metadata: authors: 0: author: Sanoh,Aly
1: author: Coulibaly,Massaoly
count: Mali
docna: 0: docna: Socioeconomic and fiscal impact of
            large-scale gold mining in Mali
docty: Policy Research Working Paper
subtopic: Primary Metals,Mining & Extractive Industry (Non-Energy),Educational Sciences,Inequality
teratopic: Poverty Reduction,Social Development
abstracts: cdata!: This paper analyzes the socioeconomic,
            fiscal, and governance impact of gold mining in Mali. The
            analysis finds that, at the national level, mining plays an
            important role by contributing to export earnings and
            overall government fiscal revenue. In 2013, the mining
            sector represented 7 percent of gross domestic product,
            contributed 1.5 percent to growth in total gross domestic
            product, and accounted for 65 percent of total export
            earnings and 25 percent of total government budget revenues.
            At the local level, despite higher population growth, there
            is some evidence that outcomes (poverty and infrastructure
            services) are marginally better in mining communes compared
            with non-mining communes. Local governments receive fiscal
            windfalls that are spent significantly on education capital
            expenditures and current expenditures (salaries and
            non-salaries). Non-salary current expenditures are 10 times
            higher in mining areas. Analysis of the political economy of
            public service provision at the local level suggests that
            technical or absorptive capacities may be the bottleneck to
            increasing the local benefit of mining instead of corruption
            or accountability.
geo_reg_key: 1927320,517191
repnme: repnme: Socioeconomic and fiscal impact of
            large-scale gold mining in Mali
display_title: Socioeconomic and fiscal impact of
            large-scale gold mining in Mali
listing_relative_url: /research/2015/10/25229832/socioeconomic-fiscal-impact-large-scale-gold-mining-mali
topicv3: Mining Services m1326410 1701,Poverty m1326687 695,Education m1327048 470,Mining Sector m1327113 554,Gold m1503518 2936
publishtoextweb_dt: 2015-10-30T00:00:00Z
historic_topic: Poverty Reduction,Social Development
no_of_pages: 37
author_keys: 0: author_key: 1220585
1: author_key: 647291
model_extractions: WPS7467 Policy Research Working Paper 7467 Socioeconomic and Fiscal Impact of Large-Scale Gold Mining in Mali _Aly Sanoh Massaoly Coulibaly_ Africa Region Office of the Chief Economist October 2015
Policy Research Working Paper 7467

# **Abstract**

This paper analyzes the socioeconomic, fiscal, and governance impact of gold mining in Mali. The analysis finds that, at the national level, mining plays an important role by contributing to export earnings and overall government fiscal revenue. In 2013, the mining sector represented 7 percent of gross domestic product, contributed 1.5 percent to growth in total gross domestic product, and accounted for 65 percent of total export earnings and 25 percent of total government budget revenues. At the local level, despite higher population growth, there is some evidence that outcomes (poverty and infrastructure services) are marginally better in mining communes compared with non-mining communes. Local governments receive fiscal windfalls that are spent significantly on education capital expenditures and current expenditures (salaries and non-salaries). Nonsalary current expenditures are 10 times higher in mining areas. Analysis of the political economy of public service provision at the local level suggests that technical or absorptive capacities may be the bottleneck to increasing the local benefit of mining instead of corruption or accountability.

This paper is a product of the Office of the Chief Economist, Africa Region. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at asanoh@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **Socioeconomic and Fiscal Impact of Large-Scale Gold Mining in Mali**<sup>**1**</sup>

Aly Sanoh World Bank Massaoly Coulibaly Groupe de recherche en économie appliquée et théorique JEL Classification: J16, J21, O13, O15, O18 Keywords: Local Impact, Natural Resources, Welfare, Governance

> 1 This is a background paper to the Socioeconomic Impact of Mining on Local Communities in Africa Project (P148422). The authors are grateful to Johannes Hoogeveen, Bryan Land, Gregory Smith, Michael Lewin, Punam Chuhan-Pole, Andrew Dabalen, Kristina Svensson, and Souleymane Traore.
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<!-- Start of picture text --> 1000 3 76 380<br>“sowo 4 yt see9 ow - -_ z a<br>ly “is 60<br>a=== 600 50€=<br>== 500 - - 404s<br>Ss +<br>5 aw ap<br>2 “es<br>“300 +<br>200 L 20<br>100 - 10<br>0 —-—-—- - om of BH es 0<br>2005-2006 200s, 200s 2008S 2010 2011 12-2013<br>Gag Artisanal Mining Industrial mining «= Share miningin total export (%)<br><!-- End of picture text --> <!-- Start of picture text --> 60 700<br>—=—_—_<br>5p. - ~ ~ - —— = 600<br>—_— =<br>=F a0 500s4<br>=25 30 4008=<br>3 3<br>z ; 3008<br>$s o<br>g 20 - 200a2<br>10 100<br>0 0<br>2008 2008 2010 2011 2012 2013<br>Price Mali (thousand feta/once) = = ©fxport (tons)<br><!-- End of picture text -->
<!-- Start of picture text --> 40<br>35<br>30<br>24.5 25.6<br>25<br>20<br>15<br>10<br>6) [<br>2005 2006 2007 2008 2009 2010 2011 2012 2013<br>@ Mining as share of total budget Mining as share of total fiscal revenues<br><!-- End of picture text -->
<!-- Start of picture text --> 4000 243 312 298 8096<br>3500 -— xz -_ 000<br>Wi r | | 6947<br>3000 mz #000 6220<br>2500 6000 | 5230 5404<br>2000 SOOO =<br>1500 | 3079 3351 3498 3500 3504 a A000 4144<br>1000 + 3000 |<br>500 |<br>2000<br>() 1 1<br>2008 2009 2010 2011 2012 2013 1000<br>Employed Nationals mTM Employed Foreign 0 2008 2009 2010 2011 2012 2013<br><!-- End of picture text -->

<!-- Start of picture text --> 11.4 a0.2<br>0 2<br>CSS<br>20 60.0<br>=Sadiola *Sitakilly **Sanso %Fourou Il Gouandiaka =Sadiola &Sitakilly :*Sanso Fourou i Gouandiaka<br><!-- End of picture text -->
<!-- Start of picture text --> 35 10<br>30 - me<br>8 —<br>2 os el _7 5<br>2 59 J (6 3<br>s a<br>3 (SF a<br>oi a &<br>z £<br>10 2 ¢<br><<br>- 2<br>><br>- 4<br>0 orm"SOs ero 0<br>2008 2009 2010 2011 2012 2013<br>Net salaries (billions fcfa) <=Average salary per worker (millions fcfa)<br><!-- End of picture text -->
<!-- Start of picture text --> 7<br>80 “ 35<br>70 67 68 65 66 69 30 30.3<br>61 co<br>co 5%<br>50 48<br>20<br>“0 15.5<br>31<br>30 15<br>20 10 8.1<br>ss<br>a 0 . a . 0<br>Sadiola Silakily Sanso Fourou Gouandiaka Sadiola sitakily sanso rouroe Gouandiaka<br>199% 2009 1998 M700<br><!-- End of picture text --> <!-- Start of picture text --> 7<br>80 “ 35<br>70 67 68 65 66 69 30 30.3<br>61 co<br>co 5%<br>50 48<br>20<br>“0 15.5<br>31<br>30 15<br>20 10 8.1<br>ss<br>a 0 . a . 0<br>Sadiola Silakily Sanso Fourou Gouandiaka Sadiola sitakily sanso rouroe Gouandiaka<br>199% 2009 1998 M700<br><!-- End of picture text --> <!-- Start of picture text --> ‘ / 6.61 . 6.68<br>aed 6.41 6.10<br>6 6<br>55 3.47 3.565 3.40) 4229 5<br>A<br>4<br>33 2.90<br>3 -<br>2<br>1 2<br>-1- Mining Neighboring islcontrol 2nd Control =Cornmunes rest 1<br>communes communes = within Cercle within Kegion — of country<br>ia)<br>Group growltirale ml Average ol growlt rales Sadiola Silakilly Sanso Tourou Gouandiaka<br><!-- End of picture text -->
<!-- Start of picture text --> 70<br>61.5 58.5 59.8 61.3<br>60<br>.<br>49.9 52.6<br>50<br>40<br>30<br>20<br>0)<br>2008 2009 2010 2011 2012 2013<br>Mining sector (%) TM Other sectors (%)<br><!-- End of picture text -->
<!-- Start of picture text --> 700 70<br>600 60<br>500 oo a : oeredet oe<br>3 an oon | S<br>Fa$ 400 nenn ee: ee°<br>g ne 3<br>3 300 FL 30 8<br>200 on ee ee<br>° °<br>2010 2011 2012 2013 2014 2015 2016 2017<br>mam Reserves (tons) — Production (tons)<br><!-- End of picture text -->

<!-- Start of picture text --> 100%<br>oe90% aeegoeF a)oeiesoeaoePores Passe] |e<br>7% | # Se ge Fe & & Ee Be<br>eerae eeeeee aeae eeece peeps eeeSee pesee eeoe<br>60%) #e ee se & & $j: Fe Be<br>ie = fe fe be<br>40%<br>10%<br>0%‘oO<br>2010 2011 2012 2013 2014 2015 2016 2017<br>@Fourou @Sadiola ¥Sanso_ & Sitakily Others<br><!-- End of picture text -->
<!-- Start of picture text --> a0 AO 1400 1288.5<br>400 » 1200<br>350300 - eS =>» a 1000 918<br>250 = 800<br>200 1, 7<br>10 16 = gag 8D 600 | 460.5 O14<br>16 = bea 307 400<br>100 = tes 189 24? 1 209 = 247-830<br>so|a ma 200(0) 85 51 112 |<br>2005 7006 «2007, 90087000) aMO 711 OTA 2.013 2008 2009 2010 2011 2012<br>Kesearch & exploration @ exploitation @ Public Mining sector<br><!-- End of picture text -->
<!-- Start of picture text --> OE mee<br>{0 ATED Sé<br>/ / a0 any, KS<br>50 “e. r A AR of<br>\ / ~ — (Pe wes:<br>v0 / SINGS yore<br>20 NN oH Sy<br>7 wae I gOS<br>ZA LBS<br>3 | |<br>kK | Mining communes pe TS<br>a ©a am3 ©8 aA8 an$ m8 +t8 ws8 8oRs we8 HO8 S aS [|__|| NeighboringControl withincommunes Cercle eyaLy acy.<br>| Control within regions<br><!-- End of picture text -->

<!-- Start of picture text --> 60 do 70 69<br>43, 61<br>50 “ 44 60 52 43<br>40 50 - 48<br>AO<br>30<br>204 48 17 * 21 19<br>12 11 20 16 15<br>10 10 6-<br>0<br>Mining communcs Neighbor 1st control within 2nd Control within o . a .<br>communes Cercle Region Sadiola Sitakily Sanso Fourou Gouandiaka<br>1998 TM 2009 1998 2009<br><!-- End of picture text -->
30 percent had access to an improved water source, and 50 percent to an improved sanitation facility (table 2). In terms of progress, between 1998 and 2009, mining communes had significant improvement with regard to services where they started from a lower base. However, in 2009, only the share of the population using an improved water source is far greater in mining areas. This may explain the better children’s health outcomes in the vicinity of mining activity (Polat et al. 2014). Beyond indicators of access, infrastructure outcomes in 2013 are not better in mining areas. For example, while paved road per capita is slightly higher in mining areas, irrigation per capita<sup>6</sup> is lower than in other areas. Mining areas have also fewer nurses and midwives per capita than other type of communes (table 3).

**Table 2 Use of infrastructure services, 1998 and 2009 (% of population)**

|||<br>199|<br>8|||<br>200|<br>9||
|---|---|---|---|---|---|---|---|---|
||Electricity<br>for lighting|Improved<br>water source|Improved<br>cooking fuel|Improved<br>sanitation|Electricity<br>for lighting|Improved<br>water source|Improved<br>cooking fuel|Improved<br>sanitation|
|Mining communes|1.88|30.31|1.73|50.62|12.97|67.16|2.10|89.25|
|Neighbor communes|2.73|17.37|1.81|47.94|11.88|42.87|0.65|84.03|
|1st control within Cercle|7.71|23.45|2.48|48.97|13.31|42.08|0.61|83.20|
|2nd Control within Region|2.62|19.38|2.11|52.35|10.54|29.06|0.47|82.08|

_Source:_ RGPH (General Population and Housing Census) 1998 and 2009.

**Table 3 Infrastructure outcomes by** **<u>group of communes, 2013</u>**

||Paved road<br>per 1,000 inh.|Irrigated area<br>per 1,000 inh.|Local health<br>centers per<br>10,000 inh.|Health<br>doctors per<br>10,000 inh.|Midwives<br>per 5,000<br>inh.|Nurse per<br>5,000 inh.|primari<br>school per<br>1000 inh.|Net primary<br>enrollement<br>(%)|
|---|---|---|---|---|---|---|---|---|
|Mining communes|6.80|0.73|0.74|0.52|0.06|0.38|0.88|73.00|
|Neighbor communes|6.56|4.84|1.17|0.41|0.14|0.82|0.90|63.75|
|1st control within Cercle|6.16|5.19|1.03|0.42|0.18|0.58|1.02|61.59|
|2nd Control within Region|10.37|6.21|1.07|0.32|0.12|0.72|0.82|55.87|

_Source:_ ODHD (Sustainable Development Observatory) database 2013.

With regard to poverty,<sup>7</sup> although mining communes had lower levels of poverty than other communes in 1998 and 2009, poverty reduction remains slightly less significant between the two dates. Over 10 years, poverty declined by 17 percentage points in mining communes compared to 19 percentage points in neighboring communes and other communes within the same Cercle (figure 13a). Furthermore, the mining communes of Sikasso region experienced an increase in the monetary poverty rates between 1998 and 2009. Sadiola and Sitakily achieved by far the biggest

> 6 However, this may reflect differences in rainfall or farming intensity.

> 7 We use two poverty maps based on the General Population and Housing Censuses of 1998 and 2009. The poverty map for 1998 was developed by GREAT (Applied and Theoretical Economics Research Group) and combines the 1998 census and the household survey ELIM (Integrated Light Household Survey) of 2006. The poverty map for 2009 is the result of collaboration between the World Bank and INSTAT (National Statistics Institute) and uses data from the 2009 census and the 2010 ELIM. One major methodological difference between the two maps is that the map of 1998 is based on a consumption model that considers urban and rural areas inside each administrative region with a total number of 17 models, whereas the map of 2009 uses only 8 regional models. In addition, the gap in years between the census and the household budget surveys is much smaller for the 2009 map.

12
<!-- Start of picture text --> 90 82 82 -<br>30 Jb 78 90 87 ga 86 a? 85<br>70 80 77<br>60 59 63 59 61 10 68 66<br>60<br>50<br>50<br>40 40<br>30 30 24 21<br>20 20<br>0 0<br>Mining com#munes Neighbor Ast control within 2nd Control within Sadiola Sitakily Sanso Fourou Gouandiaka<br>communes Cercle Region | |<br>Kayes Kayes Sikasso Sikasso Sikasso<br>1998 m 2009<br>1998 TM 2009<br><!-- End of picture text -->
related to the provision of public services.<sup>8</sup> From 1992 to 2009, five local elections have been held (1992, 1998 and 1999, 2004, and 2009). The next local elections, which were initially planned for 2014, are finally expected to take place in 2015 and, for the first time, they will be coupled with regional elections in the new context of regionalization,<sup>9</sup> under which all the deliberating bodies of local authorities (regions, cercles, and commune councils) must be elected directly by their governed citizens.

Despite some progress in the administrative and financial empowerment of local authorities, they are still to a large extent under the authority of the central government with regard to resources, and even acts and decisions related to their respective territories. The government seems to be more concerned about national unity and territorial integrity than the enhancement of the transfer of capabilities and public resources to communities. Importantly, the Malian decentralization framework includes an element called “deconcentration,” whereby agents<sup>10</sup> paid by the central government are relocated within local authorities to provide technical assistance or even approve budgets.

# _<u>Flows of resources to communes</u>_

From 2011 to 2014, central government transfers to local authorities accounted for an average of 2 percent of GDP. While the transfers to local authorities account for only 9 percent of the government’s budget expenditures, local authorities additionally benefit from other public resources within the framework of “deconcentration.” Data on transfers to local authorities in the last four years show that their share in the government total budget has varied significantly from year to year (table 4). In particular, there was a very sharp fall in direct transfers in 2012, whereas “deconcentration” transfers grew moderately each year. These transfers to all communes are much smaller than the contribution of the five mining communes to the national budget. In 2013, revenue from mining areas represented 25 percent of total government revenue. Tracking these transfers to individual communes is only a recent phenomenon in the budgeting framework of the central government. There are no clear rules for the transfers, and therefore it is not obvious whether they benefit certain groups of communes to the detriment of others.

> 8 Responsibilities transferred are in the health, education, and water sectors.

> 9 In previous elections, commune councils were directly elected by citizen’s votes, while cercles and regional councils were indirectly elected.

> 10 In practice, the agents interfere with local authorities even within their responsibilities in the areas of health, education, and water.

14
**Table 4 Transfers to local governments, 2011–14**<sup>a</sup>

||2011|2|012|2|013|2|014||
|---|---|---|---|---|---|---|---|---|
||10<sup>9</sup>fcfa|%|10<sup>9</sup>fcfa|%|10<sup>9</sup>fcfa|%|10<sup>9</sup>fcfa|%|
|Deconcentration transfers|157.1|12.4|163.1|17.7|173.7|19.6|202.5|11.2|
|Direct transfers|122.4|9.6|48.6|5.3|83.4|9.4|141.5|7.8|
|Total transfers|279.5|22|211.7|23|257.1|29.1|344|19|
|Total State budget|1 268.5||918.6||884.3||1 806.6||

_Source:_ BOOST 2014. _Note:_ a. 2014 is based on partial execution of the budget.

In 2000 new legislation mandated 60 percent of local taxes<sup>11</sup> paid by commercial mining firms and 80 percent of license fees paid by small-scale miners to the local authorities, be paid to the communes where gold production takes place. Producing Cercles should receive 25 percent and 15 percent of these taxes, respectively, while producing regions retain 15 percent and 5 percent, respectively. Commercial mining companies are exempted from paying these taxes during the first three years of operations. Unfortunately, local authorities have little possibility of determining the actual level of the license fees that are supposed to be distributed among them. License fees from mining companies are actually not distributed as required by the above-mentioned law. The share actually allocated to communes represents 73 percent of the total amounts collected instead of the 60 percent according to the law. As a result, all the other levels of local authorities receive less than the percentages required by the law, 17 percent at the Cercle level (while the law requires 25 percent) and 1 percent at the regional level (while the law requires 15 percent).

In general, Malian local authorities have a low rate of revenue collection and therefore a low level of self-financing capacity. As a result, the transfers and subsidies from the government represent the main source of revenue to support current and especially investment expenditures. The analysis of the budgetary accounts from 5 mining communes and 24 neighboring communes reveal indeed a low level of tax collection. Mining communes have an emphasis on the resources generated by license fees paid by mining companies. These license fees represented more than 50 percent of their revenues between 2011 and 2013 compared to just 2 percent for neighboring communes (figure 14a and 14b). Although mining communes are less dependent on the transfers from the government, they still remain exposed to the risks related to the closing of mines or the drop of production in this sector because license fee payments are a function of turnover.

> 11 All other mining taxes are paid to Bamako and consolidated in the national budget.

15
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<!-- Start of picture text --> 11<br>Reitetite tes ———___<br>aSaNtbaseeebencsenetesEE ===———==<br>16| _——————= 59<br>=Sadiola Sitakilly #Sanso -* Fourou & Gouandiaka<br><!-- End of picture text -->

<!-- Start of picture text -->
1.2 4.9<br>Ss ———¥<br>16.0 | a|vit —==—<$__———_—J<br>————<br>SSS 71.2<br>=Sadiola i Sitakilly =#Sanso =:Fourou # Gouandiaka<br><!-- End of picture text -->
<!-- Start of picture text --> 40000<br>17000 35000<br>10000 - 30000<br>8000 25000"0<br>20000 -<br>6000<br>15000 -<br>4000<br>10000 -<br>2000 5000 ~<br>a 0<br>Mining communes — Neighbor =—_1st control within 2nd Control within Mining communes = Neighbor —1st control within 2nd Control within<br>communes Cercle Region communes Cercle Region<br>Own revenues per capita (tcta) B Investment ({cfa/capila, 2000-08)<br>Current expendilures (fefa/capila, 2005-08)<br>20000 6000<br>18000<br>Te000 = 5000 -<br>14000 - 4000) =<br>12000 -<br>10000 3000<br>8000<br>6000 2000<br>4000<br>7000 1000<br>0 ny<br>Mining communes communes Neighbor ist contro! within contro! within within 2nd Control Control within Mining communes communes Neighbor Isl control within 2nd Control wilhin Control wilhin wilhin<br>communes Cercle Region communes Cercle Kegion<br>Education Health m Water Water m Others Salaries m Others Others<br><!-- End of picture text --> <!-- Start of picture text --> 20000 6000<br>18000<br>Te000 = 5000 -<br>14000 - 4000) =<br>12000 -<br>10000 3000<br>8000<br>6000 2000<br>4000<br>7000 1000<br>0 ny<br>Mining communes communes Neighbor ist contro! within contro! within within 2nd Control Control within Mining communes communes Neighbor Isl control within 2nd Control wilhin Control wilhin wilhin<br>communes Cercle Region communes Cercle Kegion<br>Education Health m Water Water m Others Salaries m Others Others<br><!-- End of picture text -->
We estimate these differences in budget outcomes within a regression framework to control for other factors that matter for these outcomes. The identification strategy follows (Loayza, Teran, and Rigolini 2013), which here consists of just comparing budget outcomes in _communes_ where mines are located with neighboring _communes_ with similar characteristics while controlling for other factors. This is based on the presumption that at the lowest administrative level, location of mines is determined by exogenous geological factors. Therefore, comparing nearby similar _communes_ will minimize the issue of omitted variables biases. While gold mining is taking place in only three regions of Mali, our sample includes all 705 _communes_ , including those in the capital city, which allows us to control for a range of factors such as urbanity and rainfall.

A clean econometric identification of the impact of mining activities on local government budget outcomes would require data on budgets for both mining and non-mining _communes_ during preand post-mining. However, we have annual budget data available for only 2006 to 2008, during which most of the mines had already opened. Industrial gold production started with the first site in Sadiola in 1996, just two years before the second population census of Mali. Fortunately, we are able to use the 1998 population census to fully control for some initial differences across communes, such as poverty and education levels prior to the mining boom. We also test whether larger mines have bigger impacts on budget outcomes by using the cumulative level of production measured in metric tons as an alternative measure of mining activity.

The baseline specification considers three groups of communes: _communes_ where mines are located and their neighboring _communes_ within the same region, which are the “treatment” because of their spatial proximity and possible institutional similarities. The control group is all other communes located in the rest of the country. The regression equation is as follows:

# _Outcomerc_ =  β0* _miningrc_ + β1* _neighborsrc_ + α _Xrc_ + εrc

Our outcome variables are communes’ fiscal revenues, capital, and current expenditures, all in per capita terms. Per capita capital expenditures is further broken down by education, health, water, and others, while per capita current expenditures has salaries and others as components. The variable _mining_ is an indicator equal to 1 if the commune has an industrial mine site within its boundaries, and 0 otherwise. It can also represent the continuous cumulative gold production levels within the producing communes. The variable _neighbors_ is an indicator equal to 1 if the commune directly shares a border with a mining commune, and 0 otherwise. _Xrc_ controls for pre-boom differences such as poverty and education, as well as for average rainfall within the _communes_ . Without-region fixed effects, _β0_ and _β1_ give the difference between mining and neighboring _communes_ with all other control _communes_ . We expect _β0_ to be positive and significant and _β1_ not 19
to be significant, because neighboring communes do not receive license fee payments from mining companies.

Table 6 focuses on the cross-sectional effect of mining on revenues and composition of spending. In column (1), we observe that mining communes have significantly more revenues than other communes after controlling for initial levels of poverty and education. The coefficient implies that the mean difference is about 8,500 CFA francs per capita. Note that the coefficient on the variable neighbors is not significant, confirming that mining activities cannot affect these communes through the fiscal channel. Average commune rainfall and being urban both positively and significantly affect revenue levels. From column (2) to column (6), total capital expenditure per capita is 25,000 CFA francs higher and significant in mining communes, but this is solely driven by the capital spending on education in column (3). Here, higher average rainfall negatively affects spending levels consistently. There are no significant differences between mining communes and other communes in terms of spending on health, water, and other sectors. Urban communes spend more than their rural counterparts only in terms of other capital expenditures column (6). From column (7) to column (9), total current expenditures, as well as its components (salaries and nonsalaries), are significantly higher in mining communes. These results suggest that when mining communes benefit from windfall revenues in Mali, capital expenditure on education and current expenditures including salaries go up.<sup>14</sup> In the next section, we investigate why higher levels of reported revenues and spending have not led to far better outcomes in mining areas by focusing on aspect of governance, and technical and absorptive capacities.

# **D. Mining, Governance, and Public Goods Provision**

The literature review by Aragón et al. (2014) shows there is an emerging body of empirical research on the effect of resource windfall transfers on political corruption at the local levels—that is, the political resource curse (Brollo et al. 2009; Caselli and Michaels 2013; Ferraz and Finan 2008; Morgandi 2008). These papers show that in environments of weak institutions and bad governance, resource revenue windfalls can foster corruption and rent-seeking behavior among politicians, and promote competition over funds among citizens and elite groups, all of which can impact socioeconomic outcomes by worsening inequality and poverty.

For the presence of mining activities to have any positive effect on economic welfare through the fiscal channel, it needs first to lead to an actual increase in the fiscal space and spending levels of local governments. We have shown that this has been the case in Mali, where gold mining _communes_ report both higher levels of revenues and spending per capita. Within the context of infrastructure gaps in developing countries, the literature review also tells us that more revenues

> 14 These results do not change when using intensity of production in mining communes instead of a dummy indicator. The results are available upon request.

20
are necessary but not sufficient for better provision of public goods and better local infrastructure, which ultimately will improve human welfare. What matters is whether the increased revenues are used in an efficient manner to deliver these public goods, and this is not a given in the developingcountry context. For example, Caselli and Michaels (2013) show that in Brazil, revenue windfall to oil-rich municipalities significantly failed to improve public good provision due to corruption and rent-seeking. Also in the same vein, Monteiro and Ferraz (2012) show that a revenue windfall to the same Brazilian oil-rich municipalities affects spending levels in education and health only through an increase in payrolls.

In this section of the paper, we focus on the political economy issues of translating more revenues into better outcomes in gold mining communes by investigating the relationship between governance (trust in public institutions, responsiveness or accountability to citizens, and corruption) and quality of public services. We test hypotheses related to the two most important factors—that is, political responsiveness and the technical capacities of local government that may undermine the positive effect of revenue windfalls on public good provision, and local living conditions. In the absence of objective measures of these factors at the commune level, we leverage the Afrobarometer<sup>15</sup> surveys to investigate at the individual level the effect of the proximity to gold-mining activities on perceptions of governance and quality of public services.

We use the 2005, 2012, and 2014 rounds of the nationally representative Afrobarometer survey in Mali. We focus on variables of political responsiveness, corruption, and quality of public services that are similar across the three rounds. Geocoded enumeration areas are combined with location of industrial mine sites to construct our treatment variable, which is the cumulative gold production within 20 kilometers of the mine at the time of the survey. This assumes that both distance to mines and intensity of production matter for the effect. We then estimate the following logistic regression of the form:

# _Pr_ ( _Outcomeij_ | _Production2_ 0 _kmij ,_ **Xij** ) = _logit_<sup>−1</sup> ( _α_ + _β *Production2_ 0 _kmij_ + λ* **Xij** )

Figure 17 shows the location of the enumeration areas on a map of Mali. We restricted our sample to the three regions were industrial and artisanal mining are taking place. The outcomes variables and their coding are defined in Annex 3. Our individual-level control variables _Xij_ include the respondent’s opinion of their own living conditions ( _Povertyij_ ), their education ( _Educationij_ ), age ( _Ageij_ ), gender ( _Maleij_ ), and living area ( _Ruralij_ ). We also include year dummies for the survey rounds, and the standard errors are clustered at the enumeration area level.

> 15 Data and codebook are available at www.afrobarometer.org/data/data-by-country/mali.

21
<!-- Start of picture text --> Locations of afrobarometer enumeration areas<br>°-ry.:$°é.<br>°° °<br>° . e .e . *° .<br>(o\ “4° °<br>\ @) . . ° Pod<br>= OQ ° o . . aa<br>Wa = ° ° ° 2¢ * 7; °<br>(9% 3 ooo<br>be' *?‘eoee) ry . % ° ol “3°< ‘° *.? °<br>° esofo2bafoe3° ¢° °e e<br>e c 7<br>° 2005 clusters<br>° 2012 clusters<br>¢ 2014 clusters<br>© Mining sites<br>[__] Butter 20km —<br>[__] Butter sokm<br><!-- End of picture text -->
standards in restaurants. In contrast, perceptions of financial management capacity are higher, with 70 percent of respondents approving of local government performance in revenue collection and 50 percent for spending decisions.

Regarding provision of quality education, 40 percent of respondents found public schools lacking textbooks or other supplies, and classrooms overcrowded. Regarding the quality of health services, 40 percent of respondents found the services to be too expensive, experienced lack of medicines or other supplies, and long wait time. Only 20 percent of respondents had direct experience with corruption related to illegal payments to access education or health services.

Table 8 shows the results for the governance indicators. In column (1), respondents living within 20 kilometers of an industrial mining site are significantly less likely to trust their elected local government councilors. But they are also significantly less likely to view their local councilors as widely involved in corruption (column 2), and they see them as more accountable (column 3). More-educated respondents significantly view local councilors as less trustworthy, more corrupt, and more accountable. The coefficients of the variable poverty imply that richer respondents significantly view councilors as more trustworthy and more accountable. As table 9 (columns 1– 4) indicates, respondents in mining areas are significantly less likely to view local government as performing well in terms of maintaining roads or markets, keeping the communities clean, and maintaining health standards in restaurants. They are also significantly less likely to perceive local government as performing well in revenue collection (column 5), but more likely to perceive them as performing well in spending decisions (column 6). Table 10 shows that respondents in the vicinity of mining are less likely to have experienced public schools as expensive, lacking textbooks or other supplies, having poor teaching, or having facilities in poor condition (columns 1–6). But they are more likely to have experienced illegal payments (column 7). Table 11 shows that respondents in the vicinity of mining are more likely to have experienced lack of medicines (column 2) and illegal payments (column 7) in public health services.<sup>16</sup> The above results are consistent with the emerging literature on the role of governance within local jurisdictions in moderating the effect of resource booms. Our results confirm those found in oilrich Brazilian municipalities in Caselli and Michaels (2013) and Monteiro and Ferraz (2012). Mining communes in Mali receive higher revenues and report higher capital and current expenditures spending. Capital expenditure is driven by spending in education. Reported nonsalaries current expenditure increased tenfold in mining areas. However, the differences in welfare outcomes in general are only marginal. The Afrobarometer data reveal that technical capacity and not corruption or lack of accountability may be the reason local governments are not successful in transforming revenues into better public goods and services for better welfare outcomes.

> 16 Sensitivity of the results is consistent with a declining size effects as distance from mine site increases. These results are available upon request.

23
<!-- Start of picture text --> Active artisanal mining communes, 2013<br>LIN i><br>PO MEST AO<br>ow atedes Sars<br>Rt ee RE<br>[_]@ otherIndustrial communessites Ea=; br a iyas ps fe<br>{) Artisinalions mining communes AES ie<br>C4 ss Uys€ ea‘a<br><!-- End of picture text --> <!-- Start of picture text --> Industrial and artisanal sites, 2009<br>e<br>a Industral sites PS,<br>— -<br>CT CercleRegions<br><!-- End of picture text -->
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# **Table 6a Effect of mining on revenues and spending patterns (Indicators for mining communes and neighboring communes)**

||**Revenue**<br>||**Capita **<br>|**lexpenditures**<br>|||**Cur**<br>|**rent expenditu**<br>|**res**<br>|
|---|---|---|---|---|---|---|---|---|---|
||**(1)**<br>**Revenue**|**(2)**<br>**Total **|**(3)**<br>**Education **|**(4)**<br>**Health **|**(5)**<br>**Water **|**(6)**<br>**Others**|**(7)**<br>**Total **|**(8)**<br>**Salaries**|**(9)**<br>**Others**|
|Mining communes|8,480***|25,331**|14,450***|1,124|2,422|4,583|8,416***|1,092***|5,168***|
||(1,430)|(10,818)|(3,455)|(1,273)|(4,759)|(6,878)|(1,158)|(403)|(431)|
|Neighbors|-791|-2,484|-1,836|-669|188|-198|14|-61|-86|
||(676)|(5,117)|(1,634)|(602)|(2,251)|(3,253)|(548)|(190)|(204)|
|Poverty (%) 1998|-1|72|16|37***|46|-46|-7|1|-1|
||(12)|(94)|(30)|(11)|(41)|(59)|(10)|(3)|(4)|
|Enrollment (%) 98|-22|121|7|11|41|35|-8|7|-2|
||(18)|(138)|(44)|(16)|(61)|(88)|(15)|(5)|(6)|
|Rainfall (mm) 1998–|1*|-37***|-6***|-2***|-10***|-20***|0|0|-0**|
|2008||||||||||
||(1)|(5)|(1)|(1)|(2)|(3)|(0)|(0)|(0)|
|Urban|1,455**|8,064|-1,066|17|1,771|7,418**|2,287***|1,164***|530**|
||(703)|(5,316)|(1,698)|(626)|(2,338)|(3,380)|(569)|(198)|(212)|
|Observations|692|692|692|692|692|692|692|692|692|
|R-squared|0.063|0.109|0.050|0.041|0.043|0.089|0.102|0.087|0.185|

_Note:_ Standard errors in parentheses. ***p<0.01, **p<0.05, *p<0.1.

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**Table 6b Effect of mining on revenues and spending patterns (continuous production intensity in mining communes)**

||||**Capit**<br>|**al expenditu**<br>|**res**<br>||**Cu**<br>|**rrent expenditu**<br>|**res**<br>|
|---|---|---|---|---|---|---|---|---|---|
||**(1)**<br>**Revenue**|**(2)**<br>**Total **|**(3)**<br>**Education **|**(4)**<br>**Health **|**(5)**<br>**Water **|**(6)**<br>**Others**|**(7)**<br>**Total**|**(8)**<br>**Salaries**|**(9)**<br>**Others**|
|Cum. gold produced|116***|327***|190***|13|33|57|124***|19***|71***|
||(12)|(94)|(30)|(11)|(42)|(60)|(9)|(3)|(3)|
|Poverty (%) 1998|-3|65|11|36***|46|-47|-8|1|-2|
||(12)|(93)|(29)|(11)|(41)|(59)|(9)|(3)|(3)|
|Enrollment (%) 1998|-20|125|8|9|42|36|-4|7|-0|
||(18)|(137)|(43)|(16)|(60)|(87)|(14)|(5)|(5)|
|Rainfall (mm) 98-08|1*|-37***|-6***|-2***|-10***|-19***|0|0|-0**|
||(1)|(5)|(1)|(1)|(2)|(3)|(0)|(0)|(0)|
|Urban|1,393**|7,878|-1,159|23|1,743|7,377**|2,207***|1,156***|482***|
||(676)|(5,284)|(1,670)|(625)|(2,334)|(3,374)|(527)|(195)|(176)|
|Observations|692|692|692|692|692|692|692|692|692|
|R-squared|0.129|0.117|0.078|0.040|0.044|0.089|0.227|0.115|0.434|

Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 28
**Table 7 Summary statistics**

||**Observations**|**Mean**|**Std. Dev.**|**Min**|**Max**|
|---|---|---|---|---|---|
|_Governance_||||||
|Trust|2176|0.8|0.4|0|1|
|Corruption|2103|0.8|0.4|0|1|
|Accountability|1167|0.6|0.5|0|1|
|_Local Capacity_||||||
|Road|1148|0.3|0.5|0|1|
|Market*|615|0.3|0.5|0|1|
|Health*|547|0.4|0.5|0|1|
|Cleaning|1146|0.4|0.5|0|1|
|Revenue*|511|0.7|0.4|0|1|
|Spending*|446|0.5|0.5|0|1|
|_Education Services_||||||
|Expensive|2201|0.3|0.5|0|1|
|Manuals|2207|0.4|0.5|0|1|
|Learning|2194|0.3|0.5|0|1|
|Absenteeism|2204|0.3|0.5|0|1|
|Crowded|2200|0.4|0.5|0|1|
|Equipment|2186|0.3|0.5|0|1|
|Corruption*|573|0.2|0.4|0|1|
|_Health Services_||||||
|Expensive|2207|0.4|0.5|0|1|
|Pills|2207|0.4|0.5|0|1|
|Care|2209|0.2|0.4|0|1|
|Absenteeism|2207|0.3|0.4|0|1|
|Long line|2205|0.4|0.5|0|1|
|Clean|2199|0.3|0.5|0|1|
|Corruption*|567|0.2|0.4|0|1|

_Note:_ *Variable not available in all three rounds.

29
**Table** **<u>8 Proximity to mining and governance</u>**

||**(1)**<br>**Trust**|**(2)**<br>**Corruption **|**(3)**<br>**Accountability**|
|---|---|---|---|
|Cum. gold within 20km|-0.004**<br>(0.002)|-0.006***<br>(0.001)|0.135***<br>(0.015)|
|Education|-0.171***|0.265***|0.125*|
||(0.056)|(0.056)|(0.071)|
|Age|-0.008*|-0.001|0.025***|
||(0.004)|(0.004)|(0.006)|
|Male|-0.423***|-0.061|0.421***|
||(0.120)|(0.108)|(0.143)|
|Poverty|0.137**|-0.072|0.116*|
||(0.053)|(0.050)|(0.064)|
|Rural|0.498***|-0.402*|0.623**|
||(0.183)|(0.225)|(0.294)|
|Observations|2,170|2,097|1,160|

_Note:_ Robust standard errors in parentheses. ***p<0.01, **p<0.05, *p<0.1.

**Table** **<u>9 Proximity to mining and local capacities</u>**

||**(1)**<br>**Road**|**(2)**<br>**Market**|**(3)**<br>**Health **|**(4)**<br>**Cleaning**|**(5)**<br>**Revenue**|**(6)**<br>**Spending**|
|---|---|---|---|---|---|---|
|Cum. gold within 20km|-0.066***|-0.103***|-0.119***|-0.076***|-0.694***|0.241***|
||(0.020)|(0.012)|(0.013)|(0.012)|(0.077)|(0.077)|
|Education|-0.015|0.044|0.133*|0.043|0.073|0.018|
||(0.055)|(0.077)|(0.077)|(0.063)|(0.133)|(0.092)|
|Age|-0.002|-0.002|0.010|0.005|-0.013**|-0.001|
||(0.003)|(0.007)|(0.007)|(0.005)|(0.006)|(0.005)|
|Male|-0.226*|-0.123|-0.159|-0.183|0.360*|0.414***|
||(0.129)|(0.185)|(0.192)|(0.115)|(0.217)|(0.158)|
|Poverty|0.057|0.160**|0.155*|0.106*|-0.214***|0.164*|
||(0.067)|(0.073)|(0.086)|(0.055)|(0.082)|(0.090)|
|Rural|-0.279|-0.490|-0.208|0.097|0.255|0.298|
||(0.190)|(0.367)|(0.329)|(0.207)|(0.388)|(0.305)|
|Observations|1,142|615|547|1,139|505|440|

_Note:_ Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1.

30
||**Table 10 P**<br>**(1)**<br>**Expensive**|**roximity to min**<br>**(2)**<br>**Manuals**|**ing and quality**<br>**(3)**<br>**Learning**|**of public educat**<br>**(4)**<br>**Absenteeism **|**ion services**<br>**(5)**<br>**Crowded**|**(6)**<br>**Equipment**|**(7)**<br>**Corruption **|
|---|---|---|---|---|---|---|---|
|Cum. gold within 20 km|-0.012***|-0.010**|-0.005***|-0.030|0.000|-0.003***|0.197**|
||(0.004)|(0.005)|(0.002)|(0.033)|(0.001)|(0.001)|(0.080)|
|Education|0.013|0.047|0.211***|0.181***|0.106**|0.087*|0.003|
||(0.043)|(0.042)|(0.040)|(0.045)|(0.046)|(0.045)|(0.086)|
|Age|0.001|0.004|-0.004|-0.000|0.004|0.001|-0.022***|
||(0.004)|(0.003)|(0.003)|(0.004)|(0.003)|(0.003)|(0.008)|
|Male|0.172*|0.165**|0.416***|0.387***|0.332***|0.335***|0.207|
||(0.096)|(0.082)|(0.094)|(0.099)|(0.083)|(0.088)|(0.181)|
|Poverty|-0.136***|-0.137***|-0.169***|-0.063*|-0.065*|-0.093**|0.018|
||(0.038)|(0.039)|(0.040)|(0.037)|(0.038)|(0.041)|(0.079)|
|Rural|0.130|-0.057|-0.021|-0.163|-0.278*|-0.031|-0.346|
||(0.178)|(0.146)|(0.168)|(0.179)|(0.150)|(0.183)|(0.262)|
|Observations|2,193|2,199|2,186|2,196|2,192|2,179|565|

_Note:_ Robust standard errors in parentheses. ***p<0.01, **p<0.05, *p<0.1.

31
||**Table 11**<br>|**Proximity to m**<br>|**ining and quali**<br>|**ty of public heal**<br>|**th services**<br>|||
|---|---|---|---|---|---|---|---|
||**(1)**<br>**Expensive**|**(2)**<br>**Pills**|**(3)**<br>**Care**|**(4)**<br>**Absenteeism **|**(5)**<br>**Long line**|**(6)**<br>**Clean **|**(7)**<br>**Corruption **|
|Cum. gold within 20km|-0.003|-0.007*|-0.001|-0.009|0.001|0.001|0.245**|
||(0.002)|(0.004)|(0.001)|(0.006)|(0.001)|(0.001)|(0.107)|
|Education|-0.109***|-0.007|0.072|0.126***|0.055|0.076|-0.214**|
||(0.040)|(0.044)|(0.048)|(0.048)|(0.044)|(0.047)|(0.102)|
|Age|-0.006**|-0.005|-0.007*|-0.007*|-0.007*|-0.003|-0.015**|
||(0.003)|(0.003)|(0.004)|(0.004)|(0.003)|(0.004)|(0.007)|
|Male|0.075|0.073|0.186*|0.249***|0.140*|0.221**|0.207|
||(0.079)|(0.087)|(0.099)|(0.093)|(0.078)|(0.087)|(0.233)|
|Poverty|-0.161***|-0.107***|-0.157***|-0.083*|-0.094**|-0.188***|-0.084|
||(0.039)|(0.038)|(0.045)|(0.043)|(0.038)|(0.040)|(0.085)|
|Rural|-0.262*|-0.165|-0.699***|-0.131|-0.272*|-0.480***|-0.671*|
||(0.135)|(0.166)|(0.166)|(0.166)|(0.152)|(0.172)|(0.360)|
|Observations|2,199|2,199|2,201|2,199|2,197|2,191|559|

Robust standard errors in parentheses. ***p<0.01, **p<0.05, *p<0.1.

32
# **Annex 3 List of outcome variables**

|**_Governance_**|**Afrobarometer Question**|**Coding**|
|---|---|---|
|**Trust**|How much do you trust each of the following, or haven’t you<br>heard enough about them to say: Local council? (Not at all,<br>just a little, somewhat, a lot, don’t know/haven’t heard<br>enough, and missing)|Equals to 1 if<br>“somewhat” and “a lot”<br>and zero otherwise|
|**Accountability**|How much of the time do think the following try their best to<br>listen to what people like you have to say: Local Government<br>Councilors? (Never, only sometimes, often, always, don’t<br>know, and missing)|Equals to 1 if “often”<br>and “always” and zero<br>otherwise|
|**Corruption**|How many of the following people do you think are involved<br>in corruption, or haven’t you heard enough about them to say:<br>Elected Local Government Councilors? (None, some of them,<br>most of them, all of them, don’t know/haven’t heard enough,<br>and missing)|<br> <br>Equals to 1 if “most of<br>them” and “all of them”<br>and zero otherwise|
|**_Local Capacity_**|||
|**Maintaining roads**<br>**Maintaining markets**<br>**Keeping community clean**<br>**Maintaining health standards**<br>**Collecting taxes**<br>**Spending decisions**|<br>What about local government? How well or badly would you<br>say your local government is handling the following matters,<br>or haven’t you heard enough about them to say: Maintaining<br>our roads? Maintaining local markets? Keeping our<br>community clean? Maintaining health standards in<br>restaurants? Collecting local taxes? Deciding how to spend<br>local revenues? (Very badly, fairly badly, very well, fairly<br>well, don’t know/haven’t heard enough, and missing)|Equals to 1 if “very<br>well” and “fairly well”<br>and zero otherwise|
|**_Quality of Services_**|||
|**School**<br>**Health**|Have you encountered any of these problems with your local<br>public schools during the past 12 months? Services are too<br>expensive / Unable to pay, lack of textbook or other supplies,<br>poor teaching, absent teachers, overcrowded classes, poor<br>conditions of facilities, demand for illegal payments? (never,<br>once or twice, a few times, often, no experience, don’t know,<br>missing data)<br>Have you encountered any of these problems with your local<br>public clinic or hospital during the past 12 months? Services<br>are too expensive / Unable to pay, lack of medicines or other<br>supplies, lack of attention or respect from staff, absent doctors<br>long waiting time, dirty facilities, demand for illegal<br>payments. (never, once or twice, a few times, often, no<br>experience, don’t know, missing data)|,<br>Equals to 1 if “once or<br>twice”, “a few times”<br>and “often” and zero<br>otherwise<br>Equals to 1 if “once or<br>twice”, “a few times”<br>and “often” and zero<br>otherwise|

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**Appendix 4** : the tables below show the treatment effect of mining with varying cut-off distance (0-20 to 0-50km)

Table A: <u>Varying the cut-off distance for the effect of mining on governance</u>

|||(1)<br>(2)<br>Trust<br>Corruption|(3<br> <br>Accoun|)<br>tability||
|---|---|---|---|---|---|
|0-20km|-0.00|4**<br>-0.006***|0.135**|*||
|0-30km|-0.00|6*<br>-0.004*|0.009|||
|0-40km|-0.00|4**<br>-0.001|-0.001|||
|0-50km|-0.00|2<br>0.0000|0.002|||
|TableB: V|aryingthe cut-o<br>(1)<br>Road|ffdistancefor the effectof mi<br>(2)<br>(3)<br>Market<br>Health|ning on localca<br>(4)<br>Cleaning|pacities<br>(5)<br>Revenue|(6)<br>Spending|
|0-20km|-0.066***|-0.103***<br>-0.119***|-0.076***|-0.694***|0.241***|
|0-30km|-0.008***|-0.016***<br>-0.017***|-0.013***|0.009***|-0.011***|
|0-40km|0.002|0.000<br>0.004|-0.001|0.005**|-0.003|
|0-50km|0.002|0.001<br>0.002|0.0000|0.005**|-0.003|

Table C: Varying the cut-off distance for the effect of mining on the quality of public education <u>services</u>

||(1)<br>Expensive|(2)<br>Manuals|(3)<br>Learning|(4)<br>Absenteeism|(5)<br>Crowded|(6)<br>Equipment|(7)<br>Corruption|
|---|---|---|---|---|---|---|---|
|0-20km|-0.012***|-0.010**|-0.005***|-0.030|0.000|-0.003***|0.197**|
|0-30km|-0.001|-0.002|-0.002|-0.003|0.0000|-0.003|-0.002*|
|0-40km|0.002|0.001|-0.002|-0.002|-0.001|-0.002|0.002|
|0-50km|0.000|0.000|-0.002*|0.0000|-0.001|-0.002|0.002|

- <u>Table D: Varying the cut off distance for the effect of mining on the quality of public health services</u>

||(1)<br>Expensiv|(2)<br>e<br>Pills|(3)<br>Care|(4)<br>Absenteeism|(5)<br>Longline|(6)<br>Clean|(7)<br>Corruption|
|---|---|---|---|---|---|---|---|
|0-20km|-0.003|-0.007*|-0.001|-0.009|0.001|0.001|0.245**|
|0-30km|0.001|0.0000|0.001|0.0000|0.003|0.004|0.009***|
|0-40km|0.003|0.001|0.004*|0.002|0.003*|0.002|0.009***|
|0-50km|0.001|0.0000|0.002|0.002|0.001|0.001|0.009***|

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