Reducing Regional Disparities in China: Is Investment Allocation Policy Effective?

Anping Chen,
School of Economics,
Jinan University,
Guangzhou, 510632,
Guangdong Province,Chinae-mail: anping.chen＠yahoo.com.cn
andNicolaas Groenewold,
Department of Economics,
University of Western Australia,
Crawley, WA 6009
Australiae-mail: nic.groenewold＠uwa.edu.au

We are grateful to the Australian Department of Education, Employment andWorkplace Relations and to the Business School at UWA for support for Chen to visit
UWA under the Endeavour Award scheme in 2009. We have benefited fromcomments on an early draft by our colleagues at an Economics brown bag session at
UWA in September 2009, from participants at the Western Economics Association
annual conference in Portland, Oregon in July 2010, as well as from participants at theannual conference of Chinese Economic Studies Australia at UWA in July, 2011.

1

Reducing Regional Disparities in China: Is Investment Allocation Policy Effective?
Abstract:
Inter-regional disparities in China have been an important concern for
central-government policy-makers for most of the past 60 years. One of the main
policy instruments for redressing the balance between the prosperous coastal region
and the poorer inland region has been the allocation of investment spending. Yet
there is little empirical work evaluating the response of disparities to changes in theregional distribution of investment. We help fill this gap and analyse the two-way
relationship between these variables within a VAR/VECM framework, the results ofwhich we tentatively interpret in terms of a simple demand model. We find,surprisingly, that changes in the regional allocation of investment have only a modest
positive effect on inter-regional output disparities while the effect in the opposite
direction is also positive but much larger. The effects of investment on output arelarger, though, for the post-1978 period. We find our overall results to be robust to
numerous variations in variable definition.
JEL Codes: E62, O18, O23, R58
Key words: regional disparities, regional policy, China, investment allocation

2

1. Introduction
China has experienced a high rate of economic growth in the past 30 years. Its
average growth rate in terms of real GDP has been about 9.5 per cent per annum
between 1978 and 2009. 1

This rapid growth has, however, been far from evenly

spread across its regions with the coastal region, for example, growing at a higher
average rate than the interior region. At the provincial level, the post-1978 averageannual growth rate has varied from a low of 8.4% for Qinghai province in thenorth-west of China to rates over 13% for the south coastal provinces of Zhejiang,
Jiangsu, Fujian and Guangdong.

By and large, these growth differences haveexacerbated already large disparities in per capita output levels.

Thus in 2009

Qinghai had a per capita GDP of 19,454 yuan compared to that of Zhejiang of 44,641,
Jiangsu of 44,744, Fujian of 33,840 and Guangdong of 41,166. 2
Not surprisingly, the spatial distribution of economic activity and welfare has
been the subject of considerable interest to both policy-makers and academic
researchers.

Apart from concerns about the inequity of an uneven distribution,

regional disparities have the potential to generate large inter-regional population
movements as well as social unrest stemming from resentment in the poor regions at
the obviously higher welfare in the richer provinces.
Beginning with the first Five-Year Plan (1953-1957), the central government
attempted to “correct” the inherited imbalance between the interior and coastal

1

The measurement of China’s economic growth has not been without controversy. See Woo(1998), Young(2003) and Bosworth and Collins (2008) for details.
2
Data for per capita GDP are from State Statistical Bureau, China Statistical Year Book 2010. For a more
extensive recent discussion of regional disparities in China, see Groenewold, Chen and Lee (2008) and for a recent
econometric analysis of trends in and explanations of disparities see Lau (2010).

3

regions, with the allocation of investment resources being the most important
instrument.

The investment allocated to the interior region increased from 52% to

60% during the first Five-Year Plan.

This trend continued until the end of the third

Five-Year Plan by which time the proportion of investment going to the interior had
increased further to 70%, although by then national security had become a major
factor driving this policy since the relationship with the Soviet Union had deteriorated
considerably by the mid-1960s and an important motivation for increased allocationof funds to the interior was to create several zones of economic self-sufficiency within
China for strategic purposes. The allocation of investment to the interior slowed in
the fourth Five-Year Plan (1971-1975) with China’s greater interaction with western
economies and less concern for the national security.

However, the investment shareof the interior was still above 50% and continued higher than that of the coastal region
until 1978.
Despite the increase in investment in the interior region, inter-regional income
disparities did not improve much. The ratio of GDP per capita in the interior to thatin the coast was stable at about 0.7 in the 1950s and 1960s and actually fell to 0.5 by
1977 despite the biased investment allocation.

Therefore, it appears that theinvestment allocation policy had little effect in reducing regional disparities in thepre-1978 period.
From the beginning of reforms in 1978, there was an explicit policy of
unbalanced growth, favouring the coastal region under the argument that the limited
resources of the country should be allocated to those provinces with higher

4

productivity in the expectation that the consequently higher coastal growth would pull
the rest of the country behind it.

As a result, the investment allocated to the coast

outstripped that in the interior in 1979 for the first time since 1953 and continued to
do so, rising to 65% by the end of eighth Five-Year Plan (1991-1995).

Not

surprisingly, the regional income gap widened during this time period – the ratio ofreal GDP per capita in the interior to that in the coast fell from about 0.5 in 1978 to
0.4 in 1995.
During the ninth Five-Year Plan (1996-2000), in the face of growing concern inBeijing about the unequal regional distribution of the prosperity generated by the
1978 reform and opening-up policy, a co-ordinated development strategy was
advocated by the central government in which the focus shifted back to the interior
region.

A number of special policies, such as the Great Western Experiment(announced in 1999 during the ninth Five-Year Plan), the Resurgence ofNorth-Eastern Old Industry Base and the Stimulation of the Central Region (bothduring the tenth Five-Year Plan) were implemented. The investment allocated to theinterior region increased slowly in the ninth Five-Year Plan and has accelerated since2004 with the investment share in the interior region increasing by about 10
percentage points from 1996 to 2008.

However, the regional income gap has notchanged much during the past decade; the ratio of real GDP per capita in the interior
to that of the coast even fell from 0.4 to 0.36 from 1996 to 2004 and then rose back to
0.4 by 2008.

It appears that inter-regional investment allocation policy also had little

effect in reducing the regional disparities during this period.

A word of warning is5needed, however.

In the pre-reform period, investment consisted mainly of public

investment so that the allocation of investment expenditure is clearly a reflection of
government regional policy.

After 1978, an increasingly important privatecomponent in investment might be considered to undermine the characterisation of
investment as a policy variable.

However, investment has continued to have a large

public component and, besides, even private investment is importantly affected by thegovernment, either via state-owned-bank finance or by direct government control oflarge projects so that the allocation of investment can still be regarded as substantiallyreflecting the central government’s regional policy, even after 1978.
Given the importance and persistence of regional disparities in China and thelong history of the use of investment policy to attempt to ameliorate them, it issurprising that this relationship has been the subject of so little empirical research.
Our paper sets out to begin to fill this gap in the literature by analysing therelationship between the inter-regional allocation of investment and output disparities.
It does so by distinguishing two regions – the coast and the interior.

We measureinvestment allocation policy by the ratio of per capita investment in the interior to thatin the coast while output disparities are measured by the ratio of real per capita output
in the interior to that in the coast. We analyse the dynamic relationship between
them in a time-series framework, using either a vector-auto-regressive (VAR) modelor a vector-error-correction model (VECM), depending on the properties of the data.
This modelling strategy allows us to clearly assess the effects of investment allocation
on regional disparities, both the strength and timing of these effects, as well as6analysing the possibility of reverse effects running from regional inequality toinvestment allocation. We assess the plausibility of the magnitude of the effects
generated by our empirical model by comparing them to the predictions of a simple
macro model based on the relationship of total output to its components.
The remainder of the paper is structured as follows. We briefly discuss thelimited literature in the area and set out a simple theoretical framework in section 2
and describe the data in section 3. Section 4 contains an outline of our empiricalmodel and the estimation and simulation results are reported in section 5. Sensitivity
tests are reported and discussed in section 6 and conclusions are drawn in section 7.

2. Literature and Theory
In this section we review such literature as there is relating to the relationshipbetween output and investment at the regional level in China and then go on topresent a simple theoretical framework which we use to assess the plausibility of themagnitude of the effects we find.
Although there is discussion in the literature of regional investment allocation
policy, little empirical work has been carried out on assessing the effects ofinvestment allocation on regional disparities.

Thus, e.g., Yang (1990), Fan (1995),

Wei (2000), Fujita and Hu (2001) and Yao (2003) touch on the issues but they arelargely descriptive treatments of the questions of regional disparities, policy andinvestment.

While they draw conclusions regarding investment and regional

disparities, none of these papers reports a systematic evaluation of the efficacy of7investment allocation to influence regional inequalities.

Other related papers focus

on only a small and special component of investment, viz., foreign direct investment
and its effect on disparities; examples are Zhang and Kristensen (2001), Sun andParikh (2001) and Wei, Yao and Liu (2009).
There is, therefore, little work on the effect of regional investment-allocation
policy on disparities in China, although there is a substantial literature on disparities
as such.

Much of this literature, however, is cast in terms of the convergence debate

which focuses on whether there are persistent disparities between regions, and if there
are, what are the factors that determine the equilibrium disparities. 3

While a few

papers in this literature (Chen and Fleisher, 1996; Lin, 2000; Brun, Combes andRenard, 2002; Wang and Ge, 2004) test the role of investment in generating
disparities within the convergence framework by including investment as a control
variable in growth equations, their conclusions regarding investment policy arelargely incidental to their main purpose.

Thus, they do not consider the effect onregional disparities of the re-allocation of investment from one region to another.
Not only is there little if any systematic investigation of the effect of investment
re-allocation on regional disparities, there has been no analysis of possible feedback
and dynamics within this relationship.

While the question that motivates theresearch reported in this paper is whether investment allocation affects output

3

For a selection of papers reporting research on China’s regional disparities see: Chen and Fleisher (1996),
Fleisher and Chen (1997), Kanbur and Zhang (1999), Yao and Zhang (2001a, 2001b), Demurger (2001), Chang(2002), Lu (2002), Cai, Wang and Du (2002), Yang (2002), Demurger, Sachs, Woo, Bao, Chang and Mellinger(2002), Bao, Chang, Sachs and Woo (2002), Demurger, Sachs, Woo, Bao and Chang (2002), Lai, Peng and Bao(2006), Yao (2006), Jin and Zou (2005), Zhang (2006), Candelaria, Daly and Hale (2009) and Fleisher, Li and
Zhao (2010), Chen (2010), Chen and Groenewold (2010), Lau (2010), Zhang and Eriksson (2010) and Hao andWei (2010).

8

disparities directly, it is possible that not only does investment affect disparities but
the reverse may also be true in which case the simple inclusion of an investment
variable in a regional growth equation will give biased results.

In addition,

policy-makers are likely to be interested in the dynamics of the relationship between
investment and disparities – how long do changes in investment take to affect regional
output disparities?

What is the shape of the response?

And so on. We analysesuch questions within a VAR/VECM framework which is specifically designed for the
analysis of the dynamic inter-relationship between variables.
Before specifying our empirical model, we consider a simple macroeconomic
relationship between output and its components which we will use as a framework forthinking about the likely magnitudes of the relationship between regional output
disparities and the regional allocation of investment. For region i, we have:(1)

Yi = Ci + Ii + Gi + NXi,

for i = I, C

where Y, C, I, G and NX denote output, consumption, government expenditure and net
exports (both inter-regional and international), all in real per capita terms, and thesubscripts I and C denote the interior and the coastal regions respectively.

Takingthe ratio of YI to YC, we have:
(2)

YI
C
I
G
NX I
= sC I + sI I + sG I + sNX
YC
CC
IC
GC
NX C

where the sj (j = C, I, G, NX) are the relevant output shares in the coastal region(e.g., sC = CC/YC).

The two variables of particular interest are the output ratio, YI/YC,

and the investment ratio, II/IC.

From equation (2) it follows immediately that thedirect effect on the output ratio of a change in the investment ratio is given by:

9∂ (YI / YC )= sI∂( I I / IC )(3)

Thus the simple national accounting relationship between the variables suggests thatthe effect on the output ratio of a unit rise in the investment ratio ought to be about
equal to the share of investment in total output in the coastal region, ceteris paribus.
Naturally, in practice the relationship between the two variables will be much more
complicated for at least the following reasons:(i)

The partial derivative in (3) assumes that all the other terms in the equation
are unaffected by the change in investment allocation.
be true in practice.

This is unlikely toThus, for example, it is possible that there will be

offsetting changes in one or more of the other variables.

In particular, it

may be the case that an increased government investment allocation will,in part, replace other government expenditure so that a rise in theinvestment ratio will be partially offset by a reduction in the government
expenditure ratio.

Moreover, it is likely that at least some of the inputs

required for the investment will come from other provinces so that there
will be an offsetting change in NX.

Both of these indirect effects willserve to reduce the magnitude of the overall impact on the output ratio of
the increased investment allocation.
(ii)

The expression in (3) ignores positive feedbacks via consumption, forexample, that are usually captured in the multiplier process.

The

inclusion of these would be expected to increase the size of the effect inequation (3) since the increase in interior investment would have positive

10

multiplier effects while the reduction in coastal investment would havenegative multiplier effects.
(iii)

Since investment adds to the capital stock, a higher level of investment in
the interior will build up the capital stock there which should boost
productivity, thus further increasing the output effect, although perhaps
this will take longer than the traditional multiplier effect. Also, this may
be offset in practice by the widely-cited inefficiency of investment in theinterior region in China; see, e.g., Boyreau-Debray and Wei (2004), Dollar
and Wei (2007), Bai, Hsieh and Qian (2006), Qin and Song (2007) and Wu(2009).

(iv)

Similarly, the improvement in employment opportunities expected in theinterior as a results of the higher investment would likely attract labour
from the coast (or slow down migration from the interior to the coast) andthis will reduce output per capita if there are decreasing returns to labour.

Thus, the simple national accounting relationship between components of output
which may underlie the investment allocation policy is likely to tell only a part of thestory although it provides a guide to the order of magnitude of the short-run effect on
output which might be expected to result from an investment re-allocation.

Inpractice, though, the relationship between investment allocation and the per capita
output ratio is likely to be much more complicated, something we will need to keep inmind when interpreting our empirical results later in the paper.
While the brief theoretical discussion above suggests that there are various

11channels through which the allocation of investment might influence per capita output
disparities in a regional setting, in this paper we stop well short of a full-scale
structural multi-regional model.

In fact, given the constraints of macro data at theregional level in China, particularly before 1978, we begin with the simplest possible
structure which involves only two regions and two variables, the output ratio, YI/YC,
and the investment ratio, II/IC. 4

Since the form of our empirical model (aVAR/VECM) depends on the nature of the data, we consider the data first, before
retuning to an exposition of the modelling framework.

3. The Data
The data employed are annual for the period 1953 to 2008. For inter-regional
disparities we use the ratio of real GDP per capita in the interior to that in the coast(YI/YC).

We recognise that in the analysis of distributional issues, the use of income

may be more natural than GDP.

We do not use income to compute the regional

disparity measure, however, since the State Statistical Bureau does not provide suchdata at the provincial or regional level for our sample period.

They do report data on

per capita net income of rural households and per capita disposable income of urbanhouseholds within each province and we could use these to construct a measure ofprovincial income but we do not proceed along this path since the definition ofincome for a rural household is different from that for an urban household, making
any aggregation of the two difficult to interpret.
4

Moreover, per capita GDP is by far

Since macro time-series data in China are generally available at an annual frequency except for the most recent
past, it is important when using time-series models to have a reasonably long run of data which effectively means
using data before 1978.

12

the most commonly used indicator of economic development and well-being for
China and also the most commonly used variable to compute inter-regional disparities(see, e.g., Fan and Sun, 2008).
The choice of the investment allocation variable is more problematic. The main
problem is the change in the nature of investment in China since economic reforms
and “opening-up” began in 1978.

Prior to that date, investment was a central

government choice variable and can therefore be used as a policy variable.

Since

1978, however, an increasing proportion of investment has been (notionally) private.
To exclude the private component after 1978 would create a break in the series at thatpoint as well as likely biasing the relationship between investment in the coast and theinterior since the coastal provinces have benefited much more from the government’s
opening-up policy than their inland counterparts.

While the use of total investment,on the other hand, would avoid this problem, it would include investment not subject
to direct government allocation policy after 1978.

We opted for the use of total

investment for the entire period since this provides continuity in the data coverage,
there has continued to be a sizable government component of investment after 1978
and, moreover, even private investment in the second part of the sample is subject toconsiderable government influence through, for example, government approval andincentives for major private projects as well as directed lending by the big
state-owned banks.

Thus, we use the ratio of per capita total investment in theinterior to that in the coast (II/IC) as our investment-allocation policy variable. In
contrast to the treatment of the per capita GDP ratio, we do not deflate investment

13

before taking the ratio for two reasons.

First, we would argue that the central

government allocates resources in nominal terms rather than real terms and, second,
there is no consistent regional investment price index for the whole sample period
which we could use as a deflator.
We recognise that some of our choices are not incontrovertible and we carry out
extensive robustness-testing to assess the impact of our assumptions on the
conclusions.

These include (i) the use of a GDP deflator to deflate investment

instead of using nominal investment expenditure, (ii) the use of private investment
only rather than total investment (although this requires restricting the sample to begin
after 1978), and (iii) the use of general government expenditure as an alternative toinvestment as the government policy instrument.

The effects of all these alternatives

are reported in section 6.
All of the data we use are available disaggregated by province. We use theprovincial series to compute regional equivalents for real GDP and the policy
variables and then use the regional population data to compute per capita equivalents.
We aggregate the provinces into two regions, conventionally defined as coastal andinterior. The composition of the two regions is as follows. Coast: Beijing, Tianjin,
Hebei, Guangdong, Shandong, Fujian, Zhejiang, Jiangsu, Shanghai, Liaoning,
Guangxi; Interior: Shanxi, Inner Mongolia, Jilin, Heilongjiang, Anhui, Jiangxi, Henan,
Hubei, Hunan, Sichuan, Guizhou, Yunnan, Shaanxi, Gansu, Qinghai, Ningxia,
Xinjiang. 55Note that Hainan, Chongqing and Tibet are missing. Hainan is included in Guangdong and Chongqing in
Sichuan. Tibet has been omitted altogether due to missing data.

14

We take the GDP data from China’s GDP Data 1952-95 (State Statistical Bureau,1997) via Wu (2004) and Statistical Yearbook of China (State Statistical Bureau,various years) and China Statistical Abstract 2009 (State Statistical Bureau, 2009).
The investment variable we use is “Total Investment in Fixed Assets” and data for thisvariable as well as for population and expenditure come from Comprehensive
Statistical Data and Materials on 55 Years of New China (State Statistical Bureau,2005), Statistical Yearbook of China (State Statistical Bureau, various years) andChina Statistical Abstract 2009 (State Statistical Bureau, 2009).
Before turning to a discussion of our empirical method, we test the stationarity ofthe data using the augmented Dickey-Fuller (ADF) test, the results for which arereported in Table 1. 6 They show that the levels of the per capita GDP ratio and the percapita investment ratio are non-stationary but that their first differences are stationary;i.e., both variables are I(1). The results are clearly independent of the number oflags and the deterministic specification of the testing equation.
Table 1 near here
While both variables are non-stationary, it is possible that a linear combination ofthese series may exhibit stationary behaviour so that the two variables can becharacterised by a long-run relationship. We test for such a cointegrating relationshipusing the Johansen test; see Johansen (1991, 1995). Unlike the results of the ADF test
reported in the previous section, the cointegration results are not independent of thespecification of the testing framework. We therefore tested various specifications and6

See Dickey and Fuller (1981). Many text-book treatments are available; e.g. Enders (1995).

15

concluded that two lags were necessary to remove autocorrelation from the testing
equations and that a trend is significant in the cointegrating vector. Therefore we
report the results of the Johansen tests conducted within a VECM with a trend andintercept in the cointegrating vector and an intercept and two lags in the VAR part ofthe model.

The results of the application of Johansen’s trace and maximum

eigenvalue tests within this framework are reported in Table 2.
Table 2 near here
Both statistics clearly indicate that the null of no cointegrating relationships is rejected
at the 5 per cent level of significance.

We conclude, therefore, that the two variables

are cointegrated and that there exists a long-run equilibrium relationship between theregional output gap and regional investment allocation.

The estimated cointegrating

vector normalized on the per capita output ratio is:(4)

YI/YC = 0.035 II/IC – 0.006Trend + 0.658(0.01)(0.001)

where standard errors are in parentheses. The estimated equation shows that there is apositive and significant relationship between regional output inequality andinvestment allocation.

The trend is negative, which implies that, even when theinvestment ratio is stable, there will be a slow but gradual decline in the ratio of GDP
per capita in the interior to that in the coast, i.e., a steady rise in regional income
inequality.
While the cointegration results indicate a long-run relationship between our two
variables of interest, they allow us to say nothing about the direction of causality or
the strength and timing of the relationship, something that requires the specification

16

and estimation of an empirical model, a matter we now consider.

4. Empirical Model
The preliminary analysis of the data in the previous section indicates that the two
ratios are I(1) and cointegrated, provided the VECM model in which the test is run
has two lags, a trend and an intercept in the cointegrating regression and an intercept
in the VAR part of the model. The model therefore has the form:(5)∆(YI/YC)t = α10 + α11∆(YI/YC)t-1 + α12∆(YI/YC)t-2 + β11∆(II/IC)t-1 + β12∆(II/IC)t-2+ δ1ECMt-1 + εYt(6)∆(II/IC)t = α20 + α21∆(YI/YC)t-1 + α22∆(YI/YC)t-2 + β21∆(II/IC)t-1 + β22∆(II/IC)t-2+ δ2ECMt-1 + εIt

where ∆ denotes the first difference operator, the αs and βs are the coefficients of lagsof differenced variables, ECMt-1 is the (lagged) error from the cointegrating
regression:(7)

ECMt = (YI/YC)t – [γ0 + γ1t + γ2(II/IC)t]– the error-correction term – and the δs are their coefficients; the εs are the equation
error terms.

The estimated values of the γs in equation (7) were reported earlier inequation (4).
The estimated VECM is used for two procedures (apart from testing forcointegration).

First we use it as a framework to test for short-run and long-run

Granger causality and then we use it to generate impulse response functions (IRFs)
which picture the dynamic relationships between the variables.

17

Short-run Granger causality in a VECM is a straightforward extension of thecommon concept of Granger causality in a system of stationary variables.

In a VAR

in stationary variables x and y, we say that x causes y if the lagged x variables in the yequation are jointly significant and vice versa for the causality from y to x.

In a

VECM in x and y we say that there is short-run causality from x to y if the lagged Δxvariables in the Δy equation are jointly significant and vice versa for short-run
causality from y to x.
Long-run causality is a less common concept.

We propose to use a definition ofit and an associated test based on a recent paper by Canning and Pedroni (2008). 7
Although they developed the tests for the case of causality in cointegrated panels, thetest statistics are actually derived in a single-equation context and we follow thisderivation.

At present the test appears to be available for only two-variable models

and we exposit it for this case.
The intuition behind the test is simple. The cointegrating vector describes thelong-run relationship between the two variables and the error-correction term in the
VECM consists of deviations from this long-run equilibrium relationship.

If the twovariables are to satisfy this equilibrium relationship in the long run, it must be the casethat a change in one variable will be associated, in the long run, with a change in theother in order to keep the relationship satisfied.
relationship.

But this is not necessarily a causal

It is possible, for example, that an exogenous change in x will befollowed by a change only in y or a change only in x or, more likely, by a change in

7

We note that the tests published in Canning and Pedroni (2008) have circulated for some time in unpublishedworking papers. See Canning and Pedroni (1999, 2004).

18

both x and y to ensure that equilibrium is re-established.

In the second of these cases

there is no long-run causality running from x to y, while there is such causality in theother two.

We can test which of these possibilities is valid very simply by using thesignificance of the coefficients of the error-correction terms in the VECM equations:if a deviation from long-run equilibrium caused by a change in x has a significant
effect on Δy (that is, the error-correction term in the Δy equation is significant), x
causes y in the long run and vice versa.
More formally, Canning and Pedroni say that x causes y in the long run if apermanent shock to the x structural error has a permanent effect on y.

In terms of theearlier explanation: if a permanent shock to the x structural error is adjusted to in thelong run at least partly by a change in y then x causes y in the long run.

As alreadyexplained, the Canning-Pedroni test for this is based on the significance of theerror-correction term in the VECM equations but requires an additional restriction
which is that a shock to the y innovation has a permanent effect on y itself.

In

Canning and Pedroni’s application, they derive this supplementary condition from thetheory underlying their model while we check that it holds empirically. 8
Finally, the IRFs measure the dynamic effect on each of the endogenous variablesof a shock to an equation error term.

There are various ways of computing the IRFs.

The most straightforward is simply to shock one of the εs in equations (5) and (6).
However, in practice these errors are correlated and shocking them independently
would violate this relationship.
8

For this reason it is common to transform the modelThis additional restriction appears to be ignored in many applications of the Canning-Pedroni procedure, as does
the restriction to two variables; see, for example, Basu, Chakraborty and Reagle (2003), Christopoulos and Tsionas(2005), Narayan and Smyth (2008) and Lee and Chang (2008).

19

errors into independent innovations using the Choleski decomposition of the model’s
covariance matrix, Σ, asΣ = PPtr(8)

where P is a lower triangular matrix and Ptr is its transpose.

Using this, the model

errors can be transposed as:
ut = P-1εt(9)where εt is the vector of model error terms, (εY, εI)tr and the elements of ut are
independent. 9This has the advantage that the errors can be shocked independently

and interpreted as structural shocks but the disadvantage that, since the Choleski
matrix is not unique, neither are the resulting IRFs.

In particular, the IRFs are

dependent on the order in which the variables are included in the model; thetransformation implies that the first-listed error has a contemporaneous effect on bothvariables whereas the second-listed has a contemporaneous effect only on itself.

In

this sense it is assumed that the first-listed variable is causally prior to the second.
The seriousness of the “ordering problem” is mitigated when the correlation of themodel errors is low.

5. Results
We turn first to the estimated VECM which is reported in Table 3.
Table 3 near here
In terms of specification, the LM test for autocorrelation shows that the two lags used9

See Enders (1995) for a text-book discussion of this and related points.

20

are sufficient to remove serial correlation from the residuals. The estimation results
show that the coefficient of the error-correction term in the regional disparity Δ(YI/YC)
equation is negative and significant and that in the investment allocation Δ(II/IC)
equation it is positive and significant.

Given that the coefficient of YI/YC isnormalized in the error-correction term, these coefficients show that both variables
significantly contribute to moving the system back to long-run equilibrium (capturedby the cointegrating relationship) after a shock.
In the Δ(YI/YC) equation, the first lags of the regional inequality and investment
allocation variables are both positive and significant.

Therefore, it appears that

output inequality has autoregressive characteristics and is also (positively) influenced
by the investment allocation as we would expect from our theoretical priors.

In theΔ(II/IC) equation, its own lags and the first lag of Δ(YI/YC) are significant.

Thus

regional income inequality has significant reverse effects on investment allocation.
These results are suggestive of both long-run and short-run causality between thetwo variables.

The results for a test of short-run Granger causality are reported in

the second part of Table 3.

Clearly, the null hypothesis that II/IC does not cause YI/YC

in the short run can be rejected at the 5% level of significance.

Similarly, thehypothesis that YI/YC does not cause II/IC in the short run can be rejected, although
only at the 10% level of significance and, in this sense, the evidence for causality
running in the reverse direction (from YI/YC to II/IC) is less strong than that running in
the expected direction from investment allocation to output disparities.
Statistics for testing long-run causality between the two variables in our model

21

are also set out in the second part of Table 3.
directions.

Clearly, long-run causality runs in both

The short-run and long-run causality from the investment ratio to the

GDP ratio are consistent with the basic assumption underlying the use of investment
as a policy instrument to influence the regional output distribution, although from theGranger causality tests we cannot tell what the sign and magnitude of the effects arewhich therefore need to be further investigated.
direction is more surprising.

The causality in the opposite

Indeed, our simple theory in section 2 is not

informative about this since it assumes that investment is an exogenous policy
variable.

But the estimation results make it clear that, not only does investment

allocation affect output disparities but changes in output disparities lead to changes in
the government’s investment allocation.

Thus, analysis which looks at the effects inonly one direction misses half the story.
However, given that the Granger causality results show only the joint significanceand not the magnitude or the sign of the coefficients and so tell us nothing about the
dynamics of the interaction between the variables of interest, not a great deal can belearned from these results.

Thus, for example, if coefficients of successive lags moreor less cancel each other out, there may be little effect of a shock even if there isstrongly significant causality.

In our estimation results, the first lag always has the

opposite sign to the second lag, in all but one case, and the (absolute) value ofcoefficients of Δ(YI/YC) lags is much larger than that of Δ(II/IC) lags in each of theequations. Therefore, we should be cautious in our interpretation of the causality test
results and we now turn to impulse response functions (IRFs) for more information

22

about magnitude and dynamics.
The IRFs show the dynamic interaction between the output gap and investment
allocation.

In particular, they show the effect over time on the levels of each of thetwo endogenous variables of a shock to each of the (VECM) equation error terms.
They reflect the whole model (both equations and the short-run adjustment
mechanism as well as the long-term adjustment through the error-correction term) andthe size and sign of the coefficients rather than their significance.

Given that thevariables are non-stationary but cointegrated, the effects of a shock are permanent but
should converge and this is reflected in the long-run behaviour of the IRFs.

As

mentioned in the previous section, we use the IRFs based on the Choleski
decomposition and it is well known that the results using the Choleski decomposition
are sensitive to the order in which the variables appear in the model.

However,

given that the contemporaneous correlation of the error terms in our model isrelatively low (0.29), this is not likely to be a serious problem.

Besides, we orderinvestment allocation first on the view that II/IC is more likely to affect YI/YC within
the period than vice versa. 10 We report the first IRFs in Figure 1.

The response of

the regional output gap to a one-standard-error shock to investment allocation isdenoted by YI/YC and the response of investment allocation to a shock of one standard
error to regional disparities is denoted by II/IC. 11
Figure 1 near here10

As part of our robustness tests, we also generated IRFs using the reverse variable ordering as well as thoseusing the generalized version of IRFs following Pesaran and Shin (1998) which are derived from shocks which
incorporate their historical correlation. The resulting IRFs do not change our overall conclusions.
11
To minimise clutter in the diagram, we do not include the response of a variable to its own shock since they areof no immediate interest.

23

Recall that both variables are ratios of the interior to the coast.

Following our simple

theory in section 2, we would expect a rise in the interior’s share in investment toimprove its output position relative to the coast so that a positive shock to II/IC should
lead to a positive effect on YI/YC.

Our priors regarding the reverse effect are

ambiguous; indeed, our theory assumes that the investment ratio is exogenous andtherefore is unaffected by the output ratio.

In practice it is possible that theinvestment ratio is governed by a policy rule so that a fall, say, in YI/YC will stimulate
the central government to increase its allocation of investment to the interior, so
producing a negative effectOn the other hand, a rise in YI/YC might generate

expectations of further development in the interior and so draw more investment to
the interior, resulting in a positive effect.

Inspection of the IRFs in Figure 1 showsthat, clearly, an increased allocation of investment from the coast to the interior region
has a positive effect on reducing the regional output gap but the effect appears small.
A shock to the output gap, on the other hand, has substantial positive effects on theinvestment allocation between the two regions.

In both cases the effects are more orless monotonic and are felt relatively quickly – most of the adjustment to the newlong-run equilibrium are completed within the first three or four years.
Our simple theoretical framework in section 2 suggests that the short-run effect ofa unit change in the investment ratio on the output ratio should be about the share ofinvestment in total output in the coastal region which averages about 0.22 over oursample period.

Whether the IRFs are consistent with this prediction is difficult tosee since they result from a one-standard-error shock rather than a unit shock.

24

Moreover, the relative magnitudes of the two effects may also simply reflect
differences in the size of the relevant standard errors.

This difficulty ininterpretation is easily overcome by computing the implied effects for a unit shock
and, rather than reporting adjusted IRFs, we simply report the short-run and long-run
figures for a unit shock in Table 4.
Table 4 about here
The full-sample effects in both directions are given in the first two columns of figures
and we see, first, that the relative effects of the two shocks are little changed – theeffect on the output ratio is much smaller than that in the opposite direction.

Second,the maximum effect of investment on output is approximately 0.14 which occurs two
periods after the shock with a long-run effect of about 0.05.

The short-run effecttherefore falls somewhat short of the prediction of our simple model, even at its
maximum impact.

Clearly some of the offsetting factors mentioned in section 2

must come into play in practice in the short run and, more strongly, in the long run.
Thus, it is possible that government investment allocations are to some extent offset
by opposite changes in other categories of government expenditure.

Secondly, it ispossible that net exports also fall when investment rises; this would happen, forexample, if the investment goods are ”imported” from the other region.

In the longer

term, an additional explanation is that the investment in the interior region isinefficient relative to that in the coastal provinces, as pointed out in a previous section.
Finally, it is likely that in the long run the migration of labour also plays a part – thegreater the allocation of capital to the interior the greater will wages be since the25

marginal productivity of labour is increasing in the capital stock in a standard
production function. 12

Thus, while initially per capita output rises in the interior

relative to the coast, labour is attracted over time which results in the dissipation of
the advantage, making it only temporary.
The large and positive effect in the reverse direction (from output inequality toinvestment allocation) is more difficult to explain.

The positive sign suggests that it

does not reflect a policy reaction since we would then expect a negative sign – a fallin the output ratio would call forth more investment in the interior relative to the coast.
A possible explanation is that a rise in the output ratio is taken as a sign by the private
sector of improvements in investment returns in the future, thus attracting moreinvestment. This is plausible only after the dismantling of central planning from thelate 1970s onwards.

Before that it is well-known (see the brief discussion in section

1) that much investment allocation to the interior during Mao’s time was motivated by
security considerations rather than by the desire to reduce disparities so that thepositive sign of the IRF is due to the fact that investment allocation continued tofavour the interior even as its relative output position was improving while later
investment allocation favoured the coast even as it was increasing in prosperity.

Themagnitude of the effect of YI/YC on II/IC, though, is difficult to explain since, as shownin Table 4, the long-run effect of a unit shock is almost 7.
the bounds of possibility it seems very large.

12

While this is not beyond

A numerical example might clarify this.

This effect is more likely to operate in the post-1978 period when labour movement was increasingly lessrestricted. Nevertheless, during the Mao period, while internal migration restrictions were severe, there was ageneral encouragement to move to the interior, in line with national security objectives and this may have had asimilar effect of diluting the per capita improvements in output resulting from the systematic bias of investment
allocation in favour of the interior.

26

Suppose that initially YI/YC is 100/200 = 0.5 and that the II/IC ratio is 22/44 = 0.50
(corresponding to an average investment share in output of 22%).

Suppose theoutput ratio increases by 10% to 110/200. Then a 7-fold effect of the 10% increase
in YI/YC would require the II/IC ratio to rise to 0.85 which would require a re-allocationof investment from the coast to the interior of about 8 units to make the new II/IC ratio
equal to 30/36 which is about 0.83; not impossible but very large. 13
Thus, all in all, the results from the main simulations show that the effects frominvestment to output, although somewhat small, are plausible but those in the opposite
direction are of a quite unexpected magnitude.

We go on, in the next section toconsider the robustness of our results before drawing conclusions.

6. Robustness tests
In this section, we subject the main findings to greater scrutiny by presenting theresults of some robustness tests.

6.1 Splitting the sample at 1978
Our first robustness test involves splitting the full sample into two sub-samples at1978 and re-running the simulations for the pre- and post-1978 periods separately,
given that there is a possibility of structural change in 1978 when economic reforms
began and the investment variable became increasingly private as discussed in theprevious section.

The effects of a shock to investment allocation in the sub-samples

13
From a purely mechanical point of view, of course, if the model associates an increase of 0.22 in YI/YC with aunit increase in II/IC then a unit increase in YI/YC is associated with an increase in II/IC of 1/0.22 which is about 4.5.

27

are reported in Figure 2 and those of a shock to the output ratio are pictured in Figure
3. We also include the results for the full sample in the two figures for the purpose ofcomparison. 14
Figures 2 and 3 near here
In terms of overall pattern, there is no difference between the two sub-samples:a change to the investment allocation has a relatively small positive effect on output
inequality while the regional output shock has a larger reverse effect on investment
allocation, just as we found for the full sample.
Nevertheless, there are interesting and substantial differences between the two
sub-samples and these are highlighted by the multipliers reported in Table 4.

Inparticular, the change in the investment ratio has a much stronger effect on the output
ratio in the post-1978 period than in pre-1978 period.

The multiplier calculations

show that the short-run effect of investment on output is about 0.45 and in the longrun it rises to 0.53, both considerably in excess of the prediction of 0.22 based on thesimple macro model of section 2. 15 Following on from our earlier conjectures about
the reasons for the relatively small effect for the full sample, the larger post-1978
effects might reflect smaller offsetting changes in government expenditure and net
exports as well as reinforcing effects of induced increases in consumption.

Since

investment was increasingly private during this period, it is less likely that an increase
in investment would be substituted for current government expenditure and it is also
possible that, with the greater technical capabilities of the interior provinces, less14

To make the effects comparable over the sub-periods, we scale all shocks to 0.01 which is about the order ofmagnitude of the shock to the output ratio for the full sample.
15
Although it should be noted that the relevant investment share in the post-1978 period was 0.33.

28

investment goods needed to be “imported” from the coast.

Moreover, the fact thatafter 1978 investment was increasingly private and so was more likely to be allocatedaccording to market principles means that it is more likely to go to a use with a highmarginal product thus boosting output in the longer term.

Finally, the greater

effectiveness of investment allocation in the post-Mao period might also reflect thefact that investment increasingly went to the coast during this period.
In contrast, the income gap has larger effects on investment allocation in thepre-1978 period than in the post-1978 period.

This might simply reflect the fact thatinvestment re-allocations to the interior region were much larger in Maoist period sothat relatively small changes in the output ratio were associated with large change in
the investment ratio.
In summary, although the relative magnitudes of the two IRFs of interest has notchanged, the effects of investment re-allocation after 1978 are considerably larger andmuch more in line with our theoretical priors.

6.2 Using the ratio of real investment
Recall that we use nominal investment to compute the investment ratio variable,
II/IC, in the base case since there was no suitable investment deflator.

We can,however, deflate investment using the GDP deflator and we now use this to computereal investment which we then use to compute the ratio of investment per capita andthen re-run the simulations.

We report the IRFs in Figure 4 and, again, they do notchange much from the base case and certainly do not change our overall conclusions.

29

Figure 4 near here

6.3 Excluding outliers in the sample
For most of our sample period China had a household registration or hukou
system under which permission to migrate to another location to work was seriously
restricted, although enforcement has slackened progressively since reforms began in
1978.

Official population data which are used in our measures of per capita output

and investment are based on official registered population but it is well-known that
there has been very substantial unregistered migration from the inland to the coastal
provinces so that the population data in some of the provinces are overestimated andothers are underestimated (Cai and Wang, 2010).

This problem obviously affects theaccuracy of our measures of GDP per capita and investment per capita and so that ofregional disparities and investment allocation 16.

Population data adjusted for thisproblem are not available but we conjecture that this problem is most serious for four
provinces, Guangdong, Shanghai, Sichuan and Chongqing. The former two
underestimate their population since many of their residents are without Shanghai andGuangdong hukou and so are not included in the population data. The latter two
overestimate their population since many people with Sichuan and Chongqing hukou
do not live in these two provinces but are still included in the population data.

We

attempt to get some idea of how this might affect our results by excluding these four
provinces from the sample and re-running the simulations.

16

We find that the results

However, these floating workers usually send money back home, to some extent reducing the income bias
resulting from the official population data (Kanbur and Zhang, 2005).

30

do not change much except that the responses convergence more quickly.

The IRFs

are reported in Figure 5.
Figure 5 near here

6.4 Using investment by SOEs
As we discussed in the previous section, investment in China became
increasingly private after 1978 so that there must be some doubt as to the extent towhich investment can be considered a policy instrument in the latter part of thesample period.

As a fourth robustness test, we replace total investment byinvestment by the state-owned enterprises (SOEs), which is more likely to have been
influenced by the government.
investment ratio variable.

We use SOE investment to re-compute theUnfortunately such data are available only for the

post-1978 period so that we estimate a model for the period from 1978 to 2008 andre-compute the IRFs which we report in Figure 6.

We compare these to the effects in

Figures 2 and 3 for the post-1978 sample and find that investment allocation now has
a smaller effect on regional income inequality, the output ratio also has a smaller
effect on the investment ratio but their relative magnitudes have not changed so that
our overall conclusions are not affected. 17 In broad terms, the magnitudes of theeffects are consistent with those in post 1978 sub-sample period with the original
investment variable reported in Figures 2 and 3 so that our original results are notimportantly driven by our choice of investment data..

17
For this case we estimated a VAR in the first differences of the two variables and report accumulated IRFs since
there is no cointegrating relationship between the levels. We used a common shock size of 0.01.

31

Figure 6 near here

6.5 Using the government expenditure ratio
Government expenditure is under the control of government in the entire sample
period and is clearly a policy variable.

To assess the possible weakness of using

investment which was not wholly policy-controlled in the second part of the sample,
we used the ratio of government expenditure in the interior region to that in the
coastal region, GI/GC, to measure the government allocation of resources 18. The IRFs,
reported in Figure 7, indicate that, as was the case for investment, the effect ofgovernment expenditure on output is smaller than the effect in the opposite direction.
Converting the effects to those of unit Choleski shocks shows that the short-run effectof a change in the expenditure ratio is approximately 0.47, falling to 0.44 in the longrun, both of which are plausible in terms of our simple framework.

The effects of aunit shock in the opposite direction are smaller than they were for investment: 0.68 in
the short run and 1.1899 in the long run, suggesting that government expenditure as awhole is “more exogenous” than (government) investment.
Figure 7 near here
All in all, the sensitivity tests above support our main findings that a policy ofinter-regional resource re-allocation generally has a relatively modest effect onregional output inequality while output inequality has a substantial effect on resource
allocation.
18

This conclusion is robust to choice of sample and to various alternative

It should be noted that government expenditure includes not only investment but also other non-investment
spending such as education, health care, etc. The expenditure ratio and the output ratio were found to be I(1) andcointegrated and we generated the IRFs from a VECM with one lag and a trend in the cointegrating error.

32

measures of the two variables in the model.

7. Conclusions
This paper has addressed the important policy question: has the regional
re-allocation of resources by the government in China had an appreciable effect on the
distribution of output across the regions?

We analysed this for two regions (theinterior and the coast) within the context of a vector-error-correction model (VECM)
in two variables: the ratio of real per capita GDP in the interior to that in the coast andthe ratio of per capita investment in the interior to that in the coast.

Our surprisingfinding is that, for our full sample of 1953-2008, the effect has been relatively modest:a unit change in the investment ratio has led to a change in the output ratio of about
0.14 after two years which falls to 0.05 in the long run.

This is considerably smaller

than what we would expect even on the basis of our simple model based on theapplication of the national accounting identity at the regional level so that weconclude that there must have been significant offsetting changes in other variables
such as net exports and government expenditure.
When we re-examined the relationship for the latter part of the sample(1979-2008), however, the effect is much stronger and quite consistent with our model,suggesting that smaller offsetting changes in other variables and, perhaps, greater
investment efficiency.
A second surprising finding is that the reverse effect of the output ratio on theinvestment ratio is much larger, in both the short run and the long run, than the

33

influence of investment on output.
Our overall results are found to be robust to a number of variations in thedefinition of the variables.

In particular, we assess the sensitivity of the results toboth choice of sample and choice of investment variable and find the broadconclusions to be surprisingly robust.

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Table 1 ADF Test Results
A. Per Capita Real GDP Ratio (YI/YC)
Level
With constant
Lags
With constantand trend
1
-1.24
-3.12
2
-1.32
-2.10
3
-1.13
-2.26

First differenceWith constantWith constantand trend-6.37***-6.41***-4.58***-4.61***-3.99***-4.10**

B. Per Capita Investment Ratio (II/IC)
Level
With constant
Lags
With constantand trend
1
-1.72
-2.54
2
-1.04
-1.67
3
-1.52
-1.81

First differenceWith constantWith constantand trend-6.26***-6.19***-3.72***-3.64**-2.65*-2.61

Note: *, ** and *** denote rejection of the null at 10%, 5% and 1% significance levels
respectively.

39

Table 2 Johansen Tests for Cointegration Between YI/YC and II/IC
Hypothesis
Trace statistic
Maximum eigenvalue statistic
r=0
26.09**
21.46**
r≤1
4.63
4.63
Note: r denotes the number of cointegrating vectors and ** denotes rejection of the null at a 5%
significance level.

Table 3 Vector Error-Correction Model Estimates∆ YI/YC equation
Coefficientt-Value
ECMt-1-0.311
2.55∆ (YI/YC)t-1
0.319
2.34
∆ (YI/YC)t-2-0.040-0.25∆ (II/IC)t-1
0.061
2.55
∆ (II/IC)t-2-0.016-0.67
Constant-0.004-1.44
Autocorrelation (LM Test):
Lag 1, (p-value)
Lag 2 (p-value)
Short-run causality:
II/IC does not cause YI/YC (p-value)
YI/YC does not cause II/IC (p-value)
Long-run causality
II/IC does not cause YI/YC (p-value)
YI/YC does not cause II/IC (p-value)∆ II/IC equation
Coefficientt-Value
1.989
3.21-1.456-2.10-0.530
-0.67
0.545
4.45
-0.511-4.15-0.017
-1.26
0.273
0.821
0.037
0.092
0.014
0.002

Table 4 Multipliers
Direction
II/IC to YI/YC
YI/YC to II/IC1953-2008SRLR
0.14090.05302.16096.74141953-1978
SR
LR
0.15730.08554.42307.2577

1979-2008
SRLR
0.45320.53553.07872.2472

Notes: The numbers in the table are the effects of Choleski unit shocks to the first-listed variable in thefirst column.

“SR” denotes the short-run effects (two periods after the shock) and “LR” denotes thelong-run effects (after 30 periods).

40

Figure 1 Cross Effects, Base Case, Full Sample (1953-2008)
0.14
0.12
0.1
0.08
0.06
0.04
0.02
0
1

3

5

7

9 11 13 15 17 19 21 23 25 27 29
YI/YC

II/IC

Figure 2 Effects of Shock to II/IC
0.006
0.005
0.004
0.003
0.002
0.001
0
1

3

5

7

9 11 13 15 17 19 21 23 25 27 29

YI/YC5308

YI/YC5378

YI/YC7908

Figure 3 Effects of Shock to YI/YC
0.09
0.08
0.07
0.06
0.05
0.04
0.03
0.02
0.01
0
1

3

5

7

9 11 13 15 17 19 21 23 25 27 29

II/IC5308

II/IC5378

II/IC7908

41

Figure 4 Cross Effects, with Real Investment Ratio
0.09
0.08
0.07
0.06
0.05
0.04
0.03
0.02
0.01
0
1

3

5

7

9 11 13 15 17 19 21 23 25 27 29
YI/YC

II/IC

Figure 5 Cross Effects, Excluding Outliers
0.09
0.08
0.07
0.06
0.05
0.04
0.03
0.02
0.01
0
1

3

5

7

9 11 13 15 17 19 21 23 25 27 29
YI/YC

II/IC

Figure 6 Cross Effects, with Investment by SOEs
0.02
0.015
0.01
0.005
0
1

3

5

7

9 11 13 15 17 19 21 23 25 27 29
YI/YC

II/IC

42

Figure 7 Cross Effects, with Expenditure Ratio Variables
0.025
0.02
0.015
0.01
0.005
0
1

3

5

7

9 11 13 15 17 19 21 23 25 27 29
YI/YC

GI/GC