rafmacalaba/datause-encoder-data huggingface dataset:
<mark>and eventually to planting more profitable crops. The study demonstrated that the low-cost (0.60 USD per month) information was able to change the behavior of the farmers. Similarly, the Digital Green project, which started in India and has spread to other countries including Ethiopia, used a participatory process to let farmers get access to agricultural advice by connecting farmers with experts through a local social network. By minimizing the distance between instructors and learners, the initiative increases the adoption of certain agricultural practices seven-fold over classical extension approaches (Gandhi et al 2009). In the same way, a Chilean farming cooperative (Coopeumo) uses text messages to help small-scale farmers increase productivity, especially by providing targeted planting advice and weather updates that are particularly useful to farmers at critical points such as sowing and harvest (World Bank 2011).</mark> <mark>Preventing losses through early warning systems have also been of growing interest, including climate models that raise public awareness of drought warnings, pest outbreaks, forest fire detection, and flood alerts, and thus give stakeholders enough time to react to emerging threats. Disease and meteorological information are required by farmers on a frequent basis. Without such information, farmers are unable to use timely measures to stem losses from climate shocks. Digital tools have been serving as the backbone for early warning systems to mitigate these risks and safeguard incomes.</mark><sup>3</sup> <mark>Early warning systems typically use data from multiple sources, ranging from satellite images to classic surveys. For example, desert locust outbreak systems determine rainfall and vegetation cover from satellite images, after which the results are used to send survey teams to areas where the probability of a desert locust outbreak is most likely (Ceccato et al., 2014). A range of climatic parameters can be obtained from satellite imagery, and although the accuracy of satellite data is not always at the level of measurement stations, they provide almost real-time data on rainfall, temperature, evaporation, vegetation and land cover that are especially efficient in remote areas where other measurement infrastructure is lacking.</mark> <mark>In addition to the monitoring systems that are based on collecting environmental sensor data, such as temperature and rainfall, we may, in the future, also see warning systems that are based</mark>

> 3 For example, a publicly funded pilot project in Turkey provides locally relevant information to farmers in Kastamonu province, where producers maintain orchards susceptible to frost and pests (Donovan 2011). Initially, nationally aggregated weather data collected in urban areas was used but proved to be inaccurate and of limited use to farmers in the provinces, because of differing microclimates from farm to farm and differences in temperature, humidity, precipitation, and soil fertility. Five additional meteorological stations and 14 reference farms were then established to collect data on these variables, enabling accurate pest monitoring.

16
transition back into school. For 15 to 24 year-olds who do transition out of Nininess, the transition is usually towards work rather than studying, for both men and women (Figure 5, left panel). Even among 15-18 year olds who transition out of Nininess, the norm is to start working. Roughly four out of five young males who are Ninis transition into work rather than school, while the remaining transition back into school, or school and work. For women, the percentage of transition Ninis who choose to go back to school is larger by 12 p.p., although work, and not school, remains the state young women transition into after Nininess.

<!-- Start of picture text --> Figure 5. Which states do Ninis today transition into (excluding those who remain Nini)?<br>Status at time of second interview for those who were Ninis in the first interview and who are not<br>Ninis anymore<br>(as percentage)<br>2 2<br>18 3 3<br>13<br>19<br>Women 31<br>15 to 24 y.o. 15 to 18 y.o.<br>Men<br>67<br>78<br>84<br>80<br>Work Study Study+Work Work Study Study+Work<br><!-- End of picture text --> _Notes_ : Weights are used in the estimation. A person is considered a _Nini_ if s/he is not working nor studying. This graph uses information for all cohorts included in the sample. _Source_ : Authors' calculations based on the _Encuesta Nacional de Ocupación y Empleo_ (ENOE).

Taken together, all the results presented in this section suggest that youths’ first step is to find a job, and then once they have one, they drop out of school and never return, and if they lose that job, they either enter into a persistent state of Nininess, or transition into and out of employment. These results are in line with others in the literature such as those of Cunningham and Busto Salvagno (2011).

# **3. Model and Econometric Specification**

We use Behrman et al.’s (2014) model of youths’ decisions regarding time use as the basis of our analysis. According to Behrman et al.’s (2014) model for young people aged 15 to 24, youth allocate their time across four mutually exhaustive choices: attend school { _s_ }, work in the labor market { _h_ }, do both { _b_ }, _or_ do neither of these { _n_ }. Each youth has a utility function 16
Philippines. We discuss the limitations of the study and provide some recommendations for a program of SBM evaluation in the Philippines.

First although the study found significant differences between SBM and non-SBM schools on schoollevel outcomes, the possibility of unmeasured differences influencing outcomes exists. The findings in impact evaluations are critically dependent on the choice of the comparison group. In the context of this study, although a comparison group was identified, whether the group represented a good counterfactual is more difficult to argue. SBM funds were disbursed in batches and since we had data for all schools, we chose the first batch of SBM schools as the treatment school and created a counterfactual using available administrative data.

Although our model controls for a number of important school-level factors such as student-teacher ratio, school size, school type, and school head type, it is quite possible that other unobservable variables affected the outcomes. For example, the model does not capture student-level data (e.g., socio-economic background), which literature shows to have a significant relationship to student achievement. School personnel quality may similarly play a role. Furthermore, although we rely on the assumption that propensity-score matching would have accounted for some unmeasured differences, important differences cannot be ruled out.

While quasi-experimental evaluation methods are an improvement over simple multivariate techniques, they are not a substitute for more rigorous experimental designs. Taking an experimental approach where the treatment group is randomly selected would allow for a direct test of the effects of the intervention. For instance, if those who received SBM in the first batch were randomly selected with control schools that meet the same criteria, a simple differences-in-difference method would have sufficed. It is understandable that in real world conditions, however, an experimental design can be challenging to implement. While it is difficult to identify control groups that will never receive the program benefits, it is possible to implement a pipeline approach and roll out the program in batches, using randomization or a clear assignment mechanism over time, to enable “control groups” to be built in. This would provide program leaders with an opportunity by which to assess the effectiveness of the intervention among beneficiaries and non-beneficiaries who are similar in every other respect. Such a method would be superior to the one we have presented here, where we rely on statistical methods to create a control group for our analysis.

14
integrate into the region’s educational sector or the extent to which they benefit from public services.

In this paper we analyze the educational transitions of young forced migrants in Chile. We examine students’ educational trajectories and the institutional roadblocks that could impede their transition to higher education. We use data from Chile’s Ministry of Education (2017–2020) to identify such roadblocks to access and advancement. We also employ a regression discontinuity design (RDD) to compare access to financial resources for higher education between migrants and non-migrants.

Our results show that offering financial aid effectively increases access to higher education, but there are significant gaps between native-born and migrant (and especially forced migrant) students. We also identify gaps between students in general education versus vocational schools. Moreover, and rather counterintuitively, financial aid has a greater impact on migrants in general education schools than their native counterparts.

These results advance the literature on migrants’ educational outcomes in at least three important ways. First, in addition to confirming prior national and international findings regarding the widening gap in educational enrollment between natives and migrants, we identify forced migrants as an even more vulnerable group. Second, our results complement the literature on forced migrants in continental Europe by focusing on a developing country to identify some of the problems developing nations have when accepting migrants into their already crowded public education systems. Third, we highlight the importance of information and access to financial resources in helping migrant students access higher education.

This paper is part of a World Bank series that analyzes the educational and labor market integration of migrants, forced migrants, and refugees in various countries and its effects on different forms of inequalities between groups. For instance, for the Malian case, (Foltz and Shibuya, 2021) analyzes the results of a household survey and concludes that an influx of internally displaced persons (IDPs) is associated with ’non-decreasing’ wealth and is uncorrelated with inequality and poverty. Similarly, in Switzerland, (Mullera et al., 2021) analyze the labor market integration of refugees and find that early labor market integration is vital to their long-term integration and the reduction of unequal access to the labor market between natives and migrants. In contrast to these results, (Sedova et al., 2021) identified that poorer local communities in Nigeria are negatively affected by forced migrant influxes as the competition for access to scarce welfare services increases. Similarly, (Hoseini and Jahan Dideh, 2021) argue that turbulent economic periods have larger detrimental effects for Afghan forced 3
TABLE 16 Impulse Responses of Potential Growth to Recessions (Other Measures)

|||<br>|**Recessions**<br>|**:**<br>|<br>|**Recessions**<br>|**:**<br>|
|---|---|---|---|---|---|---|---|
|**Definition of potential**||**Bas**|**eline defini**|**tion**|**Alter**|**native defi**|**nition**|
|<br>**output**|**h**|**World**|**AEs**|**EMDEs**|**World**|**AEs**|**EMDEs**|
||0|0.004|0.04|-0.056|0.012|0.041|-0.04|
||1|-0.084|-0.024|-0.189**|-0.087*|-0.058|-0.139*|
|**Exectations (CF)**|2|-0.157**|-0.127|-0.207*|-0.135**|-0.13|-0.145|
|**p**|3|-0.114|-0.07|-0.171|-0.077|-0.083|-0.067|
||4|-0.215**|-0.134*|-0.361|-0.241***|-0.224***|-0.272|
||5|-0.19**|-0.187*|-0.203|-0.214**|-0.26**|-0.124|
||0|-0.165***|-0.194***|-0.128***|-0.16***|-0.181***|-0.132***|
||1|-0.212***|-0.337***|-0.046|-0.2***|-0.298***|-0.066|
|**Hodrick-Prescott filter**|2|-0.493***|-0.664***|-0.224|-0.412***|-0.512**|-0.264|
||3|-0.32|-0.544*|0.056|-0.232|-0.35|-0.053|
||4|-0.146|-0.321|0.17|-0.072|-0.132|0.006|
||5|0.058|-0.047|0.249|0.089|0.089|0.055|
||0|-0.691***|-0.575***|-0.8***|-0.673***|-0.524***|-0.826***|
||1|-0.809***|-0.937***|-0.61***|-0.798***|-0.867***|-0.67***|
|**Christiano-Fitzgerald**|2|-1.299***|-1.572***|-0.795**|-1.193***|-1.304***|-0.956**|
|**filter**|3|-1.233***|-1.563***|-0.608|-1.061***|-1.215***|-0.749*|
||4|-1.029***|-1.419***|-0.257|-0.887***|-1.062***|-0.548|
||5|-0.685**|-0.833*|-0.406|-0.598**|-0.579|-0.666|
||0|-2.161***|-1.983***|-2.388***|-2.113***|-1.932***|-2.351***|
||1|-4.197***|-4.099***|-4.327***|-4.08***|-3.983***|-4.216***|
|**BtKi filt**|2|-3.413***|-3.607***|-3.071***|-3.132***|-3.295***|-2.843***|
|**axer-ng er**|3|-1.589***|-1.799***|-1.2**|-1.42***|-1.512***|-1.254**|
||4|-1.469***|-1.614***|-1.166**|-1.303***|-1.281***|-1.353***|
||5|-1.333***|-1.298***|-1.396***|-1.167***|-1.047***|-1.417***|
||0|-0.703***|-0.562***|-0.744***|-0.693***|-0.544***|-0.726***|
||1|-1.507***|-1.27***|-1.672***|-1.461***|-1.212***|-1.626***|
|**Bttth filt**|2|-1.419***|-1.493***|-1.078***|-1.29***|-1.307***|-1.01***|
|**uerwor er**|3|-1.103***|-1.017***|-1.05**|-0.979***|-0.813***|-1.044***|
||4|-0.792***|-0.75**|-0.784*|-0.679***|-0.554**|-0.834**|
||5|-0.443**|-0.433|-0.425|-0.378**|-0.293|-0.51*|

Source: World Bank.

Note: *** indicates significance at the 1 percent level, ** at the 5 percent level, and * at the 10 percent level. “Recessions: Baseline definition” are defined as the period from the peak preceding a business cycle trough to the trough, with the troughs defined as years of output growth that is both negative and one standard deviation below the long-term average (as in Huidrom, Kose, and Ohnsorge 2016). “Mild Recessions: Alternative definition” are defined as years of negative output growth only, regardless of the depth of the output decline. Sample includes unbalanced panel of 33 advanced economies and 77 EMDEs for 1981-2020.

62
sector (see Table 1a). Routine expenditure refers to expenditures related to general administration, including expenses on personnel, goods, and official travel, and repayment on borrowing and interest. Development expenditures refer primarily to expenditures on O&M (operations and maintenance) and capital spending. The impact of direct election is positive and significant on both aggregate development and routine expenditures.

Disaggregating by sector, we find that the significant increase in education and industry sector expenditures is attributable to increases in routine expenditures in these sectors. In contrast; the increases in administration, agriculture and housing expenditures are driven by _both_ development and routine expenditures. The story that emerges on expenditures in the key sectors of health and education is as follows. The increase in education expenditures was significantly larger in districts where direct elections were held, mainly due to increases in routine (as opposed to development) expenditures, relative to districts without direct election. In the case of health expenditures, however, districts with direct elections did not have a significantly larger increase relative to the rest. Direct elections therefore seemed to have an impact on routine expenditures in education, but not on health expenditures of any type.

The DID estimates indicate that direct elections had a positive and significant impact on the real per capita _revenue_ from own sources of district governments.<sup>20</sup> Moreover, the budget surplus in per capita terms (defined here as the simple difference between total revenues and expenditures of the district government) in treatment districts increased after the direct elections in comparison to the control districts (Table 1b).

In sum, the positive impact of direct elections on expenditures appears to have been spread across sectors, with 4 out of 8 sectors showing at least weakly significant impact. The impact is strongly significant for the key service delivery sector of education (but not for health), where increase in routine (as opposed to development) expenditures is the driving force. Since the increase in expenditures is spread across a number of sectors, direct elections do not seem to have led to significant shifts in the allocations of spending across sectors (see Annex, Table A-2a and 2b).

> 20 One important caveat for the interpretation of these results is that the reported revenue from own sources (PAD) in the SIKD data base is zero in 2004 and 2005. We have been unable to establish the reasons behind the absence of any revenues in these two years.

18
Thus, labor demand for college-educated workers with ICT-relevant skills may have already been satisfied in these cities.

Finally, differences in training quality could also explain the heterogeneity in treatment impact across locations, as a public sector organization won the bid to provide training in Abuja while private sector firms won the bids in the other cities, and reported attendance was notably higher in training sessions in Abuja. We note that such an explanation would be inconsistent with empirical evidence from a training program in Turkey, where Hirschleifer et al (2016) find that private firms were more effective than government training institutes in improving labor market outcomes, but consistent with evidence from France, where Behaghel, Crepon and Gurgand (2013) find strong performance by public providers of job training and search assistance.

Table 6 Panel D also shows that applicants applying during their NYSC internship did not benefit any more from the program than final year students, individuals not participating in the internship program, or individuals who had graduated from university much earlier (Table 6 Panel D).

Table 7 examines the extent to which gender bias mediates program impact, focusing on the contrasts developed for this study related to professionalism, working outside the home, and working in an office job. We estimate impacts for the female and male samples separately, restricting attention to ICT employment as an outcome. We also examine impact using a measure of explicit bias.

Given that applicants who took the Implicit Association Tests were significantly different than those who skipped this component along some dimensions (Appendix Table 4), we estimate the basic ITT specification on the sample of applicants who took the IAT (Table 7, Column 1) using relevant D-scores as our measures of implicit bias. The estimated impact of the program for this sample is statistically indistinguishable from the full sample estimate presented in Column 1 of Table 4.

We first see that explicit bias related to female professionalism does not alter treatment impact for either females or males (Columns 2 and 6). While difficult to interpret the units of the D-score, the results suggest large differences between females biased against female professionalism in treatment impact, where a unit increase in the D-score increases treatment impact by 7.8 percentage points (Column 3), more than doubling the average treatment effect.

18
TABLE 6. Cross Country Growth (Developed and Developing)

# DEPENDENT VARIABLE: GR6072

|N of OBS|<br>23|23|23|23|78|78|78|78|78|78|
|---|---|---|---|---|---|---|---|---|---|---|
|Const.|24.486<br>3.85-|24.194<br>3.97--|22.767<br>3.50"*|19.947<br>3.47--|5.941<br>2.94--|5.457<br>2.59--|5.942<br>2.97**|6.001<br>2.98-^|6.372<br>3.12--|5.944<br>2.83--|
|LGOP60|-2.806<br>-3.16-*|-2.759<br>-3.26-*|-2.612<br>-2.96- *|-2.255<br>-2.98^*|-0.768<br>-2.58^ *|-0.703<br>-2.25* *|-0.771<br>-2.61* *|-0.778<br>-2.62- *|-0.795<br>-2.64"-|-0.747<br>-2.83"|
|A160|0.099<br>2.06'|0.099<br>2.07-|0.115<br>2.25*|0.134<br>2.61 - *|0.095<br>2.89^ *|0.093<br>2.92" *|0.096<br>2.88" *|0.096<br>2.85"*|0.086<br>2.64"*|0.088<br>2.59*"|
|BAR60|0.101|0.097|0.070|0.023|0.259|0.245|0.257|0.252|0.252|0.230|
||0.565|0.550|0.403|0.147|2.36'|2.30'|2.34*|2.17'|2.35*|2.05'|
|EEC9||-0.230<br>-0.725||-0.639<br>-1.443|||||||
|EFTA|||.-0.41 1<br>-1-.153|-0.891<br>-1.656|||||||
|SACU||||||1.654<br>2.23*||||1.607<br>2.22'|
|CACM|||||||0.200|||0.199|
||||||||0.584|||0.502|
|LAFTA||||||||0.152||0.188|
|||||||||0.249||0.293|
|CEAO|||||||||-1.626<br>-1.96'|-1.501<br>-1.781|
|R2|0.667|0.672|0.676|0.698|0.288|0.315|0.288|0.288|0.312|0.338|
|MSE|1.042|1.064|1.057|1.050|1.903|1.879|1.915|1.915|1.883|1.886|

Notes: GR6072 = Average rate of growth of the income pec capita for the 1960-1972 period (S & H). LGDP60 = Logarithm of real GDP per capita in 1960 (S & H).

A160 = Average investment as a share of GDP 1960-1 972 (S & H).

BAR60 = Barro and Lee (1992) average schooling of the population over 25 years old in 1960.

EEC, EFTA. SACU. CACM, LAFTA, CEAO = dummy variables for each one of the respective integration schemes

* t-test significant at the 5% level.

t-test significant at the 1 % level.

38
|**Table 1: In Sample Des**<br>**Variable**|**criptive S**<br>**Mean**|**tatistics**<br>**Standard**<br>**Deviation**|**Minimum**|**Maximum**|
|---|---|---|---|---|
|Life|0.021|0.025|0.000|0.136|
|Non-Life|0.022|0.015|0.003|0.130|
|(Life + Non-Life)|0.042|0.033|0.005|0.179|
|Private Credit|0.512|0.336|0.042|1.629|
|Stock Market Turnover|0.424|0.509|0.004|3.508|
|GDP per capita growth|0.019|0.021|-0.065|0.091|
|Openness|0.040|0.005|0.026|0.054|
|Gov. Consumption|0.027|0.003|0.014|0.034|
|Inflation|0.334|1.863|-0.005|23.422|
|Human Capital|0.736|0.304|0.077|1.607|
|Terms of Trade changes|0.002|0.035|-0.110|0.205|
|Log initial GDP per capita|9.062|0.957|6.605|10.439|

As it is standard in the economic growth literature, the dependent variable is the average rate of real per capita GDP growth. The regression equation is dynamic in the sense that it includes the initial level of per capita GDP as an explanatory variable. As additional control variables, the regression includes the average rate of secondary school enrollment to account for human capital investment, the average inflation rate to account for monetary discipline, the average growth of the terms of trade ratio, and the average ratio of government consumption to GDP as a measure of government burden. Following Beck and Levine (2004), we measure banking sector development using the ratio of bank claims on the private sector by deposit money banks divided by GDP. This measure excludes credits by development banks and loans to the government and public enterprises. Regarding stock market development, the turnover ratio measure of market liquidity is use as a proxy. The latter equals the value of the trades of shares on domestic exchanges divided by the total value of listed shares.

7
������������������� where the dependent variable, yearly FDI inflows as a percentage of GDP (���),<sup>9</sup> is modeled as a function of a series of location-related, macroeconomic, institutional, and transition-related determinants (����, as discussed before. We include country fixed effects (��,) in all the specifications in order to control for unobserved country specificities. The error term is represented by ���.

In addition, the equation laid out above was estimated using an alternative method of random effects. Although the results are remarkably similar across both specification, the Hausman test confirms in most cases that estimation under fixed effects is preferred to random effects (as the latter estimator turns out to be inconsistent).

**Table 7. Im-Pesaran-Shin unit-root test results**

|Variable|P value|
|---|---|
|FDI net inflow|0.0000|
|Inflation|0.0000|
|GDP growth|0.0074|
|Openness|0.0000|
|Law and Order|0.0000|
|Bureaucracy|0.0000|
|Government stability|0.0000|
|Corruption Control|0.0005|
|Investment Profile|0.0207|
|Exchange rate risk|0.0000|
|FDI Stock|0.6377|
|Tertiary Educ enrolment|0.9501|
|Tel and mobile subscription|1.0000|
|Δ Tertiary Educ enrolment|0.0004|
|Δ Tel and mobile subscription|0.0000|

Source: authors’ calculations.

> 9 FDI inflows  (% GDP) come from the UNCTAD database, and are defined as follows: “For associates and subsidiaries, FDI flows consist of the net sales of shares and loans (including non-cash acquisitions made against equipment, manufacturing rights, etc.) to the parent company plus the parent firm ́s share of the affiliate ́s reinvested earnings plus total net intra-company loans (short- and long-term) provided by the parent company. For branches, FDI flows consist of the increase in reinvested earnings plus the net increase in funds received from the foreign direct investor”.

17