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28
Given the rising importance of identifying digital education platforms that work, this paper conducts a meta-analysis of the results of available experiments that assess the impact of VR training on learning and skills development. The study's primary purpose is to identify the extent to which VR training is conducive to learning and skills development. A secondary objective is to assess, to the extent possible, if VR training is also an efficient mechanism to deliver training. The analysis presented here relies on a total of 31 primary studies and over 90 different experiments.

There has not been a systematic assessment of the effects of VR training on learning, other than in the field of surgical education in the early 2000s (Haque, & Srinivasan, 2006). This study was conducted with a limited number of studies and focused on assessing the extent to which VR training could help students perform surgical procedures faster (i.e. improve their time-intask). To bridge the knowledge gap, this study focuses on a more recent time period (2005-2020), during which VR technology has significantly evolved, and covers other fields such as engineering, science, technical education, and general education. Moreover, this study analyzes the effects of VR more holistically as a mechanism to develop cognitive, technical, and socioemotional skills. As such, the findings of this paper represent an essential contribution to the literature and intend to guide education institutions and policy makers to have more information about the effects of VR training as they expand their offer of digital learning opportunities to students.

Based on the information available, our research shows that VR training is, on average, more effective than traditional training as a mechanism to develop students' technical, practical, and socio-emotional skills. Results are particularly promising in fields related to health and safety, engineering, and technical education. Results reveal that for each additional hour (1⁄4 hour) of VR training, students score 3 percent higher in technical (cognitive) learning assessments than 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 than their peers receiving traditional instruction. Results also suggest that students exposed to VR training are up to 30 percent more efficient using inputs, time, and/or avoiding performance errors than students exposed to traditional training, per additional hour of instruction.

4
Aryeetey et al. (2014). We make further refinements to this line of research by underscoring the need for policies that could propel aggregate productivity from growing north-south differences in intersectoral linkages and regional specialization.

The remainder of the paper is organized as follows. Section 2 provides an overview of the mining productivity shock and national and regional trends in structural transformation for Ghana. Section 3 discusses how the mining productivity shock in the south of Ghana leads to growing regional differences in the pattern of intersectoral linkages based on regional I-O tables. Section 4 shows how spatial differences in intersectoral linkages affect sectoral output and productivity based on census data for firms. Section 5 concludes.

# **2. Overview of Mining Shock and Structural Transformation in Ghana**

We begin with a brief account of the recent mining productivity shock in Ghana, followed by an overview of the annual and spatial trends in mining and manufacturing based on statistics from the GGDC-UNU-WIDER economic transformation database (ETD)<sup>3</sup> and district-level data from two censuses on Ghana firms: the 2003 National Industrial Census (NIC) and the 2014 Integrated Business Establishment Survey (IBES).<sup>4</sup> The 2003 NIC and 2014 IBES were administered by the Ghana Statistical Service (GSS). The 2003 NIC covered only industrial sectors (mining, manufacturing, public utilities, and construction), whereas 2014 IBES covered industrial as well as all services sectors (wholesale and retail trade, transport, communications, finance, real estate, government services, and private services). Both censuses were conducted in two phases. Phase II involved a detailed questionnaire, capturing workforce, wages and salaries, stocks, value of fixed assets, quantity and cost of inputs purchased, other operating costs, and sales and other income. We use the data from phase II for both surveys. The reference year for phase II of the 2003 NIC is the calendar year 2003 and the reference year for phase II of the 2014 IBES is the calendar year 2013.<sup>5</sup>

> 3 The ETD is a joint initiative of the Groningen Growth and Development Centre (GGDC) and United Nations University-World Institute for Development Economics Research (UNU-WIDER). It is publicly available at <u>https://www.wider.unu.edu/project/etd-economic-transformation-database, along with documentation on its</u> contents and construction.

> 4 For survey design and implementation details, see GSS (2006) and (2018).

> 5 Phase II of 2003 NIC was fielded between December 2004 and February 2005, and phase II of 2014 IBES was fielded between November 2015 and April 2016.

6
All firms were asked "In fiscal year 2005, would this establishment have hired or fired permanent workers had it not been for having to comply with labor regulations?" When a firm answered yes to this question, the firm was asked specifically how many workers would have been hired or fired. I also observe the number of full-time permanent employees who were employed in the last fiscal year.

Table 1 presents some basic information about the firm-level data. In total, 10,396 firms were used in the analysis. Observations per country ranged from 436 in Honduras to 1,478 in Mexico. Statistics are weighted to give estimates of the population of all firms with at least five employees not in agriculture or the financial sector. Perhaps the most striking feature is that 83.2% of firms state that neither hiring nor firing decisions were affected by rigid labor regulations. The large informal economies in these countries may explain why so many firms say that their hiring and firing decisions are unaffected by labor regulations, since firms may be able to substitute informal workers for formal ones thereby avoiding the regulatory costs. According to the calculations presented in Schneider (2007), the informal economy contributes an average of 44% to total GDP for the 14 countries studied here. Another possibility, which I will explore later in the paper, is that firms can substitute temporary workers for permanent workers.

Only 2.2% of firms said that rigid labor regulations affected their dismissal decisions but not their hiring decisions while 6.6% of firms said that rigid labor regulations affected their hiring decisions but not their dismissal decisions. Interestingly, 8.0% of firms said that rigid labor regulations affected both their hiring and their dismissal decisions. We will see later that, despite the fact that small percentages of firms state that rigid labor regulations affected their hiring or firing decisions, rigid labor regulations do have an important effect on aggregate employment.

The next step in the analysis is to calculate the job- and worker-flows statistics at the level of the country. In order to explain this procedure, I begin by defining some terms. Let the subscript c denote the country and let the subscript j denote the firm. Specifically, consider the following definitions: emplcj = the number of full-time permanent employees in the last full fiscal year t for firm j in country c.

hirecj = the number of permanent employees that firm j in country c would have hired in fiscal year t in absence of rigid labor regulations.

6
# **1 Introduction**

Using firm level data, this paper brings a new perspective to the question of who is vulnerable in volatile times. By focusing on gender differences in labor market impacts of crises, it illustrates how firm level data can benefit our understanding of who is at risk of losing their job and why. Existing studies that examine the distributional impact of crises almost exclusively rely on labor force and household level data, and consequently focus on labor supply. However, crisis induced changes in labor market outcomes are predominantly driven by changes in labor demand; labor supply responses are typically muted compared to the large contractions in labor demand that are a defining feature of crises (McKenzie, 2004; Thomas et al., 2002). In addition, micro-level studies of the distributional consequences of crises typically document significant heterogeneity in impact across broad sectors, yet fail to explore how such heterogeneity arises (see e.g. Fallon and Lucas, 2002). Moreover, they are not able to distinguish between different mechanisms that might explain why some workers are more vulnerable than others.

This paper helps redress these lacunae in the literature by examining how variations in firm responses during the East Asian crisis in Indonesia affected men‘s and women‘s relative employment vulnerability. It distinguishes between two previously unexplored mechanisms by which gender difference in impact might arise: differences in vulnerability could be the result of (i) _sorting_ by gender into firms and occupations that differ in their vulnerability to crises, and/or of (ii) of _differential treatment_ of men and women workers _within_ firms.

That employers often treat men and women differently in the workplace is widely documented (see Altonji and Blank, 1999 for a review of the literature), and there is no shortage of rhetoric asserting that women are disproportionately at risk during volatile times (Grown, 2009; ILO, 2009; World Bank, 2009).<sup>2</sup> A common concern is that women get dismissed first and hired last (Oxfam, 2009, Seguino, 2009 and UNGEI, 2009). However, the limited empirical evidence on the labor market consequences of macroeconomic crisis is distinctly ambiguous as to whether women or men were more vulnerable (Houseman and Abraham, 1993, Lim 2000, Thomas et al., 2002), begging the question as to whether claims regarding women‘s vulnerability may be exaggerated.

Moreover, many of the assertions about women‘s relative vulnerability are predicated on the idea that employers discriminate against women. While we cannot prove or disprove the existence of

> 2 See for example IFPRI, 2008; ILO, 2009; UNDP 2009; and World Bank, 2009; for discussions of the gender implications of the recent food, fuel and financial crises.

2
Table 5: Estimating the effect of migrants’ legal status on hourly wages after return, conditional on return migration

||(1)<br>Migrate|(2)<br>Illegal|(3)<br>Return|(4)<br>Log of<br>hourly wage|
|---|---|---|---|---|
|Age|0.012***|-0.077**|-0.014**|-0.044|
||(0.001)|(0.034)|(0.006)|(0.036)|
|Age squared|-0.000***|0.001**|0.000***|0.001|
||(0.000)|(0.000)|(0.000)|(0.000)|
|No education|0.019***|0.497***|-0.030|-0.398***|
||(0.004)|(0.120)|(0.021)|(0.106)|
|Primary or preparatory|0.032***|0.469***|0.049**|-0.447***|
||(0.004)|(0.129)|(0.022)|(0.103)|
|Secondary|0.017***|0.401***|0.033*|-0.140*|
||(0.003)|(0.105)|(0.017)|(0.078)|
|Rural|0.009***|0.254***|0.009|-0.020|
||(0.003)|(0.084)|(0.015)|(0.063)|
|Oil prices|0.013***<br>(0.000)||||
|Diplomatic exchange||-0.227**<br>(0.102)|||
|Confict|||0.047***<br>(0.013)||
|Illegal status||||-0.596***<br>(0.151)|
|Observations|14,879|1,955|2,071|718|
|Parental controls|Yes|Yes|Yes|Yes|
|Decades of migration dummies|Yes|Yes|Yes|No|
|Labor market controls in Egypt|No|No|No|Yes|
|Predicted log wage: Illegal mig.=|1.242; Legal mig.|=1.838|||

Robust standard errors in parentheses. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1 _Notes_ : Equation 1 is the probability of temporary migration. Equation 2 is the probability of undocumented migration. Equation 3 is the probability of return migration. Equation 4 models the logarithm of hourly wages upon return. For identification of equation (1), we use the inflation adjusted historical oil prices (in US dollars). For identification of equation (2), we use the diplomatic exchange, which is the diplomatic representation of each country of destination of Egyptian migrants in Egypt at time of migration. It is a dummy variable equal zero if there is no evidence of diplomatic exchange and is equal one if there is evidence of diplomatic exchange at the level of charg ́e d’affaires, minister, ambassador or other. For identification of equation (3), we use the number of active armed conflicts that is both country and year specific from the Uppsala Conflict Data Project (UCDP). Equation (4) estimates the effect of illegality on the logarithm of hourly wages, conditional on return migration. The predicted logarithm of hourly wages for illegal and legal migrants are reported in the last row.

39
Policy Research Working Paper 10152

# **Abstract**

As COVID-19 vaccines have become more widely available in Sub-Saharan Africa, vaccination campaigns in the region have struggled to pick up pace and trail the rest of the world. This paper presents new evidence on vaccine hesitancy, uptake, last-mile delivery barriers, and potential strategies to reach those who remain unvaccinated. The data come from high-frequency phone surveys in five countries in East and West Africa (Burkina Faso, Kenya, Malawi, Nigeria, and Tanzania). The surveys were conducted by countries’ national statistical agencies, have national scope, are cross-country comparable, and draw their samples from nationally representative sampling frames. The findings show that across the study countries, a majority is willing to get vaccinated. Still, vaccine hesitancy is non-negligible among those pending vaccination. Concerns about side effects of the vaccine are the primary reason for hesitancy. At the same time, many who are willing to get vaccinated are deterred by a lack of easy access to vaccines at the local level. Radio broadcasts have widespread reach and medical professionals have good rapport among the unvaccinated population. Furthermore, social ties and perceptions as well as intrahousehold power relations matter for vaccine take-up. Based on the findings, the paper elaborates policy options to boost vaccination campaigns in Sub-Saharan Africa.

This paper is a product of the Development Data Group, Development Economics. 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 pwollburg@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
Ppt = Pwt l txt x tpt Pct = Pwt / txt x tct

# where

Endogenous Variables Sc = Supply of cotton, India Dc = Demand of cotton, India Swc = Supply of cotton, Rest of the world Dwc = Demand of cotton, Rest of the world Xc = Export of cotton, India Sy = Supply of yarn, India Dy = Demand of yarn, India Xy = Export of yarn, India Swy = Supply of yarn, Rest of the world Dwy Demand of yarn, Rest of the world St = Supply of textiles, India Swt = Supply of textiles, Rest of the world Dwt = Demand of textiles, Rest of the world Ppc = Producer price of cotton, India Pcc = Consumer price of cotton, India Pwy = World price of yarn Ppy = Producer price of yarn, India Pcy = Consumer price of yarn, India Pwy = World price of yarn Ppt = Producer price of textiles, India Pct = Consumer price of textiles, India Pwt = World price of textiles Dt = Demand of textiles, India Xt = Export of textiles, India Exogenous Variables (Policy Option Variables) txc = Cotton export tax rate (quota equivalent), India tpc = Cotton producer tax / subsidy rate, India tcc = Cotton consumer tax / subsidy rate, India txy = Yarn export tax rate (quota equivalent), India tpy = Yarn producer tax / subsidy rate, India.

tcy = Yarn consumer tax / subsidy rate, India txt = Textiles export tax rate (quota equivalent), India tpt = Textiles producer tax / subsidy rate, india tct = Textiles consumer tax / subsidy rate, India

# **_Iniial_** _Values_

Data used to calibrate the simulation model are based on the official data in various sources (Office of the Textiles Commissioner, 1994-2000, EICA, 1995-2000, and ICAC, 1994-2000). As for the cotton production in India, the initial values are based on the data of the Cotton Advisory Board, rather than the data of the Ministry of Agriculture in order to make them consistent with other values. Although there are many varieties of cotton, the spinnable cotton market is not divided by variety in this model. This is partly due to the lack of sufficient data and partly because both long-staple cotton and medium-staple cotton, and medium-staple cotton and short-staple cotton are substituted for each other. In other words, cotton is treated as homogeneous in this model so far as it is spinnable.

While spinnable cotton is treated in one category in the model, non-spinnable cotton _(deshi_ cotton) is excluded from the simulation. The two main reasons are that the market of non-spinnable cotton is completely different from the market of spinnable cotton, and that non-spinnable cotton is freely exported 32
# **Table A32: OLS; Effects of refugee presence on household total vegetables production by change in main source of income, with analysis at cluster level and the distance weighted refugee index -** **<u>at 50km from the clusters (Panel 2009 2012)</u>**

||Log of T|otalVegetablesProd|uction(UGX)|
|---|---|---|---|
||(1)|(2)|(3)|
|Log of refugees (50km)|-0.0242|-0.0187|-0.0101|
||(0.0307)|(0.0307)|(0.0312)|
|Log of refugees(50km)#To Commercial farming|0.2820**|0.2789**|0.2855***|
||(0.1316)|(0.1386)|(0.0817)|
|Log of refugees(50km)#To Wage employment|-0.0026|-0.0159|0.0325|
||(0.0462)|(0.0457)|(0.0347)|
|Log of refugees(50km)#To Non-Agric self-employment|0.0550|0.0496|0.0853**|
||(0.0402)|(0.0399)|(0.0351)|
|HH head age||0.0643***|0.0388***|
|||(0.0128)|(0.0123)|
|Age squared||-0.0006***|-0.0004***|
|||(0.0001)|(0.0001)|
|HH head sex (male)||0.3794***|-0.0042|
|||(0.0654)|(0.0858)|
|Annual mean temperature||-0.0052|-0.0643|
|||(0.0689)|(0.1212)|
|Average 12 months total rainfall||-0.0007|0.0007|
|||(0.0009)|(0.0018)|
|Household size|||0.0491***|
||||(0.0145)|
|Experienced shocks|||0.0215|
||||(0.0855)|
|**_HH head marital status_**||||
|Married monogamously|||-0.0681|
||||(0.2809)|
|Married polygamously|||-0.0522|
||||(0.2905)|
|Separated (divorced/widowed)|||-0.3166|
||||(0.2896)|
|**_HH head education level_**||||
|Completed primary sch|||0.2295***|
||||(0.0637)|
|**_HH main source of income_**||||
|Change to Commercial farming|0.2832|0.2606|0.0899|
||(0.3182)|(0.3117)|(0.3299)|
|Change to Wage employment|-0.0935|-0.0647|-0.1240|
||(0.1346)|(0.1331)|(0.1211)|
|Change to Non-agricultural self-employment|-0.0379|-0.0591|-0.1308|
||(0.1116)|(0.1095)|(0.1226)|
|**_Other source of HH income_**||||
|Property income and investments|||-0.1350|
||||(0.1451)|
|Transfers and other benefits|||0.0197|
||||(0.0984)|
|**_HH Asset_**||||
|Own > median size of land (2.5 acres)|||0.3106***|
||||(0.0643)|
|Observations|2,957|2,956|2,511|
|R-squared|0.2343|0.2649|0.3439|

52
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