Shaping the Future of Kenya Tax Policy: Introducing the New Kenya Tax CGE Model

Kenya's Medium-Term Revenue Strategy (MTRS FY2024/25-2026/27) which aligns to the Fourth Medium Plan (MTP 2023-2027), aims at raising sufficient resources for the delivery of Government's priority programmes under the Bottom-up Economic Transformation Agenda. The strategy provides a comprehensive approach of effective tax system reforms to boost revenue collection and improve the tax system over the medium term.

In Kenya, taxes form major source of government revenue for delivery of essential public services, and a policy tool to influence economic behavior, promote equity and achieve developmental goals. For instance, in the financial year 2023/2024, taxes accounted for 85.5 percent of total government revenue (National Treasury, 2024). Given the expansion of fiscal expenditure demands, optimizing Kenya's major tax revenue sources is essential to achieving a sustainable balance between revenue generation and minimal distortion on economic welfare.

The MTRS outlines tax reforms (by tax heads) to be undertaken over the medium term. Value Added Tax (VAT, a key component of Kenya's tax revenue, is among the tax heads whose rate is to be reviewed. Currently, the VAT standard rate stands at 16 percent and as mentioned in the MTRS, this is lower than most East Africa Community member states with standard rates of 18 percent. Article 32 of the EAC Common Market Protocol foresees the need for harmonizing EAC tax regimes, to facilitate the free movement of goods, services, and capital, and the promotion of investments within the Community. Kenya has had varied standard VAT rates since its introduction in 1990. For example, in FY1989/90 the rate was 17 percent, in FY1990/91 - FY1994/95 the rate was 18 percent, in 1999/00 it was15 percent, (Omondi, 2013). From FY2003/04 to date the standard rate has been 16% except April - December 2020 at 14% during the COVID-19 pandemic.

While studies have shown that a well-designed and effectively implemented VAT could be an efficient means of raising government revenue (de la Feria & Swistak, 2024) and promoting economic and social welfare. Currently, the Kenyan government undertakes static analysis of the potential revenue impacts of tax policy reforms. This approach hardly considers the potential economy-wide impacts in the medium to long term. In this regard, an economic-wide analysis could play a critical role in informing policy decisions, including tax related policies, on domestic revenue mobilization strategies.

To bridge this gap, IFPRI, CGIAR's initiatives on National Policies and strategies(NPS) and Foresight in collaboration with Kenya Institute for Public Policy Research and Analysis (KIPPRA) and Kenya Revenue Authority(KRA) have built a Kenya Tax model[1].

The Kenya Tax model, anchored on IFPRI's dynamic RIAPA model, is designed to provide a comprehensive dynamic assessments of potential impacts of tax policy changes, especially taxes levied on products such as VAT. To showcase the kind of analysis that could be undertaken using the model, an assessment of impacts of increasing or decreasing the standard VAT rate by 2 percentage points and reducing VAT rate differential[2]by 25 percent. The preliminary results of these assessments were shared with representatives from the government, private sector, researchers and the academia through a virtual workshop event held on November 26.

Talking during the event, KIPPRA's Interim Executive Director acknowledged the support and collaborative efforts from IFPRI and the two CGIAR initiatives towards strengthening the institution's capacity and institutionalizing innovative policy analysis tools such as this Kenya Tax model. "The new tax model forms part of economic modeling tools in our newly established Economic Modelling Hub". Also, at the event there was the Chief Manager Research and Surveys, KRA who gave his opening remarks. "This is a timely model, especially now that taxes have become a highly topical issue in the country", he said. He thanked the team that worked jointly to co-create such a timely and useful analysis tool, "Now that we have a tax model that not only looks at revenue impacts but also household welfare, as revenue authority, we look forward to using it immediately for our forthcoming tax proposals", he added.

He also added that such models to be extended to cover other tax heads, especially the ones on direct taxes to reflect the current amendments made on personal tax laws. He called upon the need to incorporate the National Treasury in the collaboration. IFPRI's head of Kenya office and the NPS initiative lead applauded the institutions for the milestone achieved through this collaboration, he looked forward to more joint activities in the future.

Below is the summary of model scenarios considered in the analysis and the results.

The first two tax scenarios are 18 per cent and 14 per cent, respectively, while the third scenario shows the potential impact of reducing the differential rate of VAT by 25%. The model is run for baseline scenario and tax scenarios. The difference between the baseline and tax scenarios is the impact of the policy change.

Figures 1 and 2 show the overall and sectoral GDP changes relative to the baseline for the period 2025 and 2028. The year 2025 shows the immediate impact of the policy change.

The results suggest that the overall GDP is affected modestly at less than positive or negative 0.1 percent for the year in which the shock is introduced, the GDP is affected by 0.28 percent in 2028. While the impacts on the total economy and sectoral GDP are modest with increasing or lowering VAT rate, with the VAT rate increase, total and industrial GDP increases slightly, while agriculture and service GDP moves to the opposite direction at a much smaller scale than the increase in industrial GDP in terms of changes in percentage. Lowering the VAT gap by 25 percent leads to small changes in both national and sectoral GDP, similar effect direction as those of increasing the VAT rate scenario.

Figure 1: Overall and sectoral GDP impacts for the year 2025

Figure 1: Overall and sectoral GDP impacts for the year 2025. Source: Kenya Tax Model Simulation Results

Figure 2: Overall and sectoral GDP impacts for the year 2028

Figure 2: Overall and sectoral GDP impacts for the year 2028. Source: Kenya Tax Model Simulation Results

Results on government revenue suggests that if VAT rate is assumed to be 18 percent and 14 percent respectively VAT revenue about is positive or negative 12 percent compared to business-as-usual scenario. This is equivalent to an increase or a decrease in total indirect tax revenue by 6 percent, while the change in government total revenue is positive and negative 3.5 percent.

Regarding the household impacts the results suggest that while increasing VAT rate generates more income for the government, the income effect, measured by total real consumption, is modestly negative for households. All households are affected with most effect felt by the poorest and the richest households.

As shown in figure 3 below, the results suggest that with an increased VAT rate to 18 percent, 100,529 more people become poor in 2025 compared to the baseline. In 2028 the number is lower, about 51,860 more poor. This is an indication of an improved welfare as investment from the collected revenue affects the economy more positively in the mid-term than its immediate effect. The result is consistent with a negative impact on household income from rising VAT rate particularly among the poorer household groups, which showed that poverty increases slightly with rising VAT rate.

Figure 3: Poverty Outcomes from Increasing VAT to 18% (Differences in No. of the poor from the baseline, 2025-2028)

Figure 3: Poverty Outcomes from Increasing VAT to 18% (Differences in No. of the poor from the baseline, 2025-2028). Source: Kenya Tax Model Simulation Results

The results for food security outcomes are similar in terms of direction as that of the increasing VAT rate scenario. The worst affected are the urban households who face an increase in prices of commodities given the increased VAT rate scenario. The immediate impact is larger than that of the last period.

[1] This is part of a broader collaboration between the Kenya Institute for Public Policy Research Analysis (KIPPRA), the International Food Policy Research Institute (IFPRI) and the CGIAR Initiatives on National Polices and Strategies (NPS) and Foresight. We would like to thank all funders who supported this research through their contributions to the CGIAR Trust Fund.

[2]The difference between the pronounced standard VAT rate and rate computed from the actual tax collections.

References:

Fourth Medium Term Plan (2023-2027):https://www.planning.go.ke/wp-content/uploads/2024/03/MTP-IV-2023-2027.pdf

EAC common market protocol: https://eacj.org/wp-content/uploads/2012/08/Common-Market-Protocol.pdf

National Treasury. (2024). 2024 budget policy statement. Nairobi: The National Treasury: https://www.treasury.go.ke/budget-policy-statement/

Omondi, F. (2013). VAT reforms and revenue productivity in Kenya 1990-2010. Nairobi: University of Nairobi: https://erepository.uonbi.ac.ke/handle/11295/63135

de la Feria, R., & Swistak, A. (2024). Designing a progressive VAT. Washington, D.C.: International Monetary Fund: https://www.imf.org/en/Publications/WP/Issues/2024/04/05/Designing-a-Progressive-VAT-546923

Government of Kenya. (2023). The medium-term revenue strategy FY 2024/25 - 2026/27. Nairobi: The National Treasury and Economic Planning :https://www.treasury.go.ke/wp-content/uploads/2023/12/Medium-Term-Revenue-Strategy-2023.pdf

Authors

Lensa Omune, Research Officer, IFPRI

Juneweenex Mbuthia, Research Officer, IFPRI

This work is part of the CGIAR Research Initiative on National Policies and Strategies (NPS). CGIAR launched NPS with national and international partners to build policy coherence, respond to policy demands and crises, and integrate policy tools at national and subnational levels in countries in Africa, Asia, and Latin America. CGIAR centers participating in NPS are The Alliance of Bioversity International and the International Center for Tropical Agriculture (Alliance Bioversity-CIAT), International Food Policy Research Institute (IFPRI), International Livestock Research Institute (ILRI), International Water Management Institute (IWMI), International Potato Center (CIP), International Institute of Tropical Agriculture (IITA), and WorldFish. We would like to thank all funders who supported this research through their contributions to the CGIAR Trust Fund.

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