rafmacalaba/datause-encoder-data huggingface dataset:
had a positive economic impact on real income and poverty reduction. The benefit extends to local population not directly linked to mining, such as farmers and service workers. These results are consistent with the effects of a local shock to demand for labor in the presence of imperfect worker mobility. As suggested by Greenstone, Hornbeck, and Moretti (2010), in this case local shocks are not fully capitalized in land prices but can increase real wages.

Arag ́on and Rud (2013) also examine the effect of the fiscal revenue windfall associated with mining production. In the Peruvian case, a share of corporate taxes from mining companies is transferred to local authorities. Similar to Caselli and Michaels (2013), they also find that increased local revenue and public spending associated with the resource boom do not translate into higher household income. The authors interpret these results as suggestive evidence that the market mechanism (that is, increase in demand for local inputs) might be more effective in improving local living conditions than the fiscal channel (that is, increase in local governments’ revenue and spending). These findings, both in Brazil and Peru, cast some doubts on the effectiveness of revenue sharing schemes as a policy instrument for local communities to benefit from resource booms.

In a related study, Loayza, Mier y Teran, and Rigolini (2013) examine the socioeconomic impact of mining in Peru using a rich data set at the district level that combines census and household surveys. Similar to Arag ́on and Rud (2013), they find a positive relation between measures of living standards (such as poverty, consumption, and literacy) and mining production, but not with government transfers associated to mining tax revenue. Moreover, they find suggestive evidence that mining is associated with an increase in inequality. The authors highlight that this increase in inequality, among other factors, may explain the opposition of local communities to mining projects.<sup>20</sup> In one of the few studies focusing on Africa, Kotsadam and Tolonen (2014) examine the effect of mining on local employment. They use a rich data set at the individual level for several Sub-Saharan African countries, and implement a difference-in-differences approach exploiting the opening and closure of mines. This study finds that mine openings seem to create new employment opportunities outside agriculture, and significant structural shifts. Interestingly, these effects are differentiated by gender. Women switch toward service sectors,

> 20 A similar relation among resource booms, income, and inequality has been reported in the case of Australia (Reeson, Measham, and Hosking 2012). Interestingly, this study suggests that the relation between mining and inequality may have an inverted U-shape. 28
|Table 3. MatchingR<br>Stage|esults<br>Outcome|Matches*|Job<br>Candidates**<br>All|Firms***|Matches<br>*|Job<br>Candidates** <br>Female|Firms***|Matches<br>*|Job<br>Candidates** <br>Male|Firms***|
|---|---|---|---|---|---|---|---|---|---|---|
|Treatment Group|Unmatched|-|447|-|-|296|-|-|150|-|
|Treatment Group|Matched|1143|564|134|592|312|101|552|253|102|
|Matched|Unable to Contact Job Candidate|82|45|11|39|25|5|43|20|6|
|Matched|Job Candidate Not Interested|319|191|35|177|107|30|142|84|31|
|Matched|Firm Not Interested|627|270|74|307|143|58|320|127|59|
|Matched|Interview|115|58|14|69|37|8|47|22|6|
|Interviewed|Job Candidate Refused Job Offer|30|11|2|22|10|0|8|1|1|
|Interviewed|No Job Offer|58|33|10|31|17|5|28|17|3|
|Interviewed|Hired|24|24|17|17|17|11|7|7|7|
|Hired|Quit Within First Month|15|15|8|11|11|5|4|4|4|
|Hired<br>*Administrative re<br>a corresponding fir<br>and one continued|Continued Past First Month<br>cords on job seekers **Administrative<br>m or job candidate in the same stage,<br> past the first month, so we record thi|9<br>records on uniq<br>then we record<br>s firm as "contin|9<br>ue job seekers **<br>the lowest level re<br>ued past first mon|9<br>*Administrativ<br>sult i.e. one f<br>th" NOT both|6<br>e records<br>irm hired t|6<br>on firms **** If<br>wo candidates|6<br>a job candi<br>and one ca|3<br>date or fir<br>ndidate qu|3<br>m gets two "re<br>it within the fir|3<br>sults" from<br>st month|

*Administrative records on job seekers **Administrative records on unique job seekers ***Administrative records on firms **** If a job candidate or firm gets two "results" from a corresponding firm or job candidate in the same stage, then we record the lowest level result i.e. one firm hired two candidates and one candidate quit within the first month and one continued past the first month, so we record this firm as "continued past first month" NOT both

**Table 4: ITT Results of Impact of Job Matching on Job Candidates**

||Oct 2012<br>Emplo|May2013<br>yment|<br>Oct 2012<br>Salary conditional|May2013<br>on being employed|
|---|---|---|---|---|
|Assigned to treatment (getting matched)|0.00713<br>(0.0378)|0.0235<br>(0.0360)|-9.360<br>(20.38)|0.173<br>(12.29)|
|Control Group Mean|0.43|0.57|313|350|
|Observations|984|1,097|428|605|

Notes: Sample size for October 2012 is smaller since some job candidates joined study after this date. Salary is a monthly salary, expressed in terms of Jordanian Dinar (JD) Robust standard errors in parentheses, *, **, and *** indicate significance at the 10, 5, and 1 percent levels.

34
Second, we re-define K to include intangible capital as _Log_ ( _CapitalStock_ + _ICAPit_ ) where _ICAPit_ , is the sum of externally and internally purchased intangible capital defined in section 1.2. With these adjustments, we re-estimate markups to be: where _βv_<sup>_∗_are the output elasticities estimated using OPEX* as the variable input and</sup><sup>_Log_(</sup><sup>_CapitalStock_+</sup> _ICAPit_ ) as the capital input in equation 14.

Table 1 presents summary statistics of the main variables in our analysis. In addition to the variables discussed above we also use a proxy for firm age which is defined as the number of years since the firm first appears in Compustat following Giroud and Mueller [2010].

The mean _ROIC_ in our sample according to conventional metrics is -32.3% and for just large firms (unreported in the table) it is 24.3. Once we adjust for intangible capital, the mean _ROIC_<sup>_TOT_</sup> is 13%. By definition, 10% of our sample are classified as star firms according to both the ROIC measures, _ROIC_ Star and _ROIC_<sup>_TOT_</sup> Star. Once we take into account intangible capital, the average markup is 1.18. The average Herfindahl industry concentration is 0.09 and the average firm market share is 0.014.

# **3 Empirical Findings**

## **3.1 Is there a rise in Markups?**

There has been a great deal of controversy on the rise in market power in the US. Using marginal costs measured by COGS, De Loecker and Eeckhout [2017] document a stunning rise in markups in the US over the past three decades. Traina [2018] however argues that COGS are a declining share of firm costs and once we use operating expenses that includes COGS and SGA, there has been no rise in firm markups. This is an important policy question as it also speaks to the discussion on the rise in industrial concentration and decline in labor share (see Grullon et al. [2017] and Autor et al. [2017]).

16
<!-- Start of picture text --> 3.<br>2.6<br>2A-<br>~2.2 -<br>2 -s<br>I 18 Ms V\<br>1.6<br>1.4<br>1.2<br>N ufte d ObserCiam<br>1 3 2 4 6 7 44 8 4 9 9 4 4 8 5 8 6<br>0.6* a , I * * I T a I I I I I I I<br>1966 1967 1966 1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1960 1961 1982 1963 194<br>Yea d Otl0 fn<br>Vid Bak - 40814.2<br><!-- End of picture text --> Figure 8: Trend of Debt Service Coverige **Ratio.**
# **Quantile Regressions**

# _Model_

The underlying economic model used in the analysis will simply follow Mincer’s (1974) human capital earnings function extended to control for a number of other variables that relate to location. In particular, we apply a semi-logarithmic framework that has the form: where ln yi is the log of earnings or wages for an individual, i; xi is a measure of a number of personal characteristics including human capital variables, etc.; and zi represents location specific variables. The functional form is left unspecified in equation (1). The empirical work makes extensive use of dummy variables in order to catch nonlinearities in returns to years of schooling, tenure, and other quantitative variables. The last component, ui, is a random disturbance term that captures unobserved characteristics.

# _Quantile regressions_

Labor market studies usually make use of conditional mean regression estimators, such as OLS. This technique is subject to criticism because of several, usually, heroic assumptions underlying the approach. One is the assumption of homoskedasticity in the distribution of error terms. If the sample is not completely homogenous, this approach, by forcing the parameters to be the same across the entire distribution of individuals may be too restrictive and may hide important information.

The method applied in this paper is quantile regressions. The idea is that one can choose any quantile and thus obtain many different parameter estimates on the same variable. In this manner, the entire conditional distribution can be explored. By testing, whether coefficients for a given variable across different quantiles are significantly different, one implicitly also tests for conditional heteroskedasticity across the wage distribution. This is particularly interesting for developing countries such as Haiti where wage disparities are huge and returns to, for example, human capital may vary across the distribution.

The method has many other virtues apart from being robust to heteroskedasticity. When the error term is nonnormal, for instance, quantile regression estimators may be more efficient than least squares estimators. Furthermore, since the quantile regression objective function is a weighted sum of absolute deviations, one obtains a robust measure of location in the distribution, and as a consequence the estimated coefficient vector is not sensitive to outlier observations on the dependent variable.<sup>4</sup>

> 4 That is, if _y i_ − _x i_ ′β ˆ θ > 0 then yi can be increasing towards + ∞, or if _y i_ − _x i_ ′β ˆ θ < 0 , yi can be

> decreasing towards -∞, without altering the solution β<sup>ˆ</sup> θ . In other words, it is not the _magnitude_ of the

5
<!-- Start of picture text --> T<br>1 — (———<br>g(r,T) = (ce)<br>+r<br><!-- End of picture text --> <!-- Start of picture text --> So, under certain assumptions, the Flores et al. approach gives the same answer as the (lifetime-<br>consistent) lifetime money metric method. The challenge with the Flores et al. method (and the<br>LMMU method) is that with the typical cross-section household survey, we do not have the<br>PRE —<br>0<br>PRE == —— TG. T) + + CCposr<br>LMMU = FKOV = —————° <t(r,T) = ° t(r,T)<br>PRE t(r,T) + Cposr<br>So, for example, if -=10%, 7-1=25, C =90 and MM =10, then the regular consumption-based<br>catastrophic spending ratio is 10%, while the LMMU and FKOV measures (under the assumptions<br>we have made) are only 1.1%. In empirical work, one would of course have to make an assumption<br>loans (perhaps specifically for medical expenses), one may also have to make an assumption about<br><!-- End of picture text --> <!-- Start of picture text --> consistent with studies that find that people borrow to finance medical expenses (Demirguc-Kunt<br>and Klapper 2012) and that nonhealth consumption falls after a health shock (see e.g. Gertler and<br>Gruber 2002; Jack and Suri 2014). The evidence is consistent, in other words, with one implication<br>consumption over time, or at least plan their consumption such that consumption in period ¢+1 isa<br><!-- End of picture text -->
Marquez, Jaime, and Caryl McNeilly. 1988. Income and Price Elasticities for Exports of Developing Countries. _Review of Economics and Statistics_ 70: 306-14.

- Marquez, Jaime. 1994. "The Econometrics of Elasticities or the Elasticity of Econometrics: an Empirical Analysis of the Behavior of U.S. Imports." _Review of Economics and Statistics_ 76(3): 471-81.

- McDaniel, Christine A., and Edward J. Blistreri (2003). "A Review of Armington Trade Substitution Elasticities." _Économie international_ 94-95: 301-14.

- McDonald, Scott, Karen Thierfelder, and Terry Walmsley. 2012. "R23 Model: A SAM Based Global Macro CGE Model." Mimeo. The World Bank DEC Prospects Group.

- Mundell, Robert A. 1963. “Capital Mobility and Stabilization Policy Under Fixed and Flexible Exchange Rates.” _Canadian Journal of Economic and Political Science_ 29 (4): 475-85.

- Ostry, Jonathan D., and Andrew K. Rose. 1992. "An Empirical Evaluation of the Macroeconomic Effects of Tariffs." _Journal of International Money and Finance_ 11:63-79.

- Panagariya, Arvind, Shekhar Shah, and Deepak Mishra. 1996. "Demand Elasticities in International Trade - Are They Really Low?" World Bank Policy Research Working Paper 1712, Washington D.C.

Petri, Peter A. 1984. _Modeling Japanese-American Trade._ Cambridge: Cambridge University Press.

- Reinert, Kenneth, and David Roland-Holst. 1992. "Armington Elasticities for United States Manufacturing Sectors." _Journal of Policy Modeling_ 14: 631-639.

- Reinhart, Carmen M. (1995). "Devaluation, Relative Prices, and International Trade: Evidence from Developing Countries." _IMF Staff Papers_ 42 (2): 290-312.

- Rittenberg, Libby. 1986. "Export Growth Performance of Less-developed Countries." _Journal of Development Economics_ 24: 167-77.

- Rose, Andrew K. 1990. "Exchange Rates and the Trade Balance: Some Evidence from Developing Countries." _Economic Letters_ 34: 271-75.

- Rose, Andrew K. 1991. "The Role of Exchange Rates in Popular Models of International Trade: Does the Marshall-Lerner Condition Hold? _Journal of International Economics_ 30:1-16.

- Rutherford, Thomas. 2003. "Lecture Notes on Constant Elasticity Functions." Mimeo, University of Colorado.

- Salter, Wilfred E. G. 1959. "Internal and External Balance: The Role of Price and Expenditure Effects." _Economic Record_ , Vol 35, pp. 226-238.

- Schurenberg-Frosch, H (2015), “We Could Not Care Less about Armington Elasticities–But Should We? A Meta-Sensitivity Analysis of the Influence of Armington Elasticity Misspecification on Simulation Results”, Ruhr Economic Papers 594/2015, Ruhr Universitat Bochum, Germany.

- Senhadji, Abdelhak S., and Claudio E. Montenegro. 1999. "Time Series Analysis of Export Demand Equations: A Cross-Country Analysis." _IMF Staff Papers_ 46 (3): 259-73.

23
23

**Table 2. Monthly Real Wage Levels in U.S. Dollars for Cambodia and Sri Lanka, 2005 prices**

||Panel|A: Camb|odia||||||
|---|---|---|---|---|---|---|---|---|
||1996|1999|2004|2007|2008|2009|2010|2011|
|Mean wage in T&G, all|38|51|76|64|57|54|66|64|
|Male|48|71|85|73|54|55|81|66|
|Female|34|47|73|62|58|54|63|63|
|Mean wage in agriculture, all|41|28|78|35|37|32|34|45|
|Male|44|33|85|41|42|36|39|57|
|Female|34|21|71|30|32|27|30|34|
|Mean wage, all|67|46|77|79|78|57|77|82|
|Male|76|48|79|87|87|62|87|94|
|Female|55|43|73|67|64|50|63|67|

||||||P<br>|anel B<br>|: Sri La<br>|nka<br>||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
||1992|1993|1994|1995|1996|1997|1998|1999|2000|2001|2002|2006|2008|2011|2012|
|Mean wage in||||||||||||||||
|T&G, all|91|85|92|96|85|81|74|70|71|61|59|68|58|65|75|
|Male|115|99|112|118|114|108|96|87|94|77|79|94|70|91|92|
|Female|80|79|84|86|72|71|64|61|62|53|54|60|53|56|67|
|Mean wage in<br>agriculture, all|57|54|59|61|52|51|47|49|45|37|35|39|39|45|54|
|Male|63|59|66|69|58|57|53|55|50|42|39|44|46|50|60|
|Female|46|46|49|46|45|43|41|40|38|31|29|31|31|38|44|
|Mean wage, all|94|92|101|101|87|89|84|82|79|69|65|81|69|76|88|
|Male|99|96|106|105|93|96|90|87|84|73|68|85|72|80|92|
|Female|84|84|92|94|74|77|72|71|68|61|59|71|62|67|78|

_Source:_ Calculations based on Cambodia Socio-Economic Surveys, the Sri Lankan 2006 Household Income and Expenditure Survey, and the 1992–2002, 2008, 2011, and 2012 Labor Force Surveys. Note: T&G = textiles and garments. The local currency was transformed into the U.S. dollars using the Consumer Price Index (CPI) and the exchange rate from the World Development Indicators Database.

# **4.2 Decomposing the Gender Wage Differential**

The Oaxaca-Blinder decomposition is a very well-known technique that is generally applied in studies of gender wage differentials. Men and women may have different wages for
2.2 **The Diagnostic Framework.** Prior to procuring and implementing it is important to determine the rationale for implementing an FMIS and identify the problems that the system is intended to solve. It is also important to identify whether legal and institutional prerequisites are in place. For countries that have an operational FMIS (which is most but not all), this stage may involve identifying key system deficiencies before engaging in a second/third generation reform. Such a diagnostic should facilitate a cost-effective engagement by determining binding constraints across the various stages of the system lifecycle and take into consideration coverage and utilization questions. Similar to public investment management diagnostics (Rajaram et al. 2010), in FMIS a diagnostic can provide a ‘gap analysis’ of the actual system relative to a benchmark so as to identify structural aspects that may be weak and in need of attention. The importance of a diagnostic prior to an engagement is firmly embedded in the information systems literature (see for example Dorotinsky and Matsuda 2001, Diamond 2013, Dorotinsky and Watkins 2013, and Hashim 2014). The importance of a diagnostic (or lack thereof) also comes out firmly from recent IEG project evaluations and case studies (IEG 2016a-e) and was also documented in some World Bank project documentations (see for example experience from Bolivia, Cameroon, Colombia, the Democratic Republic of Congo, Ecuador, Honduras, Kazakhstan, Kenya, Madagascar, the Russian Federation, Sierra Leone, the Slovak Republic, Pakistan and Ukraine). There have been some efforts in developing diagnostic toolkits, including Hashim and Moon (2004) and more recently Hashim and Piatti (2016).

2.3 **The System Development Life Cycle.** There are multiple stages involved in getting an FMIS operational, which are commonly referred to as the systems development life cycle (SDLC). These include process review and design, system design, system procurement, actual systems implementation, and system maintenance (Sheti and Sharma 2013). These stages are discussed in more depth in appendix B.<sup>9</sup> Most studies recognize the importance of the SDLC and are focused on at least some of its aspects in their analysis (see for example Hashim and Allan 1999, Diamond and Khemani 2005, Khan and Pessoa 2010, Dener et al 2011, Hashim 2014, Uña and Pimenta 2015, Combaz 2015, and Laizer and Suomi 2016). As almost all World Bank FMIS investment operations are centered around the implementation of FMIS, they naturally address various aspects of the SDLC and present lessons accordingly in the project documentation.

2.4 **Coverage and utilization.** Once the system is up and running it is axiomatic that it has to be utilized to be functional. The coverage and utilization stage in the theoretical framework covers scope of the system (geographical and functional), actual budget

> 9 Appendix B can be accessed at this link.

**19**

**19**
ar V[ 1 ( 17) When , equals one, both components are equally noisy. Using this definition in (16), and noting that _s = F/(NF+F),_ total expenditure, y, will be a better measure than food expenditure, _f,_ whenever The right hand side of this inequality is always greater than one, which means that it will be advantageous to use non-food expenditure information even if it is somewhat noisier than food expenditure information. This is the benefit of aggregation. The data suggest that non-food information can, in fact, be substantially noisier and still be useful. The RHS of (18) is equal to approximately four for Ecuador and five for Pakistan, using food shares of 0.60 and 0.67, respectively (see Tables 2 and 4, rows 1 and 2). In other words, as long as the relative noise of non-food expenditure is no more than four times that of food in the case of Ecuador, and no more than five times that of food in the case of Pakistan, aggregating these two components into a comprehensive measure of consumption provides a better indicator of welfare than food alone.

The preceding values assume independence across measurement errors and, of course, a positive correlation between them would diminish the advantages of aggregation. However, there are grounds for expecting less than perfect correlation. First, questions on household expenditures are often answered by different family members. In LSMS surveys interviewers are generally instructed to ask questions of those who are best placed to answer them (Grootaert, 1986). This might imply, for instance, that the questions on food expenditures are answered by women, while transport or durable purchase expenditures are described by men. Second, at least some of the non-food expenditures which can be included in the consumption aggregate consist of imputed expenditures, eg. for housing, consumer durables, or certain public services (see for example, Hentschel and Lanjouw, 1996). The errors which these components introduce stem from the underlying imputation model, and it would seem reasonable to suppose that they are not correlated with the errors introduced by a specific household's response to questions on other consumption components.

The above values also assume that there are no household deviations from population food demand patterns. As is clear from the condition given in (15), the addition of such variation makes the use of non-food expenditure information more important in assessing the standard of living of households.

20
- -4

# 1. EXECUTIVE SUMMARY

# 1.1 Overview and General Observations

This retrospective study was carried out to review power projects financed by the Bank from 1965 to 1983. The main objectives of the study were to:

- (I) assess the performance of power projects and institutions In terms of key physical and financial Indicators, and adherence to loan covenants;

- (Q) assess sector performance In terms of the Increase In access to service, and service price, quality, and cost;

- (ill) identify issues and causal factors relating to good or poor performance; and

- (Iv) determine options and make recommendations to Improve project and sector performance.

Project Completion Reports (PCR's) and Project Performance Audit Reports (PPAR's) were the princqpal sources of data, supplemented by information from Staff Appraisal Reports (SAR's) and other specific Bank documents, where necessary.

Since both the Bank and Its borrowers have had many decades of experience In Identifying, analyzing and solving power sector problems, n Is unlikely that any new Investigation would produce hitherto unsuspected or startling conclusions. Also, broad generalizations are difficuit to make, particularly across a wide range of developing countries In different reglons, and over a long period of time. Nevertheless, this study does present fresh Insights concerning the electric sector and a better understanding of several fundamental Issues that have emerged recently. It also helps prioritize the key Issues, Identify causal factors, and determine practical options available to decisionmakers, who wish to Improve electric utility performance. Finally, the report Is designed to facilitate the pro-