rafmacalaba/data-use-mentions-extended huggingface dataset:
_L_<sup>2</sup> (Eucledian distance) penalties set on the moving average components that vanish with a weight of 1 _/√NT_ . Penalizing the _L_<sup>1</sup> norm (absolute sum), as in popularized LASSO estimations, encourages parameter vectors with many elements set to zero, which results in an unidentified problem for **b** . _L_<sup>2</sup> penalization, like in the ridge framework, encourages solutions where parameters are small, and in fact the penalty effect reduces in strength as parameters become close to zero. To reduce dimensionality, we suggest to evaluate the _AICc_ 17
Chart 2

# **Brazil: Total Debt ratio to** GDP

<!-- Start of picture text --> 0.45<br>0.40<br>0.35<br>0.30<br>0.25<br>0.20 85 _ _ 86 _ _ 87 _ _ _ 88 _ _ 89 _ _ _ 90 _ _ 91 _ _ 92 _ _ 93 _ _ _ 94 _ _ 9~5<br>1- BRTOTDVP<br><!-- End of picture text --> Sources: Foreign Debt, World Bank debt tables. Domestic Debt: Central Bank of Brazil Brazil has chronically suffered from three related ailments: lack-luster fiscal performance, high interest rate on its debt, and high velocity of circulation of money. Nonetheless, it had one thing in its favor -a relatively high rate of growth.

The next two charts demonstrate Brazil's changing strategy regarding domestic and foreign debt financing.

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2013; Helliwell et al., 2021). They offer advantages compared to widely used, objective measures of quality of life, such as per capita gross domestic product (GDP), income or expenditure from household surveys, the Human Development Index, and multidimensional poverty measures.

SWB measures capture how people evaluate the quality of their own lives, while traditional, objective measures reflect the livability of the environment or the presence of opportunities to live a good life. This distinction is important because opportunities do not necessarily materialize in life; likewise, people are sometimes able to thrive in adverse circumstances. Thus, increased income or expenditures need not indicate rising experienced welfare and increased welfare need not reflect increased incomes or expenditure. In sum, traditional economic indicators may not be able to accurately track changes in experienced welfare (Arampatzi et al., 2018).

Despite criticism by economists that SWB measures have limited validity and reliability (e.g., Bertrand and Mullainathan, 2001), recent work in behavioral and happiness economics has shown that these measures complement well objective indicators as they provide information that is useful and cannot be obtained from objective data sources (Deaton, 2008; Ravallion, 2012). First, the use of specific objective quality-of-life indicators disregards the fact that people may value differently the opportunities for a good life. These indicators do not capture people’s preferences on the extent to which objective conditions translate into outcomes of a good life (Veenhoven, 2000; 2002). According to Deaton (2008), objective aggregate measures, such as per capita GDP, reflect the subjective opinions of analysts and policy makers of what makes a good quality of life (Deaton, 2008). The assumption being that improvements in the average level of income in a country should lead to welfare increases for its citizens. Multidimensional quality-of-life measures can provide a solution because they cover non-monetary areas that matter to experienced welfare but are omitted from traditional welfare indicators based only on aggregate GDP or household income or expenditures. However, weighing
to cater to their different needs and overcome barriers related to isolation and social norms (World Bank, forthcoming).

**The vulnerability of the Roma community to multidimensional poverty in Bulgaria is pronounced, with higher probabilities of monetary poverty, low work intensity, and material and social deprivation even after accounting for other socio-economic factors.** Particularly striking is the likelihood of material and social deprivation among the Roma, surpassing that of other vulnerable groups by 12.8 percentage points. These deprivations are more concentrated in material items that cannot be afforded, instead of items associated with social inclusion, exhibiting a lack of economic resources that could be compensated with income measures or educational policies to reduce educational inequalities that enable them to have better job opportunities and financial prospects. Education emerges as a critical factor, with the vast majority of the Roma lacking access to primary education. Additionally, disparities persist in health and housing conditions, with overcrowding prevalent among the Roma population. These findings underscore the urgent need for comprehensive policy interventions tailored to address the multifaceted dimensions of poverty within the Roma community, highlighting the structural nature of their poverty experiences.

**Recommended policies to tackle persistent poverty include a wide spectrum of policies** . These include improving educational outcomes, promoting job opportunities, alleviating the childcare burden of families with many children and working adults through subsidies, increasing social protection and access to health services and as well as improving road infrastructure for those living in rural areas (Addison et al., 2008; Barrientos and Hulme, 2005; Chronic Poverty Research Center, 2011).

**Future lines of exploration could involve an in-depth analysis of material and social deprivation of children, as child poverty is a critical issue in some countries, such as Bulgaria and Romania** . Other authors ( <mark>Guio et al., 2018)</mark> have constructed and proposed a specific suitable and additive deprivation measured focused on the specific situation of children using the EU-SILC data, which could be implemented in future analysis
# **_Q. What skills are specified in job descriptions? Do these skill demands vary between industry, occupation, or gender of jobseekers?_**

# **_A. Programming skills are most frequently required (63% of job postings), followed by sales skills (12%). There are differences in skill requirements between occupations and genders. For example, finance skills are most frequently demanded for managers, while communication skills are more frequently demanded for intern- and entry-level positions. By gender, communication and Microsoft skills are more frequently required for female workers._**

The 10 most frequently required skill categories are programming, sales, designing, communication, finance, Microsoft, analytics and research, other soft skills,<sup>18</sup> leadership and management and others (Figure 13). Programming skills are required by 63% of all job postings, followed by sales skills (12%), designing skills (6%), and communication skills (4%).

**Figure 13. Required skills**

<!-- Start of picture text --> 3% 3%2%2%1%<br>4%<br>4%<br>6%<br>12% 63%<br>Programming Sales Designing<br>Communication Finance Microsoft<br>Analytical/ Research Other soft skills Leadership/ Management<br>Others<br><!-- End of picture text --> _Source_ : Authors using the data from Rozee.pk.

Figure 14 shows required skills by industries. Programming skills are most frequently required in ICT jobs (82%). Interestingly they are also required in other industries (e.g., 44% in professional, scientific, and technical jobs; 37% in administrative, social, and personal service jobs; and 26% in education and health jobs). This high demand for programming skills in all industries may indicate that employers tend
past eight years in over 100 countries with a consistent survey instrument.<sup>3</sup> The surveys try to capture business perceptions on the most important obstacles to enterprise operation and growth, but also include detailed information on management and financing arrangements of companies. Sample sizes vary between 250 and 1,500 companies per country and data are collected using either simple random or random stratified sampling. The sample includes formal and informal enterprises of all sizes, different ownership types and across nine sectors in manufacturing, construction, services and transportation. Firms from different locations, such as capital city, major cities and small towns, are included. Our empirical work relies on data for formal enterprises from 37 African countries and 49 countries from the rest of the world covering in total 35,000 firms during 2006 to 2009 (Appendix Table A1).<sup>4</sup> In addition, we focus on informal mostly micro-firms for a sample of 25 African countries.

The Enterprise Surveys offer several advantages for our purpose. First, the surveys collect comparable information for several firm characteristics across all the countries. This comparability allows us to document cross-country and within-country profiles of firms that have female ownership participation. Second, the surveys collect information on financing at the firm level as well as several other relevant firm characteristics. These include the firm size and age, human capital composition of the workforce, measures of technology adoption and firms‟ international openness, i.e. export activity and sources of capital. In addition, there is also detailed information on the firm‟s geographical location and its sector of activity (3digit-ISIC classification). Third, the surveys reach a substantial number of countries across all

> 3 See www.enterpriseseurveys.org for more details. Similar surveys were previously conducted under the leadership of the World Bank and other IFIs in Africa (RPED), and world-wide in 2000 (World Business Environment Survey).  Enterprise Surveys still go under different names in some regions, e.g.
• Enhancing the functioning of value chains, including standards A number of efforts revolve around enhancing the performance of value chains. This can span any link in the chain, ranging from design to production, assembly, packaging, marketing, distribution and consumption. Assistance in meeting quality and safety standards is particularly important to help incorporate local producers into global value chains (see Figure 26). Promoting the sustainable inclusion of small producers into global value chains is fundamental to fighting poverty: 75% of the world’s poor live in rural areas, and of these, 86% depend on agriculture (World Bank databank). If small-scale producers are able to link to the chain while at the same time obtaining assistance to help with needed certification for value-added goods (e.g., organic production), they will be able to take much better advantage of market access opportunities.

# • Facilitating trade

Trade facilitation is a major concern for the private sector as red tape and inefficiencies in border management and corridor performance can raise transport costs substantially, resulting in major delays. Initiatives and projects led by firms and industry groups range from road safety initiatives to more efficient customs processes through customized software development. Trade facilitation is typical of a public good: when roads are safer and custom’s clearance faster, it benefits the company at the origin of the capacity building efforts as much as its competitors.

# **_Minimizing the risks of participation in GVCs_**

In some instances, GVCs are severely criticized due to the predatory behavior of some global buyers or producers that tap into local human and natural resources in an irresponsible or unsustainable way or without sharing enough of the profits. For example, Oxfam observed the functioning of GVCs led by major food and beverage companies, and asked whether these lead firms were:

- improving conditions for women, small-scale farmers and farm workers;

- promoting equitable and sustainable access to and use of land and water;

- reducing emissions and helping farmers adapt to climate change; and

- being transparent
# **Table A3: World Management Survey Indicators**

|**Aggregate Index**|**Topic**|**Question**|**Score 1**|**Score 3**|**Score 5**|
|---|---|---|---|---|---|
|Monitoring|Monitoring|In what kind of ways does your Directorate track how well it is<br>delivering services? Can you give me an example?|Directorate does not track performance.|Directorate tracks a number of performance indicators. These are<br>seen and reviewed by senior management only.|<br>Full set of indicators are tracked formally and continuously. Reviews are<br>conducted regularly and involve representative of all directorate staff groups.<br>The results of the review are formally communicate to all directorate staff.|
|Monitoring|Monitoring|Are you involved in performance review for your Directorate?<br>If so, how often does this occur?|Not involved in performance review;|Bi-annually|Monthly|
|Other|Targeting|Does your Directorate have a clear set of targets derived from<br>the organization’s goals and objectives? Are they used to<br>determine your work schedule?|The directorate does not have defined<br>targets.|Targets are assigned to the directorate, as well as to the manager<br>and employee levels, and these are generally well understood by<br>mid-level staff. However the tasks assigned to staff are not always<br>related to those targets.|<br> <br> <br>Targets are clearly defined for the directorate, manager, and employee levels,<br>and are well understood by all staff. All tasks are directly derived from the<br>targets, which are regularly reviewed to ensure they remain on track.
1|20.0<br>20.0|20.0<br>20.0<br>20.0|
|<br>With aphon|e|43.0|30.4|16.6<br>18.9|19.8<br>20.8<br>23.9|
|_Weighted_<br>Full sample|Q3|52.2|29.4|21.5<br>21.0|22.2<br>19.0<br>16.3|
|AWMS R3|participants *|46.2|24.9|17.9<br>21.2|20.3<br>21.1<br>19.5|
|**Panel c. Comparison of key ch**|**aracteristics at baseli**<br>|**ne: IELFS 20**<br>|**19/20 infor**<br>|**mation** <br>|<br>|
||**Full sample**<br>**(IE-LFS weights)**|**Phone**<br>**(IE-LFS w**|**owners**<br>**eights)**|**AWMS R3 pan**<br> **(IE-LFS weigh**|**el***<br>**ts)**<br>**AWMS R3 panel***<br>**(AWMS weights)**|
|**Household Characteristics**||||||
|Household Size|7.25|7.|39|7.59|7.52|
|**Asset Ownership**||||||
|Refrigerator|16.7%|22.|7%
. Cusick and Georgieff, 2016; Walker et al., 2007) and the effects of accumulated exposure causing lasting damage in older individuals. The comparison across treatment and control households is supported by within household estimates that focusing on those exposed during the sensitive earlylife period to isolate the _Mirt_ treatment effect from confounding factors.

The observed effect on child growth is both precisely estimated and meaningful in magnitude. A wide body of evidence links early-life health to adult health, educational attainment, cognitive function, and earnings (e.g. Adair et al., 2013; Almond et al., 2018; Fogel, 2004; Glewwe and Miguel, 2008; Grimard and Laszlo, 2013; Groppo and Kraehnert, 2016; Hoddinott et al., 2008; LaFave and Thomas, 2017; Strauss and Thomas 1998; Wisniewski, 2016). For example, analysis of the Ethiopian

> 1 While child growth is often used as a marker of nutrition, a significant and growing body of work notes physiological associations between household air pollution from biomass cookstoves and reduced height-for-age (e.g. Balietti and Datta, 2018; Edwards and Langpap, 2012; Ghosh et al., 2011; Kim et al., 2016; Kyu et al., 2009; Mishra and Retherford, 2007; Pongyo et al., 2006).

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regulations. According to surveys, the coastal cities continue to benefit from more satisfactory investment climate than the rest of the country. The capital city of Tunis, followed by the 2 other economic poles Sousse and Sfax are the top 3 ranked regions in terms of business climate index where business climate is considered "very satisfactory" mainly due to the quality of infrastructures, a better living environment, the existence of a dynamic local industrial fabric and the availability of skilled labor force. In contrast, the interior regions (North-West, Center-West and South-West) are ranked at the bottom of this index well below the average, with business climate considered as "not at all satisfactory."

**Table 4.** _Regional Ranking based on Business Climate Index_

|**Regions**|**Ranking**<br>**2015**|**Index**<br>**2015**|**Ranking**<br>**2016**|**Index**<br>**2016**|**Ranking**<br>**2017**|**Index**<br>**2017**|**Ranking**<br>**2018**|**Index**<br>**2018**|
|---|---|---|---|---|---|---|---|---|
|**Grand Tunis**_(Tunis/ Ariana/_<br>_Manouba/ Ben Arous)_|**1**|4,45|**1**|2,65|**1**|3,90|**1**|4,30|
|**North East**_(Bizerte/ Nabeul/_<br>_Zaghouan)_|*
migration flows in Vietnam. Whereas they find that provinces with a higher per capita income attract more migrants, the coefficient for income in the sending province in their regression turns out to be positively significant. As a potential explanation, they suggest that the “liquidity constraint effect”<sup>12</sup> in poorer regions outweighs the “push” effect of migration in Vietnam. Relating the migration rate between pairs of provinces in one period to the change in income differential between these provinces in the next period, they also find an inequality-reducing impact of migration for the flows going to HCMC and surrounding provinces.<sup>13</sup> Nguyen-Hoang and McPeak (2010) extend the macro gravity model by introducing urban unemployment rates at origin and destination provinces as a proxy for the probability of finding employment. Whereas they find that higher unemployment at places of origin generates more out-migrants, they also find, contrary to theoretical predictions, that higher unemployment at destination is associated with more in-migrants.

Nguyen, Tran, Nguyen and Oostendorp (2008) employ a micro approach to explore the determinants of out-migration using the data on panel households in the VHLSS (2002, 2004). Using a probit model, they specify the dichotomous outcome of migration (stay or move) as a function of household characteristics and the conditions in the sending areas. They find that larger households tend to have more out-migrants; households with members of age between 15 and 25 years are more likely to have out-migrants; and higher educational attainments of household members increase the probability of out-migration. Interestingly, for “economic” migrants, they find evidence of the existence of a “migration hump” with an inverted U-shape

> 12 According to Phan and Coxhead (2010), the “liquidity constraint effect” means that poorer provinces have low capacities to finance migration costs and that this in turn leads to poverty related labor immobility. The liquidity constraint hypothesis predicts that the out-migration rate first increases with respect to a sending province’s income and then decreases
The number of the laws and decrees might not capture the most important laws and decrees. In other words, it might not reflect the depth and significance of economic reforms. We use news media to provide a robustness check by focusing on reform announcements in the press. The argument is that media chatter about reform announcements can pick up reforms that are perceived as important. Similar results are obtained when reform announcements are used instead of the number of laws and decrees. Figure II.3 shows scatterplots between the reform announcements about a policy pillar versus its lagged Distance to Policy Frontier (i.e., the distance to the policy frontier _in the previous year_ ) between 2007 and 2017. A policy pillar’s lagged distance to the frontier is not significantly correlated to reform announcements (see Appendix A2 for more detail).

## **Figure II.3: Number of Laws and Decrees Passed and Reform Announcements**

<!-- Start of picture text --> Bahrain Kuwait Oman<br>Qatar Saudi Arabia UAE<br>0 20 40 60 80 0 20 40 60 80 0 20 40 60 80<br>Distance to Frontier, Lagged (100: farthest away from the frontier)<br>Diversification Financial Market Development Goods Market Efficiency Health and Primary Education<br>Higher Education and Training Infrastructure Innovation Institutions<br>Labor Market Efficiency Macroeconomic Environment Technological Readiness<br>2<br>1.5<br>1<br>.5<br>0<br>2<br>1.5<br>1<br>Reform Announcements Index .5<br>0<br><!-- End of picture text --> _Note_ : A higher ‘Distance to Policy Frontier’ implies being farther away from policy best practices. GCI data are from 2007 to 2017 due to data constraints from WEF’s GCI database (the GCI scores are only methodically consistent and comparable across years during this period).

#
The third adjustment is related to the "Balance Sheet adjustment".22 The idea of this adjustment is that the macroeconomic impacts of the "skeletons" (old debts that were eventually repaid) occurred in the Fast. For example, the public debt issue for Banco do - - Brasil's recapitalization whose accumulated losses were threatening its solvency recognized losses derived from bad credit expansions in the past. Indeed, the debt issue was not related to deficits _during_ the recapitalization period, but to _old_ deficits, that had never been recognized until then. Thus, it i.s necessary to subtract the Balance Sheet adjustment's variation from the total net debt to obtain the debt variation concept comparable to the public deficit, namely, the "Net Fiscal Debt without Privatization".

Therefore, the following accounting identity should hold: for the fiscal statistics published by the Brazilian Central Bank.

Increase in Net Fiscal Debt without Plrivatization = Nominal Deficit.

Making the adjustments, we obtain: some state-owned enterprises held public debt as part of their assets. This debt, apparently was part of the gross debt, but, since it belonged to a slate owned enterprise, was not part of the net debt. After the privatization, it also became part of the net debt. Therefore the study of the relation between privatization and the public debt is still a work in progress.

> 22 Footnote no I of Table XXI of _Nota pcra Imprensa_ (Monetary) of _Banco Central do Brasil_ defines the "Balance Sheet Adjustment"as the following:

"(basis: Dec/95) Computes the bond issues relative to the Banco do Brasil's recapitalization, the reduction of the investment on the monetary reserve fuiid due to the court ruling involving the liquidation of the banks Comind and Auxiliar, securitization of debts, the use of "privatization money" in the PND, renegotiation of the Itaipu and Eletronorte debts with the SFN, the inclusion of constitution funds, besides the foreign debt difference, due to balance conversions, end of period
Uganda or Tanzania, but we would expect the smallest agricultural exporters to target Burundi or the DRC, where product standards are non-existent or weaker than in Rwanda.

In both the agriculture and manufacturing sectors we had, a priori, expected to observe many more small exporters. Family farms are the predominant economic activity in the country. In the informal manufacturing sector there are many tailors or seamstresses working out of their homes. However, the majority of small-scale agricultural exports to neighboring countries are unlikely to be captured in the Customs data that we work with and the same is likely to be true for small manufacturing exports.

In our dataset there are additional export transactions conducted by firms with less than 10,000 USD total exports in a given year. In many instance, when such export transactions are

> 7 A few basic production and export statistics are provided at https://en.wikipedia.org/wiki/Mining_in_Rwanda.

11