Inter-district road infrastructure and spatial inequality in rural Indonesia

Wahyuni, Ribut Nurul Tri; Ikhsan, Mohamad; Damayanti, Arie; Khoirunurrofik Article Inter-district road infrastructure and spatial inequality in rural Indonesia Economies Provided in Cooperation with: MDPI – Multidisciplinary Digital Publishing Institute, Basel Suggested Citation: Wahyuni, Ribut Nurul Tri; Ikhsan, Mohamad; Damayanti, Arie; Khoirunurrofik (2022) : Inter-district road infrastructure and spatial inequality in rural Indonesia, Economies, ISSN 2227-7099, MDPI, Basel, Vol. 10, Iss. 9, pp. 1-20, https://doi.org/10.3390/economies10090229 This Version is available at: https://hdl.handle.net/10419/328529 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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Economies 10: 229. https://doi.org/10.3390/ economies10090229 Academic Editor: Ralf Fendel Received: 29 July 2022 Accepted: 14 September 2022 Published: 16 September 2022 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). economies Article Inter-District Road Infrastructure and Spatial Inequality in Rural Indonesia Ribut Nurul Tri Wahyuni 1,2,* , Mohamad Ikhsan 1, Arie Damayanti 1and Khoirunurrofik Khoirunurrofik 1 1Faculty of Economics and Business, Universitas Indonesia, Depok 16424, Indonesia 2STIS Polytechnic of Statistics, Jakarta 13330, Indonesia *Correspondence: rnur[email protected] Abstract: Road quality plays an important role, especially in rural areas where most poor households are situated. This study aims to calculate the Rural Access Index (RAI), an indicator of rural road quality (SDG indicator 9.1.1), at the district level, to evaluate the implementation of the Nawacita programme in Indonesia from 2014–2020. The RAI describes the proportion of rural residents who live within 2 km of an all-season road. This study recommends the utilisation of road network maps, urban–rural boundary maps, three road network condition datasets, and WorldPop data to calculate the RAI. The results show that during this period, the RAI increased and its inequality decreased, specifically in the regions of priority for this programme (Papua and West Papua). The results also capture a strong pattern of regional convergence. To ensure the future success of this implementation, the government can create regulations to designate several road infrastructure projects as a national strategy, as well as increase tax collection and private sector investment as sources of road infrastructure development funding. Keywords: Rural Access Index; all-season road; inequality; regional convergence 1. Introduction Limited road connectivity can result in high transportation costs and long travel times, which may impact sectoral productivity (Bell and van Dillen 2014;Haughton and Khandker 2009), employment (Mu and Van de Walle 2011) and poverty (Dercon et al. 2012;Khandker and Koolwal 2011). A lack of access to the outside market, for instance, makes it difficult for people to find new jobs and discourages investment, especially in rural areas where most poor households are situated. Roberts et al. (2006) estimated that 68.3 per cent of rural residents lack access to the global road network. Almost a billion people reside in rural areas without access to paved national roads (Asher and Novosad 2020). As shown in Table 1, in 2011, 43.27 per cent of Indonesia’s rural areas did not have access to paved road networks. Rural road construction was also unequal. In eastern Indonesia, 77 per cent of rural areas lacked access to paved roads connecting villages. Similarly, 62.16 per cent of Borneo Island’s rural areas lacked access to paved roads connecting villages. Other islands had paved roads connecting villages in less than 46 per cent of rural areas. The government has been implementing the Nawacita programme by reducing fuel subsidies since 2014 to boost infrastructure development (Salim and Negara 2018). This policy prioritises accelerating connectivity between peripheries and growth centres so that inter-regional inequality can be reduced, particularly in rural areas and eastern Indonesia (Bappenas 2014). State spending on infrastructure has increased significantly, from 8 per cent of the total state budget in 2014 to 19 per cent of the total state budget in 2017. Moreover, the President of Indonesia has created the Committee for the Acceleration of Priority Infrastructure Delivery (KPPIP), a special task force with the responsibility of coordinating policies among various stakeholders and unblocking stalled national strategic projects and priority projects (Salim and Negara 2018). In the 2015–2019 National Economies 2022,10, 229. https://doi.org/10.3390/economies10090229 https://www.mdpi.com/journal/economies Economies 2022,10, 229 2 of 20 Medium-Term Development Plan, the government committed to building 2600 km of roads. To balance the geographic concentration of investment, at least half of the government expenditure went to areas outside the capital region (Bappenas 2014), such as outside Java. Table 1. The percentage of Indonesian rural areas by inter-village road condition and regional group. Regional Group 2011 2014 2020 Zone Paved All-Season Paved All-Season Paved All-Season Sumatra Western 54.16 88.30 58.86 84.11 78.05 91.23 Java Western 78.80 97.69 84.04 97.00 95.21 98.63 Bali and Nusa Tenggara Central 57.16 88.40 61.71 85.98 75.53 92.87 Borneo Central 37.84 68.92 42.27 66.96 57.40 72.10 Sulawesi Central 60.04 87.55 65.66 88.47 82.09 91.93 Moluccas and North Moluccas Eastern 39.49 55.70 54.21 65.80 61.96 68.74 Papua and West Papua Eastern 16.84 32.51 26.39 39.40 29.59 39.32 Indonesia 56.73 83.34 63.56 83.87 77.76 87.78 Source: Author’s calculation from The Potensi Desa (Podes) survey data, BPS-Statistics Indonesia. After the Nawacita programme’s implementation, access to paved inter-village roads in rural areas grew significantly. The percentage of Indonesia’s rural areas that did not have access to paved road networks fell to 43.27, but road inequity persisted. Eastern Indonesia has lagged behind western Indonesia in terms of rural road infrastructure development. Unfortunately, information about Indonesian rural roads’ connectivity and inequality to support this opinion, other than the data in Table 1, is currently unavailable. Few regional indicators measure rural road connectivity correctly. Conventional measurements are total road length and the proportion of paved roads (Iimi et al. 2016), which are not good predictors for rural roads (World Bank 2016). These indicators barely change over time, although the government has spent a lot of money upgrading the road network (Iimi et al. 2016). The quality of roads is often unknown and a matter of concern in developing countries (World Bank 2016). In Indonesia, besides total road length and the proportion of paved roads, the government uses steady-road condition data to indicate road connectivity. These data are only available for the national road network by province, without rural–urban separation. They are calculated from the International Roughness Index (IRI) and used as an indicator of sustainable development goals (SDGs), namely 9.1.1 (Bappenas 2017,2020), even though the United Nations (UN) recommendation uses the Rural Access Index (RAI). The objectives of this study were to calculate the RAI and its regional inequality. The RAI was used as an indicator of rural road connectivity in Indonesia. It shows the proportion of rural residents who live within 2 km, usually equal to a walk of 20–25 min, of an all-season road. The term “all-season road” refers to a road that is drivable all year round by the prevailing rural transport mode (Iimi et al. 2016;Roberts et al. 2006;Workman et al. 2019;World Bank 2016). The RAI is a new rural road connectivity measurement method based on Geographic Information Systems (GIS) data. This method resolves the limitations of conventional measurements. Iimi et al. (2016) and Mikou et al. (2019) calculated the RAI by utilising rural population distribution data from WorldPop or LandScan and road network data from the government or OpenStreetMap (OSM). The best policies for rural road access improvement require estimates for local regions, such as at the district level. This is the first study conducted in Indonesia to provide such estimates. Because the Nawacita policy places a high priority on reducing inequality in certain areas (e.g., eastern Indonesia), this study also provides rural road connectivity inequality by regional group. Indonesia is divided into seven regional groups, each with multiple provinces. Each province has a number of districts, and each district consists of several subdistricts, which include rural and urban areas. National roads are under the authority of the central government and connect the capitals of the provinces. The provin- Economies 2022,10, 229 3 of 20 cial government has the jurisdiction to construct provincial roads connecting provincial capitals to district capitals. Finally, the district government is responsible for managing local roads. Because of data limitations, this study used only national and provincial roads to calculate the RAI. This study aims to identify districts with poor rural road quality and regional groups with high rural road inequality. With these data, the government can evaluate the effects of the Nawacita programme and determine priority regions for rural road construction. 2. Methodology The first step was to calculate the RAI at the district level. The RAI needs several datasets: population distribution maps, urban–rural classification data, village maps, road maps, and road network condition data. Step-by-step procedures for calculating the RAI are shown in Figure 1. Economies2022,10,xFORPEERREVIEW4of22   Figure1.Overviewofstep‐by‐stepproceduresforcalculatingtheRAI. ThenextstepwastoexaminetheimpactoftheNawacitaprogramme,whichispart ofthesecondobjective.Thisstudyemployedthevariancecoefficient(Equation(1)),the Ginicoefficient(Equation(2)),theLorenzcurve,andtheTheilindex(Equation(3))to measureruralroadinequality.Thesemethodsarefrequentlyusedtoquantifyinequityin thetransportationsector(e.g.,Jangetal.2017;Mestre2021;SimonandNatarajan2017; Zimm2019).Byanalysingtheinequalityvaluesofthesedifferentapproaches,wecan understandhowwellIndonesia’sruralroadsarebeingconstructed.Inaddition,thisstudy alsousedthedecompositionoftheinequalityindicator(Equation4)andconvergence analysis(Equations(5)and(6))toevaluatetheimplementationoftheNawacita programme. 𝐶𝑉󰇛󰇜       where𝑠𝑒󰇛𝑅𝐴𝐼󰇜  ∑󰇛      󰇜   (1) Figure 1. Overview of step-by-step procedures for calculating the RAI. Economies 2022,10, 229 4 of 20 We used population distribution maps from WorldPop, which is the most robust dataset available, according to Mikou et al. (2019) and World Bank (2016). WorldPop uses the latest national census data and other data from countries to produce 100 m × 100 m of population distribution data. It can be downloaded freely and used in QGIS software (Workman et al. 2019). This study also conducted a robustness check by using 1 km × 1 km of population distribution data from LandScan. We applied the urban–rural classification data from the Regulation of the Head of BPS-Statistics Indonesia Number 37 Year 2010. Then, we combined the village map with the urban–rural classification data. From this combination, we chose only rural areas to create the rural map. The intersection of the population distribution data and the rural map results in the total rural population. This study utilised a road map from the Directorate General of Highways. Although Iimi et al. (2016), Li et al. (2022), Mikou et al. (2019) and Workman et al. (2019) recommend using OSM data, this study did not utilise it because Indonesian OSM data from 2014 to 2020 were inconsistent. This inconsistency is shown in Figure A1. We then buffered the road map with a 2-km radius. All-season road identification uses data from the Directorate General of Highways and refers to paved roads with an IRI of less than 6 m per kilometre, unpaved roads with an IRI of less than 13 m per kilometre, paved roads in excellent, good, or fair condition, and unpaved roads in excellent or good condition (Workman et al. 2019). We also used Podes survey data from BPS-Statistics Indonesia for all-season road identification, specifically the existence of inter-village roads that can be traversed by motorised vehicles with four or more wheels throughout the year. This study applied all methods to specify all-season national roads in 2018. The results show that when data on the surface type and roughness of regional roads are not available, we can use the last method as a substitute to identify all-season roads in Indonesia. The intersection between the road map with a 2-km radius and all-season road data produced the all-season road map. In the next step, we overlaid this map with a population layer, removed urban areas, and counted the population in the buffer (World Bank 2016). This resulted in the total rural population living within 2 km of all-season roads. Finally, the ratio between the total rural population living within 2 km of all-season roads and the total rural population resulted in the RAI. The next step was to examine the impact of the Nawacita programme, which is part of the second objective. This study employed the variance coefficient (Equation (1)), the Gini coefficient (Equation (2)), the Lorenz curve, and the Theil index (Equation (3)) to measure rural road inequality. These methods are frequently used to quantify inequity in the transportation sector (e.g., Jang et al. 2017;Mestre 2021;Simon and Natarajan 2017; Zimm 2019). By analysing the inequality values of these different approaches, we can understand how well Indonesia’s rural roads are being constructed. In addition, this study also used the decomposition of the inequality indicator (Equation (4)) and convergence analysis (Equations (5) and (6)) to evaluate the implementation of the Nawacita programme. CVt=se(RAIt) RAIt where se(RAIt) = s∑n i=1RAIit −RAIt2 n(1) Ginit=1− n ∑ i=1Xit −X(i−1)tYit +Y(i−1)t(2) Tt= n ∑ i=1 1 n RAIit RAIt ln RAIit RAIt (3) RAIt , se(RAIt) , CVt , Ginitand Tt are the mean, standard deviation, coefficient of variance, Gini coefficient and Theil index year t, respectively. Xit is the cumulative proportion of the population variable in the smaller region i= 1, . . . ,nyear twith X0t= 0 and Xnt = 1. Yit is the cumulative proportion of the RAI variable in the smaller region i= 1, . . . ,nyear twith Economies 2022,10, 229 5 of 20 Y0t= 0 and Ynt = 1. Yit should be indexed in non-decreasing order ( Yit ≥Y(i−1)t ) and Xit is generated by arranging regions in ascending order based on the RAI values. A lower variation coefficient value indicates a more equitable distribution. The Gini coefficient is a simple mathematical metric representing the overall degree of inequality, whereas the Lorenz curve is a visual representation of equality. The Gini coefficient is usually calculated from the Lorenz curve. The Gini coefficient is the ratio of the segment between the 45 ◦ line of equality and the Lorenz curve over the entire segment under the 45 ◦ line. It has a value from 0 to 1, where 0 stands for perfect equality and 1 denotes perfect inequality. The higher the Gini coefficient, the further away the Lorenz curve is from the 45 ◦ line. The Lorenz curve is a valuable and essential visualisation tool because different Lorenz curves can have the same Gini coefficient (Zimm 2019). A Gini value of less than 0.20 stands for low inequality, a value from 0.20 to 0.50 shows medium inequality, and a value above 0.50 indicates high inequality. The Theil index is part of a larger family of measures referred to as the general entropy class. If the Gini coefficient computes the deviation, the Theil index describes the entropic distance between a situation and the ideal egalitarian situation (Mestre 2021). Like the Gini coefficient, the Theil index also ranges from 0 to 1, where 0 stands for perfect equality and 1 denotes perfect inequality. The decomposition of the inequality indicator assesses the contribution of withininequality, between-inequality, and a residual term to total inequality (Bellu and Liberati 2006), as shown in Equation (4). Within-inequality captures disparity due to the variability of the RAI within each regional group. Between-inequality shows disparity due to the variability of the RAI across different regional groups. The coefficient of variance and the Gini index are not perfectly decomposable (Bellu and Liberati 2006;Cowell 2011), hence only the Theil index was decomposed. Let us assume that there are mregional groups. The Theil index can be decomposed as follows: Tt= m ∑ k=1 nk n RAIkt RAIt T(RAIkt)+TRAIt(4) nk is the number of smaller regions in the regional group k. T(RAIkt) is the Theil index of regional group kin year t. TRAIt is calculated by replacing each actual RAI of the regional group with the corresponding means, then computing the Theil index of this fictitious RAI distribution (Bellu and Liberati 2006). We also checked whether the convergence of the RAI occurred. Convergence measurements can use σ convergence (Equation (5)) and β convergence (Equation (6)). Because σ convergence cannot indicate the significance of convergence itself, this study also used β convergence. σ convergence refers to the decline in the cross-sectional dispersion (disparity) of a rural road access indicator across regions, that is, whether σconvergencet+T< σconvergencet. The concepts of σ and β convergences are related. Intuitively, we can see that if the RAI levels of 2 regions become more similar over time, it must be the case that the poor region is growing faster. As an illustration, the RAI in region A starts out being higher than the RAI in region B. There is an initial distance or dispersion between the 2 levels of the RAI. If the growth rate of the RAI in region A is smaller than the growth rate of the RAI in region B between times tand t+T, we say that there is β convergence. Because dispersion at t+Tis smaller than at time t, we also say that there is σ convergence. In other words, β convergence is a necessary condition for σconvergence (Sala-i-Martin 1996). σconvergencet=s1 n n ∑ i=1ln RAIit −ln RAIt2(5) Economies 2022,10, 229 6 of 20 Suppose that β convergence holds for a group of regions i , where i= 1, 2, . . . , n , the RAI in region iat time t, corresponding perhaps to annual data, can be approximated by: 1 TlnRAIi,t+T RAIit =α−βln RAIit +uit (6) where α is an intercept and uit is a disturbance term. The annual growth rate of RAI between tand t+T 1 TlnRAIi,t+T RAIit  is inversely related to ln RAI at time t (ln RAIit) . The negative sign of the coefficient on ln RAI exhibits convergence (Sala-i-Martin 1996). On the contrary, the positive sign of this coefficient indicates divergence. Equation (6) assumes that all regions are structurally similar. They have the same steady state and differ only in terms of their initial conditions. It depicts unconditional βconvergence (Tselios 2009). 3. Results and Discussion 3.1. Best Approach for Calculating the RAI We calculated the RAI for a selection of districts in 2018 using various population distribution data, such as WorldPop and LandScan population distribution data. Because of the absence of regional road quality data, this study utilised the national road network map from the Directorate General of Highways and the accessibility data from the Directorate General of Highways and BPS-Statistics Indonesia. The results, displayed in Table 2, show similar values. The Indonesian RAI ranged from 18.94 per cent to 25 per cent. According to WorldPop data, the proportion of the Indonesian rural population in 2018 was 60.61 per cent. In the same year, LandScan data showed that the percentage of the Indonesian rural population was 64.75 per cent. The RAI using LandScan is higher than the RAI using WorldPop data, whichever RAI methods are used, because the WorldPop dataset has the lowest concentration of population in rural areas. This result is in line with Mikou et al. (2019). In general, with the same method, RAIs using different population distribution datasets have the same pattern, as shown in Figures A2 and A3. Table 2also displays the Pearson correlations of the RAI between different population distribution datasets for each method over 0.8. Table 2. 2018 Indonesian RAI by road network condition data and population distribution data. Method Road Network Condition Data Indonesian RAI (per cent) Pearson Correlation WorldPop LandScan 1 IRI 18.94 21.67 0.8732 2 Road condition 21.21 24.17 0.8790 3 Podes 21.59 25.00 0.8747 Source: Author’s calculation. WorldPop data were chosen for the population layer because the computational process underlying the WorldPop data is fully transparent (Stevens et al. 2015), and the model is considered to be the most accurate and robust among the currently available datasets (World Bank 2016). From three methods using WorldPop data, the descriptive statistics of RAI at the district level were similar. The RAI using IRI, road condition, and Podes data had means of 23.41 per cent, 25.21 per cent, and 25.71 per cent, respectively. These data are also in line with the scatter plots in Figure 2. The Pearson correlation between RAI using Podes data and RAI using IRI data was 0.9475. Furthermore, the correlation between RAI using Podes data and RAI using road condition data was also positive, with a Pearson correlation coefficient value of 0.9833. A one-way analysis of variance (ANOVA) was also used to assess whether there were differences between the three methods. The results concluded that there were no differences between the group means (F (2,1322) = 2.01, p= 0.135)1. Economies 2022,10, 229 7 of 20 Economies2022,10,xFORPEERREVIEW7of22  WorldPopdatawerechosenforthepopulationlayerbecausethecomputational processunderlyingtheWorldPopdataisfullytransparent(Stevensetal.2015),andthe modelisconsideredtobethemostaccurateandrobustamongthecurrentlyavailable datasets(WorldBank2016).FromthreemethodsusingWorldPopdata,thedescriptive statisticsofRAIatthedistrictlevelweresimilar.TheRAIusingIRI,roadcondition,and Podesdatahadmeansof23.41percent,25.21percent,and25.71percent,respectively. ThesedataarealsoinlinewiththescatterplotsinFigure2.ThePearsoncorrelation betweenRAIusingPodesdataandRAIusingIRIdatawas0.9475.Furthermore,the correlationbetweenRAIusingPodesdataandRAIusingroadconditiondatawasalso positive,withaPearsoncorrelationcoefficientvalueof0.9833.Aone‐wayanalysisof variance(ANOVA)wasalsousedtoassesswhetherthereweredifferencesbetweenthe threemethods.Theresultsconcludedthattherewerenodifferencesbetweenthegroup means(F(2,1322)=2.01,p=0.135)1.  Figure2.ScatterplotsbetweenRAIusingPodes‐WorldPopdataandRAIusingothermethods‐ WorldPopdatain2018.Source:Author’scalculation. ProvincialroadqualitydatafromtheDirectorateGeneralofHighwayswas unavailable.Basedonpreviousresults,thisstudyusedpopulationdistributionmapsfrom WorldPop,thenationalandprovincialroadmapsfromtheDirectorateGeneralof Highways,androadnetworkconditiondatafromBPS‐StatisticsIndonesiatocalculatethe RAIin2014,2018,2019,and2020.TableA1showstheresults. 3.2.RoadInfrastructureAccessacrossDistrictsinRuralIndonesia Foranalysis,thisstudydividedIndonesiaintosevenregionalgroups2.FigureA4 showsthedistrictlocationsineachregionalgroup.TheresultsinFigure3showthatin 2020,ruralresidentsin3.31percentofdistrictsdidnotlivewithinatwo‐kilometreradius ofall‐seasonnationalandprovincialroads.Thisdatawaslowerthanthe8.56percent recordedin2014.TheRAImedianalsoincreasedfrom29.43percentin2014to33.68per centin2020.Thepairedt‐testresultsreachedthesameconclusion.The2020RAIwas significantlyhigherthanthe2014RAI,withap‐valueoflessthan0.001. Figure 2. Scatter plots between RAI using Podes-WorldPop data and RAI using other methodsWorldPop data in 2018. Source: Author’s calculation. Provincial road quality data from the Directorate General of Highways was unavailable. Based on previous results, this study used population distribution maps from WorldPop, the national and provincial road maps from the Directorate General of Highways, and road network condition data from BPS-Statistics Indonesia to calculate the RAI in 2014, 2018, 2019, and 2020. Table A1 shows the results. 3.2. Road Infrastructure Access across Districts in Rural Indonesia For analysis, this study divided Indonesia into seven regional groups 2 . Figure A4 shows the district locations in each regional group. The results in Figure 3show that in 2020, rural residents in 3.31 per cent of districts did not live within a two-kilometre radius of allseason national and provincial roads. This data was lower than the 8.56 per cent recorded in 2014. The RAI median also increased from 29.43 per cent in 2014 to 33.68 per cent in 2020. The paired t-test results reached the same conclusion. The 2020 RAI was significantly higher than the 2014 RAI, with a p-value of less than 0.001. Economies2022,10,xFORPEERREVIEW8of22         Figure3.2020IndonesianRAIbydistrict(percent).Source:Author’scalculation. ThemajorityofdistrictswithahighRAIarelocatedinfourregionalgroups:Sumatra, Java,BaliandNusaTenggara,andSulawesi.RAIwaslowinmostdistrictsinBorneo, Moluccas,NorthMoluccas,Papua,andWestPapua.Duringthesametimeperiod,77.38 percentofdistrictshadahigherRAI.ThepositivechangeinRAIoccurredindistricts withalowRAI,namelyineasternIndonesia,whichisthepriorityoftheNawacita programme(seeFigure4).ThisshowsthattheNawacitaprogrammeimplementationwas relativelysuccessful.        Figure4.ChangeintheRAIduring2014–2020bydistrict.Source:Author’scalculation. 3.3.RoadInfrastructureAccessInequalityinRuralIndonesia Thisstudyusesthefollowingindicatorsofinequalitytoestablishtheevolutionof roadinfrastructureaccessinequalityinruralIndonesiafor2014–2020:thecoefficientof variance,theGinicoefficient,andtheTheilindex.Wedecomposedtheinequality indicatorbyregionsubgroups,usingthedecompositiontechniqueofBelluandLiberati (2006);Cowell(2011)andHaughtonandKhandker(2009)toanalysethecontributionsof eachregion’sdisparitytototalinequality. TheRAIinallregionalgroupsincreasedsignificantlyaftertheNawacita programme’simplementation.BaliandNusaTenggarahadthehighestRAI,which increasedfrom37.62percentin2014to44.99percentin2020.PapuaandWestPapuahad thelowestRAI,whichreached9.23percentin2014andincreasedto10.23percentin 2020.Thepolicyhadapositiveimpact,reducingIndonesia’sinequalitybetween2014and 2020.AsdescribedinTable3,thecoefficientofvariancedecreasedfrom0.665to0.587,the Ginicoefficientdecreasedfrom0.37to0.325,andtheTheilindexwentdownfrom0.164 to0.16.Indonesia’sGinicoefficientwascategorisedas“mediuminequality”.Figure5 representstheshiftsintheLorenzcurvefrom2014to2020.Theresultsalsoindicatethat inequalityfellbetween2014and2020. Figure 3. 2020 Indonesian RAI by district (per cent). Source: Author’s calculation. The majority of districts with a high RAI are located in four regional groups: Sumatra, Java, Bali and Nusa Tenggara, and Sulawesi. RAI was low in most districts in Borneo, Moluccas, North Moluccas, Papua, and West Papua. During the same time period, 77.38 per cent of districts had a higher RAI. The positive change in RAI occurred in districts with a low RAI, namely in eastern Indonesia, which is the priority of the Nawacita programme (see Figure 4). This shows that the Nawacita programme implementation was relatively successful. Economies 2022,10, 229 8 of 20 Economies2022,10,xFORPEERREVIEW8of22         Figure3.2020IndonesianRAIbydistrict(percent).Source:Author’scalculation. ThemajorityofdistrictswithahighRAIarelocatedinfourregionalgroups:Sumatra, Java,BaliandNusaTenggara,andSulawesi.RAIwaslowinmostdistrictsinBorneo, Moluccas,NorthMoluccas,Papua,andWestPapua.Duringthesametimeperiod,77.38 percentofdistrictshadahigherRAI.ThepositivechangeinRAIoccurredindistricts withalowRAI,namelyineasternIndonesia,whichisthepriorityoftheNawacita programme(seeFigure4).ThisshowsthattheNawacitaprogrammeimplementationwas relativelysuccessful.        Figure4.ChangeintheRAIduring2014–2020bydistrict.Source:Author’scalculation. 3.3.RoadInfrastructureAccessInequalityinRuralIndonesia Thisstudyusesthefollowingindicatorsofinequalitytoestablishtheevolutionof roadinfrastructureaccessinequalityinruralIndonesiafor2014–2020:thecoefficientof variance,theGinicoefficient,andtheTheilindex.Wedecomposedtheinequality indicatorbyregionsubgroups,usingthedecompositiontechniqueofBelluandLiberati (2006);Cowell(2011)andHaughtonandKhandker(2009)toanalysethecontributionsof eachregion’sdisparitytototalinequality. TheRAIinallregionalgroupsincreasedsignificantlyaftertheNawacita programme’simplementation.BaliandNusaTenggarahadthehighestRAI,which increasedfrom37.62percentin2014to44.99percentin2020.PapuaandWestPapuahad thelowestRAI,whichreached9.23percentin2014andincreasedto10.23percentin 2020.Thepolicyhadapositiveimpact,reducingIndonesia’sinequalitybetween2014and 2020.AsdescribedinTable3,thecoefficientofvariancedecreasedfrom0.665to0.587,the Ginicoefficientdecreasedfrom0.37to0.325,andtheTheilindexwentdownfrom0.164 to0.16.Indonesia’sGinicoefficientwascategorisedas“mediuminequality”.Figure5 representstheshiftsintheLorenzcurvefrom2014to2020.Theresultsalsoindicatethat inequalityfellbetween2014and2020. Figure 4. Change in the RAI during 2014–2020 by district. Source: Author’s calculation. 3.3. Road Infrastructure Access Inequality in Rural Indonesia This study uses the following indicators of inequality to establish the evolution of road infrastructure access inequality in rural Indonesia for 2014–2020: the coefficient of variance, the Gini coefficient, and the Theil index. We decomposed the inequality indicator by region subgroups, using the decomposition technique of Bellu and Liberati (2006); Cowell (2011) and Haughton and Khandker (2009) to analyse the contributions of each region’s disparity to total inequality. The RAI in all regional groups increased significantly after the Nawacita programme’s implementation. Bali and Nusa Tenggara had the highest RAI, which increased from 37.62 per cent in 2014 to 44.99 per cent in 2020. Papua and West Papua had the lowest RAI, which reached 9.23 per cent in 2014 and increased to 10.23 per cent in 2020. The policy had a positive impact, reducing Indonesia’s inequality between 2014 and 2020. As described in Table 3, the coefficient of variance decreased from 0.665 to 0.587, the Gini coefficient decreased from 0.37 to 0.325, and the Theil index went down from 0.164 to 0.16. Indonesia’s Gini coefficient was categorised as “medium inequality”. Figure 5represents the shifts in the Lorenz curve from 2014 to 2020. The results also indicate that inequality fell between 2014 and 2020. Since 2014, as shown in Table 3, all indicators have demonstrated a consistent declining trend across all Indonesian regions. Java had the lowest level of inequality, while Papua and West Papua had the greatest. Even though Papua and West Papua’s rural regions had the lowest RAI and the greatest inequality, this value had decreased. This trend is stronger in this region than in the others. Economies2022,10,xFORPEERREVIEW10of22   Figure5.LorenzcurveoftheRAI.Source:Author’scalculation. 3.4.ConvergenceofRoadInfrastructureAccessacrossIndonesianDistricts Ourdistrictanalysiscapturedastrongpatternofregionalconvergence.Asshownin Figure6,the𝜎convergenceoftheRAIdecreasedovertime.In2014,thisvaluewas1.054, anditreached0.975in2020.Table4describestheequationof𝛽convergence.The regressionofthechangeintheRAIasafunctionofitsinitiallevelconfirmsthe𝛽 convergenceinwhichthecoefficientoftheinitialvalueisnegativeandstatistically significantatthe1percentlevel.ThismeansthattherateofincreaseintheRAIwasfaster inthedistrictwithaninitiallylowRAIandviceversa.Thenegativetrendof𝜎 convergenceandthenegativecoefficientoftheinitialvalueintheequationof𝛽 convergencereinforcethepreviousstatementthattheNawacitaprogramme implementationreducedregionalinequalityduring2014–2020.  Figure6.The𝜎convergenceofRAIacrossIndonesiandistrictsin2014–2020.Source:Author’s calculation. Table4.𝛽convergenceofRAIacrossIndonesiandistrictsin2014–2020. DependentVariable:𝟏𝟔𝒍𝒏𝑹𝑨𝑰 ,𝟐𝟎𝟐𝟎 𝑹𝑨𝑰  , 𝟐𝟎𝟏𝟒   CoefficientStd.ErrortStatisticProb. 𝑙𝑛 𝑅𝐴𝐼,−0.0599***  0.003−17.360.000 Constant0.2255***  0.01219.410.000 N429  R‐squared0.4138  F‐statistic301.48***   Note:***significantat1percent.Source:Author’scalculation. 1.054 1.122 0.976 0.975 2014 2018 2019 2020 Figure 5. Lorenz curve of the RAI. Source: Author’s calculation. Economies 2022,10, 229 15 of 20 Table A1. Cont. Code District 2014 2018 2019 2020 Code District 2014 2018 2019 2020 1705 Seluma 45.5 46.7 48.6 48.1 7202 Banggai 35.5 38.8 48.9 48.9 1706 Mukomuko 37.8 37.7 39.8 40.0 7203 Morowali 30.6 30.4 39.1 40.9 1707 Lebong 41.7 42.2 42.2 41.7 7204 Poso 42.8 41.7 46.5 46.7 1708 Kepahiang 62.3 64.6 64.0 62.9 7205 Donggala 41.3 42.4 41.3 41.6 1709 Bengkulu Tengah 45.9 54.0 49.2 49.1 7206 Toli-Toli 35.8 37.0 46.5 46.5 1771 Bengkulu 34.1 36.9 36.8 36.1 7207 Buol 41.6 44.0 46.3 46.4 1801 Lampung Barat 24.4 25.0 43.0 43.5 7208 Parigi Moutong 34.8 35.2 43.7 44.2 1802 Tanggamus 34.8 38.8 47.9 47.9 7209 Tojo Una-Una 25.6 26.6 25.1 25.1 1803 Lampung Selatan 45.3 46.3 49.4 49.4 7210 Sigi 50.1 50.9 49.8 49.4 1804 Lampung Timur 44.0 44.7 47.8 47.3 7211 Banggai Laut 0.0 0.0 0.0 0.0 1805 Lampung Tengah 42.3 42.2 44.1 43.7 7212 Morowali Utara 0.0 0.0 0.0 0.0 1806 Lampung Utara 46.5 48.4 47.5 47.0 7271 Palu 6.9 8.0 11.7 11.9 1807 Way Kanan 39.2 39.5 42.4 41.9 7301 Kepulauan Selayar 29.3 34.4 33.3 32.0 1808 Tulangbawang 28.7 28.6 33.2 33.7 7302 Bulukumba 41.0 42.7 50.0 49.5 1809 Pesawaran 52.2 53.2 45.4 44.9 7303 Bantaeng 36.0 41.0 39.3 38.3 1810 Pringsewu 48.5 49.9 59.8 59.7 7304 Jeneponto 51.8 53.0 51.5 51.2 1811 Mesuji 27.9 26.1 24.7 24.8 7305 Takalar 28.8 29.2 29.7 29.0 1812 Tulang Bawang Barat 44.2 45.2 44.5 44.1 7306 Gowa 45.0 45.3 44.6 44.6 1813 Pesisir Barat 0.0 0.0 42.2 42.3 7307 Sinjai 29.4 32.9 32.3 31.4 1871 Bandar Lampung 16.5 17.0 17.6 17.0 7308 Maros 31.9 32.4 32.4 32.0 1872 Metro 93.9 94.8 94.8 95.1 7309 Pangkajene Dan Kepulauan 33.5 35.2 33.7 33.0 1901 Bangka 35.4 36.1 35.9 35.7 7310 Barru 43.0 44.4 44.5 43.8 1902 Belitung 29.2 37.3 47.3 46.1 7311 Bone 30.9 35.5 34.5 34.1 1903 Bangka Barat 20.9 20.0 21.4 22.3 7312 Soppeng 41.2 43.5 47.4 46.4 1904 Bangka Tengah 34.3 36.0 38.2 37.3 7313 Wajo 38.6 39.1 39.1 39.0 1905 Bangka Selatan 29.1 29.4 28.9 28.7 7314 Sidenreng Rappang 48.0 50.3 48.1 46.6 1906 Belitung Timur 39.8 39.5 41.7 42.1 7315 Pinrang 32.6 33.0 34.8 34.8 1971 Pangkal Pinang 0.2 0.2 85.4 86.0 7316 Enrekang 31.8 34.9 32.7 31.8 2101 Karimun 1.8 2.3 2.2 2.1 7317 Luwu 24.3 27.7 28.0 27.8 2102 Bintan 43.7 55.7 55.5 55.0 7318 Tana Toraja 24.3 23.4 28.5 28.0 2103 Natuna 23.0 24.0 24.2 24.2 7322 Luwu Utara 14.9 15.4 14.3 14.0 2104 Lingga 0.0 6.6 6.4 6.3 7325 Luwu Timur 22.6 22.8 24.2 25.4 2105 Kepulauan Anambas 0.0 8.3 15.7 15.2 7326 Toraja Utara 15.2 17.8 19.6 18.6 2171 Batam 0.0 27.4 27.4 27.0 7371 Makassar 0.0 0.0 0.0 0.0 2172 Tanjung Pinang 5.1 51.3 48.4 47.6 7372 Parepare 6.9 73.3 72.6 72.4 3201 Bogor 17.7 18.0 19.5 19.6 7373 Palopo 53.1 54.7 53.9 53.0 3202 Sukabumi 30.8 39.0 42.0 41.2 7401 Buton 19.8 21.1 16.4 17.2 3203 Cianjur 30.2 38.2 39.8 39.2 7402 Muna 22.6 31.2 18.9 19.3 3204 Bandung 35.8 37.8 37.3 37.2 7403 Konawe 19.7 19.5 18.4 18.8 3205 Garut 37.2 45.6 46.1 45.7 7404 Kolaka 36.4 38.5 40.9 41.4 3206 Tasikmalaya 20.4 25.8 23.6 23.3 7405 Konawe Selatan 39.8 41.1 39.6 40.1 3207 Ciamis 23.2 25.0 24.4 24.4 7406 Bombana 29.2 33.1 33.9 33.9 3208 Kuningan 33.2 35.0 36.5 36.3 7407 Wakatobi 0.0 21.3 20.8 20.6 3209 Cirebon 44.6 46.1 47.9 48.2 7408 Kolaka Utara 40.8 39.8 42.9 43.8 3210 Majalengka 26.3 25.6 25.9 26.5 7409 Buton Utara 4.9 5.1 5.2 5.6 3211 Sumedang 44.7 45.1 44.2 44.6 7410 Konawe Utara 16.8 16.9 14.7 15.1 3212 Indramayu 34.5 35.4 38.3 38.5 7411 Kolaka Timur 0.0 0.0 17.4 18.2 3213 Subang 34.9 34.5 34.4 34.5 7471 Kendari 85.6 85.7 55.9 55.5 3214 Purwakarta 35.3 34.9 36.0 36.6 7472 Baubau 49.3 51.0 64.0 64.1 3215 Karawang 6.6 8.4 8.9 9.3 7501 Boalemo 34.3 35.2 35.5 36.4 3216 Bekasi 4.7 4.9 5.1 5.1 7502 Gorontalo 46.9 47.8 51.4 51.3 3217 Bandung Barat 24.7 25.4 29.8 29.8 7503 Pohuwato 38.9 43.6 45.8 46.3 3218 Pangandaran 0.0 0.0 50.1 49.5 7504 Bone Bolango 44.0 44.1 49.0 49.5 3278 Tasikmalaya 49.5 50.4 49.5 48.9 7505 Gorontalo Utara 25.0 33.8 62.4 62.6 3279 Banjar 49.5 50.1 50.2 50.4 7571 Gorontalo 0.0 0.0 99.6 99.6 3301 Cilacap 42.8 47.9 47.1 46.7 7601 Majene 17.8 18.8 48.5 49.1 3302 Banyumas 51.9 50.7 49.9 49.8 7602 Polewali Mandar 29.8 34.2 31.4 30.1 3303 Purbalingga 24.8 25.7 25.2 25.5 7603 Mamasa 15.6 21.6 29.3 30.1 3304 Banjarnegara 48.2 49.4 49.0 48.6 7604 Mamuju 30.4 33.4 34.3 34.4 3305 Kebumen 20.9 21.2 20.9 21.1 7605 Mamuju Utara 33.6 35.4 36.0 35.3 3306 Purworejo 38.0 37.4 38.5 38.7 7606 Mamuju Tengah 0.0 0.0 38.4 37.8 3307 Wonosobo 51.1 52.1 51.4 50.9 8101 Maluku Tenggara Barat 11.2 19.8 23.6 23.6 3308 Magelang 50.5 53.1 52.4 52.2 8102 Maluku Tenggara 37.2 46.2 33.2 32.1 3309 Boyolali 29.9 34.5 32.9 32.8 8103 Maluku Tengah 25.6 31.1 47.4 47.4 3310 Klaten 27.0 27.3 27.0 27.0 8104 Buru 19.4 21.7 24.1 24.5 Economies 2022,10, 229 16 of 20 Table A1. Cont. Code District 2014 2018 2019 2020 Code District 2014 2018 2019 2020 3311 Sukoharjo 29.0 30.2 29.1 29.1 8105 Kepulauan Aru 0.0 1.1 2.6 2.5 3312 Wonogiri 45.2 46.7 46.5 45.8 8106 Seram Bagian Barat 18.0 29.1 42.5 41.7 3313 Karanganyar 31.6 31.9 33.4 33.2 8107 Seram Bagian Timur 0.7 3.1 7.7 7.5 3314 Sragen 32.5 34.5 34.0 34.2 8108 Maluku Barat Daya 0.0 5.2 3.6 3.5 3315 Grobogan 44.6 45.5 44.7 43.9 8109 Buru Selatan 2.9 7.0 9.3 9.3 3316 Blora 29.9 31.6 33.2 32.9 8171 Ambon 39.9 35.2 36.5 36.9 3317 Rembang 45.0 46.7 45.9 45.1 8172 Tual 24.5 35.9 35.6 35.1 3318 Pati 35.8 36.9 37.4 37.5 8201 Halmahera Barat 15.2 17.1 16.8 16.0 3319 Kudus 40.9 44.4 44.1 44.4 8202 Halmahera Tengah 29.0 48.8 45.7 44.8 3320 Jepara 16.7 19.7 20.6 20.4 8203 Kepulauan Sula 12.4 25.0 27.9 26.8 3321 Demak 35.5 36.9 36.7 36.6 8204 Halmahera Selatan 1.3 7.0 9.1 8.8 3322 Semarang 41.8 43.2 42.4 42.4 8205 Halmahera Utara 34.7 37.1 36.4 35.5 3323 Temanggung 46.1 47.5 46.4 46.1 8206 Halmahera Timur 14.4 38.5 38.1 37.1 3324 Kendal 35.0 36.1 35.7 36.1 8207 Pulau Morotai 19.8 41.4 49.6 48.7 3325 Batang 48.1 48.1 48.9 49.0 8208 Pulau Taliabu 0.0 0.0 10.5 10.4 3326 Pekalongan 42.7 42.5 41.5 41.3 8271 Ternate 67.4 69.4 35.5 34.3 3327 Pemalang 33.7 34.0 33.7 33.5 8272 Tidore Kepulauan 53.8 58.6 57.2 56.8 3328 Tegal 36.9 38.4 38.0 37.7 9101 Fakfak 11.1 22.7 13.7 13.9 3329 Brebes 44.0 43.4 46.4 46.1 9102 Kaimana 0.0 0.6 0.6 0.6 3374 Semarang 31.9 34.8 33.5 33.3 9103 Teluk Wondama 0.0 0.0 0.9 0.9 3375 Pekalongan 0.0 0.0 14.7 14.7 9104 Teluk Bintuni 1.4 1.7 1.7 1.9 3401 Kulon Progo 83.7 83.9 84.8 84.7 9105 Manokwari 54.6 54.8 55.8 56.1 3402 Bantul 84.4 85.0 84.6 84.7 9106 Sorong Selatan 0.7 2.4 0.8 0.7 3403 Gunung Kidul 66.5 66.9 65.9 65.8 9107 Sorong 18.6 29.9 34.5 34.0 3404 Sleman 67.0 68.5 67.8 68.1 9108 Raja Ampat 0.0 0.0 1.8 1.9 3501 Pacitan 31.1 55.8 50.6 49.4 9109 Tambrauw 0.0 0.0 0.2 0.2 3502 Ponorogo 31.4 33.0 32.5 31.5 9110 Maybrat 0.0 13.3 20.0 19.8 3503 Trenggalek 38.7 41.9 41.5 40.8 9111 Manokwari Selatan 0.0 0.0 40.2 39.0 3504 Tulungagung 9.7 19.3 19.3 19.1 9112 Manokwari 0.0 0.0 0.1 0.1 3505 Blitar 13.8 24.1 23.8 23.8 9171 Sorong 77.3 88.2 31.8 31.6 3506 Kediri 23.5 23.8 23.4 23.6 9401 Merauke 1.3 2.8 3.5 3.5 3507 Malang 23.2 23.7 23.3 23.0 9402 Jayawijaya 7.0 8.8 7.5 7.8 3508 Lumajang 22.0 32.4 32.9 32.5 9403 Jayapura 26.0 30.3 29.7 29.8 3509 Jember 26.4 27.2 27.2 27.1 9404 Nabire 19.1 24.6 23.4 22.1 3510 Banyuwangi 21.9 18.3 18.0 17.8 9408 Kepulauan Yapen 11.3 12.0 13.0 12.6 3511 Bondowoso 21.4 22.2 22.0 22.0 9409 Biak Numfor 32.3 40.4 40.6 39.7 3512 Situbondo 41.4 41.6 41.1 40.8 9410 Paniai 7.3 7.9 6.8 6.5 3513 Probolinggo 29.0 33.3 33.3 33.2 9411 Puncak Jaya 0.0 1.0 2.9 3.0 3514 Pasuruan 40.1 43.0 43.5 43.5 9412 Mimika 1.5 5.3 5.6 5.8 3515 Sidoarjo 19.2 24.5 24.9 24.9 9413 Boven Digoel 4.1 3.6 4.6 4.9 3516 Mojokerto 33.6 34.7 34.7 35.0 9414 Mappi 0.0 0.0 0.0 0.0 3517 Jombang 27.5 31.3 31.2 31.5 9415 Asmat 0.0 0.0 0.0 0.0 3518 Nganjuk 28.6 31.7 31.0 30.6 9416 Yahukimo 0.0 0.6 0.5 0.5 3519 Madiun 34.0 36.5 38.7 38.3 9417 Pegunungan Bintang 0.0 0.6 1.7 1.7 3520 Magetan 22.2 22.7 22.3 21.9 9418 Tolikara 0.0 0.5 2.4 2.4 3521 Ngawi 30.3 32.1 32.4 32.1 9419 Sarmi 3.4 14.1 15.8 16.0 3522 Bojonegoro 32.9 33.1 31.7 31.0 9420 Keerom 14.9 15.8 11.0 11.0 3523 Tuban 40.3 41.3 41.2 40.8 9426 Waropen 0.7 0.7 1.5 1.6 3524 Lamongan 30.4 30.7 31.0 31.3 9427 Supiori 40.1 40.3 40.4 39.7 3525 Gresik 26.3 26.1 26.2 26.1 9428 Mamberamo Raya 0.0 0.0 0.0 0.0 3526 Bangkalan 33.4 33.7 33.4 33.1 9429 Nduga 0.0 0.0 0.0 0.0 3527 Sampang 24.4 24.3 23.9 24.3 9430 Lanny Jaya 0.0 1.8 0.9 0.9 3528 Pamekasan 23.4 24.1 23.7 23.4 9431 Mamberamo Tengah 2.9 3.1 1.4 1.5 3529 Sumenep 24.1 24.4 23.8 23.5 9432 Yalimo 16.4 16.8 18.9 19.0 3574 Probolinggo 91.4 90.0 89.2 89.7 9433 Puncak 0.0 0.0 0.0 0.0 3579 Batu 45.0 47.1 45.8 44.8 9434 Dogiyai 4.3 5.6 8.2 7.9 3601 Pandeglang 30.5 32.6 33.0 32.6 9435 Intan Jaya 0.0 0.0 0.0 0.0 3602 Lebak 34.7 37.5 38.1 37.4 9436 Deiyai 0.1 3.9 8.3 8.3 3603 Tangerang 5.7 5.7 5.9 6.1 9471 Jayapura 59.0 62.0 62.1 62.8 3604 Serang 23.1 28.3 30.0 30.6 Source: Author’s calculation. Economies 2022,10, 229 17 of 20 Economies2022,10,xFORPEERREVIEW18of22  3516Mojokerto33.634.734.735.09414Mappi0.00.00.00.0 3517Jombang27.531.331.231.59415Asmat0.00.00.00.0 3518Nganjuk28.631.731.030.69416Yahukimo0.00.60.50.5 3519Madiun34.036.538.738.39417PegununganBintang0.00.61.71.7 3520Magetan22.222.722.321.99418Tolikara0.00.52.42.4 3521Ngawi30.332.132.432.19419Sarmi3.414.115.816.0 3522Bojonegoro32.933.131.731.09420Keerom14.915.811.011.0 3523Tuban40.341.341.240.89426Waropen0.70.71.51.6 3524Lamongan30.430.731.031.39427Supiori40.140.340.439.7 3525Gresik26.326.126.226.19428MamberamoRaya0.00.00.00.0 3526Bangkalan33.433.733.433.19429Nduga0.00.00.00.0 3527Sampang24.424.323.924.39430LannyJaya0.01.80.90.9 3528Pamekasan23.424.123.723.49431MamberamoTengah2.93.11.41.5 3529Sumenep24.124.423.823.59432Yalimo16.416.818.919.0 3574Probolinggo91.490.089.289.79433Puncak0.00.00.00.0 3579Batu45.047.145.844.89434Dogiyai4.35.68.27.9 3601Pandeglang30.532.633.032.69435IntanJaya0.00.00.00.0 3602Lebak34.737.538.137.49436Deiyai0.13.98.38.3 3603Tangerang5.75.75.96.19471Jayapura59.062.062.162.8 3604Serang23.128.330.030.6      Source:Author’scalculation.  (a)(b)(c) FigureA1.OSMdatainconsistency(a)2014(b)2020(c)mapmerger.Note:Authorsonlyuse primary,primarylink,secondary,andsecondarylinkroadclassifications.Source: www.geofabrik.de(accessedon15November2021).     Figure A1. OSM data inconsistency ( a ) 2014 ( b ) 2020 ( c ) map merger. Note: Authors only use primary, primary link, secondary, and secondary link road classifications. Source: www.geofabrik.de (accessed on 15 November 2021). Economies2022,10,xFORPEERREVIEW19of22   (a)  (b)  (c) FigureA2.2018IndonesianRAIusingthenationalroadnetworkmap,WorldPop,anddifferent roadnetworkconditiondata(percent):(a)IRI(b)Roadcondition(c)Podes.Source:Author’s calculation.  Figure A2. 2018 Indonesian RAI using the national road network map, WorldPop, and different road network condition data (per cent): ( a ) IRI ( b ) Road condition ( c ) Podes. Source: Author’s calculation. Economies 2022,10, 229 18 of 20 Economies2022,10,xFORPEERREVIEW20of22   (a)  (b)  (c) FigureA3.2018IndonesianRAIusingthenationalroadnetworkmap,LandScan,anddifferentroad networkconditiondata(percent):(a)IRI(b)Roadcondition(c)Podes.Source:Author’scalculation.  Figure A3. 2018 Indonesian RAI using the national road network map, LandScan, and different road network condition data (per cent): ( a ) IRI ( b ) Road condition ( c ) Podes. Source: Author’s calculation. Economies 2022,10, 229 19 of 20 Economies2022,10,xFORPEERREVIEW21of22   FigureA4.Indonesiabyregionalgroupanddistrict’scode. Notes 1. Bartlett’sequal‐variancestesthad𝜒  󰇛2󰇜=0.1065andp‐value=0.948. 2. Sumatrahasdistrictcodes1101–2172,Javahasdistrictcodes3201–3673,BaliandNusaTenggarahavedistrictcodes5101–5371, Borneohasdistrictcodes6101–6571,Sulawesihasdistrictcodes7101–7606,MoluccasandNorthMoluccashavedistrictcodes 8101–8272,andPapuaandWestPapuahavedistrictcodes9101–9471. 3. AccordingtoMinistryofFinanceRegulationNumber120/PMK.07/2020aboutRegionalFiscalCapacityMaps,𝐷𝐹𝐶𝐼     ∑    / where𝐷𝐹𝐶  isgovernmentrevenue—(governmentrevenuethatitsalocationisdetermined+specificexpenditure) andnisthenumberofdistrictsinIndonesia.𝐷𝐹𝐶  showsthefiscalcapacityofdistricti. References (AsherandNovosad2020)Asher,Sam,andPaulNovosad.2020.RuralRoadsandLocalEconomicDevelopment.AmericanEconomic Review110:797–823.https://doi.org/10.1257/aer.20180268. (Bappenas2014)Bappenas.2014.RencanaPembangunanJangkaMenengahNasional(RPJMN)2015–2019.Jakarta:Bappenas.Available online:http://djsn.go.id/storage/app/media/RPJM/BUKU%20I%20RPJMN%202015‐2019.pdf(accessedon9September2022). (Bappenas2017)Bappenas.2017.RingkasanMetadataIndikatorTujuanPembangunanBerkelanjutan(TPB)/SustainableDevelopmentGoals (SDGs)Indonesia.Jakarta:Bappenas.Availableonline:http://sdgs.bappenas.go.id/wp‐ content/uploads/2017/09/Buku_Ringkasan_Metadata_Indikator_TPB.pdf(accessedon18October2021). (Bappenas2020)Bappenas.2020.PedomanTeknisPenyusunanRencanaAksiTujuanPembangunanBerkelanjutan(TPB)/Sustainable DevelopmentGoals(SDGs).Jakarta:Bappenas.Availableonline:http://sdgs.bappenas.go.id/buku‐pedoman‐rencana‐aksi‐sdgs/ (accessedon11June2021). (BellandvanDillen2014)Bell,Clive,andSusannevanDillen.2014.HowDoesIndia’sRuralRoadsProgramAffecttheGrassroots? FindingsfromASurveyinUplandOrissa.LandEconomics90:372–94.https://doi.org/10.3368/le.90.2.372. (BelluandLiberati2006)Bellu,LorenzoGiovanni,andPaoloLiberati.2006.PolicyImpactsonInequality:DecompositionofIncome InequalitybySubgroups.Rome:FAO.Availableonline:https://www.fao.org/3/a‐am342e.pdf(accessedon1November2021). (Cowell2011)Cowell,FrankA.2011.MeasuringInequality,3rded.NewYork:OxfordUniversityPress. https://doi.org/10.1093/acprof:osobl/9780199594030.001.0001. (Derconetal.2012)Dercon,Stefan,JohnHoddinott,andTassewWoldehanna.2012.GrowthandChronicPoverty:Evidencefrom RuralCommunitiesinEthiopia.JournalofDevelopmentStudies48:238–53.https://doi.org/10.1080/00220388.2011.625410. (HaughtonandKhandker2009)Haughton,Jonathan,andShahidurR.Khandker.2009.HandbookonPovertyandInequality. Washington,DC:TheWorldBank. (Iimietal.2016)Iimi,Atsushi,FarhadAhmed,EdwardCharlesAnderson,AdamStoneDiehl,LabanMaiyo,TatianaPeralta‐Quirós, andKulwinderRao.2016.NewRuralAccessIndex:MainDeterminantsandCorrelationtoPoverty.PolicyResearchWorking Paper.Washington,DC:WorldBankGroup.https://doi.org/10.1596/1813‐9450‐7876. (Jangetal.2017)Jang,Seongman,YoungsooAn,ChanghyoYi,andSeungilLee.2017.AssessingtheSpatialEquityofSeoul’sPublic TransportationUsingtheGiniCoefficientBasedonItsAccessibility.InternationalJournalofUrbanSciences21:91–107. https://doi.org/10.1080/12265934.2016.1235487. (KhandkerandKoolwal2011)Khandker,ShahidurR.,andGayatriB.Koolwal.2011.EstimatingtheLong‐termImpactsofRural Roads:ADynamicPanelApproach.PolicyResearchWorkingPaper5867:1–37.https://doi.org/10.1596/1813‐9450‐5867. (Lietal.2022)Li,Wanjing,QiZhou,YuhengZhang,andYijunChen.2022.VisualisingRuralAccessIndexandNotServedRural PopulationinAfrica.EnvironmentandPlanningA:EconomyandSpace54:215–18.https://doi.org/10.1177/0308518X211035786. Figure A4. 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