rafmacalaba/data-use-mentions-extended huggingface dataset:
# **3. Gender and Household Access to Financial Services**

This section explores gender differences in the use of different financial services by individuals. Unlike in the previous section, we therefore focus more on savings and payments than credit services. We consider both formal and informal financial services. This section first discusses the data and simple comparisons in the use of financial services by men and women, before turning to multivariate regressions.

# **3.1. Data and Ocular Econometrics**

To explore the relationship between gender and use of financial services, we use 11 household surveys across 9 Sub-Saharan African countries, co-branded as FinScope or FinAccess surveys. Specifically, we have data for Botswana, Kenya, Malawi, Namibia, Rwanda, South Africa, Tanzania, Uganda and Zambia. These surveys, first undertaken in South Africa in 2002, are surveys with up to 7,600 observations and sampled with cluster stratified probability. They are based on individuals rather than households. While this might reduce the accuracy in terms of financial services that the individual has indirect access to through other household members and might reduce the representativeness, it has the advantage that we can focus specifically on the gender gap (Cull and Scott, 2010). For this study, we have a total of 11 surveys available, with Kenya and Tanzania having undertaken surveys twice. While South Africa has undertaken yearly surveys, we only include the one from 2008. All surveys used in this section were undertaken between 2004 and 2009.

The FinScope surveys distinguish between four different population segments – (i) users of formal banking services, (ii) users of other formal services, such as insurance companies, mobile phone services and regulated MFIs, (iii) users informal financial services, including unregulated SACCOs, ASCAs and ROSCAs, and (iv) individuals excluded from any service. There are two ways to explore this differentiation; first, considering each strand 18
imbalances in the starting conditions between treatment and control villages. The results prove to be robust to such controls, although a small number of results lose their significance when individuallevel controls are added due to the reduction in the sample size caused by individual-level attrition.

An important characteristic of the two eastern districts is that they are predominantly Pashtun. To check whether the difference in the results for the eastern districts is driven by their ethnic composition, rather than the level of violence, we also examine whether the effect is different in the other two predominantly Pashtun districts in our sample (Balkh and Farsi). The results indicate that there is only a small difference in the effect of the program in Pashtun regions as compared with other districts, suggesting that the divergence of results in the eastern districts are driven by insecurity and/or regional specificity.

## **VI.V. Heterogeneity of Results**

To examine whether the above effects are affected by progress in implementing projects, we use data from NGOs indicating rates of project completion at the time of the follow-up survey. This data is used to construct a dummy variable which assumes a value of one for the 113 treatment villages in which at least one project was at least 80 percent complete. Estimates of interaction effects between this variable and the treatment effect indicate that for, almost all measures, the effect is stronger in villages that made more progress in project completion.

We also explore potential heterogeneity of effects on economic outcomes and attitudes at the individual level. In particular, we examine whether the effects are driven by respondents’ age, education, land ownership, unemployment status or their involvement in subsistence agriculture. The results indicate that the effect of the program is smaller for older respondents, who are more likely to be skeptical of development interventions as they have lived through over three decades of war but also less likely to be themselves actively involved or recruited in the insurgency. However, there is no evidence that any other individual characteristics that we consider have a consistent effect on
is deprived in a given year). Additional analysis of chronic poverty and chronic material and social deprivation (capturing who is consistently monetary poor/consistently deprived over time) is also undertaken in Bulgaria. Within this group, attention will be given to the Roma to understand whether they are overrepresented among them.</mark>

# III. Data and Empirical Strategy

**To analyze multiple deprivations and at-risk-of-poverty or social Exclusion (AROPE) among vulnerable groups in the EU (excluding the Roma), we rely on cross-sectional and longitudinal data from the European Union Statistics on Income and Living Conditions (EUSILC).**

**<mark>The EU-SILC is a comprehensive survey conducted in 27 European Union member states as well as Iceland, Norway and Switzerland.</mark>** <mark>The EU-SILC is composed of national probability sample surveys of individuals 16 years and older and is conducted annually. The target population comprises private households. It collects data on income, poverty, social exclusion, and living conditions of households and individuals. EU-SILC offers two primary types of data: cross-sectional data, which captures information over a specific time frame and includes variables related to income, poverty, social exclusion, and living conditions, and longitudinal data, which tracks individual-level changes over a four-year period.   Social exclusion and housing conditions data are predominantly gathered at the household level, while labor, education, and health information is obtained from individuals aged 16 and above. Detailed income components are also primarily collected from individuals within the survey. The EU-SILC relies on a stratified multi-stage (two-stage stratified) sampling with a primary sampling unit (Census</mark>

> 22 AROPE identifies individuals as multidimensionally poor if they meet any of the thresholds (union criteria), meaning that a deficiency in one dimension is enough to classify them as at risk of poverty or social exclusion.

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||All Small<br>States|Sample<br>Group|
|---|---|---|
|Average GNI per capita US$|4759|4329|
|Classified as:|||
|Low income|9|18|
|Lower middle income|48|47|
|Uppper middle income|43|35|
|Average Ratio of Public Debt to GDP|52|62|
|External public debt/GDP|34|40|
|Domestic public debt/GDP|18|22|
|With an IMF program|43|59|
|Rescheduled with external creditors|43|65|

Source: World Bank

# **Findings from the DeMPA in Small States**

The DeMPA findings confirm that most of the 17 small states assessed have rudimentary debt management functions, practices and systems. No small state in the sample group met the minimum requirement for more than twenty of the thirty five DPIs assessed and more than half of them (53 percent) met the minimum requirement for less than ten DPIs. These scores are well below those of the other 42 developing countries for which DeMPAs have been conducted (Figure 2).

**Figure 2: Debt Management Capacity in Small States and Other Developing Countries Compared**

_Percent_ <!-- Start of picture text --> % of Countries in Each Category<br>60 52.94 52.38<br>47.06<br>50<br>40 33.33<br>30<br>20 14.29<br>10<br>0.00 0.00 0.00<br>0<br> <10 >=10 & <20 >=20 & <30 >=30<br>No. of indicators out of 35 indicators that meet minimum<br>requirements<br>Non-small states Small states<br>Source: DeMPA reports<br><!-- End
-2|-7|-4|0.7|-12|-2|
|Agency (% having say in 1 or more decisions)|64%|88%|84%|97%|73%|61%|
|Decadal change (percentage points)|-6|-1|-2|-1|-10|-13|
|Spousal difference in age (yrs.)|5.3|8|4.7|3.6|4.2|4.6|
|Decadal change (months)|-6|-11|-3|-6|-4|-0.2|
|Spousal difference in education (yrs.)|2.4|0|1.5|-0.8|2.2|2.1|
|Decadal change (months)|0.1|-8|-5|-5|-0.4|0.2|
|Fertility (actual minus ideal # of children)|-0.1|0.8|0.5|-0.2|1|0.4|
|Decadal change (number)|0.3|-0.6|-0.1|-0.1|-0.1|-0.1|
|IPV experience (% reporting)|7%|13%*|6%|2%|6%|4%|
|Decadal change(percentagepoints)|-0.02|-0.04|-0.1|-1|-3|0.2|

Source: DHS rounds (see, for details, table A2 in the annex). Notes: for each outcome variable, the table reports two rows of numbers. The first row represents the most recent level of the variable, and the second row shows the decadal changes. The levels, in
the range of net transfers for different levels of pre-transfer income during the 1995 to 1998 period for households without and with migrants, respectively. Comparing the top two figures, note that more of the lower bound of transfers lies below the poverty threshold for households that do not have migrant family members. Indeed, during the earlier period, elderly households with migrant family members appear very unlikely to have income per capita below the poverty line after private transfers are included household income. Consistent with Giles (2006), the migrant labor market appears to be providing sufficient insurance against falling into poverty. Evident in the lower panel of Figure 5, households with migrant family members are actually at a greater risk of falling into poverty by the 2000 to 2003 period. Below, Table 5 summarizes the range of transfers at five multiples of the poverty line. From the values reported in Table 5, it is evident that elderly households with pre-transfer income per capita less than onehalf the nutrition-based poverty line are at particular risk of falling into poverty. From Table 3, we know that in the later period this amounts to 17.5 and 15.7 percent, respectively, of the elderly without and with migrant children. As transfers have become less responsive to pretransfer income even as risk of falling into poverty has increased, it is apparent that a positive role might be played by some type of government transfer, either the _dibao_ or a non-contributory component of a pension. Given that transfers are unlikely to completely crowd out private transfers, a low base level of support would provide insurance against falling into poverty.

When thinking about the descriptive results on financial transfers above, it is important to keep in mind that financial transfers are only one form of transfer received by elderly from their adult children. As enumerated in the RCRE dataset the value of in-kind transfers in the form of food-stuffs and other consumable goods are included among these transfers, but there is no attempt to value time spent providing instrumental care, labor input to the elderly person’s farm
It is worth noting, however, that not all types of trade openness are equivalent. An increase in the differential between export and import growth rates (that is, exports growing faster than imports) reduces the probability of the worst outcome (cluster V). It also increases the probability of being in a higher growth and lower vulnerability (G) cluster and, even more, the probability of being in a lower growth and lower vulnerability (L) cluster. Thus, a country’s export orientation reduces the worst of all possible outcomes (cluster V). It does provide a metric to gauge a country’s choice of development path.

# **Figure 4. Probability and Shocks in 2-Year Average Estimation**

# **A One Standard Deviation Increase in:**

<!-- Start of picture text --> Trade Openness L GV H<br>Export-Import Growth Diff. V H GL<br>Financial Openness G L VH<br>FDI, Net, % GDP VH GL<br>Counter-Cyclical Fiscal Policies HGV L<br>Monetary Tightening VH L G<br>Exchange Rate Flexibility VL G H<br>Capital Account Liberalization G H VL<br>Initial Income, % of G7 GDP GH L V<br>Market Volatility, VIX HL G V<br>Oil Balance, % Exports & Imports V L H G<br>-.3 -.2 -.1 0 .1 .2 .3<br>Change in the Predicted Probability<br><!-- End of picture text --> Sources: IMF’s World Economic Outlook database and authors’ calculations. Notes: The horizontal axis measures the change in the predicted probability in each individual cluster following a change (a one standard deviation shock) in the indicator specified in the vertical axis. A positive value represents an increase in the probability of being in that cluster; e.g., greater exchange rate flexibility increases the
The probability level α is assumed to be uniformly distributed between 0 and 1. When α follows other distributions, the difference between implied correlations and linear correlations is assigned different weights at different probability levels. Ang and Chen (2002), for example, chose weights proportional to the number of observations.

Under the null hypothesis that returns follow multivariate normal distribution, the linear correlation is appropriate and the theoretical implied correlations equal the theoretical linear correlation. Since the sample mean, the empirical estimates of VaR and ES are consistent estimators of their corresponding theoretical values, the estimated implied correlations converge to the theoretical implied correlations, which equal the linear correlation under _H_ 0. The alternative hypothesis is that the linear correlation underestimates (overestimates) correlations during market downturns (upturns). The null hypothesis is rejected when the test statistic is too large.

I use bootstrap to test the significance of the test statistics. The bootstrap technique provides a simple way to test statistics whose distribution is unknown, but can be simulated.<sup>6</sup> Take the _H_ statistics for example. I use the following procedure to make statistical inference. Given two assets with sample size _n_ , Step A: Calculate the test statistic _H_<sup>ˆ</sup> , sample means, standard deviations of each individual asset, and their correlation using the empirical data.

Step B: Draw _n_ pairs of data from the bivariate normal distribution using the sample mean, standard deviation, and correlation calculated in Step A.

Step C: Compute the test statistic, named _H_<sup>(1)</sup> for _H_ statistic from the simulated data.

Step D: Repeat step B and step C M times and get a sequence of test statistics, _H_<sup>(1)</sup> ,..., _H_<sup>(</sup><sup>_M_)</sup> . ˆ <u>1</u> _M_ Step E: Calculate the _p_
the tax base, and the tax liability. These tabulations also decompose this information by gender, the location of the asset (domestic versus foreign), the type of asset, the industry sector, and many wealth brackets. In addition, we use information from the personal income tax and its four components: rental income, capital income, business income, and labor income. The income tax tabulations include the number of filers and taxpayers and information on the asset value, debts, and net worth. This information is also reported by brackets of total income.

The extensive scale of Argentina’s asset disclosures and the detailed tax tabulations allow us to utilize straightforward methodologies to unpack the effects of the policy changes. Furthermore, we examine the distributional implications of the enforcement policies using the Pareto interpolation methodology to characterize, visualize, and estimate distributions of income or wealth. In particular, we use the generalized Pareto interpolation method developed by Blanchet et al. (2022) to flexibly reconstruct continuous distributions of income and assets and obtain precise series of the income and asset distribution. This method is substantially more precise than the alternatives commonly used in the literature and can often be more precise than non-exhaustive individual microdata.

# **3 The effectiveness of Argentina’s enforcement initiatives**

This section examines the effectiveness of Argentina’s tax enforcement initiatives. Section 3.1 sheds light on the prevalence and nature of tax evasion. Section 3.2 discusses the distributional patterns of tax evasion. Sections 3.3 analyzes the fiscal externalities that asset revelations have on the wealth and capital income tax bases. Finally, Section 3.4 discusses why the 2016 policy was so much more effective than previous enforcement efforts.

## **3.1 Revealing foreign and domestic assets**

Figure 2 illustrates the number of individuals who filed wealth tax declarations. The series is indexed to 100 in 2015, before the 2016 amnesty. This figure reveals several noteworthy findings. First, neither the 2009 nor the amnesties in 2013–
identification of generic reasons and forces behind decentralization, of process characterization and of success levels, just because the focus was on individual case study description. A more analytical approach would allow finding general relationships and patterns. For that one needs to have a framework that incorporates the political, institutional and economic variables and the paths by which they may influence decentralization outcome. Such framework also needs to take into account the initial and contextual conditions. An important consideration in this context is that “the lowest appropriate level” for integrated river basin management varies between basins, i.e. while full decentralization of decision-making can be optimal in one case, it can be destructive in another one. For this reason, the methodology necessarily needs to take into account hydrological, socio-economic, cultural and historical conditions in each basin (see also Saleth and Dinar, 2004).

In this paper we use the theory that has been developed within the research project and expand it to explain levels of success of decentralization processes in river basins and apply it to the global data collected from river basins around the world. The next section presents the theory and the hypotheses we plan to test. The third section presents the process of data collection and the 5
Friedberg et al., 2020; Tibshirani et al., 2018). Table 2 summarizes key features of these methods, which we describe in greater detail below. Importantly, we evaluate these methods in the context of developing countries, where census data is often unavailable or dated. One of our goals is to improve the estimation of key development outcomes in such contexts. As such, we propose using data that is widely available across the globe: remote sensing and geospatial data. In addition, we adapt and apply a two-stage residual bootstrap procedure to estimate uncertainty for the machine-learning models.

These techniques may improve on existing methods but require rigorous evaluation of their accuracy and

> 4Data gaps are a well-known challenge affecting poverty measurement in poorer countries. While census data are less available in these poorer countries, fewer household surveys are also infrequently collected. Even where household surveys are available, they likely display inconsistent quality over longer time periods. Data imputation methods have been increasingly used to help fill these data gaps (Beegle et al. (2016); Dang et al. (2019); Dang and Lanjouw (2023)).

7
would be expected from the GDP numbers, because non-wage jobs tend to be much lower productivity.<sup>10</sup>

**Figure 3: Incomes sources of households in Uganda, 1992/93-2005/06, showing that share of non-agricultural income sources grew,**

<!-- Start of picture text --> Uganda National Rural areas<br>20.9 24.2<br>Agriculture wage Agriculture wage<br>10.7 11.9<br>Public non-agriculture 6.0 Public non-agriculture 5.3<br>wage 8.4 wage 6.7<br>Private non agriculture 21.2 Private non agriculture 15.1<br>wage 12.8 wage 8.9<br>41.4 38.5<br>Non farm enterprise Non farm enterprise<br>27.7 24.3<br>77.3 86.9<br>Farm Farm<br>82.0 91.6<br>0.0 20.0 40.0 60.0 80.0 100.0 0.0 20.0 40.0 60.0 80.0 100.0<br>2005/06 1992/93 2005/06 1992/93<br><!-- End of picture text -->

**Source** : Authors‘ calculations based on the IHS 1992/93 and UNHS 2005/06

Moving to household livelihoods (which covers all employment, not just primary employment) _we see a clear trend toward income diversification into incomes sources outside-subsistence agriculture as expected._ A comparison of household sources of income in 1992/93 and 2005/06 in Figure 3 shows that the proportion of households with an income from non-farm sources increased dramatically. The proportion of households with a private non-agricultural income almost doubled while that of households with non-farm household enterprise increased by 50 percent. The number of NFEs has been rising at an annual average
impact is strongly significant for the key service delivery sector of education (but not for health), where increase in routine (as opposed to development) expenditures is the driving force. Since the increase in expenditures is spread across a number of sectors, direct elections do not seem to have led to significant shifts in the allocations of spending across sectors (see Annex, Table A-2a and 2b).

> 20 One important caveat for the interpretation of these results is that the reported revenue from own sources (PAD) in the SIKD data base is zero in 2004 and 2005. We have been unable to establish the reasons behind the absence of any revenues in these two years.

18
4 5 6 7 8 9 10 11<br>Number of external defaults<br>Serial defaulters All others<br>Mean annual real return in %<br><!-- End of picture text --> _Notes:_ This figure plots real, average total returns on country-level portfolios of sovereign external bonds against the total number of defaults since independence by that country on the horizontal axis. The data come from Table 6, covering countries with 10 years or more of bond price data. The default data from Reinhart and Rogoff (2009) and updated in Reinhart and Trebesch (2016).

# 4.5. Liquidity of the sovereign bond market over the past 200 years

This section gives a brief overview on sovereign bond market liquidity over 200 years, as proxied by bidask spreads (additional insights are provided in Appendix B6). To complement our main database of prices, haircuts and returns, we gathered a large additional dataset of bid and ask bond prices. The resulting bid-ask spread series cover all countries in our sample, but with some gaps, in particular in the first half of the 19th century (see Figure B3). In total, we could gather monthly, bond-level bid-ask spreads for 62% of our pricing sample (165,638 out of 266,134 observations). For historical bonds traded on the London 33
<!-- Start of picture text --> Figure B2.1. Average marginal probability to innovate given the use of innovation inputs across developing East Asian<br>countries<br>35.0% 32.2%<br>30.0%<br>26.3%<br>25.0%<br>21.7%<br>20.0% 19.2%18.4%<br>16.2%<br>14.9%<br>13.7%<br>15.0%<br>11.0%<br>10.1% 9.7%<br>10.0%<br>6.0%<br>4.8%<br>5.0%<br>0.0%<br>Product OR Product Process Organizational Marketing<br>process<br>R&D Trainning Technology license<br>Source: Own elaboration using ES data<br>Note: average marginal effect results from a probit model estimating the probability of introducing all types of<br>innovation (product or process; product; process; organizational; and marketing) conditional on whether the firm:<br>perform R&D activities, offer training for innovative activities, and acquire technology license (all of them<br>introduced simultaneously in the regression). Controls for firm age, size, sector, ownership, export status, city<br>size plus country fixed effects are also included as regressors. The graph shows only the coefficients/effects that<br>are statistically significant. For innovation of product or process, and product innovation, the effect of technology<br>license was not statistically significant.<br><!-- End of picture text -->

# **_3.1. Managerial capabilities as a (complementary, but key) innovation input_**