diff --git a/06-interactive.Rmd b/06-interactive.Rmd index bfd1a05..fee2736 100644 --- a/06-interactive.Rmd +++ b/06-interactive.Rmd @@ -54,3 +54,8 @@

+ +

+The interactive bar graph shows the total number of Work From Home employees change in 2020 by month. +Click on the buttons, we can explore that each sector has the lowest rate of Work From Home employees in October. +

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Chapter 7 Conclusion

7.1 Lessons Learned

This study found a number of notable associations between variables, which motivate further research.

@@ -135,13 +136,14 @@

7.2 Limitations

  • As we have discussed in the data sources sections, one of the limitation with this study is with gaps within time series data. That post significant challenge for us to take a holistic view of the fluctuation in the WFH employees’ population.

  • Additionally, it would be much better for us to investigate in this topics using real-life datasets that contains specific entries for individual employees. However, due to privacy reasons, that is not realistic for now.

  • Quarterly summarized data is not representative enough for visualization. For future studying, we will try to get more detailed data.

    • +

  • We cannot draw a causal effect conclusion on the questions that we were looking at, because these are observational data and there is no control group.

    • 7.3 Future Directions

    Several directions to consider looking into in our future research:

    diff --git a/docs/finalproj_files/figure-html/unnamed-chunk-29-1.png b/docs/finalproj_files/figure-html/unnamed-chunk-29-1.png index 4b8a593..8ad34df 100644 Binary files a/docs/finalproj_files/figure-html/unnamed-chunk-29-1.png and b/docs/finalproj_files/figure-html/unnamed-chunk-29-1.png differ diff --git a/docs/finalproj_files/figure-html/unnamed-chunk-29-2.png b/docs/finalproj_files/figure-html/unnamed-chunk-29-2.png index fc3a5c4..9b6f2a3 100644 Binary files a/docs/finalproj_files/figure-html/unnamed-chunk-29-2.png and b/docs/finalproj_files/figure-html/unnamed-chunk-29-2.png differ diff --git a/docs/finalproj_files/figure-html/unnamed-chunk-33-1.png b/docs/finalproj_files/figure-html/unnamed-chunk-33-1.png index cd1028c..45b5a42 100644 Binary files a/docs/finalproj_files/figure-html/unnamed-chunk-33-1.png and b/docs/finalproj_files/figure-html/unnamed-chunk-33-1.png differ diff --git a/docs/finalproj_files/figure-html/unnamed-chunk-33-2.png b/docs/finalproj_files/figure-html/unnamed-chunk-33-2.png index 157a152..82dffef 100644 Binary files a/docs/finalproj_files/figure-html/unnamed-chunk-33-2.png and b/docs/finalproj_files/figure-html/unnamed-chunk-33-2.png differ diff --git a/docs/finalproj_files/figure-html/unnamed-chunk-33-3.png b/docs/finalproj_files/figure-html/unnamed-chunk-33-3.png new file mode 100644 index 0000000..eae7e1b Binary files /dev/null and b/docs/finalproj_files/figure-html/unnamed-chunk-33-3.png differ diff --git a/docs/finalproj_files/figure-html/unnamed-chunk-37-1.png b/docs/finalproj_files/figure-html/unnamed-chunk-37-1.png index 4b8a593..1c6aef3 100644 Binary files a/docs/finalproj_files/figure-html/unnamed-chunk-37-1.png and b/docs/finalproj_files/figure-html/unnamed-chunk-37-1.png differ diff --git a/docs/finalproj_files/figure-html/unnamed-chunk-37-2.png b/docs/finalproj_files/figure-html/unnamed-chunk-37-2.png index fc3a5c4..8ffef40 100644 Binary files a/docs/finalproj_files/figure-html/unnamed-chunk-37-2.png and b/docs/finalproj_files/figure-html/unnamed-chunk-37-2.png differ diff --git a/docs/finalproj_files/figure-html/unnamed-chunk-41-1.png b/docs/finalproj_files/figure-html/unnamed-chunk-41-1.png index ef7296c..45b5a42 100644 Binary files a/docs/finalproj_files/figure-html/unnamed-chunk-41-1.png and b/docs/finalproj_files/figure-html/unnamed-chunk-41-1.png differ diff --git a/docs/finalproj_files/figure-html/unnamed-chunk-41-2.png b/docs/finalproj_files/figure-html/unnamed-chunk-41-2.png new file mode 100644 index 0000000..82dffef Binary files /dev/null and b/docs/finalproj_files/figure-html/unnamed-chunk-41-2.png differ diff --git a/docs/finalproj_files/figure-html/unnamed-chunk-41-3.png b/docs/finalproj_files/figure-html/unnamed-chunk-41-3.png new file mode 100644 index 0000000..eae7e1b Binary files /dev/null and b/docs/finalproj_files/figure-html/unnamed-chunk-41-3.png differ diff --git a/docs/finalproj_files/figure-html/unnamed-chunk-45-1.png b/docs/finalproj_files/figure-html/unnamed-chunk-45-1.png new file mode 100644 index 0000000..1c6aef3 Binary files /dev/null and b/docs/finalproj_files/figure-html/unnamed-chunk-45-1.png differ diff --git a/docs/finalproj_files/figure-html/unnamed-chunk-45-2.png b/docs/finalproj_files/figure-html/unnamed-chunk-45-2.png new file mode 100644 index 0000000..8ffef40 Binary files /dev/null and b/docs/finalproj_files/figure-html/unnamed-chunk-45-2.png differ diff --git a/docs/interactive-component.html b/docs/interactive-component.html index 3f38bf0..242a483 100644 --- a/docs/interactive-component.html +++ b/docs/interactive-component.html @@ -204,6 +204,10 @@

    Chapter 6 Interactive component

    +

    +The interactive bar graph shows the total number of Work From Home employees change in 2020 by month. +Click on the buttons, we can explore that each sector has the lowest rate of Work From Home employees in October. +

    diff --git a/docs/reference-keys.txt b/docs/reference-keys.txt index 81d44f1..b91629f 100644 --- a/docs/reference-keys.txt +++ b/docs/reference-keys.txt @@ -16,8 +16,6 @@ relationship-between-producitivity-and-work-from-home-employees part-iii-employment-and-earnings employee-numbers average-weekly-working-hours-and-overtime-hours -weekly-working-hours -weekly-overtime-hours average-hourly-earnings interactive-component conclusion diff --git a/docs/results.html b/docs/results.html index 145a80e..f04ee08 100644 --- a/docs/results.html +++ b/docs/results.html @@ -159,20 +159,18 @@

    5.2.4 Relationship between Produc

    5.3 Part III: Employment and earnings

    5.3.1 Employee numbers

    -

    -

    From the time series plot for different sectors and sub-sectors, we can see that for all the industries, there was an abrupt drop in employees number at the beginning of 2020, around the time of March when the pandemic started spreading across the US. The only industry that did not fluctuate much is Utilities. This shows that this industry provides stable jobs.

    +

    +

    From the time series plot for different sub-sectors, we can see that for all the industries, there was an abrupt drop in employees number at the beginning of 2020, around the time of March when the pandemic started spreading across the US. The only industry that did not fluctuate much is Utilities. This shows that this industry provides stable jobs.

    5.3.2 Average weekly working hours and overtime hours

    -
    -

    5.3.2.1 weekly working hours

    -
    -
    -

    5.3.2.2 Weekly overtime hours

    -
    +

    +

    Looking at the first graph, we can see that in general there’s a decrease in weekly working hours at the beginning of pandemic, then the hours started to increase to a new high point. Then by checking the the subsectors, it’s clear that the weekly hours for industries in goods-producing sector dropped while that for industries in private service providing sector increased since the pandemic started. When looking at the hours worked for Utilities, we can see a gradual increase. This may be the result of more people working from home causing higher needs for maintenance.

    5.3.3 Average hourly earnings

    +

    +

    From these two graphs, it’s apparent that the there’s an inflation in earnings per hour after the pandemic started, and it’s more obvious in the Private Service Providing sector. Recall that the average weekly working hours in goods-producing sector decreased while that in Private Service Providing sector, we can conclude that the employees in the Private Service Providing sector get higher payrolls after Covid-19 started spreading. Recall again from part 1 that the proportion of WFH increased since Covid-19, it’s possible that there is a positive relationship between the proportion of WFH and payrolls in the Private Service Providing sector.

    diff --git a/docs/search_index.json b/docs/search_index.json index 9753927..0cbe245 100644 --- a/docs/search_index.json +++ b/docs/search_index.json @@ -1 +1 @@ -[["index.html", "Analysis on the Telework Employees Features before and under COVID Chapter 1 Introduction", " Analysis on the Telework Employees Features before and under COVID Yucen Wang, Renyin Zhang, Zikun Dong 2021-04-12 Chapter 1 Introduction During the pandemic period, States governors published health guidance to restrict social activities and assure public safety. Hence, most employees and office workers had to shift to a remote working mode. Work-from-home (WFH), or teleworking, as a new fashion in the work arrangement type, has become a controversial topic as we walked through 2020. As graduate students, we have already collected some diverging opinions from our fellow colleagues. Some tend to easily get disturbed by different entertaining options when by themselves and have a hard time to truly focus, while others find out that studying alone helps them easily concentrate and increase their productivity while they can maintain flexible daily schedules. Students might not be the only group who face the same controversy. Therefore, it triggered our interests, as we wanted to know how work from home employees are affected by the pandemic and related policies. To solve this problem, we had to figure out how the general trends of work from home employees changed during the pandemic; whether there is a significant relationship between productivity and percentage of employees who telework; and the effect of WFH on salary within different industries and sectors over time. "],["data-sources.html", "Chapter 2 Data sources 2.1 Data Description 2.2 Issues/Problems with Data", " Chapter 2 Data sources 2.1 Data Description The data used for this project is downloaded from several different websites that contain census data related to telecommunicating population. Depending on the different facet of the problem we want to investigate from, the data sources vary. One of the website that we got our data from is the U.S Bureau of Labor Statistics (BLS), which is known as one of the largest government published databases. Specifically, we utilized the data summarized from this web-page Employed persons working on main job at home and at their workplace and time spent working at each location by occupation to get the data about annual WFH population before COVID-19 (2013-2019) by sectors, and utilized the data summarized from this webpage Effects of the coronavirus COVID-19 pandemic as the WFH data since May 2020, which measures the effect of COVID on the labor market from May 2020 to March 2021. Data downloaded from the previous website consist of 20 tables, containing 13 variables describing total employed population and WFH-only workers population within each occupation sector over years. Data downloaded from the latter website consists 11 tables, containing demographic (Race, Gender, Ages,), occupational, industrial and other characteristics measures about the total and WFH population. We accessed employment payrolls data collected in Employment, Hours, and Earnings from the Current Employment Statistics survey (National) by BLS from Employment Situation Table. The source provides information on monthly average working hours and payrolls for employees in different sectors from 2011 to 2021. All sectors belong to Total private sector, which is divided into two main sectors: Goods-producing and Private service-providing. Goods-producing includes Mining and logging, Construction, and Manufactoring, which consists of Durable goods and Nondurable goods, while Private service-providing includes Trade, transportation, and utilities, Information, Financial activities, Professional and business services, Education and health services, Leisure and hospitality, and Other services. Under Trade, transportation, and utilities, there are Wholesale trade, Retail trade, Transportation and warehousing, and Utilities sectors. We also considered U.S. BLS Beta Labs as our second resource. It provided time-series employment productivity data. We wanted to show the trend of industrial-wise productivity with its correlation with the WFH portion of the labor force, so we selected the quarterly data from 2018 to 2020 by different sectors. There were six sectors in total: Business, Non-farm business, Manufacturing, Durable goods, Non-durable goods and Non-financial corporations. In order to compare the productivity changes, we took several features: Productivity, Working hours and Unit labor costs. For data from each sector and each feature, we chose the same unit for comparison: output per hour for labor productivity, average weekly hours for working hours and unit labor costs. The source provided us with 18 raw data tables in total, we would merge and combine these data tables to come up with several visualized plots. 2.2 Issues/Problems with Data Though the data provides suitable information for our topic, it has some limitations. One issue is that for covid-concurrent data, we can only have access to the monthly summaries no earlier than May, 2020, while the before-covid datasets only have yearly summaries, which end at 2019. Since we dont have the first several months data for 2020, we cannot generate a yearly summary from the covid-concurrent table. That would give us a data gap from Jan to May of 2020. We might need to give an estimate or generate a visualization of the trend when dealing with this problem. Another issue is that there is no direct data linking employment payrolls with WFH. We will need to utilize the resources in the first two links in hope to generate some insights on the change in number of WFH laborers and its possible effect on payroll amounts. For the data extracted from U.S. BLS Beta Labs, those data sets are quarterly averaged or weekly averaged. Since data for a more specific time period was not published, we could not depict a more time-sensitive change. Also, we were not able to get the first quarter data of 2021. For work from home data, we have a missing value, but this did not influence the initial analysis. Missing values were stated in 04-missing part. Another issue was that the sources provided different definitions for industrial sectors, so the sectors for productivity and payrolls could not be uniformed. To solve this issue, we matched some of the sectors to estimate our data. "],["data-transformation.html", "Chapter 3 Data transformation", " Chapter 3 Data transformation From the websites mentioned in the Data Sources section, we downloaded our data. Data from the U.S Bureau of Labor Statistics followed certain formats and contained redundant headers. We removed those headers in order to import the data into R for further cleaning process. Also, different sectors have their own data sets for each topic. We reorganized the structure of the tables and combined some of them in order to generate desired graphs. To clean the data, we carefully dealt with the missing values corresponding different types of data. Some columns were dropped respecting certain situations. We changed data type for some primary features we are interested in analyzing. For example, features representing Time were originally stored in numeric or character format; they were transformed into Date or yearmon data types to better fit the desired graphs. "],["missing-values.html", "Chapter 4 Missing values 4.1 Missing data in WFH employees population by occupation sectors 4.2 Missing data in Productivity features", " Chapter 4 Missing values 4.1 Missing data in WFH employees population by occupation sectors Below is a table that summarized the missing data in WFH employees population by sectors over time. ## Year Construction Farm Installation Mgmt_Bus_Fin Office_Admin ## 0 0 14 1 0 0 ## Production Professional Sales Service Transport WFH_only ## 0 0 0 0 0 0 ## NOTE: In the following pairs of variables, the missingness pattern of the second is a subset of the first. ## Please verify whether they are in fact logically distinct variables. ## [,1] [,2] ## [1,] "Farm" "Installation" We can see that for the data set that describes WFH population before COVID, occupation sectors such as Farming, Fishing and Forestry contains lots of missing summary data. That is likely due to the fact that workers from Farming Sectors are likely working from home and the standard of telework is not clearly defined for them. Also, it is noticed on the documentation of the data tables that effective with January 2011 data, occupations reflect the introduction of the 2010 Census occupational classification system. Data for 2011 and later are not strictly comparable with earlier years. 4.2 Missing data in Productivity features ## Series.ID Year Period Label Value ## 8 PRS88003092 2019 Q04 2019 Qtr4 1.4 ## 9 PRS88003092 2020 Q01 2020 Qtr1 0.3 ## 10 PRS88003092 2020 Q02 2020 Qtr2 4.8 ## 11 PRS88003092 2020 Q03 2020 Qtr3 -0.6 ## Series.ID Year Period Label Value ## 9 PRS88003092 2020 Q01 2020 Qtr1 0.3 ## 10 PRS88003092 2020 Q02 2020 Qtr2 4.8 ## 11 PRS88003092 2020 Q03 2020 Qtr3 -0.6 ## 12 PRS88003092 2020 Q04 2020 Qtr4 NA For non-financial-corporation, there are only 11 rows. Productivity for the fourth quarter of 2020 is missing. However, this does not affect the analysis for productivity when plotting the time series plot. For the scatterplot, we will trimmed the WFM data to make sure two data frames are matched with no missing values. (Trimmed data is included in data transformation part.) "],["results.html", "Chapter 5 Results 5.1 Part I: WFH Employees before and after COVID-19 5.2 Part II: Productivity 5.3 Part III: Employment and earnings", " Chapter 5 Results 5.1 Part I: WFH Employees before and after COVID-19 Overall, the general population of telework employees tend to increase over the last 10 years. Among all the sectors being investigated, Service and Management,Business and Financial Sector seemed contribute to the increase in work-from-home employees population the most. The plot above shows the fluctuation of growth rate with WFH population within selected Sectors over years. We can see that some Sectors, such as Construction, Production, Office and Administration, Services and Transportation seem to have high volatility in the changes of WFH population percentages, with fluctuation ranging between -0.5% to 1%. Management, Business and Financial and Professional Sectors seem to have less fluctuation in the change of rate. In general, we can see that the increase of telework population due to COVID has a greater portion among the number of all employed workers starting from May 2020 and its proportion started to decrease as time pass by. Even though we could not get the data before May 2020, this trend may suggest that at the beginning of 2020, when COVID had just begun, the ratio of COVID-lead WFH population over the total employed population would be even higher. Another trend we can see from the graph is that the total employed workers number is gradually increasing. 5.2 Part II: Productivity 5.2.1 Labor Productivity From the time series subplots, we can see that the output has a abrupt change at 2020 first and second quarter. Manufacturing, Durable Goods and Non-Durable Goods sectors have a sudden decrease and other sectors had a sudden increase. The increasing number of Work From Home employees might be the reason that causes this change. 5.2.2 Work hours Then we check work hours from 2018-2020. Work hours have apparent decrease for all sectors in the first quarter of 2020. This shows an opposite trend with productivity. 5.2.3 Unit labor cost We check unit labor cost from 2018-2020 and find the costs increase a lot in 2020 first quarter. Since three time series plots all show that there is a sudden change at 2020 first quarter, the pandemic could be the cause. 5.2.4 Relationship between Producitivity and Work From Home employees The graph represents the percentage of Work From Home Employees change due to the pandemic. There is a decreasing trend from May to October and lines slightly back up in November and December. From the previous sections, we find that Work hours decrease and production increase in first and sector quarter of 2020. This means that the efficiency has increased, which might be caused by teleworking. Hence, we make a scatter-plot to illustrate the relationship between Productivity and WFH employees. The scatter-plot indicates that there is a positive correlation between two variables. As the percentage of WFH employees increase, the Productivity tends to increase. 5.3 Part III: Employment and earnings 5.3.1 Employee numbers From the time series plot for different sectors and sub-sectors, we can see that for all the industries, there was an abrupt drop in employees number at the beginning of 2020, around the time of March when the pandemic started spreading across the US. The only industry that did not fluctuate much is Utilities. This shows that this industry provides stable jobs. 5.3.2 Average weekly working hours and overtime hours 5.3.2.1 weekly working hours 5.3.2.2 Weekly overtime hours 5.3.3 Average hourly earnings "],["interactive-component.html", "Chapter 6 Interactive component", " Chapter 6 Interactive component Id Attributes #business { color:black; font-size:14px; font-weight:bold; text-align:center; } #durableGoods { color:black; font-size:14px; font-weight:bold; text-align:center; } #manufacturing { color:black; font-size:14px; font-weight:bold; text-align:center; } #nonDurableGoods { color:black; font-size:14px; font-weight:bold; text-align:center; } #nonFinancial { color:black; font-size:14px; font-weight:bold; text-align:center; } #nonFarm { color:black; font-size:14px; font-weight:bold; text-align:center; } Choose your interested sectors Change to Business Sector Change to Durable Goods Sector Change to Manufacturing Sector Change to Non-durable Goods Sector Change to Non-financial Corporations Sector Change to Non-farm Sector "],["conclusion.html", "Chapter 7 Conclusion 7.1 Lessons Learned 7.2 Limitations 7.3 Future Directions", " Chapter 7 Conclusion 7.1 Lessons Learned This study found a number of notable associations between variables, which motivate further research. Regarding the teleworking population, it has been continuously growing even before COVID happened. The unprecedented pandemic has suddenly increased the WFH population ratio over total employed populations. Among sectors, Construction, Professional and Management, Business and Finance tend to have rooms for increasing number of employees to telecommute on an daily basis. From the graphs of section 5.2 part II, we conclude that there tends to be a positive correlation between Productivity and percentage of employees work from home. The result leads to an opposite direction as our initial thoughts. Three sectors (Manufacturing, Durable and Non-Durable Goods) decrease in productivity, whereas other sectors increase. Work hours decrease and unit labor costs both increase at the first quarter of 2020. 7.2 Limitations As we have discussed in the data sources sections, one of the limitation with this study is with gaps within time series data. That post significant challenge for us to take a holistic view of the fluctuation in the WFH employees population. Additionally, it would be much better for us to investigate in this topics using real-life datasets that contains specific entries for individual employees. However, due to privacy reasons, that is not realistic for now. Quarterly summarized data is not representative enough for visualization. For future studying, we will try to get more detailed data. 7.3 Future Directions Several directions to consider looking into in our future research: Due to time limitation, in this project we only focused on the U.S employment records. In fact, COVID-19 as a global pandemic, has also posted huge influence on the working patterns for various countries around the globe. It might be a good direction to investigate in topics such as: Does WFH employees ratio dependent on different countries or regions? After studying the potential relationship between teleworking mode and productivity, we want to figure out the reason why productivity decrease in Manufacturing, Durable and Non-Durable Goods sectors. Hence we need to analyze the relationships between these three sectors and factory working hours. "]] +[["index.html", "Analysis on the Telework Employees Features before and under COVID Chapter 1 Introduction", " Analysis on the Telework Employees Features before and under COVID Yucen Wang, Renyin Zhang, Zikun Dong 2021-04-12 Chapter 1 Introduction During the pandemic period, States governors published health guidance to restrict social activities and assure public safety. Hence, most employees and office workers had to shift to a remote working mode. Work-from-home (WFH), or teleworking, as a new fashion in the work arrangement type, has become a controversial topic as we walked through 2020. As graduate students, we have already collected some diverging opinions from our fellow colleagues. Some tend to easily get disturbed by different entertaining options when by themselves and have a hard time to truly focus, while others find out that studying alone helps them easily concentrate and increase their productivity while they can maintain flexible daily schedules. Students might not be the only group who face the same controversy. Therefore, it triggered our interests, as we wanted to know how work from home employees are affected by the pandemic and related policies. To solve this problem, we had to figure out how the general trends of work from home employees changed during the pandemic; whether there is a significant relationship between productivity and percentage of employees who telework; and the effect of WFH on salary within different industries and sectors over time. "],["data-sources.html", "Chapter 2 Data sources 2.1 Data Description 2.2 Issues/Problems with Data", " Chapter 2 Data sources 2.1 Data Description The data used for this project is downloaded from several different websites that contain census data related to telecommunicating population. Depending on the different facet of the problem we want to investigate from, the data sources vary. One of the website that we got our data from is the U.S Bureau of Labor Statistics (BLS), which is known as one of the largest government published databases. Specifically, we utilized the data summarized from this web-page Employed persons working on main job at home and at their workplace and time spent working at each location by occupation to get the data about annual WFH population before COVID-19 (2013-2019) by sectors, and utilized the data summarized from this webpage Effects of the coronavirus COVID-19 pandemic as the WFH data since May 2020, which measures the effect of COVID on the labor market from May 2020 to March 2021. Data downloaded from the previous website consist of 20 tables, containing 13 variables describing total employed population and WFH-only workers population within each occupation sector over years. Data downloaded from the latter website consists 11 tables, containing demographic (Race, Gender, Ages,), occupational, industrial and other characteristics measures about the total and WFH population. We accessed employment payrolls data collected in Employment, Hours, and Earnings from the Current Employment Statistics survey (National) by BLS from Employment Situation Table. The source provides information on monthly average working hours and payrolls for employees in different sectors from 2011 to 2021. All sectors belong to Total private sector, which is divided into two main sectors: Goods-producing and Private service-providing. Goods-producing includes Mining and logging, Construction, and Manufactoring, which consists of Durable goods and Nondurable goods, while Private service-providing includes Trade, transportation, and utilities, Information, Financial activities, Professional and business services, Education and health services, Leisure and hospitality, and Other services. Under Trade, transportation, and utilities, there are Wholesale trade, Retail trade, Transportation and warehousing, and Utilities sectors. We also considered U.S. BLS Beta Labs as our second resource. It provided time-series employment productivity data. We wanted to show the trend of industrial-wise productivity with its correlation with the WFH portion of the labor force, so we selected the quarterly data from 2018 to 2020 by different sectors. There were six sectors in total: Business, Non-farm business, Manufacturing, Durable goods, Non-durable goods and Non-financial corporations. In order to compare the productivity changes, we took several features: Productivity, Working hours and Unit labor costs. For data from each sector and each feature, we chose the same unit for comparison: output per hour for labor productivity, average weekly hours for working hours and unit labor costs. The source provided us with 18 raw data tables in total, we would merge and combine these data tables to come up with several visualized plots. 2.2 Issues/Problems with Data Though the data provides suitable information for our topic, it has some limitations. One issue is that for covid-concurrent data, we can only have access to the monthly summaries no earlier than May, 2020, while the before-covid datasets only have yearly summaries, which end at 2019. Since we dont have the first several months data for 2020, we cannot generate a yearly summary from the covid-concurrent table. That would give us a data gap from Jan to May of 2020. We might need to give an estimate or generate a visualization of the trend when dealing with this problem. Another issue is that there is no direct data linking employment payrolls with WFH. We will need to utilize the resources in the first two links in hope to generate some insights on the change in number of WFH laborers and its possible effect on payroll amounts. For the data extracted from U.S. BLS Beta Labs, those data sets are quarterly averaged or weekly averaged. Since data for a more specific time period was not published, we could not depict a more time-sensitive change. Also, we were not able to get the first quarter data of 2021. For work from home data, we have a missing value, but this did not influence the initial analysis. Missing values were stated in 04-missing part. Another issue was that the sources provided different definitions for industrial sectors, so the sectors for productivity and payrolls could not be uniformed. To solve this issue, we matched some of the sectors to estimate our data. "],["data-transformation.html", "Chapter 3 Data transformation", " Chapter 3 Data transformation From the websites mentioned in the Data Sources section, we downloaded our data. Data from the U.S Bureau of Labor Statistics followed certain formats and contained redundant headers. We removed those headers in order to import the data into R for further cleaning process. Also, different sectors have their own data sets for each topic. We reorganized the structure of the tables and combined some of them in order to generate desired graphs. To clean the data, we carefully dealt with the missing values corresponding different types of data. Some columns were dropped respecting certain situations. We changed data type for some primary features we are interested in analyzing. For example, features representing Time were originally stored in numeric or character format; they were transformed into Date or yearmon data types to better fit the desired graphs. "],["missing-values.html", "Chapter 4 Missing values 4.1 Missing data in WFH employees population by occupation sectors 4.2 Missing data in Productivity features", " Chapter 4 Missing values 4.1 Missing data in WFH employees population by occupation sectors Below is a table that summarized the missing data in WFH employees population by sectors over time. ## Year Construction Farm Installation Mgmt_Bus_Fin Office_Admin ## 0 0 14 1 0 0 ## Production Professional Sales Service Transport WFH_only ## 0 0 0 0 0 0 ## NOTE: In the following pairs of variables, the missingness pattern of the second is a subset of the first. ## Please verify whether they are in fact logically distinct variables. ## [,1] [,2] ## [1,] "Farm" "Installation" We can see that for the data set that describes WFH population before COVID, occupation sectors such as Farming, Fishing and Forestry contains lots of missing summary data. That is likely due to the fact that workers from Farming Sectors are likely working from home and the standard of telework is not clearly defined for them. Also, it is noticed on the documentation of the data tables that effective with January 2011 data, occupations reflect the introduction of the 2010 Census occupational classification system. Data for 2011 and later are not strictly comparable with earlier years. 4.2 Missing data in Productivity features ## Series.ID Year Period Label Value ## 8 PRS88003092 2019 Q04 2019 Qtr4 1.4 ## 9 PRS88003092 2020 Q01 2020 Qtr1 0.3 ## 10 PRS88003092 2020 Q02 2020 Qtr2 4.8 ## 11 PRS88003092 2020 Q03 2020 Qtr3 -0.6 ## Series.ID Year Period Label Value ## 9 PRS88003092 2020 Q01 2020 Qtr1 0.3 ## 10 PRS88003092 2020 Q02 2020 Qtr2 4.8 ## 11 PRS88003092 2020 Q03 2020 Qtr3 -0.6 ## 12 PRS88003092 2020 Q04 2020 Qtr4 NA For non-financial-corporation, there are only 11 rows. Productivity for the fourth quarter of 2020 is missing. However, this does not affect the analysis for productivity when plotting the time series plot. For the scatterplot, we will trimmed the WFM data to make sure two data frames are matched with no missing values. (Trimmed data is included in data transformation part.) "],["results.html", "Chapter 5 Results 5.1 Part I: WFH Employees before and after COVID-19 5.2 Part II: Productivity 5.3 Part III: Employment and earnings", " Chapter 5 Results 5.1 Part I: WFH Employees before and after COVID-19 Overall, the general population of telework employees tend to increase over the last 10 years. Among all the sectors being investigated, Service and Management,Business and Financial Sector seemed contribute to the increase in work-from-home employees population the most. The plot above shows the fluctuation of growth rate with WFH population within selected Sectors over years. We can see that some Sectors, such as Construction, Production, Office and Administration, Services and Transportation seem to have high volatility in the changes of WFH population percentages, with fluctuation ranging between -0.5% to 1%. Management, Business and Financial and Professional Sectors seem to have less fluctuation in the change of rate. In general, we can see that the increase of telework population due to COVID has a greater portion among the number of all employed workers starting from May 2020 and its proportion started to decrease as time pass by. Even though we could not get the data before May 2020, this trend may suggest that at the beginning of 2020, when COVID had just begun, the ratio of COVID-lead WFH population over the total employed population would be even higher. Another trend we can see from the graph is that the total employed workers number is gradually increasing. 5.2 Part II: Productivity 5.2.1 Labor Productivity From the time series subplots, we can see that the output has a abrupt change at 2020 first and second quarter. Manufacturing, Durable Goods and Non-Durable Goods sectors have a sudden decrease and other sectors had a sudden increase. The increasing number of Work From Home employees might be the reason that causes this change. 5.2.2 Work hours Then we check work hours from 2018-2020. Work hours have apparent decrease for all sectors in the first quarter of 2020. This shows an opposite trend with productivity. 5.2.3 Unit labor cost We check unit labor cost from 2018-2020 and find the costs increase a lot in 2020 first quarter. Since three time series plots all show that there is a sudden change at 2020 first quarter, the pandemic could be the cause. 5.2.4 Relationship between Producitivity and Work From Home employees The graph represents the percentage of Work From Home Employees change due to the pandemic. There is a decreasing trend from May to October and lines slightly back up in November and December. From the previous sections, we find that Work hours decrease and production increase in first and sector quarter of 2020. This means that the efficiency has increased, which might be caused by teleworking. Hence, we make a scatter-plot to illustrate the relationship between Productivity and WFH employees. The scatter-plot indicates that there is a positive correlation between two variables. As the percentage of WFH employees increase, the Productivity tends to increase. 5.3 Part III: Employment and earnings 5.3.1 Employee numbers From the time series plot for different sub-sectors, we can see that for all the industries, there was an abrupt drop in employees number at the beginning of 2020, around the time of March when the pandemic started spreading across the US. The only industry that did not fluctuate much is Utilities. This shows that this industry provides stable jobs. 5.3.2 Average weekly working hours and overtime hours Looking at the first graph, we can see that in general theres a decrease in weekly working hours at the beginning of pandemic, then the hours started to increase to a new high point. Then by checking the the subsectors, its clear that the weekly hours for industries in goods-producing sector dropped while that for industries in private service providing sector increased since the pandemic started. When looking at the hours worked for Utilities, we can see a gradual increase. This may be the result of more people working from home causing higher needs for maintenance. 5.3.3 Average hourly earnings From these two graphs, its apparent that the theres an inflation in earnings per hour after the pandemic started, and its more obvious in the Private Service Providing sector. Recall that the average weekly working hours in goods-producing sector decreased while that in Private Service Providing sector, we can conclude that the employees in the Private Service Providing sector get higher payrolls after Covid-19 started spreading. Recall again from part 1 that the proportion of WFH increased since Covid-19, its possible that there is a positive relationship between the proportion of WFH and payrolls in the Private Service Providing sector. "],["interactive-component.html", "Chapter 6 Interactive component", " Chapter 6 Interactive component Id Attributes #business { color:black; font-size:14px; font-weight:bold; text-align:center; } #durableGoods { color:black; font-size:14px; font-weight:bold; text-align:center; } #manufacturing { color:black; font-size:14px; font-weight:bold; text-align:center; } #nonDurableGoods { color:black; font-size:14px; font-weight:bold; text-align:center; } #nonFinancial { color:black; font-size:14px; font-weight:bold; text-align:center; } #nonFarm { color:black; font-size:14px; font-weight:bold; text-align:center; } Choose your interested sectors Change to Business Sector Change to Durable Goods Sector Change to Manufacturing Sector Change to Non-durable Goods Sector Change to Non-financial Corporations Sector Change to Non-farm Sector The interactive bar graph shows the total number of Work From Home employees change in 2020 by month. Click on the buttons, we can explore that each sector has the lowest rate of Work From Home employees in October. "],["conclusion.html", "Chapter 7 Conclusion 7.1 Lessons Learned 7.2 Limitations 7.3 Future Directions", " Chapter 7 Conclusion 7.1 Lessons Learned This study found a number of notable associations between variables, which motivate further research. Regarding the teleworking population, it has been continuously growing even before COVID happened. The unprecedented pandemic has suddenly increased the WFH population ratio over total employed populations, which could potentially mean that remote working is more stable than on-site working, given the situation. Among sectors, Construction, Professional and Management, Business and Finance tend to have rooms for increasing number of employees to telecommute on an daily basis. From the graphs of section 5.2 part II, we conclude that there tends to be a positive correlation between Productivity and percentage of employees work from home. The result leads to an opposite direction as our initial thoughts. Three sectors (Manufacturing, Durable and Non-Durable Goods) decrease in productivity, whereas other sectors increase. Work hours decrease and unit labor costs both increase at the first quarter of 2020. There might be a positive correlation between salary and percentage of employees WFH in the private service providing sector, but not in goods-producing sector. This is consistent with our prediction since goods-producing usually requires employees to be on-site, while service providing does not. 7.2 Limitations As we have discussed in the data sources sections, one of the limitation with this study is with gaps within time series data. That post significant challenge for us to take a holistic view of the fluctuation in the WFH employees population. Additionally, it would be much better for us to investigate in this topics using real-life datasets that contains specific entries for individual employees. However, due to privacy reasons, that is not realistic for now. Quarterly summarized data is not representative enough for visualization. For future studying, we will try to get more detailed data. We cannot draw a causal effect conclusion on the questions that we were looking at, because these are observational data and there is no control group. 7.3 Future Directions Several directions to consider looking into in our future research: Due to time limitation, in this project we only focused on the U.S employment records. In fact, COVID-19 as a global pandemic has also posted huge influence on the working patterns for various countries around the globe. It might be a good direction to investigate in topics such as: Does WFH employees ratio dependent on different countries or regions? After studying the potential relationship between teleworking mode and productivity, we want to figure out the reason why productivity decrease in Manufacturing, Durable and Non-Durable Goods sectors. Hence we need to analyze the relationships between these three sectors and factory working hours. "]]