rafmacalaba/datause-encoder-data huggingface dataset:
would otherwise be very difficult, expensive, or impossible to access. VR simulations can provide students practical training opportunities without pressure, danger, and allowing repeated practice. Such opportunities have the potential of accelerating students’ learning curve in a simulated environment, reproducing real-life conditions and situations without time or space limitations, and with much fewer risks.

This study constitutes an attempt to assess the effects of VR instruction holistically, as a mechanism to develop students’ skills. Given its pedagogic potential and its increasing market availability, it is crucial to examine the effectiveness of emerging VR technologies for pedagogical instruction. Creating educational applications for VR could be a laborious and costly endeavor, so it is essential to investigate whether these applications are useful for learning or not and, to the extent possible, to assess their cost-effectiveness. Results capitalize from a thorough review of 31 primary studies and over 90 experiments that intend to assess the effects of VR instruction on student learning. Our findings reveal that VR instruction is, on average, more effective than traditional training as a mechanism to develop students’ skills. Results indicate that for each additional hour (1⁄4 hour) of training, students exposed to VR training score 3 percent higher in technical (cognitive) learning assessments, when compared to students exposed to the same curricular content delivered through traditional training methods. Results also indicate that students exposed to VR instruction report, on average, 30 percent higher scores in socio-emotional skills assessments after completing their training.

Results are particularly promising in fields related to health and safety, engineering, and technical education. Results from available experiments confirm systematically that VR instruction yields positive results as a mechanism to train surgeons and medical personnel. It offers trainees the opportunity to practice medical procedures safely and at a comparatively low cost. Available experiments confirm VR simulators' effectiveness to improve surgeons’ proficiency to perform procedures such as laparoscopic surgery, robotic surgery, eye surgery, transvaginal examinations, infant sedation, and bone surgery, to name a few. Some results even indicate that VR training can be conducive to much higher student learning gains, especially in topics such as emergency response, where it is otherwise hard to provide students access to real emergencies.

VR training can provide students similar-to real-life laboratories and equipment without making significant capital investments. Available experiments show that virtual laboratories can 28
**Table** 4.2. **Quality survey monitoring scores. Departure and arrival areas at selected BAA airports. 1995/96**

|HEATR|OW<br>AI~WCK1ST|ANTI|
|---|---|---|
|-Secury queue<br>4.1|4.2|4.4|
|Telephones<br>4.0|4.0|4.1|
|Ch**e**c¢-4n<br>.~z ue<br>4.0|4.0|4.3|
|4.0|4.1|4.5|
|^<br>-t<br>information - -3.9|4.0|4.0|
|Tolets<br>-<br>--- -3.9|4.0|<br>4.4|
|Tro1l~<br>,, .<br>_3.9|3.9|<br>4.2|
|id,seating---~-<br>Xl<br>3.7|3.9|<br>4.2|
|Amiou--:<br>- -;s<br>3.7|3.7|<br>4.0|
|Ched&in crowdi<br>3.6|3.8|<br>4.1|
|Laudaide seating<br>_3.5_|3.8|<br>4.1|
|flAparttirlounge crowdingi<br>3.5|3.8|<br>4.3|
|_Ave--g-_<br>_---_<br>_-_<br>_-3.82_|_3.93_|_4.22_|
|4.2|4.3|<br>_4.5_|
|Di:<br>-<br>4.0|4.0|<br>4.1|
|Trolleys<br>3.9|3.8|<br>4.2|
|Tdelhnes-0<br>i-- -3.9|4.0|<br>4.2|
|Baggage reclaimqueue<br>3.8|3.9|<br>4.0|
|Ti&ilets<br>0<br>-|3.9|;3**.**8<br> <br>4 4|
|C<br>kou crowding<br>3.5|3.8|<br>4.3|
|**_Aw-.:-e_**<br>**-i ;-;--****_.-._**<br>**_3.87_**|**_3.96_**|<br>**_4.24_**|

Note: A score I is "extremely poor", 2 is "poor", 3 is "average", 4 is "good" and 5 is "excellent". Source: Monopolies and Mergers Commission (1996).

39
**-** **<u>Table 5: Minimum Questions for Multi Topic Household Surveys</u>**

||**Tenancy Status**|**Ownership**|**Value**|
|---|---|---|---|
|**Housing (for each**<br>**dwelling)**|Form of tenancy:<br>Owned outright<br>Owned but<br>mortgaged<br>Rented<br>Right of usufruct<br> Other|Who owns this<br>dwelling? ID<br>codes<br>Whose name is<br>on the title? ID<br>codes|For how much<br>could this dwelling<br>be sold day?|
|**Land (for each**<br>**plot)**|Form of tenancy:<br>Owned outright<br>Owned but<br>mortgaged<br>Rented<br>Right of usufruct<br> Other|Who owns the<br>plot? ID codes<br>Whose name is<br>on the title? ID<br>codes|For how much<br>could this plot be<br>sold today?|
|**Livestock (for each**<br>**type of animal)**||Who owns<br>(each group of<br>animals)? ID<br>codes|For how much<br>could you sell a<br>mature animal of<br>this type at current<br>prices?|
|**Non-Farm**<br>**Business Assets**<br>**(for each business**<br>**enterprise)**||Who owns this<br>business? ID<br>codes|What is the value of<br>the assets owned by<br>the business if sold<br>today?|
|**Savings (for all**<br>**savings accounts in**<br>**a formal or**<br>**informal**<br>**institution)**||Whose name is<br>on the account?<br>ID codes|What is the current<br>balance in this<br>savings account?|
|**Other Physical**<br>**Assets (for major**<br>**consumer durables,**<br>**agricultural**<br>**equipment)**||Who owns this<br>asset? ID codes|For how much<br>could you sell this<br>asset today?|

# 5.1 Housing

_Minimal information needed_ . The minimal information that is needed for a gender analysis in the housing module is information on tenancy status, individual-level ownership, and the value of the dwelling. These questions can be easily incorporated into existing LSMS surveys.

None of the three questionnaires reviewed ask appropriate individual ownership questions. Yet, in all three it would be very simple to add “who in the household owns the dwelling,” leaving space for more than one individual to be listed as a co-owner, or to restructure the question slightly to improve the quality of the information collected (i.e., rather than only coding if the dwelling is owner-operated, specifying by whom).

In the Vietnam survey, information is obtained on the dwelling and then, if a household member owns the whole or part of the dwelling, the respondent is asked which member 30
Policy Research Working Paper 8824

# **Abstract**

Abstract: This study assesses the redistributive impact of fiscal policy––including expenditures and taxation––in the Arab Republic of Egypt. Using a broadly applied methodology, a fiscal incidence analysis is conducted using survey and government data for fiscal year 2015. Evidence shows that Egyptian fiscal policy reduces income inequality, and that among individual fiscal programs, the largest reduction is due to public expenditures on the primary education system. Compared with similar countries, Egypt’s overall fiscal policy placed it in the median of the distribution of inequality reduction. Fiscal policies in Egypt also led to a decrease in poverty, mostly from the flagship Tamween program. Poverty and inequality could be reduced more effectively if the country would shift away from spending on untargeted energy subsidies to more targeted transfers. The large gap between the government’s expenditures and revenues helps explain the positive outcomes on poverty and inequality but poses challenges in the long term.

This paper is a product of the Poverty and Equity Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at glaraibarra@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
**Table 5: Triple difference estimates of program participation impacts on scheduled castes and tribes versus other castes**

|||**DDD**||**DD**|**D and PSM **||
|---|---|---|---|---|---|---|
||**PHASE 1 DIST**|**RICTS**|||||
|**Scheduled castes and tribes**|||||||
|Consumption (Rs/year)|592|(617)||456|(571)||
|Energy intake (Kcal/day)|307|(131)|**|324|(152)|**|
|Protein intake (g/day)|4.56|(2.43)|*|4.46|(2.66)|*|
|Nonfinancial assets (Rs/year)|1,346|(489)|***|1,323|(599)|**|
|Consumption (log)|0.092|(0.061)||0.064|(0.066)||
|<br>Energy intake (log)|0.128|(0.062)|**|0.142|(0.067)|**|
|Protein intake (log)|0.093|(0.057)||0.097|(0.062)||
|Nonfinancial assets (log)|0.832|(0.186)|***|0.697|(0.202)|***|
|<br>Number of observations|438 + 41|3 = 851||432 + 38|8 = 820||
|**Other castes**|||||||
|Consumption (Rs/year)|1,602|(581)|***|1,259|(608)|**|
|<br>Energy intake (Kcal/day)|188|(99)|*|18|(96)||
|Protein intake (g/day)|1.92|(1.80)||-0.11|(1.68)||
|Nonfinancial assets (Rs/year)|442|(634)||548|(627)||
|Consumption (log)|0.108|(0.048)|**|0.067|(0.051)||
|Energy intake (log)|0.058|(0.043)||-0.009|(0.047)||
|Protein intake (log)|0.023|(0.039)||-0.018|(0.040)||
|Nonfinancial assets (log)|0.218|(0.139)||0.114|(0.137)||
|Number of observations|579 + 997|= 1,576||568 + 953|= 1,521||
||**PHASE 2**|**and 3 DISTR**|**ICTS**||||
|**Scheduled castes and tribes**|||||||
|Consumption (Rs/year)|939|(720)||1,700|(717)|**|
|Energy intake (Kcal/day)|298|(152)|**|399|(163)|**|
|Protein intake (g/day)|4.50|(2.95)||7.72|(2.88)|***|
|Nonfinancial assets (Rs/year)|-465|(577)||-89|(534)||
|Consumption (log)|0.107|(0.080)||0.200|(0.087)|**|
|Energy intake (log)|0.122|(0.071)|*|0.168|(0.077)|**|
|Protein intake (log)|0.100|(0.070)||0.171|(0.070)|**|
|Nonfinancial assets (log)|-0.500|(0.272)|*|-0.419|(0.285)||
|<br>Number of observations|271 + 51|7 = 788||263 + 4|95 = 758||
|**Other castes**|||||||
|Consumption (Rs/year)|-1,387|(978)||-682|(1019)||
|<br>Energy intake (Kcal/day)|-203|(171)||-5|(170)||
|Protein intake (g/day)|-1.96|(3.01)||2.04|(3.06)||
|<br>Nonfinancial assets (Rs/year)|-13|(909)||198|(868)||
|<br>Consumption (log)|-0.212|<br>(0.082)|***|-0.143|<br>(0.091)||
|<br>Energy intake (log)|-0.085|<br>(0.073)||-0.006|<br>(0.076)||
|<br>Protein intake (log)|-0.039|<br>(0.066)||0.033|<br>(0.068)||
|Nonfinancial assets (log)|0.359|(0.381)||0.214|(0.386)||
|Number of observations|168 + 82|8 =996||151 + 6|32 = 783||

_Source:_ Authors’ computation from household survey and administrative data. _Notes:_ All figures in per capita terms. DDD = triple difference estimation; PSM = propensity score matching. Robust standard errors are in parentheses. Significant level: *: 10%, **: 5%, ***: 1%.

20
_Absenteeism, stock-outs, informal payments and Quantitative Service Delivery Surveys (QSDS)_ Absenteeism, stock-out rates and informal payments can be measured by studies that rely on detailed facility surveys. The Quantitative Service Delivery Survey (QSDS) represents such an approach. A QSDS examines the performance of service providers by collecting data on finances, inputs, outputs, pricing, quality, and supervision from a representative sample of facilities, often combined with interviews of staff and patients.

A QSDS provides a more thorough picture of how money and resources are utilized at the service provision level than do most PETS. The QSDS approach permitted Chaudhury and Hammer (2004) to estimate absenteeism through random visits of health care facilities in multiple locations in Bangladesh over a period of months. To the extent that local health care provision is similar across countries, the measures of absenteeism, stock-outs and informal payments from the QSDS could provide reasonably comparable estimates of governance performance.

The cost of implementing a combined PETS and QSDS is approximately US$200,000.<sup>11</sup> This figure includes a survey – applied in a random representative sample of health facilities – along with a review of documentation and interviews to track the flow of funds through the public health care system.

_Issues in measuring health care worker absenteeism_ Accurate measurement of absenteeism that is comparable across countries requires attention to a number of factors. The basic facility sampling strategy should assure that the survey is representative of the chosen type of facility (e.g. all public primary health care facilities). Unlike normal facility surveys, gathering information on absenteeism also requires that the timing of the facility visit be unannounced. Finally, appropriate information has to be gathered to distinguish excused from unexcused absences. As a governance performance measure, only unexcused absences are of interest. This means it is necessary to gather enough information about facilities to know which staff members are actually supposed to be present and determine, for those who are not available, whether they have legitimate reasons for being absent – such as authorized vacations, offsite assignments, or training courses. Follow-up visits are recommended to determine the reliability of the survey.

One six-country study has shown that international comparisons of absenteeism rates is possible given a rigorous study design and standardized questionnaire (Chaudhury et al 2006). The costs for absenteeism studies by themselves without a complete QSDS are closer to US$150,000 per country.

_Issues in measuring stock-out rates_ Several approaches are available to measure stock-out rates (the absence of particular medicines from a point of health care provision). Examples include a study by Reinikka

> 11 Cost estimates are based on conversations with World Bank staff who conducted or supervised studies.

______________________________________________________________________________________

-30-
the degree of competition in the sector. These estimates provide a single set of indexes for the late 1990s. For trade, we work with bilateral merchandise trade data extracted from UNCTAD’s COMTRADE database and matched to bilateral import protection data taken from GTAP (2005). These data are for 2001. The tariff data offer the advantage of including a bottom-up concordance from detailed tariff data to aggregate bilateral trade flows, including preferential tariff rates. We also have included estimates of the trade-tax equivalent of export barriers as part of the basic trade barrier data (primarily the quota regime on textiles and clothing). In addition, bilateral export data have been adjusted to reflect estimated freight margins. For 69 countries as exporters, we have matched bilateral import data to other country-specific data for the 22 OECD importers covered by our set of OECD indexes on the distribution and freight sectors. We also incorporate data on distance, common language, and common borders from Guillaume, Mayer and Zignago (2004). Finally, as we are unable to use importer dummies for this exercise, we also include data on importer GDP and per-capita income from the World Bank (2005), following the older (pre fixed effects) gravity literature. After matching trade data to our competition data, we have 1,725 bilateral trade flows to work with involving OECD countries as importers in 2001.

Our estimating equation is a reduced-form gravity equation, utilizing the data discussed above and augmented to reflect our propositions based on equation (8). Since we are working with a single year, using exporter dummies controls for _fob_ prices, while value flows map to quantities if we normalize these prices to unity. Defining imports by country 14
||||(0.01590)||||(0.01618)||||(0.01591)||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Remittances & Others|||0.00852||||0.01148||||0.00558||
||||(0.01517)||||(0.01565)||||(0.01481)||
|Property income and investments|||0.00629||||0.00776||||0.00524||
||||(0.01397)||||(0.01421)||||(0.01389)||
|Transfers and other benefits|||0.01516||||0.01220||||0.01763||
||||(0.01488)||||(0.01524)||||(0.01499)||
|Own > median size of land (2.5 acres)|||0.01491||||0.01672||||0.01204||
||||(0.01410)||||(0.01452)||||(0.01413)||
|Observations|5,453|5,450|4,105|4,102|5,453|5,450|4,105|4,102|5,453|5,450|4,105|4,102|
|Root MSE|0.581|0.582|0.612|0.593|0.585|0.586|0.615|0.597|0.589|0.589|0.619|0.600|
|**_Included controls_**|||||||||||||
|Exogenous controls (age, agesq, sex<br>“male==1”)|_No_|_Yes_|_Yes_|_Yes_|_No_|_Yes_|_Yes_|_Yes_|_No_|_Yes_|_Yes_|_Yes_|
|Other controls (HH size, marital status,<br>education, occupation, other income sources,<br>land ownership)|_No_|_No_|_Yes_|_Yes_|_No_|_No_|_Yes_|_Yes_|_No_|_No_|_Yes_|_Yes_|
|Year fixed effects|_Yes_|_Yes_|_Yes_|_Yes_|_Yes_|_Yes_|_Yes_|_Yes_|_Yes_|_Yes_|_Yes_|_Yes_|
|Cluster fixed effects|_Yes_|_Yes_|_Yes_|_No_|_Yes_|_Yes_|_Yes_|_No_|_Yes_|_Yes_|_Yes_|_No_|
|Household fixed effects|_No_|_No_|_No_|_Yes_|_No_|_No_|_No_|_Yes_|_No_|_No_|_No_|_Yes_|

12
# The Macroeconomic Implications of a Transition to Zero Net Emissions: A Modeling Framework<sup>1</sup>

Stephane Hallegatte,<sup>a</sup> Florent McIsaac,<sup>a</sup> Hasan Dudu,<sup>a</sup> Charl Jooste,<sup>a</sup> Camilla Knudsen,<sup>a</sup> and Hans Beck<sup>a</sup> aWorld Bank

**Keywords** : Macroeconomic modeling; climate change; technological change **JEL Classification** : E60; Q43; Q54

> 1 This analysis is part of the analytical work for the Country Climate and Development Report (CCDR) on Türkiye and benefited from the contributions of the entire CCDR team, especially the many colleagues who developed the sectoral roadmaps on which this work is based. It also benefited from the multiple reviews done on the CCDR. The authors thank in particular the peer reviewers of the CCDR who provided insightful comments that enhanced the quality of this paper. We also thank Etienne Espagne for his helpful comments. All remaining errors and opinions are our own. The authors thank the multi-donor <u>Climate Support Facility</u> for funding part of this work.
# **Contents**

|**1**|**Intr**|**oduction**<br>**4**|
|---|---|---|
|**2**|**Con**|**fict and Economic Growth**<br>**6**|
||2.1|The Per Capita Model of Violence . . . . . . . . . . . . . . . . . . . . .<br>7|
||2.2|Confict Incidence and Economic Performance . . . . . . . . . . . . . .<br>9|
||2.3|Economic Impact at the Ethnic Group Level . . . . . . . . . . . . . . .<br>13|
|**3**|**Rec**|**overy from Confict**<br>**17**|
||3.1|Empirical Evidence for Economic Recoveries . . . . . . . . . . . . . . .<br>18|
||3.2|Unstable Peace as a Central Selection Problem . . . . . . . . . . . . . .<br>20|
|**4**|**Inte**|**rnal Conficts and Refugees**<br>**28**|
|**5**|**Con**|**fict and Long Term Productivity**<br>**32**|
||5.1|A Measure of Aggregate Productivity Loss . . . . . . . . . . . . . . . .<br>34|
||5.2|Macro Evidence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .<br>37|
|**6**|**The**|**Role of Expectations for Investment**<br>**42**|
||6.1|The Literature<br>. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .<br>42|
||6.2|Political Risk in Enterprise Surveys . . . . . . . . . . . . . . . . . . . .<br>43|
||6.3|Foreign Investment During Recovery<br>. . . . . . . . . . . . . . . . . . .<br>45|
||6.4|Expectations and Investment: A Model . . . . . . . . . . . . . . . . . .<br>53|
||6.5|The Role of Expectations: Evidence . . . . . . . . . . . . . . . . . . . .<br>54|
|**7**|**Acc**|**ess to Power and Institutions**<br>**60**|
|**8**|**Con**|**clusions**<br>**64**|
||8.1|Measure Expectations<br>. . . . . . . . . . . . . . . . . . . . . . . . . . .<br>64|
||8.2|Alleviate the Humanitarian Crisis . . . . . . . . . . . . . . . . . . . . .<br>64|
||8.3|Ensure Stability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .<br>65|
|||8.3.1<br>The Role of Aid in Stabilization . . . . . . . . . . . . . . . . . .<br>65|
|||8.3.2<br>The Role of Peace Missions<br>. . . . . . . . . . . . . . . . . . . .<br>67|
|||8.3.3<br>Political Exclusion<br>. . . . . . . . . . . . . . . . . . . . . . . . .<br>68|
|||8.3.4<br>Trust and Confict<br>. . . . . . . . . . . . . . . . . . . . . . . . .<br>69|
|**A**|**ppen**|**dix**<br>**80**|
||Rob|ustness Check on Confict Incidence and Economic Performance . . . . .<br>80|
||Reco|very after Civil Confict at the Local Level<br>. . . . . . . . . . . . . . . .<br>85|
||Cou|ntry-level Economic Performance and Recovery Length . . . . . . . . . .<br>86|
||Rob|ustness Check on Internal Conficts and Refugee Flows . . . . . . . . . .<br>87|
||Reco|very after Confict and Peace Stability . . . . . . . . . . . . . . . . . . .<br>89|

1
cherry-coffee crops. Thus, and similar to the main rainy season, there are fewer opportunities for monitoring child labor activities in the harvesting season relative to the short rainy season.

We thus estimate the following multivariate equation, _yijt_ = α + β 1 _SRij_ + β 2 _Gij_ + β 3 ( _SRij_ × _Gij_ ) + β 4 ( _SRij_ × _rainit_ ) + β 5 ( _SRij_ × _harvit_ ) + β 6 ( _Gij_ × _rainit_ ) (2) + β 7 ( _Gij_ × _harvit_ ) + β 8 ( _SRij_ × _Gij_ × _rainit_ ) + β 9 ( _SRij_ × _Gij_ × _harvit_ ) + _X ijt_ ' γ + _St_ + _c j_ + ε _ijt_ where _rainit_ is and indicator for the main rainy season and _harvit_ is an indicator for the harvesting season. The coefficients of interest are β 8 and β 9 . We formally test the null that β 8 = β 9 = 0

# **4. Results**

# **4.1 Differences in child labor statistics by respondent type and by season**

Table 2 presents differences in means for child labor indicators by treatment assignment and disaggregated by gender for the pooled data and each season separately. The upper panel shows means at the extensive margin while the lower ones at the intensive margin. In each case, we test for differences in means between self- and proxy statistics based on a 30-day recall period. Descriptive statistics for the 7-day recall period depict similar patterns and thus are not discussed but are available in the Appendix Table A.1.2.

By looking at the left panel for the pooled data, we observe that participation in farm work obtained from the child (60.4%) is statistically significantly different from the one obtained from the proxy-respondent (56.1%). This discrepancy is driven by the girls’ subsample that reports 7.2 percentage points (or 15%) higher rate of participation than the mean participation obtained from the proxy. On the contrary, the difference in reports is marginal and it is not statistically significant 16