Persistent Regional Income Inequalities in Kenya During the COVID-19 Pandemic: Evidence from a Bi-Dimensional Inequality Decomposition Analysis

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While some studies find that downturns reduce regional income inequality, others report persistent or worsening disparities. Kenya, the seventh most populous nation in Africa, comprises 47 counties with diverse ethnic and geographical landscapes, where regional income inequality has attracted considerable public attention. This study examined the spatial distribution of per capita Gross County Product (GCP) together with the regional and industrial factors that influenced it both during and after the COVID-19 pandemic. The bi-dimensional inequality decomposition revealed that the industrial factors driving inequality differed regionally. In the wealthier two regions, the financial and business service sector in Central region and the transportation and communication sectors in North Coast region were the primary contributors to overall inequality. In Rift Valley region, agriculture played the most prominent role. These findings aid in identifying targeted policy interventions. Despite the significant variations in per capita GCP growth rates across counties during the pandemic and subsequent recovery, the study’s analysis confirmed that the regional inequality structure remained stable over the years. While the pandemic did not significantly increase regional disparities, the nearly 10-fold difference between the minimum and maximum GCP per capita highlights the persistently high levels of inequality. This underscores the urgent need for government intervention to promote more balanced regional development. JEL: D63 N97 O18 P25 R11 R12 Economic downturns COVID-19 pandemic regional income inequality inequality decomposition Kenya Figures Figure 1 Figure 2 Figure 3 Figure 4 1. INTRODUCTION Economic downturns affect subnational regions unevenly, highlighting regional income disparities. Studies demonstrate mixed effects on inequality within countries. Some studies find a reduction in regional income inequality, as seen during Japan's economic crisis after the bubble burst in the early 1990s (Kataoka 2008 ), Indonesia's Asian crisis in 1997-98 (Akita 2001), and the Great Recession across 200 OECD regions (Royuela et al. 2018). Conversely, other studies have highlighted persistent or worsening disparities, as seen in 295 cities across 31 Chinese provinces during the COVID-19 pandemic (Shen et al. 2021 ). These examples highlight the complex and dynamic nature of regional economic disparities. Kenya, Africa’s seventh-most populous country and 23rd largest by land, adopted a new constitution via referendum after the 2013 elections, leading to significant institutional reforms. The reforms established a subnational structure of 47 counties with an elected governor, replacing the previous system of eight provinces and 46 districts. Since then, the economy has grown steadily, recording an average annual real Gross Domestic Product (GDP) increase of approximately 5% by 2019 (Zalengera et al. 2020 ). Kenya's geography significantly influences its regional economic structure. Figure 1 depicts the Gross County Product (GCP) per capita (GCP) per capita for 2019, with a range from Kenyan shilling (KES) 45,200 to KES 472,200. The ratio of over 10 between the minimum and maximum highlights a significant disparity. Kenya is divided into five regions: Central, Eastern, North Coast, Nyanza Western, and Rift Valley. Nairobi in Central region, a major economic hub with a subtropical climate, is strongly clustered with its neighboring counties, while Mombasa in North Coast region, an international port, has a weaker regional cluster despite its strategic coastal location. Counties such as Elgeyo Marakwet (Rift Valley region) and Nyandarua (Central region) rely on subsistence agriculture because of the fertile soil and favorable climate. The three lowest-income counties—Garissa, Wajir, and Mandera—are located in North Coast region near the Somali border and have experienced conflict due to Somali refugees, resulting in lower development (Haider 2020 , Mbaka 2022 ). Each region and county includes nine industrial sectors: (1) Agriculture (AGR); (2) Mining (MIN); (3) Manufacturing (MAN); (4) Electricity, gas, and water (EGW); (5) Construction (CNS); (6) Trade, hotels, and restaurants (THR); (7) Transport and communication (TIC); (8) Finance and business services (FBS); and (9) other services (OSV). [ Figure 1 near here] Kenya reported its first COVID-19 case on March 13, 2020, leading to nationwide curfews, lockdowns in high-risk counties such as Nairobi and Mombasa, and travel restrictions. The first peak occurred in July 2020, with over 20,000 cases, and restrictions were eased by the end of 2020. Restrictions were tightened again during the second wave in March 2021, but some measures were eased by May 2021. This included partial relaxation of curfews. Vaccination efforts that started in March 2021 helped reduce cases, though the Delta variant caused a spike in August 2021 (Pape et al. 2021). After a brief Omicron surge in December 2021, cases had dropped sharply by February 2022, leading to the lifting of curfews and lockdowns by mid-2022. These led to a − 0.3% decline in GDP growth in 2020, followed by a strong recovery of 7.5% in 2021. In 2022, GDP growth slowed to 4.8%, driven primarily by the services sector (World Bank, 2023 ). AGR grew by 1.6%, despite drought and rising costs, rebounding from a 0.4% contraction in 2021. AGR remains significant, contributing 21.2% to GDP. Industry accounted for 17.7% and services dominated at 61.1% in 2022, with key sectors such as finance, transport, and communication driving growth (Kenya National Bureau of Statistics [KNBS], 2023). Regional income inequality in Kenya remains underexplored due to limited regional income data, leading early studies to focus on non-income statistics. Court and Prewitt ( 1974 ) compared local public services across provinces, finding that Nyanza and Western regions lagged behind Central regions. Miguel ( 2001 ) linked ethnic diversity to lower funding for primary education, while Ajulu ( 2002 ) showed how tribal politics maintained regional disparities. Later studies used labor force and household survey data to analyze income disparities across population subgroups. Gîthînji ( 2000 ) emphasized the significant urban-rural income disparities, particularly the pronounced variations in rural inequality. In contrast, Bigsten and Durevall ( 2006 ) demonstrated that trade openness significantly reduced wage inequality between agriculture and manufacturing. Mbaka ( 2022 ) and Mutono et al. ( 2022 ) researched spatial inequalities in access to energy and water. However, no studies examined industrial factors’ role in county-level regional income inequality or the spatial income distribution during the pandemic in Kenya. We fill the gap by analyzing per capita GCP between 2019 and 2021 using bi-dimensional inequality decomposition (BDID). We make two key contributions: (1) examining the impact of the pandemic shock on regional income inequality, and (2) applying BDID to identify the regional and industrial factors driving this inequality. 2. METHOD AND DATA 2.1 Bi-dimensional income inequality decomposition method Shorrocks’ ( 1980 , 1982 ) inequality decomposition methods, disaggregating overall inequality by regional subgroups and industry sector components, have been widely used in regional studies. Akita and Miyata ( 2010 ) developed a BDID method, combining both methods in their analysis of Indonesia's region-province structure with multiple industries. A nation has a dual-layered region-county structure with m regions, each region i further subdivided into \(\:{n}_{i}\) counties. As county \(\:j\) is located in region \(\:i,\) the corresponding GCP per capita is denoted as \(\:{\text{y}}_{\text{i}\text{j}}\) . We then express the arithmetic mean values in region \(\:i\) and all regions, respectively, as: \(\:\:{\mu\:}_{i}=\frac{1}{{n}_{i}}{\sum\:}_{\text{i}=1}^{{n}_{i}}{\text{y}}_{\text{i}\text{j}}\) and \(\:\mu\:=\frac{1}{m}{\sum\:}_{\text{i}=1}^{\text{m}}{\mu\:}_{i}\) . The squared CV is additively decomposed to regional subgroups as: $$\:{CV}^{2}=\frac{1}{{\mu\:}^{2}}\frac{1}{n}{\sum\:}_{i=1}^{m}{\sum\:}_{j=1}^{{n}_{i}}{\left({y}_{ij}-\mu\:\right)}^{2}={\sum\:}_{i=1}^{m}\frac{{n}_{i}}{n}{\left(\frac{{\mu\:}_{i}}{\mu\:}\right)}^{2}\:{CV}_{i}^{2}+{\sum\:}_{i=1}^{m}\frac{{n}_{i}}{n}{\left(\frac{{\mu\:}_{i}-\mu\:}{\mu\:}\right)}^{2}={\sum\:}_{i=1}^{m}{CV}_{Wi}^{2}+{CV}_{B}^{2}$$ 1 where \(\:{CV}_{i}^{2}=\frac{1}{{n}_{i}}{\left(\frac{1}{{\mu\:}_{i}}\right)}^{2}{\sum\:}_{j=1}^{{n}_{i}}{\left({y}_{ij}-{\mu\:}_{i}\right)}^{2}\) , \(\:{CV}_{Wi}^{2}=\frac{{n}_{i}}{n}{\left(\frac{{\mu\:}_{i}}{\mu\:}\right)}^{2}\:{CV}_{i}^{2}\) and \(\:{CV}_{B}^{2}={\sum\:}_{i=1}^{m}\frac{{n}_{i}}{n}{\left(\frac{{\mu\:}_{i}-\mu\:}{\mu\:}\right)}^{2}\) . \(\:{CV}_{Wi}^{2}\) and \(\:{CV}_{B}^{2}\) represent the within-region inequality component in region i and between-region inequality component. GCP per capita of county j in region i consists of K sectors, defined as \(\:{y}_{ij}={\sum\:}_{\text{k}=1}^{K}{y}_{ijk}\) where \(\:{y}_{ijk}\) is GCP per capita in sector k of county j in region i . Thus, we respectively express the sectoral component k’s arithmetic mean values of GCP per capita in region i and in all counties as \(\:{\mu\:}_{ik}=\frac{1}{{n}_{i}}{\sum\:}_{\text{j}=1}^{{n}_{i}}{\text{y}}_{\text{i}\text{j}\text{k}}\) and \(\:{\mu\:}_{k}=\frac{1}{\text{m}}\frac{1}{{n}_{i}}{\sum\:}_{i=1}^{m}{\sum\:}_{\text{j}=1}^{{n}_{i}}{\text{y}}_{\text{i}\text{j}\text{k}}\) . Using the definition \(\:{\mu\:}_{i}={\sum\:}_{\text{k}=1}^{K}{\mu\:}_{ik}\) , and multiplying \(\:\left(\frac{{\mu\:}_{ik}}{{\mu\:}_{ik}}\right)\) , \(\:{CV}_{i}^{2}\) can be expressed as $$\:{CV}_{i}^{2}={\sum\:}_{k=1}^{K}\left(\frac{{\mu\:}_{ik}}{{\mu\:}_{i}}\right)\left(\frac{1}{{{\mu\:}_{ik}\mu\:}_{i}}\right)\left[\frac{1}{{n}_{i}}{\sum\:}_{j=1}^{{n}_{i}}\left({y}_{ijk}-{\mu\:}_{ik}\right)\left({y}_{ij}-{\mu\:}_{i}\right)\right]={\sum\:}_{k=1}^{K}\left(\frac{{\mu\:}_{ik}}{{\mu\:}_{i}}\right)\left(\frac{{cov}_{ik}}{{\mu\:}_{ik}{\mu\:}_{i}}\right)$$ 2 where notation \(\:{cov}_{ik}=\left[\frac{1}{{n}_{i}}{\sum\:}_{j=1}^{{n}_{i}}\left({y}_{ijk}-{\mu\:}_{ik}\right)\left({y}_{ij}-{\mu\:}_{i}\right)\right]\) is a covariance between \(\:{y}_{ijk}\) and \(\:{y}_{ij}\) . Multiplying \(\:\left(\frac{{\mu\:}_{k}}{{\mu\:}_{k}}\right)\) , the between-region component in Eq. ( 1 ) is expressed as: $$\:{CV}_{B}^{2}={\sum\:}_{k=1}^{K}\left(\frac{{\mu\:}_{k}}{\mu\:}\right)\left(\frac{1}{{\mu\:}_{k}\mu\:}\right)\left[\frac{{n}_{i}}{n}{\sum\:}_{i=1}^{m}\left({\mu\:}_{ik}-{\mu\:}_{k}\right)\left({\mu\:}_{i}-\mu\:\right)\right]={\sum\:}_{k=1}^{K}\left(\frac{{\mu\:}_{k}}{\mu\:}\right)\left(\frac{{cov}_{k}}{{\mu\:}_{k}\mu\:}\right)$$ 3 where notation \(\:{cov}_{k}\left(=\frac{{n}_{i}}{n}{\sum\:}_{i=1}^{m}\left({\mu\:}_{ik}-{\mu\:}_{k}\right)\left({\mu\:}_{i}-\mu\:\right)\right)\) is a covariance between \(\:{\mu\:}_{ik}\) and \(\:{\mu\:}_{i}\) . Finally, we substitute Equations ( 2 ) and ( 3 ) into Eq. ( 1 ) and obtain: $$\:{CV}^{2}={\sum\:}_{i=1}^{m}{CV}_{Wi}^{2}+{CV}_{B}^{2}={\sum\:}_{i=1}^{m}{\sum\:}_{k=1}^{K}\frac{ni}{n}{\left(\frac{{\mu\:}_{i}}{\mu\:}\right)}^{2}\left(\frac{{\mu\:}_{ik}}{{\mu\:}_{i}}\right)\left(\frac{{cov}_{ik}}{{\mu\:}_{ik}{\mu\:}_{i}}\right)+{\sum\:}_{k=1}^{K}\left(\frac{{\mu\:}_{k}}{\mu\:}\right)\left(\frac{{cov}_{k}}{{\mu\:}_{k}\mu\:}\right)$$ 4 Dividing the above by \(\:{CV}^{2}\) : $$\:1=\sum\:_{i=1}^{m}\sum\:_{k=1}^{K}{c}_{ik}\:+\:\sum\:_{k=1}^{K}{c}_{k}$$ 5 where \(\:{c}_{ik}=\left[\frac{ni}{n}{\left(\frac{{\mu\:}_{i}}{\mu\:}\right)}^{2}\left(\frac{{\mu\:}_{ik}}{{\mu\:}_{i}}\right)\left(\frac{{cov}_{ik}}{{\mu\:}_{ik}{\mu\:}_{i}}\right)\right]/{CV}^{2}\) and \(\:\:{c}_{k}=\left[\left(\frac{{\mu\:}_{k}}{\mu\:}\right)\left(\frac{{cov}_{k}}{{\mu\:}_{k}\mu\:}\right)\right]/{CV}^{2}\) . Notation \(\:{c}_{ik}\) refers to the contribution of region i ’s within-region inequality for sector k to overall inequality, whereas \(\:{c}_{k}\) is the contribution of the between-region inequality for sector k to overall inequality. In Kenya, a nation consists of five regions (m = 5), and each unit has nine sectors (K = 9), we decompose the overall regional income inequality into 45 (= \(\:\left(m+1\right)\times\:K\) ) components. We don’t apply further decomposition in \(\:{c}_{ik}\) to our analysis, as expanding it to three components by sector is empirically impractical. 2.2 Data We used GCP per capita, considering a dual-layered regional structure: region and county. We define that the nation consists of five regional units: (1) Central, (2) Eastern, (3) North Coast, (4) Nyanza Western, and (5) Rift Valley (see Appendix Table 1). Each region and county has nine industrial sectors, as listed earlier. Our balanced panel dataset of aggregate and sectoral GDP covers all 47 counties for the period 2019–2021 at 2016 constant prices, sourced from the 2023 Gross County Report (KNBS 2023). Population data is from the U.S. Census Bureau (2023). Table 1 Descriptive statistics of per capita GDP, 1,000 KES (n = 47) Year Min Median Max Mean CV Skewness Outliers 2019 45.2 107.0 472.2 123.6 0.57 2.96 MM, NB 2020 46.9 106.8 456.5 122.1 0.56 2.89 MM, NB 2021 54.9 120.0 537.9 139.2 0.57 3.05 MM, NB AGR 0.9 31.1 109.0 37.7 0.62 1.22 NN, EM MIN 0.1 0.5 8.6 1.2 1.38 2.82 KW, MC MAN 0.9 4.8 52.5 8.0 1.41 2.88 MM, NB EGW 0.4 0.9 21.9 2.9 1.72 2.97 MB, EB, NK CNS 0.4 3.5 43.1 6.5 1.31 2.74 NB, MM THE 1.0 5.6 73.9 10.3 1.20 3.41 NB, MM TIC 2.1 13.6 86.2 17.5 0.93 2.96 NB, MM FBS 2.0 6.6 166.6 13.2 1.87 5.43 NB OSV 18.1 24.9 42.5 26.4 0.22 0.95 NB, LM Note: Outliers if the standardized values exceed the absolute value of 2.5. Appendix Table 1: List of counties and regional classification Table 1 summarizes GDP per capita statistics, showing the COVID-19 shock and subsequent recovery in 47 counties between 2019 and 2021. The minimum and median remained stable during 2020 but grew significantly in 2021, indicating resilience and recovery. Contrastingly, the maximum and average dropped slightly in 2020, reflecting the downturn in wealthier counties. It, however, rebounded sharply in 2021. Overall county-level inequality in GCP per capita, measured by CV, changed insignificantly. Skewness slightly increased in 2021, showing faster recovery in wealthier counties. The data shows resilience during the pandemic and a strong recovery in 2021. Wealthier counties recovered faster, slightly widening inequalities. The minimum to maximum GCP per capita ratio remains nearly 10, indicating an unacceptable disparity level. Nairobi (NB) and Mombasa (MM) are outliers as economic hubs. The table’s bottom part highlights significant spatial differences by industrial sectors. AGR plays an important role with the highest mean income, contributing nearly one-third of the average GDP per capita (0.304 = 37.7/123.6) in 2019, followed by OSV. Driven by local demand, OSV has the lowest variation, while all sectors show positive skewness. [ Table 1 near here] 3. EMPIRICAL RESULTS Figure 2 illustrates the large variations in per capita GCP growth rates across counties during both the pandemic and recovery periods. In 2020, 30 counties experienced economic regression, with Mombasa experiencing the largest decline at − 5.8%. There was a turnaround in 2021, with 46 counties recovering, particularly low-income Garissa (24.0%) and Wajir (18.9%) in North Coast region. These highlight the uneven economic impact of the pandemic and the different paths to recovery. [ Figure 2 near here] Figure 3 shows the percentage contribution of the between- and within-region inequality components, based on Eq. ( 1 ). The blue area represents within-region inequality, accounting for over 75% of the total. Within-inequality components are significantly present in Central and North Coast regions. This pattern remained consistent throughout the study period, indicating the persistent contribution of regional hubs to overall inequality. Central, Eastern, and Rift Valley regions have shown rising within-region inequalities over time, highlighting a growing intra-region gap in per capita GCP between richer and poorer counties. [ Figure 3 near here] Since between-region inequality contributes less than 25% to overall inequality, we next examine the sectoral component within each region's inequality, denoted by \(\:\left(100\times\:{c}_{ik}\right)\) in Eq. ( 5 ), in Fig. 4 . Central, North Coast, and Rift Valley regions consistently show substantial within-region inequality across all three years. AGR, MFG, CNS, THR, TIC, and FBS contribute significantly to inequality in those regions. However, the primary sector forming the within-region inequality varies by region. In Central region, FBS was the most significant, followed by TIC, THR, MFG, and CNS. They contribute to the rise of within-region inequality and are more unevenly performed. Conversely, AGR remained consistently negative throughout the study period, which clearly indicates that it helps reduce within-region inequalities in the Central region. This is evidenced by the fact that counties with higher (lower) incomes have a lower (higher) agricultural presence. In North Coast region, TIC is the most significant, followed by FBS, THR, MFG, and CNS. They contribute to the rise of within-region inequality. This reflects Mombasa's influence as an international logistics hub, driving trade, tourism, and port activity. AGR remained consistently negative throughout the study period. In Rift Valley, AGR played the most significant role in increasing inequality. These findings identify key sectors that drive regional inequality and guide targeted policy interventions. In Central and North Coast regions, policies should promote more equitable development in FBS and TIC by expanding their presence beyond the regional hubs in Nairobi and Mombasa. In the Rift Valley, prioritizing the diffusion of agricultural benefits to neighboring counties can help mitigate their impact on regional inequality. Despite initial expectations, year-to-year shifts in the regional inequality structure remained unchanged. Our variance comparison test confirmed that variations across the BDID 45 inequality components were consistent year-to-year, indicating the resilience of the inequality structure. 4. CONCLUSIONS Despite significant variations in per capita GCP growth rates across counties during the COVID-19 pandemic and recovery, our analysis found no substantial changes in overall inter-county inequality. BDID analysis showed regional differences in industrial factors contributing to inequality. In the wealthier Central and North Coast regions, the FBS and TIC sectors were the primary contributors, while AGR was the most significant in Rift Valley. The regional inequality structure remained stable, indicating that the pandemic did not drastically increase regional disparities. However, the nearly 10-fold ratio between the minimum and maximum GCP per capita highlights substantial inequality, underscoring the need for government intervention to promote balanced regional development. Given Kenya's role as a major vegetable exporter to the EU and its strong horticulture sector, focusing on agricultural development in less wealthy regions could be a viable strategy (Tyce 2020 ). Our study has some limitations. First, we did not examine the growth transition path on multiple convergences. Using Phillips and Sul’s ( 2007 ) club convergence analysis with long-term county data could address this issue. Second, incorporating spatial autocorrelation and spillovers into the analysis of regional income inequality is a promising direction. Additionally, exploring causal mechanisms with a supply-side growth model using production factor data could improve policy insights. Declarations Author Contribution Dr. Kataoka wrote the main manuscript text and prepared Figures 1-4 and all tables. Mr. Kirugi collected the county-level data employed in this study and reviewed the geographical background and policy implications in Kenya. Acknowledgement This work is supported by Grant-in-Aid for Scientific Research C (21K01469) from the Japan Society of the Promotion of Science. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5101160","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":374940665,"identity":"d1b6e5fc-0d4e-4a83-89ce-39931717d270","order_by":0,"name":"Mitsuhiko Kataoka","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8ElEQVRIiWNgGAWjYDACHjCZwMDPwNjM2AAVNCCgBaQwgUGygWQtBgcYmOFa8ALdnsPPH/yoSJM3vn242XBmmw0Df/sBhuICPFrMzrYZNvacyTHcdi6xOXFjWxqDxJkEBuMZ+LScZzBs4G2rYNx2hrH54MO2wwwMNxgYjHnwamH/2Pi3rcJ+cw9UizxBLWd7DJt523ISN/Awghx2mMGAoJYzZwpny5xJS54BdJjhjHNpPIZnEhvw++VM+oaPbyqSbft72B9L9pTZyMkdP3zMGF+IoQJGNlBqYGwzJloHA8MfMMn8mAQto2AUjIJRMPwBAO/RUhtcb8mUAAAAAElFTkSuQmCC","orcid":"","institution":"Rikkyo University","correspondingAuthor":true,"prefix":"","firstName":"Mitsuhiko","middleName":"","lastName":"Kataoka","suffix":""},{"id":374940666,"identity":"4eb475db-3fe6-419e-9d91-e8e599c68f7e","order_by":1,"name":"Geoffrey Mwenda Kirugi","email":"","orcid":"","institution":"Ministry of Industry, Trade and Investment, Kenya","correspondingAuthor":false,"prefix":"","firstName":"Geoffrey","middleName":"Mwenda","lastName":"Kirugi","suffix":""}],"badges":[],"createdAt":"2024-09-17 06:54:49","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5101160/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5101160/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s12076-025-00399-x","type":"published","date":"2025-03-18T15:58:27+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":72332031,"identity":"e9691ef5-7afe-4cf0-9aef-357d09d0a338","added_by":"auto","created_at":"2024-12-25 14:45:48","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":238955,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend.\u003c/p\u003e","description":"","filename":"F1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5101160/v1/3c4ae4de5714b2a95cfb0275.jpg"},{"id":72332030,"identity":"6614794c-8c18-4881-94de-45f57818aafb","added_by":"auto","created_at":"2024-12-25 14:45:48","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":350981,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend.\u003c/p\u003e","description":"","filename":"F2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5101160/v1/9854b8d7d944b13937d585b8.jpg"},{"id":72332305,"identity":"ffd644ce-df1c-400c-b47e-9bc672143428","added_by":"auto","created_at":"2024-12-25 14:53:48","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":245336,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend.\u003c/p\u003e","description":"","filename":"F3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5101160/v1/c5dac713ae91023c9e5277db.jpg"},{"id":72332034,"identity":"5531ff47-5eaa-4a8f-b96e-47c1e35ce6fd","added_by":"auto","created_at":"2024-12-25 14:45:48","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":219193,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend.\u003c/p\u003e","description":"","filename":"F4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5101160/v1/19583ac32ca6eb98252bbce9.jpg"},{"id":79120755,"identity":"54bb86a8-d6b2-431e-87d2-e9a612ba64b5","added_by":"auto","created_at":"2025-03-24 16:11:20","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1593373,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5101160/v1/bb0111ce-05ba-4312-9e04-520139b5e8ac.pdf"},{"id":72332033,"identity":"472afd17-09b9-4f13-8503-c9836a13758a","added_by":"auto","created_at":"2024-12-25 14:45:48","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":27391,"visible":true,"origin":"","legend":"","description":"","filename":"02Tables.docx","url":"https://assets-eu.researchsquare.com/files/rs-5101160/v1/7446d67404d0a5e7d3939651.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Persistent Regional Income Inequalities in Kenya During the COVID-19 Pandemic: Evidence from a Bi-Dimensional Inequality Decomposition Analysis","fulltext":[{"header":"1. INTRODUCTION","content":"\u003cp\u003eEconomic downturns affect subnational regions unevenly, highlighting regional income disparities. Studies demonstrate mixed effects on inequality within countries. Some studies find a reduction in regional income inequality, as seen during Japan's economic crisis after the bubble burst in the early 1990s (Kataoka \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), Indonesia's Asian crisis in 1997-98 (Akita 2001), and the Great Recession across 200 OECD regions (Royuela et al. 2018). Conversely, other studies have highlighted persistent or worsening disparities, as seen in 295 cities across 31 Chinese provinces during the COVID-19 pandemic (Shen et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). These examples highlight the complex and dynamic nature of regional economic disparities.\u003c/p\u003e \u003cp\u003eKenya, Africa\u0026rsquo;s seventh-most populous country and 23rd largest by land, adopted a new constitution via referendum after the 2013 elections, leading to significant institutional reforms. The reforms established a subnational structure of 47 counties with an elected governor, replacing the previous system of eight provinces and 46 districts. Since then, the economy has grown steadily, recording an average annual real Gross Domestic Product (GDP) increase of approximately 5% by 2019 (Zalengera et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eKenya's geography significantly influences its regional economic structure. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e depicts the Gross County Product (GCP) per capita (GCP) per capita for 2019, with a range from Kenyan shilling (KES) 45,200 to KES 472,200. The ratio of over 10 between the minimum and maximum highlights a significant disparity. Kenya is divided into five regions: Central, Eastern, North Coast, Nyanza Western, and Rift Valley. Nairobi in Central region, a major economic hub with a subtropical climate, is strongly clustered with its neighboring counties, while Mombasa in North Coast region, an international port, has a weaker regional cluster despite its strategic coastal location. Counties such as Elgeyo Marakwet (Rift Valley region) and Nyandarua (Central region) rely on subsistence agriculture because of the fertile soil and favorable climate. The three lowest-income counties\u0026mdash;Garissa, Wajir, and Mandera\u0026mdash;are located in North Coast region near the Somali border and have experienced conflict due to Somali refugees, resulting in lower development (Haider \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2020\u003c/span\u003e, Mbaka \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Each region and county includes nine industrial sectors: (1) Agriculture (AGR); (2) Mining (MIN); (3) Manufacturing (MAN); (4) Electricity, gas, and water (EGW); (5) Construction (CNS); (6) Trade, hotels, and restaurants (THR); (7) Transport and communication (TIC); (8) Finance and business services (FBS); and (9) other services (OSV).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003e[\u003c/b\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e \u003cb\u003enear here]\u003c/b\u003e\u003c/p\u003e \u003cp\u003eKenya reported its first COVID-19 case on March 13, 2020, leading to nationwide curfews, lockdowns in high-risk counties such as Nairobi and Mombasa, and travel restrictions. The first peak occurred in July 2020, with over 20,000 cases, and restrictions were eased by the end of 2020. Restrictions were tightened again during the second wave in March 2021, but some measures were eased by May 2021. This included partial relaxation of curfews. Vaccination efforts that started in March 2021 helped reduce cases, though the Delta variant caused a spike in August 2021 (Pape et al. 2021). After a brief Omicron surge in December 2021, cases had dropped sharply by February 2022, leading to the lifting of curfews and lockdowns by mid-2022.\u003c/p\u003e \u003cp\u003eThese led to a \u0026minus;\u0026thinsp;0.3% decline in GDP growth in 2020, followed by a strong recovery of 7.5% in 2021. In 2022, GDP growth slowed to 4.8%, driven primarily by the services sector (World Bank, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). AGR grew by 1.6%, despite drought and rising costs, rebounding from a 0.4% contraction in 2021. AGR remains significant, contributing 21.2% to GDP. Industry accounted for 17.7% and services dominated at 61.1% in 2022, with key sectors such as finance, transport, and communication driving growth (Kenya National Bureau of Statistics [KNBS], 2023).\u003c/p\u003e \u003cp\u003eRegional income inequality in Kenya remains underexplored due to limited regional income data, leading early studies to focus on non-income statistics. Court and Prewitt (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e1974\u003c/span\u003e) compared local public services across provinces, finding that Nyanza and Western regions lagged behind Central regions. Miguel (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2001\u003c/span\u003e) linked ethnic diversity to lower funding for primary education, while Ajulu (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2002\u003c/span\u003e) showed how tribal politics maintained regional disparities. Later studies used labor force and household survey data to analyze income disparities across population subgroups. G\u0026icirc;th\u0026icirc;nji (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2000\u003c/span\u003e) emphasized the significant urban-rural income disparities, particularly the pronounced variations in rural inequality. In contrast, Bigsten and Durevall (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2006\u003c/span\u003e) demonstrated that trade openness significantly reduced wage inequality between agriculture and manufacturing. Mbaka (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and Mutono et al. (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) researched spatial inequalities in access to energy and water. However, no studies examined industrial factors\u0026rsquo; role in county-level regional income inequality or the spatial income distribution during the pandemic in Kenya. We fill the gap by analyzing per capita GCP between 2019 and 2021 using bi-dimensional inequality decomposition (BDID). We make two key contributions: (1) examining the impact of the pandemic shock on regional income inequality, and (2) applying BDID to identify the regional and industrial factors driving this inequality.\u003c/p\u003e"},{"header":"2. METHOD AND DATA","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Bi-dimensional income inequality decomposition method\u003c/h2\u003e \u003cp\u003eShorrocks\u0026rsquo; (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e1980\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e1982\u003c/span\u003e) inequality decomposition methods, disaggregating overall inequality by regional subgroups and industry sector components, have been widely used in regional studies. Akita and Miyata (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) developed a BDID method, combining both methods in their analysis of Indonesia's region-province structure with multiple industries.\u003c/p\u003e \u003cp\u003eA nation has a dual-layered region-county structure with \u003cem\u003em\u003c/em\u003e regions, each region \u003cem\u003ei\u003c/em\u003e further subdivided into \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{n}_{i}\\)\u003c/span\u003e\u003c/span\u003e counties. As county \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:j\\)\u003c/span\u003e\u003c/span\u003e is located in region \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:i,\\)\u003c/span\u003e\u003c/span\u003e the corresponding GCP per capita is denoted as \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{y}}_{\\text{i}\\text{j}}\\)\u003c/span\u003e\u003c/span\u003e. We then express the arithmetic mean values in region \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:i\\)\u003c/span\u003e\u003c/span\u003e and all regions, respectively, as:\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\:{\\mu\\:}_{i}=\\frac{1}{{n}_{i}}{\\sum\\:}_{\\text{i}=1}^{{n}_{i}}{\\text{y}}_{\\text{i}\\text{j}}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\mu\\:=\\frac{1}{m}{\\sum\\:}_{\\text{i}=1}^{\\text{m}}{\\mu\\:}_{i}\\)\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eThe squared CV is additively decomposed to regional subgroups as:\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:{CV}^{2}=\\frac{1}{{\\mu\\:}^{2}}\\frac{1}{n}{\\sum\\:}_{i=1}^{m}{\\sum\\:}_{j=1}^{{n}_{i}}{\\left({y}_{ij}-\\mu\\:\\right)}^{2}={\\sum\\:}_{i=1}^{m}\\frac{{n}_{i}}{n}{\\left(\\frac{{\\mu\\:}_{i}}{\\mu\\:}\\right)}^{2}\\:{CV}_{i}^{2}+{\\sum\\:}_{i=1}^{m}\\frac{{n}_{i}}{n}{\\left(\\frac{{\\mu\\:}_{i}-\\mu\\:}{\\mu\\:}\\right)}^{2}={\\sum\\:}_{i=1}^{m}{CV}_{Wi}^{2}+{CV}_{B}^{2}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{CV}_{i}^{2}=\\frac{1}{{n}_{i}}{\\left(\\frac{1}{{\\mu\\:}_{i}}\\right)}^{2}{\\sum\\:}_{j=1}^{{n}_{i}}{\\left({y}_{ij}-{\\mu\\:}_{i}\\right)}^{2}\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{CV}_{Wi}^{2}=\\frac{{n}_{i}}{n}{\\left(\\frac{{\\mu\\:}_{i}}{\\mu\\:}\\right)}^{2}\\:{CV}_{i}^{2}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{CV}_{B}^{2}={\\sum\\:}_{i=1}^{m}\\frac{{n}_{i}}{n}{\\left(\\frac{{\\mu\\:}_{i}-\\mu\\:}{\\mu\\:}\\right)}^{2}\\)\u003c/span\u003e\u003c/span\u003e. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{CV}_{Wi}^{2}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{CV}_{B}^{2}\\)\u003c/span\u003e\u003c/span\u003e represent the within-region inequality component in region \u003cem\u003ei\u003c/em\u003e and between-region inequality component.\u003c/p\u003e \u003cp\u003eGCP per capita of county \u003cem\u003ej\u003c/em\u003e in region \u003cem\u003ei\u003c/em\u003e consists of \u003cem\u003eK\u003c/em\u003e sectors, defined as \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{y}_{ij}={\\sum\\:}_{\\text{k}=1}^{K}{y}_{ijk}\\)\u003c/span\u003e\u003c/span\u003e where \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{y}_{ijk}\\)\u003c/span\u003e\u003c/span\u003e is GCP per capita in sector \u003cem\u003ek\u003c/em\u003e of county \u003cem\u003ej\u003c/em\u003e in region \u003cem\u003ei\u003c/em\u003e. Thus, we respectively express the sectoral component k\u0026rsquo;s arithmetic mean values of GCP per capita in region \u003cem\u003ei\u003c/em\u003e and in all counties as \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\mu\\:}_{ik}=\\frac{1}{{n}_{i}}{\\sum\\:}_{\\text{j}=1}^{{n}_{i}}{\\text{y}}_{\\text{i}\\text{j}\\text{k}}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\mu\\:}_{k}=\\frac{1}{\\text{m}}\\frac{1}{{n}_{i}}{\\sum\\:}_{i=1}^{m}{\\sum\\:}_{\\text{j}=1}^{{n}_{i}}{\\text{y}}_{\\text{i}\\text{j}\\text{k}}\\)\u003c/span\u003e\u003c/span\u003e. Using the definition \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\mu\\:}_{i}={\\sum\\:}_{\\text{k}=1}^{K}{\\mu\\:}_{ik}\\)\u003c/span\u003e\u003c/span\u003e, and multiplying \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\left(\\frac{{\\mu\\:}_{ik}}{{\\mu\\:}_{ik}}\\right)\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{CV}_{i}^{2}\\)\u003c/span\u003e\u003c/span\u003e can be expressed as\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$$\\:{CV}_{i}^{2}={\\sum\\:}_{k=1}^{K}\\left(\\frac{{\\mu\\:}_{ik}}{{\\mu\\:}_{i}}\\right)\\left(\\frac{1}{{{\\mu\\:}_{ik}\\mu\\:}_{i}}\\right)\\left[\\frac{1}{{n}_{i}}{\\sum\\:}_{j=1}^{{n}_{i}}\\left({y}_{ijk}-{\\mu\\:}_{ik}\\right)\\left({y}_{ij}-{\\mu\\:}_{i}\\right)\\right]={\\sum\\:}_{k=1}^{K}\\left(\\frac{{\\mu\\:}_{ik}}{{\\mu\\:}_{i}}\\right)\\left(\\frac{{cov}_{ik}}{{\\mu\\:}_{ik}{\\mu\\:}_{i}}\\right)$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere notation \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{cov}_{ik}=\\left[\\frac{1}{{n}_{i}}{\\sum\\:}_{j=1}^{{n}_{i}}\\left({y}_{ijk}-{\\mu\\:}_{ik}\\right)\\left({y}_{ij}-{\\mu\\:}_{i}\\right)\\right]\\)\u003c/span\u003e\u003c/span\u003e is a covariance between \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{y}_{ijk}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{y}_{ij}\\)\u003c/span\u003e\u003c/span\u003e. Multiplying \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\left(\\frac{{\\mu\\:}_{k}}{{\\mu\\:}_{k}}\\right)\\)\u003c/span\u003e\u003c/span\u003e, the between-region component in Eq.\u0026nbsp;(\u003cspan refid=\"Equ1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) is expressed as:\u003cdiv id=\"Equ3\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ3\" name=\"EquationSource\"\u003e\n$$\\:{CV}_{B}^{2}={\\sum\\:}_{k=1}^{K}\\left(\\frac{{\\mu\\:}_{k}}{\\mu\\:}\\right)\\left(\\frac{1}{{\\mu\\:}_{k}\\mu\\:}\\right)\\left[\\frac{{n}_{i}}{n}{\\sum\\:}_{i=1}^{m}\\left({\\mu\\:}_{ik}-{\\mu\\:}_{k}\\right)\\left({\\mu\\:}_{i}-\\mu\\:\\right)\\right]={\\sum\\:}_{k=1}^{K}\\left(\\frac{{\\mu\\:}_{k}}{\\mu\\:}\\right)\\left(\\frac{{cov}_{k}}{{\\mu\\:}_{k}\\mu\\:}\\right)$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e3\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere notation \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{cov}_{k}\\left(=\\frac{{n}_{i}}{n}{\\sum\\:}_{i=1}^{m}\\left({\\mu\\:}_{ik}-{\\mu\\:}_{k}\\right)\\left({\\mu\\:}_{i}-\\mu\\:\\right)\\right)\\)\u003c/span\u003e\u003c/span\u003e is a covariance between \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\mu\\:}_{ik}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\mu\\:}_{i}\\)\u003c/span\u003e\u003c/span\u003e. Finally, we substitute Equations (\u003cspan refid=\"Equ2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) and (\u003cspan refid=\"Equ3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) into Eq.\u0026nbsp;(\u003cspan refid=\"Equ1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) and obtain:\u003cdiv id=\"Equ4\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ4\" name=\"EquationSource\"\u003e\n$$\\:{CV}^{2}={\\sum\\:}_{i=1}^{m}{CV}_{Wi}^{2}+{CV}_{B}^{2}={\\sum\\:}_{i=1}^{m}{\\sum\\:}_{k=1}^{K}\\frac{ni}{n}{\\left(\\frac{{\\mu\\:}_{i}}{\\mu\\:}\\right)}^{2}\\left(\\frac{{\\mu\\:}_{ik}}{{\\mu\\:}_{i}}\\right)\\left(\\frac{{cov}_{ik}}{{\\mu\\:}_{ik}{\\mu\\:}_{i}}\\right)+{\\sum\\:}_{k=1}^{K}\\left(\\frac{{\\mu\\:}_{k}}{\\mu\\:}\\right)\\left(\\frac{{cov}_{k}}{{\\mu\\:}_{k}\\mu\\:}\\right)$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e4\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eDividing the above by \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{CV}^{2}\\)\u003c/span\u003e\u003c/span\u003e:\u003cdiv id=\"Equ5\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ5\" name=\"EquationSource\"\u003e\n$$\\:1=\\sum\\:_{i=1}^{m}\\sum\\:_{k=1}^{K}{c}_{ik}\\:+\\:\\sum\\:_{k=1}^{K}{c}_{k}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e5\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{c}_{ik}=\\left[\\frac{ni}{n}{\\left(\\frac{{\\mu\\:}_{i}}{\\mu\\:}\\right)}^{2}\\left(\\frac{{\\mu\\:}_{ik}}{{\\mu\\:}_{i}}\\right)\\left(\\frac{{cov}_{ik}}{{\\mu\\:}_{ik}{\\mu\\:}_{i}}\\right)\\right]/{CV}^{2}\\)\u003c/span\u003e\u003c/span\u003e and\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\:{c}_{k}=\\left[\\left(\\frac{{\\mu\\:}_{k}}{\\mu\\:}\\right)\\left(\\frac{{cov}_{k}}{{\\mu\\:}_{k}\\mu\\:}\\right)\\right]/{CV}^{2}\\)\u003c/span\u003e\u003c/span\u003e. Notation \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{c}_{ik}\\)\u003c/span\u003e\u003c/span\u003e refers to the contribution of region \u003cem\u003ei\u003c/em\u003e\u0026rsquo;s within-region inequality for sector \u003cem\u003ek\u003c/em\u003e to overall inequality, whereas \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{c}_{k}\\)\u003c/span\u003e\u003c/span\u003e is the contribution of the between-region inequality for sector \u003cem\u003ek\u003c/em\u003e to overall inequality. In Kenya, a nation consists of five regions (m\u0026thinsp;=\u0026thinsp;5), and each unit has nine sectors (K\u0026thinsp;=\u0026thinsp;9), we decompose the overall regional income inequality into 45 (=\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\left(m+1\\right)\\times\\:K\\)\u003c/span\u003e\u003c/span\u003e) components. We don\u0026rsquo;t apply further decomposition in \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{c}_{ik}\\)\u003c/span\u003e\u003c/span\u003e to our analysis, as expanding it to three components by sector is empirically impractical.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Data\u003c/h2\u003e \u003cp\u003eWe used GCP per capita, considering a dual-layered regional structure: region and county. We define that the nation consists of five regional units: (1) Central, (2) Eastern, (3) North Coast, (4) Nyanza Western, and (5) Rift Valley (see Appendix Table\u0026nbsp;1). Each region and county has nine industrial sectors, as listed earlier. Our balanced panel dataset of aggregate and sectoral GDP covers all 47 counties for the period 2019\u0026ndash;2021 at 2016 constant prices, sourced from the 2023 Gross County Report (KNBS 2023). Population data is from the U.S. Census Bureau (2023).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescriptive statistics of per capita GDP, 1,000 KES (n\u0026thinsp;=\u0026thinsp;47)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMin\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMedian\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMax\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCV\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSkewness\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eOutliers\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e45.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e107.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e472.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e123.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMM, NB\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e46.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e106.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e456.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e122.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMM, NB\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e54.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e120.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e537.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e139.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMM, NB\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAGR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e31.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e109.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e37.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNN, EM\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMIN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eKW, MC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMAN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e52.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMM, NB\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEGW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e21.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMB, EB, NK\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e43.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNB, MM\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTHE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e73.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNB, MM\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTIC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e86.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e17.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNB, MM\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFBS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e166.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e13.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e5.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNB\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOSV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e24.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e42.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e26.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNB, LM\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eNote: Outliers if the standardized values exceed the absolute value of 2.5.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u003cspan refid=\"Sec7\" class=\"InternalRef\"\u003eAppendix Table\u0026nbsp;1: List of counties and regional classification\u003c/span\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e summarizes GDP per capita statistics, showing the COVID-19 shock and subsequent recovery in 47 counties between 2019 and 2021. The minimum and median remained stable during 2020 but grew significantly in 2021, indicating resilience and recovery. Contrastingly, the maximum and average dropped slightly in 2020, reflecting the downturn in wealthier counties. It, however, rebounded sharply in 2021. Overall county-level inequality in GCP per capita, measured by CV, changed insignificantly. Skewness slightly increased in 2021, showing faster recovery in wealthier counties. The data shows resilience during the pandemic and a strong recovery in 2021. Wealthier counties recovered faster, slightly widening inequalities. The minimum to maximum GCP per capita ratio remains nearly 10, indicating an unacceptable disparity level. Nairobi (NB) and Mombasa (MM) are outliers as economic hubs. The table\u0026rsquo;s bottom part highlights significant spatial differences by industrial sectors. AGR plays an important role with the highest mean income, contributing nearly one-third of the average GDP per capita (0.304\u0026thinsp;=\u0026thinsp;37.7/123.6) in 2019, followed by OSV. Driven by local demand, OSV has the lowest variation, while all sectors show positive skewness.\u003c/p\u003e \u003cp\u003e \u003cb\u003e[\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e \u003cb\u003enear here]\u003c/b\u003e\u003c/p\u003e \u003c/div\u003e"},{"header":"3. EMPIRICAL RESULTS","content":"\u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e illustrates the large variations in per capita GCP growth rates across counties during both the pandemic and recovery periods. In 2020, 30 counties experienced economic regression, with Mombasa experiencing the largest decline at \u0026minus;\u0026thinsp;5.8%. There was a turnaround in 2021, with 46 counties recovering, particularly low-income Garissa (24.0%) and Wajir (18.9%) in North Coast region. These highlight the uneven economic impact of the pandemic and the different paths to recovery.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003e[\u003c/b\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e \u003cb\u003enear here]\u003c/b\u003e\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows the percentage contribution of the between- and within-region inequality components, based on Eq.\u0026nbsp;(\u003cspan refid=\"Equ1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The blue area represents within-region inequality, accounting for over 75% of the total. Within-inequality components are significantly present in Central and North Coast regions. This pattern remained consistent throughout the study period, indicating the persistent contribution of regional hubs to overall inequality. Central, Eastern, and Rift Valley regions have shown rising within-region inequalities over time, highlighting a growing intra-region gap in per capita GCP between richer and poorer counties.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003e[\u003c/b\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e \u003cb\u003enear here]\u003c/b\u003e\u003c/p\u003e \u003cp\u003eSince between-region inequality contributes less than 25% to overall inequality, we next examine the sectoral component within each region's inequality, denoted by \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\left(100\\times\\:{c}_{ik}\\right)\\)\u003c/span\u003e\u003c/span\u003e in Eq.\u0026nbsp;(\u003cspan refid=\"Equ5\" class=\"InternalRef\"\u003e5\u003c/span\u003e), in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. Central, North Coast, and Rift Valley regions consistently show substantial within-region inequality across all three years. AGR, MFG, CNS, THR, TIC, and FBS contribute significantly to inequality in those regions. However, the primary sector forming the within-region inequality varies by region.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn Central region, FBS was the most significant, followed by TIC, THR, MFG, and CNS. They contribute to the rise of within-region inequality and are more unevenly performed. Conversely, AGR remained consistently negative throughout the study period, which clearly indicates that it helps reduce within-region inequalities in the Central region. This is evidenced by the fact that counties with higher (lower) incomes have a lower (higher) agricultural presence. In North Coast region, TIC is the most significant, followed by FBS, THR, MFG, and CNS. They contribute to the rise of within-region inequality. This reflects Mombasa's influence as an international logistics hub, driving trade, tourism, and port activity. AGR remained consistently negative throughout the study period. In Rift Valley, AGR played the most significant role in increasing inequality. These findings identify key sectors that drive regional inequality and guide targeted policy interventions. In Central and North Coast regions, policies should promote more equitable development in FBS and TIC by expanding their presence beyond the regional hubs in Nairobi and Mombasa. In the Rift Valley, prioritizing the diffusion of agricultural benefits to neighboring counties can help mitigate their impact on regional inequality.\u003c/p\u003e \u003cp\u003eDespite initial expectations, year-to-year shifts in the regional inequality structure remained unchanged. Our variance comparison test confirmed that variations across the BDID 45 inequality components were consistent year-to-year, indicating the resilience of the inequality structure.\u003c/p\u003e"},{"header":"4. CONCLUSIONS","content":"\u003cp\u003eDespite significant variations in per capita GCP growth rates across counties during the COVID-19 pandemic and recovery, our analysis found no substantial changes in overall inter-county inequality. BDID analysis showed regional differences in industrial factors contributing to inequality. In the wealthier Central and North Coast regions, the FBS and TIC sectors were the primary contributors, while AGR was the most significant in Rift Valley. The regional inequality structure remained stable, indicating that the pandemic did not drastically increase regional disparities. However, the nearly 10-fold ratio between the minimum and maximum GCP per capita highlights substantial inequality, underscoring the need for government intervention to promote balanced regional development. Given Kenya's role as a major vegetable exporter to the EU and its strong horticulture sector, focusing on agricultural development in less wealthy regions could be a viable strategy (Tyce \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOur study has some limitations. First, we did not examine the growth transition path on multiple convergences. Using Phillips and Sul\u0026rsquo;s (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) club convergence analysis with long-term county data could address this issue. Second, incorporating spatial autocorrelation and spillovers into the analysis of regional income inequality is a promising direction. Additionally, exploring causal mechanisms with a supply-side growth model using production factor data could improve policy insights.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eDr. Kataoka wrote the main manuscript text and prepared Figures 1-4 and all tables. Mr. Kirugi collected the county-level data employed in this study and reviewed the geographical background and policy implications in Kenya.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThis work is supported by Grant-in-Aid for Scientific Research C (21K01469) from the Japan Society of the Promotion of Science.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003e10.6084/m9.figshare.27043354\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAjulu, R.: Politicized ethnicity, competitive politics and conflict in Kenya: A historical perspective. Afr. 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Sci. \u003cb\u003e10\u003c/b\u003e(3), 270\u0026ndash;289 (2020). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s13412-020-00608-7\u003c/span\u003e\u003cspan address=\"10.1007/s13412-020-00608-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"letters-in-spatial-and-resource-sciences","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"lsrs","sideBox":"Learn more about [Letters in Spatial and Resource Sciences](http://link.springer.com/journal/12076)","snPcode":"12076","submissionUrl":"https://submission.springernature.com/new-submission/12076/3","title":"Letters in Spatial and Resource Sciences","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Economic downturns, COVID-19 pandemic, regional income inequality, inequality decomposition, Kenya","lastPublishedDoi":"10.21203/rs.3.rs-5101160/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5101160/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eEconomic downturns affect regions unevenly, highlighting regional income disparities. While some studies find that downturns reduce regional income inequality, others report persistent or worsening disparities. Kenya, the seventh most populous nation in Africa, comprises 47 counties with diverse ethnic and geographical landscapes, where regional income inequality has attracted considerable public attention. This study examined the spatial distribution of per capita Gross County Product (GCP) together with the regional and industrial factors that influenced it both during and after the COVID-19 pandemic. The bi-dimensional inequality decomposition revealed that the industrial factors driving inequality differed regionally. In the wealthier two regions, the financial and business service sector in Central region and the transportation and communication sectors in North Coast region were the primary contributors to overall inequality. In Rift Valley region, agriculture played the most prominent role. These findings aid in identifying targeted policy interventions. Despite the significant variations in per capita GCP growth rates across counties during the pandemic and subsequent recovery, the study’s analysis confirmed that the regional inequality structure remained stable over the years. While the pandemic did not significantly increase regional disparities, the nearly 10-fold difference between the minimum and maximum GCP per capita highlights the persistently high levels of inequality. This underscores the urgent need for government intervention to promote more balanced regional development.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eJEL: D63 N97 O18 P25 R11 R12\u003c/strong\u003e\u003c/p\u003e","manuscriptTitle":"Persistent Regional Income Inequalities in Kenya During the COVID-19 Pandemic: Evidence from a Bi-Dimensional Inequality Decomposition Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-25 14:45:44","doi":"10.21203/rs.3.rs-5101160/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-11-06T15:56:42+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-11-06T13:49:26+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"125842550130016826255407752950504731156","date":"2024-11-04T18:08:28+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-09-20T23:43:07+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"155031674792154532517887823444834330288","date":"2024-09-20T22:52:53+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-09-18T13:38:58+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-09-18T05:44:52+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-09-18T05:44:09+00:00","index":"","fulltext":""},{"type":"submitted","content":"Letters in Spatial and Resource Sciences","date":"2024-09-17T06:53:20+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"letters-in-spatial-and-resource-sciences","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"lsrs","sideBox":"Learn more about [Letters in Spatial and Resource Sciences](http://link.springer.com/journal/12076)","snPcode":"12076","submissionUrl":"https://submission.springernature.com/new-submission/12076/3","title":"Letters in Spatial and Resource Sciences","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"d079c0c8-054a-42bb-93fe-d8635debbe28","owner":[],"postedDate":"December 25th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-03-24T16:07:46+00:00","versionOfRecord":{"articleIdentity":"rs-5101160","link":"https://doi.org/10.1007/s12076-025-00399-x","journal":{"identity":"letters-in-spatial-and-resource-sciences","isVorOnly":false,"title":"Letters in Spatial and Resource Sciences"},"publishedOn":"2025-03-18 15:58:27","publishedOnDateReadable":"March 18th, 2025"},"versionCreatedAt":"2024-12-25 14:45:44","video":"","vorDoi":"10.1007/s12076-025-00399-x","vorDoiUrl":"https://doi.org/10.1007/s12076-025-00399-x","workflowStages":[]},"version":"v1","identity":"rs-5101160","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5101160","identity":"rs-5101160","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}