rafmacalaba/datause-encoder-data huggingface dataset:
with more than 100 employees, bribes as a percentage of the output of small plants and big plants, among the plants who paid bribes, and bribes as a percentage of the total output of small plants and big plants regardless of bribes being paid. I use the average of the 2009-2014 data for the plant size distribution moments and I use the 2013 WBES for bribery moments. Tables 6 and 7 summarize the parameter values and the calibration performance.

The assumed distribution function is able to generate the U.S. plant size distribution. Moreover, although there is some room for improvement with more data and careful parametrization, the model is able to generate not only the plant size distribution of Türkiye but also the relative bribery payments of different size groups. The estimated average effective tax rates for small and big plants are 28.3% which implies that bribing is optimal for all plants at the Türkiye’s benchmark calibration. Even if one cannot directly relate the fraction of the plants that accept that they paid bribes in the data and the probability of being assigned to the corrupt official in the model, the inferred probabilities are different from the fractions that are calculated from the Enterprise Survey data.

# **6 Results of the Experiments**

In this section, the results of the experiments are presented. First, I introduce the size dependent distortions and the bribery opportunities to the U.S. economy. Then I run experiments with Türkiye’s economy, which is calibrated with the size-dependent distortions and the bribery opportunities. These involve analyzing the effects of changing the size dependency of the distortions, the return on bribes, and the probability of meeting with a corrupt official. Then I discuss the consequences of removing bribery and distortions. I compare the new steady-state values of the output, mean size, the employment share of big enterprises, the tax wedge, average effective tax rate, and bribery expenditure with the benchmark steady-state values. Specifically, the tax wedge is defined as following: where _y_ ̄ and _b_<sup> ̄</sup> indicate average values of output and bribe, respectively. It was first defined by Guner _et al._ (2015) and has the following interpretation: The tax wedge compares the distortion rate for the manager who produces 5 times the average output with the distortion rate for the manager who produces the average output level. If the distortions are the same for all managers ( _τ =_ 0), the tax wedge is equal to one. Also, the tax wedge decreases with the 14
**Table 4. Contextual continuous variables by student gender, TIMSS 2019**

||**Grade 4**|**Grade 8**||
|---|---|---|---|
||**Male**<br>**Female**<br>**_d_**|**Male**<br>**Female**|**_d_**|
|**Student-level variables**|<br>**_m_**<br>**_SD min max m_**<br>**_SD min max_**|**_m_**<br>**_SD min max m_**<br>**_SD min max_**||
|_Home resources for learning (grade 4)_|9.3 1.41 3.8 14.9 9.5 1.34 3.8 14.9 0.109|||
|_Home educational resources (grade 8)_||9.4 1.59 4.6 13.5 9.5 1.51 4.6 13.5|0.058|
|_Literacy and numeracy readiness for school_|10.1 2.01 3.1 14.6 10.5 1.91 3.1 14.6 0.235***|||
|_Student likes learning mathematics_|10.3 2.03 3.9 13.1 11.3 1.88 3.9 13.1 0.501***|10.3 1.97 5.1 13.9 10.0 2.08 5.1 13.9|0.143**|
|_Student likes learning science_|10.0 2.33 2.7 13.2 11.3 2.15 2.7 13.2 0.575***|10.4 2.09 3.9 13.5 10.7 2.15 3.9 13.5|0.146*|
|_Student confident in mathematics_|10.1 2.08 2.8 14.4 11.1 2.17 2.8 14.4 0.470***|10.5 1.88 3.3 15.9 10.5 2.01 3.3 15.9|0.010|
|_Student confident in science_|10.0 2.01 3.4 13.3 11.0 2.05 3.4 13.3 0.517***|10.5 1.92 3.3 14.8 10.9 2.02 3.5 14.8|0.223***|
|_Student sense of school belonging_|10.0 2.33 3.1 12.8 11.1 2.01 3.1 12.8 0.533***|10.2 2.09 3.9 13.3 10.3 1.92 3.9 13.3|0.075|
|_Bullying_|8.9 2.25 2.9 12.7 10.2 2.14 2.9 12.7 0.619***|9.8 2.35 2.0 12.9 10.6 1.97 3.6 12.9|0.360***|
|**Class/school-level variables**||||
|_School mean of home resources for learning_<br>_(grade 4)_|9.2 0.83 6.3 11.3 9.5 0.68 7.2 11.3 0.409**|||
|_School mean of home educational resources_<br>_(grade 8)_||9.4 0.69 7.9 11.4 9.5 0.67 8.3 11.6|0.132|
|_Safe and orderly schools (mathematics teacher)_|10.8 1.83 5.8 13.4 11.9 1.71 7.5 13.4 0.644***|11.2 1.96 7.2 13.9 11.9 1.92 6.3 13.9|0.376*|
|_Safe and orderly schools (science teacher)_|10.9 2.10 5.1 13.4 11.7 1.95 3.9 13.4 0.390*|10.9 1.88 6.3 13.9 11.7 2.11 4.9 13.9|0.410**|
|_School emphasis on academic success_|10.8 2.12 4.2 16.4 11.8 2.14 7.2 16.4 0.446**|10.8 1.89 6.7 16.4 11.5 2.03 5.8 16.4|0.337*|
|_School discipline_|9.5 2.40 3.7 12.8 10.6 2.15 3.7 12.8 0.443*|10.0 2.68 4.1 14.0 11.6 2.37 4.3 14.0|0.616**|
|_Teacher years of experience (mathematics_<br>_teacher)_|15.9 8.43 0.0 31.0 14.4 7.70 0.0 33.0 0.183|10.6 7.29 0.0 30.0 12.2 7.27 1.0 30.0|0.216|
|_Teacher years of experience (science teacher)_|15.3 9.36 0.0 36.0 12.8 7.86 0.0 31.0 0.295|13.1 7.43 0.0 36.0 12.6 7.53 0.0 30.0|0.066|
|_Teacher job satisfaction (mathematics teacher)_|10.6 1.36 6.4 11.7 11.2 0.87 8.3 11.7 0.569***|10.7 1.50 5.3 11.8 11.3 0.74 7.2 11.8|0.465**|
|_Teacher job satisfaction (science teacher)_|10.6 1.50 5.8 11.7 11.3 0.88 6.9 11.7 0.553***|10.5 1.65 5.6 11.8 11.5 0.64 8.5 11.8|0.783***|
|_Principalyears of experience_|9.9 6.65 0.0 30.0 8.6 7.09 0.0 33.0 0.185|9.4 7.56 0.0 32.0 8.6 8.30 0.0 39.0|0.102|

_Note:_ * _p_ < .05; ** _p_ < .01; *** _p_ < .001.

14
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# **4 Data**

# **_4.1 Data collection tools, training, and fieldwork_**

The data collection in Egyptian Kindergartens was designed to further two goals: (1) to do the first ever in-depth diagnosis of the quality of teaching and learning in Kindergartens, and (2) to pilot and validate a nimble monitoring tool that Egypt’s Ministry of Education can afford to administer periodically as part of its QAS. There were thus two overlapping data collection efforts, what we refer to as “the diagnostic research tool” and “the QAS tool.”

# _4.1.1 The diagnostic research tool_

The diagnostic research tool used as its starting point the Measuring Early Learning Quality Outcomes (MELQO) tools (UNESCO 2017). The MELQO tools were developed in order to measure child development and quality of early childhood education in LMICs (Raikes et al. 2019).<sup>5</sup> These tools were locally adapted to the Egyptian context, including to reflect the Egyptian KG curriculum. The curriculum had recently been updated as part of the “Education 2.0” reforms (Moustafa et al. 2022). There are two main parts to the MELQO tools: (1) the Measure of Early Development and Learning (MODEL) measures the development of children aged 3-6, and (2) the Measure of Early Learning Environments (MELE) measures learning environments and their quality.

For this study, we use MODEL data collected through a child direct assessment, a parent report of child development (which includes family background), and a teacher report of child development. We use MELE data collected through classroom observation, a teacher interview, and the school director interview.

# _4.1.2 The QAS tool_

The starting point for Egypt’s KG QAS tool was the Brief Early Childhood Quality Inventory (BEQI).<sup>6</sup> The BEQI was built on the MELQO tools, but was designed to be a simpler tool than MELQO, integrated into monitoring systems or used for formative assessment (ECD Measure 2022). As an example of simplification, items that were measured on a four-point scale in MELQO were typically transformed to yes/no for the BEQI. Some items, however, were identical across the two tools. The BEQI was adapted to the Egyptian context, including Egypt’s recently adopted KG quality standards (see Appendix 1), as discussed in more detail below. A summary of the features of the diagnostic research tool and QAS tool is provided in Appendix 2.

# _4.1.3 Tool adaptation_

The BEQI and MELQO tools were adapted to the Egyptian context and curriculum in collaboration with the MoETE, Kindergarten teachers, and Kindergarten supervisors. Both tools were adapted in a May 2019 workshop. For data collection, tools were programmed into Android

> 5 The MELQO tools have been used and validated in other LMIC contexts (Raikes et al. 2020).

> 6 The BEQI has been used in other country contexts as well (Raikes et al. 2023).

8
# **5.1 Kenya**

Table 5 and Figure 10 show the county-level GDP estimates for Kenya. Based on specification (1), we estimate total 2013 GDP for Kenya at $26.8 billion (2005 USD), close to its actual level of $26.9 billion (WDI, 2014). On county-level, we find that Nairobi had the highest overall GDP in 2013 ($3.4 billion), followed by Kiambu ($3.0 billion), Nakuru ($2.3 billion), Nyeri ($1 billion), and Kilifi ($1 billion). Counties with the lowest GDP are the sparsely populated counties of Isiolo ($56 million), Lamu ($58 million), Samburu ($67 million), Elgeyo Marakwet ($108 million), and Tharaka Nithi ($109 million). Nairobi has the highest contribution to national GDP (13 percent), followed by Kiambu (11 percent), Nakuru (9 percent) and Nyeri (4 percent – see Figure 9).

When we control for population by looking at GDP per capita, there are substantial changes (Figure 11). Kiambu is the best-off county, with a per capita GDP of $1,785 (2005 USD), followed by Nyeri ($1,503), Kajiado (1,466), Nakuru ($1,413), and Kwale ($1,406). Due to its large population, Nairobi does not make it to the top five with an estimated per capita GDP of $1,081 (ranked 8<sup>th</sup> among the 47 counties, one spot in front of Mombasa). Note that Nairobi and Mombasa have zero agricultural GDP because the 2009 census classified these counties as 100 percent urban. This may underestimate their GDP if agricultural activity does take place in these counties.

17
The _no dependents_ group tends to be richer than average and when married or cohabitating (more frequent than single adults with no dependents), the primary woman is less likely (on average) to be working. _Single adults with dependents_ are also richer than average, and single women with a dependent are far more likely to work. _Married couples with dependents_ have younger children while _married couples with other adults have older children;_ both groups are poorer than average.

The “fiscal” subcategories (Figure 4) consider the same household demographic dimensions, but each dimension is considered from a different perspective:

- Children matter: the amount of education benefits received will depend upon how many children are in the household and whether they are enrolled in a public school (without concern for the child’s age).

- Elderly matters: the amount of pension benefits will depend upon how many elderly members there are in the household and whether they are receiving a public pension.

- The working status of married couples matters: the relative value of dependent-based income tax deduction will depend upon whether both, one, or neither of a married couple are working.

Figure 4. Fiscal Categories in Jordan <!-- Start of picture text --> No workers, 1<br><!-- End of picture text --> <!-- Start of picture text --> No workers, 1<br>children in school<br>14<br>35 No workers, 1<br>23<br>Children in school 4<br>11<br>Pensioners 3<br>28<br>no dependents 2<br>1 no workers: no workers: 3 pensioners include: pensioners include: 4 children in school children in school<br>Single head of No children No worker includes:<br>household is not in school One worker<br>working workers • +1 children<br>public pension in school,<br>couple is working 1+ pensioner<br><!-- End of picture text --> <!-- Start of picture text --> 1 no workers: no workers:<br>Single head of<br>household is not<br>working<br>couple is working<br><!-- End of picture text --> <!-- Start of picture text --> No children<br>in school<br>public pension<br><!-- End of picture text --> <!-- Start of picture text --> 3 pensioners include: pensioners include:<br>No worker<br>One worker<br> workers<br><!-- End of picture text --> <!-- Start of picture text --> 4 children in school children in school<br>includes:<br>• +1 children<br>in school,<br>1+ pensioner<br><!-- End of picture text --> _Source:_ Rodriguez, Wai-Poi, and Woodham (2022).

24
Policy Research Working Paper 7472

# **Abstract**

Hygiene information and practices play a critical role in preventing diseases, particularly among children. Hygiene behaviors practiced in the household have been linked to development outcomes such as socio-emotional skills. This paper exploits data from impact evaluation surveys of a hygiene information campaign conducted in Senegal, where the randomized design suffered from contamination between comparison groups. The variations in exposure and intensity to hygiene information campaigns captured in the surveys were used to understand contamination biases. Such variations were interacted with the presence of household communication assets to explore potential effects on children’s socio-emotional scores. In the presence of contamination biases, the study exploited the longitudinal sample of children in the surveys to reduce time-dependent biases. For robustness, statistical matching was applied between the impact evaluation surveys and Demographic and Health

- Surveys conducted in 2008 and 2011. Socio-emotional out comes were the imputed into Demographic and Health surveys to expand sample sizes. By applying matching techniques and imputing outcomes into a larger sample, impacts were non-negligible. Double-difference estimates showed that children’s socio-emotional scores were higher when intervention status was interacted with the presence of communication assets within households. Without the presence of communication assets in the households the impacts were close to zero. Evaluating the effect of hygiene campaigns on children’s socio-emotional skills is challenging because of the biases from contamination that exist when information flows between comparison groups. Targeted hygiene information to the poorest households is relevant for reducing risks of recurrent infections and enables better conditions for socio-emotional development of children.

This paper is a product of the Water Global Practice 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 cborjavega@ 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
so.

fiscal policies. **_£_** Finally, there is the cash or observed deficit which is pacticularly useful when tracing the movement of financial resources between the central government and the rest of the er.onomy. 'The view is now widely held that the ogeradjonalg deficit best reflects the abso.pdion of real resources by a government, and thus is the most appropriate measure of fiscal plicy within the famework of stabilization and adjustment." # 6. The analysis which follows Is anchored in the govemnment budget identity which acknowledges that the sum of the real non-interest defcit (d), including the real inerest due on domestic debt (ib) and foreign debt ([be), is equal to the sum of financing from all soum es. Financing could take the form of real external debt (b'), domestic debt (b) or central bank credit to the government (dc). Formally, where i is the real domestic intarest rate, i^ is the real foreign interest rate, e is the real exchange rate, and " ' indicates the change in the specified variable.

7. Eq. (1) would come closer to reality if it were to include deficit financing which may not be reflected in the account of the central government. It is conceivable, for istance, that some foreign debts of the central government could be handled by the central bank without being adequately recorded in the budget. To cover such cases, consider a simplified balance sheet of a central bank. The assets therein are real credit to the central goverment, dc, and real net foreign assets (valued in local currency), nfae. The liabilities consist of currency held by the public, Cu, and commercial bank reserves, RR, so that: M/P **=** (Cu + RR)IP o where P is the domsstic price level and M/P is the real value of the central bank's nominal base money or the central bank's net liability to the p,ivate sector. The balancing item is assumed to be nw, the real net worth of the central bank, where: Eq. (3) indicates that the net worth and base money are used, in this illustration, to extend credit to the central government and to obtain foreign assets. Intuitively,:

# nw ifwe ... (4)

that is, that the real value of interest earnings on foreign assets Increases the central bank's real net woeth.

S. Eq. I can now be rewritten to reflect the inc!usion of the central bank in the budget constraint:

**_I/_** Tis claim makes th simplisic aumpion tha no **idtrs** payments oa deb conttd during the ourent period all due within dha patiod.

**I/ Catamba at i.** (t989). p. **4.**
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Policy Research Working Paper 6745

# **Abstract**

This paper presents a systematic and comprehensive meta-analysis of the literature on financial education interventions. The analysis focuses on financial education studies designed to strengthen the financial knowledge and behaviors of consumers. The analysis identifies 188 papers and articles that present impact results of interventions designed to increase consumers’ financial knowledge (financial literacy) or skills, attitudes, and behaviors (financial capability). These papers are diverse across a number of dimensions, including objectives of the program intervention, expected outcomes, intensity and duration of the intervention, delivery channel used, and type of population targeted. However, there are a few key outcome indicators where a subset of papers are comparable, including those that address savings behavior, defaults on loans, and financial skills, such as record keeping. The results from the meta analysis indicate that financial literacy and capability interventions can have a positive impact in some areas (increasing savings and promoting financial skills such as record keeping) but not in others (credit default).

This paper is a product of the Finance and Private Sector Development Team, Development Research Group; and the Financial Inclusion and Infrastructure Practice, Financial and Private Sector Development. 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 corresponding authors may be contacted at mmiller5@worldbank.org and bzia@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
26

# III. EXPERIENCE OF BRADY DEALS

We now discuss the five most recent menu-based deals: Mexico,<sup>Philippines, Costa Rica,</sup> Venezuela, and Uruguay. The agreements are described in Annex<sup>table 1 (more detail can be</sup> found in World Bank 1990 and 1991). Negotiations were<sup>relatively slow in Mexico, the</sup> Philippines and Costa Rica. In each of the three cases, the approach<sup>followed was an innovation</sup> relative to previous deals. Mexico's strategy was to spread the needed<sup>debt reduction among a</sup> large group of banks, to develop a simple menu, and to work hard<sup>on contractual issues--such</sup> as the novation clause--in order to produce marketable securities.<sup>The lack of a buyback option</sup> in the Mexico deal was due to the fact that many of the smaller banks had<sup>already exited in</sup> previous deals. The Philippines negotiations proceeded in<sup>parallel, but the approach followed</sup> was rather different. The main goal there seems to have<sup>been the exit of a large fringe of small</sup> banks, with the expectation that a sizable amount of new money<sup>could be extracted from the</sup> remaining large banks who could (for strategic or financial reasons) afford<sup>to take a longer view.</sup> The Costa Rica negotiations were also slowed down by the search for<sup>innovative solutions. The</sup> main problem there was how to treat arrears in the context<sup>of a menu deal. On the other hand,</sup> the negotiations proceeded quite briskly for Venezuela<sup>and Uruguay, where the degree of</sup> innovation was modest.

# _ne extent_ **_of_** _debt reduction_

Table 1 reports for each of the countries the eligible<sup>commercial and total debt before</sup> the exchange. In all cases, non-commercial claims, short<sup>term debt as well as bonds were left</sup> out from the eligible base. While the official sector shared<sup>in the debt relief burden by</sup> providing loans or reschedulings, short term debt and<sup>bonds were not subjected to rescheduiings</sup>