Figure 5. Percent of adult population (ages 15+) without a financial account by gender, 2017 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% 98% 89% 80% 74% 80% 73% 84% 83% 73% 72% 71% 73% 66% 67% 61% 59% 54% 44% 44% 43% 40% 29% Yemen Iraq Syria (2011) West Bank & Gaza Egypt Morocco Tunisia Algeria Jordan Lebanon Libya Male Female Source: The Global Findex Database 2017. Several factors contribute to the gender disparity in financial inclusion. One is that women in the MENA region are 9 percent less likely than men to have a mobile account. This translates into 25 million fewer women than men owning a mobile phone (Rowntree 2019). Barriers to phone ownership include affordability and lack of digital skills. Lower levels of phone ownership can compound gender inequalities, limiting economic opportunities for women. Women are also less likely on average to use mobile internet (Rowntree 2019). Cultural and economic norms also contribute to the gap in financial account ownership. In 2017, the rate of women’s participation in the labor force in the MENA region was only 22 percent, reflecting traditional roles for men as breadwinners in the family (Rachidi 2019). Women also face gender-specific barriers accessing loans from banks due to property laws that affect their use of collateral and discrimination from bank officials (Vital Voices 2012). Many women in the Arab region lack freedom to move outside the home, limiting their ability to sign up for accounts or visit agents to conduct transactions (GIZ 2017). Additional barriers to account ownership include women’s lower levels of education, with a gender gap in literacy of 14 percentage in the MENA region (Wahdwa 2019). Egyptian policy makers are taking steps to increase financial account ownership by women. Egypt’s Vision 2030 sets women’s financial inclusion and economic empowerment at the “heart of the national development reform agenda” (CBE and AFI 2019). Compared with men, women are 10 percentage points less likely in Egypt to have a financial account (Xxxxxxxx-Xxxx et al. 2018). To address the gender gap, the Central Bank of Egypt has developed a roadmap for women’s financial inclusion. Priority areas include: obtaining accurate gender-disaggregated data from banks, expanding the reach of DFS, and encouraging the use of e-payments (CBE and AFI 2019). The Central Bank and the National Council for Women signed a Memorandum of Understanding (MoU) to cooperate in empowering Egyptian women economically and financially, and...
Figure 5. Conditional inference tree, predicting the empirical log-odds ratios; accuracy with subject-verb agreement in 2SG and 3SG contexts in main clauses with inversion (AdvVS) and without inversion (SVO). HomeLanguage L2Proficiency VerbForm Child 0.00 0.05 0.10 0.15 Variable importance Figure 6: Variable importance plot of predictors (random forest); accuracy with subject-verb agreement in 2SG and 3SG contexts in main clauses with inversion (AdvVS) and without inversion (SVO).
Figure 5. Infant mortality rate per 1,000 live births: Bulgaria and comparators; 1980-2012 Figure 6: Maternal mortality ratio per 100,000 live births: Bulgaria and comparators; 1990-2010
Examples of Figure 5 in a sentence
Note that a portion of the bank in Reach MM that was not initially identified as a candidate bank area will be included in the proposed BFC sampling as it is adjacent to two other candidate banks and situated within the same geomorphic unit as the adjacent banks (see Figure 5).
Figure 5: Main dialog of Contract Wizard Because .NET supports overloading it is possible that a class contains more than one feature with the same name but different signature.
Figure 4 Status of SMEs in Ireland relative to EU average Source: 2014 SBA Factsheet Ireland Figure 5 Employment in SMEs Investment in SMEs presents synergies with a range of thematic objectives to drive the Irish economy and impact upon employment and welfare levels.
Binary recursive partitioning (Figure 5) indicates that home language was the primary predictor for children with an L2 proficiency score equal to or lower than 23.
Due to the length of the Reach LL/MM bank (see Figure 5), sub-samples from each vertical composite collected within each of the six model grids within this area will be combined and archived for potential future analysis (if necessary).
More Definitions of Figure 5
Figure 5. Byzantine k-set agreement based on SMV-broadcast, and local random coins (Algorithm 5) 39 Each round r executed by a process pi is made up of two phases. During the first phase of round r, 40 each correct process pi invokes SMV_broadcast(esti) (multiset version) and stores the multiset returned 41 by this invocation in viewi[r, 1]. Let us remind that this multiset contains only values SMV-broadcast 42 by at least one correct process. The aim of this phase is to build a global set 2, denoted AUX [r], which 44 contains at most (k + 1) values, such that at most k of them are contributed by correct processes, and 45 the other one is the default value ⊥. To this end, each correct process pi checks if there is a value v 46 that appears “enough” (say W ) times in the multiset viewi[r, 1]. If there is such a value v, pi adopts it 47 (assignment aux → v), otherwise it adopts the default value ⊥ (line 5). 48 The set AUX [r] is made up of the aux variables of all the correct processes. For AUX [r] to contain 50 at most k non-⊥ values, W has to be such that (k +1)W > n (there are not enough processes for (k +1) 51 different values such that each of them was contributed by W processes3. Hence, W > n/(k + 1).4 53 2While the value of this set could be known by an external global observer, its value can never be explicitly known by a 54 correct process. However, a process can locally build an approximation of it during the second phase. 55 3Let us remind that, due to the ND-broadcast used in the algorithm implementing SMV-broadcast, two correct processes 56 cannot ND-deliver different values from the same Byzantine process.
Figure 5. Timeline of initiatives' implementation for Pillar 2 Moving forward, we depict the distribution of the initiatives of Pillar 2 according to their “Feasibility” and “Necessity” scoring and we identify the set of “Quick Wins”, which refer to initiatives that demonstrate high Feasibility (therefore they are easier to be implemented) and high Necessity (therefore they are expected to have a significant impact towards Greek Industry’s rotation to Industry 4.0). These initiatives are the ones that fall under Q2 as also explained in the Figure below.
Figure 5. The induced voltage in the primary winding versus time.
Figure 5. Correlation of the type-specific stressor index with the multimetric index LIMCO (Littoral Invertebrate Multimetric Index based on Composite Sampling) for the 4 biogeographical regions (D = Germany / DK = Denmark, IRL = Ireland / GB = United Kingdom, S = Sweden / FIN = Finland, IN = northern Italy / IC = central Italy). Table 7: Selected candidate metrics for the 4 biogeographical regions (D = Germany / DK = Denmark, IRL = Ireland / GB = United Kingdom, S = Sweden / FIN = Finland, IN = northern Italy / IC = central Italy). X denotes core metrics for the final MMI variant: TFC = taxonomic and functional composition, D = diversity, A = abundance, DST = disturbance sensitive taxa. AC = abundance class.] Biogeographical region D/DK IRL/GB S/FIN IC/IN Rho -0.70 -0.47 -0.39 -0.49 Candidate metric Metric type Margalef Diversity D X X X No. Families X Shredders % AC TFC X Gatherer/Collectors % X Gatherer/Collectors % AC X r/K relationship X Odonata % A,TFC X Chirononomidae % AC X Diptera % AC X Crustacea % AC X No. ETO Taxa DST X X No. EPTCBO Taxa X No. Odonata Taxa X
Figure 5. Vehicle Licence without Licence Disc and Roadworthy Certificate
Figure 5. Response of a field faceting search The HTTP request below provides an example for query faceting. The query searches for records containing the term "xxxxx" and asks faceting counts based on different file size ranges. The file size has been configured as a numeric value (of type long) in the field "size" of the index schema. xxxx://XXXXXXX/xxxx/xxxxxxx/xxxxxx?x=xxxxx&xxxxx=xxxx&xxxxx.xxxxx=xxxx:[*%00XX%0000 00]&facet.query=size:[1000%20TO%2010000]&facet.query=size:[10000%20TO%20100000] &facet.query=size:[100000%20TO%20*]
Figure 5. Tag cloud based on frequency of different categories of response to question 10.