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American Journal of  Applied 
Statistics and Economics (AJASE)

Impact of  Macroeconomic Factors on Government Spending in Ghana
Tobias Kwame Adukpo¹*, Jacob Obeng Bethel²

Volume 4 Issue 1, Year 2025
ISSN: 2992-927X (Online)

DOI: https://doi.org/10.54536/ajase.v4i1.5833
https://journals.e-palli.com/home/index.php/ajase

Article Information ABSTRACT

Received: July 30, 2025

Accepted: September 01, 2025

Published: October 09, 2025

This study examines how key macroeconomic variables such as GDP growth, inflation, 
interest rates, government debt and unemployment influence government spending in 
Ghana. While many studies rely on traditional econometric models, this research applies 
a Bayesian regression framework, which allows for the incorporation of  prior information 
and a clearer assessment of  uncertainty in the estimates. The results show that GDP growth, 
public debt and unemployment are positively associated with government spending, while 
higher interest rates constrain fiscal expenditure. The effect of  inflation remains uncertain. 
These findings provide new evidence on the dynamics of  fiscal behavior in Ghana and 
highlight the need for policymakers to balance growth and debt considerations while 
managing the risks of  rising interest rates. The study contributes to the broader debate on 
how macroeconomic conditions shape fiscal policy in emerging economies.

Keywords

Bayesian Analysis, Economic 
Indicators, Economic Modeling, 
Fiscal Policy, Government 
Spending, Macroeconomic Factors, 
Public Expenditure

1 Department of  Accounting, University for Development Studies, Ghana
² Department of  Economics, University of  Ghana, Ghana
* Corresponding author’s e-mail: adukpotobias@gmail.com

INTRODUCTION
Economic policy is significantly supported by the 
presence of  government spending as it carries a lot of  
influence in the progress of  any given country (Stiglitz 
& Rosengard, 2015). For a nation like Ghana, which is 
actively striving to have a strong and inclusive growth, 
understanding the factors that influence government 
expenditure is not just academic research but an important 
investigation in achieving proper fiscal management and 
long-term financial sustainability. Ghana has had a long-
documented history of  having to deal with a complex set 
of  fiscal challenges, such as the constant budget deficits, 
rising public borrowing and the fine juggling act of  
finding the means to fund development projects against 
available revenue (Asiama et al., 2014). These challenges 
show the persistent instability in Ghana’s fiscal space, 
which makes the dynamics of  governmental expenditures 
an important topic to study.
Although traditional econometric analyses help determine 
economic relationships, sometimes these analyses are not 
comprehensive enough in grasping the complexity and 
uncertainties that exist in the fiscal policy of  a developing 
economy. These constraints include limitations in 
integrating previous theoretical information, working 
with relatively medium sample sizes and the extensive 
probabilistic interpretation of  parameter estimations 
(Koop & Korobilis, 2010). This article fills these gaps 
by depending on a Bayesian econometric approach 

to examine the impacts of  macroeconomic factors on 
government spending in Ghana. The Bayesian framework 
has distinct benefits: it gives the ability to explicitly 
incorporate prior information and economic theory, 
provides a richer understanding of  parameter uncertainty 
by summarizing information in full posterior distributions 
and it is especially resilient in situations when the quantity or 
quality of  data is relatively sparse, or when some structural 
changes occur (An & Schorfheide, 2007).
The primary objective of  this research was to understand 
the complex relationships that exist between the most 
significant macroeconomic indicators, such as real GDP, 
inflation rate, exchange rates, interest rates, public debt 
levels and unemployment levels on the dynamics of  
government spending in Ghana. Through a more stringent 
Bayesian approach, we presented a more informed and 
probabilistically oriented assessment regarding how these 
macroeconomic processes influence the fiscal decision 
process and outcomes in Ghana. The insight drawn from 
this examination is essential to policymakers in Ghana and 
helps develop better, more robust and responsive fiscal 
policies, which improve the way the state handles finances 
and eventually this change will help steer Ghana to a scenario 
of  long-term economic success, regardless of  the changes 
in the global and local economy. The study is part of  the 
large body of  literature regarding fiscal policy in developing 
economies and provides a methodological framework that 
other researchers can use in a similar setting.



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LITERATURE REVIEW
Understanding Government Spending
Government spending, also known as public expenditure, 
refers to the spending made by a government to finance 
its operations and functions (Stiglitz & Rosengard, 2015). 
It includes current consumption expenditure for daily 
operations and public servant salaries, capital investments 
aimed at long-term asset creation like infrastructure for 
social welfare programs and income redistribution and 
crucially, interest payments on government borrowing 
(Musgrave & Musgrave, 1980). These expenditures 
serve as vital tools of  fiscal policy that directly influence 
macroeconomic variables such as aggregate demand, 
economic growth and employment (Keynes, 1937; Barro, 
1990; Afonso & Furceri, 2010). In developing countries 
like Ghana, government expenditure is of  much concern, 
since it supports the establishment of  necessary public 
services, fosters the creation of  supportive infrastructure 
and enables the establishment of  social protection 
networks. Consequently, a comprehensive understanding 
of  its dynamics is of  paramount importance for effective 
economic governance and the achievement of  national 
development goals.

Theoretical Foundations of  Government Spending
Wagner’s Law (Law of  Increasing State Activity)
This hypothesis was proposed by Adolph Wagner 
in the late 1800s and has been the subject of  several 
studies. The hypothesis suggests that as economic 
development indicators such as rising per capita income, 
industrialization and public sector expansion improve, 
government expenditure is also expected to increase 
correspondingly. With a rise in the number of  nations 
that are getting richer, there is a growing need for 
public goods and services (education, healthcare, and 
infrastructure, etc.), as well as the nature of  administrative 
and legal operations, which have also seen government 
expenditures rise (Wagner, 1883). In some regions around 
the world, not everyone has supported this theoretical 
evidence, although some studies conducted in Ghana 
have supported the use of  this law, which implies that 
the growth of  the economy results in higher levels of  
government spending in the Ghanaian economy (Keho, 
2016).

Peacock-Wiseman Hypothesis (Displacement Effect)
Contrary to the smooth rise that Wagner suggested, 
Peacock and Wiseman (1961) proposed that spending by 
governments does not rise progressively but in a sequential 
manner in jumps. The causes of  these jumps are mostly 
social upheavals such as wars, natural disasters, or a major 
economic crisis. In times like these, those societies can 
live with increased taxation to fund higher government 
spending. After the crisis passes, increased government 
spending and taxation made during its high points are 
likely to continue, as people get accustomed to a greater 
governmental role and the new services the government 
provides. This displacement effect, in addition to an 

inspection effect (reassessment of  acceptable levels of  
taxes by the people) and a concentration effect (central 
government increases its limits), results in a higher 
permanent plateau of  government spending (Peacock & 
Wiseman, 1961; Ocran, 2011). The history of  economic 
shocks and structural adjustment programs in Ghana 
proves an excellent testing arena for this hypothesis.

Keynesian Theory
In the Keynesian approach, government spending is 
considered an exogenous variable and a method of  
stabilizing the economy. The expansion in government 
expenditure increases aggregate demand when there 
is no growth and aggregate demand is at its lowest, 
particularly during a recession, by way of  a multiplier 
effect, thus affecting economic development and 
employment (Keynes, 1937). It means that fiscal policy 
actively participates in economic cycle management, 
which proposes an idea of  countercyclical spending to 
counterbalance the fluctuations in the private sector 
(IMF, 2014).

Empirical Review
The connection between government expenditure and 
macroeconomic measures has been studied extensively, 
with studies frequently using a diversity of  econometric 
methods to ensure a variety of  national economies 
(Gemmell et al, 2011; Gupta et al., 2005). GDP growth 
has always been associated with government expenditure. 
To illustrate this, Afonso and Jalles (2016), in a panel VAR 
model of  28 EU countries, discovered that economic 
growth is usually accompanied by higher government 
expenditure, which implies procyclical fiscal behaviour 
in most of  the EU member states. Similarly, Gali and 
Perotti (2003) used a structural VAR model across 
OECD countries, including Germany, France, the UK 
and the US, to argue that fiscal policies moderate toward 
the direction of  expansion during economic booms 
as governments spend on government services and 
infrastructure. In Ghana, Mensah and Adukpo (2025) 
used a multiple regression model and found that capital 
expenditure is a significant determinant of  economic 
growth, though recurrent expenditure increased 
economic growth positively but was not significant. These 
implications highlight the point that the growth in GDP 
leads to not only widened fiscal space but also a shift in 
policy inclinations that could be achieved by increasing 
investment on a larger scale in the public sector.
Inflation, on the other hand, has played a more complex 
role in government spending. High inflation erodes the 
real value of  government budgets and complicates long-
term fiscal planning. For instance, Lithuania et al. (2012), 
using VECM and Granger causality tests, established that 
inflation volatility contributed to fiscal uncertainty and 
constrained capital investment. Their study showed a 
negative correlation between inflation and developmental 
spending, particularly in liberal economies with high 
fiscal balance requirements. Similarly, Anagaw (2023), 



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emphasized that persistent inflation undermines 
macroeconomic stability, reduces long-term growth 
potential, disproportionately affects low-income and 
unemployed groups. These findings highlight how 
inflation not only depreciates the real value of  government 
resources but also generates substantial distortions that 
hinder both fiscal planning and sustainable economic 
growth.
Interest rates also influence government spending since 
they influence the price of  borrowing. High interest rates 
increase the burden of  debt service payments, thereby 
reducing the fiscal space available for other crucial 
expenditures. In a case study conducted across 216 
countries, Peña (2023) used system GMM and Granger 
causality analysis, revealing a strong negative correlation 
between interest rate and aggregate government 
expenditure. Their findings show that an increase in 
interest rates acts as a deterrent towards the borrowing 
of  essential areas like infrastructure and education. The 
impact of  this effect is especially high in developing 
economies, where the payment of  interest displaces funds 
that should be used in social and economic programs.
Another crucial aspect that determines government 
expenditure is public debt. In a panel fixed-effects model 
and analysis of  high-debt OECD countries like Italy, 
Greece, and Portugal, Alesina et al. (2019) found that rising 
public fund levels of  a country tend to tighten its fiscal 
policies, most commonly through capital expenditure cuts. 
Backing this, the IMF (2015) indicated that a high level of  
public debt usually forces governments to transfer funds, 
which should have been used to develop the country, to 
pay the debt. These findings support the debt overhang 
hypothesis, that there exists debt or burden in the form 
of  debt that can restrict governments from stimulating 
the economy by spending.
Unemployment also acts as a major determinant of  
fiscal policy. During periods of  economic downturn, 
governments often increase spending on social protection 
programs to mitigate the impact of  job losses. A time-
varying VAR model based on U.S. data by Klein and 
Linnemann (2020) demonstrated that the increase in 
unemployment rates leads to an increase in spending by 
governments on social protection programs. Their finding 
demonstrates how automatic stabilizers, which include 
unemployment benefits, increase government spending 
during economic downturns at varying magnitudes at 
different points in time. Complementing this perspective, 
Nojeem (2020) employed an Auto Regressive Distributed 
Lag (ARDL) bounds testing approach to Nigerian data 
from 2010 to 2020. Their results identified an inverse 
relationship between unemployment and economic 
growth, with unemployment further associated with 
rising crime rates. All these findings emphasize both 
the countercyclical role of  fiscal policy and the wider 
macroeconomic and social implications of  persistent 
unemployment.
On the methodological side, Afonso and Sousa (2012) 

employed a Bayesian Structural VAR model based on 
data in the US, UK, Germany and Italy and their results 
indicated that fiscal shocks had a modest but persistent 
effect on GDP and private consumption. Such methods 
based on Bayesian conditioning have been preferred 
because of  their capability to use prior knowledge and 
yield more consistent estimates, especially in complex and 
uncertain environments (Koop & Korobilis, 2010).
To conclude, the literature consistently attests that 
macroeconomic factors such as GDP growth, inflation, 
interest rates, public debt and unemployment play huge 
roles in determining the pattern of  government spending. 
However, the magnitude and directions of  such effects 
vary depending on the economy and the model employed. 
This study builds on this foundation by using a Bayesian 
multiple regression model and provides a more in-depth 
analysis of  these relationships in Ghana. The Bayesian 
approach is advantageous in its capacity to ensure robust 
modelling, which is highly flexible because it combines 
prior information with explicit consideration of  the 
parameters’ uncertainty.

MATERIALS AND METHODS
This study employs a Bayesian multiple regression 
method to estimate the impact of  the macroeconomic 
variables on government spending in Ghana. Bayesian 
methods are chosen based on their potential robustness 
in dealing with model uncertainty and incorporating prior 
information and their ability to produce full posterior 
distributions of  the estimated parameters. Using this is 
especially favorable in macroeconomic cases when one of  
the main problems is data limitation and multicollinearity. 
The analysis was also based on secondary annual 
data covering the period from 2000 to 2024. The key 
variables included in this research were government 
spending, GDP growth, inflation, interest rates, public 
debt and unemployment rates. These data were sourced 
from institutions such as the Bank of  Ghana (BoG), 
International Monetary Fund (IMF), Ghana Statistical 
Service (GSS) and the World Bank. These sources were 
selected for their credibility, consistency and relevance 
to Ghana’s macroeconomic landscape. Additionally, 
all monetary figures were converted into real terms to 
neutralize inflationary effects, which ensures that our 
analysis captures genuine economic impacts.

Model Specification
The empirical model is defined as a linear regression 
in which the dependent variable is the government 
expenditure and the independent variables are GDP 
growth rate, inflation rate, interest rate, public debt and 
unemployment rate. The model is presented as:
GOVEXPt = βo + β1GDPGRt + β2INFt + β3INTt + β4 
PUBDEt + β5UNEMPt + εt
Where,
GOVEXPt= Government expenditure at time t
GDPGRt = GDP growth rate at time t



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INFt = Inflation rate at time t
INTt = Interest rate at time t
PUBDEt = Public debt at time t
UNEMPt = Unemployment rate at time t 
βo is the intercept
β1,β2…β5 are the coefficients for the explanatory variable
εt is the error term 
The equation above can be written in matrix form as:
y = Xβ+ϵ, the error term is assumed to follow a normal 
distribution (i.e. ϵ~Nn (0,σ2 I)),
β=(β0,β1…βk-1)

T, is the n×k vector of  parameters and 
ϵ=(ϵ1, ϵ2…ϵn)

T the n×1 vectors of  errors.
In contrast, Bayesian regression involves prior beliefs 
or knowledge in the study by use of  prior distributions 
as opposed to traditional frequentist regression, which 
merely uses observed data. In this study, non-informative 
priors were used to ensure that the data itself  primarily 
influences the results, which allows for an unbiased 
estimation process. The likelihood function is assumed 
to be normally distributed, which aligns with standard 
regression assumptions. To determine the posterior 
distributions of  the model parameters, the Markov 
Chain Monte Carlo (MCMC) method was used with a 
focus on the Gibbs sampling algorithm. This approach 
allows the problem of  approximating complex posterior 
distributions with efficiency and credibility, mostly in 
situations where an analytical solution is not possible 
(Kruschke, 2018).

Estimation Technique
In Bayesian inference, the goal is to estimate the posterior 
distribution of  the model parameters β=(β1,β2…βk) and 

σ2 conditional on the observed data. This study models 
the relationship between government spending and five 
key macroeconomic variables: GDP growth, inflation 
rate, interest rate, public debt and unemployment rate. 
The Bayesian estimation process involves three key steps:
First, we define the prior distributions for each β. Given 
that we lack strong prior information about the exact 
magnitude of  the effects for the β’s, we used weakly 
informative priors. The prior distribution for each 
coefficient (β) is assumed to follow a normal distribution 
with mean m and variance V ( β~N(m,V)) to allow 
flexibility.
The prior density function for each β is

The prior distribution for σ2 is also assumed to follow an 
inverse gamma distribution with hyperparameters a and 
b. The prior density function for σ2 is:

Secondly, we specify the likelihood distribution of  the 
observed y. The likelihood function was derived based 
on the observed data and the assumed error distribution. 
The Likelihood of  the joint density function of  the 
observed yi’s is:

Figure 1: Histogram of  Government spending with a normal curve

Because the purpose of  the study is to find out how the 
macroeconomic indicators affect government spending 
trends, the histogram (figure 1 above) reveals that the 
data on government spending is approximately normally 
distributed, with a slight skew in the right direction. This 
is in support of  the likelihood function of  the Bayesian 
model, which implies that a Gaussian likelihood is suitable. 

The consistency between the data with the normal curve 
indicates a well-specified inference model.
Thirdly, using MCMC sampling techniques, the posterior 
distributions of  the parameters (β’s) are obtained, 
which allows for robust estimation and uncertainty 
quantification. The posterior density function for β and 
σ2  were derived using the formula:



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Data Analysis and Discussions
This study employed a Bayesian multiple regression 
framework to examine how key macroeconomic variables, 
including GDP growth, inflation rate, interest rates, 

public debt and unemployment rate, affect government 
spending. Through the adoption of  a Bayesian 
approach, the analysis does not capture the direction and 
strength of  these relationships only but also provides a 

Table 1: Posterior Estimates of  the Bayesian Regression Model
Macroeconomic 
Indicators

Mean    SD HDI 3%  HDI 97%  MCSE Mean  MCSE SD 

Intercept 27.498 6.681 14.352 39.458 0.115 0.081
GDP growth 1.681 0.555 0.654 2.707 0.012 0.008
Inflation rate 0.300 0.184 -0.021 0.676 0.003 0.002
Interest rate      -1.797 0.399 -2.580 -1.071 0.009 0.006
Public debt 0.242 0.065 0.117 0.362 0.001 0.001
Unemployment rate 3.358 3.358 1.491 5.106 0.022 0.016
Sigma 4.518 0.669 3.188 5.700 0.015 0.010

Source: Authors' calculations



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comprehensive understanding of  the uncertainty around 
each estimate through full posterior distributions. The 
results, as presented in Table 1 (Posterior Means and 
HDIs) and Table 2 (MCMC Diagnostics), offer clear 
and probabilistically informed insights into how each 
factor contributes to shifts in government expenditure; 
however, the credible intervals reflect the confidence we 
place in those findings.
The intercept, which represents the estimated baseline 
level of  government spending when all macroeconomic 
factors are held constant, was found to be 27.498, with 
a 94% Highest Density Interval (HDI) ranging from 
14.352 to 39.458. This wide interval reflects a degree of  
uncertainty around the baseline level of  expenditure. This 
is due to unobserved factors or structural shifts in fiscal 
policy over the study period. GDP growth exhibited a 
strong positive relationship with government spending. 
The posterior mean coefficient was 1.681, with a 94% 
HDI of  (0.654 to 2.707), which suggests that a one-unit 
increase in GDP growth is associated with an approximate 
1.68 unit increase in government spending. This finding 
aligns with Wagner’s Law, where higher economic growth 
enhances revenue mobilization, enabling greater fiscal 
space for government expenditures.
Inflation demonstrated a marginal and uncertain effect. 
The posterior mean coefficient was 0.300, but the 94% 
HDI of  (-0.021 to 0.676) includes zero. This indicates 
a potential lack of  robust influence, which suggests that 
inflation has a neutral or slightly ambiguous effect on 
fiscal policy decisions, depending on prevailing monetary 
and price stabilization measures or through adaptive or 
nominal adjustments that insulate aggregate spending 
from direct inflationary shocks. Interest rates showed 

a significant inverse relationship with government 
spending. The posterior mean coefficient was -1.797, 
with a 94% HDI of  (-2.580 to -1.071), which indicates 
that as interest rates increase, government spending tends 
to decrease. This is attributed to the rising cost of  debt 
servicing and a contraction in fiscal space, which limits 
the government’s capacity for discretionary spending.
Public debt had a positive and statistically significant effect 
on government spending, with a posterior mean of  0.242 
and a 94% HDI of  (0.117 to 0.362). This relationship 
suggests that increases in public debt, primarily through 
borrowing, enable governments to finance higher levels 
of  expenditure. Borrowed funds are often directed toward 
capital investments, social programs or economic stimulus 
packages, especially in developing economies like Ghana, 
where revenue bases are limited. The unemployment 
rate displayed a strong and positive relationship with 
government spending. The posterior mean coefficient 
was 3.358, and the 94% HDI of  (1.491 to 5.106) was 
entirely positive. This indicates that higher unemployment 
levels lead to greater government spending. This is likely 
due to increased allocations toward social protection, 
unemployment benefits, skills training initiatives and job 
creation initiatives aimed at mitigating social distress and 
stimulating economic activity.
Lastly, the posterior mean of  sigma, which captures 
the standard deviation of  the model’s residuals, was 
4.518, with a 94% HDI of  (3.188 to 5.700). The Monte 
Carlo Standard Errors (MCSE) for all parameters were 
consistently low. This confirms adequate convergence of  
the Markov Chain Monte Carlo (MCMC) simulations and 
reliability of  the estimated posterior distributions.
The convergence of  the Markov Chain Monte Carlo 

Table 2: Posterior Estimates of  the Bayesian Regression Model
ESS bulk  ESS tail  R hat  

Intercept 3384.0 4067.0 1.0
GDP growth 2191.0 3816.0 1.0
Inflation rate 2885.0 4084.0 1.0
Interest rate      1892.0 2788.0 1.0
Public debt 2795.0 3681.0 1.0
Unemployment rate 1932.0 2885.0 1.0
Sigma 2105.0 2599.0 1.0

Source: Authors' calculations

(MCMC) chains was thoroughly assessed using R hat 
and Effective Sample Size (ESS) diagnostics. All R hat 
values were 1.0, indicating excellent convergence for all 
parameters. Though the ESS values, which ranged from 
1892.0 to 3384.0 for ESS bulk and 2599.0 to 4084.0 
for ESS tail, were generally satisfactory, slightly lower 
ESS bulk values were observed for interest rates and 
unemployment rates. However, the overall ESS values 
were deemed sufficient to ensure the reliability and 
robustness of  the posterior estimates. This confirms 
that the MCMC procedure yielded well-converged and 

dependable parameter estimates.
The convergence and reliability of  the Bayesian regression 
were further assessed through visual inspection of  
the posterior distributions and trace plots (Figure 1). 
Consistent with the R hat values of  1.0, the trace plots 
for all parameters exhibited good mixing, which indicated 
successful convergence of  the MCMC chains. The 
posterior distributions displayed in the left column of  
Figure 1 visually represent the uncertainty surrounding 
each parameter estimate. For instance, the posterior 
distribution for the interest rate was centered on a 



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Figure 2: 
Source: Authors’ calculations

negative value, and this confirms the negative relationship 
observed in Table 1, with a mean of  -1.797. Similarly, the 
positive relationships for GDP growth (mean 1.681), 
public debt (mean 0.242) and unemployment rate (mean 
3.358) were reflected in the location of  their respective 
posterior distributions. The wider distribution for the 
inflation rate, compared to other parameters, mirrored 
the greater uncertainty indicated by its wider 94% HDI 
(-0.021 to 0.676) in Table 1.

CONCLUSION
This research employs a Bayesian multiple regression 
framework to examine how key macroeconomic factors, 
including GDP growth, inflation, interest rates, public debt 
and unemployment influence government spending. The 
Bayesian approach allowed for the incorporation of  prior 
knowledge and provided an insightful understanding by 
capturing the uncertainty surrounding each relationship 
through probability distributions. The analysis revealed 
several meaningful insights. GDP growth, public 



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debt and unemployment were found to have positive 
associations with government spending. This suggests 
that as the economy grows or faces higher unemployment, 
governments tend to increase their expenditure, either 
to sustain growth or cushion the social impact of  job 
losses. The positive effect of  public debt reflects the 
reality that governments often rely on borrowing to 
finance increased spending, especially during times of  
economic pressure or investment-driven policy goals. On 
the other hand, interest rates showed a strong negative 
relationship with government spending. This is consistent 
with economic theory that says that as higher interest 
rates raise the cost of  borrowing, it can constrain fiscal 
space and reduce the incentive or ability of  governments 
to expand spending. Inflation rate, however, presented 
an inconclusive relationship; its credible interval included 
zero, which indicates uncertainty about its true effect. 

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