rafmacalaba/datause-encoder-data huggingface dataset:
In this section we will present an econometric analysis on the impact of the loan on student behavior, using both the SOFES database and the survey data. It should be noted that in the latter case the treatment effect refers to an effect along, as we call it, the “external” margin, i.e. being a SOFES recipient or not. In contrast, the impact of SOFES using the RD-design should be interpreted as an effect along the “internal” margin, i.e. between SOFES-recipients with different credit levels. Econometric results may differ due to unobserved heterogeneity and non-linearity.

# _6.2 Impact on student performance_

Does the level of student support impact on the academic performance of students? On the one hand, students receiving a large loan might be enticed to put in less effort. Universities carry the default risk, and this could lead to moral hazard among students (see Section 2). On the other hand, peer pressure to show good performance on students eligible for a SOFES credit might lead to an opposite effect: students receiving a large loan perform better than students receiving a smaller credit, everything else being equal. In addition, and perhaps more importantly, the loan program is contingent on student performance: a student will receive renewal of the loan only after passing the previous term of the program. To investigate the relationship between loans and student performance, we estimate a model of the following form, where GPA is the grade point average achieved in the previous term of the program.

Results using the SOFES database are presented in Table 3A (Bachelor-students) and 3B (Masterstudents). The table should be read as follows: The cut-off at ENF=0.08 is denoted by DS=0.08 (DS stands for discontinuity sample). We want to compare the change in the outcome variable close to the point of discontinuity. To that end, four subsamples are defined within a narrow range around the cut-off point, but with an increasing width, e.g. DS±0.005, DS±0.01, DS±0.015, and DS±0.02.<sup>15</sup> So the first row in Table 3A uses the subsample 0.075<ENF<0.085, and the second row 0.07<ENF<0.09, etc. More observations become available when the range is widened, but the groups below and above the threshold become more dissimilar. Column (2) denotes the average of the variable of interest in the region below the cut-off (denoted by “<RD”), and Column (3) denotes the average of the variable of interest in the region above the cut-off (denoted by “>RD”). Results

> 15 Because of small numbers of observations, we cannot exploit the discontinuity at DS=0.50. For similar reasons, we sometimes omit RD estimation for the smallest subsample, we sometimes use a broader band width, and we cannot provide a complete sensitivity analysis (including the complete set of control variables) in all cases.

19
As seen in the table, across the three categories of households, more than 90% are not aware of nearby shelters. Even during the extreme floods of July 2005, these households did not move to shelters. More than 90% also do not receive any warning about floods. Mumbai does not have an effective early warning system and the only warning households get is through the community alarm and radio and television network. Community alarm simply means the network within the community who spreads information on the impending floods. The households mostly get flood warning 15 minutes prior. When floods occur, poor households approach only the local corporator (elected representative with the local government) since they are known to provide formal and informal support to slums and other poor communities. Some of the non‐poor households either approach the MCGM ward office or the disaster control room. Majority of the poor and non‐poor households have not received any training on coping with floods. Among those who have received some form of training, it has been organized mainly by NGOs and academic institutions for BPL families and MCGM and NGOs for others.

As regards the financial support and loans in the aftermath of floods, both poor and non‐poor households approach banks and/or family and friends. Banks are the major source of support for all since the penetration of banking in the city is very high. It must also be noted that the poor usually do not approach the scheduled commercial banks for loans but the local cooperative banks or chit funds. All these sources have been clubbed together under the heading of loans from banks to indicate that the sources are organized in nature.

# 4.3.1 Short‐term responses

The poor and non‐poor households have a limited ability to respond to recurrent floods since they do not have adequate financial resources or access to a formal early warning system and training. With the limited ability, the households, both poor and non‐poor, undertake various measures every year on the onset of monsoon through their own initiative and funding. Some of these measures are given in Table 7. All these measures are recurrent with recurrent costs and put financial burden on the poor families. The BPL families have to undertake repairs for roofs and houses in larger numbers than the non‐poor, simply because their houses are constructed poorly or might be temporary structures.

Table 7: Recurrent measures undertaken by households in the pre‐monsoon period

|**Measures taken by HHs**|**BPL (%)**|**Poor (%)**|**Non‐poor**|**Cost per HH**|
|---|---|---|---|---|
||||**(%)**|**(in Rs.)**|
|Cleaning house surroundings|70.0|55.7|68.2|200|
|Cleaning nullah (canal or gutter)|50.0|48.4|56.8|200|
|Repairing roof|50.0|35.2|25.0|1300|
|Overhauling vehicle|5.0|5.7|4.5|600|
|Repairs inside house|35.0|22.1|13.6|800|

Source: Calculations based on data obtained from 200 households 21
- 23 -

owners with high cost at a time when they are facing only modest prospects for growth in seatrade--and thus improved freight earnings.

Table 5: INTERNATIONAL CONTAINER TRAFFIC Vessel Capacity Utilization observed and expected load factors in percent

|Year|**Trans**<br>|**atlantic**<br>|**Tran**<br>|**spacific**<br>|**Europe**|**-** Far East|
|---|---|---|---|---|---|---|
||eastbound|Twes|boun<br>eastbound|d<br> <br>westbound|eastbound|<br>westbound|
|1991|62.2|54.4|71.7|60.7|53.1|85.7|
|1992|62.8|56.4|76.1|63.8|51.3|84.2|
|_1993_|_62.6_|_56.3_|_77.9_|_64.9_|_50.2_|_82.1_|
|_1994_|_62.7_|_56.4_|_78.5_|_65.1_|_50.0_|_81.4_|
|_1995_|_64.7_|_58.5_|_79.1_|_65.5_|_49.0_|_77.6_|
|_1996_|_66.8_|_60.8_|_79.3_|_65.5_|_51.1_|_78.7_|

Source: For observed indicators: _"Containerization International"._ Various issues. London For expected load factors: _'International Transport Journal"._ Issue 49, 1992. Basle 35. Because of continued poor revenue earnings, shipowners had to cut cost wherever possible. In growing numbers they opted for outflagging their tonnage to FOC countries which reduced their tax burden and enabled them to hire low-wage personnel. Unfortunately, however, cost cutting also meant in many cases neglect or deference of maintenance which caused accelerated physical deterioration of shipping assets and has to be seen as a main cause for increasing ship casualties. Conmmercial pressures on masters to improve the revenue earning perfonnance of their ships have induced reckless loading practices in ports and the operation of ships at speeds beyond design limits with deleterious effects on hull structures. Low-wage personnel often implied unqualified seafarers. Human error has been identified as a principal reason for accidents at sea and in ports.

36. Another defensive strategy which has been increasingly adopted by many owners is to keep existing shipping assets beyond commonly accepted economic life spans, which are typically between 15 and 20 years. Considering current prices of newbuildings and the freight rates the market is willing to pay, a decision to continue with aged tonnage was more than ever deemed superior--from a corporate management point of view--to a combination of scrapping and acquisition of new tonnage. But for much of this kind of aged tonnage major repairs would be required before such vessels could continue to operate safely and profitably.

37. Ship upgrading or conversion have become other attractive propositions through which older ships are either technologically improved, e.g. refitted with more powerful and fuel efficient engines, or physically transformed to serve other trades than the ones for which they were originally designed and built. The upgrading of vessels leads to better operational performance--by permitting faster steaming and shorter cargo handling times--and thus to enhanced revenue earnings. Shifting of converted tonnage to other trades, for which the growth prospects are more promising, has already enabled many operators
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33
out labor effort by members of the household other than the participant, 2) whether or not there are gains from participating in the program six months after the program has finished, and 3) whether or not there are gains in household consumption, so as to assess the role of the program as an insurance mechanism. The authors found that the program positively influenced number of hours worked and both individual and household labor income. Household income increased more than individual’s income, indicating that the program had some positive externality on the other members of the household.

A third application of these methods is in the evaluation of job training. One study, by Card et al (2007), presents the results of the first impact evaluation based on an experimental design for a job training program in Latin America. Between 2001 and 2005, the government of the Dominican Republic implemented a job training program for low income youth population (Juventud y Empleo) consisting of classroom training followed by an internship in a private sector firm. The eligible population was randomly selected to training, and information was gathered 10-14 months after graduation for both trainees and members of the control group. Training was found not to have a significant impact on the likelihood of having a job. However, a marginally significantly impact was detected for wages and also for having health insurance coverage conditional on being employed. The results suggest significant heterogeneity of impacts, with male teens being the group that benefits from the program; impacts were not found for women or for young adults. Regional differences also seem to be present. Although small, the impact on wages (if maintained over time) coupled with no discernible employment effect implies that the costs of the program are recovered in two years.

Another randomized evaluation of a training program is reported by Attanasio, Kugler and Meghir (2007) for Colombia. Contrary to Card et al’s findings for the Dominican Republic, this study found that job training in Colombia had large, widespread effects on women, but fewer and less pronounced effects on men.

These studies illustrate the power of randomized experimental data of labor market programs. Undoubtedly more such evaluations will be conducted in the future.

6. Qualitative data. Analysis of qualitative data has a role to play in informing us about labor market issues. Kanbur (2003) has identified five dimensions in which qualitative and quantitative approaches differ from one another: 40
repeat the analysis (with a bandwidth of 50) on the sub-sample of regions with very similar threshold scores, i.e. regions 3, 4, 5 and 6 (see appendix tables A3a, b).

# _Spillover Effects and Evaluation Bias_

Another potential explanation for the observed patterns of impact obtained using the RDD method may be due to the inadequacy of the comparison group used by the RDD.<sup>23</sup> It is conceivable, for example, that after the start of the PROGRESA program in the treatment villages, non-eligible households in these villages may have altered their behavior by enrolling their children to school or withdrawing them from the labor market either due to “peer effects” within these small rural communities or due to expectations that this behavior may increase their chances of becoming eligible for the program’s benefits. Whatever the reason, as long as non-eligible households change their behavior because of the presence of the program in their community they may cease to provide an appropriate comparison group for the evaluation of the program. This may also impact on the performance of the RDD relative to the benchmark experimental estimates.

In order to investigate this latter possibility in more detail, we conduct two tests based on two different comparison groups. Firstly, we examine whether the program had any impact on children from non-eligible households just above the threshold point in group C using as a comparison group children from non-eligible households that are also just above the threshold score in the control villages (group D in table 2). Secondly, we examine program impact on group C using as a comparison group children from group B. Since none of these groups of 23 Bobonis and Finan (2002), for example, in their analysis of the spillover effects of PROGRESA, find that the program did have an impact on the non-eligible households, and that this impact occurred primarily in round 3 the first year after the start of the program.

22
If analyzed from the sectoral point of view, investigations follow clear-cut international patterns. First come steel products; second, chemicals, and third, textiles and clothing (Figure 4).

**Figure 4 - Investigations Broken Down by Sector, 1987-2002**

<!-- Start of picture text --> Basic metals 87<br>Chemicals 67<br>Textiles 30<br>Machinery & Equip. 23<br>Other industries 14<br> Food 10<br>Agriculture 9<br>Timber 7<br>Minerals 3<br>Mining 1<br>0 10 20 30 40 50 60 70 80 90 100<br><!-- End of picture text --> Source: Based on data from UPCI’s _Informe de Labores_ , 2002.

**Table 6 - Investigations and Duties by Type of Imported Good**

||**Number of**<br>**investigations**|**Percentage of**<br>**total number of**<br>**investigations**|**Number of**<br>**duties in**<br>**force**|**Percentage of duties**<br>**in force vis-à-vis the**<br>**total number of**<br>**investigations**|
|---|---|---|---|---|
|Consumergoods|56|22.31%|14|25.00%|
|Intermediategoods|187|74.50%|63|33.69%|
|Capitalgoods|8|3.19%|2|25.00%|
|Total|251|100.00%|79|31.47%|

Source: UPCI’s _Informe Anual de Labores_ , 2002, and DOF’s Resolutions, 2003.

Table 6 shows how investigations are distributed per type of good. Intermediate products recorded the highest number of investigations (74%), followed by consumer goods (22%) and capital goods (8%). With regard to the number of duties in force, intermediate goods rank first as well.

22
processing and apparel), and other Services (including all other firms, excluding retail). We use this sector structure of the data for most of the analysis in this paper. Additional sector stratification that were country specific included: motor vehicles (ISIC 29); Leather (ISIC 15), Pharmaceutical (ISIC 21), and Motor vehicles (ISIC 29); and Land transport (ISIC 49), Finance (ISIC 64), and Health (ISIC 86).<sup>31</sup> In the geographic stratification, we use sub-national regions.

To calculate the optimal distribution of the sample, we followed a similar methodology as described by the World Bank (2009). The sample size for each country was aligned with the degree of stratification of the sample.

The data used in this paper corresponds to the first and second phase of the survey implementation. The surveys were administered between June 2019 and the end of 2021 by the World Bank in partnership with public or private local agencies across ten countries: Bangladesh, Brazil (the state of Ceará), Senegal, and Vietnam in the first phase until January 2020. In the second phase, conducted during the COVID-19 pandemic, after January 2020, included Burkina Faso, India (the states of Tamil Nadu and Uttar Pradesh), Ghana, Kenya, Poland, and the Republic of Korea. The mode of data collection was face-to-face before the pandemic and mostly on the telephone during the pandemic.

Table A.16: Year and mode of data collection

|Country|Year|Mode|
|---|---|---|
|Bangladesh|2019|Face-to-face|
|Brazil|2019|Face-to-face|
|Burkina Faso|2021|Telephone|
|Cambodia|2022|Telephone|
|Chile|2022|Telephone|
|Croatia|2023|Online|
|Ethiopia|2022|Face-to-face|
|Georgia|2022|Online & Telephone|
|Ghana|2021|Telephone|
|India|2020/23*|Face-to-face|
|Kenya|2020|Telephone|
|Korea, Rep.|2021|Telephone|
|Poland|2021|Telephone|
|Senegal|2019|Face-to-face|
|Vietnam|2019|Face-to-face|

> 31These specific stratifications were taken into consideration when determining sampling weights.

100
|**Table 6. Elite capture and targeting, Arusha**|(1)<br>**vs. Kilimanjaro**<br>Aru|(2)<br><br>sha|(3)<br>Kilim|(4)<br>anjaro|
|---|---|---|---|---|
||2008<br>beneficiaries|2010 new<br>beneficiaries|2008<br>beneficiaries|2010 new<br>beneficiaries|
|Related to government officials or influentials|-0.004|-0.004|0.032*|0.051|
||(0.896)|(0.904)|(0.097)|(0.381)|
|Eligible in 2008/09 season|-0.083***|0.074**|-0.028|-0.068|
||(0.003)|(0.016)|(0.142)|(0.219)|
|Observations|734|666|544|256|
|R-squared|0.012|0.009|0.011|0.011|
|Mean dep. var. non eligible non related<br>Note: Data come from baseline and follow-up Villager<br>Kilimanjaro (also in the north) were not part of the whole s|0.193<br> survey. Arusha (<br>tudy. Beneficiary s|0.155<br>north) villages wer<br>tatus is self-reported|0.054<br>e part of the study<br>by survey responde|0.244<br>, while villages in<br>nts.|
<!-- Start of picture text --> 2<br>1.5<br>0.5<br>-0.5<br>-1.5<br>1997 2003 2009 2015 2021 2027 2033 2039 2045 2051 2057 2063 2069<br>-PAYG Reforms Before Second Pillar -Multipillar Reforms with 6% Contribution<br><!-- End of picture text --> projected to generate small surpluses at the end of the decade, even considering the revenue losses, but turns into deficits again in the second decade, with the retirement of the baby boom generations.

The PAYG deficit peaks around 2022 but then improves, as the first significant cohorts to receive first pillar benefits in the new system retire (i.e. those in their mid-thirties in 1998). The difference between the PAYG balances with and without the opt out to the second pillar increases until about that time and narrows thereafter. By 2040 the deficit in the multi-pillar system is actually smaller than the PAYG deficit had the second pillar not been introduced. These results are driven by two factors. First, the replacement ratio of the first pillar of the new system is about three fourths of the replacement ratio in the reformed PAYG. Thus, the temporary imbalances between replacement ratios and contributions created by the opt out starts to taper off after the cohorts in the mixed system begin to retire. Second, the reform involves a reduction of about one fourth of the accrued rights of workers who opt for the new system. As a result, the valuation for the years of contribution under the old system are lower than what would have been generated by the old benefit formula.

To determine the first-order impact of the reform on national savings the public and private pension savings need to be combined, as shown in Figure 12. The PAYG balances with the second pillar opt out are reproduced in Figure 12, together with the net private contributions to the second pillar (gross contributions plus interests minus redemptions), and the sum of the two balances. Total pension savings peak at around 2011, decline thereafter, following the decline in the PAYG balances, but increase again after 2020, when the first significant numbers of workers begin to retire in the new system. The contribution of the pension system to savings decline in the third decade due to demographic aging. The private scheme starts maturing, with increasing redemptions and smaller net positive contributions, while the public scheme shifts into deficits. The net result is a decline in total pension savings to around I percent of GDP. In order to eliminate the public deficits and increase total savings, the authorities would have to increase further the retirement age and/or shift to price indexation.

"S