rafmacalaba/data-use-mentions-extended huggingface dataset:
# **Figure 1.2. Female adolescents reporting forced sexual initiation, as a percent of those reporting having had sex (Population-Based Surveys 1993-1999)**

<!-- Start of picture text --> New Zealand 7%<br>United States 9%<br>Mozambique 19%<br>Ghana 21%<br>South Africa 28%<br>United Republic of Tanzania 29%<br>Cameroon 37%<br>Peru 40%<br>Caribbean 48%<br><!-- End of picture text -->

**Source:** Population-Based Surveys 1993-1999. All figures cited in Jewkes, Sen, and Garcia Moreno, 2002.

Sexual violence within marriage is also common, with approximately 10-13 percent of women reporting having been forced by a partner to have sex against their will at some point (Heise, Ellsberg, and Gottemoeller, 1999). Sexual violence often accompanies physical battery by intimate partners, and in many settings, women and men believe that men have the right to beat their partners for refusing sex (Figure 1.3). Women who experience physical violence by intimate partners may be less able to negotiate when and how they have sex. Nonetheless, large variations exist in patterns and prevalence levels between and within countries. For example, a study from León, Nicaragua found that nearly all women who reported sexual violence had also experienced physical violence (Ellsberg et al, 2000). In contrast, research from Indonesia suggests that sexual violence often occurs outside the context of physical violence and may be even more common than physical violence (Hakimi et al., 2001).

**Figure 1.3. Percentage of men and women who say that men have the right to beat their wives when they refuse to have sex.**

<!-- Start of picture text --> 81%<br>57%<br>Men<br>43%<br>Women<br
9 These require that consumption or income be converted into the unit used for the poverty line. The World Bank uses poverty lines in international dollars, so expenditures have to be converted to 2011 prices using the country’s consumer price index (CPI) and then into 2011 international (PPP) dollars. The International Comparison Program is an important source of data on PPPs. CPI and PPP series are available from the World Bank and IMF databases.

## <u>Computation</u>

The computation of the first two measures – expenditure in absolute terms and dispersion – is straightforward, the only complication being that Q90/Q50 is undefined if median out-of-pocket expenditures are zero, which is quite possible. The computation of the budget share is also straightforward, the only complication being what to do if the denominator is negative, which is possible if it is household income. Progressivity is easily computed as the difference between the Gini coefficient (for income or consumption) and the concentration index for out-of-pocket expenditures, both of which can be computed straightforwardly in Stata using any user-developed section that computes inequality measures such as CONINDEX for Stata (O'Donnell et al. 2015) and INEQ for R (Zeileis and Kleiber 2014). The incidence of and inequality in catastrophic expenditures can both be computed using the Stata module FPRO (Eozenou and Wagstaff 2018), as can the headcountand poverty gap-based measures of impoverishment. Further guidance on the computation issues is provided in O’Donnell et al. (2008).

# **New global estimates of out-of-pocket health expenditures – data**

In the rest of the paper we present new global estimates of out-of-pocket health expenditures for each of the measures listed in Table 1. We present in this section details of the data we use.

Our data are drawn from the 2019 version of the World Bank’s Health Equity and Financial Protection Indicators (HEFPI) database (Wagstaff et al. 2018a; Wagstaff et al. 2019). In generating the database, we identified potentially
The idea of turning Sudan into the Middle East’s or Africa’s ‘breadbasket’ has been repeatedly proposed by Sudanese leaders, regional leaders, and development organizations for the last 50 years. However, since Sudan experienced rapid nationalization followed by rapid privatization and has been subject to repeated droughts, desertification, famines, and conflict, the dream of turning Sudan into the region’s breadbasket has yet to be achieved (Calkins et al. 2015; Verhoeven 2015). In contrast to many of its African neighbors which have transitioned, to one degree or another, from agriculture to industry and services, much of Sudan’s labor force continues to be engaged in agriculture. As mentioned earlier in this report, the share of the employed population working in agriculture increased between 2011 and 2014, with almost half of the labor force employed in agriculture in 2014. The data also show a significant gender dimension, with 60 percent of female labor force participation being in agriculture, compared to 39 percent of male labor force participation. This is even more pronounced in rural areas where four out of every five women working outside the home are engaged in agriculture (World Bank 2019c).

Despite the large share of women in Sudan’s agricultural labor force, female farmers generally earn less than male farmers for a number of reasons including, but not limited to, the smaller size of their plots. A multicountry study in Africa found that women farmers not only face individual-level obstacles that limit their access to inputs and impede their productivity, but they also face a complex set of restrictive social norms, market failures, and regulatory or institutional constraints that affect policies and programs intended to support women farmers (O’Sullivan et al. 2014). In 1992, the Government of Sudan launched a Ten Year Comprehensive National Strategy that aimed, among other things, to increase the country’s

> 23 (Musa 2012) This is aligned with the findings from the 2014 Enterprise Survey, where 54 percent of female-led firms had majority
Figure 13: An increase in the progressivity of the wealth tax in 2019 <!-- Start of picture text --> Effective Tax<br>Rate (%)<br>2.25% 2013-15 2016<br>Amnesty Amnesty<br>Top 0.01%<br>p99.95-p99.99<br>p99.9-p99.95<br>1.25% p99.5-p99.9<br>0.75%<br>p99-p99.5<br>0.5%<br>0.25%<br>0<br>2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022<br><!-- End of picture text --> _Notes:_ this figure compares the wealth tax liability expressed as a share of total (taxable and non-taxable) assets by the wealthiest 1% of adults (aged 20 and above) separately by bins of increasing assets. The progressivity of the wealth tax increased in 2019 when Argentina taxed offshore assets at higher rates. _Source:_ authors’ calculations using data from AFIP statistical yearbooks.

Table 1: Domestic and foreign assets in 2014, 2015, and 2016

|||**Dom**|**estic assets**|||**Fore**|**ign assets**||
|---|---|---|---|---|---|---|---|---|
||**2015**<br>US$|**2015–14**<br>_%_∆_(pre)_|**2016–15**<br>_%_∆_(post)_|**2016–15**<br>_% of total_∆|**2015**<br>US$|**2015
although the gender gap is smallest for those with post-secondary education. Conversely, for any level of educational attainment, the share of women who are employed is always lower than that for men. This may be explained in part by the fact that some women leave the labor force to be a full-time housewife.

**Figure 13. Labor force indicators by sex and educational attainment**

<!-- Start of picture text --> 80%<br>5%<br>70% 8% 12%<br>60% 10%<br>22%<br>50%<br>20% 16%<br>40% 11%<br>30% 9% 10%<br>4%<br>2%<br>20% 4% 15% 12% 2% 41%<br>34% 4%<br>10% 18% 10%<br>13% 14%<br>4%<br>0%<br>Male Female Male Female Male Female<br>Employed Unemployed Out of Labor force<br>No qualification Some/Completed Primary Secondary Post-Secondary and above<br><!-- End of picture text --> _Source:_ World Bank staff calculations based on NHBPS 2014/15.

The labor market seems to demand low-skilled labor as there is a general pattern where there are better job opportunities for people without education than those with any education. Women and youth with some or completed primary education have 38 and 65 percent lower chances to be in the labor force and employed, respectively, compared to those with no education (Annex 2). Men with some or completed primary education have a 58 and 52 percent lower probability to be a labor force participant and employed, respectively, compared to men with no education. On the other hand, women and adults with higher education attainment (post-secondary and above) are better-off than those with no education, with
known about capital incomes in developing countries, since many income surveys do not even attempt to collect this information.

Finally, and closely related, the measurement of top incomes needs to be improved in developing countries. The administrative records that have been used in developed countries are not available for the developing world. The most innovative approaches to inequality measurement combine income information from administrative records with household surveys for other questions.<sup>30</sup> Broad-based income taxes will generate these administrative records, similar to Piketty’s proposal

> 27 Roughly speaking, when the average income in China exceeds the global average, growth in China will have a disequalizing effect on global inequality. This is likely to happen very soon, as Milanovic (2016) explains in detail. 28 While it is not unprecedented for individual countries over a 20-year period, this is unlikely to happen in all countries in the world.

> 29 This does not necessarily apply to the measurement of absolute poverty, such as the $1 per day poverty estimated by the World Bank. Even though the importance of the agricultural sector is declining in these emerging economies, many of the poorest individuals will continue to live in rural areas and work in agriculture. Furthermore, monetary and non-monetary transfers may be underreported at the bottom in an income survey. For example, the US Current Population Survey fails to capture many transfer payments at the very bottom, when compared with administrative records (Meyer et al., 2015). As a result, consumption is often surprisingly high for individuals on very low or even zero incomes (e.g. Brewer et al., 2013 for the UK).

> 30 For example, France recently began using register-based information (including tax records) for some questions in its EU-SILC survey (Burricand, 2013).

17
# _Wage Responses to Higher Food Prices_

In principle, the indicator of agricultural GDP used in table 3 includes farm wage incomes as part of value added. However, higher food prices might also result in increases in non-farm wages. Yet, as we noted in section II, only a small empirical literature has focused on these issues. Rarely has this literature used sufficiently high frequency data to accurately inform the question of how long it takes for wages to react to food price increases, and only one paper has examined the responses of urban wages to higher food prices (Headey et al. 2011). We therefore revisit short-run adjustment issues using high frequency data on rural and urban wage indices for Bangladesh from 2001 to 2011.<sup>15</sup> Although the data are monthly, the noise in the data prompts us to aggregate the data into quarters, which also more closely approximates Bangladesh’s different agricultural seasons ( _aman, aus, boro_ ). Our expectation was that labor demand and supply may be less likely to respond in the current season but may respond more substantially in subsequent seasons as planting decisions constitute an important behavioral response to higher food prices.

Figure 3 shows trends in these nominal rural and urban wage indices along with the nominal producer price of rice, which is easily the most important crop in Bangladesh. Nominal rice prices roughly doubled from July 2006 to July 2008 in close accordance with international prices. Nominal rural and urban wages also increased, but rural wages increased much faster than urban wages. Indeed, from July 2006 to January 2011, the ratio of rural to urban wages increased rapidly from 59 percent to 82 percent. This suggests two important conclusions. First, rural and urban wages (and hence labor markets) may not be very closely integrated in the short term. Second, it is plausible that higher food prices were the leading factor behind the declining rural-urban wage gap, though there are other possible explanations such as accelerated out-migration from rural areas (Zhang et al. 2013)
Data and methodology

# **<u>High Frequency Phone Surveys and Global Monitoring Database</u>**

This section describes the data sources and the methodology used to examine the impact of Covid-19 across the welfare distribution in 14 countries in Sub-Saharan Africa. We rely on two different databases for our analysis: High Frequency Phone Surveys (HFPS) implemented since the pandemic began and the Global Monitoring Database (GMD), a database of household surveys implemented prior to the pandemic, compiled and managed by the World Bank. We rely on survey-to-survey imputation, which entails developing a model of household consumption in the GMD for each country, and using the model to impute the welfare status of households in each wave of that country’s HFPS.

Since Spring 2020, the World Bank has supported national statistical offices in developing countries to design and implement phone surveys to examine various aspects of Covid-19 on lives and livelihoods.<sup>2</sup> The rapid pace at which the Covid-19 pandemic has affected countries has made access to timely knowledge on the impacts of Covid-19 critical to effectively design, target and evaluate policy interventions. However, implementing face-to-face interviews was severely restricted in the context of widespread government-imposed social distancing practices. Phone surveys filled this gap by eliciting information from individuals and households rapidly and at low cost. In all 14 of the countries we consider, phone surveys were implemented as follow-up surveys from previous nationally representative surveys. These previous surveys were used to reweight the phone survey to make it more representative of the national population of households. However, the phone surveys disproportionately interview household heads, meaning that individual level characteristics such as the ability to work at usual were not representative of the underlying population of workers. Nonetheless, phone surveys can be helpful in tracking the responses to and impacts of the pandemic, particularly if they are combined with traditional household surveys. Table 1 lists the countries and waves of HFPS that were used for this analysis.

**Table 1: Sample size of HFPS waves and the
# **3. EVIDENCE OF TENURE CHOICE**

# **Data and Methodology**

The data used in the study and presented in Table 1 are collected from the LSMS and HBS, nationally representative surveys developed to evaluate and track national living standards with the assistance of the World Bank and other donors.<sup>18</sup> The HBS includes more questions on household budgets than the LSMS; however, both surveys include information for creating a socioeconomic profile of households. Both have an advantage for housing sector analysis in that they focus on households, not physical housing stock units, so that questions about residuals arising from census data are of minor relevance.<sup>19</sup> The study team has analyzed the survey datasets available for five of the six studied countries for the most recent available year. For Lithuania, a specific household survey was performed in 2002 in cooperation with the World Bank with detailed housing sector questions, which qualifies it as a comparator.<sup>20</sup>

**Table 1. Household Survey Data Descriptors**

|**Country**|**Year**|**Survey**|**Households**|**Households**<br>**in urban**<br>**apartments**|**Income proxy**<br>**used**|
|---|---|---|---|---|---|
|Armenia|2001|Integrated Living Standards<br>Survey|4,037|1,738|Expenditure|
|Serbia|2002|Living Standards<br>Measurement Survey|6,386|1,063|Consumption|
|Romania|2002|Household Budget Survey|32,285|11,053|Consumption|
|Russia|2003|NOBUS|44,493|25,223|Expenditure|
|Poland|2000|
a resolutely empirical perspective and focus on the performance of individual firms. While it is undeniable that firms in some countries have been able to benefit greatly from GVCs, the rapid development of the Chinese economy is a case in point, not all countries have had the same experience. We study the economy of Rwanda and focus especially on the manufacturing sector, while also presenting some evidence for other tradable goods sectors, namely agriculture and mining.

The unique perspective of our analysis is that we have access to information for the universe of formally registered firms in Rwanda. We have merged information from three administrative datasets at the firm-level: from payroll, business income taxation, and custom’s. The exhaustive nature of our data allows us to reach conclusions about the breath of engagement in GVCs for the entire set of active firms.

One important finding that surfaces is the remarkable concentration of exports in only a few firms. Freund and Pierola (2015) already highlighted this pattern for 32 developing countries, but our more detailed evidence for Rwanda highlights this even further. Given that we not only observe international trade engagement at the firm-level, but also employment and sales statistics, we are able to show in what way trading firms are different from firms only active domestically. One surprising finding is that sales per employee, a coarse proxy for labor productivity, is not a predictor of export market participation. That is to say, firms which pay payroll taxes, and thus firms for which we observe formal employment, are 2
8 television) and the characteristics of the household’s home (e.g. type of floor and roof) as proposed by Filmer and Pritchett (1999, 2001). The 2012 database also expanded the range of the health data: it included indicators of adult health, including noncommunicable disease (NCD) indicators, covering: health status, e.g. arthritis; risky behavior, e.g. smoking; preventive care, e.g. cervical cancer screening; and receipt of curative care, e.g. inpatient admissions. The 2012 database also expanded the range of countries, going from 95 countries in the developing world to 109 countries at all levels of development. Finally, the 2012 database expanded the scope of the exercise from just health equity to health equity and financial protection: the new indicators included covered both catastrophic health expenditures and impoverishing health expenditures (cf. Wagstaff and van Doorslaer 2003).

<u>Figure 1: Evolution of the World Bank’s Data on Health Equity and Financial Protection</u>

|**2000**|**2007**|**2012**|**2018**|
|---|---|---|---|
|42 countries – all<br>developing<br>countries|56 countries – all<br>developing<br>countries|109 countries –<br>incl. some high‐<br>income|193 countries –<br>goal is global<br>coverage|
|42 surveys – all|95 surveys – all|285 surveys –|1,654 surveys –|
|DHS|DHS|DHS, MICS &<br>WHS|DHS, MICS, WHS,<br>LSMS, HBS, etc.|
|34 indicators – all<br>services or<br>outcomes|115 indicators –<br>all services or<br>outcomes|73 indicators –
the democracy score, hence, the resulting unified polity scale ranges from +10 (strongly democratic) to - 10 (strongly autocratic). The Rule of law Indicator is the lagged rule of law indicator which is one of the six Worldwide Governance Indicators. It captures perceptions of the extent to which agents have confidence in and abide by the rules of society, and in particular the quality of contract enforcement, property rights, the police, and the courts, as well as the likelihood of crime and violence.

We also check the robustness of our results to the informal sector. Blanchard and Leigh (2013) find that errors in official forecasts of fiscal consolidation effects are prevalent across many countries. Such errors, according to Pappa et al. (2015), are caused by the absence of the informal sector in standard models. When taxes increase in these models, agents may opt to work and invest less. Agents do, however, have an alternative option in an economy with an informal sector, which is to shift their activities to the shadow economy. This makes it easier for more resources to leave the formal sector, which causes recorded output and tax income to decline sharply. A recession may turn out to be worse than forecasted, but it may also not be as bad as it seems because the resources that leave the formal sector are employed in the informal sector. As a result, the decline in true (formal plus informal) output can be much smaller than that in recorded output. Dellas et al. (2017) found that the standard modeling practice of overlooking the informal sector is largely responsible for the significant forecast errors associated with the fiscal consolidation in Greece during the Euro Area Debt Crisis. In this sense, informality can affect GDP growth forecast errors, and we account for it using self-employment as a proxy, which is defined as the lag of total self-employed as a percentage of the total employment based on modeled ILO estimates.

Our findings are robust to the inclusion of natural disasters.
<!-- Start of picture text --> Poverty rate (%) ve<br>[_]0- 10 Asa<br>[_j= aa10 -: =20 a \<br>Hm 50-60 oe<br>HM 40 - 50 y<br>Hm 60-70 aA.<br>HiHl 8070-80- 90 ho'? z<br>Hm 90 - 100 es ;<br>conte i»<br>2 ae<br><!-- End of picture text --> <!-- Start of picture text --> L | nationalLower thanpoverty level<br>| | National poverty level<br>‘| Higher than i<br>national poverty level<br>sia!<br>: he<br><!-- End of picture text -->
income share for the top 0.1% of the income distribution saw a remarkable increase, soaring from 5% to over 20%. In contrast, the next 0.4% witnessed a more modest rise in their capital income share, while the capital income share remained relatively stable for the subsequent 0.5%. Due to the increased disclosure of capital income, the income tax paid by the top 0.1% surged by 60% during the period from 2015 to 2021. _Source:_ authors’ calculations using data from AFIP statistical yearbooks.

35
# **5 Concluding remarks**

Despite the increase in quality of household surveys in recent years, most variables of interest to welfare analysts suffer from measurement error: the `outdetect` command focuses on outliers, that is, on values that are so large or so small that they seem “too far away” from the rest of the data. While ‘outlier’ is not necessarily a synonym for ‘error’, in practice their investigation often leads to the identification of gross errors; and even when outliers turn out to be legitimate values, their analysis is informative, and is integral to the initial stages of any data analysis. The literature provides three sets of results that motivate our interest in developing `outdetect` as a tool for detecting outliers and assessing their potential impact on poverty and inequality measures. First, the body of theoretical results that demonstrate the extent to which different inequality and poverty measures are sensitive to the presence of extreme values (Cowell and Victoria-Feser 1996a, 1996b; Victoria-Feser 2000; Cowell and Flachaire 2007, 2015; Cowell and van Kerm 2015). In general, welfare indices have been shown to be highly sensitive to extreme values in the tails of the distribution of income. Second, extreme values are omni-present in household surveys, both in low- and high-income countries (Hlasny and Verme 2018, Hlasny, Ceriani and Verme 2021). Third, while common experience and anecdotal evidence suggest that analysts always inspect their data, and often adjust them in some way, to protect their analyses from the impact of extreme values, these practices are rarely documented. Based on the examination of the documentation accompanying the release of some 200 official poverty and inequality estimates by national statistical offices, Mancini and Vecchi (2022) find that the overwhelming majority does not even mention whether outliers were dealt with, and how.

`outdetect` does not purport to solve the complex and long-standing problem of outlier identification and treatment once and for all. In fact, `outdetect` does not include options to