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.


The G.17 press releases on industrial production (IP) and capacity utilization published on June 15, 2021, and on July 15, 2021, both noted that shortages of semiconductors have held down the production of motor vehicles. A number of press reports have pointed out that many almost-complete vehicles have been parked in lots awaiting the arrival and installation of components requiring semiconductors before being made available for purchase to consumers.

Are these almost-complete vehicles included in the IP indexes for a given month? Or are the vehicles only counted as production when they are shipped to dealers?
Posted: 07/22/2021

The almost-complete vehicles are included in the IP indexes. The organizations that provide data on motor vehicle assemblies have generally indicated that vehicles leaving the production line are counted toward unit production figures, even if further modification of the vehicle (such as the installation of components requiring semiconductor chips) is required.

The industrial production (IP) index for motor vehicles and parts jumped 120.8 percent in May 2020. How much did that add to overall IP growth?
Posted: 06/16/2020

Broadly speaking, the rate of change from one month to the next for any aggregate IP index can be calculated as a weighted average of the rates of change of its components. The weights used for aggregation are based on estimates of nominal value added for the components in the earlier of the two months over which the change is calculated.

In the case of motor vehicles and parts, as a result of the almost-complete shutdown of motor vehicle assembly facilities in April, motor vehicles and parts accounted for only 1.12 percent of the nominal value added for total IP, a far lower share than its 2019 average of 5.5 percent. The contribution of motor vehicles and parts to the rate of change for total IP in May is its rate of change in May (120.8 percent) multiplied by its value-added share for April (1.12 percent)—about 1.35 percentage points.

A precise description of the IP aggregation calculation is available in the IP Explanatory Notes on the Federal Reserve's website. The monthly shares of the implicit value added for total IP are called "relative importance weights" and are available through the Federal Reserve's Data Download Program and in a text file.

How were the IP estimation procedures for March 2020 updated in the G.17 release published on May 15, 2020?
Posted: 05/15/2020

The initial estimates for March in the G.17 release on industrial production and capacity utilization published on April 15, 2020, included adjustments to the standard procedures as outlined in an earlier Technical Q&A. These adjustments were necessary because the initial estimates for many IP indexes rely on data—namely, Bureau of Labor Statistics (BLS) Current Establishment Survey (CES) measures of production-worker hours for the pay period that contains the 12th of March—that were likely unrepresentative of the month, as a large number of establishments slowed or suspended production in late March to protect workers from the spread of COVID-19. The CES survey period for April is thought to be representative of the month, so no special adjustments were made to the procedures for estimating April IP.

After the publication of the G.17 release in April, officials from the BLS suggested that information on initial claims for unemployment insurance (UI) by detailed industry could be helpful in refining the estimates of output. The BLS shared with the Federal Reserve estimates of weekly industry-level UI claims for 43 states plus the District of Columbia; for the remainder of the states, estimates were imputed by the Federal Reserve using employment shares from the U.S. Census Bureau’s County Business Patterns report.

For each industry, the most recent CES estimates describe employment and hours for the pay periods containing March 12 and April 12. In nearly all cases, activity in the week containing April 12 was much lower than the week containing March 12. In order to improve the estimate of activity during the whole of March, estimates of weekly employment and hours were constructed based on the weekly UI figures. These weekly estimates reflected the weekly pattern of the declines between March and April for a particular industry. Adjusted values of employment and hours for March were constructed as the average over all weeks in the month. In the standard IP calculations, these adjusted measures replaced the standard measure that reflected only employment and hours during the pay period containing the 12th of the month.

The level of activity implied by the April survey week data appears representative of the month as a whole, so this new procedure allows for a more precise allocation of the overall decline from February to April in industry activity between declines in March and declines in April. In practice, the adjustment used CES values for February and April in addition to March because those months served as endpoints for the interpolations that produce the weekly series.

The UI estimates were incorporated into the IP indexes using the following methodology.

  1. At the 4-digit NAICS level, the monthly readings from the CES were treated as if they were for the Sunday–Saturday week typically used as the BLS survey period.
  2. For the weeks after the survey week in February (March), an initial estimate of 4-digit employment was calculated by subtracting the initial claims from the initial estimate of the previous week’s employment. Of course, this method did not yield a value for employment in the March (April) survey week that aligned exactly with the actual report in the CES, so the initial weekly estimates of employment were multiplied by adjustment factors, as described in steps three and four.
  3. For the March (April) survey week, the ratio of employment from the CES to the UI-adjusted estimate of employment from step two was calculated.
  4. A weekly series was computed that interpolated between 1.0 (for the February (March) survey week) and the ratio computed for the March (April) survey week in step three.
  5. The initial weekly estimate of 4-digit employment from step two was multiplied by the weekly series from step four to produce the final weekly adjusted measure of employment. It should reflect the pattern of UI claims—and, likely, reductions in production—in the industry.
  6. A survey week series for the ratio of production-worker hours to employment was computed, and the intervening weeks were interpolated using the weekly adjusted employment series from step five.
  7. A weekly series for production-worker hours was computed as the product of the ratio constructed in step six times employment from step five.
  8. The monthly average of the weekly series from step seven should reflect average employment and production-worker hours over the month and should represent production for the month more accurately than a straight-read of the information from the CES survey period.
  9. For each industry, the final adjustment to the IP index was the ratio of average weekly production-worker hours for March divided by the CES value for production-worker hours.
How were the industrial production indexes for April 2020 adjusted to account for the new production of ventilators and personal protective equipment (PPE) by companies in unrelated industries that had not previously manufactured these products?
Posted: 05/15/2020

The IP indexes for April 2020 were adjusted to account for the production of ventilators at facilities that produce motor vehicle parts. This adjustment was unusual because, in general, the IP index for a particular industry reflects overall output of the establishments in that industry, even if those establishments also produce secondary and tertiary products associated with other industries.

In the current environment, some motor vehicle parts manufacturers are producing ventilators at previously idled establishments. Using the standard method to account for an industry's output, these ventilators would be considered to be a secondary product of the establishments producing them (unless their production ends up exceeding that of motor vehicle parts in that establishment for the year as a whole) and counted toward overall output of the motor vehicle parts industry. However, because of the atypical nature and volume of this production, the IP indexes have been adjusted to reassign the output of ventilators from otherwise idle factories that normally produce motor vehicle parts (NAICS 3363) to the IP index for electromedical and electrotherapeutic apparatus (NAICS 334510).

This adjustment is expected to continue for as long as ventilators are reported to constitute a non-negligible share of output for NAICS 334510. The amount and timing of ventilator production was estimated from government announcements and press reports. When annual benchmark data for 2020 become available from the U.S. Census Bureau, these benchmarks will also be adjusted to reassign ventilator production from NAICS 3363 to NAICS 334510.

IP indexes were not adjusted to account for PPE (such as masks, hand sanitizers, goggles, or other garments or equipment) that was produced at establishments that do not typically manufacture these products. The information needed to quantify these adjustments is unavailable, and it is expected that secondary production of this type was relatively small compared with the output of establishments that manufacture these products as primary output.

How did the COVID-19 Pandemic affect the IP estimation procedures for March?
Posted: 04/15/2020

To protect individuals against the spread of COVID-19, a large number of states and localities issued “stay-at-home” orders directing “non-essential” businesses to suspend most operations. Most of these orders were introduced in the second half of March, but at different times in different jurisdictions. The suspension of production had a large, negative effect on industrial production for the month. The magnitude of the effect of the COVID-19 pandemic on industrial output was estimated using procedures comparable to those employed previously to assess the effects on production of significant weather events, such as Hurricanes Sandy and Katrina.

The Federal Reserve measures of industrial production are built from indexes for around 300 individual industries. For some industries, timely high-frequency physical product data are available for the initial estimate of production for a month and should reflect the imprint of the pandemic without any further adjustment. For other industries, physical product data lag a month or more, so the estimate of the pandemic effect needs to be estimated from other sources until the physical product data are received. Finally, there is a set of industries for which physical product data do not exist, and production is estimated based on Bureau of Labor Statistics (BLS) data on production worker hours and an estimate of productivity. The BLS data on production worker hours are based on payrolls for the pay period that contains the 12th of March, but for many companies, that pay period will not be representative of the month because it preceded the stay-at-home orders issued by state and local governments.

For industries without timely high-frequency product data, the effects of the pandemic were incorporated into the IP indexes using the following methodology.

  1. The starting dates for stay-at-home orders by state and local governments were compiled.
  2. For industries that were not exempt from the stay-at-home orders, it was assumed that all of the establishments in the jurisdiction issuing the order were shut down from the first day specified through the end of March.
  3. Industries were exempt from these orders if the jurisdiction designated them as essential; much of the industrial sector fell into this category. However, if additional information indicated otherwise, partial shutdowns were assumed for some of these essential industries.
  4. Information from the U.S. Census Bureau's County Business Patterns report provided the share of an industry's activity in a particular county. These shares were used to cumulate for each industry the proportion of monthly output that was affected by stay-at-home orders and by partial shutdowns for essential industries. The IP indexes were reduced by these industry-specific effects.

For some of these industries, the estimates of the pandemic effect will be replaced once direct measures of output become available in future months.

Q: How are the seasonal factors for light vehicle sales calculated?
Posted: 07/31/2018

A: Each summer the dataset of raw light vehicle unit sales used to estimate the seasonal factors is updated with data through April. As of 2018, the dataset extends back to January 2000, when the current configuration of the auto sales announcement calendar was introduced. (Earlier vintages of the seasonal factors were based on sales data back to 1977.) These data are reported by WardsAuto each month and reflect manufacturers' announcements or Wards estimates. 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 X13-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 and holidays, a step that captures the variation in the length of each selling month, the composition of weekdays within each selling month, and the placement of some holidays that float between sales months from year to year. (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. The only holiday for which we have found it necessary to include an adjustment is Labor Day, which floats between August and September on the auto sales calendar.

Second, the seasonal component is estimated from the trading-day and holiday-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, the seasonal factors posted by the BEA may vary slightly from the factors posted here, reflecting small adjustments to align annual totals of the seasonally adjusted and not seasonally adjusted data. The updated factors are posted on our website.

Q: A set of price indexes for the communications equipment industry were published on the Federal Reserve's website on June 1, 2018. How do these differ from the price indexes posted previously?
Posted: 06/01/2018

A: The data published on the Federal Reserve's website on June 1, 2018, span the full output of NAICS 3342, "Communications Equipment Manufacturing," whereas the earlier data covered only a portion of the industry.

Previously, the Federal Reserve published quarterly- and annual-frequency price indexes for the 2002-2016 period for four broad categories of products in the communications equipment industry. The quarterly indexes were constructed from a variety of data sources and were benchmarked to annual-frequency price indexes.

In contrast, the price indexes published on June 1, 2018, include information for all components of NAICS 3342, and this information is provided in two different ways. First, the Federal Reserve provides price indexes for NAICS 3342 by three levels of product classes: High-level product classes (three classes), Mid-level product classes (seven classes), and Low-level product classes (19 classes). Each set of product classes covers the entirety of NAICS 3342. Second, the Federal Reserve provides price indexes for each of the six-digit NAICS industries (NAICS 334210, NAICS 334220, and NAICS 334290) that together compose NAICS 3342; furthermore, price indexes are provided for primary products, secondary products, and miscellaneous receipts for each of these six-digit NAICS industries.

In addition to the price indexes that were published on June 1, 2018, the Federal Reserve published weights for each individual index so that data users can aggregate to broader categories according to their own interests.

Most of the newly published price indexes are annual and cover the 1987-2016 period. However, some quarterly price indexes are provided. The full set of price indexes first published on June 1, 2018, are the result of reinvestigation (and in some cases, small modifications) of the sources and methods described in Byrne and Corrado (2015). A detailed description of the Federal Reserve's data, sources, and methods is available at https://www.federalreserve.gov/releases/g17/commequip_price_indexes.htm

References
Byrne, David M., and Carol A. Corrado (2015). "Prices for Communications Equipment: Rewriting the Record," Finance and Economics Discussion Series 2015-069. Board of Governors of the Federal Reserve System (U.S.). Available at http://dx.doi.org/10.17016/FEDS.2015.069.

The Federal Reserve's measures of capacity for the motor vehicle industry are constructed differently from the measures for other industries. Capacity for motor vehicles is constructed at the plant level and aggregated to an industry total. For most other industries, the Federal Reserve uses industry-level source data in its capacity estimates.

Q: How are the capacity indexes for the motor vehicles series created? How does the Federal Reserve's measurement of motor vehicle capacity differ from other industry sources?
Posted: 05/03/2018

A: The Federal Reserve's measures of capacity and utilization use a definition of capacity that seeks to capture the concept of sustainable maximum output: The greatest level of output an industry can maintain within the framework of a realistic work schedule, after factoring in normal downtime and assuming sufficient availability of inputs to operate the capital in place. Other definitions of capacity are sometimes used in industry reports of capacity, with the resulting estimates necessarily different. Capacity estimates for light motor vehicles reflect, for each assembly plant and model year, data on the line speed (the number of vehicles produced per hour), the workweek (the number of shifts operated per day multiplied by the number of hours per shift), and the number of workdays per year. The Federal Reserve obtains data on assembly plant shifts and line speeds from Ward's Automotive Yearbooks and other sources. (Changes in line speeds are inferred from the changes in plant capacity when line speeds are not directly reported.) The Federal Reserve's estimates of capacity assume that the capacity line speed for a plant is its maximum line speed observed over a 10 year window. For the workweek, all plants are assumed capable of running two 8-hour shifts per weekday; the capacity workweek is higher for factories that have been observed running with three shifts or four crew configurations in the past. Taking into account holidays and the shutdowns in the summer and at the end of the year, there are 235 production days in a typical model year. The Federal Reserve aggregates the plant-level capacity estimates using weights based on the mix of models produced within each plant and model-level prices.

Other sources of capacity estimates for the motor vehicle sector may use definitions of capacity that are based on the assembly plants' current line speeds and workweek configurations rather than their maximum sustainable levels. For example, some industry capacity estimates are adjusted down right away if a plant reduces a shift or trims its line speed, whereas the Federal Reserve estimates of capacity would typically not be affected as quickly, if at all. All else equal, this methodological difference would result in other sources' estimates of motor vehicle capacity being lower, and operating rates higher, than those published by the Federal Reserve.


Q: When a new automotive plant comes online, how and when is that reflected in the Federal Reserve's capacity index?
Posted: 05/03/2018

A: The Federal Reserve's estimates of motor vehicle capacity are updated to reflect changes in plant-level capacity at the time of the publication of the G.17 release on Industrial Production and Capacity Utilization published in February, and again with the annual revision. New information may also be incorporated in a mid-year update of capacity that occurs with the G.17 release published in July.

Q: What are the effects on Industrial Production of Hurricanes Harvey, Irma, and Nate?
Posted: 11/16/2017

A: The table below summarizes the effects of the hurricanes in August, September, and October on industrial activity. Row 1 shows the most recently published rates of change for total IP and manufacturing IP. Rows 2 through 6 show estimates of the hurricane-related contributions to total and manufacturing IP by the industry groups that were the most affected by the storms. The total hurricane effect (row 7) is subtracted from the published rate of change (row 1) to estimate the change in IP excluding hurricane effects (row 8). Below the table is a detailed explanation of the values in each row.

For more information about how the hurricane effects were constructed, please see the related Technical Q&A or FEDS Note. The values in the table below reflect newly available data and differ slightly from the previously published estimates.

Note: The table below has been updated to correct an error in an earlier version. For September, the change in total industrial production excluding hurricane effects is 0.55%; previously, this was reported as 0.25%.

Estimated Contributions to the Percentage Change in IP (percentage point)
August September October
Total Mfg. Total Mfg. Total Mfg.
1 Published Change in IP (11/16/2017) -0.46% -0.19% 0.40% 0.37% 0.94% 1.26%
Hurricane Harvey effects on
2 Crude oil and natural gas drilling and extraction -0.10 ... 0.10 ... ... ...
3 Oil refineries, petrochemical plants, and plastic resin facilities -0.30 -0.40 -0.45 -0.60 0.75 1.00
4 All other industries -0.25 -0.25 0.25 0.25 ... ...
5 Hurricane Irma effects on all industries ... ... -0.05 -0.05 0.05 0.05
6 Hurricane Nate effects on all industries ... ... ... ... -0.20 ...
7 Total hurricane effects -0.65 -0.65 -0.15 -0.40 0.60 1.05
8 Change in Industrial Production excluding hurricane effects. 0.19% 0.46% 0.55% 0.77% 0.34% 0.21%

Crude oil and natural gas extraction was suppressed by the shuttering of rigs on the mainland and in the Gulf of Mexico in late August because of Hurricane Harvey (row 2). The rigs were mostly back on-line by early September, thus raising total IP growth in September by 0.10 percentage point.

Many oil refineries, petrochemical plants, and plastic resin facilities shut down in late August because of Hurricane Harvey, and some were closed through much of September (row 3). In fact, more output was lost in September than in August, which lowered IP growth in September. As these establishments came back online and returned to producing at normal levels in October, they provided a boost of 0.75 percentage point to the growth in total IP.

Outside of crude oil and natural gas extraction and production at oil refineries, petrochemical plants, and plastic resin facilities, industries in the Houston area were assumed to have lost 5 days of production in late August, lowering IP growth for by ¼ percentage point (row 4). It was assumed that producers returned to normal operations in September, raising IP growth in that month. For these industries, Hurricane Harvey had no effect on IP in October.

Hurricane Irma suppressed output, but since it occurred in the first part of September, it was assumed that producers made up much of the lost output by the end of the month, with the net effect being a loss of only ½ day of production, lowering the rate of change for total IP by 0.05 percentage point (row 5). The return to normal production for these industries then raised the rate of change for total IP for October by 0.05 percentage point.

In October, Hurricane Nate caused a sharp but short-lived decline in oil and gas drilling and extraction in the Gulf of Mexico, suppressing total output growth by 0.20 percentage point (row 6). No other industries appeared to be affected measurably.

Excluding hurricane effects, total IP increased in each of August, September, and October. Manufacturing IP apart from the hurricane effects increased solidly in August and September, and it advanced more modestly in October.

Q. The G.17 press release on industrial production (IP) and capacity utilization published on October 17, 2017, noted that the "continued effects of Hurricane Harvey and, to a lesser degree, the effects of Hurricane Irma combined to hold down the growth in total production in September by 1/4 percentage point." Can you quantify the effects of Hurricanes Harvey and Irma on the rates of change for industrial production in August and September 2017?
Posted: 10/20/2017

A. The table below provides additional information on the effects of Hurricanes Harvey and Irma on the industrial sector. The rows show estimates of the hurricane-related contributions to the rates of change for IP for August and September for several groupings of industries. The total of the hurricane effects is subtracted from the published rate of change to create the estimate of the change in IP excluding the hurricane effects, which is shown in the bottom row of the table. For more information about how the hurricane effects were constructed, please see the related Technical Q&A or FEDS Note.

  Estimated Contributions to the Percentage Change in IP (percentage point)
August September
Total Mfg. Total Mfg.
Published Change in IP (10/17/2017) -0.73% -0.24% 0.28% 0.10%
Crude oil and natural gas extraction was suppressed by the shuttering of rigs on the mainland and in the Gulf of Mexico in late August because of Hurricane Harvey. The rigs were mostly back on-line by early September, thus raising total IP growth in September. -0.15 ... 0.10 ...
Many oil refineries, petrochemical plants, and plastic resin facilities shut down in late August because of Hurricane Harvey, and some were closed through much of September. More output was lost over all of September than in August, thus lowering IP growth in September. -0.30 -0.40 -0.50 -0.65
Hurricane Harvey lowered the output of other industries in late August. It was assumed that they lost 5 days of production in August but were back to normal operations for September, thus raising IP growth. -0.25 -0.25 0.25 0.25
Hurricane Irma suppressed output, but since it occurred in the first part of September it was assumed that producers made up much of the lost output by the end of September, with the net effect being a loss of only 1/2 day of production. ... ... -0.05 -0.05
Total hurricane effects -0.70 -0.65 -0.20 -0.45
Excluding hurricane effects, total IP was little changed in August and increased in September. Manufacturing IP apart from the hurricane effects increased in both months. -0.03% 0.41% 0.48% 0.55%
Q. The G.17 press release from September 15, 2017, reported that output in the industrial sector was held down by 3/4 percent because of disruptions caused by Hurricane Harvey. How were the storm effects calculated?
Posted: 9/15/2017

A. The effect of the severe weather on industrial output was estimated using data and procedures comparable to those employed to assess the effects on production of previous significant weather events. In summary, for some industries, there exist timely high-frequency physical product data that reflect, without any further adjustment, the imprint of the severe weather. Where such data are not available, industry output was adjusted in a procedure that uses employment data from the U.S. Census Bureau's County Business Patterns (CBP) report to determine the prevalence of a particular industry in the storm-affected region; this information is combined with assumptions about the duration of outages to estimate a storm effect.

The effects of Hurricane Harvey were concentrated in the Gulf Coast region, home to much of the nation's output of oil and natural gas (NAICS 211111) and of related downstream industries, including petroleum refining (NAICS 32411), organic chemicals excluding ethanol (NAICS 32511 and NAICS 32519 other than NAICS 325193), and plastics materials and resins (NAICS 325211).

For the first estimate of IP for August, the indexes for crude oil extraction, natural gas extraction, and petroleum refining were based on preliminary weekly frequency data that covered the period that included the storm; these preliminary estimates will be updated once more comprehensive data become available in subsequent months. So, for these industries, the standard IP source data were adequate and no supplemental information or adjustment was required.

Although the first estimate of the IP index for organic chemicals is also typically based on preliminary weekly data, more-detailed alternative data from PetroChem Wire--a daily news source for organic chemicals and plastic resins--became available in late August and were used to supplement the weekly readings. During the storm and its continued aftermath, PetroChem Wire provided daily reports on the plant-level status of operations. These reports include information on each plant's capacity as well as whether the plant was shut down, in the process of restarting, or fully operational. The plant-level information was aggregated to the industry level and used to inform industrial activity for August. In contrast to the IP index for organic chemicals, the primary source data for plastics materials and resins are not available for the first estimate of IP, so the plant-level PetroChem Wire reports were used to help assess industry output for August. PetroChem Wire continues to produce these reports, which will help guide the IP estimates for September.

For industries where high-frequency data on output are not available--including mining oil and gas field machinery (NAICS 33313), which is heavily represented in the Harvey-affected region--the IP indexes were adjusted for storm-related disruptions using a procedure detailed in a Federal Reserve Bulletin article from 2009, an Annual Revision article for 2013, and in Technical Q&As from November 2012 and February 2014. Briefly, this three-step procedure determines the storm effect for each industry by first identifying the counties affected by the storm; using employment shares from the CBP to determine the portion of each industry's output that occurs in those counties; and combining assumptions about the duration of outages with the information from the first two steps to estimate the overall effect on the IP index for each industry. As actual measures of industry output become available over time, the initial measures are updated, just like IP estimates in non-storm months.

With the G.17 release issued on April 18, 2017, the Federal Reserve began publishing estimates of the reliability of the levels and the rates of change (monthly and quarterly) of the reported production indexes for total industry, manufacturing, mining, and utilities. The reliability estimates will be published for those months and quarters for which either new or updated estimates were issued in that release.

Q. What are the reliability estimates and how are they constructed?
Posted: 4/18/2017

A. The reliability estimates are measures designed to give data users a sense the typical range into which a statistic will likely end up after its final (fifth) revision in a monthly release. The first estimate of a production index for a month is published around the 15th of the following month. The estimate is preliminary and subject to revision in each of the subsequent five months as new or revised source data become available. For example, the G.17 release published on April 18, 2017, includes the first estimate of output for March 2017, the second estimate for February 2017, and so on, to the sixth estimate (5th revision) of output for October 2016. Reliability measures are reported for the estimates of total industrial production, manufacturing, mining, and utilities for each of these months, and for the quarters that contain them.

The reliability estimate to be published for an index level or rate of change reports the typical range for that figure after all five monthly revisions are complete, given the current value for the measure. The bottom of the range is computed as the current estimate plus the 15th percentile revision to the real-time estimates dating back to 2008; the top of the range is the current estimate plus the 85th percentile revision. To calculate the bottom of the range for a particular (first, second or any subsequent) estimate of the index level (or rate of change) for IP, the history of revisions between the index level (or rate of change) for a particular estimate and the index level (or rate of change) for the sixth estimate are sorted, and the value chosen is the one that is above only 15 percent of the observations. This 15th percentile value is then added to the current estimate to determine the bottom of the range. The top of the range is computed in the same way, based on a revision value that is higher than 85 percent of the observations.

In general, the initial estimate of an IP index is subject to the most revision in subsequent months. As monthly information accrues from the first estimate to the second, from the second to the third, and so on, there is less scope for revisions, and the computed range consequently narrows. Because the sixth estimate of a month is final other than for changes from annual revisions, the range for the statistic in that month only includes the current estimate.


Q. Where can I find the reliability estimates on the Board's website? Can I download the data? Are they available from the Download Data Program (DDP)?
Posted: 4/18/2017

A. The reliability estimates are issued in table 15 of the G.17 release, available on the Board's website at https://www.federalreserve.gov/releases/g17/Current/default.htm. A text file that contains the estimates is also available on the Federal Reserve's website and can be found at https://www.federalreserve.gov/releases/g17/ipdisk/revh_sa.txt .

The reliability estimates are not available for download from the Board's DDP site because they are not standard time series in the same way as the other data on the DDP. The nature of the estimates is closer to the "vintage" data available in text files elsewhere on the Board's website, so the reliability measures are packaged with those data.


Q. Is it possible to construct reliability estimates for the rates of change from the reliability estimates from the index levels?
Posted: 4/18/2017

A. No. Because the revisions to monthly rates of change may be correlated, the confidence intervals for the index levels cannot be directly computed from the confidence intervals for the rates of change.


Q. Do the published reliability estimates depend on the state of the economy or the amount of data available for a particular month?
Posted: 4/18/2017

A. No. The interval defined by the bottom and top of the ranges for a particular month is not conditional on either the state of the economy in that month or the amount of data available for that month's estimate; the span of the range is derived from population percentiles using every month since the beginning of 2008. The reliability estimates are only designed to provide the user a sense of the range in which the IP indexes are likely to fall after all five monthly revisions are complete.


Q. Do the reliability estimates account for the revisions to the index levels and rates of change that occur with annual revisions to industrial production?
Posted: 4/18/2017

A. No. These reliability estimates focus on the revisions that result from incorporating monthly data. The annual revisions to industrial production incorporate more comprehensive annual estimates of production that can affect the entire history of the index.


Q. Why do the reliability estimates only begin in 2008?
Posted: 4/18/2017

A. The reliability estimates begin in 2008 because the IP system began publishing with a 6-month window on April 16, 2008, the first monthly G.17 release following the 2008 annual revision. Previously, the monthly G.17 releases published data with a 4-month window.


Q. Why are the top and bottom of the ranges defined as the 85th and 15th percentiles, respectively?
Posted: 4/18/2017

A. These values were chosen to cover a broad range of likely revisions.


Q. Why are the reliability estimates published to 2 decimal places but IP is only published to one decimal place?
Posted: 4/18/2017

A. The reliability estimates are published to 2 decimal places to give a finer definition on likely ranges of revisions in the coming months.


Q. Why are there only reliability estimates for total industry, manufacturing, mining, and utilities?
Posted: 4/18/2017

A. The reliability estimates are only published for the major aggregates because these are thought to be the indexes where data users would have the most interest in having additional information.

Q. How are the seasonal factors for light vehicle sales calculated?
Posted: 7/30/2015 (Updated: 7/31/2018)

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?
Posted: 10/16/2012

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.

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Last Update: July 22, 2021