rafmacalaba/data-use-mentions-extended huggingface dataset:
- Emran, M. S. and Shilpi, F. J. (2019). Economic approach to intergenerational mobility: Measures, Methods, and Challenges in Developing Countries. Working Paper Series 98, UNU-WIDER.

- Emran, S. and Sun, Y. (2015). Magical transition? intergenerational educational and occupational mobility in rural China : 1988?2002. Policy Research Working Paper Series 7316, The World Bank.

- Fan, Y., Yi, J., and Zhang, J. (2020). Rising Intergenerational Income Persistence in China. _American Economic Journal: Economic Policy_ .

- Febriana, M., Nurkamto, J., Rochsantiningsih, D., and Muhtia, A. (2018). Teaching in Rural Indonesian Schools: Teachers Challenges. _International Journal of Multicultural and Multireligious Understanding_ , 5:11–20.

- Frankema, E. (2014). Why was the Dutch legacy so poor? Educational development in the Netherlands Indies, 1871-1942. CGEH Working Paper Series 54, Universiteit Utrecht.

- Frankenberg, E., Karoly, L. A., Gertler, P., Achmad, S., Agung, I., Hatmadji, S. H., and Sudharto, P. (1995). The 1993 indonesian family life survey: Overview and field report.

- Friedman, J. (2005). How Responsive is Poverty to Growth? A Regional Analysis of Poverty, Inequality, and Growth in Indonesia, 1984-1999. In Kanbur, R. and Venables, A., editors, _Spatial Inequality and Development_ . Oxford University Press.

- Goldin, C. and Katz, L. (2008). _The Race Between Education and Technology_ . Belknap Press.

- Hertz, T. and Jayasundera, T. (2007). School Construction and Intergenerational Mobility in Indonesia. Working Papers 2007-18, American University, Department of Economics.

- Hertz, T., Jayasundera, T., Piraino, P., Selcuk, S
website, it is possible to gather all relevant information to compute grouped-data poverty estimates at international poverty lines for 2007 and for 2009, hence to update the available pre-conflict baseline poverty series (Box 1).

> 6 Information from the HIES 1997 was collected between October 1996 and September 1997; for the HIES 2003 information was collected between July 2003 and June 2004; for the HIES 2007, information was collected between November 2006 and October 2007.

> 7 El Laithy, Abu-Ismail (2005).
delays. Most often, trucks are stuck in long lines. Weighbridges should normally require 3 minutes for transit. According to the East African Business Council survey, however, trucks on average spend 92 minutes and some weighbridges actually require up to five hours. There are also frequent unwarranted roadblocks and checkpoints. Inspections are notorious for their lack of procedural transparency. Officials regularly deviate from the commonly-agreed inspection procedures and drivers are subjected to administrative harassment and outright extortion.

The key problems that plague border crossings have been extensively documented and include: excessive documentary requirements and anachronistic official procedures; insufficient use of ICT systems; questionable due process--lack of transparency, predictability and consistency in customs activities and determinations; unclear demarcation of responsibilities and lack of efficient cooperation among customs and other governmental agencies.

Customs authorities and procedures in East Africa, like those in other parts of the continent, are opaque and unpredictable. Firms have to spend considerable time searching for information and frequently have to pay bribes, penalties, and fees for administrative or judicial appeals. All of these 18
to reduce the likelihood of success, perhaps due to the intricate political difficulties involved in enacting such reforms. However, the paper concludes by noting that increases in leadership and coordination from the Bank at the project level generally have a positive effect on outcome. In a recent book chapter, Enyinda (2017) assesses the role of quantitative risk analysis on project management, examining the ways in which ex-ante risk analysis can mitigate poor outcome ratings on completion.

Region- and sector-specific works related to project success are too numerous to cite here with any degree of meaningful exhaustiveness. That being said, several works with a specific eye to issues that may be seen as delivery challenges can be highlighted here. Diallo and Thuillier’s (2004) examination of success factors in African projects presents an insightful analysis of the challenges posed to project success in Africa. Vawda et al. (2003) test the performance of education projects that have been subjected to economic analysis at the time of appraisal, showing a strong link between the project outcome and cost-benefit analysis. This suggests that project design is a strong indicator of ultimate performance. At a regional level, Blanc et al. (2016) use data from World Bank projects in the East Asia and Pacific regions to model the importance of project supervision, implementation progress, and the achievement of development objectives during project supervision. Similarly, Noorbakhsh and Paloni (2007) provide a novel take on the lessons from structural adjustment programs and the role of selectivity in Africa. Along more prescriptive lines, Ika (2012) offers a broad assessment of the general factors that may be leading to project failure on the continent, concluding with a list of prescriptions for improving under-performing projects.

In more sector-specific assessments of project ratings, Vickland and Nieuwenhuijs (2005) provide an interesting take on the primary factors affecting the modernization of public financial management information systems in Bosnia and Herzegovina, and Chauvet, Collier, and Duponchel (2010) analyze the elements that affect aid project success in post-conflict areas more generally. Finally
1986|–2009|1993|–2009|1986|–2009|1986|–2009|

_Source:_ Authors’ estimation using ECH and National Meteorology Office data. _Note:_ Estimates are obtained using a Papke & Wooldridge Fractional Logit Model. Marginal effects are reported, and robust standard errors clustered at the city level are reported in parentheses. All models include city fixed effects. *, **, and *** indicate statistical significance at the 10 percent level, 5 percent level, and 1 percent level, respectively.<sup>a</sup> Treatment Units (three cities): Maldonado, San Carlos, and Pan de Azúcar; Control Units (32 cities): Artigas, Bella Unión, Canelones, Carmelo, Colonia, Dolores, Durazno, Florida, Fray Bentos, Lascano, Libertad, Melo, Mercedes, Minas, Montevideo, Paso de los Toros, Paysandú, Periferia Canelones, Rivera, Rocha, Rosario, Río Branco, Salto, San José de Mayo, San Ramón, Santa Lucía, Sarandí del Yí, Sarandí Grande, Tacuarembó, Tranqueras, Treinta y Tres, Trinidad, and Young.<sup>b</sup> Treatment Unit (one city): Maldonado; same Control Units as in A.

> <sup>c</sup> Treatment Unit (one city): Maldonado; Control Units (17 cities): Artigas, Canelones, Colonia, Durazno, Florida, Fray Bentos, Melo, Mercedes, Minas, Paysandú, Rivera, Rocha, Salto, San José de Mayo, Tacuarembó, Treinta y Tres, and Trinidad.

29
Policy Research Working Paper 7528

# **Abstract**

The World Bank has used an income classification to group countries for analytical purposes for many years. Since the present income classification was first introduced 25 years ago there has been significant change in the global economic landscape. As real incomes have risen, the number of countries in the low income group has fallen to 31, while the number of high income countries has risen to 80. As countries have transitioned to middle income status, more people are living below the World Bank’s international extreme poverty line in middle income countries than in low income countries. These changes in the world economy, along with a rapid increase in the user base of World Bank data, suggest that a review of the income classification is needed. A key consideration is the views of users, and this paper finds opinions to be mixed: some critics argue the thresholds are dated and set too low; others find merit in continuing to have a fixed benchmark to assess progress over time. On balance, there is still value in the current approach, based on gross national income per capita, to classifying countries into different groups. However, the paper proposes adjustments to the methodology that is used to keep the value of the thresholds for each income group constant over time. Several proposals for changing the current thresholds are also presented, which it is hoped will inform further discussion and any decision to adopt a new approach.

This paper is a product of the Data Group, Development Economics Vice Presidency. 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 nfantom@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
The main objectives of this study are first, to estimate total factor productivity at the production system level in the period since the advent of the Green Revolution in Pakistan's Punjab Province, and second, to relate productivity trends to changes in resource quality. Earlier studies have estimated resource productivity at the national or state level (e.g., Rosegrant and Evenson, 1992), or have been restricted to one crop in a region (e.g., Cassman and Pingali, 1995). Since sustainability is likely to relate to underlying agronomic and socioeconomic characteristics of the farming or production system, such as crop rotation, an important contribution of this study is to quantify productivity trends at the production system level defined in terms of the dominant cropping pattern. Considerable resources were invested in collecting the large amount of data on individual crop and livestock products, inputs, and prices that are required for the system-level analysis.

Further, a major contribution of the study is to quantify trends in resource quality and then to econometrically relate trends in productivity to indicators of resource quality. To do this, we establish a second comprehensive data set on soil and water quality variables based on records of tests in farmers' fields. This enables us to at least partially decompose productivity trends into the effects of technological change, improvements in human resources and infrastructure, and natural resource degradation.<sup>2</sup> This study focuses on the measurement of agricultural productivity change in the irrigated agriculture of Pakistan's Punjab province which is the agriculturally dominant province in the country, with a farming population of over 60 million people, and often described as Pakistan's bread basket. Over 80% of the cropped area of the province is irrigated. The Punjab was one of the earliest beneficiaries of the Green Revolution with the introduction of modern varieties of wheat and rice in the 1960s. However, recent studies have questioned the sustainability of current intensification strategies (Byerlee and Siddiq, 1994). In light of recent record imports
create this variable. Analogously, the Non-investment grade dummy variable refers to firms that had an issuer rating below BBB-/Baa3 on March 17, 2020. In our sample, 9.9% of firms had an investment grade rating, while 4.6% of had a non-investment grade rating. The remaining firms had no issuer rating.

Firms that rely more heavily on bond finance are potentially more strongly affected by the PEPP, as the PEPP is restricted to purchasing marketable debt securities. To represent a firm’s bond issuance, Bonds/assets is calculated as the ratio of the sum of the principal amounts of all outstanding bonds as of the most recent reporting date in 2019 available in S&P’s Capital IQ (most commonly, end of Q2 or Q4 in 2019) divided by total assets. Bonds are defined to include commercial paper and notes. Assets for the same reporting date as bonds are obtained from Compustat. We matched data from Capital IQ and Compustat with data from Eikon based on ISIN security numbers. In our sample, firms finance on average 5.75% of assets using bonds.

The implications of monetary policy for a firm depend, among other things, on whether it serves to alleviate credit constraints and debt overhang. Some firms no doubt already were subject to credit constraints and debt overhang before the COVID-19 crisis, while the occurrence of this crisis can only have aggravated these problems for many firms, even if to different extents. To proxy for potential pre-existing credit constraints and debt overhang, we consider a range of firm-level variables similarly to Ehrmann and Fratzscher (2004) and Maio (2014), measured as of the end of 2019 and thus predating the COVID-19 crisis.

Among these, Leverage is the ratio of liabilities to assets. Highly leveraged firms potentially experience greater credit constraints and debt overhang that could be alleviated by the PEPP. The sample mean of Leverage in our sample is 53.1%. Log assets is the natural logarithm of total assets, and it
Table E3: Share of Significant Rainfall Coefficients in Niger

||CHIRPS|CPC|MERRA-2|ARC2|ERA5|TAMSAT|
|---|---|---|---|---|---|---|
|Mean Daily Rainfall|0.67|0.67|0.42|0.67|0.83|0.33|
|Median Daily Rainfall|0.00|0.00|0.58|0.00|0.00|0.58|
|Variance of Daily Rainfall|0.75|0.83|0.50|0.67|0.58|0.25|
|Skew of Daily Rainfall|0.42|0.33|0.42|0.42|0.42|0.33|
|Total Rainfall|0.67|0.67|0.42|0.67|0.83|0.33|
|Deviation in Total Rainfall|0.58|0.92|0.83|0.67|0.67|0.25|
|Z-Score of Total Rainfall|0.58|0.83|0.83|0.67|0.50|0.17|
|Rainy Days|0.42|0.42|0.58|0.75|0.67|0.67|
|Deviation in Rainy Days|0.58|0.42|0.50|0.25|0.00|0.67|
|No Rain Days|0.50|0.33|0.58|0.67|0.58|0.67|
|Deviation in No Rain Days|0.58|0.42|0.50|0.25|0.00|0.67|
|% Rainy Days|0.
account past performance and expected losses (Abedifar et al., 2013).

The second dimension, volatility, is quantified through the standard deviation in credit growth and deposits growth at the country level. We consider both price and volume growth. We first compute the year on year growth rate at the country level in bank lending rate, commercial banks outstanding loans, deposit interest rate and commercial banks outstanding deposits. We then compute the standard deviation of the growth rate at the country level. In this way we are able to quantify the uncertainty and risk deriving by both the variability in the cost and provisioning of credit, and the cost and volume of funding.

The third dimension captures the cost of crises, calculated using data from Laeven and Valencia (2013). We consider measures of banking crises including the output loss (the cumulative loss in income relative to pre-crisis trends), the costs of government intervention to mitigate and resolve the crises (direct fiscal outlays due to financial sector rescue packages), and the peak level of realized credit risk (the peak ratio of NPLs to total loans).

Figure 3 reports the distribution of the various indicators used to measure financial stability arranged by country income-group. It also highlights the median, the minimum and the maximum of the distribution by income group.

The distributions of the financial stability indicators show that the most advanced countries score relatively worse than the other countries in some indicators. For instance, the median value of high income countries appears the lowest for capital ratios and the highest for the variability in the deposit rate growth.

> 7 See for instance, Lowe (2002) for a discussion of macroeconomic consideration for risk measurement, Danielsson et al. (2015) for the link between volatility and financial crises, and Acharya et al. (2010) for the importance of measuring tail risks.

> 8 While the Z-score contains equity to assets, which is very similar to the capital ratio, the two indicators deliver statistically different information. They are uncorrelated based on the
exporters and small states, of which only a few are UMICs, have experienced the steepest increase in domestic debt.

**Figure 8. Foreign Currency Public Debt, by Income Group** _(% GDP)_

<!-- Start of picture text --> 60<br>50<br>40<br>30<br>20<br>10<br>0<br>2007 2013 2018 2007 2013 2018 2007 2013 2018 2007 2013 2018<br>Low-Income Countries Lower-Middle Income Upper-Middle Income Sub-Saharan Africa<br>Countries Countries<br><!-- End of picture text --> _Note: The columns represent group simple averages whereas red dots (diamonds) represent the group medians. Source: World Bank; IMF – World Economic Outlook Database, April 2018_ The increase in foreign currency public debt, on the other hand, was particularly pronounced in lower-middle-income countries: LMCs have recorded substantial increases in foreign currency denominated debt since 2013 and display the highest foreign currency debt to GDP share in 2018 ( **Figure 8** ) **.** During the recent episode of accelerated borrowing, median foreign currency debt increased by 12 percentage points in LMCs, from 27 to 39 percent of GDP. One emerging factor behind this increase was the ability of some of the LMCs in the region to tap international capital markets. During the past few years, Sub-Saharan African countries have issued international market-based bonds at an increasing pace. Most of the countries that issued such bonds were LMCs, such as Angola, Ghana, Côte d'Ivoire, Kenya, and Nigeria.

9
# **5.** **<u>Data</u>**

# (a) <u>Students’ census evaluation</u>

The experiment was designed to assess the impact of the ‘GYM package’ using results from the annual students’ census evaluation ( _Evaluación Censal de Estudiantes_ , ECE) administered by MINEDU. The strategy of asking teachers from grades seven and eight to implement the ‘GYM session’ generated two separate evaluation cohorts: the eighth-grade cohort assessed in the ECE 2015, and the seventh-grade cohort assessed in the ECE 2016. First introduced at the secondary education level in 2015, the ECE administers _Mathematics_ and _Reading Comprehension_ tests to all students enrolled in grade eight.<sup>17</sup> In 2016, the evaluation included a _History, Geography and Economics_ test. These are our key outcomes measures.

The ECE in 2015 and 2016 were administered in mid-November. All pupils were requested to sit their exams on the same dates throughout the entire Peruvian geography. Test content and marking were carried out centrally by MINEDU, while the administration of the tests was under the supervision of a bespoke team of enumerators. The ECE data set provides individual test scores for all participating students. Based on these scores, students are classified into four groups according to their level of learning: ‘Before beginning’, ‘Beginning’, ‘In process’ and ‘Satisfactory’. Only those in the ‘Satisfactory’ group have achieved a learning level consistent with their current grade. In 2015, this accounted for 9.5% in _Mathematics_ and 14.7% in _Reading Comprehension_ for all the student populations in the eighth grade.

Apart from test scores, the ECE also collects pupil background data via a self-administered questionnaire, which includes standard modules on household, parental and student socioeconomic characteristics, one question on education expectations (¿What is the highest education level you expect to achieve?) and, in the ECE 2015 only, two Likert scales to measure students
underlying growth determinants for the 20 countries in Latin America and the Caribbean come from Loayza _et al_ . (2005).

We test each growth determinant for a unit root, I(1). The unit root tests include a trend implying that the test has unit root as the null hypothesis. This is consistent with the presumption that we ‘accept’ the null hypothesis of a unit root unless confronted with strong evidence to the contrary. To account for possible heteroskedasticity we compute White heteroskedasticity-consistent standard errors. In most cases the changes to the standard errors were insignificant.

Instead of considering that a variable has a quadratic trend or a unit root plus trend, we consider the more plausible specification that the variable is I(2). It is possible to test whether there is a significant trend in the presence of a unit root. However, such a model is rarely used since it is difficult to justify that the growth rate shrinks or grows in a linear fashion. Often, a naïve curve fitting exercise (ignoring unit roots) will find a quadratic trend term that is significant (and perhaps higher order terms). Although the higher-order trend terms allow you to fit the data better ex post, the model will often perform poorly in out of sample forecasting (Sims, 1999).

A few growth determinants were treated in a different way. We assume that the number of years in which a country underwent a systemic banking crisis remains the same over the next decade. We believe that this is the best we could do given that the occurrence of a banking crisis is difficult to predict even with sophisticated models. Loayza _et al_ . (2005) use a more sophisticated technique to forecast this variable,<sup>35</sup> but their technique tends to result in a similar outcome as ours. They estimate low probabilities of new banking crises in countries

> 35 They use a panel data model to estimate the probability of crisis based on an index of real exchange rate overvaluation and the previous occurrence of
Anchoring civil registration in a regulatory framework is the first step towards not only completeness and accuracy of information about the life events of each citizen, but also towards ensuring access and ample registration to all and safeguarding basic human rights.

_Living Life’s_ Civil Registration and Identification focus area includes data on registering births and deaths as well as data on obtaining IDs. The topic covers the legal framework for each of the civil registration processes including procedures and requirements, time and cost to complete certain steps, the type of information collected at the time of registration, late registration, and the digitalization of registration mechanisms.

The study revealed that all four economies have mandatory legal provisions for civil registration, although the sanctions for non-compliance and procedures vary. While India and Chile have moved towards including digital components to civil registration and identification systems, Vietnam and Ghana rely on more traditional processes. The efficiency of identification systems measured by time taken to obtain a national identity card (NID) or passport also varies across economies.

# **3.1.1. Legal requirements**

Birth and death registration is legally required in all four economies. Relevant laws also contain a specific time period within which records on births and deaths should be registered. These periods vary across different countries (Figure 3.1). Generally, countries establish shorter timeframes for death registration as opposed to birth registration. While a death should be registered within one day in Ghana, in India this time limit is 21 days. A birth should be registered within 21 days in both Ghana and India and within 60 days in Chile and Vietnam.

**Figure 3.1: Time Limits for Birth and Death Registration (in days)**

<!-- Start of picture text --> Time limits for birth and death registration<br>70<br>60 60<br>60<br>50<br>40<br>30<br>21 21 21<br>20 15<br>10<