rafmacalaba/datause-encoder-data huggingface dataset:
Policy Research Working Paper 10965

# **Abstract**

This paper investigates the determinants of mining projects, with a focus on green minerals. The research question is the effect of political risk on investment decisions, the size of projects, the volume of ore mined, and the ensuing resource rents captured by the host country. The paper shows the challenges of measuring and capturing resource rents, using a mathematical model of resource rent maximization for the host country under the constraint of a positive after-tax cash flow for investors. The analysis finds that the optimal approach for taxing extraction is a progressive profit tax on mining revenues that generates revenues for the country while minimally deterring investment. Alternatively, taxing cash flow, which can be non-distortionary, can be implemented. Using the S&P Capital IQ database, the analysis finds that the low-quality of governance, institutions, infrastructure, skills, and services dampens the exploration and exploitation of copper, a key mineral for green energy. The opportunity cost in terms of unexplored or underexploited deposits translates into suboptimal global copper production and forgone revenues for the poorest host countries. To unlock exploration, the paper proposes measures to mitigate political risk, including investing in geological surveys and institutions and designing stable tax systems. For underexploited projects, it proposes that countries not only invest in infrastructure, skills, and services, but also improve governance and institutions. This would lower the metal grade at which investors would be willing to commit, ultimately producing more metal from identified mineral deposits. Interventions from international financial institutions can help to alleviate all country risks, including political risks, that hinder credible intertemporal commitments between investors and countries.

This paper is a product of the Macroeconomics, Trade and Investment Global Practice. 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://www.worldbank.org/prwp. The authors may be contacted at gdavis@mines.edu; cbouhabib@worldbank.org; gaute.solheim@hotmail.com; and mlokanc@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
71% of employment in the census.

Figure 1: Share of formal and informal establishments in each size category. 2018.

<!-- Start of picture text --> 100<br>80<br>60<br>Formal<br>Not registered<br>40 Not registered and non-response<br>20<br>0<br>Number of workers<br>Share of formal establishments<br>1-5 6-10 11-50 51-100 101+<br><!-- End of picture text -->

**Notes:** Formal establishments are registered with SAT, IMSS, or both, as self-reported by the respondent. Establishments that are not formal are informal.

Table 2 shows the size distribution and the employment size distribution of formal and informal establishments in the 2018 cross-section. Micro-establishments (with 1-10 workers) amount to 90% of establishments and account for 26% of employment among formal plants. Among informal plants, micro-establishments amount to 99% of plants and account for 90% of employment. Large establishments (with 101 or more workers) account for almost 50% of employment among formal plants but only 4% of employment among informal establishments. Informal establishment size averages 2.03, while average formal establishment size is 10.1 workers.

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|||**Power purc**<br>|**hases (GWh)**<br>||**Power purchas**<br>|**es (as % dispatch**<br>|**ed onto nation**<br>|**al grid)**<br>|
|---|---|---|---|---|---|---|---|---|
||**Private**<br>**purchases**<br>**(excluding**<br>**EPP)**|**Emergency**<br>**power**<br>**purchases**|**Import**<br>**purchases**|**Total power**<br>**purchases**|**Private**<br>**purchases**<br>**(excluding**<br>**EPP)**|**Emergency**<br>**power**<br>**purchases**|**Import**<br>**purchases**|**Total power**<br>**purchases**|
||_GWh_|_GWh_|_GWh_|_GWh_|_%_|_%_|_%_|_%_|
|Mauritius_2013|1,436.3|0.0|0.0|**1,436.3**|55|0|0|55|
|Mozambique_2014|8.7|93.5|190.2|**292.4**|0|2|4|6|
|Namibia_2013|0.0|0.0|2,888.0|**2,888.0**|0|0|68|68|
|Niger_2014|47.6|109.2|727.0|**883.7**|5|12|78|94|
|Nigeria_2014|—|0.0|0.0|**0.0**|n/a|0|0|0|
|Rwanda_2013|10.2|125.2|88.0|**223.4**|2|24|17|43|
|São Tomé and Príncipe_2014|8.7|0.0|0.0|**8.7**|10|0|0|10|
|Senegal_2013|379.0|442.0|308.5|**1,129.5**|12|15|10|37|
|Seychelles_2014|0.0|0.0|0.0|**0.0**|0|0|0|0|
|Sierra Leone_2012|0.0|0.0|0.0|**0.0**|0|0|0|0|
|Somalia_2011|0.0|0.0|0.0|**0.0**|0|0|0|0|
|South Africa_2014|6,022.0|0.0|10,731.0|**16,753.0**|3|0|5|7|
|South Sudan_2013|0.0|0.0|0.0|**0.0**|0|0|0|0|
|Sudan_2014|0.0|0.0|467.9|**467.9**|0|0|4|4|
|Swaziland_2014|47.8|0.0|930.2|**978.0**|4|0|77|81|
|Tanzania_2015|2,915.2|344.8|92.4|**3,352.4**|31|4|1|36|
|Togo_2013|0.0|5.5|833.5|**839.0**|0|1|76|77|
|Uganda_2014|3,222.7|0.0|32.7|**3,255.4**|99|0|1|100|
|Zambia_2014|0.0|0.0|50.7|**50.7**|0|0|0|0|
|Zimbabwe_2012|0.0|0.0|1,113.6|**1,113.6**|0|0|11|11|

_Sources_ : Utility annual reports and other documents. _Notes_ : Data are for power purchases from IPPs, EPPs, and imports. Purchases from state-owned generation companies are not included. Imports include purchases from regional SPV generation utilities, even if the generation facility is located within the borders of a given country. The year following each country name is the reference year for countries included in the QFD analysis, or the latest year with available power purchase data for countries not included in the QFD analysis.

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> - 1.58 -

Figure I.C.12 COCOA ACREAGE ADJUSTMENT POPULATION AND INCOME GROWTH <!-- Start of picture text --> (Comtant Ws)<br>6-<br>3<br>*=37 1997 2007 2017 2027 2037<br>PERIOD MIDOINTS<br>o W2 + 140 o 0 IG a IG a<br><!-- End of picture text -->
relies on sequer:e. It is on<sup>the basis of sequence that reciprocal effects are</sup> estimated. For example, the model<sup>contains parameters that represent both the</sup> impact of weaning on the hazard of<sup>next pregnancy and the onset of the next</sup> pregnancy on the hazard of weaning.<sup>The measurement of ages in the EFS no doubt</sup> contains inaccuracies. The most<sup>glaring evidence of this is the high degree of</sup> heaping on cercain ages (multiples<sup>of six months) evident in the information on</sup> age at death and age at weaning (Eid<sup>and Casterline, 1988; Akin,</sup><sup>_et al.,_</sup> 1988). The shifting of reported ages from<sup>true values to certain convenient values can</sup> reduce the reliability of the ordering<sup>of the three ages of interest.</sup> The reliance on the sequence of events<sup>has further implications. For</sup> state A to influence state B, state<sup>Amust occur first. When two events occur</sup> at the same age, effects of one event<sup>on the other are not plausible, even</sup> though both events occur. This<sup>point is especially pertinent to estimation of</sup> the relationship between death and weaning. When<sup>death occurs prior to weaning,</sup> retrospective survey data may show that death<sup>and weaning occur in the same</sup> month. In reality, death has censored<sup>weaning, and the child remains unweaned</sup> even though the child is dead.<sup>It is important to recognize that death can</sup> censor weaning; failure to recognize<sup>this fact can lead to estimation of effects</sup> of breastfeeding on mortality<sup>that are seriously upwardly-biased. The problem</sup> is potentially acute in Egypt: because<sup>of the long durations of nursing, a</sup> large proportion of infant deaths<sup>are likely to occur prior to weaning (i.e.,</sup> death curtails breastfeeding)<sup>The observed weaned children are, therefore,</sup> selected with respect to survival,<sup>so weaning will appear to reduce the risk of</sup> dying. To mitigate this selection<sup>problem, in this analysis the models for</sup> mortality contain a parameter for<sup>the equality of breastfeeding duration and age</sup> at death. In effect, those cases<sup>for which the two ages are equal have been</sup> 18
# **Figure 1. Organizational Structure of the Federations**

<!-- Start of picture text --> SPMS SVAWTC<br><!-- End of picture text --> <!-- Start of picture text --> Federation Federation<br>Thrift Cooperative<br>SHG<br><!-- End of picture text --> <!-- Start of picture text --> KVK<br><!-- End of picture text --> <!-- Start of picture text --> SMMS<br><!-- End of picture text --> <!-- Start of picture text --> Block Federation Mandal<br>Federation<br>Cluster<br>Village<br>Development<br>Association Organization<br>SHG SHG<br><!-- End of picture text -->

- 37 -
- Moav, O. (2005), ”Cheap children and the persistence of poverty,” Economic Journal, 115: 88-110.

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Table 13. Women and men: OLS HH fixed effects regressions on association of asset values with time use

||Wom<br> <br>(obs =|en: minutes<br>(OLS HH FE r<br>1,832;num|in last 24 hour<br>egressions)<br>ber of HH = 1,34|s<br>8)|<br>(obs|Men: minute<br>(OLS HH F<br>= 1,550;nu|s in last 24 hour<br>E regressions)<br>mber of HH = 1.|s<br>194)|
|---|---|---|---|---|---|---|---|---|
||Unpaid:<br>cook/clean|Unpaid:<br>childcare|Off-farm or<br>agr. activity|Leisure|Unpaid:<br>cook/clean|Unpaid:<br>childcare|Off-farm or<br>agr. activity|Leisure|
||(1)|(2)|(3)|(4)|(1)|(2)|(3)|(4)|
|(1) Log total asset value|||||||||
|Log asset value*not working in either|||||-|-|-|-|
|agriculture or off-farm work|0.7|5.0***||-1.1|||||
|Log asset value*in agricultural work|1.3|1.5|1.6|0.5|0.5|-0.7|3.2|1.1|
|Log asset value*in off-farm work|-1.6|-0.1|5.3*|-3.0**|-0.9|-2.0***|1.9|1.4|
|R-squared|0.299|0.31|0.587|0.245|0.094|0.152|0.486|0.135|
|(2) Share of household asset value|||||||||
|Share of asset value*not working in either|||||||||
|agriculture or off-farm work|-0.1|7.2||-6|||||
|Share of asset value*in agricultural work|10.7|-19.8|57.3|15.7|-16.5|-24.1|50.1|9|
|Share of asset value*in off-farm work|-23.4|15.7|13.9|-4.1|-5.9|-41.6**|49.4|-6|
|R-squared|0.297|0.284|0.583|0.236|0.094|0.162|0.488|0.133|
|(3) Log value of assets: asset categories separately<br>|||||||||
|_Interactions with not working in either_|||||||||
|<br>_agriculture or off-farm work_<sup>(2)</sup>|||||||||
|Mobile phone|-0.5|9.1***||-2.7|-|-|-|-|
|Financial accounts|-5.7|0.3||7.1|-|-|-|-|
|Livestock|-0.7|4.9||-4.3*|-|-|-|-|
|Motorized vehicles|-1.9*|1.7||-3.3**|-|-|-|-|
|Non-residential land|-1.1|-3.8||1.2|-|-|-|-|
|Residential land|1.8*|0.9||3.7**|-|-|-|-|
|_Interactions with working in agriculture_<sup>(2)</sup>|||||||||
|Mobile phone|1|-0.2|-3.3|-0.7|1.9***|-2.2**|0.3|0.2|
|Financial accounts|2.5|2.8|0.1|2.8|-1.5*|0.4|5.1|-4.4|
|Livestock|1.1|1.8|2.2|0.7|-1.2**|1.3|2.4|-2.8*|
|Motorized vehicles|-2.3**|-0.9|0.4|-0.1|0.8|-0.5|2.8|0|
|Non-residential land|1.1|-1|0.2|-0.3|0.2|-1.3*|2|-1.6|
|Residential land|-0.6|-0.6|2.4|0.4|-0.2|-0.6|0.7|2.7*|
|_Interactions with being in off-farm work_<sup>(2)</sup>|||||||||
|Mobile phone|-2.0*|-0.2|4.5|-0.7|-0.9|0.7|-2.4|2.8|
|Financial accounts|-5.1***|3.3|-0.4|-0.7|1.4*|-1.1|-1.9|0|
|Livestock|-0.5|-2|-1.2|0.6|1.2*|0.2|-9.9***|2.9*|
|Motorized vehicles|-1.2|-1.1|4.1**|-2.3***|-0.8|-2.9***|4.6**|-0.4|
|Non-residential land|1.3|0.3|-3.3|1|0|1|1.2|-0.4|
|Residential land|-0.5|0.8|-1.8|0.4|-0.1|0.1|2|-2.5**|
|R-squared|0.331|0.358|0.602|0.278|0.137|0.215|0.411|0.166|

Notes:(1) Regressions weighted by household sampling weight. Standard errors are clustered at the enumeration area level. Significant effects are indicated by asterisks; ***p<0.01 **p<0.05 *p<0.10 . (2) As reported in labor module. Interactions with not working in either agriculture or off-farm activity were not included for the sample of men, because of the very limited number of men who fell into this category. (3) Results for OLS specification (A), controlling for household variables, district and interview FE are available in Appendix Table A5.

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methods. However, its implementation is time-consuming and burdensome, and is often unfeasible in the context of national household surveys and censuses.

As in the GPS measurement, this method requires that the enumerator and respondent travel to the plot, and clear its boundaries from obstacles to the extent possible. Before the measurement can begin the farmer must pace the perimeter of the plot with the enumerator in tow to ensure the measurement captures the proper area. The enumerator will note the corners of the plot, where clearly available. When plots are irregularly shaped enumerators use their best judgment in declaring the corner points. The boundary of bushy plots will need to be cleared (with the permission of the farmer) prior to commencing measurement in order for the ranging poles to be visible. Only then can the enumerator start the task of measuring the plot. An example of instructions for completing the compass and rope measurement can be found in FAO (1982), while an example questionnaire format can be found in Carletto et al. (2016).

To some degree, the measurement error associated with traversing is observable. The closure (or closing) error is an important element of quality control in the compass and rope method. It is a measure of the gap between the reported start and end points of the constructed polygon, and gives an indication of the accuracy of the measurement. If the closing error is calculated while in the field, the measurement can be conducted again when found to be above a pre-determined threshold. The closing error will not confirm that the plot corners have been accurately assessed, however, only that the bearings and distances recorded form a full closed figure. The precision of the measurement is still subject to human error as identifying the plot corners can be a burdensome task on its own, particularly for irregularly shaped plots.

When corners are not clearly defined (as is the case in many irregularly shaped plots) enumerators must plot the “best” corner they can, and take the compass bearings. With each additional corner, therefore, there is additional room for error in the misreading of the compass or the measurement of the distance between two corners. Misreading of the compass by one or two degrees on one corner is not likely to result in material changes to the area measurement. However, aggregated over several plot corners, these small deviations add up, implying that the area calculation is not for the true plot boundaries. While field protocols usually include closing error thresholds beyond which the measurement needs to be retaken, this adds to the time necessary to take the measurement.

The limitations to the compass and rope method lie primarily in the burdensome nature and time required to complete the measurement. The time requirements will vary by plot size but the compass and rope method is expected to be consistently and significantly more time-intensive than GPS. However, when properly implemented this method is considered the gold standard and, as such, it offers the benchmark against which to evaluate the trade-offs with competing, less time-intensive methods.

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activity. Services also benefit but to a lesser extent (Figure 4). This is more evident during the periods of faster growth in the stock of electricity capital. Qualitatively, this is the general equilibrium effect captured by the OLS results in Tables 2 and 3. Quantitatively, the model simulations show a larger impact that the OLS estimates. This is, however, not surprising as the OLS estimates suffer from several well-known econometric problems.

Let us move now to the second row of panels in Figure 4. The left panel shows that the average number of employees increases in all industries by between 1.61 and 12.66 on average (see Table 7), with the largest increase in the manufacturing sector. Expression (43) implies that this change can be a consequence of wages, threshold productivity or grid capacity. However, the effect of the latter two factors move in opposite directions and partially offset one another. The one that dominates is the impact of wages, which fall by 23 percent when _Gt_ does not change compared to the benchmark case, thus increasing the average size of firms. In the manufacturing sector, this effect is further amplified by the negative electricitycapital elasticity of firm size. The second row of the right panel shows the evolution of the real level of production by sector (i.e., the sectoral real gross value added). It is computed as the GVA share of the industry times GDP divided by the sector’s relative price. It says that the decrease in infrastructure hurts production in all sectors, but specially the manufacturing one that falls by 34.08 percent; production in agriculture and services decrease by 17.26 and 19.40 percent, respectively.

The two charts located in the third row of Figure 4 give information about the evolution of GDP (left panel) and the expenditure shares (right panel). The loss of the grid capacity reduces GDP by 32.31 percent. It also reduces private investment, because of the complementarity between the two types of capital. The result is that the expenditure shares that benefit are the ones of consumption and the fixed costs. In particular, the former rises by 6.04 and the latter by 0.09 percentage points. The private and public investment shares fall by 4.74 and 1.39 percentage points, respectively.

In the previous experiment, some public capital investment is directed to maintaining 45
the estimate of Baland et al. (2010), which uses the 1995/1996 survey. Our estimate is also 17% below those for Dolakha District by Kandel et al. (2016). We therefore see our estimate of a 1-percent decrease in fuelwood collections (in tons) as reasonable, but erring on the conservative side, because each 1% reduction implies less wood reduced than in our consumption data and in much of the literature.

Only preservation of non-renewable biomass generates additional carbon savings and as we saw in the previous section, in our sample CFs implement a variety of restrictions to increase sustainability. We cannot know the sustainability implications of each household’s reduction in fuelwood collections or even the average portion of collections that are currently sustainable across our sample. To estimate the percentage of biomass harvests that are currently unsustainable, we use estimates from Bailis et al. (2015) and Drigo et al. (2014), who calculate nonrenewable biomass estimates by country and region within country using the Woodfuel Integrated Supply/Demand Overview Mapping (WISDOM) model. The estimate for Nepal is 52%, which is the estimate we employ in this paper, implying that slightly more than half of fuelwood harvest reductions generate additional carbon savings.

Each ton of unsustainably harvested fuelwood that is not collected would have been burned to generate heat for cooking and perhaps heating. Not all the biomass in fuelwood is carbon and for our analysis we use the IPCC (2013) emissions factor (0.50) to convert fuelwood not burned to carbon not emitted. We then apply the standard factor of 3.67 tons of CO2 per ton of carbon to convert carbon to CO2, which is the metric for international carbon transactions.

In Nepal, only about 4% of households who cook with fuelwood use improved biomass cookstoves (Nepal et al., 2011). Much of the rest use traditional, inefficient stoves, such as the Nepali _chulo_ , which is made of mud and stone, and typically vents waste gases within the home. Such 19