metadata: authors: 0: author: Devarajan, Shantayanan
1: author: Swaroop, Vinaya
2: author: Heng-fu Z
count: World
docna: 0: docna: What do governments buy? The composition of
            public spending and economic performance
docty: Policy Research Working Paper
subtopic: Economic Growth,Economic Theory & Research,Industrial Economics,Educational Sciences,Public Financial Management,Public Sector Economics,Public Finance Decentralization and
            Poverty Reduction,Macro-Fiscal Policy,Economic Adjustment and Lending,Health Care Services Industry
teratopic: Macroeconomics and Economic Growth,Industry,Public Sector Development,Education
abstracts: cdata!: The authors develop a simple analytical
            framework that shows how the composition of public spending
            affects economic growth.  Distinguishing between productive
            and unproductive government spending (that which complements
            private sector productivity and that which does not), they
            show that increasing the share of productive spending leads
            to a higher steady-state economic growth rate.  They use
            data from 69 developing countries over 20 years to determine
            which components of public spending are productive. They
            find that an increase in the share of current spending has
            positive and statistically significant effects on growth.
            Otherwise, the news is mainly negative.  The relationship
            between the capital component of public spending and per
            capita growth is negative.  The same is true of the share of
            spending on transport and communications.  The shares spent
            on health and education have no significant impact, although
            parts of those shares - the parts spent on preventative care
            and "other education" - do.  The results raise the
            question whether public spending actually leads to a flow of
            public goods and services.
repnme: repnme: What do governments buy? The composition of
            public spending and economic performance
display_title: What do governments buy? The
            composition of public spending and economic performance
listing_relative_url: /research/1993/02/699103/governments-buy-composition-public-spending-economic-performance
topicv3: Economic Growth m1326241 471,Public Finance Management m1326316 716,Government Expenditure m1326649 1112,Education m1327048 470,Health m1504661 3480
publishtoextweb_dt: 2010-07-01T00:00:00Z
historic_topic: Macroeconomics and Economic Growth,Industry,Public Sector Development,Education
origu: Policy Research Department (PRD)
author_keys: 0: author_key: NA
1: author_key: NA
2: author_key: NA
model_extractions: <!-- Start of picture text --> Polloy Research<br>Publilo Economlos<br>WPS 1082<br><!-- End of picture text -->

> This paper **-** a product of the Public Economics Division, Country Economics Department **-** is part of

> a larger effort in the department to analyze public expenditure policies in developing countries. Copies of

> the paper are available free from the World Bank, **1818** H Street NW, Washington, **DC** 20433. Please contact Carlina Jones, room **NIO-063,** extension **37699** (February **1993,** 40 pages).

Devarajan, Swaroop, and Zou develop a simple analytical framework **that** shows how the composition of public spending affects economic growth.

Distinguishing between productive and unproductive government spending (that which complements private sector productivity and that which does not), they show that increasing the share of productive spending leads to a higher steady-state economic growth rate.

They use data from **69** developing countries over 20 years to determine which components of public spending are productive. They find that an increase in the share of current spending has

> positive and statistically significant effects on growth.

> Otherwise, the news is mainly negative. The relationship between the capital component of public spending and per capita growth is negative. The same is true of the share of spending on transport and communications. The

> shares spent on health and education have no significant impact, although parts of those shares **-** the parts spent on preventive care and "other

> education" **-** do.

These results raise tie question whether public spending actually leads to a flow of public goods and services.

The Policy Research Working Paper Series disseminates the findings of work under _way_ in the Bank. An objective of the series is to get these findings out quickly, even if presentations are less than fully polished. The findings, interpretations, and conclusions in these papers do not necessarily represent official Bank policy.

Produced **by** the Policy Research Disseednation Center
What Do Governments Buy? The Composition of Public Expenditure and Economic Performance

**by**

> Shantayanan Devarajan Vinaya Swaroop Heng-fu Zou*

*Public Economics Division, Country Economics Department, World Bank. We are indebted to Min Zhu for exceptionally able research assistance. We received helpful comments from Alberto Alesina, Gerry Helleiner, Manny Jimenez, Lant Pritchett, Mike Stevens, Mike Walton, and seminar participants at the World Bank and at a conference on "Africa: From Stabilization to Growth," held in Marstrand, Sweden.
# Table of Contents

|**1.**Introduction**...........................................**|**...**<br>**1**|
|---|---|
|2.TheM**odel...........................................**|**....**<br>**3**|
|**3.EmpiricalAnalysis....................................**|**......**<br>**7**|
|**3.1**DataandChoiceofVariables**........................**|**....**<br>**10**|
|**3.2**Sample StatisticsandCorrelationAnalysis**................**|**.....**<br>**11**|
|**3.3**RegreisionAnalysis**..............................**|**....**<br>**13**|
|3.4AlternativeSpecificationsandSamples**..................**|**.....**<br>**17**|
|3.4.1FixedEffectsModel**........................**|**....**<br>**18**|
|3.4.2NonlinearSpecificationandOtherVariables**........**|**......**<br>**19**|
|3.4.3Generalvs.CentralGovernmentSpending**.........**|**......**<br>21|
|4.Conclusion**..........................**<br>**............**|**......**<br>**23**|

**23**
# **1.** Introduction

Governments in developing countries spend an average of **26** percent of **GDP** on goods and services, a figure which has risen **by** eight percentage points over the last fifteen years (World Bank **[1991]).** The magnitude and growth of this figure has prompted a fair amount of research on the relationship between the size of government and economic growth'. Much less is known about how the _composition_ of public expenditure affects a country's growth rate?. For at least three reasons, this question is becoming increasingly important. First, after a decade of fiscal adjustment, during which many of the "white elephants" in government budgets were weeded out, some developing countries are faced with hard choices when undertaking further fiscal restraint. Which component of public expenditure should be cut? Health? Education? Infrastructure? Defense? Before taking a decision, policymakers need to know the relative contributions of these different components to the country's economic performance. Second, the World Bank periodically undertakes Public Expenditure Reviews of its client countries where, among other things, the mix of public expenditures is evaluated. **A** systematic analysis of how this mix affects economic growth would lend much-needed support to these evaluations. Third, reviews of the experience with structural adjustment have shown that adjusting countries have followed a different composition of public expenditure from non-adjusting countries (see World Bank **[1992]).** What are the implications of this difference for the future prospects of these countries? The purpose of this paper, therefore, is to shed light on the relationship

> 'For a survey, see Lindauer and Velenchik **[19911.**

> Me only systematic study is Diamond **[19891.**

1
> between the composition of public expenditure and economic growth.

Before proceeding, we note that governments undertake expenditures to pursue a variety of goals, only one of which may be an increase in per-capita income. We focus on growth because (i) inasmuch as growth is one of the objectives of a government, it is useful to know the contribution of different components of expenditure to this objective as a means of assessing the cost of pursuing other goals; and (ii) per-capita income is easier to measure than some of the other objectives of government.

Neither economic theory nor empirical evidence provides clear-cut answers to the question of how the composition of public expenditure affects economic growth. The theory develops a rationale for government provision of goods and services based on the failure of markets to provide public goods, internalize externalities and cover costs when there are significant economies of scale. Furthermore, when there is a failure in one market, government intervention in a related market can be justified. Sound as they are, these theoretical notions do not translate easily into operational rules about which component of public expenditure to be cut. We need to know the _relative_ contribution of each component to allocative efficiency before deciding on which to reduce.

> On the empirical front, a few researchers have tried linking particular components of government expenditure to private-se-tor productivity and economic growth but most of these

> efforts lack a rigorous theoretical framework and are therefore removed from the underlying

> rationale for government's role in the economy (Diamond **[1989]).** Others (Ahmed **[1986],** Barro **[1981, 1987])** have emphasized the distinction between transitory and permanent changes

> in the _level_ of government purchases of goods and services for explaining movements in output

2
> among other macro variables. Much of the traditional 'disctission of fiscal policy centers around

> the financing decisions of a particular spending level. The recent revival of interest in the expenditure-composition issue (Aschauer **[1989],** Morrison **[1991],** Holtz-Eakin **[1991])** has been

> based on theoretical models but the focus has been on the productivity of public expenditures in the United States.

In this paper, we develop (in section 2) an analytical framework which links the composition of public expenditure with economic growth. Government expenditures are divided into two categories: "productive" and "unproductive". The former complements private-sector productivity while the latter does not. We show that an increase in the share of productive expenditure leads to a higher steady-state growth rate of the economy. Next, in section **3,** we estimate the model with different components of public expenditure as candidates for productive expenditure. Using data on **69** countries over twenty years, we assess whether current or capital expenditure on the one hand, and health, education, transport and communications, or defense expenditure on the other, can be classified as productive in the sense defined above. Section 4 presents our concluding remarks.

# **2. The Model**

Since the 1960's, researchers have been looking at the relationship between fiscal policy and the economy's growth rate. The seminal contribution was **by** Arrow and Kurz **[1970],** who developed a model where consumers derive utility from private consumption as well as the public capital stock. In addition, pri-ate production benefits from the services **of** this capital

**3**
> stock. Arrow and Kurz assumed (implicitly) that all government investment was productive. Furthermore, their model was in the neoclassical tradition where public spending only affected

> the economy's transitional growth rate; the steady-state growth rate remained unaltered.

The recent explosion of work on "endogenous growth" has generated a number of models linking public spending with the economy's long-term growth rate. **A** particularly simple version is Barro's **[1990],** which takes current government expenditure to be complementary with private production. Like Arrow and Kurz, Barro assumes that all government spending is productive in this sense.

> Meanwhile, the empirical literature on the same topic has highlighted the distinction

> between productive and unproductive government spending (Landau **[1983],** Aschauer **[1989]**

> and Barro **[1990]). A** major finding of these cross-country studies is that output growth is

> negatively correlated with the share of government consumption in **GDP.** Aschauer and Barro

> find a positive relationship between public investment and output growth.

> In this paper, we combine the above empirical observation with the earlier theoretical framework **by** postulating a model in which there are two types of government expenditure, productive and unproductive. **A** variant of Barro's **[1990]** model, ours expresses the difference between productive anO unproductive expenditures **by** how they affect the aggregate production

> function of the economy. We assume the function has three arguments: private capital stock **(k),** productive government spending **(g,)** and unproductive government spending **(g)**<sup>**3**</sup> **. If** the

> 3As is typical of these models, we leave out labor as a separate argument in the production function. **If** the

> economy in question has surplus labor, then labor is not **a** binding constraint and can be left out of the production

> function. Alternatively, we can consider the capital factor, **k,** to reflect human as well as physical capital.

4
> functional form is Cobb-Douglas, then the relationship can **be** expressed as:

v **_= Ak, gl, g.) = k*gPg2Y_** (1) _where_ **_a >_ 0,** p > **0, y < 0, a +** p **+ y** = 1.

The government finances its expenditure **by** levying a flat tax,<sup>7,</sup> on<sup>income4:</sup>

**lv=** g, **_+_** g2 (2)

Finally, the government chooses the share **(4)** of total government expenditure which will go towards productive expenditure:

**g1=** *ryl 92 = (1-4)ry **(3)**

Taking the government's decision as given, the single agent in the economy maximizes his welfare <!-- Start of picture text --> U = fu(c)e-Pdt (4)<br><!-- End of picture text --> subject to

**_k_** = _(1-r)y_ **_-_** _c_ **_(5)_**

where _c_ is private consumption and **p** the rate of time preference.

In order to get analytical solutions, it is useful to specialize the utility function to the With this assumption, we are abstracting from all issues of financing government expenditures. For a lucid treatment of this issue. see Easterly **[1989].**

**_5_**
## isoelastic form:

For a given total level of government expenditure **_g (g = g, +_ ge),** maximizing the objective functional (4) subject to **(1)** and (2) yields the familiar equation for the growth rate of consum,otion: _e/c_ [( ) 1- **p]/a**

> where the first term in the square **brackets** is the marginal productivity of capital.

Call the steady-state growth rate of consumption **X,** and assume that along the steady-state growth path, the tax rate r (and hence **_g/y)_** is constant. **It** follows that **_g/k_** is a constant which, **by** simple manipulation of (2), is given **by:**

# **(8)**

The steady-state growth rate of consumption, then, is: A = {a(1 -r)4 (1-4)'[v( **(1-)YP'** - **pI/o (9)** From equation **(9),** we can derive a relationship between the steady-state growth rate, **X,** and the share of government expenditure devoted to productive uses:

**_dA_** - [p(1-0)-#y]a(1-r)## _'(1-4)- 1(k) */o_ **(10)** d4o

Since ***y** is negative and **0 <** 4 **< 1,** it follows that **dX/d4 > 0.** In other words, an increase in the _share_ of public expenditures going towards productive uses raises the economy's steadystate growth rate.

**_6_**
Note that this increase in the growth rate was achieved with no change in _total_ government expenditure. In fact, the effect of an increase in the latter on the growth rate is ambiguous. To see this, consider the response of X to an increase in _r_ (since r **= g/y,** this is equivalent to an increase in the share of government expenditure in **GDP).** Some manipulation leads to the result and conversely if r **> 6 + y.** This is intuitive given our balanced-budget assumption: an increase in total government spending, since it has to **be** financed **by** taxes, will raise the steadystate growth rate only if the productivity of that government spending **(8 + y)** exceeds the taxes required to pay for it.

Clearly the model can **be** extended in several ways. We now consider two. First, the number of components of government expenditure can be increased from just two. This extension only makes the algebra more cumbersome without improving our knowledge of the growth process. **If** there are **N** types of government expenditure, each with its own exponent, Bi, in the production function, then the effect on growth of increasing the share of government expenditure going to the i-th component depends on the sign of **Bi. If** it is positive, then increasing the share raises the growth rate, and conversely if it is negative (i.e., unproductive). Second, not all components of government expenditure affect the production function; some **--** such as transfers **- -** ,re intended to affect consumer welfare. In our<sup>model,this can be</sup> incorporated **by** including these components in the consumer's<sup>utility function,and allowing their</sup>

> he ambiguity in the sign of d/dr is confirmed **by** our empirical results.

**7**
exponent in the production function to be zero. The rest of the analysis follows as before.

Finally, in this model, we are taking the government's spending decisions **--** on both the level and composition of expenditure **--** as given, rather than deriving them from some optimizing framework. Thus, we are postulating a positive, rather than normative, approach to public spending, avoiding altogether the issue of the government's objective function.

> Despite _its_ simplicity, the model described above yields a striking conclusion: **by** shifting the mix of government expenditure in favor of productive activities, the economy can increase its long-run growth rate. However, the formal framework begs the question of which

> government expenditures are productive and which are not. In the next section, we attempt to answer this question **by** examining empirically how the growth performance of developing countries over time was affccted **by** the composition of their pi-blic expenditures. We ask the data to tell us which components of expenditure contributed to faster economic growth in the long-run.

# **3.** Empirical Analysis

Our empirical analysis focuses on the link between various components of government expenditure and economic growth in developing countries. Aschauer and Greenwood **(1985),** Barro **(1990),** and others emphasize the distinction between public goods and services that enter into the household's utility function and those that complement private sector production. The former, which they argue would include much of government consumption, are likely to have negative growth effects. While it provides utility to households, government consumption lowers

**8**
> economic growth because the higher taxes needed to finance the consumption expenditure reduce returns on investments and the incentive to invest. This is confirmed **by** Grier and Tullock **(1987).** Using pooled cross-section/time-series data **(115** countries including 24 **OECD** countries in the post-World War II period), they find a significantly negative relationship between the growth rate of real **GDP** and government consumption's share of **GDP. By** contrast, government investment expenditure, such as the provision of infrastructure services, is thought to provide the enabling environment for growth. Aschauer **(1989)** finds that "core infrastructure" **-** streets, highways, airports, mass transit and other public capital **--** has the most explanatory power for private sector productivity in the United States over the period **1949-85.** For other categories of public spending, there appears to be some disagreement over whether they constitute "productive" expenditure. While Kormendi and Meguire **_(1985),_** Grier and Tullock **(1987),** Summers and Heston **(1988)** classify defense and education as government consumption and hence unproductive, Barro **(1990)** models them as productive. He considers spending on public education as investing in human capital. Similarly, defense spending helps protect property rights which increases the probability that an investor will receive the marginal product of capital. Based on data on **98** countries, Barro **(1990)** finds that an increase in resources devoted to non-productive government consumption is associated with lower per capita growth<sup>6</sup>

In our analysis, we refrain from an a priori classification of public expenditures into "productive" and "unproductive". Instead, we allow the data to tell us which components 6Based on a 119-country (developed and developing) sample, Levine and Renelt **(1992)** have analyzed the relationship between a diverse collection of fiscal policy indicators and growth. They find that,<sup>whilethereare</sup> econometric specifications that yield significant coefficient estimates between specific fiscal-policy indicators and growth, the relationship is not "robust".

**9**
conform to our definition of productive expenditure. Furthermore, since ours is a pooled, crosssection/time-series data set, we are able to capture some of the lags involved in translating productive public expenditures into economic growth. Finally, our study is unique inasmuch as it focuses exclusively on developing countries. Other studies use a mixed sample of developed and developing counties, or examine developed countries only. As we will show, the results change dramatically when the sample is restricted to developing countries.

# **3.1** Data and Choice of Variables

Disaggregated spending figures at the level of consolidated general government (including public sector enterprises) are required to examine the full impact of public expenditures on economic growth. Unfortunately, such data do not exist in sufficient quantity for the majority of developing countries. For this reason, the data used in this paper are confined to central government expenditures. The operations of state and local governments as well as expenditures of government owned or controlled public sector enterprises are not included<sup>7</sup> The empirical analysis uses annual data on **69** countries (see the data appendix for a list of countries included) from **1970** through **1990** to examine the link between components of government expenditure and economic growth. The primary source of data on public expenditure variables is the _Government Finance Statistics_ **_(GFS),_** published annually **by** the International Monetary Fund. Not all observations are available for all the countries. The pooled data include total government expenditures (including the **GFS** classification of current and capital), expenditures for defense, education, health, and transport and communication. The

> 7 As a check on our results, we repeat our analysis for the sub-sample of countries for which there are data on consolidated government expenditures (see below).

**10**
latter expenditure variable is used as a proxy for expenditure in economic infrastructure.

The model in section 2 developed a link between **4,** the share of government expenditure devoted to productive activities, and the long-term growth rate of the economy. In the empirical analysis, we test whether the share allocated to different components of government expenditure (capital, current, health, education, defense, and transport and communication) is associated with higher growth. Thus, our key explanatory variable is the share of each component in total government expenditure. To control for level effects, we also include the share of government expenditure in **GDP.** This also allows us to control for the effects of financing government expenditure (which is a function of the level) on growth. In addition, we attempt to control for two other factors which determine a country's growth rate but are not necessarily linked to the composition of public expenditure: external shocks and domestic policies. The latter is measured **by** the premium in the parallel market for foreign exchange. To **be** sure, the premium captures both policy-induced distortions, such as trade restrictions, capital controls, taxes and regulation, as well as economic and political instability. Finally, the dependent variable is the five-year, forward moving average of per capita real **GDP** growth. The five-year forward lag is chosen to reflect the fact that public expenditures often take time before their effects on output growth can be registered. We use a moving average to eliminate short-term fluctuations induced **by** shifts in public expenditure (Keynesian multiplier effects).

# **3.2** Sample Statistics and Correlation Analysis

Before proceeding to the regression analysis, we present some sample statistics<sup>and</sup> correlation coefficients of the variables. The most striking<sup>feature of theexpendituresharesis</sup>

**11**
their variation across countries. The average share of capital expenditure is about 22 percent, but it ranges between one percent (Bolivia, **1982)** and **71** percent (Nepal, **1989).** Within the functional classification, defense's share is the most volatile, ranging between half a percent (Botswana, **1976)** and **_53_** percent (Oman, **1978).** Despite this variability, there appears to be no systematic difference in the average expenditure shares ot slow- and fast-growing economies (Table 2). The current and capital expenditure shares are almost identical. The shares of defense is higher, and those of health and education lower in the fast-growing economies.

The comparison of averages masks how these shares vary with growth rates. **A** first cut at the latter question is in Table **3,** which shows the correlation coefficients among the different variables. Note that current expenditure has a positive correlation, and capital a negative one, with average per-capita growth five years later. Furthermore, the correlation between transport and communication's share and per-capita **GDP** growth is negative and statistically significant. In looking at either cross-section/time-series averages (Table 2) or sample correlations (Table **3),** we leave out many factors that should be controlled for in order to establish any causal relationship. In the next subsection we attempt to control for some of these factors **by** undertaking a regression analysis of the relationship between expenditure composition and economic growth.

12
# **3.3** Regression Analysis

The method of ordinary least squares is used to estimate<sup>the followingequation:</sup>

**_GGRPCGDP,(.. 4_** _=_ aIDI **+ a2+** **_a 3D 3_** 4C **+** **_a,D +_** _a 6(G1TE),_ (12) **+ a** **_7BMPO + aSHOCKt +_ pu**

where the variables are: (i) GRPCGDPj,0+1.,+: Five year forward moving average of per capita real **GDP** for country (ii) **Dj :** Continental dummy variables; **j = 1,** 2, **3,** 4,<sup>and</sup><sup>**_5_**correspondtoEastAsia, South</sup> Asia, Sub Saharan Africa, Latin America, and Europe,<sup>Middle EastandNorth Africa</sup> respectively; (iii) **(G/TE),, : A** vector of public expenditure ratios for country i:

- **NCURETE =** current net of interest/total expenditure

- **CAPETE** = capital/total expenditure

- DEFTE = defense/total expenditure

- HLTHTE **=** health/total expenditure

- **EDTE** = education/total expenditure

- **TACTE** = transportation and communication/total expenditure

(iv) BMPi,, **:** Premium in the parallel market for foreign exchange<sup>incountry</sup><sup>**_i;_**calculatedas</sup> <!-- Start of picture text --> BMP BMERJ - OER)O 100 (13)<br>OERJ<br><!-- End of picture text --> where BMERI,, **=** Black market exchange rate; and OER., **=** Official<sup>exchangerate</sup>

**13**
(v) **SHOCK,: A** variable constructed for each country. It measures terms of trade, interest rate shocks; calculated as <!-- Start of picture text --> SHOCK, = (R.=t.s - t-44) *(DEB77GDP),<br>-(PXt.1;tS - Pxt- 4 )*(X/GDP)t (14)<br>-<br>+(P-*t.jt.5s PM,-4/)*(M/GDP),<br><!-- End of picture text -->

# where

R = (i-dP/P)/(1 +dP/P) i= INTALL/DEBTALL INTALL **=** total interest payment **= INTPPG + INTPNG** INTPPG **=** public and public guaranteed debt interest payment INTPNG **=** private and non-public guaranteed debt interest payment **=** (DEBTALL-DEBTPPG)*(Annualized 3-month LIBOR **+ 1 %)** DEBTPPG **=** public and public guaranteed debt DEBTALL= total debt dP/P **=** World inflation rate measured **by** percentage change in **GDP** deflator of **US** Px = deflator for exports Pm = deflator for imports X = total export M = total import

**GDP =** gross domestic product

(vi) 4 An error term.

Table 4 contains the estimates of the above equation. Equation **(4.1)** shows a positive and statistically significant relationship between the five-year, forward moving average of per capita 14
real **GDP** growth' and the ratio of current (net of interest spending) to total expenditure.' **A** unit increase in this ratio increases the per capita real **GDP** growth rate **by** **_.05_** percentage points. Clearly, this finding appears to be counterintuitive. For example, Barro **(1989, 1990)** finds that consumption expenditure (current expenditure less education and defense expenditure) is associated with _lower_ per-capita growth. Furthermore, our result cuts against the grain of policy advice received **by** developing countries, which prescribes cutting current, rather than capital, expenditures in order to foster long-term growth. In the next sub-section, we report on various attempts to test the robustness of these results, to ensure that they are not just some statistical anomaly. Since the results appear to be robust to these tests, in the final section, we offer some interpretations about what is driving them.

The level effect of total government expenditure"o on per-capita growth is positive but statistically insignificant. This is consistent with our model's prediction: an increase in total government spending, since it has to be financed **by** distortionary taxes, will raise the steady state growth rate only if the productivity of that government spending exceeds the deadweight loss associated with the taxes required to pay for it.

The relationship between the capital component of public expenditure and per capita growth rate is negative and significant as illustrated in equation (4.2). Once again this belies the standard hypothesis. Public expenditure on capital goods is supposed to add to the country's The choice of five year forward moving average was somewhat arbitrary. Intuition suggests that lagged expenditure variables would have growth effects. We also tried seven and ten year forward moving averages of the growth variable; the results were marginally different.

Even when the budgetary share of total current expenditure (i.e. including interest spending) is used, the coefficient is positive and statistically significant.

**1**<sup>**0**</sup> This variable in the regression controls for the level effect of public expenditure as we are primarily interested in examining the link between the composition of public expenditure and economic growth.

**15**
physical capital (mainly infrastructure **-** roads, bridges, dams, ports, power plants etc.). Intuition suggests that the resulting stock of infrastructure capital would complement private sector productivity and hence, should have favorable growth effects.

Equation (4.3) indicates that the defense and economic infrastructure components of public spending are negatively related to per capita growth rate. Public spending in health and education also have negative coefficients though they are statistically insignificant. As economic infrastructure expenditures in general have a high proportion of capital expenditures, the finding that it has a negative correlation with per capita real **GDP** growth is consistent with the negative correlation found between capital expenditures and per capita growth rate in equation (4.2). However, the issue of interest is how to explain this statistically significant negative relationship given the implicit understanding that government spending on infrastructure services complements private-sector productivity.

In equation (4.4), public spending on health care is disaggregated into expenditure on (i) hospital affairs and services; (ii) clinics (providing mainly outpatient services); and (iii) public health affairs and services (mainly of a preventive nature), applied research and experimental development related to the health and medical delivery system. Notwithstanding the reduced number of observations with this specification of the health expenditure variable, we find that the coefficient of the share of expenditure on public health affairs and services, etc. is significantly positive for per capita growth. The other two components of health expenditures have statistically insignificant coefficients. **A** unit increase in per capita health expenditure is however, associated with a decline in the per capita growth rate. Thus, the finding indicates that neither health expenditure per capita nor total public health expenditure as a share of total

**16**
expenditure is positively related with per capita growth rate. It is the share of health expenditure on preventive care and research and development that has growth effects.

In equation _(4.5),_ we disaggregate the education variable into expenditure on (i) administration, management, inspection, operation of pre-primary, primary and secondary education; (ii) of tertiary education; and (iii) other education. As reported in<sup>equation</sup><sup>_(4.5),_this</sup> last component of education expenditure is positively and significantly<sup>relatedwithpercapita</sup> growth rate. This category of spending on education includes subsidiary services<sup>toeducation</sup> (transportation, food, lodging, medical and other such services to<sup>students),programunits</sup> engaged in administering, supporting, or carrying out applied research into<sup>teaching methods and</sup> objectives, into learning theory and curriculum development, etc.<sup>**A**</sup> unit<sup>increasein theshare</sup> of this category of education spending leads to an increases of **0.63** percentage points<sup>in per</sup> capita real **GDP.** The level of education expenditure (measured<sup>**by**percapita realeducation</sup> expenditure) has negative growth effects.

As for the other variables in the regressions, note that the black-market premium<sup>is</sup> negative and statistically significant in almost all the equations. The<sup>signis whatwouldbe</sup> expected: the higher the premium, the more distorted the economy,<sup>theworseitsgrowth</sup> performance. Interestingly, the shock variable is not<sup>statistically significant.It is possible that</sup> most of the contribution of this variable is being picked up **by** the regional dummies,<sup>which are,</sup> for the most part, statistically significant.

# 3.4 Alternative **Specifications and** Samples

Given the surprising nature of these results,<sup>especially thosehavingtodo withcurrent</sup>

**17**
> and capital expenditures, we now subject them to a series of tests, to ensure that they are not due to some statistical fluke. The tests are not formal ones. Rather, they are based on our

> views on possible factors which could be driving these results but were not connected with the productivity of public spending.

# 3.4.1 **Fixed Effects Model**

The regression results reported in subsection **3.3** are based on panel data with the implicit assumption that there are no individual cross-sectional effects. It is likely, however, that there are country-specific characteristics that might influence per capita growth. While the countryspecific characteristics are generally difficult to measure (e.g. cultural factors), simply running pooled regression may bias the coefficient estimates. One simple way to account for country specific characteristics is to introduce country dummies. Given that we have **69** countries in the sample, this correction would result in a significant reduction in the degrees of freedom. Alternatively, we can apply the fixed-effects method which takes into account country-specific characteristics and models them as fixed effects within the country. In such a case we estimate the following individual-mean corrected regression model: where the variable X consists of all the independent variables of equation **(11).** The computational procedure (see Hsiao, **1992)** for estimating the parameters requires transforming the observed variables **by** subtracting out the appropriate time-series means, and then applying

**18**
the least-squares method to the transformed data.

Table **5** contains the estimates of the above equation. The issue of interest is: How do the results presented in Table 4 change when the fixed-effects method controls for the country specific characteristics? Equation **(5.1)** in Table **5** shows that the coefficient on the budgetary share of current expenditure (net of interest) continues to be weakly positive and statistically significant. Similarly, the coefficient on capital expenditure's share is negative and statistically significant. The most significant change is the statistical significance of the coefficient on the share of transport and communication. In all but one of four specifications, the negative relationship between transport and communications and per capita growth is statistically insignificant. Our earlier interpretation linking the sign on capital expenditure with that on transport and communications appears not to be valid. AnAther interesting feature of this fixed effects model is that the shock variable, which was previously insignificant, now becomes<sup>**highly**</sup> significant, and the black-market premium does the reverse. Evidently, the black-market premium was picking up country-specific characteristics (political instability, etc.). Once these characteristics were explicitly accounted for, the premium loses significance. **By** contrast,<sup>the</sup> external shock variable's role appears to have strengthened, since it now<sup>capturesthose</sup> determinants of growth not incorporated in the country-specific characteristics.

# 3.4.2 Nonlinear Specification and Other **Variables**

In this subsection we discuss the regression results based on other<sup>specificationsof the</sup> basic model reported in equation **(11).** In the first instance<sup>we attempt anonlinearspecification</sup> of the model. It is possible that expenditure ratios and growth<sup>havesome sort of "Laffer curve"</sup>

**19**
relationship. Intuition suggests that the budgetary share of capital expenditure will have a positive association with growth, but as this share keeps rising, decreasing returns to scale set in and eventually, the relationship between the two variables turns negative. Similarly,<sup>one can</sup> visualize that the share of current expenditure would be positively related to<sup>growthat least</sup> when the share is low. **A** well-paid but streamlined bureaucracy would efficiently<sup>manage public</sup> administration which in turn would complement private sector productivity.<sup>Table</sup><sup>**6**reportsthe</sup> nonlinear regression model. As reported in equation **(6.1),** the growth<sup>rateisanincreasing</sup> function of the share of current expenditure (net of interest spending)<sup>in budgetand a decreasing</sup> function of the square term. While the first variable is strongly significant (t value **=**<sup>**2.39),**the</sup> square term is insignificant at the conventional **_5_** percent<sup>level.Thereis one clearexplanation</sup> of this result: Most of the data points are clustered around the positive and upward<sup>slopingpart</sup> of the functional relationship. Therefore, it is likely that the linear relationship<sup>givesa better</sup> fit. The nonlinear specification for the capital expenditure ratio<sup>is</sup> reported<sup>in equation</sup><sup>**(6.2).**</sup> The function attains a maximum when the ratio is around **18** percent.<sup>Whilethe coefficient on</sup> the square term is statistically significant, the coefficient on<sup>theothervariableisnot.Once</sup> again these results corroborate our earlier findings reported<sup>in Table 4.In this case most of the</sup> data points cluster around the downward sloping negative part of the functional<sup>relationship.</sup> There is always the possibility that the results obtained<sup>inTable4 areduetocertain</sup> variables left out of the regression equation. While we have attempted<sup>to include those variables</sup> we believe are important in determining growth<sup>(andconsistent with thetheoreticalmodelin</sup> section 2), we present below the result of including one more variable. That variable<sup>is a proxy</sup> for the level of development of the country<sup>at thebeginningof the period.Previousstudents</sup> 20
of the growth process (e.g., Chenery and Syrquin **[1985])** have found this variable to be an important factor in determining the relationship between, say, openness and growth. We include it here mainly as a check on our results, rather than as part of a more elaborate model of the relationship between public expenditure and growth. The variable we use as a proxy for the level of development is the country's per-capita **GDP** in **1969.** When this variable is included, the results reported in Table 4 remain unchanged. The variable itself has a negative sign and is statistically insignificant".

# 3.4.3 General vs. Central Government Spending

As stated earlier, our data set covers the operations of only the central government. Ideally, one would like to examine the impact of total government expenditures that includes the operations of state and local governments as well as expenditures of government owned or controlled public sector enterprises, on economic growth. This may be particularly important in the case of health and education expenditures, where in some federal systems, the bulk of these expenditures are carried out **by** sub-national governments. To our knowledge such _comprehensive and consistent_ expenditure series (across countries and time) are not available. However, there are a few countries for which consolidated general government expenditures (i.e., operations of central, state and local governments) are reported in the **GFS.** In order to determine whether or not including the state and local government expenditure data qualitatively and quantitatively affects our results, we do a few diagnostic<sup>tests.</sup><sup>**Of**the</sup><sup>**69**</sup> countries in our sample, there are 12 (see data appendix for the list) for which consolidated

> "The _results_ for this specification are not reported in the paper.

21
> general government expenditure data are reported in the **GFS.** We take this sample of 12 countries to ascertain whether the expenditure ratios used in our analysis are statistically different for general government from central government in these countries. Table **7** presents the sample

> statistics for the expenditure ratios. In comparing the statistics for the two different levels of government, a couple of interesting facts emerge: as defense is primarily the rL.soonsibility of the central government, the ratio of defense to total expenditure decreases for the general government; the share of education expenditure is larger for the general government indicating that state and local government allocate a higher budgetary share for education. The expenditure ratios presented in Table **7** also seem to indicate that state and local governments spend more

> money on capital but less on current expenditure. Based on - paired t test, we find that all

> expenditure ratios but transport and communication based on general government data are

> statistically different (significant at **99%** level) from the ratios based on central government data.<sup>**12**</sup>

> To test whether or not the relationship between composition of expenditure and economic growth is different when expenditure shares based on general government data are used, we run the same regression model based on each of the two data sets. The regression results are reported in Table **8.** While the signs and magnitudes of the coefficients are similar for both data sets, the coefficients are statistically insignificant. **A** paired t test, however, indicates that the difference between the coefficients is statistically insignificant. Hence, the coefficient estimates of the growth equations based on general government expenditure and central government

> expenditure are statistically the same.

> 1<sup>2</sup> For space considerations these results are not reported. The results are available from the authors.

22
# 4. Conclusion

The purpose of this paper was to investigate the relationship between the composition of pubic expenditure and economic growth. Using a simple, analytical model, we showed how a change in the mix of public spending in favor of productive activities could lead to a higher steady-state growth rate for the economy. The empirical implementation of the model, however, yielded some surprising results. **All** of the standard candidates for productive expenditure **--** capital, transport and communication, health and education **--** had either a negative or insignificant relationship with economic growth. The only broad category which was associated with higher economic growth was current expenditure. Finally, some expenditures within the health and education sectors **--** preventive care and "other education" **--** had a positive coefficient in the regression with economic growth.

At least two interpretations suggest themselves. One is that our model is misspecified, or our data inaccurate, so that we are not capturing the "true" link between these components of public expenditure and growth. However, we have attempted to control for several of the factors which may affect economic growth: external shocks, policy distortions, region-specific effects, and development index. We also report a nonlinear specification for the expenditure variables. Thus, the charge of model misspecification rests on the existence of some other factor which both affects long-term economic growth and is systematically related to public expenditure composition. Similarly, while public expenditure data are notoriously poor, we have no reason to believe there are any systematic biases in them which would yield the above results. The one exception is the importance of sub-national government spending in education and health, in particular, in some of the larger, federal countries. We addressed this issue **by** examining the

**23**
> coefficient estimates of the growth equations based on general government expenditure and

> central government expenditure for the countries for which data on both were available. We

> found them to be statistically the same.

> The second inerpretation is that our results reflect a problem in the link between public expenditures and outcomes. Earlier work has established that the stock of educated and healthy people, and of public infrastructure capital, are positively associated with economic growth. What we may be capturing is the fact that public _expenditures_ in these sectors do not necessarily

> lead to increases in the stock of human and physical capital, so that the connection with

> economic growth is severed. One reason could be the efficiency with which public resources are used. Expenditure on capital goods does not necessarily lead to more capital goods. **A**

> second reason could be that the standard categories of public spending **--** current and capital expenditure **-** do not capture the difference between capital-stock-enhancing and consumption

> expenditures. For example, operations and maintenance expenditures often make a capital good

> productive, but they are classified as current expenditures. Similarly, some capital investment projects (everyone has his favorite example) end up being consumption goods for powerful

> members of society, rather than productivity-increasing activities. **A** third possibility is that

> governments use current expenditure to placate politically volatile groups. The attendant political stability, in turn, leads to higher economic growth. Regardless of which of these three possibilities is the reason, the basic message arising from this paper is that the traditional view of the link between the composition of public expenditures and economic growth is not borne out **by** the historical experience of developing countries.

24
# Table **I**

# Sample Statistics for Pooled Data

**(in** percent)

> **(69** countries, **1971-90]**

|Variable|Observations|Mean|Std.Dev.|Maximum|Minimum|
|---|---|---|---|---|---|
|_Curfe_|**1013**|**77.09**|12.02|**100.00**|**29.16**|
|_Capfe_|**1008**|**22.37**|11.28|70.84|**1.11**|
|_Deflfe_|**931**|**13.52**|**_9.51_**|_53.04_|**0.16**|
|_Hhfe_|**995**|**5.69**|**3.62**|**32.77**|0.54|
|_Edufe_|**998**|14.04|**_5.95_**|34.71|1.02|
|_TaCTe_|**957**|**8.69**|_5.54_|**48.27**|**0.08**|
|_Other/Te_|**887**|**58.39**|**12.19**|**89.73**|**26.13**|

_Notes: CurlTe_ **=** Ratio of current to total expenditure; _Cap/Fe_ **=** Ratio of capital to total expenditure; _Deffe_ **=** Ratio of defense to total expenditure; _HIhfFe_ **=** Ratio of health to total expenditure; _EdulTe_ **=** Ratio of education to total expenditure; _TaclTe_ **=** Ratio of transport and communication to total expenditure; _Other/Te_ **=** Ratio of other to total expenditure.

**25**
Table 2 Economic Growth and Public Expenditure **Mix** (in percent) [Cross-section/Time-scries data, **69** countries, **1971-90]**

|Range|Slow-Growth|Fast-Growth|
|---|---|---|
|_Mean GrowthRate_|**-1.97**|**3.23**|
||**(163)**|(146)|
|_Te/GDP_|24.76|**22.85**|
||**(163)**|(146)|
|_Curefle_|**_75.92_**|**75.61**|
||**(163)**|(146)|
|_Ncurefle_|**68.61**|**68.87**|
||**_(155)_**|**(139)**|
|_Capefle_|24.03|24.21|
||**(160)**|(145)|
|_DeflTe_|**12.75**<br>(140)|14.92<br>**(131)**|
|_HIMte_|**6.33**|4.87|
||**_(156)_**|**(137)**|
|_EdlTe_|**_15.05_**|**_13.55_**|
||**(156)**|**(138)**|
|_Tacle_|**10.10**|**8.67**|
||**(152)**|**(130)**|
|_HospfTe_|**_5.16_**|**2.96**|
||**(83)**|**_(66)_**|
|_InhkhlTe_|**_0.56_**|**0.77**|
||**(26)**|(44)|
|_OthkhlTe_|1.41|**_1.55_**|
||**(66)**|**(72)**|
|_SchIlTe_|**8.60**|**8.99**|
||**(83)**|**(78)**|
|_UnivlTe_|**3.03**|**2.85**|
||(82)|**(76)**|
|_OthedlTe_|2.42|**2.65**|
||**(71)**|**(77)**|

_Notes: a)_ Growth rate is 5-year forward moving average of per capita real **GDP.**

**b)** Mean growth is **0.5.** Past-growth refers to the periods<sup>where growthisgreater thanthe mean</sup> growth; Slow-growth is less than the mean growth.

**c)** Numbers in parentheses are observations.

**26**
Table **3** Correlation Coefikients

||_GRPCGDP_|_Cur/Te_|_Cap/Te_|_Def/Te_|_Edu/Te_|_Hfhfe_|_Tac/Te_|_Other/Te_|
|---|---|---|---|---|---|---|---|---|
|_GRPCGDP_<br>_C/T_||0.043<br>**(0.36)**|**-0.065**<br>**(0.18)**<br>|**0.18**<br>**(0.001)**|_-0.054_<br>**(0.26)**|-0.2<br>**(0.001)**|-0.14<br>**(0.006)**|0.04<br>(0.41)|
|_ure_<br>|||**-0.85**<br>**(0.001)**|**0.08**<br>**(0.01)**|**0.1**<br>(0.002)|<br>**0.13**<br>**(0.001)**|<br>**-0.38**<br>**(0.0c,**|**0.006**<br>**(0.87)**|
|_Cap/Te_<br>||||-0.02<br>(0.54)|**-0.07**<br>**(0.03)**|<br>**-0.09**<br>**(0.005)**|<br>0.47<br>**(0.001)**|-0.12<br>**(0.001)**|
|_Def/Te_<br>_Ed/T_|||||**-0.18**<br>**(0.001)**|**-0.32**<br>**(0.001)**|<br>**-0.08**<br>(0.02)|**_-0.56_**<br>**(0.001)**|
|_ue_<br>_Hh/T_||||||<br>**0.38**<br>**(0.001)**|<br>**0.10**<br>(0.002)|**_-0.52_**<br>**(0.001)**|
|_fe_<br>_T/T_|||||||<br>**0.068**<br>**(0.003)**|**-0.29**<br>**(0.001)**|
|_ace_<br>_Other/Te_||||||||-0.47<br>**(0.001)**|

_Note:_ Parenthesis indicate the level of significance required to reject the hypothesis that the Pearson correlation coefficient is zem.

**27**
Table 4

|(Dependentva|Compositi<br>riable**=**GRP|onofGovern<br>CGDP, 5-year|mentExpend<br> forwardmovi|itureandEco<br>ngaverageof|nomicGrowth<br> percapitareal|**GDP**growthrate)*|
|---|---|---|---|---|---|---|
|Equation|(4.1)|(4.2)|(4.3)|(4.4)|_(4.5)_|(4.6)|
|**_E._**_Asia_|1.22<br>**(0.93)**|**5.09**<br>**_(5.62)_**|**7.29**<br>**(6.23)**|**3.70**<br>**(0.91)**|**6.66**<br>**(3.81)**|8.21<br>(1.46)|
|**_S._**_Asia_|1.14<br>**(0.92)**|4.89<br>**(6.03)**|**5.89**<br>(6.46)|**2.61**<br>**(0.61)**|7.46<br>**(3.96)**|**7.86**<br>(1.44)|
|_SubSaharanAfrica_|-2.00<br>**(-1.62)**|**2.03**<br>**(2.63)**|**3.66**<br>(3.47)|0.28<br>**(0.09)**|**2.93**<br>(1.82)|4.33<br>**(0.81)**|
|_LatinAmerica_|**-2.61**<br>(-2.12)|**1.35**<br>**(1.93)**|**2.03**<br>(2.14)|**6.32**<br>**(1.93)**|4.28<br>(2.44)|**7.86**<br>**_(1.59)_**|
|_EMENA_|-0.02<br>(-0.02)|**3.63**<br>(3.46)|**_5.27_**<br>**(3.86)**|4.22<br>**(1.26)**|**3.91**<br>(2.41)|**5.78**<br>**(1.05)**|
|_Te/GDP_|**0.016**<br>**(0.80)**|**0.003**<br>**(0.16)**|**-0.033**<br>(-1.43)|||**-0.039**<br>_(-0.46)_|
|_NcurfTe_|**0.039**<br>**(2.91)**||||||
|_CaplTe_||**-0.037**<br>**(-2.62)**|||||
|_DeflTe_|||**-0.053**<br>**(-2.27)**|**0.093**<br>(1.04)|**-0.053**<br>(-1.21)|**-0.006**<br>**(-0.06)**|
|_HIthffe_|||-0.024<br>(-0.47)||**-0.50**<br>(-2.94)||
|_EdlTe_|||-0.021<br>**(-0.62)**|**0.017**<br>**(0.17)**|||
|_TnCTe_|||-0.145<br>**_(-5.13)_**|**-0.33**<br>**_(-5.31)_**|-0.22<br>**_(-5.11)_**|**-0.30**<br>**(-3.92)**|
|_SchIlTe_|||||**0.075**<br>**(0.88)**|-0.02<br>**(-0.08)**|
|_UnivlTe_|||||**0.38**<br>**_(1.52)_**|**0.39**<br>**(1.00)**|
|_OthedlTe_|||||**0.63**<br>(3.64)|**-0.56**<br>**(-1.00)**|
|_Hospfle_||||**0.29**<br>(0.47)||**-0.70**<br>**_(-1.59)_**|
|_Inhkhtle_||||0.02||0.02|
|||||**(0.03)**||(0.02)|

**28**
# Table 4 (Cont'd)

|Equation|(4.1)|(4.2)|(4.3)|(4.4)|_(4.5)_|_(4.6)_|
|---|---|---|---|---|---|---|
|_Onhkth1Te_||||**1.03**<br>(1.47)||**_1.05_**<br>**(1.27)**|
|_HkhCap_||||**-0.16**<br>**(-1.96)**|||
|_EdCap_|||||-0.025<br>**_(-2.05)_**||
|_Pvlnv/Gdp_|**-0.038**<br>**(-1.17)**|**-0.037**<br>(-1.14)|||||
|_Black_|**-0.013**|-0.014|**-0.010**|**0.003**|**-0.010**|**-0.009**|
||**(-3.95)**|(-4.17)|**(-2.92)**|(0.12)|_(-1.54)_|**(-0.31)**|
|_Shock_|**-0.05**|**-0.06**|**0.008**|**_0.005_**|**-0.01**|**_-0.051_**|
||(-1.48)|**(-1.70)**|(0.22)|(0.04)|**(-0.13)**|**(-0.33)**|
|**_Adj._**_R-sq._|**0.32**|**0.30**|**0.37**|**0.81**|**_0.53_**|**0.79**|
|<br>_Obs._|**286**|**297**|**266**|54|121|_54_|
|_DW_|**_0.56_**|**0.56**|**0.66**|**0.92**|0.84|**0.83**|

't-statistics in parentheses

**29**
Table **S** Composition of Government Expenditure and Economic Growth (Fixed-Effeots Model) IDependent variable **=** GRPCGDP, _5-year_ forward moving average of per capita retl **GDP** growth rate)

|Equation|**(5.1)**|_(5.2)_|**_(5.3)_**|_(5.4)_|**_(5.5)_**|**_(5.6)_**|
|---|---|---|---|---|---|---|
|_Intercept_|0.041<br>(0.42)|**0.1**<br>(1.04)|0.048<br>(0.46)|**-0.11**<br>(-0.43)|**-0.023**<br>(-0.14)|**_-0.15_**<br>(-0.46)|
|_Te/GDP_|0.002<br>**(0.07)**|**-0.003**<br>**(-0.13)**|**_-0.015_**<br>(-0.47)|||**0.035**<br>**(0.3)**|
|_Ncur/Te_|**0.035**<br>**(2.7)**||||||
|_Cap/Te_||**_-0.059_**<br>(-3.41)|||||
|_DeflTe_|||**0.053**<br>(1.42)|**-0.13**<br>**(-1.23)**|**0.016**<br>**(0.27)**|**-0.11**<br>**(-0.97)**|
|_HkhlTe_|||**-0.013**<br>**(-0.30)**||0.14<br>**(0.62)**||
|_Ed/Te_|||**0.006**<br>(0.14)|**-0.16**<br>**(0.11)**|||
|_Tac/Te_|||**-0.037**<br>(-1.14)|-0.14<br>(-1.24)|-0.04<br>**(-0.88)**|**-0.13**<br>**(-1.00)**|
|_SchlTe_|||||**0.16**<br>(1.40)|**-0.29**<br>**(-1.37)**|
|_UnivlTe_|||||**0.09**<br>_(0.45)_|**0.23**<br>(0.58)|
|_OthedlTe_|||||**0.16**<br>**(0.81)**|-0.14<br>(-0.24)|
|_HosplTe_||||**_0.75_**<br>**(0.90)**||0.46<br>(0.48)|
|_InhithlTe_||||0.43<br>**(0.70)**||0.21<br>**(0.26)**|
|_OthkhlTe_||||**2.26**<br>(2.48)||2.14<br>**(2.06)**|
|_HkhCap_||||**-0.39**<br>(-3.24)|||
|_EdCap_|||||**-0.091**<br>**(-3.88)**||

**30**
# Table **5** (Cont'd)

|Equation|**_(5.1)_**|(5.2)|**_(5.3)_**|_(5.4)_|**_(5.5)_**|**_(5.6)_**|
|---|---|---|---|---|---|---|
|_Black_|0.0004|**0.0005**|**0.001**|**_-0.009_**|**0.001**|**_-0.009_**|
||(0.44)|**(0.61)**|**(1.00)**|**(-0.36)**|**(0.18)**|**(-0.33)**|
|_Shock_|**-0.096**|**_-0.095_**|-0.12|**0.017**|**-0.096**|**-0.065**|
||**(-3.67)**|**(-3.67)**|**(-3.86)**|**(0.16)**|**(-1.78)**|**_(-0.50)_**|
|**_Adj._**_R-sq._|**0.06**|**0.08**|**_0.05_**|**0.26**|**_0.15_**|**0.06**|
|**_Obs._**|294|**_305_**|**266**|_54_|121|_54_|
|_DW_|**0.96**|**1.05**|1.04|0.84|**1.03**|**1.01**|

> t-statistics _in_ parenthescs

**31**
|T<br>CompositionofGovernment <br>(Nonlinear M<br>(Dependentvariable=GRPCGDP,5-year forwa<br>Eti<br>|able**6**<br>Expenditure**and**EconomicGrowth<br>odelSpeoification)<br>rdmovingaverageofpercapitareal**GDP**growthrate)<br>|
|---|---|
|quaon<br>**(6.1)**|**(6.2)**|
|**_E._**_Asia_<br>-6.41<br>**(1.83)**|**2.76**<br>**(2.87)**|
|**_S._**_Asia_<br>**-6.18**<br>**(-1.76)**|2.88<br>**(2.87)**|
|_SubSaharanApka_<br>**-9.37**<br>**(-2.69)**|0.04<br>**_(0.05)_**|
|_LatinAmerica_<br>**-10.26**<br>**(-2.96)**|**-1.06**<br>(-1.2)|
|**_EMENA_**<br>**_-7.55_**<br>**(-2.09)**|**_1.35_**<br>**(1.09)**|
|_Te/GDP_<br>0.02<br>**(0.97)**|**0.008**<br>(0.41)|
|_Ncurlfe_<br>0.24<br>**(2.39)**||
|_(Ncur/7e)sq_<br>**-0.001**<br>**_(-1.95)_**||
|_CaplTe_|**0.11**<br>**(1.80)**|
|_(Capffe)sq_|**-0.003**<br>**(-2.62)**|
|_Black_<br>**-0.013**|-0.014|
|(-4.0)|_(-4.58)_|
|_Shock_<br>-0.048|**0.059**|
|**(-1.37)**|**(-1.7)**|
|**_Adj._**_R-sq._<br>**0.33**<br>|**0.32**|
|**_Obs._**<br>294|**_305_**|
|_DW_<br>**_0.57_**|**0.59**|

t-statistics in parentheses

**32**
Table **7**

> Sample Statistics for Central and General Government Expenditure Shares [12 countries, **1971-90]**

|Variable|Observ<br>|ations<br>|Mean||Std.De|v.|Maxim|um|Minim|um|
|---|---|---|---|---|---|---|---|---|---|---|
||**CC**|**CG**|**CC**|**CG**|**CC**|**CG**|**CC**|**CG**|**CC**|**CG**|
|_CurITO_|184|**135**|**79.46**|**76.99**|12.64|14.16|**98.05**|**97.73**|47.15|_45.44_|
|_NcrlTe_|184|**135**|**70.70**|**68.85**|**12.90**|14.28|**91.86**|**93.07**|**35.12**|**32.11**|
|_Capffe_|184|**_135_**|**20.55**|**20.97**|**12.62**|**12.09**|**_52.85_**|**51.95**|**1.95**|**1.95**|
|_DefiTe_|145|121|**11.95**|**8.67**|**5.70**|**5.93**|26.24|**20.76**|**1.69**|**0.01**|
|_HwiT_|182|**126**|**6.17**|**6.62**|**3.73**|**3.79**|**19.83**|19.44|**1.09**|2.01|
|_EdUle_|182|**126**|11.82|13.43|5.94|**3.53**|24.00|24.31|1.46|**6.52**|
|_TaclTe_|**179**|**125**|**8.63**|**_8.85_**|6.40|**6.02**|48.27|**27.99**|**0.90**|**1.96**|

_Notes:_ _a)_ **CC =** Consolidated Central Government

> **CG =** Consolidated General Government

> **b)** 12 countries are: Argentina, Chile, Ethiopia, Gambia, Greece, Hungary, Indonesia, India, Kenya, Malawi, Panama, Zimbabwe

**33**
Table **8**

> Composition of General Government Expenditure and Economic Growth (Dependent variable **=** GRPCGDP, 5-year moving average per capita real **GDP** growth rate)e

|Equation|**(ClP**|**(GlP**|(T-test)|**(C2)**|**(G2)**|(t-test)|**(C3)**|**(G3)**|_(t-test)_|
|---|---|---|---|---|---|---|---|---|---|
|_E.Asia_<br>**_S_**_Ai_|_4.53_<br>**(2.25)**<br>|4.88<br>(2.52)<br>|**0.023**<br>|**1.17**<br>**(0.53)**<br>|**2.09**<br>**(0.88)**|**-0.286**|**-1.62**<br>(-0.54)|1.41<br>**(0.68)**|**-0.83**|
|**_._**_sa_<br>**_Sb_**_Sh _|4.92<br>(2.04)<br>|5.14<br>**(1.97)**<br>|**0.086**<br>|2.54<br>**(2.36)**<br>|2.48<br>**(1.67)**<br>|**0.035**|<br>**1.695**<br>**(0.88)**|0.84<br>(0.48)|**0.33**|
|**_u_**_aaranAfrica_<br>_LiAi_|**2.92**<br>**(0.98)**<br>|**2.52**<br>**(0.75)**<br>|**0.11**<br>|0.28<br>**(0.16)**<br>|**-0.06**<br>**(-0.03)**<br>|**0.132**|-6.41<br>(-1.46)|**-3.60**<br>**(-1.37)**|**_-0.55_**|
|_atnmerca_<br>|4.68<br>(1.47)<br>|4.65<br>**_(1.35)_**<br>|**0.072**<br>|**2.25**<br>**(1.18)**|**1.92**<br>**(0.97)**|**_0.119_**|**-2.78**<br>**(-0.83)**|-2.01<br>**_(-0.75)_**|**-0.18**|
|**_EMIENA_**|**0**|**0**|**0**|**0**|**0**|**0**|<br>**0**|**0**|**0**|
|_TeIGDP_<br>_Ncur/Te_<br>|**-0.025**<br>(-0.41)<br>-0.024<br>**(-0.91)**|**-0.026**<br>(-0.44)<br>**-0.027**<br>**(-0.98)**|-0.048<br>**0.065**|**-0.023**<br>**(-0.39)**|-0.02<br>**(-0.35)**|**-0.036**|**-0.011**<br>**(-0.13)**|-0.012<br>(-0.164)|**0.0087**|
|_Cap/Te_<br>_DflT_||||0.043<br>**(1.35)**|**0.033**<br>**(0.92)**|**0.215**||||
|_ere_<br>_HIhT_|||||||**0.016**<br>(0.194)|**-0.086**<br>**(-1.11)**|**0.89**|
|_e_<br>|||||||<br>**0.066**<br>**(0.336)**|<br>0.149<br>**(0.91)**|**-0.32**|
|**_Ed/Te_**<br>|||||||<br>**0.223**<br>**(1.35)**|**0.152**<br>**(1.33)**|**0.35**|
|_TaclTe_<br>_Blk_|||||||<br>**_0.175_**<br>**(2.17)**|**0.062**<br>(1.14)|**1.15**|
|_ac_<br>|-0.014<br>(-1.45)<br>|-0.012<br>**(-0.91)**<br>|**0.127**<br>|**-0.015**<br>**_(-1.56)_**<br>|**-0.015**<br>**(-1.19)**|**0.0**|<br>**-0.0079**<br>(-0.441)|**-0.00008**<br>**(-0.005)**|-0.345|
|_Shock_|**-0.03**<br>|**-0.017**<br>|**-0.27**|-0.046<br>|**0.018**<br>|**-0.505**|<br>0.042|0.049|**-0.056**|
||(-0.34)<br>|**(-0.18)**<br>||(-0.54)|(0.2)||(0.46)|**(0.57)**||
|**_Adj._**_R-sq._<br>|0.44<br>|0.47<br>||0.45|0.46||<br>**0.52**|**0.62**||
|_Obs._<br>|**60**<br>|**57**<br>||**60**|**_57_**||**_51_**|46||
|_DW_<br>_Notes:_|**0.96**<br>*t-statistics|**1.00**<br> _in_arenthese|s**IC ** **C**|**_0.95_**<br>l|**0.97**<br>||1.02|1.46||

> * t-statistics _in_ parentheses; **I C** **_=_ C** entral Government; **G =** General Government; **t** -Test **=** t-test for the differences between **ka.** and **f,**

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