rafmacalaba/datause-encoder-data huggingface dataset:
# **J Survey Instrument**

# **I. Immersion**<sup>23</sup>

1. How much did you feel the experience of the Venezuelan migrant was an extension of yourself?

- a. Completely

- b. Quite a bit

- c. A little

- d. None

2. How aware were you of outside distractions around you during the activity?

- a. Very aware

- b. Aware

- c. Unaware

- d. Very unaware

# **II. Comprehension assessment**

_Game treatment recipients:_

1. What was the name of the main character of the story?

- a. Mile

- b. Lina

- c. Diana

- d. Paola

2. The main character and her husband have to decide between arriving to:

- a. C ́ucuta and Bucaramanga

- b. Bucaramanga and Bogot ́a

- c. Riohacha and Bogot ́a

- d. Bucaramanga and Riohacha

3. Where is the main character from?

- a. Caracas

- b. C ́ucuta

- c. Maracaibo

- d. Manzanillo

_Video treatment recipients:_

1. What sentence does describe your experience with the internet connection:

- a. I watched the video without interruptions.

- b. The video was interrupted only once.

- c. The video was interrupted more than once.

- d. I could not watch the video because it was frequently interrupted.

> 23Adapted from Behm-Morawitz, Lewallen and Miller (2015), applied only to individuals assigned to treatment. Question 2 was, due to a programming error, only applied to the game treatment and therefore we do not use it for the analysis)

63
**Table 9: Average TFPC by airport categories (Annual %)**

|**Airport categories**|**1995-1999**|**1999-2003**|**2003-2007**|**ALL**|
|---|---|---|---|---|
|<br> <sup>**6**</sup>|**Non-w**|**eighted**|||
|**Size (10 passengers)**|||||
|**< 5.0**|- 5.6|- 1.8|3.5|0.4|
|**5.0 to 7.5**|-|- 4.3|3.7|0.5|
|**7.5 to 10.0**|8.9|1.7|7.0|5.4|
|**> 10.0**|8.5|0.9|1.8|3.4|
|**Private-Public**|||||
|**Private**|- 23.0|- 1.6|3.4|0.7|
|**Public**|5.3|- 0.8|4.5|2.9|
|**ALL**|2.7|- 1.2|3.9|1.9|
||**Weig **|**hted***|||
|**Private-Public**|||||
|**Private**|- 23.2|- 0.5|2.7|1.3|
|**Public**|6.1|0.2|4.4|3.2|
|**ALL**|5.5|0.0|3.7|2.6|

* Weighted by WLU.

Public airports: BSB, CGH, GIG, GRU, MAO and VCP (Brasil); SAL (El Salvador); MEX (México); and PTY (Panamá). Private airports: AEP, EZE, FTE (Argentina); SCL (Chile), BAQ and CLO (Colombia); SJO (Costa Rica); GYE (Ecuador); CUN GDL and MTY (México); and LIM (Perú). Airport size: < 5.0: BAQ, CLO, FTE, GYE, MAO, PTY, SAL, SDQ and SJO; 5.0-7.5: AEP, EZE, GDL, LIM, MTY and SCL; 7.5-10.0: BSB, CUN and GIG; > 10.0: CGH, GRU and MEX.

# **6. Conclusions**

To the best extent of our knowledge, this paper is the first to conduct a comprehensive efficiency calculation of Latin American airports. Our results indicate that technical efficiency in Latin American airports shows notable variations: from airports on the frontier (with a value of 1) to airports that have technical efficiency scores close to 0. When variable returns to scale are assumed (which implies that the scale of operation is out of the control of airport managers, a sensible assumption) of the 22 LAC airports in the sample, 6 are on the frontier. However, when constant returns to scale are considered, only two airports are on the frontier.

On average, Latin American airports are less efficient than Asian and North American airports when constant returns to scale are assumed, but more efficient than European airports. However, when boarding bridges are excluded and not considered as a proxy for capital 22
<mark>Twenge, J. M., & Campbell, W. K. (2001). Age and birth cohort differences in self-esteem: A cross-temporal meta-analysis.</mark> _<mark>Personality and social psychology review, 5</mark>_ <mark>(4), 321-344.</mark> <mark>Villaseñor, P. (2018). The different ways that teachers can influence the socio-emotional development of their students: A literature review.</mark> _<mark>World Development Report</mark>_ <mark>.</mark> <mark>Vugt, M.V., Cremer, D.D., and Janssen, D.P. (2007). Gender Differences in Cooperation and Competition: The Male-Warrior Hypothesis.</mark> _<mark>Psychological Science,</mark>_ <mark>18(1):19-23.</mark> Wagner, H. L., Buck, R., & Winterbotham, M. (1993). Communication of specific emotions: Gender differences in sending accuracy and communication measures. _Journal of Nonverbal Behavior_ , _17_ (1), 2953.

Willer, R., Wimer, C., and Owens, L.A. (2015). What drives the gender gap in charitable giving? Lower empathy leads men to give less to poverty relief. _Social Science Research_ . <u>http://dx.doi.org/10.1016/j.ssresearch.2014.12.014</u> Wilson, F., Kickul, J., & Marlino, D. (2007). Gender, entrepreneurial self–efficacy, and entrepreneurial career intentions: Implications for entrepreneurship education. _Entrepreneurship theory and practice, 31_ (3), 387-406.

Woolley, A., Chabris, C., Pentland, A., Hashmi, N., and Malone, T. (2010). Evidence for a Collective Intelligence Factor in the Performance of Human Groups. _Science 330_ (6004): 686-688.

Yeager, D. S., & Walton, G. M. (2011). Social-psychological interventions in education: They’re not magic. _Review of educational Research_ , _81_ (2), 267-301.

40
Table A.3: Eect of a direct SLM training intervention on contact farmers' use of demonstration plots and access to extension agents

||Ctrl.|Mean|ITT|N|R<sup>2</sup>|
|---|---|---|---|---|---|
|Used demonstration plot during the last year|0.845||-0.034<br>[0.066]|347|0.043|
|EA visited CF at least once/month|0.512||0.001<br>[0.073]|347|0.033|
|EA visited CF at least once/half year|0.631||0.055<br>[0.123]|347|0.047|
|EA visited CF at least once/year|0.667||0.143<br>[0.123]|347|0.048|

Sources: Contact farmer survey, 2010, 2012, 2013; Household survey, 2012, 2013. Notes: Regressions include the following variables: a constant, age, a completed primary school dummy, a single dummy, number of children, total landholdings, the number of rooms in the dwelling, baseline CF's number of years since formal training, a dummy for missing the baseline CF variable, district indicators, an incentive treatment dummy, and an endline dummy. ***, **, and * indicate signicance at the 1, 5, and 10 percent critical level. CF=contact farmer; EA=extension agent; ITT=intent-to-treat eect.

46
Consolidating the network of general secondary education (GSE) schools has become an ever urging issue in Ukraine considering the demographic and financial situation in the country. Due to the demographic crisis the number of students in Ukrainian schools fell by 40 percent since independence while the number of schools only fell by 7 percent and the number of teachers by only 4 percent<sup>16</sup> . As a consequence, Ukraine is maintaining a large network of small schools that have become smaller and smaller because of the severe drop in birth rates: 30 percent of level I schools have less than 10 students 21 percent of level I-II schools - less than 40 students; 17 percent of level I-III schools - less than 100 students<sup>17</sup> . Overall 19.2 percent of schools are small as defined above and its overwhelming majority is located in rural areas. As a result budget resources while increasing over time mainly support the large network of institutions rather than being directed towards quality-enhancing inputs<sup>18</sup> .

**_Figure 1: Trends in number of students teachers and schools in GSE in Ukraine 1990-2010_**

<!-- Start of picture text --> 120<br>110<br>100<br>90<br>80<br>70<br>60<br>50<br>schools teachers students<br>Index, 1990 = 100<br>1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010<br><!-- End of picture text --> 16 Around 510 thousand teachers work in the sector with 4.2 million students (compared to 537 thousand teachers and 7.1 million students in 1990/1991 school year).

17 World Bank calculations based on a school-level database compiled on the basis of information provided by each of the oblasts and related jurisdictions for the 2009/2010 school year.

18 See for details the Ukraine Public Finance Review (phase II) in FY 2007-08. Currently education sector expenditures account for almost a quarter of consolidated budget (24.8 percent in 2010) and 7.1 percent of GDP. In 2010, expenditures from the consolidated budget for education reached UAH 77.9 billion - a ten-time increase over the 2000 level. This increase exceeded the growth rate of the nominal amount of expenditures of the consolidated budget for these years which multiplied 6.5 times. The bulk of the consolidated budget is earmarked for salaries and utilities which are mainly intended to maintain budgetary institutions and thus crowd out other expenditures. At the same time wages in education remain lower than in many other sectors of the economy including manufacturing, transport, communications, financial services, retailing, etc. In Bulgaria, for example, resources saved thanks to consolidation of schools allowed to increase wages in the education sector by 46 percent between 2006 and 2008. Efficiency gains allowed also allocation of more resources for capital investment within the sector (http://www.worldbank.bg/WBSITE/EXTERNAL/COUNTRIES/ECAEXT/BULGARIAEXTN/0,,contentMDK:226 <u>99182~menuPK:305444~pagePK:2865066~piPK:2865079~theSitePK:305439,00.html).</u> 11
- 10 -

Secondly large commercial creditors increasingly promote debt conversion and voluntary debt reduction ,VDR). As a large part of VDR is transacted in the secondary market for loans, turnover in that market has risen substantially. For 1988 as a whole, total turnover, including interbank trading, is estimated at around $36 billion. VDR clauses have also been included in recent agreements between Bolivia, Brazil, Chile, and Mexico and their commercial creditors. The growing volume of VDR, through debt-equity swaps and debt repurchases has enabled a number of debtors to capture some of the secondary loan market's discounts.

Nevertheless, activity remains concentrated in the debts of Argentina, Brazil, Chile and Mexico. These four countries account for 90 percent of all secondary-market transactions and debt conversion to-date. For the other debtors the asymmetry the between heavily-discounted secondarymarket prices for their debt and the contractual value of their debt-service obligations remains an aggravating fact as they have generally been unable to take advantage cf prevailing discounts. The reason for this this is that a debtor's unilateral repurchase of part of its debt (a "buyback") is the legal equivalent of prepayment. "Sharing provisions" that are customary in loan agreements usually prevent such buvbacks. 10/. This explains why market discounts can generally only be passed through to debtors in the context of debt-reduction transactions that are structured to deal with legal problems, such as those that arise from sharing clauses.

Further headway with VDR may broaden the emerging consensus that debt reduction need not prevent future commercial lending if it can be explicitly linked to the borrowers' adjustment efforts and economic performance. As discussed in the 1988-89 edition of the World Debt Tables, this could set the stage for a more pragmatic debt strategy that blends greater use of VDR by commercial banks, renewed adjustment efforts by debtor countries, and an enhanced catalytic role of the multilateral financial institutions. To be successful, such a strategy would need to have a demonstrable impact on the level of negative resource transfers by the HICs and translate into higher investment levels. Otherwise it would merely "buy time" and ultimately raise the geopolitical dimensions of the debt crisis. III. Can 'DR Be Expanded Sufficiently to Offset the Shortfall in Lending The recent surge in the volume of VDR has made it a central tenet of the evolving debt strategy. To evaluate the potential of sustained expansion in the volume of market-based debt reduction, to the point that it can have a major impact on resource transfers, a number of factors have to be taken into account.

10/ Sharing provisions specify that any prepayment of part of a syndicated loan to any ##?member(s) of the syndicate has to be "shared" with the cther creditors in that syndicate, unless they have waived their right to share.
disability (i.e., age of father and the district proportion of disabled people). The probit regressions are reported in Table A.4 in the Appendix.

Table 4 presents the estimate of ATT for different child groups. We are not able to reject the equality of the impact estimates between different groups. However, Table 4 shows some interesting findings. The point estimate shows a stronger (negative) effect of parental disability on boys than on girls, given that culturally females are more likely to care for people in need, this may suggest that the main driver for lower enrollment may be resulting more from the need to undertake livelihood production to account for the extra costs associated with disability. This is also consistent with the finding that the biggest impact on enrollment is for children aged 15 to 17.

These results are also consistent with the descriptive data presented in Table 1, in which there is a large gap in the schooling rate between girls and boys in households with parental disability. The adverse effect of parental disability on child education is found larger in urban areas and Kinh children.

Table 4: The impact of parental disability on child schooling rate for different groups

|Model specification|Groups|Y1|Y0|ATT|
|---|---|---|---|---|
||All|0.731***|0.816***|-0.086***|
|Mdl ildi||(0.024)|(0.011)|(0.023)|
|oe ncung<br>interaction between|Boy|0.684***|0.810***|-0.126***|
|child gender and<br>disabilit of arents||(0.032)|(0.014)|(0.030)|
|y p|Girl|0.776***|0.822***|-0.047|
|||(0.030)|(0.014)|(0.030)|
||All|0.731***|0.818***|-0.087***|
|Ml ili||(0.024)|(0.011)|(0.023)|
|ode ncudng<br>interaction between|Urban|0.823***|0.870***|-0.046|
|urbanity and<br>disability of parents||(0.048)|(0.021)|(0.051)|
||Rural|0.708***|0.805***|-0.096***|
|||(0.027)|(0.013)|(0.026)|
||All|0.731***|0.817***|-0.087***|
|Model including||(0.024)|(0.011)|(0.024)|
|interaction between<br>ethnic minorit of|Ethnic minorities|0.662***|0.713***|-0.051|
|y<br>children and||(0.049)|(0.028)|(0.051)|
|disability of parents|Kinh|0.750***|0.850***|-0.101***|
|||(0.026)|(0.011)|(0.026)|
|Model including<br>|All|0.731***|0.822***|-0.091***|
|interaction between<br>children’s age and||(0.024)|(0.011)|(0.023)|
|<br>disability of parents|Aged 6-10|0.920***|0.945***|-0.024|

14
17 percentile—and therefore per capita consumption would track mainly changes in household size. Because the dependent variable in the consumption equation is per household and not per capita, total household expenditure as an independent variable is also per household. In Kenya, where six questions related to LPG use were asked, three alternative definitions of LPG users (see appendix 2) were also tested, two of which allowed more LPG-using households to be entered in the selection equation than the 436 households in the model reported in Table 5: (1) households using LPG as their primary or secondary cooking fuel (498 households), (2) households using LPG in the last year (643), and (3) households using LPG cylinders of different sizes in the previous month (414). The third model did not yield a statistically significant positive coefficient for household expenditure in the consumption model. The first two models yielded nearly identical consumption equations to that in Table 5, and similar selection equations but with the appearance of one extra independent variable in the first model and disappearance of one price variable each in both the first and second models; the numerical findings are not reported in this paper.

Table 5: Heckman model for LPG in countries with household-level prices

|**Independent variable**|**Guatemala**|**India**|**Indonesia**|**Kenya **|**Pakistan**|**Sri Lanka**|
|---|---|---|---|---|---|---|
||**Selec**|**tion equat**|**ion(probit)**||||
|Log of household<br>expenditure|0.54<br>(0.044)|1.5<br>(0.016)|1.3<br>(0.068)|0.78<br>(0.051)|0.31<br>(0.042)|1.2<br>(0.033)|
|Log of LPG price|—|—|–1.5<br>(0.27)|–0.76<br>(0.23)|–2.3<br>(0.65)|—|
|Log of firewood price|0.16<br>(0.030)|0.18<br>(0.013)|NA|—|—|0.079<br>(0.031)|
|Log price of charcoal|NA|—|—|0.27<br>(0.13)|—|NA|
|Log of price of kerosene<sup>a</sup><br>|—|0.88<br>(0.073)|0.65<br>(0.15)|—|—|0.25<br>(0.11)|
|Number of rooms<sup>b</sup>|—|NA|0.0017<br>(0.0003)|0.073<br>(0.021)|0.10<br>(0.014)|0.098<br>(0.014)|
|Dummy for car ownership|—|0.16<br>(0.037)|0.47<br>(0.057)|—|—|0.44<br>(0.061)|
|Household size|–0.13<br>(0.012)|–0.31<br>(0.006)|–0.22<br>(0.027)|–0.11<br>(0.016)|—|–0.26<br>(0.029)|
|Household size squared|—|0.0066<br>(0.0003)|0.004<br>(0.001)|—|—|0.010<br>(0.003)|
|Dummy for engaging in|–0.72|–0.20|–0.18|–0.27|—|–0.59|
|agriculture<sup>c</sup>|(0.045)|(0.016)|(0.068)|(0.091)||(0.033)|
|Highest level of education for<br>men|0.016<br>(0.008)|0.083<br>(0.003)|0.026<br>(0.006)|0.042<br>(0.011)|0.020<br>(0.005)|0.065<br>(0.006)|
|Highest level of education for<br>women|0.054<br>(0.007)|0.11<br>(0.003)|0.041<br>(0.006)|0.058<br>(0.012)|0.023<br>(0.006)|0.078<br>(0.006)|
|Age of household head|—|0.002<br>(0.0005)|—|—|–0.005<br>(0.001)||
|Dummy for male household<br>head|—|—|—|—|—|0.071<br>(0.036)|
|Dummy for indigenous<br>household head|–0.57<br>(0.047)|NA|NA|NA|NA|NA|
|Dummy for urban residence|0.61<br>(0.044)|0.48<br>(0.013)|—|0.22<br>(0.099)|–0.34<br>(0.049)|0.64<br>(0.030)|
|Dummy for electricity|0.87|0.82|0.82|0.59|—|0.85|
|connection|(0.070)|(0.020)|(0.21)|(0.076)||(0.12)|
separate business and household responsibilities; (v) how business bank accounts allow for the mental and physical separation of household and business funds and (vi) practical examples of using bank accounts for business purposes. At the end of the second day, NBS Bank offered a recently launched business bank account, which had a lower minimum balance (MWK 500) than previous products offered by the bank. This business bank account was available to all firms in Malawi with a BRC.

# **3.7 Sources of Data for Measuring Impacts**

Currently we have two sources of data for measuring impacts of these interventions. The first are data from our administrative records of program take-up. This includes information on which firms we assisted to get business registration certificates and a Tax Payer Identification Number, as well as information on attendance at the bank information sessions and on which firms signed up for business bank accounts at the conclusion of these sessions.

The second source of data is the baseline and the two first rounds of follow-up surveys. A first follow-up survey took place between November 2012 and March 2013, on average 4 months post-intervention. Attrition was 5.7 percent, and uncorrelated with treatment status<sup>23</sup> . A second follow-up survey took place between November 2013 and March 2014, on average 16 months post-intervention. Attrition for this second follow-up survey was 9.4 percent, and also uncorrelated with treatment status (see Appendix 1). Although attrition rates were low, nine and sixteen percent of the firms interviewed at the first and second follow-up surveys respectively had closed their businesses and not started a new one. This reduces the number of people in our samples that currently operate firms, but there are no differences between groups in closure rates.

# **4. Results**

Table 4 provides take-up results based on the BRC and TPIN certificates delivered with our assistance. Overall take-up of business registration was 75 percent for those offered just the BRC. The take-up of the BRC was 85 percent among those also invited to bank sessions on separating household and business money, and 69 percent among those offered BRC plus the TPIN (since they could opt for the BRC while declining the TPIN). These differences in take-up

> 23 The BRC treatment group attrition was statistically different from the control group.

17
In Table 1.1 above, there are obvious differences in growth rate estimates among the three sources; however, there is consistency in some time periods and certain trends are apparent. The estimates of all three sources show Europe's population growing considerably faster than that of Asia from 1000 to 1500. In the next two centuries (16th and 17th), the growth rate worldwide slows considerably - most likely due to epidemics of infectious disease (primarily bubonic plague), war and urbanization. During this time, the difference between the two regions' growth rates narrows but for Europe the slowdown is greater so that Asia's growth exceeds that of Europe (according to two of the three sources). In the 1 8 th century, Asia's growth rate jumps to between 40 percent and 77 percent, but only Maddison shows a faster rate for Asia than for Europe.

Table 1.2 <!-- Start of picture text --> Comparative Populatlon Share of World Total<br>1000 1500 1600<br>Radlon 6;ndtt| Clark:| Meddlson Bennett Clark | Maddlon Bennett Clark IMaddbon<br>Eurq;e? 15% 14% 12% 15% 16% 16% 18% 17% 1s<br>Al Asia 61% 63% 68% 57% 54% 65% 60% 61% 66f<br>Chhia 25S 21% 22% 28% 23% 24S 29% 30% 29<br>India 179% 25% 28% 12% 19S% 25% 14% 20% 24<br>Japan 1% 4% 3% 4% 4% 2% 4% 4% 3%<br>Wcdd 100% 100% 100% 100%9 100% 100%O 100% 100% 100%<br>1700 1800<br>Rion SeBennet Clark Maddlson Bennrtt Clark Maddbeon'<br>Eurape 19% 17% 17% 20% 19% 16%<br>Al Asia 65% 6f% 67% 67% 66%J 68%<br>China 33% 23% 23% 38% 35% 37%<br>India 16% 31% 27% 17% 21%7 20%<br>Japan 4% 4% 5%9 3 3% 3<br>WaId 100%S 100%9 1001% 100% 100% 100%<br>Gtovlh rate coim 1700-182D for Maddison<br>klk-d both Eastern and Western Eurpe<br>Indides Ed, West and Sotth Asia.<br>Sagia td frn Maddisan (2001) Frank (1998), Bennett (1954), and Clark (1977)<br><!-- End of picture text -->

Asia's share of world population far exceeded that of Europe in all time periods. (See Table 1.2.) The esitimates for population share from the three sources (Bennett, Clark and Maddison) are much more consistent than those for population growth.'<sup>0</sup> Asia's share of world population in the year 1000 was already four to five times greater than that of Europe so that although faster growth in later periods allowed Europe to increase its proportion, Asia retained the predominant share. In fact, Asia's population share has remained above 50 percent for 2,000 years and was, by most estimates, between 60 and 70 percent up to the end of the 1 9 th century.

The major proportion of Asia's population has been located in China and India, which historically have had a combined 70 to 85 percent of the total population of Asia, or 40 to 60 percent of the world's population. " 1 The speed of population growth in Asia is thus strongly affectedl by growth in these two countries, and secondarily by growth in Japan and Indonesia. For example, according to Maddison's estimates in Table H. I (Historical Appendix), in the 18 th century, East Asia's phenomenal 80 percent rise in population was led by China's increase of 176 percent. Similarly, in the following 3