rafmacalaba/datause-encoder-data huggingface dataset:
-

- Wolf, J., Hunter, P. R., Freeman, M. C., Cumming, O., Clasen, T., Bartram, J., ... & Prüss Ustün, A. (2018). Impact of drinking water, sanitation and handwashing with soap on childhood diarrhoeal disease: updated meta‐analysis and meta‐regression. _Tropical medicine & international health_ , _23_ (5), 508-525.

- World Bank. (2018). Promising Progress: A Diagnostic of Water Supply, Sanitation, Hygiene, and Poverty in Bangladesh. WASH Poverty Diagnostic. World Bank, Washington, DC.

# 8. Annex

**Annex Figure 1: Proportion of households with E.Coli contamination at source and household points, by type of drinking water source**

<!-- Start of picture text --> E.Coli Contamination at Consumption Point by Type of Water<br>Source<br>82%<br>Other<br>78%<br>Piped 67%<br>Water 74%<br>40%<br>Tubewells<br>61%<br>0% 20% 40% 60% 80% 100%<br>E.Coli Contamination (%)<br>Source Household<br>Water Source<br><!-- End of picture text -->

**Annex Figure 2: Urban‐rural breakdown of E. Coli risk level at household point**

**Urban‐Rural Breakdown of E.Coli Risk at Household Point**

<!-- Start of picture text --> Urban 36% 19% 20% 26%<br>n = 119<br>Rural 38% 23% 26% 13%<br>n = 697<br>0% 20% 40% 60% 80% 100%<br>E.Coli Risk Level at Household Point (%)<br>Low Medium High Very High<br><!-- End of picture text --> 24
related to these DDSR operations were provided to eligible countries from official sources (including the World Bank and the International Monetary Fund). For the low-income countries, the World Bank established the Debt Reduction Facility for IDA-only countries (i.e., those World Bank member countries that were eligible for concessional lending only). This Facility provided resources, along with other official donors, to execute a buyback of all uninsured commercial debts (owed to private creditors) at substantial discounts on the face value of these claims.

# **III. And towards the 21st Century the Challenge Continues...**

The rapidly changing nature of development finance in the 1990s has brought with it new concerns and questions among policy makers that still need to be addressed. The surge of private capital flows has brought with it concerns about the volatility of asset prices and volume of capital inflows, especially portfolio equity inflows, in developing countries. It has raised concerns about the appreciation of the real exchange rate that may undermine ongoing reform efforts of the recipient countries. It has heightened the need to understand the destabilizing effects of these surges of private capital flows (in and out of a country) on the domestic financial system. The involuntary reserve accumulation (and depletion) that often accompany these surges have led to concerns among policy makers about the loss of independence of monetary policy under a very volatile environment.

Efforts within academia to answer these policy questions in the context of the socalled "emerging markets" were delayed by the need to first obtain reliable and consistent information on the size and composition of private capital flows. Several recipient countries have only until recently begun to put in place the institutional machinery needed to monitor these transactions effectively, especially those involving portfolio investment by foreign residents. Recently available portfolio flows data compiled by the World Bank (Gooptu (1993), World Bank (1994)) and the International Monetary Fund (IMF 1994, 1995) have permitted econometric studies to be undertaken in this regard. However, important contributions have been made through analytical work produced in the last two 14
# **7. Conclusions and Policy Implications**

Despite the popularity of financial literacy workshops among policymakers and financial institutions, voluntary participation rates are typically very low for these programs. Working with a half-day long training course in Mexico City that aims to teach participants the importance of savings and responsible debt and credit card use, we use randomized experiments to investigate the lack of demand.

We find very little interest in participating in financial education among samples of financial institution clients and Facebook users. Screening our sample to focus on those individuals who express interest in attending training, we find the majority of them also do not attend. Experiments to increase take-up suggest that this low participation rate is not mainly due to high discount rates, time-inconsistency, or lack of information, but rather appear to be due to individuals thinking that the benefits of such training are not high enough. Monetary incentives which increase the benefits lead to more attendance. A follow-up survey conducted about six months after the course enable us to measure just what these benefits are. We find participating in the course leads to increases in financial knowledge and savings, but this increase in savings appears to dissipate quickly, and there is no evidence that the training changed credit card usage or borrowing behavior. The lack of interest in training therefore appears to be a rational choice, since users see relatively little benefit from it.

One natural response to the modest impacts of training measured here is to note that the training is only a few hours long, and to thus argue that much longer and intensive training sessions are needed to really affect financial behavior. However, our study shows that most of the general population has very little interest in attending even a short financial literacy course, and that it is very difficult to even get individuals who state they are interested in participating to show up and attend. Monetary incentives did help boost attendance, but even an immediate payment of $72 got fewer than 40 percent of people who had expressed interest in a financial literacy program to attend. Longer and more intensive programs would appear likely to have even more trouble attracting participants. Moreover, Kim et al. (2003) provides an example to suggest that even 18 months of adult credit counseling has not been effective.

We caution that our study measures local treatment effects, showing the effect of training for people who are induced to attend it as a result of our interventions, but would not attend it without the information, logistical support, and incentives we provided. It seems plausible that the impacts for these individuals who have self-selected into not attending a program that is available to anyone who wants to participate will have less benefit from such training than the people who choose to attend of their own accord. However, as our study notes, fewer than two 27
Table 3: Investment in digital technologies and fixed assets

|||Has investe|d in digital|||Has invested|in fxed assets||
|---|---|---|---|---|---|---|---|---|
||(1)<br>Conditional|(2)<br>Cont. initial<br>productivity|(3)<br>Conditional|(4)<br>Cont. initial<br>productivity|(5)<br>Conditional|(6)<br>Cont. initial<br>productivity|(7)<br>Conditional|(8)<br>Cont. initial<br>productivity|
||||**Panel A. B**|**aseline estimat**|**ions**||||
|Informality|-0.251**|-0.288**|||-0.144**|-0.126**|||
||(0.0201)|(0.0333)|||(0.0343)|(0.0407)|||
||||**Panel B. By**|**type of inform**|**ality**||||
|Fully formal|||_Baseline_|_Baseline_|||_Baseline_|_Baseline_|
|Necessity|||-0.0976**|-0.118**|||0.000347|-0.0195|
||||(0.0133)|(0.0165)|||(0.0263)|(0.0294)|
|De Soto|||-0.0996**|-0.107**|||-0.00524|-0.0221|
||||(0.0114)|(0.0142)|||(0.0225)|(0.0248)|
|Parasite|||-0.129**|-0.0564**|||-0.102**|-0.0607*|
||||(0.0119)|(0.0203)|||(0.0240)|(0.0327)|
|Digital readiness||0.0537**||0.0539**|||||
|||(0.00495)||(0.00494)|||||
||||**Panel C. By **|**margin of infor**|**mality**||||
|_PCA 1_|-0.0988**|-0.145**|||-0.0109|0.00416|||
||(0.0167)|(0.0291)|||(0.0272)|(0.0321)|||
|_PCA 2_|-0.168**|-0.146**|||-0.187**|-0.186**|||
||(0.0157)|(0.0215)|||(0.0298)|(0.0349)|||
|Panel controls|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Other controls|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Initial productivity|No|Yes|No|Yes|No|Yes|No|Yes|
|Digital readiness|No|Yes|No|Yes|No|No|No|No|
|Observations|14532|9702|14816|9813|6391|5222|6477|5279|

**N** otes: All continuous variables are winsorized at the 1% and 99%. Robust standard errors in parentheses

* p<0.10, ** p<0.05, ** p<0.01

-Parasite firms exhibit a statistically significant lower probability of investing at the 5% level of significance in Panel B, col. 3, but, controlling for initial productivity, a higher probability than other types of informal at the 1% level in col. 4.

-In col. 7, parasite firms show a lower probability vs. other types of informal at the 1% level. In col. 8, the differences are not statistically significant. -The coefficients of PCA 1 and PCA 2 are statistically different at the 1% level in Panel C, cols. 1, 5, and 6; and are not statistically different in col. 2. Source: Authors’ calculations using data from the World Bank Business Pulse Survey (BPS).
_Figure 15: Factors of well‐being worsening by vulnerable and non‐vulnerable groups, % of respondents_ <!-- Start of picture text --> 40%<br>35%<br>other<br>30% inflation<br>25% loan servicing<br>20% health<br>change of HH composition<br>15%<br>loss of additional income<br>10%<br>retirement<br>5%<br>lower salary<br>0% job loss<br>vulnerable average vulnerable average vulnerable average vulnerable average<br>2014 2015 2016 2017<br><!-- End of picture text --> Source: Authors’ estimates based on HSS data.

There were differences in the way the crisis affected labor income of different social groups:

- Job losses were more commonly reported as a factor of well‐being deterioration by individuals with high education levels rather than those with secondary specialized education or lower education levels. On the contrary, the latter faced a higher risk of falling nominal wages.

- Those identified as partly employed faced a higher risk of well‐being reduction due to loss of job or nominal wages reduction than other social groups. On the contrary, retirees were mostly affected by real decreases in their income (pensions) that took place in 2016 and 2017.

- Households with children appeared to be more exposed to the risk of nominal wages reductions, on average, while single parent households were also vulnerable to inflation, as they have fewer possibilities to optimize their consumption structure compared to larger households with more diversified income sources and larger scale effect.

The economic recovery of 2017 allowed to moderate some of the losses for affected households. Well‐being improvements were reported by 11.8 percent of households ‐‐ significantly higher than in 2016 (Figure 16). In particular, a larger share of the population reported well‐being gains from improved terms of employment and higher wages. Still, the share of those benefiting from wage increases in 2017 was lower than in the pre‐ crisis period. Moreover, the difference between the share of the population enjoying well‐being improvements in 2014 and 2017 is related pensions ‐‐ given a slower pace of pensions recovery compared to wages, fewer opportunities for continued employment in retirement age during the crisis, and the increasing pension age, only a small share of the population indicated pension as a key factor behind their well‐being improvements.

In general, a higher share of vulnerable households report well‐being improvements compared to non‐ vulnerable households (Figure 16). Most of the difference is related to employment (including better terms of employment). This tendency was observed both pre‐crisis and during the crisis years. However, in the post‐crisis 2017 year there are also differences related to wages ‐‐ 5.5 percent of respondents vulnerable to the risk of poverty reported well‐being gains due to wages increase in 2017 (3.4 percent among non‐ vulnerable households).

14
O.A.M.D.G.

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**Cohen Jacqueline and Tita George:** “Diffusion in homicide: exploring general method for detecting spatial diffusion processes” in _Journal of Quantitative Criminology, Vol 15 No 4_ , 1999

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43
Learning from Developing Country Power Market Experiences: The Case of Peru <!-- Start of picture text --> 2015 USD/MWh<br>120.00<br>100.00<br>80.00<br>60.00<br>40.00<br>20.00<br>0.00<br>2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017<br>Marginal cost Liberalized contracts price Regulated price<br><!-- End of picture text --> Figure 5-1 Evolution of wholesale and retail electricity prices in Peru (inflation adjusted to 2015 US$/MWh).

Source: Worldbank Table 5-1 Yearly evolution of retail and wholesale prices (in 2015 USD/MWh).

|**Year**|**Average Wholesale**<br>**Pool Prices**<sup>**1**</sup><br>**(2015 US$/MWh)**|**Average Liberalized**<br>**Prices**<sup>**2**</sup><br>**(2015 US$/MWh)**|**Regulated Price**<sup>**3**</sup><br>**(2015 US$/MWh)**|
|---|---|---|---|
|**2005**|76.6|67.2|56.1|
|**2006**|80.1|63.3|52.0|
|**2007**|43.4|62.0|49.7|
|**2008**|97.1|59.1|46.6|
|**2009**|35.6|60.8|57.4|
|**2010**|23.3|59.2|52.2|
|**2011**|25.1|59.2|51.7|
|**2012**|31.9|59.2|60.5|
|**2013**|27.0|55.4|48.9|
|**2014**|25.3|54.8|49.1|
|**2015**|14.7|54.5|58.6|
|**2016**|21.15|51.90|N/D|
|**2017**|9.22|46.93|N/D|
|**2005-17**|**-88%**|**-25%**|**5%**|

1Average of wholesale energy prices in the power pool.

2Average of energy prices for liberalized customers.

3Average of energy prices for regulated customers. Data not available for years 2016-17.

Source: Worldbank 17
# **111. Data** Issues

Sources: This paper relies on World Bank and IMF data, and ptiblished data. (The only unpuiblislhed data is the currency-adjuistment factor.) The IMF Balance of Payments Yearbook (BoPY) is the source for almost all BOP cuirrent and capital-accounlit itenms. Data from thie BoPY were taken fromii the 13 March 1993 update of the IMF BoPYtape. The World Bank Debtor Reporting System (DRS) is the source for all external debt statistics. The external debt figures comile froml thie DRS-data base (as frozeni on 29 January 1993) **and** used for the putblished World Debt Tables 1992/93. The IMF Direction Of Trade (DOT) statistics provide the trade data. Data froin DOT were taken from the 16 March 1993 update of the tape. The one-year US-Treasury interest rate information came from the IMF International Financial Statistics (IFS) data set. IFSdata were also used for the change in the deposit money banks foreign assets. Data from the IFS were taken from the 12 March 1993 update of the tape.

As data are constantly revised, variations can arise becauise of different vintages of data and differences between the published versions of data and tapes (and other computerized data sources). The effect of such variations on calculations, however, should be small.

# IMF Balance of Payments Yearbook (BoPY) Data

Although no adjustments were made to any of the BoPY-figures, some conventions are worth mentioning: a) Signs: Items in the IMF BoPY are prefixed by signs: + for debits (for example, exports), - for credits (for example, imports). In the capital account, assets (lending) and liabilities (borrowing) are stocks which increase (-/+) or decline (+/-). Net figures (flows) and net balances (stocks) are calculated not hy subtractinig debits from credits, but by adding them, for example, exports + imports = the trade balance. Thus, the capital account, reserves, and errors and oimissionls are added to (rather than subtracted from) the currenit accounlt to arrive at the overall balance.

b) Aggregated versus Detailed Presentation: The Aggregated Presentation in the BoPY provides capital account and reserves figures net of counterpart items (valuation changes in reserves, {de}monetization of gold, and SDR's), exceptional financing, and liabilities constituting foreign authorities reserves, which are slhown separately. As a result, the current account. capital account, Limange in reserves, and errors and omissions in the aggregated accouints do not add to zero. They do, however, balance in the Detailed Presentation, which incluides counterpart items. Throughout, this paper uses the Detailed Presentation.
# ANNEXES

|ANNEX|1: Research|components of World Bank Education Projects, 1982-1989<br>. .<br>24|
|---|---|---|
|ANNEX|2: Researc<br>completed|h components of World Bank Educatica Projects, 1972-1982,<br>after 1982 .......................................<br>66|
|ANNEX|3: .<br>...|..................................................<br>82|
||Table 1:|Distribution of studies by type and by region, 1982-1989, as a<br>percent of studies within region .......................<br>83|
||Table 2:|Distribution of studies and completion rates by size of<br>commitments to research components, 1982-1989 ....<br>......<br>84|
||Table 3:|Distribution of projects by size of commitments to research<br>compontnts, by year, 1982-1989 .......................<br>85|
||Table 4:|Commitments to research components per project and per<br>planned study, by yeas (UJS$ millions) ..................<br>86|
||Table 5:|Commitments to research components per project and per<br>planned study, by region (US$ millions) .................<br>87|
|ANNEX|4: .........|.....<br>..............<br>......................<br>88|
||Memorand<br>comp|um sent to Division Chiefs requesting information on research<br>onents .............<br>.............................<br>89|
||List of Div|ision Chiefs to whom memorandum was sent ....<br>..........<br>91|

ii
Table 7: Heterogeneous effects of class compositions. Restricted Sample

||D|ependent vle: Te|st score in Spani|sh|
|---|---|---|---|---|
||Lower<br>(1)|Lower Middle<br>(2)|Upper middle<br>(3)|Upper<br>(4)|
|Prop peers in L||-.055<br>(.009)<sup>_∗∗∗_</sup>|-.114<br>(.017)<sup>_∗∗∗_</sup>|-.098<br>(.019)<sup>_∗∗∗_</sup>|
|Prop peers in LM|.039<br>(.009)<sup>_∗∗∗_</sup>||-.077<br>(.014)<sup>_∗∗∗_</sup>|-.044<br>(.015)<sup>_∗∗∗_</sup>|
|Prop peers in UM|.073<br>(.015)<sup>_∗∗∗_</sup>|.020<br>(.011)<sup>_∗_</sup>||.020<br>(.016)|
|Prop peers in U|-.002<br>(.016)|-1.12e-06<br>(.011)|-.062<br>(.016)<sup>_∗∗∗_</sup>||
|Obs.|107,523|130,721|50,303|44,720|
|e(N-g)|6,274|6,444|6,052|5,846|
|_R_<sup>2</sup>|.047|.06|.068|.06|
|Test LM-UM (p-value)|.021||||

Note: Standard errors are robust and clustered at the school level. Other variables included in the regressions are the student’s number of books at home, gender, repeat, class size and age. Source: ONE 2000. Argentina But the tendency for a child at the bottom to perform better diminishes when the social distance is too large.

Finally, for robustness I ran the previous three regressions using alternative definitions of the dependant variable, the main peer variable, and type of school attended, to see the robustness of results found. The results are summarized in table 8 with regressions results in the appendix (tables 18 to 21).

One could argue that the rules of randomization across classes are more relevant in the context of public (state) schools than in private ones. When registering a new student into a school, principals are not meant to request information other than date of birth and formal education history of the child. Private institutions are not subject to such regulation. Indeed, some private establishments request the candidates to take tests, to provide information of parents education and occupation, and to include references. Additional information of candidate’s ability and social background can be use to assign pupils into classes. I therefore run the same analysis exclusively for public schools. All the previous results hold.

A second set of robustness analysis involves using alternative measures 24