@ -236,5 +236,5 @@ The amount of overall development contributions to electrification increases fro
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.
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.
@ -83,13 +83,13 @@ 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 [@WorldBank2022c].
Only 10% of rural poor inhabitants live close (under 1km) to a health facility [@Ibarra2020].
### Gender inequalities in livelihood opportunities
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) [@Mendiratta2019].
### Gender inequalities in livelihood opportunities
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].
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% [@Mendiratta2019].
@ -99,6 +99,18 @@ where 22.3% of all firms have female participation in ownership and only 14.2% a
and both salaried employment and agricultural employment are male-dominated
(though agricultural work only with a slight and shrinking difference of 4%) [@WorldBank2022d].
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 [@WorldBank2020],
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 [@WorldBank2022f],
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 [@WorldBank2021a, @WorldBank2022g].
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 [@WorldBank2022f] ---
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) [@WorldBank2022g].
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.
@ -172,12 +184,12 @@ The primary type of development assistance provided are grants, with loans makin
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,
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 in 2020.
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,
be decreasing almost as significantly again the following year.
but decreasing almost as significantly again the following year.
```{python}
#| label: fig-dji-aid-donortype
@ -288,7 +300,7 @@ The table is broken down into four sectors of development aid which drive the po
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.
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,
@ -296,7 +308,7 @@ through capacity strengthening and household-level food security programmes, sho
excluding emergency food assistance measures (such as for disaster crisis affected households).
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,
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.
@ -48,9 +48,9 @@ with Western Ugandan households increasing in poverty while Northern and Eastern
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.
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 @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.
<!-- TODO find a source for the national poverty line being too low (quant data is already in vandeVen2021) -->
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 @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.
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].
<!-- endowment/assets: education, ..? -->
Esaku [-@Esaku2021; -@Esaku2021a] finds a somewhat circular driving relationship between Ugandan inequality, poverty and working in what calls the shadow economy:
@ -206,7 +206,7 @@ The Official Development Assistance overall further increased to over 2.2bn USD
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.
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.
@ -244,7 +244,7 @@ Source: Author's elaboration based on OECD ODA CRS (2022).
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,
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.
```{python}
@ -300,7 +300,7 @@ Source: Author's elaboration based on OECD ODA CRS (2022).
The breakdown of development aid to water supply infrastructure and education projects can be seen in @tbl-uga-aid-watersupply.
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.
Basic water supply improvement, large water supply improvement and education and training.
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 ---
@ -313,7 +313,7 @@ with larger-scale sewage pumping stations and trunk sewers, as well as smaller o
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.
The split shows that while basic water supply infrastructure projects have tended to be contributed to between 10m USD and 20m USD,
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,
@ -27,6 +27,7 @@ concomitant with low education and skills, more prevalent dependency on subsiste
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 [@Le2021].
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].
For Gini coefficients estimated using consumption per capita, see @fig-vnm, 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 [@Ebrahim2021].
@ -276,8 +277,8 @@ Source: Author's elaboration based on OECD ODA CRS (2022).
Bilateral donor contributions make up the largest part of development aid contributions to Vietnam, as can be seen in @fig-vnm-aid-donortype.
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.
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,
@ -356,7 +357,7 @@ 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.
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.
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.
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 .. ..
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,..,..
,,,,,,,,,,,,,,,,,,,,,,,
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Data from database: Gender Statistics,,,,,,,,,,,,,,,,,,,,,,,
Last Updated: 06/23/2022,,,,,,,,,,,,,,,,,,,,,,,
1
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]
2
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
3
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
4
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
5
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
6
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
7
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
8
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
..
..
..
..
..
..
..
..
..
..
9
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
..
..
..
..
..
..
..
..
..
..
10
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
..
..
11
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
..
..
12
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
..
..
13
Credit card ownership, female (% age 15+)
fin7.t.a.2
Djibouti
DJI
..
..
..
..
..
..
..
..
..
3.49554419517517
..
..
..
..
..
..
..
..
..
..
14
Credit card ownership, male (% age 15+)
fin7.t.a.1
Djibouti
DJI
..
..
..
..
..
..
..
..
..
4.55562686920166
..
..
..
..
..
..
..
..
..
..
15
Credit card ownership (% age 15+)
fin7.t.a
Djibouti
DJI
..
..
..
..
..
..
..
..
..
3.96891236305237
..
..
..
..
..
..
..
..
..
..
16
Debit card ownership (% age 15+)
fin2.t.a
Djibouti
DJI
..
..
..
..
..
..
..
..
..
7.60348653793335
..
..
..
..
..
..
..
..
..
..
17
Debit card ownership, female (% age 15+)
fin2.t.a.2
Djibouti
DJI
..
..
..
..
..
..
..
..
..
5.89633560180664
..
..
..
..
..
..
..
..
..
..
18
Debit card ownership, male (% age 15+)
fin2.t.a.1
Djibouti
DJI
..
..
..
..
..
..
..
..
..
9.71940994262695
..
..
..
..
..
..
..
..
..
..
19
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
..
..
20
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
..
..
21
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
..
..
22
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
..
..
23
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
..
..
24
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
..
..
25
Financial institution account (% age 15+)
fin1.t.a
Djibouti
DJI
..
..
..
..
..
..
..
..
..
12.2738819122314
..
..
..
..
..
..
..
..
..
..
26
Financial institution account,female(% age 15+)
fin1.t.a.2
Djibouti
DJI
..
..
..
..
..
..
..
..
..
8.76278209686279
..
..
..
..
..
..
..
..
..
..
27
Financial institution account,male(% age 15+)
fin1.t.a.1
Djibouti
DJI
..
..
..
..
..
..
..
..
..
16.6257038116455
..
..
..
..
..
..
..
..
..
..
28
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
29
Number of female business owners
IC.WEF.LLCO.FE
Djibouti
DJI
..
..
..
..
..
..
..
..
..
..
..
..
..
..
..
..
..
..
..
..
30
Number of male business owners
IC.WEF.LLCO.MA
Djibouti
DJI
..
..
..
..
..
..
..
..
..
..
..
..
..
..
..
..
..
..
..
..
31
Proportion of time spent on unpaid domestic and care work, female (% of 24 hour day)
SG.TIM.UWRK.FE
Djibouti
DJI
..
..
..
..
..
..
..
..
..
..
..
..
..
..
..
..
..
..
..
..
32
Proportion of time spent on unpaid domestic and care work, male (% of 24 hour day)
SG.TIM.UWRK.MA
Djibouti
DJI
..
..
..
..
..
..
..
..
..
..
..
..
..
..
..
..
..
..
..
..
33
Saved any money in the past year, male (% age 15+)
fin18.t.d.1
Djibouti
DJI
..
..
..
..
..
..
..
..
..
..
..
..
..
..
..
..
..
..
..
..
34
Saved any money in the past year, female (% age 15+)
fin18.t.d.2
Djibouti
DJI
..
..
..
..
..
..
..
..
..
..
..
..
..
..
..
..
..
..
..
..
35
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
..
..
36
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
..
..
37
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
..
..
38
Share of female business owners (% of total business owners)
IC.WEF.LLCO.FE.ZS
Djibouti
DJI
..
..
..
..
..
..
..
..
..
..
..
..
..
..
..
..
..
..
..
..
39
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
..
..
40
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
..
..
41
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
..
..
42
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
43
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
44
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
..
..
45
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
..
..
46
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
..
..
47
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
..
..
48
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
..
..
49
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
..
..
50
Wage and salaried workers, male (% of male employment) (modeled ILO estimate)
<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 <spanclass="citation"data-cites="Bui2019">(<ahref="#ref-Bui2019"role="doc-biblioref">Bui & Imai, 2019</a>)</span>.</p>
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<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 <spanclass="citation"data-cites="WorldBank2012 McCaig2011 Le2022">(<ahref="#ref-Le2022"role="doc-biblioref">N. V. T. Le et al., 2022</a>; <ahref="#ref-McCaig2011"role="doc-biblioref">McCaig, 2011</a>; <ahref="#ref-WorldBank2012"role="doc-biblioref">World Bank, 2012</a>)</span>. While the rate of decline slowed since the mid-2000s <spanclass="citation"data-cites="VASS2006 VASS2011">(<ahref="#ref-VASS2006"role="doc-biblioref">VASS, 2006</a>, <ahref="#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 <spanclass="citation"data-cites="Benjamin2017">(<ahref="#ref-Benjamin2017"role="doc-biblioref">Benjamin et al., 2017</a>)</span>. On the other hand, Le et al. <spanclass="citation"data-cites="Le2021">(<ahref="#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 <spanclass="citation"data-cites="Baulch2012">(<ahref="#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 <spanclass="citation"data-cites="Kozel2014">(<ahref="#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 <spanclass="citation"data-cites="Le2021">(<ahref="#ref-Le2021"role="doc-biblioref">Q. H. Le et al., 2021</a>)</span>. For Gini coefficients estimated using consumption per capita, see <ahref="#fig-vnm">Figure1</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 <spanclass="citation"data-cites="Ebrahim2021">(<ahref="#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 <spanclass="citation"data-cites="Le2021">(<ahref="#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 <spanclass="citation"data-cites="WorldBank2022e">(<ahref="#ref-WorldBank2022e"role="doc-biblioref">World Bank, 2022d</a>)</span>. For Gini coefficients estimated using consumption per capita, see <ahref="#fig-vnm">Figure1</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 <spanclass="citation"data-cites="Ebrahim2021">(<ahref="#ref-Ebrahim2021"role="doc-biblioref">Ebrahim et al., 2021</a>)</span>.</p>
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<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>
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<p>Bilateral donor contributions make up the largest part of development aid contributions to Vietnam, as can be seen in <ahref="#fig-vnm-aid-donortype">Figure3</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 <ahref="#fig-vnm-aid-donortype">Figure3</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>
<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>
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<p>The breakdown of project-based development aid for water supply infrastructure and disaster risk reduction in Vietnam can be seen in <ahref="#tbl-vnm-aid-water">Table1</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>
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<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 <spanclass="citation"data-cites="Atamanov2022">(<ahref="#ref-Atamanov2022"role="doc-biblioref">World Bank, 2022f</a>, see also <ahref="#fig-uga"role="doc-biblioref">Figure4</a>)</span>, while <spanclass="citation"data-cites="Lwanga-Ntale2014">Lwanga-Ntale (<ahref="#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 <spanclass="citation"data-cites="Lwanga-Ntale2014">Lwanga-Ntale (<ahref="#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 <spanclass="citation"data-cites="Atamanov2022">(<ahref="#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 <spanclass="citation"data-cites="Atamanov2022">(<ahref="#ref-Atamanov2022"role="doc-biblioref">World Bank, 2022h</a>, see also <ahref="#fig-uga"role="doc-biblioref">Figure4</a>)</span>, while <spanclass="citation"data-cites="Lwanga-Ntale2014">Lwanga-Ntale (<ahref="#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 <spanclass="citation"data-cites="Lwanga-Ntale2014">Lwanga-Ntale (<ahref="#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 <spanclass="citation"data-cites="Atamanov2022">(<ahref="#ref-Atamanov2022"role="doc-biblioref">World Bank, 2022h</a>)</span>.</p>
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<p>Source: Author’s elaboration based on UNU-WIDER WIID (2022).</p>
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<!-- poverty -->
<p>The World Bank <spanclass="citation"data-cites="Atamanov2022">(<ahref="#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 --><spanclass="citation"data-cites="Ssewanyana2012">Ssewanyana & Kasirye (<ahref="#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 <spanclass="citation"data-cites="vandeVen2021">van de Ven et al. (<ahref="#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 <spanclass="citation"data-cites="Atamanov2022">(<ahref="#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 --><spanclass="citation"data-cites="Ssewanyana2012">Ssewanyana & Kasirye (<ahref="#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 <spanclass="citation"data-cites="vandeVen2021">van de Ven et al. (<ahref="#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 <spanclass="citation"data-cites="WorldBank2022e">(<ahref="#ref-WorldBank2022e"role="doc-biblioref">World Bank, 2022d</a>)</span>.</p>
<!-- endowment/assets: education, ..? -->
<p>Esaku <spanclass="citation"data-cites="Esaku2021 Esaku2021a">(<ahref="#ref-Esaku2021"role="doc-biblioref">2021b</a>, <ahref="#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><spanclass="citation"data-cites="Cali2014">Cali (<ahref="#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) <spanclass="citation"data-cites="Atamanov2022">(<ahref="#ref-Atamanov2022"role="doc-biblioref">World Bank, 2022f</a>)</span>.</p>
<p>The World Bank <spanclass="citation"data-cites="WorldBank2022">(<ahref="#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. <spanclass="citation"data-cites="Datzberger2018">Datzberger (<ahref="#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><spanclass="citation"data-cites="Cali2014">Cali (<ahref="#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) <spanclass="citation"data-cites="Atamanov2022">(<ahref="#ref-Atamanov2022"role="doc-biblioref">World Bank, 2022h</a>)</span>.</p>
<p>The World Bank <spanclass="citation"data-cites="WorldBank2022">(<ahref="#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. <spanclass="citation"data-cites="Datzberger2018">Datzberger (<ahref="#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>
<h3class="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 <spanclass="citation"data-cites="Atamanov2022">(<ahref="#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 <spanclass="citation"data-cites="Naiga2015">(<ahref="#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 <spanclass="citation"data-cites="Atamanov2022">(<ahref="#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 <spanclass="citation"data-cites="Atamanov2022">(<ahref="#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 <spanclass="citation"data-cites="Naiga2015">(<ahref="#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 <spanclass="citation"data-cites="Atamanov2022">(<ahref="#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 <spanclass="citation"data-cites="Mulogo2018">(<ahref="#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 <spanclass="citation"data-cites="Mulogo2018">(<ahref="#ref-Mulogo2018"role="doc-biblioref">Mulogo et al., 2018</a>)</span>. <!-- TODO check validity --><spanclass="citation"data-cites="Naiga2015">Naiga et al. (<ahref="#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>
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<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>
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<p>Overall Ugandan development aid reception is high, with over 1.5bn USD granted as official development assistance in 2011 as seen in <ahref="#fig-uga-aid-financetype">Figure5</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 <ahref="#fig-uga-aid-financetype">Figure5</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>
<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>
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<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>
<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>
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<p>The breakdown of development aid to water supply infrastructure and education projects can be seen in <ahref="#tbl-uga-aid-watersupply">Table2</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 <ahref="#tbl-uga-aid-watersupply">Table2</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>
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<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 <spanclass="citation"data-cites="WorldBank2022b">(<ahref="#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 <ahref="#fig-ben">Figure7</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 <spanclass="citation"data-cites="WorldBank2022b">(<ahref="#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 <spanclass="citation"data-cites="WorldBank2022b">(<ahref="#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 <ahref="#fig-ben">Figure7</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 <spanclass="citation"data-cites="WorldBank2022b">(<ahref="#ref-WorldBank2022b"role="doc-biblioref">World Bank, 2022e</a>)</span>.</p>
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<p>Source: Author’s elaboration based on UNU-WIDER WIID (2022).</p>
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<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 <spanclass="citation"data-cites="WorldBank2022b">(<ahref="#ref-WorldBank2022b"role="doc-biblioref">World Bank, 2022c</a>)</span>. Looking at the effect of income growth on the time to exit poverty, <spanclass="citation"data-cites="Alia2017">Alia (<ahref="#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. <spanclass="citation"data-cites="Djossou2017">Djossou et al. (<ahref="#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 <spanclass="citation"data-cites="WorldBank2022b">(<ahref="#ref-WorldBank2022b"role="doc-biblioref">World Bank, 2022e</a>)</span>. Looking at the effect of income growth on the time to exit poverty, <spanclass="citation"data-cites="Alia2017">Alia (<ahref="#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. <spanclass="citation"data-cites="Djossou2017">Djossou et al. (<ahref="#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>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 <spanclass="citation"data-cites="WorldBank2022a">World Bank (<ahref="#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, <spanclass="citation"data-cites="McNabb2018">McNabb (<ahref="#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 — <spanclass="citation"data-cites="Gruijters2020">Gruijters & Behrman (<ahref="#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 <spanclass="citation"data-cites="WorldBank2022a">World Bank (<ahref="#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, <spanclass="citation"data-cites="McNabb2018">McNabb (<ahref="#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 — <spanclass="citation"data-cites="Gruijters2020">Gruijters & Behrman (<ahref="#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>
<h3class="anchored"data-anchor-id="inequalities-in-access-to-electricity">Inequalities in access to electricity</h3>
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<p>One of the foremost examples of the effects of inequal endowments can have is brought by <spanclass="citation"data-cites="VanDePoel2009">Van De Poel et al. (<ahref="#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 <spanclass="citation"data-cites="WorldBank2021">(<ahref="#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 <spanclass="citation"data-cites="VanDePoel2009">Van De Poel et al. (<ahref="#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 <spanclass="citation"data-cites="WorldBank2021">(<ahref="#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) <spanclass="citation"data-cites="Jaglin2019">(<ahref="#ref-Jaglin2019"role="doc-biblioref">Jaglin, 2019</a>)</span>.</p>
<p><spanclass="citation"data-cites="Rateau2022">Rateau & Choplin (<ahref="#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 <spanclass="citation"data-cites="Barry2020">(<ahref="#ref-Barry2020"role="doc-biblioref">Barry & Creti, 2020</a>)</span>. The former, physical access, is argued by <spanclass="citation"data-cites="Djossou2017">Djossou et al. (<ahref="#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. <spanclass="citation"data-cites="Golumbeanu2013">Golumbeanu & Barnes (<ahref="#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>
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<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>
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<p><ahref="#tbl-ben-aid-electricity">Table3</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>
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<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 <spanclass="citation"data-cites="WorldBank2022c">(<ahref="#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 <spanclass="citation"data-cites="WorldBank2022c">(21.1%, <ahref="#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 <spanclass="citation"data-cites="WorldBank2022c">(<ahref="#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 <spanclass="citation"data-cites="WorldBank2022c">(21.1%, <ahref="#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>
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<p>Source: Author’s elaboration based on UNU-WIDER WIID (2022).</p>
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<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) <spanclass="citation"data-cites="Mendiratta2019 WorldBank2022c">(<ahref="#ref-Mendiratta2019"role="doc-biblioref">World Bank, 2019</a>, <ahref="#ref-WorldBank2022c"role="doc-biblioref">2022d</a>)</span>. Furthermore, there is a significant spatial disparity between poverty rates. <spanclass="citation"data-cites="Ibarra2020">World Bank (<ahref="#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<ahref="#fn2"class="footnote-ref"id="fnref2"role="doc-noteref"><sup>2</sup></a> area <spanclass="citation"data-cites="Ibarra2020">(<ahref="#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 <spanclass="citation"data-cites="Mendiratta2020">(<ahref="#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) <spanclass="citation"data-cites="Mendiratta2019 WorldBank2022c">(<ahref="#ref-Mendiratta2019"role="doc-biblioref">World Bank, 2019</a>, <ahref="#ref-WorldBank2022c"role="doc-biblioref">2022f</a>)</span>. Furthermore, there is a significant spatial disparity between poverty rates. <spanclass="citation"data-cites="Ibarra2020">World Bank (<ahref="#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<ahref="#fn2"class="footnote-ref"id="fnref2"role="doc-noteref"><sup>2</sup></a> area <spanclass="citation"data-cites="Ibarra2020">(<ahref="#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 <spanclass="citation"data-cites="Mendiratta2020">(<ahref="#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 <spanclass="citation"data-cites="Mendiratta2019">(<ahref="#ref-Mendiratta2019"role="doc-biblioref">World Bank, 2019</a>)</span>. <spanclass="citation"data-cites="Emara2020">Emara & Mohieldin (<ahref="#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>
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<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 <spanclass="citation"data-cites="Mendiratta2019">(<ahref="#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 <spanclass="citation"data-cites="WorldBank2022c">(<ahref="#ref-WorldBank2022c"role="doc-biblioref">World Bank, 2022d</a>, see also <ahref="#fig-dji"role="doc-biblioref">Figure10</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 <spanclass="citation"data-cites="Ibarra2020">(<ahref="#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 <spanclass="citation"data-cites="Mendiratta2019">(<ahref="#ref-Mendiratta2019"role="doc-biblioref">World Bank, 2019</a>)</span>. <spanclass="citation"data-cites="Mendiratta2019">World Bank (<ahref="#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 <spanclass="citation"data-cites="WorldBank2022d">(<ahref="#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 <spanclass="citation"data-cites="Mendiratta2019">(<ahref="#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 <spanclass="citation"data-cites="WorldBank2022c">(<ahref="#ref-WorldBank2022c"role="doc-biblioref">World Bank, 2022f</a>, see also <ahref="#fig-dji"role="doc-biblioref">Figure10</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 <spanclass="citation"data-cites="Ibarra2020">(<ahref="#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 <spanclass="citation"data-cites="Mendiratta2019">(<ahref="#ref-Mendiratta2019"role="doc-biblioref">World Bank, 2019</a>)</span>. <spanclass="citation"data-cites="Mendiratta2019">World Bank (<ahref="#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 <spanclass="citation"data-cites="WorldBank2022d">(<ahref="#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 <spanclass="citation"data-cites="WorldBank2022c">(<ahref="#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 <spanclass="citation"data-cites="WorldBank2022c">(<ahref="#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 <spanclass="citation"data-cites="Mendiratta2019">(<ahref="#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 <spanclass="citation"data-cites="Ibarra2020 Mendiratta2019">(<ahref="#ref-Mendiratta2019"role="doc-biblioref">World Bank, 2019</a>, <ahref="#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 <spanclass="citation"data-cites="ElKhamlichi2022">(<ahref="#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 <spanclass="citation"data-cites="WorldBank2022c">(<ahref="#ref-WorldBank2022c"role="doc-biblioref">World Bank, 2022d</a>)</span>. Only 10% of rural poor inhabitants live close (under 1km) to a health facility <spanclass="citation"data-cites="Ibarra2020">(<ahref="#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) <spanclass="citation"data-cites="Mendiratta2019">(<ahref="#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 <spanclass="citation"data-cites="WorldBank2022c">(<ahref="#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 <spanclass="citation"data-cites="WorldBank2022c">(<ahref="#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 <spanclass="citation"data-cites="Mendiratta2019">(<ahref="#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 <spanclass="citation"data-cites="Ibarra2020 Mendiratta2019">(<ahref="#ref-Mendiratta2019"role="doc-biblioref">World Bank, 2019</a>, <ahref="#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 <spanclass="citation"data-cites="ElKhamlichi2022">(<ahref="#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 <spanclass="citation"data-cites="WorldBank2022c">(<ahref="#ref-WorldBank2022c"role="doc-biblioref">World Bank, 2022f</a>)</span>. Only 10% of rural poor inhabitants live close (under 1km) to a health facility <spanclass="citation"data-cites="Ibarra2020">(<ahref="#ref-Ibarra2020"role="doc-biblioref">World Bank, 2020b</a>)</span>.</p>
<h3class="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) <spanclass="citation"data-cites="WorldBank2022d">(<ahref="#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% <spanclass="citation"data-cites="Mendiratta2019">(<ahref="#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%) <spanclass="citation"data-cites="WorldBank2022d">(<ahref="#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) <spanclass="citation"data-cites="Mendiratta2019">(<ahref="#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) <spanclass="citation"data-cites="WorldBank2022d">(<ahref="#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% <spanclass="citation"data-cites="Mendiratta2019">(<ahref="#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%) <spanclass="citation"data-cites="WorldBank2022d">(<ahref="#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 <spanclass="citation"data-cites="WorldBank2020">(<ahref="#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 <spanclass="citation"data-cites="WorldBank2022f">(<ahref="#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 <spanclass="citation"data-cites="WorldBank2021a">World Bank (<ahref="#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 <spanclass="citation"data-cites="WorldBank2022f">(<ahref="#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) <spanclass="citation"data-cites="WorldBank2022g">(<ahref="#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. <spanclass="citation"data-cites="Brass2008">Brass (<ahref="#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 <ahref="#fig-dji-aid-financetype">Figure11</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>
<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 <ahref="#tbl-dji-aid-projects">Table4</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>
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</section>
</section>
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Marty Oehme