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16 changed files with 561 additions and 119 deletions
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@ -236,5 +236,5 @@ The amount of overall development contributions to electrification increases fro
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with significant increases in 2013 and 2015 for loans and 2019, 2020 for grants.
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While there is a steady increase to the overall development aid toward electrification,
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increases in grants tend to lag behind increases in loans for Benin,
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with grants exceeding 10mil. USD for the first time in 2019 while loans already reached 18.90mil. USD in 2013.
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with grants exceeding 10m USD for the first time in 2019 while loans already reached 18.90m USD in 2013.
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Over the complete period of 2011 to 2020, however, grants for the transmission and distribution of electric power in Benin have consistently been lower than loans.
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@ -83,13 +83,13 @@ leaving a budget of 5% for health and 3% for social expenditures,
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spendings which looks diminutive compared to its over 30% expenditures on public infrastructure [@WorldBank2022c].
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Only 10% of rural poor inhabitants live close (under 1km) to a health facility [@Ibarra2020].
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### Gender inequalities in livelihood opportunities
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While still facing reduced rates of labor market participation, the country has expended effort on increasing women's opportunity for education:
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Having overall lower literacy rates for women still,
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the overall literacy rates in younger cohorts (10-24 years old) is significantly higher compared to older ones,
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and the gaps have decreased from 24% difference between the genders (40-60 years old) to 10% (15-24 years old) and 2% (10-14 years old) [@Mendiratta2019].
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### Gender inequalities in livelihood opportunities
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Women's lower secondary completion rate grew from 28.6% in 2009 (compared to 35.2% men) to 56.3% in 2021 (54.0% for men) [@WorldBank2022d].
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However, for 2017, women's upward educational mobility was still significantly worse than men's,
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with non-poor men having an upward mobility of 53%, non-poor women 29%, poor men 19% and poor women only 10% against the national average of 36% [@Mendiratta2019].
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@ -99,6 +99,18 @@ where 22.3% of all firms have female participation in ownership and only 14.2% a
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and both salaried employment and agricultural employment are male-dominated
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(though agricultural work only with a slight and shrinking difference of 4%) [@WorldBank2022d].
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The official number of procedures to register a business are the same for men and women,
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as are the time and cost required for business start-up procedures [@WorldBank2020],
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however, there are factors which may further inhibit equal female business participation and ownership:
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while women have the same legal rights in access to credit, contractual and financial instruments as men [@WorldBank2022f],
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women have an overall lower account ownership rate at financial institutions with 8.8% compared to men's 16.6% (2011)
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reflecting itself especially in a lower access to debit cards at institutions [@WorldBank2021a, @WorldBank2022g].
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As mentioned above, women have a lower participation rate on the labor market with an especially stark gender difference in the industrial sector ---
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a sector of the economy in which women in Djibouti do not have the same rights to participate in as men, especially in jobs deemed dangerous [@WorldBank2022f] ---
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with service being the sector that makes up the greatest share of female labor participation (71.1% of all female labor compared to 56.0% of all male labor 2019),
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a sector which is also driving the high share of women in vulnerable employment (41.4% of female labor in 2019) [@WorldBank2022g].
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Overall it seems, however, that past growth in the country's GDP is likely not favorable for an inclusive growth path,
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with its large-scale infrastructure investments mostly creating demand for skilled workers
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and neglect of social spending not allowing the buffers and social safety nets that prevent further drift into inequality.
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@ -172,12 +184,12 @@ The primary type of development assistance provided are grants, with loans makin
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Grants have trended slowly upwards from just over 100m USD in 2011 to 135m in 2014,
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before fluctuating around this level until 2017,
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and finally increasing more significantly to over 200m USD in 2020.
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Loans had a more significant jump earlier, from there relatively stagnant level of under 40m USD in 2014 to 80m USD in 2015,
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Loans had a more significant jump earlier, from a relatively stagnant level of under 40m USD in 2014 to 80m USD in 2015,
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with a similarly significant jump from 2018 to 2019,
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before decreasing slightly again to just over 110m in 2020.
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before decreasing slightly again to just over 110m USD in 2020.
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While largely comprising less than 10m USD until 2018,
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other official flows (non-export credits) had a large increase to over 75m USD in 2019,
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be decreasing almost as significantly again the following year.
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but decreasing almost as significantly again the following year.
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```{python}
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#| label: fig-dji-aid-donortype
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@ -288,7 +300,7 @@ The table is broken down into four sectors of development aid which drive the po
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First, trade development encompasses trade policy and administrative management, trade facilitation, regional trade agreements, multilateral trade negotiations, trade-related adjustments and trade education and training.
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Second, business growth is the combination of business policy and administrative management, privatization, business development services as well responsible business conduct ---
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meaning the establishing of policy reform, implementation and enforcement of responsible business conduct, including, among others, implementation of guidelines for human rights support.
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meaning the establishing of policy reform and implementation and enforcement of responsible business conduct, including, among others, implementation of guidelines for human rights support.
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Third, and specifically aimed at the inclusion of women in economic activities,
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is the support for women's rights which includes the establishment of, and assistance for, women's rights organizations and institutions to enhance their effectiveness, influence and sustainability.
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And last, the provision for and protection of food security for those in vulnerable positions,
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@ -296,7 +308,7 @@ through capacity strengthening and household-level food security programmes, sho
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excluding emergency food assistance measures (such as for disaster crisis affected households).
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The amount of aid contributions into these sectors of inclusive growth in Djibouti is small in comparison with development assistance to the other countries analyzed.
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The absolute amount of contributions has consistently stayed under 10m USD per year for all four sectors combined,
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The absolute amount of contributions has consistently kept under 10m USD per year for all four sectors combined,
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though an overall growth trend is visible from 0.5m USD in 2011 to 1.6m USD in 2016 and more rapid growth in 2020 to just under 10m USD.
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Most of this recent growth in 2020 is driven by contributions to trade development with 7.7m USD,
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while business growth and women's rights support are seeing much smaller contributions yet.
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@ -48,9 +48,9 @@ with Western Ugandan households increasing in poverty while Northern and Eastern
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Additionally, they find that while transient poverty is more common than chronic poverty in Uganda,
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nearly 10% of households continue to live in persistent material deprivation.
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Lastly, for a long time it has been seen as an issue that Uganda puts its national poverty line too low with the line being put between 0.94 USD PPP and 1.07 USD PPP depending on the province (lower than the international live of 1.90 USD PPP),
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while @vandeVen2021 estimate a living income of around 3.82 USD PPP would be required for a national poverty line that meets basic human rights for a decent living.
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<!-- TODO find a source for the national poverty line being too low (quant data is already in vandeVen2021) -->
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Lastly, for a long time it has been seen as an issue that Uganda puts its national poverty line too low with the line being put between 0.94 USD (2011 PPP) and 1.07 USD (2011 PPP) depending on the province (lower than the international live of 1.90 USD PPP),
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while @vandeVen2021 estimate a living income of around 3.82 USD (2011 PPP) would be required for a national poverty line that meets basic human rights for a decent living.
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In absolute terms, the bottom 40% of Uganda had a median daily income of 1.28 USD (2011 PPP) in 2016 which kept stable to 2019 [@WorldBank2022e].
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<!-- endowment/assets: education, ..? -->
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Esaku [-@Esaku2021; -@Esaku2021a] finds a somewhat circular driving relationship between Ugandan inequality, poverty and working in what calls the shadow economy:
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@ -206,7 +206,7 @@ The Official Development Assistance overall further increased to over 2.2bn USD
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before rapidly increasing in 2020 to over 3.0bn USD.
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The overall trend of increasing aid money is largely due to increases in development grants which especially increased from 2015 to 2017.
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In general, development loans play a smaller role in absolute terms:
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Whereas in 2011 around 1.2bn USD funds came in the form of grants, only around 0.3bn USD were in the form of loans.
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Whereas in 2011 around 1.2bn USD funds came in the form of grants, only around 300m USD were in the form of loans.
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The absolute portion of loans slowly increased until 2019 to just over 500m USD,
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before significantly increasing in 2020, tripling to almost 1.5bn USD.
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@ -244,7 +244,7 @@ Source: Author's elaboration based on OECD ODA CRS (2022).
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In terms of predominant donor types, bilateral aid to Uganda was much higher than multilateral aid to the country until 2019.
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In 2011 only about 400m USD were provided through multilateral donors while almost 1.2bn USD were provided via bilateral donors,
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though the multilateral contributions quickly grew to over 600m USD in 2013.
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Despite a significant decrease of multilateral aid in 2014, the amount of multilateral aid kept generally stagnant until 2018,
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Despite a single significant decrease of multilateral aid in 2014, the amount of multilateral aid kept generally stagnant until 2018,
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when the amount first increased to 800m USD in 2019 and subsequently to over 1.7bn in 2020.
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```{python}
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@ -300,7 +300,7 @@ Source: Author's elaboration based on OECD ODA CRS (2022).
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The breakdown of development aid to water supply infrastructure and education projects can be seen in @tbl-uga-aid-watersupply.
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It shows that overall the contributions to improve water access have been increasing, starting at 42.27m USD in 2011 and climbing to 146.43m USD by 2020.
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The development funds are broken down into three categories:
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Basic and large water supply improvement and education and training.
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Basic water supply improvement, large water supply improvement and education and training.
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Education and training encompasses training for both professionals in the field itself and service providers.
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Water supply improvement is broken down into funds for large systems ---
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@ -313,7 +313,7 @@ with larger-scale sewage pumping stations and trunk sewers, as well as smaller o
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This is due to most infrastructure projects missing the concrete dimensions to separate water supply from sanitation in the data,
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either due to infrastructural overlap or missing data points.
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The split shows that while basic water supply infrastructure projects have tended to be contributed to between 10m USD and 20m USD,
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The split shows that while basic water supply infrastructure projects tended to see contributions between 10m USD and 20m USD,
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with little overall increase from 2011 to 2020.
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Large-scale water supply and sanitation projects have, however, seen a significant increase over time, starting at a contribution of 17m USD in 2011 and receiving a 125.15m USD contribution in 2020.
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This may speak to the necessity of larger infrastructure in place before more basic water supply infrastructure can make use of it,
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@ -27,6 +27,7 @@ concomitant with low education and skills, more prevalent dependency on subsiste
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The country's overall estimated Gini coefficient for income fluctuates between 0.42 and 0.44 between 2010 and 2018, with the highest levels of income inequality observed in the Central Highlands in 2016,
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though absolute income may be rising, with the top quintile having 9.2 times the income of the lowest quintile in 2010 and 9.8 times in 2016 [@Le2021].
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On the other hand, the bottom 40% experienced a slight absolute rise in mean income per capita from 4.00 USD (2011 PPP) in 2014 to 5.00 USD (2011 PPP) in 2018 [@WorldBank2022e].
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For Gini coefficients estimated using consumption per capita, see @fig-vnm, which shows similar trends of increasing inequality, with 2010 constituting a significant increase.
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Economic inequality and poverty in Vietnam thus underlies an intersectional focus, between ethnic minorities, regional disparities, rural-urban divides and gendered lines,
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one which exogenous shocks can rapidly exacerbate as the example of the COVID-19 pandemic has recently shown [@Ebrahim2021].
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@ -276,8 +277,8 @@ Source: Author's elaboration based on OECD ODA CRS (2022).
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Bilateral donor contributions make up the largest part of development aid contributions to Vietnam, as can be seen in @fig-vnm-aid-donortype.
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Both bilateral and multilateral contributions increase from 2011 to 2014 and subsequently begin decreasing.
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While bilateral contributions do not increase in absolute amounts afterwards until 2020,
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multilateral contributions increase again from 2019 to 2020.
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While bilateral contributions do not increase in absolute amounts afterwards, until 2020,
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multilateral contributions do increase again from 2019 to 2020.
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Nevertheless, bilateral contributions are consistently higher than multilateral,
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having around a 1.5 times higher share of absolute USD contribution,
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though growing to just over 2 times the share in 2017,
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@ -356,7 +357,7 @@ From the level of 96m USD in 2011,
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access to basic water supply saw significant increases to its contributions from 2013 to 2016,
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with 154m USD contributed at its peak in 2016 and shrinking drastically the following years to 39m USD in 2019,
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its lowest contribution year.
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Large water supply project contributions see a similar if less drastic curve, with contributions increasing from 105m USD in 2011 to 252m USD at their in 2018, before decreasing slightly over the next two years.
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Large water supply project contributions see a similar if less drastic curve, with contributions increasing from 105m USD in 2011 to 252m USD at their peak in 2018, before decreasing slightly over the next two years.
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Thus, the contribution curves to basic and large-scale water supply projects somewhat follows the overall development aid contribution curve to Vietnam,
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with peaks between 2016 and 2018 before more or less drastic drops in aid contributions.
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4
data/cleaned/WB-GPSP/selected_countries.tsv
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data/cleaned/WB-GPSP/selected_countries.tsv
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@ -0,0 +1,4 @@
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Global Database of Shared Prosperity and Median Income/Consumption, circa 2014-2019
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As of April 30, 2022
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EAP VNM Vietnam 2014-2018 c 5.753333 6.253831 6.463288 4.0007582 6.8647929 8.3938988 5.0040145 8.7499553 10.783587 2011 2014 2018
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SSA UGA Uganda 2016-2019 c 0.0490267 0.3071832 -0.2575038 1.2790791 2.2091904 3.2032306 1.2810492 2.2305694 3.1774018 2011 2016.5 2019.64
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Can't render this file because it has a wrong number of fields in line 2.
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data/cleaned/WB-GenderStatistics/empty_removed.tsv
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data/cleaned/WB-GenderStatistics/empty_removed.tsv
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@ -0,0 +1,48 @@
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Series Name Series Code Country Name Country Code 2002 [YR2002] 2003 [YR2003] 2004 [YR2004] 2005 [YR2005] 2006 [YR2006] 2007 [YR2007] 2008 [YR2008] 2009 [YR2009] 2010 [YR2010] 2011 [YR2011] 2012 [YR2012] 2013 [YR2013] 2014 [YR2014] 2015 [YR2015] 2016 [YR2016] 2017 [YR2017] 2018 [YR2018] 2019 [YR2019] 2020 [YR2020] 2021 [YR2021]
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A woman can get a job in the same way as a man (1=yes; 0=no) SG.GET.JOBS.EQ Djibouti DJI 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
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A woman can register a business in the same way as a man (1=yes; 0=no) SG.BUS.REGT.EQ Djibouti DJI 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
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A woman can sign a contract in the same way as a man (1=yes; 0=no) SG.CNT.SIGN.EQ Djibouti DJI 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
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A woman can work at night in the same way as a man (1=yes; 0=no) SG.NGT.WORK.EQ Djibouti DJI 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
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A woman can work in a job deemed dangerous in the same way as a man (1=yes; 0=no) SG.DNG.WORK.DN.EQ Djibouti DJI 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
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A woman can work in an industrial job in the same way as a man (1=yes; 0=no) SG.IND.WORK.EQ Djibouti DJI 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
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Account ownership at a financial institution or with a mobile-money-service provider, female (% of population ages 15+) FX.OWN.TOTL.FE.ZS Djibouti DJI .. .. .. .. .. .. .. .. .. 8.76278209686279 .. .. .. .. .. .. .. .. .. ..
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Account ownership at a financial institution or with a mobile-money-service provider, male (% of population ages 15+) FX.OWN.TOTL.MA.ZS Djibouti DJI .. .. .. .. .. .. .. .. .. 16.6257038116455 .. .. .. .. .. .. .. .. .. ..
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Cost of business start-up procedures (% of GNI per capita) IC.REG.COST.PC.ZS Djibouti DJI .. .. .. 279.6 267 251.6 245.2 240.1 214.9 214.8 195.7 214.2 202.7 196.6 156 58.8 41.9 39.7 .. ..
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Cost of business start-up procedures, female (% of GNI per capita) IC.REG.COST.PC.FE.ZS Djibouti DJI .. .. .. 279.6 267 251.6 245.2 240.1 214.9 214.8 195.7 214.2 202.7 196.6 156 58.8 41.9 39.7 .. ..
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Cost of business start-up procedures, male (% of GNI per capita) IC.REG.COST.PC.MA.ZS Djibouti DJI .. .. .. 279.6 267 251.6 245.2 240.1 214.9 214.8 195.7 214.2 202.7 196.6 156 58.8 41.9 39.7 .. ..
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Credit card ownership, female (% age 15+) fin7.t.a.2 Djibouti DJI .. .. .. .. .. .. .. .. .. 3.49554419517517 .. .. .. .. .. .. .. .. .. ..
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Credit card ownership, male (% age 15+) fin7.t.a.1 Djibouti DJI .. .. .. .. .. .. .. .. .. 4.55562686920166 .. .. .. .. .. .. .. .. .. ..
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Credit card ownership (% age 15+) fin7.t.a Djibouti DJI .. .. .. .. .. .. .. .. .. 3.96891236305237 .. .. .. .. .. .. .. .. .. ..
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Debit card ownership (% age 15+) fin2.t.a Djibouti DJI .. .. .. .. .. .. .. .. .. 7.60348653793335 .. .. .. .. .. .. .. .. .. ..
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Debit card ownership, female (% age 15+) fin2.t.a.2 Djibouti DJI .. .. .. .. .. .. .. .. .. 5.89633560180664 .. .. .. .. .. .. .. .. .. ..
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Debit card ownership, male (% age 15+) fin2.t.a.1 Djibouti DJI .. .. .. .. .. .. .. .. .. 9.71940994262695 .. .. .. .. .. .. .. .. .. ..
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Employment in services, female (% of female employment) (modeled ILO estimate) SL.SRV.EMPL.FE.ZS Djibouti DJI 52.2400016784668 53.2999992370605 54.3699989318848 55.4500007629395 56.5999984741211 57.7799987792969 58.9799995422363 60.0200004577637 61.1199989318848 62.3300018310547 63.4199981689453 64.4899978637695 65.5999984741211 66.7399978637695 67.8300018310547 68.8499984741211 69.9899978637695 71.0599975585938 .. ..
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Employment in services, male (% of male employment) (modeled ILO estimate) SL.SRV.EMPL.MA.ZS Djibouti DJI 44.2700004577637 44.8800010681152 45.4700012207031 46.0900001525879 46.8499984741211 47.6399993896484 48.4799995422363 48.939998626709 49.5900001525879 50.439998626709 51.060001373291 51.6599998474121 52.4000015258789 53.1599998474121 53.8699989318848 54.4700012207031 55.2799987792969 56.0499992370605 .. ..
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Employment in industry, male (% of male employment) (modeled ILO estimate) SL.IND.EMPL.MA.ZS Djibouti DJI 16.8600006103516 16.7399997711182 16.6499996185303 16.5699996948242 16.5400009155273 16.5200004577637 16.5200004577637 16.5300006866455 16.5799999237061 16.6700000762939 16.7700004577637 16.9099998474121 17.0799999237061 17.25 17.4099998474121 17.5599994659424 17.7000007629395 17.8099994659424 .. ..
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Employment in industry, female (% of female employment) (modeled ILO estimate) SL.IND.EMPL.FE.ZS Djibouti DJI 8.60999965667725 8.47999954223633 8.35000038146973 8.22999954223633 8.10000038146973 7.96999979019165 7.82000017166138 7.76000022888184 7.65999984741211 7.53000020980835 7.44000005722046 7.34999990463257 7.28999996185303 7.19000005722046 7.09999990463257 7.03999996185303 6.90000009536743 6.80000019073486 .. ..
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Employment in industry (% of total employment) (modeled ILO estimate) SL.IND.EMPL.ZS Djibouti DJI 13.7299995422363 13.5799999237061 13.4399995803833 13.3400001525879 13.2700004577637 13.210000038147 13.1599998474121 13.1400003433228 13.1300001144409 13.1300001144409 13.1499996185303 13.1800003051758 13.25 13.3000001907349 13.3500003814697 13.3999996185303 13.4200000762939 13.4399995803833 .. ..
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Employment in agriculture, male (% of male employment) (modeled ILO estimate) SL.AGR.EMPL.MA.ZS Djibouti DJI 38.8699989318848 38.3699989318848 37.8800010681152 37.3300018310547 36.5999984741211 35.8400001525879 35 34.5299987792969 33.8400001525879 32.8899993896484 32.1599998474121 31.4300003051758 30.5200004577637 29.5900001525879 28.7299995422363 27.9699993133545 27.0200004577637 26.1399993896484 .. ..
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Financial institution account (% age 15+) fin1.t.a Djibouti DJI .. .. .. .. .. .. .. .. .. 12.2738819122314 .. .. .. .. .. .. .. .. .. ..
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Financial institution account,female(% age 15+) fin1.t.a.2 Djibouti DJI .. .. .. .. .. .. .. .. .. 8.76278209686279 .. .. .. .. .. .. .. .. .. ..
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Financial institution account,male(% age 15+) fin1.t.a.1 Djibouti DJI .. .. .. .. .. .. .. .. .. 16.6257038116455 .. .. .. .. .. .. .. .. .. ..
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Men and women have equal ownership rights to immovable property (1=yes; 0=no) SG.OWN.PRRT.IM Djibouti DJI 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
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Self-employed, female (% of female employment) (modeled ILO estimate) SL.EMP.SELF.FE.ZS Djibouti DJI 49.4900016784668 49.2599983215332 49.0299987792969 48.9099998474121 48.5400009155273 48.1500015258789 47.6599998474121 47.1599998474121 46.9300003051758 46.4300003051758 45.7999992370605 45.6599998474121 45.1199989318848 44.4700012207031 43.7200012207031 43.1500015258789 42.5099983215332 41.8899993896484 .. ..
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Self-employed, male (% of male employment) (modeled ILO estimate) SL.EMP.SELF.MA.ZS Djibouti DJI 37.7400016784668 37.810001373291 37.8300018310547 37.7299995422363 37.439998626709 37.060001373291 36.5900001525879 36.4599990844727 36.0200004577637 35.2799987792969 34.7900009155273 34.0900001525879 33.3800010681152 32.5999984741211 31.9099998474121 31.3199996948242 30.3999996185303 29.5599994659424 .. ..
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Self-employed, total (% of total employment) (modeled ILO estimate) SL.EMP.SELF.ZS Djibouti DJI 42.2000007629395 42.2099990844727 42.1599998474121 42.060001373291 41.75 41.3600006103516 40.8699989318848 40.5900001525879 40.2400016784668 39.5900001525879 39.060001373291 38.6100006103516 37.9799995422363 37.2700004577637 36.560001373291 36 35.2000007629395 34.4500007629395 .. ..
|
||||
Start-up procedures to register a business (number) IC.REG.PROC Djibouti DJI .. .. .. 11 11 11 11 11 11 11 11 9 7 7 7 7 6 6 .. ..
|
||||
Start-up procedures to register a business, female (number) IC.REG.PROC.FE Djibouti DJI .. .. .. 11 11 11 11 11 11 11 11 9 7 7 7 7 6 6 .. ..
|
||||
Start-up procedures to register a business, male (number) IC.REG.PROC.MA Djibouti DJI .. .. .. 11 11 11 11 11 11 11 11 9 7 7 7 7 6 6 .. ..
|
||||
The law prohibits discrimination in access to credit based on gender (1=yes; 0=no) SG.LAW.CRDD.GR Djibouti DJI 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
|
||||
The law prohibits discrimination in employment based on gender (1=yes; 0=no) SG.LAW.NODC.HR Djibouti DJI 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
|
||||
Time required to start a business (days) IC.REG.DURS Djibouti DJI .. .. .. 44 44 44 37 37 37 37 37 17 14 14 14 14 14 14 .. ..
|
||||
Time required to start a business, female (days) IC.REG.DURS.FE Djibouti DJI .. .. .. 44 44 44 37 37 37 37 37 17 14 14 14 14 14 14 .. ..
|
||||
Time required to start a business, male (days) IC.REG.DURS.MA Djibouti DJI .. .. .. 44 44 44 37 37 37 37 37 17 14 14 14 14 14 14 .. ..
|
||||
Vulnerable employment, male (% of male employment) (modeled ILO estimate) SL.EMP.VULN.MA.ZS Djibouti DJI 36.6299986839295 36.7199988365174 36.7400007247925 36.6000015735626 36.3399982452393 35.9899997711181 35.5599989891053 35.4399998188019 34.9400007724762 34.1899998188019 33.7299997806549 32.9100008010864 32.2100005149842 31.4600002765655 30.8199992179871 30.2600009441375 29.3400005102158 28.5099991559982 .. ..
|
||||
Vulnerable employment, female (% of female employment) (modeled ILO estimate) SL.EMP.VULN.FE.ZS Djibouti DJI 48.9999995231628 48.7800016403198 48.5499992370605 48.4100003242493 48.0599980354309 47.6700010299682 47.1899995803833 46.6899991035461 46.4500002861023 45.9299983978271 45.3099985122681 45.1299982070923 44.5799984931946 43.9400012493133 43.210001707077 42.6399986743927 41.9999985694885 41.369998216629 .. ..
|
||||
Wage and salaried workers, female (% of female employment) (modeled ILO estimate) SL.EMP.WORK.FE.ZS Djibouti DJI 50.5099983215332 50.7400016784668 50.9700012207031 51.0900001525879 51.4599990844727 51.8499984741211 52.3400001525879 52.8499984741211 53.0699996948242 53.5699996948242 54.2000007629395 54.3400001525879 54.8800010681152 55.5299987792969 56.2799987792969 56.8499984741211 57.4900016784668 58.1100006103516 .. ..
|
||||
Wage and salaried workers, male (% of male employment) (modeled ILO estimate) SL.EMP.WORK.MA.ZS Djibouti DJI 62.2599983215332 62.189998626709 62.1699981689453 62.2700004577637 62.560001373291 62.939998626709 63.4099998474121 63.5499992370605 63.9799995422363 64.7200012207031 65.2099990844727 65.9100036621094 66.620002746582 67.4000015258789 68.0999984741211 68.6800003051758 69.5999984741211 70.4400024414063 .. ..
|
||||
|
||||
|
||||
|
||||
Data from database: Gender Statistics
|
||||
Last Updated: 06/23/2022
|
||||
|
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data/raw/WB-GPSP/GPSP-and-Median-Income-2014-2019-Final.xlsx
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data/raw/WB-GPSP/GPSP-and-Median-income-2014-2019-Final.pdf
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|
|
@ -0,0 +1,55 @@
|
|||
Series Name,Series Code,Country Name,Country Code,2002 [YR2002],2003 [YR2003],2004 [YR2004],2005 [YR2005],2006 [YR2006],2007 [YR2007],2008 [YR2008],2009 [YR2009],2010 [YR2010],2011 [YR2011],2012 [YR2012],2013 [YR2013],2014 [YR2014],2015 [YR2015],2016 [YR2016],2017 [YR2017],2018 [YR2018],2019 [YR2019],2020 [YR2020],2021 [YR2021]
|
||||
A woman can get a job in the same way as a man (1=yes; 0=no),SG.GET.JOBS.EQ,Djibouti,DJI,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
|
||||
A woman can register a business in the same way as a man (1=yes; 0=no),SG.BUS.REGT.EQ,Djibouti,DJI,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
|
||||
A woman can sign a contract in the same way as a man (1=yes; 0=no),SG.CNT.SIGN.EQ,Djibouti,DJI,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
|
||||
A woman can work at night in the same way as a man (1=yes; 0=no),SG.NGT.WORK.EQ,Djibouti,DJI,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
|
||||
A woman can work in a job deemed dangerous in the same way as a man (1=yes; 0=no),SG.DNG.WORK.DN.EQ,Djibouti,DJI,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
|
||||
A woman can work in an industrial job in the same way as a man (1=yes; 0=no),SG.IND.WORK.EQ,Djibouti,DJI,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
|
||||
"Account ownership at a financial institution or with a mobile-money-service provider, female (% of population ages 15+)",FX.OWN.TOTL.FE.ZS,Djibouti,DJI,..,..,..,..,..,..,..,..,..,8.76278209686279,..,..,..,..,..,..,..,..,..,..
|
||||
"Account ownership at a financial institution or with a mobile-money-service provider, male (% of population ages 15+)",FX.OWN.TOTL.MA.ZS,Djibouti,DJI,..,..,..,..,..,..,..,..,..,16.6257038116455,..,..,..,..,..,..,..,..,..,..
|
||||
Cost of business start-up procedures (% of GNI per capita),IC.REG.COST.PC.ZS,Djibouti,DJI,..,..,..,279.6,267,251.6,245.2,240.1,214.9,214.8,195.7,214.2,202.7,196.6,156,58.8,41.9,39.7,..,..
|
||||
"Cost of business start-up procedures, female (% of GNI per capita)",IC.REG.COST.PC.FE.ZS,Djibouti,DJI,..,..,..,279.6,267,251.6,245.2,240.1,214.9,214.8,195.7,214.2,202.7,196.6,156,58.8,41.9,39.7,..,..
|
||||
"Cost of business start-up procedures, male (% of GNI per capita)",IC.REG.COST.PC.MA.ZS,Djibouti,DJI,..,..,..,279.6,267,251.6,245.2,240.1,214.9,214.8,195.7,214.2,202.7,196.6,156,58.8,41.9,39.7,..,..
|
||||
"Credit card ownership, female (% age 15+)",fin7.t.a.2,Djibouti,DJI,..,..,..,..,..,..,..,..,..,3.49554419517517,..,..,..,..,..,..,..,..,..,..
|
||||
"Credit card ownership, male (% age 15+)",fin7.t.a.1,Djibouti,DJI,..,..,..,..,..,..,..,..,..,4.55562686920166,..,..,..,..,..,..,..,..,..,..
|
||||
Credit card ownership (% age 15+),fin7.t.a,Djibouti,DJI,..,..,..,..,..,..,..,..,..,3.96891236305237,..,..,..,..,..,..,..,..,..,..
|
||||
Debit card ownership (% age 15+),fin2.t.a,Djibouti,DJI,..,..,..,..,..,..,..,..,..,7.60348653793335,..,..,..,..,..,..,..,..,..,..
|
||||
"Debit card ownership, female (% age 15+)",fin2.t.a.2,Djibouti,DJI,..,..,..,..,..,..,..,..,..,5.89633560180664,..,..,..,..,..,..,..,..,..,..
|
||||
"Debit card ownership, male (% age 15+)",fin2.t.a.1,Djibouti,DJI,..,..,..,..,..,..,..,..,..,9.71940994262695,..,..,..,..,..,..,..,..,..,..
|
||||
"Employment in services, female (% of female employment) (modeled ILO estimate)",SL.SRV.EMPL.FE.ZS,Djibouti,DJI,52.2400016784668,53.2999992370605,54.3699989318848,55.4500007629395,56.5999984741211,57.7799987792969,58.9799995422363,60.0200004577637,61.1199989318848,62.3300018310547,63.4199981689453,64.4899978637695,65.5999984741211,66.7399978637695,67.8300018310547,68.8499984741211,69.9899978637695,71.0599975585938,..,..
|
||||
"Employment in services, male (% of male employment) (modeled ILO estimate)",SL.SRV.EMPL.MA.ZS,Djibouti,DJI,44.2700004577637,44.8800010681152,45.4700012207031,46.0900001525879,46.8499984741211,47.6399993896484,48.4799995422363,48.939998626709,49.5900001525879,50.439998626709,51.060001373291,51.6599998474121,52.4000015258789,53.1599998474121,53.8699989318848,54.4700012207031,55.2799987792969,56.0499992370605,..,..
|
||||
"Employment in industry, male (% of male employment) (modeled ILO estimate)",SL.IND.EMPL.MA.ZS,Djibouti,DJI,16.8600006103516,16.7399997711182,16.6499996185303,16.5699996948242,16.5400009155273,16.5200004577637,16.5200004577637,16.5300006866455,16.5799999237061,16.6700000762939,16.7700004577637,16.9099998474121,17.0799999237061,17.25,17.4099998474121,17.5599994659424,17.7000007629395,17.8099994659424,..,..
|
||||
"Employment in industry, female (% of female employment) (modeled ILO estimate)",SL.IND.EMPL.FE.ZS,Djibouti,DJI,8.60999965667725,8.47999954223633,8.35000038146973,8.22999954223633,8.10000038146973,7.96999979019165,7.82000017166138,7.76000022888184,7.65999984741211,7.53000020980835,7.44000005722046,7.34999990463257,7.28999996185303,7.19000005722046,7.09999990463257,7.03999996185303,6.90000009536743,6.80000019073486,..,..
|
||||
Employment in industry (% of total employment) (modeled ILO estimate),SL.IND.EMPL.ZS,Djibouti,DJI,13.7299995422363,13.5799999237061,13.4399995803833,13.3400001525879,13.2700004577637,13.210000038147,13.1599998474121,13.1400003433228,13.1300001144409,13.1300001144409,13.1499996185303,13.1800003051758,13.25,13.3000001907349,13.3500003814697,13.3999996185303,13.4200000762939,13.4399995803833,..,..
|
||||
"Employment in agriculture, male (% of male employment) (modeled ILO estimate)",SL.AGR.EMPL.MA.ZS,Djibouti,DJI,38.8699989318848,38.3699989318848,37.8800010681152,37.3300018310547,36.5999984741211,35.8400001525879,35,34.5299987792969,33.8400001525879,32.8899993896484,32.1599998474121,31.4300003051758,30.5200004577637,29.5900001525879,28.7299995422363,27.9699993133545,27.0200004577637,26.1399993896484,..,..
|
||||
Financial institution account (% age 15+),fin1.t.a,Djibouti,DJI,..,..,..,..,..,..,..,..,..,12.2738819122314,..,..,..,..,..,..,..,..,..,..
|
||||
"Financial institution account,female(% age 15+)",fin1.t.a.2,Djibouti,DJI,..,..,..,..,..,..,..,..,..,8.76278209686279,..,..,..,..,..,..,..,..,..,..
|
||||
"Financial institution account,male(% age 15+)",fin1.t.a.1,Djibouti,DJI,..,..,..,..,..,..,..,..,..,16.6257038116455,..,..,..,..,..,..,..,..,..,..
|
||||
Men and women have equal ownership rights to immovable property (1=yes; 0=no),SG.OWN.PRRT.IM,Djibouti,DJI,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
|
||||
Number of female business owners,IC.WEF.LLCO.FE,Djibouti,DJI,..,..,..,..,..,..,..,..,..,..,..,..,..,..,..,..,..,..,..,..
|
||||
Number of male business owners,IC.WEF.LLCO.MA,Djibouti,DJI,..,..,..,..,..,..,..,..,..,..,..,..,..,..,..,..,..,..,..,..
|
||||
"Proportion of time spent on unpaid domestic and care work, female (% of 24 hour day)",SG.TIM.UWRK.FE,Djibouti,DJI,..,..,..,..,..,..,..,..,..,..,..,..,..,..,..,..,..,..,..,..
|
||||
"Proportion of time spent on unpaid domestic and care work, male (% of 24 hour day)",SG.TIM.UWRK.MA,Djibouti,DJI,..,..,..,..,..,..,..,..,..,..,..,..,..,..,..,..,..,..,..,..
|
||||
"Saved any money in the past year, male (% age 15+)",fin18.t.d.1,Djibouti,DJI,..,..,..,..,..,..,..,..,..,..,..,..,..,..,..,..,..,..,..,..
|
||||
"Saved any money in the past year, female (% age 15+)",fin18.t.d.2,Djibouti,DJI,..,..,..,..,..,..,..,..,..,..,..,..,..,..,..,..,..,..,..,..
|
||||
"Self-employed, female (% of female employment) (modeled ILO estimate)",SL.EMP.SELF.FE.ZS,Djibouti,DJI,49.4900016784668,49.2599983215332,49.0299987792969,48.9099998474121,48.5400009155273,48.1500015258789,47.6599998474121,47.1599998474121,46.9300003051758,46.4300003051758,45.7999992370605,45.6599998474121,45.1199989318848,44.4700012207031,43.7200012207031,43.1500015258789,42.5099983215332,41.8899993896484,..,..
|
||||
"Self-employed, male (% of male employment) (modeled ILO estimate)",SL.EMP.SELF.MA.ZS,Djibouti,DJI,37.7400016784668,37.810001373291,37.8300018310547,37.7299995422363,37.439998626709,37.060001373291,36.5900001525879,36.4599990844727,36.0200004577637,35.2799987792969,34.7900009155273,34.0900001525879,33.3800010681152,32.5999984741211,31.9099998474121,31.3199996948242,30.3999996185303,29.5599994659424,..,..
|
||||
"Self-employed, total (% of total employment) (modeled ILO estimate)",SL.EMP.SELF.ZS,Djibouti,DJI,42.2000007629395,42.2099990844727,42.1599998474121,42.060001373291,41.75,41.3600006103516,40.8699989318848,40.5900001525879,40.2400016784668,39.5900001525879,39.060001373291,38.6100006103516,37.9799995422363,37.2700004577637,36.560001373291,36,35.2000007629395,34.4500007629395,..,..
|
||||
Share of female business owners (% of total business owners),IC.WEF.LLCO.FE.ZS,Djibouti,DJI,..,..,..,..,..,..,..,..,..,..,..,..,..,..,..,..,..,..,..,..
|
||||
Start-up procedures to register a business (number),IC.REG.PROC,Djibouti,DJI,..,..,..,11,11,11,11,11,11,11,11,9,7,7,7,7,6,6,..,..
|
||||
"Start-up procedures to register a business, female (number)",IC.REG.PROC.FE,Djibouti,DJI,..,..,..,11,11,11,11,11,11,11,11,9,7,7,7,7,6,6,..,..
|
||||
"Start-up procedures to register a business, male (number)",IC.REG.PROC.MA,Djibouti,DJI,..,..,..,11,11,11,11,11,11,11,11,9,7,7,7,7,6,6,..,..
|
||||
The law prohibits discrimination in access to credit based on gender (1=yes; 0=no),SG.LAW.CRDD.GR,Djibouti,DJI,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
|
||||
The law prohibits discrimination in employment based on gender (1=yes; 0=no),SG.LAW.NODC.HR,Djibouti,DJI,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
|
||||
Time required to start a business (days),IC.REG.DURS,Djibouti,DJI,..,..,..,44,44,44,37,37,37,37,37,17,14,14,14,14,14,14,..,..
|
||||
"Time required to start a business, female (days)",IC.REG.DURS.FE,Djibouti,DJI,..,..,..,44,44,44,37,37,37,37,37,17,14,14,14,14,14,14,..,..
|
||||
"Time required to start a business, male (days)",IC.REG.DURS.MA,Djibouti,DJI,..,..,..,44,44,44,37,37,37,37,37,17,14,14,14,14,14,14,..,..
|
||||
"Vulnerable employment, male (% of male employment) (modeled ILO estimate)",SL.EMP.VULN.MA.ZS,Djibouti,DJI,36.6299986839295,36.7199988365174,36.7400007247925,36.6000015735626,36.3399982452393,35.9899997711181,35.5599989891053,35.4399998188019,34.9400007724762,34.1899998188019,33.7299997806549,32.9100008010864,32.2100005149842,31.4600002765655,30.8199992179871,30.2600009441375,29.3400005102158,28.5099991559982,..,..
|
||||
"Vulnerable employment, female (% of female employment) (modeled ILO estimate)",SL.EMP.VULN.FE.ZS,Djibouti,DJI,48.9999995231628,48.7800016403198,48.5499992370605,48.4100003242493,48.0599980354309,47.6700010299682,47.1899995803833,46.6899991035461,46.4500002861023,45.9299983978271,45.3099985122681,45.1299982070923,44.5799984931946,43.9400012493133,43.210001707077,42.6399986743927,41.9999985694885,41.369998216629,..,..
|
||||
"Wage and salaried workers, female (% of female employment) (modeled ILO estimate)",SL.EMP.WORK.FE.ZS,Djibouti,DJI,50.5099983215332,50.7400016784668,50.9700012207031,51.0900001525879,51.4599990844727,51.8499984741211,52.3400001525879,52.8499984741211,53.0699996948242,53.5699996948242,54.2000007629395,54.3400001525879,54.8800010681152,55.5299987792969,56.2799987792969,56.8499984741211,57.4900016784668,58.1100006103516,..,..
|
||||
"Wage and salaried workers, male (% of male employment) (modeled ILO estimate)",SL.EMP.WORK.MA.ZS,Djibouti,DJI,62.2599983215332,62.189998626709,62.1699981689453,62.2700004577637,62.560001373291,62.939998626709,63.4099998474121,63.5499992370605,63.9799995422363,64.7200012207031,65.2099990844727,65.9100036621094,66.620002746582,67.4000015258789,68.0999984741211,68.6800003051758,69.5999984741211,70.4400024414063,..,..
|
||||
,,,,,,,,,,,,,,,,,,,,,,,
|
||||
,,,,,,,,,,,,,,,,,,,,,,,
|
||||
,,,,,,,,,,,,,,,,,,,,,,,
|
||||
Data from database: Gender Statistics,,,,,,,,,,,,,,,,,,,,,,,
|
||||
Last Updated: 06/23/2022,,,,,,,,,,,,,,,,,,,,,,,
|
||||
|
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|
|
@ -34,7 +34,9 @@
|
|||
copyright = {Copyright La Francaise de Financement et d'Edition (FFE) Nov
|
||||
2019},
|
||||
langid = {eng ; fre},
|
||||
file = {/home/marty/Zotero/storage/TSPFYLEQ/RINDU1_194_0105.pdf},
|
||||
keywords = {status::skimmed},
|
||||
file = {/home/marty/Zotero/storage/TSPFYLEQ/Aden2019_The role of Ports and
|
||||
Free Zones in the Development of Africa.pdf},
|
||||
}
|
||||
|
||||
@article{Alia2017,
|
||||
|
|
@ -64,7 +66,7 @@
|
|||
author = {Asaba, Richard Bagonza},
|
||||
date = {2013},
|
||||
publisher = {{National University of Ireland, Maynooth (Ireland)}},
|
||||
keywords = {country::Uganda,irrelevant::thesis,topic::water},
|
||||
keywords = {country::Uganda,irrelevant::,topic::water},
|
||||
file = {/home/marty/Zotero/storage/WRASMP5W/Asaba2013_Gender, power and
|
||||
local water governance in rural Uganda.pdf},
|
||||
}
|
||||
|
|
@ -203,7 +205,7 @@
|
|||
|
||||
@incollection{Benjamin2004,
|
||||
title = {Agriculture and Income Distribution in Rural {{Vietnam}} under
|
||||
Economic Reforms: A Tale of Two Regions},
|
||||
Economic Reforms: {{A}} Tale of Two Regions},
|
||||
booktitle = {Economic {{Growth}}, {{Poverty}} and {{Household Welfare}} in {
|
||||
{Vietnam}}},
|
||||
author = {Benjamin, Dwayne and Brandt, Loren},
|
||||
|
|
@ -267,7 +269,7 @@
|
|||
personal observation and secondary material.},
|
||||
copyright = {Copyright Adonis \& Abbey Publishers Ltd Jun-Dec 2016},
|
||||
langid = {english},
|
||||
keywords = {next},
|
||||
keywords = {status::skimmed},
|
||||
file = {/home/marty/Zotero/storage/J6Z53UN5/Bereketeab2016_Djibouti.pdf},
|
||||
}
|
||||
|
||||
|
|
@ -398,8 +400,8 @@
|
|||
influence of value judgments on the assessment of inequality.},
|
||||
langid = {english},
|
||||
keywords = {country::Vietnam,index::Atkinson,inequality::education,
|
||||
inequality::health,inequality::housing,next,topic::consumption,
|
||||
topic::education,topic::health,topic::housing},
|
||||
inequality::health,inequality::housing,status::skimmed,
|
||||
topic::consumption,topic::education,topic::health,topic::housing},
|
||||
file = {/home/marty/Zotero/storage/IXPIBKJR/Bui2020_Multidimensional
|
||||
Inequality in Vietnam, 2002–2012.pdf},
|
||||
}
|
||||
|
|
@ -526,7 +528,7 @@
|
|||
}
|
||||
|
||||
@report{Cao2008,
|
||||
title = {Urban and Rural Dimensions of Income Inequality in Vietnam},
|
||||
title = {Urban and Rural Dimensions of Income Inequality in {{Vietnam}}},
|
||||
author = {Cao, Thi Cam Van and Akita, Takahiro},
|
||||
date = {2008},
|
||||
series = {Economic {{Development}} \& {{Policy Series}}},
|
||||
|
|
@ -735,8 +737,8 @@
|
|||
issn = {0377-7332, 1435-8921},
|
||||
doi = {10.1007/s00181-021-02022-6},
|
||||
langid = {english},
|
||||
keywords = {country::Vietnam,inequality::income,next,topic::agriculture,
|
||||
topic::livestock},
|
||||
keywords = {country::Vietnam,inequality::income,status::skimmed,
|
||||
topic::agriculture,topic::livestock},
|
||||
file = {/home/marty/Zotero/storage/ED6YGVJ6/Do2022_Livestock production and
|
||||
income inequality in rural Vietnam.pdf},
|
||||
}
|
||||
|
|
@ -760,7 +762,7 @@
|
|||
involvement of Western countries has on the region.},
|
||||
copyright = {Copyright University Constantin Brancusi of Târgu-Jiu 2017},
|
||||
langid = {rum},
|
||||
keywords = {next},
|
||||
keywords = {status::skimmed},
|
||||
}
|
||||
|
||||
@article{Ebrahim2021,
|
||||
|
|
@ -795,8 +797,8 @@
|
|||
north-west Vietnam. It discusses early experiences and learning
|
||||
on pivoting projects, funded by the Government of Australia, to
|
||||
be COVID-19 responsive and contribute to longer-term recovery.},
|
||||
keywords = {inequality::gender,next,topic::agriculture,topic::covid19,
|
||||
topic::poverty},
|
||||
keywords = {inequality::gender,status::skimmed,topic::agriculture,
|
||||
topic::covid19,topic::poverty},
|
||||
file = {/home/marty/Zotero/storage/YU72GAUG/Ebrahim2021_Women’s economic
|
||||
empowerment and COVID-19.pdf},
|
||||
}
|
||||
|
|
@ -988,8 +990,8 @@
|
|||
|
||||
@article{Fesselmeyer2010,
|
||||
title = {Urban-Biased {{Policies}} and the {{Increasing Rural-Urban
|
||||
Expenditure Gap}} in {{Vietnam}} in the 1990s: {{URBAN-BIASED
|
||||
POLICIES IN VIETNAM IN THE 1990S}}},
|
||||
Expenditure Gap}} in {{Vietnam}} in the 1990s: {{Urban-biased}}
|
||||
Policies in {{Vietnam}} in the 1990s},
|
||||
shorttitle = {Urban-Biased {{Policies}} and the {{Increasing Rural-Urban
|
||||
Expenditure Gap}} in {{Vietnam}} in the 1990s},
|
||||
author = {Fesselmeyer, Eric and Le, Kien T.},
|
||||
|
|
@ -1014,7 +1016,7 @@
|
|||
date = {2005},
|
||||
institution = {{DFID Vietnam}},
|
||||
location = {{Hanoi}},
|
||||
keywords = {country::Vietnam,inequality::,next,review::synthesis},
|
||||
keywords = {country::Vietnam,inequality::,review::synthesis,status::skimmed},
|
||||
file = {/home/marty/Zotero/storage/Z4A2KQWV/Fritzen2005_Vietnam inequality
|
||||
report 2005.pdf},
|
||||
}
|
||||
|
|
@ -1127,7 +1129,7 @@
|
|||
reflection, innovation, and anticipation, all of which are key
|
||||
elements of the adaptation process. Source: TROVE},
|
||||
langid = {english},
|
||||
keywords = {country::Vietnam,inequality::environmental,next,
|
||||
keywords = {country::Vietnam,inequality::environmental,status::skimmed,
|
||||
topic::climate_change},
|
||||
}
|
||||
|
||||
|
|
@ -1166,7 +1168,8 @@
|
|||
for women to reduce gender wage gap in Vietnam.},
|
||||
copyright = {Copyright © Taylor \& Francis Group, LLC},
|
||||
langid = {english},
|
||||
keywords = {country::Vietnam,inequality::income,next,topic::gender},
|
||||
keywords = {country::Vietnam,inequality::income,status::skimmed,
|
||||
topic::gender},
|
||||
file = {/home/marty/Zotero/storage/EFB6LJVX/ContentServer.pdf},
|
||||
}
|
||||
|
||||
|
|
@ -1256,7 +1259,7 @@
|
|||
circumstances.},
|
||||
copyright = {2014 Taylor \& Francis 2014},
|
||||
langid = {english},
|
||||
keywords = {inequality::environmental,inequality::gender,next,
|
||||
keywords = {inequality::environmental,inequality::gender,status::skimmed,
|
||||
topic::climate_change,topic::river},
|
||||
file = {/home/marty/Zotero/storage/8GCHSZTY/Huynh2014_Women's differentiated
|
||||
vulnerability and adaptations to climate-related.pdf},
|
||||
|
|
@ -1290,7 +1293,7 @@
|
|||
issn = {0269-2171, 1465-3486},
|
||||
doi = {10.1080/02692171.2010.483471},
|
||||
langid = {english},
|
||||
keywords = {country::Vietnam,inequality::ethnicity,next},
|
||||
keywords = {country::Vietnam,inequality::ethnicity,status::skimmed},
|
||||
file = {/home/marty/Zotero/storage/4HH88HQY/Imai2011_Poverty, inequality and
|
||||
ethnic minorities in Vietnam.pdf},
|
||||
}
|
||||
|
|
@ -1317,7 +1320,7 @@
|
|||
@article{Jafino2021,
|
||||
title = {Accounting for Multisectoral Dynamics in Supporting Equitable
|
||||
Adaptation Planning: {{A}} Case Study on the Rice Agriculture in the
|
||||
Vietnam Mekong Delta},
|
||||
{{Vietnam Mekong}} Delta},
|
||||
author = {Jafino, B.A and Kwakkel, J.H and Klijn, F and Dung, Nguyen Viet
|
||||
and van Delden, Hedwig and Haasnoot, Marjolijn and Sutanudjaja,
|
||||
Edwin H},
|
||||
|
|
@ -1483,7 +1486,7 @@
|
|||
location = {{Washington, D.C.}},
|
||||
url = {https://openknowledge.worldbank.org/handle/10986/20074},
|
||||
keywords = {inequality::ethnicity,inequality::income,inequality::regional,
|
||||
next,topic::poverty},
|
||||
status::skimmed,topic::poverty},
|
||||
file = {/home/marty/Zotero/storage/HBSGJ5K7/Kozel2014_Well Begun but Not Yet
|
||||
Done.pdf},
|
||||
}
|
||||
|
|
@ -1560,8 +1563,8 @@
|
|||
issn = {0003-6846, 1466-4283},
|
||||
doi = {10.1080/00036846.2019.1588943},
|
||||
langid = {english},
|
||||
keywords = {country::Vietnam,inequality::income,next,topic::modernization,
|
||||
topic::poverty,topic::trade_liberalization},
|
||||
keywords = {country::Vietnam,inequality::income,status::skimmed,
|
||||
topic::modernization,topic::poverty,topic::trade_liberalization},
|
||||
file = {/home/marty/Zotero/storage/3S5CUK6U/Le2019_Trade liberalisation,
|
||||
poverty, and inequality in Vietnam.pdf},
|
||||
}
|
||||
|
|
@ -1604,7 +1607,7 @@
|
|||
education and improving human capital, which not only can reduce
|
||||
income inequality but also can attract more FDI inflows.},
|
||||
langid = {english},
|
||||
keywords = {country::Vietnam,inequality::income,next,topic::FDI},
|
||||
keywords = {country::Vietnam,inequality::income,status::skimmed,topic::FDI},
|
||||
file = {/home/marty/Zotero/storage/RJ3NP2U8/Le2021_The Impact of Foreign
|
||||
Direct Investment on Income Inequality in Vietnam.pdf},
|
||||
}
|
||||
|
|
@ -1647,7 +1650,7 @@
|
|||
education and improving human capital, which not only can reduce
|
||||
income inequality but also can attract more FDI inflows.},
|
||||
langid = {english},
|
||||
keywords = {next},
|
||||
keywords = {status::skimmed},
|
||||
file = {/home/marty/Zotero/storage/DIRG96MR/Le2021_The Impact of Foreign
|
||||
Direct Investment on Income Inequality in Vietnam.pdf},
|
||||
}
|
||||
|
|
@ -1666,8 +1669,8 @@
|
|||
issn = {1369-6866, 1468-2397},
|
||||
doi = {10.1111/ijsw.12482},
|
||||
langid = {english},
|
||||
keywords = {country::Vietnam,inequality::income,next,topic::modernization,
|
||||
topic::poverty,topic::trade_liberalization},
|
||||
keywords = {country::Vietnam,inequality::income,status::skimmed,
|
||||
topic::modernization,topic::poverty,topic::trade_liberalization},
|
||||
file = {/home/marty/Zotero/storage/L8HM9TCJ/Le2022_Growth, inequality and
|
||||
poverty in Vietnam.pdf},
|
||||
}
|
||||
|
|
@ -1800,7 +1803,7 @@
|
|||
issn = {00221996},
|
||||
doi = {10.1016/j.jinteco.2011.05.007},
|
||||
langid = {english},
|
||||
keywords = {inequality::rural,next,topic::poverty,
|
||||
keywords = {inequality::rural,status::skimmed,topic::poverty,
|
||||
topic::trade_liberalization},
|
||||
file = {/home/marty/Zotero/storage/94NXHFS2/McCaig2011_Exporting out of
|
||||
poverty.pdf},
|
||||
|
|
@ -1965,7 +1968,8 @@
|
|||
for reducing income inequality across provinces and across
|
||||
districts within provinces in Vietnam.},
|
||||
langid = {english},
|
||||
keywords = {country::Vietnam,inequality::income,next,topic::education},
|
||||
keywords = {country::Vietnam,inequality::income,status::skimmed,
|
||||
topic::education},
|
||||
file = {/home/marty/Zotero/storage/EMUDX7L5/Minh Ho2021_DOES GOVERNMENT
|
||||
SPENDING ON EDUCATION AFFECT PROVINCIAL INCOME INEQUALITY IN.pdf},
|
||||
}
|
||||
|
|
@ -2076,7 +2080,7 @@
|
|||
answer to the question whether or not they are really mutually
|
||||
beneficial.},
|
||||
langid = {english},
|
||||
keywords = {next},
|
||||
keywords = {status::skimmed},
|
||||
file = {/home/marty/Zotero/storage/QA48TT2X/Mormul2016_Ethio‑Djiboutian
|
||||
relations in the 21st century – towards new african cooperation.pdf},
|
||||
}
|
||||
|
|
@ -2193,7 +2197,7 @@
|
|||
author = {Natuhwera, Justus},
|
||||
date = {2019},
|
||||
institution = {{Kampala international University, School of Law}},
|
||||
keywords = {country::Uganda,irrelevant::thesis,topic::water},
|
||||
keywords = {country::Uganda,irrelevant::,topic::water},
|
||||
file = {/home/marty/Zotero/storage/9VNT58MW/Natuhwera2019_Rights of women to
|
||||
property, a case study of Isingiro district, Uganda.pdf},
|
||||
}
|
||||
|
|
@ -2233,8 +2237,8 @@
|
|||
issn = {08189935},
|
||||
doi = {10.1111/apel.12219},
|
||||
langid = {english},
|
||||
keywords = {country::Vietnam,inequality::income,next,topic::modernization,
|
||||
topic::poverty},
|
||||
keywords = {country::Vietnam,inequality::income,status::skimmed,
|
||||
topic::modernization,topic::poverty},
|
||||
file = {/home/marty/Zotero/storage/7JLMIQA8/Asian-Pac Economic Lit - 2018 -
|
||||
Nguyen - Economic growth inequality and poverty in Vietnam.pdf},
|
||||
}
|
||||
|
|
@ -2251,7 +2255,8 @@
|
|||
issn = {03014215},
|
||||
doi = {10.1016/j.enpol.2019.06.001},
|
||||
langid = {english},
|
||||
keywords = {country::Vietnam,inequality::,next,topic::energy,topic::poverty},
|
||||
keywords = {country::Vietnam,inequality::,status::skimmed,topic::energy,
|
||||
topic::poverty},
|
||||
file = {/home/marty/Zotero/storage/BS8J9JT6/Nguyen2019_Energy transition,
|
||||
poverty and inequality in Vietnam.pdf},
|
||||
}
|
||||
|
|
@ -2319,7 +2324,7 @@
|
|||
substandard houses are not only more vulnerable to disasters but
|
||||
take longer to recover. Source: TROVE},
|
||||
langid = {english},
|
||||
keywords = {country::Vietnam,inequality::housing,next},
|
||||
keywords = {country::Vietnam,inequality::housing,status::skimmed},
|
||||
file = {/home/marty/Zotero/storage/FD3NAYXR/NGUYEN2020_Essays on housing
|
||||
affordability and housing quality dilemmas in Vietnam.pdf},
|
||||
}
|
||||
|
|
@ -2377,7 +2382,7 @@
|
|||
on the slopes in Vietnam.},
|
||||
copyright = {Springer Nature B.V. 2020},
|
||||
langid = {english},
|
||||
keywords = {next},
|
||||
keywords = {status::skimmed},
|
||||
file = {/home/marty/Zotero/storage/7A3JX6JC/Nguyen2020_Diversified responses
|
||||
to contemporary pressures on sloping agricultural land.pdf},
|
||||
}
|
||||
|
|
@ -2439,7 +2444,7 @@
|
|||
isbn = {92-64-08383-9},
|
||||
langid = {english},
|
||||
organization = {{OECD}},
|
||||
keywords = {next},
|
||||
keywords = {status::skimmed},
|
||||
file = {/home/marty/Zotero/storage/6PGYN69I/OECD2009_Djibouti.pdf},
|
||||
}
|
||||
|
||||
|
|
@ -2483,7 +2488,7 @@
|
|||
isbn = {978-92-64-20102-6},
|
||||
langid = {english},
|
||||
organization = {{OECD}},
|
||||
keywords = {next},
|
||||
keywords = {status::skimmed},
|
||||
file = {/home/marty/Zotero/storage/CUJQ3HYN/OECD2013_Aid, trade and
|
||||
development indicators for djibouti.pdf},
|
||||
}
|
||||
|
|
@ -2509,7 +2514,7 @@
|
|||
isbn = {978-92-64-19536-3 978-92-64-19534-9},
|
||||
langid = {english},
|
||||
pagetotal = {162},
|
||||
keywords = {country::Vietnam,next,topic::agriculture,topic::food},
|
||||
keywords = {country::Vietnam,status::skimmed,topic::agriculture,topic::food},
|
||||
file = {/home/marty/Zotero/storage/KKZDH3D8/Organisation for Economic
|
||||
Co-operation and Development2013_Global food security.pdf},
|
||||
}
|
||||
|
|
@ -2525,7 +2530,7 @@
|
|||
isbn = {978-3-030-41513-6 978-3-030-41512-9 978-3-030-41515-0},
|
||||
langid = {english},
|
||||
pagetotal = {649},
|
||||
keywords = {next},
|
||||
keywords = {status::skimmed},
|
||||
}
|
||||
|
||||
@article{Petrosino2012,
|
||||
|
|
@ -2601,7 +2606,7 @@
|
|||
conditions.},
|
||||
copyright = {2020 Elsevier B.V.},
|
||||
langid = {english},
|
||||
keywords = {next},
|
||||
keywords = {status::skimmed},
|
||||
file = {/home/marty/Zotero/storage/TLWQQZEI/Pham2021_Natural hazard's effect
|
||||
and farmers' perception.pdf},
|
||||
}
|
||||
|
|
@ -2782,14 +2787,15 @@
|
|||
climate change in the Northern Mountainous Region of.pdf},
|
||||
}
|
||||
|
||||
@article{Ssewanyana2012,
|
||||
@report{Ssewanyana2012,
|
||||
title = {Poverty and Inequality Dynamics in {{Uganda}}: {{Insights}} from
|
||||
the {{Uganda}} National {{Panel Surveys}} 2005/6 and 2009/10},
|
||||
shorttitle = {Poverty and Inequality Dynamics in {{Uganda}}},
|
||||
author = {Ssewanyana, Sarah and Kasirye, Ibrahim},
|
||||
date = {2012},
|
||||
publisher = {{Unknown}},
|
||||
doi = {10.22004/AG.ECON.148953},
|
||||
institution = {{EPRC - Economic Policy Research Centre}},
|
||||
url = {https://ageconsearch.umn.edu/record/148953},
|
||||
urldate = {2022-08-16},
|
||||
abstract = {While Uganda has made significant efforts in reducing the
|
||||
proportion of individuals and households living below the
|
||||
absolute poverty line, nearly 10 percent of the households
|
||||
|
|
@ -2892,7 +2898,7 @@
|
|||
eventtitle = {Vietnam {{Update Conference}}},
|
||||
isbn = {978-981-230-275-5 978-981-230-254-0},
|
||||
pagetotal = {392},
|
||||
keywords = {country::Vietnam,inequality::,next},
|
||||
keywords = {country::Vietnam,inequality::,status::skimmed},
|
||||
}
|
||||
|
||||
@article{ThanhThiPham2020,
|
||||
|
|
@ -2908,7 +2914,7 @@
|
|||
issn = {22120963},
|
||||
doi = {10.1016/j.crm.2020.100215},
|
||||
langid = {english},
|
||||
keywords = {next},
|
||||
keywords = {status::skimmed},
|
||||
file = {/home/marty/Zotero/storage/VZJ2IRQK/Thanh Thi Pham2020_Vulnerability
|
||||
assessment of households to flash floods and landslides in the.pdf},
|
||||
}
|
||||
|
|
@ -2955,7 +2961,7 @@
|
|||
url = {
|
||||
http://documents.worldbank.org/curated/en/237751646144451455/Djibouti-Economic-Monitor-Navigating-through-the-Pandemic-and-Regional-Tensions
|
||||
},
|
||||
keywords = {country::Djibouti,next,topic::poverty},
|
||||
keywords = {country::Djibouti,status::skimmed,topic::poverty},
|
||||
file = {/home/marty/Zotero/storage/BM6ZY8AB/Tsouck Ibounde2020_Tsouck
|
||||
Ibounde,Rick Emery Wes,Marina Mohammed,Nadir Le Borgne,Eric.pdf},
|
||||
}
|
||||
|
|
@ -2989,7 +2995,7 @@
|
|||
number = {HS/029/20E},
|
||||
institution = {{United Nations High Commissioner For Refugees}},
|
||||
location = {{Geneva}},
|
||||
keywords = {country::Uganda,next,topic::refugee},
|
||||
keywords = {country::Uganda,status::skimmed,topic::refugee},
|
||||
file = {/home/marty/Zotero/storage/2NPXANQ6/UNHCRNakivale Settlement
|
||||
profile.pdf},
|
||||
}
|
||||
|
|
@ -3001,7 +3007,7 @@
|
|||
series = {Inter-{{Agency Situation Report}}},
|
||||
institution = {{United Nations High Commissioner For Refugees}},
|
||||
location = {{Geneva}},
|
||||
keywords = {country::Uganda,next,topic::refugee},
|
||||
keywords = {country::Uganda,status::skimmed,topic::refugee},
|
||||
file = {/home/marty/Zotero/storage/LX2SGCK9/UNHCR2022_Uganda refugee
|
||||
emergency.pdf},
|
||||
}
|
||||
|
|
@ -3188,7 +3194,7 @@
|
|||
date = {2006},
|
||||
institution = {{Vietnam Academy of Social Sciences}},
|
||||
location = {{Hanoi}},
|
||||
keywords = {country::Vietnam,next,topic::poverty},
|
||||
keywords = {country::Vietnam,status::skimmed,topic::poverty},
|
||||
}
|
||||
|
||||
@report{VASS2011,
|
||||
|
|
@ -3198,7 +3204,7 @@
|
|||
date = {2011},
|
||||
institution = {{Vietnam Academy of Social Sciences}},
|
||||
location = {{Hanoi}},
|
||||
keywords = {country::Vietnam,next,topic::poverty},
|
||||
keywords = {country::Vietnam,status::skimmed,topic::poverty},
|
||||
file = {/home/marty/Zotero/storage/TMVA9NCG/VASS2011_Poverty Reduction in
|
||||
Vietnam.pdf},
|
||||
}
|
||||
|
|
@ -3257,7 +3263,7 @@
|
|||
empowerment and for development.},
|
||||
copyright = {COPYRIGHT 2021 Tennessee State University},
|
||||
langid = {english},
|
||||
keywords = {country::Uganda,next},
|
||||
keywords = {country::Uganda,status::skimmed},
|
||||
file = {/home/marty/Zotero/storage/7AQXA3SR/Walker2021_Role of women in
|
||||
economic development.pdf},
|
||||
}
|
||||
|
|
@ -3282,7 +3288,7 @@
|
|||
Phung Duc and Cuong, Nguyen Viet and Vu, Linh Hoang and Wells Dang
|
||||
, Andrew},
|
||||
editoratype = {collaborator},
|
||||
keywords = {next},
|
||||
keywords = {status::skimmed},
|
||||
file = {/home/marty/Zotero/storage/KC72ZFUP/Badiani-Magnusson2012_Vietnam
|
||||
poverty assessment.pdf},
|
||||
}
|
||||
|
|
@ -3296,11 +3302,21 @@
|
|||
url = {
|
||||
https://thedocs.worldbank.org/en/doc/381951474255092375-0010022016/Uganda-Poverty-Assessment-Report-2016
|
||||
},
|
||||
keywords = {next},
|
||||
keywords = {status::skimmed},
|
||||
file = {/home/marty/Zotero/storage/NUGTKD2Y/WorldBank2016_Uganda Poverty
|
||||
Assessment Report 2016.pdf},
|
||||
}
|
||||
|
||||
@report{WorldBank2020,
|
||||
title = {Doing {{Business}}},
|
||||
author = {{World Bank}},
|
||||
date = {2020},
|
||||
institution = {{World Bank}},
|
||||
location = {{Washington, D.C.}},
|
||||
url = {https://doingbusiness.org/},
|
||||
keywords = {topic::poverty},
|
||||
}
|
||||
|
||||
@report{WorldBank2021,
|
||||
title = {Tracking {{SDG}} 7: {{The Energy Progress}} {{Report}}},
|
||||
author = {{World Bank}},
|
||||
|
|
@ -3309,11 +3325,21 @@
|
|||
location = {{Washington, D.C.}},
|
||||
editora = {World Bank and IEA and IRENA and UNSD and WHO},
|
||||
editoratype = {collaborator},
|
||||
keywords = {next},
|
||||
keywords = {status::skimmed},
|
||||
file = {/home/marty/Zotero/storage/F8CBQJZA/World Bank2021_Tracking SDG
|
||||
7.pdf},
|
||||
}
|
||||
|
||||
@report{WorldBank2021a,
|
||||
title = {Global {{Findex Database}}},
|
||||
author = {{World Bank}},
|
||||
date = {2021},
|
||||
institution = {{World Bank}},
|
||||
location = {{Washington, D.C.}},
|
||||
url = {https://www.worldbank.org/en/publication/globalfindex/},
|
||||
keywords = {topic::poverty},
|
||||
}
|
||||
|
||||
@report{WorldBank2022,
|
||||
title = {Uganda - {{Learning Poverty Brief}}},
|
||||
author = {{World Bank}},
|
||||
|
|
@ -3388,6 +3414,41 @@
|
|||
Landscape.pdf},
|
||||
}
|
||||
|
||||
@report{WorldBank2022e,
|
||||
title = {Global {{Database}} of {{Shared Prosperity}} (9th Edition, circa
|
||||
2014–19)},
|
||||
author = {{World Bank}},
|
||||
date = {2022},
|
||||
institution = {{World Bank}},
|
||||
location = {{Washington, D.C.}},
|
||||
url = {
|
||||
https://www.worldbank.org/en/topic/poverty/brief/global-database-of-shared-prosperity
|
||||
},
|
||||
keywords = {topic::poverty},
|
||||
}
|
||||
|
||||
@report{WorldBank2022f,
|
||||
title = {Women, {{Business}} and the {{Law}} 1971-2022},
|
||||
author = {{World Bank}},
|
||||
date = {2022},
|
||||
institution = {{World Bank}},
|
||||
location = {{Washington, D.C.}},
|
||||
url = {https://wbl.worldbank.org/},
|
||||
keywords = {topic::poverty},
|
||||
}
|
||||
|
||||
@dataset{WorldBank2022g,
|
||||
title = {Gender {{Statistics}} – {{Version}} 23 {{June}} 2022},
|
||||
author = {{World Bank}},
|
||||
date = {2022-06-30},
|
||||
publisher = {{World Bank}},
|
||||
location = {{Washington, D.C.}},
|
||||
doi = {10.35188/UNU-WIDER/WIID-300622},
|
||||
langid = {english},
|
||||
keywords = {country::Benin,country::Djibouti,country::Ethiopia,
|
||||
country::Uganda,country::Vietnam},
|
||||
}
|
||||
|
||||
@book{WorldBankWashingtonDistrictofColumbia2020,
|
||||
title = {Poverty and {{Shared Prosperity}} 2020: Reversals of Fortune},
|
||||
shorttitle = {Poverty and {{Shared Prosperity}} 2020},
|
||||
|
|
|
|||
Binary file not shown.
|
|
@ -2,7 +2,7 @@
|
|||
<html xmlns="http://www.w3.org/1999/xhtml" lang="en" xml:lang="en"><head>
|
||||
|
||||
<meta charset="utf-8">
|
||||
<meta name="generator" content="quarto-1.1.147">
|
||||
<meta name="generator" content="quarto-1.1.251">
|
||||
|
||||
<meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=yes">
|
||||
|
||||
|
|
@ -2912,7 +2912,7 @@ vertical-align: -.125em;
|
|||
<p>Vietnam’s economy is now firmly in the third decade of ongoing economic reform (<em>Doi Moi</em>) as a market-based economy, which lead to remarkable growth phases through opening the economy to international trade while, seen over the bulk of its population, attempting to keep inequality rates managed through policies of controlling credit and reducing subsidies to state-owned enterprises <span class="citation" data-cites="Bui2019">(<a href="#ref-Bui2019" role="doc-biblioref">Bui & Imai, 2019</a>)</span>.</p>
|
||||
<!-- poor/poverty <40%; mention low social mobility: different social insurances [@Bui2019] -->
|
||||
<p>Poverty in Vietnam is marked by a drastic reduction in absolute terms over this time with some of the decline directly attributable to the liberalization of markets over the country’s growth more generally <span class="citation" data-cites="WorldBank2012 McCaig2011 Le2022">(<a href="#ref-Le2022" role="doc-biblioref">N. V. T. Le et al., 2022</a>; <a href="#ref-McCaig2011" role="doc-biblioref">McCaig, 2011</a>; <a href="#ref-WorldBank2012" role="doc-biblioref">World Bank, 2012</a>)</span>. While the rate of decline slowed since the mid-2000s <span class="citation" data-cites="VASS2006 VASS2011">(<a href="#ref-VASS2006" role="doc-biblioref">VASS, 2006</a>, <a href="#ref-VASS2011" role="doc-biblioref">2011</a>)</span>, it continued declining in tandem with small income inequality decreases. The overall income inequality decrease that Vietnam experienced from the early 2000s suggests that economic growth has been accompanied by equity extending beyond poverty reduction <span class="citation" data-cites="Benjamin2017">(<a href="#ref-Benjamin2017" role="doc-biblioref">Benjamin et al., 2017</a>)</span>. On the other hand, Le et al. <span class="citation" data-cites="Le2021">(<a href="#ref-Le2021" role="doc-biblioref">2021</a>)</span> suggest a slight increase in overall income distribution from 2010-2018. At the same time, the population groups most affected by poverty through welfare inequalities stay unaltered, as do largely the primary factors accompanying it: There is severe poverty persistence among ethnic minorities in Vietnam <span class="citation" data-cites="Baulch2012">(<a href="#ref-Baulch2012" role="doc-biblioref">Baulch et al., 2012</a>)</span>, concomitant with low education and skills, more prevalent dependency on subsistence agriculture, physical and social isolation, specific disadvantages which become linked to ethnic identities and a greater exposure to natural disasters and risks <span class="citation" data-cites="Kozel2014">(<a href="#ref-Kozel2014" role="doc-biblioref">Kozel, 2014</a>)</span>.</p>
|
||||
<p>The country’s overall estimated Gini coefficient for income fluctuates between 0.42 and 0.44 between 2010 and 2018, with the highest levels of income inequality observed in the Central Highlands in 2016, though absolute income may be rising, with the top quintile having 9.2 times the income of the lowest quintile in 2010 and 9.8 times in 2016 <span class="citation" data-cites="Le2021">(<a href="#ref-Le2021" role="doc-biblioref">Q. H. Le et al., 2021</a>)</span>. For Gini coefficients estimated using consumption per capita, see <a href="#fig-vnm">Figure 1</a>, which shows similar trends of increasing inequality, with 2010 constituting a significant increase. Economic inequality and poverty in Vietnam thus underlies an intersectional focus, between ethnic minorities, regional disparities, rural-urban divides and gendered lines, one which exogenous shocks can rapidly exacerbate as the example of the COVID-19 pandemic has recently shown <span class="citation" data-cites="Ebrahim2021">(<a href="#ref-Ebrahim2021" role="doc-biblioref">Ebrahim et al., 2021</a>)</span>.</p>
|
||||
<p>The country’s overall estimated Gini coefficient for income fluctuates between 0.42 and 0.44 between 2010 and 2018, with the highest levels of income inequality observed in the Central Highlands in 2016, though absolute income may be rising, with the top quintile having 9.2 times the income of the lowest quintile in 2010 and 9.8 times in 2016 <span class="citation" data-cites="Le2021">(<a href="#ref-Le2021" role="doc-biblioref">Q. H. Le et al., 2021</a>)</span>. On the other hand, the bottom 40% experienced a slight absolute rise in mean income per capita from 4.00 USD (2011 PPP) in 2014 to 5.00 USD (2011 PPP) in 2018 <span class="citation" data-cites="WorldBank2022e">(<a href="#ref-WorldBank2022e" role="doc-biblioref">World Bank, 2022d</a>)</span>. For Gini coefficients estimated using consumption per capita, see <a href="#fig-vnm">Figure 1</a>, which shows similar trends of increasing inequality, with 2010 constituting a significant increase. Economic inequality and poverty in Vietnam thus underlies an intersectional focus, between ethnic minorities, regional disparities, rural-urban divides and gendered lines, one which exogenous shocks can rapidly exacerbate as the example of the COVID-19 pandemic has recently shown <span class="citation" data-cites="Ebrahim2021">(<a href="#ref-Ebrahim2021" role="doc-biblioref">Ebrahim et al., 2021</a>)</span>.</p>
|
||||
<div class="cell" data-execution_count="8">
|
||||
<details>
|
||||
<summary>Code</summary>
|
||||
|
|
@ -3075,7 +3075,7 @@ vertical-align: -.125em;
|
|||
<div data-custom-style="caption">
|
||||
<p>Note: Values shown are for all Official Development Assistance flows valid under the OECD ODA data, split into bilateral development donor countries (dac) and multilateral donors (mlt), as constant currency (2020 corrected) USD millions. Source: Author’s elaboration based on OECD ODA CRS (2022).</p>
|
||||
</div>
|
||||
<p>Bilateral donor contributions make up the largest part of development aid contributions to Vietnam, as can be seen in <a href="#fig-vnm-aid-donortype">Figure 3</a>. Both bilateral and multilateral contributions increase from 2011 to 2014 and subsequently begin decreasing. While bilateral contributions do not increase in absolute amounts afterwards until 2020, multilateral contributions increase again from 2019 to 2020. Nevertheless, bilateral contributions are consistently higher than multilateral, having around a 1.5 times higher share of absolute USD contribution, though growing to just over 2 times the share in 2017, before quickly shrinking down to just 1.3 times the share of multilateral contributions in 2018. This gap may close further in the future, with multilateral contributions being on an increase and bilateral contributions still decreasing.</p>
|
||||
<p>Bilateral donor contributions make up the largest part of development aid contributions to Vietnam, as can be seen in <a href="#fig-vnm-aid-donortype">Figure 3</a>. Both bilateral and multilateral contributions increase from 2011 to 2014 and subsequently begin decreasing. While bilateral contributions do not increase in absolute amounts afterwards, until 2020, multilateral contributions do increase again from 2019 to 2020. Nevertheless, bilateral contributions are consistently higher than multilateral, having around a 1.5 times higher share of absolute USD contribution, though growing to just over 2 times the share in 2017, before quickly shrinking down to just 1.3 times the share of multilateral contributions in 2018. This gap may close further in the future, with multilateral contributions being on an increase and bilateral contributions still decreasing.</p>
|
||||
<div class="cell page-columns page-full" data-execution_count="12">
|
||||
<details>
|
||||
<summary>Code</summary>
|
||||
|
|
@ -3228,7 +3228,7 @@ vertical-align: -.125em;
|
|||
<p>Note: Values shown are for all Official Development Assistance flows valid under the OECD CRS data, calculated as constant currency (2020 corrected) USD millions. The categories under analysis are large- and small-scale water supply and sanitation infrastructure projects as well as disaster risk reduction which includes improved flooding prevention infrastructure. Source: Author’s elaboration based on OECD ODA CRS (2022).</p>
|
||||
</div>
|
||||
<p>The breakdown of project-based development aid for water supply infrastructure and disaster risk reduction in Vietnam can be seen in <a href="#tbl-vnm-aid-water">Table 1</a>. It shows the funds broken down into their use for three categories: First, contributions to provide access to basic water supply and sanitation, which subsumes building and maintaining handpumps, gravity wells, rainwater collection systems, storage tanks, and small, often shared, distribution systems. Second, contributions to large-scale water supply and sanitation, including potable water treatment plants, intake works, large pumping stations and storage, as well as the transmission and distribution through large-scale systems. And last, contributions towards disaster risk reduction which is a larger umbrella concept aimed at building local and national capacities, but includes infrastructure measures (e.g. flood protection systems), preparedness measures (such as early warning systems), and normative prevention measures (such as closer adherence to building and structural codes), as well as risk transfer systems (insurance schemes or risk funds). This constitutes the closest category to flood risk management itself, which is part of the overarching disaster risk management dimension.</p>
|
||||
<p>While overall aid contributions to Vietnam’s water supply and risk management sectors have slightly increased over time from 206m USD in 2011 to their peak of 422m USD in 2016, they have largely stagnated around the level of 300m to 350m USD per year since then. From the level of 96m USD in 2011, access to basic water supply saw significant increases to its contributions from 2013 to 2016, with 154m USD contributed at its peak in 2016 and shrinking drastically the following years to 39m USD in 2019, its lowest contribution year. Large water supply project contributions see a similar if less drastic curve, with contributions increasing from 105m USD in 2011 to 252m USD at their in 2018, before decreasing slightly over the next two years.</p>
|
||||
<p>While overall aid contributions to Vietnam’s water supply and risk management sectors have slightly increased over time from 206m USD in 2011 to their peak of 422m USD in 2016, they have largely stagnated around the level of 300m to 350m USD per year since then. From the level of 96m USD in 2011, access to basic water supply saw significant increases to its contributions from 2013 to 2016, with 154m USD contributed at its peak in 2016 and shrinking drastically the following years to 39m USD in 2019, its lowest contribution year. Large water supply project contributions see a similar if less drastic curve, with contributions increasing from 105m USD in 2011 to 252m USD at their peak in 2018, before decreasing slightly over the next two years.</p>
|
||||
<p>Thus, the contribution curves to basic and large-scale water supply projects somewhat follows the overall development aid contribution curve to Vietnam, with peaks between 2016 and 2018 before more or less drastic drops in aid contributions. Disaster risk reduction contributions, however, show the least similarity to the general trend, with contributions being only 4m USD in 2011 before increasing year-over-year (with the exception of 2018) to reach their peak with 63m USD in 2020. The most significant increases happened between the years 2014 and 2016, as well as again in 2020. While the other contribution sectors follow a shrinking contribution in the years following 2014, then, disaster risk reduction instead keeps on reaching an increase in its absolute contribution amounts, perhaps pointing to a continued necessity for development in the sector.</p>
|
||||
<div style="page-break-after: always;"></div>
|
||||
</section>
|
||||
|
|
@ -3246,7 +3246,7 @@ vertical-align: -.125em;
|
|||
</ul>
|
||||
<hr>
|
||||
<!-- intro/overall -->
|
||||
<p>Uganda generally has a degree of inequality that fluctuates but over time seems largely unchanged, as does the share of people below its poverty line in recent years. The long-term level of income inequality in the country stayed relatively stagnant, with a Gini coefficient for the consumption per capita of 0.36 calculated for the 1992/93 census and a World Bank calculation of 0.43 for the year 2019, with the coefficient rising slighly in the years 2002/03 and 2009/10 during its fluctuation <span class="citation" data-cites="Atamanov2022">(<a href="#ref-Atamanov2022" role="doc-biblioref">World Bank, 2022f</a>, see also <a href="#fig-uga" role="doc-biblioref">Figure 4</a>)</span>, while <span class="citation" data-cites="Lwanga-Ntale2014">Lwanga-Ntale (<a href="#ref-Lwanga-Ntale2014" role="doc-biblioref">2014</a>)</span> finds a slight upward trend over time. However, the aggregation masks several important distinctions: Rural inequality overall is lower than urban inequality, with <span class="citation" data-cites="Lwanga-Ntale2014">Lwanga-Ntale (<a href="#ref-Lwanga-Ntale2014" role="doc-biblioref">2014</a>)</span> finding Gini coefficients of 0.35 and 0.41 for 2012/13 respectively. Additionally, he sees inequalities between income quintiles primarily driven by the highest (0.25) and lowest (0.14) quintiles, whereas middle-income show lower Gini coefficients (0.05-0.07). These inequality levels remained mostly unchanged between 2012/13 and 2019/20 but hide qualitative dimensions such as the shift out of a lower-income agricultural livelihood predominantly taking place among older men who have at least some level of formal education and are from already more well-off households <span class="citation" data-cites="Atamanov2022">(<a href="#ref-Atamanov2022" role="doc-biblioref">World Bank, 2022f</a>)</span>.</p>
|
||||
<p>Uganda generally has a degree of inequality that fluctuates but over time seems largely unchanged, as does the share of people below its poverty line in recent years. The long-term level of income inequality in the country stayed relatively stagnant, with a Gini coefficient for the consumption per capita of 0.36 calculated for the 1992/93 census and a World Bank calculation of 0.43 for the year 2019, with the coefficient rising slighly in the years 2002/03 and 2009/10 during its fluctuation <span class="citation" data-cites="Atamanov2022">(<a href="#ref-Atamanov2022" role="doc-biblioref">World Bank, 2022h</a>, see also <a href="#fig-uga" role="doc-biblioref">Figure 4</a>)</span>, while <span class="citation" data-cites="Lwanga-Ntale2014">Lwanga-Ntale (<a href="#ref-Lwanga-Ntale2014" role="doc-biblioref">2014</a>)</span> finds a slight upward trend over time. However, the aggregation masks several important distinctions: Rural inequality overall is lower than urban inequality, with <span class="citation" data-cites="Lwanga-Ntale2014">Lwanga-Ntale (<a href="#ref-Lwanga-Ntale2014" role="doc-biblioref">2014</a>)</span> finding Gini coefficients of 0.35 and 0.41 for 2012/13 respectively. Additionally, he sees inequalities between income quintiles primarily driven by the highest (0.25) and lowest (0.14) quintiles, whereas middle-income show lower Gini coefficients (0.05-0.07). These inequality levels remained mostly unchanged between 2012/13 and 2019/20 but hide qualitative dimensions such as the shift out of a lower-income agricultural livelihood predominantly taking place among older men who have at least some level of formal education and are from already more well-off households <span class="citation" data-cites="Atamanov2022">(<a href="#ref-Atamanov2022" role="doc-biblioref">World Bank, 2022h</a>)</span>.</p>
|
||||
<div class="cell" data-execution_count="13">
|
||||
<details>
|
||||
<summary>Code</summary>
|
||||
|
|
@ -3267,17 +3267,17 @@ vertical-align: -.125em;
|
|||
<p>Source: Author’s elaboration based on UNU-WIDER WIID (2022).</p>
|
||||
</div>
|
||||
<!-- poverty -->
|
||||
<p>The World Bank <span class="citation" data-cites="Atamanov2022">(<a href="#ref-Atamanov2022" role="doc-biblioref">2022f</a>)</span> report goes on to examine the share of people below the poverty line in Uganda: around 30% of households are in a state of poverty in 2019/20, which once again fluctuated but roughly reflects the share of 30.7% households in poverty in 2012/13. Two surges in rural household poverty in 2012/2013 and 2016/17 can be linked to droughts in the country, with an improvement in 2019/20 conversely being linked to favorable weather conditions. <!-- TODO find citation or put Atamanov --> <span class="citation" data-cites="Ssewanyana2012">Ssewanyana & Kasirye (<a href="#ref-Ssewanyana2012" role="doc-biblioref">2012</a>)</span> find that in absolute terms poverty fell significantly (from 28.5% in 2005/06 to 23.9% in 2009/10) but there are clear relative regional differences emerging, with Western Ugandan households increasing in poverty while Northern and Eastern households reduced their share of households below the poverty line. Additionally, they find that while transient poverty is more common than chronic poverty in Uganda, nearly 10% of households continue to live in persistent material deprivation.</p>
|
||||
<p>Lastly, for a long time it has been seen as an issue that Uganda puts its national poverty line too low with the line being put between 0.94 USD PPP and 1.07 USD PPP depending on the province (lower than the international live of 1.90 USD PPP), while <span class="citation" data-cites="vandeVen2021">van de Ven et al. (<a href="#ref-vandeVen2021" role="doc-biblioref">2021</a>)</span> estimate a living income of around 3.82 USD PPP would be required for a national poverty line that meets basic human rights for a decent living. <!-- TODO find a source for the national poverty line being too low (quant data is already in vandeVen2021) --></p>
|
||||
<p>The World Bank <span class="citation" data-cites="Atamanov2022">(<a href="#ref-Atamanov2022" role="doc-biblioref">2022h</a>)</span> report goes on to examine the share of people below the poverty line in Uganda: around 30% of households are in a state of poverty in 2019/20, which once again fluctuated but roughly reflects the share of 30.7% households in poverty in 2012/13. Two surges in rural household poverty in 2012/2013 and 2016/17 can be linked to droughts in the country, with an improvement in 2019/20 conversely being linked to favorable weather conditions. <!-- TODO find citation or put Atamanov --> <span class="citation" data-cites="Ssewanyana2012">Ssewanyana & Kasirye (<a href="#ref-Ssewanyana2012" role="doc-biblioref">2012</a>)</span> find that in absolute terms poverty fell significantly (from 28.5% in 2005/06 to 23.9% in 2009/10) but there are clear relative regional differences emerging, with Western Ugandan households increasing in poverty while Northern and Eastern households reduced their share of households below the poverty line. Additionally, they find that while transient poverty is more common than chronic poverty in Uganda, nearly 10% of households continue to live in persistent material deprivation.</p>
|
||||
<p>Lastly, for a long time it has been seen as an issue that Uganda puts its national poverty line too low with the line being put between 0.94 USD (2011 PPP) and 1.07 USD (2011 PPP) depending on the province (lower than the international live of 1.90 USD PPP), while <span class="citation" data-cites="vandeVen2021">van de Ven et al. (<a href="#ref-vandeVen2021" role="doc-biblioref">2021</a>)</span> estimate a living income of around 3.82 USD (2011 PPP) would be required for a national poverty line that meets basic human rights for a decent living. In absolute terms, the bottom 40% of Uganda had a median daily income of 1.28 USD (2011 PPP) in 2016 which kept stable to 2019 <span class="citation" data-cites="WorldBank2022e">(<a href="#ref-WorldBank2022e" role="doc-biblioref">World Bank, 2022d</a>)</span>.</p>
|
||||
<!-- endowment/assets: education, ..? -->
|
||||
<p>Esaku <span class="citation" data-cites="Esaku2021 Esaku2021a">(<a href="#ref-Esaku2021" role="doc-biblioref">2021b</a>, <a href="#ref-Esaku2021a" role="doc-biblioref">2021a</a>)</span> finds a somewhat circular driving relationship between Ugandan inequality, poverty and working in what calls the shadow economy: inequality increases the size of the informal economy, as a large subsistence sector creates revenue tax shortfalls, undermines the governments efforts to attain equitable income distributions in the economy and the creation of social safety nets for the poort, who, in turn, have to turn to the informal economy to secure their livelihoods, increasing its size both short- and long-term and feeding back into the cycle.</p>
|
||||
<p><span class="citation" data-cites="Cali2014">Cali (<a href="#ref-Cali2014" role="doc-biblioref">2014</a>)</span> finds that, already, one of the primary determinants of income disparity in more trade-exposed markets of Uganda in the 1990s were the increasing education differences leading to more disparate wage premiums. Additionally, slow structural change — further impeded by the onset of the COVID-19 pandemic, which pushed both urban and rural residents back into poverty — leaves a low-productivity agricultural sector which becomes, in combination with a lack of education, the strongest predictor of poverty: the poverty rate in households with an uneducated household head (17% of all households) is 48% (2019/20), while already households with a household head possessing primary education (also 17% of all) nearly cuts this in half with 25% poverty rate (2019/20) <span class="citation" data-cites="Atamanov2022">(<a href="#ref-Atamanov2022" role="doc-biblioref">World Bank, 2022f</a>)</span>.</p>
|
||||
<p>The World Bank <span class="citation" data-cites="WorldBank2022">(<a href="#ref-WorldBank2022" role="doc-biblioref">2022e</a>)</span> calculated a Learning Poverty Indicator for Uganda which finds that 82% of children at late primary age are not proficient in reading, 81% of children do not achieve minimum proficiency level in reading at the end of primary schooling, and 4% of primary school-aged children are not enrolled in school at all. <span class="citation" data-cites="Datzberger2018">Datzberger (<a href="#ref-Datzberger2018" role="doc-biblioref">2018</a>)</span> argues these problems primarily exist in Uganda due to choosing an approach to education that is primarily assimilation-based, that is, intended to effect change at the individual-level through fostering grassroots education throughout society at large, instead of looking into more transformative policy approaches which would operate on a more systemic level, removing oppressive structures of inequality in tandem with government institutions at multiple levels.</p>
|
||||
<p><span class="citation" data-cites="Cali2014">Cali (<a href="#ref-Cali2014" role="doc-biblioref">2014</a>)</span> finds that, already, one of the primary determinants of income disparity in more trade-exposed markets of Uganda in the 1990s were the increasing education differences leading to more disparate wage premiums. Additionally, slow structural change — further impeded by the onset of the COVID-19 pandemic, which pushed both urban and rural residents back into poverty — leaves a low-productivity agricultural sector which becomes, in combination with a lack of education, the strongest predictor of poverty: the poverty rate in households with an uneducated household head (17% of all households) is 48% (2019/20), while already households with a household head possessing primary education (also 17% of all) nearly cuts this in half with 25% poverty rate (2019/20) <span class="citation" data-cites="Atamanov2022">(<a href="#ref-Atamanov2022" role="doc-biblioref">World Bank, 2022h</a>)</span>.</p>
|
||||
<p>The World Bank <span class="citation" data-cites="WorldBank2022">(<a href="#ref-WorldBank2022" role="doc-biblioref">2022g</a>)</span> calculated a Learning Poverty Indicator for Uganda which finds that 82% of children at late primary age are not proficient in reading, 81% of children do not achieve minimum proficiency level in reading at the end of primary schooling, and 4% of primary school-aged children are not enrolled in school at all. <span class="citation" data-cites="Datzberger2018">Datzberger (<a href="#ref-Datzberger2018" role="doc-biblioref">2018</a>)</span> argues these problems primarily exist in Uganda due to choosing an approach to education that is primarily assimilation-based, that is, intended to effect change at the individual-level through fostering grassroots education throughout society at large, instead of looking into more transformative policy approaches which would operate on a more systemic level, removing oppressive structures of inequality in tandem with government institutions at multiple levels.</p>
|
||||
<!-- water access -->
|
||||
<section id="inequalities-in-access-to-drinking-water" class="level3">
|
||||
<h3 class="anchored" data-anchor-id="inequalities-in-access-to-drinking-water">Inequalities in access to drinking water</h3>
|
||||
<p>Such personal circumstances as access to a timely education play decisive role in life and human capital development — circumstances to which decent housing as well as access to clean water are equally fundamental building blocks <span class="citation" data-cites="Atamanov2022">(<a href="#ref-Atamanov2022" role="doc-biblioref">World Bank, 2022f</a>)</span>. In 1990 a policy initiative to shift from a supply-driven to a demand-driven model for rural drinking water provision was enacted which, over time, improved rural safe water coverage slightly but also made operation and maintenance of improved water sources pose a challenge that could impede long-term access to safe water.</p>
|
||||
<p>In the country, access to improved water sources rose from 44% in 1990 to 60% in 2004 and 66% in 2010 <span class="citation" data-cites="Naiga2015">(<a href="#ref-Naiga2015" role="doc-biblioref">Naiga et al., 2015</a>)</span>. In 2019, access to improved sources of drinking water in the country is at a level of 87% in urban areas and 74% in rural areas, with relatively little inequality in rural regions between poor and non-poor households <span class="citation" data-cites="Atamanov2022">(<a href="#ref-Atamanov2022" role="doc-biblioref">World Bank, 2022f</a>)</span>.</p>
|
||||
<p>Such personal circumstances as access to a timely education play decisive role in life and human capital development — circumstances to which decent housing as well as access to clean water are equally fundamental building blocks <span class="citation" data-cites="Atamanov2022">(<a href="#ref-Atamanov2022" role="doc-biblioref">World Bank, 2022h</a>)</span>. In 1990 a policy initiative to shift from a supply-driven to a demand-driven model for rural drinking water provision was enacted which, over time, improved rural safe water coverage slightly but also made operation and maintenance of improved water sources pose a challenge that could impede long-term access to safe water.</p>
|
||||
<p>In the country, access to improved water sources rose from 44% in 1990 to 60% in 2004 and 66% in 2010 <span class="citation" data-cites="Naiga2015">(<a href="#ref-Naiga2015" role="doc-biblioref">Naiga et al., 2015</a>)</span>. In 2019, access to improved sources of drinking water in the country is at a level of 87% in urban areas and 74% in rural areas, with relatively little inequality in rural regions between poor and non-poor households <span class="citation" data-cites="Atamanov2022">(<a href="#ref-Atamanov2022" role="doc-biblioref">World Bank, 2022h</a>)</span>.</p>
|
||||
<p>Health care facilities in rural areas are generally well connected to improved sources with 94% of facilities having access to public stand posts, protected spring technology, deep boreholes and some to rain harvesting tanks, gravity flow schemes or groundwater-based pumped piped water supplies <span class="citation" data-cites="Mulogo2018">(<a href="#ref-Mulogo2018" role="doc-biblioref">Mulogo et al., 2018</a>)</span>. Thus, individual households are generally less well connected than health care facilities, and rural households in turn less well than urban households.</p>
|
||||
<!-- Isingiro district -->
|
||||
<p>The same study found for the Isingiro district in Western Uganda on the other hand, in 2010, only 28% of households had access to improved water <span class="citation" data-cites="Mulogo2018">(<a href="#ref-Mulogo2018" role="doc-biblioref">Mulogo et al., 2018</a>)</span>. <!-- TODO check validity --> <span class="citation" data-cites="Naiga2015">Naiga et al. (<a href="#ref-Naiga2015" role="doc-biblioref">2015</a>)</span> investigated the characteristics of improved water access in the Isingiro district, finding that whereas the national average distance to travel for a water source is 0.2km in urban and 0.8km in rural locations, in Isingiro it is 1.5km, and of the fewer existing improved water sources, only 53% were fully functional, with 24% being only partly functional (having only low or intermittent yield) and 18% not being functional at all. Additionally, they found blocked drainage channels in some of the sources which could in turn lead to a possible health risk due to contamination of the source.</p>
|
||||
|
|
@ -3345,7 +3345,7 @@ vertical-align: -.125em;
|
|||
<div data-custom-style="caption">
|
||||
<p>Note: Values shown are for all Official Development Assistance flows valid under the OECD CRS data, split into the type of financing flow, calculated as constant currency (2020 corrected) USD millions. Source: Author’s elaboration based on OECD ODA CRS (2022).</p>
|
||||
</div>
|
||||
<p>Overall Ugandan development aid reception is high, with over 1.5bn USD granted as official development assistance in 2011 as seen in <a href="#fig-uga-aid-financetype">Figure 5</a>. The Official Development Assistance overall further increased to over 2.2bn USD in 2019, before rapidly increasing in 2020 to over 3.0bn USD. The overall trend of increasing aid money is largely due to increases in development grants which especially increased from 2015 to 2017. In general, development loans play a smaller role in absolute terms: Whereas in 2011 around 1.2bn USD funds came in the form of grants, only around 0.3bn USD were in the form of loans. The absolute portion of loans slowly increased until 2019 to just over 500m USD, before significantly increasing in 2020, tripling to almost 1.5bn USD.</p>
|
||||
<p>Overall Ugandan development aid reception is high, with over 1.5bn USD granted as official development assistance in 2011 as seen in <a href="#fig-uga-aid-financetype">Figure 5</a>. The Official Development Assistance overall further increased to over 2.2bn USD in 2019, before rapidly increasing in 2020 to over 3.0bn USD. The overall trend of increasing aid money is largely due to increases in development grants which especially increased from 2015 to 2017. In general, development loans play a smaller role in absolute terms: Whereas in 2011 around 1.2bn USD funds came in the form of grants, only around 300m USD were in the form of loans. The absolute portion of loans slowly increased until 2019 to just over 500m USD, before significantly increasing in 2020, tripling to almost 1.5bn USD.</p>
|
||||
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|
||||
<details>
|
||||
<summary>Code</summary>
|
||||
|
|
@ -3382,7 +3382,7 @@ vertical-align: -.125em;
|
|||
<div data-custom-style="caption">
|
||||
<p>Note: Values shown are for all Official Development Assistance flows valid under the OECD ODA data, split into bilateral development donor countries (dac) and multilateral donors (mlt), as constant currency (2020 corrected) USD millions. Source: Author’s elaboration based on OECD ODA CRS (2022).</p>
|
||||
</div>
|
||||
<p>In terms of predominant donor types, bilateral aid to Uganda was much higher than multilateral aid to the country until 2019. In 2011 only about 400m USD were provided through multilateral donors while almost 1.2bn USD were provided via bilateral donors, though the multilateral contributions quickly grew to over 600m USD in 2013. Despite a significant decrease of multilateral aid in 2014, the amount of multilateral aid kept generally stagnant until 2018, when the amount first increased to 800m USD in 2019 and subsequently to over 1.7bn in 2020.</p>
|
||||
<p>In terms of predominant donor types, bilateral aid to Uganda was much higher than multilateral aid to the country until 2019. In 2011 only about 400m USD were provided through multilateral donors while almost 1.2bn USD were provided via bilateral donors, though the multilateral contributions quickly grew to over 600m USD in 2013. Despite a single significant decrease of multilateral aid in 2014, the amount of multilateral aid kept generally stagnant until 2018, when the amount first increased to 800m USD in 2019 and subsequently to over 1.7bn in 2020.</p>
|
||||
<div class="cell page-columns page-full" data-execution_count="17">
|
||||
<details>
|
||||
<summary>Code</summary>
|
||||
|
|
@ -3531,9 +3531,9 @@ vertical-align: -.125em;
|
|||
<div data-custom-style="caption">
|
||||
<p>Note: Values shown are for all Official Development Assistance flows valid under the OECD CRS data, calculated as constant currency (2020 corrected) USD millions. The categories under analysis are large- and small-scale water supply and sanitation infrastructure projects as well as education and training for the management of water supply infrastructure. Source: Author’s elaboration based on OECD ODA CRS (2022).</p>
|
||||
</div>
|
||||
<p>The breakdown of development aid to water supply infrastructure and education projects can be seen in <a href="#tbl-uga-aid-watersupply">Table 2</a>. It shows that overall the contributions to improve water access have been increasing, starting at 42.27m USD in 2011 and climbing to 146.43m USD by 2020. The development funds are broken down into three categories: Basic and large water supply improvement and education and training.</p>
|
||||
<p>The breakdown of development aid to water supply infrastructure and education projects can be seen in <a href="#tbl-uga-aid-watersupply">Table 2</a>. It shows that overall the contributions to improve water access have been increasing, starting at 42.27m USD in 2011 and climbing to 146.43m USD by 2020. The development funds are broken down into three categories: Basic water supply improvement, large water supply improvement and education and training.</p>
|
||||
<p>Education and training encompasses training for both professionals in the field itself and service providers. Water supply improvement is broken down into funds for large systems — potable water treatment plants, intake works, large pumping stations and storage, as well as large-scale transmission and distribution systems — and more individual-level basic water supply, such as handpumps, gravity wells, rainwater collection systems, storage tanks, and smaller, often shared, distributions systems. The basic water supply encompasses a more endpoint-oriented collection of measures, often situated in rural locations. Both the large and small scale categories encompass sanitation, with larger-scale sewage pumping stations and trunk sewers, as well as smaller on-site disposal and sanitation systems, latrines and alternative systems. This is due to most infrastructure projects missing the concrete dimensions to separate water supply from sanitation in the data, either due to infrastructural overlap or missing data points.</p>
|
||||
<p>The split shows that while basic water supply infrastructure projects have tended to be contributed to between 10m USD and 20m USD, with little overall increase from 2011 to 2020. Large-scale water supply and sanitation projects have, however, seen a significant increase over time, starting at a contribution of 17m USD in 2011 and receiving a 125.15m USD contribution in 2020. This may speak to the necessity of larger infrastructure in place before more basic water supply infrastructure can make use of it, or the provision of large infrastructure at the cost of implementations at smaller scales.</p>
|
||||
<p>The split shows that while basic water supply infrastructure projects tended to see contributions between 10m USD and 20m USD, with little overall increase from 2011 to 2020. Large-scale water supply and sanitation projects have, however, seen a significant increase over time, starting at a contribution of 17m USD in 2011 and receiving a 125.15m USD contribution in 2020. This may speak to the necessity of larger infrastructure in place before more basic water supply infrastructure can make use of it, or the provision of large infrastructure at the cost of implementations at smaller scales.</p>
|
||||
<p>Education and training for water infrastructure management and service provision, while still receiving contributions of 14.53m USD and 12.40m USD in 2011 and 2012 respectively, significantly decrease over the next years to amounts continuously under one million. The monetary focus for aid provision thus lies on large-scale water supply and sanitation projects for these years.</p>
|
||||
<div style="page-break-after: always;"></div>
|
||||
</section>
|
||||
|
|
@ -3551,7 +3551,7 @@ vertical-align: -.125em;
|
|||
</ul>
|
||||
<hr>
|
||||
<!-- intro/overall -->
|
||||
<p>Benin in recent years has seen fairly stable real GDP growth rates and downward trending poverty levels in absolute terms. Its growth rate averaged 6.4% for the years 2017 to 2019 and, with a decrease during the intermittent years due to the Covid-19 pandemic, has recovered to a rate of 6.6% in 2021 <span class="citation" data-cites="WorldBank2022b">(<a href="#ref-WorldBank2022b" role="doc-biblioref">World Bank, 2022c</a>)</span>. There only exists sporadic and fluctuating data on the country’s overall inequality, with the World Bank Development Index noting a Gini coefficient of 38.6 for the year (2003) before rising to 43.4 (2011) and up to 47.8 (2015), though decreasing below the 2003 level to 37.8 (2018) in its most recent calculation, see <a href="#fig-ben">Figure 7</a>. At the same time, the country’s poverty rate, even measured based on the international line, only decreased at a very slow rate in its most recent years, from a share of households in poverty at 18.8% in 2019, to 18.7% in 2020 and 18.3% at the end of 2021, with the reduction threatened to be slowed further through increased prices on food and energy <span class="citation" data-cites="WorldBank2022b">(<a href="#ref-WorldBank2022b" role="doc-biblioref">World Bank, 2022c</a>)</span>.</p>
|
||||
<p>Benin in recent years has seen fairly stable real GDP growth rates and downward trending poverty levels in absolute terms. Its growth rate averaged 6.4% for the years 2017 to 2019 and, with a decrease during the intermittent years due to the Covid-19 pandemic, has recovered to a rate of 6.6% in 2021 <span class="citation" data-cites="WorldBank2022b">(<a href="#ref-WorldBank2022b" role="doc-biblioref">World Bank, 2022e</a>)</span>. There only exists sporadic and fluctuating data on the country’s overall inequality, with the World Bank Development Index noting a Gini coefficient of 38.6 for the year (2003) before rising to 43.4 (2011) and up to 47.8 (2015), though decreasing below the 2003 level to 37.8 (2018) in its most recent calculation, see <a href="#fig-ben">Figure 7</a>. At the same time, the country’s poverty rate, even measured based on the international line, only decreased at a very slow rate in its most recent years, from a share of households in poverty at 18.8% in 2019, to 18.7% in 2020 and 18.3% at the end of 2021, with the reduction threatened to be slowed further through increased prices on food and energy <span class="citation" data-cites="WorldBank2022b">(<a href="#ref-WorldBank2022b" role="doc-biblioref">World Bank, 2022e</a>)</span>.</p>
|
||||
<div class="cell" data-execution_count="18">
|
||||
<details>
|
||||
<summary>Code</summary>
|
||||
|
|
@ -3572,14 +3572,14 @@ vertical-align: -.125em;
|
|||
<p>Source: Author’s elaboration based on UNU-WIDER WIID (2022).</p>
|
||||
</div>
|
||||
<!-- poverty -->
|
||||
<p>Based on its national poverty line, Benin’s overall poverty rate is 38.5%, though it hides a strong spatial disparity in the incidence of poverty between rural (44.2%) and urban (31.4) areas <span class="citation" data-cites="WorldBank2022b">(<a href="#ref-WorldBank2022b" role="doc-biblioref">World Bank, 2022c</a>)</span>. Looking at the effect of income growth on the time to exit poverty, <span class="citation" data-cites="Alia2017">Alia (<a href="#ref-Alia2017" role="doc-biblioref">2017</a>)</span> finds a general negative correlation with stronger growth indeed leading to shorter average exit times (7-10 years for a household at a per capita growth rate of 4.2%), though this aggregate also hides a large heterogeneity primarily determined by a households size, its available human capital and whether it is located rurally. So while the study does conclude for an overall equitable pro-poor growth in Benin, rural households, beside already being relatively more poverty stricken, are in danger of being left further behind during periods of overall growth. <span class="citation" data-cites="Djossou2017">Djossou et al. (<a href="#ref-Djossou2017" role="doc-biblioref">2017</a>)</span> find similar pro-poor growth with spatial disparities but surprisingly see urban households potentially benefiting less than rural households from additional growth, with efforts to open up communities to harness the benefits of growth often primarily targeted at rural communities.</p>
|
||||
<p>Based on its national poverty line, Benin’s overall poverty rate is 38.5%, though it hides a strong spatial disparity in the incidence of poverty between rural (44.2%) and urban (31.4) areas <span class="citation" data-cites="WorldBank2022b">(<a href="#ref-WorldBank2022b" role="doc-biblioref">World Bank, 2022e</a>)</span>. Looking at the effect of income growth on the time to exit poverty, <span class="citation" data-cites="Alia2017">Alia (<a href="#ref-Alia2017" role="doc-biblioref">2017</a>)</span> finds a general negative correlation with stronger growth indeed leading to shorter average exit times (7-10 years for a household at a per capita growth rate of 4.2%), though this aggregate also hides a large heterogeneity primarily determined by a households size, its available human capital and whether it is located rurally. So while the study does conclude for an overall equitable pro-poor growth in Benin, rural households, beside already being relatively more poverty stricken, are in danger of being left further behind during periods of overall growth. <span class="citation" data-cites="Djossou2017">Djossou et al. (<a href="#ref-Djossou2017" role="doc-biblioref">2017</a>)</span> find similar pro-poor growth with spatial disparities but surprisingly see urban households potentially benefiting less than rural households from additional growth, with efforts to open up communities to harness the benefits of growth often primarily targeted at rural communities.</p>
|
||||
<!-- drivers: endowment/assets: education, ..? -->
|
||||
<p>Using the Learning Poverty index, which combines the share of school deprivation (the share of primary-aged children out-of-school) and learning deprivation (share of pupils below a minimum proficiency in reading), a <span class="citation" data-cites="WorldBank2022a">World Bank (<a href="#ref-WorldBank2022a" role="doc-biblioref">2022a</a>)</span> report shows that 56% of children at late primary age in Benin are not proficient in reading, 55% do not achieve minimum proficiency levels at the end of primary school and 3% of primary school-aged children are not enrolled in school at all. <!-- TODO These levels are higher than in Uganda, though, since ... gender dimension? --> Looking purely at attendance rates, <span class="citation" data-cites="McNabb2018">McNabb (<a href="#ref-McNabb2018" role="doc-biblioref">2018</a>)</span> finds that the primary household-level determinants of attendance are the wealth of a household, its religion, as well as the education level of its household head. Here, gender disparities persist, however, with girls continuously less likely to attend and adopted girls being at the greatest disadvantage, while boys tend to face higher opportunity costs than girls due to often working in the fields in which case the distance to a school begins to play an important role. While the household-level variables do play a role — through the availability of educational resources at home, differences in schooling quality and overall health and well-being — <span class="citation" data-cites="Gruijters2020">Gruijters & Behrman (<a href="#ref-Gruijters2020" role="doc-biblioref">2020</a>)</span> find that most of the disparity stems from the community-level: the difference in school quality is large, marked by high socio-economic segregation between schools, and primarily determined through an unequal distribution of teaching resources including teachers and textbooks.</p>
|
||||
<p>Using the Learning Poverty index, which combines the share of school deprivation (the share of primary-aged children out-of-school) and learning deprivation (share of pupils below a minimum proficiency in reading), a <span class="citation" data-cites="WorldBank2022a">World Bank (<a href="#ref-WorldBank2022a" role="doc-biblioref">2022b</a>)</span> report shows that 56% of children at late primary age in Benin are not proficient in reading, 55% do not achieve minimum proficiency levels at the end of primary school and 3% of primary school-aged children are not enrolled in school at all. <!-- TODO These levels are higher than in Uganda, though, since ... gender dimension? --> Looking purely at attendance rates, <span class="citation" data-cites="McNabb2018">McNabb (<a href="#ref-McNabb2018" role="doc-biblioref">2018</a>)</span> finds that the primary household-level determinants of attendance are the wealth of a household, its religion, as well as the education level of its household head. Here, gender disparities persist, however, with girls continuously less likely to attend and adopted girls being at the greatest disadvantage, while boys tend to face higher opportunity costs than girls due to often working in the fields in which case the distance to a school begins to play an important role. While the household-level variables do play a role — through the availability of educational resources at home, differences in schooling quality and overall health and well-being — <span class="citation" data-cites="Gruijters2020">Gruijters & Behrman (<a href="#ref-Gruijters2020" role="doc-biblioref">2020</a>)</span> find that most of the disparity stems from the community-level: the difference in school quality is large, marked by high socio-economic segregation between schools, and primarily determined through an unequal distribution of teaching resources including teachers and textbooks.</p>
|
||||
<p>Thus, while growth is generally pro-poor in Benin, its primary determinants do not cluster only at the household level, but are comprised of partly household-level but especially community-level differences.</p>
|
||||
<section id="inequalities-in-access-to-electricity" class="level3">
|
||||
<h3 class="anchored" data-anchor-id="inequalities-in-access-to-electricity">Inequalities in access to electricity</h3>
|
||||
<!-- electricity access -->
|
||||
<p>One of the foremost examples of the effects of inequal endowments can have is brought by <span class="citation" data-cites="VanDePoel2009">Van De Poel et al. (<a href="#ref-VanDePoel2009" role="doc-biblioref">2009</a>)</span> when they look at the determinants of rural infant death rates in Benin among others and find that environmental factors — such as access to a safe water source, quality housing materials and electricity — are the primary determinants, ahead even of access to a health facility in the community. Access to electricity in the country especially underlies a large heterogeneity based on location. The overall level of electrification of Benin has been rising slowly — though outpacing population growth — from 22% in 2000 to 26% in 2005, 34% in 2010, a decline to 30% in 2015 and then a faster increase to 40% in 2019, although a broad difference in electrification levels between urban (65%) and rural (17%) regions remain <span class="citation" data-cites="WorldBank2021">(<a href="#ref-WorldBank2021" role="doc-biblioref">World Bank, 2021</a>)</span>.</p>
|
||||
<p>One of the foremost examples of the effects of inequal endowments can have is brought by <span class="citation" data-cites="VanDePoel2009">Van De Poel et al. (<a href="#ref-VanDePoel2009" role="doc-biblioref">2009</a>)</span> when they look at the determinants of rural infant death rates in Benin among others and find that environmental factors — such as access to a safe water source, quality housing materials and electricity — are the primary determinants, ahead even of access to a health facility in the community. Access to electricity in the country especially underlies a large heterogeneity based on location. The overall level of electrification of Benin has been rising slowly — though outpacing population growth — from 22% in 2000 to 26% in 2005, 34% in 2010, a decline to 30% in 2015 and then a faster increase to 40% in 2019, although a broad difference in electrification levels between urban (65%) and rural (17%) regions remain <span class="citation" data-cites="WorldBank2021">(<a href="#ref-WorldBank2021" role="doc-biblioref">World Bank, 2021b</a>)</span>.</p>
|
||||
<p>In rural areas there are generally three approaches to electrification that work outside of a connection to the main grid, individual installation of solar panels or generators for smaller electric appliances, collective solutions like kiosks offering electric charging for some cost, or autonomous mini-grids powering a portion of a more densely populated rural area (though often requiring permits or licenses if above certain sizes) <span class="citation" data-cites="Jaglin2019">(<a href="#ref-Jaglin2019" role="doc-biblioref">Jaglin, 2019</a>)</span>.</p>
|
||||
<p><span class="citation" data-cites="Rateau2022">Rateau & Choplin (<a href="#ref-Rateau2022" role="doc-biblioref">2022</a>)</span> see one of the primary reasons for off-grid electrification in either physical unavailability in rural areas or a prohibitively high cost for connection to the grid. However, these more individualized solutions are often only targeted at credit-worthy customers and can lead to a further increase in inequalities between income percentiles, leaving behind households which are already neglected within the field of energy access <span class="citation" data-cites="Barry2020">(<a href="#ref-Barry2020" role="doc-biblioref">Barry & Creti, 2020</a>)</span>. The former, physical access, is argued by <span class="citation" data-cites="Djossou2017">Djossou et al. (<a href="#ref-Djossou2017" role="doc-biblioref">2017</a>)</span> as well, emphasizing the need for continued infrastructure expansion to more households, in order to provide access to more durable goods (fridges, mobile phones and internet) which can help decrease the inequality gap. The latter, prohibitively high costs, should not be disregarded in such an infrastructure expansion as well, however.</p>
|
||||
<p>One of the major obstacles to main grid connection remains the high charge a customer is expected to pay with solutions requiring continued political commitment to identify, examine and implement more low-cost electrification processes as well as financing solutions. <span class="citation" data-cites="Golumbeanu2013">Golumbeanu & Barnes (<a href="#ref-Golumbeanu2013" role="doc-biblioref">2013</a>)</span> point out the main obstacles that need to be addressed here: the lack of incentives to increase electrical affordability, a weak utilities commitment toward providing broad electricity access with focus often lying more on high-consumption urban markets, often overrated technical specifications for low loads, too great distances between households and distribution poles in an area, and an overall lack of affordable financing solutions.</p>
|
||||
|
|
@ -3787,7 +3787,7 @@ vertical-align: -.125em;
|
|||
<p>Note: Values shown are for all Official Development Assistance flows valid under the OECD CRS data, split into the type of financing flow, calculated as constant currency (2020 corrected) USD millions. The category under analysis is Electric Power transmission and distribution (centralized grids) within the data. Source: Author’s elaboration based on OECD ODA CRS (2022).</p>
|
||||
</div>
|
||||
<p><a href="#tbl-ben-aid-electricity">Table 3</a> shows the amounts of project-bound development aid to Benin for the transmission and distribution of electric power within its centralized grid. The category subsumes grid distribution from the power source to end users and transmission lines. It also includes storage of energy to generate power (e.g. batteries) and projects to extend grid access, especially in rural areas. For development aid to the electrification of Benin, the monetary contributions are smaller but increasing and show trends quite different to that of overall development aid to the country.</p>
|
||||
<p>The amount of overall development contributions to electrification increases from 2011 to 2020, with significant increases in 2013 and 2015 for loans and 2019, 2020 for grants. While there is a steady increase to the overall development aid toward electrification, increases in grants tend to lag behind increases in loans for Benin, with grants exceeding 10mil. USD for the first time in 2019 while loans already reached 18.90mil. USD in 2013. Over the complete period of 2011 to 2020, however, grants for the transmission and distribution of electric power in Benin have consistently been lower than loans.</p>
|
||||
<p>The amount of overall development contributions to electrification increases from 2011 to 2020, with significant increases in 2013 and 2015 for loans and 2019, 2020 for grants. While there is a steady increase to the overall development aid toward electrification, increases in grants tend to lag behind increases in loans for Benin, with grants exceeding 10m USD for the first time in 2019 while loans already reached 18.90m USD in 2013. Over the complete period of 2011 to 2020, however, grants for the transmission and distribution of electric power in Benin have consistently been lower than loans.</p>
|
||||
<div style="page-break-after: always;"></div>
|
||||
</section>
|
||||
</section>
|
||||
|
|
@ -3804,7 +3804,7 @@ vertical-align: -.125em;
|
|||
</ul>
|
||||
<hr>
|
||||
<!-- intro -->
|
||||
<p>Djibouti occupies a somewhat singular position, being a tiny country with an economy focused primarily around its deep-water port, trying to establish itself as a regional hub for trade and commerce. The country’s GDP has averaged roughly 6% per year before the Covid-19 pandemic greatly reduced those growth rates <span class="citation" data-cites="WorldBank2022c">(<a href="#ref-WorldBank2022c" role="doc-biblioref">World Bank, 2022d</a>)</span>. However, the country’s inequality levels are high (Gini coefficient 41.6) and its poverty rates are extreme <span class="citation" data-cites="WorldBank2022c">(21.1%, <a href="#ref-WorldBank2022c" role="doc-biblioref">World Bank, 2022d</a>)</span>. Additionally in many cases there is a lack of data or the data itself are lacking in several socio-economic dimensions which hinders analysis and policy design.</p>
|
||||
<p>Djibouti occupies a somewhat singular position, being a tiny country with an economy focused primarily around its deep-water port, trying to establish itself as a regional hub for trade and commerce. The country’s GDP has averaged roughly 6% per year before the Covid-19 pandemic greatly reduced those growth rates <span class="citation" data-cites="WorldBank2022c">(<a href="#ref-WorldBank2022c" role="doc-biblioref">World Bank, 2022f</a>)</span>. However, the country’s inequality levels are high (Gini coefficient 41.6) and its poverty rates are extreme <span class="citation" data-cites="WorldBank2022c">(21.1%, <a href="#ref-WorldBank2022c" role="doc-biblioref">World Bank, 2022f</a>)</span>. Additionally in many cases there is a lack of data or the data itself are lacking in several socio-economic dimensions which hinders analysis and policy design.</p>
|
||||
<div class="cell" data-execution_count="23">
|
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<details>
|
||||
<summary>Code</summary>
|
||||
|
|
@ -3825,19 +3825,21 @@ vertical-align: -.125em;
|
|||
<p>Source: Author’s elaboration based on UNU-WIDER WIID (2022).</p>
|
||||
</div>
|
||||
<!-- poverty -->
|
||||
<p>Poverty in Djibouti is high and marked by high deprivation: Using the national poverty line of around 2.18USD (2011 PPP) the poverty rate for the overall country by consumption is estimated at 21.1% in 2017, while 17% live in extreme poverty under the international poverty line of 1.90USD (2011 PPP) and 32% of the population are still under the international lower middle income poverty line of 3.20USD (2011 PPP) <span class="citation" data-cites="Mendiratta2019 WorldBank2022c">(<a href="#ref-Mendiratta2019" role="doc-biblioref">World Bank, 2019</a>, <a href="#ref-WorldBank2022c" role="doc-biblioref">2022d</a>)</span>. Furthermore, there is a significant spatial disparity between poverty rates. <span class="citation" data-cites="Ibarra2020">World Bank (<a href="#ref-Ibarra2020" role="doc-biblioref">2020a</a>)</span> estimate only 15% of Djibouti’s overall population living in rural areas, with 45% of the country’s poor residing in rural areas while 37% reside in the Balbala<a href="#fn2" class="footnote-ref" id="fnref2" role="doc-noteref"><sup>2</sup></a> area <span class="citation" data-cites="Ibarra2020">(<a href="#ref-Ibarra2020" role="doc-biblioref">World Bank, 2020a</a>)</span>. The study goes on to describe the high levels of deprivation for the rural poor, with the country’s highest dependency ratios, lowest participation in the labor force, very low levels of employment in the households’ heads and very low school enrollment, and while urban poor face similar restrictions they have better access to public services and higher school attendance rates. Access to basic amenities and services in Djibouti is low (42.1%) and 15.5% of the population have no access to both electricity and sanitation, and all people in monetary poverty are also deprived along multiple dimensions <span class="citation" data-cites="Mendiratta2020">(<a href="#ref-Mendiratta2020" role="doc-biblioref">World Bank, 2020b</a>)</span>.</p>
|
||||
<p>Poverty in Djibouti is high and marked by high deprivation: Using the national poverty line of around 2.18USD (2011 PPP) the poverty rate for the overall country by consumption is estimated at 21.1% in 2017, while 17% live in extreme poverty under the international poverty line of 1.90USD (2011 PPP) and 32% of the population are still under the international lower middle income poverty line of 3.20USD (2011 PPP) <span class="citation" data-cites="Mendiratta2019 WorldBank2022c">(<a href="#ref-Mendiratta2019" role="doc-biblioref">World Bank, 2019</a>, <a href="#ref-WorldBank2022c" role="doc-biblioref">2022f</a>)</span>. Furthermore, there is a significant spatial disparity between poverty rates. <span class="citation" data-cites="Ibarra2020">World Bank (<a href="#ref-Ibarra2020" role="doc-biblioref">2020b</a>)</span> estimate only 15% of Djibouti’s overall population living in rural areas, with 45% of the country’s poor residing in rural areas while 37% reside in the Balbala<a href="#fn2" class="footnote-ref" id="fnref2" role="doc-noteref"><sup>2</sup></a> area <span class="citation" data-cites="Ibarra2020">(<a href="#ref-Ibarra2020" role="doc-biblioref">World Bank, 2020b</a>)</span>. The study goes on to describe the high levels of deprivation for the rural poor, with the country’s highest dependency ratios, lowest participation in the labor force, very low levels of employment in the households’ heads and very low school enrollment, and while urban poor face similar restrictions they have better access to public services and higher school attendance rates. Access to basic amenities and services in Djibouti is low (42.1%) and 15.5% of the population have no access to both electricity and sanitation, and all people in monetary poverty are also deprived along multiple dimensions <span class="citation" data-cites="Mendiratta2020">(<a href="#ref-Mendiratta2020" role="doc-biblioref">World Bank, 2020c</a>)</span>.</p>
|
||||
<p>Over half the working-age population does not participate in the labor force with employment being estimated at 45% in 2017, lower than the 46.3% estimated for 1996, despite the country’s economic growth <span class="citation" data-cites="Mendiratta2019">(<a href="#ref-Mendiratta2019" role="doc-biblioref">World Bank, 2019</a>)</span>. <span class="citation" data-cites="Emara2020">Emara & Mohieldin (<a href="#ref-Emara2020" role="doc-biblioref">2020</a>)</span> look at the overall impact of financial inclusion on poverty levels but find that, first, Djibouti is way above its targeted poverty levels, second, it is not only one of the only countries in the region (together with Yemen) to not achieve a 5% poverty level target yet, but not even on track to achieve this target by 2030 solely through improvements in financial inclusion.</p>
|
||||
<!-- inequality -->
|
||||
<p>Inequality in Djibouti is high, with the lowest decile only making up 1.9% of total consumption while the richest decile enjoy 32% of the total consumption, 16 times as much as those at the lowest decile <span class="citation" data-cites="Mendiratta2019">(<a href="#ref-Mendiratta2019" role="doc-biblioref">World Bank, 2019</a>)</span>. The country has an estimated Gini coefficient for consumption per capita of 41.6 in 2017, making it one of the most unequal countries in the region <span class="citation" data-cites="WorldBank2022c">(<a href="#ref-WorldBank2022c" role="doc-biblioref">World Bank, 2022d</a>, see also <a href="#fig-dji" role="doc-biblioref">Figure 10</a>)</span>. More of its inequality hides in a large spatial and gendered heterogeneity. Urban poor face high deprivation but higher access to public services and schooling compared to the rural poor, who have only 41% access to improved water sources, 10% access to sanitation, 3% access to electricity, and with only one third living close (under 1km) to a primary school <span class="citation" data-cites="Ibarra2020">(<a href="#ref-Ibarra2020" role="doc-biblioref">World Bank, 2020a</a>)</span>.</p>
|
||||
<p>While in general over half the working-age population does not participate in the labor force, the makeup is 59% of men and only 32% of women who participate, mirroring unemployment rates with an estimated third of men and two thirds of women being unemployed <span class="citation" data-cites="Mendiratta2019">(<a href="#ref-Mendiratta2019" role="doc-biblioref">World Bank, 2019</a>)</span>. <span class="citation" data-cites="Mendiratta2019">World Bank (<a href="#ref-Mendiratta2019" role="doc-biblioref">2019</a>)</span> also find the labor market itself highly unequal, with its dichotomy of a public administrative sector (drawing mainly highly skilled workers) and informal private sector making up 90% of the overall labor market, the majority of women working in the informal sector and almost half of the jobs for women in this sector consisting of one-person ‘self-employed’ enterprises. Nearly 41% of working-age women find themselves in positions of vulnerable employment <span class="citation" data-cites="WorldBank2022d">(<a href="#ref-WorldBank2022d" role="doc-biblioref">World Bank, 2022b</a>)</span>.</p>
|
||||
<p>Inequality in Djibouti is high, with the lowest decile only making up 1.9% of total consumption while the richest decile enjoy 32% of the total consumption, 16 times as much as those at the lowest decile <span class="citation" data-cites="Mendiratta2019">(<a href="#ref-Mendiratta2019" role="doc-biblioref">World Bank, 2019</a>)</span>. The country has an estimated Gini coefficient for consumption per capita of 41.6 in 2017, making it one of the most unequal countries in the region <span class="citation" data-cites="WorldBank2022c">(<a href="#ref-WorldBank2022c" role="doc-biblioref">World Bank, 2022f</a>, see also <a href="#fig-dji" role="doc-biblioref">Figure 10</a>)</span>. More of its inequality hides in a large spatial and gendered heterogeneity. Urban poor face high deprivation but higher access to public services and schooling compared to the rural poor, who have only 41% access to improved water sources, 10% access to sanitation, 3% access to electricity, and with only one third living close (under 1km) to a primary school <span class="citation" data-cites="Ibarra2020">(<a href="#ref-Ibarra2020" role="doc-biblioref">World Bank, 2020b</a>)</span>.</p>
|
||||
<p>While in general over half the working-age population does not participate in the labor force, the makeup is 59% of men and only 32% of women who participate, mirroring unemployment rates with an estimated third of men and two thirds of women being unemployed <span class="citation" data-cites="Mendiratta2019">(<a href="#ref-Mendiratta2019" role="doc-biblioref">World Bank, 2019</a>)</span>. <span class="citation" data-cites="Mendiratta2019">World Bank (<a href="#ref-Mendiratta2019" role="doc-biblioref">2019</a>)</span> also find the labor market itself highly unequal, with its dichotomy of a public administrative sector (drawing mainly highly skilled workers) and informal private sector making up 90% of the overall labor market, the majority of women working in the informal sector and almost half of the jobs for women in this sector consisting of one-person ‘self-employed’ enterprises. Nearly 41% of working-age women find themselves in positions of vulnerable employment <span class="citation" data-cites="WorldBank2022d">(<a href="#ref-WorldBank2022d" role="doc-biblioref">World Bank, 2022c</a>)</span>.</p>
|
||||
<!-- drivers -->
|
||||
<p>Djibouti’s economy is primarily, and within its formal sector almost exclusively, driven by its strategic location and possession of a deep-water port so it can act as a regional refueling, trading and transport shipment center <span class="citation" data-cites="WorldBank2022c">(<a href="#ref-WorldBank2022c" role="doc-biblioref">World Bank, 2022d</a>)</span>. At the same time, this interconnected economic nature and the country’s heavy reliance on food and energy imports marks a key vulnerability and makes it immediately dependent on the stability of global trade and export markets, a stability which was recently disrupted through a global pandemic <span class="citation" data-cites="WorldBank2022c">(<a href="#ref-WorldBank2022c" role="doc-biblioref">World Bank, 2022d</a>)</span>.</p>
|
||||
<p>Likewise, Djibouti depends on regional stability, since its economic growth is tightly coupled with the Ethiopian economy, sourcing around 70% of its port trade from this landlocked neighbor <span class="citation" data-cites="Mendiratta2019">(<a href="#ref-Mendiratta2019" role="doc-biblioref">World Bank, 2019</a>)</span>. A series of droughts in the country threatened the livelihood of its nomadic and pastoralist population, with many fleeing to neighboring countries, some becoming sedentary in village or city outskirts, and the overall nomadic population decreasing by nearly three quarters from 2009 to 2017 <span class="citation" data-cites="Ibarra2020 Mendiratta2019">(<a href="#ref-Mendiratta2019" role="doc-biblioref">World Bank, 2019</a>, <a href="#ref-Ibarra2020" role="doc-biblioref">2020a</a>)</span>.</p>
|
||||
<p>Additionally, during the early waves of Covid-19 Djibouti had one of the highest infection rates in the region, and though it had a high recovery rate, it also had one of the highest fatality rates, possibly due to deficiencies in its healthcare system <span class="citation" data-cites="ElKhamlichi2022">(<a href="#ref-ElKhamlichi2022" role="doc-biblioref">El Khamlichi et al., 2022</a>)</span>. The country’s rising costs of now fast-maturing debts made the government leave social spending behind, leaving a budget of 5% for health and 3% for social expenditures, spendings which looks diminutive compared to its over 30% expenditures on public infrastructure <span class="citation" data-cites="WorldBank2022c">(<a href="#ref-WorldBank2022c" role="doc-biblioref">World Bank, 2022d</a>)</span>. Only 10% of rural poor inhabitants live close (under 1km) to a health facility <span class="citation" data-cites="Ibarra2020">(<a href="#ref-Ibarra2020" role="doc-biblioref">World Bank, 2020a</a>)</span>.</p>
|
||||
<p>While still facing reduced rates of labor market participation, the country has expended effort on increasing women’s opportunity for education: Having overall lower literacy rates for women still, the overall literacy rates in younger cohorts (10-24 years old) is significantly higher compared to older ones, and the gaps have decreased from 24% difference between the genders (40-60 years old) to 10% (15-24 years old) and 2% (10-14 years old) <span class="citation" data-cites="Mendiratta2019">(<a href="#ref-Mendiratta2019" role="doc-biblioref">World Bank, 2019</a>)</span>.</p>
|
||||
<p>Djibouti’s economy is primarily, and within its formal sector almost exclusively, driven by its strategic location and possession of a deep-water port so it can act as a regional refueling, trading and transport shipment center <span class="citation" data-cites="WorldBank2022c">(<a href="#ref-WorldBank2022c" role="doc-biblioref">World Bank, 2022f</a>)</span>. At the same time, this interconnected economic nature and the country’s heavy reliance on food and energy imports marks a key vulnerability and makes it immediately dependent on the stability of global trade and export markets, a stability which was recently disrupted through a global pandemic <span class="citation" data-cites="WorldBank2022c">(<a href="#ref-WorldBank2022c" role="doc-biblioref">World Bank, 2022f</a>)</span>.</p>
|
||||
<p>Likewise, Djibouti depends on regional stability, since its economic growth is tightly coupled with the Ethiopian economy, sourcing around 70% of its port trade from this landlocked neighbor <span class="citation" data-cites="Mendiratta2019">(<a href="#ref-Mendiratta2019" role="doc-biblioref">World Bank, 2019</a>)</span>. A series of droughts in the country threatened the livelihood of its nomadic and pastoralist population, with many fleeing to neighboring countries, some becoming sedentary in village or city outskirts, and the overall nomadic population decreasing by nearly three quarters from 2009 to 2017 <span class="citation" data-cites="Ibarra2020 Mendiratta2019">(<a href="#ref-Mendiratta2019" role="doc-biblioref">World Bank, 2019</a>, <a href="#ref-Ibarra2020" role="doc-biblioref">2020b</a>)</span>.</p>
|
||||
<p>Additionally, during the early waves of Covid-19 Djibouti had one of the highest infection rates in the region, and though it had a high recovery rate, it also had one of the highest fatality rates, possibly due to deficiencies in its healthcare system <span class="citation" data-cites="ElKhamlichi2022">(<a href="#ref-ElKhamlichi2022" role="doc-biblioref">El Khamlichi et al., 2022</a>)</span>. The country’s rising costs of now fast-maturing debts made the government leave social spending behind, leaving a budget of 5% for health and 3% for social expenditures, spendings which looks diminutive compared to its over 30% expenditures on public infrastructure <span class="citation" data-cites="WorldBank2022c">(<a href="#ref-WorldBank2022c" role="doc-biblioref">World Bank, 2022f</a>)</span>. Only 10% of rural poor inhabitants live close (under 1km) to a health facility <span class="citation" data-cites="Ibarra2020">(<a href="#ref-Ibarra2020" role="doc-biblioref">World Bank, 2020b</a>)</span>.</p>
|
||||
<section id="gender-inequalities-in-livelihood-opportunities" class="level3">
|
||||
<h3 class="anchored" data-anchor-id="gender-inequalities-in-livelihood-opportunities">Gender inequalities in livelihood opportunities</h3>
|
||||
<p>Women’s lower secondary completion rate grew from 28.6% in 2009 (compared to 35.2% men) to 56.3% in 2021 (54.0% for men) <span class="citation" data-cites="WorldBank2022d">(<a href="#ref-WorldBank2022d" role="doc-biblioref">World Bank, 2022b</a>)</span>. However, for 2017, women’s upward educational mobility was still significantly worse than men’s, with non-poor men having an upward mobility of 53%, non-poor women 29%, poor men 19% and poor women only 10% against the national average of 36% <span class="citation" data-cites="Mendiratta2019">(<a href="#ref-Mendiratta2019" role="doc-biblioref">World Bank, 2019</a>)</span>. <!-- trade and inclusion --> Such differences reflect themselves in firm ownership structures and on the labor market, where 22.3% of all firms have female participation in ownership and only 14.2% a female top manager, and both salaried employment and agricultural employment are male-dominated (though agricultural work only with a slight and shrinking difference of 4%) <span class="citation" data-cites="WorldBank2022d">(<a href="#ref-WorldBank2022d" role="doc-biblioref">World Bank, 2022b</a>)</span>.</p>
|
||||
<p>While still facing reduced rates of labor market participation, the country has expended effort on increasing women’s opportunity for education: Having overall lower literacy rates for women still, the overall literacy rates in younger cohorts (10-24 years old) is significantly higher compared to older ones, and the gaps have decreased from 24% difference between the genders (40-60 years old) to 10% (15-24 years old) and 2% (10-14 years old) <span class="citation" data-cites="Mendiratta2019">(<a href="#ref-Mendiratta2019" role="doc-biblioref">World Bank, 2019</a>)</span>.</p>
|
||||
<p>Women’s lower secondary completion rate grew from 28.6% in 2009 (compared to 35.2% men) to 56.3% in 2021 (54.0% for men) <span class="citation" data-cites="WorldBank2022d">(<a href="#ref-WorldBank2022d" role="doc-biblioref">World Bank, 2022c</a>)</span>. However, for 2017, women’s upward educational mobility was still significantly worse than men’s, with non-poor men having an upward mobility of 53%, non-poor women 29%, poor men 19% and poor women only 10% against the national average of 36% <span class="citation" data-cites="Mendiratta2019">(<a href="#ref-Mendiratta2019" role="doc-biblioref">World Bank, 2019</a>)</span>. <!-- trade and inclusion --> Such differences reflect themselves in firm ownership structures and on the labor market, where 22.3% of all firms have female participation in ownership and only 14.2% a female top manager, and both salaried employment and agricultural employment are male-dominated (though agricultural work only with a slight and shrinking difference of 4%) <span class="citation" data-cites="WorldBank2022d">(<a href="#ref-WorldBank2022d" role="doc-biblioref">World Bank, 2022c</a>)</span>.</p>
|
||||
<p>The official number of procedures to register a business are the same for men and women, as are the time and cost required for business start-up procedures <span class="citation" data-cites="WorldBank2020">(<a href="#ref-WorldBank2020" role="doc-biblioref">World Bank, 2020a</a>)</span>, however, there are factors which may further inhibit equal female business participation and ownership: while women have the same legal rights in access to credit, contractual and financial instruments as men <span class="citation" data-cites="WorldBank2022f">(<a href="#ref-WorldBank2022f" role="doc-biblioref">World Bank, 2022i</a>)</span>, women have an overall lower account ownership rate at financial institutions with 8.8% compared to men’s 16.6% (2011) reflecting itself especially in a lower access to debit cards at institutions <span class="citation" data-cites="WorldBank2021a">World Bank (<a href="#ref-WorldBank2022g" role="doc-biblioref">2022a</a>)</span>.</p>
|
||||
<p>As mentioned above, women have a lower participation rate on the labor market with an especially stark gender difference in the industrial sector — a sector of the economy in which women in Djibouti do not have the same rights to participate in as men, especially in jobs deemed dangerous <span class="citation" data-cites="WorldBank2022f">(<a href="#ref-WorldBank2022f" role="doc-biblioref">World Bank, 2022i</a>)</span> — with service being the sector that makes up the greatest share of female labor participation (71.1% of all female labor compared to 56.0% of all male labor 2019), a sector which is also driving the high share of women in vulnerable employment (41.4% of female labor in 2019) <span class="citation" data-cites="WorldBank2022g">(<a href="#ref-WorldBank2022g" role="doc-biblioref">World Bank, 2022a</a>)</span>.</p>
|
||||
<p>Overall it seems, however, that past growth in the country’s GDP is likely not favorable for an inclusive growth path, with its large-scale infrastructure investments mostly creating demand for skilled workers and neglect of social spending not allowing the buffers and social safety nets that prevent further drift into inequality. <span class="citation" data-cites="Brass2008">Brass (<a href="#ref-Brass2008" role="doc-biblioref">2008</a>)</span> argues even that the country leadership’s policy decisions carry increased weight in this, towards a path of ever increasing economic dependence and into a predicament of economic diversification requiring a more educated population, but a more educated population without already accompanying diversified economy likely enacting a successful policy or governmental opposition.</p>
|
||||
<!-- conclusion -->
|
||||
<p>Thus, Djibouti represents a country with an overall solid growth rate but accompanying high inequalities and poverty rates, from which path it does not seem to detach without more policy intervention. It is a country with one of the highest poverty rates in the region and an enormous spatial disparity in poverty between the prime sectors of Djibouti city and the rest of the country. The rural sectors face high levels of deprivation, economic disparity and largely lacking infrastructure, and the majority of its population not participating in the labor force. The country’s labor market is to the largest degree dichotomized in the public administrative sector, comprised of mostly skilled workers, and a large private informal sector comprised mostly of unskilled workers, many of which are women. The overall economy is dependent on high levels of regional and global stability which was recently undermined by droughts, Ethiopian conflict and the Covid-19 pandemic. Nomadic and pastoralist people in the country’s rural regions were hit especially hard, with the nomadic population decreasing by nearly three quarters and many fleeing or becoming sedentary. Women face less opportunity in the country with worse upward educational mobility, less participation in the labor force, higher unemployment rates, and a continuing, if closing, gender literacy gap. Djibouti is set to miss most of its poverty target levels and move along a growth pathway that does not lend itself to inclusion unless active policy measures changing its economic investment and growth strategies are examined.</p>
|
||||
|
|
@ -3892,7 +3894,7 @@ vertical-align: -.125em;
|
|||
<p>Note: Values shown are for all Official Development Assistance flows valid under the OECD CRS data, split into the type of financing flow, calculated as constant currency (2020 corrected) USD millions. Source: Author’s elaboration based on OECD ODA CRS (2022).</p>
|
||||
</div>
|
||||
<p>The amount of Official Development Assistance to Djibouti has generally been increasing since 2011, first steadily and, since 2017, more rapidly, as can be seen in <a href="#fig-dji-aid-financetype">Figure 11</a>. With just under 150m USD in assistance contributions 2011 and just over 320m USD at its peak in 2020, Djibouti has received less overall ODA funds than the other countries surveyed in this study.</p>
|
||||
<p>The primary type of development assistance provided are grants, with loans making up between half and one third of the absolute grant amount in USD between 2011 and 2020. Grants have trended slowly upwards from just over 100m USD in 2011 to 135m in 2014, before fluctuating around this level until 2017, and finally increasing more significantly to over 200m USD in 2020. Loans had a more significant jump earlier, from there relatively stagnant level of under 40m USD in 2014 to 80m USD in 2015, with a similarly significant jump from 2018 to 2019, before decreasing slightly again to just over 110m in 2020. While largely comprising less than 10m USD until 2018, other official flows (non-export credits) had a large increase to over 75m USD in 2019, be decreasing almost as significantly again the following year.</p>
|
||||
<p>The primary type of development assistance provided are grants, with loans making up between half and one third of the absolute grant amount in USD between 2011 and 2020. Grants have trended slowly upwards from just over 100m USD in 2011 to 135m in 2014, before fluctuating around this level until 2017, and finally increasing more significantly to over 200m USD in 2020. Loans had a more significant jump earlier, from a relatively stagnant level of under 40m USD in 2014 to 80m USD in 2015, with a similarly significant jump from 2018 to 2019, before decreasing slightly again to just over 110m USD in 2020. While largely comprising less than 10m USD until 2018, other official flows (non-export credits) had a large increase to over 75m USD in 2019, but decreasing almost as significantly again the following year.</p>
|
||||
<div class="cell page-columns page-full" data-execution_count="26">
|
||||
<details>
|
||||
<summary>Code</summary>
|
||||
|
|
@ -4102,8 +4104,8 @@ vertical-align: -.125em;
|
|||
<p>Note: Values shown are for all Official Development Assistance flows valid under the OECD CRS data, calculated as constant currency (2020 corrected) USD millions. Source: Author’s elaboration based on OECD ODA CRS (2022).</p>
|
||||
</div>
|
||||
<p>The sector-based breakdown of aid contributions for inclusive business growth in Djibouti can be seen in <a href="#tbl-dji-aid-projects">Table 4</a>. It shows that overall development assistance to the necessary inclusive growth sectors in Djibouti is still small in absolute terms, especially for those in vulnerable positions. The table is broken down into four sectors of development aid which drive the potential for inclusive growth in trade and business:</p>
|
||||
<p>First, trade development encompasses trade policy and administrative management, trade facilitation, regional trade agreements, multilateral trade negotiations, trade-related adjustments and trade education and training. Second, business growth is the combination of business policy and administrative management, privatization, business development services as well responsible business conduct — meaning the establishing of policy reform, implementation and enforcement of responsible business conduct, including, among others, implementation of guidelines for human rights support. Third, and specifically aimed at the inclusion of women in economic activities, is the support for women’s rights which includes the establishment of, and assistance for, women’s rights organizations and institutions to enhance their effectiveness, influence and sustainability. And last, the provision for and protection of food security for those in vulnerable positions, through capacity strengthening and household-level food security programmes, short- or long-term, excluding emergency food assistance measures (such as for disaster crisis affected households).</p>
|
||||
<p>The amount of aid contributions into these sectors of inclusive growth in Djibouti is small in comparison with development assistance to the other countries analyzed. The absolute amount of contributions has consistently stayed under 10m USD per year for all four sectors combined, though an overall growth trend is visible from 0.5m USD in 2011 to 1.6m USD in 2016 and more rapid growth in 2020 to just under 10m USD. Most of this recent growth in 2020 is driven by contributions to trade development with 7.7m USD, while business growth and women’s rights support are seeing much smaller contributions yet. The business growth sector, though seeing small contributions in absolute terms, has seen a continued increase in contributions from 0.3m USD in 2011 to 1.7m USD in 2020, with almost 2m USD at its peak in 2019. Women’s rights support, on the other hand, has seen some increase from its small contributions of not even 0.1m USD in 2011 to almost 0.8m USD in 2016, but overall assistance to the sector stays stagnant at only around 0.25m USD in recent years. Lastly, food security remains almost completely without Official Development Assistance contributions, with barely 0.05m USD being contributed at its peak in 2019. Thus, development contributions to Djibouti’s trade sector itself are increasing, though at the same time contributions to inclusive growth specifically, aimed at vulnerable populations and an inclusive business environment, are growing slowly at best and stagnant for protection measures for those in vulnerable groups.</p>
|
||||
<p>First, trade development encompasses trade policy and administrative management, trade facilitation, regional trade agreements, multilateral trade negotiations, trade-related adjustments and trade education and training. Second, business growth is the combination of business policy and administrative management, privatization, business development services as well responsible business conduct — meaning the establishing of policy reform and implementation and enforcement of responsible business conduct, including, among others, implementation of guidelines for human rights support. Third, and specifically aimed at the inclusion of women in economic activities, is the support for women’s rights which includes the establishment of, and assistance for, women’s rights organizations and institutions to enhance their effectiveness, influence and sustainability. And last, the provision for and protection of food security for those in vulnerable positions, through capacity strengthening and household-level food security programmes, short- or long-term, excluding emergency food assistance measures (such as for disaster crisis affected households).</p>
|
||||
<p>The amount of aid contributions into these sectors of inclusive growth in Djibouti is small in comparison with development assistance to the other countries analyzed. The absolute amount of contributions has consistently kept under 10m USD per year for all four sectors combined, though an overall growth trend is visible from 0.5m USD in 2011 to 1.6m USD in 2016 and more rapid growth in 2020 to just under 10m USD. Most of this recent growth in 2020 is driven by contributions to trade development with 7.7m USD, while business growth and women’s rights support are seeing much smaller contributions yet. The business growth sector, though seeing small contributions in absolute terms, has seen a continued increase in contributions from 0.3m USD in 2011 to 1.7m USD in 2020, with almost 2m USD at its peak in 2019. Women’s rights support, on the other hand, has seen some increase from its small contributions of not even 0.1m USD in 2011 to almost 0.8m USD in 2016, but overall assistance to the sector stays stagnant at only around 0.25m USD in recent years. Lastly, food security remains almost completely without Official Development Assistance contributions, with barely 0.05m USD being contributed at its peak in 2019. Thus, development contributions to Djibouti’s trade sector itself are increasing, though at the same time contributions to inclusive growth specifically, aimed at vulnerable populations and an inclusive business environment, are growing slowly at best and stagnant for protection measures for those in vulnerable groups.</p>
|
||||
<div style="page-break-after: always;"></div>
|
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