global conflict risk index: new variables in...
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HALKIA Matina, FERRI Stefano, THOMAKOS
Dimitrios, HAS Silvan, SAPORITI Francesca
Global Conflict Risk Index: New variables in 2018
2018
EUR 29501 EN
This publication is a Technical report by the Joint Research Centre (JRC), the European Commission’s science
and knowledge service. It aims to provide evidence-based scientific support to the European policymaking
process. The scientific output expressed does not imply a policy position of the European Commission. Neither
the European Commission nor any person acting on behalf of the Commission is responsible for the use that
might be made of this publication.
Contact information
Name: Matina Halkia
Address: Joint Research Centre, Via Enrico Fermi 2749, TP 480, 21027 Ispra (VA), Italy
Email: [email protected]
Tel.: +39 0332786242
JRC Science Hub
https://ec.europa.eu/jrc
JRC113181
EUR 29501 EN
PDF ISBN 978-92-79-97697-1 ISSN 1831-9424 doi:10.2760/258293
Luxembourg: Publications Office of the European Union, 2018
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How to cite this report: Halkia, S., Ferri, S., Thomakos, D., Has, S., Saporiti, F., Global Conflict Risk Index:
New variables in 2018, EUR 29501, Publications Office of the European Union, 2018, ISBN 978-92-79-97697-1,
doi:10.2760/258293, JRC113181
Global Conflict Risk Index: New variables in 2018
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Table of contents
Executive Summary ............................................................................................... 5
1. Introduction ................................................................................................. 6
2. New GCRI variables....................................................................................... 7
2.1. Internally Displaced People ..................................................................... 7
2.1.1. Description......................................................................................... 7
2.1.1.1. Bibliography review ....................................................................... 8
2.1.2. GCRI Integration ................................................................................ 9
2.2. Climate Change ................................................................................... 11
2.2.1. Description....................................................................................... 12
2.2.1.1. Bibliography review ..................................................................... 13
2.2.2. GCRI Integration .............................................................................. 13
2.3. Food security (new) ............................................................................. 16
2.3.1. Description....................................................................................... 17
2.3.1.1. Bibliography review ..................................................................... 17
2.3.2. GCRI Integration .............................................................................. 18
2.3.2.1. Construction of the Food Security 4 indicators (2 reconstructed) ....... 18
2.4.2.2 Metrics and validation process ........................................................... 25
2.4.2.3 Anomaly Hotspot of Agricultural Production (ASAP) .............................. 26
3. Discussion.................................................................................................. 30
4. Conclusion ................................................................................................. 30
4. References ................................................................................................. 32
5. List of figures ............................................................................................. 37
6. List of tables .............................................................................................. 37
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Acknowledgments
The authors wish to thank Paulo Barbosa and Gustavo Naumann for their scientific and
technical assistance in the development of the climate indicator.
We would also like to thank Negre Thierry, Rembold Felix, Justin Ginnetti and Leonardo
Milano for their contribution to the data analysis in the food security and internal
displacement topics.
JRC would also like to thank FPI.2, Marc Fiedrich, Giovanni Squadrito, Roberta Gentile,
and Paula Valente Correia, as well as EEAS/PRISM, Rene Van Nes, Gosia Sendrowska,
Pavla Danisova, and Silvia Costantini, for their unwavering support to the development of
the Global Conflict Risk Index.
Authors
HALKIA Matina1, FERRI Stefano1, THOMAKOS Dimitrios2, HAS Silvan1, SAPORITI
Francesca3
1 European Commission, Joint Research Centre (JRC), Ispra, Italy 2 Arhs Developments S.A., 2b rue Nicolas Bové, L-1253, Luxembourg 3 Piksel Ltd Italian Branch, Via Breda 176, Milano (MI), Italy
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Executive Summary
The Global Conflict Risk Index (GCRI) is an early warning system designed to give policy
makers a global risk assessment based on economic, social, environmental, security and
political factors.
The GCRI is composed of two statistical models: the regression model, that quantifies the
probability and the intensity of national and subnational conflicts occurring in the next one
to four years, and the composite model, whose aim is to provide an overview of the factors
contributing to conflict at country level. Both models are based on twenty-four individual
variables, whose raw data are open-source.
The nature of conflict is evolving and the diversity of conflict drivers today has been widely
acknowledged. Exploring new triggers of instability, such as climate change or the role of
internally displaced populations in predicting armed conflicts, (aside from being a
consequence of violence) would help to get a better understanding of the drivers of
conflicts and potentially improve the accuracy of the GCRI regression model.
While it is generally agreed that political and socio-economic variables are the most
relevant ones for conflict risk modelling, other variables and their linkages with armed
conflicts have received growing attention from both academics and policy makers in recent
years. The most striking example is climate variability. Kelley et al. (2015) have provided
evidence on the contribution of the 2007-2010 severe drought to the recent Syrian
Conflict, while the European Union has acknowledged the implications of climate change
for international security and stability4. It is therefore profoundly important and urgent to
address climate change in the GCRI.
This report focuses on these possible new variables and additionally demonstrates a new
method in order to reconstruct the ‘food’ variable, addressing the issue of limited
availability of data provided by FAO.
The implementation of IDPs is not currently feasible in the model −a problem to be
addressed in the short term by employing artificial intelligence and machine learning
techniques in the GCRI. On the other hand, drought as a proxy for climate change will be
included in the next GCRI release. Finally, the method devised for reconstructing the food
indicators not provided anymore by FAO, despite its limitations, permits food security to
be retained in the GCRI.
4 Council of the European Union (2018), Council Conclusions on Climate Diplomacy, 26
February 2018, available at: http://data.consilium.europa.eu/doc/document/ST-6125-
2018-INIT/en/pdf.
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1. Introduction
The Global Conflict Risk Index (GCRI) is an early warning system designed to give policy
makers a global risk assessment based on economic, social, environmental, security and
political factors. It provides input to the EU early warning framework, one input to the EU
Conflict Early Warning System (EWS), developed by the European External Action Service
(EEAS) in close partnership with the European Commission to enhance the EU's conflict
risk prevention capabilities. The GCRI is composed of two statistical models: the regression
model, that quantifies the probability and the intensity of national and subnational conflicts
occurring in the next one to four years, and the composite model, whose aim is to provide
an overview of the factors contributing to conflict at country level. Both models are based
on twenty-four individual variables, whose raw data are open-source. The GCRI variables
and models are described in detail in the technical reports “Conflict Risk Indicators:
Significance and Data Management in the GCRI” (doi. 10.2760/44005) and “The Global
Conflict Risk Index (GCRI) Regression model: data ingestion, processing, and output
methods” (doi. 10.2760/303651).
In an effort to stay abreast with new conflict drivers, emerging policy needs and latest
modelling technologies, the Disaster Risk Management Unit, and more specifically the
Peace and Stability Team, has conducted a study on climate-induced conflict risk factors,
as well as migration, with a view to integrating new variables in GCRI. Furthermore, an
in-depth study is underway on defining and modelling conflict resilience using both
theoretical and date-driven approaches.
The current report presents a research study that has been conducted to analyse the
possible integration of new variables into the GCRI. This research is the first step towards
a future integration or implementation of those variables that have been proven influential
on conflict risk. The report is structured in three chapters: the first two present the
internally-displaced-people indicator along with the climate change variable, and the third
one is concerned with the update of the already existing variable “FOOD”. While the first
two chapters are about topics new to the GCRI, the last one describes the challenges
posed by quantitative raw data that have changed due to data provider choices. Expert
consultations, bibliographical research, and statistical analysis were necessary to the
reconstruction of the food security variable.
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2. New GCRI variables
GCRI is developed in close collaboration with the GCRI experts’ groups, which meet
annually to review development objectives and latest research supporting conflict risk
modelling, more specifically conflict risk modelling methods and new conflict indicators. In
the context of the latest two GCRI workshops of 20175 and 2018, there were in-depth
technical discussions on IDP/conflict risk theory data, climate/conflict nexus and the food
security variable construction.
2.1. Internally Displaced People
According to the 1998 OCHA Guiding Principles, internally displaced persons are those
people, or groups of people, who have been forced or obliged to flee or to leave their
homes or places of habitual residence6. These situations are caused by armed conflict or
the effects of armed conflict, by situations of generalized violence, by violations of human
rights, or by natural or human-made disasters (Word Bank Metadata).
2.1.1. Description
According to the Internal Displacement Monitoring Centre (IDMC) millions of people
around the world are displaced every year within their countries, as a result of conflicts,
persecution and human rights violations (see Figure 1). The fact that the number of
Internally Displaced Persons (IDPs) exceeds that of refugees (i.e. those relocated to a
foreign country) demonstrates the magnitude of the problem. However, unlike refugees,
who enjoy the rights granted to them by the UN Convention of 1951 and the protection of
a specialized UN agency (the UNHCR), internally displaced people lack predictable
structures of support. Therefore, they may face even greater hardships than the refugees,
taking also into account the fact that the authorities of their own country are often
responsible for their displacement.
As their socio-economic reintegration is very difficult, IDPs can further destabilize an
economy, exacerbate social tensions or even undermine the security and cohesion of a
state, particularly if their displacement is protracted, lasting years or decades.
5 Halkia et al. (2017), 3rd Workshop on the EU Global Conflict Risk Index. JRC109042.
6 http://www.internal-displacement.org/publications/1998/ocha-guiding-principles-on-
internal-displacement
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Internal displacement has become not only one of the more pressing humanitarian and
human rights issues, but also a modern security problem affecting countries and the
international community at large.
Figure 1 - Displacement in 2017
Source: Food Security Information Network
http://www.fsincop.net/fileadmin/user_upload/fsin/docs/global_report/2018/GRFC_2018_Full_rep
ort_EN_Low_resolution.pdf
2.1.1.1. Bibliography review
Studies have demonstrated that the displacement of people within their own country has
important political, economic and societal repercussions that undermine peace and
stability and in many cases has contributed to the eruption or intensification of conflicts.
IDPs can increase the pressure on an already difficult situation like when resource scarcity
exists, overwhelming in this way the institutional capacity of host communities. Neglected
or poorly managed displacement, particularly protracted displacement, can exacerbate
situations of conflict and fragility.
In Iraq, millions of people were forced to leave their homelands as a result of the War and
the violent insurgency that followed the US intervention of 2003. According to Riera and
Harper (Forced Migration Review, University of Oxford, 2007), the internally displaced
Iraqis strained the “already heavily burdened social services and local infrastructure”
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creating serious discontent and tension within the receiving communities. The latter
regarded the IDPs as competitors for “the scarce resources and responsible for the rising
cost of food, fuel and housing”. Furthermore, the IDPs in Iraq alternated the traditionally
mixed composition of the population of certain regions resulting to the emergence of
‘sectarian cantons’ (Ibid. and Lischner, 2008). The fragmentation of a country undermines
its stability and encourages secession movements that lead with high probability to the
eruption of conflict (Buhaug 2006, p. 698) and to foreign intervention.
In a similar fashion, the creation of Turkish-Cypriot enclaves in Cyprus following the
intercommunal strife of 1963-64, undermined the fledgling Republic, as these became a
serious source of instability the years to come. The enclaves ultimately facilitated the
Turkish military intervention of 1974 that led thousands of people from both communities
to flee their homes becoming IDPs. Their protracted displacement that lasts until today
threatens peace and security on the island and the entire region.
IDPs militarization is another aspect of the security implications of forced displacement.
In the bibliography there are numerous examples in Syria, Somalia, Sudan (Aspa, 2011),
Liberia (Achvarina and Reich, 2006) and Uganda (Muggah, 2006), where the IDPs played
a significant role in the spread of conflict.7 In Turkey, during the violent struggle between
the Kurdistan Workers’ Party (PKK) and the Turkish Army, hundreds of thousands Kurds
were displaced to cities that were not equipped to absorb this flow. Unemployment and
marginalization heightened the PKK’s attraction to many young Kurds that were recruited
and took part in the hostilities (Barkey and Fuller, 1997).
2.1.2. GCRI Integration
The Internal Displacement Monitoring Centre (IDMC) provides data for displaced people
due to conflicts both as “Stock” and as “New Displacement”. In the first case IDMC
classifies as stock the number of people living in displacement as of the end of each year,
while in the second case they refer to the number of new cases of displacement recorded,
rather than the number of people displaced.
We analysed first the “Stock”, then the “New Displacement”, and we observed that the
data availability is quite limited, both in time and geographically. In fact the historical time
series dates back only until 2009, and there are many cases of missing data even in the
time span 2009-2016. This type of data are not a good fit for a linear regression model
since little amount of data and a short time series do not allow full training of the model.
7 (Bohnet et al, 2013).
10
With other methods used in artificial intelligence and machine learning, such as complex
systems, random forest or deep neural networks IDP data might more readily exploited.
Table 1 - New Displacement (Data availability per year)
Figure 2 - New Displacement (Data availability per country)
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NEW DISPLACEMENT
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Table 2 - Displacement Stock (Data availability per year)
Figure 3 - Displacement Stock (Data availability per country)
2.2. Climate Change
The United Nations Framework Convention on Climate Change (UNFCCC) defines “climate
change” as “a change of climate which is attributed directly or indirectly to human activity
that alters the composition of the global atmosphere and which is in addition to natural
climate variability observed over comparable time periods (Art.1)”8. Addressing climate
8http://unfccc.int/files/essential_background/background_publications_htmlpdf/application/pdf/co
nveng.pdf
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DISPLACEMENT STOCK
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change has been identified as one of the main priorities of the European Union’s Foreign
and Security Policy.9 Being a signatory of the Paris Agreement and in line with the
Sustainable Development Goals (SDGs) set by the United Nations, the European Union
remains committed to mitigate global warming by reducing greenhouse gas emissions,
having acknowledged “that climate change has direct and indirect implications for
international security and stability”10. The EU’s commitment to address the destabilising
effects and risks of climate change was reiterated at the high-level event on climate and
security that was held on 22 June 2018 in Brussels at the initiative of EU High
Representative for Foreign and Security Policy Federica Mogherini.
For the purpose of modelling climate change in GCRI as a conflict risk variable, we chose
to use drought as one consequence of climate change that contributes to violent conflict
(von Uexkull et al., 2016), because of its slow-burning, long-term damaging effect,
especially for states whose economy depends on agricultural production, or where other
structural constraints exist (e.g. ethnic segregation)11.
2.2.1. Description
Drought is a slow-burning climate phenomenon. It originates from a deficit in precipitation
over a prolonged period of time or from the inadequate timing or ineffectiveness of
precipitation, often combined with high temperatures and increased water demand (Carrao
et al., 2014). To model drought, we use a multi-scalar drought index (SPEI) based on
climate data (see 2.2.2).
Drought is a complex phenomenon that can occur everywhere and might affect directly or
indirectly many social and economic sectors, such as agriculture. As rainfall varies
significantly among different regions, the importance and impact of droughts may differ
locally. The likelihood of impacts of a certain event depends on the quantity of assets
exposed and their coping capacity (irrigation, fertilizer consumption, etc.). An indicator
based on the exposed crop areas (and possibly extended to grazelands) to droughts might
9 Shared Vision, Common Action: A Stronger Europe. A Global Strategy for the European
Union’s Foreign And Security Policy. June 2016.
10 Council of the European Union (2018), Council Conclusions on Climate Diplomacy, 26
February 2018, available at: http://data.consilium.europa.eu/doc/document/ST-6125-
2018-INIT/en/pdf.
11JRC is grateful for the insights shared by Prof. Nina von Uexkull (Uppsala University) on this point.
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help capturing the potential of conflicts that could be triggered by water allocation permits,
rural labour instability and ultimately food security.
2.2.1.1. Bibliography review
Researchers (Kelley et al. 2015) have provided evidence that the severe drought of 2007-
2010 contributed to the Syrian Conflict that followed. The Potsdam Institute for Climate
Impact Research in Germany carried out a statistical analysis of the outbreak of armed
conflicts and climate-related natural disasters between 1980 and 2010. Their findings
suggest that war should be added to the usual list of problems likely to be caused by global
warming, such as crop failures, water shortages and floods. It has been years since
environmentalists have first warned that the rise of temperatures over the next century
could result in large areas of the planet becoming uninhabitable, forcing millions of people
to migrate elsewhere and significantly increase the risk of conflicts breaking out.
Significant contributions in the literature on the climate-conflict nexus (Homer-Dixon,
1991 and 1999), (Barnett and Adger, 2007), (Baechler, 1999), (Devitt and Tol, 2012),
(Raleigh and Urdal, 2007) and more recently (Kahl, 2016), underline climate as an
emerging conflict driver globally.
According to Naumann (2018) droughts are known to affect wide areas and a large number
of people over long periods. Droughts can impact on population’s health and safety, can
cause conflicts between people when water restrictions are in place and may also trigger
unwanted migrations. Furthermore, other studies based on empirical data evaluated
linkages between the accumulation period of drought indicators and impact on various
sectors (Sepulcre-Cantó et al., 2012, Trambauer et al., 2014, Naumann et al., 2015,
Bachmair et al., 2016, Blauhut et al., 2016).
2.2.2. GCRI Integration
Drought impacts certain land cover categories more than others, especially those devoted
to rainfed agriculture. Therefore, for the implementation of a Climate Change variable that
covers the effect of drought we need two different types of data: One would detect
decreased precipitation with increased evaporation conditions with a high spatial resolution
and the other would provide information on whether or not a certain region is used for
agriculture.
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For the implementation of the drought indicator in the GCRI two datasets are used. The
first dataset is the Standardised Precipitation-Evapotranspiration Index (SPEI). By using
this index, it is possible to register the effects of temperature variability and temperature
extremes beyond the context of global warming (SPEI; Vicente-Serrano et al., 2010). The
dataset takes into account effects of precipitation and evaporation and standardizes the
drought effects to a scale with mean 0 and standard deviation 1. The SPEI values range
from -3 to 3, where negative values translate to low precipitation/ high evapotranspiration.
It provides a spatial resolution of 0.5 degrees12 and a time resolution that goes from
monthly to yearly. For the GCRI we use the SPEI12 (see Figure 2), which aggregates the
weather conditions only for one month and therefore has a high time resolution. Indices
like the SPEI6 or SPEI3, which aggregate the data over longer periods of time are good
indicators to measure for example rise and fall of groundwater levels. Blauhut et al.,
(2016) shows that the SPEI for a 12-month accumulation period performs best at
predicting the impact of climate events across different sectors and regions in Europe.
Figure 4 - SPEI Global Drought Monitor
Source: http://spei.csic.es/map/maps.html#months=1#month=3#year=2018
12 This results in 259,200 spatial observation points worldwide.
15
Since the GCRI is only interested in the occurrence of drought, the dataset is truncated so
that only the negative values from 0 to -3 are considered.
Figure 5 - SPEI
Source: http://spei.csic.es/
The second dataset is the “Global agricultural lands in the year 2000”, which is a data
collection that represents the proportion of land area used as cropland in the year 2000
(Ramankutty et al. 2008). Satellite data from MODIS13 and SPOT-VEGETATION14 were
combined with agricultural inventory data to create the agricultural layer. The next step
consists in analysing the impact of dry weather conditions on agricultural areas,
intersecting the SPEI12 with the crop layer. If a certain geographical area is used as
cropland, the drought data is used. On the different case, the information on drought is
dropped and set to 0 (no drought)15. After having identified the areas of interest, an
average per year per country is calculated, then rescaled from 0 (no drought) to 10
(drought).
13 https://modis.gsfc.nasa.gov/
14 http://www.vgt.vito.be/
15 The focus is on the impact of drought in those regions that are relevant to food
production.
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Table 3 – Temporal data availability for the GCRI drought variable per year
Figure 6 – Geographical data availability for the GCRI drought variable
2.3. Food security (new)
The final report of the 1996 World Food Summit states that food security "exists when all
people, at all times, have physical and economic access to sufficient, safe and nutritious
food to meet their dietary needs and food preferences for an active and healthy” (Patel
2013, and Rome Declaration available at:
http://www.fao.org/docrep/003/w3548e/w3548e00.htm).
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2.3.1. Description
Food security is one of the 24 already existing GCRI indicators; in the GCRI it used to be
composed of four different indices (Diet, Price Level, Nourishment, and Volatility) all
provided by the FAO (Halkia et at. 2017). However, FAO has stopped producing two of the
four indicators, namely Price Level and Volatility. Currently the FAO is “reviewing
underlying methodologies and data to produce these series”16 and price and volatility data
cannot be expected to be published again soon. These circumstances obliged us to exploit
alternative ways to model the food security concept.
2.3.1.1. Bibliography review
In conflict literature it is widely agreed that food prices may be a causal factor of internal
conflicts, since an increase of the price level is likely to result in social unrest ranging from
riots to a civil war. For example, Bellemare’s (2015) research results show that food prices
indeed cause social agitation, a finding also confirmed by Arezki and Bruckner’s study
(2011). The latter was conducted back in 2011 using annual food price data and
demonstrated that an increase in food prices affects political stability in low income
countries and causes intrastate conflicts, a phenomenon not observed in high income
countries. Moreover Goldstone (1982) stresses that the changing effect of food prices on
social unrest is especially high, when combined with high unemployment.
Even though literature is concord on which effect food prices have on conflicts, food price
volatility must be addressed as a separate issue. In fact the link between price volatility
and internal conflict is not that straight forward, and scientific evidence points to a
completely different direction than the one for price level.
First of all, food price volatility has been decreased since 1970s, as Barrett and Bellemare
(2011) have demonstrated (see also Jacks et al. 2011, Gilbert and Morgan 2010). In
addition, economic research has shown that consumers are less affected by volatility than
by food prices themselves, since volatile prices can often be smoothed by consumption of
different goods.
Second, Bellemere (2013) found in a statistical analysis of the FAO data set that volatility
either reduces social unrest or has at best no effect at all. On the other hand, Jacks et al
(2011), in their study concerning Sub-Saharan Africa, argue that the casual relation works
in the exact opposite way, presenting that conflicts increase food price volatility and not
vice versa (see also IMF and UNCTAD 2011, p. 58).
16 Mail received from “Food Security Statistics-FAO” on February, 13th 2018.
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Note that there is some ambiguity about the expression “Food Price Volatility” in the
literature. For the GCRI we define price volatility as frequent change in the prices, usually
measured using standard deviation.
As a conclusion, the food price volatility variable can better be replaced by a variable
measuring for example change rate in food price.
2.3.2. GCRI Integration
2.3.2.1. Construction of the Food Security 4 indicators (2
reconstructed)
As explained above, this year we had to replace both the Price Level and the Volatility
datasets. The food price dataset was built using an overall food price level and then
adjusting this data over the years addressing also the changes in food prices. Hence, if
the food price doubles within a year, and the overall price level doubles, the food price
level is considered to remain constant. Furthermore, it is normalized so that all food prices
are relative to the one of the United States, which has a constant level of 1. The food price
volatility variable was built so to measure the average standard deviation in food prices.
For each month of the year the standard deviation is computed from its previous 8 months.
Then the average of all monthly standard deviations is taken to get an annual value for
food price volatility.
The first approach we decided to adopt, to an alternative modelling way, was based on
the experts’ advices received in May 2017 during the 3rd GCRI workshop. The experts’
group had suggested to integrate into the model international food prices, because
“international and domestic prices are not necessary identical and have different impacts
on different parts of the population” (Halkia et al, 2017). However, even in cooperation
with the FAO it was not possible to find data suitable for the purpose of the GCRI.
As a consequence of not finding satisfactory replacement data and of having conflicting
technical arguments in view, we decided to concentrate our efforts in finding only domestic
price dataset. Our decision to exclude international food prices was reinforced by studies
shown that the former do not have any economic impact on countries like Ethiopia, Yemen,
Somalia that are not integrated in the global economy (Alemu et al., 2008).
We first analysed the Global Information and Early Warning System (GIEWS), which
actually provides data both on domestic price (DP) and international ones (IP). However
even in this case some difficulties were present. After analysing the DP dataset, it appeared
19
evident that the data were available only for 90 countries, and that the data coverage
differed quite a lot from country to country (e.g. the starting date for one country could
be the 1990 but for one other the 2013). An additional problem was that the data were
disaggregated by type of commodity and market, which imposes a decision on how to
aggregate them. Analysing the IP dataset we realized that: the data availability was quite
small, less than 20 countries; that the frequency was monthly, and that the coverage
presented the same problem as for the DP.
As a consequence we discarded the idea of using this data and we searched for an
alternative dataset.
The FAO has other freely available data17, which consists of monthly data for food price
level and overall price level. Since our original aim was to maintain comparability with the
previous GCRI versions, we tried to reconstruct the FAO Food Price Index as well as Food
Price Volatility data from the raw price data. Rest assured, the reconstruction method is
meant to be applied only to 2015, 2016 and 2017, meaning, to those years that had
missing data in the original FAO dataset.
The first step to reconstruct the original food price variable was done using the following
ratio for each country:
𝐹1 =𝐹𝑜𝑜𝑑 𝑃𝑟𝑖𝑐𝑒𝐿𝑒𝑣𝑒𝑙
𝑂𝑣𝑒𝑟𝑎𝑙𝑙 𝑃𝑟𝑖𝑐𝑒 𝐿𝑒𝑣𝑒𝑙
Then, as second step, 𝐹1 is standardized to the United States price level for every year:
𝐹2 =𝐹1
𝐹𝑈𝑆𝐴
The third step is to use a linear regression to transform F2, country by country, into the
scale used by the FAO:
𝐹𝑂𝑂𝐷 = 𝛽0 + 𝛽1 ∗ 𝐹2
The performance of this method is good and manages to reconstruct the FAO food price
index very well for 130 out of the 146 countries covered by the original FAO Food Price
Index (𝑅2 > 0.87). For some countries there was no or too little data available to estimate
the transformation coefficients 𝛽0 and 𝛽1. Using this method it is possible to maintain
backwards comparability of the GCRI to a very high degree.
17 http://www.fao.org/faostat/en/#data/CP,
20
We also tried to reconstruct the Volatility Variable following the same procedure as for the
Price level index. The reconstruction of the volatility variable took the above computed
standardized ratio 𝐹2 as basis (monthly data). For every month of each calendar year we
computed the variation coefficient from the previous 8 months. Then, the average of all
variation coefficients of the year was taken. Hence, for every year we got 12 values for
the variation coefficient, over which we took the mean to compute the overall food price
volatility value. Again, we used a linear regression to fit our scale to the one used by the
FAO for their food price volatility index. The performance of this method was significantly
lower than for the food price index. We could reconstruct the volatility index for 124
countries and get an average goodness of fit of 𝑅2 ≈ 0.47. Although, the value of 𝑅2 is not
as high as it is for the food price (0.87), the utilisation of up-to-date data is still a better
approximation compared to data imputation of constant values. The latter would reduce
the performance of the model.
In May 2017 we received two more recommendations by the GCRI panel of experts: a) to
drop the hunger and nourishment-related indicators because “hunger do not present
strong linkages with risk of conflict, as opposed to food prices changes. […] there is also
little evidence to suggest that undernourishment is an important explanation for current
armed conflicts.” (Halkia et al. 2017); and b) to consider if the country is dependent either
on food import or on food export. On the FAO website is possible to find such data, however
with a time series that ends in 2013.
Even though we reconstructed the two indicators, we also built a new food security variable
to take into consideration current data availability as well as expert and literature opinion.
Having a composite variable is not an ideal approach. The weighting of variables in a
composite variable is set exogenously instead of computing it inside the GCRI model. This
can weaken the variable and in extreme cases it might even lose all of its explanatory
power. Moreover, reconstructing the missing FAO datasets from raw price data is a short
term solution only meant to ensure comparability of the GCRI with its previous versions.
Hence, we aim at replacing the current composite variable with a variable focusing, at the
moment, purely on food price changes, as the one that follows.
The new variable uses the same data source as the one used to rebuilding the FAO Food
Price Index. Hence, both data on food price level and overall price level were available.
We computed the relative change of this ratio to the previous year, creating in this way
the new index for each country:
𝐹𝑂𝑂𝐷𝑁𝐸𝑊(𝑌𝐸𝐴𝑅) = 𝐹1(𝑌𝐸𝐴𝑅)
𝐹1(𝑌𝐸𝐴𝑅 − 1)
Data availability for the new variable is comparably good. For many countries we can
impute older values using a linear transformation of the previous FAO Food Price Index as
21
described above. Furthermore, data availability and consistency in the future is secured
since we rely on basic data not an index published by a third party.
Table 4 - Temporal data availability for the FAO Diet variable per year
Figure 7 – Geographical data availability for the FAO Diet variable
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
19
89
19
90
19
91
19
92
19
93
19
94
19
95
19
96
19
97
19
98
19
99
20
00
20
01
20
02
20
03
20
04
20
05
20
06
20
07
20
08
20
09
20
10
20
11
20
12
20
13
20
14
20
15
20
16
20
17
DIET
22
Table 5 - Temporal data availability for the FAO Nourishment variable per year
Figure 8 – Geographical data availability for the FAO Nourishment variable
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
19
89
19
90
19
91
19
92
19
93
19
94
19
95
19
96
19
97
19
98
19
99
20
00
20
01
20
02
20
03
20
04
20
05
20
06
20
07
20
08
20
09
20
10
20
11
20
12
20
13
20
14
20
15
20
16
20
17
NOURISHMENT
23
Table 6 - Temporal data availability for the FAO Price Level variable per year
Figure 9 – Geographical data availability for the FAO Price Level variable
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
19
89
19
90
19
91
19
92
19
93
19
94
19
95
19
96
19
97
19
98
19
99
20
00
20
01
20
02
20
03
20
04
20
05
20
06
20
07
20
08
20
09
20
10
20
11
20
12
20
13
20
14
20
15
20
16
20
17
PRICE LEVEL
24
Table 7 - Temporal data availability for the FAO Volatility variable per year
Figure 10 – Geographical data availability for the FAO Volatility variable
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
19
89
19
90
19
91
19
92
19
93
19
94
19
95
19
96
19
97
19
98
19
99
20
00
20
01
20
02
20
03
20
04
20
05
20
06
20
07
20
08
20
09
20
10
20
11
20
12
20
13
20
14
20
15
20
16
20
17
VOLATILITY
25
2.4.2.2 Metrics and validation process
For scientific purposes and completeness of the work, we checked how strong the impact
on the model of a food indicator composed only by Diet and Nourishment would be.
Therefore we computed first the metrics for 2017 using the former GCRI 2418 (Table 8),
then for 2017 using an adaptation of the former GCRI19 (Table 9).
Table 8 - 4 food indicators
FOOD 4 INDICATORS
METRICS_VC_NP METRICS_VC_SN METRICS_HVC_NP METRICS_HVC_SN
MSE 25.09 22.78 60.66 65.37
RMSE 5.01 4.77 7.79 8.08
Sensitivity or TPR 0.96 0.93 1 1
Specificity or TNR 0.49 0.4 0.39 0
Precision or PPV 0.26 0.33 0.12 0.09
NPV 0.98 0.94 1 -
fall-out or FPR 0.5 0.59 0.6 1
FNR 0.04 0.07 0 0
accuracy 0.57 0.53 0.44 0.091
Table 9 - 2 food indicators
FOOD 2 INDICATORS
METRICS_VC_NP METRICS_VC_SN METRICS_HVC_NP METRICS_HVC_SN
MSE 25.1 22.8 60.04 65.3
RMSE 5.01 4.77 7.77 8.08
Sensitivity or TPR 0.962 0.934 0.991 1
Specificity or TNR 0.496 0.404 0.391 0
Precision or PPV 0.262 0.335 0.128 0.09
NPV 0.986 0.95 0.998 -
fall-out or FPR 0.504 0.596 0.609 1
FNR 0.377 0.065 0.008 0
accuracy 0.569 0.533 0.441 0.091
As clearly visible, the changes on the accuracy, sensitivity, and specificity of the models
are minimal, if not inexistent. These two tests, that have been done using the same input
18 Former GCRI 24=23 indicators plus food security composed as Price Level, Volatility,
Diet, and Nourishment.
19 Former GCRI 24 adapted=23 indicators plus food security composed as Diet and
Nourishment.
26
but different methodology for calculating the FOOD, demonstrate that the controlled
redefinition of the FOOD variable has no impact on the forward-looking capacity of the
GCRI models. The impact of food security on GCRI may be limited, however, as an integral
part to conflict risk theory (see paragraph 2.3.1.1), given the extensive literature providing
evidence of the contribution of food insecurity to conflict risk, food security remains a GCRI
variable.
2.4.2.3 Anomaly Hotspot of Agricultural Production (ASAP)
Besides price level and volatility, a third study was undertaken on an additional indicator;
choosing to evaluate the effect of decreased precipitation with increased evaporation
conditions on agricultural production. JRC experts, consulted on the topic, confirmed that
drought has a high impact on food productivity, even though these phenomena are less
destructive than before. Technology has helped countries to progress and implement
measures to fight against floods, hurricane, drought etc., becoming in this way more
resilient to natural disasters. Nevertheless, agricultural drought, with its negative effects
on agricultural production, is still one of the main causes of food insecurity. With the
continuously increasing demand for agricultural production in order to satisfy the food
needs and dietary preferences of an increasing world population, drought is one of the
climate events with the highest potential of negative impact on food availability and
societal development (see Figure 11). Droughts aggravate the competition and conflicts
for natural resources in those areas where water is already a limiting factor for agriculture,
pastoralism and human health. Climate change may further deteriorate this picture by
increasing drought frequency. In 2017 persistent drought played a major role, causing
consecutively poor harvests in countries already facing high food insecurity such as Kenya,
Somalia and Uganda, and in southern Africa (FSIN, 2018). Therefore monitoring crop and
rangeland conditions are highly relevant for early warning and response planning in food
insecure areas of the world. On one side satellite remote sensing makes available
information on vegetation status in such areas where ground data are scattered, non-
homogenous, or frequently unavailable. On the other hand rainfall estimates provide an
outlook of the drivers of vegetation growth.
27
Figure 11 - Climate shocks in 2017
Source: Food Security Information Network.
http://www.fsincop.net/fileadmin/user_upload/fsin/docs/global_report/2018/GRFC_2018_Full_rep
ort_EN_Low_resolution.pdf
“ASAP is an online decision support system for early warning about hotspots of agricultural
production anomaly (crop and rangeland), developed by the JRC for food security crises
prevention and response planning anticipation”20. It provides information on agricultural
production, signalling which countries are suffering of difficult condition, through warning
of critical issue (see Figure 12). The system classifies each sub-national administrative
unit (Gaul 1 level, i.e. first sub-national level) into five possible warning levels, ranging
from “none” to level 4. This classification is based on an automatic standard analysis,
carried on during crop growing season. The assumption that drives the analysis is that
rainfall estimates, and remotely sensed biophysical status of the vegetation are two
indicators closely linked to biomass development and thus, to crop yield and rangeland
production.
20 European Commission, EU science HUB, About ASAP, available at:
https://mars.jrc.ec.europa.eu/asap/asap-info.php
28
Figure 12 - ASAP
Source: https://mars.jrc.ec.europa.eu/asap/map.php
More specifically the classification system is built on: two rainfall-based indicators (the
Standardized Precipitation Index computed at 1 and 3-month scale), one biophysical
indicator (the anomaly of the cumulative Normalized Difference Vegetation Index from the
start of the growing season), and the timing during the growing cycle at which the anomaly
occurs.
Every ten days, ASAP verifies if a level of 0, 1, 2, 3, or 4 is registered, for each country
whose crop season is currently active. Then at the end of every year, it sums the
occurrences that have values equal to 2, 3, or 4 in order to get the total amount of decades
that had relevant warnings. For each country, the number of decades is divided for the
duration of the growing season, so to get the percentage of time with more than 25% of
crop area in distress. These values are then rescaled before GCRI integration.
29
Table 10 - Data availability per year
Figure 13 - Data availability per country for ASAP
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
19
89
19
90
19
91
19
92
19
93
19
94
19
95
19
96
19
97
19
98
19
99
20
00
20
01
20
02
20
03
20
04
20
05
20
06
20
07
20
08
20
09
20
10
20
11
20
12
20
13
20
14
20
15
20
16
20
17
ASAP
30
3. Discussion
This technical report investigated the integration of new variables into the GCRI, namely
the Internally Displaced Persons (IDPs) and climate change. In addition, as FAO, a GCRI
input data provider, has stopped data publication for two indicators (Price Level and
Volatility) that were used for constructing the ‘Food Security’ variable in the GCRI model,
a new approach was devised in order to reconstruct them.
Due to data availability, the implementation of the IDPs variable in the GCRI is not
currently feasible as the latter employs linear regression techniques. Nevertheless, the
dataset provided by the IDMC could be valuable using artificial intelligence and machine
learning −methods that the JRC is considering to put in practice.
Integrating climate change into the GCRI using drought as a proxy variable is very
important and may improve the accuracy of the GCRI model taking into consideration
recent research that provides evidence on the contribution of the severe drought of 2007-
2010 to the Syrian Conflict (Kelley et al. 2015).
Despite the limitations of the new method devised to reconstruct the two missing
indicators from FAO, the utilisation of up-to-date data for price level and volatility is a
better approximation compared to imputed data.
4. Conclusion
In the context of continued development and optimization of methods for conflict risk
modelling, also following the GCRI experts’ group recommendations, we investigated the
potential of internal displacement and climate variability indicators in the GCRI. Migration
data availability is not suitable for immediate integration in the GCRI regression model,
nevertheless, it will be possible to exploit the potential of IDP data with the advent of new
GCRI modelling developments. However, the next GCRI release will include the 25th
variable of climate, whose implementation will be detailed in a dedicated report. Finally,
food security, although of limited impact to the GCRI continues to be considered as a key
variable for conflict risk modelling.
31
Table 11 - Summary of the new variables to be integrated in the GCRI.
Indicator Source Name of dataset Years covered URL
Internally Displaced People Word Bank Total displaced
New displacement 2009-2016
https://data.worldbank.org/indicator/VC.IDP.TOCV
https://data.worldbank.org/indicator/VC.IDP.NWCV
Drought Standardised Precipitation-
Evapotranspiration Index (SPEI) SPEI12 1901-2015 http://digital.csic.es/handle/10261/153475
Food Security
(Former)
Food and Agriculture
Organization (FAO)
Domestic food price index 2000-2014
http://www.fao.org/fileadmin/templates/ess/foodsecurity/ Food_Security_Indicators.xlsx (release: 16 December 2016)
Domestic food price volatility 2000-2014
Prevalence of Undernourishment 1990-92 2014-16
Average dietary energy supply
adequacy 1990-92 2014-16
Food Security
2 indicators
Food and Agriculture
Organization (FAO)
Prevalence of Undernourishment 1999-01 2014-16 http://www.fao.org/fileadmin/templates/ess/foodsecurity/ Food_Security_Indicators.xlsx (release: 15 September 2017) Average dietary energy supply
adequacy 1999-01 2014-16
Food Security
4 indicators
(2 reconstructed)
Food and Agriculture
Organization (FAO)
Domestic food price index
(reconstructed) 1990-2017
http://www.fao.org/economic/ess/ess-fs/ess-fadata/en/ http://www.fao.org/faostat/en/#data/CP Domestic food price volatility
(reconstructed) 1990-2017
Prevalence of Undernourishment 1999-01 2014-16 http://www.fao.org/fileadmin/templates/ess/foodsecurity/ Food_Security_Indicators.xlsx (release: 15 September 2017) Average dietary energy supply
adequacy 1999-01 2014-16
Food Security
(ASAP)
Anomaly Hotspots of Agricultural
Production ASAP 2004-2017 https://mars.jrc.ec.europa.eu/asap/
32
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index. J Clim, 23(7), pp. 1696-1718
Von Uexkull N., Croicu M., Fjelde H., Buhaug H. (2016). Civil conflict sensitivity to growing-
season drought. Proc. Nat. Acad. Sci., USA, 113(44):12391–12396.
Wischnathand G., Buhaug H. (2014). Rice or riots: On food production and conflict severity
across India. Political Geography, pp. 6–15.
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5. List of figures
Figure 1 - Displacement in 2017.............................................................................. 8
Figure 2 - New Displacement (Data availability per country) ..................................... 10
Figure 3 - Displacement Stock (Data availability per country) ................................... 11
Figure 4 - SPEI Global Drought Monitor .................................................................. 14
Figure 5 - SPEI ................................................................................................... 15
Figure 6 – Geographical data availability for the GCRI drought variable ...................... 16
Figure 7 – Geographical data availability for the FAO Diet variable ............................ 21
Figure 8 – Geographical data availability for the FAO Nourishment variable ................ 22
Figure 9 – Geographical data availability for the FAO Price Level variable ................... 23
Figure 10 – Geographical data availability for the FAO Volatility variable .................... 24
Figure 11 - Climate shocks in 2017 ........................................................................ 27
Figure 12 - ASAP ................................................................................................. 28
Figure 13 - Data availability per country for ASAP ................................................... 29
6. List of tables
Table 1 - New Displacement (Data availability per year) ........................................... 10
Table 2 - Displacement Stock (Data availability per year) ......................................... 11
Table 3 – Temporal data availability for the GCRI drought variable per year ............... 16
Table 4 - Temporal data availability for the FAO Diet variable per year ....................... 21
Table 5 - Temporal data availability for the FAO Nourishment variable per year .......... 22
Table 6 - Temporal data availability for the FAO Price Level variable per year ............. 23
Table 7 - Temporal data availability for the FAO Volatility variable per year ................ 24
Table 8 - 4 food indicators .................................................................................... 25
Table 9 - 2 food indicators .................................................................................... 25
Table 10 - Data availability per year ...................................................................... 29
Table 11 - Summary of the new variables to be integrated in the GCRI. ..................... 31
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