LEVERAGING MICROFINANCE: A TOOL TO AID GROUND
COMMANDERS IN DISTRIBUTING ECONOMIC DEVELOPMENT FUNDS
Isaac Faber, Max Gordon, Nick George, Colin Fisk, Lance Parker, and Braden Schoenlein
Department of Systems Engineering
United States Military Academy, West Point, NY 10996
ABSTRACT
Military leaders do not currently have an effective method to guide the allocation of economic
development funds. The purpose of this article is to present a tool that will help guide how a
ground commander can best allocate available funds in order to raise a community’s economic
output. The tool is data driven using information from a microfinance organization, Kiva. Data
from microfinance loans are utilized due to the similar purpose with that of financial aid
provided by ground commanders through economic development funds. The Kiva dataset is
consolidated to five sectors: Industry, Services, Agriculture, Health, and Education. These
sectors act as the individual stocks that make up an ‘investment’ portfolio; the sectors then can
be analyzed for optimization in terms of long term economic growth. Given a country of
operation, the tool will return a portfolio recommendation with percent allocation by economic
sector to be used as guidance in economic development fund distribution. This tool serves to
provide much needed guidance in providing a base of economic knowledge of a country of
interest and a proposal about the distribution of development funds.
BACKGROUND
Economic development is crucial aspect in completing the military’s mission. This
objective is included in the mission statements and the funds allocated for both large recent
conflicts in Iraq and Afghanistan. From 2002 to 2010, $61 billion was distributed to Iraq and $62
billion was given to Afghanistan for humanitarian efforts (Poole, 2011). US foreign assistance
funds, under economic development, are distributed through the Department of Defense, with
60% of these funds channeled for Afghanistan alone (Poole, 2011). Foreign assistance has
played a pivotal role in stability and reconstruction efforts in the Middle East as the military
engages in ongoing conflict. The United States Military has acknowledged the need to target
quality of life in occupied countries as a means of accomplishing the mission by including
economic development as an objective within the mission statement for Iraq and Afghanistan:
“MNF-I Conducts stability operations to support the establishment of government, the
restoration of essential services, and economic development to set the conditions for a transfer of
sovereignty to designated follow-on authorities”
- MNF-I Mission Statement
“In support of the Government of the Islamic Republic of Afghanistan, ISAF conducts
operations in Afghanistan to reduce the capability and will of the insurgency, support the growth
in capacity and capability of the Afghan National Security Forces (ANSF), and facilitate
improvements in governance and socio-economic development in order to provide a secure
environment for sustainable stability that is observable to the population.”
-
ISAF in Afghanistan Mission Statement
Economic development funds are intended to provide U.S. military commanders the option
to designate an amount of money for relief and humanitarian efforts within their respective Area
of Operation (AO) in hopes of enhancing the local populace’s support for coalition forces
(Marsh, 2011)). Despite the importance placed on economic development through mission
statements and funding, ground commanders have not been provided a method to accurately
utilize these funds. Without allocation guidance on these funds current military expenditure of
economic development funds has been primarily directed at improving infrastructure.
Commanders are using economic development funds to repair and create schools, water
purification plants, sewers, and only a small portion has been allocated to promote small business
within an AO (Clay, 2009). Throughout the current conflict in the Middle East the infrastructure
projects financed by a commander’s economic development funds have proven costly.
Additionally, such projects are difficult to complete, leading to wasted or unused funds. As of
2011, US Forces-Afghanistan (USFOR-A) reported that approximately $38.4 million had been
lost due to outstanding unliquidated obligations (Marsh, 2011). This loss in funds is due to
improper termination of such projects and a failure to properly takeover said projects by
incoming ground commanders during a deployment rotation. The failure to properly terminate
and transfer the burden of large infrastructure projects may be a result of the lack of knowledge a
ground commander has on supervising large construction operations (Adams, 2013). Although
infrastructure projects are intended to help a large portion of the population within an AO, these
projects create targets for the enemy, and are usually outsourced to contractors outside of the
AO. These types of projects fail to provide the intended beneficial outcomes of bettering the
civil-military relationship, improving the economic status as a whole, and allowing the
community to operate on its own (Poole, 2011). Therefore it is imperative that US Ground
Forces consider targeting an AO’s economy in a more holistic manner.
A drastic range exists for the size of economic development funds given to ground
commanders who are left to allocate the funds at their discretion. Numerous studies have shown
that small economic development projects, those less than $50,000, tend to be more successful
than larger projects because they are better informed and create more incentives for a local
community to work together (Clay, 2009 and Berman et al, 2013). While ground commanders
must adhere to guidelines and rules on how they can spend the economic development funds,
there is little education to guide them on how to best allocate their funds. Much of the training
employed about how to allocate funds revolved around employing it as a combat enhancement.
In fact the U.S. Army’s Center for Lessons Learned handbook for general guidance on allocating
economic development funds states: “that monies should not be used to support local business”
but suggests instead “employ as many Iraqi’s as possible” (Clay, 2009).
This approach leads to a significant amount of wasteful spending and does little to
alleviate poverty or promote long term economic stability within a region. Even worse, at least
10% of all funds distributed throughout Afghanistan ended up in the hands of insurgents, thus
working at cross purposes with the desired effectiveness of economic development funds
(Marsh, 2011). Additionally, even if commanders have a basic understanding often improper
allocation to projects can lead to more economic harm than good (Angelucci et. al, 2013). To
counter such shortcomings, the US military should employ a strategy that answers the following
question: How can we encourage stability through growth in local economies? The answer is to
invest in the right businesses. Invest, in the case of military expenditure on communities in an
AO, does not refer to a ground commander reaping a financial gain, but rather gaining a return
through an improved community and civil military relationship with the local populace. Through
the use of a micro-grant program that is driven by the theory of portfolio optimization and long
term optimal growth, a ground commander can acquire a base of knowledge of where best to
place their funds in the local business sectors in order to raise a community’s economic output
within a current AO.
APPROACH
In theory, a sound strategy for employing development funds can effectively increase a
community’s economic output. The implied task is that the ground commander must ensure that
they allocate this funding appropriately in order to obtain this successful result (Adams, 2013).
By viewing local community’s economic sectors as a portfolio an optimization analysis can be
conducted. This approach will determine what economic sectors produce the greatest amount of
growth over a period of time. In turn, the ground commander can choose to place the economic
development funds in the higher growth sectors in order to raise the status of living. However, in
order to conduct a portfolio optimization analysis significant amount of data is needed. This has
been a challenge as many military units did not collect or consolidate this type of information
while deployed so most datasets are incomplete (Berman et al, 2013). One of the primary
contributions of this paper is to leverage microfinance data to help support the optimization
program.
The portfolio optimization is driven by historical economic data collected by
microfinance institutions (MFIs) via Kiva. Kiva is an intermediary organization that helps find
donors for MFIs. MFIs issue small sized loans, ranging from $50.00 to $10,000.00, to local
business owners and entrepreneurs in impoverished communities in order to provide them with
the financial assistance to operate their business. Considering the operations of MFIs, the method
in which the grants (given by a ground commander) from the economic development funds are
allocated parallel the loans issued by an MFI (Schmidt et al., 2009 and Eversole 2000).
A ground commander will be able to issue portions of funds based on the optimization
recommendation. These will go to individuals of the community operating businesses within
specific sectors. In order to create the optimization program, data pertaining to loan
performance, return on investment, loan amount, amount of loans issued, and the economic
sector is needed. This introduces the assumption that grants act like loans and that the individuals
seeking grants are the same as the individuals seeking loans. Although a seemingly large
assumption, through research on grant performance and loan performance by the Joint Poverty
Action Lab (JPAL) historical data shows negligible difference. The implementation of a vetting
process to the distribution of grants will eliminate individuals who do not intend on using the
grant appropriately (Walsh, 2013). Additionally, conducting follow up meetings with those
individuals who have taken a grant, possibly through the use of patrols, a ground commander can
ensure that the individuals who have been given said grants are utilizing them as intended, as
well as track the performance of the grant.
Datasets
The portfolio optimization application leverages microfinance data to develop
‘investment’ portfolio recommendations. The primary data set used is a record of Kiva
microloans from the years of 2005 to 2013 from dozens of MFIs. This data set contains
geographic, demographic and financial loan data points from over 1 million loans. Initially, the
Kiva dataset separated loan data on an individual level in sixteen economic sectors based on a
specific country.
The Kiva dataset is extensive; however countries that lack diversity in their economy fail
to provide substantial data in certain sectors. This lack of information in various sectors
motivated a consolidation based on data collected by World Bank. The micro loan data found in
the World Bank datasets covers over a 100 different variables. In reviewing the collected data it
was found that there were five primary variables that underpinned the rest and that the
subordinate variables were rooted. These five core variables are: Industry; Services; Agriculture;
Education; and Health. The consolidation of the sixteen Kiva sectors is as follows:
Figure 1 Consolidation of Kiva economic sectors
In Kiva’s dataset the loan sizes range from as small as $50 and as large as $10,000. This
large variation in loan size has the potential to skewed the output. The Kiva data was trimmed to
only include loans of $2,500 or less to avoid this nuance. The trimming of the Kiva dataset
considered the micro grant approach. The assumption is made that grants no greater than $2,500
would be given to any individual in a ground commander’s AO.
In conducting interviews with pundits in the field of microfinance, it was discovered that
MFIs defined success as the availability impoverished communities have to loans (Lehman,
2013). This definition of success does not provide hard data concerning the performance of
individual loans, and thus cannot be used to determine the performance of the application or the
growth of the local economy. When speaking with former ground commanders it was determined
that a grant based approach was the best method for issuing economic development funds
(Adams, 2013). A grant based approach implies that a return on the issued portion of the
economic development fund is not expected by a ground commander but instead goes to the
business proprietor.
Since a means of success has not been widely defined by the microfinance community in
a manner that is conducive for analytical use, this paper develops its own measure. The
approach interprets the difference between loan sizes (average dollar amount of a loan) from year
to year in a specific sector to determine the net economic performance. This logic implies that if
loan sizes increase from the previous year within a sector, then that sector has undergone a
positive gain, or return. The opposite holds true as well. If the loan sizes have decreased from the
previous year in a sector then that specific sector has undergone a loss, or negative return.
Figure 2 Kiva Loan Amount by Year from 2005 to 2013
Figure 2 shows the change in loan size over time for the entire Kiva dataset. The loan
amount in the x-axis displays the loan size taken out by year (y-axis) within a specific sector.
The general performance of the sector relationship between is observable. The observation that
changes in loan amounts increase over time does not in and of itself justify its use as a
performance measure. Further observation is required to validate this assumption. Figure 3 is a
pairs plot of the rates of changes of loan amounts compared against the rates of changes of
macroeconomic variables reported by the world bank for the country of Kenya.
Figure 3 Rates of Change of Loan Amount vs. Macro Counterparts in Kenya
If the rate of change between loan amounts are significantly correlated with known
macroeconomic variables that would lend credence to the assumption that they represent a useful
source of information. From a cursory view of Figure 3 there does appear to be a positive
relationship. It is also obvious that the microloans have much greater variation than do the
macroeconomic variables. In order to test for significance a hypothesis test of correlation is
conducted with the results given in Table 1.
Table 1 Test of Correlation of Rates of Change
R
N
Alpha
Test Statistic
Result
0.356301
35
.05
2.19
Reject Null Hypothesis (R =0)
The result of the test demonstrates statistical significance between the two variables at
95% confidence. This observation supports the use of changes in loan amounts as a proxy for
economic conditions. The sample correlation of .35 suggests that the two variables are not
strongly related. This is to be expected as microloans are targeted at the lower echelons of an
economy as opposed to the country level concentration that the world bank reports. There is also
friction concerning the size of the loans as MFIs have varying practices in determining and
changing loan sizes over time. However, using averages on a time series loan amount changes
offers a positive first effort at measuring the performance of micro economic sectors. It is these
sectors that a ground commander may have the best chance of influencing.
METHODOLOGY
The approach used to leverage the microfinance data will rely on the dynamics of the
dollar size of loans. In order to infer portfolio performance a major assumption used is that a
loan amount (aggregate not individual) will follow a log normal distribution. Cursory
observation of the loan size by sector given in figure 4 support the distribution assumption as a
stylized fact.
Figure 4 Distribution of Loan Size for Entire Data Set
Though the data has been trimmed there is a clear trend in reduction of frequency of loan size as
the value increases from mode of $500. The parameters for the distribution (given in equations 1
and 2) (𝑔, 𝜎) are defined by statistical estimates on a parsed selection from the data set. Later
sections in this paper will show that it is the changing dynamics of these parameters that will
serve the basis for portfolio selection. The parameter estimates from the total data set (all
countries over all years) is given in Table 2.
Table 2 Log Normal Parameter Estimates
Estimate
Standard Error
Log Mu
6.5437885001
0.0008844527
Log SD
0.7750747967
0.0006254025
The values in Table 2 give an indication that there is a relatively low error associated with the
estimates at the aggregate level. However, the data set is ‘thin’ for some countries resulting in
less reliable estimates when parsed. It will be an important consideration for any decision maker
when allocating funds for economic improvement to know how reliable the recommendations
are. The tool developed for use in this regard will contain the proper warnings in the event of
small sample sizes with high standard error.
The assumption of log normality allows for the use of models intended for equity pricing. In
particular the expectation and variance of a given loan is assumed to follow from equations 1 and
2.
𝐸[𝐿(𝑡)] = 𝐿(0)e
1
(g+ 𝜎2 )𝑡
2
1 2
)𝑡
𝑉𝑎𝑟[𝐿(𝑡)] = (𝐿(0)e(gt+2𝜎
2
Equation 1
1 2
2
(𝑒 𝜎 𝑡 − 1) )
Equation 2
In the expressions 𝐿(𝑡) is loan size and 𝑔 is the log normal growth rate. Describing the loans
size as lognormal is important because it allows for the development of optimal growth
allocation (Luenberger 1997). A loan size after 𝑛 time periods can be described in equation 3
𝐿𝑛 = 𝐺𝑛 𝐿𝑛−1
Equation 3
Where 𝐺𝑛 is a random return factor given as 𝐺𝑛 = 1 + 𝑔. Under these conditions loan size
growth process can be described in equation 4
Equation 4
𝐿𝑛 = 𝐺𝑛 𝐺𝑛−1 … 𝐺2 𝐺1 𝐿0
Taking the logarithm of equation 4 and performing trivial manipulation yields equation 5
1
𝐿𝑛 𝑛
1
𝑙𝑛 ( ) = ( ) ∑𝑙𝑛𝐺𝑛 = E(lnG1 ) = 𝑣
𝐿0
𝑛
Equation 5
Then
Equation 6
𝐿𝑛 = 𝐿0 𝑒 𝑣𝑛
In equation 5 it is demonstrated that the growth rate, (same as continuously compounded) is
described by 𝑣, is the same as the expected logarithm of 𝐺1 . So maximizing the expected
logarithm is the same as maximizing the growth rate in loan size.
The assumption of log normality allows for the extension of modeling the change in the loan size
over time. The loan size is described in terms of Brownian motion given in equations 7 and 8.
𝑑 𝑙𝑛[𝐿(𝑡)] =
𝑑𝐿(𝑡)
𝐿(𝑡)
1
𝑑𝐿(𝑡)/𝐿(𝑡) = (𝑔 + ( ) 𝜎 2 ) 𝑑𝑡 + 𝜎𝑑𝑧
2
Equation 7
Equation 8
1
In equation 4 the term (𝑔 + (2) 𝜎 2 ) is defined as the growth rate of a given loan category. This
process describes the growth of a loan size over time but does not allow for the elaboration of
several categories of loans. Because the tool developed in this paper allocates funds based on
sector. The sectors become equivalent to asset categories (indexed as ‘𝑖’). Each asset can be
described by its growth terms from equation 7.
𝐿𝑖 (𝑡)
1
𝐸 [ln (
)] = (𝑔 + ( ) 𝜎 2 ) 𝑡 = 𝑣𝑖 𝑡
𝐿𝑖 (0)
2
Equation 9
It is demonstrated that equation 9 and equation 5 relate to express the growth rate over time. If
weights are incorporated then the result is given in equation 10 for expectation and equation 11
for variance
𝐿𝑖 (𝑡)
1
𝐸 [ln (
)] = 𝑣𝑖 𝑡 = ∑𝑤𝑖 𝑔𝑖 𝑡 − ( ) 𝑤𝑖 𝜎𝑖𝑗 𝑤𝑗 𝑡
𝐿𝑖 (0)
2
𝐿𝑖 (𝑡)
𝑉𝑎𝑟 [ln (
)] = ∑𝑤𝑖 𝜎𝑖𝑗 𝑤𝑗 𝑡
𝐿𝑖 (0)
Equation 10
Equation 11
Where 𝜎𝑖𝑗 is the variance (where 𝑖 = 𝑗) or covariance (where 𝑖 ≠ 𝑗). It follows that 𝑣 can be
contorled by selecting weigths for 𝑤𝑖 , 𝑤𝑖−1 … 𝑤2 , 𝑤1. So in order to optimize the growth rate of a
set of loan sectors the non-linear program in equations 12 and 13 should be used.
1
𝑚𝑎𝑥𝑖𝑚𝑖𝑧𝑒: ∑𝑤𝑖 𝑔𝑖 − ( ) ∑𝑤𝑖 𝜎𝑖𝑗 𝑤𝑗
2
𝑠𝑢𝑏𝑗𝑒𝑐𝑡 𝑡𝑜 ∑𝑤𝑖 = 1
Equation 12
Equation 13
One important addition to the program is to restrict negative weights. The reason for this is that
loans should (can) not be short sold. So the constraint given in equation 14 is included.
𝑠𝑢𝑏𝑗𝑒𝑐𝑡 𝑡𝑜 ∑𝑤𝑖 ≥ 0
Equation 14
Including the constraint in equation 14 eliminates linear combinations of growth efficient
portfolios. So growth optimal points will need to be calculated independently. This will be
reviewed in the results section with an application to the country of Kenya.
CASE STUDY: KENYA
The country of Kenya has benefited from numerous microfinance and micro grant
programs. The amount of microloan information associated with Kenya within the Kiva dataset
is significant. This provides an opportunity for a proof-of-concept. In respect to the Kiva data
concerning microloans in Kenya, a growth chart was produced (Figure 5) in order to show the
growth rate for each individual sector in comparison to the growth rate of the optimal portfolio
(Kenya specific output from equation 12). Kenya’s Industry sector has the greatest growth rate.
However, this superior growth rate does not indicate that the ground commander should allocate
their funds solely to the sector. The ground commander should instead place a proportional
amount of their funds into a sector based on the value the sector can provide to optimal growth.
Figure 5 Growth Rate over Risk chart for the Kenya’s sectors and optimal portfolio
The program results in the portfolio with the proportional distribution recommendation listed in
Table 3.
Table 3 Kenya Fund Distribution by sector based on optimal growth algorithm
Sector
Allocation
Education
Health
Industry
0
.25
0
Services
Agriculture
0
.75
As seen in the Kenya portfolio, the funds are to be distributed between the sectors of
Health and Agriculture. This result is, in a way, counter intuitive because health that the lowest
growth rate. However, the optimization program considers the relationship that the sectors share
( 𝜎𝑖𝑗 ), and controls the growth rate by the individual sector weights (𝑤𝑖 ). This results in a
portfolio that seeks to optimize long term growth.
The portfolio program recommends a distribution of funds in order to achieve long term
growth. In doing so, the concept of ‘over-betting’ must be taken into consideration. This concept,
refers to wasting funds or unintentionally harming the local economy despite the good intention
to improve it. In the world of card counting and Black Jack, this same concept exists, in which a
gambler places too great of a bet on a hand they know has a strong probability of being in their
favor. Although, instinctively it makes sense to win as much as possible when given the
opportunity, in regards to long term optimal growth this is dangerous. With that said, it behooves
the gambler and commander to bet proportionally considering the probabilities they have of
winning the next hands. In the case of Kenya it would be counterproductive to either only invest
in Industry or evenly distribute funds. Both of these approaches would result in a negative
(wealth destroying) growth rate.
WEB APPLICATION
To assist a ground commander in conducting this complex portfolio analysis on the
country they are operating within, a web application was created. This web application, presents
the ground commander with a user-friendly interface, with easy to understand instructions
(Shown in Figure 6). The application has four tabs that consider the user’s country of operation
and the amount of economic development funds available for their use.
The first tab, labeled “Results”, provides the ground commander with a Loan amount by
year chart. This chart represents the change in the amount of loans taken out by year. Below the
Loan amount by year chart the user will find the weights that the portfolio program has assigned
to the economic sectors for the country of choice. The second tab, labeled “Asset Calculator”,
provides a visual representation of the funds to be distributed in raw monetary form ($US).
Additionally, the user will find a similar chart that displays the monetary allocation of the funds
in the specific sectors based on the weights that have been assigned through the algorithm and
the user’s input.
The third tab, labeled “Compare Your Plan”, allows the user to input their own allocation
of the funds into the five economic sectors. The application then runs a comparative analysis on
the performance of the optimal portfolio (the portfolio determined by the optimal growth
algorithm) and the user’s portfolio (the manually inputted allocation of the funds). This tab also
provides the user with the growth rate of the optimal portfolio and the user’s portfolio. The
fourth and final tab, labeled “Credit Check”, provides a ground commander with a general means
of vetting grant applicants based on country, economic sector, and gender. This is a simplistic
means of determining an individual’s success if provided a grant, and by no means should be the
only factor considered when providing the local populace with grants. The web application is
open source and can be found at the following web address:
https://analysticspro.shinyapps.io/capston-app/
Figure 6 Screenshot of web application main tab
BENEFITS OF IMPLEMENTATION
The idea of leveraging microfinance to provide ground commanders with an
understanding on how best to allocate their economic development funds has three beneficial
outcomes. An immediately noticeable benefit of the approach is, the stimulating of the
microfinance community to seek a means to collect data on loan performance. Currently,
microfinance institutions base loan success on accessibility; the ease and convenience that an
impoverished community has in obtaining a microcredit loan. In socializing the idea of
leveraging microfinance, it became more apparent that the concept of success based on loan
performance has not been strongly emphasized. Through implementing this theory in modern
military strategy and current microfinance operations, the microcredit community will have an
outlet in which such loan information can be utilized; motivating the change in how success is
defined in the microcredit world. The implementation of this theory and the means of collecting
beneficial data will also improve civil-military relationships.
The web application requires updated data in order to be useful in the future. There are
various means in which this data can be collected, but there are two specifically that will better
the military’s relationship with the civilian world. The first means of collecting this data is
through microfinancial institutions. MFI’s, which currently collect data on loans (despite the
lacking success variable), could supply the military with this loan information. In doing so, the
military will be able to branch out and form a working relationship with NGO’s; creating a
positive image and strengthening the military’s force. More importantly, the gathering of the
needed data can better the military’s relationship with the local populace within a ground
commander’s AO.
Rather than conducting presence patrols, through the implementation of a grant program
aided by the web application patrols can focus on collecting performance information on the
distributed grants. Through conducting patrols focused on tracking grant performance the
ground commander can show the local populace the genuine care the military has in their
individual performance, while still collecting data that can increase the accuracy of the
application. As a result of data collection and an increasingly accurate application coupled with
patrols focused on tracking the performance of grants, a local economy can be improved and
boost the creation of new businesses. Each new data point entered into the applications database
amplifies the usefulness it can provide to a ground commander, as it can better determine the
optimal portfolio for an economy. Subsequently, as a grant based program guided by the
application gains momentum through updates, more individuals can be affected by these grants;
thus boosting current businesses as well as encourage the creation of new businesses.
FUTURE WORK
The current status of the application can provide a ground commander with a substantial
base of knowledge concerning the economic performance of their country of operation. As this
application is operationalized in current theaters it will increase in accuracy; resulting in greater
applicability through regionalization and continuously updated data. The application can be
operationalized through three phase: Initial Deployment; Collecting Local Data; and Updating
the Portfolio.
During the initial deployment phase the application will provide the ground commander
with a portfolio analysis based on the current Kiva data. This data pulls from microcredit
information, and although it is not regionalized, it provides a ground commander with significant
guidance on the performance of the economy and economic sector allocation. In the follow on
phase of collecting local data, current grant performance will be tracked by the unit operating in
the community. In doing so, the ground commander can ensure that the grants are being used
appropriately, portray a vested interest to the local populace through presence in the economy,
and provide additional data for the application. The means of accomplishing these objectives is
through the creation and implementation of a data collection method. This opens the door for
future work, in which a data collection method will be created. There are existing methods in
operation today, such as the data collection method used in Sri Lanka after the devastating
tsunami (De Mel, 2008).
This collection method was a required form that loan applicants had to complete prior to
reserving the financial aid from the MFI. It allowed the MFI’s in Sri Lanka to gain an insight at
depth of the individuals that requested loans, intended use of the requested loan, the success of
previous loans (if applicable), etc. With a similar means of collecting data, a ground commander
will be provided with a greater understanding of the individuals in the community, their needs,
possible areas of focus, and more. This collected data can then be inputted into a larger database
that is continuously updated. As this database grows in size with each additional input from
ground commanders across the globe, it can be coupled with the current microeconomic
information fueling the application to create a more accurate portfolio analysis. Additionally, as
this information is collected from lower level ground commanders the application can transfer
the current focus on country level to regional level. The end state is a portfolio analysis that
sources from data that relates to a ground commanders method of grant distribution at a
regionalized level.
REFERENCES
Adams, Greg. Personal Interview With Special Forces Team Leader . Harvard Business School.
20 Oct. 2013.
Angelucci, Manuela, Dean Karlan, and Jonathan Zinman. Win some lose some? Evidence from a
randomized microcredit program placement experiment by Compartamos Banco. No. w19119.
National Bureau of Economic Research, 2013.
Berman, Eli, et al. Modest, Secure and Informed: Successful Development in Conflict Zones.
No. w18674. National Bureau of Economic Research, 2013.
Colonel Clay, T.A. 2009. Commander’s Guide to Money as a Weapons System. Center for Army
Lessons Learned, 23-26.
Colonel Mains, Steven. PRT Playbook. Center for Army Lessons Learned, 61.
De Mel, S., Mckenzie, D., Woodruff, C. 2008. Returns to Capital in Microenterprises: Evidence
from a Field Experiment, The Quarterly Journal of Economics, Vol. CXXIII, Issue 4, 13351355.
Eversole, Robyn. Beyond microcredit—the trickle up program. Small Enterprise Development
11.1 (2000): 45-58.
International Security Assistance Force. 2013. Mission Statement: Afghanistan. NATO.
www.isaf.nato.int/mission.html
Lehman,Joyce. Personal Interview. United States Military Academy. 15 Nov. 2013
Luenberger, David G. Investment science. OUP Catalogue (1997).
Marsh, Patricia A. 2011. Management Improvements needed in Commander’s Emergency
Response program in Afghanistan, Inspector General United States Department of Defense, No.
DODIG-2012-023, 1-11.
Poole, Lydia. 2011. AFGHANISTAN: Tracking major resource flows, Conflict & the Military,
Ver. 1, 4-20.
Schmidt, M., et al. Micro grants as a stimulus for community action in residential health
programmes: a case study. Health promotion international 24.3 (2009): 234-242.
Walsh, Claire. Personal Interview. Joint Poverty Action Lab. 20 Oct. 2013.