rafmacalaba/datause-encoder-data huggingface dataset:
migrants do not always consent to paying. For example, a study of migrants in Mexico notes that criminals kidnap migrants in order to extort money from their families (Vogt, 2013). When individuals being extorted will not reveal the information about their families, many are abused in an effort to force them to reveal contact information for family members (MSF, 2017). We also find in our interviews that some individuals are denied food and water or are held in a place until they pay additional money, potentially as a result changing coyotes en route. Overall, extortion during migration frequently involves both violence and familial indebtedness.

It is important to note that illegal migration in other parts of the world involves similar abuse. For example, individuals fleeing to Europe experience physical, sexual, psychological, and verbal abuse (Dempsey, 2020). UNHCR estimates that thousands of people die and go missing at sea on this journey to Europe (UNHCR, 2020). Similarly, research in MSF clinics in Serbia suggests that more than a quarter of refugees had experienced violence during their journey (Arsenijevi ́c et al., 2017).

Although little work has been done on the topic of individuals returning to Guatemala after deportation, studies from other areas of Latin America and the world more broadly can shed light on many of the issues faced by individuals forced to return to their birth country via deportation. For example, in a study of life after forced deportation to Mexico, Slack (2019) finds that returnees are easy targets for violence because they often do not have strong social networks in their country of origin and are more closely tied to the United States. Some scholars have even called individuals deported from the United States with strong ties to that country “deported Americans” (Caldwell, 2019) or the “new American diaspora” (Kanstroom, 2012). Indeed, in a study of rejected asylum seekers who did not obtain residence permits to six different countries, Ruben et al. (2009) conclude that returnees who were able to develop social contacts in their countries of origin were more successful on their return. Employment and independent housing were also helpful. Beyond establishing social ties, deportees face other challenges. For example, research on deported Afghans and deported Salvadorans indicates that debt can fuel cycles of migration, deportation, and remigration (Schuster and Majidi, 2013; Heidbrink, 2019a). As such, many deported migrants face extensive challenges once they return to their home countries, on top of the challenges which forced them to leave that country in the first place, violence which they may have experienced while migrating, and the deportation experience. Understanding deportees’ political engagement in their home country is crucial for conceptualizing re-integration and re-migration.

4
29 percent of deleveraging were to occur via the loan portfolio, the impact would be €501 billion, or 30 percent of EMDE credit (Scenario 2). If banks are able to raise equity by 20 percent―as they did after the 2007–08 crisis―and the residual deleveraging occurs fully by loan reduction, the estimated decline in EMDE credit could amount to €676 billion, or 41 percent of EMDE credit (Scenario 3). Scenario 4 shows the impact if half of the asset shedding occurs through loans (€338 billion, or 21 percent).

**Figure 21. European Banks' EMDE Credit (€billion and percent)**

<!-- Start of picture text --> ASIA, 241.1 CIS, 99.1<br>15% 6%<br>MENA, 132.8<br>8%<br>CEE, 529<br>32%<br>AFR, 125.5<br>8%<br>LAC, 519,<br>31%<br><!-- End of picture text --> Source: IMF, Financial Soundness Indicators.

**Figure 22. EBA Recapitalization Impact on EMDE Credit across Simulation Scenarios**

<!-- Start of picture text --> Result of EBA Capital Shortfall (€billion)<br>300.0<br>€300<br>LAC<br>€250 245.1 250.0 Middle East<br>€200 1 Asia<br>200.0<br>109.0 CIS<br>€150<br>2 130.5 150.0 CEE<br>€100<br>Africa<br>3<br>€50 4 100.0 56.7<br>5 27.1<br>€0 50.0 96.4 29.1<br>10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 53.0<br>28.1 11.7<br>Necessary asset deleveraging via loan book (%) Fraction via asset deleveraging: 50% 0.0 11.3 4.7 4.5<br>Fraction via asset deleveraging: 100% Scenario 1 Scenario 2 Scenario 3 Scenario 4 Scenario 5<br>Fraction via average deleveraging: 10%<br>Scenario Scenario Scenario Scenario Scenario<br>1 2 3 4 5<br>Fraction of shortfall reached via deleveraging (%) 100 50 50 10 10<br>Fraction of deleveraging via loans (percent) 100 100 50 100 40<br>Credit reduction in EMs (bn)<br><!-- End of picture text --> Source: Authors‘ calculations.
<mark>where w</mark> m m hi <mark>denotes the weights after the differences in the distribution of observable</mark> ii <mark>characteristics vanish.</mark>

# **3.2** **<mark>Instrumental variables</mark>**

<mark>The instrumental variables methodology consists of a two-step regression. In the first step we regress our endogenous variables o</mark> n all covariates and a vector of valid instruments,<sup>6</sup> which are correlated with the decision of being a teacher but uncorrelated with scores in Saber Pro. Then, in the second step we use the estimated values and � , and estimate ii,1 ıı,1 ıı,0 tt̂ tt ∗yy � 0 1 2 3 4 . (5) ii,1 ii ii,1 ii,0 ıı,1 ıı,0 ii,1 Analogously for Equation (2), we plug yy = ββ + ββ XX + ββ, tt̂ � + ββ , yyh<sup>�</sup> + ββ and �tth ∗yy� �+ εε –obtained from the ii,1 ıı,1 ıı,0 ii,1 ıı,1 ıı,0 corresponding first step regressions- into it and estimate tt̂ tt ∗yy ∗yy 0 1 2 3 4 � 5h<sup>�</sup> 6�h � . (6) ii,1 ii ii,1 ii,0 ıı,1 ıı,0 ii,1 ıı,1 ıı,0 ii,1 yy = ββ + ββ XX + ββ tt̂ + ββ yy + ββ �tt ∗yy �+ ββ + ββ ∗yy �+ εε <mark>Our instruments correspond to two dummy variables: The first one takes the value of 1 if the student moved to another administrative unit to pursue his/her college studies, 0 otherwise. The second one takes the value of 1 if the student studied in a superior normal school, 0 otherwise.</mark> <mark>There is no reason to think that moving to another administrative unit is correlated to Saber Pro scores. In Colombia, although the main cities offer a broad spectrum of possibilities for higher education, there is high within-administrative unit variation in the quality of higher education programs.</mark><sup>7</sup> <mark>Indeed, we find that the correlation between scores in quantitative reasoning, foreign language and native language and our instrument is below 0.008; using Saber 11 scores we find that this correlation is below 0.01.</mark>

> 6 Given that is endogenous for modelling students’ skills, is also endogenous. Also remember that any valid instrument for it can also be used as a valid instrument for . 7 See: http://www.icfes.gov.co/resultados/ttii,1 ttii,1 ∗yyii,0 ii,1 ii,1 ii,0 tt tt ∗yy

15
Wurlod, J. D., & Noailly, J. (2018). The impact of green innovation on energy intensity: An empirical analysis for 14 industrial sectors in OECD countries. _Energy Economics_ , _71_ , 4761. <u>https://doi.org/10.1016/j.eneco.2017.12.012</u> Würzburg, K., Labandeira, X., & Linares, P. (2013). Renewable generation and electricity prices: Taking stock and new evidence for Germany and Austria. _Energy Economics_ , 40, S159S171. https://doi.org/10.1016/j.eneco.2013.09.011 Xylia, M., Silveira, S., & Morfeldt, J. (2017). Implications of an energy efficiency obligation scheme for the Swedish energy-intensive industries: an evaluation of costs and benefits. _Energy Efficiency_ , _10_ (1), 151-169. https://doi.org/10.1007/s12053-016-9446-7 Zhang, D., & Vigne, S. A. (2021). How does innovation efficiency contribute to green productivity? A financial constraint perspective. _Journal of Cleaner Production_ , _280_ , 124000. https://doi.org/10.1016/j.jclepro.2020.124000 Zhang, D., Du, W., Zhuge, L., Tong, Z., & Freeman, R. B. (2019). Do financial constraints curb firms’ efforts to control pollution? Evidence from Chinese manufacturing firms. _Journal of Cleaner Production_ , _215_ , 1052-1058. https://doi.org/10.1016/j.jclepro.2019.01.112

- Zhang, D., Zhuge, L., & Freeman, R. B. (2020). Firm dynamics of hi-tech start-ups: Does innovation matter? _China Economic Review_ , _59_ , 101370. <u>https://doi.org/10.1016/j.chieco.2019.101370</u>

- Zheng, X., Wu, C., & He, S. (2021). Impacts of China's differential electricity pricing on the productivity of energy-intensive industries. _Energy Economics_ , _94_ , 105050. <u>https://doi.org/10.1016/j.eneco.2020.105050</u>

Zhou, Z., Zhang, S., Wang, C., Zuo, J., He, Q., & Rameezdeen, R. (2016). Achieving energy efficient buildings via retrofitting of existing buildings: a case study. _Journal of Cleaner Production_ , 112, 3605-3615. https://doi.org/https://doi.org/10.1016/j.jclepro.2015.09.046 Zhu, X., Chen, Y., & Feng, C. (2018). Green total factor productivity of China's mining and quarrying industry: A global data envelopment analysis. _Resources Policy_ , _57_ , 1-9. <u>https://doi.org/10.1016/j.resourpol.2017.12.009</u> Zuberi, M. J. S., & Patel, M. K. (2019). Cost-effectiveness analysis of energy efficiency measures in the Swiss chemical and pharmaceutical industry. _International Journal of Energy Research_ , _43_ (1), 313-336. https://doi.org/10.1002/er.4267 56
- 37 -

## 4. REAL OVERVALUATION AND THE TAXATION OF AGRICULTURE IN SUDAN

## 4.1 Equilibrium RER and Overvaluation

Now we can proceed to compute an index for ERER using the estimates of equation (3.8') above for given 'sustainable' or 'permanent' values of the fundamentals. The permanent components of the fundamentals are obtained by using the time series technique introduced by Beveridge and Nclson(1981) and further elaborated upon by Nelson and Plosscr (1982), Cuddington and Winters (1987), and Cuddington and Urzua (1989). Beveridge and Nelson (1981) show that any variable x, with an integrated process (ie. a unit root) can be decomposed into a random walk with drift and stationary component, ie.

# (4.1) **_x,_** = **_x,-l + pt_** + **_C(L) et_**

This technique is desirable for the problem at hand because unlike the trend stationary model based decomposition it allows the steady state growth path of the time series to shift upwards or downwards over time. Fluctuations around the (shifting) permanent path reflect cyclical effects. The stochastic and growing nature of the permanent fundamentals predicted by this procedure is indeed a minimal identification condition for the derived ERER to be consistent with the concept of RER equilibrium as outlined in section 2 above.

The decomposition procedure is based on the following steps. The first step is to take logarithms of the individual variables and then first difference to obtain stationary series. Sccond, the Box-Jenkins techr 'que is used to identify and estimate an ARIMA model. The results of the estimated models are shown in the appendix table (A.4.1 ). Third, using the steady state "gain function" approach (Cuddington and Winter (1987)), a First order difference equation in the permanent componcnt of the series can be obtained as a linear function of the corresponding
ECCAS’S INFRASTRUCTURE: A REGIONAL PERSPECTIVE Beyond basic connectivity, it is also important to evaluate the convenience and velocity of air travel. Cameroon and Gabon have the greatest frequency of flights, both direct and indirect. Cameroon serves as a hub for the CEMAC countries. São Tomé and Príncipe and the Central African Republic have three or fewer direct <u><mark>Angola</mark></u> flights per week. Lower velocity of air <u><mark>Cameroon</mark></u> travel is associated with lengthy travel <u><mark>Central African Republic 1</mark></u> times (table 2.10b). The flights <u><mark>Congo, Dem. Rep. of</mark></u> <mark>1</mark> originating from the Central African <u><mark>Equatorial Guinea 2 3</mark></u> Republic to Equatorial Guinea and from <u><mark>Gabon 18</mark></u> São Tomé and Príncipe to Gabon have <mark>São Tomé and Príncipe 1</mark> low velocities, implying that passengers spend a longer amount of travel time, in part due to waiting for connecting flights.

**Table 2.9. All flights within ECCAS, one week in November 2007**

|||Angola|Cameroon|D<br>Central African Republic|estinati<br>Congo , Dem. Rep. of|on<br>Equatorial Guinea|Gabon|São Tomé and Príncipe|
|---|---|---|---|---|---|---|---|---|
||Angola||||1|4||1|
||Cameroon|||1|1|3|19||
|n<br>|Central African Republic||1||||||
|Origi<br>|Congo,Dem. Rep.of||1|||3|||
|<br>|Equatorial Guinea|2|3||2||2||
||Gabon||18|||4||2|
||São Tomé and Príncipe|1|||||2||

**Table 2.10 . Proportion of direct flights and speed of air service in East Africa**

|**a. Directness of servi**|**ce**|**b. Speed of service**|||||||
|---|---|---|---|---|---|---|---|---|
|Angola|All flights<br>Direct<br>flights<br>6<br>4|||D<br>on<br>African<br>|estinati<br>Dem.|on<br>al||é and<br>|
|Cameroon|24<br>23||ola|ero<br>tral<br>ublic|go,<br>. of|atori<br>ea|on|Tom<br>cipe|
|Central African<br>Reublic|1<br>1||Ang|Cam<br>Cen<br>Rep|Con<br>Rep|Equ<br>Guin|Gab|São<br>Prín|
|p<br>C D R||Angola||||443|559||
|ongo, em. ep.<br>of|4<br>3|Cameroon|||||||
|Equatorial Guinea|9<br>4|in<br>Central African Republic|||559|**_230_**|348|402|
|Gabon|24<br>22|Orig<br>Congo, Dem. Rep. of||559|||||
|São Tomé and|<br>|Equatorial Guinea||646|||514||
|Príncipe|3<br>3|Gabon|436|348||533||**_204_**|
|||São Tomé and Príncipe||392|||**_143_**||

_Source:_ Bofinger 2009.

CEMAC’s air transport market is considered among the most liberalized in Africa, though minor restrictions remain. By instituting free pricing and lifting capacity and frequency restraints, the region has made significant progress toward implementing the Yamoussoukro Decision (YD). Specifically, the number of flights flown under fifth-freedom arrangements (that is, by carriers that are not registered either in the origin or destination country) is on the rise. This progress puts the CEMAC region ahead of most of east and southern Africa.

28
achievement. As one example, Zentall & Morris (2010) examined student responses to various scenarios illustrating student failure. First, students received scenarios in which hypothetical students completed drawings and teachers responded with generic (“You are a good drawer”) versus nongeneric praise (“You did a good job drawing”). Later in these scenarios, teachers looked at “failed” drawings and responded with, “That doesn’t look like a cat; it has no ears.” Then the children continued working on the cat pictures. After completion, teachers said, “You found a really good way to draw the cat. I see that it is black and has ears.” Various forms of these scenarios were repeated to examine consistent versus inconsistent use of type of praise (generic versus nongeneric). When the majority of the praise students received was nongeneric, students reported feeling happy about the scenarios, suggesting the emergence of a mastery orientation toward learning. When the majority of the praise was generic, students reported feeling sad about the vignettes (corresponding to helplessness in learning). Teachers’ feedback in failure situations conveys important information to students that can motivate them to do better in the future.

Student self-efficacy is an important driver of students’ approach to failure, especially in mathematics instruction, which is fraught with opportunities for student mistakes. For that reason, various studies have examined the types of teaching practices that contribute to high self-efficacy. In one study, high levels of emotional support (i.e., positive climate, teachers’ sensitivity to students’ point of view) was found to relate to higher student self-efficacy in mathematics (a 1.0 SD increase in emotional support was associated with a 0.14 SD increase in student self-efficacy [Blazar & Kraft, 2017]). In another study, fifth grade students low in self-efficacy showed lower engagement in learning in math. However, when teachers were emotionally supportive, students low in math self-efficacy were just as engaged as those students who were high in math self-efficacy (Martin & Rimm-Kaufman, 2015). Yet another study linked emotionally supportive classrooms (e.g., students’ perception of emotional support from teachers, academic support from peers, encouragement from teachers to discuss their work) to student achievement with academic self-efficacy and engagement with peers in academic work as important explanatory factors contributing to student achievement (Patrick, Ryan, & Kaplan, 2007).

A rich literature exists on how teachers can cultivate positive attitudes toward student struggle and failure. Effective teachers focus on the process of learning, not just product. In one study, fifth and sixth grade students were given instructions for a computerized problemsolving task. Some students were given instructions that drew attention to the task and away from concerns about performance (i.e., “It doesn’t matter at all how much you get 34
and Todd (2011) report similar impacts on children‟s working behavior in their nonexperimental evaluation of the _Oportunidades_ program in urban areas.<sup>25</sup>

# **6.1.2  Effects of** **_Oportunidades_ in Rural Areas on Alcohol Abuse and Domestic Violence**

Angelucci (2008) evaluates the impact of _Oportunidades_ on alcohol abuse and on domestic violence in rural areas. If income shortage is a major cause of domestic arguments, then the provision of extra income through CCT programs may reduce domestic violence. On the other hand, if program funds are used to purchase more alcohol and increased alcohol consumption is positively associated with domestic violence, then the program could increase violence against women. The shift in bargaining power resulting from the income transfers being given to women could also lead to increased violence or to marital instability.

Angelucci (2008) finds that the program on average led to a 13-fold increase in wife income, because wives hardly had any income before the program, given their low rates of formal labor market participation. The program led to a decrease in the share of spousal income earned by the husband from 97 to 62%. For households receiving relatively small amounts of transfers, Angelucci (2008) finds that the program led to a 15% decrease in alcohol abuse and a 37% decrease in drunken violence. However, in households in which the wife was entitled to large transfers (usually due to larger families with age-eligible children) and where husbands had low education levels (about 17% of the sample), she finds evidence of increased violence and aggressive behavior, as reported by the wife. The increase in violence appears to have been larger the older the husband was relative to the wife.

Thus, her findings suggest that CCT programs targeted at women are beneficial for the majority of them, but that there is a subset of families with low-education male heads who tend to hold more traditional values for whom violence increases.

# **6.1.3  Effects on Marriage and Fertility-related Behaviors of Adolescents**

Gulametova-Swan (2009) studies the effect of the Mexican _Oportunidades_ program on adolescent girls‟ decisions about marriage and fertility. Adolescent youth enrolled in the program were required to attend health talks, where topics such as prevention of pregnancy were discussed. Gulametova-Swan‟s (2009) analysis is based on data from the 2002–2004 urban _Oportunidades_ evaluation data samples, which were nonexperimental. Program participation was

> 25 The impacts of _Oportunidades_ in urban areas were assessed using propensity score matching estimators.

50
up of employed workers aged 20 to 24, as reported in the aggregates of the ENEU of January 1994.

The results of these evaluations concluded that CONALEP graduates actively participated in the labor market at a much higher rate than the similar age cohort of the general population, and at a much higher rate than graduates from traditional technical high schools. On average, CONALEP graduates found jobs faster than control individuals, and about two-thirds of CONALEP graduates worked in jobs related to the specialization they had studied. Using crosscohort comparison, these evaluations also suggested that CONALEP graduates' earnings increased rapidly within the first two to three years of employment.

These conclusions are as expected, although the magnitudes of the participation rate and the increase in earnings in comparison to the magnitudes in traditional technical high schools and the general population are surprising-thirty percent in Lane and Tan, and forty percent in Lee. The results should be considered with caution, since these studies failed to control for possible self-selection bias that could account for different labor market outcomes between the CONALEP group and the comparison groups. In addition, some of these evaluations do not fully explain how the control groups were constructed.

A fourth evaluation, aimed at understanding the background experience and goals of CONALEP students, conducted a survey with five percent of the senior students (ready to graduate) and freshmen students, the control group. The sample was 4,930 third year students and 725 first year students who, on the basis of their responses, were then divided into three groups using a socioeconomic status indicator. The results confirm the assumption that close to one-third of the students from CONALEP come from a low socioeconomic background. Another 20
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