rafmacalaba/data-use-mentions-extended huggingface dataset:
educational profile today than 10/15 years ago. Finally, the cases of Georgia and Turkey are less clear because we find a rise in the prevalence of workers with secondary education but together with a decrease in the share of workers at the tertiary level.

Figure 24: Education profile of Part-time employees. (Early 2000s/Mid-2010s) <!-- Start of picture text --> 100%<br>80%<br>60%<br>40%<br>20%<br>0%<br>Russia Georgia Kyrgyzstan Turkey Armenia Moldova<br>Without Instruction (SP) Without Instruction (EP) Primary (SP)<br>Primary (EP) Secondary (SP) Secondary (EP)<br>Tertiary (SP) Tertiary (EP)<br><!-- End of picture text --> Source: Own calculations based on Household surveys Note: SP indicates the starting point of the analysis and EP states de ending point.

A similar picture is registered in the case of temporary employment. Armenia is the most evident case of improvement in the educational profile of part-time workers because it is observed a rise in the share of workers with secondary and tertiary education. Kyrgyzstan shows a decrease in the prevalence of workers at the secondary level, but this decrease is compensated by the greater incidence of tertiary workers. Lastly, Georgia and Turkey are the countries where the improvement of the educational profile of temporary workers is less clear because there is observed a rise in the prevalence of workers with secondary education compensated, in part by a decrease in the share of workers at the tertiary level. Anyway, even in this last two cases, taken together, the share of workers with secondary and tertiary education increased within temporary workers in detriment of workers with lower levels of education.

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# **2 Learning-Adjusted Years of Schooling Framework**

Learning-adjusted years of schooling for a given country—what we call macro-LAYS—are the product of years of schooling and a measure of schooling quality (Filmer et al., 2020). Specifically, they are produced by scaling the country’s average schooling by its test-score performance relative to a global high-performance benchmark.<sup>5</sup> Figure 1 shows an example using data from the World Bank Human Capital Index. For example, Singapore’s average student test scores are closer to the high-performance benchmark than any other country’s scores. As a result, its 14 average years of schooling are discounted only slightly, to 13 LAYS. By contrast, South Africa has 10 years of schooling but only about 5 LAYS, because its test scores are only about half of Singapore’s. In other words, macro-LAYS are produced at the country level by adjusting average schooling in a given country by the amount of learning in that country (relative to a high-performance benchmark). Expressing national education levels in terms of macro-LAYS provides a unified and user-friendly measure for a variety of education outcomes.

In this section, we show how LAYS can also be used at the micro level to compare education interventions. The number of rigorous studies evaluating the effect of interventions on educational outcomes is growing, with nearly 300 impact evaluations focused on learning outcomes in low- and middle-income countries as of 2016 (World Bank, 2018). An improved and more comparable metric would enable better evidence synthesis and clearer policy recommendations. As described below, we aim to address many of the challenges that limit current comparisons—most notably, that access and learning impacts are often discussed separately, and that learning gains can be expressed only relative to local performance. We do this by expressing education outcomes from interventions and policies in terms of LAYS units that offer a single, internationally comparable, and policy-salient metric. Hereafter, we will refer to LAYS gained from an intervention or policy
Policy Research Working Paper 9826

# **Abstract**

Despite the many simultaneous deprivations faced by forcibly displaced communities, such as food insecurity, inadequate housing, or lack of access to education, there is little research on the level and composition of multidimensional poverty among them, and how it might differ from that of host communities. Relying on household survey data from selected areas of Ethiopia, Nigeria, Somalia, South Sudan, and Sudan, this paper proposes a Multidimensional Poverty Index (MPI) that captures the overlapping deprivations experienced by poor individuals in contexts of displacement. Using the MPI, the paper presents multi-country descriptive analysis to explore the relationships between multidimensional poverty, displacement status, and gender of the household head. The results reveal significant differences across displaced and host communities in all countries except Nigeria. In Ethiopia, South Sudan, and Sudan, female-headed households have higher MPIs, while in Somalia, those living in male-headed households are more likely to be identified as multidimensionally poor. Lastly, the paper examines mismatches and overlaps in the identification of the poor by the MPI and the $1.90/ day poverty line, confirming the need for complementary measures when assessing deprivations among people in contexts of displacement.

This paper is a product of the Gender Global Theme. 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 atybogale@worldbank.org, sabina.alkire@qeh.ox.ac.uk, uekhator@worldbank.org, fanni.kovesdi@qeh.ox.ac.uk, juliethsa@iadb.org, and sophie. scharlin-pettee@qeh.ox.ac.uk.

_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
. 2008. “Biofuels, Poverty and Growth: A Computable General Equilibrium Analysis of Mozambique”. IFPRI Discussion Paper 803. Washington, D.C. International Food Policy Research Institute.

- Beghin, J., Timilsina, G, van der Mensbrugghe, D. and Mevel, S. 2010. “The Impacts of Biofuels Targets on Land-Use Change and Food Supply: A Global CGE Assessment” Unpublished World Bank manuscript.

- Birur, D, Hertel, T., and Tyner, W. 2008. “Impact of Biofuel Production on World Agricultural Markets: A Computable General Equilibrium Analysis”. GTAP Working Paper No. 53.

- Boeters, S. Veenendaal,P. van Leeven, N. Rojas-Ramagoza, H. 2008. “The Potential for Biofuels alongside the EU-ETS”. Paper for Presentation at the Eleventh Annual GTAP Conference „Future of Global Economy‟, Helsinki, June 12-14.

Bussolo, M., R. de Hoyos, and D. Medvedev. 2008. “Economic Growth and Income 31
Four months later, in February 2013, a follow‐up survey was conducted on all baseline respondents to collect longer‐term data on attitudes, intentions and behaviors with respect to saving and borrowing activities to assess the longevity of any impacts identified at the screenings.

# **4. Outcome Measures, Baseline Balance and Attrition**

## **_Outcome Measures_**

The main outcome measures are aligned with the essential messages of the Nollywood movie. They can be divided into four categories that capture (i) perceptions of MFBs, (ii) perceptions of women, (iii) intentions to save or borrow, and (iv) savings and borrowing behavior.

Regarding the perceptions of MFBs, the survey asked the micro‐entrepreneurs if they agree or disagree with statements such as, “I would trust an MFB to keep my money safe”, “MFBs treat people with respect”, “If I apply to an MFB for a loan, my application will be accepted”. Since the movie focused on female entrepreneurs as the main protagonists, we also explore self‐reported perceptions of female business competence and access to financial opportunities. Questions designed to explore perceptions of women as business owners or financial decision makers ask respondents if they agree or disagree with statements like “Women can run businesses just as well as men”, “Women make better financial decisions than men”, “It is easier for men to receive loans than for women”. The intention to save or borrow questions capture whether respondents agree with statements such as “I plan to apply for a loan in the next 6 months” or “I will save some money next month”. Self‐reported savings and borrowing behavior is captured through responses to questions such as “I saved money last month”, the amount of total savings relative to the monthly income earned, savings kept at MFBs, savings at commercial banks, outstanding loans from commercial banks, MFBs, suppliers, money lenders, or family/friends. Actual savings
significant). This is in contrast with workers with university education: relative to small firms, the difference of being employed by a small, medium, or large firm is 7.3 percent, 13.4 percent, and 20.6 percent, respectively. Comparable results hold contrasting workers with completed primary education with those that have completed high school: for the former, the difference between working for a large firm relative to a small one is 3.2 percent; for the latter, 13.1. More generally, there is a premium be to match employer-employee data over time, which is not available for Mexico. The magnitude of this bias, if any -particularly in terms of its relative importance across educational levels- should not affect the counterfactual exercise.

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identify distinct segments with the WSME population and tailor interventions accordingly. As more sex-disaggregated data become available, machine learning techniques may offer better predictions and be used in combination with expert</mark> analysis, peers’ and trainers’ evaluations to conduct this segmentation.

<mark>Most of the evaluations reviewed in this paper focus on interventions that address only a single group of constraints. However, the multifaceted nature of the challenges faced by WMSMEs necessitates a comprehensive approach that combines various types of support. For instance, access to finance can be coupled with capacity-building initiatives, and its effect may be enhanced with expanded access to markets. Additionally, networking opportunities and mentorship can provide peer support and guidance. Research into how these interventions can be bundled effectively will help to identify the most effective</mark> combinations that drive growth and sustainability in WMSMEs.

<mark>Finally, the implementation and uptake of interventions are heavily influenced by the context in which WMSMEs operate. Contextual constraints, such as gender norms, regulatory environments, and the availability of enabling services like childcare, can either facilitate or hinder the success of proposed solutions. There is a need for more evidence on how addressing these contextual constraints can impact WMSMEs’ performance. Furthermore, it is crucial to conduct thorough assessments of these contextual constraints to ensure that programs are designed with sensitivity to local realities. Adapting implementation strategies to align with the specific needs and challenges of WMSMEs in different regions or communities will increase the likelihood of achieving meaningful and lasting outcomes.</mark> 20
. The global distribution of economic activity: nature, history, and the role of trade. _The Quarterly Journal of Economics_ , _133_ (1), pp.357-406. Herrendorf, B., Rogerson, R. and Valentinyi, A., 2014. Growth and structural transformation. In _Handbook of economic growth_ (Vol. 2, pp. 855-941). Elsevier.

Herrendorf, B., Rogerson, R. and Valentinyi, Á., 2021. Structural Change in Investment and Consumption—A Unified Analysis. _The Review of Economic Studies_ , _88_ (3), pp.1311-1346.

Heyman, F., Sjöholm, F. and Tingvall, P.G., 2007. Is there really a foreign ownership wage premium? Evidence from matched employer–employee data. _Journal of International Economics_ , _73_ (2), pp.355-376. Hu, C., 2019. State-Owned Enterprise Reform, Employment Decline and Tertiary Education in Urban China.

Jacobs, W., Koster, H.R. and van Oort, F., 2014. Co-agglomeration of knowledge-intensive business services and multinational enterprises. _Journal of Economic Geography_ , _14_ (2), pp.443-475.

Judson, R.A. and Owen, A.L., 1996. Estimating dynamic panel models: A practical guide for macroeconomists. _Federal Reserve Board of Governors mimeo_ .

Kis-Katos, K., Pieters, J. and Sparrow, R., 2018. Globalization and social change: Gender-specific effects of trade liberalization in Indonesia. _IMF Economic Review_ , _66_ (4), pp.763-793.

Kongsamut, P., Rebelo, S. and Xie, D., 2001. Beyond balanced growth. _The Review of Economic Studies_ , _68_ (4), pp.869-882.

Lawrence, R.Z. and Edwards, L., 2013. US employment deindustrialization: insights from history and the international experience. _Policy Brief_ , (13-27).

Leight, J. and Pan, Y.,
tenure insecurity suggest that, with tenure of some 43% or 25% of agricultural parcels perceived to be insecure or very insecure, levels of tenure insecurity on agricultural land are higher on state (52%) rather than customary (42%) land but that the depth of insecurity is higher on the latter (25% on customary and 23% on state land), i.e., insecurity may be more acute. Demand for title is 47% overall (47% on agricultural and 46.4% on residential parcels) among those who perceive their land rights as secure, in line with the notion that, although titles can reduce insecurity, they perform other functions (e.g., reducing the cost of transfers among strangers). Of those who perceive their rights as insecure, 51% of agricultural and 40% of residential parcels would like to acquire title, suggesting that formal title is not perceived as a panacea to deal with tenure insecurity. Substantively, this suggests that other measures to secure tenure might need to be explored. Methodologically, it implies that, in situations where access to formal documents is incomplete, adding questions on title demand could greatly increase household surveys’ policy relevance.

# **4. Regression analysis and comparison to the Prindex global poll**

Regressions for determinants of transferability, perceived tenure insecurity, and demand for title using LFS data allow us to explore heterogeneity of outcomes, including whether having title is associated with greater female decision-making. As similar regressions for Prindex data produce very different results, we discuss potential reasons in terms of data quality and use these to identify ways to improve this approach to ensure better complementarity and avoid confusion.

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**Table A.3. Uncertainties affecting** **<u>population risk and economic risk.</u>**

|**Uncertainty**|**Population**<br>**Risk**|**Economic**<br>**Risk**|
|---|---|---|
|Increase in Rainfall Intensity|X|X|
|Increase in Saigon River Levels|X|X|
|Total Ho Chi Minh CityPopulation|X||
|Geographic Distribution of Population|X||
|PovertyRate|X||
|Average Annual Economic Growth||X|
|Geographic Distribution of Economic<br>Growth||X|
|Population Vulnerability|X||
|Economic Vulnerability||X|

# **_Uncertainties Associated with Future Hazard_**

The first two factors relate to the future hazard: the increase in intensity of future 3- hour rainfalls and the increase in the mean level of the Saigon River.

## **Change in Rainfall Intensity**

The IPCC Special Report on Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation (SREX) provides an analysis of how future rainfall intensity may change. For Southeast Asia, global climate models suggest that the intensity of a daily precipitation event in the time period 2045-2065 could be similar to that in the period 1981-2000 or may increase by as much as 35%. We use this as the basis for developing the range of potential rainfall intensities for future 2, 5, and 10-year events. For each return period, we consider a wide range of intensities. To increase confidence that we include the full range of plausible futures, our range exceeds that of the IPCC SREX report. Intensities range from the historical baseline to a 60% increases in intensity, as shown in Table A.4

**Table A.4. Range of intensities (mm) for 3-hour rainfalls with 2, 5
midst of the ongoing 2016-2018 Demographic and Health Survey (DHS). The survey consisted of a household survey, and then separate surveys for eligible men and women in the household. In all households in the survey, all women who were aged 15-49 and who were either usual residents of the household or who spent the night before in the household were eligible to be interviewed; and for every second household in the survey, all men who were aged 15-49 and who were either usual residents of the household or who spent the night before in the household were eligible to complete the men’s questionnaire. In total, 16,021 households were surveyed with a common questionnaire, 15,198 women were administered the women’s questionnaire,<sup>16</sup> and 7,333 men were administered the men’s questionnaire.<sup>17</sup> The survey is nationally and provincially representative, and can report estimates at the rural and urban levels. The fieldwork was conducted in four waves between October 2016 and December 2018.<sup>18</sup> However, the households interviewed were not randomized across waves. Rather, the survey began in the most urban areas in each province, and then progressively surveyed more rural and remote areas (DHS 2019).<sup>19</sup> All households to be interviewed were selected prior to the beginning of the survey in 2016, and no households were allowed to be replaced. But data collection was difficult and fieldwork could not be completed in 33 of the 800 census units originally selected in the sample design, reducing the overall sample size from the initial 19,200 households selected to the 16,012 households that were ultimately included in the survey. Reasons for the delays and for not completing work in each census unit include difficulties in handling the terrain in the country, adverse weather, and security issues (e.g., DHS 2019).

Importantly, the disaster did affect the survey. The
in the MENA region, and especially in Saudi Arabia. The paper explores the surprisingly large and consistent performance gap observed between boys and girls in the region. As shown in figure 1, boys in the region tend to achieve noticeably lower scores than girls in reading, mathematics, and science, with this difference being larger than those in most of the other countries participating in PISA 2018. A further feature of the paper is its examination of two large-scale assessments that have a shared focus on the same domains of study and the same grade levels in a complementary fashion, with data from NALO used to complement findings arising from the analysis of TIMSS data. This paper, therefore, provides important insights for policy makers, both in Saudi Arabia and in other countries in the region, regarding the factors that may contribute to the observed gender gaps in achievement across a range of subjects.

Secondly, Saudi Arabia offers a unique setting in which boys and girls attend separate schools on a universal basis starting from grade 1, being educated only by male and female teachers, respectively, in effect inhabiting parallel education systems, as shown in figure 2, which presents the distribution of grade 4 and grade 8 students in single-gender or mixed education among countries participating in TIMSS 2019.<sup>3</sup> Although gender-segregated schools are not uncommon in the MENA region, students do not usually attend single-gender schools until the end of primary education. The unique structure of the Saudi education system provides an opportunity to examine, in a multilevel framework, how variance in systemlevel factors applying only to boys or to girls contributes to the observed individual differences in achievement. This analysis exploits the existence of parallel gender-segregated school environments that operate within a shared overarching cultural context, where expectations and practices outside school also vary significantly between boys and girls. While this paper exploits this feature of the Saudi education system in its analysis, it also acknowledges potential difficulties in interpreting findings due to this extreme degree of separation as teacher and school characteristics are confounded with gender
originating from the 2010 release of the _Database on Political Institutions_ (DPI).<sup>11</sup> The resulting panel dataset consists of an unbalanced panel of 201 countries for the period 1991 – 2009.

# **5. Fossil Fuel Energy Subsidies, Public Good Provision and Governance Institutions: Empirics**

## _5.1 Fossil fuel energy subsidies and public good provision_

In order to get a notion of what the data can tell us on the relationship between the provision of public goods by the central government and fossil fuel subsidies we first averaged the panel data over time. For the fossil fuel variables the mean for the period 2006 – 2010 was

> 9 In Table 1, means are arithmetic across countries in each group; not weighted by country-specific consumption levels.

> 10 Kaufman et.al, 2006.

> 11 Keefer, 2010; Beck _et.al_ , 2000. These are objective indicators of political institutions.

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the omission of certain categories. Beegle et al. (2012) found that using a collapsed, or aggregated, list of consumption items results in significantly lower estimates of total consumption relative to the gold standard for measuring consumption (a personal diary with frequent supervision). On the contrary, research on the measurement of microenterprise profits by de Mel, McKenzie, and Woodruff (2009) concluded that asking respondents about profits in aggregate, as opposed to asking several detailed questions on revenues and expenses, resulted in more precise estimates of profits. The degree and direction of bias introduced by the aggregation of land to the individual level in the measurement of tenure rights remains an open empirical question.

Using data from the Armenia Land Tenure and Area (ALTA) study and in the context of land tenure rights, this paper addresses questions on: (i) the data quality implications associated with respondent strategy; (ii) the data quality implications associated with level of data collection; (iii) the interaction of biases stemming from the use of proxy respondents and aggregated level data collection, as relevant; and ultimately, (iv) the implications of these design decisions on the computation and monitoring of SDGs 1.4.2 and 5.a.1.

The ALTA study was implemented with the technical guidance of the lead author by the International Centre for Agribusiness Research and Education (ICARE) in partnership with the Statistical Committee of the Republic of Armenia (ArmStat) and with the financial support of the 50x2030 Initiative. ALTA included 1,200 scientifically sampled households which were randomly assigned to one of four treatment arms, each of which received a specific version of the land tenure module differing by respondent approach and level of data collection. This experimental design provides a unique opportunity to identify the biases resulting from these particular survey design choices and the implications for SDG indicator computation.

Findings from this study will inform future survey design around these particular SDG indicators and land tenure data in general, as well as alert policy makers to the implications