Pretty Vacant: Using job vacancies to understand labour market mismatch and the determinants of UK growth

Transcription

1 Pretty Vacant: Using job vacancies to understand labour market mismatch and the determinants of UK growth Arthur Turrell*, Bradley Speigner*, Jyldyz Djumalieva, James Thurgood*, David Copple* *Bank of England NESTA ESCoE Conference on Economic Measurement - London Views expressed are not those of the Bank of England or its policy committees and should not be reported as such.

2 Motivation How much would aggregate output and productivity increase if mismatch in the labour market were eliminated?

3 Motivation How much would aggregate output and productivity increase if mismatch in the labour market were eliminated?

4 Motivation How much would aggregate output and productivity increase if mismatch in the labour market were eliminated? Patterson et al.: occupational mismatch can explain up to two thirds of the deviations from trend-growth in UK labor productivity since 2007, and a similar fraction of output. Patterson, C., Şahin, A., Topa, G., & Violante, G. L. (2016). Working hard in the wrong place: A mismatch-based explanation to the UK productivity puzzle. European Economic Review, 84,

5 Data: 15 million individual job vacancies posted online from on reed.co.uk Why use data on job vacancies from a recruiter? Current ONS statistic is a survey available by sector at quarterly frequency Data are naturally occurring, having been posted online at daily frequency, and are split according to region, sector, and offered wage Only similar data are from JobCentre Plus, which was discontinued at end of 2012

6 Aggregate data Re-weight by sector to reduce bias and improve coverage

7 Data labelling (hard)

8 Data labelling (hard) Want jobs split by region, sector and occupation

9 Data labelling (hard) Want jobs split by region, sector and occupation Existing sector field manually mapped to ONS classification

10 Data labelling (hard) Want jobs split by region, sector and occupation Existing sector field manually mapped to ONS classification Latitude and longitude easily mapped into NUTS statistical regions

11 Data labelling (hard) Want jobs split by region, sector and occupation Only possible with our regionlabelled vacancy data used in tandem with ONS data on (un)employment Existing sector field manually mapped to ONS classification Latitude and longitude easily mapped into NUTS statistical regions Labour market tightness (v/u)

12 Data labelling (hard) Want jobs split by region, sector and occupation Only possible with our regionlabelled vacancy data used in tandem with ONS data on (un)employment Existing sector field manually mapped to ONS classification Latitude and longitude easily mapped into NUTS statistical regions No information on occupation! But a lengthy job description for each vacancy Labour market tightness (v/u)

13 Putting SOCs on

14 Take text of each description of a SOC code, d, to vector vd based on composition and frequency of terms in document Putting SOCs on apple tf-idf(t, d) =tf(t) idf(t, d) =tf(t) ln 1+D 1+df(t, d) +1

15 Take text of each description of a SOC code, d, to vector vd based on composition and frequency of terms in document Putting SOCs on apple tf-idf(t, d) =tf(t) idf(t, d) =tf(t) ln 1+D 1+df(t, d) +1 Calculate same vector for each job, v

16 Putting SOCs on Job vector Take text of each description of a SOC code, d, to vector vd based on composition and frequency of terms in document Dimension N ~v 0 apple 1+D tf-idf(t, d) =tf(t) idf(t, d) =tf(t) ln 1+df(t, d) +1 ~v d Calculate same vector for each job, v Find the value of d (=a SOC code) which maximises the Euclidean norm of the two Closest SOC match vectors arg max d nˆ~v0 ˆ~v d o ~v e Dimension 2 Poorly matching SOC Dimension 1

17 Putting SOCs on

18 Putting SOCs on Final step: Use fuzzy matching amongst the top 5 SOC code titles and the job title to pick a final winner for the SOC code

19 Putting SOCs on Final step: Use fuzzy matching amongst the top 5 SOC code titles and the job title to pick a final winner for the SOC code Example job_title job_description job_sector Physicist Make calculations about the universe, do research, perform experiments and understand the physical environment. Professional, scientific & technical activities

20 Putting SOCs on Final step: Use fuzzy matching amongst the top 5 SOC code titles and the job title to pick a final winner for the SOC code Example job_title job_description job_sector Physicist Make calculations about the universe, do research, perform experiments and understand the physical environment. Professional, scientific & technical activities SOC Natural and Social Science Professionals The code to assign job descriptions to SOC codes will be made available as part of the project: look out for occupationcoder on github.

21 What is mismatch in the labour market?

22 What is mismatch in the labour market? Demand for labour Supply of labour Market 1 Market 2

23 What is mismatch in the labour market? Demand for labour Supply of labour Market 1 Market 2 {,,z}

24 What is mismatch in the labour market? Demand for labour Supply of labour Market 1 Market 2 Matching efficiency {,,z}

25 What is mismatch in the labour market? Demand for labour Supply of labour Market 1 Market 2 Tightness (=V/U) Matching efficiency {,,z}

26 What is mismatch in the labour market? Demand for labour Supply of labour Market 1 Market 2 Tightness (=V/U) Matching efficiency {,,z} Productivity (output per worker)

27 Allows to go from aggregate view of labour market

28 to a disaggregated view Only possible with our occupationlabelled vacancy data used in tandem with ONS data on (un)employment

29 Effect of mismatch: counter-factual analysis with output maximising social planner Social planner choosing how to distribute any unemployed to search for jobs. Can choose different allocation across sub-markets than occurred in reality. No cost to moving unemployed across submarkets. What you need to know: Social planner s value function 8 Output >< z X } { 9 >= V (u t,e t ; t ) = max {u i,t } >: i z i,t (e i,t + h i,t ) u t + E [V (u t+1,e t+1 ; t+1 )] >; Matching function h i (v i,u i )= i M(v i,u i )= i u 1 i v i z = productivity; e = employment; = matching e ciency; h =newhires

30 Empirical results: occupational mismatch Output per worker Matching efficiency Anti-correlated

31 Simulation results: occupational mismatch

32 Simulation results: occupational mismatch Employment increases substantially, perhaps implausibly so.

33 Simulation results: occupational mismatch Employment increases substantially, perhaps implausibly so.

34 Simulation results: occupational mismatch Employment increases substantially, perhaps implausibly so. but it barely explains any of the difference in output level.

35 Results: occupational mismatch As the social planner optimises for output, productivity is actually lower in the counterfactual occupational mismatch may have been a factor in productivity puzzle up to 2013 but since then it has not been a plausible driver of it

36 Empirical results: regional mismatch Output per worker Matching efficiency

37 Simulation results: regional mismatch

38 Simulation results: regional mismatch

39 Simulation results: regional mismatch Employment increases substantially

40 Simulation results: regional mismatch Employment increases substantially

41 Simulation results: regional mismatch Employment increases substantially and output can, in some scenarios, get back to its implied pre-crisis level.

42 Counter-factuals do seem to imply that regional mismatch has weighed down on productivity growth. Results: regional mismatch Harmonising tightness, match efficiency, and productivity between regions could lead to a significant level boost in aggregate output and productivity

43 Conclusion How to take naturally occurring text data and map it into official statistical classifications to yield timely vacancy data on a large scale

44 Conclusion How to take naturally occurring text data and map it into official statistical classifications to yield timely vacancy data on a large scale By occupation, the effects of mismatch on productivity and output are small, and do not account for the productivity puzzle

45 Conclusion How to take naturally occurring text data and map it into official statistical classifications to yield timely vacancy data on a large scale By occupation, the effects of mismatch on productivity and output are small, and do not account for the productivity puzzle Unwinding regional mismatch in 2008Q1 would have allowed output and productivity to reach the levels implied by the the pre-crisis period trends

46 Conclusion How to take naturally occurring text data and map it into official statistical classifications to yield timely vacancy data on a large scale By occupation, the effects of mismatch on productivity and output are small, and do not account for the productivity puzzle Unwinding regional mismatch in 2008Q1 would have allowed output and productivity to reach the levels implied by the the pre-crisis period trends Take-away message: Close attention should be paid to the heterogeneous aspects of the labour market. Composition of jobs important for determining output, productivity, unemployment dynamics

47 Extra slide: Estimation of the matching function hi,t Vi,t 1 ln =ln i + ln + i,t + d t U i,t 1 U i,t 1 1-digit SOC 2-digit SOC 3-digit SOC 1-digit NUTS Aggregate data Elasticity parameter ( ) Point estimate (least squares) Standard error Point estimate (IV) Standard error Cross-sections Observations All significant at the 1% level.

48 Effect of mismatch on output Channel I - heterogeneous supply and demand Demand for labour Supply of labour Unemployment Market 1 Market 2 Vacancies

49 Effect of mismatch on output Channel II - heterogeneous matching efficiency Demand Supply Demand Supply Market 1 Market 2

50 Effect of mismatch on output Channel II - heterogeneous matching efficiency Demand Supply Demand Supply Market 1 Market 2

51 Effect of mismatch on output Channel II - heterogeneous matching efficiency Demand Supply Demand Supply Market 1 Market 2 " Higher matching efficiency

52 Effect of mismatch on output Channel III - workers and productivity allocated differently Employment Employment Market 1 Market 2

53 Effect of mismatch on output Channel III - workers and productivity allocated differently Employment Employment Market 1 z 1 Market 2

54 Effect of mismatch on output Channel III - workers and productivity allocated differently Employment Employment Market 1 z 1 z Market 2 2 Higher productivity