balanced technical progress within South, we return to the ZT economy. But aslong as technical progress occurs at different rates in Inland and Coast, it creates extra effects. For

10instance, if Inland lags Coast in productivity growth, then at constant wages Inland becomes lessproductive relative to Coast. Therefore, slower income growth in Inland results in a trade surplus for thisarea with Coast. To restore trade balance requires Inland to produce less, while Coast needs to increase
its variety of goods. This leads to a rise in z2, and a fall in z1.
When wages are permitted to change, slower technical progress in Inland results in a lower
regional skill premium, while wage inequality in Coast is reduced by that region’s expansion into lessskill-intensive production. Thus faster technical progress in Coast compared to the other regions has two
opposing effects on its wage inequality. Technical progress and expanded production raise both skilled
and unskilled wages, and the relative wage outcome depends on which effect dominates. In Inland, bycontrast, both wage changes are indeterminate, so technical progress and reduced production lines have
opposing effects (in the special case of a lack of technical progress, both wages decrease). But in any
case, returns to both types of labor rise faster in Coast than in Inland.
Labor mobility
Up to this point, our model has departed from its predecessors only in the regional decomposition of theSouth economy. We now partially relax the restriction of labor immobility by allowing skilled labor tomove freely within South. This asymmetry in relative labor mobility has robust empirical support.
Within China, most permanent migrants are skilled workers. Their moves are facilitated by a special
residence certificate known as the "blue stamp hukou," introduced in 1992. The criteria for obtaining a
blue stamp hukou include a large investment or home purchase, as well as age, education, and skills.
Most low-skill or rural migrants are ineligible for this type of certificate, or cannot afford it. Thus,
despite large numbers of unskilled internal migrants, skilled workers move in proportionally far greaternumbers because of the relative ease with which they can relocate themselves and their families.
Returning to the model, we now assume free mobility of skilled labor within the South economy.
So HC + HI = HS, and we define HC/ HS = η, i.e., the proportion of skilled labor in Coast. Now, a11technology shock in one region has two kinds of effect: it causes production to move as before, but it
also induces skilled labor movement, thereby also altering regional factor endowments. Both effects
alter regional incomes and thus their import demands: for example, when skilled labor moves out of
Inland (i.e, η rises), import demand decreases because of a reduction of local population and income,and in order to keep trade balanced, Inland’s exports also decrease. These changes result in new tradebalance conditions, shown in full in the appendix.
Also, with skilled labor mobile the labor market clearing condition in South becomes

∫

z1

0

1/ LI ( z )YN + YC + YI
α ( z)H I ( z ) dz = (1 − η ) H S
1 + ωI hI ( z )
wLI

,

(8)

and with all else constant, ωI is decreasing in 1 − η . Thus Inland wage inequality is a decreasing
function of the number of skilled workers in that region.
The relative demand for skilled labor in Inland is:
⎤
⎤
z1 ⎡
z1 ⎡
YN + YI + YC
YN + YI + YC
S I ( z ) ≡ ∫ ⎢α ( z )dz − ∫ ⎢α ( z )⎥⎥dz = 0 .
00wHI H S (1 − η ) ( lI ( z ) / ωI + 1) ⎦⎥wHI LS ( lI ( z ) / ωI + 1) ⎦⎥
⎣⎢
⎣⎢

(9.a)

Similarly, in Coast,⎤z2 ⎡(Y + YI + YC ) LC / η H S ⎤dz − z2 ⎡α z
YN + YI + YC
S C ( z1 ) ≡ ∫ ⎢α ( z ) NlC ( z ) ⎥dz = 0
(
)
⎥
⎢
∫
z1
z1
wHCη H S ( lC ( z ) / ωC + 1) ⎥⎦wHC LC ( lC ( z ) / ωC + 1)
⎢⎣
⎢⎣
⎥⎦

(9.b)

The asymmetric treatment of labor mobility is a substantial step away from earlier North-South models.
It introduces a new channel for trade and trade-related phenomena to influence income distribution. As
productivity rises in Coast due to trade and/or FDI, each region undergoes both a change in production
structure—with Inland losing capacity to produce its most skill-intensive goods—and a change in
relative factor endowments, which further widens the technological gap.
Proposition 3: As Coast catches up with North, and Inland has slower technical progress than Coast,12
some skill-intensive production moves from North to Coast, and Inland loses its most skill-intensive
goods to Coast. Wage inequality rises in both North and Inland, while that in Coast can go either way.
Proposition 4: When Inland has slower technical progress than Coast, unskilled wages in Coast increase
more than those of Inland. Skilled wages in South increase, and regional inequality also increases.
Proofs: see appendix.
Propositions 3 and 4 tell us the following. First, holding endowments constant, the situation isexactly as in Proposition 1, i.e., Coast’s production range expands and Inland’s shrinks. Wage inequality
in Inland falls; that for Coast may rise or fall, but we know for certain that dωI / dt > dωC / dt . Second,
the rising skill premium draws Inland skilled labor to Coast, thereby reducing relative wages in Coast.
The net effect on inequality is ambiguous. In Inland, the relative demand for skilled labor decreases, as
does the relative wage. Outflows of skilled labor boost the skill premium in this region until skilledwages equalize in both Coast and Inland. So the skill premium rises in both Coast and Inland.
Thus far we have abstracted from the consequences of factor mobility on changes in economic
structure. The inflow of skilled labor helps Coast to expand production at the expense of both Inland andNorth. In turn, demand for both types of labor decreases in Inland, while demand for labor in Coast
increases even more. This second-round effect of labor mobility causes both skilled and unskilled wages
to fall in Inland and to rise in Coast. In equilibrium, we know that inequality in Inland must be higher
than that of Coast, and unskilled laborers in Inland certainly earn less than those in Coast.
3 EMPIRICS
In this section we test the foregoing analytical predictions using data from China. We test only
propositions 3 and 4, the cases in which skilled workers are mobile but unskilled workers are not.
Several features of China make this highly relevant. First, it is a very large country whose provinces
have widely varying geography, technology levels and skill endowments. Second, as mentioned, there
is restricted labor mobility, especially for those with few skills. Third, after opening to trade, regions

13
have had different access to FDI and its associated productivity-enhancing technologies and networks.
Thus, if a worker with skill level si in province j earns wage wij = m j ( si ) , m j (.) is a provincial functionthat maps labor skills into wages. This m j (.) is itself a function of export composition, relative skill
stock and other province-specific characteristics. Our model predicts that intra-province inequality
increases with z, its marginal good: dω Si / dz j > 0, j = I, C . An increase in z is associated with higher
wages for both skilled and unskilled workers: dw j / dz > 0, j =L,H . On the other hand, technicalprogress (lower unit costs) leads to higher z, and higher z is associated with higher wage inequality, sod ω j / dC j > 0 .
Data and estimation methods
The database covers 29 provinces over 1993–2005 (data for Tibet and Chongqing are unavailable).
There are inevitably some compromises to be made between ideal and available datasets. Ideally wetwould have measures of skilled and unskilled wages ( wLjt and wHj) in province j and year t. Then intrat/ wLjt , and interprovincial inequality at time t
provincial wage inequality in province j would be ω tj = wHj

between provinces i and j would be wLit / wLjt , as in our analytical model. Unfortunately, provincial wage
data by occupation are incomplete. Instead, we use the Gini coefficient of disposable income per worker
to capture inequality. Using the Gini as a proxy for the skill premium has potential problems: therelation between the two is nonlinear, and could even be non-monotonic. However, a review of trends
in provincial human capital stocks reveals relatively smooth changes over time. Therefore, nonmonotonicity issues should not greatly affect the dataset.
There are other limitations too. First, the urban labor force survey is restricted to households
holding urban hukou, and so excludes migrants working illegally in cities. The survey also excludes
rural wage workers. However, since most wage employment is in urban areas and most wage-workers
are urban residents, the data should nonetheless capture major changes in wage inequality. Second,

14
working hours are not reported, precluding construction of hourly wage rates. Third, we must treat
different workers from the same household as if they had the same skill level.
For interprovincial wage comparisons we use average manufacturing wages, since it is most
likely that a province with a higher average manufacturing wage has higher returns to both unskilled andskilled labor. Using the average wage to represent either skilled or unskilled labor is problematic, since
the skill composition of the labor force may vary in some regions due to the relative immobility ofunskilled labor. For example, when coastal provinces experience faster technical progress than inland
provinces, not only do unskilled wages in inland provinces decrease, but these regions also become
more skill-scarce. So our estimate of the effect of coastal growth on average inland income may bebiased upward -- albeit with the correct sign. Annual data on the quintile distribution of disposable
income and average manufacturing wages come from provincial Statistical Yearbooks (1995-2005).
We have two equations to estimate:ln G jt = β1 ln z jt + β 2 ln ( H jt / L jt ) + β3 ln X 1jt(10.a)

ln W jt = γ 1 ln z jt + γ 2 ln ( H jt / L jt ) + γ 3 ln X 2jt(10.b)

where H jt / L jt is relative skill stock, or the ratio of skilled to unskilled workers at time t in province j.
X .jt represents all other variables taken into account. Following Barro and Lee (2000), we define skilledworkers as having completed at least college education; all others are classed as unskilled. Data on thecomposition of employment by educational attainment is from the China Labor Statistical Yearbook(1998-2005). From the model, we expect β1 , β 2 , γ 1 and γ 2 to be positive.
On the other hand, z jt itself is also function of technology level and variables such as FDI inflow:ln z jt = λ1 ln Pjt + λ2 ln X 3jt

(11)

15
where Pjt is technology level of province j at time t, and is calculated by dividing total value added bytotal employment (China Industry Economy Statistical Yearbook 1994-2006). X 3jt includes FDI inflows
as a percentage of GDP, and arable land per capita. The former represents technology spillover fromadvanced countries, while the latter can be used to control for natural resources. Resource abundance ina region makes it cheap to produce agricultural and other primary products; other things equal, therefore,
its skill intensity of exports should be low. In regressions we lag FDI once, to allow for delays between
new capital installation and its impact on production and exports. This also resolves a potential
endogeneity problem. Data for these two variables are from provincial Statistical Yearbooks.
Provincial commodity export data are also from provincial Statistical Yearbooks (1993-2005),
and the Yearbook of China's Foreign Economic Relations and Trade (1992-2006). At this highly
aggregated level (2-digit HS), there is no clear-cut z jt above which a province supplies no exports.
Instead, we use the skill-intensity of exports as a proxy. Because an increase in z shifts the economy's
export share towards more skill-intensive goods, zˆ jt = ∫ ∫ x jt ( z ) dzdx . Using UN classifications, we
1

z

0 0

sort industries into 4 groups by skill intensity (Coxhead and Li 2008, and see appendix table A-1). Thenthe corresponding provincial export data are classified accordingly. For each province, we define theskill-intensity of its exports in each year as the value of its high-skill exports divided by the value of its
total exports. Table 1 gives summary statistics of this variable and others to be used in regression
analysis.
INSERT TABLE 1 HERE
Unit roots in panel data can dramatically affect the asymptotic properties of regression estimates
and test statistics (Levin and Lin 1992; Im et al. 2003; Levin et al. 2002; Maddala and Wu 1999). This
problem may apply to the wage and income variables (export skill intensity and the Gini coefficient lie
in the interval [0, 1] and so are automatically stationary). Prior to estimation, we perform Levin-Lin-Chu

16panel data unit root tests on these variables. The tests reject the null of nonstationarity when a first orderlag is allowed (see appendix for details). When no lag is included, we cannot reject the null hypothesisthat the average manufacturing wage has a unit root. We address this problem in estimation by adding a
one-period lagged dependent variable.

Results
First, we confirm that faster technical progress leads to an increase in the sophistication of exports.
Column 1 of Table 2 displays province fixed-effect (FE) estimates of equation 11. The coefficient on thetechnology variable is highly significant and shows that a 1% increase in technical progress leads to a
0.204% increase in the skill intensity of exports. The elasticity of export skill intensity with respect to
FDI is 0.107. Arable land per capita has a significant negative effect on the dependent variable (-0.54),
suggesting that provincial exports follow comparative advantage. The random effects (RE) results(column 2), are very similar to the fixed effects, and this is confirmed by a Hausman test.
INSERT TABLE 2 HERE
Learning-by-exporting arguments hold that productivity in exporting firms can be improved overtime, either because FDI transfers technology to firms that introduce new export products, or because
the possibility of selling in export markets stimulates firms to improve their own capabilities (Westphal2002). Thus the lagged skill intensity of exports may be a determinant of skill intensity in the current
stage, which introduces a dynamic element. We therefore implement an AR(1) specification in the
disturbance terms. The FE and RE results are reported in columns 3 and 4. The AR(1) specification
makes RE results more reliable, so even though the Hausman test rejects RE in favor of FE, we discuss
the RE results. In these, the technology, FDI and natural resource variables retain their significance at
1%, and their magnitudes are comparable with those obtained in the static model.
Another means to deal with dynamics is to include a lagged dependent variable among theregressors, so we have yit = α yit −1 + xit' β + μi + eit . In this case, however, the FE estimator is inconsistent17
because the sample mean of yit −1 is correlated with that of eit . One solution is to combine firstdifferencing with IV, i.e., Δyit = αΔyit −1 + Δxit' β + Δeit , using yit − 2 as an instrument for Δyit −1 (Anderson
and Hsiao 1981). A frequently preferred alternative is the Arellano-Bond (A-B) estimator (Wooldridge
2001: 304). The A-B estimates are shown in Table 2, column 5, and the pooled IV model in firstdifferences in column 6. These differ somewhat from the AR(1) results. The natural resource variable
loses significance, and the technology variable is significant only in the A-B model, with a smaller
elasticity (0.063). The FDI variable retains a high level of significance, however, and its elasticity
increases somewhat, to 0.259 in the A-B estimates and 0.176 in the IV estimates.
We next turn to inequality, using provincial Gini coefficients as discussed above. We want to test
whether greater skill-intensity of exports is associated with higher within-province inequality. The firsttwo columns of Table 3 show estimates of Eq. (10.a). The estimated coefficient on export skill intensity
is positive as expected. It predicts that a 1% higher skilled export share increases the intra-provincial
Gini by 0.361%. When an economy concentrates its production and exports on skill-intensive products,
skilled labor demand growth exceeds that for unskilled labor. The skill stock variable is also positive(0.108). In these regressions we also include the GDP share of government expenditure as a control for
institutional (i.e. non-market) influences on inequality. Until very recently China’s non-military public
expenditures were focused almost exclusively on growth, rather than on safety nets or redistributive
programs. Public investments in infrastructure largely supported coast-based investments in tradable
industries, thus complementing FDI and trade-based sources of growth. The government spending share
has an estimated elasticity of 0.262, indicating that other things equal, the distributional impact of such
expenditure has been regressive. Of course, this says nothing about its impact on absolute well-being.
INSERT TABLE 3 HEREWe also address dynamics. Columns 3 and 4 of Table 3 show AR(1) estimates; these are similar
to those in columns 1 and 2. However, the first-differencing and A-B estimates tell a different story. In

18
the first-differenced regression neither the human capital nor the public spending variables are
significant. In the A-B estimates, human capital is weakly significant with an elasticity of 0.162. But inboth cases, the skill intensity of exports remains significant, with elasticity values of 0.629 and 0.290 in
the first-differenced and A-B regressions, respectively.
Our model (and the Chinese reality) is of limited labor mobility in response to changing labordemand, and in particular, of greater mobility among skilled than unskilled workers. Thus trade-relatedchanges such as in the skills composition of exports could affect both intra-provincial inequality in otherregions, and also inter-regional inequality. We now address both of these issues.
Does an increase in skilled labor demand in one region raise inequality in others? In Table 4, we
re-estimate the inequality model on coastal and inland data subsets. In Proposition 3 we hypothesizedthat a rise in the skill-intensity of exports in one region will attract relatively more skilled than unskilledworkers from others, thereby raising the wage premium. To test this, we include variables for the
average export skill intensity of provinces in coastal and inland regions in the inequality regressions forthe inland and coastal regions, respectively. These variables are robustly positive in all models, so we
reject the hypothesis of no inter-provincial effects from inter-regional productivity changes.
INSERT TABLE 4 HERE
Finally, we examine interregional inequality. The two dependent variables available for thispurpose are the average manufacturing wage and the income share of the lowest 20% of the income
distribution in each province. In the first four columns of Table 5 we see that in both specifications, theskill-intensity of coastal region exports has a negative effect on average inland wages. The elasticity (inthe A-B model) is -0.041, meaning that a 1% increase in coastal export skill intensity leads to a 0.04%
reduction in inland manufacturing wages. Conversely, higher skill-intensity of inland exports reduces
the coastal average wage (the elasticity is -0.013). These results support our prediction that rapid

19
technical progress (and therefore higher skill-intensity of exports) in one region has a negative effect on
average wages in other locations within the same skilled labor market.
INSERT TABLE 5 HERE
Finally, as a proxy for the incomes of the poor we use disposable income per worker in thebottom 20% of the provincial wealth distribution. Columns 5-8 in Table 5 show first that higher skillintensity of coastal exports is associated with lower incomes of the poor in inland, and second thathigher skill-intensity of inland exports has a negative effect on the incomes of the coastal region’s poor.
A 1% increase in the skill intensity of exports from an average coastal province is associated with a
0.56% decrease in the incomes of the poor in inland provinces. On the other hand, higher export skill
intensity in inland provinces lowers the incomes of the poor in coastal areas with an elasticity of -0.693.
These results imply a rather elastic response of incomes of the poor to a change in the composition ofexports from other regions. In the presence of restrictions on the interprovincial mobility of low-skill
workers, growth in one part of the country may actually impose welfare costs on other parts.
In summary, the estimates confirm our prediction that technical progress shifts export shares
towards more skill-intensive goods. In turn, this shift increases intra-province inequality and theprovincial average wage. These results mean that technical progress contributes to rising inequality.
Also, as predicted, there are interactions between dynamic and lagging regions that may also affect both
intra- and inter-province inequality and the income of the poor in each region.
4 CONCLUSION
Many economists now maintain that greater openness to trade in developing economies is associatedwith higher inequality, a view sharply at odds with Heckscher-Ohlin predictions. Analysis of this issue
is complicated by market imperfections, specifically those that inhibit factor mobility and cause
differential rates of technical progress, since these are also sources of spatial inequality. Our theoreticaland econometric analyses both show how interactions between factor mobility limitations and different

20
regional rates of technical progress can explain the evolution of inequality in response to trade and FDI.
As favored regions benefit more from globalization, they drain skilled workers from less favored areas.
In a dynamic sense, the latter lag further behind; average income growth in favored regions is faster.
Moreover, unbalanced regional growth may even exert downward pressure on the absolute incomes of
unskilled and immobile workers in disadvantaged areas.
It is often observed that exports are positively correlated with economic growth, but that not all
forms of export are equal in this respect. Hausmann et al. (2007) show that specialization in higher skillintensity exports leads to faster growth, and that policies that foster skills upgrading in exports havepositive growth effects. We have found that in a heterogeneous economy with some labor immobility,
high-skill export growth may worsen both interregional and intraregional inequality. Therefore, theaggregate growth effect observed in the literature is in fact a net gain, in which rapid, export-driven
expansion in one region of the economy masks slower growth or even contractions in other regions.
Finally, while space limits prevent a detailed policy discussion, it is important to recall thatgovernments also contribute to changes in regional inequality. In order to maximize aggregate growth
possibilities, government may prefer to allocate public investments mainly in rapidly growing regions.
But with partial labor immobility this will likely exacerbate interregional inequality—and political
discontent. The mismatch in China between coast-intensive capital investments and an inland-intensive(and only partially mobile) labor force is also a source of economic inefficiency, and thus a drag on
aggregate growth. Since 2000, the Chinese government has responded to these domestic tensions with amassive campaign of public-sector investment, business subsidies, and income tax breaks directed atinland provinces. This so-called “Go West” campaign is a belated attempt to equalize regional rates of
output and employment growth and thus to close the development gap. Given the country’s geography,
however, it seems likely that coastal locations will continue to offer higher returns on trade-oriented

21
private capital investments, and thus that state-funded redistributive measures are clearly second-best
relative to policy reforms that liberalize the movement of workers from inland to coast.
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Variable
FDI
Public Spending
Skill intensity of exports
Human capital
Arable land
Gini Coefficient
Income of the poor
Manufacturing wage
Value added per worker

Unit

Mean

MedianStd. Dev% of GDPshare of GDP

7.800
0.156
0.093
6.004
0.101
0.225
7522
9238
58426

3.070
0.146
0.060
5.070
0.075
0.218
6668
8364
47651

11.792
0.052
0.090
4.704
0.069
0.037
2760
4188
38127

% of labor force
ha /person
RMB yuan
RMB yuan
RMB yuan

Min

Max

Obs

0.326 77.100 275
0.074
0.500 339
0.004
0.532 339
0.000 30.940 309
0.015
0.321 355
0.144
0.360 208
3775 18428 207
3611 29743 330
9864 243447 271

Table 1: Summary statistics of key variables

Dependent variable

Skill Intensity of Exports

MethodLagged skillintensity of exports
Technology level
FDI
Arable land per capita

AR(1)
FE

RE

FE

RE

0.204***(0.0442)0.107**(0.0494)-0.544**(0.2772)

0.187***(0.0404)
0.138***(0.0452)-0.783***(0.1519)0.104*(0.0595)
0.128*(0.0725)-0.014(0.3695)

0.192***(0.0465)
0.144***(0.0472)-0.780***(0.1456)

Hausman test
Chi-square
2.40
28.45
Observations
290
290
261
290
Groups29
29
29
29
Adj R-square:
Within0.18780.18490.03810.1850Between0.60020.59690.46600.5982Overall
0.56110.5636
0.41310.5652Standard errors in parentheses* significant at 10%; ** significant at 5%; *** significant at 1%

Table 2: Estimation results: skill intensity of exports

ArellanoBond
0.132**(0.0669)
0.063*(0.0360)
0.259***(0.0595)
0.159(0.3291)First
differencing
0.889(0.7566)
0.106(0.0798)
0.176***(0.0605)-0.152(0.3533)

254
29

254
0.0459Dependent variable

Gini Coefficient

MethodPublic spending
Skill stock
Lagged Gini
coefficient
Skill intensityof exports
Hausman test
Chi-square
Observations
GroupsAdj R-square:
WithinBetweenOverall

AR(1)

First

Arellano-

FE

RE

FE

RE

differenceBond

0.262***(0.0772)
0.108***(0.0339)0.146**(0.0573)
0.084***(0.0245)0.270***(0.0975)0.089**(0.0403)0.140**(0.0567)0.080***(0.0243)

0.361***(0.0938)

0.057(0.0353)

0.362***(0.1155)

0.055(0.0346)

0.166(0.1319)0.004(0.0398)
0.378(0.0803)0.629***(0.1937)

0.162(0.1931)0.097*(0.0506)-0.352***(0.1359)0.290*(0.1531)

213
29

25.65
184
29

213
29

153

153
28

89.93
213
29

0.14730.35600.00990.02510.32980.04170.038

0.26590.02430.0084Standard error in brackets* significant at 10%; ** significant at 5%; *** significant at 1%

Table 3: Estimation results: Gini coefficient (whole sample)

3297
0.04180.038

Dependent variable
MethodGini CoefficientFE

FE

InlandCoast

FE AR(1)InlandCoast

Public spending

First differencing

Arellano-Bond

InlandInland

Coast

Coast

0.193**
0.062
0.199
0.080
-0.139 -0.741*** 0.352-0.083(0.0893) (0.1347) (0.1257) (0.1404) (0.1562) (0.1804) (0.2925) (0.2069)
Skill stock
0.039
0.089*-0.034 0.099*** -0.284
0.075 1.350** 0.208(0.0464) (0.0454) (0.0466) (0.0461) (0.3301) (0.0673) (0.4437) (0.0991)
Coastal skill0.504***
0.555***
1.433*
1.682**
intensity of exports (0.1373)(0.2116)(0.7332)(0.7574)
Inland skill0.767***
0.848***
0.346**
1.016***
intensity of exports(0.1460)(0.1572)(0.1721)(0.2331)
Lagged Gini
-0.1120.050 -0.303** -0.728***
coefficient(0.1216 (0.1245) (0.1275) (0.1233)
Skill intensity-0.0400.132
0.026 0.238*** 0.172
0.467-0.110 0.486*
of exports(0.0315) (0.1058) (0.0370) (0.1156) (0.1914) (0.5075) (0.3300) (0.2638)
Observations
125
88
108
76
90
63
90
63
Groups
17
12
17
12
16
12
Adj R-square:
0.0686 0.2157
Within0.33630.61260.1616
0.6060Between0.04290.00050.04180.0033Overall
0.08130.17250.02070.0778
Standard error in brackets* significant at 10%; ** significant at 5%; *** significant at 1%

Table 4 Estimation results: Gini coefficient (by region)Dependent Variable
MethodManufacturing wage
First differencing
InlandCoast

Arellano-Bond
InlandCoast

Public spending

Bottom 20% income
First differencing
InlandCoast

Arellano-Bond
InlandCoast-0.0140.080-0.0080.002 -0.385** 0.104 -0.347* 0.203(0.0591) (0.0634) (0.0424) ( 0.0455) (0.1825) (0.2088) (0.1453) (0.1440)
Skill stock
0.022 0.587*** 0.018*
0.023-0.120-0.0800.022
0.005(0.0185) (0.1430) ( 0.0107) (0.0199 ) (0.3401) (0.0764) (0.0543) (0.0602)
Coast skill-0.387**-0.041**0.512-0.559*intensity of exports (0.1561)(0.0191)(0.6003)(0.2961)
Inland skill-0.025*-0.013*-0.811***-0.693***intensity of exports(0.0127)(0.0067)(0.1967)(0.1612)
Lagged0.548*** 0.943*** -0.309*** -0.359*** 0.876*** 0.895*** 0.037 0.291***dependent variable (0.0917) (0.1634) (0.0785) (0.0959) (0.1047) (0.0665) (0.2339) (0.1030)
Skill intensity
0.300*** 0.587*** 0.042*
0.014*-0.1200.451
0.300 0.397***of exports(0.1033) (0.1430) (0.0235) (0.0059) (0.3401) (0.5757) (0.2367) (0.1070)
Observations
153
108
153
108
90
63
90
63
Groups
16
12
16
12
2
0.7527 0.7691
0.4883 0.7733
Adjusted R
Standard error in brackets* significant at 10%; ** significant at 5%; *** significant at 1%

Table 5: Estimation results: wages and poverty