rafmacalaba/datause-encoder-data huggingface dataset:
time difference between _L_<sup>_w_</sup> _ct_<sup>and</sup><sup>_Lw_</sup> _c_ ( _t−_ 1)<sup>inthesameequations.6</sup> The _Learningc_ component of the _RBF_ formula has the same structure as the _Literacyc_ component. It also considers _levels_ and _time variation_ at the municipal level but, instead of _L_<sup>_w_</sup> _c_<sup>and</sup><sup>_Lw_</sup> _ct_<sup>,the</sup> formula considers weighted test scores in 5<sup>_th_</sup> grade named _Tc_<sup>_w_and</sup><sup>_T_</sup> _ct_<sup>_w_.All underlying calculations</sup> to find _ILearning,ct_ follow the same procedures as for _Iliteracy,ct_ . But different from _Literacyc_ , this component includes pass rates ( _Enroll_ % _,c_ ). More explicitly, the final _Learningc_ indicator becomes: _Enroll_ % _<u>,c</u> Ilearning,c_ ∆ _Ilearning,ct Learningc_ = 0 _._ 2 _·_ <u>�</u> _c_<sup>_Enroll_%</sup><sup>_,c_+ 0</sup><sup>_._8</sup><sup>_·_</sup> �0 _._ 4 _·_ <u>�</u> _c_<sup>_Ilearning,c_+ 0</sup><sup>_._6</sup><sup>_·_</sup> <u>�</u> _c_<sup>∆</sup><sup>_Ilearning,ct_</sup> �.

In summary, the original _RBF_ model shows that municipalities with greater improvements in literacy rates, pass rates, and learning scores will receive a higher fraction of _Quota − Parte_ than municipalities with lower overall performance. However, if better test scores are achieved by restricting the participation of students in _SPAECE_ or by widening the gap between high and low performers, municipalities are penalized with a lower _Quota−Parte_ . Overall, the _RBF_ model intends to generate incentives to local authorities to amplify the coverage of students participating in standard exams and, at the same time, reduce learning disparities.

# **2.2 The Reformulation of the RBF Model**

The _RBF_ formula described above was partially reformulated in 2012 by the state government of _Ceará_ . The new decree, 30,796/2011, took effect in 2013 and focused on the _Educc_ component, with _Healthc_ and _Environmentc_ unaltered. In this decree, four reformulations were implemented: (i) _Enroll_ % _,c_ was removed from _Learningc_ and added to the _Educc_ component (weighting 0 _._ 05); (ii) _Literacyc_ replaced the weight for _levels_ by 0 _._ 75 and for _time variation_ by 0 _._ 25, instead of 6Explicitly, the _Literacyc_ equation is illustrated in the RBF mechanism: where ∆ _Iliteracy,ct_ = _L_<sup>_w_</sup> _ct_<sup>_−Lw_</sup> _c_ ( _t−_ 1)<sup>.</sup><sup>_Enroll_%</sup><sup>_,c_=</sup><sup>_<u>NA</u>_</sup> _NEc_<sup>_<u>c</u>NAm_and</sup><sup>_NEm_arethenumberofpupilsassessedand</sup> enrolled in the municipal network, respectively.

9
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**Figure 10:  Long-run Fees over Assets for Average, Highest and Lowest Cost MPF**

<!-- Start of picture text --> 14%<br>12%<br>10%<br>8%<br>6%<br>4%<br>1.66%<br>2%<br>1.18%<br>0.49%<br>0%<br>Average Cost MPF Lowest Cost MPF Highest Cost MPF<br>1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030<br><!-- End of picture text --> Note: Real wage growth = 4%, rate of return = 8%. Source: Own calculations on HFSA data.

48. An alternative way to examine fees in Hungary entails the examination of the long-run equivalence of fees over contributions and assets, building on the general methodology developed in Diamond (2001). Figure 11 shows the compensating variation in fees of contributions that would be required to maintain the final balance constant if the fees on assets is set to 1 percent for an individual with a wage growth of 5%, a working career of 40 years ( _T_ = 40), and no interruption in service ( _T_<sup>*</sup> = 40). It shows that fees on assets of 1 percent are equivalent to fees of 15 to 30 percent on contributions, depending on the net rate of return (gross rate minus fees). These results are consistent with the simulations shown in Figure 7 and Figure 8, and illustrate the long-run impact of asset fees charged during the whole accumulation phase. In particular, the long-run total fee over assets of 1.18 percent for the average MPF would translate in a long run contribution fee of 17 – 35 percent.<sup>17</sup>

> 17 The methodology is developed in Appendix B.  Note that the projected fees on contributions in Figure 7 are higher because the average fees on assets charged during the accumulation phase are higher than 1 percent and wage growth assumption is lower.
# **4.1 Data**

This paper uses equity return indexes of the G7 countries from January 1, 1973 to December 31, 2015. To avoid time difference, I use weekly returns. The weekly frequency avoids market microstructure of using daily returns, yet provides sufficient observations in the tails. The data is acquired from Datastream and includes 2313 observations.

Panel A of Table 2 presents the summary statistics of returns for the whole period. The mean and standard deviation of returns are annualized by multiplying returns by 52. The average returns of all the countries, except Japan, are around 10%. Japan has an average return of only about 7%. Standard deviations of returns vary from 1.14 to 1.69. The row labeled "Skewness" reports the results of the D’Agostino test of skewness (D’Agostino, 1970). It shows that the equity returns in all countries, except the UK and Italy, are skewed to left at 5% significance level and higher. While the equity returns of the UK are significantly skewed to right, the returns of Italy do not exhibit significant skewness. The row labeled "Kurtosis" reports the results of the Anscombe-Glynn test (Anscombe & Glynn, 1983) and implies that all equity returns have acute peaks and tend to be heavy-tailed. The results of the Anderson-Darling test (Stephens (1986) and Thode (2002)) and the Shapiro-Wilk test (Shapiro and Wilk (1965) and Royston (1982)), reported in rows labeled "AD test" and "Shapiro test", indicate that the equity returns do not follow the normal distribution.

To see how the 2008 financial crisis affects the financial market, I divide the whole sample period into two subperiods. The first period includes the first 30 years from January 1, 1973 to December 31, 2002. The second period includes the 2008 financial crisis and extends from January 1, 2003 to December 31, 2015. Panel B and Panel C in Table 2 report the summary statistics for the two subperiods. Except Germany and Japan, the average return of all the countries decreases in the second period. In the first subperiod, returns of most countries, except the UK, Italy, and Japan, are skewed to the left, while in the second period, returns of all countries are skewed to the left, suggesting the possibility of having large negative returns in the second subperiod. Results from the kurtosis, Anderson-Darling test and Shapiro-Wilk test imply that equity returns of all the countries in the two subperiods are not normally distributed.

# **4.2 Empirical results**

In total, there are _C_ (7, 2) =<sup><u>7×</u></sup> 2<sup><u>6</u>= 21 correlation coefficients between the seven countries.To save</sup> space, only correlations between the US and the other G7 countries are reported. The weight vector used to compose the portfolio is (0.5, 0.5) in this section.

Figure 4 plots the performance of the estimated linear correlation, VaR-implied correlation and ES-implied correlation in the whole sample period, where low quantiles correspond to bad market situations and high quantiles correspond to good market situations.

18
# **C Details on outcome measures**

## Outcome Variable Descriptions

### **Psychological Well-being**

|PHQ9|The standardized total score of 9 questions from the Patient Health<br>Questionnaire-9 (PHQ9)|
|---|---|
|Life Satisfaction Index|A standardized average of survey responses to four questions from Di-<br>ener’s standardized scale, responses made along a seven-point Likert<br>scale.|
|Stress Index|The standardized total score from three elements of adapted from the<br>Cohen Stress scale. “How many of the last 7 days have you [been able<br>to fall asleep peacefully / felt nervous / felt frustrated]?”|
|Sociability (Total)|The total number of conversations in the past day with adults.|
|Sociability (Positive)|The total number of conversations in the past day with adults that the<br>respondent felt were positive.|
|Self-Worth Index|The standardized total score from the responses on a scale from 1 to 10<br>to two questions: “Think of a person you know who you most respect<br>and who brings greatest value to your [family / community]. If that<br>person is a 10, where would you put yourself?”|
|Locus of Control|The standardized total score from responses to four locus of control<br>questions. “In the last 7 days, how many days did you feel that to a<br>great extent your life is controlled by accidental/chance happenings...”|
|Allocation Decision Game|Indicator (yes / no) for response to an ofer to participate an allocation<br>committee to decide how money is spent. Participants are ofered the<br>opportunity to make a resource allocation decision for their community<br>or have another individual (an NGO worker, an “expert”, or another<br>refugee) make the decision.|
|Stability Index|The standardized total score from responses to two stability questions<br>using a Cantril ladder. “How secure [do you feel / think you will feel]<br>[at present / fve years from now]”|
|Physiological Index|An inverse-covariance weighted average of PHQ, Stress, Life Satisfac-<br>tion, Sociability (Total), Self-Worth, Locus of Control, and Stability<br>indices.|
|**Gender Dynamics**||
|Gender Perceptions - Work|The standardized total score of two questions regarding women’s work,<br>“How often would you agree that women should be allowed to work<br>for a living [inside /outside] the block?”|

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ratio of tha financial investment cost to the same adjusted _for_ tariff diltlies on imported equipment and for the high prices of local equipment (the first two price fActors described in the previous paragraph).<sup>10</sup> 20. As noted in para. 16 and Annex 1, the simple formula desin4<sup>ing the CEPR</sup> is valid provided that the share "s" of labor costs in VAw is relatively small. Parameter "s" was estimated for each sample project by adding to the direct labor cost the "overheads" costs (which comprise not only non-direct labor but also certain services inputs such as telephone,...). The estimated values of "s' represent thus an upper limit of the labor content of Value Added VAw. It is cle .- that "s" is indeed small in these projects, as shown by the following table which summarizes the basic parameters of the samp'e: TABLE 1: BASIC PARAMETERS OF THE PROJECT SaMPLE /a

|SubsectrqMs|Sample sha<br>Nc.proi.|re oX)<br> <br>VAw|ICOR|Factor s<br>(X|Factor NPCk|
|---|---|---|---|---|---|
|Heavy Chemicals|17|20|3.2|12|1.36|
|Synthetic Yarns|7|15|3.1|12|1.54|
|Basic Steel Goods|12|7|2.4|19|1.46|
|Heavy Miscellns|9|13|4.1|21|1.42|
|Avera3.e Intermdtes|45|55|3.3|15|1.43|
|Light Chemicals|5|3|1.6|19|1.33|
|Food Industries|7|6|1.9|16|1.36|
|Electronics|17|12|1.3|25|1.25|
|Other Engineering|26|24|1.5|17|1.45|
|Avge Final Goods|55|45|1.5|20|1.38|
|Overall Average|100|100|2.5|17|1.42|

/a All parameters in this table are weighted averages.

# Source: Annex 2

21. The sample data confirm that: (i) investment costs in India are substantially higher than international costs, by about 42X on account of the price of capital goods; and (ii) selected processes and technologies are capital-intensive with a low share "s" of labor costs in the value added VAw (17X on average, with a maximum of **25X** in electronics). The data reveal also the presence in t-v sample of two distinct groups of industries: (i) the first group consists oi heavy industries producing intermediates and inputs, with ICORs generally above 3 and a low "s" averaging 15X; and (ii) the second group comprises less capital-intensive industries producing final goods, with ICORs around 1.5 and a slightly higher "s" averaging 20X.

_10_ A second estimate NPCk2 is the ratio between the financial investment cost to the leconomic" invastment cost (used for ERR computation), which captures not only the effect of capital goods prices but also the price distortions of other goods and factors (e.g., cement). This second estimate represento an upper limit of t i cumulative effects of all forms of investment taxation in India. Results corresponding to NPCk2 are given in the Annexes.
Policy Research Working Paper 7054

# **Abstract**

Although previous research has demonstrated the health benefits of water treatment programs, relatively little is known about the effect of water treatment on education. This paper examines the educational benefits to rural youth in China of a major drinking water treatment program started in the 1980s, perhaps the largest of such programs in the world. By employing a cross-sectional data set (constructed from a longitudinal data set covering two decades) with more than 4,700 individuals between 18 and 25 years old, the analysis finds that this health program has improved the individuals’ education substantially, increasing the grades of education completed by 1.08 years. The qualitative results hold when the analysis controls for local educational policies and resources, village dummies, and distance of villages to schools, and by instrumenting the water treatment dummy with villages’ topographic features, among others. Moreover, three findings render support to the brawn theory of gender division of labor: girls benefit much more from water treatment than boys in schooling attainment; youth with an older brother benefit more than youth with an older sister; and boys gain more body mass than girls do from having access to treated water. The program can account for the gender gap in educational attainment in rural China in the sample period. Young people that had access to treated plant water in early childhood (0–2 years of age) experienced significantly higher gains in education than those who were exposed to treated water after early childhood. The estimates suggest that this program is highly cost-effective.

This paper is a product of the Finance and Private Sector Development Team, Development Research 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 lxu1@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
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3. _The data·_ This· study uses income statement and balance sheet data of commercial banks from the BankScope data base provided by IBCA (for a complete list of data sources and variable definitions, see the Appendix). Coverage by IBCA is comprehensive, with banks included roughly accounting for 90 percent of the assets of banks in each country. We start with the entire universe· of commercial banks, with the exception that for France, Germany and the United States only several hundred commercial banks listed as 'large' are included. To ensure reasonable coverage for individual countries, we include only countri~s where there are at least three banks in a country for a given year. This yields a data set covering 80 colintries during the years 1988-1995, with about 7900 individual commercial bank observations. This data set includes all OECD countries, as well as many developing countries and economies in transition. For a list of countries, see Table 1.

First, we consider the extent of foreign bank penetration in national banking markets. Table 1 presents two measures of foreign bank penetration: the share of banks that: is foreign-owned, and the share of foreign bank assets in total bank assets. The number penetration measure· is an appropriate measure, if the number of domestic and · foreign banks determines competitive conditions. This is the case, if ·domestic banking firms adjust their operations as soon as foreign entry occurs to prevent the foreign entrants from ever capturing significant market share. Alternatively, the share penetration measure is appropriate, if foreign banks start to have an impact only after they have gairied sub~tantial size. Foreign banks may indeed have to be sizable for' there to be any significant transfer of banking technology to the domestic banking sector. Note that either penetration n;ieasure is a measure of actual foreign banking penetration, and thus does not capture the disciplining effects on domestic banks of potential foreign bank entry. The threat of foreign bank entry, however, may not be credible in the absence of actual entry.

From Table 1, we see that for most countries the number foreign penetration measure exceeds the asset penetration measure (this is the case for France, Germany, Italy, the U.K. and the U.S., but not Japan). This reflects that foreign banks tend to be smaller than domestic banks. Either penetration measure is zero for Finland, Guatemala, , Haiti, India, Malta, Oman, and Yemen, reflecting regulatory barriers to foreign bank entry. At the other extreme, Nepal and Swaziland only have foreign-owned banks in our sample. Other countries with a large foreign bank presence (with both foreign penetration measures of at least 75 percent) are Bahrain, Botswana~ Luxembourg, and New Zealand. A colonial past or the presence of a large neighboring county can explain some of these high ratios. Among the developed countries, Denmark, Finland, Italy, Sweden and the United States have relatively insulated banking markets, with foreign penetration measures below 10 percent. The last column for each country reports· the total 'number of banks in the sample for 1995. · 5
from including an initial value of consumption as a control variable did not affect our findings regarding the impact of a prime age death in the household.

A final concern with the results presented in Tables 3 and 4 using IHHFE is that the treatment and comparison groups may not be comparable: even though split-offs originate from the same initial household, there may be some non-random sorting into particular groups, making the evaluation of the impact of adult mortality flawed. For example, it may be that baseline individuals in a household were split in a group of relatives and non-relatives of the deceased before the mortality shock. This could be either in anticipation of a period of hardship in the household or unrelated to it. For example, in anticipation of an adult dying, household splits may also involve the placement of the ill person with relatives only, and not randomly across the split-offs. Our regressions already control for a range of variables, like age fixed effects, sex, years of schooling and number of biological children of the respondent living outside the household at baseline. In order to specifically accommodate these concerns, we restricted our sample to create more directly comparable control and treatment groups. We report on three restricted sub-samples: children of the deceased, parents and children of the deceased, and spouses, parents and children of the deceased. The variation used to identify the impact of the adult mortality shock is then based on those households splitting so that these different types of blood relatives are spread across different households. While this may not solve all problems of (endogenous) placement across split-off households, it forces the comparison to be done between 2004 households with similar blood relatives. Table 6 reports the results, while Table 7 shows the number of ‘useful’ treatment observations from which the results are derived. In all specifications, the impact of adult death is strongly significant, and larger in size compared to the earlier results in Table 3. It would appear that households of children living with a parent that 20
# **Can Information and Alternatives to Irregular Migration Reduce**

# **“Backway” Migration from The Gambia?**<sup>*****</sup>

Tijan L. Bah<sup>†</sup> , Catia Batista<sup>‡</sup> , Flore Gubert<sup>§</sup> and David McKenzie<sup>**</sup>

**Keywords:** Irregular migration, migration deterrence, information interventions, vocational training, cash transfer, randomized experiment.

**JEL codes:** O15, F22, J61

* We thank the editor and two anonymous referees for helpful comments on our stage 1 registered report, our formal discussant Giacomo Battiston and participants in various seminars for comments on the results, the migration experts and Gambian stakeholders who provided predictions of our treatment effects, Mam Jarra Njie and Vitor Cavalcanti for excellent research assistance and support of field activities, YAIM, and the Ministry of Youths and Sports, Gambian Embassy in Dakar, Senegal, The Gambian Migrant Associations in Senegal and Italy, and the Ministry of Foreign Affairs. This work was supported by the European Union grant number AMIF-2017-AG-INFO 824432. The content represents the views of the authors only and is their sole responsibility. The European Commission does not accept any responsibility for use that may be made of the information it contains. Ethical approval was granted by the Nova School of Business and Economics on March 1, 2019. The study was pre-registered in the AEA RCT registry as AEARCTR-0003998 on March, 2019. <u>http://www.socialscienceregistry.org/trials/3998</u>

† Navarra Center for International Development, University of Navarra, University of The Gambia and NOVAFRICA. Email: tbah@unav.es ‡ Nova School of Business and Economics and NOVAFRICA. Email: catia.batista@novasbe.pt § IRD, UMR LEDa-DIAL, PSL, Université Paris-Dauphine, CNRS. Email: gubert@dial.prd.fr

** World Bank. Email: dmckenzie@worldbank.org