rafmacalaba/data-use-mentions-extended huggingface dataset:
the current account balance indicates that devaluation would be an important step toward reversing the downward spiral in the current account balance.

# **6.    Major Policy Trade-offs of Nominal Devaluation**

# **6.1    Will a devaluation of the birr cause inflation?**

Nominal exchange rate depreciation did not cause inflation during 2004-13 in Ethiopia (Haile, 2015a) and thus future devaluation measures will not necessarily trigger higher inflation. Perhaps the foremost cause for concern for policy makers is that rapid exchange rate adjustment will be counter-productive by increasing domestic prices and possibly leading to an inflationary spiral. Nominal depreciation drives up import prices, which feeds into higher domestic production costs and, if left unchecked, might cause higher inflation rates. Nonetheless, imported goods account for a fairly small share of the consumer price index and thus the impact of nominal devaluation on inflation is likely to be less marked. The analysis finds that, over the past decade, inflation was mainly caused by strong economic growth, monetary expansion, and international oil and food price rises (Haile, 2015a).

Demand-driven inflation and supply shocks were the dominant forces behind the inflation surges since the mid-2000s. The empirical results indicate that excessive growth in aggregate demand gave rise to an inflationary gap and that the demand-pull inflation was at least partly monetary in origin, a phenomenon commonly referred to as “too much money chasing too few goods.” The finding that expansionary monetary policy partly explains the dramatic increase in inflation is unsurprising as rapid growth of base money characterized the post-2004 period. The average annual growth in the monetary base jumped from 6 percent in 1992-2003 to about 20 percent in 2014-13, during which inflation reached astronomical proportions. In addition, significant increases in global food and oil prices contributed to the higher inflation rates.

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# **Appendix 3: Tests Items**

# **Table A3.1 Description of the levels of skills addressed in the Science Test**

|Basic skills|Medium skills|Higher-order skills|
|---|---|---|
|Recall of scientific knowledge|Describe scientific phenomena.|<br>Identify research questions.|
|Read information from tables.|Interpret conclusions from<br>Science experiments.|Design Science experiments to<br>test hypothesis.|
|||Explain more complex Science<br>phenomena (such as the<br>integration of the systems in<br>the Human Body).|

Author’s analysis based on experimental data generated for this study.

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) for a discussion of the use of banking services in transition economies using the LITS data set.

12. See Beck, Demirguc-Kunt, and Martinez Peria (2007). In addition, Honohan (2008) and World Bank (2008a) used these indicators as well as other data to estimate a headline indicator of access. In a separate exercise Beck, Demirguc-Kunt, and Martinez Peria (2008) documented cross-country eligibility, affordability, and geographic access barriers by surveying banks.

**7**

MEASURING FINANCIAL INCLUSION
from control group catch-up as opposed to the treatment group falling back. For example, for having a written budget this is 6.4/15.4 _=_ 0.42.

This then raises two questions: why did the treatment group stop doing some of these practices, and how was the control group able to adopt some of them without receiving training? We speculate that one reason may be related to the churn in business practices that we observe, which may reflect both the types of individuals who applied to the program and changes in the economy taking place as Mexico came out of the COVID-19 pandemic. Women who responded to advertisements about a business training program may be people who are looking to make changes in their business. In addition, changes in the economy during the pandemic may have made business owners also seek to do something new. This could explain why the control group adopts new practices over time. However, some of these planning and accounting practices may be ones where firms don’t do them all the time, but rather, at some point, decide they need to take a snapshot of what is happening in their business. Training may then have just accelerated this process of trying some of these new practices. In the qualitative surveys, some owners in the treatment group acknowledged that they had had the discipline to implement new practices while training was taking place, but then spoke of losing their ‘discipline’ and reverting back to their old ways once training was over.

Another reason that firm owners may stop doing practices is that they are unable to detect changes in business performance from using these practices, given the amount of volatility and other factors driving sales and profits. Our analysis finds that even with a sample of over 2,000 firms, it is difficult to statistically detect an impact of a 10-13 percent increase 11
policies against problem loans.

Whatever the primary motivation, countries that have developed these tools have tended to find them useful for a wider variety of reasons that often go beyond their original thinking. This paper reviews many of the current uses and discuses the relationship between these competing roles and the design of PCRs. There are also potential pitfalls related to PCR design and management

> 4 Not all countries replied to the World Bank survey that found PCRs in 58 countries.  The actual number is then likely to be significantly more than 58 worldwide.

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announced fiscal policies worth approximately 10% of GDP, while Ghana and Nigeria show the lowest magnitude of intervention at 1.4% of GDP and 0.8% of GDP, respectively. Overall, for the countries for which information is available, the data shows the extraordinary policy reaction to the spread of COVID-19 although for African countries this was on a smaller scale. The data from policy announcements in reaction to COVID-19 suggest three main conclusions. First, the banking sectors in African countries were less affected by international economic turbulence as they are not well-integrated with the rest of the world, as was also seen in the Global Financial Crisis (see section 2.1.2). Second, COVID-19 had mild negative effects on African banking sectors, at least in part because it was a less severe health crisis in Africa.<sup>12</sup> Third, as suggested in Feyen et al. (2021), the lack of policy space (fiscal, monetary, and financial) limited the number of interventions of governments in reaction to COVID-19.

To this point, this survey has documented the shallowness, inefficiency, and costliness of African financial markets, none of which are surprising given the region’s low levels of economic development and the four characteristics mentioned above—small size, informality, volatility, and institutional governance problems. However, many of the non-African low- and lowermiddle-income countries suffer from similar problems. Is there an Africa-specific element to financial underdevelopment? This issue is addressed next.

> 12 Data collected by the World Health Organization on the cumulative number of infections and deaths as per December 2021 indicate that the total cumulative number of infections per 100,000 people in African developing countries was 276, compared to 2,167 for the rest of the developing world. The cumulative number of deaths per 100,000 people was 5 for African countries and around 40 for the rest of the developing world.

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Alexa’s ranking, which is web traffic data-based metric.<sup>14</sup> An automated data acquisition mechanism was set up to scrape and store daily information on job postings from all selected portals. Extracted data were pre-processed (e.g., string cleaning, language detection, avoid multiple entries per job ad and harmonization of company name and sectoral affiliation) before being used for the analysis. Advanced machine learning techniques were employed for deduplication. After deduplication, a total of 17,812 job vacancies were collected. Most job vacancies cover occupations such as sales and purchasing agents and brokers, administrative and specialized secretaries, administration professionals, transport and storage laborers, and information and communications technology professionals. A combination of Natural Language Processing and Name Entity Extraction/Recognition methods was adopted to extract the core information from the job postings. Examples of job posting fields extracted through this process include job title, job description, job category, location, job type, contract type, experience, qualification, employer, employer type, firm location, firm size. The extracted data cover the period from January 2020 through June 2020.

> 14 The the two highest-ranked job portals according to Alexa (www.alexa.com) are kariera.gr and jobfind.gr. Alexa’s traffic data take into account websites’ unique visitors and page views. A recent assessment conducted by Cedefop also lists kariera.gr and jobfind.gr among the top private job portals in Greece based on different sources, including a study conducted by ELSTAT in 2017 and based on number of advertisements, number of monthly visitors and Alexa ranking (Cedefop, 2018).

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several neighboring villages. This is most notably the case of the distribution of transfers in temporary and mobile outposts, located in hub localities, which serve an additional function to assist beneficiaries and disseminate information on the program. Hence, program beneficiaries from different neighboring villages can interact in a number of places.

# _Sample Description_

We combine the geo-referenced locality data mentioned above with three of the five rounds of the evaluation survey collected in October 1997 (from the baseline targeting ENCASEH survey), October 1998 (second round of the ENCEL evaluation surveys), and November 1999 (fourth round of the ENCEL surveys).<sup>8</sup> The resulting dataset contains detailed information on the outcomes of children and socioeconomic characteristics of a panel of households that reside within the evaluation localities.

The evaluation survey was intended to cover all inhabitants of the localities under study. However, a small share of the population was not interviewed at baseline, and there were some changes in the village populations so that the total number of households observed in the data is 24,077 in October 1997, 25,846 in October 1998, and 26,972 in November 1999. Some attrition occurred due, in part, to migration out of the villages and, in part, to errors in identification codes that occurred for a few enumerators: 8.4 percent of the 1997 households cannot be followed and matched in all three rounds of the survey. Yet, this is unrelated to the treatment assignment.

At baseline (October 1997), 60 percent of the households in evaluation localities were classified as eligible to receive program benefits. In this paper, we study the schooling decisions of the children of those eligible households.<sup>9</sup> Our main outcome of interest is school enrollment, for which we also use the term “school participation” interchangeably.

8. We have discarded the March 1998 and June 1999 rounds of the survey because we only have information on the roll-out of the program at the end of
where _p0_ – proportionof non-smokers _p1_ – share of smokers at the moment RR – the relative risk of diseases in smokers in relation to non-smokers.

The following algorithm was used to calculate the **cost of inpatient treatment in 24-hours** and **day care hospitals** for diseases caused by smoking:

1. define average base rate per inpatient treatment case in 24-hours and day care hospitals (averaging rates specified in regional tariff agreements);

2. extract information from statistical survey forms on the total number of cases of treatment for ICD10 codes in question; 3. defining the values of RR based on available open sources 4. relate the disease classes with PAR indicators- the shares of diseases associated with smoking by disease (attributable risk of smoking); 5. determine the number of treatment cases triggered by smoking (the number of treatment cases under ICD-10 codes, multiplied by the relative attributable risk of smoking); 6. on diseases of the circulatory system, respiratory system, and malignant neoplasms: determination of the clinical profile group on the basis of decoding the DRGs provided in hospital and day hospitals, and the determination of the DRG weight on the profile of diseases; 7. for diabetes – determination of DRGs, which include the treatment of diabetes and the calculation of the average DRG weight for the disease; 8. calculate price per treatment case based on profile group (average base rate multiplied by average disease-profile weight); 9. determine the cost of treatment for diseases associated with tobacco smoking as the number of treated cases caused by tobacco smoking, multiplied by price per case under respective DRG profile group and multiplied by PAR.

Overall, the cost is determined by formula: where _Cost_ – the cost of inpatient treatment for diseases associated with smoking; NзаболМКБ – number of registered diseases according to the ICD-10 code; Кз<sup>КПГ</sup> – average weight of DRG profile groups;

- _БС_ – average DRG base rate for inpatient care;

- PAR – population attributable risk for ICD-10
Policy Research Working Paper 6015

# **Abstract**

The paper examines the link between poverty, the middle class and institutional outcomes using a new crosscountry panel dataset on the distribution of income and expenditure. It uses an econometric methodology to gauge whether a larger middle class has a causal effect on policy and institutional outcomes in three areas: social policy in health and education, marketoriented economic structure and quality of governance. The analysis finds that when the middle class becomes larger (measured as the proportion of people earning more than US$10 a day), social policy on health and education becomes more progressive, and the quality of governance (democratic participation and official corruption) also improves. This trend does not occur at the expense of economic freedom, as a larger middle class also leads to more market-oriented economic policy on trade and finance. These beneficial effects of a larger middle class appear to be more robust than the impact of lower poverty, lower inequality or higher gross domestic product per capita. That may be linked to the evolution of the middle class: they are more enlightened, more likely to take political actions and have a stronger voice. They also share preferences and values for policy and institutional reforms, as well as higher stakes in property rights and wealth accumulation.

This paper is a product of the Office of the Chief Economist, Latin America and the Caribbean Region; and Macroeconomics and Growth Team, Development Research Group. 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://econ.worldbank.org. The authors may be contacted at jrigolini@worldbank. org and nloayza@worldbank.org, respectively.

_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
Berulava argues that developing an efficient service infrastructure represents a strategic and underexploited source of export enhancement for policy-makers. Further reforms and liberalization in the services sectors would stimulate export performance of manufacturing industries by reducing the costs of doing business in downstream industries. The reforms should include creating favorable conditions for attracting foreign direct investment in services. Moreover, he argues that entrepreneurs should realize that investments in innovation, research and development, and employment of advanced technologies are likely to lead to additional exports. Finally, his results confirm earlier studies that show that reducing trade related costs, through trade and customs procedures facilitation, will increase exports.

# **Cross-Country Differences in Credit Market Liberalization Outcomes, by Petar Stankov**

Petar Stankov employs cross country growth regressions to assess the impact of a specific form of services liberalization on growth, namely credit market liberalization. Stankov uses a dataset of 108 countries to test the hypothesis that countries that liberalized credit faster grew faster. Following Estevadeordal and Taylor (2008), Stankov divides his dataset into two time periods: 1975 to 1990; and 1990 to 2005. Based on his data on credit market liberalization, he assigns countries to one of four groups: those that liberalized in the early period only; those that liberalized in the later period only; those that liberalized in both periods; and those that did not liberalize in either period.

His key finding is that the earlier and more consistent is the credit market liberalization reform, the stronger is the positive impact on per capita GDP. That is, countries that either did not reform in either period or reformed only in the later period had lower per capita GDP levels than the early reformers and those countries that reformed extensively in both periods.

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- (land reform)?

- • To what degree do different people in households have access to and control over consumption goods and services?

- –

- <u>Society household and kinship groups:</u> • Have there been any changes in the roles that men and women (of different groups) have traditionally played? Why have these changes occurred and what are the impacts of these changes?

- Are women better or worse off today compared to the past? In what ways? Are women of different groups (differentiated by class, age, ethnicity, religion, etc.) better or worse off today compared to the past? In what ways?

-

- <u>Society community:</u> • How many and how diverse are the membership organizations that exist? What are the “rules” of community membership groups?

- • To what degree does social identity affect membership of community groups and associations?

- • Is there conflict between different groups in the community?

Analyzing the qualitative data normally produced using participatory tools can be difficult, especially when trying to incorporate the data into the predominantly statistical analysis of the individual survey. The analysis of this data often relies to a certain extent on interpretation and reflection. However, generating numbers from, or quantifying, the qualitative outputs of participatory approaches and tools is possible and can help when trying to combine the analysis with the data from the individual survey. Given that the information being analyzed will be from focus groups spanning a number of different sites, recoding the information should be systematic and should use consistent pre-determined formats and terminology where possible.

The analysis of the focus group discussion data will be crosschecked with data from the individual interviews. Crosschecking may produce contradictions that will also need to be explained or resolved.

Diversity is another important factor to consider when analyzing the data from focus groups and interviews. Diversity in responses can be seen as an indicator of empowerment, with diversity of behavior at the population level being a gross indicator of
In order to interpret these results it is important to keep in mind that our estimates are coming from a country fixed effects regression. In this way we are comparing growth in a country during conflict to other years in the same country without conflict. The hope is that factors which affect the country generally are captured by the fixed effect. However, our empirical strategy also implies that if conflict affects growth in the postconflict period then _β_<sup>ˆ</sup> will provide an underestimate of the true economic costs of conflict. We will return to this point below.

# **2.3 Economic Impact at the Ethnic Group Level**

As a final piece of evidence, we draw from economic, population and ethnic conflict data compiled in the GROWup dataset. This dataset provides satellite night light data as a proxy for economic activity at the ethnic group level. Conflict incidence in the GROWUp data is defined through more than 25 fatalities, i.e. a fairly low threshold, in ethnic conflicts. This dataset provides information on 502 ethnic groups for the period 1992 to 2013. We run a regression of the following form: where _git_ is night light per capita growth of group _i_ in year _t_ , _incidenceit_ is conflict incidence, _θi_ are a set of 502 group fixed effects and _ηt_ are year fixed effects. The fixed effects in this context control for group-specific growth trends in the data. Note that we do not use the per capita model in this specification as violence seems to be a pure public bad locally.

Table 2 reports the results. We find a negative and statistically significant effect of incidence on per capita light growth. Light per capita growth is reduced by around 7 percentage points in years a group is engaged in conflict. This effect is robust to controlling for lagged light growth in column (2). In columns (4) and (5) we show that these reductions in light per capita growth are composed by two factors; light growth falls dramatically and this is partially
the much more difficult circumstances as the 1990s boom disintegrated. This also makes it harder to establish positive effects (eg on investment) than for telecoms.

For electricity, there is also, as yet, no database comparable to the World Bank/ITU database for telecoms. The data position is substantially weaker for railways and other transport infrastructure industries, water and sewage and, to some extent, for natural gas, primarily because there has been much less DTE privatization, unbundling or establishment of independent regulatory agencies. To our knowledge, virtually all of the relevant systematically collected data for these industries in DTE countries derives from Latin America via the Guasch et al World Bank data set on concession contracts and the Calderon-Serven data set (which ends in 1998)<sup>33</sup> .

**_A fundamental point to note is that_** **_<u>none</u> of these data sets includes data on the informal attributes of regulation (ie observations on transparency and other elements of regulatory practice).  This is in contrast to many of the estimates which have been made of the effect of independent central banks on macro-economic outcomes._** The implication is that all the econometric results based on these data sets for telecoms, electricity and other utility industries may provide downward-biased

> 33 See Caderon, Easterly and Serven (2002) for a full description of this data set.

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