Discussion Papers In Economics
And Business

Intergovernmental Fiscal Relationships in China:
A Simple Model Based on the Nonsymmetric Nash Solution

Mototsugu FUKUSHIGE and Yingxin SHI

Discussion Paper 13-21

Graduate School of Economics andOsaka School of International Public Policy (OSIPP)
Osaka University, Toyonaka, Osaka 560-0043, JAPANIntergovernmental Fiscal Relationships in China:
A Simple Model Based on the Nonsymmetric Nash Solution

Mototsugu FUKUSHIGE and Yingxin SHI

Discussion Paper 13-21
August 2013

Graduate School of Economics andOsaka School of International Public Policy (OSIPP)
Osaka University, Toyonaka, Osaka 560-0043, JAPANIntergovernmental Fiscal Relationships in China:
A Simple Model Based on the Nonsymmetric Nash Solution *Mototsugu FUKUSHIGE
Graduate School of Economics, Osaka University
1-7, Machikaneyama-cho, Toyonaka, Osaka 560-0043, Japan

Yingxin SHI
Department of Economics & Management, Dalian Nationalities University
18, Liaohe West Road, Jinzhou New Area, Dalian 116600, China

Abstract
We propose a new empirical approach to analyzing fiscal decentralization and apply it to Chineseintergovernmental fiscal relationships between the central government and provincial governments.
In calculating budgetary revenue and expenditure shares, we include extra budgetary revenue andexpenditure. We find that although an increase in either income inequality or real per capita GDP
lowers local governments’ bargaining power within the budgetary system, local governments canoffset this by obtaining more bargaining power over extra budgetary expenditures. Another finding isthat although urbanization increases provincial governments’ budgetary revenues, it also restricts thescope for further budgetary expenditure.

JEL Classification: H77, D72, P35.
Keywords: nonsymmetric Nash bargaining, intergovernmental fiscal relationships, China*

Shi is grateful to SEAC (2013-GM-022) and DLNU (ZJ13RWYB002) for their financial support of
the joint research on this topic with Fukushige and Fukushige is grateful to the Japan Society for the
Promotion of Science (Grant-in-Aid for Scientific Research A 23243038) for their financial support.
Fukushige is also grateful to Professor Noritsugu Nakanishi for his helpful suggestions about
nonsymmetric Nash Bargaining solution.

1

1. Introduction
Having been established after World War Two, (the People’s Republic of) China has ahistory of only 60 years, as have its intergovernmental relationships, which have been changing
rapidly, as Man (2011) has pointed out.

China has five layers of government, andintergovernmental transfers flow from higher to lower levels of government. If we limit ourattention to central–provincial intergovernmental relations, there are several types ofintergovernmental fiscal transfer systems, such as the fiscal contract systems that existed between
1979 and 1993, and the fiscal sharing system that has existed since 1994. Revenue andexpenditure shares have changed dramatically following the decentralization of the 1980s and byrevenue recentralization and expenditure decentralization from 1994. Figure 1 illustrates thetrends in local government’s shares of revenue and expenditure, both with and without extra
budgetary revenues and expenditures. These historical changes complicate the assignment ofcentral and local government responsibilities. (See, for example, Martinez-Vazquez and Qiao
(2011) for a discussion.)
Several researchers have investigated these historical changes and their determinants.
Of course, no researcher can explain these changes consistently or establish a rule in relation tothem. Huang and Chen (2012) pointed out that there is no systematic rule for distributing fiscal

2transfers from the central to provincial governments. As Zhang and Zou (1998) pointed out,
variations among provinces are too large to form a simple rule or to introduce some incentive
mechanism; for example, based on theoretical insights from fiscal federalism.
In this paper, we develop a new approach to analyzing nonsymmetric Nash bargaining.
Our approach involves restricting bargaining between the central government and provincial
governments over shares of fiscal revenues and expenditures. This simple approach differs fromthose adopted by researchers on fiscal federalism, or decentralization, but enables us to shed newlight on intergovernmental fiscal relationships.
The outline of the paper is as follows. In Section 2, we survey the literature on fiscal
federalism, or decentralization, and intergovernmental relationships in China. In Section 3, wedevelop a two-person bargaining game and use its first-order condition to develop a simple
econometric model. In Section 4, we conduct an empirical investigation. In Section 5, weconclude the paper and discuss remaining issues.

2. Literature Survey of Intergovernmental Fiscal Relationships

Much theoretical and empirical investigation of fiscal federalism, or decentralization,
followed the seminal paper on fiscal federalism by Oates (1968). Weingast (2009) categorizedthese studies into first- and second-generation studies based on their theoretical assumptions.

3However, from the empirical researcher’s point of view, there are two types of studies of fiscal
federalism, or decentralization.

In one type, the consequences of fiscal federalism, or

decentralization, are investigated. In the other type, degrees and causes of fiscal decentralization
are investigated.
Examples of the former include the study of Woller and Phillips (1998), whoinvestigated the effect of decentralization on less developed countries’ economic growth, andDavoodi and Zou (1998), who investigated the same effect by using panel data on 46 countries.
In the Chinese context, Jin et al. (2005), Chen (2013), and Zhang and Zou (1998), who all use
panel data, and Zhang (2006), who uses so-called growth regression, investigated the relationshipbetween the fiscal decentralization and economic growth.

Feltenstein and Iwata (2005)

investigated the effects of economic and fiscal decentralization on macroeconomic indicatorssuch as economic growth and inflation. In the context of China’s recent rapid economic growth,
Qian and Roland (1998) concluded that China’s recent good economic performance was led by
the combination of political centralization and the fiscal decentralization of the Chinese Central
Government. Zhang (2006) and Zheng et al. (2013) also stress the role of political considerationsin China’s recent high economic growth. Tochkov (2007) and Tsui (2005) investigated theeffects on the dispersion of local governments’ expenditures of fiscal transfers by the central

4government to local governments. Wei (1996), Kanbur and Zhang (2005), and Huang and Chen
(2012) investigated the effects of central government transfers on regional income inequality.
Oates (1972, Chapter 5), who investigated the revenue share of the central government,
was one of the first to investigate the degrees and causes of fiscal decentralization. For the
purpose of making international comparisons, Panizza (1999) used cross-sectional data on 57
countries to investigate the determinants of fiscal centralization. Arzaghi and Henderson (2005)
used panel data on 48 countries to conduct a similar analysis. In relation to the Chinese case, only
Zhang and Zheng (2011) and Lin (2011) have investigated the rules governing central
government fiscal transfers to local governments.

There has so far been no empiricalinvestigation of the factors enabling such fiscal decentralization, or recentralization, or of theeconomic determinants of such historical movements.

3. The Nonsymmetric Nash Solution and Empirical Modeling

Although Tsui and Wang (2008) developed a game theoretic model of Chinese
decentralization, theirs is a theoretical study that lacks empirical investigation. We know of noempirical study in which central and local government fiscal shares are determined based on agame theoretic analysis in which several local governments participate in the bargaining, andeach government, including the central government, tries to maximize its objective function.

5

Such a bargaining model is not easy to implement empirically because of the difficulty of forming
a simple estimable equation. In this paper, to simplify bargaining between the central governmentand several local governments, we develop a two-person bargaining game, in which the central
government is one player, and the group of provincial governments is the other player. Then, we
use the nonsymmetric Nash solution for the budgetary shares of the central government and thegroups of local governments. Denoting the local governments’ budget share bycentral government’s share is (implies that the). Based on these shares, we set up a nonsymmetric Nashsolution as the maximization of the following objective function:

(

)

.

(1)

This type of function was originally introduced by Kalai (1977). (For details, please refer toMyerson, 1991, pp. 390–394.) However, in this paper, we set both the central and local
governments’ threshold points to zero. In this model,

represents the relative bargaining powerof the local government group. Hence, the central government’s relative bargaining power is. The first-order condition for maximizing the objective function is(2)

6

It is assumed thatis between zero and unity. Given the history of power politics between

central and local government,

should vary over time. Hence, we write p as

specification of the estimating equation, we further assume that. To simplify thecan be approximated by using

the following cumulative logistic distribution function:(3)

In addition, we assume thatis a linear function of our independent variables, as follows:

(4)

where

and

are column vectors of the independent variables and their coefficients,

respectively. By using equations (3) and (4), equation (2) can be rewritten as

Logarithmic transformation and the addition of an error term yield

(

)

(5)

This is the well-known logistic transformation from econometrics. The dependent variable issimilar to the logarithmically transformed decentralization index used by Zhang and Zou (1998).
To be specific, they used the ratio of the local governments’ expenditures to central government

7

expenditures as an index of decentralization in their regression model of China’s economic
growth. Although the index in (5) is not mentioned in the comprehensive discussions ofdecentralization indices by Zhang and Zou (1998) and Vo (2008), it is intuitively appealing as anindex of decentralization.

Therefore, estimating equation (5), and its expenditure version

described in the next section, constitutes empirical research on the determinants of fiscal
decentralization. This is confirmed by the fact thatis the parameter that determines the fiscal

share of local governments in our bargaining model.

4. Estimation of Intergovernmental Relationships

4.1 Econometric Model

The two shares that represent intergovernmental budgetary relationships are the revenue
share and the expenditure share. We restate the revenue share of the local governments group as
and define the expenditure share of the local governments group as . To simplify theequations, we rewrite equation (5) in terms of the dependent variables

(

)

(

and

as follows:)

These two equations form the following system of equations for the budgetary shares:

8

(6)

which can be estimated by using the Seemingly Unrelated Regressions (SUR) estimator or
another simultaneous-equations estimator. If any of the independent variables are correlated withthe error terms, we should apply the instrumental variables (IV) method.

To deal withheteroscedasticity and moving average (MA)-type serial correlation in the error terms, we apply
generalized method of moments (GMM) estimation to this system of equations.

4.2 Data
We initially calculated the shares of central and local government’s fiscal expenditureand revenue for the period 1982–2008 from the Finance Yearbook of China 2012. Defining thesefiscal shares requires careful handling of extra budgetary revenues and expenditures. Tsui (2005)
and Tochkov (2007) pointed out the reverse effects of central government budgetary transfers andextra budgetary funds on local governments’ expenditures. In the next section, we estimate
models based on including and excluding extra budgetary items from the shares.
We use eight independent variables: population; real per capita gross domestic product(GDP); real economic growth; inflation; the central government’s fiscal deficit; trade openness;the degree of urbanization; and regional income inequality. Below, we explain the rationale forusing these independent variables.

9

Population: This is the national population. Based on cross-sectional analysis of internationaldata, Panizza (1999) found that population size positively affects fiscal decentralization.

Real per capita GDP: Based on cross-sectional analysis of international data, Panizza (1999)
found that national income positively affects fiscal decentralization. To allow this variable tohave a nonlinear effect on the dependent variable, we used not only real per capita GDP but also
its square. Real per capita GDP is calculated by dividing nominal per capita GDP (in 100 million
yuan) by the consumer price index (CPI) (1950 = 100).

Economic growth: Because economic growth may affect the central governments’ fiscal
transfers, we include the annual growth rate of real GDP.

Inflation: Cukierman et al. (1992), for example, pointed out that seigniorage and taxation are
substitutable. Hence, inflation affects the bargaining power of the central government through thecentral government’s seigniorage.

Central government’s fiscal deficit: Among others, Ahmad (1997, p. 643) and Bahl (1999, p.
75) pointed out that local governments are not permitted to run deficits legally. Although thelocal governments can bypass this restriction to finance deficits by borrowing money from themarket, the central government’s fiscal deficit still affects its relative bargaining power.

10

Trade openness: Trading activity by China’s coastal provinces has contributed to its recent rapid
economic growth. Therefore, trade promotion may affect the relative bargaining power of thecentral government and the local government group. There are several ways of measuring trade
openness, or trade liberalization (see, for example, Harrison, 1996, and Yanikkaya, 2003).
Because of data availability, we use the trade share as a measure of trade openness.

Degree of urbanization: Because Wagner’s Law (see Bird, 1971) suggests that urbanization
increases the needs for public spending, the degree of urbanization may affect the relative
bargaining power of the central government and the local government group.

Regional income inequality: Under fiscal federalism, an important role of the central
government is to ease regional economic inequality. If China’s central government is to achieve
its goal of a “Harmonious Society”, then not only income redistribution but also fiscal
redistribution is important. To measure regional income inequality, we use Atkinson’s (1970)
inequality index. To measure income inequality between China’s provinces, given population
weights for each region ( wi , i  1,2,..., M ), the Atkinson measure is1

 1 M  x 1  1
A  1    w i  i   , for   0 and   1,
 N i1    

11

1 M
 x 
A  1  exp  w i ln  i  , for   1,N
  
 i1where w i and xi are the population weights and the average income of the ith province,
respectively,  is national average income, and N is the total population. The parameter 
represents the degree of inequality aversion. Atkinson’s original measure of  is based on thesocial welfare function and represents the degree of inequality aversion, which reflects relative
sensitivity to income transfers at different income levels. When  is high, the weight assigned to
an individual with a relatively low (high) level of income is large (small). When   0, which isan extreme case, individuals are indifferent to income redistribution. Treating the social welfare
function in this way is an advantage of the Atkinson measure. We obtained the required data fromthe China Compendium of Statistics 1949–2008, compiled by the Department of Comprehensive
Statistics of the National Bureau of Statistics (2010). For estimation, we calculated A based on
three different values (   0.1, 0.5, and 0.9 ). We also used

to allow for a potentiallynonlinear effect of the Atkinson measure.
Table 1 defines the independent variables and reports their data sources, and Table 2reports their summary statistics and those of the dependent variable.

124.3 Estimation Results

To estimate the system of equations given by (6), we used SUR and selected the
independent variables based on the Akaike Information Criterion (AIC). To check the robustnessof the estimation results, we estimated the chosen model by using the IV method and GMM. To
apply the IV method in three stages, we first constructed the instruments, then estimated eachequation, and then estimated the system of equations. (The method is often referred to as “threestage least squares”.) For both IV and GMM estimation, we used lagged independent variables as
instruments. We used one lag of the independent variables for IV and used two lags for GMM;
this is because we needed one MA lag for the error terms. We used two types of fiscal shares forthe dependent variables: budgetary revenue and expenditure shares based on excluding andincluding extra budgetary revenues and expenditures. Figure 1 illustrates both. They exhibit
similar trends, but there are apparent differences over time. As mentioned earlier, Tsui (2005)
and Tochkov (2007) pointed out the reverse effects of central government budgetary transfers andextra budgetary funds on local governments’ expenditures
Table 3 reports the estimation results for the budgetary shares, and Table 4 reports theresults of the robustness checks. The sample period covered is from 1982 to 2008, primarily
because of data availability on Atkinson’s measure and on extra budgetary revenues and13expenditures. Table 5 reports estimation results based on including extra budgetary revenues andexpenditures in the shares, and Table 6 reports the results of the robustness checks. In all cases,
the model that includes the Atkinson measure “Atkins09” generates the lowest AIC, so we report
only these results. Before discussing the sources of relative bargaining power, we make twopoints. The first relates to the goodness of fit of the estimated equations. Whether extra
budgetary items are included or excluded, the estimated revenue functions do not fit the data well.
This may be because the shares changed dramatically between 1993 and 1994. Unobservable
political factors may have affected these shares. When extra budgetary items are included, the fitof the revenue function deteriorates. This suggests that political factors might have played a rolein determining extra budgetary revenue. The second point concerns the robustness of theestimation results. According to the robustness checks reported in Tables 4 and 6, with some
exceptions, the estimated coefficients have the same signs, are of similar magnitudes, and areequally statistically significant. Exceptions are the coefficients of “Def” in the budgetary
expenditure function and the revenue function based on including extra budgetary items. In thebudgetary share function, the robustness checks generate larger coefficients of “Growth”, “Infl”,
and “Atkins09” than before. In the expenditure function based on including extra budgetary
items, following the robustness checks, the coefficient of “Pgdp” increases, and that of “Growth”

14decreases. These changes might signal some correlation between the independent variables andthe error term.
Comparing the estimated coefficients based on including and excluding extra budgetary
items reveals three main findings.

First, in the expenditure functions, the coefficients of“Atkins09” are negative when excluding extra items but positive when including them. (Thevariable is not selected for the revenue functions.) This suggests that an increase in income
inequality does not affect revenue shares but does reduce local governments’ budgetary
expenditure shares. This may be because greater income inequality lowers local governments’bargaining power within the budgetary system. However, this reduced bargaining power is offset
by gaining more bargaining power over extra budgetary expenditures. This is consistent with
Tochkov’s (2007) finding relating to the smoothing of provincial expenditures. Second, thecoefficients of “Pgdp” are negative in the budgetary expenditure functions when excluding extra
budgetary items but are positive when including them. Our interpretation of this is based on
consideration of the coefficient of “Growth” in the expenditure function based on including extra
items. As with “Atkins09,” although an increase in real per capita GDP lowers local governments’relative bargaining power within the budgetary system, this decrease is offset by the extra
bargaining power over extra budgetary expenditures. However, the latter additional bargaining
power is abated when macroeconomic performance is good (when economic growth is high). The

15

third finding relates to the coefficients of “UrbanR”. This variable has a positive coefficient inboth the budgetary revenue function (without extra items) and the budgetary expenditure function(including extra items), but otherwise, the variable is not selected by the AIC. This suggests that
although urbanization increases budgetary revenues for provincial governments, it also narrows
the scope for extra budgetary expenditures; hence, urbanization lowers local governments’relative bargaining power over extra budgetary expenditure.

5. Conclusion

In this paper, we proposed a new empirical approach to analyzing fiscal decentralization
and applied it to Chinese intergovernmental fiscal relations. In calculating the budgetary sharesof the central and provincial governments, we included extra budgetary revenues andexpenditures. We obtained a number of important findings. First, an increase in income
inequality reduces the relative bargaining power of local governments within the budgetarysystem. However, this reduction is offset by additional bargaining power over extra budgetary
expenditures. Second, an increase in real per capita GDP reduces local governments’ relative
bargaining power within the budgetary system. This reduction is offset by additional bargaining
power over extra budgetary expenditures but to a lesser extent when macroeconomic performanceis good. Third, although urbanization increases budgetary revenues for provincial governments,16

it also narrows the scope for extra budgetary expenditures, and hence, urbanization lowers therelative bargaining power of local governments in determining extra budgetary expenditures.
Our analysis has limitations. First, we used a simple model to analyze bargaining
between the central government and a group of local governments. A model that explains thebargaining process between the central government and local governments simultaneously would
be more complex but more applicable to actual data. We plan to do this in the future. A second
problem relates to the short period covered by our study (1982–2008), which was limited by data
availability on extra budgetary shares and the Atkinson measure of inequality. If more past dataon extra budgetary shares become available, and as more data are accumulated in the future, we
can reestimate our models and obtain more robust results. This remains a task for future research.

17

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Table 1. Data Sources
Data for Independent Variables

Definition

SourcePopulation (Pop)National population

China Statistical Yearbook)

China Statistical Yearbook

Economic growth rate (Growth)()
Annual rate of change in real GDP

China Statistical Yearbook

Inflation rate (Infl)

Annual rate of change in CPI (1950 = 100)

China Statistical Yearbook

Real per capita GDP (Pgdp)(

Central government’s fiscal deficit (Def)China Statistical Yearbook

Economic openness (Open)

China Statistical Yearbook

Degree of urbanization (UrbanR)

Proportion of urban population

China Statistical Yearbook

Regional income inequality (Atkins0x)

Given in main text

China Compendium of Statistics
1949–200821

Table 2. Summary Statistics
Variables

MeanStandard Deviation

Maximum

Minimum

0.24570.4471–0.22901.26460.39080.26290.04711.33610.76580.3338–0.1559

1.30580.85050.35990.13411.3900ln(Pop)11.69420.0793

11.5425

11.7966

Pgdp

1.12720.74550.34232.9896Growth

0.10240.0440–0.04270.1768Infl

0.0621

0.0685–0.01400.2408Def

0.06700.0444–0.0309

0.1568Open0.37870.14860.14420.6652

UrbanR

0.32300.07580.21620.4568Atkins01

0.01070.00120.00830.0127

Atkins05

0.05060.00620.03870.0607

Atkins09

0.08570.01140.06510.1038(((()): including extra budgetary revenues)): including extra budgetary expenditures

22

Table 3. Estimation Results for the Budgetary Shares
Variables
Constantln(Pop)
Pgdp
2

Pgdp

Growth
Infl
Def
Open
UrbanR
Atkins09
Atkins09

2

Full Model
Revenue
Expenditure
Function
Function–70.9584*–75.1135**

Model Selected by AIC
Revenue
Expenditure
Function
Function–80.7280**–85.6846**(–2.017)(–4.819)(–2.861)(–12.229)

5.277966.33183**

6.86135**

7.50318**(1.701)(4.508)(2.825)(12.327)–9.32810*–2.02503–6.28150**–.284383**(–2.269)(–1.544)(–4.041)(–4.608).340996.340996

1.13467**(1.281)(1.281)(3.934)2.79449–.628439

3.00981**(1.746)(–1.215)(2.596).949975.621916.968167**(.812)(1.295)(4.162)–4.39256–3.76381**–3.82925–3.07697**(–1.746)(–4.610)(–1.899)(–4.751).911166.203543(.570)(.453)

32.7653*

5.4020917.6969**(2.413)(1.423)(2.714)138.00542.6128–76.6489**(.940)(1.449)(–3.973)–768.463–14392.6(–.925)(1.281)

2

.7619

.9555

.7323

.9481

S.E.

.2139

.0689

.2268

.0745

LM-hetero

.0453

.996

.258

2.716

Durbin–Watson

2.233

2.019

1.915

1.755

R

AIC

–30.105–42.572

Log likelihood

37.05234.176

Notes: ** and * denote statistical significance at 1% and 5%, respectively.
Total number of observations is 26.
Log likelihood value is produced by the LSE calculation in TSP 5.0.

23

Table 4. Robustness Checks for the Budgetary Shares
Instruments
No. of MA lags
NMA)
Variables
Constantln(Pop)

Pgdp

Pgdp2

GrowthIV Method
Lagged independent variables with
one lag in Full Model in Table 3

GMM
Lagged independent variables with twolags in Full Model in Table 3–

NMA = 1

Revenue
Function–114.604**

Expenditure
Function–78.7209**

Revenue
Function–63.3838*

Expenditure
Function–80.2603*(–2.867)(–7.150)(–2.511)(–2.511)9.63791**

6.89898**

5.34143*7.04700**(2.816)(7.256)(2.428)(7.987)–7.59352**–.203628–5.57883**–.204070*(–3.528)(–1.959)(–4.744)(–2.335)

1.15342**.969575**(2.899)(4.737)4.74727*

6.48473**(2.396)(2.619)Infl

Def

UrbanR

1.54848**

1.54857**(4.253)(4.710)–11.2193*–2.17757–4.49608**–1.59382(–2.370)(1.680)(–2.882)(–1.386)27.5069**

16.4978**(2.796)(2.732)

Atkins09–84.5667**–105.915**(–3.188)(–5.240)

R2

.6239

.9391

.6054

.9321

S.E.

.2886

.0810

.2897

.0864

Durbin–Watson

2.042

1.598

1.785

1.512

Test ofoveridentifying
restrictions

–

4.910

Note: ** and * denote statistical significance at 1% and 5%, respectively.

24

Table 5. Estimation Results for Shares based on Inclusion of Extra Budgetary Items
Variables
Constantln(Pop)
Pgdp
2

Pgdp

Growth
Infl
Def
Open
UrbanR
Atkins09
Atkins09

2

Full Model
Revenue
Expenditure
Function
Function–62.1524*–76.2038**

Model Selected by AIC
Revenue Function
Expenditure
Function–67.5556**(–2.311)(–5.259)(–10.697)

5.07422*6.61348**.056502**5.90843**(2.145)(5.073)(9.436)(10.620)–3.89849–.070671–.239437**.342816**(–1.244)(–.057)(–4.594)(3.082).687235.069068(1.120)(.277)

1.31638–1.32909**–1.80108**(1.078)(–2.744)(–5.457).741687.352423(.829)(.788)–3.37549–3.05842**–2.39801**(–1.754)(–3.989)(–4.145)–.775042–4.13882(–.637)(–1.165)

11.3221–4.13882–6.31107**(1.098)(–1.165)(–5.135)

58.5486

19.4555

15.2672**(.532)(.718)(3.082)–264.791–1285.89(–.426)(–.164)

2

.5971

.9663

.4419

.9615

S.E.

.1636

.0647

.1925

.0692

LM-hetero

4.372*

.188

.226

.056

Durbin–Watson

2.499

2.469

2.108

2.269

R

AIC

–49.440–66.023

Log likelihood46.72042.011

Notes: ** and * denote statistical significance at 1% and 5%, respectively.
Total number of observations is 26.
Log likelihood value is produced by the LSE calculation in TSP 5.0.

25

Table 6. Robustness Checks for Shares based on Inclusion of Extra Budgetary Items
Instruments
No. of MA lags
(NMA)
Variables

IV Method
Lagged independent variables with
one lag in Full Model in Table 3

GMM
Lagged independent variables with
two lags in Full Model in Table 3–

NMA = 1

Revenue
Function

Expenditure
Function–61.8018**

Constant

Revenue
Function

Expenditure
Function–67.6795**(–7.965)ln(Pop)Pgdp(–10.248).056445**5.43945**.056237**5.95254**(9.424)(8.069)(27.890)(10.247)–.238845**.548079**–.232930**.521002**(–4.581)(2.918)(–12.424)(5.899)GrowthDef
UrbanR
Atkins09–2.26641**–2.04969**(–4.679)(–3.648)–1.10426–1.39878*(–.880)(–2.315)–7.80057**–7.97662**(–4.444)(–7.678)

14.5818**

14.1752**(4.163)(7.870)

R2

.4419

.9486

.4419

.9552

S.E.

.1925

.0800

.1926

.0747

Durbin–Watson

2.108

2.162

2.105

2.237

Test ofoveridentifying
restrictions–

10.004

Note: ** and * denote statistical significance at 1% and 5%, respectively.

26

0.85
0.80
0.75
0.70

0.65
0.60
0.55
0.50

0.45

Budgetary Revenue
Budgetary Expenditure

Budgetary Revenue Based on Inclusion of Extra Budgetary Items
Budgetary Expenditure Based on Inclusion of Extra Budgetary Items

Figure 1. Trends in Budgetary Shares based on Inclusion of Extra Budgetary Items

27

2008

2007

2006

2005

2004

2003

2002

2001

2000

1999

1998

1997

1996

1995

1994

1993

1992

1991

1990

1989

1988

1987

1986

1985

1984

1983

0.40