rafmacalaba/datause-encoder-data huggingface dataset:
# **I INTRODUCTION**

Over the past decade, the number of individuals forcibly displaced worldwide has more than doubled, surpassing 110 million by 2023 (UNHCR 2023). Notably, children and adolescents under 18 of make 45 of international forced years age up percent migrants (UNICEF 2023). Forced displacement exposes these young individuals to severe wellbeing challenges and traumatic experiences, placing them at heightened vulnerability. This is particularly concerning as such events unfold during critical developmental stages that significantly influence their future life paths. Despite the urgency, our understanding of the impact of forced displacement on this demographic is still limited.

This challenge is partly due to data restrictions that obstruct the diagnosis of problems and the design of effective aid strategies. This issue is particularly acute for forced migrants living in local communities in developing countries, where an estimated 80% of the world’s refugees reside (Climate Center 2022). These individuals often face trust issues and may lack the formal documentation needed to establish their legal status within host communities. Consequently, they may fear detection by authorities when asked to respond to surveys or participate in public initiatives.<sup>1</sup> Furthermore, collective representative and data on a with longitudinal forcibly displaced migrants, population high mobility rates, is difficult and costly (Ib ́a ̃nez et al. 2024). This complexity is compounded when focusing on children and adolescents, given the need for enumerators to receive specific training to interact with such a vulnerable demographic and for migrant parents to authorize their children’s involvement despite prevailing distrust issues.

To address this knowledge gap, we launched the Venezuelan Refugee Panel Study for Kids (VenRePs-Kids) in Medell ́ın, Colombia. VenRePs-Kids is a longitudinal study representative of forcibly displaced Venezuelan and Colombian children and adolescents aged 5 to 17. To our knowledge, it is the first study to gather panel data specifically on forcibly

> 1This concern is also prevalent among undocumented migrants in the United States, as highlighted by Amuedo-Dorantes and Lopez (2015).

2
aforementioned laboratory measures of social capital. The mean effect is calculated from the average standardized effect (i.e. _z_ -scores of the effect) from each individual measure. As before the point estimate is negative and statistically insignificant.

The final two columns in the upper panel of Table 4 present the ATTs of our measures of risk preferences and the discount rate (i.e., patience). We report estimates of the effect of the program on these variables because they may be important confounders. Persons with higher risk premiums or lower discount rates may appear to be less trusting when in fact their behavior is driven by their attitudes toward risk or their discount rate (or both). Thus, if there were significant differences between the treated and control communities in this regard, we would have cause for concern about confounding. The results in the last two columns of the upper panel in Table 4 show there is no such cause for concern. The attitudes toward risk and the discount rates are statistically identical in the treated and control communities. The average of the villagers’ lottery choices is 2.8 in both the treated and the control communities and the average of villagers’ time-preference choice was category 4 in both the treated and control communities.

Finally while not reported in teh main text we did check for the possibility that the program may have had impacts on sub-groups such as women or youth. We estimated the effects of the program on these various subsamples using interactive affects. In no case did those estimates indicate that people in the treated communities behaved more pro-socially than did people in the control communities. Thus the program did not cause greater prosociality even in subsamples of our subjects. The estimates from these specifications are exhibited in the appendix in Table A.2.

In summary the results using the behavioral measures from games conducted in the laboratory are quite clear in indicating that the program had no impact on individuals’ pro-social behavior. The estimates of effect were very close to zero. Skeptics may still raise power concerns, however the fact that all of the point estimates were negative—the opposite of the hypothesized sign—assuages power concerns. Our results are not due to large standard 26
relies on 386,244 tests with loan repayment data, including tests from Latin America, where further refinements have been made according to the local context.

The EFL tool is designed to be similar to the qualitative assessments that loan officers perform. In practice, the SME owner who is in charge of the business decisions takes the test on a tablet, smartphone or PC. The application does not require access to the internet and thus allows the lenders to administer the tool either at a branch or in the field. The application uses many common techniques to prevent fraud, such as designing questions with no obvious right or wrong answers, randomizing the content of the application and the order in which questions appear to make each application different, or analyzing whether answers display unusual and unlikely patterns, to detect if for example, loan officers are assisting applicants.<sup>10</sup> The EFL application generates a 3-digit score that ranks the relative credit risk of the person who took the test. Lenders can use this score in different ways, such as setting cutoffs for approvals, or modifying the price, size, or other margins of a loan. Some examples of the types of questions that are asked in the EFL application are illustrated in appendix table A1.

In March 2012, our partner bank started to pilot EFL’s psychometric credit-scoring model in Peru, with the objective of expanding commercial lending to SMEs. At the time, our partner bank had only a small SME portfolio and they were interested in testing whether the EFL tool could help them enhance their credit approval process. SMEs who applied for a working capital loan (up to 18 months in duration with an average loan size of $3,855) were screened by the EFL tool as part of the application process. The EFL application took on average 45 minutes to complete (the current version takes 25 minutes). Applicants who achieved a score on the EFL application higher than a threshold defined by our partner bank were offered a loan, independently of whether or not they would have been offered a loan based on the conventional screening method used by the bank. Figure A1 in the appendix shows the distribution of EFL scores, below and above the selected threshold, for the SME loan applicants in our sample.

Applicants with a score below the EFL threshold were also offered a loan if they were approved under the institution’s conventional screening method. This conventional screening method relied on a credit score from Equifax Peru and a site visit to the SME. All applicants had an Equifax credit score, but for unbanked individuals, i.e. those who do not have a credit history, this credit score is primarily based on demographic information. Only SMEs that were rejected

> 10 Information from EFL website (https://www.eflglobal.com/).

8
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18

# <u>Table 2: Health equity: Service coverage (treatment)</u>

|||Indicator|Definition|Main Data Source|
|---|---|---|---|---|
|||Births attended by skilled health<br>staff (% of total)|Percentage of most recent births in last 2 years attended by any skilled health personnel (women<br>age 18-49 at the time of the survey). Definition of_skilled_varies by country and survey but always<br>includes doctor, nurse, midwife and auxiliarymidwife).|DHS, MICS, WHS|
||**_MDG_**|Acute respiratory infections treated<br>(% of children under 5 with cough<br>_and_rapid breathing)|Percentage of children under 5 with cough and rapid breathing in the two weeks preceding the<br>survey (DHS, WHS) who had a consultation with a formal health care provider (excluding<br>pharmacies and visits to ‘other’ health care providers). MICS data points use sample of children<br>under 5 with cough and rapid breathing in the 2 weeks preceding the survey which_originated_<br>_from the chest_. The definition of_formal_health care providers varies by country and data source.|DHS, MICS|
|**_ENT_**||Diarrhea treatment (% of children<br>under 5 with diarrhea who received<br>ORS)|Percentage of children under 5 with diarrhea in the 2 weeks before the survey who were given<br>oral rehydration salts (ORS)|DHS, MICS|
|**_TREATM_**|**_SDG_**|Inpatient care use in last 12 months<br>(% of population age 18 and older)|Percentage of population age 18 and older using inpatient care in the last 12 months|DHS, ECHP,<br>ENAHO,<br>Eurobarometer,<br>EHIS, ISSP, LSMS,<br>MCSS, US-NHIS,<br>SLC, SUSENAS, UK-<br>GHS, WHS|
|||Treated for high blood pressure (%<br>of adultpopulation)|Percentage of adult population being treated for high blood pressure (age-range may vary)|Eurobarometer,<br>EHIS|
|||Treated for raised blood glucose or<br>diabetes (% of adult population)|Percentage of adult population being treated for raised blood glucose or diabetes (age-range may<br>vary)|DHS,<br>Eurobarometer,<br>EHIS|
disbursements could be adjusted according to the results of regular assessments of the local implementation of the program. Rewards to municipalities that demonstrate effective reductions in local social infrastructure gaps could help establish appropriate incentives for the use of FAIS resources. In addition, the lack of monitoring of social infrastructure and the absence of social infrastructure in the allocation formula fail to create incentives for local governments to increase investments through other sources of income, thereby increasing their fiscal capacity. Thus, although improving the targeting of resources through modifications in the allocation formula is important, a bigger issue is the need to increase the capacity of local governments to raise tax revenue, which has been shown to exert a larger impact on the provision of public goods. Empirical evidence from Brazil (Gadenne 2017), Colombia (Martínez 2016), and Indonesia (Kis-Katos and Sjahrir 2017) demonstrates that higher tax revenues, accompanied by greater accountability, have a much larger effect than transfers on the provision of education, health care, and basic services even if resources are earmarked in some cases. Local governments with more fiscal autonomy show greater convergence (Smith 2018), which could explain why the analysis finds a greater positive effect on welfare in urban municipalities with larger populations and more fiscal capacity. Thus, the framework of analysis applied in this paper could be useful for other countries with similar programs and facing comparable challenges. The changes in the indicators and rules outlined above provide examples of how allocation formulas can be redesigned to create incentives to invest more efficiently.

21
much lower coverage among rms than pure information reporting, especially for the self-employed. D150 and D153 records are available for only 5.0% and 5.8% of the self-employed and 8.4% and 11.% of corporations respectively.<sup>22</sup> 98.5% of D150 records are submitted by state institutions, meaning that withholding by private non-nancial rms is minimal. A signicant share of informative declarations cannot be matched with income tax records, suggesting that a large number of rms covered by third-party information or withholding are incompliant on the extensive margin.

In addition to the tax returns and informative declarations, we use the D140 and D141 registration and deregistration records for 2006-2014 to construct snapshots of the tax register for each scal period. Firms use the D140 form both for registration purposes, as well as for modication and deregistration. If the government deregisters a rm de ocio, which happens if a rm has not led taxes for at least three years, a D141 form is used.

# 4 Anatomy of Compliance

This section presents the anatomy of tax compliance in Costa Rica, identifying mis-reporting through discrepancies between two data reports on the same tax base, as applied by Fisman & Wei (2004). We start with the extensive margin, estimating the share of non-lers by matching tax declarations led to the set of tax liable rms as constructed from the tax register and available third-party reports. We then consider the intensive margin, estimating misreporting by comparing third-party reported and self-reported sales and costs. Finally, we estimate compliance with the payment obligation, comparing tax returns with payment receipts. We compare compliance by the self-employed and by corporations, as the two groups face dierent tax schedules and the selfemployed have been identied in the literature as a particularly evasion-prone segment of taxpayers.

## 4.1 Extensive Margin Compliance

To examine compliance on the extensive margin, we construct the set of tax liable rms and compare it to the self-assessment declarations led for the income tax and the sales tax. A rm is considered income tax liable for scal year _t_ if it fullls at least one of the following conditions: (i) the rm is in the tax register in year _t_ , (ii) has led income tax in _t_ , (iii) is covered by at least one third-party informative declaration in _t_ ,<sup>23</sup> (iv) has led income tax in _t −_ 1 and has not deregistered since, or (v) has registered in the last year and has not deregistered since. For the sales tax, we consider only rms that have either (i) led sales tax between month _m_ and _m −_ 4 , (ii) registered as liable for the

> 22As indicated by the percentages in squared brackets in Table III, the coverage of D153 declarations among sales-tax-liable rms is higher, since they constitute only a small subsample of income taxpayers.

> 23We exclude information from D151 cost reports, which could pertain to wage-earning individuals who purchased goods from a rm.

14
**Table A.6: RIF decomposition of the Gender Differential in Agricultural Productivity with region fixed effects - Food crops only (2016)**

||**Y= RIF es**|**timate of Lo**|**g [ Self-repor**|**ted Producti**|**vity (XOF/ha**|**)]-Food crops**||||
|---|---|---|---|---|---|---|---|---|---|
||Q10|Q20|Q30|Q40|Q50|Q60|Q70|Q80|Q90|
|**overall**||||||||||
|Male headed households|10.048***|10.747***|11.153***|11.400***|11.671***|11.934***|12.236***|12.563***|13.101***|
||(0.091)|(0.064)|(0.046)|(0.046)|(0.045)|(0.046)|(0.043)|(0.047)|(0.071)|
|Female Headed households|9.924***|10.606***|10.959***|11.196***|11.518***|11.775***|12.123***|12.428***|13.192***|
||(0.249)|(0.098)|(0.094)|(0.091)|(0.094)|(0.090)|(0.090)|(0.104)|(0.195)|
|difference|0.124|0.141|0.194*|0.204**|0.154|0.159*|0.114|0.135|-0.092|
||(0.261)|(0.116)|(0.102)|(0.097)|(0.097)|(0.095)|(0.094)|(0.109)|(0.200)|
|**% gap**|**12%**|**13%**|**18%**|**18%**|**14%**|**15%**|**11%**|**13%**|**-10%**|
|<br>explained|-0.018|0.235*|0.091|0.159|0.022|-0.078|-0.129|-0.228|-0.528**|
||(0.224)|(0.127)|(0.100)|(0.103)|(0.107)|(0.116)|(0.115)|(0.139)|(0.217)|
|unexplained|0.142|-0.094|0.103|0.045|0.132|0.237*|0.243*|0.363**|0.436*|
||(0.310)|(0.162)|(0.129)|(0.127)|(0.130)|(0.141)|(0.135)|(0.159)|(0.265)|
|**explained**||||||||||
|<br>Log [Total land area cultivated in|-0.426***|-0.255***|-0.282***|-0.224***|-0.276***|-0.283***|-0.280***|-0.278***|-0.509***|
|<br>ha]|(0.096)|(0.064)|(0.048)|(0.047)|(0.049)|(0.051)|(0.055)|(0.069)|(0.110)|
|Household head age (years)|0.025|0.019|0.009|0.008|-0.013|0.008|0.007|-0.015|0.019|
||(0.044)|(0.026)|(0.020)|(0.018)|(0.018)|(0.018)|(0.018)|(0.019)|(0.031)|
|Married|0.383*|0.107|0.041|0.026|0.038|0.034|-0.029|-0.026|0.170*|
||(0.208)|(0.104)|(0.080)|(0.074)|(0.072)|(0.073)|(0.078)|(0.076)|(0.099)|
|Divorced/Separated|-0.086*|0.005|0.015|0.000|-0.021|-0.015|0.002|-0.015|-0.083*|
||(0.044)|(0.029)|(0.022)|(0.020)|(0.021)|(0.021)|(0.019)|(0.022)|(0.045)|
|Widowed|-0.292|-0.072|-0.063|0.061|0.013|-0.035|-0.010|-0.029|-0.210|
||(0.244)|(0.111)|(0.089)|(0.083)|(0.080)|(0.079)|(0.082)|(0.088)|(0.132)|
|Household head has Primary|-0.017|-0.008|-0.014*|-0.012*|-0.007|-0.004|0.002|0.002|0.007|
|<br>education|(0.014)|(0.009)|(0.008)|(0.007)|(0.006)|(0.006)|(0.006)|(0.007)|(0.011)|
|Household head has Secondary|-0.026|-0.025|-0.016|-0.011|-0.004|-0.003|-0.008|-0.015*|-0.003|
|<br>education|(0.026)|(0.016)|(0.010)|(0.010)|(0.010)|(0.010)|(0.009)|(0.009)|(0.015)|
|Household head has Tertiary|-0.003|-0.005|-0.006|-0.003|-0.004|-0.003|-0.003|-0.003|0.000|
|<br>education|(0.007)|(0.005)|(0.004)|(0.003)|(0.003)|(0.003)|(0.003)|(0.003)|(0.006)|
|Household head disability|<br>-0.013|<br>0.001|<br>-0.005|<br>-0.007|<br>0.002|<br>0.001|<br>-0.002|<br>-0.001|<br>-0.020|
||(0.013)|(0.010)|(0.008)|(0.008)|(0.008)|(0.008)|(0.007)|(0.007)|(0.015)|
|Dependency ratio|-0.007|-0.016|0.001|-0.017|-0.010|-0.021|-0.028*|-0.023|-0.009|

47
deadline for renewal of the loan), and what the student thinks about the performance of the local SOFES representative. The general message from this block is that most students are satisfied. Only 5.5% answered that they regarded the information provided by SOFES as insufficient, and 90% considers the performance of the local SOFES representatives at the universities as good.

# **5. Impact of Loans on Accessibility to Higher Education**

# _5.1 Background_

The SOFES student loan program seeks to solve credit market imperfections in the higher education sector. So we would expect that SOFES contributes to the accessibility of university programs for the low and middle income students. To get a first impression, we asked the surveyed students whether the SOFES credit has affected their university enrollment decision. Almost half of the students, 48%, responded affirmative (cf. Table 3). We also asked what would have happened if the student would not have received a credit from SOFES: 29% would not have enrolled in university, 9% would have enrolled in another institution, 39% would have required more time to complete the program, 12% would not have been able to finish the program, and only 11% replied that it would not have affected them. Finally, about 70% of the respondents indicated that they know people that did not go to university for economic reasons. These findings suggest that the SOFES credit played an important role in the enrollment decision.

Do we indeed observe an increase in accessibility in the data? To study the impact of student loans on accessibility of higher education, information is needed on people who face the question whether to enroll in higher education or not. Typically, these are people in the age range 18-24 who completed high school. When information is available on academic abilities (for instance grade point averages at upper secondary school), socio-economic background, tuition fee levels, college premium, and financial support, and the outcome of their enrollment decision (enrollment or no enrollment) we could estimate enrollment elasticities with regard to tuition fee levels and financial support. Unfortunately, the SOFES database and the questionnaire held in the summer of 2003 only provide information on people who already are enrolled in a higher education program. In order to get an idea of the impact of financial support on student enrollment, we explore information from the Mexican household survey ENIGH 2000 in this section.

12
12 each cross-section, it imposes more restrictions on the number of instrumental variables that should be used for each period and makes identification of the parameters more difficult. Given the small number of cross-section observations (33 countries) and the potential pattern of long lagged effects, it may be very difficult to test for stationarity. I thus proceed with the maintained stationarity assumption regarding the fixed effects and all the other parameters and address this point from a different angle. After estimating the model using the whole 1970-87 data, it is reestimated with two subsamples, 1970-78 and 1979-87, which correspond to different trends in food and commodity prices. The sample is disaggregated to two groups of countries and again reestimated and the results compared. Finally, I attempt to allow for a countryspecific coefficient representing the effect of food aid on food production.

The correct lag length is determined by initially seleoting an arbitrary -- but long enough -- lag length. The system is then reestimated with a shorter lag. The increase in the sum of squared residuals can be used to test which lag length is appropriate. Changing the lag length can be treated as linear constraints that can be tested by noting that the difference in the constrained and unconstrained sum of squared residuals has a X<sup>2</sup> distribution.

Food Production. An equation is estimated with three lags of each of the right-hand side variables: in terms of the notation, m-3. This lag length is used to estimate the covariance matrix necessary to test for this and other longer or shorter lag lengths. The first differenced version, then, has four lags. Given that in the data T-18, m-3 implies that parameters can be estimated only for the last 13 years in the data set; that is, t-13 1975-87. When equations for these years are estimated jointly using the three-stage procedure described, the minimized value of the X2 test statistic, denoted by Q, is equal to 183.35 and has 96 degrees of freecom (see table 2).
**Ovca&anglShoage-** 4

pressure, but simply postponed it until a later price liberalization. Remedies suggested for this inflationary pressure, or 'rubIe overhang", can be organized into "supply-side' and demand-side' policies. On the demand side, McKinnon (1991) suggested the raising of interest rates on deposits to convert forced saving into desired saving and to encourage the reduction in currency hoarding. On the supply side, proposals induded the exchange of government assets and gold holdings for the excess currency and deposits. 1E January 1991 the Soviet govaement undertook a draconian supplyside policy by declaring large-denomination bank notes to be no longer legal tender. Holders were allowed to exchange them for smaller bills up to a maximum governed by a worker's monthly salary or a fixed quantity, whichever was less. Household saving accounts were frozen, with individuals allowed to withdraw only 500 rubles per monha3 The republics softened these regulations somewhat in subsequent actions.

In 1991 the aggregate fiscal deficit of the members of the Soviet Union reached 26 percent of GDP.' Price controls were relaxed somewhat, and inflation reached previously unimagined rates: 142 percent on retail goods and 236 percent on wholesale products. This inflation induced the govermment to provide transfers to wage-earners, pensioners and savers to index partially the value of their incomes and fiancial assets. However, due to a lack of goods and services the households retained the transfers in forced saving of deposits or currency holding. The real value of household deposits was reduced somewhat by the inflation, while the ratio of currecy in circulation to GDP remained nearly constant.

The former republics of the Soviet Union cooperated nearly unanimously on the liberalization of the majority of wholesale and retail prices on 2 January 1992. Those not liberalized were nearly 3 These details are reported in McKinnon (1991, p. 157).

4 The information in these two paragraphs is drawn from World Bank (1992).