Division of Economics
AJ Palumbo School of Business Administration
Duquesne University
Pittsburgh, Pennsylvania
IS THE INFLUENCE OF GOVERNMENT SIZE ON SOCIAL WELFARE
DIFFERENT AMONG LESSER DEVELOPED, DEVELOPING AND
MORE DEVELOPED NATIONS? AN ECONOMIC PANEL ANALYSIS
James Vogelgesang
Submitted to the Economics Faculty
in partial fulfillment of the requirements for the degree of
Bachelor of Science in Business Administration
December 2008
Faculty Advisor Signature Page
Matthew Marlin, Ph.D.
Date
Professor of Economics
2
IS THE INFLUENCE OF GOVERNMENT SIZE ON SOCIAL WELFARE
DIFFERENT AMONG LESSER DEVELOPED, DEVELOPING AND MORE
DEVELOPED NATIONS? AN ECONOMIC PANEL ANALYSIS
James Vogelgesang, BSBA
Duquesne University, 2008
Abstract
Previous research has examined the relationship between the size of a country’s
government and its GDP growth. In this paper, I conduct an analysis modeling the
impact of a government’s size against several social welfare indices. The regression will
be empirically analyzed using generalized method of moments with two staged least
squares in a panel data framework. I hypothesize that higher levels of social welfare
occur with a larger government in lesser developed nations compared to developing or
developed nations.
The results of this analysis find that the impact of government size varies
significantly for lesser developed, developing, and developed nations. Evidence shows
that as a country becomes more developed, the presence of a larger government
decreases the level of social welfare.
3
Table of Contents
I. Introduction……………………………………………………………………..........
5
II. Literature Review…………………………………………………………………….
5
III. Methodology……………………………………………………………………........ 13
IV. Results …………….………………………………………………………………… 19
V. Analysis of Impact on Human Development Index…………………………………. 21
VI. Analysis of Impact on Social Welfare Function…………………………………….. 25
VII. Economic Implications………………………………………………………............ 29
VIII. Suggestion for further research………………………………………………............ 30
IX. Conclusion………………..…...…………………………………………………….. 31
X. References……………………………………………………………………............ 32
XI. Appendix…………………….………………………………………………............
35
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I. Introduction
Beyond economic status, social welfare research has become increasingly popular
as a method of determining the well-being of individuals in a given country. The need
for an appropriate measure of social welfare has brought much debate and different
perspectives. The most commonly cited measurement for government size is government
expenditure as a percentage of GDP. This has been used to determine its impact on
economic growth while neglecting to take into account how it affects a country’s social
welfare. This paper will analyze it’s affect on the country’s social welfare.
This paper is an empirical analysis of the effect of the size of government on
social welfare for lesser developed, developing and developed nations. Using two
separate measurements of social welfare, the Human Development Index (HDI) and a
Social Welfare Function (SWF) developed by Amartya Sen, I will compare the effects of
government size on countries with different levels of development. I expect that increases
in government size will influence a country’s social welfare differently depending on
their level of development. I hypothesize that a lesser developed nation will require a
greater amount of government to maximize their social welfare while a larger
government in developed nations will have a negative impact on social welfare based on
findings of Yavas (1998) and Hetger (2001).
II. Literature Review
a. Size of Government
Previous research into the optimal size of government falls somewhere on the
spectrum from Keynesian models, which states that increases in government spending
5
will increase aggregate demand and lead to economic growth, to Neoclassical models,
that claim that increases in government spending lead to decreases in economic growth.
Ram (1986) uses a cross sectional analysis of 115 countries over the period of
1960-1980. To measure government size on economic growth Ram uses OLS
regressions to test the effect of government size on economic growth1. From his results,
Ram argues that, for most countries, larger governments are associated with increased
economic growth in both the 1960s and the 1970s. Along with a positive effect on
growth, Ram finds that governments become more productive. Although countries of
every level of development show economic growth, evidence for increased growth with
greater government size is strongest for lesser-developed countries. Yuk (2005) finds
similar results for the United Kingdom in the years 1830-1993. He finds structural
breaks in the data, which he then divides into four separate subgroups2. These groups
allow Yuk to examine government size on growth to observe separate periods and test for
cointegration across time. Yuk finds that government size Granger-causes growth for all
three of the four subgroups. The subgroup of 1830-1867 only shows causality for
increased government spending resulting in increased GDP.
Loizides and Vamvoukas (2005) take the relationship between government size
and growth a step further by testing for correlation as well as Granger-causality. The
study uses data on economies for the UK, Greece, and Ireland. They define the UK as a
developed nation and consider Greece and Ireland to be developing nations. Their study
examines the three countries from the years 1950-1990 using bivariate and trivariate
models. The analysis, uses government expenditure as a percent of GNP as a proxy for
1
Government size is measured by government consumption divided by GDP in domestic dollars while
economic growth is calculated as the growth in GDP for a country
2
Periods include 1830-1868, 1869-1930, 1930-1993, 1830-1993
6
size, and find that an increase in government size results in an increase of real GNP per
capita for all three countries. They also conclude, for all three countries, that an increase
in real GNP per capita results in an increase in the size of government. The results of
these studies are consistent with the Keynesian theory.
In contrast Barro (1991) examines economic growth in a cross sectional study of
98 countries for the years 1960-1985. He finds a negative relationship between
government consumption expenditure and per capita growth in GDP. This study shows
that countries with higher human capital, measured in GDP per capita, experience higher
investment in GDP. Maddison (1987) finds similar results in an analysis that looks at six
developed countries from 1913-1984. He uses separate OLS regressions for each country
and theorizes that increased government spending is used to improve the quality of life
within these countries and is a significant contributor to the decrease in GDP growth.
Landau (1985) performs an OLS cross sectional analysis of 65 countries to test
the effects of government consumption, education, investment, financial capital, military,
and transfer payments on countries’ growths in per-capita RGDP. Landau’s results show
that all five government expenditures cause negative growth. He also finds that private
investment has a significant positive impact on growth in per capita GDP while all
measures of public expenditure result in decreased per capita GDP growth. Landau states
that for less developed countries, government support used to help the private sector was
only beneficial if it promotes economic growth and does not protect it from competition.
Saxton (1998) expands on the Neoclassic model through his use of the Armey
Curve in the United States3. Saxton’s use of the Armey Curve predicts that a country
with low per capita output will increase its per capita output with government input. As
3
A graphical technique popularized by Arthur Laffer and Richard Armey
7
the country continues to develop, the need for government input decreases and at a
certain point begins to decrease output per capita. Saxton estimates the maximum size
for the US government from the years 1801-1996 to be 13.42%. From his findings he
points out that when government output increased to 16.28% in 1956, the United States
experienced a decade of slower growth. Although the United States is a developed
nation, one can make the argument within the period of the data set that the United States
was at one point considered a developing nation and experienced diminishing returns
from the government output.
Recent research has discovered that government size affects countries economic
growth differently based on their level of development. Yavas (1998) models the effects
countries in a steady-state output level to changes in government size. His findings show
that countries with a low-steady state that increasing government size increases the
steady-state level4. Conversely, those countries with a high steady-state will have a lower
steady-state with increased government5. Yavas points out several shortcomings in the
modeling done by Ram and Landau and believes that his adjustments allow for a better
interpretation of the effects of government size.
In another study of the effects of government size on economic growth Heitger
(2001) hypothesizes that increased government spending will allow for the building of
the private sector in lesser developed nations while increased government spending in a
developed nation will crowd out private sector investment. He confirms his hypothesis
by using a 21-country panel study of European nations from 1960-2000. Heitger
concludes that countries, which are under-producing, will have increased growth with
4
5
Low steady-state is considered an lesser developed nation
High steady-state is considered a developed nation
8
greater government-provision of public goods provided; while more developed nations
will have decreased growth with high government expenditure. Heitger also subdivides
expenditure to examine investment expenditure alone. This reveals different effects on
growth, but overall still negative. According to Heitger, the decrease in growth occurred
due to increased taxes that took money away from private investment and resulted in an
overproduction of public goods.
The reasoning given by Heitger follows the logic that Joseph Stiglitz notes for
lesser developed nations. Stiglitz (2002) argues that lesser developed countries forced to
sell off their public sector caused their failing economies. He states that these countries’
private sectors do not have capable financial systems and that without the protection and
guidance of the government, their private sector will fail. Decreasing the public sector
works for developed nations with a mature and stable private sector, but it has been
proven fatal for lesser developed economies.
While debates on what size of government enable economic growth in a country
to continue, it must be noted that policy makers alone do not make this decision. Voters
in a democratic society elect officials who determine the tax rates and spending by the
government. Voters choose officials based on policies that they believe will benefit
themselves. Meltzer and Richards (1981) argue that the upper or lower classes do not
decide the income redistribution by the government, but the middle class does, and over
time, the size of government has increased because of their decisive votes. In a
democratic society, a person’s vote is his or her idea on what size government will
maximize his or her own social welfare.
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While most studies on government size measure its effects on government
growth, very few studies have looked at government size’s influence on social welfare.
Davies (2008) discusses the differences in the effects of increased GDP compared to
increased HDI. He argues that countries with higher levels of GDP do not necessarily
have higher levels of HDI, and that the criterion for a good government is to maximize
the social welfare of the individuals in that country. Davies concludes that there is a
relationship between government size and social welfare.
b. Social Welfare
The study of social welfare indicators began in the mid-1960s when Raymond
Bauer originally investigated this topic. He defined social welfare indicators as
“statistics, statistical series, and all other forms of evidence that enable us to assess where
we stand and are going with respect to our values and goals.” The United Nations later
stated that:
Social indicators can be defined as statistics that usefully reflect important social
conditions and facilitate the process of assessing those conditions and their
evolution. Social indicators are used to identify social problems that require
action, to develop priorities and goals for action and spending to assess the
effectiveness of programmers and policies.
It is difficult to determine what actually determines a good “quality of life.” Many
studies find that measures for subjective quality of life, where measurements experienced
at the individual level, are the best indicators (Noll 2004).
Bjornskov (2003) does a cross sectional analysis to determine the factors
influencing life satisfaction across various countries. Data for life satisfaction is gathered
10
from World Value’s Survey and tested on 32 countries across Europe. The purpose of his
analysis is to determine the effectiveness of social capital on life satisfaction. 6 Bjornskov
concludes that high levels of life satisfaction result from decreased levels of income, and
those countries with the highest level of social capital have higher levels of life
satisfaction. He also states that the countries with the highest level of social capital are
not the countries with the greatest level of GDP.
Using the same indicator of social welfare, The World Value’s Survey,
Bjornskov, Dreher and Fischer (2006) create a cross sectional analysis of 70 countries
using over 90,000 observations to find the best indicators of social welfare by conducting
robustness tests. Their robustness tests indicate how well an independent variable
predicts the dependent variable. They find that variables showing a person’s ability to
move up in status are important, while indicators such as GDP per capita, by itself, do not
pass their robustness test for being a good indicator.
Stroup (2006) tests the relationship between economic freedom and social welfare
is examined. For the measurement of social welfare, Stroup individually uses life
expectancy, mortality rates, literacy rates, grades, water availability, and shot availability.
As a result, Stroup argues that it is beneficial for a country to increase its level of
economic freedom or decrease government intervention to increase a country’s social
welfare. He also concludes that market forces and the private sector allow for the best
allocation of goods that help improve social welfare.
Wickrama and Mulford (1996) take into account the level of political democracy
in a country and test its impact on a country’s well being. They test this using a crosssectional analysis in 82 countries, excluding oil-exporting economies. To measure social
6
Bjornksov defines social capital as ones perceived trust in the government
11
welfare, Wickrama and Mulford use the Human Development Index because of its
inclusion of life expectancy, adult literacy, and purchasing power. They find that social
welfare is determined not only by economic growth, but also by the level of democracy.
These findings expand on Richards (1998) study, stating that by increasing the voters
ability to choose there will be an increase in social welfare.
Previous literature looks at the use of GDP per capita or income as a measurement
of social welfare. Welsch (2006) argues that by only observing income you cannot
determine the level of happiness in a country. Although income may be a good
supporting variable to determining the quality of life, it cannot be the only indicator. He
argues that variables such as air pollution, which can be controlled by government
regulations, are better determinants of one’s social welfare. Welsch gives the example
that when air pollution increases, it subjects people to higher health risks and lower life
expectancy. His findings show that indicators that affect people are better at explaining
social welfare than measurements such as income.
In contrast to this study, Dipietro and Anorou (2006) argue that GDP per capita is
a better measure of a country’s social welfare compared to HDI. To determine this they
use results from quality of life surveys as the dependent variable and test to see which
measurement of social welfare is a better predictor across countries. Their findings show
that GDP per capita is a better predictor of happiness across time than HDI. They note
that part of the criticism in using GDP per capita is that it is not a measurement of
welfare.
Expanding on this idea, Amartya Sen develops a social welfare function based on
three main criteria. The first states that social welfare function must have act
12
consequentialism. This means that the conclusion of utility must be a function of what is
actually happening, and in this case, what is actually happening in the country. Second, it
must include welfarism, meaning it must be based on an individual’s social welfare
defined by the level of individual utility. Lastly, it must contain sum ranking, which is
the sum of the individual utilities (Atkinson 1999). The Social Welfare Function created
by Sen uses an individual’s income, GDP per capita, as a function of the income
distribution in a country.
Prior literature is unclear on what size of government is needed to increase the
economic growth in a country, and the amount of government necessary to improve
social welfare. Because previous literature fails to agree on the best measure of social
welfare, the models in this paper test two separate measures of social welfare. Doing this
allows one to reach a better understanding of the effects of government size on social
welfare for countries in different development stages. This method accounts for
disagreements in measurements for social welfare to produce an overall conclusion about
the effects of government size’s influence on social welfare for countries in different
development stages.
III. Methodology
a. Dependent Variables
Due to the uncertainty of the best measure for social welfare, this paper uses two
of the most widely accepted measurements for social welfare. I estimate the two
measures separately as dependent variables. The first measure comes from United
Nations Development Programme: the Human Development Index (HDI). This index is
13
calculated from 1975 to 2005 for 177 countries. The second measure of social welfare is
a Social Welfare Function developed by Sen using GDP per capita and the GINI
coefficient.
The HDI is a composite measure of a country’s social welfare that is remeasured every five years. The index includes measurements of literacy and educational
attainment, life expectancy, and standard of living. The United Nations uses the
following calculation to build their HDI in their 2007/2008 report:
𝐿𝑖𝑓𝑒 𝐸𝑥𝑝𝑒𝑐𝑡𝑎𝑛𝑐𝑦 𝐼𝑛𝑑𝑒𝑥 (𝐿𝐸𝐼) =
𝐿𝑖𝑓𝑒 𝐸𝑥𝑝𝑒𝑐𝑡𝑎𝑛𝑐𝑦−25
85−25
2 𝐴𝑑𝑢𝑙𝑡 𝐿𝑖𝑡𝑒𝑟𝑎𝑐𝑦 𝑅𝑎𝑡𝑒−0
1 𝐺𝑟𝑜𝑠𝑠 𝑒𝑛𝑟𝑜𝑙𝑙𝑚𝑒𝑛𝑡 𝑟𝑎𝑡𝑖𝑜−0
+ (3)
100−0
100−0
𝐸𝑑𝑢𝑐𝑎𝑡𝑖𝑜𝑛 𝐼𝑛𝑑𝑒𝑥 (𝐸𝐼) = (3)
𝐿𝑜𝑔(𝐺𝐷𝑃)−𝐿𝑜𝑔(100)
(0.1)
(0.2)
𝐺𝐷𝑃 𝐼𝑛𝑑𝑒𝑥 (𝐺𝐷𝑃𝐼) 𝐿𝑜𝑔(40,000)−𝐿𝑜𝑔(100)
(0.3)
1
1
1
HDI     LEI      EI      GDPI 
 3
 3
 3
(0.4)
When building each dimension of the index, the United Nations creates a minimum and
maximum for each measurement to determine a value between zero and one allowing
them to create a composite index. The final measurement of the index is scaled between
zero and one with one being the highest level of social welfare. Due to the five year
increments of data I follow the works of Davies and Quinlavin (2006), and construct a
straight-line annual progression between the intervening years from one measurement to
the next.
The second measure of a country’s social welfare is the social welfare function
(SWF) developed by Sen. Following his previously outlined criterion, he develops his
SWF using an individual’s average income for a country, allowing it to be weighted by
the inequality of distribution of income within a country and is calculated as:
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SWFit = 𝐺𝐷𝑃 𝑝𝑒𝑟 𝑐𝑎𝑝𝑖𝑡𝑎𝑖𝑡 ∗ (1 − 𝐺𝐼𝑁𝐼𝑖𝑡 )
(1.5)
GDP per capita is the average level of income in a given country i at time t. GINI is the
most commonly used measurement of income inequality for country i at time t.
The combined measurement developed by Sen follows the definitional guidelines
given by the United Nations and Bauer (Noll 2004). It also meets the criterion of a good
measurement of social welfare according to Dipietro and Anorou (2006), and Welsh
(2006)
b. Independent variables
Following the works of Heitger (2001) and Davies (2008) I expect to find
different impacts from the two different measures of government: government investment
expenditure, Iit, and government consumption expenditure, Cit, each measured as a
percent of GDP for country i and year t.
GINVit =
GCONit =
𝐺𝑜𝑣𝑒𝑟𝑛𝑚𝑒𝑛𝑡 𝐼𝑛𝑣𝑒𝑠𝑡𝑚𝑒𝑛𝑡 𝐸𝑥𝑝𝑒𝑛𝑑𝑖𝑡𝑢𝑟𝑒𝑖𝑡
𝐺𝑟𝑜𝑠𝑠 𝐷𝑜𝑚𝑒𝑠𝑡𝑖𝑐 𝑃𝑟𝑜𝑑𝑢𝑐𝑡𝑖𝑡
𝐺𝑜𝑣𝑒𝑟𝑛𝑚𝑒𝑛𝑡 𝐶𝑜𝑛𝑠𝑢𝑚𝑝𝑡𝑖𝑜𝑛 𝐸𝑥𝑝𝑒𝑛𝑑𝑖𝑡𝑢𝑟𝑒𝑖𝑡
𝐺𝑟𝑜𝑠𝑠 𝐷𝑜𝑚𝑒𝑠𝑡𝑖𝑐 𝑃𝑟𝑜𝑑𝑢𝑐𝑡𝑖𝑡
(1.6)
(1.7)
Data for government expenditures and Gross domestic product come from the
International Financial Statistics March 2007.
Table 1 includes all other independent variables included in the analysis.
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Table 1
Variable
GWGDPit
CORRit
GDPPCit
POPit
PUBLICit
HDIi(t-1)
SWFi(t-1)
Description
Growth in world GDP
Freedom from
Corruption
GDP per capita
Unit
Growth rate
Growth in Population
Public Rights
Lagged HDI
Lagged Social Welfare
Function
Growth Rate
First difference
First Difference
First Difference
Source
Penn World Tables
Index of Economic
Freedom7
International Monetary
Foundation
Penn World Tables
Freedom House
UNDP
Penn World Tables and
UNU-WIDER
The analysis includes control variables I found to be significant in previous
literature. Paloni and Zanardi (2007) use lagged HDI, growth in world population, and
GDP per capita to control for HDI. The use of population and corruption as control
variables were found significant in work done by Antic (2004) who used GDP per capita
as a dependent variable for social welfare. I also include public rights as a measurement
of democracy in a nation following the findings of Wikrama and Mulford (1996). The
lagged dependent variable included is similar to the work by Davies (2008) as well as
Paloni and Zanardi because I expect a country’s current HDI to be similar to its previous
HDI level and extend the idea to a country’s level according to Sen’s SWF. Lastly, I
include a squared term for government expenditure as I expect a non-linear relationship,
similar to findings by Saxton (1998) with the Armey Curve.
Other control variables were considered for the regressions, but were not included
because of an insufficient number of observations for countries, or were not found to be
7
Calculations are based off of the transparency index
16
significant. Measures of investment and consumption were analyzed but omitted because
they were not found significant.
c. Method Specifications
In this analysis I estimate 12 different regression models. For each dependent
variable, HDI and SWF, I examine government consumption expenditure as a percentage
of GDP as well as government investment expenditure as a percent of GDP for lesser
developed, developing and developed nations. I estimate the level of nation’s
development based on their level of GDP per capita similar to the method used by the
United Nations Development Programme. Lesser developed nations include countries
with an annual GDP per capita less than $2000. Developing nations include countries
with a GDP per capita between $2,000 and $8,000 while developed nations have a GDP
per capita greater than $8,000.
I estimate the following general equations:
𝐻𝐷𝐼𝑖𝑡 = 𝛽1 𝐺𝐶𝑂𝑁𝑖𝑡 + 𝛽2 𝐺𝐶𝑂𝑁𝑖𝑡 2 + 𝛽3 𝐺𝑊𝐺𝐷𝑃𝑖𝑡 + 𝛽4 𝐶𝑂𝑅𝑅𝑖𝑡 + 𝛽5 𝑃𝑈𝐵𝐿𝐼𝐶𝑖𝑡 +
𝛽6 𝐺𝐷𝑃𝑃𝐶𝑖𝑡 + 𝛽7 𝐻𝐷𝐼𝑖(𝑡−1)
(1.8)
𝐻𝐷𝐼𝑖𝑡 = 𝛽1 𝐺𝐼𝑁𝑉𝑖𝑡 + 𝛽2 𝐺𝐼𝑁𝑉𝑖𝑡 2 + 𝛽3 𝐺𝑊𝐺𝐷𝑃𝑖𝑡 + 𝛽4 𝐶𝑂𝑅𝑅𝑖𝑡 + 𝛽5 𝑃𝑈𝐵𝐿𝐼𝐶𝑖𝑡 +
𝛽6 𝐺𝐷𝑃𝑃𝐶𝑖𝑡 + 𝛽7 𝐻𝐷𝐼𝑖(𝑡−1)
(1.9)
𝑆𝑊𝐹𝑖𝑡 = 𝛽1 𝐺𝐶𝑂𝑁𝑖𝑡 + 𝛽2 𝐺𝐶𝑂𝑁𝑖𝑡 2 + 𝛽3 𝐺𝑊𝐺𝐷𝑃𝑖𝑡 + 𝛽4 𝐶𝑂𝑅𝑅𝑖𝑡 + 𝛽5 𝑃𝑈𝐵𝐿𝐼𝐶𝑖𝑡 +
𝛽6 𝑃𝑂𝑃𝑖𝑡 + 𝛽7 𝑆𝑊𝐹𝑖(𝑡−1)
(1.10)
𝑆𝑊𝐹𝑖𝑡 = 𝛽1 𝐺𝐼𝑁𝑉𝑖𝑡 + 𝛽2 𝐺𝐼𝑁𝑉𝑖𝑡 2 + 𝛽3 𝐺𝑊𝐺𝐷𝑃𝑖𝑡 + 𝛽4 𝐶𝑂𝑅𝑅𝑖𝑡 + 𝛽5 𝑃𝑈𝐵𝐿𝐼𝐶𝑖𝑡 +
𝛽6 𝑃𝑂𝑃𝑖𝑡 + 𝛽7 𝑆𝑊𝐹𝑖(𝑡−1)
(1.11)
Where:
HDI
SWF
GCON
GINV
GWGDP
= First difference of the human development index
= First difference of social welfare function
= Government consumption expenditure as a percent of GDP
= Government investment expenditure as a percent of GDP
= Growth in world GDP
17
CORR
PUBLIC
GDPPC
POP
HDI(t-1)
SWF(t-1)
= Freedom from corruption
= Public Rights
= First difference of GDP per capita
= Growth in population
= Lagged first difference of human development index
= Lagged first difference of the social welfare function
Due to the panel data and the lagged dependent variable on the right sides of the
equations in (1.8) through (1.11) I model these equations as geometric lag models and
estimate them using generalized method of moments (GMM) with two staged least
squares similar to works done by Davies and Quinlavin (2006) and Davies (2008). As
instruments in the two staged least squares I used lagged GWGDP, lagged CORR, lagged
PUBLIC, lagged GDPPC, lagged GCON or GINV and lagged GCON2 or GINV2.
d. Data issues
Before estimating the regression, variables were tested for non-stationarity. Due
to the bounded nature of HDI, public rights, and freedom from corruption, they are by
definition non-stationary and cannot be adjusted. I have adjusted world GDP into a
growth variable in order to correct for non-stationarity. GDP per capita also showed
signs of non-stationarity and is corrected using the first difference. Data was also found
to have low levels of multicollinearity through observation in a correlation matrix.
Though difficult to estimate the presence of heteroskedasticity in panel data, it is
reasonable to assume an unequal variance across periods for countries of similar levels of
development. To correct for problems of an increasing level of variance across periods, I
use period weights with cross sectional weights.
Within the panel data sets, it is reasonable to expect variance in error terms due to
economic shocks across time. Because of the use of GDP in the dependent variable, it is
18
reasonable to assume autocorrelation will occur. In addition, because countries are
separated by levels of development, each regression may not experience similar levels of
autocorrelation. To test for this, I examine the correlogram of the residuals as well as
examine the Durbin-Watson statistic and adjust levels of AR process accordingly
e. Expected results
I expected to find different results for different levels of development. For lesser
developed countries, I expect that with an increasing size of government, ceterius
paribus, there will be an increasing level of social welfare. Accordingly, I expect to find
similar results for developing nations, to a certain point, when the benefit of the increased
size of government begins to decrease the level of the country’s social welfare. Lastly, I
expect to find an increase in a developed country’s size of government to result in a
decrease in its social welfare.
IV. Results
Table 2 displays regression output for HDI and SWF using government consumption
expenditure as a percentage of GDP estimating the size of government.
19
Table 2: Government Consumption Expenditure
HDI
Constant
SWF
Developed
Developing
Lesser developed
Developed
Developing
Lesser developed
-0.001*
0.00096*
0.001912*
5.538477*
-603.292*
2875.12*
GCON
0.008572**
-0.004563**
-0.011889*
-1.92065***
6450.076*
-45352.39*
2
-2.17E-02**
0.009229***
0.024651**
-1.77147***
-17721.91*
174845.3*
0.018973*
-0.008426*
-1.57E-02*
3.136259*
n/a
-1166.061*
6.10E-08***
1.41E-07**
3.48E-06*
n/a
n/a
n/a
Public Rights
Freedom from
Corruption
n/a
n/a
n/a
n/a
n/a
21.23803**
n/a
n/a
n/a
n/a
6.135581*
n/a
Population
n/a
n/a
n/a
1.658049***
31480.62**
n/a
HDIt-1
1.036111*
0.864282*
0.766298*
n/a
n/a
n/a
SWFt-1
n/a
n/a
n/a
-1.239949
0.580824*
-0.383811*
Ar(1)
n/a
0.068832*
n/a
-7.437871*
-0.481283*
n/a
Ar(2)
n/a
n/a
n/a
n/a
n/a
0.257446**
Ar(3)
n/a
n/a
n/a
n/a
0.094035*
n/a
GCON
World GDP
GDP Per
Capita
*Significant at 0.01, **Significant at 0.05, ***Significant at 0.1
Table 3 displays regression output for HDI and SWF using government
investment expenditure as a percentage of GDP estimating the size of government.
Table 3: Government Investment Expenditure
HDI
SWF
Developed
Developing
Lesser developed
Developed
Developing
Lesser developed
Constant
0.000107
0.000378***
0.008712*
44.03926
166.8547*
117.9228**
GINV
0.000887***
0.000715***
-0.03088**
2692.886**
661.7471*
-3196.928*
2
-0.00211***
-0.002546***
0.106969***
-5858.065**
-936.9448**
15591.05*
World GDP
GDP Per
Capita
0.003331***
-0.008081
-1.19E-05*
7472.793
n/a
n/a
1.15E-07*
1.80E-07**
-0.024346**
n/a
n/a
n/a
Public Rights
Freedom from
Corruption
4.26E-05*
n/a
n/a
n/a
-37.82781*
26.86719*
n/a
n/a
n/a
n/a
n/a
n/a
Population
n/a
n/a
n/a
19810.22***
25862.42*
-2981.123**
HDI(t-1)
0.91402*
0.88045*
-0.411296**
n/a
n/a
n/a
GINV
20
SWF(t-1)
n/a
n/a
n/a
0.470918*
0.648701*
-0.201326***
Ar(1)
0.009684*
0.059632**
0.498915*
-1.083752*
-0.937574*
n/a
Ar(2)
n/a
n/a
n/a
-0.556258*
-0.278309*
n/a
*Significant at 0.01, **Significant at 0.05, ***Significant at 0.1
V. Analysis of impact on HDI
a. Table of results for first difference in HDI
Table 4 shows the estimating equations for the change in the first difference of
HDI for government consumption expenditure as well as government investment
expenditure.
Table 4: The Impact of Government on HDI
Δ in First difference HDIit = f(GCON)
2
Δ in First difference HDIit = f(GINV)
Lesser developed
- 0.012GCON + .025GCON
-0.031GINV + 0.107GINV2
Developing
0.004GCON + .009GCON2
0.0007GINV -0.0026GINV2
Developed
0.009GCON - .022GCON2
0.0009GINV - 0.002GINV2
b. Effect of government consumption expenditure on HDI
Figure 1 uses the estimating equations for the impact of government consumption
expenditure on HDI while taking the average of all other independent variables and using
them as constants. The graph shows the impact of the first difference on HDI with
respect to amount of government consumption expenditure as a percent of GDP.
21
Figure 1
HDI as a function of Government
Consumption Expenditure
Impact on first difference HDI
0.007
0.006
0.005
0.004
Lesser Developed
Developing
0.003
Developed
0.002
0.001
0
1% 5% 9% 13% 17% 21% 25% 29% 33% 37% 41% 45% 49%
The results for a lesser developed nation show that when government
consumption expenditure is less than 24.1% of the total GDP, the level of HDI decreases.
When the government consumption expenditure exceeds 24.1% of total GDP, the level of
HDI for the lesser developed country will begin to increase and will continue to increase
as the amount of government consumption expenditure increases.
Developing nations experience similar effects from government consumption
expenditure as lesser developed nations but to a lesser degree. The results for a
developing nation show that when government consumption expenditure as a percent of
GDP is less than 24.7% it will have a decreased level of HDI, compared to more than
22
24.7% where an increase in government consumption expenditure gives a developing
country an increased level of HDI.
In contrast, when a developed nation increases its government consumption
expenditure as a percent of total GDP to 19.7%, it will experience in increase in HDI.
When the government consumption expenditure surpasses 19.7% of its total GDP, it will
begin to decrease the HDI in the country.
c. Effect of government investment expenditure on HDI
Figure 2 shows estimates of government investment expenditures impact on the
first difference of HDI, holding all other variables constant. The graph shows the impact
of the first difference on HDI with respect to amount of government investment
expenditure as a percent of GDP.
Figure 2
HDI as a function of Government
Investment Expenditure
0.016
Impact on first difference HDI
0.014
0.012
0.01
Lesser Developed
0.008
Developing
0.006
Developed
0.004
0.002
0
1% 5% 9% 13% 17% 21% 25% 29% 33% 37% 41% 45% 49%
23
Results for a lesser developed nation show that until government investment
expenditure reaches 14.4% of total GDP, the government will have a negative impact on
the country’s HDI. When the investment expenditure becomes higher than 14.4% the
lesser developed nation will see a positive effect on its HDI.
For a developing nation, the amount of government investment expenditure does
not have much impact on HDI and will result in only 1.1% growth until government
investment expenditure reaches 14%. After this point, the impact of the investment
expenditure will have a negative impact on HDI.
The impact of government investment expenditure in developed nations is similar
to developing nations. The government investment expenditure will have a positive
impact on a developed country’s HDI until it reaches 21% where it begins to decrease the
country’s level of HDI. Similar to developing nations, the amount of government
investment expenditure does not have much impact on a country’s level of HDI and only
improves HDI by 3.4%.
d. Comparison of Government Consumption and Investment Expenditure on HDI.
Similar to findings by Saxton (1998) and Davies (2008) I find that the effect of
government consumption and investment expenditures on a country’s HDI has different
impacts on lesser developed, developing and developed nations.
Lesser developed nations will benefit greatly from high levels of both government
consumption expenditure and government investment expenditure. When countries have
low levels of either type of expenditure, their level of HDI will be lower.
24
For developing nations, a high level of government consumption expenditure will
benefit the country and will raise its level of HDI compared to investment expenditure. A
developing nation with government consumption expenditure that exceeds 24.7% of GDP
will have higher levels of HDI.
A developed nation will benefit more from government consumption expenditure,
than it will from government investment expenditure. The developed country will
improve its level of HDI with in increased amount of government consumption
expenditure until it reaches 19.7% of GDP.
VI. Analysis of Impact on the SWF
a. Table of results for first difference in SWF
Table 5 shows the estimating equations for the change in the first difference of the
SWF for government consumption expenditure as well as government investment
expenditure.
Table 5
Δ in First difference SWFit = f(GCON)
2
Δ in First difference SWFit = f(GINV)
Lesser developed
-45352.39GCON + 174845.3GCON
-3196.928GCON + 15591.05GCON2
Developing
6450.076GCON - 17721.91GCON2
661.747GCON - 936.945GCON2
Developed
-1.921GCON - 1.771GCON2
2692.886GCON - 5858.065GCON2
b. Effect of Government consumption expenditure on SWF
Figure 3 shows the impact of government consumption expenditure on the first
difference of SWF, holding all other variables constant. The graph shows the impact of
the first difference on SWF with respect to amount of government consumption
expenditure as a percent of GDP.
25
Figure 3
SWF as a function of Government
Consumption Expenditure
30000
Impact on first difference SWF
25000
20000
15000
Lesser Developed
10000
Developing
Developed
5000
0
1% 5% 9% 13% 17% 21% 25% 29% 33% 37% 41% 45% 49%
-5000
-10000
Results for a lesser developed nation show that until government consumption
expenditure reaches 13% of total GDP, the country will not be allocating enough toward
investment expenditure resulting in a decreased SWF. Once a lesser developed nation’s
government expenditure exceeds 13%, they will experience higher levels as measured by
the SWF.
Developing nations will benefit from increased levels of government consumption
expenditure until it reaches 18.2% of the total GDP where the impact of the government
consumption investment on the SWF begins to decrease.
For a developed nation, any amount of government consumption expenditure will
result in a negative impact on a country’s SWF.
26
c. Effect of government investment expenditure on SWF
Figure 4 shows the impact of government investment expenditure on the first
difference of SWF, holding all other variables constant. The graph shows the impact of
the first difference on SWF with respect to amount of government investment
expenditure as a percent of GDP.
Figure 4
SWF as a function of Government
Investment Expenditure
3000
Impact on first difference SWF
2500
2000
Lesser Developed
1500
Developing
1000
Developed
500
0
1% 5% 9% 13% 17% 21% 25% 29% 33% 37% 41% 45% 49%
-500
For lesser developed nations, government investment expenditure results in a
positive impact on a country’s SWF when it is greater than 10.3% of its total GDP.
Before that point, the government investment expenditure negatively affects the country’s
SWF.
27
When a country is developing, government investment expenditure affects its
level of SWF differently than it does for lesser developed nations. As the amount of
government investment expenditure increases to 35.3% of GDP, the country experiences
higher SWF. After this point, the government investment expenditure results in a
negative impact on the SWF.
For developed nations, government investment expenditure will result in a
positive impact on SWF until it reaches 23% of the total GDP. Any increase in
government investment expenditure after 23% will have a negative impact on the SWF.
d. Comparison of government consumption and investment expenditure on SWF
Government consumption and investment expenditures impact the first difference
of SWF differently for lesser developed, developing, and developed nations.
Lesser developed nations benefit from both high levels of government
consumption expenditure and high levels of government investment expenditure. The
results show that when there are low levels of government consumption or investment
expenditure there is a negative impact on the country’s SWF.
For developing nations, both government consumption expenditure and
government investment expenditure will have a positive impact on a country’s SWF until
a certain threshold. Once government consumption expenditure reaches 18.2% of total
GDP it begins to have a negative impact while government investment expenditure
continues to have a positive impact until it reaches 35.3% of total GDP.
A developed nation will not want to use government consumption expenditure,
because it only results in a negative impact on the SWF. The developed nation will
28
benefit from government investment expenditure until it reaches 23% of total GDP,
where it too begins to have a negative impact on the country’s SWF.
VII. Economic Implications
Obtaining higher levels of social welfare in a country is a main concern for every
government. By measuring the amount of expenditure appropriate to obtain higher levels
of social welfare policy makers will be able adjust their policies domestically. It is
important to recognize that social welfare improvements are important for not only lesser
developed nations, but developing and developed nations as well.
Using two different social welfare, measurements also can aide policy makers.
Although there is debate on what type of measurement is best, these findings show that
both measurements of social welfare react similarly to increases in government size.
Knowing this, policy makers will be able to make better government decisions.
The current findings also show evidence supporting the works of Yavas (1998),
Heitger (2001) and Stiglitz (2002). The results from HDI and SWF for lesser developed
countries illustrate that an increased amount of government does lead to an increase in the
social welfare of the country. This agrees with the Keynsian point of view. Following
the logic of Stiglitz, this implies that lesser developed nations will require higher levels of
governance to increase social welfare because the private sector is not capable of
improving social welfare alone. On the other hand, as a country continues to develop and
eventually becomes a developed nation, a larger size of government results in decreased
levels of social welfare. This implies for more developed nations that the private sector is
29
able to sustain higher levels of social welfare, and by increasing the public sector, the
private sector is crowded out resulting in diminishing marginal returns.
VIII. Suggestions of future research
Further research may investigate different types of government expenditure
programs and analyze their individual effects on a country’s social welfare as opposed to
an aggregate perspective. Doing this would help to estimate whether certain programs
are being over or under-funded and what type of government expenditure may lead the
largest amount of growth for lesser developed nations.
Another perspective, with data availability, would be to observe the levels of
government decentralization and its impact on the social welfare for lesser developed,
developing, and developed nations. Results could indicate different levels of
decentralization should be utilized for countries in different stages of development.
Because we see that increases in the sizes of government affect countries in different
development stages differently, it would be useful to examine how the government
expenditure could be divided between central and lower levels of government.
Lastly, future research should further examine the relationship between
government expenditure and social welfare in lesser developed nations. Findings in this
paper conclude that after a certain point an increase in government expenditure always
increases social welfare. By reviewing this, one could argue that once a lesser developed
country reaches a certain level of social welfare, it has become a developing nation. If
30
this were the case then it too would experience decreasing marginal returns to
government expenditure.
IX. Conclusion
The purpose of this analysis was to test differences in the size of governments’
spending on a country’s social welfare in lesser developed, developed, and developed
nations through a multi-year and multi-country panel analysis. Findings suggest that
countries respond differently to government consumption expenditure and government
investment expenditure, and that different levels of development require different
amounts of expenditure for higher levels of social welfare.
This paper provides evidence that less developed nations, or lesser developed
nations, need higher amounts of government consumption expenditure as well as
government investment expenditure as a percent of their total GDP. Developing nations,
also need higher levels of government expenditure, although at a certain point, higher
levels of government expenditure have a negative impact on a country’s social welfare.
Lastly, developed nations require lower amounts of government expenditure to increase
social welfare, while higher amounts of both government consumption expenditure and
government investment expenditure result in negative impacts on a country’s social
welfare.
31
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34
Appendix
a. Eviews output
HDI results for a developed country using consumption expenditure
𝐻𝐷𝐼𝑖𝑡 = 𝑐 + 𝛽1 𝐺𝐶𝑂𝑁𝑖𝑡 + 𝛽2 𝐺𝐶𝑂𝑁𝑖𝑡 2 + 𝛽3 𝐺𝑊𝐺𝐷𝑃𝑖𝑡 + 𝛽4 𝐺𝐷𝑃𝑃𝐶𝑖𝑡 +𝛽5 𝐻𝐷𝐼𝑖(𝑡−1) + 𝜀
Coefficient Probability
Constant
0.001912
0.0001
GCON
-0.011889
0.0094
2
GCON
0.024651
0.0377
GWGDP
-1.57E-02
0.0085
GDPPC
3.48E-06
0.0007
HDI(-1)
0.766298
0
R-squared
0.755593
Adjusted R-Squared
0.753842
Durbin-Watson
1.747193
HDI results for developing nations using consumption expenditure
𝐻𝐷𝐼𝑖𝑡 = 𝑐 + 𝛽1 𝐺𝐶𝑂𝑁𝑖𝑡 + 𝛽2 𝐺𝐶𝑂𝑁𝑖𝑡 2 + 𝛽3 𝐺𝑊𝐺𝐷𝑃𝑖𝑡 + 𝛽4 𝐺𝐷𝑃𝑃𝐶𝑖𝑡 +𝛽5 𝐻𝐷𝐼𝑖(𝑡−1) + 𝐴𝑅(1) + 𝜀
Coefficient Probability
C
0.00096
0
GCON
-0.004563
0.0296
GCON2
0.009229
0.0894
GWGDP
-0.008426
0.0075
GDPPC
1.41E-07
0.0144
HDI1(-1)
0.864282
0
AR(1)
0.068832
0
R-squared
0.798378
Adjusted R-squared
0.797815
Durbin-Watson stat
2.075484
HDI results an lesser developed country using consumption expenditure
𝐻𝐷𝐼𝑖𝑡 = 𝑐 + 𝛽1 𝐺𝐶𝑂𝑁𝑖𝑡 + 𝛽2 𝐺𝐶𝑂𝑁𝑖𝑡 2 + 𝛽3 𝐺𝑊𝐺𝐷𝑃𝑖𝑡 + 𝛽4 𝐺𝐷𝑃𝑃𝐶𝑖𝑡 +𝛽5 𝐻𝐷𝐼𝑖(𝑡−1) + 𝜀
Coefficient Probability
C
-0.000999
0.0071
GCON
0.008572
0.0277
GCON2
-2.17E-02
0.0325
GWGDP
0.018973
0
GDPPC
6.10E-08
0.0605
HDI1(-1)
1.036111
0
R-squared
0.90934
Adjusted R-squared
0.907673
Durbin-Watson stat
2.043563
35
HDI results for a developed country using investment expenditure
𝐻𝐷𝐼𝑖𝑡 = 𝑐 + 𝛽1 𝐺𝐼𝑁𝑉𝑖𝑡 + 𝛽2 𝐺𝐼𝑁𝑉𝑖𝑡 2 + 𝛽3 𝐺𝑊𝐺𝐷𝑃𝑖𝑡 + 𝛽4 𝐺𝐷𝑃𝑃𝐶𝑖𝑡 +𝛽5 𝑃𝑈𝐵𝐿𝐼𝐶 + 𝛽6 𝐻𝐷𝐼𝑖(𝑡−1) + 𝐴𝑅(1) +
𝜀
Coefficient Probability
C
0.000107
0.1672
INV
0.000887
0.0664
2
INV
-0.00211
0.0875
GDPPCF
1.15E-07
0.0002
GWGDP
0.003331
0.0576
PUBLIC
4.26E-05
0.0044
HDI(-1)
0.91402
0
AR(1)
0.009684
0.0001
R-squared
0.913814
Adjusted R-squared
0.912536
Durbin-Watson stat
2.020094
HDI results for a developing country using investment expenditure
𝐻𝐷𝐼𝑖𝑡 = 𝑐 + 𝛽1 𝐺𝐼𝑁𝑉𝑖𝑡 + 𝛽2 𝐺𝐼𝑁𝑉𝑖𝑡 2 + 𝛽3 𝐺𝑊𝐺𝐷𝑃𝑖𝑡 + 𝛽4 𝐺𝐷𝑃𝑃𝐶𝑖𝑡 +𝛽5 𝐻𝐷𝐼𝑖(𝑡−1) + 𝐴𝑅(1) + 𝜀
Coefficient Probability
C
0.000378
0.076
INV
0.000715
0.0898
2
INV
-0.002546
0.067
GDPPCF
1.80E-07
0.0455
GWGDP
-0.008081
0.2177
HDI(-1)
0.88045
0
AR(1)
0.059632
0.0333
R-squared
0.810779
Adjusted R-squared
0.810055
Durbin-Watson stat
2.054619
36
HDI results for an lesser developed country using investment expenditure
𝐻𝐷𝐼𝑖𝑡 = 𝑐 + 𝛽1 𝐺𝐼𝑁𝑉𝑖𝑡 + 𝛽2 𝐺𝐼𝑁𝑉𝑖𝑡 2 + 𝛽3 𝐺𝑊𝐺𝐷𝑃𝑖𝑡 + 𝛽4 𝐺𝐷𝑃𝑃𝐶𝑖𝑡 +𝛽5 𝐻𝐷𝐼𝑖(𝑡−1) + 𝐴𝑅(1) + 𝜀
Coefficient Probability
C
0.008712
0
INV
-0.03088
0.0497
2
INV
0.106969
0.0747
GDPPCF
-1.19E-05
0.0148
GWGDP
-0.024346
0.0005
HDI(-1)
-0.411296
0.0436
AR(1)
0.498915
0
R-squared
0.927005
Adjusted R-squared
0.880775
Durbin-Watson stat
2.15893
SWF results for a developed country using consumption expenditure
𝑆𝑊𝐹𝑖𝑡 = 𝑐 + 𝛽1 𝐺𝐶𝑂𝑁𝑖𝑡 + 𝛽2 𝐺𝐶𝑂𝑁𝑖𝑡 2 + 𝛽3 𝐺𝑊𝐺𝐷𝑃𝑖𝑡 + 𝛽4 𝐺𝑃𝑂𝑃𝑖𝑡 +𝛽5 𝑆𝑊𝐹𝑖(𝑡−1) + 𝐴𝑅(1) + 𝜀
Coefficient Probability
C
2226.666
0
GCON
-4414.251
0.0564
2
GCON
-17553.68
0.0783
GWGDP
10009.85
0.002
GPOP1
9148.104
0.0991
SWF(-1)
-0.243733
0.2167
AR(1)
-0.504648
0
R-squared
0.418256
Adjusted R-squared
0.313407
Durbin-Watson stat
1.978902
37
SWF results for a developing country using consumption expenditure
𝑆𝑊𝐹𝑖𝑡 = 𝑐 + 𝛽1 𝐺𝐶𝑂𝑁𝑖𝑡 + 𝛽2 𝐺𝐶𝑂𝑁𝑖𝑡 2 + 𝛽3 𝐶𝑂𝑅𝑅𝑖𝑡 + 𝛽4 𝐺𝑃𝑂𝑃𝑖𝑡 +𝛽5 𝑆𝑊𝐹𝑖(𝑡−1) + 𝐴𝑅(1) + 𝐴𝑅(3) 𝜀
Coefficient Probability
C
-603.292
0.0001
GCON
6450.076
0.0001
2
GCON
-17721.91
0.0005
CORR
6.135581
0.0001
GPOP
31480.62
0.0227
SWF(-1)
0.580824
0
AR(3)
0.094035
0.003
AR(1)
-0.481283
0
R-squared
0.507977
Adjusted R-squared
0.477225
Durbin-Watson stat
1.795114
SWF results for an lesser developed country using consumption expenditure
𝑆𝑊𝐹𝑖𝑡 = 𝑐 + 𝛽1 𝐺𝐶𝑂𝑁𝑖𝑡 + 𝛽2 𝐺𝐶𝑂𝑁𝑖𝑡 2 + 𝛽3 𝐺𝑊𝐺𝐷𝑃𝑖𝑡 + 𝛽4 𝑃𝑈𝐵𝐿𝐼𝐶𝑖𝑡 +𝛽5 𝑆𝑊𝐹𝑖(𝑡−1) + 𝐴𝑅(2) + 𝜀
Coefficient Probability
C
2875.12
0.0093
GCON
-45352.39
0.0087
2
GCON
174845.3
0.0065
PUBLIC
21.23803
0.0226
GWGDP
-1166.061
0.0005
SWF(-1)
-0.383811
0
AR(2)
0.257446
0.0183
R-squared
0.489845
Adjusted R-squared
0.254388
Durbin-Watson stat
2.344068
SWF results for a developed country using investment expenditure
𝑆𝑊𝐹𝑖𝑡 = 𝑐 + 𝛽1 𝐺𝐼𝑁𝑉𝑖𝑡 + 𝛽2 𝐺𝐼𝑁𝑉𝑖𝑡 2 + 𝛽3 𝐺𝑊𝐺𝐷𝑃𝑖𝑡 + 𝛽4 𝐺𝑃𝑂𝑃𝑖𝑡 +𝛽5 𝑆𝑊𝐹𝑖(𝑡−1) + 𝐴𝑅(1) + 𝐴𝑅(2) + 𝜀
Coefficient Probability
C
44.03926
0.5854
INV
2692.886
0.0365
INV^2
-5858.065
0.0351
GWGDP
7472.793
0.2306
GPOP
19810.22
0.0616
SWF(-1)
0.470918
0
AR(1)
-1.083752
0
AR(2)
-0.556258
0.0001
R-squared
0.306005
Adjusted R-squared
0.272033
Durbin-Watson stat
2.091921
38
SWF results for a developing country using investment expenditure
𝑆𝑊𝐹𝑖𝑡 = 𝑐 + 𝛽1 𝐺𝐼𝑁𝑉𝑖𝑡 + 𝛽2 𝐺𝐼𝑁𝑉𝑖𝑡 2 + 𝛽3 𝐺𝑊𝐺𝐷𝑃𝑖𝑡 + 𝛽4 𝑃𝑈𝐵𝐿𝐼𝐶𝑖𝑡 +𝛽5 𝑆𝑊𝐹𝑖(𝑡−1) + 𝐴𝑅(1) + 𝐴𝑅(2) + 𝜀
Coefficient Probability
C
166.8547
0
INV
661.7471
0
INV^2
-936.9448
0.0586
GPOP1
25862.42
0.05
PUBLIC
-37.82781
0
SWF(-1)
0.648701
0
AR(1)
-0.937574
0
AR(2)
-0.278309
0.0001
R-squared
0.538999
Adjusted R-squared
0.515949
Durbin-Watson stat
1.674956
SWF results for an lesser developed country using investment expenditure
𝑆𝑊𝐹𝑖𝑡 = 𝑐 + 𝛽1 𝐺𝐼𝑁𝑉𝑖𝑡 + 𝛽2 𝐺𝐼𝑁𝑉𝑖𝑡 2 + 𝛽3 𝐺𝑃𝑂𝑃𝑖𝑡 + 𝛽4 𝑃𝑈𝐵𝐿𝐼𝐶𝑖𝑡 +𝛽5 𝑆𝑊𝐹𝑖(𝑡−1) + 𝜀
Coefficient Probability
C
117.9228
0.023
INV
-3196.928
0.0003
INV^2
15591.05
0.0001
PUBLIC
26.86719
0.0011
GPOP
-2981.123
0.0449
SWF(-1)
-0.201326
0.0673
R-squared
0.693628
Adjusted R-squared
0.636893
Durbin-Watson stat
1.870397
39