The State Exports, Cash Receipts Estimates and State Trade by Country of Origin and Destination datasets measure different aspects of “State trade” using different data and methods. The State Trade by Country of Origin and Destination dataset uses data from the U.S. Department of Commerce, Bureau of the Census, which provides a picture of agricultural trade from the State where commodities are loaded or unloaded for shipment. As such, agricultural exports tend to be larger in States with export ports such as Louisiana, California, and Washington. Alternatively, the State Exports, Cash Receipts Estimates dataset estimates the value of the State’s agricultural production that is exported, ignoring where the commodities were loaded for export. The approach used to create each of these datasets is detailed below.
The State Exports, Cash Receipts Estimates and the State Trade by Country of Origin and Destination datasets are intended to be similar in scope and coverage. However, the nature of the data creates some important differences in terms of geography, time, and the commodity groupings used.
The State Trade by Country of Origin and Destination (COD) dataset reports State-level imports and exports of the top five commodities detailing the top five trading partners of those commodities. This dataset uses data that are made public by the U.S. Department of Commerce, Bureau of the Census. The dataset covers all 50 States as well as Puerto Rico, Washington, DC, and approximately 230 international trading partners. Due to file size limitations, only the five most recent years are provided and aggregated by fiscal quarter. Accordingly, the dataset is updated quarterly. The commodities are grouped according to the 6-digit Bulk, High-value Intermediate and Consumer-Oriented (BICO) product grouping as defined in the USDA, Foreign Agricultural Service’s Global Agricultural Trade System (GATS) documentation. This results in 45 import commodity groups and 48 export commodity groups.
The State Exports, Cash Receipts Estimates (CR) files provide the calendar year (i.e., January—December) State export estimates starting in 2000 and are updated on an annual basis following the release of the Annual cash receipts by commodity, U.S. and States data by USDA, ERS. To match U.S. agricultural exports with farm receipts, exports are grouped according to the USDA, ERS farm sales commodity groupings (e.g., livestock products, vegetables, fruits, grains, etc.), which are further detailed in Farm Income and Wealth Statistics' documentation section. The result is data reporting exports for each of the 50 States by 24 commodity groupings. This data product does not associate States with specific international trading partners and does not include State-level imports.
State Trade by Country of Origin and Destination
The COD data provide a detailed view of commodity trade between the United States and its main trading partners. The export data that underlie the COD data are originally compiled by the U.S. Department of Commerce, Bureau of the Census (Census Bureau) from the Electronic Export Information (EEI) filed by the exporter (or more accurately the U.S. Principal Party in Interest) or their agents through the Automated Export System (AES). Each EEI represents a shipment of one or more kinds of merchandise from one exporter to one foreign importer on a single carrier. Filing the EEI directly to the Census Bureau is mandatory under Chapter 9, Title 13, United States Code.
Data on U.S. imports are compiled primarily from automated data submitted through the U.S. Customs' Automated Commercial System. Data are also compiled from import entry summary forms, warehouse withdrawal forms, and Foreign Trade Zone documents as required by law to be filed with the U.S. Department of Homeland Security, U.S. Customs and Border Protection.
The aggregated data are made publicly available in the Bureau of the Census’ State Data Series, which identifies commodities using the convention of the International Harmonized Commodity Coding and Classification System (HS) at the 6 digit level (HS6). Specifically for this data product, the HS6 State-Level Export Data and HS6 State-Level Import Data from the Census are used. Key dimensions of this data include country code identifying trading partner countries; State of import or export; year of shipment; month of shipment; and consumption value by month, which denotes that re-exported goods—goods not for domestic consumption—are excluded. The datasets also include other values, which are not used here such as, trade values that include re-exports, trade volumes, and mode of transport. For more information about trade data, see the Foreign Agricultural Trade of the United States (FATUS) documentation page.
Several operations are conducted to transform the data. The primary transformation is to group the HS6 commodities into the desired agricultural aggregations. The commodities are grouped to the Bulk, high-value Intermediate and Consumer-Oriented (BICO) definition maintained by USDA, Foreign Agricultural Service (FAS), which is provided as part of the GATS database documentation. The BICO grouping used in COD may vary slightly in some instances from FAS documentation for data usability. One example of this is the decomposition of the “fresh fruit” import category into four subcategories (avocados, berries, bananas, and other). All HS6 agricultural commodities are assigned an HS6 BICO grouping code and are summed to obtain the total trade value of all commodities in that BICO grouping.
To maintain modern HS codes that are in alignment with other trade code systems, HS codes are regularly updated. Such changes include the addition of new codes, the retirement of outdated codes, and the reassignment of existing codes, which are documented by GATS database documentation. To keep in alignment with these changes, the BICO aggregations are updated in parallel, and each quarterly release of COD data incorporates recent changes. While this ensures that commodity groupings are kept current, it is important to note that the commodity groupings change over time and may not contain the exact same bundle of HS codes from one period to another. This is especially important when using the trade data as a long-term time series. To minimize any such discrepancies, code changes are applied to historical data retroactively. In other words, all historical data are continually updated to the current set of HS codes so historical values may change over time.
Commodity groups in the database are further associated with country codes and corresponding country names. The U.S. Census Bureau provides a list of country codes (Schedule C). USDA, FAS' GATS database also contains a list of individual countries.
Lastly, the monthly data is grouped into fiscal year (October 01–September 30) quarters for the most recent 5 years. With these groupings, the top 5 countries importing or exporting each of the top 5 commodities is queried for each State. These data are produced as a .csv file, which is used to create a Microsoft Excel file intended to aid the user in the sorting and parsing of the dataset by State and time period of interest.
State Exports, Cash Receipts Estimates
The COD data focus on the export of commodities from the point where the commodities are assembled for shipment (e.g. port). However, many are interested in the value of a States’ production that is exported, which is estimated in the CR data.
Data necessary to directly track agricultural export products back to their original source of production are not widely available. Recording the State of production for exported farm products would be difficult due to the lack of documentation or specific source information for processed products or mixed shipments. A large portion of U.S. agricultural commodities—such as grains and soybeans—are produced in inland States such as Iowa, Nebraska, and Kansas. These bulk commodities are typically sold first to a local elevator, which may mix and sell them to various larger elevators where they may comingle with similar commodities from other States before arriving at the port of export. Tracking the source State is even more complicated for processed agricultural products. Processors and manufacturers may use raw materials from several States, and final processed products may undergo multiple processing steps in different States before reaching the port to be shipped internationally. Instead of tracing commodities or products back to their original State of production, it is more feasible to estimate exports via shares of production value.
Using farm cash receipts to compute State export shares provides data consistency as both the estimated export values and the farm receipts data are expressed in dollar terms. USDA, ERS uses on-farm cash receipts data to estimate the sales revenue U.S. farmers receive for their commodities. These receipts are calculated from production quantities and prices received by farmers for each U.S. State during the calendar year, which are derived from farm survey data collected by USDA’s National Agricultural Statistics Service (NASS). As such, farm cash receipts provide the primary value of all agricultural production sold in the United States with the national sales equaling the sum of each State’s farmgate sales. For more details on the calculation of cash receipts, see the Documentation for the Farm Sector Cash Receipts Estimation page.
A State’s estimated export value for a commodity is determined by its share of the total U.S. farm receipts from sales of that commodity. To match U.S. agricultural exports with farm receipts, export data is aggregated according to the USDA, ERS farm sales commodity groupings (e.g., livestock products, vegetables, fruits, grains, etc.). Closely matching these groups is important as a State’s export estimate for a commodity, and the products processed from it, depends on the State’s share of total U.S. farm sales for that commodity. A concordance of these groups is provided in the “Commodity key” tab of the data product Excel files.
National export data are sourced from U.S. Department of Commerce, Bureau of the Census. Due to the need for multiple agricultural aggregation schemes, the data are accessed through USDA, FAS' GATS database.
State export-value estimates for a commodity or commodity group are calculated by multiplying each State’s share of total U.S. farm receipts for that commodity, or group, by the U.S. export dollar value corresponding to the same commodity or group. For example, using California’s 32.4 percent share of U.S. farm receipts for rice in 2020 and U.S rice exports of $1.9 billion for the same year, the estimate of rice exports from California in 2020 is $612 million.
For commodity groups, which include processed products, value-added from processing can inflate the estimated export value for that commodity group since corresponding farm receipts do not account for processing costs. As such, the sum of estimated exports for a group of commodities may exceed a commodity group’s estimated export value. To ensure consistency with actual U.S. agricultural export values, the sum of all States’ export estimates is calibrated to equal the actual U.S. export values for each commodity and commodity group.
Strengths and Limitations
Although both methods have respective strengths and limitations, the principal concern of the State Trade Data Series is how trade is attributed to a particular State, either where the commodity is consolidated for shipment or where the commodity is produced.
State Trade by Country of Origin and Destination
The COD data are directly collected from shippers, accounting for all shipping activity and should represent an accurate picture of the movement of commodities both nationally and by the individual States. Furthermore, the use of the data allows significant detail as to the commodity grouping, time frame, and geography of the transactions. However, as previously mentioned, the COD dataset defines trade as the movement from the point at which the products are assembled for shipment or the destination where they are disassembled after shipping. As a result, larger export values are generally attributed to States where ports are located such as Louisiana, California, and New York, although this varies by commodity and method of transportation.
Cash Receipts-based Method
Many users are interested in understanding a State’s share of its production value that is exported. Unfortunately, no data are available to directly measure the value of the commodities produced in a State that are ultimately exported. Normalizing the national exports by States’ share of cash receipts is a straightforward method of estimating that value. Cash receipts have several appreciable properties as a basis for calculating the share of production.
- Farm cash receipts account for differences in the quality and price of commodities produced in each State. Information on price per unit in each State is used to compute cash receipts.
- Farm cash receipts account for commodities’ changing farm and export prices over time.
- Farm cash receipts provide information on the amount of commodities sold by farms, excluding those retained for farm use. Farm use, farm-owned stocks, and USDA’s Commodity Credit Corporation purchases are not accounted for in the calculation.
Despite the value of using cash receipts to estimate the States’ value of production that is exported, it has limitations.
- The primary limitation of the cash receipts approach is the assumption that States’ propensity to export is homogenous across all States. For example, Arkansas is assumed to export the same percent of its soybean crop as South Dakota does, despite Arkansas’ comparative ease of accessing export markets via the Mississippi River. This likely results in a significant underestimation of the Arkansas’ soybean exports and an overestimation of South Dakota’s exports. The magnitude of this error is hard to observe and varies between commodities and States making it difficult to account for.
- Another limitation is the difficulty in accounting for States’ roles in processing—or other value-adding procedures to—exported products between farm and export locations. The export value of processed foods and agricultural products is apportioned to States based on where the raw commodity was produced, not where the product was processed. Data on value-added by commodity are not available by State, only value-added by manufacturing industry is available.
- Export values or free alongside ship (f.a.s.) include costs for inland freight, insurance, and other charges incurred while delivering the commodity to the U.S. port of exportation, which are not attributable to the commodity’s farm production value.
- The calculations using cash receipts preclude the ability to provide information on the countries of destination for each State’s exports.
- There is no similar estimation method for imports at the State level.
Although several methods for estimating export values by State are available, with varying levels of sophistication, no methods provide a direct measure of actual export value. Each method has its own weaknesses when allocating export earnings by State because tracing products back to their original farm of production is difficult, whether in raw or processed form.
Comparing State Trade by Country of Origin and Destination and State Exports, Cash Receipts Estimates
Since COD focuses on where a commodity is assembled for export and the CR method focuses on where the commodities are produced, the two approaches offer different viewpoints of State exports. In general, COD data will result in comparatively more exports from States with export ports, as they are commonly the point of assembly for many agricultural products. However, this varies across commodities.
The estimation differences are especially large for bulk commodities, which are commonly loaded for export at the port. Using soybean exports in 2021 as an example, in the COD data, Louisiana and Washington together accounted for 71 percent of total soybean export value. However, these two States were only responsible for 1 percent of U.S. soybean farm level cash receipts so only 1 percent of exports in the CR dataset. Conversely, the top two States in soybean farm-level cash receipts (Illinois and Iowa) accounted for 28 percent of the national total but only 8 percent of exports according to the COD data.
The disparity tends to be less for products that are more commonly consolidated for shipment where they are produced or manufactured. However, this varies across States, products, and processing industries. For example, milled grain products are often processed and consolidated for shipment inland and transported to the export port via rail or truck. According to the 2021 COD data, Illinois exported 15 percent of U.S. milled grain products. The CR dataset shows Illinois exporting a comparable 11 percent as it has a large milling and shipping capacity. However, Washington exported 23 percent of U.S. milled grain products according to the COD data, but only 1 percent according to the CR data. Washington has both significant processing capacity as well as a dominant export port, but little commodity production. Distilled spirits are an example of a product that is typically packaged by the manufacturer for export. According to the 2021 COD data Kentucky and Tennessee were responsible for 54 percent of distilled spirit exports, despite being inland. The above differences serve to highlight the separate intent of the two datasets.
USDA, ERS's farm cash receipts by commodity and State are computed from USDA’s National Agricultural Statistics Service (NASS) production data and unit prices of farm products in each State.
Bulk, high-value Intermediate and Consumer-Oriented (BICO) product grouping definitions used in the State Trade by Country of Origin and Destination are obtained from the USDA, Foreign Agricultural Service’s Global Agricultural Trade System (GATS).
State Trade by Country of Origin and Destination values are compiled using the U.S. Department of Commerce, Bureau of the Census’ State and Metropolitan Area Data Series.
U.S. Department of Agriculture, Economic Research Service. State Agricultural Trade Data.