[Federal Register Volume 88, Number 174 (Monday, September 11, 2023)]
[Notices]
[Pages 62402-62406]
From the Federal Register Online via the Government Publishing Office [www.gpo.gov]
[FR Doc No: 2023-19486]


-----------------------------------------------------------------------

DEPARTMENT OF LABOR

Bureau of Labor Statistics


Comment Request

AGENCY: Bureau of Labor Statistics, Department of Labor.

ACTION: Request for comments on proposed action.

-----------------------------------------------------------------------

SUMMARY: The Department of Labor, through the Bureau of Labor 
Statistics (BLS) and, specifically, the International Price Program 
(IPP), is soliciting comments on its plan to improve the Import and 
Export Price Indexes (MXPI) estimates by using administrative trade 
data acquired from the U.S. Census Bureau. IPP is responsible for the 
estimation and publication of the U.S. Principal Federal Economic 
Indicator of Import and Export Price Indexes (MXPI). The IPP collects 
data from companies on import and export prices and estimates price 
indexes for nearly all goods trade and some service trade for the 
United States. The data are primarily collected with a business survey. 
After completion of extensive research, and in response to a decline in 
data collected through traditional survey methods, BLS plans to 
implement improvements to the quality and quantity of import and export 
price indexes in fiscal year 2025 by replacing data directly collected 
from the business survey with administrative trade records for select 
homogeneous product areas.

DATES: Written comments must be submitted to the office listed in the 
Address section of this notice on or before October 26, 2023.

ADDRESSES: Written comments may be submitted by postal mail to Susan E. 
Fleck, International Price Program, U.S. Bureau of Labor Statistics, 
Room 2150, Postal Square Building, Massachusetts Avenue NE, Washington, 
DC 20212, or by email to: <a href="/cdn-cgi/l/email-protection#501900000f16021e10323c237e373f26"><span class="__cf_email__" data-cfemail="dd948d8d829b8f939dbfb1aef3bab2ab">[email protected]</span></a>.

FOR FURTHER INFORMATION CONTACT: Susan Fleck, International Price 
Program, Bureau of Labor Statistics, by phone at 202-691-6043 or by 
email at <a href="/cdn-cgi/l/email-protection#125b42424d54405c52707e613c757d64"><span class="__cf_email__" data-cfemail="d69f86868990849896b4baa5f8b1b9a0">[email protected]</span></a>.

SUPPLEMENTARY INFORMATION: 

I. Introduction

    The Department of Labor, through the Bureau of Labor Statistics, is 
responsible for the development and publication of Import and Export 
Price Index statistics through the International Price Program (IPP). 
Currently, monthly estimates of import and export price indexes for 
merchandise goods are published for approximately 740 industry and 
product classification areas, including the Harmonized System, Bureau 
of Economic Analysis (BEA) End Use System, and North American Industry 
Classification System (NAICS). Every month, approximately 17,000 prices 
for merchandise goods are collected from businesses using the 
International Price Survey. The participating businesses are selected 
based on a statistically representative sample of import and export 
goods trade.
    The International Price Program has developed an approach to 
maintain and expand the number of publishable price indexes of 
merchandise goods for the Import and Export Price Indexes. IPP plans to 
replace approximately a third of the sample of merchandise goods trade 
with administrative trade transaction records. The improvement is 
focused on homogeneous products; monthly prices are calculated from 
detailed unit values derived from timely trade transaction records. 
These administrative records are reported by companies for regulatory 
purposes and are used by the BLS for statistical purposes only. The 
administrative records are compiled by the U.S. Census Bureau to 
publish official international trade statistics. The Census Bureau is 
collaborating with BLS to share the records for use in calculation of 
the MXPI. These records have not been used previously to calculate 
monthly price indexes. Rather, they have been used by BLS at an 
aggregate level on an annual basis to establish the sample frame for 
the International Price Survey and to calculate annual trade weight 
shares.
    This new process is the culmination of a long-standing BLS 
objective to mitigate the decline in the number of items whose prices 
support the published indexes. In a multi-year, multi-project 
initiative that began in FY2018, the following proposed improvements to 
import and export price indexes for homogeneous products have been 
validated and are scheduled to be implemented:
    <bullet> Replace prices collected using the business survey with 
unit values of trade transaction records for the subset of homogeneous 
merchandise goods. This is accomplished by introducing the following 
improvements:
    [cir] Revision to sample selection process to replace directly 
collected prices from select sampled product areas with current-period 
transaction values of administrative trade records for similar goods.
    [cir] Application of a matched-model approach to administrative 
trade records to create unique product varieties that are consistently 
traded over time by:
    [ssquf] Applying a rigorous approach to define unit values and 
product varieties that mitigates unit value bias.
    [ssquf] Using coefficient of variation and other statistics to 
evaluate and rank homogeneity of product varieties and product 
categories.
    [ssquf] Grouping transactions into unique product varieties within 
detailed product categories by implementing a product match-adjusted R-
squared method that statistically ranks each combination of descriptors 
in transaction records. The best combination results in product 
varieties that are continuously traded and prices that are closest to 
the mean price of a variety.
    [ssquf] Filtering outliers that could cause large fluctuations in 
monthly price.
    <bullet> Improve representativity of price index by accounting for 
current period trade with current period price and

[[Page 62403]]

quantity from trade transaction records for the subset of homogeneous 
goods.
    <bullet> Reduce bias of price indexes by implementing a superlative 
index methodology to calculate lower-level unit value indexes for the 
homogeneous product categories, using same-period price and quantity 
information. This methodology improves the relevance and quality of 
price indexes by accounting for new and disappearing goods. The 
methodology also accounts for the seasonality and lumpiness of trade by 
calculating a mid-term relative between the current period unit price 
and the previous year's average unit price for each product variety.
    <bullet> Provide historic time series that allow data users to 
independently evaluate the comparability of planned and current 
official price indexes using proposed and current data sources.
    <bullet> Update the list of publishable import and export price 
indexes to expand the coverage from a current 700 to approximately 
1,200 detailed product and industry price index series, and 
additionally to expand coverage of country-specific price indexes.
    To assure data users that this transition to use administrative 
data and unit values provides for comparable price index estimates to 
the current approach, BLS has provided historical comparisons by 
calculating a research data series of the detailed 5-digit BEA End Use 
import and export price indexes for 2012 to 2021. BLS will continue to 
update the detailed research to current periods and will provide an 
overlap of the research data series with the official data series, once 
the transition to including administrative trade records occurs in 
2025. The research data series is posted to the MXPI research web page 
(http://www.bls.gov/mxp/data/research.htm).

II. Background

    The import and export price indexes are calculated with a modified 
Laspeyres formula, using current period prices and fixed trade weights 
that reflect trade quantities at the time of sampling, and that are 
adjusted annually. The target population for coverage of these price 
indexes is merchandise trade, excluding military goods, works of art, 
used items, charity donations, railroad equipment, items leased for 
less than a year, rebuilt and repaired items, and custom-made capital 
equipment. The measures are presented at a national level and are 
published using three classification systems; by product with the BEA 
End Use Classification System and the Harmonized System (HS), and by 
industry according to the North American Industry Classification System 
(NAICS). The estimates are based on the useable monthly prices of 
sampled items provided by company respondents to the International 
Price Survey. The data collected are based on a sample drawn from the 
frame of administrative trade data provided by importers and exporters 
to the U.S. government for regulatory purposes. BLS uses these data 
solely for statistical purposes.
    The number of companies and prices that support the price indexes 
has declined over time. In the 5-year period from 2017 to 2022, there 
was a 20-percent decline in the monthly number of prices collected, 
from 21,800 to 17,000. While the quality of the top-level price indexes 
has been sustained, the reduction in the number of prices has 
negatively impacted publishability of detailed price indexes and thus 
the relevance of the statistical measure for data users. An initiative 
to evaluate the unit prices of administrative trade records to replace 
prices reported in the directly collected survey was begun in FY 2018 
in response to the decline in prices collected. The research initiative 
has successfully shown that unit values from Census administrative 
trade records can be used in estimating import and export price indexes 
for many homogeneous product categories, because the price indexes 
using the new source and method show similar trends to the current 
official measures. The new approach also mitigates bias in the indexes 
and significantly reduces respondent burden.

III. Differences in Concepts and Methods Using Census Administrative 
Trade Data Source for Homogeneous Product Categories

    Using the data source of administrative trade transaction records 
in a new way to estimate prices requires changes to concepts, design, 
and calculation methods for this subset of the target population. This 
change in the source introduces a major expansion of coverage of 
homogeneous product categories while also reducing respondent burden. 
Because unit value price concepts are used for administrative trade 
data, the focus of the improvement is on homogeneous product 
categories. The changes to concepts and methods introduced by the 
change in source data are consistent with internationally recognized 
approaches to calculating price indexes, and the concepts and methods 
used complement those used for the directly collected business survey. 
The changes to concepts are: (1) price concepts, and (2) units and 
periodicity of collection. The new concepts are relevant only to the 
subset of homogeneous product categories. The change to design affects 
the subset of the target population of merchandise goods whose price 
source is administrative trade records; these product categories will 
no longer be sampled. The changes to methods are relevant only to the 
subset of homogeneous product categories; furthermore, these new 
methods are only for calculations of the unpublished lower-level price 
indexes for 10-digit Harmonized System (HS) product classification 
groups. New calculation approaches for the unit value indexes for the 
subset of homogeneous products cover (1) calculation of unit value 
indexes and aggregation, including treatment of outliers, (2) 
substitution procedures, (3) imputation, (4) starting a series, (5) 
variance estimates, and (6) sources of error. There are no changes to 
the aggregation method of calculating price indexes from the lower 
level to the published strata.
    Price concepts for administrative trade data source. The current 
preferred price concept for directly collected prices is a transaction 
price in the currency traded excluding fees, taxes, and duties. The new 
price concepts for the administrative trade data source are dependent 
on the regulatory requirements for data entry. All prices are border 
transaction prices. Prices are reported in U.S. dollars. The reporting 
requirements specify that the dollar value of the shipment is to be 
recorded, excluding insurance, freight, and duties. This dollar value, 
in international commercial (INCO) accounting terms, aligns with the 
free on board (f.o.b.) cost basis for imports, and the free alongside 
ship (f.a.s.) basis for exports, both of which exclude insurance, 
freight, and duties.
    In addition, the price definition used for the administrative trade 
data is a unit price, and the lower-level index calculated from the 
unit price is the unit value index. The unit price is an average price 
of a subset of administrative trade transactions grouped by similar 
characteristics to create unique matched-model product varieties that 
are then able to be consistently priced over time. Grouping 
administrative records into product varieties adheres to international 
best practices, which establish that unit values should relate to a 
single homogenous product whose specifications should remain constant.
    The new concept of unit price is based on the data fields reported 
in the administrative trade data. Each record reports the product 
quantity traded and total trade dollar value for a specific shipment by 
a specific company for a

[[Page 62404]]

specific 10-digit HS product category, for a point in time (i.e. the 
date of arrival or departure from the U.S. port). The unit price for 
each individual shipment is a product's total trade dollar value 
divided by the quantity. The shipment records are grouped by data 
fields into product varieties. The selection of the data fields to 
group records into distinct product varieties uses a match-adjusted R-
squared approach (MARS); data field combinations are ranked based on 
the explained variance in product unit prices with product match over 
time, using a stratification scheme based on the 10-digit HS product 
classifications that include a 5-digit BEA End Use product category. 
Product varieties are established as a combination of characteristics 
by BEA End Use strata using the MARS analysis. Once the characteristics 
are selected, records with the same characteristics are grouped into a 
unique product variety to calculate a quantity-weighted average unit 
price. The average unit price of each unique product variety is 
aggregated into a larger product category by HS classification to 
calculate a unit value index. (See New and enhanced methods to 
calculate and aggregate unit value indexes.) The unit value index is 
equivalent to a directly collected item price for calculation purposes. 
The characteristics of product varieties will be reviewed when major 
revisions occur in the HS product classification structure. Any change 
in HS product classification or product variety will be linked to 
continue a time series.
    Units and periodicity of collection. The current concept of 
periodicity of collection for the directly collected survey is that the 
preferred price for items reported by a respondent is the transaction 
price for an item traded in the reference month as near as possible to 
the first day of the month. The new concept for the subset of 
homogeneous products using administrative trade records is to account 
for all transactions throughout the reference month and to calculate a 
weighted average unit price for each detailed product variety. The 
reporting requirements for trade data extend beyond the calendar month, 
so that the preliminary estimate of MXPI will not include all trade 
during the reference month. Subsequent revisions to the MXPI will 
incorporate all transaction records for the reference month that meet 
data quality verification criteria.
    New and enhanced methods to calculate and aggregate unit value 
indexes for homogeneous product MXPI. Unit value indexes are reliably 
estimated using an estimation approach that incorporates new methods, 
enhancements to current methods, and continuation of other methods 
currently in use.
    New approach to calculation of unit value indexes. The current 
approach to account for those homogeneous product categories that use 
an average, spot, or unit price, for homogeneous product types such as 
grains, metals, and crude petroleum, is 1) to record the price for the 
homogeneous product category as a unit price for an item, and 2) to use 
the corresponding trade dollar value for the product category for 
aggregation. For crude petroleum imports, specifically, the current 
method is more refined; using the administrative data of imported crude 
petroleum collected by the U.S. Energy Information Agency, BLS 
calculates a weighted average unit price of each unique crude oil 
stream, all of which are then aggregated to a single unit value index 
for the crude petroleum product category.
    This current approach to using unit prices is enhanced for use with 
administrative trade data. At the index calculation level of published 
strata, the current approach for estimating published strata with 
average, spot, or unit prices remains the same. A new method has been 
implemented to calculate the unit prices of administrative trade 
transactions and to aggregate these transactions into unit value 
indexes. The new method accounts for the availability of current period 
quantity data in the administrative trade data. The new method results 
in a significant quality improvement that mitigates new goods and 
substitution bias by using the current period trade weights in a 
superlative index formula.
    The superlative index formula used for calculating the unit value 
indexes is a Tornqvist formula. A Tornqvist price index first 
calculates a geometric average of the price relatives of the current to 
base period prices. Current period prices are calculated for each of 
the 4 months of the revision period. Base period prices are the 
arithmetic average of all prices of the previous year. The ratio of 
current-period price to previous-year price, also called a mid-term 
relative (MTR), is calculated for each month. The Tornqvist calculation 
then weights the MTR price relatives of the product varieties by the 
arithmetic average of the value shares for the two periods to calculate 
the unit value index for each 10-digit HS product classification group. 
The index levels in each month are then linked to calculate month-to-
month price changes for each classification group. Using an entire year 
for the base period implies that any product variety that was traded 
the previous year contributes to the index, even if they were not 
traded the previous month. This approach greatly increases the number 
of product variety prices used in the unit value index estimation. The 
unit value index, once calculated, is then treated as a unique item 
price and then aggregated to the publication-level industry or product 
import or export price index using the current modified Laspeyres index 
method.
    New approach to aggregation. The current method to estimate the 
published Import and Export Price Indexes uses the monthly prices of 
directly collected items to calculate each item's price change, as well 
as sample weights and company weights, to aggregate to a 10-digit HS 
product classification group. The next step then aggregates the price 
change of the 10-digit classification group with annual trade weights 
from the calendar year ended 2 years prior to the current calendar year 
to calculate a modified Laspeyres price index for each classification 
system. The aggregation uses the concordance between the Harmonized 
System and the other two classification systems of BEA End Use and 
NAICS. With the new data source, aggregation does not require sample or 
company weights. Each unit value index is equivalent to an item price 
in the calculation of import and export price indexes. Thus the item 
prices that are aggregated to the published indexes are composed of 
directly collected prices and unit value indexes. Together they form 
two non-overlapping subsets of item prices that cover the target 
population of merchandise goods trade. The first subset consists of the 
monthly prices of directly collected items for product categories that 
do not meet the quality criteria for unit value indexes. The second 
subset consists of the unit value indexes for product categories that 
meet the quality criteria for use. The primary product classification 
is the BEA End Use product classification, and the detailed 5-digit BEA 
End Use import and export price indexes will be based on either the 
survey data or administrative trade data. However, at the higher levels 
of aggregation and for other classifications, most other published 
indexes will be composed of some combination of the two data sources.
    The subset of country-specific NAICS price indexes, called locality 
of origin and locality of destination price indexes, are used to 
measure U.S. competitiveness with trading partners. The current 
sampling approach does not account for locality, but the locality price 
indexes are quality-reviewed for

[[Page 62405]]

publication. The revised approach to calculating and publishing 
locality price indexes will blend directly collected items with 
locality-specific unit value indexes. Product varieties will be grouped 
by country and locality before their prices are aggregated to unit 
value indexes. Locality-specific unit value indexes are weighted by the 
locality-specific dollar value of trade from the transaction to the 
unit value index level. Each locality-specific unit value index is 
mapped to a classification group and then aggregated to the locality-
specific 6-digit NAICS industry category using the current modified 
Laspeyres index method. Some published indexes will be composed of some 
combination of the two data sources.
    New treatment of outliers. A new approach has been developed to 
assure fitness for use of the transactions comprising each 10-digit HS 
product category that will replace the directly collected survey data. 
This approach eliminates transactions that are not useable and excludes 
outliers at the tail ends of the distribution of price and quantity. 
Excluding outliers mitigates the occurrence of unit value bias. 
Previous research has identified the fitness for use of 10-digit HS 
product categories by comparing multiyear trends of price indexes that 
are composed of current data sources and administrative trade data, 
respectively. When price index trends are shown to be statistically 
consistent across years and months, administrative trade data are 
selected to replace current data sources. Subsequently, once the 
administrative trade data are in place in the official price indexes, 
procedures must be in place to evaluate and eliminate those 
transactions that are outliers, i.e., that differ greatly from the 
average trade transaction that make up a 10-digit HS product category. 
The exclusion of outliers will reduce the occurrence of unit value bias 
by limiting the variability that contributes to the bias and will 
assure the quality of the price indexes.
    The administrative trade data are filtered to exclude missing data 
and outliers using automated microdata review processes. Regarding 
missing data, transactions with null data fields are excluded. 
Transactions with a null quantity data field for which the quantity is 
imputed are excluded from unit price calculation. However, the dollar-
value weight is included for unit value index calculation. Regarding 
trimming outliers, four procedures are implemented progressively to 
trim quantities and filter unit prices and price changes to apply the 
matched-item approach and mitigate unit value bias. First, unit prices 
for each transaction are calculated, after which a set percent of the 
quantity is trimmed equally from both tails of the unit price 
distribution within the product variety; then the transaction unit 
prices are weighted using the trimmed quantities to calculate an 
average weighted unit price for the product variety. Thus, the largest 
and smallest transaction unit prices will have less impact on the 
weighted unit price of a product variety, which mitigates unit value 
bias. Second, the coefficient of variation value of the weighted unit 
price of each product variety is calculated; for any product variety's 
price whose coefficient of variation is over a set threshold, that 
product variety displays unit value bias, and thus is excluded from the 
unit value index calculation. The exclusion is conditional on the 
dollar-value weight of the product variety not exceeding 10 percent of 
the trade dollar value of the detailed BEA End Use stratum to which the 
variety's corresponding 10-digit HS product classification is mapped. 
Thus, this step excludes the product variety prices that show unit 
value bias while assuring representativeness. Third, the mid-term 
relatives (MTRs) are calculated for each product variety, using the 
average unit price in the reference month and the variety's base price 
from the previous year. The MTRs of all product varieties that comprise 
each 5-digit BEA End Use strata product grouping are sorted by 
magnitude, and MTRs on the tails of the distribution are trimmed for 
those values that extend beyond a previously established outlier 
threshold; the corresponding trade weights are also excluded in the 
index aggregation. This step uses historic research data to establish 
the outlier threshold. Fourth, in monthly production, automated flags 
identify outliers of product variety prices based on established 
thresholds relating to larger than average price movements. Individual 
product variety prices are compared over time and across varieties to 
determine statistical validity. Data values that do not meet 
established parameters are excluded.
    Enhanced method for substitution. Current substitution procedures 
and practices in the survey allow for item substitution, in which a 
previously traded item is replaced with a new item from the same 
company and within the same commodity classification group. Current 
imputation procedures and practices allow for an imputed or estimated 
price to be entered when there are missing price data.
    The new approach for items comprising HS product classification 
groups using administrative trade data does not substitute items. 
However, the new approach will immediately account for substitution in 
trade. There is no substitution procedure to replace items missing in 
trade, because the administrative trade data account for the natural 
occurrence of all trade. For unit value indexes, which are mapped to HS 
product classification groups, the lack of an observation is not an 
indication of missing data, it is rather an indication of no trade in 
that period.
    Enhanced method for imputation. The current imputation approach is 
to impute or estimate a price when there are missing price data and 
when starting a series. The new approach for items comprising HS 
product classification groups using administrative trade data depends 
on the level of calculation. At the level of product varieties, 
imputation is not used when a product variety has no unit price, 
because the lack of a unit price indicates an absence of trade. 
However, imputation is used when starting a series for a new product 
variety. A new product variety naturally occurs in trade, which is 
characterized by a not-previously defined combination of shared 
characteristics for the selected data fields in an HS product 
classification category.
    Enhanced method for starting a series. The current method for 
starting a price series, or initialization, is to impute the first 
price of an item based on the value of the index for the weight group. 
The enhanced method for starting a price series is to impute the first 
price of a product variety from the unit value index that is calculated 
from all other product varieties in the same HS-product category. The 
mid-term relative (MTR) method is then used to calculate the current 
period price relative. The current imputation approach for imputing a 
missing price at the classification group level does not change.
    Variance estimates for administrative trade data. The current 
approach for calculating variance estimates will not be revised; 
variance is calculated for price indexes that consist of sampled prices 
and are not calculated for price indexes that consist of prices 
collected from non-sample sources. For those price indexes that will be 
calculated with administrative trade data in place of directly 
collected survey data, no variance estimate will be calculated.
    Sources of error in administrative trade data. The current sources 
of error for survey data are a combination of sampling and nonsampling 
error. Sampling error is not relevant to price indexes calculated using 
administrative trade data because these are not sample data. With the 
new administrative data

[[Page 62406]]

source, there are a few potential sources of error. Processing error is 
one source of nonsampling error that is introduced with the use of the 
administrative trade data. Among the transaction records processed by 
the Census Bureau, some records have incomplete data and are not used 
in BLS calculations. Additionally, there is measurement error in 
assuming that the characteristics that make up a product variety 
adequately explain the month-to-month price change movements. 
Furthermore, other records are analyzed and excluded from calculation 
because they are at the tails of the distribution of prices or 
quantities and are excluded in order to reduce the variability of unit 
prices and unit value indexes. The exclusion of transactions with 
missing data and estimates at the tails of the distribution may result 
in bias or a skewed result if there is a repeatable pattern in either 
set of data, such that certain companies have more transactions with 
missing data or with widely variable prices. These nonsampling errors 
cannot be measured with current methods and there is little actual 
research on this topic for administrative data that represents the full 
population; however, research has begun and is ongoing to evaluate 
sources of error. This research includes methods to adequately explain 
mean square error for index estimates that are constructed from the 
integration of administrative data and sampled survey data.
    Publication of official MXPI. Current publication procedures for 
price indexes require an annual review of statistical robustness that 
include sample representativeness and that assure the protection of 
respondent and company identifiable information. Revised publication 
procedures for price indexes calculated with administrative trade data 
will be put in place. Current procedures limit publication of indexes 
that represent commodity areas with a minimum dollar value of annual 
import or export trade value. New procedures for administrative trade 
data will not require a minimum dollar value for publication. 
Protection of respondent and company identifiable information will 
remain in place and thus not all price indexes using administrative 
trade data will be published separately.
    Modified publication procedures are in place to evaluate the price 
indexes selected for inclusion in the aggregation. Up to the date of 
publication, a research data series using the new methods will be 
calculated to compare monthly and long-term variability and skewness 
relative to the official price indexes using current methods to assure 
quality and consistency before incorporating the administrative trade 
data in the official data release. When a detailed product area is 
either under-represented in the sample or difficult to collect, a price 
index representing commodity areas with smaller dollar values may use 
administrative trade data. This approach increases efficiency and 
mitigates respondent burden, even if some bias exists, as long as the 
bias does not have an impact on the upper-level indexes.
    Publication. Another important improvement is that the methods 
allow for an expansion of the number of publishable price indexes. The 
enhanced procedure will convert roughly 7 million transaction records 
for homogeneous product areas into hundreds of thousands of product 
varieties, which subsequently will be used to calculate thousands of 
unit value indexes for 10-digit HS product categories. These unit value 
indexes are integrated as item prices into the calculation of the MXPI. 
There is no change to the method of calculating the monthly estimates 
of price indexes. When the transition to using the administrative trade 
data occurs, the price indexes currently published will not have a 
break in series. Under current procedures, new items brought into the 
price indexes replace discontinued items. With the introduction of the 
administrative trade data source, new items based on the administrative 
trade data will be brought in to completely replace directly collected 
survey data within classification groups that have been determined to 
meet criteria of homogeneity. Whether a published price index includes 
administrative trade data will be determined by the concordance between 
HS classification groups and each product and industry classification. 
The new approach treats directly collected and administrative data 
equally, and no distinction will be made in publication of the data 
source. The transition to using administrative trade data in the 
official news release will be announced in advance.
    This detailed description of the current and redesign approaches 
complements the research data series that are available at the BLS MXPI 
website http://www.bls.gov/mxp/home.htm.

IV. Desired Focus of Comments

    This notice is a general solicitation of comments from the public 
on the technical approach to this major change in the concepts, 
sources, and methods of the Import and Export Price Indexes.

    Signed at Washington, DC, this 5th day of September 2023.
Eric Molina,
Acting Chief, Division of Management Systems, Bureau of Labor 
Statistics.
[FR Doc. 2023-19486 Filed 9-8-23; 8:45 am]
BILLING CODE 4510-24-P


</pre><script data-cfasync="false" src="/cdn-cgi/scripts/5c5dd728/cloudflare-static/email-decode.min.js"></script></body>
</html>