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Minimum Wage Arises- $15 per Hour for the People

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By Paul Tulloch- Living Work Analytics

(click here for PDF version)

Introduction

The Ontario Government under Premier Wynne has announced its plan to raise the minimum wage level from the current of $11.40 per hour to $15 per hour by 2019. Research from many sources have shown that over the past 30 years there has been an increasing level of income polarization. In fact, the minimum wage level over the past 30 years have barely kept pace with inflation – meaning many of the lowest paid workers have been witnessing a stagnant wage. As stated in a 2016 Statistics Canada report- “In 2013, the minimum wage was around $10 in all provinces. In constant dollars, this rate was similar to the rate observed in the late 1970s”. The Ontario government along with several other regions across North America have implemented, or are considering, a substantial raise of the minimum wage level. This of course has many in the business community up in arms. Several economists and business interest groups are making the claim that such minimum wage hikes will unfairly push their “bottom line”. They warn such actions will result in both massive layoffs and inflationary price pressures throughout the economy. It has many in the country talking about the rise in the minimum wage with some serious questions. With that in mind research was undertaken to answer some of these questions. How many workers are affected and what are the demographics of such workers. What will it cost in economics terms and what are the benefits with such a rise in the minimum wage.

After extensive data wrangling and a rigorous estimation method- it was calculated for 2016 that approximately 1 out of 4 workers in Canada currently work for $15/hour or less with a similar proportion in Ontario. In this representative population estimate- the major findings noted that a significantly higher proportion of lower waged workers are women, and that a majority of low wage workers are older than 25 years of age. In the second part of the research using a simulation of 2016 data, it was found that the for Ontario the direct cost of raising employees to the new $15/hour minimum wage would have added $6 billion annually in wages – or a mere 1.71% of the total wages paid to all Ontario based employees in 2016. It also simulates the minimum wage rise to $15/hour for all provinces in Canada and estimates the total direct cost would have added $14.7 Billion in annual wages or 1.6% of the total wages paid in the country for 2016.

Methods and Data Quality

The following report makes use of a custom dataset of employees making $15 or less built on data from the Labour Force Survey (LFS). The data is provided by Statistics Canada through the public use micro file (PUMF). This allows a database to be built from the individual respondents rather than the traditional aggregates. It will also uses selected tables for aggregates of total wages paid and Gross Domestic Product (GDP) provided by Statistics Canada’s CANSIM aggregation and dissemination vehicle. (Statistics Canada LFS PUMF anonymizes the data by removing all identifying tombstone data.) Data accuracy and reliability measures are adhered to allowing a data science approach with statistically robust methods. For further information on survey methods and data collection please consult Statistics Canada’s “Guide to the Labour Force Survey”.

As with most measures of income and wage rates- there are different pathways used in the calculation of income and benefits a worker receives for work performed. For example, there are large variances in how a worker is paid- by the hour- by the month- piece work, salaried etc. There is also a large variance in reporting how many hours a worker works in a week, month, or year. The labour force survey attempts to resolve such reporting variances by asking for a per hour rate- and when that is not provided they receive what information they can- and use a standardized algorithm to produce hourly estimates. In terms of hours work- the LFS asks the respondent an estimate of “usual hours of work” as well as the “actual hours worked” during the reference week. It is through these two metrics that the wage rate is determined- and an estimate of hourly compensation is derived.

It should be noted that the question of compensation is only asked to respondents who declare they are employees- i.e. they are not self- employed. This poses a problem from an underestimate perspective- as there are known groups of workers- especially some of the more precarious like homecare workers, nannies, temp agency workers and others who are declared self-employed and therefore excluded from these counts. Obviously, this means the estimates produced using this data source- and those released by Statistics Canada using similar methods- will be underestimates of the actual number of low wage workers. It poses a challenge from a measurement standpoint- but one must clearly lay out what is being measured and inform the users of the information. More on the self- employed later.

How many workers are working for $15/hour and below?

Using the hourly metric noted above- it is estimated that in 2016 the annual number of Canadians earning $15/hour or less were 3.99 Million workers or 26% of the workforce who defined themselves as employees. For Ontario- the number of workers estimated to be making $15/hour or less in 2016 was 1.63 million or 27.7% of the workforce who defined themselves as employees. (table 1) That evaluates to a bit more than 1 in 4 workers who are working for wages that many activists and policy makers have defined as earning wages below a living wage.

Table 1- Workers Earning $15 and Below- 2016

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Source: Statistics Canada Public Use Micro File

(The estimated total percentages were verified with those reported by Statistics Canada quoted in an article by Armine Yalnizyan, published with McLean’s online, June 2, 2017)

Demographics of Lower Waged Workers

In providing a brief description of who works at $15/hour and bellow, the micro data were aggregated into demographics of age group and the sex of worker. The data clearly show that a much higher proportion of low wage workers are women. In Canada for 2016 it is estimated that 57.8% or 2.30 million low wage workers were women, the proportion for Ontario women was almost the same at 57.6% or 944,000 employees. As stated in the Government of Ontario’s press release over its new minimum wage proposal- this policy would help prime age women more than other groups- so they obviously did their research.

Oddly enough, there has been a long-held belief that pervades the public knowledge over labour markets in which a majority of lower waged workers tend to be younger as in high school aged in which most of the wages earned are defined as discretionary spending. Yet as shown in the data- a much larger proportion of workers earning below $15/hour for both Canada 58.8% and Ontario 58.8% are aged 25 years and older. Such popular misinformed notions need to be displaced- as they continue to be used by some groups to marginalize the importance of minimum wage legislation in helping fight poverty. It is a critical point to be made- as income and wealth becomes more polarized- the income for many workers in the prime age category increasingly becomes reliant on minimum wages. There is a question regarding component of family income that is reliant on low income work that remains to be answered. It will be followed up in future research.

Adding another layer of demographics and examining the age and sex groupings the difference in the lower wage category between the age groupings is significantly greater for women greater than 25 years of age.

 

Expanding the age categories by sex into finer levels of age groupings shows that women in the older age categories increasingly make up more of the total low wage workers. It is actually quite pronounced for women between the age of 35- 54 where nearly 2/3 of low wage workers are women. Again, clearly showing that low wages do impact prime aged women more than any other age and sex group. Such

 

 

 

 

statistical facts provide some very important information.  

This specifically when considering child care polices and other efforts to mitigate low wages and their impacts on families. This brings to light a critical understanding of jobs and work that demographic facts underline. Low wage work has been transforming the meaning of dollar values and wage levels at this end of the wage rate spectrum that has a much more encompassing potential causation effect on many sociological as well as economic outcomes. Minimum wage legislation is not just about younger people and disposable income that once defined much of this terrain. After 30 years of polarization in wages- low waged workers and their dollar values and purchasing decisions have a much more transformative meaning. As expenditure for such workers are more likely to be focused on basic living needs than discretionary spending. This is especially true for low wage workers who are in single earning families- or multiple income low wage families.

 

Workers Earning $15/hour and Below, Ontario- December 2016

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Workers Earning $15/hour and Below, Canada- December 2016

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The Costs and Benefits of Giving Low Wage Workers a Raise.

Minimum wage legislation has long been a part of the policy tools used by governments to protect workers from the vagrancies of markets. Minimum rates of pay act as a wage floor in which fundamentally determine the standard of living for many workers. It is in this light that the meaning of a minimum wage transcends the economics of production and industry to become a new form of safety net under neo-liberal economics. Government have been attempting to set minimum level of living standards through taxation and income transfers for last couple hundred years. However minimum wage legislation- has less of a history. Indeed, there is a very lengthy class struggle in wage determination in terms of collective organizing, unions and class interests but rarely has the state in Canada directly pushed this far into the wage rate setting mechanism. In many ways, it has been the failure of the state to effectively empower labour unions to represent workers that has potentially led to such new interests and policy outcomes on the minimum wage legislations. Unfortunately, this new minimum wage legislation could come at the expense of unions- and potentially worker outcomes. Under a union model it could be argued that much like the post war era and the onset of Keynesian economics- a much more efficient wage and bargaining outcome mechanism for workers could be achieved through traditional collective bargaining. However, that would mean much greater legislative changes to collective representation and empowering unions to organize these low wage workers. But given the choices for effecting demand management policies- the new much higher minimum wage laws- could be the beginnings of a new- “new deal” similar to that of the post war era- but with potentially less desirable outcomes for workers.

Therefore, as the state becomes a more direct actor in such wage setting policy, the leap from the economic realm in the minimum wage debate will become ever more politicized and barbed in ideological overtones- and more so at election time. As the new reality of such policy direction becomes more prominent- it pits the wages of workers through the state- against the profits of employers in a much more significant manner. And hence why such measures of cost and further understanding of the workings of such laws and wage setting economics are required.

The power to set pay scales within a capitalist planned economy has long served as one of the fundamental “management rights” in the social bargain. It has been a long-guarded principle of private property and the rights of corporations versus the workers that has defined the historical aspects of an adversarial industrial relations system. And as such the wage setting mechanisms become more embedded within a state versus corporate- cultural understanding within such a history of debate, rhetoric and hyperbole in which class struggle has been defined.

In estimating, what the costs and benefits of such a new minimum wage levels might be- it must be clearly delineated what one is measuring. On the cost side, the rise in a minimum wage can have a direct and indirect impact on wages that have to be paid by companies while concurrently setting the living standards for many more workers.

The following framework demarcates many such costs and benefits.

Costs- Direct and Indirect

  1. The direct costs – are the amount of new wages that would be incurred by all industries to raise all low waged workers to the $15/ hour level.
  2. The indirect costs focus on the secondary wage push that results from a rise in the minimum wage. These are not automatic- as these indirect wage gains are related to wage bargaining power- be it unionization or other mechanism. It would difficult to assess what these wages might be given the variances in bargaining power of such workers. However, some methods using historical minimum wage data have proven some level of effectiveness in such estimation.
  3. Net loss of Jobs- as a result of such wage hikes. Many business groups state that the proposed minimum wage hike will cost the Ontario economy thousands of jobs. Other groups of economists see a net gain in the number of jobs- as new spending brought about by the minimum wage levels will expand the aggregate demand within the economy. Other economists- most recent Card Et al- have noted a neutral net effect.
  4. Inflationary price pressures- and the wage and pricing mechanism can pressure costs in the economy to rise. Most low wage jobs are located in non-export sectors- as well in sectors that have a low input/ output connection in terms of intermediate goods and value adding- therefore prices will be restricted to sectors for the most part.

Benefits:

  1. Reduction of individuals caught within poverty or as some refer to these as the working poor.
  2. Increased incentives for working will improve the labour force participation rate
  3. Net Jobs created- this due to increased aggregate wages being paid
  4. All the direct and indirect costs of poverty on social outcomes and health
  5. Reduce gender and other wage discrimination practices.

As can be imagined- to produce a reliable cost- benefit analysis of the above framework would prove to be a massive undertaking- and in many cases- it would be difficult to measure. One of the larger issues in the debate surrounding the minimum wage is a veritable lack of measurement outcomes for the benefits to workers. This is typically caused by the short-sighted measures designed to measure cost and most cases in terms of dollar values. If one were to have better designed sources of data on the benefit side of measurement it would allow for a much wider, balanced and fair assessment. However much of the costs of poverty are not directly measured and therefore quite difficult to place anything but a qualitative measure on- such as reduced poverty and the numbers of workers within such space. A large amount of research and from many studies have shown empirically that poverty is negatively correlated to health, education, and other positive social outcomes.

Estimating the Direct Cost of a $15 Minimum wage

With such shortcomings in data availability in mind, the research process started by measuring the direct cost to employers. That is, what would it have cost employers in terms of new wages paid to bring all workers up to the new minimum wage of $15/hour in 2016 at the usual hours worked. A majority of economic modelling relating to wage and price interactions are based upon past relationships and derived from historical data. There are a variety of techniques that have proven to be useful and can aid in providing some significant insights into predicting the future behavior and outcomes of actors within the economy. Through such- it was decided that the modelling would make use of a simulation based method in which a hybrid agent based aggregation based approach would be simulated. That is, the simulation was set up with initial parameters and then the simulation was run- statistical measures were aggregated from such actor behavior and the changes inherent to the inner workings of the markets. Given the goal is to measure the direct costs of raising wages – the agents within the model would be designed in a quite rudimentary fashion- that is the employer and worker agents will keep prices and wages fixed. That is, prices and wages other than the minimum wage hike- will remain constant and will ignore any changes to the demand for labour or product outputs. One could develop a more rigorous model and simulate elasticities for price changes using empirical data on the many minimum wage increases over the past. Such advanced modelling is planned for implementation in part 2 of this research using occupational and industrial data variables as well as providing more decision autonomy for the agents based upon historical actions and probabilities.

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A data file from the twelve-monthly files of 2016 was combined containing all workers reporting $15 per hour and less. Monthly data was used rather than annual – due to the presence of significant seasonality of some industries containing low wage workers. Also note that all self-employed workers were not included in the study- due to wage data not being collected in the Labour Force Survey for the self-employed. The histogram above is for all workers and wage levels by $/hour earned from a cross section in Dec. 2016.

The goal of part one is to estimate the following scenario- simulate a new minimum wage implemented on January 1, 2016- agents within the simulation are companies and workers – prices and wages are fixed at those in which the agents experienced historically throughout the 2016 year- save for the implementation of a new minimum wage hike. The model then aggregates the wages at this new level for the usual hours worked for all workers that fell below the $15/hour. The simulation was then run 12 times, once for each month to estimate the seasonal aspects. The time frame was then estimated at an annual basis. Equation 1 provides the details. (this was performed first at the Canada level and then at the Ontario level- with quality assurance measures and reliability checks implemented at appropriate points)

Equation 1- Direct Cost Weekly Wage = ∑ (Min. Wage- Worker Wage) * Usual Hrs work* LFS Weight

Using the above criteria- table 1 provides the monthly estimates of the cost to employers to raise all worker’s wages to the $15/ hour threshold. The simulation was run at the Canada level- as well as the Ontario level.

As can be seen in table 2- the total cost for the entire 2016 year to bring all workers below the $15/ hour threshold to the proposed minimum wage for Ontario is about $500 Million per month. It would add $6.1 billion to the annual wage bill for all employers. This would affect over 1.6 million low wage workers. It is definitely quite a small amount when one considers that the total wages paid to all workers in Ontario for the 2016 year estimated from CANSIM table 384-0037 was $358 billion. That represents 1.7% of the entire wages paid to all workers for Ontario in 2016. Amazing that such a small amount of money could bring so many workers up to such a possible living wage.

Table 2- Direct Cost of $15/ hour Minimum Wage Ontario 2016 Simulation

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The simulation was run again this time on the entire LFS micro records representing all Canadians. Again, the minimum wage was set at $15 per hour for every province and jurisdiction. The direct cost to employers was estimated at a monthly average of $1.2 billion in added wages to bring all workers up to the $15/hour level. Or on an annual basis $14.8 billion to bring 3.9 million workers to a more livable wage level. CANSIM table 384-0037 estimates that the annual wage bill for all workers in Canada for 2016 was $905 Billion. The $14 billion-direct cost to employers to raise the minimum wage to $15/hour was a mere 1.6% of the entire wages paid to workers in 2016.

Table 3- Direct Cost of $15/ hour Minimum Wage Canada 2016 Simulation

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Considering the total estimated direct cost are low compared to the entire wages paid out in the economy- it is a difficult process to understand why so many economists and business lobbyist have raised such alarm bells. In fact, when one thinks about the dynamics of such a minimal amount of dollars in the midst of a such large economic numbers as Gross Domestic Product (GDP)- one starts to question the ideological aspects behind the motivations of this debate. Think about the Canada level- GDP for the Canadian economy was estimate at $1.6 Trillion in 2016 that makes the direct cost of a country wide pay hike to $15/hour amount to $14 Billion and in a $1.6 trillion economy- it is a mere 0.8% of GDP for Canada. For the province of Ontario in 2016 it had a GDP of $798.2 Billion which means the direct cost of such a wage increase would amount to 0.77% of Ontario’s GDP in 2016. When one gather the amounts that are allocated on other areas like healthcare, police services, education- it does make you wonder how businesses allocate so little to so many people who work for a living. It does bring a lot of questions to many aspects of how wages are formed in society and about the functional aspects of the economy and priorities.

Conclusion and Potential Outcomes

In terms of the estimated job losses or jobs gained as discussed by many economists- given the low level of the direct costs as calculated above it is difficult to envision much of a change in the number of jobs. The amount of extra spending for workers or wage costs for employers caused by such additional dollars in the economy will have a small marginal effect on job creation and job destruction. The dollars involved are just not enough to create much in the way of economic shock in positive or negative direction. However-the benefit for  nearly 4 million workers at the Canada level is invaluable in terms of making progress towards a livable wage.  

Indeed, there is the potential for some sectoral adjustment. However, given low wages are concentrated in a handful of low productivity sectors- the overall effect should be small and contained. Few export sectors have significant amounts of low wage workers- therefore international trade flows will be marginally impacted and mainly by indirect wage pressures. Additionally, sectors that have high economic multipliers contain a small amount of low wage workers like manufacturing, resource extraction, professions etc. The combination of such factors would again predict that such multipliers in terms of jobs created or destroyed would be minimal. A quite interesting recent study byCengiz,, Arindrajit Dube, Attila Lindner and Ben Zipperer using a new wage binning method for statistical wage measurement  for estimating minimum wage effect on jobs – estimates over a 40 year period of different minimum wage hikes across the United States concluded that there had been very little impact on job creation or destruction due to minimum wage increases.

With regard to inflationary pressures- given the low level of estimated wage increases in the simulation of $6 billion in Ontario and $14 Billion at the Canada level-  would suggest that inflationary pressures would be minimal and contained within sectors. This however assumes that we operate within an economy that has some degree of competitiveness that would prevent price increases. But that ignores the fact that current industrial organization structures and inherent market powers in terms of oligopoly functionality- are the norm for most major industries in Canada. Rarely do such industries act as pure price takers in which much of the orthodox theory predicts. For many industries, are more generally oriented towards pricing behavior suited to that described as price givers. Of course, this will be hotly contested by the orthodoxy- competitiveness as laid out in the theory of many economic textbooks- rarely describes the actually existing of the economy.  This essentially predicts that most industries will not absorb the wage costs as denoted above- and instead will pass them on to consumers. Potentially a more dangerous threat to inflation than the actual wage increases paid to low wage workers and the benefits they accrue, are the Industrial organization structures and how it could produce undesirable outcomes. We could witness the economic machinery use such minimum wage hikes as an excuse with their oligopoly power to increase prices willy nilly to capture increased wealth effects of the wage increase.  There is and has always been opportunism within the profit seeking behavior of oligopoly structures. We do not exist in a state of perfect competition- our reality is much more fitting to what Anwar Shaikh describes as “Real Competition” in his latest book that encapsulates his 30 years in the economics profession called –  “Capitalism: Competition Conflict Crises” – which is a mix of Post- Keynesian/Kalecki with Baran and Sweezy  like market model of imperfect competition.

 

That said- there are industries with enough competitiveness in which companies cannot pass all or part of the costs on to the consumers. Such companies   will be forced through the competitive process to find cost savings through a variety of means. There are options in finding savings through internal mechanisms such as innovations, in the form of work organization, automation, expanding markets, pressuring supply chains and finally renegotiating fixed costs such as commercial property leasing costs etc. Of course, for some service industries like many parts of the food services- there is a limit to economic adjustment on the shop floor. As the recently deceased economist William Baumol theorized in terms of what he called the cost disease for service sector industries. That is – where a production process is mainly reliant on activities that must be performed by humans- traditional automation and innovation strategies to replace labour costs cannot help such entities bring costs down. Over time persistent wage increases and lack of productivity enhancements due to a lack of automation push the costs of such services up relative to other industries. These businesses face an increasingly difficult adjustment without raising prices and hence face a declining market share because of rising prices and the resulting shrinkage of demand.   Some businesses could face bankruptcy, downsizing or market share loss- but given the smaller size of the overall wage increase- such pressures should be small.  

Another factor that will play a role in the inflationary effects will be the role of wage compression. The term wage compression will soon become a foundational aspect to most corporate newspeak for many companies.  That is- the wage push and pressure that the new minimum wage will create on all workers with wages above the current minimum wage. Traditionally when minimum wages rise- employees working above the previous minimum wage levels make demands for pay raises to keep pace with the rise to a new minimum wage. There is indeed a decent set of empirical data to examine such wage distributional changes over time, but rarely have minimum wages jumped to such higher levels with such a speed. It is difficult to determine whether these past indirect wage pressures can be used to predict the future.  Unionized workers and some more organized workers have shown to be more successful in obtaining historical wage gains which would create some wage inflationary pressures.  Similarly, more organized sectors on the employer side will be much more effective at compression of wages and cost containment when faced with such minimum wage related cost pressures.

Many workers will be demanding a higher wage due to the minimum wage hike. However- given the decline in unionization rates in the private sector- it is difficult to see the forces that will push employers to accommodate such wage demands. As stated- we could see an unprecedented level of wage compression across quite a number of occupations and industries.  Much of the inflationary pressures are effected by the corporate response than the wage response. If the larger more concentrated corporate sectors use the minimum wage hike to raise prices –  regardless of labour costs and instead search for higher profits caused by the increased purchasing power- then inflation could be pushed to higher levels in a price spiral. If however the corporate sector tries to accommodate these wage pressures with cost savings through innovation and other mean- then inflationary pressures will be less of a problem.

Small businesses and the self-employed could bear the brunt of such costs. Many small businesses will find it more difficult to pass costs onto consumers- and will try to emulate the behavior of larger companies- finding savings internally- within their supply chain or innovate with new technology. However, these measures are much more expensive and difficult to implement for small business as most are price takers and do not have the investments required to innovate. There is also a potential window for an expanded black market- especially for small businesses and the self-employed- where regulatory and monitoring costs are much higher.

Lastly with the concerns about costs and inflation- any potential small inflationary outcomes will be outweighed by the many workers who now have access to a higher wage- and hence a potentially higher standard of living. It is not the aggregate of these wages- but the effect that each dollar has on these low wage individuals will have. The marginal rate of benefit is at a much higher level for these workers for every dollar than at other parts of the wage curve. The benefits to these individuals and society – need to be held up in the same light when debating these very complicated but necessary economic matters. For many it will mean more security and independence- towards a new standard of living- that is difficult to quantify. But the research has empirically swayed heavily in favour of the argument that less poverty in society is better for all.

 

 

Future Activities

Also coming in part two of this research- the project will expand the research into estimating the indirect costs as outline above- and also attempt to measure the net affect such will have on jobs. And lastly part two will also attempt to provide some measures of benefit for workers- beyond the increased wages and purchasing power. We will attempt to look at who in terms composition of family income and low wage earners and also look into the racialized aspects of low wage work.  

Part two of the research will also expand the coverage of the employees to encompass the self-employed. As indicated the report above excludes the self-employed which number over 3 million workers at the Canada level. This given there is no wage data collected for the self-employed in fact there is very little data collected on the self-employed by Statistics Canada. It is a large data gap indeed. However, LivingWork Analytics has constructed a Neural Network binary based prediction system to determine whether a worker is low wage or not. The neural network is a multilayered perceptron with 37 input variables from the LFS micro file. The network was designed with two hidden layers one with 120 nodes and the second with 54 nodes. It was trained on over 250,000 employees from the LFS micro file- to identify low wage workers. Using a relu and sigmoid function on the hidden layers- the neural network achieved a 91% accuracy rating on the training file- and a 88% accuracy rating on the test file. That is it successfully predicted the workers as being low waged or high waged on the known labelled training set. Such predictive powers are quite efficient. The stated neural network will be employed to estimate the low wage status of the self-employed. The NN is still being trained to achieve even higher levels of efficiency. More information on this model is available at LivingWork.ca.

 

New Quarterly Statistical Time Series to Measure Lower Wage Workers

As a follow, up to this research- LivingWork.ca will be publishing a new quarterly measure of workers making $15 per hour and below – using both the LFS method of excluding the self- employed as well as using the Neural Network enhanced approach noted above to include the self-employed. It will publish the data by several demographics variables and make the data available to the public for download. The new measure will be available in for the 3rd quarter of 2017.

 

 

 

 

References:

Galarneau, Diane and Eric Fecteau, “The Ups and Downs of minimum Wage.” Statistic Canada- July 2014.

Brennan, Jordan. “$15 Minimum Wage Should Be Something All Ontarians Can Agree On.” Huffington Post, July 5, 2017

Yalnizyan, Armine. “Why a $15 Minimum Wage Is Good for Business.” Macleans.ca. N.p., 03 June 2017. Web. 06 July 2017.

Jackson, Andrew. “The Return of the Gilded Age: Consequences, Causes and Solutions.” Broadbent Institute. Web. 06 July 2015.

Walks, Alan. “Income Inequality and Polarization in Canada’s Cities: An Examination and New Form of Measurement.” Cities Centre, University of Toronto, August 2013.

Thomas, Jasmin. “Trends in Low-Wage Employment in Canada: Incidence, Gap and Intensity, 1997-2014.” Center For the Study of Living Standards, July 2016.

Government of Ontario, “2014 Minimum Wage Advisory Panel.” Minster of Labour, Jan, 2014.

C. Michael Mitchell and John C. Murray, “Changing Workplaces Review: Final Report.” Government of Ontario, 2017.

Schenk, Christopher, “From Poverty Wages to a Living Wage.” Ontario Federation of Labour, November 2001.

Card, David and Alan B. Krueger, Myth and measurement: the new economics of the minimum wage, New Jersey: Princeton University Press 1995.

Workers’ Action Center, “Building Decent Jobs from the Ground Up.” Workers’ Action Center/Parkdale Community Legal Services – Toronto, September 2016.

Ivanova, Iglika, Seth Klein and Pamela Reano, “Working For a Living Wage 2017.” Canadian Center for Policy Alternatives, April 2017

Vosko, Leah F. Temporary Work: The Gendered Rise of a Precarious Employment Relationship. Toronto: University of Toronto Press, 2000

Vosko, Leah F., John Grundy, et al, “Closing the Employment Standards Enforcement Gap.” Closing the Gap Policy Forum, June 2017.

Morissette, René, Garnett Picot, and Yuqian Lu, “The Evolution of Canadian Wages over the Last Three Decades.” Statistics Canada, March 2013.

Schimtt, John, “Why Does the Minimum Wage Have No Discernible Effect on Employment?”, Center for Economic and Policy Research, Washington, D.C. Feb. 2013.

Cengiz, D, Arindrajit Dube, Attila Lindner and Ben Zipperer, “The effect of minimum wages on the total number of jobs: Evidence from the United States using a bunching estimator.” Journal of Labor Economics- website. http://www.sole-jole.org/17722.pdf

Baumol, William; William Bowen. Performing Arts, The Economic Dilemma: a study of problems common to theater, opera, music, and dance. New York: Twentieth Century Fund, 1966

Shaikh, Anwar, Capitalism: Competition, Conflict, Crises. Oxford University Press, 2016

Report Shows Job Growth Highest in Low Wage Industries.

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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. ChInd2

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.

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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.

Call:
lm(formula = sephm$diff07 ~ sephm$X2014.y, weights = (abs(sephm$diff07)))

Weighted Residuals:
Min 1Q Median 3Q Max
-13652457 -1896031 -1035852 -484681 35907579

Coefficients:
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.

For questions or comments please use the contact us form to send feedback.

 

 

 

Employment Insurance Levels on the Rise- this is more than a “technical recession”!

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caneiThe latest GDP numbers have confirmed that Canada has officially entered into its second recession in less than 10 years. Without much but denial from the federal government- many questions remain on the nature and extent of this new recession. How long will this recession last? How will it impact different sectors and regions? How many workers and families will it affect? As with most economic questions – we must look deeper into the data for clues to make such predictions. One such measure is the Employment Insurance (EI) claims data. The Employment Insurance statistics are an administrative data source.  The level of EI claims are a very sensitive indicator on the health of the labour market. The Conference Board of Canada uses EI claims as one of the single inputs of the labour market into their modelling of a  leading indicator index on the economy. The EI data is far from perfect and excludes many of the unemployed especially those in short term unemployment- but it can provide a strong timely indicator of the functioning of the economy. Given it is administrative data, it goes beyond the data quality of unemployment numbers estimated by the Labour Force Survey which are subject to large sampling errors. The data looks at EI claimants rather than those actually receiving benefits.  The waiting  and processing period of EI can delay the statistical outcome of being counted as a person collecting benefits for up to a four months. Therefore focusing on claimants provides a timelier look at the economy.

(Employment Insurance Claimants are defined as those that have filed a claim and are awaiting a decision to determine their eligibility. The waiting period attached to this process can range from 4-8 weeks. Claimants have a high probability of eventually becoming beneficiaries – for more information on this table see the notes for CANSIM table 276-0004)

Analysis of the Employment Insurance data provides the following summary highlights:

1) The number of EI claims have risen 17% in a year over year change from June 2014 to June 2015, and notably 14% over the first 6 months of this year (latest data available is June 2015). With many new more stringent Employment Insurance eligibility requirements- as compared to previous periods- this has undoubtedly biased the number of claimants downwards. So one must take the 17% as an underestimate when comparing to earlier recessionary periods in our recent economic history.

 

2) A regional breakdown of the EI Claims shows that several provinces have experienced a rise in the number of claimants. This indicates the recession is digging a wider hole in the economy beyond that of the oil sector and Alberta. Year over year change from June of 2014 to June of 2015 in EI claims have risen in Alberta 42.3%, Saskatchewan 12.6% and Ontario 9.2%. (using data from CANSIM table 276-0004 in which are Statistics Canada seasonally adjusted counts). Given the newer rules of EI eligibility, especially those relating to seasonal workers, it will be difficult to fully assess the regional aspects of EI levels as compared over time, as we know some areas have higher concentrations of seasonal workers such as in Eastern provinces, and Northern areas of the country . However a more complete analysis of the data focusing on the level of seasonality of the data from these provinces could provide some evidence. Such a task is beyond the scope of this short article.

mprov

3) The third summary point that arises from the data focuses on the trend in EI claimants as compared to past recessions. As mentioned in the last point, the EI data contains a large seasonal component making it difficult assess the raw data. In making the analysis somewhat clearer, the trend or signal in the monthly EI time series was extracted (blue line in graphs). The data reveals that the relative economic impact compared to previous recessions is beyond a technical recession that pundits have labelled. The evidence is quite clear- that so far in this early stage of the recession, at least according to the growth in EI, this current recession is larger than the 2002 recession that was the result of the Dot.com meltdown. However it is not as great as that witnessed during the Great recession of 2008. As can be seen in the trend line- the acceleration has not changed from its upward trajectory and therefore we are definitely not at the end of this recession. So it is difficult to compare given this recession has just started. The message is fairly obvious from the trend line- this is much more than a technical recession. (The blue trend line or signal was extracted using the raw non-seasonally adjusted data from CANSIM table 276-0004. The algorithm to extract the trend was the STL with LOESS seasonal decomposition method which used localized polynomial regression combined with a moving average function. This algorithm is similar to the ARIMA method- but is less susceptible to outliers. However it can be more difficult to obtain the greater sensitivity of the ARIMA method. Given the task was to merely create a visual display the STL was chosen.)

reccanei

 

4) The coverage rate of Employment Insurance Beneficiaries as a proportion of the unemployed is the last measure that was calculated. This measure is quite important in determining the overall effectiveness of the EI program in reaching its functional goals in providing relief to those experiencing job loss. As can be seen in the last chart, the coverage rate has declined substantively from the past levels and reached a low point of 38% in 2011.(calculated using Employment Insurance Regular Beneficiaries over the total unemployment using data from CANSIM tables 276-0040 and 282-0087) This due to the continued dismantling of the program, where now less than 2 of 5 unemployed workers actually qualify for this job loss insurance- a tragic outcome for workers. These lows in EI benefit payouts occurring during the longest economic stagnation and recession prone times in the history of Canada. The coverage rate has increased a small amount in the past year- but this is mainly due to the uptick in unemployment- and not due to any new more worker friendly policy.

 

covrate

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Summary- Employment Insurance claims have shown a dramatic rise in response to the recessionary period that Canada entered in the first half of the 2015. Given the new more stringent eligibility rules for collecting EI benefits, the rise in the number of claimants is under representing the extent of job loss when compared to previous recessionary periods. This is contrary to what many have concluded- that the Canadian economic recession was merely- a “technical recession”. The question that many ask- why has the unemployment rate not spiked in a traditional manner when entering this recession. The historical linkage and loss of protective power in EI benefits may actually be part of the reason. That is, as benefits have been cut back in terms of benefits paid, as well as the much tighter eligibility rules- the lack of insurance benefits forces many who face job loss- to find some income protection in jobs that are low paying, part-time, self-employment and other necessary non-traditional employment transition and workforce adjustment mechanisms. In the short term- such ad hoc work force adjustment may be less costly in terms of short term outlays- however the longer term inefficiencies and social outcomes measured from a skill development, training, and many related social outcomes to job loss much more costly to the economy and society.  Wider labour market measures- seem to suggest that this is the new trend within the process of workforce adjustment and transition mechanisms. The EI claim data also point to a much wider recession across more sectors and regions of the economy- wider than the oil sector, and more decentralized than Alberta as many economists have suggested. Lastly- the data in terms of trend predicts that the job loss and EI claims associated with it, will remain high for at least the next several months.

The Limits of Women’s Work or Did Women just lose 400,000 Jobs- Employment Rates and the Great Recession

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New Explorations into the Canadian Workforce

As part of a larger research project being coordinated by the Canadian Center for Policy Alternatives, entitled Working Across Canada, I have been volunteering my time researching various dimensions of labour markets in Canada with the intention of creating a new measure to evaluate the nature of employment quality- or as some call it at the International Labour Organization and elsewhere – a Good Jobs Index (I am not sure what to call it).  As I work through this project I thought it would be constructive to write up some of the more interesting findings that are uncovered along the way. I also thought it might be constructive to evaluate some new web based software that allow users to interact and explore data in which was loaded up for this project. This interactive aspect will hopefully allow readers a chance to dig deeper into the research and explore the data, slicing, dicing, rolling up and segmenting with ease and adventure. Just click on the included link below to interact with the data chart. (you can use the software online CLICK HERE, or you can download the reader here and use it offline.) Mainly I want to bring light to some of the data artifacts that are uncovered and try and fit some reasoning and limited analysis to the facts.

Part 1- The Limits of Women’s Work or Did Women just lose 400,000 Jobs- The Great Recession and Employment Rates in Canada

Employment, or having access to a means of the production is the key to a person’s survival and well-being within a market based economy. For women in many developed nations, the past thirty years have served as an unprecedented period of entry into the waged workforce of the formal economy. It has been heralded by some as the great exodus out of the chains of the informal economy into the “freedom” of the waged workforce- as one artist famously put it in the ‘70s, moving away from “being a slave of a slave”.

Examining chart 1 and comparing the employment rate of women over the past thirty years verifies that this transformation has been ongoing in a substantive and hurried process. Only briefly interrupted by two recessions the upwards rate of women workers into the formal economy marched steadily onward from less than 50% in the 70’s to an employment rate that has women workers now approaching that of men. After nearly thirty years of steady and consistent employment rate growth, the great recession of 2008 ravaged the economy and the velocity of change in women employment rates for prime age women aged 25-54, came to a very sudden halt. Upon hitting the employment wall- the rate has stalled for the last 6 years at a historic (non-war time) high of 77%. These past six years of stagnation has been the longest period of non- growth in the employment rate of women in more than 30 years. As we move through this unprecedented period, the question must be asked- are we witnessing a historical maximum for women’s employment in the Canadian workforce? Have we reached an upper bound of women workers in waged work?

Capture2

If we are not at this upper bound, then much of what has been written about the great recession has to be rewritten as the pundits have forgot to mention the 400,000 plus jobs that women have lost during this period. Indeed if one is to run the trend for women workers using the employment rate and its robust growth rate over the past 15 years, then we can estimate with econometric forecasting that women have lost over 400,000 jobs during the past 6 years of stagnation. (See graph and calculations using an additive model of exponential smoothing to forecast an average expected Women’s Employment Rate of 83% which equate to roughly 400,000 jobs in 2014)  Suddenly the great recession seems much more traumatic for women and turns the popular notion of a “he-session”  coined by media depicting this great recession as being more difficult for men- on its head. (to explore this data visually click here)

The debate of who lost more, is of course a loaded question chalk full of the political dimensions of bias and would simply result in the divide and conquer mentality. So rather than focus on a gender divisive debate, given the numbers, one can conclude both genders have suffered greatly but differently. As can be seen in chart 1 women’s employment rate has been growing at a much higher rate over the past 30 years than men, as women entered into the waged workforce in droves.  The employment rate for men on the other hand has slowly declined over this period in a very awkward but evidently painful recession induced jagged downward trend. Each of the three major recessions over the past thirty years has been quite painful for both genders but for men it has meant a permanent adjustment to a lack of waged work for an increasingly larger proportion of the workforce.

Starting in the 70’s the employment rate of prime aged males was averaging above 91% – then after a massive carnage of job loss  in the early 80’s recession due to high interest rates and the beginning of the neo-con assault on workers, recovered to stabilize around 87% for much of the 80’s. After which the early 90’s recession took its toll and again male workers dropped off and recovered to stabilize at a lower 85% employment rate. Facing the great recession of 2008, males were hit quite suddenly with substantive jobless and they seem to have recovered ever so slightly to stabilize again at a lower rate at 83%. Obviously, given these are prime aged workers, many have to adjust to life without employment, as either discouraged workers or in some other activity (training, house husbands, return to school, early retirement). The focal point for men has been a three decade long adjustment to a lower and lower equilibrium of life without waged work.

Considering this 30 year linear climb for women, the velocity and scale of such growth over is historic and an impressive display of the market’s ability to find such space for waged workers during a neo-liberal era of uneven economic growth. Recall in retrospect that we are witnessing an almost doubling of the labour force for women in just 30 years- yet we have still maintained an unemployment rate of below ten percent (outside of the recessions and depending on how you measure unemployment). Of course as impressive as that sounds it says very little about the quality of a high proportion of jobs that were and continue to be created for women- more on that in another paper.

Since the end of the early 90’s recession, women have been entering into waged work at somewhat slower velocity then previous periods, however the acceleration is still positive and consistent up until the great recession hit in 2008 and then it flat lined. So the logic is clearly evident, we are either at a maximum of women’s grand entry into employment- or alternatively women have suffered massive loss of forgone jobs through the recession. This does not mean women actually lost all 400,0000 jobs, as in the case of men who actually did experience plenty of job loss, but it does mean that for women the pain of the recession was in terms of lost actual and potential jobs and was differently realized then men. That is women, were not hired, but most likely would have been, given the strength of the underlying historical trend in growth of women’s employment. And that loss actually does count as a dead weight loss to society given the strength of the relationship prior to the slowdown. In summary, we need to be mindful that lost opportunities must be factored into the damage the recession unleashed. Oddly enough if we look at the participation rate of women it is does not quite reflect this notion, in terms of proportion, or fluidity. As one would have expected a large increase in unemployment to match this employment flattening trend. However unemployment falls short of that which we would have expected and is only partially made up for in the pattern that was witnessed in the participation rate. So what does that mean- it means a whole lot of women workers either left the labour market in discouragement or indeed we have reached the height of women’s entry into the workforce? It is actually a very odd finding given the timing.

So what is going on?

Given the ongoing stagnation in the economy and recessionary winds it would be premature to say that women’s historical employment rate has peaked at 77%, a full five plus points below men. So that begs the question should we expect a difference between women and men employment rates?  Is there some systemic discriminatory disincentive to waged work operating independently or dependently on gender to explain such a difference?  One could suggest differences in job quality, pay rates, precarious work, career opportunity, and/or unwaged labour demands are all undoubtedly some factors.

You can explore the data yourself. Have a look and compare different age groups, participation rates, or other aspects of the labour force and see how these measures reacted in previous recessions. Of course the employment rate is different from that of the participation rate that is often used to measure waged workers participation into the workforce. Employment rate includes discouraged workers who fall into the numerator. Also recall that we are referring to relative increases, and as the population increases we will see more women enter into employment, but given the flatness over the past six years means that employment for women is constant with the total amount of people employed.

This is indeed a very big question and only the future holds clarity for outcomes, but if this current employment rate of 77% is to become a permanent fixture of the labour market for women and we have reached a maximum, then it will unleash some very massive changes in other areas of the labour market and society. All of which have been affected by the almost constant rate of increase in women’s commodification into the waged labour of the market, and the dynamics that are intricately woven through the fabric of society.  It will mean a lot of change on many other connected issues, and will have a significant slowdown in everything from day care to food items in the grocery store. We have become so accustomed to this  large ongoing change of the women into employment, that without that growth much will have to adjust to the  relative stability of natural population growth.

If we are not at this point, then we must reconsider and rewrite that the recession had a massive impact on the employment loss for women workers, and rather than being the “he-cession” that many labelled this last recession- it will mean over 400,000 jobs will have been lost by women workers- as that is what the trend would have predicted.

(Note- the employment rates above are measured for prime aged workers, between the ages of 25-54.  Other segments of the population are not considered, but you can explore them with the data software and compare click here. Other age groups over such long historical periods have flows out of the stock of employment that produces a greater variance due to retirement, returning to school, retraining, etc. The segment of the population aged 25-54 has the highest probability to be part of the waged workforce and therefore was used to guide the exploration process. This is not to discount the experiences of other aged workers, but simply to clarify the trends and bring more focus to a labour market in transition)

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