rafmacalaba/datause-encoder-data huggingface dataset:
# **1. Introduction**

‘The poor can only rely on their labor as their main asset’ is a common claim stressing that the poor generally do not have land or other assets, but can engage in (paid or unpaid) work to meet their needs and hope to escape poverty. In this paper, we will focus on those situations where this is not enough, that is, situations in which people are consumption poor despite devoting very substantial time to work activities. This is what we define as ‘time poverty’—the need to spend long hours working (in either the labor market or in domestic work) because the alternative would be (even deeper) consumption poverty. This approach recognizes that poverty is multidimensional, and that lack of time aggravates consumption or income poverty. Because time can be measured and recorded at the individual level more easily than income or consumption can, this approach also allows for an individual-based measurement of poverty, at least as it relates to time, rather than the traditional household-level one.

In Sub-Saharan Africa, the issue of time use and its relationship to consumption poverty is especially important because of the high workload carried by many. Households have a high probability of being consumption poor, so that any occasion to enable them to make a better livelihood, for example by shifting time from low- to high-productivity activities should be pursued. Furthermore, time use issues have strong gender dimensions, as African women often have to work long hours for domestic chores and the collection of water and wood apart from working in the fields or in other labor market activities. Essentially, when considering domestic work as well as labor market work, the identification of who is time poor is reversed—women become much more time poor than men, and this matters for policy interventions.

The aim of this paper is to refine the definition of ‘time poverty’ that we used in previous work (author’s work, 2006) by combining the concept of time poverty as in ‘working long hours’ (our old definition) with consumption poverty. We obtain measures of time poverty for a population as a whole and for various groups of individuals. Specifically, using data from a 2002-03 survey for Guinea, this paper compares estimates and profiles (univariate and multivariate) of time poverty under our new approach with the estimates that we obtained previously without the monetary poverty constraint imposed as a condition to be time poor. A special focus is placed in the paper on comparisons by gender. We find that women are more likely to be time poor than men, and even more so according to our new definition. We also compare time poverty for different types of individuals, and conduct a simple regression analysis 2
# **1. Introduction**

Financial inclusion, understood as access to and use of affordable financial services by enterprises and households, has gradually become one of the top priorities of recent development and international policy agendas.<sup>2</sup> This interest derives from the recognition of macro and micro socio-economic benefits of financial inclusion, including economic growth, poverty alleviation, inequality reduction and aggregate consumption smoothing (Beck and Demirguc-Kunt, 2008; Bruhn and Love, 2014; Aslan et al., 2017; Burgess and Pande, 2005; Bhattacharya and Patnaik, 2016 among others).

A cursory view of data on global financial inclusion portrays that there has been significant progress in expanding financial inclusion. The World Bank Global Findex report (2021) suggests that account ownership around the world increased by 50 percent in the 10 years spanning 2011 to 2021. This implies that 76 percent of the global adult population in 2021, compared to 51 percent in 2011, have accounts at banks, other financial institutions or mobile money service providers.

However, financial inclusion is not gender-neutral, with large gaps in access between men and women. Worldwide, in 2021, only 74 percent of women had an account at a formal financial institution compared with 78 percent of men. This gender gap of 4 percent in financial access is systematic and persistent (Demirguc-Kunt et al., 2022). Thus, about 740 million women (13 percent of all adults globally and 54 percent of the unbanked) still do not have access to any bank services. These disparities are worrisome for women’s empowerment, including their labor force participation (Gonzales et al., 2015), family welfare (Swamy, 2014) and household savings (Dupas et al., 2018).

Given this statistical evidence, it is crucial to shed light on the factors that might explain the cross-country variation in the financial exclusion of women. Extant literature mostly focuses on assessing microeconomic drivers of female financial inclusion, such as sociodemographic characteristics (Zins and Weill, 2016), financial literacy (Grohmann et al., 2018), individual preferences (Beck et al., 2018) or educational level, employment status and income (Aterido et al., 2013; Ghosh and Vinod, 2017). Nonetheless, country-level factors – and more

> 2 For instance, the foundational document of the global Alliance for Financial Inclusion (AFI), the 2011 Maya Declaration, asserts that all its members (more than 80 economies) “recognize the critical importance of financial inclusion to empowering and transforming the lives of all our people, especially the poor, its role in improving national and global financial stability and integrity and its essential contribution to strong and inclusive growth in developing and emerging market countries.”

2
25 This paper proposes two alternative statistical approaches to circumvent this missing data problem, for those cases where a Demographic and Health Survey (DHS) is available. The first approach relies on the DHS alone, and uses a “wealth index” as the Roemer _advantage_ variable. This index is computed as a principal component of a vector of assets and durable goods owned, housing characteristics and access to amenity indicators. The second approach relies on an additional, ancillary survey, and imputes a measure of consumption from that survey into the DHS.

Once these advantage variables are constructed, we apply an intuitive measure of inequality of opportunity developed in a companion paper (Ferreira and Gignoux, 2008) to their distributions: the “between-type inequality share”. The measure relies on a partition of the population by a small set of observed circumstances which can be confidently interpreted as completely independent of individual choices: region and area of birth, the educational attainment of both parents, mother tongue, and the number of siblings a person grew up with. Because this is an incomplete set of circumstances, the inequality share is interpreted as a lower bound on inequality of opportunity.

Since the wealth index and the imputed consumption distributions are rather different statistical constructs, different versions of the between-type inequality share are calculated for each indicator. A ratio of variances is used for the zero-mean wealth-index distribution, while a ratio of mean-log deviations is used for the distribution of imputed consumption. These measures are estimated both parametrically and non-parametrically, but the parametric approach yields preferable lower-bound estimators, given sample-size restrictions.

In an application of these methods to the sample of ever-married women aged 3049 in Turkey, we found that inequality of opportunity accounts for at least 26% of total inequality in predicted consumption, and 31% of total inequality in the wealth index. We attribute the difference between these two numbers primarily to the greater transitory or unexplained heterogeneity that is present in the consumption, but not in the wealth measure. This is consistent with the fact that the between-type inequality share is much higher for imputed _predicted_ consumption (i.e. without imputed residuals). Nonparametric estimates are higher for both advantage indicators.
considered in a wider range of data collection. It also hints that there are some glitches when translating the experience of research guidelines in clinical environments into field work undertaken by economic and other social sciences.<sup>1</sup> We group these issues into three broad categories, cautioning the reader that more often than not, we pose questions but have relatively little to say definitive about potential solutions.

Our first category considers the objectives of balancing transparency and confidentiality. Researchers familiar with the act of conducting a survey in a remote village are aware that there is seldom a single “respondent”. Even in the confines of his or her home, the survey respondent typically faces the entire family, with neighbors “dropping in” and a curious set of faces looking in from the window. How to maintain confidentiality in these settings, and what confidentiality _means_ in a context where certain information is public—and therefore “OK” to share—but other information is not (and the researchers seldom knows which is which) is an ethically complicated question. The ethical ambiguity is heightened in cases where such confidential information implicates certain members of the family in behaviors that, at least in the eyes of the surveyor, fall outside the moral norms of society. We present a specific case study—surveys on domestic abuse— that takes on this issue.

The second category relates to trust between the surveyor and the surveyed and again goes to the issue of informed consent, and the sharing of information from the survey with the surveyed population. There are three fundamental issues that we address in this context. First, surveys are never isolated from the social history of the surveyed population. For instance, _any_ survey on vaccinations among certain populations in India will have to tackle the surveyed populations’ perceptions, and in particular, whether they believe that the survey will lead to

> 1 Stark (2012), however, reports that the question of obtaining signatures of informed consent – not of consent _per se_ – was also controversial when the National Institute of Health was drafting its guidelines for research on human subjects.

3
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annual fuel cost in line 12. Combining lines 12 and 14, we calculate the total direct opportunity cost per year (line 15).

To make the calculations for the ICS, we apply the heat-equivalence method described in footnote 9. Lines 3 and 5 give the energy content and the stove heat transfer efficiency ( _HC_ and _SE_ ) of the traditional stove and ICS. Applying this equation along with the total fuel use for the traditional stove in line 10 (the parameter Q0 in footnote 9), we can calculate the total fuel use per year for the ICS. From there the calculations proceed as described above. Of special note is the importance of the implicit (i.e., “shadow”) value of cooking time. Women spend a lot of time on cooking and therefore small changes in daily cooking time, depending on the value used, can have big effects. In some scenarios improved stoves reduce cooking time sufficiently to offset higher levels of other costs.

Table 3 presents levelized direct costs for six different combinations of stoves and fuels, including electric stoves, based on the information in Jeuland and Pattanayak (2012). The columns labeled stove+fuel costs omit the opportunity cost of cooking time, in order to highlight the importance of that component.

**Table 3.  Comparison of Annual Direct Costs for Different Fuel-and-Stove Combinations**

|Technology|Stove+F|uel Cost ($|/yr)|Total<br>Cookin|Direct Cost<br>g Time ($/yr)|including|
|---|---|---|---|---|---|---|
||Low|Mid|High|Low|Mid|High|
|Traditional wood|14|74|420|109|293|1150|
|Improved wood|25|103|470|92|311|1565|
|Traditional charcoal|8|91|451|64|255|1181|
|Improved charcoal|8|81|303|65|245|1033|
|Kerosene|15|67|200|63|220|857|
|Propane|63|123|250|107|269|907|
|Electricity|108|374|789|142|512|1446|

Calculations based on Jeuland and Pattanayak (2012).

As can be seen from Table 3, the ranges of potential direct costs for each technology are quite large. The figures suggest that in general more advanced technologies tend to be less costly, because of improved thermal efficiency and higher energy density, even though capital costs and fuel costs may be higher. Less anticipated perhaps is the very wide range of costs. The figures also emphasize the substantial impact of cooking times on opportunity costs.

Tables 4 and 5 present estimated differences in the cost of acute respiratory illness induced by exposure to household air pollutants. Table 4 shows the relative capabilities to reduce acute respiratory illness of the other technologies in Table 3, compared to traditional wood stoves. To translate these relative risk reductions into economic benefits per household, we need the baseline incidence of acute respiratory illness <u>(number of cases per person per year), to which the</u> **10 |** P a g e
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36
progressions over the span of our data, in contrast to lower grades, which is similar to trends in senior management pay observed in the United States over the period.

**Table 1: Summary of Salaries by Grade**

|||||**Fisc**|**al Year**|||
|---|---|---|---|---|---|---|---|
|**Grade**||1987-90|1991-95|1996-00|2001-05|2006-10|2011-15|
|**GA**|mean salary (GA in 1987 = 100)|100|103|96|100|101|99|
||Number of staff|122|62|72|74|34|8|
||p90-p10 % difference|36%|27%|30%|29%|26%|13%|
|**GB**|mean salary (GA in 1987 = 100)|112|116|116|118|121|119|
||Number of staff|2600|3099|1828|1068|381|253|
||p90-p10 % difference within grade|32%|27%|25%|23%|26%|26%|
|**GC**|mean salary (GA in 1987 = 100)|141|150|148|149|154|153|
||Number of staff|4,859|6,212|6,159|5,156|4155|3718|
||p90-p10 % difference within grade|41%|38%|38%|33%|33%|36%|
|**GD**|mean salary (GA in 1987 = 100)|171|185|185|182|188|189|
||Number of staff|1093|2088|2478|3642|3506|3084|
||p90-p10 % difference within grade|36%|33%|38%|39%|36%|36%|
|**GE**|mean salary (GA in 1987 = 100)|193|218|217|214|219|220|
||Number of staff|2618|2719|2917|4000|3917|4007|
||p90-p10 % difference within grade|37%|32%|40%|38%|37%|33%|
|**GF**|mean salary (GA in 1987 = 100)|256|275|273|278|282|284|
||Number of staff|1822|2944|3522|6,017|7,084|8,615|
||p90-p10 % difference within grade|33%|23%|26%|29%|28%|28%|
|**GG**|mean salary (GA in 1987 = 100)|362|378|372|377|389|390|
||Number of staff|7,644|10,746|10,323|9,973|11,813|13,842|
||p90-p10 % difference within grade|43%|42%|39%|32%|33%|36%|
|**GH**|mean salary (GA in 1987 = 100)|457|492|484|501|534|539|
||Number of staff|2901|4428|5407|6,161|6,741|8,008|
||p90-p10 % difference within grade|24%|26%|33%|31%|31%|35%|
|**GI**|mean salary (GA in 1987 = 100)|547|610|610|644|698|712|
||Number of staff|548|743|920|1168|1160|1224|
||p90-p10 % difference within grade|20%|17%|21%|22%|22%|26%|
|**GJ**|mean salary (GA in 1987 = 100)|619|706|704|782|851|877|
||Number of staff|69|100|160|188|152|163|
||p90-p10 % difference within grade|23%|9%|16%|19%|17%|17%|

Figure 4 shows the evolution of mean salaries over time for different grades at the WBG more clearly, normalizing each salary to a base of 100 in 1987. Like in Table 1, mean real salaries have increased more for grades GI, GJ and GK compared to GF and GG staff. Annual real salaries increased by 3/10th of 1 14
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