Winners and Losers of Service O shoring: Micro Level Evidence · • shares of blue collars service...

Introduction Data and Stylized Facts Firm-Level Analysis Worker-Level Analysis Conclusions

Winners and Losers of Service Offshoring:

Micro Level Evidence

Andrea Ariu1

J. Bradford Jensen2

Katariina Nilsson Hakkala3

Saara Tamminen4

March 2018

1LMU Munich, ifo and CESifo2McDonough School of Business, Georgetown University3ETLA & Aalto university4VATT Institute for Economic Research


Service offshoring

• Service trade increased more than trade in goods in the past tenyears and it accounts today for about 30% of trade amongdeveloped countries (WTO, 2017)

• Most of this increase comes from business services which representalmost exclusively intermediates for other firms (Berlingieri, 2013)

• Peculiarity of service offshoring:

- exposes also white collar and high-skilled workers to the threatof relocation

- recent phenomenon scarcely studied


The paper

We use micro level data from Finland to understand the effect ofservice offshoring on:

• Firms:1. Workforce composition (educational levels, occupations)2. Performance (size, sales, productivity, etc.)

• Workers:1. Wages2. The probability of separation (unemployment or switch

firm/sector)

–> 2SLS to solve the endogeneity of the offshoring choice–> New narrow offshoring measure for services–> extensive and intensive margin


Results

Following service offshoring:1. workforce composition changes:

• share and number of low skilled shrink• share and number of high skilled increase• shares of blue collars service workers decrease• increase the share of managers

2. firm performance:• sales, average wages and total assets increase• employment mildly affected (negatively)

3. workers:• increase risk of separation for all workers, especially for

blue-collar service and professionals• mixed results on total earnings


Contribution

• Manufacturing: Negative effects on salaries and employment of lowskilled (e.g. Autor et al. 2013, 2014, Firpo et al., 2011, Baumgartenet al., 2013, Hummels et al., 2014, Ebenstein et al., 2014, NilssonHakkala and Huttunen, 2016)

• Service offshoring: Weak negative effect on the employment size(Amiti and Wei, 2005 and 2006; Gorg and Hanley, 2005; Eppinger,2017). Weak positive effect on the relative demand for (top tail of)high skilled, negative for lower & medium skilled (Crino 2007 and2010; Liu and Trefler, 2011; Anderson et al. 2016, and 2016)

• Rough classifications of workers (low/high skilled or white/bluecollar)

• Most studies at the industry level


Data


Data

Panel 2002-2012 by combining various micro-databases:• Annual firm-level inquiry on international trade in services

(Statistics Finland)- Around 2000-3000 firms annually, from all industries- All EBOPS services expect tourism and transport, all partners

• Goods trade statistics by commodity and partner (FinnishCustoms)

• Finnish Longitudinal Employer Employee Database, all Finnishemployees (SF)

• Financial statement panel, all Finnish firms (SF)


Data: samples by broad industry groups

Number of individual firms Share of Finnish firms (min 5 employees)

Industry group NSO starters All service importers NSO starters All service importers

Manufacturing 173 481 2,4% 5,7%Wholesale & Retail 91 155 1,1% 1,6%IT services 52 124 4,3% 9,0%Other business services 79 252 1,7% 3,7%Other services 71 155 0,8% 1,5%

Total 466 1167

By firm size


Data: samples’ representativeness

Table: Samples of firms compared to total Finnish economy annually

NSO Starters All service importers

Year Revenue, % Employment % Value added,% Revenue, % Employment % Value added, %2002 9.7 3.1 3.6 32.7 12.8 17.72003 9.2 3.2 3.6 34.6 12.9 18.12004 10.8 3.8 4.4 40.2 13.7 20.02005 13.4 4.1 4.8 44.7 14.5 20.92006 13.8 4.4 5.8 48.5 13.7 21.82007 14.7 4.4 5.7 49.4 13.6 22.92008 15.0 4.3 5.6 55.1 14.8 22.62009 13.3 4.0 4.9 44.8 14.3 17.62010 14.3 4.0 5.5 48.5 13.9 19.62011 15.8 4.0 5.6 50.9 13.2 19.02012 16.1 4.0 5.6 48.2 12.7 16.9


Data: samples’ characteristics

NSO starters All service importersMeasure (2002-2012 averages) Obs Mean Std.dev Obs Mean Std.dev

Employees (ln) 466 4.5 1.3 8490 4.8 1.5Value added per employee (ln) 441 11.0 0.8 7877 11.2 0.8Turnover (ln) 454 16.8 1.6 8284 17.2 1.7Capital per employee (ln) 461 10.0 1.9 8366 10.5 2.2Average wage bill (ln) 459 10.5 0.4 8336 10.6 0.5Services exporter, % 466 56.7 49.6 8495 67.0 47.0


Stylized fact 1: service imports increased over time


Stylized fact 1: service imports increased over time

Table: Finnish service imports by commodity & year, % of total

Year 2002 2004 2006 2008 2010 2012

Freight and Postal 5.5 5.5 9.3 9.2 8.9 12.1Information and Telecommunication 6.1 4.0 4.5 2.4 2.8 2.8Construction and Mining 4.0 7.9 1.3 1.5 1.6 1.3Finance 0.0 0.7 0.9 0.8 3.0 1.6Computer services 5.8 8.2 10.7 10.2 12.8 11.8IPR 10.4 9.7 12.6 10.7 7.6 9.4Trade related services 8.5 7.2 4.9 3.4 2.5 2.3Legal, Advertising, Consulting 19.0 17.2 18.8 19.5 19.4 18.3R&D & technical 29.6 27.4 25.1 28.4 26.7 25.6Services BW Related Companies 11.1 12.0 11.8 13.9 14.5 14.7Personal, Health, Education and Government 0.1 0.2 0.2 0.1 0.1 0.1


Stylized fact 2: importing services is associated to

employment compositional changes

Table: 2004-2012 changes in total number of employees by occupation

Panel a: entry-exit firmsManufacturing Services

Blue-Collar Production -24% -50%Blue-Collar Service -36% 120%Blue-Collar Professionals 42% 26%White-Collar Professionals 153% -30%Managers 117% 44%

Panel b: surviving firmsManufacturing Services

Blue-Collar Production -29% -1%Blue-Collar Service -49% -3%Blue-Collar Professionals -19% 47%White-Collar Professionals -20% 10%Managers 18% 4%

Occupational classification


Stylized fact 2: importing services is associated to

employment compositional changes

Table: Employment compositional changes for starter firms

t HS MS LS Blue-Collar Blue-Collar Blue-Collar White-Collar Managers ObsProduction Service Professional Professional

-2 49.4% 36.3% 14.3% 20.0% 22.4% 29.3% 20.2% 8.1% 87-1 50.1% 36.3% 13.7% 20.8% 19.7% 28.1% 21.7% 9.7% 870 50.8% 36.5% 12.6% 20.5% 19.6% 28.9% 20.6% 10.3% 871 51.4% 36.6% 11.9% 19.8% 19.3% 28.9% 20.6% 11.5% 872 52.6% 35.9% 11.4% 20.1% 16.3% 32.1% 20.8% 10.7% 873 52.6% 36.2% 11.2% 20.1% 17.1% 31.2% 20.6% 10.4% 874 54.8% 35.1% 10.1% 19.3% 16.5% 31.1% 21.7% 11.4% 87


Firm-Level Analysis


Firm-level regressions

Yit = ↵1NSOit + ↵2NGOit + �0Xi,t�1 + µi + ✏it

• Where Yit represents:

- firm performance variables (e.g. employees, productivity,turnover, K/L - ratio, average wage, various profitability andprofit measures, total assets, etc.)

- share or number of employees by education and occupation

• NSOft = Log of narrow services imports

• NGOit = Log of narrow goods imports

• Xit matrix of controls: dummies for services and goods exporting,industry-year trends (from total data, excluding each firm itself)

NSO


Instrumental strategy

World Export Supply (WES) as instrument for narrow services andgoods imports:

WESjt =X

p

✓(XWorld

p,t �MFINp,t ) ⇤

✓MFIN,jpt�1

MFIN,jt�1

◆◆

• j=industry, p=commodity/service, t=time, X=exports,M=imports

• Data from UN Comtrade at EBOPS 3 digit and HS 6 digitlevel


Firm performance - starters

(1) (2) (3) (4) (5) (6) (7) (8)Employees Productivity Turnover Capital Average NPM EBITDA Total value

intensity wage of assets

NSO 0.000 0.009 0.038*** 0.002 0.025** 0.003 0.064* 0.045***(0.008) (0.019) (0.010) (0.013) (0.011) (0.005) (0.035) (0.010)

NGO -0.018 0.000 -0.001 -0.007 -0.002 0.025 -0.158*** 0.008(0.027) (0.008) (0.021) (0.035) (0.008) (0.026) (0.058) (0.011)

Observations 3,059 2,880 2,993 3,027 3,017 3,020 1,353 3,060Kleibergen-Paap Wald stat 84.26 38.86 77.75 87.56 20.31 86.28 49.64 60.17

* p < 0.1, ** p < 0.05, *** p < 0.01. All regressions include firm fixed effects, dummies for goods and services export status and

IND*year controls. Firm clustered standard errors in parentheses.


Firm performance - all service importers



NSO -0.065** 0.009 0.083*** -0.002 0.053** 0.001 0.004 0.130***(0.027) (0.030) (0.027) (0.038) (0.025) (0.015) (0.093) (0.030)

NGO 0.029** -0.001 0.022* -0.005 -0.009* 0.007 -0.098 -0.002(0.014) (0.011) (0.012) (0.016) (0.005) (0.008) (0.062) (0.012)


* p < 0.1, ** p < 0.05, *** p < 0.01. All regressions include firm fixed effects, dummies for goods and services export status and

IND*year controls. Firm clustered standard errors in parentheses.


Number of employees by education and occupation - starters

(1) (2) (3) (4) (5) (6) (7) (8)By education level By occupational group

High Medium Low Blue-Collar Blue-Collar Blue-Collar White-Collar Managersproduction services professionals professional

NSO 0.014* -0.005 -0.040*** -0.007 -0.060*** 0.017 0.005 0.023***(0.008) (0.010) (0.013) (0.013) (0.012) (0.012) (0.011) (0.009)

NGO -0.006 -0.011 -0.030 0.066 -0.016 0.020 -0.031 -0.011(0.019) (0.035) (0.032) (0.042) (0.028) (0.026) (0.029) (0.013)


* p < 0.1, ** p < 0.05, *** p < 0.01. All regressions include firm fixed effects, dummies for goods and services export status and IND*year

controls. Firm clustered standard errors in parentheses.


Number of employees by education and occupation - all

service importers



NSO 0.007 -0.120*** -0.258*** -0.165*** -0.311*** 0.003 -0.062** 0.028(0.020) (0.035) (0.060) (0.042) (0.064) (0.030) (0.027) (0.021)

NGO 0.017* 0.047** 0.054* 0.107*** 0.035 0.016 0.011 0.000(0.009) (0.020) (0.030) (0.025) (0.028) (0.012) (0.014) (0.009)





Share of employees by education and occupation - starters



NSO 0.007*** -0.001 -0.008*** -0.001 -0.007*** 0.006** -0.003 0.003**(0.002) (0.001) (0.001) (0.001) (0.002) (0.002) (0.002) (0.001)

NGO -0.002 -0.001 0.000 0.001 -0.003 -0.003 0.002 0.007***(0.004) (0.003) (0.002) (0.005) (0.004) (0.006) (0.004) (0.003)





Share of employees by education and occupation - all service

importers



NSO 0.043*** -0.008*** -0.029*** -0.011*** -0.021*** 0.017*** -0.002 0.013***(0.011) (0.003) (0.007) (0.004) (0.005) (0.006) (0.004) (0.004)

NGO -0.013** 0.002 0.005** 0.006** 0.002 0.001 0.004** 0.005***(0.006) (0.002) (0.002) (0.003) (0.003) (0.004) (0.002) (0.002)





Summary of the firm-level results

• Following service offshoring, firms:- become bigger in terms of sales and assets- employment levels decrease slightly and composition of

workforce changes:. decrease the number and share of low-skilled workers

(especially for blue-collar service workers)

. increase the number and share of managers


Robustness

• Same results when:- excluding intra-firm trade in services

. Starters

Firms’ performance Employment levels Employment shares

. All service importers

Firms’ performance Employment levels Employment shares

- using alternative weights


Worker-Level Analysis


Worker-level regressions

Fixed effects and IV regressions:

Separationwit = ↵1NSOit + ↵2OCC/EDUsit + ↵3NSOit ⇤ OCC/EDU

sit

+ �0Xwij,t�1 + µi + µw + ✏wit

• s=Educational and occupational groups, w=jobspell/worker, i=firm,

t=time

• Separation=1 if the employee is not employed in the same firm next year

• Xiwt matrix of controls: e.g. age, sex, number of small children, goods

offshoring (no instrument needed), exporting dummies, IND*year trends

(excluding firm i)


Risk of separation (=1) by worker type - starters



NSO 0.089*** 0.018*** 0.011 0.020*** 0.021*** 0.025*** 0.040*** 0.023***(0.031) (0.004) (0.008) (0.005) (0.005) (0.005) (0.008) (0.005)

NSO*Occ/Edu dummy -0.082** 0.054 0.111 0.030** 0.020** -0.004 -0.041*** 0.004(0.032) (0.034) (0.088) (0.013) (0.009) (0.005) (0.011) (0.017)


Joint coefficient 0.007 0.071** 0.122 0.050*** 0.042*** 0.020*** -0.001 0.027

* p < 0.05, ** p < 0.01, *** p < 0.001. All results with fixed effects, IND*year controls and robust standard errors (in parentheses).


Risk of separation (=1) by worker type - all service importers



NSO -0.075*** 0.178*** 0.323*** -0.054 0.125*** 0.227*** 0.358*** 0.448***(0.028) (0.016) (0.061) (0.115) (0.028) (0.045) (0.072) (0.118)

NSO*Occ/Edu dummy 0.222*** -0.186*** 0.209** 0.386** -0.097*** 0.046*** 0.039*** 0.093**(0.034) (0.034) (0.090) (0.156) (0.010) (0.007) (0.014) (0.040)

Observations 1,080,857 1,080,857 1,080,857 1,061,342 1,061,342 1,061,342 1,061,342 1,061,342Kleibergen-Paap Wald stat 43.74 45.59 25.57 6.278 55.22 29.00 18.17 9.358

Joint coefficient 0.147*** -0.008 0.532*** 0.332*** 0.0278 0.274*** 0.397*** 0.541***



Total earnings - starters



NSO -0.036 -0.006 0.005 -0.005 -0.005 -0.012** -0.016** -0.007(0.024) (0.005) (0.008) (0.005) (0.005) (0.006) (0.008) (0.005)

NSO*Occ/Edu dummy 0.037 -0.008 -0.116 -0.019** -0.031*** 0.011** 0.022** 0.018(0.025) (0.026) (0.074) (0.009) (0.011) (0.005) (0.010) (0.017)


Joint coefficient 0.000 -0.014 -0.111 -0.024*** -0.036* -0.002 0.006 0.011



Total earnings - all service importers



NSO 0.604*** 0.146*** 0.089*** 2.181 0.132*** 0.110*** 0.058* -0.024(0.102) (0.021) (0.034) (1.913) (0.027) (0.028) (0.033) (0.040)

NSO*Occ/Edu dummy -0.405*** 0.242*** -0.495*** -2.577 0.059*** -0.025*** -0.029*** -0.088***(0.111) (0.073) (0.082) (2.527) (0.010) (0.005) (0.006) (0.016)

Observations 1,080,857 1,080,857 1,080,857 1,061,482 1,061,482 1,061,482 1,061,482 1,061,482Kleibergen-Paap Wald stat 17.84 21 58.16 0.555 85.42 64.52 45.76 31.69

Joint coefficient 0.199*** 0.388*** -0.406*** -0.397 0.191*** 0.085*** 0.029 -0.112*



Conclusions

• first comprehensive analysis on the impacts of services offshoringwith firm- and worker-level data

• firm becomes more high-skilled intensive increasing the number andshare of managers and decreasing the number and share of whitecollar service workers

• firm performance improves in terms of sales and total assets whileemployment decreases slightly

• employees in the occupations that shrink have higher risk of leavingthe firm

• mixed results on wages


Thank you!


Data: samples by firm size

Size Micro Small Medium Large

NSO starters 22 126 226 92All service importers 35 210 340 266

Back


Occupational classification

Blue-Collar prod Blue-Collar serv Blue-Collar ProfStationary plant and machine operators Secretaries Commercial sales representatives

Assemblers Receptionists Process control techniciansDrivers and mobile plant operators Sales clerks Trade brokers

White-Collar Prof ManagersScience and engineering professionals Administrative and commercial managers

Doctors Production and specialised services managersTeaching professionals Hospitality, retail and other services managers

Back


Firm performance - no intra-firm - starters



NSO 0.000 -0.008 0.046*** -0.012 0.015 0.003 0.096* 0.054***(0.010) (0.015) (0.011) (0.017) (0.010) (0.006) (0.049) (0.011)

NGO 0.010 0.006 0.011 0.019 -0.008 0.016 -0.144*** 0.010(0.020) (0.010) (0.016) (0.027) (0.005) (0.016) (0.055) (0.010)



controls. Robust standard errors in parentheses.

Back


Firm performance - no intra-firm - all service importers



NSO -0.145* 0.012 0.129** 0.000 0.120 -0.002 -0.007 0.189***(0.084) (0.049) (0.060) (0.076) (0.116) (0.034) (0.328) (0.072)

NGO 0.056 -0.001 -0.011 -0.011 -0.034 0.010 -0.122 -0.046*(0.034) (0.022) (0.024) (0.031) (0.030) (0.011) (0.142) (0.028)



controls. Robust standard errors in parentheses.

Back


Number of employees by education and occupation - no

intra firm trade - starters



NSO 0.014 -0.004 -0.043*** -0.020 -0.069*** 0.020 0.003 0.015(0.009) (0.011) (0.016) (0.015) (0.016) (0.015) (0.013) (0.010)

NGO 0.011 0.014 0.015 0.091*** 0.010 0.022 -0.007 -0.003(0.015) (0.026) (0.026) (0.034) (0.019) (0.020) (0.025) (0.011)

Observations 2,864 2,864 2,864 2,716 2,869 2,871 2,871 2,865Kleibergen-Paap Wald stat 51.69 39.69 30.22 52.03 48.60 56.64 56.54 56

* p < 0.1, ** p < 0.05, *** p < 0.01. All regressions include firm fixed effects, dummies for goods and services export status and IND*year controls.

Robust standard errors in parentheses.


Number of employees by education and occupation - no

intra firm trade - all service importers



NSO 0.027 -0.218** -0.402** -0.253*** -0.531*** 0.008 -0.067 0.056(0.041) (0.097) (0.157) (0.087) (0.192) (0.054) (0.055) (0.040)

NGO 0.009 0.094** 0.120 0.167*** 0.123 0.013 0.015 -0.008(0.015) (0.048) (0.074) (0.048) (0.079) (0.018) (0.023) (0.015)




Back


Share of employees by education and occupation - no intra

firm- starters



NSO 0.008*** -0.001 -0.010*** -0.003* -0.007** 0.006 -0.001 0.002(0.002) (0.001) (0.002) (0.002) (0.003) (0.004) (0.002) (0.001)

NGO -0.005 -0.000 0.002 0.004 -0.004 0.000 0.001 0.004**(0.003) (0.002) (0.002) (0.003) (0.003) (0.005) (0.002) (0.002)




Back


Share of employees by education and occupation - no intra

firm- all service exporters



NSO 0.116 -0.021** -0.049** -0.024** -0.044** 0.026* 0.007 0.034**(0.075) (0.009) (0.025) (0.010) (0.018) (0.015) (0.010) (0.015)

NGO -0.050 0.008* 0.014* 0.013** 0.008 -0.001 0.003 -0.001(0.037) (0.004) (0.007) (0.006) (0.008) (0.008) (0.004) (0.005)




Back


Narrow offshoring measures

• Services:- Finnish input-output tables in 2002-2003 on average- information on which service inputs are used for the production

Narrow offshoring of servicesIndustry TOL_02 Sector EBOPS Names of services

code group codes

Mining of coal and peat 10 1 281,283,274,285 Agri & mining services, Other agri serv., Other business serv., SBREMan of pulp & paper 21 4 274,285 Other business serv., SBREMan of coke & petroleum 23 6 274,279,285 Other business serv.,R&D, SBREMan of chemicals 24 6 274,279,285 Other business serv.,R&D, SBREMan of machinery & equipment 29 8 274,279,285 Other business serv.,R&D, SBRE

• Goods:- Feenstra Hanson (1999)

Back

Date post:	02-Jun-2020
Category:	Documents
Upload:	others
View:	0 times
Download:	0 times

Winners and Losers of Service O shoring: Micro Level Evidence · • shares of blue collars service...

Documents