rafmacalaba/datause-encoder-data huggingface dataset:
constructed. When we use changes in short-term borrowing (Balance Sheet data item #34) as a measure of credit, we find 45.63% of our sample to be Phoenix Miracles – these firms had a positive increase in sales without a positive increase in short-term borrowing from 2009 to 2010.<sup>22</sup> Even amongst these candidate Phoenix Miracles, we find that 90% (unreported in the table) of the firms had an adequate cash balance in their balance sheet to cover their liquidity crunch. That is, the ratio of the absolute value of changes in short-term borrowing from 2009 to 2010 to their balance sheet Cash and Short-term Investments position in 2010, was <1 for 90% of the firms. When we look at the firms that are not candidate miracles, 27.2% of the firms had a positive increase in sales and short-term borrowing, 9.29% had a decline in sales but an increase in short-term borrowing and 17.88% of firms had a decline in sales and short-term borrowing.

The percentage of Phoenix Miracles is reduced to 8.96% when we consider the changes in Long-term Debt (Balance Sheet data item #9), Net Capital Stock Issuance ( _Sale of Common and Preferred Stock,_ data item #108 – _Purchase of Common and Preferred Stock,_ data item #115), Cash from Dividends ( _Cash Dividends,_ data item #127), and Other Financing activities ( _Financing Activities-Other,_ data item #312). And of these firms, 59% (unreported in the table) had adequate cash balances to cover their liquidity crunch.

In Panel C we examine the different accounts in the Statement of Cash flows to identify alternate sources of liquidity for the firms during the recovery period. First, we re-define credit to not only include net financing from changes in short-term debt, long-term debt, equity issuances, and other financing activities but also cash from other sources such as that which could have been saved by cutting dividends, capital expenditures, and acquisitions. Specifically, when we include cash that could have been generated by cutting Dividends (data item #21), Capital Expenditures (data item #128), Net Investments ( _Sale of Investments_ data item #109- _Increase in Investments_ data item #113), Changes in Short-term Investments (data item #309) and Other Investing Activities (data item #310) and cash generated from Sale of Property, Plant and Equipment (data item #107) and Acquisitions (data item #129), we find that the percentage of candidate phoenix miracles in our sample decreases to 5.54%. This further reduces to 2.76% (92 firms out of 3338) once we include the Cash Flow from Operating Activities (data item #308).

> 22 We prefer to use the balance sheet data for constructing changes in short-term and long-term borrowing over 2009-2010 because the corresponding items in the Statement of cash flows are missing for a large portion of the sample.

25
_16_ _Estimates and Projections of Mortality by Cause_ Table 7. Percentage distrihution of deaths by major cause in 1985 and incremental changes expected by 2000 and 2015 from three factors, world and developed and developing countries

||_1985_|_Perc_<br>|_entage_<br>_point c_<br>_by 2000 due t_|_hange_<br>_o_|_Perc_<br>|_entage_<br>_point c_<br>kg_2015 due t_|_hange_<br>_o_|
|---|---|---|---|---|---|---|---|
|_p_<br>_Region and cause di_|_ercentage_<br>_sttibution_|_Age-sex_<br>_structure_|_Mortality_<br> <br>_change_|_Distrtiu-_<br>_tion change_|<br>_Age-sex_<br>_stmeture_|<br>_Mortality_<br>_change_|_Distdbu-_<br>_tion change_|
|_World_||||||||
|Infections|29.6|-1.1|-2.0|-5.2|-1.4|-3.3|-8.4|
|Neoplasms|9.8|0.6|0.8|1.1|1.1|1.1|2.6|
|<br>Circulatory|26.5|2.2|2.9|2.9|3.3|4.4|5.3|
|<br>Pregnancy|0.5|0.1|-0.1|0.0|0.1|-0.2|0.0|
|<br>Perlnatal|6.7|0.8|-1.2|0.3|-1.8|-1.6|0.6|
|Injury|7.4|-0.8|-0.4|0.4|-1.1|-0.7|1.1|
|<br>Other|19.6|-0.3|0.0|0.4|-0.1|0.2|-1.2|
|_Developed_<br>_Countries_||||||||
|<br>Infections|8.8|-1.1|-0.2|-0.6|-1.1|-0.2|-0.6|
|Neoplasms|18.2|0.6|0.0|-0.9|0.8|-0.2|-0.7|
|<br>Clrculatory|50.4|2.5|1.1|-1.8|2.8|1.5|-1.8|
|<br>Pregnancy|0.1|0.0|0.0|0.0|0.0|0.0|0.0|
|<br>Perlnatal|1.2|-0.3|-0.2|0.4|-0.4|-0.2|0.3|
|Injury|6.1|-05|-0.6|0.9|-0.9|-0.8|1.0|
|<br>Other|15.3|-1.2|-0.1|2.0|-1.2|-0.1|1.9|
|_Devlopig Countne_|_s_|||||||
|<br>Infections|<br>36.2|-1.9|-1.8|-6.6|-3.0|-3.0|-10.8|
|Neoplasms|7.2|1.0|0.7|1.7|1.8|1.0|3.6|
|<br>Crculatory|18.8|3.2|2.6|4.3|5.2|3.9|7.4|
|<br>Pregnancy|0.6|0.1|-0.1|0.0|0.1|-0.2|0.0|
|<br>Perlnatal|8.4|-1.1|-1.3|0.3|-2.6|-1.8|0.7|
|Injury|7.8|-0.9|-0.3|0.2|-1.2|-0.6|1.1|
|<br>Other|21.0|-0.3|0.2|-0.1|-0.2|0.6|-2.1|

Table 7 indicates that the three components can operate in the same or in opposite directions. For instance, population aging, lower mortality (especially at younger ages), and changes in the distribution of deaths all contribute to the decline in infectious diseases. On the other hand, population aging and lower mortality reduce the importance of injury and poisoning, but distribution change raises their importance in relation to other causes of death.

WWch of the three components exerts the greatest effect varies considerably from cause to cause. The largest effect overall is that of distrbutional change on infectious and parasitic diseases, but in this case the other two components mostly operate in the same direction, namely to reduce the proportion of deaths from this cause. The three components are also consistent in their effect on raising the importance of circulatory system diseases in developing countries, but in developed countries the components work in opposite directions. Therefore, both demographic and epidemiological change will have roles in modifying mortality by cause in the future.
trast, the transitions in Mexico (where the formal sector grew much less) are more evenly spread according to the origin or destination wage distributions.

There are two reasons why the contribution of movements from self-employment to formal wage employment probably played a small role in the observed changes in inequality in the three countries. First, the self-employed who became formal are much rarer. In Argentina and Mexico, they represent 17 and 6 percent of the total transitions, respectively, and in Brazil they reached 26 percent. Second, the movers from self-employment to formal employment are more uniformly distributed in terms of the origin and destination wage distributions, perhaps with the exception of Mexico where a strong positive selection with respect to the origin wage distribution was observed.

Table 4: Distribution of Formal Employment and Workers Who Transition to Formality, by Wage Quartile (percent)

||Informal employees who became<br>formal employees|Self-employed who became formal<br>employees|
|---|---|---|
|Wage quartile|Informal<br>employees’ wage<br>distribution<br>Formal<br>employees’ wage<br>distribution|Self-employed<br>wage distribution<br>Formal<br>employees’ wage<br>distribution|
||_a. Argentina_||
|1|17.4<br>46.5|21.9<br>39.3|
|2|24.8<br>26.7|34.0<br>23.8|
|3|27.9<br>18.5|18.8<br>16.8|
|4|29.9<br>8.3|25.3<br>20.2|
|Share in new formal|77.1|16.6|
||_b. Brazil_||
|1|20.5<br>42.5|22.4<br>29.9|
|2|25.0<br>27.0|23.9<br>22.0|
|3|25.9<br>19.8|24.7<br>24.1|
|4|28.6<br>10.7|29.1<br>24.0|
|Share in new formal|66.4|25.7|
||_c. Mexico_||
|1|16.3<br>31.2|11.5<br>23.5|
|2|24.6<br>26.3|12.0<br>23.6|
|3|27.9<br>22.1|29.2<br>24.2|
|4|31.3<br>20.4|47.3<br>28.6|
|Share in new formal|58.1|6.5|

_Source_ : Authors’ calculations based on data from the 12-months Survey Panels from Labor Force Surveys. _Note:_ The table reports average yearly transitions to the formal sector from informal wage employment (columns 2 and 3) and self-employment (columns 4 and 5). Columns 2 and 4 classify movers according to their quartile in the wage distribution of origin: informal employees in column 2 and self-employed in column 4. Columns 3 and 5 classify movers according to their quartile in the wage distribution of destination (i.e., the wage distribution of formal workers). The “share in new formal” is defined as the share of those who formalize who were originally informal employees and who were originally self-employed. The sum does not add to 100 percent, as some unemployed and workers out of the labor force at the beginning of the panel also formalized during the 12-month window considered. See supplementary online appendix S1.2 for details on the construction of the panel data sets.

22
Policy Research Working Paper 9974

# **Abstract**

Issues of data availability and incomparability in the measurement of household consumption arise frequently when measuring poverty trends over time. Yet, understanding these trends is key to guide national and international policy makers in their poverty reduction efforts. This paper aims to estimate a long-run poverty trend for Nigeria, a country whose poverty trends are crucial for regional and global estimates. In 2020, the Nigerian National Bureau of Statistics released the first official poverty estimates for Nigeria in almost a decade, calculated using the 2018/19 Nigerian Living Standards Survey. Yet the official poverty estimates from the 2018/19 Nigerian Living Standards Survey cannot technically be compared with those from the 2009/10 Harmonized Nigerian Living Standards Survey—the previous official household consumption survey—given key differences in the way household consumption was measured and concerns around data quality in the 2009/10 survey.

To address this challenge, this paper uses two distinct methodologies to construct a poverty trend for Nigeria in the decade before the COVID-19 crisis. First, it uses sector-level gross domestic product growth rates combined with microdata from the 2018/19 Nigerian Living Standards Survey to “backcast” poverty rates back to 2009. Second, it uses survey-to-survey imputation methods and data collected throughout the decade through the General Household Survey panel. Despite their very different foundations, these two approaches produce very similar results, suggesting that there was a small reduction in poverty at the beginning of the decade, followed by a period of stagnation or even a slight uptick in poverty following the 2016 economic recession. The paper estimates a poverty rate of between 42.2 and 46.3 percent in 2009, translating into a reduction in the poverty headcount rate of between 3 and 7 percentage points between 2009 and 2018/19.

This paper is a product of the Poverty and Equity 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 jlain@worldbank.org or mschoch@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. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
Table 1: Summary Statistics and Tests of Randomization

||(1)<br>|(2)<br>|(3)<br>Migrant and<br>|(4)<br>|(5)|(6)|(7)|(8)|(9)|(10)|(11)|
|---|---|---|---|---|---|---|---|---|---|---|---|
||Migrant-Only<br>Training (A)|Family-Only<br>Training (B)|Family<br>Training (C)|Control Group<br>(D)|P-value<br>A = B|P-value<br>A = C<br>|<br>P-value<br>A = D|<br>P-value<br>B = C|<br>P-value<br>B = D|P-value<br>C = D<br>|<br>P-value<br>A = B = C = D|
|Follow-up 1:||||||||||||
|Respondent is Female|0.34|0.31|0.31|0.36|0.612|0.509|0.924|0.952|0.484|0.45|0.815|
|Respondent Age|43.33|41.99|41.45|43.68|0.434|0.236|0.868|0.75|0.316|0.171|0.457|
|Migrant is his/her Spouse|0.17|0.16|0.15|0.24|0.9|0.668|0.236|0.739|0.205|0.111|0.421|
|Migrant is his/her Parent|0.36|0.36|0.39|0.31|0.968|0.673|0.427|0.684|0.424|0.227|0.667|
|Follow-up 2:||||||||||||
|Respondent is Female|0.36|0.36|0.37|0.38|0.99|0.934|0.796|0.945|0.807|0.864|0.994|
|Respondent Age|44.66|44.06|42.59|45.83|0.731|0.227|0.502|0.422|0.343|0.081<br>*|0.355|
|Migrant is his/her Spouse|0.18|0.17|0.17|0.25|0.927|0.877|0.193|0.949|0.167|0.154|0.444|
|Migrant is his/her Parent|0.36|0.35|0.32|0.29|0.889|0.581|0.332|0.681|0.41|0.688|0.763|
|Follow-up 3:||||||||||||
|**Respondent is Female**|0.38|0.35|0.38|0.38|**0.762**|**0.985**|**0.909**|**0.78**|**0.678**|**0.895**|**0.979**|
|Respondent Age|45.87|44.42|42.69|45.97|0.428|0.082 *|0.957|0.356|0.399|0.073 *|0.238|
|Migrant is his/her Spouse|0.17|0.17|0.17|0.23|1|0.978|0.323|0.978|0.323|0.317|0.704|
|Migrant is his/her Parent|0.33|0.35|0.28|0.3|0.759|0.477|0.595|0.311|0.403|0.854|0.723|

This table presents demographic summary statistics for respondents in all follow-up survey rounds by treatment status -- Columns (1) - (4); as well as p-values for equality of means tests across treatments -- Columns (5) - (11). Statistically significant p-values are highlighted by: * (10% significance level), ** (5% significance level), and *** (1% significance level).
The role of corruption, the abuse of public office for private gain, in limiting human development is now well established. However, the ability to measure changes in corruption over time and across countries, in a valid and reliable manner, remains a contested issue. This is despite the proliferation of indicators purporting to do just that. The overarching concern of this paper is to critically evaluate the different measures or indicators of corruption that might logically be used to inform policy makers about changes in corruption across countries and over time. This evaluation is used in determining whether it is possible to associate changes in anti-corruption efforts/institutional features that may incentivize more or less corruption (independent variable of interest) to variations in reported or perceived corruption (dependent variable of interest).

The paper achieves this objective by critically reviewing the strengths and weaknesses of different objective and subjective indicators of corruption, in order to identify appropriate indicators. This objective is achieved by using the standard criteria of validity and reliability in order to discriminate between these potential indicators. This is an important exercise because the new Sustainable Development Goals are explicitly committed to measuring aspects of corruption over time, and therefore identifying robust indicators to do so will be critical to realizing this endeavor.

More specifically, this paper critically reviews the validity, consistency, and substantive focus of the following indicators of corruption: (1) the Corruption Perceptions Index (CPI); (2) the Control of Corruption Governance Metric (CC); (3) the Government Effectiveness Governance Metric (GE); (4) the Global Corruption Barometer; (5) the UN Survey of Crime Trends; and (6) evidence on the propensity of diplomats from different countries to break the law (hereafter ‘Tickets’). The paper finds, consistent with the large literature in the field, that the aggregate survey-based indicators of corruption, especially the CPI and the CC, despite some important reservations and limitations, are the most valid measures of the magnitude of overall corruption in many country contexts. In every case, though, initial results using one indicator should be cross checked with the use of the other indicator, as there are some minor differences between how the two indicators are constructed, and in practice it is difficult to establish, _a priori_ , which indicator is marginally more efficient. Furthermore, despite a narrower focus, because comparative information on the tickets data is also available, it is also possible to cross check the results of these subjective 2
# **Quality Regulation Creates and Reallocates Trade**<sup>∗</sup>

Lucas Zavala<sup>b</sup> , Ana Fernandes<sup>a</sup> , Ryan Haygood<sup>c</sup> , Tristan Reed<sup>b</sup> , Jose-Daniel Reyes<sup>b</sup> Originally published in the Policy Research Working Paper Series in _November 2023_ . This version is updated in _February 2026_ .

To obtain the originally published version, please email prwp@worldbank.org.

<u>JEL Codes:</u> F13, F14, L11, O19.

<u>Keywords:</u> Quality regulation, trade policy, non-tariff measures, imports, market concentration, trade reallocation.

> ∗We thank Penny Goldberg, Juan Carlos Hallak, Hiau-Looi Kee, and Ariel Weinberger for comments, as well as seminar participants at the World Bank, UNCTAD, Princeton, and Yale. This paper was supported by the Development Research Group Research Support Budget, the Knowledge for Change Program III for funding, the Umbrella Facility for Trade trust fund (financed by the governments of the Netherlands, Norway, Sweden, Switzerland and the United Kingdom), the Structural Transformation and Economic Growth Small Research Grant, and the Economic Growth Center SYLFF Research Grant. Jan Gabriel Oledan and Paula Suarez provided excellent research assistance. Alejandro Forero assisted with tariff data. Computational reproducibility was verified by DIME Analytics. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the World Bank or its affiliated organizations, or those of the Executive Directors of the World Bank, their Managements, or the governments they represent.

a: Corresponding author, World Bank, 1818 H St NW, Washington, DC, 20433, afernandes@worldbank.org; b: World Bank; c: Yale University.
**Figure 3. School Safety Index by Category of Urbanization among MENA and Non-MENA Countries in** Source: TIMSS 2015, 8th Grade

<!-- Start of picture text --> 0.4<br>0.3<br>0.2<br>0.1<br>0<br>‐0.1<br>‐0.2<br>‐0.3<br>‐0.4<br>‐0.5<br>‐0.6<br>Urban, densely Suburban, on fringe of Medium size city or Small town or village Remote rural<br>populated urban area large town<br>MENA Other countries<br><!-- End of picture text --> Note: School safety has been estimated using factor decomposition. The index is centered on 0 with a standard deviation of 1.

Teachers display more hostility in MENA countries. A measure of the hostility of teachers toward students is derived from the items illustrated in Figure 4.<sup>7</sup> The figure shows that three MENA countries and Turkey have much higher shares of students reporting hostile behaviors from their teacher, with an average of 22 percent in the MENA countries and Turkey compared to 17 percent in other countries. More concerning is the fact that the gap between the MENA and non-MENA countries is larger for more serious incidents such as teachers insulting students in front of others. These results corroborate evidence found in schools in Jordan for example where 11 percent of school children report experiencing corporal punishment from teachers while 18 percent report experiencing verbal violence during the 2015/2016 academic year (UNICEF 2016).

Teacher hostility is clearly more directed toward boys in the three MENA countries for which data are available (Table 2). These findings mirror international trends which indicate a gender gap of around 0.25 of a standard deviation globally. However, in these three MENA countries, the gender gap is almost twice as large as in the rest of the world, with boys being more likely than girls to be the subject of negative comments, humiliation, insults, and harsh discipline. It is to be noted that the fact that boys and girls may be segregated in different classes does not seem to play an obvious role. In Tunisia where boys and girls are sharing classes, the gender gap is indeed higher than in the United Arab Emirates where gender segregation is still the norm.

**Table** 2 _._ **Gender and Teacher’s Hostility index**

||Boys|Girls|Gender gap|
|---|---|---|---|
|United Arab Emirates|0.12|0.55|0.43|
|Qatar|0.13|0.59|0.46|
|Tunisia|-0.03|0.47|0.50|

7 The index is reliable with a Cronbach’s alpha of 0.79 and all items being relevant with satisfactory KMO tests.

7
killed, made homeless, affected and financial losses for more than 16,000 sudden-onset (such as floods, storms, earthquakes) and slow-onset (drought) events from 1900 to the present. Data are generally collected from various sources, including UN agencies, nongovernmental organizations, insurance companies, research institutes and press agencies. The other database is the proprietary Munich Re NatCat Service database, which mainly serves to inform insurance and reinsurance pricing. This database contains fewer entries focusing on the about 300 largest events since 1950, yet data exhibit a higher reliability as they are often crosschecked with other information. We focus on the monetary losses listed in constant 2000 USD terms. In both datasets, loss data follow no uniform definition and are collected for different purposes such as assessing donor needs for relief and reconstruction, assessing potential impacts on economic aggregates and defining insurance losses (see Provention, 2002). The sample comprises of sudden and slow onset events. Key sudden-onset events are extreme geophysical events (earthquakes) and hydrometeorological events such as tropical cyclones, floods and winterstorms. Slowonset hydrometeorological disasters are either of a periodically recurrent or permanent nature; these are mostly droughts, extreme temperature events and forest fires. Table 1 classifies the events in our sample according to cause and type of event.

<u>Table 1: Classification of events in sample</u>

|<br>**Hazard**|<br>**Cause**|**Type**|
|---|---|---|
|Flood|Hydrometeorological|Sudden onset|
|Storm|Hydrometeorological|Sudden onset|
|Mass movementwet|Hydrometeorological|Sudden onset|
|Drought|Hydrometeorological|Slowonset|
|Extreme temperature|Hydrometeorological|Slow onset|
|Wildfire|Hydrometeorological|Slowonset|
|Earthquake|Geophysical|Sudden onset|

Loss data may refer to direct and indirect loss, or stocks and flows, in unknown proportions. As for our analysis only the direct stock losses are of importance, we resort to assumptions and refer to evidence on the share of the direct capital stock losses in productive sectors and infrastructure in different events in Latin America, for which good information was at hand. Based on information listed on Table 2 and Table I-2 in the Appendix for which a simple average would amount to 33%, given uncertainty around 10
# **Figure 10: Out‐of‐pocket expenditure for hospital delivery, 2003‐2008**

<!-- Start of picture text --> 117<br>2000 120<br>108<br>105<br>2008 Hospital<br>1600 89 100 delivery average out‐<br>of‐pocket payment<br>(after NRCMS<br>80 reimbursement)<br>1200 2003 Hospital<br>delivery out‐of‐<br>60 pocket payment<br>(without NRCMS<br>800<br>coverage)<br>40<br>Hospital delivery<br>out‐of‐pocket<br>400 20 payment in 2008<br>relative to 2003 (%)<br>0 0<br>Large Medium Small Type I Type II Type III Type IV<br>cities Cities Cities<br>Urban Rural<br>Hospital delivery oop<br>2008 OOP as % of 2003 OOP<br><!-- End of picture text --> Source: NHSS 2008 22