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Vietnam’s economy is now firmly in the third decade of ongoing economic reform (Doi Moi) 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 (Bui & Imai, 2019).

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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 (N. V. T. Le et al., 2022; McCaig, 2011; World Bank, 2012). While the rate of decline slowed since the mid-2000s (VASS, 2006, 2011), it continued declining in tandem with small income inequality decreases. The overall income inequality decrease that Vietnam experienced from the early 2000s suggests that its growth has been accompanied by equity extending beyond poverty reduction (Benjamin et al., 2017). On the other hand, Le et al. (2021) suggest a slight increase in overall income distribution from 2010-2018. At the same time, the ones most affected by poverty through welfare inequalities stay unaltered, as do largely the primary factors accompanying it: There is severe persistent poverty among ethnic minorities in Vietnam (Baulch et al., 2012), 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 (Kozel, 2014). The country’s overall estimated Gini coefficient for income per capita fluctuates between 0.42 and 0.44 between the years 2010 and 2018, with the highest inequality in the 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 (Q. H. Le et al., 2021). Economic inequality and poverty in Vietnam thus underlies an intersectional focus, between ethnic minorities, regional situations, rural-urban divides and gendered lines, one which exogenous shocks can rapidly exacerbate as the example of the COVID-19 pandemic has recently shown (Ebrahim et al., 2021).

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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 (N. V. T. Le et al., 2022; McCaig, 2011; World Bank, 2012). While the rate of decline slowed since the mid-2000s (VASS, 2006, 2011), it continued declining in tandem with small income inequality decreases. The overall income inequality decrease that Vietnam experienced from the early 2000s suggests that its growth has been accompanied by equity extending beyond poverty reduction (Benjamin et al., 2017). On the other hand, Le et al. (2021) suggest a slight increase in overall income distribution from 2010-2018. At the same time, the ones most affected by poverty through welfare inequalities stay unaltered, as do largely the primary factors accompanying it: There is severe persistent poverty among ethnic minorities in Vietnam (Baulch et al., 2012), 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 (Kozel, 2014). The country’s overall estimated Gini coefficient for income per capita fluctuates between 0.42 and 0.44 between the years 2010 and 2018, with the highest inequality in the 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 (Q. H. Le et al., 2021). For Gini coefficients estimated using consumption per capita, see Figure 1, 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 situations, rural-urban divides and gendered lines, one which exogenous shocks can rapidly exacerbate as the example of the COVID-19 pandemic has recently shown (Ebrahim et al., 2021).

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plot_consumption_gini_percapita_ruralurban(vnm)
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Gini index of consumption per capita for Vietnam. Source: Author’s elaboration based on UNU-WIDER WIID (2022).

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Figure 1: Gini index of consumption per capita for Vietnam. Source: Author’s elaboration based on UNU-WIDER WIID (2022).

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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 overall level of welfare inequality in the country had a slight upward trend, with a Gini coefficient 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 significantly in the years 2002/03 and 2009/10 during its fluctuation (Lwanga-Ntale, 2014; World Bank, 2022d). However, the overall aggregation masks several important distinctions: Rural inequality on the whole is lower than urban inequality, with Lwanga-Ntale (2014) finding coefficients of 0.35 and 0.41 for 2012/13 respectively. Additionally, he sees quintile inequalities primarily driven by the highest quintile (0.25) with the middle-incomes less affected (0.05-0.07), also finding a significantly higher coefficient for the first quintile (0.14), however. These inequality levels remain mostly unchanged from 2012/13 to 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 (World Bank, 2022d).

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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 welfare inequality in the country had a slight upward trend, 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 (Lwanga-Ntale, 2014; World Bank, 2022d, see also Figure 2). However, the overall aggregation masks several important distinctions: Rural inequality on the whole is lower than urban inequality, with Lwanga-Ntale (2014) finding coefficients of 0.35 and 0.41 for 2012/13 respectively. Additionally, he sees quintile inequalities primarily driven by the highest quintile (0.25) with the middle-incomes less affected (0.05-0.07), also finding a significantly higher coefficient for the first quintile (0.14), however. These inequality levels remain mostly unchanged from 2012/13 to 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 (World Bank, 2022d).

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plot_consumption_gini_percapita(uga)
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Gini index of consumption per capita for Uganda. Source: Author’s elaboration based on UNU-WIDER WIID (2022).

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Figure 2: Gini index of consumption per capita for Uganda. Source: Author’s elaboration based on UNU-WIDER WIID (2022).

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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 (World Bank, 2022b). There only exists sporadic and highly 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. 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 relative rate 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 (World Bank, 2022b).

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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 (World Bank, 2022b). 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 Figure 3. 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 relative rate 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 (World Bank, 2022b).

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plot_consumption_gini_percapita(ben)
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Gini index of consumption per capita for Benin. Source: Author’s elaboration based on UNU-WIDER WIID (2022).

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Figure 3: Gini index of consumption per capita for Benin. Source: Author’s elaboration based on UNU-WIDER WIID (2022).

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plot_consumption_gini_percapita(dji)
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Gini index of consumption per capita for Djibouti. Source: Author’s elaboration based on UNU-WIDER WIID (2022).

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Figure 4: Gini index of consumption per capita for Djibouti. Source: Author’s elaboration based on UNU-WIDER WIID (2022).

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Poverty in Djibouti is both very 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) (World Bank, 2019, 2022c). Furthermore, there is an enormous spatial disparity between poverty rates. World Bank (2020) 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 Balbala1 area (World Bank, 2020). The report 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. 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 (World Bank, 2019). Emara & Mohieldin (2020) 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.

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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 (World Bank, 2019). The country has an estimated Gini coefficient of 41.6 in 2017, making it one of the most unequal countries in the region (World Bank, 2022c). 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 (World Bank, 2020). 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 (World Bank, 2019). World Bank (2019) 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 (World Bank, 2022a).

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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 (World Bank, 2019). 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 (World Bank, 2022c, see also Figure 4). 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 (World Bank, 2020). 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 (World Bank, 2019). World Bank (2019) 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 (World Bank, 2022a).

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 (World Bank, 2022c). 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 (World Bank, 2022c). Likewise, Djibouti depends on more regional stability, since its economic growth is tightly coupled with the Ethiopian economy, sourcing around 70% of its port trade from this landlocked neighbor (World Bank, 2019). A series of droughts in the country threatened the livelihood of its nomadic and pastoralist populate, 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 (World Bank, 2019, 2020). 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 (El Khamlichi et al., 2022). 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 (World Bank, 2022c). Only 10% of rural poor inhabitants live close (under 1km) to a health facility (World Bank, 2020).

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) (World Bank, 2019). 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) (World Bank, 2022a). 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% (World Bank, 2019). Such differences reflect themselves in firm team and 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%) (World Bank, 2022a). 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. Brass (2008) 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.

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