rafmacalaba/datause-encoder-data huggingface dataset:
Kimball, M.S. and Shumway, T. (2010). Investor sophistication and the home bias, diversification, and employer stock puzzles. _Working Paper, University of Michigan._ Krause, B. L., McCarthy, A. S., and Chapman, D. (2016). Fuelling financial literacy: estimating the impact of youth entrepreneurship training in Tanzania. _Journal of Development Effectiveness,_ 8(2): 234–256 _._ Loke, V., Choi, L., and Libby, M. (2015). Increasing youth financial capability: An evaluation of the mypath savings initiative _. Journal of Consumer Affairs_ , 49(1): 97–126.

Lusardi, A. (2002). Preparing for retirement: The importance of planning costs. _National Tax Association Proceedings–2002_ : 148–154.

Lusardi, A. (2005). Financial education and the saving behavior of African American and Hispanic households, _Report_ , _US Department of Labor, Employee Benefits Security Administration._ Lusardi, A. and Mitchell, O.S. (2007). Financial literacy and retirement planning: New evidence from the Rand American life panel. _Michigan Retirement Research Center Research Paper No. WP 2007-157._ Lusardi, A., Samek, A.S., Kapteyn, A., Glinert, L., Hung, A., and Heinberg, A. (2015). Visual tools and narratives: New ways to improve financial literacy. _Forthcoming in Journal of Pension Economics and Finance._ Lührmann, M., Serra-Garcia, M., and Winter, J. (2015). Teaching teenagers in finance: Does it work? _Journal of Banking and Finance_ , 54: 160–174.

Maki, D. (2004), Financial education and private pensions, in Gale, W., Shoven, J. and Warshowsky, M. (editors.), _Private Pensions and Public Policies_ , Washington, DC: Brookings Institution Press: 126-139.� Mandell, L. (2006). Teaching young dogs old tricks:The effectiveness of financial literacy intervention in pre-high school grades. P _aper Presented at the Academy of Financial Services 2006 Annual Conference Salt Lake City._ Mandell, L. (2009a). Starting younger: Evidence supporting the effectiveness of personal financial education for pre-high school students. _Working Paper, Aspen Institute and University of Washington._ Mandell, L. (2009b). The impact of financial education in high school and college on financial literacy and subsequent financial decision making. _Paper presented at the American Economic Association Meetings, San Francisco, CA._ Mandell, L. and Schmid Klein, L. (2009). The impact of financial literacy education on subsequent financial behavior. _Journal of Financial Counseling and Planning_ , 20(1): 15–24.

Mills, G., Patterson, R. Orr, L., and DeMarco, D. (2004). _Evaluation of the american dream demonstration: Final evaluation report_ , Cambridge, MA.

Muller, L. (2003). Does retirement education teach people to save pension distributions? _Social Security Bulletin,_ 64 (4), 48–65.

Pang, M.F. (2010). Boosting financial literacy: benefits from learning study. _Instructional Science,_ 38(6): 659–677.

Peng, T.-C.M., Bartholomae, S., Fox, J.J., and Cravener, G. (2007). The impact of personal finance education delivered in high school and college courses. _Journal of Family and Economic Issues_ , 28(2): 265–284.

73
from Ethiopia, Malawi, Niger, Nigeria, and Uganda (see also Section 4) combined with high-resolution precipitation data to study the effects of cumulative climate shocks on long-term migratory flows in SubSaharan Africa. Overall they find evidence of a persistent impact of droughts on rural households in these countries, which translates into a much larger effect on migration compared to the period in the aftermath of the shock as the impacts accumulate over time. The authors also detect the existence of a relationship between rainfall shortage and accelerating urbanization trends in four of the five countries considered in their analysis. At the country level, their findings contrast with previous studies that use similar multicountry micro-level data sets to examine the effect of climate shocks on rural out-migration, which found no significant or consistent migration-inducing effect of droughts in the short- and long-run. Conversely, the authors notice that their results are in line with those of macro-level cross-country studies that corroborate the contribution of rainfall deficits to faster urban development.

Overall, this review confirms that the relationship between migration and climate change in Sub-Saharan Africa is far from univocal, with some studies considering migration as a direct consequence of weather shocks and climatic changes, and others that do not find that these factors exert a clear or significant impact on people’s mobility. The majority of the SSA literature focuses on slow-onset rainfall and temperature events, whereas only a few studies specify the type of migration. As an aside, we notice that there is unequal country coverage in this literature, with repercussions in terms of external validity. For instance, the Sahel region is particularly underrepresented, despite being one of the areas identified among the hotspots of climate change (IPCC, 2014).

# **3. Open empirical issues and data gaps**

## _3.1 Open empirical issues_

From the conceptual discussion above, a number of key open empirical issues stem:

- How to disentangle, from an empirical point of view, short-run elasticities of weather shocks on migration from the compounding effects of slow-onset, long-run, eventually permanent changes in climate and progressive warming? Most of the current works only investigate the short-run weather effects of migration and then extrapolate with respect to climate change. But this poses the problem of the already mentioned external validity gap between weather shocks and climatic change. To empirically investigate the latter, one needs to look either at longer time series in a longitudinal setting, using several lags of the weather parameters or long-run (e.g., 30-year,

16
# Table 7: **Which Curriculum Is the Most Effective? - Social-Emotional Skills and Emotion Regulation** _Intention-to-Treat Estimates_

|||Estimated<br>Coeffcient<br>(1)|Standard<br>Error<br>(2)|_P-_value<br>(3)|_P_-value<br>Wild Bootstrap<br>(4)|Mean<br>Clubs Group<br>(5)|P-value<br>_Virtue_=_Mindful_<br>(6)|Observations<br>(7)|
|---|---|---|---|---|---|---|---|---|
|SES index|_Virtue_|-0.000|(0.014)|(0.996)|[0.995]|0.000|0.381|755|
||_Mindful_|0.007|(0.014)|(0.608)|[0.685]||||
|Short-term perseverance|_Virtue_|0.004|(0.010)|(0.736)|[0.725]|0.000|0.902|759|
||_Mindful_|0.005|(0.008)|(0.576)|[0.580]||||
|Self-control|_Virtue_|-0.008|(0.012)|(0.497)|[0.530]|0.000|0.469|776|
||_Mindful_|-0.013|(0.011)|(0.233)|[0.230]||||
|Risk aversion|_Virtue_|0.025**|(0.012)|(0.032)|[0.020]|0.000|0.066|773|
||_Mindful_|0.004|(0.011)|(0.709)|[0.715]||||
|Fluid intelligence|_Virtue_|-0.116|(0.080)|(0.152)|[0.210]|0.000|0.244|733|
||_Mindful_|0.037|(0.132)|(0.780)|[0.815]||||
|Fluid intelligence - after negative stimuli|_Virtue_|-0.119|(0.095)|(0.217)|[0.280]|0.000|0.005|733|
||_Mindful_|0.220**|(0.089)|(0.016)|[0.010]||||
|Fluid intelligence - after positive stimuli|_Virtue_|-0.068|(0.091)|(0.456)|[0.555]|0.000|0.531|733|
||_Mindful_|0.009|(0.132)|(0.944)|[0.930]||||
|Arousal|_Virtue_|0.027|(0.111)|(0.811)|[0.845]|0.000|0.617|622|
||_Mindful_|-0.059|(0.171)|(0.731)|[0.720]||||
|Valence|_Virtue_|-0.270**|(0.106)|(0.013)|[0.030]|0.000|0.792|622|
||_Mindful_|-0.211|(0.219)|(0.338)|[0.360]||||

_Notes:_ This table shows the estimated impacts of each type of psychology-based curriculum with a social-emotional-learning component. Descriptions of all dependent variables are available in Section 3.2. _Virtue_ and _Mindful_ are dummies equal to 1 if the student was enrolled in a school that was randomly assigned to the _Virtue_ or _Mindful_ intervention, respectively, and 0 if assigned to _Clubs_ . In column (1), we present the estimated coefficients of the interaction between the treatment dummies and the _Post_ indicator. Clustered standard errors, unadjusted _p_ -values, and _p_ -values adjusted using a wild bootstrap procedure (adjusted for small number of clusters) are shown in columns (2), (3), and (4), respectively. _Mean Clubs Group_ in column (5) is the mean of the outcome for the group that only participates in _Clubs_ , without any extracurricular activity. Column (6) presents the _p_ -value for the test for differences between estimated coefficients for _Virtue_ and _Mindful_ presented in column (1). Sample size in each specification presented in column (7) varies according to the number of observations available for each outcome. Estimations include all individual controls for which there is imbalance at baseline. All regressions include randomization-block (strata) fixed effects. Strata are defined as country and violence level (high or low) of the community in which the school is located.<sup>_∗∗∗_</sup> _p <_ 0 _._ 01 _,_<sup>_∗∗_</sup> _p <_ 0 _._ 05 _,_<sup>_∗_</sup> _p <_ 0 _._ 1
# **Graph (16a): Scaled Residuals of Unrestricted Open Economy VECM:**

<!-- Start of picture text --> 0.2 r Drm2<br>0.0<br>-0.2<br>1980 1985 1990 1995 2000 2005 2010 2015<br>0.2<br>r Drgdp<br>0.1<br>0.0<br>-0.1<br>1980 1985 1990 1995 2000 2005 2010 2015<br>r DDISC<br>2.5<br>0.0<br>-2.5<br>1980 1985 1990 1995 2000 2005 2010 2015<br><!-- End of picture text -->

# **Graph (16b): Long Run Stability Test of Unrestricted Open Economy VECM:**

<!-- Start of picture text -->
1.00 Roots of companion matrix<br>0.75<br>0.50<br>0.25<br>0.00<br>-0.25<br>-0.50<br>-0.75<br>-1.0 -0.8 -0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 0.8 1.0<br><!-- End of picture text -->

78
We are also interested in the impact of the regulatory and institutional framework on the deposit insurance and systemic risk relationship. The adverse consequence of deposit insurance may well depend on the institutional environment and can potentially be mitigated through bank regulation. For instance, better bank supervision may limit the extent to which banks can engage in correlated risk taking activities in the presence of deposit insurance.

We consider a bank supervisory quality index, which measures whether the supervisory authorities have the power and the authority to take specific preventive and corrective actions such as replacing the management team. This variable comes from the banking surveys conducted by Barth, Caprio, and Levine (2008). The surveys were conducted in the years 1999, 2002, and 2005. Because country level regulations change slowly over time, we use the previously available survey data until a new survey becomes available.

Our dependent variable is bank _i_ ’s systemic risk, whose calculation is described in Section 2.3. The regression specifications and control variables are the same as those used in Section 3.2. Because the full coverage dummy is expected to lead to the most egregious moral hazard problem during good times, we examine whether bank supervision can mitigate such an unintended consequence of full deposit insurance coverage.

Table 6 reports the coefficient estimates. We first partition our sample by the bank supervisory quality index. Column (1) of Table 6 shows the regression results for the subsample where bank supervisory quality is greater than or equal to sample median, and Column (2) of Table 6 presents that the regressions coefficients for the subsample where bank supervisory quality is lower than sample median. We find that, in Column (1) of Table 6, full coverage dummy is associated with lower bank systemic risk during global crisis years and has no adverse impact on bank systemic risk in non-crisis years, when bank supervision is strong. In contrast, Column (2) of Table 6 indicates that, full coverage increases bank systemic risk in all years when bank supervision is weak. Overall, the results in Table 6 suggest that better bank supervision may be able to mitigate the adverse consequences of deposit insurance while weak bank supervision exacerbates the negative impact of deposit insurance on bank stability.

# **4. Conclusion**

As part of the International Monetary Fund’s best practice recommendations to developing countries, deposit insurance has received increased attention from both academic 15
- 14 -

_HYDRO_ is defined as the monthly discharge (in million cubic meters (m<sup>3</sup> )) of each river at the project site. The data come from each of feasibility studies. Temperature and precipitation data depend on the NOAA GHCN Monthly Database Version 2; the data from the observatory closest to the project location are used.<sup>4</sup> In the NOAA database, however, there is no available comprehensive data for Southern Vietnam. Alternatively, the observed regional weather time series provided by the IPCC Data Distribution Centre (DDC) are borrowed.<sup>5</sup> But unlike the NOAA database, these time series are available only up to 1990, and may have poor spatial representation. Thus, when using them, we may risk

> underestimating the most recent impacts of global warming. Whereas _TEMP_ is defined as the

> monthly average temperature converted to _Fahrenheit_ in order to avoid taking logarithms of

> negative numbers, _PREC_ is total monthly rainfall measured in millimeters (mm).<sup>6</sup> When there is no precipitation in a particular month, it is set to a very small positive number, but not zero.

How are these three variables related to each other? Table 4 shows simple correlations. The extent to which hydrology is linked to climate differs among project locations. Precipitation may be most relevant to hydrological series. However, temperature may be positively or negatively associated with river flow. There is no serious multicollineality problem in our data.

Table 4. Simple Correlation

||Vishnugad<br>Pipalkoti<br>(India)|Upper<br>Kotmale<br>(Sri Lanka)|Thac Mo<br>(Vietnam)|
|---|---|---|---|
|(ln_HYDRO_, ln_TEMP_)|0.774|-0.360|0.465|
|(ln_HYDRO_, ln_PREC_)|0.271|0.285|0.849|
|(ln_TEMP_, ln_PREC_)|0.117|0.030|0.551|
|No of obs.|295|496|240|

4 The Mukteshwar observatory (lat. 29.5° north; long. 79.7° east) provides data to the Vishnugad Pipalkoti case; and data from Colombo (lat. 6.9° north; long. 79.9° east) are borrowed.

5 The latitude and longitude of the specified point are 12° north and 107° east, respectively.

6 The NOAA database provides precipitation time series per day, which is thus converted to monthly series in the Thac Mo case.
only to look at the migratory flows causally linked to weather anomalies and climatic disasters but also, and perhaps especially so, to the relationship between such events and the presence of potential immobility traps associated with adaptation failures. A useful tool in this respect would be an _ad hoc_ section on migration-as-adaptation embedded within a broader Adaptation Module, with particular emphasis on questions investigating the reasons (e.g., the role of liquidity constraints) that hampered the implementation of household- or individual-level coping strategies, including those migration-related.

Migration can be seen as one among a pool of household adaptation strategies in the face of environmental change. The study of _in-situ_ adaptation strategies, therefore, should be seen as complementary to the adaptive decision to migrate. McCarthy (2011) provided a set of key indicators and modules to supplement LSMS and LSMS-ISA survey instruments based on a taxonomy of household agricultural practices and investments that can contribute to adapting to climate change (the so-called sustainable land management, SLM). These include agroforestry investments, reduced or zero tillage, use of cover crops, and various soil and water conservation structures. There are often long-term benefits to households from adopting such activities in terms of increasing yields and reducing the variability of yields, making the system more resilient to changes in climate.

The implementation of a comprehensive _Adaptation_ module capturing these aspects as well as other insights from resilience and vulnerability studies (Magrini et al., 2018; D’Errico et al., 2019) would represent a clear added value for household surveys. It would allow one to distinguish between migration as adaptation mainly implemented as a strategic risk diversification strategy by richer and better-endowed households _vs_ survival migration adopted as a last-resort option by the poorer households, and hence enhance our understanding of the nature of migration as voluntary or involuntary. It would also have important implications for studying adaptation policies to climate change in rural developing contexts in Sub-Saharan Africa. For example, the systematic inclusion, in the roster of potential answers to the question on coping strategies, of the option “ _Drought, flood or other weather-related condition_ ” as in question Q.3.18 of the earlier rounds of the Uganda collection, which we mentioned above, would be a straightforward way to collect more information on climatic push factors. Even if a module should not be seen as a viable solution, the collection of additional questions and to make up for the current lack of an integrated framework on adaptation in household surveys such as the LSMS-ISA is in any case strongly encouraged.

31
|||MANUA|L||EE.ECTRONIC||
|---|---|---|---|---|---|---|
|Cogeston Charging<br>Meebanisms|7||2||3|4|
|Manual and lecltronic Toll<br>Colecdon<br>plus<br>Congestion Charging<br>Mechanism|Maua<br>Toll<br>Collection<br>(24 hour)|Cordo<br>Pricing<br>(Combinato<br>of Maa<br>and<br>Reseved<br>Lane)|Licenn (Single<br>Zone)<br>|Eleronic Tol<br> <br>Collection (24<br>hour) with AVI<br>Technoloy2|actonic Road<br>Priin (ERP)<br>with Autmatc<br>Vehicle<br>Idenfication|Electronic<br>Road<br>Picing<br>(E)<br>with<br>Smat Cad<br>Technology|
|8. Secuntray<br>andEnfonmnt:<br>a. Protection from Theft|_9_<br>*(||**U**<br>*||_.s_<br>0|**U**|
|b. Protection fromFrud|O(|OI||0|0_a3_||
|9. Provision for Occasonal<br>Vwoisi|O|O|**32**<br>3|O|3|O|
|10. 'Market' Prce as an<br>Investmt Signal|O0||||0|0|
|11. Passage of Rvenue-Cost<br>Test|case-<br>Specific|14<br>0<br>6.3:1|3<br>6.9:1to11.8:1|0<br>5.9:1 to 2.2:1|0<br>7.9:1 to 10.4:1|El<br>2.6:1|
|_FromSocdy's PohAtof Vw(12_|_-20):_||||||
||||1~~~<br>**14**|3~~~~|~7|is|
|12. Passage of Benefit-Cost<br>Test|Case-<br>specific|**Ldd.**|0|**U**<br>0.2:1 to 2.5:1|14.7:1 to 17.8:1|(3<br>4.5:1|
|_Long-mm Cost per T_<br>__vadi_<br>_(US cenXt_in1990 figus)|_ox_Case-<br>spDcific|_11.00_|5.6-9.90|21.20|6.60|_12.50_|
|13. Minimum of Road Work<br>andEnviromentalntruuion|||||||
|14. Provision for Mixed Traffic|O|0||(3|||
|15.**HEadlin**of Transtional<br>Phse|_3|||0|(|(3|
|Jordan|8.0|8.0|8.0|8.0|
|---|---|---|---|---|
|Kuwait|10.6|18.8|22.2|22.2|
|Lebanon|2.0|2.0|2.0|2.0|
|Libya|17.0|20.0|25.0|25.0|
|Morocco|1.0|5.0|5.0|5.0|
|Oman|38.0|38.0|34.0|34.0|
|West Bank and Gaza|0.4|0.8|1.0|1.0|
|Qatar|209.0|259.0|259.0|259.0|
|Saudi Arabia|132.0|132.0|132.0|132.0|
|Sudan|0.1|0.1|0.1|0.1|
|Syrian Arab Republic|3.0|11.0|11.0|11.0|
|Tunisia|6.0|6.0|5.0|5.0|
|United Arab Emirates|90.0|76.0|64.0|64.0|
|Yemen, Rep.|0.4|2.9|6.3|6.3|

Sources: World Bank (2020).

# **4e. Renewable energy capacity factors**

|**Economy**|**Wind**|**PV**|**CSP without storage**|**Hydro**|
|---|---|---|---|---|
|Algeria|20%|23%|15%|13%|
|Bahrain|36%|21%|12%|-|
|Egypt, Arab Rep.|31%|24%|16%|55%|
|Iraq|17%|23%|16%|19%|
|Jordan|30%|26%|20%|57%|
|Kuwait|16%|21%|13%|-|
|Lebanon|16%|24%|19%|-|
|Libya|16%|23%|16%|49%|
|Morocco|21%|22%|17%|17%|
|Oman|31%|26%|19%|-|
|West Bank and<br>Gaza|20%|22%|16%|-|
|Qatar|22%|22%|14%|-|
|Saudi Arabia|38%|26%|19%|-|
|Sudan|13%|24%|16%|32%|
|Syrian Arab<br>Republic|13%|23%|18%|21%|
|Tunisia|18%|22%|16%|12%|
|United Arab<br>Emirates|14%|23%|15%|-|
|Yemen, Rep.<br>Sources: IHA (2017)|18%<br>for hydro, Solar|27%<br>GIS (https://sola|20%<br>rgis.com)for solar.|-|

Sources: IHA (2017) for hydro, SolarGIS (https://solargis.com) for solar.

# **4.4. Cross-Border Interconnections**

The existing and committed (i.e., under construction) cross-border transmission interconnections are provided in Figure 1. Among the several transmission interconnections, the 3,000MW Egypt-Saudi Arabia interconnection to be commissioned in 2022 is the largest crossborder transmission interconnection in the MENA region by 2035. The current total cross-border 15
<!-- Start of picture text --> 1000 120<br>900<br>@ 700 20 =<br>cal]=e 600 =<br>2 nn<br>& 500 60 D2<br>oo -_<br>5= 400 AQ z<br>S& 300 F<br>E 200 2<br>Pad 70 +=<br>100<br>0 0<br>2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016<br>e=§—= meme Customers e=§—= meme Investment<br><!-- End of picture text -->
There is now robust evidence that higher levels of GDP per capita is associated with lower corruption (La Porta et al., 1999; Ades and Di Tella, 1999; Svensson, 2005; Fan et al., 2009). If the level of income also varies systematically with country size, our main results could suffer from the omitted variable bias problem. Thus, we control for (log of) GDP per capita (PPP adjusted and at constant 2011 international dollars) taken from WDI. We complement the GDP per capita control with the annual growth rate of GDP per capita to capture any possible impact of recent changes in economic activity on the level of corruption. The data source for the variable is WDI.

There is some work on the impact of education attainment on civil participation and punishing the corrupt officials. Most studies in the area suggest that education lowers corruption. That is, education leads to a more vigilant political participation and “elite-challenging” behavior that identifies and punishes corrupt behavior (see for example OECD, 2007; Eicher et al., 2009). Empirical studies that confirm this prediction include for example, Galston (2001), Carpini and Keeter (1996), Popkin and Dimock (1999), Glaeser and Saks (2006), Goel and Nelson (2011), Nie et al. (1996).<sup>7</sup> Thus, our main results could be biased if the level of education attainment also varies systematically with the country size. To guard against this possibility, we control for education attainment using the gross enrollment rate in primary education (Primary Education). Due to missing data, we use average values of the variable over the last two years (prior to the date of ES) for which data are available. The data source for the variable is WDI.

The degree of urbanization might also play a role in the level of corruption in a country. Meier and Holbook (1992) argue that urbanization fosters the necessary conditions for corruption by loosening the social controls of family and religion, and by concentrating government programs and resources. Their empirical evidence shows a strong positive correlation between urbanization

> 7 A few studies provide a counter view that higher education attainment increases output and therefore corruption rents; this implies a positive impact of education on corruption (see for example Eicher et al., 2009).

14