excess investment and efficiency loss during reforms: the case of provincial-level fixed-asset...

7/28/2019 Excess Investment and Efficiency Loss During Reforms: The Case of Provincial-level Fixed-Asset Investment in Peo

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FOREWORD

The ERD Working Paper Series is a forum for ongoing and recentlycompleted research and policy studies undertaken in the Asian DevelopmentBank or on its behalf. The Series is a quick-disseminating, informal publicationmeant to stimulate discussion and elicit feedback. Papers published under thisSeries could subsequently be revised for publication as articles in professionaljournals or chapters in books.


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CONTENTS

Abstract vii

I. Introduction 1

II. Investment Inefficiency: A Conceptual Framework 4III. Allocative Inefficiency and Institutional Constraints 7

IV. Empirical results 10

V. Conclusion 21

APPENDIX 22

REFERENCES 24


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ABSTRACT

A method is proposed to estimate efficiency of aggregate investment in atransitional economy, using provincial panel data from the Peoples Republic ofChina (PRC) as an experimental case. Inefficiency is defined on the basis ofdisequilibrium investment. It is further decomposed into allocative and productioninefficiency. Allocative inefficiency is related to policy/institutional factors. The

main findings are: the PRC investment demand hardly responds to capital pricingsignals, whereas it is strongly receptive to expansionary fiscal policies andinterprovincial network effect. Once institutional factors are separated out, thereare clear signs of increasing allocative efficiency and receding growth in regionalinvestment disparity. The estimates on production efficiency are broadly in linewith regional development.


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1 This is implicitly confirmed by Sun (1998) and Song et al. (2001), who find that the PRCs short-run aggregate investmentadjusts at very slow speeds to the long-run disequilibrium investment with respect to GDP.

2 Investment hunger is regarded as a key feature of CPEs, described initially by Kornai (1980).

I. INTRODUCTION

Capital investment plays a key role in economic growth, especially in an economy where thereis relatively abundant labor supply. However, excess investment occurs when economic growth lagsbehind investment growth, due to lack of accompanying growth in capital productivity. Such inefficiencyof investment at an aggregate level used to plague centrally planned economies (CPEs) (see, forexample, Begg et al. 1990). An interesting question arises whether economic reforms of formerCPEs have alleviated excess investment, or whether investment efficiency has improved as the marketgrows and prevails during economic transition. In the present study, we propose a model to explainhow much institutional factors associated with CPEs affect aggregate investment efficiency in atransitional economy and estimate the model using a panel data set from Peoples Republic of China(PRC).

Several phenomena stand out concerning fixed-asset investment in the PRC over the last decade(see Figures 1 and 2). First, fixed-asset investment has been growing faster than gross domesticproduct (GDP), with an average rate of around 14 percent per annum in real terms as against nearly10 percent in real GDP during 1990-2002; capital formation has also risen in terms of its GDPcomposition, from around 25 percent in 1990 to 36 percent in 2000. Such an outgrowth in capitalformation is obviously unsustainable in the long run, implying the possibility of excess investmentor decreasing capital productivity. Second, growth in capital formation has been volatile. As investment

bears high adjustment costs, such a volatile movement must have incurred very high social costs, 1let alone its adverse effects on inflation and output growth stability. Thirdly, investment growthfell sharply in the mid-1990s leaving total bank savings exceeding total bank loans for the first timesince 1950. It makes us wonder if this symbolizes the end of investment hunger2 or persistentinvestment shortage. If so, should this imply that aggregate investment in the PRC has finally becomemainly responsive to market conditions and thus more efficient than before? Notice, however, thatthe surplus in bank savings seems to have helped encouraging nationwide government deficit financingat both the central and the provincial levels. It is unclear how much efficiency improvement a mixtureof government-induced and market-induced investment activities can make in comparison with thesituation under CPEs. What is clear is that the recent concern over banking sector reforms in thePRC, especially over problems of bad debts, relates closely to the problems of excess investment

and of underutilized capital in production.


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EXCESS INVESTMENTAND EFFICIENCYLOSS DURINGREFORMS: THE CASEOF PROVINCIAL-LEVEL FIXED-ASSET INVESTMENTIN PEOPLES REPUBLICOF CHINA

DUO QINAND HAIYAN SONG

2 OCTOBER 2003

Recent studies on the PRCs aggregate investment lack conclusive views on the above questions.For example, Wang and Fan (2000) maintain that investment shortage is not yet over because ofsizeable waste in past investment, which is reflected in unbalanced investment structure, policy-induced impulsive investment behavior, soft loans of the banking system, and relatively poor performancein a sizeable part of the state-owned sector. However, they reckon some signs of improvement ininvestment efficiency since the reforms, such as rising transformation rates from investment to capitalformation, and increasing shares of investments by the nonstate-owned sector and the foreign sector.Zhang (2002) is very critical of the positive contribution of capital investment to the PRCs long-term growth. He regards investment outpacing GDP growth as a sign of excessive investment and

of deterioration in investment efficiency. By showing decelarating growth in total factor productivityand diminishing investment returns during the 1990s, Zhang maintains that the PRCs overall investmentin fixed assets has gone too far, especially with regard to its labor resource. He ascribes the problemsmainly to institutional distortion, which induces a mixture of old tendency of excess investment withregional overcompetition for capital as a result of fiscal decentralization. The latter factor has attractedincreasing attention in recent years. For instance, Zhang and Zou (1996) demonstrate empiricallythat a higher degree of fiscal decentralization is associated with lower provincial growth. They thus


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3ERD WORKINGPAPER SERIESNO. 47

SECTION I

INTRODUCTION

infer that fiscal decentralization must have caused severe capital shortage for infrastructureinvestment at the national level, which is vital for rapid economic growth. The problem is moreextensively examined by Young (2000), who demonstrates that decentralization has resulted insignificant fragmentation of internal markets and therefore worsened efficient allocation of resources.

But these empirical findings are somewhat at odds with Huang's detailed analysis of the politicaleconomy of central-local relations in investment controls (1996). Huang argues that the PRCspresent de facto federal system, in which economic responsibilities are delegated to the localgovernments while the central government keeps political responsibilities, can have the meritsof reducing coordination costs and improving economic governance. The economic role of federalismis further theorized by Qian and Roland (1998), who postulate two main effects. The first iscompetitive effect of federalism, which could lead to regional investment distortion; the second


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4 OCTOBER 2003

is checks-and-balance effect of federalism, which should result in hardening soft budgets for state-owned firms. Unfortunately, these postulates lack rigorous empirical support.

In fact, there lacks systematic methods of measuring and evaluating efficiency of aggregateinvestment in the literature. This is reflected in a gap between the theoretical and empirical discussions

on possible inefficiency in the PRCs aggregate investment. While theorists are most concerned withpossible misallocation of financial resources due to imperfectly reformed economic systems,3 empiricalevidence is focused on production efficiency, such as productivity changes of capital in aggregationproduction functions or changing shares of capital to labor inputs. The problem, we believe, liesmainly in the different economic environments in which the issue has been considered. In a marketeconomy, investment decisions are mostly made at the firm level and therefore the issue of investmentefficiency falls formally in the realm of microeconomics; whereas in a transitional economy, the marketis far from perfect and micro investment decisions are still significantly affected by various institutionalfactors.

The present study is an attempt to measure and evaluate inefficiency of aggregate investmentin a transitional economy. We adapt capital input demand theory and measures of investment efficiency

in standard microeconomics to the case of aggregate investment. We extend the theory and the measuresto cover a transitional economy. We disentangle investment efficiency into two types: efficiency ininvestment allocation and efficiency in capital utilization during production. We are particularly interestedin identifying and estimating how institutional factors have contributed to excess investment viainefficient investment allocation. We experiment with our model using data of 30 provinces in thePRC over the period 1989-2000.4 The arrangement of the paper is as follows: Section II presentsa general theoretical framework for defining and measuring investment inefficiency; Section III extendsthe framework to transitional economies where there exist important institutional factors affectinginvestment decision making; empirical model results are reported and analyzed in Section IV, andthe final section concludes the paper.

II. INVESTMENT INEFFICIENCY: A CONCEPTUAL FRAMEWORK

In this section a simple model base is set up for the purpose of estimating inefficiency inaggregate investment. The model is adapted from microeconomics. The problem of aggregationis disregarded for simplicity, following the normal practice of most of the empirical macro modelsof investment (see Caballero 1999). Our key focus is on how to measure inefficiency. We start bydefining excess investment demand as deviations of actual investment from the desired investmentdriven by cost-minimizing factor demand for capital input. This enables us to exploit two availablemeasures of efficiencyallocative efficiency and technical or production efficiency, and to relateinvestment deviations to these measures. The next section discusses the issue of how to link thesemeasures to a transitional economy where there are serious institutional constraints to a perfect

market situation.

3 Bai et al. (1997) point out that improvement in production efficiency in terms of total factor productivity may not leadto more efficient resource allocation in a mixed market where firms are not solely profit maximizers.

4 Beijing, Shanghai, and Tianjin are counted as provinces, but Chongqing, the new autonomous municipality, is still regardedas part of Sichuan.


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Standard welfare economics dictates that perfect market equilibrium is the most efficient state.By this criterion, inefficiency in investment should arise largely from deviations of actual investment,I, from the market desired investment I*. In the time-series context, we have:

t = lnItlnI*t (1)

where t>0 reflects excess investment and t


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An AE measure can then be defined by ratio of the actual price ratio to the equilibrium priceratio:7

PP Pl k

P P P* * *

/, , ,

/

= = = =jk l kkl jlk l j (6 )

Obviously, full AE means Zkl = Zk = Zl= 1. Notice that there are two aspects of price distortion in(6) namely own-price distortion and relative price distortion. Since all prices are relative, the own-price distortion can be seen as one factor price distortion with respect to the general price level.Notice also that Zkl = 1 can be achieved when both labor and capital prices are distorted at thesame rate, as it only reflects AE with respect to resource allocation between the two factors. In

practice, P*j is unobservable. Zs are thus often viewed as a set of parametric correction in input

factor prices. The set can be estimated either directly from the secondary price space of firms cost-minimizing function constrained by a production function, or indirectly from the primal goods spaceof firms input demand function conditional on cost minimization by means of input distance function

(see Atkinson and Cornwell 1994, Atkinson and Primont 2002).8

In the present context, we are only interested inZI and/orZk. If we choose the primal goodsspace, the AE measure ofZI amounts to the disequilibrium in (1):

*I

Iln = lnI - lnI

I* = =I (7 )

The equation reveals that the cointegration method can be used as an empirical AE measure.

Let us now assume a CES (constant elasticity of substitution) function for (4) with constantreturns to scale under equilibrium:

1

Y K 1 L* * *[ ( ) ] = + 1

0 1 1

= < (8 )

where is the substitution parameter mapping into , the elasticity of substitution. The factordemand function for the long-run K* corresponding to (8) when it is subject to cost minimization,i.e., min(Pkk + PlL), becomes:

y1

k

PK Y

P

** *

*

=

(9 )

where P*y

is the minimum unit cost of output (see, for example, Varian 1992, chapter 4). Combining(9) and (3) into (1), we get:

7 The actual market price ratio is more frequently used in equation (5) in the empirical literature. Under that context,firm-specific shadow prices are employed in contrast with market prices, e.g., see Baos-Pino et al. (2001).

8 A detailed explanation of duality of the two approaches can be found in Fre and Primont (1995).


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SECTIONIII

ALLOCATIVEINEFFICIENCYANDINSTITUTIONAL CONSTRAINTS

[ ]

[ ]

I A Y C

IA C A

Y

ln ln ln

ln ln , ln ln ln

= + +

= + = +

1

t t 1 t 2 t

=1

2 tt

(10)

where 1 is the inverse of the returns to scale parameter and hence is expected to be unity,2 = -< 0, and Cdenotes user cost of capital relative to output cost, the standard specificationof which is:

P PrC

P 1 P

( )

( )

+= =

k I

y y(11)

where r is the real interest rate for investment loans and is the tax rate. In view of our paneldata set of 30 provinces, we can rewrite (10) as:

[ ]

[ ]

I A Y C i 1 30

IA C A

Y

ln ln ln , ,

ln ln , ln l n ln

= + + =

= + = +

1

it it i 1 it 2 it

=1

i 2 it i i i iit

(10)

Equation (10) presents us with a convenient vehicle to estimate both measures of efficiency.According to the established procedure (see Greene 1997), PE corresponds to the fixed individualeffect i in Ai of (10). Equation (10) tells us that identification ofi depends on knowing i, which

have to be estimated via the production function (8) unless either = 0 or = i i , providedwe have data for

i. We also need to consider the possibility of rapid technological progress in i.

This is dealt with here via the following alternative specifications:

{ }texp = +i 0 i (common trend plus individual effect) (12a){ }exp = +i 0t i (common random time effect plus individual effect) (12b)

As for AE, full efficiency indicates it=0. Discernibly, it can be easily identified with the residualterm derivable from regressing lnIit on lnYit and lnCit. However, a key conceptual weakness of thisidentification is that any structural interpretation of the residual term entails substantially oversimplifiedassumptions, cf. Qin and Gilbert (2001). Since AE forms our major concern, we devote the next sectionto ways of improving this measure.

III. ALLOCATIVE INEFFICIENCY AND INSTITUTIONAL CONSTRAINTS

As mentioned before, most of the concern over the PRCs excess investment demand relates tofinancial resource misallocation due to imperfect market environment. The theoretical framework ofthe previous section does not cover this concern explicitly. Here, we argue that there are two typesof AE regarding investment demand in a transitional economy. One results from those institutionalarrangements that distort pure market demand conditions for investment. The other is the usual


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8 OCTOBER 2003

type due to firms decision errors, assuming that their investment decisions are already conditionedupon an imperfect market environment. Obviously, it of (10) does not allow us to identify thetwo types, except probably for the case when the estimated 2 = 0,

9 i.e., investment demand isinsensitive to price signals. We can interpret this as the actualCbeing significantly different from

market-equilibrating C*

, and thus infer the presence of imperfect market.In this section, we propose two ways of modifying it. The first is to modify the cost function

to incorporate in it market-disequilibrating institutional effects. It is commonly recognized thatmany state-owned firms have objectives other than profit maximization (see, for example, Liu 2001,Dong and Putterman 2002). For instance, ideological concern for spatial equality and defenceconsideration used to be among the key objectives in state investment plans, see Ma and Wei (1997).These objectives are hard to achieve unless budget constraints are soft. In other words, a mixed-goal objective-maximizing function should correspond to a cost-minimizing function mixed with softbudget constraints. A common route to incorporate these institutional features is disaggregation,i.e., to formulate a two-sector model with different behavioral rules for the state-owned sector andthe nonstate-owned sector. However, this route may not fully reflect the fact that it is becomingharder to differentiate firms economic behavior simply by ownership in the PRC, since many firmssuffer from incompletely specified property rights, or have their ownership diversified due to thegradual privatization programme, not to mention the extra cost of data requirement. An alternativeis to specify soft-budget constraints by the degree of their capacity to alleviate hard cost constraintsat the aggregate level. We adopt this approach and attach a multiplicative term (x) to the standardcost function:

(PkK+PlL) (x) (13)

wherexdenotes a set of disequilibrating soft budget indicators such that (0) = 1. For practicalpurposes, we choose the exponential function:

{ }( ) exp

= x jj (14)

Substituting (14) into (13) and minimizing it subject to (8), we arrive at the following alternativeto equation (10):

I A Y C xln ln ln = + + + it it i 1 it 2 it j jitj (15)

The difference between (10) and (15) gives us an explicit AE measure caused by (x). Thismeasure has the advantage of directly evaluating both the positive and negative contributions ofthe institutional factors toward AE. It brings empirical model results closer to testing theoreticalpostulates concerning efficiency and evolving institutions during reforms.10

The second modification is to adapt the original interpretation of AE to it

, i.e., to try tointerpret it as a measure of allocative inefficiency due to firms decision errors in investment demand,while their decisions are already conditioned on a mixed market situation, as described in (13).

9 Notice that (9) collapses into a simple acceleration model when this happens, i.e., when =0.10 Theorization of efficiency and institutional changes is still in the making (see, e.g., Yao 2002), and desires better

interactions with applied studies.


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Two considerations guide our adaptation. One is that regression residuals are mixed with all sortsof misspecification and/or measurement errors. These should be filtered out before we attemptinferring it as decision errors. The other consideration relates to the dynamics of error-correctingadjustment. If a measure of AE turns out to follow a white-noise process, as is normally assumed

of the residual term of a regression model, we would always come to the conclusion that thereis virtually no persistent allocative inefficiency. An interesting AE measure should thus be expectedto follow a stationary process, which encompasses, rather than is identical to, a white-noise process.In the context of investment demand, corrections of financial resource misallocation are expectedto be rather slow because of very high adjustment costs. This implies that the AE measures arelikely to exhibit significant autocorrelation, and leads us to exploit the separate specification ofa static, error-correction component from an innovative, nonstructural error term in time-serieseconometrics. More specifically, we adopt the normal practice of specifying (15) into anautoregressive-distributed lag (ADL) model, denoting its residual term as it. We propose to filterit out from

it before interpreting it as an AE measure..

We are now in the position to define two measures of AE. One measure, z, captures theinstitutional aspect of AE and the other,zm, the conventional AE due to nonoptimal firm decisions:

z

z

=

=

it it it

mit it it

(16)

Before moving on to empirical modelling, we need to consider how to select x. Two generalprinciples underlie the selection. These variables must embody institutional disequilibrating effects,and they must satisfy (0) = 1. We take especially into consideration those factors that have beensuggested repeatedly in the relevant literature, such as regional factors arising from decentralization.A number of indicators are constructed and experimented, which cover national and local governmentfiscal policies, interprovincial competition, changes in firms debt-asset ratios and in bank loan-depositratios. Four variables have survived the selection experiment, one at the national level and the otherthree at the regional level. More precisely,

x1 denotes the nationwide effect of deficit-financing fiscal policies, which is taken as logarithmof the net government debt including both the central and the local governments;

x2i represents the local government expansionary fiscal policy effect, which is taken as logarithmof the ratio of provincial government expenditure to revenue;11

x3i is designed to capture the tendency of over-investment due to provincial competition,in addition to whatx2i captures, which is defined as one-period lagged deviation of provincialexcess investment from its average regional level; and

x4i is aimed at capturing regional growth effect, which is defined as one-period lagged deviationof provincial per capita GDP from its average regional level.

11 Notice that post-1994 data on x2i do not represent as drastic an increase in provincial government deficit as Figure2 suggests. This is because a new system of tax division was introduced in 1994, which entails part of the tax collectednationally to be returned to provinces by a certain formula, whereas the published local government revenue accountdoes not contain this part. Nevertheless, local government deficit financing is mainly responsible for the nationwidegovernment debt, as shown in Figures 1 and 2.

12 Here, we adopt the division of three broad regions by the National Bureau of Statistics; see also Song et al. (2001).

SECTIONIII



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10 OCTOBER 2003

Detailed definition of these variables and the division of three regions12 are given in theAppendix. The debt-asset ratios and bank loan-deposit ratios have turned out to be insignificantand hence dropped out. This can be explained by the facts that few firms use bank loans exclusivelyfor fixed capital investment, that the available debt-asset ratio data only cover the period of 1993-

1999, and that most of these banking related series fluctuate far less than those fiscal policyvariables.

IV. EMPIRICAL RESULTS

To estimate disequilibrium investment based on (10), we use the following model:

0 iI a a Y Cln ln ln = + + + +it 1 it 2 it it (10a)

where i denotes individual effect and its various specifications are given in Table 1. The datasample covers 30 provinces of 12 years, 1989-2000. Essentially, (10a) is expected to constitute

a homogeneous long-run equilibrating, possibly cointegrating, relation. it should therefore bea stationary and probably nonwhite-noise process. We have to choose appropriate estimation methodsaccordingly. Considering that all the time series involved in (10a) are likely to exhibit nonstationaryproperties, we use two estimation methods: feasible generalized least squares (FGLS) method directlyon (10a) and the dynamic panel model estimation method of combined generalized method ofmoments (GMM) on a first-order ADL version of (10a): 13

I a a b I b Y b Y b C b C

b b

1 b

b b

1 b

ln ln ln ln ln ln

= + + + + + + +

+=

+

=

it 0 i 0 it-1 10 it 11 it-1 20 it 21 it-1 it

10 111

0

20 21

2 0

(10a)

In order to check if these long-run coefficients withstand the rapid changes in the economy,we also conduct two subsample estimations in addition to full-sample estimation. The main estimationresults are reported in Table 1.14 As expected, the residuals of static model (10a) show strongautocorrelation, suggesting a very slow disequilibrium correcting process, whereas the residuals ofthe dynamic model are serially uncorrelated.15

It is noticeable from Table 1 that there is strong evidence supporting the postulate of constantreturns to scale, i.e., 1=1. Unsurprisingly, the estimates of this parameter are sensitive to the

13 The estimation is carried out by PcGive 10. We use ordinary least squares (OLS) residuals as the weights of the FGLS

estimator. For the GMM method, we choose one-step estimator since residual heteroscedasticity should not be a significantproblem once the individual effects have been filtered out; see Arellano and Bover (1995), and also Blundell andBond (1998).

14 Since some sample observations of the cost variable are negative because of large negative real interest rates, weshift the real interest rate net of the depreciation rate upward by adding one to the whole series before taking logtransformation. This adjustment should only affect the magnitude of the constant term.

15 The significant first-order serial correlation is an expected feature of the GMM method. See Doornik and Hendry(2001, Chapter 7) for details of the residual autocorrelation test.


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i = i +ot i = i i = i +otFGLS89-2000 92-2000 94-2000 89-2000 92-2000 94-2000 89-2000 92-2000 94-2000

1 1.17(0.1002)

0.847(0.1706)

0.7795(0.2694)

1.152(0.0213)

1.144(0.0367)

1.126(0.0546)

1.135(0.1068)

0.7546(0.1922)

0.831(0.288)

2 -0.186(0.0976)

-0.202(0.1131)

-0.178(0.1091)

-0.191(0.0942)

-0.155(0.1106)

-0.192(0.1088)

-0.092(0.1909)

-0.034(0.2056)

-0.223(0.1985)

0 -0.002(0.0111)

0.0329(0.0184)

0.0335(0.0255)

Joint test ofi2(30)

1858[0.000]

1486[0.000]

1643[0.000]

1928[0.000]

1479[0.000]

1767[0.000]

1911[0.000]

1486[0.000]

1754[0.000]

Joint test of0t2(sample size)

6.914[0.806]

7.853[0.448]

4.285[0.638]

Test: no residual autocorrelation (AR)AR(1) N(0,1) 14.80

[0.000]12.73[0.000]

7.073[0.000]

14.88[0.000]

12.91[0.000]

7.26[0.000]

15.30[0.000]

14.80[0.000]

2.353[0.019]

AR(2) N(0,1) 5.426[0.000]

3.643[0.000]

0.5536[0.58]

5.471[0.000]

3.755[0.000]

0.6647[0.506]

5.647[0.000]

5.426[0.000]

-2.399[0.016]

AR(3) N(0,1) -1.311[0.19]

-2.439[0.015]

-3.803[0.000]

-1.368[0.171]

-2.28[0.023]

-3.892[0.000]

-1.329[0.184]

-1.311[0.19]

-4.506[0.000]

AR(4) N(0,1) -5.719[0.000]

-5.745[0.000]

-5.717[0.001]

-5.804[0.000]

-5.579[0.000]

-5.873[0.000]

-5.824[0.000]

-5.719[0.000]

-3.291[0.001]

Estimated long-run coefficients of (10a)

i = i +ot i = i i = i +otGMM89-2000 89-2000 92-2000 94-2000 89-2000

1 -0.2445(0.4627)

1.2043(0.0869)

1.4556(0.1707)

0.9185(0.3291)

0.1673(0.6621)

2 -0.6557(0.3865)

-0.177(0.3797)

-0.724(0.552)

2.7155(2.6075)

-0.9847(0.5811)

0 0.1647(0.0514)

specification of time effects, especially in the dynamic panel model, since the time series of bothinvestment and output are heavily trended. The closeness of FGLS estimates to the GMM long-runestimates without time effects implies cointegration of both series at 1=1, which corroborates thefindings by Sun (1998) and Song et al. (2001). It is found that the time effects remain largely

insignificant in the form of either a deterministic trend or random effect. We henceforth drop thetime effect specification. Another noticeable result is the very low significance level in 2 estimates.There are two possibilities. Either , the elasticity of substitution, is virtually zero, or the actualCit has not been perceived as cost-minimizing signals. We are inclined to the latter based on theobservation that bank loan rates and investment prices remained rather low during excess investmentpeaks in the sample period. To further verify this possibility, we carry out two experiments. We firstinvestigate whether there are different responses to different components in C. Then, we examinewhether there are nonhomogeneous responses to these signals. For the first experiment, we take1=1 and separate the cost variable in (11) into three parts:

SECTIONIII


TABLE 1: MAIN ESTIMATION RESULTSFOR (10A)

Note: Standard errors in round brackets and p value in squared brackets.


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12 OCTOBER 2003

( ) ( )IiY

PIa a r 1

Y Pln ln ln ln

= + + + + + +

0 i 20 21 22 it ittit it

(10b)

We expect that 20


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Next, we estimate the following version of (15):

( )I a a Y 1 x x x xln ln ln = + + + + + + + +it 0 i 1 it 22 it 1 1t 2 2it 3 3it-1 4 4it-1 it (15a)

as well as a first-order ADL of it similar to (10a). Table 3 reports the main estimation results. The

residual test results resemble closely those reported in Table 1.

It is evident from Table 3 that 1=1 is strengthened. In other words, the slight tendency ofdecreasing return to scales, i.e., 1>1 under i = i in in Table 1 has disappeared and is very probablyexplained by one of the institutional variables. The estimates of 22 fall and turn to wrong sign aswe reduce sample size. This reinforces the earlier finding that investment demand hardly respondsto capital price signals, implying that these signals are far from reflecting the real costs of investment.Notice that the combination of1=1 and 2=0 enables us to simply view the ratio of investment toGDP as disequilibrium or mandated investment. Figure 4 plots the panel of this ratio. We see thatBeijing, Shanghai, Gangdong, Hainan, and Tibet are among the most prominent for excessinvestment while Guizhou, Yunnan, and Guangxi are for underinvestment.

Now, let us look at the results of the institutional variables. Table 3 shows considerabledifferences between the FGLS and GMM coefficient estimates of these variables, and the latterestimates are mostly insignificant. This is because these variables are defined in rate, whichdifferentiates them from flow variables in terms of time-series properties. In fact, the dynamicpanel model estimation reveals that the way both xi1 and xi3 impact on investment are in first-order difference form. We thus respecify (15a) into a restricted dynamic model incorporating 1=1and 2=0:

TABLE 3: MAIN ESTIMATION RESULTSFOR (15a)

Note: Standard errors in round brackets and p value in squared brackets; GMM estimates are based on a first-order ADL of (15a).

SECTIONIII


89-2000 92-2000 94-2000 89-2000 92-2000 94-2000FGLS GMM

10.9711

(0.0484)

0.8782

(0.0603)

0.9403

(0.0579)

1.0378

(0.0719)

1.0038

(0.1931)

0.9114

(0.2316)22

0.3603(0.1427)

0.2761(0.1594)

-0.0805(0.1614)

0.0871(0.4613)

-0.7739(0.5455)

-1.0534(0.616)

10.0524

(0.0222)0.058

(0.0236)0.0459

(0.0208)0.0058

(0.0507)-0.0245(0.074)

-0.0208(0.0772)

20.0964

(0.0293)0.1973

(0.0433)0.1174

(0.0735)0.2129(0.116)

0.2985(0.2268)

0.3118(0.2789)

3-0.6727(0.0698)

-0.6431(0.0792)

-0.6308(0.0755)

-0.0572(0.2746)

0.0755(0.3748)

-0.1396(0.2535)

40.1556

(0.0729)0.1555

(0.0869)0.0195(0.089)

0.0941(0.2809)

-0.2023(0.5723)

-0.0269(0.4244)

Test: No residual autocorrelation (AR)AR(1)

N(0,1)

9.693

[0.000]

8.616

[0.000]

6.479

[0.000]

-3.829

[0.000]

-3.481

[0.000]

-3.706

[0.000]AR(2)N(0,1)

2.555[0.011]

2.235[0.025]

1.193[0.233]

0.9459[0.344]

1.679[0.093]

1.82[0.069]

AR(3)N(0,1)

-1.631[0.103]

-1.702[0.089]

-3.202[0.001]

0.9[0.368]

0.0661[0.947]

-0.0616[0.951]

AR(4)N(0,1)

-4.821[0.000]

-4.884[0.000]

-5.804[0.000]

-1.655[0.098]

-0.5315[0.595]

-0.959[0.338]


20/41



14 OCTOBER 2003

t

Ia a x x x x

Yln

= + + + + + +

0 i 1 1t 2 2i 3 3it-1 4 4it-1 itit

(15b)

Table 4 reports the main estimation results. Model (15b) has the advantage of explicitlyexplaining disequilibrium investment exclusively in terms of institutional factors. Results fromboth models show that both fiscal policy variables have positively encouraged disequilibriuminvestment. Notice thatxi1 exerts its impact in a growth rate form. This suggests that changes infiscal policies at the national level directly affect disequilibrium investment. As the rising governmentdebt is due to deficit financing of many local governments, we infer that the positive impact ofxi2 helps to explain away the slight tendency of decreasing return to scales found in the estimates

of model (10a). In other words, the part of persistent excess investment with respect to GDP, whichleads to the inference of decreasing return to scale, can actually be accounted for by rising localgovernment deficit spending. This suggests that investment induced by government deficit-financingpolicy is likely to encourage underutilization of capital, as judged by the expected long-runequilibrium 1=1. The highly robust negative coefficient estimates for xi3 are confirmatory of theview that provinces have been competing with each other to invest more if they notice that theyhave fallen behind their neighbors in the investment race. As forxi4, its declining significance whenwe move to more recent subsample periods indicates that unequal regional allocation of investmentdue to unequal regional economic development has been gradually subsiding. Notice that thisvariable is somewhat negatively correlated with xi2. This implies that provincial governmentexpansionary investment policies may have contributed to the lessening of investment disparityto some extent, and that it is very difficult, if at all possible, for government to achieve efficiencyand equality at the same time.

Let us now turn to the question of whether the institutional factors encourage allocativeefficiency. First, we calculate z of (16) using the following two residual series from full-sampleFGLS estimation:


21/41


TABLE 4: MAIN ESTIMATION RESULTSFOR (15b)

Ia a

Y

Ia a x x x x

Y

ln

ln

=

=

itit 0 i

it

itit 0 i 1 1t 2 2it 3 3it-1 4 4it-1

it (16a)

Figure 5 plots the calculatedz for individual provinces, the cross-section means with 95 percentconfidence interval bars over the sample period, and cross provincial covariance. Interestingly, mostprovinces show a risingz, and the rises are most prominent around 1993-1994 and in late 1990swhen the PRC experienced major expansionary fiscal policy boosts. A slight rise is also discerniblefrom the time series of cross-section means, notwithstanding the fact that both panel series of thetwo residuals in (16a) have zero means. The results show that institutional factors are likely todisencourage efficient allocation of investment, especially when a balanced fiscal policy is severelysuppressed. Moreover, the dominance of positive over negative correlation between provincialz inthe covariance graph shows that provinces tend to suffer together from institution-induced allocativeinefficiency, and that macro policy factors still exert great impact in the regional distribution ofinvestment funds.

Next, we calculate zm of (16) using it of (16a) and the residual series of the ADL modelGMM full-sample estimation as it (column 5 in Table 3). The white-noise assumption of it isfurther confirmed by most of the normality test results at the provincial level reported in Table5. Figure 6 plots the calculatedzm for individual provinces, the cross-section means with 95 percentconfidence interval bars over the sample period, and covariance between individual provinces.

SECTIONIII


FGLS 89-2000 92-2000 94-2000 Correlation coefficients

1Restrict to 1 Restrict to 1 Restrict to 1 1

2

3

4

1 0.0716(0.0203) 0.0466(0.0202) 0.0422(0.0184) 1 0.057 0.000 0.000

2 0.1463(0.0266)0.2224

(0.0351)0.158

(0.075)1 -.033 -.139

3 -0.4831(0.0827)-0.5256(0.0848)

-0.4678(0.0828)

1 0.005

4 0.2006(0.0785)0.2065

(0.0901)0.145

(0.0956)1

Test: No residual autocorrelation (AR)AR(1)N(0,1)

13.11[0.000]

10.22[0.000]

7.103[0.000]

AR(2)N(0,1)

6.414[0.000]

4.657[0.000]

1.35[0.177]

AR(3)N(0,1) -0.6271[0.531] -0.4858[0.627] -3.433[0.001]AR(4)N(0,1)

-5.606[0.000]

-5.301[0.000]

-6.293[0.000]

Note: Standard errors in round brackets and p value in squared brackets; GMM estimates are based on a first-order ADL of(15b) without restricting 1=1.


22/41



16 OCTOBER 2003


23/41


BJ TJ HB SX NM LN JL HLJ SH JS

2.5475[0.2798]

5.911[0.0521]

3.5021[0.1736]

2.6254[0.2691]

0.8144[0.6655]

7.4409[0.0242]

3.6455[0.1616]

2.4106[0.2996]

6.6222[0.0365]

7.1045[0.0287]

ZJ AH FJ JX SD HN HUB HUN GD GX

1.7466[0.4176]

4.0828[0.1298]

2.8665[0.2385]

0.5696[0.7522]

6.4875[0.039]

0.3909[0.8225]

8.6756[0.0131]

0.5158[0.7727]

2.766[0.2508]

2.5587[0.2782]

HAN SC GZ YN XZ SHX GS QH NX XJ

7.0721[0.0291]

0.514[0.7734]

1.2515[0.5349]

0.6505[0.7224]

0.3539[0.8378]

6.3848[0.0411]

0.0637[0.9686]

0.0544[0.9732]

2.1116[0.3479]

1.7747[0.4117]

TABLE 5: NORMALITY TESTSON it OF (15A): 2(2)

We see from the graphs that, in contrast toz, there is a trend ofzm moving toward zero for manyprovinces, suggesting a general improvement of allocative efficiency in firms aggregate investmentdemand as reforms proceed and the institutional effects have been filtered out. There is also anoteworthy contrast between the part ofzm of around 1993-1994 and the part in the late 1990sfor many provinces. While we can see a strong policy impact in the first part, i.e., firms AE wentup with that of the institution-induced AE around 1993-1994, the policy impact of the secondpart is hardly discernible from firms AE in the late 1990s. The provinces with deteriorating firmsAE in late 1990s are concentrated in the less developed western and central regions (see Figure4). All these demonstrate significant progress of decentralization and enhancing market conditions.The time series of cross-section means remain around zero and the covariance between provincesare more evenly distributed around zero, indicating that some provinces improve their firms AEtogether while others are squeezed out by competition.

Notice that the contrasting trends in the two AE indices can be viewed as strong confirmatoryevidence for the competitive effect and the checks-and-balance effect postulated by Qian and Roland(1998). To facilitate further comparison, we have calculated various rank correlation coefficients

of the two AE measures (see Table 6). It is interesting to see from the rank autocorrelation coefficientsthat evolution of the institutional AE measure follows closely macro changes in the PRCs politicaleconomy whereas improvement of firm-level AE is more gradual and persistent. Correlation betweenz andzmover time shows certain sign of disassociation, suggesting that firms investment decisionshave become less affected by institutional considerations as reforms deepen.

Finally, we calculate three versions of PE using the following equation based on (10): 16

{ }{ }

{ }{ }

{ }{ }

0

a

a

a

exp

max

exp

max

exp

max

=

=

=

=

=

i

i i ii

i

i ii

i i

i

(17)

16 Most of the PE indices use the negative of the fixed individual effects to reflect the degree of technological inefficiency.Our indices denote PE directly.

SECTIONIII



24/41



18 OCTOBER 2003


25/41


TABLE 6: RANK CORRELATION COEFFICIENTSOF EFFICIENCY MEASURES (STANDARD DEVIATION: 0.1857)

Note: All rank correlation coefficients use Spearmans formula. In the calculation of rank correlation coefficients between zm and i,we take provincial means ofzm for the appropriate sample size first before ranking them. Sample size is 30.

17 We think it more appropriate to use the one-period lagged estimates of a i when calculating i of (17). The two setsof estimated ai are from full sample of 1989-2000 and subsample 1992-2000, respectively.

wheredenotes sample mean and the index is normalized by max{i}. The second line of (17)is based partly on the observation that depreciation rate data show little difference across provinceand time, and partly on the consideration that officially defined depreciation rates can be markedlydifferent from the effective depreciation rates required by theoretical models like (11). As for i,we choose to use the estimates from model (10a) rather than (15b) for the reason that the estimatedi in (15b) are likely to contain certain heterogeneous effects due to the institutional factors.

Since we are unable to estimate via the cost-minimization route because of the insignificanceof the cost variable, we have to estimate i via a production function if 0 . Regarding the factthat most empirical studies of the PRCs aggregate production function assume = 1, we follow suitfor simplicity and specify (8) by the Cobb-Douglas type of production function in a mixed panel

form:

YI LI KI uln ln ln = + + +30

it 0 1 it i it iti=1

(8)

Due to lack of aggregate data on capital, we use data of the industrial sector here and assumethat the spatial pattern of the estimated i applies to all the other sectors. Since the variablesof (8) usually exhibit strong nonstationary property, i are taken as the long-run solution of afirst-order ADL version of (8), similarly specified as (10a), and estimated by the combined GMMmethod. Two sets ofi are calculated, one for sample 1988-1999 and the other for 1991-1999.

17

Correspondingly, two sets ofi are calculated under the three situations of (17) respectively, namely=1, i = , and =0. The results are plotted in Figure 7, where the order of graphs goes with

the line order of (17). We see from the figure that there is no considerable change in the generalpattern ofi between different situations. The pattern appears to be in accord with what is usuallyperceived, namely coastal and southern provinces tend to be technologically more efficient thaninland and western provinces. In particular, our result does not contradict Yaos estimates of

SECTIONIII


1st-Order Autocorrelation

Year (90,91) (91,92) (92,93) (93,94) (94,95) (95,96) (96,97) (97,98) (98,99) (99,2000)

z

0.0425 0.1399 0.1199 0.0189 -.1591 0.1693 0.3001 0.4478 0.4087 0.5453zm 0.5835 0.3771 0.4656 0.8162 0.7219 0.6974 0.5907 0.6801 0.9350 0.7602

Betweenzand zm

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000

-.00645 -.1484 0.1858 0.180 0.0963 -.2156 0.0345 -.0145 0.1026 -.1057 -.0643

Betweenzmand iFull sample: -0.08343 Sub sample (92-2000): 0.04605


26/41



20 OCTOBER 2003

technological inefficiency using micro firm data (2001). Moreover, provinces with relatively highPE tend to show better performance in their AE indices by and large. Rank correlation betweeni andz

m shows (see Table 6) that the two efficiency measures hardly relate to each other, verifyingthe postulate by Bai et al. (1997) that PE may not imply AE when firms objectives are more

complicated than profit maximization due to imperfect market environment.


27/41


V. CONCLUSION

Excess investment at the aggregate level is a common phenomenon in CPEs. Has it disappearedduring extensive economic reforms in many formerly CEPs? Evidence from the PRC suggests that the

capital formation has outpaced GDP growth in the PRC over the last decade, especially during theperiods of 1990-1994 and 1997-2000. Is this evidence of excess investment? In this study, weset up a model to estimate inefficiency in aggregate investment and to attribute the inefficiencyto various economic and institutional factors. The model is used to analyze the PRC aggregateprovincial-panel data.

Primarily, we have identified disequilibrium investment, i.e., deviations of actual investmentfrom the market desired investment, to the ratio of investment to GDP, a result that confirms theprevious findings by Sun (1998) and Song et al. (2001). The user cost of capital is found to playa negligible role, indicating that capital prices have not emitted market-clearing signals to reflectthe true costs of investment demand. This finding is consistent with Stigilitz's observation (1996,97) that, unlike in a pure market economy, firm managers in a transition economy tend to undertakegrandiose investment projects, because their decisions generally do not bear the risks or costs ofmistakes that they might make, but may, however, get credit for any achievement under their direction.A major factor sustaining this kind of behavior is incomplete and ambiguous property rights, whichstill prevails in the PRC firms.

Noticeably, our model design enables us to uncover how much disequilibrium investment canbe explained by nonmarket-equilibrating institutional variables, which act as proxies of soft budgetconstraints. Fiscal deficit is found to contribute significantly to excess investment demand. In particular,provincial fiscal deficit appears to explain a slight and gradual declining return to scales observedfrom aggregate data. A network effect is also found to exacerbate disequilibrium investment, suggestingthat provinces will not curb their investment desire until they join ranks with their regional leadersof excess investment. Both findings are consistent with the federalism argument by Huang (1996)and Qian and Roland (1998), as well as with the evidence previously presented by Zhang and Zou(1996) and Young (2000).

The essential advantage of our modelling approach is embodied in three clearly defined andestimable measures of investment efficiency. These measures enable us to draw distinction not onlybetween inefficiency caused by resource misallocation and by underutilization of capital assetsin production, but also between allocative inefficiency caused by imperfect market system andby firms nonoptimal investment decisions. Estimates of the two AE measures suggest that severeunderutilization of investment recourses is closely associated with governments major attemptsto stimulate aggregate demand and boost economic growth, and that there have been signs ofimprovement in firms AE as the reforms deepen. These results support the views that decentralizationand federalization generates mixed welfare effects (see Huang 1996, and Qian and Roland 1998).The third measure on PE is broadly in line with the pattern of regional development, with southern

and coastal provinces more efficient than western provinces at large.

We must acknowledge that our efficiency measures have limitations. For example, the standardefficiency criterion that these measures are based on does not take into account the possibly positiveexternality of government nonprofit-seeking investment demand. Moreover, the measures have notexplicitly allowed for the role of future expectation. In other words, our AE measures may not beable to indicate whether inefficient investment allocation at present will eventually become efficient

SECTIONIV

CONCLUSION


28/41



22 OCTOBER 2003

APPENDIX

Main data sources:

National Bureau of Statistics: Statistical Yearbook of China (SYC), Industrial Economic StatisticalYearbook of China (IESYC), Statistics on Investment in Fixed Assets of China (SIFAC), ProvincialStatistical Yearbook (PSY), various issues.

China Finance Ministry: Financial Yearbook of China (FYC), various issues.

People's Bank of China: Almanac of China's Finance and Banking (ACFB), various issues.

Variable definition and source:

I: Fixed-asset investment at provincial level, SYC and SIFAC, adjusted to constant price by PI

Y: GDP at provincial level, SYC, adjusted to constant price by PYPI: Price index of fixed-asset investment at provincial level, SYC

PY: Price index of GDP at provincial level, SYC

r: Real interest rate calculated by 3-5 year loan rates net of the growth rate of PI

of one-yearlag (proxy for expected inflation of investment goods), SYC and ACFB

: Depreciation rate of fixed assets of state-owned industrial firms at provincial level, FYC andPSY (data for 1999 and 2000 unavailable, calculated using previous observations together withdata of the net gross asset values of state-owned industries at provincial level from IESYC)

: Tax is derived from total pre-tax profits minus total after-tax profits of industrial firms withindependent accounting systems at provincial level, tax rate is then calculated using tax dividedby value-added of the firms, SYC

x1: Logarithm of net government debt, i.e., total government debt incurred minus total retirementof debt and interest payments (the net debt amounts approximately to the total governmentdeficit); a series of central government deficit is also calculated, SYC

x2: Logarithm of the ratio of provincial government expenditure to revenue, SYC

x3: One-period lagged provincial Ii/Yi minus its regional average I/Y, standardized by the nationalaverage of I/Y

in the sense that it enhances the PRCs potential for future development. However, this kind ofdynamic effect should not significantly bias our general results, as our measures are built upondisequilibrium investment from its long-run path. Efficiency is a normative concept after all. Model-based definitions and estimable measures should at least help to clarify previously confused views

and disorganized evidence, and hopefully to reduce the gap between theoretical and empiricalstudies on the welfare implications of institutional changes in transitional economies.


29/41


APPENDIX

x4 : One-period lagged provincial per capita GDP minus its regional per capita GDP, standardizedby the national per capital GDP, SYC, and PSY

YI: Value-added of Industry at provincial level, IESYC, 1989-1999

LI: Average employment of Industry at provincial level, IESYC, 1989-1999

KI: Net fixed assets of Industry at provincial level, IESYC, 1989-1999

Abbreviation of provinces by region:

Coastal region Central region Western region

BJ Beijing SX Shanxi SC Sichuan

TJ Tianjin NM Inner Mongolia GZ Guizhou

HB Hebei JL Jilin YN Yunnan

LN Liaoning HLJ Heilongjiang XZ TibetSH Shanghai AH Anhui SHX Shaanxi

JS Jiangsu JX Jiangxi GS Gansu

ZJ Zhejiang HN Henan QH Qinghai


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24 OCTOBER 2003

FJ Fujian HUB Hubei NX Ningxia

SD Shandong HUN Hunan XJ Xinjiang

GD Guangdong

GX GuangxiHAN Hainan

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Douglas H. Brooks, Emma Xiaoqin Fan,and Lea R. Sumulong

April 2003

No. 39 The Political Economy of Good Governance for

Poverty Alleviation Policies

Narayan LakshmanApril 2003

No. 40 The Puzzle of Social CapitalA Critical Review

M. G. QuibriaMay 2003

No. 41 Industrial Structure, Technical Change, and theRole of Government in Development of the

Electronics and Information Industry in

Taipei,ChinaYeo LinMay 2003

No. 42 Economic Growth and Poverty Reductionin Viet Nam

Arsenio M. Balisacan, Ernesto M. Pernia, andGemma Esther B. Estrada

June 2003No. 43 Why Has Income Inequality in Thailand

Increased? An Analysis Using 1975-1998 Surveys

Taizo MotonishiJune 2003

No. 44 Welfare Impacts of Electricity Generation SectorReform in the Philippines

Natsuko TobaJune 2003

No. 45 A Review of Commitment Savings Products in

Developing CountriesNava Ashraf, Nathalie Gons, Dean S. Karlan,and Wesley Yin

July 2003No. 46 Local Government Finance, Private Resources,

and Local Credit Markets in Asia

Roberto de Vera and Yun-Hwan KimOctober 2003July 2003

No. 47 Excess Investment and Efficiency Loss During

Reforms: The Case of Provincial-level Fixed-AssetInvestment in Peoples Republic of China

Duo Qin and Haiyan SongOctober 2003


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ERD POLICY BRIEF SERIES (PBS)

(Published in-house; Available through ADB Office of External Relations; Free of charge)

No. 1 Is Growth Good Enough for the Poor?

Ernesto M. Pernia, October 2001No. 2 Indias Economic Reforms

What Has Been Accomplished?What Remains to Be Done?

Arvind Panagariya, November 2001No. 3 Unequal Benefits of Growth in Viet Nam

Indu Bhushan, Erik Bloom, and Nguyen MinhThang, January 2002

No. 4 Is Volatility Built into Todays World Economy?

J. Malcolm Dowling and J.P. Verbiest,February 2002

No. 5 What Else Besides Growth Matters to PovertyReduction? Philippines

Arsenio M. Balisacan and Ernesto M. Pernia,February 2002

No. 6 Achieving the Twin Objectives of Efficiency andEquity: Contracting Health Services in Cambodia

Indu Bhushan, Sheryl Keller, and BradSchwartz,March 2002

No. 7 Causes of the 1997 Asian Financial Crisis: What

Can an Early Warning System Model Tell Us?

Juzhong Zhuang and Malcolm Dowling,June 2002

No. 8 The Role of Preferential Trading Arrangementsin Asia

Christopher Edmonds and Jean-Pierre Verbiest,July 2002

No. 9 The Doha Round: A Development Perspective

Jean-Pierre Verbiest, Jeffrey Liang, and LeaSumulongJuly 2002

No. 10 Is Economic Openness Good for RegionalDevelopment and Poverty Reduction? The

PhilippinesE. M. Pernia and P. F. Quising

October 2002No. 11 Implications of a US Dollar Depreciation for Asian

Developing Countries

Emma FanJuly 2002

No. 12 Dangers of Deflation

D. Brooks and P. F. QuisingDecember 2002

No. 13 Infrastructure and Poverty ReductionWhat is the Connection?

I. Ali and E. PerniaJanuary 2003

No. 14 Infrastructure and Poverty ReductionMaking Markets Work for the Poor

Xianbin YaoMay 2003

No. 15 SARS: Economic Impacts and Implications

Emma Xiaoqin FanMay 2003

No. 16 Emerging Tax Issues: Implications of Globalization

and Technology

Kanokpan Lao ArayaMay 2003

No. 17 Pro-Poor Growth: What is It and Why is ItImportant?

Ernesto M. PerniaMay 2003

No. 18 PublicPrivate Partnership for Competitiveness

Jesus Felipe

June 2003No. 19 Reviving Asian Economic Growth Requires Further

Reforms

Ifzal AliJune 2003

No. 20 The Millennium Development Goals and Poverty:

Are We Counting the Worlds Poor Right?

M. G. QuibriaJuly 2003

No. 21 Trade and Poverty: What are the Connections?

Douglas H. BrooksJuly 2003

No. 22 Adapting Education to the Global Economy

Olivier DupriezSeptember 2003

SERIALS

(Co-published with Oxford University Press; Available commercially through Oxford University Press

Offices, Associated Companies, and Agents)

1. Asian Development Outlook (ADO; annual)

$36.00 (paperback)

2. Key Indicators of Developing Asian and Pacific Countries (KI; annual)

$35.00 (paperback)

JOURNAL

(Published in-house; Available commercially through ADB Office of External Relations)

1. Asian Development Review (ADR; semiannual)

$5.00 per issue; $8.00 per year (2 issues)


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MONOGRAPH SERIES

(Published in-house; Available through ADB Office of External Relations; Free of charge)

EDRC REPORT SERIES (ER)

No. 1 ASEAN and the Asian Development BankSeiji Naya, April 1982

No. 2 Development Issues for the Developing East

and Southeast Asian Countriesand International CooperationSeiji Naya and Graham Abbott, April 1982

No. 3 Aid, Savings, and Growth in the Asian RegionJ. Malcolm Dowling and Ulrich Hiemenz,

April 1982No. 4 Development-oriented Foreign Investment

and the Role of ADBKiyoshi Kojima, April 1982

No. 5 The Multilateral Development Banksand the International Economys MissingPublic SectorJohn Lewis, June 1982

No. 6 Notes on External Debt of DMCsEvelyn Go, July 1982

No. 7 Grant Element in Bank Loans

Dal Hyun Kim, July 1982No. 8 Shadow Exchange Rates and Standard

Conversion Factors in Project Evaluation

Peter Warr, September 1982No. 9 Small and Medium-Scale Manufacturing

Establishments in ASEAN Countries:

Perspectives and Policy IssuesMathias Bruch and Ulrich Hiemenz,

January 1983No. 10 A Note on the Third Ministerial Meeting of GATT

Jungsoo Lee, January 1983No. 11 Macroeconomic Forecasts for the Republic

of China, Hong Kong, and Republic of KoreaJ.M. Dowling, January 1983

No. 12 ASEAN: Economic Situation and Prospects

Seiji Naya, March 1983No. 13 The Future Prospects for the Developing

Countries of Asia

Seiji Naya, March 1983No. 14 Energy and Structural Change in the Asia-

Pacific Region, Summary of the Thirteenth

Pacific Trade and Development ConferenceSeiji Naya, March 1983

No. 15 A Survey of Empirical Studies on Demand

for Electricity with Special Emphasis on PriceElasticity of DemandWisarn Pupphavesa, June 1983

No. 16 Determinants of Paddy Production in Indonesia:1972-1981A Simultaneous Equation ModelApproachT.K. Jayaraman, June 1983

No. 17 The Philippine Economy: EconomicForecasts for 1983 and 1984J.M. Dowling, E. Go, and C.N. Castillo,

June 1983No. 18 Economic Forecast for Indonesia

J.M. Dowling, H.Y. Kim, Y.K. Wang,and C.N. Castillo, June 1983

No. 19 Relative External Debt Situation of AsianDeveloping Countries: An Application

of Ranking MethodJungsoo Lee, June 1983

No. 20 New Evidence on Yields, Fertilizer Application,

and Prices in Asian Rice ProductionWilliam James and Teresita Ramirez, July 1983

No. 21 Inflationary Effects of Exchange Rate

Changes in Nine Asian LDCsPradumna B. Rana and J. Malcolm Dowling,

Jr., December 1983

No. 22 Effects of External Shocks on the Balanceof Payments, Policy Responses, and DebtProblems of Asian Developing Countries

Seiji Naya, December 1983No. 23 Changing Trade Patterns and Policy Issues:

The Prospects for East and Southeast Asian

Developing CountriesSeiji Naya and Ulrich Hiemenz, February 1984

No. 24 Small-Scale Industries in Asian Economic

Development: Problems and ProspectsSeiji Naya, February 1984

No. 25 A Study on the External Debt Indicators

Applying Logit AnalysisJungsoo Lee and Clarita Barretto,

February 1984No. 26 Alternatives to Institutional Credit Programs

in the Agricultural Sector of Low-IncomeCountriesJennifer Sour, March 1984

No. 27 Economic Scene in Asia and Its Special FeaturesKedar N. Kohli, November 1984

No. 28 The Effect of Terms of Trade Changes on the

Balance of Payments and Real NationalIncome of Asian Developing CountriesJungsoo Lee and Lutgarda Labios, January 1985

No. 29 Cause and Effect in the World Sugar Market:Some Empirical Findings 1951-1982Yoshihiro Iwasaki, February 1985

No. 30 Sources of Balance of Payments Problemin the 1970s: The Asian ExperiencePradumna Rana, February 1985

No. 31 Indias Manufactured Exports: An Analysisof Supply SectorsIfzal Ali, February 1985

No. 32 Meeting Basic Human Needs in AsianDeveloping CountriesJungsoo Lee and Emma Banaria, March 1985

No. 33 The Impact of Foreign Capital Inflowon Investment and Economic Growthin Developing Asia

Evelyn Go, May 1985No. 34 The Climate for Energy Development

in the Pacific and Asian Region:

Priorities and PerspectivesV.V. Desai, April 1986

No. 35 Impact of Appreciation of the Yen on

Developing Member Countries of the BankJungsoo Lee, Pradumna Rana, and Ifzal Ali,

May 1986No. 36 Smuggling and Domestic Economic Policies

in Developing CountriesA.H.M.N. Chowdhury, October 1986

No. 37 Public Investment Criteria: Economic Internal

Rate of Return and Equalizing Discount RateIfzal Ali, November 1986

No. 38 Review of the Theory of Neoclassical Political

Economy: An Application to Trade PoliciesM.G. Quibria, December 1986

No. 39 Factors Influencing the Choice of Location:

Local and Foreign Firms in the PhilippinesE.M. Pernia and A.N. Herrin, February 1987

No. 40 A Demographic Perspective on Developing

Asia and Its Relevance to the BankE.M. Pernia, May 1987

No. 41 Emerging Issues in Asia and Social Cost

Benefit AnalysisI. Ali, September 1988


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No. 42 Shifting Revealed Comparative Advantage:

Experiences of Asian and Pacific DevelopingCountriesP.B. Rana, November 1988

No. 43 Agricultural Price Policy in Asia:Issues and Areas of ReformsI. Ali, November 1988

No. 44 Service Trade and Asian Developing EconomiesM.G. Quibria, October 1989

No. 45 A Review of the Economic Analysis of Power

Projects in Asia and Identification of Areas

of ImprovementI. Ali, November 1989No. 46 Growth Perspective and Challenges for Asia:

Areas for Policy Review and ResearchI. Ali, November 1989

No. 47 An Approach to Estimating the PovertyAlleviation Impact of an Agricultural ProjectI. Ali, January 1990

No. 48 Economic Growth Performance of Indonesia,the Philippines, and Thailand:The Human Resource Dimension

E.M. Pernia, January 1990No. 49 Foreign Exchange and Fiscal Impact of a Project:

A Methodological Framework for Estimation

I. Ali, February 1990No. 50 Public Investment Criteria: Financial

and Economic Internal Rates of Return

I. Ali, April 1990No. 51 Evaluation of Water Supply Projects:An Economic Framework

Arlene M. Tadle, June 1990No. 52 Interrelationship Between Shadow Prices, Project

Investment, and Policy Reforms:An Analytical Framework

I. Ali, November 1990No. 53 Issues in Assessing the Impact of Project

and Sector Adjustment Lending

I. Ali, December 1990No. 54 Some Aspects of Urbanization

and the Environment in Southeast Asia

Ernesto M. Pernia, January 1991No. 55 Financial Sector and Economic

Development: A Survey

Jungsoo Lee, September 1991No. 56 A Framework for Justifying Bank-Assisted

Education Projects in Asia: A Reviewof the Socioeconomic Analysis

and Identification of Areas of ImprovementEtienne Van De Walle, February 1992

No. 57 Medium-term Growth-Stabilization

Relationship in Asian Developing Countriesand Some Policy ConsiderationsYun-Hwan Kim, February 1993

No. 58 Urbanization, Population Distribution,

and Economic Development in AsiaErnesto M. Pernia, February 1993No. 59 The Need for Fiscal Consolidation in Nepal:

The Results of a SimulationFilippo di Mauro and Ronald Antonio Butiong,

July 1993No. 60 A Computable General Equilibrium Model

of Nepal

Timothy Buehrer and Filippo di Mauro,October 1993

No. 61 The Role of Government in Export Expansion

in the Republic of Korea: A RevisitYun-Hwan Kim, February 1994

No. 62 Rural Reforms, Structural Change,

and Agricultural Growth inthe Peoples Republic of ChinaBo Lin, August 1994

No. 63 Incentives and Regulation for Pollution Abatementwith an Application to Waste Water TreatmentSudipto Mundle, U. Shankar,and Shekhar Mehta, October 1995

No. 64 Saving Transitions in Southeast AsiaFrank Harrigan, February 1996

No. 65 Total Factor Productivity Growth in East Asia:

A Critical Survey

Jesus Felipe, September 1997No. 66 Foreign Direct Investment in Pakistan:

Policy Issues and Operational Implications

Ashfaque H. Khan and Yun-Hwan Kim,July 1999

No. 67 Fiscal Policy, Income Distribution and Growth

Sailesh K. Jha, November 1999


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No. 1 International Reserves:Factors Determining Needs and Adequacy

Evelyn Go, May 1981No. 2 Domestic Savings in Selected Developing

Asian Countries

Basil Moore, assisted byA.H.M. Nuruddin Chowdhury, September 1981

No. 3 Changes in Consumption, Imports and Exports

of Oil Since 1973: A Preliminary Survey ofthe Developing Member Countriesof the Asian Development Bank

Dal Hyun Kim and Graham Abbott,September 1981

No. 4 By-Passed Areas, Regional Inequalities,

and Development Policies in SelectedSoutheast Asian CountriesWilliam James, October 1981

No. 5 Asian Agriculture and Economic DevelopmentWilliam James, March 1982

No. 6 Inflation in Developing Member Countries:An Analysis of Recent Trends

A.H.M. Nuruddin ChowdhuryandJ. Malcolm Dowling,March 1982

No. 7 Industrial Growth and Employment in

Developing Asian Countries: Issues and

Perspectives for the Coming DecadeUlrich Hiemenz, March 1982

No. 8 Petrodollar Recycling 1973-1980.Part 1: Regional Adjustments andthe World Economy

Burnham Campbell, April 1982No. 9 Developing Asia: The Importance

of Domestic Policies

Economics Office Staff under the directionof Seiji Naya, May 1982

No. 10 Financial Development and Household

Savings: Issues in Domestic ResourceMobilization in Asian Developing CountriesWan-Soon Kim, July 1982

No. 11 Industrial Development: Role of SpecializedFinancial InstitutionsKedar N. Kohli, August 1982

No. 12 Petrodollar Recycling 1973-1980.Part II: Debt Problems and an Evaluationof Suggested Remedies

Burnham Campbell, September 1982No. 13 Credit Rationing, Rural Savings, and Financial

Policy in Developing Countries

William James, September 1982No. 14 Small and Medium-Scale Manufacturing

Establishments in ASEAN Countries:

Perspectives and Policy IssuesMathias Bruch and Ulrich Hiemenz, March 1983

No. 15 Income Distribution and EconomicGrowth in Developing Asian Countries

J. Malcolm Dowling and David Soo, March 1983No. 16 Long-Run Debt-Servicing Capacity of

Asian Developing Countries: An Application

of Critical Interest Rate Approach

Jungsoo Lee, June 1983No. 17 External Shocks, Energy Policy,

and Macroeconomic Performance of AsianDeveloping Countries: A Policy AnalysisWilliam James, July 1983

No. 18 The Impact of the Current Exchange RateSystem on Trade and Inflation of SelectedDeveloping Member Countries

Pradumna Rana, September 1983No. 19 Asian Agriculture in Transition: Key Policy Issues

William James, September 1983No. 20 The Transition to an Industrial Economy

ECONOMIC STAFF PAPERS (ES)

in Monsoon AsiaHarry T. Oshima, October 1983

No. 21 The Significance of Off-Farm Employmentand Incomes in Post-War East Asian GrowthHarry T. Oshima, January 1984

No. 22 Income Distribution and Poverty in SelectedAsian CountriesJohn Malcolm Dowling, Jr., November 1984

No. 23 ASEAN Economies and ASEAN EconomicCooperationNarongchai Akrasanee, November 1984

No. 24 Economic Analysis of Power ProjectsNitin Desai, January 1985

No. 25 Exports and Economic Growth in the Asian Region

Pradumna Rana, February 1985No. 26 Patterns of External Financing of DMCs

E. Go, May 1985No. 27 Industrial Technology Development

the Republic of KoreaS.Y. Lo, July 1985

No. 28 Risk Analysis and Project Selection:

A Review of Practical IssuesJ.K. Johnson, August 1985

No. 29 Rice in Indonesia: Price Policy and Comparative

Advantage

I. Ali, January 1986No. 30 Effects of Foreign Capital Inflows

on Developing Countries of AsiaJungsoo Lee, Pradumna B. Rana,

and Yoshihiro Iwasaki, April 1986No. 31 Economic Analysis of the Environmental

Impacts of Development ProjectsJohn A. Dixon et al., EAPI,

East-West Center, August 1986No. 32 Science and Technology for Development:

Role of the Bank

Kedar N. Kohli and Ifzal Ali, November 1986No. 33 Satellite Remote Sensing in the Asian

and Pacific Region

Mohan Sundara Rajan, December 1986No. 34 Changes in the Export Patterns of Asian and

Pacific Developing Countries: An Empirical

OverviewPradumna B. Rana, January 1987

No. 35 Agricultur

excess investment and efficiency loss during reforms: the case of provincial-level fixed-asset...

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