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 fig. 1). 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. 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). 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. 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. 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. Prepositioning of emergency food aid reduces the average delivery time frame for USAID’s food aid shipments for WFP and other cooperating sponsors. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. In contrast, the average delivery time frame for the 938 standard shipments was 156 days. 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. 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. 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. 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; 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. 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. 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 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. 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. 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 app. IV for more information.) 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. 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 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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 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. 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. 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) 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 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. 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. 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) 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. 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. 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 app. 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 app. 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. Using USAID criteria, we evaluated USAID’s efforts to monitor its prepositioning program. 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. 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. We collected shipment data from the WFP and six freight forwarders. 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, 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—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. We excluded shipments from our analysis that were outside our scope, 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. 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. For a comparison group, we used standard shipments included in the three freight forwarders’ data. 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. 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. 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. 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. 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 Y = α + βPrepo + δX + ε for the second column in each table, where Y is the delivery time frame of shipment i, Prepo is a dummy for whether shipment i is from prepositioning, and X are the control variables listed in column 1. We estimated Y = α + βDomesticPrepo + βOverseasPrepo + δX + ε for the third column, where Y is the delivery time frame of shipment i, DomesticPrepo is a dummy for when shipment i is from domestic prepositioning, OverseasPrepo is a dummy for when shipment i is from overseas prepositioning, and X are the control variables listed in column 1. 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. 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. 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 Y= α + β + β + βOverseasPrepo + δX + ε for the second column, where Y is the delivery time frame of shipment i; Divert is a dummy for when shipment i is diverted; DomesticPrepo is a dummy for when shipment i is from domestic prepositioning; OverseasPrepo is a dummy for when shipment i is from overseas prepositioning; and X are the control variables listed in column 1. 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. 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. 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). 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). 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. These products can be downloaded from the links shown or can be found on the GAO website at http://www.gao.gov . Global Food Security: USAID Is Improving Coordination but Needs to Require Systematic Assessments of Country-Level Risks. GAO-13-809. Washington, D.C.: September 17, 2013. E-supplement GAO-13-815SP. International Food Assistance: U.S. Nonemergency Food Aid Programs Have Similar Objectives but Some Planning Helps Limit Overlap. GAO-13-141R. Washington, D.C., December 12, 2012. International Food Assistance: Improved Targeting Would Help Enable USAID to Reach Vulnerable Group. GAO-12-862. Washington, D.C.: September 24, 2012. World Food Program: Stronger Controls Needed in High-Risk Areas. GAO-12-790. Washington, D.C.: September 13, 2012. Farm Bill: Issues to Consider for Reauthorization. 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. GAO-11-636. Washington, D.C.: June 23, 2011. International Food Assistance: Better Nutrition and Quality Control Can Further Improve U.S. Food Aid. 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. GAO-11-544. Washington, D.C.: May 19, 2011. International Food Assistance: A U.S. Governmentwide Strategy Could Accelerate Progress toward Global Food Security. GAO-10-212T. Washington, D.C.: October 29, 2009. International Food Assistance: Key Issues for Congressional Oversight. 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. 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. GAO-08-680. Washington, D.C.: May 29, 2008. Foreign Assistance: Various Challenges Impede the Efficiency and Effectiveness of U.S. Food Aid. GAO-07-560. Washington, D.C.: April 13, 2007. 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.

Summary: 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. 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. 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. 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.