rafmacalaba/datause-encoder-data huggingface dataset:
# **7. References**

Angrist, Joshua D.; Lavy, Victor. 1999 “Using Maimonides’ Rule to Estimate the Effect of Class Size on Scholastic Achievement.” _The Quarterly Journal of Economics_ 114 (2), 533-575.

Angrist, Joshua D.; Lavy, Victor; Leder-Luis, Jetson; Shany, Adi. 2017. “Maimonides’ Rule Redux.” NBER Working Paper No. 23486.

Asim, S., J. Chimombo, D. Chugunov, and R. Casley Gera (2019). “Moving teachers to Malawi’s remote communities: A data-driven approach to teacher deployment.” _International Journal of Educational Development_ 65 (2019) 26–43 Asim, S. and Casley Gera, R. 2024. _What Matters for Learning in Malawi? Insights from a Longitudinal School Survey._ Washington, D.C.: World Bank Publications.

Azim Premji Foundation. 2014. “Pupil-Teacher Ratios in Schools and their Implications.” Available at: https://www.issuelab.org/resources/23973/23973.pdf [1.27.22] Bashir, S., Lockheed, M., Ninan, E., Tan, J., 2018 _. Facing Forward: Schooling for Learning in Africa._ Washington, D.C.: World Bank Publications.

Checchi, D. and De Paola, M. 2017. “The Effect of Multigrade Classes on Cognitive and NonCognitive Skills: Causal Evidence Exploiting Minimum Class Size Rules in Italy.” IZA DP No. 11211. Bonn: Institute of Labor Economics.

Chin, Aimee (2005). Can redistributing teachers across schools raise educational attainment? Evidence from Operation Blackboard in India. _Journal of Development Economics_ 78(2), 0–405.

Cummings, Clare and Ali Bako M. Tahirou with Hamissou Rhissa, Falmata Hamed, Hamadou Goumey, and Idi Mahamadou Mamane Noura. 2016. “Collective action and the deployment of teachers in Niger: A political economy analysis.” Overseas Development Institute. Available at: <u>https://cdn.odi.org/media/documents/10303.pdf [2.3.22]</u> Duflo, Esther; Dupas, Pascaline; Kremer, Michael. 2015. “School governance, teacher incentives, and pupil-teacher ratios: Experimental evidence from Kenyan primary schools.” _Journal of Public Economics_ (123), 92-110.

Hoxby, Caroline M. 2000. “The Effects of Class Size on Student Achievement: New Evidence from Population Variation.” _The Quarterly Journal of Economics_ 115 (4), 1239-1285.

Jacob, Verghese; Kochar, Anjini; Reddy, Suresh. 2008. “School Size and Schooling Inequalities.” Stanford Center on Global Poverty and Development Working Paper 354.

Krueger, Alan B. 1999. “Experimental Estimates of Education Production Functions.” _The Quarterly Journal of Economics_ 114 (2), 497-532.

Majgaard, K., and A. Mingat. 2012. Education in Sub-Saharan Africa: A Comparative Analysis. A World Bank Study. Washington, DC: World Bank.

23
Table 6: Differences Across Populations

||All Chiapas<br>Mean (SD)|Second Experiment<br>Chiapas vs. Second Experiment<br>Mean (SD)<br>_p_-value|
|---|---|---|
|||Panel A: Community Characteristics|
|Number of households|34.625|29.329<br>0.486|
||(109.863)|(50.234)|
|Total population|140.293|121.389<br>0.494|
||(394.371)|(240.562)|
|Share economically active|0.298|0.303<br>0.361|
||(0.073)|(0.070)|
|Water connection (Y/N)|0.033|0.023<br>0.454|
||(0.178)|(0.151)|
|Sewer system (Y/N)|0.018|0.009<br>0.346|
||(0.134)|(0.096)|
|Share of illiterates|0.268|0.270<br>0.832|
||(0.175)|(0.167)|
|Share of dwellings with dirt foor|0.328|0.363<br>0.126|
||(0.319)|(0.322)|
|Garbage collection (Y/N)|0.025<br>(0.158)|0.023<br>0.842<br>(0.151)|
|||Panel B: School Characteristics|
|Average test score (Spanish) 2010|425.173|431.340<br>0.158|
||(57.245)|(60.810)|
|Average test score (Math)|415.998|421.333<br>0.363|
||(76.967)|(80.895)|
|Number of students|14.023|14.770<br>0.205|
||(8.403)|(7.069)|
|Number of local instructors|1.216<br>(0.449)|1.279<br>0.054<br>(0.514)|
|Share students over-age|3.125|3.620<br>0.264|
||(6.285)|(5.629)|
|Observations|1,475|230|

_Notes_ : Means and standard deviations in parentheses for various characteristics collected before the introduction of the API program. The last column shows asymptotic _p_ -values for mean differences between the overall population and the experimental sample. Panel A shows community-level characteristics from the population census (2010), whereas Panel B displays school-level variables from the school census (2010). See Appendix A.1 for more details on the data sources.

high “marginality,” as defined according to the National Population Council or CONAPO (Poverty 1), and whether the community was targeted by an anti-poverty program (Poverty 2).<sup>13</sup> In the sample of schools in the second experiment, approximately one-third satisfy the Poverty 1 criterion, 70 percent satisfy the Poverty 2 criterion, and 25 percent satisfy both criteria. We run separate regression models for three summary outcomes of the intervention on both students and parents: the overall index of student achievement, the indicator for enrollment in seventh grade, and the overall index of parental engagement. Estimation

> 13For details on the Poverty 1 index, refer to `https://www.gob.mx/cms/uploads/attachment/file/ 685308/Nota_t_cnica_IML_2020.pdf` , accessed on August 2022.

22
# Economic and Fiscal Impacts of Venezuelan Refugees and Migrants in Brazil<sup>∗</sup>

Mrittika Shamsuddin<sup>1</sup> , Pablo Ariel Acosta<sup>1</sup> , Rovane Battaglin Schwengber<sup>1</sup> , Jedediah Fix<sup>2</sup> and Nikolas Pirani<sup>2</sup> 1World Bank 2UNHCR

**Keywords: fiscal impacts, labor market impacts, Venezuelan refugees and migrants, forced displacement JEL Codes: J21, J31, J61, F22, F15, O15, R23, H20, H50**

> ∗ **Funding Disclaimer:** This work is part of the program “Building the Evidence on Protracted Forced Displacement: A Multi-Stakeholder Partnership”. The program is funded by UK aid from the United Kingdom’s Foreign, Commonwealth and Development Office (FCDO), it is managed by the World Bank Group (WBG) and was established in partnership with the United Nations High Commissioner for Refugees (UNHCR). The scope of the program is to expand the global knowledge on forced displacement by funding quality research and disseminating results for the use of practitioners and policy makers. We further thank FCDO for additional funding support through its Knowledge for Change (KCP) program. This work does not necessarily reflect the views of FCDO, the WBG or UNHCR.
national poverty rate using 1998 data and 0.7 percent using 2004 data. These results support the finding of Edmonds and Pavcnik (2005) that increases in rice prices would reduce poverty, and is more optimistic than the finding by Minot and Goletti (2000, pxiii and p64) that an increase in rice prices would lower rural poverty rates, but raise national poverty rates slightly in Vietnam. A key influence on these findings appears to be the relatively egalitarian distribution of land in Vietnam, and the absence of a large class of poor landless laborers (Ravallion and van der Walle 2008). In both years, increases in the price of poultry reduce poverty, and the size of this favorable impact doubled between 1998 and 2004, from 0.1 percent to 0.2 percent.

The overall average poverty impacts presented at the bottom of Table 2 show that, on average, increases in the prices of all of the commodities considered would increase poverty. The increases in urban poverty are generally larger than those in rural areas—unsurprisingly, since it is much less likely that urban households will be net sellers of these goods. For all commodities except beef, price increases raise both rural and urban poverty.

The results for changes in the poverty gap in Table 3 complement the estimates for the poverty rate by taking into account the depth of poverty. As for the poverty headcount, there are substantial differences in the effects by commodity and between rural and urban areas. However, the poverty gap numbers tend to be less volatile than the poverty rate impacts and, particularly, less affected by the initial poverty rate. In terms of the overall average, the picture is very similar to that discussed above for the poverty rate. Increases in the prices of all of these staple foods increase the poverty gap more in urban than in rural areas, but increase the poverty gap in both, and raise the national poverty rate. The one exception at the commodity level is beef, for which price rises reduce the poverty gap both in rural areas and nationally, albeit by a small amount.

13
|||0.49|0.42|0.43||-0.31|-0.26|-0.27||0.12|0.14|0.27|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Average Wage 2009||-0.001***|-0.001***|-0.001***||-0.001***|-0.002***|-0.001***||-0.001|-0.001|-0.001|
|||-3.72|-4.01|-4.01||-4.04|-4.19|-4.06||-1.06|-1.14|-1.09|
|Population||0.007***|0.005***|0.005***||0.005***|0.005***|0.004**||0.091***|0.079***|0.081***|
|||4.03|3.04|3.06||2.95|2.65|2.49||5.38|4.48|4.74|
|Share Ukrainian||0.052***|0.052***|0.052***||0.046***|0.046***|0.046***||0.047***|0.047***|0.047***|
|||12.41|12.39|12.39||8.34|8.35|8.33||7.01|7.06|7.1|
|Participation Ratio||||-0.002||||0.022**||||-0.015**|
|||||-0.33||||2.52||||-2.07|
|_cons|154.661**<br>*|155.621**<br>*|158.498**<br>*|158.593**<br>*|155.297**<br>*|157.593**<br>*|162.157**<br>*|160.874**<br>*|149.420**<br>*|150.028**<br>*|151.062**<br>*|151.739**<br>*|
||246.41|197.65|154.34|144.47|173.78|140.19|87.32|81.47|101.52|93.1|81.19|79.35|
|R Adj sq.|0.153|0.169|0.171|0.171|0.295|0.311|0.314|0.316|0.046|0.055|0.056|0.057|
|N|11086|11086|11086|11086|5137|5137|5137|5137|5949|5949|5949|5949|

The regression also includes oblast dummies, dummies for different types of schools and evening schools. The full table can be found in excel tables which can be made available upon request. *** means significant at the 1% level, ** at the 5 % level and * at the 10 percent level.T statistics are based on robust standard errors for the OLS specifications.

38
Table 2 shows that gender inequality in education exists both in Burkina Faso and in Togo, although the situation has significantly improved since the early 1990s. Moreover, inequality increases with education level. For instance in Togo, while the gross enrollment ratio in primary education was 119% and 111% in 2009, respectively for boys and girls, these numbers were respectively 54% and 28% in secondary education. The situation is less marked in Burkina Faso where the ratios seem to be very low compared with Togo.

<u>Table 2: Gross enrollment ratios (%) in Burkina Faso and Togo</u>

|School level||Burki|na Faso|T|ogo|
|---|---|---|---|---|---|
|||_Male_|_Female_|_Male_|_Female_|
|Pi|1991|41|26|115|75|
|rmary|2009|83|75|119|111|
|d|1991|-|-|30|10|
|Seconary|2009|24|19|54|28|
|i|1991|1|0|4|1|
|Tertary|2009|5|2|-|-|

<u>Source: World Bank (2011a)</u>

# **_2.5 Employment_**

Employment remains the main source of income for households in the world. In order to eradicate extreme poverty and hunger as pursued by the first MDG, one major target is to achieve full employment and decent work for all individuals, including women. This is in line with the initial objective of the International Labor Organisation, which is to provide an adequate living wage. This objective is reinforced by the Declaration of Philadelphia in 1944, which mandates the International Labor Organisation to continue to promote full employment and the improvement of standards of living (Luebker, 2011). As stressed by Lugo (2007), even though employment is not a new dimension of wellbeing, it is often forgotten, unlike education and health, in human development and poverty reduction analyses.

Beyond addressing the lack of employment for all, assessing gender disparities in African labor markets is a great challenge (Kolev and Sirven, 2010). In fact, it is recognized that 10
# **Table A15: Second stage: Log Refugees at 100km instrumented by distance-based IV with decay function represented (i.e. IV generated using 1)mean border distance,** **<u>2)square of mean border distance and 3) square root of mean border distance) including Kampala district</u>**

|||||L|og of welfare|(consumption|aggregate per a|dult equivalen|t)||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|
|Log of refugees (100km) predicted by Log of<br>Distance IV (mean border distance)|0.04644<br>(0.07479)|0.04615<br>(0.07469)|0.12475*<br>(0.07169)|0.11818*<br>(0.06507)|||||||||
|Log of refugees (100km) predicted by Log of<br>Distance IV (square of mean border distance)|||||0.04941|0.04965|0.14825*|0.14460**|||||
||||||(0.07568)|(0.07562)|(0.07744)|(0.07080)|||||
|Log of refugees (100km) predicted by Log of<br>Distance IV (square-root of mean border<br>distance)|||||||||0.06123|0.06058|0.11354|0.10909*|
||||||||||(0.07631)|(0.07618)|(0.07158)|(0.06519)|
|HH head age||0.00197|0.01170**|-0.00187||0.00197|0.01165**|-0.00236||0.00195|0.01172**|-0.00170|
|||(0.00487)|(0.00563)|(0.01664)||(0.00487)|(0.00564)|(0.01669)||(0.00487)|(0.00562)|(0.01662)|
|Age squared||-0.00002|-0.00008|0.00000||-0.00002|-0.00008|0.00001||-0.00002|-0.00008|0.00000|
|||(0.00005)|(0.00005)|(0.00014)||(0.00005)|(0.00005)|(0.00014)||(0.00005)|(0.00005)|(0.00014)|
|HH head sex (male)||0.00649|0.02245|-0.07945||0.00647|0.02218|-0.07980||0.00641|0.02258|-0.07932|
|||(0.03284)|(0.03776)|(0.06333)||(0.03284)|(0.03777)|(0.06348)||(0.03284)|(0.03775)|(0.06328)|
|Household size|||-0.02635***||||-0.02636***||||-0.02634***||
||||(0.00540)||||(0.00540)||||(0.00540)||
|Married monogamously|||-0.42451***||||-0.42360**||||-0.42495***||
||||(0.16463)||||(0.16473)||||(0.16456)||
|Married polygamously|||-0.37119**||||-0.37032**||||-0.37160**||
||||(0.17217)||||(0.17226)||||(0.17212)||
|Separated (divorced/widowed)|||-0.41535**||||-0.41499**||||-0.41552**||
||||(0.17266)||||(0.17280)||||(0.17259)||
|Completed primary sch|||0.22170***||||0.22178***||||0.22166***||
||||(0.03701)||||(0.03699)||||(0.03702)||
|Commercial farming|||0.10633||||0.10593||||0.10653||
||||(0.08967)||||(0.08975)||||(0.08961)||
|Wage employment|||-0.00117||||-0.00121||||-0.00115||
||||(0.04833)||||(0.04833)||||(0.04834)||

21
models for this type of exercise. Many authors, including Mwabu et al. (1993) and Leonard et al. (2002), use a conditional logit but assume the patient knows the facilities it bypassed. Mwabu et al. (1993) arbitrarily choose for each type (government, mission, private, self-treatment) of facility not chosen by the patient, the nearest such facility as the bypassed facility. This is arbitrary because it may well be that the patient had no knowledge of a closer facility, as a matter of fact the patient may have chosen that theoretically bypassed facility had she known about it.

# **8.1. Estimation of Household Incomes**

A serious limitation of our data concerns the patient’s household income. The patient or the person who accompanies her was asked about the total income of her household. However, we do not know the relationship of the interviewees to their head of household, it is therefore not clear whether either of them is the best person to answer such a question. For these reasons, we chose to discard the income reports from the patients and instead estimated those incomes using data from a nationally representative household survey which was being conducted around the same time. This survey, ECOSIT II, covered 6,697 households and 39,356 individuals across the country.

In order to link the surveys, we incorporated the module on asset holdings and dwelling characteristics of the household survey’s questionnaire into our patient’s questionnaire. There is common information on eight assets (appliances and vehicles), seven characteristics of the household’s dwelling, further broken down into 16 dummy variables. We then use small area estimation methods to estimate the income of our patients’ households. Specifically, using the ECOSIT II we run the regression Inch=ΣαiAih + ΣβjRjh + εh where Inch is household’s h income, Aih is the number of asset i that household h holds, and Rjh are regional dummies and household size to estimate the α ˆ and β<sup>ˆ</sup> . We then use the estimated coefficients to predict our patients’ household income.

## 8.2. Building Quality Indices

Because the quality of health care provided by a health center is likely to influence its demand and the likelihood that it is bypassed it is among the main variables of 21
in columns (1) and (4). Between April and June 2020, by contrast, the possibility of remote labor no longer had a mitigating effect.

The durability of a sector’s products mattered less strongly in shaping the negative trade effects from demand shocks in partner countries, as reported in columns (2) and (4), during February and April, whereas its mitigating role mattered much more during April to June. Both results are intuitive. The possibility of remote work increases in importance under more severe lockdowns. Similarly, during a global downturn, consumers withhold their expenditures, including on durable goods, given the uncertainty they are facing.

Regarding the GVC channel, both the positive influence of an exporting country’s reliance on imported inputs in buffering domestic shocks and the negative influence of GVC participation in a partner country’s sector were smaller between February and April, as reported in columns (1), (2) and (4), while their role was enhanced between April and June. By contrast, the upstream shock showed a magnified effect on bilateral trade growth during the first three months of our time period, as reported in columns (3) and (4), whereas the relationship between the upstream shock and bilateral export growth was no longer significant between April and June. These results imply that shocks in an exporter’s source countries represented stronger bottlenecks to export during the early period, whereas shocks in the partner countries were less disruptive. The smaller role of an exporter’s reliance on imported inputs in mitigating domestic shocks between February and April could also reflect simultaneous production shocks in key source countries such as China and Germany which made importing inputs less viable. The insignificant upstream shock during April and June, by contrast, could point to reduced bottlenecks in supply as production picked up in China.

**Table 4: Supply, demand and third-country shocks and bilateral export growth, the role of sector characteristics, OLS regression results, February to April 2020 only**

|FRAMEWORK|VARIABLES|(1)<br>**_growth_**_ijkt_|(2)<br>**_growth_**_ijkt_|(3)<br>**_growth_**_ijkt_|(4)<br>**_growth_**_ijkt_|
|---|---|---|---|---|---|
|**Exporter supply**|**_work mobilityit*remoteik_**|**1.616*****|||**1.847*****|
|**sector**||(0.371)|||(0.390)|
||**_work mobilityit*gvcil_**|**0.911***|||**1.204****|
|||(0.535)|||(0.539)|
|**Partner demand**|**_retail mobilityjt*durablek_**||**0.401*****||**0.346*****|
|**sector**|||(0.102)||(0.107)|
||**_retail mobilityjt*gvc partnerjl_**||**-0.168****||**-0.178****|
||||(0.080)||(0.081)|
|**Third country**|**_competition shockijkt_**|||-0.160|0.358*|

22
percent, 52 percent, and 41 percent, respectively. These numbers suggest that domestic migrants typically come from the upper range of the education distribution, while international migrants tend to originate from the middle part of the distribution.

Within the same three migration categories, I primarily used the 1998 KMS data set to derive the household characteristics shown in Table 1. These can be viewed as baseline or pre-migration variables. The exceptions—which I obtained from the 2003 KMS data set—are the number of dependents (e.g., children and spouse) who moved overseas and within India between survey rounds, the number of returning migrants after 1998, and the change in the number of employed household members in Kerala between 1998 and 2003.<sup>20</sup> Over this five-year period, households with one or more overseas migrants saw a larger decline in the average number of working members living in Kerala than households with one or more domestic migrants. This suggests that these internationalmigrant households became increasingly reliant on remittance income. Households with one or more members who migrated overseas after 1998 were also more likely to have a member who was already working abroad in 1998: 40 percent of such households were recorded as having at least one member in a foreign workforce. Similarly, 27 percent of households with one or more individuals who departed after 1998 for work within India already had a family member in another state’s labor force as of 1998.

Overseas migrants tended to emerge from larger households (mean of 6.4 members), while domestic migrants came from households similar in size to non-migrant families (mean of 5.2). Households with members leaving to work abroad over the five-year interval also had more male members between the prime ages of 19 and 40 years (mean of 2.3) than households with domestic migrants (mean of 1.9) and non-migrant families (mean of 1.6). Nearly 50 percent of the migrants

> 20 Only 5 percent of households with members moving overseas for work had accompanying dependents, compared with 10 percent for households with domestic migrants.

11
trickle in over the first five years and then begin to increase after year five, finding their peak at the seventh year (Figure 3).

**Figure 3: Total Number of Private Infrastructure Investments and PPPs in Post-conflict Countries**

<!-- Start of picture text --> 20<br>15<br>10<br>5<br>0<br>0 1 2 3 4 5 6 7 8 9<br>Year After Conflict<br>Number of Projects<br><!-- End of picture text --> Source: PPI Database. Authors’ estimations.

When the data are viewed by sector, however, the story becomes more intriguing. In the first four years, with only a few exceptions, only telecom investments have found their way into countries that have just emerged from conflict. These are almost entirely from mobile licenses and related investments. This single sector concentration may be because the cost recovery period for mobile investments is extremely low, the technology sufficiently diffused and the price elasticity of demand sufficiently high for mobile operators to accept higher levels of country risk. Mobile investors have been active in countries like Somalia during a time when there is little government structure, or Iraq in a matter of weeks after the country was last invaded.

As can be seen in Figure 4 , other sectors with larger investment requirements, longer costrecovery periods, and greater sensitivity to user willingness to pay—such as toll roads, electricity and water utilities—have longer lag times. Private investors in those sectors do not enter conflict-affected countries until six years have passed with only a few exceptions.

By focusing on a sector that has longer-term cost recovery periods, it is easier to see the effects of conflict on investment. In energy, among the 31 countries studied, there is only one case of a private investment in the first five years post-conflict. Disaggregating the sub-sectors of energy, it is clear that the majority of investments are in power generation. In these cases, off-take agreements for power purchasing can minimize exposure to commercial risk—as can other credit enhancements, including political risk insurance. This is consistent with the regressions run on the relationship of country risk to greenfield projects versus concessions. In addition, the assets can be physically protected and secured more easily than distribution networks. Out of 28 total energy projects in these 31 countries, 19—or two-thirds of the total—are in electricity generation. Only one electricity distribution investment was made in the first six years from the 18