California Department of Food & Agriculture
Specialty Crop Block Grant
BI-ANNUAL PROGRESS REPORT
CFDA# 10.170
PROGRESS REPORTS
Project title: Measuring and understanding the pattern of margins between farm and retail prices for Calilfornia specialty crops to increase growers returns
Dec 2011
Data Compilation
As indicated in the last report, our goal in this project was to complete data collection and be ready for data analysis. In this period of the report, we completed the data compilation and began data analysis. As outlined in the previous report, in addition to commodity price information, it is also important to acquire other industry related information specific to commodity, including domestic production, marketing methods, and international trade. In light of this, our data consist of three broad categories including prices, production, and trade. Price data are vast. Price data include detailed crop specific fruit prices, along with vegetable prices which can be used as comparison. These data are collected from various sources including Bureau of Labor Statistics, National Agricultural Statistics Service and Economic Research Service. Price data include prices at different marketing stages (farmgate and retail), and at different time intervals (annual and monthly). We collected the time series data from as far back in time as is available. These data goes back as far as 1913, but for some commodities only begin in 1996 (data time period depends on the specific crop). Production data are mostly collected from USDA sources. Given California is the major producer for fruits and vegetables, which are the commodities of our particular interest, the geographical region for production data is limited to California. Production data include production and acreage of major fruit crops and some representative vegetables. Trade data are collected from Foreign Agricultural Service, USDA. From the Service’s Global Agricultural Trade System, U.S. import and export quantities and values are collected for most major fruits including peaches, apples, nectarines, strawberries, kiwis, avocadoes, oranges for the period of 1989-2009. More detailed information is provided in the appendix.
Data Analysis
To summarize our output (thus far) on descriptive data analysis, we present two figures (in the appendix) which describe the historical movement of aggregate price indices. The first figure presents annual Consumer Price Indexes (CPI) for urban consumers for general food and two different food bundles, fresh fruit and fresh vegetables for the period of 1950-2009 with the index values based on the prices of 1982-84. All three CPIs increased together at a relatively low rate until the early 1980s. However, since then all three indexes increases at considerably different rates, showing that fresh produce prices rose far more than general food prices. Compared to the price of 1982, the CPIs for fresh fruit and vegetables rose 3.5 times and 3.1 times in 2008, respectively, while the general food index rose 2.3 times. The second figure depicts farmgate and retail spreads for fresh fruit prices over the same period. Up to the early 1980s, the producer price index (PPI) (or farmgate prices) is slightly higher than the consumer price index (CPI). However, after this period, the CPI rose very rapidly while there was little change in PPI. As a result, the gap between CPI and PPI widens. These two graphs show the central point of our project—for the past two decades, fresh fruit retail prices have risen more than any other food groups. On the other hand, producer received prices have either decreased or changed little.
We are currently investigating historical prices for specific crops in relation to different marketing stages (farmgate and retail), crop seasons (based on domestic crop seasons) and the importance of international trade. Our crop specific analysis includes all major fruits. However, price analysis at different marketing stages is limited to the crops where data are available.
After preliminary investigation on historical trend of data, our data analysis is based on the two levels of hypotheses. The first is to verify whether the price gap between retail and producer levels are the relevant price gap for domestic producers. That is, CPI is calculated based on the prices of in season as well as off season while PPI is based on only in season prices. Thus, CPI relevant to our study (with the focus on farm income implications) would be the CPI for in season prices. This investigation is done using monthly prices. The second set of hypotheses is developed to understand why the prices have evolved as they are. Those hypotheses include: a. differential market power that has changed over time, b. differential changes in degree of marketing services, c. differential changes in the prices of marketing services for which some products are more intensive (including waste), d. change in the elasticity of demand for fresh produce relative to other items so prices have changed given some market power, e. differential changes in the farm price because of costs of production evolving differently (because of input prices), f. differential changes in costs regionally (California relative to Iowa?) where different products are grown in different ratios in different regions, g. differential changes in technical change by crop, and h. differential changes in product characteristics that are costly to supply. We also investigate the possibility of the availability of offseason crops and quality adjusted (i.e., fresher or other improved characteristics) products.
Provide a comparison of actual accomplishments with the goals established for the reporting period.
As was outlined in the previous report, our project started late due to the administrative delay in funding transfer. However, we steadily achieved the ojectives of our workplan. Our work schedule indicates that by this term we would be finishing data collection and conducting preliminary descriptive data analysis, where the data summary will be prepared at various levels, by broad level of crops, by specific crop, and by specific marketing channel. Given this, our progress in this project closely follows the workplan schedule.
Present the significant contributions and role of project partners in the project.
I have been working with Dr. Daniel Sumner closely in each stage of work. Dr. Sumner provided his insight in developing hypotheses and other stages of project.
Clearly convey progress toward achieving outcomes by illustrating baseline data that has been gathered to date and showing the progress toward achieving set targets.
For data collection, as outlined above, we have collected a large amount of data from many different sources. They are compiled to be ready for data analysis. Data analysis work is already under way. Preliminary data analysis is almost completed. Further data analysis specific to the hypotheses mentioned above are being formulated. Our targets are specified in the workplan and we are closely following the workplan schedule.
Apr 2011
In this period, our major activities can be summarized broadly in two areas, including additional data collection and data analysis. More detailed description of activities conducted in each area is provided below.
On-going Data Analysis: This on-going analysis is conducted using annual time series of product price indexes. We have analyzed the time trend of price indexes for fresh fruit as an aggregate compared to food or vegetable price indexes. The time series indexes are mostly developed from data reported by the Bureau of Labor Statistics (BLS). Our investigation indicates that the rate of increase in the historical price of the aggregate of fresh fruit has been comparable to other food prices up to the early 1980s. However, since then, fresh fruit prices have risen much more rapidly than general food prices. On the other hand, the producer price index (representing wholesale prices) of fresh fruit has increase much less, resulting in the falling share of farmgate price in retail value for fresh fruit.
In the second stage of our analysis, we have expanded our investigation into specific crops. In this crop specific study, our analysis uses more detailed data for each crop. The main purpose of this second stage analysis is to investigate the farm margin at the crop specific level using more detailed price data. So far, we have relied on annual price data published by the BLS. However, BLS generates annual price data by aggregating the monthly figures using the simple averaging method. This means that the prices, regardless they are collected during the season or off-season, are treated with the same weight in the annual price calculation.
These simple average annual prices may be biased for two main reasons. First, we have witnessed an increase in imports of fresh fruit in the last two decades. Therefore, products are available in the market during the traditional “off-season” and off-season market prices are tied to import prices. Second, introduction of early varieties and plant hybrid technology extended the season of domestic production. The annual fruit prices data published by BLS reflect prices of the product supplied from domestic as well as import sources. Moreover, the annual price data may be biased upward relative to the information from two or three decades ago because the price of early season crops, which is generally higher than the mid-season price, is treated with the same weight in annual price calculation, even though production in the early season may be in small quantity. In addition, even if the weights were correctly assigned in the annual averages, the comparison with historical data might be misleading because the nature of the products has changed. Simply stated “early-season” fruit is not really the same product as a “mid-season” fruit on either the buyer or seller side of the market and the “early-season” price is likely to be higher. That means, the aggregate price could rise, because early season fruit became a larger share, even if the price of neither type of fruit increased. We will continue to investigate this data issue. Given that this study focuses on the farm share in retail value in the fruit sector, our objective in the next step of analysis is to investigate the issues surrounding farm share. We next construct more relevant price data and analyze them.
Construction of annual prices which reflect domestic supplies during the season: Our objective is to construct price data which account for various supply flows during the season. To do so, we began with monthly price data. These monthly price data are the most disaggregated data available from public sources on a consistent basis. We also collected shipment information from Agricultural Marketing Service of USDA (we collected the data as much as possible from Agricultural Marketing News). Then, using each month’s share of shipment during the season as a weight, we constructed weighted season prices for the period of 1980-2010 (1980 was the earliest year for which monthly retail prices were available from BLS). When shipment data are not available, we use the simple average of the months which are defined as harvest months by UC-Davis crop specialists. When it is hard to define the season due to wide geographic production areas, we used the monthly import and export data to define the season.
We have also begun to investigate in detail potential anomalies in the individual commodity price indexes because of changes in product definitions, oddly timed base periods and changes in products covered in aggregations.
Analysis: We initially applied the weighted price method for five commodities, including fresh apples, fresh strawberries, navel oranges, fresh peaches, and fresh grapes. We have summarized the initial results in graphic form and presented the results for three commodities. We selected these three for presentation because each represents unique characteristics in the market. Fresh apples in the U.S. market are mostly supplied from domestic sources and strawberries have a season which is long relative to other fruits while table grapes are one of the fruits which represent a large import share. Currently, about half of U.S. annual consumption is supplied from foreign sources. While the prices of apples and strawberries were weighted using shipment data, shipment data for table grapes separately were not available and thus prices of grapes were calculated as a simple season average. Figures A-C present retail and farm prices for these three crops. The first and second graphs in each figure describe nominal and deflated prices at the retail and grower level. For all these three crops, real grower prices had not declined as was previously believed. Even though they fluctuated to some extent, the real grower price for fresh apples has been steady over the three decades. On the other hand, the real price for fresh strawberries has declined about 20-25 percent (judging from the linear trend line). However, given the strawberry yield has more than doubled over the same period, a decline of real price per unit by 20 percent is relatively a small decline. The real price for fresh grapes shows steady trend with wide fluctuation.
Figure D presents the farm share in retail value for each of three commodities considered. The farm share for fresh apples has fluctuated at around 17 percent, whereas the farm share for strawberries has clearly trended downward over time. The farm share for fresh grapes has fluctuated widely with no clear trend.
SCBGP
Revised 12/08/2009
Attachment 4
Page 1 of 15
California Department of Food & Agriculture
Specialty Crop Block Grant
BI-ANNUAL PROGRESS REPORT
CFDA# 10.170
Figure A. Fresh Apples: Historical Weighted Average Prices in Current and Real Value
SCBGP
Revised 12/08/2009
Attachment 4
Page 1 of 15
California Department of Food & Agriculture
Specialty Crop Block Grant
BI-ANNUAL PROGRESS REPORT
CFDA# 10.170
SCBGP
Revised 12/08/2009
Attachment 4
Page 1 of 15
California Department of Food & Agriculture
Specialty Crop Block Grant
BI-ANNUAL PROGRESS REPORT
CFDA# 10.170
Figure B. Fresh Strawberries: Historical Weighted Average Prices in Current and Real Value
SCBGP
Revised 12/08/2009
Attachment 4
Page 1 of 15
California Department of Food & Agriculture
Specialty Crop Block Grant
BI-ANNUAL PROGRESS REPORT
CFDA# 10.170
SCBGP
Revised 12/08/2009
Attachment 4
Page 1 of 15
California Department of Food & Agriculture
Specialty Crop Block Grant
BI-ANNUAL PROGRESS REPORT
CFDA# 10.170
Figure C. Fresh Grapes: Historical Weighted Average Prices in Current and Real Value
SCBGP
Revised 12/08/2009
Attachment 4
Page 1 of 15
California Department of Food & Agriculture
Specialty Crop Block Grant
BI-ANNUAL PROGRESS REPORT
CFDA# 10.170
SCBGP
Revised 12/08/2009
Attachment 4
Page 1 of 15
California Department of Food & Agriculture
Specialty Crop Block Grant
BI-ANNUAL PROGRESS REPORT
CFDA# 10.170
Figure D. Historical Farm Shares
SCBGP
Revised 12/08/2009
Attachment 4
Page 1 of 15
California Department of Food & Agriculture
Specialty Crop Block Grant
BI-ANNUAL PROGRESS REPORT
CFDA# 10.170
Provide a comparison of actual accomplishments with the goals established for the reporting period.
Compared to the goals stated in the plan, we had to spend more time and effort in collecting data. That was due to the lack of already published data because they did not best suit for our research purpose. This was not anticipated. Nevertheless, we are in good shape in achieving the goals stated in the workplan. Our work schedule indicates that by this term we would be finishing data collection and major analysis work is under way. Our current progress in this project closely follows the workplan schedule.
Present the significant contributions and role of project partners in the project.
BLS is probably one of the most important data sources. USDA collects its own (price) data at the grower level prices, but for retail prices, BLS is the only source. Probably the most widely used data by applied researchers in the time series context would be annual time series. However, it is very important to understand that BLS usually uses the simple average method to construct annual data, rather than the quantity weighted method. (However, USDA uses the weighted average method in the construction of annual grower received prices. Shipment data are supplied from USDA’s Agricultural Marketing Services.) The consequence of this simple average method used in annual retail price calculation is the prices which are higher than otherwise. The implication of these higher prices in farm share calculation is serious, leading to erroneously lower farm shares.