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Bank of Uganda
Working Paper Series
Working Paper No. 01/2016
Private and Public Investments and Economic Growth in
Uganda: A Multiplier Model Analysis
Thomas Bwire¶1, Wilson Asiimweظ, Grace, A. Tinyinondi¶2, Priscilla Kisakyeظ, and Denise,
M. Ayume¶1
Ministry of Finance, Planning and Economic Developmentظ ¶1Research Department, Bank of Uganda ¶2Statistics Department, Bank of Uganda
July 2016
Working papers describe on-going research by the author(s) and are published to elicit comments and to further debate. The views expressed in the working paper series are those of the author(s) and do not in any way represent the official position of the Bank of Uganda. This paper should not therefore be reported as representing the views of the Bank of Uganda or its management
Bank of Uganda WP No. 01/2016
\
Private and Public Investments and Economic Growth in Uganda
A Multiplier Model Analysis
Prepared by
Thomas Bwire¶1, Wilson Asiimweظ, Grace, A. Tinyinondi¶2, Priscilla Kisakyeظ, and Denise, M. Ayume¶1
Ministry of Finance, Planning and Economic Developmentظ ¶1Research Department, Bank of Uganda ¶2Statistics Department, Bank of Uganda
July 2016
Abstract
This paper applies a multiplier model on Uganda’s 2009/10 Social Accounting Matrix
(SAM) to investigate the impact on growth of private and public investments. A positive
impact of general investment on growth is found, but with varying degree of sectoral
intensity. Infrastructural investments depict the highest income multiplier effect on
growth than the general industry and services sectors, while excess capacity exist in the
agricultural sector and the labor market and returns to capital from each unit of
investment more than double the returns to labor.
Capital is increasingly driving growth, but at the expense of the agriculture sector and
labor market. It appears necessary that capital utilization needs to be complemented with
strategies to utilize the excess capacity in agriculture and the labor market, if growth is to
be all inclusive. One way to address the labor market challenge is to use policy to steer
the productive system towards adopting capital-led labor intensive technologies
simultaneously with increasing government infrastructural investment.
JEL Classification: C02, C22, C67, E22, H30, J20
KEYWORDS: Public investment; Private sector investment, GDP, Cointegration, Multiplier model,
Social Accounting Matrix and Economic growth
To cite this article,
Bwire, T., Asiimw, W., Tinyinondi, A. G., Kisakye, P. and Ayume, M.D. (2016), “Private and Public
Investments and Economic Growth in Uganda: A Multiplier Model Analysis”, Bank of Uganda
Working Paper No. 01/2016.
Correspondence Address: Research Department, Bank of Uganda, P.O. Box 7120, Kampala, Tel. 256 414 230791, Fax. 256 414 230791. Authors E-Mail addresses: [email protected]; [email protected], [email protected]; [email protected] and [email protected] in the order above. The
views as expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the Bank of Uganda
and the Ministry of Finance, Planning and Economic Development of Uganda and its affiliated organizations.
1 | P a g e
1.0 Introduction
In the past decade, Uganda’s economic growth has averaged 6.7 percent per annum, though this trend
deteriorated in recent years to an annual average of 4.3 percent in the past four years. Amidst
retarding growth, the annual growth in private sector credit (PSC) continues to be robust and
improved from an annual average of 7 percent in the past decade to 13.1 percent between FY 2011/12
and 2014/15. Credit offered by commercial banks to the personal and household sector has declined,
while increasing credit is offered to the agriculture, trade and real estate sectors. Government has
recently intensified investment in infrastructural developments in a bid to boost economic growth.
The share of development expenditure in Uganda’s budget has steadily increased from an average of
4.3 percent of GDP during FY 2002/03 to 2007/08 to 7.6 percent of GDP in FY 2008/09 to 2014/15,
with the bulk of it directed to the energy and transport sectors.
Government has also, together with the central bank, implemented reforms in the financial sector
aimed to foster rapid expansion of the banking system and improve the business and macroeconomic
environment for private sector investment. Despite these developments, real GDP growth has
remained far below the historical trend levels of 7 percent. Export growth also declined during FY
2012/13-2014/15 to an average of about 13 percent of GDP from about 15 percent in the three years
to 2012/13, despite Uganda’s membership to many regional economic blocks and export promotion
zones, weighing down further on economic growth.
It might be the case that the weakening response of growth to private and government investments is
indicative of weak inter-linkages within the income-multiplicative process, i.e. the general
equilibrium interactions of the economic blocks and its capacity to multiplicatively generate incomes
from a unit of investment has slowed down. The paper therefore investigates the amount of national
income generated from each unit of investment in each sector. The paper uses a multiplier model
analysis, which is complemented with suitable time series techniques to investigate the impact of
public and private investments on economic growth and to establish a comparative contribution of
public infrastructural and private investments to economic growth. It also assesses the existing
strategies to revive economic growth to its long-run potential level.
The rest of the paper is organized as follows. Section 2 provides a review of the relevant literature
while Section 3 explains the methodology. Empirical results are given in section 4 while section 5
provides a conclusion.
2 | P a g e
2.0 Literature Review
There is growing interest in analysis of the relative effects of public and private investment on
growth. The recent literature on long-run growth and convergence in real per capita incomes across
countries emphasizes the role of aggregate investment (Khan and Kumar, 1997; Warner 2014, among
others). In the case that public and private investments have different impacts on growth, it bears
significant implications for the determination of the steady-state growth path (Khan and Kumar,
1997). While studying a sample of 95 developing countries for the period 1970 to 1990, Khan and
Kumar use a suite of cross sectional and time series pooled data estimation techniques and establish a
substantial difference in the impact of private and public sector investment on growth. Private
investment was found to bear a much larger impact than public investment especially during the
1980s. In related studies on sub-Saharan Africa, Tokunbo and Lloyd (2010) find a positive
relationship between foreign private investments; domestic investment and net-export growth and
economic growth in Nigeria. In Uganda, Adam et al. (2016) show that public investments in
infrastructure is key for sustainable private sector growth.
Warner (2014) studies whether public investment could drive economic growth on a sample of 124
lower and middle income countries during the period 1960 to 2011. He shows that the public
investment boom variable had a consistent and negative association with private investment rates,
while financial depth was associated with higher private investment rates than with overall growth.
Inflation and export growth showed little association with private investment. However, these
findings run counter to the assumption in models that use Cobb-Douglas specifications that public
capital will stimulate private capital formation, and also undermines a key selling point of public
investment booms – that they will stimulate private investment and thus have a magnified impact on
the economy.
Notwithstanding the positive impact on growth of investment in public goods, elsewhere growth has
been found to be a reducing function of public investment in infrastructure. In South Africa, Fedderke
and Garlick (2008) show that relative to Sub-Saharan standards, transport infrastructure quality is
high, but the quality and accessibility of the infrastructure varies noticeably between the urban and
rural areas. This suggests that the impact on growth of infrastructure investment could be negligible,
though not strictly negative.
3 | P a g e
Using the vector autoregressive (VAR) approach, Ghani and Din (2006) investigate the relationship
between public investment and economic growth in Pakistan over the period 1973 to 2004. They
show that public investment crowds out private investment, which is surprising because public
investment should be complementary to private investment. They also uncover both ways causality
between private investment, public consumption and economic growth and one way causality running
from public investment to growth. The results also showed that growth was largely driven by private
investment, with negative but insignificant impact from public investment.
Llop Llop (2005) uses the Social Accounting Matrix (SAM) model to decompose the endogenous,
exogenous and leakages and assess the factors that contribute to the changes in the SAM multipliers
for the Catalan economy. They used two SAMs, for 1990 and 1994. The SAM multiplier model was
extended by decomposing the multipliers using both the additive and the multiplicative
decomposition approaches. In this paper, the endogenous accounts are categorized as input-output
relations, private consumption, factors of production, factorial income distribution and the interactive
term. The paper established that changes in the multipliers are mainly driven by the structural
coefficients.
Pyatt and Round (1979) used the SAM to develop a dis-integrated multiplier model. The SAM is first
used to deduce the structure of the economy at the base date. The multipliers are decomposed into the
three matrices including; one that captures the transfer effects, second the circular effects and lastly
that captures the cross-effects of the multiplier process, where injections into the economic system
has impacts on the rest of other sectors or economic agents. The analytical work in the paper shows
that decomposition of the structure of the economy can be directly derived from the accounting
balances. The paper captures the fixed price aspects which represent a single step beyond the
structure of the accounting balances.
Breisinger et al. (2009) discuss both the theoretical and practical application of the SAM-based
analysis with a number of exercises for scholars. The paper discusses the economic linkages and
multiplier effects in the SAM framework. The paper shows that for every shock, there are both the
direct and indirect effects. The indirect effects are in the form of consumption and production
linkages. The production linkages are categorized into backward and forward linkages. All these
effects are categorized into multiplier effects of a unit of exogenous investment. In this paper, the
Multiplier model is further decomposed into constrained multipliers and unconstrained multipliers.
Unconstrained multipliers assume prices to be fixed and that any changes in demand will lead to
4 | P a g e
changes in physical output rather than prices. However, this would require an additional assumption
that the economy‘s factors resources are unlimited or unconstrained so that any changes in demand
can be matched by an increase in supply. In the constrained multipliers, the supply responses are
unconstrained by fixing the level of output in certain areas. This approach generates the SAM based
multiplier formula of the constrained multiplier matrix.
Punt et al. (2003) use the SAM to design the SAM-based multiplier model. The authors postulate that
the SAM alone does not constitute an economic model though it can be used to present economic
theory. The paper shows that the SAM approach to modelling is a useful way of showing how the
model statement (price and demand system, behavioral equations and macroeconomic
balances/closures) of a macroeconomic or general equilibrium model can be developed. The paper
adds that the SAM approach of modelling clearly shows that the various properties of a SAM,
particularly the fact that the account row and column total are balanced, are very important when a
SAM is used as the database for a general equilibrium model.
This paper applies a multiplier model on the 2009/10 SAM for Uganda to investigate the impact on
growth due to public and private investments and analyzes the amount of national income generated
from each unit of investment in each sector.
3.0 The Multiplier Model
3.1 The Model Set up
A multiplier model analysis is applied on Uganda’s 2009/10 Social Accounting Matrix (SAM) to
assess the contribution of private and public infrastructural investments to economic growth by sector
and economic agent. To facilitate easy reference, the paper categorizes structural coefficients building
the SAM into policy instrument variables (exogenous), policy target variables (endogenous) and
leakage variables (earnings to government and the rest of the world, ROW- hereafter). This
disintegration of the income creation (multiplicative) process or the structural multipliers will capture
the contributions of sectoral private sector investments to economic growth. The multiplicative
process will quantify the amount of income (direct and indirect) created from each unit of investment
per sector. The direct and indirect effects of private sector investment on growth, is further
categorized into backward and forward linkages of each unit of growth. This approach will also
provide detailed information of the indirect effects on growth, that is, income created by a given
5 | P a g e
sector due to private investment in other sectors. The private investment is also categorized by its
origin that is domestically sourced investments and foreign or externally sourced investments. We
shall use the same method to capture the contribution of other sources of investments (like
government) and shocks to economic growth. The application of this multiplier model on the Social
Accounting Matrix (SAM) will reveal the detailed contributions of each sector’s private investment to
economic growth.
An exogenous expenditure shock, e.g. an increase in government demand for construction services is
associated with both direct and indirect effects (Breisinger et al., 2010). The effect on the
construction sector itself is the direct effect, while that on other sectors, other than construction is the
indirect effect. The indirect linkages encompass production and consumption linkages. The
production linkages are a series of agglomeration of backward and forward linkages. A combination
of direct and indirect effects is what constitutes the multiplier effects. This SAM multiplier analysis
model used in this paper follows the approaches proposed by Llop Llop (2005), Round (2006), Pyatt
and Round (1979), Punt et al., (2003), Bellù (2012), Stone R. (1978) and Breisinger et al., (2010).
3.2 Structural analysis
Using the Row-Column framework of the SAM, we denote payments made by columns as j and
receipts received (rows) as i. As in Asiimwe and Kisakye (2015), the SAM matrix is then denoted Zij
with i rows and j columns. However, for economic and statistical consistency, payments and receipts
are equal per sector (matrix element) and the row-column totals are equal. For instance the row and
column equlity of ROW) account is expressed as;
n
jjROW
n
iROWi zz
1,
1,
(1)
Embedding this in the SAM, we derive the technical coefficients, while differentiating the policy
objectives from policy instruments and leakages. The technical coefficients are derived as:
j
ij
ijz
z
(2)
6 | P a g e
Where ijZ is the individual SAM element and jz is the column totals. We then categorized the
corresponding matrix of technical coefficients into endogenous accounts (policy objectives), ij ;
exogenous accounts (policy instruments or shocks), i and leakages, jl in matrix ijZ . The resultant
ijZ matrix is:
n
n
n
nn
n
n
i
n
m
m
m
mm
m
mmmm
nm
nm
m
mm
m
mm
ij
z
z
z
ll
z
zi
z
z
z
z
z
z
z
z
z
z
z
ll
z
ll
z
ll
z
z
z
z
z
z
z
z
z
z
z
z
z
z
z
z
z
z
Z
1
...
1.....11
.....
.....
............
.....
.....
1
2
11
2
2
1
1
2
22
1
11
2
22
1
11
22
2
2222
1
2121
11
2
1212
1
1111
The total receipts of each economic account say row account in the SAM is a summation of the
endogenous and exogenous expenditure denoted as:
n
j
ijiji zz1
(3)
Where jz is the row total of the SAM, i is a vector of exogenous variables and the rest is the
multiplicity of technical coefficients and the column totals. The right hand side of this equation
should be able to reproduce the original SAM with equality with the sum of the columns and rows.
Satisfying this condition, the technical coefficients can now be used for deriving economic
multipliers using the multiplier model.
3.3 The Multiplier Model Analysis
The multiplier model uses the above technical coefficients ( ij ) to generate economic multipliers
which then define the amount of growth (or income) derived from each unit of investment per sector.
Given that the row and column totals are equal, (3) becomes:
7 | P a g e
)...3,2,1(,
1
mji
n
j
ijiji zz
(4)
Eqn. (4) represents a series of simultaneous equations that define the SAM. The endogeneity across
these equations is captured by their respective technical coefficients denoted by ij . The nominal
flow of income from one economic agent to another, denoted by jz must equate to the receipts
received by each of the given economic agent iz . Dropping subscripts from (4) for ease, we derived
the following simultaneous equation:
zz (5)
Where z represents income and expenditures flows, represents the coefficient matrix and is the
shock component of the SAM. Collecting like terms we derive the following multiplier model.
1)(Iz (6)
The matrix of multipliers ( ) is used to derive the impact of sectoral investment on growth. It’s these
multipliers that are used to derive the backward and forward linkages resulting from each unit of
investment by sector. The sectoral investment that creates more income is considered to contribute
more to the economic system and thus to growth (Roberts, 1998). Thus the sector with the highest
multipliers is the one that can rejuvenate growth prospects in the medium term. The multiplier effect
can be decomposed into direct and indirect effects. Following, Alarcó et al., (2011) the indirect
effects are categorized as backward and forward linkages through the respective column and row
summations. If we define the elements of the multiplier matrix ( ) as ji , , the sector receiving the
investment shock as jsis, , and excluding the multiplier element of the sector receiving investment, the
remaining sectors becomeji~
,~ . The multiplier elements can be expressed as:
jijsisji ~,
~,, (7)
In the equation the direct effect of investment is captured by jsis, whereas the indirect effect is
captured byji~
,~ . It is then possible to decompose the indirect effect into backward and forward
linkages and it takes the form:
8 | P a g e
isn
ijijBW
~~
,~ (8)
and
jsn
jjiiFW
~~
,~ (9)
Where jBW and jFW are the backward and forward linkages, respectively. Thus sectors with strong
backward and forward linkages are those that stimulate growth faster than any other (Breisinger et
al., 2010; Jorge et al., 2011).
3.4 The Data
The paper uses the 2009/10 SAM alongside its corresponding Supply and Use Table (SUT) for the
same year to capture the contribution to growth emanating from a unit of general investment in each
of the sectors and disintegrate the multiplier effect of general investment on growth. The theoretical
structure of the SAM is shown in Appendix 2. The Ministry of Finance, Planning and Economic
Development (MoFPED) in collaboration with the Uganda Bureau of Statistics (UBOS) finalized the
construction of the 2009/10 SAM that replaces the 2002 SAM. It is the corresponding Supply and
Use Table (2009/10) that was used as a basis for rebasing GDP to the FY 2009/10. The economic
sectors in the 2009/10 SAM were increased to 161 compared to 61 sectors in the 2002 SAM, but
these are merged in this study to focus the analysis to addressing the set out objectives. The Supply
and Use Table (SUT) and the SAM comprise a summary table of payments and receipts between
economic agents in a specified period of time usually a year (Stuttard and Frogner, 2003). In this
paper, the 2009/10 Uganda’s SAM was mapped to thirty two (32) accounts as shown in Appendix 3.
As a robust check, a Vector Error Correction Model (VECM) based on quarterly time series data for
the period 1997Q1-2015Q3 is estimated. The VECM consists of five variables: an indicator of output
growth ( tGDP ), development expenditure (Devtexp), exports (Xt), imports (Mt) and private sector
credit (PSCt). GDP is measured in billions of Uganda Shillings in constant 2009/10 prices. Devtexp is
used to proxy public sector investment while PSC, a measure of financial institutions contribution to
the private sector is used to proxy private sector investment. In Uganda, commercial banks hold about
80 percent of the total assets of the financial system and as a result, most private sector firms seeking
finance for investment have to rely on loan finance, for which the most important source is the
banking system.
9 | P a g e
As a share of GDP, growth in PSC has been improving steadily from an annual average of 7 percent
in the past decade to 13.1 percent in the most recent four years. PSC may be an inadequate measure
of private fixed capital formation, but the available aggregate data on private sector investment dates
only to 2009 and on annual basis. We therefore acknowledge the use of PSC as an appropriate price
to pay, despite the fact that it is a significant under estimate of private sector investment. Quarterly
GDP data is from UBOS, Devtexp data is from MoFPED, while data on exports, imports and PSC is
from BoU. All data are in logarithms and are given in levels and in first differences in Figure 1. The
series are also adjusted for seasonality effects, but only where it has been detected.
Figure 1: Series in level and first difference
-800
-400
0
400
800
98 00 02 04 06 08 10 12 14
DEVTEXP_DIFF
4.5
5.0
5.5
6.0
6.5
7.0
7.5
98 00 02 04 06 08 10 12 14
LDEVTEXP
-200
-100
0
100
200
300
400
98 00 02 04 06 08 10 12 14
EXPORTS_DIFF
4
5
6
7
8
98 00 02 04 06 08 10 12 14
LEXPORTS
-400
-200
0
200
400
600
800
98 00 02 04 06 08 10 12 14
IMPORTS_DIFF
5
6
7
8
9
98 00 02 04 06 08 10 12 14
LIMPORTS
-200
0
200
400
600
800
98 00 02 04 06 08 10 12 14
PSC_DIFF
5
6
7
8
9
10
98 00 02 04 06 08 10 12 14
LPSC
-2,000
0
2,000
4,000
6,000
98 00 02 04 06 08 10 12 14
QGDP_DIFF
7.2
7.6
8.0
8.4
8.8
9.2
9.6
98 00 02 04 06 08 10 12 14
LQGDP
Source: Uganda Bureau of Statistics, UBOS, Ministry of Finance Planning and Economic Development,
MoFPED and Bank of Uganda, BoU
The statistical description of the variables used in the VECM is presented in Table 1. The small
coefficient of variation (at most a sixth of the total sample) suggests small dispersion of the data
points from the mean for all the series. The Jarque-Bera (J-B) statistics (Mukherjee, White and
Wuyts, 1998) suggests normal distribution is rejected for all the variables. Nonetheless, as these are
to be analysed in a system framework of the VAR, estimates of a VAR model are robust to deviations
10 | P a g e
from normality, providing system residuals are not auto correlated (Juselius, 2006), as is indeed the
case as we show later.
Table 1: Descriptive Statistics
LQGDP LEXPORTS LDEVTEXP LIMPORTS LPSC
Mean 8.502 6.279 5.895 6.966 7.584 Std. Dev. 0.799 0.956 0.713 0.923 1.096 Coefficient of variation (%) 9.397 15.226 12.093 13.249 14.457 Skewness -0.135 -0.037 0.488 -0.062 0.131 Kurtosis 1.528 1.446 2.012 1.586 1.599 Jarque-Bera 6.813 7.364 5.863 6.132 6.177
Probability 0.033 0.025 0.053 0.047 0.046
Observations 73 73 73 73 73
Notes: LQGDP is the log of quarterly GDP; LEXPORTS is the log of exports; LDEVTEXP is the log of development expenditure;
LIMPORTS is the log of imports and LPSC is the log of Private Sector Credit.
4.0 Empirical Results
The results are generated from the 2009/10 SAM using a multiplier model. The SAM was
compressed to 28 sectors so as to focus the analysis on addressing the contribution of private and
public investment on growth. In this analysis, we presume that private firms receive income from
sales and subsidies from government. The expenditure of producing firms is composed of
intermediate consumption, wages and salaries, payment for use of capital, taxes on activity and
depreciation of capital. Thus the impact of investment on GDP growth mainly follows the input-
output structure embedded in the 2009/10 SAM.
4.1 General Contribution of sector investment to growth
As noted in sections 2.0 and 3.1 of this paper, the impact of each unit of investment on growth is
categorized into direct and indirect effect, for instance an investment in a given sector, increases both
income in the direct beneficiary sector as well as incomes of other sectors interlinked to it through
backward and forward mechanisms as is shown in Figure 2.
11 | P a g e
Figure 2: Sectoral direct and indirect impacts
Source: Author’s computation
It is evident in Figure 1 that investment in all sectors generates income over and above the initial
value of investment, since all the respective multipliers are above unity (total impact). The industrial
sector possesses the highest direct multipliers (1.59) and also indirect (0.9) impacts on growth. The
service sector is second to the industrial sector in steering growth directly (1.51) and indirectly (0.74),
while agricultural sector has the least direct multiplier (1.05) and indirect (0.5) impact. This implies a
unit of investment in the industrial sector generates more income and yields more economic growth
than does a unit of investment in the service and agricultural sectors. The direct multiplier and
indirect impact due to industrial sector on growth that we detect from Figure 1 is however too general
an impact and as is, is difficult to tell the contribution of private and public investment. So in what
follows, we decompose growth impact of industrial investment by source that is ‘private’ and
‘public’.
4.2 Public and private investment contribution to economic growth
The key drivers of economic growth are those economic agents with the highest multipliers. Sectors
with the highest backward linkages possess capacity to propel stronger economy wide growth, mainly
because they are inter-linked to other sectors in their production process through their demand of raw
materials. Conversely, sectors with the higher forward linkages generate more market potentials from
each unit of investment. Thus, investment in sectors with the highest backward and forward linkages
would boost growth back to its potential level. The breakdown of multipliers into public and private
investments is presented in Table 2, whereof the columns are sectors that receive the investment
whereas the rows are endogenous sectors that benefit from the income multiplicative process.
12 | P a g e
Table 2: Comparative impact of private and public investment 2009/10 SUT/SAM Multipliers Public Goods &Infrastructure Investments
Agriculture Industry Services Govt_InvestPublicGds Prv’t_InvestPublicGds
Agriculture 1.05 0.21 0.15 0.08 0.09
Industry 0.20 1.59 0.59 1.45 1.60
Services 0.31 0.70 1.51 1.21 1.01
Total sectoral income 1.56 *2.50 2.25 *2.73 2.70
Memo:
Backward linkages 0.51 *0.90 0.74
Forward linkages 0.35 0.80 **1.01
* Highest level of contribution
Source: Author’s computations.
As can be seen, general investment in industrial sector drives growth faster (2.5), followed by the
services sector (2.25) and then the agricultural sector (1.56). The industrial sector has strong
backward linkages (0.9) compared to services (0.74) and agriculture (0.51). However, the industrial
backward linkages are stronger with the service sector (0.7) compared to its linkages with the
agricultural sector (0.21). This shows that economic growth is mainly driven by the industry and
service sectors given their strong input-output inter-linkages.
Investment in the agricultural sector currently yields less comparative impact on economic growth,
implying, in part, the sector possess high levels of excess capacity. It is probably this dismal
economic viability that makes it less attractive for capital investment, attracting only 0.9 percent of
the Foreign Direct Investment (FDI) in 2013 (Bank of Uganda, 2015).
In addition, government investment in public goods and infrastructure generate more economic
growth (2.73) compared to investment in any other sector, the highest in the category being industry
(2.5). This indicates that the economy is ‘infrastructure hungry’.
4.3 Accelerating Growth
As revealed in the previous section, a significant portion of the current economic growth is
shouldered mainly around government infrastructural investments. This is ‘good news’ for an
infrastructure ‘hungry’ economy like Uganda but could be ‘bad news’ for other sectors, as any
deterioration in a sector’s interlinkage with the rest of the economy is not felt; though it does not add
up to an internal Dutch disease effect. For instance, deterioration of the agriculture sector interlinkage
with other sectors could be compensated by a coincidental increase in infrastructure investments, thus
delimiting potential growth from agriculture. In such circumstances, growth may not reduce in the
short run but hurt the economy through a weakened agricultural sector in the long-run. This generates
13 | P a g e
perpetual excess capacity in the agricultural sector as it’s for the case depicted in the 2009/10 Social
Accounting Matrix (SAM). We now turn to the detailed approach of revamping agricultural sector.
Khan and Kumar (1997) note increasing acknowledgement that public investment in infrastructure
projects may not lead to an increase in growth in some cases. For example, in Latin America in the
1970s and Africa and Asia in the 1980s, which were characterized by several large projects
undertaken of somewhat questionable quality including electric power plants, roads and railways,
built at exorbitant costs and ended up either incomplete or under-utilized. In addition, these projects,
if financed through increased taxes, could drive up costs of inputs and lead to an adverse effect on
growth and investment in an economy.
4.3.1 Revamping agricultural contribution to growth
The rejuvenation of agriculture’s contribution to growth is key and investment in the sector is a
necessary but not sufficient condition to maximize growth returns. The sufficient condition is to
improve the backward and forward linkages of the agricultural sector with the rest of the economy
(industry and service sector). Thus, investment in agricultural sector should be complemented with
investments in the sub-sectors of industry and services sector that have strong backward and forward
linkages with the agriculture sector. In the services sector, Hotels and restaurants have strong
backward linkages with agriculture, whereas in the industrial sector the ‘agro-processing’ possess
strong linkages with the agricultural sector as is shown in Table 3.
Table 3: Investment Drivers of agriculture growth
Agriculture sector Agro-Processing
1) Productive sectors Agriculture Cash crops
Other
Agric
Industry
Food_
Processing
Agro_
processing
Agriculture 1.05 1.05 1.05 0.21 **0.49 0.45
Industry 0.20 0.31 0.18 1.59 1.37 1.50
Services 0.31 0.49 0.27 0.70 0.52 0.49
Total sectoral impact 1.56 **1.84 1.51 **2.50 2.38 2.43
2) Factor payments
Labor 0.16 **0.22 0.15 **0.27 0.21 0.20
Capital 0.88 0.83 0.89 0.86 0.86 0.86
Total factorial impact 1.04 **1.05 1.04 **1.13 1.07 1.06
**highest level of contribution
The sub-sectors of agriculture show that the linkage between agricultural sector and the rest of the
economy is mainly driven by the cash-crops production. The total impact of investment in cash crop
production is at 1.84 units compared to 1.51 units of the rest of agriculture. The cash crops agro-
14 | P a g e
processing dominate agro-processing industry thus the respective strong backward and forward
linkages.
Also as shown in the table, investment in non-agro-processing industries generates more economic
growth than investments in agro-processing. However, investment in agro-processing and food
processing within the agro-processing generate stronger backward linkages with agriculture (0.45 and
0.49, respectively) than with general industry (0.21). Since 66 percent of the working population is
employed in agriculture (UBOS, 2010), it’s eminent to diagnose the contribution of these sectoral
investments to employment and income. As shown in Table 3, labor receives more income from
investments in cash-crop production (0.22) compared to that received from investments in the rest of
agricultural sector (0.15). Thus, agricultural sub-sector linked to agro-processing provides stronger
employment benefits compared to the rest of agriculture.
4.3.2 Revamping labor market contribution to economic growth
Private and public investments have consequential impacts on both economic growth and factor
employment. Sectors that generate more factor income from a unit of investment are considered to
have great potential in rejuvenating national income towards its long run potential. Table 4 shows
that sectoral investment generates more income to capital than it does to labor. This information may
not be conclusive to assert whether Uganda is a capital intensive country or the capital cost in
production is extremely higher than the unit labor cost. What we can conclude is that a unit of
investment yields more labor income to the nation if and only if the investment is in the service sector
(0.29). However, this could reflect the notion that the service sector in Uganda is labor intensive, thus
any unit of investment is translated to a larger wage bill to the labor employed.
Next to services is investment in the industry sector, which generates 0.27 units of marginal labor
income and the agriculture sector is the least paying with marginal labor income of 0.16 units. This
probably explains the rampant rural-urban migration and the youth disorientation in agriculture
opting for activities in urban areas which puts pressure on social services provided.
Table 4: Factor multiplicative income
Agricultural sector Industrial sector Services Sector
Factor payments Agric Cash crops
Industry Agro-
Processing
Services
Public
Admin Transport
Labor 0.16 *0.22 *0.27 0.20 0.29 *0.67 *0.40 Capital 0.88 0.83 0.86 0.86 0.86 0.82 0.75
Total impact 1.04 1.05 1.13 1.06 1.15 1.49 1.15
Source: Author’s computations.
15 | P a g e
Table 4 also shows that returns to capital are higher with investment in the agricultural sector (0.88)
but the same for the industrial and service sectors (0.86). In general, returns to capital are more than
twice the wage income resulting from each unit of investment across all sectors. Thus, capital
endowments are crucial in boosting economic growth. This is ‘good news’ for economic growth but
could be harboring ‘bad news’ for the inclusive growth agenda because capital is driving growth at
the expense of agriculture and labor efforts. In addition, the capacity of the economic system to
multiply income depends on the ratio of the return re-invested in the economy to that repatriated
abroad. The portion of these returns that is repatriated abroad acts as a leakage that discounts the
income multiplicative system which incapacitates the ability of the economic system to propel
economic growth further.
This evidence is consistent with the findings contained in the Uganda National Household Survey for
2009/10 (UBOS, 2010). The survey shows that 66 percent of the working population is employed in
agriculture, a sector which receives the least wage return (0.16) from each unit of investment as
expressed in Table 3. The service sector which has the highest wage returns (0.29) from each unit of
investment employs only 28 percent of the total working population (UBOS, 2010). This shows the
magnitude of excess capacity existing in the use of labor resources tied up in the agricultural sector
thus resulting in unemployment of labor as their exist rigidities for it to join the service and industrial
sectors. It is further shown that the literacy rate of persons above 10 years is 73 percent at national
level and 69 percent for the rural areas. This shows that a large share of the excess capacity in labor
market possess skills that can be utilized to boost economic growth.
4.4 Robustness checks
4.4.1 Vector Error Correction Model (VECM) Analysis
The generality of the result for the relative impact of private and public sector investment on growth
is qualified using a vector error correction model which is estimated within the Johansen’s (1988) and
Granger Non causality (Granger 1969) frameworks. This is applied on a 5-dimensional vector
autoregressive model: tx ( tGDP , tDevtexp , tX , tM , tPSC ) where (for year t), GDP is gross
domestic product, Devtexp is development expenditure, X is exports, M is imports and PSC is private
sector credit, covering the period 1997Q1-2015Q3. Details of measurement of these variables, their
time series characterization, including choices of the lag length are discussed in the online Appendix.
16 | P a g e
The presence of one equilibrium (stationary) relation, even when corrected for small sample bias
among the variables at the conventional 5 percent level of significance, could not be rejected, and the
roots of the companion matrix and graphs of the potential cointegrating relations (available on
request) suggested that r = 1seems reasonably well supported by the data. Unless otherwise stated,
the only existing cointegrating relation, as shown in Table 5, is normalized for the quarterly change
of GDP in order to interpret the estimated coefficients. Consistent with economic theory and
estimates of the multiplier model, results of the long-run analysis reveal that DEVTEXP, exports and
PSC have a positive significant long-run correlation with GDP, and in addition, the correlation of
GDP with imports is negative. The estimated coefficients imply, ceteris paribus, over the long-run a 1
percentage point increases in DEVTEXP, EXPORTS and PSC increases GDP by about 1.7, 5.1 and
4.2 percentage points, respectively, while 1 percentage point reduction in IMPORTS stimulates GDP
growth by 7.8 percentage points.
Table 5: Normalized co-integrated equation
LGDP LIMPORTS LPSC LDEVTEXP LEXPORTS
β
1.000
-7.858 (-4.385)
4.258 (8.946)
1.700 (2.678)
5.065 (3.342)
α 0.008 (0.628) 0.039 (4.705) -0.205 (-4.630) 0.001 (0.031) 0.019 (1.716)
In parentheses are p-values
Source: Author’s Computations. Estimates from E-Views
The error correction term is statistically significant with a correct sign in the PSC equation, adjusting
by about 20 percent of the previous quarter’s deviation from equilibrium. This suggests, for the
system variables considered here that once the system is pushed out of the long-run steady state,
equilibrium is reinstated through adjustments in the PSC. It is not surprising therefore that wherever
the government is considering possible means of funding its development expenditure, it is always
cautious not to hurt PSC growth.
Turning to the question of direction of causality, two elements are of interest to us: i) Have measures
of public and private investment collectively influenced GDP growth in Uganda so that causation
runs from investment to Growth?, or ii) it is the robust and sustained GDP growth over the last two
decades that Uganda has achieved that has been an impetus to its observed investment trends. If it
happens to be the case that we fail to reject both (i) and (ii) simultaneously, it may well be case that a
nexus exists in Uganda; where public and private investment are key in accelerating the desired rate
of growth and at the same time, growth is a stimulus to public and private investment. Results of both
pairwise and block exogeneity are shown in Table 6.
17 | P a g e
Table 6: VAR Granger Causality/Block Exogeneity Wald Tests
Macroeconomic indicators
Development Expenditure
Private Sector Activity
GDP
Development Expenditure --- 4.920 (0.178) 12.467 (0.006)
Private Sector Activity 15.889 (0.001) --- 7.889(0.048)
GDP
4.875 (0.181)
4.087 (0.252)
---
ALL 13.778 (0.032) 10.444 (0.107) 15.395 (0.017)
Notes: “---”denotes dependent variable, and p-values in parentheses
Source: Author’s Computations. Estimates from E-Views
Collectively, the null hypothesis that the system variables considered here do not influence growth in
the short-term is rejected at the conventional 5 percent level of significance; so that in general, this set
of variables, over the short-term are growth enhancing. Individually, public development expenditure
has a more pronounced growth enhancing effect in the short-term than does private sector investment.
The result on government development expenditure though consistent with the multiplier model
results, is somewhat surprising because development expenditure is expected to be associated with a
lagged/delayed impact on output growth. In this case however, it reflects in part the multiplier effect
it has on aggregate demand which, over the short-term, may distort or stimulate economy-wide
output. The level of national output does not seem to drive, in the short run, development expenditure
and private sector activity.
5.0 Conclusion and Policy Implications
The paper investigated the impact on growth due to private and public investments in Uganda using a
suite of multiplier model based on the 2009/10 Social Accounting Matrix (SAM) and the vector error
correction (VECM) and Granger non-causality frameworks for the period Q3-1997 to Q3-2015.
Based on the multiplier model, government investment in public goods and infrastructure generate
more economic growth than does private investments in public goods like education, health and
others, implying that government investment is shouldering substantial growth out turns compared to
private investment. This general result is robust to VECM and Granger non-causality evidences
where a steady-state long-run relationship between economic growth, public and private sector
investment, and exports and imports of goods and services is found, and a strong unidirectional
causality running from both public and private investments to growth is uncovered, with public
investment having a more pronounced growth enhancing effect.
Disaggregating the sectoral effect, the paper uncovers excess capacity in both the agricultural sector
and the use of labor and that the current economic growth is shouldered by industry and service
sectors. We find that the industry sector is mainly driven by government infrastructure investments
18 | P a g e
and that it possesses the highest growth multiplicative income effect. This suggests that to maximize
economic growth, investments should concentrate in industrial sector. However, the two key growth
driving sectors in general and the industry sector in particular, have weak backward linkages with
agriculture sector, except for ‘agro-processing’ and ‘hotels and restaurants’.
The excess capacity in the agricultural sector implies that investment in industry is a necessary but
not sufficient condition. The sufficient condition would require utilizing the excess capacity in
agriculture to boost growth. This requires scaling up investments in the agricultural production as
well as improving on the inter-linkages between agriculture and industrial sector through promotion
of agro-processing. The impact on growth is maximized when investments are directed to agricultural
sub-sectors that provide raw materials to the agro-processing industries. In addition, policies to
expedite the inter-linkages between agricultural sector and the rest of the industrial sector need to
focus on investing more in agro-processing with special concentration in food-agro-processing. The
results also carry the implication that the capacity of the industry sector to revive agricultural growth
strongly relies in the agro-processing sub-sector that demand raw materials from the agricultural sub-
sector of interest.
The study also finds that factor income response to each unit of investment is skewed more towards
capital returns than wage returns, and the returns to labor from investment is comparatively weaker in
agriculture sector compared to the service sector. Capital driving growth at the expense of agriculture
and labor efforts is good news, but this may hurt inclusive growth agenda particularly because of the
economic importance of agriculture. Therefore, in addition to promoting agro-processing,
infrastructural development and agro-steered agriculture, the economy needs to also devise a policy to
utilize the excess capacity in labor market. Since less leakages exist in the labor market, a holistic
approach could be followed where a capital-driven labor-intensive technologies are adopted in the
development process as investments in agro-processing is scaled up. For instance in infrastructure
development, as capital is employed, it could be combined with more labor to boost both productivity
and employment of labor as well as increase the capacity of the income multiplicative system to
generate or steer more economic growth.
Overall, accelerating growth will require three simultaneous approaches. First, promoting
industrialization and in particular agro-processing and ensuring a non-declining government
infrastructure investments at least in the short- to the medium-term. This is because government
investments have strongest multiplier effect for each unit of investment and is strongly linked to
19 | P a g e
industrial and service sectors. Agro-processing has strong linkages with the agriculture sector which
employs more than 66 percent of Uganda’s workforce. The second approach is to strengthen the inter-
linkages within the economic system. Currently the inter-linkages in the economy are low especially
between agricultural sector and the rest of the economy. Still a large share of the economy’s
intermediate inputs are imported and input-output relationships in the economy are still strong
between industry and service but weak with agricultural sector with exception of the agro-processing
industry. The third policy approach is to endeavor to rejuvenate the productivity in agriculture by
promoting agro-based demand-driven agriculture output. Revamping agriculture will require a
holistic approach including strengthening its linkages to other sectors as well as focusing on import
substitution of imported raw agricultural materials.
Finally, the paper also finds that a substantial portion of investment in Uganda is sourced from the
rest of the world, thus increasing a potential risk for leakages from the economic system and this is
constraining the multiplicative process. The impact on economic growth, of the leakage-injection of
foreign capital on the multiplicative income should be judged based on the net effect of these
investments against the cost of repatriation. A suitable candidate for extension of this analysis could
be to establish the net-effect of foreign capital and repatriation on economic growth.
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Appendix 1: Mapping of economic sectors
SUB SET SUB-SET ELEMENT
SUB SET SUB-SET ELEMENT
Ag
ricu
ltu
re
1 cashcrop c-cotton, c-flowers, c-
coffee, c-tea, c-
othcashcrops
Ind
ust
ry
17 TransptEqvip c-transportequipment
18 OtherManuf c-othmanufact
2 Grainseeds c-maize, c-rice, c-
othcereal, c-oilseeds, c-
potatoes, c-cassava, c-
beans, c-othlegumes, c-
bananas
19 govInvst c-wtsngov, c-oinfagr, c-
oinfroadtp, c-oinfwatertp, c-
oinfrailtp, c-oinfenerg, c-
oinfoth, c-healthgov, c-
edupgov, c-edusgov, c-
edutgov 3 other_agric c-othagr
28 Pvt_Invest c-wtsnprv, c-edupprv, c-
edusprv, c-edutprv, c-
healthprv
4 Animal_Husbandry c-cattlebuffaloes, c-
dairyfarming, c-othanimal
21 Construction c-construct
5 Forestry c-forestry
Ser
vic
e
20 OthrServ c-othsvc
6 Fish c-fishing 22 Trade c-trade
Ind
ust
ry
7 Petroleum c-crudepetroleumgas, c-
oilref
23 TrsprtServ c-transport
8 Mining c-othmining 24 HotelRest c-hotelsrest
9 foodProcesg c-processedmeat, c-
processedfish, c-dairy, c-
grainmillstarch, c-bakery,
c-manufactsugar, c-
manufactcoffee, c-
manufacttea, c-othfood
25 Telecom c-telecomict
26 FinServices c-financialsvc
27 gov_cur c-publicadminist
29 labour f-labn, f-labs, f-labt
30 capital f-capprv, f-land, f-timber, f-
fish, f-ext-
crudepetroleumgas, f-ext-
othminerals, f-ext-quarrying
10 Oils&fats c-edibleoilsfats
11 Animal_feed c-animalfeeds
12 Text_tobacco c-bebtob, c-textiles HH_Rural h-rur
13 Chem_RubPlast c-chemplastrubber HH-Urban h-urb
14 NonMetal c-nonmetprod
15 Metal c-metalprod
16 Energy c-electequipment, c-
electgasaircond
22 | P a g e
Online Appendix
Unit root and specification of the Data Generating Process (dgp)
Results of the Augmented Dickey Fuller (ADF) unit root test (Dickey and Fuller, 1979) are provided
in Table 7 and indicate that all variables are non-stationary, but become stationary upon first
differencing at the conventional 5 percent level of significance.
Table 7: The Augmented Dickey-Fuller (ADF) Unit root test ADF Test
Level First difference
Macrovariables Ho: 0 Lag-length Inference Ho: 0 Inference
LDEVTEXP -1.670(-3.475) 2 Accept -17.856 (-2.904) Reject
LX
-2.654 (-3.473) 0 Accept -8.066 (-2.903) Reject
LM -2.257 (-3.473) 0 Accept -2.600 (-2.904) Reject
LPSC -2.145 (-3.473) 0 Accept -3.396 (-2.903) Reject
LGDP -1.819 (-3.474) 0 Accept -8.907 (-2.903) Reject
Notes: L = Log; Akaike Information criterion [AIC], Schwarz Bayesian criterion [SC] and Hannan-Quinn criterion [HQ] were used
(maximum set at 6 lags). An unrestricted intercept and restricted linear trend were included in the ADF equation when conducting unit
root test of all the series in the levels. Numbers in parentheses are the 5 percent critical values, unless otherwise stated. All unit-root
non-stationary variables are stationary in first differences.
Source: Author’s Computations. Estimates from E-Views
On the basis of unit root testing and the fact that all variables are integrated of the same order, i.e. I(1)
implies they could be cointegrated. The 5-dimentional vector autoregressive model is estimated with
an unrestricted constant. Including an unrestricted constant allows for linear trends in both
cointegrating space and in the variables in levels and produces a non-zero mean in the cointegrating
relation. Furthermore, it avoids creation of quadratic trends in levels, which would arise if both the
constant and the trend are unrestricted (Bwire et al., 2016; Juselius, 2006). The lag-length is
determined as the minimum number of lags that meets the crucial assumption of time independence
of the residuals, based on a Lagrange Multiplier (LM) test, starting with 5 lags. Akaike, Schwarz and
Hannan-Quinn information criteria all favour one lag, but LM suggests a lag of 3, and it is this that
we adopt for our forward model estimation.
An assessment of VAR (3) system residual misspecification test reveals that although the residuals
meet the crucial assumption of no auto-correlation and residual heteroskedasticity test is satisfactory
[ 2 (450) = 444.2[0.569]], the assumption of multivariate normality of the residuals is not met
[ 2 (10) = 98.2 [0.000]]. Rejection of joint non-normality of residuals notwithstanding, estimates of
the VAR model are robust to deviations from normality provided residuals are not auto-correlated
(Bwire et al., 2013b).
Johansen’s Cointegration Test
Having determined the appropriate specification of the d.g.p, we then evaluated the existence of long-
run equilibrium relation (s) using the Johansen (1988) trace statistic test for cointegration. The
determination of the cointegrating rank, r , relies on a top-to-bottom sequential procedure; this is
asymptotically more correct than the bottom-to-top Max-Eigen statistic alternative [Juselius, 2006:
131-134]. The trace-test has been shown to have finite sample bias when the number of observations
is as small as in the present study with the implication that it often indicates too many cointegrating
relations so that the test is oversized (Juselius, 2006; Cheung and Lai, 1993b; Reimers 1992). The
trace-test in the current study is adjusted for finite sample bias using the correcting procedure
23 | P a g e
suggested by Reimers (1992) as shown in Harris and Sollis (2005:122-124) for 71 included
observations after adjustment. Based on the results in Table 8, the presence of one equilibrium
(stationary) relation, even when corrected for small sample bias among the variables at the
conventional 5 percent level of significance, cannot be rejected. Even more, roots of the companion
matrix and graphs of the potential cointegrating relations (available on request) suggest that
1r seems reasonably well supported by the data.
Table 8: Johansen’s Cointegration test and Long-run Analysis
Hypothesized No. of CE(s) Eigen value Trace Statistic Trace Statistic
# 0.05 Critical Value
None * 0.4588 86.995 70.603 69.819
At most 1 0.2847 44.633 36.221 47.856
At most 2 0.1729 21.512 17.458 29.797
At most 3 0.1131 8.4155 6.828 15.495
At most 4 0.0019 0.1308 0.107 3.841
Notes: i) Trace test indicates 1 cointegrating eqn(s) at the 0.05 level; ii) * denotes rejection of the hypothesis at the 0.05
level; iii) # is the small sample corrected test statistic;
Source: Author’s Computations. Estimates from E-Views