Methods to Clarify Drivers of Conflict: Social Science in Zharay and Maiwand Districts, Kandahar Province, Afghanistan
The purpose of this paper is to describe social science research methods employed in Afghanistan during 2011-2012 and to report their use to clarify social issues related to conflict. The area of operations (AO) comprised Zharay and Maiwand Districts in Kandahar Province of south Afghanistan. Previously Zharay was part of the Maiwand District and the birthplace of the Taliban and home to its leader Mullah Omar. The AO experienced one of the highest levels of ISAF combat actions and casualties in all Afghanistan. Both districts are generally arid, but have the Arghandab River as a southern border providing a major water resource for agriculture of vineyards, orchards and poppy for opium.
The social science unit was Afghanistan Human Terrain Team 19 (AF19) assigned to the AO brigade headquarters of the 3rd Brigade Combat Team (BCT)/10th Mountain Division and later 4th BCT/82nd Airborne Division. The primary mission of the Human Terrain Team (HTT) is to collect and analyze the local population for operationally relevant information for the brigade. HTT did not collect intelligence as does (S-2) intelligence units. In US Army terms, HTT collects information on the “White” neutral population while S-2 focuses on the “Red” enemy population. Sometimes there are cross-overs, but a concerted effort by HTT is to “stay in their lane.”
Like most Human Terrain Teams, AF19 consisted of a Colonel as team leader, social scientists, research managers, and analysts. Most of AF19 team members were military veterans. The social scientists were generally Ph.D.s of various social science disciplines: economics, political science, and urban planning. Social scientists prepared research designs including research methods, conducted research, and wrote reports on findings. Research managers assisted social scientists, maintained data bases, and often provided security for the team. The analysts assisted in field data collection.
While different social science disciplines often have similar backgrounds in research methods, e.g., quantitative and qualitative analysis, each particular discipline, e.g., economics or political science, has unique research methods. TRADOC’s Human Terrain System (HTS) recruits and trains Human Terrain Teams (HTTs) and prefers anthropology social scientists to advise brigades on cultural aspects of local nationals (LNs). However, anthropological skills are complimented with other social science disciplines that provide additional perspectives of societal activity in an AO such as economics, governance, LN sociology and attitudes, and development. While understanding cultural attributes is important in conflict areas, the other issues of economic development, governance, attitudes, and development are elements of the counterinsurgency strategy and often military unit “Lines of Effort.” (FM 3-24, 2006 p.2-1).
The social science methods specifically employed included observation, interviews, small sample surveys, statistics, geographic information systems (GIS) including satellite imagery analysis, sociology (Maslow Hierarchy, Cohort-Survival demographic model), economic models (Harrod-Domar and Solow-Swan model), and a spatial model (gravity model). The various models were not mathematically applied but rather provided a framework to clarify observations on LN attitudes and patterns of life, and as a guide for strategy. Mathematical modeling was prevented by a lack of data, and most importantly combat time-constraints. Most of these theories, models, and methods are common in economic geography. Otherwise, they are scattered within other social science disciplines such as urban planning, economics, sociology, and political science.
Most data collection was done with social scientists embedded in military foot patrols. This approach immediately raises the issue of biased responses from LNs who could be intimidated by the US military forces and the Taliban who observe them cooperating with the US military. To minimize this issue, social scientists often conducted interviews out of sight and during normal patrol activities as opposed to offensive operations. Social scientists had to comply with U.S. law and regulations regarding research on human subjects, and always asked permission of the LNs before interviewing them, and never recorded their name unless they were a “public person,” i.e., elected officials. A few times LNs said “no” to an interview, and in those few cases, fear was observed of Taliban reprisals or the person seemed to be Taliban. This was confirmed by US Army Female Engagement Teams (FETs) who could only interview women in their homes, out of sight of the Taliban and informers. On several occasions after interviews, FET members reported that in the protected environment women were much more vocal about village issues.
Observation, Interviews, Surveys, and Focus Groups
Social science observation was very useful to clarify general characteristics of villages. Signs of economic prosperity could be observed by amount of livestock, solar lights, building construction materials, number and type of businesses (in particular small factories), and number of vehicles. Astutely observing physical capital sometimes indicated enemy presence. On one occasion the social scientist spotted a radio tower that was discovered to be a Taliban communication relay station and was clear evidence of significant Taliban presence in the village.
Also, observing patterns of life as to where LNs did or did not congregate provided clues as to enemy presence or intimidation in a village. In one village, overhead air support reported villagers moving away from the patrol to the frustration of the social scientist trying to interview them. Long observation clarifies normal patterns of life and timing such as farmers moving into fields just after sunrise and leaving before the heat of the afternoon. If adults were going to the fields in the late afternoon they were likely not farmers but rather insurgents preparing Improvised Explosive Devices (IEDs) or positions.
Interviewing and short surveys were the most common methods used to clarify drivers (attitudes and issues) of conflict or instability. While interviewing does provide some in-depth information it is often skewed to understand village dynamics. In-depth interviews were only done with elected officials to clarify their backgrounds and interests (the Brigade commander rightly called it their “equities” or what were their investments of effort and issue interests). The results were very valuable to the brigade commander and staff to better understand who they were negotiating with.
In most situations, AF19 employed random-sampled, short surveys sufficient for small sample statistical analysis. In small villages the goal was to gather more than 30 surveys for a statistically valid small sample. If the village was larger, a town, an estimate of the population was used to determine a sample size to have statistically valid results. Surveys of LNs were done at intervals of 50 meters to get a more random sample. This technique also kept the patrol moving and less likely to be attacked. Also, moving from person to person reduced the growth of curious crowds who could interrupt or influence responses. The survey was designed to be completed within 10 minutes. This was done purposely to reduce standing time and the likelihood of an attack.
The format of the survey was done in accordance with US Army PEMSII (Political, economic, military, social, infrastructure, and information) elements. Using a PEMSII format was beneficial for two reasons: it was the general approach of the US Army relative to counterinsurgency so the information was relevant; secondly, it provided a holistic assessment of a village. Too often in an unstructured interview there is a driving topic obscuring other important issues. A five level Likert Scale (person’s opinion: worst to best) was used for most questions with an open-ended questions at the end such as, “Is there anything important I did not ask you?”
Native-speaking interpreters translated survey questions. To ensure correct translation the social scientists reviewed questions with the interpreters before going on patrols. Using a survey list of questions also made the interviews go faster because the interpreters would memorize the questions.
The result of this survey data collection was statistically significant and quantifiable information about LNs by village for the brigade commander and staff. The information was highly valued and an attempt was made to increase data collection by using soldiers. Unfortunately, soldier-executed surveys did not have good results for several reasons. First, the soldiers were unfamiliar with “human subject” research requirements. While the soldiers were exempt from human subject research requirements, the social scientists were not. The social scientists analyzed the data and would be violating federal law if they could not ensure Human Subject research protocols. A second practical problem was the number of interpreters on a patrol. Most patrols only had one interpreter so only one survey could be done at a time. Social scientists had their own interpreters in addition to the patrol’s interpreter. A third issue was that the patrol units often could not do the correct number of surveys and ensure randomness due to higher priority tasks. This resulted in very poor survey responses that could not be properly analyzed. After several attempts with limited success, using soldiers to do surveys was discontinued.
Formal surveys with statistical analysis were also done for special groups such as the Afghanistan National Police (ANP). The results of that analysis were very valuable to the brigade and the Stability Support Team in recruiting, vetting and training ANP.
Focus group program of Afghanistan people divided into separate focus groups by gender and age were done to discover in-depth motivations and attitudes. In one program, to answer a brigade commander’s question of HTT as to what were important themes for the brigade public information campaign, a group of 30 men were recruited and divided into six focus groups with men over 30 in three groups and men under 30 in three groups. The results revealed appropriate public information messaging and graphics. For example, to discourage young men from joining the Taliban, Afghanistan men reported that appeals to take care of mothers was the best message, and graphics should use the color green and photos of nature to best appeal to Islamic preferences.
Analysis by Statistics and GIS
According to the US Army Colonels involved, AF19 switching to surveys with statistical analysis was a major improvement of valued information. Using the software Excel and Statistical Package for the Social Sciences (SPSS), collected data could be statistically validated and analyzed. Social scientists could state to brigade staff that certain variables had statistical validity to 95% confidence levels or other variables were not valid. Furthermore, surveys were done in a village over time. Social scientists could provide a time-series type analysis of a village to document LN changing attitudes. This particular information allowed the brigade command to reallocate resources to better fit counterinsurgency goals by village over time.
Statistical profiles of village attributes were visualized using GIS to clarify the geography of LN characteristics and attitudes across the AO. The profiles were in a PEMSII bar chart format to quickly clarify issue status by village in the AO. This GIS visualization was very useful to the brigade staff and assisted in allocating time and resources across the AO.
GIS with satellite imagery was also used to clarify environmental, infrastructure, and population characteristics. GIS was used to view environmental soil conditions. Areas with arid soils were most often areas of conflict due to poor farming output, causing low incomes, and creating social strife. Well-vegetated areas provided stable farming that reduced insurgent tendencies. However, well-vegetated areas were also good cover and concealment for the enemy.
The GIS was also used by social scientists to assess local infrastructure of roads, wells, and homes. While on patrol social scientists collected GPS coordinates for infrastructure to input to the GIS. This particular information was also useful to the brigade engineers.
A unique social science method used GIS to estimate village populations. Village survey research was used to calculate the average number of persons per home. Using GIS and satellite imagery, home roof-tops were counted to sum the number of homes in a village. The average persons per home was multiplied by the home count to estimate total population in a village. This technique was compared to official government census data and was revealed to be very accurate. Estimated village populations were summed for a district population. That sum was within 2% of the official census that for the district. The social scientists provided a previously unknown geography of population within the brigade AO.
Use of Social Science Models
Poverty and lack of education have been continually exploited by insurgent leaders. Insurgent leaders Vladimir Lenin, Joseph Stalin, Mao, and then Fidel Castro urged impoverished peasants to revolt and this was the main tactic of their insurgency strategy (Schell and Delury 2013). Rural poverty was a key factor allowing those ideologues to incite insurgency. Both Lenin and Mao also said the peasantry were ignorant and needed guidance. To address these causes of conflict, the counterinsurgency strategy includes economic development as a key effort.
AF19 social scientists used the Harrod-Domar and Solow-Swan models of economic growth as a guide to clarify economic development factors stimulating conflict. These two validated models substantiate that increases in capital and technological progress are the drivers of economic growth. The main ingredients are increases in physical capital and improvements in human capital. Furthermore, a challenge in all developing countries is the capacity to sustain international aid improvements such as equipment, e.g., machines due to human capital shortcomings. In collaborating with and advising brigade and battalion Civil Affairs (S-9), AF19 social scientists recommended economic development lines of effort focused on improving sustainable physical capital such as roads or structures. Over time, the brigade S-9 organized LN work crews to build and repair bazaars and government facilities rather than “make work” programs such as cleaning streets. The development of bazaars to attract and encourage commerce succeeded within a few months with new businesses and crowds of customers.
An additional AF19 economic development recommendation was to support women entrepreneurship with women’s centers or other entrepreneurial support programs. Women (underused human capital) who could increase household income by selling chicken eggs or handmade articles reduced the economic stress in a household. Also, Afghanistan men were more tolerant of women adding to household income and less likely to commit domestic violence.
AF19 social scientists’ long-term economic recommendation was for US military Civil Affairs and US Agency for International Development to invest in low-technology factories. In-depth economic development research revealed that to break the cycle of poverty in developing countries, a significant capital investment, in particular for factories, broke the cycle (Banerjee and Duflo 2011). In the case of southern Afghanistan there were opportunities in building juice factories to capitalize on the extensive grape and pomegranate fruit agriculture. Such factories previously existed in Kandahar City, but were disrupted by conflict and closed.
Maslow’s Hierarchy of Needs was used by AF19 social scientists to clarify a potential driver of conflict. Maslow’s Hierarchy describes how persons achieve satisfaction in life as different physical, personal, social (family and friendship), and esteem needs are met. Basic needs include food, shelter, sex, companionship, and social standing. AF19 social scientists interviewed dozens of young Afghanistan men and learned that many, if not most, joined the Afghanistan army, police, and local militia to earn money to get married. Being married provided sex, companionship, and social standing, but to marry an Afghanistan man and his family had to pay for a bride price and a wedding. Furthermore, in the Afghanistan culture a wedding is a milestone social event in which all friends must be invited, adding to its cost. AF19 social scientists also learned that this issue was also true for young men who joined the insurgency. Joining the insurgency was a job to the end of achieving life needs.
The practical application of Maslow’s Hierarchy was to provide jobs and also a plan to have a structure for weddings. None was ever built due to time limitations, but two brigade commanders endorsed the concept. Interestingly, there was a very successful wedding hall in Kandahar City catering to the need for lavish weddings.
The Cohort-Survival Model for projecting population was used to estimate AO current population and the probable number of military-age men in the AO (This is a common model that divides a population growth trends by cohorts of age and gender to estimate total population increase, and is used by the US Census Bureau to project population). Using GIS and satellite imagery housing compounds were summed for all AO villages and multiplied by the average number of persons per household obtained by random surveys in the AO. The Afghanistan Central Office of Statistics had calculated an age and gender cohort population pyramid for the Kandahar Province. (A population pyramid graphically presents a population categorized by cohorts of age and gender). Using the Kandahar Province population pyramid percentages of persons by age and gender, the AO population pyramid was estimated. The result was a probable estimate of the number of military-age male LNs in the AO. This information was useful for the brigade and later became of interest to ISAF headquarters for use in other AOs.
Economic geography includes many social science methods useful to clarifying drivers of conflict, and GIS is a useful tool to visualize the results of the models. A useful spatial model is the gravity model derived from physics (gravity pull of planets on each other) in the 1930’s to determine geographic trade areas of competing markets. Repeatedly validated, the gravity model accurately calculates the distance of influence of one trade area to another. There were two practical applications in using the gravity model relative to AO conflict. The first use was to clarify optimal distance between bazaars for maximum economic development. While AF19 was too late in advising where to build bazaars in the AO, the gravity model was used to explain why certain bazaars were not economically successful.
Another value of the gravity model was in explaining the geography of AO conflict. With good data the gravity model calculates the distance of influence of a village and the boundaries of weakest influence. In one case AF19 social scientists were asked why combat actions were occurring in a part of the AO. Using the gravity model as a guide, it was quickly determined that a city in the adjacent AO seemed the likely source of enemy infiltrating into our AO. In another part of the AO there was significant enemy activity that was in between US forces “centers of gravity.” While these observations are intuitive, the gravity model is useful to clarify the potential geography of conflict prior to experiencing it. However, the gravity model is probably only useful for mapping insurgency activity and not advanced warfare.
Several social science research methods have been shown useful to clarify drivers of conflict in brigade AOs and possibly higher unit AOs. Also, the various social science models can be useful in guiding counterinsurgency Lines of Effort. Social science research methods and models used for a conflict’s “White” population should be assessed for value in “Red” intelligence gathering programs such as in S-2 HUMINT and geo-spatial units, and possibly taught at appropriate military training schools.
Banerjee, Abhijit and Esther Duflo (2011). Poor Economics: A Radical Rethinking of the Way to Fight Global Poverty. Publisher: Public Affairs. ISBN 1586487981.
FM 3-24 (2006) Counterinsurgency. Headquarters US Department of the Army.
Schell, Orville and John Delury (2013). Wealth and Power: China’s Long March to the Twenty-first Century. Publisher: Random House ISBN 067964378.