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entitled 'International Food Aid: Prepositioning Speeds Delivery of 
Emergency Aid, but Additional Monitoring of Time Frames and Costs Is 
Needed' which was released on March 5, 2014. 

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United States Government Accountability Office: 
GAO: 

Report to the Chairwoman, Committee on Agriculture, Nutrition, and 
Forestry, U.S. Senate: 

March 2014: 

International Food Aid: 

Prepositioning Speeds Delivery of Emergency Aid, but Additional 
Monitoring of Time Frames and Costs Is Needed: 

GAO-14-277: 

GAO Highlights: 

Highlights of GAO-14-277, a report to the Chairwoman, Committee on 
Agriculture, Nutrition, and Forestry, U.S. Senate. 

Why GAO Did This Study: 

Through Title II of the Food for Peace Act, the United States provides 
U.S. agricultural commodities to meet emergency food needs in foreign 
countries. In fiscal years 2007 to 2012, USAID delivered $9.2 billion 
in emergency food aid to recipient countries through cooperating 
sponsors. In 2000, Congress authorized USAID to order, transport, and 
store food for prepositioning in both overseas and domestic locations. 
Through prepositioning, the agency orders food before it is requested 
and stores it in warehouses in or near regions with historically high 
needs. 

GAO was asked to examine U.S. international food aid procurement. This 
report examines (1) the effects of prepositioning on emergency food 
aid delivery time frames, (2) the effects of prepositioning on the 
costs of the food aid, and (3) the extent to which the agency monitors 
prepositioning to maximize time savings and cost effectiveness. GAO 
analyzed data on delivery time frames and costs; reviewed agency 
documents; and interviewed agency officials and representatives from 
WFP, other cooperating sponsors, and ocean freight contractors. 

What GAO Found: 

The U.S. Agency for International Development (USAID) reduces the 
average delivery time frame for emergency food aid by prepositioning 
food domestically—that is, in warehouses in the United States—and 
overseas. GAO estimates that compared with USAID's standard shipping 
process, which can take several months, prepositioning food aid 
shortened delivery time frames by an average of almost a month for 
shipments to the World Food Program (WFP). GAO also estimates that 
prepositioning shortened delivery time frames by an average of more 
than 2 months for other organizations—“cooperating sponsors”—that 
receive USAID grants. In addition, USAID reduces delivery time frames 
when it diverts shipments en route to overseas prepositioning 
warehouses to areas with immediate needs. For all cooperating 
sponsors, GAO estimates that diversions saved, on average, about 2 
months. 

Figure: Illustration of Time Savings from USAID Prepositioning of 
Emergency Food Aid: 

[Refer to PDF for image: illustration] 

Depicts: 
Time elapsed after food aid request; 
Time savings from prepositioning process; 
Warehouse. 

Process: Standard shipping; 
USAID orders food: 
Food shipped overseas: 
Food arrives in discharge port/recipient country: 

Process: Overseas prepositioning; 
USAID orders food: 
Food shipped overseas: 
Food arrives in discharge port/recipient country: 

Process: Domestic prepositioning; 
USAID orders food: 
Food shipped overseas: 
Food arrives in discharge port/recipient country: 

Process: Diversions (redirection); 
USAID orders food: 
Food shipped overseas: 
Food arrives in discharge port/recipient country: 

Source: GAO analysis of U.S. Agency for International Development 
(USAID) data. 

Note: For illustrative purposes; not drawn to scale. 

[End of figure] 

Prepositioning food can increase the cost of emergency food aid 
because of additional warehouse, shipping, and commodity costs. For 
example, in fiscal year 2012, USAID paid approximately $8 million for 
its overseas and domestic prepositioning warehouses. USAID also paid 
$13 million to ship food by ocean freight from overseas prepositioning 
warehouses to recipient countries, in addition to the cost of shipping 
from the United States to the warehouses. Further, USAID generally 
paid higher weighted annual average prices for domestically 
prepositioned commodities than for standard shipment commodities. U.S. 
officials and vendors noted that factors such as limited commodity 
supplies and few participating suppliers may have contributed to 
higher prices. 

USAID has taken some steps to evaluate prepositioning, but the agency 
does not collect and analyze data needed to systematically monitor 
delivery time frames for prepositioned commodities. In addition, some 
available data are unreliable. Further, USAID does not systematically 
monitor the total cost of prepositioning. According to USAID policy 
and federal internal control standards, the agency should monitor its 
programs by collecting and analyzing data to guide higher-level 
decision making and allocate resources. Without such monitoring, USAID 
is limited in its ability to assess prepositioning's impact on 
delivery time frames and costs and to maximize emergency food aid's 
timeliness and cost effectiveness. 

What GAO Recommends: 

GAO recommends that USAID systematically collect, and ensure the 
reliability of, data for prepositioned food aid and systematically 
monitor and assess the effectiveness of food aid's delivery time 
frames and costs. USAID concurred with the recommendations and is 
working to improve both the collection of reliable data and its 
monitoring of prepositioning. 

View GAO-14-277. For more information, contact Thomas Melito at (202) 
512-9601 or MelitoT@gao.gov. 

[End of section] 

Contents: 

Letter: 

Background: 

USAID's Prepositioning of Emergency Food Aid Shortens Delivery Time 
Frames: 

Prepositioning Emergency Food Aid Involves Additional Costs: 

USAID Does Not Systematically Monitor Prepositioning to Maximize Time 
Savings and Cost Effectiveness: 

Conclusions: 

Recommendations for Executive Action: 

Agency Comments: 

Appendix I: Objectives, Scope, and Methodology: 

Appendix II: Delivery Time Frames Regression Analysis: 

Appendix III: Examples of Additional Ocean Shipping Costs for Overseas 
Prepositioning: 

Appendix IV: Analysis of Weighted Annual Average Prices for Domestic 
and Overseas Prepositioned Commodities: 

Appendix V: Related GAO Products about International Food Aid: 

Appendix VI: Comments from the U.S. Agency for International 
Development: 

Appendix VII: GAO Contact and Staff Acknowledgments: 

Tables: 

Table 1: Estimated Days Saved by USAID's Prepositioning of Emergency 
Food Aid for the World Food Program, Compared with Standard Shipping, 
Fiscal Years 2009-2012: 

Table 2: Estimated Days Saved by USAID's Prepositioning of Emergency 
Food Aid for Nine Cooperating Sponsors, Compared with Standard 
Shipping, Fiscal Years 2007-2012: 

Table 3: Estimated Days Saved by USAID's Diversion of Prepositioning 
of Emergency Food Aid Shipments, Compared with Standard Shipping, 
Fiscal Years 2007-2012: 

Table 4: Annual Targets and Total Expenditures for USAID Overseas 
Prepositioning Warehouses, Fiscal Years 2011 and 2012: 

Table 5: Percentages and Costs of Ocean Shipping of Emergency Food Aid 
from Overseas Prepositioning Warehouses, Fiscal Year 2012: 

Table 6: Numbers of Emergency Food Aid Shipments Represented in Data 
Included in GAO Analysis: 

Table 7: Numbers of Emergency Food Aid Shipments Represented in Data 
Excluded from GAO Analysis and the Reasons for Exclusion: 

Table 8: Average Delivery Time Frames (in Days) for Emergency Food Aid 
Shipments: 

Table 9: Estimated Days Saved by USAID's Prepositioning Emergency Food 
Aid for the World Food Program, Relative to Standard Shipping, Fiscal 
Years 2009-2012: 

Table 10: Estimated Days Saved by USAID's Prepositioning Emergency 
Food Aid for Nine Cooperating Sponsors, Relative to Standard Shipping, 
Fiscal Years 2007-2012: 

Table 11: Estimated Days Saved by USAID's Diversion of Prepositioning 
Emergency Food Aid Shipments, Relative to Standard Shipping, Fiscal 
Years 2007-2012: 

Table 12: Illustrative Examples of Additional Costs for Ocean Shipping 
from Djibouti in Fiscal Year 2012, per Metric Ton: 

Table 13: Numbers of Domestic Prepositioning and Standard Shipment 
Purchases for Six Key Commodities, Fiscal Years 2007-2012: 

Figures: 

Figure 1: USAID Processes for Delivering Emergency Food Aid: 

Figure 2: Percentage Differences between Weighted Annual Average 
Prices That USAID Paid for Six Domestically Prepositioned Commodities 
and Standard Shipment Commodities, Fiscal Years 2007-2012: 

Figure 3: Percentage Differences between Weighted Annual Average 
Prices for USAID's Domestically and Overseas Prepositioned Commodities 
Relative to Standard-Shipment Commodities, Fiscal Years 2007-2012: 

Abbreviations: 

Title II: Title II of the Food for Peace Act: 

UNICEF: United Nations Children's Fund: 

USAID: U.S. Agency for International Development: 

USDA: U.S. Department of Agriculture: 

WFP: United Nations World Food Program: 

[End of section] 

United States Government Accountability Office: 
GAO:
441 G St. N.W. 
Washington, DC 20548: 

March 5, 2014: 

The Honorable Debbie Stabenow: 
Chairwoman: 
Committee on Agriculture, Nutrition, and Forestry: 
United States Senate: 

Dear Madam Chairwoman: 

U.S. international emergency food aid assists nations affected by 
severe ongoing crises, such as the long-term food shortage in 
Ethiopia, as well as unanticipated crises, such as the 2010 earthquake 
in Haiti. In fiscal years 2007 to 2012, the United States donated 
about $9.2 billion in emergency food aid to recipient countries under 
Title II of the Food for Peace Act (Title II).[Footnote 1] The U.S. 
Agency for International Development's (USAID) Office of Food for 
Peace, which is responsible for managing the U.S. emergency food aid 
program, delivers this assistance through cooperating sponsors such as 
the World Food Program (WFP), Catholic Relief Services, and Save the 
Children.[Footnote 2] In 2005, to expedite the delivery of emergency 
food aid, USAID began to stockpile, or preposition, food commodities, 
such as lentils, peas, and vegetable oil, in or near regions of the 
world with historically high emergency food needs. From 2007 to 2012, 
USAID increased the tonnage of commodities that it ordered for 
prepositioning under Title II from 6 percent to 22 percent of all 
emergency food aid commodities. To address immediate needs, USAID can 
also redirect, or divert, commodities intended for prepositioning and 
ship them directly to areas in crisis. 

In 2000, Congress authorized USAID to order, transport, and store food 
for prepositioning in the United States and in foreign countries. 
[Footnote 3] Since 2008, Congress has authorized up to $10 million 
annually for storage of food in overseas prepositioning sites. 
[Footnote 4] The Farm Bill signed by the President in February 2014 
increased the authorization for overseas prepositioning storage costs 
to $15 million annually.[Footnote 5] For this report, you asked us to 
assess several issues related to U.S. international food aid 
procurement. This report examines the (1) effects of USAID's 
prepositioning on delivery time frames for emergency food aid 
shipments,[Footnote 6] (2) the effects of prepositioning on the costs 
of emergency food aid, and (3) the extent to which the agency monitors 
prepositioning to maximize time savings and cost effectiveness. 

To determine the effects of USAID's prepositioning on the delivery 
time frames and costs of emergency food aid, we analyzed data for 
3,785 emergency food aid shipments in fiscal years 2007 through 
2012.[Footnote 7] We used characteristics such as commodity type, 
month and year the commodity was requested, and discharge port to 
control for the differences between standard and prepositioned 
emergency food aid shipments. We conducted separate analyses, and 
present separate findings, on the effects of prepositioning for WFP 
and the other cooperating sponsors because of differences in time 
periods and shipment dates tracked.[Footnote 8] We also analyzed cost 
data related to emergency food shipments in fiscal years 2007 through 
2012. To determine the extent to which USAID has monitored 
prepositioning, we reviewed agency documents to assess whether USAID's 
monitoring was consistent with its policy and regulations. We 
interviewed officials of U.S. agencies, WFP and one other cooperating 
sponsor, and freight forwarders--contractors that manage the supply 
chain of emergency food aid for USAID and its cooperating sponsors--in 
Washington, D.C. Appendixes I though IV contain additional information 
on our scope and methodology. 

We conducted this performance audit from July 2013 to March 2014 in 
accordance with generally accepted government auditing standards. 
Those standards require that we plan and perform the audit to obtain 
sufficient, appropriate evidence to provide a reasonable basis for our 
findings and conclusions based on our audit objectives. We believe the 
evidence obtained provides a reasonable basis for our findings and 
conclusions based on our audit objectives. 

Background: 

To respond to food aid emergencies, USAID can provide emergency food 
aid commodities through four general delivery processes: standard 
shipping, overseas prepositioning, domestic prepositioning, or 
diversion (see figure 1).[Footnote 9] USAID determines the most 
appropriate of these four processes by analyzing the request for food 
aid, the nature of the emergency, and the availability of commodities 
and resources. USAID aims to maintain a combined total of up to 
100,000 metric tons of food in its prepositioning supply chain at any 
given time.[Footnote 10] USAID currently maintains prepositioning 
warehouses in six overseas locations (Colombo, Sri Lanka; Djibouti, 
Djibouti; Dubai, United Arab Emirates; Durban, South Africa; Las 
Palmas, Spain; and Mombasa, Kenya) and two domestic locations (Jacinto 
Port, Texas, and Miami, Florida).[Footnote 11] 

Figure 1: USAID Processes for Delivering Emergency Food Aid: 

[Refer to PDF for image: process illustration] 

Standard shipping: 
Once the cooperating sponsor requests the food, USAID orders and 
arranges to have the food transported to the U.S. port. The 
cooperating sponsor then ships the food to the foreign discharge port 
and transports it to the recipient country. 

Overseas prepositioning: 
Once the cooperating sponsor requests the food, the cooperating 
sponsor picks up the food at the overseas prepositioning warehouse and 
transports it to the recipient country. USAID has already ordered and 
arranged to have the food transported to the overseas prepositioning 
warehouse before the request. 
Overseas sites (6): 
* Colombo, Sri Lanka; 
* Djibouti, Djibouti; 
* Dubai, United Arab Emirates; 
* Durban, South Africa; 
* Las Palmas (Canary Islands), Spain; 
* Mombasa, Kenya. 

Domestic prepositioning: 
Once the cooperating sponsor requests the food, the cooperating 
sponsor picks up the food at the domestic prepositioning warehouse, 
ships the food to the foreign discharge port, and then transports it 
to the recipient country. USAID has already ordered and arranged to 
have the food transported to the domestic prepositioning warehouse 
before the request. 
Domestic sites (2): 
* Jacinto Port, Texas; 
* Miami, Florida. 

Diversion: 
Once the cooperating sponsor requests the food, USAID diverts the food 
originally intended for an overseas prepositioning warehouse to the 
foreign discharge port where the cooperating sponsor picks up the food 
and transports it to the recipient country. USAID has already ordered 
and arranged to have the food transported to the overseas 
prepositioning warehouse before the request. The cooperating sponsor's 
request for the food was submitted while the food was en route to an 
overseas prepositioning warehouse. 

Source: GAO analysis; Map Resources (map). 

Notes: The descriptions shown for each stage of the process are 
illustrative examples and do not reflect all emergency food aid 
shipments. For instance, diversions can occur at any point in the 
procurement, transportation, and shipping process. 

[End of figure] 

USAID's two domestic warehouses are in Jacinto Port, Texas, and Miami, 
Florida. 

USAID's six overseas warehouses are in Colombo, Sri Lanka; Dubai, 
United Arab Emirates; Durban, South Africa; Djibouti, Djibouti; Las 
Palmas (Canary Islands), Spain; and Mombasa, Kenya. Before 2013, USAID 
also had prepositioning warehouses in Lake Charles, Louisiana, and 
Lomé, Togo; the agency stopped using these warehouses for 
prepositioning in 2007 and 2012, respectively. 

In 2007, we reported that U.S. food aid delivery is generally too time 
consuming to be sufficiently responsive in emergencies, requiring 4 to 
6 months on average, including time required for procurement and 
transportation of the commodities.[Footnote 12] We recommended, among 
other things, that USAID conduct a cost-benefit analysis of 
prepositioning to improve its food aid logistical planning. In 2008, 
USAID commissioned a cost-benefit analysis of the U.S. government's 
food prepositioning activities. The commissioned analysis did not 
compare the timeliness of domestic and overseas prepositioning; 
however, it recommended that USAID consider increasing the amount of 
food aid prepositioned domestically, to improve its response times to 
critical emergency program needs.[Footnote 13] In 2013, a report by 
USAID's Inspector General found that the agency had not determined 
whether the benefits of overseas prepositioning in the Horn of Africa 
outweighed the costs or whether overseas prepositioning saved time in 
comparison with domestic prepositioning.[Footnote 14] 

USAID's Prepositioning of Emergency Food Aid Shortens Delivery Time 
Frames: 

Prepositioning of emergency food aid reduces the average delivery time 
frame for USAID's food aid shipments for WFP and other cooperating 
sponsors.[Footnote 15] For WFP, prepositioning food aid in overseas 
and domestic warehouses shortened delivery time frames by an average 
of almost a month. For nine of USAID's other cooperating sponsors, 
prepositioning food aid in domestic and overseas warehouses shortened 
delivery time frames by an average of more than 2 months.[Footnote 16] 
In addition, diversion of emergency food aid from the prepositioning 
process shortened delivery time frames by, on average, about 2 months 
for WFP and the other sponsors.[Footnote 17] 

For WFP, Prepositioning Emergency Food Aid Shortened Average Delivery 
Time Frame by Almost a Month: 

We estimated, using statistical modeling to control for various 
factors, that prepositioning food in overseas and domestic warehouses 
shortened the average delivery time frame for shipments for WFP by 28 
days in fiscal years 2009 through 2012 compared with USAID's standard 
shipping process. As table 1 shows, during this period, the 472 
prepositioned shipments for WFP had an average delivery time frame of 
about 102 days. In contrast, the 1,665 standard shipments for WFP had 
an average delivery time frame of 135 days.[Footnote 18] According to 
WFP officials, USAID's prepositioning of food in warehouses has helped 
WFP provide food aid more quickly. WFP officials also stated that 
because prepositioned food is available for immediate collection from 
warehouses, prepositioning helps ensure a sufficient food aid supply 
to meet spikes in demands due to unforeseen emergencies.[Footnote 19] 

Table 1: Estimated Days Saved by USAID's Prepositioning of Emergency 
Food Aid for the World Food Program, Compared with Standard Shipping, 
Fiscal Years 2009-2012: 

Standard shipping: 
Number of days saved (regression results): [Empty]; 
Margin of error at 95 percent confidence (days): [Empty]; 
Average delivery time frame (days): 135; 
Number of shipments: 1,665. 

All prepositioning: 
Number of days saved (regression results): 28[A]; 
Margin of error at 95 percent confidence (days): 15; 
Average delivery time frame (days): 102; 
Number of shipments: 472. 

Overseas prepositioning: 
Number of days saved (regression results): 41[A]; 
Margin of error at 95 percent confidence (days): 17; 
Average delivery time frame (days): 92; 
Number of shipments: 262. 

Domestic prepositioning: 
Number of days saved (regression results): 16[B]; 
Margin of error at 95 percent confidence (days): 17; 
Average delivery time frame (days): 114; 
Number of shipments: 210. 

Source: GAO analysis of World Food Program (WFP) data. 

Notes: 

For all prepositioning, overseas prepositioning, and domestic 
prepositioning, we used regression analysis to estimate the number of 
days saved, controlling for various factors such as commodity type and 
discharge port. As a result, the numbers of days saved do not equal 
the differences between average delivery time frames. For example, the 
number of days saved for all prepositioning is 28 days, but the 
difference between average delivery time frames for standard shipping 
and all prepositioning is 33 days. 

Delivery time frames begin with WFP and USAID's signing a grant 
agreement and end with the delivery of the food aid at the discharge 
port. 

Data provided by WFP do not distinguish diversions from other types of 
shipments. 

[A] The coefficient is statistically significant at the 0.01 level; 
that is, there is a 1 percent or lower chance that no relationship 
exists between prepositioning and delivery time frames. 

[B] The coefficient is statistically significant at the 0.10 level; 
that is, there is a 10 percent or lower chance that no relationship 
exists between prepositioning and delivery time frames. 

[End of table] 

Although both overseas and domestic prepositioning shortened delivery 
time frames for WFP, the estimated time savings were larger for 
overseas prepositioning. As table 1 shows, we estimated, using 
statistical modeling to control for various factors, that 
prepositioning food in overseas warehouses saved an average of 41 days 
compared with USAID's standard shipping process. We found that 
prepositioning food in domestic warehouses saved fewer days--an 
estimated average of 16.[Footnote 20] 

For Other Cooperating Sponsors, Prepositioning Food Aid Shortened 
Average Delivery Time Frame by More than 2 Months: 

We estimated, using statistical modeling to control for various 
factors, that prepositioning food aid commodities in overseas and 
domestic warehouses in fiscal years 2007 through 2012 saved an average 
of about 67 days for nine of USAID's cooperating sponsors, compared 
with USAID's standard shipping process.[Footnote 21] As table 2 shows, 
during this period, the average delivery time frame for the 141 
prepositioned shipments for these cooperating sponsors was about 87 
days. In contrast, the average delivery time frame for the 869 
standard shipments was 161 days.[Footnote 22] 

Table 2: Estimated Days Saved by USAID's Prepositioning of Emergency 
Food Aid for Nine Cooperating Sponsors, Compared with Standard 
Shipping, Fiscal Years 2007-2012: 

Standard shipping: 
Number of days saved (regression results): [Empty]; 
Margin of error at 95 percent confidence (days): [Empty]; 
Average delivery time frame (days): 161; 
Number of shipments: 869. 

All prepositioning: 
Number of days saved (regression results): 67[A]; 
Margin of error at 95 percent confidence (days): 9; 
Average delivery time frame (days): 87; 
Number of shipments: 141. 

Overseas prepositioning: 
Number of days saved (regression results): 73[A]; 
Margin of error at 95 percent confidence (days): 9; 
Average delivery time frame (days): 98; 
Number of shipments: 52. 

Domestic prepositioning: 
Number of days saved (regression results): 62[A]; 
Margin of error at 95 percent confidence (days): 13; 
Average delivery time frame (days): 80; 
Number of shipments: 89. 

Source: GAO analysis of freight forwarder data. 

Notes: 

For all prepositioning, overseas prepositioning, and domestic 
prepositioning, we used regression analysis to estimate the number of 
days saved, controlling for factors such as commodity type and 
discharge port. As a result, the numbers of days saved do not equal 
the differences between average delivery time frames. For example, the 
number of days saved for all prepositioning is 67 days, but the 
difference between the average delivery time frame for standard 
shipping and for all prepositioning is 74 days. 

The nine cooperating sponsors are CARE, Catholic Relief Services, the 
government of Pakistan, Norwegian People's Aid, Relief Society of 
Tigray, SHARE Guatemala, Save the Children, Save the Children--United 
Kingdom, and UNICEF. Data for these nine cooperating sponsors are 
collected and tracked by three freight forwarders. 

Delivery time frames begin with the cooperating sponsor's request for 
food aid and end with delivery of the requested food at the discharge 
port for 25 percent of the sponsors and end with delivery of the 
requested food to the recipient country for 75 percent of the sponsors. 

[A] The coefficient is statistically significant at the 0.01 level--
that is, there is a 1 percent or lower chance that no relationship 
exists between prepositioning and delivery time frames. 

[End of table] 

Both overseas and domestic prepositioning resulted in time savings, 
with larger savings from overseas prepositioning. As table 2 shows, 
after controlling for various factors, we estimated that 
prepositioning food for these cooperative sponsors in overseas 
warehouses saved an average of 73 days relative to USAID's standard 
shipping process. We also estimated that, relative to the standard 
shipping process, prepositioning food in domestic warehouses saved 
USAID an average of 62 days. [Footnote 23] 

While prepositioning of food aid shortens delivery time frames, it has 
some disadvantages, according to one cooperating sponsor's 
representative whom we interviewed. For example, the representative 
noted that although overseas prepositioning makes commodities 
available for immediate collection and thus saves time, handling of 
prepositioned commodities by multiple parties can lead to losses due 
to damage during the transit from prepositioning warehouse to 
discharge port. In addition, prepositioned commodities may be 
comingled in the warehouse, making it difficult to identify 
infestation problems in a particular shipment of commodities. The 
cooperating sponsor's representative further noted that prepositioned 
commodities may have to be fumigated several times if they remain in 
the warehouse for months; if the commodities are overexposed to the 
fumigation chemicals, they can no longer be used for food aid. 
[Footnote 24] 

Diversion of Food Aid Intended for Prepositioning Shortened Delivery 
Time Frames by About 2 Months: 

We estimated, using statistical modeling to control for various 
factors, that diversion of emergency food aid from the prepositioning 
process shortened the average delivery time frame by about 64 days in 
fiscal years 2007 through 2012 compared with USAID's standard shipping 
process (see table 3). During this period, the average delivery time 
frame for the 568 diverted shipments was about 76 days.[Footnote 25] 
In contrast, the average delivery time frame for the 938 standard 
shipments was 156 days.[Footnote 26] 

Table 3: Estimated Days Saved by USAID's Diversion of Prepositioning 
of Emergency Food Aid Shipments, Compared with Standard Shipping, 
Fiscal Years 2007-2012: 

Standard shipping: 
Number of days saved (regression results): [Empty]; 
Margin of error at 95 percent confidence (days): [Empty]; 
Average delivery time frame (days): 156; 
Number of shipments: 938. 

Diversion[A]: 
Number of days saved (regression results): 64[B]; 
Margin of error at 95 percent confidence (days): 22; 
Average delivery time frame (days): 76[C]; 
Number of shipments: 568. 

Source: GAO analysis of freight forwarders' data. 

Notes: 

We used regression analysis to estimate the number of days saved, 
controlling for various factors such as commodity type and discharge 
port. As a result, the number of days saved does not equal the 
difference between average delivery time frames. 

Delivery time frames begin with the cooperating sponsor's request for 
food aid and end with delivery of the requested food at the discharge 
port for some shipments and with delivery of the requested food to the 
recipient country for other shipments. 

[A] The data shown for diversions were provided primarily by one 
freight forwarder that handles all USAID diversions of emergency food 
aid. The data include diversions of shipments for WFP and eight other 
cooperating sponsors. 

[B] The coefficient is statistically significant at the 0.01 level--
that is, there is a 1 percent or lower chance that no relationship 
exists between prepositioning and delivery time frames. 

[C] This average is calculated in reference to the regression analysis. 

[End of table] 

Side bar: 

2013 Philippines Typhoon: 
In November 2013, the Typhoon Haiyan affected 16 million people in the 
Philippines. To help meet immediate needs in the typhoon's aftermath, 
USAID diverted 5,000 metric tons of rice that it had purchased for its 
Colombo prepositioning warehouse and provided 1,020 metric tons of 
rice from the Colombo warehouse. Additionally, USAID airlifted 
approximately 55 metric tons of emergency food aid commodities from a 
domestic prepositioning warehouse in Miami, Florida. These 
commodities--nutrition-dense food bars and other items that did not 
require cooking--were sufficient to feed 15,000 adults and 20,000 
children for 5 days. 

[End of side bar] 

According to USAID officials, prepositioning of emergency food aid 
allows for greater flexibility to divert food assistance when 
necessary to meet immediate needs. The officials further stated that, 
because the U.S. government retains ownership of prepositioned food 
aid, USAID does not have to replace diverted commodities. In contrast, 
when food aid is shipped under the standard process, non-governmental 
cooperating sponsors usually take ownership of commodities before they 
are shipped, and depending on the availability of commodities and 
resources, USAID may decide to replace diverted shipments. 

USAID has diverted commodities before and after they left the U.S. 
port and at all points in the procurement, transportation, or shipping 
process.[Footnote 27] Our analysis shows that in fiscal years 2007 
through 2012, an average of 80 days elapsed between USAID's ordering 
commodities for prepositioning and diverting the commodities. The 
smallest number of days between USAID's ordering and diverting 
commodities was 6 days, and the largest number of days was 163. Our 
analysis also shows that larger number of days between USAID's 
ordering and diverting commodities are associated with shorter 
delivery time frames. 

Prepositioning Emergency Food Aid Involves Additional Costs: 

USAID pays additional costs for prepositioning of emergency food aid, 
compared with standard shipping. For both overseas and domestic 
prepositioning, USAID incurs additional warehouse costs that it does 
not incur for standard shipments. USAID also incurs additional 
shipping costs due to a second leg of ocean shipping from overseas 
prepositioning warehouses to foreign discharge ports. Furthermore, 
USAID often pays higher weighted annual average prices for 
domestically prepositioned commodities than it does for standard 
shipment commodities.[Footnote 28] 

USAID Incurs Additional Costs for Prepositioning Warehouses: 

USAID incurs various additional costs to store prepositioned food in 
overseas and domestic prepositioning warehouses, depending on the 
quantity of food stored and the duration of the storage. In contrast, 
for standard shipments of emergency food aid, USAID transports the 
food directly from U.S. ports to recipient countries. The cost per day 
for food storage at USAID's six overseas prepositioning warehouses 
averaged $0.25 per ton in fiscal years 2011 and 2012;[Footnote 29] the 
longer food is stored at the prepositioning warehouses, the higher the 
storage costs. 

In fiscal years 2011 and 2012, USAID expended approximately half of 
the annual $10 million authorized by the fiscal year 2008 Farm Bill 
for overseas warehouse costs.[Footnote 30] Warehouse costs include, in 
addition to food storage, storage of nonfood items (e.g., bags, 
pallets, and cartons) and payment of warehouse operators for handling 
commodities (e.g., unloading trailers and loading bulk commodities). 
To help ensure that its total costs for overseas prepositioning 
warehouses do not exceed the ceiling authorized by Congress, USAID 
sets annual cost targets for each warehouse that total $8.5 million. 
In fiscal year 2011, USAID expended approximately $5.4 million--about 
54 percent of its annual ceiling--for food storage and other costs for 
its overseas prepositioning warehouses. In fiscal year 2012, USAID 
expended about $4.6 million--about 46 percent of its annual ceiling. 
Table 4 shows USAID's annual targets and total expenditures for its 
overseas prepositioning warehouses in fiscal years 2011 and 2012. 

Table 4: Annual Targets and Total Expenditures for USAID Overseas 
Prepositioning Warehouses, Fiscal Years 2011 and 2012: 

Colombo: 
Annual target[A]: $1,000,000; 
Fiscal year 2011 expenditures: $15,282; 
Fiscal year 2012 expenditures: $459,996. 

Djibouti: 
Annual target[A]: 2,500,000; 
Fiscal year 2011 expenditures: 2,475,208; 
Fiscal year 2012 expenditures: 1,381,622. 

Durban: 
Annual target[A]: 2,000,000; 
Fiscal year 2011 expenditures: 2,783,236[B]; 
Fiscal year 2012 expenditures: 1,236,265. 

Las Palmas: 
Annual target[A]: 1,000,000; 
Fiscal year 2011 expenditures: [Empty]; 
Fiscal year 2012 expenditures: 184,508. 

Lomé: 
Annual target[A]: 1,000,000; 
Fiscal year 2011 expenditures: 105,975; 
Fiscal year 2012 expenditures: 81,272. 

Mombasa: 
Annual target[A]: 1,000,000; 
Fiscal year 2011 expenditures: 1,041; 
Fiscal year 2012 expenditures: 1,213,364[C]. 

Total: 
Annual target[A]: $8,500,000; 
Fiscal year 2011 expenditures: $5,380,743; 
Fiscal year 2012 expenditures: $4,557,026. 

Source: USAID. 

[A] To help ensure that its total expenditures for overseas 
prepositioning warehouses did not exceed the $10 million authorized by 
the fiscal year 2008 Farm Bill, USAID sets annual cost ceilings for 
each warehouse. 

[B] Total expenditures for the Durban warehouse exceeded USAID's 
annual target in fiscal year 2011. According to USAID, it used the 
Durban warehouse more than expected because of demand for emergency 
food aid from Zimbabwe and Somalia. However, USAID stayed within the 
overall annual ceiling of $10 million for 2011. 

[C] USAID's expenditures at the Mombasa warehouse exceeded its annual 
target in fiscal year 2012. According to USAID, it used the Mombasa 
warehouse more than expected because of demand for emergency food aid 
from Somalia. However, USAID's total overseas warehouse expenditures 
in fiscal year 2012 did not exceed the 2008 Farm Bill's annual ceiling 
of $10 million. 

[End of table] 

In addition, USAID expended $2.0 million in 2011 and $3.1 million in 
fiscal year 2012 for its domestic prepositioning warehouse in Jacinto 
Port. However, these expenditures did not count against the $10 
million ceiling set by the 2008 Farm Bill. 

USAID Incurs Additional Shipping Costs for Overseas Prepositioning: 

USAID incurs additional shipping costs when overseas prepositioning 
requires a second leg of ocean shipping, from the overseas 
prepositioning warehouses to the final discharge ports. Although the 
first leg of ocean shipping--from a U.S. port to the overseas 
warehouse--is comparable to ocean transport for standard shipping, the 
second leg does not exist in the standard shipping process.[Footnote 
31] For example, a prepositioned shipment might undergo a first leg of 
ocean shipping to the Djibouti warehouse and a second leg of ocean 
shipping from Djibouti to Beira, Mozambique; in contrast, a standard 
shipment would travel directly from a U.S. port to Beira. As table 5 
shows, in fiscal year 2012, 75 percent of shipments from overseas 
prepositioning warehouses--including 100 percent of shipments from 
Colombo, Las Palmas, and Lomé--traveled via ocean freight and thus 
involved costs for the second leg of ocean shipping. In fiscal year 
2012, USAID paid a total of $13 million, averaging $143 per metric 
ton, for the second leg of ocean shipping for overseas prepositioned 
food aid.[Footnote 32] 

Table 5: Percentages and Costs of Ocean Shipping of Emergency Food Aid 
from Overseas Prepositioning Warehouses, Fiscal Year 2012: 

Warehouse: Colombo; 
Total metric tons shipped out: 12,710; 
Metric tons shipped by ocean freight: 12,710; 
Percentage shipped by ocean freight: 100%; 
Cost of ocean shipping: $6,456,255. 

Warehouse: Djibouti; 
Total metric tons shipped out: 45,795; 
Metric tons shipped by ocean freight: 26,027; 
Percentage shipped by ocean freight: 57%; 
Cost of ocean shipping: $2,159,507. 

Warehouse: Durban; 
Total metric tons shipped out: 39,836; 
Metric tons shipped by ocean freight: 34,616; 
Percentage shipped by ocean freight: 87%; 
Cost of ocean shipping: $2,876,716. 

Warehouse: Mombasa; 
Total metric tons shipped out: 15,850; 
Metric tons shipped by ocean freight: 9,450; 
Percentage shipped by ocean freight: 60%; 
Cost of ocean shipping: $653,917. 

Warehouse: Las Palmas; 
Total metric tons shipped out: 9,300; 
Metric tons shipped by ocean freight: 9,300; 
Percentage shipped by ocean freight: 100%; 
Cost of ocean shipping: $1,062,216. 

Warehouse: Lomé; 
Total metric tons shipped out: 1,673; 
Metric tons shipped by ocean freight: 1,673; 
Percentage shipped by ocean freight: 100%; 
Cost of ocean shipping: $241,769. 

Warehouse: Total; 
Total metric tons shipped out: 125,164; 
Metric tons shipped by ocean freight: 93,776; 
Percentage shipped by ocean freight: 75%; 
Cost of ocean shipping: $13,450,380. 

Source: GAO analysis of USAID procurement and shipping data. 

[End of table] 

USAID Generally Paid Higher Prices for Domestically Prepositioned 
Commodities Than for Standard Shipment Commodities: 

In fiscal years 2007 to 2012, USAID generally paid higher weighted 
annual average prices for commodities that it purchased for domestic 
prepositioning than for similar commodities that it purchased for 
standard shipping. Six commodities--corn-soy blend, cornmeal, pinto 
beans, vegetable oil, yellow split peas, and sorghum--accounted for 76 
percent of USAID's domestic prepositioning purchases in those years. 
Figure 2 shows the percentage differences between the weighted annual 
average prices that USAID paid per ton for domestically prepositioned 
and standard shipment commodities in fiscal years 2007 through 2012. 
In figure 2, the bars above the zero line show that USAID paid a 
higher weighted annual average price for domestically prepositioned 
commodities in 24 instances, and the bars below the zero line show 
that USAID paid a lower weighted annual average price in 8 instances. 
USAID generally did not pay higher weighted annual average prices for 
overseas prepositioning of these commodities during this period. (See 
appendix IV for more information.) 

Figure 2: Percentage Differences between Weighted Annual Average 
Prices That USAID Paid for Six Domestically Prepositioned Commodities 
and Standard Shipment Commodities, Fiscal Years 2007-2012: 

[Refer to PDF for image: vertical bar graph] 

Year: 2007; 
Corn-soy blend bag-HP-25 kg: 13%; 
Cornmeal bag-HP-25 kg: -8%; 
Beans, pinto bag-50 kg: 3%; 
Oil, vegetable can-6/4 liter: 14%; 
Peas, yellow, split bag-50 kg: -3%; 
Sorghum bag-50 kg (domestic): 5%. 

Year: 2008; 
Corn-soy blend bag-HP-25 kg: 20%; 
Cornmeal bag-HP-25 kg: 0.2%; 
Beans, pinto bag-50 kg: -5%; 
Oil, vegetable can-6/4 liter: 10%; 
Peas, yellow, split bag-50 kg: 35%; 
Sorghum bag-50 kg (domestic): -9%. 

Year: 2009; 
Corn-soy blend bag-HP-25 kg: 4%; 
Cornmeal bag-HP-25 kg: -4%; 
Beans, pinto bag-50 kg: 4%; 
Oil, vegetable can-6/4 liter: 8%; 
Peas, yellow, split bag-50 kg: 2%; 
Sorghum bag-50 kg (domestic): 3%. 

Year: 2010; 
Corn-soy blend bag-HP-25 kg: 5%; 
Cornmeal bag-HP-25 kg: not purchased; 
Beans, pinto bag-50 kg: 31%; 
Oil, vegetable can-6/4 liter: 17%; 
Peas, yellow, split bag-50 kg: 34%; 
Sorghum bag-50 kg (domestic): 22%. 

Year: 2011; 
Corn-soy blend bag-HP-25 kg: 12%; 
Cornmeal bag-HP-25 kg: not purchased; 
Beans, pinto bag-50 kg: 18%; 
Oil, vegetable can-6/4 liter: 3%; 
Peas, yellow, split bag-50 kg: -7%; 
Sorghum bag-50 kg (domestic): 6%. 

Year: 2012; 
Corn-soy blend bag-HP-25 kg: 0.3%; 
Cornmeal bag-HP-25 kg: not purchased; 
Beans, pinto bag-50 kg: not purchased; 
Oil, vegetable can-6/4 liter: 15%; 
Peas, yellow, split bag-50 kg: 7%; 
Sorghum bag-50 kg (domestic): -6%. 

Source: GAO analysis of USAID data. 

Note: The graph shows the percentage differences between the weighted 
annual average price per ton for a given domestically prepositioned 
commodity and the weighted annual average price per ton for the same 
commodity procured for standard shipment in the same year. If the 
percentage difference is greater than 0, the weighted annual average 
price for the domestically prepositioned commodity was higher than the 
price for the standard shipment commodity. If the percentage 
difference is less than 0, the weighted annual average price for the 
domestically prepositioned commodity was lower than the price for the 
standard shipment commodity. 

HP = high performance bags. 

[End of figure] 

Many factors may have contributed to the higher weighted annual 
average prices paid for domestically prepositioned commodities. For 
example, limited commodity supply and limited numbers of suppliers for 
domestic prepositioning purchases are two possible factors, according 
to U.S. officials and commodity vendors whom we interviewed.[Footnote 
33] First, if USAID purchases commodities for domestic prepositioning 
after commodity vendors have fulfilled their monthly orders for 
standard shipment and overseas prepositioning, vendors may have a 
reduced supply of commodities available for domestic prepositioning 
and may therefore charge higher prices. Second, because domestically 
prepositioned commodities--unlike standard shipment and overseas 
prepositioned commodities--are delivered to only two preselected U.S. 
ports, vendors for some commodities may be unwilling to compete with 
vendors who are geographically closer to the port and who thus can 
deliver the commodities to the port more cheaply. 

USAID Does Not Systematically Monitor Prepositioning to Maximize Time 
Savings and Cost Effectiveness: 

USAID has taken some steps to evaluate timeliness and costs associated 
with prepositioning. However, the agency does not collect and assess 
data needed to systematically monitor delivery time frames for 
prepositioned commodities to maximize time savings. Further, USAID 
does not systematically monitor the costs of prepositioning to 
maximize the program's cost-effectiveness. According to USAID 
guidance, the agency should monitor programs through various 
activities, including collecting and analyzing data to make necessary 
program adjustments and to guide higher-level decision making and 
resource allocation. Additionally, federal internal control standards 
indicate that monitoring should assess the quality of performance over 
time and ensure that the findings of these assessments are promptly 
resolved.[Footnote 34] 

USAID Has Taken Some Steps to Evaluate Prepositioning: 

USAID has taken some steps to evaluate prepositioning of emergency 
food aid by examining timeliness and costs. For example, in 2008, 
USAID commissioned a cost-benefit analysis of prepositioning.[Footnote 
35] The analysis found that prepositioning food aid reduced delivery 
time frames of emergency shipments from an average of 177 days for 
standard shipments of food aid to an average of 26 days. The analysis 
also found that the average cost for domestically prepositioned food 
aid amounted to an additional $23 per metric ton, and the average cost 
for overseas prepositioned food aid amounted to an additional $164 per 
metric ton. However, the methodology for the 2008 analysis had several 
limitations. First, the analysis did not include data on delivery time 
frames for WFP, USAID's largest cooperating sponsor for emergency food 
aid. Second, the analysis included data on delivery time frames up to 
the date when the shipments were discharged at the foreign ports but 
did not include any data up to the date when the shipments arrived in 
the recipient countries. Third, the evaluation did not compare 
shipments of prepositioned food with standard shipments that had 
similar characteristics, such as the recipient country. Because 
differences in such characteristics can contribute to differences in 
delivery time frames, the evaluation may not have accurately isolated 
the effects of prepositioning. 

In 2009, USAID also developed a framework for prepositioning that 
outlines the purpose of the program. However, the framework is not up-
to-date and does not guide current prepositioning practices, according 
to USAID officials. Additionally, the framework does not outline 
guidelines for evaluations on timeliness or costs. 

In 2013, USAID's Inspector General found that the agency had not 
determined whether the benefits of overseas prepositioning in the Horn 
of Africa outweighed the costs and whether overseas prepositioning 
saved time compared with domestic prepositioning. The report 
recommended, among other things, that USAID conduct another 
independent evaluation of the cost and timeliness of 
prepositioning.[Footnote 36] In November 2013, USAID released a 
solicitation for a third-party vendor to conduct an independent 
evaluation of prepositioning; the contract was awarded in January 
2014. According to USAID, the evaluation will be finalized in March 
2014 and will include an analysis of the cost, benefits, and 
effectiveness of prepositioning.[Footnote 37] 

USAID Does Not Collect and Assess Data Needed to Systematically 
Monitor Delivery Time Frames for Prepositioned Commodities: 

Although USAID's goal for prepositioning is to improve the timeliness 
of emergency food aid, USAID does not collect or assess data needed to 
systematically monitor delivery time frames for prepositioned 
emergency food aid shipments. According to guidance in USAID's 
Automated Directives System, the agency should monitor programs 
through various activities, including collecting and analyzing data to 
make necessary program adjustments and to guide higher-level decision 
making.[Footnote 38] Moreover, USAID's evaluation policy states that 
programs can best manage for results by collecting and analyzing 
information to track progress toward planned results and by ensuring 
that implementing partners collect relevant monitoring data. The 
policy also states that monitoring can reveal whether desired results 
are occurring and whether implementation is on track.[Footnote 39] 

USAID officials told us that the agency would be able to calculate 
delivery time frames of prepositioned food only if its cooperating 
sponsors provided data on all emergency food aid shipments. According 
to the officials, these sponsors are responsible for tracking food 
shipments once the shipments leave U.S. loading ports. However, 
according to USAID, the terms of its agreements with its cooperating 
sponsors do not require these sponsors to collect or provide 
comprehensive data to USAID. As a result, USAID lacks the ability to 
determine whether prepositioning is achieving its primary goal of 
shortening response times or to identify possible time savings 
relative to standard emergency food aid shipments. 

In addition, some emergency food aid data that are currently available 
from WFP and USAID's other cooperating sponsors have limitations that 
constrain their usefulness for monitoring delivery time frames. We 
collected data on emergency food aid shipments, including 
prepositioned shipments, from 19 cooperating sponsors; however, 
because of the following limitations, we did not include some of these 
data in our analysis of delivery time frames. 

* Limitations in WFP data. We did not use data provided by WFP for our 
analysis of diversion's effect on delivery time frames, because WFP's 
data on emergency food aid shipments do not distinguish diversions 
from other standard or prepositioned shipments. WFP also was unable to 
provide data for years before 2009 because of changes in its data 
management systems. As a result, we used data provided by a freight 
forwarder for our analysis of WFP diversions. 

* Limitations in some data from several other cooperating sponsors. 
Some data from five cooperating sponsors that received emergency food 
aid in fiscal years 2007 through 2012 do not distinguish emergency 
food aid shipments from other types of food aid shipments. In 
addition, some data for these five cooperating sponsors and three 
others were incomplete, missing some shipments and providing only 
partial information for others. As a result, we were unable to use 
some of the data from these eight sponsors. 

USAID Does Not Systematically Monitor the Total Cost of Prepositioning 
to Maximize Cost Effectiveness: 

Although USAID collects data on the cost of its international food aid 
programs, it does not use these data to systematically monitor the 
total cost of prepositioning in order to maximize the program's cost 
effectiveness. According to USAID's Automated Directives System, the 
agency should collect and analyze data to make necessary program 
adjustments and to guide higher-level decision making and resource 
allocation.[Footnote 40] Further, federal internal controls indicate 
that monitoring should assess the quality of performance over time and 
ensure that the findings of these assessments are promptly resolved. 
USAID collects data on the total costs of its overseas prepositioning 
warehouses to ensure that the costs do not exceed the limit 
established by Congress.[Footnote 41] However, USAID does not analyze 
this information to determine whether the locations of these 
warehouses are cost effective. Further, the agency does not 
systematically or routinely monitor and analyze prepositioning 
commodity and shipping costs to determine the cost-effectiveness of 
prepositioning.[Footnote 42] 

USAID collects data on prepositioning commodity and shipping costs, 
according to agency officials. However, USAID does not store these 
data in a single system or spreadsheet, where they would be easily 
accessible for monitoring purposes. Instead, according to USAID, 
various offices collect these data and store them in a number of 
different systems or spreadsheets.[Footnote 43] In addition, agency 
officials told us that because prepositioning is not a separate 
program but is part of USAID's emergency food aid program, the agency 
does not monitor prepositioning costs separately. These officials also 
stated that the agency does not monitor prepositioning's total costs, 
because the primary goal of prepositioning is rapid response rather 
than financial savings. 

As a result of the lack of monitoring of prepositioning costs, USAID 
does not conduct analyses that could help it manage its resources and 
improve the program's cost-effectiveness. For example, the agency does 
not know the causes of the commodity price differences that we 
identified and therefore is limited in its ability to ensure that the 
procurement of prepositioned commodities is conducted in a cost-
effective manner. 

Conclusions: 

The emergency food aid that USAID provides helps to save lives in 
countries where ongoing or unanticipated crises have severely 
disrupted food supplies. Our analysis of prepositioning showed that 
this approach can have a significant effect on the United States' and 
its cooperating sponsors' ability to deliver food quickly in response 
to humanitarian emergencies. However, without reliable data on the 
delivery time frames of prepositioned shipments, USAID lacks 
management tools that could help it assess and improve 
prepositioning's effect on emergency response times. Such data could 
also help USAID assess the tradeoffs between prepositioning's 
timeliness and its additional warehouse, shipping, and commodity 
costs. Furthermore, although cost savings are not USAID's primary goal 
for the prepositioning program, without systematic monitoring of the 
total cost of prepositioning the agency has limited ability to 
maximize the resources available for addressing emergency food crises. 

Recommendations for Executive Action: 

To strengthen USAID's ability to help ensure that its food aid 
prepositioning program meets the goal of reducing delivery time frames 
in a cost-effective manner, we recommend that the USAID Administrator 
take the following three steps to systematically: 

1. collect, and ensure the reliability and validity of, data on 
delivery time frames for all emergency food aid shipments, including 
prepositioned food aid shipments; 

2. monitor and assess data on delivery time frames for prepositioned 
food aid shipments; and: 

3. monitor and assess costs associated with commodity procurement, 
shipping, and storage for prepositioned food aid shipments. 

Agency Comments: 

We provided a draft of this report for comment to USAID. 

USAID provided written comments on a draft to this report, which we 
reprinted in appendix VI. In its comments, USAID concurred with our 
recommendations. In addition, USAID stated that it is working to 
identify actions needed to ensure the collection of reliable data on 
delivery time frames for all emergency food aid. USAID also stated 
that it is revising its prepositioning program strategy to provide a 
framework for monitoring and assessing data on the timeliness and cost 
effectiveness of the prepositioning program. 

In addition to providing copies of this report to your offices, we 
will send copies to interested congressional committees. We will make 
copies available to others on request. In addition, the report will be 
available at no charge on the GAO Web site at [hyperlink, 
http://www.gao.gov]. 

If you or your staff have any questions regarding this report, please 
contact me at (202) 512-9601 or MelitoT@gao.gov. Contact points for 
our Offices of Congressional Relations and Public Affairs may be found 
on the last page of this report. GAO staff who made key contributions 
to this report are listed in appendix VI. 

Sincerely yours, 

Signed by: 

Thomas Melito: 
Director, International Affairs and Trade: 

[End of section] 

Appendix I: Objectives, Scope, and Methodology: 

We examined (1) the effects of USAID's prepositioning on delivery time 
frames for emergency food aid shipments, (2) the effects of 
prepositioning on the costs of emergency food aid, and (3) the extent 
to which the agency monitors prepositioning to manage program 
resources effectively. 

To examine the effects of prepositioning on delivery time frames, we 
collected shipment-level data from the World Food Program (WFP) and 
six freight forwarders for 19 other cooperating sponsors.[Footnote 44] 
We assessed the reliability of these data by asking WFP and the six 
freight forwarders how the data were collected, what quality checks 
were performed, and what other internal controls were in place. We 
determined that the data were sufficiently reliable for tracking the 
delivery time frames of emergency food aid for WFP and three of the 
freight forwarders that manage the food aid supply chain for nine 
cooperating sponsors. For the remaining three freight forwarders, the 
data were not sufficiently reliable for tracking delivery time frames 
because of a number of limitations, including incomplete or missing 
data and, in one freight forwarder's data, a lack of distinction 
between emergency and nonemergency shipments. 

The data that we included in our analysis of the effects of 
prepositioning on delivery time frames include the date when the 
cooperating sponsor requested the food (or the closest approximation) 
[Footnote 45] and the date when the food arrived at the discharge port 
or in the recipient country.[Footnote 46] In addition, the data 
indicate whether the shipment was from prepositioning. The data also 
include other shipment characteristics--for example, the loading port, 
the discharge port, and the commodity shipped--and indicate whether 
the shipment was an emergency or nonemergency shipment and whether it 
was bulk or packaged. 

We separately discuss our analysis of data provided by WFP and the 
other cooperating sponsors' three freight forwarders because WFP's and 
these freight forwarders' data differ in the time periods covered and 
their treatment of food aid diversions. 

* Time periods covered. Data provided by WFP cover shipments in fiscal 
years 2009 through 2012, while data provided by the three freight 
forwarders cover shipments in fiscal years 2007 through 2012. 

* Treatment of food aid diversions. Data provided by WFP do not 
distinguish shipments that USAID diverted from the prepositioning 
process from shipments that it prepositioned, while data provided by 
the freight forwarders do identify diversions. Despite this 
difference, our analysis suggests that using WFP's data to estimate 
the number of days saved from prepositioning would not have affected 
our estimates significantly. 

To analyze delivery time frames, we calculated the number of days 
between a cooperating sponsor's request for the commodities and the 
commodities' arrival at the discharge port or in the recipient 
country. To compare delivery time frames for overseas and domestic 
prepositioning with those for standard shipping for WFP and for other 
cooperating sponsors, we estimated ordinary least squares regression 
models to control for characteristics of prepositioning and standard 
shipments that would allow us to isolate the effect of prepositioning. 
[Footnote 47] 

To examine the effects of USAID's diversions of emergency food aid 
shipments on delivery time frames, we used data primarily from the 
freight forwarder responsible for tracking all diverted shipments for 
World Food Program (WFP) and for eight other cooperating sponsors. 
[Footnote 48] This freight forwarder's data include all diverted food 
aid shipments from fiscal years 2007 through 2012. For the comparison 
group of standard shipments, we used data from this freight forwarder 
in addition to data from two other freight forwarders. We determined 
that these data were sufficiently reliable for tracking delivery time 
frames. Like the data that we included in our analysis for 
prepositioning, the diversions data are at the shipment level and 
include the date when WFP and other cooperating sponsors requested the 
food (or the closest approximation)[Footnote 49] and the date when the 
food arrived at the discharge port or in the recipient country. In 
addition, the data indicate whether the shipment was diverted. The 
data also include other shipment characteristics--for example, the 
loading port, the discharge port, and the commodity shipped--and 
indicate whether the shipment was bulk or packaged and was an 
emergency or nonemergency shipment. GAO analysts collected the dates 
USAID authorized food aid diversions by reviewing one freight 
forwarder's shipment documents.[Footnote 50] 

To analyze additional costs associated with prepositioning, we looked 
at three types of cost that are unique to prepositioned food: costs 
for prepositioning warehouses, costs for ocean shipping from overseas 
prepositioning warehouses to discharge ports, and costs for 
procurement for prepositioning commodities. 

* Warehouse costs. To analyze prepositioning warehouse costs, we 
obtained warehouse contracts from USAID. Each contract identifies the 
annual expenditure target that USAID had established for the warehouse 
as well as the daily storage charge per metric ton and outlines other 
charges related to handling the commodities. 

* Ocean shipping costs. We estimated the additional cost due to 
prepositioning using the actual cost of the second leg of ocean 
shipping, because the data required to re-construct the hypothetical 
cost of direct shipping for every prepositioned shipment were not 
available to us. Although the first leg of ocean shipping--from a U.S. 
port to the overseas warehouse--is comparable to ocean transport for 
standard shipping, the second leg does not exist in the standard 
shipping process.[Footnote 51] We obtained data from USAID that track 
the mode of transportation of shipments from each of the overseas 
warehouses, the metric tonnage, and the freight cost. (See appendix 
III for examples of the additional ocean transportation costs.) 

* Commodity procurement costs. To analyze prepositioning commodity 
procurement costs, we focused on six commodities that accounted for 76 
percent of total prepositioning purchases. We compared the weighted 
annual average prices of commodities purchased for domestic 
prepositioning, overseas prepositioning, and standard shipping. To 
understand the possible reasons for the observed differences in prices 
for prepositioned commodities, we interviewed U.S. Department of 
Agriculture (USDA) officials as well as commodity vendors. (See 
appendix IV for the results of the price comparison.) 

To examine the extent to which USAID monitors prepositioning to 
maximize time savings and cost effectiveness, we reviewed USAID 
documentation related to prior, current, and future efforts to monitor 
its prepositioning program, specifically efforts to monitor the 
delivery time frames and program costs. We also interviewed USAID 
officials in Washington, D.C., to discuss the extent to which the 
agency has taken steps to monitor the program. We also interviewed 
WFP, one other cooperating sponsor, and freight forwarders in 
Washington, D.C. We reviewed evaluations and reports on USAID's 
prepositioning, such as a 2008 evaluation of the program and a 2013 
USAID Inspector General report on prepositioning.[Footnote 52] Using 
USAID criteria, we evaluated USAID's efforts to monitor its 
prepositioning program.[Footnote 53] We also analyzed and assessed the 
reliability of data on emergency food aid shipments that we collected 
from cooperating sponsors' freight forwarders to determine whether 
these data could be used to monitor delivery time frames of 
prepositioned food. We found that the data provided by three freight 
forwarders were not sufficiently reliable for tracking delivery time 
frames for emergency food aid shipments owing to a number of 
limitations, including incomplete or missing data and, in one freight 
forwarder's data, lack of distinction between emergency and 
nonemergency shipments. We also reviewed relevant legislation to 
determine where there are any statutory requirements to monitor or 
evaluate USAID's prepositioning program. 

We conducted this performance audit from July 2013 to March 2014 in 
accordance with generally accepted government auditing standards. 
Those standards require that we plan and perform the audit to obtain 
sufficient, appropriate evidence to provide a reasonable basis for our 
findings and conclusions based on our audit objectives. We believe the 
evidence obtained provides a reasonable basis for our findings and 
conclusions based on our audit objectives. 

[End of section] 

Appendix II: Delivery Time Frames Regression Analysis: 

A variety of factors besides prepositioning and food aid diversions 
may affect delivery time frames, therefore we developed a statistical 
model--a linear regression model--to control for factors that are 
likely to be associated with delivery time frames, helping to isolate 
the effect of prepositioning and food aid diversions. 

Data Sources: 

We collected shipment data from the WFP and six freight forwarders. 
[Footnote 54] The data indicate whether the shipment was from 
prepositioning or was standard and include the date (or the closest 
approximation) when the cooperating sponsor requested the food, 
[Footnote 55] the date of the food's arrival at the discharge port or 
in the recipient country, and other shipment characteristics such as 
the commodity shipped. We assessed the reliability of these data by 
asking WFP and the six freight forwarders how the data were collected, 
what quality checks were performed, and whether other internal 
controls were in place. We determined that the data from WFP and three 
of the freight forwarders[Footnote 56]--74 percent of the data we 
collected--were sufficiently reliable for tracking delivery time 
frames and comparing standard, prepositioning, and diverted shipments. 
Table 6 shows the numbers of prepositioned, standard, and diverted 
shipments represented in the data that we deemed sufficiently reliable 
for our analysis. 

Table 6: Numbers of Emergency Food Aid Shipments Represented in Data 
Included in GAO Analysis: 

Data source: WFP; 
Number of shipments: 2,137; 
Percentage of total: 42%; 
Prepositioning shipments: 472; 
Standard shipments: 1,665. 

Data source: Three freight forwarders[A]; 
Number of shipments: 1,010; 
Percentage of total: 20%; 
Prepositioning shipments: 141; 
Standard shipments: 869. 

Data source: Diverted shipments[B]; 
Number of shipments: 568; 
Percentage of total: 11%; 
Prepositioning shipments: [Empty]; 
Standard shipments: [Empty]. 

Data source: WFP shipments[C]; 
Number of shipments: 70; 
Percentage of total: 1%; 
Prepositioning shipments: [Empty]; 
Standard shipments: [Empty]. 

Data source: Subtotal; 
Number of shipments: 3,785; 
Percentage of total: 74%; 
Prepositioning shipments: 613; 
Standard shipments: 2,603. 

Data source: Not included in GAO analysis; 
Number of shipments: 1,357; 
Percentage of total: 26%. 

Data source: Total shipments represented; 
Number of shipments: 5,142. 

Source: GAO analysis of WFP's and freight forwarders' data. 

Notes: 

[A] The three forwarders manage the Title II emergency food aid supply 
chain for nine of USAID's cooperating sponsors. 

[B] WFP's data do not identify diversions from other types of 
shipments. 

[C] One freight forwarder included data on 69 standard shipments and 1 
domestically prepositioned shipment for WFP. 

[End of table] 

We excluded shipments from our analysis that were outside our scope, 
[Footnote 57] were duplicated in two freight forwarders' data, or did 
not have a discernible delivery time frame. In addition, we found data 
from the remaining three freight forwarders to be insufficiently 
reliable for tracking delivery time frames of emergency food aid owing 
to a number of limitations, including incomplete or missing data and, 
in one freight forwarder's data, a lack of distinction between 
emergency and nonemergency shipments. Table 7 shows the number of 
shipments that we excluded from our analysis and the reasons for their 
exclusion. 

Table 7: Numbers of Emergency Food Aid Shipments Represented in Data 
Excluded from GAO Analysis and the Reasons for Exclusion: 

Reason for exclusion: Outside our scope[A]; 
Number of shipments: 572. 

Reason for exclusion: Duplicate shipments[B]; 
Number of shipments: 29. 

Reason for exclusion: Shipment with indiscernible delivery time frame; 
Number of shipments: 1. 

Reason for exclusion: Not sufficiently reliable; 
Number of shipments: 755. 

Reason for exclusion: Total; 
Number of shipments: 1,357. 

Source: GAO analysis of WFP's and freight forwarders' data. 

[A] These shipments include shipments ordered in fiscal year 2013 and 
shipments ordered by USAID for the prepositioning warehouses. 
Shipments ordered for the prepositioning warehouses do not fulfill a 
cooperating sponsor's request, but instead comprise the food stored in 
prepositioning warehouses that are later used to fulfill cooperating 
sponsors' requests. 

[B] These shipments were included in two freight forwarders' data, but 
are more accurately captured in one set of data. We then exclude the 
duplicate shipments in the other set of data. 

[End of table] 

We separately analyzed data provided by WFP and the three freight 
forwarders that we included in our analysis, because the two groups of 
data differ in the time periods covered and their treatment of food 
aid diversions. WFP's data cover shipments in fiscal years 2009 
through 2012, while the three freight forwarders' data cover shipments 
in fiscal years 2007 through 2012. In addition, WFP's data do not 
distinguish between diversions of food aid and other shipments, 
preventing our estimation of the number of days saved from diversions 
of food aid using WFP's data. 

We also separately analyzed the effect of food aid diversions on 
delivery time frames. All food aid diversions, including diversions to 
WFP's programs, are managed by one freight forwarder.[Footnote 58] For 
a comparison group, we used standard shipments included in the three 
freight forwarders' data.[Footnote 59] We did not include WFP's data 
in the comparison group because of the differences between WFP's and 
the three freight forwarders' data. GAO analysts collected the dates 
USAID authorized food aid diversions by reviewing one freight 
forwarder's shipment documents.[Footnote 60] 

Limitations: 

Because of differences between the data sources, we used slightly 
different start and end dates to estimate delivery time frames. In 
addition, our results from the linear regression model do not cover 
2007 and 2008 for WFP's shipments or other freight forwarders' 
shipments not included in our analysis. 

* Start dates. Although we defined delivery time frame as the number 
of days between a cooperating sponsor's request for food and arrival 
at a discharge port or recipient country, the start dates we collected 
were the best available approximations of the date of request and 
differed slightly in data provided by WFP and the freight forwarders. 
However, USAID and WFP officials stated that the number of days 
between the start dates and WFP's food request dates does not differ 
for prepositioning and standard shipments. USAID and freight forwarder 
officials also stated that the number of days between the freight 
forwarder start dates and the dates they request the food does not 
differ for prepositioning and standard shipments. For comparisons of 
prepositioning and standard shipments, the difference between the 
start dates we collected and the request dates should not affect our 
results. However, these differences may affect the average delivery 
time frames we calculated, which should be considered as context for 
our regression analysis rather than exact estimates. For diversions, 
we also used the best available approximations of the date of request--
usually the date when USAID authorized the diversion. According to 
USAID officials, the authorization date is generally a few days after 
the request date. Therefore, for comparisons of diversions and 
standard shipments, our estimates should be accurate to within a few 
days. 

* End dates. Data provided by WFP include only the date of the food's 
arrival at the discharge port. Data provided by the freight forwarders 
include, for some freight forwarders, only the date of the food's 
arrival at the discharge port for some shipments and, for other 
freight forwarders, only the date of the food's arrival in the 
recipient country when inland transportation is required. Although 
this affects the average delivery time frames we calculated, it should 
be considered as context for our regression analysis rather than exact 
estimates. Moreover, we found that combining these shipments did not 
affect our regression estimates of number of days saved from 
prepositioning; inland transport from the discharge port to the 
recipient country is similar for prepositioned and standard shipments. 
In addition, we controlled for the port of arrival (either a discharge 
port or in the recipient country) in our analysis. See table 8 for 
average delivery time frames for shipments with dates of arrival at 
the discharge port and in the recipient country. 

* Treatment of diversions. Data provided by WFP do not distinguish 
diversions from standard or prepositioning shipments. However, we 
estimate that this limitation may affect our estimates of number of 
days saved by prepositioning by only a few days.[Footnote 61] 

* Nongeneralizable results. Our estimates are not generalizable beyond 
the time periods covered by the data (fiscal years 2009 through 2012 
for data provided by WFP and fiscal years 2007 through 2012 for the 
data provided by the freight forwarders) or beyond WFP and the three 
freight forwarders' data that we included in our analysis. 

Average Delivery Time Frames: 

Table 8 shows the average delivery time frames for two groups of 
emergency food aid shipments. The first group comprises shipments 
shown in WFP's data, with delivery time frame defined as the number of 
days between the date when WFP and USAID signed a grant agreement and 
the date when the shipment arrived at the discharge port. The second 
group comprises shipments shown in the freight forwarders' data, with 
the delivery time frame defined as the number of days between the date 
when the cooperating sponsor requested the food and the date when the 
shipment arrived at the discharge port or recipient country. 

Table 8: Average Delivery Time Frames (in Days) for Emergency Food Aid 
Shipments: 

WFP (fiscal years 2009-2012)[A]: 

Average delivery time frame: 
Standard shipments: 135; 
All prepositioning shipments: 102; 
Overseas prepositioning shipments: 92; 
Domestic prepositioning shipments: 114. 

Number of shipments: 
Standard shipments: 1,665; 
All prepositioning shipments: 472; 
Overseas prepositioning shipments: 262; 
Domestic prepositioning shipments: 210. 

Three freight forwarders (fiscal years 2007-2012) 

Data with date of arrival in the recipient country (includes land 
transport): 

Average delivery time frame; 
Standard shipments: 170; 
All prepositioning shipments: 110; 
Overseas prepositioning shipments: 103; 
Domestic prepositioning shipments: 120. 

Number of shipments; 
Standard shipments: 681; 
All prepositioning shipments: 79; 
Overseas prepositioning shipments: 47; 
Domestic prepositioning shipments: 32. 

Data with date of arrival at the discharge port (does not include land 
transport): 

Average delivery time frame; 
Standard shipments: 129; 
All prepositioning shipments: 58; 
Overseas prepositioning shipments: 54; 
Domestic prepositioning shipments: 58. 

Number of shipments; 
Standard shipments: 188; 
All prepositioning shipments: 62; 
Overseas prepositioning shipments: 5; 
Domestic prepositioning shipments: 57. 

Source: GAO analysis of WFP's and three freight forwarders' data. 

Note: The three forwarders manage the Title II emergency food aid 
supply chain for nine of USAID's cooperating sponsors. 

[A] Data provided by WFP do not identify diversions from other types 
of shipments. 

[End of table] 

Prepositioning: 

To isolate the effect of prepositioning on delivery time frames for 
emergency food aid, we estimated ordinary least squares regression 
models that control for characteristics of prepositioned and standard 
shipments for WFP and for the nine other cooperating sponsors 
represented in the three freight forwarders' data. Tables 9 and 10, 
respectively, show the results of our regression analysis of the data 
collected from WFP and the three freight forwarders, demonstrating the 
average number of days saved by prepositioning relative to standard 
shipping delivery time frames. Specifically, we estimated Yi = á + 
âPrepoi + äXi + åi for the second column in each table, where Yi is 
the delivery time frame of shipment i, Prepoi is a dummy for whether 
shipment i is from prepositioning, and Xi are the control variables 
listed in column 1. We estimated Yi = á + â1DomesticPrepoi + 
â2OverseasPrepoi + äXi + åi for the third column, where Yi is the 
delivery time frame of shipment i, DomesticPrepoi is a dummy for when 
shipment i is from domestic prepositioning, OverseasPrepoi is a dummy 
for when shipment i is from overseas prepositioning, and Xi are the 
control variables listed in column 1.[Footnote 62] We clustered the 
standard errors by the discharge port or recipient country. For WFP, 
we included the discharge port, the year of the commodity request, the 
month of the commodity request, and the commodity type as control 
variables. For the nine cooperating sponsors, we included the freight 
forwarder, the cooperating sponsor, the discharge port or recipient 
countries, the year of the commodity request, the month of the 
commodity request, and the commodity type as control variables. These 
variables are known to USAID when cooperating sponsors submit requests 
for commodities.[Footnote 63] Controlling for these variables allowed 
us to compare shipments that fulfilled requests with similar 
characteristics. We did not control for the shipment's tonnage, the 
port of loading, and other variables that USAID determines when it 
decides whether to fulfill a sponsor's request with commodities 
sourced from the standard shipping process or from prepositioned 
warehouses.[Footnote 64] 

Table 9: Estimated Days Saved by USAID's Prepositioning Emergency Food 
Aid for the World Food Program, Relative to Standard Shipping, Fiscal 
Years 2009-2012: 

Coefficients and standard errors for prepositioning: 

Prepositioning (domestic or overseas); 
Prepositioning regression: -27.504***; 
Domestic and overseas prepositioning regression: [Empty]. 

Standard error; 
Prepositioning regression: <7.419>; 
Domestic and overseas prepositioning regression: [Empty]. 

Domestic prepositioning; 
Prepositioning regression: [Empty]; 
Domestic and overseas prepositioning regression: -15.652***. 

Standard error; 
Prepositioning regression: [Empty]; 
Domestic and overseas prepositioning regression: <8.497>. 

Overseas prepositioning; 
Prepositioning regression: [Empty]; 
Domestic and overseas prepositioning regression: -40.692***. 

Standard error; 
Prepositioning regression: [Empty]; 
Domestic and overseas prepositioning regression: <8.258>. 

Sample size; 
Prepositioning regression: 2137; 
Domestic and overseas prepositioning regression: 2137. 

Adjusted R squared; 
Prepositioning regression: 0.36; 
Domestic and overseas prepositioning regression: 0.37. 

Coefficients of other control variables: 

Discharge ports: 

Jomi, Tajikistan; 
Prepositioning regression: Dropped; 
Domestic and overseas prepositioning regression: Dropped. 

Abidjan, Cote d'Ivoire; 
Prepositioning regression: 6.635**; 
Domestic and overseas prepositioning regression: -0.685. 

Adale, Somalia; 
Prepositioning regression: 80.198***; 
Domestic and overseas prepositioning regression: 90.836***. 

Aden, Yemen; 
Prepositioning regression: -8.507; 
Domestic and overseas prepositioning regression: -8.015. 

Alexandria, Egypt; 
Prepositioning regression: -68.645***; 
Domestic and overseas prepositioning regression: -68.837***. 

Ashdod, Israel; 
Prepositioning regression: 5.573; 
Domestic and overseas prepositioning regression: 5.923. 

Bangkok, Thailand; 
Prepositioning regression: -23.516***; 
Domestic and overseas prepositioning regression: -23.700***. 

Barranquilla, Columbia; 
Prepositioning regression: 3.177; 
Domestic and overseas prepositioning regression: 3.252. 

Beira, Somalia; 
Prepositioning regression: 22.448***; 
Domestic and overseas prepositioning regression: 22.335***. 

Benghazi, Libya; 
Prepositioning regression: -46.713***; 
Domestic and overseas prepositioning regression: -45.725***. 

Berbera, Somalia; 
Prepositioning regression: -15.124**; 
Domestic and overseas prepositioning regression: -5.162. 

Bosasso, Somalia; 
Prepositioning regression: 52.818***; 
Domestic and overseas prepositioning regression: 64.966***. 

Calcutta, India; 
Prepositioning regression: 13.542*; 
Domestic and overseas prepositioning regression: 14.604*. 

Cap-Haitien, Haiti; 
Prepositioning regression: -18.186**; 
Domestic and overseas prepositioning regression: -20.480**. 

Chittagong, Bangladesh; 
Prepositioning regression: 2.585; 
Domestic and overseas prepositioning regression: 5.188. 

Colombo, Sri Lanka; 
Prepositioning regression: -1.512; 
Domestic and overseas prepositioning regression: 0.127. 

Cotonou, Benin; 
Prepositioning regression: -11.132**; 
Domestic and overseas prepositioning regression: -13.402**. 

Dakar, Senegal; 
Prepositioning regression: -30.519***; 
Domestic and overseas prepositioning regression: -31.210***. 

Dar Es Salaam, Tanzania; 
Prepositioning regression: 10.183**; 
Domestic and overseas prepositioning regression: 9.598*. 

Djibouti, Djibouti; 
Prepositioning regression: -7.882; 
Domestic and overseas prepositioning regression: -8.732. 

Douala, Cameroon; 
Prepositioning regression: 5.39; 
Domestic and overseas prepositioning regression: 3.714. 

Durban, South Africa; 
Prepositioning regression: 7.453; 
Domestic and overseas prepositioning regression: 4.476. 

Fort Dauphin, Madagascar; 
Prepositioning regression: 9.796; 
Domestic and overseas prepositioning regression: 16.189*. 

General Santos City, Philippines; 
Prepositioning regression: 49.923***; 
Domestic and overseas prepositioning regression: 50.207***. 

Gonaives, Haiti; 
Prepositioning regression: -1.479; 
Domestic and overseas prepositioning regression: 0.499. 

Guayaquil, Ecuador; 
Prepositioning regression: -20.077**; 
Domestic and overseas prepositioning regression: -19.961**. 

Hodeiyah, Yemen; 
Prepositioning regression: 19.804***; 
Domestic and overseas prepositioning regression: 18.562***. 

Jacmel, Haiti; 
Prepositioning regression: -53.479***; 
Domestic and overseas prepositioning regression: -51.501***. 

Karachi, Pakistan; 
Prepositioning regression: -4.945; 
Domestic and overseas prepositioning regression: -3.551. 

Laem Chabang, Thailand; 
Prepositioning regression: -37.967***; 
Domestic and overseas prepositioning regression: -40.693***. 

Lome, Togo; 
Prepositioning regression: -22.372***; 
Domestic and overseas prepositioning regression: -24.901***. 

Manila, Philippines; 
Prepositioning regression: 5.152; 
Domestic and overseas prepositioning regression: 7.905. 

Matadi, Democratic Republic of Congo; 
Prepositioning regression: 15.063**; 
Domestic and overseas prepositioning regression: 14.688*. 

Misurata, Libya; 
Prepositioning regression: -12.642; 
Domestic and overseas prepositioning regression: -10.867. 

Mogadishu, Somalia; 
Prepositioning regression: 98.367***; 
Domestic and overseas prepositioning regression: 110.285***. 

Mombasa, Kenya; 
Prepositioning regression: 14.631***; 
Domestic and overseas prepositioning regression: 14.954***. 

Monrovia, Liberia; 
Prepositioning regression: -31.973***; 
Domestic and overseas prepositioning regression: -38.442***. 

Nouakchott, Mauritania; 
Prepositioning regression: -32.020***; 
Domestic and overseas prepositioning regression: -30.749***. 

Oran, Algeria; 
Prepositioning regression: 17.651***; 
Domestic and overseas prepositioning regression: 15.388***. 

Polloc, Philippines; 
Prepositioning regression: 36.141***; 
Domestic and overseas prepositioning regression: 39.586***. 

Port-Au-Prince, Haiti; 
Prepositioning regression: -26.111***; 
Domestic and overseas prepositioning regression: -34.224***. 

Port Sudan, Sudan; 
Prepositioning regression: 16.398***; 
Domestic and overseas prepositioning regression: 18.560***. 

Port Qasim, Pakistan; 
Prepositioning regression: -22.322***; 
Domestic and overseas prepositioning regression: -21.066***. 

Santo Tomas, Guatemala; 
Prepositioning regression: -30.379**; 
Domestic and overseas prepositioning regression: -31.267***. 

Tema, Ghana; 
Prepositioning regression: -49.695***; 
Domestic and overseas prepositioning regression: -43.731***. 

Toliara, Madagascar; 
Prepositioning regression: 17.957***; 
Domestic and overseas prepositioning regression: 23.456***. 

Trincomalee, Sri Lanka; 
Prepositioning regression: -23.951***; 
Domestic and overseas prepositioning regression: -22.696***. 

Year of order: 

Ordered 2008; 
Prepositioning regression: 56.024***; 
Domestic and overseas prepositioning regression: 52.333***. 

Ordered 2009; 
Prepositioning regression: 21.312***; 
Domestic and overseas prepositioning regression: 17.922**. 

Ordered 2010; 
Prepositioning regression: 10.169**; 
Domestic and overseas prepositioning regression: 7.774. 

Ordered 2011; 
Prepositioning regression: 11.609***; 
Domestic and overseas prepositioning regression: 9.876***. 

Ordered 2012; 
Prepositioning regression: Dropped; 
Domestic and overseas prepositioning regression: Dropped. 

Month of order: 

Ordered January; 
Prepositioning regression: Dropped; 
Domestic and overseas prepositioning regression: Dropped. 

Ordered February; 
Prepositioning regression: 7.361; 
Domestic and overseas prepositioning regression: 8.926. 

Ordered March; 
Prepositioning regression: 25.336***; 
Domestic and overseas prepositioning regression: 26.426***. 

Ordered April; 
Prepositioning regression: 20.218***; 
Domestic and overseas prepositioning regression: 20.951***. 

Ordered May; 
Prepositioning regression: 11.224*; 
Domestic and overseas prepositioning regression: 13.165**. 

Ordered June; 
Prepositioning regression: 4.923; 
Domestic and overseas prepositioning regression: 5.932. 

Ordered July; 
Prepositioning regression: 18.563**; 
Domestic and overseas prepositioning regression: 20.328***. 

Ordered August; 
Prepositioning regression: 7.747; 
Domestic and overseas prepositioning regression: 9.484. 

Ordered September; 
Prepositioning regression: -6.654; 
Domestic and overseas prepositioning regression: -8.178. 

Ordered October; 
Prepositioning regression: 12.974***; 
Domestic and overseas prepositioning regression: 14.667***. 

Ordered November; 
Prepositioning regression: 13.676; 
Domestic and overseas prepositioning regression: 14.626. 

Ordered December; 
Prepositioning regression: 17.383; 
Domestic and overseas prepositioning regression: 18.921. 

Commodity: 

Beans; 
Prepositioning regression: Dropped; 
Domestic and overseas prepositioning regression: Dropped. 

Bulgur; 
Prepositioning regression: 17.683*; 
Domestic and overseas prepositioning regression: 21.491**. 

Corn soya blend; 
Prepositioning regression: -10.246; 
Domestic and overseas prepositioning regression: -5.679. 

Lentils; 
Prepositioning regression: -16.341; 
Domestic and overseas prepositioning regression: -16.984. 

Maize; 
Prepositioning regression: 15.303**; 
Domestic and overseas prepositioning regression: 15.467**. 

Peas; 
Prepositioning regression: -5.21; 
Domestic and overseas prepositioning regression: -4.053. 

Rice; 
Prepositioning regression: 3.044; 
Domestic and overseas prepositioning regression: 3.976. 

Sorghum; 
Prepositioning regression: -32.612***; 
Domestic and overseas prepositioning regression: -31.433***. 

Vegetable oil; 
Prepositioning regression: -19.131***; 
Domestic and overseas prepositioning regression: -16.100***. 

Wheat; 
Prepositioning regression: -16.190*; 
Domestic and overseas prepositioning regression: -15.305*. 

Wheat flour; 
Prepositioning regression: 24.436***; 
Domestic and overseas prepositioning regression: 25.118***. 

Wheat soya blend; 
Prepositioning regression: 31.108***; 
Domestic and overseas prepositioning regression: 29.090***. 

Bulk/packaged commodity; 
Prepositioning regression: 4.114; 
Domestic and overseas prepositioning regression: 5.982. 

Source: GAO analysis of WFP data. 

Notes: 

Data provided by WFP do not distinguish diversions from other types of 
shipments. 

In each category of control variables, one variable is dropped from 
the regression to serve as the comparison group for the other dummy 
variables in that category. 

* = significant at the 10 percent significance level. 

** = significant at the 5 percent significance level. 

*** = significant at the 1 percent significance level. 

[End of table] 

Table 10: Estimated Days Saved by USAID's Prepositioning Emergency 
Food Aid for Nine Cooperating Sponsors, Relative to Standard Shipping, 
Fiscal Years 2007-2012: 

Coefficients and standard errors for prepositioning: 

Prepositioning (domestic or overseas); 
Prepositioning regression: -66.788***; 
Domestic and overseas prepositioning regression: [Empty]. 

Standard error; 
Prepositioning regression: <4.445>; 
Domestic and overseas prepositioning regression: [Empty]. 

Domestic prepositioning; 
Prepositioning regression: [Empty]; 
Domestic and overseas prepositioning regression: -61.912***. 

Standard error; 
Prepositioning regression: [Empty]; 
Domestic and overseas prepositioning regression: <6.301>. 

Overseas prepositioning; 
Prepositioning regression: [Empty]; 
Domestic and overseas prepositioning regression: -72.910***. 

Standard error; 
Prepositioning regression: [Empty]; 
Domestic and overseas prepositioning regression: <4.726>. 

Sample size; 
Prepositioning regression: 1010; 
Domestic and overseas prepositioning regression: 1010. 

Adjusted R squared; 
Prepositioning regression: 0.64; 
Domestic and overseas prepositioning regression: 0.64. 

Coefficients of other control variables: 

Freight forwarder: 

Freight Forwarder 1; 
Prepositioning regression: Dropped; 
Domestic and overseas prepositioning regression: Dropped. 

Freight Forwarder 2; 
Prepositioning regression: -119.973***; 
Domestic and overseas prepositioning regression: -123.149***. 

Freight Forwarder 3; 
Prepositioning regression: 36.516***; 
Domestic and overseas prepositioning regression: 35.071***. 

Cooperating sponsor: 

CARE; 
Prepositioning regression: Dropped; 
Domestic and overseas prepositioning regression: Dropped. 

Catholic Relief Services; 
Prepositioning regression: 181.208***; 
Domestic and overseas prepositioning regression: 182.788***. 

Government of Pakistan; 
Prepositioning regression: -17.235; 
Domestic and overseas prepositioning regression: -17.975. 

Norwegian People's Aid; 
Prepositioning regression: 46.588***; 
Domestic and overseas prepositioning regression: 42.273***. 

Relief Society of Tigray; 
Prepositioning regression: 63.317***; 
Domestic and overseas prepositioning regression: 62.116***. 

SHARE Guatemala; 
Prepositioning regression: 76.939***; 
Domestic and overseas prepositioning regression: 76.419***. 

Save the Children; 
Prepositioning regression: -11.008**; 
Domestic and overseas prepositioning regression: -10.411**. 

Save the Children - United Kingdom; 
Prepositioning regression: 35.602**; 
Domestic and overseas prepositioning regression: 34.279**. 

Discharge ports: 

Jomi, Tajikistan; 
Prepositioning regression: Dropped; 
Domestic and overseas prepositioning regression: Dropped. 

Addis Ababa, Ethiopia; 
Prepositioning regression: 47.108***; 
Domestic and overseas prepositioning regression: 47.365***. 

Bujumbura, Burundi; 
Prepositioning regression: 153.000***; 
Domestic and overseas prepositioning regression: 153.000***. 

Chittagong, Bangladesh; 
Prepositioning regression: 23.739**; 
Domestic and overseas prepositioning regression: 23.370**. 

Corinto, Nicaragua; 
Prepositioning regression: -51.946***; 
Domestic and overseas prepositioning regression: -51.695***. 

Dessie, Ethiopia; 
Prepositioning regression: 44.067***; 
Domestic and overseas prepositioning regression: 45.072***. 

Dire Dawa, Ethiopia; 
Prepositioning regression: 23.175***; 
Domestic and overseas prepositioning regression: 24.418***. 

Djibouti, Djibouti; 
Prepositioning regression: 16.804***; 
Domestic and overseas prepositioning regression: 17.575***. 

Dushanbe, Tajikistan; 
Prepositioning regression: -41.595**; 
Domestic and overseas prepositioning regression: -42.900**. 

Garm, Tajikistan; 
Prepositioning regression: -9.79; 
Domestic and overseas prepositioning regression: -11.37. 

Gonaives, Haiti; 
Prepositioning regression: -92.977***; 
Domestic and overseas prepositioning regression: -93.953***. 

Harare, Zimbabwe; 
Prepositioning regression: 10.627; 
Domestic and overseas prepositioning regression: 18.830*. 

Kombolcha, Ethiopia; 
Prepositioning regression: 26.080***; 
Domestic and overseas prepositioning regression: 27.132***. 

Kur-Tiube, Tajikistan; 
Prepositioning regression: 5.991; 
Domestic and overseas prepositioning regression: 4.505. 

Les Cayes, Haiti; 
Prepositioning regression: -21.876***; 
Domestic and overseas prepositioning regression: -23.158***. 

Mekele, Ethiopia; 
Prepositioning regression: 28.829***; 
Domestic and overseas prepositioning regression: 30.218***. 

Mombasa, Kenya; 
Prepositioning regression: 12.519**; 
Domestic and overseas prepositioning regression: 13.609***. 

Multiple destinations, Niger; 
Prepositioning regression: 51.022***; 
Domestic and overseas prepositioning regression: 50.727***. 

Multiple destinations, Sudan; 
Prepositioning regression: 120.180***; 
Domestic and overseas prepositioning regression: 118.659***. 

Multiple destinations, Zimbabwe; 
Prepositioning regression: 64.023***; 
Domestic and overseas prepositioning regression: 65.813***. 

Nairobi, Kenya; 
Prepositioning regression: 43.667***; 
Domestic and overseas prepositioning regression: 43.667***. 

Nazareth, Ethiopia; 
Prepositioning regression: 18.562**; 
Domestic and overseas prepositioning regression: 19.793***. 

Port-Au-Prince, Haiti; 
Prepositioning regression: -33.714***; 
Domestic and overseas prepositioning regression: -34.662***. 

Port Sudan, Sudan; 
Prepositioning regression: 13.667***; 
Domestic and overseas prepositioning regression: 13.667***. 

Santo Tomas, Guatemala; 
Prepositioning regression: -54.899***; 
Domestic and overseas prepositioning regression: -55.877***. 

Segou, Mali; 
Prepositioning regression: 17.957**; 
Domestic and overseas prepositioning regression: 14.550*. 

Shodecosa, Haiti; 
Prepositioning regression: -53.898***; 
Domestic and overseas prepositioning regression: -54.770***. 

Woldiya, Ethiopia; 
Prepositioning regression: 61.329***; 
Domestic and overseas prepositioning regression: 63.357***. 

Year of order: 

Ordered 2006; 
Prepositioning regression: 12.691*; 
Domestic and overseas prepositioning regression: 16.002***. 

Ordered 2007; 
Prepositioning regression: 41.919***; 
Domestic and overseas prepositioning regression: 42.865***. 

Ordered 2008; 
Prepositioning regression: 32.387***; 
Domestic and overseas prepositioning regression: 34.037***. 

Ordered 2009; 
Prepositioning regression: 40.613***; 
Domestic and overseas prepositioning regression: 42.361***. 

Ordered 2010; 
Prepositioning regression: 25.187***; 
Domestic and overseas prepositioning regression: 26.850***. 

Ordered 2011; 
Prepositioning regression: 32.450***; 
Domestic and overseas prepositioning regression: 34.547***. 

Ordered 2012; 
Prepositioning regression: Dropped; 
Domestic and overseas prepositioning regression: Dropped. 

Month of order: 

Ordered January; 
Prepositioning regression: Dropped; 
Domestic and overseas prepositioning regression: Dropped. 

Ordered February; 
Prepositioning regression: -19.309**; 
Domestic and overseas prepositioning regression: -20.253**. 

Ordered March; 
Prepositioning regression: 7.177; 
Domestic and overseas prepositioning regression: 7.217. 

Ordered April; 
Prepositioning regression: -19.993***; 
Domestic and overseas prepositioning regression: -20.565***. 

Ordered May; 
Prepositioning regression: -14.418**; 
Domestic and overseas prepositioning regression: -15.988**. 

Ordered June; 
Prepositioning regression: -10.922; 
Domestic and overseas prepositioning regression: -12.936. 

Ordered July; 
Prepositioning regression: -3.362; 
Domestic and overseas prepositioning regression: -5.401. 

Ordered August; 
Prepositioning regression: -4.318; 
Domestic and overseas prepositioning regression: -5.577. 

Ordered September; 
Prepositioning regression: 23.483***; 
Domestic and overseas prepositioning regression: 22.069**. 

Ordered October; 
Prepositioning regression: 18.902**; 
Domestic and overseas prepositioning regression: 17.937**. 

Ordered November; 
Prepositioning regression: 23.542*; 
Domestic and overseas prepositioning regression: 23.791*. 

Ordered December; 
Prepositioning regression: -0.257; 
Domestic and overseas prepositioning regression: -1.333. 

Commodity: 

Beans; 
Prepositioning regression: Dropped; 
Domestic and overseas prepositioning regression: Dropped. 

Bulgur; 
Prepositioning regression: 2.23; 
Domestic and overseas prepositioning regression: 2.624. 

Corn soya blend; 
Prepositioning regression: 2.288; 
Domestic and overseas prepositioning regression: 2.621. 

Lentils; 
Prepositioning regression: 1.028; 
Domestic and overseas prepositioning regression: 1.553. 

Maize; 
Prepositioning regression: -20.599***; 
Domestic and overseas prepositioning regression: -22.827***. 

Peas; 
Prepositioning regression: 1.483; 
Domestic and overseas prepositioning regression: 1.175. 

Rice; 
Prepositioning regression: 6.563***; 
Domestic and overseas prepositioning regression: 6.810***. 

Sorghum; 
Prepositioning regression: -32.716***; 
Domestic and overseas prepositioning regression: -32.758***. 

Vegetable oil; 
Prepositioning regression: -22.292***; 
Domestic and overseas prepositioning regression: -21.729***. 

Wheat; 
Prepositioning regression: -43.782***; 
Domestic and overseas prepositioning regression: -44.036***. 

Wheat flour; 
Prepositioning regression: 11.16; 
Domestic and overseas prepositioning regression: 11.567. 

Bulk/packaged commodity; 
Prepositioning regression: -11.237**; 
Domestic and overseas prepositioning regression: -11.298**. 

Source: GAO analysis of cooperating sponsors' freight forwarders' data. 

Notes: 

In each category of control variables, one variable is dropped from 
the regression to serve as the comparison group for the other dummy 
variables in that category. Other variables may also be dropped due to 
insufficient number of shipments. 

* = significant at the 10 percent significance level. 

** = significant at the 5 percent significance level. 

*** = significant at the 1 percent significance level. 

[End of table] 

Food Aid Diversions: 

To isolate the effect of diverted shipments on the delivery time frame 
of emergency food aid, we estimated ordinary least squares regression 
models that control for characteristics of diverted shipments, 
prepositioning shipments, and standard shipments. Table 11 shows the 
results of our regression analysis, demonstrating the average number 
of days saved by diversions relative to standard shipping time frames. 
Specifically, we estimate Yi = á + â1Diverti + â2DomesticPrepoi + 
â3OverseasPrepoi + äXi + åi for the second column, where Yi is the 
delivery time frame of shipment i; Diverti is a dummy for when 
shipment i is diverted; DomesticPrepoi is a dummy for when shipment i 
is from domestic prepositioning; OverseasPrepoi is a dummy for when 
shipment i is from overseas prepositioning; and Xi are the control 
variables listed in column 1.[Footnote 65] We clustered the standard 
errors by the discharge port or recipient country. We include the 
freight forwarder, the cooperating sponsor, the discharge port or 
recipient countries, the year of the commodity request, the month of 
the commodity request, and the commodity type. These variables are 
known to USAID when cooperating sponsors submit requests for 
commodities.[Footnote 66] Controlling for these variables allowed us 
to compare shipments fulfilling requests with the same 
characteristics. We did not control for the shipment's tonnage, the 
port of loading, and other variables that USAID determines when 
deciding whether to fulfill a sponsor's request with commodities 
sourced from the standard shipping process or with food aid 
diversions.[Footnote 67] 

Table 11: Estimated Days Saved by USAID's Diversion of Prepositioning 
Emergency Food Aid Shipments, Relative to Standard Shipping, Fiscal 
Years 2007-2012: 

Coefficients and standard errors for prepositioning and diversion: 

Diversion; 
Diversion, domestic, and overseas prepositioning regression: -
63.885***. 

Standard error; 
Diversion, domestic, and overseas prepositioning regression: <11.023>. 

Domestic prepositioning; 
Diversion, domestic, and overseas prepositioning regression: -
68.780***. 

Standard error; 
Diversion, domestic, and overseas prepositioning regression: <7.073>. 

Overseas prepositioning; 
Diversion, domestic, and overseas prepositioning regression: -
72.867***. 

Standard error; 
Diversion, domestic, and overseas prepositioning regression: <5.883>. 

Sample size; 
Diversion, domestic, and overseas prepositioning regression: 1648. 

Adjusted R squared; 
Diversion, domestic, and overseas prepositioning regression: 0.71. 

Coefficients of other control variables: 

Freight forwarder: 

Freight Forwarder 1; 
Diversion, domestic, and overseas prepositioning regression: Dropped. 

Freight Forwarder 2; 
Diversion, domestic, and overseas prepositioning regression: 10.446. 

Freight Forwarder 3; 
Diversion, domestic, and overseas prepositioning regression: 16.281. 

Cooperating sponsor: 

ACDI/VOCA; 
Diversion, domestic, and overseas prepositioning regression: Dropped. 

Adventist Development and Relief Agency; 
Diversion, domestic, and overseas prepositioning regression: -
30.571***. 

CARE; 
Diversion, domestic, and overseas prepositioning regression: -3.332. 

Counterpart International; 
Diversion, domestic, and overseas prepositioning regression: -6.686. 

Catholic Relief Services; 
Diversion, domestic, and overseas prepositioning regression: 24.096. 

Feed the Hungry; 
Diversion, domestic, and overseas prepositioning regression: 40.335***. 

Government of Pakistan; 
Diversion, domestic, and overseas prepositioning regression: -19.811*. 

Norwegian People's Aid; 
Diversion, domestic, and overseas prepositioning regression: 30.096. 

Relief Society of Tigray; 
Diversion, domestic, and overseas prepositioning regression: 35.514**. 

SHARE Guatemala; 
Diversion, domestic, and overseas prepositioning regression: 48.243***. 

Save the Children; 
Diversion, domestic, and overseas prepositioning regression: -17.524**. 

Save the Children - United Kingdom; 
Diversion, domestic, and overseas prepositioning regression: 10.192. 

UNICEF; 
Diversion, domestic, and overseas prepositioning regression: -25.773. 

WFP; 
Diversion, domestic, and overseas prepositioning regression: 9.039. 

World Vision; 
Diversion, domestic, and overseas prepositioning regression: 24.761**. 

Discharge ports: 

Addis Ababa, Ethiopia; 
Diversion, domestic, and overseas prepositioning regression: Dropped. 

Aqaba, Jordan; 
Diversion, domestic, and overseas prepositioning regression: -9.877. 

Bangkok, Thailand; 
Diversion, domestic, and overseas prepositioning regression: -
31.090***. 

Beira, Mozambique; 
Diversion, domestic, and overseas prepositioning regression: -
23.063***. 

Bujumbura, Burundi; 
Diversion, domestic, and overseas prepositioning regression: 
110.909***. 

Calcutta, India; 
Diversion, domestic, and overseas prepositioning regression: -10.841. 

Chittagong, Bangladesh; 
Diversion, domestic, and overseas prepositioning regression: -16.858**. 

Colombo, Sri Lanka; 
Diversion, domestic, and overseas prepositioning regression: -
48.876***. 

Corinto, Nicaragua; 
Diversion, domestic, and overseas prepositioning regression: -
73.778***. 

Cotonou, Benin; 
Diversion, domestic, and overseas prepositioning regression: -
37.861***. 

Dakar, Senegal; 
Diversion, domestic, and overseas prepositioning regression: -
94.186***. 

Dar Es Salaam, Tanzania; 
Diversion, domestic, and overseas prepositioning regression: -
30.353***. 

Dessie, Ethiopia; 
Diversion, domestic, and overseas prepositioning regression: -0.695. 

Dire Dawa, Ethiopia; 
Diversion, domestic, and overseas prepositioning regression: -
22.083***. 

Djibouti, Djibouti; 
Diversion, domestic, and overseas prepositioning regression: -
53.199***. 

Douala, Cameroon; 
Diversion, domestic, and overseas prepositioning regression: -
42.541***. 

Doutchi, Niger; 
Diversion, domestic, and overseas prepositioning regression: 19.166**. 

Durban, South Africa; 
Diversion, domestic, and overseas prepositioning regression: -
31.926***. 

Dushanbe, Tajikistan; 
Diversion, domestic, and overseas prepositioning regression: -
58.383***. 

Garm, Tajikistan; 
Diversion, domestic, and overseas prepositioning regression: -25.068*. 

Gonaives, Haiti; 
Diversion, domestic, and overseas prepositioning regression: -
25.847***. 

Harare, Zimbabwe; 
Diversion, domestic, and overseas prepositioning regression: -
49.705***. 

Kampala, Uganda; 
Diversion, domestic, and overseas prepositioning regression: -
42.091***. 

Karachi, Pakistan; 
Diversion, domestic, and overseas prepositioning regression: -
46.621***. 

Kombolcha, Ethiopia; 
Diversion, domestic, and overseas prepositioning regression: -
19.891***. 

Kur-Tiube, Tajikistan; 
Diversion, domestic, and overseas prepositioning regression: -11.474. 

Laffiteau, Haiti; 
Diversion, domestic, and overseas prepositioning regression: 
-125.802***. 

Las Palmas, Canary Islands; 
Diversion, domestic, and overseas prepositioning regression: -
44.887***. 

Les Cayes, Haiti; 
Diversion, domestic, and overseas prepositioning regression: -
55.181***. 

Lome, Togo; 
Diversion, domestic, and overseas prepositioning regression: -
53.503***. 

Mekele, Ethiopia; 
Diversion, domestic, and overseas prepositioning regression: -
16.300***. 

Mombasa, Kenya; 
Diversion, domestic, and overseas prepositioning regression: -
32.409***. 

Monrovia, Liberia; 
Diversion, domestic, and overseas prepositioning regression: -
26.006***. 

Multiple destinations, Niger; 
Diversion, domestic, and overseas prepositioning regression: 9.328. 

Multiple destinations, Sudan; 
Diversion, domestic, and overseas prepositioning regression: 85.267***. 

Multiple destinations, Zimbabwe; 
Diversion, domestic, and overseas prepositioning regression: 18.261***. 

Nairobi, Kenya; 
Diversion, domestic, and overseas prepositioning regression: 1.575. 

Nampo, North Korea; 
Diversion, domestic, and overseas prepositioning regression: -
45.988***. 

Nazareth, Ethiopia; 
Diversion, domestic, and overseas prepositioning regression: -
24.744***. 

Nouakchott, Mauritania; 
Diversion, domestic, and overseas prepositioning regression: -
95.186***. 

Port-Au-Prince, Haiti; 
Diversion, domestic, and overseas prepositioning regression: -
81.237***. 

Port Sudan, Sudan; 
Diversion, domestic, and overseas prepositioning regression: -
28.425***. 

Port Qasim, Pakistan; 
Diversion, domestic, and overseas prepositioning regression: -
57.161***. 

Santo Tomas, Guatemala; 
Diversion, domestic, and overseas prepositioning regression: -
86.743***. 

Segou, Mali; 
Diversion, domestic, and overseas prepositioning regression: -
29.550***. 

Shodecosa, Haiti; 
Diversion, domestic, and overseas prepositioning regression: -
75.984***. 

Tanout, Niger; 
Diversion, domestic, and overseas prepositioning regression: -4.354. 

Tema, Ghana; 
Diversion, domestic, and overseas prepositioning regression: -
39.412***. 

Woldiya, Ethiopia; 
Diversion, domestic, and overseas prepositioning regression: 12.5. 

Yemen; 
Diversion, domestic, and overseas prepositioning regression: -
45.658***. 

Year of order: 

Ordered 2006; 
Diversion, domestic, and overseas prepositioning regression: 1.175. 

Ordered 2007; 
Diversion, domestic, and overseas prepositioning regression: 27.133***. 

Ordered 2008; 
Diversion, domestic, and overseas prepositioning regression: 13.920*. 

Ordered 2009; 
Diversion, domestic, and overseas prepositioning regression: 20.852***. 

Ordered 2010; 
Diversion, domestic, and overseas prepositioning regression: 12.236*. 

Ordered 2011; 
Diversion, domestic, and overseas prepositioning regression: 14.157**. 

Ordered 2012; 
Diversion, domestic, and overseas prepositioning regression: Dropped. 

Month of order: 

Ordered January; 
Diversion, domestic, and overseas prepositioning regression: Dropped. 

Ordered February; 
Diversion, domestic, and overseas prepositioning regression: -11.198**. 

Ordered March; 
Diversion, domestic, and overseas prepositioning regression: -2.652. 

Ordered April; 
Diversion, domestic, and overseas prepositioning regression: -14.918**. 

Ordered May; 
Diversion, domestic, and overseas prepositioning regression: -9.713**. 

Ordered June; 
Diversion, domestic, and overseas prepositioning regression: -9.365. 

Ordered July; 
Diversion, domestic, and overseas prepositioning regression: -5.312. 

Ordered August; 
Diversion, domestic, and overseas prepositioning regression: -2.149. 

Ordered September; 
Diversion, domestic, and overseas prepositioning regression: 30.048***. 

Ordered October; 
Diversion, domestic, and overseas prepositioning regression: 15.184***. 

Ordered November; 
Diversion, domestic, and overseas prepositioning regression: 5.523. 

Ordered December; 
Diversion, domestic, and overseas prepositioning regression: 2.506. 

Commodity: 

Beans; 
Diversion, domestic, and overseas prepositioning regression: 6.556. 

Bulgur; 
Diversion, domestic, and overseas prepositioning regression: 5.425. 

Corn soya blend; 
Diversion, domestic, and overseas prepositioning regression: 5.597. 

Lentils; 
Diversion, domestic, and overseas prepositioning regression: -14.277. 

Maize; 
Diversion, domestic, and overseas prepositioning regression: 6.924. 

Peas; 
Diversion, domestic, and overseas prepositioning regression: 10.011. 

Ready-to-use therapeutic food; 
Diversion, domestic, and overseas prepositioning regression: -25.402**. 

Rice; 
Diversion, domestic, and overseas prepositioning regression: -7.06. 

Sorghum; 
Diversion, domestic, and overseas prepositioning regression: -
37.349***. 

Vegetable oil; 
Diversion, domestic, and overseas prepositioning regression: 8.157. 

Wheat; 
Diversion, domestic, and overseas prepositioning regression: -9.729**. 

Wheat flour; 
Diversion, domestic, and overseas prepositioning regression: Dropped. 

Bulk/packaged commodity; 
Diversion, domestic, and overseas prepositioning regression: 10.446. 

Source: GAO analysis of cooperating sponsors' freight forwarders' data. 

Notes: 

In each category of control variables, one variable is dropped from 
the regression to serve as the comparison group for the other dummy 
variables in that category. Other variables may also be dropped due to 
insufficient number of shipments. 

* = significant at the 10 percent significance level. 

** = significant at the 5 percent significance level. 

*** = significant the 1 percent level. 

[End of table] 

[End of section] 

Appendix III: Examples of Additional Ocean Shipping Costs for Overseas 
Prepositioning: 

To derive examples of the additional cost of the second leg of ocean 
shipping for prepositioning, we first estimated the costs of shipping 
from the United States to the prepositioning warehouse in Djibouti and 
from the Djibouti warehouse to three discharge ports--Mombasa, Kenya; 
Dar es Salaam, Tanzania; and Beira, Mozambique--in fiscal year 2012. 
We then compared those estimates with the estimated cost of shipping 
from U.S. ports directly to the three ports. (See table 12).[Footnote 
68] 

Table 12: Illustrative Examples of Additional Costs for Ocean Shipping 
from Djibouti in Fiscal Year 2012, per Metric Ton: 

Discharge port: Mombasa, Kenya; 
Estimated cost of shipping from United States to discharge port via 
Djibouti warehouse[A]: $293; 
Estimated cost of shipping directly from United States to discharge 
port[B]: $174; 
Estimated additional cost of shipping from Djibouti warehouse to 
discharge port[C]: $119. 

Discharge port: Dar es Salaam, Tanzania; 
Estimated cost of shipping from United States to discharge port via 
Djibouti warehouse[A]: $322; 
Estimated cost of shipping directly from United States to discharge 
port[B]: $173; 
Estimated additional cost of shipping from Djibouti warehouse to 
discharge port[C]: $149. 

Discharge port: Beira, Mozambique; 
Estimated cost of shipping from United States to discharge port via 
Djibouti warehouse[A]: $300; 
Estimated cost of shipping directly from United States to discharge 
port[B]: $172; 
Estimated additional cost of shipping from Djibouti warehouse to 
discharge port[C]: $129. 

Source: GAO analysis of USAID data. 

[A] To estimate the cost of shipping from the United States to the 
discharge ports via Djibouti, we used average rates for shipments from 
U.S. ports to USAID's prepositioning warehouse in Djibouti and actual 
rates for shipments from the Djibouti warehouse to the discharge ports 
in fiscal year 2012. 

[B] To estimate the cost of shipping directly from the United States 
to the discharge ports, we used average rates for such shipments in 
fiscal year 2012. 

[C] To estimate the additional cost of shipping from the Djibouti 
warehouse to the discharge ports, we subtracted the estimated cost of 
shipping directly from the United States to the discharge ports from 
the estimated cost of shipping from the United States to the discharge 
ports via Djibouti. 

[End of table] 

[End of section] 

Appendix IV: Analysis of Weighted Annual Average Prices for Domestic 
and Overseas Prepositioned Commodities: 

To identify additional commodity costs associated with USAID's 
domestic and overseas prepositioning of emergency food aid, we 
compared the weighted average annual prices paid for six key 
prepositioned commodities with the weighted average annual prices paid 
for each of those commodities for standard shipments of emergency food 
aid in fiscal years 2007 through 2012. We found that, relative to 
standard shipments, the weighted average annual prices were higher 
relative to standard shipments for domestically prepositioned 
commodities more often than for overseas prepositioned commodities. 

As figure 3 shows, for domestically prepositioned commodities, the 
weighted average annual prices were higher relative to standard-
shipment commodities in 24 instances and lower in 8 instances. For 
overseas prepositioned commodities, the prices relative to standard-
shipment commodities were higher in 13 instances and lower in 9 
instances. Additionally, the size of the percentage differences 
between prices for prepositioned commodities relative to standard-
shipment commodities was generally larger for domestic prepositioning 
than for overseas prepositioning. For domestically prepositioned 
commodities, the difference exceeded 15 percent in 7 instances; for 
overseas prepositioned commodities, the difference reached 15 percent 
in only one instance. Weighted annual average prices were consistently 
higher for two domestically prepositioned commodities, corn-soy blend 
(2007-2011) and vegetable oil (2007-2012). 

Figure 3: Percentage Differences between Weighted Annual Average 
Prices for USAID's Domestically and Overseas Prepositioned Commodities 
Relative to Standard-Shipment Commodities, Fiscal Years 2007-2012: 

[Refer to PDF for image: 2 vertical bar graphs] 

Domestic: 

Year: 2007; 
Corn-soy blend bag-HP-25 kg: 13%; 
Cornmeal bag-HP-25 kg: -8%; 
Beans, pinto bag-50 kg: 3%; 
Oil, vegetable can-6/4 liter: 14%; 
Peas, yellow, split bag-50 kg: -3%; 
Sorghum bag-50 kg (domestic): 5%. 

Year: 2008; 
Corn-soy blend bag-HP-25 kg: 20%; 
Cornmeal bag-HP-25 kg: 0.2%; 
Beans, pinto bag-50 kg: -5%; 
Oil, vegetable can-6/4 liter: 10%; 
Peas, yellow, split bag-50 kg: 35%; 
Sorghum bag-50 kg (domestic): -9%. 

Year: 2009; 
Corn-soy blend bag-HP-25 kg: 4%; 
Cornmeal bag-HP-25 kg: -4%; 
Beans, pinto bag-50 kg: 4%; 
Oil, vegetable can-6/4 liter: 8%; 
Peas, yellow, split bag-50 kg: 2%; 
Sorghum bag-50 kg (domestic): 3%. 

Year: 2010; 
Corn-soy blend bag-HP-25 kg: 5%; 
Cornmeal bag-HP-25 kg: not purchased; 
Beans, pinto bag-50 kg: 31%; 
Oil, vegetable can-6/4 liter: 17%; 
Peas, yellow, split bag-50 kg: 34%; 
Sorghum bag-50 kg (domestic): 22%. 

Year: 2011; 
Corn-soy blend bag-HP-25 kg: 12%; 
Cornmeal bag-HP-25 kg: not purchased; 
Beans, pinto bag-50 kg: 18%; 
Oil, vegetable can-6/4 liter: 3%; 
Peas, yellow, split bag-50 kg: -7%; 
Sorghum bag-50 kg (domestic): 6%. 

Year: 2012; 
Corn-soy blend bag-HP-25 kg: 0.3%; 
Cornmeal bag-HP-25 kg: not purchased; 
Beans, pinto bag-50 kg: not purchased; 
Oil, vegetable can-6/4 liter: 15%; 
Peas, yellow, split bag-50 kg: 7%; 
Sorghum bag-50 kg (domestic): -6%. 

Foreign: 

Year: 2007; 
Corn-soy blend bag-HP-25 kg: 10%; 
Cornmeal bag-HP-25 kg: not purchased; 
Beans, pinto bag-50 kg: not purchased; 
Oil, vegetable can-6/4 liter: 2%; 
Peas, yellow, split bag-50 kg: -4%; 
Sorghum bag-50 kg (domestic): not purchased. 

Year: 2008; 
Corn-soy blend bag-HP-25 kg: 1%; 
Cornmeal bag-HP-25 kg: not purchased; 
Beans, pinto bag-50 kg: not purchased; 
Oil, vegetable can-6/4 liter: 4%; 
Peas, yellow, split bag-50 kg: -10%; 
Sorghum bag-50 kg (domestic): not purchased. 

Year: 2009; 
Corn-soy blend bag-HP-25 kg: 2%; 
Cornmeal bag-HP-25 kg: not purchased; 
Beans, pinto bag-50 kg: not purchased; 
Oil, vegetable can-6/4 liter: 6%; 
Peas, yellow, split bag-50 kg: -4%; 
Sorghum bag-50 kg (domestic): 14%. 

Year: 2010; 
Corn-soy blend bag-HP-25 kg: -4%; 
Cornmeal bag-HP-25 kg: not purchased; 
Beans, pinto bag-50 kg: not purchased; 
Oil, vegetable can-6/4 liter: 13%; 
Peas, yellow, split bag-50 kg: 9%; 
Sorghum bag-50 kg (domestic): 15%. 

Year: 2011; 
Corn-soy blend bag-HP-25 kg: 1%; 
Cornmeal bag-HP-25 kg: not purchased; 
Beans, pinto bag-50 kg: not purchased; 
Oil, vegetable can-6/4 liter: -1%; 
Peas, yellow, split bag-50 kg: 13%; 
Sorghum bag-50 kg (domestic): -5%. 

Year: 2012; 
Corn-soy blend bag-HP-25 kg: -5%; 
Cornmeal bag-HP-25 kg: not purchased; 
Beans, pinto bag-50 kg: not purchased; 
Oil, vegetable can-6/4 liter: 4%; 
Peas, yellow, split bag-50 kg: 1%; 
Sorghum bag-50 kg (domestic): -5%. 

Source: GAO analysis of USAID data. 

Notes: 

The graph shows the percentage differences between the weighted annual 
average prices per ton for six key prepositioned commodities and the 
weighted annual average prices per ton for the same standard-shipment 
(i.e., nonprepositioned) commodities purchased in the same year. A 
percentage difference higher than 0 indicates a higher weighted 
average price for the prepositioned commodity than for the standard-
shipment commodity. A percentage difference lower than 0 indicates a 
lower weighted annual average price for the prepositioned commodity 
than for the standard-shipment commodity. 

The listed commodities represented 76 percent of commodities purchased 
for domestic prepositioning in fiscal years 2007 through 2012. 

[End of figure] 

To further examine price differentials between prepositioned and 
standard-shipment commodities, we analyzed the number of purchases per 
year in fiscal years 2007 through 2012. For commodities that the U.S. 
government purchased infrequently, the differences in the average 
prices might be a result of monthly commodity price fluctuations. For 
example, the U.S. government made only one purchase of pinto beans in 
2008 and two purchases in 2009 for domestic prepositioning. In 
comparison, there were 81 purchases of pinto beans in 2008 and 75 in 
2009 for standard shipments. Table 13 lists the numbers purchases for 
domestic prepositioning and for standard shipment in fiscal years 2007 
to 2012. 

Table 13: Numbers of Domestic Prepositioning and Standard Shipment 
Purchases for Six Key Commodities, Fiscal Years 2007-2012: 

Pinto beans (50 kg bag): 

Domestic prepositioning; 
2007: 9; 
2008: 1; 
2009: 2; 
2010: 24; 
2011: 20; 
2012: 0. 

Standard shipment; 
2007: 75; 
2008: 81; 
2009: 75; 
2010: 30; 
2011: 15; 
2012: 37. 

Corn-soy blend (25 kg bag): 

Domestic prepositioning; 
2007: 6; 
2008: 14; 
2009: 9; 
2010: 7; 
2011: 11; 
2012: 2. 

Standard shipment; 
2007: 117; 
2008: 62; 
2009: 68; 
2010: 62; 
2011: 54; 
2012: 44. 

Cornmeal (25 kg bag: 

Domestic prepositioning; 
2007: 20; 
2008: 25; 
2009: 12; 
2010: 0; 
2011: 0; 
2012: 0. 

Standard shipment; 
2007: 195; 
2008: 89; 
2009: 100; 
2010: 67; 
2011: 65; 
2012: 57. 

Vegetable oil (6/4 liter can): 

Domestic prepositioning; 
2007: 5; 
2008: 10; 
2009: 12; 
2010: 7; 
2011: 5; 
2012: 3. 

Standard shipment; 
2007: 159; 
2008: 132; 
2009: 76; 
2010: 65; 
2011: 62; 
2012: 49. 

Yellow split peas (50 kg bag): 

Domestic prepositioning; 
2007: 12; 
2008: 7; 
2009: 15; 
2010: 7; 
2011: 8; 
2012: 17. 

Standard shipment; 
2007: 114; 
2008: 146; 
2009: 135; 
2010: 96; 
2011: 93; 
2012: 127. 

Sorghum (50 kg bag): 

Domestic prepositioning; 
2007: 2; 
2008: 4; 
2009: 6; 
2010: 2; 
2011: 3; 
2012: 1. 

Standard shipment; 
2007: 6; 
2008: 3; 
2009: 7; 
2010: 5; 
2011: 7; 
2012: 5. 

Source: GAO analysis of USAID data. 

Note: The listed commodities represented 76 percent of commodities 
purchased for prepositioning in fiscal years 2007 through 2012. 

[End of table] 

[End of section] 

Appendix V: Related GAO Products about International Food Aid: 

These products can be downloaded from the links shown or can be found 
on the GAO website at [hyperlink, http://www.gao.gov]. 

Global Food Security: USAID Is Improving Coordination but Needs to 
Require Systematic Assessments of Country-Level Risks. [hyperlink, 
http://www.gao.gov/products/GAO-13-809]. Washington, D.C.: September 
17, 2013. E-supplement [hyperlink, 
http://www.gao.gov/products/GAO-13-815SP]. 

International Food Assistance: U.S. Nonemergency Food Aid Programs 
Have Similar Objectives but Some Planning Helps Limit Overlap. 
[hyperlink, http://www.gao.gov/products/GAO-13-141R]. Washington, 
D.C., December 12, 2012. 

International Food Assistance: Improved Targeting Would Help Enable 
USAID to Reach Vulnerable Group. [hyperlink, 
http://www.gao.gov/products/GAO-12-862]. Washington, D.C.: September 
24, 2012. 

World Food Program: Stronger Controls Needed in High-Risk Areas. 
[hyperlink, http://www.gao.gov/products/GAO-12-790]. Washington, D.C.: 
September 13, 2012. 

Farm Bill: Issues to Consider for Reauthorization. [hyperlink, 
http://www.gao.gov/products/GAO-12-338SP]. Washington, D.C.: April 24, 
2012. 

International Food Assistance: Funding Development Projects through 
the Purchase, Shipment, and Sale of U.S. Commodities Is Inefficient 
and Can Cause Adverse Market Impacts. [hyperlink, 
http://www.gao.gov/products/GAO-11-636]. Washington, D.C.: June 23, 
2011. 

International Food Assistance: Better Nutrition and Quality Control 
Can Further Improve U.S. Food Aid. [hyperlink, 
http://www.gao.gov/products/GAO-11-491]. Washington, D.C.: May 12, 
2011. 

International School Feeding: USDA's Oversight of the McGovern-Dole 
Food for Education Program Needs Improvement. [hyperlink, 
http://www.gao.gov/products/GAO-11-544]. Washington, D.C.: May 19, 
2011. 

International Food Assistance: A U.S. Governmentwide Strategy Could 
Accelerate Progress toward Global Food Security. [hyperlink, 
http://www.gao.gov/products/GAO-10-212T]. Washington, D.C.: October 
29, 2009. 

International Food Assistance: Key Issues for Congressional Oversight. 
[hyperlink, http://www.gao.gov/products/GAO-09-977SP]. Washington, 
D.C.: September 30, 2009. 

International Food Assistance: Local and Regional Procurement Can 
Enhance the Efficiency of U.S. Food Aid, but Challenges May Constrain 
Its Implementation. [hyperlink, 
http://www.gao.gov/products/GAO-09-570]. Washington, D.C.: May 29, 
2009. 

International Food Security: Insufficient Efforts by Host Governments 
and Donors Threaten Progress to Halve Hunger in Sub-Saharan Africa by 
2015. [hyperlink, http://www.gao.gov/products/GAO-08-680]. Washington, 
D.C.: May 29, 2008. 

Foreign Assistance: Various Challenges Impede the Efficiency and 
Effectiveness of U.S. Food Aid. [hyperlink, 
http://www.gao.gov/products/GAO-07-560]. Washington, D.C.: April 13, 
2007. 

[End of section] 

Appendix VI: Comments from the U.S. Agency for International 
Development: 

GAO received USAID letter on February 25, 2014. 

USAID: 
From The American People: 

Thomas Melito: 
Director, International Affairs and Trade: 
U.S. Government Accountability Office: 
Washington, DC 20548: 

Dear Mr. Melito: 

I am pleased to provide USAID' s formal response to the Government 
Accountability Office (GAO) draft report entitled "International Food 
Aid: Prepositioning Speeds Delivery of Emergency Aid, but Additional 
Monitoring of Time Frames and Costs Is Needed" (GAO-14-227). 

This letter, together with the enclosed USAID comments, is provided 
for incorporation as an appendix to the final report. 

Thank you for the opportunity to respond to the GAO draft report and 
for the courtesies extended by your staff in the conduct of this audit 
review. 

Sincerely, 

Signed by: 

Angelique M. Crumbly: 
Assistant Administrator: 
Bureau for Management: 
U.S. Agency for International Development: 

Enclosure: a/s. 

USAID Comments On GAO Draft Report: 
No. GAO-14-227. 

Recommendation 1: To strengthen USAID's ability to ensure that its 
food aid prepositioning programs meet the goal of reducing delivery 
time frames in a cost-effective manner, we recommend that the 
Administrator systematically collect, and ensure the reliability and 
validity of, data on delivery time frames for all emergency food aid 
shipments, including prepositioned food aid shipments. 

We agree with this recommendation. Therefore, USAID's Office of Food 
for Peace (FFP) is currently reviewing what actions will be needed to 
ensure that reliable data for delivery time frames for all emergency 
time frames are collected from cooperating sponsors, including the
modification of current contracts, transfer authorizations, and/or 
cooperative agreements. 

Recommendation 2: To strengthen USAID's ability to ensure that its 
food aid prepositioning programs meet the goal of reducing delivery 
time frames in a cost-effective manner, we recommend that the 
Administrator systematically monitor and assess data on delivery time
frames for prepositioned food aid shipments. 

Recommendation 3: To strengthen USAID's ability to ensure that its 
food aid prepositioning programs meet the goal of reducing delivery 
time frames in a cost-effective maImer, we recommend that the 
Administrator systematically monitor and assess costs associated with
commodity procurement, shipping and storage for prepositioned food aid 
shipments. 

USAID agrees with these recommendations and is taking the following 
actions. FFP recently awarded a contract for the independent 
evaluation of the prepositioning program; the scope of this evaluation 
includes both timeliness and cost-effectiveness of prepositioning 
activities. Information and recommendations from the independent 
evaluation, the USAID Office of Inspector General's Audit of USAID's 
Internal Controls Over Prepositioned Food Assistance for the Horn of 
Africa, and this GAO report will inform the ongoing revision to FFP's 
strategy for the prepositioning program. This document will provide a 
framework for the monitoring and assessment of data on the timeliness 
and cost effectiveness of the prepositioning program. 

[End of section] 

Appendix VII: GAO Contact and Staff Acknowledgments: 

GAO Contact: 

Thomas Melito, (202) 512-9601 or melitot@gao.gov: 

Staff Acknowledgments: 

In addition to the contact named above, Valérie Nowak (Assistant 
Director), Farahnaaz Khakoo-Mausel, Teresa Abruzzo Heger, Ming Chen, 
Fang He, Rhonda Horried, Carol Bray, Martin De Alteriis, Justin 
Fisher, Mark Dowling, Reid Lowe, Todd Anderson, Sushmita SriKanth, 
John O'Trakoun, Barbara Shields, Patrick Hickey, Gergana Danailova-
Trainor, Gezahegne Bekele, and Etana Finkler made key contributions to 
this report. 

[End of section] 

Footnotes: 

[1] See 7 U.S.C. § 1721 et seq. Section 3001 of Pub L. No. 110-246, 
the Food, Conservation, and Energy Act of 2008, renamed the underlying 
legislation, the Agricultural Trade Development Aid Act of 1954 (also 
known as P.L. 480), as the Food for Peace Act. 

[2] Cooperating sponsors are organizations that have received USAID 
grants for delivery of emergency food aid to recipient countries. 
Shipments of cooperating sponsors' emergency food aid are tracked by 
freight forwarders--contractors that manage the supply chain of these 
shipments. 

[3] Pub. L. No. 106-472, §310(b). 

[4] The Food Conservation and Energy Act of 2008, Pub. L. No. 110-246, 
increased the authorized level of funding for overseas storage from $2 
million to $10 million 

[5] Pub. L. No. 113-79, §3009. 

[6] We calculated delivery time frames as the number of days between a 
cooperating sponsor's request for emergency food aid commodities and 
the commodities' arrival at the discharge port or in the recipient 
country. We chose the request for food as the starting point for 
delivery time frames because the goal of prepositioning is to expedite 
the delivery of requested emergency food aid. 

[7] We collected data for 5,142 emergency food aid shipments and 
analyzed data for 3,785 (74 percent) of these shipments. Our analysis 
excludes data for the remaining shipments because of a number of data 
limitations (see appendix II). Our estimates are not representative of 
the shipments not included in our analysis. 

[8] See appendix II for full description of our methodology and 
results. 

[9] In the food aid community, "diversion" generally refers to loss or 
theft of food aid. However, USAID and cooperating sponsors also use 
"diversion" to refer to the redirection of food aid from some stage of 
the prepositioning process to a foreign port to meet immediate needs. 
In this report, "diversion" has the latter meaning. 

[10] According to USAID, the agency varies the inventory of each 
warehouse throughout the year to align prepositioned food aid with 
anticipated program needs, availability of funding, and warehouse 
space. 

[11] Two of USAID's current prepositioning warehouses--in Dubai, 
United Arab Emirates, and Miami, Florida--were opened in 2013. 
According to USAID, these two warehouses are used only to preposition 
specialized emergency food products and are managed separately from 
USAID's other prepositioning warehouses. Since the scope of our review 
is 2007 through 2012, we did not include the Dubai and Miami 
warehouses in our analysis. Before 2013, USAID also operated 
prepositioning warehouses in Lake Charles, Louisiana, and Lomé, Togo, 
but the agency no longer uses these warehouses for prepositioning. 
Because data were available for the Lomé warehouse, we included these 
data in our analysis. Data were not available for the Lake Charles 
warehouse, which closed in 2007. Jacinto Port is located in Houston, 
Texas. 

[12] GAO, Foreign Assistance: Various Challenges Impede the Efficiency 
and Effectiveness of U.S. Food Aid, [hyperlink, 
http://www.gao.gov/products/GAO-07-560] (Washington, D.C.: Apr. 13, 
2007). 

[13] Janet R. van der Vaart, P.L. 480 Title II Pre-positioning Cost/ 
Benefit Analysis (2008). 

[14] U.S. Agency for International Development, Office of Inspector 
General, Audit of USAID's Internal Controls Over Prepositioned Food 
Assistance for the Horn of Africa, Audit Report No. 4-962-13-004-P 
(Jan. 7, 2013). 

[15] We conducted analyses, and discuss our findings, of delivery time 
frames for USAID food aid shipments for WFP and nine other cooperating 
sponsors separately because the two groups of data differ in the time 
periods covered and in their treatment of food aid diversions. 

[16] The nine cooperating sponsors are CARE, Catholic Relief Services, 
the government of Pakistan, Norwegian People's Aid, Relief Society of 
Tigray, SHARE Guatemala, Save the Children, Save the Children--United 
Kingdom, and the United Nations Children's Fund (UNICEF). Shipment 
data for these nine cooperating sponsors are collected and tracked by 
three freight forwarders. We collected shipment data for additional 
cooperating sponsors from three other freight forwarders; however, we 
did not find these data to be sufficiently reliable for our purposes 
and did not include them in our analysis. See appendixes I and II for 
a detailed discussion of our methodology and results. 

[17] Our estimate of delivery time frames of diverted shipments for 
WFP and other cooperating sponsors uses data obtained primarily from a 
single freight forwarder. These data do not display the differences we 
identified between the data for WFP and the data for the other 
cooperating sponsors. We found these data to be reliable for 
estimating the effect of emergency food aid diversions on delivery 
time frames for both WFP and the other sponsors. See appendixes I and 
II for a detailed discussion of our methodology and results. 

[18] We used regression analysis to estimate the number of days saved, 
controlling for various factors such as commodity type and discharge 
port. As a result, the numbers of days saved does not equal the 
difference between the average delivery time frames. 

[19] Our analysis showed that in fiscal years 2009 through 2012, USAID 
directed 83 percent of food stored in prepositioned warehouses to WFP. 

[20] This estimate is statistically significant at the 10 percent 
level. 

[21] We analyzed data from three freight forwarders for the following 
nine cooperating sponsors: CARE, Catholic Relief Services, the 
government of Pakistan, Norwegian People's Aid, Relief Society of 
Tigray, SHARE Guatemala, Save the Children, Save the Children--United 
Kingdom, and UNICEF. We also collected data from three freight 
forwarders that included eight of cooperating sponsors; however, we 
did not find these data to be sufficiently reliable for our purposes 
and did not include them in our analysis. See appendixes I and II for 
a detailed discussion of our methodology and results. 

[22] We used regression analysis to estimate the number of days saved, 
controlling for factors such as commodity type and discharge port. As 
a result, the numbers of days saved do not equal the difference 
between the average delivery time frames. 

[23] See appendix II, table 8, for disaggregation of the delivery time 
frames by delivery endpoint. 

[24] In reviewing a draft of this report, USAID noted that it had 
never experienced a situation where a commodity could no longer be 
used due to overexposure to fumigant. 

[25] The diversions data that we collected showed that in fiscal years 
2007 through 2012, 947 shipments were designated for prepositioning 
warehouses and 568 (60 percent) of these shipments were diverted. For 
further details on the differences in our data sources, see appendix 
II. 

[26] We used regression analysis to estimate the number of days saved, 
controlling for various factors such as commodity type and discharge 
port. As a result, the number of days saved does not equal the 
difference between the average delivery time frames. 

[27] According to USAID, diversions cannot be ordered until an ocean 
freight carrier has been identified that is willing and able to 
deliver the diversion to the desired discharge port. Ocean freight 
contracts may include a window of time when the shipment can be 
diverted, although the terms vary by contract and carrier. USAID can 
request diversions after the window has closed, and the carrier may 
grant the request based on the situation. Some of the larger carriers 
allow for diversions after the commodity has been loaded on the ocean 
vessel and the vessel has set sail. 

[28] We calculated weighted annual average prices by dividing the 
total commodity cost in a particular year by the total commodity 
tonnage. The weighted averages take into account the quantity of the 
purchases, giving more weight to purchases with larger quantities. 

[29] Storage costs at the six warehouses range from $0.0 per day at 
the Las Palmas warehouse to $0.49 per day at the Lomé warehouse. USAID 
incurs no daily storage cost for the Las Palmas warehouse, because 
storage space at the warehouse is donated by the Las Palmas Port 
Authority and the Spanish government. 

[30] The most recent Farm Bill which became law in February 2014 
increased the ceiling to $15 million. Pub. L. No. 113-79, §3009. 

[31] Because the data required to reconstruct the hypothetical cost of 
direct shipping for every prepositioned shipment were not available to 
us, we estimated the additional cost due to prepositioning using the 
actual cost of the second leg of ocean shipping. (See appendix III for 
some examples of how the cost of the second leg of ocean shipping 
compares with the estimated cost differential between prepositioning 
and direct shipment). In some cases, costs associated with the second 
leg of ocean shipping, from a prepositioning warehouse to a discharge 
port, might differ from the reconstructed cost differential. 

[32] In addition, prepositioning can increase shipping costs because 
of cargo preference requirements, which mandate that at least 50 
percent of total food aid be shipped on U.S. flag carriers. Because of 
the lack of U.S. flag carriers from the overseas prepositioning ports, 
USAID has to increase the share of food aid shipped from U.S. ports on 
U.S. flag carriers in order to meet the cargo preference requirement. 
Because U.S. flag carriers, on average, have higher rates than non-
U.S. flag carriers, the overall food aid shipping cost may therefore 
increase as a result of the increased use of prepositioning. 

[33] In addition to these two factors, other reasons, such as the 
frequency of purchases, may account for the differences between the 
weighted annual average prices for domestically prepositioned 
commodities and for standard shipment commodities. See appendix IV for 
the number of annual purchases of domestically prepositioned 
commodities. 

[34] See GAO, Standards for Internal Control in the Federal 
Government, [hyperlink, 
http://www.gao.gov/products/GAO/AIMD-00-21.3.1] (Washington, D.C.: 
November 1999). 

[35] Janet R. van der Vaart, P.L. 480 Title II Pre-positioning Cost/ 
Benefit Analysis (2008). 

[36] U.S. Agency for International Development, Office of Inspector 
General, Audit of USAID's Internal Controls Over Prepositioned Food 
Assistance for the Horn of Africa, Audit Report No. 4-962-13-004-P ( 
Jan. 7, 2013). 

[37] The contract specifies that a draft of the evaluation will be 
available 60 days from the start of the contract. 

[38] U.S. Agency for International Development, Automated Directives 
System (ADS) Chapter 203: Assessing and Learning (Washington D.C.: 
Feb. 10, 2012). 

[39] U.S. Agency for International Development, USAID Evaluation 
Policy (Washington D.C.: January 2011). 

[40] U.S. Agency for International Development, Automated Directives 
System (ADS), Chapter 203. 

[41] See 7 U.S.C. § 1736a(c)(4)(A). 

[42] According to USAID, an upcoming independent evaluation on 
prepositioning will analyze the cost effectiveness of prepositioning. 

[43] According to USAID, all contracts related to the costs of the 
prepositioning program are reviewed and evaluated by USAID contracting 
officials. 

[44] Cooperating sponsors are organizations that have received USAID 
grants for delivery of emergency food aid to recipient countries. 
Freight forwarders are contractors that manage USAID's and other 
cooperating sponsors' food aid supply chain. WFP, which is also a 
cooperating sponsor, maintains its own shipment data and was able to 
provide this information. 

[45] Data provided by WFP and the three freight forwarders approximate 
the dates when the cooperating sponsors requested the food from USAID. 
Data provided from one of the three freight forwarders included the 
date when USAID approved the request, up to a few weeks afterward. 
This should not affect our estimates of days saved. According to 
USAID, the number of days between the request for food and USAID's 
approval date is similar for prepositioned and standard shipments. 
WFP's data provide the date when WFP and USAID signed a grant 
agreement, which could be 5 days or less before the date when WFP 
requested the food. However, both USAID and WFP officials noted that 
whether a shipment was sourced from a prepositioning warehouse or the 
standard shipping process did not affect the number of days between 
the request for food date and USAID's grant agreement date. Therefore, 
in comparisons of prepositioned and standard shipments, the days 
between the request for food and USAID's grant agreement should not 
affect our estimates of days saved by prepositioning. 

[46] Data provided by WFP include only the date of the shipment's 
arrival at the discharge port. Data provided by the freight forwarders 
include the date of arrival at the discharge port for some shipments 
and the date of arrival in the recipient country for others (even if 
the discharge port was outside the recipient country). While the 
average delivery time frames differ for shipments with the date of 
arrival in the recipient country compared with shipments with the date 
of arrival at the discharge port, we did not find evidence that 
combining these shipments would affect our estimates of number of days 
saved from prepositioning; inland transport from the discharge port to 
the recipient country is similar for prepositioned and standard 
shipments. See table 8 in appendix II for average delivery time frames 
for shipments with the date of arrival at the discharge port and in 
the recipient country. 

[47] See appendix II for further details and results of the regression 
models. 

[48] While almost all diversion data analyzed were provided by one 
freight forwarder responsible for tracking all diverted shipments, 
data for 39 diverted shipments were analyzed using data from two other 
freight forwarders because their information better approximated the 
date the cooperating sponsors requested food. Cooperating sponsors 
included in the diversion data are ACDI/VOCA, Adventist Development 
and Relief Agency, CARE, Counterpart International, Catholic Relief 
Services, Food for the Hungry, Save the Children-U.S, and World Vision. 

[49] For diversions, when the date of the cooperating sponsor's 
request for the food was unavailable, we used as an approximation the 
date when USAID authorized the diversion. According to USAID, USAID 
generally authorizes diversions a few days after the cooperating 
sponsor requests the food. 

[50] While we may have introduced measurement error to the regression 
model with our data collection, measurement error should not affect 
our estimate of the number of days saved from diversions compared with 
standard shipments. 

[51] In some cases, costs associated with the second leg of ocean 
shipping, from a prepositioning warehouse to a discharge port, might 
not be the same as the reconstructed cost differential. 

[52] Janet R. van der Vaart, P.L. 480 Title II Pre-positioning Cost/ 
Benefit Analysis (2008); and U.S. Agency for International 
Development, Office of Inspector General, Audit of USAID's Internal 
Controls Over Prepositioned Food Assistance for the Horn of Africa, 
Audit Report No. 4-962-13-004-P (Jan. 7, 2013). 

[53] U.S. Agency for International Development, Automated Directives 
System (ADS) Chapter 203: Assessing and Learning (Washington D.C.: 
Feb.10, 2012); USAID Evaluation Policy (Washington D.C.: January 2011). 

[54] In fiscal years 2007 through 2012, seven freight forwarders 
managed USAID's and its cooperating sponsors' supply chain. We 
collected data from six of these contractors; we did not collect data 
from WFP's freight forwarder, instead collecting data directly from 
WFP. We did not include in our analysis data from three of the other 
six freight forwarders, because we found that data were incomplete or 
missing in these three contractors' data and we could not distinguish 
emergency from nonemergency shipments in one of these contractors' 
data. 

[55] See appendix I for a discussion of dates included in WFP and the 
freight forwarders' data. 

[56] These three freight forwarders manage the Title II emergency food 
aid supply chain for nine of USAID's cooperating sponsors. 

[57] These shipments include shipments ordered in fiscal year 2013 and 
shipments ordered by USAID for the prepositioning warehouses. 
Shipments ordered for the prepositioning warehouses do not fulfill a 
cooperating sponsor's request; instead, their contents are stored in 
prepositioning warehouses and are later used to fulfill cooperating 
sponsors' requests. 

[58] Almost all of the diversion data that we analyzed were provided 
by one freight forwarder responsible for tracking all diverted 
shipments. However, for 39 diverted shipments, we analyzed data 
provided by two other freight forwarders, because these data better 
approximated the date when the cooperating sponsors requested food. 

[59] One freight forwarder's data include 69 standard shipments and 1 
shipment from domestic prepositioning that were destined for WFP's 
programs, which we also included in the comparison group. 

[60] While our data collection may have introduced measurement error 
to the regression model, measurement error should not affect our 
estimate of the number of days saved by diversions compared with 
standard shipments. 

[61] To estimate the effect of this limitation on our results for WFP, 
we estimated the amount of commodities diverted to WFP programs using 
data from the freight forwarder that tracks all diversions. We made 
the conservative assumption that all of these diversions are standard 
shipments in WFP's data. Using the estimated number of days saved by 
diversions, based on the freight forwarders' data, we were then able 
to estimate the bias introduced by this limitation. 

[62] These regression results are robust to removing outliers and 
influential shipments. Removing the slowest and fastest shipments and 
influential shipments yields similar regression results. In addition, 
estimating a propensity score matching model yields similar results. 

[63] Cooperating sponsors' requests also include the tonnage 
requested. However, USAID decides how much tonnage to include in each 
shipment when it decides whether to source the shipment from the 
standard shipping process or prepositioned warehouses. USAID may 
source part of the request from prepositioned warehouses and the rest 
from the standard shipping process. 

[64] We estimated models controlling for the shipment's tonnage and 
the flag of the ocean vessel which yielded similar regression results. 

[65] These regression results are robust to removing outliers and 
influential shipments. Removing the slowest and fastest shipments and 
influential shipments yields similar regression results. 

[66] Cooperating sponsors' requests also include the tonnage 
requested. However, USAID decides how much tonnage to include in each 
shipment when it decides whether to source the shipment from the 
standard shipping process or prepositioned warehouses. USAID may 
source part of the request from food aid diversions and the rest from 
the standard shipping process. 

[67] We estimated models controlling for the shipment's tonnage and 
the flag of the ocean vessel which yielded similar regression results. 

[68] We selected these three routes as illustrative examples to 
demonstrate how the second leg of transportation can represent an 
additional cost. In some cases, costs associated with the second leg 
of ocean shipping, from a prepositioning warehouse to a discharge 
port, might not be the same as the reconstructed cost differential. 

[End of section] 

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