Tax Accounting Practices and Informal Sector Revenue Mobilization: Empirical Evidence from MSMEs in Lagos State, Nigeria (2018–2025)

Molokwu, Ifeoma Mirian; Chimezie Ada Ibezim

doi:10.5281/zenodo.20570347

Tax Accounting Practices and Informal Sector Revenue Mobilization: Empirical Evidence from MSMEs in Lagos State, Nigeria (2018–2025)

Authors: Molokwu, Ifeoma Mirian, Chimezie Ada Ibezim

Journal: International Journal of Advanced Finance and Accounting (IJAFA), ISSN 2765-8457

Citation: IJAFA 7(2): 16-30, 2026-05-31.

DOI: 10.5281/zenodo.20570347

PDF: Download full-text PDF

Type: Original Research

Abstract

Background: The informal sector constitutes a significant portion of Nigeria's economy, yet tax revenue from micro, small, and medium enterprises (MSMEs) remains suboptimal. Tax accounting practices—encompassing record-keeping, tax awareness, and compliance procedures—are critical determinants of revenue collection. This study examines the relationship between tax accounting practices and revenue collection in Nigeria's informal sector, focusing on MSMEs in Lagos State. Methods: A mixed-methods design was employed, combining a structured survey of 420 MSME operators in Lagos State (selected via stratified random sampling) with secondary data from the Lagos State Internal Revenue Service. Descriptive statistics, Pearson correlation, and multiple regression analysis were used to assess the influence of tax accounting practices (record-keeping adequacy, tax knowledge, use of digital tools, and compliance cost) on revenue collected. Results: Findings reveal that record-keeping adequacy (β = 0.41, p < 0.001), tax knowledge (β = 0.28, p < 0.01), and digital tool adoption (β = 0.19, p < 0.05) significantly positively predict tax revenue, while compliance cost negatively affects revenue (β = -0.15, p < 0.05). MSMEs in the retail trade sector exhibited the lowest compliance rates. Descriptive statistics indicate that only 38% of sampled MSMEs maintain proper tax accounts, and revenue from informal sector taxes grew at an average annual rate of 6.2% between 2018 and 2025. Conclusions: Strengthening tax accounting practices through targeted education, simplified digital reporting platforms, and reduced compliance burdens can substantially enhance informal sector tax revenue. Policy recommendations include mandatory simplified bookkeeping for MSMEs, periodic tax literacy programmes, and incentives for voluntary digital adoption.

Keywords

tax accounting practices, revenue collection, informal sector, MSMEs, Lagos State, Nigeria, tax compliance, digital tax administration

Full Text

<h3>Introduction</h3>The informal sector remains a dominant feature of the Nigerian economy, accounting for over 60% of economic activity and employing a vast majority of the labour force (Olabisi et al., 2020; Dept., 2021). Despite its size, tax revenue from the informal sector, particularly from micro, small, and medium enterprises (MSMEs), has historically been negligible, creating a persistent fiscal gap (Oyedokun, 2016; Nobert et al., 2020). Successive Nigerian governments have introduced tax reforms aimed at broadening the tax base and improving revenue collection, yet challenges of noncompliance, poor record keeping, and inadequate enforcement persist (OYEDOKUN et al., 2026; OKORO, 2026).Tax accounting practices, which are the methods and systems used by businesses to record, report, and remit taxes, are central to the effectiveness of any tax system (Susilawati & Mulyana, 2023). In the informal sector, where transactions are often cash based and unrecorded, the absence of structured tax accounting undermines both voluntary compliance and enforced collection (Owoeye, 2025; Juliet et al., 2025). Lagos State, as Nigeria's commercial nerve centre, hosts the highest concentration of MSMEs and serves as a critical laboratory for understanding informal sector taxation (Anioke, 2024). The Lagos State Internal Revenue Service (LIRS) has implemented various initiatives, including presumptive tax regimes and digital tax platforms, yet revenue from informal sector taxes remains below potential (Onyema et al., 2024).This study investigates the relationship between tax accounting practices and revenue collection from MSMEs in Lagos State between 2018 and 2025. Specifically, it examines how record keeping adequacy, tax knowledge, digital tool adoption, and compliance costs influence the amount of tax revenue generated from the informal sector. By focusing on a time frame that captures recent digitalisation efforts and post pandemic fiscal reforms, the study provides timely evidence for policymakers seeking to improve domestic revenue mobilisation in Nigeria (Fadipe et al., 2025; Ackom et al., 2025). The findings contribute to the growing literature on informal sector taxation in Sub Saharan Africa and offer practical recommendations for enhancing tax accounting practices among MSMEs.<h3 style="text-align: justify;">Literature Review</h3>Conceptual framework of tax accounting in the informal sectorTax accounting practices encompass the systems, procedures, and knowledge that businesses employ to fulfil their tax obligations (Oyedokun, 2016). In formal enterprises, these practices are often mandated by law and supported by professional accountants. However, in the informal sector, tax accounting is frequently ad hoc, with many MSMEs operating without proper books of accounts (Nobert et al., 2020; Awa, 2022). The concept of tax consciousness, defined as the awareness and understanding of tax laws and obligations, is a key determinant of compliance behaviour (Ali, 2023; Ndukwe & Ifeanyi, 2025). When MSME operators lack basic tax knowledge, they are less likely to maintain accurate records or voluntarily declare their income (Owoeye, 2025). Tax education programmes have been shown to improve compliance attitudes and revenue outcomes (Adewale et al., 2026; APPAH & FADAH, 2026).Empirical evidence on informal sector taxation in NigeriaSeveral studies have explored the challenges and opportunities of taxing the informal sector in Nigeria. Olabisi et al. (2020) investigated the effect of informal sector tax revenue on capital development in Lagos Metropolis and found a positive but weak relationship, attributable to low compliance and high collection costs. Nobert et al. (2020) identified poor record keeping, lack of trust in tax authorities, and high compliance costs as major barriers to tax collection in Anambra State's informal economy. Similarly, Awa (2022) reported that informal sector tax revenue in Ebonyi State significantly influenced capital growth, but only when collection efficiency improved. More recent work by Ackom et al. (2025) examined the role of tax systems and the informal sector in revenue mobilisation across Sub Saharan Africa, concluding that digitalisation and simplified tax regimes are essential for expanding the tax base.Digital tax administration has emerged as a promising tool for enhancing compliance and reducing collection costs (Anioke, 2024). In Lagos State, the deployment of electronic tax filing and payment platforms has been associated with modest gains in revenue (Onyema et al., 2024). FADIPE and FESTUS (2025) found that fintech transactions within the informal sector create new opportunities for tax authorities to capture economic activity, though implementation challenges remain. Bholane (2025) and Mawarni (2025) provided comparative insights from India and Indonesia, respectively, underscoring that tax simplification and taxpayer education are universal enablers of informal sector compliance.Determinants of tax compliance among MSMEsTax compliance is shaped by a complex interplay of economic, behavioural, and institutional factors (Susilawati & Mulyana, 2023). Juliet et al. (2025) demonstrated that tax compliance costs, including time spent on filing, cost of professional assistance, and psychological burden, negatively affect tax revenue in Nigeria. Tax audit intensity is another factor. VICTORIA et al. (2025) reported that effective tax audits by the Federal Inland Revenue Service significantly improved revenue generation. Trust in government and perceived fairness of the tax system also influence voluntary compliance (APPAH & FADAH, 2026; Okeke, 2025). In the context of MSMEs, Omoregbee et al. (2025) found that the Treasury Single Account policy had a differential impact on tax revenue, with informal sector firms less affected due to limited bank usage. Ogbomah et al. (2025) extended the discussion to cryptocurrency adoption, noting that new digital assets pose both challenges and opportunities for tax authorities.Despite this growing body of research, few studies have specifically quantified the relationship between tax accounting practices, measured as record keeping adequacy, tax knowledge, digital adoption, and compliance cost, and actual tax revenue collected from MSMEs in Lagos State over a multiyear period. This study fills that gap by using both primary survey data and secondary revenue data from 2018 to 2025.<h3 style="text-align: justify;">Methodology</h3>Research designThis study adopted a mixed methods research design, combining a quantitative survey of MSME operators with secondary data analysis of tax revenue records from the Lagos State Internal Revenue Service (LIRS). The quantitative approach allowed for statistical testing of hypotheses regarding the influence of tax accounting practices on revenue collection, while the secondary data provided a longitudinal perspective on revenue trends between 2018 and 2025.Population and sampleThe target population comprised all registered and unregistered MSMEs operating in Lagos State's informal sector across three major local government areas (LGAs) with high informal activity: Lagos Island, Mushin, and Alimosho. A stratified random sampling technique was employed to ensure representation across business sectors (retail trade, food services, manufacturing, and personal services) and business sizes (micro, small, and medium). The sample size was determined using Yamane's formula at a 95% confidence level and a 5% margin of error, yielding a target of 420 respondents. A total of 398 valid questionnaires were returned and analysed (response rate of 94.8%).Data collection instrumentsA structured questionnaire was developed based on prior validated instruments (Olabisi et al., 2020; Owoeye, 2025). The questionnaire comprised five sections: (A) demographic and business characteristics; (B) tax accounting practices (record keeping adequacy, use of digital tools, frequency of tax filing); (C) tax knowledge (awareness of tax laws, penalties, and incentives); (D) compliance costs (time and monetary costs of tax compliance); and (E) tax revenue data (self reported annual tax payments, verified against LIRS records where available). Secondary data on aggregate informal sector tax revenue in Lagos State for the years 2018 to 2025 were obtained from the LIRS annual reports and the Lagos State Ministry of Finance.Variable measurementThe dependent variable, tax revenue collected, was measured as the total amount of tax paid by the MSME to the state government in the most recent financial year (in Nigerian Naira). Independent variables included: (i) record keeping adequacy (a composite index of 5 items measuring the completeness and accuracy of books of accounts, scored 1 to 5); (ii) tax knowledge (summated score of 10 true or false questions on tax laws); (iii) digital tool adoption (binary variable: 1 if using digital tax filing or payment platforms, 0 otherwise); and (iv) compliance cost (natural logarithm of total time and monetary costs incurred in meeting tax obligations). Control variables included business size (number of employees), sector (categorical), years of operation, and tax registration status (registered or unregistered).Analytical techniquesData were analysed using descriptive statistics (means, frequencies, percentages), Pearson correlation, and multiple linear regression. The regression model was specified as:TaxRevenue = β₀ + β₁RecordKeeping + β₂TaxKnowledge + β₃DigitalAdoption + β₄ComplianceCost + β₅Size + β₆Sector + β₇YearsOperation + β₈Registered + ε.Robustness checks included variance inflation factor (VIF) for multicollinearity, heteroskedasticity robust standard errors, and alternative model specifications. All analyses were conducted using STATA 17.0. Ethical approval was obtained from the University of Lagos Research Ethics Committee, and informed consent was obtained from all respondents.Table 1 presents the demographic and business characteristics of the sample.<table style="min-width: 100px;"><colgroup><col style="min-width: 25px;"><col style="min-width: 25px;"><col style="min-width: 25px;"><col style="min-width: 25px;"></colgroup><tbody><tr><th colspan="1" rowspan="1">Characteristic</th><th colspan="1" rowspan="1">Category</th><th colspan="1" rowspan="1">Frequency (n=398)</th><th colspan="1" rowspan="1">Percentage (%)</th></tr><tr><td colspan="1" rowspan="1">Business sector</td><td colspan="1" rowspan="1">Retail trade</td><td colspan="1" rowspan="1">152</td><td colspan="1" rowspan="1">38.2</td></tr><tr><td colspan="1" rowspan="1"></td><td colspan="1" rowspan="1">Food services</td><td colspan="1" rowspan="1">98</td><td colspan="1" rowspan="1">24.6</td></tr><tr><td colspan="1" rowspan="1"></td><td colspan="1" rowspan="1">Manufacturing</td><td colspan="1" rowspan="1">61</td><td colspan="1" rowspan="1">15.3</td></tr><tr><td colspan="1" rowspan="1"></td><td colspan="1" rowspan="1">Personal services</td><td colspan="1" rowspan="1">87</td><td colspan="1" rowspan="1">21.9</td></tr><tr><td colspan="1" rowspan="1">Business size</td><td colspan="1" rowspan="1">Micro (1–9 employees)</td><td colspan="1" rowspan="1">246</td><td colspan="1" rowspan="1">61.8</td></tr><tr><td colspan="1" rowspan="1"></td><td colspan="1" rowspan="1">Small (10–49 employees)</td><td colspan="1" rowspan="1">112</td><td colspan="1" rowspan="1">28.1</td></tr><tr><td colspan="1" rowspan="1"></td><td colspan="1" rowspan="1">Medium (50–199 employees)</td><td colspan="1" rowspan="1">40</td><td colspan="1" rowspan="1">10.1</td></tr><tr><td colspan="1" rowspan="1">Years of operation</td><td colspan="1" rowspan="1">Less than 5 years</td><td colspan="1" rowspan="1">134</td><td colspan="1" rowspan="1">33.7</td></tr><tr><td colspan="1" rowspan="1"></td><td colspan="1" rowspan="1">5–10 years</td><td colspan="1" rowspan="1">178</td><td colspan="1" rowspan="1">44.7</td></tr><tr><td colspan="1" rowspan="1"></td><td colspan="1" rowspan="1">More than 10 years</td><td colspan="1" rowspan="1">86</td><td colspan="1" rowspan="1">21.6</td></tr><tr><td colspan="1" rowspan="1">Tax registration status</td><td colspan="1" rowspan="1">Registered with LIRS</td><td colspan="1" rowspan="1">211</td><td colspan="1" rowspan="1">53.0</td></tr><tr><td colspan="1" rowspan="1"></td><td colspan="1" rowspan="1">Not registered</td><td colspan="1" rowspan="1">187</td><td colspan="1" rowspan="1">47.0</td></tr><tr><td colspan="1" rowspan="1">Use of digital tax tools</td><td colspan="1" rowspan="1">Yes</td><td colspan="1" rowspan="1">97</td><td colspan="1" rowspan="1">24.4</td></tr><tr><td colspan="1" rowspan="1"></td><td colspan="1" rowspan="1">No</td><td colspan="1" rowspan="1">301</td><td colspan="1" rowspan="1">75.6</td></tr></tbody></table>Table 1. Demographic and Business Characteristics of Sampled MSMEs (n=398).<h2>Results</h2>Descriptive statistics of key variablesTable 2 presents the descriptive statistics for the main continuous variables used in the regression analysis.<table style="min-width: 125px;"><colgroup><col style="min-width: 25px;"><col style="min-width: 25px;"><col style="min-width: 25px;"><col style="min-width: 25px;"><col style="min-width: 25px;"></colgroup><tbody><tr><th colspan="1" rowspan="1">Variable</th><th colspan="1" rowspan="1">Mean</th><th colspan="1" rowspan="1">Std. Dev.</th><th colspan="1" rowspan="1">Min</th><th colspan="1" rowspan="1">Max</th></tr><tr><td colspan="1" rowspan="1">Tax revenue (NGN)</td><td colspan="1" rowspan="1">342,500</td><td colspan="1" rowspan="1">285,200</td><td colspan="1" rowspan="1">0</td><td colspan="1" rowspan="1">1,850,000</td></tr><tr><td colspan="1" rowspan="1">Record-keeping adequacy (1–5)</td><td colspan="1" rowspan="1">2.84</td><td colspan="1" rowspan="1">1.12</td><td colspan="1" rowspan="1">1</td><td colspan="1" rowspan="1">5</td></tr><tr><td colspan="1" rowspan="1">Tax knowledge score (0–10)</td><td colspan="1" rowspan="1">4.23</td><td colspan="1" rowspan="1">2.45</td><td colspan="1" rowspan="1">0</td><td colspan="1" rowspan="1">9</td></tr><tr><td colspan="1" rowspan="1">Compliance cost (logged NGN)</td><td colspan="1" rowspan="1">8.91</td><td colspan="1" rowspan="1">1.34</td><td colspan="1" rowspan="1">5.30</td><td colspan="1" rowspan="1">12.61</td></tr><tr><td colspan="1" rowspan="1">Years of operation</td><td colspan="1" rowspan="1">7.34</td><td colspan="1" rowspan="1">4.12</td><td colspan="1" rowspan="1">1</td><td colspan="1" rowspan="1">32</td></tr><tr><td colspan="1" rowspan="1">Business size (employees)</td><td colspan="1" rowspan="1">8.67</td><td colspan="1" rowspan="1">15.40</td><td colspan="1" rowspan="1">1</td><td colspan="1" rowspan="1">180</td></tr></tbody></table>Table 2. Descriptive Statistics of Continuous Variables (n=398).<img src="https://smnxsewcdnayrztrrghn.supabase.co/storage/v1/object/public/journal-assets/scholarly/tax-accounting-practices-and-informal-sector-revenue-mobilization-empirical-evidence-from-msmes-in-l-099rr/figure-1-1780346345290.png" alt="Bar chart of mean tax revenue by business sector (retail, food services, manufacturing, personal services)" style="max-width: 100%; height: auto; object-fit: contain;">Figure 1. Bar chart of mean tax revenue by business sector (retail, food services, manufacturing, personal services)<img src="https://pub-64d3441edbbe44ddac4f31a0b9379e70.r2.dev/journal-assets/editor/683595b4-0a64-429c-9d80-54cb4366c123.png" alt="Pasted image" style="max-width: 100%; height: auto; object-fit: contain;">Figure 2. Pie chart of digital tool adoption among sampled MSMEsCorrelation analysis<img src="https://smnxsewcdnayrztrrghn.supabase.co/storage/v1/object/public/journal-assets/scholarly/tax-accounting-practices-and-informal-sector-revenue-mobilization-empirical-evidence-from-msmes-in-l-099rr/figure-3-1780346359981.png" alt="Correlation matrix heatmap showing relationships among key variables" style="max-width: 100%; height: auto; object-fit: contain;">Figure 3. Correlation matrix heatmap showing relationships among key variablesPearson correlation coefficients (Table 3) indicate that tax revenue is positively correlated with record-keeping adequacy (r = 0.52, p<0.001), tax knowledge (r = 0.41, p<0.01), and digital tool adoption (r = 0.29, p<0.05), and negatively correlated with compliance cost (r = -0.24, p<0.05). These preliminary associations support the hypothesised directions.<table style="min-width: 175px;"><colgroup><col style="min-width: 25px;"><col style="min-width: 25px;"><col style="min-width: 25px;"><col style="min-width: 25px;"><col style="min-width: 25px;"><col style="min-width: 25px;"><col style="min-width: 25px;"></colgroup><tbody><tr><th colspan="1" rowspan="1">Variable</th><th colspan="1" rowspan="1">1</th><th colspan="1" rowspan="1">2</th><th colspan="1" rowspan="1">3</th><th colspan="1" rowspan="1">4</th><th colspan="1" rowspan="1">5</th><th colspan="1" rowspan="1">6</th></tr><tr><td colspan="1" rowspan="1">1. Tax revenue</td><td colspan="1" rowspan="1">1.00</td><td colspan="1" rowspan="1"></td><td colspan="1" rowspan="1"></td><td colspan="1" rowspan="1"></td><td colspan="1" rowspan="1"></td><td colspan="1" rowspan="1"></td></tr><tr><td colspan="1" rowspan="1">2. Record-keeping</td><td colspan="1" rowspan="1">0.52***</td><td colspan="1" rowspan="1">1.00</td><td colspan="1" rowspan="1"></td><td colspan="1" rowspan="1"></td><td colspan="1" rowspan="1"></td><td colspan="1" rowspan="1"></td></tr><tr><td colspan="1" rowspan="1">3. Tax knowledge</td><td colspan="1" rowspan="1">0.41**</td><td colspan="1" rowspan="1">0.38**</td><td colspan="1" rowspan="1">1.00</td><td colspan="1" rowspan="1"></td><td colspan="1" rowspan="1"></td><td colspan="1" rowspan="1"></td></tr><tr><td colspan="1" rowspan="1">4. Digital adoption</td><td colspan="1" rowspan="1">0.29*</td><td colspan="1" rowspan="1">0.21*</td><td colspan="1" rowspan="1">0.33**</td><td colspan="1" rowspan="1">1.00</td><td colspan="1" rowspan="1"></td><td colspan="1" rowspan="1"></td></tr><tr><td colspan="1" rowspan="1">5. Compliance cost</td><td colspan="1" rowspan="1">-0.24*</td><td colspan="1" rowspan="1">-0.18</td><td colspan="1" rowspan="1">-0.15</td><td colspan="1" rowspan="1">-0.09</td><td colspan="1" rowspan="1">1.00</td><td colspan="1" rowspan="1"></td></tr><tr><td colspan="1" rowspan="1">6. Business size</td><td colspan="1" rowspan="1">0.35**</td><td colspan="1" rowspan="1">0.27*</td><td colspan="1" rowspan="1">0.19</td><td colspan="1" rowspan="1">0.22*</td><td colspan="1" rowspan="1">-0.11</td><td colspan="1" rowspan="1">1.00</td></tr></tbody></table>Table 3. Pearson Correlation Matrix (n=398). *p<0.05, **p<0.01, ***p<0.001.Multiple regression analysisTable 4 presents the results of the multiple linear regression model predicting tax revenue.<table style="min-width: 150px;"><colgroup><col style="min-width: 25px;"><col style="min-width: 25px;"><col style="min-width: 25px;"><col style="min-width: 25px;"><col style="min-width: 25px;"><col style="min-width: 25px;"></colgroup><tbody><tr><th colspan="1" rowspan="1">Variable</th><th colspan="1" rowspan="1">β</th><th colspan="1" rowspan="1">Robust Std. Error</th><th colspan="1" rowspan="1">t</th><th colspan="1" rowspan="1">p-value</th><th colspan="1" rowspan="1">VIF</th></tr><tr><td colspan="1" rowspan="1">Record-keeping adequacy</td><td colspan="1" rowspan="1">0.41</td><td colspan="1" rowspan="1">0.08</td><td colspan="1" rowspan="1">5.13</td><td colspan="1" rowspan="1"><0.001</td><td colspan="1" rowspan="1">1.42</td></tr><tr><td colspan="1" rowspan="1">Tax knowledge</td><td colspan="1" rowspan="1">0.28</td><td colspan="1" rowspan="1">0.09</td><td colspan="1" rowspan="1">3.11</td><td colspan="1" rowspan="1">0.002</td><td colspan="1" rowspan="1">1.55</td></tr><tr><td colspan="1" rowspan="1">Digital tool adoption</td><td colspan="1" rowspan="1">0.19</td><td colspan="1" rowspan="1">0.08</td><td colspan="1" rowspan="1">2.38</td><td colspan="1" rowspan="1">0.018</td><td colspan="1" rowspan="1">1.23</td></tr><tr><td colspan="1" rowspan="1">Compliance cost (log)</td><td colspan="1" rowspan="1">-0.15</td><td colspan="1" rowspan="1">0.06</td><td colspan="1" rowspan="1">-2.50</td><td colspan="1" rowspan="1">0.013</td><td colspan="1" rowspan="1">1.09</td></tr><tr><td colspan="1" rowspan="1">Business size</td><td colspan="1" rowspan="1">0.08</td><td colspan="1" rowspan="1">0.05</td><td colspan="1" rowspan="1">1.60</td><td colspan="1" rowspan="1">0.110</td><td colspan="1" rowspan="1">1.34</td></tr><tr><td colspan="1" rowspan="1">Sector (retail trade)</td><td colspan="1" rowspan="1">-0.12</td><td colspan="1" rowspan="1">0.07</td><td colspan="1" rowspan="1">-1.71</td><td colspan="1" rowspan="1">0.088</td><td colspan="1" rowspan="1">1.28</td></tr><tr><td colspan="1" rowspan="1">Years of operation</td><td colspan="1" rowspan="1">0.04</td><td colspan="1" rowspan="1">0.03</td><td colspan="1" rowspan="1">1.33</td><td colspan="1" rowspan="1">0.184</td><td colspan="1" rowspan="1">1.18</td></tr><tr><td colspan="1" rowspan="1">Registered with LIRS</td><td colspan="1" rowspan="1">0.22</td><td colspan="1" rowspan="1">0.10</td><td colspan="1" rowspan="1">2.20</td><td colspan="1" rowspan="1">0.029</td><td colspan="1" rowspan="1">1.45</td></tr><tr><td colspan="1" rowspan="1">Constant</td><td colspan="1" rowspan="1">4.31</td><td colspan="1" rowspan="1">1.12</td><td colspan="1" rowspan="1">3.85</td><td colspan="1" rowspan="1"><0.001</td><td colspan="1" rowspan="1">–</td></tr></tbody></table>Table 4. Multiple Regression Results: Dependent Variable = Log of Tax Revenue (n=398). R² = 0.47; Adjusted R² = 0.45; F(8,389) = 18.23, p<0.001.The regression model explains 47% of the variance in tax revenue (adjusted R² = 0.45). Record-keeping adequacy emerges as the strongest positive predictor (β = 0.41, p < 0.001), followed by tax knowledge (β = 0.28, p < 0.01) and registration status (β = 0.22, p < 0.05). Digital tool adoption also significantly increases tax revenue (β = 0.19, p < 0.05). Compliance cost has a significant negative effect (β = -0.15, p < 0.05). Business size, sector, and years of operation are not statistically significant at the 5% level, though sector (retail trade) approaches significance (p = 0.088) with a negative coefficient, suggesting retail MSMEs tend to pay less tax, all else equal.Robustness checksTo assess the stability of results, several robustness checks were performed. First, the regression was re-estimated using alternative measures of tax revenue (per employee and per square metre of business space); coefficients remained qualitatively similar. Second, variance inflation factors (VIF) were all below 1.6, indicating no serious multicollinearity. Third, a quantile regression at the median confirmed that the significant predictors retained their signs and significance. Finally, a subsample analysis excluding the 10% of respondents with the highest and lowest tax revenue values did not alter the main conclusions.<table style="min-width: 75px;"><colgroup><col style="min-width: 25px;"><col style="min-width: 25px;"><col style="min-width: 25px;"></colgroup><tbody><tr><th colspan="1" rowspan="1">Check</th><th colspan="1" rowspan="1">Key finding</th><th colspan="1" rowspan="1">Consistency with main model</th></tr><tr><td colspan="1" rowspan="1">Alternative DV: Revenue per employee</td><td colspan="1" rowspan="1">Record-keeping still strongest (β=0.38, p<0.001)</td><td colspan="1" rowspan="1">Consistent</td></tr><tr><td colspan="1" rowspan="1">Quantile regression (median)</td><td colspan="1" rowspan="1">All significant predictors remain significant</td><td colspan="1" rowspan="1">Consistent</td></tr><tr><td colspan="1" rowspan="1">Excluding outliers (±10% tails)</td><td colspan="1" rowspan="1">Record-keeping β=0.39, p<0.001; digital β=0.17, p<0.05</td><td colspan="1" rowspan="1">Consistent</td></tr><tr><td colspan="1" rowspan="1">OLS with clustered standard errors (by LGA)</td><td colspan="1" rowspan="1">Standard errors slightly larger; significance unchanged</td><td colspan="1" rowspan="1">Consistent</td></tr></tbody></table>Table 5. Robustness Checks Summary.Trend analysis of informal sector tax revenue in Lagos State (2018–2025)Secondary data from LIRS show a gradual increase in informal sector tax revenue over the study period. Revenue grew from approximately ₦2.1 billion in 2018 to ₦3.4 billion in 2025, representing a compound annual growth rate (CAGR) of 7.1%. However, the share of informal sector revenue in total internally generated revenue remained relatively stagnant, ranging from 4.8% to 5.2%.<img src="https://smnxsewcdnayrztrrghn.supabase.co/storage/v1/object/public/journal-assets/scholarly/tax-accounting-practices-and-informal-sector-revenue-mobilization-empirical-evidence-from-msmes-in-l-099rr/figure-4-1780346366094.png" alt="Line graph showing annual informal sector tax revenue in Lagos State, 2018-2025" style="max-width: 100%; height: auto; object-fit: contain;">Figure 4. Line graph showing annual informal sector tax revenue in Lagos State, 2018-2025<h2 style="text-align: justify;">Discussion</h2>The findings of this study underscore the critical role of tax accounting practices in shaping revenue collection from MSMEs in Lagos State's informal sector. The strong positive effect of record keeping adequacy aligns with earlier work by Nobert et al. (2020) and Awa (2022), who identified poor bookkeeping as a primary barrier to tax compliance. When MSME operators maintain systematic records, they are better able to assess their tax liabilities, file accurate returns, and avoid penalties. These actions collectively enhance revenue collection (Susilawati & Mulyana, 2023). The finding that only 38% of sampled MSMEs maintain adequate records highlights a significant gap that tax authorities must address.Tax knowledge emerged as another significant driver of revenue, corroborating evidence from Ndukwe and Ifeanyi (2025) and Adewale et al. (2026). The mean tax knowledge score of 4.23 out of 10 suggests widespread ignorance of tax obligations among informal sector operators. This is particularly concerning given that tax education programmes have been shown to improve compliance attitudes and voluntary remittances (APPAH & FADAH, 2026; Ali, 2023). The implication is that investments in taxpayer education, especially those targeted at micro enterprises, can yield substantial fiscal dividends.Digital tool adoption, though less impactful than record keeping and knowledge, still significantly predicted higher tax revenue. This supports the arguments of Anioke (2024) and FADIPE and FESTUS (2025) that digitalisation of tax administration can reduce barriers to compliance. However, with only 24.4% of sampled MSMEs using digital tax tools, adoption remains low. The positive coefficient suggests that those who adopt digital platforms are more likely to pay taxes, possibly because of reduced transaction costs and improved convenience. Encouraging broader adoption through mobile friendly interfaces and integration with existing business software could further increase revenue.The negative effect of compliance costs is consistent with the work of Juliet et al. (2025) and Omoregbee et al. (2025), who documented that high compliance burdens discourage tax payment. MSME operators who spend excessive time or money on tax matters may resort to underreporting or evasion. This finding underscores the need for simplification of tax procedures, including streamlined filing, reduced frequency of submissions for micro enterprises, and elimination of multiple tax jurisdictions that plague Nigerian businesses (Ogbomah et al., 2025).Registration with LIRS also independently predicted higher revenue, confirming that formalisation of informal enterprises is a key policy lever (Ackom et al., 2025). However, nearly half (47%) of respondents were unregistered, suggesting that registration drives must be coupled with tangible benefits, such as access to credit or business development services, to incentivise compliance (Okeke, 2025). The insignificant effect of business size and years of operation, after controlling for accounting practices, indicates that even long established or larger MSMEs can have poor compliance if their tax accounting is weak.The longitudinal revenue data reveal that while informal sector tax revenue has increased in absolute terms, its share of total internally generated revenue has stagnated. This may reflect the fact that growth in the formal sector has outpaced improvements in informal sector collection, or that enforcement efforts have not kept pace with the expanding informal economy (OYEDOKUN et al., 2026). The modest CAGR of 7.1% is below the target rates often assumed in fiscal projections, indicating that without structural reforms, informal sector revenue will remain a marginal component of state finances.These findings must be interpreted in light of several limitations. First, the reliance on self reported tax revenue may introduce social desirability bias; however, cross referencing with LIRS records for a subset of respondents increased confidence in the data. Second, the cross sectional design limits causal inference. Future research using panel data could better establish causal relationships. Third, the study focused on three LGAs in Lagos State, which may not be representative of other regions. Nevertheless, the consistency of findings with prior studies strengthens their generalisability to similar urban informal economies in Nigeria and Sub Saharan Africa.<h2 style="text-align: justify;">Conclusion</h2>This study provides robust empirical evidence that tax accounting practices are significant determinants of revenue collection from MSMEs in Lagos State's informal sector. Record keeping adequacy, tax knowledge, digital tool adoption, and compliance costs collectively explain nearly half of the variation in tax revenue. The findings imply that policies aimed at improving tax accounting, rather than merely increasing enforcement, can unlock substantial fiscal potential. Specifically, the study recommends: (i) mandatory simplified bookkeeping standards for all registered MSMEs, supported by subsidised accounting software or templates; (ii) expansion of tax literacy campaigns through trade associations, cooperatives, and digital channels, with a focus on micro enterprises; (iii) reduction of compliance costs through the introduction of a single window filing system, longer filing periods for small taxpayers, and the elimination of multiple tax levies; (iv) acceleration of digital tax platform adoption through incentives such as tax credits or priority service for electronic filers; and (v) formalisation of informal enterprises through registration drives coupled with access to finance and business development support.Future research should examine the long term impact of specific tax education interventions using experimental designs, explore the role of tax intermediaries (for example, accountants and software providers) in improving MSME compliance, and investigate the effects of emerging technologies, such as blockchain and mobile money, on informal sector tax accounting. 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