rafmacalaba/datause-encoder-data huggingface dataset:
covered to prevent contact with other vectors; health effects will clearly also depend on hygiene practices. In the case of water, a growing literature suggests that there are large differences between the quality of water at the source and the quality of water at the point of use (for an overview see Wright, 2004; Zwane and Kremer, 2007). While community standpipes and covered ground-water wells and pumps offer clean water, the transport of water to the household as well as the storage of water in the household heavily expose water to contamination, substantially lowering the average quality of water consumed in the household relative to the quality of the water at the (public) source.

The rules we applied for classifying water and sanitation infrastructure are very similar to the three-rung ladder for drinking-water and the four-rung ladder for sanitation suggested by the WHO/UNICEF Joint Monitoring Program (JMP) for Water Supply and Sanitation (see Table 2).<sup>8</sup> The first distinction we make is between _unimproved_ and _improved_ water sources. Surface water and traditional (unprotected) wells and springs are coded as unimproved water source, while protected springs and wells, boreholes and public standpipes are coded as an improved water source.<sup>9</sup> On top of these two categories, piped water into the household is considered as a separate category to capture the lower risk of water contamination through transport and storage. The distinction is obviously also important from a cost-benefit perspective, as the additional capital investment required for private access is substantial and may not be warranted by additional improvements in child health.

Following a similar logic, we also divide sanitation into three broad categories. We code (flush) toilets connected to either a septic tank or a central sewage system as the (first-) best technology available. The second-best option is improved latrines (private or shared); all other traditional sanitation practices are considered _unimproved._ The Joint Monitoring Program (JMP) further distinguishes between unimproved sanitation techniques (e.g. bucket latrines) and open defecation. We investigated this distinction empirically without finding any significant difference across the two groups in our previous paper (Günther and Fink, 2010), and therefore decided to collapse the two categories for the purpose of this paper. We also deviate marginally from the JMP ladder with respect to improved sanitation. Whereas the JMP groups private flush toilets to a sewage system/septic tank together with improved onsite latrines we distinguish between these two categories, since connected private toilets are

> 8 http://www.wssinfo.org/definitions/ladders.html

> 9 Several DHS surveys do not distinguish between protected and unprotected wells and springs. Whenever it was not clear from the data whether a spring or well was improved or not we made the assumption that it is unimproved. We therefore might somewhat underestimate the access to improved water technologies and overestimate the costs to reach MDG4 with MDG7 in our estimations (see Section 3).

8
3 socially vulnerable groups. Internal CG mechanisms should be designed to ensure that the interests of all these stakeholders’ are looked after. Broader institutional arrangements, or external mechanisms, would complement internal arrangements and enhance their effectiveness.

Section A of this paper reviews prevailing internal and external CG arrangements to protect stakeholders’ financial interests in IIFS and identifies shortcomings. Section B suggests measures to overcome these shortcomings.

# **_A. Protecting Stakeholders’ Financial Interests: Current Practice and Shortcomings_**

## **1. Internal Arrangements**

IIFS generally put in place CG structure and systems similar to those of BCFS to handle traditional agency problems between _shareholders_ and management. Although not specific to IIFS, the protection of small shareholders may be equally important for them. Indeed, concentrated ownership and control may be more widespread in IIFS than with BCFS. Table I shows that out of a sample of 21 IIFS for which shareholder information was exhaustive, 9 appear to be family owned and controlled, about 43% of the sample.<sup>9</sup> This translates into a concentration of control of executive decisions and a monolithic board of directors that may be biased in favor of specific interests, unless adequately checked. Small shareholders as well as other stakeholders may accordingly be at risk. For example, management may use discretion in the funds they commingle to finance specific investments and provide better yields to dominant shareholders.<sup>10</sup>

> 9 Data were collected through IIFS websites as well as the Bankscope and Capital Intelligence databases. Control is defined as effective control power over the enterprise. Further research may want to analyze more rigorously ownership structure differences between IIFS and BCFS. In particular, research may want to explore the ratio of independent directors to all directors in IIFS to verify whether family ownership effectively leads to episodes of board capture.

> 10 Paradoxically, family ownership in non-financial firms may solve the problems deriving from Bearle and Means’ (1932) separation of ownership from control. However, in financial firms, where substantial funds are contributed by depositors and not equity holders, this type of ownership worsens the position of nonshareholder stakeholders. Given the importance attached by IIFS to stakeholders, this represents a relevant problem for them.
and economic sector. It also presents an opportunity to understand the behavior of financial institutions towards risks. The experience of Chile’s FOGAPE in gathering data about credit rejection (as described in Box 2) is useful to understand the types of companies that are not getting credit. From the policy perspective, that information is useful for policy makers to assess the desirability of offering other tools for supporting specific sectors of the economy. These analyses require PCG administrators to have in place adequate IT resources that allow them to process this information on a timely basis.

# c. Self-assessment of obstacles

The challenges to the operations of PCGs in the LAC region highlight the importance of learning from experiences outside the region. Not surprisingly, the complications seen in LAC also arose in several PCGs across the world as these institutions were used as a lifeline for the local firms facing the crisis. In that regard, several European PCGs have successfully addressed many of these issues, as described in Box 1.

The most salient (self-reported) difficulties faced by PCGs in the LAC region were related to the need to fast-track procedures and to streamline documentation requirements. The majority of schemes (5) mentioned the urgent need for accelerating application procedures and implementing new IT platforms to automate different procedures within their operations as a critical challenge during the implementation of COVID-19 related PCG scherms. Other elements that affected the implementation capacity of PCG schemes included the approval rate by the financial intermediaries (see Box 2), and the need to adjust either regulation or coordination between government branches in order to speed up the procedures. Moreover, three PCGs stressed the need for additional support from their governments, either through capitalization of the guarantee funds or through the provision of counter-guarantees. Finally, only one scheme highlighted the need to support the participating financial intermediaries with technical assistance.

25
# **Table A7 – Share of households living in usufruct or free of charge in European countries by wealth quintiles (average values in the period 2014-2021)** _(percentages)_

|**Country**||**Househ**|**old net wealthq **|**uintiles**||
|---|---|---|---|---|---|
||**First**|**Second**|**Third**|**Fourth**|**Fifth**|
|**Austria**|9.8|15.7|7.6|1.9|1.0|
|**Belgium**|4.8|4.1|1.3|1.3|1.2|
|**Cyprus**|63.9|15.1|5.3|4.3|3.6|
|**Czechia**|19.5|5.0|0.6|0.9|0.2|
|**Germany**|5.2|6.8|6.5|1.2|1.0|
|**Estonia**|41.6|10.1|4.5|2.6|2.4|
|**Spain**|25.2|6.8|2.3|1.7|1.8|
|**Finland**|2.1|2.5|0.5|0.4|0.2|
|**France**|4.9|5.7|1.8|1.5|0.8|
|**Greece**|23.8|6.8|2.0|1.5|1.1|
|**Croatia**|46.4|6.2|0.9|0.7|0.8|
|**Hungary**|19.7|3.3|2.0|1.2|0.9|
|**Ireland**|3.4|3.9|0.4|0.3|0.2|
|**Italy**|29.3|15.5|2.7|1.5|1.5|
|**Lithuania**|15.6|0.9|0.7|0.2|0.4|
|**Luxembourg**|8.5|5.8|1.7|0.6|1.0|
|**Latvia**|24.1|7.2|2.9|1.0|0.6|
|**Malta**|15.3|1.7|0.8|0.1|0.1|
|**Netherlands**|1.4|1.1|0.2|0.1|0.2|
|**Poland**|53.2|5.1|3.1|1.7|0.9|
|**Portugal**|22.6|9.2|2.5|2.0|1.6|
|**Slovenia**|50.1|10.9|3.0|2.9|1.8|
|**Slovak Republic**|21.0|1.2|1.1|0.5|0.4|
|**Total**|16.0|7.5|3.3|1.4|1.1|

Source: Our elaboration on HFCS data.
# **3.5. Results**

Table 5 presents the regression using the per capita electricity generating capacity. First, we see that the standard results of growth regressions are present: initial per capita GDP are strongly significant, with the right, negative sign indicating convergence conditional on the other variables. Similarly, education and investment variables are significant with the right, positive sign expect for primary enrolment. These results are robust across Tables 5 to 9.

**<u>Table 5 : Electricity</u>**

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|---|---|---|---|---|---|---|---|
||OLS|OLS|OLS|OLS|OLS|2SLS|2SLS|
||pcgdpgrowth|pcgdpgrowth|pcgdpgrowth|pcgdpgrowth|pcgdpgrowth|pcgdpgrowth|pcgdpgrowth|
|Constant|-0.015|-0.013|-0.248|-0.014|-0.285|-0.012|-0.377|
||(0.016)|(0.017)|(0.071)***|(0.017)|(0.077)***|(0.024)|(0.150)**|
|pcgdp71|-0.000|-0.000|-0.000|-0.000|-0.000|0.000|-0.000|
||(0.000)***|(0.000)**|(0.000)***|(0.000)*|(0.000)***|(0.000)|(0.000)***|
|school_enrol_secondary|0.001|0.001|0.000|0.001|0.001|0.001|0.000|
||(0.000)***|(0.000)***|(0.000)***|(0.000)***|(0.000)***|(0.000)**|(0.000)|
|school_enrol_primary|-0.000|-0.000|-0.000|-0.000|-0.000|-0.000|0.000|
||(0.000)|(0.000)|(0.000)|(0.000)|(0.000)*|(0.000)|(0.000)|
|inv_gdp|0.149|0.154|0.110|0.151|0.141|0.128|0.094|
||(0.052)***|(0.054)***|(0.048)**|(0.052)***|(0.040)***|(0.065)*|(0.043)**|
|Pcegc|0.001|0.001||0.001||-0.019||
||(0.002)|(0.002)||(0.002)||(0.013)||
|electric_power_transloss||-0.000||||||
|||(0.001)||||||
|pcegc_gr|||0.222||0.263||0.343|
||||(0.065)***||(0.070)***||(0.143)**|
|pcegc_mena||||-0.001||||
|||||(0.002)||||
|pcegc_grmena|||||-0.013|||
||||||(0.006)**|||
|Observations|48|47|46|48|46|38|37|
|R-squared|0.51|0.52|0.63|0.51|0.69|||
|Wu-Hausman F||||||0.01|0.35|
|test, p-value||||||||
|Robust standard error<br>Instruments are begin<br>Variables are in per ca|s in parenthe<br>ning of the p<br>pita terms ex|ses. * signific<br>eriod indicato<br>cept where v|ant at 10%;<br>rs for: infras<br>ariable name|** significan<br>tructure, agri<br>does not be|t at 5%; *** s<br>/gdp, populat<br>gin with ‘pc’|ignificant at<br>ion density,<br>|1%<br>population.|

16
# **2.1.** **<mark>Differences in socio-demographic characteristics between teachers and other professionals</mark>**

<mark>Table 2 presents socio-demographic characteristics for students of education majors and students of other programs. The share of females studying to become teachers is higher than in other programs. Also, students in education majors are more likely to come from larger families; they have less educated parents/guardians; they are less likely to migrate to another administrative unit to pursue their studies; they are more likely to have studied in public schools; and also they are more likely to enroll in public universities and in programs with lower tuition fees. This reaffirms the idea that teachers are more likely to come from a disadvantaged socioeconomic position compared to their peers (Denzler and Wolter, 2008; Aksu et al., 2010).</mark>

**Table 2. Descriptive statistics**

# **a. Characteristics at the student level**

|**Variables**|**Other professionals**|**Teachers**|**Difference between**<br>|
|---|---|---|---|
||||**groups**|
|**Socio-demographic characteristics (%)**||||
|Gender (Female, as measured by Saber 11 and Saber Pro)|58.10<br>(0.23)|66.60<br>(0.73)|***|
|Familiy size (more than 5 persons, as measured by Saber|14.30|22.30|***|
|Pro)|(0.16)|(0.65)||
|Max education of the parents/guardians (as measured by<br>Saber Pro)||||
|Secondary incomplete or less|19.70|39.70|***|
||(0.18)|(0.76)||
|Secondary complete or tertiary incomplete|28.10|34.20|***|
||(0.21)|(0.74)||
|Technical or technician education complete|12.10|10.30|***|
||(0.15)|(0.47)||
|Universitary education complete|40.00|15.80|***|
||(0.23)|(0.57)||
|The student moved to another administrative unit for his/her|22.59|14.58|***|
|higher education (as measured by a difference in the<br>administrative unit of residence in Saber 11 and Saber Pro)|(0.19)|(0.55)||
|Semester of study in current university program (as<br>measured by Saber Pro)||||
|7 or 8|11.20|9.10|***|
||(0.15)|(0.45)||
|9 or 10|77.70|81.80|***|
||(0.19)|(0.6)||
|11 or 12|11.10|9.10|***|
||(0.15)|(0.45)||

7
of product‐lines subject to TTBs by 25% during the crisis, most of this is the result of developing economies that combined to increase their stock of product coverage by 40%. Perhaps surprisingly, high‐income economies increased their stock of products affected by TTBs by only 5% during the crisis.

Furthermore, these changes during the global economic crisis are consistent with pre‐crisis trends in the data on TTB use. Developing countries have been increasing their use of TTBs prior to the crisis at such a rate that it is difficult to claim empirically that the 2008‐2009 crisis _caused_ developing economies to increase their stock of TTBs above what the simple time trend would predict even in the absence of a major global recession. On the other hand, the U.S. and EU have reduced the stock of imported products they subject to TTBs by up to 50% over the last fifteen years, which is consistent with the crisis data, indicating a muted response to political calls for new TTBs.

Also significant are the trends in the data from the perspective of the exporting economies. By 2009, China had roughly four times as many products subject to foreign‐imposed TTBs as the second most‐targeted economies. Furthermore, the share of China’s exports to other _developing_ economies is subject to much more foreign‐imposed TTBs than its share of exports to developed economies, and it is also growing at a faster rate. My evidence confirms that this particular feature is not unique to China but is shared by a number of other major developing economy exporters, thus deepening the concern that such discriminatory trade barriers are increasingly a “South‐South” phenomenon.

Finally, I conclude by commenting on how the lack of a substantial increase in new import protection resulting from the 2008‐2009 crisis beyond that predicted from pre‐crisis data raises important questions for research. If the world trading system does ultimately escape the 2008‐2009 crisis relatively unscathed with respect to new and extraordinary protectionist initiatives, an open and fundamentally important question is, why?

The facts that I have generated from the use of TTBs during the 2008‐2009 crisis suggest many potential contributing causes that should form the basis for future research. One possibility is that the WTO system’s architecture was well constructed to handle the crisis. I.e., perhaps by having a system in place which allowed for TTB use, domestic political pressure for new trade barriers was allowed to escape via a “safety valve.” These nontrivial, though relatively small (in product coverage terms) increases in import protection could have prevented greater market‐closing forces from emerging and the overall multilateral system from falling apart. On the other hand, the lack of a major protectionist response may be unrelated to WTO rules. It could be the result of the nature of the political economy of trade policy changing in a way which actually makes the WTO redundant (Blanchard, forthcoming).

32
destination 2, even though destination 1 would have maximized income.<sup>7</sup> If we were concerned that failure to maximize income reflects errors made from fatigue, we would expect to see more of this behavior for those individuals randomly assigned to be asked Game 1 first, compared to those who get randomly assigned it as part of the middle or last block module. However, we cannot reject that question ordering is not related to this behavior (p=0.977). Taken together, this qualitative and quantitative evidence supports the idea that non-income-maximization was the desired choice, and not simply the result of participants making random choices or hitting the wrong button.

# **4.3 Introducing Liquidity Constraints and Risk**

There are no liquidity constraints in either Sjaastad (1962) or Borjas (1987), with all those who wish to migrate being able to do so. We introduce liquidity constraints in Game 4 by increasing the cost of moving to 100, and otherwise keeping the game the same as in Game 2. Recall that endowments are randomly assigned as {50, 100}* _s_ . Therefore, all of those with skill level 1 that receive an endowment of 50 are not able to migrate, even though they would like to.

Introducing liquidity constraints in this way halves the migration rate of those in the Lisbon sample with skill level 1 from 72% in Game 2 to 35% in Game 4 (p<0.001); and also halves the rate in the Nairobi sample from 56% in Game 2 to 27% in Game 4 (p<0.001). The higher cost also lowers the return to migration, so that individuals with skill level 2 and unobserved skill 0 are now indifferent between the two destinations. This lowers the migration rate in Lisbon of skill level 2 from 70% in Game 2 to 52% in Game 4 (p=0.019), and in Nairobi from 42% in Game 2 to 30% in Game 4 (p=0.016). In contrast, it should not affect the migration decisions of those with skill level 3, and indeed the migration rate for them is similar in Game 4 (28%) and Game 2 (32%) in Lisbon (p=0.16), although the rate is slightly lower in Game 4 (30%) than in Game 2 (37%) in Nairobi (p=0.057). The overall result is that liquidity constraints have lowered migration rates, and made migration less negatively selected than the standard Borjas model would predict.

> 7 We also examined whether non-game characteristics of individuals help predict this behavior by running a probit of not maximizing income as a function of the characteristics in Table 1. We cannot reject that these characteristics are jointly orthogonal to whether or not someone does not maximize income (p=0.501). In particular, we do not find that the math score or expected value score are predictive of not income maximizing, which suggests it is not a case of individuals with lower numeracy misunderstanding.

13
The practice of assigning different courses of action to different groups and comparing outcomes dates back thousands of years. In the Old Testament, King Nebuchadnezzar orders a group of his subjects to eat rich meat and drink wine while another group is made to adhere to vegetarianism in order to evaluate the merits of the two diets (1 Daniel 11–16, New International Version). Versions of this approach have since been used in countless other efforts to evaluate competing hypotheses, from eighteenth century studies of scurvy treatment to the A/B testing now common in technology firms.

One particular version of this approach is the randomized controlled trial (RCT). An RCT is usually a large-scale study, prospectively designed to test a small set of hypotheses by randomly assigning treatment(s) to participants. Medical scientists have implemented hundreds of thousands of RCTs since the mid-1940s to test hypotheses about treatment options and inform care decisions<sup>1</sup> . In the 1980s, several studies showed that RCTs in medical research yielded less biased treatment effect estimates than observational studies. The method has been adopted in several scientific fields, as well as by the US Food and Drug Administration (FDA) and other government agencies, as the “gold standard” of empirical evidence (Vader 1998).

Not all RCTs are created equal, however. Meta-analysis of thousands of medical RCTs has revealed several pitfalls that skew effect estimates and lead to erroneous conclusions (Jüni, Altman, and Egger 2001). Broader conclusions based on flawed studies have led to the use of drugs or procedures that bring no measurable benefit and, in some cases, even cause harm (Goldacre 2014). In the past two decades, medical

> 1. There were 183,991 trial protocols registered in the US Government’s Clinical Trials database (www.clinicaltrials.gov) as of 11 February 2015. It is perhaps impossible to know how many trials have ever been conducted, as public registration of trial protocols was not common practice until the late 1990s.

2
# **Household Data**

The first dataset includes information on individual and household characteristics. Among the 474 eligible program recipients, we stratified by neighborhood and randomly selected 251 program recipients to participate in the household survey. A baseline survey was conducted in August/September 2011, prior to the distribution of the first transfer, with follow-up surveys in November 2011 (after the second transfer) and March 2012 (after the third transfer). Each survey included modules on household demographics, asset ownership, shocks, income-generating activities and food expenditures. For the follow-up surveys, we also included modules on the uses of the cash transfer or voucher. As female program recipients primarily worked as laborers or transporters for non-IDP households, with relatively long distances of travel, we were mindful of the time burden on respondents. As a result, the household surveys did not include a full income and expenditure module. While this somewhat constrains our analysis, we feel that data on transfer use approximates households’ expenditures, as we argue below.

Typically, attrition is a concern in any humanitarian context, as violence is frequent and populations are highly mobile. Immediately prior to the second round of the survey, violence intensified in the area, and approximately half of the IDP households fled into the surrounding hills. While most households were present in the camp during the third survey round, attrition raises concerns about the external and internal validity of our findings. If the types of households who stayed were different from those who fled, this further affects the external validity of the findings. Furthermore, if the characteristics of the remaining households differed between the _voucher_ and _cash_ groups, this could affect the internal validity of our findings despite the randomized design.<sup>17</sup> Appendix S2 formally tests whether there is differential attrition at different rounds of data collection. While 46 percent of households were missing in November 2011 (the second round), there was not a statistically significant difference in attrition rates between the _cash_ and _voucher_ households. Attrition in March 2012 (the third round) was significantly lower, with 27 percent of households missing. Similar to

> 17 For example, _cash_ households might have been able to take the cash with them, as compared with _voucher_ households, who would have needed to transport (or sell) their goods.

11