rafmacalaba/data-use-mentions-extended huggingface dataset:
markets. This at least partially, and often substantially, offsets the more direct losses from erosion of bilateral preference margins. Thus, preference erosion will be offset by the compensatory effect of broad-based multilateral liberalization, including by emerging market economies and by beneficiary countries themselves. However, research suggests that what matters most in terms of own reform by LDCs is the pursuit of complementary reforms and public investments that enhance the productivity of firms and farmers. Additional trade reforms on their own will not generate significant benefits in terms of poverty reduction (World Bank and IMF, 2005).

Finally, implementation and transition periods also matter, as does the depth and scope of the reforms. Total erosion is unlikely to happen in a short span—and any MFN reforms will be implemented gradually over several years. What follows briefly discusses some recent studies that quantify the potential income effects of preference erosion.<sup>8</sup> Focusing on the LDCs and using a global general equilibrium model and the latest version of the Global Trade Analysis Project (GTAP) database that incorporates data on the major OECD preference programs (Bouet et al. 2004), Francois, Hoekman and Manchin (2005) conclude that complete preference erosion due to MFN reforms in the EU—including in agriculture—would impose a welfare (real income) loss of some $460 million on African LDCs and an additional $100 million on Bangladesh. This assumes away compliance costs. Limão and Olarreaga (2005) also undertake an analysis of the welfare effects of complete preference erosion. They calculate what the income transfer to LDCs would need to be so as to be equivalent to the transfer implied by existing preference programs. They conclude that for LDCs the figure is $266 million. This is a one-year, short-run effect—all else equal the net present value will be several times higher. This brings their results in line with those of Francois, Hoekman and Manchin (2005), although the results are not strictly comparable given that Limão and Olarreaga use partial
# **1. Introduction**

COVID-19 led to strict lockdown measures, which included school closures in most countries. As a result, more than 1.5 billion students were out of school for weeks or months (UNESCO, 2022). The loss of schooling is expected to negatively impact children's cognitive development, even if distance learning modes are enacted. The loss of in-person teaching could also lead to inequality since the only remaining relevant input is parental involvement during school closures (Agostinelli et al., 2022).

Most studies document significant learning loss. In Europe, the average learning loss is almost a quarter of a school year, but the estimates are available mainly for Western European countries (Donnelly and Patrinos, 2021). Worldwide the loss is even greater, especially in lower-income countries (Patrinos et al., 2022). Poland is an interesting case because it represents countries in Eastern Europe where school closures lasted longer, and research on learning loss is scarce.

# **2. Measuring learning loss using PISA as a benchmark**

Most countries do not have standardized assessments that allow comparison of student results over time. To measure learning loss in Poland, we implemented an assessment of students in mathematics, science, and reading, that reports results on a scale comparable to OECD's PISA international study. The results are based on a representative random sample of students in 2021 from grades 10 to 12 and compared to PISA results that are available for a random sample of students from Warsaw collected between 2003 and 2018. In each comparison, the sample sizes are greater than 1,000 students (Jakubowski et al., 2022).

2
declined substantially for adult men (135 calories) and women (86) calories over the 1983-2012 period but by less for children (30). Activity levels for adult men and women fell over 6%, and these decreases were only partly offset by their rising REE. For the 1998-1999 TUS, the decrease in AL is equivalent to about one third of the urban-rural gap for adult men and one half for women.

Although the estimated decline in AL for adult men and women appears plausible and quite large, it is instructive to consider what variables in the NSS-E drive these results. Descriptive statistics for all years using the NSS employment survey are reported in Appendix Table S.5. Agriculture is one of the more physically active industries/occupations and declined considerably as a share of employment, from 60% to 43% as a share of main household industry; however, there was large and partly offsetting increase in construction work, from 3% to 12%, which is also very physically active. Within agriculture, the probability that individuals engage in specific manual labor intensive agricultural task in the previous week has only fallen slightly, e.g. 12% to 9% for harvesting activities, 2% to 1% for plowing. The share of women engaged mostly in domestic duties remains very high. Although some of these duties are very physically active, most have declined only slightly, with the largest effect coming from the decline in the probability of bringing water from outside the household premises from 11% to 5%, smaller effects coming from decreases in food processing and animal husbandry, and no change in the probability of collecting firewood or cooking/heating fuel (around 7% throughout the period). The predictive equations used adopt a more flexible specification, but the linear estimates from Table 2 are also instructive. The 3.3 year increase in years of schooling for adults and the over 50% increase in real per capita expenditure imply only a modest decrease in AL over this period (about 3%
Unfortunately, the paucity of WBES data limits the cases in which firm fixed effects can be applied.

Finally, we confirm that in all cases we get similar results if we use OLS methods instead of tobit and probit. For firm-level analysis, we also confirm that the results remain qualitatively the same if we do not control for the labor productivity measure, which increases the sample size by 15 percent, and if we use a categorical measure of the tax administration obstacle instead of a binary variable.

# **5. Conclusion**

This paper is the first attempt to assess the impact of e-government adoption on public sector capacity cross-nationally. We exploit the new _Global e-Government Systems Database_ , developed by the World Bank, and the measures of tax compliance and public procurement competitiveness from the _Doing Business_ and _Enterprise Survey_ databases. Our results show that e-government can substantially improve government capacity, but the estimated effects vary by the type and functionality of e- government systems adopted, government activity and country context.

E-filing systems can significantly reduce the number of tax payments and the time required to prepare and pay taxes. They can also reduce the likelihood and frequency of visits by tax officials, thereby lowering tax compliance costs. Since these costs are frequently cited as a major impediment of the business climate, investments in e-filing can have potentially significant economic returns. However,

> 15 We estimate all regressions for the ECA region using OLS method.

14
blocks. Randomization was stratified by the 9 sub-districts in Belo Horizonte, and by whether the block had above or below the median firm density (measured by the number of listed informal firms divided by the area of the block).<sup>9</sup> This resulted in 201 blocks being chosen as control blocks, 202 inspector blocks, and 202 communication blocks (Table 1).

# **4.3. Baseline Survey of Control and Communication Blocks**

All of the firms listed in the communication and control blocks were then targeted for a baseline survey which took place between May and August 2011 and was carried out by the same firm (Gauss) as had done the listing. Out of these 5,419 firms, 1,455 were found to be formal (through presentation of

> 8 For each street which formed part of more than one block (144 streets in total), we calculated the median street number in each block. We then took the difference in median street numbers across blocks for each street, and for all blocks where this difference was smaller than 250 street numbers, combined the firms that were on the same street but in different blocks into a new block.

> 9 Since firm density (area of the block) was not known for the blocks that we had reclassified to avoid having neighboring firms in different blocks, we had three strata within each subdistrict: above median density, below median density, and reclassified block.

12
micro levels. The results of this study confirm that firms in Egypt, especially small and medium firms, would benefit from increased access to finance which is likely to support firm investment into productive assets (i.e. physical savings) and stimulate growth.

Based on this analysis, the main policy recommendation from the study reaffirms the importance of improving access to financial services in Egypt. It is recommended to undertake further studies on constraints to financial access and include data on financial savings in future 31
and more recently regression discontinuity design. As in social experiments, both of these “quasi-experimental” methods attempt to equalize the selection bias present in treatment and comparison groups so as to yield unbiased estimates of parameters measuring program impact, such as the average treatment effect, the treatment of the treated effect and the local average treatment effect (Blundell and Costa-Diaz, 2002). One critical question associated with these non-experimental approaches is the extent to which they result in impact estimates that are comparable to those that would be obtained with an experimental approach.

With these considerations in mind, this paper uses a unique data set from rural Mexico collected for the purposes of evaluating the impact of the PROGRESA poverty alleviation program to examine the performance of the Regression Discontinuity Design (RDD). The PROGRESA program and its impact on a variety of welfare indicators have been extensively evaluated using the experimental design of the sample.<sup>2</sup> In this paper, we evaluate a nonexperimental estimator rather than the impact of the program itself, by examining how different impact estimates would be if one were to use the RDD as the estimator for evaluating program impact on two key indicators: child school attendance and child work.

The RDD estimator in the economic literature has been recently used by Van Der Klaauw, 2002; DiNardo and Lee, 2002; Lee, 2001; Black, 1999; Pitt and Khandker, 1998; and Angrist and Lavy, 1999, while the identification and estimation of treatment effects with an RD design are discussed in Hahn, Todd and Van der Klaauw, 2001. In the case of PROGRESA the particular method of selecting households eligible for the program benefits provides the basis for the RD design. As discussed in more detail later, all households within a region are ranked media campaign of opposition parties in Mexico prior to the elections of 2000. 2 Skoufias (2001) provides a detailed discussion of PROGRESA, the evaluation design and the experimental estimates impacts of the program.

2
-Teachers-Non-teachers Pay OMffermnces 17-

These findings are consistent with those of Bourguignon (1986) for Cote d'Ivoire and with those of Psacharopoulos (1987) for Brazil. The conclusion was unchanged when allowance was made for the longer vacation time enjoyed by teachers, for the higher minimum education requirements recently applied to teachers in Cote d'Ivoire, and for the existence of different types of teachers. We also obtained that earnings-experience profiles for non-teachers are steeper than for teachers revealing that possibilities of earnings growth are more available to non-teachers than to teachers. Therefore, much caution should be observed when considering salary policy options in a context of budgetary restri:tions in Cote d'Ivoire.

While our findings could be viewed as supporting a reduction in teachers' base salary and a reallocation within the total compensation package towards in-kind benefits, they do indicate that a cut in total earnings would make the teaching profession less attractive than others, from a strict salary point of view. (6) This conclusion is of course only valid given the current amount of human capital embodied in the average teacher, which as we have seen significantly exceeds that of other professions. The question whether the current entry requirements into the teaching profession in terms of years of education could not be lowered without jeopardizing the quality of teaching is an important topic for further investigation. Since the Cote d'Ivoire Living Standards data collection is ongoing we look forward to investigating this issue further on a larger database. Such analysis will allow.to expand the scope of the present study and to draw specific policy conclusions that were not possible with the current sample.
17 In related research, key informants in each of these same markets were surveyed to test the hypothesis that missing prices are related to seasonal availability, and to estimate the bias measurement error in missing prices would have on a least-cost diet estimate. The nutrientadequate diet was estimated in that study for the case of a woman of reproductive age (Kaiyatsa et al. 2021). Comparing the 2013 – 2017 price data to the survey results, the authors found that 79.1% of the price records were concordant with the reported availability (of which only 6.2% were missing and the missingness was explained by seasonality and the rest had a price record for the markets and months where they were reported to be usually available). Seventeen percent of the price observations were missing when reported available (“discordant missing”) and the remainder (3.9%) had a price observation when reported usually unavailable. The discordant missing prices were not meaningfully explained by food item, time, market, or their interactions. While statistically significant, food item and month only explained a fraction of a percent of the discordant missing prices, and therefore measurement error is a likely explanation. In markets with higher-than-average discordant missing prices (pooling all items and months), the authors estimated the least-cost diet was likely biased upwards by approximately 6.4% (Kaiyatsa et al. 2021). These results lend confidence that even where missing prices are due to measurement error, the magnitude of bias introduced into the cost estimate is reasonably small.

We also remind the reader that as a data envelopment technique, missing or erroneous data would only affect the linear optimization results if that item _would have otherwise been selected into the diet_ were the true data value known. Additionally, where we find solutions to the leastcost diets under the individualized scenario but not under the shared scenario, the difference is due to the nutrient requirement constraints since the input data (prices and nutrient composition)
, overall, the highest occurrence of conflict events. The insurgency, however, has been active elsewhere in the country, particularly in the North Central area. We also find that events related to property, such as theft or the destruction of assets, are more detrimental to food insecurity and consumption than are violent events.

We also were interested in studying mental health as an outcome of conflict because economic well-being and mental health are strongly connected (Alloush 2019). Becoming a victim of crime and violence can lead to trauma. Recent studies have established a link between conflict exposure and mental health (Singhal 2018; Jamison et al. 2018). For our measure of mental health, we use the Center for Epidemiologic Studies Depression Scale (CES-D), which was introduced in wave 3 of the General Household Survey (GHS) (Jamison 4
PDs.

**Table 5: Table matching internal credit ratings and Moody’s ratings**

|**Internal risk ratings **|**Moody's ratings**|**Rating definitions**|
|---|---|---|
|1<br>2<br>3<br>4|Aaa<br>Aa2<br>A2<br>Baa2|Highest credit quality with the smallest degree of risk<br>High credit quality and are subject to low credit risk<br>Upper-medium grade and are subject to moderate credit risk<br>Mediumgrade and are subject to moderate credit risk|
|5<br>6|Ba2<br>B2|Obligations have questionable credit quality<br>Obligations are speculative and subject to high credit risk|
|7<br>8|Caa2<br>Ca|Obligations are of poor standing and subject to very high credit risk<br>Obligations are highly speculative and are usually in default on their obligations|
|9|C|Obligations are lowest rated class and are in default|

_Source: National Treasury of South Africa_ NTSA is currently working on estimating the economic value of guarantees; at the moment, the annual expected losses are calculated for a three‐year period.

Annual expected losses are the product of exposures, PDs, and loss given default (LGD). NTSA is using annual debt service payments to represent exposure,<sup>26</sup> and annual PDs derived from PD tables (such as Table 6). LGDs depend on the ability of the government to recover payments made under a guarantee

> 24 The time horizon chosen will depend on the objective. It may be the entire maturity of the loan or bond guaranteed or a shorter period for fiscal planning.

> 25 In Moody’s default frequency tables, an entity defaulting in year one is no longer represented in the sample of entities in year two (i
Finally, COVID-19 changes the health conditions _H_ . The risk of COVID-19 lowers health conditions at both home and abroad, but is likely to have done so most in Europe, where infection rates are highest.

Taking these pieces together, COVID-19 has lowered earnings opportunities at home, but this is offset by lower earnings opportunities abroad, higher costs of migrating, less ability to pay these costs, and worse health conditions abroad. This leads us to predict that the intention to migrate will fall for migration to both Europe and Senegal. This theory also offers suggestions of which youth will have their intentions most affected. Youth from poorer households are more likely to find the increase in costs and reduction in wealth to make the budget constraint binding. Youth who were never planning on migrating in the first place should only have this intention reinforced, and youth for whom the gain in utility from migrating was extremely high may still find their migration decision unaffected. This appears more likely to occur for Europe as a destination, given the gains from Senegal migration are more modest. The largest change in migration intention should therefore be for youth for whom the initial migration decision is close - e.g., those who are at the margin of wanting to migrate to Europe, or who are considering migrating to Senegal, where the gains are not as large.

# **3. Data and Experimental Methodology**

# **3.1 Setting and Sample**

The Gambia provides a useful setting for studying how migration intentions have changed with the COVID-19 pandemic. Prior to the pandemic, it was the African country with the highest rate of irregular migration to Europe according to Frontex data. This migration typically takes place through what is referred to as “the backway” – an overland journey through West Africa, across the Sahara Desert, into Libya, and then across the Mediterranean to Italy and other European destinations. This is a costly and dangerous journey, but, as noted above, the potential earnings gains if successful are very high
on both the lending and borrowing entity, the size of the loan, the year the loan was agreed and confirmation that the loan had a repayment period beyond a single year.

Our data set is by no means comprehensive, and there are certainly more RBLs which match our criteria but for which insufficient information was readily accessible. The data set, and therefore the analysis, is limited by the information that is publicly available. As described further in Section 5b, the RBL landscape remains largely opaque, with limited information publicly available about the terms, and at times even the existence, of RBLs.

# 4. Results: Key characteristics of RBLs

## a. Overview of the RBLs analyzed

Our research identified 30 RBLs in Africa signed between 2004 and 2018. Included in our review were 11 African countries which borrowed RBLs during the period: Angola, Chad, the Democratic Republic of Congo (DRC), Ghana, Guinea, Niger, the Republic of Congo, São Tomé and Príncipe, South Sudan, Sudan, and Zimbabwe. The relevant RBLs totaled USD$ 46.5 billion agreed in this period, of which Angola accounted for nearly half the total.6, 7

> 5 <u>http://ResourceContracts.org</u>

> 6 For the purpose of this paper, commitment is defined as amounts committed under signed loan agreements. In the cases of large credit lines that support multiple projects, disbursed amounts are used when available.

> 7 All dollar signs ($) refer to United States dollars (USD) in this paper unless otherwise specified.

5
The resulting measure of expected school years approximates the number of years of school that a child can expect to attain by her 18<sup>th</sup> birthday if she starts school at age 4, for a maximum of 14 years. Data on this measure of expected years of school are reported in the top panel of **Figure 2** .

Conceptually this calculation corresponds to the measure of “school life expectancy” (SLE) calculated by UNESCO Institute for Statistics.<sup>6</sup> However, the implementation here differs in that UIS uses gross enrollment rates to calculate school life expectancy, whereas here total net enrollment rates are used wherever possible. This reflects a tradeoff. On the one hand, gross enrollment rates are more widely available and typically have longer time‐series coverage in the UIS data. On the other hand, total net enrollment rates conceptually correspond more closely to the age‐specific enrollment rates in Equation (5). A further reason to use total net enrollment rates is that for some countries the repetition‐adjusted gross enrollment rates reported by UNESCO are – sometimes implausibly – well above 100 percent. While gross enrollment rates (the number of students enrolled at a given grade level as a fraction of children of the theoretical age for that grade level) can in principle exceed 100 percent if some children start school early or late, it is difficult for this timing effect to generate gross enrollment rates that are persistently much above 100 percent.<sup>7</sup> A practical consequence of using gross enrollment rates above 100 percent to calculate SLE is that SLE can then exceed the statutory duration of school. This is the case for primary and secondary SLE as reported by UIS for about one‐quarter of 6 When more granular data such as enrollment rates by age are available, UNESCO uses them to calculate SLE. When they are not, UNESCO calculates SLE using school level‐specific gross enrollment rates (which are the most widely available in