Leading pollsters have indicated that the economy has become the number one issue during the election campaign of 2015. It is not surprising, given the economy has recently entered its 2nd recession since 2007. Beyond the headline numbers, the question arises- who can Canadians trust to build a future economy? The Harper government maintains that they have been great economic managers and Canadians should trust them (campaigning on this logic during a recession is surely a sign of how bad things have gotten.) The following report finds that contrary to these claims the economy in terms of job growth has stagnated and become recession prone. So much so that some of the fastest growing industries in Canada are actually rooted in industries that help the growing levels of poverty. For example the 2nd fastest growing industry in Canada since the recession of 2007 is NAICS444 –“Rooming and boarding houses”. Also making it into the top 30 high growth industries are “Used Merchandise stores”, “ Community food and housing, and emergency and other relief services”. Other top growth industries are related to the increasingly privatization of the health care sector and elder care sectors.
Quite ironically there is not one technology or manufacturing related industry in the top 30 growth industries as measured by job creation rates over this period. However there many technology and manufacturing industries in the top 50 declining industries. The conclusion being that there is qualitative side to job creation as the actual nature of job growth and decline under Harper’s government is leading Canada down a pathway that could make the current recession more pronounced. In fact looking at the data- it is hard to fashion any plan or direction in job growth- save for privatization of government services and a declining resource extraction, technology and manufacturing industries.
So what exactly has Mr. Harper’s much advertised “action plan” produced for the Canadian economy. The following report, produces a detailed accounting of what industries produced the highest rates of growth and decline since the great recession measured in terms of jobs. For now these jobs will be treated as “any job”- in future the report with segment these into low wage and high wage jobs. What industries have flourished and which industries have fallen off since the great recession of 2008.
The uniqueness and power of this report rests on the facts and the detail of where Canada’s economy has shown growth and decline. This based upon a very powerful but underutilized data source from Statistics Canada. The report uses detailed industrial classifications from an administrative Payroll Data file of all business entities that is maintained by Statistics Canada- (also known as the Survey of Employment, Payroll and Hours (SEPH). The data is a very powerful administrative data source- meaning it is not subject to sampling errors, and because the data source rides on the back of the payroll file and is classified using the very large and extensive Business Register at Statistics Canada- it has a great ability to encapsulate Industrial Classification and monitor employment and wages. A very powerful but sadly underused data source. The data source does have some gaps, for example the data does not include the self- employed, and has some large groups of unclassified businesses. (however many of these are undoubtedly own account employers or self employed with intentions of hiring or potentially laid off workers)
Part One: What Industries are Growing- A Qualitative view of the Quantitative
As can be seen in the first chart- job growth has been mixed within several sectors- and job declines have been focused in the manufacturing sector. The box plots show the broader NAICS industry by the percentage of job growth and decline. Each dot within the industry box plot represents the number of jobs gained or lost within that sub-industry so it affords a size dimension for each industries absolute job gains or losses. As can be seen in the manufacturing sector many of the jobs lost in this critical sector have been widespread. This suggests that a macro scale dynamic is at work- namely the appreciation of the dollar over this period has produced a widespread downsizing withing the industry. This across the board disadvantage meaning the affect of a high dollar can impact all companies- even those showing highly innovative capabilities. Possibly verifying what many leading economists have been concluding about economic development strategy- macro policy trumps micro. This has some serious implications for firms struggling to use innovation strategies to survive and prosper and could have a large impact on future productivity development.
The graph also shows broader increases in public sevices, educa and health as well as trade. The expansion of public services could lead to some future growth in productive capacity and that is a positive outcome. However- looking at the more detailed job growth and decline at the 4 digit industries, one gains a qualitative perspective.
As can be seen- in some of the top job creation industries it raises concern and signifies just how bad the economy is doing – that is industries have grown fastest growing industries are not in those one would classify as a healthy sustainable economy on many fronts. For example some of the leading growth industries are within those that are helping people adapt to economic hardship such as “Rooming and Boarding houses”, “Community Food and Housing and Emergency Services”, and “Used Merchandise Stores”. Several
[embeddoc url=”http://www.livingwork.ca/wp-content/uploads/2015/09/sephwage.xlsx” download=”all” viewer=”microsoft”]
Part 2: Exploring Industry Growth and Decline by Average Weekly Wage
The analysis next moves onto examine what industries have increased or decreased in size based upon the average weekly wage as measured by the SEPH survey vehicle. The next part of the analyis rates industries in terms of absolute gains or losses in number of jobs. A plot was performed on the 4 digit NAICS level on the count of job change over the 2008 to 1014 period by the average weekly wage paid in 2014. A linear regression algorithm was used to explore whether there was a relationship between size of industry job change and wage level. the regression model used a standard least squares algorithm, and was weighted by the absolute value of the size change in jobs.
As can be seen their is a negative linear association between the two variables- which suggests that as the size of jobs created within an industry grows positive the lower the wage rate becomes and the reverse for higher wages. Note that the graphical interpretation is a bit hidden because of the weighting structure of the industries. So imagine the larger dots in the plot having much more power in minimizing the variance of the estimate when determining the location of the regression line.
Also note that the large industry in the top left corner- i.e. low wage but large increase in jobs is the Full service Restaurant and semi service restaurant industry- which by far created the most jobs.
Here is the Regression output- as you can see the p value is significant and the r value is .12 which means the relationship is signficant.
lm(formula = sephm$diff07 ~ sephm$X2014.y, weights = (abs(sephm$diff07)))
Min 1Q Median 3Q Max
-13652457 -1896031 -1035852 -484681 35907579
Estimate Std. Error t value Pr(>|t|)
(Intercept) 61777.67 7438.46 8.305 1.76e-14 ***
sephm$X2014.y -41.05 7.76 -5.290 3.32e-07 ***
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 3602000 on 192 degrees of freedom
(49 observations deleted due to missingness)
Multiple R-squared: 0.1272, Adjusted R-squared: 0.1227
F-statistic: 27.98 on 1 and 192 DF, p-value: 3.32e-07
Please note that this is in early stages of research- I will be adding wages to each of the industries and quantifying industry growth by wage classifications. And finally I will be performing some machine learning on the time series over this period on the monthly data to determine what industries had similarity in terms of growth and decay patterns. The routines will use a k-means clustering algorithm for times series data. I will be adding more analysis as time moves forward.
Update: Work has commenced on expanding this analysis. The goal will be to include an analysis of wage growth within each of these high growth industries- to determine if we can quantity and classify the wage level of jobs being created within these 3 digit NAICS industries.
A second goal will be to explore the nature of job growth between industries that have grown versus those that have declined with a special emphasis on time series analysis. Adding into this analysis will be several additional varablies in which the study will use to expand the understanding of the high growth industries and those in decline.
Lastly the outputs from this study will be to generate some user friendly and accessible outputs to aid and develop a more granular understanding of the actually existing industrial growth and decline since the great recession of 2008.
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