rafmacalaba/datause-encoder-data huggingface dataset:
We suggest interpreting the local effects as being additional to the district-level effects; that is, the mine affects the mining district predominantly through the fiscal channel, and local mining communities mainly through employment generation, and other localized factors. Overall, the results are more robustly estimated at the district level than at the individual level, and we find no indications of positive spillover effects across districts. This is in line with a public spending hypothesis, where mining districts benefit more than adjacent non-mining districts through the fiscal revenue channel, since 10 percent of mining royalties are redistributed to mining districts.

# **2 Gold mining in Ghana**

Ghana has a long tradition of gold mining and has produced a substantial portion of the world’s gold for over 1,000 years (see Hilson [2002] for an extensive overview of gold production in Ghana). During colonial British rule, the country was named the Gold Coast Colony, and gold production was booming. The first gold rush occurred between 1892 and 1901, and the second after World War I. Gold production decreased at the dawn of independence in 1957, and remained low until the 1980s. Over the last 20 years, Ghana has been experiencing its third gold rush. During this period, annual gold production has increased by 700 percent, as shown in Figure 1. It is the expansion that has happened during this recent gold rush that is used in this analysis to understand the socioeconomic effects of mining. The high international gold price was a driving factor in the expansion of small-scale mining, such as the 2,700% increase in gold mining territory around the Offin River between 2008 and 2012 (Hausermann et al., 2018). Between 2006 and 2012, two large-scale mines opened in Ghana, but no mine closed down (Table 1) possibly due to the high gold price increasing profitability and extending life length.

The expansion across artisanal small-, medium-, and large-scale mining contributed to an increase in total production that rose from 541,147 oz in 1990 to 3,119,823 oz in 2009 according official Ghana statistics (Bloch and Owusu, 2012). This production increase led to an increased sector contribution to GDP from 4,83% (1990) to 5,78% (2009), alongside export value of US$304m in 1990, US$702m in 2000, and US$2246 m in 2008, reaching 43% of national exports in 2008. Mining related foreign direct investment (FDI) also rose from US$165m to US$762m between 1995 and 2009. Mining was the dominating sector with between 48% and 94% of mining FDI to total FDI from 1995 to 2007, until the country saw an incredible increase in non-mining foreign direct investment (Bloch and Owusu, 2012), following the discovery of oil in 2007.

4
# **Figure A.8. Changes in firms’ performance (%)**

# **a. Educational attainment b. Expertise, networking, employee discipline**

<!-- Start of picture text --> 40 20 13.51 13.19 14.68 14.88 15.08 15.99<br>32.9 34.21<br>35 32.9 10<br>30 0<br>25<br>-10<br>20<br>-20<br>15 9.95<br>10 7.74 7.56 6.42 -30<br>1.67<br>5 1.22 -40<br>-39.72 -39.39<br>0 -50 -41.00<br>Post-primary Secondary Can read Financial Business Discipline<br>education education Knowledge association problem<br>Top-performers Gazelles Survivalists Top-performers Gazelles Survivalists<br><!-- End of picture text --> _Source:_ a: Calculations based on OLS estimates from performance drivers regression (1) (column 1, table 11). b: Calculations based OLS estimates from performance drivers regression (3) (column 3, table 11).

40
13

- _(5c) dLIae_ = ac(w,e)/ae - X(8Q/ae), (5d) aL/aeb = at(e,)/aeB- X(aQ/aes), (5e) dL/AX = Q - Q(X,e,%e).

The Lagrangian multiplier X is the shadow price of the output. When X = P, the market price of the output, then (5b)-(5d) satisfy the condition of profit maximization that the value of the marginal product of each input equals its marginal cost.

Rates of technological substitution (RTS) between any pair of inputs are obtained from (5b) through (5d). They are (6a) RTS(Xo,,Xj) = (aQ/aXoJ/(aQ/8Xoj) = pi/pj, for any two primary inputs i and j, (6b) RTS(X02,e) = _(aQ/IaXj)/(Q/aX.)_ = pi/[ac/ae], for any primary input i, (6c) RTS(eB,e) = (aQ/ae.)/(8Q/re) = [at/aeB]/[ac/ae].

(6d) RTS(Xoi,XCj) = (aQ/aX..)f[(aQ/ae) (ae/aX,)] = p1/wj, for any primary input i, and any embedded input j Labor appears in both the primary and embedded production functions. However, since the skills required in the two settings may differ, the wage rates may differ between the two. Consequently, we treat labo, in the two settings as distinct inputs.

It should be noted that NEPA and PLN quantity discounts and scale economies in embedded production (falling marginal costs of e and eB) mean that the cost of power is concave in e and **eB** which causes the isocost curve between e and eB (keeping X. constant) to be nonlinear and convex to the origin. However, as long as the isoquants are convex to the origin and do not cut the axes (because of our assumption that both e and C9 are essential inputs), the tangency between the isocost curve and the isoquant insures an interior solution. The TRS
Mexico should also consider the impact that large companies can have on the results. If we separate the income accruing to PEMEX and to the rest of the private sector, then divide these incomes by the respective stocks of capital plus inventories, we obtain separate rates of return for each subsector. This process allows us to assess the contribution of PEMEX—a large stateowned enterprise that has very high returns and is also heavily taxed by the state—to the rates of return in the economy. These results can have an impact on the calculations of the EOCK when using the traditional method (top-down approach using national accounts data under different scenarios). In addition, the analysis in separate accounts is useful for the discussion of the bottom-up approach to the EOCK calculation for public sector projects in Mexico.

# **3.2 Estimation of the gross rates of return for invested private sector capital, 2003– 2011**

**Based on the discussion in section 3.1, this section focuses on the estimation of the EOCK and the necessary intermediate calculations for the 2003–2011 period, using SHCP data for those years.** The base year for the nominal and real values used in the calculations is 2003.

## **_3.2.1 Rates of return to private sector capital_**

**The national accounts data allow us to differentiate our assessment of income accruing to capital versus income accruing to labor.** We obtain GDP at factor costs from the national accounts data by taking GDP at market prices and deducting taxes on output. We then obtain the amounts accruing to labor, capital, and indirect taxes. The data shows that the income accruing to capital in the national accounts is, on average, more than double the income accruing to labor (see Table 1). Depreciation is reported as part of the gross operating surplus.

**Table 1 National accounts: Shares of income to factors of production, 2003–2011** **<u>(million pesos)</u>**

|Year|GDP<br>(nominal)|_Less_:<br>Indirect<br>taxes on<br>output|Gross value<br>added (GVA)<br>at factor<br>costs|Labor income|Indirect<br>taxes paid<br>out of GVA|Capital<br>income|Depreciation|Gross<br>operating<br>surplus<br>(= GVA less<br>labor income)|
|---|---|---|---|---|---|---|---|---|
|2003|7,555,803|393,030|7,162,773|2,370,474|304,878|4,487,421|683,354|4,792,299|
|2004|8,574,823|390,210|8,171,095|2,540,339|399,461|5,231,295|759,183|5,630,756|
|2005|9,251,737|395,564|8,825,085|2,729,235|519,324|5,576,526|804,314|6,095,849|
|2006|10,379,091|400,971|9,943,093|2,955,305|579,641|6,408,148|883,697|6,987,788|
|2007|11,320,836|436,368|10,854,384|3,172,663|614,088|7,067,633|972,737|7,681,721|
|2008|12,181,256|315,664|11,837,772|3,409,255|971,440|7,457,077|1,095,587|8,428,517|
|2009|11,937,250|499,027|11,394,220|3,474,162|580,608|7,339,451|1,264,601|7,920,058|
|2010|13,071,597|543,592|12,485,511|3,678,700|722,627|8,084,184|1,306,076|8,806,811|
|2011|14,395,833|507,736|13,843,758|3,955,335|932,639|8,955,784|1,382,400|9,888,423|

_Source:_ National accounts for Mexico, SHCP (2013) and INEGI.

14
**Figure 1**

# **“What is the most effective policy to improve access to finance among low-income borrowers?”**

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# CONTENTS

|1. i|<br>:E<br>1.1<br>1.2<br>1.3|xecutive<br>Summary ..............................................................................<br>4<br>Overview and General Observations ........................................4<br>Summary of Specific Findings ...................................................<br>8<br>Conclusion and Recommendations ........................................<br>f 2|
|---|---|---|
|I1.|Pow<br>21|er Project and Sector Performance and Options .......................<br>29<br>Introduction<br>29|
||.<br>2.2<br>2.3<br>2.4<br>2.5<br>2.6<br>|.............................................................................<br><br>Access, Quality and Cost of Service and Price ......................<br>3<br>Project Composition ..................................................<br>38<br>P<br>r e<br>c<br>mplementatlon ...............................................<br>39<br>Economic Aspacts of Project and Sector Performance.........<br>48<br>em and<br>Foreca s..................................<br>51<br>|
||2.7|FinancIal Performance of Power Projects<br>and the Sector ..................................<br>52|
||2.8<br>2.9|Instiutional Performance**................................................h.....**69<br>Conformity With Loan Covenants ...............................**....**....71|
|Ill.|Res<br>3.1<br>3.2<br>3.3|sons for Project and Sector Performance .................................74<br>Exogenous Factors ..... a..............................<br>74<br>Potentiasly Controliable Factors<br>In the National Environment ..................................... .... 77<br>Factors Within the Utility ..........................................................<br>79|
|IV.|Issu<br>4.1<br>4.2|es, Options and Recommendatlons<br>............................................<br>83<br>Implications of the Retrospective Review ..............................<br>83<br>Options and Recommendations .............................................<br>90|
|AN|NEX 1.|Sources of Data for Review of Power<br>Project and Sector Performance<br>..............................................<br>97|
||A.1<br>A.2<br>A.3|<br>Dat Sources .........., r.... .<br>s 7 ........<br>oes.e.97<br>Quslity of Data Sources ............................................................<br>99<br>Ulstof Projects ............................<br>102|
# Box 3.2. Details of application of the price-cup at Manchester<sup>airport</sup>

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new data about the distribution of factor income for labor and capital, indirect and commodity taxes, and external accounts are incorporated; and iv) using the maximumentropy method for estimating and balancing a SAM developed by Arndt, Robinson, and Tarp (2002), the 2009 SAM is derived by targeting key aggregates such as GDP and its final demand components, aggregate labor and capital income, tax revenue, exports and imports figures etc. In addition to having more recent data, the new SAM (albeit an estimated one) offers another advantage – it corrects the low level and share of capital income in the value added by sector in the 2004 SAM. The capital income reported in the 2004 Niger SAM is quite low – 5.5 percent of value added. Such a low figure is contrary to most empirical results from growth accounting, which tend to give relatively higher factor income shares to capital in low-income countries (as much as 50 percent or higher) and relatively lower share for wage income. The primary reasons for this pattern relate to the fact that capital is scarce and expensive in developing countries while labor, especially the unskilled in agriculture and services, are abundant and inexpensive. Partly for this reason, we update the 2004 matrix to 2009 in order to obtain better estimates of the factor income shares.

**Table 8: Sector aggregation for Niger 2009 SAM**

|**Aggregate Sector**|**Components from the 2004 SAM**|
|---|---|
|Food agriculture (subsistence agriculture)|Agriculture vivrière irriguée<br>Agriculture vivrière non irriguée<br>Elevage et pêche<br>Forêt|
|Other Agriculture|Agriculture de rente irriguée<br>Agriculture de rente non irriguée|
|Mines|Industrie d'extraction|
|Petroleum|Produitspétroliers|
|Food manufacturing|Fabrication aliments et boissons|
|Other manufacturing|Textiles et habillement<br>Industrie chimique<br>Autres manufactures<br>Production des métaux|
|Construction|Construction|
|Utilities|Electricité, gaz,eau|
|Services|Transport et communication<br>Services financiers<br>Services immobiliers et aux enterprises<br>Hôtels et restaurants<br>Autres services<br>Services communautaires etpersonnels|
|Public Services|Administrationpublique|
|Education & Health|Education<br>Santé et services sociaux|

_Note_ : The original 2004 SAM was provided courtesy of the International Food Policy Research Institute (IFPRI).

12
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- **Rossin-Slater, Maya, Christopher J. Ruhm, and Jane Waldfogel.** 2013. “The Effects of California’s Paid Family Leave Program on Mothers’ Leave-Taking and Subsequent Labor Market Outcomes.” _Journal of Policy Analysis and Management_ , 32(2): 224–245.

- **Rovigatti, Gabriele, and Vincenzo Mollisi.** 2020. “PRODEST: Stata Module for Production Function Estimation Based on the Control Function Approach.” _Statistical Software Components, Boston College Department of Economics_ .

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- **Stearns, Jenna.** 2018. “The Long-Run Effects of Wage Replacement and Job Protection: Evidence from Two Maternity Leave Reforms in Great Britain.” _Mimeo_ .

- **Sun, Liyang.** 2021. “EVENTSTUDYINTERACT: Interaction Weighted Estimator for Event Study.”

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- **Ulyssea, Gabriel.** 2018. “Firms, Informality, and Development: Theory and Evidence from Brazil.” _American Economic Review_ , 108(8): 2015–2047.

- **Ulyssea, Gabriel.** 2020. “Informality: Causes and Consequences for Development.” _Annual Review of Economics_ , 12(1): 525–546.

- **Williams, Colin C.** 2013. “Evaluating Cross-National Variations in the Extent and Nature of Informal Employment in the European Union.” _Industrial Relations Journal_ , 44(5-6): 479–494.

- **Williams, Colin C., and Jo Padmore.** 2013. ““Envelope Wages” in the European Union.” _International Labour Review_ , 152(3-4): 411–430.

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15
14 States manufacturing industries.'<sup>8</sup> This index takes a value of 100 if the item is produced with a capital intensity that is average for the U.S., with increasing values indicating increasingly capital intensive production. Table 3 shows that a relatively small number of products are important in accounting for the difference between the core and UNDO (or GATT(2)) definitions of manufactures with refined petroleum products being a key item. Refined petroleum accounts for 47 percent of the total trade in non-core products and also accounts for one-quarter or more of the exports of 39 of the 72 developing countries. Two factors arguing for inclusion of refined petroleum in manufactures are similarities to petroleum based chemicals in SITC _5_ (which are included in IBRD/UNCTAD/GATI core definitions) and the fact that it has a capital intensity more than three times high than the U.S. average for industrial production. Aside from refined petroleum, preserved fish, fruit, vegetables and meat -ombined account for about 18 percent of the total trade in the added products, while prepared animal feeds adds a further 7 percent. All six items have a capital intensity ratio equal to or above the U.S. average for industrial products.

# IV. The Influence on Country Classifications

For various analytical purposes, economists utilize country groupings like "newly industrialized countries", "exporters of manufactures", or "major developing country exporters of manufacturese. Argentina, Brazil, Hong Kong, Republic of Korea, Singapore, Taiwan (China), and Yugoslavia are generally included in '8 The capital intensity index (C.) is based on 1982 U.S. Census of Manufact data and is defined as J (1) C. = _(V./N.)_ / (V,/N 100 where V. and Vt represent value added in industry j and all U.S. manufacturing respectively, while N. and N represent the number of workers in the industry and all U.S. manufacturing activity. These data can ie usefu for evaluating UNIDOs inclusion of each item in :he organization's definition of manufactures since the production of manufactured goods often involves relatively high capital inputs. The reader should note that most of the items listed in Table 3 are included in the U.S. Census and are therefore considered to be "manufactures" by the Department of Commerce.
Table A10: Relationship between management (all sub-scores) and energy intensity in value (1)-(5) and quantity (6)-(10) terms (split by energy intensity for manufacturing and knowledge intensity for service firms).

||(1)<br>High EI|(2)<br>Moderate EI|(3)<br>Low EI|(4)<br>High KI|(5)<br>Low KI|(6)<br>High EI|(7)<br>Moderate EI|(8)<br>Low EI|(9)<br>High KI|(10)<br>Low KI|
|---|---|---|---|---|---|---|---|---|---|---|
|Targets z-score|-0.058|-0.780<sup>_∗∗∗_</sup>|-0.440<sup>_∗_</sup>|-0.083|0.533|0.352<sup>_∗_</sup>|-0.132|0.124|0.035|0.211|
||(0.426)|(0.193)|(0.229)|(0.244)|(0.451)|(0.194)|(0.108)|(0.138)|(0.119)|(0.196)|
|Monitor z-score|-0.165|-0.008|-0.080|0.042|-0.778<sup>_∗_</sup>|0.299|0.189<sup>_∗_</sup>|0.408<sup>_∗∗∗_</sup>|0.071|0.041|
||(0.376)|(0.162)|(0.230)|(0.215)|(0.407)|(0.183)|(0.099)|(0.146)|(0.111)|(0.167)|
|Operations z-score|-0.268|-0.024|0.215|0.069|0.154|-0.082|0.154<sup>_∗_</sup>|-0.048|0.209<sup>_∗∗∗_</sup>|-0.073|
||(0.420)|(0.161)|(0.204)|(0.167)|(0.345)|(0.145)|(0.084)|(0.144)|(0.080)|(0.118)|
|Incentives z-score|0.146|0.387<sup>_∗_</sup>|-0.202|-0.002|0.079|-0.288|0.027|-0.121|0.059|-0.106|
||(0.499)|(0.225)|(0.283)|(0.257)|(0.462)|(0.227)|(0.113)|(0.171)|(0.122)|(0.199)|
|No. Employees|0.697|0.841<sup>_∗∗∗_</sup>|0.979<sup>_∗∗∗_</sup>|1.311<sup>_∗∗∗_</sup>|1.360<sup>_∗∗∗_</sup>|0.313|0.318<sup>_∗∗∗_</sup>|0.014|0.387<sup>_∗∗∗_</sup>|0.498<sup>_∗∗∗_</sup>|
||(0.434)|(0.253)|(0.316)|(0.234)|(0.440)|(0.212)|(0.123)|(0.163)|(0.116)|(0.186)|
|Capital Stock|0.667<sup>_∗∗∗_</sup>|0.508<sup>_∗∗∗_</sup>|0.691<sup>_∗∗∗_</sup>|||0.380<sup>_∗∗∗_</sup>|0.174<sup>_∗∗∗_</sup>|0.333<sup>_∗∗∗_</sup>|||
||(0.204)|(0.096)|(0.143)|||(0.090)|(0.051)|(0.076)|||
|Log(Sales)|-1.435<sup>_∗∗∗_</sup>|-1.760<sup>_∗∗∗_</sup>|-1.754<sup>_∗∗∗_</sup>|-1.954<sup>_∗∗∗_</sup>|-2.084<sup>_∗∗∗_</sup>|-0.656<sup>_∗∗∗_</sup>|-0.604<sup>_∗∗∗_</sup>|-0.446<sup>_∗∗∗_</sup>|-0.607<sup>_∗∗∗_</sup>|-0.579<sup>_∗∗∗_</sup>|
||(0.311)|(0.184)|(0.279)|(0.197)|(0.333)|(0.147)|(0.081)|(0.120)|(0.077)|(0.118)|
|3-digit sector|✓|✓|✓|✓|✓|✓|✓|✓|✓|✓|
|Age|✓|✓|✓|✓|✓|✓|✓|✓|✓|✓|
|Country|✓|✓|✓|✓|✓|✓|✓|✓|✓|✓|
|Observations|582|1680|688|1305|750|362|991|421|702|433|
|Adjusted _R_<sup>2</sup>|0.182|0.228|0.244|0.191|0.440|0.409|0.297|0.357|0.326|0.278|
|df<br>~~m~~|46|49|54|46|56|45|49|47|45|49|

Standard errors in parentheses Capital, labor, and output enter in logs. _∗ ∗∗ ∗∗∗ p <_ 0 _._ 1, _p <_ 0 _._ 05, _p <_ 0 _._ 01

**52**
the West Bank and Gaza, while contributing to inequality reduction. However, their coverage is still limited in deciles 2 and 3. Improving social protection coverage among deciles 2 and 3 to compensate for taxes they pay could reduce inequality and poverty in the West Bank and Gaza. Using targeted programs such as the flagship CTP (targeted through the PMT) could represent the most effective way to redistribute resources in the country.

# **10.2 Next steps**

**Future extensions of the current study should aim to reassess the incidence of public health benefits based on actual access to health services.** This could be done using secondary household surveys such as the Demographic and Health Survey or the next PECS, for which data are being collected in 2023.

**Future study should also aim to include the simulation of the flagship and targeted CTP.** In the PECS 2016/2017, social protection transfers are aggregated as government cash assistance versus government near-cash assistance, making it impossible to disaggregate the impacts of individual social programs. The current analysis infers that the CTP is the reported cash assistance program in the PECS. The PECS 2023 will have more disaggregated data of social protection programs to refine this analysis. On the other hand, having access to the PMT targeting formula of the CTP would allow to perform other policy-reform simulations, such as expansions of coverage of the CTP beyond that reported in the PECS. This could be useful to analyze the impact of social protection compensation measures.

**Finally, the fiscal incidence model we developed for the West Bank and Gaza could be used to inform likely distributional effects for new PA policies and reforms.** Examples of these reforms include adjusting the electricity tariff structure to increase progressivity and decrease costs to the poor, reducing the fuel subsidy to increase fiscal space, updating PIT tax brackets, and expanding the number of municipalities applying property tax. Furthermore, the PECS 2023 survey has been updated to address some of the limitations highlighted in this analysis. For example, the PECS 2023 is better able to disaggregate social protection programs’ coverage and payments to, and use of, different healthcare facilities.

61