1. ICT, Jobs and Task Content of Occupations in Chilean firms
Rita Almeida
World Bank and IZA
Ana Fernandes
World Bank
Mariana Viollaz
CEDLAS-FCE-UNLP
2017 ASSA Conference
Chicago, IL, USA
January 8th 

2. ICT are expanding fast and their impacts on skills and jobs are vastly unknown
ICT adoption is changing the world of work:
Task composition of jobs → ICT are replacing routine tasks (e.g. clerical work) and complementing (expanding) non-routine tasks (e.g. doing research) in developed countries.
Skills that are useful for employers → ICT are creating a higher demand for ICT skills, cognitive and socio-emotional skills.
There is still some uncertainty about the sign and magnitude of impacts on productivity gains, aggregate employment, income inequality and quality of jobs.

3. Previous evidence on ICT impacts and our hypothesis (I)
Previous literature has considered the hypothesis of workplace computerization and automation of routine tasks (cognitive and manual) performed by middle-educated workers:
ICT adoption measures: use of computer, use of internet, IT capital stock.
In developed countries, ICT complement abstract/non-routine cognitive tasks and substitute for workers in routine tasks (Autor et al., 2008; Akerman et al., 2015; Gaggl & Wright, 2016).
Consequence: polarization of labor markets (Acemoglu & Autor, 2011; Frey & Osborne, 2013).
Evidence is scarcer in developing countries: occupations using ICT intensively demand more cognitive skills and less routine and manual skills (Santos et al., 2015; Aedo et al., 2013). Weak evidence of labor polarization (Messina et al., 2016; Maloney & Molina, 2016).

4. Previous evidence on ICT impacts and our hypothesis (II)
The pace of technological innovation is still increasing and computerization is spreading to non-routine/cognitive domains, e.g. sophisticated algorithms are gradually taking tasks performed by lawyers (Brynjolfsson & McAfee, 2011).
We consider a different type of automation led by the use of advanced software (our ICT adoption measure):
Advanced software allows the automation of complex routine-cognitive and abstract task performed by high educated workers, e.g. engineers planning the level of production based on expected demand and stocks and estimating production costs.
Examples of advanced software: product pricing, improvement of the speed of purchase transactions, calculation of costs at each step of the production process, forecast of agricultural production.

5. What we do
What are the firm-level labor market impacts of ICT adoption (use of advanced software)?
We explore a unique administrative data on firms in Chile – Encuesta Longitudinal de Empresas over the period to estimate the ICT impacts on:
Employment composition, task content of occupations, labor productivity, and labor costs.
We analyze which types of workers (managers, administrative or production workers) and which type of tasks disproportionately appropriate the benefits (share of employment and labor costs) from ICT adoption, and which bear the cost.

6. Previous evidence for Chile and our contribution
Previous evidence for Chile is limited analyzing only the links between ICT and firm innovation (Santoleri et al., 2015; Alvarez, 2016), innovation and wages (Cirillo, 2016), innovation and skill upgrading (Pavcnik, 2003), and computer wage premium (Benavente et al., 2011).
Our paper’s contribution:
Addresses a novel question for Chile (labor market impacts of firms’ ICT adoption) using different measures of tasks and considering firms in all sectors of the Chilean economy.
Addresses labor market impacts of firms’ ICT adoption using a novel ICT measure: use of advanced software.
Estimates potentially different results compared to previous literature.

7. Our main results
We explore changes in ICT adoption over time combining firm fixed effects and an IV strategy to assess the impact of using advanced software at the firm level on a series of firm labor outcomes.
Our results show that using advanced software:
Increases total employment, reduces the share of skilled production workers and increases the share of unskilled production workers in total employment.
Reduces the abstract task index and increases the routine and manual indexes.
Increases labor productivity (gross income per worker)
No change in labor costs per worker.
Results are robust to different sets of control variables and different methodologies for constructing task indexes.

8. The data we use (I) Encuesta Longitudinal de Empresas (ELE):
Longitudinal survey of Chilean firms in 2007, 2009, and 2013.
Representative of all sectors and sale segments of the formal Chilean economy.
We use labor-related outcome variables, ICT-related variables, and firm characteristics for 2007 and 2013.
Comparability problems in 2009 in the definition of employment categories so data for 2009 cannot be used for main analysis.
Comparability problems across ELE rounds for ICT measures other than use advanced software

9. ELE sample, sectoral and size distribution

10. The data we use (II)
Program for the International Assessment of Adult Competencies (PIAAC):
Measures key cognitive and workplace skills across occupations. Implemented to 3578 individuals in Chile in 2014.
We identify abstract, routine-cognitive, routine-manual and manual task content for each occupation.
We combine the information related to each task in PIAAC into single task measures using two alternative methodologies:
Using the first component of a Principal Component Analysis (Autor & Handel, 2013).
Using a standardization procedure (Acemoglu & Autor, 2011).
We match ISCO-08 in PIAAC (1,400 occupations) with 4 employment categories in ELE: managers, administrative workers, skilled and unskilled production workers.

11. Task variables available in PIAAC

12. Our main firm-level outcome variables
Task indexes: abstract, routine-cognitive, routine-manual, manual
For each firm j and task k, we multiply the share of each employment category c in the firm’s total employment by the PIAAC content of task k in employment category c.
Employment: share of each category (managers, administrative workers, skilled and unskilled production workers) in total employment and log of total employment.
Productivity: log of gross income per worker.
Labor costs: total labor costs per worker by employment category.

13. Main outcome variables – Descriptive stats (I)
Content of tasks (taskck) by employment category (PIAAC)

14. Main outcome variables – Descriptive stats (II)
Content of tasks (taskck) by employment category - Acemoglu & Autor (2011) method
When firms adopt an advanced software, we expect:
↓ in relative demand for workers performing complex routine-cognitive and abstract tasks (substitution effect) → skilled prod. workers.
↑ in the relative demand for workers performing complementary non- routine tasks (analytic, creative tasks) → skilled prod. workers.
Change in relative demand for managers and adm. workers is not clear: abstract and rout-cog tasks are the most and least important for them.
Possible increase in relative demand for unskilled prod. workers (services workers included in this category).
Task indexes should change according to changes in shares of employment.

15. Main outcome variables – Descriptive stats (III)
Task indexes – Acemoglu & Autor (2011) method

16. Main outcome variables – Descriptive stats (IV)
Employment shares and labor productivity (ELE)

17. ICT adoption variable
ICT adoption variable from ELE: use of advanced software for firm main activity.
Variable with a consistent definition over ELE rounds; exhibits variation across sectors and regions.
Advanced software defined as use of business or client management software.
Example of tasks performed by an advanced software:
Planning of the level of production based on expected demand and stocks.
Product pricing.
Estimation of production costs.
Processes control and optimization.

18. Which firms adopt an advanced software?
Firms switching their ICT adoption status between 2007 and 2013

19. ICT adoption variable – Regional variation

20. ICT adoption variable – Sectoral variation

21. Specification and challenges of using OLS (I)
Reduced-form specification for firm j, in sector s, region r, and year t:
ICT measures firm technology adoption (use of advanced software).
Using OLS: Firms may make their ICT adoption and employment decisions jointly and based on unobserved characteristics, e.g., managerial quality → Endogeneity problem for ICT variable and biased estimates for β.
Strategy: use ELE’s panel structure to control for time-invariant unobserved firm heterogeneity via firm fixed effects (Ij).
Challenge: ICT may still be correlated with unobserved firm characteristics or shocks that vary over time.

22. Specification and challenges of using OLS (II)
Strategy: Use IV in addition to inclusion of firm fixed effects.
Proposed instrument as in Iacovone et al. (2016): regional measure of computer access by households * measure of sectoral intensity of ICT use.
Rationale: Zrst as a region-sector technology development measure. Regional use of computers must be highly correlated with ICT adoption by firms (e.g. both benefit from technology price reductions) but from an individual firm’s perspective access to computers by households in its region is exogenous.
We add vectors of firm characteristics, regional development variables, and region-time trends.

23. Required additional sources of data
2003 Chilean Input-Output matrix:
We construct the sectoral propensity to use ICT as the share of ICT inputs in total inputs value in each sector.
Chilean national household survey (CASEN):
Representative at the regional level available for 2006 and We use: regional share of households with computer as well as regional level of development variables (share of urban households, avg. number of years of education and avg. households per capita income). All 15 regions of Chile covered.

24. Required additional data
Sectoral ICT intensity measure and Regional share of hhlds with computer

25. First-stage regression
ICTjsrt: use of advanced software for firm j, in sector s, region r and year t.
RegComprt: share of households with computer in region r and year t.
ints: sectoral intensity of ICT use.
Control for firm characteristics (Xjrst ) - size, age, indicators for exporting firm and foreign ownership, manager characteristics, and regional development variables (Wrt) -log avg. household per capita income, share of urban population, avg. number of years of education.
Firm, year fixed effects and region-time trends. Standard errors clustered at region and sector level.

26. Positive, significant and robust first-stage results

27. Main results – Employment
Estimated changes in employment shares – 10% Confidence Interval
Most complete IV specification

28. Main results – Task indexes (I)
Estimated changes in task indexes – 10% Confidence Interval
Autor & Handel (2013) method. Most complete IV specification

29. Main results – Task indexes (II)
Estimated changes in task indexes – 10% Confidence Interval
Acemoglu & Autor(2011) method. Most complete IV specification

30. Main results – Labor costs and labor productivity
Estimated changes in labor costs and productivity – 10% Confidence Interval
Most complete IV specification

31. Summary and interpretation (I)
We considered a novel ICT adoption measure (use of advanced software) able to automate complex routine-cognitive and abstract tasks performed by high educated workers.
Findings - The use of advanced software:
Reduces the share of skilled prod. workers in total employment → Category intensive in abstract and rout-cog tasks.
Increases the share of unskilled prod. workers in total employment → Category intensive in rout-manual and manual tasks. Probably explained by sale and services workers.
No effect on the share of managers and adm. workers → Abstract and rout-cog tasks are the most and least important for them.
Tasks indexes change accordingly:
Reduction in the abstract index.
Increases in the routine and manual indexes.

32. Summary and interpretation (II)
Total employment increases, labor productivity (gross income per worker) increases with no changes in labor costs per worker.
Our interpretation:
More sophisticated software technologies are entering labor markets with potentially different impacts than previous computerization and automation of routine tasks.
Advanced software has a skill component and may substitute for high-educated workers. Our findings for Chile show a significant reduction in the share of skilled production workers.
The lack of adjustment in labor costs per worker is in line with the large share of micro and small firms (price-taker firms) and the large aggregation of employment categories.

33. Thank you!

34. Appendix

35. Employment categories
Managers: owners and partners without fixed remuneration working at least 15 hours per week. Their role is the administration, planning, organization, monitoring, and running of the firm.
Adm. workers: workers with direct contact with public (except sellers). Their role includes keeping accounting books, data entry and processing, clerk services, and customer support.
Skilled prod. workers: professionals and technicians directly related with the main activity of the firm. Their role includes analysis and research, application of production methods and techniques, workers supervision, legal, economic, social, commercial services.
Unskilled prod. workers: non professional workers performing simple and routinary tasks directly related to the main activity of the firm. Includes sales and services workers.

36. Matching between PIAAC and ELE occupational classification

37. Changes in employment shares (I)

38. Changes in employment shares (II)

39. Changes in employment shares (III)

40. Changes in employment shares (IV)

41. Estimated coefficients - Most complete specification
Panel IV estimations
Estimated coefficients - Most complete specification
Note: Standard errors clustered at the region and sector level. Task indexes using Autor & Handel methodology (2013).

42. Estimated coefficients- Most complete specification
Panel OLS estimations
Estimated coefficients- Most complete specification
Note: Standard errors clustered at the region and sector level. Task indexes using Autor & Handel methodology (2013).