rafmacalaba/datause-encoder-data huggingface dataset:
The sectoral distribution of working children by area of residence (given in table2) suggests that during the months of May-December the proportion of children employed in the agriculture sector in rural areas increased and there was a decrease in employment in all other sectors consistent with figure 2. In urban areas there was an increase in the proportion of children working in the personal services sector and shops, hotels and transport sectors. In both areas there is a decrease in the proportion of children in the manufacturing sector, however, the magnitude of the fall is smaller in urban areas. This pattern seems to be reflective of the fact that children’s work in the agriculture sector as well as the domestic services sector is not prohibited by the EoCA 1991 (detailed tables showing the employment rates of children in different occupations in the year 1991 are given in Appendix B).

**Table 2: Sectoral distribution of working children by region (age 10-14)**

<mark>(Percent of employed children working in each sector)</mark>

||**_Rur_**|**_al_**|**_Ur_**|**_ban_**|**_To_**|**_tal_**|
|---|---|---|---|---|---|---|
|**_Sector_**|**_Jan-April_**|**_May-Dec._**|**_Jan-April_**|**_May-Dec._**|**_Jan-April_**|**_May-Dec._**|
|Agriculture|55.1|70.7|17.1|13.0|39.9|56.8|
|Manufacturing|12.2|6.3|26.7|23.9|18.0|10.5|
|Personal and consumer|8.3|2.6|10.5|15.8|9.2|5.8|
|services|||||||
|Construction|2.6|2.4|5.7|1.6|3.8|2.2|
|Shops/hotels and|17.3|12.9|32.4|38.6|23.4|19.1|
|transport|||||||
|Undefined|4.5|5.2|7.6|7.1|5.7|5.6|
||100|100|100|100|100|100|

# **_3.2 - Methodology_**

The objective of this paper is to test whether the EoCA 1991 has been effective in reducing the employment of children using a quasi-experimental approach. A quasi-experiment (or natural experiment) is caused by an external exogenous effect, which changes the environment in which individuals operate (Stock _et al_ 2003; Meyer, 1995). Such natural experiments may be induced by introduction of, or changes in legislation, or a policy change. Thus a particular policy change may give rise to treatment and control groups and the variation 11
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|Servicescategory|1995-2000|2008-2013|
|---|---|---|
|Financial|14.1%|24.7%|
|Information Technology|2.1%|1.5%|
|Otherservices|9.0%|13.6%|
|Personal, cultural and recreation|0.8%|1.1%|
|Businessand Professional|7.5%|14.2%|
|Repair and Maintenance|0.9%|0.9%|
|Retail and wholesaletrade|63.4%|41.5%|
|Transport|1.2%|1.4%|
improved in line with the improvements in FY14 outcome ratings for the region, and reached the Bank average of 14 percent as of January 2016 (Figure 3).<sup>3</sup>

**Figure 3. Candor Gap by Region (IBRD and IDA)**

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# Shortcomings in Existing Performance Monitoring

While the candor gap is useful for identifying the scale of the performance problem and assessing the level of over-optimism among task teams, it does not help to identify which projects may need attention during implementation to reduce the likelihood of a less than satisfactory rating upon closing. For that purpose, the Bank uses two other tools: disbursement tracking and ratings for implementation progress (IP) and development outcome (DO) in ISR reports that are prepared about every six months during the life of the project. Both methods have limitations.

## Disbursement tracking underestimates projects likely to be rated moderately unsatisfactory

The Bank’s disbursement monitoring systems track “disbursement ratios” for countries and regions as well as tracking slow-disbursing projects. Currently, a monthly senior management meeting identifies and reviews all IBRD and IDA projects more than 5 years old and with undisbursed balances greater than 60 percent. As of the end of 2015, EAP had only three slow-disbursing projects and only 5 percent of EAP projects were delayed by 24 months or more. This substantially underestimates the percentage projects likely to be rated moderately unsatisfactory or lower at exit, which is currently on the order of 30 percent. Moreover, regression studies carried out by others (see bibliography) have failed to demonstrate a strong negative correlation between the disbursement delays and project outcome.

> 3 If there was no lack of candor or over optimism in reporting by task team leaders (TTLs), one could argue that the underestimation and overestimation of the project risks would cancel each other out, leading to a non-biased estimate. However, this is not the case as easily demonstrated by the very low amount of negative disconnects when looking at the difference between the IEG ratings and the final ISR rating, i.e., when IEG rates a project moderately satisfactory or higher that had been rated moderately unsatisfactory or lower in the last ISR.

3
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9

# Table 2 **Mlnlmum** Crude Price for Economic Viability of Anhydrous Ethanol Distilleries In Brazil in Constant 1984 USS/barrel

||Sa6 Paulo|Northeast|
|---|---|---|
|Build New Distillery|23-28|24-34|
|Operate Existing Distillery|17-23|20-28|

28. The CIF price of crude in November 1985 in Santos, Brazil was US$ 29.40/bbl. The CIF Santos price had fallen below US$ 15/bbl by August, 1986 and was nearly US$ 17/bbl in February, 1988. Hence, since mid 1986, the ethanol program in Brazil could be only marginally justified in a strict economic sense. Because of this the Government of Brazil has announced a suspension of further expansion in fuel alcohol distilling capacity.

29. As anhydrous ethanol substitutes for imported gasoline, the economic viability of ethanol production is robustly sensitive to the cost of imported gasoline. The Swaziland proposal summarized above is for a plant designed to produce ethanol at roughly 21¢/liter, while representative production costs in Brazil range between 18e and 26e/liter.'° If the economic cost of gasoline remains above these prices, all else being equal, ethanol production makes good economic sense. Many proposed ethanol installations have been at locations far inland, where the transport component of gasoline costs are high and the export value of molasses is low or nil. Consequently, at such installations (as in Swaziland), the actual landed cost of gasoline will not have dropped as much as the drop in international FOB prices. Hence, the Mhlume proposal still appears viable using actual summer '86 gasoline import prices as delivered to Matsapha. while certain installations in Brazil are on the margin of economic justification.

# **Wind** Electricity Generation

30. To properly assess the place of wind electricity generation capacity in an electrical grid, planners must consider the uncertainty of wind power supply, daily and seasonal wind profiles and how they match load profiles, fuel savings due to use of wind turbines, as well as possible deferrals of future capital investment in conventional generation capacity. Hence, the economic value of ",ind power is determined not so much by average cost considerations as by long run marginal cost and wind time distribution characteristics.

IO/ SAR: **Brazil** Alcohol Rationalization and **Efficiency** Project, Industry Department, **World** Bank, **1985.** Quoted **costs are** average 1983 **costs** of alcohol production in constant 1984 **USS.**
Using this information, the park VR experiences for Treatment-1 (T1) and Treatment-3 (T3) were digitally enhanced with the following elements: (i) additional entry/exit to the park; (ii) street lights; (iii) tea kiosk; (iv) emergency phone; (v) group tables and benches; (vi) signals promoting respect of others; (vii) picnic area for families; (viii) removal of trash; (ix) garbage bins; (x) shady trees and tall grass; (xi) play fixtures for children; and (xii) greener grass.<sup>6</sup> To add social diversity to the park for Treatment-2 (T2) and Treatment-3 (T3), 22 actors were hired to populate the park in the VR experience. The actors differed in sex (men and women), age (children, young adults and older adults) and ethnicity (Sindhi, Pashto and Muhajir). They were shown walking around and interacting with each other. Although the actors did not speak, their physical characteristics and clothes matched the visual traits of various ethnic groups in Karachi.<sup>7</sup> These characteristics were wellnoticed by those who participated in the VR experience: 93 percent of those who were exposed to the diversity treatments (T2 or T3) reported seeing men; 92 percent reported seeing women; 73 percent reported seeing Sindhis; 72 percent reported seeing Pashtuns; and 84 percent reported seeing Muhajirs.

# **_3.2. Data_**

The data used in this study were collected through a computer-assisted survey conducted right after the participants were exposed to the VR intervention. The survey included questions about the park and the people seen in the VR experience, as well as demographic information about the participants.

In total, 2,010 respondents participated in the study by experiencing one of the four VR interventions and completing the follow-up survey. However, during data analysis, it became clear that some participants may have been too hasty to respond to the survey, whereas others took an anomalously long time in their response time. The sample was trimmed to exclude respondents whose response duration was below the 1<sup>st</sup> percentile (3.12 minutes) and above the 99<sup>th</sup> percentile (29.35 minutes) to mitigate potential biases that may emanate from the responses of these individuals (see Appendix C for summary statistics). In addition, a few observations were dropped to ensure that the working sample had

> 6 The enhancements were constrained by the available 360-degree VR technology, as well as by the park’s natural layout and the angle of the three-dimensional video recording.

> 7 For example, Pashtun actors wore a traditional hat; Sindhi actors wore a traditional scarf; and Muhajir actors wore more casual, less traditional clothes.

8
Finally, trade data base has some gaps, especially in measuring intra developing country trade. If the trade flows are with other developing, especially with low-income countries, trade statistics tend to underestimate them. For this study, we did not use individual country information other than what is available in the international trade database.

# **III. Net Food Status: Food Narrowly Defined**

Table 1 shows the distribution of the countries in terms of their income status and their net food balance for the “raw food” definition. For the years 2004/2005, there were 196 countries in the database with imports and exports of raw food items.<sup>11</sup>

**Table 1: Country Classifications by Raw Food Trade (2004/05)**

||No. ofC<br>|ountriesin RawFoo<br>|dTrade<br>|
|---|---|---|---|
|CountryGroup|Net Exporter|Net Importer|Total|
|Industrial Countries|13|20|33|
|Middle-income, all|36|69|105|
|Oil Exporters|3|17|20|
|Civil Conflict States|1|3|4|
|Small Islanders|5|25|30|
|Other Middle-income|27|24|51|
|Low-income, all|16|42|58|
|Oil Exporters|2|5|7|
|Civil Conflict States|1|7|8|
|Other Low-income|13|30|43|
|World,Total|65|131|196|

Notes: Food is defined as raw food in SITC Revision 2, excluding all cash crops, processed food and seafood (see detailed classifications in Appendix Table 1). The classification of country groups is provided in Appendix Tables 2 and 3.

Source: Based on partner data from UN COMTRADE Statistics.

As expected, most of the countries in the world are net food importers, i.e. 131 out of 196. This ratio is consistent with any other product group where the exporters tend to be more specialized than the importers. 69 middle-income countries out of 105 and 42 out of 58 low-income countries are net food importers.

In terms of percentages, 39 percent of industrial countries, 34 percent of middle-income countries, and 28 percent of low-income countries are net food exporters. If we exclude oil exporters, countries in conflict, and island states, these ratios increase to almost 51 percent

> 11 There are some islands and territories that belong to industrial countries such as US or British Virgin Islands etc. We have excluded these countries.

6
hi = hi(w1, ..., wn, Y) i = 1, ..., n (5b) which can be given an empirical specification to obtain estimates of household parameters.

Empirical work in the developed economy literature typically estimates this system on household survey data that contains information on market hours of work, wage rates and non-labor incomes at the level of the individual and on market consumption expenditures at the level of the household. Data on non-market time uses, and therefore on pure leisure and time inputs to household production, as well as on outputs from household production, are missing. The convention is to compute non-market time, the li in equation (1), as the difference between market labor supply and the total time available for each individual.<sup>16</sup> The “leisure” variables therefore represent time spent on activities for own consumption and for the benefit of other household members.

Given this empirical implementation, logical consistency requires that the model should be specified as containing a household production system of the following kind. Let yi be the aggregate amount of a household good consumed by all household members, and produced by the i’th member, according to the simple linear production function where ki is a domestic productivity parameter. The opportunity cost or price, pi, of domestic good i is then Since data on the yi are missing, they must be constructed. In effect, the standard household model does this by setting ki = 1, which implies that each household member of type i is equally productive across all households, producing one unit of good i per hour, priced at wi. In other words this is an implicit choice of the units in which yi is measured, but one which constrains productivity in household production to be identical

> consumptions.

> 16 Note that since data on total time allocated to non-market activity are not collected other than in time use surveys, there is an endogeneity problem associated with computing li from the time constraint.

11
|lnPopDen1|122|4.084|1.260|2.418|3.446|4.240|4.867|5.660|
|---|---|---|---|---|---|---|---|---|
|**City level variables**|||||||||
|Corruption obstacle|638|0.489|0.276|0.092|0.279|0.493|0.697|0.878|
|Overdraft facility|636|0.377|0.263|0.070|0.146|0.325|0.585|0.771|
|Trade credit|638|0.427|0.198|0.178|0.272|0.414|0.574|0.706|
|Inf competition|633|0.499|0.208|0.246|0.338|0.478|0.659|0.783|
|Skilled labor obstacle|642|0.408|0.223|0.125|0.224|0.388|0.567|0.712|
|Labor regulation obstacle|638|0.282|0.200|0.058|0.140|0.243|0.402|0.554|
|Tax rate obstacle|642|0.582|0.224|0.267|0.446|0.594|0.757|0.870|
|Land access obstacle|638|0.334|0.212|0.100|0.177|0.284|0.469|0.664|
|Outage|638|0.575|0.269|0.220|0.340|0.578|0.832|0.933|
|Web Intensity|642|0.440|0.239|0.114|0.241|0.448|0.618|0.766|
|Security Cost|642|0.018|0.018|0.004|0.008|0.014|0.022|0.037|
|Capacity agglomeration|648|0.268|0.146|0.077|0.161|0.260|0.354|0.456|

In order to include pollution measures in our analysis, we need access to the GPS location of each of the firms of the survey. Because this information is confidential, we only have access to a masked location for each firm, randomly assigned within a radius of 2km of the true firm location. Although not all firms in the WBES data set have GPS locations, comparing the distribution of the key variables between the whole sample and the sample of geo-referenced firms shows no significant differences (see Annex I for details). We have a sample of 56,977 georeferenced firms.

Originally, we have air pollution data for two pollutants, nitrogen dioxide (NO2) from NASA OMI satellite and PM 2.5, from NASA MODIS and MISR Aerosol Optical Depth. Both data sets are in raster format, with a resolution of 0.1x0.1 degrees, approximately 11km by 11km at the equator. Each pixel contains the annual mean of the corresponding pollutant. For nitrogen dioxide, the data span between 1997 and 2011, while for PM 2.5, the period is 1998 to 2015. Because different models may differ in how they estimate the PM 2.5 surface concentration levels, we complement NASA data with PM 2.5 raster data (with the same resolution) estimated by van Donkelaar et al. (2016). Their work uses geographically weighted regression to calibrate ground-based PM 2.5 concentration levels using satellite data and ground level data were available.

We overlay WBES masked firm locations with pollution data rasters and extract NO2 and PM 2.5 levels for each location. WBES data include a cross section of firms surveyed between 2006 9
# **4.3 Additional interpretation of results on earnings**

The lack of impact on earnings can be further interpreted in light of the relative effects on savings and investments. On average, the cash grants provide 95,000 CFA. Among cash grant recipients, 18,000 CFA is invested in start-up capital and 27,000 CFA is saved. Among recipients of cash grant with repayment, 15,000 CFA is invested in start-up capital, 12,000 CFA is saved, and 15,000 CFA is repaid. This leaves 50,000 CFA from the cash grant and 53,000 CFA from the cash grant with repayment for other investments (e.g. for business operation) or expenditures. First, the observed investments do not appear sufficient to increase profits.<sup>44</sup> We cannot rule out that part of the grants was used for consumption or other household expenditures when they were received, although we do not detect impacts on food security, education expenditures, or household durables at endline. Second, the lack of impact on earnings also seems partly explained by a substantial share of the capital injection being saved. As mentioned above, this may reflect precautionary savings motives, in particular in a post-conflict setting and in the context of an intervention that did not provide regular consumption support.

What about the contribution of the cross-cutting training? The pooled treatment induces improvements in an index of business skills and an index of business practices that are statistically significant and in the order of 0.2 standard deviations (Table A13, columns 3-8).<sup>45</sup> Most of the literature on traditional entrepreneurship training finds changes in practices that are not sufficient to increase profits (McKenzie and Woodruff (2014)), and our results are consistent with this pattern.

Heterogeneity analysis reveals limited differences in impacts between subgroups such as women, youth, native or urban populations (Tables A14 - A17). There is some evidence that youth benefit slightly more in some dimensions compared to older individuals (Table A14), but the patterns are not fully robust. Notably, there is no subgroup exhibiting a statistically significant increase in profits.

> 44We also note that the timeline at which the study assesses impacts is similar to other graduation studies that find large effects on earnings (such as Bossuroy et al. (2022)). As such, it is not likely that it is too early for investments to generate profits. Most activities for which business plans were developed are not active at follow-up, which also makes it unlikely that larger returns would arise in the future.

> 45The increase in business knowledge is larger for the cash grant interventions, and the increase in business practices is larger for the cash grant with repayment modality that involved some additional post-training follow-up. The magnitude is similar to another experiment in Cˆote d’Ivoire where microentrepreneurship training was provided to public works participants (Bertrand et al. (2021)).

24
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