rafmacalaba/data-use-mentions-extended huggingface dataset:
# **1. Introduction**

In cities around the world, the COVID-19 pandemic introduced changes in travel patterns and disruptions in urban mobility, such as reduction in trips by public transport and changes in work and study-related trips, as well as increase in e-commerce related trips. While some of these changes were driven by specific regulatory measures, such as capacity limits to public transport use, others were related to changes in personal preferences that may or may not be limited to the specific time period retrospectively referred to as “the pandemic”. The traditional transport planning data sources and methods – such as periodic and timeconsuming household travel surveys – were no longer sufficient to understand and act on the new dynamics associated with the pandemic, such as decrease in public transport use and changes in travel during the hours commonly thought of as “peak”. The pandemic introduced new preferences, restrictions, and incentives for travel that could no longer be explained solely by population and economic growth.

The _Big Bang_ of so-called “big data”, advanced analytics and new technologies are transforming not only mobility but also the ability of transport planners to identify important mobility patterns at new scales and resolutions and to act on these findings in real time. Telecommunications and digital technology provide an opportunity to measure mobility; especially in data poor settings with low smartphone penetration, call detail records (CDR) have the highest coverage. In Argentina, there were over 56.3 million cellular subscriptions registered in 2019,<sup>2</sup> and smartphone adoption is expected to increase from 59 percent in 2019 to 82 percent by 2025, while in Colombia the respective figures are 65 percent and 77 percent.

The current study took advantage of the very high mobile phone penetration rates in Bogota and the Buenos Aires Metropolitan Area (AMBA) and the associated availability of high frequency and spatial resolution mobility data to understand how mobility evolved during the pandemic. While the pandemic impacted numerous mobility indicators that matter for the system’s environmental and financial sustainability, only
5 regime. We find that one year after UC, the effect of the program on the probability of being too ill to work is -0.004; this effect rises to -0.007 after two years and stays at the same level in the third year since implementation. We find much larger effects among those aged 65 and over.

Our effects are mostly statistically significant at the 10% level if not the 5% level. But are they large in economic terms? In part to answer this question, we take into account and report the effects on self-reported health limitations of another major public program that was introduced around the same time as UC, namely the Village Fund (VF). This provided Thai villages with seed money (around $22,500 per village) to set up a microcredit scheme; survey data suggest villagers used the money mostly to fund agricultural activities, though the VF was also used by significant numbers of villagers to finance daily expenses and non-farm businesses (cf. Boonperm _et al._ 2012). While not a health intervention, the VF might have been expected to have had some beneficial effects on health status, by for example increasing food consumption and giving households extra income to purchase medicines and other medical goods not covered by the UC scheme. We find that in practice the effects of the VF on the probability of someone reporting themselves as too ill to work is zero in the year of implementation and in the following two years; only in the third year after implementation do we see an effect. On average, the UC effect is around four times the size of the VF effect.
- Philippines (Cagas et al., 2006; Salas, 2006);

- Poland (Sznajderska, 2014; Greszta et al., 2011,Nojkovi ́c and Petrovi ́c, 2015);

- Thailand (Taguchi and Wanasilp, 2018; McCauley et al., 2006);

- Turkey (Gozgor, 2012;Kaytanci, 2009);

- And South Africa (Naraidoo and Paya, 2012; Ortiz and Sturzenegger, 2007; Naraidoo and Raputsoane, 2011; Steinbach et al., 2009; Aron et al., 2000).

The Taylor Rule used in this paper is specified as: where _it_ is the nominal policy rate, and responds to the deviation of inflation ( _πt_<sup>_e_)fromitstarget</sup> ( _πt_<sup>_∗_) and the output gap ( ̃</sup><sup>_yt_).The rule specified above is a forward looking version of the rule by Taylor</sup> (1993). This paper uses private sector inflation forecasts ( _πt_<sup>_e_)asthevariabletargetedbythecentral</sup> bank because it is alternative source of information than model expectations, and since the model is more parsimonious. Although EMDE central banks have different horizons to achieve their inflation targets and can target slightly different definitions of annual inflation, the Taylor Rule specified above should be a good approximation of most central banks’ behavior. Taylor Rules have also been extended in a number of directions for use in EMDEs including nonlinear specifications (Caporale et al., 2018) and additional variables such as the exchange rate (Taylor, 2001; Batini et al., 2003). While individual central banks’ behavior may be better defined by a non-linear rule or one that includes other variables, the Taylor Rule as specified above should indirectly capture other variables including the exchange rate through its impacts on inflation and output.

The Taylor Rule is embedded in an unobserved components model that estimates the output gap based on: in conjunction with equations 5
poverty estimates calculated from the national household budget survey across 25 Districts in Sri Lanka and 25 Regions in Tanzania. Bottom row shows the unweighted MSE times 10,000 and mean CV for non-monetary poverty estimates that incorporating geospatial data across 331 subdistricts in Sri Lanka and 159 Districts in Tanzania, benchmarked to survey estimates for 25 districts in Sri Lanka and 25 regions in Tanzania._ 30 In Sri Lanka, it is not possible to rescale the direct estimates to achieve 95 percent coverage because the arealevel poverty and variance estimates are zero for more than 5 percent of the sub-districts.

28
# **1. Introduction**

Since the early 1990s, the economic weight of the “South” or “Sur” (countries outside the G7 and Western Europe) has risen sharply. Several studies document that the South accounts for a growing share of global economic activity (IMF 2017; UNCTAD 2018; World Bank 2018) and international trade in goods (Hanson 2012). But the literature has focused less on the rise of the South in global finance.

In this paper, we provide a systematic and comprehensive analysis of international financial investments from and to the South and contrast them with those from and to the North. To do so, we combine information from various databases on bilateral international investments, including bank loans and deposits, portfolio investment in debt and equity, foreign direct investment (FDI), and international reserves. The bilateral nature of these data allows us to shed new light on international financial integration. First, we examine who integrates with whom by splitting cross-border investment into four blocks: South-to-South, South-to-North, North-to-South, and North-to-North. Second, we analyze the process of financial integration along the intensive margin (the value of investment) and the extensive margin (the number of bilateral links) for each investment type across the four blocks.

We assemble global bilateral data covering most of the countries in the world from 2001 to 2018. The data sources jointly make up the bulk of countries’ international investment positions (IIPs): (i) international bank loans and deposits from the Locational Banking Statistics of the Bank for International Settlements (BIS); (ii) portfolio investment in debt and equity from the Coordinated Portfolio Investment Survey (CPIS) of the International Monetary Fund (IMF); (iii) FDI data from the Bilateral FDI Statistics provided by the United Nations Conference on Trade and Development (UNCTAD) and the Coordinated Direct Investment Survey (CDIS) of the IMF; and (iv) international reserves data constructed by combining the International Financial Statistics (IFS) with the Currency 1
Policy Research Working Paper 10754

# **Abstract**

Changes in the sentiment of migration-related news published in destination countries affect the timing of migrants’ journeys to these countries. Using geo-localized data on migrants in Libya and the complete record of news articles in their country of destination, this paper shows that a worsening news sentiment leads to migrants staying longer in Libya, slowing down their journeys to their final destinations. The paper validates these results by showing that the effect is concentrated in locations with internet connections.

The results indicate that changes in news sentiment have a significant impact only for some groups of migrants and under specific conditions, suggesting a limited effect on overall migrant movements. Finally, the paper provides suggestive evidence that a worsening news sentiment in the preferred destination induces substitution across destination countries, yet it does not make migrants return to their country of origin.

This paper is a product of the Office of the Chief Economist, Middle East and North Africa Region. 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 michele.dimaio@uniroma1.it, nelmallakh@worldbank.org, and valerio.leonesciabolazza@uniroma1.it.

_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
32 country. But what is the implication of having consumption cities for growth?

Comparing the returns to experience for the UT and UNT sectors might be one way to answer it. Indeed, if the returns to experience capture human accumulation at work (Lagakos et al., 2018; Jedwab et al., 2020a), and if there is more human capital accumulation at work in the UT sector, we should observe higher returns in UT than in UNT. However, we do not find that this is the case.<sup>49</sup> Yet, in some specifications we found significantly lower urban returns to experience in the aggregate in NRXGDP and DEINDU countries, which may be due to more general factors in consumption cities.<sup>50</sup> Another way to answer the question about the growth implications of consumption cities, especially large ones, would be to globally estimate separately the agglomeration economies (AEs) for the two sectors, UT and UNT. We cannot estimate AEs by sector using the I2D2 because we cannot identify cities in the database (only or the number of the cluster are Since this regions survey given). paper identifies production/consumption city-ness (PCC) for many urban areas in a large number of countries, we could obtain from the literature and compare the estimates of AEs for countries whose cities are mostly consumption cities and the AEs of countries whose cities are mostly production cities. One can draw on many such urban studies, which consider one at a time and for a of cities or typically country sample regress log wages log productivity on the log of population, population density or employment. Chauvin et al. (2017) estimated the AEs for China – a country which our analysis shows has mostly production cities – at more than 16%, for India and the United States – two countries that we show have a mix of production, consumption and neutral cities – at 8% and 5%, respectively, and for Brazil – a country with mostly
|---|---|---|---|---|---|---|---|---|
|Pre-1991|0|2|2|0|2|0|0|6|
|1991-2000|0|21|16|1|0|2|10|50|
|2001-2010|15|18|6|8|0|4|11|62|
|2011-2020|2|6|6|1|1|0|16|32|
|2021-2024|2|1|0|0|0|0|0|3|
|**Total**|**19**|**48**|**30**|**10**|**3**|**6**|**37**|**152**|

Source: (Akcura 2024) _Unbundling:_ One of the key sector reforms has been separating network from generation and retail functions. Transmission unbundling in particular has been pursued as countries transitioned out of VIU into SBM and WRC structures, although the degree of unbundling varies between countries (Figure 12). Based on Global Power Market Structures Database, as of July 2024, 93 economies have instituted some type of unbundling of their power transmission. Most have unbundled transmission in phases starting with separating the financial accounts of the VIU for each segment, then to organizationally separate functions, and progressing to create separate legal entity responsible for transmission which formalizes the separation created through accounting and functional unbundling. In some countries, primarily in Western Europe, ownership unbundled transmission such that the company owning and operating a network is not allowed to be active in any competitive segment of the electricity supply chain nor have an interest with respect to those activities. This ensures that there is no conflict between those who control the network and market participants (IPPs, traders, retailers
_-_**_judicial independence with_**<br>index of law & order,<br>_-formal constraints on executive_<br>_discretion_<br>(federalist state dummy,<br>dual executive/legislature<br>dummy, bicameral legislatures<br>dummy, index of legislators’<br>effectiveness,<br>index of military role in<br>government, index of gov.<br>particip. by org. religion,<br>degree of developm. of pol.<br>parties),<br>_-informal_<br>_constraints_<br>_on_<br>_executive discretion_<br>(index of quality of bureaucracy,<br>index of corruption in gov., index<br>of racial/nationalist tensions).<br>-political index (2) - as above but<br>index of law & order replaced<br>with the ‘tenure of high/supreme|-GDP per capita (log)<br>(CL)<br>-industry share in GDP<br>(log)<br>(CL)<br>-urbanisation % (log)<br>(CL)<br>-energy share of hydro<br>(log)<br>(SE)|The analysis uses data from a<br>total of 91 developed and<br>developing countries for<br>1987. The size of the actual<br>samples used in different<br>models are 38 and 87 or 86<br>countries depending on data<br>availability for variables.|

53
300 surveys in over 90 countries.

78 MICS is an initiative of UNICEF that assists countries in collecting and analyzing health and education data in order to fill data gaps for monitoring the situation of children and women.

79 Many refugee hosting countries issue a form of identification, either specific to refugees or based on national identification documents or those issued to non-national residents. In many cases where such documents are not issued, refugee identity cards are issued in collaboration with UNHCR.

80 A population register provides a mechanism for the continuous recording of selected data on the resident population including a unique identification number, date of birth, sex, marital status, place of birth, place of residence, citizenship and language and possibly also socio-economic data, such as occupation or education.
In Sub-Saharan Africa 5 percent of adults (and 14 percent of savers) report having saved using both formal and community-based methods in the past year. Globally, slightly less than half of all adults who report having saved in the past 12 months using an informal savings club or person outside the family also report having saved using a formal financial institution.

While many savers in the developing world blend formal and informal methods, an even larger share use only community savings clubs. In Sub-Saharan Africa 34 percent of savers report having saved using a community savings club (and not a formal financial institution) in the past 12 months.

The popularity of savings clubs speaks to their advantages, but these arrangements also have downsides. Their essential characteristic—informality—is accompanied by risks of fraud and collapse (although formal accounts are not immune to these risks where explicit government-run deposit insurance is absent or inadequate, as it is in many developing economies). In addition, the cyclical nature of contributions and disbursements can be too rigid for some people and out of sync with their needs to deposit surplus income or quickly withdraw funds for an emergency.

Community-based savings methods and formal financial institutions are not the only options for saving. A large share of adults around the world who report having saved or set aside money in the past 12 months do not report having done so using a formal financial institution, informal savings club, or person outside the family. While the Global Findex survey did not gather data on these alternative

**34**

MEASURING FINANCIAL INCLUSION
# **5. Conclusions**

In this paper, we used data from the 2006 VHLSSs and probit regressions to estimate the effect of parental disability on education of children age from 6 to 17. Our estimates indicate that disability among parents reduces the school attendance rate of their children around 8.1 percentage points from 81.3 percent to 73.2 percent. The effect tends to be larger for boys, Kinh children, and children in urban areas. We found an especially strong effect of parental disability on children aged from 15 to 17, i.e., upper secondary school age children. However, disability of parents does not have a significant effect on the academic performance of their children in school.

There can be several reasons why children whose parents are disabled are less likely to attend school. First, parental disability can reduce the education as well as income of parents, which then decreases child education. Second, children might have increased responsibilities at home if their disabled parents cannot generate a sufficient livelihood or if they require personal assistance that they cannot access outside the family. Third, children might be reliant on their parents to physically get to school, which disabled parents – e.g., facing transportation barriers – cannot do. Fourth, the increased costs and demands on disabled parents‟ time might leave them less time and energy to look after their children‟s education. This fourth reason, however, is not supported by the fact that once they attend school, children of disabled parents have the same level of achievement as their peers.

These findings suggest clear policy implications. To achieve the Millennium Development Goal of universal primary school as well as increased coverage of secondary education, the government should have policies and programs supporting the education of children whose parents have a disability. One approach would be to implement education supports, such as a reduction or exemption of tuition fees and contributions or possibly assistance with transportation. But an approach which gets more directly at the costs of disability would be
to poverty-induced vulnerability that indicates the quota of urban Dakar is 84 percent higher than it would have been under the old RNU system. Rural areas are still assigned consistently higher quotas because of their higher poverty rates (56.8 percent of the population). However, the effect of accounting for riskinduced vulnerability in the total quota in rural areas, which represent more than half the country’s population, is marginal compared with the effect in urban areas. These results hold if the analysis is based on communes instead of population averages.

# 6. Conclusions

About 55 percent of the population in Senegal was vulnerable in 2018/19. Of the vulnerable, two-thirds were poor (poverty-induced vulnerability), while the remaining one-third were people with expenditures above the poverty line, but facing a high probability of becoming poor (risk-induced vulnerability). Relying only on the information from the latest household survey provides valuable insights into the characteristics and regional distribution of the poor and vulnerable. However, it cannot provide highly detailed spatial information that is crucial for supporting the implementation of targeted social programs. This paper addresses the related knowledge gap by combining two separate, but linked strands in the pertinent literature: the small area estimation of poverty and the estimation of vulnerability. The combination enables the estimation of highly disaggregated poverty rates and the determination of the share of nonpoor households at risk of falling into poverty.

Poverty- and risk-induced vulnerability underline different economic challenges: deprivation versus variations in welfare. From a public policy perspective, it is crucial to understand how overlapping factors contribute to (or limit) the capacity of households to increase their incomes and improve their resilience to adverse shocks. If structural poverty is the primary concern, the most suitable interventions are likely to involve cash transfer programs or initiatives that improve the delivery of basic services and facilitate investments in physical and human capital. In contrast, if vulnerability is predominantly risk induced, then implementing a program that addresses the risk may be essential (Skoufias, Vinha, and Beyene 2024). For
data. When weather is a critical component of the analysis, similar evidence of robustness should be provided regarding the choice of weather metrics. The researcher should be able to answer the question of whether their results hinge on the choice of a particular metric to measure precipitation or temperature, or whether their results reflect a broader relationship between precipitation, temperature, and the outcome of interest.

- Researchers should be cautious in using temperature data to predict agricultural outcomes. When looking at agricultural production in the LSMS-ISA data, temperature tends to be a poor predictor. Whether this is because remote sensing products mismeasure temperature or because temperature is not important to agricultural production in tropical Africa is uncertain. Obviously, there are situations that call for the use of temperature in the analysis but simply adding temperature to a regression because the data is available risks overcontrolling. Common temperature metrics for temperate regions, like GDD, are particularly poor predictors of agricultural production in the LSMS-ISA data, and should be avoided.

- Researchers must carefully choose which remote sensing source to use in their analysis. Despite the volume of precipitation and the temperature in a given location on a given day being objective facts, remote sensing products differ in how they measure these objective facts. Because of this, remote sensing products can and do disagree on what the weather was. At least for those using the LSMS-ISA data, researchers should not use rainfall data from MERRA-2 or ERA5. Without evidence to the contrary, one cannot assume that the measurement error in MERRA-2 or ERA5 is only limited to the LSMS-ISA, meaning researchers may want to avoid them when using household data from other Sub-Saharan African countries or from countries on other continents.

- Researchers may want to demonstrate the robustness of their results to the choice of weather data drawn from different remote sensing products. Again, when weather data is critical to the identification strategy, results should not be sensitive to the choice of remote sensing product. While it is unlikely that the choice between CHIRPS, CPC, ARC2, and TAMSAT will matter,