Ansah, John, "Negotiating Fiscal Sustainability against Socio-Economic Development: A Model-Based Policy Analysis", 2010 July 25-2010 July 29

Negotiating Fiscal Sustainability against Socio-Economic
Development: A Model-Based Policy Analysis

John Pastor Ansah
System Dynamics Group, School of Social Sciences
University of Bergen, Norway

Abstract

Fiscal policy can be multidimensional in nature. On the one hand, it addresses socio-
economic development, and on the other, it deals with ensuring fiscal sustainability. The
ability of the government to design fiscal policies to achieve the twin goal of socio-economic
development and fiscal sustainability requires understanding the social, economic and public
debt impact of the fiscal policy. This can only be achieved by considering the complex
relationships between the social sector, the economic sector and the public sector. Even
though existing models and techniques offer insights about the impact of fiscal policy on
socio-economic development and fiscal sustainability, they lack sufficient causal explanation
of the dynamic impact of fiscal policy on socio-economic development and fiscal
sustainability. This paper develops a causal socio-economic model to help analyze the impact
of fiscal policy on socio-economic development and fiscal sustainability. Results from the
policy analyses found that expansionary fiscal policy is the best policy to achieve socio-
economic development. Contractionary fiscal policy is found to be the best policy for
ensuring fiscal sustainability. We conclude that the balanced budget fiscal policy is the most
workable fiscal policy for the government to achieve the twin goal of socio-economic

development and fiscal sustainability.

Keywords: Fiscal Sustainability, Socio-economic development, Modeling and Simulation

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1. ‘Introduction

Over the past decade the emergence of the public debt crisis in many developing countries
has been attributed to the growth of public spending (Tanzi and Blejer 1986). Guided by the
economic models suggesting that growth can be stepped-up by increasing resources for
investment, governments of developing countries have often resorted to borrowing to
supplement revenue. The borrowed resource is often used for investment purposes and/or
consumption smoothing (Campbell 1989). However, for a variety of reasons, including
increased spending resulting from population growth, external shocks, bad economic
management, low productivity, low competition in the world market, as well as output
decline and slow recovery thereafter, government spending consistently exceed its revenue,
hence causing continued borrowing (Lindauer and Velenchik 1992; Jha 2001; Ghatak and
Sanchez-Fung 2007).

It is therefore not surprising that the economic program, i.e. structural adjustment program
designed to deal with the public debt crisis in developing countries, had fiscal adjustment as a
critical component. The aim of the fiscal adjustment is to reform the government expenditure
management aimed at reducing total government expenditure and to correct the imbalances in
government finances through tax reform aimed at improving the efficiency and effectiveness
of the tax system and tax administration (Konadu-A gyemang 2001). However, due to the
inability of government to increase tax revenue significantly, government expenditure was
reduced to ensure fiscal balance. Empirical studies (Konadu-Agyemang 2001; SAPRIN
2002) have concluded that the social sector expenditure, i.e. education and health suffered
most from the expenditure cut while in some countries the economic sector was not exempted
from the cut in expenditure. Balancing the art of achieving socio-economic development and
ensuring fiscal sustainability has been the dilemma of governments over the years. In most
developing countries, these two goals appear to be in conflict because the only way to
increase socio-economic development from beyond the current stage of development is to
increase government expenditure. Increased expenditure will increase budget deficit and,
consequently, public debt, defeating the goal of fiscal sustainability. The ability of the
government to design fiscal policies to achieve the twin goal of socio-economic development
and fiscal sustainability requires understanding the social, economic and public debt impact
of the fiscal policy. This can only be achieved by taking into account the complex

relationships between the social sector, the economic sector and the public finance sector.

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Even though existing models and techniques offer insights about the impact of fiscal policy
on socio-economic development and fiscal sustainability, they lack sufficient causal
explanation of the dynamic impact of fiscal policy on socio-economic development and fiscal

sustainability. This is the content-wise shortcoming we address in this paper.

Our contribution is as follows; first, we have developed a structure-behavior oriented model-
based socio-economic framework that represent the complex interactions between the social,
economic and public finance sectors based on an adaptation of system dynamics to the
social, economic and public finance theories and an empirical observations. Second, we offer
causal explanation for the dynamics observed in the social, economic and public finance
sectors of the economy and their evolution over time. Lastly, we conduct a policy analysis
from 2000 to 2080 to determine what socio-economic development and fiscal sustainability

would be as a result of the four experimented fiscal policies’.

The policy analysis demonstrates that; the expansionary fiscal policy is the best policy if one
intends to increase and enhance socio-economic development; however, the down side of this
policy is that it builds up high public debt that makes fiscal policy unsustainable. The
simulation result concludes that contractionary fiscal policy is the best policy to significantly
reduce the public debt burden in Ghana; but the trade-off for this policy is that it reduces
socio-economic development. We find that the balanced budget fiscal policy is the second
best policy to increase socio-economic development and reduce debt-GDP ratio. Lastly, the
result of the combined fiscal policy” result demonstrates that the policy is the third best if one
intends to increase socio-economic development and reducing debt-GDP ratio. We therefore

recommend the balanced budget fiscal policy as the most workable fiscal policy for the

! The fiscal policies are; expansionary fiscal policy, contractionary fiscal policy, balanced
budget fiscal policy and combined fiscal policy.

? This policy is a combination of expansionary fiscal policy in the medium term and
contractionary fiscal policy in the long term. The combined policy ensures that government
expenditure exceeds its revenue in the medium term to build human and physical capital to
increase production and socio-economic development. When the foundation of the economy
is perceived to be strong, government fiscal policy is changed to contractionary policy to

ensure that previous deficit are financed by future surplus.

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government of Ghana to pursue in its attempt of achieving the twin goal of socio-economic
development and fiscal sustainability. Though contractionary fiscal policy reduces debt-GDP
ratio much more significantly than the balanced budget fiscal policy does, the simple fact that

it reduces socio-economic development do not make the policy the most desirable policy.

This paper is organized as follows. In section 2 we review the social and economic
implications of public debt. Section 3 represents the methods for assessing fiscal
sustainability and socio-economic development and also the assumptions of the system
structure of the socio-economic model. Section 4 describes the socio-economic model in
detail. Section 5 represents the validation of the model. Section 6 represents the base run
behavior explanations. Section 7 represents the policy analysis and discussion. Section 8

represents the scenario analysis. Section 9 represents the conclusion.

2. Literature Review
This section reviews literature on social and economic implications of public debt in

developing countries. The purpose of the literature review is: first and foremost to outline
some of the empirical finding from studies in developing countries concerning the impact of
public indebtedness on social and economic development. Second, we want to offer structure
information to be imbedded in the model. Lastly, we want to refer the literature to structure
documentation. We want to establish through validation if the behavior pattem from the

model coincide with empirical findings.

2.1 Social and Economic Implications of Public Debt
According to (Mahdavi 2004), two main issues arise in relation to efforts aimed at containing

the fiscal deficits. The first issue is the distribution of the deficit reduction between
government expenditure cuts and higher taxes to increase revenues. Empirical evidence and
findings from studies (Konadu-A gyemang 2001; SAPRIN 2002) indicates that, government
expenditure is more likely to be reduced in times of deficit reduction than increases in taxes.
This is because raising taxes may be perceived as a policy with distortionary effects
(Mahdavi 2004). The second issue is the distribution of government expenditure cuts among
various functional spending. Mahdavi argued that if spending cuts mainly fall on expenditure

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categories that affect current income and consumption levels of large segments of population,
they will have adverse welfare (poverty) implications (Mahdavi 2004). This may result in a
rise in the levels of public discontent and political instability. On the other hand, if the
expenditure reduction is bore by sectors that maintain and enhance the economy’s
productive capacity, it may well endanger the long-term economic growth. This is
particularly true in developing countries where the public sector is the main source of
investment in infrastructure, education and health. During the structural adjustment program
era, these concems were expressed by many groups opposed to the structural adjustment

program.

Various studies (Pattillo, Poirson et al. 2004; Lora and Olivera 2006) have concluded that
high public debt severely constraints developing countries ability to provide social services
such as education and health. A recent study by Lora and Olivera (2006) indicates that a
higher debt ratio reduce social expenditure not just because interest payment on debt
constrained social spending, but because high public debt is associated with cuts in total
expenditure that affect the social sector. They established that debt displaces social
expenditure mainly because it reduces the room (or the appetite) for further indebtedness.
Mahdavi (2004) assess how the external debt burden may influence the composition of
government spending and concluded that there is adverse effect of debt burden on capital
expenditure, and on current expenditures other than wages and salaries. He explained that
since a large part of social expenditure takes place in the form of wages and salaries paid to
public servants in the education and health public sectors, this finding may suggest that social
expenditure are shielded from the adverse effects of the debt burden (Mahdavi 2004).

Public debt has both social and economic consequences for every economy. However, there
seems to he little agreement as to whether public debt affect social and economic sector of the
economy negatively or positively. The debt overhang theory (Krugman 1988; Sachs 1989)
concludes that high public debt negatively affect social and economic growth. On the other
hand, debt irrelevance theory (Cordella, Ricci et al. 2005) concludes that the debt overhang
argument holds for non-HIPC (countries who are not highly indebted), but that for HIPC
(highly indebted countries) there is no evidence of the debt overhang argument.

A number of studies (Krassowski 1974; Sachs 1984; Krugman 1988; Olukoshi 1989; Sachs
1989; Sachs 2002; Pattillo, Poirson et al. 2004) have dealt with the public debt-economic

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growth relationship during the last two decades. Both theory and policy discussion indicate
that the effect of public debt on economic growth could occur through both the main sources
of growth, i.e. the capital accumulation channel and factor productivity growth channel. The
capital accumulation channel argument is supported by the debt overhang theory which
implies that when external debt grows large, investors lower their expectations of investment
returns in anticipation of higher and progressively more distortionary taxes required to repay
debt (Pattillo, Poirson et al. 2004). Consequently, new domestic and foreign investment is
discouraged, which in tu, slows capital stock accumulation. In the highly indebted poor
countries (HIPC), it is argued that investors hold back, given the uncertainties about what
portion of the debt will actually be serviced with the countries own resources (Pattillo,
Poirson et al. 2004). The factor productivity growth channel proponents argue that
governments of indebted countries may be less willing to undertake difficult and costly
reforms if it is perceived that the future benefit in terms of higher output will accrue partly to
foreign creditors. They speculate that the poorer policy environment, in tum, is likely to
affect the efficiency of investment and productivity. In addition, it is believed that
uncertainties and instabilities related to debt overhang are likely to hinder incentives to

improve technology or to use resources efficiently (Pattillo, Poirson et al. 2004).

According to the debt irrelevance theory, generous official assistance helped highly indebt
poor countries (HIPCs) to service their debt, so that they never experience the crowding out
of resources that preceded the emerging market debt crisis of the 1980s. Moreover, the debt
irrelevance theory suggests that net official transfers to HIPCs have grown together with the
debt stock from the 1970s, and that donors/creditors have continued to transfer to HIPCs

resources in excess of those needed to service growing debt.

3. Methodology

3.1 Fiscal Sustainability
The definition of fiscal sustainability derives from the budget constraint of the public sector.

Many authors (Buiter 1985; Blanchard, Chouraqui et al. 1990; Home 1991; Ghatak and
Sanchez-Fung 2007) have discussed the concept and meaning of fiscal policy sustainability.
The idea of fiscal sustainability is intimately related to the public debt dynamics. According
to Ghatak and Sanchez-Fung (2007), fora fiscal policy to be sustainable, every deficit should

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be financed by future surplus. To illustrate, let D, be the stock of total public debt
outstanding at the end of period; Ex, the non-interest government expenditure, that is,
government expenditure excluding total interest payments on the public debt; R, the

government revenue and grants; i the rate of interest on public debt and mis the debt

maturity. Then, the budget constraint for the public sector can be written as:
dD ’
ay =(at)Ex, —(ar)R, +(at)D,, Hat) (1)

Equation (1) shows that the change in public debt will be equal to the difference between
non-interest government expenditures and government revenues and grants, plus total interest
payment on public debt and repayment of principal. This budget constraint ignores public

revenues arising from the creation of money. Equation (1) can be rewritten as:
D, =(dt)pd, +(dt)ds, (2)

Here, pd,is the primary deficit ie. the difference between non-interest expenditure and
government revenue and grants, and ds,is debt servicing which consist of total interest

payments and repayment of public debt. By iterating the government budget constraints
forward, a fiscal policy is considered to be sustainable in infinity if the condition expressed in
equation (3) holds:

= ((at) ((at)pa, +(at)ds, ) (3)

a4

Equation (3) is a condition for fiscal sustainability. It says that the value of future primary
surpluses must be equal to the total public debt. Therefore, for the purpose of this study, a
fiscal policy is said to be sustainable if the total budget surplus over time

T
[= ((dt)pd, +(dt)ds, ] is large enough to maintain or reduce total public debt over the time
tal

frame of the analysis.

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According to the World Bank, fiscal policy is sustainable if debt-GDP ratio does not increase
continuously and/or creates financing needs that cannot be adequately met by the supply of
funds available to the public sector (Bank 2004). Blanchard (1990) defines sustainable fiscal
policy as a policy that ensures that the ratio of debt to GDP converges back towards its initial
level (Blanchard, Chouraqui et al. 1990). However, judgments about fiscal sustainability and
particularly about excessive debt-GDP ratio are hard to make. Economic theory provides
little guidance on this because it was generally believed (before the 1982 Latin American
debt crises) that countries do not go broke. A common approach, therefore, is to rely on a
simple rule that specifies, for example, that the debt ratio should not rise or exceed a specific
limit (e.g. is the approach used by the World Bank and IMF for the highly indebted poor

countries initiative).

For the purpose of this paper, a fiscal policy is perceived to be sustainable if;
1. The total budget surplus over time is large enough to reduce total public debt

2. Debt-GDP ratio decreases or remains constant as a result of the fiscal policy.

3.2. Socio-economic Development Index
Assessment of the development level in this study is based on the socio-economic

development index (SEDI) proposed in this section. The SEDI consist of various indicators
of education, health, social services and income. The SEDI is formulated to be as all-
inclusive socio-economic indicator of development as practicable. The estimation of the

SEDI is influenced by human development index (HDI)? from United Nations and socio-

° The human development index (HDI) is a composite index that measures the average
achievements in a country in three basic dimensions of human development: a long and
healthy life as measured by life expectancy at birth; knowledge, as measured by the adult
literacy rate and the combined enrolment ratio for primary, secondary and tertiary schools;
and a decent standard of living as measured by gross domestic product (GDP) per capita in
purchasing power parity (PPP) US dollars

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economic development index from the EU* (Mehrotra and Peltonen 2005). The SEDI
estimation includes the following variables; primary school enrollment rate, secondary school
enrollment rate, tertiary school enrollment rate, infant mortality, life expectancy, average
access to basic health, per capita food availability, per capita community/social expenditure,

unemployment rate and GNI per capita.

The SEDI is calculated by estimating the various components or index° of SEDI. However,

each index consists of variables. Table 1 shows the SEDI indicators and its component

variables.
Values Dimension Value”
SEDI Index (Variables) Source of Value
Max | Min Max Min
Education
Primary enrollment rate (%) 1 0.2 1 0 HDE’
Secondary enrollment rate (%) 0.8 0.15 1 0 Author's Estimation
Tertiary enrollment rate (%) 0.6 0.05 1 0 Author's Estimation
Health
Infant mortality 10 400 1 0 Author's Estimation
Life expectancy 85 20 1 0 HDE
Average access to health (%) 1 0.1 1 0 Author's Estimation
Per capita food availability 3 0.5 1 0 Author's Estimation
Social Services
Per capital Com/Social exp. 400 10 1 0 Author's Estimation
Unemployment rate (%) 5 40 1 0 Author's Estimation
Income
GNI per capita 40000 100 1 0 HDE

Table 1: Goalposts for Calculating Socio-Economic Development index (SEDI)

4 The index consists of different indicators of health, infrastructure, environment and
education taking into account the public/private sector nature of the variables and data
limitations

> Index represents the main categories of the SEDI. As shown in table 1, the SEDI index are
education, health, social services and income

5 The estimated values are expressed as dimension values between 1 and 0.

T Human Development Estimate by the United Nations

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The equation for representing the index is represented as;

y E var
Index, = A (4)

Here, Index, is the estimated value for each category® of the SEDI, E var is the estimated

value of the variable and nis the number of variables under each category. E var is calculated

as:
E var, =(var —min, )/(max, —min, ) (5)

Here, var is the value of the variablei, min, is the minimum value of variable iand max, is

the maximum value of the variablei . The socio-economic index at any time t is obtained by

an arithmetic average of;

index,
SED, == // (6)

Here, nis the number of variables. It is important to note that the calculation of the SEDI
follows quite closely the human development index (HDI) of the United Nations (UN).

3.3 Model Boundary
Sterman (2000) defines model boundary as the separation between the system being modeled

and the rest of the universe. The boundary is set to keep the model manageable without
compromising its purpose. Table 2 shows the socio-economic model’s boundary indicating

the ignored, exogenous and endogenous variables respectively. A model is but a

8 Education, health, social services and income

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representation of reality. A representation carefully selected to serve a particular purpose. The
purpose of this model is to develop a structure-behavior oriented model-based socio-
economic framework that represents the complex interactions between the social, the
economic and the public sector. The purpose of the model served as a guide to carefully
select variables to be determined endogenously, exogenous and ignored in the model. The
endogenous variables listed in table 2 are the variables defined by the feedback structure of
the model. The exogenous variables affect the state development (behavior) of the model but
are not affected by it. The exogenous variables are either constants or historical tables that are

specified as input to the model. The ignored variables are completely omitted from the model.

Ignored Variables Exogenous Variables

+ Politics ; * Price Endogenous Variables

+ International relations + — Interest Rate Social

* Monetary Sector * Inflation * Population

* Exchange Rate * Births
* Rest of the World * Deaths
+ Education
* Access to basic health
Production
+ Agriculture
+ Industry
* Services
+ Investment
+ Employment, human
capital
+ Households
Public Finance

* Government expenditure
* Government revenue
° Public debt

Table 2: Boundary diagram of the Socio-Economic Model

3.4 Simulation Methodology
3.4.1 Rationale

We selected the methodology of System Dynamics to create our socio-economic model.

System Dynamics is an adequate methodology to capture the relevant system structure to

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explain the system’s outcome behavior (Radzicki 1992; Sterman 2000; Wheat 2007 ).
Fundamental is the idea that all socio-economic actions take place within “feedback loops”.
According to Forrester, “the feedback loop is the closed path that connects an action to its
effect on the surrounding conditions, and these resulting conditions in turn come back as
information to influence further action” (Forrester 1973). Two types of feedback loops are
differentiated; the positive or reinforcing feedback loop (R), and the balancing or
counteracting loop (C). A reinforcing loop represents a mechanism by which an action
produces a result which causes more of the same action thus resulting in the reinforcement of
a growth or decline of a system’s state. A counteracting loop attempts to stabilize some
current state at a desired level through an action of resistance; it reacts to any change and
works as a dampening mechanism. The assumptions we represent by the system structure in
our socio-economic model are explained in the following section. To portray these
assumptions, we use causal loop diagram (Sterman 2000). A causal loop diagram is important
tool in the SD modeling approach for representing the feedback loop structure of systems and

for communicating the important feedbacks underlying the system.

3.4.2 Assumptions about Model System Structure
Figure 1 depicts the assumptions about the system structure of the socio-economic model.

Forrester proposed that it is especially important to review, revise and document the structure
of the model because structure is just as important as the assumed numerical initial and
parameter values in determining the modes of behavior that a system can exhibit (Forrester
1973). The causal diagram in figure 1 helps to review, examine and observe the feedback
structure of the socio-economic model. We have identified twelve reinforcing feedback loops

and ten counteracting feedback loops.

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consumption
leerte Ps ee \
cA
+ .
He 5 & a production sectotl physical RE
5 ee x
Nea ead (-

vestment— public interest payment
* consumption

fe

capacity gap

Figure 1: Complete Causal Structure of the Socio-economic Model interrelating the Social, Economic and
Public Finance sectors of the economy

3.4.3 Social causal structure

The social causal structure consists of the demography, education and health. They are

explained below:

Demography
The demographic causal structure consists of the following feedback loops; the births loop

(R1), the deaths loop (C1), the migration loop (C2), and the under-five mortality loop (R2).

These loops are explained below:

Births loop
Feedback loop (R1) represents the mechanism that generates addition to the population,

causing population to grow. Loop (R1) includes births, population and sexually active

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women. According to (R1), births at any point in time depend on the population of sexually
active women’, and the total fertility rate of sexually active women. An increase in births
causes an increase in population and, consequently, the population of sexually active women,

which further create the condition for births to increase.

Deaths loop
Feedback loop (C1) represents one of the mechanisms responsible for population decrease.

While births reinforce population growth as shown by (R1), deaths counteract such growth.
Loop (C1) includes deaths and population. Loop (C1) represents the fact that a rise in
population causes deaths to increase which, in tum, causes a decrease in the population the

next time round.

Migration loop
The feedback loop (C2) represents second mechanism responsible for population decrease.

Feedback loop (C2) includes migration and population. Counteracting feedback loop (C2)
represents the fact that whereas a population increase positively affects migration an increase

in migration in turn causes the population to decrease.

Under-five mortality loop
The feedback loop (R2) represents the mechanism through which under-five mortality affects

the population dynamics. Loop (R2) includes under-five mortality, total fertility, births,
population and deaths. As deaths increase, due to many factors, such as lack of access to
basic health care, under-five mortality is expected to increase as well. Increase in the under-
five mortality rate will increase the desired number of children to compensate the possibility
of infant death consequently, total fertility rate increases. As the total fertility rate increases,
birth and population is expected to increase which feeds back to increase death and under-
five mortality rate.

Education
The education causal structure is governed by; the education loop (R3), the educational

capacity loop (C5), and literacy loop (R11). These loops are explained as follows;

° Female population between the ages of 15years-49years

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Education loop
The education feedback structure is shown by the reinforcing loop (R3). This loop represents

the feedback process between government education spending, human capital and production.
Loop (R3) includes educational expenditure, educational capacity addition, educational
capacity, enrolled population, literate population, educational attainment of labor force,
average years of formal schooling, human capital, sectoral production, gross domestic
product, government revenue and government expenditure. Government finances expenditure
by taxing income (GDP). Among other expenditures, a fraction of the tax revenue is spent on
education. It is postulated that as education spending increases, educational capacity
increases over time which increases the ability of the educational system to enroll more
students. As the students eventually graduate with different educational attainments, labor
force is made available to the economy for employment. The educational attainment of the
labor force determines the quality of the labor force which is used to estimate the human
capital. As human capital increases, we posit that GDP increases which, consequently,

increases the tax revenue of government the next year round.

Educational capacity loop
Feedback loop (C5) represents the mechanism that makes sure that educational capacity does

not exceed the desired capacity. This loop consists of educational capacity, educational
capacity gap and educational capacity addition. Educational capacity is always compared to
the desired capacity and the capacity gap is used to adjust educational capacity to ensure that

we do not over or under capitalize education.

Literacy rate loop
Feedback loop (R11) represents the effect of literacy on the population dynamics. This loop

includes literate population, literacy fraction, total fertility rate, births, population, population
of school going age, educational capacity and enrolled population. As the literate population
increases, the literacy fraction increases. Supported by demographic literature we hypothesize
that an increase in literacy fraction will decrease the total fertility rate. Therefore, as the
literacy fraction increases the birth rate and over time, the population decreases as well. In
particular, the population within the school going age decreases. As a result, the educational
capacity available is able to accommodate the population more effectively, which in tum,

increases the literate population.

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Health

Access to basic health care loops
Feedback loops (R8, R12, C8 and C10) represents the mechanism responsible for determining

accessibility to health care. In the model, two factors determine access to basic health care.
These factors are physical access to health facility, which is represented by loops (R8 and
C10) and physician-population ratio, which is represented by loops (R12 and C8). Loop (R8)
includes health care spending, health care center, population-health care center ratio, physical
access to health facility, access to basic health care, deaths, population, population of school
going age, enrolled population, literate population, educational attainment of labor force,
average years of formal schooling, human capital, sectoral production, gross domestic
product, goverment revenue and government expenditure. This loop shows that as
government expenditure increases due to GDP increase, it is expected that health care
spending will increase. As health care spending increases, health care centers are expected to
increase which in turn reduces the population per health center (i.e. population-health center
ratio), consequently, access to basic health care increases. An increase in access to basic
health care is postulated to decrease deaths and, thus, increase population which,
consequently, will increase the labor force to yield more employment and thus increase
production. As production increases, revenue to government is expected to increase which
will, subsequently, increase health care spending the next year round. For (R8) to work,
health care center growth must exceed population growth. If not, access to basic health care
will decrease. This is represented by loop (C10). Loop (C10) includes population, population-
health care center ratio, physical access to basic health facility, access to basic health care and
deaths. As population increases, all things being equal, the pressure on the existing health
care centers increases, consequently, population-health care center ratio increases which in
tum, decreases access to basic health care. As access to basic health care decreases over time,

deaths are hypothesized to increase which will in turn decrease population.

Feedback loop (R12) includes health care spending, physicians, physician-population ratio,
access to basic health care, deaths, population, population of school going age, enrolled
population, literate population, educational attainment of labor force, average years of formal
schooling, human capital, sectoral production, gross domestic product, government revenue
and government expenditure. As government expenditure increases due to income (GDP)

increase, it is assumed that health care spending will increase. As health care spending

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increases, more physicians are hired which increases the number of physicians. As physicians
increase, physician-population ratio will decrease which will increase access to basic health
care, over time, deaths is assumed to decrease as access to basic health care increases, which
in tum increases population. Population increase will consequently increase the labor force to
yield more employment and thus increase production. As production increases, revenue to
government is expected to increase which will, subsequently, increase health spending the
next year round. For (R12) to work, therefore, physicians’ growth must exceed population
growth; else, access to basic health care will decrease. This is represented by feedback loop
(C8). Loop (C8) includes population, physician-population ratio, access to basic health care
and deaths. This loop implies that as population increase, physician-population ratio
increases which consequently decrease access to basic health care. As access to basic health
care decreases, over time deaths is expected to increase which will in tum, decrease

population.

3.4.4 Economic causal structure

Employment loop
The feedback loops (R4, C3 and C4) represents the mechanism that generates employment.

Loop (R4) includes gross domestic product, expected demand, desired labor, employment,
human capital and sectoral production. The reinforcing loop (R4) represents how gross
domestic product creates demand of goods and services which is produced by labor
(employment). As employment increases, over time, human capital increases which in tum
causes sectoral production and gross domestic product to increase. The counteracting
feedback loop (C3) causes employment to increase as desired labor increases. Consequently,
as employment increases, desired labor is expected to decrease the next year round, assuming
desired labor remains constant. The counteracting feedback loop (C4) postulates that, if
unemployment rises, wages will fall. As a result, desired labor will increase which will
initiate the hiring of labor to increase employment over time. As employment increases

eventually, unemployment decreases.

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Public investment loop
The feedback loop (R5) represents how public investment creates physical capital that is used

for production. Loop (R5) includes public investment, investment, sectoral physical capital,
sectoral production, gross domestic product, government revenue and government
expenditure. As public investment increases, investment goes up, which accumulates physical
capital and consequently increase production. As production increases, government revenue
is expected to increase which is used to finance all government expenditure. As a result
government expenditure increases which in tum increase private investment the next time

round.

Private investment loop
The feedback loop (R6) represents the mechanism that creates private investment. Loop (R6)

includes gross domestic product, household income, disposable income, savings, private
investment, sectoral physical capital and sectoral production. As households’ income
increases as a result of increased income from production (GDP), disposable income
increases. As disposable income increases, it is expected that savings will rise, which in tum
will increase private investment with a delay and, consequently, investment. In reality, rise in

investment increases physical capital accumulation. As a result, GDP increases.

Labor Productivity loop
The feedback loop structure of labor productivity is governed by the reinforcing loop (R7)

and the counteracting feedback loop (C7). Loop (R7) includes sectoral physical capital,
capital-labor ratio, labor productivity, sectoral production, gross domestic product,
government revenue, government expenditure, public investment and investment. The
reinforcing loop (R7) represents how an increase in physical capital, relative to employment,
causes capital-labor ratio to increase, which feeds back to increase labor productivity,
production and thus physical capital accumulation. The counteracting feedback (C7) includes
employment, sectoral employment, capital-labor ratio, labor productivity, sectoral
production, gross domestic product, expected demand and desired labor. Loop (C7)
represents how an increase in employment relative to physical capital causes capital-labor
ratio to decrease, consequently, causing labor productivity to fall. As labor productivity

decline, production decreases which feed back to dampening the increase in employment.

Page 18 of 88
3.4.5 Public debt causal structure

The accumulation of public debt is embodied in the reinforcing feedback loops (R9, R10, and
C9). Government finances its spending in part by taxing income (GDP); and finances any gap
remaining, as is the case in developing countries’, by raising public debt through borrowing.
The feedback loops (R9) and (R10) represents the debt trap phenomenon (Saeed 1993) which
represents the basic mechanism responsible for debt accumulation in developing countries.
The reinforcing loop (R9) includes the following variables: Budget deficit, borrowing, public
debt, interest payments due and interest payments. As the budget deficit increases due to
government excess spending over revenue and grants, the deficit is financed through
borrowing. As borrowing increases, public debt accumulates, - consequently the interest
payments build-up. As interest payments increases, so does the budget deficit due to
increased spending. The deficit must, in part, be financed by additional borrowing which in
tum increase public debt the next time round. The reinforcing loop (R9) illustrates the deep-
seated structure of the debt accumulation process. The reinforcing loop (R10) includes public
debt repayment and borrowing. As public debt increases, repayment increases over time. Due
to pervasive deficit spending by government, repayment increase causes the need to borrow
to finance repayment, which in turn increases public debt the next year round. The public
debt growth is contained by the counteracting feedback loop (C9) striving to reduce the
public debt stock and other measures such as debt relief and interest relief that are not
represented in the feedback loop structure of this model. Loop (C9) includes public debt and
repayment. As public debt increases, repayment increases which then decreases public debt

the next time round.

4. Description of Stock and Flow Structure of the Modules

4.1 Population Module

Since population size, composition and growth have a major influence on socio-economic
development, a long-term socio-economic model requires a population module that generates
these outputs. The significant importance of population in a country’s economic growth

makes its presence in such model essential and fundamental not the least, because population

Page 19 of 88
provides one of the two major inputs to economic production, i.e. labor and the other one
being technology. The population module represents the total population and population age-
distribution disaggregated by sex. The conceptual framework of the population module is
based on the demographic transition theory (Thompson 1928) and empirical observations
which suggest that births, deaths and net migration are the three determinants of changes in
the size of population over time. The stock and flow structure of the population module is
shown in figure 3 below.

total net migratio1
NET MIGRATION PER eRe ran
1000 HABITANTS
FUNCTION
INITIAL adult crude death
POPULATION rate
inet migration

secondary student

total adult deaths crude death rate

primary student
crude death rate

share of population

under five y per gender
mortality rate allan: patton total deaths .. —
lati cohorts sl leath per age total population:
population group oD
effective death rates
population under per age group

five

Figure 3: The stock and flow structure of the population module

The population stock is disaggregated into male and female genders and 82 age cohorts. i.e.
(new born, age 0 to 79, 80+) for each gender. The ageing process is conceptually
straightforward, as illustrated in Figure 2. Babies bom (births) is moved to a stock called new
bom, which is immediately moved to age 0-1 cohort. At the end of each year, the surviving
population in each age cohort is moved to the next age cohort, except for the last age cohort

(age 80+). The population in the last age cohort remains in the cohort until they die.

Oo QO Qo fon

death at bith
i leath at 1 deaths te 2 deaths be 79 deaths lage 80+ deaths

births ——*) new bom |

ageO-1 fmt age 2 fn | age 78-79 [|] age 80+

Figure 2: Ageing process in the population module

Page 20 of 88
In the population module, the equation that shifts the population from one cohort to the other

at the end of each year (minus deaths) is:

Population cohort shift [sex] =SHIFT IF TRUE (population [sex, new born], MODULO
(time, 1) <time step/2, age 80 and over, 1, 0)

When the condition MODULO (time, 1) <time step/2 is true (when time reaches the end of
each year), the subscripted variable population shifts from one age cohort to the next. The
second to last parameter of the equation causes the function to add (not replace) the age 79
cohort to the last cohort age 80+. The last parameter of the function (0) is used to replace the
first age cohort, i.e. to set the first cohort to zero at the end of each year to store new births

the next year.

We initialize the population stock by estimating the population for each age (age 0- 80+)
using the 5 year age group population data of the World Population Prospects provided by
United Nations (UNDP 2002). We postulate a diminishing stock variable for the population
ageing process where a given population declines as it moves from one age cohort to the
other due to deaths. Therefore, the formula for disaggregating the 5 year age group

population data to initialize the model is as follows; using age 0-4 as an example:

AGE, =X 9-4 +2* (X54 —X50)/5 (7)
AGE, =X oq +(Xo4 —X5-9)/5 (8)
AGE, =X,4 =f (9)
AGE, =Xqy (Xo 4 —X59)/5 (10)
AGE, =X 9-4 —2*(X oy —X5-)/5 (11)

Here, X,_, is the mean population of age group (age 0-4), X;., is the mean population of
age group (age 5-9) and P,_, is the sum of the population age group (age 0-4).

Page 21 of 88
Equation (7) to (11) illustrates the estimation of initial population into the age 1-4 cohort. The
approach and sequence is followed for the successive age groups to disaggregate the
population into each age cohort to initialize the population module.

4.2 Births Module

The study of population dynamics must begin with fertility (McFalls 2003). The determinants
of fertility have engaged the interest of economists for sometime. Adam Smith noted that
families were larger in settings where labor was scarce and child labor was especially
valuable to parents (Smith 1776). However, Malthus viewed fertility not as an individual
choice but as an outcome of social institutions. Malthus assumed with some justification that
fertility is determined primarily by two variables, age at marriage and the frequency of
coition during marriage (Malthus 1798). According to Becker, the development and spread of
knowledge about contraceptive during the last century greatly widened the scope of family
size decision-making, for it has separated the decision to control births from the decision to
engage in coition (Becker 1960). However, such a widening of the scope of decision-making
has increased the importance of other factors. In every society a variety of cultural, economic
and health factors interfere with the process of human reproduction (McFalls 2003).
Demographic literature has identified countless factors that affect births. Any population
model that tries to account for each factor soon becomes hopelessly confusing (Meadows,
Randers et al. 1972). To avoid confusion without compromising the fundamental objective of
the model, we assume that births depend on three variables namely, the number of sexually
active women, the total fertility rate and age specific fertility distribution. The choice of these
variables is consistent with the proposed factors by Malthus (Malthus 1798) incorporating
Becker's approach to fertility rate estimation (Becker 1960). These causal variables were
found in a review of literature on fertility see (Smith 1776; Malthus 1798; Kamerschen 1972;
McFalls 2003; Schultz 2007). The structure of the birth module is as shown in figure 5:

Page 22 of 88
EFFECT OF ADULT
LITERACY ON DESIRED
NUMBER OF CHILDREN INITIAL DESIRED
TABLE NUMBER OF CHILDREN

<average litera effect of adult literacy on PER'WOMAN
fraction» —~® desired number of children
per woman

relative po gdp
> effect of economic ccritiin hy 7
EFFECT OF ECONOMIC ondesired number of chidren —-pe- desired number of sexually active

CONDITION ON DESIRED per woman children per woman women proportion of
NUMBER OF CHILDREN babies per sex

TABLE ve
<under five 7 conciously controlled
mortality> ———m Perceived Under fertility fraction

Five Mortality
ee

TIME TO PERCEIVE unconciously
CHANGE IN UDER FIVE determined fertility
MORTALITY fraction

total fertility rate —®> births ———— total births

age specific fertility
UNCONSCIOUSLY 4 distibution
DETERMINED FERTILITY contraceptive (ERLCONDTTON
RATEFUNCTION prevalence fraction FERTILITY DISTRIBUTION
Pd ADJUSTMENT PARAMETER
relative adult female ___effect of female literacy rate
literacy fraction —® on contraceptive prevalence AGE SPECIFIC FERTILITY
fraction DISTRIBUTION FUNCTION
INITIAL CONTRACEPTIVE
EFFECT OF FEMALE LITERACY PREVALENCE FRACTION
FRACTION ON CONTRACEPTIVE
PREVALENCE FRACTION TABLE

Figure 5: The Structure of Birth Module

(i) Sexually active women
This is the female population between the age cohorts 15-49 years. It is assumed that females

between these age cohorts are the womb of the nation. As the female population in the 15-49

years increases, births are more likely to increase.

(ii) Total Fertility Rate
In the births module, the total fertility rate is positively influenced by the desired number of

children and negatively affected by the contraceptive prevalence fraction. Leaving the
contraceptive prevalence fraction for later discussion, consider the desired number of
children: It is suggested that income (GDP), education and perceived under-five mortality are
the determinants of desired number of children. It is assumed that human nature no longer
guarantees that a growth in income appreciably above the subsistence level results in a large
inadvertent increase in fertility (Becker 1960). In other words we stipulate a negative
relationship between income and desired number of children. Moreover, we postulate that, as
literacy rate increases and women increasingly become active economically, the desired

number of children will decrease over time. Lastly, we assume that a decline in under-five

Page 23 of 88
mortality would induce a corresponding decline in desired number of children. Considering
the contraceptive prevalence fraction, the growth of knowledge about contraception has
greatly widened the scope of decision-making concerning family sizes. We would expect this
to have reduced the relative fertility rate among users. We assume a positive causal
relationship between relative adult literacy and contraception use. A rise in literacy rate is
postulated to increase contraception use which subsequently reduces fertility. Consciously
controlled fertility fraction is defined as desired number of children per woman by
contraceptive prevalence fraction. On the other hand, unconsciously determined fertility
fraction is determined by unconsciously determined fertility fraction by the fraction of adult
population not using contraception (1-contraceptive prevalence fraction). Thus, total fertility
rate is defined as consciously controlled fertility fraction plus unconsciously determined
fertility fraction. Therefore, births are determined by total fertility rate, sexually active
women, age specific fertility distribution. Births are disaggregated by proportion of babies
per sex. The age specific fertility distribution function used in the model is shown in figure 4.

(iii) Age Specific Fertility
Figure 4 shows the fertility distribution by age. In the model this is taken as exogenous
variable based on data from Ghana population data analysis report (Ghana Statistical Service

2005).

us eS
od “\

IN
Va

oy 5 0 5 30 35 40 45 50

Figure 4: Age Specific Fertility rate

Page 24 of 88
4.3 Deaths Module
In recent history, there has been a substantial decline in mortality in the poorer countries of

the world. One manifestation of this mortality decline has been labeled the population
explosion, in that the large increase in the numbers of people has been associated with drops
in deaths, rather than increases in fertility. Extensive research review shows two main
arguments about the causes of mortality decline. Increasing success in the application of
communicable disease control technologies through public activities is argued to be the main
reason for the decline in mortality. On the other hand, Krishnan (1975), indicated that the
decline in mortality had been the result of general economic improvement, particularly in the
elimination of famines and improved nutrition available to the people of the less developed
countries (Grosse and Perry 1982). The relationship between social and economic well-being
and health status has long been recognized, but it has been argued that the association
between economic levels and health status has weakened in recent decade. The structure of

the death module is as shown in figure 7.

INITIAL GROSS

NATIONAL INCOME
NORMAL LIFE
EXPECTANCY
TABLE
Average Gross
‘National Income
TIME FOR INCOME average real pc income
CHANGES TO AFFECT in usd in ppp
LIFE EXPECTANCY ppp = — romlllife
PARAMETER MALE FEMALE LE SP&tancy
DIFFERENCE

EFFECT OF BASIC HEALTH

EXPEDTARCY TATE ————» Effect Of Basic Health indicated life

Care On Life Expectancy expectancy
TIME FOR CHANGES IN. __-——

ACCESS TO BASIC HEALTH
CARE TO AFFECT LIFE Effect Of Adult Literacy
EXPECTANCY <access to basic Rate On Life Expectancy

health care>

——> life expectancy

EFFECT OF ADULT
LITERACY RATE ON LIFE
EXPECTANCY TABLE

TIME FOR CHANGE IN
ADULT LITERACY TO
AFFECT LIFE EXPECTANCY

Figure 7: The Structure of Death Module

Page 25 of 88
In the model income, health and literacy rate are the three main factors determining deaths
through life expectancy. We postulate a positive relationship between income and life
expectancy. However, it is important to note that this relationship is nonlinear, where, at a
certain level of income, the effect is flattened (see figure 6). Access to basic health care is the
proxy for health. We assume a positive causal relationship between access to basic health
care and life expectancy. As the percentage of the population with access to health care
increases, it is expected that life expectancy will increase which will subsequently reduce
mortality. Lastly, the literacy rate is believed to contribute to increase life expectancy. As the
population becomes more literate, their way of life and level of awareness about health issues
increases. This directly or indirectly affects life expectancy positively. Figure 6 shows the
postulated relationship between income and life expectancy.

Graph Lookup - normal life expectancy table

This table function represents the relationship between income and life expectancy. Iis based on _ Export,
p.15 of World Resources 1888-99: A guide to the global envionment, and we assumed that beyond Pint

Input Outout aed

a a

[a0 [a

jess [moss 1

(tea [seas

foo

[moo [7 f

|eves_ [7578

[aesooa [7a
pm
t—

co

il

:

WF

|
New

I fy
Inpotvals | xin] Jae8195 e793 %maie|20000 | Reset Seaing

Ok | ClearPonis | ClewAlPonis | CursRet| _Clea'Reference | RefsCur| _ Cancel |

Figure 6: Income and Life Expectancy

4.4 Education Module
The history of education in Ghana indicates that government efforts in education lagged

behind the missions (religious groups), and between 1847 and 1923, there was no colonial
education policy per se (Finlay 1971). After independence, the education system grew and
access to education was a response to African aspirations, their desire for parity with
European elites, and their recognition of where mobility opportunities resided (Foster 1965).
As primary education expanded, the concomitant effect was increased pressure for

development of secondary schools, partly because it was the logical extension of existing

Page 26 of 88
education and would meet employment needs and opportunities (Finlay 1971). The stock and
flow structure of the educational module is as shown figure 8.

SEX ENROL
FRACTION

Popubion With

a | ad ot “eetny Euan
earn | PP ~~ Way at

Figure 8: Stock and Flow Structure of Education Module

The education module models the educational capacity, the education system and the
recruitment of labor. Leaving the educational system and the recruitment of labor for later
discussion, consider the educational capacity: The input to the educational capacity sector is
the total education expenditure which comprises of public and private education expenditure.
Total education expenditure is used to finance capital and recurrent education expenditure.
The capital expenditure finances capital spending that creates educational capacity to support
the education system at each level. The allocation of capital spending to primary, secondary
and tertiary levels of education is based on a decision variable “educational capital
expenditure function”. This decision variable reflects government education priorities. We
used subscripts to disaggregate educational capacity into the three educational levels
identified in the model, i.e. primary, secondary and tertiary.

Considering the educational system, this sector of the module models the process of students
going through schooling from the primary to the secondary and to the tertiary level. Students
are disaggregated by both stage and gender with subscripts. The primary education has six

Page 27 of 88
stages, each stage is assumed to last for one year, while secondary education has six stages,
comprising of junior secondary, i.e. three stages, and senior secondary, i.e. three stages, with
each stage duration of one year. Moreover, tertiary education is assumed to comprise of four
stages with each stage lasting for a year. The output of each stage and level of education
serves as an input to the subsequent stage and level. We assume age seven as the age for
starting primary school. The main input to the educational system sector is the age 7
population. The stock of student at each level of education is increased by enrollment and
decreased by graduation and dropouts. The following equations are used to represent student
population at each level. The equation representing the primary students is:

$,” =5,.” +b,” -9,"}-[5,."*a,") (12)

Where, S,_,”° are the previous year primary students, e,”is the primary enrollment, g,”* is
the primary graduation and dr,’’is the primary dropout fraction. The equation representing
the secondary students is:

s," =5,." +," -g,")5,,.°*4r,") (13)

Here, S,_,“ are the previous year secondary students, e,” is the secondary enrollment, g,” is

the secondary graduation and dr,“ is the secondary dropout fraction. The equation
representing the tertiary students is:

," =8,." +e," -g,") 4s, ,°* ar") (14)

Here, S,_,"are the previous year tertiary students, ¢,"is the tertiary enrollment, g,"is the

tertiary graduation, and dr,"*is the tertiary dropout fraction.

Considering the recruitment of labor, the model account for the population disaggregated by

levels of education. This is important for the labor recruitment process, because the

Page 28 of 88
educational attainment of the labor force is differentiated. In the model, the following
equations are used to represent the population with different levels of education. The equation

representing the population with no formal education is:

Pp” =P," +(p,’ ~e,"")+d -(p" aa ad ) (15)

Here, P.," is the previous year population with no formal education, p,’is the age 7

population, e,”°is the primary enrollment, dis the primary dropout and 1," is the death
fraction of the population with no formal education. The equation for representing the

population with primary education is:
pe =P” ag,” 0) 40 fp hen) 09

Where, P,_,”" is the previous year population with primary education only, g,” is the primary
graduation, e,’is the secondary enrollment, d‘*is the secondary dropout andr,” is the death
fraction of the population with primary education only. The equation representing the

population with secondary education is:
P” =P." +(9," -2,")+d" -(P“*1,") (17)

Here, P,,*’is the previous year population with secondary education, g,”is the secondary

graduation, ¢,"is the tertiary enrollment, dis the tertiary dropout and 1,“ is the death

fraction of the population with secondary education. The equation representing the population
with tertiary education is:

BY =P" +9," ("#7") (18)

Page 29 of 88
Here, P,,"is the previous year population with tertiary education, g,"is the tertiary

graduation, and r,"is the death fraction of the population with tertiary education.

We assume age 18 to 60 years as the working age. Therefore the total workforce in the model
consist of the total workforce with no formal education, 18 years to 60 years, total workforce
with primary education, 18 years to 60 years, total workforce with secondary education, 18
years to 60 years and total workforce with tertiary education, 18 years to 60 years. The

equation representing the total workforce is;

L, =1" +1. H.° 4," (19)

Where L, is the total workforce, 1," is the workforce with no formal education, 1,”° is the

workforce with primary education only, 1,*° is the workforce with secondary education, 1," is

the workforce with tertiary education. The workforce with no formal education is represented

by equation (20) as:
lt =Sagel8 <P", <age60 (20)
tal
Equation (21) represents the workforce with primary education only as:

1," =Sagel8 <P: <age60 (21)
ta

Representation of the workforce with secondary education is as shown in equation (22):

1" =Yagel8 =P“: <age60 (22)
tal

Page 30 of 88
The workforce with tertiary education is represented by equation (23) as follows:

1° =Siagel8 <P", <age60 (23)
ta

4.5 Access to Basic Health Care Module
The stock and flow structure of access to health care module is as shown in figure 9.

TINETO ADU
STUDENT PHYSICIAN

etme, MITE
geduates an
eal e
mero
(| Ram
: i _-— "
5 = '
mactoN os \ 5 nine Ps
Guero SNL roses Ee] rey ream TV pe
SEM ipsam =< J J) meres
a TR sit Ue) eet
wero anuet —TMETOAOWUST__ srw a rel
peer nant a FT ontceitin
guctonson veurcumm ton TS tN thie N PN, ma eset
Maman | MURCaR ; Scoala hes | <a
; a

CARE pysians POPULATION ys }
cots Ga SHRI —T nines
pits etd a pepsin ath sot popaonheathcae ysl

sons j vemmoeaucaneceren Spacer 7 t

4 } { | Es stetf tes

| Suet \ Nastinecinimasay “hae

— - ages oa

Re ne 2) \
“ | f

pepe SUPPLY

Figure 9: Stock and Flow Structure of Access to Basic Health Care Module

The access to basic health care module represents health capacity. It includes the population
of physicians and the process of student’s physicians going through schooling and eventually
becoming practicing physicians to contribute to health care delivery. The model also accounts
for the distribution of health capacity and practicing physicians within the urban and rural
parts of the country and its effects on health care access. In the module, we assume that
capital expenditure on health contribute to the creation of health capacity. Therefore, as
capital health expenditure increases it’s presupposed that health capacity will increase. It has
been established that in most developing countries, including Ghana, the per capita social

services is more skewed towards the urban areas compared to the rural areas. We therefore

Page 31 of 88
assume that a higher proportion of health centre’s will be located in the urban areas resulting,

in lower population-health centre ratio in the urban areas than, what we find in the rural areas.

In the module, we assume a fraction of the students going to the university to enroll into
medical school and become student physicians. Based on the medical school educational
structure in Ghana, we assume 6 years delay from enrollment to medical school to becoming
a practicing physician. The population of student's physicians increases by enrollment and
decreases by graduation or dropout rate. The population of practicing physicians increases by
graduates from the medical school, and immigration of physicians whereas retirement and
out-migration decreases the population of practicing physicians. It has been established in
Ghana that there are more physicians per capita in the urban areas as compared to the rural
areas. The model incorporates this vital information by assigning a higher proportion of
practicing physicians per capita to the urban area. Access to basic health care depends on
physical access to health facility and effect of population-physician ratio on access to basic
health. Physical access to health facility is determined by population-health centre ratio and
average distance to access health centre. It is estimated that the rural population travels an
average distance of 10km to access a health facility which causes their physical access to
health services to be low compared to the urban population with an average distance of 2km.
Moreover, it is hypothesized that, as the population to health centre ratio, increases, access to
health decreases due to the inability of the limited facility to take proper care of such a larger
population.

4.6 Government Revenue/E xpenditure Module
The structure of the government expenditure and revenue module is as shown in figure 10

below:

Page 32 of 88
BUDGETARY <

EXPENDITURE
TABLE cn ‘.
v orodi tax revere —® budgetary revenue — revenue and grants
nme
expenditure
GENERAL SERVICES <grants>
general services desired general EXPENDITURE AS SHARE
expenditure License OF BUDGET
EDUCATION
desired education EXPENDITURE AS
Peseinie cewpentins’ SHARE OF BUDGET
HEALTH _—-?-
desired heath EXPENDITURE 4S. ————» belepadie
belt penton expenditure SHARE OF BUDGET fractia
weet COMMUNITY AND SOCIAL
expenditure D A
= > desired social __j_____ SERVICES EXPENDITURE AS
community and social services expenditure SHARE OF BUDGET
services expenditure
desired economic ECONOMIC SERVICES
—
economic services. services expenditure EAEENDITURE foe
expenditure
UNALLOCATED
unallocated | __esivd other__ EXPENDITURE AS
expenditure expel SHARE OF BUDGET

Figure 10: Structure of the Government Expenditure and Revenue Module

This module represents government expenditure and revenue accounting identities underlying
the government fiscal policy. Government expenditure is defined as the budgetary
expenditure fraction of gross domestic product. Budgetary expenditure fraction is a decision
variable, where the government decides on the total expenditure for each fiscal year. The total
budgetary expenditure fraction is estimated exogenously from historical data by computing
the ratio of government expenditure as a fraction of GDP. Non-interest expenditure is the
difference between government expenditure and real interest payments. The total government
expenditure consists of six functional expenditures. The functional expenditures are; general
services, education, health, community/social services, economic services and unallocated
expenditure. The functional expenditure share of non-interest expenditure is estimated from
historical data. The allocation of the functional expenditure is taken as a decision variable
where government based on the expenditure priorities decides the fraction of non-interest
expenditure to be spent on each area. Public investment is captured in the functional
expenditure by the share of government expenditure for economic services. In the
government expenditure accounting, for instance, desired general services expenditure is the
actual government expenditure on general services and general services expenditure is the
actual expenditure. It is hypothesized in the government expenditure accounting that desired

expenditure equals the real expenditure.

Page 33 of 88
Considering government revenues the tax revenue is estimated as a function of the effective
tax rate and gross domestic product. The effective tax rate is the actual tax fraction of income
(GDP) actually collected by the tax authorities, estimated from historical data. Grants are

exogenous variable from historical data. Revenues and grants is the sum of tax revenues and

grants.

4.7 Public Debt Module
The conceptual framework of the public debt module is a SD-based adaptation of the

government budget constraint literature (Christ 1968 ; Blinder and Solow 1973). This
literature sets out that the fiscal deficit must equal the sum of domestic borrowing, foreign
borrowing and seignorage and considers the impact of deficit financing on output (Islam and
Wetzel 1991). Guided by the economic models suggesting that growth can be stepped-up by
increasing resources for investment, governments of developing countries have often resorted
to borrowing to supplement revenue. The borrowed resource is often used for investment
purposes and/or consumption smoothing (Campbell 1989). However, due to various reasons,
including increased spending resulting from population growth, external shocks, bad
economic management, as well as output decline and its slow recovery thereafter,
government spending consistently exceed its revenue, hence causing a continued borrowing
(Lindauer and Velenchik 1992; Jha 2001; Ghatak and Sanchez-Fung 2007). As a

consequence, debt accumulates causing a heavy debt burden.

The public debt model demonstrates transparently the mechanisms that generate debt. Public
debt disaggregates into domestic and foreign sources. Subscripts!® are used to separate
domestic debt from foreign debt. Principal relief and interest relief in the model are only
applicable to foreign debt, and are represented by exogenous variables generated from
historical data.

10 The following variables: ( borrowing, borrowing fraction, public debt, total public debt,
repayment, debt maturity, interest payments due, interest payments, obligatory interest rate,
interest rate, interest addition, accrued interest, interest subtraction) in the public debt model

as shown in figure 1a are subscript variables separated into domestic and foreign.

Page 34 of 88
We assume that government finances its budget deficit by borrowing from domestic and

foreign sources and depict it as a result of a government budget constraint:

Pd, +044 4i,D hea 4D 4D lo =Bd, =9B," +98," (21)
Where pd, is primary deficit, i,’ is the domestic interest rate, D“.4 is the domestic public
debt of the previous year, i,‘ is foreign interest rate, D‘:1 is the foreign public debt of
previous year, m* is the domestic debt maturity, , m is the foreign debt maturity, Bd, is the

budget deficit, gB,° is the domestic borrowing and gB,’ is the foreign borrowing.

We express the stock of total public debt (D,) from the government budget constraint
equation and the public debt model in figure 14 as follows:

D, =D* +D‘: (22)

Dis [Divs +(at)gB,? “(ar(P*vf )] +[Arss +(at)la%s +(at uit, -(at)is“.] (23)

Di, =a fdlgp! ai, ‘| fara Hatha’ Hatui, fadts'|4o4(¢ -£.,)]Hadeo'] (24)

Here AI‘:4 is domestic accrued interest obligation of the previous year, (dt)Ia‘: is the
domestic interest obligation accrual, (at)ui*, is the domestic unpaid interest payments,
(dt)Is*, is domestic interest obligation elimination, Al‘, is the foreign accrued interest
obligation of the previous year, (dt)Ia‘, is foreign interest obligation accrual, (dtUi', is
foreign unpaid interest payments, (dt)Is‘: is foreign interest obligation elimination, f, is the
current average exchange rate per year, f,_, is average exchange rate of the previous year and

(dt)rD ‘4 is the foreign public debt forgiven per year.

Equation (22) demonstrates that total public debt consists of domestic public debt and
foreign public debt. Equation (23) defines the domestic public debt, where the first term of
the equation represents the integration of domestic borrowing and domestic repayment into

the domestic public debt. The second term characterizes the integration of domestic interest

Page 35 of 88
obligation accrual, domestic unpaid interest payments and domestic interest obligation
elimination into the domestic accrued interest obligation. In equation (24), the first term of
the equation represents the integration of foreign borrowing and foreign repayment into
foreign public debt. The second term represents the integration of foreign interest obligation
accrual, foreign unpaid interest payments and foreign interest obligation elimination into
foreign accrued interest obligation. The third term represents the foreign debt adjustment!!,
where the change in exchange rate is multiplied by the foreign debt. The last term represents
debt relief.

The public debt model adopted the ‘co-flow structure’ (Sterman 2000) to account for
‘accrued interest obligation’. As government borrows, it attracts an interest obligation, which
is referred to as ‘interest obligation accrual’ (see figure 12). The ‘interest obligation accrual’
is stored into a stock of ‘accrued interest obligation’. ‘Accrued interest obligation’ represent
the total obligatory interest to be serviced per year. On the other hand, when repayment on
debt is made, it decreases ‘accrued interest obligation’ through ‘interest obligation
elimination’. In sum, the co-flow structure helps us to keep track of ‘accrued interest
obligation’ as an attributes of public debt.

Obligatory interest rate is estimated as accrued interest obligation divided by public debt.
Assuming that government is able to service all interest payment due, where interest
payments due equals’ interest payment. In that situation, ‘obligatory interest rate’ (as is being
referred to in this model) would be known as ‘average interest rate’. However, in the model,
we postulate that depending on government debt burden (measured by debt-GDP-ratio),
interest payments due can be rescheduled for future payment. The addition of unpaid interest
payments to the stock of accrued interest obligation create imbalance between public debt
and accrued interest obligation, therefore, the average interest rate on public debt is referred

to as ‘obligatory interest rate’ .

Unpaid interest is the difference between interest payments due and interest payment. The
model separates interest payments due from interest payments because when goverment
debt burden is high, deficit spending is reduced by rescheduling interest payments which

inadvertently reduces borrowing. Interest payments due is estimated as public debt multiplied

" Foreign debt adjustment is the foreign debt incurred due to changes in exchange rate.

Page 36 of 88
by obligatory interest rate. Interest payments are estimated as interest payment due multiplied
by effect of debt-GDP-ratio on interest payments. The non-linear function of the effect of
debt-GDP-ratio on interest payment is as shown in figure 11 below. According to figure 11,
as debt-GDP-ratio increases, governments’ ability to service interest payments is reduced; as

a result a fraction of the interest payments due is actually paid.

1.05

0 0.2 0.4 0.6 0.8 1 12
DebtGDP-ratio

Figure 11: Non-Linear effect of Debt-GDP-Ratio on Interest Payment

The foreign debt adjustment is most often unaccounted for in many studies such as
(Simonsen 1985; Meijdam and Stratum 1989; Saeed 1993; Senhadji 1997; Helbling, Mody et
al. 2004). In many developing countries, foreign debt adjustment is a significant debt
component that is often not recognized. The foreign debt adjustment in the public debt
module captures the debt incurred due to exchange rate changes. The adoption of a Structural
Adjustment policy of currency exchange liberalization and the resulting devaluation, as well
as currency devaluation policies significantly increased the exchange rate in many developing
nations, Ghana included, in the 1980s after many years of a fixed exchange rate policy
regime (Islam and Wetzel 1991; Konadu-Agyemang 2001). Since foreign loans are
contracted in foreign currency, the sudden increase in the exchange rate following the
liberalization of the exchange market significantly increased the foreign debt in local
currency equivalence. This is captured in the model to account for the exchange rate effect on
foreign debt accumulation. As the exchange rate increases”, ceteris paribus foreign debt is

increased by the debt incurred from the exchange rate increase (foreign debt adjustment). It is

12 Exchange rate is the current market price of 1 US dollar to 1 Cedi (Ghanaian currency). An
increase in exchange rate is when more cedi is needed to exchange for 1 US dollar. That is to

say that the local currency (cedi) has depreciated or the US dollar has appreciated.

Page 37 of 88
important to note that foreign debt adjustment arises only when the exchange rate changes. In
cases where exchange rate remains stable, there is no foreign debt adjustment. The stock and

flow structure of the public debt module is as shown in figure 12.

TIMETO ADJUST
EXHCANGE RATE

TIMETO ADJUST

ORCA DT me
ADJUSTMENT xcl | ™
ange eae ve indicated EXCHANGE RATE
me exchange rate“ — "TIME SERIES
Foreign Debt s /
‘Adjustment | 2etctange in orem
debt adjustment ‘ real exchange rate
change
{ 7 indicateed forign#—— © <<prncpal relief
1 debt adjustment borrowing fraction -a— time series>
real otal foreign = pecans
debt Sg
"ie ug ee Public Debt
7 repayment.
oe total public debt ae i iy “
_ indicated
’ EB’
pennant — interest payments ine
expendiure spending i
<wni etre huge J yet
pha iol iget interest rate
, —™ primary deficit |
\ revenue anc grants>" Pima de ‘a detcit | interest payments | f\
tax revenue rans
\ GRANTS TIME
SERIES

th effect of debt-qdp-rato

‘on interest payment
Interest
dp -rat | interest obligation Obligation | interest obligation
EFFECT OF DEBT-GDP-RATIO accrual elimination
ON INTEREST PAYMENT
TIME SERIES: at
interest rate We | <interest relief time
series>

Figure 12: Stock and Flow Structure of Public Debt

4.8 Households Module
In the household module, real total household income is calculated as the sum of income

(GDP) and total net transfers. In other words, real total household income equals the sum of
income from production, private transfers, private income and interest payment from
domestic loans. Private transfer and private income are exogenous variables from historical
data. Disposable income to households is the sum of after-tax income. It is assumed in the
model that, the household use their disposable income for consumption and or savings. The
consumption decision of households in the model assumes that households will spread

existing resources to achieve a smooth consumption profile and the excess resources will go

Page 38 of 88
for savings. We speculate that as disposable income rises, consumption as well as savings
increases. Over time, savings are used for investment which builds-up capital for production
whereas; increased consumption can move aggregate demand upward consequently leading

to more production to meet demand for goods and services. The structure of the household

module is as shown in figure 13.
publ
PRIVATE FACTOR _—
INCOME TABLE INITIAL
PROPENSITY TO ‘consumption
saa pie factor CONSUME a satg
TRANSFERS TABLE consumption
pra tafe |
* a eal disposable
—
ames domestic ————?” Households income’ fieons |
produ a Sa
— oa private savings __._ private domestic
. investment —-m private
Bae investment
fri dc So
FOREIGN DIRECT
INVESTMENT TABLE
nai

Figure 13: Structure of Household Module

4.9 Land Module
The land module represents the urban land, agricultural land in use and other land’? and

account for the factors responsible for the changes in these categories of land. In the model,
urban land increases as agricultural land and other land are converted for urban land use due
to population increase in urban areas. In the model we assume a per capita space for each
person in the urban area. Therefore, desired urban land is defined as urban population by per
capita space. Agricultural land increases as more other land is transformed into agricultural
land and decreases as agricultural land is moved to other land due to land fertility decline and
transformation of agricultural land to urban land due to urbanization. Desired agricultural
land is calculated as indicated per capita land for agriculture by rural population. Here,
desired agricultural land is the land desired by farmers to support their livelihood. It is
assumed that agricultural activities occur only in the rural areas. Indicated per capita land is
defined as normal per capita land for agriculture and effect of fertilizer use on agricultural
land expansion. It is postulated that as fertilizer use decreases due to increase in fertilizer

prices, per capita land for agriculture increases due to reduction in land fertility. Therefore,

1S Land other than for agriculture and urban land use

Page 39 of 88
for a farmer to achieve the production targets, land size must be increased. The stock and

flow structure of the land module is as shown in figure 14.

INITIAL URBAN
LAND
PER CAPITA
SPACE Sin ond
|
desired urban an
ne ee FRACTION OF LAND
<popiletion in the FROM FALLOW 10
urban areas> desired change in URBAN LAND
whan land > other to wan
EFFECT OF FERTILIZER
USE ON AGRIC LAND agri to wan land
INITIAL FERTILIZER “EXPANSION TABLE
CONSUMPTION
INITIAL OTHER
BAND total arable land
Tehtive fertilizer eae le
FERTILIZER _ x comsumption Ofer dans

CONSUMPTION

TABLE effect of fertilizer use on

agric land expansion agtic to fallow
Pea land
indicated per capita Ew aan
land for agtic pa
* TIME FOR LAND
desired agric land desired agric fand EXHAUSTABILITY
changes
“ a giicuttral Land In Use
NORMAL PER CAPITA
LAND FOR i e
AGRICULTURE INITIAL
AGRICULTURAL
LAND IN USE

Figure 14: Stock and Flow Structure of Land Module

Page 40 of 88
4.10 Employment Module
The stock and flows structure of the employment module is shown in figure 16.

NUMBER OF
YEARS STUDIED

years of schooling

education

5 average years of TIMETO ADJUST
co -e attainment ratios Schooing HUMAN CAPITAL
Haman
HIRING indicated human Capital
ADJUSTMENT TIME capita = human capital
pe cane
INITIAL TIMETO ADJUST
= PLOYMENT EXPECTED DEMAND
—~ Expected
Demand net change in
expected demand
WAGE
=~. YMENT TIME
LABOUR aggregate demand
INCOME SHARE =
whet co in Wages

wages a,
inidicated nominal _ Bae
wages

=

UNEMPLOYMENT —
EFFECT ON WAGES.
TABLE

TIMETOADJUST _TIMETO ADJUST
UNEMPLOYMENT FORWA GES FOR PRICE
WAGES

Figure 16: Stock and Flow Structure of Employment Module

Neoclassical economics postulate that employment depends on both the supply of and
demand for labor. Demand for labor is represented in the model by a variable called “desired
labor”. As desired labor changes, employment adjusts through net hiring. Desired labor
depends positively on expected aggregate demand for goods and services and negatively on
wages. Net hiring is the difference between desired labor and employment divided by the
hiring adjustment time. The total workforce is defined as the working age population (age 18-
60 years). Wages is the income share of labor (income is taken to be equal to GDP). In the
model, wages is formulated by exponential smoothing. Indicated wages are the negotiated
wages. The real wages adjusts to their indicated values with a delay (wages payment time).
This adjustment time determines how rapidly wages respond to the result of negotiations. In

the model, wages payment time is monthly, adjustment can take place at a minimum of 0.08

Page 41 of 88
years. Indicated (negotiated) wages is a function of gross domestic product and labor income
share adjusted by the unemployment effect on wages. The equation for representing

employment is as follows;

Ep, =Ep, +(dt)nh, (25)

Where Ep, is current employment, Ep,_, is previous year employment, nh, is net hiring. Net

hiring is represented by equation (34) as;
nh, =If [wf, >Ep,, (dL, —Ep, )/,0] (26)

Here wf, is total workforce, dL, is desired labor, and © is time delay to adjust net hiring.
Equation (35) represents the desired labor as;

_xd,(1-a)
at, = iw, em

Here, xd, is expected demand, (1 -a) is labor income share, and Nw, is nominal wage.

The supply of labor is referred to as total workforce. The unemployment fraction is the
unemployment relative to the total workforce. Assume that the unemployment fraction
increases unexpectedly. This will push wages downwards, thus keeping wages lower than
when the economy is in equilibrium, i.e. normal. The effect of the unemployment fraction on
wages is represented by the nonlinear function portrayed in figure 15. Figure 15 shows that,
as the unemployment fraction increases, wages are adjusted downwards because the
unemployed labor would rather work for less wages than being unemployed. We assume that,

decreasing wages will stimulate demand for labor causing an increase in employment.

Page 42 of 88
° 0.1 0.2 0.3 0.4 0.5
Unemployment fraction

Figure 15: Non-linear effect of unemployment fraction on wages

The employment module postulates that employment opportunities are filled by workforce
with the highest educational attainment respectively. In other words, it is assumed that
workforce with tertiary education attainment is the first category of workforce to be
employed, followed by workforce with secondary education attainment, primary education
attainment and lastly, non formal education respectively. The acquisition of human capital is
represented by an exponential smoothing. The real human capital adjusts to its indicated
value with a delay (time to adjust human capital). Indicated human capital is a function of
average years of schooling and employment. The representation of human capital in the
model is based on Loening’s human capital estimation method (Loening 2005). The equation

representing human capital is as shown below:

he, =he,, +(dt)che, , (28)

Here, hcis the human capital and chcis the change in human capital. The change in human

capital is calculated in the model as:

che, =(ihe,, —he,_, )/the (29)

Here, ihc,_,is the previous year indicated human capital and thcis the time to adjust human
capital. The indicated human capital is calculated in the model as:

ihc, =Ep,_, * ays, , (30)

Page 43 of 88
Here, Ep,_,is previous year employment and ays,_, are the previous year average years of
schooling. The average years of schooling is defined as:

ays, =SUM (ear, , *nys } (31)

Here, ear,_, is the previous year education attainment ratio and nys is the number of years of

studies. The educational attainment ratio measures the level of education among the

workforce.

4.11 Agriculture Module
Agricultural production in developing countries has virtually been dominated by small-scale

farmers who are known to produce up to 90% of food consumed in some countries in A frica
(Lambert 1989; IFAD 1993). In Africa, it is estimated that small-scale farmers make up at
least 73% of the farming population (Garrison 1990; IFAD 1993; Odulaja and Kiros 1996)
and it is expected to be about 90% in Ghana. The stock and flow structure of the agricultural

sector is as shown in figure 17.

. AVERAGE LIFE OF
TIME TO ADJUST CAPITAL

CAPITAL ACQUISITION 5
AGRICULTURE AGRICULTURE
Mi Capit EFFECT OF RELATIVE
CAPITAL ON YEILD PER
capital acquisition capital depreciation. HECTA TABLE AGRIC CAPITAL

agriculture aggicure J {ee ELASTICITY
Ye INITIAL CAPITAL effect of relative capital VALUE ADDED

AGRICULTURE Loy onvielperhecla —REFERENCEYEILD AGRICULTURE
relative capital PER HECTARE |

COST OF AGRIC eae omoite
CAPITAL MANAGEMENT yield per hecta. ines
—_— “i

ELASTICITY ——® rebate. § f ‘aggicitural yield —a» chs

‘production.
ee ‘ee
INITIAL Value added per

MANAGEMENT INITIAL, ‘ai

In
rmanagennt ENVIRONMENT 4

<Agricultural Lane

food availabilty

<Human Capitals | FRACTION OF AGRIC
AGRIC PRODUCTION AS per capita food
ENVIRONMENT FOOD avaiabilty
annual rainfall environttent . y
a ELASTICITY
ADJUSTMENT TIME ‘total populion>
effect of fertiizer
RAINFALL TIME rehtive effect of rainfall on consumption of
SERIES a = environment
i EFFECT OF FERTILIZER
CONSUMPTION ON
BARE, ENVIRONMENT TABLE
EFFECT OF RAINFALL ; :
(ON ENVIRONMENT

Figure 17: Stock and Flow Structure of Agricultural Module

Page 44 of 88

The agricultural production function in the module reflects the characteristics described. The
production function used in the agricultural module is based on the agricultural production
function by Odulaja and Kiros (1996)! with modification. The modification here is that, we
introduce a fourth factor of production, i.e. capital, which represents investment in
agricultural research and extension services '°. In most developing countries in Africa,
agricultural activities are small-scale, which make little use of agricultural machinery.
However, government investment in agriculture through agricultural research departments to
produce high-breed seeds, to come out with best farming practices and advice farmers on

cropping pattem is considered here as the capital in the agriculture model. In the module,
agricultural production (Y“:) is calculated as a function of yield per hectare ( yh, ) and land

area Cultivated (cl, ).

Y*, =yh, *cl, (32)
Where yield per hectare ( yh) is portrayed as a function of reference yield per hectare (ry ),
relative environmental effect (en), relative management effect (rm), relative capital effect
(K*) represented as;

yh, =ry*en*rm* K* (33)

In the module, we define relative environment effect (, en ) as;

en =(" 4.) ty y op

Here, rf, is average annual rainfall, and rf,_,is the initial average annual rainfall, ft,is the
current fertilizer consumption, ft,,is the initial fertilizer consumption and fis the agric

environment elasticity.

Relative management effect (rm ) is portrayed in the module as;

wa ¥ =f(L)g(E)h(M). Where L is land size, E is environmental effects and M is

management effects and f, g, h are functions relating to L, E and M respectively.
15 Ts the application of scientific research and new knowledge to agricultural practices

through farmer education

Page 45 of 88
mK)

Here, hc*:is the current human capital in agricultural sector, and hc*:is the initial human

capital in agricultural sector, and 6 is the agric management elasticity.

Lastly, relative capital is portrayed as;

Here Va, is the current agricultural capital, i.e. investment in agricultural research and

agricultural extension staff, Va,_, is the initial agricultural capital and €is agric capital
elasticity.

In the agricultural module, agricultural production is driven by the increase in availability of
the production factors i.e. land, environment, management and capital. Agricultural value
added is the price of agricultural products and agricultural production is calculated as
agricultural yield by value added to agriculture.

4.12 Industry Module
The stock and flow structure of the industrial module is as shown in figure 18.

Page 46 of 88
INITIAL INDUSTRIAL
AVERAGE CAPITAL-LABOR RATIO
GROWTH RATE GROWTH
TIME
INITIAL INDUSTRIAL industrial capital-labor-——a
effect of industrial
CAPITAL GROWTH ratio growth cantal Ebor proructly
yweh
<Employment>——™ industry ="
capital labor rai effect of industrial
capital growth Seg
INITIAL LABOUR
industrial capital indicated industrial labor  _ PRODUCTIVITY
INDUSTRY CAPITAL growth productivity INDUSTRY
ACQUISITION DELAY
> industrial Capital Industrial Labor]
sina ap aed change in labour Productivity
industrial capital industrial capital depreciation productivity
acquisition i \
INITIAL CAPITAL i
f \ INDUSTRY AVERAGE LIFE TIME TO ADJUST
CAPITAL
: TADUSTEE: PRODUCTIVITY
: . per unit cost of
industrial capital industrial factors of industry
inpustry ™ Production =—Wproduction
CAPITAL
ELASTICITY
<Human Capital>

Figure 18: Stock and Flow Structure of Industrial Module

The industry module employs a Cobb-Douglas production function to represent industrial
production. Industrial capital is accumulated through industrial capital acquisition and
industrial capital depreciation. Industrial capital acquisition is defined by industrial
investment divided by per unit cost of industrial capital. Industrial capital depreciation is
based on the perpetual inventory method with a common geometric depreciation rate of 4%
(Collins and Bosworth 1996). This gives an average life of industrial capital of 25 years.
Industrial human capital represents the human capital accumulation in the industrial sector of

the economy. Industrial production is represented as;
yi =(Kia) * hes) pis (37)
Here, Y':is the industrial production, K':is the industrial capital, hc’: is the human capital

in the industrial sector, lp'sis the industrial labor productivity and siis the industrial
physical capital elasticity.

Page 47 of 88

Labor productivity calculation in the module is based on the labor productivity function
(Sargent and Rodriguez 2000). In the industrial module, we define labor productivity as;

Ip =Ip'+(dt)elp,_, (38)

Here, Ip’: is the industrial labor productivity, clp,, is the change in industrial labor
productivity. Change in the industrial labor productivity is the difference between indicated
labor productivity and labor productivity adjusted by time. Indicated labor productivity is a
function of initial industrial labor productivity, effect of industrial labor-capital ratio growth
and effect of industrial capital growth.

4.13 Service Module
The stock and flow structure of the service module is as shown in figure 19.

<AVERAGE
GRC 1 RATE
TIME:
INITIAL SERVICE INITIAL SERVICE
TECHNOLOGY \ CAPITAL-LABOUR
GROWTH service capital labour RATIO GROWTH
ratio growth =
effect of service
<Employmeni> " __ service capital labor ratio
capital- labour rat effect of service growth
SERVICE CAPITAL wee capital growth
ACQUISITION
DELAY service capital

INITIAL CAPITAL growth INITIAL LABOR

SERVICES indicated service labor ——PRODUCTIVITY

productivity SERVICE
Services
| Capital eel
« sercive capital depreciation Service Labor
service capital See oe »~ change in labour Productivity
acquisition AVERAGE LIFE productivity service
Ry CAPITAL SERVICE oA
PER UNIT COST OF seNViog Eeboie
. is service factors of SERVICE LABOUR
sondces CAPITAL SERVICE ‘production PRODUCTIVITY
inves : seRVICE”” —— 4
SERVICES
CAPITAL services
ELASTICITY production:
<Human Capital

Figure 19: Stock and Flow Structure of Service Module

The service module like the industrial module uses the Cobb-Douglas production function to
estimate the service production. Service capital accumulates through service capital
acquisition and service capital depreciation. Service capital acquisition is the delayed capital
acquisition which is service investment divided by per unit cost of capital service. Service

capital depreciation follows the perpetual inventory estimation of capital with 6.67%

Page 48 of 88
depreciation rate which translates to 15 years of service capital life. Service labor
productivity follows the definition of Sargent and Rodriguez (2000) as explained in the

industrial sector. The service production is defined as;

ye, =(K va * (he's) * Ip. (39)

Here Y “tis the service production, K *:is the service capital, hc*:+ is the human capital in
the service sector, lp*:is the service labor productivity and s is the service physical capital

elasticity.

4.14 Investment/Aggregate Production Module
The investment module simply represents how agricultural, industrial and service

investments are calculated. In the model, investments are apportioned to the three sectors of
the economy (agriculture, industry and services) by a decision variable, investment share. We
assume that 10 percent of investment goes to the agricultural sector, 40 percent to the
industrial sector and 50 percent to the service sector. Investment to the various sectors is
portrayed as investment multiplied by investment share. Gross domestic product is the sum of
industrial production, service production and agricultural production. The sector GDP ratio
accounts for the ratio of each sector of production to the total production. The structure of

investment and production is shown in figure 20.

agricultural

ee investment ——

<investment>—____»»_ industrial J INVESTMENT
investment" SHARE

services investment

<industry sectoral
—_
production> production
<services ‘i
production> sector gdp tan6
<agriculbire gross domestic
— >

production> product

Figure 20: Structure of Investment and A ggregate Production Module

Page 49 of 88
5. Validation
Validation of the model is an integral part of our model development (Forrester and Senge

1980; Sterman 1984; Barlas 1996; Sterman 2000; Schwaninger and Grosser 2008). First, the
model is firmly grounded in empirical research on economic growth (Smith 1776; Solow
1956; Romer 1990; Stem 1991; Solow 1994; Ranis, Stewart et al. 2000), demographic
transition theory (Thompson 1928), SD-based adaptation of the government budget constraint
literature (Christ 1968 ; Blinder and Solow 1973) and the debt trap theory (Saeed 1993).
Second, the validation of the parameter values utilized draw on variety of data sources such
as; quarterly digest of statistics from Ghana Statistical Service, world development indicators
from the World Bank, international financial statistics from IMF, world population prospects
from UNDP, demographic and health surveys in Ghana and expert opinions from interviews.
Third, the model has been extensively analyzed using structure-oriented behavior test (e.g.,
extreme condition test, boundary adequacy test and verification test). Finally, in the behavior
reproduction tests we compared the simulation output with several historical times series
(e.g., total population, life expectancy (female), life expectancy (male), under-five mortality,
government expenditure, gross domestic product, household income, disposable income,
foreign debt and domestic debt). The result from the behavior reproduction tests (Theil
inequality) is presented as follows:

5.1 Behavior reproduction test
In a behavior reproduction tests we compare the simulation output with several historical

times series. Summary statistics of historical fit (Sterman 1984) has become a standard
validity test for system dynamics models (Oliva 1998). The summary statistics proposed by
Sterman (2000), apply mean-square-error (MSE) to the measurement and interpretation of
forecast errors by comparing the model output to historical data. The MSE is defined as:

2

1 n
— >, (s, -A,) (40)

Nn ‘ta

Where nis the number of observations, tis time, S, is the simulated value at time t, and A, is

the historical value at time t.

Page 50 of 88
Failure to fit the historical data with simulated value may be caused by a poor model or by a
large degree of randomness in the historical data (Sterman 1984). Sterman employs the Theil
statistics (Theil 1966) to decompose the MSE. The ‘inequality proportions’ (Theil statistics)
derived from the MSE are:

ys = -4) (41)
1 —a~ yt
=> (8, -A)

Here, U™ is the fraction of MSE due to bias, S is the mean of the simulated values and A is
the mean of the historical values.

2
US = (s, -S,) (42)

1

=> (8, -A, 7

n
Here, U ‘is the fraction of MSE due to unequal variance, S, is the standard deviation of
simulated values and S, is the standard deviation of historical values.

_ aQ-r) $8,
*¥ (5, -A)

ue (43)

Here, U‘is the fraction of MSE due to unequal covariance and ris the correlation

coefficient.

U™ +U%+4U° =1. Table 3 present the error decomposition of the socio-economic model.

Page 51 of 88
Inequality Statistics
Variable RMSE | U™ us US R?
Total population 0.018 | 0.013 | 0.320} 0.667] 0.998
Life expectancy (female) 0.020 0.192 0.760 0.048 0.998
Life expectancy (male) 0.025 0.437 0.537 0.026 0.998
Under-five mortality 0.190 | 0.573 | 0.332 | 0.096 | 0.952
Goverment expenditure 0.433 | 0.352 | 0.056} 0.591] 0.759
Gross domestic product 0.115 | 0.167] 0.167] 0.666] 0.849
Household income 0.119 | 0.185] 0.298} 0.517 | 0.871
Disposable income 0.122 | 0.172 | 0.230 | 0.598] 0.827
Foreign debt 0.800 | 0.128} 0.043 | 0.829] 0.773
Domestic debt 0.286 | 0.407] 0.158] 0.435] 0.856

Table 3: Eror Decomposition (Theil Inequality)

Table 3 demonstrates that the model reproduces these variables with a high accuracy ranging
fromr’ =0.75tor? =0.998. This indicates that there is a strong correlation between the
model output and historical data. On the behavior validity, table 3 shows the results of total
error from MSE breaks down into bias (UM), unequal variance (US) and unequal covariance
(U‘). The result in table 3 indicates that most of the variables except ie. government
expenditure, foreign debt and domestic debt have RMSE above 20%. This strongly indicates
that the model endogenously tracks major variables quite well. Moreover, all the variables
except i.e. life expectancy (female), life expectancy (male) and under-five mortality, shows
that the major part of the error is with the covariation component as compared to bias and
unequal variance which are relatively small. This clearly shows that simulated variables
tracks the underlying trend well, but diverges point-by-point. This might indicate that the
majority of the error is unsystematic with respect to the purpose of the model, and it should

not therefore be rejected for failing to match the data points.
The high error associated with bias i.e. life expectancy (female), life expectancy (male) and

under-five mortality might be due to the few data points available for historical data of these

variables.

Page 52 of 88
6. Model behavior: the base run
This section presents a base run simulation that is intended to replicate historical

development and the future development base on the present. The simulation time frame is
from 1960 to 2080.

6.1 Demographic transition
Figure 21 shows the evolution of total population, crude births rate, crude deaths rate and out-

migration rate on the left hand side and infant mortality rate, life expectancy and total fertility
rate on the right hand side from 1960 to 2080.

60M 200

60 100

49 §
0.006

0 0

0

0 et

0 0

4960 1975 1990 2005 2020 2035 2050 2065 2080 1960 1975 1990 2005 2020 2085 2050 2065 2080
Time (Y ear) Time (Y ear)

total population : basen, —_—_—_ infant mortaliy : basen

crude birth rate[FEMA LE] : baserun. ————— expectany :

crude deaths rate : basen. mite lly oe

“out-migration rate": baserun

Figure 21: Base run: demographic transition

Ghana’s total population have been increasing from approximately 6.8 million people in 1960
to around 18.9 million people in 2000 and by 2080, total population will be 52.1 million if
current trend continues. The net annual population growth rate decreased from 2.5% in 1960
to 2.3% in 2000 and will be 0.59% in 2080. The main determinants of population growth, i.e.
mortality and fertility have been declining over the years and will continue amidst some
slowdown, which is consistent with the demographic transition theory. Out-migration on the

other hand continues to rise as population increases over time.
Mortality, measured by the crude deaths rate shows a decreasing trend from 1960 to 2045,

after which crude deaths rate started increasing. The crude deaths rate of 21 deaths per 1000
people in 1960 fell to about 8.7 deaths per 1000 people in 2000 and will decrease further to

Page 53 of 88
6.1 by 2045 before increasing again to 8.7 by 2080 if the current trend continues. The
significant decrease in mortality is an indication of fundamental improvement in Ghana's
human health which is also evident in the improvement of the infant mortality rate and the
life expectancy as shown on the right hand side of figure 21. The infant mortality rate was
179 infant deaths per 1000 live births in 1960 and has fallen over the years to about 85 by
2000 and will fall further to around 24 deaths per 1000 live births in 2080 if current trend
continues. Life expectancy at birth has increased from 43 years in 1960 to approximately 62
years in 2000 and will be 82 years by 2080. The decline in mortality and increase in life
expectancy at birth is caused by a higher literacy rate, increased access to basic health care
and increased real per capita income in USD PPP"°. As per capita income in USD PPP
increases, normal life expectancy!” to be achieved increases as well. Moreover, as literacy
rate and access to basic health care increases, its effect on normal life expectancy is assumed

to be positive, thus further increasing life expectancy at birth.

The crude birth rate remained above 40 births per 1000 people from 1960 until about 1985
when it started reducing. The crude birth rate declined from 45 births per 1000 people in
1960 to 35 births in 2000 and will be 18.2 births per 1000 people in 2080 if current trend
continues. The crude birth rate remained higher than the crude death rate from 1960 to 2080,
and this explains the population increase. While the decline in crude birth rate is substantial,
it is far above the replacement level fertility rate of 8.7 births per 1000 people (crude death
rate in 2080). The total fertility rate - the average number of children a woman would have
during her life given the current fertility rate, fell substantially from 5.5 children in 1960 to 4
children in 2000 and will be 2.2 children in 2080. The factors responsible for the decline in
total fertility rate are increase in the contraceptive prevalence rate, and a decrease in desired
number of children per woman due to decline in under-five mortality rate, increased literacy
rate and increased per capita income. As contraceptive usage increases due to an increase in
literacy rate, and promotion of contraceptive use through public education, it is postulated
that the total fertility rate will decline because of the reduction in the probability of unwanted
children being born. In addition, as under-five mortality rate decrease, total fertility rate is
expected to decrease due to the positive relationship between under-five mortality and total

1© United States Dollars in Purchasing Power Parity
” Normal life expectancy is the life expectancy that considers income as the only

determining variable.

Page 54 of 88
fertility rate. Also, an increase in literacy rate and per capita gross national income is
assumed to decrease total fertility rate.

In summary, in Ghana, birth rate has continuously been higher than the death rate over the
years; consequently, the population has grown consistently from 1960 to 2000. The base run
simulation indicates that birth rate will continue to exceed birth rate by 2080.

6.1.1 Age-sex structure
Figure 22 shows the age-sex structure distribution of Ghana’s population for 1960, 2000,

2040 and 2080. The male population is on the left hand side and the right hand side portrays
the female population. The population pyramid shows the male and female population into 5
years age cohorts from age 0 to age 80 and over. The x-axis represents the population size
and the bar represents the population size in each age cohort.

baserun

bese

Population Pyramid for 1960
Male Female

Population Pyramid for 2000
Male Female

80.and over
7 to 79
70 to 74
65 to 69
60 to 64
55 to 59
50 to 54
45 to 49
40 to 44
35 to 39
30 to 34
25 to 29
20 to 24
15 to 19
10 to 14
5to 9
Oto4

2M 15M 1M 500,000 0

baserun
Population Pyramid for2040

Male Female

0 500,0001M 15M 2M

2M 15M 1M 500,000 0 0 500,0001M 15M 2M

basenun

Population Pyramid for2080
Female

80.and over
Bto

2M 15M 1M 500,000 0 0 500000 1M 15M 2M

Male
80 and over
75 to 79
‘1 to 74
65 to 69
60 to 64
55 to 59
50 to 54
45 to 49
40 to 44
35 to 39
30 to 34
25 to 29
20to 24
15to 19
10to 14
5to 9
Oto 4
2M 1M 0

3M 0 1M 2M 3M 4M

Figure 22: Population pyramid for 1960, 2000, 2040 and 2080

Page 55 of 88
Ghana’s population bears a youthful structure with a broad base consisting of large numbers
of children and a conical top of a small number of elderly persons. The population pyramid
shows that Ghana’s population continues to expand because of the tremendous momentum
built into its young age structure, as larger numbers of people are bom each year. The
structure of the population in Ghana as shown in figure 22 implies that even if the average
number of children born per woman falls substantially as compared to what is it now, the
young age structure will generate growth in population for decades to come as successively
larger number of people enters their childbearing age. The shape of the population pyramids
shows the transitional nature of Ghana’s population from 1960 to 2080. The population is
experiencing a transition from high births and deaths rates to a rapid and continuous drop in
deaths rates without a corresponding reduction in births rate. Consequently, the proportion of
the population within 15-60 years as well as the elderly (60+) years is expected to increase

over time.

Table 4 shows the share of the population in absolute and relative terms in the various age
cohorts.

Age Cohorts 1960 1980 2000 2020 2040 2060 2080

0-14 3.13E+06 | 4.92E+06 | 7.73E+06 9.65E +06 1.05E+07 1.18E+07 | 1.34E+07

15-59 3.40E+06 | 5.92E+06 | 1.01E+07 1.65E+07 2.24E+07 2.64E+07 | 2.91E+07

60+ 2.85E+05 | 5.27E+05 | 9.87E+05 1.96E+06 3.61E +06 6.79E+06 | 9.68E+06

Total 6.82E+06 | 1.14E+07 | 1.89E+07 2.81E+07 3.66E+07 | 4.50E+07 | 5.21E+07

0-14 0.46 0.43 0.41 0.34 0.29 0.26 0.26
15-59 0.50 0.52 0.54 0.59 0.61 0.59 0.56
60+ 0.04 0.05 0.05 0.07 0.10 0.15 0.18
Total 1.00 1.00 1.00 1.00 1.00 1.00 1.00

Table 4: Population by Age Cohorts in absolute and relative terms

Leaving the share of population in relative terms for later discussion, we consider the share of
population in absolute terms. The simulation outcome indicates significant increase in all age
cohorts from 1960 to 2080. This is a reflection of the continuous increase in population over
the years. Considering the share of population in relative terms (as a proportion of total
population), table 4 shows that, the share of the population under 15 years (0-14) has been
decreasing from 46% in 1960 to 41% in 2000 and will be 26% of the population in 2080. The

relative reduction of the under 15 population is very significant and is mainly the result of a

Page 56 of 88

fertility decline. The age 15-59 has increased from 50% in 1960 to 54% in 2000 and is likely
to be 56% in 2080. The relative increase in the age 15-59 is an indication of the improvement
in the human health condition in Ghana and the significant increase in life expectancy at
birth. The share of the elderly (60 and older) in the population increased slightly from 4% in
1960 to 5% in 2000 and is expected to increase significantly to 18% by 2080 due to the
transition that the population is going through. This indicates that the aging process is slowly
creeping in because of the improvement in health status which, consequently, increases the
life expectancy at birth. The growth of the working age population and the elderly is also
evident in the gradual broadening of the top of the population pyramid.

In summary, the population of Ghana is increasing and the increase is expected to continue
due to the rapid drop in deaths rates without a corresponding rapid reduction in births rate.
Moreover, the changes of the age structure are the outcomes of the dynamics of fertility and
mortality through the demographic transition.

6.2 Education
Figure 23 shows the base run simulation of the educational expenditure, the educational

capacity (schools), the enrollment fraction and the literacy rate from 1960 to 2080. The graph
on the top left hand side shows the government educational expenditure and the capital
educational expenditure used for building up the educational capacity for the three
educational levels (primary, secondary and tertiary) in the model. On the top right hand side
of figure 23 shows the simulation of the educational capacity available for the primary, the
secondary and the tertiary education. On the bottom left hand side of figure 23, the simulation
shows the average enrollment rate for the primary, the secondary and the tertiary education.
Lastly, the bottom right hand side shows the literacy rate among the female and the male
population.

Page 57 of 88
10B
4 B 50,000
600M 200
0
0 0
0
0 0
1960 1990 2020 2050 2080 1960 1990 2020 2050 2080
Time (Y ear) Time (Y ear)
Pinecone Ene C eRe _—————_—_—— Educational Capacity[PRIMA RY] : baserun
font expenditure alallonlPRIMA enn Educational Capacity[SECONDA RY] : aserun
ae ete chctouTERTIARY]beece = _Edvcational Capacty(TERTIA RY]: baserun

08 a a
05 ” | | rer |

0
1960 1990 2020 2050 2080

0

nett 1960 1990 2020 2050 2080
ime (Lea) Time (Y ear)

average primary enrollment faction :aserun s :

average secondary enrollment fraction : baserun. —————_—_— literacy rate[FEMALE] : baserm ————————._ mn
average tertiary enrollment fraction : basen ———__— literacy rate[MALE] : basen ——————————._ dm

Figure 23: Base run: Educational expenditure, educational capacity, enrollment and literacy fraction

The behavior of the educational expenditure indicates that the government of Ghana’s
expenditure on education is very significant and this has been consistent over the years and is
expected to continue to 2080. Historical data indicates that 11% of government's non-interest
expenditure went to the educational sector in 1960 and increased to 14% in 2000 and is
therefore assumed to remain constant over time. However, capital expenditure distribution to
the three educational levels clearly shows that the governments’ educational policy places
more emphasis on primary education, followed by secondary education and tertiary
education. It is estimated that 25% of the govemments’ educational expenditure goes to
capital expenditure for primary education, while as 14% and 5% goes to the secondary and
the tertiary education, respectively. Consequently, educational capacity in the primary level is
expected to be higher as compared to the secondary and the tertiary education. The
educational capacity simulation shows that by 2080, we expect primary education capacity to

Page 58 of 88
be approximately 48830 as compared to 1800 in 1960. The secondary and the tertiary
educational capacity are simulated to be around 8806 and 157, respectively, by 2080. The
increased primary educational capacity implies that more children within the primary school
going age can be enrolled. This will then increase the primary enrollment fraction as well as
the literacy rate. On the other hand, the relatively small secondary and tertiary educational
capacity implies that not all the primary school graduates and the secondary school graduates

can access secondary and tertiary education, respectively, due to inadequate capacity.

The enrollment simulation shows that the primary enrollment fraction decreased from 55% in
1960 to 49% in 2000 due to the inability of the primary educational capacity to keep pace
with the increase in the population of primary school going age (age 7 populations). The
primary enrollment fraction is expected to increase significantly from 49% in 2000 to 100%
in 2080 due to an increase in government education expenditure. This will, subsequently,
increase capital expenditure for primary education assuming goverment educational policy
remains unchanged. It is important to note that educational capacity is calculated only based
on the cumulative government capital expenditure on education. Therefore an increase in the
educational capital expenditure subsequently increases educational capacity. The secondary
enrollment fraction increased from 12% in 1960 to 38% in 2000 and will be 90% by 2080.
The increase in the secondary enrollment rate is caused by a combination of increased
secondary education capacity and a declining number of primary school graduates as a result
of decreasing primary enrollment. Tertiary enrollment increased shapely from 15% in 1960 to
25% in 2000. However, the tertiary enrollment fraction will be 37% by 2080 due to lack of
significant investment in tertiary education which, consequently, results in limited capacity
and enrollment. The literacy rate simulation shows that in Ghana, there are more literate
males than females. The literacy rate among the female population increased insignificantly
from 31% in 1960 to 34% in 2000 and is expected to be 56% by 2080. On the other hand, the
literacy rate among the male population decreased significantly from 59% in 1960 to 52% in
2000 and will be 74% by 2080.

To summarize, using educational spending as a measure of governments’ educational policy
focus, it is established that the governments’ focus historically has been on primary
education. Therefore, investment in tertiary education significantly falls short of the desired
investment. By 2080, the government is likely to achieve 100% enrollment fraction at the

primary level, while secondary enrollment fraction is likely to be around 90%. However, only

Page 59 of 88
37% of the secondary graduates will have access to tertiary education by 2080 due to lack of
investment in tertiary education. It is recommended that government refocus its educational
priorities on secondary and tertiary education to build the needed capacity to facilitate

economic growth which will consequently have a positive effect on social development.

6.3 Health
The base run simulation in figure 24 shows some selected indicators that portrays the

dynamics of the health sector in this socio-economic model. On the left hand side of figure
24, the graph shows health care spending (public and private), the number of health care
centre, population-health care centre ratio and physical access to health care facility from
1960 to 2080. On the right hand side, we have simulation results for practicing physicians,

population-physician ratio and access to basic health care.

2B 20,000
4.000 40,000
400.000 8,000
60,000 08

a 08

i 0

4 0

04 04

 s960 1975 1990 2005 20m 203 2050 2065 2080 1960 1975 1990 2005 2020 2035 2050 2065 2080

Tine (Year) Time (Year)

talc Syne bamng Practicing Physicians baserun
Hal CanCeua tan “population-physician ratio rua”: baserun
‘plinth eo RURAL|: basen ————____*population-physican ratio urban: baserun
SOE chee RiN(RURAl} ea tess to basic heath car RURAL]: baserun
‘ical ate halt cin [U3BAN), asa __@ocess to basic health careURBA.N]-aserun

Figure 24: Base run: Educational expenditure, educational capacity, enrollment and literacy rate

Health care spending fluctuated from 1960 to 2000 as a result of government health spending.
Government health care spending oscillated between 4% and 7% of government non-interest
expenditure over the period. It is assumed that from 2000 to 2080, government health care
spending will remain at 7% of non-interest expenditure. As absolute health care spending
increases, a fraction of it would go to capital expenditure on health. Therefore, health
capacity (health care centre) is expected to increase. According to the model, health care

Page 60 of 88
centre’s increased from 60 in 1960 to approximately 367 in 2000 and is expected to be
around 3289 by 2080. The population-health care centre ratio illustrates the average
population per health centre. The population-health care centre ratio is disaggregated into
tural and urban areas due to the observed differences in the distribution of health facilities to
rural and urban areas. The population-health care centre ratio in the rural areas decreased
from 265,270 people per health centre in 1960 to 117,710 in 2000 and it will decrease further
to approximately 35,362 by 2080. The population-health care centre ratio in the urban areas
decreased significantly from 48,723 people per health centre in 1960 to 21,620 in 2000. Itis
expected that population-health care centre ratio in the urban areas will reduce further to
approximately 6,544 people per health centre by 2080. The high population-health care centre
ratio in the rural areas compared to the urban areas establishes the skewed distribution of
health facilities in the urban areas. The drastic expected reduction in population-health care
centre ratio in rural is caused by the increase in health centre’s due to increase in health care
spending. On the other hand, the reduction in population-health care centre ratio in the urban
areas is due to the significant increase in health centre’s to accommodate the increase in
population in the urban areas due to increase in health care spending coupled with the
existing skewed distribution of health facilities in favor of urban areas. By 2080, population-

health care center ratio in urban areas will be 5 times better than in the rural areas.

Physical access to health care facility measures the access to health care facility using a
combination of the effect of population-health care center ratio to physical access to health
care facility and the effect of distance on physical access to health care facility. Experience
from studies in developing countries indicates that distance to health care facility is one of the
critical factors that determine the use of the facility. The model simulation establishes that the
fraction of population that have physical access to health care facilities in the rural areas
increased from 30% in 1960 to 49% in 2000 and it will increase further to 77% by 2080. In
the urban areas, the fraction of population that have physical access to health care facilities
increased from 55% in 1960 to 63% in 2000 and it will increase to 99% by 2080. We can
conclude that, the low physical access to health care facility in the rural areas from 1960 to
2000 is due to the longer distance to a health care facility as a result of low health care
facility density’? in rural areas.

18 Health care facility density refers to the number of persons per square kilometer of land
area.

Page 61 of 88
Practicing physicians increased from 400 in 1960 to approximately 2066 in 2000 and will
increase to around 12832 by 2080. This gives a population-physician ratio of 39,790 in the
rural areas and 7,308 in the urban areas in 1960. The population-physician ratio decreased
sharply in the early 1960s due to the sharp increase in physicians and the population-
physician ratio then increased again from 1980. During the period of 1960 to 1980,
population-physician ratio in the rural areas decreased from 39,790 to 18,417 while that of
the urban areas decreased from 7,308 to 3,382. From 1980, the population-physician ratio
begins to increase, reaching a peak of 22146 in year 2010 in the rural areas and 4067 in year
2011 in the urban areas. The increase in the population-physician ratio is attributed to
population increase in both urban and rural areas surpassing the increase in physicians in both
areas. It is expected that by 2080, the population-physician ratio in the rural areas will have
fallen to 9,134 and that of the urban areas is expected to fall to around 1,677.

From the forgoing discussion, it is expected that access to basic health care in rural Ghana
will lag behind that of the urban Ghana due to better physical access to health care facility
and low population-physician ratio in the urban Ghana. Access to basic health care was 20%
in the rural areas in 1960 and 44% in the urban areas. However, access to basic health care in
the rural areas increased from 20% in 1960 to 37% in 2000 and will increase to 62% by 2080.
The urban areas also experience an increase in access to basic health care from 44% in 1960
to 50% in 2000 and will increase to 80% by 2080.

6.4 Labor
The dynamics of the labor sector is shown by figure 25. The left hand side demonstrates the

expected demand for goods and services and employment for the three sectors of the
economy, i.e. agriculture, industry and services. The right hand side shows total expected
demand for goods and services, total employment, total work force and unemployment

fraction for the economy in total.

Page 62 of 88
Szzun0
i
5

eccooco

1960 1975 1990 2005 2020 2035 2050 2065 2080 1960 1990 2020 2050 2080
Time (Vat) Time (Y ear)

Expected Demand{AGRI); basen), total expected demand : baserun
Expected Damani} = sen

total employment ; baserun
total workforce : baserun
unemployment fraction : baserun
cc average per capita wages : baserun

Figure 25: Base run: expected demand, employment, unemployment and total work force

The simulation result shows an increasing trend for expected demand in the agriculture, the
industrial and the service sectors of the economy. As expected demand increase, the
demanded labor to meet the demand for goods and services increases as well. Hence
employment increases. The employment in the agricultural, the industrial and the service

sectors of the economy increased from 1960 to 2080.

Total expected demand increased from approximately 3.7 billion in 1960 to around 8.1
billion in 2000 and will increase to 122.14 billion in 2080. The increase in production is a
reflection of an increase in total expected demand. The total work force was 2.4 million in
1960 of which 1.6 million are employed. By 2000, the total work force had increased to 9.12
million, of which 7.75 million were employed. The simulation result indicates a slow-down
of growth in total work force from 2035. This is attributed to the demographic transition
which is changing the age structure of the population. Total employment increases from
1960 up to 2035. After the year 2035, the total employment increases at a decreasing rate.
The increase in total employment is insufficient to reduce unemployment due to a more
significant increase in the work force. The increase in total employment is caused by an
increase in total expected demand and the reduction in average per capita wages. Production
increase causes employment to increase. Also, a decline in average per capita wages causes
employment to increase as more employees can be paid with the same amount of wages.
Average per capita wages decreased significant from 1960 to 2000. After the year 2000,

average per capita wages then started a gradual increase, and is expected to continue to do so

Page 63 of 88
up to the year 2050, when the average wages will be equal the 1960 wage level. After 2050,
average per capita wages is expected increase significantly. The 1960 to 2000 decline in
average per capita wages is attributed to unemployment. As unemployment increases, the
effect of unemployment on wages decreases nominal wages resulting in a reduction in

average per capita wages.

The unemployment rate is a reflection of the difference between total work force and total
employment. The unemployment rate decreased sharply in the early 1960s, i.e. from 33.4% to
15.5% in 1973 and oscillated between 16.8% and 14% from 1973 to 2010. Unemployment
rate is expected to decrease from 14% in 2010 to 10.4% by 2080. The decline in
unemployment is caused by an expected continuous increase in total expected demand, and a

slow-down in the increase of the total work force.

6.5 Production
The base run behavior of the agriculture, industry and service of the economy is shown in

figures 26, 27 and 28 respectively. They are explained below:

6.5.1 Agriculture
The simulation result of the agriculture is evident in figure 26. The graph on the top left hand

side of figure 26 indicates that from 1960 until 2000, the agricultural yield increased amidst
some fluctuations. The fluctuation in agricultural yield is explained mainly by the variations
observed in the yield per hectare. However, the increase in agricultural yield is predominantly

a result of the increase in agricultural land in use.

Page 64 of 88
200 M 6
40M

400 2

208 2

0

3 0

g 08

0 08

H960 Tag, 1880) 25, ned ss 2050; 2065. 2080 1960 1975 1990 2005 2020 2035 2050 2065 2080
ng Cea Time (Y ear)

agricultural yield : basen. ———_ Sil
Agricultural Land In Use : basern. relative capital agriculture : baserun
value added per unit : basen, =< $ $$ @&@<@<$<_$_—$<———— relative management : baserun

yield perhecta: baserun

agriculture production :baserun relative environment : baserun

Figure 26: Base run behavior of agricultural sector

400
40,000

200
20,000

0
0

1960 1975 1990 2005 2020 2035 2050 2065 2080
Time (Y ear)

annual rainfall : baserun
fertilizer consumption : baserun

Figure 26: Base run behavior of agricultural sector

The graph on the top right hand side of figure 26 shows the three main factors that account
for the variation in the yield per hectare. The three factors are management, agriculture
capital and environment. The increase observed in management is due to improvement in
human capital among the farmers. As the educational attainment increase as explained in the
education sector for all workers in Ghana, it is expected that agricultural labor will duly
benefit from an increase in knowledge. Also, the increase in agricultural capital is as a result
of an increase in government investment in agricultural research and extension services. As
agricultural research increases, the findings of the research are disseminated directly to
farmers to help improve yield. Moreover, increase in extension services increases farm visit

to advice farmers on new and better ways of farming. The fluctuations observed in the

Page 65 of 88
environment, is explained by the graph in the bottom of figure 26, which shows the behavior
over time of annual rainfall and fertilizer consumption based on historic data. We confirm
that the unstable rainfall and general reduction of fertilizer consumption account for the
variation in the environment and, consequently, the yield per hectare from 1960 to 2000. It is
important to note that, agriculture in Ghana is highly dependent on rainfall. The use of
irrigation water for agriculture is very insignificant. The pattern of the fertilizer consumption
clearly shows that fertilizer use for farming intensified in Ghana in the mid 1970s, then
oscillated during the late 1970s and early 1980s. By the mid 1980s, fertilizer consumption
declined significantly. Various studies in Ghana (Frimpong-Ansah 1991; Ayittey 1992;
SAPRIN 2002) have implied that one of main factors that explain the reduction in fertilizer
consumption is the removal of agricultural subsidies by the govemment since the
implementation of structural adjustment program. The late 1990s saw a sharp increase and
immediate fall of fertilizer consumption and we hypothesize that fertilizer consumption will

stabilize at the current consumption level due to assumption of stable price.

The graph on the top left hand side shows the behavior over time of the value added per unit
of agriculture product. It is evident that value added to agriculture, i.e. the real price of
agriculture raw materials is generally decreasing over time. In 2000, the real price of
agriculture raw materials stands at only 50% of the 1960 price. As explained by Junne (1991)
the IMF and World Bank policies have undoubtedly contributed to the decrease in agriculture
raw material prices (Junne 1991). The immediate effect of the currencies devaluation policy
implemented by the IMF and World Bank in developing countries is that exporters receive
more in terms of the domestic currency for the goods than before. This signal to farmers in
most countries to increase their output for exportation and the resulting glut has helped to
bring about the fall in the world market price. The export orientation of the IMF and World

Bank policies for agriculture therefore made things worse.
Thus, it is not surprising that even though agricultural yield increased in general over the

years, agricultural production, i.e. total output multiplied by the value added did not increase
correspondingly. This is due to the price reduction of agricultural product.

Page 66 of 88
6.5.2 Industry
The base run behavior of the industrial sector shows that industrial production increased over

the 1960 to 2000 period.

1960 1975 1990 2005 2020 2035 2050 2065 2080
Time (Y ear)

Industrial Capital : baserun
Human Capital[IND] : baserun
Industrial Labor Productivity : baserun
“industry capital-labor ratio" : baserun
industry production : baserun

Figure 27: Base run behavior of industrial sector

The increase in industrial production is as a result of an increase in human capital. However,
from 1960 to 2000, industrial capital and industrial labor productivity declined. A high
industrial capital depreciation compared to the industrial capital acquisition explains the
decline in the industrial capital level. The relatively low industrial capital acquisition is due to
inadequate investment in industries and the high cost of industrial capital. The falling
industrial labor productivity is explained by the declining capital-labor ratio due to low
physical capital base of the industrial sector and declining technology. As growth in
employment surpasses the growth in physical capital, capital available per employee, i.e.
capital-labor ratio declines. As the capital-labor ratio decreases, coupled with low technology
growth (in part due to a low physical capital growth) industrial labor productivity decreased.
On the other hand, the steady increase in human capital is due to combination of gradual
improvement in the average years of formal schooling among the industrial workforce and an
increase employment. As investment in education increases, the educational capacity enabled
an increase in the recruitment of educated people. Over time a more educated workforce

become available for employment which, consequently, increases the average years of formal

Page 67 of 88
schooling among the workforce and, accordingly, the human capital in use. The industrial

sector duly benefit from the increase in human capital.

We established that the slow growth in the industrial sector from 1960 to 2000 is explained
by the low physical capital base of the industrial sector. This, consequently, affected labor
productivity: hence the observed decline in labor productivity in the industrial sector.

6.5.3 Service
The service in the economy exhibits similar behavior as the one explained for the industry.

As shown in figure 28, services production increased along side human capital from 1960 to
2000 and the increase continued to 2080. The increase in human capital is as a result of an
increase in educational attainment, i.e. average years of formal schooling due to an
improvement in education. However, service capital and service labor productivity declined
from 1960 to 2005 as shown in figure 28. The decline in service capital is attributed to a high
service capital depreciation compared to the service capital acquisition due to inadequate
investments. On the other hand, the service labor productivity decline is attributed to a
declining capital-labor ratio and technology growth.

1960 1975 1990 2005 2020 2035 2050 2065 2080
Time (Y ear)

services production : baserun
Services Capital : baserun
Human Capital[SERV] : baserun
Service Labor Productivity : baserun

Figure 28: Base run behavior services sector

Page 68 of 88
6.6 Public Debt
Figure 29 shows the behavior of the total debt over time, budget deficit, interest’s payment

and foreign debt adjustment on the left hand side and the domestic and foreign interest rates
and the real exchange rate change on the right hand side.

600B 2
20B
20B : ;
2B
0 0
0 0
400, ood “hone
1960 1975 1990 2005 2020 2035 2050 2065 2080 igo ad wey an 2080
Time (Y ear)
Time (Y ear)
real total debt :baserun obligatory interest rate[DOMESTIC] : basen
weal deficit ee ————— obligatory interest rate[FO REIGN] : baserun
Teese ——— ;
Torign Debt Adhstrent:basenn Teal exchange rate change : baserun

Figure 29: Base run behavior showing government revenue, expenditure, public debt, foreign debt adjustment,
interest payments and interest rates

The behavior of the budget deficit indicates that government expenditure consistently
exceeded revenues and grants from 1960 to 2000 and the base run simulation indicates that
budget deficit will continue to increase to the year 2080. The gap between expenditures and
revenues (budget deficit) is closed by borrowing, consequently, debt builds up. The total debt
increased from 1960 to 1980 amidst some fluctuations, and then decreased suddenly in the
early 1980s, but started to increase again from the early 1990s and is expected to increase to
the year 2080. As total debt increases, interest payments on debt increase which, in tum,
increase government expenditures and the budget deficit. Domestic and foreign interest rates
on the right hand side of figure 29 shows that the interest rate charged on public debt from

both domestic and foreign sources.

The obligatory interest rate is separated into that for domestic debt and that associated with
foreign debt. The domestic obligatory interest rate dropped sharply from 0.11 in 1960 to 0.04
in 1961. From 1961 to 1980, the obligatory domestic interest rate increased gradually from
0.04 to 0.12. The period 1960s to 1980 was an era when the financial sector in Ghana was
characterized by a fixed ceiling on interest rates, credit guidance for different sectors and

fixed ceiling on credits. Interest rate controls ensures that the government governs the interest

Page 69 of 88
rate. This explains, perhaps, the slow increase in interest rate from 1960 to 1980 (Mensah
1997) regardless of the high inflation rate in the economy, - well above the interest rate. The
domestic obligatory interest rate then rose sharply from 0.12 in 1980 to 0.41 in 1992. This
sharp increase is attributed to the significant accrual of interests in the early 1980s and the
high interest rate charged on domestic borrowing. From 1980, the domestic interest rate
increased from 0.10 to 0.24 in 1992. The interest rate rose due to the gradual deregulation of
the financial sector when the Financial Sector Structural Adjustment Program (FINSA P) was
adopted (Mensah 1997). As a result, the interest rate increased towards the market
conditions. From 1992, the domestic obligatory interest rate was briefly reduced until it
increased once more and reached 0.48 in 2000 and is expected to be 1.23 by the year 2080.
On the other hand, foreign obligatory interest rate decreased from 0.05 in 1960 to 0.02 in
1976. This reduction is attributed to the shift of government borrowing from private
borrowing in the financial market to concessional loans from bilateral and multilateral
sources. The relatively low interest rate on concessional loans compared to private credit,
(Krassowski 1974; Killick 1978) ensures that the obligatory interest rate decreases during the
period. The foreign obligatory interest rate increased significantly during the period 1977 to
1990, from 0.02 to 0.06. The increase in the obligatory interest rate from 1977 to 1990 is
ascribed to the combined effect of an increase in the interest rate on concessional loans and
increase in accrual of interest. The interest rate on concessional foreign borrowing increased
during the period 1977 to 1990 from 0.02 to 0.03 as a response to the rise in the interest rate
worldwide. Moreover, the increase in accrual of interest stepped up the accumulation of
accrued interest which, invariably, increased the obligatory interest rate. The obligatory
interest rate decreased slightly from the 1991 level of 0.05 to 0.04 in 2000. This follows the
reduction of interest rate on concessional loans. The obligatory interest rate is expected to be
0.1 by the year 2080

The graph in figure 29 i.e. right hand side shows that from 1960 to 1980, the change in the
real exchange rate was negative. This is due to the implementation of a fixed currency
exchange regime (Islam and Wetzel 1991; Boafo-Arthur 1999). From 1980, the change in the
real exchange rate became positive and then increased sharply between 1983 and 1986. This
sharp increase is attributed to the deregulation of the currency exchange market during the
Structural Adjustment era (Boafo-Arthur 1999). Subsequently the exchange rate adjusted to
the market rate as a result of the deregulation amidst some fluctuations after 1990, - based on
the strength of Ghana’s balance of payment.

Page 70 of 88
7. Policy Analysis and Discussion

7.1 Policy Analysis
In this study, we have subjected four fiscal policies to an experimental investigation. The

main focus of the policy analysis is by seeking the most successful fiscal policy that
facilitates the improvement of socio-economic development (SEDI) and ensure fiscal
sustainability (cumulative budget deficit and debt-GDP ratio). The experiments are supposed
to guide our selection of fiscal policy to achieve these twin goals. We conducted an ex-ante
simulation analysis from 2000 to 2080 to examine the impact of each fiscal policy (overall
policy) and policy combinations (distributional policies) on the twin goals. Three
distributional policy’? areas were tested for each fiscal policy (overall policy) to further
explore their impact. The permutation of the distributional policy and the overall policy gave
twelve policy combinations alternatives, which were tested to evaluate their impact on socio-
economic development and ensure fiscal sustainability. The twelve policy combinations are:
Pibr is expansionary policy with base case expenditure pattern, Plei is the expansionary
policy with economic investment focus, Plsi is the expansionary policy with social
investment focus, P2br is the contractionary policy with base case expenditure pattern, P2ei
is the contractionary policy with economic investment focus, P2si is the contractionary policy
with social investment focus, P3br is the balanced budget policy with base case expenditure
pattern, P3ei is the balanced budget policy with economic investment focus, P3si is the
balanced budget policy with social investment focus, P4br is the combined policy with base
case expenditure pattern, P4ei is the combined policy with economic investment focus, and

P4si is combined policy with social investment focus. Table 5 shows the policy setup.

19 The distributional policy are; base case (br), economic investment (ei) and social

investment (si)

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Policy Expansionary policy | Contractionary Policy | Balanced Budget Combined Policy
(31) 2) 4)

Policy (3)
Expendit
Fractional expenditure | Fractional expenditure | Fractional expenditure | Fractional expenditure
Plbr Plei Pisi P2br P2ei P2si P3br P3ei P3si P4br Pdei Pasi

Functional Expend™
1.General services” 0.11 0.11 0.11 0.11 0.11 0.11 0.11 0.11 0.11 0.11 0.11 0.11
2.Com/soc. Services” 0.23 0.23 0.23 0.23 0.23 0.23 0.23 0.23 0.23 0.23 0.23 0.23
3.Economic services” 0.23 0.38 0.23 0.23 0.38 0.23 0.23 0.38 0.23 0.23 0.38 0.23

4.Education 014 [014 | 021 | 014 | 014 021 |o14 | 014 | 021 | 014 | 014 | 021
5.Health 0.04 | 0.04 ) 012 | 0.04 | 0.04 | 0.12 | 0.04 | 0.04 | 0.12 | 0.04 | 0.04 | 0.12
6.Unallocated expend | 0.25 | 0.10 | 0.10 | 0.25 | 010 | 0.10 | 0.25 | 010 | 0.10 | 0.25 | 0.20 | 010
Expenditure Policy

Medium tem
1, Expend.® (% GDP) 0.35 0.22 0.25 0.40
2.Tax rev""(% GDP) 0.25 0.25 0.25 0.25,

Long term
1.Expend (% GDP) 0.40 0.25 0.30 0.25
2.Tax rev (% GDP) 0.30 0.30 0.30 0.30

Table 5: Policy Setup and Analysis

The four policies and the distributional policy areas experimented are described as follows:

Expansionary Fiscal Policy (P1): This policy will ensure that goverment increases

expenditure to facilitate economic and social investment. The assumption underlying this

2° Functional expenditure

*1 General services expenditure consists of the following spending: general public service,
defense, public order and safety

?2 Community and social services expenditure consists of the following spending: social
security and welfare services, housing and community amenities, recreational, cultural and
religious services

3 Economic services expenditure consists of the following spending: fuel and energy,
agriculture, forestry and fishing, mining, manufacturing and construction, roads and railways,
other transportation and communication and other economic services.

*4 Unallocated expenditure consists of the following spending: transfers to other levels of
government, social efficiency fund, others

*5 Expenditure

°° Tax revenue

Page 72 of 88
policy is that as government increases expenditures, investment and social services increase,
which helps to stimulate the economy. Consequently, production increases which result in
increased tax revenue. However, this policy is associated with a high budget deficit resulting
in high public debt due to government expenditure consistently exceeding tax revenue. In the
ex-ante policy experimentation, this policy is implemented by increasing expenditure from
34% of GDP in 2000 to 35% by 2010 (medium term) and to 40% by 2080 (long term). On the
other hand, tax revenue is demonstrated to increase from 19% of GDP in 2000 to 25% by
2010 and to 30% by 2080.

Contractionary Fiscal Policy (P2): With this policy, government expenditure is
reduced to decrease budget deficit and consequently public debt. This policy is often used or
recommended in times of public debt crisis. The underpinning argument for the
contractionary policy is that, as public debt increases, government spending should be
reduced to generate budget surplus to service the public debt. On the other hand, the
contractionary policy is argued to focus mainly on fiscal balance ignoring social and
economic development, which suffers as a result of the cut in government spending. In the
ex-ante policy experimentation, this policy is implemented by decreasing expenditure from
34% of GDP in 2000 to 22% in 2010 and to increase to 25% by 2080. It is important to note
that tax revenue for 2010 is assumed to be 25% of GDP by 2010 and is to increase to 30% by
2080.

Balanced Budget Fiscal Policy (P3): The balanced budget policy ensures that
government spending is fully funded by tax revenue. This policy ensures that government
avoids the possibility of overspending which results in public debt. In the ex-ante policy
analysis, this policy is implemented by ensuring that govemment spending and tax revenue
are always equal. That is government expenditure is assumed to reduce from 34% of GDP in
2000 to 25% by 2010 and increase to 30% by 2080.

Combined Policy (P4): This policy is a combination of expansionary fiscal policy in the
medium term and contractionary fiscal policy in the long term. The combined policy ensures
that government expenditure exceeds its revenue in the medium term to build human and
physical capital to increase production and socio-economic development. When the
foundation of the economy is perceived to be strong, government fiscal policy is changed to a

contractionary policy to ensue that the previous deficit is financed through a future surplus.

Page 73 of 88
This policy is implemented by increasing government expenditures from 34% of GDP in
2000 to 40% by 2010 and then reducing it to 25% by 2080. However, government tax
revenue is assumed to be 25% of GDP by 2010 and to 30% by 2080.

Distributional Policy
Three distributional policies are identified and tested to evaluate their impact on socio-

economic development and fiscal sustainability. They are;

Base case (br): This distributional policy maintains the status quo by ensuring that the
functional expenditure distribution of government expenditure remains unchanged from the

pervious year.

Economic Investment (ei): This distributional policy ensures that government
expenditure prioritizes economic investment as the engine of growth. This policy is
implemented by assuming that 38% of the government expenditure is earmarked for
economic investment. This is achieved by reducing the fraction of government expenditure
for unallocated expenditure from 25% to 10% and shifting the 15% difference to economic
investment to make it 38%.

Social Investment (si): The social investment distributional policy will ensure that
government expenditure focuses on education and health. This policy is implemented by
reducing the fraction of government expenditure for unallocated expenditure from 25% to
10% and shifting the 15% to education and health. As a result, the education expenditure
fraction increases to 21% and the health expenditure to 12%.

7.2 Policy Discussion
The policy discussion describes the future consequence of the policy interventions on the

socio-economic development and fiscal sustainability in Ghana. Table 6 shows the results of
the policy experimentation. For the purpose of this analysis, the success of a policy depends
on its ability to increase socio-economic development indicator, i.e. SEDI, increase

cumulative budget surplus and to reduce debt-GDP ratio.

Page 74 of 88
Expansionary policy (P1) | Contractionary Policy (P2) | Balanced Budget Policy (P3) | Combined Policy (P4)
Policy

Indicators Pibr Piel Pisi P2br P2ei P2si P3br P3ei Psi Pabr Pai Pasi
Impact on:

ser” 0.60 061 061 0.56 057 057 057 059 059 057 058 059
Fiscal Sustainability

‘.Cumbud def."*(+/-) | 3.3EH1 | 3.6E411 | 346411 | -L1E411 | -1.26+11 | -1.2E+11 | 6.1E409 | 6.16409 6.16409 | 44E410 | 43e40 | 4.3410
2debt-GDP Ratio 3.30 3.5 3.27 0.02 0.02 0.02 0.12 0.10 oa 0.89 0.82 0.86

Table 6: Result of Policy Analysis

The outcome of the first fiscal policy (P1) is evident in the simulation result portrayed in
figure 30. Considering the impact of P1 on SEDI, the P lei and P1si produced slightly higher
SEDI as compared to P1br. As shown in table 6, it is expected that the implementation of
P1br will increase SEDI to 0.60, while that of P lei will give SEDI of 0.61 and P 1si resulting
in SEDI of 0.61. On the other hand, the impact of P1 on debt-GDP ratio indicates that P lei
gives slightly lower debt-GDP ratio, i.e. 3.15. The implementation of P1si gives debt-GDP
ratio of 3.27, while that of Pibr indicate a debt-GDP ratio of 3.30. Considering the
cumulative budget deficit, policy P 1ei generate the highest budget deficit (3.6E+11) as shown
in table 6, while policy P1si generates budget deficit of (3.4E+11), and policy P1br results in
a budget deficit of (3.3E+11).

Evaluating the expansionary policy (P1) to the base run simulation as shown in figure 30
indicates that P1 increases SEDI slightly as compared to the base run simulation and
significantly decreased the debt-GDP ratio over time. This indicates that the expansionary

policy performs better as compared to the current policies pursued by the government.

27 Socio-economic development indicator consists of various indicators of education, health,
social services and income as a measure of socio-economic development or progress.

8 Cumulative budget deficit

Page 75 of 88

Expansionary Policy (SEDI) Expansionary Policy (Debt-GDP Ratio)

08 6

04 ee ane 3

0 0

1960 1975 1990 2005 2020 2035 2050 2065 2080 © 1960 1975 1990 2005 2020 2035 2050 2065 2080
‘Time (Y ear) ‘Time (Year)

sedi :baserun "debt-gdp -atio® : baserun
sedi: Plbr “debt-gdp ratio" : Plbr

sedi: Plet "debt-gdp -ratio® : Pei —— $$
sedi: Pisi "debt -gdp atio® Pls; —

Figure 30: Results of Expansionary Policy Runs

Figure 31 shows the expected outcome from the simulation of the contractionary fiscal policy
(P2). It is evident that policies P2br, P2ei, P2si reduce SEDI slightly as compared to the base
run simulation also shown in figure 31, but significantly reduce the debt-GDP ratio.
Moreover, as shown in table 6, the cumulative budget surplus from implementing P 2br, P2ei,
and P2si is very significant compared to what is obtained under expansionary policies. The
simulation outcome from P2 shows that, P2ei and Psi policies give the slightly higher SEDI
(0.57) between the three contractionary policies, while P2br produce a SEDI of 0.56.
Moreover, the three contractionary policies, i.e. P2br, P2ei, and P2si considerably reduce the
debt-GDP ratio from 0.88 in 2000 to 0.02 by 2080. The effect of the contractionary policies
on the budget deficit is reported in table 6. The implementation of policies P2br, P2ei, and
P2si results in a cumulative budget surplus of 1.1E+11, 1.2E+11, 1.2E+11 respectively. The
simulation outcome of P2 shows that policy P2ei and P2si yields the highest cumulative
budget surplus followed by P2br.

Comparing the simulation analysis of the contractionary policies to the expansionary policies
and the base case simulation clearly shows that, if the main goal of government is to achieve
socio-economic development, then the expansionary policies is the best fiscal policy option
to pursue. If on the other hand, the main goal of government is to ensure fiscal sustainability,
then the contractionary policy is the best policy option to pursue as compared to the

expansionary fiscal policy and the base case simulation.

Page 76 of 88
Contractionary Policy (SEDI) Contractionary Policy (Debt-GDP Ratio)

06 6
03 3
0 oN
1960 1975 1990 2005 2020 2035 2050 2065 2080 «19601975 1990 2005 2020 2035 2050 2065 2080
Time (Year) Time (Y ear)
sedis basenun *debt-gdp -ratio® basenun
Sedi: abr "debt -qdp ratio” :PDbr

sedi: Pei "debt -gdp -ratio" : P2ei
sedi: P2si "debt -gdp -ratio” : P2si

Figure 31: Results of Contractionary Policy Runs

The outcome of the balanced budget fiscal policy is apparent in the simulation of figure 32.
The simulation result evidently shows that the balanced budget policy did not significantly
change SEDI compared to the base case simulation. But, the balanced budget policy
significantly reduces the debt-GDP ratio and the accumulated budget deficit over the
simulation period. Policies P3br, P3ei, and P3si yield SEDI of 0.57, 0.59 and 0.59
respectively. The simulation result indicates that P3ei reduces debt-GDP ratio from 0.88 in
2000 to 0.10 by 2080. This indicates that the best balanced budget fiscal policy with respect
to the debt-GDP ratio is P3ei. Policies P3si, and P3br shows debt-GDP ratio of 0.11 and 0.12
respectively. The cumulative budget deficit from the balanced budget policies indicates that
all the three policies i.e. P3br, P3ei, and P3si produced a similar cumulative budget deficit of
6.1E+09 over the simulation period.

Comparing the balanced budget fiscal policies to the other policies, as portrayed in table 6,
shows that the balance budget policy is the second best policy with regard to the socio-
economic development. Also, the balanced budget policy is the second best policy to reduce
debt-GDP ratio significantly. Moreover, apart from the expansionary fiscal policy that
generates budget surpluses, the balanced budget policy is the fiscal policy with the least
accumulated budget deficit over the policy simulation period. This makes the balanced fiscal
policy an attractive fiscal policy in pursuit of the twin goal of socio-economic development
and fiscal sustainability.

Page 77 of 88
Balanced Budget Policy (SEDI) Balanced Budget Policy (Debt-GDP Ratio)

0.6 6

03 3

0 0

1960 1975 1990 2005 2020 2035 2050 2065 2080 1960 1975 1990 2005 2020 2035 2050 2065 2080

‘Time (¥ ear) ‘Time (Y ear)

sedi: baserun " debt-gdp -ratio" :baserun

sedi : P3br "debt-gdp -ratio" :P3br

sedi: P3ei "debt-gdp -ratio" :P2ei

sedi: P3si "debt -gdp -ratio" :P3si

Figure 32: Results of Balanced Budget Policy Runs

The simulation in figure 33 shows the outcome of the combined policy experiment. The
simulation result shows that SEDI increases slightly as a result of P4 compared to the base
run. However, the debt-GDP ratio is significantly decreased by P4. The results portrayed in
table 6 indicate that at the end of the simulation period, the combined policy accumulates a
budget deficit. Policies P4br, P4ei, P4si shows SEDI values of 0.57, 0.58, and 0.59
respectively. This is slightly higher than the base case simulation. The result for the debt-
GDP ratio also indicates that policies P4br, P4ei, P4si reduce the debt-GDP ratio from 0.88
in 2000 to 0.89, 0.82 and 0.86, respectively. The cumulative budget deficit from the
combined policy is 4.4E+10 for P4br, 4.3E+10 for P4ei, and 4.3E+10 for P4si.

Comparing the combined policy results with the other policies, we may conclude that the
combined policy result for SEDI is quite comparable to the good results from the balanced
budget fiscal policy. This makes the combined policy the third best policy to increase socio-
economic development. On fiscal sustainability, the combined policy result is only better than
the expansionary policy and less desirable to the contractionary and balanced budget fiscal
policies. That is, apart from the expansionary policy, the combined fiscal policy gives higher
debt-GDP ratio than the contractionary and the balanced budget policies. Moreover, the
combined policy is the policy with the second largest cumulative budget deficit. This makes
the combined policy the third desirable policy with respect to the cumulative budget deficit.

Page 78 of 88
Combined Policy (SEDI) Combined Policy (Debt-GDP Ratio)

0.6 6
03 3
0 0
1960 1975 1990 2005 2020 2035 2050 2065 2080 1960 1975 1990 2005 2020 2035 2050 2065 2080
Time (Year) Time (Y ear)
sedi :basenun "debt -gdp ratio" : baserun
sedi : Pabr "debt -gdp -rtio" : Pabr
sedi : Pei "debt -gdp -ratio" : Pei
sedi : Psi "debt -gdp -ratio" : Pasi

Figure 33: Results of Combined Policy Runs

Ranking of Policy Indicators
Policies Overall
SEDI Fiscal Sustainability ranking
Expansionary policy (P1) ile 4 ae
Contractionary Policy (P2) 4 it a
Balanced Budget Policy (P3) 28 # i#
Combined Policy (P4) af an Ps

Table 7: Policy Ranking

To summarize, the policy simulation results, (as shown in table 7) indicates that the
expansionary fiscal policy is the best policy to increase and enhance socio-economic
development in Ghana. With the expansionary fiscal policy, as government expenditure
increases, social and economic investment increases, which, consequently, increase
production and access to social services, i.e. health and education. On fiscal sustainability, the
simulation results lead us to conclude that the contractionary fiscal policy is the best policy in
that it significantly reduces the public debt burden in Ghana. Conceming the contractionary
fiscal policy, as the government expenditures reduce; budget surplus is accumulated over
time to service outstanding public debt. This evidently decreases the debt-GDP ratio. The
balanced budget deficit was identified as the second best fiscal policy with respect to the
socio-economic development and debt-GDP ratio. The combined policy is the third best
policy with respect to improving socio-economic development and in reducing the debt-GDP

ratio.

Page 79 of 88
From the forgoing analysis, the best policy aimed at achieving the twin goal of enhancing
socio-economic development and ensures fiscal sustainability in Ghana is the balanced
budget fiscal policy. The balanced budget fiscal policy is the most workable policy for the
government of Ghana to pursue because it guarantees significant improvement in the socio-
economic development while, at the same time, ensuring fiscal sustainability. Though a
contractionary fiscal policy reduces debt-GDP ratio much more, the simple fact that it
reduces socio-economic development slightly does not make the contractionary policy the

most desirable policy.

To elaborate on the policy choice, i.e. the balanced budget fiscal policy, it is expected that the
government expenditures is funded by government revenues and grants to avoid debt
accumulation. We believe that if the government adopts this fiscal policy, innovative ways to
generate revenue for the government will be pursued to increase government revenues. This
is based on the empirical evidence that significant revenue due to the government is not
realized due to an ineffective tax system and revenue collection structures, especially at the
local level. Moreover, because a significant part of the economy in Ghana is informal, tax
evasion is very prevalent and, by instituting the right structures, government revenues is
expected to increase significantly to finance expenditures. It is important to note that, this
study is not against goverment borrowing for effective investment that will increase

economic growth.

8. Scenario Analysis
The scenario analysis simulates the impact of two experimented scenarios on the four fiscal

policies discussed as part of the policy analysis section. The two scenarios are debt

forgiveness and currency exchange rate increase.

Foreign interest rate increase scenario (S1): The foreign interest rate increase
scenario is based on the fact that current government borrowings are concessionary
borrowing from the IMF and the World Bank. This scenario assumes that if loans are
acquired from the private financial market, interest on loans will exceed the current interest

rate on foreign borrowing. The scenario is implemented by assuming that by 2080, foreign

Page 80 of 88
interest rate will increase from 2.9% to 10%. This scenario assesses the impact of increasing

tax revenue on socio-economic growth and fiscal sustainability.

Currency exchange rate increase scenario (S2): The currency exchange rate
increase scenario is informed by the observed constant devaluation of local currency due to
factors such as bad macroeconomic management, high trade deficit and devaluation policies
from IMF and the World Bank. For example, in 1990, the Ghanaian currency (cedi) did
exchange for the US dollar at a rate of 1 US dollar to 326.3 cedi. By 2000, 1 US dollar was
exchanged for 5455 Ghana cedi. As explained in the public debt module, the constant
increase in exchange rate of Ghana cedi to US dollars has a significant effect on public debt
accumulation. This scenario is implemented by assuming that by 2080, 1 US dollar will
exchange for 15455 cedi. This scenario assesses the impact of further increase in exchange

rate of Ghana cedi to US dollar on socio-economic development and fiscal sustainability.

Scenarios Expansionary Policy | Contractionary Policy | Balanced Budget Policy | Combined Policy
@1) 2) (3) 4)

Scenario 1

SEDI 0.55 0.56 0.57 0.55
Fiscal Sustainability

1.Cumm budget deficit (+/-) 31EH1 “AEH 6.1E409 6.0E+10
2.Debt-GDP Ratio 4.15 0.02 0.13 1.23
Scenario 2

SEDI 0.60 0.56 0.57 0.57
Fiscal Sustainability

1,.Cumm.budget deficit (+/-) 3.3E+11 -LAIE+11 6.1E +09 5.9E+10
2.Debt-GDP Ratio 3.46 0.02 0.12 0.94

Table 8: Result of Scenario Analysis

The outcome from the scenario 1 simulation as shown in table 8 and figure 34 establishes that
the balanced budget and contractionary fiscal policy are the two best fiscal policies to be
implemented to ensure stable socio-economic development and fiscal sustainability in an
event that interest on foreign debt increases. As shown in table 8, under scenario 1 the
balanced budget fiscal policy (P3) yields a SEDI of 0.57 and a debt-GDP ratio of 0.13 with
an accumulated budget deficit of 6.1E+09. On the other hand, contractionary policy (P2)
yields a SEDI of 0.56 and a debt-GDP ratio of 0.02 with an accumulated surplus of 1.1E+11.

Under scenario 1, if the main concem of the government is fiscal sustainability, then, the

Page 81 of 88
contractionary policy is the best policy because it produces the least debt-GDP ratio as
compared to balanced budget policy. However, if the objective of the government is to
improve socio-economic development under scenario 1, then the balanced budget policy is

best policy to achieve socio-economic development growth.

Scenario 1 Analysis (SED]) Scenario 1 Analysis (Debt-GDP Ratio)
06 8
03 4
0 oN
1960 1975 1990 2005 2020 2035 2050 2065 2080 ©1960 1975 1990 2005 2020 2035 2050 2065 2080
Time (Y eat) Time (Y ear)

sedi S1 ‘debt gdp ato": 1

‘eebt gdp tio" - SIP
*eebt “pep ato”: S1P2
“bt gdp aio": S1P3
"bt gdp tio" : SIP

Figure 34: Results of Scenario 1 Simulation

The outcome of the scenario 2 simulation is evident in table 8 and figure 35. We established
that in an event of currency exchange rate increase, the balanced budget and contractionary
fiscal policy will be the best fiscal policies to pursue to avoid accumulating new public debt
to increase the public debt burden and at the same time ensures sustainable improvement of
socio-economic development. The balanced budget and contractionary fiscal policy proved to
be the fiscal policies that maintains the gradual improvement in socio-economic development
and also ensures fiscal sustainability. Table 8 indicates that, under scenario 2, the balanced
budget fiscal policy yields a SEDI of 0.57 and a debt-GDP ratio of 0.12 with a cumulative
budget deficit of 6.1 E+09, while the contractionary fiscal policy yields a SEDI of 0.56 and a
debt-GDP ratio of 0.02 with a cumulative budget surplus of 1.1E+11. The expansionary fiscal
policy guarantees the highest socio-economic development (SEDI of 0.60) with a
significantly high debt-GDP ratio which makes the policy unsustainable.

Page 82 of 88
Scenario 2 Analysis (SEDI) Scenario 2 Analysis (Debt-GDP Ratio)

06 8

03 4

i 0

1960 19751990 200820202035 205020652080 «1960 «1975 1990 2005 2020 2035 2050 2065 2080
Time (¥ eat) Time (Year)

Prt "dtp -to®: St
oi “at-adp

sud: SIP2 “aet-ap 2
su: 13 abt gpa: SPD
oud: IPA “abt-qdp sao": SIPA

1

Figure 35: Results of Scenario 2 Simulation

In summary, the scenario analysis demonstrates that it is important for the government to
maintain fiscal discipline when interest rate on public debt increases. Furthermore, we
established that the only way for the government to achieve fiscal sustainability during
continuous currency exchange rate increase is to avoid over spending i.e. to ensure that
expenditure are funded by revenues and grants. When the government manages to operate a
balanced budget, it is believed that the government can achieve the twin goal of socio-

economic development and fiscal sustainability.

9. Conclusion
This paper presents a dynamic socio-economic model for assessing the impact of government

fiscal policy on socio-economic development and fiscal sustainability. The model captures
the interactions between the social, economic and public finance sectors of the economy. The
base run simulation of the model establishes the theory explaining the observed behavior and
evolution of the major variables. We designed a method for estimating socio-economic
development and fiscal sustainability which utilized the synthetic data from the model to
assess the desirability of the fiscal policy experimented. An ex-ante policy analysis was
conducted to understand and assess the impact of each alternative fiscal policy proposed on
socio-economic development and fiscal sustainability. In addition, a scenario analysis was
conducted to assess the impact of each scenario on the proposed fiscal policies.

To summarize, the policy simulation results indicates that an expansionary fiscal policy is the

preferred policy when one needs to increase and enhance socio-economic development. On

Page 83 of 88
fiscal sustainability, the simulation result concludes that contractionary fiscal policy is the
best policy to significantly reduce the public debt burden in Ghana. The balanced budget
policy was found to be the second best fiscal policy to increase the socio-economic
development and decrease the debt-GDP ratio. The combined policy is the third best policy in
terms of increasing socio-economic development and reducing the debt-GDP ratio. We,
therefore, recommended the balanced budget fiscal policy as the most workable fiscal policy
for the government of Ghana to pursue in order to achieve the twin goal of socio-economic
development and fiscal sustainability. Though a contractionary fiscal policy reduces debt-
GDP ratio much more significantly than the balanced budget fiscal policy, the simple fact
that it reduces socio-economic development does not make the contractionary policy the most
desirable one.

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