rafmacalaba/datause-encoder-data huggingface dataset:
and total CERs to be generated by the end of the first commitment period by each major project type and methodology.

As can be seen from table 1, about 62 percent of the projects in the CDM pipeline are renewable resource based power generating projects accounting for 43.65 percent and 37.67 percent of the annual and total abatement during the first commitment period, respectively. Methane avoidance, coal bed/mine and cement is the second largest category in terms of number (17.04 percent) and annual abatement (18.32 percent), but HFCs, PFCs, and N2O reduction is the second largest category in terms of 2012 abatement (22.61 percent). Transport is the smallest and forestation is the second smallest category in terms of both number of projects and abatement. Large scale methodologies (AM) are applied to 6.87 percent of the projects, which account for 27.11 percent of the total annual abatement by the projects in the pipeline. Large scale consolidated (ACM) and small scale (AMS) methodologies are applied to 45.97 and 46.18 percent of the projects that account for 62.16 and 10.09 percent of annual abatement, respectively.

Following UNEP Risoe Center, the expected issuance of CERs for each individual project in each year over the life of the project is calculated by adjusting the annual average emissions abatement with an increment or decrement as reported in the database.<sup>10</sup> The crediting period is either 20 or 30 years for the afforestation and reforestation projects and either 7 or 10 years for all other types of projects. We consider the number of credit years as the duration of the project. In addition to CERs, some projects generate additional electricity output (in addition to the capacity of the baseline). The pipeline database reports the additional electricity generation capacity and expected hours of operation for individual projects. Using this data, the expected electricity output measured in megawatt hours (MWh) are calculated for each year of the project, as well as discounted-weighted lifetime totals.

Individual CDM projects widely vary across types in terms of expected annual average CERs and electricity output. The smallest project in the CDM pipeline is expected to generate only 400 CERs per

> 10 The CDM pipeline database reports the annual increment or decrement in a variable namely ‘slope.’ The expected CER issuance over the life of a project is approximated with a straight line that goes through the annual average value in the mid-year. For a positive (negative) value of ‘slope,’ expected CERs increases (decreases) each year by the ‘slope’ amount. There is no change in expected CERs over time when ‘slope’ is zero.

11
**Caregiver handwashing practices:** These were collected based on self-reported information and structured observations. Given the potential courtesy bias associated with self-reported measures, we consider self-reports as reflecting knowledge rather than practice. For structured observations we measure whether the caregiver uses water and soap to wash her own or her child’s hands in conjunction with the following exposure events:

- **After fecal contact:** (i) after defecating; (ii) after toileting; (iii) after cleaning child posttoileting

- **Before handling food:** (i) before cutting or preparing food; (ii) before eating; (iii) before serving food; (iv) before breastfeeding.

**Diarrhea** : From the household data we use a caregiver-reported symptom-based measure defining 7 day diarrhea prevalence as having 3 or more loose/watery stools in a 24 hour period or having a stool with blood or mucus (Baqui _et al._ , 1991). To ensure consistency with the 2010 DHS, in the listing survey we ask caregivers of 34,045 children (under 5 years) a question on whether children have had diarrhea in the past 14 days.

**Anemia** : Anemia status (iron deficiency) was collected using a HemoCue<sup>TM</sup> Hb 201+ photometer, which measures blood hemoglobin levels in real time. These biomarkers were collected for all children between 6 months and 5 years old in the household. In line with WHO guidelines, we consider children to be anemic if their hemoglobin concentration is below 110 g/L, (WHO, 2011).

**Anthropometry** : Child height, weight and head circumference were collected by specially trained enumerators with a nursing background, who followed WHO anthropometric data collection protocols to assess malnutrition levels. Results are transformed into height-for-age, weight-for-age and head circumference-for-age standardized z-scores based on WHO international growth standards.

# **5. Econometric Specification**

We estimate the intention-to-treat (ITT) estimator as the difference between average outcomes across treatment and control groups. The basic specification is: Where, _Yij_ is the outcome of interest for household or individual _i_ in ward _j_ , _Tjk_ is a dummy variable equal to 1 in wards assigned to receive treatment _k_ where _k_ = {1,2,3} for HWWS, TSSM and the combined intervention respectively. _βk_ is the estimate of the average effect of treatment _k_ . _S_ **_jl_** is a dummy variable equal to one if Ward _j_ is included in block _l,_ representing the 17
survey runs only every second year. If mistargeted transfers are treated as more socially costly than one cent in the dollar, the benefit-cost ratio for spending money to get the needed spatial price data is even higher. If we use results from 2012, when the Engel curve does not overstate the poverty gap so much, the difference in the total value of the poverty gap is US$1.3b, and it still greatly exceeds the cost of the survey even at a one percent social loss rate.

While these are little more than back of the envelope calculations, they have some basis in the history of the SCOLI surveys in Vietnam. A growing concern about unreliable poverty results due to spatial deflators being formed from inappropriate temporal price indexes caused the World Bank and the GSO to invest in a new program of surveys. This program of work was of such use that Vietnam self-funded the 2012 survey since it also helped answer other policy questions, such as setting cost-of-living adjustments for public sector wages in major cities. Moreover, simple as they are, these calculations give an order of magnitude to the question of how costly it could be for a country if a “no-price” analysis was treated seriously by a government that had the wherewithal to undertake large scale transfer programs.

Even if a government did not design transfers based on deflators coming from a food Engel curve, there is a hidden cost when researchers develop and use “no-price” methods. In our opinion, a researcher is implicitly saying “we don’t need price data” when they use “no-price” methods like the food Engel curve method of deflation. This reduces the demand that is placed on the statistics agency to provide higher quality and more extensive price data and price indexes. Governments respond to pressure from constituents, including economists and other researchers. If more demands were made for the right sort of price data, and if the cost of not having such data was shown, better outcomes may result than those that come from using “no-price” methods.

33
- 10 -

Our data are broadly supportive of the theoretical proposition. Figure 3 shows that the probability distributions of entrant bids only. While the distributions conditional on the existence of incumbent bidder(s) in an auction are shown in the upper charts, those conditional on the absence of incumbent(s) are shown in the lower charts. Except for the water sector, it seems that fringe bidders respond with aggressive bids when they compete against incumbent bidders in that auction. Especially in electricity procurement auctions, the probability distribution is concentrated on the left hand side, if one or more incumbents are present.

<!-- Start of picture text --> Figure 3. Probability Distribution of Normalized Fringe Bids<br>(Roads; faced with incumbents) (Water; faced with incumbents) (Electricity; faced with incumbents)<br>0 .5 1 1.5 2 2.5 3 0 .5 1 1.5 2 2.5 3 0 .5 1 1.5 2 2.5 3<br>Bid / Engineering cost estiamte Bid / Engineering cost estiamte Bid / Engineering cost estiamte<br>(Roads; without incumbents) (Water; without incumbents) (Electricity; without incumbents)<br>0 .5 1 1.5 2 2.5 3 0 .5 1 1.5 2 2.5 3 0 .5 1 1.5 2 2.5 3<br>Bid / Engineering cost estiamte Bid / Engineering cost estiamte Bid / Engineering cost estiamte<br>Source: Author’s calculation.<br>.3 .3 .3<br>.25 .25 .25<br>.2 .2 .2<br>.15 .15 .15<br>Fraction Fraction Fraction<br>.1 .1 .1<br>.05 .05 .05<br>0 0 0<br>.3 .3 .3<br>.25 .25 .25<br>.2 .2 .2<br>.15 .15 .15<br>Fraction Fraction Fraction<br>.1 .1 .1<br>.05 .05 .05<br>0 0 0<br><!-- End of picture text --> Figure 4 shows the cumulative probability distribution functions of entrant bids. The electricity sector exhibits the stochastic dominance of entrant bids in the presence of incumbent firms, as expected by Maskin and Riley (2002). In road procurements, entrant bids with incumbent rival’s presence may partly dominate those without incumbents, but it cannot be applied to the entire range of observed bids. The water sector looks inconsistent with theory; however, these comparisons are informal; there are many uncontrolled factors, some of which are accounted for in the following sections.
**<u>Table 4: Predictions within one-third of a notch</u>**

|**Country**|**S&P 2012**|**Predicted 2012**|**_S&P June 2013_**|
|---|---|---|---|
|India|BBB- o-|BBB- os||
|Colombia|BBB- o+|BBB o-|_BBB os_|
|Zambia|B+ os|B+ os||
|Lithuania|BBB os|BBB os||
|Belarus|B- os|B- os|_B-o+_|
|Georgia|BB- os|BB- os||
|Nigeria|BB- os|BB- os||
|Mexico|BBB os|BBB o-|_BBBo+_|
|Vietnam|BB- os|BB- o-|_BB- os_|
|Serbia|BB- o-|B+ o+|_BB-o-_|
|Cape Verde|B+ os|B+ o-|_B+ os_|
|Botswana|A- os|A- o-|_A- os_|

Notes: “os” stands for stable outlook, “o+” for positive outlook and “o-” for negative outlook for actual ratings. Nomenclature for predicted ratings are deliberately kept different and unabbreviated.

The distribution of divergences of the predicted ratings for 2012 from the actuals is displayed in Figure 5. This shows a strong central tendency and a longer tail for negative divergences related to cases where the predicted rating is better than the actual rating arising largely due to the persistence of the 2008 shock for peripheral European economies. In certain cases the predicted rating is within one-third of a notch (that is close to the precise outlook state) (see Table 4).

**Table 5: Prediction higher than actual rating**<sup>**6**</sup>

|**Country**|**S&P 2012**|**Predicted 2012**|**_S&P June 2013_**|
|---|---|---|---|
|Turkey|BB os|BBB+positive|_BB+os_|
|Portugal|BB o-|BBBpositive|_BB os_|
|Hungary|BB os|BBBpositive|_BBo-_|
|Costa Rica|BB os|BBB os|_BB os_|
|Greece|B- os|BB- negative|_B-os_|
|Iceland|BBB- os|BBB+positive|_BBB-os_|
|Saudi Arabia|AA- os|AA+positive|_AA- o+_|
|Ghana|B os|BB-positive|_Bos_|
|Uruguay|BBB- os|BBB+ os|_BBB- os_|
|Dominican Rep|B+ os|BB os|_B+os_|
|Ecuador|B os|BB- os|_B os_|
|Bahrain|BBB o-|BBB+positive|_BBB os_|

Notes: “os” stands for stable outlook, “o+” for positive outlook and “o-” for negative outlook for actual ratings. Nomenclature for predicted ratings are deliberately kept different and unabbreviated.

In some cases the predicted rating is higher than the actual rating (Table 5). Some of these cases pertain to unanticipated political shocks while others relate to the continuation of the European crisis. In the latter case, it is possible that rating agencies are lagging in restoring ratings, as has been discussed in the past in the literature (Ferri, Liu and Stiglitz

> 6 Shadow ratings for some countries could not be calculated due to unavailability of data on ‘general government gross debt’. Instead, if ‘government net lending/borrowing’ (from IMF World Economic Outlook) is used in the regressions, the resulting shadow ratings are: Bangladesh B positive for 2012, lower than the actual rating of BB- stable; Republic of Korea AA positive for 2012, higher than the actual rating of A+ stable; Sri Lanka BB- negative for 2012, higher than the actual rating of B+ stable; and Samoa BB stable shadow rating for 2012.

14
# **References**

Barro, R. and J.W. Lee **(** 2013). "A New Data Set of Educational Attainment in the World, 1950-

- 2010." _Journal of Development Economics_ 104: 184-198.

- Barro, R. (2013). "Education and Economic Growth." _Annals of Economics and Finance_ 14: 301328.

- Bazzi, S. and M. Clemens (2013). “Blunt Instruments: Avoiding Common Pitfalls in Identifying the Causes of Economic Growth”. _American Economic Journal: Macroeconomics_ 5:152‐186.

- Blanchard, O. and R. Perotti (2002). "An Empirical Characterization of the Dynamic Effects of Changes in Government Spending and Taxes on Output." _Quarterly Journal of Economics_ 117: 1329-1368.

- Bound,  J., D. Jaeger, and R. Baker (1995). "Problems with Instrumental Variables Estimation When the Correlation between the Instruments and the Endogenous Explanatory Variables is Weak." _Journal of the American Statistical Association_ 90: 443-50.

- Brueckner, M. (2013). “On the Simultaneity Problem in the Aid and Growth Debate”. _Journal of Applied Econometrics_ 28: 102-125.

- Brueckner, M. and D. Lederman (2015). “Effects of Income Inequality on Aggregate Output.” World Bank Working Paper 7317.

- Brueckner, M., K. Gerling, and H. Gruener (2010). "Wealth Inequality and Credit Markets: Empirical Evidence from Three Industrialized Countries." _Journal of Economic Growth_ 15: 155-176.

- Brueckner, M., E. Dabla Norris, M. Gradstein (2015). "National Income and Its Distribution." _Journal of Economic Growth_ 20: 149-175.

- Castelló‐Climent, A. (2010).  “Inequality and Growth in Advanced Economies:  An Empirical Investigation”. _Journal of Economic Inequality_ 8:293‐321

- Dabla‐Norris, Era, Kalpana Kochhar, Frantisek Ricka, Nujin Suphaphiphat, and Evridiki Tsounta

23
- Csereklyei, Z., and D. Stern. 2015. “Global Energy Use: Decoupling or Convergence?” _Energy Economics_ 51(September): 633‐641.

- Cuddington, J., and D. Jerrett. 2008. “Super Cycles in Real Metal Prices?” _IMF Staff Papers_ 55 (4): 541‐565.

- Dahl, C. 2012. ʺMeasuring Global Gasoline and Diesel Price and Income Elasticities.ʺ _En‐ ergy Policy_ 41 (February): 2‐13.

- Dahl, C., and C. Roman. 2004. _Energy Demand Elasticities—Fact or Fiction: A Survey Update_ . Golden, CO, Colorado School of Mines, Division of Economics and Business.

- Dale S. and B. Fattouh. 2018. “Peak Oil Demand and Long Run Oil Prices.” _Energy Insight_ 25. The Oxford Institute for Energy Studies.

- Deaton, A., and J. Muellbauer. 1980. _Economics and Consumer Behavior._ Cambridge Univer‐ sity Press, Cambridge.

- Djukanovic, G. 2016. “Copper vs. Aluminum—Substitution Slows but Continues.” _Alumi‐ num Insider_ , https://aluminiuminsider.com/copper‐vs‐aluminium‐substitution‐slows‐ <u>but‐continues/</u> accessed January 9, 2020.

- Drollas, L.P. 1984. “The demand for Gasoline: Further Evidence.” _Energy Economics_ 6(1): 71‐82.

- Eggert, R.G. 1990. “The Passenger Car Industry.” In _World Metal Demand: Trends and Pro‐ spects_ , ed. J. E. Tilton. Resources for the Future Press, New York.

-

- IEA (International Energy Agency). 1979. _<u>Principles for IEA Action on Coal: Decision on Pro</u> ʹ_

- _<u>cedures for Review of IEA Countries Coal Policies</u>_ <u>. Press Release, Paris, France.</u>

- <u>.</u>

- EIA, U.S. Energy Information Administration. 2016. _<u>Carbon Dioxide Emissions Coefficients</u>_ Washington, DC, Energy Information Administration.

- Engel, E. 1857 “Die productions‐ und consumtionsverhaltnisse des königreichs sachsen”, Zeitschrift des Statistischen Bureaus des Königlich‐Sächsischen Ministerium des In‐ neren, 8/9, 1–54 [“The production and consumption relations of the Kingdom of Sax‐ ony”, _Journal of the Statistical Office of the Royal Saxon Ministry of the Interior_ ].

- Erdogdu, E. 2010. “Natural Gas Demand in Turkey.” _Applied Energy_ 87(1): 211‐219.

- Erten, B., and J.A. Ocampo. 2013. ʺSuper Cycles of Commodity Prices Since the Mid‐Nine‐ teenth Century.ʺ _World Development_ 44(C): 14‐30.

- Evans M. and A.C. Lewis. 2005. “Dynamics Metal Demand Model.” _Resources Policy_ 30: 55‐69.

- Fernandez, V. 2018a. “Price and Income Elasticity of Demand for Mineral Commodities.” _Resources Policy_ 59: 160–183.

- Fernandez, V. 2018b. “Mineral Commodity Consumption and Intensity of Use Re‐as‐ sessed.” _International Review of Financial Analysis_ 59 (10): 10‐16.

- Focacci, A. 2005. “Empirical Relationship between Total Consumption‐GDP Ratio and Per Capital Income for Different Metals of a Series of Industrial Nations.” _International_

_— 26 —_
**Table 9: Double difference regression of land investment effects of NREGS** **<u>participation</u>**

||**Entire**|**Sample**|**Scheduled**<br>**Tri**|**Castes and**<br>**bes**|**Othe**|**r Castes**|
|---|---|---|---|---|---|---|
||**2006/08**|**2004/06**|**2006/08**|**2004/06**|**2006/08**|**2004/06**|
||0.222***|-0.00754|0.226***|-0.00469|0.220***|-0.0128|
|If household participated in NREGS|(-8.87)|(-0.42)|(-6.21)|(-0.18)|(-6.92)|(-0.53)|
||-0.00595|-0.00763|0.00458|-0.0402|-0.0116|0.0164|
|If household located in hamlet|(-0.20)|(-0.33)|(-0.12)|(-1.20)|(-0.32)|(-0.54)|
|Being very poor|0.0341|-0.0579**|0.0525|-0.0780*|0.0249|-0.0554*|
||(-1.37)|(-2.42)|(-1.09)|(-1.75)|(-0.79)|(-1.83)|
|Being poor|0.0288|-0.0386*|0.0383|-0.0481|0.0274|-0.0397|
||(-1.25)|(-1.84)|(-0.79)|(-1.12)|(-0.97)|(-1.58)|
|Scheduled tribe|<br>0.0393|<br>0.0102|<br>0.0957**|<br>-0.0311|||
||(-1.47)|(-0.47)|(-2.07)|(-1.00)|||
|Scheduled caste|-0.0544|0.0206|||||
||(-1.15)|-0.57|||||
|Nonbackward caste|<br>-0.0718**|-0.0408|||-0.0619*|-0.0391|
||(-2.30)|(-1.56)|||(-1.95)|(-1.48)|
|If any member can write|<br>-0.0552**|<br>0.0133|-0.0343|0.0107|<br>-0.0734**|<br>0.0164|
||(-2.12)|(-0.67)|(-0.85)|(-0.39)|(-2.24)|(-0.60)|
|Female headed|-0.0216|0.0871***|-0.0472|0.0838**|0.000008|0.0959***|
||(-0.70)|(-3.71)|(-0.98)|(-2.15)|(0.00)|(-3.36)|
|Number of female adults|0.0195|-0.0214|0.00101|-0.0169|0.0279|-0.0214|
||(-1.06)|(-1.28)|(-0.030)|(-0.69)|(-1.14)|(-1.02)|
|Number of male adults|<br>0.0155|<br>0.0141|<br>0.0401*|<br>0.0524**|<br>0.0014|<br>-0.00812|
||(-1.06)|(-1.1)|(-1.72)|(-2.48)|(-0.08)|(-0.53)|
|Household size|0.027|-0.0530***|0.00548|-0.0560**|0.0418|-0.0405*|
||-1.31|(-3.22)|(-0.17)|(-2.42)|(-1.57)|(-1.89)|
||-0.0021|0.00482***|-0.0000219|0.00500**|-0.0034|0.00353*|
|Household size squared|(-1.16)|(-3.00)|<br>(-0.01)|(-2.23)|(-1.47)|(-1.72)|
||<br>-00226***|<br>-00354***|<br>-00724**|<br>-00758***|<br>-00142*|<br>-00305***|
|Irrigated area of land owned|.<br>(-2.91)|.<br>(-4.50)|.<br>(-2.46)|.<br>(-2.98)|.<br>(-1.75)|.<br>(-3.69)|
||<br>-0.0197***|<br>-0.025***|<br>-0.00622|<br>-0.0371**|<br>-0.0202**|<br>-0.0235**|
|Nonirrigated area of land owned|(-2.77)|(-3.07)|(-0.39)|(-2.16)|(-2.55)|(-2.51)|
||0.00020**|0.00090***|0.000151|0.0032***|0.000061|0.00079***|
|Square term of total land owned|<br>(-2.23)|<br>(-3.33)|<br>(-1.00)|<br>(-3.41)|<br>(-0.74)|<br>(-2.71)|
|District dummies and constant term|Yes|Yes|Yes|Yes|Yes|Yes|
|Number of observations|3,390|3,547|1,291|1,338|2,099|2,209|

_Source:_ Authors’ computation from household survey and administrative data. _Notes_ : Robust standard errors are in parentheses. Significance level: *: 10%, **: 5%, ***: 1%.

24
_Gender Equality and Economic Development: The Role for ICTs_ 13 expenditure can increase gender equality in education, whether it does so in reality very much depends on how the additional resources are spent, which is likely to differ from country to country and from year to year. As such, the resultant effects of public education expenditure on gender equality a priori are at best indeterminate.

# **5.2 Determinants of Gender Inequality in Employment**

## <u>Economic Development</u>

Economic development may influence gender inequality in the labor market through the effects of the wages. As mentioned above, wages tend to increase with economic development and that this increases the opportunity cost of leisure or not working. As the opportunity cost increases, more people who were formerly not working will be induce to participate in the labor force. In a society with gender inequality in the labor market, the majority of the persons not working would be women, and hence it is likely that majority of the new entrants into the labor market would be women. Consequently, as the economy development, it is expected the gender equality in terms of labor force participation rates would increase.

## <u>Level of General Education</u>

As above in gender inequality in education.

## <u>Gender Equality in Education</u>

Gender equality in education is postulated to have positive effects on gender equality in employment. Given that education tends to increase one’s human capital and thus productivity, it is intuitive that the more women are educated relative to men, the more women will be working relative to men. In addition, given the likely high positive correlation between gender equality in education and gender equality in employment, controlling for education gender equality in the employment gender equality regression ensures that the estimated coefficients of the independent variables are not simply reflecting effects of the independent variables on education gender equality.

## <u>Urbanization Rate</u>

People residing in rural areas are generally more conservative and therefore have more rigid views with regard to traditional gender roles dictated by customs and norms. As such, increased urbanization rates may lead to higher levels of gender equality.

## <u>Unemployment Rate</u>

In some countries, women tend to serve as secondary breadwinners of the family. In such cases, labor force participation rates of women may actually increase in period of high unemployment if the primary breadwinner is unemployed. Also, firms may prefer to hire female workers during economic recessions, as they are more likely to accept lower wages as compared to male workers. However, many women, especially in