rafmacalaba/datause-encoder-data huggingface dataset:
detail, learners may not engage adequately with the essential materials that trigger cognitive processing and learning (Mayer, 2009; Mayer, 2014).

Finally, other authors claim that the usefulness of VR for general education might also depend on the type of subject of learning. Indeed, VR simulations may not necessarily be equally suitable for all subject areas. For example, it might be less beneficial for learning to play a musical instrument that requires tactile feedback, such as arts education. Still, it may be particularly helpful for teaching subjects where it is important to visualize the learning materials in 3D (e.g., biology or geometry) (Allcoat, & von Mühlenen, 2018). Other authors argue that technologies themselves, such as VR, do not directly cause learning but can afford specific tasks that themselves may result in learning (Dalgarno, & Lee, 2010).

# **III. Data and Methodology**

A first step for conducting an informative meta-analysis is to gather relevant studies and accurately extract and report information from these primary studies (Uttl, White, & Gonzalez, 2017). Data were collected through a review of available studies assessing the impacts of VR training on learning and skills development. Studies included in the meta-analysis follow some predetermined criteria. First, they need to be published in a peer-reviewed journal or as a doctoral thesis, as a proxy for research quality. Second, to account for significant technological developments in access and quality of VR simulators (hardware and software), the sample only includes studies conducted within the last 15 years (2005-2020 period). Third, studies included in the meta-analysis assess the impact of VR training on student learning through value-added experiments or experimental evaluations that use randomized control trials (RTC).<sup>3</sup> Fourth, studies included in the sample assess skills development using objective and clearly measurable metrics, such as learning assessments or performance evaluations (pre- and post-test). The review of studies relied on web and academic databases, such as ACM Digital Library, IEEE Xplore, Web of Science, ERIC, and Scopus. Data obtained from these primary studies were compiled systematically, including the following information:

> 3 RCT experiments are defined as those where individuals are allocated at random (by chance alone) to receive one or several interventions. One of these interventions is the standard of comparison or control. The control may be a standard practice or no intervention at all. Value-added studies quantify changes in desired outcomes (for instance, skills development of student learning) by quantifying these outcomes before and after individuals benefit from the intervention.

9
# **Table A1: Alternative specifications of panel model**

# _Panel A: Poverty_

|Dependent variable:<br>Poverty rate at $1.90|Adding<br>country-<br>specific<br>linear time<br>trend|Adding<br>region-<br>specific<br>linear time<br>trend|Adding<br>temperature<br>change|Adding<br>temperature<br>squared<br>term|Adding<br>temperature cubic<br>term|Adding<br>temperature<br>squared term and<br>temperature<br>change|Adding<br>temperature<br>interaction<br>term|
|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|Temperature|0.867***|0.316***|0.879***|0.873***|0.719*|0.523***|0.524***|
||(0.129)|(0.107)|(0.211)|(0.321)|(0.370)|(0.129)|(0.129)|
|∆Temperature|||-0.046|||0.044*|0.074|
||||(0.030)|||(0.024)|(0.047)|
|Temperature squared||||-0.007|-0.003|-0.002|-0.002|
|||||(0.006)|(0.012)|(0.002)|(0.002)|
|Temperature cubic|||||0.000<br>(0.001)|||
|Temperature*∆Tempearture|||||||-0.003<br>(0.005)|
|Controlling for rainfall|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Subnational FE|Yes|Yes|Yes|Yes|Yes|Yes|No|
|Year FE|Yes|Yes|Yes|Yes|Yes|Yes|No|
|Number of countries|134|134|134|134|134|134|134|
|Number of regions|1,594|1,594|1,594|1,594|1,594|1,594|1,594|
|Observations|4,972|4,972|4,972|4,972|4,972|4,972|4,972|

_Notes:_ Robust standard errors in parentheses. Standard errors are clustered at the subnational level. Regressions are weighted by region population. Poverty rate is measured by the Subnational Poverty Headcount Ratio at $1.90 a day. *** p<0.01, ** p<0.05, * p<0.1 46
# **Acknowledgements**

We received invaluable advice and support from Andrew Foster, Duncan Thomas, and Ira Wilson. We would also like to thank Anna Aizer, Daniel Bj ̈orkegren, Joseph Camardo, Caitlin Cohen, Jishnu Das, Seydou Doumbia, Chris Gill, Rachel Glennerster, Jessica Goldberg, Bentley McLeod, Yusuf Neggers, Emily Oster, Simone Schaner, Dan Silverman, and Dean Yang. Special thanks go to Hamadoun Bocoum, Zorina Curry, Judith Kom, and Fred Wensing, Mali Health and its staff and Board, and Innovations for Poverty Action. We are grateful for funding from Brown University through the Brown Seed Fund Award, the Population Studies and Training Center, and the Rhodes Center, from the Aga Khan Foundation, and from the ESRC/DFID Development Frontiers Award (ES/K01207X/1). The research protocol was approved by the IRB of Brown University (protocol number #1205000629) and the Comit ́e National d’Ethique pour la Sant ́e et les Sciences de la Vie (CNESS) (protocol No. 13/MSHP-SG/CNESS). The study is registered in the AEA RCT Registry with ID AEARCTR-0006603. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.
more limited: the average import content of U.S. exports was 13 percent in 2014, well below the average for other advanced economies (27 percent). However, in some U.S. industries, imports account for more than 20 percent of inputs. These include apparel and leather products, motor vehicles, and computers and electronics (U.S. International Trade Commission 2011). Imports are often essential components that do not have readily available domestic substitutes.

# **5.3 Multinational corporations**

Much global value chain activity is conducted through U.S. multinational corporations and their affiliates abroad. Although U.S. multinationals account for less than 1 percent of the total number of U.S. firms, since 1990, they accounted for one-third of U.S. real GDP growth and almost half of U.S. labor productivity growth (McKinsey Global Institute 2010). As part of global supply chains, U.S. multinationals rely heavily on exports and imports; in fact, the largest U.S. exporters are multinationals (Moran and Oldenski 2016). Multinationals’ presence in financial markets is large; for example, they account for about 85 percent of the stock market capitalization of the S&P500.

About 43 percent of total U.S. trade occurs within multinational firms (intra-firm trade), especially in the case of U.S. trade with advanced economies. Since the global financial crisis, intra-firm trade has continued to grow robustly (especially with EMDEs) whereas arm’s-length trade has slowed sharply. Access to foreign markets has also benefited domestic U.S. activity. For example, a 10 percent increase in foreign direct investment by U.S. multinationals abroad was accompanied by 2.6 percent greater domestic investment in the United States (Desai, Foley, and Hines 2009). In turn, foreign multinationals operating in the United States provided 10 percent of U.S. employment and 19 percent of U.S. exports, on average, during 2010-13 (Figure 11).

# **5.4 Global**

Financial linkages between the United States and the rest of the world, including emerging market economies, have grown rapidly over the past decade, potentially leading to two-way spillovers. Financial market stress or sharp growth slowdowns in the rest of the world can put pressure on the U.S. financial system (IMF 2013; 2014). For example, financial stress that raises risk premia and widens output gaps by 1 percent in some major economies, could widen the U.S. output gap by 0.1-0.35 percent (IMF 2013).

A significant appreciation of the U.S. dollar, which could be driven by increasingly divergent monetary policies with other reserve currencies, weakening growth prospects in the rest 22
# FORtWORD

World Bank work on population began two decades ago when popular literature caricatured the problem of rapid population growth with threats of famine and social decay. Thanks to analyses over the past decade, particularly those included in the _World Development Reports_ of i 380, 1984, and 1990, our knowledge about how population dynamics and economic development interact has grown more specific and less apocalyptic. Population growth at rates above two percent per annum exacerbates the problems of development in the poorest countries by limiting poor families' chances to provide good health and education to their children. Moreover, birth spacing and fertility limitation can offer an antidote to poverty as they lessen the burden of chiidbearing on women and result in better nutrition and physical development for children. Experience with family planning programs, and the complementary efforts to enhance the role of women in the development process by offering them better educaticn and work opportunities, demonstrates that governments can take actions that slow popu!ation growth and improve the prospects for development and poverty reduction.

It is within that context that Steven Sinding, Population Adviser at the World Bank from July 1990 through June 1991, prepared the accompanying analysis and recommendations aimed at strengthening the Bank's population and family planning work. The report was discussed at a meeting of the President's Council in June 1991; some of its recommendations are being implemented, and others are under review.

We were fortunate to have Steven Sinding with us for a year and to have his contribution to Bank work summarized here. He brought to the task twenty years of experience in the fields of population and international donor assistance, most recently, before joining the Bank, as USAID mission director in Kenya. He left the Bank to become the Director of Population Sciences at the Rockefeller Foundation, but his work continues to stimulate useful discussion within the Bank of how best to pursue its population and family planning objectives.

Ann 0. Hamilton Director Population and Human Resources Department
paid for government services and taxpayers generally are sensitive to the way the government uses tax revenues. Therefore, taxpayers perceive their relationship with the state not only as a relationship of coercion, but also as one of exchange. Individuals will feel cheated if taxes are not spent efficiently. If citizens perceive that their interests (preferences) are properly represented in political institutions and they receive an adequate supply of public goods, their identification with the state increases alongside their willingness to contribute. On the other hand, an inefficient state where corruption is rampant will spawn citizens with little trust in authority and thus a low incentive to cooperate. In other words, a more encompassing and legitimate state increases citizens‘ willingness to contribute. Such a state may tend to increase taxpayers‘ positive attitudes and commitment to the tax system, with an accompanying positive effect on tax compliance (see, e.g., Smith, 1992). Hayoz and Hug (2007, p. 10) stress: ―The state being considered as a part of the community and not as the hostile other or ―they‖ can reasonably expect to secure tax compliance. This is also true about governance, about citizen participation in the management of the rules of the game. In some regions of Eastern Europe such experiences were never made: here a ―culture of distrust‖ imply also a ―culture of tax evasion‖. The more people are involved in establishing rules, the stronger is their sense of obligation (Kidder and McEwen, 1989; Cialdini, 1989; McEwen and Maiman, 1986; Lempert, 1972). There are various tax compliance studies that indicate, e.g., that giving individuals the chance to vote on setting the rules increases compliance (Alm, Jackson and McKee, 1993; Feld and Tyran, 2002; Torgler and Schaltegger, 2005). Voting procedure and public discussions prior to votes, creates a sense of civic duty, as taxpayers become aware of the importance to contribute to public goods. Voting possibilities also provide utility in itself. Citizens value the right to participate, because it produces a kind of procedural utility as the opportunity set increases. It leads to an outcome (acceptance of the amnesty or not) more favourable compared to the situation where no such voting possibility exists.

If the government and the administration have a great discretionary power over the allocation of resources, corruption is enhanced where the rulers are non-benevolent. A sustainable tax system is based on a fair tax system and responsive government, achieved with a strong connection between tax payments and the supply of public goods (Bird et 13
Lederman, Daniel, and Marwane Zouaidi. 2020. “Incidence of the Digital Economy and Frictional Unemployment: International Evidence.” Policy Research Working Paper 9170, World Bank, Washington, DC.

Lee, Jinhyung, Jeffrey S. McCullough, and Robert J. Town. 2013. “The Impact of Health Information Technology on Hospital Productivity.” _RAND Journal of Economics_ 44 (3): 545–68.

Lendle, Andreas, Marcelo Olarreaga, Simon Schropp, and Pierre-Louis Vézina. 2016. “There Goes Gravity: eBay and the Death of Distance”, _Economic Journal_ 126 (591): 406–41.

<mark>Levine, Ross. 2005. “Finance and Growth: Theory and Evidence.” In</mark> _<mark>Handbook of Economic Growth, Volume 1A</mark>_ <mark>, edited by Philippe Aghion and Steven N. Durlauf, 865–934. Amsterdam: North-Holland Publishing Company.</mark> Lewis-Faupel, Sean, Yusuf Neggers, Benjamin A. Olken, and Rohini Pande. 2016. “Can Electronic Procurement Improve Infrastructure Provision? Evidence from Public Works in India and Indonesia.” _American Economic Journal: Economic Policy_ 8 (3): 258–83.

Liu, Min, Shi Min, Wanglin Ma, and Tianjin Liu. 2021. “The Adoption and Impact of E commerce in Rural China: Application of an Endogenous Switching Regression Model.” _Journal of Rural Studies_ 83: 106–16.

Lin, Jeffrey. 2011. “Technological Adaptation, Cities, and New Work.” _The Review of Economics and Statistics_ 93 (2): 554–74.

Livingston, Jeffrey A. 2005. “How Valuable Is a Good Reputation? A Sample Selection Model of Internet Auctions.” _Review of Economics and Statistics_ 87 (3): 453–65.

Lo, Kai Lisa, Jiaojiao Zhang, and Fei Xia. 2023. “Does Digital Technology Innovation Work Better for Industrial Upgrading? An Empirical Analysis of Listed Chinese Manufacturing Firms.” _Applied Economics Letters_ 30 (18): 2504–509.

Loeprick, Jan, and Michael Engelschalk. 2011. “Simplified Risk Scoring for SMEs.” In _RiskBased Tax Audits: Approaches and Country Experiences_ , ed. Sultan Khwaja Munawer, Awasthi Rajul and Jan Loeprick, 45–54. Washington, DC: World Bank.

Loveman, Gary W. 1993. “An Assessment of the Productivity Impact of Information Technologies.” In _Information Technology and the Corporation of the 1990s: Research Studies_ , edited by Thomas J. Allen and Michael S. Scott Morton, 84–110. Oxford: Oxford University Press.

Lu, Yingying, and Yixiao Zhu. 2021. “A Review on the Economics of Artificial Intelligence.” _Journal of Economic Surveys_ 35 (4): 1045–72.

<mark>Lucking-Reiley, David, Doug Bryan, Naghi Prasad, and Daniel Reeves. 2007. “Pennies from eBay: The Determinants of Price in Online Auctions.”</mark> _<mark>Journal of Industrial Economics</mark>_ <mark>55 (2): 223–33.</mark> 36
kerosene. On the other hand, the higher-output packages on offer are costlier and may be demanded mainly by comparatively well-off households. This hypothetically creates a differentiation in impact across households ranked by economic condition (wealth, assets, income, etc.). One needs to understand if this is the case; and if there are such differentials in impact, how pronounced they are. In any case, such differentiation also means that better-off households may be less exposed to indoor pollution and its consequences.

Very poor households may not have any designated covered spaces, as kitchen and cooking may be done in open space. Lighting from solar home systems is of little relevance in the context of indoor pollution in such cases. On the other hand, where there is a separate covered space such as a kitchen, in some cases it is located at a distance from the main rooms. As the normal solar home system operates well only within a given distance from the charge controller, which is usually located in one of the main rooms, kitchens that are located farther may go without any SHS lighting. Also, as the most popular modules usually offer only a few connection points, kitchens may again often go without lighting from solar electricity. In any case, this illustrates the importance of understanding both the number and the location of points of connection for lighting. The scope for kitchen lighting with solar electricity thus may be somewhat problematic.

The new lighting facility immediately extends waking and working hours in the evening and may have several types of benefits. Better lighting provides more time to do household chores and/or productive works probably at a somewhat unhurried pace. The better lighting facility may also provide more time for reading and study by students as well as non-students. Social interactions may increase, as may the sense of security due to lighting. Longer daylight hours may alter the time use pattern of both men and women, but possibly more for women. Some of these issues need to be investigated, keeping in mind the seasonal variations in daylight.

The provision of enough electricity for running a black and white TV may provide scope for entertainment as well as information which may be of value. Again, time use data will reveal if time for amusements has increased. One other potential benefit is that mobile phones, which are now ubiquitous even in villages, may now be charged at home. Prior to electrification, the owners of mobile phones would have to 16
digits) allowing for a finer classification of NTMs. The structure of the NTM nomenclature at the highest degree of aggregation is in Table 1.

|||**Table 1. Non-Tariff Measures Classification**|
|---|---|---|
||**Technical**<br>**Measures**|A. Sanitary and Phytosanitary Measures (SPS)<br>B. Technical Barriers to Trade (TBT)<br>C. Pre-Shipment Inspection and other Formalities(PSI)|
|**Imports**|**Non technical**|D. Contingent Trade-Protective Measures<br>E. Non-Automatic Licensing, Quotas and other quantitative controls (QC)<br>F. Price Controls (PC)<br>G. Financial Measures<br>H. Measures affecting Competition<br>I. Trade-Related Investment Measures|
||**Measures**|J. Distribution Restrictions<br>K. Restrictions on Post-Sales Services<br>L. Subsidies (Excluding Export Subsidies under P7)<br>M. Government Procurement Restrictions<br>N. Intellectual Property<br>O. Rules of Origin|
||**Exports**|P. Export-Related Measures|

Source: UNCTAD 2012 Data on NTMs is traditionally quite fragmented and incomplete. The World Trade Report 2012 finds that “ _existing data sources are compromised by large gaps in country coverage, intermittent data collection and a lack of shared terminology_ ”. This applies even to data collected by the WTO; the WTO framework itself is incomplete despite containing more than 200 different legal notification requirements. Specifically, notification requirements do not cover three of the 16 categories of international classification measures (finance measures, distribution restrictions and restrictions on post-sales services). The exclusion of these is not surprising; the notification system is not designed to create a comprehensive database. For example, the SPS and TBT notification requirement is intended so that early drafts of regulations can be viewed by partner countries at the stage when amendments are still possible. Countries are therefore not required to report regulations that predate the introduction of the SPS or TBT Agreements, or new regulations in their final form (WTO, 2012). In addition, because notifications are voluntary with no sanction mechanism, governments may simply choose not to comply with notification requirements, either due to logistical difficulties (Bacchetta et al, 2012) or concern that notification will expose their NTMs to scrutiny or objections. Finally, the quality of notifications varies widely. In many cases, even when NTMs are reported, precise information on certain dimensions of the measures, such as product coverage or the time period during which the measure will remain in place is not provided.

Alterative inventories of NTMs have been obtained through concerns or complaints registered by traders, with sources including direct surveys and WTO Trade Policy Reviews. However, these databases are not comprehensive and tend to be biased as countries vary widely in terms of the administrative procedures required to file complaints against third parties (Beghin and Bureau, 2001).

7
Croke et al. (2016) containing infected individuals, creates an apparent paradox, since if there are effects of deworming among those known to be infected then one would expect a smaller but still positive average effect of MDA in endemic populations. A simple exercise suggests that this hypothesis cannot be rejected using TMSDG's data. Under the assumption that MDA does not affect weight in uninfected children,<sup>12</sup> the expected average effect of MDA on weight is given by multiplying prevalence by the expected effect on infected children. Thus we calculate each study’s expected MDA treatment effect by multiplying the studyspecific STH prevalence by TMSDG’s random effects estimate for the impact of deworming on infected children, which is 0.75 kg.<sup>13</sup> We generate a standard error for this expected MDA treatment effect by multiplying the study-specific prevalence by TMSDG’s standard error of 0.26 kg. These hypothetical data represent MDA effects that would be reasonable to expect in each setting, with standard errors that reflect our uncertainty about the expected treatment effects.<sup>14</sup> We apply a random effects model to these expected MDA effects, which yields a point estimate of 0.20 kg and a confidence interval of (0.13 kg to 0.28 kg). We cannot reject that this expected average effect of MDA is different from TMSDG’s estimate of 0.08 kg (with p=0.23). Thus, TMSDG’s MDA results are consistent with their estimates of the effect of deworming on infected

> 12 This assumption may not hold if deworming creates epidemiological externalities and also affects weight, but it will hold under the null hypothesis that worms have no impact on weight.

> 13 For the expected effect on infected children, we use TMSDG’s random effects estimate of 0.75 kg (95% CI: 0.24, 1.26). Note that these calculations use estimated prevalence for two studies in TMSDG's sample that do not report prevalence within their study – Alderman et al. (2006) and Awasthi et al. (2008). See Section 4 for more details. 14 In particular, suppose that our beliefs about the average treatment effect on infected children are informed by TMSDG’s results and are characterized by ). Under the assumptions of this exercise, in an MDA setting with prevalence , our beliefs about the MDA treatment effect would be characterized by ). The hypothetical data we consider, then, are NNP NNSSNN(0.75, 0.26<sup>2</sup> consistent with our beliefs about MDA treatment effects. Results we obtain using these hypothetical pp data are results that we consider reasonable to expect given TMSDG’s results on infected children.NNP NNSSNN(0.75pp, 0.26<sup>2</sup> pp<sup>2</sup>

13