Layer One Conflict Vulnerability bubdomains and indicators
|
Indicator |
Definition & Brief Conceptualization |
Source, Year |
|---|---|---|
|
Maximum Conflict Intensity (MEPV) |
The systematic and sustained use of lethal violence by organized groups that result in at least 500 directly-related deaths over the course of the episode.[a] The greater the intensity of conflict, the greater the vulnerability of the security domain.[b] |
MEPV, 2015. |
|
Best Estimate from Death Toll from State-Based, One-Sided and Non-State Violence |
An actor-year dataset with information on intentional attacks on civilians by governments and formally organized armed groups.[c] The number of deaths from listed types of violence is used as an indication of the overall status of security in a country.[d] |
Uppsala Conflict Data Program, Time-Series data since 1970s. |
|
Global Terrorism Index |
The GTI measures the impact of terrorism [non-state actors] in 162 countries. To account for the lasting effects of terrorism, each country is given a score that represents a five year weighted average.[e] The higher the impact of terrorism, the less control a state has over its security. |
GTI, 2014. |
|
Political Terror Scale (PTS) |
The PTS measures the levels of state-sanctioned or state perpetrated violence. This includes assassinations of political challengers or police brutality. The PTS represents a five point scale whereby countries are classified on the degree to which the population suffers from political violence.[f] Political terror represents how susceptible a state is to using political violence. |
PTS, 2014. |
|
Refugees Produced |
Refugee movement here is interpreted as an indicator of the status of living in the host country. This can be interpreted two-fold: a) Refugees produced as an indicator of political/security strife; b) Refugees produced as an indicator of economic scarcity. The outflow of refugees illustrates the lack of security people feel in a country, acting as a push factor to leave.[g] |
World Development Indicators (WDI), 2014.[h] |
|
Indicator |
Definition & Brief Conceptualization |
Source, Year |
|---|---|---|
|
Polity4 Score |
The Polity4 Score displays periods of “factionalism” and important Polity change events such as autocratic backsliding, executive auto-coup or autogolpe, revolution, collapse of central authority (state failure), and successful military coups.[a] The greater the degree of political change and factionalism the less cohesive the political system is. |
Center for Systemic Peace, between 1800-2014.[b] |
|
Variance in Polity4 Score |
The variance shows the actual degree of volatility at a country level which gives an indication of how likely change is going to arise, with continuous change characterized as instability. The variance shows the actual degree of change as an indicator of political volatility. |
Center for Systemic Peace, 1996-2014. |
|
Factionalism Dummy |
Countries with political factions that regularly compete for political influence in order to promote particularist agendas and favor group members.[c] The greater the degree of factional competitiveness of political participation the greater the likelihood of domestic conflict. |
Center for Systemic Peace, 2015.[d] |
|
Rule of Law |
As part of Freedom House’s Freedom in the World, this indicator assesses the degree of Rule of Law. There is a points system in place (0-16) - the higher the figure the higher the degree of rule of law.[e] The greater the rule of law the less vulnerable and more cohesive the political and legal system is. |
Freedom House, 2015.[f] |
|
Control of Corruption |
Control of Corruption includes the “perceptions of the extent to which public power is exercised for private gain, including both petty and grand forms of corruption, as well as “capture” of the state by elites and private interests.”[g] Control of Corruption conveys the political cleavages between government and the populace.[h] The greater the corruption, the more political vulnerability. |
WDI, 2014. |
|
Indicator |
Definition & Brief Conceptualization |
Source, Year |
|---|---|---|
|
Infant Mortality |
The number of infants dying before reaching one year of age, per 1,000 live births in a given year.[a] Infant Mortality is used as an indicator of the overall health infrastructure in a country. |
WDI, 2015. |
|
Life Expectancy at Birth |
The number of years a newborn infant would live if contemporary patterns of mortality at the time of its birth were to stay consistent throughout its life.[b] The greater the life expectancy at birth, the more capable (and less vulnerable) a state is in regards to ensuring the well-being of its people. |
WDI, 2014. |
|
Human Development Index |
Composite indicator which illustrates how having a long and healthy life, being knowledgeable, and having a decent standard of living improve development. Human development Index is used as a representation of the overall level of social and demographic development in a country – the greater the degree of social development, the less vulnerable a state is to conflict. |
United Nations Development Program, 2014. |
|
Ethnic Fractionalization |
Ethnic fractionalization[c] reflects the probability that two randomly selected people from a given country will not share a certain characteristic, the higher the number the less probability of the two sharing that characteristic.[d] |
Alesina et al., 2003. |
|
Female Labor Participation |
The number of women participating in the labor force and are economically active. Higher levels of female labor participation are found to be significant in reducing the likelihood of intrastate armed conflict in some studies |
WDI, 2014. |
Layer Two Climate Change Vulnerability subdomains and indicators
|
Indicator (Sub-Domain) |
Definition & Brief Conceptualization |
Source, Year |
|---|---|---|
|
Changes in Average Precipitation- Coefficient of Variation (Precipitation Sub-Domain) |
Precipitation is the amount of water received by a country in the form of precipitation as a mean value over the course of a year. The Coefficient of Variation (the ratio of standard deviation to the mean) will highlight precipitation volatility. Studies have been conducted which provide a more methodologically astute measure of volatility, however our data is based on yearly annuals and aims to convey the digression from historical trends as an indicator of climate change vulnerability. The greater the variation, the more volatile the changes in average precipitation, the more vulnerable a state is.[a], [b] The Difference from Absolute Values conveys the degree of change from the beginning of the time series data to the end. This does not take into account the direction of change (as in increase or decrease) but merely the extent of the change. The greater the change, the more vulnerable.[c] |
World Bank, 1960-2014.[d] |
|
Changes in Average Precipitation- Difference in Absolute Values (Precipitation Sub-Domain) |
||
|
Population Living Below Five Metres Above Sea-Level (Sea Sub-Domain) |
Population Living Below Five Metres Above Sea-level is the number of people living below five metres above sea-level. With a greater proportion of the population living below sea-level comes more vulnerability to the state. |
WDI, 2010. |
|
Water Stress (Water Sub-Domain) |
“Water stress occurs when the demand for water exceeds the available amount during a certain period or when poor quality restricts its use. Water stress causes deterioration of fresh water resources in terms of quantity.”[e] The more exposed a country is to water stress, the more the domestic population is competing for limited water supplies. In terms of vulnerability, the more competition the more vulnerable a state is to the impact of climate change due to the deterioration of water volume.[f] |
World Resource Institute, 2020.[g] |
|
Renewable Internal Freshwater Resources Per Capita (Water Sub-Domain) |
“Renewable internal freshwater resources flows refer to internal renewable resources in the country.”[h] This is an indicator of the available natural freshwater bodies in a country. In taking the data “per capita” the monitor illustrates how much freshwater an individual in the country has access to. The greater the access to internal freshwater, the less vulnerable a state is to climate change as climate change is expected to impact internal freshwater levels. |
World Bank, 2014.[h] |
|
Definition & Brief Conceptualization |
Source, Year |
|
|---|---|---|
|
Percentage of Desert of a Country (Land Sub-Domain) |
With a lack of sufficient data on desertification and the rate of desertification, it was decided to use the percentage of desert land of a country as a proxy for desertification. “Land degradation in arid, semiarid and dry sub-humid areas resulting from various factors, including climatic variations and human activities.”[a] |
Nunn and Puga, 2012.[b] |
|
Arable Land (Land Sub-Domain) |
The amount of land under temporary crops (double-cropped areas are counted once), temporary meadows for mowing or for pasture, land under market or kitchen gardens, and land temporarily fallow, per person in a country.[c] With arable land being measured in hectares per person as opposed to as the percentage of total land, there is more emphasis on a per capita understanding of vulnerability.[d] |
WDI, 2013. |
|
Vulnerability to Weather-related Disasters (Drought, Floods, Storms & Extreme Temperatures) (Disaster Sub) |
Borrowed from EM-DAT, this indicator is a composite indicator incorporating drought, floods and extreme temperatures as a percentage of the average population. Instead of using a single indicator for drought[e], floods and extreme temperatures respectively, this indicator provides an overall sense of vulnerability in regards to weather-related disasters by factoring in storms, with a ten year moving average. In assessing the vulnerability to weather-related disasters, “[HCSS] consider[s] the number of people that were either killed or wounded or became homeless as a result of weather-related disasters as a percentage of the overall population over the last two decade.”[f] The indicator illustrates that extreme weather changes caused by climate change are increasing. It relates to vulnerability as what was previously regarded as single weather-related disaster episodes is now becoming more frequent and less abnormal. |
EM-DAT, 2015.[g] |
Layer Three Low Carbon Risk indicators
|
Indicator |
Definition & Brief Conceptualization |
Source, Year |
|---|---|---|
|
Rents from the Following Resources: Oil, Gas, Mineral, Forests and Coal |
The rents from different resources are calculated by subtracting the production costs of resources from their market prices. [a] The higher the rents from resources, the more dependent a country is on their production and export, which indicates the economic effect that a potential transition to a low carbon economy would have. |
|
|
Electricity Production from Sources of Oil |
Different sources of oil are diverse, ranging from crude oil to petroleum products. The use of electricity is crucial in improving local quality of life. However, electricity use can also damage the environment. High proportion of electricity production from oil sources indicates high cost of transition to renewable energy production.[g] |
WDI, 2014.[h] |
|
Renewable Energy Consumption |
Proportion of renewable energy consumption from total energy consumption. High renewable energy consumption indicates readiness for low carbon economic model. |
WDI, 2014.[i] |
|
Greenhouse Gas Emissions |
Greenhouse gas (CO2, CH4, N2O, F-gases) emissions in CO2 equivalent kilotons in 2010. High greenhouse gas emissions indicate the cost of restructuring to low carbon economy |
Data from the JRC EDGAR between 1990, 2010.[j] |
|
Greenhouse Gas Emissions (change from 1990) |
Proportion of greenhouse gas emissions in 2010 compared to 1990 levels (2010 value divided by 1990 value). Changes in greenhouse gas emissions indicates steps taken in recent years to decrease carbon footprint. |
Data from the JRC EDGAR between 1990, 2010.[k] |
Layer Four Economic Resilience indicators
|
Indicator |
Definition & Brief Conceptualization |
Source, Year |
|---|---|---|
|
GDP per Capita, PPP (Current International $) |
GDP per capita is “based on purchasing power parity (PPP). PPP GDP is gross domestic product converted to international dollars using purchasing power parity rates.”[a] The greater the GDP PPP, the more resilient a country is in mitigating climate change. |
CIA Factbook, 2015.[b] |
|
External Debt ($) |
Total external debt is debt owed to non-residents repayable in currency, goods, or services.[c] External indebtedness affects a country’s credit rating and investor perceptions, thus mitigating their economic resilience. |
IMF, 2015.[d] |
|
Economic Complexity Index |
The Economic Complexity Index is the degree to which a country’s economy is diverse and ubiquitous, these being desirable traits in economic resilience. “The complexity of an economy is related to the multiplicity of useful knowledge embedded in it.”[e] The greater the complexity, the more economic resilience.[f] |
MIT Media Lab Macro Connections group, 2015. |
|
Credit Rating |
The credit rating is the evaluation of risk of a potential debtor, evaluating their ability to pay back the debt. The higher the credit rating the greater the level of economic resilience. |
Trading Economics, 2015.[g] |
|
Labour Force |
“The labour force is the supply of labour available for producing goods and services in an economy. It includes people who are currently employed and people who are unemployed but seeking work as well as first-time job-seekers.”[h] The larger the labour force, the more people a state has to mobilize its resources. |
WDI, 2014.[i] |
|
Index of Economic Freedom |
The Index covers 10 freedoms – from property rights to entrepreneurship – in 186 countries. “In an economically free society, individuals are free to work, produce consume and invest in any way they please”, while governments allow the processes thereof to occur without constraint of liberty beyond the “extent necessary to protect and maintain liberty itself.”[j] The greater the economic freedom, the greater the ease of doing business in a country. |
Heritage Foundation, 2015.[k] |
Criteria set
Prior to determining the indicator sets in the analytical framework, guidelines which stipulate the prerequisites that each indicator must have before inclusion were prepared. The following section outlines these prerequisites. The selected indicators provide an important source of information for policymakers and business professionals and help guide decision making for the private sector. There are many benefits in providing a robust set of procedures on selecting relevant indicators in regards to a particular study. Most indicator selection is driven by the historical practices in choosing these indicators based on previous studies and on the degree to which each indicator meets a set of criteria individually. Due to the integrative nature of the analytical framework, we incorporated the relatability of each indicator within its respective layer as well as its place in the overall framework.[94]
Specificity - Firstly, the indicator has to be conceptualized in a manner which expresses the gist of the layer. For instance, volatile precipitation has to capture climate change vulnerability. Without a clear and unambiguous definition the indicator will be more susceptible to scrutiny, challenging the relevance of the indicator to the layer.[95]
Measurability - Indicators must be precisely defined so that their measurement is clear.[96] This relates to how the data is interpreted by the monitor users. Generally, this means that quantitative data must be easily interpreted by the user. Data measurements must have the capacity to be subject to methodological changes in the case of aggregating the data and developing a composite indicator.[97]
Integrative Components - In light of the fact that the monitor will contain aggregates of various indicators, the relatability of each indicator to one another remains crucial.[98] As such, we consider that each indicator must be conceptually relatable to other indicators within the same layer.
Reliability - Is the data consistent over time? Is the data easy to quantify and presentable? Reliability includes the relevance of the chosen time scale, the gaps in data and the structure of the data. It is often the case that when something is more reliable then it is more quantifiable, therefore resulting in improved overall consistency of a measure.
Methodological notes
The quantitative research was undertaken in the following manner: firstly, we developed a conceptual framework of the quantitative layers involved in the study and how they would express data in relation to one another. Secondly, those layers were then divided into subdomains in order to allow users of the monitor to further capture a particular aspect. This process also took into account data availability. Thirdly, we decided on a set of indicators which we would explore based on the criteria outlined in the previous page. The data and indicators had to be reliable and valid, and preference was given to data sources that had application programming interfaces (APIs). We then acquired the data, and employed a number of tidying and transformation techniques to create a structure and format that could best capture the specificity of each layer and the aggregation of all layers. We used the R programming environment for all of our data collection, tidying, transformation, imputation and visualization tasks. We then assessed the fullness of the indicator datasets and chose ones with the best data coverage and lowest number of missing values. As a number of datasets we used did not have data for countries with populations of less than 500,000 people, we left those countries out of our analysis.
Our next phase of research dealt with any remaining missing values. Layers One and Two have a very limited number of missing values that do not follow any particular pattern. After all the data of a particular layer was joined into one data frame, missing values were then imputed using multiple imputation (predictive mean matching with 5 imputations). For the first two layers, the number of missing values per indicator ranged from two (in the cases of precipitation and disasters data) to 13 missing values (for water stress data). For Layers Three and Four, the number of missing values was higher for some variables, so in order to avoid biases, missing values were not imputed.
The data for each layer were then normalized between 0 and 1 by using percentile ranks, with 1 indicating the least desirable figure (1 being the most vulnerable or most at risk, or least resilient). The indicator data was aggregated to a subdomain level using arithmetic means, which was previously conceptualized based on the similarity of the elements as expressed by indicators. There was a small difference in calculation between Layers One and Two when compared to Layers Three and Four: namely, in the first two layers the missing values had been replaced by imputed values, however these missing values remained in Layer Three and Four. The latter two layers’ mean was taken over all non-missing values, and in the first two layers imputed values were included in the calculation. Next, the domain index was calculated by taking the arithmetic means of subdomain index values, to give a general understanding of the layers used in the analysis. Finally, an overall composite of all layers, as well as a composite of the first three layers, was calculated using the same arithmetic means as before.
The resulting data was then visualized on an interactive choropleth monitor, with one separate map for each layer, as well as one for overall composite, and one for the composite of three vulnerability layers (which omitted the resilience layer). Countries used in the analysis were color-coded according to the index values of the layer, with red indicating high vulnerability or risk, yellow implying medium risk or vulnerability and green denoting low values of those indices. In a pop-up format, monitor users can see the vulnerability by indicator, illustrated by a spotlight graph. On a bar chart next to the map, users can observe country vulnerability rankings by layer, with different subdomains color-coded.