rafmacalaba/data-use-mentions-extended huggingface dataset:
to reduce inequalities (SDG10). Ensuring women’s access to the internet is a necessary but not sufficient condition to transform the digital divide into a digital dividend and ensure that women benefit from being online. Bridging the digital divide requires ensuring the relevance of internet content, trust in internet privacy, and female e-inclusion policies. Moreover, closing the gender gap in labor force participation in the MENA region requires implementing policies and programs that address gender norms regarding girls’ and women’s mobility.

Finally, it is worth noting that the analysis has limitations that should be considered for future research. First, long panel data encompassing more countries and more than four years would allow researchers to consider factors that vary across countries in the region and across years. Second, digital solutions could help segments of women to better manage and balance familial obligations with paid work, based on the nature of work and economic activity. Besides, the extent of formal and informal employment in an economy affects interactions between the gender digital divide and the gender employment gap. Future research should consider how an inclusive internet environment would affect the different labor market outcomes in the MENA region.

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In EU countries, losses were greater for weaker students and those experiencing longer school closures. This is in line with other studies (Goldhaber et al. 2022; Jack et al. 2022; Patrinos 2023). Losses are larger in all ECA+ countries, compared to EU countries only.

Our results are similar to national micro-studies conducted before the publication of PIRLS 2021 data (Patrinos 2023) and are in line with analyses of the same PIRLS data for the rest of the world (Jakubowski et al. 2023).

While the data go back to 2016, it is likely safe to say that most of the deviation from over time trends is due to the school closures induced by COVID-19. The analyses conducted correspond to the analysis plan set out in our pre-registered protocol (https://osf.io/nrbgz/).

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mobile capital; sector-specific capital in imperfectly competitive sectors; and primary inputs imported by multinational service providers, reflecting specialized management expertise or technology of the firm. The existence of sector specific capital in the imperfectly competitive sectors implies that there are decreasing returns to scale in the use of the mobile factors and supply curves in these sectors slope up. In our central model, we assume that 50 percent of the capital in each of the imperfectly competitive sectors in sector specific. We conduct sensitivity analysis with respect to this share by allowing 25 percent and 75 percent of the capital in each sector to be sector specific.

We build on the model and data set for Tanzania in Jensen, Rutherford and Tarr (2009). As in their model, there are three categories of firms in the model: (1) perfectly competitive goods and services sectors: (2) imperfectly competitive goods sectors; and (3) imperfectly competitive services sectors with foreign direct investment. The cost, production and pricing structures in the three categories differ widely. The principal extension is that we disaggregate the rest of the world region of JRT into three regions: (1) the European Union; (2) the union of the East African Customs Union and SADC, which we call our African region; and (3) the Rest of the World. In the imperfectly competitive sectors, this requires introducing different firm types with distinct cost structures for each region. We retain the small open economy model framework, so only Tanzania is modeled fully.

# **Perfectly Competitive Goods and Services Sectors**

Regardless of sector, all firms minimize the cost of production. In the competitive goods and services sectors, goods or services are produced under constant returns to scale and where price equals marginal costs with zero profits. This includes all 19 of the agriculture sectors, some food processing sectors such as meat and dairy products and grain milling, and services such as construction, hotels and restaurants, postal communication, real estate, public administration, health and education
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. We exclude individuals who had more than two migration episodes. 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, unconditional on return migration. The predicted logarithm of hourly wages for non-migrants, illegal migrants and legal migrants are reported in the last row.

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CÔTE D’IVOIRE’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE Côte d’Ivoire could take many decades to reach the infrastructure targets. However, if inefficiencies were addressed, the goals could be reached within 20 years, even without increased spending.

# The continental perspective

The Africa Infrastructure Country Diagnostic (AICD) has gathered and analyzed extensive data on infrastructure across almost all African countries, including Côte d’Ivoire. The results have been presented in reports covering different sectors of infrastructure (ICT, irrigation, power, transport, water and sanitation) and different policy areas (including investment needs, fiscal costs, and sector performance).

This report presents the key AICD findings for Côte d’Ivoire, allowing the country’s infrastructure situation to be benchmarked against that of its African peers. A social and economic crisis in Côte d’Ivoire has crippled its growth trajectory, which had been that of a middle-income country. It will therefore be compared to low-income countries (fragile and non-fragile groups) and middle-income countries, as well as immediate regional neighbors in West Africa.

The study presented several methodological issues. 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 effects of short-term fluctuations. Second, the indicators and analyses were standardized to ensure that comparisons across countries were consistent. As a result, 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

During the five years from 2003 to 2007, Côte d’Ivoire’s economy grew at an average annual rate of 1.5 percent, considerably lower than the 2.1 percent recorded in the previous decade. Infrastructure contributed 1.3 percentage points to per capita economic growth over this period. This contribution came mainly
Uncertainty and ambiguity brought about by new products and business processes can undermine essential considerations for cost-effectiveness and allow for higher extra profits for the corrupt. When pricing is more uncertain, corrupt buyers can collude with firms to push prices unreasonably high, yet claim that no corrupt overpricing took place (Lundberg and Marklund, 2013; Halonen, 2021). Applying these arguments to Bulgaria where corruption is endemic and hence established corrupt networks are likely to take advantage of opportunities presented by GPP, we hypothesize: _H2 Green public procurement is expected to increase corruption risks, through enabling more restrictive tendering conditions._ Third, green considerations in public procurement are expected to impact firm productivity in the short term through at least two channels: selection and investment. Less competitive and higher corruption risk in procurement has been shown to lead to less allocative efficiency and thus lower productivity growth in the economy (Brugués, Brugués and Giambra 2024). Thus, if GPP presents a market entry barrier and increases corruption risks, it can contribute to the selection of less productive firms as suppliers of green products. By contrast, if GPP follows more competitive procurement procedures and practices, it can thus enhance the economy’s allocative efficiency by channeling more resources to more productive firms. Moreover, the compliance with green product and process criteria in public procurement contracts may require more sophisticated technology, which may attract more productive firms to compete for green contracts. The relationship between GPP and firm productivity is thus ambiguous, unless the allocation of GPP contracts follows more competitive practices that are less prone to corruption risk. Building on the previous two hypotheses, we hypothesize: _H3: If green public procurement is associated with less competition and high corruption risks, it is expected to allocate public resources to less productive firms._

# 3. Methodology

## 3.1 Data

Two public procurement data sources were used for the analysis, following Fazekas et al. (2024). First, all tenders and contracts were collected from the historic national e- procurement portal, AOP (
<u>https://openknowledge.worldbank.org/handle/10986/25045</u>

- Blum, J. R. (2014). _What Factors Predict How Public Sector Projects Perform? A Review of the World Bank's Public Sector Management Portfolio_ (Policy Research Working Paper No. 6798). World Bank, Washington, DC. https://doi.org/10.1596/1813-9450-6798

- Bold, T., Kimenyi, M., Mwabu, G., Ng’ang’a, A., & Sandefur, J. (2018). Experimental evidence on scaling up education reforms in Kenya. _Journal of Public Economics_ , _168_ , 1–20. <u>https://doi.org/10.1016/j.jpubeco.2018.08.007</u>

Briggs, R. C. (2020). Results from single-donor analyses of project aid success seem to generalize pretty well across donors. _The Review of International Organizations_ , _15_ (4), 947–963. <u>https://doi.org/10.1007/s11558-019-09365-x</u>

- Bulman, D., Kolkma, W., & Kraay, A. (2017). Good countries or good projects? Comparing macro and micro correlates of World Bank and Asian Development Bank project performance. _The Review of International Organizations_ , _12_ (3), 335–363. https://doi.org/10.1007/s11558-016-9256-x

- Buntaine, M. T., & Parks, B. C. (2013). When Do Environmentally Focused Assistance Projects Achieve their Objectives? Evidence from World Bank Post-Project Evaluations. _Global Environmental Politics_ , _13_ (2), 65–88. <u>https://doi.org/10.1162/GLEP_a_00167</u>

Cameron, D. B., Mishra, A., & Brown, A. N
in SSA have been created in the informal sector. A study by Kruse, Mensah, Sen, and de Vries (2023) based on the same ETD dataset suggests that these SSA countries experienced a ‘renaissance’ of their manufacturing employment share between 2000 and 2018, driven by unregistered firms that likely serve domestic or regional demand. On the other hand, Diao, Ellis, McMillan, and Rodrik (2021) find that large manufacturing firms in SSA are productive and participate in GVCs, but do not expand employment. The large role of informal activity in creating jobs may be rooted in differences in the enabling environment, a bias against low-wage workers in GVCs, or a combination thereof. A recent study suggests that while GVC participation and manufacturing output shares are positively associated in a large sample of 91 countries, this is driven by HMICs and is insignificant for many low-income countries many of which being commodity exporters. The study 10
l POLICY RESEARCH WORKING PAPER 2068

# Summary findings

## Combining microeconomic evidence with

macroeconomic theory, Krebs and Maloney present an integrated approach to wage and employment determination in an economy where firms pay above market "efficiency wages" to prevent trained workers from quitting.

The model offers predictions about the behavior of formal employment, labor turnover, and segmentation in response to formal sector productivity shocks (including economic growth and tax reductions), changes in the desirability of self-employment (formal sector tax rates), and the cost of training a new worker.

They use panel data from Mexican labor surveys to estimate the quit function derived from the model and the results support their view that transitions from formal salaried work to informal self-employtnent are quits rather than fires. (Quitting is positively related to the mean self-employment income and the probability of being rehired and negatively related to the mean formal salaried wage.) They then use the parameters estimated from the quit function to calibrate the model economy and simulate the impacts of economic shocks and policy innovations and find the impact on employment, turnover, and segmentation to be substantial.

- This paper a product of the Poverty Reduction and Economic Management Sector Unit, Latin America and Caribbean - Region is part of a larger effort in the region to understand the functioning of developing country labor markets. Copies of the paper are available free from the World Bank, 1818 H Street NW, Washington, DC 20433. Please contact Tania Gomez, room 18-102, telephone 202-473-2127, fax 202-522-2119, Internet address tgomez@worldbank.org. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/htmVdec/Publications/WVorkpapers/ home.html. The authors may be contacted at tkrebs@uiuc.edu or wmaloney@worldbank.org. February 1999. (39 pages)

_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,
- [115] McGuirk, E. F. & Nunn, N. Transhumant Pastoralism, Climate Change, and Conflict in Africa. National Bureau of Economic Research Working Paper 28243 (2021).

- [116] Schon, J. & Koren, O. Introducing AfroGrid, a unified framework for environmental conflict research in Africa. _Scientific Data_ **9** , 116 (2022).

- [117] Stearns, J. & Vogel, C. The Landscape of Armed Groups in Eastern Congo: Fragmented, politicized networks (2017). URL https://reliefweb.int/report/democratic-republic-congo/ landscape-armed-groups-eastern-congo-fragmented-politicized.

- [118] OECD and Sahel and West Africa Club. _Urbanisation and Conflicts in North and West Africa_ (2023). URL https://www.oecd-ilibrary.org/content/publication/ 3fc68183-en.

**Acknowledgments.** We thank Jonas Geldmann for sharing METT data, Vivek Sakhrani for sharing asset wealth data, Sahiti Sarva for facilitating the transfer of asset wealth data, Reece Thornley for sharing the Chinko Nature Reserve shapefile, and Mathias D’haen for sharing the Siniaka-Minia Reserve shapefile. 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. SD and GE acknowledge generous funding from the Knowledge for Change Program, whose donors are the Swedish International Development Cooperation Agency (SIDA), Agence Fran ̧caise de D ́eveloppement (AFD) - French Development Agency, the Government of Japan, and the European Union.

**Data Availability.** All data is available in figshare at https://doi.org/10.6084/m9. figshare.25560351 [52]. The only exception is the raw asset wealth data, obtainable directly from Atlas AI. However, we provide in figshare the asset wealth values for each
# **5.1** **_Empirical Strategy_**

Because we have a panel data set, we are better equipped to assess the effect of financial literacy on a set of outcomes than previous studies. First, we use 2009 measures of financial inclusion and outcomes and 2008 values of financial literacy and other explanatory variables. We use past values of the independent variables to account for both the potential simultaneity between financial literacy and financial outcomes, along with the potential endogeneity of the financial literacy measure.

Second, we use IV estimation to assess the impact of financial literacy on financial behavior. Two instruments are used: (a) the number of newspapers in circulation per two-digit region (both regional and national) and (b) the total number of universities per two-digit region (both public and private). The two variables can be expected to be correlated with financial literacy in terms of ―exposure‖ to newspaper readership (either directly or through family members and neighbors who read the paper) and higher education of peers in the region. The experience of others is not under the control of the respondent and is thus exogenous with respect to his or her actions, but respondents can learn from those around them, thus increasing their own literacy. Several other studies have documented that individuals learn about financial matters from peers (Duflo and Saez, 2003 Hong, Kubik, and Stein, 2004; and Brown, Ivkovic, Smith, and Weisbenner, 2008). Studies have also documented the importance of proximity to a university as an exogenous measure of financial knowledge (Christiansen, Joensen, and Rangvid, 2008).

The bottom of Table 1 shows that the average number (by two-digit region) of newspapers is 56 and the average number of universities is 18 (11 public and 7 private).

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. For example, allowance in Beijing is 260 yuan every month, relatively high, the National average standard is 200 Yuan in 1993. A national survey in 1998 shows that 93.2% of the Xiagang workers have received the monthly allowance, ranging from 140 yuan to 380 yuan in different cities.

'9 The department of Labor predicted that form 1995 to 2000, the number of laid off worker will reach 18 million.

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duct, processing and analysis of country health studies and involved in dissemination and application of findings from them, rather than from attempts to collect internationally comparable information on health.

- (4) Country health studies should be set in the context of and supportive of the existing health information system. It is vital to involve Ministries of Health as well as national statistical offices. Major efforts should be put into drawing together, and perhaps conducting further analyses of, existing data before fielding any survey.

- (5) Country health studies should aim to provide complementarv information to existing initiatives to develop routine data collection, and other progra_es of surveys and methodologies for project evaluation. In many countries this is likely to be achieved by conducting health interview surveys that focus on morbiditv and children's nutritional status, but retrospective data on mortality should also be collected.

- (6) The variety of health problems and priorities in developing countries suggests that any core questionnaire should be fairly brief. Reviews of existing data should feed into the development of a design and questionnaire that reflect health problems, service provision and information needs in each country and will provide the data needed for planning.

- (7) Although much useful information can be collected in singleround surveys, other topics require a prospective studv or the conduct of medical examinations. Part of a WHS programme should consist of in-depth studies of health issues of particular importance in the countries concerned.

- (8) Experience of conducting and analysing general-purpose health surveys is limited. Although their feasibility in now established and many of the instruments designed, the WHS will need to devote adequate funds to methodological research. One

45
radiant energy of lightning flashes (Dewan et al., 2017). They analyze lightning flash counts to examine annual, monthly and diurnal patterns of flash activity in Bangladesh over the study period (1998–2014). Overall, a total of 83,641 flashes were estimated to have occurred within the territory of Bangladesh, of which 86% occurred over land and the rest was over the Bay of Bengal. Figure 2.2 demonstrates that the seasonality of SLCS and lightning strikes is indeed strongly correlated.

## 2.3. Control variables

In addition to the climate and outage data, the analysis also includes auxiliary state-level control variables, in particular GDP per capita, annual power generation, the number of electricity customer accounts, surface area, and forest coverage (as a share of the state’s total area). In Bangladesh electrification rates are also taken into account. The data is obtained from the World Bank’s World Development Indicators (2018) and Hidden Dimensions of Poverty databases (Hansen _et al._ , 2013).

# 3. Outage patterns

Data from 28 countries enable an analysis of the trends and main causes of power outages. This section shows that the vulnerability of electricity systems to natural shocks varies significantly across countries. The share of overall outages that is caused by natural shocks typically lies anywhere between 10% and 70%, though subnational disaggregation (e.g. by federal states of the USA) yields an even wider range. Figure 3.2 summarizes these results and shows that natural shocks are indeed a significant (and often 8
# **6 Targeting: The way forward**

Because direct transfers to households are increasingly used as emergency or long-term development instruments, the selection of beneficiary households – targeting – is also increasingly discussed in policy and academic settings. Recurrent shocks related to climate change, forced displacement and the Covid-19 pandemic have highlighted the need to deliver benefits rapidly, cost-efficiently and at scale with limited information and infrastructure to identify vulnerable households. This paper aimed to make conceptual and empirical contributions to this discussion by focusing on the Sahel, one of the poorest and most shockprone regions globally. Consistent with previous reviews, our study emphasizes how difficult it is to compare targeting performance across countries. However, by conducting a harmonized primary analysis across nine data sets from six countries, we are able to contribute to the heated policy debate on targeting, and to make some general remarks and recommendations regarding the design and study of targeting mechanisms.

By relying on multiple performance indicators, we show that the definition of the well-being metric, the share of beneficiaries selected, and the indices used to evaluate targeting play a decisive role in targeting performance. Our analysis suggests that a much greater importance needs to be paid, explicitly, to defining these parameters in a clear and policy-relevant manner.

Even though PMT is more successful at selecting households with the lowest per capita consumption based on a binary classification of households as “poor” and “non-poor,” PMT does not generate results that differ greatly from budget-neutral alternatives such as CBT, or random or universal delivery of benefits, when considering distribution-sensitive measures of performance. The high levels of poverty and relatively low levels of inequality, combined with the geographical targeting applied before household-level targeting, may be driving these results. Also, our analysis shows that while simulations of PMT schemes are widely used by practitioners and academics, ignoring geographical targeting and arbitrarily defining selection thresholds could potentially lead to large biases in the simulated targeting performance.

Our analyses also allow some conclusions on CBT, which are often ignored by studies based on simulated targeting