The PPI Analysis Project involved extracting index data for five-digit and six-digit NAICS industries from 2008 to 2013. The data was displayed graphically showing percentage changes in indexes starting from 2008. Correlation coefficients were calculated to measure how well six-digit industries moved with five-digit industries. The original project examined characteristics like sector and size that help explain how long establishments report prices to the PPI survey before quitting. Regression analysis found these characteristics impact attrition.

1. PPI Analysis Project
The PPI Analysis Project involved extracting index data from December 2008
to December 2013 for the five-digit NAICS industries and their corresponding
six-digit industries within the section of nondurable goods. The data were to
be displayed graphically, with the indexes for each NAICS five-digit industry,
the corresponding NAICS six-digit industries, and Finished Goods on the
same graph. I used Excel to calculate and graph the percentage change in
the indexes starting from December 2008. To get a numerical indicator of
how well the six-digit industries move with their five-digit industries and
Finished Goods, I calculated the correlation coefficients.
About the Producer Price Index
The PPI is a group of indexes that measures the average change in the
prices received by producers in the U.S. over time. The indexes are
classified by industry, commodity, or commodity-based final demand-
intermediate-demand (FD-ID). The industry classification system measures
the changes in prices received for the net output for each of the North
American Industry Classification System’s (NAICS) six-digit industries. The
commodity classification system measures changes in prices received for
products or services based on similarity or material composition, regardless
of the establishment’s industry classification. The FD-ID classification
system regroups commodity indexes for goods and services according to the
type of buyer and the amount of physical processing the products have
undergone. PPI data for a particular month are made available in the
following month in the PPI News Release and the PPI Detailed Report.
Original Project: Attrition among Establishments Participating
in the PPI Survey
This project involved designing an original project about something I found
interesting during my internship. The PPI sample includes over 25,000
establishments and participation in the PPI survey is voluntary. Some
establishments agree to participate but quit reporting prices before the next
resampling period, which is typically every five to seven years. For my
original project, I decided to identify characteristics of establishments that
help explain the length of time that reporters continue reporting prices. The
dependent variable is the number of months that the establishment reports
good prices and the independent variables include the establishment’s
sector, size, reporting method, region, reporting frequency, and number of
items for which the establishment reports prices The data came from the PPI
database and contains information about establishments and the items for
which they report prices. I used R to merge the data files, create the
variables, and estimate the model. The multiple linear regression model was
estimated using Ordinary Least Squares. My results indicate that the
establishment’s sector, size, reporting method, region, and reporting
frequency impact attrition.
Internship at the Bureau of Labor Statistics
Ashley El Rady, Centre College
Bureau of Labor Statistics
This summer, I completed an internship at the
Bureau of Labor Statistics in Washington, DC. An
independent statistical agency within the U.S.
Department of Labor, the Bureau of Labor
Statistics collects, analyzes, and distributes data
on the labor market, working conditions, and price
changes in the U.S. economy. I worked with the
economists in the Nondurables Section of the
Producer Price Index (PPI). In addition to
learning about how the PPI is developed, I
completed a PPI analysis project and a
pharmaceutical revenue data project.
Additionally, I researched 3D printing/additive
manufacturing and Factoryless Goods Producers
(FGPs). Finally, I completed an original project
about attrition among establishments that are
sampled and agree to participate in the PPI
survey.
Pharmaceutical Revenue Data Project
This project involved researching revenue data for pharmaceutical
manufacturers. The PPI will resample the pharmaceutical manufacturing
industry in the next six months, which involves first sampling specific drugs
and then sampling establishments. In order to sample the drugs, the PPI
needs a sampling frame with item weights. To avoid purchasing an
expensive dataset with this information, the PPI is seeking to find the
information online. I reviewed company websites, annual reports, and
financial reports and recorded the 2013 revenues for the drugs included in
the Food and Drug Administration’s list of domestically produced drugs.
While I was only able to find revenue data for 65 drugs on the list, those 65
drugs account for about two-thirds of the total drug revenue. This is a good
start to building the item sampling frame and perhaps the PPI can purchase
a less costly set of data to receive the rest of the revenue information.
3D Printing/Additive Manufacturing Research Project
This project involved developing a bibliography of articles and reports about
3D printing/additive manufacturing, as well as a list of contacts that the PPI
may contact for more information. In the last several years, the adoption of
3D printing and additive manufacturing technologies has increased rapidly as
the applications are expanding. I compiled a list of articles, websites, and
conferences and events about 3D printing/additive manufacturing. The
information collected for this project is intended to help the PPI decide how to
classify products made from 3D printing/additive manufacturing.
Factoryless Goods Producers Research Project
The project consisted of creating a list of news/blog articles about the
Factoryless Goods Producers (FGP) topic. In recent years, firms have
begun outsourcing manufacturing transformation activities while maintaining
control over the production process. These firms, called Factoryless Goods
Producers (FGPs), are not clearly classified under the North American
Industry Classification System (NAICS). The lack of guidance on classifying
FGPs has led to differences in classification practices across statistical
programs. In order to provide consistent classification guidelines, the
Economic Classification Policy Committee (ECPC) has recommended that
FGPs be classified in the manufacturing sector. I reported why the ECPC
recommends classifying FGPs in the manufacturing sector, as well as
concerns that people have expressed regarding this classification.