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Economy 
Vol. 12, No. 2, 146-155, 2025 

ISSN(E) 2313-8181/ ISSN(P) 2518-0118 
DOI: 10.20448/economy.v12i2.7632 

© 2025 by the authors; licensee Asian Online Journal Publishing Group 

 
 

 
 
 
Effects of macroeconomic variables on unemployment in Kenya 

 
Penina Anyango1  
Maurice Ombok2  

Benedict Troon3  

Christopher Maokomba4  

                                                                                                                            

               
( Corresponding Author) 

 
1,2,3,4School of  Business and Economics, Maasai Mara University, Kenya.        
1Email: anyangopenina2018@gmail.com  
2Email: ombokmaurice74@gmail.com  
3Email: troonbenedict@gmail.com  
4Email: maokomba@mmarau.ac.ke  

 
Abstract 

Unemployment remains a major global challenge, with uneven progress across regions towards the 3% target. In 
Kenya, despite various interventions since independence, the issue remains unresolved and persistent. The aim of 
the study was to examine the effects of macroeconomic variables (economic growth, lending rate, development 
expenditure, and VAT) on unemployment in Kenya and provide empirical insights for designing policies to create 
employment. The study employed a time series research design to assess how changes in the macroeconomic 
variables under review influenced unemployment. The study adopted a two-regime Markov switching model with 
all parameters switching on secondary data for the period 1991-2024. Regime 1 represents a period of stagnating 
unemployment, while regime 2 represents a   period of trend unemployment. The findings established that in both 
regimes, while economic growth significantly reduced unemployment, development expenditure was found to 
significantly increase unemployment. Conversely, the lending rate reduced unemployment, but the effect was only 
significant in regime 2. Similarly, VAT significantly increased unemployment only in regime 2. The findings imply 
that policymakers should promote sustainable and inclusive growth, while strategically allocating development 
funds to sectors that are labor-intensive and have high employment potential to create more employment 
opportunities and reduce unemployment. Additionally, they should enhance access to credit and consider targeted 
VAT reforms, such as exemptions or reductions of VAT rates, especially during periods of trend unemployment. 

 
Keywords: Kenya, macroeconomic variables, unemployment, Markov switching model, regimes, time series design, Keynesian theory. 

 
Citation | Anyango, P., Ombok, M., Troon, B., & Maokomba, C. 
(2025). Effects of macroeconomic variables on unemployment in 
Kenya. Economy, 12(2), 146–155. 10.20448/economy.v12i2.7632 
History:  
Received: 24 September 2025 
Revised: 15 October 2025 
Accepted: 20 October 2025 
Published: 31 October 2025 
Licensed: This work is licensed under a Creative Commons 

Attribution 4.0 License  
Publisher:  Asian Online Journal Publishing Group 
 

Funding: This study received no specific financial support. 
Institutional Review Board Statement: Not applicable. 
Transparency: The authors confirm that the manuscript is an honest, 
accurate, and transparent account of  the study; that no vital features of  the 
study have been omitted; and that any discrepancies from the study as planned 
have been explained. This study followed all ethical practices during writing. 
Competing Interests: The authors declare that they have no competing 
interests. 

Authors’ Contributions: All authors contributed equally to the conception 
and design of  the study. All authors have read and agreed to the published 
version of  the manuscript. 

 
Contents 
1. Introduction .................................................................................................................................................................................... 147 
2. Literature Review .......................................................................................................................................................................... 148 
3. Methodology ................................................................................................................................................................................... 150 
4. Results and Discussion ................................................................................................................................................................. 151 
5. Discussion ........................................................................................................................................................................................ 153 
6. Conclusions ..................................................................................................................................................................................... 154 
7. Recommendations .......................................................................................................................................................................... 154 
References ............................................................................................................................................................................................ 154 
 

 

 

 

mailto:anyangopenina2018@gmail.com
mailto:ombokmaurice74@gmail.com
mailto:troonbenedict@gmail.com
mailto:maokomba@mmarau.ac.ke
https://creativecommons.org/licenses/by/4.0/
https://creativecommons.org/licenses/by/4.0/
https://www.doi.org/10.20448/economy.v12i2.7632
https://orcid.org/0000-0002-3226-7821
https://orcid.org/0009-0004-3150-6386


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Contribution of  this paper to the literature 
This paper contributes to the existing literature by utilizing the Markov switching model to examine the 
effects of macroeconomic variables on unemployment in Kenya. This study narrows its focus to VAT, 
development expenditure, and lending rates, in contrast to previous studies that examined broader 
indicators such as taxation, government expenditure, and general interest rates. 

 

1. Introduction 
Unemployment remains a persistent global challenge and a key concern for policymakers. In tracking progress 

towards achieving SDG Goal 8, specific thresholds have been established. The target is considered met if the 
unemployment rate is 3% or less (United Nations, 2022). However, Sodergren, Kettler, Sulak, and Payne (2023) 
report that this target remains unmet both globally and across all regions. 

Although the global unemployment rate appears to be low, significant regional disparities exist. Asia and the 
Pacific are relatively close to achieving the target, while the Arab States and Africa lag far behind due to high and 
increasing unemployment, as shown in Figure 1. 
 

 
Figure 1. Trends of  global and regional unemployment rates. 

     Source: ILO database. 

 
Since independence, some of the interventions undertaken by Kenya to address unemployment included short-

term employment interventions, such as public works programs and targeted youth funds, while medium-term 
strategies focused on sectoral policy reforms. These were complemented by broader macroeconomic initiatives to 
modernize agriculture, boost industrialization, and improve labour market efficiency through education and 
regulatory updates (Omondi, 2013).  

Despite adopting these interventions, Kenya experienced a sharp and unexplained increase in the 
unemployment rate since 2017, which peaked in 2021 as shown in Figure 2. 

 

 
Figure 2. Trend of Kenya's unemployment rate from 1991 to 2024. 

Source:  World Bank data bank. 

 



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While factors like public sector downsizing, election uncertainty, development plan blueprints, and pandemics 
such as COVID-19 provide partial context, the precise impact of key macroeconomic drivers on unemployment in 
Kenya remains unclear. For instance, the sharp unemployment spike between 2017 and 2022 occurred despite 
favorable macroeconomic indicators; rising development expenditure, stable VAT rates, and falling lending rates, 
as shown in Figure 3. This may indicate a disconnect between macroeconomic performance and labour market 
outcomes, suggesting deeper investigations into how macroeconomic variables interact to influence unemployment 
in the Kenyan context. 

 

 
Figure 3. Trend of  Kenya’s unemployment rate and macroeconomic variables under review. 

 
Despite extensive research, no consensus exists on the precise impact of the variables above on unemployment. 

Furthermore, previous studies have focused on broad policy tools such as overall tax revenue, government 
expenditure, and general interest rates. Therefore, this study fills the gap by providing a deeper empirical analysis 
of how VAT, development expenditure, and the commercial lending rate influence unemployment trends in Kenya. 

The general aim of this study is to determine the effect of the selected macroeconomic variables on 
unemployment in Kenya. To achieve this, the study will be guided by four specific objectives: first, to determine the 
effect of commercial lending rates on unemployment; second, to examine the impact of development expenditure; 
third, to evaluate the influence of Value Added Tax (VAT); and finally, to analyze the effect of economic growth on 
unemployment trends in Kenya. 
 

2. Literature Review 
The study was guided by Keynesian theory and Okun’s law. Keynesian theory postulates that, in the short run, 

employment is determined by effective demand, implying that unemployment is caused by a deficiency in aggregate 
demand. Since labour demand is a ‘derived’ demand, high unemployment can be solved by increasing the demand 
for goods through an increase in disposable income, a reduction in interest rates, and increasing government 
spending. On the other hand, Okun’s law was used to investigate the link between the unemployment rate and 
economic growth. The theory argues that a 3 per cent increase in output reduces the unemployment rate by 1 per 
cent. 
 

2.1. Empirical Literature 
Several studies have been conducted in an attempt to understand the impacts of macroeconomic variables on 

the unemployment rate using different methodologies and empirical approaches. 
 

2.1.1. Lending Rate and Unemployment Rate 
Lloyd (2024) studied the effects of inflation, GDP, interest rate, and policy rate on unemployment in Ghana 

using time series data for the period 2000-2021. The study employed a multiple regression analysis and found that 
the lending rate had a positive and significant effect on unemployment. Musiita, Kijjambu, and Katarangi (2024), 
using annual data from 1987 to 2019, analyzed the impacts of input costs (lending rate, global crude oil prices, and 
GDP) on unemployment in Uganda. The study employed the Autoregressive Distributed Lag (ARDL) model and 
found that lending rates had a short-term negative impact on unemployment, while in the long run, a positive 
relationship was established between the lending rate and unemployment. Chinonye (2021) studied the impact of 
fiscal (government expenditure and taxes) and monetary policy (interest rate and money supply) on unemployment 
in Nigeria. The study utilized a Vector Autoregressive model on time series data for the period 1981-2020 and 
found that interest rates had a negative and significant effect on unemployment at lag 2. Maijama’a and Musa 
(2021) in the study on the nexus between crude oil, interest rates, and unemployment in Nigeria applied Toda and 



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Yamamoto's long-run Granger causality on time series data from 1991 to 2019. The findings indicated a one-way 
causality running from unemployment to interest rates. Mahadika and Wibowo (2021) assessed the influence of 
monetary policy (real interest rate, real exchange rate) and economic growth on the unemployment rate in 
Indonesia using time series data for the period spanning 1975-2016. The study employed Autoregressive 
Distributed Lag (ARDL) and established that the real interest rate at lag 1 had a negative and significant effect on 
the unemployment rate in the short run. AboElsoud, AlQudah, and Paparas (2021) examined the dynamic 
relationship between London Interbank Offered Rate (LIBOR), the unemployment rate, and economic growth in 
the United Kingdom. The study employed the Vector Autoregressive model and Granger causality test on 
quarterly data for the period 1992 to 2016 and established no short-run relationship between LIBOR and the 
unemployment rate. Furthermore, the Granger causality test indicated no directional causality between LIBOR 
and unemployment.  
 

2.1.2 Economic Growth and Unemployment Rate 
Abdisalan (2024) empirically examined the relationship between GDP and unemployment in Somalia using 

secondary data from 2000 to 2021. The study applied Ordinary Least Squares (OLS) and established that economic 
growth had a negative and insignificant effect on unemployment. In addition, modified ordinary least squares 
(FMOLS), canonical cointegrating regression (CCR), and dynamic ordinary least squares (DOLS) were employed, 
and the relationship between the GDP gap and unemployment was not strong enough to be considered statistically 
significant. Lestari (2023) assessed the effect of economic growth and labor force on unemployment in Langsa City, 
Indonesia. The study employed multiple linear regression analysis on secondary data spanning 2011-2020 and 
found that economic growth had a negative and insignificant effect on unemployment. Tembo (2023) studied the 
effects of economic growth, real effective exchange rate, external debt, and inflation rate on unemployment in 
Zambia. The study used Vector Error Correction Model (VECM) on quarterly time series data from 1990 to 2020, 
and findings revealed that in the short run, economic growth had a negative significant effect on unemployment at 
lag 2. Chenini, Ayad, Attouchi, and Dahmani (2023) analyzed the existence of Okun’s law in Algeria from 1970 to 
2020. The study employed both the gap and differences model and established that economic growth had no 
significant effect on unemployment. Leasiwal, Oppier, Tutupoho, and Palloma (2022) investigated the effect of 
economic growth, minimum wage, and the human development index on unemployment in Indonesia by utilizing 
secondary data from 2001 to 2020. The study employed Vector Error Correction Model (VECM), and the findings 
showed that both in the short and long run, economic growth had a positive and significant impact on the 
unemployment rate. Sotonye, Zeb-Obipi, and Konya (2021) examined the effect of Gross National Product (GNP), 
Gross Domestic Product (GDP), and Per Capita Income (PCI) on unemployment reduction in Nigeria using time 
series data for the period 1995-2019. The study employed Ordinary Least Squares and established that Gross 
Domestic Product (GDP) had a positive but insignificant effect on unemployment. Hjazeen, Seraj, and Ozdeser 
(2021) examined the relationship between the unemployment rate, economic growth, education, female population, 
and urban population in Jordan. The study employed Auto-regressive distributed lag (ARDL) model on data 
spanning 1991–2019 and established that economic growth had a negative and statistically significant effect on 
unemployment. Katumo and Maingi (2020) analyzed the relationship between youth unemployment and economic 
growth, inflation rate, FDI, and minimum wage. The study employed the Granger causality test and regression 
analysis on secondary data for the period 1991-2015 and found a unidirectional relationship between youth 
unemployment and economic growth, with causality running from economic growth to youth unemployment, 
while regression output indicated that economic growth had positive and significant effects on youth 
unemployment. Usha (2020) assessed the relationship between unemployment and economic growth in Mauritius. 
The study adopted Autoregressive distributed lag (ARDL), ARDL error-correction model (ARDL-ECM) using the 
ordinary least squares (OLS) approach and Okun’s law-gap version on annual data for the period spanning 1983-
2017. The findings indicated that both in the long run and short run, there is a negative and insignificant 
relationship between economic growth and unemployment, whereas Okun’s coefficient predicted that a 4 percent 
increase in GDP would reduce unemployment by 1 percent. 
 

2.1.3. Development Expenditure and Unemployment Rate 
Wandile, Semosa, and Ogujiuba (2024), in a study on the effect of socio-economic variables (government 

expenditure, economic growth, and population growth) on unemployment in South Africa, a Vector Error 
Correction Model (VECM) was employed on time series data covering the years 1980 to 2020. The findings 
established a positive and significant effect of government expenditure on unemployment. Ibrahim (2023) examined 
the impact of fiscal policy tools (Tax revenue, government expenditure, Foreign direct investment (FDI), and 
domestic investment) on the unemployment rate in Nigeria between 1991 and 2021. The study applied the 
Autoregressive Distributed Lag Model (ARDL) and established that in the long run, government expenditure had 
a positive and significant effect on unemployment. In the short run, lagged government expenditure had a positive 
impact on the unemployment rate. Hammad et al. (2023) assessed the effect of public spending on the 
unemployment rate in Iraq using quarterly time series data for the period 2004-2021. The study used the 
autoregressive distributed lag model (ARDL) and found that in the short run, public spending had a positive and 
insignificant effect on unemployment at lags 1 and 2. However, at lags 3 and 4, public spending had a negative and 
insignificant effect on unemployment. Further, in the long run, public spending had a negative and insignificant 
effect on unemployment. Kinuthia (2022) assessed the validity of the Phillips curve in the Kenyan economy by 
examining the effect of inflation, money supply, and government expenditure on unemployment. The study 
employed Auto-Regressive Distributed Lag (ARDL) and Error Correction Model (ECM) on annual secondary time 
series data from 1991 to 2020. Government expenditure had a negative and insignificant effect in the short run, 
while in the long run, it had a positive but insignificant effect on unemployment. Enyoghasim and Hycenth (2022) 
analyzed the effect of fiscal policy tools (inflation rate, interest rate spread, gross fixed capital formation, 
government recurrent expenditure, government capital expenditure, and debt servicing) on unemployment in 
Nigeria by applying Autoregressive Distributed Lag (ARDL) on annual data for the period 1981 to 2011. The 



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findings indicated a negative and insignificant effect of government recurrent expenditure on unemployment, while 
government capital expenditure had a positive and significant effect on unemployment. Abdullahi and Haruna 
(2021) studied the impact of fiscal policy instruments (external debt, recurrent expenditure, capital expenditure, 
and tax revenue) on unemployment in Nigeria using data from 1986 to 2020, and employed the Autoregressive 
Distributed Lag (ARDL) model. The results revealed that recurrent expenditure had a positive but insignificant 
effect, while capital expenditure had a positive and significant impact on unemployment in Nigeria. Mungai and 
Korir (2020) analyzed the effect of fiscal policy (government expenditure), inflation, population, and economic 
growth on unemployment in Kenya using time series data over the period 1986-2017. The study employed OLS 
and found that government expenditure had a positive and significant effect on unemployment. Saraireh (2020) 
empirically examined the effect of government expenditure, private investment, development assistance, and Gross 
Fixed Capital Formation (GFCF) on unemployment in Jordan. The study utilized autoregressive distributed lag 
(ARDL) on annual data for the period 1990 to 2019, and findings revealed that in the long run, government 
expenditure had a negative and significant effect on unemployment, while in the short run, it had a positive and 
significant effect on unemployment.    
 

2.1.4 VAT on Unemployment 
Olabiyi, Etong, Olaniyan, and Akinrinde (2024) assessed the impacts of VAT on unemployment in Nigeria but 

included the inflation rate and manufacturing output as control variables. The study employed the ARDL model on 
time series data for the period 1994-2021 and found that, in the short run, VAT had positive and significant effects 
on unemployment, while in the long run, VAT had a negative and significant effect on unemployment. Peter, 
Olaolu, and Nneka (2021) focused on the effects of tax revenues (with VAT as one of the variables) on 
unemployment in Nigeria. The study utilized ARDL-ECM on secondary data spanning 1994-2020 and established 
that VAT had a positive and significant effect on the unemployment rate in Nigeria. Kadenge (2021) assessed the 
effects of taxes (VAT, income tax, customs, and excise duty) on economic performance in Kenya. The study 
employed OLS on secondary data for the period 2010-2020 and found that VAT could reduce unemployment 
through its positive and insignificant effects on GDP. Enueshike, Dele, and Nwala (2021) examined the effect of tax 
revenue (corporate taxes, customs tax, excise duty, and VAT) on unemployment in Nigeria from 1994 to 2020. The 
study used ARDL-Error Correction Model (ECM) and established that VAT had a positive and significant effect 
on unemployment in Nigeria. Anichebe (2019) examined the effect of tax policy (specifically company income tax, 
personal income tax, and customs and excise duty) on unemployment in Nigeria. The study employed OLS on time 
series data from 1981 to 2017 and established a positive and significant effect of VAT on the unemployment rate. 
 

3. Methodology  
The study employed a time series research design due to its suitability in detecting long-term patterns and the 

impact of policy changes and economic shocks. This will enable the study to examine how macroeconomic variables 
influence unemployment trends over the review period. 
 

3.1. Model Specification  
Unemployment was defined as a function of lending rate, VAT, development expenditure, and economic 

growth, as shown below: 

Unemployment = f(lending rate, VAT, development expenditure, economic growth)      (1) 
Log transformation was applied to the development expenditure to reduce data skewness. 

The study employed time series data, and Equation 1 will be rewritten as: 

𝑌𝑡 = 𝛽0 + 𝛽1𝑋1𝑡 + 𝛽2𝑋2𝑡 + 𝛽3𝑋3𝑡 + 𝑙𝑛𝛽4𝑋4𝑡 + 휀, 휀~𝑁(0, 𝛿2)                            (2) 
Where: 
Y represents unemployment, X1 represents the commercial lending rate, X2 represents the log of development 

expenditure, X3 represents the economic growth rate, and X4 represents VAT. 
To correct the autocorrelation issue, an AR (1) term was introduced into Equation 2 and rewritten as: 

𝑌𝑡 = 𝛽0 + 𝛽1𝑋1𝑡 + 𝛽2𝑋2𝑡 + 𝛽3𝑋3𝑡 + 𝛽4𝑋4𝑡 + ∅𝑌𝑡−1 + 휀                                        (3) 
Since a break was anticipated, the study employed a Markov switching model (Hamilton, 1989)  to account for 

the break. Let St represent the unobserved state. A Markov switching linear model can be stated as follows: 

Y𝑠𝑡 = 𝛽0,𝑠𝑡 + 𝛽1,𝑠𝑡𝑋1𝑡 + 𝛽2,𝑠𝑡𝑋2𝑡 + 𝛽3,𝑠𝑡𝑋3𝑡 + 𝛽4,𝑠𝑡𝑋4𝑡 + ∅𝑠𝑡𝑌𝑡−1 + 휀𝑖𝑡  ,       ε𝑡𝑖 ~𝑁(0, 𝜎𝑠𝑡
2 ).                      (4) 

Where; 

𝑆𝑡 𝜖{1 … 𝐾}       (5) 
Assuming there are K interdependent regimes and that the Markov property holds, such that the future state 

St+1 depends only on the current state St, then a first-order Markov process’s transition probabilities will be given 
as: 

𝑃(𝑆𝑡+1 = 𝑗|𝑆𝑡 = 𝑖) = 𝑃𝑖𝑗     (6) 

Under the assumptions given, the aggregated state transitions will be described with a transition probability 
matrix: 

𝑃 = [(

𝑝𝑖1 ⋯ 𝑝1𝑖

⋮ ⋱ ⋮
𝑝1𝑖 ⋯ 𝑝𝑖𝑖

)]     (7) 

The transition probability matrix and the coefficients of the Markov switching model were estimated using the 
maximum likelihood method with the EM algorithm. 
 

3.2. Data Type and Data Sources 
This study employed secondary data from multiple sources between 1991 and 2024. Unemployment rates were 

obtained from the World Bank Development Indicators database, while data on economic growth, lending rates 
and government expenditure were sourced from the annual Economic Survey reports published by the Kenya 



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National Bureau of Statistics (KNBS). For Value-Added Tax (VAT) rates, the study compiled a comprehensive time 
series by combining three sources: Karingi and Wanjala (2005) provided historical VAT data for 1991–2004,  
Omondi (2020) covered 2005–2016, and more recent records (2016–2024) were extracted from KRA. 
 

3.3. Data Analysis 
3.3.1. Pre-Estimation Tests 

These are tests conducted on the data before fitting the model. Descriptive statistics provided an overview of 
the observed data and included: means, standard deviations, minimums, and maximums. Since the study employed 
time series data, a stationarity test was conducted. A stationarity test is done to prevent spurious results and help 
guide the model to be applied in the study. The study employed the Phillips-Perron test to assess the stationarity 
of the variables. 

A structural break test was conducted using the Bai-Perron test due to its ability to detect multiple breaks. The 
optimal number of breaks was selected via the lowest BIC value. 
 

3.3.2. Post-Estimation Test 
After model estimation, post-estimation tests were conducted to ensure the reliability and validity of the model, 

as they influence the accuracy and usefulness of the estimates. To assess the validity of the Markov Switching 
model, various diagnostic tests were performed. The Ljung-Box test was employed to test for autocorrelation since 
it can detect higher-order autocorrelation. The normality assumption was tested using the Jarque-Bera test. The 
ARCH test was used to check for ARCH effects on both residuals and squared residuals of the Markov switching 
model. To check if the coefficients are significantly different, 95% confidence intervals were computed for each 
coefficient in regimes 1 and 2. 
 

4. Results and Discussion 
Table 1 gives the descriptive summary on key variables under review, including measures of central tendency 

(mean and median), and variability (minimum, maximum, and standard deviation). 
 
Table 1. Descriptive statistics of  the variables for the period 1991-2024. 

 Economic growth 
rate 

Lending rate Development 
expenditure 

VAT Unemployment 
rate 

Minimum -0.200 -3.000 9076 14.00 2.650 
Maximum 7.600 31.500 133655 18.00 5.707 
Mean 3.847 10.380 239682 16.35 3.352 
Median 4.650 8.720 625780 16.00 2.855 
Standard deviation 2.208 7.799 1.460 1.041 1.054 

 
The analyzed variables displayed a wide range of volatility, with VAT and unemployment rates remaining 

relatively stable while commercial lending rates were highly volatile. Economic growth averaged 3.9%, with its 
peak of 7.6% attributed to post-lockdown recovery and its low of -0.2% caused by sectoral poor performance and 
high inflation. The lending rate averaged 10.4%, swinging from -3% to 31.5%, and development expenditure also 
showed significant variation between its high and low points. Finally, the VAT rate demonstrated low volatility, 
fluctuating only between 14% and 18% with an average of 16.4%. 

The results of the Phillips-Perron (PP) test are presented in Table 2. The results indicate that economic 
growth, lending rate, and VAT were stationary at the level, while development expenditure became stationary only 
after first differencing. However, after differencing the unemployment rate ten times, it remained non-stationary. 
This suggests a potential structural break in the unemployment rate data. 
 
Table 2. Results of stationarity test. 

Variable Phillips-Perron 
statistic at level 

Phillips-Perron 
statistic after 
differencing 

P-value at 
level 

P-value after 
differencing 

Order of  
integration 

Unemployment -1.200 -4.071 0.883 0.873 Unknown 
Economic growth -24.888  0.01  I(0) 
Lending rate -5.218  0.01  I(0) 
VAT -20.631  0.026  I(0) 
Development 
expenditure 

-8.087 -42.094 0.613 0.01 I(1) 

 
Given the unemployment rate's persistent non-stationarity despite differencing, the study employed the Bai-

Perron multiple structural break test to identify structural breaks in the unemployment rate series and the results 
are presented in Table 3. 
 
Table 3. Results of the structural break test. 

Breaks (m) Breakdates RSS BIC 

0 – 36.672 106.113 

1 2017 2.313 19.201 
2 2012, 2017 2.275 25.696 
3 2004, 2012, 2017 2.165 31.067 
4 1995, 2004, 2012, 2017 2.161 38.048 
5 1995, 2000, 2005, 2012, 2017 2.173 45.289 

 



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Model selection criteria indicated a single statistically significant breakpoint in 2017, supported by a BIC value 
of 19.201 (the lowest) and a significant reduction in the Residual Sum of Squares (RSS) observed between the null 
model (m = 0) and the single-break model (m = 1). 
 

4.1. Markov Switching Model 
A Markov switching model was fitted to capture potential regime-dependent dynamics in the data. A baseline 

linear regression model was fitted to the data before estimating a Markov switching model, and results are 
presented in Table 4. 
 
 
 
Table 4.  Results of  the Baseline Linear Regression model. 

Variables  Estimate Std. error t-value P-value 

Intercept  -2.016 4.219 -0.478 0.636 
GDPG  -0.104 0.092 -1.132 0.267 
CLR 0.005 0.022 0.218 0.829 
lnDevExp  0.494 0.167 2.963 0.006 ** 
VAT       -0.0004 0.183 -0.002 0.998 

AIC=97.903 BIC=107.062 log Lik. = -42.952 

Residual standard error: 0.927  
Note: Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1. 

 

 
A two-regime MS-AR model with all parameters switching was fitted. Regime 1 indicates a period of 

stagnating unemployment, whereas regime 2 represents a period of trend unemployment. Table 5 presents the 
results of the fitted Markov Switching model. 
 
Table 5. Results of Markov switching model with all variables switching. 

Regime 1 model 

Variables Estimate Std. Error t-value P-values 

Intercept (S)   -0.042 0.174 -0.261 0.812 
GDPG (S)  -0.026 0.004 -6.023 1.709e-09 *** 
CLR (S) -0.001 0.001 -0.889 0.374 
lnDevExp (S)  0.026 0.01 3.321 0.001 *** 
VAT(S)   0.002 0.008 0.200 0.841 
UNEMPR_1(S)   0.94 0.010 95.959 < 2.2e-16 *** 
Residual standard error: 0.03521215 
Note: S=switching implying that the variable is switching 
Regime 2 model 

Variables Estimate Std. Error t-value P values 

Intercept (S)   -21.209 0.218 -97.513 < 2.2e-16 *** 
GDPG (S)  -0.066 0.009 -7.152 8.540e-13 *** 
CLR (S)       -0.020 0.005 -4.533 5.807e-06 *** 
lnDevExp (S)  1.682 0.028 59.660 < 2.2e-16 *** 
VAT(S)       0.079 0.030 2.629 0.009 ** 
UNEMPR_1(S)     0.703 0.009 76.424 < 2.2e-16 *** 
Residual standard error: 0.006 
AIC= -104.642       BIC= -44.77         
Transition probability matrix 
 Regime 1 Regime 2 

Regime 1 0.924 0.283 
Regime 2 0.076 0.717 

Note: S=switching implying that the variable is switching 
 

Diagnostic checks were performed on the model assumptions to ensure that the results were valid and reliable, 
and the results are presented in Table 6. The results show that the model satisfied all the assumptions. 
 
Table 6. Diagnostic test results for the Markov switching model with all variables switching. 

Diagnostic Test Statistic P-value Conclusion 

Normality 0.559 0.756 Residuals normally distributed 
Autocorrelation 12.297 0.266 No Autocorrelation 
ARCH test on residuals 8.701 0.728 No ARCH effect on residuals 
ARCH test on squared residuals 7.117 0.850 No ARCH effect on squared residuals 

 
The study proceeded to interpret the model since it satisfied all the assumptions. The Markov switching model 

equation is given by: 

UNEMPt = −0.042 − 0.026GDPGt − 0.001CLRt + 0.026lnDevExpt + 0.002VATt + 0.940UNEMPt−1, St = 1   (8) 

UNEMPt = −21.21 − 0.066GDPGt − 0.020CLRt + 1.682lnDevExpt + 0.079VATt + 0.703UNEMPt−1, 𝑆𝑡 = 2   (9) 
Holding other factors constant, a 1% increase in economic growth reduces unemployment by 0.026% and 

0.066% in Regime 1 and Regime 2, respectively. While economic growth is significant in both regimes, it has a 
stronger impact on unemployment in Regime 2 compared to Regime 1. 



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The lending rate coefficients of -0.001 in Regime 1 and -0.020 in Regime 2 imply that an increase in the 
lending rate by 1% decreases the unemployment rate by 0.001% in Regime 1 and 0.020% in Regime 2, holding 
other factors constant. The lending rate was statistically significant in Regime 2 but insignificant in Regime 1. 

An increase in development expenditure by 1% increased unemployment by 0.00026% in regime 1 and 0.0168% 
in regime 2, holding other factors constant. It was significant at 0.05 in both regimes. 

On the other hand, an increase in VAT rate by 1% in Regime 1 and Regime 2 increased unemployment by 
0.0016% and 0.079%, respectively. However, the impact of VAT on unemployment is significant in Regime 2 at 
0.05 but insignificant in Regime 1. 

In regime 1, unemployment in the previous period increases current unemployment by 0.94%, and by 0.70% in 
regime 2. Both effects are significant at the 0.05 level in both regimes. 

The transition probability matrix in Table 5 indicates that when the process is in regime 1, the probability of 
remaining in that regime is 92.4%, while the probability of transitioning to regime 2 is 28.3%. Conversely, when 
the process is in regime 2, the probability of remaining in regime 2 is 71.7%, while the probability of transitioning 
to regime 1 is 7.6%. Furthermore, the expected duration for regimes 1 and 2 is 13.6 and 3.5 periods, respectively. 
This suggests that regime 1 is highly persistent, whereas regime 2 is short-lived. 

Significant differences in parameter estimates across regimes were assessed using 95% confidence intervals, and 
the results are presented in Table 7. 
 
Table 7. A 95% non-overlapping confidence interval. 

Variable Regime 1 CI 
[Lower, upper] 

Regime 2 CI 
[Lower, upper] 

Decision Conclusion 

Intercept [-20.72, -20.00] [-0.38, 0.26] No Overlap in CI Significant difference 
GDPG [-0.075, -0.064] [-0.035, -0.018] No Overlap in CI Significant difference 
CLR [-0.014, -0.010] [-0.002, 0.001] No Overlap in CI Significant difference 
LnDevExp [1.59, 1.61] [0.013, 0.044] No Overlap in CI Significant difference 
VAT [0.074, 0.105] [0.013, 0.016] No Overlap in CI Significant difference 
UNEMPR_1 [0.707, 0.725] [0.919, 0.958] No Overlap in CI Significant difference 

 
The results on unemployment rate projections, presented in Table 8, show a slow and consistent decline in 

unemployment rates over the next 5 years. On the other hand, regime probability estimates indicate that while 
unemployment is likely to stay in the stagnating regime, the probability falls while the probability of shifting to the 
trend regime increases. 
 
Table 8. 5-year period unemployment rate and probabilities forecast. 

Unemployment rate forecast 

Period 1 Period 2 Period 3 Period 4 Period 5 

-0.0378 -0.036 -0.035 -0.035 -0.034 
Regime probabilities forecast 

 Regime 1 Regime 2 

Period 1 0.766 0.234 
Period 2 0.653 0.347 
Period 3 0.593 0.408 
Period 4 0.557 0.443 
Period 5 0.536 0.464 

 

5. Discussion 
5.1. Economic Growth Rate and Unemployment  

The study found that economic growth had a negative and statistically significant effect on unemployment in 
both regimes. This is consistent with Okun’s Law, which suggests that an increase in economic growth results in 
the creation of jobs and lower unemployment rates. The results align with findings of Tembo (2023), Leasiwal et al. 
(2022), and Hjazeen et al. (2021).  
 

5.2. Commercial Lending Rate and Unemployment 
Contrary to the economic theory that a high lending rate increases the unemployment rate, the study 

established that a high commercial lending rate significantly reduced unemployment only in Regime 2. This may 
be attributed to improved access to credit through the growth of digital loans, mobile money, and the 2019 repeal 

of the 2016 interest rate cap. The results agree with the findings of Musiita et al. (2024) and Chinonye (2021). 
 

5.3. Development Expenditure and Unemployment 
Development expenditure had a positive and significant effect on unemployment in both regimes, with a 

stronger effect in Regime 2. This contradicts Keynesian theory, which postulates that an increase in government 
expenditure could reduce unemployment by boosting demand. The positive effect may be attributed to lag effects, 
mostly due to the time lag between project implementation and results realization in the form of job creation. 
Further, funds may be allocated to capital-intensive projects. In addition, some projects provide short-term 
employment opportunities or jobs dominated by foreigners, or projects are stalled, further increasing 
unemployment. The results align with the findings of Wandile et al. (2024), Ibrahim (2023), Enyoghasim and 
Hycenth (2022), Abdullahi and Haruna (2021), and Mungai and Korir (2020). 

5.4. VAT and Unemployment 
VAT had a positive effect on unemployment, though statistically significant only in Regime 2. The results 

align with economic theory that a high VAT rate reduces consumer spending by decreasing purchasing power, 



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thus causing firms to cut production and lay off workers, increasing unemployment. The results resonate with the 
findings of Peter et al. (2021), Enueshike et al. (2021), and Anichebe (2019). 
 

6. Conclusions 
Economic growth significantly reduces unemployment in both regimes, highlighting the crucial role played in 

job creation. Therefore, policymakers should prioritize growth-led policies. On the other hand, lending rates were 
found to reduce unemployment in both regimes, although the effect was significant only in regime 2. This suggests 
that increased access to credit facilities may offset the traditional adverse effect of high lending rates. Contrary to 
Keynesian theory, development expenditure significantly increased unemployment in both regimes. This may 
indicate inefficiencies in development expenditure, possibly due to time lag or allocation of funds to capital-
intensive projects or projects that generate short-term employment. VAT rates were found to increase 
unemployment; however, the effect was significant only in Regime 2. This suggests VAT can be harmful during 
trend unemployment, as it reduces demand. The non-overlapping confidence intervals, which confirmed that all 
coefficients differ significantly between Regime 1 and Regime 2, underscore that unemployment in Kenya varies 
depending on the state of the economy. Hence, uniform policy measures may not be effective in addressing 
unemployment across different regimes. Furthermore, based on the forecast, the study concludes that although a 
slow but steady decline in the unemployment rate is expected over the next 5 years, the probability of Kenya 
switching to trend unemployment in the next 5 years is increasing. This calls for long-term policies that will 
ensure the decline in unemployment is sustained and inclusive. 

 

7. Recommendations 
The study examined the impact of lending rates, development expenditure, VAT, and economic growth on 

unemployment in Kenya from 1991 to 2024. Based on the findings, the study proposes several key 
recommendations. First, policymakers should implement growth-led policies designed to promote sustainable and 
inclusive economic expansion, with a specific priority given to sectors that have a high capacity for labour 
absorption. To further stimulate job growth, targeted VAT reforms such as introducing exemptions or reducing 
rates during periods of high unemployment could boost consumption and create jobs. Furthermore, development 
funds must be strategically allocated to labour-intensive sectors with high employment potential. This should be 
accompanied by robust frameworks for project monitoring, assessment, and transparency to ensure that public 
investment effectively translates into tangible job creation. In the financial sector, efforts should focus on 
promoting financial inclusion and implementing targeted credit policies, such as lowering lending rates for high-
employment-potential sectors during economic downturns. The analysis also reveals a significant difference in 
variable coefficients between economic regimes, suggesting that policymakers should adopt flexible, regime-specific 
measures rather than applying uniform policies. Finally, given the forecast of only a slow decline in the 
unemployment rate over the next five years, proactive policies are urgently needed to accelerate this projected 
decrease and achieve a more rapid improvement in the labour market. 

In light of these findings, future studies should undertake a comparative analysis of the effects of these 
macroeconomic variables on unemployment, consider how other types of taxes (corporate tax and Pay As You 
Earn) affect unemployment, and examine how interest rates (commercial bank rates such as savings and deposit 
rates, and overdraft and central bank rates such as treasury bill rates, Central Bank Rate (CBR), repo rates, and 
interbank rates) influence unemployment. 
 

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