rafmacalaba/datause-encoder-data huggingface dataset:
<mark>decisions.</mark> _<mark>Climatic change</mark>_ <mark>,</mark> _<mark>138</mark>_ <mark>(3), 439-451.</mark> **[1]** <mark>Kraly, E. P., & Hovy, B. (2020). Data and research to inform global policy: the global compact for safe, orderly and regular migration.</mark> _<mark>Comparative Migration Studies</mark>_ <mark>,</mark> _<mark>8</mark>_ <mark>(1), 1-32.</mark> **[2]** <mark>Kubik, Z., & Maurel, M. (2016). Weather shocks, agricultural production and migration: Evidence from Tanzania.</mark> _<mark>The Journal of Development Studies</mark>_ <mark>,</mark> _<mark>52</mark>_ <mark>(5), 665-680.</mark> **<mark>[</mark> 1]** <mark>Laczko, F., & Aghazarm, C. (2009).</mark> _<mark>Migration, Environment and Climate Change: assessing the evidence</mark>_ <mark>. International Organization for Migration (IOM).</mark> **<mark>[</mark> 1]** <mark>Lai, S., zu Erbach-Schoenberg, E., Pezzulo, C., Ruktanonchai, N. W., Sorichetta, A., Steele, J., ... & Tatem, A. J. (2019). Exploring the use of mobile phone data for national migration statistics.</mark> _<mark>Palgrave communications</mark>_ <mark>,</mark> _<mark>5</mark>_ <mark>(1), 1-10.</mark> **<mark>[</mark> 2]** <mark>Letta, M., Montalbano, P., & Tol, R. S. (2018). Temperature shocks, short-term growth and poverty thresholds: Evidence from rural Tanzania.</mark> _<mark>World Development</mark>_ <mark>,</mark> _<mark>112</mark>_ <mark>, 13-32.</mark> **[4]** <mark>Lilleør, H. B., & Van den Broeck, K. (2011). Economic drivers of migration and climate change in LDCs.</mark> _<mark>Global Environmental Change</mark>_ <mark>, 21, S70-S81.</mark> **<mark>[</mark> 1]** <mark>Liu, M. Y., Shamdasani, Y., & Taraz, V. (2022). Climate change and labor reallocation: Evidence from six decades of the Indian Census.</mark> _<mark>American Economic Journal: Economic Policy</mark>_ <mark>, forthcoming.</mark> **<mark>[</mark> 1]** <mark>Lu, X., Wrathall, D. J., Sundsøy, P. R., Nadiruzzaman, M., Wetter, E., Iqbal, A., ... & Bengtsson, L. (2016). Detecting climate adaptation with mobile network data in Bangladesh: anomalies in communication, mobility and consumption patterns during cyclone Mahasen.</mark> _<mark>Climatic Change</mark>_ <mark>,</mark> _<mark>138</mark>_ <mark>(34), 505-519.</mark> **<mark>[</mark> 2]** <mark>Lucas, R. E. (2021).</mark> _<mark>Crossing the Divide: Rural to Urban Migration in Developing Countries</mark>_ <mark>. Oxford University Press.</mark> **<mark>[</mark> 2]** <mark>Magrini, E., Montalbano, P., Winters L. A. (2018). H</mark> ouseholds’ vulnerability from trade in Vietnam <mark>, World Development, 112. pp. 46-58. ISSN 0305-750X.</mark> **<mark>[</mark> 4]** <mark>Marchiori, L., Maystadt, J. F., & Schumacher, I. (2012). The impact of weather anomalies on migration in Sub-Saharan Africa.</mark> _<mark>Journal of Environmental Economics and Management</mark>_ <mark>,</mark> _<mark>63</mark>_ <mark>(3), 355-374.</mark> **<mark>[</mark> 1]** 45
# Meta-Analysis Assessing the Effects of Virtual Reality Training on Student Learning and Skills Development

Diego F. Angel-Urdinola, Catalina Castillo-Castro, Angela Hoyos The World Bank<sup>1</sup> JEL Classification: I20, I24

**Keywords:** Virtual reality, Education, Learning, Skills Development. **Corresponding Author:** Diego F. Angel-Urdinola (dangelurdinola@worldbank.org)

> 1 This paper is a product of the Education Global Practice. The paper was prepared as a background paper for the _Pilot Program to Test Virtual Reality Training Programs for Technological and Technical courses in Higher Education (TF0A8313)_ , supported by the Korea World Bank Partnership Facility (KWPF). The authors acknowledge useful comments and support from Christine H. Joo, Robert Hawkins, and Michael Trucano.
of the world, or relatively sizable fiscal stimulus in the United States, could have a negative impact on U.S. growth as well. For example, a 10 percent appreciation of the trade-weighted U.S. dollar, could reduce U.S. GDP from baseline by over 1 percent after three years, assuming no change in monetary policy (Fischer 2015). The adverse effect would materialize only gradually, with over half of the impact occurring after more than a year. Monetary policy accommodation could substantially ease the impact of a strengthening dollar to about one-half to two-thirds of its direct trade<sup>15</sup>

# **5.5 Consumer and labor markets**

About one-third of U.S. consumer spending is on goods, of which about one-sixth is on imported goods. The share of imports in consumption expenditures is larger for durable goods (29 percent)—especially durable household equipment, motor vehicles, and recreational goods—and clothing and footwear (32 percent). The United States hosts the world’s largest number of immigrants (Chandy and Seidel 2016). Immigrants accounted for 17 percent of the U.S. civilian labor force, on average, in 2015, and more than one-quarter in some parts of the United States. Immigrants originate from all over the world, but mainly from Mexico, China, and India.<sup>16</sup>

# **5.6 Spillovers from the world to the United States**

Because of strengthening multidimensional linkages between the United States and the rest of the world, U.S. business cycles are highly synchronized with the global business cycle. Global developments account for a sizable fraction of variation in business cycles in the United States. According to a dynamic factor model estimated over the period 1985-2015, close to 40 percent of the variance of U.S. growth can be attributable to a global factor (Figure 11; Hirata, Kose, and Otrok 2013; World Bank 2016a).

In addition, growth shocks originating in other economies, especially in other advanced economies, have a significant impact on activity in the United States (Bems, Johnson, and Yi 2010).<sup>17</sup> A vector autoregression model including GDP or industrial production growth in the United States, other advanced economies and EMDEs, as well as 10-year U.S. bond

> 15See Erceg, Guerrieri, and Gust (2006); Laforte and Roberts (2014); Brayton, Laubach, and Reifschneider (2014).

> 16Immigration generally appears to raise aggregate wages and lower prices as well as stimulate investment and innovation (Peri 2010; Greenstone and Looney 2012; Hunt and Gaultier-Loiselle 2010; Chellaraj, Maskus and Mattoo 2008).

> 17Some recent studies examine the impact of shocks originating in other countries on activity in the United

23
Policy Research Working Paper 6321

# **Abstract**

The proliferation of mobile phones in developing countries has generated a wave of interest in collecting high-frequency socioeconomic surveys using this technology. This paper considers lessons from one such survey effort in a difficult environment—the South Sudan Experimental Phone Survey, which gathered data on living conditions, access to services, and citizen attitudes via monthly interviews by phones provided to respondents. Non-response, particularly in later rounds of the survey, was a substantial problem, largely due to erratic functioning of the mobile network. However, selection due to non-response does not appear to have markedly affected survey results. Response rates were much higher for respondents who owned their own phones. Both compensation provided to respondents in the form of airtime and the type of phone (solarcharged or traditional) were varied experimentally. The type of phone was uncorrelated with response rates and, contrary to expectation, attrition was slightly higher for those receiving the higher level of compensation. The South Sudan Experimental Phone Survey experience suggests that mobile phones can be a viable means of data collection for some purposes, that calling people on their own phones is preferred to handing out phones, and that careful attention should be given to the potential for selective non-response.

This paper is a product of the Poverty Reduction and Economic Management Unit, Africa Region. 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://econ.worldbank.org. The author may be contacted at gdemombynes@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
10 desertion. At the margin, the supplemental actions provided by the program should have the greatest impact amongst this population.

**Table 3.  Distribution of Students by School Type and State**

||**Chiapas**|**Guerrero**|**Hidalgo**|**Oaxaca**|**Sub-total:**<br>**Experimen**<br>**tal**|**Michoacan**<br>**: Control**|**Total**|
|---|---|---|---|---|---|---|---|
|Urban|398|107|257|357|1,119|361|1,480|
|Rural|200|202|175|239|816|208|1,024|
|Native|197|114|122|259|692|205|897|
|Community|19|11|29|59|118|27|145|
|**Total**|814|434|583|914|2,745|801|3,546|
|Source: PA|RE's databa|se.||||||

The sample consists of students from 198 schools randomly chosen from four different types of schools from five different states (Table 4).

**Table 4. Sample of Schools by Type and State**

|**State**|**Urban**|**Rural**|**Native**|**CONAFE**|**Total**|
|---|---|---|---|---|---|
|Chiapas|6|13|14|5|38|
|Guerrero|4|14|12|4|34|
|Hidalgo|3|11|12|8|34|
|Oaxaca|7|17|15|12|51|
|Michoacan|7|15|10|9|41|
|Total|27|71|64|44|198|
|Source: PAR|E's database|.||||

The sample means and standard deviations of selected variables, which are defined operationally in the annex, are presented in Table 4 below by stratum. It clearly shows how disadvantaged the children in indigenous and rural school are in terms of their learning achievement, socioeconomic background (e.g. mother’s education); and the quality of the their teachers and school directors.
variables (Biggs, 1992). The use of proxy respondents may amplify recall errors or affect the reporting of hours of work, especially in the case of irregular or multiple activities (Hussmanns, Mehran, and Verma, 1990). Moreover, the use of proxy respondents is also considered to potentially be a source of sex biases in the form of underestimation of women’s participation in economic activity (ILO, 1982).

The survey experiment we conducted and analyze here seeks to inform the method by which labor statistics are collected in household surveys in low-income countries, and, therefore, the information base for analytical work on employment. We hope that this work leads to improvements in the measurement of, among others, labor force participation, the nature of work in terms of type and intensity (particularly work that occurs in household enterprises and farms), the changing patterns in employment over time, and nuanced changes in labor market activity that could be missed when using existing data collection instruments.

# **3. The Survey Experiment**

Whether changes in measurement have an effect on the statistics they produce is, ultimately, an empirical question. We designed and implemented a survey experiment focusing on two key dimensions of labor survey design: the level of detail of the questionnaire (specifically the screening questions to establish employment status) and the type of respondent (Table 1). Households were randomly selected for the survey, and, after being selected, randomly assigned to one of the four survey assignments based on these two dimensions.<sup>10</sup> Eligible persons to respond to the

> 10 An alternative approach to randomly assigning households to different surveys to study mismeasurement is to test existing data for abnormalities based on prior distributional assumptions about

10
# Natural Gas Production in Russia

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_53_
in probability is small, however, and specially so for the native population (a mere 3 percent increase).

Due to the lack of adequate and sufficient number of instruments we could not sort out the intriguing findings posed by the sign or significance of some of the variables. No doubt, in all the models the experimental variable was significant and positive.

# **4. Cost-effectiveness of the PARE program**

# 4.1 Costs in the PARE program

It is very difficult to estimate the true costs of the PARE program. The program, financed by CONAFE, is not independent of actions taken by SEP in its usual activities of finding and supervising basic education, as explained in Section 2. It could be, for example, that teachers in a school benefiting from the PARE program become more motivated and assiduous simply because they perceive the threat (or reward) of closer supervision by the educational authorities. The costs of the PARE program, as reported by the C.E.E, are shown in Table 11. Expenditure on native schools was nearly 60% higher compared to rural schools and 786% higher respect to urban schools.<sup>5</sup> The largest cost items were infrastructure and materials. Expenditure on teacher training and wage incentives accounted for less than 14 % of total spending.

**Table 11. Per pupil expenditure, 1994.**

|**A**|**lI**schools*||**PARE****||**Cos**|**t**increas|e|
|---|---|---|---|---|---|---|---|
|||Native|Rural|Urban|Native|Rural|Urban|
|Chiapas|1,983|605.7|338.1|<br>210.7|<br>30.5%|17.0%|<br>10.6%|
|Guerrero|2,253|749.1|764.2|<br>62.8|33.2%|33.9%|<br>2.8%|
|Hidalgo|2,143|1,127|636.8|<br>51.0|52.6%|29.7%|<br>2.4%|
|Oaxaca|1,770|624.1|229.7|<br>23.8|35.3%|13.0%|<br>1.3%|
|**Average**|**2,037**|**776.4**|**492.2**|<br>**87.1**|<br>**_38.1%_**|**_24.2%_**|<br>**4.3%**|

Source: PARE's database.

* Unit cost for primary schools in native communities, SEP.

** See Table 12.

> 5 The percentages are obtained as follows: the difference in cost increase between native and rural areas (and native and urban areas) is divided by the cost increase in rural (urban) strata.

14
# **Global Value Chains in the Electronics Industry: Was the Crisis a Window of Opportunity for Developing Countries?**

by Timothy J. Sturgeon MIT Industrial Performance Center <u>sturgeon@mit.edu</u> and Momoko Kawakami Institute of Developing Economies, JETRO <u>momoko@ide.go.jp</u>

**Keywords:** Global Value Chains, Value Chain Modularity, Electronics Industry, Offshoring, Outsourcing, Globalization, Electronics Manufacturing, China

> **JEL Codes:** L63, N65, 014, P3
**Table 1.** The Effect of the Devaluation on Perceived Living Conditions

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|
|---|---|---|---|---|---|---|---|---|---|---|---|
|_Panel A: Least Squares_||||_Dependent v_|_ariable: Subj_|_ective evalu_|_ation of livin_|_g conditions_||||
|Treatment|-0.16***|-0.14***|-0.09***|-0.12***|-0.16***|-0.13***|-0.16***|-0.15***|-0.12***|-0.14***|-0.07***|
||(0.035)|(0.035)|(0.028)|(0.025)|(0.032)|(0.027)|(0.042)|(0.059)|(0.028)|(0.030)|(0.025)|
|Botswana economic condition|||||||||0.11***<br>(0.012)||0.09***<br>(0.013)|
|Own past economic situation||||||||||0.08***|0.05***|
|||||||||||(0.007)|(0.008)|
|Male dummy|||||||||||-0.03*<br>(0.012)|
|Urban dummy||0.05***|||||||||<br>0.00|
|||(0.022)|||||||||(0.015)|
|Gaborone dummy|||0.12***<br>(0.026)|||||||||
|Poverty|||||||||||0.25***<br>(0.024)|
|Age|||||||||||-0.01*<br>(0.002)|
|Age squared|||||||||||0.00*<br>(0.000)|
|Unemployment|||||||||||<br>-0.02*<br>(0.012)|
|No children|||||||||||0.02|
||||||||||||(0.029)|
|District fixed effects|No|No|No|Yes|No|No|No|No|No|No|No|
|Tribal fixed effects|No|No|No|No|Yes|No|No|No|No|No|No|
|Occupational FE|No|No|No|No|No|Yes|No|No|No|No|No|
|Sample centred on discontinuity|No|No|No|No|No|No|Yes|Yes|No|No|No|
|Observations|1,198|1,198|1,198|1,198|1,198|1,198|375|216|1,152|1,188|1,109|
|R-squared|0.053|0.061|0.061|0.105|0.105|0.113|0.070|0.068|0.267|0.150|0.362|
|_Panel B: Ordered logit_||||||||||||
|<br>Treatment|-1.07***|-0.89***|-0.62***|-0.91***|-1.12***|-0.91***|-0.97***|-0.97***|-0.93***|-0.97***|-0.65***|
||(0.206)|(0.207)|(0.167)|(0.212)|(0.202)|(0.158)|(0.230)|(0.314)|(0.174)|(0.178)|(0.195)|

_Source_ : Authors’ analysis based on data sources discussed in text. Treatment denotes the Pula devaluation. Days before devaluation are coded as 0; days after devaluations are coded as 1. All models contain a constant term (not reported to save space). Robust standard errors clustered at the region level in parentheses. *** _p_ < .01, ** _p_ < .05, * _p_ <.1.

19
the two components is not available. Moreover, these series tend to fluctuate significantly over time, and these fluctuations are not always easy to interpret as changes in quality as opposed to, say, measurement errors or “abnormal” shocks. More importantly in the present case, these indicators were not all available for our group of countries, and when they were, many data points were missing. Despite using a combination of local and international sources—including the _World Development Indicators_ of the World Bank and Canning’s (1998) database on physical infrastructure stocks—we were unable to “piece together” complete series.

We therefore followed another approach, which consisted in, first, defining two alternative quality indicators and, second, combining them to create a composite indicator. Our first individual indicator is an “ICOR-based” measure. Aggregate ICORs (calculated as the ratio of total domestic investment divided by the change in output) are commonly viewed as a measure of the efficiency of investment. Here we apply this idea to public infrastructure, by calculating an ICOR coefficient defined as public capital expenditure on infrastructure divided by the change in GDP. We then invert this measure and take a 3-year moving average, in order to smooth out the behavior of the series over time. Figure 3 shows the behavior of our ICOR-based quality indicator. The results suggest that whereas quality seems to have improved in recent years in Tunisia, it has deteriorated in Egypt and Jordan.

Our second indicator is an “excess demand” measure. Our premise is that, if growth in the demand for infrastructure services tends to exceed growth in supply, pressure on the existing public capital stock will intensify and quality will deteriorate. To construct these indicators we proceeded in two steps. First, we calculated individual indicators of “excess demand” for three categories of infrastructure services: electricity; telephone mainlines; and paved roads. To estimate demand for infrastructure service _h_ , we applied the annual growth rate of real GDP per capita to the stock of public capital in _h_ at the base period. We used elasticity values of unity in Esfahani and Ramírez (2003, p. 446) also note the existence of a close correlation between stocks of infrastructure capital and quality in their sample.

16
Table 4.25. OLS regression. Dependent variable: Number of clinic visits.

||Pre-|crsis||Post|-crisis|
|---|---|---|---|---|---|
||Coef.|Std.Err||Coef.|Std.Err|
|Othertowns|0.1523|0.3114||-0.0012|0.2523|
|Rural|-0.4927|0.2850|*|-0.2966|0.2333|
|Age|0.0208|0.0206||0.0231|0.0145|
|Agesq.|-0.0002|00002||-0.0002|0.0001|
|Headfemale|00342|0.1116||0.2294|0.1628|
|Single|-0.4094|0.1961|**|-0.1208|0.2052|
|Separate|0.1276|0.6226||-0.3988|0.2241|
|Widowed|-0.3082|0.1536|**|-0.2800|0.1812|
|Divorced|-0.3207|0.2227|.|-0.4891|0.1835|
|Illiterate|-0.2646|02088||-0.1261|0.2127|
|Pnmaryeducation|-0.1081|0.1564||-0.1606|0.0844|
|Highereducation|0.4455|0.2551|*|0.0751|0 1333|
|Farmers|-0.0405|0.1903||0.0983|0.1516|
|Hiredinagriculture|-0.0806|0.1582||0.1701|0.1792|
|Selfemployed|0.5200|0.4316||0.3567|0.5641|
|Pensioners|0.0758|0.2060||-0.0079|0.1767|
|Other|0.0868|0.3269||0.1210|0.2837|
|Householdsize (log)|0.1638|0.1957||-0.0003|0.1692|
|Numberunder6|0.1298|0.1082||0.1524|0.1211|
|Numberaged6-14|00015|0.0762||-0.0167|0.0819|
|Numberaged15-17|00827|0.1269||0.2452|0.1586|
|Numberaged18-25|0.1037|0.0909||0.2046|0.1385|
|Number over64|0.1758|0.1264||0.1197|0.1028|
|Numbereamers|-01864|0.0794|**|-0.0472|0.0823|
|Plotsize|-00502|1.0932||-0.2716|0.4068|
|Fractionagncult|-0.3016|0.6136||-0.5062|0.5638|
|<br>Housearea|0.0002|0.0018||0.0046|0.0023|
|Fraction**living**area|0.0387|0.4173||-0.4313|0.4458|
|Housingownership|0.2190|0.1930||-0.2073|0.1938|
|Yearquarter1|01047|0.1044||0.0866|0.1053|
|Yearquarter2|-0.0048|0.1226||-0.0724|0.1229|
|Consumption(log)|01531|0.0822|*|0.2062|0.0738|
|Agncultural society|0.1574|0.1257||-0.0789|0.1161|
|Socialbenefits|0.1047|0.1230||0.2177|0.0998|
|Pnvatetransfers|01046|0.1002||0.0564|0.0835|
|Distancetoclinic|-0.2050|0.0862|**|0.0745|0.0671|
|Distance tohospital|0.0263|0.0104|**|0.0199|0.0112|
|Timeto climc|0.0038|0.0075||-0.0048|0.0037|
|Timetohospital|-00032|0.0030||0.0036|0.0031|
|Constant|0.0144|1.0019||-0.3477|0.8578|
|R-squared|0.0728|||0.0635||

Left-out vanables are dumnues for large cities, married, secondary education, hired in non-agriculture, number aged 26-64, year quarter 4. Robust standard errors reported. (*) Significant at the 10% level. (**) Significant at the 5% level.

40
|Variable|N|Mean|Std. Dev.|Min|Max|
|---|---|---|---|---|---|
|R,thd.hryvnas|40440|8001.91|16948.02|0.08|548158.70|
|L, workers|40440|171.57|265.52|1|6779|
|K,thd.hryvnas|40440|3111.47|6964.35|0.07|183732.00|
|M,thd.hryvnas|40440|6971.82|17583.37|0.1|706991.3|
|I,thd.hryvnas|30357|693.39|2112.28|0|89370.69|
|Serv. Lib.|40440|0.36|0.57|0|4.85|
|Serv. Lib. (FDI)|40440|0.34|0.64|0|29.31|
|Exporter, Yes=1|40440|0.34|0.47|0|1|

Table 2: Descriptive statistics services (Arnold et al., 2011; Fernandes and Paunov, 2011) liberalization on productivity. We follow a similar identification strategy. The strategy relies on the assumption that manufacturing firms that use services more intensively gain more from services liberalization. For each period _t_ , we construct a firm-specific index of services liberalization by interacting a subsector-specific index of services liberalization with firm- and sub-sector-specific intensity of services use. We further look at the _within_ firm variation in TFP and relate it to the changes in the firm-specific index of services liberalization. To recover the TFP measure, we estimate the production function for each manufacturing industry (1-digit NACE classification) by the Olley-Pakes procedure (Olley and Pakes, 1996), controlling for sub-industry-specific demand and price shocks as suggested by De Loecker (2011). We identify demand and price shocks by exploiting variation in sub-industry (4-digit NACE classification) output at time _t_ and by controlling for sub-industry and time fixed effects. Under the constant elasticity of substitution (CES) demand system, unobserved prices are picked up by the variation in inputs and by aggregate demand and do not reflect differences in technology within an industry. If this assumption fails, we still are able to estimate the impact of services liberalization on productivity because our identification strategy relies on within firm variation in services intensity and time invariant differences in technology are not important.

15