rafmacalaba/data-use-mentions-extended huggingface dataset:
evidence about the effectiveness of different policies. The paper concludes with a discussion of policy implications and directions for future research.

# **2. Data**

We rely on data from the first wave of the Business Pulse Surveys (BPS) developed by the World Bank Group (WBG) to measure the impact of the COVID-19 pandemic on the private sector (ApedoAmah et al., 2020), as well as the COVID-19 follow-up rounds of the World Bank Enterprise Surveys.<sup>2</sup> This novel harmonized data set offers the most comprehensive assessment of the short-term impact of the shock (from April through August 2020). The sample includes more than 120,000 businesses in 60 low-, middle-, and high-income countries in the six regions where the WBG is present.<sup>3</sup> The sample covers micro, small, medium, and large businesses across all main sectors (agriculture, manufacturing, retail, and other services, including construction).

The questionnaire collects information on business performance through the COVID-19 shock on some critical economic dimensions: operations of the business, sales, liquidity and insolvency, labor adjustments, firms’ responses, and expectations and uncertainty about the future (Apedo-Amah et al., 2020). Businesses are asked about their preferred mechanisms of support during the pandemic. We grouped these mechanisms in the following categories of policy instruments: monetary transfers, deferral of payments, access to finance, support with tax obligations, wage subsidies, and others.<sup>4</sup> Businesses are also surveyed on whether they have received any of these mechanisms of support

> 2Source: Enterprise Surveys, The World Bank, http://www.enterprisesurveys.org.

> 3The survey covers East Asia and Pacific (EAP), Europe and Central Asia (ECA), Latin America and the Caribbean (LAC), Middle East and North-Africa (MNA), South Asia (SAR), and Sub-Saharan Africa (SSA).
. (2010) Changes in extreme high water levels based on a quasi-global tide-gauge data set. Journal of Geophysical Research 115. doi:10.1029/2009JC005997 Mooyaart, L.F. and Jonkman, S.N. (2017) Overview and Design Considerations of Storm Surge Barriers. Journal of Waterway, Port, Coastal, and Ocean Engineering, 143, doi:10.1061/(ASCE)WW.1943-5460.0000383
*|
|---|---|
|Top5performers(2021)|Cabo Verde,Rwanda,Kenya,Senegal and Ghana|
|Bottom 5 performers (2021)|South Sudan, Somalia, Sudan, Guinea-Bissau, Central African Republic|
|Most positive trend since 2005<br>(>=0.5pt)|Zimbabwe, Togo, Côte d’Ivoire, Rwanda|
|Most negative trend since 2005 (<=-<br>0.5 pt)|Sudan, Tanzania, Mali, Madagascar, Guinea-Bissau, Eritrea|
|No change since 2005|Niger, Nigeria, Senegal|

Data source: CPIA Africa Report 2022.

11
MALAWI’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE reports covering different areas of infrastructure—ICT, irrigation, power, transport, WSS—and different policy areas including investment needs, fiscal costs, and sector performance.

This report presents the key AICD findings for Malawi, allowing the country’s infrastructure situation to be benchmarked against that of its African peers. Malawi is benchmarked against both low- and middle-income African peers that, respectively, provide close comparators and a more aspirational point of reference. Detailed comparisons will also be made with immediate regional neighbors in the Southern African Development Community (SADC).

Several methodological issues should be borne in mind. First, because of the cross-country nature of data collection, a time lag is inevitable. The period covered by the AICD runs from 2001 to 2006. Most technical data presented are for 2006 (or the most recent year available), while financial data are typically averaged over the available period to smooth out the effect of short-term fluctuations. Second, in order to make comparisons across countries, the indicators and analysis had to be standardized so that everything was done on a consistent basis. This means that some of the indicators presented here may be slightly different from those that are routinely reported and discussed at the country level.

# Why infrastructure matters

Over the decade 1997–2007, Malawi’s economy grew at an average annual rate of only 2.9 percent, placing it among the slower-growing nations on the continent. This performance falls well short of the sustained 7 percent per year needed to meet the MDGs. About 0.8 percentage points of southern Africa’s improved per capita growth performance during the 2000s can be credited to improved structural and stabilization policies (Calderón 2008), while 1 percent is related to improvements in the countries’ infrastructure platforms. In the case of Malawi, most of the boost was due to the ICT revolution, while power and roads contributed very little. Simulations suggest that if Malawi’s infrastructure could be improved across the board to the
**Figure 3.8 Governance of Extractive Resources**

<!-- Start of picture text --> 80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>2017 Resource Governmance Index.<br>Turkmenistan Gabon Saudi Arabia Sudan Bahrain Libya Equatorial Guinea Cuba United Arab Emirates Oman Qatar Iran Bangladesh Chad Uzbekistan Algeria South Sudan Uganda Kuwait Myanmar Democratic Republic of Congo Egypt Congo Russia Venezuela Timor-Leste Malaysia Azerbaijan Yemen Angola Nigeria Ecuador Iraq China Kazakhstan Vietnam Argentina Colombia Cameroon Tunisia Cote d'Ivoire Ukraine Bolivia Brazil Mexico Indonesia Trinidad and Tobago Ghana Tanzania United States of America (Gulf of Mexico) Mozambique Canada (Alberta) United Kingdom India Norway<br><!-- End of picture text --> Source: Natural Resource Governance Institute, _2017 Resource Governmance Index._ Note: Governance of Extractive Resources covers the governance of allocating extraction rights, exploration, production, environmental protection, revenue collection and state-ow ned enterprises. Score 0-100 where 100=best. If a country scores "No data / Not applicable" it w ill not show in the bar chart.

Exclusively state-owned corporations tend not to separate control from management. That typically limits the empowerment of managers, who are key not only to the success of the firm but to economic development in general. In fact, several developing countries have in the past criminalized business mistakes, especially for top executives of state-owned enterprises, which discourages employees from risk-taking and economic initiative.

In the past decades, there has been an important shift in opening the capital of state-owned oil corporations, including in China, Brazil and Mexico. In December 2017, Abu Dhabi National Oil Corporation (ADNOC) successfully launched an initial public offering (IPO), one of the first among large Middle East oil exporters. Saudi Arabia, as part of its ambitious plan to transform its economy, has announced that it would sell 5 percent of the state-owned
|(0.0573)|(0.0620)|(0.0908)||(0.0218)|(0.0224)|(0.0390)|
|IDN exports per worker *||||0.0324||||0.238||||0.000657|
|Share of hhlds with computer||||(0.407)||||(0.368)||||(0.138)|
|Observations|165|165|165|165|165|165|165|165|165|165|165|165|
|R-squared|0.927|0.927|0.927|0.927|0.975|0.975|0.975|0.975|0.967|0.968|0.968|0.968|

Source: Authors’ calculations based on Sakernas 2005-2010 and 2011-2015, Susenas 2005-2010, 2015-2016 Philippines STEP survey, and 2011-2015, and UN Comtrade database 2005-2010.

Notes: IV regressions. IV: Global demand for foreign products excluding Indonesian products. Indonesian exports per worker in thousands of USD at 2010 prices and global demand for foreign products in millions of USD at 2010 prices. ICT variable at the province level is the share of households with computer. Province controls include the age structure of the population, share of urban population, real GDP per capita, minimum wage level, gender and educational composition of employment. All models control for province and year fixed effects. Robust standard errors between parentheses clustered by province. ***, **, and * indicate significance at 1%, 5%, and 10% confidence levels, respectively.

36
36 whether the payment was made in full, or in part, or whether it was to pay for arrears. Nevertheless, a large percentage of grid‐connected households reporting zero expenditures could signal many households not paying fully, if at all, for electricity.

The results, summarized in Figure 11, show that Angola, Botswana, Ethiopia, Malawi, South Africa, Uganda, and Zambia had one‐fifth or more of households connected to the grid reporting zero expenditures on electricity, with Angola (46 percent), Zambia (38 percent), and Botswana (31 percent) leading. By contrast, Nigeria—where bill collection losses have been documented to be very high—significantly under‐reported non‐payment, showing a non‐payment rate of only 4 percent. The survey in Senegal, which asked for expenditures over the last 12 months, found no households connected to the grid not having made payments. The results by quintile are shown in Table A.20 in the annex. Generally, the share of grid‐ connected households reporting zero expenditures declined with quintile, but Ethiopia and Uganda showed the opposite trend, with the top quintile having the highest proportion of grid‐connected households reporting zero expenditures. There are several potential explanations. The rich may be more likely to get jobs with generous benefits, including free electricity. They may be more likely to rent houses with electricity included in the rent. Or they are able to manipulate meters or bribe utility employees to avoid bill payment, in which case they should be targeted for reducing commercial and bill collection losses.

Figure 11: Share of households connected to the grid with zero expenditures on electricity <!-- Start of picture text --> 80<br>Urban Rural Total<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>% of households<br><!-- End of picture
level, an extensive volume of research has examined the effects of FDI on host countries in a variety of dimensions including productivity, employment, wage rate, and export performance. The productivity effect of FDI, in particular, has attracted considerable empirical attention.

In this sub-section, we discuss the literature evaluating the impacts of inward FDI in the context of BRI countries. The literature presented here is based primarily on analysis of historical FDI data in OBOR countries prior to the OBOR initiative. Table A.1 in the appendix provides a summary of studies discussed in this Section.

An extensive empirical literature assesses the existence of productivity spillover from multinational to domestic firms. Such spillover could occur to domestic firms acquired by foreign multinational firms, domestic firms competing with the foreign multinationals, as well as those sharing vertical production linkages.

- **Foreign acquisition.** The impacts of foreign acquisition on targeted firms have been shown in terms of wage premium, employment growth, and productivity increase. In terms of wage premium, Lipsey and Sjholm (2004) use Indonesian manufacturing firm-level data to show that foreign firms tend to hire more educated workers and pay a wage premium even

> 4 A prominent literature led by Helpman, Melitz and Yeaple (2004) introduces firm heterogeneity into the decision between exports and horizontal FDI and shows that not only are the most productive firms most likely to engage in FDI, FDI sales relative to exports are also larger in sectors with more firm heterogeneity, higher transportation cost, higher tariff, and greater capital intensity. The work by Helpman, Melitz and Yeaple (2004) is extended in numerous studies including, for example, Yeaple (2009) and Chen and Moore (2010).

6
sectors and restricts the equalization of labor productivity (nominal value-added per worker) across sectors. Introducing time-scripts, the labor allocation p ]′ can be written as a function of final consumption shares p , trade shares pLL, and productivity gaps = [llaappaa, llmmppaa, llssppaa p p p p p CC<sup>�</sup> � (7)NN<sup>�</sup> TT where and denote the time-invariant matrices of labor shares pp pp and sectoral linkages .<sup>3</sup> LL = FF�CC<sup>�</sup> , NN<sup>�</sup> , TT �BB , MM Therefore, function pp pp which combines equations (5) and (6) helps solve for the resulting labor jjpp jjkkpp BB MM ββ μμ allocation when one or more vectors among FF(. ) p and p are counterfactually altered. jjpp CC<sup>�</sup> , NN<sup>�</sup> TT

# **3   Data**

The quantitative application of equation (7) requires time-series data on four variables– employment shares p , final consumption shares p , trade shares p and labor market wedges p . Equation (6) allows the construction of LL p using CC<sup>�</sup> p and p . Hence, in essence, the analysis hinges on the NN<sup>�</sup> TT availability of p , p , p , and TT p . LL VV<sup>�</sup> In addition, I also need the estimates of time-invariant labor share vector LL CC<sup>�</sup> NN<sup>�</sup> VV<sup>�</sup> and intermediate share matrix . Equation (7) allows quantifying the impact of over-time changes in pp and on BB sectoral allocation. However, the unavailability of these data for Central American economies hinders pp pp pp
|0.06<br>(0.085)|-0.13<br>(0.085)|0.10|0.05<br>(0.079)|-0.16*<br>(0.091)|0.08|
|Observations|908|1,016||1,034|890||
|Control Mean|-0.03|0.03||0.15|-0.18||
|Selected Controls|X|X||X|X||

Notes: Robust standard errors are shown in parentheses. ***p<0.01 **p<0.05 *p<0.1 Second, Table 4 shows the effects of the intervention on the “perceptions toward outgroups index” and its components, measuring how happy and relaxed the respondents felt when thinking about outgroups. Again, none of the treatment arms has any effect on the index, but there is a statistically significant impact on perceptions toward the opposite sex at the indicator level. Specifically, exposure to diverse social groups in the VR experience is estimated to cause between 0.07 and a 0.08 percentage points reduction in the respondent’s perceptions toward the opposite sex (Columns 5-6), meaning that young women felt less relaxed or happy when thinking about the men they saw in the public space.

When looking at the subgroups, the only statistically significant results pertain to Urdu and nonUrdu speakers, taken as a proxy for Muhajir ethnic group versus other ethnicities. Table 4.A reports the effect of the intervention on “perceptions toward outgroups index” by ethnic affiliation. Exposure to urban design elements is associated with an increase of 0.31 standard deviation on the “perceptions toward outgroups index” among non-Urdu speakers. The difference between Urdu and non-Urdu is statistically different (p-value<0.01), suggesting that non-Urdu speakers report positive perceptions toward other ethnic groups,
# **3. Trying to track employment outcomes in SSA: Lessons from Uganda**

## **3.1 Evolution of the UNHS survey and the labor force/employment module**

Uganda’s National Household Surveys (UNHS) (formerly the Integrated Household Survey, or IHS) has been conducted by the Uganda Bureau of Statistics (UBOS) at regular intervals since 1988. The UNHS are multipurpose household surveys, originally developed as a tool for poverty monitoring and analysis. They have been conducted on a random sample of households over a 12 month period since 1992. Core modules include a household demographic module, health and education modules, questions measuring household living conditions assets and access to infrastructure, and a standard LSMS-type (recall) consumption module.<sup>12</sup> The analysis in this paper focuses on the 1992/93 surveys onwards. This series of household surveys is noteworthy for the consistency of the overall design and quality of execution in the field. UBOS was one of the first offices in SSA to regularly make clean, anonymized unit record data available to researchers<sup>13</sup> , despite numerous obstacles such as funding gaps and the regional conflict in the early 1990s.

The consistency of the overall design of the household surveys did not extend to the labor force and employment modules. These modules have been altered in each survey, displaying a range of good and not so good practices. Some changes in the employment module were made to shorten the questionnaire, making room for a topic given a higher priority that year, while more recent changes were made to improve the measurement of labor force participation and employment outcomes (e.g. more extensive screening questions) as more attention was focused on better capturing labor outcome indicators in the country. Consequently the information content differs across surveys in terms of scope and content. This has meant that the methodology used to compute variables on the measurement of labor market outcomes - levels and trends –changed over time as
# Modeling the Impacts of climate Change on Future Vietnamese Households: A Micro‐Simulation Approach

Julie Rozenberg, Stéphane Hallegatte World Bank Group, Washington DC

**<mark>Keywords</mark>** <mark>: development, poverty, climate change, shared socio‐economic pathways, scenarios, uncertainty, inequalities</mark> JEL: I30, I32, Q54, Q56, O10, O40

**Acknowledgements** : This work is part of the programmatic AAA on Vietnam Climate Resilience and Green Growth (P148188) and was developed under the oversight of Christophe Crepin. It contributed to the global program on Climate Change and Poverty (P149919) under the oversight of Stephane Hallegate. The results for Vietnam presented here where extracted from a global work published in (Rozenberg and Hallegatte 2015). The authors would like to thank, in no particular order, Syud Amer Ahmed, Marcio Cruz and Israel Osorio‐ Rodarte for the GIDD database and precious advice on micro‐simulation modeling; Jan Kwakkel for the scenario discovery tools; Kris Ebi for her knowledge of climate change impacts on health; Petr Havlik and Hugo Valin for the agriculture scenarios; Cyril Caminade and Tamaro Kane for malaria numbers; Simon Lloyd for inputs on stunting; Francisco Ferreira, Eeshani Kandpal, Gabriel Demombynes, Marianne Fay, Adrien Vogt‐Schilb, Pamela McElwee, and Maurice Rawlins for comments on previous versions of this work.
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**Figure 5. The income gap in account ownership remains large in some countries in Europe and Central Asia**

<!-- Start of picture text --> 100%<br>90%<br>80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>B40 T60 B40 T60 B40 T60 B40 T60 B40 T60 B40 T60 B40 T60 B40 T60 B40 T60 B40 T60<br>Central Central Eastern Northern Russian South Southern Turkey Western Western<br>Asia Europe & Europe Europe Federation Caucasus Europe Balkans Europe<br>the Baltics<br>2011 2017<br>Percent of adults with account<br><!-- End of picture text --> _Source_ : Global Findex database.

_Note_ : The gap is between adults living in the wealthiest 60 percent (T60) and poorest 40 percent of households (B40) within economies.

Unbanked adults are more likely than people with accounts to have low levels of education. In the developing world, about half of all adults have no more than a primary school education; among unbanked adults, the share is close to two‐thirds. In developing economies in ECA, 34 percent of unbanked adults have no more than a primary school education. However, there is wide variation. In Albania and Kosovo, 67 percent of the unbanked have no more than a primary school education. By contrast, in Armenia this share was only 12 percent in 2017, thanks to the huge increase in account ownership among low‐education adults, which rose from 7 percent in 2011 to 50 percent in 2017.

Being unbanked in ECA is also associated with lack of labor force participation. About 60 percent of unbanked adults in developing ECA are out of the labor force. The share of account ownership among people out of the labor force was 12–16 percentage points
mation to measure the quality of the physical environment and materials, qualification and training of facilitators, parenting education sessions and parental engagement, classroom practices and interactions. More information on the MELE instrument is presented A. in Appendix _Variables for LASSO Controls._ In addition to recording the age and gender of the target child, at baseline we collected anthropometric measures for target children (height and weight) using international standardized procedures and measuring equipment (UNICEF MICS, 2019). For the primary caregiver, we collected the following sociodemographic data: age, sex, education, employment status, and language spoken. We also collected information on other household characteristics, such as household size and the receipt of remittances. This information was provided by the primary caregiver at baseline and follow-up.

# **4.3 Baseline Summary Statistics**

Table 1 displays the mean and standard deviation (hereafter, SD) at baseline for the key characteristics of the target children, caregivers and households in panels A, B and C, respectively. Columns (1) and (2) show these summary statistics for the control group, and columns (3) and (4) for the treatment group. The average baseline age of the target children is 47 months, while the average age of their primary caregiver is 32 years. Figure A2 presents a histogram of the age distribution of the target children at baseline, depicting substantial variation in ages between 36 and 59 months.

The baseline characteristics depict a high level of acute deprivation among the children and families in our sample. Anthropometric measurements at baseline show that the average target child is stunted, with height-for-age that is more than two standard deviations below the World Health Organization (WHO) child growth standards median. The medium of instruction in primary school, Portuguese, is spoken only by 15% of the caregivers. The vast majority of caregivers are illiterate (81%) and 45% never went to school,

> school coordinators, and caregivers; and parental engagement in the escolinha.

15