rafmacalaba/datause-encoder-data huggingface dataset:
Table 4 shows the value of the traffic guarantees and the disbursements in terms of the UF, a constant value unit of account whose value ranged from USD 38-44 during the period.<sup>42</sup> We also show the values as a percentage of GDP.

The first two columns show the actual payments to PPPs from contingent debt. On average they add to 0.01% of GDP, with a standard deviation of 0.0044%; the value corresponding to 2019 was USD 63MM. The third and fourth columns are derived from a model developed by the Ministry of Finance and the World Bank and represent the expected value of guarantees.<sup>43</sup> These values are small, with a maximum value of about USD 600MM, or 0.21% of PGB, with an average of 0.18% of PGB and a standard deviation of 0.04%. The last two columns of the table represent the cost of the guarantees if there were no revenue at all from PPPs: no traffic in highways and no demand for airport services. This is the maximum possible exposure of the guarantees, and the payments would be spread over the term of the PPPs.

As we can see from Table 4, traffic guarantees in PPPs were not an important source of fiscal risk in Chile. There were several reasons for this. First, it was a period of high economic growth, so traffic volumes grew fast in most projects. Second, from around 2007 onwards, most projects were of the PVR type and did not require guarantees. Third, as the projects age, the number of remaining years in which guarantees might be paid gets smaller.

The risk and outlays associated to renegotiations were much larger, but these were never included in the Contingency report, presumably due to the difficulties in estimation. According to David Duarte (personal communication), the Budget Office used the rule of thumb that it would have to pay 20% of the value of the project in renegotiations, at least until the 2010 reform of the statute. Indeed, in some years renegotiated amounts equal the stock of contingent guarantees and until 2012 were often 10 times the size of annual outflows from guarantees. Since the proceedings in renegotiation agreements are very bureaucratic, there is a lag of 2-3 years between the initial renegotiation and the time when the final amount is determined. As we will see in what follows, three years after the law was changed in 2010, the amounts renegotiated decreased substantially.

> 42 We use the UF because it does not suffer from currency gyrations and is constant in terms of buying value in Chile.

> 43 The model makes predictions of future traffic flows given growth expectations, growth volatility and the correlation between GDP and traffic flows. It then uses Montercarlo simulations to estimate the probability distribution of payments. The associated predictions of revenues are adjusted by risk, leading to guarantee payments adjusted by risk. These are then discounted at the risk-free rate to obtain the value for the guarantees. The calculations include the fact that if revenues grew beyond predetermined bounds, the state would receive a payment from the private party.

20
**ENUMERATOR:**

**IF C2_emptype1=1, 2, 3, 4, 5, 6 OR 7 (THAT IS, IF THE RESONDENT INDICATED THAT CURRENT JOB IS A PAID JOB OR APPRETICESHIP WORKING FOR SOMEONE ELSE), FILL OUT C14-C22 FOR CURRENT JOB.**

**IF C2_emptype1=8, 9, 10 OR 11 (THAT IS, IF THE RESONDENT INDICATED THAT CURRENT JOB IS IN SELF-EMPLOYMENT OR AS A PAID OR UNPAID FAMILY WORKER IN A HOUSEHOLD BUSINESS), GO TO C24 FOR CURRENT JOB.**

C14a_contract1 Do you have a job contract or formal letter of agreement in this MAIN job?

1. Yes, permanent contract à **Go to C15a** 2. Yes, contract that is not permanent à **Go to C14c** 3. No 888. Don’t know 999. Refused

C14b_verbal1 Do you have a verbal agreement about terms and conditions in this MAIN job?

1. Yes, verbal agreement that includes permanent appointment à **Go to C15a**

2. Yes, verbal agreement that does not include permanent appointment à **Go to C14c** 3. No 888. Don’t know 999. Refused C14c_contractlength1 What is the length of your contract or verbal agreement?

**[If length not specified in contract or verbal agreement, put “Not specified” and go to C15a] [If don’t know then put “Don’t Know” and go to C15a] [If refused then put “Refused” and go to C15a]**

C14d_contractunit1

**[Specify unit in which length of contract or agreement is given]**

1. days

2. weeks 3. months 4. years 888. Don’t know 999. Refused

**Go to** à **C15a**

5
|**Country**|**Intervention**|**Method**|**Test**|**Effect**<br>**Sizes**|**Reference**|
|---|---|---|---|---|---|
||Synthetic Phonics: decoding and||Letter matching test|0.22||
||<br>synthetic phonics pedagogy + teacher<br>||Sound blending word test|0.72||
||training||Dictation|0.29||
|India|The Shishuvachan programme: reading<br>classes (teacher training) and provision of<br>a library|RCT|**Normalised reading level**<br>**(Hindi, Marathi, Urdu**)|**0.13**|He, Linden and Macleod,<br>2009|
||||English letter knowledge|‐0.02||
||||**Swahili passage reading**<br>**fluency (words per minute**)|**0.13**||
|Kenya|Health and Literacy Intervention (HALI):<br>teaching manuals and training|RCT|**Swahili passage reading**<br>**comprehension**|**0.11**|Jukes and Dubeck, 2015|
||||Swahili letter sounds|0.33||
||||Swahili word identification<br>(words per minute)|0.15||
||Reading to Learn (RtL): teacher<br>dhl ldhi||**Written literacy exam**|**0.02**|l|
|Kenya|prepareness,scoo eaersp,<br>classroom learning environments|RCT|Oral literacy exam|0.05|Lucas et a. , 2014|
||||Kiswahili letter sound fluency|0.76||
|Kenya|The Primary Math and Reading (PRIMR)<br>Rural Expansion Programme|RCT|Kiswahili syllable fluency|0.31|RTI International, 2015|
||||Kiswahili decoding fluency|0.19||

29
**Reducing Regulatory Barriers to Private-Sector Participation in Latin America's Water and Sanitation Services**

<!-- Start of picture text --> Barbara Richar4&ck L,,,L<br>Thelma.--T.riche 4* for e<br>A S EP 0 O i)9,<br><!-- End of picture text --> _Report of a study carried out by_ _Apogee Research, Incorporated,_ _under contract to_

> _The Water and Sanitation Division Transport, Water, and Urban Development Department The World Bank_
Living Standards Survey (VHLSSs), while data from India are synthetic panels that are constructed from cross sections of the National Sample Surveys (NSSs). To maximize comparability across countries, we focus on the same time period for all three countries: 2004-2008 for the US and Vietnam; and 2004/05- 2009/10 for India.<sup>6</sup> Detailed calculations and estimation results are drawn from two companions to the present paper (Dang and Lanjouw, 2014, 2015).

This note consists of four sections. We briefly review the growth experience for India, the US, and Vietnam in the next section, before delving into the analysis of shared prosperity in Section III. Section IV concludes with further thoughts on research directions.

# **II. Overview on Growth Experience of Three Countries**

Table 1 provides two measures of economic growth, one is growth in GDP per capita, and the other is growth in household consumption per capita (total household income for the US). The latter measure is shown both for the whole population and the bottom 40 percent. While these measures can be qualitatively similar, they can offer different estimates of the speed of growth (e.g., Deaton, 2005). Thus combining the two can provide a more comprehensive picture. Indeed, Table 1 shows that growth rates are different, depending on whether the first or the second measure is employed.

A couple of observations are in order for this table. First, India had the largest annual growth rate for GDP per capita, which is then followed by Vietnam and the US for the considered periods. This order is, however, different when growth is defined in terms of household consumption. By this measure, overall growth rates in India and Vietnam were the same, and were higher than in the US. Second, while GDP per capita for the US had positive growth over this time period, surveybased income growth was even negative for the US. This suggests that this economy might in fact

> 6 We use the 2007 and 2009 rounds of the PSID, whose income data are for the previous tax year. Thus we refer to these two rounds by the tax year for convenience.

6
23 Table 4t Correlation between Capital Intensity of Product Groups and their Share in each Supplier's Total Exports to the EC during 1985-1989: Pearson Correlation Coefficients (***, ** and * denote, respectively, significance at 1%, 10% and 25% levels)

|Yugoslavia|0.74***|Czechoslovakia|-0.08|
|---|---|---|---|
|Romania|0.55***|Macau|-0.11|
|Poland|0.55*I*|Korea|-0.13|
|Hungary|0.38**|Taiwan (China)|-0.21*|
|Singapore|0.19|Philippines|-0.24|
|Bulgaria|0.16|China|-0.27*|
|Malaysia|0.07|India|-0.30|
|Hong Kong|0.06|Brazil|-0.40|
|||Thailand|-0.47**|
|||Sri Lanka|-0.74*|

Sources: See Figure 2.

Note: The number of MFA product groups subject to quotas in which each supplier had exports to the EC varied considerably. Peru was excluded from this correlation analysis as it had only two product groups subject to quotas.

# Degree of Specialization

There were 19 suppliers whose shipments subject to MFA quotas accounted for about 50 percent or more of their total exports to the EC. Based on this, we constructed an ad hoc concentration index reflecting the exporters' **degree** of product specialization. We first ranked (in descending order of<sup>their</sup> value), then counted the number of MFA product groups which accounted for 50 percent of each supplier's total exports of textiles and clothing to the EC (during 1985-89).

In Figure 3, the horizontal axis depicts the values of this **index** while the per capita GNPe of the exporters are measured along the vertical axis. The highest income East Asian exporters are in the upper left corner of the diagram with index values of 3 for Singapore and Macau, and 5 for Hong Kong, showing an extremely high degree of specialization. The other two high income
months of fieldwork, only about one‐half of the intended sample of households was interviewed. While the disruptions caused by the conflict have had impact on the data collected, consistency checks suggest that this impact was relatively small. In addition, any introduced sample selection bias due to the conflict is likely to be a downward bias leading to under‐estimation of poverty.

The HFS presents a rare data point in a fragile setting. Only very few similar surveys have managed to collect comprehensive data on welfare and livelihoods in such a complicated and volatile context. Indeed, the HFS documents some staggering levels of deprivation, which are also corroborated by accounts from a multitude of organizations operating in the country. The methodology employed to estimate poverty in the HFS is based on the best available methodologies specifically adapted to the context of fragility. The estimation is also entirely reproducible through the publicly available code and data published in the World Bank MicroData Library.<sup>38</sup> Overall, the HFS provides an extremely detailed picture of welfare and livelihoods for the South Sudanese population between 2015 and 2017. This is especially true when combined with the other three waves conducted between 2015 and 2017, as in the South Sudan Poverty Assessment (World Bank, 2018).

Finally, the satellite imputation, although limited in scope and means, provides an additional glimpse of livelihoods across the country. Although the results are only a proof‐of‐concept, it remains a useful exercise to complement the survey‐based data rather than assuming a national average for inaccessible areas. Much research has already gone into the field of small area estimation, which is likely to benefit enormously from the recent availability of cheaper and more encompassing – geospatial – data sets. Although such models are not likely to replace survey data, as these are needed to train the models, they can be used to supplement data collection and provide information either at more frequent intervals or for hard‐to‐reach areas. One particular area for future research that might be especially relevant would be to explore how such sources of data can be leveraged to estimate outcomes during rapidly evolving and dynamic events, exactly when representative surveys and other traditional data collection exercises are especially difficult to implement.

> 38 See: http://microdata.worldbank.org/index.php/catalog/2914

22
The disruption and very large social costs witnessed in the aftermath of economic crises have highlighted the need to address the social vulnerability exposed **by** the crisis. Without such attention, the consequence of the crisis would be excessively large adjustment costs, especially for the poor and the vulnerable segments of society with long-term, and often irreversible impacts such as the destruction of human capital, of social institutions, and of political stability. Reduction of social costs and an increase in resilience to crisis require a broad approach in developing countries, spanning the creation of market insurance against unemployment, self-insurance (for example, through protections to individual savings of the poor), to self-protection against becoming poor (e.g., health insurance and increased education) (see de Ferranti, et al, 2000 and World Bank, _WDR,_ 2000c). The most important step that each developing country can take is to establish an institutional framework to minimize the risks and severity of economic downturns at times of crises, and then respond to the particular needs of the poor during cnses.

# Policies include:

- Better information on the poor and their exposure to economic risk;

- Targeting of scarce fiscal subsidies on programs for the poor, especially during crisis;

- Improving evaluation of spending programs to ensure a positive pro-poor focus, particularly in education; and

- Automatic programs-such as unemployment insurance or stay in school programs-

- which provide more predictable institutional responses to economic downturn, regardless of cause.

43
39

# Sex Differences in Mathematics Achievement

The investigation of gender differences in mathematics achievement is a popular research topic. Such research has generally concluded that secondary-level females score considerably below their male counterparts in achievement in mathematics, while few differences are observed before Grade 8.L/ Explanations for these differences range from genetic to environmental.AI Environmental explanations include differential coursetaking in mathematics among college-bound high school students, differential within-class socialization experiences, and differential attitudes toward the usefulness and gender-appropriateness of mathernatics. Genetic explanations center on spatial visualization "deficits" in females.

Almost all of the research on gender and mathematics has centered on American students, however. At the very least, research on the topic has been confined to developed countries.46/ However, in this study we have found that girls outscore boys in mathematical achievement in Nigerian schools, and a similar study using IEA mathematics achievement data in Thailand showed females' mathematics achievement superior to that of males before statistical adjustment.AL After statistical adjustment for the differences between the types of students who attend different types of Nigerian schools (Table 3), there remained only a small and statistically non-significant sex difference in mathematics achievement favoring males. This is particularly noteworthy, since the average age of these Nigerian sttdents (between 15 and 17 years old) is about the same at which the maximum sex differences in mathematics achievement are seen in American adolescents.

Lockheed and her colleauges have argued that one important feature of American pre-secondary education, in comparison with secondary and
not collected on these people. This can introduce a significant selection bias, which complicates the estimation of the treatment effect even for studies which start out with experimental data.<sup>4</sup> In our Malawi study, we experience similarly high rates of drop-out among youth randomly assigned to receive training, but we anticipated the problem and tracked down a significant fraction of the drop-outs in our follow-up surveys. This allows us to report intent-totreat and treatment-on-treated results (accounting for the drop-out decision) that are closer to the pure experimental estimates. The main effects if training we report – which are large, significant increases in the self-reported skills and knowledge that the training was meant to impart, and improvements in trainees’ subjective measures of well-being - are not sensitive to the way dropouts are handled.

More importantly, we conduct a detailed analysis of _why_ trainees chose to drop out. Since dropping out is a commonly observed phenomenon across such programs in both developing and developed nations, it is useful to identify its causes and consequences, in order to better understand the direction of bias in existing evaluations of training programs stemming from this specific source of attrition. Our data allow us to characterize whether drop-outs in training are positively or negatively selected. Why potential participants drop out and do not take advantage of a program designed to build their human capital, and offered to them at deeply discounted rates (or even for free) is an important puzzle worth exploring.<sup>5</sup> We collect data on alternative opportunities and unanticipated shocks around the time of program inception to understand better the conditions under which people choose to not partake in training.

These opportunities and constraints happen to affect men and women differently, which leads to the third main contribution of this paper: developing a better understanding of why training and other development programs may have heterogeneous effects across gender. A growing literature documents differential treatment effects by gender (e.g. see Bergemann and van den Berg 2008; Attanasio, Kugler, and Meghir 2011; Blattman, Fiala, and Martinez 2012).

> 4 A large body of literature has devised non-experimental methods to correct for these biases {{1225 Lee,David S. 2009;1147 Heckman,James J. 2000;1302 Heckman,James J. 1999;1199 Manski,Charles F. 1989/psee: ;1213 Horowitz,Joel L. 2000;1211 Manski,Charles F. 1990;1198 Horowitz,Joel L. 1998; 1312 DiNardo,John 2006}}.

> 5 This is related to a literature that identifies technologies designed to meet pressing needs are often not adopted at rates commensurate with their potential benefits {{965 Miller,Grant 2012; 162 Meredith, Jennifer 2011}}. The case of training appears related to a literature that suggests that the low take-up may signal that the product or service does not work as well as anticipated, given local conditions. {{165 Miguel, Edward 2004/pe.g. see ;1153 Mobarak,Ahmed Mushfiq 2012;1154 Ashraf,Nava 2009;502 Duflo, Esther 2012}}.

4
# **Figure 2.3 Distribution of the working age population by education level. 16 LA countries, c.1989-c.2009**

<!-- Start of picture text --> ARG BOL BRA CHL<br>90 95 00 05 10 95 00 05 10 90 95 00 05 10 90 95 00 05 10<br>COL CRI ECU HND<br>95 00 05 10 90 95 00 05 10 02 04 06 08 10 90 95 00 05 10<br>MEX NIC PAN PER<br>90 95 00 05 10 90 95 00 05 90 95 00 05 10 95 00 05 10<br>PRY SLV URY VEN<br>95 00 05 10 90 95 00 05 90 95 00 05 10 90 95 00 05<br>Less than high school High school degree Some tertiary<br>100<br>50<br>0<br>100<br>50<br>0<br>100<br>50<br>0<br>100<br>50<br>0<br><!-- End of picture text --> Source: Own computations based on SEDLAC (CEDLAS and World Bank).

**Figure 2.4 Gini coefficient, poverty based on the 4USD international line and GDP. Average for Latin America, 1992-2009**

<!-- Start of picture text --> 2.5<br>2.0<br>The 1990s Growth<br>1.5<br>1.0<br>0.5<br>0.0 Crisis<br>-0.5<br>-1.0 Stagnation<br>-1.5<br>-2.0<br>-2.5<br>1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009<br>GDP Poverty Inequality<br><!-- End of picture text --> Source: Source: own computations based on SEDLAC (CEDLAS and World Bank) and World Development Indicators.

24
remittances promote financial development, measured by the ratio bank deposits to GDP, and the share of bank credit to GDP. Gupta et al. (2009) use a similar methodology as Aggarwal et al. and find analogous results for a sample of Sub-Saharan African countries. Focusing exclusively on Mexico and using municipality-level data, Demirgüç-Kunt, Lopez-Cordova, Martínez Pería, and Woodruff (2011) find that municipalities where a higher proportion of households receive remittances have a higher number of bank branches and accounts per capita, and larger shares of deposits to GDP.

Our paper contributes to the study of the impact of remittances on the financial sector in several ways. First, to our knowledge, this is the first paper to analyze the impact of remittances on financial inclusion directly. While previous studies have looked at the link between remittances and aggregate deposits and credit amounts, this is the first study to examine whether remittances foster the use of deposit accounts and credit by households. This is important given the literature that has identified beneficial effects from financial inclusion. Second, by looking at whether households apply for loans, this study is able to examine the impact of remittances on the demand for credit and not purely on credit outcomes. This is useful because it can allow us to begin to assess to what extent remittances might relax credit constraints. Third, by using household-level panel data, this study can circumvent some of the limitations of the previous studies. In particular, the survey data used in this study has the potential to capture remittances flows received through formal and informal channels, minimizing concerns about measurement error in remittances.<sup>4</sup> Also, because we use household-level panel data we are able to control for unobserved household characteristics that can affect both remittances and financial inclusion, something that was not possible in the earlier studies. Finally, our study offers evidence on the impact of remittances on financial inclusion for a new country - El Salvador – for which remittances represent a very significant share of GDP.

We conduct probit estimations of the likelihood of using deposit and credit services, allowing for department-, municipality-, or household-level fixed effects to control for factors other than remittances that might affect financial inclusion at the household level.<sup>5</sup> Also, to deal

> 4 This is a significant concern in the study by Aggarwal et al. (2011) that uses balance of payment data on remittances.

> 5 El Salvador is divided into 14 departments and 262 municipalities.

4