rafmacalaba/datause-encoder-data huggingface dataset:
Policy Research Working Paper 10104

# **Abstract**

Education is a powerful tool for social mobility and cultural integration. However, it is one of the largest hurdles for migrants—particularly for forcefully displaced migrants, given their more vulnerable condition and lack of resources to pay for private education. This paper explores educational gaps between migrants and natives in Chile, a country that provides free public education to newcomers. The paper analyzes an administrative data set that includes all students in the Chilean educational system and follows students from 2017 to 2018. Using a research discontinuity design around the cut-off for financial aid to tertiary education, this paper investigates whether access to financial aid generates incentives for forced migrants to enroll in tertiary education. This research confirms previous findings that show that migrants have lower advancement and enrollment rates than natives at every school level. Moreover, it finds that financial aid applications constitute a major roadblock preventing migrant students from accessing higher education. Furthermore, the paper presents suggestive evidence showing that the interaction between the type of school (vocational vs. technical) and the migrant condition affects applications for financial aid.

This paper is a product of the Social Sustainability and Inclusion Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at fjmenese@gmail.com.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
Walloon in the Brussels region of Belgium). Our approach was to include those but flag them in the dataset so they can be dropped in robustness tests since the logic of competing sovereignty movements in the same region might be different from the model we propose here. We treat in analogous fashion cases where the same group is active in adjoining regions since in these cases, our region-based ethnic concentration variable might not capture the effect of ethnic difference on sovereignty demand. These could be cases where the group is too broad (e.g. Aborigines) and we have no data on smaller subgroups; or the group is large and has a historical homeland that does not map onto current administrative boundaries.

# **_Income & Inequality_**

We use household surveys to obtain regional per capita _disposable income_ . Thus, we have a fully comparable statistic across regions and countries. Previous country-level studies of regional separatism have used rough measures of differences in regional GDP per capita within countries. However, GDP data include income from production of natural resources, which does not necessarily remain within the region, limiting the usefulness of that measure for a test of our hypothesis that wealthier regions will have higher demand for sovereignty. A region may appear rich because it produces a valuable commodity (say, oil) while its people are in reality poor. Our household survey data show the actual income level of people living in each region.<sup>38</sup> Relative regional income is the ratio between regional mean per capita income and nation-wide mean per capita income. Income matters only in relative terms –i.e. regional income compared to the rest of the country matters, not income in absolute terms. Obviously, the betteroff regions will have a value greater than 1. We have data for 684 regions with values ranging from 0.08 (Magadan Oblast in Russia) to 2.8 (Moscow). We also use income share of each

> 38 A small problem with household survey data may be in advanced industrial countries in which unemployment income or pensions are partly covered through central transfers.

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Where ∆��∗ ��∑�� ��∆��∗ ��������� �������� ��∑�� ���� ��� _w_ is an element of the spatial-weighting matrix. _λ_ and _ρ_ are spatial autoregressive parameters in the dependent variable and error term, respectively. _ε_ is an idiosyncratic error distributed independently and identically. Under the normality assumption, this can be estimated by the conventional maximum likelihood estimation procedure (e.g., Anselin, 1988; Amaral and Anselin, 2011; Shehata and Michaiel, 2013).

For the spatial weighting matrix, inverse distances between two locations _s_ and _t_ are used. The distance is calculated using the Euclidean distance between the two locations. The intuition is that two locations are more closely related to each other if they are located closely. This follows the Tobler’s first law of geography: “everything is related to everything else, but near things are more related than distant things” (Tobler 1970).

For transport connectivity, three indices are generated using spatial data and software: (i) Market Access Index ( _MAI_ ), (ii) Farm Accessibility Index ( _FAI_ ), and (iii) Port Accessibility Index ( _PAI_ ). The MAI is primarily expected to measure the economic proximity of each agrobusiness to the output market, which is formally defined by the following equation: This is the sum of purchasing power or market capacity, _y_ , inversely weighted by the degree of impediment between two locations, _d_ .<sup>3</sup> For _y_ , the city population is used as a proxy of the market capacity. _d_ is measured by estimating transportation costs from commune _i_ and large city _k_ .<sup>4</sup> Given the georeferenced road condition data, transport costs to bring one unit of goods to a major market are calculated by spatial software minimizing the total road user costs. In principle, the costs would likely be higher when the road distance is longer and the condition is poor. The index is normalized to zero to one. The MAI clearly shows that in

> 3 This is based on a conventional gravity framework. The literature also uses the negative exponential weights. See for instance Elbadawi, Mengistae and Zeufack (2006) and Lall and Mengistae (2005). 4 In this paper, 60 cities and towns that have more than 20,000 population are considered.
**Table 6: Regression results: Secondary market** Dependent <u>variable: Log of spread-to-benchmark</u>

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
||After60 days|After90 days|After 120 days|After 180 days|
|IG dummy (Sov. rating)|0.030|0.083|0.090|-0.079|
||(0.22)|(0.61)|(0.69)|(-0.52)|
|S&P Sovereign rating|0.119***|0.122***|0.127***|0.101***|
||(5.81)|(4.87)|(5.61)|(2.83)|
|Log(size)|0.111*|0.067|0.043|0.003|
||(1.75)|(1.04)|(0.67)|(0.04)|
|Years to Maturity|-0.015**|-0.013**|-0.012**|-0.005|
||(-2.62)|(-2.19)|(-2.03)|(-1.16)|
|U.K. Law|0.307***|0.304***|0.313***|0.325***|
||(3.71)|(3.19)|(3.41)|(3.34)|
|VIX|0.019***|0.016***|0.021***|0.017***|
||(3.55)|(4.55)|(4.55)|(2.94)|
|GDP growth (_launch-1_)|-0.006|-0.001|-0.000|-0.003|
||(-0.74)|(-0.11)|(-0.02)|(-0.35)|
|EAP|-0.109|-0.112|-0.100|-0.088|
||(-0.75)|(-0.69)|(-0.63)|(-0.69)|
|ECA|0.142|0.134|0.157|0.166|
||(1.19)|(1.03)|(1.24)|(1.66)|
|LAC|-0.109|-0.174|-0.097|-0.048|
||(-0.82)|(-1.25)|(-0.74)|(-0.42)|
|SAS|0.107|0.106|0.077|0.229**|
||(1.01)|(0.88)|(0.64)|(2.09)|
|SSA|0.235**|0.200*|0.183|0.237**|
||(2.18)|(1.70)|(1.55)|(2.38)|
|Observations|122|122|122|121|
|R-squared|0.739|0.711|0.725|0.753|

Robust t-statistics in parentheses * significant at 10%; ** significant at 5%; *** significant at 1% Year dummies and a constant term were included in all specifications, but not reported. The default region is Middle East & North Africa (MNA); the regional dummies represent East Asia & Pacific (EAP), Europe & Central Asia (ECA), Latin America & Caribbean (LAC), South Asia (SAS) and Sub-Saharan Africa (SSA).

# 4. Explaining the difference in launch spreads

In this section we evaluate several plausible explanations of the spread difference. First, we report in Table 7 the results from the addition of a CAC dummy.<sup>11</sup> In the pre-crisis period the CAC dummy turns out to be significant, but the governing law is not. This switches after the crisis, and governing law becomes the key explanatory variable. The results confirm the findings from Table 5, and point to the conclusion that collective action clauses do not lie behind the effect of jurisdiction on spreads. This result is not surprising in view of the fact, already noted, that collective action clauses have become a common feature of issuances

> 11 Unfortunately, Bloomberg has CAC information for a somewhat smaller number of bonds than we included in the benchmark regression. Whenever the added explanatory variable restricted the sample, both the benchmark regression and the alternative were run for the full sample, and for the pre and post-crisis periods. In all cases, the benchmark results held before addition of the new variable. These are not reported here for brevity.

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**Table 7: Sensitivity Analysis of the Impact of Parents’ occupation [AET (2005)]**

|**Sample**|**Year**||**Correlation**|**of the Error Te**|**rms in the Tria**|**ngular Model**||
|---|---|---|---|---|---|---|---|
|||0<br>|0.05<br>|0.10<br>|0.15<br>|0.20<br>|0.25<br>|
|||**0.12**|**0.09**|**0.05**|**0.03**|0.02|0.00|
||1988|(3.73)***|(3.11)***|(2.42)**|(1.69)*|(0.82)|(0.03)|
|Daughters||||||||
||2002|‐0.10|_|_|_|_|_|
|||(‐3.18)|_|_|_|_|_|
||1988|**0.12**|**0.10**|**0.07**|0.04|0.01|‐0.02|
|Sons||(3.49)***|(3.17)***|(2.24)**|(1.31)|(‐0.34)|(‐0.62)|
||2002|0.04|0.03|0.001|‐0.03|_|_|
|||(1.42)|(1.31)|(0.18)|(‐1.32)|_|_|

NOTES

1. t statistics in parentheses;

2. Adult children’s occupation dummy: 1=participation in Non‐farm; 3. Parent’s occupation dummy, 1=at least one of parents is in Non‐farm; 4. “*” significant at 10 percent; “**” significant a 5 percent; “***” significant at 1 percent; 5. The estimation uses STATA routine of AET (2005); 6. The samples are: 3231 (1988, daughters), 3363 (1988, sons), 2091 (2002 daughters), 3573 (2002, sons).

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# **<mark>5. Conclusions</mark>**

The model of power sector reform that emerged during the 1990s placed considerable emphasis on the creation of a regulatory framework, often centered on an independent regulatory agency. The purpose of regulation was to minimize political interference in tariff-setting through a technical determination of prices that cover efficient costs so as to safeguard the financial viability of the sector. The agency would also protect consumers from abuse of monopoly power and hold utilities accountable for providing the stipulated quality of service. In exercising its functions, the regulator could draw upon regulatory innovations for tariff-setting, such as price caps, which had been newly developed at that time.

While there was widespread uptake of regulation, with some 70 percent of developing countries creating regulatory agencies between 1990 and 2015 (Foster et al., 2017), the implementation of the full regulatory vision proved to be challenging in the developing country context. Some of the main conclusions to emerge from this literature review are as follows.

## **First, even where regulators were created they were seldom as independent as had**

**been envisaged** . The design of the regulatory governance framework often fell short on many dimensions of independence. For example, a survey of African electricity regulators found that they scored less than 50 out of 100 on an index of good practice institutional design features (World Bank, 2010). Moreover, even when institutions were well designed on paper, the day-to-day practice of regulation often belied a different reality (Rodriguez Pardina, 2008). A number of studies highlight the divergence between formal and informal regulatory practice in Latin America (Correa et al., 2006; Andres et al., 2007) and even to some extent in Western Europe (Gillardi and Maggetti, 2010).

## **Second, this has led to a recognition that regulators in developing countries are**

**often operating under various intermediate models.** Many developing country regulators function primarily as “advisory regulators” meaning that their main role is to provide technical support to the ultimate political decision-makers, who may or may not act on their advice (Brown et al., 2006). Where the requisite capacity and commitment for independent regulation are lacking, it may be more realistic for developing countries to adopt such intermediate models, including advisory regulators, regulation by contract, or expert panels (Eberhard, 2006).

## **Third, there is some evidence that regulation has had a positive performance impact particularly in the context of privatized utilities and middle income countries.**

Despite the limitations noted above, there is empirical evidence to the effect that the presence of a regulator has helped to support more stable privatization contracts (Guasch, 2004) and led to a larger performance impact, particularly in Latin America (Andres et al., 2008), and in larger middle income countries (Vagliasindi, 2013).

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Table 11: Test results

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
||Oral<br>cognitive|Oral<br>curriculum|Written<br>curriculum|Oral<br>cognitive|Oral<br>curriculum|Written<br>curriculum|
|Treatment|-0.03|0.07|0.02|-0.02|0.10|0.05|
||(0.09)|(0.09)|(0.12)|(0.09)|(0.08)|(0.11)|
|Experience (PF1)|0.15|0.12|0.25|0.12|-0.08|0.01|
||(0.07)**|(0.07)*|(0.09)***|(0.09)|(0.08)|(0.11)|
|Experience(PF1)||||0.08|0.47|0.55|
|*Treatment||||(0.13)|(0.12)***|(0.16)***|
|Training (PF2)|-0.01|0.00|0.05|-0.01|-0.01|0.04|
||(0.09)|(0.10)|(0.13)|(0.09)|(0.08)|(0.12)|
|Classroom useable|0.07|-0.08|-0.11|0.06|-0.12|-0.16|
|in the rain|(0.09)|(0.09)|(0.12)|(0.09)|(0.08)|(0.11)|
|Ethnic fractiona-|-0.20|-1.03|-1.59|-0.19|-0.96|-1.50|
|lization|(0.32)|(0.34)***|(0.46)***|(0.33)|(0.30)***|(0.41)***|
|Girl|-0.15|-0.13|0.00|-0.15|-0.13|0.00|
||(0.05)***|(0.05)***|(0.05)|(0.05)***|(0.05)***|(0.04)|
|Female head of|0.11|0.09|0.13|0.11|0.08|0.12|
|household|(0.09)|(0.09)|(0.08)|(0.09)|(0.09)|(0.08)|
|Nr children <6yrs|-0.03|0.00|-0.02|-0.03|0.00|-0.01|
|in household|(0.02)|(0.02)|(0.02)|(0.02)|(0.02)|(0.02)|
|Age 7|0.60|0.65|0.60|0.60|0.66|0.61|
||(0.06)***|(0.06)***|(0.06)***|(0.06)***|(0.06)***|(0.06)***|
|Age 8|1.01|1.14|0.95|1.01|1.14|0.95|
||(0.06)***|(0.06)***|(0.06)***|(0.06)***|(0.06)***|(0.06)***|
|Constant|-0.42|-0.34|-0.10|-0.42|-0.35|-0.11|
||(0.13)***|(0.13)***|(0.16)|(0.13)***|(0.12)***|(0.15)|
|Observations|1350|1357|1326|1350|1357|1326|
|Number of schools|49|49|49|49|49|49|
|Sigma u|0.24|0.26|0.38|0.24|0.22|0.34|
|Sigma e|0.88|0.83|0.80|0.88|0.83|0.80|
|Rho|0.07|0.09|0.18|0.07|0.06|0.15|
|R2 overall|0.17|0.24|0.24|0.17|0.26|0.27|
|R2 between|0.12|0.31|0.44|0.13|0.50|0.57|
|R2 within|0.18|0.23|0.18|0.18|0.23|0.18|

Notes: The dependent variable is the normalized test score on the indicated test component. Experience (PC1) and Training (PC2) are the ...rst and second principal component of a rotated principal components analysis of the following indicators: teacher’s year of experience, years of formal education, an indicator of whether the teacher had taken a 2-year on the job training course in early childhood education, an indicator of whether the teacher had taken short courses in early childhood education. Ethnic fractionalization is de...ned in Table 1. The regression model is linear random e¤ects at the school level. Standard errors are reported in parentheses.* signi...cant at 10 percent; ** signi...cant at 5 percent; *** signi...cant at 1 percent 32
communicated back to the respective field interviewers. These crosschecks were performed daily throughout the duration of the survey.

# **Table AP_B1. Means of Original and Substitute Household Characteristics**

|Variable|(1)<br>Original<br>Household|(2)<br>Substitute<br>Household|(3)<br>_P_-val.<br>Difference|
|---|---|---|---|
|**_PANEL A. Household Characteristics_**||||
|Household size (N)|3.756|3.543|0.052|
|Dependency ratio (%)|0.259|0.287|0.071|
|Households in rural area (%)|0.470|0.606|0.000|
|Households with an assets index above the median (%)|0.482|0.434|0.215|
|Moderate to severe food insecurity (%)|0.372|0.335|0.307|
|Remittances received (%)|0.366|0.308|0.110|
|Household has a non-farm enterprise (%)|0.333|0.299|0.337|
|Household works in agriculture (%)|0.545|0.529|0.679|
|Observations|787|221||
|**_PANEL B. Individual Characteristics_**||||
|Female (%)|0.546|0.559|0.612|
|Age (years)|35.513|35.387|0.853|
|High school or higher education (%)|0.284|0.285|0.967|
|Read or write (%)|0.876|0.854|0.201|
|Never married (%)|0.332|0.345|0.587|
|Access to mobile phone (%)|0.887|0.856|0.055|
|Access to internet (Wi-Fi) (%)|0.177|0.218|0.036|
|Observations|1,979|501||

_Notes:_ This table compares the mean of the characteristics of the original (Column [1]) and substitute (Column [2]) households included in our sample. Column (3) presents the _p-_ values for the test of the differences in means between the two groups.

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18 dollar by extra fees. The analysis showed that if fees are more pro‐rich than utilization, the proportionality assumption yields the most pro‐rich distribution of subsidies, the constant‐cost assumption the least pro‐rich distribution, and the third special case lies somewhere between these two extremes. Section III explored the incidence of government health spending in Vietnam under different assumptions. Previous studies have made the constant‐cost assumption but have set to zero negative imputed subsidies. This turns out to make a difference between subsidies being pro‐rich (the result reported in these studies) and pro‐poor (the result that would have been obtained if negative subsidies had been retained). When the constant‐ cost assumption is replaced by the proportionality assumption, subsidies turn to being pro‐rich; in the more general case, when unit costs and fees are linearly related, they are still pro‐rich albeit less so. In this latter case, when general revenues are used to focus on subsidizing a basic unit of care, subsidies still emerge as pro‐rich, more so in fact than in previous studies when negative imputed subsidies have been set to zero.

There is a complication not considered in the above, or indeed in other BIA studies, namely health insurance. Lack of data makes it impossible to allow for it, but one can speculate on the likely direction of bias introduced by not allowing for it. Eqn (1) assumes that providers cover their costs through subsidies or fee income levied on patients. In Vietnam, as in many other countries, providers (or at least hospitals) receive fee income but also income from insurers. In Vietnam, with the exception of insurance for school‐children, there is just one government insurer that provides cover (at the taxpayer’s expense) to the poor (and other vulnerable groups), (at the employer’s and employee’s expense) to formal‐sector workers, and (at the taxpayer’s expense) to civil servants, war veterans and other “people of merit”. The insurer reimburses hospitals according to the same scale that the hospital uses to charge uninsured patients. A more complete analysis would extend eqn (1) to include insurance
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