afd-development-contexts/_drivers-of-inequality-djibouti.qmd

## Djibouti-Ethiopia

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* Stable average GDP growth rates recently greatly reduced through local and global economic instabilities.
* Enormous poverty rate with much disparity between Djibouti city and other country regions and country set to miss many future poverty rate goals.
* High levels of deprivation for rural poor, little participation in labor force and lacking rural infrastructure.
* Labor market highly dependent on trade and internal dichotomy of public administration sector and large informal sector comprised of predominantly unskilled workers.
* Women facing fewer opportunities for upward educational mobility, less labor market participation, higher unemployment rates, a persisting (but closing) literacy gap.
* Rural nomadic and pastoralist people highly vulnerable after droughts and regional instability, leading many to flee country or become sedentary and greatly reducing their numbers.

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<!-- intro -->
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 [@WorldBank2022c].
However, the country's inequality levels are high (Gini coefficient 41.6) and its poverty rates are extreme [21.1%, @WorldBank2022c].
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.

```{python}
#| label: fig-dji
#| fig-cap: "Gini index of consumption per capita for Djibouti. Source: Author's elaboration based on UNU-WIDER WIID (2022)."
gni_cnsmpt = dji[dji['resource'].str.contains("Consumption")]
gni_cnsmpt_percapita = gni_cnsmpt[gni_cnsmpt['scale'].str.contains("Per capita")]
gini_plot(gni_cnsmpt_percapita)
```

<!-- poverty -->
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) [@Mendiratta2019; @WorldBank2022c].
Furthermore, there is a significant spatial disparity between poverty rates.
@Ibarra2020 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[^balbala] area [@Ibarra2020].
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.
Acess 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 [@Mendiratta2020].
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 [@Mendiratta2019].
@Emara2020 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.

[^balbala]: The Balbala area comprises the 4th and 5th district out of the five districts of Djibouti city.

<!-- inequality -->
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 [@Mendiratta2019].
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 [@WorldBank2022c, see also @fig-dji].
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 [@Ibarra2020].
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 [@Mendiratta2019].
@Mendiratta2019 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 [@WorldBank2022d].

<!-- drivers -->
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 [@WorldBank2022c].
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 [@WorldBank2022c].
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 [@Mendiratta2019].
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 [@Ibarra2020; @Mendiratta2019].
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 [@ElKhamlichi2022].
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 [@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].
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].
<!-- 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%) [@WorldBank2022d].
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.
@Brass2008 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.

<!-- conclusion -->
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.

<!-- development assistance -->
### Development assistance to Djibouti

```{python}
# Load CRS data
dfsub1 = pd.read_csv('data/raw/OECD_CRS/CRS1_Djibouti_11-13_05092022210301944.csv', parse_dates=True, low_memory=False)
dfsub2 = pd.read_csv('data/raw/OECD_CRS/CRS1_Djibouti_14-16_05092022210632022.csv', parse_dates=True, low_memory=False)
dfsub3 = pd.read_csv('data/raw/OECD_CRS/CRS1_Djibouti_17-20_05092022210913679.csv', parse_dates=True, low_memory=False)
df = pd.concat([dfsub1, dfsub2, dfsub3], ignore_index=True)
df = df.rename(columns={'\ufeff"DONOR"': 'DONOR'})
```

```{python}
#| label: fig-dji-aid-financetype
#| fig-cap: "Total ODA for Djibouti per year, by financing type"
#| column: page
totals = df.loc[
    (df['SECTOR'] == 1000) & # Total
    (df['CHANNEL'] == 100) &
    (df['AMOUNTTYPE'] == 'D') &
    (df['FLOWTYPE'] == 112) &
    (df['AIDTYPE'] == "100") # contains mixed int and string representations
    ]
financetotals = totals.copy()
financetotals = financetotals[financetotals['DONOR'] < 20000] # drop all 'total' aggregations

## count amount of development aid financing instruments (grants/loans) by year and display
## count USD amount of development aid financing instumrnets by year and display
financetotals_grouped = financetotals.groupby(['Flow', 'Year']).agg({'Value': ['sum']})
financetotals_grouped = financetotals_grouped.reset_index(['Flow', 'Year'])
financetotals_grouped.columns = financetotals_grouped.columns.to_flat_index()
financetotals_grouped.columns = ['Financetype', 'Year', 'Value']

fig = px.line(financetotals_grouped, x='Year', y='Value', color='Financetype', labels={"Value": "Development aid, in millions"}, markers=True, template="seaborn")
fig.show()
```

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).

```{python}
#| label: fig-dji-aid-donortype
#| fig-cap: "Total ODA for Djibouti per year, separated by donor type"
#| column: page
totals = df.loc[
    (df['SECTOR'] == 1000) & # Total
    (df['FLOW'] == 100) &
    (df['CHANNEL'] == 100) &
    (df['AMOUNTTYPE'] == 'D') &
    (df['FLOWTYPE'] == 112) &
    (df['AIDTYPE'] == "100") # contains mixed int and string representations
    ]
donortotals = totals.copy()
donortotals["Donortype"] = donortotals["DONOR"].map(donortypes)
donortotals = donortotals[(donortotals["Donortype"] != "nondac")]

donortotals_grouped = donortotals.groupby(['Donortype', 'Year']).agg({'Value': ['sum']})
donortotals_grouped = donortotals_grouped.reset_index(['Donortype', 'Year'])
donortotals_grouped.columns = donortotals_grouped.columns.to_flat_index()
donortotals_grouped.columns = ['Donortype', 'Year', 'Value']
fig = px.line(donortotals_grouped, x='Year', y='Value', color='Donortype', labels={"Value": "Development aid, in millions"}, markers=True, template="seaborn")
fig.show()
```

Note: Values shown are for all Official Development Assistance flows valid under the OECD ODA data, split into bilateral development donor countries (dac), bilateral non-DAC countries (nondac) and multilateral donors (multilateral), as constant currency (2020 corrected) USD millions.
Source: Author's elaboration based on OECD ODA CRS (2022).

```{python}
#| label: tbl-dji-aid-electricity
#| tbl-cap: "ODA for transmission and distribution of electric power in Djibouti per year, separated by financing type"
#| column: page
totals = df.loc[
    ((df['SECTOR'] == 23630) | (df['SECTOR'] == 23631)) & # Total
    (df['CHANNEL'] == 100) &
    (df['AMOUNTTYPE'] == 'D') &
    ((df['FLOW'] == 11) | (df['FLOW'] == 13)) & # Total
    (df['FLOWTYPE'] == 112) &
    (df['AIDTYPE'] == "100") # contains mixed int and string representations
    ]
electricityaid = totals[totals['DONOR'] < 20000] # drop all 'total' aggregations

pd.options.display.float_format = "{:.2f}".format

el_grouped = electricityaid.groupby(['Year', 'Flow']).agg({'Value': ['sum']})
el_grouped.style.format(escape="latex")
el_grouped
```

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).

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