Tuesday, November 1, 2016

Emerging Business Networks in a Developing Context

I do not plan to take the second half of the course at present.

Having lead the first-ever implementation of the AMR (Automated Meter Reading) System in Bhutan, I have had multiple opportunities to analyze the crucial metrics that impact the nature of the outcome of the solution itself. The interplay between the diversely qualified stakeholders chiefly based on the respective group-specific interests shape the trajectory of the complex decision making processes that lead the idea to execution.


Many of the previously labeled ‘developing’ countries are stepping onto the next wave of transformation which is heralding fundamental changes in the Utilities sector. This is the front-line for spreading the combined benefits of Technology and Commerce for enriching and elongating the life of the natural resources that are present within these nations. The Innovation-driven and Need-based focus of the rising Entrepreneurial ecosystem in these regions (largely lead by the youth of the population) acts as a catalyst for this change. I firmly believe that the existing value chain, that which functions to create such impact is being disrupted by these contextual factors.
Due to the primary involvement of third-party agencies such as the Asian Development Bank there is a gradual increase in the amount of regulation and participatory dialogue between the private sector and the government. My goal is identifying and measuring those areas of convergence that lead to overall growth.

Questions to Consider

What are the Key Performance Indicators for ensuring success in the above scenarios? What is the criticality of the measure of communication as a principal area of consideration in this regard? Which are the stakeholder groups leading this conversation? What is the role (if any) is played by the Host government to facilitate this discussion? What is the extent of the role played by Social Media in this situation? How effective is the participation of the nation’s Entrepreneurial Network to leverage these connections?


In accordance to my core understanding of the evolving relations between the different groups of participants, I foresee a strong trend of collaboration between the various government bodies and the international institutions that roll-out the road map for the end-user implementation. The degree of co-operative decision making processes serves as a sign of the effective ‘lobbying’ tactics that will prove to be indispensable from a entrant’s point of view. The unknown part of the equation remains the extent of awareness and involvement by the rising startup network within this scheme of things.s


The design of the survey instruments for Data collection will be a key determinant of the effectiveness of this research. Although there is the presence of preliminary data artifacts that could point to the pertinent questions that need to be addressed, the effectiveness of the same must be determined. A lot of this information is no longer relevant due to discontinued learning practices which has led to obsolete data sources. The questions need to be reframed and re-presented to the parties for understanding the present trends.
Principal Attributes: Region, Role, Project Experience, Pre-Project Credentials, Frequency of Inter-Group Conversation and finally the Level of Accepted Regulatory Procedures.


1.       Identify the major individuals/organizations/participating governments that constitute the end-to-end value chain which is fueling this pipeline of development projects. Here the growing business community (largely startups) and all the other relevant groups will be placed on either side.
2.       Create a common set of questionnaire which basically enumerates the frequency of conversation coupled with individually customized reference queries for each of the stakeholder sections. For instance, the host government’s ‘Utility Administration Departments’ need to understand their intrinsic level of acquaintance with pertinent regional issues and the amount of ready expertise available to start tackling them. It also needs to gauge the aid that is available as loans/grants and the regulatory practices that are inherently tied with its utilization.
3.       Analyzing further I would aim to be guided by the principal criteria of frequency of conversation that would lead me to segregate the principal actors. For instance, the bigger question is the level of co-operation that exists between these sides across regions. Building on top of the previous answer, I would attempt to delve deeper into the primary groups (companies/international bodies) that are co-existent across multiple regions.
4.       Once we can narrow down on the list of organizations that are driving growth globally, we can look internally to ‘network’ identify the potential leaders that are indispensable to the flow of communication as measured by their degree of Centrality( Eigen-Vector and In-Degree/Out-Degree values)
5.        Moving a step further, we can also attempt to understand the fluidity of communication that emanates from these interest groups with the corresponding governments to answer questions such as ‘Which are the Public-Sector bodies that are at the front of this change and how are they communicating with the participating bodies?’


The objective is to formulate a strategic framework built on the foundation of social network measurements to not only understand the key players in Development project-work but to also understand the larger trends that are changing the game for future involvement and efficiency of operations.


Friday, October 28, 2016

The inter-connected economy problem

While the twentieth century was all about globalization and an attempt to create a truly world economy, the twenty-first century seems to be going in the reverse direction. Various decisions like Brexit, Greece default etc. often questions the success of a globalized world.
Based on such thoughts, the following questions pop up:
  • How well connected are world economies?
  • What would happen if an economy falls?
  •  Should a country be worried about a possible default by a country with which does not have any trade relations?

While a decaying and troubling economies have numerous impact of the human life, my area of concern is primitively financial. It is essential to understand what is the impact of such failures of government on the value of currency for other countries.

As per Professor Gustav Cassel, of Sweden, the Stock Exchange has often been represented as an astonishingly sensitive barometer, which indicates beforehand what is going to happen in economic life.

Based on such an interpretation regarding the stock exchange I would like to measure the network that exists between the various stock exchanges of the world and to understand whether a domino effect exists within this network, i.e. if one of the exchanges fall, the other fall along with it as well.

To understand this network, we must understand the working of the stock exchange. A stock exchange refers to a market place where the equity shares and bonds of companies can be traded. The price of these shares is determined by the demand and supply of these securities. The demand and supply is determined by the profitability of the company (both current and future). With globalization, many companies are registered on multiple stock exchanges across borders. Further, many companies have procured investment or made investment in other countries. Also, many global companies act as a supplier or an intermediary to local businesses and therefore, there exist a mesh of international transactions within various companies.

Based on such understanding, we try to understand how a certain stock exchange can impact economies other than its home economy. This study cannot be done at an instance of time but has to be down over a period of time. For instance, if the study is done for a date which is prior to 2000, it could end with a result of China being a fringe economy, which in current timeline is not the case. Therefore, multiple events over the period needs to be consider to assess the impact of stock exchanges. Few of such events identified are as follows:
  • Brexit referendum
  • Greece default
  •  Lehman Brother bankruptcy
  •  Enron scandal
  •  Dot com burst
  • September’11 terrorist attack

Now, on to the trickiest part, that is creating a network between the various exchanges. All the exchange operates based on an index which measures the performance. If the companies listed are doing well, the index goes up otherwise, the index falls. To measure the impact of any event, we measure the change in the index of other countries with relation to the movement of the index of the home country. For instance, in event of the Brexit referendum, what was the degree of rise and fall in the world stock exchanges as compared to the London Stock exchange, was the change by the same degree, was there no impact or whether the index moved in the other direction.

An important factor to consider in this regard is the time zone and operating hours of the stock exchanges. Therefore, it would be essential not just to see the change in the movement on Day 0, that is the day on which the event happened but also the following 2-3 days which give an understanding on how the event is affecting the investment in the area. Another factor to consider is the market understanding of the scenario. With advent of technology and faster communication methods, analysts around the world make predictions about the outcomes of multiple events, while event like recession, terrorists act cannot be forecasted, analysts do predict on decisions on referendum etc. While it is difficult to determine when the analysts make these predictions and factor them into various investment being done, it can be said that an analysis of +- 7 days of the Day 0 is essential to understand the impact a certain event can have on the world economies.

While I have decided to take some additional time in putting this data together and defer my enrollment for the second module, I wish to social network analysis and machine learning techniques to create a system which can enable countries to identify their direct and in-direct dependency on various economy. This would allow them to better understand the forces impacting the global economy and take the necessary corrective steps.

Bilateral Aid in support of Gender Equality and Women’s Empowerment in South Asia

Bilateral Aid in support of Gender Equality and Women’s Empowerment in South Asia

Sanna Bedi 
I will be taking 2nd module

Background: According to the International Labour Organisation, ‘gender equality is considered a critical element in achieving decent work for all women and men, in order to effect social and institutional change that leads to sustainable development with equity and growth. Gender equality refers to equal rights, responsibilities and opportunities that all persons should enjoy, regardless of whether one is born male or female.(1)

The South Asia region comprises eight countries, including Afghanistan, Bangladesh, Bhutan, India, Maldives, Nepal, Pakistan, and Sri Lanka. Critical of the development of the region are the women who hold more than 50 percent of total population. However, women and girls in South Asia are severely suffering from rampant gender based violence, and discrimination in education, nutrition, health and employment paralysing their empowerment and disrupting gender equality. Gender equality is an essential indicator for advancing development and reducing poverty. In order to achieve sustainable development, issues of women’s empowerment and gender equality need to be addressed urgent. 

Over the past few decades there is a greater recognition of such issue. Official development aid focused on gender equality has tripled from USD 8 billion in 2002 to USD 24 billion in 2012.(2) An analysis of the past two decades reveals the crucial role of the Millennium Development Goals in channelising foreign aid towards issues like women’s education and health. Goal 5 of the Sustainable Development Goals is aimed at achieving Gender Equality and Women’s Empowerment for all women and girls and all other goals have gender sensitive features. 

Given the prevailing conditions, an understanding of the key countries providing development aid for specific gender related issues to South Asia will be of immense value. Further, a comparison of the foreign aid received and the gender inequality index can provide a context for optimum allocation of aid.

Primary Guiding Question: Which countries are most likely to invest in specific gender projects in the South Asian Region?

Secondary Questions: Which countries provide maximum aid for gender equality and women’s empowerment to each of the South Asian countries? And which sectors within gender equality and women’s empowerment receives the maximum aid w.r.t. to each of the South Asian countries? 

What is the co-relation between the gender-inequality index and the aid-recipient country? 

Hypothesis: There are specific countries that are more likely to provide aid to the South Asian region. I believe that different countries provide aid for different sector specific issues within gender equality and women’s empowerment. Some countries are more likely to provide aid for more controversial issues like women’s abortion rights. Sector specific aid could provide a context of policies affecting donor decisions. Furthermore, I predict that certain countries in South Asia receive more than others ranked lower the gender inequality index, simply because of their bilateral relationships. 

Data and Methodology: The research will begin by constructing a dataset of all countries that provide development aid to South Asian counties. The aid provided will be classified at three levels to include 1) aid that was not targeted to gender related issues, 2) aid that has a significant target to gender related issues,  and 3) principal target of aid was to address gender related issues. Aid provided between 2010-2015 will be assessed. The data is readily available from public reports by donor countries and international organisation tracking development aid. 

The attribute dataset can be expanded to include specific sectors within gender equality and women’s empowerment, scale of the aid provided and the duration of the project. The two-mode dataset of aid-recipients and aid-provider can be further analysed as one-mode matrices. Then, a review of the sector specific aid provided to each of the countries can be studied. Analysing the aid provided over a period of five years can indicate a change in sectors to which aid is provided.

Finally, the use of a second data set of the gender inequality index can be added to increase the scope of analysis. This will help develop co-relations with the countries that need most aid versus those that actually receive more aid. At this stage, a deeper dive on the duration of aid provided to a country can help understand the sustainability of the aid being received. 

Centrality measures will be important to assess countries receiving sector specific aid. Any formation of cliques based on issues will reveal the most crucial issues that receive aid in the region and can be compared to critical issues actually effecting each country. Cohesion measures could be helpful in revealing information of the likelihood of donors providing aid to a country to belong to the same region or countries competing to forge stronger relations with or influence on the aid-recipient.

Conclusions: The social network analysis will help in creating a go-to list to know which countries are most likely to invest in specific gender projects in the South Asian Region. Trends can be observed about factors that influence a countries likelihood to provide aid to a specific country. 

Sources: Previous SNA work on this question was not identified.

(1) ILO, ABC of Women Worker’s Rights and Gender Equality. 2000.

Tuesday, October 25, 2016

Information Warfare and Identity Formation: Fuel to Conflict Escalation on the International and Domestic Scene?
(I will not be taking the 2nd half of the course)

Introduction & Background:
Much has been written on the Social Networks of Russian “twitter-bots” and paid information warriors, as well as the ISIS propaganda machine. Analysis of how they operate through dense connections reinforcing one another to amplify their message, and fill any gaps that emerge as accounts may be banned or removed show the utility of SNA for understanding the nature of their behavior, and the success of their self-propagation. This paper will seek to bring the rich analysis exemplified in work on these two groups to bear on the understanding of developing political-extremist views in the United States, specifically those espoused by Cliven Bundy and often supporters of Donald Trump. Using the Circle of Equity Framework, as detailed below, two case study comparisons of Russia’s modern information warriors and ISIS’s online propaganda machine will be compared and contrasted and the conclusions further applied to the message and strategy of political-extremist movements in the United States.

Identity and the Circle of Equity Framework:
The central feature of each group’s strength comes from its ability to tap into a shared identity and associated ideology among its various adherents both deepening and spreading the core tenets of this identity. In order to do this successfully, leaders and contributors to each group’s message must communicate in a way that is recognized as legitimate by the other members of the group. Communication that is off message, either from new or less central members, or from groups trying to hijack the group’s social network and communication, are sidelined and removed, as they could potentially harm the group. This inherently means that certain ideas and styles are more central to each group than others for maintaining and building legitimacy as well as strengthening and expanding the group. To understand this complex rubric of legitimacy, the concept of the Circle of Equity will be borrowed from historian John Vander Lippe and sociologist Pinar Batur (“Nexus of Legitimization” publication forthcoming 2016).

The basic idea in this framework is that there are four distinct poles, or primary concepts, that constitute the social order:  Authority, Legitimacy, Equity, and Social Justice. Each pole is dependent upon the others and in constant contest for a greater stake in society. Authority depends on Legitimacy to maintain power, Legitimacy upon Social Justice determined by the Equity of society, which is maintained by Authority and so on again with no one pole completely independent of another. It is the contest and overlap between these four poles that creates the ups and downs and nuances to any society, as the balance between them may be different at any given time.

Each of these groups is essentially its own separate society, or subset social group of a larger society reacting to what it perceives as imbalances in this framework. Understanding this, and further using it to examine the messaging style of each group allows for a greater understanding of each group’s identity, how this is propagated, and potentially, whom might be susceptible to its message.

Data Collection:
Data on Russian information warfare and ISIS propaganda networks are heavily studied at this time. Sources such as the George Washington University Program on Extremism, NATO, and myriad independent researchers have produced ample analysis of these social networks, enough that acquiring additional data for this paper should be unnecessary. A thorough review of these studies, and a synthesis of their conclusions using the Circle of Equity framework should be sufficient.

Little work has been done specifically on the social networks emerging from the rhetoric of the US presidential campaign around issues central to politically-extremist views, though much has been published on the possible causes of it. As a starting point for data collection, news around the Bundy ranch stand-off in 2014 and the Malheur National Wildlife refuge will be examined to build a network map of names. Further news and publicly available social media content will be examined to look for additional links and common phrases for deeper investigation of Twitter, or Facebook data, pending appropriate permissions. Overlap in these phrases with statements from politicians associated with this movement, including their tweets and other available social media content, will be included. These will be synthesized in the same fashion as the Russian information warriors’ activities and ISIS propagandists in order to be labeled appropriately in the Circle of Equity framework.

Tweets and comments, along with common phrases will be weighted as to how much they adhere to each of the four poles of the framework, and assigned to that pole (Authority, Legitimacy, Equity, and Social Justice) to which they most strongly relate. Once this scoring is done, a two-mode network consisting of these phrases and comments and the list of names compiled from research will be set up. An additional one-mode dataset where the lines between these individuals will be the phrases and comments will also be examined.


From a cursory look, each group appears to have a different goal driving its use of propaganda. The Russian information warriors appear to be using the messages to confuse and slow their opponents and sow discord, as well as maintain a certain level of cohesion back home. ISIS appears to be using its messaging primarily to gain followers and esteem. In the US, outside of the election competition itself, messaging around the types of political-extremist ideas exemplified by the Bundy family appears to be aimed at making an alternative/downtrodden voice heard and expressing divergent opinions from the main stream, as well as to coordinate like-minded individuals. It will be interesting to see if these apparent different goals lead to different uses of communication across networks, as well as a different balancing of the Circle of Equity across their messaging.

Saturday, October 22, 2016

Building Better Mental Health Support for Peace Corps Volunteers

Kelsey Goodman
Not taking the 2nd module

Can the Peace Corps use social network analysis to provide better mental health support to volunteers? 

The Peace Corps is an international volunteer program, funded by the US Congress, that sends American volunteers to host countries abroad to provide technical assistance and to promote global friendship and cooperation. Since its founding in 1961, over 220,000 volunteers have served in 141 countries in program sectors like Education, Small Business Development, Environment, Youth Development and more.[1]

The Peace Corps Morocco program – where I served from 2013-2016 – transitioned in 2012 from a multisector program with Environment, Small Business Development, Health, and Youth Development volunteers to a single sector Youth Development program. Under this new arrangement with the Moroccan government, Peace Corps Morocco was officially hosted via formal partnership with the Moroccan Ministry of Youth and Sports (MYS).

The YD transition resulted in several programmatic changes:

1.     Moroccan program managers who oversaw volunteers based on sector were transitioned to “regional” managers who oversaw volunteers based on geographical location.
2.     Two to three intakes of 30-40 volunteers a year became one intake a year of 90-100 volunteers
3.     New sites were developed to accommodate larger intakes
4.     New sites were developed in bigger cities/towns because more volunteers needed to be in sites with MYS community centers (In towns < 5,000 inhabitants, community centers are typically either run by local civic associations or do not exist.)

I arrived one year after the youth development transition was implemented. My intake started off as 95 volunteers. Over the course of our two year service[2], about 21% of my intake (20 volunteers) terminated their service early, either as an “early termination” (volunteer chooses to leave) or a “medical separation” (volunteer develops a medical condition that Peace Corps cannot medically accommodate; mental health issues are considered a medical condition.)

This termination rate is consistent with other the post-YD transition intakes, however it is a higher termination rate than the multisector intakes from before the YD transition[3]. While some of these termination incidences were due to unforeseen family or medical emergencies, many were related to termination related to mental health reasons.

According to Peace Corps Morocco staff, correlated with the higher rates of termination, were also increased reports of unwanted attention, harassment, sexual assault[4],[5], as well as requests for mental health counselling[6].

Combined, these three developments signaled to Peace Corps that something was amiss with Peace Corps Morocco program.

Peace Corps Morocco attempted to address these issues in order to reduce termination rates, protect volunteers, and improve the resiliency and mental health of volunteers. First, they allocated special funds for an in-country therapist. However, there was difficulty finding a therapist in Morocco who was willing to accept volunteers as patients who could commit to working with Peace Corps in the long-term (American expatriates in Rabat are often transitional and temporary.) Second, they tried to re-transition to smaller sites with the hopes that volunteers would be exposed to less unwanted attention and harassment. Once again, Peace Corps was constrained by the resources in Morocco. The partnership with MYS states that volunteers must be supervised by a MYS employee, which limits volunteers to sites with a MYS community center.

While good site development and accessibility to mental health services are important, it is equally important to develop the resiliency of volunteers by strengthening their volunteer support networks.

Existing mental health research finds a correlation between weak support network of friends and higher occurrences of anxiety and depression.

A study of mental health in post-conflict Kosova demonstrated that only one type of social network – contact with friends – was associated with mental health outcomes. On a multivariate logistic regression, only those who “sometimes/never contacted friends” had a statistically significant association with anxiety and depression.[7]

In a study of a different population, researchers found that weak social support networks among older women were associated with feelings of loneliness.[8]

Furthermore, Peace Corps knows that volunteers with robust support networks are happier, more resilient volunteers. Peace Corps data shows that globally 78% of volunteers worldwide list “Spend time with friends” as an activity they do to manage stress and 77% “Contact others by phone, text, email, etc.” These activities are second only to “Read” and “Listen to Music”.[9]

This is why Peace Corps Morocco pays for a cellphone plan that includes free calling between volunteers and schedules all volunteers’ mandatory yearly flu shot in Rabat the day after Thanksgiving (so that the volunteers can all gather for Thanksgiving, and travel expenses can be covered by Peace Corps’ medical transportation budget.)

Research Questions

Is there an association between weak social support networks and terminations and/or mental health issues?

If so, how can Peace Corps use social network analysis to strengthen volunteers’ networks?

How diverse are volunteers’ networks? Are cliques formed by training location, site, or another attribute like age or race?

Who are the bridges between cliques? Could Peace Corps use these volunteers to build stronger networks?


Disregarding unexpected family and medical emergencies, volunteers who are more connected within the Peace Corps community are more likely to complete their entire 27 months of service.

Friendships between Peace Corps volunteers are formed under 3 circumstances: 1) Community Based Training (CBT) 2) Regional proximity 3) Similar backgrounds (homophilies)

Members of committees are more likely to be trusted for advice/seen as leaders than volunteers not involved in committees.

New volunteers trust the advice of old volunteers, but not the other way around.

Data Collection

In a perfect world without the constraints of things like the passage of time, I would be able to conduct an analysis of the social networks of pre-YD transition and post-YD transition volunteers, and compare them for connectivity as well as mental health

Since that is not possible, instead, I would like to conduct a survey of all volunteers, 7-9 months into the service of the most recent intake groups (i.e., if we survey volunteers in May 2017, one intake will have been in the country for 21 months, and one the other 9 months.) This time frame is optimal because it is after the newest volunteers have settled into a routine in their permanent site, but not so late that the older volunteers are busy closing their service and non-response rates would most likely be higher.

Because of HIPAA and Peace Corps confidentiality regulations, most of the data would need to be collected via survey. In my personal experience, volunteers have regular access to the internet and a lot of free time. Volunteers have the capability to complete the survey, 
however achieving high response rates on the survey requires buy-in by volunteers to the importance of this type of research. Respondents will be assigned pseudonyms to encourage truthfulness in their responses.

The one data point we will not collect through data is termination rate. That will be collected via Peace Corps Morocco simply by comparing the data from the survey with terminations as they happen. Peace Corps staff will be able to determine if the hypothesis is correct and if isolates are more likely to terminate than connected volunteers.

Network Questions
Question 1: Friendships – On a social basis, I talk to this person regularly (4); On a social basis, I talk to this person occasionally (3); On a social basis, I rarely talk to this person (2); I have never talked to this person (1), (valued)

Question 2: Leadership – I trust this person’s advice relating to Peace Corps policies or activities (4); I somewhat trust this person’s advice relating to Peace Corps policies or activities (3); I do not trust this person’s advice relating to Peace Corps policies and activities (2); I have never talked to this person (1). (valued)

Question 3: Collaboration – I worked on a work-related project, camp, or program with this person. (y) (n) (binary)

Attribute Questions
Question 3: What year did you begin your service? (Which intake)

Question 4: Where was your community based training site?

Question 5: What is your region?

Question 6: Of which of the following committees are you a member: GAD, VSN, VAC, SPSN, SHC, Wellness Retreat

Question 7: Diversity: Do you self-identify as minority in terms of race, color, national origin, religion, sex, gender identity (including gender expression), sexual orientation, disability, age, marital status, family/parental status, income derived from a public assistance program, political beliefs, or reprisal or retaliation for prior civil rights activity.[10] (y) (n)

Question 8: Throughout your service, have you experienced feelings of loneliness, isolation, anxiety, or depression?
·      Yes – intense/severe
·      Yes – moderate
·      Yes – mild
·      No


To perform the social network analysis, first we would visualize the data. Looking at how the friendship, leadership, and collaboration networks compare and looking for any correlations between attributes and clustering. We would also compare networks by intake and across intake.

Looking at whole network measures, we would analyze density and average distance of the networks. This gives us a feel for the level of interconnectedness of the intake (this is where it would be great to compare to density and average distance of the pre-YD transition intakes to the post-YD transition intakes.)

After looking at whole network measures, we would analyze the networks by subgroups. Analyzing subgroups by faction or Newman Girvan, we could draw out clusters of friends or colleagues and see if there is a correlation between certain attributes and ties. With a deeper dive, we could eyeball the factions for cliques.

Are there friendship cliques? Are cliques regional? Formed by training site? By minority status?
Are there cliques formed by happiness (i.e., do volunteers experiencing no or mild feelings of loneliness, isolation, anxiety and depression flock together vs. volunteers experiencing moderate or intense/severe feelings of loneliness, etc.?)

Comparing the friendship network to the collaboration network, we could see if volunteers choose to collaborate on project based on friendship or another factor (such as regional proximity.)

By comparing in-degrees across the friendship network and the leadership network we can see if volunteers’ perception of leaders is correlated with their friendships, or if there are certain individuals who are seen as leaders regardless of whether or not they are friends with each other. Looking at eigenvector we can determine the most influential volunteers to the network. These are the volunteers that Peace Corps could target to lead diversity trainings, wellness retreats or could encourage volunteers to embrace new policy or programmatic changes.

Looking at node centrality measures, we would look at whether or not a lower number of connections is correlated with more intense feelings of loneliness, isolation, anxiety, or depression. If so, is there any other patterns we can see in the data? Do these volunteers have certain similar attributes? Perhaps they are located in a region with poor transportation, making it hard to visit friends in other sites.

Do nodes with high eigenvectors have no or mild feelings of loneliness, isolation, anxiety, or depression? If so, this would support the hypothesis that well-connected volunteers are more likely to complete their service (absence of unforeseen circumstances).

Are there any nodes with high eigenvectors that have severe/intense feelings of loneliness, isolation, anxiety, or depression? If so, are they directly connected to other nodes with severe/intense feelings of loneliness? Does this suggest a “negativity” path or that there are certain “negativity influencers”?

Analyzing subgroups based on attribute data can illuminate areas of focus in which Peace Corps can improve its inclusion and training. If we compare the density of networks by region, we can see if certain regions have greater levels of support and collaborations than others. Does this correlate with better mental health?

Similarly, we could look at the network of volunteers who are self-identified as belonging to minority groups as a subgroup and as part as the network whole. By analyzing by Newman Girvan faction the friendship, leadership, and collaboration networks with a focus on the attribute of diversity, we can see if there are clusters formed by diversity. If these clusters appear in the friendship network, but not in the leadership or collaboration networks, this suggests that volunteers who self-identify as belonging to minority groups are well-connected within the whole network, but perhaps draw their emotional support from volunteers who have experienced similar challenges as them. If clusters appear in the friendship network and the leadership and/or collaboration networks, then this suggests that there are much more serious problems with diversity and inclusion in the Peace Corps network.

Finally, to look at the correlation between social network density, mental health, and termination, we would track the volunteers who terminate after the survey is complete. Who was in their egonet? Did they have ties to other volunteers who terminated? Is there a path of volunteers who terminated (domino effect?)  


Since this is an observational study, we cannot make any claims of causation. However, by analyzing the social networks of Peace Corps volunteers, we can make recommendations to Peace Corps concerning ways to improve the mental health support of volunteers which would hopefully reduce rates of termination and make volunteers happier, more productive, and more effective to their communities and to the mission of the Peace Corps.

For instance, we if can determine that isolates or volunteers with low degrees of connectivity are more likely to have severe/intense feelings of loneliness, isolation, anxiety, or depression then Peace Corps could consider using its discretionary programming funds to subsidize the wellness-focused social gatherings, like reimbursing volunteers for travel to the volunteer-run Wellness Retreats.

If we can identify the volunteers with the highest eigenvectors on the leadership network, then Peace Corps will know who they could ask to lead diversity and inclusion trainings at pre-service trainings and regional meetings so that volunteers will be more engaged in the content of the trainings because they will trust the importance of it. These are also the volunteers who are important when enacting program changes. For instance, if these volunteers embraced changes in policies for working in summer camps outside of their regions, then other volunteers perhaps would be more likely to follow their lead and comply instead violating the rules.

I am particularly interested in the support networks of volunteers who self-identify as members of minority groups (people of color, the LGBTQ+ community, religious minorities, senior citizens, people with special needs.) While all Peace Corps volunteers tend to get a certain degree of unwanted attention within their host countries simply because they are foreigners, volunteers who belong to certain minority groups often experience different forms unwanted attention or harassment and often deal with different stereotypes than volunteers who fit host country nationals’ perception of “America". While I believe Peace Corps staff in Washington and Morocco recognize the specific challenges that volunteers from different backgrounds face, in my experience volunteers were not always supportive or empathetic to the challenges that other volunteers faced (for instance, male volunteers saying female volunteers are overreacting when complaining about sexual harassment or volunteers minimizing the challenges that Muslim volunteers face because Morocco is a Muslim country.)

Challenges & Limitations

There are two missing pieces of the puzzle in terms of evaluating social support networks: social network within friends and family in the United States and social network within their communities with Moroccan friends and colleagues.

Unforeseen changes with friends and family in the United States (births, deaths, ultimatums issued by long-distance girlfriends) usually are factors in volunteers’ decisions to terminate. It would be interesting to analyze the data on those network, however for the scope of this study, it would not be feasible.

Relating to the other social network not studied, I hypothesize that the happiest volunteers are actually the ones whose main support network is located in their Moroccan communities, not necessarily to other volunteers. In fact, being too well-connected to the Peace Corps community might indicate that a volunteer travels frequently out of site and is not well-connected within their community.

Finally, to better understand the correlation between the density of social network and certain attributes, we would need to do a regression analysis on the node centrality measures and the attribute dataset. I do not believe this is possible to do in UCINET, thus for a more robust study, we need to utilize other analytical tools.

The content of this website is mine alone and does not necessarily reflect the views of the U.S. Government, the Peace Corps, or the Moroccan Government.

[1] http://files.peacecorps.gov/multimedia/pdf/about/pc_facts.pdf
[2] For those who noticed an inconsistency in timeline: a normal Peace Corps service is 27 months. I extended for a year (including one-month home leave), so my total service length was 40 months.
[3] While statistics on termination rates (as well as statistics on crimes and unwanted attention) are monitored by Peace Corps, the public available information is not broken down by country in publicly available information. I am aware of the termination rates from the 2011, 2012, 2013, and 2014 intakes because my service overlapped with those intakes and, out of curiosity, track of the termination rates. These numbers are confirmed by Peace Corps Morocco staff.
[4] The lurking variable in these statistics is the passage of the Kate Puzey Volunteer Protection Act of 2011 which changed the way sexual assault was reported and processed by Peace Corps security and medical staff – specifically that groping and fondling were now categorized as sexual assault, not sexual harassment. With the change in sexual assault reporting, Peace Corps saw increasing rates of sexual assault worldwide, not just in Peace Corps Morocco. (Statistical Report of Crimes Against Volunteers 2014, pg 7) However, this does not take into account the increasing reports of unwanted attention and sexual harassment as the Kate Puzey act actually narrowed the definition of unwanted attention and sexual harassment by reclassifying groping and fondling as sexual harassment.
[5] This information is not publicly available, but is confirmed by Peace Corps Morocco staff
[6] Once again, this information is not publicly available, but is confirmed by Peace Corps Morocco staff

[7] Nakayama, Risa et. al. “Social networks and mental health in post-conflict Mitrovica, Kosova.” BMC Public Health. 17 November 2014.

[8] Gibney, S and M McGovern. “Social support networks and mental health: evidence from share.” J Epidemiol Community Health August 2011 Vol 65 Suppl 1

[10] This data could also be collected with a demographics survey; however, I would be concerned that volunteers may be sensitive about responding with information that will make them too easily identifiable.