rafmacalaba/data-use-mentions-extended huggingface dataset:
terms of gross output. Value added within the manufacturing sector is only around 25% of total output, while it averages between 40 and 65% for the other sectors. This is important because export shares are calculated as a fraction of gross output. Combining information on the value added share of each sector with its share of GDP and its total export share, we can calculate each sector’s importance for total exports. This ranking, averaging over all countries, is: (1) manufacturing 25%, (2) mining and quarrying (21%), (3) agriculture 20%, (4) business services (16%), and (5) trade 10%.

# **3.5 Domestic value added in gross exports**

The previous calculations illustrate what fraction of output is exported for each country and what fraction of national production that each country accounts for, but this only gives a partial view on the importance of trade in value creation. It only summarizes international trade linkages in the final production step. For example, when inputs are sourced domestically, not all of their value added will be added by domestic sectors.

Johnson and Noguera (2012) have argued that the usual trade statistics provide a misleading picture on the importance of international trade as countries’ relative or absolute trade exposure is influenced by the intermediate inputs that are often imported first and later leave the country embedded in exports. They proposed to calculate a value added in gross output (VAX) ratio which divides the domestic value added that is embedded in a country’s exports by the directly observed gross export flows. There are several reasons why this ratio could deviate from unity. Exports can contain imported intermediates, either directly or through the inputs sourced from other sectors in the country. Greater integration of production chains internationally is likely to raise export flows more rapidly than the amount of value added that crosses borders.

These embedded imported inputs give rise to two measurement problems: reflection and redirection. A fraction
.8|19.3|22.6|18.5|19.5|

Source: Authors’ own estimate based on NSSO data. Note: ‘Married’ refers to currently married females and ‘not married’ refers to females who are currently not married (e.g., never married, widowed, etc.).

This multidimensional dissection of the data on women’s labor force participation in India leads us to several important conclusions. First, there has been a national decline in FLFP, and the bulk of this drop occurred in rural areas. Second, an increase in educational enrollment can successfully 25
<!-- Start of picture text --> V.3 Emerging market credit standards have been V.4 Local and international funding conditions for<br>tightening since 2011Q1. Demand slowed down and is banks in emerging markets deteriorated and only<br>virtually stagnant since 2011Q4 stabilized as recently as 2012Q3<br>Emerging Market Credit Standards and Demand Emerging Market Funding Conditions<br>Diffusion index, below 50 indicates tightening relative to Diffusion index, below 50 indicates tightening relative<br>previous quarter to previous quarter<br>65 80<br>70<br>60<br>60<br>55<br>50<br>50<br>40<br>45<br>30<br>40 20<br>Credit Standards Demand for Loans Local International<br>Source: IIF Lending Survey 2013Q1 Source: IIF Lending Survey 2013Q1<br><!-- End of picture text -->

# **VI. European Bank Lending Activity outside the Euro Area**

**As part of their pre-crisis expansion outside Europe, European banks regularly extended more new loans outside the Euro Area than within** (Exhibit VI.1). Indeed, consolidated foreign claims<sup>33</sup> of European banks to EMDEs experienced extremely high pre-crisis annual growth rates of over 40 percent while they reached over 25 percent to other advanced economies (Exhibit VI.2). Particularly in certain EMDEs, European banks have come to play a critical role in financial markets exposing these countries vulnerable to European shocks.

**As European credit conditions weakened, European banks disproportionately scaled back their external lending flows, especially in 2008 when they turned deeply negative (Exhibit VI.1). Moreover, as the financial crisis spread and the global economy slowed down, growth rates of European claims on EMDEs have recently turned negative again.** As a result of European bank retrenchment, their total foreign claims outside Europe have been falling
connection; titling non-SOE, titling non-SOE * political connection. Here “political connection” is either _bureaucrats on board_ or _the party member ratio_ . The results are consistent. For SOEs, the titling effect does not hinge on further political connections—that is, state ownership is enough. For non-SOEs, the titling effect highly hinges on the existence of political connections.

What are the _channels_ through which political connections help connected firms to take advantage of the titling program while effectively prevent the non-connected ones from doing so? We search for evidence on the roots of the lack of responses of non-SOEs directly from the annual reports of those titling firms. We checked their annual reports for information on their progress in the land titling program. When non-SOEs explain the slow progress of their application for titles, they do not mention financial constraints and investment opportunities. In contrast, they frequently cite dealing with legal and administrative barriers. Firms usually state that the slow progress of their application for land title is due either to their disputes with the government (or the court) or to the disputes with other firms. This is consistent with international evidence that government discretion in enforcing rules is substantial, and that the government’s treatments of regulatory rules differ greatly within countries among firms (Hallward-Driemeier and Pritchett 2015).

We therefore explain whether the titling firm has a dispute with the government or other firms over its land. We estimate the linear probability model with the dependent variable being the dummy variable of the firm having a dispute with government or other firms over the land, using the sample of titling firms. The result is in Table 11. In Column (1), the probability of having disputes with either the government or other firms are presented. The negative and large coefficient of the SOE dummy suggests that those SOEs are much (i.e., 37 percent) less likely to have a dispute with either government or other firms, rendering them a significant advantage to implement the titling program quickly. The results remain robust when we
The multiplicity of principles, attributes, conditions and functions used to approach social sustainability in the academic literature has prevented the emergence of a dominant consensus definition. Instead, we have vague, partial, or long definitions, which are difficult to articulate in precise measurements. By contrast, the evolution in the definition and measurement of poverty offers relevant insights. Even a pragmatic consensus on what constitutes monetary poverty and how to measure it—through the USD 1 per person per day and subsequent updates—has allowed decades of sprawling comparable statistics, monitoring, and research. The operational consensus on a simple definition of monetary poverty has further encouraged alternative definitions and measurements. In fact, the literature on poverty has made significant strides beyond monetary poverty concepts and measurement, leading to the increasingly influential conceptualization and measurement of multidimensional poverty. This evolving consensus has delivered an impressive body of evidence—including dissenting work—contributing to further developments in concepts (e.g., relative vs absolute poverty); definitions (national vs international poverty lines), measurement (MPI vs MODA multidimensional poverty measurements), and analytics (from impact evaluations to fiscal incidence analysis). Social sustainability has lacked a similar conceptual, definitional, and empirical development.

Rather than providing yet another contested definition of social sustainability, we propose focusing on meaningful indicators that capture social sustainability comprehensively and flexibly. The result is a global data set of social sustainability indicators, the Social Sustainability Global Database (SSGD), which aims to shift the focus away from elusive definitions and towards insightful analytics on social sustainability itself.

# **3. Constructing the Social Sustainability Global Database (SSGD)**

Generating a global database of social sustainability indicators contributes to expanding the current evidence in four ways: one, it allows the _benchmarking_ of those indicators between countries; two, it describes social sustainability conditions or _profiles_ within a given country; three, it expands the _analytics_ around social sustainability; and four, it _monitors_ country, regional, or global social sustainability trends over time.
The regulatory issues cover the stipulation of basic conduct rules, the creation of a centralized database, the regulation of broker commissions, the prevention of deceptive pricing practices, and the prohibition of perverse marketing campaigns.

# **6.2 Basic Conduct Rules**

The first requirement of an effective regulation of marketing in a decentralized competitive market is compliance with basic conduct rules, such as the „know-your-customer‟ rule and an adequate disclosure of the terms and conditions of different products. However, because life annuity products are highly complex, there is also a need for extensive training of agents and brokers. In addition to adequate training, brokers need to pass a certification test as well as the standard „fit and proper‟ test. Licensed brokers must be legally obligated to represent their clients, must generate their income from commissions on the sale of annuities, and must not be permitted to accept volume-related remuneration from insurers.

# **6.3 Creation of Centralized Databases**

Adopting a centralized electronic quotation system, such as the one introduced in Chile in 2004 (Rocha and Thorburn, 2007; Reyes and Stewart, 2008), merits serious consideration. This is a centralized service that compiles and validates individual data on retiring workers and solicits quotes from participating institutions. Such a system reduces the influence of brokers, lowers the search costs of retiring workers, enhances the quality of information available to them, and ensures broad access to competitively-priced annuities.

An electronic quotation system is essential in the case of fixed nominal or real annuities as well as escalating annuities, but it can also be useful for all kinds of variable payout annuities. It can facilitate the comparability of different offers and underscore the various types of guaranteed benefits and the charges that are levied for each type of benefit.

However, to strengthen competition in the market for variable payout annuities, the central register should also compile comparative data on a consistent and informative basis on the performance and bonus policies of different providers. This will enhance the transparency of the system.
(Senegal) (Figures 2a and 2b). From 2007 onward, transit through Abidjan started to recover but even by 2016, it was well below its pre-CIV crisis level for Burkina.<sup>7</sup> While the effect of political instability on maritime trade is evident from Figures 2a and 2b, volumes of trade that took place among neighboring countries are not recorded officially by routes. But the disruption of trade along the Abidjan route is expected to have similar adverse effects on the inland trade as well. It is clear from Figures 2a and 2b that shock to trade routes coincided with the CIV political crisis and the impact of shock lasted about 6 years before it started to recover. The shock was thus temporary but not too short-lived as to make no difference. We exploit this negative shock to transit through the BamakoAbidjan and Ouagadougou-Abidjan routes as a source of exogenous variation in market access concentrated in a particular region in Mali and Burkina Faso.<sup>8</sup>

# **(4) Conceptual Framework**

We develop a model that incorporates two distinguishing features of a poor developing economy. First, the production structures of poorer developing countries are dominated by agriculture. The share of non-agricultural employment in Mali and Burkina was less than 10 percent in the pre-CIV crisis period.<sup>9</sup> Second, roads are sparse and paved roads are rare. The high costs of transportation mean that not all areas trade with each other and pockets of autarkic subsistence economies are more of a rule than an exception.

# **(4.1) The Basic Set-up**

## **Geography and Endowment**

We take the location of cities and the transport network as given, and then examine how spatial distribution of population and sectoral employment shares respond to a shock to transport costs. We consider a country which consists of two cities: _K_ and _J_ . City _K_ uses

> 7For Mali, we do not have access to more recent transit data.
and the highly inadequate supply of housing. Most households are living in slums and chawls<sup>16</sup> . The number of such dwelling units can only grow through subdivision of the existing floor space or densification by adding additional floors. This illustrates the difficulties created by freezing so much of the housing stock for redevelopment through reservations or rent controls. (See box 1 above) Most of the scope for

> 13 Residual land value is calculated by taking market price per square foot of housing space, subtracting cost of construction, then calculating land value using the assumption that land area would 16% more than the surface area. These calculations are illustrative only and would have to be redone based on field surveys in the city. We also assume that a household could afford a home 2.4 times its income. Again, such figures should be validated with field measurements when actually used for establishing the program in a city.

> 14 Dharavi is well located , but is still one of the cheapest housing solutions per sq foot. Figures 8-9 illustrate that it is necessary to move more than 50km from the city centre to achieve such a low price. It is quite unrealistic to expect that many poor households will accept to stay at these long distances from their work—even if they are provided with a home there.

> 15 These calculations should not be interpreted as a costing of a minimalist program of housing improvement. Such a costing would need to include, among others,  infrastructure upgrading, both tertiary improvements for individual households and secondary and primary network upgrades to ensure the capacity of the system can handle the expansion of new connections.

> 16 Chawls are low standard housing once provided to factory workers by their employers. They do not meet current standards for formal housing.

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# **Figure 2B. Quantile Regression Estimates of the Differences between Countries Depending on the Total Number of Weeks of Full and Partial School Closures (UNESCO Data), All ECA+ Countries**

<!-- Start of picture text --> 0<br>-5<br>-10<br>-15<br>-20<br>-25<br>-30<br>1 2 3 4 5 6 7 8 9<br>Reading achievement decile<br>10 weeks closure 25 weeks closure 50 weeks closure<br>Departure from achievement trend<br><!-- End of picture text --> How do these estimates compare to previous national estimates of learning loss? There are robust estimates of learning loss from 18 ECA countries published prior to PIRLS between 2020 and 2023 (Patrinos 2023). Learning losses range from 0 in Sweden (no school closures) to 0.37 SD in Turkey (40 weeks of closures, the longest in our sample). The average learning loss is 0.17 SDs for those studies; very close to the reading loss of 0.20 SDs recorded in our analysis of PIRLS over time.

Globally, using the same PIRLS data but for all countries – 55 countries and regions, representing more than 16 million 4<sup>th</sup> graders worldwide – the losses are estimated at 0.33 SDs, or just over a year’s worth of learning (Jakubowski et al. 2023). Again, losses are larger for students in schools that faced relatively longer closures and for lower-achieving students. The economic losses may translate into a 0.68 percentage point reduction of GDP growth totaling a global loss of more than $80 trillion.

# **Summary and conclusions**

We estimate the impact of COVID-19 on student reading on standardized tests over time for EU and ECA+ countries. We model the effect of closures on achievement by predicting the deviation of the
from users who have turned on the ‘Location History’ setting on their phone and show how visits and length of stay at different types of places change compared to the median value, for the corresponding day of the week, during the 5-week period from January 3, 2020 to February 6, 2020. That is, mobility is indexed relative to a “baseline” level

> Wave 7 was conducted in the last 2 weeks of September 2021. Appendix C includes further methodological details of the Future of Business survey.

> 16Over 90 million businesses estimated to be represented in this sampling frame (Facebook, 2019).

> 17UNESCO records the evolution of national school closures and re-openings on a daily basis.

> 18School closure status was fixed according to the first day of data collection for each survey wave e.g. for wave 7 on September 15, 2021.

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, teachers, and members of community committees; and interviews and testing of students in both a cohort and longitudinal sample (see below). Testing is conducted under classroom conditions in a primarily multiple-choice format supervised by the class teacher as well as MLSS enumerators. For more details of the MLSS instruments and procedures, see Appendix A.

For our difference-in-difference analysis, we compare the data from MLSS baseline and endline. MLSS baseline data collection was conducted in a sample of 559 schools between May and September 2016, just prior to the commencement of MESIP (Asim and Casley Gera, 2024). Endline data collection took place between April 2021 and February 2022, following the completion of the main MESIP activities.

> 14 EMIS 2020 data is the last collected prior to COVID-19. COVID-19 introduced significant disruption to schooling (see Asim et al., 2022) and to EMIS data collection; we use the last available pre-COVID data to mitigate these potential confounding factors. This is a conservative approach which is expected to potentially underestimate the full extent of impacts from MESIP activities.

> 15 The first visit to each school is unannounced to help capture the real situation of the school in terms of infrastructure, school performance, school and classroom management practices, student and teacher absenteeism and student learning outcomes. If required to complete all instruments, a second visit is made on an announced or pre-scheduled basis.

14
say, the 90th percentile gives the true return to education for high ability people, purged of ability bias. The same caution is given in Arias, Hallock and Sosa-Escudero (2001), who cite QR studies of returns to education (Buchinsky 1994; Machado and Mata 2001; Schultz and Mwabu 1998) and say that the results of these studies should be interpreted with caution since they do not handle the problems of endogeneity bias.

> ** However, the data for Greece do not allow for a straightforward comparison with the other countries, as they are based on net wages.  Progressive taxation is likely to have a stronger impact in eroding the returns to education at the top of the distribution than at its bottom. This may explain the Greek results.

11
Participation and perceptions of empowerment are core elements of social cohesion.<sup>3</sup> Jenson (1998) describes five dimensions of social cohesion: belonging/isolation, inclusion/exclusion, participation/non-involvement, recognition/rejection, and legitimacy/illegitimacy. The extent to which individuals feel empowered to participate in both the social and political life is – at least partially – a manifestation of all five dimensions of social cohesion. Especially in the context of displacement, where the sudden influx may cause a backlash effect among host populations towards displacees (Dinas, Matakos, Xefteris & Hangartner 2019, Marbach & Ropers 2018, Hangartner, Dinas, Marbach, Matakos & Xefteris 2019), the ability to participate and feel empowered is an important step to building more sustainable livelihoods and may help lessen tension between displaced and non-displaced communities. Promoting empowerment among displaced communities may also help alleviate some of the mental (Pham, Fozouni, Al-Saiedi, Coughlin & Vinck 2021), social (Stevens 2016), and political stress on these communities (Carrillo 2009).

We examine when and why displacees become politically and socially engaged by analyzing a number of surveys of IDPs and relevant comparison groups in Iraq, the Philippines, and Uganda.<sup>4</sup> We collected these surveys in a series of projects between 2005 and 2019 analyzing the political, social, and health consequences of large scale forced displacement crises within each case. By bringing these data together, we explore how IDPs behave in and perceive their environments across different political systems, time periods, and continents. Unifying these contextually specific surveys into a single project enables us to focus on which findings (or do not) translate across contexts while respecting the specifics of the displacement experience within each case.

The insights from our surveys advance our understanding of the behavioral consequences of displacement in a number of ways. The overwhelming majority of existing research on the political and social ramifications of displacement focuses on refugees in the global North,<sup>5<
**Loan repayment.** We observe repayment of loans in our experiment using administrative data provided by the lender. Based on this data, we construct our main outcome variable _loan repaid_ . Figure A.2 in the Supplementary Appendix shows a stylized timeline of loan repayment. It is worth noting that making loan payments in full and on time is indeed a choice variable in our setting. The vast majority of customers make payments using a digital payments link each month (81 percent). Only a small fraction of customers (11 percent) use the lender’s automatic payment option and a similarly small number of customers make payments in cash (3 percent).

Our primary measure of loan repayment is the variable _loan repaid_ , which is an indicator equal to one if a loan has been repaid in full. The lender measures repayment one day after a borrower is scheduled to make their final monthly payment. If the lender receives this payment the loan is closed and reported as repaid to the credit bureau. Because borrowers who accept one of the moratorium offers in our experiment not only pause payments, but also have the tenor of their loan extended by three months, we measure repayment for all loans three months after the initial due date of the last payment for each loan.<sup>16</sup> We construct the outcome _loan repaid_ for loans in the experiment and for all loans from other lenders that appear in credit bureau data of borrowers in our sample.

**Borrower beliefs.** The second group of outcomes we use in our analysis are measures of borrower beliefs. Examining the impact of moratorium offers on borrower beliefs allows us to test for the precise mechanisms through which forbearance can give rise to moral hazard, for example by generating expectations of lenient credit enforcement or future forbearance.

The main survey we use for this purpose is an incentivized elicitation exercise in which borrowers who received one of our experimental treatments are asked whether they expect banks or the regulator to offer another moratorium in the event