rafmacalaba/data-use-mentions-extended huggingface dataset:
destination country. They include policy variables that are amenable to change in the short to medium term, as well as country fundamentals such as per capita GDP or population size. While these indicators may not be under the control of policy makers, their inclusion nevertheless has an important policy dimension to insert a reality check into sectoral FDI strategies. For instance, countries may find that policy initiatives intended to promote FDI in a certain industry are doomed to fail if country fundamentals are detrimental to the development of such an industry in the country. In other cases, there might be short-term initiatives that policy makers can consider to compensate for shortcomings on particular fundamentals, such as a more open immigration regime to make up for skills shortages or spatial solutions to reduce transport costs and disadvantages related to remote location.

_Table 1. Indicators categories_

|Category|Sub-category|
|---|---|
|DEMAND|Demand|
|PRODUCTION FACTORS|Labor and skills|
|PRODUCTION FACTORS|Geographyand natural resources|
|PRODUCTION FACTORS|Existingcapabilities|
|INPUTS|Energy|
|INPUTS|Transport|
|INPUTS|Finance|
|INSTITUTIONS|Regulatorybarriers and taxation|
|INSTITUTIONS|Rule of law andpropertyrights|
|INSTITUTIONS|Market contestability|
|INSTITUTIONS|Macroeconomic andpolitical stability|

# Empirical Design and Approach

# Data preparation

The initial country indicators data list included 594 potential indicators for 217 countries, collected over the 1960-2019 period. Not surprisingly, the country coverage is limited until the most recent periods. Due to large number of missing data points per individual indicators, we try to maximize the trade-off between including the maximum number of indicators while maintaining adequate country data coverage. After thorough data examination and cleaning, the final balanced data set includes 190 potential indicators which are available for 116 countries over the 2010-19 period, the period which matches the availability of FDI data while being representative in terms of the number of countries selected (Annex 3 provides the full list).

We average the data for indicators over two non-overlapping five-year periods (2010-2014 and 201519
- End of picture text --> Source: Own elaboration based on data from the 2020 ENVIF-VCM.

The prevalence rates of all forms of violence were analyzed according to individual characteristics of women, characteristics of partners or ex-partners, and attitudes towards inequitable gender norms. Appendix B provides details on these statistics.

The data show that the prevalence of intimate partner violence is higher among women belonging to younger age groups. In fact, the prevalence of partner violence reaches almost 41 percent in the group aged 15 to 18, and 36 percent in the 19-to-25 age group. This trend remains after disaggregating the data by type of violence for all forms of violence except for economic violence, where no significant differences are observed between the different age groups.

Additionally, more foreign women report economic violence than their Chilean counterparts (18 percent versus 10 percent). No significant differences are observed in the other types of violence. The prevalence of both psychological and sexual violence is higher among indigenous women than among women who do not identify as indigenous (23 percent versus 16 percent and 5 percent versus 3 percent, respectively). In the other types of violence, no significant differences are observed. Economic violence is also more prevalent among women with a disability than among those without one (16 percent versus 11 percent), as is the prevalence of physical violence (6 percent versus 3 percent).

Significant differences in prevalence by education and socioeconomic levels exist in all forms of intimate partner violence. The prevalence of psychological violence among women who have completed primary education or less is 21 percent, while among women who have a tertiary education, it is 12 percent. These figures are 15 percent and 7 percent for economic violence, 5 percent and 2 percent for physical violence, and 4 percent and 3 percent for sexual violence, respectively. Similarly, the prevalence of psychological violence among women of low socioeconomic status is 22 percent, while that of women of high socioeconomic status is 8 percent. These figures, respectively, are 13 percent and 4
Khandker, Shahidur R, Hussain A Samad, Rubaba Ali, and Douglas F Barnes. 2014. "Who benefits most from rural electrification? Evidence in India." _The Energy Journal_ :75-96.

Khanna, Madhulika, Nishtha Kochhar, and Nethra Palaniswamy. 2015. "A retrospective impact evaluation of the Tamil Nadu empowerment and poverty alleviation (Pudhu Vaazhvu) project." _The Journal of Development Studies_ 51 (9):12101223.

Khatun, Dilruba, and BC Roy. 2012. "Rural livelihood diversification in West Bengal: determinants and constraints." _Agricultural Economics Research Review_ 25 (347-2016-16910):115.

Lance, Peter M, David K Guilkey, Aiko Hattori, and Gustavo Angeles. 2014. "How do we know if a program made a difference? A guide to statistical methods for program impact evaluation." Macours, Karen, Patrick Premand, and Renos Vakis. 2012. "Transfers, Diversification and Household Risk Strategies: Experimental evidence with lessons for climate change adaptation." _World Bank Policy Research Working Paper_ (6053).

Majumdar, Shruti, and Vijayendra Rao. 2017. "On the frontlines of scaling-up: a qualitative analysis of implementation challenges in a CDD project in rural India." Ministry of Rural Development. 2019. "Deendayal Antyodaya Yojana - NRLM." Accessed 25-02-2019. <u>https://aajeevika.gov.in/.</u> Parajuli, Dilip, Gayatri Acharya, Nazmul Chaudhury, and Bishnu Bahadur Thapa. 2012. "Impact of social fund on the welfare of rural households: evidence from the Nepal poverty alleviation fund." Prennushi, Giovanna, and Abhishek Gupta. 2014. "Women's empowerment and socio-economic outcomes: impacts of the Andhra Pradesh rural poverty reduction program." _World Bank Policy Research Working Paper_ (6841).

Rosenbaum, Paul R, and Donald B Rubin. 1983. "The central role of the propensity score in observational studies for causal effects." _Biometrika_ 70 (1):41-55.

Sanghi,
involve deaths are excluded because, otherwise, this would duplicate entries in the SIM death registry. Information on the reason for hospital admittance is provided in the ICD-10, which the study used to identify the hospitalizations for assault based on the same codes as in the homicide data. Though the primary cause typically indicates the location of the injury (for example, trauma in the knee or leg), the secondary cause often clarifies the incident associated with the injury (such as accident or assault).

The study confirmed that all municipalities in the sample had at least one hospitalization of any type, which indicates that all cities in the comparable sample have access to hospitals. The lowest number of hospitalizations in any municipality in any year

> 5 See “Sistema de Informações de Mortalidade,” DATASUS, Ministerio da Saude, Brasília, http://tabnet.datasus.gov.br/cgi/deftohtm.exe?sim/cnv/obt10br.def.

> 6 See “Sistema de Comunicação de Informação de Hospitalar e Ambulatorial,” DATASUS, Ministerio da Saude, Brasília, http://www2.datasus.gov.br/DATASUS/index.php?area=0203&id=6926.

8
non-host components in column (3). All, moreover, are highly significant. In the FDI data, in contrast, while the coefficients on the statutory rate terms take the expected sign they are in all cases not only much lower but also insignificant. In all columns the host _KF_ - _METR_ enters with the expected negative sign, and is again insignificant in the FAI data. It proves significant, however, in the FDI data—though only moderately so.

While this result—that tax effects are stronger using FAI data than FDI data—is not wholly unexpected given some previous work, it may nonetheless seem counter-intuitive: Might one not, to the contrary, expect the tax-motivated use of conduit arrangements, picked up directly in FDI statistics but not in FAI, to generate tax effects that are more marked, not less, in FDI data?

Some explanation can be found, however, in the narrative around Table 1 in Section 3 and Figure 1. Suppose, for instance, that the statutory rate in host country _H_ is reduced, so that real investment there by the parent in _UP_ increases; and suppose moreover that this is financed by increasing both direct investment from _UP_ to _H_ and conduit investment from _UP_ through _IP_ to _H_ . In the FAI data the aggregate of these two flows, FAI from _UP_ to _H_ , is indeed identified as responding to the tilting of the tax differential in favor of investment in _H_ . In the FDI data, the increased FDI flows from both _UP_ and _IP_ to _H_ are also in line with the shift in tax differentials. But the FDI data also picks up the increased flow from _UP_ to _IP_ : that is, the conduit country _IP_ experiences an increase in inward FDI even though in tax terms it has become less attractive. In this way, FDI flows through conduit countries can dilute the apparent impact of taxation on investment. And, given the significance of conduit activities, the downward bias this implies in measured tax effects may be sizable.

# **
# <u>Brazil</u>

The Brazil data includes a sample of 1,641 firms distributed across 13 states (Table 5). To make the analysis comparable with China, 930 firms from five industry groups: Textiles, Apparel, Shoe and Leather Products, Electronics and Auto Parts, were selected. Table 6 shows that unlike the China sample, almost half of the observations are from a single industry group: apparel. Also, unlike the China sample, only a very small share of firms is state-owned.

As Table 7 shows, with the exception of the Auto Parts industry group, most firms do not export. Most notably, about 85% of the firms in the garment industry group produce exclusively for domestic production. This lack of export orientation is found even in the most economically advanced states of S **ã** o Paolo and Rio de Janeiro (Table 8).

**Table 5:  Brazil Observations by State**

|**State**|**Number of**<br>**Firms**|**Percentage of**<br>**Total Firms**|
|---|---|---|
|**São Paulo**|359|21.9|
|**Rio de Janeiro**|122|7.4|
|**Minas Gerais**|232|14.1|
|**Santa Catarina**|175|10.7|
|**Rio Grande do Sul**|191|11.6|
|**Paraná**|179|10.9|
|**Goiás**|82|5.0|
|**Mato Grosso**|38|2.3|
|**Ceará**|90|5.5|
|**Paraíba**|47|2.9|
|**Maranhão**|22|
and does not burn<br>garbage|Yes|No|
|||Exposure to<br>farm animals|Household member owns or manages<br>livestock|Yes|No|
||Not in<br>questionnaire|Handwashing<br>facilities|Availability of handwashing facilities,<br>water, soap & detergent|No|No|
|Community-level<br>environment|NNS Form<br>1.2|**Community**<br>**level**<br>**sanitation**|**At least 75% of households in**<br>**municipality have improved**<br>**sanitation**|**Yes**|**Yes**|
|Behaviors that support<br>hygiene, sanitation and|NNS Form<br>1.2|Safely<br>manages<br>drinking water|Boils, chlorinates or filters water or<br>purchases bottled mineral water|Yes|No|
|<br>disease prevention|Not in<br>questionnaire|Feces disposal|Child’s stools are disposed into an<br>improved sanitation facility|No|No|

Access to improved WSH infrastructure is in general very high: 95 percent of children under-2 have access to improved drinking water, 85 percent have access to improved sanitation and 82 percent live in a municipality where at least three-quarters of the households have improved sanitation. Nationally, close to 3 in 4 children under-2 live in a household with adequate WSH infrastructure.

Several indicators associated with the household environment but not related to water and sanitation were identified in the NNS data. Whether the household has access to electricity or uses improved cooking fuel and does not burn garbage has a bearing on indoor air quality, and how efficiently food can be prepared. Whether there is a functioning refrigerator has a bearing on the household’s ability to preserve foods that are prone to spoilage, including meat
# **1. Introduction**

<mark>Financial inclusion, or the extent to which people have access to useful and affordable financial products that meet their financial needs, is a cornerstone of economic development (DemirgucKunt et al. 2022). Access to financial products at banks and other brick-and-mortar institutions, as well as digital-only services—such as with a mobile money or other fintech provider—can allow individuals to formally save, borrow, make payments, and manage financial emergencies.</mark> While formal financial services can benefit households, adults need to understand the risks and costs of these services to use them effectively. Fraud, over-indebtedness, and misunderstandings about account terms and fee structures pose serious risks to users who lack proper knowledge and financial education (Garz et al. 2021).

Using data from the Global Findex 2021 survey for 79 middle- and low-income (“developing”) countries, we present descriptive statistics on unbanked adults (who do not have an account at a formal financial institution, including mobile money services) and on adults with an inactive account (who did not make a deposit, withdrawal, or digital payment in the past year). We also present new data on financial capability, and discuss the role improved financial capability could play in motivating and enabling adults to use financial services safely and to their benefit. These findings are supported by results from multivariate regressions.

Our paper makes three contributions. First, we contribute to the literature by quantifying the number of unbanked around the world and identifying who the unbanked are in terms of individual characteristics such as gender, income, educational attainment, age, and employment status. Understanding the demographic and income characteristics of the unbanked in a specific economy can help focus research and educational outreach to the populations most likely to 2
gas plant and supplied through pipeline, fuelwood harvested from natural forests and supplied through trucks, electricity supplied through distribution network) compete. It finds the optimal mix of these networks to ensure the least cost to deliver a unit of energy service (heat for cooking here). While finding the optimal solution, environmental or policy constraints can be incorporated. For example, if the policy is to adopt more and cleaner cooking, the model can incorporate it by setting a higher share for LPG, natural gas or electricity instead of wood fuel or biomass for cooking.

The base year for the CGE is 2012, whereas it is 2015 in the TIMES model. The TIMES model used historical data for the 2012-2015 period, whereas it uses CGE projection afterward.

49
understanding, highlighting that climate change impacts on inequality can vary greatly depending on country-specific socioeconomic and environmental contexts.

# **3.3. Other Outcomes/ Channels**

Studies that explicitly examine the mechanisms linking climate change and disasters with poverty and inequality are still relatively few, perhaps due to data limitations. The handful of studies that explore these mechanisms often converges on a few key themes, with agriculture standing out as a primary driver (Azzarri and Signorelli, 2020; Paglialunga _et al._ , 2022). Climate change impacts tend to be amplified in countries with large rural populations or a high proportion of the workforce in agriculture. Such demographic characteristics not only exacerbate the vulnerability of these populations to climate change but also enhance the potential for climate change to aggravate inequality.

Extreme climatic events may have differing impacts depending on the rural-urban context. While rising temperatures seem to exacerbate inequality more in largely rural countries (or regions), extreme precipitation events are associated with increased disparities in predominantly urban areas. Paglialunga _et al._ (2022) show that precipitation deviation can trigger flood risks in urban areas, damaging infrastructure and overloading drainage systems. Such events can disrupt food availability and inflate prices, disproportionately affecting the urban poor who rely more on markets for food. Combining household surveys with climatic datasets, our analyses of Chile, Colombia, and Indonesia, Nakamura _et al._ (2023) find that while households in large metropolitan areas are more likely to escape from poverty, climate shocks such as extreme rainfalls and high flood risks significantly reduce upward mobility, thus offsetting such benefits of urban agglomerations.

In terms of adaptation mechanisms, social safety nets such as unemployment insurance can mitigate the negative impacts of natural disasters on income (Pleninger, 2022). However, these 24
Finally, Jakubovsky and Sakowski (2006) find a small positive effect of school size and an inverse u-shaped effect of class size.

Kallai and Maniu (2004) estimate educational production functions for Romania. They found a _positive effect of school size but no effect of class size on students’ test scores_ . Porta (2011) using PISA data for Romania also finds that the _smallest schools (first quintile) perform less well than bigger schools_ , but that the size of this effect is small and significant only for reading scores. A similar study for Serbia (MacDonald et al, 2009) found similar _small but significant effects for reading, math and science scores_ . Hermann (2004b) finds a negative effect of primary school size on the likelihood to entering a better secondary school in Hungary.

Summarizing, the large literature on the effects of class size and school size suggest that while class and school size might have an effect, these effects are unlikely to be large. For transition countries, school size, however, appears to be more significant than class size as a determinant of educational performance and, increasing school size appears to be, if any, beneficial for educational performance. At the same time, the literature also suggests that effects vary from one country to another, pointing to the need for more country-specific studies.

# **III. The Educational System of Ukraine - Some Background**

In Ukraine the state is the main provider of education services, at all levels including general secondary education<sup>14</sup> (GSE). Public schools constitute 99 percent of general secondary institutions educating more than 99.5 percent of students. According to the data of the State Statistics Service of Ukraine the school network in the 2009/2010 school year consisted of 20.368 institutions: 2.177 level I schools (grades 1-4); 5.397 level I-II schools (grades 1-9); 12.312 level I-III schools (grades 1-12)<sup>15</sup>
Nevertheless, the fact remains that Latvian enterprises currently tend not to conduct research and they tend not to conduct or purchase R&D. In this respect, even innovating Latvian enterprises are relatively inert. They prefer turnkey technology solutions in the form of technology embedded in imported machinery and equipment. In this respect, they are no different from enterprises in other CC countries.

**Figure 23 Innovation Expenditures in Latvia in 2001**

<!-- Start of picture text --> 7,4%<br>13,7%<br>Intramural reserch and experimental development<br>11,1%<br>6,8% Acquisition of R&D from other enterprises/ organisations<br>(extramural R&D)<br>Acquisition of machinery and equipment<br>Acquisition of other external knowledge<br>Training, market introduction of innovations, design of<br>other preparations for production<br>61%<br>Source : Latvia Survey of Innovation Activities<br>Figure 24 Innovation Expenditures in Manufacturing<br>1 0 0 %<br>9 0 %<br>8 0 %<br>7 0 %<br>6 0 % O th e r<br>5 0 % M a c h in & e q p m .<br>4 0 % R & D<br>3 0 %<br>2 0 %<br>1 0 %<br>0 %<br>9<br>Romania Turkey Poland Russia Slovakia Slovenia EU<br><!-- End of picture text --> _Source_ : Slavo Radosevic, “(Mis)match Between Demand and Supply for Technology: Innovation, R&D, and Growth Issues in Countries of Central and Eastern Europe,” processed 2003.

37
of total natural resource rents. No resource rents data for Sao Tome and Principe. Source: World Bank staff estimates based on UNU-WIDER (2021) and The Changing Wealth of Nations 2021.

10 Two important caveats are necessary to interpret this finding. First, the measure of rents is likely subject to sizeable uncertainty and measurement error. It relies on measures of both total revenues – which typically uses on world prices rather than the actual price paid for a unit of the commodity – and an estimated measure of costs – which are held confidentially by the industry, and vary significant by project, by country, by geology, and even by the level of prices for the commodity (Toews and Naumov 2015). Second, the relationship between what the government ‘ought’ to collect, i.e. the rents value once a reasonable cost and return on capital is deducted, and what it actually collects is complicated by numerous factors. Taxes and contracts might be poorly configured or badly negotiated, either due to bad luck, significant information asymmetries, lack of relevant expertise, or deliberate (and potentially corrupt) actions by decision makers. Alternatively, government may be unable to recover more of the rents without deterring investors due to additional risk to capital, like political risks, associated with operating in particular context. This is also very hard to evaluate on a general basis.

17
the past 5-<br>years.|2/12|3|MICS|

# **3.2 Risk analysis and quantification**

We perform a risk analysis, evaluating damages to housing, infrastructure assets and agricultural land. A risk analysis combines data on the spatial extent of the hazard with the location of exposed assets and land-use, after which the resulting damages are evaluated using specific vulnerability curves and reconstruction costs (Meyer et al., 2013). For the vulnerability curves and reconstruction costs, we have used country-specific data where possible, supplemented by data from countries with comparable characteristics as Bangladesh in terms of asset vulnerability ( _e.g._ engineering standards, building types). A brief overview with more details is included in Appendix D.

# **_3.2.1 Hazard data_**

We consider two types of hazards; cyclone-induced coastal flooding and extreme wind. For coastal flooding, we utilize modeled flood water levels from the Institute of Water Modelling (IWM) in Bangladesh (IWM, 2018). They developed a hydrodynamic model to simulate how extreme storm surges of different magnitudes propagate through the tidal channels in the coastal zone. The modeled flood water levels are then overlayed with a digital elevation model, assuming a complete inundation of unprotected areas and inundation of embanked areas (see below) above their design standards. This approach is known to be simplistic, in particular in terms of the inundation dynamics of the polders and the probability of polders breaching before design conditions are reached. However, given a lack of more refined data to include this failure 13