ROUTINE JOBS, EMPLOYMENT AND TECHNOLOGICAL INNOVATION IN GLOBAL VALUE CHAINS

Transcription

1 ROUTINE JOBS, EMPLOYMENT AND TECHNOLOGICAL INNOVATION IN GLOBAL VALUE CHAINS (Preliminary & Incomplete) Luca Marcolin, OECD (STI/EAS) Sébastien Miroudot, OECD (TAD/TSD) Mariagrazia Squicciarini, OECD (STI/EAS) 3 rd PIAAC Conference Madrid November 2016

2 Outline Motivation & contribution Empirical framework Data: PIAAC & non-piaac Results Policy discussion

3 The role of GVCs and technology on employment Concerns about the impact on some categories of workers of (i) the fragmentation of production, and (ii) technological change. Objective: labour demand Objective: routine workers, i.e. if follow a set of welldefined rules or sequences. Production (and trade) according to tasks.

4 The role of GVCs and technology on employment Concerns about the impact on some categories of workers of (i) the fragmentation of production, and (ii) technological change. Objective: labour demand Objective: routine workers, i.e. if follow a set of welldefined rules or sequences. Production (and trade) according to tasks. Routine tasks may be easier to offshore. Routine tasks may be more sensitive to substitution by ICT. But also: Routine tasks are performed by workers of different skills. Not all innovations are labor-substituting.

5 Why would we care? Economic polarization seems to affect political polarization: Autor et al. (2016) : in regions more severely hit by Chinese trade shock, political polarization rises (Congressional elections 2002 vs 2010) Future of work. What levers should we activate? 21/11/2016

6 This paper: The question: what drives demand of routine vs non-routine employment? GVCs vs technology

7 This paper: The question: what drives demand of routine vs non-routine employment? GVCs vs technology Cross-country, cross-industry analysis over , but also aggregated microdata (PIAAC, Microdata Lab) 27 EU countries + U.S., 27 industries. Different modes of GVC participation: Trade in Value Added Different ways of proxying technology Contributions: New measure of routine intensity of sectors. Based on all occupations, and at a high level of disaggregation. Assess several shifters of labour demand, beyond manufacturing firms + Do it in GVC context.

8 This paper: The question: what drives demand of routine vs non-routine employment? GVCs vs technology Cross-country, cross-industry analysis over , but also aggregated microdata (PIAAC, Microdata Lab) 27 EU countries + U.S., 27 industries. Different modes of GVC participation: Trade in Value Added Different ways of proxying technology Contributions: New measure of routine intensity of sectors. Based on all occupations, and at a high level of disaggregation (PIAAC). Assess several shifters of labour demand, beyond manufacturing firms + do it in GVC context.

9 (Ultra-quick) Literature Determinants of labor demand by skill: offshoring (e.g. Feenstra & Hanson, 1999; Blinder & Krueger, 2013) or ICT (e.g. Michaels et al., 2014) Routine & polarisation: Autor et al., 2003, Goos & Manning, Determinants of job polarisation: Country-industry : Autor & Dorn, 2013; Goos et al., 2014 at the firm level (L.E.E.D., Kerr, et al., 2015; Harrigan et al., 2016); Individual level: Keller & Utar (2015)

10 The empirical specification Ln Empl q,i,k,t = β 0 + β 1 TECH i,k,t + β 2 GVC i,k,t + + β 2 X i,k,t +dummies + error q,i,k,t X i,k,t = {VA, K, Hours, Wage, Wage & Hours by skill} NOT about usual polarisation. Effects of policies. Similar to Berman et al and Feenstra & Hanson (1996), modify short-run labor demand equation with technology and GVC. Estimation with SUR + IV

11 The empirical specification Ln Empl q,i,k,t = β 0 + β 1 TECH i,k,t + β 2 GVC i,k,t + + β 2 X i,k,t +dummies + error q,i,k,t X i,k,t = {VA, K, Hours, Wage, Wage & Hours by skill} NOT about usual polarisation. Similar to Berman et al and Feenstra & Hanson (1996), modify short-run labor demand equation with technology and GVC. Estimation with SUR + IV

12 (1) Technology Intutition: substitution vs complementarity with different types of labor. ICT : Ideally: ICT GFCF (SNA). Note: coverage issue. Share of workers involved in ICT & engineering business functions in the industry. Using LFS occupation-sector matrix (Miroudot, 2016) Number of patent families in the industry (OECD Microdata Lab). Van Looy et al. (2014): mostly manufacturing Matching (Dernis et al. 2013): selected countries.

13 (2) Trade in Value Added (TiVA) New methodology to split VA of exports between directly produced, imported, or produced by other domestic suppliers. From Noguera and Johson (2012), Koopman, Wang and Wei (2014), Timmer, Los, Stehrer and de Vries (2013). Final product (TiVA): 62 Countries, 34 industries (16 manuf, 14 services), (2013) Assumes existence of Inter Country Input Output matrix, integrating national IO or SUT with import-related ones.

14 Importing Foreign Value Added Foreign value added of exports Source: OECD-WTO TiVA Database.

15 Trade in Value Added: this paper Intuition: GVCs as labor demand shifters: Efficiency gains from within industry reallocation, Different substitutability of routine types depending on how much of production is outsourced. input outsourcing: intermediate consumption of service inputs over gross output

16 Trade in Value Added: this paper Intuition: GVCs as labor demand shifters: Efficiency gains from within industry reallocation, Different substitutability of routine types depending on how much of production is outsourced. Ad-hoc estimators: (narrow) offshoring: imported intermediate inputs (narrow) / total intermediate consumption (narrow) domestic outsourcing: same as offshoring, for domestic prod. (narrow) offshoring of assembly: imports of VA in final goods only Service input outsourcing: intermediate consumption of service inputs over gross output

17 (3) Measuring routine intensity Previous measures: expert judgement (Blinder 2006, 2009), or Dictionary of Occupational Titles (DOT/O NET) Autor et al. 2003, Calculated for the USA in the 70s-90s. Or alternative national classifications (e.g. Spitz-Oener 2006)

18 Measuring routine intensity (II) How can we improve on it? Not ad-hoc association, asking workers directly. perception-based vs experts judgement? Country specific. More on-the-point questions. Finger dexterity: Routine (manual) tasks? Distinct from skills (abstract reasoning). (Time variant)

19 Measuring routine intensity (III) Routine intensity: the extent to which tasks are carried out in a codifiable and repetitive way 4 PIAAC questions on individuals own assessment of: 1. Their degree of freedom in establishing the sequence of their tasks (sequentiability) 2. Their degree of freedom in deciding the type of tasks to be performed on the job (flexibility) 3. The frequency with which they plan their own activities (plan_own) 4. The frequency with which they organise their own time (organise_own)

20 The Routine Intensity Index For each individual k, RII k,i,o = w seq Sequentiability k,i,o + w flex Flexibility k,i,o + w planown Plan_own k,i,o + w orgown Organise_own k,i,o The higher, the more routine intensive (1-5). Weights sum up to 1. Several choices: For the weight of each variable. For the attribution of each weight to each variable: PCA; dispersion-based (more precision = higher weight). Obtain 14 different indexes per individual

21 The Routine Intensity Index For each individual k, RII k,i,o = w seq Sequentiability k,i,o + w flex Flexibility k,i,o + w planown Plan_own k,i,o + w orgown Organise_own k,i,o The higher, the more routine intensive (1-5). Weights sum up to 1. Several choices: For the weight of each variable. For the attribution of each weight to each variable: PCA; dispersion-based (more precision = higher weight). Obtain 14 different indexes per individual

22 The Routine Intensity Index For each individual k, RII k,i,o = w seq Sequentiability k,i,o + w flex Flexibility k,i,o + w planown Plan_own k,i,o + w orgown Organise_own k,i,o The higher, the more routine intensive (1-5). Weights sum up to 1. Several choices: For the weight of each variable. For the attribution of each weight to each variable: PCA; dispersion-based (more precision = higher weight). Obtain 14 different indexes per individual

23 From individuals to occupations and sectors (i) Choose one RII. (ii) Aggregate over all individuals in the 3- digit occupation. (iii) Rank occupations by median value. Classification of 3-digit ISCO2008 occupations in Quartiles

24 Examples in common across country Low routine-intensive occupations (Q1) Legislators and senior officials; Managing directors and chief executives; Sales and purchasing agents and brokers; Authors, journalists and linguists. Medium-low routine-intensive occupations (Q2) Secondary education teachers; Hotel and restaurant managers; Administrative and specialized secretaries; Hairdressers, beauticians and related workers. Medium-high routine-intensive occupations (Q3) Machinery mechanics and repairers; Shop salespersons; Medical and pharmaceutical technicians; Other clerical support workers. High routine-intensive occupations (Q4) Assemblers; Food preparation assistants; Tellers, money collectors and related clerks; Metal processing and finishing plant operators. 24

25 From individuals to occupations and sectors (i) Choose one RII. (ii) Aggregate over all individuals in the 3- digit occupation. (iii) Rank occupations by median value. Classification of 3-digit ISCO2008 occupations in Quartiles Assume 2011 classification applies to all years. => Changes in routine content driven by occupational s. Apply to (3dig occupation)*(2dig sector) employment data EULFS + CPS + SES. Data hurdle. Intensity of 2-digit SECTORS in routine quartiles

26 Percentage of employment by quartile of routine intensity (average ) 100% NR: non-routine intensive MR: medium routine intensive LR: low routine intensive HR: high routine intensive 90% 80% 70% 60% 50% 40% 30% 20% 10% 0%

27 % Routine jobs in the crisis Employment growth by routine-intensity. Selected European countries and United States, Employment growth (left-hand scale): Non-routine occupations Routine-intensive occupations Total economy Selected European countries Changes in employment (right-hand scale): Non-routine occupations Routine-intensive occupations Thousands of persons Change in classification in % United States Thousands of persons Source: OECD calculations based on PIAAC & other databases. STI Scoreboard 2015

28 The empirical estimation Reminder: Ln Empl q,i,k,t = β 0 + β 1 TECH i,k,t + β 2 GVC i,k,t + β 2 X i,k,t + dummies + error q,i,k,t About 3050 observations , with gaps USA + EU27

29 Results : SUR Manufacturing Services Low routine Medium routine High routine Nonroutine Nonroutine Low routine Medium routine ICT intensity Patents High routine Skill intensity (+) Domestic outsourcing of inputs Offshoring of inputs + + Offshoring of final assembly Service Outsourcing Also included throughout : value-added; capital; wages; hours worked; industry, year, country dummies.

30 Results : SUR, cont d Manufacturing Services Low routine Medium routine High routine Nonroutine Nonroutine Low routine Medium routine ICT intensity Patents High routine Skill intensity (+) Domestic outsourcing of inputs Offshoring of inputs + + Offshoring of final assembly Service Outsourcing Also included throughout : value-added; capital; wages; hours worked; industry, year, country dummies.

31 Extra results Splitting between transition and G7 countries Robust: ICT capital. Robust : patents (matched). Robust: Skills by quartile: always positively correlated with employment, for all quartiles. Robust: Entire time series (WIOD). Robust: Restricting to PIAAC countries only.

32 Instrumental Variables (preliminary) Fixed Effects + IV (extension to entire time series) Lagged terms (Anderson & Hsiao, 1981), & Bartik IV (Bartik, 1991) Non-routine Manufacturing Low routine Medium routine High routine ICT intensity + + Patents Skill intensity Domestic outsourcing of inputs Offshoring of inputs + + Service Outsourcing

33 Preliminary conclusions Complex interactions between skills, technology and trade => no clear losers and winners in GVCs on the basis of the routine-intensity of occupations. Most important is the specialisation of countries through technology and skills, vs no strong ground for GVC. In line with literature (e.g. Michaels et al. 2014) ICT can displace HR workers. Innovation as employment enhancer. BUT may be missing general equilibrium role of GVCs (e.g. through incentives to innovation, and viceversa)

34 Policy discussion (I) Routine intensity not necessarily problematic. Aggregate efficiency gains from relocation or reallocation of resources. Does not always imply offshorability (face-face interaction, strict sequentiality, cost of restructuring). Does not always imply automation (e.g. nurses, cost of capital). egree of routineness (tech progress, in-work organization). => Rather influence the leverages.

35 Policy discussion (I) Routine intensity not necessarily problematic. Aggregate efficiency gains from relocation or reallocation of resources. Does not always imply offshorability (face-face interaction, strict sequentiality, cost of restructuring). Does not always imply automation (e.g. nurses, cost of capital). Hard to influence degree of routineness (tech progress, in-work organization). => Rather influence the levers (ex ante) and adjust for the consequences.

36 Policy discussion (II) (assuming that trade and technological progress are irreversible): 1. Shock absorption (reallocation). Strengthening welfare and active labor market policies. Re-training. 2. Technology, skills & innovation more important than trade (although possibly complementary). A. Skills for innovation and ICT (digital skills): Training. Skill anticipation. Bundles of skills.

37 Policy discussion (III) B. Innovation positively correlated to employment, also for routine workers. It can mean further monetary support (R&D incentives, procurement). BUT insufficient. : No blanket support. Target activities with high spillover potential (e.g. intangible assets only partially excludable). Public private partnership. Evaluation, evaluation, evaluation. Non-patent innovation, e.g. managerial capabilities. Not in a vacuum: right policy mixes (e.g. skills, business environment). OECD Innovation Strategy (2015)

38 THANK YOU