rafmacalaba/data-use-mentions-extended huggingface dataset:
the 12 months prior to the COVID-19 pandemic.

# **3.2 Tax Administrative Data**

The main data source used in this paper are monthly administrative records of TOT filing and payments between 2015 and 2024. Given the nature of TOT, monthly declarations mainly include the identity of taxpayers and their total declared turnover – taxes are then automatically calculated. We complement these filing data with information on TOT payments<sup>12</sup> and also characteristics of taxpayers, such as their declared location and business type.

In Table 1, we provide key descriptive statistics of our sample of interest. We highlight several stylized facts. First, we observe a somewhat stable number of businesses declaring ToT in the 2016 - 2018 period (between 6,000 and 7,000 per year), followed by a decline in 2020 during the COVID-19 pandemic and a rebound thereafter. In 2023, the number of businesses filing increases by almost 70% to over 17,000. As shown in Figure 3, that increase was particularly strong starting in August-September, when the number of monthly declarations climbed from 10,000 to 14,000 by November. This expansion coincides with the roll-out of a strong registration campaign by KRA involving the hiring of new agents (Revenue Service Assistants - RSAs) to improve the authority’s presence throughout the country. Despite the expansion of registered small businesses as TOT payers, it should be noted that these numbers are still very likely only a fraction of total potential taxpayers: less than 3,000 taxpayers filing ToT in 2023 are registered as Nairobi residents, a city of over 4 million people. Furthermore, Nairobi City Council has over 200,000 businesses in its business licensing regime, many of which should be also part of the TOT regime. This suggests that subnational/county governments may have a stronger knowledge of the existence of unregistered businesses and the ability to tax them than KRA.

> 12Most commonly, we observe payments being linked to one declaration. In some cases,
economic activity which is assumed to have remained outside the tax net. These studies have arrived at a wide range of tax-loss estimates, from 0.7 to 3.8 percent of GDP per annum (see Tatariyanto, 2014; Mulyawan, 2017; Nizar and Purnomo, 2011; Ramadhan, 2019; Indupurnahayu and Walujadi, 2019).

The revenue authority in Indonesia, the Directorate General of Taxation (DGT), has

> 6Based on a joint World Bank and Directorate General of Taxation exercise conducted in 2017. It analyzed a sample of business tax returns, comparing income tax estimated by taxpayers with that estimated by tax auditors.

6
dimension: where the regulatory framework is not competitive with other countries, reforms should be undertaken. The DB studies can help governments to identify reform areas based on their countries’ performance as compared to other countries. Table 1 lists the nine core indicators and their constituent components.

**<u>Table 1:</u>** <u>The Doing Business Indicators</u>

|<br>**1.**<br>**Starting a**<br>Procedures<br>(number)|<br>**4.**<br>**Getting**|Legal Rights Index|**7.**<br>**Trading**|Documents for<br>export(number)|
|---|---|---|---|---|
|**Business**<br>Time (days)|**Credit**|Depth of Credit<br>Information Index|**Across**<br>**Borders**|Time for export<br>(days)|
|Cost (% of income<br>per capita)||Public registry<br>coverage<sup>*</sup>||Cost to export<br>(US$ /container)|
|Min. capital (% of<br>incomeper capita)||Credit bureau<br>coverage<sup>*</sup>||Documents for<br>import(number)|
|**2. Dealing**<br>**with**<br>Procedures<br>(number)|**5.**<br>**Protecting**|Disclosure Index||Time for import<br>(days)|
|**Licenses**<br>Time (days)|**
calculated by dividing the tax liabilities by the turnover using two different measures of tax liabilities: the self-reported taxes and imputed taxes.<sup>2</sup> Estimating the effective tax rate is relevant because high tax rates could discourage businesses from registering and complying with payments. For example, a survey of informal and formal firms in Ethiopia found high tax rates to be a disincentive for informal firms to formalize and pay taxes (World Bank 2016). Similarly, a study in Pakistan showed that increasing tax rates resulted in formal firms to become informal (Waseem 2018), and in Brazil, lowering tax rates encouraged more firms to formalize (Rocha, Ulyssea, and Rachter 2018).

There are three key findings from this study. First, the effective tax rates for the smallest businesses are high because the tax-free threshold is not available for some subsectors. Using the tax code, the effective tax rates for businesses in the lowest turnover quartile are 4.3% of turnover compared to 1.5% for businesses in the highest quartile, using the most recent data, resulting in a regressive system. Second, the effective tax rates in the lowest quartile are higher for male businesses than for female businesses because the former tend to operate in sectors _without_ a tax-free threshold. Third, the effective tax rates for businesses in food and beverage services – a female-dominated sector, and in transit services – a male-dominated sector, are high due to the lack of a tax-free threshold for these sectors. Given the regressivity of the tax code, this paper conducts a policy simulation by estimating the tax burden of a hypothetical presumptive tax regime by exempting _all_ businesses with a revenue of 50,000 birr or less and taxing a fixed rate on turnover for businesses earning above the threshold. This paper finds that a fixed tax rate of 2.5 percent on turnover and exemptions for low revenue firms would be simpler for tax assessment and more progressive.

This paper makes a contribution to the literature in three ways. First, the
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Table III provides the frequency distribution of our sample with respect to distress and closure. Almost 42 percent of the institutions in our sample experienced distress, while by July 1999 only 82 of the 120 distressed institutions survived with some type of assistance. Of the whole sample, 38 out of 283 institutions in existence in July 1997, or 13 percent, were closed over this period. Indonesia is the country with the most financial system distress, in terms of absolute numbers of distressed and closed institutions, followed by Korea and Thailand, but the rank is the opposite in terms of the percentage of institutions in distress (Table IV). Malaysia has fewer distressed institutions and did not close any financial institution. The Philippines has the least distressed institutions, four, but did close two nonbank institutions.

We use only financial information as of the end of 1996-the year preceding the crisis-thus avoiding any risk of contaminating our estimations with the occurrence of distress and closure processes themselves.1<sup>2</sup> We collect end-1996 financial data for the different dimensions of a CAMEL-type risk analysis, as used by supervisors in many countries. Table V lists the independent variables, their corresponding CAMELcategories and their definition. Some CAMEL variables were not available for all countries. For instance, two indicators of financial institution risk, the ratio of nonperforming loans to total loans and the capital risk-adjusted adequacy ratio, were not available in a consistent manner for all the five countries under study.'<sup>3</sup> We end up using the following variables: equity to gross loans, loan loss reserves to capital, loan growth, operational expenses to revenues, return on assets, net interest revenues as a share of total revenues, and loans to borrowings. Although not strictly a CAMEL variable, size has usually been added in early warning studies as a proxy for "too big to fail" situations.'<sup>4</sup> In the specific case of the East Asian countries, following Kane (1998
male-headed households engaged in non-farm activities. From the participation data, we know that by 1996 there was a large increase in female participation in non-farm activities as a secondary activity, especially among female spouses, thereby calling into question the significance of income averages at the household level.

Looking at changes in shares of income provides another perspective. For Ghana, in Table 1 1, self-employment accounted for a larger percentage of total incomes earned by women (26% ) than that for men (9%) in 1987. The proportion of self-employment income increased to 31% for women and 11% for men. Table 11 also shows how selfemployment income may affect intra-household relations. First, as noted above, women's income share in self-employment income-a high-earning sector-increased, and it decreased in farming, the lowest earning sector. In contrast, men' s income shares are almost stable in all three sectors over the period. Compared to women as a whole, married women had the same starting income share from self-employment in 1987 (25%) but by 1991 had a higher share (34%) than for women as a whole (3 1%). Married women also had a sharper decline in the share of income from farming. In contrast, married men had a smaller increase in the share of self-employment, but their share of farm income remained high and stable at 70% while their wage income share declined. Married women appear to have gained more than married men, more than men as a group, and slightly more than women as a group.

Examining the trends for the other household members in Ghana, men's income share patterns differed little across the categories with the exception of the "other men" category, men who are neither household heads nor married. Compared to men as a group, these men have much higher and growing income shares from farming, from 86 to 18
complaint handling procedures in these projects and programs were designed as general reviews of transactions with end users. For instance, the Kyrgyz Republic Health and Social Protection Project focuses on serious allegations on the part of auditors, and public complaints regarding the imposition of informal fees, rather than on the variety of complaints end users might have. A concern regarding complaints handling procedures that focus primarily on donor-financed operations, to the exclusion of more general complaints, is that they may weaken the complaints handling capacity of the government more generally, either by drawing staff and talent away, or undermining client confidence in the government‘s processes.

A noteworthy grievance redress procedure is that incorporated into the newly expanded Philippines‘ Conditional Cash Transfer Program, whose focus is grievances expressed regarding the broader government program, rather than within a Bank-supported project per se. Grievances are entered into a publicly accessible database that tracks the nature, origin, location, and status of complaints, including targeting errors, payment irregularities, fraud, and corruption. The database takes in complaints from text, various websites, and a hotline. In the first quarter of 2010, some 13,500 complaints were received. A survey of one region found that 13% of the population had complaints about the program.<sup>25</sup> This is not surprising given the rapid expansion of the program, which went from 376,000 households in 2008 and expanded further at the beginning of 2009 to cover one million households beneficiaries , or approximately 20 percent of the poor. The complaints system provides an important information source for course corrections as the program expands.

As the section on design principles, above, indicated, redress procedures within line agencies should address issues concerning confidentiality, transparency of the redress process, accessibility, speed and responsiveness, and authority. The publication of regular reports on the number and nature of appeals is a useful way to monitor the transparency of the redress process. For instance, Manitoba‘s Family Services and Consumer Affairs department, for example,
same community characteristics as in Table 1 used for the first stage.

Because there is no information in our survey data on migration of women the definition of access to family planning implicitly assume that a woman has spent her entire life in the area where she was found during the survey. This does not seem to be a problematic assumption. Data from the 2005 National Labour Force Survey show that 70 percent of women 15 to 45 have always lived in their current location and that another 15 percent have resided there for 10 years or more, presumably a move associated with marriage and the onset of the women’s entrance into family formation.

> 16This also makes them attractive for women who do not want to reveal to their partner that they are using contraceptives (Ashraf et al. 2010).

> 17The descriptive statistics for the full sample is available on request.

> 18For comparison the equivalent number for Guatemala is 2.8 and Guatemala has one of the highest total fertility rates in Latin America (P ̈ortner 2008). See also World Bank (2010) for TFR for other countries.

11
2007/08<br>–20 0 20 40 60 80 100 120 140 160 180<br>Percent<br><!-- End of picture text --> Source: Firpo and Pieri, Chapter 7 in this book. Note: The bars correspond to growth rates for the whole period indicated. For example, for the period 1950–2005, using Groningen (GGDC) data, labor productivity resulting from the within-sector and structural change effects grew by 132 percent and 24 percent, respectively. GGDC = Groningen Growth Development Centre; PNAD = Pesquisa Nacional por Amostra de Domicílios.

25
# **Annex A: Sample description and representativeness**

To obtain information on changes in welfare, production, and productivity in the small and medium-scale farm sector between 2021 and 2022, a nationwide phone survey of such farms in areas controlled by Ukraine was implemented from October to December 2022, in cooperation with the Ministry of Agricultural Policy and Food (MAPF), the Kyiv International Institute of Sociology, and the Kyiv School of Economics.<sup>13</sup> Although the original intent was to construct a sample frame using data from the State Statistics Service of Ukraine (SSSU), complemented by the company registry, these sources cover only registered legal entities whose registration details are often no longer current. To capture informal farms, which, based on expert estimates, cultivate 32% of Ukraine’s agricultural area (Nivievskyi et al. 2021), a decision was taken to use the State Agrarian Registry (SAR) as a sample frame instead.

The SAR is an electronic registry that was established in August 2022, with the objective of transferring support to small and medium-scale farmers in a transparent yet expeditious way. It allows farmers of any legal status (a registered legal entity, a family-owned business (FOP), or individuals) to sign up at the SAR website (https://www.dar.gov.ua/) using their electronic signature and providing a minimum of personal information. The system then gathers information on all the land parcels to which the farmer has registered rights from the registry of rights and the cadaster and adds information on the farm from other registries.<sup>14</sup> The MAPF or any authorized entity can use information in the SAR to target programs to support the agriculture sector and interact electronically with potential farmers. Farmers can carry out the required actions digitally rather than by filling paper forms, including uploading scanned documents and photos or providing authorization for providers of certain services to access specific types of personal information stored on the system. Use
tween 2007 and 2017. Table 7 shows the number of active regulations in 2017 by country and NTM-type and its share implemented after 2007, the first year of NTM data. The majority of NTMs reported in 2017 are implemented after 2007. For the whole sample, this is the case for 53% of SPS measures, 48% of TBTs and 59% of quantity control measures. Costa Rica and Panama stand out in particular as they have implemented more than 86% of their SPS measures and TBTs since 2007. Given the prominence of technical measures, the empirical section will focus on the effect of SPS, TBTs, quantity control and PSIs measures.

Table 7: Regulations implemented since 2007 and effective in 2017

||SPS|measures||TBTs|Pre-shi|pment Inspections|Quantit|y control measures||Other|
|---|---|---|---|---|---|---|---|---|---|---|
||2017|Since 2007|2017|Since 2007|2017|Since 2007|2017|Since 2007|2017|Since 2007|
|ARG|143|41%|265|26%|9|22%|10|40%|18|28%|
|BOL|31|23%|37|19%|1|0%|5|60%|8|50%|
|BRA|199|54%|491|63%|3|67%|36|50%|7|29%|
|CHL|260|45%|194|49%|5|40%|4|50%|13|31%|
|CRI|163|86%|115|56%|0|0%|8|75%|6|33%|
|CUB|24|50%|40|25%|1
Communities qualify for a PROHECO school if they meet the following criteria: (1) they are located in a rural area, (2) there are at least 25 pre-school and primary-school age children able to attend, (3) the nearest school is at least 3 km away, and (4) the village is located in an area that was affected by hurricane Mitch. Once communities are identified, a government employee visits them to inform them of the project and, if they agree to participate, helps them elect a school council. The government employee is also responsible for providing ongoing advice and capacity building on organizational matters and administrative and legal procedures. Training and support are crucial since communities are not familiar with carrying out the functions that the decentralization reform devolves to them.

# _Measuring Empowerment in PROHECO_

PROHECO’s impact evaluation seeks to measure empowerment in the context of two power relationships that are potentially affected by the education reform. These are power relations (1) among different community members, and (2) between the school council and education authorities, including school staff. Related hypotheses state that the project empowers different community members to participate in and have a voice in school council meetings, irrespective of their gender, socio-economic status, or ethnicity; and that the reform empowers school councils to have greater decision-making authority and autonomy in relation to education authorities and school staff.

Identifying these power relations and where they play out helped select the sub-domains and levels where empowerment is measured. It also helped determine the units and locus of data collection and analysis. The PROHECO evaluation addresses both the service delivery sub-domain (assessing whether the school council is able to carry out the devolved tasks) and the household and community sub-domains (assessing whether different household and community members are able to participate in the school council).

Similar to WDIP and RWSS, the PROHECO evaluation uses a mixed-methods approach, applying institutional and household surveys at the quantitative stage as well as key informant and community interviews during the qualitative stage. The evaluation uses both
migrants in Iran when compared to the native population.

From this series, our results align most with those from (Kovac et al., 2021) as the authors explore the effects of forced displacement on the educational attainment of voluntary migrants, IDPs, and refugees in Croatia and Bosnia. For Croatia they use a similar dataset to ours and for Bosnia a representative survey. Overall, they find that greater war exposure to conflict negatively affects the educational attainment of migrants. For Bosnia they identify some educational and economic advantages of external migration, but these advantages diminish or become negative for individuals who were directly forced to move. In Croatia, they find a small convergence in educational outcomes between the refugee and host groups.

The reminder of the paper is structured as follows. In the next section, we describe the Chilean case and identify some of the major educational roadblocks for forced migrants, migrants, and native students. We then present a brief literature review and outline our main hypotheses. Afterwards, we describe our research design and report our results. Finally, we discuss the results and propose some public policy recommendations.

# **Context**

## **Migration and educational inclusion in Chile**

Chile has made remarkable progress in terms of economic growth and access to public education compared with other Latin American countries over the last 40 years. This success has made it an attractive destination for migrants and forced migrants from elsewhere in the region. Chile’s foreign-born population increased by more than 19 percent from 2018 to 2019, when it comprised 7.5 percent of the population). It is thus a unique case for studying migrant educational outcomes.

Half of Chile’s current immigrant population has arrived since 2015 (INE, 2017). Nearly 80 percent of the country ’s migrant population as of 2020 comes from five countries: Venezuela, (30.7 percent), Peru (16.3 percent), Haiti (12.5 percent), Colombia (11.4 percent)
Table A3: Schools in _Mais Educação_ in the period 2008–2011 with missing information

||Panel|A: Fifth-grade schools||Schools in<br>Panel|B: Ninth-grade sch|ools|
|---|---|---|---|---|---|---|
||Schools in_Mais_||||Schools in_Mais_||
|Variable|<br>_Educação_with<br>complete<br>information|Schools in_Mais_<br>_Educação_with<br>missing information|Difference<br>(1)−(2)|_Mais_<br>_Educação_<br>with complete<br>information|<br>_Educação_with<br>missing<br>information|Difference<br>(4)−(5)|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Portuguese grade|164|163|1.0|222|218|4.3***|
|Mathematics grade|180|179|0.8|230|227|3.2***|
|Dropout rate|5.9|8.4|-2.5***|12.0|16.2|-4.2***|
|∆ Portuguese grade|1.0|-1.0|2.0*|5.0|3.4|1.5|
|∆ Mathematics grade|10.5|6.9|3.6***|1.6|1.1|0.6|
|IDEB Index|3.5|3.3|0.2***|2.9|2.6|0.2***|
|∆ IDEB|0.4|0.3|0.0|0.