Add cross-references to Gini figures

_drivers-of-inequality-benin.qmd

@@ -14,14 +14,14 @@
 <!-- intro/overall -->
 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 [@WorldBank2022b].
-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.
+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 @fig-ben.
 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 [@WorldBank2022b].
 
 ```{python}
-#| label: gini-ben
+#| label: fig-ben
 #| fig-cap: "Gini index of consumption per capita for Benin. Source: Author's elaboration based on UNU-WIDER WIID (2022)."
 plot_consumption_gini_percapita(ben)
 ```

_drivers-of-inequality-djibouti.qmd

@@ -18,7 +18,7 @@ However, the country's inequality levels are some of the highest in the world an
 Additionally in many cases there is a lack of data or the data itself are lacking in several dimensions which hinders creating a cohesive picture or plan.
 
 ```{python}
-#| label: gini-dji
+#| label: fig-dji
 #| fig-cap: "Gini index of consumption per capita for Djibouti. Source: Author's elaboration based on UNU-WIDER WIID (2022)."
 plot_consumption_gini_percapita(dji)
 ```
@@ -43,7 +43,7 @@ but not even on track to achieve this target by 2030 solely through improvements
 
 <!-- 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 [@Mendiratta2019].
-The country has an estimated Gini coefficient of 41.6 in 2017, making it one of the most unequal countries in the region [@WorldBank2022c].
+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,

_drivers-of-inequality-uganda.qmd

@@ -14,9 +14,9 @@
 <!-- intro/overall -->
 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-Ntale2014; @Atamanov2022].
+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-Ntale2014; @Atamanov2022, see also @fig-uga].
 However, the overall aggregation masks several important distinctions:
 Rural inequality on the whole is lower than urban inequality, with @Lwanga-Ntale2014 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),
@@ -24,7 +24,7 @@ also finding a significantly higher coefficient for the first quintile (0.14), h
 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 [@Atamanov2022].
 
 ```{python}
-#| label: gini-uga
+#| label: fig-uga
 #| fig-cap: "Gini index of consumption per capita for Uganda. Source: Author's elaboration based on UNU-WIDER WIID (2022)."
 plot_consumption_gini_percapita(uga)
 ```

_drivers-of-inequality-vietnam.qmd

@@ -26,11 +26,12 @@ There is severe persistent poverty among ethnic minorities in Vietnam [@Baulch20
 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 [@Kozel2014].
 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 [@Le2021].
+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 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 [@Ebrahim2021].
 
 ```{python}
-#| label: gini-vnm
+#| label: fig-vnm
 #| fig-cap: "Gini index of consumption per capita for Vietnam. Source: Author's elaboration based on UNU-WIDER WIID (2022)."
 plot_consumption_gini_percapita_ruralurban(vnm)
 ```