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63fcdf3bf8
12 changed files with 141 additions and 358 deletions
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@ -2,7 +2,7 @@
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-----
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* A stable and increasing real GDP growth rates but slow decrease in poverty levels.
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* A stable and increasing real GDP growth rates but slow decrease in relative poverty levels.
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* Poverty affects households in poorly educated households in rural areas to much higher levels than urban areas.
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* Education disparities happen mainly along community-level dimensions through high socio-economic segregation of schools and different access to resources.
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* Large disparity of access to electricity between urban and rural households, which directly negatively affects the environmental conditions of individual rural households.
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@ -17,21 +17,17 @@ Its growth rate averaged 6.4% for the years 2017 to 2019 and, with a decrease du
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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),
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though decreasing below the 2003 level to 37.8 (2018) in its most recent calculation, see @fig-ben.
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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,
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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,
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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,
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with the reduction threatened to be slowed further through increased prices on food and energy [@WorldBank2022b].
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```{python}
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#| label: fig-ben
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#| fig-cap: "Gini index of consumption per capita for Benin"
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#| fig-cap: "Gini index of consumption per capita for Benin. Source: Author's elaboration based on UNU-WIDER WIID (2022)."
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gni_cnsmpt = ben[ben['resource'].str.contains("Consumption")]
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gni_cnsmpt_percapita = gni_cnsmpt[gni_cnsmpt['scale'].str.contains("Per capita")]
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gini_plot(gni_cnsmpt_percapita)
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```
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::: {custom-style="caption"}
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Source: Author's elaboration based on UNU-WIDER WIID (2022).
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:::
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<!-- poverty -->
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Based on its national poverty line, Benin's overall poverty rate is 38.5%,
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though it hides a strong spatial disparity in the incidence of poverty between rural (44.2%) and urban (31.4) areas [@WorldBank2022b].
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@ -52,7 +48,7 @@ a @WorldBank2022a report shows that 56% of children at late primary age in Benin
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Looking purely at attendance rates, @McNabb2018 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.
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Here, gender disparities persist, however,
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with girls continuously less likely to attend and adopted girls being at the greatest disadvantage,
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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.
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while boy 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.
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While the household-level variables do play a role ---
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through the availability of educational resources at home, differences in schooling quality and overall health and well-being ---
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@Gruijters2020 find that most of the disparity stems from the community-level:
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@ -60,19 +56,18 @@ the difference in school quality is large,
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marked by high socio-economic segregation between schools,
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and primarily determined through an unequal distribution of teaching resources including teachers and textbooks.
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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.
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### Inequalities in access to electricity
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<!-- electricity access -->
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Thus, while growth is generally pro-poor in Benin, its primary determinants do not cluster only at the household level,
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but are comprised of partly household-level but especially community-level differences.
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One of the foremost examples of the effects of inequal endowments can have is brought by @VanDePoel2009 when they look at the determinants of rural infant death rates in Benin among others and find that environmental factors ---
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such as access to a safe water source, quality housing materials and electricity ---
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are the primary determinants, ahead even of access to a health facility in the community.
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Access to electricity in the country especially underlies a large heterogeneity based on location.
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The overall level of electrification of Benin has been rising slowly ---
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though outpacing population growth ---
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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 [@WorldBank2021].
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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, altough a broad difference in electrification levels between urban (65%) and rural (17%) regions remain [@WorldBank2021].
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In rural areas there are generally three approaches to electrification that work outside of a connection to the main grid,
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individual installation of solar panels or generators for smaller electric appliances,
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collective solutions like kiosks offering electric charging for some cost,
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@ -85,7 +80,6 @@ leaving behind households which are already neglected within the field of energy
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The former, physical access, is argued by @Djossou2017 as well, emphasizing the need for continued infrastructure expansion to more households,
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in order to provide access to more durable goods (fridges, mobile phones and internet) which can help decrease the inequality gap.
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The latter, prohibitively high costs, should not be disregarded in such an infrastructure expansion as well, however.
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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.
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@Golumbeanu2013 point out the main obstacles that need to be addressed here:
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the lack of incentives to increase electrical affordability,
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@ -142,15 +136,11 @@ financetotals_grouped.columns = financetotals_grouped.columns.to_flat_index()
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financetotals_grouped.columns = ['Financetype', 'Year', 'Value']
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fig = px.line(financetotals_grouped, x='Year', y='Value', color='Financetype', labels={"Value": "Development aid amount, in millions of USD"}, markers=True, template="seaborn")
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# 0-figure
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fig.update_yaxes(rangemode="tozero")
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fig.show()
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```
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::: {custom-style="caption"}
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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.
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Source: Author's elaboration based on OECD ODA CRS (2022).
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:::
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The total amount of development aid for Benin registered by the OECD Creditor Reporting System has been fluctuating, with an overall upward trend since 2011:
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The aid broken down by financing type can be seen in @fig-ben-aid-financetype and shows that money has predominantly been given by way of ODA grants, with roughly double the absolute monetary amount of ODA loans per year.
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@ -181,15 +171,11 @@ donortotals_grouped = donortotals_grouped.reset_index(['Donortype', 'Year'])
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donortotals_grouped.columns = donortotals_grouped.columns.to_flat_index()
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donortotals_grouped.columns = ['Donortype', 'Year', 'Value']
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fig = px.line(donortotals_grouped, x='Year', y='Value', color='Donortype', labels={"Value": "Development aid amount, in millions of USD"}, markers=True, template="seaborn")
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# 0-figure
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fig.update_yaxes(rangemode="tozero")
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fig.show()
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```
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::: {custom-style="caption"}
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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.
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Source: Author's elaboration based on OECD ODA CRS (2022).
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:::
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The total amount of development aid for Benin registered by the OECD Creditor Reporting System,
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broken down into individual donor types can be seen in @fig-ben-aid-donortype.
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@ -212,18 +198,12 @@ totals = df.loc[
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electricityaid = totals[totals['DONOR'] < 20000] # drop all 'total' aggregations
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el_grouped = electricityaid.groupby(['Year', 'Flow']).agg({'Value': ['sum']})
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el_grouped = el_grouped.reset_index(['Year', 'Flow'])
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el_grouped.columns = el_grouped.columns.to_flat_index()
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el_grouped.columns = ['Year', 'Flow', 'Value']
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crosstab = pd.crosstab(el_grouped['Year'], el_grouped['Flow'], margins=True, values=el_grouped['Value'], aggfunc='sum')
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Markdown(tabulate(crosstab.fillna("0.00"), headers="keys", tablefmt="github", floatfmt=".2f"))
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el_grouped.style.format(escape="latex")
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el_grouped
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```
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::: {custom-style="caption"}
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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.
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Source: Author's elaboration based on OECD ODA CRS (2022).
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:::
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@tbl-ben-aid-electricity shows the amounts of project-bound development aid to Benin for the transmission and distribution of electric power within its centralized grid.
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The category subsumes grid distribution from the power source to end users and transmission lines.
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@ -231,7 +211,6 @@ It also includes storage of energy to generate power (e.g. batteries) and projec
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especially in rural areas.
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For development aid to the electrification of Benin,
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the monetary contributions are smaller but increasing and show trends quite different to that of overall development aid to the country.
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The amount of overall development contributions to electrification increases from 2011 to 2020,
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with significant increases in 2013 and 2015 for loans and 2019, 2020 for grants.
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While there is a steady increase to the overall development aid toward electrification,
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@ -19,16 +19,12 @@ Additionally in many cases there is a lack of data or the data itself are lackin
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```{python}
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#| label: fig-dji
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#| fig-cap: "Gini index of consumption per capita for Djibouti"
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#| fig-cap: "Gini index of consumption per capita for Djibouti. Source: Author's elaboration based on UNU-WIDER WIID (2022)."
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gni_cnsmpt = dji[dji['resource'].str.contains("Consumption")]
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gni_cnsmpt_percapita = gni_cnsmpt[gni_cnsmpt['scale'].str.contains("Per capita")]
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gini_plot(gni_cnsmpt_percapita)
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```
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::: {custom-style="caption"}
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Source: Author's elaboration based on UNU-WIDER WIID (2022).
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:::
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<!-- poverty -->
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Poverty in Djibouti is high and marked by high deprivation:
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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,
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@ -39,9 +35,8 @@ Furthermore, there is a significant spatial disparity between poverty rates.
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with 45% of the country's poor residing in rural areas while 37% reside in the Balbala[^balbala] area [@Ibarra2020].
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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,
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and while urban poor face similar restrictions they have better access to public services and higher school attendance rates.
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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,
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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,
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and all people in monetary poverty are also deprived along multiple dimensions [@Mendiratta2020].
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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].
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@Emara2020 look at the overall impact of financial inclusion on poverty levels but find that,
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first, Djibouti is way above its targeted poverty levels,
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@ -57,7 +52,6 @@ More of its inequality hides in a large spatial and gendered heterogeneity.
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Urban poor face high deprivation but higher access to public services and schooling compared to the rural poor,
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who have only 41% access to improved water sources, 10% access to sanitation, 3% access to electricity,
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and with only one third living close (under 1km) to a primary school [@Ibarra2020].
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While in general over half the working-age population does not participate in the labor force,
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the makeup is 59% of men and only 32% of women who participate,
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mirroring unemployment rates with an estimated third of men and two thirds of women being unemployed [@Mendiratta2019].
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@ -69,12 +63,10 @@ Nearly 41% of working-age women find themselves in positions of vulnerable emplo
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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].
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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,
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a stability which was recently disrupted through a global pandemic [@WorldBank2022c].
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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].
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A series of droughts in the country threatened the livelihood of its nomadic and pastoralist population,
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with many fleeing to neighboring countries, some becoming sedentary in village or city outskirts,
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and the overall nomadic population decreasing by nearly three quarters from 2009 to 2017 [@Ibarra2020; @Mendiratta2019].
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Additionally, during the early waves of Covid-19 Djibouti had one of the highest infection rates in the region,
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and though it had a high recovery rate, it also had one of the highest fatality rates,
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possibly due to deficiencies in its healthcare system [@ElKhamlichi2022].
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@ -83,13 +75,12 @@ leaving a budget of 5% for health and 3% for social expenditures,
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spendings which looks diminutive compared to its over 30% expenditures on public infrastructure [@WorldBank2022c].
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Only 10% of rural poor inhabitants live close (under 1km) to a health facility [@Ibarra2020].
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### Gender inequalities in livelihood opportunities
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While still facing reduced rates of labor market participation, the country has expended effort on increasing women's opportunity for education:
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Having overall lower literacy rates for women still,
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the overall literacy rates in younger cohorts (10-24 years old) is significantly higher compared to older ones,
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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].
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### Gender inequalities in livelihood opportunities
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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].
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However, for 2017, women's upward educational mobility was still significantly worse than men's,
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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].
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@ -98,7 +89,6 @@ Such differences reflect themselves in firm ownership structures and on the labo
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where 22.3% of all firms have female participation in ownership and only 14.2% a female top manager,
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and both salaried employment and agricultural employment are male-dominated
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(though agricultural work only with a slight and shrinking difference of 4%) [@WorldBank2022d].
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Overall it seems, however, that past growth in the country's GDP is likely not favorable for an inclusive growth path,
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with its large-scale infrastructure investments mostly creating demand for skilled workers
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and neglect of social spending not allowing the buffers and social safety nets that prevent further drift into inequality.
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@ -158,16 +148,13 @@ fig = px.line(financetotals_grouped, x='Year', y='Value', color='Financetype', l
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fig.show()
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```
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::: {custom-style="caption"}
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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.
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Source: Author's elaboration based on OECD ODA CRS (2022).
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:::
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The amount of Official Development Assistance to Djibouti has generally been increasing since 2011,
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first steadily and, since 2017, more rapidly, as can be seen in @fig-dji-aid-financetype.
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With just under 150m USD in assistance contributions 2011 and just over 320m USD at its peak in 2020,
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Djibouti has received less overall ODA funds than the other countries surveyed in this study.
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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.
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Grants have trended slowly upwards from just over 100m USD in 2011 to 135m in 2014,
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before fluctuating around this level until 2017,
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@ -203,10 +190,8 @@ fig = px.line(donortotals_grouped, x='Year', y='Value', color='Donortype', label
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fig.show()
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```
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::: {custom-style="caption"}
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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.
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Source: Author's elaboration based on OECD ODA CRS (2022).
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:::
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The primary donor type of development assistance to Djibouti has been through bilateral donors for the majority of time between 2011 and 2020,
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see @fig-dji-aid-donortype.
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@ -220,92 +205,3 @@ multilateral contributions kept increasing significantly to over 170m USD in 202
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For the first time in 2020, then, multilateral contributions provided a significantly larger share of development assistance to Djibouti than bilateral contributions,
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a trend which may move even further apart if bilateral contributions keep decreasing while multilateral ones increase.
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```{python}
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#| label: tbl-dji-aid-projects
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#| tbl-cap: "ODA projects of advancing inclusive growth, separated by project type"
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#| column: page
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totals = df.loc[
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(
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(df['SECTOR'] == 15170) |
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(df['SECTOR'] == 15180) |
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(df['SECTOR'] == 25010) |
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(df['SECTOR'] == 25020) |
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(df['SECTOR'] == 25030) |
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(df['SECTOR'] == 25040) |
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(df['SECTOR'] == 33110) |
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(df['SECTOR'] == 33120) |
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(df['SECTOR'] == 33130) |
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(df['SECTOR'] == 33140) |
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(df['SECTOR'] == 33150) |
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(df['SECTOR'] == 33181) |
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(df['SECTOR'] == 43071) |
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(df['SECTOR'] == 43072)
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) &
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(df['CHANNEL'] == 100) &
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(df['AMOUNTTYPE'] == 'D') &
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((df['FLOW'] == 11) | (df['FLOW'] == 13)) & # Total
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(df['FLOWTYPE'] == 112) &
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(df['AIDTYPE'] == "100") # contains mixed int and string representations
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]
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aid = totals[totals['DONOR'] < 20000] # drop all 'total' aggregations
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# Aggregate different measures of topics
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aid.loc[aid[(aid['SECTOR'] == 15170) | (aid['SECTOR'] == 15180)].index, 'SECTOR'] = 15170
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aid.loc[aid[(aid['SECTOR'] == 15170)].index, 'Sector'] = "Women's rights support"
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aid.loc[aid[(aid['SECTOR'] == 25010) | (aid['SECTOR'] == 25020) | (aid['SECTOR'] == 25030) | (aid['SECTOR'] == 25040)].index, 'SECTOR'] = 25010
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aid.loc[aid[(aid['SECTOR'] == 25010)].index, 'Sector'] = "Business growth"
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aid.loc[aid[(aid['SECTOR'] == 33110) | (aid['SECTOR'] == 33120) | (aid['SECTOR'] == 33130) | (aid['SECTOR'] == 33140) | (aid['SECTOR'] == 33150) | (aid['SECTOR'] == 33181)].index, 'SECTOR'] = 33110
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aid.loc[aid[(aid['SECTOR'] == 33110)].index, 'Sector'] = "Trade development"
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aid.loc[aid[(aid['SECTOR'] == 43071) | (aid['SECTOR'] == 43072)].index, 'SECTOR'] = 43071
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aid.loc[aid[(aid['SECTOR'] == 43071)].index, 'Sector'] = "Food security"
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# Group by sector per year and sum all values
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aid_grouped = aid.groupby(['Year', 'Sector']).agg({'Value': ['sum']})
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aid_grouped = aid_grouped.reset_index(['Year', 'Sector'])
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aid_grouped.columns = aid_grouped.columns.to_flat_index()
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aid_grouped.columns = ['Year', 'Sector', 'Value']
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crosstab = pd.crosstab(aid_grouped['Year'], aid_grouped['Sector'], margins=True, values=aid_grouped['Value'], aggfunc='sum')
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# Rename and reorder columns
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crosstab = crosstab[['Trade development', 'Business growth', "Women's rights support", 'Food security', 'All']]
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Markdown(tabulate(crosstab.fillna("0.00"), headers="keys", tablefmt="github", floatfmt=".2f"))
|
||||
```
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||||
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||||
::: {custom-style="caption"}
|
||||
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).
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||||
:::
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The sector-based breakdown of aid contributions for inclusive business growth in Djibouti can be seen in @tbl-dji-aid-projects.
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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:
|
||||
|
||||
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).
|
||||
|
||||
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.
|
||||
|
|
|
|||
|
|
@ -2,7 +2,7 @@
|
|||
|
||||
-----
|
||||
|
||||
* Poverty and inequality in Uganda are at a fluctuating level in Uganda, with poverty levels staying roughly stable and inequality slowly trending upward.
|
||||
* Poverty and inequality in Uganda are at a fluctuating level in Uganda, with relative poverty staying roughly stable and inequality slowly trending upward.
|
||||
* National poverty line set very low, potentially hiding additional households in states of deprivation and those in danger of reverting to poverty.
|
||||
* Inequality, poverty and informal economy in close circular relation in Uganda, presenting a vicious circle for those captive within.
|
||||
* Education levels of poor people are consistently low, with those of rural population more so.
|
||||
|
|
@ -26,16 +26,12 @@ These inequality levels remained mostly unchanged between 2012/13 and 2019/20 bu
|
|||
|
||||
```{python}
|
||||
#| label: fig-uga
|
||||
#| fig-cap: "Gini index of consumption per capita for Uganda"
|
||||
#| fig-cap: "Gini index of consumption per capita for Uganda. Source: Author's elaboration based on UNU-WIDER WIID (2022)."
|
||||
gni_cnsmpt = uga[uga['resource'].str.contains("Consumption")]
|
||||
gni_cnsmpt_percapita = gni_cnsmpt[gni_cnsmpt['scale'].str.contains("Per capita")]
|
||||
gini_plot(gni_cnsmpt_percapita)
|
||||
```
|
||||
|
||||
::: {custom-style="caption"}
|
||||
Source: Author's elaboration based on UNU-WIDER WIID (2022).
|
||||
:::
|
||||
|
||||
<!-- poverty -->
|
||||
The World Bank [-@Atamanov2022] 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,
|
||||
|
|
@ -47,7 +43,6 @@ with an improvement in 2019/20 conversely being linked to favorable weather cond
|
|||
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.
|
||||
|
||||
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) -->
|
||||
|
|
@ -58,7 +53,6 @@ inequality increases the size of the informal economy, as a large subsistence se
|
|||
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.
|
||||
|
||||
@Cali2014 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 ---
|
||||
|
|
@ -66,7 +60,6 @@ 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) [@Atamanov2022].
|
||||
|
||||
The World Bank [-@WorldBank2022] 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.
|
||||
@Datzberger2018 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,
|
||||
|
|
@ -79,10 +72,8 @@ Such personal circumstances as access to a timely education play decisive role i
|
|||
circumstances to which decent housing as well as access to clean water are equally fundamental building blocks [@Atamanov2022].
|
||||
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.
|
||||
|
||||
In the country, access to improved water sources rose from 44% in 1990 to 60% in 2004 and 66% in 2010 [@Naiga2015].
|
||||
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 [@Atamanov2022].
|
||||
|
||||
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 [@Mulogo2018].
|
||||
Thus, individual households are generally less well connected than health care facilities,
|
||||
and rural households in turn less well than urban households.
|
||||
|
|
@ -95,7 +86,6 @@ only 28% of households had access to improved water [@Mulogo2018].
|
|||
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.
|
||||
|
||||
@Naiga2018 argues that some reasons for the low access to working improved water sources is the absence of many of the organizational characteristics prescribed by the design principles of community-managed water infrastructure management ---
|
||||
unclear social boundaries, missing collective-choice arrangements and a lack of sanctions or conflict resolution mechanisms ---
|
||||
in other words, a policy failure resulting in lack of sufficient self-governance arrangements.
|
||||
|
|
@ -108,7 +98,6 @@ Looking into the effects of climate change and its accompanying increase in clim
|
|||
@Nagasha2019 find that it gender roles adapt while gender inequalities tend to increase,
|
||||
with men participating more in firewood collection and water fetching but generally focused on assuming a single reproductive role while women played multiple roles simultaneously.
|
||||
Two effects they found of this exacerbation were the women often being forced to engage their children in work activities to manage the simultaneous workload, and women, due to their exclusion from landownership in the region, being brought further into a state of dependence and thus made even more vulnerable to future climate change effects.
|
||||
|
||||
Water supply use seems to experience little change during emergency situations, and people's willingness (or ability) to pay for water is also too small to maintain water revenue without addressing the disparity in socio-economic attributes of households [@Sempewo2021; @Sempewo2021a].
|
||||
Taken together, this hints at one possibility of subsequent health disparity increases due to prior income inequalities and poverty during emergency situations such as climate shocks.
|
||||
|
||||
|
|
@ -117,7 +106,6 @@ In Uganda, @Cooper2016 investigate the vulnerability of rural farmers to climate
|
|||
many responses only protect against very specific events (45.4%) and most had no response at all to coping with rainfall variability:
|
||||
while farmers with more land, education, access to government extensions and non-farm livelihoods have more capacity to buffer the shock,
|
||||
both wealthier farmers (droughts as highest perceived risk) and poor farmers (extreme rainfall as highest) perceive themselves most vulnerable to rainfall-based events.
|
||||
|
||||
In the Isingiro district, @Twongyirwe2019 find that most farmers (68.6%) perceive food insecurity as a problem with the overwhelming majority seeing droughts as the major contributory issue to this food insecurity (95.6%).
|
||||
They also find that mainly higher-income and larger farms see it as less of a problem,
|
||||
while 13% of all farmers report that they did not, or could not, do anything to respond to the drought effects.
|
||||
|
|
@ -133,7 +121,6 @@ the circumstances in neighboring refugee camps have more received more quantific
|
|||
with only 67% of the Kyangwali refugee camp having access to improved water sources and only 46% access to sanitation service facilities [@Calderon-Villarreal2022].
|
||||
Little access to sanitation sites can in turn negatively affect access to clean water if no improved water sources are nearby,
|
||||
as was the case with a prolonged cholera outbreak in Kyangwali due to a contaminated stream in 2018 [@Monje2020].
|
||||
|
||||
Such resource scarcity can also be a gendered problem,
|
||||
with predominantly girls and young women experiencing an increased amount of sexual and gender based violence as access to resources (especially water, food and firewood) becomes more scarce [@Logie2021].
|
||||
In Nakivale refugee camp growing numbers of refugees have arrived throughout 2022,
|
||||
|
|
@ -149,7 +136,6 @@ Thus, while Uganda's poverty and inequality are not trending towards drastically
|
|||
hidden disparities bring its issues in focus once disaggregated:
|
||||
Nationally, poverty is a looming transient affair for many households, more if increasing the country's very low national line of poverty.
|
||||
Inequality derives itself partly from this poverty, making it necessary for many to accept informal work which, taken at large, in turn fosters further national inequality.
|
||||
|
||||
The role education plays in Uganda's allocation of poverty cannot be overstated, with especially many rural children not having adequate opportunity to access timely education.
|
||||
This disparity could be exacerbated by poor quality access to clean water through improved water sources,
|
||||
which in turn worsens food securities, retrenches gender role inequalities and precludes more children from their education.
|
||||
|
|
@ -191,15 +177,11 @@ 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 amount, in millions of USD"}, markers=True, template="seaborn")
|
||||
# 0-figure
|
||||
fig.update_yaxes(rangemode="tozero")
|
||||
fig.show()
|
||||
```
|
||||
|
||||
::: {custom-style="caption"}
|
||||
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).
|
||||
:::
|
||||
|
||||
Overall Ugandan development aid reception is high, with over 1.5bn USD granted as official development assistance in 2011 as seen in @fig-uga-aid-financetype.
|
||||
The Official Development Assistance overall further increased to over 2.2bn USD in 2019,
|
||||
|
|
@ -231,15 +213,11 @@ 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 amount, in millions of USD"}, markers=True, template="seaborn")
|
||||
# 0-figure
|
||||
fig.update_yaxes(rangemode="tozero")
|
||||
fig.show()
|
||||
```
|
||||
|
||||
::: {custom-style="caption"}
|
||||
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).
|
||||
:::
|
||||
|
||||
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,
|
||||
|
|
@ -289,24 +267,21 @@ crosstab = pd.crosstab(wateraid_grouped['Year'], wateraid_grouped['Sector'], mar
|
|||
crosstab.columns = ['Basic water supply', 'Education and training', 'Large water supply', 'All']
|
||||
crosstab = crosstab[['Basic water supply', 'Large water supply', 'Education and training', 'All']]
|
||||
|
||||
Markdown(tabulate(crosstab.fillna("0.00"), headers="keys", tablefmt="github", floatfmt=".2f"))
|
||||
crosstab
|
||||
```
|
||||
|
||||
::: {custom-style="caption"}
|
||||
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).
|
||||
:::
|
||||
|
||||
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.
|
||||
|
||||
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.
|
||||
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.
|
||||
|
|
@ -318,7 +293,6 @@ 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.
|
||||
|
||||
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.
|
||||
|
|
|
|||
|
|
@ -24,7 +24,6 @@ On the other hand, Le et al. [-@Le2021] suggest a slight increase in overall inc
|
|||
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 [@Baulch2012],
|
||||
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 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].
|
||||
For Gini coefficients estimated using consumption per capita, see @fig-vnm, which shows similar trends of increasing inequality, with 2010 constituting a significant increase.
|
||||
|
|
@ -33,7 +32,7 @@ one which exogenous shocks can rapidly exacerbate as the example of the COVID-19
|
|||
|
||||
```{python}
|
||||
#| label: fig-vnm
|
||||
#| fig-cap: "Gini index of consumption per capita for Vietnam"
|
||||
#| fig-cap: "Gini index of consumption per capita for Vietnam. Source: Author's elaboration based on UNU-WIDER WIID (2022)."
|
||||
gni_cnsmpt = vnm[vnm['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")]
|
||||
|
|
@ -41,10 +40,6 @@ gni_cnsmpt = gni_cnsmpt[gni_cnsmpt['areacovr'].str.contains("All")]
|
|||
gini_plot(gni_cnsmpt)
|
||||
```
|
||||
|
||||
::: {custom-style="caption"}
|
||||
Source: Author's elaboration based on UNU-WIDER WIID (2022).
|
||||
:::
|
||||
|
||||
<!--
|
||||
* estimated Gini coeff, overall income distribution: [@Le2021]
|
||||
* fluctuating 0.42-0.44 (2010-2018)
|
||||
|
|
@ -59,7 +54,6 @@ There are two complementary views on the primary dimensions of rural inequalitie
|
|||
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. [-@Nguyen2007] 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 [@Fesselmeyer2010].
|
||||
|
|
@ -68,7 +62,6 @@ On the other, Thu Le and Booth [-@ThuLe2014] argue that the urban-rural inequali
|
|||
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 [@Fritzen2005; @ThuLe2014].
|
||||
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 [-@Cao2008] note.
|
||||
|
||||
In a recent study, Bui and Imai [-@Bui2019] build on this earlier work,
|
||||
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 find that, as within-sector became more pronounced again after 2010,
|
||||
|
|
@ -77,7 +70,6 @@ Early income studies generally highlighted the important role of agricultural in
|
|||
Benjamin et al. [-@Benjamin2017] 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.
|
||||
|
|
@ -103,7 +95,6 @@ a finding he suggests waws created due to environmental and structural differenc
|
|||
Baulch et al. [-@Baulch2012] find that between 1993 and 2004, the welfare gap between ethnic minorities and the ethnic majority had increased by 14.6%, two-fifths of which were due to endowments such as demographic structure and education while geographic variables make up less than one-fifth.
|
||||
They additionally suggest some drivers of the inequality being the lack of ability speaking the Vietnamese language or the distance to a commune or district center amplifying isolating effects, though a large part of the change was linked to temporal changes of unobservable factors -
|
||||
which the study conjectures to be due to negative ethnic stereotyping, a poor understanding of ethnic customs and culture and further (unobserved) variations in household-level endowments.
|
||||
|
||||
While in 2002 the ethnic minority population living in rural areas was below 15%, it rose to over 18% in 2014 - both due to higher fertility among minorities and ethnic majority Kinh urbanizing at a higher rate - and the ratio of Kinh to minority incomes rose to more than 2.0 in 2014 [@Benjamin2017].
|
||||
The same study finds that income inequality rose even more sharply *within* ethnic minorities, while that of rural Kinh, though increasing from 2002 to 2014, fell back to 2002 levels around 2014.
|
||||
These findings suggest that the primary drivers of rural income inequality are a growing gap between Kinh and minorities while at the same time a similar rising inequality develops among minority rural populations themselves.
|
||||
|
|
@ -122,10 +113,8 @@ In the same vein as the urban-rural divide, Nguyen et al. [-@Nguyen2007] thus ar
|
|||
While the effect of agriculture on inequality outcomes is an equalizing one,
|
||||
its future growth, and that of agricultural livelihoods, is threatened by vulnerability to risks such as natural disasters and environmental degradation, exacerbated through climate change [@Kozel2014].
|
||||
Kozel [-@Kozel2014] goes on to argue the continuous precarity of poor households against economy-wide shocks (such as the effect of climate change on rainfall and temperatures) but also highlights the danger of vulnerable households *falling* into poverty through generated inequalities.
|
||||
|
||||
Looking at the particularities of flood risk management in the Ninh Binh province, Mottet and Roche [-@Mottet2009] find that most areas within the region are vulnerable.
|
||||
They find the strengths of current management lying in prevention with existing dykes designed to channel high waters, effective monitoring of weather conditions (rainfall or typhoons) and consolidation or elevation of existing residences, while the weaknesses are mainly centered around insufficient information given to inhabitants over flood risks, few compensation systems for flood victims and construction policies continuing to allow building in flood-endangered zones.
|
||||
|
||||
Sen et al. [-@Sen2021] estimate that the main barriers to better information are farmers' lack of trust toward formal climate-related services, their lack of perceived risk from climate change itself and difficulties in balancing both climate adaptation and economic benefits of interventions.
|
||||
They argue that, while ethnicity itself is not a barrier to information access with all farmers receiving information through informal channels ---
|
||||
friends neighbors and market actors instead of agricultural departments or mass media ---
|
||||
|
|
@ -144,18 +133,14 @@ The results are further intensification of inequality along existing social line
|
|||
The effects of inequalities mainly affecting ethnic minorities are illustrated by Son and Kingsbury [-@Son2020],
|
||||
with droughts impacting yield losses between 50% and 100%, cold snaps leading to loss of livestock and floods damaging residential structures but even more importantly disrupting livelihoods through landslides, crop destruction and overflowing fish ponds.
|
||||
Locally employed coping strategies, they argue, are always conditional on the strength and foresight of institutions and implemented preventative policies along local but also regional and central levels.
|
||||
|
||||
Similarly, Ylipaa et al. [-@Ylipaa2019] analyze impacts mainly across the gender dimension to find that,
|
||||
resulting inequalities may be exacerbated with differentiated rights and responsibilities leading to unequal opportunities and, especially, decreased female mobility in turn increasing their vulnerability to climate impacts with a reduced capacity to adapt.
|
||||
Hudson et al. [-@Hudson2021] along the same dimension find that,
|
||||
while the set of relevant variables is largely similar with age, social capital, internal and external support after the flood and the perceived severity of previous flood impacts having major impacts,
|
||||
women tend to show longer recovery times and psychological variables can influence recovery rates more than some adverse flood impacts.
|
||||
|
||||
While the quantitative evidence for impacts of such shock events are relatively sparse, Jafino et al. [-@Jafino2021] lament the overuse of aggregate perspectives,
|
||||
instead disaggregating the local and inter-sectoral effects to find out that flood protection efforts in the Mekong Delta often predominantly support large-scale farming while small-scale farmers can be harmed through them.
|
||||
They find that measures decrease the aggregate total output and equity indicators by disaggregating profitability indicators into inundation, sedimentation, soil fertility, nutrient dynamics and behavioral land-use in an assessment which sees within-sector policy responses often having an effect on adjacent sectors,
|
||||
increasing the inter-district Gini coefficient.
|
||||
|
||||
While the quantitative evidence for impacts of such shock events are relatively sparse, Jafino et al. [-@Jafino2021] lament the overuse of aggregate perspectives, instead disaggregating the local and inter-sectoral effects to find out that flood protection efforts in the Mekong Delta often predominantly support large-scale farming while small-scale farmers can be harmed through them.
|
||||
They find that measures decrease the aggregate total output and equity indicators by disaggregating profitability indicators into inundation, sedimentation, soil fertility, nutrient dynamics, behavioral land-use in an assessment which sees within-sector policy responses often having an effect on adjacent sectors, increasing the inter-district Gini coefficient.
|
||||
Adaptation during these catastrophic events reinforces the asset and endowment drivers of non-shock event times,
|
||||
with impacts levels often depending on access to non-farm income sources, access to further arable land, knowledge of adaptive farming practices and mitigation of possible health risks such as water contamination [@Son2020].
|
||||
Karpouzoglou et al. [-@Karpouzoglou2019] make the point that, ultimately, the pure coupling of flood resilience into infrastructural or institutional interventions needs to take care not to amplify existing inequalities through unforeseen consequences ('ripple effects') which can't be escaped by vulnerable people due to their existing immobility.
|
||||
|
|
@ -169,7 +154,6 @@ 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.
|
||||
|
|
@ -216,33 +200,26 @@ 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 amount, in millions of USD"}, markers=True, template="seaborn")
|
||||
# 0-figure
|
||||
fig.update_yaxes(rangemode="tozero")
|
||||
fig.show()
|
||||
```
|
||||
|
||||
::: {custom-style="caption"}
|
||||
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).
|
||||
:::
|
||||
|
||||
Official Development Assistance (ODA) to Vietnam reached its highest point in 2014 with almost 5bn USD but generally decreased in the intervening years, as can be seen in @fig-vnm-aid-financetype.
|
||||
Decreasing continuously after 2014,
|
||||
development assistance reached its lowest point of the last ten years in 2019 when it fell to just under 2.5bn USD,
|
||||
before increasing slightly to just above 2.5bn USD in 2020.
|
||||
Development aid to Vietnam is primarily driven by ODA loans instead of ODA grants.
|
||||
|
||||
While grants were just under 1bn USD in 2011 and decreased slightly over the following years to 600m USD in 2019,
|
||||
decreasing loans were also the primary driver of the overall development aid contributions,
|
||||
with the overall monetary curve closely following that of loan contributions.
|
||||
Thus, while loans constituted almost triple the USD amount of grants to Vietnam in 2011,
|
||||
this number even climbed to almost 5 times the amount in 2014,
|
||||
before falling to just over 2.5 times the amount of USD in loans compared to grants in 2020.
|
||||
A large share of development aid contributions to Vietnam are also made up from other official flows[^officialflows],
|
||||
A large share of development aid contributions to Vietnam are also made up from other official flows, non-export credits,
|
||||
a share which started equal to the absolute grant amount of USD contributed, rose steeply in 2013 to double the amount and fell equally steeply back to its original level in 2019.
|
||||
|
||||
[^officialflows]: Other official flows, per OECD CRS definition, describe contributions that do not meet the ODA criteria. These can include grants for primarily representational or commercial purposes, contributions having a grant element under the required share of 25% or primarily export-facilitating contributions. See https://doi.org/10.1787/6afef3df-en for a full definition.
|
||||
|
||||
```{python}
|
||||
#| label: fig-vnm-aid-donortype
|
||||
#| fig-cap: "Total ODA for Vietnam per year, separated by donor type"
|
||||
|
|
@ -264,15 +241,11 @@ 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")
|
||||
# 0-figure
|
||||
fig.update_yaxes(rangemode="tozero")
|
||||
fig.show()
|
||||
```
|
||||
|
||||
::: {custom-style="caption"}
|
||||
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).
|
||||
:::
|
||||
|
||||
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.
|
||||
|
|
@ -329,13 +302,12 @@ crosstab = pd.crosstab(floodaid_grouped['Year'], floodaid_grouped['Sector'], mar
|
|||
crosstab.columns = ['Basic water supply', 'Disaster risk reduction', 'Large water supply', 'All']
|
||||
crosstab = crosstab[['Basic water supply', 'Large water supply', 'Disaster risk reduction', 'All']]
|
||||
|
||||
Markdown(tabulate(crosstab.fillna("0.00"), headers="keys", tablefmt="github", floatfmt=".2f"))
|
||||
#crosstab.style.format(escape="latex")
|
||||
crosstab
|
||||
```
|
||||
|
||||
::: {custom-style="caption"}
|
||||
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).
|
||||
:::
|
||||
|
||||
The breakdown of project-based development aid for water supply infrastructure and disaster risk reduction in Vietnam can be seen in @tbl-vnm-aid-water.
|
||||
It shows the funds broken down into their use for three categories:
|
||||
|
|
@ -357,7 +329,6 @@ access to basic water supply saw significant increases to its contributions from
|
|||
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.
|
||||
|
||||
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,
|
||||
|
|
|
|||
29
_quarto.yml
29
_quarto.yml
|
|
@ -7,8 +7,7 @@ project:
|
|||
|
||||
format:
|
||||
html:
|
||||
standalone: true
|
||||
embed-resources: true
|
||||
self-contained: true
|
||||
toc: true
|
||||
code-fold: true
|
||||
code-tools: true
|
||||
|
|
@ -16,19 +15,19 @@ format:
|
|||
docx:
|
||||
echo: false
|
||||
reference-doc: tools/justified.docx
|
||||
pdf:
|
||||
echo: false
|
||||
papersize: A4
|
||||
geometry:
|
||||
- left=2cm
|
||||
- right=2.5cm
|
||||
- top=2.5cm
|
||||
- bottom=2.5cm
|
||||
indent: true
|
||||
linestretch: 1.25
|
||||
fontfamily: lmodern
|
||||
fontsize: "12"
|
||||
toc: true
|
||||
# pdf:
|
||||
# echo: false
|
||||
# papersize: A4
|
||||
# geometry:
|
||||
# - left=2cm
|
||||
# - right=2.5cm
|
||||
# - top=2.5cm
|
||||
# - bottom=2.5cm
|
||||
# indent: true
|
||||
# linestretch: 1.25
|
||||
# fontfamily: lmodern
|
||||
# fontsize: "12"
|
||||
# toc: true
|
||||
jupyter: python3
|
||||
title: "Drivers of Inequality"
|
||||
author:
|
||||
|
|
|
|||
|
|
@ -35,11 +35,6 @@ uga = df.loc[df['c3'] == "UGA"]
|
|||
vnm = df.loc[df['c3'] == "VNM"]
|
||||
```
|
||||
|
||||
```{python}
|
||||
from IPython.display import Markdown
|
||||
from tabulate import tabulate
|
||||
```
|
||||
|
||||
```{python}
|
||||
# Set up the data extraction and figure drawing functions
|
||||
import plotly.express as px
|
||||
|
|
|
|||
Binary file not shown.
Binary file not shown.
|
|
@ -54,7 +54,8 @@
|
|||
issn = {09680802},
|
||||
doi = {10.1002/sd.1674},
|
||||
langid = {english},
|
||||
keywords = {country::Benin,inequality::income,status::read,topic::poverty},
|
||||
keywords = {country::Benin,inequality::income,status::skimmed,topic::poverty
|
||||
},
|
||||
file = {/home/marty/Zotero/storage/5NKMQYPC/Alia2017_Progress Toward The
|
||||
Sustainable Development Goal on Poverty.pdf},
|
||||
}
|
||||
|
|
@ -110,7 +111,7 @@
|
|||
Takaaki and Myers, Cara Ann Granguillhome and Ochoa, Rogelio and
|
||||
Sinha, Nistha},
|
||||
editoratype = {collaborator},
|
||||
keywords = {country::Uganda,status::read},
|
||||
keywords = {country::Uganda,status::skimmed},
|
||||
file = {/home/marty/Zotero/storage/DI29L2WE/Atamanov2022_Uganda Poverty
|
||||
Assessment.pdf},
|
||||
}
|
||||
|
|
@ -179,7 +180,7 @@
|
|||
issn = {01409883},
|
||||
doi = {10.1016/j.eneco.2020.104843},
|
||||
langid = {english},
|
||||
keywords = {country::Benin,status::read,topic::electricity,topic::rural},
|
||||
keywords = {country::Benin,status::skimmed,topic::electricity,topic::rural},
|
||||
file = {/home/marty/Zotero/storage/BERQHNQT/Barry2020_Pay-as-you-go
|
||||
contracts for electricity access.pdf},
|
||||
}
|
||||
|
|
@ -196,7 +197,7 @@
|
|||
issn = {1360-0818, 1469-9966},
|
||||
doi = {10.1080/13600818.2011.646441},
|
||||
langid = {english},
|
||||
keywords = {inequality::ethnicity,status::read,topic::rural},
|
||||
keywords = {inequality::ethnicity,status::skimmed,topic::rural},
|
||||
file = {/home/marty/Zotero/storage/K4W8VPTE/Baulch2012_Decomposing the
|
||||
Ethnic Gap in Rural Vietnam, 1993–2004.pdf},
|
||||
}
|
||||
|
|
@ -232,7 +233,7 @@
|
|||
doi = {10.1007/s10888-016-9341-7},
|
||||
langid = {english},
|
||||
keywords = {country::Vietnam,index::Gini,index::Theil,inequality::income,
|
||||
status::deepread,topic::equity},
|
||||
status::read,topic::equity},
|
||||
file = {/home/marty/Zotero/storage/FQJMZJSG/Benjamin2017_Growth with
|
||||
equity.pdf},
|
||||
}
|
||||
|
|
@ -267,7 +268,6 @@
|
|||
personal observation and secondary material.},
|
||||
copyright = {Copyright Adonis \& Abbey Publishers Ltd Jun-Dec 2016},
|
||||
langid = {english},
|
||||
keywords = {next},
|
||||
file = {/home/marty/Zotero/storage/J6Z53UN5/Bereketeab2016_Djibouti.pdf},
|
||||
}
|
||||
|
||||
|
|
@ -301,7 +301,7 @@
|
|||
Finally, even tiny countries can provide scholars and policy
|
||||
makers with new insights.},
|
||||
langid = {english},
|
||||
keywords = {country::Djibouti,status::read,topic::modernization,
|
||||
keywords = {country::Djibouti,status::skimmed,topic::modernization,
|
||||
topic::poverty,topic::trade_liberalization},
|
||||
file = {/home/marty/Zotero/storage/X6YSKQYR/Brass2008_Djibouti's unusual
|
||||
resource curse.pdf},
|
||||
|
|
@ -338,7 +338,7 @@
|
|||
copyright = {2017 John Wiley \& Sons Ltd},
|
||||
langid = {english},
|
||||
keywords = {country::Uganda,inequality::ethnicity,inequality::income,
|
||||
status::deepread,topic::consumption},
|
||||
status::read,topic::consumption},
|
||||
file = {/home/marty/Zotero/storage/KYBLB9YI/Brunori2018_Consumption dynamics
|
||||
and inequality of opportunity with an application to Uganda.pdf},
|
||||
}
|
||||
|
|
@ -358,8 +358,8 @@
|
|||
issn = {0022-0388, 1743-9140},
|
||||
doi = {10.1080/00220388.2018.1536265},
|
||||
langid = {english},
|
||||
keywords = {country::Vietnam,inequality::rural,status::deepread,topic::rural
|
||||
,topic::urban},
|
||||
keywords = {country::Vietnam,inequality::rural,status::read,topic::rural,
|
||||
topic::urban},
|
||||
file = {/home/marty/Zotero/storage/7GHFRSL8/Bui2019_Determinants of
|
||||
Rural-Urban Inequality in Vietnam.pdf},
|
||||
}
|
||||
|
|
@ -398,7 +398,7 @@
|
|||
influence of value judgments on the assessment of inequality.},
|
||||
langid = {english},
|
||||
keywords = {country::Vietnam,index::Atkinson,inequality::education,
|
||||
inequality::health,inequality::housing,next,topic::consumption,
|
||||
inequality::health,inequality::housing,topic::consumption,
|
||||
topic::education,topic::health,topic::housing},
|
||||
file = {/home/marty/Zotero/storage/IXPIBKJR/Bui2020_Multidimensional
|
||||
Inequality in Vietnam, 2002–2012.pdf},
|
||||
|
|
@ -463,7 +463,7 @@
|
|||
women of reproductive age.},
|
||||
langid = {english},
|
||||
keywords = {country::Bangladesh,country::Kenya,country::South Sudan,
|
||||
country::Uganda,status::read,topic::refugee,topic::water},
|
||||
country::Uganda,status::skimmed,topic::refugee,topic::water},
|
||||
file = {/home/marty/Zotero/storage/HXMCVQ5J/Calderon-Villarreal2022_Social
|
||||
and geographic inequalities in water, sanitation and hygiene access
|
||||
in.pdf},
|
||||
|
|
@ -483,7 +483,7 @@
|
|||
issn = {1468-2702, 1468-2710},
|
||||
doi = {10.1093/jeg/lbu001},
|
||||
langid = {english},
|
||||
keywords = {country::Uganda,inequality::income,status::deepread,
|
||||
keywords = {country::Uganda,inequality::income,status::skimmed,
|
||||
topic::education,topic::trade_liberalization},
|
||||
file = {/home/marty/Zotero/storage/MPYD83JI/Cali2014_Trade boom and wage
|
||||
inequality.pdf},
|
||||
|
|
@ -519,7 +519,7 @@
|
|||
as a complement.},
|
||||
copyright = {2001 Elsevier Science Ltd},
|
||||
langid = {english},
|
||||
keywords = {country::Uganda,inequality::income,status::read,
|
||||
keywords = {country::Uganda,inequality::income,status::skimmed,
|
||||
topic::agriculture,topic::gender,topic::modernization},
|
||||
file = {/home/marty/Zotero/storage/JKRD3UJR/Canagarajah2001_Non-farm income,
|
||||
gender, and inequality.pdf},
|
||||
|
|
@ -531,7 +531,7 @@
|
|||
date = {2008},
|
||||
series = {Economic {{Development}} \& {{Policy Series}}},
|
||||
institution = {{GSIR}},
|
||||
keywords = {inequality::income,inequality::rural,status::read,
|
||||
keywords = {inequality::income,inequality::rural,status::skimmed,
|
||||
topic::agriculture,topic::modernization,
|
||||
topic::trade_liberalization},
|
||||
file = {/home/marty/Zotero/storage/EXXELF4W/Cao2008_Urban and rural
|
||||
|
|
@ -597,7 +597,7 @@
|
|||
climate risk.},
|
||||
copyright = {Springer-Verlag Berlin Heidelberg 2016},
|
||||
langid = {english},
|
||||
keywords = {country::Uganda,inequality::environmental,status::read,
|
||||
keywords = {country::Uganda,inequality::environmental,status::skimmed,
|
||||
topic::climate_change},
|
||||
file = {/home/marty/Zotero/storage/8WPKV5IC/Cooper2016_Rural household
|
||||
vulnerability to climate risk in Uganda.pdf},
|
||||
|
|
@ -636,7 +636,7 @@
|
|||
doi = {10.1007/s11205-018-1896-1},
|
||||
langid = {english},
|
||||
keywords = {country::Vietnam,inequality::ethnicity,inequality::horizontal,
|
||||
inequality::regional,status::deepread},
|
||||
inequality::regional},
|
||||
file = {/home/marty/Zotero/storage/RIVNVNEQ/Dang2019_Does Horizontal
|
||||
Inequality Matter in Vietnam.pdf},
|
||||
}
|
||||
|
|
@ -696,7 +696,7 @@
|
|||
statistical abstracts inform the analysis.},
|
||||
copyright = {2018 Elsevier Ltd},
|
||||
langid = {english},
|
||||
keywords = {country::Uganda,inequality::education,status::deepread,
|
||||
keywords = {country::Uganda,inequality::education,status::skimmed,
|
||||
topic::poverty},
|
||||
file = {/home/marty/Zotero/storage/5LNQ7X6P/Datzberger2018_Why education is
|
||||
not helping the poor.pdf},
|
||||
|
|
@ -717,8 +717,8 @@
|
|||
issn = {19442858},
|
||||
doi = {10.1002/pop4.199},
|
||||
langid = {english},
|
||||
keywords = {country::Benin,inequality::income,status::deepread,topic::gender
|
||||
,topic::poverty,topic::rural},
|
||||
keywords = {country::Benin,inequality::income,status::skimmed,topic::gender,
|
||||
topic::poverty,topic::rural},
|
||||
file = {/home/marty/Zotero/storage/BGZ6SI3H/Djossou2017_Is Growth Pro-Poor
|
||||
in Benin.pdf},
|
||||
}
|
||||
|
|
@ -735,7 +735,7 @@
|
|||
issn = {0377-7332, 1435-8921},
|
||||
doi = {10.1007/s00181-021-02022-6},
|
||||
langid = {english},
|
||||
keywords = {country::Vietnam,inequality::income,next,topic::agriculture,
|
||||
keywords = {country::Vietnam,inequality::income,topic::agriculture,
|
||||
topic::livestock},
|
||||
file = {/home/marty/Zotero/storage/ED6YGVJ6/Do2022_Livestock production and
|
||||
income inequality in rural Vietnam.pdf},
|
||||
|
|
@ -760,7 +760,6 @@
|
|||
involvement of Western countries has on the region.},
|
||||
copyright = {Copyright University Constantin Brancusi of Târgu-Jiu 2017},
|
||||
langid = {rum},
|
||||
keywords = {next},
|
||||
}
|
||||
|
||||
@article{Ebrahim2021,
|
||||
|
|
@ -795,7 +794,7 @@
|
|||
north-west Vietnam. It discusses early experiences and learning
|
||||
on pivoting projects, funded by the Government of Australia, to
|
||||
be COVID-19 responsive and contribute to longer-term recovery.},
|
||||
keywords = {inequality::gender,next,topic::agriculture,topic::covid19,
|
||||
keywords = {inequality::gender,topic::agriculture,topic::covid19,
|
||||
topic::poverty},
|
||||
file = {/home/marty/Zotero/storage/YU72GAUG/Ebrahim2021_Women’s economic
|
||||
empowerment and COVID-19.pdf},
|
||||
|
|
@ -832,7 +831,7 @@
|
|||
issn = {22124268},
|
||||
doi = {10.1016/j.jobcr.2021.10.004},
|
||||
langid = {english},
|
||||
keywords = {country::Djibouti,status::read,topic::covid19},
|
||||
keywords = {country::Djibouti,status::skimmed,topic::covid19},
|
||||
file = {/home/marty/Zotero/storage/H9SGGQDQ/El Khamlichi2022_Comparative
|
||||
study of COVID-19 situation between lower-middle-income countries.pdf
|
||||
},
|
||||
|
|
@ -903,8 +902,8 @@
|
|||
financial inclusion in addressing extreme poverty by 2030, the
|
||||
year UN member states set as a target for achieving the SDGs.},
|
||||
langid = {english},
|
||||
keywords = {country::Djibouti,inequality::income,status::read,topic::poverty
|
||||
},
|
||||
keywords = {country::Djibouti,inequality::income,status::skimmed,
|
||||
topic::poverty},
|
||||
file = {/home/marty/Zotero/storage/X4KLTJFE/Emara2020_Financial inclusion
|
||||
and extreme poverty in the MENA region.pdf},
|
||||
}
|
||||
|
|
@ -944,7 +943,7 @@
|
|||
copyright = {2021 The Author(s). This open access article is distributed
|
||||
under a Creative Commons Attribution (CC-BY) 4.0 license. 2021},
|
||||
langid = {english},
|
||||
keywords = {country::Uganda,status::read},
|
||||
keywords = {country::Uganda,status::skimmed},
|
||||
file = {/home/marty/Zotero/storage/SYR6AV8U/Esaku2021_Does the shadow
|
||||
economy increase income inequality in the short- and long-run.pdf},
|
||||
}
|
||||
|
|
@ -980,7 +979,7 @@
|
|||
copyright = {2021 The Author(s). Published by Informa UK Limited, trading as
|
||||
Taylor \& Francis Group 2021},
|
||||
langid = {english},
|
||||
keywords = {country::Uganda,inequality::income,status::deepread,
|
||||
keywords = {country::Uganda,inequality::income,status::skimmed,
|
||||
topic::formality},
|
||||
file = {/home/marty/Zotero/storage/5ZQH59RL/Esaku2021_Does income inequality
|
||||
increase the shadow economy.pdf},
|
||||
|
|
@ -1001,7 +1000,7 @@
|
|||
issn = {13513958},
|
||||
doi = {10.1111/j.1467-8381.2010.02034.x},
|
||||
langid = {english},
|
||||
keywords = {country::Vietnam,inequality::rural,status::deepread,
|
||||
keywords = {country::Vietnam,inequality::rural,status::skimmed,
|
||||
topic::education,topic::urban},
|
||||
file = {/home/marty/Zotero/storage/AB2Z7CPT/Fesselmeyer2010_Urban-biased
|
||||
Policies and the Increasing Rural-Urban Expenditure Gap in
|
||||
|
|
@ -1014,7 +1013,7 @@
|
|||
date = {2005},
|
||||
institution = {{DFID Vietnam}},
|
||||
location = {{Hanoi}},
|
||||
keywords = {country::Vietnam,inequality::,next,review::synthesis},
|
||||
keywords = {country::Vietnam,inequality::,review::synthesis},
|
||||
file = {/home/marty/Zotero/storage/Z4A2KQWV/Fritzen2005_Vietnam inequality
|
||||
report 2005.pdf},
|
||||
}
|
||||
|
|
@ -1029,8 +1028,8 @@
|
|||
institution = {{World Bank}},
|
||||
location = {{Washington, D.C.}},
|
||||
url = {https://openknowledge.worldbank.org/handle/10986/15871},
|
||||
keywords = {country::Benin,inequality::rural,status::read,topic::electricity
|
||||
,topic::rural},
|
||||
keywords = {country::Benin,inequality::rural,status::skimmed,
|
||||
topic::electricity,topic::rural},
|
||||
file = {/home/marty/Zotero/storage/MYW9JFER/Golumbeanu2013_Connection
|
||||
Charges and Electricity Access in Sub-Saharan Africa.pdf},
|
||||
}
|
||||
|
|
@ -1070,7 +1069,7 @@
|
|||
most countries in the region could improve equity as well as
|
||||
overall performance by “raising the floor” in school quality.},
|
||||
langid = {english},
|
||||
keywords = {country::Benin,inequality::education,status::deepread,
|
||||
keywords = {country::Benin,inequality::education,status::skimmed,
|
||||
topic::education,topic::poverty},
|
||||
file = {/home/marty/Zotero/storage/IL6LR44L/Gruijters2020_Learning
|
||||
Inequality in Francophone Africa.pdf},
|
||||
|
|
@ -1127,8 +1126,8 @@
|
|||
reflection, innovation, and anticipation, all of which are key
|
||||
elements of the adaptation process. Source: TROVE},
|
||||
langid = {english},
|
||||
keywords = {country::Vietnam,inequality::environmental,next,
|
||||
topic::climate_change},
|
||||
keywords = {country::Vietnam,inequality::environmental,topic::climate_change
|
||||
},
|
||||
}
|
||||
|
||||
@article{HongVoDuc2021TDoG,
|
||||
|
|
@ -1166,7 +1165,7 @@
|
|||
for women to reduce gender wage gap in Vietnam.},
|
||||
copyright = {Copyright © Taylor \& Francis Group, LLC},
|
||||
langid = {english},
|
||||
keywords = {country::Vietnam,inequality::income,next,topic::gender},
|
||||
keywords = {country::Vietnam,inequality::income,topic::gender},
|
||||
file = {/home/marty/Zotero/storage/EFB6LJVX/ContentServer.pdf},
|
||||
}
|
||||
|
||||
|
|
@ -1208,7 +1207,7 @@
|
|||
socio-environmental contexts in Germany and Vietnam.},
|
||||
copyright = {info:eu-repo/semantics/openAccess},
|
||||
langid = {english},
|
||||
keywords = {country::Vietnam,inequality::environmental,status::read,
|
||||
keywords = {country::Vietnam,inequality::environmental,status::skimmed,
|
||||
topic::climate_change,topic::flooding},
|
||||
file = {/home/marty/Zotero/storage/AUV5KVF6/Hudson2021_Self-stated recovery
|
||||
from flooding.pdf},
|
||||
|
|
@ -1274,7 +1273,7 @@
|
|||
url = {
|
||||
http://documents.worldbank.org/curated/en/203361579888116251/Location-Matters-Welfare-Among-Urban-and-Rural-Poor-in-Djibouti
|
||||
},
|
||||
keywords = {country::Djibouti,status::deepread,topic::poverty},
|
||||
keywords = {country::Djibouti,status::skimmed,topic::poverty},
|
||||
file = {/home/marty/Zotero/storage/MPGT5NQI/Ibarra2020_Location Matters.pdf},
|
||||
}
|
||||
|
||||
|
|
@ -1308,7 +1307,7 @@
|
|||
issn = {1564-698X, 0258-6770},
|
||||
doi = {10.1093/wber/lhs009},
|
||||
langid = {english},
|
||||
keywords = {country::Vietnam,inequality::income,status::read,
|
||||
keywords = {country::Vietnam,inequality::income,status::skimmed,
|
||||
topic::modernization},
|
||||
file = {/home/marty/Zotero/storage/FXT2NQW3/Imbert2013_Decomposing the Labor
|
||||
Market Earnings Inequality.pdf},
|
||||
|
|
@ -1359,7 +1358,7 @@
|
|||
prepare additional measures to aid the worse-off actors.},
|
||||
copyright = {info:eu-repo/semantics/openAccess},
|
||||
langid = {english},
|
||||
keywords = {country::Vietnam,inequality::environmental,status::read,
|
||||
keywords = {country::Vietnam,inequality::environmental,status::skimmed,
|
||||
topic::agriculture,topic::climate_change,topic::river},
|
||||
file = {/home/marty/Zotero/storage/Q9YPVAP2/Jafino2021_Accounting for
|
||||
multisectoral dynamics in supporting equitable adaptation.pdf},
|
||||
|
|
@ -1400,7 +1399,7 @@
|
|||
relatively poor households.},
|
||||
copyright = {Copyright Taylor \& Francis Group, LLC 2012},
|
||||
langid = {english},
|
||||
keywords = {country::Uganda,status::deepread},
|
||||
keywords = {country::Uganda,status::skimmed},
|
||||
file = {/home/marty/Zotero/storage/JWE37R5N/Jagger2012_Environmental income,
|
||||
rural livelihoods, and income inequality in western Uganda.pdf},
|
||||
}
|
||||
|
|
@ -1416,7 +1415,7 @@
|
|||
pages = {291--310},
|
||||
publisher = {{Wiley}},
|
||||
location = {{Hoboken, NJ}},
|
||||
keywords = {country::Benin,status::read,topic::electricity,topic::rural,
|
||||
keywords = {country::Benin,status::skimmed,topic::electricity,topic::rural,
|
||||
topic::urban},
|
||||
file = {/home/marty/Zotero/storage/MQ94YYD3/Jaglin2019_Electricity autonomy
|
||||
and power grids in Africa.pdf},
|
||||
|
|
@ -1468,7 +1467,7 @@
|
|||
copyright = {2019 The Authors},
|
||||
issue = {March 2018},
|
||||
langid = {english},
|
||||
keywords = {country::Vietnam,inequality::,status::deepread,topic::river},
|
||||
keywords = {country::Vietnam,inequality::,status::skimmed,topic::river},
|
||||
file = {/home/marty/Zotero/storage/24YV5JI5/Karpouzoglou2019_Unearthing the
|
||||
ripple effects of power and resilience in large river deltas.pdf},
|
||||
}
|
||||
|
|
@ -1483,7 +1482,7 @@
|
|||
location = {{Washington, D.C.}},
|
||||
url = {https://openknowledge.worldbank.org/handle/10986/20074},
|
||||
keywords = {inequality::ethnicity,inequality::income,inequality::regional,
|
||||
next,topic::poverty},
|
||||
topic::poverty},
|
||||
file = {/home/marty/Zotero/storage/HBSGJ5K7/Kozel2014_Well Begun but Not Yet
|
||||
Done.pdf},
|
||||
}
|
||||
|
|
@ -1541,7 +1540,7 @@
|
|||
comprehensive national UHMIS to inform policy planning and
|
||||
evidence-based decision-making.},
|
||||
langid = {english},
|
||||
keywords = {country::Uganda,status::read,topic::refugee,topic::water},
|
||||
keywords = {country::Uganda,status::skimmed,topic::refugee,topic::water},
|
||||
file = {/home/marty/Zotero/storage/M9FTQ6TN/Kyozira2021_Integration of the
|
||||
UNHCR Refugee Health Information System into the National.pdf},
|
||||
}
|
||||
|
|
@ -1560,7 +1559,7 @@
|
|||
issn = {0003-6846, 1466-4283},
|
||||
doi = {10.1080/00036846.2019.1588943},
|
||||
langid = {english},
|
||||
keywords = {country::Vietnam,inequality::income,next,topic::modernization,
|
||||
keywords = {country::Vietnam,inequality::income,topic::modernization,
|
||||
topic::poverty,topic::trade_liberalization},
|
||||
file = {/home/marty/Zotero/storage/3S5CUK6U/Le2019_Trade liberalisation,
|
||||
poverty, and inequality in Vietnam.pdf},
|
||||
|
|
@ -1604,7 +1603,7 @@
|
|||
education and improving human capital, which not only can reduce
|
||||
income inequality but also can attract more FDI inflows.},
|
||||
langid = {english},
|
||||
keywords = {country::Vietnam,inequality::income,next,topic::FDI},
|
||||
keywords = {country::Vietnam,inequality::income,topic::FDI},
|
||||
file = {/home/marty/Zotero/storage/RJ3NP2U8/Le2021_The Impact of Foreign
|
||||
Direct Investment on Income Inequality in Vietnam.pdf},
|
||||
}
|
||||
|
|
@ -1647,7 +1646,6 @@
|
|||
education and improving human capital, which not only can reduce
|
||||
income inequality but also can attract more FDI inflows.},
|
||||
langid = {english},
|
||||
keywords = {next},
|
||||
file = {/home/marty/Zotero/storage/DIRG96MR/Le2021_The Impact of Foreign
|
||||
Direct Investment on Income Inequality in Vietnam.pdf},
|
||||
}
|
||||
|
|
@ -1666,7 +1664,7 @@
|
|||
issn = {1369-6866, 1468-2397},
|
||||
doi = {10.1111/ijsw.12482},
|
||||
langid = {english},
|
||||
keywords = {country::Vietnam,inequality::income,next,topic::modernization,
|
||||
keywords = {country::Vietnam,inequality::income,topic::modernization,
|
||||
topic::poverty,topic::trade_liberalization},
|
||||
file = {/home/marty/Zotero/storage/L8HM9TCJ/Le2022_Growth, inequality and
|
||||
poverty in Vietnam.pdf},
|
||||
|
|
@ -1747,7 +1745,7 @@
|
|||
the importance of developing health enabling social contexts with
|
||||
and for refugee adolescents and youth.},
|
||||
langid = {english},
|
||||
keywords = {country::Uganda,status::read,topic::refugee,topic::water},
|
||||
keywords = {country::Uganda,status::skimmed,topic::refugee,topic::water},
|
||||
file = {/home/marty/Zotero/storage/PEAXZ8P9/Logie2021_Exploring resource
|
||||
scarcity and contextual influences on wellbeing among young.pdf},
|
||||
}
|
||||
|
|
@ -1766,7 +1764,7 @@
|
|||
issn = {1011-6370, 1461-7072},
|
||||
doi = {10.1057/dev.2015.44},
|
||||
langid = {english},
|
||||
keywords = {country::Uganda,index::Gini,inequality::income,status::read,
|
||||
keywords = {country::Uganda,index::Gini,inequality::income,status::skimmed,
|
||||
topic::consumption,topic::poverty},
|
||||
file = {/home/marty/Zotero/storage/WRXL7XN9/Lwanga-Ntale2014_Inequality in
|
||||
Uganda.pdf},
|
||||
|
|
@ -1800,8 +1798,7 @@
|
|||
issn = {00221996},
|
||||
doi = {10.1016/j.jinteco.2011.05.007},
|
||||
langid = {english},
|
||||
keywords = {inequality::rural,next,topic::poverty,
|
||||
topic::trade_liberalization},
|
||||
keywords = {inequality::rural,topic::poverty,topic::trade_liberalization},
|
||||
file = {/home/marty/Zotero/storage/94NXHFS2/McCaig2011_Exporting out of
|
||||
poverty.pdf},
|
||||
}
|
||||
|
|
@ -1818,7 +1815,7 @@
|
|||
location = {{Cambridge, MA}},
|
||||
doi = {10.3386/w19616},
|
||||
langid = {english},
|
||||
keywords = {country::Vietnam,status::read,topic::modernization,
|
||||
keywords = {country::Vietnam,status::skimmed,topic::modernization,
|
||||
topic::trade_liberalization},
|
||||
file = {/home/marty/Zotero/storage/N6FT2KCY/McCaig2013_Moving out of
|
||||
Agriculture.pdf},
|
||||
|
|
@ -1835,7 +1832,7 @@
|
|||
location = {{Cambridge, MA}},
|
||||
doi = {10.3386/w20455},
|
||||
langid = {english},
|
||||
keywords = {country::Vietnam,status::read,topic::modernization,
|
||||
keywords = {country::Vietnam,status::skimmed,topic::modernization,
|
||||
topic::trade_liberalization},
|
||||
file = {/home/marty/Zotero/storage/Y52395K2/McCaig2014_Export Markets and
|
||||
Labor Allocation in a Low-income Country.pdf},
|
||||
|
|
@ -1866,7 +1863,7 @@
|
|||
formalization. Fourth, formalization coincides with occupational
|
||||
upgrading.},
|
||||
langid = {english},
|
||||
keywords = {country::Vietnam,status::read,topic::formality,
|
||||
keywords = {country::Vietnam,status::skimmed,topic::formality,
|
||||
topic::modernization},
|
||||
file = {
|
||||
/home/marty/Zotero/storage/B9PDGNH5/mccaig2015.pdf.pdf;/home/marty/Zotero/storage/WXU5WNWQ/zAppendix.pdf
|
||||
|
|
@ -1888,7 +1885,7 @@
|
|||
issn = {0964-5292, 1469-5782},
|
||||
doi = {10.1080/09645292.2018.1426732},
|
||||
langid = {english},
|
||||
keywords = {country::Benin,inequality::education,status::deepread,
|
||||
keywords = {country::Benin,inequality::education,status::skimmed,
|
||||
topic::education,topic::rural},
|
||||
file = {/home/marty/Zotero/storage/GGSIGNY3/McNabb2018_Exploring regional
|
||||
and gender disparities in Beninese primary school attendance.pdf},
|
||||
|
|
@ -1906,7 +1903,7 @@
|
|||
url = {
|
||||
http://documents.worldbank.org/curated/en/449741576097502078/Challenges-to-Inclusive-Growth-A-Poverty-and-Equity-Assessment-of-Djibouti
|
||||
},
|
||||
keywords = {country::Djibouti,status::read,topic::poverty},
|
||||
keywords = {country::Djibouti,status::skimmed,topic::poverty},
|
||||
file = {/home/marty/Zotero/storage/64DR8Z8S/Mendiratta2019_Challenges to
|
||||
Inclusive Growth.pdf},
|
||||
}
|
||||
|
|
@ -1922,7 +1919,7 @@
|
|||
url = {
|
||||
http://documents.worldbank.org/curated/en/272691596006234817/The-Multi-Dimensional-Nature-of-Poverty-in-Djibouti
|
||||
},
|
||||
keywords = {country::Djibouti,status::deepread,topic::poverty},
|
||||
keywords = {country::Djibouti,status::skimmed,topic::poverty},
|
||||
file = {/home/marty/Zotero/storage/TU49848D/Mendiratta2020_The
|
||||
Multi-Dimensional Nature of Poverty in Djibouti.pdf},
|
||||
}
|
||||
|
|
@ -1965,7 +1962,7 @@
|
|||
for reducing income inequality across provinces and across
|
||||
districts within provinces in Vietnam.},
|
||||
langid = {english},
|
||||
keywords = {country::Vietnam,inequality::income,next,topic::education},
|
||||
keywords = {country::Vietnam,inequality::income,topic::education},
|
||||
file = {/home/marty/Zotero/storage/EMUDX7L5/Minh Ho2021_DOES GOVERNMENT
|
||||
SPENDING ON EDUCATION AFFECT PROVINCIAL INCOME INEQUALITY IN.pdf},
|
||||
}
|
||||
|
|
@ -2036,7 +2033,7 @@
|
|||
increasing latrine coverage, and provision of safe water by the
|
||||
District and entire High Commission for refugees.},
|
||||
langid = {english},
|
||||
keywords = {country::Uganda,status::read,topic::refugee,topic::water},
|
||||
keywords = {country::Uganda,status::skimmed,topic::refugee,topic::water},
|
||||
file = {/home/marty/Zotero/storage/CGVFN6AB/Monje2020_A prolonged cholera
|
||||
outbreak caused by drinking contaminated stream water,.pdf},
|
||||
}
|
||||
|
|
@ -2076,7 +2073,6 @@
|
|||
answer to the question whether or not they are really mutually
|
||||
beneficial.},
|
||||
langid = {english},
|
||||
keywords = {next},
|
||||
file = {/home/marty/Zotero/storage/QA48TT2X/Mormul2016_Ethio‑Djiboutian
|
||||
relations in the 21st century – towards new african cooperation.pdf},
|
||||
}
|
||||
|
|
@ -2095,7 +2091,7 @@
|
|||
doi = {10.4000/vertigo.7782},
|
||||
issue = {Volume 8 Numéro 3},
|
||||
langid = {french},
|
||||
keywords = {country::Vietnam,status::read,topic::flooding,topic::river},
|
||||
keywords = {country::Vietnam,status::skimmed,topic::flooding,topic::river},
|
||||
file = {/home/marty/Zotero/storage/NAZTWGZW/Mottet2009_L’urbanisation de la
|
||||
ville de Ninh Binh dans le delta du fleuve rouge (Vietnam).pdf},
|
||||
}
|
||||
|
|
@ -2109,7 +2105,7 @@
|
|||
journaltitle = {Journal of environmental and public health},
|
||||
volume = {2018},
|
||||
publisher = {{Hindawi}},
|
||||
keywords = {country::Uganda,status::deepread,topic::water},
|
||||
keywords = {country::Uganda,status::skimmed,topic::water},
|
||||
file = {/home/marty/Zotero/storage/P4LYXIC5/Mulogo2018_Water, sanitation,
|
||||
and hygiene service availability at rural health care.pdf},
|
||||
}
|
||||
|
|
@ -2152,8 +2148,8 @@
|
|||
climate services to local communities should consider gender in
|
||||
decision making, access to resources, information and knowledge.},
|
||||
langid = {english},
|
||||
keywords = {country::Uganda,inequality::gender,level::local,status::deepread
|
||||
,topic::climate_change,topic::water},
|
||||
keywords = {country::Uganda,inequality::gender,level::local,status::read,
|
||||
topic::climate_change,topic::water},
|
||||
file = {/home/marty/Zotero/storage/VS54U4XB/Nagasha2019_Effect of climate
|
||||
variability on gender roles among communities surrounding.pdf},
|
||||
}
|
||||
|
|
@ -2166,7 +2162,7 @@
|
|||
journaltitle = {International Journal of the Commons},
|
||||
volume = {9},
|
||||
number = {1},
|
||||
keywords = {country::Uganda,status::read,topic::water},
|
||||
keywords = {country::Uganda,status::skimmed,topic::water},
|
||||
file = {/home/marty/Zotero/storage/8MLNSETL/Naiga2015_Challenging pathways
|
||||
to safe water access in rural Uganda.pdf},
|
||||
}
|
||||
|
|
@ -2181,8 +2177,8 @@
|
|||
number = {2},
|
||||
pages = {110--135},
|
||||
publisher = {{SAGE Publications Sage India: New Delhi, India}},
|
||||
keywords = {country::Uganda,inequality::regional,level::local,status::read,
|
||||
topic::water},
|
||||
keywords = {country::Uganda,inequality::regional,level::local,
|
||||
status::skimmed,topic::water},
|
||||
file = {/home/marty/Zotero/storage/4YUD22W9/Naiga2018_Conditions for
|
||||
successful community-based water management.pdf},
|
||||
}
|
||||
|
|
@ -2213,7 +2209,7 @@
|
|||
doi = {10.1016/j.jdeveco.2006.04.006},
|
||||
langid = {english},
|
||||
keywords = {country::Vietnam,inequality::ethnicity,inequality::rural,
|
||||
status::read,topic::education},
|
||||
status::skimmed,topic::education},
|
||||
file = {/home/marty/Zotero/storage/WN23U92I/Nguyen2007_A quantile regression
|
||||
decomposition of urban–rural inequality in Vietnam.pdf},
|
||||
}
|
||||
|
|
@ -2233,7 +2229,7 @@
|
|||
issn = {08189935},
|
||||
doi = {10.1111/apel.12219},
|
||||
langid = {english},
|
||||
keywords = {country::Vietnam,inequality::income,next,topic::modernization,
|
||||
keywords = {country::Vietnam,inequality::income,topic::modernization,
|
||||
topic::poverty},
|
||||
file = {/home/marty/Zotero/storage/7JLMIQA8/Asian-Pac Economic Lit - 2018 -
|
||||
Nguyen - Economic growth inequality and poverty in Vietnam.pdf},
|
||||
|
|
@ -2251,7 +2247,7 @@
|
|||
issn = {03014215},
|
||||
doi = {10.1016/j.enpol.2019.06.001},
|
||||
langid = {english},
|
||||
keywords = {country::Vietnam,inequality::,next,topic::energy,topic::poverty},
|
||||
keywords = {country::Vietnam,inequality::,topic::energy,topic::poverty},
|
||||
file = {/home/marty/Zotero/storage/BS8J9JT6/Nguyen2019_Energy transition,
|
||||
poverty and inequality in Vietnam.pdf},
|
||||
}
|
||||
|
|
@ -2319,7 +2315,7 @@
|
|||
substandard houses are not only more vulnerable to disasters but
|
||||
take longer to recover. Source: TROVE},
|
||||
langid = {english},
|
||||
keywords = {country::Vietnam,inequality::housing,next},
|
||||
keywords = {country::Vietnam,inequality::housing},
|
||||
file = {/home/marty/Zotero/storage/FD3NAYXR/NGUYEN2020_Essays on housing
|
||||
affordability and housing quality dilemmas in Vietnam.pdf},
|
||||
}
|
||||
|
|
@ -2377,7 +2373,6 @@
|
|||
on the slopes in Vietnam.},
|
||||
copyright = {Springer Nature B.V. 2020},
|
||||
langid = {english},
|
||||
keywords = {next},
|
||||
file = {/home/marty/Zotero/storage/7A3JX6JC/Nguyen2020_Diversified responses
|
||||
to contemporary pressures on sloping agricultural land.pdf},
|
||||
}
|
||||
|
|
@ -2413,7 +2408,7 @@
|
|||
negative in less democratic countries and poor countries, but
|
||||
positive in more democratic countries and rich countries.},
|
||||
langid = {english},
|
||||
keywords = {country::Djibouti,status::read,topic::trade_liberalization},
|
||||
keywords = {country::Djibouti,status::skimmed,topic::trade_liberalization},
|
||||
file = {/home/marty/Zotero/storage/Y8EAC6RC/Nosier2018_The Indirect Effect
|
||||
of Democracy on Economic Growth in the MENA Region.pdf},
|
||||
}
|
||||
|
|
@ -2439,7 +2434,6 @@
|
|||
isbn = {92-64-08383-9},
|
||||
langid = {english},
|
||||
organization = {{OECD}},
|
||||
keywords = {next},
|
||||
file = {/home/marty/Zotero/storage/6PGYN69I/OECD2009_Djibouti.pdf},
|
||||
}
|
||||
|
||||
|
|
@ -2464,7 +2458,7 @@
|
|||
langid = {english},
|
||||
organization = {{Organization for Economic Cooperation and Development
|
||||
(OECD) Staff Corporate Author}},
|
||||
keywords = {country::Uganda,status::read},
|
||||
keywords = {country::Uganda,status::skimmed},
|
||||
file = {/home/marty/Zotero/storage/AN7SU5YY/OECD2013_Co-operation Report
|
||||
2013.pdf},
|
||||
}
|
||||
|
|
@ -2483,7 +2477,6 @@
|
|||
isbn = {978-92-64-20102-6},
|
||||
langid = {english},
|
||||
organization = {{OECD}},
|
||||
keywords = {next},
|
||||
file = {/home/marty/Zotero/storage/CUJQ3HYN/OECD2013_Aid, trade and
|
||||
development indicators for djibouti.pdf},
|
||||
}
|
||||
|
|
@ -2509,7 +2502,7 @@
|
|||
isbn = {978-92-64-19536-3 978-92-64-19534-9},
|
||||
langid = {english},
|
||||
pagetotal = {162},
|
||||
keywords = {country::Vietnam,next,topic::agriculture,topic::food},
|
||||
keywords = {country::Vietnam,topic::agriculture,topic::food},
|
||||
file = {/home/marty/Zotero/storage/KKZDH3D8/Organisation for Economic
|
||||
Co-operation and Development2013_Global food security.pdf},
|
||||
}
|
||||
|
|
@ -2525,7 +2518,6 @@
|
|||
isbn = {978-3-030-41513-6 978-3-030-41512-9 978-3-030-41515-0},
|
||||
langid = {english},
|
||||
pagetotal = {649},
|
||||
keywords = {next},
|
||||
}
|
||||
|
||||
@article{Petrosino2012,
|
||||
|
|
@ -2601,7 +2593,6 @@
|
|||
conditions.},
|
||||
copyright = {2020 Elsevier B.V.},
|
||||
langid = {english},
|
||||
keywords = {next},
|
||||
file = {/home/marty/Zotero/storage/TLWQQZEI/Pham2021_Natural hazard's effect
|
||||
and farmers' perception.pdf},
|
||||
}
|
||||
|
|
@ -2641,8 +2632,8 @@
|
|||
markers of urban Africa, providing information on inequalities in
|
||||
access to electricity.},
|
||||
langid = {english},
|
||||
keywords = {country::Benin,country::Nigeria,status::deepread,
|
||||
topic::electricity,topic::urban},
|
||||
keywords = {country::Benin,country::Nigeria,status::read,topic::electricity,
|
||||
topic::urban},
|
||||
file = {/home/marty/Zotero/storage/MDMADPTM/Rateau2022_Electrifying urban
|
||||
Africa.pdf},
|
||||
}
|
||||
|
|
@ -2673,8 +2664,8 @@
|
|||
number = {1},
|
||||
pages = {100230},
|
||||
publisher = {{Elsevier}},
|
||||
keywords = {country::Uganda,inequality::health,status::read,topic::covid19,
|
||||
topic::water},
|
||||
keywords = {country::Uganda,inequality::health,status::skimmed,
|
||||
topic::covid19,topic::water},
|
||||
file = {/home/marty/Zotero/storage/ZJVIT5DE/Sempewo2021_Assessing
|
||||
willingness to pay for water during the COVID-19 crisis in
|
||||
Ugandan.pdf},
|
||||
|
|
@ -2690,7 +2681,7 @@
|
|||
number = {5},
|
||||
pages = {2489--2504},
|
||||
publisher = {{IWA Publishing}},
|
||||
keywords = {country::Uganda,status::read,topic::covid19,topic::water},
|
||||
keywords = {country::Uganda,status::skimmed,topic::covid19,topic::water},
|
||||
file = {/home/marty/Zotero/storage/CHMK2BB6/Sempewo2021_The impact of
|
||||
COVID-19 on households’ water use in Uganda.pdf},
|
||||
}
|
||||
|
|
@ -2738,7 +2729,7 @@
|
|||
the limitations of using devices for information dissemination.},
|
||||
copyright = {2021 The Author(s)},
|
||||
langid = {english},
|
||||
keywords = {country::Vietnam,status::read,topic::agriculture,
|
||||
keywords = {country::Vietnam,status::skimmed,topic::agriculture,
|
||||
topic::climate_change},
|
||||
file = {/home/marty/Zotero/storage/TE5D79FK/Sen2021_Farmers’ barriers to the
|
||||
access and use of climate information in the.pdf},
|
||||
|
|
@ -2776,7 +2767,7 @@
|
|||
issn = {1022-5706, 2158-1762},
|
||||
doi = {10.1080/10225706.2019.1701507},
|
||||
langid = {english},
|
||||
keywords = {country::Vietnam,inequality::environmental,status::read,
|
||||
keywords = {country::Vietnam,inequality::environmental,status::skimmed,
|
||||
topic::climate_change},
|
||||
file = {/home/marty/Zotero/storage/CBYPLX4P/Son2020_Community adaptation and
|
||||
climate change in the Northern Mountainous Region of.pdf},
|
||||
|
|
@ -2823,7 +2814,7 @@
|
|||
the poorest in a way that expands their opportunities.},
|
||||
langid = {english},
|
||||
keywords = {country::Uganda,index::Gini,index::Theil,inequality::education,
|
||||
inequality::income,status::read},
|
||||
inequality::income,status::skimmed},
|
||||
file = {/home/marty/Zotero/storage/B4AJQX4A/Ssewanyana2012_Poverty and
|
||||
inequality dynamics in Uganda.pdf},
|
||||
}
|
||||
|
|
@ -2849,7 +2840,7 @@
|
|||
Unexpectedly, differences in land endowment do not strongly
|
||||
correlate with regional income disparity because better access to
|
||||
land in a region was partially offset by lower returns.},
|
||||
keywords = {inequality::environmental,inequality::regional,status::read,
|
||||
keywords = {inequality::environmental,inequality::regional,status::skimmed,
|
||||
topic::education,topic::river},
|
||||
file = {/home/marty/Zotero/storage/KFCNGEDE/TakahashiSources of regional
|
||||
income disparity in rural Vietnam.pdf},
|
||||
|
|
@ -2892,7 +2883,7 @@
|
|||
eventtitle = {Vietnam {{Update Conference}}},
|
||||
isbn = {978-981-230-275-5 978-981-230-254-0},
|
||||
pagetotal = {392},
|
||||
keywords = {country::Vietnam,inequality::,next},
|
||||
keywords = {country::Vietnam,inequality::},
|
||||
}
|
||||
|
||||
@article{ThanhThiPham2020,
|
||||
|
|
@ -2908,7 +2899,6 @@
|
|||
issn = {22120963},
|
||||
doi = {10.1016/j.crm.2020.100215},
|
||||
langid = {english},
|
||||
keywords = {next},
|
||||
file = {/home/marty/Zotero/storage/VZJ2IRQK/Thanh Thi Pham2020_Vulnerability
|
||||
assessment of households to flash floods and landslides in the.pdf},
|
||||
}
|
||||
|
|
@ -2925,7 +2915,7 @@
|
|||
issn = {00346586},
|
||||
doi = {10.1111/roiw.12051},
|
||||
langid = {english},
|
||||
keywords = {inequality::rural,status::read,topic::education},
|
||||
keywords = {inequality::rural,status::skimmed,topic::education},
|
||||
file = {/home/marty/Zotero/storage/VYQT833H/Thu Le2014_Inequality in
|
||||
Vietnamese Urban-Rural Living Standards, 1993-2006.pdf},
|
||||
}
|
||||
|
|
@ -2955,7 +2945,7 @@
|
|||
url = {
|
||||
http://documents.worldbank.org/curated/en/237751646144451455/Djibouti-Economic-Monitor-Navigating-through-the-Pandemic-and-Regional-Tensions
|
||||
},
|
||||
keywords = {country::Djibouti,next,topic::poverty},
|
||||
keywords = {country::Djibouti,topic::poverty},
|
||||
file = {/home/marty/Zotero/storage/BM6ZY8AB/Tsouck Ibounde2020_Tsouck
|
||||
Ibounde,Rick Emery Wes,Marina Mohammed,Nadir Le Borgne,Eric.pdf},
|
||||
}
|
||||
|
|
@ -2976,7 +2966,7 @@
|
|||
issn = {22120947},
|
||||
doi = {10.1016/j.wace.2019.100201},
|
||||
langid = {english},
|
||||
keywords = {country::Uganda,level::local,status::read,topic::agriculture,
|
||||
keywords = {country::Uganda,level::local,status::skimmed,topic::agriculture,
|
||||
topic::climate_change,topic::water},
|
||||
file = {/home/marty/Zotero/storage/KLIPN9XR/Twongyirwe2019_Perceived effects
|
||||
of drought on household food security in South-western Uganda.pdf},
|
||||
|
|
@ -2989,7 +2979,7 @@
|
|||
number = {HS/029/20E},
|
||||
institution = {{United Nations High Commissioner For Refugees}},
|
||||
location = {{Geneva}},
|
||||
keywords = {country::Uganda,next,topic::refugee},
|
||||
keywords = {country::Uganda,topic::refugee},
|
||||
file = {/home/marty/Zotero/storage/2NPXANQ6/UNHCRNakivale Settlement
|
||||
profile.pdf},
|
||||
}
|
||||
|
|
@ -3001,7 +2991,7 @@
|
|||
series = {Inter-{{Agency Situation Report}}},
|
||||
institution = {{United Nations High Commissioner For Refugees}},
|
||||
location = {{Geneva}},
|
||||
keywords = {country::Uganda,next,topic::refugee},
|
||||
keywords = {country::Uganda,topic::refugee},
|
||||
file = {/home/marty/Zotero/storage/LX2SGCK9/UNHCR2022_Uganda refugee
|
||||
emergency.pdf},
|
||||
}
|
||||
|
|
@ -3074,7 +3064,7 @@
|
|||
at both the community and household levels to correct such
|
||||
deficiencies.},
|
||||
langid = {english},
|
||||
keywords = {country::Benin,status::read,topic::electricity,topic::health,
|
||||
keywords = {country::Benin,status::skimmed,topic::electricity,topic::health,
|
||||
topic::rural,topic::water},
|
||||
file = {/home/marty/Zotero/storage/T48RNP2K/Van De Poel2009_What explains
|
||||
the rural-urban gap in infant mortality.pdf},
|
||||
|
|
@ -3137,7 +3127,7 @@
|
|||
researcherid-numbers = {Mellisse, Beyene/AHB-8424-2022 Giller, Ken
|
||||
E/K-2799-2012 Descheemaeker, Katrien/F-3041-2010},
|
||||
unique-id = {WOS:000573406200001},
|
||||
keywords = {country::Uganda,level::local,status::read,topic::poverty},
|
||||
keywords = {country::Uganda,level::local,status::skimmed,topic::poverty},
|
||||
file = {/home/marty/Zotero/storage/WWZJBHZB/van de Ven2021_Living income
|
||||
benchmarking of rural households in low-income countries.pdf},
|
||||
}
|
||||
|
|
@ -3155,8 +3145,8 @@
|
|||
issn = {03043878},
|
||||
doi = {10.1016/S0304-3878(01)00133-X},
|
||||
langid = {english},
|
||||
keywords = {country::Vietnam,inequality::ethnicity,status::read,topic::rural
|
||||
},
|
||||
keywords = {country::Vietnam,inequality::ethnicity,status::skimmed,
|
||||
topic::rural},
|
||||
file = {/home/marty/Zotero/storage/LDMZ9SGR/van de Walle2001_Sources of
|
||||
ethnic inequality in Viet Nam.pdf},
|
||||
}
|
||||
|
|
@ -3175,7 +3165,7 @@
|
|||
issn = {0967-0750, 1468-0351},
|
||||
doi = {10.1111/j.0967-0750.2004.00178.x},
|
||||
langid = {english},
|
||||
keywords = {country::Vietnam,inequality::income,status::read,
|
||||
keywords = {country::Vietnam,inequality::income,status::skimmed,
|
||||
topic::modernization,topic::poverty},
|
||||
file = {/home/marty/Zotero/storage/GHYIGV7U/van de Walle2004_Is the emerging
|
||||
non-farm market economy the route out of poverty in Vietnam.pdf},
|
||||
|
|
@ -3188,7 +3178,7 @@
|
|||
date = {2006},
|
||||
institution = {{Vietnam Academy of Social Sciences}},
|
||||
location = {{Hanoi}},
|
||||
keywords = {country::Vietnam,next,topic::poverty},
|
||||
keywords = {country::Vietnam,topic::poverty},
|
||||
}
|
||||
|
||||
@report{VASS2011,
|
||||
|
|
@ -3198,7 +3188,7 @@
|
|||
date = {2011},
|
||||
institution = {{Vietnam Academy of Social Sciences}},
|
||||
location = {{Hanoi}},
|
||||
keywords = {country::Vietnam,next,topic::poverty},
|
||||
keywords = {country::Vietnam,topic::poverty},
|
||||
file = {/home/marty/Zotero/storage/TMVA9NCG/VASS2011_Poverty Reduction in
|
||||
Vietnam.pdf},
|
||||
}
|
||||
|
|
@ -3257,7 +3247,7 @@
|
|||
empowerment and for development.},
|
||||
copyright = {COPYRIGHT 2021 Tennessee State University},
|
||||
langid = {english},
|
||||
keywords = {country::Uganda,next},
|
||||
keywords = {country::Uganda},
|
||||
file = {/home/marty/Zotero/storage/7AQXA3SR/Walker2021_Role of women in
|
||||
economic development.pdf},
|
||||
}
|
||||
|
|
@ -3282,7 +3272,6 @@
|
|||
Phung Duc and Cuong, Nguyen Viet and Vu, Linh Hoang and Wells Dang
|
||||
, Andrew},
|
||||
editoratype = {collaborator},
|
||||
keywords = {next},
|
||||
file = {/home/marty/Zotero/storage/KC72ZFUP/Badiani-Magnusson2012_Vietnam
|
||||
poverty assessment.pdf},
|
||||
}
|
||||
|
|
@ -3296,7 +3285,6 @@
|
|||
url = {
|
||||
https://thedocs.worldbank.org/en/doc/381951474255092375-0010022016/Uganda-Poverty-Assessment-Report-2016
|
||||
},
|
||||
keywords = {next},
|
||||
file = {/home/marty/Zotero/storage/NUGTKD2Y/WorldBank2016_Uganda Poverty
|
||||
Assessment Report 2016.pdf},
|
||||
}
|
||||
|
|
@ -3309,7 +3297,6 @@
|
|||
location = {{Washington, D.C.}},
|
||||
editora = {World Bank and IEA and IRENA and UNSD and WHO},
|
||||
editoratype = {collaborator},
|
||||
keywords = {next},
|
||||
file = {/home/marty/Zotero/storage/F8CBQJZA/World Bank2021_Tracking SDG
|
||||
7.pdf},
|
||||
}
|
||||
|
|
@ -3323,7 +3310,7 @@
|
|||
url = {
|
||||
http://documents.worldbank.org/curated/en/099021407212243534/IDU01dbf45100704f046410bb6f03c4c1cb85588
|
||||
},
|
||||
keywords = {country::Uganda,inequality::education,status::deepread,
|
||||
keywords = {country::Uganda,inequality::education,status::read,
|
||||
topic::education},
|
||||
file = {/home/marty/Zotero/storage/ILZVZJLF/WorldBank2022_Uganda - Learning
|
||||
Poverty Brief.pdf},
|
||||
|
|
@ -3338,7 +3325,7 @@
|
|||
url = {
|
||||
http://documents.worldbank.org/curated/en/099021407212243534/IDU01dbf45100704f046410bb6f03c4c1cb85588
|
||||
},
|
||||
keywords = {country::Benin,inequality::education,status::deepread,
|
||||
keywords = {country::Benin,inequality::education,status::read,
|
||||
topic::education},
|
||||
file = {/home/marty/Zotero/storage/LY2GR4JD/WorldBank2022_Benin - Learning
|
||||
Poverty Brief.pdf},
|
||||
|
|
@ -3353,7 +3340,7 @@
|
|||
url = {
|
||||
http://documents.worldbank.org/curated/en/099930404182210208/IDU0ef8057e509b5f0432c0b50d00f85b54deb33
|
||||
},
|
||||
keywords = {country::Benin,status::deepread,topic::poverty},
|
||||
keywords = {country::Benin,status::read,topic::poverty},
|
||||
file = {/home/marty/Zotero/storage/EWU6G456/WorldBank2022_Macro Poverty
|
||||
Outlook for Benin.pdf},
|
||||
}
|
||||
|
|
@ -3367,7 +3354,7 @@
|
|||
url = {
|
||||
https://documents.worldbank.org/en/publication/documents-reports/documentdetail/099310104232265208/idu08979c8f809e1604dc70be93050dce6a02a23
|
||||
},
|
||||
keywords = {country::Djibouti,status::deepread,topic::poverty},
|
||||
keywords = {country::Djibouti,status::read,topic::poverty},
|
||||
file = {/home/marty/Zotero/storage/GP5993W5/World Bank2022_Macro Poverty
|
||||
Outlook for Djibouti.pdf},
|
||||
}
|
||||
|
|
@ -3382,8 +3369,7 @@
|
|||
url = {
|
||||
http://documents.worldbank.org/curated/en/099929206302212659/IDU068dce0c7003280435b099f8040232925d37f
|
||||
},
|
||||
keywords = {country::Djibouti,inequality::gender,status::deepread,
|
||||
topic::gender},
|
||||
keywords = {country::Djibouti,inequality::gender,status::read,topic::gender},
|
||||
file = {/home/marty/Zotero/storage/LR8Z2RKE/World Bank2022_Djibouti Gender
|
||||
Landscape.pdf},
|
||||
}
|
||||
|
|
@ -3437,8 +3423,8 @@
|
|||
pages = {1--12},
|
||||
publisher = {{BioMed Central}},
|
||||
keywords = {country::Uganda,inequality::education,inequality::health,
|
||||
status::read,topic::climate_change,topic::poverty,topic::rural,
|
||||
topic::water},
|
||||
status::skimmed,topic::climate_change,topic::poverty,topic::rural
|
||||
,topic::water},
|
||||
file = {/home/marty/Zotero/storage/43YUJHEM/Yikii2017_Prevalence of
|
||||
household food insecurity in wetland adjacent areas of Uganda.pdf},
|
||||
}
|
||||
|
|
@ -3485,7 +3471,7 @@
|
|||
Notwithstanding the ProQuest Terms and Conditions, you may use
|
||||
this content in accordance with the terms of the License.},
|
||||
langid = {english},
|
||||
keywords = {country::Vietnam,inequality::gender,status::read,
|
||||
keywords = {country::Vietnam,inequality::gender,status::skimmed,
|
||||
topic::climate_change},
|
||||
file = {/home/marty/Zotero/storage/5RH9PBTL/Ylipaa2019_Climate change
|
||||
adaptation and gender inequality.pdf},
|
||||
|
|
|
|||
18
poetry.lock
generated
18
poetry.lock
generated
|
|
@ -1028,17 +1028,6 @@ build = ["cython (>=0.29.26)"]
|
|||
develop = ["cython (>=0.29.26)"]
|
||||
docs = ["sphinx", "nbconvert", "jupyter-client", "ipykernel", "matplotlib", "nbformat", "numpydoc", "pandas-datareader"]
|
||||
|
||||
[[package]]
|
||||
name = "tabulate"
|
||||
version = "0.8.10"
|
||||
description = "Pretty-print tabular data"
|
||||
category = "main"
|
||||
optional = false
|
||||
python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
|
||||
|
||||
[package.extras]
|
||||
widechars = ["wcwidth"]
|
||||
|
||||
[[package]]
|
||||
name = "tenacity"
|
||||
version = "8.0.1"
|
||||
|
|
@ -1135,7 +1124,7 @@ python-versions = ">=3.7"
|
|||
[metadata]
|
||||
lock-version = "1.1"
|
||||
python-versions = "^3.9"
|
||||
content-hash = "9a8889c9ff9cf08f3529c276b3d41da95fcfdc871cacea6ab96d55ec3e8d3e0a"
|
||||
content-hash = "dcd0e115c0c55bebb2a464655fe91c749ef16ed875ebfdf0d3b04f8ee1bb8ccc"
|
||||
|
||||
[metadata.files]
|
||||
appnope = [
|
||||
|
|
@ -2014,11 +2003,6 @@ statsmodels = [
|
|||
{file = "statsmodels-0.13.2-cp39-cp39-win_amd64.whl", hash = "sha256:39daab5a8a9332c8ea83d6464d065080c9ba65f236daf6a64aa18f64ef776fad"},
|
||||
{file = "statsmodels-0.13.2.tar.gz", hash = "sha256:77dc292c9939c036a476f1770f9d08976b05437daa229928da73231147cde7d4"},
|
||||
]
|
||||
tabulate = [
|
||||
{file = "tabulate-0.8.10-py3-none-any.whl", hash = "sha256:0ba055423dbaa164b9e456abe7920c5e8ed33fcc16f6d1b2f2d152c8e1e8b4fc"},
|
||||
{file = "tabulate-0.8.10-py3.8.egg", hash = "sha256:436f1c768b424654fce8597290d2764def1eea6a77cfa5c33be00b1bc0f4f63d"},
|
||||
{file = "tabulate-0.8.10.tar.gz", hash = "sha256:6c57f3f3dd7ac2782770155f3adb2db0b1a269637e42f27599925e64b114f519"},
|
||||
]
|
||||
tenacity = [
|
||||
{file = "tenacity-8.0.1-py3-none-any.whl", hash = "sha256:f78f4ea81b0fabc06728c11dc2a8c01277bfc5181b321a4770471902e3eb844a"},
|
||||
{file = "tenacity-8.0.1.tar.gz", hash = "sha256:43242a20e3e73291a28bcbcacfd6e000b02d3857a9a9fff56b297a27afdc932f"},
|
||||
|
|
|
|||
|
|
@ -12,7 +12,6 @@ openpyxl = "^3.0.10"
|
|||
matplotlib = "^3.5.3"
|
||||
plotly-express = "^0.4.1"
|
||||
kaleido = "^0.2.1,!=0.2.1.post1"
|
||||
tabulate = "^0.8.10"
|
||||
|
||||
[tool.poetry.dev-dependencies]
|
||||
|
||||
|
|
|
|||
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Loading…
Reference in a new issue