Estimation of the sub-national fiscal potential of WAEMU countries using satellite images of nighttime lights data

Estimation of the sub-national fiscal potential of WAEMU countries using satellite images of nighttime lights data | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Estimation of the sub-national fiscal potential of WAEMU countries using satellite images of nighttime lights data LAWIN Laifoya Moise This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6329840/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This study assesses the sub-national fiscal potential of eight (8) West African Economic and Monetary Union (UEMOA) countries from 2012 to 2022, using satellite data on total night lighting (TNL). Three-step method have been used : (i) estimation of national fiscal potential using the Stochastic Frontier Analysis (SFA) method; (ii) examination of the relationship between TNL and fiscal potential using the Granger causality test and the GMM model in panel data; (iii) estimation of sub-national fiscal potential. The results reveal the existence of a causal link and a positive and significant effect of night lighting on fiscal potential. Findings are showing that night lighting is a reliable indicator for estimating and allocating fiscal potential at sub-national level. The existence of bidirectional causality between TNL and fiscal potential underlines the need for tax administration in WAEMU countries to implement an integrated approach that takes into account both economic and institutional dynamics. The persistence of fiscal potential indicates that tax policies in UEMOA countries need to be designed for the long term to maximize their impact on economic development. Tax Law Development Economics Fiscal potential tax effort WAEMU countries satellite images nighttime lights Introduction In recent years, mobilizing domestic public resources to support development has become a major challenge for developing countries, in the face of tightening external financing (Benitez et al., 2023 ). West African Economic and Monetary Union (UEMOA) countries, like many other developing countries, are facing growing challenges in terms of domestic resource mobilization. Statistics (tax revenues mobilized as a % of GDP) are stagnating, calling for reforms and interventions in which the contribution of all players (public and private sector) is critical. Data from the Central Bank of West African States (CBWAS) show that tax revenues as a proportion of GDP averaged between 9.68% and 13.5% in the WAEMU over the period 2001–2022, compared with a minimum convergence threshold set by WAEMU countries at 20% of GDP. This low level of tax revenue mobilized by developing countries further increases budget deficits and public debt, and slows the speed toward Sustainable Development Goals achievements. To understand and mitigate these challenges, it is essential to examine the fiscal potential of WAEMU countries on a daily basis, in order to better coordinate efforts to mobilize national public resources. Alfirman ( 2003 ) defines fiscal potential as the tax ratio that would result from an economy's use of all its resources and its capacity to collect all possible tax revenues. From a technical point of view, fiscal potential corresponds to the maximum amount or capacity of tax revenues that an economy can mobilize, given its institutional, demographic and economic characteristics (Caldeira et al., 2019 ). Existing literature reveals that in developing countries, tax revenues are influenced by the preponderance of the informal sector in the economy, generous tax incentives, governance problems, structural bottlenecks, tax policy and administrative limitations (Mawejje et al., 2019), inadequate administrative infrastructure (Mascagni et al, 2014 , Doghmi, 2020 ; Mallick, 2021 ) and poor use of information and communication technologies (ICT) in the revenue mobilization process (Mascagni et al, 2014 ; Canares, 2016 ). A major limitation of the existing literature is that most studies are focused on national tax potential, neglecting sub-national disparities in tax potential. And yet, for better mobilization of tax revenues, it is essential to have a finer estimate of tax potential at level two and three of administrative divisions. However, the unavailability of disaggregated economic data is an obstacle to this sub-national estimation of tax potential. How can fiscal potential be estimated and distributed at sub-national level in the context where the lack of disaggregated economic data is prevailing? Specifically, to what extend can night lights, commonly used to estimate sub-national GDPs, constitute a relevant proxy for breaking down national fiscal potential between administrative divisions? This question highlights the problem of sub-national estimation of fiscal potential in a context of unavailability of disaggregated data. This study tends to respond whether night lights can be used as a proxy for sub-national fiscal potential estimation. In literature, the assessment of fiscal potential is generally carried out at national level and justified by the gap of data at lower administrative levels. This constraint on sub-national data availability often prevents a more refined analysis of sub-national fiscal disparities. To overcome this gap in the literature, we employ exploiting night-light data as a proxy to map local or sub-national economic activity. This approach is based on the idea that satellite-captured light intensity is correlated with economic activity and, by extension, potential fiscal capacity. By using sub-national night lights to break down national fiscal potential between administrative divisions, we propose an empirical solution that address the challenge of lack of disaggregated data, while providing a better understanding of territorial fiscal disparities. Total night lighting (TNL) offers the potential to measure economic activity beyond formal GDP (Farzanegan et al., 2021, Farzanegan et al., 2019; Ghosh et al., 2009 ; Tanaka et al., 2017). TNLs can measure regional inequalities in public services in real time (Zhou et al., 2015 ; Li et al., 2021) and have been used to predict or estimate national and sub-national GDP and industrial sector output in several countries (Henderson, 2012; Farzanegan et al., 2019; Tanaka et al., 2017; Gu et al., 2022 ; Liang et al., 2020 ). In addition to its contribution to the literature, this study provides countries with a tool that can be used to determine the level of tax revenue to be mobilized by region of the country. In this study, we adopt a standardized plan. After the introduction, we present the literature review, followed by the methodological approach used. In a joint-section, results are presented and discussed before conclusion and recommendation. Literature review This section will present theorical and empirical literature review Theorical literature review The tax potential or tax frontier is based on a number of theories, notably : 1. theory of taxpaying capacity, 2. theory of tax efficiency (or tax effort), 3. theory of tax decentralization, and 4. theory of tax modernization and technological innovation. Theory of taxpaying capacity Advocated by Musgrave et al. (1989), the tax capacity theory is based on the principle that each agent or economic entity should contribute to tax revenues according to its financial capacity. For Musgrave et al. (1989), fiscal potential is determined by income level, wealth and overall economic activity. Theory of tax efficiency Tax efficiency (or tax effort) theory is based on the idea that fiscal potential is defined in terms of a state's ability to maximize revenue by optimizing tax administration and reducing tax evasion. Theory of tax decentralization The theory of fiscal decentralization presented by Bahl et al. (2008). Oates ( 1972 ) emphasizes the distribution of fiscal resources between levels of administration (national, regional, local) and stresses the importance of fiscal autonomy for efficient public finance management. Theory of tax modernization and technological innovation Developed by Besley et al. (2013) and Prichard ( 2016 ), this theory highlights the rise of digital technologies and computer systems as a factor in improving the assessment of tax potential, particularly in countries where economic data is limited. Empirical literature review Determinants of fiscal potential A large body of literature addresses various factors that can affect tax potential. At the macroeconomic level, the contribution of sectors to the formation of national wealth (GDP) influences tax revenues. In their analysis, Caldeira et al. ( 2019 ), Mawejje et al. (2019), and Benitez et al. ( 2023 ) have shown that the primary sector (agriculture, mining) has a negative effect on tax revenue formation. These authors' analysis is based on the idea that in developing countries, the primary sector is characterized by low productivity and a predominance of the informal sector (Benitez et al, 2023 ), suggesting that the higher the contribution of the primary sector to GDP, the less able the economy is to mobilize tax revenues. Studies have also shown that industrial sector development is an asset for expanding fiscal potential and mobilizing tax revenues. Mawejje et al. (2019), Teref et al. (2018) and Lawin ( 2023 ) found in their study that the development of the industrial sector has a positive and significant effect on tax revenue formation. Most of these authors justify their results by the fact that the industrialization process is associated with the transformation of commodities with apparent added value. This industrialization process as a whole generates opportunities for tax mobilization within the supply chain particularly through: (i) income tax on people employed in the industrial process; (ii) corporate tax when the industrialization process is sanctioned by a positive result in terms of profits and (iii) value-added tax (VAT). Some scientific works have found a positive effect, while others affirm negative and insignificant effects between service sector development and taxation. Teref et al. (2018) showed that, for East African countries over the period 1992–2015, the service sector has a positive and significant impact on fiscal capacity. Doghmi ( 2020 ) works support this point of view with a panel of 76 developing countries over the period 1980–2017, that the services sector is positively associated with fiscal capacity. But this relationship is not statistically significant. In contrast, Lawin ( 2023 ) reveals that the effect of the services sector on fiscal capacity is negative and insignificant for UEMOA countries. This result is justified by the preponderance of informal activities in the service sector in UEMOA countries. Other factors influence tax revenues and tax potential in UEMOA zone. These include GDP per capita (Mallick, 2021 ; Lawin, 2023 ; Benitez et al., 2023 ,), trade openness (Gordon et al., 2009; Mallick, 2021 ), institutional factors (Canares, 2016 ) and technological factors (Mallick, 2021 ; Adegboye et al., 2022 ). While the literature highlights several determinants of national fiscal potential, such as structural, economic, technological and institutional factors, one of the main challenges remains the measurement of this potential at sub-national level. In this context, numerous research studies have explored the use of satellite data, in particular night lights, as a proxy for local economic activity. This approach, initially developed to estimate sub-national GDPs in the absence of detailed economic statistics, offers an alternative for breaking down national fiscal potential between administrative divisions. Total night lighting: indicator of economic activity Satellite data on nighttime light intensity are produced by the Line Scan Operational System of the US Department of Defense's Satellite Weather Program. They record the brightness of light emissions from buildings, roads, industrial areas, parking lots and other artificially lit sources (Elvidge et al., 1997 ), with higher values indicating more intense economic activities (Bagan et al., 2015; Keola et al., 2015 ; Tong et al., 2018 ). Non-urban activities such as agriculture and burning can produce light emissions captured by TNL sensors, urban areas are the main source of light related to housing, land and energy consumption, and other development-related activities (He, Ma, Liu, & Zhang, 2014 , Tong et al., 2018 ). Several studies have established a strong correlation between night-time brightness and GDP. Gu et al. ( 2022 ) found a strong correlation between TNL and GDP. Liang et al ( 2020 ) estimated the GDP of a Chinese province using TNL data, Galimberti ( 2020 ) proved the effectiveness of TNL data in predicting regional GDP growth in France. Dai et al. ( 2017 ) and Ma et al. ( 2019 ) used TNL data to estimate industrial sector output. González et al. ( 2021 ) examined the impact of regional disasters on economic growth using TNL data. Overall, TNL has demonstrated its effectiveness in predicting macroeconomic aggregates. This study focuses on the pertinence of TNL to predict fiscal potential in the countries of the West African Economic and Monetary Union. In the following section, data source and methods will be presented. Data and Methods Data Source This study focuses on the West African Economic and Monetary Union countries (Benin, Togo, Niger, Burkina-Faso, Mali, Guinea-Bissau, Senegal and Côte d'Ivoire) over the period 2012–2022. Tax revenues (% GDP) and GDP per capita are taken from the database of the Central Bank of West African States (BCEAO), while value added in the industrial, primary and tertiary sectors, trade openness, Internet users (% of population) and mobile phone subscriptions (per 100 inhabitants) are taken from the World Bank database (WDI). The choice of variables was inspired by the empirical literature, and especially works of Canares ( 2016 ), Mallick ( 2021 ), Benitez et al. ( 2023 ). The data on total night light or Visible Infrared Imaging Radiometer Suite (VIIRS) comes from AIDDATA. These data are collected using the satellite from space and concern light emanating from economic activity, ship fleets and auroral activity (Henderson et al., 2012 ; Hansen et al., 2013 ; Bundervoet, 2015). Table 1 below presents some descriptive statistics on the data used in this study. Table 1 Descriptive Statistics Variable Source Mean Std. Dev. Min Max Fiscal (%GDP) BCEAO 12.699 2.723 6.5 17.7 GDP per capita BCEAO 486,218.92 238,390.52 232,623.4 111,2798.4 trade openness World Bank database 56.16 9.952 36.1 83 tertiary sectors World Bank database 29.055 12.757 12.3 58.1 primary World Bank database 21.467 4.9 11.8 29 value added in the industrial World Bank database 49.448 15.209 16.5 74.8 Internet users (% of population) World Bank database 19.409 14.944 1.1 68.9 mobile phone subscriptions (per 100 inhabitants) World Bank database 89.212 29 30.1 174.8 Total night light (Visible Infrared Imaging Radiometer Suite (VIIRS)) https://geo.aiddata.org/query/#!/ 49,525.623 63,572.823 637.849 351,678.77 Gouvernance index Mo Ibrahim database 51.702 6.326 37.2 62.3 Source: BCEAO and World Bank data, author's estimation Table 1 shows that over the 2012–2022 period, tax revenue (%GDP) averages 12.7 in the WAEMU region, with a standard deviation of 2.72% of GDP and a maximum of 17.7% of GDP. It also shows that the average contribution of the industrial sector to GDP formation is 49.45% over the same period 2012–2022, while that of the services sector is 29.05%. As shown in Table 1 , trade openness (exports + imports) averages 56.2% of GDP for WAEMU countries, with a standard deviation of 9.95% of GDP. It should be noted that the average Internet penetration or access rate in the WAEMU over the period 2012–2022 is 19.40%, with a standard deviation of 14.94%. Methods The sub-national fiscal potential is estimated using a three-step approach. First, we estimate the national fiscal potential using Stochastic Frontier Analysis (SFA) (Schimidt et al., 1977). Then, we evaluate the predictive power of total night-time light at national level on fiscal potential. Finally, we estimate subnational fiscal potential using subnational nightlight data and based on the approach proposed by Lopez-Ruiz and Hassanov (2019). The advantage of using nightlight data is that it is available for all countries and smaller geographical units (provincial, communal, prefectural, departmental units). First stage: estimating national fiscal potential Two approaches are often used to assess performance in tax revenue mobilization: a parametric approach and a non-parametric approach. The non-parametric method Data Envelopment Analysis (DEA) is based on optimization using minimal extrapolation, whereas parametric approaches use classical statistical principles, in particular the maximum likelihood principle (Peter et al., 2011). In both approaches, actual observations are used to estimate tax potential and tax effort. The approach used in this study is the Stochastic Frontier Analysis (SFA) method, the most widely used by researchers and international institutions. This approach takes into account the limitations of the DEA model as well as random phenomena that could occur over time, independently of the tax system apparatus. The Stochastic Frontier Analysis method is based on an optimization program in which the efficiency frontier function has a specific functional form: \(\:y=f\left(x,\:\beta\:\right)exp\left(v\right)exp(-u)\) where \(\:u\) denotes the efficiency term and \(\:v\) the error term (Peter et al., 2011). The logarithmic form of the model is given by : \(\:{y}^{k}=f\left({x}^{k}\:,\:\beta\:\right)+{v}^{k}-\:{u}^{k}\) where \(\:{v}^{k}\:\text{N}(0\:;\:{}_{\text{v}}²)\) et \(\:{u}^{k}\:{\text{N}}_{+}(0\:;\:{}_{\text{u}}²)\) ; k = 1, … K The term v takes into account the stochastic nature of the tax revenue formation process and any errors in measuring inputs and outputs, and the term u is any inefficiency (Peter et al., 2011). The inputs correspond to all factors likely to contribute to the formation of tax revenues, which is the output. Given the availability of data, we used as inputs GDP per capita, value added in the industrial, primary and service sectors, trade openness, digital development or ICT (Internet users (% of population), mobile phone subscriptions (per 100 inhabitants) and as output tax revenues (as a % of GDP). The choice of these variables was based on the empirical literature (Teref et al., 2018; Caldeira et al., 2019 ; Mawejje et al., 2019; Doghmi, 2020 ; Mallick, 2021 ; Adegboye et al., 2022 ; Benitez et al., 2023 ; and Lawin, 2023 ) Efficiency is measured by the formula : $$\:D\left({x}^{0},{y}^{0}\right)=\frac{\text{T}\text{a}\text{x}\:\text{r}\text{e}\text{v}\text{e}\text{n}\text{u}\text{e}\:\text{a}\text{c}\text{t}\text{u}\text{a}\text{l}\text{l}\text{y}\:\text{r}\text{a}\text{i}\text{s}\text{e}\text{d}}{\text{M}\text{a}\text{x}\text{i}\text{m}\text{u}\text{m}\:\text{e}\text{x}\text{p}\text{e}\text{c}\text{t}\text{e}\text{d}\:\text{T}\text{a}\text{x}\:\text{r}\text{e}\text{v}\text{e}\text{n}\text{u}\text{e}\:}=\:\frac{f\left({x}^{0},\:\beta\:\right)-{u}^{0}}{f\left({x}^{0},\:\beta\:\right)}$$ Second stage: Examination of the effect of night-time luminaires on fiscal potential The second step consists of estimating the explanatory power of night lights on the fiscal potential of the eight (08) countries. We used a panel data model specified in log-linear form in order to interpret the estimated parameters in terms of elasticity. At the beginning, we examine the presence of unit roots in the series used. The presence of a unit root was examined using the Levin-Lin-Chu (2002) test. This test, commonly used for panel data, enables us to test whether series are stationary or follow an autoregressive (AR) process. Under the null hypothesis, the Levin-Lin-Chu test assumes the presence of a unit root in the series for all countries (i.e., the series for all countries is non-stationary) (Levin et al., 2002 ). Conversely, the alternative assumption is that at least one series is stationary. The results of both tests show the presence of a unit root in the countries' tax potential and TNL series. This result leads us to introduce the first difference series into the model. Causality link The examination of the causal relationship between TNL and fiscal potential is inspired by the test procedure of Granger ( 1969 ) and Dumitrescu and Hurlin ( 2012 ) and the work of Weinhold (1996). This procedure takes into account possible heterogeneity across countries. The basic specification of the Dumitrescu and Hurlin ( 2012 ) test is given by the model below. $$\:{Y}_{i,t}={\lambda\:}_{i}+\:{\sum\:}_{k=1}^{K}{\alpha\:}_{1i}^{\left(k\right)}\:{Y}_{i,t-k}+\:{\sum\:}_{k=1}^{K}{\beta\:}_{1i}^{\left(k\right)}\:{X}_{i,t-k}\:+\:{\epsilon\:}_{1i,t}\:\:\:\:\:\:\:\:\:\:i=1,\dots\:,\:N\:;t=1,\dots\:,\:T\:\left(1\right)$$ Under the null hypothesis of homogeneous non-causality, there is no causality from X to Y for all the cross-sectional units in the panel. $$\:{H}_{0}\::\:{\beta\:}_{i}=0\:\:\:\:\:\forall\:\:\:i=1,\dots\:,\:N$$ 2 The alternative hypothesis assumes the existence of causality from X to Y for at least one country. $$\:{H}_{1}\::\:{\beta\:}_{i}=0\:\:\:\:\:\forall\:\:\:i=1,\dots\:,\:{N}_{1}$$ 3 $$\:{\beta\:}_{i}\:\ne\:0\:\:\:\:\:\forall\:\:\:i={N}_{1},\dots\:,\:N$$ 4 To test these hypotheses, Dumitrescu and Hurlin ( 2012 ) propose a procedure that consists of running the N individual regressions of the model and performing F-tests of the K linear hypotheses \(\:{\beta\:}_{i1}=\dots\:=\:{\beta\:}_{iK}=0\) to recover the individual Wald statistic W_and finally to calculate the average Wald statistic \(\:\underset{\_}{W}\:\) : $$\:{\underset{\_}{W}}_{NT}=\frac{1}{N}{\sum\:}_{i=1}^{N}{W}_{iT}$$ 5 where \(\:{W}_{iT}\) are the individual Wald statistics for the Granger causality test (Dumitrescu and Hurlin, 2012 ). Assuming that the \(\:{W}_{iT}\) statistics are independent and identically distributed, we calculate a standardized statistic, Z-bar : $$\:\underset{\_}{Z}=\sqrt{\frac{N}{2K}}\:\:\:{\left(\underset{\_}{W}-K\right)}_{T,N\:\to\:{\infty\:}\to\:}^{\:\:\:\:\:\:\:d}\:\:N(0,\:1)$$ 6 Methods The bidirectional causality between TNL and fiscal potential raises an endogeneity bias. To take into account this endogeneity, we used the Generalized Method of Moments (GMM) proposed by Arellano and Bond ( 1991 ) and Blundell and Bond (1998). This method offers an advantage compared to other regression models. Indeed, this method provides more consistent results in the presence of endogeneity problems (Ullah, et al., 2018 ), takes into account unobserved individual effects, persistence of the dependent variable and yields efficient estimators (Arellano and Bond, 1991 ; Wooldridge, 2010 ). We used the system GMM method due to the persistence of the dependent variable (Blundell et al., 1998; Ullah et al., 2018 ) (correlation coefficient of the one-period lagged variable of fiscal potential is equal to 0.98, positive and significant prob = 0.000). In addition, the GMM system method is more efficient than the GMM difference method and also reduces the problem of weak instruments (Blundell et al., 1998; Ullah, et al., 2018 ). The model specification is as follows: $$\:{\text{l}\text{n}(Fiscalpotentiel}_{it})={\beta\:}_{0}+{\beta\:}_{1}{\text{l}\text{n}(TNL}_{it})+{\epsilon\:}_{it}$$ Where i represents the country and t the year. Following the example of Arellano and Bond ( 1991 ), we introduced endogenous instruments (TNL) and lags of the dependent variable (fiscal potential). We also introduced an exogenous instrument (governance index). The choice of governance as an exogenous instrument is justified. First, governance is a key determinant of economic performance and institutional quality, directly affecting investment, productivity and growth. Acemoglu et al. ( 2001 , 2005 ) have shown that effective governance and good institutional quality have a causal effect on investment and growth. Henderson et al. ( 2012 ) have also shown that night light data capture well the effects of institutional policies on development. Third stage: estimation of the regional fiscal potential of each WAEMU country Having shown that total night lights is a appropriate indicator for measuring fiscal potential, we assess the sub-national fiscal potential of countries using the method proposed by Lopez-Ruiz et al. ( 2019 ). This method considers, in fact, the share of each region's night lights in national night lights as the share or its contribution to the formation of national wealth or value added in the national economy. This relationship is a consequence of the existence of a linear relationship between economic activity and night lights (Lopez-Ruiz and Hassanov, 2019). $$\:{FiscalpotentielR\text{é}gi}_{ijt}={FiscalpotentielNatio}_{it}\text{*}\frac{{TNLR\text{é}gi}_{ijt}}{{TNLNatio}_{ijt}}$$ \(\:{FiscalpotentielR\text{é}gi}_{ijt}\) : the regional tax potential of region j in country i in period t ; \(\:{FiscalpotentielNatio}_{it}\:\) : the national fiscal potential of country i in period t \(\:\:\) ; \(\:{TNLR\text{é}gi}_{ijt}\:\) : night-time light intensity of region j in country i at period t \(\:\:\) ; \(\:{TNLNatio}_{ijt}\:\) : global light intensity of country i at period t Results and discussion The national fiscal potential of WAEMU countries was assessed, and analysis reveals fluctuating trends of tax revenue (as a % of GDP) over the period 2012–2022 (Fig. 1 in annexes). However, several WAEMU countries have improved their performance in tax revenue mobilization over 2020–2022. Burkina-Faso, Benin, Côte d'Ivoire, Senegal and Togo have seen a slight increase in their tax burden, bringing them closer to the tax frontier (Fig. 1 in annexes). This performance is underpinned by the investments made by the various countries in digitizing and modernizing tax and customs administration. Despite the actions taken by WAEMU countries to cover the full fiscal potential, a significant gap remains looking at the tax revenue actually mobilized. Over the period 2012–2022, the fiscal potential or tax frontier remained far above the tax burden (tax revenue as a % of GDP) for each of the eight (08) WAEMU countries (Fig. XXX? in annexes). The tax gap or tax effort averaged 3.7% of GDP per year for Côte d'Ivoire, 4.8% of GDP per year for Benin, 3.4% of GDP per year for Burkina Faso, 2.6% of GDP per year for Mali, 6.2% of GDP per year for Niger, 4.6% of GDP per year for Senegal, 3.5% of GDP per year for Guinea-Bissau and 5.7% of GDP per year for Togo. Examining the relationship between TNLs and national fiscal potential Causality link We then examined the correlation between national fiscal potential and national annual total night lights. The result of Pearson's correlation test reveals the existence of a positive and significant correlation between fiscal potential and total night lights, at the 1% threshold. The correlation coefficient is estimated at 0.982 (see Table 12 in annexes). Similarly, the Granger causality test shows the existence of a two-way causal relationship between fiscal potential and total night-time light. In fact, the null hypotheses of non-causality between tax potential and total night lights are rejected with a probability of less than 5% (see Table 2). This result means that total night-time light has predictive power over the fiscal potential of WAEMU countries, and conversely, knowledge of fiscal potential provides information on total night-time light. According to Henderson et al. (2018) and Ghosh et al. (2013), such a result is well justified. Indeed, an increase in light intensity reflects increased economic activity, which translates into a broader tax base and higher potential tax revenues (Henderson et al., 2018). For Ghosh et al. (2013), increased fiscal capacity enables greater investment in infrastructure and public services, which in turn can drive economic development and urbanization, thus contributing to an increase in light intensity. Michalopoulos and Papaioannou (2014) also justify this result on the grounds that public financial resources are likely to stimulate investment in urban infrastructure, generating a virtuous circle between economic development and tax revenue mobilization. Table 2 Results of the Dumitrescu and Hurlin (2012) panel causality test W-bar Z-bar p-value H0: TNL does not Granger-cause LFPotentieles 3.7855 5.5711 0.0000 LFPotentieles does not Granger-cause TNL 2.9977 3.9953 0.0001 Source: BCEAO and World Bank data, author's estimation The bidirectional causal relationship between TNL and tax potential raises a problem of endogeneity. We have estimated a fixed-effect or random-effect model (see annexes), and the result of the Hausman test confirms the presence of an individual effect. The results of the GMM model are presented in Table 3 below. Table 3 Results of estimations RUMS Dependent variable: Rate of use of microfinance services (% adult population) TUSM. LFPotentieles (-1) 0.92*** (0.0294) TNL 0.08*** (0.0295) Number of countries 8 Number of instruments 8 Wald test statistic 1.86e + 06*** AR(1) p value 0.033 AR(2) p value 0.735 Hansen J-test p value 0.489 *** p < .01, ** p < .05, * p < .1 Source: Authors, World Bank and BCEAO databases The results of the GMM model show that the past value of fiscal potential has a strong positive and significant effect on the present value of potential. The analysis also shows that night lights have a positive and significant effect on fiscal potential, but the coefficient is lower than in the static models (fixed effect or random effect). The results of the Arellano-Bond auto-correlation test show that the hypothesis of no first-order auto-correlation is rejected (with probability p = 0.033), while the hypothesis of no second-order auto-correlation (AR(2)) is accepted (prob = 0.735). Furthermore, the results of the Hansen test (p = 0.489) reveal that the instruments are valid. Our results show that night lights (TNL) have a positive and significant influence on tax potential. This finding aligns with other previous work demonstrating that night-time light intensity is a predictor of key economic variables, such as subnational GDP (Henderson et al., 2012; Chen et al., 2015) and household consumption. Our results also show the persistence of fiscal potential. This result is consistent with the literature on fiscal inertia, which stresses that tax systems in developing countries tend to evolve slowly due to institutional and administrative rigidities (Fenochietto et al., 2013). Fiscal potential of the different regions of UEMOA countries Having demonstrated that total night lights are an indicator for measuring national fiscal potential, we assessed the fiscal potential of the first administrative divisions of UEMOA countries using the approach of Lopez-Ruiz et al. (2019). The results of the estimates for the regions are presented in the tables below. Table 4 Fiscal potential of Togo's regions (as % of GDP) Année 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 Savanes Region 0,72 0,83 0,67 0,96 1,01 1,33 1,25 1,17 1,22 1,26 1,44 Kara Region 1,82 1,72 1,53 1,60 1,57 1,61 1,67 1,57 1,70 1,54 1,67 Centrale Region 0,89 0,96 0,87 0,99 0,88 0,97 0,96 0,88 0,97 1,12 1,09 Plateaux Region 1,12 1,62 1,60 2,44 2,18 3,31 3,85 3,58 4,38 5,58 6,42 Maritime Region 14,33 16,42 16,35 14,49 13,91 12,90 12,91 12,32 12,28 12,22 12,36 Source: BCEAO and World Bank data, author's estimation Analysis of Table 4 shows that over the period 2012–2022, Togo's fiscal potential will average 20.64% of GDP. At sub-national level, fiscal potential varies on average between 0.96% of GDP and 13.68% of GDP. The Maritime Region leads with an average fiscal potential of 13.68% of GDP over the period 2012–2022, followed by the Plateaux Region (3.28% of GDP), the Kara Region (1.64% of GDP), the Savanes Region (1.08% of GDP) and the Centrale Region (0.96% of GDP). Table 5 Fiscal potential of Senegal's regions (as % of GDP) Année 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 Dakar 9,28 9,03 9,29 9,81 9,31 8,65 8,47 8,26 7,15 6,57 6,24 Diourbel 2,70 2,70 2,43 2,45 2,28 2,26 2,33 2,66 2,81 3,32 3,54 Fatick 0,30 0,33 0,27 0,26 0,25 0,35 0,36 0,42 0,55 0,69 0,95 Kaffrine 0,11 0,13 0,11 0,10 0,10 0,13 0,16 0,22 0,28 0,33 0,40 Kaolack 0,75 0,83 0,71 0,77 0,70 0,76 0,78 0,97 1,12 1,34 1,65 Kolda 0,31 0,33 0,25 0,17 0,14 0,15 0,16 0,16 0,20 0,20 0,28 Louga 0,74 0,78 0,63 0,66 0,65 0,89 0,89 0,87 1,00 1,17 1,29 Matam 0,18 0,20 0,17 0,17 0,18 0,31 0,31 0,29 0,38 0,33 0,32 Saint Louis 0,95 1,01 0,86 0,80 0,80 1,10 1,13 1,01 1,15 1,08 0,99 Sedhiou 0,06 0,06 0,04 0,04 0,03 0,06 0,08 0,10 0,13 0,16 0,22 Tambacounda 0,24 0,24 0,24 0,26 0,26 0,25 0,24 0,29 0,27 0,35 0,38 Thies 3,58 3,84 4,00 4,04 4,08 4,44 5,25 5,16 4,71 5,36 5,47 Ziguinchor 0,30 0,33 0,36 0,29 0,25 0,30 0,27 0,29 0,34 0,32 0,37 Kedougou 0,51 0,37 0,33 0,36 0,36 0,38 0,49 0,43 0,42 0,54 0,45 Source: BCEAO and World Bank data, author's estimation For Senegal, the results show that over the same underlying period, fiscal potential averaged 20.58% of GDP. The results also show that fiscal potential varies between 0.09% and 8.37% of GDP for Senegal's regions. The country's economic capital is the region with the highest fiscal potential, with a potential of 8.37% of Senegal's GDP, followed by the regions of Thies (4.54% of GDP), Diourbel (2.68% of GDP), Saint Louis (0.99% of GDP), Kaolack (0, 94% of GDP), Louga (0.87% of GDP), Fatick (0.43% of GDP), Kedougou (0.42% of GDP), Ziguinchor (0.31% of GDP), Tambacounda (0.27% of GDP), Matam (0.26% of GDP), Kolda (0.21% of GDP), Kaffrine (0.19% of GDP) and the Sedhiou region (0.09% of GDP). Table 6 Fiscal potential of Burkina Faso's regions (as % of GDP) Année 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 Centre 9,55 9,37 9,84 9,52 9,58 8,73 8,53 8,70 8,91 8,89 8,97 Boucle du Mouhoun 0,58 0,65 0,64 0,82 0,85 0,96 0,87 0,76 0,79 0,95 0,85 Cascades 0,33 0,35 0,32 0,39 0,39 0,39 0,48 0,52 0,66 0,83 0,76 Centre-Est 0,54 0,55 0,63 0,65 0,58 0,68 0,70 0,60 0,70 0,80 0,77 Centre-Nord 0,50 0,62 0,60 0,63 0,77 0,94 0,92 0,80 0,77 0,77 0,65 Centre-Ouest 0,74 0,99 0,90 0,88 0,77 0,81 0,80 0,72 0,75 0,82 0,83 Centre-Sud 0,18 0,18 0,18 0,19 0,19 0,28 0,28 0,33 0,31 0,43 0,41 Est 0,37 0,37 0,38 0,36 0,35 0,44 0,65 0,55 0,48 0,55 0,47 Hauts-Bassins 3,06 3,00 2,96 3,15 3,13 3,00 2,80 2,63 2,74 2,94 2,96 Nord 0,46 0,44 0,48 0,49 0,52 0,59 0,70 0,68 0,75 0,73 0,65 Plateau Central 0,25 0,26 0,29 0,29 0,27 0,33 0,33 0,30 0,52 0,79 0,95 Sahel 0,54 0,57 0,68 0,65 0,59 0,56 0,59 0,58 0,52 0,48 0,43 Sud-Ouest 0,21 0,22 0,21 0,26 0,22 0,26 0,36 0,31 0,33 0,39 0,41 Source: BCEAO and World Bank data, author's estimation The analysis shows that Burkina-Faso's fiscal potential averages 18.15% of GDP over the period 2012–2022, with a variation of 0.27–9.14% between regions. The Centre region has a higher fiscal potential (9.14% of GDP on average over the period 2012–2022). Hauts-Bassins is the second region with the highest fiscal potential in Burkina, at 2.94% of GDP. Table 7 Fiscal potential of Niger's regions (as % of GDP) Année 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 Tahoua/Agadez 2,46 2,25 2,34 2,78 2,41 2,53 2,19 2,20 2,20 2,42 2,35 Dossa 0,33 0,31 0,32 0,56 0,48 0,55 0,48 0,44 0,51 0,61 0,68 Niamey 4,83 4,31 4,50 6,38 6,53 6,04 7,10 7,95 7,30 6,76 6,02 Tillaberi 0,51 0,49 0,48 0,80 0,72 0,94 0,94 1,08 1,37 1,37 1,39 Maradi 0,66 0,64 0,68 1,10 1,04 1,13 0,95 0,92 0,93 1,13 1,32 Zinder/Diffa 9,72 11,26 9,99 7,02 5,54 4,40 3,43 2,95 3,16 3,89 3,14 Source: BCEAO and World Bank data, author's estimation Over the period 2012–2022, fiscal potential averaged 16.75% of GDP at national level in Niger, with a variation of between 0.92% and 6.16% of GDP between regions. The Niamey region has a fiscal potential of 6.16% of GDP on average over the study period; 5.86% of GDP on average for the Zinder/Diffa region, 2.38% of GDP for the Tahoua/Agadez region, 0.95% of GDP for the Maradi region, 0.92% of GDP for the Tillaberi region and 0.48% of GDP for the Dossa region. Table 8 Fiscal potential of Mali's regions (as % of GDP) Année 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 Bamako 7,27 7,70 7,46 6,81 6,48 5,58 5,61 5,73 5,53 5,09 4,89 Gao 0,01 0,03 0,18 0,22 0,24 0,24 0,26 0,25 0,26 0,24 0,25 Kayes 2,29 2,27 2,07 2,06 2,10 2,08 2,11 2,14 2,24 2,31 2,20 Kidal 0,14 0,11 0,10 0,11 0,13 0,11 0,12 0,15 0,17 0,17 0,20 Koulikouro 2,82 2,94 3,06 3,33 3,57 3,96 4,21 4,58 5,11 5,29 5,32 Mopti 0,52 0,58 0,72 0,65 0,52 0,54 0,53 0,47 0,51 0,54 0,48 Segou 1,05 1,08 1,12 1,18 1,11 1,35 1,27 1,21 1,26 1,20 1,26 Sikasso 1,70 1,86 1,70 1,68 1,50 1,62 1,70 1,66 1,78 1,74 1,87 Tombouctou 0,01 0,01 0,30 0,40 0,35 0,34 0,38 0,32 0,28 0,27 0,31 Source: BCEAO and World Bank data, author's estimation At national level, Mali's fiscal potential averaged 16.44% over the study period, with a variation of between 0.14% and 6.20% of GDP between Mali's regions. The regions of Bamako, Koulikouro, Kayes, Sikasso, Segou, Mopti, Timbuktu, Gao and Kidal have a fiscal potential of 6.20% of GDP, 4.02% of GDP, 2.17% of GDP, 1.71% of GDP, 1.19% of GDP, 0.55% of GDP, 0.27% of GDP, 0.20% of GDP and 0.14% of GDP respectively, on average over the period 2012–2022. Table 9 Fiscal potential of Côte d'Ivoire's regions (as % of GDP) Année 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 Bas-Sassandra 0,76 0,72 0,63 0,65 0,60 0,70 0,73 0,68 0,54 0,66 0,88 Denguele 0,15 0,15 0,18 0,33 0,38 0,51 0,51 0,53 0,38 0,38 0,43 District d'Abidjan 4,72 4,63 4,85 4,59 4,41 4,14 4,41 4,29 2,92 2,75 2,99 District de Yamoussoukro 1,13 1,06 1,10 1,03 0,97 0,82 0,84 0,72 0,47 0,47 0,49 Goh-Djiboua 1,29 1,16 0,97 0,94 0,85 0,91 0,90 0,81 0,60 0,63 0,71 Lacs 1,29 1,19 1,03 1,13 0,99 1,07 1,09 0,93 0,83 0,88 0,93 Montagnes 0,38 0,39 0,38 0,45 0,42 0,47 0,51 0,56 0,49 0,66 0,85 Sassandra-Marahoue 1,11 0,99 0,84 0,81 0,75 0,80 0,80 0,77 0,72 0,86 0,99 Savanes 1,28 1,60 1,99 1,98 1,89 2,10 2,08 2,42 2,12 1,92 2,04 Valle du Bandama 1,00 1,06 1,32 1,31 1,23 1,31 1,32 1,34 1,41 1,43 1,47 Woroba 0,26 0,28 0,27 0,42 0,45 0,60 0,57 0,62 0,66 0,74 0,84 Zanzan 0,38 0,35 0,33 0,38 0,43 0,53 0,60 0,63 0,60 0,61 0,66 Lagunes 1,07 1,00 0,82 0,74 0,66 0,76 0,86 0,79 0,65 0,69 0,73 Comoe 0,90 0,86 0,75 0,81 0,90 0,98 1,08 1,00 0,76 0,83 0,87 Source: BCEAO and World Bank data, author's estimation The estimates show that for Côte d'Ivoire, fiscal potential is estimated at 15.16% of GDP on average over the period 2012–2022. This potential varies between 0.36% and 4.06% of GDP for the regions of Côte d'Ivoire. The District of Abidjan has a fiscal potential of 4.06% of Côte d'Ivoire's GDP, followed by the District of Savanes (1.95% of GDP), Valle Du Bandama (1.29% of GDP), Lacs (1.03% of GDP), Goh-Djiboua (0.89% of GDP) and Comoe (0.89% of GDP), Sassandra- Marahoue (0.86% of GDP), District De Yamoussoukro (0.83% of GDP), Lagunes (0.80% of GDP), Bas-Sassandra (0.69% of GDP), Woroba (0.52% of GDP), Montagnes (0.51% of GDP), Zanzan (0.50% of GDP) and Denguele (0.36% of GDP). Table 10 Fiscal potential of Benin's regions (as % of GDP) Année 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 Alibori 0,18 0,19 0,25 0,24 0,22 0,34 0,33 0,40 0,38 0,37 0,42 Borgou 1,32 1,35 1,39 1,71 1,69 1,89 2,03 1,94 1,61 1,39 1,55 Atakora 0,47 0,46 0,40 0,54 0,51 0,47 0,43 0,46 0,43 0,48 0,51 Donga 0,44 0,51 0,45 0,56 0,61 0,68 0,56 0,52 0,54 0,55 0,57 Collines 0,30 0,36 0,49 0,46 0,37 0,47 0,49 0,46 0,54 0,47 0,52 Plateau 0,21 0,27 0,37 0,55 0,54 0,61 0,59 0,55 0,45 0,44 0,43 Zou 0,81 0,78 0,87 0,94 1,00 0,94 0,87 0,83 0,86 0,97 1,00 Kouffo 0,15 0,19 0,19 0,16 0,18 0,28 0,24 0,25 0,22 0,24 0,30 Atlantique 3,03 3,14 3,58 3,41 3,61 3,85 3,95 4,30 3,86 4,27 4,35 Littoral 3,48 3,36 3,35 2,92 3,21 2,71 3,08 3,20 2,50 2,52 2,30 Mono 0,62 0,79 0,77 0,75 0,75 0,88 0,77 0,77 0,65 0,71 0,70 Ouémé 3,09 3,33 3,15 2,95 3,16 2,96 2,87 2,94 2,64 2,64 2,61 Source: BCEAO and World Bank data, author's estimation In Benin, the results show that the departments of Atlantique, Ouémé, Littoral, Borgou and Zou have the highest fiscal potential in Benin. Fiscal potential averages 3.76%, 2.97%, 2.94%, 1.63% and 0.90% of GDP, respectively for the departments of Atlantique, Littoral, Ouémé, Borgou and Zou, over the period 2012–2022. This result can be explained by the concentration of economic activity in these areas. In addition, the development of the Glo-Djigbé industrial zone (GDIZ) in the Atlantic department and the development of the special economic zone at Sèmè Podji in the Ouémé department offer these two (02) departments higher tax potential than the other departments. Table 11 Fiscal potential of Guinea Bissau's regions (as % of GDP) Année 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 Bissau 8,18 8,61 9,60 9,01 9,36 9,07 9,27 9,24 8,81 9,12 9,03 Bafatá 0,11 0,16 0,27 0,31 0,28 0,33 0,34 0,26 0,16 0,15 0,07 Biombo 0,81 0,94 1,25 1,40 1,24 1,91 1,96 1,85 1,89 2,25 2,49 Bolama 0,01 0,02 0,02 0,02 0,16 0,17 0,18 0,09 0,05 0,02 0,01 Cacheu 0,07 0,11 0,10 0,41 0,37 0,27 0,28 0,20 0,16 0,09 0,19 Gabu 0,09 0,14 0,27 0,22 0,22 0,27 0,28 0,22 0,23 0,21 0,18 Oio 0,05 0,10 0,13 0,07 0,18 0,29 0,29 0,09 0,05 0,06 0,08 Quinara 0,14 0,17 0,22 0,12 0,12 0,23 0,23 0,20 0,06 0,05 0,04 Tombali 0,09 0,21 0,11 0,16 0,13 0,20 0,21 0,16 0,19 0,10 0,09 Source: BCEAO and World Bank data, author's estimation For Guinea Bissau, the results show a fiscal potential of 11.79% of GDP, averaged over the period 2012-2022. Estimates show that the Bissau region has a fiscal potential of 9.03% of GDP. This potential amounted to 1.64% of GDP for the Biombo region, 0.22% of GDP for the Bafatá region, 0.21% of GDP for the Gabu region, 0.20% of GDP for the Gabu region, 0.15% of GDP for the Tombali region, 0.14% of GDP for the Quinara region, 0.13% of GDP for the Oio region and 0.07% of GDP for the Bolama region. Overall, an examination of the fiscal potential of the regions of UEMOA countries reveals significant differences between countries and within national territories. This disparity can be explained by several factors, including economic structure, urbanization, infrastructure (Krugman, 1991), and socio-political dynamics (Alesina and Perotti, 1996). Our results show that, in most WAEMU countries, economic capitals and industrial centers have the highest levels of fiscal potential. In terms of hierarchy of fiscal potential, Senegal and Côte d'Ivoire are far ahead with Dakar (8.37% of GDP) and Abidjan (4.06% of GDP), confirming their role as regional economic hubs. Benin and Burkina Faso follow with respectively Atlantic (3.76% of GDP) and Central (9.14% of GDP) regions. Mali and Niger show a concentration of fiscal potential in Bamako (6.20%) and Niamey (6.16%). Guinea-Bissau, on the other hand, has a more modest fiscal potential, but is highly centralized in Bissau (9.03% of GDP). Togo also stands out for the importance of the Maritime region (13.68%), where Lomé is located, confirming the role of port trade. A number of factors explain the disparity in the fiscal potential of WAEMU countries. On one hand, political instability and economic crises have a direct impact on states' ability to mobilize their fiscal resources. Alesina and Perotti (1996), using the theory of political instability and development, underline that political uncertainties slow down private investment and reduce economic growth, which directly affects fiscal potential. On other hand, security crises influence fiscal potential and the tax administration's ability to collect taxes (Collier and Hoeffler, 2004). As our results show, the Gao region of Mali (0.25% of GDP) and the Sahel region of Burkina Faso (0.43% of GDP) have the lowest levels of fiscal potential. This is linked to the fact that terrorist attacks in the Sahel (Burkina Faso, Mali, Niger) have limited economic activity in certain rural regions. This result corroborates the findings of Collier and Hoeffler (2004) who, through their work on the economics of conflict, have shown that persistent instability reduces tax collection due to the destruction of infrastructure and the reduction of trade. In addition, economic reforms and infrastructure development explain the disparity in fiscal potential (Krugman, 1991). Our results show how Côte d'Ivoire and Senegal, within improvement of policies, industrialization and infrastructure (e.g. Port of Dakar, Zone industrielle uof Yopougon in Abidjan), have seen their fiscal potential maintained despite economic shocks. Similarly, Benin has benefited from the creation of special economic zones such as Glo-Djigbé and Sèmè-Podji, increasing resource mobilization in these regions. These findings are in line with the theory of economic concentration, which emphasizes that infrastructure and economic density enhance fiscal capacity (Krugman, 1991). Conclusions and recommendations This study evaluates the sub-national fiscal potential of the eight (08) WAEMU countries using satellite data on infrared imagery night lights. To achieve this goal, we used a three-step approach. First, we estimated the national fiscal potential using the Stochastic Frontier Analysis method proposed by Schimidt et al (1977). Next, we examine the relationship between total night lights and national fiscal potential using the panel data regression model, fixed effects model and the Feasible Generalized Least Squares (FGLS) method and finally we estimate the fiscal potential at the regional level. The results show that over the period 2012–2022, fiscal potential is on average 20.64% of GDP for Togo, 20.58% of GDP for Senegal, 18.15% of GDP for Burkina Faso, 16.75% of GDP for Niger, 16.44% of GDP for Mali, 15.37% of GDP for Benin, 15.16% of GDP for Côte d'Ivoire and 11.79% of GDP for Guinea-Bissau. The gap between fiscal potential and tax revenue actually mobilised averages 3.7% of GDP per year for Côte d'Ivoire, 4.8% of GDP per year for Benin, 3.4% of GDP per year for Burkina Faso, 2.6% of GDP per year for Mali, 6.2% of GDP per year for Niger, 4.6% of GDP per year for Senegal, 3.5% of GDP per year for Guinea-Bissau and 5.7% of GDP per year for Togo. The results of the regression model reveal that night lights have a positive and significant effect on fiscal potential, with a correlation coefficient of 0.98 and an R² of 84%. Sub-national fiscal potential varies between 0.09% and 8.37% of GDP for the regions of Senegal, 0.96% and 13.68% of GDP for the regions of Togo, 0.27% and 9.14% of GDP for Burkina, 0.92% and 6.16% of GDP for the regions of Niger, 0.14% and 6.20% of GDP for the regions of Mali, 0.22% and 3.76% of GDP for the regions of Benin, 0.36% and 4.06% of GDP for the regions of Côte d'Ivoire and between 0.07% and 9.03% of GDP for the regions of Guinea Bissau. The results suggest that night lights can be used to improve the forecasting and allocation of fiscal resources at a sub-national level. Our results suggest that improving infra-national fiscal capacity can be a strategic lever for fostering local economic development. This study recommends that tax revenue mobilization targets be set by the administrative divisions of WAEMU countries, in order to mobilize tax revenues up to the level of fiscal capacity. An awareness-raising program can also be set up to communicate on tax civic-mindedness or the fiscal responsibility of economic agents. Information and communication technologies can be used to achieve these objectives. This study offers an unprecedented opportunity for real-time monitoring of tax mobilization forcasting. We believe that this study can be successfully carried out in other countries as the satellite imagery night light data is freely available to all countries. One limitation of this study is the lack of tax data by administrative division of the countries that could enable a comparative analysis between the tax potential of the administrative divisions and their tax effort. Including only TNL as an explanatory variable in the GMM model can be seen as a limitation of this study. Further work could examine the influence of other factors on tax potential or tax effort. Declarations Conflict of interest statement The author states that there is no conflict of interest. Ethical approval The data used for the estimates do not include confidential information about individuals or animals that may raise ethical concerns. Acknowledgement We thank all those who have contributed to the improvement of the quality of this paper. Special thanks to all reviewers. Consent for publication The author grants his consent for publication of this paper. Author contribution The author contributed alone to the paper. Funding The writing of this paper has not been funded or sponsored. It was done at the author’s expense. Data availability statement The data used in this paper is fully available and can be accessed upon request. References Acemoglu, D., Johnson, S., & Robinson, J. A. (2005). Institutions as the fundamental cause of long-run growth. Handbook of Economic Growth, 1, 385-472. https://doi.org/10.1016/S1574-0684(05)01006-3 Acemoglu, D., Johnson, S., & Robinson, J. A. (2001). The colonial origins of comparative development: An empirical investigation. American Economic Review, 91(5), 1369-1401. https://doi.org/10.1257/aer.91.5.1369 Adegboye, A., Uwuigbe, U., Ojeka, S., Uwuigbe, O., Dahunsi, O., & Adegboye, K. (2022). Driving information communication technology for tax revenue mobilization in Sub-Saharan Africa. Telecommunications Policy, 46(7), 102329. https://doi.org/10.1016/j.telpol.2022.102329 Aigner, D., Lovell, C. K., & Schmidt, P. (1977). Formulation and estimation of stochastic frontier production function models. Journal of econometrics , 6 (1), 21-37. https://doi.org/10.1016/0304-4076(77)90052-5 Alesina, A., & Perotti, R. (1996). Income distribution, political instability, and investment. European Economic Review, 40(6), 1203-1228. https://doi.org/10.1016/0014-2921(95)00030-5 Alfirman, L. (2003). Estimating stochastic frontier tax potential: Can Indonesian local governments increase tax revenues under decentralization? Discussion Papers in Economics, Working Paper No. 03-19. Boulder: Center for Economic Analysis, Department of Economics, University of Colorado at Boulder. https://www.colorado.edu/economics/sites/default/files/attached-files/wp03-19.pdf Arellano, M., & Bond, S. (1991). Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations. The Review of Economic Studies, 58(2), 277-297. https://doi.org/10.2307/2297968 Bagan, H., & Yamagata, Y. (2015). Analysis of urban growth and estimating population density using satellite images of nighttime lights and land-use and population data. GIScience & Remote Sensing, 52(6), 765-780. https://doi.org/10.1080/15481603. 2015.1072400. Bahl, R., & Bird, R. (2008). "Subnational Taxes in Developing Countries: The Way Forward". Public Budgeting & Finance, 28(4), 1-25. https://doi.org/10.1111/j.1540-5850.2008.00914.x Benitez, J. C., Mansour, M., Pecho, M., & Vellutini, C. (2023). Building Tax Capacity in Developing Countries. International Monetary Fund. https://pacerplus.org/assets/Building-Tax-Capacity.pdf Besley, T., & Persson, T. (2013). Taxation and development. Handbook of Public Economics, 5, 51-110. https://doi.org/10.1016/B978-0-444-53759-1.00002-9 Bundervoet, T., Maiyo, L., & Sanghi, A. (2015). Bright lights, big cities: measuring national and subnational economic growth in Africa from outer space, with an application to Kenya and Rwanda. World Bank Policy Research Working Paper, (7461). https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2682850 Caldeira, E., Compaore, A., Dama, A. A., Mansour, M., & Rota-Graziosi, G. (2019). Effort fiscal en Afrique subsaharienne: les résultats d’une nouvelle base de données 1. Revue d’économie du développement, 27(4), 5-51. https://uca.hal.science/hal-03182352/document Canares, M. P. (2016). Creating the Enabling Environment for more Transparent and Better-resourced Local Governments: A Case of E-taxation in the Philippines. Information Technology for Development, 22(sup1), 121-138. https://doi.org/10.1080/02681102.2015.1121857 Chen, X., & Nordhaus, W. (2015). Using luminosity data as a proxy for economic statistics. Proceedings of the National Academy of Sciences, 112(14), 5080-5085. https://doi.org/10.1073/pnas.1017031108 Collier, P., & Hoeffler, A. (2004). Greed and grievance in civil war. Oxford Economic Papers, 56(4), 563-595. https://doi.org/10.1093/oep/gpf064 Dai, Z., Hu, Y., & Zhao, G. (2017). The suitability of different nighttime light data for GDP estimation at different spatial scales and regional levels. Sustainability, 9(2), 305. https://doi.org/10.3390/su9020305 Dumitrescu, E.-I., & Hurlin, C. (2012). Testing for Granger non-causality in heterogeneous panels. Economic Modelling, 29(4), 1450-1460. https://doi.org/10.1016/j.econmod.2012.02.014 Doghmi, H. (2020). La capacité de mobilisation des recettes fiscales au Maroc (No. 2020-1). Bank Al-Maghrib, Département de la Recherche. Elvidge, C. D., Baugh, K. E., Kihn, E. A., Kroehl, H. W., Davis, E. R., & Davis, C. W. (1997). Relation between satellite observed visible-near infrared emissions, population, economic activity and electric power consumption. International Journal of Remote Sensing, 18(6), 1373-1379. https://doi.org/10.1080/014311697218485 Farzanegan, M. R., & Fischer, S. (2021). Lifting of international sanctions and the shadow economy in Iran a view from outer space. Remote Sensing, 13(22), 4620.https://doi.org/10.3390/rs13224620 Farzanegan, M. R., & Hayo, B. (2019). Sanctions and the shadow economy: Empirical evidence from Iranian provinces. Applied Economics Letters, 26(6), 501-505. https://doi.org/10.1080/13504851.2018.1486981 Fenochietto, M. R., & Pessino, M. C. (2013). Understanding countries’ tax effort. International Monetary Fund. Galimberti, J. K. (2020). Forecasting GDP growth from outer space. Oxford Bulletin of Economics and Statistics, 82(4), 697-722. https://doi.org/10.1111/obes.12361 Ghosh, T., Anderson, S., Elvidge, C., & Sutton, P. (2013). Using Nighttime Satellite Imagery as a Proxy Measure of Human Well-Being. Sustainability, 5(12), 4988-5019. https://doi.org/10.3390/su5124988 Ghosh, T., Anderson, S., Powell, R. L., Sutton, P. C., & Elvidge, C. D. (2009). Estimation of Mexico’s informal economy and remittances using nighttime imagery. Remote Sensing, 1(3), 418-444. https://doi.org/10.3390/rs1030418 González, F. A. I., London, S., & Santos, M. E. (2021). Disasters and economic growth: evidence for Argentina. Climate and Development, 13(10), 932-943.https://doi.org/10.1080/17565529.2021.1873724 Gordon, R., & Li, W. (2009). Tax structures in developing countries: Many puzzles and a possible explanation. Journal of public Economics, 93(7-8), 855-866. https://doi.org/10.1016/j.jpubeco.2009.04.001 Granger, C. W. (1969). Investigating causal relations by econometric models and cross-spectral methods. Econometrica: journal of the Econometric Society, 424-438. https://doi.org/10.2307/1912791 Gu, Y., Shao, Z., Huang, X., & Cai, B. (2022). GDP forecasting model for China’s provinces using nighttime light remote sensing data. Remote Sensing, 14(15), 3671. https://doi.org/10.3390/rs14153671 Hansen, T. B., Jensen, T. I., Clausen, B. H., Bramsen, J. B., Finsen, B., Damgaard, C. K., & Kjems, J. (2013). Natural RNA circles function as efficient microRNA sponges. Nature, 495(7441), 384-388. https://doi.org/10.1038/nature11993 He, C., Ma, Q., Liu, Z., & Zhang, Q. (2014). Modeling the spatiotemporal dynamics of electric power consumption in Mainland China using saturation-corrected DMSP/OLS nighttime stable light data. International Journal of Digital Earth, 7(12), 993–1014. https://doi.org/10.1080/17538947.2013.822026. Henderson, V., Squires, T., Storeygard, A., & Weil, D. (2018). The Global Distribution of Economic Activity: Nature, History, and the Role of Trade. Quarterly Journal of Economics, 133(1), 357-406. https://doi.org/10.1093/qje/qjx030 Henderson, J. V., Storeygard, A., & Weil, D. N. (2012). Measuring economic growth from outer space. American economic review, 102(2), 994-1028. https://doi.org/10.1257/aer.102.2.994 Keola, S., Andersson, M., & Hall, O. (2015). Monitoring economic development from space: using nighttime light and land cover data to measure economic growth. World Development, 66, 322-334. https://doi.org/10.1016/j.worlddev.2014.08.017. Krugman, P. (1991). Increasing returns and economic geography. Journal of Political Economy, 99(3), 483-499. http://google.comwww.rrojasdatabank.info/krugman1991.pdf Lawin, L. M. (2023). ICTs Development and Tax Revenue Mobilization in West African Economic and Monetary Union Countries. American Journal of Economics, 13(1), 1-12. http://article.sapub.org/10.5923.j.economics.20231301.01.html Levin, A., Lin, C. F., & Chu, C. S. J. (2002). Unit root tests in panel data: asymptotic and finite-sample properties. Journal of econometrics, 108(1), 1-24. https://doi.org/10.1016/S0304-4076(01)00098-7 Li, X., Cai, G., & Luo, D. (2020). GDP distortion and tax avoidance in local SOEs: Evidence from China. International Review of Economics & Finance, 69, 582-598. https://doi.org/10.1016/j.iref.2020.06.042 Liang, H., Guo, Z., Wu, J., & Chen, Z. (2020). GDP spatialization in Ningbo City based on NPP/VIIRS night-time light and auxiliary data using random forest regression. Advances in Space Research, 65(1), 481-493. https://doi.org/10.1016/j.asr.2019.09.035 Lopez-Ruiz, H., Blazquez, J., & Hasanov, F. (2019). Esfimafing Saudi Arabia’s regional GDP using satellite nighttime light images (No. ks--2019-dp80). Ma, C., Niu, Z., Ma, Y., Chen, F., Yang, J., & Liu, J. (2019). Assessing the distribution of heavy industrial heat sources in India between 2012 and 2018. ISPRS International Journal of Geo-Information, 8(12), 568. https://doi.org/10.3390/ijgi8120568 Mallick, H. (2021). Do governance quality and ICT infrastructure influence the tax revenue mobilisation? An empirical analysis for India. Economic Change and Restructuring, 54(2), 371-415. https://doi.org/10.1007/s10644-020-09282-9 Mascagni, G., Moore, M., & McCluskey, R. (2014). Tax Revenue Mobilistation In Developing Countries: Issues and Challenges. European Union (Belgium). https://www.europarl.europa.eu/RegData/etudes/etudes/join/2014/433849/EXPO-DEVE_ET(2014)433849_EN.pdf Mawejje, J., & Sebudde, R. K. (2019). Tax revenue potential and effort: Worldwide estimates using a new dataset. Economic Analysis and Policy, 63, 119-129. https://doi.org/10.1016/j.eap.2019.05.005 Michalopoulos, S., & Papaioannou, E. (2014). National Institutions and Subnational Development in Africa. Quarterly Journal of Economics, 129(1), 151-213. https://doi.org/10.1093/qje/qjt029 Musgrave, R. A. & Musgrave, P. B. (1989). Public Finance in Theory and Practice. McGraw-Hill. https://www.nispa.org/files/publications/ebooks/Public-Finance-Theory-and-Practice.pdf Oates, W. E. (1972). Fiscal Federalism. Harcourt Brace Jovanovich. Peter B., Lars O. (2011). Benchmarking with DEA, SFA, and R. International Series in Operations Research and Management Science, Vol. 157, 368 pp. Prichard, W. (2016). Taxation, Responsiveness and Accountability in Sub-Saharan Africa: The Dynamics of Tax Bargaining. Cambridge University Press. https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=18b0189596c828f9c502ecabc814bfe5a2353691 Tanaka, K., & Keola, S. (2017). Shedding light on the shadow economy: A nighttime light approach. The Journal of Development Studies, 53(1), 32-48. https://doi.org/10.1080/00220388.2016.1171845 Terefe, K. D., & Teera, J. (2018). Determinants of tax revenue in East African countries: An application of multivariate panel data cointegration analysis. Journal of Economics and International Finance, 10(11), 134-155. https://doi.org/10.5897/JEIF2018.0924 Tong, L., Hu, S., & Frazier, A. E. (2018). Mixed accuracy of nighttime lights (TNL)-based urban land identification using thresholds: Evidence from a hierarchical analysis in Wuhan Metropolis, China. Applied Geography, 98, 201-214. https://doi.org/10.1016/j.apgeog.2018.07.017 Ullah, S., Akhtar, P., & Zaefarian, G. (2018). Dealing with endogeneity bias: The generalized method of moments (GMM) for panel data. Industrial Marketing Management , 71 , 69-78. https://doi.org/10.1016/j.indmarman.2017.11.010 Wooldridge, J. M. (2010). Econometric analysis of cross section and panel data. MIT press. Zhou, Y., Ma, T., Zhou, C., & Xu, T. (2015). Nighttime light derived assessment of regional inequality of socioeconomic development in China. Remote Sensing, 7(2), 1242-1262. https://doi.org/10.3390/rs70201242 Additional Declarations The authors declare no competing interests. Supplementary Files ResearchHighlights.docx Research Highlights Annexe.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-6329840","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":435469329,"identity":"344e6835-3810-46d3-888c-67b90ee87876","order_by":0,"name":"LAWIN Laifoya Moise","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA00lEQVRIiWNgGAWjYBACPiA+AMQ8DAzMIFpChqAWNoQWtgSQFh6itEABjwEDRC8hLexnDA8X5tjIyLf3fH51o8aCh4H98NENeLXw5BgcnrktjcfgzNlt1jnHgA7jSUu7gd9huRsO8247zGMgkbvNOIcNqEWCxwy/Fv63EC3y8988M875R4wWCagtDDd4mB/nthGl5f0HoBaQX9LMmHP7JHjYCPmFnz8t+TPvNht7+fbDjz/nfKuT42c/fAyvFlQbwSSxykGA+QMpqkfBKBgFo2DkAACso0KpNi63OgAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0003-2406-3826","institution":"","correspondingAuthor":true,"prefix":"","firstName":"LAWIN","middleName":"Laifoya","lastName":"Moise","suffix":""}],"badges":[],"createdAt":"2025-03-28 16:45:41","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-6329840/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6329840/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":79666266,"identity":"d579035e-9d26-4d65-b0f8-088722c4cf53","added_by":"auto","created_at":"2025-04-01 10:16:49","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1697551,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6329840/v1/517e49fc-0bb9-4f38-804a-b6f7ae1bffb6.pdf"},{"id":79661688,"identity":"63623822-c59c-45db-9cdb-ea80995bc499","added_by":"auto","created_at":"2025-04-01 09:44:47","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":16171,"visible":true,"origin":"","legend":"\u003cp\u003eResearch Highlights\u003c/p\u003e","description":"","filename":"ResearchHighlights.docx","url":"https://assets-eu.researchsquare.com/files/rs-6329840/v1/9384f4d3b1975c8df85074b4.docx"},{"id":79661687,"identity":"18320ee2-f00f-4976-bc0c-35ac8cff3160","added_by":"auto","created_at":"2025-04-01 09:44:47","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":115464,"visible":true,"origin":"","legend":"","description":"","filename":"Annexe.docx","url":"https://assets-eu.researchsquare.com/files/rs-6329840/v1/b377449cb5e78226bb803b5a.docx"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eEstimation of the sub-national fiscal potential of WAEMU countries using satellite images of nighttime lights data\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eIn recent years, mobilizing domestic public resources to support development has become a major challenge for developing countries, in the face of tightening external financing (Benitez et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). West African Economic and Monetary Union (UEMOA) countries, like many other developing countries, are facing growing challenges in terms of domestic resource mobilization. Statistics (tax revenues mobilized as a % of GDP) are stagnating, calling for reforms and interventions in which the contribution of all players (public and private sector) is critical. Data from the Central Bank of West African States (CBWAS) show that tax revenues as a proportion of GDP averaged between 9.68% and 13.5% in the WAEMU over the period 2001\u0026ndash;2022, compared with a minimum convergence threshold set by WAEMU countries at 20% of GDP. This low level of tax revenue mobilized by developing countries further increases budget deficits and public debt, and slows the speed toward Sustainable Development Goals achievements. To understand and mitigate these challenges, it is essential to examine the fiscal potential of WAEMU countries on a daily basis, in order to better coordinate efforts to mobilize national public resources.\u003c/p\u003e \u003cp\u003eAlfirman (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2003\u003c/span\u003e) defines fiscal potential as the tax ratio that would result from an economy's use of all its resources and its capacity to collect all possible tax revenues. From a technical point of view, fiscal potential corresponds to the maximum amount or capacity of tax revenues that an economy can mobilize, given its institutional, demographic and economic characteristics (Caldeira et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Existing literature reveals that in developing countries, tax revenues are influenced by the preponderance of the informal sector in the economy, generous tax incentives, governance problems, structural bottlenecks, tax policy and administrative limitations (Mawejje et al., 2019), inadequate administrative infrastructure (Mascagni et al, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2014\u003c/span\u003e, Doghmi, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Mallick, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and poor use of information and communication technologies (ICT) in the revenue mobilization process (Mascagni et al, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Canares, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eA major limitation of the existing literature is that most studies are focused on national tax potential, neglecting sub-national disparities in tax potential. And yet, for better mobilization of tax revenues, it is essential to have a finer estimate of tax potential at level two and three of administrative divisions. However, the unavailability of disaggregated economic data is an obstacle to this sub-national estimation of tax potential.\u003c/p\u003e \u003cp\u003eHow can fiscal potential be estimated and distributed at sub-national level in the context where the lack of disaggregated economic data is prevailing? Specifically, to what extend can night lights, commonly used to estimate sub-national GDPs, constitute a relevant proxy for breaking down national fiscal potential between administrative divisions? This question highlights the problem of sub-national estimation of fiscal potential in a context of unavailability of disaggregated data. This study tends to respond whether night lights can be used as a proxy for sub-national fiscal potential estimation.\u003c/p\u003e \u003cp\u003eIn literature, the assessment of fiscal potential is generally carried out at national level and justified by the gap of data at lower administrative levels. This constraint on sub-national data availability often prevents a more refined analysis of sub-national fiscal disparities. To overcome this gap in the literature, we employ exploiting night-light data as a proxy to map local or sub-national economic activity. This approach is based on the idea that satellite-captured light intensity is correlated with economic activity and, by extension, potential fiscal capacity. By using sub-national night lights to break down national fiscal potential between administrative divisions, we propose an empirical solution that address the challenge of lack of disaggregated data, while providing a better understanding of territorial fiscal disparities.\u003c/p\u003e \u003cp\u003eTotal night lighting (TNL) offers the potential to measure economic activity beyond formal GDP (Farzanegan et al., 2021, Farzanegan et al., 2019; Ghosh et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Tanaka et al., 2017). TNLs can measure regional inequalities in public services in real time (Zhou et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Li et al., 2021) and have been used to predict or estimate national and sub-national GDP and industrial sector output in several countries (Henderson, 2012; Farzanegan et al., 2019; Tanaka et al., 2017; Gu et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Liang et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In addition to its contribution to the literature, this study provides countries with a tool that can be used to determine the level of tax revenue to be mobilized by region of the country.\u003c/p\u003e \u003cp\u003eIn this study, we adopt a standardized plan. After the introduction, we present the literature review, followed by the methodological approach used. In a joint-section, results are presented and discussed before conclusion and recommendation.\u003c/p\u003e"},{"header":"Literature review","content":"\u003cp\u003eThis section will present theorical and empirical literature review\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eTheorical literature review\u003c/h2\u003e \u003cp\u003eThe tax potential or tax frontier is based on a number of theories, notably : 1. theory of taxpaying capacity, 2. theory of tax efficiency (or tax effort), 3. theory of tax decentralization, and 4. theory of tax modernization and technological innovation.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eTheory of taxpaying capacity\u003c/h3\u003e\n\u003cp\u003eAdvocated by Musgrave et al. (1989), the tax capacity theory is based on the principle that each agent or economic entity should contribute to tax revenues according to its financial capacity. For Musgrave et al. (1989), fiscal potential is determined by income level, wealth and overall economic activity.\u003c/p\u003e\n\u003ch3\u003eTheory of tax efficiency\u003c/h3\u003e\n\u003cp\u003eTax efficiency (or tax effort) theory is based on the idea that fiscal potential is defined in terms of a state's ability to maximize revenue by optimizing tax administration and reducing tax evasion.\u003c/p\u003e\n\u003ch3\u003eTheory of tax decentralization\u003c/h3\u003e\n\u003cp\u003eThe theory of fiscal decentralization presented by Bahl et al. (2008). Oates (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e1972\u003c/span\u003e) emphasizes the distribution of fiscal resources between levels of administration (national, regional, local) and stresses the importance of fiscal autonomy for efficient public finance management.\u003c/p\u003e\n\u003ch3\u003eTheory of tax modernization and technological innovation\u003c/h3\u003e\n\u003cp\u003eDeveloped by Besley et al. (2013) and Prichard (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), this theory highlights the rise of digital technologies and computer systems as a factor in improving the assessment of tax potential, particularly in countries where economic data is limited.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eEmpirical literature review\u003c/h2\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003eDeterminants of fiscal potential\u003c/h2\u003e \u003cp\u003eA large body of literature addresses various factors that can affect tax potential. At the macroeconomic level, the contribution of sectors to the formation of national wealth (GDP) influences tax revenues. In their analysis, Caldeira et al. (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), Mawejje et al. (2019), and Benitez et al. (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) have shown that the primary sector (agriculture, mining) has a negative effect on tax revenue formation. These authors' analysis is based on the idea that in developing countries, the primary sector is characterized by low productivity and a predominance of the informal sector (Benitez et al, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), suggesting that the higher the contribution of the primary sector to GDP, the less able the economy is to mobilize tax revenues. Studies have also shown that industrial sector development is an asset for expanding fiscal potential and mobilizing tax revenues.\u003c/p\u003e \u003cp\u003eMawejje et al. (2019), Teref et al. (2018) and Lawin (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) found in their study that the development of the industrial sector has a positive and significant effect on tax revenue formation. Most of these authors justify their results by the fact that the industrialization process is associated with the transformation of commodities with apparent added value. This industrialization process as a whole generates opportunities for tax mobilization within the supply chain particularly through: (i) income tax on people employed in the industrial process; (ii) corporate tax when the industrialization process is sanctioned by a positive result in terms of profits and (iii) value-added tax (VAT).\u003c/p\u003e \u003cp\u003eSome scientific works have found a positive effect, while others affirm negative and insignificant effects between service sector development and taxation. Teref et al. (2018) showed that, for East African countries over the period 1992–2015, the service sector has a positive and significant impact on fiscal capacity. Doghmi (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) works support this point of view with a panel of 76 developing countries over the period 1980–2017, that the services sector is positively associated with fiscal capacity. But this relationship is not statistically significant. In contrast, Lawin (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) reveals that the effect of the services sector on fiscal capacity is negative and insignificant for UEMOA countries. This result is justified by the preponderance of informal activities in the service sector in UEMOA countries. Other factors influence tax revenues and tax potential in UEMOA zone. These include GDP per capita (Mallick, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Lawin, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Benitez et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2023\u003c/span\u003e,), trade openness (Gordon et al., 2009; Mallick, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), institutional factors (Canares, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) and technological factors (Mallick, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Adegboye et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWhile the literature highlights several determinants of national fiscal potential, such as structural, economic, technological and institutional factors, one of the main challenges remains the measurement of this potential at sub-national level. In this context, numerous research studies have explored the use of satellite data, in particular night lights, as a proxy for local economic activity. This approach, initially developed to estimate sub-national GDPs in the absence of detailed economic statistics, offers an alternative for breaking down national fiscal potential between administrative divisions.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e\n\u003ch3\u003eTotal night lighting: indicator of economic activity\u003c/h3\u003e\n\u003cp\u003eSatellite data on nighttime light intensity are produced by the Line Scan Operational System of the US Department of Defense's Satellite Weather Program. They record the brightness of light emissions from buildings, roads, industrial areas, parking lots and other artificially lit sources (Elvidge et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e1997\u003c/span\u003e), with higher values indicating more intense economic activities (Bagan et al., 2015; Keola et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Tong et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Non-urban activities such as agriculture and burning can produce light emissions captured by TNL sensors, urban areas are the main source of light related to housing, land and energy consumption, and other development-related activities (He, Ma, Liu, \u0026amp; Zhang, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2014\u003c/span\u003e, Tong et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSeveral studies have established a strong correlation between night-time brightness and GDP. Gu et al. (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) found a strong correlation between TNL and GDP. Liang et al (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) estimated the GDP of a Chinese province using TNL data, Galimberti (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) proved the effectiveness of TNL data in predicting regional GDP growth in France. Dai et al. (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) and Ma et al. (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) used TNL data to estimate industrial sector output. González et al. (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) examined the impact of regional disasters on economic growth using TNL data. Overall, TNL has demonstrated its effectiveness in predicting macroeconomic aggregates. This study focuses on the pertinence of TNL to predict fiscal potential in the countries of the West African Economic and Monetary Union. In the following section, data source and methods will be presented.\u003c/p\u003e "},{"header":"Data and Methods","content":"\u003ch2\u003eData Source\u003c/h2\u003e\u003cp\u003eThis study focuses on the West African Economic and Monetary Union countries (Benin, Togo, Niger, Burkina-Faso, Mali, Guinea-Bissau, Senegal and Côte d'Ivoire) over the period 2012–2022. Tax revenues (% GDP) and GDP per capita are taken from the database of the Central Bank of West African States (BCEAO), while value added in the industrial, primary and tertiary sectors, trade openness, Internet users (% of population) and mobile phone subscriptions (per 100 inhabitants) are taken from the World Bank database (WDI). The choice of variables was inspired by the empirical literature, and especially works of Canares (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), Mallick (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), Benitez et al. (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The data on total night light or Visible Infrared Imaging Radiometer Suite (VIIRS) comes from AIDDATA. These data are collected using the satellite from space and concern light emanating from economic activity, ship fleets and auroral activity (Henderson et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Hansen et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Bundervoet, 2015). Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e below presents some descriptive statistics on the data used in this study.\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\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\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSource\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStd. Dev.\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMin\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMax\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFiscal (%GDP)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBCEAO\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.699\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.723\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.5\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e17.7\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGDP per capita\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBCEAO\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e486,218.92\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e238,390.52\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e232,623.4\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e111,2798.4\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003etrade openness\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWorld Bank database\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e56.16\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.952\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e36.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e83\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003etertiary sectors\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWorld Bank database\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29.055\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.757\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e58.1\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eprimary\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWorld Bank database\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21.467\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.9\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.8\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003evalue added in the industrial\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWorld Bank database\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e49.448\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15.209\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16.5\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e74.8\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInternet users (% of population)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWorld Bank database\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19.409\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14.944\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e68.9\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emobile phone subscriptions (per 100 inhabitants)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWorld Bank database\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e89.212\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e30.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e174.8\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal night light (Visible Infrared Imaging Radiometer Suite (VIIRS))\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://geo.aiddata.org/query/#!/\u003c/span\u003e\u003cspan address=\"https://geo.aiddata.org/query/#!/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e49,525.623\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e63,572.823\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e637.849\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e351,678.77\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGouvernance index\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMo Ibrahim database\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e51.702\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.326\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e37.2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e62.3\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSource: BCEAO and World Bank data, author's estimation\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows that over the 2012–2022 period, tax revenue (%GDP) averages 12.7 in the WAEMU region, with a standard deviation of 2.72% of GDP and a maximum of 17.7% of GDP. It also shows that the average contribution of the industrial sector to GDP formation is 49.45% over the same period 2012–2022, while that of the services sector is 29.05%. As shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, trade openness (exports + imports) averages 56.2% of GDP for WAEMU countries, with a standard deviation of 9.95% of GDP. It should be noted that the average Internet penetration or access rate in the WAEMU over the period 2012–2022 is 19.40%, with a standard deviation of 14.94%.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eThe sub-national fiscal potential is estimated using a three-step approach. First, we estimate the national fiscal potential using Stochastic Frontier Analysis (SFA) (Schimidt et al., 1977). Then, we evaluate the predictive power of total night-time light at national level on fiscal potential. Finally, we estimate subnational fiscal potential using subnational nightlight data and based on the approach proposed by Lopez-Ruiz and Hassanov (2019). The advantage of using nightlight data is that it is available for all countries and smaller geographical units (provincial, communal, prefectural, departmental units).\u003c/p\u003e\u003ch2\u003eFirst stage: estimating national fiscal potential\u003c/h2\u003e\u003cp\u003eTwo approaches are often used to assess performance in tax revenue mobilization: a parametric approach and a non-parametric approach. The non-parametric method Data Envelopment Analysis (DEA) is based on optimization using minimal extrapolation, whereas parametric approaches use classical statistical principles, in particular the maximum likelihood principle (Peter et al., 2011). In both approaches, actual observations are used to estimate tax potential and tax effort.\u003c/p\u003e\u003cp\u003eThe approach used in this study is the Stochastic Frontier Analysis (SFA) method, the most widely used by researchers and international institutions. This approach takes into account the limitations of the DEA model as well as random phenomena that could occur over time, independently of the tax system apparatus.\u003c/p\u003e\u003cp\u003eThe Stochastic Frontier Analysis method is based on an optimization program in which the efficiency frontier function has a specific functional form: \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:y=f\\left(x,\\:\\beta\\:\\right)exp\\left(v\\right)exp(-u)\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:u\\)\u003c/span\u003e\u003c/span\u003e denotes the efficiency term and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:v\\)\u003c/span\u003e\u003c/span\u003e the error term (Peter et al., 2011). The logarithmic form of the model is given by :\u003c/p\u003e\u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:{y}^{k}=f\\left({x}^{k}\\:,\\:\\beta\\:\\right)+{v}^{k}-\\:{u}^{k}\\)\u003c/span\u003e \u003c/span\u003e where \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{v}^{k}\\:\\text{N}(0\\:;\\:{}_{\\text{v}}²)\\)\u003c/span\u003e\u003c/span\u003e et \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{u}^{k}\\:{\\text{N}}_{+}(0\\:;\\:{}_{\\text{u}}²)\\)\u003c/span\u003e\u003c/span\u003e ; k = 1, … K\u003c/p\u003e\u003cp\u003eThe term v takes into account the stochastic nature of the tax revenue formation process and any errors in measuring inputs and outputs, and the term u is any inefficiency (Peter et al., 2011). The inputs correspond to all factors likely to contribute to the formation of tax revenues, which is the output. Given the availability of data, we used as inputs GDP per capita, value added in the industrial, primary and service sectors, trade openness, digital development or ICT (Internet users (% of population), mobile phone subscriptions (per 100 inhabitants) and as output tax revenues (as a % of GDP). The choice of these variables was based on the empirical literature (Teref et al., 2018; Caldeira et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Mawejje et al., 2019; Doghmi, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Mallick, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Adegboye et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Benitez et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; and Lawin, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2023\u003c/span\u003e)\u003c/p\u003e\u003cp\u003eEfficiency is measured by the formula :\u003c/p\u003e\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:D\\left({x}^{0},{y}^{0}\\right)=\\frac{\\text{T}\\text{a}\\text{x}\\:\\text{r}\\text{e}\\text{v}\\text{e}\\text{n}\\text{u}\\text{e}\\:\\text{a}\\text{c}\\text{t}\\text{u}\\text{a}\\text{l}\\text{l}\\text{y}\\:\\text{r}\\text{a}\\text{i}\\text{s}\\text{e}\\text{d}}{\\text{M}\\text{a}\\text{x}\\text{i}\\text{m}\\text{u}\\text{m}\\:\\text{e}\\text{x}\\text{p}\\text{e}\\text{c}\\text{t}\\text{e}\\text{d}\\:\\text{T}\\text{a}\\text{x}\\:\\text{r}\\text{e}\\text{v}\\text{e}\\text{n}\\text{u}\\text{e}\\:}=\\:\\frac{f\\left({x}^{0},\\:\\beta\\:\\right)-{u}^{0}}{f\\left({x}^{0},\\:\\beta\\:\\right)}$$\u003c/div\u003e\u003c/div\u003e\u003ch2\u003eSecond stage: Examination of the effect of night-time luminaires on fiscal potential\u003c/h2\u003e\u003cp\u003eThe second step consists of estimating the explanatory power of night lights on the fiscal potential of the eight (08) countries. We used a panel data model specified in log-linear form in order to interpret the estimated parameters in terms of elasticity.\u003c/p\u003e\u003cp\u003eAt the beginning, we examine the presence of unit roots in the series used. The presence of a unit root was examined using the Levin-Lin-Chu (2002) test. This test, commonly used for panel data, enables us to test whether series are stationary or follow an autoregressive (AR) process. Under the null hypothesis, the Levin-Lin-Chu test assumes the presence of a unit root in the series for all countries (i.e., the series for all countries is non-stationary) (Levin et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). Conversely, the alternative assumption is that at least one series is stationary.\u003c/p\u003e\u003cp\u003eThe results of both tests show the presence of a unit root in the countries' tax potential and TNL series. This result leads us to introduce the first difference series into the model.\u003c/p\u003e\u003cp\u003eCausality link\u003c/p\u003e\u003cp\u003eThe examination of the causal relationship between TNL and fiscal potential is inspired by the test procedure of Granger (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e1969\u003c/span\u003e) and Dumitrescu and Hurlin (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) and the work of Weinhold (1996). This procedure takes into account possible heterogeneity across countries. The basic specification of the Dumitrescu and Hurlin (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) test is given by the model below.\u003c/p\u003e\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:{Y}_{i,t}={\\lambda\\:}_{i}+\\:{\\sum\\:}_{k=1}^{K}{\\alpha\\:}_{1i}^{\\left(k\\right)}\\:{Y}_{i,t-k}+\\:{\\sum\\:}_{k=1}^{K}{\\beta\\:}_{1i}^{\\left(k\\right)}\\:{X}_{i,t-k}\\:+\\:{\\epsilon\\:}_{1i,t}\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:i=1,\\dots\\:,\\:N\\:;t=1,\\dots\\:,\\:T\\:\\left(1\\right)$$\u003c/div\u003e\u003c/div\u003e\u003cp\u003eUnder the null hypothesis of homogeneous non-causality, there is no causality from X to Y for all the cross-sectional units in the panel.\u003c/p\u003e\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:{H}_{0}\\::\\:{\\beta\\:}_{i}=0\\:\\:\\:\\:\\:\\forall\\:\\:\\:i=1,\\dots\\:,\\:N$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e\u003cp\u003eThe alternative hypothesis assumes the existence of causality from X to Y for at least one country.\u003c/p\u003e\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$$\\:{H}_{1}\\::\\:{\\beta\\:}_{i}=0\\:\\:\\:\\:\\:\\forall\\:\\:\\:i=1,\\dots\\:,\\:{N}_{1}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e3\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equ3\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ3\" name=\"EquationSource\"\u003e\n$$\\:{\\beta\\:}_{i}\\:\\ne\\:0\\:\\:\\:\\:\\:\\forall\\:\\:\\:i={N}_{1},\\dots\\:,\\:N$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e4\u003c/div\u003e\u003c/div\u003e\u003cp\u003eTo test these hypotheses, Dumitrescu and Hurlin (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) propose a procedure that consists of running the N individual regressions of the model and performing F-tests of the K linear hypotheses \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\beta\\:}_{i1}=\\dots\\:=\\:{\\beta\\:}_{iK}=0\\)\u003c/span\u003e\u003c/span\u003e to recover the individual Wald statistic W_and finally to calculate the average Wald statistic \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\underset{\\_}{W}\\:\\)\u003c/span\u003e\u003c/span\u003e :\u003c/p\u003e\u003cdiv id=\"Equ4\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ4\" name=\"EquationSource\"\u003e\n$$\\:{\\underset{\\_}{W}}_{NT}=\\frac{1}{N}{\\sum\\:}_{i=1}^{N}{W}_{iT}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e5\u003c/div\u003e\u003c/div\u003e\u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{W}_{iT}\\)\u003c/span\u003e\u003c/span\u003e are the individual Wald statistics for the Granger causality test (Dumitrescu and Hurlin, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Assuming that the \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{W}_{iT}\\)\u003c/span\u003e\u003c/span\u003e statistics are independent and identically distributed, we calculate a standardized statistic, Z-bar :\u003c/p\u003e\u003cdiv id=\"Equ5\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ5\" name=\"EquationSource\"\u003e\n$$\\:\\underset{\\_}{Z}=\\sqrt{\\frac{N}{2K}}\\:\\:\\:{\\left(\\underset{\\_}{W}-K\\right)}_{T,N\\:\\to\\:{\\infty\\:}\\to\\:}^{\\:\\:\\:\\:\\:\\:\\:d}\\:\\:N(0,\\:1)$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e6\u003c/div\u003e\u003c/div\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eThe bidirectional causality between TNL and fiscal potential raises an endogeneity bias. To take into account this endogeneity, we used the Generalized Method of Moments (GMM) proposed by Arellano and Bond (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e1991\u003c/span\u003e) and Blundell and Bond (1998). This method offers an advantage compared to other regression models. Indeed, this method provides more consistent results in the presence of endogeneity problems (Ullah, et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), takes into account unobserved individual effects, persistence of the dependent variable and yields efficient estimators (Arellano and Bond, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e1991\u003c/span\u003e; Wooldridge, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). We used the system GMM method due to the persistence of the dependent variable (Blundell et al., 1998; Ullah et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) (correlation coefficient of the one-period lagged variable of fiscal potential is equal to 0.98, positive and significant prob = 0.000). In addition, the GMM system method is more efficient than the GMM difference method and also reduces the problem of weak instruments (Blundell et al., 1998; Ullah, et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe model specification is as follows:\u003c/p\u003e\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e\n$$\\:{\\text{l}\\text{n}(Fiscalpotentiel}_{it})={\\beta\\:}_{0}+{\\beta\\:}_{1}{\\text{l}\\text{n}(TNL}_{it})+{\\epsilon\\:}_{it}$$\u003c/div\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eWhere i represents the country and t the year.\u003c/p\u003e\u003cp\u003eFollowing the example of Arellano and Bond (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e1991\u003c/span\u003e), we introduced endogenous instruments (TNL) and lags of the dependent variable (fiscal potential). We also introduced an exogenous instrument (governance index). The choice of governance as an exogenous instrument is justified. First, governance is a key determinant of economic performance and institutional quality, directly affecting investment, productivity and growth. Acemoglu et al. (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2001\u003c/span\u003e, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2005\u003c/span\u003e) have shown that effective governance and good institutional quality have a causal effect on investment and growth. Henderson et al. (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) have also shown that night light data capture well the effects of institutional policies on development.\u003c/p\u003e\u003ch2\u003eThird stage: estimation of the regional fiscal potential of each WAEMU country\u003c/h2\u003e\u003cp\u003eHaving shown that total night lights is a appropriate indicator for measuring fiscal potential, we assess the sub-national fiscal potential of countries using the method proposed by Lopez-Ruiz et al. (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). This method considers, in fact, the share of each region's night lights in national night lights as the share or its contribution to the formation of national wealth or value added in the national economy. This relationship is a consequence of the existence of a linear relationship between economic activity and night lights (Lopez-Ruiz and Hassanov, 2019).\u003c/p\u003e\u003cdiv id=\"Equd\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equd\" name=\"EquationSource\"\u003e\n$$\\:{FiscalpotentielR\\text{é}gi}_{ijt}={FiscalpotentielNatio}_{it}\\text{*}\\frac{{TNLR\\text{é}gi}_{ijt}}{{TNLNatio}_{ijt}}$$\u003c/div\u003e\u003c/div\u003e\u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:{FiscalpotentielR\\text{é}gi}_{ijt}\\)\u003c/span\u003e \u003c/span\u003e : the regional tax potential of region j in country i in period t ;\u003c/p\u003e\u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:{FiscalpotentielNatio}_{it}\\:\\)\u003c/span\u003e \u003c/span\u003e: the national fiscal potential of country i in period t\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\:\\)\u003c/span\u003e\u003c/span\u003e; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{TNLR\\text{é}gi}_{ijt}\\:\\)\u003c/span\u003e\u003c/span\u003e: night-time light intensity of region j in country i at period t\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\:\\)\u003c/span\u003e\u003c/span\u003e; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{TNLNatio}_{ijt}\\:\\)\u003c/span\u003e\u003c/span\u003e: global light intensity of country i at period t\u003c/p\u003e"},{"header":"Results and discussion","content":"\u003cp\u003eThe national fiscal potential of WAEMU countries was assessed, and analysis reveals fluctuating trends of tax revenue (as a % of GDP) over the period 2012–2022 (Fig. 1 in annexes). However, several WAEMU countries have improved their performance in tax revenue mobilization over 2020–2022. Burkina-Faso, Benin, Côte d'Ivoire, Senegal and Togo have seen a slight increase in their tax burden, bringing them closer to the tax frontier (Fig. 1 in annexes). This performance is underpinned by the investments made by the various countries in digitizing and modernizing tax and customs administration. Despite the actions taken by WAEMU countries to cover the full fiscal potential, a significant gap remains looking at the tax revenue actually mobilized. Over the period 2012–2022, the fiscal potential or tax frontier remained far above the tax burden (tax revenue as a % of GDP) for each of the eight (08) WAEMU countries (Fig. XXX? in annexes). The tax gap or tax effort averaged 3.7% of GDP per year for Côte d'Ivoire, 4.8% of GDP per year for Benin, 3.4% of GDP per year for Burkina Faso, 2.6% of GDP per year for Mali, 6.2% of GDP per year for Niger, 4.6% of GDP per year for Senegal, 3.5% of GDP per year for Guinea-Bissau and 5.7% of GDP per year for Togo.\u003c/p\u003e\n\u003cdiv id=\"Sec19\"\u003e\n \u003ch2\u003eExamining the relationship between TNLs and national fiscal potential\u003c/h2\u003e\n \u003cp\u003eCausality link\u003c/p\u003e\n \u003cp\u003eWe then examined the correlation between national fiscal potential and national annual total night lights. The result of Pearson's correlation test reveals the existence of a positive and significant correlation between fiscal potential and total night lights, at the 1% threshold. The correlation coefficient is estimated at 0.982 (see Table 12 in annexes). Similarly, the Granger causality test shows the existence of a two-way causal relationship between fiscal potential and total night-time light. In fact, the null hypotheses of non-causality between tax potential and total night lights are rejected with a probability of less than 5% (see Table 2). This result means that total night-time light has predictive power over the fiscal potential of WAEMU countries, and conversely, knowledge of fiscal potential provides information on total night-time light. According to Henderson et al. (2018) and Ghosh et al. (2013), such a result is well justified. Indeed, an increase in light intensity reflects increased economic activity, which translates into a broader tax base and higher potential tax revenues (Henderson et al., 2018). For Ghosh et al. (2013), increased fiscal capacity enables greater investment in infrastructure and public services, which in turn can drive economic development and urbanization, thus contributing to an increase in light intensity. Michalopoulos and Papaioannou (2014) also justify this result on the grounds that public financial resources are likely to stimulate investment in urban infrastructure, generating a virtuous circle between economic development and tax revenue mobilization.\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 2\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eResults of the Dumitrescu and Hurlin (2012) panel causality test\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003eW-bar\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003eZ-bar\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eH0: TNL does not Granger-cause LFPotentieles\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e3.7855\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e5.5711\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eLFPotentieles does not Granger-cause TNL\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e2.9977\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e3.9953\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0001\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cem\u003eSource: BCEAO and World Bank data, author's estimation\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003eThe bidirectional causal relationship between TNL and tax potential raises a problem of endogeneity. We have estimated a fixed-effect or random-effect model (see annexes), and the result of the Hausman test confirms the presence of an individual effect. The results of the GMM model are presented in Table 3 below.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec20\"\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 3\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eResults of estimations\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003eRUMS\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003eDependent variable: Rate of use of microfinance services (% adult population) TUSM.\u003c/p\u003e\n \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eLFPotentieles (-1)\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.92*** (0.0294)\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eTNL\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.08*** (0.0295)\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eNumber of countries\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eNumber of instruments\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eWald test statistic\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e1.86e + 06***\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eAR(1) p value\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.033\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eAR(2) p value\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.735\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eHansen J-test p value\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0.489\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003e*** p \u0026lt; .01, ** p \u0026lt; .05, * p \u0026lt; .1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"2\"\u003eSource: Authors, World Bank and BCEAO databases\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eThe results of the GMM model show that the past value of fiscal potential has a strong positive and significant effect on the present value of potential. The analysis also shows that night lights have a positive and significant effect on fiscal potential, but the coefficient is lower than in the static models (fixed effect or random effect).\u003c/p\u003e\n \u003cp\u003eThe results of the Arellano-Bond auto-correlation test show that the hypothesis of no first-order auto-correlation is rejected (with probability p = 0.033), while the hypothesis of no second-order auto-correlation (AR(2)) is accepted (prob = 0.735). Furthermore, the results of the Hansen test (p = 0.489) reveal that the instruments are valid.\u003c/p\u003e\n \u003cp\u003eOur results show that night lights (TNL) have a positive and significant influence on tax potential. This finding aligns with other previous work demonstrating that night-time light intensity is a predictor of key economic variables, such as subnational GDP (Henderson et al., 2012; Chen et al., 2015) and household consumption. Our results also show the persistence of fiscal potential. This result is consistent with the literature on fiscal inertia, which stresses that tax systems in developing countries tend to evolve slowly due to institutional and administrative rigidities (Fenochietto et al., 2013).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec21\"\u003e\n \u003ch2\u003eFiscal potential of the different regions of UEMOA countries\u003c/h2\u003e\n \u003cp\u003eHaving demonstrated that total night lights are an indicator for measuring national fiscal potential, we assessed the fiscal potential of the first administrative divisions of UEMOA countries using the approach of Lopez-Ruiz et al. (2019). The results of the estimates for the regions are presented in the tables below.\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 4\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eFiscal potential of Togo's regions (as % of GDP)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003eAnnée\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003e2012\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003e2013\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003e2014\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003e2015\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003e2016\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003e2017\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003e2018\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003e2019\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003e2020\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003e2021\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003e2022\u003c/p\u003e\n \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eSavanes Region\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,72\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,83\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,67\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,96\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e1,01\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e1,33\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e1,25\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e1,17\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e1,22\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e1,26\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e1,44\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eKara Region\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e1,82\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e1,72\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e1,53\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e1,60\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e1,57\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e1,61\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e1,67\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e1,57\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e1,70\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e1,54\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e1,67\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eCentrale Region\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,89\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,96\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,87\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,99\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,88\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,97\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,96\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,88\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,97\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e1,12\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e1,09\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003ePlateaux Region\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e1,12\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e1,62\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e1,60\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e2,44\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e2,18\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e3,31\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e3,85\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e3,58\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e4,38\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e5,58\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e6,42\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eMaritime Region\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e14,33\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e16,42\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e16,35\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e14,49\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e13,91\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e12,90\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e12,91\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e12,32\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e12,28\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e12,22\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e12,36\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"12\"\u003eSource: BCEAO and World Bank data, author's estimation\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eAnalysis of Table 4 shows that over the period 2012–2022, Togo's fiscal potential will average 20.64% of GDP. At sub-national level, fiscal potential varies on average between 0.96% of GDP and 13.68% of GDP. The Maritime Region leads with an average fiscal potential of 13.68% of GDP over the period 2012–2022, followed by the Plateaux Region (3.28% of GDP), the Kara Region (1.64% of GDP), the Savanes Region (1.08% of GDP) and the Centrale Region (0.96% of GDP).\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 5\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eFiscal potential of Senegal's regions (as % of GDP)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003eAnnée\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003e2012\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003e2013\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003e2014\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003e2015\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003e2016\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003e2017\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003e2018\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003e2019\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003e2020\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003e2021\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003e2022\u003c/p\u003e\n \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eDakar\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e9,28\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e9,03\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e9,29\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e9,81\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e9,31\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e8,65\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e8,47\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e8,26\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e7,15\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e6,57\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e6,24\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eDiourbel\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e2,70\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e2,70\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e2,43\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e2,45\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e2,28\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e2,26\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e2,33\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e2,66\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e2,81\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e3,32\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e3,54\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eFatick\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,30\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,33\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,27\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,26\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,25\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,35\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,36\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,42\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,55\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,69\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,95\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eKaffrine\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,11\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,13\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,11\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,10\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,10\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,13\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,16\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,22\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,28\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,33\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,40\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eKaolack\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,75\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,83\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,71\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,77\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,70\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,76\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,78\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,97\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e1,12\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e1,34\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e1,65\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eKolda\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,31\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,33\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,25\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,17\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,14\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,15\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,16\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,16\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,20\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,20\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,28\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eLouga\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,74\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,78\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,63\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,66\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,65\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,89\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,89\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,87\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e1,00\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e1,17\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e1,29\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eMatam\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,18\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,20\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,17\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,17\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,18\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,31\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,31\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,29\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,38\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,33\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,32\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eSaint Louis\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,95\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e1,01\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,86\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,80\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,80\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e1,10\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e1,13\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e1,01\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e1,15\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e1,08\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,99\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eSedhiou\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,06\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,06\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,04\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,04\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,03\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,06\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,08\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,10\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,13\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,16\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,22\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eTambacounda\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,24\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,24\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,24\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,26\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,26\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,25\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,24\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,29\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,27\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,35\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,38\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eThies\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e3,58\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e3,84\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e4,00\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e4,04\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e4,08\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e4,44\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e5,25\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e5,16\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e4,71\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e5,36\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e5,47\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eZiguinchor\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,30\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,33\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,36\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,29\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,25\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,30\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,27\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,29\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,34\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,32\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,37\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eKedougou\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,51\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,37\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,33\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,36\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,36\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,38\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,49\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,43\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,42\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,54\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,45\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"12\"\u003eSource: BCEAO and World Bank data, author's estimation\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eFor Senegal, the results show that over the same underlying period, fiscal potential averaged 20.58% of GDP. The results also show that fiscal potential varies between 0.09% and 8.37% of GDP for Senegal's regions. The country's economic capital is the region with the highest fiscal potential, with a potential of 8.37% of Senegal's GDP, followed by the regions of Thies (4.54% of GDP), Diourbel (2.68% of GDP), Saint Louis (0.99% of GDP), Kaolack (0, 94% of GDP), Louga (0.87% of GDP), Fatick (0.43% of GDP), Kedougou (0.42% of GDP), Ziguinchor (0.31% of GDP), Tambacounda (0.27% of GDP), Matam (0.26% of GDP), Kolda (0.21% of GDP), Kaffrine (0.19% of GDP) and the Sedhiou region (0.09% of GDP).\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab7\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 6\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eFiscal potential of Burkina Faso's regions (as % of GDP)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003eAnnée\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003e2012\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003e2013\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003e2014\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003e2015\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003e2016\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003e2017\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003e2018\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003e2019\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003e2020\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003e2021\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003e2022\u003c/p\u003e\n \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eCentre\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e9,55\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e9,37\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e9,84\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e9,52\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e9,58\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e8,73\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e8,53\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e8,70\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e8,91\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e8,89\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e8,97\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eBoucle du\u003c/p\u003e\n \u003cp\u003eMouhoun\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,58\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,65\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,64\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,82\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,85\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,96\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,87\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,76\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,79\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,95\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,85\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eCascades\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,33\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,35\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,32\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,39\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,39\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,39\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,48\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,52\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,66\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,83\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,76\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eCentre-Est\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,54\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,55\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,63\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,65\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,58\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,68\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,70\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,60\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,70\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,80\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,77\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eCentre-Nord\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,50\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,62\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,60\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,63\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,77\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,94\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,92\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,80\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,77\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,77\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,65\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eCentre-Ouest\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,74\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,99\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,90\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,88\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,77\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,81\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,80\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,72\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,75\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,82\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,83\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eCentre-Sud\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,18\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,18\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,18\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,19\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,19\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,28\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,28\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,33\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,31\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,43\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,41\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eEst\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,37\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,37\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,38\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,36\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,35\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,44\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,65\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,55\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,48\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,55\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,47\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eHauts-Bassins\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e3,06\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e3,00\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e2,96\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e3,15\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e3,13\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e3,00\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e2,80\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e2,63\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e2,74\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e2,94\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e2,96\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eNord\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,46\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,44\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,48\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,49\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,52\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,59\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,70\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,68\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,75\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,73\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,65\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003ePlateau Central\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,25\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,26\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,29\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,29\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,27\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,33\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,33\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,30\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,52\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,79\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,95\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eSahel\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,54\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,57\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,68\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,65\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,59\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,56\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,59\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,58\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,52\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,48\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,43\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eSud-Ouest\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,21\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,22\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,21\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,26\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,22\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,26\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,36\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,31\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,33\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,39\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,41\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"12\"\u003eSource: BCEAO and World Bank data, author's estimation\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eThe analysis shows that Burkina-Faso's fiscal potential averages 18.15% of GDP over the period 2012–2022, with a variation of 0.27–9.14% between regions. The Centre region has a higher fiscal potential (9.14% of GDP on average over the period 2012–2022). Hauts-Bassins is the second region with the highest fiscal potential in Burkina, at 2.94% of GDP.\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab8\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 7\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eFiscal potential of Niger's regions (as % of GDP)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003eAnnée\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003e2012\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003e2013\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003e2014\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003e2015\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003e2016\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003e2017\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003e2018\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003e2019\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003e2020\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003e2021\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003e2022\u003c/p\u003e\n \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eTahoua/Agadez\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e2,46\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e2,25\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e2,34\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e2,78\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e2,41\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e2,53\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e2,19\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e2,20\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e2,20\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e2,42\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e2,35\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eDossa\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,33\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,31\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,32\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,56\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,48\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,55\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,48\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,44\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,51\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,61\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,68\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eNiamey\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e4,83\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e4,31\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e4,50\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e6,38\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e6,53\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e6,04\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e7,10\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e7,95\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e7,30\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e6,76\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e6,02\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eTillaberi\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,51\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,49\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,48\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,80\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,72\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,94\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,94\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e1,08\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e1,37\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e1,37\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e1,39\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eMaradi\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,66\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,64\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,68\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e1,10\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e1,04\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e1,13\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,95\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,92\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,93\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e1,13\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e1,32\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eZinder/Diffa\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e9,72\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e11,26\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e9,99\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e7,02\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e5,54\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e4,40\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e3,43\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e2,95\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e3,16\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e3,89\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e3,14\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"12\"\u003eSource: BCEAO and World Bank data, author's estimation\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eOver the period 2012–2022, fiscal potential averaged 16.75% of GDP at national level in Niger, with a variation of between 0.92% and 6.16% of GDP between regions. The Niamey region has a fiscal potential of 6.16% of GDP on average over the study period; 5.86% of GDP on average for the Zinder/Diffa region, 2.38% of GDP for the Tahoua/Agadez region, 0.95% of GDP for the Maradi region, 0.92% of GDP for the Tillaberi region and 0.48% of GDP for the Dossa region.\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab9\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 8\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eFiscal potential of Mali's regions (as % of GDP)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003eAnnée\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003e2012\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003e2013\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003e2014\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003e2015\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003e2016\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003e2017\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003e2018\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003e2019\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003e2020\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003e2021\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003e2022\u003c/p\u003e\n \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eBamako\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e7,27\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e7,70\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e7,46\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e6,81\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e6,48\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e5,58\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e5,61\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e5,73\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e5,53\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e5,09\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e4,89\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eGao\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,01\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,03\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,18\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,22\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,24\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,24\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,26\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,25\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,26\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,24\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,25\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eKayes\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e2,29\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e2,27\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e2,07\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e2,06\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e2,10\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e2,08\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e2,11\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e2,14\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e2,24\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e2,31\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e2,20\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eKidal\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,14\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,11\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,10\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,11\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,13\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,11\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,12\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,15\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,17\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,17\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,20\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eKoulikouro\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e2,82\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e2,94\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e3,06\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e3,33\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e3,57\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e3,96\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e4,21\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e4,58\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e5,11\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e5,29\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e5,32\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eMopti\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,52\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,58\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,72\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,65\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,52\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,54\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,53\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,47\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,51\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,54\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,48\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eSegou\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e1,05\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e1,08\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e1,12\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e1,18\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e1,11\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e1,35\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e1,27\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e1,21\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e1,26\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e1,20\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e1,26\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eSikasso\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e1,70\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e1,86\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e1,70\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e1,68\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e1,50\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e1,62\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e1,70\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e1,66\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e1,78\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e1,74\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e1,87\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eTombouctou\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,01\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,01\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,30\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,40\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,35\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,34\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,38\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,32\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,28\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,27\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,31\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"12\"\u003eSource: BCEAO and World Bank data, author's estimation\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eAt national level, Mali's fiscal potential averaged 16.44% over the study period, with a variation of between 0.14% and 6.20% of GDP between Mali's regions. The regions of Bamako, Koulikouro, Kayes, Sikasso, Segou, Mopti, Timbuktu, Gao and Kidal have a fiscal potential of 6.20% of GDP, 4.02% of GDP, 2.17% of GDP, 1.71% of GDP, 1.19% of GDP, 0.55% of GDP, 0.27% of GDP, 0.20% of GDP and 0.14% of GDP respectively, on average over the period 2012–2022.\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab10\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 9\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eFiscal potential of Côte d'Ivoire's regions (as % of GDP)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003eAnnée\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003e2012\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003e2013\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003e2014\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003e2015\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003e2016\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003e2017\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003e2018\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003e2019\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003e2020\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003e2021\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003e2022\u003c/p\u003e\n \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eBas-Sassandra\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,76\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,72\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,63\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,65\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,60\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,70\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,73\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,68\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,54\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,66\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,88\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eDenguele\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,15\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,15\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,18\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,33\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,38\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,51\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,51\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,53\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,38\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,38\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,43\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eDistrict d'Abidjan\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e4,72\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e4,63\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e4,85\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e4,59\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e4,41\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e4,14\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e4,41\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e4,29\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e2,92\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e2,75\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e2,99\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eDistrict de Yamoussoukro\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e1,13\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e1,06\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e1,10\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e1,03\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,97\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,82\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,84\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,72\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,47\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,47\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,49\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eGoh-Djiboua\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e1,29\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e1,16\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,97\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,94\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,85\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,91\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,90\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,81\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,60\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,63\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,71\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eLacs\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e1,29\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e1,19\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e1,03\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e1,13\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,99\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e1,07\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e1,09\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,93\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,83\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,88\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,93\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eMontagnes\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,38\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,39\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,38\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,45\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,42\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,47\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,51\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,56\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,49\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,66\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,85\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eSassandra-Marahoue\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e1,11\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,99\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,84\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,81\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,75\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,80\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,80\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,77\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,72\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,86\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,99\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eSavanes\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e1,28\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e1,60\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e1,99\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e1,98\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e1,89\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e2,10\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e2,08\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e2,42\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e2,12\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e1,92\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e2,04\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eValle du Bandama\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e1,00\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e1,06\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e1,32\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e1,31\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e1,23\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e1,31\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e1,32\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e1,34\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e1,41\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e1,43\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e1,47\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eWoroba\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,26\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,28\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,27\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,42\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,45\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,60\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,57\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,62\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,66\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,74\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,84\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eZanzan\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,38\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,35\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,33\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,38\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,43\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,53\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,60\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,63\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,60\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,61\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,66\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eLagunes\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e1,07\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e1,00\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,82\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,74\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,66\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,76\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,86\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,79\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,65\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,69\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,73\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eComoe\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,90\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,86\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,75\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,81\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,90\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,98\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e1,08\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e1,00\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,76\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,83\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,87\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"12\"\u003eSource: BCEAO and World Bank data, author's estimation\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eThe estimates show that for Côte d'Ivoire, fiscal potential is estimated at 15.16% of GDP on average over the period 2012–2022. This potential varies between 0.36% and 4.06% of GDP for the regions of Côte d'Ivoire. The District of Abidjan has a fiscal potential of 4.06% of Côte d'Ivoire's GDP, followed by the District of Savanes (1.95% of GDP), Valle Du Bandama (1.29% of GDP), Lacs (1.03% of GDP), Goh-Djiboua (0.89% of GDP) and Comoe (0.89% of GDP), Sassandra- Marahoue (0.86% of GDP), District De Yamoussoukro (0.83% of GDP), Lagunes (0.80% of GDP), Bas-Sassandra (0.69% of GDP), Woroba (0.52% of GDP), Montagnes (0.51% of GDP), Zanzan (0.50% of GDP) and Denguele (0.36% of GDP).\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab11\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 10\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eFiscal potential of Benin's regions (as % of GDP)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003eAnnée\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003e2012\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003e2013\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003e2014\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003e2015\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003e2016\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003e2017\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003e2018\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003e2019\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003e2020\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003e2021\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003e2022\u003c/p\u003e\n \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eAlibori\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,18\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,19\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,25\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,24\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,22\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,34\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,33\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,40\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,38\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,37\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,42\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eBorgou\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e1,32\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e1,35\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e1,39\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e1,71\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e1,69\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e1,89\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e2,03\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e1,94\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e1,61\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e1,39\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e1,55\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eAtakora\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,47\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,46\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,40\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,54\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,51\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,47\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,43\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,46\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,43\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,48\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,51\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eDonga\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,44\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,51\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,45\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,56\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,61\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,68\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,56\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,52\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,54\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,55\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,57\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eCollines\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,30\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,36\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,49\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,46\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,37\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,47\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,49\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,46\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,54\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,47\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,52\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003ePlateau\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,21\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,27\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,37\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,55\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,54\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,61\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,59\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,55\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,45\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,44\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,43\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eZou\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,81\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,78\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,87\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,94\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e1,00\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,94\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,87\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,83\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,86\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,97\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e1,00\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eKouffo\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,15\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,19\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,19\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,16\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,18\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,28\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,24\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,25\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,22\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,24\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,30\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eAtlantique\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e3,03\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e3,14\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e3,58\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e3,41\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e3,61\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e3,85\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e3,95\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e4,30\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e3,86\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e4,27\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e4,35\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eLittoral\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e3,48\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e3,36\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e3,35\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e2,92\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e3,21\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e2,71\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e3,08\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e3,20\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e2,50\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e2,52\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e2,30\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eMono\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,62\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,79\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,77\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,75\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,75\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,88\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,77\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,77\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,65\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,71\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,70\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eOuémé\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e3,09\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e3,33\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e3,15\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e2,95\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e3,16\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e2,96\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e2,87\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e2,94\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e2,64\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e2,64\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e2,61\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"12\"\u003eSource: BCEAO and World Bank data, author's estimation\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eIn Benin, the results show that the departments of Atlantique, Ouémé, Littoral, Borgou and Zou have the highest fiscal potential in Benin. Fiscal potential averages 3.76%, 2.97%, 2.94%, 1.63% and 0.90% of GDP, respectively for the departments of Atlantique, Littoral, Ouémé, Borgou and Zou, over the period 2012–2022. This result can be explained by the concentration of economic activity in these areas. In addition, the development of the Glo-Djigbé industrial zone (GDIZ) in the Atlantic department and the development of the special economic zone at Sèmè Podji in the Ouémé department offer these two (02) departments higher tax potential than the other departments.\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab12\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 11\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eFiscal potential of Guinea Bissau's regions (as % of GDP)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003eAnnée\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003e2012\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003e2013\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003e2014\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003e2015\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003e2016\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003e2017\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003e2018\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003e2019\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003e2020\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003e2021\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003e2022\u003c/p\u003e\n \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eBissau\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e8,18\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e8,61\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e9,60\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e9,01\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e9,36\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e9,07\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e9,27\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e9,24\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e8,81\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e9,12\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e9,03\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eBafatá\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,11\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,16\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,27\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,31\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,28\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,33\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,34\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,26\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,16\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,15\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,07\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eBiombo\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,81\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,94\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e1,25\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e1,40\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e1,24\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e1,91\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e1,96\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e1,85\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e1,89\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e2,25\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e2,49\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eBolama\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,01\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,02\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,02\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,02\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,16\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,17\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,18\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,09\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,05\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,02\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,01\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eCacheu\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,07\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,11\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,10\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,41\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,37\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,27\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,28\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,20\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,16\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,09\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,19\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eGabu\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,09\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,14\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,27\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,22\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,22\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,27\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,28\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,22\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,23\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,21\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,18\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eOio\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,05\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,10\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,13\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,07\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,18\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,29\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,29\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,09\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,05\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,06\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,08\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eQuinara\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,14\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,17\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,22\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,12\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,12\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,23\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,23\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,20\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,06\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,05\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,04\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eTombali\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,09\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,21\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,11\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,16\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,13\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,20\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,21\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,16\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,19\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,10\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e0,09\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"12\"\u003eSource: BCEAO and World Bank data, author's estimation\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eFor Guinea Bissau, the results show a fiscal potential of 11.79% of GDP, averaged over the period 2012-2022. Estimates show that the Bissau region has a fiscal potential of 9.03% of GDP. This potential amounted to 1.64% of GDP for the Biombo region, 0.22% of GDP for the Bafatá region, 0.21% of GDP for the Gabu region, 0.20% of GDP for the Gabu region, 0.15% of GDP for the Tombali region, 0.14% of GDP for the Quinara region, 0.13% of GDP for the Oio region and 0.07% of GDP for the Bolama region.\u003c/p\u003e\n \u003cp\u003eOverall, an examination of the fiscal potential of the regions of UEMOA countries reveals significant differences between countries and within national territories. This disparity can be explained by several factors, including economic structure, urbanization, infrastructure (Krugman, 1991), and socio-political dynamics (Alesina and Perotti, 1996). Our results show that, in most WAEMU countries, economic capitals and industrial centers have the highest levels of fiscal potential. In terms of hierarchy of fiscal potential, Senegal and Côte d'Ivoire are far ahead with Dakar (8.37% of GDP) and Abidjan (4.06% of GDP), confirming their role as regional economic hubs. Benin and Burkina Faso follow with respectively Atlantic (3.76% of GDP) and Central (9.14% of GDP) regions. Mali and Niger show a concentration of fiscal potential in Bamako (6.20%) and Niamey (6.16%). Guinea-Bissau, on the other hand, has a more modest fiscal potential, but is highly centralized in Bissau (9.03% of GDP). Togo also stands out for the importance of the Maritime region (13.68%), where Lomé is located, confirming the role of port trade.\u003c/p\u003e\n \u003cp\u003eA number of factors explain the disparity in the fiscal potential of WAEMU countries. On one hand, political instability and economic crises have a direct impact on states' ability to mobilize their fiscal resources. Alesina and Perotti (1996), using the theory of political instability and development, underline that political uncertainties slow down private investment and reduce economic growth, which directly affects fiscal potential. On other hand, security crises influence fiscal potential and the tax administration's ability to collect taxes (Collier and Hoeffler, 2004). As our results show, the Gao region of Mali (0.25% of GDP) and the Sahel region of Burkina Faso (0.43% of GDP) have the lowest levels of fiscal potential. This is linked to the fact that terrorist attacks in the Sahel (Burkina Faso, Mali, Niger) have limited economic activity in certain rural regions. This result corroborates the findings of Collier and Hoeffler (2004) who, through their work on the economics of conflict, have shown that persistent instability reduces tax collection due to the destruction of infrastructure and the reduction of trade.\u003c/p\u003e\n \u003cp\u003eIn addition, economic reforms and infrastructure development explain the disparity in fiscal potential (Krugman, 1991). Our results show how Côte d'Ivoire and Senegal, within improvement of policies, industrialization and infrastructure (e.g. Port of Dakar, Zone industrielle uof Yopougon in Abidjan), have seen their fiscal potential maintained despite economic shocks. Similarly, Benin has benefited from the creation of special economic zones such as Glo-Djigbé and Sèmè-Podji, increasing resource mobilization in these regions. These findings are in line with the theory of economic concentration, which emphasizes that infrastructure and economic density enhance fiscal capacity (Krugman, 1991).\u003c/p\u003e\n\u003c/div\u003e\n"},{"header":"Conclusions and recommendations","content":"\u003cp\u003eThis study evaluates the sub-national fiscal potential of the eight (08) WAEMU countries using satellite data on infrared imagery night lights. To achieve this goal, we used a three-step approach. First, we estimated the national fiscal potential using the Stochastic Frontier Analysis method proposed by Schimidt et al (1977). Next, we examine the relationship between total night lights and national fiscal potential using the panel data regression model, fixed effects model and the Feasible Generalized Least Squares (FGLS) method and finally we estimate the fiscal potential at the regional level.\u003c/p\u003e\u003cp\u003eThe results show that over the period 2012–2022, fiscal potential is on average 20.64% of GDP for Togo, 20.58% of GDP for Senegal, 18.15% of GDP for Burkina Faso, 16.75% of GDP for Niger, 16.44% of GDP for Mali, 15.37% of GDP for Benin, 15.16% of GDP for Côte d'Ivoire and 11.79% of GDP for Guinea-Bissau. The gap between fiscal potential and tax revenue actually mobilised averages 3.7% of GDP per year for Côte d'Ivoire, 4.8% of GDP per year for Benin, 3.4% of GDP per year for Burkina Faso, 2.6% of GDP per year for Mali, 6.2% of GDP per year for Niger, 4.6% of GDP per year for Senegal, 3.5% of GDP per year for Guinea-Bissau and 5.7% of GDP per year for Togo. The results of the regression model reveal that night lights have a positive and significant effect on fiscal potential, with a correlation coefficient of 0.98 and an R² of 84%. Sub-national fiscal potential varies between 0.09% and 8.37% of GDP for the regions of Senegal, 0.96% and 13.68% of GDP for the regions of Togo, 0.27% and 9.14% of GDP for Burkina, 0.92% and 6.16% of GDP for the regions of Niger, 0.14% and 6.20% of GDP for the regions of Mali, 0.22% and 3.76% of GDP for the regions of Benin, 0.36% and 4.06% of GDP for the regions of Côte d'Ivoire and between 0.07% and 9.03% of GDP for the regions of Guinea Bissau.\u003c/p\u003e\u003cp\u003eThe results suggest that night lights can be used to improve the forecasting and allocation of fiscal resources at a sub-national level. Our results suggest that improving infra-national fiscal capacity can be a strategic lever for fostering local economic development. This study recommends that tax revenue mobilization targets be set by the administrative divisions of WAEMU countries, in order to mobilize tax revenues up to the level of fiscal capacity. An awareness-raising program can also be set up to communicate on tax civic-mindedness or the fiscal responsibility of economic agents. Information and communication technologies can be used to achieve these objectives. This study offers an unprecedented opportunity for real-time monitoring of tax mobilization forcasting. We believe that this study can be successfully carried out in other countries as the satellite imagery night light data is freely available to all countries. One limitation of this study is the lack of tax data by administrative division of the countries that could enable a comparative analysis between the tax potential of the administrative divisions and their tax effort. Including only TNL as an explanatory variable in the GMM model can be seen as a limitation of this study. Further work could examine the influence of other factors on tax potential or tax effort.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eConflict of interest statement\u003c/p\u003e\n\u003cp\u003eThe author states that there is no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Ethical approval\u003c/p\u003e\n\u003cp\u003eThe data used for the estimates do not include confidential information about individuals or animals that may raise ethical concerns.\u003c/p\u003e\n\u003cp\u003eAcknowledgement\u003c/p\u003e\n\u003cp\u003eWe thank all those who have contributed to the improvement of the quality of this paper. Special thanks to all reviewers.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author grants his consent for publication of this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author contributed alone to the paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe writing of this paper has not been funded or sponsored. It was done at the author\u0026rsquo;s expense.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data used in this paper is fully available and can be accessed upon request.\u003c/p\u003e\n"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAcemoglu, D., Johnson, S., \u0026amp; Robinson, J. A. (2005). Institutions as the fundamental cause of long-run growth. Handbook of Economic Growth, 1, 385-472. https://doi.org/10.1016/S1574-0684(05)01006-3\u003c/li\u003e\n\u003cli\u003eAcemoglu, D., Johnson, S., \u0026amp; Robinson, J. A. (2001). The colonial origins of comparative development: An empirical investigation. American Economic Review, 91(5), 1369-1401. https://doi.org/10.1257/aer.91.5.1369 \u003c/li\u003e\n\u003cli\u003eAdegboye, A., Uwuigbe, U., Ojeka, S., Uwuigbe, O., Dahunsi, O., \u0026amp; Adegboye, K. (2022). Driving information communication technology for tax revenue mobilization in Sub-Saharan Africa. Telecommunications Policy, 46(7), 102329. https://doi.org/10.1016/j.telpol.2022.102329\u003c/li\u003e\n\u003cli\u003eAigner, D., Lovell, C. K., \u0026amp; Schmidt, P. (1977). Formulation and estimation of stochastic frontier production function models. \u003cem\u003eJournal of econometrics\u003c/em\u003e, \u003cem\u003e6\u003c/em\u003e(1), 21-37. https://doi.org/10.1016/0304-4076(77)90052-5\u003c/li\u003e\n\u003cli\u003eAlesina, A., \u0026amp; Perotti, R. (1996). \u003cem\u003eIncome distribution, political instability, and investment.\u003c/em\u003e European Economic Review, 40(6), 1203-1228. https://doi.org/10.1016/0014-2921(95)00030-5\u003c/li\u003e\n\u003cli\u003eAlfirman, L. (2003). Estimating stochastic frontier tax potential: Can Indonesian local governments increase tax revenues under decentralization? Discussion Papers in Economics, Working Paper No. 03-19. Boulder: Center for Economic Analysis, Department of Economics, University of Colorado at Boulder. https://www.colorado.edu/economics/sites/default/files/attached-files/wp03-19.pdf \u003c/li\u003e\n\u003cli\u003eArellano, M., \u0026amp; Bond, S. (1991). Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations. The Review of Economic Studies, 58(2), 277-297. https://doi.org/10.2307/2297968\u003c/li\u003e\n\u003cli\u003eBagan, H., \u0026amp; Yamagata, Y. (2015). Analysis of urban growth and estimating population density using satellite images of nighttime lights and land-use and population data. GIScience \u0026amp; Remote Sensing, 52(6), 765-780. https://doi.org/10.1080/15481603. 2015.1072400.\u003c/li\u003e\n\u003cli\u003eBahl, R., \u0026amp; Bird, R. (2008). \u0026quot;Subnational Taxes in Developing Countries: The Way Forward\u0026quot;. Public Budgeting \u0026amp; Finance, 28(4), 1-25. https://doi.org/10.1111/j.1540-5850.2008.00914.x\u003c/li\u003e\n\u003cli\u003eBenitez, J. C., Mansour, M., Pecho, M., \u0026amp; Vellutini, C. (2023). Building Tax Capacity in Developing Countries. International Monetary Fund. https://pacerplus.org/assets/Building-Tax-Capacity.pdf \u003c/li\u003e\n\u003cli\u003eBesley, T., \u0026amp; Persson, T. (2013). Taxation and development. Handbook of Public Economics, 5, 51-110. https://doi.org/10.1016/B978-0-444-53759-1.00002-9\u003c/li\u003e\n\u003cli\u003eBundervoet, T., Maiyo, L., \u0026amp; Sanghi, A. (2015). Bright lights, big cities: measuring national and subnational economic growth in Africa from outer space, with an application to Kenya and Rwanda. World Bank Policy Research Working Paper, (7461). https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2682850 \u003c/li\u003e\n\u003cli\u003eCaldeira, E., Compaore, A., Dama, A. A., Mansour, M., \u0026amp; Rota-Graziosi, G. (2019). Effort fiscal en Afrique subsaharienne: les r\u0026eacute;sultats d\u0026rsquo;une nouvelle base de donn\u0026eacute;es 1. Revue d\u0026rsquo;\u0026eacute;conomie du d\u0026eacute;veloppement, 27(4), 5-51. https://uca.hal.science/hal-03182352/document \u003c/li\u003e\n\u003cli\u003eCanares, M. P. (2016). Creating the Enabling Environment for more Transparent and Better-resourced Local Governments: A Case of E-taxation in the Philippines. Information Technology for Development, 22(sup1), 121-138. https://doi.org/10.1080/02681102.2015.1121857\u003c/li\u003e\n\u003cli\u003eChen, X., \u0026amp; Nordhaus, W. (2015). Using luminosity data as a proxy for economic statistics. Proceedings of the National Academy of Sciences, 112(14), 5080-5085. https://doi.org/10.1073/pnas.1017031108\u003c/li\u003e\n\u003cli\u003eCollier, P., \u0026amp; Hoeffler, A. (2004). \u003cem\u003eGreed and grievance in civil war.\u003c/em\u003e Oxford Economic Papers, 56(4), 563-595. https://doi.org/10.1093/oep/gpf064\u003c/li\u003e\n\u003cli\u003eDai, Z., Hu, Y., \u0026amp; Zhao, G. (2017). The suitability of different nighttime light data for GDP estimation at different spatial scales and regional levels. Sustainability, 9(2), 305. https://doi.org/10.3390/su9020305\u003c/li\u003e\n\u003cli\u003eDumitrescu, E.-I., \u0026amp; Hurlin, C. (2012). Testing for Granger non-causality in heterogeneous panels. Economic Modelling, 29(4), 1450-1460. https://doi.org/10.1016/j.econmod.2012.02.014\u003c/li\u003e\n\u003cli\u003eDoghmi, H. (2020). La capacit\u0026eacute; de mobilisation des recettes fiscales au Maroc (No. 2020-1). Bank Al-Maghrib, D\u0026eacute;partement de la Recherche.\u003c/li\u003e\n\u003cli\u003eElvidge, C. D., Baugh, K. E., Kihn, E. A., Kroehl, H. W., Davis, E. R., \u0026amp; Davis, C. W. (1997). Relation between satellite observed visible-near infrared emissions, population, economic activity and electric power consumption. International Journal of Remote Sensing, 18(6), 1373-1379. https://doi.org/10.1080/014311697218485\u003c/li\u003e\n\u003cli\u003eFarzanegan, M. R., \u0026amp; Fischer, S. (2021). Lifting of international sanctions and the shadow economy in Iran a view from outer space. Remote Sensing, 13(22), 4620.https://doi.org/10.3390/rs13224620\u003c/li\u003e\n\u003cli\u003eFarzanegan, M. R., \u0026amp; Hayo, B. (2019). Sanctions and the shadow economy: Empirical evidence from Iranian provinces. Applied Economics Letters, 26(6), 501-505. https://doi.org/10.1080/13504851.2018.1486981\u003c/li\u003e\n\u003cli\u003eFenochietto, M. R., \u0026amp; Pessino, M. C. (2013). Understanding countries\u0026rsquo; tax effort. International Monetary Fund. \u003c/li\u003e\n\u003cli\u003eGalimberti, J. K. (2020). Forecasting GDP growth from outer space. Oxford Bulletin of Economics and Statistics, 82(4), 697-722. https://doi.org/10.1111/obes.12361\u003c/li\u003e\n\u003cli\u003eGhosh, T., Anderson, S., Elvidge, C., \u0026amp; Sutton, P. (2013). Using Nighttime Satellite Imagery as a Proxy Measure of Human Well-Being. Sustainability, 5(12), 4988-5019. https://doi.org/10.3390/su5124988\u003c/li\u003e\n\u003cli\u003eGhosh, T., Anderson, S., Powell, R. L., Sutton, P. C., \u0026amp; Elvidge, C. D. (2009). Estimation of Mexico\u0026rsquo;s informal economy and remittances using nighttime imagery. Remote Sensing, 1(3), 418-444. https://doi.org/10.3390/rs1030418\u003c/li\u003e\n\u003cli\u003eGonz\u0026aacute;lez, F. A. I., London, S., \u0026amp; Santos, M. E. (2021). Disasters and economic growth: evidence for Argentina. Climate and Development, 13(10), 932-943.https://doi.org/10.1080/17565529.2021.1873724\u003c/li\u003e\n\u003cli\u003eGordon, R., \u0026amp; Li, W. (2009). Tax structures in developing countries: Many puzzles and a possible explanation. Journal of public Economics, 93(7-8), 855-866. https://doi.org/10.1016/j.jpubeco.2009.04.001\u003c/li\u003e\n\u003cli\u003eGranger, C. W. (1969). Investigating causal relations by econometric models and cross-spectral methods. Econometrica: journal of the Econometric Society, 424-438. \u003cu\u003ehttps://doi.org/10.2307/1912791\u003c/u\u003e\u003c/li\u003e\n\u003cli\u003eGu, Y., Shao, Z., Huang, X., \u0026amp; Cai, B. (2022). GDP forecasting model for China\u0026rsquo;s provinces using nighttime light remote sensing data. Remote Sensing, 14(15), 3671. https://doi.org/10.3390/rs14153671\u003c/li\u003e\n\u003cli\u003eHansen, T. B., Jensen, T. I., Clausen, B. H., Bramsen, J. B., Finsen, B., Damgaard, C. K., \u0026amp; Kjems, J. (2013). Natural RNA circles function as efficient microRNA sponges. Nature, 495(7441), 384-388. https://doi.org/10.1038/nature11993 \u003c/li\u003e\n\u003cli\u003eHe, C., Ma, Q., Liu, Z., \u0026amp; Zhang, Q. (2014). Modeling the spatiotemporal dynamics of electric power consumption in Mainland China using saturation-corrected DMSP/OLS nighttime stable light data. International Journal of Digital Earth, 7(12), 993\u0026ndash;1014. https://doi.org/10.1080/17538947.2013.822026.\u003c/li\u003e\n\u003cli\u003eHenderson, V., Squires, T., Storeygard, A., \u0026amp; Weil, D. (2018). The Global Distribution of Economic Activity: Nature, History, and the Role of Trade. Quarterly Journal of Economics, 133(1), 357-406. https://doi.org/10.1093/qje/qjx030\u003c/li\u003e\n\u003cli\u003eHenderson, J. V., Storeygard, A., \u0026amp; Weil, D. N. (2012). Measuring economic growth from outer space. American economic review, 102(2), 994-1028. https://doi.org/10.1257/aer.102.2.994\u003c/li\u003e\n\u003cli\u003eKeola, S., Andersson, M., \u0026amp; Hall, O. (2015). Monitoring economic development from space: using nighttime light and land cover data to measure economic growth. World Development, 66, 322-334. https://doi.org/10.1016/j.worlddev.2014.08.017.\u003c/li\u003e\n\u003cli\u003eKrugman, P. (1991). \u003cem\u003eIncreasing returns and economic geography.\u003c/em\u003e Journal of Political Economy, 99(3), 483-499. http://google.comwww.rrojasdatabank.info/krugman1991.pdf \u003c/li\u003e\n\u003cli\u003eLawin, L. M. (2023). ICTs Development and Tax Revenue Mobilization in West African Economic and Monetary Union Countries. American Journal of Economics, 13(1), 1-12. http://article.sapub.org/10.5923.j.economics.20231301.01.html \u003c/li\u003e\n\u003cli\u003eLevin, A., Lin, C. F., \u0026amp; Chu, C. S. J. (2002). Unit root tests in panel data: asymptotic and finite-sample properties. Journal of econometrics, 108(1), 1-24. https://doi.org/10.1016/S0304-4076(01)00098-7 \u003c/li\u003e\n\u003cli\u003eLi, X., Cai, G., \u0026amp; Luo, D. (2020). GDP distortion and tax avoidance in local SOEs: Evidence from China. International Review of Economics \u0026amp; Finance, 69, 582-598. https://doi.org/10.1016/j.iref.2020.06.042\u003c/li\u003e\n\u003cli\u003eLiang, H., Guo, Z., Wu, J., \u0026amp; Chen, Z. (2020). GDP spatialization in Ningbo City based on NPP/VIIRS night-time light and auxiliary data using random forest regression. Advances in Space Research, 65(1), 481-493. https://doi.org/10.1016/j.asr.2019.09.035\u003c/li\u003e\n\u003cli\u003eLopez-Ruiz, H., Blazquez, J., \u0026amp; Hasanov, F. (2019). Esfimafing Saudi Arabia\u0026rsquo;s regional GDP using satellite nighttime light images (No. ks--2019-dp80).\u003c/li\u003e\n\u003cli\u003eMa, C., Niu, Z., Ma, Y., Chen, F., Yang, J., \u0026amp; Liu, J. (2019). Assessing the distribution of heavy industrial heat sources in India between 2012 and 2018. ISPRS International Journal of Geo-Information, 8(12), 568. https://doi.org/10.3390/ijgi8120568\u003c/li\u003e\n\u003cli\u003eMallick, H. (2021). Do governance quality and ICT infrastructure influence the tax revenue mobilisation? An empirical analysis for India. Economic Change and Restructuring, 54(2), 371-415. https://doi.org/10.1007/s10644-020-09282-9 \u003c/li\u003e\n\u003cli\u003eMascagni, G., Moore, M., \u0026amp; McCluskey, R. (2014). Tax Revenue Mobilistation In Developing Countries: Issues and Challenges. European Union (Belgium). https://www.europarl.europa.eu/RegData/etudes/etudes/join/2014/433849/EXPO-DEVE_ET(2014)433849_EN.pdf \u003c/li\u003e\n\u003cli\u003eMawejje, J., \u0026amp; Sebudde, R. K. (2019). Tax revenue potential and effort: Worldwide estimates using a new dataset. Economic Analysis and Policy, 63, 119-129. https://doi.org/10.1016/j.eap.2019.05.005\u003c/li\u003e\n\u003cli\u003eMichalopoulos, S., \u0026amp; Papaioannou, E. (2014). National Institutions and Subnational Development in Africa. Quarterly Journal of Economics, 129(1), 151-213. https://doi.org/10.1093/qje/qjt029\u003c/li\u003e\n\u003cli\u003eMusgrave, R. A. \u0026amp; Musgrave, P. B. (1989). Public Finance in Theory and Practice. McGraw-Hill. https://www.nispa.org/files/publications/ebooks/Public-Finance-Theory-and-Practice.pdf \u003c/li\u003e\n\u003cli\u003eOates, W. E. (1972). Fiscal Federalism. Harcourt Brace Jovanovich.\u003c/li\u003e\n\u003cli\u003ePeter B., Lars O. (2011). Benchmarking with DEA, SFA, and R. International Series in Operations Research and Management Science, Vol. 157, 368 pp.\u003c/li\u003e\n\u003cli\u003ePrichard, W. (2016). Taxation, Responsiveness and Accountability in Sub-Saharan Africa: The Dynamics of Tax Bargaining. Cambridge University Press. https://citeseerx.ist.psu.edu/document?repid=rep1\u0026amp;type=pdf\u0026amp;doi=18b0189596c828f9c502ecabc814bfe5a2353691 \u003c/li\u003e\n\u003cli\u003eTanaka, K., \u0026amp; Keola, S. (2017). Shedding light on the shadow economy: A nighttime light approach. The Journal of Development Studies, 53(1), 32-48. https://doi.org/10.1080/00220388.2016.1171845\u003c/li\u003e\n\u003cli\u003eTerefe, K. D., \u0026amp; Teera, J. (2018). Determinants of tax revenue in East African countries: An application of multivariate panel data cointegration analysis. Journal of Economics and International Finance, 10(11), 134-155. https://doi.org/10.5897/JEIF2018.0924 \u003c/li\u003e\n\u003cli\u003eTong, L., Hu, S., \u0026amp; Frazier, A. E. (2018). Mixed accuracy of nighttime lights (TNL)-based urban land identification using thresholds: Evidence from a hierarchical analysis in Wuhan Metropolis, China. Applied Geography, 98, 201-214. https://doi.org/10.1016/j.apgeog.2018.07.017\u003c/li\u003e\n\u003cli\u003eUllah, S., Akhtar, P., \u0026amp; Zaefarian, G. (2018). Dealing with endogeneity bias: The generalized method of moments (GMM) for panel data. \u003cem\u003eIndustrial Marketing Management\u003c/em\u003e, \u003cem\u003e71\u003c/em\u003e, 69-78. https://doi.org/10.1016/j.indmarman.2017.11.010\u003c/li\u003e\n\u003cli\u003eWooldridge, J. M. (2010). Econometric analysis of cross section and panel data. MIT press.\u003c/li\u003e\n\u003cli\u003eZhou, Y., Ma, T., Zhou, C., \u0026amp; Xu, T. (2015). Nighttime light derived assessment of regional inequality of socioeconomic development in China. Remote Sensing, 7(2), 1242-1262. https://doi.org/10.3390/rs70201242\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Fiscal potential, tax effort, WAEMU countries, satellite images, nighttime lights","lastPublishedDoi":"10.21203/rs.3.rs-6329840/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6329840/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study assesses the sub-national fiscal potential of eight (8) West African Economic and Monetary Union (UEMOA) countries from 2012 to 2022, using satellite data on total night lighting (TNL). Three-step method have been used : (i) estimation of national fiscal potential using the Stochastic Frontier Analysis (SFA) method; (ii) examination of the relationship between TNL and fiscal potential using the Granger causality test and the GMM model in panel data; (iii) estimation of sub-national fiscal potential. The results reveal the existence of a causal link and a positive and significant effect of night lighting on fiscal potential. Findings are showing that night lighting is a reliable indicator for estimating and allocating fiscal potential at sub-national level. The existence of bidirectional causality between TNL and fiscal potential underlines the need for tax administration in WAEMU countries to implement an integrated approach that takes into account both economic and institutional dynamics. The persistence of fiscal potential indicates that tax policies in UEMOA countries need to be designed for the long term to maximize their impact on economic development.\u003c/p\u003e","manuscriptTitle":"Estimation of the sub-national fiscal potential of WAEMU countries using satellite images of nighttime lights data","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-01 09:44:42","doi":"10.21203/rs.3.rs-6329840/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"97f73ae6-8111-40ac-b113-d1307d9eae6a","owner":[],"postedDate":"April 1st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":46372791,"name":"Tax Law"},{"id":46372792,"name":"Development Economics"}],"tags":[],"updatedAt":"2025-04-01T09:44:42+00:00","versionOfRecord":[],"versionCreatedAt":"2025-04-01 09:44:42","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6329840","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6329840","identity":"rs-6329840","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}