General Question on data analysis

I am analyzing the economic contribution of the logging sector (IMPLAN code-16) in East Texas ( by adding 38 counties) vs Texas state in the year 2019 and dollar year also 2019. 

Pooja,

Without knowing your explicit Project/Region setup I would be unable to say for sure, but I do think that this would make sense. While the concentration of this Industry may be stronger in East Texas relative to the rest of the state, running a contribution at the state level would include East Texas in addition to North, South, and West Texas. What I can see on my end is that for Industry 16, there are 250 counties in Texas that have some level of Output in 2019, meaning that restricting your contribution to only the 38 East Texas counties would naturally lead to smaller Direct Results relative to the state level analysis (due to the 212 counties not included in the East Texas run).

Hope this helps!

Michael Nealy 

Hello Michael,

You can see the data I have posted in question section. I can provide you all the information that you need to understand these. The only thing I didn't understand is the difference in the data. I accept that the other state of Texas has also contributed to some extent to the Texas's economy but having only 2.4 million in the rest of Texas out of 14.2 million acres. How can they generate almost 2500 more jobs as compared to east Texas? 

Good afternoon Pooja!

I may have misunderstood how your Regions were set up: Are you comparing Results from these 38 counties to the Texas totals, or to Texas less the 38 East Texas Counties? If you could specify which 38 Counties you are using for your East Texas Region, I can take a deeper look at what may be causing the discrepancy in what you expect to see with this contribution analysis vs. what you actually see in the Results. Just for a note that may help, I went ahead and looked at the occupation data for Industry 16 - Commercial Logging in Texas via IMPLAN's Data Library - and found that this Industry supports 29 different occupations for Wage and Salary Workers at the minor level of detail:

While a majority of these workers are likely employed at/reside close to the timberlands you are referring to, it is also possible that many of them will be employed in locations different than that of the timberlands themselves. 

Best,

Michael Nealy

Hello Michael, 

I really appreciate all the efforts and help. 

I am comparing the results between 38 East Texas Counties and Texas total. The name of 38 counties that I have included in my project are Anderson, Angelina, Bowie, Cherokee, Cass, Camp, Delta, Franklin, Gregg, Harrison, Henderson, Hardin, Houston, Hopkins, Jefferson, Jasper, Lamar, Morris, Marion, Newton, Nacogdoches, Orange, Polk, Panola, Red River, Rusk, Rains, San Jacinto, Sabine, San Augustin, Smith, Shelby, Tyler, Trinity, Titus, Upshur, Van Zandt and Wood. 

It not only the case of employment. For other section such as output, having the impact more in Texas than east Texas is fine. But you can see the difference, how large it is. Having little more forest land in Texas as compared to East Texas, how the impacts can be so large. I couldn't understand well so. 

I have one more question. I remember you replying to my COVID-19 quarterly related question. This is the section of your reply which I do not understand. I would note that although these are based off of quarterly data, the Q2 dataset for example is annualized and seasonally adjusted, meant to represent the 2020 economy as if the second quarter of 2020 was representative of the entirety of the year 2020. What is the meaning of the term annualized and seasonally adjusted. 

What I notice after analyzing the data of COVID-19 is the economic contribution has increased from 2019 to 2020 but the economic contribution within 2020 fluctuated. In the second quarter of 2022, the economic contribution was increasing and after that in third quarter the economic contribution declined. But I remember you mentioning that each quarter is the representative of 2020. How can quarterly data be representative of 2020 as it has different data and got different results while analyzing with each of them. 

One more thing. I could download the data about how the logging sector has impacted to all 546 sectors in terms of employment, output, and value added but could download/see the data for labor income. I have attached below the picture for your reference. 

With regards,

Pooja Chhetri. 

Hello Pooja!

I am happy to help! So regarding the Covid-19 quarterly data, IMPLAN used the best publicly available quarterly data from 2020 to produce IMPLAN datasets (U.S., state, county, congressional district, and zip code models) meant to represent the pandemic-altered 2020 U.S. economy. These datasets have the same data points and capabilities as regular annual IMPLAN data. These datasets are annualized and seasonally adjusted, meant to represent the 2020 economy as if that particular quarter of 2020 was representative of the entirety of the year 2020.

This means that to produce this data, our Data Team relied on a smaller set of source data points (by quarter) to project the latest regular annual IMPLAN data to 2020. This is because many of our usual sources did not have 2020 data available until the end of 2021. Because of this, you can compare 2019 levels to the Covid quarterly Data to see differences in your Region. However, being that the 2020 annual IMPLAN Data Year is already available in the IMPLAN Cloud at this point in time, I would utilize that instead of the quarterly data to analyze events that occurred in 2020. 

Regarding your Results tables, there is no Labor Income Impact by Industry table available in the IMPLAN Results. However, under the Value Added Results tab there is a table for each of the components of Labor Income, Employee Compensation and Proprietor Income. 

Hope this helps!

Michael Nealy

Thank you for your response Michael. These information helps me to understand things better.