rafmacalaba/data-use-mentions-extended huggingface dataset:
acquisition or construction of regional accounts. This challenge is amplified by two factors. First, regional accounts constructed from primary data are rare. Most regional analysts must rely on one of several methods that can be applied to generate regional accounts from corresponding national accounts using a smaller set of region-specific data to estimate counterparts to national parameters. Second, whereas most of the tools that have been developed for national or regional input-output accounts operate on industry-by-industry (or interindustry) accounting frameworks, it is a reality of the data collection and reporting conventions that most nations publish accounts not in interindustry format, but in what is known as a supply-use framework. Interindustry accounts can be derived from supply-use data, but the necessary data manipulations are not always straightforward and intuitive.

As an aid to regional analysts, computer software applications have been developed primarily for the purpose of facilitating the construction of regional accounts. For example, IO-Snap, described further below, supports accounts generation and standard final demand impacts assessment, but more extensive analytical capabilities beyond multiplier analysis, descriptive economic statistics, and impacts assessment are still being added. While additional analytical features are under development, however, IO-Snap can be used to generate regional interindustry accounts that can then be exported for use with software applications like REAL-I-O that provide a much richer suite of analytical tools. IO-Snap and REAL-IO are described briefly, below.

# **IO-Snap**

IO-Snap, which stands for “Input-Output – State and National Analysis Program,” is a Windowsbased computer software program that was developed to facilitate the use of input-output data from U.S. national supply and use tables. In the process of assembling the various utilities that support national and state-level input-output analysis, the developers also created an environment that facilitates inter-state comparisons of input-output related variables such as employment and worker compensation. Annual U.S. IO tables from the Bureau of Economic Analysis (BEA) for 1998 through the most recently published year are included with the software. In addition to default data covering the U.S. 50 states
have higher chances to achieve higher performance, are the most attractive ones for teachers with higher education. This entails in an overestimation of the treatment effects due to reverse causality.<sup>13</sup> Does being enrolled in treated schools imply better student performance? Or do characteristics related to the performance of students imply more treatment schools?

In this sense, it is necessary to compare the performance of treated schools with a group of schools that have similar chances to be treated, even if they were not. We do this by applying difference-indifference estimation on matched samples obtained by propensity score matching, which allows us to control for several observed and unobserved school characteristics that may contribute to selection bias. Adapted from Cameron and Trivedi (2005), in a model of differences-in-differences, considering a school panel of two periods, the _ATT_ can be obtained from the  parameter in equation (1):

> 13 It is expected that better schools attract or select higher performance teachers. As an example, Hanushek et al (2004) find that teachers in Texas tend to move to schools with more proficient students, especially the more experienced ones, who often have the option of going through districts and choose the school within the district in which they teach. Thus, it is necessary to isolate the characteristics of students and schools in order to guarantee that the odds of attracting/retaining teachers with higher education are similar for both treatment and control schools. In this sense, the treatment status between schools with same characteristics would be random (conditional independence assumption).

12
-- End of picture text --> _Notes:_ The data comes from the Afghanistan Welfare Monitoring Survey conducted for this study in the fall of 2021. The Figure includes the 5,592 individuals using in the main regressions. Red lines represent 95% confidence intervals for the mean. Employed is [=1] if the individual has worked for remuneration during the past 7 days. Student is restricted to the population aged between 6 to 24 years old.

50
maneuver would not affect the taxation mechanism for this group. Table A- 10 shows how the informality status was defined for these two categories of workers using the survey questions. Since one individual may have more than one job, some individuals may be classified as both employee and the self-employed and receive both formal and informal income at different jobs.

For this study, we opted for a wide definition of informality where a worker is defined as informal if it has any of the following jobs: (i) a main job without contract, or with contract but part of its payment is not subject to SIC and PIT (i.e., envelope payments); (ii) a second job as self-employed or in the corporate sector but without contract, or (iii) an occasional job without a labor contract. According to RLMS data, as of 2016, the share of informally employed amounted to 19.8 percent at the main job and 44.2 percent at the second job, among employees. Among the self-employed, 63 percent were classified as informal workers at the main job. Among those who reported occasional selfemployment 89 percent were informal. According to this definition, 32.6 percent of all jobs (either primary, secondary or occasional) are informal. If focusing on workers, rather than jobs, 33.4 percent

> 14 In this study we focus only on informal employment, not the informal sector. Hence, accounting and behavioral simulations focus on individual workers and their jobs, not on firms. This is due to our use of a household survey, rather than firm data, as well as our methodological approach. An analysis of informal firms would require different data sources and methods. For an analysis of firms and informality, see Galiani & Weinschelbaum (2012) and Ulyssea (2018). For an explanation of the distinction between informal employment and informal sector, see Chapter 25 in “Informal aspects of the economy” in _System of National Accounts 2008_ (United Nations Department of Economic
|64%|36%|
|Non-overcrowding,0-5|48|59|1.2%|62%|38%|
|Telephone|14|71|6.3%|78%|22%|
|Assisted birth delivery|64|84|2.5%|74%|26%|
|Post-natal care,0-5|19|28|1.1%|91%|9%|
|Prenatal care,0-4|58|78|2.6%|76%|24%|
|Immunization vaccines,0-4|87|85|-0.2%|12%|88%|
|Non-wasting,0-4|88|75|-1.6%|79%|21%|
|Non-stunting,2-17|69|69|0.0%|---|---|
|Non-underweight,0-17|80|85|0.6%|60%|40%|
|_Aggregate HOIs_||||||
|IOHa9-15-HousingServices|35|72|4.1%|66%|34%|
|IOHa0-4-HousingServices|49|83|3.8%|64%|36%|
|IOHa0-4-Nutrition & ECD|14|14|0.0%|--|--|
|<br>Note: (*) Decomposition is omitted for trivi<br>_Source_: HIECS 2000 and 2009 surveys, ED|<br>al cases in<br>HS 2000 an|<br>which the<br>d 2008,|<br>HOI chang<br>And authors|<br>e is negli<br>’ calculat|<br>gible.<br>ions.|

_Uneven Improvement across Sectors._ Figure 2 shows that improvements in the opportunity indices of basic housing services (BHS) and early childhood development (ECD) were impressive (18 and 12 points respectively). The rates
Policy Research Working Paper 8925

# **Abstract**

This paper provides new evidence of factors, other than GDP per capita, that correlate with the development of insurance markets. Based on 20 years of insurance premium data from 180 countries, and a similar wealth of data on institutions and financial market development, the paper presents important correlates of insurance market development. Although the analysis cannot identify which factors directly cause insurance market growth, the results suggest that interventions aimed at stimulating insurance supply and demand should take enabling factors, such as the quality of institutional governance and the degree of financial market development, into consideration.

This paper is a product of the Development Research Group, Development Economics and the Finance, Competitiveness and Innovation Global Practice. 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 xgine@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
# **SHOULD CONSUMPTION SUB-AGGREGATES BE USED TO MEASURE POVERTY?**

# LUC CHRISTIAENSEN

_World Bank_ ETHAN LIGON _University of California, Berkeley_ THOMAS PAVE SOHNESEN _World Bank_

> . _Date_ : June 30, 2020.

> . _Keywords._ Consumption sub-aggregates, poverty, Engel curves. . JEL Classification: D11, D12, I32.
Policy Research Working Paper 9824

# **Abstract**

During protracted displacement, women and girls often face serious gender-specific challenges and vulnerabilities, including adverse norms and institutional barriers. Yet, quantitative evidence on gendered drivers and differentials during protracted displacement remains scarce. Using survey data from 18,533 displaced and non-displaced individuals in El Fasher, Sudan, this paper documents that livelihood outcomes are significantly shaped by strong and complex intersectionality between long-term displacement and gender. Being female and being a long-term displaced person have separate negative impacts on work and welfare. Therefore, being a long-term displaced woman is particularly challenging: internally displaced women work more than non–internally displaced women but are poorer, on average. For men, there is no such difference in employment between the internally displaced and non–internally displaced. These outcomes are the result of the ‘double burden of female displacement’: women are disadvantaged by norms and institutions both at their destination (due to being a displaced person) and their place of origin (due to their gender). The double burden is strongest for older displaced women. In contrast, protracted displacement can be an opportunity for younger displaced women. Future policies should address the challenges stemming from the intersectionality of gender and displacement and develop targeted programs.

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 brueck@isdc.org and stojetz@isdc.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
where Lt0 is the overall index; pj0 and pjt are the prices for the consumption component _j_ ( _m_ is the number of consumption components) in the reference period _0_ and period _t_ ; and, sj0 is the share of consumption component _j_ for the reference basket evaluated at the prices in period _0_ : The aggregate price change between periods _t_ and _t-1_ can be derived as a ratio of two indices. It t−1 has the same structure as (1), except for the hybrid weights sj reflecting the consumption shares of the reference basket, which are evaluated at prices of period _t-1_ : Similar to (3), the inflation index for a household _i_ can be defined as: and Cj,i is the consumption expenditure of household _i_ on goods from consumption category _j_ .<sup>3</sup> Assuming the same prices are used to calculate both indexes, the difference between the two indexes can be decomposed as the covariance of the difference between the CPI and householdt−1 specific shares sj,i −sj and the deviations of the relative component prices from their mean (e.g., Hagemann 1982):

> Laspeyres price index, the quantities that make up the basket must be the actual quantities of the price reference period. In practice, the expenditures and quantities used for CPIs typically come from household budget surveys undertaken some years before the price reference period for the CPI (Hill 2010).

> 3 Although (4) has a similar functional form as (1) and (3), the interpretation of this index is slightly different, as it no longer refers to a fixed quantity vector priced at one of the periods being compared (t or t-1).

7
|Sweden|1992|-1.66|-1.42|-2.22|3.34|-9.21|-2.38|-23.06|-6.23|
|USA|1989|3.82|3.36|1.23|-0.93|5.63|5.35|0.23|-2.10|

_Source:_ IMF, International Financial Statistics. * For Spain: Year when the Deposit Guarantee Fund was granted legal powers for bank restructuring.

12
Google are also critical inputs into small area estimation, as they are both strong predictors of welfare and also essential for aggregating predictions to higher administrative levels.

Information on building footprints is also valuable when they can be obtained. Worldpop has made statistical information on building footprints available for much of Africa (Dooley et al, 2020); these are derived by Ecopia using Maxar imagery. The Microsoft planetary computer also now contains building footprint data for a variety of countries, including most of Europe and the Americas, and parts of Africa and Southeast Asia. Google recently released a new version of its Open buildings layer covering Africa and Southeast Asia, and the German Aerospace Center recently released the World Settlement Footprint global database of 3-D building footprints (Esch et al, 2023). Liu et al. (2023) recently showed that building footprints can be modeled accurately using Sentinel 1 and Sentinel 2 imagery, but the resulting indicator data have not yet been publicly released. Dynamic information on building footprints should become increasingly available in the near future. In addition, a variety of data pertaining to agriculture and food security are posted online through FAO’s hand-in-hand geospatial platform, which contains information on food security, crops and vegetation. Recent subnational estimates of crop type and yield estimates are only available for a few countries at this time, but coverage will likely expand significantly in the coming years. Overall, an impressive amount of geospatial imagery and indicators is already publicly available, and more should be coming online in the next few years.

b. Geospatial data predicts poverty and wealth accurately across space Several studies have examined how predictions of wealth or poverty derived from linking survey and geospatial data compare with either survey or census-based measures of poverty and welfare. Accuracy is often assessed using R<sup>2</sup> , defined as: Where i is the target area, yi is the reference measure of poverty or welfare for target area i , ŷi is the predicted value for target area i, and y̅i is the
linkages in banking activities over the past three decades, we instrument the extent of branch networks with the strength of branch networks in geographically close countries.<sup>5</sup> This approach allows us to identify a causal link between financial development and the share of informal output in GDP.

This paper documents two main findings. First, in line with former studies, less pervasive informality is associated with greater financial development. Second, the negative impact of financial development (especially banking sector development) on informality is causal. This causal link is stronger in countries with greater trade openness and capital account openness. The findings are robust to the use of alternative indicators of informality and financial development.

As such, this paper makes two contributions to the literature: it is the first study to document a causal link between financial development and informal economic activity in a multi-country setting. The only previous studies that established such a causal link were specific to single countries and relied on microdata or sectoral data. Capasso and Jappelli (2013) use data on loan applications to show that access to finance increased formal activity in Italy; Catão, Pages, and Rosales (2009) and Moron, Salgado, and Seminario (2012) use household survey data to show that financial deepening increased formal employment in sectors that were most dependent on external finance in Brazil and Peru, respectively.

Second, this study introduces a novel instrumental variable for domestic banking sector development: banking sector development in other (neighboring) countries. We establish that cross-country banking sector links can go beyond those established for lending (K ı l ı nç, Seven, Yetkiner 2017; Bahadir and Valev 2015; Sever and Yucel 2021; Csonto et al. 2018), efficiency (Matousek et al. 2015; Nurboja and Košak 2017), interest rates (Rughoo and Sarantis 2014), or profitability (Olson and Zoubi 2017). Significant cross-country links exist even for banking system access, such as that captured by the extent of branch networks.

The next section
**Table A1.** Variables associated to each skill measure using the Philippines STEP survey and O*Net <u>database</u>

|Skill|Variable from STEP|Variables from O*Net|
|---|---|---|
||Type of document read|Getting Information|
||Length of longest document typically read<br>Filling forms<br>Math tasks<br>Frequency of thinking for at least 30 minutes to do tasks <br>|Identifying Objects, Actions, and Events<br>Estimating the Quantifiable Characteristics of Products,<br>Events, or Information<br>Judging the Qualities of Things, Services, or People<br> Processing Information<br>Evaluating Information to Determine Compliance with<br>|
||Frequency of learning new things|Standards|
|Non-routine<br>analytical||Analyzing Data or Information<br>Making Decisions and Solving Problems<br>Thinking Creatively<br>Updating and Using Relevant Knowledge<br>Developing Objectives and Strategies<br>Scheduling Work and Activities<br>Organizing, Planning, and Prioritizing Work<br>Drafting, Laying Out, and Specifying Technical Devices,<br>Parts, and Equipment|
|||Interacting With Computers|
||Supervising coworkers<br>Making presentations<br>Contact with people other than coworkers|Interpreting the Meaning of Information for Others<br>Communicating with Supervisors, Peers, or Subordinates<br>Communicating with Persons Outside Organization|
||Frequency of collaborating with coworkers|<br>Establishing and Maintaining Interpersonal Relationships<br>Assisting and Caring for Others<br>Selling or Influencing Others|
|||Resolving Conflicts and Negotiating with Others|
|Non-routine<br>interpersonal||Performing for or Working Directly with the Public<br>Coordinating the Work and Activities of Others<br>Developing and Building Teams<br>Training and Teaching Others<br>Guiding
; Martinez-Bravo and Stegmann, 2022). Since the outset of the pandemic there has been some experimental work in high-income countries about ways to boost COVID-19 vaccine acceptance (e.g., Kaplan and Milstein, 2021), but in LICs and LMICs existing publicly available studies have been almost exclusively descriptive (e.g., Kanyanda et al., 2021). An exception is a study by Hoy et al. (2021) in Papua New Guinea (PNG) that also uses social media to 4
# **_Summing up: Preferences—the good, the bad and the ugly_**

The bottom line is that the research suggests that preferences are widely used by many countries, but that the benefits are reduced by a variety of factors, whether by design, incidentally, or by domestic supply constraints. Much depends on how binding rules of origin are and how high are MFN tariffs. A recent analysis suggests that if one assumes away preferences – i.e., only MFN barriers are assumed to apply – developing countries would confront _higher_ protection in the OECD than other countries, i.e., the current structure of protection implies a _negative_ preference margin. This is illustrated by the darker bars in Figure 1, which reports an overall trade restrictiveness index (OTRI).<sup>29</sup> Alternatively, if it is assumed preferences are fully utilized – that is, we assume away rules of origin and other constraints, which are known in practice to reduce the utilization of preferences, and it is assumed that all rents created by preferences accrue to exporters – there is still much to be done to remove the negative effect of trade barriers.

**Figure 1. OECD Protection and Preferences**

<!-- Start of picture text --> Full utilization No utilization<br>40<br>30<br>20<br>10<br>0<br>-10 LIC LDC<br>-20<br>-30<br>-40<br>-50<br>Preference margin<br><!-- End of picture text --> _Source:_ IMF and World Bank, 2004.

The average reduction in OECD trade barriers due to the preference programs is not more than 20-30 percent of MFN rates (IMF and World Bank, 2004). In part this is due to (i) the effect of NTBs that apply to all imports, (ii) the fact that OECD countries

>
Table 1. Skill Content by Occupational Groups

|||Non-routine||<br>Ro|<br>utine|
|---|---|---|---|---|---|
|||||||
||Analytical|nerper-<br>sonal|Manual|oune<br>manual|oune<br>cognitive|
|Administrative and managerial workers|0.80|1.11|-1.24|-0.63|-1.31|
|Professionals and technicians|1.45|1.29|-1.15|0.33|-0.96|
|Clerical workers|0.27|0.08|-0.10|-1.69|-0.04|
|Sales and services workers|-0.83|-0.92|0.96|0.78|1.36|
|Agriculture, animal husbandry, forestry workers, fishermen, and hunters|-0.62|-0.71|0.87|0.99|0.61|
|Production workers, transport and equipment operators, and laborers|-1.06|-0.85|0.65|0.22|0.34|

Source: Authors’ calculations based on the 2015-2016 Philippines STEP survey. Notes: The skills measures are constructed based on Acemoglu and Autor (2011) methodology described in the Appendix.

Table 2. Summary Statistics on ICT Use, Skill Measures, Aggregate Labor Market Indicators, and Province and Industry Characteristics

||20|05|20|10|2|011|2|015|
|---|---|---|---|---|---|---|---|---|
||Mean|Std. Dev|Mean|Std