Internship at the Bureau of Labor Statistics by Ashley El Rady
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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.
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Transcript of Internship at the Bureau of Labor Statistics by Ashley El Rady
PPI Analysis ProjectThe 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 IndexThe 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 StatisticsAshley El Rady, Centre College
Bureau of Labor StatisticsThis 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 ProjectThis 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 ProjectThis 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 ProjectThe 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.
