rafmacalaba/datause-encoder-data huggingface dataset:
Table A6: Price Index of Imported Materials/ Price Index of Domestic Materials

|Industry Sector||||Ye|ar||||
|---|---|---|---|---|---|---|---|---|
||2000|2001|2002|2003|2004|2005|2006|2007|
|04:beverages & spirit (16-24)|1|0.980|0.975|1.075|1.067|1.092|1.187|1.220|
|06:chemical products (28-38)|1|0.981|1.028|1.145|1.219|1.385|1.564|1.657|
|07:plastics & rubber (39-40)|1|0.997|1.053|1.139|1.183|1.288|1.418|1.526|
|08:raw hides & skins (41-43)|1|1.000|0.997|1.098|1.125|1.192|1.279|1.355|
|09:wood & articles (44-46)|1|0.960|0.991|1.077|1.112|1.162|1.233|1.262|
|10:pulp of wood (47-49)|1|0.998|1.024|1.116|1.168|1.241|1.332|1.486|
|11:textiles (50-63)|1|0.995|1.004|1.087|1.108|1.153|1.228|1.253|
|12:footwear & headgear, etc. (64-67)|1|0.994|1.019|1.101|1.150|1.234|1.328|1.396|
|13:stone, plaster, cement, etc. (68-70)|1|0.996|1.007|1.095|1.197|1.356|1.510|1.659|
|14:precious metals (71)|1|0.985|0.960|1.048|1.094|1.208|1.316|1.403|
|15:base metals (72-83)|1|0.978|0.991|1.043|1.112|1.256|1.403|1.488|
|16:machinery, mech, elect eqmt (84-85)|1|1.021|1.115|1.237|1.305|1.431|1.572|1.890|
|17:vehicles & aircraft (86-89)|1|1.044|1.053|1.136|1.245|1.390|1.547|1.890|
|18:optical, photographic, etc. (90-92)|1|1.015|1.120|1.299|1.416|1.541|1.672|2.022|
|20:misc manufacturing (94-96)|1|0.992|1.009|1.105|1.175|1.286|1.413|1.563|

Source: China’s Customs Trade Data and National Bureau of Statistics Manufacturing Survey. Both prices are normalized to 1 for year 2000.

Table A7: Percentage of Foreign Content in Domestic Materials

|Industry Sector||||Ye|ar||||
|---|---|---|---|---|---|---|---|---|
||2000|2001|2002|2003|2004|2005|2006|2007|
|04:beverages & spirit (16-24)|0.727|0.795|0.960|1.176|1.560|2.032|2.029|2.084|
|06:chemical products (28-38)|0.670|0.744|0.921|1.151|1.595|2.134|2.183|2.318|
|07:plastics & rubber (39-40)|0.386|0.433|0.544|0.691|0.975|1.312|1.374|1.466|
|08:raw hides & skins (41-43)|0.718|0.788|0.972|1.210|1.652|2.169|2.210|2.291|
|09:wood & articles (44-46)|1.110|1.209|1.465|1.826|2.518|3.353|3.352|3.493|
|10:pulp of wood (47-49)|0.892|1.012|1.286|1.680|2.389|3.211|3.374|3.549|
|11:textiles (50-63)|1.058|1.163|1.443|1.800|2.436|3.226|3.288|3.426|
|12:footwear & headgear, etc. (64-67)|0.927|1.027|1.290|1.631|2.263|3.023|3.133|3.293|
|13:stone, plaster, cement, etc. (68-70)|1.204|1.338|1.662|2.094|2.944|3.967|4.103|4.381|
|14:precious metals (71)|0.918|1.024|1.276|1.607|2.249|3.053|3.188|3.450|
|15:base metals (72-83)|1.146|1.282|1.602|2.026|2.857|3.907|4.122|4.511|
|16:machinery, mech, elect eqmt (84-85)|1.089|1.230|1.544|1.974|2.737|3.689|3.939|4.375|
|17:vehicles & aircraft (86-89)|1.414|1.586|1.981|2.528|3.564|4.855|5.134|5.657|
|18:optical, photographic, etc. (90-92)|0.730|0.820|1.028|1.311|1.829|2.466|2.617|2.877|
|20:misc manufacturing (94-96)|1.015|1.129|1.412|1.787|2.502|3.366|3.513|3.759|

Source: From Koopman, Wang, and Wei (2012) and authors’imputation based on the growth rate of the number of ordinary importers 22
Not all countries will have adopted the GFSM 2001 functional classification, COFOG, and necessitating a code-mapping exercise as described earlier in the Guide. The basis for this code-mapping exercise should be fully described in the country report. The best way to do this is through a tabular presentation (as shown for Kenya in table 3.5 and for Rwanda in table 3.6).

An assessment of the practical problems that officials face in presenting data from the government‘s existing budget presentation, aligned with the GFSM 2001 reporting framework, should consider whether these problems arise from poor data capture (on the part of the Ministry of Finance, for example), from weak accounting systems, or simply from weak data reporting through the budget structure.

# _Pre-field templates._

In the report embed<sup>11</sup> the Excel files containing the pre-field templates:

- Template 1. Jurisdictional responsibilities in infrastructure service delivery

- Template 2. Special funds financing infrastructure service delivery

- Template 3. Private participation in infrastructure

- Template 4. ODA in infrastructure service delivery

_Qualitative field templates._ In the report embed the Excel files containing the qualitative templates:

- Template 5. Basic budgetary institutions, national level

- Template 6. Budgetary cycle diagram, national level

- Template 7. Governance of public operators

_Quantitative field templates._ In the report embed the Excel files containing the quantitative templates:

- Template 8. Macroeconomic parameters for normalization

- Template 9. Macroeconomic parameters for budgetary context of infrastructure spending

- Template 10. Functional and economic classification of government expenditures (central and local)

- Template 11. Public operators‘ financial data

## **d. Concluding remarks**

The report should provide concluding remarks on the data collection process, emerging messages and other country-specific issues that might be of interest, as next steps for the country in terms of data generation, institutional transparency, etc.

11 To embed an Excel file into a Word file:

> Insert  Object  Select ―Create from File‖ tab  (click on ―display as icon‖ option)  Browse your file  Select the file to embed by pressing ―Insert‖  select ―Change Icon‖  Type the name of the file as specified in the Guide in the box ―Caption‖  click ―OK‖  click ―OK.‖

55
Tablle 6: Operating Performance of Registered Pension Funds (1997-2001)

|(percent)|1997|1998|1999|2000|2001|
|---|---|---|---|---|---|
|OperatingExpenses/Contributions|3.82|4.81|6.47|7.56|5.86|
|OperatingExpenses/AverageAssets|0.53|0.56|0.61|1.06|0.66|
|InvestmentIncome/AverageAssets|8.66|9.09|7.24|9.61|4.71|
|Benefits/Contributions|42.3|46.0|54.4|46.1|45.3|
|Benefits/TotalInflows|26.1|25.8|30.3|11.8|31.4|
|InvestmentIncome/TotalInflows|38.3|43.7|43.0|17.5|29.0|
|GrowthRateofTotalAssets|19.2|22.9|12.6|15.4|7.8|

Source: Estimated on the basis of date collected by the FSC and Registrar of Associations.

Table 7: Operating Perforrmance of Occupational Pensionm Funds, 2001

||OperExp/<br>Contr|OperExp/<br>AverAssets|Inv Inc/<br>Aver Assets|Inv Inc/<br>TotInflow|NetFlow/<br>AverAssets|
|---|---|---|---|---|---|
|LargePensionFunds|4.24|0.49|4.27|26.5|10.3|
|MediumPensionFunds|11.40|0.97|5.75|40.1|7.6|
|SmallPensionFunds|12.07|2.15|7.69|29.5|20.9|
|TotalRegistered|5.86|0.66|4.71|29.0|10.3|
|SugarIndustryPF|6.80|0.43|6.92|52.4|5.5|
|Registered&SIPF|6.14|0.56|5.67|38.0|8.2|
|NPF|9.22|0.48|11.16|68.3|14.3|

Source: Estimated on the basis of data collected by the FSC and the Registrar of Associations.

|Table8:Comparative Oper<br>|atingand <br>|Investme<br>|ntPerfor<br>|mance(1<br>|997-2001<br>|)<br>|
|---|---|---|---|---|---|---|
|(percent)|1997|1998|1999|2000|2001|Average*|
|OperatingExpenses|||||||
|LargeRegistered PensionFunds|0.39|0.41|0.44|0.97|0.49|0.56|
|MediumRegisteredPension Funds|0.78|0.92|0.96|1.03|0.97|0.97|
|Small Registered PensionFunds|2.41|1.95|1.96|2.35|2.15|2.13|
|AllRegisteredPensionFunds|0.53|0.56|0.61|1.06|0.66|0.72|
|NationalPensionsFund|0.77|0.70|0.58|0.53|0.48|0.61|
|InvestmnentReturns|||||||
|LargeRegisteredPensionFunds|9.03|9.07|7.13|10.27|4.27|7.93|
|MediumRegisteredPension Funds|6.87|9.23|7.37|7.81|5.75|7.40|
|SmallRegisteredPension Funds|9.04|8.72|8.58|8.94|7.69|8.59|
|AllRegisteredPensionFunds|8.66|9.09|7.24|9.61|4.71|7.85|
|NationalPensionsFund|10.00|14.26|10.57|10.56|11.16|11.30|
|Insured/AdministeredPensionFunds|10.88|13.38|10.94|10.21|9.49|10.97|

* The average investment return is the compounded average for the five-year period. All reported rates are weighted averages.

Source: Estimated on the basis of date collected by the FSC, NPF and Registrar of Associations.

12
Social Development Department Environmentally and Socially Sustainable Development Network The World Bank

# Child Labor in C8te d'Ivoire: Incidence and Determinants

## Christiaan Grootaert*

### * Senior Economist, Social Development Department, The World Bank.

The author would like to thank the National Institute of Statistics of C8te d'Ivoire for making the data set available for this study. Thanks also are due to Gi-Taik Oh, who managed the data base and programmed all the computer calculations, to Kimberly Cartwright and Harry Patrinos-colleagues in the comparative study "Child Labor: What Role for Demand-side Interventions" - (RPO 680-64) for many helpful discussions on the topic of child labor, and to Ravi Kanbur, Roger Key, Felicia Knaul and John Mcintire who provided valuable comments on an earlier draft of this paper.
**TABLE 1: National Regulatory Agencies in the ERGI and the four main indexes**

||ER|GI|AUTON|OMY|TRANS|PARENCY|ACCOUN|TABILITY|TOO|LS|
|---|---|---|---|---|---|---|---|---|---|---|
||Position|Score|Position|Score|Position|Score|Position|Score|Position|Score|
|Argentina|7|0.80|6|0.85|8|0.71|10|0.71|8|0.83|
|Barbados|8|0.76|10|0.82|8|0.71|4|0.83|10|0.59|
|Bolivia|3|0.84|**1**|**0.912**|5|0.80|3|0.84|4|0.78|
|Brazil|2|0.85|5|0.87|6|0.79|2|0.87|2|0.90|
|Chile|18|**0.56**|**19**|**0.57**|12|0.63|**16**|**0.50**|12|0.52|
|Colombia|9|0.75|18|0.67|5|0.8|6|0.79|7|0.71|
|Costa Rica|12|0.74|8|0.84|7|0.74|10|0.71|10|0.59|
|D. Republic|10|0.75|3|0.90|8|0.71|8|0.74|12|0.51|
|Ecuador|17|0.60|17|0.70|15|0.57|12|0.65|13|0.40|
|El Salvador|5|0.82|7|0.84|2|0.86|5|0.81|7|0.71|
|Guatemala|6|0.79|12|0.80|13|0.62|2|0.87|**1**|**0.93**|
|Honduras|**19**|**0.56**|16|0.70|**16**|**0.53**|14|0.54|**14**|**0.37**|
|Jamaica|15|0.72|14|0.78|10|0.68|13|0.62|3|0.86|
|Mexico|14|0.72|15|0.75|4|0.83|7|0.75|**14**|**0.37**|
|Nicaragua|11|0.74|2|0.91|11|0.66|9|0.72|9|0.63|
|Panama|16|0.63|11|0.81|14|0.59|15|0.52|11|0.54|
|Peru|4|0.83|4|0.90|3|0.85|7|0.75|3|0.86|
|Trinidad & T|**1**|**0.88**|9|0.82|**1**|**0.92**|**1**|**0.97**|5|0.76|
|Uruguay|13|0.72|13|0.80|9|0.69|11|0.67|6|0.72|

_Source:_ LAC Electricity Regulatory Governance Database. The World Bank. 2007. Extracted from Andres _et al._ (2007b).

# **4. EMPIRICAL APPROACH**

Ideally, to evaluate the impact of private sector participation and the characteristics of the regulation on the performance of a utility, privatized utilities should be compared to the performance of its “counterfactual”, a comparable firm in a similar environment that is still operated by the government and is not regulated. In most cases, it is hard to identify a comparable firm; hence, most of the literature compares the evolution of selected indicators before and after the change in ownership and regulation.

The majority of the literature focuses mainly on the impact of change in ownership. For these evaluations, two methodologies have been identified. The first methodology began with Megginson _et al._ (1994); and several subsequent studies have used the same approach to measure means and medians of the periods before and after change and to test the significance of the change between the periods. This methodology has also been applied in studies that considered different samples of state owned enterprises (SOEs) among countries and compared their performance to privatized firms **.** The second methodology is found in another branch of literature that assumes privatization policies to be an intervention. Following the literature of program evaluation (see Heckman _et al._ , 1985), this approach proposes a dummy for those periods where the SOE was privately owned, and checks the significance of this dummy, as well as the significance of various interactions specific to each paper (for example, Boardman and Vining,1989 and Ros, 1999).

- 10 -
from government and international NGOs is significantly largest (i.e., >85%) in the TillabEri region; not so for the other regions.<sup>14</sup> Next, figure 6 explores the distribution of social assistance across different mix of wealth brackets, regions, and poverty classes. All illustrations in this figure are entirely correlational; but a few hypothetical features are evident. In particular, figure 6a shows the probability of receiving social assistance across different regions; 6b does the same but allows this probability to vary further across quartiles of _per capita_ consumption's distribution. The figures show significant inequalities of receiving assistance. TillabEri households are significantly more likely to receive social support; and households within the second quartile of the distribution of consumption expenditures are by far more probable to receive social assistance. In addition, households in the upper quartiles of income or expenditure distribution are more likely to receive social support in the regions of Diffa, Dosso and Zinder. This is surprising and inconsistent with the usual targeting standards of formal social programs. To investigate this further, we present in 6c and 6d estimates for the probability of receiving social assistance conditional on the poverty class of households. To draw linkages of these with observed poverty trends, the results are presented for different regions. Two working definitions of poverty are employed, throughout the document: either food-poor or absolute-poor. Guided by the Nigerien National Poverty lines in 2011, a household is classified food-poor if the _per capita_ food consumption expenditure is strictly less than 123,410 CFA. The household is however classified absolute-poor if the _per capita_ food consumption expenditure is less than 189,233 CFA. In some case, poor households are less probable to receive social transfers. The results in 6c and 6d corroborate the earlier findings.

Examining trends in poverty, we find that the Dosso, Maradi, and TillabEri regions have identical share of absolute-poor households using the 2011 National Absolute Poverty Line of 189,233 CFA. The share of food-poor households is similar, but on average households in Dosso

> 14 To ensure that the results in 5b are not driven by differences in the number of representative households, we rather examined _per capita_ total assistance across the regions. The latter re-affirms the previous evidence: the TillabEri region accounts for over 85% of total assistance; this is followed by the Tahoua region which accounts for less than 5% —not shown. Additional diagnostics on the reported transfers reveal limited evidence of outliers —this helps to rule out potential issues of outliers and data mis-reporting.

26
healthcare. In addition, over-indebtedness has material costs, such as late fees and, in default cases, seizure of assets. Moreover, the resulting loss of creditworthiness can limit access to credit and cause liquidity problems. These consequences may occur despite the fact that microfinance is meant to alleviate poverty through income generation activities. This suggests that there may be a thin line between the debt trap and poverty alleviation as far as microfinance effects are concerned.

Our long-term panel data analysis confirms that continuous microcredit participation or borrowing from multiple sources generally adds more to assets than to liabilities over time. This perhaps shows that microloans of various types as developed by MFIs in Bangladesh support both investment and consumption, including creating a liquidity buffer for emergency needs in situations of health problems or other unanticipated expenses. In fact, barriers to access to financial services by the poor may cause over-indebtedness, since consumers’ lack of access to credit can force many of them to borrow from high-cost informal sources which increase their risk of falling into over-indebtedness. Thus, as we also find, borrowing from multiple sources is not necessarily a pathway to being over-indebted. However, in order to achieve the desired effects, it is important for both lenders and borrowers to invest in sectors where returns are sufficient.

While long-term borrowing from MFIs, coupled with productive investments, helps households avert the debt trap, there are certainly many borrowers who are not so lucky.<sup>18</sup> Risk and vulnerability are everyday realities for the poor, including microcredit borrowers. Over time, they need to develop strategies for managing both expected and unexpected risks by

> 18 This paper, however, did not analyze who these borrowers are and why they are not as fortunate as others, because of lack of sufficient information about their enterprises and other characteristics. This is why a separate in-depth study is required on this process of indebtedness.

28
# Too Much Energy The Perverse of Low Fuel Prices on Firms<sup>∗</sup>

Massimiliano Cal`ı<sup>†</sup> , Nicola Cantore<sup>‡</sup> , Leonardo Iacovone<sup>§</sup> , Mariana Pereira-L ́opez,<sup>¶</sup> Giorgio Presidente<sup>‖</sup>

**Keywords:** Fuel subsidy, energy prices, productivity, technology adoption, Indonesia, Mexico. **JEL codes :** O13, Q4, Q55

> ∗We thank Antoine Coste, Aufa Doarest, Ana Fernandes, Frederico Gil-Sander, Bill Maloney, Denis Medvedev, Vikram Nehru, Jun Rentschler and seminar participants at the GGKP Annual Conference and the World Bank Indonesia seminar series for useful comments, Taufik Hidayat, Muhammad Hazmi Ash Shidqi and Nabil Rizky Ryandiansyah for excellent research assistance and Julio Valle for his advisory on Mexico’s Energy Sector. All remaining errors are our own.

> †World Bank, email: mcali@worldbank.org

> ‡UNIDO, email: n.cantore@unido.org

> §World Bank, email: liacovone@worldbank.org

> ¶World Bank & Universidad Iberoamericana, email: mpereiralopez@worldbank.org

> ‖World Bank, email: giopresidente@gmail.com (Corresponding author)
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_Public and professional scrutiny is essential for the credibility of governance indicators_ . Virtually all of the governance indicators listed in Table 1 are publiclyavailable, either commercially or at no cost to users. This transparent feature is central to their credibility as tools to monitor governance. Open availability permits broad scrutiny and public debate about the content and methodology of indicators and their implications for individual countries. Many indicators are also produced by nongovernment actors, making it more likely that they are immune from either the perception or the reality of self-interested manipulation on the part of governments being assessed. Scholarly peer review can also help to strengthen the quality and credibility of governance indicators. For example, a number papers describing the methodology of the Doing Business indicators, the Database of Political Institutions, and the Worldwide Governance Indicators have all appeared in peer-reviewed professional journals. Transparency with respect to details of methodology and its limitation is also essential for credible use of governance indicators. It is important that users of governance indicators understand fully the characteristics of the indicators they are using, including any methodological changes over time as well as time lags between the collection of data and publication.

It is thus of concern that some proposed and existing indicators of governance are as yet insufficiently open to public scrutiny. While the recent disclosure of the World Bank's CPIA ratings for low-income countries is a very useful step, these indicators are now being disclosed for only about half of the roughly 130 countries for which they are prepared each year, and not at all for the historical data prior to 2005. Similarly, historical data on the CPIA assessments of the African Development Bank and Asian Development Bank have not been disclosed publicly. This is unfortunate given that the decision to only selectively disclose recent CPIA data, and not to disclose historical CPIA data, is made by the executive boards of these organizations and so reflects the desire of the very governments these ratings are supposed to assess. Regarding transparency, it is also of concern that although the Public Expenditure and Financial Accountability initiative, which has been ongoing for seven years, has so far resulted in indicators and reports being constructed for just 42 countries as of March 2007, for only one period per country, and only nine of these are publicly available. Moreover, since these reports are prepared in collaboration with the governments in question, their credibility would not be the same as those associated with purely third-party indicators.

32
- Lipsey, M.W., D.C. Farran, and K. Durkin. 2018. "Effects of the Tennessee Prekindergarten Program on children’s achievement and behavior through third grade." _Early Childhood Research Quarterly_ 45: 155-176.

- Martinez, S., S. Naudeau, and V. Pereira. 2017. _Preschool and child development under extreme poverty: evidence from a randomized experiment in rural Mozambique._ Washington D.C.: The World Bank.

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- Ozler, B., L. C. H. Fernald, P. Kariger, C. McConnell, M. Neuman, and E. Fraga. 2018. "Combining preschool teacher training with parenting education: A clusterrandomized controlled trial." _Journal of Development Economics_ (The World Bank.) 133: 448–467.

- Phillips, D., M. Lipsey, K. Dodge, R Haskins, D. Bassok, M. Burchinal, G. Duncan, M. Dynarski, K. Magnuson, and Ch. Weiland. 2017. _The Current State of Scientific Knowledge on PreKindergarten Effects._ Washington D.C.: The Brookings Institute.

- Raver, C.C., S.M. Jones, C. Li-Grining, F. Zhai, M.W. Metzger, and B. Solomon. 2009. "Targeting children's behavior problems in preschool classrooms: A cluster-randomized controlled trial." _Journal of Consulting and Clinical Psychology_ 77 (2): 302.

- Rossin-Slater, M., and M. Wüst. 2020. "What is the Added Value of Preschool for Poor Children? LongTerm and Intergenerational Impacts and Interactions with an Infant Health Intervention." _American Economic Journal: Applied Economics_ 12 (3): 255-86.

- Schady, N., J. Behrman, M.C. Araujo, R. Azuero, R. Bernal, D. Bravo, F. Lopez-Boo, et al. 2014. _Wealth gradients in early childhood cognitive development in five Latin American countries._ Washington D.C.: The World Bank.

- Schultz, T.P. 2010. "Population and Health Policies." In _Handbook of Development Economics 5_ , edited by Mark Rosenzweig and Dani Rodrik, 4785-4881.