rafmacalaba/datause-encoder-data huggingface dataset:
2,630 100% 100% 100% Low and middle income

# _Source: REFD (2024)_

As shown in Figure J, in 2021 over half of ODA for refugee education in LICs and MICs was allocated to countries in the Middle East and North Africa region, with all other regions receiving far less financing despite their significant refugee populations.

inclusive education for refugees by 17 percent in 2021.<sup>21</sup> By contrast, less than 3 percent of the costs of inclusive refugee education in Latin America and the Caribbean were financed by ODA for refugee education, with Latin America and the Caribbean the most underfunded region in 2021. Significant disparities also exist in Sub-Saharan Africa, Europe and Central Asia, and South Asia, where approximately 50, 30 and 16 percent (respectively) of the costs of inclusive refugee education were financed by ODA in 2021, with donors more likely to finance refugee education in the Middle East and North Africa over any other region.

_Figure J: Allocation of ODA for refugee education, costs of inclusive education, and school-age refugees (5-17), by region_ <!-- Start of picture text --> 3,000 3500<br>3000<br> 2,500<br>2500<br> 2,000<br>2000<br> 1,500<br>1500<br> 1,000<br>1000<br> 500<br>500<br> - 0<br>Middle East & Europe & Sub-Saharan Latin America South Asia East Asia &<br>North Africa Central Asia Africa & Caribbean Pacific<br> Education ODA Education Costs School-age refugees (5-17)<br>2,730<br>2,936<br>2,162<br>1,798<br>1,437<br>1,548<br>1,226<br>1,082 918<br>Education Funds (US$ millions) 665<br>271 519 68 44 274 5 170 121 School-age refugees (Thousands)<br><!-- End of picture text -->

# _Source: REFD (2024)_

countries, with donor financing far exceeding costs in some countries Sixty-nine LICs and MICs were recipients of ODA for refugee and host community education in 2021,<sup>22</sup> with additional ODA allocated to a host of regional and international organizations. Figure K shows the number of school-age refugees in each country and the sum of ODA for refugee education for the 25 largest refugee hosting LICs and MICs. As this Figure shows, there

> 21 education systems. The provision of education through parallel systems is likely to face higher costs. 22 The OECD (2023) dataset records the first implementing partner only, and so the final destination of aid delivered to multilateral organizations by donors is unknown.

18
# **Regression Table 1(a)** : Long-term relation between crises and Gini

Dependent <u>variable: Gini coefficient (series of Table 3a)</u>

|Equation:|1.A||1.B||1.C||1.|D|1.|E|
|---|---|---|---|---|---|---|---|---|---|---|
||Coeff.|t-Stat|Coeff.|t-Stat|Coeff.|t-Stat|Coeff.|t-Stat|Coeff.|t-Stat|
|Constant|5.35|0.6|10.3|**6.6|7.5|0.9|14.4|1.8|13.3|1.6|
|Inflation|0.081|1.8|0.080|1.8|0.101|*2.2|||||
|Institutions (Economic freedom)|10.1|**3.6|9.4|**3.3|9.6|**3.5|8.2|**2.9|8.1|**2.9|
|Sub-Saharan Africa|14.9|**4.0|11.8|**3.8|11.8|**3.8|12.4|**3.9|12.5|**3.9|
|Latin America|12.0|**2.9|9.3|*2.5|8.4|*2.3|11.5|**3.2|11.3|**3.1|
|Middle East and North Africa|7.0|1.5|||||||||
|End of crisis dummy|||||4.1|1.5|||2.4|0.9|
|_Countries excluded_|_Trans_||_Trans_||_Trans_||_Trans_||_Trans_||
|R-squared / NOBS|0.504|43|0.473|43|0.504|43|0.430|43|0.442|43|
|Adjusted R-squared|0.437||0.418||0.437||0.387||0.384||
|S.E. of regression|8.4||8.6||8.43||8.81||8.8||
|Loglikelihood|-149.5||-150.8||-149.5||-152.5||-152.0||

# **Regression Table 1(b)** : _Long-term relation between crises and Gini_

## Dependent <u>variable: Gini coefficient (series of Table 3a)</u>

||Equation:|1.F<br>|<br>|1.G<br>|<br>|1.H<br>|<br>|1.I<br>|<br>|1.<br>|J<br>|
|---|---|---|---|---|---|---|---|---|---|---|---|
|||Coeff.|t-Stat|Coeff.|t-Stat|Coeff.|t-Stat|Coeff.|t-Stat|Coeff.|t-Stat|
|Constant||-35.2|*2.4|-34.1|*2.3|-200.8|0.3|-18.8|1.3|-18.9|1.3|
|Inflation||0.080|2.0|0.092|*2.2|0.092|2.0|||||
|Institutions (Economic f|reedom)|6.1|*2.3|6.4|*2.4|6.3|*2.3|||||
|Sub-Saharan Africa||16.0|**5.4|15.8|**5.3|15.7|**5.1|15.5|**5.0|9.8|**4.9|
|Latin America||6.1|*2.3|7.1|*2.1|6.8|2.0|9.8|**3.0|15.5|**3.0|
|GNI per cap (PPP, log)||6.7|**3.6|6.3|**3.3|6.3|**3.3|6.9|**3.9|7.0|**3.7|
|End of crisis dummy||||2.3|1.0|1.8|0.6|||-0.2|0.1|
|Date||||||0.084|0.3|||||
|_Countries excluded_||_Trans_||_Trans_||_Trans_||_Trans_||_Trans_||
|R-squared / NOBS||0.610|43|0.620|43|0.621|43|0.486|45|0.486|45|
|Adjusted R-squared||0.557||0.556||0.545||0.448||0.434||
|S.E. of regression||7.5||7.5||7.6||8.2||8.3||
|Loglikelihood||-144.3||-143.8||-143.7||-156.4||-156.4||

22
# **Table 7B: First stage estimations instrumenting the per capita amount of remittances received**

This table shows the results of the first stage regressions instrumenting the per capita amount of remittances received. The dependent variable is the per capita amount of remittances received from overseas. The instruments used are _Real GDP per capita in US states_ and _Poverty in US states_ . Columns (1)-(3) are the first stage regressions for likelihood that the household has a deposit account. Columns (4)-(6) are the first stage regressions for the likelihood of receiving a loan, while columns (7)-(9) show the first stage regressions for the likelihood of applying for a loan. All independent variables are defined in Table 1. Department fixed effects are estimated but not reported. Robust z-statistics are shown in brackets. *,**, and *** denote significance at 10, 5 and 1 percent, respectively.

|Variables|||Depe|ndent variable:|per capita am|ount of remittan|ces|||
|---|---|---|---|---|---|---|---|---|---|
|||Deposit accou|nt||Loan received|||Loan requeste|d|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|
|Real GDP per capita in US states|0.036||0.009|0.036||0.006|0.036||0.005|
||[4.31]***||[0.87]|[4.21]***||[0.32]|[4.23]***||[0.31]|
|Poverty in US states||-8.509|-6.965||-8.394|-7.389||-8.387|-7.444|
|||[-5.43]***|[-3.30]***||[-5.31]***|[-2.27]**||[-5.32]***|[-2.36]**|
|Real per capita income|-0.008|-0.007|-0.007|-0.009|-0.008|-0.008|-0.009|-0.008|-0.008|
||[-0.84]|[-0.75]|[-0.77]|[-0.95]|[-0.86]|[-0.88]|[-0.95]|[-0.86]|[-0.88]|
|House and land ownership|0.115|0.111|0.112|0.116|0.112|0.113|0.117|0.113|0.114|
||[2.86]***|[2.76]***|[2.78]***|[2.87]***|[2.78]***|[2.80]***|[2.90]***|[2.81]***|[2.83]***|
|Adults average education|0.010|0.011|0.011|0.013|0.013|0.013|0.011|0.011|0.011|
||[1.11]|[1.14]|[1.13]|[1.33]|[1.37]|[1.36]|[1.19]|[1.22]|[1.22]|
|Agriculture|-0.006|-0.007|-0.008|-0.004|-0.005|-0.006|-0.003|-0.005|-0.005|
||[-0.10]|[-0.12]|[-0.13]|[-0.07]|[-0.09]|[-0.10]|[-0.06]|[-0.08]|[-0.09]|
|Electricity|0.234|0.245|0.241|0.234|0.245|0.242|0.236|0.247|0.244|
||[5.74]***|[6.11]***|[5.93]***|[5.68]***|[6.06]***|[5.77]***|[5.74]***|[6.12]***|[5.84]***|
|Returned migrant|0.668|0.657|0.656|0.678|0.667|0.667|0.665|0.654|0.654|
||[4.02]***|[3.99]***|[3.98]***|[4.05]***|[4.02]***|[4.01]***|[4.01]***|[3.97]***|[3.97]***|
|Constant|-1.363|0.968|0.477|-1.321|0.949|0.627|-1.353|0.946|0.641|
||[-4.6]***|[4.11]***|[0.83]|[-4.31]***|[4.22]***|[0.63]|[-4.53]***|[3.99]***|[0.66]|
|Observations|2289|2289|2289|2261|2261|2261|2267|2267|2267|
|Wald test chi<sup>2</sup>|574.5|573.9|573.8|57.9|64.9|62.6|64.3|78.6|75.7|
|Wald test p-value|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|
|Cragg-Donald weak inst. F-stat|18.42|29.21|14.69|17.61|28.00|14.08|17.75|28.09|14.12|

27
<!-- Start of picture text --> reer tby rrate_us<br>.0015 .003 .001<br>.001 .002 .0005<br>.0005 .001 0<br>0 0 −.0005<br>0 5 10<br>gdp govexp<br>.01 .004<br>.003<br>.005 .002<br>.001<br>0 0<br>rrate gdp_us<br>.0005 .003<br>0<br>.002<br>−.0005<br>−.001 .001<br>−.0015<br>0<br>0 5 10 0 5 10<br>step<br>95% CI Orthogonalized IRF<br><!-- End of picture text --> We find a few contrasting results for fiscal rule(s) adopters and non-adopters. First, a positive shock to the US real interest rate leads to an increase in GDP for the countries with fiscal rule(s) in place, but to a fall in GDP in non-adopters. Second, shocks to GDP tend to be larger in nonadopters than in adopters, although the degree of persistence of these shocks is similar for the two groups. Third, positive shocks to government expenditures raise GDP in both groups of countries, but have larger effects on GDP in countries with no fiscal rules than in those with fiscal rules in place. Fourth, shocks resulting in higher domestic real interest rate have contractionary effects on GDP in the countries with fiscal rules, but have no significant effects in non-adopters. Lastly, a positive foreign demand shock and a shock that appreciates the real exchange rate, both raise GDP in fiscal rule(s) adopters, but has no significant effect in countries without fiscal rule(s). Given a small number of non-adopters in our data set, it is not surprising that most of the responses for this group are not statistically significant.

23
# **1 Introduction**

The property tax is increasingly recognized as a key component of equitable domestic revenue mobilization, with significant untapped potential for generating revenue (World Bank, 2021; Benitez, Mansour, Pecho, and Vellutini, 2023; Dom, Custers, Davenport, and Prichard, 2022; Maloney, Zambrano, Vuletin, Beylis, and Garriga, 2024). In many countries, real estate emerges as a primary repository of wealth, positioning property taxation as a potential tool for redistribution. However, as this tax grows in importance, taking into consideration its gender dimension is a central concern (Scot, Flores Ibarra, Moura, Feinmann, and Rocha, 2023; Komatsu, Ambel, Koolwal, and Yonis, 2021). Despite its relevance, the relationship between property taxation and gender outcomes remains largely understudied.

This paper provides new gender-focused empirical evidence on property ownership and taxation using administrative tax micro-data from Tres de Febrero, a large urban municipality in Argentina with over 360,000 inhabitants and about 120,000 households. Residents from Tres de Febrero are required to pay a monthly tax on their real estate, locally known as _Tasa por Servicios Generales_ (TSG), which accounts for most of the local revenues in Argentine municipalities. We leverage rich tax administrative data and exogenous variation to show how gender interacts with several fundamental issues: the distribution of property ownership, tax evasion, and tax enforcement.

The analysis unfolds in three stages. First, we examine the distribution of residential ownership, presenting novel descriptive evidence on property ownership across the municipality, with a particular focus on gender disparities and the assessed values of properties. A significant challenge for such analyses is that property ownership records often lack explicit information on owners’ gender. However, a unique feature of our setting is the structure embedded within national personal tax identifiers, which can be used to infer gender. Following methodological guidance from our team, the municipality’s staff utilized this structural feature to infer the gender assigned at birth and the owners’ ages for over 100,000 properties annually.

Our analysis reveals significant gender disparities in residential property ownership, primarily at the higher end of the property value distribution, where women own a smaller share than men. At the lower end of the property value distribution, up to the 40th percentile, ownership is fairly balanced, with women, men, and co-owned properties each accounting for about onethird of the residences. However, this balance shifts markedly as property values rise. While men’s ownership share remains relatively stable throughout the distribution, women’s share declines sharply for higher-value properties. This disparity is most pronounced in the top 1% of property values, where co-owned properties account for 50%, men own about 30%, and women’s 1
# **Figure 5 Does Selective Migration Influence Observed Patterns in Years of Schooling of the Urban Population in CULS Provinces?** Evidence from the 2000 Census

<!-- Start of picture text --> Shanghai<br>14.0<br>13.0<br>12.0<br>11.0<br>10.0<br>9.0<br>8.0<br>1940 1944 1948 1952 1956 1960 1964 1968 1972 1976 1980<br>Year<br>Current Residents with Urban Hukou Individuals Born in Shanghai<br>Individuals Born in Shanghai and Living in Shanghai<br>Liaoning<br>13.0<br>12.0<br>11.0<br>10.0<br>9.0<br>8.0<br>1940 1944 1948 1952 1956 1960 1964 1968 1972 1976 1980<br>Years<br>Current Residents with Urban Hukou Individuals Born in Liaoning<br>Individuals Born in Liaoning and Living in Liaoning<br>Shaanxi<br>14.0<br>13.0<br>12.0<br>11.0<br>10.0<br>9.0<br>8.0<br>1940 1944 1948 1952 1956 1960 1964 1968 1972 1976 1980<br>Birth Year<br>Current Residents with Urban Hukou Individuals Born in Shaanxi<br>Individuals Born in Shaanxi and Living in Shaanxi<br>Years of Schooling<br>Years of Education<br>Years of Schooling<br><!-- End of picture text --> <!-- Start of picture text --> Hubei<br>14.0<br>13.0<br>12.0<br>11.0<br>10.0<br>9.0<br>8.0<br>1940 1944 1948 1952 1956 1960 1964 1968 1972 1976 1980<br>Years<br>Current Residents with Urban Hukou Individuals Born in Hubei<br>Individuals Born in Hubei and Living in Hubei<br>Fujian<br>13.0<br>12.0<br>11.0<br>10.0<br>9.0<br>8.0<br>1940 1944 1948 1952 1956 1960 1964 1968 1972 1976 1980<br>Birth Year<br>Current Residents with Urban Hukou Individuals Born in Fujian<br>Individuals Born in Fujian and Living in Fujian<br>Years of Education<br>Years of Schooling<br><!-- End of picture text --> Source: 2000 Population Census.

31
average precipitation is 643 mm, yet varies from 250 mm in the central part to 3000 mm in the Eastern Black Sea region. Seventy-five percent of annual rain falls during the winter season. Annual rainfall is less than 500 mm in the inland Thrace and in the Eastern Anatolia regions. This diverse precipitation structure emphasizes the crucial importance of irrigation.

Generally, agricultural production is adversely affected by the shortage and inconsistency of rainfall during the growing season. Solar energy makes it possible to grow arid and semi-arid crops such as bananas and citrus. Moreover, it is possible to grow 2 to 3 different crops in irrigated areas that have crop growing seasons for a period of 270 days. However, some crops may be harvested before maturation, particularly in Eastern Anatolia with its 60 to 90 growing days. The southeast region has a very low humidity level. The coastal regions are humid with high precipitation rates. Inevitably, the topographic features are main factors shaping the distribution. The long-term annual evaporation rates indicate a high rate, particularly in the southeast region, which receives almost no rainfall during the summer, and reaches more than 2000 mm per year in the Southeastern region (Kanber et al., 2005).

The average annual precipitation of the country corresponds to a water potential of 501 km<sup>3</sup> per year, of which 274 km<sup>3</sup> are lost to evapotranspiration, 69 km<sup>3</sup> feed aquifers and 158 km<sup>3</sup> flow through the rivers to the sea or lakes. The gross total surface and ground water potential of Turkey amounts to 234 km<sup>3</sup> (Table 2.1).

**<u>Table 2.1. Turkey: Water Resources Potential and Use</u>**

|Surface water|Groundwater|Total|
|---|---|---|
|Surface flow<br>158km<sup>3</sup>|Feeding groundwater<br>69 km<sup>3</sup>|Mean annual precipitation<br>501 km<sup>3</sup>(603mm)|
|Surface runoff<sup>a</sup><br>193km<sup>3</sup><br>|Recharge<br>41 km<sup>3</sup>|Renewable water potential<br>234 km<sup>3</sup>|
|Usable surface runoff<sup>b</sup><br>98km<sup>3</sup>|Safe yield<br>14 km<sup>3</sup>|Usable (net)<br>112 km<sup>3</sup>|
|Consumption<br>31|Consumption<br>12|Consumption<br>43|

Notes:<sup>a</sup> includes the contributions from underground (28 km<sup>3</sup> ) and from the neighboring countries (7 km<sup>3</sup> ); b includes the usable flow of 3 km3 from the neighboring countries. _Sources: DSI, 2008a._ The amount of surface water utilized for consumption purposes is in the range of 98 km<sup>3</sup> per year, including the contributions from the neighboring countries. According to the studies based on groundwater resources, the total safe yield of groundwater resources is estimated to be 9
annual grade point average are able to achieve 81% specificity and a 25% false-positive rate in two school districts in the U.S.

While these results assess dropout prediction based on performance in-sample, the true test for the accuracy of prediction models is performance outside of the sample on which the models are estimated. Using administrative data on attendance, behavioral violations, standardized test scores, and demographic characteristics from the state of Wisconsin, Knowles (2015) estimates a range of models, from standard logistic regression to support vector machines, on a “training sample” and then tests their accuracy out of sample, finding that logistic regression models perform very well relative to more complex algorithms. Similarly, using rich administrative data in Denmark, Sara et al (2015) show that machine learning algorithms “trained” on one data set can achieve sensitivities over 90% (with false-positive rates around 20%) out of sample when predicting high school dropout over three-month intervals.

Overall, then, evidence is substantial that who will drop out can be accurately predicted using highquality administrative data in the U.S. as well as Europe. We are not aware, however, of any studies investigating this question in the context of a developing country, where data requirements have only recently been met and dropout is often a much more widespread issue. This paper therefore provides one of the first applications of dropout prediction methodologies in lower middle-income education systems, made possible by the remarkable efforts of the ministries of education in both Guatemala and Honduras. Furthermore, unlike much of the existing U.S. research, we estimate models at the national level instead that at the state/province level. This is possible because in both countries the administrative data are harmonized, aggregated, and analyzed at the federal level.

7
Table 1: Sample selection and observation numbers This table summarizes the different numbers of observations used in the different analyses based on the sample selection criteria. In each case, for a particular combination ( _i, t_ ) the CPIA, FD (or all its constituents), and the controls need to be available. The sample selection relates to the number of countries _i_ and the number of observations <u>(</u> _i, t_ <u>).</u>

|required|linear|clustering|clustering|clustering|clustering|
|---|---|---|---|---|---|
|full|panel|(2-dim)|with|(7-dim)|with|
|1998–2019|model||controls||controls|
|CPIA||yes||yes||
|FD||yes||||
|FD consituents||||yes||
|controls||||||
|nr. countries|137|125|118|49|54|
|nr. observations|2896|2750|2236|1078|1068|

for our clustering analyses without controls, we only require the CPIA and financial development proxies to exist. Given that clustering is performed with time-varying cluster means, we require the variables to be present for all times. For the panel data analysis, we take all observations into account for which we observe the CPIA, financial development proxies, and the other controls.

Averages of CPIA and FD are presented in Figure 1. The time-series averages per country in Figure 1a show some expected differences, such as higher financial development scores in economies such as for instance the Republic of Korea, Brazil, China, India, and South Africa, and lower scores in most of Sub-Saharan Africa.

The CPIA scores are only partially correlated with the FD scores, at best. Some countries that have relatively low FD scores can at the same time, have relatively high CPIA scores, and the other way around. This result is in line with Barajas et al. (2021). Notably the largest part of Sub-Saharan Africa has low FD scores, but at the same time shows much more variety in the 12
Comparing (2) and (3) provides a clear picture of the simplifying assumptions that are required for the NBR based measure to be robust. First, it assumes that there is no demand response to consumption price changes through substitution eliminating the cross-elasticity term (the second term) in equation (2). Second, the proportional price changes are assumed the same for both consumer prices and producer prices, and therefore the first and the third terms in equation (2) are combined. Third, it is also assumed that producers do not exhibit any supply responses allowing the fourth term to be ignored. Finally, there are no significant changes in input prices including wage rates thereby ignoring the last two terms of the second-order approximation in (2).

In practice, household survey data limitations often constrain the extent to which these assumptions can be relaxed in the welfare analysis. Even though the micro-econometric approach described above provide many insights, the partial equilibrium nature of this analysis fails to account for various macroeconomic factors that could change the welfare incidence of food price spikes. For instance, economy-wide changes in relative prices, factors affecting structure of production and wage rates in the economy can alter households’ real income. Past work has found that while certain sections of the population are affected negatively in the short-run due to rising food prices, the long-run effect could be zero or positive after accounting for changes in wage rates (Rashid, 2002; Ravallion, 2000).

Understanding the macroeconomic processes is particularly relevant to the 2016 Mozambique case given the underlying currency market shocks. Studies employing general equilibrium methods range from simple simulations to full scale CGE models (Wodon & Zaman (2008) provide a literature overview). In particular, micro-simulation approaches, in which survey households are embedded in the CGE model, can provide a more comprehensive picture of the welfare and poverty impacts of food price spikes (De Janvry & Sadoulet, 2008). In this approach, a CGE model is used to estimate changes in the average income for representative household groups. These changes are then passed down to individual households within each group and consumption expenditures are recalculated to estimate the welfare incidence. Nouve & Wodon (2008) carry out such an analysis for the case of Mali. Particularly relevant for this study is the work by Arndt et al., (2008) discussed later in this section.

7
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