Managerial knowledge spillovers and firm productivity. Marie Le Mouel

Transcription

1 Managerial knowledge spillovers and firm productivity Marie Le Mouel February 6, 2018

2 What is managerial knowledge? General definition Managerial knowledge is the know-how needed to combine human and capital resources to produce goods and services Operational definition following Bloom and Van Reenen (2007), Bloom et al. (2017a) It involves implementing structured practices relating to: Performance monitoring: tracking what goes on inside the firm Target setting: setting appropriate targets, measuring outcomes, and taking action when the two do not match Incentives and talent retention: promoting and rewarding employees based on performance, and systematically seeking to hire and retain the best employees

3 Managerial practices matter for firm performance Results from the World Management Survey suggest: Large heterogeneity in the implementation of these practices across countries, industries and firms A higher management score associated with higher productivity, profitability, survival rates, growth rates Differences in management scores explain differences in productivity; same importance as R&D spending and twice as important as IT spending. Drivers of management scores are: competition, ownership, human capital and learning spillovers Source: Bloom et al. (2017a), Bloom et al. (2017b)

4 From managerial practices to managerial occupations Evidence from task surveys suggests that 1 Managerial practices can be broken down into specific tasks 2 These tasks are the central activities of managerial occupations Figure : Tasks associated with managerial practices, from PIAAC and ONET surveys Source: Le Mouel and Squicciarini (2015)

5 Learning-by-Hiring The literature on Learning-by-Hiring differs according to the definition of movers and of performance being analysed My paper focuses on the mobility of managers and on firm productivity Type of knowledge Definition of movers Unspecified or general knowledge Scientific or technical knowledge Export-related knowledge All employees with higher education All employees coming from a more productive firm All employees in R&D functions Managers mea- Performance sure Reference Productivity Parrotta & Pozzoli (2012) Stockinger & Wolf (2016) Stoyanov & Zubanov (2012, 2014) Serafinelli (2017) Productivity Maliranta, Mohnen & Rouvinen (2009) Patenting Kaiser, Kongsted & Roende (2014) Participation in export markets Mion & Oppromolla (2014)

6 Research question Research question To what extent is managerial mobility a vector of transmission for managerial knowledge? Contributions of the paper: 1 Focus on managers as repositories of managerial knowledge 2 Include a measure of costs of mobility and explore trade-offs between interests of hiring and sending firms 3 Look into characteristics of hiring firms, movers and sending firms

7 Implementation Data Finnish Linked Employer-Employee Data Detailed employee information: occupation, experience, qualification Detailed firm information: employee composition, industry, position on productivity distribution Methodology Estimate a structural model of production following Ackerberg et al. (2015) and De Loecker and Warzynski (2012) Model managerial mobility (arrivals and departures) as affecting the productivity process of firms

8 Preview of results On average, hiring managers boosts productivity, whereas internally promoting workers to managerial positions has no effect Departures have a strong negative impact on productivity The size of the hiring effect depends on the productivity of the hiring firm - larger effects are observed for the least productive firms Experience and qualification matter Managerial knowledge is not industry-specific and appears to be of a more general nature

9 The Finnish Linked Employer-Employee Data Employee data Keep employees for whom a firm ID is recorded in the last week of the year Identify managers according to the ISCO classification Employer data Drop firms with less than 5 employees, in their year of creation, in mining, financial services and real estate 30,000 firm observations per year Construct transition matrices between sending and hiring firms Observe arrival and departure of every employee in each firm Observe the origin and destination of all transitions

10 Key variables Elements of the main vector of interest- mobility it 1 Spillover potential 2 Cost of mobility 3 Benchmark for external knowledge 4 Firm growth dynamics

11 Key variables Elements of the main vector of interest- mobility it 1 Spillover potential Manager Hire it = 2 Cost of mobility Manager Departure it = 3 Benchmark for external knowledge Internal Promotion it = 4 Firm growth dynamics Non-Managerial Hire it = number of managerial hires from another firm total number of managers number of managerial departures to another firm total number of managers number of stayers switching to managerial status total number of managers number of non-managerial hires total number of employees

12 Descriptive statistics Very micro firms have a different structure and account for a negligeable fraction of overall managerial hires Table : Managerial composition and mobility, by firm size % firms % of firms with Firm with % of Managerial Internal Survival Sizeclass Empl. Man. Man. Hire Depart. Prom. Rate N. Obs Very Micro ,588 Micro ,518 Small ,569 Med. Small ,662 Medium ,187 Med. Large ,315 Large ,743 Source: Author s calculations

13 Descriptive Statistics Firms with no managerial departure tend to promote internally and hire managers to the same extent Firms with a managerial departure are more likely to hire than to promote internally Figure : Share of firms with managerial hires and internal promotions, by managerial departure and size in percentage Source: Author s calculations

14 Table : Transition matrix between quartiles of labour productivity, by managerial hiring Panel A: Firms without a managerial hire Destination Origin Quartile Bottom 2nd 3rd Top of Labour Productivity Exit Quartile Quartile Quartile Quartile Bottom Quartile nd Quartile rd Quartile Top Quartile Panel B: Firms with a managerial hire Destination Origin Quartile Bottom 2nd 3rd Top of Labour Productivity Exit Quartile Quartile Quartile Quartile Bottom Quartile nd Quartile rd Quartile Top Quartile Source: Author s calculations

15 Theoretical model of production Production function y it = f (l it, k it, m it ; α) + ω it + ɛ it (1) References Olley & Pakes (1996), Levinsohn & Petrin (2003), Ackerberg, Caves and Frazer (2015), De Loecker & Warzynski (2012)

16 Theoretical model of production Production function y it = f (l it, k it, m it ; α) + ω it + ɛ it (1) Demand for intermediary inputs m it = h t (k it, l it, ω it, HC it, s it, w it, mobility it ) (2) References Olley & Pakes (1996), Levinsohn & Petrin (2003), Ackerberg, Caves and Frazer (2015), De Loecker & Warzynski (2012)

17 Theoretical model of production Production function Demand for intermediary inputs y it = f (l it, k it, m it ; α) + ω it + ɛ it (1) m it = h t (k it, l it, ω it, HC it, s it, w it, mobility it ) (2) Under the right assumptions ω it = h 1 t (k it, l it, m it, HC it, s it, w it, mobility it ) (3) References Olley & Pakes (1996), Levinsohn & Petrin (2003), Ackerberg, Caves and Frazer (2015), De Loecker & Warzynski (2012)

18 Theoretical model of production Productivity process ω it = g(ω it 1, P it 1, mobility it 1 ) + ξ it (4) Key assumption Evolution of productivity follows first-order Markov process References Olley & Pakes (1996), Levinsohn & Petrin (2003), Ackerberg, Caves and Frazer (2015), De Loecker & Warzynski (2012)

19 Estimation strategy Stage 1 Recover predicted productivity ˆω up to production coefficients α y it = f (l it, k it, m it ; α) + ht 1 (.) + σ t + ɛ it = ϕ t (k it, l it, m it, HC it, s it, w it, mobility it ) + σ t + ɛ it ˆω it = ˆϕ t (.) ˆσ t f (l it, k it, m it ; α) Stage 2 Recover predicted survival probabilities ˆP, from a probit model of survival Pr(Survival = 1 mobility it, k it, l it, HC it, startups it, w it, σ i, σ t )

20 Estimation strategy Stage 3 Recover production coefficients ˆα GMM estimation built from the error term of the productivity process ξ it E[ξ it I it 1] = 0 E[ˆω it g(ˆω it 1, ˆP it 1, mobility it 1 ) I it 1 ] = 0 Instruments: l it, k it, m it 1 and respective squared and interaction terms Stage 4 Recover productivity ˆω and re-estimate the productivity process ˆω it = δ 1 ˆω it 1 + δ 2 ˆω 2 it 1 + δ 3 ˆPit 1 + δ 4 ˆP2 it 1 + δ 5 ˆω it 1 ˆPit 1 + δ 6 mobility it 1 + ξ it ˆω it = δ 1 ˆω it 1 + δ 2 ˆω 2 it 1 + δ 3 ˆPit 1 + δ 4 ˆP2 it 1 + δ 5 ˆω it 1 ˆPit 1 + δ 6 mobility it 1 + δ 7 mobility it 1 ˆω it 1 + ξ it (5)

21 Table : Average output elasticities of labour, capital and materials, by industry Industry θ l θ k θ m θ l θ k θ m Total Economy Services Agriculture Construction Manufacturing Trade Food Manuf Logistics Textile Manuf Hospitality Wood & Paper Publishing Chemicals Telecoms Rubber & Plastics ICT services Basic Metals Legal & Acct Computer & Elect R&D Services Electrical equipmt Admin. Act Machinery Education Transport Health Furniture Social services Energy supply Entertainment Water supply Other services Survival Probabilities

22 Table : Law of motion of productivity, by managerial experience Dep. variable Baseline Experience ω (1) (2) (3) (4) Man. Hire *** *** ( ) ( ) Man. Hire*ω ** (0.0325) Man. Departure *** *** *** *** ( ) ( ) ( ) ( ) Man. Departure*ω * * (0.0289) (0.0314) Exp Man. Hire *** *** ( ) (0.0107) Exp Man. Hire*ω * (0.0978) Ext. Promotion *** *** ( ) ( ) Ext. Promotion*ω ** (0.0287) Int. Promotion *** *** *** *** ( ) ( ) ( ) ( ) Int. Promotion*ω (0.0207) (0.0170) Non-Man. Hire *** *** ( ) ( ) ( ) ( ) Non-Man. Hire*ω *** *** (0.0293) (0.0239) Observations 211, , , ,679 R-squared All independent variables lagged one period. All models include polynomial of order 2 in lagged productivity & survival probability, unreported. Block bootstrapped std errors in brackets. * p < 0.1, ** p < 0.05, *** p < 0.01

23 Figure : Average effect of managerial hires and departures, by productivity quartiles Source: Own calculations, FLEED

24 Figure : Average effect of managerial, by experience and productivity quartiles Source: Own calculations, FLEED

25 Table : Law of motion of productivity, by mover characteristics Dep. variable Industry of origin Qualification ω (5) (6) (7) (8) Same det. ind *** *** ( ) ( ) Same det. ind.*ω ** (0.0932) Same broad ind *** *** ( ) ( ) Same broad ind.*ω (0.0559) Diff broad ind *** *** ( ) ( ) Diff broad ind.*ω (0.0236) No Qualif Man. Hire *** *** ( ) ( ) No Qualif Man. Hire*ω (0.0352) Med Qualif Man. Hire *** *** ( ) ( ) Med Qualif Man. Hire*ω (0.0776) Adv Qualif Man. Hire *** *** ( ) ( ) Adv Qualif Man. Hire*ω (0.0416) Observations 211, , , ,679 R-squared All independent variables lagged one period. All models include polynomial of order 2 in lagged productivity & survival probability, & mobility vector & interactions with lagged ω, unreported. Block bootstrapped std errors in brackets. * p < 0.1, ** p < 0.05, *** p < 0.01

26 Figure : Average effect of managerial, by qualification and productivity quartiles Source: Own calculations, FLEED

27 Table : Law of motion of productivity, by sender characteristics Dependent variable Human Capital ω Quartiles ω (1) (2) (3) (4) Share Med Qualif Sender ** *** ( ) ( ) Share Med Qualif Sender*ω (0.0879) Share Adv Qualif Sender *** *** (0.0117) (0.0109) Share Adv Qualif Sender*ω (0.124) Sender in Laggard Quartile *** ** ( ) ( ) Laggard*ω (0.0747) Sender in Frontier Quartile ( ) ( ) Frontier*ω (0.165) Med Qualif Man. Hire *** *** ** ( ) (0.0102) ( ) ( ) Med Qualif Man. Hire*ω * * (0.118) (0.0809) Adv Qualif Man. Hire *** *** *** *** ( ) ( ) ( ) ( ) Adv Qualif Man. Hire*ω (0.0582) (0.0642) Observations 186, , , ,767 R-squared All independent variables lagged one period. All models include polynomial of order 2 in lagged productivity & survival probability, & mobility vector & interactions with lagged ω, unreported. Block bootstrapped std errors in brackets. * p < 0.1, ** p < 0.05, *** p < 0.01

28 Conclusion New evidence Hiring managers from other firms boosts productivity on average, low performing firms benefit the most Managerial departures have a strong negative impact on productivity Characteristics that matter the most are managerial experience, qualifications, and coming from a knowledge-intensive firm Industry experience and coming from a frontier firm do not seem to matter

29 Conclusion Managerial implications Hiring the right managers can be an effective way to increase the productivity of poorly performing firms Trade-off between interests of hiring and sending firms suggests that: Firm should invest in making managerial knowledge more sticky Scope to increase overall supply of qualified managers There can exist more important sources of growth for frontier firms (e.g. innovation) Open questions Are managers improving processes (i.e. quantity productivity) or market positioning (i.e. prices)? - Observing prices would permit disentangling quantity from price effects What are the overall welfare implications?

30 Thank you for your attention! Marie Le Mouel - mlemouel@diw.de DIW Berlin Deutsches Institut für Wirtschaftsforschung e.v. Mohrenstraße 58, Berlin

31 References Additional slides References Ackerberg, D., Caves, K., and Frazer, G. (2015). Identification properties of recent production function estimators. Econometrica. Bloom, N., Sadun, R., and Van Reenen, J. (2017a). Management as a technology? NBER Working Paper Bloom, N., Brynjolfsson, E., Foster, L., Jarmin, R., Patnaik, M., saporta-eksten, I., and Van Reenen, J. (2017b). What drives differences in Management? NBER Working Paper Bloom, N. and Van Reenen, J. (2007). Measuring and explaining management practices across firms and countries. Quarterly Journal of Economics. De Loecker, J. and Warzynski, F. (2012). Markups and firm-level exports. American Economic Review. Kaiser, U., Kongsted, H., Ronde, T., (2014). Does the mobility of R&D labor increase innovation? Journal of Economic Behavior & Organization. Le Mouel, M. and Squicciarini, M. (2015). Cross-country estimates of employment and invest- ment in organisational capital: A task-based methodology using piaac data. OECD Science, Technology and Industry Working Papers. Levinsohn, J. and Petrin, A. (2003). Estimating production functions using inputs to control for unobservables.review of Economic Studies, 70: Maliranta, M., Mohnen, P., and Rouvinen, P. (2009). Is interfirm labor mobility a channel of knowledge spillovers? evidence from a linked employer employee panel. Industrial and Corporate Change Mion, G. and Opromolla, L. (2014). Managers mobility, trade performance and wages. Journal of International Economics Olley, S. and Pakes, A. (1996). The dynamics of productivity in the telecommunications equipment industry. Econometrica, 64(6): Parrotta, P. and Pozzoli, D. (2012). The effect of learning by hiring on productivity. RAND journal of economics. Serafinelli, M. (2017). Good firms, worker flows and productivity. Journal of Labor Economics. Stockinger, B. and Wolf, K. (2016). The productivity effect of worker mobility between heterogeneous firms. IAB Discussion paper. Stoyanov, A. and Zubanov, N. (2012). Productivity spillovers across firms through labour mobility. American Economic Journal: Applied Economics.

32 References Additional slides Table : Marginal effects of managerial hiring on survival probabilities Marginal Standard Effect Error t-stat p-value Man. Hire Exp. Man Hire Ext. Promotion Same det. Industry Same broad industry Diff broad industry No Qualif Man. Hire Med. Qualif Man. Hire Adv Qualif Man. Hire Share HE Sender Share advhe Sender Q1 ω Sender Q2 ω Sender Q3 ω Sender Q4 ω Sender Output elasticities

33 References Additional slides Table : Reduced form evidence with labour productivity Dependent Variable (1) (2) (3) (4) Log Labour Productivity Baseline Experience Ind. Origin Qualif. Man. Hire *** ( ) Exp Man. Hire *** ( ) Ext. Promotion *** ( ) Same det. Industry ** ( ) Same broad industry *** ( ) Diff broad industry *** ( ) No HE Man. Hire ( ) HE Man. Hire *** ( ) Adv HE Man. Hire *** (0.0113) Int. Promotion *** *** *** *** ( ) ( ) ( ) ( ) Man. Departure *** *** *** *** ( ) ( ) ( ) ( ) Non-Man. Hire *** *** *** *** ( ) ( ) ( ) ( ) Observations 214, , , ,579 R-squared All independent variables are lagged one period. All sepcifications include one lag of labour productivity and survival

34 References Additional slides Figure : Marginal effect of managerial hires and departures, by industry and ω quintiles

35 References Additional slides Recovering Markups Following DeLoecker & Warzynski (2012) 1 Firm-specific output elasticity of materials θ mit = α m + 2α mm m it + α lm l it + α km k it (6) 2 Firm-specific markups according to ( ) M 1 it µ it = θ mit. (7) Y it /exp(ɛ it ) 3 Regress logged markups on hiring behaviour, labour and capital inputs, and time and industry fixed effects

36 References Additional slides Table : Effect of mangerial hires on markups and market-shares Baseline Experience Industry of Origin Qualification Dependent variable: Logµ Log Mkt Logµ Log Mkt Logµ Log Mkt Logµ Log Mkt Share Share Share Share Man. Hire *** 0.126*** ( ) ( ) Exp Man. Hire *** 0.202*** ( ) (0.0129) Ext. Promotion *** *** ( ) ( ) Same det. Industry *** 0.232*** ( ) (0.0121) Same broad industry ** *** (0.0101) (0.0157) Diff broad industry *** *** ( ) (0.0109) HE Man. Hire *** 0.184*** ( ) (0.0118) Adv HE Man. Hire *** 0.298*** (0.0104) (0.0183) Int. Promotion *** *** *** *** *** *** *** *** ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) Man. Departure *** *** * *** *** * ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) Non-Man. Hire *** *** *** *** *** *** *** *** ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) Observations 211, , , , , , , ,609 R-squared All independent variables relating to mangerial hires and mobility are lagged one period. Models include labour and capital, industry and time fixed-effects. Standard errors obtained from block bootstrap in parentheses. * p < 0.1, ** p < 0.05, *** p < 0.01