rafmacalaba/data-use-mentions-extended huggingface dataset:
Clear patterns emerge in saving across age groups. In both developing and highincome economies the share of those who report having saved formally in the past year varies little across income quintiles among young adults (those ages 15–24) and among older adults (those age 65 and above), who are likely to be retired. And there is little difference between adults in these age groups and adults ages 25–64, who are most likely to be in the workforce. But among these working-age adults the share of those who report formal saving more than doubles from the poorest to the richest income quintile in all economies on average—and rises from 10 percent to 38 percent in developing economies.

###

## **Savings behavior among account holders**

Adults with a formal account by savings behavior in the past year (%) <!-- Start of picture text --> 100<br>DID NOT SAVE<br>80<br>SAVED<br>USING OTHER<br>60 METHODS ONLY<br>40<br>SAVED<br>FORMALLY<br>20<br>0<br>EUROPE MIDDLE EAST LATIN SOUTH EAST ASIA HIGH- SUB-<br>& CENTRAL & NORTH AMERICA ASIA & PACIFIC INCOME SAHARAN<br>ASIA AFRICA & CARIBBEAN ECONOMIES AFRICA<br>Source: Demirguc-Kunt and Klapper 2012.<br><!-- End of picture text --> This is unsurprising: working-age adults could be expected to have a greater propensity to save formally—and this propensity could be expected to rise with income. Yet the Global Findex data also show a gap of about 28 percentage points between developing and high-income economies in the share of working-age adults who report having saved formally in the past year—a gap that persists across all income quintiles.

# **How does savings behavior vary among account holders?**

Having a formal account does not necessarily imply formal saving; even among account holders there is great variation
data collections are more infrequent, with the COVID-19 pandemic contributing to further disruptions (United Nations & World Bank, 2021).

Although efforts have been made in analyzing gaps and limitations of existing data sources to provide recommendations for improving methodologies and tools (Bilsborrow et al., 1997; Lucas, 2000; De Brauw and Carletto, 2012; Bilsborrow, 2016; Global Migration Group, 2017; Kirchberger, 2021; Tjaden, 2021), at present, the lack of accurate, meaningful data on migration flows – especially on international migration – at the global, regional and, in some contexts, national level is still one of the main constraints to study different types of mobility patterns. Moreover, much of the emphasis for improving migration data has focused on the quantification aspect, positing census data as the gold standard for computing reliable migration rates. Qualifying migration, that is, determining its drivers and impacts at a fine scale, should be equally important and an essential complement to accurately estimate “how much” migration is happening and is expected to happen.

A detailed review of the issues with defining migration goes beyond the scope of this paper.<sup>7</sup> However, it is clear that old and new challenges apply when it comes to producing definitions and measurements in the domain of “climate migration”, which is itself an umbrella term encompassing different circumstances such as forced displacement, planned relocation, or even involuntary immobility.<sup>8</sup> Multiple (push and pull) mediating factors contribute to determining different types of mobility responses – and associated impacts – in the face of climate change-induced shocks. To be able to disentangle these dynamics and assess the effects on different groups of individuals (e.g., women, marginalized groups, etc.), rich, granular, contextual data and accurate weather and climate information are needed, in addition to data on actual movements of people. This type of information is generally not available in population censuses or administrative records/
# **2.2 Data on BLAs**

The data we use for the BLAs is from Chilton and Woda (2022). This dataset contains a comprehensive set of 1,222 BLAs between two countries since World War II. The dataset contains information on the year the BLA was signed and the two countries between which the treaty was signed. The dataset is the most comprehensive dataset on BLAs to date and builds on the data compiled by Chilton and Posner (2018), Peters (2019 _a_ ) and Peters (2019 _b_ ). A richer description of the dataset is presented in Appendix A.

We transform the dataset to match the format of the migration dataset. That is, for each BLA in this dataset, we identify the country of origin, the country of destination, and assign it to the subsequent year in migration data.<sup>4</sup> That is, a BLA signed between 1970 to 1979 is assigned to _t_ = 1980. To simplify categorization, we label the country with a lower GDP per-capita as the origin country and the other as the destination country. The origin-destination-year cells without a BLA is assumed to have no BLA.

# **2.3 Descriptive statistics**

The combined dataset used in this paper has information on BLAs and migration from 198 countries to 197 potential destinations (i.e., 39,006 origin-destination corridors) for 7 periods from 1960 to 2020 at ten-year intervals. A common feature of the bilateral migration dataset, as seen in table 1, is that most (61 percent) of the corridors do not have any migration. About 12 percent of the corridors have at least 10 observed migrants across all time periods; we term these as _regular_ corridors. The shares of non-empty corridors and regular corridors are similar across the key subsets used in the analysis.

Likewise, BLAs are also relatively rare in the data. About 1.6 percent of corridors have a BLA signed between them, with the share lower for African
While NA estimates of average PC private consumption are closer to those in household survey compared to per capita GDP, the result are reversed when looking at growth rates. From 2003 to 2009, the average annual growth of per capita GDP was 1.6 percent, which closely aligns with the 2 percent estimated from household surveys (Table 9).

_Table 9: Average annual growth rate 2003-2009_

||**A**<br>|**verage Growth**<br>|<br>|
|---|---|---|---|
||**2003-2009**|**2003-2007**|**2007-2009**|
|**Per capita Consumption from household surveys**|-0.2%|-0.7%|0.4%|
|**Per capita GDP**|1.6%|1.8%|1.3%|
|**Per capita Private consumption from NA**|3.4%|2.0%|5.6%|

_Source_ : World Bank staff calculations, based on WDI and CBS data Pre-conflict data already revealed a substantial discrepancy between the growth-rate of per-capita welfare estimates based on NA private consumption and those coming from household surveys.<sup>23</sup> Over time, such discrepancy is expected to have further increased together with the deteriorating capacity of NA aggregates to reflect changes in the economy as a result of conflict. In particular, the likely increase of the informal economy as a result of conflict might have created an additional source of measurement error in available NA indicators. In principle, it could be assumed that an increase in informality would affect measurement error of GDP more compared to household final expenditure. However, as household final consumption expenditure in Syria’s NA is computed as a residual,<sup>24</sup> it could be assumed that the loss in reliability of
4%|12.2%|23.1%|18.5%|3.6%|4.9%|61.6%|
|**Memo:**||||||||||
|**Free trade zone**|39.1%|54.5%||||||||
|**USA reported imports CIF**|62.4%|68.1%|66.3%|||||||

_Note_ : Interpretation of this table is as follows. The table should be read column -wise, where each row represents the share in total column -countries exports. As an example, the top-left figure indicates that exports from Costa Rica to El Salvador represent 2.3% of Costa Rica’s total exports.

Source: Direction of Trade Statistics

**Table 6. Central America’s Trade Structure: Bilateral Exports/GDP**

|**Bilateral Exports / GDP (avera**|**ge: 1995 - 2001)**<br>|||||||||
|---|---|---|---|---|---|---|---|---|---|
||**Costa Rica El**|**Salvador Gu**|**atemala**<br>**H**|**onduras **|**Nicaragua**|**Argentina**|**Mexico**|**Canada**|**France**|
|**Costa Rica**||0.8%|0.7%|0.6%|1.2%|0.01%|0.05%|0.01%|0.01%|
|
both urban and rural areas. In areas along the grid, bringing road networks 10 km closer to a rural location marginally decreases the likelihood of working 56 percentage points in the DHS sample (column 4); in urban areas, bringing road networks 10 km closer increases the likelihood of working in a low-skilled occupation by 10 percentage points in the DHS sample (column 3). Similarly, in areas along a main road, bringing an electric grid 10 km closer in rural areas marginally decreases the likelihood of working in a low-skilled occupation by 27.3 percentage points in the DHS sample. The joint effect of roads and electricity is negative in rural areas, but positive in urban areas.

There are significant employment gains in high-skilled employment in response to the expansion of roads and electricity in both urban and rural areas, with few exceptions. In rural areas that are connected to electricity, bringing roads 10 km closer marginally increases this type of employment by 19.4percentage points in the LSMS sample, and by 13.0 percentage points in the DHS sample. In urban areas, the effect of increasing proximity of roads 10 km is positive in the LSMS sample (7 percentage points) but negative in the DHS (-2 percentage points), although the latter effect is not statistically significant. In rural areas with high access to roads, bringing electric grids 10 km closer marginally increases the probability of high-skilled employment by over 99 percentage points in the LSMS sample, and by 2.4 percentage points in the DHS sample. In urban areas, the corresponding effects are 14.7 (LSMS) and 1.5 (DHS) percentage points, respectively. In both urban and rural areas, the joint effect of expansion of the two types of infrastructure is positive and statistically significant, except in the DHS sample where it is negative and insignificant in urban areas. These findings are consistent with those of the OLS estimates (Table 14).

The findings imply that in urban areas, investment in road and electricity infrastructure
Wealth Distribution

Thus far we have focused on the challenges when measuring the top of the income distribution. But much of the preceding discussion applies directly to wealth. In this section we focus on aspects of measuring the top of the wealth distribution where there is not a simple correspondence between the literatures on measuring the top of the income and wealth distributions.

All of the challenges that we have discussed above in measuring the top of the income distribution using household surveys also apply to measuring the wealth distribution, although some are even more of a concern. The ex-ante solutions we discussed above can also be used to solve wealth challenges, as can the ex-post solutions using survey data. But other solutions that researchers have used have relied on novel types of data. We discuss these issues in this section.

> 5 This is not an issue if a survey asks about earnings over the last year to all individuals, including those not currently employed, as the US CPS does. In countries where most employed people are employed throughout the year, this may also not be a substantial concern.

22
in developing countries. The paper makes two contributions to the literature on gender and taxation. First, it provides a more comprehensive analysis of gender differences in business income tax burden by focusing on a broader set of indicators, including taxpayer behavior (declarations) and payments. This is made possible because of a rich set of administrative data on business characteristics, reported income and liabilities, and tax payments. This allows the paper to answer the question of whether the business income tax burden differs for men and women due to their own choices (as shown in their declarations) or as a result of tax enforcement decisions. Second, the paper examines gender differences in exiting the tax net for the first time, which can be linked to the tax burden. Future research can fill the gap by explaining the reasons for the differences in tax declarations and payments.

The paper is organized as follows. Section 2 reviews the structure of Ethiopia’s tax policy and its implication for gender differences relying on the literature on gender and taxation. Section 3 discusses the data. Section 4 presents multivariate analysis results. Section 5 concludes.

# **2. The Structure of the Tax System and Its Implications for Gender**

According to Ethiopian law, the business income tax applies to selfemployed individuals. As per the law, businesses generate income from conducting business, operation, stock disposal, and services provision (see FDRE, 2016a). Self-employed individuals are liable to pay income tax on profits, which is the income left after deducting the cost of goods and services sold, as well as other expenses.

An important aspect of the Ethiopian business income tax administration is segmentation, a common approach to simplify tax administration in many countries (see Cleary et al., 2017 ). In Ethiopia, taxpayers are categorized into three groups, known as registration categories A, B, and C, based on their expected annual gross income (turnover) when they start their businesses. These categories are updated as new data becomes available: 6
# **I. INTRODUCTION**<sup>**1**</sup>

Over the past decade international taxation has become headline news. Leaked information on private wealth and corporate profits hidden in Switzerland, Luxembourg, Panama, and small island jurisdictions, caused public outcry. Anecdotes of aggressive tax evasion complement macro assessments of the “scourge of tax havens” (Zucman 2015), looking at offshore household wealth (Alstadsaeter and others, 2017) and corporate profit shifting. Yet, despite increasing attention, public scrutiny of policies enabling or facilitating an outsize role of investment hubs<sup>2</sup> in the global economy remains limited,<sup>3</sup> which is likely due to the opacity of the international tax architecture.

In this context, we investigate the role of tax treaties, an aspect of tax policy making that can contribute to the strength of the business model in ‘offshore jurisdictions’ or ‘investment hubs’. Double Tax Treaties (DTTs) are intended to facilitate cross-border trade and investment by demarcating taxing rights of countries. They affect returns to foreign investment by determining when a branch is liable for income tax, the treatment of capital gains, or by changing the taxation of dividend, interest and royalty flows from the country where the investment is made, to the country where it has been initiated. While DTTs serve more purposes and often also come with a range of complementary political objectives, the avoidance of double taxation is at their core. However, these bilaterally negotiated agreements can, by restricting taxing rights and through the abuse of treaty networks using shell companies, also contribute to the loss of a country’s tax base and facilitate tax avoidance.

Our paper is focused on tax treaty policy in Sub-Saharan Africa. Building on a panel data set of 41 African economies from 1985 to 2015, we examine the effects of concluding DTTs with investment hubs empirically. In Africa, Mauritius has positioned itself as a conduit country for investment into the continent
I POLICY RESEARCH WORKING PAPER 2286

# Summary findings

Mehrez and Kaufmann investigate how transparency affects the probability of a financial crisis.

They construct a model in which banks cannot distinguish between aggregate shocks and government policy, on the one hand, and firms' quality, on the other. Banks may therefore overestimate firms' returns and increase credit above the level that would be optimal given the firms' returns.

Once banks discover their large exposure, they are likely to roll over loans rather than declare their losses. This delays the crisis but increases its magnitude.

The empirical evidence, based on data for 56 countries in 1977-97, supports this theoretical model. The authors find that lack of transparenicy increases the probability of a crisis following financial liberalization. This implies that countries should focus on increasing transparency of economic activity and government policy, as well as increasing transparency in the financial sector, particularly during a period of transition such as financial liberalization.

- - This paper a product of Governance, Regulation, and Finance, World Bank Institute is part of a larger effort in the institute to research governance and transparency and apply the findings in learning and operational programs. (For details, visit www.worldbank.org/wbi/gac.) Copies of the paper are available free from :he World Bank, 1818 H Street, NW, Washington, DC 20433. Please contact Diane Bouvet, room J3-273, telephone 2C2-473-5818, fax 202-334-8350, email address dbouvetrworldbank.org. Policy Research Working Papers are also posted on the Web at www.worldbank.org/ research/workingpapers. The authors may be contacted at gmehrez@worldbank.org or dkaufmann@worldbank.org. February 2000. (33 pages)

> _The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas abouit 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
, such as when on-the-job search is present, the Burda model presented above demonstrates that despite the drawbacks, such active labor market policies are crucial to avoiding low level, high unemployment equilibria.

19. The final category of medium-term goals consists of measures to increase administrative capacity. Some advantages to be gained are administrative cost savings through more effective contribution collection, faster and more reliable delivery of services and benefits, which encourages greater participation, and increased capacity for control, which will diminish fraud. All these contribute to reduce the costs of the system. The steps which will achieve these benefits are time- and labor-intensive, but can pay handsomely in terms of improved system efficiency. Fundamental steps include (a) replacing the enterprise as a purveyor of benefits, which will result in more equity, greater transparency of benefit levels, and less fraud and leakage from the system; (b) establishing a system of social insurance records by individual worker and firm (e.g. issuing a social security identification number); (c) computerizing record-keeping in a central database that can be linked with other social assistance program databases and used for cross-checking; and (d) improving the delivery of benefits.
# Appendix B: An extension: Remoteness and dispersion at the province level

The conceptual approach that we have outlined for measuring local-area level remoteness and countrywide population dispersion can also be applied at a sub-national regional level. For example, we can calculate the remoteness of each local area within a provincial context, and the associated dispersion of the population within each province. We can also derive a province-level indicator of the remoteness of each province within a country’s broader context.

In the analysis that follows, we derive indicators of remoteness and dispersion at the sub-national level for seven of our case-study PICs (Figure B1). We do not complete the exercise for Tuvalu due to its small size. We reconceptualize community-level remoteness as remoteness within the provincial context. Dispersion of the province population is then calculated as the population-weighted average remoteness of each local area within the province; that is, the average distance from that local area, to all other local areas (within the province), weighted by those areas’ populations. Remoteness of a province as a whole is conceptualized within the broader country context. For the calculations, we anchor the location of each province (or sub-national area) at the provincial capital (or other regional ‘urban hub’<sup>14</sup> ), and measure province remoteness as the (province) population-weighted average distance from that provincial capital to province capitals across the country.

For many countries, the results indicate that the more central provinces tend to have less dispersed (and often larger populations), while the most remote provinces often have widely dispersed (and small) populations. This likely reflects both geographical characteristics and (endogenous) settlement patterns, and the (endogenous) way in which administrative boundaries are determined. In Fiji, for example, Rewa Province (which captures the urban area of Suva, and had a population of around 101,000 at the time of the 2007 census) has a remoteness estimate (population-weighted average distance to all provinces) of 82km and an internal
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Policy Research Working Paper 6186

# **Abstract**

In the last two decades more than 120 countries have adopted a version of a Medium-Term Expenditure Framework (MTEF). These are budget institutions whose rationale it is to enable the central government to make credible multi-year fiscal commitments. This paper analyzes a newly-collected dataset of worldwide MTEF adoptions during 1990–2008. It exploits withincountry variation in MTEF adoption in a dynamic panel framework to estimate their impacts. The analysis finds that MTEFs strongly improve fiscal discipline, with more advanced MTEF phases having a larger impact. Higherphase MTEFs also improve allocative efficiency. Only top-phase MTEFs have a significantly positive effect on technical efficiency.

This paper is a product of the Public Sector & Governance Unit, Poverty Reduction and Economic Management Network. 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://econ.worldbank. org. The authors may be contacted at zmills@worldbank.org and mverhoeven@worldbank.org.

_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