diff --git a/outputs/index.docx b/outputs/index.docx index ed1d4d0..bfa0e88 100644 Binary files a/outputs/index.docx and b/outputs/index.docx differ diff --git a/outputs/index.html b/outputs/index.html index c3242fa..83096da 100644 --- a/outputs/index.html +++ b/outputs/index.html @@ -2516,7 +2516,7 @@ vertical-align: -.125em;
-

Drivers of Inequality

+

Drivers of Inequality

@@ -2559,11 +2559,40 @@ vertical-align: -.125em; - +
+
+Code +
# Set up the data extraction and figure drawing functions
+import plotly.express as px
+import plotly.io as pio
+
+def gini_plot(country_df):
+    if svg_render:
+        pio.renderers.default = "png"
+
+    fig = px.line(country_df, x="year", y="gini", markers=True, labels={"year": "Year", "gini": "Gini coefficient"}, template="seaborn", range_y=[30,60])
+    fig.update_traces(marker_size=10)
+    fig.show()
+
+def plot_consumption_gini_percapita(country_df):
+    gni_cnsmpt = country_df[country_df['resource'].str.contains("Consumption")]
+    gni_cnsmpt_percapita = gni_cnsmpt[gni_cnsmpt['scale'].str.contains("Per capita")]
+    gini_plot(gni_cnsmpt_percapita)
+
+def plot_consumption_gini_percapita_ruralurban(country_df):
+    gni_cnsmpt = country_df[country_df['resource'].str.contains("Consumption")]
+    gni_cnsmpt = gni_cnsmpt[gni_cnsmpt['scale'].str.contains("Per capita")]
+    gni_cnsmpt = gni_cnsmpt[gni_cnsmpt['source'].str.contains("World Bank")]
+    gni_cnsmpt = gni_cnsmpt[gni_cnsmpt['areacovr'].str.contains("All")]
+    gini_plot(gni_cnsmpt)
+
+
+
+
+Code +
svg_render = True
+
+

Vietnam

@@ -2578,7 +2607,21 @@ vertical-align: -.125em;

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).

-

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 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).

+

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).

+
+
+Code +
plot_consumption_gini_percapita_ruralurban(vnm)
+
+
+
+
+

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

+

+
+
+
+

In the 1990s, as the initial stages of the Doi Moi reform bore fruit with economic growth, the first amplifications of inequalities along new rural-urban boundaries became equally visible. There are two complementary views on the primary dimensions of rural inequalities. On the one hand, the urban-rural divide may be driven by structural effects: the welfare returns to education and agricultural activities changed dramatically from, and with it the requirements on policy adaptations required for stemming inequality. Nguyen et al. (2007) argue this for the period of 1993-1998, with their findings that income returns to education improved dramatically over this time and arguing through this that suggested development policies had a strictly urban bias — on the whole they would benefit both from better education and vastly benefit from the restructuring of Vietnam’s economy. This view was in turn confirmed when Theil Index decomposition found within-sector inequality remaining largely stable while between-sector inequality rose significantly (Fesselmeyer & Le, 2010). On the other, Thu Le and Booth (2014) argue that the urban-rural inequality continued to increase over the years due to both covariate effects and the returns to those covariate effects, primarily education age structures and labor market activities, but also geographic location.

-

The gap between urban and rural sectors grew, a gap which would continue to widen until 2002, when within-sector rural inequalities started to become more important for inequalities than those between the sectors (Fritzen et al., 2005; Thu Le & Booth, 2014). In the time of within-sector inequality becoming more pronounced many studies, while important contributions to continued inequality research, had a tendency to mask those inequalities in favor of continued analysis of between-sector trends — often to the detriment of the high degree of heterogeneity depending on geographic characteristics such as remoteness or cultural factors, as Cao and Akita (2008) note. In a recent study, Bui and Imai (2019) build on the insights of these viewpoints and also find access to basic education the linchpin of improving rural welfare while its lack combined with economic restructuring precluded many from equal opportunities toward human capital improvement. They found that, as within-sector became more pronounced again after 2010, the large proportion of uneducated heads of households in rural sectors and low social mobility of rural poor combine to increase within-sector inequality while the economy overall changing toward salaried work compounded within-rural and urban-rural disparities. Early income studies generally highlighted the important role of agricultural incomes in reducing, or at the very not exacerbating, income inequality (Benjamin & Brandt, 2004). Benjamin et al. (2017) expand on this over a longer time-frame by decomposing different household income sources underlying Vietnam’s structural economic changes. They find that, while there is an overall decrease in income inequality throughout Vietnam between 2002 and 2014 and the urban-rural divide also continued its downward trend, rural inequality indeed increased over this time. Wage income and family business income were the main drivers of overall inequality in 2002 (accounting for over 30% of income but 60% of inequality) and remittances add a small share on top, which, while decreased in effect (risen to 42% of total income), remain majorly correlated with income distributions and thus income inequality. Bui & Imai (2019) confirm this with a Gini coefficient of 0.36 to 0.39 between 2008 and 2010 which, decomposed into Theil indices for between rural and urban and within rural sectors show that rural-urban inequalities are smaller and decreasing, while within-rural inequalities are large and increasing. Thus, while the study points to both more prevalent and equally distributed labor markets and wage labor opportunities, these effects apply to the overall population and not just within-rural inequalities which are driven in large part by ethnicity, education and environmental factors.

+

The gap between urban and rural sectors grew, a gap which would continue to widen until 2002, when within-sector rural inequalities started to become more important for inequalities than those between the sectors (Fritzen et al., 2005; Thu Le & Booth, 2014). In the time of within-sector inequality becoming more pronounced many studies, while important contributions to continued inequality research, had a tendency to mask those inequalities in favor of continued analysis of between-sector trends — often to the detriment of the high degree of heterogeneity depending on geographic characteristics such as remoteness or cultural factors, as Cao and Akita (2008) note. In a recent study, Bui and Imai (2019) build on the insights of these viewpoints and also find access to basic education the linchpin of improving rural welfare while its lack combined with economic restructuring precluded many from equal opportunities toward human capital improvement. They found that, as within-sector became more pronounced again after 2010, the large proportion of uneducated heads of households in rural sectors and low social mobility of rural poor combine to increase within-sector inequality while the economy overall changing toward salaried work compounded within-rural and urban-rural disparities. Early income studies generally highlighted the important role of agricultural incomes in reducing, or at the very not exacerbating, income inequality (Benjamin & Brandt, 2004). Benjamin et al. (2017) expand on this over a longer time-frame by decomposing different household income sources underlying Vietnam’s structural economic changes. They find that, while there is an overall decrease in income inequality throughout Vietnam between 2002 and 2014 and the urban-rural divide also continued its downward trend, rural inequality indeed increased over this time. Wage income and family business income were the main drivers of overall inequality in 2002 (accounting for over 30% of income but 60% of inequality) and remittances add a small share on top, which, while decreased in effect (risen to 42% of total income), remain majorly correlated with income distributions and thus income inequality. Bui & Imai (2019) confirm this with a per capita income Gini coefficient of 0.36 to 0.39 between 2008 and 2010 which, decomposed into Theil indices for between rural and urban and within rural sectors show that rural-urban inequalities are smaller and decreasing, while within-rural inequalities are large and increasing. Thus, while the study points to both more prevalent and equally distributed labor markets and wage labor opportunities, these effects apply to the overall population and not just within-rural inequalities which are driven in large part by ethnicity, education and environmental factors.

Inequality in Vietnam, then, is slowly rising across the whole population distribution, runs the danger of increasing due to schisms opening within individual sectors of vulnerable groups. Rural populations experience a trend towards increasing inequality within their sector, driven primarily by the social exclusion and geographic isolation of ethnic minorities, its most precarious population. Ethnic minorities’ inequality is slowly increasing due to receiving worse returns to their existing assets (especially human capital and land) and generally worse access to endowments in the first place (land and educational infrastructure). The restructuring of the economy, turning the labor force toward urban areas and within them wage work in manufacturing and service industries, leaves behind immobile rural populations whose ability to be employed for non-farm shrink further. All these factors are at risk of experiencing large negative shocks as climate change exacerbates existing extreme environmental conditions, which in turn threaten to increase economic inequalities for both the rural population at large, ethnic minorities and women especially. Women in rural areas experience worse mobility and fewer economic opportunities and are thus less able to adapt to environmental degradation. While inequality as an aggregate is kept relatively low Vietnam’s growth rate, both ethnic minorities and the rural female population are thus at risk of being left behind economically.

+

Uganda

@@ -2629,6 +2677,20 @@ vertical-align: -.125em;

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).

+
+
+Code +
plot_consumption_gini_percapita(uga)
+
+
+
+
+

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

+

+
+
+
+

The World Bank (2022d) 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. Ssewanyana & Kasirye (2012) 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, while transient poverty is more common than chronic poverty in Uganda, nearly 10% of households continue to live in persistent or chronic poverty. 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 van de Ven et al. (2021) 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.

@@ -2658,6 +2720,20 @@ vertical-align: -.125em;

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).

+
+
+Code +
plot_consumption_gini_percapita(ben)
+
+
+
+
+

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

+

+
+
+
+

Based on its national poverty line, Benin’s overall poverty rate is 38.5%, though it hides a strong spatial disparity between rural and urban households with 44.2% to 31.4% households in poverty respectively (World Bank, 2022b). Looking at the effect of income growth on the time to exit poverty, Alia (2017) 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. Djossou et al. (2017) 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.

@@ -2682,6 +2758,20 @@ vertical-align: -.125em;

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 (World Bank, 2022c). However, the country’s inequality levels are some of the highest in the world and its poverty rates are extreme. 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.

+
+
+Code +
plot_consumption_gini_percapita(dji)
+
+
+
+
+

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

+

+
+
+
+

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.

@@ -2703,7 +2793,7 @@ vertical-align: -.125em;

References

-Alia, D. Y. (2017). Progress Toward The Sustainable Development Goal on Poverty : Assessing The Effect of Income Growth on The Exit Time from Poverty in Benin: Exit Time Out of Poverty in Benin. Sustainable Development, 25(6), 495–503. https://doi.org/10.1002/sd.1674 +Alia, D. Y. (2017). Progress Toward The Sustainable Development Goal on Poverty: Assessing The Effect of Income Growth on The Exit Time from Poverty in Benin: Exit Time Out of Poverty in Benin. Sustainable Development, 25(6), 495–503. https://doi.org/10.1002/sd.1674
Barry, M. S., & Creti, A. (2020). Pay-as-you-go contracts for electricity access: Bridging the “last mile” gap? A case study in Benin. Energy Economics, 90, 104843. https://doi.org/10.1016/j.eneco.2020.104843 @@ -2721,7 +2811,7 @@ Benjamin, D., Brandt, L., & McCaig, B. (2017). Growth with equity: Income in Brass, J. N. (2008). Djibouti’s unusual resource curse. The Journal of Modern African Studies, 46(4), 523–545. https://doi.org/10.1017/S0022278X08003479
-Bui, T. P., & Imai, K. S. (2019). Determinants of Rural-Urban Inequality in Vietnam: Detailed Decomposition Analyses Based on Unconditional Quantile Regressions. The Journal of Development Studies, 55(12), 2610–2625. https://doi.org/10.1080/00220388.2018.1536265 +Bui, T. P., & Imai, K. S. (2019). Determinants of Rural-Urban Inequality in Vietnam: Detailed Decomposition Analyses Based on Unconditional Quantile Regressions. The Journal of Development Studies, 55(12), 2610–2625. https://doi.org/10.1080/00220388.2018.1536265
Cali, M. (2014). Trade boom and wage inequality: Evidence from Ugandan districts. Journal of Economic Geography, 14(6), 1141–1174. https://doi.org/10.1093/jeg/lbu001 @@ -2736,7 +2826,7 @@ Cooper, S. J., & Wheeler, T. (2016). Rural household vulnerability to climat Datzberger, S. (2018). Why education is not helping the poor. Findings from Uganda. World Development, 110, 124–139. https://doi.org/10.1016/j.worlddev.2018.05.022
-Djossou, G. N., Kane, G. Q., & Novignon, J. (2017). Is Growth Pro-Poor in Benin? Evidence Using a Multidimensional Measure of Poverty: Is Growth Pro-Poor in Benin? Poverty & Public Policy, 9(4), 426–443. https://doi.org/10.1002/pop4.199 +Djossou, G. N., Kane, G. Q., & Novignon, J. (2017). Is Growth Pro-Poor in Benin? Evidence Using a Multidimensional Measure of Poverty: Is Growth Pro-Poor in Benin? Poverty & Public Policy, 9(4), 426–443. https://doi.org/10.1002/pop4.199
Ebrahim, C., Jack, A., & Jones, L. (2021). Women’s economic empowerment and COVID-19: The case of vulnerable women with intersectional identities in Indonesia and Vietnam. Enterprise Development and Microfinance, 32(1), 44–56. https://doi.org/10.3362/1755-1986.21-00007 @@ -2763,7 +2853,7 @@ Fritzen, S., Brassard, C., & Bui, T. M. T. (2005). Vietnam inequality re Golumbeanu, R., & Barnes, D. (2013). Connection Charges and Electricity Access in Sub-Saharan Africa (No. 6511; Policy Research Working Paper). World Bank. https://openknowledge.worldbank.org/handle/10986/15871
-Gruijters, R. J., & Behrman, J. A. (2020). Learning Inequality in Francophone Africa: School Quality and the Educational Achievement of Rich and Poor Children. Sociology of Education, 93(3), 256–276. https://doi.org/10.1177/0038040720919379 +Gruijters, R. J., & Behrman, J. A. (2020). Learning Inequality in Francophone Africa: School Quality and the Educational Achievement of Rich and Poor Children. Sociology of Education, 93(3), 256–276. https://doi.org/10.1177/0038040720919379
Hudson, P., Pham, M., Hagedoorn, L., Thieken, A. H., Lasage, R., & Bubeck, P. (2021). Self-stated recovery from flooding: Empirical results from a survey in Central Vietnam. Journal of Flood Risk Management, 14(1), 1–15. @@ -2808,10 +2898,10 @@ Nagasha, J. I., Mugisha, L., Kaase-Bwanga, E., Onyuth, H., & Ocaido, M. (201 Naiga, R. (2018). Conditions for successful community-based water management: Perspectives from rural Uganda. International Journal of Rural Management, 14(2), 110–135.
-Naiga, R., Penker, M., & Hogl, K. (2015). Challenging pathways to safe water access in rural Uganda: From supply to demand-driven water governance. International Journal of the Commons, 9(1). +Naiga, R., Penker, M., & Hogl, K. (2015). Challenging pathways to safe water access in rural Uganda: From supply to demand-driven water governance. International Journal of the Commons, 9(1).
-Nguyen, B. T., Albrecht, J. W., Vroman, S. B., & Westbrook, M. D. (2007). A quantile regression decomposition of urban–rural inequality in Vietnam. Journal of Development Economics, 83(2), 466–490. https://doi.org/10.1016/j.jdeveco.2006.04.006 +Nguyen, B. T., Albrecht, J. W., Vroman, S. B., & Westbrook, M. D. (2007). A quantile regression decomposition of urban–rural inequality in Vietnam. Journal of Development Economics, 83(2), 466–490. https://doi.org/10.1016/j.jdeveco.2006.04.006
Rateau, M., & Choplin, A. (2022). Electrifying urban Africa: Energy access, city-making and globalisation in Nigeria and Benin. International Development Planning Review, 44(1), 55–80. https://doi.org/10.3828/idpr.2021.4 @@ -2835,10 +2925,16 @@ Ssewanyana, S., & Kasirye, I. (2012). Poverty and inequality dynamics in Thu Le, H., & Booth, A. L. (2014). Inequality in Vietnamese Urban-Rural Living Standards, 1993-2006. Review of Income and Wealth, 60(4). https://doi.org/10.1111/roiw.12051
-Twongyirwe, R., Mfitumukiza, D., Barasa, B., Naggayi, B. R., Odongo, H., Nyakato, V., & Mutoni, G. (2019). Perceived effects of drought on household food security in South-western Uganda: Coping responses and determinants. Weather and Climate Extremes, 24, 100201. https://doi.org/10.1016/j.wace.2019.100201 +Twongyirwe, R., Mfitumukiza, D., Barasa, B., Naggayi, B. R., Odongo, H., Nyakato, V., & Mutoni, G. (2019). Perceived effects of drought on household food security in South-western Uganda: Coping responses and determinants. Weather and Climate Extremes, 24, 100201. https://doi.org/10.1016/j.wace.2019.100201 +
+
+UNU-WIDER. (2022a). World Income Inequality Database (WIID) – Version 30 June 2022 (pp. Version 30 June 2022) [Data set]. United Nations University World Institute for Development Economics Research. https://doi.org/10.35188/UNU-WIDER/WIID-300622 +
+
+UNU-WIDER. (2022b). World Income Inequality Database (WIID) Companion – Version 30 June 2022 (pp. Version 30 June 2022) [Data set]. United Nations University World Institute for Development Economics Research. https://doi.org/10.35188/UNU-WIDER/WIIDcomp-300622
-Van De Poel, E., O’donnell, O., & Van Doorslaer, E. (2009). What explains the rural-urban gap in infant mortality: Household or community characteristics? Demography, 46(4), 827–850. https://doi.org/10.1353/dem.0.0074 +Van De Poel, E., O’donnell, O., & Van Doorslaer, E. (2009). What explains the rural-urban gap in infant mortality: Household or community characteristics? Demography, 46(4), 827–850. https://doi.org/10.1353/dem.0.0074
van de Ven, G. W. J., de Valenca, A., Marinus, W., de Jager, I., Descheemaeker, K. K. E., Hekman, W., Mellisse, B. T., Baijukya, F., Omari, M., & Giller, K. E. (2021). Living income benchmarking of rural households in low-income countries. FOOD SECURITY, 13(3), 729–749. https://doi.org/10.1007/s12571-020-01099-8 @@ -2850,50 +2946,34 @@ van de Walle, D., & Gunewardena, D. (2001). Sources of ethnic inequality in VASS. (2006). Vietnam Poverty Update Report 2006: Poverty and Poverty Reduction in Vietnam 1993-2004. Vietnam Academy of Social Sciences.
-VASS. (2011). Poverty Reduction in Vietnam: Achievements and Challenges. Vietnam Academy of Social Sciences. +VASS. (2011). Poverty Reduction in Vietnam: Achievements and Challenges. Vietnam Academy of Social Sciences.
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-World Bank. (2012). Vietnam poverty assessment: Well begun, not yet done - Vietnam ’s remarkable progress on poverty reduction and the emerging challenges. World Bank. http://documents.worldbank.org/curated/en/563561468329654096/2012-Vietnam-poverty-assessment-well-begun-not-yet-done-Vietnams-remarkable-progress-on-poverty-reduction-and-the-emerging-challenges +World Bank. (2012). Vietnam poverty assessment: Well begun, not yet done - Vietnam’s remarkable progress on poverty reduction and the emerging challenges. World Bank. http://documents.worldbank.org/curated/en/563561468329654096/2012-Vietnam-poverty-assessment-well-begun-not-yet-done-Vietnams-remarkable-progress-on-poverty-reduction-and-the-emerging-challenges
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Yikii, F., Turyahabwe, N., & Bashaasha, B. (2017). Prevalence of household food insecurity in wetland adjacent areas of Uganda. Agriculture & Food Security, 6(1), 1–12. @@ -3056,18 +3136,31 @@ window.document.addEventListener("DOMContentLoaded", function (event) { } });
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