Table 6
Linear probability model estimates of the determinants of being re-employed (1981 to 1985, 1990 to 1995, 2008 to 2011)

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Linear probability model estimates of the determinants of being re-employed (1981 to 1985, 1990 to 1995, 2008 to 2011): Part 1


Table summary
This table displays the results of linear probability model estimates of the determinants of being re-employed (1981 to 1985 1981 to 1985, 1990 to 1995 and 2008 to 2011, calculated using coefficient and standard error units of measure (appearing as column headers).
  1981 to 1985 1990 to 1995 2008 to 2011
  coefficient standard error coefficient standard error coefficient standard error
Variables  
Female -0.018 0.0100  Note ** 0.025 0.0090  Note *** 0.013 0.015
Age/10 0.052 0.0230  Note * 0.034 0.021 -0.035 0.036
Age squared/100 -0.009 0.0030  Note *** -0.007 0.0030  Note *** 0 0.004
Education attainment (reference:
below university)		  
University 0.052 0.0220  Note * 0.052 0.0170  Note *** 0.052 0.0180  Note ***
Job tenure (reference: 24 months
or less)		  
 25 months to 60 months 0.036 0.0130  Note *** 0.04 0.0120  Note *** 0.026 0.02
More than 60 months 0.092 0.0140  Note *** 0.064 0.0110  Note *** 0.077 0.0170  Note ***
Expected to be recalled at layoff 0.217 0.0160  Note *** 0.242 0.0140  Note *** 0.145 0.0240  Note ***
Tenure group × expected
to be recalled		  
 25 months to 60 months 0.004 0.029 0.023 0.026 0.044 0.043
More than 60 months 0.067 0.0250  Note *** 0.055 0.0220  Note * 0.034 0.034
Region (reference: Ontario)  
Atlantic Provinces -0.098 0.0120  Note *** -0.04 0.0100  Note *** -0.043 0.0160  Note ***
Quebec -0.059 0.0120  Note *** 0.006 0.011 -0.003 0.019
Prairie Provinces -0.013 0.014 0.029 0.0130  Note * 0.089 0.0180  Note ***
Alberta 0.046 0.0190  Note * 0.063 0.0140  Note *** 0.033 0.021
British Columbia 0.009 0.015 0.112 0.0150  Note *** -0.014 0.021
Industry (reference: primary
industries and construction)		  
Manufacturing 0.019 0.013 0.042 0.0120  Note *** 0.004 0.022
Retail trade, accommodation,
and food services		 0.027 0.0150  Note ** 0.003 0.013 0.01 0.024
High-skill services 0.038 0.0180  Note * 0.011 0.016 0.013 0.025
Public services 0.1 0.0150  Note *** 0.17 0.0130  Note *** 0.219 0.0220  Note ***
Other service-producing industries 0.037 0.0160  Note * 0.03 0.0130  Note * 0.048 0.0220  Note *
Month of the first layoff (reference:
month 2)		  
Month 3 -0.031 0.0110  Note *** -0.033 0.0100  Note *** 0.009 0.017
Month 4 -0.079 0.0130  Note *** -0.067 0.0110  Note *** -0.078 0.0180  Note ***
Month 5 -0.19 0.0120  Note *** -0.177 0.0100  Note *** -0.212 0.0180  Note ***
Constant 0.331 0.0420  Note *** 0.305 0.0410  Note *** 0.569 0.0720  Note ***
p<0.05            
p<0.10            
p<0.01            
Note(s): A linear probability model is used. The estimating equation regresses the binary dependent variable of whether the displaced workers were re-employed with a paid job at the last month of the Labour Force Survey interview on different sets of controls. Sample weights are used to provide estimates for the entire population. Standard errors account for complex survey design (stratification, clustering, and non-response).            
             

Linear probability model estimates of the determinants of being re-employed (1981 to 1985, 1990 to 1995, 2008 to 2011): Part 2


Table summary
This table displays the results of linear probability model estimates of the determinants of being re-employed (1981 to 1985 1981 to 1985, 1990 to 1995 and 2008 to 2011
  1981 to 1985 1990 to 1995 2008 to 2011
Diagnostic statistics  
Number of observations 22,152 34,926 9,961
R squared 0.089 0.102 0.091
p<0.05      
p<0.10      
p<0.01      
Note(s): A linear probability model is used. The estimating equation regresses the binary dependent variable of whether the displaced workers were re-employed with a paid job at the last month of the Labour Force Survey interview on different sets of controls. Sample weights are used to provide estimates for the entire population. Standard errors account for complex survey design (stratification, clustering, and non-response).      
       
*
p<0.05
**
p<0.10
***
p<0.01
Note(s):
A linear probability model is used. The estimating equation regresses the binary dependent variable of whether the displaced workers were re-employed with a paid job at the last month of the Labour Force Survey interview on different sets of controls. Sample weights are used to provide estimates for the entire population. Standard errors account for complex survey design (stratification, clustering, and non-response).
Source(s):
Statistics Canada, Labour Force Survey.
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