WAGE DIFFERENTIALS, EMPLOYMENT, AND ... - Semantic Scholar

WAGE DIFFERENTIALS, EMPLOYMENT, AND GLOBALISATION: EVIDENCE FROM AN INTERNATIONAL PANEL

Guglielmo Maria Caporale Centre for Economic Forecasting London Business School and Mohammad Fazal Haq Department of Economics London Guildall University

Discussion Paper No. 21-98

October 1998

We acknowledge financial support from the Leverhulme Trust Grant No. F/124/N, “Growth, Innovation and Competitiveness: The Challenge of Asia for the West”. We are also grateful to Michael Funke, Stephen Hall, Joe Pearlman and Paul Temple for useful comments and suggestions, and to Steve Machin for kindly supplying the data.

This paper presents some empirical evidence on the factors determining changes in wage differentials between skilled and unskilled workers and in their employment. An “analysis of variance” (ANOVA) model is estimated using UN data for five OECD countries in order to assess the relative importance of industry-specific, country-specific and international shocks (as well as interactions between industry and country effects). It is found that both international shocks and national policies affect wage differentials over the whole sample, whilst skill-biased technical change (SBTC) was a significant factor in the seventies. Employment responds mainly to international shocks, although national policies and SBTC were important determinants in the seventies and eighties respectively. Some of the results are found to be sensitive to the technology level of the industries considered.

Keywords:

Analysis of Variance (ANOVA), Growth, International Trade, Skilled and Unskilled Workers, Technical Progress, Wage Differentials, Employment

JEL Classification: C23, F10, F43

ISSN No 0969-6598

CONTENTS

1.

Introduction ....................................................................................................1

2. 2.1 2.2

Data and methodology....................................................................................5 Data sources and definitions...........................................................................5 The model.......................................................................................................6

3. 3.1 3.2

Empirical results.............................................................................................8 Wage differentials...........................................................................................8 Employment..................................................................................................10

4.

Conclusions ..................................................................................................11

References..............................................................................................................17

1. INTRODUCTION Numerous studies have documented the fall in the relative wages and employment of less skilled workers, both in US and in other OECD countries, since the beginning of the eighties (see, e.g., Murphy and Welch (1982, 1983), Katz and Murphy (1992), Freeman and Katz (1994), Katz and Revenga (1989), Nickell and Bell (1995)). Two main explanations for this phenomenon have been offered in the literature. The first is increased international competition with unskilled workers in developing countries (see Wood (1994, 1995), and, for a more sceptical view, Freeman (1995) and Richardson (1995)), the second is skill-biased technical change (SBTC - see, e.g., Berman et al (1994)).

Most of the empirical studies attempting to discriminate between these two alternative hypotheses use US data. They conclude that the observed increase in the wage differential between the former and the latter reflects supply shifts, rather than a shift in demand towards skilled (nonproduction) workers. Typically, they analyse the effects of international trade on relative industry wages by calculating the quantities of factors embodied in trade volumes or trade deficits. Therefore, they rely on the Factor Price Equalisation (FPE) Theorem, which is concerned with changes in wage rates caused by changes in factor supplies, and is based on the small-country assumption that external product prices are fixed. As Leamer (1994) points out, this is not the right theorem to use for studying the impact of international trade on relative wages. Trade deficits per se have no necessary relationship to factor returns, as they depend on both production and consumption activity. As also stressed by Bhagwati (1991), the key variable is relative price changes. The appropriate theorem is consequently the Stolper-Samuelson (SS) theorem (see Stolper and Samuelson (1941)), which states that an increase in the price of a product raises the return to factors used relatively intensively in the production of that good and lowers the return to factors used relatively sparsely. In other words, international trade redistributes income by changing the terms of trade.

Lawrence and Slaughter (1993) examine the relative contributions of trade and technology to shifts in labour demand in the US by considering a general production function with three factors of production (capital, skilled and unskilled labour) and Hicks-neutral technological progress. They then derive factor demands, where each factor price is equal to the product of the exogenously given price of output, the technology parameter and the partial derivative of

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the production function with respect to that factor of production. Their aim is to test the empirical implications of the SS theorem, namely that an increase in the relative wage of skilled workers should result in (i) a fall in all industries in the ratio of skilled to unskilled labour employed, and (ii) an increase in the international price of skilled-labour-intensive products relative to those of unskilled-labour-intensive.

Using US data on two- and three-digit industries, they look at the relationship between changes in relative wages and relative employment of skilled and unskilled workers, and at the relationship between import prices by industry and skilled-labour intensity of industries. They conclude that the SS effect was dominated by other effects resulting in a fall in the relative employment of non-production workers, and that there was not much influence of the SS process on relative wages either.

To establish whether Hicks-neutral technological change affected real wages, they then examine whether (i) there was a fall in all industries in the ratio of skilled to unskilled workers, and (ii) there was greater Hicks-neutral technological progress for skilled-labourintensive products relative to unskilled-labour-intensive products. Their analysis suggests that the growth pattern of Hicks-neutral technology was not a predominant influence either on relative wages or on employment - it is possible, instead , that technological change was “biased” (see also Gregg and Manning (1997), who argue that relative wages should be included in the labour supply function in order to account for the observed patterns of wage inequality and unemployment, which are reported, for instance, by Nickell and Bell (1995), Machin et al (1996), Machin and Van Reenen (1997)). Consistent with the SBTC hypothesis is also the evidence of within-sector shifts in employment towards skilled labour which is observed in most US industries, notwithstanding the increase in the relative wage of skilled workers (see, e.g., Katz and Murphy (1992)); and the presence of within-sector correlations between indicators of technological change and increased demand for skilled workers (see, e.g., Berndt et al (1994), and Machin et al (1996)).

Note that in the Hecksher-Ohlin (H-O) model with small open economies and two factors of production skill-biased technological change cannot affect the wage structure unless it is also sector-biased. However, pervasive skill-biased technological change will (see Krugman (1995)). Pervasive SBTC has two testable implications: (i) within sector shifts away from

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unskilled labour should occur throughout the developed world; (ii) they should be concentrated in the same industries in different countries. Berman et al (1997) find that the empirical results in ten OECD countries are consistent with the theory.

The evidence from other studies is mixed. For instance, Berman et al (1994) use US data to carry out a within/between sectors decomposition, and to estimate a nonproduction wage share equation which also includes computers as a share of total investment as an indicator of technological change. Their conclusion is that production labour-saving technological change is the major cause of skill upgrading. By contrast, Revenga (1992) estimates quasi-reducedform equations for changes in employment and wages of production workers, and finds that changes in import prices have significant effects on both employment and wages in the US.

Martins (1994) examines the relationship between industry wages and openness to trade by estimating industry relative wage rate as a function of market structure specific fixed-effects, industry relative value-added per worker, relative import penetration and export intensity. He finds that in twelve OECD countries both market structure and trade variables affect industry wages significantly.

Machin and Van Reenen (1997) estimate a skilled wage bill (employment) share equation where relative wages have been replaced by country specific time dummies to capture common macroeconomic shocks. Educational measures of skill, and an index of computer use across industries, are also used as an alternative to shares and the ratio of R&D expenditures to value added respectively. The growth rate of imports and exports to value added are included as additional regressors to examine the importance of trade effects. They use data for seven OECD countries, and report that, although SBTC accounts for a sizeable fraction of the changes in skill structure, labour market institutions also play a role, whilst trade effects are completely insignificant.

Finally, Slaughter (1997) points out that trade can affect labour-demand elasticities (without changing wages) by making output markets more competitive and domestic labour more substitutable with foreign factors. He finds that in the US demand for production labour in manufacturing has become more elastic, whilst demand for non-production labour has grown less elastic, and that there is mixed evidence that trade has played a role.

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This paper takes an “analysis of variance” (ANOVA) approach in order to provide estimates of the relative importance of international, country- specific and industry-specific shocks as the driving force of changes in wage differentials between skilled and unskilled workers and in their employment. In this way, the adequacy of alternative theories aiming to explain observed trends in factor returns and employment can be assessed, albeit only indirectly. The layout of the paper is as follows. Section 2 describes the data and the estimated model. Section 3 presents the empirical results. Section 4 offers some concluding remarks.

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2. DATA AND METHODOLOGY

2.1. Data Sources and Definitions

For this study we use annual series which have been extracted from the United Nations Industrial Statistics Database (UNISDB) which is maintained by the Statistical Division of the United Nations. These data cover the manufacturing industry at approximately three-digit level and include information on the aggregate wage bill and numbers employed for all workers and for operators (skilled production workers). 1 Thus in total we have 28 industries classified according to the International Standard Industrial Classification (ISIC). In Table 1 below we present these industries by grouping them as having low, medium or high technological content.

Consistent series were not available for every country in the dataset, and hence to create a balanced panel we were restricted to including the following 5 countries: Great Britain, United States, Denmark, Sweden and Finland. The sample period covers the years from 1971 to 1990, and the panel as a whole contains 2,800 observations. Given the choice of countries the panel provides a fairly accurate picture of the developed economies in the northern hemisphere, and the results of this study should be interpreted accordingly. To examine the impact of macroeconomic shocks on the aggregate wage differential and employment share of skilled workers we construct two dependent variables. With regard to wage differentials we use the first difference of the natural log of the share of the wage bill of skilled workers in the total wage bill. For employment share we take the first difference of the natural log of the proportion of skilled employees among total employed in a particular industry.

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Machin and Van Reenen (1997) also construct education-based skill measures as an alternative to the nonproduction/production distinction using national data sources. Further details on data availability can be found in Machin et al (1996), Machin and Van Reenen (1997), and Cameron (1997).

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Table 1 Three-digit manufacturing industries covered in this study Technology level:

ISIC code:

Description:

Low:

3110 3130 3140 3210 3220 3230 3240 3310 3320 3410 3420 3530 3540 3610 3620 3690 3710 3810

Food products Beverages Tobacco Textiles Wearing apparel Leather and products Footwear Wood products Furniture, fixtures Paper and products Printing and publishing Petroleum refineries Petroleum, coal products Pottery, china etc. Glass and products Non-metal products nec Iron and metal Metal products

Medium:

3510 3520 3550 3560 3720 3840 3900

Industrial chemicals Other chemical products Rubber products Plastic products nec Non-ferrous metals Transport equipment Other industries

High:

3820 3830 3850

Machinery including computers Electrical machinery Professional goods, instruments

2.2. The Model

Using the conceptual framework of Stockman (1988) we carry out an analysis of variance of the dependent variables for wage differentials and employment share (see Funke et al (1997) for an application to OECD two-digit industry output data, and Caporale et al (1998) for an analysis of the determinants of productivity growth at the one-digit level). The underlying model is as follows:

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∆ ln(Y ) i , j ,t = µ +

1990

∑τ

t =1971

28

t Dt + ∑

1990

∑α

i =1 t =1971

5

it Dit + ∑

1990

∑β

j =1 t =1971

28

5

jt D jt + ∑ ∑ γ ij Dij + ε i , j ,t

(1)

i =1 j =1

where i = 1,...,28 represents the three-digit industries under consideration, j = 1,...,5 are the countries in the panel and t = 1971,...,1990 is the overall sample period. µ , τ , α , β and γ represent coefficients of the model. ∆ ln(Y ) i , j ,t is the dependent variable representing change in either wage differentials or employment share for skilled labour. Dt is a time dummy which takes the value 1 for time period t and 0 otherwise - this part of the model measures the contribution of the pure time effect in explaining variation in growth, and corresponds to international shocks which are common across all countries and industries. Dit = 1 for industry i and time period t and is 0 otherwise - this component of the model measures the interaction between time and industry, and thus represents shocks which are specific to the industries and are therefore technology or productivity based, but are common across all countries. D jt = 1 for country j and time period t and is 0 otherwise and explains that part of the variation in the dependent variable which corresponds to country-specific shocks. Finally, Dij = 1 for industry i and country j and takes the value 0 otherwise - this part of the model represents the interaction between industry and country which is unrelated to time. The random disturbance term in the model is denoted by ε i , j ,t .

An ANOVA (analysis of variance) based decomposition of the above model was carried out, and all sums of squares for the various components of the model were computed using the ordinary least squares criteria. In the estimation the United States, the industrial sector 3900 representing other industries, and the year 1990 have been used arbitrarily as base reference groups. The results of this study are in no way sensitive to the choice of the base reference groups. The empirical results are discussed below.

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3. EMPIRICAL RESULTS

3.1. Wage Differentials

Consider first the issue of whether changes in wage differentials between skilled and unskilled workers respond to shocks. In examining changes in wage differentials the central issue is to what extent the distribution of returns to skilled labour responds to international, countryspecific and industry-specific shocks. The results of estimating equation (1) across the panel for the sample period as a whole from 1971 to 1990 are presented in Table 2. Only international shocks and country-specific shocks are statistically significant. The overall explanatory power of the model is 32.9%, out of which international shocks account for 3.8 percentage points, while country-specific shocks contribute 10.4 percentage points. The fact that industry-specific shocks are statistically insignificant over the sample period implies that wage differentials between skilled and unskilled workers adjust at different rates across countries rather than across industries. One possible explanation for this result is that there are differences in monetary and fiscal policy between countries, and that their evolution over time is also different. Alternatively, one could argue that industry-specific technology or productivity shocks affect different countries in a different manner because of differences in the distribution of factors of production, which include skilled and unskilled workers. This will also cause country-specific shocks to be statistically significant.

To test the stability of the above results we estimate the model over two sub-samples: from 1971 to 1980 and from 1981 to 1990. The results are presented in Table 3 and 4 respectively. International shocks are found to be highly significant in both decades. Furthermore, the magnitude of their contribution to the explanatory power of the model for each of the subsamples is similar to that for the model estimated for the sample as a whole. Thus international shocks play a very significant role in determining the change in the wage differential between skilled and unskilled workers. In all other aspects the results of the two sub-samples are very different. While country-specific shocks remain statistically significant during the seventies and the eighties, their contribution to the explanatory power of the model is lower in the seventies. Thus during the seventies country-specific shocks play a less important role in determining the changes in the wage differential between skilled and unskilled workers than in the eighties. On the other hand, industry-specific shocks, while

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being insignificant during the eighties, represent the main reason for changes in the wage differential between skilled and unskilled workers in the seventies. During the seventies industry-specific or technology-based shocks contribute 22.6 percentage points to the total R 2 of 38.1%, and therefore there is strong direct evidence of skilled biased technical change which drives changes in the wage differential between skilled and unskilled workers.

The robustness of the results for wage differentials for the sample as a whole can also be examined by estimating equation (1) separately for low-, medium- and high-technology industries. These results are presented in Table 5, 6 and 7 respectively. From low- to hightechnology industries changes in wage differentials between skilled and unskilled workers respond in a statistically significant manner to international shocks and country-specific shocks. However, industry-specific shocks are significant only for industries which fall in the medium-technology band. Industry-specific shocks for the medium-technology group provide the highest contribution of 18.6 percentage points to the total R2 of the model. This constitutes some direct evidence that across medium-technology groups changes in wage differentials between skilled and unskilled workers are predominantly driven by skill biased technical change. The magnitude of the impact of international shocks on wage differentials changes as we move from low- to high-technology industries. For low-technology industries international shocks explain only 3.1% of total variation in wage differentials. This rises to 12.8% for medium-technology industries, and 17.8% for high-technology industries. Thus, the higher the level of technology for a particular industry the greater will be the response of changes in wage differentials between skilled and unskilled workers to international shocks. A similar pattern emerges for country-specific shocks also. In low-technology industries countryspecific shocks contribute 11.6 percentage points to the total R2, while for medium- and hightechnology groups this rises to 16.7 and 35.0 percentage points respectively. This implies that the higher the level of technology the more likely it is that industry-based technology change will affect different countries differently and will have corresponding effects on their wage differentials between skilled and unskilled workers.

3.2. Employment

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The above estimation process is repeated for the change in the share in total employment of skilled workers. The results of estimating equation (1) for the sample period as a whole and for the sub-samples for 1971 to 1980 and for 1981 to 1990 are presented in Table 8, 9 and 10 respectively. The consistent result which emerges is that international shocks are statistically significant for the overall sample and the two sub-samples, at least at the 10% level of significance. The effect of these international shocks was larger during the seventies than in the eighties. Country-specific shocks are statistically significant only over the sub-sample period from 1971 to 1980, whereas industry-specific shocks are never significant.

The sensitivity of the above results for the change in share of employment for skilled workers was also explored by estimating the model separately for low-, medium- and high-technology industries. These results are reported in Table 11, 12 and 13 respectively. For the lowtechnology group none of the components of the model are statistically significant even at the 10% level. Hence it appears that changes in the share of employment of skilled workers in low-technology industries are not affected by macroeconomic shocks. In medium-technology industries international shocks and country-specific shocks are statistically significant, whilst for high-technology industries only international shocks are statistically significant.

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4. CONCLUSIONS

This paper has presented some empirical evidence on the factors determining changes in wage differentials between skilled and unskilled workers and in their employment. The approach taken consists in estimating an “analysis of variance” (ANOVA) model which, although not lending itself to direct testing of hypotheses suggested by economic theory, does provide measures of the relative importance of industry-specific, country-specific and international shocks (as well as possible interactions between industry and country effects). The analysis has been carried out using a panel including data at the three-digit level for five OECD countries, and indicates the following.

In the case of wage differentials, country-specific shocks appear to be the main driving force over the sample as a whole, although international shocks are also significant. The former can naturally be thought of as resulting from the implementation of national policies, whilst industry-specific shocks can be interpreted as disturbances to technology (see Stockman (1988)). Therefore, these findings suggest that SBTC is not the main factor determining changes in wage differentials as argued, inter alia, by Berman et al (1994). Similarly, they are not supportive of theories relying on trade flows as the main explanation of such changes (see, e.g., Wood (1994)), as international shocks seem to have only small explanatory power. On the contrary, they are consistent with the idea that monetary and fiscal policies, which affect most or all industries in a particular country, play a major role in the determination of factor returns. However, SBTC was a very important factor in the seventies, and is the main determinant of changes in wage differentials in medium-technology industries. Also, in more technologically advanced industries, differentials are affected to a greater extent by both international trade and national policies.

Concerning the share in employment of skilled workers, this appears to be influenced predominantly by international factors, consistently with theories attributing increased inequality in income distribution in the West to international competition adversely affecting the unskilled (see, once again, Wood (1994)). By contrast, there is no evidence that SBTC is a significant factor (as argued, instead, by Machin and Van Reenen (1997)), whilst national policies only had an impact in the seventies. Finally, globalisation is an even more important determinant of changes in relative employment in high-technology industries.

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In brief, this study suggests that trends in relative wages are accounted principally by national stabilisation policies, although international factors also make a contribution, whereas the increasing economic integration of the world is the main force behind changes in relative employment. Neither of the two rival hypotheses put forward in the theoretical literature is therefore given full support by our findings. Future empirical research should also consider differences in policies across countries as a possible explanation for observed changes in relative factor returns and in their employment.

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Table 2 ANOVA table for the sample period as a whole from 1971 to 1990 Dependent variable: First difference of the log of share of operators’ wages in total wages. Sum of Squares:

R2 :

F - value:

International shocks Industry specific shocks Country specific shocks Country and industry factors

0.164 0.756 0.448 0.046

0.038 0.176 0.104 0.011

6.13 1.00 3.98 0.30

Total explained variation Total variation

1.414 4.298

0.329

Source of variation:

Degrees of Freedom:

Prob Value:

(19, 2052) (540, 2052) (80, 2052) (108, 2052)

(0.000) (0.495) (0.000) (1.000)

Table 3 ANOVA table for the sub-sample period from 1971 to 1980 Dependent variable: First difference of the log of share of operators’ wages in total wages. Sum of Squares:

R2 :

F - value:


0.055 0.316 0.084 0.078

0.039 0.226 0.060 0.056

6.85 1.31 2.35 0.81


0.533 1.401

0.381


Degrees of Freedom:

Prob Value:

(9, 972) (270, 972) (40, 972) (108, 972)

(0.000) (0.002) (0.000) (0.918)

Table 4 ANOVA table for the sub-sample period from 1981 to 1990 Dependent variable: First difference of the log of share of operators’ wages in total wages. Sum of Squares:

R2 :

F - value:


0.091 0.440 0.365 0.125

0.032 0.153 0.127 0.043

5.31 0.85 4.77 0.60


1.021 2.880

0.355


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Degrees of Freedom: (9, 972) (270, 972) (40, 972) (108, 972)

Prob Value: (0.000) (0.948) (0.000) (0.999)

Table 5 ANOVA table for the sample period as a whole from 1971 to 1990 - Low Technology Dependent variable: First difference of the log of share of operators’ wages in total wages. Sum of Squares:

R2 :


0.111 0.594 0.419 0.030

0.031 0.165 0.116 0.008


1.154 3.606

0.320


F - value:

3.08 0.92 2.76 0.23


Prob Value: (0.000) (0.827) (0.000) (1.000)

Table 6 ANOVA table for the sample period as a whole from 1971 to 1990 - Medium Technology Dependent variable: First difference of the log of share of operators’ wages in total wages. Sum of Squares:

R2 :

F - value:


0.046 0.067 0.060 0.004

0.128 0.186 0.167 0.011

6.09 1.39 1.87 0.41


0.177 0.360

0.492



Prob Value: (0.000) (0.009) (0.000) (0.995)

Table 7 ANOVA table for the sample period as a whole from 1971 to 1990 - High Technology Dependent variable: First difference of the log of share of operators’ wages in total wages. Sum of Squares:

R2 :


0.057 0.032 0.112 0.007

0.178 0.100 0.350 0.022


0.208 0.320

0.650


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Fvalue: 4.06 1.08 1.90 1.21


Prob Value: (0.000) (0.361) (0.000) (0.298)

Table 8 ANOVA table for the sample period as a whole from 1971 to 1990 Dependent variable: First difference of the log of share of operators’ employment in total employment. Sum of Squares:

R2 :

F - value:

Degrees of Freedom:


0.060 0.656 0.106 0.082

0.018 0.198 0.032 0.025

2.69 1.03 1.13 0.65

(19, 2052) (540, 2052) (80, 2052) (108, 2052)


0.904 3.316

0.273


Prob Value: (0.000) (0.328) (0.206) (0.998)

Table 9 ANOVA table for the sub-sample period from 1971 to 1980 Dependent variable: First difference of the log of share of operators’ employment in total employment. Sum of Squares:

R2 :

F - value:


0.030 0.136 0.053 0.034

0.039 0.176 0.069 0.044

6.29 0.95 2.49 0.60


0.253 0.772

0.328



Prob Value: (0.000) (0.694) (0.000) (0.999)

Table 10 ANOVA table for the sub-sample period from 1981 to 1990 Dependent variable: First difference of the log of share of operators’ employment in total employment. Sum of Squares:

R2 :

F - value:


0.030 0.521 0.052 0.162

0.012 0.205 0.020 0.064

1.81 1.05 0.71 0.82


0.765 2.545

0.301


15


Prob Value: (0.064) (0.300) (0.912) (0.909)

Table 11 ANOVA table for the sample period as a whole from 1971 to 1990 - Low Technology Dependent variable: First difference of the log of share of operators’ employment in total employment. Sum of Squares:

R2 :

F - value:

Degrees of Freedom:


0.042 0.557 0.118 0.070

0.015 0.193 0.041 0.024

1.36 1.01 0.91 0.64

(19, 1292) (340, 1292) (80, 1292) (68, 1292)


0.787 2.886

0.273


Prob Value: (0.137) (0.447) (0.699) (0.991)

Table 12 ANOVA table for the sample period as a whole from 1971 to 1990 - Medium Technology Dependent variable: First difference of the log of share of operators’ employment in total employment. Sum of Squares:

R2 :

F - value:


0.030 0.036 0.035 0.004

0.125 0.150 0.146 0.017

5.28 1.01 1.47 0.53


0.105 0.240

0.438



Prob Value: (0.000) (0.461) (0.009) (0.967)

Table 13 ANOVA table for the sample period as a whole from 1971 to 1990 - High Technology Dependent variable: First difference of the log of share of operators’ employment in total employment. Sum of Squares:

R2 :

F - value:


0.023 0.022 0.053 0.005

0.125 0.120 0.288 0.027

2.31 1.03 1.24 0.91


0.103 0.184

0.560


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Prob Value: (0.003) (0.434) (0.129) (0.512)

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