rafmacalaba/data-use-mentions-extended huggingface dataset:
Data**

The WIOD data set is a widely used Global input-output tables source. We use the the information contained in the 2016 release which covers 43 countries and 56 ISIC Rev. 4 two-digit (primary production, manufacturing, and services). In order to match the WIOT data with the information contained in ORBIS, we aggregate the 56 sectors up so as to obtain 38 sectors. Moreover, we group the countries in 22 major world regions<sup>1</sup> according to the detailed United Nations geoscheme and substitute coefficients for those countries in ORBIS which are not specifically contained in WIOT by the respective annual group average.

For a more formal account of the WIOT-data construction for our purpose, let us closely follow the notation in Antr`as and Chor (2018) and define a world economy with _J_ countries (indexed by _i_ or _j_ ) and _S_ sectors (indexed by _r_ or _s_ ). Also let us use _Zij,t_<sup>_rs_as total value of inputs used by country</sup><sup>_j_’s sector</sup><sup>_s_originating from country</sup> _i_ ’s sector _r_ in year _t_ ; _Fi,t_<sup>_r_and</sup><sup>_Y_</sup> _i,t_<sup>_r_asthetotalvalueofthefinalgoodssoldandthe</sup> gross output by industry _r_ in country _j_ , respectively.

These basic definitions serve to define three measures which are informed by and reflective of a country-sector pair’s positioning in the global value chain. These

> 1Northern America, Central America, Caribbean, South America, Northern Africa, Western Africa, Middle Africa, Eastern Africa, Southern Africa, Southern Europe, Western Europe, Northern Europe, Eastern Europe, Western Asia, Central Asia, Southern Asia, Eastern Asia, Southeastern Asia, Australia and New Zealand, Micronesia, Polynesia, Melanesia

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use of insurance products for health care and agriculture. (See the questionnaire for the survey questions.)<sup>2</sup>

## **Data coverage**

The Global Findex indicators are drawn from survey data collected over the 2011 calendar year, covering more than 150,000 adults in 148 economies and representing about 97 percent of the world’s population. The survey was carried out by Gallup, Inc. in association with its annual Gallup World Poll, which since 2005 has surveyed about 1,000 people annually in each of up to 157 economies,<sup>3</sup> using randomly selected, nationally representative samples.<sup>4</sup> The target population is the entire civilian, noninstitutionalized population age 15 and above. Surveys are conducted in the major languages of each economy. (For details on the data collection dates, sample sizes, excluded populations, and margins of error, see the annex to this methodology section.) The 148 economies covered by the Global Findex indicators include both high-income economies and developing (low- and middle-income) economies. The regional and income group classifications are those used by the World Bank, available at http://data.worldbank.org/about/country-classifications. The regions exclude high-income economies.

**8**

MEASURING FINANCIAL INCLUSION
## _Through opportunities_

Existing opportunities play a substantial role in nurturing aspirations. For instance, in Japan, where boys tend to have higher expectations and aspirations than girls, gender gaps are notably smaller in regions with higher female employment rates (Molina and Usui 2023). In these areas, parents invest more in their daughters financially and in time, resulting in better education achievements among girls compared with other areas. Using survey data on social norms and role models, Molina and Usui (2023) rule them out as the primary mechanisms for the relationships that they document and hypothesize instead that labor market conditions affect aspirations by changing individuals’ predictions about future returns to the relevant outcomes.

Specific interventions that target opportunities may also contribute to raising aspirations. Programs designed for adult workers may indirectly affect beneficiaries' aspirations regarding their children's future. For example, an on-the-job training program focusing on enhancing socioemotional skills improved the productivity of adult women workers in India's largest ready-made garment export firm (Adhvaryu, Kala, and Nyshadham 2023).<sup>24</sup> It also had cascading effects because it increased the savings and aspirations among the women with regard to their children’s education.

Similarly, in India, women in villages who were provided with three years of recruiting services for jobs in the business process outsourcing industry were significantly less likely to marry or have children during that period (Jensen 2012). Instead, they entered the labor market or pursued more schooling or post-school training. These women also expressed a desire to have fewer children and maintain more stable careers throughout their lifetimes, indicating an increase in their aspirations for a career. In Poland, an intervention aimed at helping women develop work portfolios for the information technology industry as an affordable alternative to mentoring resulted in a nine percentage point increase in the likelihood of finding a job in information technology (Athey and Palikot 2023).

# **3.3. Avoiding frustrated aspirations**

This subsection reviews mechanisms to prevent frustration from multiple sources
# **Abstract**

Educated parents tend to have educated children. But is intergenerational transmission of human capital more nature, more nurture, or both? This paper uses household survey data from Rwanda that contains a large proportion of children living in households without their biological parents. The data should allow us to separate genetic from environmental parental influences. The non-random placement of children is controlled by including the educational attainment of the absent biological parents and the type of relationship that links the children to their “adoptive” families. The results of the analysis suggest that the nurture component of the intergenerational transmission of human capital is important for both parents, contrary to recent evidence proposed by Behrman and Rosenzweig (2002) and Plug (2004). They conclude that mothers’ education had no environmental impact on children’s schooling. Interestingly, mothers’ education matters more for girls, while fathers’ education is more important for boys. Finally, an important policy recommendation in the African context emerges from the analysis: the risk for orphans or abandoned children to lose ground in their schooling achievements is minimized if they are placed with relatives.

ii
health worker training, to support a particular health intervention or a myriad of other potential development-oriented government actions that the World Bank finances. In some cases, projects simply take the form of budget support to receiving countries. A document describing the project is prepared by World Bank staff and includes a proposed amount of World Bank funding. An important ingredient in this initial document is a statement of the project’s “development objective,” which summarizes what the project intends to achieve. This development objective is important because the evaluation data we use rates the performance of the project relative to this objective. Once the project is approved by the Board of Executive Directors of the World Bank, it is implemented over several years, with the project spending financed by disbursements on loans provided by the World Bank.

On the World Bank side, each project is staffed by a project team and led by a task manager (formally known as a "task team leader"). At least twice a year, World Bank task managers are required to report on the status of the projects for which they are responsible, by completing an Implementation and Status Results (ISR) report (previously known as a Project Status Report (PSR)). As discussed below, these ISRs provide us with a rich set of project-level variables measured over the life of the project. Naturally, the World Bank’s information systems also capture the annual disbursements on loans associated with each project over its duration. We use this information to construct disbursement rates on projects. Once a project is concluded, the responsible task manager produces an Implementation Completion Report (ICR) (previously known as a Project Completion Report (PCR)), which provides a comprehensive review of various dimensions of project outcomes. As we discuss further below, the reports form the basis of several of our outcome measures.<sup>2</sup> All of the results that follow are representative only of the set of World Bank projects actually implemented over the past 30 years and
(e.g. regulation, restructuring, and privatisation) on the performance of the sector. Steiner (2001) tests whether the regulatory environment, the degree of vertical integration and the degree of private ownership have an impact on efficiency and on prices. Efficiency is measured by capacity utilisation rate and reserve margin in electricity generation. Liberalising regulation, restructuring and private ownership are expected to lead to improved efficiency, and lower industrial electricity prices and industrial/residential price ratios. These hypotheses are tested for a panel dataset of 19 OECD countries for the period 1987-1996. There are controls for previous commitment to generation technology and the control for GDP serves as proxy for electricity market size. There are no controls for institutions or for macroeconomic policy.

Steiner (2001) finds that utilisation rate is positively and significantly correlated with both private ownership and unbundling of generation and transmission. The coefficient on third-party-access, however, is not significant. The results also confirm the hypothesis for the reserve margin, with the exception of third-party-access, which is again statistically insignificant. With respect to the impact on prices, the estimated coefficients on unbundling of generation and transmission, and on third party access are not significant. Contrary to expectations, the coefficient on ownership is positive and significant, which suggests that private ownership is not necessarily correlated with increased competition. The establishment of a spot market was found to lead to lower prices. The results of the price ratio indicate that the benefits of reform are 14
22 model is providing such an accurate measure. In effect, the regulators 'piggyback' on a bank's existing risk-management model to determine levels of risk capital to be held. At the heart of this approach lies the VAR model described above.

# _IV 2.2. The IMA Process_<sup>_3 8_</sup>

Setting capital adequacy standards under this regime is a three-stage process. Firstly, the regulators set the quantitative standards (risk parameters) for capital calculation which are the following39:

- the model must cover all material risks in the trading book and must have a minimum number of thirteen (six for United States banks, under the corresponding Fed proposal) risk factors (maturity bands). Moreover, it must be able to account for the non-linear pricing characteristics of option instruments;

- a 99% one-sided 'conservative' confidence interval, in order to account for adverse movements only. This amounts to a risk estimate of three standard deviations away from the mean of a normal distribution of portfolio value changes;

- a ten trading-day (that is, two weeks) holding period. This has been imposed to extend the period sufficiently to be of interest to regulators, and can be justified by appealing to concerns about illiquidity and the inability to wind down positions during extreme market movements;

- a minimum of one year as the observation period for historical data to be used in calculating volatility, to be updated at least once a quarter. This is intended to resolve problems of differential volatilities and correlations arising from the choice of the size of the sample period;

- all correlations are allowed, both within and across different asset classes (risk categories), to be estimated with equally-weighted daily data;

38 See the _Federal Register (25_ July 1995) for a more detailed description of the BSM and the BIMA as proposed to be applied to the United States.

39 Quantitative standards were placed in an attempt to make consistent estimates across institutions. This was in response to important differences in model practice
Tables A.11 to A.15, remain qualitatively similar with lower significance for Model 9 in betweenyear imputations. Further meta-analysis that controls for various modeling characteristics such as the imputation methods, the poverty lines, the treatment arms, and the source of deflator data confirms that employing the poverty line that is consistent with the base survey offers better results for between-year imputation (Appendix A, Figure A.2).<sup>17</sup>

# _Alternative modeling options_

We consider two alternative modelling options, which are multiple imputation (MI) and machine learning (ML). It is useful to note that the poverty imputation literature is broadly related to a larger literature on missing data (or multiple imputation in statistics; see, e.g., Little and Rubin, 2019; Carpenter _et al_ ., 2023). Certain differences, however, exist between the two literatures. One difference is that MI studies tend to employ Bayesian techniques for their estimation, which can require more computational time for drawing from posterior distributions. Another difference is that economists tend to use economic theory rather than statistical theory for model selection.<sup>18</sup> We employ two common MI techniques, linear model and predictive mean matching (PMM).

On the other hand, ML methods have been useful additions to the economists’ toolbox <mark>(Mullainathan and Spiess, 2017; Athey and Imbens, 2019). However, unlike our proposed imputation approach that builds imputation models based on economic theory (and perhaps closer</mark>

> non-food components differ. The 2019/20 poverty line includes information from earlier NPS rounds, while the 2020/21 poverty line includes data from later rounds, notably incorporating imputed rents, the value of durable goods, and clothing. To ensure consistency, we employ the appropriate consumption aggregate definition aligned with the respective poverty line.

> 17 This result is consistent with earlier observations for various contexts in Dang _et al._ (2019) and Dang and
the analysis splitting the sample into shorter time windows and excluding some countries. No important differences arise regarding the main conclusions of the study. 10. The priority date of applications is used to allocate individuals in time. By “priority date” I mean the first year the patent was applied worldwide.

11. In order to make these figures comparable, it is worth looking at differences with other migration datasets. While 8.62% of inventors of PCT patents have a migratory background in 2000, data compiled by Beine et al. (2007) show that general migration rates in 2000 for populations aged 25 years and over 14
# **Figure 6**

Contribution of the Business Cycle to the Variance Driven by Income of Each Indicator, World Sample <!-- Start of picture text --> 60<br>Components of the HDI<br>50<br>40<br>30<br>20<br>10<br>0<br>Unemployment Rate Monetary Poverty HDI Income Education Life Expectancy<br>Explained by the Business Cycle (as percentage)<br>Share of the Variance of Social Indicators Driven by Income<br><!-- End of picture text --> Notes: Poverty line of 5.50 dollars per person a day (2011 PPP). The figures reported correspond to the median value across countries. Black lines denote two standard errors intervals (weighted by Real GDP). Estimations of the contribution of the cyclical component based on a bivariate model.

Sources: Authors’ estimations based on PovcalNet (March 2019), SEDLAC (CEDLAS and World Bank), Human Development Report Office (United Nations), and WEO (October 2018).

22
informal firms, both inter- and intra-county outdegree increase for the median county as well as on average. However, for 83% of counties, the increase in intra-county outdegrees exceeds the increase in inter-county links.<sup>25</sup> If informal firms source an even higher share of their inputs locally, the predicted shift towards intra-county trade represents a lower bound. We will discuss this in Section 6.1.

Table 5: The importance of Kenya’s primary cities in a revised network

|**Nairobi**|**Momb**|**asa**<br>in %|**Other counties**|
|---|---|---|---|
|Population<br>9|3||88|
|GDP<br>25|5||70|
|GDP w/o agriculture<br>33|7||60|
|**Number of outlin**|**ks**|||
|_Model ft_||||
|Administrative data<br>69|11||20|
|Model predicted network<br>70|11||19|
|_Revised network: informal frms ≈small frms_||||
|Default scenario<br>65|10||25|
|_p_(_θ_) using employ. share only<br>66|10||23|
|_Revised network: alternative linking patterns for _|_informal _|_frms_||
|0% sales, 50% input to/from formal<br>59|9||32|
|0% sales, 75% input to/from formal<br>63|10||28|
|25% sales, 50% input to/from formal<br>59|9||32|

The above table documents the share of overall firm-to-firm links which have a supplier based in Nairobi, Mombasa or
non-EU countries, among which, Yugoslavia. Second, there are specific policy variables within each period. During the 1981-1995 period, general quotas on total annual new work permits are taken into account in two different ways: Lagged log of quota ( _Lquotat-1_ ) and lagged unemployment rate in Switzerland as a proxy for quota changes and also for the probability of finding a job for a prospective migrant ( _UrateCHt-1_ ). The introduction of the 3-circle policy with priority to EU citizens and the classification of Balkan countries into the third circle (nonEU countries with no cultural similarities with Switzerland) is captured by a dummy ( _3-circle policy_ ) which is the product of _Pol3c_ , dummy variable defined as 1 from 1990 on (0 otherwise) and _Non-EU_ , another dummy with value 1 for countries not in the EU (0 otherwise).

Finally, the skill and occupation characteristics of permanent migrants to Switzerland are quite different depending on the country of origin. Statistics on the employed foreign population show a consistent correlation between skill level and region of origin. In particular, in 2003, 57.9% of employed foreigners from Northern and Western Europe had tertiary education. Similarly, 55.5% of new EU-members and 47.9% of non-Europeans had tertiary education. In comparison, only 11.3% of Southern European migrants and 11.6% of citizens from the Western Balkans and EU-candidates (Turkey and Yugoslavia in our sample) were highly educated (OFS, 2004b, Table G4). For decades, Switzerland filled its deficit in unskilled workers through immigration while simultaneously attracting large foreign direct investment (FDI) because of its skilled workforce. The main investing countries are the US, Germany, France, the UK, and the Netherlands (together 80.4% of the stock of foreign direct investments at the end of 2003, BNS, 2003, Table 2.2.). Accordingly, the countries from the sample are divided into two geographical areas, North and South, and we test for the possibility of 18
the largest gender gap being in Armenia and Ukraine (37-40 percent).<sup>65</sup> As with the all-countries regressions, the gender coefficient for Bolivia, Colombia, Georgia, and Vietnam loses a few percentage points when measures of skills are taken into account, suggesting that it takes higher cognitive and noncognitive skills for women in those countries to narrow the gender wage gap.

**Table 10. Female disadvantage in log earnings regressions, constrained and** **<u>unconstrained models</u>**

||**Constr**<br>|**ained**<br>|**Unconst**<br>|**rained**<br>|
|---|---|---|---|---|
||**[1]**|**[2]**|**[3]**|**[4]**|
|Armenia|-0.360***|-0.368***|-0.089|-0.104|
||(0.060)|(0.065)|(0.162)|(0.163)|
|Bolivia|-0.312***|-0.296***|-0.452***|-0.441***|
||(0.063)|(0.068)|(0.164)|(0.167)|
|Colombia|-0.437***|-0.418***|-0.488***|-0.527***|
||(0.060)|(0.063)|(0.132)|(0.136)|
|Georgia|-0.674**|
# _4.3 Financial Policy Response Activity Index in EMDEs_

Table 4 shows Poisson regressions with the FPRAI as the dependent variable. Model 1 documents that EMDEs with higher GDP per capita implemented more financial policy measures. This perhaps suggests that financial supervisors in more developed countries may have more resources to do so. The statistical significance of population size suggests that scale economies may also matter for financial supervisors. These results appear economically meaningful. For example, using the estimated coefficients in Table 4, column 4, a one-percent increase in GDP per capita increases the mean value of the total policies by 0.56 percent. Similarly, a one-percent increase in population size increases the mean value of total policies by 0.35 percent. In contrast, the spread of COVID-19 does not appear to be a significant factor. Model 2 indicates that macro-financial factors do not play a significant role either. Further, Model 3 shows there is no significant association between financial policy activity and COVID-19 containment and fiscal support policies. Model 4 confirms these findings.

24
formal or informal. It is possible for formal production units to have informal employees, and for informal units to have formal employees. Finally, there is a distinction between a job and an employee as one employee can hold several jobs. Hence informal employment refers to “...the total number of informal jobs, whether carried out in formal sector enterprises, informal sector enterprises, or households, during a given reference period...”<sup>3</sup> The primary reason for focusing on informal employment in this paper is that the information available from household surveys allows for identification of informal employment from the workers’ side. Importantly, a comprehensive analysis of the informal sector would need to consider firm-level decisions and the use of firm-level data, which we do not use in this paper. Throughout the paper, we rely on variations of legalistic definitions of informal employment, vis-à-vis

> 3 For a full description on concepts and definitions on informality, see Chapter 25 “Informal aspects of the economy” in _System of National Accounts 2008_ (United Nations Department of Economic and Social Affairs, 2010) and see Chapter 8 “Employment in the Informal Economy” in Key Indicators <u>of the Labour Market 2015 (ILO, Geneva, 2016).</u>

3/67 Informal Employment and Worker’s Wellbeing in Russia
# **Long Audit Form**

# **Department**

Laboratory Group Respondent: Last name Name Patronymic name Tel.: e-mail

# **1. SUBDIVISION DATA**

- **1.1. Basic fields of subdivision activity**

- **1.2. Methods and tools being used in the subdivision that can be claimed for by exterior users.**

- **1.3. Software support, databases, etc., worked out in the subdivision that can be claimed for by exterior users.**

**2. PROSPECTS OF PROJECTS UNDER ANALYSIS**

(results commercialization)

- **2.1 Which of projects are especially perspective for commercialization?**

- **2.2 Which of your future researches are especially perspective?**

- **2.3 What sources of finance can be used by commercialization?**

- **2.4 Is there a direct availability of financing your researches by industrial enterprises?**

# **3. RESPONDENT DATA**

- **3.1 Basic fields of your scientific interests**

- **3.2 Conferences, exhibitions you have taken part in (3 last years)**

- **3.3 List of scientific works (3 last years)**

- **3.4 Applications and patents where you are an author or an applicant for a patent (proprietor).**

- **3.5 List of works you have taken part in as a consultant (3 last years). Please, name the customer.**

- **3.6 Would you like to take an active part in industrial application of the project achievements or their commercialization?**

(or do you prefer to entrust with this task somebody else in order to concentrate yourself on your researchers)

- **3.7 Would you like to be a co-founder of the company, created for a commercialization of your