Licensing imposes barriers to entry in an occupation, effectively restricting the supply of licensed workers in the occupation, and driving prices up. We evaluate the effects of introducing mandatory licensing in all construction trades in a Canadian province, Ontario. Evidence based on vacancies and wage premia suggests the construction trades are in short supply in Ontario. We estimate the deadweight loss for Ontario’s construction industry resulting from higher prices and decreased construction output. Using an elasticity of labour demand of 31%, we obtain a deadweight loss ranging between $19 million and $75 million, depending on projected wage increases ranging between 10% and 20%. We ignore here potential benefits resulting from the increased human capital of construction workers.
When mandatory licensing is introduced for an occupation, the rationale is typically related to increased quality in that occupation’s output. In practice, occupation licensing is implemented at the initiative and under the administration of the current association of workers in that occupation. Licensing imposes barriers to entry in that occupation, effectively restricting the supply of licensed workers in the occupation, driving their wages up. This in turn increases the price of the final good or service produced in that occupation.
We will show first the theoretical channels through which occupation licensing affects workers, consumers, employers and the economy, and also empirical evidence from the literature regarding the magnitude of those effects. The theoretical implications are evaluated in the case of Ontario, a Canadian province where the government is contemplating to introduce mandatory licensing for all construction occupations.
The paper is organized as follows. Section 2 provides a general analysis on occupational licensing and its impact on consumers, workers, firms, and the economy. We discuss both theoretical channels and empirical evidence of their magnitude. Section 3 provides evidence of the skills shortage in Ontario, and Section 4 details the computation of the deadweight loss resulting from restricting construction inputs through licensing. Section 5 makes a conclusion.
We document the costs and benefits of occupational licensing for customers, for the workers getting licensed, for firms and industries, and for the economy as a whole. Note that there is a timing issue involved: following the decision to introduce mandatory licensing in an occupation, it may take a while until new workers get trained and licensed, while existing workers are typically grandfathered into the licensed occupation. It has been documented that wages increase “once and for all” for workers who are grandfathered into a newly implemented occupational licensing system (Perloff, 1980 [
The best known effect of occupational licensing is that it restricts the number of workers in a given occupation, leading to increased wages for the workers. In turn, the higher labour costs is passed on to the consumer through a higher price of the good or service (Rottenberg, 1980 [
The benefits for consumers from occupational licensing can be twofold: (i) a reduction in consumer uncertainty regarding the quality of a product or service, and (ii) an increase in the quality of the good or service itself. The uncertainty about quality refers to a scenario where the buyer (i.e. the consumer) cannot distinguish between a high quality good or service and a low quality one, while the seller knows it. This informational asymmetry problem was first identified by Akerloff (1970) [
The primary cost of occupational licensing for workers is the financial and opportunity costs of studying for and passing (or failing) an exam (Rottenberg, 1980 [
As discussed above, occupational licensing restricts the number of workers in a given occupation providing rents in the form of increased wages for these workers. The literature on this effect dates as far back as Adam Smith, who, in The Wealth of Nations, wrote about “the ability of crafts to lengthen apprenticeship programs and limit the number of apprentices per master, thereby ensuring higher earnings for persons in these occupations (Adam Smith, as cited in Kleiner, 2006 [
However, panel data and time series analysis have shown that, while occupational licensing does increase wages, the effect documented in cross-sectional studies may be higher than the true effect of licensing. Due to factors such as the selection of better workers into the training required for licensing (Kleiner, 2006 [
Occupational licensing has been found to increase the incentive for workers to pursue additional training, even if it is not mandated by the licensing body, given that those without training, who may produce a lower- quality product or service, could be barred from the market (Akerloff, 1970 [
The demand for goods and services from licensed workers may either increase or decrease, depending on the nature of the market. Demand may increase as consumers are more certain of the quality of goods or services, which would benefit the industry, but it may also decrease if some consumers demand lower-quality goods or services that are barred after the implementation of occupational licensing (Shapiro, 1986 [
The literature is lacking empirical analysis on the effects of occupational licensing on firm profits. On the one hand, increased demand or increased output prices would increase profits, whereas on the other hand increased wages for workers would decrease profits for firms. Occupational licensing can give an industry increased control over the supply of workers through manipulating the passing rates on the licensing exam (Maurizi, 1974 [
Licensing will restrict entrance into the licensed occupation. The welfare implications are similar to those of a standard monopoly story, where, as wages go up and employment goes down along the labour demand curve, the lost income (welfare loss) can be measured as the triangle area below the demand curve. Income gets reallocated from consumer to licensee through a transfer of higher output price into higher wages.
Like in Kleiner, 2006 [
The magnitude of the output (deadweight) loss can be computed if one knows, or can estimate, the elasticity of labor demand. Kleiner (2006) gives the following example. Assuming a wage increase of 10% as a result of introducing licensing (the low end of the 10% to 15% estimate), and assuming that the licensing applies to 20% of the workforce, this gives a 0.10 × 0.20 = 2% increase in the total wage bill for the overall economy relative to a non-licensed market. He gives the example of the U.S. where the total wage income is 5.3trillion, therefore in the increase in the total wage bill is 0.02 × 5800 billion = 116 billion. Assuming further a wage elasticity of demand of 0.3, the wage bill loss due to the employment change alone would be 116 × 0.3 = 34.8 billion, which would lead to a deadweight loss of half of 2% × 34.8 billion = 348 million overall for the economy. A similar calculation for Ontario is performed later on in this report. While the annual loss for the Construction sector depends on the values assumed for the labour demand elasticity and for the wage increase brought up by certification, the deadweight loss for Ontario due to mandating trade certification would amount to somewhere around 60 to 70 million per year.
Another potential downside in the context of the construction industry, which we have not found mentioned in the literature, is that legitimate construction activity may migrate to the black market to avoid paying the increased cost. While there is plenty of anecdotal evidence on construction and the black market, we do not know of any academic evidence to this effect; more research in this area would be welcome.
There are also benefits to the society associated with licensing, such as the increased quality of the product or the emergence of standardization. In theory, this could help minimize consumer loss and reduce exposure to risk. While these are theoretical channels for which we do not provide empirical evidence, we wanted to mention them here because, in theory, certification could lead to gains through the risk reduction channel. Even though we do not measure it, we should still be aware of this positive effect of certification.
Employers and public opinion seem confident there is a shortage of skilled labour in Canada, yet economists tend to disagree. Overall, at the macroeconomic level, all available evidence, which we summarize in the next subsection, seems to point out the contrary, that there is no shortage of skill.
One puzzling issue in this context refers to training. If indeed skills are not available to be hired in the labour market, then why don’t employers train those workers who are available? One reason for low training levels is that access to training is controlled by industry associations and unions. Apprenticeship positions are kept in short supply because of mandatory ratios, e.g. 3 journeymen to 1 apprentice. This pushes smaller companies out of the game.
Employers may not want to train workers for fear that the trained workers will get poached by a competitor, after they have invested in training. This is only possible if the human capital acquired by training is, to a large extent, general rather than firm-specific, implying that skills are somewhat transferable. This is one of the main arguments justifying direct government intervention in training, the other one being that employers would cherry-pick the best workers for training, leaving the disadvantaged workers with no training options. This is also one of the main theoretical arguments in favour of mandating occupational training and licensing.
One remaining question is why the workers themselves do not decide to get extra training at their own cost. The answer to this question is the same one that justifies the need for occupational licensing, as it relates to market failure. If expected skilled wages are high enough, workers will have an incentive to get trained and become licensed on their own accord. Besides the monetary cost of tuition, training would also involve significant wage loss during training, when time is devoted away from production in favour of skill acquisition. If workers do not have access to credit markets to finance training, or if they discount the future too much, workers will also under-invest in training.
A few other explanations can help bridge the apparent disconnect between evidence and public opinion. For instance, it is possible that employers have unrealistic expectations about the types of workers they can attract; or, if governments agree that there are labor shortages, this can be used to justify policies that reduce businesses’ labor costs.
To be able to document skill shortages, we need data from both sides of the labour market, the worker and the firm. On the worker side, a gap in the availability of skill should result in increased wages for the skilled labour, and yet we do not see any evidence of that in the data. “The fact that some of the most highly demanded occupations in the job market―chief among them trades, technicians and many professional groups―have not recorded above-average wage increases is at odds with the perception that there is a large-scale skills mismatch in Canada’s labour market. More research in Canada is required to better understand the underlying dynamics at play between current (and future) labour supply and demand. […]” (Derek Burleton, TD Vice President and Deputy Chief Economist, 2013 [
Another reason for the discrepancy between the views of employers and politicians and what the evidence suggests could be that we simply do not have good enough evidence regarding unfilled positions. While it is true that the wages of under-supplied skills should go up, this is an equilibrium outcome, and it is not very informative regarding the vacancy side: how long does it take for employers to fill a vacancy? Do vacancies stay posted a long time? How many applicants are there for each vacancy? We do not have information on most of these issues. Statistics Canada has been collecting some data until now, but they will only release aggregates over a few very broadly defined industries or regions.
The evidence summarized below, together with what is reported by other sources [
In
Plesca and Summerfield, 2014 [
In
In the subsequent plots in
Continuing with this analysis by province, it is more difficult to interpret the results because of no data availability over certain periods of time or across provinces. For those provinces where some data is available, the conclusion seems to hold: vacancies do not increase or even decline over all industries, but they increase in construction; employment increases over all industries.
This conclusion is confirmed by other studies. “Construction-related trades in particular posted double-digit increases in their shares since the late 1990s and now account for almost 5% of total Canadian employment” (TD Report, 2013 [
The long-run trend in occupational shortages is less clear. The CIBC report (CIBC, 2013 [
25 Occupations Showing Signs of Skills Shortages Managers in Engineering, Architecture, Science & Info Systems Managers in Health, Education, Social and Community Services Managers in Construction and Transportation Auditors, Accountants and Investment Professionals Human Resources and Business Service Professionals Professional Occupations in Natural and Applied Sciences Physical Science Professionals Life Science Professionals Civil, Mechanical, Electrical and Chemical Engineers Other Engineers Professional Occupations in Health Physicians, Dentists and Veterinarians Optometrists, Chiropractors and Other Health Diagnosing and Treating Professionals Pharmacists, Dietitians and Nutritionists Therapy and Assessment Professionals Nurse Supervisors and Registered Nurses Technical and Related Occupations in Health Medical Technologists and Technicians (Except Dental Health) Technical Occupations in Dental Health Care Other Technical Occupations In Health Care (Except Dental) Psychologists, Social Workers, Counsellors, Clergy and Probation Officers Supervisors, Mining, Oil and Gas Underground Miners, Oil and Gas Drillers and Related Workers Supervisors in Manufacturing Supervisors, Processing Occupations |
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20 Occupations Showing Signs of Labour Surplus Managers in Manufacturing and Utilities Clerical Supervisors Clerical Occupations Clerical Occupations, General Office Skills Office Equipment Operators Finance and Insurance Clerks Mail and Message Distribution Occupations Secondary & Elementary Teachers and Counsellors Sales and Service Supervisors Cashiers Occupations in Food and Beverage Services Tour & Recreational Guides and Amusement Occupations Other Attendants in Travel, Accommodation and Recreation Technical Occupations in Personal Service Other Occupations in Personal Service Butchers & Bakers Upholsterers, Tailors, Shoe Repairers, Jewellers and Related Occupations Fishing Vessel Masters and Skippers and Fishermen/Women Machine Operators & Related Workers in Metal and Mineral Products Processing Machine Operators & Related Workers in Pulp & Paper Production and Wood Processing |
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difficulties associated with reliable forecasting. Likewise, we hear economists periodically predicting a correction in the Ontario housing market, but there is still no sign of that happening yet.
We report here results from analyzing data from the 2013 Labour Force Survey. We focus on hourly wages in four trade-related occupations. According to the National Occupational Classification (NOC) codes, we consider the following two-digit occupation codes: 18 “Contractors”, 19 “Trades in Construction”, 20 “Other Trades”, and 22 “Helpers in Trades”.
At a first glance, the data seem to indicate that Ontario is lagging behind, with average skilled trades wages trailing below the national average. It would thus seem that certification is actually a welcome idea for Ontario, because the skilled trades wage is lower in Ontario than the average for Canada.
What we do here is run for each month and for each province a wage regression where on the right-hand side we condition on other productivity characteristics, such as education and age, as well as an indicator for the traded occupation. The coefficient on the Trades variable will measure the wage returns to being in a Trades occupation. That is, it measures the wage gap between for two hypothetical, average individuals who are similar in all other respects (same education and age), with the only difference being that one works in Trades, while the other one does not. In Figures 4-7 we plot this coefficient for every month, as a measure of the wage premium
Occ. 18 | Occ. 19 | Occ. 20 | Occ. 22 | |
---|---|---|---|---|
Contractor | Constr. Trades | Other trades | Helpers | |
Newfoundland | 31.1 | 24.0 | 27.1 | 18.5 |
PEI | 22.0 | 18.2 | 19.9 | 14.7 |
NovaScotia | 25.9 | 20.9 | 22.6 | 16.9 |
NewBrunswick | 24.8 | 21.8 | 23.1 | 16.9 |
Quebec | 27.8 | 25.8 | 23.3 | 18.6 |
Ontario | 28.9 | 23.9 | 25.2 | 18.8 |
Manitoba | 26.6 | 20.8 | 23.5 | 17.1 |
Saskatchewan | 31.4 | 24.0 | 27.5 | 19.6 |
Alberta | 36.4 | 28.6 | 31.6 | 21.8 |
BritishColumbia | 32.1 | 24.5 | 28.5 | 21.2 |
30.0 | 24.4 | 25.9 | 19.1 |
for being in Trades, all else equal.
In the graphs reported here, we plot the returns to each of the four trade occupations relative to the rest of occupations, separately for two Canadian provinces, Ontario and Alberta. Interestingly ? and perhaps somewhat unexpectedly ? once we control for educational attainment and labour market experience, the returns to Construction Trades (Occupation 19) are high and increasing, even more clearly so in Ontario than Alberta.
In the late 1990s the returns of Construction Trades relative to other occupations were about 10% and they are currently above 15% in Ontario, and around 15% in Alberta. This is further evidence that currently there is demand for Construction Trades, as reflected by their wage premium.
Contractors, Occupation 18, have even higher returns relative to other occupations, more than 20%, and again the trend is increasing, even more clearly so in Ontario than Alberta. Labourers (Occupation 19) have lower returns, but still positive at about 5%, while Other Trades (Occupation 20) have high returns, even higher than 20% and more clearly increasing in Alberta, which makes sense given that these are likely to be workers on the oil patch.
Preliminary exploration of Census Data (2011 National Household Survey) on wages of very detailed occupational codes allows us to investigate the wage patterns for more refined occupations, at the four digit level. We focus here on five trades: carpenters, electricians, pipe-fitters, supervisors, and labourers. Interestingly enough, all trades workers have labour force participation rates much higher than the average: 90% participation rates, compared to about 65% in the population, result which holds even when focusing only on males. Employment rates are also higher for workers in construction trades compared to employment rates for other occupations. Within Ontario, there seems to be a sizeable variation in the incomes of these five trade groups across Census Metropolitan Areas; nevertheless, there is little variation in unemployment rates.
Another interesting observation is that, while carpenters have higher annual incomes than labourers, they have lower hourly wages, because they work more hours per year compared to labourers. We had not expected this hourly wage result, because carpenters are supposed to have more education and skill than labourers. The higher skill of carpenters should command a bigger reward, thus higher hourly wages, when compared to labourers. Continuing with this example for Ontario, pipe-fitters and electricians have incomes above the provincial average, and above the average for most other provinces except for Alberta and to some extent Saskatchewan and British Columbia. These observations do not indicate conclusively nether the presence nor the absence of a shortage of trades in Ontario when looking at very refined occupations; on the whole though, as discussed earlier, construction trades workers seem to be in short relative supply in Ontario.
The small literature on licensing building trades reported a few case studies, without going into cost-benefit details (Arkani et al., 2003 [
To add numerical values to the magnitude of adjustments, we use the formula derived in Section 2.4. For the wage bill, current employment, and wages, we have exact data from the Labour Force Survey 2014. For the Elasticity of Labour Demand and for the projected wage increase due to certification we need to use numbers established elsewhere in the literature: Bruno et al. (2003) [
To illustrate results, we focus on a mid-range labour demand elasticity of 31% - in other words, a one percent
Wage Increase | Demand Elasticity | Total Construction Excl. Other Trades | Contractors + Supervisors | Construction Trades | Other Trades | Helpers Construction |
---|---|---|---|---|---|---|
10% | 31% | $18,782,114 | $4,783,638 | $6,881,291 | $22,785,004 | $7,117,185 |
15% | $9,088,120 | $2,314,663 | $3,329,657 | $11,025,002 | $3,443,799 | |
53% | $32,293,118 | $8,224,771 | $11,831,380 | $39,175,508 | $12,236,967 | |
75% | $45,501,185 | $11,588,748 | $16,670,482 | $55,198,511 | $17,241,955 | |
20% | 31% | $75,128,455 | $19,134,551 | $27,525,163 | $91,140,018 | $28,468,741 |
15% | $36,352,478 | $9,258,654 | $13,318,627 | $44,100,009 | $13,775,197 | |
53% | $129,172,472 | $32,899,083 | $47,325,522 | $156,702,030 | $48,947,867 | |
75% | $182,004,740 | $46,354,994 | $66,681,927 | $220,794,043 | $68,967,820 | |
Average | $66,040,335 | $16,819,888 | $24,195,506 | $80,115,016 | $25,024,941 |
Demand Elasticity | Total Construction | Contractors + Supervisors | Construction Trades | Other Trades | Helpers Construction |
---|---|---|---|---|---|
0.31 | $41,567,118.06 | $4,783,637.81 | $6,881,290.70 | $22,785,004.42 | $7,117,185.13 |
0.416 | $55,780,390.69 | $6,419,333.32 | $9,234,248.17 | $30,576,005.94 | $9,550,803.27 |
0.459 | $61,546,152.23 | $7,082,870.18 | $10,188,749.78 | $33,736,506.55 | $10,538,025.72 |
0.533 | $71,468,625.58 | $8,224,770.82 | $11,831,380.46 | $39,175,507.61 | $12,236,966.69 |
0.583 | $78,172,999.46 | $8,996,325.30 | $12,941,266.06 | $42,850,508.32 | $13,384,899.77 |
0.737 | $98,822,471.01 | $11,372,713.12 | $16,359,713.70 | $54,169,510.52 | $16,920,533.68 |
0.751 | $100,699,695.70 | $11,588,748.38 | $16,670,481.66 | $55,198,510.72 | $17,241,954.94 |
0.881 | $118,131,067.79 | $13,594,790.04 | $19,556,184.22 | $64,753,512.57 | $20,226,580.96 |
0.909 | $121,885,517.16 | $14,026,860.55 | $20,177,720.15 | $66,811,512.97 | $20,869,423.49 |
0.961 | $128,858,066.00 | $14,829,277.22 | $21,332,001.17 | $70,633,513.72 | $22,063,273.90 |
1.056 | $141,596,376.38 | $16,295,230.74 | $23,440,783.81 | $77,616,015.07 | $24,244,346.76 |
Wage increase 10%. Demand elasticities from Navaretti et al. (2003) | |||||
0.24 | $32,180,994.63 | $3,703,461.53 | $5,327,450.86 | $17,640,003.43 | $5,510,078.81 |
0.36 | $48,271,491.95 | $5,555,192.30 | $7,991,176.30 | $26,460,005.14 | $8,265,118.21 |
0.43 | $57,657,615.38 | $6,635,368.58 | $9,545,016.13 | $31,605,006.14 | $9,872,224.53 |
0.58 | $77,770,737.02 | $8,950,032.04 | $12,874,672.92 | $42,630,008.28 | $13,316,023.79 |
0.59 | $79,111,611.80 | $9,104,342.93 | $13,096,650.04 | $43,365,008.42 | $13,545,610.41 |
0.63 | $84,475,110.91 | $9,721,586.52 | $13,984,558.52 | $46,305,008.99 | $14,463,956.87 |
0.65 | $87,156,860.46 | $10,030,208.32 | $14,428,512.76 | $47,775,009.28 | $14,923,130.11 |
0.68 | $91,179,484.79 | $10,493,141.01 | $15,094,444.12 | $49,980,009.70 | $15,611,889.96 |
0.7 | $93,861,234.34 | $10,801,762.80 | $15,538,398.36 | $51,450,009.99 | $16,071,063.19 |
Wage increase 10%. Demand elasticities from Slaughter (2001) | ||||||
---|---|---|---|---|---|---|
0.04 | $5,363,499.11 | $617,243.59 | $887,908.48 | $2,940,000.57 | $918,346.47 | |
0.06 | $8,045,248.66 | $925,865.38 | $1,331,862.72 | $4,410,000.86 | $1,377,519.70 | |
0.09 | $12,067,872.99 | $1,388,798.07 | $1,997,794.07 | $6,615,001.28 | $2,066,279.55 | |
0.11 | $14,749,622.54 | $1,697,419.87 | $2,441,748.31 | $8,085,001.57 | $2,525,452.79 | |
0.15 | $20,113,121.64 | $2,314,663.46 | $3,329,656.79 | $11,025,002.14 | $3,443,799.26 | |
Wage increase 10%. Demand elasticities from Bruno et al. (2003) | ||||||
Deadweight Loss Due to Mandatory Certification. Sensitivity to demand elasticities. Wage increase 10%. | ||||||
0.31 | $166,268,472.26 | $19,134,551.25 | $27,525,162.80 | $91,140,017.70 | $28,468,740.51 | |
0.416 | $223,121,562.77 | $25,677,333.29 | $36,936,992.66 | $122,304,023.75 | $38,203,213.07 | |
0.459 | $246,184,608.93 | $28,331,480.72 | $40,754,999.12 | $134,946,026.20 | $42,152,102.89 | |
0.533 | $285,874,502.30 | $32,899,083.28 | $47,325,521.85 | $156,702,030.43 | $48,947,866.75 | |
0.583 | $312,691,997.83 | $35,985,301.22 | $51,765,064.24 | $171,402,033.28 | $53,539,599.09 | |
0.737 | $395,289,884.05 | $45,490,852.49 | $65,438,854.79 | $216,678,042.07 | $67,682,134.70 | |
0.751 | $402,798,782.80 | $46,354,993.51 | $66,681,926.66 | $220,794,042.87 | $68,967,819.76 | |
0.881 | $472,524,271.16 | $54,379,160.16 | $78,224,736.87 | $259,014,050.29 | $80,906,323.84 | |
0.909 | $487,542,068.66 | $56,107,442.21 | $80,710,880.60 | $267,246,051.89 | $83,477,693.95 | |
0.961 | $515,432,264.01 | $59,317,108.87 | $85,328,004.69 | $282,534,054.86 | $88,253,095.59 | |
1.056 | $566,385,505.50 | $65,180,922.96 | $93,763,135.22 | $310,464,060.28 | $96,977,387.04 | |
Wage increase 10%. Demand elasticities from Navaretti et al. (2003) | ||||||
0.24 | $128,723,978.52 | $14,813,846.13 | $21,309,803.46 | $70,560,013.70 | $22,040,315.24 | |
0.36 | $193,085,967.79 | $22,220,769.19 | $31,964,705.19 | $105,840,020.55 | $33,060,472.85 | |
0.43 | $230,630,461.52 | $26,541,474.31 | $38,180,064.53 | $126,420,024.55 | $39,488,898.13 | |
0.58 | $311,082,948.10 | $35,800,128.14 | $51,498,691.69 | $170,520,033.11 | $53,264,095.15 | |
0.59 | $316,446,447.20 | $36,417,371.73 | $52,386,600.17 | $173,460,033.68 | $54,182,441.62 | |
0.63 | $337,900,443.62 | $38,886,346.09 | $55,938,234.08 | $185,220,035.96 | $57,855,827.49 | |
0.65 | $348,627,441.84 | $40,120,833.26 | $57,714,051.04 | $191,100,037.11 | $59,692,520.43 | |
0.68 | $364,717,939.15 | $41,972,564.03 | $60,377,776.47 | $199,920,038.82 | $62,447,559.83 | |
0.7 | $375,444,937.36 | $43,207,051.21 | $62,153,593.42 | $205,800,039.96 | $64,284,252.77 | |
Wage increase 10%. Demand elasticities from Slaughter (2001) | ||||||
0.04 | $21,453,996.42 | $2,468,974.35 | $3,551,633.91 | $11,760,002.28 | $3,673,385.87 | |
0.06 | $32,180,994.63 | $3,703,461.53 | $5,327,450.86 | $17,640,003.43 | $5,510,078.81 | |
0.09 | $48,271,491.95 | $5,555,192.30 | $7,991,176.30 | $26,460,005.14 | $8,265,118.21 | |
0.11 | $58,998,490.16 | $6,789,679.48 | $9,766,993.25 | $32,340,006.28 | $10,101,811.15 | |
0.15 | $80,452,486.58 | $9,258,653.83 | $13,318,627.16 | $44,100,008.56 | $13,775,197.02 | |
Wage increase 10%. Demand elasticities from Bruno et al. (2003) | ||||||
year | Contractors + Supervisors | Construction Trades | Other Trades | Helpers Construction | Total ONTARIO | |
---|---|---|---|---|---|---|
NOC_S (2006) | 18 | 19 | 20 | 22 | ||
2010 | Wage | $62,664.1 | $44,554.3 | $50,536.8 | $35,455.4 | $44,786.3 |
Nb. Workers | 48,681 | 88,030 | 245,651 | 112,989 | 5,100,393 | |
Wage bill | 3,050,551,052 | 3,922,112,388 | 12,414,425,283 | 4,006,071,320 | 228,427,527,000 | |
2011 | Wage | $63,145.5 | $46,958.2 | $51,214.0 | $36,485.8 | $46,268.9 |
Nb. Workers | 47,717 | 84,797 | 267,418 | 119,908 | 5,189,831 | |
Wage bill | $3,013,112,392.0 | $3,981,916,181.3 | $13,695,532,081.1 | $4,374,944,102.7 | $240,127,667,759.3 | |
2012 | Wage | $61,857.0 | $48,639.2 | $51,394.4 | $36,542.9 | $47,431.6 |
Nb. Workers | 45,868 | 86,188 | 256,822 | 123,669 | 5,219,457 | |
Wage bill | $2,837,257,334.7 | $4,192,116,231.5 | $13,199,215,165.0 | $4,519,227,610.2 | $247,566,987,862.9 | |
2013 | Wage | $63,125.6 | $48,081.8 | $52,805.7 | $37,829.7 | $47,849.1 |
Nb. Workers | 46,537 | 79,868 | 265,766 | 132,308 | 5,297,378 | |
Wage bill | $2,937,676,977.9 | $3,840,194,806.4 | $14,033,949,035.6 | $5,005,167,978.4 | $253,474,822,633.6 | |
2014 | Wage | $63,807.0 | $49,678.8 | $53,672.8 | $37,164.1 | $47,964.6 |
Nb. Workers | 48,368 | 89,365 | 273,882 | 123,553 | 5,350,118 | |
Wage bill | $3,086,217,943.4 | $4,439,542,387.4 | $14,700,002,854.3 | $4,591,732,340.7 | $256,616,376,825.2 |
increase in wages would result in a 0.31 percent decline in labour demand. Assuming a 10% increase in wages we compute a deadweight loss from certification of about $18.8 million for Ontario’s construction industry, excluding those Trades that are already certified such as electricians. This deadweight loss has about $4.8 million loss coming from Contractors, $6.9 million from Construction Trades, and $7 million from Helpers in construction (Occupations 18, 19 and 22 in the North-American Occupation Classification System, 2006). If instead we allow for a 20% wage increase following certifications, the loss numbers increase to $75 million overall (excluding Other trades), and $19 million for Contractors and about $28 million each for Construction Trades and for Helpers. These are substantive costs which must be factored in by any government considering the introduction of mandatory licensing. As a reminder, while this deadweight loss from decreased production is a large cost in the analysis of mandatory licensing, we do not quantify here some potential benefits to the society such as those resulting from better educated and skilled workers.
Finally,
The paper has analyzed the channels at work when a mandatory occupational licensing program is introduced. We have identified both costs and benefits associated with occupation licensing and certifications. The cost is coming from restricting access to the occupation, and the benefit may come from the increased human capital resulting from the mandated training. Furthermore, we have quantified the potential cost in the form of deadweight output loss when the labour input becomes more expensive, with an application to the construction industry in Ontario. While the magnitude of this cost is sensitive to assumptions made about the elasticity of labour demand and about the size of the wage increase following certification, under all plausible scenarios, this deadweight loss is substantive.
The paper also has an immediate policy message, as reported in Dawson Strategic (2014) [
Future work can look more carefully into a general cost-benefit analysis, done separately for workers, for firms, and for the economy. At the aggregate macro level, the benefits for the society, such as resulting from a better educated workforce in the trade occupations, should also be accounted for. Quantifying these costs and benefits will be complicated by measurements issues. It may be difficult to quantify, for instance, productivity improvements resulting from the better skilled trades, or the increase in consumer demand from improved product quality.
Arguably, a better outcome could be achieved by encouraging volunteer certification, rather than mandatory licensing. Mandating training to all levels of trade people would reduce the quality signal, while decreasing the number of practitioners reduces overall quality of service. Certification could provide the same benefits as licensing in terms of training, except it would be undertaken only by the better workers, allowing more skilled trade workers to differentiate themselves from the less skilled ones both in terms of output quality and in terms of pricing, while avoiding the costs stemming from restricting supply.
We are very grateful to Scott Strickland for fantastic research assistance. We thank two anonymous referees who have provided excellent comments and suggestions. This paper is based on work provided for Dawson Strategic for the report “Modernizing Ontario’s Skilled Trades Apprenticeship and Training System Building New Opportunities through Governance and Regulatory Reform”.
MianaPlesca, (2015) The Impact of Introducing Mandatory Occupational Licensing. Modern Economy,06,1309-1326. doi: 10.4236/me.2015.612124