Introduction Data Estimation Methodology Results Model Policy More Charts The Impact of Macroeconomic Fluctuations on Training Decisions Vincenzo Caponi Ryerson University, IZA, RCEA Cevat Burc Kayahan Acadia University Miana Plesca University of Guelph CLSRN − HRSDC Workshop 18-19 November 2008, Toronto
Introduction Data Estimation Methodology Results Model Policy More Charts Motivation • Training should take place when output is low. − smaller opportunity costs; deJong and Ingram (RED 2001) Dellas and Sakellaris (1996) Devereux (JOLE 2000). • Training should take place when output is high. − adoption of new technologies which may require training to operate; − financing easier, outside options better; King and Sweetman (RED 2002).
Introduction Data Estimation Methodology Results Model Policy More Charts Our contribution • We provide a unifying framework where both channels are identified • Investigate how training decisions are affected by • Aggregate macroeconomic fluctuations • Relative sectoral output fluctuations • Idea: the persistence of idiosyncratic sectoral shocks should be higher than that of the aggregate shocks; this links with the training decision.
Introduction Data Estimation Methodology Results Model Policy More Charts Main findings 1. Aggregate macroeconomic fluctuations have a negative impact on firms’ propensity to train. 2. Relative sectoral fluctuations have a positive impact on firms’ propensity to train. We illustrate these two channels in a search model with random matching, human capital acquisition, and sectoral and aggregate shocks. Policy implications.
Introduction Data Estimation Methodology Results Model Policy More Charts Data: Aggregate statistics and WES Aggregate output statistics (StatsCan) 1980-2007 HP filtered Real 2000 dollars WES 1999-2005 Unit of analysis: the firm WES follows NAICS with small differences in aggregation Timing of WES: March 31st to April 1st Definition of training: formal classroom training
Introduction Data Estimation Methodology Results Model Policy More Charts GDP , HP-filtered GDP and HP trend 1.200e+12 GDP 1.000e+12 gdp 8.000e+11 6.000e+11 1980 1990 2000 2010 year 0 1 + e 0 0 .0 0 2 1 + e 0 0 .0 1 1 _ p d g 0 _ P H 0 1 + e 0 0 .0 1 0- 1 + e 0 0 0 . 2 1980 1990 2000 2010 - year 2 1 + e 0 0 2 1 . 2 1 + e 0 0 1 0 m _ 1 . s _ p d g 1 _ 1 P + e H 0 0 0 . 8 1 1 + e 0 0 0 . 6 1980 1990 2000 2010 year
Introduction Data Estimation Methodology Results Model Policy More Charts Table: Sectors Considered in the Analysis Sector Size (%) Forestry and Mining 0.05 Construction 0.13 Transportation, Warehouse, Wholesale Trade 0.13 Information, Communication and Utilities 0.10 Finance and Insurance 0.07 Real Estate 0.06 Business Services 0.10 Education and Health 0.04 Manufacturing 0.21 Retail Trade and Consumer Services 0.11 N. Obs. 5535
Introduction Data Estimation Methodology Results Model Policy More Charts Charts Sector to GDP ratio 0.2 0.18 0.16 0.14 0.12 0.1 0.08 0.06 0.04 0.02 0 1999 2000 2001 2002 2003 2004 2005 Foresty & Mining Construction Transportation, Warehouse, Wholesale Trade Information, Communication and Utilities Finance and Insurance Real Estate Bussiness Services Education & Health Manufacturing Retail Trade and Consumer Services
Introduction Data Estimation Methodology Results Model Policy More Charts Training incidence 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 1999 2000 2001 2002 2003 2004 2005 Foresty & Mining Construction Transportation, Warehouse, Wholesale Trade Information, Communication and Utilities Finance and Insurance Real Estate Bussiness Services Education & Health Manufacturing Retail Trade and Consumer Services Average
Introduction Data Estimation Methodology Results Model Policy More Charts Sector to GDP ratios (2b) 0.065 0.06 0.055 0.05 0.045 0.04 Construction 17.68% 18.26% 18.87% 17.54% 17.37% 17.03% Foresty & Mining Transportation, Warehouse, Wholesale Trade Bussiness Services Manufacturing
Introduction Data Estimation Methodology Results Model Policy More Charts Training incidence (2b) 0.5 0.45 0.4 0.35 0.3 0.25 0.2 1 2 3 4 5 6 7 Transportation, Warehouse, Wholesale Trade Bussiness Services Manufacturing Foresty & Mining
Introduction Data Estimation Methodology Results Model Policy More Charts Estimation framework D it binary training indicator P it the probability of firm i to train workers in period t P it | ω t = Pr ( D it = 1 | ω t ) = E [ D i | ω t ] ω t a collection of Z t firm characteristics X t sector or overall economy characteristics. We model the conditional expectation using the logistic distribution E [ D i | ω t ] = Λ( α i + β Z it + δ X it + u it ) [Further Evidence]
Introduction Data Estimation Methodology Results Model Policy More Charts Table: Sample Statistics Variable Description Mean Std Dev Classroom Training Indicator 0.340 0.474 % Workforce Trained 0.213 0.446 Overall Training Indicator 0.577 0.494 Firm size Number of workers employed by the workplace 16.7 49.7 Innovation Adoption of innovation and/or new technology by the workplace 0.489 0.499 Unionized Indicator whether the workplace is unionized 0.057 0.232 Multiple loc. Indicator whether the workplace belongs to a multiple-location firm 0.455 0.498 Market The most dominant sales market of the firm Local 0.855 0.351 Canada 0.095 0.292 World 0.049 0.217 Skill % of workforce in skill groups Administrative 0.197 0.283 Managers 0.202 0.231 Others 0.074 0.225 Professionals 0.059 0.169 Sales 0.122 0.249 Technicians 0.148 0.263 Production 0.198 0.312
Introduction Data Estimation Methodology Results Model Policy More Charts Main results Table: The Impact of Aggregate Fluctuations and Sectoral Deviations on the Incidence of Training Variables Coefficients P-value Innovation 0.584 0 Market Canada 0.086 0 Market World 0.472 0 Firm size 0.559 0 Multiple locations 0.156 0 Unionized -0.12 0 % Administrative 0.552 0 % Managerial 0.573 0 % Other 1.145 0 % Sales 0.283 0 % Production 0.637 0 % Technical 0.13 0 GDP deviations -0.008 0 Sector to GDP ratio 0.03 0 Number of observations 8881
Introduction Data Estimation Methodology Results Model Policy More Charts Table: Impact of Fluctuations on Training Incidence: Controlling for Previous Training Benefits Variables Coefficients P-value Innovation 0.721 0.000 Market Canada 0.361 0.000 Market World 0.502 0.000 Firm size 0.602 0.000 Multiple locations 0.094 0.000 Unionized -0.099 0.000 %Administrative 1.669 0.000 %Managerial 1.395 0.000 %Other 1.616 0.000 %Sales 0.819 0.000 %Production 1.521 0.000 %Technical 0.914 0.000 GDP deviations -0.009 0.000 Sector to GDP ratio 0.049 0.000 MB t − 1 0.104 0.001 MB t − 2 0.277 0.000
Introduction Data Estimation Methodology Results Model Policy More Charts Table: Impact of Fluctuations on Training Intensity: Continuous Training Measure (% workforce trained) Variables Coefficients P-value Innovation 0.604 0.000 Market Canada 0.080 0.000 Market World 0.457 0.000 Firm size 0.536 0.000 Multiple locations 0.094 0.000 Unionized -0.118 0.000 % Administrative 0.552 0.000 % Managerial 0.535 0.000 % Other 1.127 0.000 % Sales 0.268 0.000 % Production 0.630 0.000 % Technical 0.112 0.000 GDP deviations -0.006 0.000 Sector to GDP ratio 0.001 0.000
Introduction Data Estimation Methodology Results Model Policy More Charts Table: Impact of Fluctuations on Training Incidence: Adding OJT to CT in the Definition of Training Variables Coefficients P-value Innovation 0.49 0.00 Market Canada 0.328 0.00 Market World -0.356 0.00 Firm size 0.352 0.00 Multiple locations -0.077 0.00 Unionized -0.307 0.00 % Administrative 0.209 0.00 % Managerial 0.051 0.00 % Other -0.037 0.00 % Sales 0.464 0.00 % Production 0.337 0.00 % Technical -0.048 0.01 GDP deviations -0.0001 0.59 Sector to GDP ratio 0.061 0.00 Number of observations 6427
Introduction Data Estimation Methodology Results Model Policy More Charts Simple Mortensen-Pissarides model with sectoral and aggregate shocks and training • Random meeting between vacancies v and searchers u governed by the meeting function m ( v , u ) . • One firm is one sector. • Once there is a meeting between a worker and a firm the match-specific productivity x is realized. (Note: x is the sector-specific idiosyncratic shock). • The training decision takes place, simultaneous with the decision whether to form a productive match. • Training increases human capital according to the human capital function exp( H ( x )) at a cost c ( X ) . • If no training is offered, H ( x ) = 0. • If α ( x ) = x · exp ( H ( x )) is above the reservation productivity value, a match is created.
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