rafmacalaba/datause-encoder-data huggingface dataset:
|P157801|2017|2020|2/15/2020|6 Active|Maldives|
|---|---|---|---|---|---|
|P157801|2017|2020|6/30/2020|7 Active|Maldives|
|P157804|2017|2020|7/29/2019|1 Closed|Sri Lanka|
|P157809|2018|2019|7/16/2018|1 Active|Egypt,Arab Republic o|
|P157809|2018|2019|1/22/2019|2 Active|Egypt,Arab Republic o|
|P157809|2018|2020|7/22/2019|3 Active|Egypt,Arab Republic o|
|P157809|2018|2020|1/29/2020|4 Active|Egypt,Arab Republic o|
|P157809|2018|2021|7/21/2020|5 Active|Egypt,Arab Republic o|
|P157827|2020|2020|6/30/2020|1 Active|Afghanistan|
|P157836|2018|2018|5/23/2018|1 Active|India|
|P157836|2018|2019|10/11/2018|2 Active|India|
|P157836|2018|2019|3/19/2019|3 Active|India|
|P157836|2018|2020|10/11/2019|4 Active|India|
|P157836|2018|2020|6/15/2020|5 Active|India|
|P157858|2017|2018|9/6/2017|1 Active|Cambodia|
|P157858|2017|2018|6/12/2018|2 Active|Cambodia|
|P157858|2017|2019|1/29/2019|3 Active|Cambodia|
|P157858|2017|2020|9/19/2019|4 Active|Cambodia|
|P157858|2017|2020|3/25/2020|5 Active|Cambodia|
|P157861|2016|2018|1/27/2018|1 Closed|Jordan|
|P157861|2016|2020|6/22/2020|2 Closed|Jordan|
|P157891|2017|2017|6/29/2017|1 Active|Nigeria|
|P157891|2017|2018|12/20/2017|2 Active|Nigeria|
|P157891|2017|2018|6/21/2018|3 Active|Nigeria|
|P157891|2017|2019|12/21/2018|4 Active|Nigeria|
|P157891|2017|2019|6/21/2019|5 Active|Nigeria|
|P157891|2017|2020|1/9/2020|6 Active|Nigeria|
|P157891|2017|2020|6/30/2020|7 Active|Nigeria|
|P157892|2017|2017|6/22/2017|8 Active|Mali|
|P157892|2017|2018|3/22/2018|9 Active|Mali|
|P157892|2017|2019|12/6/2018|10 Active|Mali|
|P157892|2017|2019|6/28/2019|11 Active|Mali|
|P157892|2017|2020|2/10/2020|12 Active|Mali|
|P157892|2017|2020|5/13/2020|13 Active|Mali|
|P157909|2017|2017|6/26/2017|1 Active|Madagascar|
|P157909|2017|2018|1/26/2018|2 Active|Madagascar|
|P157909|2017|2019|9/6/2018|3 Active|Madagascar|
|P157909|2017|2019|4/22/2019|4 Active|Madagascar|
|P157909|2017|2020|12/27/2019|5 Active|Madagascar|
|P157909|2017|2020|6/29/2020|6 Active|Madagascar|
|P157918|2017|2018|6/29/2018|12 Active|St. Vincent and the Gr|
|P157918|2017|2019|1/22/2019|13 Active|St. Vincent and the Gr|
|P157918|2017|2020|7/25/2019|14 Active|St. Vincent and the Gr|
|P157918|2017|2020|3/12/2020|15 Active|St. Vincent and the Gr|
|P157918|2017|2021|7/15/2020|16 Active|St. Vincent and the Gr|
|P157919|2016|2017|4/5/2017|1 Closed|Tunisia|
|P157919|2016|2018|1/2/2018|2 Closed|Tunisia|
What is particular of the benchmark effect is that the form benchmarks are calculated guarantees that the response of flows to an own-country shock through benchmarks is positive, and it is negative for shocks to the returns to other countries. For the nonbenchmark component, the sign of these responses is indeterminate.

# **6. Conclusions**

This paper shows how benchmarks affect asset allocations and capital flows across countries using a novel dataset of well-known benchmark indexes and mutual funds from around the world investing in equities and bonds. We find that benchmarks have important effects on both asset allocations and capital flows, not only because funds explicitly declare a benchmark to compare their performance, but also because funds with different degrees of activism tend to follow their benchmark asset allocation closely, though to different extents. Given that benchmarks are based on market capitalization, they instantaneously absorb any return shock to the countries in the index. Benchmark weights also receive frequent, exogenous revisions by the companies that construct them. These benchmark changes affect the mutual fund portfolios, their reallocations, and their sensitivity to injections or redemptions. The effects of benchmarks on mutual fund allocations are significant even after controlling for industry effects, country-time effects, macroeconomic fundamentals, potential reverse causality, and other important micro and macro factors that drive country portfolios. The decisions about allocations impact capital flows through different channels.

These results can explain some of the findings documented in the literature, as well as counterintuitive and unexpected movements in cross-country investments and capital markets. First, the reclassification of countries across benchmarks is likely to have significant direct reallocation effects on capital flows, given that assets under management and country weights differ across benchmarks and types of funds. For example, advanced emerging countries tend to have larger weights in emerging market indexes than in developed market ones. Therefore, countries might face capital outflows when upgraded and capital inflows when downgraded.

Second, sensitivity, amplification, and contagion effects can occur even when fundamentals or the absolute returns of a country do not warrant them. For example, during global crises, some countries might suffer the curse of being large or having done relatively well. That is, during large retrenchments, countries with larger weights will suffer more withdrawals, although in some cases their larger market capitalization might 28
period 2007-2009, the amount of appropriations for payments in Structural Funds is €740 mn, in current prices.

**Table 6: Payments Schedule for Structural Funds**

|**Type of Appropriations in € mn, current prices**||**2007**<br>Advance(%|**2008**<br>of total envel|**2009**<br>ope)|
|---|---|---|---|---|
|Total Commitment Appropriations,2007-09|1441|10%|6%||
|||Reimbursemen|ts(% of ann.|avrg.)|
|Annual Average Commitment Appropriations,2007-09|480|3%|40%|63%|
|_TotalCalculated Payments Appropriations:_||**159**|**279**|**303**|

_Note:_ Disbursements are made over 7 years.

_Cohesion Fund_ allocations are disbursed differently than the _Structural Funds_ and according to the scheme in Table 7. To calculate the amount of payments in each year, we sum up the allocated share of commitments for each of the three years multiplied by the actual size of commitment appropriations for the respective year. For example, only 3% of the committed €191 mn will be appropriated as payments in 2007, while for 2008 payments will be €48 mn (21% of the 2007 commitments of €191 mn added to 3% of the 2008 commitments of €274 mn). The size of payments appropriations for Cohesion Fund for the three year period after accession is €165 mn in current prices.

**Table 7: Payments Schedule for the Cohesion Fund**

|**Type of Appropriations, in € mn, curr**|**ent prices**|**2007**|**2008**|**2009**|
|---|---|---|---|---|
|CommitmentAppropriations2007|191|3%|21%|22%|
|CommitmentAppropriations2008|274||3%|21%|
|CommitmentAppropriations2009|369|||3%|
|_Total Calculated Payment Ap_|_propriations:_|**6**|**48**|**111**|

The size of the payments under _Structural Actions_ from the EU to Bulgaria is 0.6% of GDP in 2007, and goes up to 1.4% in GDP in 2009, reflecting the assumed progressive increase of absorption capacity of the country. It should be taken into consideration that the size of the financial envelope which captures both Structural Funds and the Cohesion Fund, however, is capped at 4% of national GDP in any given year.

It should be noted that Structural Funds are intended to be used as incremental budget resources. They are subject to the principle of additionality and may not substitute national expenditures, but should rather lead to new projects (see Box 1).<sup>9</sup> Although monitoring is rather soft and noncompliance does not invoke sanctions, additionality is a legal requirement and as such it affects our calculations. This principle implies that fiscal space needs to be made for the effective and efficient absorption of the inflow of money from the EU budget (not only Structural Funds) without unnecessary distortions. It is imperative to ensure that the underlying incentive structure does not encourage the mere generation of projects with no economic justification. In contrast, the principle of additionality does not apply to the Cohesion Fund, internal policies or transitional expenditures like budgetary compensations or nuclear safety payments.

> 9 See Article 11(1) of Council Resolutions No. 1260/99, Official Journal L 161 of 26/06/1999.

8
23 limited general equilibrium iLodel. They measure the impact of the Indian Green Revolution and the effect of alternative government policies on inconme distribution.

# b. Time Series of Indicators

A second method, used by Behrman and Deolalikar (1989) in a study on Jamaica, analyzes time series of indicators. They estimate the secular growth of available indicators and test whether significant shifts in the relation occurred during the adjustment period.<sup>2 2</sup> In a rough way, this method identifies whether the adjustment period differed from the secular trend, as opposed to being a period of poor performance as part of a longer experience. They find that controlling for past trends leads to a much leas negative assessment of adjustment impacts than other authors had found. Their analysis is a tough attempt to try to separate the counterfactual from adjustment policies, but it cannot clearly establish causal effects of adjustment programs, because there is no control for other variables that may affect the in-lcator of interest. They supplement these regressions with labor market analysis and real wage analysis and also look at trends in transfers. Finally they examine food and nutrition, education, health and anthropometric evidence more closely. They regress expenditure relations for food and calculate food and nutrition elasticities, and use these to calculate the nutritional effects from changes in income.

# c. Fiscal Incidence Studies

22That is, they test whether the adjustment period was just part of a longer experience of bad years by regressing the variable on time, time squared and a dummy for the adjustment period and testing the significance of the dummy.
# **Table A7: Reasons of well‐being improvement**

|||**Job**|**loss**|||**Salary re**|**duction**||
|---|---|---|---|---|---|---|---|---|
||**2014**|**2015**|**2016**|**2017**|**2014**|**2015**|**2016**|**2017**|
|Average|5.88|4.56|3.41|5.20|5.94|2.99|1.91|3.97|
|Vulnerable|8.42|5.68|4.24|5.87|5.58|3.56|1.78|5.59|
|Official poverty|5.43|6.39|7.67|8.10|5.84|1.53|2.33|2.39|
|Moderatepoverty|4.57|4.21|4.63|6.24|4.77|2.19|1.89|3.33|
|Minsk|5.28|5.28|3.10|5.89|6.59|3.81|1.61|3.65|
|Large cities|5.33|3.74|4.10|6.49|6.86|3.06|2.76|4.22|
|Small cities|6.71|5.40|3.10|4.66|5.24|2.30|1.21|2.96|
|Rural area|6.35|4.68|2.85|3.14|4.84|2.77|1.36|4.50|
|Child|7.87|5.24|4.56|6.62|6.72|3.76|2.57|5.34|
|Working age|6.97|5.59|4.09|6.42|7.34|3.69|2.12|5.15|
|Retiree|2.19|2.04|1.31|2.17|2.52|1.07|1.04|1.05|
|Employed|7.06|5.36|3.48|5.88|7.51|3.71|2.28|5.09|
|Partly employed|9.84|8.56|7.04|12.39|5.99|2.29|1.47|3.70|
|Nonemployed|4.99|4.56|4.60|6.21|5.88|3.30|1.55|3.89|
|High education|6.73|5.76|3.93|6.60|8.39|4.33|3.07|5.71|
|Secondary specialized|6.48|5.47|3.28|5.59|7.39|4.34|2.04|5.26|
|Other|7.45|5.25|4.92|6.87|6.69|2.88|1.51|4.66|
|HH without children|4.10|3.59|2.46|3.44|5.44|1.98|1.30|2.80|
|Retiree HH|1.61|0.90|0.81|0.87|1.54|0.83|0.64|0.36|
|HH with children|7.65|5.53|4.38|6.95|6.43|4.02|2.52|5.14|
|1 child|7.50|5.81|3.90|7.54|6.11|4.37|2.55|4.75|
|2 children|6.48|4.92|5.40|6.60|7.17|3.77|2.58|5.73|
|3 and more children|12.55|5.84|3.89|3.66|5.73|2.35|2.12|5.74|
|singleparent|3.27|3.00|4.71|2.48|6.82|3.18|2.19|3.12|

Source: Authors’ estimates based on HSS data.

34
# **Modeling migration dynamics in Albania: A hazard function approach**

# **Carlo Azzarri and Calogero Carletto**

_World Bank_

**Keywords** : Albanian migration, duration analysis, hazard function, migration networks, return migration.

**JEL Classification** : C41, O15, R23

This paper—a product of the Poverty Cluster of the Development Research Group. —is part of a larger effort in the department to study the impact of migration on poverty and other outcomes in source countries. Policy Research Working Papers are also posted on the Web at <u>http://econ.worldbank.org. The authors may be contacted at gcarletto@worldbank.org and</u> cazzarri@worldbank.org.
**Table 6: Impact of Plan Nacer on Prenatal Care and Birth Outcomes**

||Number<br>prenatal care<br>visits|Tetanus<br>toxoid<br>vaccine|Cesarean<br>section|Birth weight<br>(grams)|Low birth<br>weight<br>(<2500 gr)|
|---|---|---|---|---|---|
|A. Intent-to-treat (differen<br>|ce-in-difference)<br>|||||
|Clinic on Plan Nacer|0.125**|0.018**|-0.019***|17.11***|-0.005**|
||(0.043)|(0.008)|(0.006)|(4.88)|(0.002)|
|Mean of nontreated|4.486|0.776|0.246|3267.730|0.072|
|B. Treatment-on-treated (|IV difference-in-di|fference)||||
|Plan Nacer beneficiary|0.679***|0.056**|-0.052**|48.95***|-0.014**|
||(0.151)|(0.027)|(0.022)|(14.78)|(0.006)|
|Mean of nontreated|4.401|0.774|0.247|3,266.39|0.074|
|_F_-statistic for first stage|113.47|132.05|153.53|231.91|231.91|
|<br>C. Treatment-on-treated<br>|with spillover (IV d<br>|ifference-in-<br>|difference)<br>|||
|Plan Nacer beneficiary|2.283**|0.300|0.113|-17.55|0.001|
||(1.058)|(0.370)|(0.091)|(63.33)|(.028)|
|Clinic on Plan Nacer|-0.805**|-0.082|-0.053|22.70|-0.005|
||(0.283)|(0.127)|(0.029)|(21.13)|(0.009)|
|_F_-statistic for first stage|15.798|2.348|12.437|19.98|19.98|
|Number observations|228,656|108,535|282,042|274,078|274,078|
|Number clinics|1,838|1,810|1,898|1,897|1,897|

_Notes:_ The table reports the estimated impact of Plan Nacer on outcomes indicated by the columns. Panel A reports the estimated coefficients and standard errors on the clinic treatment indicator from difference-in-difference models specified in equation (1). Panel B reports the estimates for beneficiary status using the clinic status variable and the length (in months) since the clinic was incorporated into Plan Nacer as instruments for beneficiary status in the difference-in-difference model specified in equation (2). Panel C reports the estimated impact of Plan Nacer on beneficiaries and on nonbeneficiaries (clinic on Plan Nacer) using the length (in months) since the clinic was incorporated into Plan Nacer as instrument for beneficiary status in the difference-in-difference model specified in equation (3). All of the models control for clinic fixed effects, time-province fixed effects, maternal age, and number of previous births. Standard errors are clustered at the clinic level.

22
# **I.   INTRODUCTION**

Bank regulation and supervision has been the subject of much recent debate and attention, due in large part to the global financial crisis that started in the late 2000s. A number of studies have pointed to weaknesses in regulation and supervision as one of the factors leading to the crisis (Dan, 2010; Lau, 2010; Levine, 2010; Merrouche and Neir, 2010; and Barth et al., 2012). Not only did the crisis raise important questions on the appropriateness of the regulatory and supervisory approaches pursued in the run-up to the crisis, but also it prompted regulators to consider important changes in regulation and supervision. Despite the high level of interest in the topic and extensive work on the global regulatory framework (exemplified for instance by the various initiatives of the Basel Committee on Banking Supervision), there is a surprising lack of consistent and up-to-date information on the regulatory and supervisory approaches pursued in countries around the world and the changes brought about by the crisis.

This paper presents and examines the World Bank’s 2011–12 Bank Regulation and Supervision Survey (BRSS). The survey is an updated and substantially expanded version of earlier surveys of the same name, released by the World Bank in 2001, 2003, and 2007.<sup>2</sup> The current, fourth iteration of the survey provides information on bank regulation and supervision for 143 jurisdictions (Table 1) and includes close to 300 questions. It covers the period 2008–10, and therefore, allows us to examine the recent state of bank regulation and supervision in a wide range of countries and to compare it to the pre-crisis situation.

Using the BRSS, this paper addresses two main questions: What are the features of the regulatory and supervisory frameworks of countries that were directly hit by the global financial crisis, and did they differ from the rest of the world? How have national regulatory and

> 2 Note that the three previous surveys captured information as of 1999, 2001, and 2005, respectively.

2
Table 6 Average Carbon per Hectare by Forest (kg) <u>Standard deviations in parentheses</u>

||Hill|_Terai_|AllCF/NCF|
|---|---|---|---|
|CF|76091.67|118327.4|89737.05|
||(71102.69)|(102999.5)|(84310.32)|
|NCF|72068.45|101988.1|95083.6|
||(70414.55)|(65616.93)|(67397.78)|
|All Hill/_Terai_|75068.82|106820.9||
||(70342.33)|(78111.61)||

Table 7

# Average Carbon per Hectare (kg) by Forest for NCFs only <u>Standard deviations in parentheses</u>

||Hill|_Terai_|All ID FUG form<br>year|
|---|---|---|---|
|Can ID FUG<br>formyear|122056.4<br>(74800.52)<br>n=7|116354.2<br>(61143.91)<br>n=30|117433<br>(62843.28)<br>n=37|
|Cannot ID FUG<br>formyear|28329<br>(20865.43)<br>n=8|80439.07<br>(67697.46)<br>n=20|65550.48<br>(62550.86)<br>n=28|
|All Hill/_Terai_|72068.45<br>(70414.55)<br>N=15|101988.1<br>(65616.93)<br>n=50||

We see that forest groups that can identify the formation year of their forest user group have more carbon per hectare on average than forests without an identifiable formation year. Whether forests are located in the hills, the _Terai_ or in total, average carbon per hectare is greater if a formation year is identifiable. In the _Terai_ , for example, forests without an identifiable formation year have only 70% of the carbon of those with an identifiable year. Overall, though, the value is about 55%.

Tables 8 and 9 present the forest level OLS regression results for our four dependent variables with and without the CF dummy and the CanIDFUGyear dummy.

**24 |** P a g e
effort away from the less visible activity of preparing for classes and towards the more visible activity of after-school tutoring.

This paper consists of eight sections. In the next section, we briefly review the related literatures on teacher incentives and effort with a focus on developing countries. Section III, which provides the analytical framework, is followed by a description of teacher incentives in Lao PDR and our data in Section IV, and Section V discusses estimation results. Sections VI and VII provide further investigation of teacher provision of tutoring and the effect of PTAs on teacher effort. Section VIII summarizes our findings and discusses policy implications.

# **II. Literature Review**

While ample evidence exists on teacher incentives in richer countries,<sup>6</sup> much less is known about teacher incentives in developing countries. It is tempting simply to apply the findings in richer countries to poorer countries, but there are important differences between rich countries and developing countries, including shortage of trained teachers, poor teacher quality and a weak monitoring system (Glewwe and Kremer, 2006). In this section, we briefly review the most relevant studies on teacher incentives in developing countries.

Teacher effort is difficult to measure in part because it happens behind classroom doors, away from the eyes of school inspectors and even school principals, and in part because effort has several dimensions. Absenteeism has been used as a proxy measure of effort in previous studies, but it is typically an unreliable indicator because it runs into problems associated with self-reporting and possible collusion among teachers and other school actors. To overcome this measurement problem, a survey of schools conducted in Bangladesh, Ecuador, India, Indonesia, Peru, and Uganda used _unannounced_ school visits and checks of teachers present against a roster

> 6 See Podgursky (2011) and Neal (2011) for recent reviews.

5
27 the nation as a whole. Tanzania needs strong growth in rural output per capita to make a substantial dent in the national poverty rate and attain the national poverty MDG. This requires sustained growth in rural GDP, a reduction in the rate of population increase or a combination of both.

# **7 References**

Datt, G., K. Ramadas, D. van der Mensbrugghe, T. Walker, and Q. Wodon [2003], “Predicting the effect of aggregate growth on poverty,” in F. Bourguignon and L. Pereira da Silva (eds), _The Impact of Economic Policies on Poverty and Income Distribution: Evaluation Techniques and Tools,_ The World Bank and Oxford University Press.

- Datt, G. and T. Walker [2002], _Povstat 2.12, A Poverty Projection Toolkit, User’s Manual,_ The World Bank, October 14, 2002, mimeo.

Datt, G. and Ravallion, M. [1992]. "Growth and Redistribution Components of Changes in Poverty: A Decomposition with applications to Brazil and China in 1980s", _Journal of Development Economics_ 38: 275-295

- Huppi, M. and Ravallion, M. [1991] "The Sectoral Structure of Poverty During an Adjustment Period. Evidence for Indonesian the Mid-1980s", _World Development_ 19: 1653-1678

Ravallion, M. and Chen, S. [2002] "Measuring pro-poor growth", The World Bank.

Ravallion, M. [2003], “Measuring Aggregate Welfare in Developing Countries: How Well do National Accounts and Surveys Agree?” _Review of Economic Statistics_ , 85(3): 645-652.

United Republic of Tanzania, President’s Office [2002], _Economic Survey 2002._ United Republic of Tanzania, National Bureau of Statistics [2002], _Household Budget Survey 2000/01._
only quizzes counted toward the receipt of the final completion certificate, given this incentive structure we expect a lower usage of the written exercises and video pitch uploads than multiple‐choice quizzes.

# _Communication and Reminders_

During the deployment of the intervention our team sent weekly motivational announcements to the students on the platform and on their email address, the aim of the announcements was to promote learning and active participation. They were structured as progress reports where we showed the top ten performing firms in the last week in terms of correct answers in submitted quizzes, and explained the reasons why it is important to take part in the course. Firms were told that going through the modules would both help provide matching of their businesses with judges who had sectoral expertise in their business, and that going through the contents of the modules would likely increase their chances of getting a higher score in the semi‐finals and getting selected for the finals.

# _Usage_

Appendix Figure 5.1 summarizes the proportion of students that submitted assignments (either quizzes or written exercises <mark>), each bar corresponds to an assignment. O</mark> ut of the 120 participants that connected at least once to the online platform, 63 (36.6% of the total) actively participated in one of the quizzes, with 45 of them completing the threshold of 70% correct answers. For the non‐graded written exercises, the technology description was completed by 40 participants, the technology brief by 20, benefits exercise by 29 and the competition exercise by 22 participants. Lastly, only 8 students uploaded a video of their pitch.

# **<mark>Appendix Figure 5.1: Participation of the Control Group in Online Course Content</mark>**

<!-- Start of picture text --> Proportion of students submitted assignemnts<br>Technology Brief Exercise<br>Competition Exercise<br>Benefits Exercise<br>Technology Description Exercise<br>Prepare a Pitch<br>Competition Quiz<br>Tech Description Quiz<br>Market Validation Quiz<br>Intellectual Property 2 Quiz<br>Intellectual Property 1 Quiz<br>Development Status Quiz<br>Benefits Quiz<br>% 20% 40% 60% 80% 100%<br><!-- End of picture text -->

# _Satisfaction_

A short survey was administered after the semi‐finals to assess their satisfaction with different elements of the program. Respondents are therefore only the entrepreneurs that participated in the semi‐finals. The survey was answered by 102 treated group firms (92.7% of the treated semifinalists) and 87 control 61