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.