rafmacalaba/datause-encoder-data huggingface dataset:
Table 2 summarizes the distribution of country-level data points for each of the six indicators in 2007. The 2007 WGI are based on a total of 11,852 country level data points (after averaging multiple questions from individual sources), of which 43 percent come from commercial business information providers. The remaining data points are fairly evenly distributed between the remaining three types of data providers.

This year, we continue the practice we started in 2006 of reporting the underlying data from virtually all of the individual data sources that go into our aggregate indicators. The sources we have made available on our website are noted in Table 1. A number of our data sources, such as Freedom House and Reporters Without Borders have always been publicly available through the publications and/or websites of their respective organizations. Several of our other sources provided by commercial risk rating agencies and commercial survey organizations have only been available for a fee. In the interests of greater transparency, these organizations have kindly agreed to allow us to report their proprietary data in the form in which it enters our governance indicators. As mentioned above and as documented in detail in Appendix A and B, we in some cases use a simple average of multiple questions from the same source as an ingredient in our governance indicators. We do this when we find more than one question from a single data source that is relevant to one of the dimensions of governance that we measure. On the interactive part of our website we report either the individual question, or the average of individual questions, from each source that enters into our governance indicators. All the individual variables have been rescaled to run from zero to one, with higher values indicating better outcomes.

These individual indicators can be used to make comparisons of countries over time, as all of our underlying sources use reasonably comparable methodologies from one year to the next. They also can be used to compare different countries' scores on each of the individual indicators, recognizing however that these types of comparisons too are subject to margins of error. We caution users however not to compare directly the scores from different sources for a single country, as they are not comparable. To take a specific example, it does not make sense to compare a question rated on a 1-10 scale from a data source covering only developing countries with a similar question rated on a similar scale, but covering developed countries, as the distribution of true governance is likely different in the two groups. For example, the same score of 7 out of 10
<!-- Start of picture text --> Figure 10. Unemployment Rate by Educational Attainment<br>(% of the labor force)<br>—@Less than a Primary School Diploma Primary Shool Diploma<br>“&-Middle School Diploma —@® More than a high school diploma<br>Total 2<br>B50 eens 7<br>f \<br>3.39 QA AD<br>3.12<br>ON a RERE ee FL<br>Fp I I eee erereereye 1.36oe I<br>0<br>FPFTg FF LFFPMFPF PPPPM PPPPPPPPPPg ggPIsg _P_ge L oh_W ghLW’ gs"<br><!-- End of picture text --> Figure 11. Unemployment Rate by Gender (% of the labor force of each group

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Having established that this intervention was able to register an impact on enrollment and children’s development, we turn next to the question of whether this impact was sufficiently large to help narrow early achievement gaps between richer and poorer children.

# **The ECED Project and Early Achievement Gaps between Richer and Poorer Children**

Our findings in the previous sections suggest that children from poorer households make more progress in a number of child outcomes compared to all children. Thus, in order to examine the extent to which the ECED project is able to reduce early achievement gaps, we investigate whether the project narrows the gap in child outcomes between children from richer households (those with a wealth Z-score ≥ 0) and poorer households (those with a wealth Z-score <0).<sup>14</sup> We modify our DiD model to estimate the progress made by poor children in batch 3 villages compared to those in the comparison group without the ECED project. We interact the DiD variables with an indicator variable for poorer children as follows: (3) yit = β0 + Diδ1+Ttδ2 + Piδ3 + DiTtδ4 + PiTtδ5 + DiPtδ6 + DiTtPiδ7 + xiβ+ uit.

As in equation (1), yit is enrollment (1 if ever-enrolled and 0 if never-enrolled) or child development outcomes at baseline ( _t_ =1) and follow-up ( _t_ =2) for a child _i_ . Di is the dummy variable indicating 1 for batch 3 and 0 for the comparison group while Tt is the dummy variable indicating 1 for baseline and 0 for follow-up. Pi is the dummy variable indicating 1 for poorer children and 0 for richer children. Thus, δ1 represents the difference between batch 3 and the comparison group, δ2 represents the progress due to age, δ3 represents the difference between poor and rich children at the baseline in the comparison group, and their interactions are captured in δ4 to δ6. In this regression model, δ7 captures the progress made by poor children in batch 3 because they were exposed to the ECED project.

Figure 11 depicts several observations from such an analysis. First, it shows (A) the change in the rich-poor gap in child outcomes between baseline and follow-up for children in batch 3 villages. This gap decreases for enrollment and most child outcome measures. More specifically, for children in batch 3 villages, the rich-poor gap in social competence, communications and general knowledge and pro-social behavior decreases between baseline and

> 14 Throughout this paper – rich or poor should be read as relatively richer and poorer. All households in this sample are rural Indonesian households and are broadly representative of the typical rural Indonesian household which is not rich by most definitions. However, within these rural households there are those that are relatively richer and those that are relatively poorer.

18
West Forest, which more than offset the decline in poverty among export crop farmers in the Savannah (table 17).20 The large increase in poverty among export crop farmers in West Forest is puzzling, and is considered below in more detail. The reduction in poverty was greater among export crop farmers whose major source of export crop revenue is coffee than among those who depend most heavily on cocoa, as expected.

**Table 16: Headcount Index of Poverty by Socioeconomic Group**

_(Poverty Line= 75, 000 CFAF per year in 1995 CFAF)_

||_Headc_|_ount Index_|_Weig_<br>_popu_|_hted_<br>_lation_|
|---|---|---|---|---|
|_Socioeconomic Group_|_1993_|_1995_|_1993_|_1995_|
|Export crop farm|0.394|0.432|16576|1582|
|Food crop farmer|0.492|0.584|13507|1149|
|Agriculture worker|0.531|0.364|843|237|
|Public sector employee|0.076|0.140|6062|446|
|Private formal sector employee|0.071|0.075|4137|431|
|Private informal employee|0.254|0.310|2464|976|
|Self-employed formal sector|0.078||125||
|Self-employed informal sector|0.248|0.293|8229|267|
|Unemployed|0.202|0.528|661|60|
|Inactive|0.271|0.191|4096|312|

**Table 17 Household Expendit re Per Capita and Headcount Index of Poverty Among Farm Households**

||_East_|_Forest_|_West_|_Forest_|_Sava_|_nnah_|
|---|---|---|---|---|---|---|
||___<br>_1993_|_1995_|_1993_|_1995_|_1993_|_1995_|
|_Expenditure per capita_|||||||
|_(in 1985 CFAFperyear)_<br>Export crop farmer|96768|99169|110082|94840|82372|89929|
|Food crop farmer|98157|83564|92117|90038|.81088|87115|
|_Headcount index_|||||||
|Export crop farmer|0.407|0.406|0.340|0.515|0.524|0.406|
|Food crop farmer|0.414|0.567|0.503|0.594|0.532|0.577|

Poverty decreased among agricultural workers, though in view of the small sample size the difference may not be judged significant. However, there are some reports that labor has become more difficult to mobilize in rural areas, and that after the devaluation there was a sharp increase in labor demand primarily for weeding coffee. One publication, based on evidence from a few villages, reported that nominal wages had increased by 60 percent which would imply a significant real wage increase on the order of about 20 percent in early 1995, greater than the real increase in cocoa prices, though less than the real coffee price increase.21 If indeed agricultural workers did benefit from wage increases of 20 Poverty also declined among export crop farmers in Other Cities, but they accounted for only 5 percent of the export farmer population in 1995.

21 Wages increased by 60 percent in the villages of Gobarza and Kremouye post devaluation (Pallix, 1995). The precise date was not specified.

20
In t.s HISTORICAL DATA model we copy each of the original data tables obtained from Turkish sources in a separate worksheet. We design formulas to map the original data in the squired flow of funds format. In this way future updates, revisions, or additional historical data collection are rendered automatic. It is sufficient to add or change the data in the original worksheet and the model will automatically (re-) calculate a consistent flow of funds. Separating the data collection and transformation from the actual projection model not only has the advantage of saving space and increasing speed but it avoids the potential confusion that might arise from the difference between the historical closure of the model (where the private sector is the residual, and the simulation closure (solving for selected endogenous variables). Appendix I explains in detail the construction of the historical flow of funds and the mapping of actual data into it.

The RMSM-X projection model only requires historical data for a few periods in order to provide initial values for the simulations. In most cases, the base year data are sufficient since the economic relations between two variables seldom exceed a one year lag. Sometimes if parameters are calibrated on historical data it may be useful to add a few more historical periods in order to check the stability of these parameters. A detailed description of the RMSM-X model is provided in the next section of the paper.

External debt and creditworthiness are key variables for the country as well as for the Bank's work. Therefore, we decided to 4
– Table A.18 Treatment effects on women’s empowerment and well-being

||||Eq (1)||Eq (2)|||
|---|---|---|---|---|---|---|---|
||(1)<br>Control<br>Mean|(2)<br>Control<br>SD|(3)<br>Treatment|(4)<br>T1:<br>Cash &<br>Training<br>Alone|(5)<br>T2:<br>Cash &<br>Joint<br>Training|(6)<br>p-value<br>T1 = T2|(7)<br>N|
|**Women empowerment**||||||||
|Women agency index|-0.000|1.000|-0.059|-0.049|-0.069|0.737|1,824|
||||(0.045)|(0.053)|(0.054)|[0.619]||
||||[0.246]|[0.398]|[0.252]|||
|Partners’ views index|-0.001|0.999|.|.|0.051<br>(0.068)|.|830|
||||||[0.464]|||
|**Women fnancial index**|0.000|1.000|0.413***|0.399***|0.427***|0.852|1,824|
||||(0.084)|(0.134)|(0.091)|[0.681]||
||||[0.001]|[0.026]|[0.001]|||
|**Women wellbeing**||||||||
|Cantrill’s ladder||||||||
|Current life satisfaction|2.356|1.470|0.227***|0.264***|0.191**|0.512|1,824|
||||(0.074)|(0.092)|(0.093)|[0.503]||
||||[0.023]|[0.029]|[0.103]|||
|Life satisfaction one year ago|2.411|1.494|0.211***|0.268***|0.153*|0.286|1,824|
||||(0.074)|(0.092)|(0.091)|[0.334]||
||||[0.029]|[0.029]|[0.153]|||
|Predicted life satisfaction in three years|3.411|2.125|0.179*|0.226*|0.132|0.526|1,824|
||||(0.103)|(0.128)|(0.126)|[0.506]||
||||[0.145]|[0.142]|[0.340]|||
|Relative wealth|2.821|1.656|0.190**|0.254**|0.126|0.284|1,824|
||||(0.082)|(0.103)|(0.100)|[0.334]||
||||[0.079]|[0.065]|[0.256]|||
|Mental health (MHI-5 score)|43.154|19.500|1.216|2.590**|-0.158|0.030|1,824|
||||(0.911)|(1.095)|(1.121)|[0.095]||
||||[0.242]|[0.078]|[0.691]|||

Notes: This table reproduces the results from Table 2 excluding control variables and enumerator fixed effects. Robust standard errors are reported in parentheses. FDR q-values are reported in brackets. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1.

61
climate and the future climate. However, in the future, more powerful storms become slightly more frequent. The bulk of these more powerful storms strike Florida and the Gulf Coast states.

The storms in each climate scenario have different properties. As temperatures warm, hurricane wind speeds increase and minimum pressure decreases, indicating more powerful storms. However, the average change is quite small. Except for the CNRM scenario, average wind speed increases from 4 to 7 percent across the climate models. Average minimum pressure falls 0.01 to 0.33 percent. The overall frequency of hurricanes changes only slightly, except in the GFDL model where it almost doubles. Although the average features of storms do not change significantly, the distribution of storms changes quite a lot. There is a noticeable increase in the intensity of the largest storms. So although all hurricanes do not change, the most powerful storms appear to become even more powerful.

The amount of damage caused by a storm depends on the intensity of the storm and what is in harm’s way. Two damage functions are estimated using aggregate damages per storm and storm characteristics at landfall from US storms since 1960 (NOAA 2009). Although earlier storm data is available, there is some question about its validity. Aggregate damages include insured and uninsured private property losses as well as infrastructure losses. Fatalities are not included. Partly, this is because fatalities are low in the US because of sophisticated warning and evacuation plans (Anbarci et al 2005; Dash and Gladwin (2007); Kahn 2005; Lindell et al 2005) and partly because the residual US fatalities appear to be random events. Whether or not losses are insured is beyond the scope of this paper (see Gares 2002; Kriesel and Landry 2004; Kunreuther and Pauly 2004). The paper also does not measure long term effects on real estate (Beracha and Prati 2008) or labor markets (Belasen and Polachek 2008) as these effects would be double counting the damages included.

We estimate two measures of storm intensity: wind speed and barometric pressure<sup>3</sup> . We also examine the population and income of the five coastal counties surrounding the eye of the storm. Many past studies assume that the damages are

> 3 We also examine a two stage model that first predicts wind speed on the basis of barometric pressure and then uses the predicted wind speed. However, we do not present this result since it is almost identical to the barometric pressure model.

9
Table 5. The Average Marginal Effects from the Probit Regression of a WUG’s Manager Appointment Model

|VARIABLES|(1)<br>Full sample model|(2)<br>Sub-sample model|
|---|---|---|
|Volumetric priceof reservoir water|7.482<br>|15.26***|
||(1.497)<br>|(2.635)|
|Volumetric priceof reservoir water squared|-52.16|-83.36*|
||(-1.529)|(-1.876)|
|Group size|||
|No. of parcels|0.000600|0.00198|
||(0.525)|(1.246)|
|Homogeneity|||
|Gini coefficient of parcel size|-0.0638|0.123|
||(-0.165)|(0.225)|
|Infrastructure condition|||
|Simplicity of water flow direction|0.980|3.570**|
||(1.232)|(2.381)|
|Earth canal length|-0.00299|-0.0852|
||(-0.0982)|(-1.558)|
|Exit options|||
|No. of ponds per member (pre-reform)|-0.0477|0.564**|
||(-0.433)|(2.266)|
|No. of pumps per member (pre-reform)|-0.0405|-0.138|
||(-0.345)|(-0.723)|
|Threshold value: the price level that gives the max<br>probability of the dependent variable|0.072|0.092|
|Observations|67|33|

_Note_ : _z_ -statistics are in parentheses. *** p<0.01, ** p<0.05, * p<0.1. _Source:_ Authors’ analysis based on data sources discussed in the text.

27
16 the boards Is Intended to encourage the participation<sup>of employers and union officials in curiculum</sup> development and in monitoring program quality.

Table 3

# Short-Term Impacts on Employment and Eamin%s Outcomes of the MFSP Demonstration Projects

|Selected<br>outcomes|Atlanta|San Jose|Providence|Washington|
|---|---|---|---|---|
|Ave. monthly<br>employment rate|-0.7%/|9.9%S|0.1%|-0.9%h|
|Ave. monthly<br>earnings|-$1|$133**|$8|-$18|
|Ave. monthly<br>welfare benefit|-$2|-$15|$14|$29**|

# Source: Burghardt and Gordon (1990: Appendix Table 2).

'Estimates are for the fourth quarter after sample enrollment<sup>and are regression adjusted for each site</sup> to control for baseline characteristics and quarter of program<sup>enrollment.</sup>

** and * signify statistically significant at the 1% and 5% levels,<sup>respectively.</sup>

The AMU offers about 450 curricula available in all<sup>100 skill centers, and each of the 25</sup> regions may further develop additional curricula of its<sup>own. The bulk of the training courses are</sup> vocationally oriented, but there are also basic education<sup>courses In Swedish, mathematics, and English.</sup> (Displaced workers in Sweden are frequently immigrants.)<sup>Since the training centers are independent</sup> of the regular school system, there Is no need for fixed<sup>entrance dates in September and January and</sup> for courses of fixed lengths measured in terms of semesters<sup>or quarters. Rather, there is open entry and</sup> exit into AMU courses, and trainees receive individual<sup>study plans and work to a substantial extent with</sup> self-instructing materials. In additon, the skill centers are<sup>staffed by well-paid, experienced instructors</sup> who provide in-depth instruction utilizing modem equipment<sup>and state-of-the-ar</sup><sup>**t**echnology. It should</sup>
- Chen, Y., & Ma, Y. (2021). Does green investment improve energy firm performance? _Energy Policy_ , _153_ , 112252. https://doi.org/10.1016/j.enpol.2021.112252

- Chen, Z., Zhang, X., & Chen, F. (2021). Do carbon emission trading schemes stimulate green innovation in enterprises? Evidence from China. _Technological Forecasting and Social Change_ , _168_ , 120744. <u>https://doi.org/10.1016/j.techfore.2021.120744</u>

- Chesser, M., Hanly, J., Cassells, D., & Apergis, N. (2019). Household energy consumption: A study of micro renewable energy systems in Ireland. _Economic and Social Review_ , 50(2), 265-280.

- Çiftçioğlu, G. A., Kadırgan, F., Kadırgan, M. A. N., & Kaynak, G. (2020). Smart agriculture through using cost-effective and high-efficiency solar drying. _Heliyon_ , 6(2), e03357. <u>https://doi.org/https://doi.org/10.1016/j.heliyon.2020.e03357</u>

Colombelli, A., Ghisetti, C., & Quatraro, F. (2020). Green technologies and firms’ market value: a micro-econometric analysis of European firms. _Industrial and Corporate Change_ , 29(3), 855-875. <u>https://doi.org/10.1093/icc/dtaa003</u>

- Comello, S., Glenk, G., & Reichelstein, S. (2021). Transitioning to clean energy transportation services: Life-cycle cost analysis for vehicle fleets. _Applied Energy_ , 285, 116408. <u>https://doi.org/10.1016/j.apenergy.2020.116408</u>

Cox, B., Bauer, C., Mendoza Beltran, A., van Vuuren, D. P., & Mutel, C. L. (2020). Life cycle environmental and cost comparison of current and future passenger cars under different energy scenarios. _Applied Energy_ , 269, 115021. <u>https://doi.org/10.1016/j.apenergy.2020.115021</u>

- Dangelico, R. M., & Pujari, D. (2010). Mainstreaming green product innovation: Why and how companies integrate environmental sustainability. _Journal of Business Ethics_ , 95(3), 471486. https://doi.org/10.1007/s10551-010-0434-0

- Davis, S., & Boundy, R. G. (2021). _Transportation Energy Data Book: Edition 39_ . Oak Ridge, Tennessee: Oak Ridge National Laboratory (ORNL).

- Dreier, D., Silveira, S., Khatiwada, D., Fonseca, K. V. O., Nieweglowski, R., & Schepanski, R. (2019). The influence of passenger load, driving cycle, fuel price and different types of buses on the cost of transport service in the BRT system in Curitiba, Brazil. _Transportation_ , 46(6), 2195-2242. https://doi.org/10.1007/s11116-018-9925-0

44
proportionally fewer women in the lower and upper age groups responding to the fertility module of the 1993 VLSS than there are in the VLSS as a whole. Since death rates among children tend to be higher among women aged 15-19, 20-24 and 40-45 than among women in the age groups in between, the VLSS will tend to produce lower estimates of child mortality than is warranted unless some adjustment is made for the under-representation of younger and older women. A simple way of correcting for this is to weight respondents according to their degree of under-representation in the fertility data-i.e., by a number that is the ratio of the number of women in the age group in the VLSS generally to the number of women in the age group responding to the fertility questions. This was done for 1993 and produced estimates that differed very little from those in Table 2. The reason for this is that the fertility rates among younger and older women in Vietnam are relatively low, with only 0.036 of births in 1993 being to women aged 15-19 and only 3.79 of births to women aged 40-45. The relatively small number of births occurring to young women is probably due in part to there being a fairly widely accepted consensus about the ideal age to start a family and in part to the widespread availability of abortion, while the relatively small number of births to older women is probably in part a reflection of the official policy of discouraging more than two children per family.

There is a second issue involving weighting. Unlike the 1993 VLSS sample, the 1998 VLSS sample is not nationally representative. Of the 6000 households surveyed in the 1998 VLSS, 4305 were included in the 1993 VLSS sample. To make the households surveyed in 1998 nationally representative the VLSS designers have included householdlevel weights or expansion factors. These were applied to the fertility data throughout.

# **References**

The word "processed" describes informally reproduced works that may not be commonly available through library systems.

- Benefo, K., and T. Schultz. 1996. "Fertility and child mortality in C6te d'Ivoire and Ghana." _World Bank Economic Review_ 10(1): 123-58.

- Central Census Steering Committee. 2000. _1999 Population and Housing Census._ Hanoi: The Gioi Publishers.

- Dahlgren, G. 2000. "Issues of equity and effectiveness in equity-oriented health care policies: An introduction." In P. Hung, I. Minas, Y. Liu, G. Dahlgren and W.

32