Industrial Production and Capacity Utilization - G.17
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This page provides additional information about data in the Board of Governors' statistical release on Industrial Production and Capacity Utilization (G.17). Most of the information is of a technical nature and represents answers to questions that may be of interest to a range of analysts and researchers. The page will be updated as such questions arise.
Documentation for the statistics in the G.17 release is available on the About page.
Q. How are the seasonal factors for light vehicle sales calculated?Posted: 7/30/2015
A. Each summer the dataset of raw light vehicle unit sales used to estimate the seasonal factors is updated with data through April. The dataset extends back to January 1977. These data are reported by the manufacturers each month. Sales are split into four market segments: (1) domestic autos, (2) imported autos, (3) domestic light trucks, and (4) imported light trucks. Each segment has been found to have its own seasonal pattern for various reasons, such as differences in the model-year calendars for domestic vehicle brands and imported vehicle brands. Monthly seasonal factors for these raw data are calculated with the X12-ARIMA seasonal adjustment program available from the U.S. Census Bureau. This program estimates the effects of the calendar on sales in two steps.
First, the monthly sales data are adjusted for "trading days," a step that captures the variation in the length of each selling month as well as in the composition of weekdays within each selling month. (For example, a particular selling month may include four or five Saturdays, depending on the configuration of the calendar.) We assume that a selling month ends on the business day prior to the industry's announcement date for sales in that month. This assumption means that vehicle selling months can differ in length from the corresponding calendar months. We assume that sales can occur on every day of the sales month, and we have not found it necessary to introduce special treatments for holidays that could potentially float between months.
Second, the seasonal component is estimated from the trading-day-adjusted sales data, a step that captures the tendency for sales to be higher at certain times of the year than at other times of the year. The seasonal component is estimated along with the long-run trend after outliers have been detected and removed. The seasonal component can vary over time.
The final published seasonal factors are the product of the trading-day component and the seasonal component. The final published seasonal factors are updated each summer with a revision window that extends back about three years and are used by the Bureau of Economic Analysis (BEA) in the national income and product accounts. For completed calendar years, seasonal factors for vehicle sales posted by BEA may very slightly from the factors issued by the Federal Reserve because of small adjustments made by BEA that align the seasonally adjusted and not seasonally adjusted sales totals. The updated factors are posted on our website.
Q. The G.17 releases of March 17, 2014, and February 14, 2014, reported that part of the decline in industrial production (IP) in January 2014 reflected the effect on output of extreme weather. Severe storms also occurred in February 2014. Did these storms affect IP, and how was this effect incorporated into the IP indexes?Posted: 3/17/2014
A. The swings in manufacturing output, which fell 0.9 percent in January and rose 0.8 percent in February, were exaggerated by weather events. In January, production curtailments arose from extremely cold weather in the Midwest and Northeast throughout much of the month and from a snow storm in the Southeast late in the month. In February, output was affected by a mid-month snow storm on the East Coast. The incorporation of these weather events into IP used procedures comparable to those employed for previous significant weather events. A technical Q&A from February 14, 2014, described these procedures.
One difference between the weather event in February and those in January is the timing of the event in the month. Importantly, short-lived storms in early or mid-month allow producers time to make up any lost production within the month. In contrast, for storms that occur late in the month, producers may not recover lost production until the subsequent month.
The timing of a storm also affects the procedures used to incorporate its effect into IP. For most industries, the data used as monthly indicators cover production during an entire month. For other industries--about 40 percent of IP--the primary indicators of output are data on production worker hours from the Bureau of Labor Statistics (BLS), which measure hours worked during a single pay period in the middle of a month; in addition, the production-worker-hour data are used as a preliminary indicator for slightly more than 30 percent of IP until they are supplanted by later-arriving product data. Because of their timing, the production-worker-hour data may not accurately depict the effects of severe weather during the month as a whole.
The BLS data for January likely did not capture the full effect of the cold weather in the month because the survey was conducted during a period of particularly mild weather, even as much of the rest of the month was characterized by atypically adverse weather. In contrast, the worst weather in February coincided with the BLS survey period. Consequently, the estimated IP indexes for January were reduced to incorporate additional weather effects beyond those reflected in the BLS data, whereas the IP estimates for February were boosted to reduce the negative impact of the weather effects reflected in the BLS data that month.
Relative to what would have been implied by a direct reading of the BLS data, the lower level of manufacturing IP in January combined with the higher level in February led to a substantially stronger rate of change in IP in February.
As noted above, the procedure to incorporate the effects of weather into the IP index for February was similar to that used for January and for other months with significant weather events. This procedure followed four basic steps. First, weather data from the National Oceanographic and Atmospheric Administration were used to identify counties with snowfall that fell disproportionately during the BLS survey period. Second, an average duration of the production shutdown was estimated. Third, the share of each industry’s employment that occurred in the high-snowfall counties was determined using the U.S. Census Bureau’s County Business Patterns data. Fourth, the hours measures used as indicators for the IP indexes were boosted by an amount proportionate to the loss of hours due to the storm.
For February, the industries reported by the BLS to have recorded the largest declines in average weekly hours--textiles, textile products, apparel, and furniture--were the same as those determined by the procedure outlined above.
In the coming months, as late-arriving physical product data and other information become available, the IP indexes will more accurately reflect the effects of the inclement weather. When the G.17 release was issued on March 17, 2014, about 15 percent of total IP for January was covered by newly arriving physical product data that replaced the weather-adjusted hours-based estimates published the previous month. The January indexes were little affected by the incorporation of the physical product data.
Q. In the G.17 release from February 14, 2014, it was reported that part of the decline in industrial production (IP) in January 2014 reflected the effect on output of severe weather. How was this effect incorporated into the IP indexes?Posted: 2/14/2014
A. The effect of the severe weather on industrial output was incorporated using procedures comparable to those employed previously to assess the effects on production of other significant weather events, such as Hurricanes Sandy and Katrina. For January, the IP indexes account for disruptions to normal operations that resulted from extremely cold weather in the Midwest and Northeast throughout much of the month and a snow storm in the Southeast late in the month.
For some industries, timely high-frequency physical product data exist that reflect the imprint of severe weather or natural disasters without any further adjustment. For other industries, the available high-frequency indicators of production, such as Bureau of Labor Statistics data on production worker hours, may not represent activity for the entire month and therefore may not accurately capture the effects of severe weather during the month as a whole. (For example, the production worker hour data are based on payrolls for only one pay period in a month; they will underestimate weather effects if the worst weather is outside the survey period for these data and overstate the effects if the worst weather is concentrated during the survey period.) For these other industries, the effects of extreme weather are incorporated into the IP indexes using the following methodology.
- Daily measures of temperatures and snowfall at weather stations throughout the country from the National Oceanographic and Atmospheric Administration (NOAA) are used to determine which counties were affected by the extreme weather.
- The U.S. Census Bureau’s County Business Patterns data are used to measure each industry’s share of employment located in the affected counties.
- The extent of the disruptions at facilities in the affected areas is estimated based on the severity of the weather event in that county as measured by the NOAA data and is supplemented by anecdotes received from industry experts, Federal Reserve District contacts, and news reports. In addition, the estimates of the adverse effects on production allow for some of the lost production to be recovered before the end of the month: that is, a day of downtime early in the month is more likely to be made up during the month than is a day of downtime late in the month.
- Given this information, an estimate of the magnitude of the disruption is constructed for each industry.
In subsequent months, as late-arriving physical product data and other information become available, the IP indexes will more accurately reflect the effect of the inclement weather.
Related information on estimating disaster effects on industrial production can be found in a Federal Reserve Bulletin article from 2009, an Annual Revision article from 2013, and a previous technical Q&A.
Q. What were the effects of the partial shutdown of the federal government on data availability for industrial production (IP)?Posted: 11/04/2013
A. The G.17 Statistical Release on Industrial Production and Capacity Utilization is compiled from both public and private data sources. The partial shutdown of the federal government last month delayed the availability of all relevant data reports from public sources as well as some reports from private sources. Consequently, the publication of the G.17 statistical release that was initially scheduled to be issued on October 17, 2013, was delayed until October 28, 2013.
This 11-day delay in the publication of the G.17 allowed for the incorporation of most of the data that would normally have been available for a mid-October release: On a value-added basis, the typically available data for only about 2 percent of IP were not received in time for the October 28 release. In addition, the later-than-normal publication enabled the incorporation of a substantial amount of data--for about 15 percent of IP on a value-added basis--that would have arrived too late for a mid-October release.
The next G.17 release will be published as originally scheduled on November 15, 2013. It is anticipated that only a very small amount of data that is normally available for inclusion in IP will be delayed past the publication date.
Q. How were the effects of Hurricane Sandy on industrial production calculated?Posted: 11/16/2012
A. In the G.17 release from November 16, 2012, it was reported that the disruptions related to Hurricane Sandy subtracted nearly 1 percentage point from the rate of change in industrial production. The effect of Hurricane Sandy on industrial output was estimated using the same procedures employed to assess the effects of previous natural disasters.
For some industries, timely high-frequency physical product data that reflect the imprint of natural disasters on industrial output exist. For other industries, estimates of natural-disaster effects are constructed using the following methodology. First, information from the Federal Emergency Management Agency (FEMA) is used to determine which counties were affected by the disaster; FEMA issues Major Disaster Declarations and Emergency Declarations based on the needs of the counties. Second, the U.S. Census Bureau?s County Business Patterns data are used to measure each industry?s share of employment located in the affected counties. Third, the duration that facilities in the affected areas were idled is estimated based on the declaration type assigned to each county by FEMA. Fourth, given this information, an estimate of the magnitude of the disruption is constructed for each industry, and the industry-specific effects are aggregated using industrial production weights to obtain an overall estimate of the effect on top-line industrial production and on the major industry aggregates. In subsequent months, as physical product data and other information become available, the disaster effects are further updated and refined.
Related information on estimating disaster effects on industrial production can be found in a Federal Reserve Bulletin article from 2009.
Q. In the G.17 press release for both September and October, it was mentioned that precautionary idling of production in late August along the Gulf of Mexico in anticipation of Hurricane Isaac likely reduced the rate of change in industrial production in August by 0.3 percentage point.
The press release for October stated, "...part of the rise in September is a result of the subsequent resumption of activity at idled facilities."
Why was the "bounceback" in September not quantified?
A. It is much more straightforward to estimate the initial reduction in the rate of change in industrial production (IP) that results from a natural disaster, such as a hurricane, than it is to quantify the effect on IP of the subsequent recovery in the affected geographic areas. The reason is that the initial shock to output is much more focused in time and is much more likely to be apparent in the source data used to estimate IP, while the recovery can either be quick or be very drawn out (such as after the hurricanes in 2005 and in 2008). As a result, the post-disaster recovery is more easily confounded in the data by other events occurring at the time including, for example, any shifting of production to unaffected producers.
The methods used to estimate IP are independent of the amount of activity that is attributed to a post-disaster recovery. In other words, different assumptions about the pace at which affected producers resume operations would lead to different estimates of the portion of current activity attributable to the recovery, but the level of IP would be largely invariant to those assumptions.