rafmacalaba/data-use-mentions-extended huggingface dataset:
Consolidating the network of general secondary education (GSE) schools has become an ever urging issue in Ukraine considering the demographic and financial situation in the country. Due to the demographic crisis the number of students in Ukrainian schools fell by 40 percent since independence while the number of schools only fell by 7 percent and the number of teachers by only 4 percent<sup>16</sup> . As a consequence, Ukraine is maintaining a large network of small schools that have become smaller and smaller because of the severe drop in birth rates: 30 percent of level I schools have less than 10 students 21 percent of level I-II schools - less than 40 students; 17 percent of level I-III schools - less than 100 students<sup>17</sup> . Overall 19.2 percent of schools are small as defined above and its overwhelming majority is located in rural areas. As a result budget resources while increasing over time mainly support the large network of institutions rather than being directed towards quality-enhancing inputs<sup>18</sup> .

**_Figure 1: Trends in number of students teachers and schools in GSE in Ukraine 1990-2010_**

<!-- Start of picture text --> 120<br>110<br>100<br>90<br>80<br>70<br>60<br>50<br>schools teachers students<br>Index, 1990 = 100<br>1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010<br><!-- End of picture text --> 16 Around 510 thousand teachers work in the sector with 4.2 million students (compared to 537 thousand teachers and 7.1 million students in 1990/1991 school year).

17 World Bank calculations based on a school-level database compiled on the basis of information provided by each of the oblasts and related jurisdictions for the 2009/2010 school year.

18 See for details the
**_Voting behavior and perceptions_**||
|Checked voter list after February 5th<br>(1=Yes)|Dummy variable equal to 1 if after February 5th woman or a family member checked to see if she was on the voter list.|
|Voted in last elections (local) (1=Yes)|Dummy variable equal to 1 if woman voted in the last local body elections.|
|Voted in Feb 08 (1=Yes)|Dummy variable equal to 1 if respondent voted in this national elections.|
|Witnessed or heard of any type of<br>violence in/near village (1=Yes)|Dummy variable equal to 1 if woman personally witnessed or heard of any type of violence in/near her village.|
|Elections were free and fair (1=Yes)|Dummy variable equal to 1 if woman believes that the national elections held in February 18th 2008 were "Completely free and fair" or "Free and fair, but<br>with minor problems" instead of "Free and fair but with minor problems" and "Not free and fair".|
|Index of opinion on democracy|Disagreement with the following statement: "Only educated should vote" and average disapproval of the following forms of government: "Only one party is<br>allowed to stand for election and hold office. ", "The army comes in to govern the country", "There are no elections, no MPA or MNA and the president|
|**_Recall of Intervention_**||
|Visit before elections (1=Yes)|Dummy variable equal to 1 if woman confirmed in the follow-up survey that she was visited before the elections to tell her about the importance of the<br>elections and voting.|
|Present in visit before elections (1=Yes)|Dummy variable equal to 1 if woman confirmed in the follow-up survey that she was present in the visit before the elections
minimum wage increase in 2018 was the highest since 2001, when Korea was recovering from a deep economic recession associated with the Asian financial crisis. Relatively low inflation rates in recent years confirm that the minimum wage growth in _real_ terms was also the highest in 20 years. Panel (b) compares Korea’s steep increase in the minimum with that in other countries. Korea’s minimum faster than wage wage grew in most advanced countries or in emerging market economies such as the Czech Republic and Hungary. According to the OECD Statistics Database, the ratio of minimum to median wages in Korea rose from 50% in 2016 to 63% in 2019 (see panel (c)), the highest percentage point increase among OECD countries. This suggests that the minimum wage increases indeed exerted strong cost push shocks on Korean firms.

> 12The minimum wage law applies to entire establishments in Korea with a single rate. That is, the minimum wage rate in Korea does not have region, industry, or occupation variations. The minimum wage must be determined by August 5th for the next year according to Article 8, Paragraph 1 of the Minimum Wage Act and is effective from January 1st in the next year.

6
Figure 2: Primary-to-Secondary education Transition rate, and Repetition rates, 1997-2016 <!-- Start of picture text --> Primary & Secondary education Transition and Repetition Rates<br>80<br>60<br>40<br>20<br>0<br>1995 1997 2000 20052006 2010 2012 2015 2020<br>Year<br>Primary−to−Secondary school Transition rate Primary school repetition Rate<br>Secondary school repetition rate<br><!-- End of picture text --> _Source_ : Own representation using data from UN Institute of Statistics.

As of 2013, there were about 13 higher education institutions, 8 (i.e. 62 percent) of which are public institutions. The National University of Lesotho (NUL) is the main tertiary institution in Lesotho, and remained the only local bachelor’s and master’s degree-awarding institution until 2007. The university admits about 44 percent<sup>5</sup> and 89 percent of Lesotho’s undergraduate and postgraduate students’ population, respectively (Council on Higher Education, 2013).

# **2.2 Policy background**

Lesotho instituted the FPE program in 2000 in order to meet the Millennium Development Goal (MDG) of ensuring that primary education is free and available to all (UNESCO, 2000). As mentioned earlier, Lesotho’s implementation strategy was different from that followed by other African countries. School fees were eliminated sequentially on a grade-by-grade basis starting with grade one in 2000 such that by 2006, all seven primary school grades were under FPE. The main reason for this implementation strategy was to cushion FPE’s financial impact on the public budget (Urwick, 2011).

The FPE policy is an amalgamation of several program components that address both demandand supply-side constraints to schooling. On the demand-side, the policy involves elimination of some private schooling costs such as school fees, stationery and textbooks’ costs. On the supply-

> 5This is as a percent
technical green skills. This paper leaves out of consideration those indirect or neutral jobs, as it is assumed that advanced labor market information systems would provide the signals to the workforce of which jobs are in demand and hence worth pursuing. This paper also excludes the worker's protection layer from the definition since it is not the main objective of the skills policy, and it is addressed by another set of policy instruments.

This note contributes to the understanding of skills policy in the context of the green transition as follows. Section 2 presents a conceptual framework to identify green jobs, departing from the state-ofthe-art definition of the production function and resulting in four measurement approaches. Then, it discusses their suitability for skills policy along with their data requirements and limitations when applied in advanced and developing countries, becoming a reference point in the literature. Section 3 discusses the methodologies that can be used to identify green occupations under the task approach and introduces one based on text analysis. Specifically, the proposed methodology constructs a dictionary of green terms and applies text analysis to an occupational database containing task statements with the objective of measuring the green task intensity of occupations. This section also discusses the strengths and weaknesses of the methodology vis-à-vis relying on the O*NET Green Economy Program, a well-recognized list of green occupations in the U.S. Finally, section 4 presents analytic applications of the green occupation taxonomy obtained to the case of Indonesia to illustrate the potential of the proposed methodology. More specifically, it describes selected green occupations' characteristics: gender, education, in-demand skills, and wage premium. Figure 1 visually summarizes the organization of this note.

The intended audience of this paper is analysts and policy makers working on skills policy around the world. The proposed definition and measurement methodology can be deployed in any country that relies on ISCO-08 or any other country that uses an occupational classification system containing task statements. We are making available to the community of analysts a toolkit that includes: (i) a readme file; (ii) the
as the extent to which access to good jobs is contingent on the socioeconomic background one is born into.

Our database makes it possible, for the first time, to paint a truly global picture of intergenerational mobility. This in turn allows us to test whether insights from the existing literature, which has mostly focused on developed countries, generalize to developing countries. We test two main hypotheses: First, whether absolute mobility is lower in the developed world where education levels are higher on average, making it harder for the next generation to surpass the education levels of their parents. Second, whether relative mobility increases with national income levels. Richer countries have the necessary resources to fund public interventions that compensate for private disadvantages (e.g. provide high quality public education for children born into disadvantaged backgrounds). This would be consistent with the findings of Chetty et al. (2014), who show that lower levels of intergenerational mobility are more likely to be found in the lagging areas of the United States.

> 2 In parallel to our study, Brunori et al. (2021) developed the “Equal Chances” database that includes estimates of intergenerational income mobility for 27 countries as well as estimates of inequality of opportunity for 47 countries. Our estimates of intergenerational mobility in education for 153 countries are also included in this database.

> 3 Alesina et al. (2021), Neidhöfer et al. (2018), Asher et al. (2018), and Hertz et al. (2007) similarly focus on education, arguably for the same reasons.

3
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process that fails to address the structural factors that prevent the chronically poor from escaping poverty.

Before concluding this subsection, we revisit briefly the finding, already noted by Dang and Lanjouw (2018), of mobility changes within the 2004/05–2011/12 interval. Tables A.3 and

> 18 Synthetic panels constructed over longer intervals are generally more tentative, as time-invariant regressors in the prediction models are less readily available, and the models accordingly have less explanatory power. Cruces et al. (2015) indicate, however, that they may not perform too badly; in their validation of the synthetic panel method with long-term panel data from Chile, they find that welfare transition estimates remain quite accurate.

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We have documented that in the developing world millions of people are purchasing energy intensive durables. In the previous section, we documented that even California features a large durable set of fossil fueled vehicles. In this section, we present additional evidence from Europe documenting that within each of the continent’s nations there are geographic clusters of higher carbon footprint places. High emissions regions have incentives to lobby their local officials to protect them.

Previous research on the United States has documented how local carbon emissions influence carbon politics. Eyer and Kahn (2020) document efforts by coal states such as West Virginia to use local policies to protect coal miner jobs. Cragg et al. (2013) document that Congressional voting on enacting Cap and Trade legislation is associated with the area’s local per-capita carbon footprint. Representatives are more likely to vote in favor of carbon pricing if their district’s emissions are lower.

Within nations, there can also be geographic areas that oppose carbon pricing because the local economy is carbon intensive. A region can have a large carbon footprint because of the industries concentrated there, the area’s low population density, and the types of power generation used to generate power. To explore this point, we use data from jurisdictions within European nations. The raw data are from https://openhgmap.net/data.<sup>3</sup> In Table 6, we report the average carbon footprint and the coefficient of variation across cities within the same nation in Europe. World carbon dioxide emissions per-capita in 2018 was 4.5 tons.<sup>4</sup> These results indicate that there are high emitting regions even in relatively low carbon nations such as France and Germany. Poland features a high coefficient of variation indicating that there are high carbon areas. The people and the political leaders of these regions will be less likely to support the low carbon agenda and they are likely to argue that Pigouvian policies are elitist unless there are spatial transfers to such regions. The
highly attractive to populations living in these villages, but the program aimed to allocate the RMG spots to the poorest households in the villages.

The village heads played an important role in this allocation. First, the village heads were responsible for publicizing the program and inviting households to the registration event, acting to some extent as gatekeepers for information about RMGs. Baird, MacIntosh and Ozler (2013) provide an example of informational elite capture. Their analysis of targeting effectiveness of Tanzania’s flagship CDD program suggests that wealth, education, access to media, and political engagement are positively correlated with awareness of the program, which is a necessary condition for benefitting from it. Such lack of access to information results in only mildly pro-poor targeting.

Second, the village heads contributed to the selection of beneficiaries through updating their poverty ranking. Specifically, beneficiaries for the RMG program were selected through a two-step process in June 2018. In the first step, those eligible to participate in the program were identified. In the second step, as demand for the program exceeded the number of RMG jobs, a lottery (lucky draw) was carried out to

> 4 PRF is housed under the Government Office of Lao PDR. Its mission is to apply a community driven development approach to reduce poverty in Lao PDR, with a focus on rural populations, by improving infrastructure and access to services and resources.

> 5 Authors’ calculations using the baseline data for the RMG impact evaluation and LECS V. Available upon request.

4
that when they have the choice, villagers can ―shop‖ for the best shalish and this perhaps enhances its role as an honest adjudicator. Conversely, it is likely that in those areas where only a single type of (traditional) shalish operates and there is no competition among informal dispute resolution systems, there is also greater likelihood of elite capture. In our own focus groups we found a plethora of examples of shalish from groups in Dinajpur and Mymensingh, relatively fewer from Satkhira and very few examples from Sunamgonj. Focus groups of women too were much more vocal on shalish in the former two sites than they were in the latter two. While it is difficult to judge adequacy of services without an indication of demand, our data on conflict seem to indicate that there is roughly similar level of demand for mediation across regions.

_NGO facilitated shalish:_ Longstanding efforts spearheaded by NGOs and supported by multiple international donors focus on strengthening and reforming these systems to make them more equitable for women and the poorest. Bangladesh civil society‘s innovation in community-level ADR is held up as an international example. The reformed ADR forums include an equal number of women as shalishkars, and often address violations of women‘s rights such as violence, dowry demands, abandonment and maintenance. The NGOs also assist parties in accessing the formal system when shalish is either not appropriate or not satisfactory. The Bangladesh Rural Advancement Centre (BRAC) probably runs one of the largest ADR systems in the world. The activities of BRAC and legal aid organizations (Bangladesh Legal Aid and Services Trust, Ain-o-Shalish Kendro and Nagorik Uddyong) which also run the ―reformed shalish‖ are laid out in a table in Annex 3. The example in box 5 shows the evolution of the shalish facilitated by an NGO in Satkhira district. The reach of the reformed ADR is speculative 12
From Figure 5, we can see that most small PICs perform better than or at similar levels to what would be expected, when their income and population size is taken into account. The extent to which these countries are disadvantaged by population size is clear, with a population “penalty” of more than one half of a score (the difference between a “C” and a “C+”) experienced by all countries except Fiji.<sup>4</sup> Results presented above test for a constant relationship between population size and PEFA scores. The assumption of linearity of the marginal effect of population on PEFA scores may not be valid if only very small countries are disadvantaged or if only very large countries are advantaged. This indeed seems to be the case when we rerun our regression on subsamples of countries of different sizes. Results are presented in Table 2. We find that the population effect is significant and largest for countries with populations of less than 500,000, whereas it is insignificant and very close to zero for larger countries.

The positive relationship between population and PEFA performance seems to be confined to small countries. We return to potential interpretations of these results in subsequent sections of this paper.

**Table 2: Testing linearity of population effect on PEFA score**

||(1)<br>Pop < 500k|(2)<br>500k > pop > 10m|(3)<br>Pop > 10m|(4)<br>Full sample|
|---|---|---|---|---|
|LGDPit (log income)|0.487*|0.575***|0.526***|0.391***|
||_(0.081)_|_(0.000)_|_(0.007)_|_(0.000)_|
|LPOPit (log population
Policy Research Working Paper 9881

# **Abstract**

This study empirically investigates the drivers of inflation in Ethiopia using monthly data from July 1998 to September 2020. It explores short-run and long-run effects of domestic and external determinants of inflation—including demand-side, supply-side, and structural factors—using the cointegration and vector error correction methodology. Four measures of inflation are considered: cereals, food, nonfood, and all items consumer price index inflation. A key contribution to the existing literature is the investigation of the role of the fiscal sector in modeling inflation, a topic that has been neglected in the existing studies on inflation in Ethiopia. The empirical results show that disequilibria in the monetary sector, grains sector, and food markets have long-run effects on inflation. In the short run, inflation is driven by structural factors (notably, cereal output gaps and imported inflation) as well as demand-side factors (notably, money growth and public sector borrowing). The results hold when the analysis is limited to the high growth period from 2005 onward, following the end of the International Monetary Fund program in the country. The evidence provides valuable insights in the context of ongoing macroeconomic policy reforms in Ethiopia.

This paper is a product of the Macroeconomics, Trade and Investment Global Practice. 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 msanchezmartin@worldbank.org.

_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.
for older children, what kinds of solid foods are given, how frequently and how diverse they are. The WHO recommends exclusive breastfeeding for children between the ages of 0 and 5 completed months of age, the introduction of solid or semi-solid foods between the ages of 6 and 8 completed months and continued breastfeeding along with complementary foods through 2 years of age. The WHO also has guidelines on dietary frequency and diversity for children above the age of 6 months. Other important and related aspects of childcare in the home include the hygienic preparation of food and whether the child receives cognitive stimulation. A set of six indicators were identified in the NNS data that map to childcare and feeding practices at home, of these two are considered necessary evidence of an adequate childcare and feeding environment at home: the child is age-appropriately (breast) fed and the mother always handwashes before food preparation and feeding, before and after eating and after toilet use (Table 4). Three ideal indicators of child feeding practices that were not available for this study include minimum meal frequency, minimum dietary diversity and minimum acceptable diet for children 6 months and older.

**Table 4**

|**Category**|**Dataset**|**Driver**|**Definition**|**Available**<br>**in NNS**<br>**dataset?**|**Required**<br>**for**<br>**adequacy**<br>**in**<br>**domain?**|
|---|---|---|---|---|---|
||||**Mom always handwashes**|||
|Hygiene|NNS Form<br>3.1|**Handwashing habit**|**before food prep and feeding,**<br>**before and after
years)|9.72|15.26|18.70|11.30|

_Source_ : Authors’ calculation using Rozee.pk data, Enterprise Survey 2013, and Labor Force Survey 2014 – 15, except for the female proportion and salary in the far right columns. They are based on Pakistan Bureau of Statistics (2015).

Wages are very different between the Rozee.pk and the entire labor market. The average and median annual salaries of the labor force participants in Rozee.pk are PKR 25,362 and PKR 16,781, respectively, while the national average in 2014/15 is PKR 12,271 (Figure 2).<sup>12</sup> This difference may be in part because of the higher education of jobseekers in Rozee.pk. On the demand side, employers post higher paying jobs at Rozee.pk. According to the distribution of monthly salaries advertised in job postings (Figure 2), the average and mean advertised salaries are PKR 33,883 and PKR 22,590, respectively. These are much higher than the national mean salary, PKR 12,271.

> 12 At the website of Rozee.pk, job seekers have an option to enter the information of current and desired salaries. Of job seekers, 45% enter the information of current salary, and 55% enter the information of desired salary.

5
described in Section 3. Mobility patterns associated with our estimated poverty line are examined when we turn to our description of mobility patterns based on the NSS-based synthetic panels. 15 Recall that our reference population comprises only that population that belonged to households in which the head was aged between 25 and 55 years in the base year. Overall poverty estimates may therefore differ somewhat from conventionally reported rates for the total population. The underlying regressions are provided in Table A.1 in Appendix A.

19