rafmacalaba/datause-encoder-data huggingface dataset:
|(8)<br>0.107**<br>(0.0451)<br>-0.0146<br>(0.0237)<br>-0.0221*<br>(0.0124)<br>-0.0279**<br>(0.0120)<br>-0.00209<br>(0.0352)<br>-0.00915<br>(0.0161)<br>-0.0199<br>(0.0193)<br>7.917***<br>(1.586)<br>4.103***<br>(1.414)<br>1.246<br>(1.145)<br>2.219<br>(2.044)<br>0.696<br>(0.868)<br>-67.08***<br>(16.30)<br>17,068<br>l mixed efects<br>ses. ***, **, *|
|---|
|(7)<br>0.109**<br>(0.0457)<br>-0.0151<br>(0.0237)<br>-0.0226*<br>(0.0125)<br>-0.0267**<br>(0.0121)<br>-0.00391<br>(0.0353)<br>-0.00792<br>(0.0157)<br>-0.0384<br>(0.0259)<br>8.402***<br>(1.623)<br>4.369***<br>(1.466)<br>1.351<br>(1.167)<br>-66.46***<br>(16.53)<br>17,068<br>g a multileve<br>el in parenthe|
|(6)<br>0.121***<br>(0.0457)<br>-0.0190<br>(0.0246)<br>-0.0230*<br>(0.0129)<br>-0.0410***<br>(0.0130)<br>-0.0340<br>(0.0371)<br>-0.0300*<br>(0.0170)<br>-69.04***<br>(16.19)<br>17,068<br>tax evasion usin<br>untry-sector lev|
|(5)<br>0.122***<br>(0.0455)<br>-0.0201<br>(0.0243)<br>-0.0236*<br>(0.0128)<br>-0.0459***<br>(0.0140)<br>-0.0339<br>(0.0374)<br>-68.03***<br>(15.69)<br>17,089<br>cies on frms’ <br>tered at the co|
|(4)<br>0.122***<br>(0.0455)<br>-0.0201<br>(0.0243)<br>-0.0235*<br>(0.0128)<br>-0.0457***<br>(0.0140)<br>-68.11***<br>(15.63)<br>17,089<br>ctures and poli<br>dard errors clus|
|(3)<br>0.127***<br>(0.0448)<br>-0.0198<br>(0.0244)<br>-0.0230*<br>(0.0128)<br>-69.15***<br>(14.91)<br>17,100<br>nary infrastru<br> Robust stan|
|(2)<br>0.132***<br>(0.0445)<br>-0.0193<br>(0.0240)<br>-70.71***<br>(14.38)<br>17,250<br>s of distortio<br>r fxed efects.|
|(1)<br> evasion<br>0.137***<br>(0.0406)<br>-62.79***<br>(16.34)<br>19,490<br>he estimates of the efect<br> country, sector, and yea<br>, and 10 percent level.|
|Dependent variable: Tax<br>Distortions<br>Management Time<br>Access to fnance<br>Foreign share<br>Government share<br>Sales exported<br>Age<br>Microenterprise<br>Small<br>Medium<br>Young<br>Mature<br>Institutional Q.<br>Observations<br>**Notes.** The table presents t<br>model. All regressions include<br>denote signifcance at the 1, 5|

12
16 consequently we have to drop the price variable).<sup>18</sup> Given that price data is only available since the mid-1960s for many natural resources (and even later for some), we lose a fairly large number of observations in columns (2)–(4).<sup>19</sup> This, combined with the demanding nature of the year-by-resource fixed effects, makes column (5) our preferred specification of Table 2.

**Table 2: The Impact of Liberalization on Resource Discoveries (OLS)**

|VARIABLES|(1)<br>Number of<br>Discoveries|(2)<br>Number of<br>Discoveries|(3)<br>Number of<br>Discoveries|(4)<br>Number of<br>Discoveries|(5)<br>Number of<br>Discoveries|
|---|---|---|---|---|---|
|SW/Wacziarg Openness, lagged|0.00716***<br>(0.00179)|0.0171***<br>(0.00442)|0.0169***<br>(0.00442)|0.0137***<br>(0.00392)|0.00875***<br>(0.00185)|
|(log) Price, lagged||0.0146**<br>(0.00677)|0.0150***<br>(0.00533)|0.0103**<br>(0.00449)||
|Stock of Discoveries, lagged|||‐0.000839|0.0260*|0.0382***|
||||(0.00503)|(0.0141)|(0.00500)|
|Stock of Discoveries squared, lagged||||‐0.000451** <br>(0.000176)|‐0.000284***<br>(8.26e‐05)|
|Country FE|/|/|/|/|YES|
|Year FE|YES|YES|YES|YES|/|
|Natural Resource FE|/|/|/|/|/|
|Country by Natural Resource FE|YES|YES|YES|YES|/|
|Year by Natural Resource FE|/|/|/|/|YES|
|Observations|161,160|57,976|57,976|57,976|161,160|
|R‐squared|0.272|0.328|0.328|0.339|0.226|
|Estimation|OLS|OLS|OLS|OLS|OLS|
||Country by|Country by|Country by|Country by|Country by|
|Error Clustering|Natural|Natural|Natural|Natural|Natural|
||Resource|Resource|Resource|Resource|Resource|

Note: The table reports results for regressions of the number of natural resource discoveries by country, year and resource on countries’ SW/Wacziarg openness indicator as well as a number of controls. The number of observations in columns (1) and (5) are larger than in the other columns because columns (2) – (4) include resource prices as a control and price data are not available for certain years and resources. Column (5) uses country as well as year-resource fixed effects instead of the year and country-resource fixed effects employed in columns (1) – (4). *** denotes significance at the 1% level, ** at the 5% level, and * at the 10% level.

In terms of quantification, this specification also provides what we see as a safe lower bound – first it is a demanding specification and second, as we will see below, we believe the OLS estimates to be biased downwards given that the 2SLS estimates are substantially larger. The

> 18 For some resources, price data have limited time series information. Appendix B provides detailed information on available price data by natural resource.

> 19 In the shorter time period covered by regressions (ii)-(iv) the point estimates are significantly larger.
between six -highly clean- and zero -highly corrupt-); quality of the bureaucracy, as measured by the Bureaucracy Quality Index produced by International Country Risk Guide (which ranges between four -high quality- and zero -low quality-); the stock of the external debt (outstanding and disbursed, in US dollars); GDP per capita (in US dollars); a political dummy variable that takes the value of one if the country is under an IMF agreement; and population density. Summary statistics are presented in Table 1.

Our sample is representative of the energy sector in development and transition countries. It covers the following countries: Argentina (22 firms supplying electricity to approximately 75% of the total number of customers in the country), Azerbaijan (5, 100%), Belize (1, 100%), Bolivia (7, 88%), Botswana (1, 100%), Brazil (57, 99%), Burkina Faso (1, 100%), Cameroon (1, 100%), Cape Verde (1, 100%), Central African Republic (1, 100%), Colombia (11, 74%), Costa Rica (8, 100%), Czech Republic (8, 84%), Djibouti (1, 100%), Ecuador (20, 100%), El Salvador (5, 100%), Eritrea (1, 100%), Estonia (1, 85%), Ethiopia (1, 100%), Georgia (1, 32%), Ghana (1, 100%), Guatemala (3, 100%), India (5, 20%), Ivory Coast (1, 100%), Kenya (1, 100%), Malawi (1, 100%), Malaysia (2, 99%), Mali (1, 100%), Mauritania (1, 100%), Mauritius (1, 100%), Mexico (2, 100%), Moldova (5, 100%), Morocco (1, 81%), Mozambique (1, 77%), Namibia (1, 20%), Nicaragua (2, 99%), Niger (1, 100%), Panama (3, 100%), Paraguay (2, 100%), Peru (15, 96%), Philippines (1, 20%), Poland (2, 23%), Russia (3, 4%), Senegal (1, 100%), Slovak Republic (3, 100%), South Africa (1, 99%), Tanzania (1, 96%), Uganda (1, 99%), Uruguay (1, 100%), Zambia (1, 100%), and Zimbabwe (1, 100%).

# **3. Methodology**

5
productive. Better workforce skills are crucial for firms to employ more efficient technologies, produce more sophisticated and higher quality products and services (e.g., Verhoogen 2008) and make the implementation of managerial and organizational practices more effective (Bloom & Van Reenen 2010).

Productivity returns to the education of managers are even higher. Many studies show a clear link between the education of a manager and firm performance in both industrialized countries (see Van der Sluis et al. 2008 for a review) and in low income countries (see e.g., Van der Sluis et al. 2005). Gennaioli et al. (2013) estimate that the return to productivity is close to 30 percent for an extra year of education of a manager. One source of these productivity gains is that more highly educated managers are also more likely to adopt better management practices (Bloom & Van Reenen 2010), suggesting that they are either more aware of what good management practices are or are more effective in adopting them. Informality is also strongly associated with education: according to the 2018 Enterprise Survey, less than five percent of owners of informal firms in Mozambique have completed tertiary education, against 45 percent of owners of formal firms, in line with results from other countries (La Porta & Shleifer 2014).

# _Interventions to improve capabilities_

While the importance of business practices on firm performance is well established (McKenzie and Woodruff, 2017; Bloom & Van Reenen, 2007), there is not enough evidence on how to change business practices effectively. A wide range of interventions aiming to improve firm capabilities of firms through business skills trainings have had at best mixed results (McKenzie & Woodruff 2013; McKenzie et al. 2020; McKenzie 2020). Grover, Medvedev & Olafsen (2018) review 15 randomized controlled trials (RCTs) of business trainings (none in Mozambique), and find that 8 of them had significant and positive impacts on sales, while the remaining 7 had insignificant impacts. Most of these studies targeted micro-sized firms with an average size between 1 and 2 employees. Most of these trainings include components on accounting, financial planning, pricing and costing and separating household and business financing. More encouraging mechanisms of building capabilities include developing non-cognitive skills of managers to have an entrepreneurial mindset (Batista & Seither 2018).

6
**Table 5: Sexual behavior and AIDS related practices, by gender and age group Round 11 (11/99-9/2000), Medical Research Council, General Population Cohort.** Individuals aged 18 and above. **Logit: Mean of Marginal Effects, observation by observation**

|**Dependent**|**variable:**|**Ever used**<br>**a condom?**|**Used condom**<br>**last**<br>**intercourse?**|**Number partners**<br>**last year**<br>**if active(*)**|**Age at**<br>**first sex**<br>**(*)**|
|---|---|---|---|---|---|
|**Males**|Years of|**0.023**|**0.013**|**0.022**|**0.09**|
||education|[6.86]|[3.38]|[1.56]|[2.71]|
||N =|1419|1095|1338|1337|
||Mean|0.351|0.126|1.651|17.6|
||R-square|0.19|0.21|0.11|0.2|
|**Males**|Years of|**0.028**|**0.015**|**0.012**|**0.064**|
|**Age 18-29**|education|[4.37]|[3.39]|[0.32]|[1.74]|
||N =|578|551|494|529|
||Mean|0.578|0.272|1.971|16.67|
||R-square|0.09|0.19|0.11|0.18|
|**Males**|Years of|**0.017**|**0.013**|**0.024**|**0.113**|
|**Age 30**|education|[2.97]|[1.25]|[2.24]|[2.41]|
|**and above**|N =|841|380|844|808|
||Mean|0.225|0.048|1.457|18.23|
||R-square|0.2|0.14|0.17|0.18|
|**Females**|Years of|**0.022**|**0.018**|**-0.006**|**0.2**|
||education|[5.61]|[4.02]|[-2.29]|[9.79]|
||N =|1598|1328|1478|1703|
||Mean|0.182|0.057|1.04|16.22|
||R-square|0.17|0.3|0.07|0.16|
|**Females**|Years of|**0.023**|**0.016**|**-0.009**|**0.156**|
|**Age 18-29**|education|[4.18]|[3.00]|[-1.62]|[5.53]|
||N =|762|723|703|689|
||Mean|0.313|0.109|1.054|16.26|
||R-square|0.16|0.33|0.07|0.15|
|**Females**|Years of|**0.014**|**0.006**|**-0.004**|**0.219**|
|**Age 30**|education|[2.27]|[1.49]|[-1.64]|[8.32]|
|**and above**|N =|809|464|775|1014|
||Mean|0.098|0.025|1.028|16.19|
|Note:<br>Age and|R-square<br>village dummie|0.16<br>s, controls for mar|0.24<br>ital status, religion an|0.08<br>d wealth included.|0.2|

<u>Note: Age and village dummies, controls for marital status, religion and wealth included.</u> Robust z-stat in parentheses. (*) = linear regression. Age dummies reduce slightly the sample size, as at old ages, the outcome is predicted perfectly. "Did you ever used a condom" was asked to all individuals who ever had sex, while condom use at the last intercourse was asked to all individuals sexually active in the last year.

43
Table 4 Heavily-indebted Countries: Actual and Sustainable Values of the Primary Surplus, 1982 (Percent of GDP)

||Actual 1/|Sustainable|
|---|---|---|
|Argentina<br>|-7.6<br>|7.3<br>|
|Bolivia<br>|-6.5<br>|8.3<br>|
|Brazil<br>|-5.5<br>|1.9|
|Chile<br>|1.9<br>|6.9|
|Colombia<br>|-3.4<br>|1.0|
|Cote d'lvoire<br>|-7.3<br>|6.7|
|Ecuador<br>|-4.5<br>|1.8<br>|
|Mexico<br>|-4.7<br>|2.7<br>|
|Morocco<br>|-8.6|6.3<br>|
|Nigeria<br>|n.a.<br>|2.2<br>|
|Peru<br>|-5.2<br>|3.1|
|Philippines<br>|-4.7|3.6|
|Uruguay<br>|n.a.<br>|2.6|
|Venezuela<br>|2.6|1.9|
|Yugoslavia|n.a.|1.5|

1/ For Argentina, Chile, Mexico, and Peru these are averages for 1974-82. For the Philippines, the average is for 1981-82 only. All remaining countries use averages for the period 1976-82. Sources of fiscal data are given in the appendix.
**Figure 3. 8  Location of cashing pay checks**

<!-- Start of picture text --> 100%<br>90% 83%<br>80%<br>70%<br>60% 53%<br>47%<br>50%<br>40%<br>30%<br>20% 13%<br>10% 1% 5%<br>0%<br>Cash it in a bank Other Deposit in account<br>Banked Unbanked<br>%<br><!-- End of picture text --> _Source DANE household study of Bogotá, 2003_ 15. **The unbanked are also disadvantaged when making payments.** Colombian banks process payments for public services (water, sanitation, electrical energy, garbage collection, telephones etc.) for both deposit holders and non-deposit holders. Banked individuals can make payments using traditional means such as check or cash, or instruments such as ATM, debit, credit cards, internet and telephone payments. Public utility payments can also be made with debit, credit and ATM cards using data phones at a wide variety of places such as supermarkets, video stores and malls. However, the unbanked who need to pay in cash are obliged to travel to Administrative Centers of Services<sup>44</sup> . Commercial banks only accept payments in cash for public utilities at specified hours and days. In addition, only one line is available for the unbanked to make payments in banks and processing late or overdue payments bears additional costs.

16. **Both the banked and unbanked receive income from remittances.** Approximately 6% of Bogotá’s population receives remittances from other cities (3.2%) and from abroad (2.4%). Of these, 58% of recipients are banked, while 42% are unbanked. When receiving domestic remittances, 60% of the banked rely on safer and cheaper wire transfers. In contrast, the majority of the unbanked receive domestic remittances in hand carried cash. Interestingly, both the banked and unbanked receive remittances from abroad mainly through remittance agencies. However, the banked individuals use direct wire transfers and checks in another 40% of the cases, while the unbanked rely on cash for another 43% of the cases.

> 44 Special District Attention Centers (CADES, or Centros de Atención del Distrito Especial), are administrative offices located in different neighborhoods of Bogotá and its periphery, where citizens pay taxes and basic public services.

51
|y(1998). All<br>ient between<br>unrestricted<br>(7)<br>0.250<br>(2.80)<br>-0.079<br>(2.94)<br>0.005<br>(2.59)<br>-0.005<br>(2.93)<br>0.001<br>(2.81)<br>0.000<br>0.549<br>0.822<br>0.255<br>0.533<br>0.604<br>0.482|0.433|
|---|---|
|and Kraa<br>n coeffc<br>stimated<br>(6)<br>0.280<br>(3.29)<br>-0.067<br>(2.44)<br>0.004<br>(2.39)<br>-0.004<br>(2.55)<br>0.000<br>0.526<br>0.814<br>0.293<br>0.521<br>0.555<br>0.496|0.436|
|riscoll<br>rrelatio<br>th the e<br>(5)<br>0.181<br>(2.38)<br>-0.064<br>(2.50)<br>0.000<br>0.530<br>0.650<br>0.305<br>0.521<br>0.538<br>0.509|0.352|
|ing<br>rors ofD<br>rts the co<br>lumn wi<br>(4)<br>0.000<br>(0.84)<br>0.525<br>0.593<br>0.515<br>0.529<br>0.526<br>0.530|0.098|
|isk shar<br>dard er<br>le repo<br>each co<br>(3)<br>0.000<br>(1.37)<br>0.521<br>0.639<br>0.505<br>0.528<br>0.523<br>0.532|0.126|
|tes of r<br>ent stan<br>the tab<br>sion in<br>(2)<br>-0.072<br>(2.67)<br>0.525<br>0.628<br>0.260<br>0.502<br>0.472<br>0.524|0.189|
|covaria<br>consist<br>t row in<br>e regres<br>(1)<br>0.221<br>(2.61)<br>0.561<br>0.645<br>0.446<br>0.548<br>0.601<br>0.509|0.270|
|Table 3: NLS estimates of the<br>stics in parentheses, using the spatial-correlation<br>include also a constant not reported here. The las<br>l-country risk-sharing parameters predicted by th<br>partial risk sharing.<br>account openness (Chin-Ito)<br>l GDP<br>reign assets+foreign liabilities/GDP<br>+ exports / GDP<br>sector credit / GDP<br>**st of joint signifcance**<br>**risk sharing coeffcients**<br>an<br>mum<br>mum<br>n-All countries<br>Advanced countries<br>Developing countries|**tion with unrestricted risk sharing coeffcients**|
|Robust t-stati<br>specifcations<br>the individua<br>coeffcients of<br>Capital<br>Log rea<br>Total fo<br>Imports<br>Private<br>**Wald te**<br>**Implied**<br>Medi<br>Maxi<br>Mini<br>Mea|**Correla**|

28
|**Table 3: Confirming Randomization for Control Vs**|**Inspector Bl**<br>F|**ocks**<br><br>ull Sample in|<br>Listing|Sample|Interviewed|at Follow-up|
|---|---|---|---|---|---|---|
|||Inspector|Control in||Inspector|Indirectly|
||Control<br>Mean|Assigned <br>Difference|Inspector Block<br>Difference|Control<br>Mean|Assigned<br>Difference|Inspected<br>Difference|
|_Listing Variables_|||||||
|In commerce|0.47|0.0230|-0.000767|0.46|0.0288|0.00567|
|In services|0.45|-0.0248|-0.00635|0.47|-0.0278|-0.0162|
|Has a sign outside|0.39|0.0382|0.0407|0.40|-0.00622|0.0324|
|Num. Employees|1.09|-0.107|0.191*|1.24|-0.192*|0.171|
|Area (square meters)|60.4|3.932|0.682|58.6|1.208|-7.665|
|_Time-invariant Variables from Follow-up Survey_|||||||
|Owner is Female||||0.42|-0.00978|0.00941|
|Owner's Age||||42.4|0.162|0.855|
|Owner has primary or lower education||||0.09|-0.0104|0.00498|
|Owner has completed high school||||0.49|0.0271|0.0851**|
|Father of owner has at most primary||||0.32|-0.0579*|0.00551|
|Father of owner completed high school||||0.14|0.0125|-0.00858|
|Sample Size<br>Notes: *, **, and *** indicate statistically differen<br>randomization strata and clustering at the block le|1398<br>t from contr<br>vel. Sampli|577<br>ol mean at t<br>ng weights u|593<br>he 10, 5 and 1% le<br>sed to account for|788<br>vels respe<br>uneven sa|329<br>ctively, after<br>mpling prob|295<br>controlling for<br>abilities.|

35
survey. Second, some of these additional variables may also be endogenous and therefore the coefficients should be interpreted as nothing more than conditional correlations.

The top of the table shows the estimates for the variables in (2), which are qualitatively similar to the ones in Tables 1-3. EPA’s budget per capita, GDP per capita, and the geographydetermined trade to GDP ratios have a positive and statistically significant effect on exports per capita in both samples. The trade restrictiveness of the rest of the world faced by exporters and the volatility of the exchange rate have a negative and statistically significant effect on exports per capita in both samples. In contrast with earlier results, the burden of export regulations has a negative and significant effect on exports per capita.

The bottom of the table reports estimates for the additional variables capturing EPA modalities. In all regressions, exports increase with the share of the EPA executive board seats that are held by the private sector. But there is also weak evidence that exports also increase with the share of EPA funding coming from the public sector (although this effect is significant only when we use the 2SLS estimator). This suggests that agencies that are directed by the private sector, but have public funding are the best performers. After all, the rationale for export promotion is about externalities, and it may be difficult to raise private sector funding when benefits are diffuse.

The proliferation of agencies dedicated to export promotion within a country (”Degree of decentralization of agencies”) is negatively correlated with exports. A single and strong EPA seems to be more effective than multiple agencies with overlapping responsibilities.<sup>18</sup> Note, however, that the OLS coefficient is not statistically significant.

The allocation of expenditures between country image, export support services, marketing and market research do not have any statistically significant correlation with exports. We also have no evidence regarding EPA targeting of large versus small firms, nor about targeting established exporters versus non exporters. The same is true for the presence of EPA representation offices abroad or the overall strategy of the EPA (sectoral focus versus broad

> 18This is a discrete variable that takes the value 1 if there is only one EPA in the country, 2 if there is one large, but many small agencies, 3 if there are two mayor agencies and several small, and 4 if there are more than two large agencies and several small agencies.

17
**FIGURE 2: COMMODITY PRICE INDICES AND CRUDE OIL PRICE PASS-THROUGH**

<!-- Start of picture text --> NON-ENERGY<br>A: 0.16<br>B: 0.14<br>C: 0.10<br>METALS AGRICULTURE FERTILIZERS<br>A: 0.11 A: 0.17 A: 0.33<br>B: 0.14 B: 0.14 B: 0.24<br>C: 0.43 C: 0.08 C: 0.07<br>BEVERAGES FOOD RAW MATERIALS<br>A: 0.26 A: 0.18 A: 0.04<br>B: 0.23 B: 0.12 B: 0.04<br>C: -0.11 C: 0.14 C: 0.20<br>CEREALS FATS & OILS OTHER FOOD<br>A: 0.18 A: 0.19 A: 0.17<br>B: 0.13 B: 0.13 B: 0.10<br>C: 0.14 C: 0.13 C: 0.12<br><!-- End of picture text -->

**Notes** : The numbers following the letters A, B, and C refer to elasticities for the 1960-2005, 1972-2005, and 1984-2005 periods, respectively. The numbers in row A are the same as the ones reported in Table 1.

**Source:** Author’s estimates

**_— 19 —_**
subject to the firm maximizing: There is not a unique solution to this problem. An infinite number of taxation schedules and mechanisms could achieve the equality in equation (13). Nor does the tax obtain all of the project resource rent because some resource rent is destroyed via the distortionary taxation mechanism. Once again, we must emphasize that the quantity of resource rent at the project cannot be known, and this is an attempt to tax the surplus to the fullest extent possible.

It appears from studies commissioned by ICMM, the mining industry body, that an average effective income tax rate of around 32% is where governments and mining firms have settled in equilibrium in equation (12). This takes some 60% of project NPV at the development stage, according to the Latin American study by Davis and Smith (2020), and even more at project initiation. ττ<sup>25</sup> At this tax rate some projects will be cancelled at the exploration stage, some will proceed with the expectation that all rents are being taxed away, and some will proceed with the expectation that the project owner will retain some resource rent. It could be viewed as a feasible tax rate that allows both the sector to continue to operate and the host governments to collect project rents. It is a feasible outcome that sustains mining project exploration and development while giving governments billions of dollars in tax revenue.

This has been an extremely simplified exposition of resource rent and resource rent taxation. Entire books have been written on the subject, and we have neglected many details, such as capital depreciation allowances in profit taxation, royalties, dividends taxes, worker profit sharing, loss carryforwards, and asset taxes.<sup>26</sup> Nor have we allowed for project participation by host governments through carried interests, which is another mechanism by which resource rents can be taxed away.

The simplified analysis allows three points to be made. First, resource rent is a stock concept, a present value at the project’s inception, which in theory has an infinite number of ways to be taxed as flows during the project’s life. A project’s resource rent does not equal profit during operations. Nor is it approximated by profit. Second, the proper calculation of resource rent requires an intertemporal estimation of project

> 25 _ICMM Members’ Tax Contribution - Focusing on Corporate Income Tax and Royalties – 2022 Update._ ICMM, December 2023. The effective tax rate is the ratio of total corporate income tax payments and royalty payments across 25 member companies making up one-third of global mine and mineral production divided by total company profits before tax, impairments, other exceptional items, and royalties charged above the profit before tax line for these same member companies. ICMM does not provide a range across the sample, although it does show that the average is relatively stable from year to year.

> 26 The most useful surveys of mining project and company taxation include Boadway and Keen (2015 and 2010), and Guj (2012).

19