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“The relationship between the climate and conflict is one of immense policy interest, and is a topic that has received substantial recent attention from researchers around the world.”(Hsiang and Burke, 2014.) Despite the fact that the climate has always had a direct influence on the life of human beings, the current rate of climate change with higher seas, decreased ice in the Arctic, melting glaciers, extreme rainfall variability and more frequent and intense storms are events humans haven’t experienced before (Werrell and Femia, 2018). In the so-called “Solana report” climate change is defined as a “threat multiplier which exacerbates existing trends, tensions, and instability” (Solana and Ferrero-Waldner, 2008). This threat overburdens all countries, in particular already fragile and conflict-prone states and regions. In this context, Africa is viewed as “one of the continents most vulnerable to climate change because of multiple stress and low adaptive capacity” (Solana and Ferrero-Waldner, 2008). The Sahel region includes some of the most fragile states in the world, like Somalia, Sudan, Ethiopia, Mali, states that have been affected by numerous challenges even before the climate change became a factor (Werrell and Femia, 2018). This semi-arid region of the southern margin of the Sahara desert is particularly vulnerable to natural variability. On top of that, the region is often recognized as a hotspot of violent conflicts. Most recently, this image was reinforced by the jihadist violence and the attacks by groups associated with the Islamic State of Iraq and the Levant (ISIL) and AL Qaeda in Mali, Boko Haram in Niger and Al-Shabaab in Somalia (Benjaminsen, 2018).
This proposal aims to answer two questions:
- Do changes in the climate conditions, namely increased rainfall variability and the rise in temperature, impact state instability and contribute to the increasing violence in the Sahel region?
- Is there any correlation between the rise of political violence in Mali and climate change?
A number of studies find consistent support for a causal relationship between climatological changes and various conflict outcomes (Hsiang and Burke 2014.) But what they don’t agree on is what specific climate variable is more important for predicting conflict, is it the increase in rainfall or the rise of temperature. For instance, in his article “Does climate change cause conflicts in the Sahel?” Tor A. Benjaminsen writes in his article that global climate change leads to drought and desertification that lead to resource scarcity and migration which than causes new conflicts or triggers already existing unrest. He further argues that it is not the drought and desertification that will lead to violence in the long run since the current rainfall trends show more abundant but also more delayed and concentrated rainfall in the future (2018).
A null hypothesis is “a statement that there is no actual relationship between variables” (Sendil and Sethuraman, 2017). Accordingly, the null hypothesis of this proposal read as following:
– The rise in temperature has no effect on the increase in the number of battles or on the increased occurrence of the violence against civilians that took place in Mali over the last ten years.
– There is no relationship between the increased rainfall variability and the increased number of battles or the increased occurrence of the violence against civilians that occurred in Mali over the last ten years.
“An alternative hypothesis is a statement that suggests a potential outcome that the researcher may expect “(Sendil and Sethuraman, 2017). Therefore, the alternative hypothesis of this proposal read as following:
– The rise in temperature has an effect on the increase in the number of battles or on the increased occurrence of the violence against civilians that took place in Mali over the last ten years;
– There is no relationship between the increased rainfall variability and the increased number of battles or the increased occurrence of the violence against civilians that occurred in Mali, over the last ten years.
The hypothesis is a formal statement which expresses the relationship between two or more measurable variables. It is an abstract idea, a concept, which we measure with variables. Those measurable variables are the independent variable, the one that is suspected of being a cause in the causal relationship, and the dependable variable which is the outcome influenced by the independent variable. As above mentioned, the hypothesis can be classified into two types, the null and the alternative hypothesis (Sendil and Sethuraman, 2017).
In conducting a quantitative research study, a goal is to determine a causal relationship between independent and dependent variable. According to the level of measurement, variables can be either categorical (binary, normal or ordinal) or continuous (interval, ratio) (Field, 2009). Thus, in this proposal, climate change as a cause is identified as the independent continuous variable. Climate change can be measured by various measures like the rise of sea level, the frequency of tornados etc. In order to increase the reliability of the measures, I will use two measures, the rise in temperature and the increased rainfall variability. The political violence, on the other hand, as an outcome influenced by climate change, is identified as a dependent variable.
Study design and data collection method
“An ecological study is an observational study defined by the level at which data are analyzed, namely at the population or group level, rather than individual level”. (Levin, 2006). Further, according to Levin, an ecological study design is used when the purpose of the study is to make largescale comparisons, e. g, comparisons between countries (2006).
To support a causal association between climate change and the violent conflicts in Mali, one of the fragile states in the Sahel, I propose a descriptive ecological study. The data used for this purpose are secondary data gathered from prominent sites that provide valuable statistical information on a variety of climate, development and security-related topics.
The whole world is affected by global warming. Security problems exist in different areas around the world and many of them can be linked to the changes in climate, for example, tensions over fisheries in the South China Sea. However, due to structural fragilities and the significant exposure to climate change the Sahel region is particularly vulnerable and exhibits some of the clearest indications of a connection between climate change and conflict (Werrell and Femia, 2018). Moreover, since 2010 violent conflicts have broken out in most countries in the Sahel. For the purpose of this research, I am looking at Mali as a sample that represents a wider population of the Sahelian countries in which violent conflicts broke out in the last decade.
A large volume of data on climate change, violence and conflict have been gathered by established institutions. To investigate the research question of this proposal, data on violence and conflict will be collected from the Armed Conflict Location & Event Data Project (ACLED) while the Climate Change Knowledge Portal (CCKP) will be used as a source for the climate change related data. Use of the secondary data provided by the above-mentioned institutions is appropriate as they produce systematically collected regularly updated high-quality data.
ACLED data are available in three forms: “a Microsoft Excel sheet which contains data on all coded events which occur in states or continents; a shapefile for the entire African continent based on the Excel file; and as files for particular event aggregations” (Raleigh and Dowd, 2015).
On the other hand, “CCKP consists of spatially referenced data visualized on a Google Maps interface. Users are able to evaluate climate-related vulnerabilities, risks, and actions for a particular location on the globe by interpreting climate and climate-related data at different levels of detail.” (Charney, 2018) For every region there are information about climate, impacts and vulnerability.
In this proposal, I use the terms climate change and political violence to describe broad classes of independent and dependent variables. The climate change refers to observations of climatic variables, the rise of temperature and the increased rainfall variability. For the political violence, I use the ACLED definition according to which “political violence is the use of force by a group with a political purpose or motivation” (Raleigh and Dowd, 2015). The term is defined through its constituent events with the aim of producing a comprehensive overview of all forms of political violence within and across states (Hsiang and Burke, 2014.) Even though many of these forms are potentially related this proposal examines two types of political violence, battles, and violence against civilians, ACLED defines them as “violent clashes between at least two armed groups” and as “violent attacks on unarmed civilians” (Raleigh and Dowd, 2015).
Sahel is African area that lies between 12°N and 20°N. The area covers all or part of 12 countries from the Atlantic coast to the Red Sea: Mauritania, Senegal, the Gambia, Mali, Burkina Faso, Niger, Nigeria, Chad, Sudan, Ethiopia, Eritrea, and Djibouti. (Heinrigs, 2010.)
To describe data collected from the ACLED and CCKP datasets I will use descriptive statistics. In a quantitative study features of a collected data can be described either by inferential or descriptive statistics. While inferential statistics are used with the aim of reaching conclusions that extend beyond the immediate data alone, descriptive statistics are used simply to describe what is going on in our data (Trochim, 2006).
Analysis of the data depends on the type of data collected. Therefore, different types of variables generated in the research need to be classified first (Field, 2009). Both climatic and political violence variables are classified in accordance with the Bryman definitions as continuous interval/ratio variables.
Secondly, three characteristics of each of the variable in the study need to be described, namely the distribution, the central tendency, and the dispersion. Depending on the number of variables that are being analyzed at a time we can apply three different analysis: univariate, bivariate and multivariate (Bryman, 2016).
For analyzing only one variable at a time we use the univariate analysis, as the name suggests. Two commonest approaches of this analysis are frequency tables and diagrams. To describe, for example, the increase of rainfall intensity as an interval/ratio variable I will use a frequency table format where categories of rainfall would be grouped by “mm”, the amount of rain per square meter in an hour. However, diagrams are the most frequently used methods of displaying quantitative data and for this reason, for violence against civilians histogram would be employed (Bryman, 2016).
To measure the central tendency of violence against civilians (or any other variable of this proposal since they are all interval/ratio variables) I will use the arithmetic mean as a form of average to summarise the distribution of values (Bryman, 2016). On the other hand, to measure the dispersion of violence against civilians, or the spread of the values around the central tendency (Trochim, 2006) I could use either the range or “the difference between the maximum and the minimum value in a distribution of values associated with interval/ratio variable” (Bryman, 2016, p.339) or the standard deviation “which is essentially the average amount of variation around the mean” (Bryman, 2016, p.339)
To simultaneously analyze all the variables the multivariable analysis would be employed. In addition to demonstrating that there is a relationship between the variables, it is important to prove that this relationship is not spurious, in other words, that it is real. “A relationship between two variables is said to be spurious if it is being produced by the impact of a third, confounding variable on each of two variables that form the spurious relationship.” (Bryman, 2016, p.345) Since it is hard to identify a variable that would cause both climatic and political violence variables of this proposal we can infer that a causal connection between them is not produced by a relationship of each of them to a third variable. Or to put it in another way, we can conclude that the relationship between the variables is real, non-spurious.
In this research proposal, my aim is to explore the link between the rise in temperature and increased rainfall variability and the two forms of political violence, battles, and violence against civilians that keep reoccurring in the Sahel region.
STRENGTHS AND WEAKNESSES
I will try to answer my research questions by developing an ecological study design. As already mentioned in Part I, an ecological study is an observational study defined by the level at which data are analyzed, namely at the population or group, rather than individual level. In spite of the fact that they are easy to carry out since they use routinely collected data, ecological study designs are prone to bias and confounding. Also, because they are area-level studies, care must be taken when extrapolating either to individuals within the area level of measurement or to a higher population level (Levin, 2006).
There are several reasons for considering secondary analysis as a serious alternative to collecting new data in a primary research study (Bryman, 2016). First, cost and time. “The most obvious advantage of the secondary analysis of existing data is the low cost. There is sometimes a fee required to obtain access to such datasets, but this is almost always a tiny proportion of what it would cost to conduct an original study” (Cheng and Phillips, 2014). Second, high-quality data. Most frequently majority of the data sets employed for secondary analysis are of extremely high quality (Bryman, 2016). Those data sets are usually cleaned by professionals who often provide detailed documentation about the data collection and data cleaning process. Moreover, teams which conduct large-scale population-based surveys that are made available to others normally “employ statisticians to generate ready-to-use survey weights and design variables – something that most users of the data are unable to do – so this helps users make necessary adjustments to their estimates” (Cheng and Phillips, 2014). Third, secondary analysis provides an opportunity for longitudinal analysis. “The increasing availability of such data online encourages the creative use and cross-linking of information from different data sources” (Cheng and Phillips, 2014). Further advantages of the secondary analysis are subgroup analysis, opportunities for cross-cultural analysis and new interpretations of collected data, more time for data analysis and the wider obligations of the social researcher who enhance the possibility of valid use of the data by making it available (Bryman, 2016).
On the other hand, limitations of the secondary analysis are not as numerous as the advantages, but nevertheless, they exist. The researchers who analyze the data are not usually the same individuals as those involved in the data collection process. For this reason, “they are probably unaware of study-specific nuances or glitches in the data collection process that could be of relevance to the interpretation of specific variables in the dataset” (Cheng and Phillips, 2014). Bryman refers to this limitation as to the lack of familiarity with data (2016). “Sometimes, the amount of documentation is daunting (particularly for complex, large-scale surveys conducted by government agencies), so users may miss important details unless they are prominently presented in the documents. Succinct documentation of important information about the validity of the data (by the provider) and careful examination of all relevant documents (by the user) can mitigate this problem” (Cheng and Phillips, 2014). Moreover, researchers have no control over data quality and the quality of data should never be taken for granted although it is reasonably assured (Bryman, 2016). Last disadvantage is the absence of key variables. “Inherent to the nature of the secondary analysis of existing data, the available data are not collected to address the particular research question or to test the particular hypothesis. It is not uncommon that some important third variables were not available for the analysis. Similarly, the data may not be collected for all population subgroups of interest or for all geographic regions of interest” (Cheng and Phillips, 2014).
PRACTICAL DIFFICULTIES IN CONDUCTING A RESEARCH
The Sahel is vast and even though the climate over the region has very similar features it cannot be said with certainty that all the countries share the same climatic variability (Buontempo, Booth and Moufouma-Okia, 2010). In order to identify the similarities and the differences of the climate across the region, it would be necessary to gather data from all the twelve countries situated in the Sahel. But, due to increased insecurity and on-going violence in some of the countries it is extremely hard to gather data that would help explain the mechanism of climate change.
Also, variables of political violence and its dynamics are complex and all of them should be taken into consideration when trying to establish an impact of the climate change of the rise of violence. Moreover, political and historical factors of the conflicts should not be overlooked since the “environmental variables are of secondary importance at best compared to political, historical and economic variables” (Heinrigs, 2010).
THREATS TO VALIDITY AND RELIABILITY
Reliability and validity are the two most important and fundamental features in the evaluation of any measurement instrument or tool for a good research. While validity concerns what an instrument measures, and how well it does so, reliability, on the other hand, deals with the faith that one can have in the data obtained from the use of an instrument. Reliability is referred to as to the stability of findings, whereas validity represents the truthfulness of findings (Mohajan, 2018).
Validity, as the most important criterion of research, is concerned with the integrity of the conclusions generated from a research. The main types of validity are the measurement, internal, external and ecological validity (Bryman, 2016).
Measurement or construct validity asks if the measure devised of a concept really reflects the concept it is supposed to be denoting (Bryman, 2016). It goes without saying that the CCKP data really reflect changes in climate and the vulnerability that could be influenced by expected climate changes. “The CCKP doesn’t only provide data derived from three different sources, all quality-controlled by leading institutions in the field, but it also explores future projections. These are the most comprehensive physically-based models of climate change available and used in the Intergovernmental Panel on Climate Change (IPCC) 4th Assessment Report” (Anon., 2018).
Internal validity relates to causality and questions the causal connection between variables rather than just mere relationship. To establish whether there is a causal relationship between variables we need to be confident that the independent variable contributes to the variation identified in the dependent variable (Bryman, 2016). In this proposal, internal validity imposes the question whether or not we can be sure that the changes in climate really increase the risk of political violence. The conclusion of examining relationships between these variables is that the causal connection between them is real, non-spurious since it is not produced by an apparent relationship of each variable to a third one. Ecological studies, like all observational studies, are prone to confounding and to avoid it the use of regression analysis is recommended (Levin, 2006). Be it as it may, I didn’t identify any confounding variable and therefore didn’t propose regression to describe the ways in which variables relate to one another.
The third type of validity is the external validity also referred to as generalizability. Results of a study are generalizable if the findings can be applied beyond the confines of the particular context in which the research is conducted (Bryman, 2016). We can argue if these climate changes will affect the increase of political violence in all the countries around the world or only in the fragile ones already weakened by political and economic instability. Nevertheless, the findings of this proposal can be applied beyond the borders of Mali and the Sahel region and therefore we can say that they are generalizable.
Last but not least is the ecological validity which is concerned with the question if the findings of a study are applicable to people’s everyday life or natural social settings (Bryman, 2016). Findings of this proposal could, for instance, be used for the purpose of developing flood and drought management strategies or even conflict resolution strategies and as a consequence of the implementation in one of these strategies the results would have an influence on people’s everyday life.
As a degree to which a measure of a concept is stable, reliability relates to the consistency of a measure of a concept. A measure is reliable if the three conditions are met, namely the stability, internal reliability and inter-observer consistency (Bryman, 2016). Are the rise of temperature and the increased rainfall stable over time? Since the climate is changing the results of measuring these two variables in ten years most likely will not be the same, but the climate will most certainly always be measured by them. The requirements of internal reliability and the inter-observer consistency of this proposal are also met since it is highly unlikely that there was subjective judgment in, for example, recording observations about the rise of temperature or that the scores of measuring this rise on any indicator trend will not be related to the scores on another indicator.
ETHICAL ISSUES RAISED BY THIS PROPOSAL
Discussions around the ethics in social research revolve around issues such as how the people on whom the research is conducted should be treated by the researches. There are four main areas of ethical principles: whether there is a harm to participants, whether there is a lack of informed consent or an invasion of privacy and finally whether deception is involved (Bryman, 2016).
As already mentioned, secondary analysis refers to the use of existing research data. With the arrival of new technologies, the fundamental ethical issues related to secondary use of research data have become more serious. “Concerns about secondary use of data mostly revolve around potential harm to individual subjects and issue of return for consent” (Tripathy, 2013). However, for this proposal, I haven’t used any data that contains identifying information on participants or information which could be linked to identify participants but the data on countries so there is no ethical concern of this kind.
Further, “the data obtained should be adequate, relevant but not excessive. In secondary data analysis, the original data was not collected to answer the present research question. Thus the data should be evaluated for certain criteria such as the methodology of data collection, accuracy, period of data collection, purpose for which it was collected and the content of the data.” (Tripathy, 2013)
Use of the ACLED data doesn’t impose ethical issues since the ACLED is “the highest quality, most widely used, real-time data and analysis source on political violence and protest in the developing world. Practitioners, researchers, and governments depend on ACLED for the latest reliable information on current conflict and disorder patterns.” (Anon., 2018) Nevertheless, “a core challenge for the design, targeting, delivery and assessment of efficient, effective, high-quality humanitarian operations in conflict-affected contexts is the absence of, and access to, reliable, timely and accessible data on political violence which is comparable across time periods and geographic contexts.” (Anon., 2018)
Anon., 2018. The World Bank Climate Change Knowledge Portal. [online], Available at: http://sdwebx.worldbank.org/climateportal/index.cfm?page=why_climate_change
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