effects of innovation on employment in latin america: the microeconometric evidence by IDB

Science and Technology Division Effects of Innovation on Employment in Latin America: the microeconomic evidence Comparative results Gustavo Crespi and Ezequiel Tacsir

9th GLOBELICS Conference. Buenos Aires, Argentina November15th, 2011

http://www.iadb.org The Inter-American Development Bank Discussion Papers and Presentations are documents prepared by both Bank and non-Bank personnel as supporting materials for events and are often produced on an expedited publication schedule without formal editing or review. The information and opinions presented in these publications are entirely those of the author(s), and no endorsement by the Inter-American Development Bank, its Board of Executive Directors, or the countries they represent is expressed or implied. This presentation may be freely reproduced.

Employment and Innovation in LAC • The potential for inclusive growth in the region depends on its capacity to generate good quality employment. • There are high expectations by regional Policy Makers on the potential of STI to trigger inclusive growth. • However, we know very little on the transmission mechanisms and dynamics between innovation and job performance. • Results will help us to better understand a very complex phenomena (very little studied in the region) and to generate inputs for the design of public policies that maximize the employment generation impacts of innovation and at the same time mitigate its negative consequences.

Motivation  Our own work on the link between innovation and productivity research shows that the lack of innovation negatively affects productivity levels. How this affects labor market outcomes?  However, we still lack knowledge on the relation between employment and innovation. Evidence from more developed countries or regions might not be applicable. Need to provide evidence for policy-making.  In Latin America the production structure is strongly dominated by small and medium enterprises (SMEs). Indeed, SMEs’ innovation is strongly dominated by informal search routines and learning from already available knowledge and technologies.  Importance of assessing the effects on employment (level and composition). It is not only quantity what matters, but also quality.

Focus  This paper focuses on the (short term) link between innovation and employment. Based on national country studies performed by different research teams.  Coverage: Comparative research in four countries: Argentina, Chile, Costa Rica and Uruguay.  Novel features (as a project): Emphasis on SMEs, sectoral differences (low/high tech sectors). Extensions of the project during this session and following session. To further increase comparability and homogeneity among countries possibility to use Enterprise Surveys for LAC.

Research questions (1)How different types of innovations (product, process, organizational) create or displace employment. (2) How different types of business innovation strategies (make or buy) influence the capacity of innovation to generate or destroy employment. (3) To analyze how (1) and (2) might render differential impacts across firms of different size and sectors? (4) To assess how different types of innovations and innovation strategies affect the quality of employment measured by types of skills.

Employment effects of innovation: A Conceptual framework  The relationship between innovation and employment at the firm level is not straightforward. It is usually believed that – Process innovation destroys jobs by substituting labor – Product innovation creates jobs by an increase in demand

But  If process innovation increase productivity firms can reduce prices and increase the demand for their products  If the firm with a new product gains market power, it can be the case that it is more profitable to increase price, reduce the quantity sold , and employment accordingly.  Harrison, Jamandreu, Mairesse, and Peters (2008) (HJMP, from now onwards) provide an empirical framework to address the issue at the firm level.

Employment effects of innovation: A Conceptual framework Displacement

Compensation

Process Innovation

Productivity effect (-): less labor for a given output

Price effect (+): cost reduction, passed on to price, expands demand

Depends Dependsofof firmsâ&#x20AC;&#x2122; firmsâ&#x20AC;&#x2122; behavior behavior

Product Innovation

Productivity differences of the new product (- or +)

Demand enlargement effect (+)

Depends Dependson on competition competition

Innovation Innovation activities activities

Methodology  Data: Research is based on innovation surveys (collected in several countries in the region, normally repeated cross-sections using similar methodologies). Need to match with other surveys (i.e., annual manufacturing surveys) or register data (social security). – Argentina: Second Innovation Survey (1998-2001), – Chile: 4 waves of National survey of Innovation (EIT) (1995, 1998, 2001, 2007), and National Annual Manufacturing Survey (ENIA) (1993-2007) – Costa Rica: Innovation survey for Costa Rica from 2006 to 2007. Matched for performance with Manufacturing survey and social security records – Uruguay: 4 waves of (Manufacturing) Innovation Surveys (MIS) (1998-2000, 2001-2003, 2004-2006 and 2007-2009) + Annual Economic Activity Surveys (EAS) for the period 1998-2007. Both surveys have the same sample and statistical framework.

Effects on employment quantity  the research setting assumes that a firm can introduce: (i) product or process innovations and (ii) produce new and old products.  2 types of products are distinguished: existing products and the production of new products. The change in employment is then decomposed into the part due to the increased efficiency in production of old products (related to process & organizational innovations) and the part due to the introduction of new products (product innovations)  HMJP (2008) suggests the following regression to estimate the effects of innovation on employment: Where l is total employment growth, g1 is the nominal growth in sales of old products, g2 is the nominal growth in sales of new products (product innovations) and d captures the introduction of process innovations in the production of old products. If process innovations in the production of old products displace employment, α1< 0. If product innovations create employment, β>0. β also captures the relative efficiency in the production of old/new products (if <1 new are more efficient than old).

Foundations of the empirical model: – Multiproduct production function (two products, CRS, etc)

Yit = θit F ( K it , Lit , M it ) eη +ωit

i = 1, 2; t = 1, 2

– Production function: K:

capital;

labour;

material;

θjt:

technological efficiency;

ωit: unobserved idiosynchratic productivity shocks η:

unobserved firm-specific productivity shocks

Foundations of the empirical model:

 Cost function:

C ( w1t , w2t , Y1t , Y2t ,θ1t , θ 2t ) = c( w1t )

Yit Y + c( w2t ) 2t θ1t θ 2t

 Assumption of constant returns to scale  Conditional labour demand for old products: L1t = cwL ( w1t )

Y1t θ1t eη +ω1t

t = 1, 2

 Conditional labour demand for new products L22 = cwL ( w22 )

Y22 θ 22 eη +ω22

if Y22 = 0

and L22 = 0 otherwise

cwL ( w11 ) = cwL ( w12 ) = cwL ( w22 )  Simplifying assumption:  Given that the production of new products at the beginning of the Y21 = 0is nil ( period ) so we can approximate the employment growth decomposition as follows: ∆L  θ −θ   Y − Y  θ Y l= = − 12 11  +  12 11  + 11 22 L  θ11   Y11  θ 22 Y11

Foundations of the empirical model:  Decomposition of employment growth into growth of employment due to production of the old products and the new products: ∆L L12 − L11 + L22 L12 − L11 L22 ~ L12 L22 = = + = ln + L L11 L11 L11 L11 L11

 Leads to:

l = −  lnθ12 − lnθ11 ÷ +  ln Y12 − ln Y11 ÷ + θ11 θ22 ÷×Y22 Y11 ÷ − ω12 −ω11 ÷ 1 4 4 4 4 2 4 4 4 43

Efficiency gain in production of old products (-)

1 4 4 44 2 4 4 4 43

Rate of change in demand for old products (+/-)

1 4 4 4 4 4 2 4 4 4 4 43

Starting the production of the new product (+) ⇓ depends on efficiency ratio between both technologies

1 4 4 2 4 4 3

Productivity shocks (+/-)

Identification strategy  If v is correlated to innovation, OLS estimates are inconsistent. Productivity is omitted and therefore v could be correlated to innovation. Innovations are the result of investment decisions (such as R&D) and those decisions depend on the firm’s productivity. If productivity is in the error term (because it is an omitted variable) the error term will be correlated (hence endogeneity problem). 

Productivity can be thought as wit = wi + uit – If the correlation is through time invariant characteristics wi (e.g. managerial capabilities), endogeneity is less important because the equation is in first differences – If the correlation is through the time varying part of productivity (productivity shock), uit, timing in the relation innovation-productivity becomes crucial. If investments decisions are taken in advance innovations variables won’t be related with the error term (OLS is consistent). If not, innovation outputs might become endogenous.

Identification strategy  Another source of endogeneity is the presence of measurement error in g1 and g2.  Ideally, we would use growth in real production but we only observe nominal output. Hence, the growth in prices (of both old and new) are left in the error term. Correlation between growth in prices and g2 can create an attenuation bias in the estimation of β 

Hence (and since we generally do not have firm level prices), we use industry price indexes and a proxy for the growth in prices of old products. We use IV correlated with real growth in the production of new products but uncorrelated with its nominal growth.

 IV methods are used to correct these issues. Variety of instruments (but preferred is the increased range of products). 2 conditions: partially correlated with product innovation but not correlated with the error term.

Descriptive statistics Argentina

Chile

Costa Rica

Uruguay

1,415

2049

208

2532

Non-innovators (no process or product innovations)

36.0

30.7

22.0

48.1

Process only innovators (non product innovators)

15.0

4.0

19.4

Product innovators

48.0

53.4

74.0

32.5

Number of observations Distribution of firms (%)

Number of employees at the beginning of (each) survey

233

215

182

Foreign Ownership (10% or more) (%)

20.0

12.5

14.9

13.2

Located in the capital of the country (%)

64.0

52.0

57.7

81.0

All firms

-4.0

-0.2

3.3

-0.7

Non-innovators (no process or product innovations)

-6.0

0.8

3.5

-3.4

Process only innovators (non product innovators)

-3.9

2.1

7.4

1.7

Product innovators

-2.5

-0.5

3.0

1.8

All firms

-9.0

6.5

23.7

5.5

Non-innovators (no process or product innovations)

-12.5

2.9

27.3

1.7

Process only innovators (non product innovators)

-8.1

7.1

11.7

9.6

Product innovators

-6.7

8.5

23.7

8.7

Old products

-45.3

-5.7

-54.9

-21.2

New products

38.7

14.2

78.6

29.9

Employment growth (%) (yearly rate)

S ales growth (%)(nominal growth) (yearly rate)

of which:

(a)

Labor productivity growth (%) (yearly rate) All firms

-5.0

6.7

20.5

6.2

Non-innovators (no process or product innovations)

-6.5

2.1

23.8

5.1

Process only innovators (non product innovators)

-4.3

4.9

4.3

7.9

Product innovators

-4.2

9.0

20.4

6.9

(b)

Prices growth (%) All firms

-2.0

5.0

14.3

6.8

Non-innovators (no process or product innovations)

-2.3

3.8

14.1

6.8

Process only innovators (non product innovators)

-1.9

5.0

11.8

6.8

Product innovators

-1.9

5.6

14.6

6.8

Source: Own elaboration based on country studies

Argentina (AR)-Innovation Survey 1998-2001; Chile (CH): pooled regressions for the innovation surveys 1995, 1998, 2001, 2007;Costa Rica (CR): Innovation survey 2006-2007 Uruguay: pooled regressions for the surveys 1998-2000, 2001-2003 and 2004-2006.

Descriptive statistics (small firms) Argentina

Chile

Costa Rica

Uruguay

417

652

119

1353

Non-innovators (no process or product innovations)

56.0

57.5

29.4

62.2

Process only innovators (non product innovators)

12.0

3.7

5.9

14.0

Product innovators

32.0

28.8

64.7

23.7

26.1

25.7

26.2

Number of observations Distribution of firms (%)

Number of employees at the beginning of (each) survey Foreign Ownership (10% or more)

5.8

6.7

6.2

Located in the capital of the country

48.2

63.9

76.7

All firms

-3.5

1.4

3.6

-3.7

Non-innovators (no process or product innovations)

-5.8

1.5

3.7

-5.3

Process only innovators (non product innovators)

1.5

4.5

5.4

-1.5

Product innovators

-1.2

1.8

3.3

-1.0

All firms

-9.7

5.3

20.0

3.6

Non-innovators (no process or product innovations)

-12.8

3.1

23.1

1.2

-3

7.6

12.8

9.4

-6.6

8.5

19.3

6.4

Employment growth (%) (yearly rate)

S ales growth (%)(nominal growth) (yearly rate)

Process only innovators (non product innovators) Product innovators of which: Old products

-49.2

-5.7

-46.1

-25.1

New products

42.6

14.2

66.1

31.5

-6.2

3.9

16.5

7.3

-7

1.6

19.4

6.5

Process only innovators (non product innovators)

-4.5

3.1

7.4

10.9

Product innovators

-5.4

6.6

16.0

7.4

-2

3.6

13.5

7.7

Non-innovators (no process or product innovations)

-2.2

1.6

14.1

7.4

Process only innovators (non product innovators)

-1.4

3.3

9.7

9.0

Product innovators

-1.9

6.4

13.6

7.7

Labor productivity growth (%) All firms

(a)

(yearly rate)

Non-innovators (no process or product innovations)

(b)

Prices growth (%) All firms

Source: Own elaboration based on country studies

Employment effects of types of innovation (OLS) S ector Regression

Manufacturing

Manufacturing small firms

4.139***

1.997**

-1.616

2.662***

2.739

3.136**

-0.845

1.757**

(0.836)

(0.825)

(5.241)

(0.555)

(1.685)

(1.326)

(6.650)

(0.775)

-0.601

-2.780**

8.175

-4.002***

-2.489

-3.346

5.726

-4.127**

(1.004)

-1.275

(6.539)

(1.06)

(2.425)

(2.717)

(8.770)

(1.686)

0.959***

0.833***

0.887***

0.853***

0.963***

0.706***

0.932***

0.826***

(0.013)

(0.034)

(0.042)

-0.018

(0.03)

(0.084)

(0.059)

(0.028)

0.989

-0.13

0.950

3.990*

3.064

10.083

(0.854)

(1.231)

(5.161)

(1.9)

(4.309)

(8.525)

-3.962***

-0.275

6.672*

1.655

-3.441

-2.318

2.112

-3.048

(0.905)

(0.889)

(3.884)

(1.181)

(3.682)

(1.718)

(5.949)

(2.51)

Yes

R-squared

0.83

0.27

0.632

0.441

0.785

0.163

0.615

0.369

Number of firms

1,415

2,049

208

2,532

1,415

652

119

1,353

Constant Process only innovator (d ) Sales growth due to new products (g2) Located in the capital Foreign owned (10% or more) 2-digit industry dummies Time dummies

Notes: (i) Robust standard errors, (ii) Significance level: *** 1%, ** 5%, and * 10%,

Employment effects of types of innovation

(IV) All firms in Manufacturing

S ector Regression

S mall firms in Manufacturing

-0.994

-2.016

-12.160**

1.402**

-0.684

-2.125

-7.571

0.267

(3.236)

(3.00)

(5.170)

-0.662

(4.44)

(4.701)

(6.088)

-0.907

1.398

0.333

18.413*

-2.716**

-2.542

-3.38

15.415

-2.595

(1.673)

(2.572)

(10.076)

-1.104

(2.691)

(2.921)

(12.655)

-1.772

1.170***

1.751***

1.015***

0.961***

1.140***

2.141*

1.051***

0.998***

(0.125)

(0.653)

(0.050)

-0.04

(0.218)

(1.205)

(0.068)

-0.063

1.623

-0.36

1.361

4.690*

5.208

7.194

(0.998)

(1.449)

(5.503)

(2.22)

(4.699)

(11.113)

-5.467***

0.048

6.680*

1.371

-5.412

0.865

-0.319

-3.162

(1.349)

(1.04)

(3.843)

-1.186

(5.132)

(3.394)

(6.049)

-2.502

2-digit industry dummies

Yes

Time dummies

Yes

F test, g2 equation

14.32

35.91

78.16

170.8

6.2

6.94

51.12

89.62

Pvalue

0.002

0.000

0.013

0.000

13.79

10.39

0.784

2.23

7.15

10.66

Constant Process only innovator (d ) Sales growth due to new products (g2 ) Located in the capital Foreign owned (10% or more)

Davidson-M acKinnon3.319 test of exogeneity 2.71 for g2 Pvalue

0.069

0.0999

0.00027

0.0013

0.376

0.1354

0.0088

0.0011

R-squared

0.785

0.2471

0.652

0.42

0.724

0.1392

0.583

0.338

1,415

2,049

208

2,532

417

652

119

1,353

Number firms

Employment effects by types of sectors (IV) S ector

Low Tech

High Tech

Low Tech

High Tech

Low Tech

High Tech

-0.201

5.697**

1.697

-2.733

1.115

1.670*

(1.961)

-1.946

(4.206)

-3.368

-0.944

-0.929

0.849

-1.45

-0.517

-0.076

-2.524

-2.897**

(1.884)

(1.628)

(3.787)

-2.835

-1.813

-1.407

1.105***

0.910***

1.403*

1.695**

0.956***

0.958***

(0.066)

(0.071)

(0.846)

(0.728)

-0.056

Located in the capital

Yes

Foreign owned (10% or more)

Yes

2-digit industry dummies

Yes

13.14

23.57

94.37

Regression Constant Process only innovator (d) Sales growth due to new products (g2)

Time dummies F test, g2 equation

8.65

Pvalue

Davidson-M acKinnon exogeneity for g2

test

5.17 0

81.97 0

4.857

0.261

0.4293776

1.98238

4.31788

0.028

0.61

0.5125

0.1594

0.038

0.0192

2.451

1.773

4.442

0.49

3.349

3.58

Pvalue (degrees of freedom)

0.874

0.939

0.1085

0.7823

0.646

0.611

R-squared

0.811

0.748

0.2568

0.2464

0.423

0.419

632

1417

1068

1464

Pvalue Sargan-Hansen overidentification

Number of firms

test

672

743

5.496489

Implications  Results show that the introduction of new products is associated with employment growth at the firm level, with similar efficiency between the production of old and new products. Hence, no evidence that product innovation displace employment (due to increased efficiency) being prevalent the creation effect of employment.  Only in Uruguay (for all firms and in the high tech sectors), process innovations present displacement effects.  In both Chile and Costa Rica, the compensation effects due to new products imply employment growth even when the replacement of old products is taken into account.

Robustness checks ď&#x201A;§ d as endogenous: similar coefficients but with a general loss in precision. ď&#x201A;§ Overidentification by including further instruments: weak instruments are not a concern. ď&#x201A;§ Allowing for change in the slope of product innovation if these innovations were introduced together with process innovation (interaction term between g2 and a dummy): generally, there is evidence that the positive impact on employment growth is weaker when products and process are introduced jointly.

Employment growth decomposition  We decompose the employment growth observed in each country (and type of firm) over four different components. Using our preferred specification, we can write employment growth for each firm in the following way:

– 1st component: measures the change in its employment attributable to the (industry specific) productivity trend in production of old products; – 2nd component: estimates the change in employment associated with the gross productivity effect of process innovation in the production of old products; – 3rd component: corresponds to the employment change associated with output growth of old products for firms that do not introduce new products; – 4th component: gives the net contribution of product innovation (i.e., contribution after allowing for any substitution of new products for old products). – The last term is a zero-mean residual component.

Employment growth decomposition Manufacturing

Firms employment growth

-4.0

-0.2

3.3

-0.7

Productivity trend in production of old products

-0.1

0.9

-8.4

1.1

Gross effect of process innovation in production of old products

0.1

-0.1

0.8

-0.2

-4.6

0.0

2.9

-1.9

0.6

-0.9

8.1

0.3

? Output growth of old products for non product innovators Net contribution of p roduct innovation Contribution of old products by product innovators

-21.1

-6.0

-51.4

-9.1

Contribution of new products by product innovators

21.7

5.1

59.5

9.4

S mall firms manufacturing

Firms employment growth

-3.5

1.4

3.6

-3.7

Productivity trend in production of old products

3.8

0.8

-6.7

0.3

Gross effect of process innovation in production of old products

-0.4

-0.1

0.9

-0.1

-6.1

0.0

2.8

-3.8

-0.8

0.8

6.6

-0.2

? Output growth of old products for non product innovators Net contribution of p roduct innovation Contribution of old products by product innovators

-15.1

-3.1

-38.8

-7.8

Contribution of new products by product innovators

14.3

3.9

45.4

7.6

Source: Own elaboration based on country studies. IV estim ates

Effects on employment quality: two approaches ď&#x201A;§ Based on equation (1), we can split the growth rate of employment in both skilled (ls) and unskilled workers (lus). Therefore, we can study the impact of both process and product innovation on skilled and unskilled labor growth:

ď&#x201A;§ Once again we use instrumental variables as discussed before in order to address the identification problem related to correlation between d and g2 and the error term. ď&#x201A;§ In addition, and as robustness check, we also follow different variations of Berman, Bound and Griliches (1994) to estimate the relative demand of skilled labor:

Descriptive statistics: skill composition Argentina

Chile

Costa Rica

Uruguay

All firms

34.0

34.5

32.3

9.5

Non-innovators (no process or product innovations)

28.0

35.7

33.0

7.4

Process only innovators (non product innovators)

34.0

35.1

25.2

10.4

Product innovators

39.0

33.7

32.5

12.5

All firms

-4.0

-18.1

3.3

9.5

Non-innovators (no process or product innovations)

-6.0

79.6

3.5

3.3

Process only innovators (non product innovators)

-3.9

212.0

7.4

6.2

Product innovators

-2.5

-51.7

3.0

7.6

All firms

-1.4

26.7

4.5

10.2

Non-innovators (no process or product innovations)

-3.9

48.0

6.1

6.3

Process only innovators (non product innovators)

-1.1

261.8

3.5

13.4

Product innovators

0.2

9.4

4.1

14.1

All firms

-5.3

-62.4

4.4

5.1

Non-innovators (no process or product innovations)

-6.7

94.3

2.2

4.1

Process only innovators (non product innovators)

-4.6

111.3

13.0

5.2

Product innovators

-4.6

-118.5

4.5

6.8

S hare of skilled labor

Employment (total) growth (%)

S killed labor growth (%)

Unskilled labor growth (%)

Source: Own elaboration based on country studies

Effects on skills S ector Regression Constant Process only innovator (d ) Sales growth due to new products (g2)

S killed

Unskilled

S killed

Unskilled

S killed

Unskilled

S killed

Unskilled

-1.179

-1.975

-6.378

5.873

-11.580**

-12.283**

2.934*

0.225

-4.353

-3.848

-6.786

-6.452

(5.873)

(6.099)

(1.748)

(1.100)

3.048

2.448

14.931

-29.793

10.465

26.260**

2.379

-3.373*

-2.291

-2.01

-28.568

-27.162

(11.446)

(11.887)

(2.822)

(1.780)

1.308***

1.126***

1.702*

1.54

1.010***

1.020***

1.087***

0.929***

-0.174

-0.153

-1.029

(0.978)

(0.057)

(0.059)

(0.120)

(0.075)

Located in the capital

Yes

Foreign owned (10% or more)

Yes

2-digit industry dummies

Yes

Time dummies

Yes

11.47

32.12

78.16

64.87

0.001

0.6661196

0.8668128

17.26771

6.233251

5.370168

1.163053

F test, equation Pvalue

Davidson-M acKinnon5.992 test of exogeneity 1.427 for g2 Pvalue Number firms

0.015

0.232

0.5138

0.4205

0.000049

0.0134

0.019

0.28

1,209

1,973

208

1037

Effects on skill composition ď&#x201A;§ In Argentina there is weak evidence suggest that product innovations are more skill intensive (one side test. Similar for small firms), the opposite for small firms in Uruguay. In all other countries, there is no evidence of skill bias on product innovation. ď&#x201A;§ In Chile we find a negative effect of process innovation on unskilled employment, except in low tech sector. In Costa Rica and Uruguay there is evidence of displacement effect in the case of unskilled for the whole sample, but not for small firms. No biases from process in the case of Argentina.

Conclusions  Relationship between innovation and employment is complex. Innovation could trigger different effects at different levels of aggregation and the relation depends on the transmission mechanisms.  Idiosyncratic nature of innovation in the region means that the recent evidence in developed countries cannot be simply extrapolated. This project allow to fill the knowledge gap on the effects of innovation on employment (both quantity and quality).  The evidence presented supports the idea that the negative performance in the labor market tend to be related to the lack of product innovators rather than to the introduction of innovations (either process or product).  Considering that innovation positively affect employment generation, these results provide support for the current emphasis to the promotion of firm-level innovation.