rafmacalaba/datause-encoder-data huggingface dataset:
||Prod|**Educati**<br>uctivity|**on and**|**Table 2**<br>**Occupat**|<br>**ion Differ**<br>Wages|**entials**|||
|---|---|---|---|---|---|---|---|---|
||Coef.|Std. Err.|<br>t|P>Itl|Coef.|Std. Err.|t|P>Itj|
|jobman|-0.194|0.141|-1.382|0.167|1.112|0.069|16.229|0.000|
|jobamd|0.060|0.110|0.548|0.584|0.687|0.053|12.858|0.000|
|jobcom|-0.092|0.156|-0.588|0.557|0.390|0.076|5.138|0.000|
|jobsup|0.040|0.104|0.384|0.701|0.499|0.051|9.847|0.000|
|jobtech|0.105|0.215|0.487|0.626|0.882|0.105|8.410|0.000|
|duni|-0.088|0.261|-0.340|0.734|0.501|0.127|3.944|0.000|
|dvoc|0.125|0.244|0.512|0.609|0.232|0.119|1.949|0.052|
|dsec|0.126|0.070|1.786|0.074|0.172|0.034|5.023|0.000|

The estimated wages of a manager, an administrative or a clerk, a commercial or salesperson, a supervisor or a technician are all significantly different from the wages of the reference group (workers in maintenance, production, support staff, and trainees). The wage premia are 204 percent, 99 percent, 47 percent, 65 percent and 42 percent, respectively.

After analyzing the impact of education and occupation on wages we now turn to their impact on productivity. The findings reveal no occupational productivity differential (see Table 2, second column). The estimated productivity of managers, administratives or clerks, commercials or sales people, supervisors or technicians is surprisingly<sup>not</sup> statistically significant from that of the reference group. The same holds for workers that have completed vocational and higher education. However, there exists a positive and marginally significant production differential by completed secondary education. The workers with completed secondary education are 13 percent more productive than their co-workers with no or completed primary education.

Are the above mentioned estimated impacts different in the wage and productivity equations? We find that the productivity differentials by education fall behind wage differentials by education. Furthermore, significantly so for completed secondary 14
use district ...xed e¤ects, and thus do not need to control for the historical district level malaria prevalence directly.

An important implication of our theoretical model is that the e¤ects of land restrictions gradually die out with an increase in the distance from urban center. For identi...cation of this interaction e¤ect between land restrictions and distance from the urban center, a critical issue is whether distance (travel time) to the urban center is potentially endogeneous in the wage regressions. There are good reasons to expect that geographic location may, in fact, be enodegenous. For example, if the urban centers historically emerge around villages with better economic potential, then distance (travel time) and wages will be negatively correlated in the data even in the absence of any causal relation. This spurious negative correlation is likely to bias the estimate of the interaction e¤ect. To address the potential endogeneity bias arising from travel time, we rely on the exogeneous variations in travel time created by variations in topography. This is motivated by a large body of transport engineering literature on the e¤ects of topography on road placement and travel time (see, for example, Myer, 2004, American Association of State Highway and Transportation O¢ cials, 2001). We use the deviation of slope of a DSD from the average slope of other DSDs in a district as an instrument for travel time. The results do not depend on whether we control for the own slope of the DSD itself in the IV regressions. The interaction of land restrictions with travel time is thus instrumented with the interaction of the instrument for travel time (deviation in slope) with an instrument for land restrictions (inland water*malaria). For a detailed discussion of the identi...cation strategy, please see section (4) below.

The IV results show that the e¤ect of the land restrictions on the equilibrium wage is substantial; a 1 percent increase in the incidence of land restrictions reduce the wage by about 6.6 percent (average of di¤erent IV estimates and evaluated at the median). The interaction e¤ect between land restrictions and location of a sub-district (DSD) is also important; the negative e¤ects of land market restrictions become smaller as we move away from the urban center. A 1 percent increase in the land under LDO restrictions leads to a reduction in equilibrium wage by about 8.5 percent when the DSD is located about half an hour from the relevant urban center, but it declines to 4 percent when the DSD is about three and a half hours away from the urban center. The e¤ects of land restrictions become e¤ectively zero after about 6 hours of travel time.

8
Table 6: Savings

||(1)<br>Has Saved<br>(last 2 yrs)|(2)<br>Participate<br>in a VSLA<br>(currently)|(3)<br>Participate<br>in a ROSCA<br>(currently)|(4)<br>Savings<br>stock<br>(Total)|(5)<br>Savings<br>stock :<br>VSLA|(6)<br>Savings<br>stock :<br>ROSCA|
|---|---|---|---|---|---|---|
|Panel A. Pooled Estimates|||||||
|Treatment (ITT)|0.05**|0.19***|-0.04|5713.39|7615.26**|-603.14|
||(0.02)|(0.03)|(0.03)|(10699.54)|(3180.46)|(9444.95)|
|PDS Lasso selected controls|Yes|Yes|Yes|Yes|Yes|Yes|
|Department X (Urban/Rural)|Yes|Yes|Yes|Yes|Yes|Yes|
|Mean in Control|81.8%|16.8%|30.3%|59410.76|4983.39|29913.08|
|Observations|2,620|2,620|2,620|2,618|2,620|2,618|
|Panel B. Treatment Arm Estimates|||||||
|VSLA (T1) (ITT)|0.06**|0.37***|-0.12***|-11124.12|9504.65***|-16469.79*|
||(0.03)|(0.04)|(0.03)|(10329.50)|(2601.07)|(8907.05)|
|Cash Grant with repayment (T2) (ITT)|0.03|0.07**|0.01|11832.10|8143.30|4169.25|
||(0.03)|(0.03)|(0.03)|(13903.51)|(5257.53)|(11747.82)|
|Cash Grant (T3) (ITT)|0.07**|0.07|0.02|26693.63*|2496.31|21465.64*|
||(0.03)|(0.05)|(0.04)|(14127.84)|(2434.49)|(12985.60)|
|PDS Lasso selected controls|Yes|Yes|Yes|Yes|Yes|Yes|
|Department X (Urban/Rural)|Yes|Yes|Yes|Yes|Yes|Yes|
|Mean in Control|81.8%|16.8%|30.3%|59410.76|4983.39|29913.08|
|p-value T1=T2|0.19|0.00|0.00|0.04|0.76|0.02|
|p-value T2=T3|0.20|0.92|0.74|0.34|0.22|0.20|
|p-value T1=T3|0.85|0.00|0.00|0.00|0.00|0.00|
|p-value T1=T2=T3|0.30|0.00|0.00|0.00|0.01|0.00|
|Observations|2,620|2,620|2,620|2,618|2,620|2,618|

Robust standard errors clustered at locality level. Savings stock is in CFA franc and winsorized at 99%. The specification includes control variables chosen using post double selection (PDS) Lasso based on the set in Table A5. _∗ p < ._ 1, _∗∗ p < ._ 05, _∗∗∗ p < ._ 01
TABLES & FIGURES

# **Table 3 - Impact of rainfall shocks for agricultural households**

|Definition of“in agriculture”|(1)|(2)<br>Growing<br>of crops|(3)<br>(2) +<br>farming<br>of animals<br>and ag.<br>services|(4)<br>(3) +<br>hunting<br>and<br>forestry|(5)|(6)|
|---|---|---|---|---|---|---|
|Positive rainfall shock|0.014|0.018|0.018|0.018|0.016|0.017|
||(0.011)|(0.012)|(0.012)|(0.012)|(0.011)<br>-|(0.012)|
|Negative rainfall shock|-0.049***|-0.048***|-0.054***|-0.056***|0.044***|-0.035*|
||(0.015)|(0.017)|(0.017)|(0.017)|(0.014)|(0.018)|
|Negative rainfall shock*HH Hd in|||||||
|<br>agriculture|0.022|0.010|0.021|0.026|||
||(0.021)|(0.023)|(0.022)|(0.021)|||
|HH Hd in agriculture|-0.389***|-0.212***|-0.233***|-0.244***|||
||(0.010)|(0.014)|(0.013)|(0.012)|||
|Negative rainfall shock*Log of|||||||
|Remittances|||||0.003|0.002|
||||||(0.002)|(0.002)|
|Log of remittances|||||0.044***<br>(0.001)|0.044***<br>(0.001)|
|Negative rainfall shock*Log of domestic<br>transfers||||||-0.002<br>(0.002)|
|Domestic transfers||||||-0.007***<br>(0.001)|
|Constant|5.709***|5.634***|5.653***|5.660***|5.466***|5.491***|
||(0.007)|(0.008)|(0.008)|(0.008)|(0.006)|(0.006)|
|Observations|65,355|65,355|65,355|65,355|65,355|65,355|
|R-squared|0.425|0.402|0.403|0.404|0.443|0.446|
|MunicipalityFE|YES|YES|YES|YES|YES|YES|

The estimates control for year dummies. Standard errors are clustered at the municipality level.

*** p<0.01, ** p<0.05, * p<0.1

24
Table 1: Sourc,es of Quantitative Measures of Governance

|Code|Source|Publication|Nature|Country<br>Coverage|Coverage<br>Index|
|---|---|---|---|---|---|
|BERI|Business<br>Environment Risk<br>Intelligence|Business Risk<br>Setrvice|Poll|50 mostly<br>developed<br>countries|0.44|
|CEER|Wall Street Journal|Central European<br>Economic Review|Survey|27 transition<br>economies|0.85|
|DRI|Standard and Poors<br>DRI/McGraw-Hill|Country Risk Review|Poll|106 developed<br>and developing<br>countries|0.23<br>t|
|EBRD|European Bank for<br>Reconstruction<br>and<br>Development|Transition Report|Poll|26 transition<br>economies|0.85|
|EIU|Economist<br>Intelligence Unit|Country Risk<br>Service, & Country<br>Forecast|Poll|114 developed<br>and developing<br>countries|0.19*|
|FH|Freedom House|Fredom in the World<br>Nations in Transit|Poll<br>Poll|172 developed<br>and developing<br>countries<br>28 transition<br>economies|0.03*<br>0.82|
|GALLUP|Gallup Intemational|50th Anniversary<br>Survey|Survey|44 mostly<br>developed<br>countries|0.50|
|GCS|World Economic<br>Forum|Global<br>C:ompetitiveness<br>S;urvev|Survey|54 developed<br>and developing<br>countries|0.42|
|GCSA|World Economic<br>Forum|Global<br>Competitiveness<br>Survey, Africa|Survey|23 African<br>countries|0.73|
|HFWSJ|Heritage<br>Foundation/Wall<br>Street Joumal|Economic Freedom<br>Index|Poll|154 developed<br>and developing<br>countries|0.06*|
|PERC|Political Economic<br>Risk Consultancy|Asia Intelligence|Survey|11 Asian<br>countries|0.83|
|PRS|Political Risk<br>Services|Intemational Country<br>Risk Guide|Poll|140 developed<br>and developing<br>countries|0.10*|
|WCY|Institute<br>Management<br>Development|World<br>Competitiveness<br>Yearbook|Survey|46 primarily<br>developed<br>countries|0.59|
|WDR|World Bank|World Development<br>Report|<br>Survey|74 developed<br>and developing<br>countries|0.25*|

Notes: Poll stands for "polls of experts" and Survey for "survey of entrepeneurs" (except gallup, which is a survey of citizens) (*) Representative source 21
# Policy Research Working Paper Series

|Title|Author|Date|Contact<br>for paper|
|---|---|---|---|
|WPS1746 The Role of Long-Term Finance:<br>Theory andEvidence|Gerard Caprio, Jr.<br>**Asli**DemirgOt-Kunt|April 1997|P. Sintim-Aboagye<br>38526|
|WPS1747 Protection and Trade in Services:<br>A Survey|Bernard Hoekman<br>Carlos A. Primo Braga|April 1997|J. Ngaine<br>37947|
|WPS1748 Has Agricultural Trade Liberalization<br>Improved Welfare in the Least-Devel<br>Countries? Yes|Merlinda D. Ingco<br>oped|April 1997|J. Ngaine<br>37947|
|WPS1749 Applying Economic Analysis to<br>Technical Assistance Projects|Gary McMahon|April 1997|C. Bernardo<br>37699|
|WPS1 750 Regional Integration and Foreign<br>Direct Investment: A Conceptual<br>Framework and Three Cases|Magnus Bl6mstrom<br>Ari Kokko|April 1997|J. Ngaine<br>37947|
|WPS1751 Using Tariff Indices to Evaluate<br>Preferential Trading Arrangements:<br>An Application to Chile|Eric Bond|April 1997|J. Ngaine<br>37947|
|WPS1 752 Ghana's Labor Market (1987-92)|Sudharshan Canagarajah<br>Saji Thomas|April 1997|B. Casely-Hayford<br>34672|
|WPS1753 Can Capital Markets Create<br>Incentives for Pollution Control?|Paul Lanoie<br>Benoit Laplante|April 1997|R. Yazigi<br>37176|
|WPS1754 Research on Land Markets in South<br>Asia: What Have We Learned?|Rashid Faruqee<br>Kevin Carey|April 1997|C. Anbiah<br>81275|
|WPS1755 Survey Responses from Women<br>Workers in Indonesia's Textile,<br>Garment, and Footwear Industries|Mari Pangestu<br>Medelina K. Hendytio|April 1997|J. Israel<br>85117|
|WPS1756 World Crude Oil Resources:<br>Evidence from Estimating Supply<br>Functions for 41 Countries|G. C. Watkins<br>Shane Streifel|April 1997|J. Jacobson<br>33710|
|WPS1757 Using Economic Policy to Improve<br>Environmental protection in Pakistan|Rashid Faruqee<br>|April 1997|C. Anbiah<br>81275|
|WPS1758 The Restructuring of Large Firms<br>in Slovakia|Simeon Djankov<br>Gernard Pohl|April 1997|F. Hatab<br>35835|
|WPS1759 institutional Obstacles to Doing<br>Business: Region-by-Region Results<br>from a Worldwide Survey of the<br>Private Sector|Aymo Brunetti<br>Gregory Kisunko<br>Beatrice Weder|April 1997|M. Geller<br>31393|
L; Ls (nisi) dyasft - Sta(eins) axesang } <!-- Start of picture text --> Lgl) Ep Ep almG wi) amary, fips amary, fipsary, fips fips<br><!-- End of picture text --> <!-- Start of picture text --> ERSees)leisy)Sees)leisy)leisy) Tydveldehldysdveldehldysldys —-- Let)SESE Cw) AxleteiplteGteiplteG<br><!-- End of picture text -->

> ERSees)leisy)Sees)leisy)leisy) Tydveldehldysdveldehldysldys —-- Let)SESE Cw) AxleteiplteGteiplteG == Lgl) Ep Ep almG wi) amary, fips amary, fipsary, fips fips
36 Table 3: Reduced Form Fstimate of RER Model (FY 1975-FY 1990) Dependent Variable is the log of the Real Exchange Rate (RER) Index

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|---|---|---|---|---|---|---|---|
|i<br>Megsgre of|RERT|RPT|RPX|RPM|RPT1|RPXl|RPM1|
|Constant|4.34|1.73|1.51|1.60|-3.35|-3.57|-3.97|
|Capital<br>Exp./Total<br>Govt. Exp.|-0.04<br>(-0.07)|-0.70**<br>(-2.53)|<br>-0.70**<br>(-2.63)|-0.69**<br>(-2.61)|-0.88*<br>(-2.15)|-0.87*<br>(-2.13)|-0.87**<br>(-2.28)|
|Total Govt.<br>Exp/GDP|-7.30***<br>(-3.85)|<br>-3.51***<br>(-3.73)|-3.52***<br>(-3.93)|<br>-3.56***<br>(-3.94)|<br>-6.10***<br>(-4.38)|-6.10***<br>(-4.39)|-6.14***<br>(-4.35)|
|Log Terms of<br>Trade (Price<br>of Exports<br>to imports)|<br>-0.04<br> <br>(0.21)<br> <br>_|-0.03<br>(-0.23)<br>_|-0.80***<br>(-8.82)<br>_|<br>0.20**<br>(2.29)|-0.03<br>(-0.21)|-0.81***<br>(-5.75)|0.19*<br>(1.40)|
|Productivity<br>|rowth|<br>0.12<br>(0.55)|0.39***<br>(3.58)|<br>0.41***<br>(3.92)|<br>0.39***<br>(3.74)|<br>0.44***<br>(2.72)|<br>0.46**<br>(2.82)|0.44***<br>(2.80)|
|Log GDP<br>l_____________|-0.23<br>(0.55)|-0.31**<br>(-2.26)|<br>-0.31**<br>(-2.92)|-0.31**<br>(-2.36)|0.32<br>(1.59)|0.32<br>(1.57)|0.32<br>(1.64)|
|Log real<br>aid|-0.03<br>(-0.11)|0.43***<br>(3.11)|<br>0.47***<br>(3.57)|<br>0.45***<br>(3.36)|<br>0.13<br>(0.64)|0.17<br>(0.83)|0.15<br>(0.74)|
|R<sup>2</sup>|_.84|0.92|_.96|0.93|0.91|0.93L|0_.9_2|
|F Statistic|<br>13.8|30.5|54.8|35.9|25.2|32.3|28.6|
|D. W.<br>Statistic|2.12|2.17|2.19|2.14|2.25|2.21|2.25|
|No. of<br>Observations|16<br> I<br>I<br>_|16<br>|16|16|16|16|16|

Notes: 1. Where the GDP share of current account deficit is assumed to be a function of real exchange rate, real aid disbursement and real GDP.
includes administrative costs of running the program (e.g. verifying employment status, transfer fees). We additionally consider benefits both on the extensive and intensive margin. One factor that decreases the cost of our intervention is that a substantial share of eligible youths do not complete the paperwork required to receive the incentive. Despite receiving SMS reminders and replacing lost debit cards, only 46.2% of youths with formal employment completed the registration and 38.3% submitted pay stubs (Appendix Table A4). While these figures are substantially higher than those of many related programs that pay wage subsidies to firms (e.g. Galasso et al. (2004); Levinsohn et al. (2014)), it raises the question of how the process can be simplified to increase program take-up. When we ask eligible beneficiaries in the endline survey why they did not complete the paperwork, the most frequent responses include that the registration process was too complicated (23%), people forgetting about it (21%), and that they lost the payment card or contact information (20%).

Focusing on vocational school graduates, we find a narrow (broad) cost-benefit measure of US$1,177 (US$1,404) per _additional_ person with formal sector work experience compared to the control group over the first two years. The corresponding figure for permanent formal sector experience is US$739 (US$882) per person. These figures compare favorably to alternative ALMPs such as vocational training, which tend to cost at least $10,000 per additional person with formal work experience. Other interventions such as search and matching assistance tend to be cheaper but in most cases fail to lead to significant employment gains (see McKenzie (2017) for a review).

Turning to our cumulative results, we find that the program costs US$63 (US$75) per additional month with formal sector work experience and US$5.3 (US$6.4) per additional dollar earned from formal work. While the extensive margin effects are stable between the first and second year, one limitation of these cumulative measures is that treatment effects likely continue to increase past our two year study period, leading us to underestimate the costof our intervention.

# **7 Concluding remarks**

We study the effect of a temporary wage incentive in a population of secondary school graduates. We find substantial increases in formal employment for graduates of vocational schools 37
18 extraregional markts or, at the very least, allowed extraregional exports (consisting mainly of primary goods) to be valued higher than intraregional exports. As a result of diverging stabilization policies and '.;e ensuing diverging official exchange rates of national currencies in relation to the U.S. dollar, national currencies became over and sometimes under-valued. Member countries with the least overvalued (or most undervalued) currency have benefitted the most from intraregional trade. At the same time, however, regional deficits accumulated rapidly and prevented further intraregional trade expansion. The monetary union could, in some cases, prevent national economies from different degrees of devaluation, but at an unprecedentedly high price. In the case of UDEAC (which, since 1948, had a fixed rate of 1:50 between the French franc and the CFA), the region became chronically uncompetitive, its production highly distorted, and the official intraregional trade practically negligible. In turn, unrecorded trade, i.e. smuggling, is estimated to have reached figures several times higher than those revealed by official trade statistics.

Although practically all national economies have been challenged by dramatic world economic changes, not all of them have been equally affected. Indeed, very different national economic policies have been formulated as a response to the changing environment. But even if economic policies had been based on the same principles, the economic and social backgrounds for their implementation would still have been substantially different, and, consequently, would have led to different developments from country to country. The emerging differences were then compounded by the technical failures of policy implementation. In sum, the international economy tended to hinder rather than support the harmonizati **o** n fnational economic policies in regional integrations.
World Bank Institute, World Bank, Washington DC. <u>http://www1.worldbank.org/wbiep/decentralization/Topic07_Printer.htm.</u>

- Bird, Richard M. 1999. "Rethinking Tax Assignment: The Need for Better Subnational Taxes," Draft paper, Fiscal Affairs Department, International Monetary Fund.

- Burki, Shaid Javed, Guillermo Perry, and William Dillinger 1999. _Beyond the Center: Decentralizing the State_ . World Bank: Washington, D.C.

- Chattopadhyay, R. and E. Duflo 2003. “Women as Policy Makers: Evidence from a India-Wide Randomized Policy Experiment.” MIT Department of Economics, Working Paper.

- Chaudhury, Nazmul and Jeffrey S. Hammer 2003. “Ghost Doctors: Absenteeism in Bangladeshi Health Facilities.” Background Paper for _World Development Report 2004_ .

- Davoodi, Hamid and Heng-fu Zou. 1998. “Fiscal Decentralization and Economic Growth: A Cross-Country Study.” _Journal of Urban Economics_ 43.

- Diaz-Cayeros, Alberto, Beatriz Magaloni, and Barry Weingast, 2001. “Democratization and the Economy in Mexico: Equilibrium (PRI) Hegemony and its Demise.” Stanford University. Processed.

- Drèze, Jean and Amartya Sen 1996. _Indian Development: Selected Regional Perspectives_ , New Delhi: Oxford University Press

- Drèze, Jean and Haris Gazdar 1996. “Uttar Pradesh: The Burden of Inertia.” In Drèze, J. and A. Sen (eds.) _Indian Development: Selected Regional Perspectives._ Oxford University Press.

- Faguet, J. 2001. “Does Decentralization Increase Responsiveness to Local Needs? Evidence from Bolivia.” Policy Research Working Paper No. 2516, The World Bank, Washington, D.C.

- Filmer, Deon and Lant H. Pritchett, 1999a. “The Impact of Public Spending on Health: Does Money Matter?” _Social Science and Medicine_ 49(10): 309-23.

- Filmer, Deon and Lant H. Pritchett 1999b. “The Effect of Household Wealth on Educational Attainment: Evidence from 35 Countries.” _Population and Development Review_ 25(1): 85-120.

- Filmer, Deon, Jeffrey S. Hammer and Lant H. Pritchett 2000. “Weak Links in the Chain: A Diagnosis of Health Policy in Poor Countries.” _World Bank Research Observer_ 17(1) 47-66.

- Foster, A. and M. Rosenzweig 2001. “Democratization, Decentralization, and the Distribution of Local Public Goods in a Poor Rural Economy.” Working Paper, Brown University, http://adfdell.pstc.brown.edu/papers/democ.pdf.

- Government of India 2004 _. Report of the Task Force on Implementation of the Fiscal Responsibility and Budget Management Act, 2003_ . Ministry of Finance, Government of India, July 2004.

25
ODA for refugee education (Türkiye, Lebanon, West Bank and Gaza, and Jordan) received 75% of all ODA for refugee education in LICs and MICs, despite hosting just 29% of the total schoolage refugee population in LICs and MICs (REFD, 2024; UNHCR, 2023).<sup>23</sup> _Figure K: ODA for refugee education relative to school-age refugee population (5-17), 2021_ <!-- Start of picture text --> 700 600<br>Financing for refugee education<br>600<br> School-age refugees (5-17) 500<br>500<br>400<br>400<br>300<br>300<br>200<br>200<br>100<br>100<br>0 0<br>ODA (US$ millions)<br>Number of school-age refugees (Thousands)<br><!-- End of picture text --> _Source: REFD (2024)_ In Figure L, the costs of inclusive refugee education are compared with ODA for refugee education for the 25 largest refugee hosting LICs and MICs to determine the proportion of inclusive refugee education costs met by ODA.<sup>24</sup> This analysis shows that in three LICs and MICs – Jordan, South Sudan and Ethiopia – ODA for refugee education exceeds or meets the resources required to finance inclusive refugee education in the country, while in all other countries a financing deficit exists. Financing exceeded costs by the greatest margin in Jordan (by 71%), followed by South Sudan (13 percent), and Ethiopia (4 percent). In South Sudan and Ethiopia, the relatively low costs of inclusive education in these countries meant that only modest ODA allocations were required to meet the costs of inclusive education. The relatively higher costs of education in Jordan meant that significant financial resources were required to meet the costs of inclusive refugee education in the country.

As Figure L shows, many large host LICs and MICs received only a fraction of the costs of inclusive refugee education as ODA, with 11 of the largest 25 host countries receiving less than 5 percent of the costs required to finance inclusive refugee education as ODA.<sup>25</sup> This gulf in ODA is most pronounced in Brazil, Mexico, the Islamic Republic of Iran, Chad and Peru, which

> 23 This population includes all school-age refugees in LICs and MICs in 2021 under UNHCR mandate and the 545,000 children enrolled in United Nations Relief and Works Agency for Palestine Refugees schools in 2021 (UNRWA, n.d.).

> 24 (per school-age refugee) to estimate the financing gap in each country and the proportion of refugee education costs met by ODA.

> 25

19