rafmacalaba/datause-encoder-data huggingface dataset:
attending and participating in CO meetings. According to the baseline survey, FAs regularly worked overtime hours, and thus they likely faced trade-offs between devoting their time towards credit- or social-related activities.<sup>8</sup> FAs work individually or in teams of two or three individuals. FAs working in teams co-manage a group of COs by dividing the monthly workload among them. Each team member is responsible for attending the meetings of the COs assigned to him or her that month and for collecting the repayments from CO members due that month. A given CO, therefore, will be managed by different FAs over time. Teamwork among FAs is encouraged by NRSP management to ensure continuity in case the FA falls sick, leaves NRSP, or is promoted. Teamwork also provides NRSP a useful way to train inexperienced FAs while they are on the job, by partnering them with relatively more experienced FAs. A detailed description of team formation is provided in Section 4 and in Appendix C.

Until recently, all FAs had been working directly from the branch (FU) with the direct supervision of both the CrO and SO, while earning a fixed salary. At the time of the study, NRSP had transferred 85 percent FAs from FUs to village branches in an effort to have FAs be closer to the field. FAs continued to work alone or in teams, but this decentralization meant that direct supervision by the supervisors was no longer possible. Since the management was concerned that a fixed salary might no longer be optimal in absence of adequate monitoring, NRSP was willing to explore other ways of remunerating the staff. During this time, NRSP had also been scaling up its microcredit program thanks to its partnership with the Pakistan Poverty Alleviation Fund (PPAF), which provided financial support through both grants and loans. The health of the microcredit program was crucial for NRSP’s growth and survival, as the program funds were loans from the PPAF that needed to be repaid. But this microcredit expansion raised concerns among NRSP management about its implications for the quality of COs that were being formed and managed by FAs.

# 2.1 Bonus intervention

We worked with NRSP to design and implement two pay-for-performance schemes for FAs to evaluate the cost and production complementarities on FA effort across credit and social activities. The study was conducted in all 35 FUs located in 15 districts across Sindh, Punjab, and Khyber Pakhtunkhwa provinces, where NRSP was active in March 2005. This provided us with a sample of 162 FAs who were working with NRSP at the time. FAs were randomly divided into three groups (two treatment groups, and one control group). To ensure that all FAs under a given CrO-SO management team were provided with the same bonus scheme, the randomization was done at the FU level. FAs

> 8In the baseline survey, the self-reported average daily overtime among study-sample FAs is 2 hours (see Table 1).

7
The VaR of asset _i_ is where _VaR_ ( _Zi_ )αis the VaR of the noise variable _Zi_ at α confidence level.

Substituting (3) into leads to the standardized VaR-implied correlation Campbell et al. (2002) removed the standard distributions in equation (5) and pioneered the VaR-implied correlation: Campbell et al. (2002) used the VaR-implied correlation to examine whether returns are from normal distribution or not.

VaR is well known for not considering losses beyond itself and not being coherent; however, ES remedies these problems. Therefore, this paper develops a correlation measure using ES. ES at confidence level α is defined as the average of losses beyond the VaR, i.e., when the distribution of _L_ is continuous.<sup>5</sup> Similarly, given the loss _Li_ = μ _i_ + σ _iZi_ , where _ES_ ( _Zi_ )α is the ES of the noise variable _Zi_ at α confidence level.

Substituting (8) into equation (4) leads to the standardized ES-implied correlation,

> 5When the distribution is discontinuous, _ES_ ( _L_ )α =<sup>_E_</sup><sup><u>(</u></sup><sup>_L_</sup><sup><u>;</u></sup><sup>_L_</sup><sup><u>≥</u></sup><sup>_VaR_</sup><sup><u>(</u></sup><sup>_L_</sup><sup><u>)</u></sup> <u>α</u><sup><u>)+</u></sup><sup>_VaR_</sup><sup><u>(</u></sup> 1<sup>_L_</sup> −<sup><u>)</u></sup> αα<sup><u>(1−α−</u></sup><sup>_Pr_</sup><sup><u>(</u></sup><sup>_L_</sup><sup><u>≥</u></sup><sup>_VaR_</sup><sup><u>(</u></sup><sup>_L_</sup><sup><u>)</u></sup> <u>α</u><sup><u>))</u></sup> . See Acerbi and Tasche (2002) and McNeil et al. (2005).

5
Table 1: Comparative Growth Decompositions for East Asia

|||Sarel**(**|**1978-96)**||Bos|worth**and C**|**ollins (1974**|**-94)**|
|---|---|---|---|---|---|---|---|---|
|_|Output<br>Growth<br>per<br>Person<br>__________<br>_________|Growth<br>due to<br>Capital<br>per<br>person<br>__<br>__person|Growth<br>due to<br>effective<br>Labor<br>per|Growth<br>due to<br>TFP|Output<br>Growth<br>per<br>Worker|Growth<br>due to<br>Capital<br>per<br>Worker|Growth<br>due to<br>Educ.<br>per<br>Worker<br>_|Growth<br>due to<br>TFP|
|Indonesia<br> <br>|4.74<br> <br> <br> <br>|8.97<br>(62)<br>|0.93<br>(13)|1.16<br>(25)|4.0|2.8<br>(70)|_0.5_<br>(13)|0.7<br>(18)|
|.. . . ....<br>........ ....... ....<br>Korea|....., _..,**....**_.<br>. ,**.**_. .**.**.. _.__<br>__<br>__<br>__,<br>_.,_,.....|_,,._|||5.8|3.4<br>(59)|0.7<br>(12)|1.6<br>(28)|
|Malaysia|4.54|6.86<br>(47)|0.58<br>(9)|2.0<br>(44)|3.7|2.3<br>(62)|0.5<br>(14)|0.9<br>(24)|
|Philippines|0.19|1.8|0.62|-0.78|_0.5_|1.1|_0.5_|-1.1|
|,**.**,-**.**<br>. .<br>__**.**,_**..**<br>**....**<br>_**.**_<br>. . .... .,_**_.**.<br>. _...<br>,**_**_<br>,.<br>**.**....**.....**<br>_. _ ___-<br>- ,<br>.....<br>..<br>Singapore|.<br>,,<br>. .. _**.**<br>. ..... .<br>5.09|6.45<br>(42)|1.06<br>14)|2.23<br>(44)|5.1|2.7<br>_(53)_|0.4<br>(8)|2.0<br>(39)|
|Thailand|5.24|7.32|1.51|2.03|5.2|2.3|0.6|2.1|
|||(41)|(21)|(39)||(44)|(12)|(40)|

Figures in parentheses are the contributions to output growth.

Effective labor per person corrects for age related productivity differences of the workforce but not for education.

14
<!-- Start of picture text --> co) eaeaaeen<br>iT)v a<br>20 30 40 50<br>Woman's age at the time of the survey<br>------=-- 95% C| ————= share with first-borngirl<br>Pooled NDHS 1990, 99, 03, 08. Survey weights used. First births from women's fertility histories<br><!-- End of picture text -->

<!-- Start of picture text --> o dagen sees ae eee<br>reeitoaetlll Tee,<br>no oe<br>o ee<br>r'9) wee<br>20 30 40 50<br>Woman's age at the time of the survey<br>-------- 95% C] =———= sharew/ first girl “== sharew/ higher order girl<br>Pooled NDHS 1990, 99, 03, 08. Survey weights used. Births from women's fertility histories.<br><!-- End of picture text -->
# **Table 1: Respondent’s demographics and enumerator’s demographics**

||||Respondent c<br>|haracteristi<br>|cs<br>||
|---|---|---|---|---|---|---|
||Age|Gender|Marital status|Can read|Can write|Education level|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Enumerator age|0.055|0.001|-0.002|0.001|-0.001|-0.012|
||(0.079)|(0.003)|(0.002)|(0.004)|(0.004)|(0.031)|
|Enumerator gender (male)|-0.674|0.022|-0.014|-0.034|-0.031|0.290|
||(0.522)|(0.020)|(0.014)|(0.033)|(0.035)|(0.191)|
|Enumerator education|1.131|0.035|-0.023|-0.036|-0.018|0.148|
||(1.326)|(0.030)|(0.023)|(0.040)|(0.039)|(0.268)|
|Observations|6,693|6,692|6,691|6,691|6,690|6,693|
|R-squared|0.021|0.007|0.011|0.137|0.133|0.150|
|F-test|1.40|1.23|1.23|0.57|0.32|0.82|

_Source_ : Data for the study comes from a survey of farmers in northern Uganda. The survey was conducted by Innovations for Poverty Action.

_Notes_ : Team (language group) fixed effects and a constant are included in all regressions but not shown. Robust standard errors in parenthesis are clustered at the enumerator level.

26
Table 1: Procurement and budget execution coverage

|State|#||Pr|ocurement|||Budget Exe|cution||
|---|---|---|---|---|---|---|---|---|---|
||Munic.|Tender|Tender|Tender|Temporal|Commitment|Verifcation|Payments|Temporal|
||||Item|Participants|Coverage||||Coverage|
|CE|184|✓|✓|✓|2009-2021|✓|✓|✓|2009-2021|
|MG|853|✓|✓|✓|2014-2021|✓|✓|✓|2014-2021|
|PB|223|✓||✓|2014-2020|✓|✓|✓|2003-2021|
|PE|185|✓||✓|2012-2021|✓|✓|✓|2012-2020|
|PR|399|✓|✓|✓|2013-2021|✓|✓|✓|2013-2021|
|RS|497|✓|✓|✓|2016-2021|✓|✓|✓|2010-2021|
|SP|644|||||✓|✓|✓|2008-2021|
|Total|3,076||||2009-2021||||2008 - 2021|

**Notes** : This table reports temporal and geographical coverage of our dataset. For procurement data, the number of municipalities for PR is 392 due to problems with the conversion of xml files. In the budget execution data, the original data does not include the municipalities of _Quixabá_ and _Santa Teresinha_ in the state of PB. We could not obtain the data for the São Paulo municipality (state capital), which is supervised by a separate audit court.

Table 2: Descriptive statistics - public procurement

||CE|MG|PB|PE|PR|RS|Total|
|---|---|---|---|---|---|---|---|
|Number of distinct tenders|271,345|643,442|133,201|220,025|735,632|417,348|2,420,993|
|Deserted tenders (%)|-|-|-|3.1|2.6|0.5|1.2|
|Unsuccessful tenders (%)|-|-|0.0|6.3|3.0|0.6|1.6|
|Non-competitive tenders (%)|27.6|31.8|38.3|38.0|45.0|45.5|38.6|
|Non-competitive tenders value (%)|10.0|18.0|12.0|15.0|19.0|12.0|14.9|
|Has item information (%)|93.3|100|-|-|92.0|100|82.2|
|Avg. number of items per tender|25.1|25.4|-|-|32.2|12.4|23.8|
|Has participant information (%)|94.4|96.5|100|100|92.7|92.2|94.9|
|Number of distinct participants|189,738|330,745|50,236|94,864|175,024|119,322|959,929|
|Number of distinct suppliers|137,997|301,447|43,548|66,466|154,301|91,609|795,368|
|Firms among suppliers (%)|55.4|83.2|47.8|66.4|71.6|79.0|72.3|
|**_Competitive tenders_**||||||||
|Avg. number of participants per tender|2.8|2.3|2.6|3.9|3.2|3.6|3.1|
|Avg. number of suppliers per tender|1.5|2.1|2.0|1.4|2.2|2.2|1.9|
|Number of distinct municipalities|184|853|223|184|392|497|2333|

**Notes** : This table presents the descriptive statistics from the public procurement dataset. Deserted tenders refers to a situation where no proposals were submitted by potential bidders in response to a tender notice, while unsuccessful tenders occur when proposals were submitted but didentifier not meet the requirements or the tender process was cancelled or revoked. The variables “Has item information” and “Has participant information” refer to the percentage of tender identifiers with any information related to items or participants, respectively. The definition of non-competitive tender encompasses both _dispensa_ and _inexigibilidade_ . To calculate the percentage of “Non-competitive tenders value ” we use the variable valor_corrigido winsorized at percentiles 0.01 and 99.9. Additionally, the percentage of firms among suppliers is calculated as the number of distinct firms divided by the number of distinct suppliers, where firms are those whose identifier has 14 digits.

32
<!-- Start of picture text --> Central Gov. General Gov. Public Sector<br>EAP 6.2 EAP 7.4 EAP 9.7<br>ECA 7.3 ECA 17.0 ECA 22.8<br>LAC 3.7 LAC 12.5 LAC 14.0<br>MENA 15.5 MENA 21.2 MENA 23.4<br>NA 2.2 NA 14.3 NA 17.9<br>SA SA 5.7 SA 12.3<br>SSA 7.4 SSA 11.2 SSA 13.6<br>0 5 10 15 0 5 10 15 20 0 5 10 15 20 25<br>Mean Mean Mean<br><!-- End of picture text --> Notes: Simple averages for each region. The Appendix has weighted averages by the size of the labor force. The regions in the case of general government employment are constructed in the following way<sup>6</sup> : EAP includes Fiji; Hong Kong SAR, China; Indonesia; Japan; Republic of Korea; Macau; New Zealand; Philippines; Singapore; Thailand; and Timor-Leste.

ECA includes Albania, Armenia, Austria, Azerbaijan, Belarus, Belgium, Bulgaria, Croatia, Cyprus, Czech Republic, Denmark, Estonia, Finland, France, Georgia, Germany, Greece, Hungary, Ireland, Italy, Latvia, Lithuania, Luxembourg, Macedonia, Moldova, Netherlands, Norway, Poland, Portugal, Russian Federation, San Marino, Slovak Republic, Slovenia, Spain, Sweden, Switzerland, Turkey, Ukraine, and United Kingdom. LAC includes Argentina, Aruba, Belize, Bolivia, Brazil, Costa Rica, Cuba, Dominican Republic, Guatemala, Mexico, Nicaragua, Panama, Paraguay, Trinidad and Tobago, and Uruguay.

MENA includes Arab Republico f Egypt, Israel, Malta, Oman, Qatar, and United Arab Emirates. NA includes Canada and the United States. SA includes Afghanistan and Sri Lanka.

SSA includes Botswana, Cabo Verde, Ethiopia, Guinea, Madagascar, Mauritius, Senegal, Seychelles, South Africa, Tanzania, and Zimbabwe. Source: ILO Stats Figure 7: Employment (%LF) over time, by region

> 6 Our total sample consists of 88 countries, but central government employment data are reported for only for 56 countries, while public sector employment data are reported for 113 countries.

10
Table 26. Growth poverty elasticity

||2007-2008|2008-2009|2009-2010|2010-2011|
|---|---|---|---|---|
|TR10|21.28|-0.25|4.06|-0.01|
|TR21|2.09|1.18|1.87|-1.20|
|TR22|0.86|1.08|-0.02|-1.52|
|TR31|-46.03|-0.13|-1.92|-0.45|
|TR32|-688.00|-0.60|-6.07|-1.14|
|TR33|-0.17|-0.51|-9.43|-0.80|
|TR41|-7.63|-0.87|-42.72|-1.49|
|TR42|7.36|-1.32|0.20|-1.41|
|TR51|-4.10|-0.91|28.50|-1.99|
|TR52|-2.62|-0.61|2.20|-1.12|
|TR61|1.10|3.46|-0.85|0.47|
|TR62|-22.42|2.50|-0.88|0.33|
|TR63|1.05|1.60|-0.75|0.19|
|TR71|1.87|0.46|-2.14|-0.94|
|TR72|1.57|1.83|-1.18|-1.39|
|TR81|-1.17|1.69|29.28|-0.72|
|TR82|-41.21|1.89|-2.13|-3.61|
|TR83|0.35|0.84|-4.02|-1.99|
|TR90|2.28|-0.80|-0.37|-3.70|
|TRA1|1.84|-7.49|1.76|0.99|
|TRA2|10.11|-3.42|-0.30|1.06|
|TRB1|15.70|-3.47|2.20|-0.95|
|TRB2|2.37|-1.07|0.80|6.58|
|TRC1|-1.96|3.61|-0.29|-0.77|
|TRC2|-2.26|-2.18|-0.41|-0.96|
|TRC3|-9.86|-1.54|-0.32|-1.23|
|Average|-29.14|-0.19|-0.11|-0.68|

_Notes_ : GVA growth – to- poverty elasticity. Poverty line is $5/day per capita PPP. _Source_ : LFS, SILC 2007-2011; authors’ calculations.

55
<!-- Start of picture text --> Subnational Doing Business Index score*- Distance to National Frontier<br>0.95<br>Romania (2017)<br>0.95<br>0.74<br>Poland (2015)<br>0.78<br><!-- End of picture text --> <!-- Start of picture text --> 0.80<br>Italy (2013)<br>0.69<br>0.72<br>Spain (2015)<br>0.69<br>0.60 0.65 0.70 0.75 0.80 0.85 0.90 0.95 1,00<br>M@Non-lagging @ Lagging<br><!-- End of picture text -->
tactless payment technology to the first and second containment shocks in response to the COVID19 epidemic in France using a proprietary data set on card-sales transactions from a representative sample of 94,199 merchants of all banks in France.<sup>2</sup> Surprisingly, I find that the lockdown due to the COVID-19 outbreak promote card sales growth of merchants with the contactless payment technology, especially small businesses, and new entrepreneurs. This result suggests that merchants with contactless payment technology are more resilient to epidemic shocks where barrier gestures is required than those still not using contactless payment technology.

By 2020, France was affected by the COVID-19 epidemic. In order to stop the spread of the virus in France, the government imposed the first containment from mid-March to mid-May 2020. In addition, in mid-May 2020, French banks increased the limit for contactless payment from 30 euros to 50 euros in order to reduce physical contact and promote the use of contactless cards instead of contact cards and cash. Following a significant increase in coronavirus contamination and deaths in October 2020, the French authorities decided again to impose a second containment. I use these three staggered shocks to study the resilience of merchants using contactless payment technology and their effects on merchant sales as well as substitution between payment methods.

I estimate a distributed lag model using the shocks as the exogenous event and obtain the impulse response of card sales. My empirical identification strategy relies on the differential benefits of the contactless payment convenience across merchants with similar characteristics. Utilizing all card and mobile payments for 17,376 matched offline merchants from a set of essential sectors during a two-year period of 2019:01 to 2020:12, I document a significant spillover effect in merchant’s sales through card payment. My findings are summarized as follows. First, compared to merchants without the latest digital payment technology, merchants with contactless payment technology experienced an average of 8.3 percent (10.2 percent) more monthly card-sales amount (count) in the 10-month period following the first containment in France in March 2020. Prior to the first shock, there is no difference in the card-sales trend between the treatment group and the control group. Second, the adjustment was rapid, with the effect already very pronounced in the period immediately following the month of the first lockout shock and after the month in which the contactless payment limit was increased. Third, I observe a strong effect of shocks for small businesses, new entrepreneurs, and merchants who make small value per transaction like bakeries. Fourth, using the same difference-in-differences specification, I find consistent results that the number of new

> 2These data were made available thanks to a partnership with Groupement des Cartes Bancaires CB, and I exploit the card payments data in accordance with the EU General Data Protection Regulation, in application of Article 89. I use the abbreviation ‘CB’ to indicate the source of the card payments.

2
Table 1: Summary Statistics, MDIC course demand applications

|**Variable**|**Mean**|**Std. Dev.**|**Obs**|
|---|---|---|---|
|Whether course was approved [0/1]|0.50|0.50|16,782|
|Number of seats requested|37.8|178.1|16,782|
|Number of seats requested — approved|43.6|82.3|8,340|
|Requestor is worker/industry association [0/1]|0.17|0.37|16,782|
|Whether requestor found in RAIS — frm|0.97|0.17|13,969|

Source: Authors’ calculations using MDIC course demand data.

Table 2: Summary Statistics, MDIC-requested courses

|**Variable**|**Mean**|**Std. Dev.**|**Obs**|
|---|---|---|---|
|Whether course was approved [0/1]|0.74|0.44|35,834|
|Number of seats _|_ approved|16.0|36.5|22,198|
|Course hours _|_ approved|198.4|44.6|26,666|

Source: Authors’ calculations using MEC course data. Note: Sample comprised of records matching course-municipality of requests to MDIC. Less than 1% of approved requests were among courses with greater than 150 seats.

30
<!-- Start of picture text --> 80% 16% 70%<br>70%<br>60%<br>60%<br>50% 43%<br>40%<br>30%<br>20%<br>10%<br>0%<br>LIC (6 countries) LMIC (13 UMIC (13 HIC (4 countries)<br>countries) countries)<br><!-- End of picture text -->
wage legislation, more (or less) liberal severance pay, reform in the pension regimes etc. These accompanying domestic reforms often concern labor—whether they are “anti” or “pro” labor. Sometimes “anti” labor legislation accompanies openness reforms because it is felt that liberalization in the foreign arena can be emptied of content (or cannot produced the desired results) if there is no improvement in the domestic legislation, i.e. if the latter is deemed too restrictive. Mexico provides one such example (Robertson, 2000 and Hanson and Harrison, 1999). Alternatively, labor policies can, for a segment of labor force at least, become more generous if that is the short-term cost the government needs to pay in order to convince trade unions not to wreck the reforms. In that case, more generous severance pay, low interest loans to start own businesses, early retirement schemes can all be used to reduce the resistance to reforms and to “buy off” potential losers. In addition to labor reforms, there may be also “accompanying” financial reforms: liberalization of interest rates, increased competition in the banking sector etc. All of this complicates any attempt to isolate the impact of trade reform on wage inequality. We shall therefore try to control for some of these other policies (labor markets, social transfers).

To measure labor market conditions, we use the Labor Market Data Base constructed by Martin Rama and Rachel Artecona (see Rama and Artecona, 2002).<sup>27</sup> Their database has, at five-year intervals (year 1975, 1980 etc.) a number of labor-related measures such as social security contributions (in percentage of gross salary), unemployment rate, replacement rate in case of unemployment etc. For our purposes, that is bearing in mind the model described above, two variables are of particular interest: share of labor force covered by collective agreements, and share of the unionized labor force. They do not allow us to distinguish between the type of unionization (skill- or industry-based) which we considered in our model, but they do allow us to proxy the power of trade unions and organized labor.

> 27 The data have been kindly supplied by Martin Rama.

32
<!-- Start of picture text --> PB a RT U 5<br>|i [Units Seale Database, Series_Code Descriptor Notes 2004 2005 2006 2007 20 Ge<br>a2 |Countr Nigeria. Oil rich Firstprojections year of eeoe<br>| 4 | Oil revenues and prices ce<br>National Currency Billions nla Met oil revenues 2,899.9 4.3 5,686.7 6044.2 6.304<br>National Currency Billions: tila Reference price-oil revenue Oil revenues at Bi 1814.5 2,385.0 2557.9 3487 3.331<br>National Currency Billions: ta Oil stabilization Furd 1085.3, 1732.4 228.8 2,895.5 2,973<br>Oil prices<br>U.S, dallars Us¢rbbl nia Market prices WEO Average Oil 383 542 605 56.5 bree<br>| 48 | U.S. dollars US ¢ibsHE b lI nind a BaselineAlternative scenario 1 BudegetPrice at minimum prices 25.0 30.0542 3325 .0 30.0255 2 086<br>4a ue<br><!-- End of picture text -->
Policy ReseaRch WoRking PaPeR 4510

# **Abstract**

Bangladesh has experienced the largest mass poisoning of a population in history owing to contamination of groundwater with naturally occurring inorganic arsenic. Continuous drinking of such metal-contaminated water is highly cancerous; prolonged drinking of such water risks developing diseases in a span of just 5-10 years. Arsenicosis–intake of arsenic-contaminated drinking water–has implications for children's cognitive and psychological development. This study examines the effect of arsenicosis at school and at home on cognitive achievement of children in rural Bangladesh using recent nationally representative school survey data on students. Information on arsenic poisoning of the primary source of drinking water—tube wells—is used to ascertain arsenic exposure. The findings show an unambiguously negative and statistically significant correlation between mathematics score and arsenicosis at home, net of exposure at school. Split-sample analysis reveals that the effect is only specific to boys; for girls, the effect is negative but insignificant. Similar correlations are found for cognitive and non-cognitive outcomes such as subjective well-being, that is, a self-reported measure of life satisfaction (also a direct proxy for health status) of students and their performance in primary-standard mathematics. These correlations remain robust to controlling for school-level exposure.

This paper—a product of the South Asia Human Development Unit—is part of a larger effort in the unit to promote research and impact evaluations in the education sector of the South Asia Region. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at nchaudhury@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