MODERN MANAGEMENT AND THE DEMAND FOR ......modern management practices and the demand for different occupational skills, and in particular technical skill, in the manufacturing sector

Yong Suk Lee Stanford University

September, 2018

Working Paper No. 18-031

MODERN MANAGEMENT AND THE DEMAND FOR TECHNICAL SKILL

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Modern Management and the Demand for Technical Skill

YONG SUK LEE*

Stanford University

September 18, 2018

Abstract This paper examines the relationship between modern management practices and the demand for different occupational skills utilizing a unique context in South Korea after the Asian financial crisis. Management practices in South Korea had traditionally emphasized the organizational harmony over individual performance, and firm growth over short-term profits. However, as South Korea opened up to foreign firms after the financial crisis, domestic firms started to adopt western or more “modern” management practices. Using the industry level variation in management practices generated by the average industry management index of five advanced economies (the US, Britain, France, Germany, and Italy), I find that modern management increases the demand for technical skill. Moreover, modern management practices help achieve various organizational changes that utilize information technology. I also find that performance measured as the return on asset increases with modern management practices, and document the complementarity between modern management practices and technical workers in increasing the return on assets. In short, this paper finds that modern management practices may increase the earnings difference between skilled - in particular, technically skilled - and unskilled workers. Keywords: modern management, management-technology complementarity, skill premium JEL Codes: M54, J31, J24

!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!* Lee: Freeman Spogli Institute for International Studies, Stanford University, Encina Hall E309, 616 Serra St, Stanford, CA 94305, USA. Phone: 1-650-723-9741. Fax: 1-650-725-6530. Email: [email protected].

I thank Nick Bloom, Raffaella Sadun, Kathryn Shaw, Mitchell Hoffman, Shai Bernstein, Adriana Kugler, Chiaki Moriguchi, Mu-Jeong Yang, and participants at the Society of Labor Economist Meeting, Empirical Management Conference, Korea-America Economic Associations Workshop, and Stanford Economics Junior Faculty Workshop for comments.

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1. Introduction

The Asian financial crisis, which had originated in Southeast Asia in 1997, unexpectedly

and abruptly spread to South Korea, and by the end of that year a liquidity crisis was looming in

the then third largest economy in Asia. The International Monetary Fund (IMF) agreed to

provide an emergency loan package conditional on South Korea making progress on various

structural reforms surrounding financial openness and corporate governance. The domestic

market became more open to foreigners, and many US and European entities, taking advantage

of the deflated prices, invested in South Korean firms, equities, and assets (Kim 2006; Choi et al.

2007). Naturally, more foreigners were in managerial positions and western management

practices spread to many firms. Management practices in South Korea had traditionally

emphasized the organization over the individual worker. Individual salary contracts were rare

and bonuses were usually shared among the employees. Family owners had disproportionately

large control over shareholders and managers regarding firm operations. Firms tended to focus

on growth over short-term profits. However, many of these features changed around this period

as more firms adopted western or “modern” management practices that emphasize individual

performance and corporate profits.

Utilizing data from this unique period, this paper examines the relationship between

modern management practices and the demand for different occupational skills, and in particular

technical skill, in the manufacturing sector. The World Management Survey and research by

Bloom and Van Reenen (2007) have triggered an expanding volume of research that explores

how management practices affect the productivity and performance of firms and organizations.

However, there is relatively little research on how modern management practices affect the

demand for different occupational skills and, hence, inequality. Modern management practices

are a set of generally perceived best practices, such as, whether workers are incentivized and

compensated accordingly, whether targets are initially identified and later assessed, and whether

procedures to minimize production errors are in place. These practices could differentially affect

the demand for occupational skill groups, e.g., managers, technical workers, office workers,

production workers, etc., if modern management practices complement or substitute each

occupational skill to varying degrees.

However, estimating the impact of management practices on the demand for skill is

difficult because of endogeneity. Firms decide whether or not to adopt modern management

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practices based on the projected benefits and costs of adopting such practices, which are often

determined by unobservable firm specific characteristics. Without a randomized control trial or a

convincing quasi-experimental design it is difficult to estimate causal effects, but generating

exogenous variation in management practices is difficult. The goal of this paper is to holistically

show - through multiple correlational analyses, 2SLS regressions, and evidence of the

complementarity between modern management practices and technical skill – that modern

management practices play a role in increasing the demand for technical skill.

I adopt the questions developed by Bloom and Van Reenen (2007) and construct a

management index for South Korea using survey data from the early 2000s. In addition to firm

level variation, I use the industry level variation in management practices to examine the demand

for occupational skills. Management practices could vary across industries because of various

reasons, including the difference in technologies, multinational firm activity, labor unionization,

etc. As Figure 1 documents, there is substantial variation in management practices across

industries within countries. I construct an industry management frontier, i.e., the industry level

management practices of five advanced western economies, by averaging the industry level

management indexes of the US, Britain, France, Germany, and Italy using the World

Management Survey. Firms from this group of 5 (hereafter, G5) countries often invest and

operate in other countries. Hence, the industry management frontier is likely to be more

correlated with the management practices in countries with large western firm presence, which

was increasingly the case for post-financial crisis South Korea.

OLS regressions find that the management index is positively related to the employment

of skill - i.e., managers, technical workers, and office workers - relative to the employment of

production workers and simple manual laborers. The 2SLS regressions that use the variation

from the industry management frontier and further controls for industry level competition and

productivity find a positive impact of modern management practices on the employment of

skilled workers. The positive effect holds for relative wages as well. However, other industry

characteristics that affect the demand for occupational skill could still be correlated with the

industry management frontier. Technological change is considered a leading cause of the recent

rise in inequality and employment polarization (Acemoglu and Autor 2011; Autor and Dorn

2013), and researchers have found that technology and aspects of modern management, such as

decentralization or incentive-based human resource management, complement each other

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(Bresnahan, Brynjolfsson and Hitt 2002; Bloom, Sadun and Van Reenen 2012). Globalization,

corporate governance, and unionization could also be correlated with management practices and

influence the demand for different types of workers. When I control for technology using the

number of computers in the establishment, modern management increases the relative

employment and wage of technical workers only. Controlling for exports, corporate governance,

and union status, however, have virtually no effect on the estimates once technology is controlled

for. The empirical results suggest that modern management is likely one factor behind the

increase in the employment and wages of the high-skilled technical workforce. Finally, I show

that the complementarity between modern management practices and technical skill helps

achieve organizational change and improve firm performance. Such complementarity is likely

one of the underlying reason behind modern management’s demand for technical skill.

This paper is related to the literature that examines how organizational characteristics are

related to aspects of inequality. Caroli and Van Reenen (2001) find that delayering and multi-

tasking increases the demand for skill. Bandiera et al. (2007) and Lemieux et al. (2009) find that

performance pay increases wage inequality within firms. I believe this is the first paper that

examines how overall management practices, rather than a specific aspect of organizational

change, affect the demand for skill. The complementarity between modern management practice

and technical skill found in this paper is related to studies that show that the productivity gains

from technology differ based on the organizational characteristics of the firm. Bloom, Sadun and

Van Reenen (2012) find that information technology productivity is higher in firms that

implement incentive based human resource management, such as performance based pay and

promotion. Bresnahan, Brynjolfsson and Hitt (2002) show that productivity gains from

information technology are higher in more decentralized organizations.

Also related is the literature that examines labor market polarization by occupational skill,

where employment has been increasing in the high-skilled and low-skilled occupation categories

but decreasing in the middle-skilled occupation groups (Autor et al. 2003; Autor et al. 2006;

Autor and Dorn 2013; Michaels et al. 2014). In particular, Autor and Dorn (2013) show that the

reduced costs of routine and codifiable tasks from technological change disproportionately hurt

both blue- and white-collar workers in the middle-skill categories, relative to the low-skilled

service sector workers and high-skilled managerial, professional, and technical workers. I show

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that another factor - i.e., management practices - can explain employment polarization where

technical workers increase relative to unskilled workers.

Finally, this paper contributes to the expanding economics literature on management.

That literature has largely focused on the impact of management practices on the performance of

firms and organizations. For example, scholars have examined how management practices affect

firms (Bloom and Van Reenen 2011; Bloom et al. 2013), the health sector (McConnell et al.

2013; Bloom, Propper, Seiler, and Van Reenen 2015), schools and universities (McCormack et

al. 2014; Bloom, Lemos, Sadun, and Van Reenen 2015), and bureaucracies (Kahn et al. 2016;

Rasul and Rogger 2017). In this paper, I show that modern management not only improves firm

performance but also the earnings difference between the technically skilled and low skilled

workers.

2. The Context

The Asian Financial Crisis, which started with the sudden drop in the value of the Thai

baht in 1997, quickly spread to neighboring Southeast Asian countries, as well as to South

Korea.1 Multiple South Korean banks and corporations that took out short-term loans in foreign

currencies were on the verge of going bankrupt and South Korea was unexpectedly in need of

cash to provide short-term liquidity into its financial sector. The IMF agreed to provide an

emergency loan package of $58.4 billion, but payments were to be made conditional on South

Korea making progress on a wide range of structural reforms. South Korea without much

alternative agreed to the conditional loans and embarked on a series of radical reforms. South

Korea's equity market became more open to foreign investors. Constraints on the purchase of

domestic assets by foreigners were relaxed. Corporate boards were instituted to enhance

accountability and transparency at banks and large firms. At the same time, US and European

firms, taking advantage of the deflated prices, purchased and invested in South Korean firms and

assets (Kim 2006; Choi et al. 2007). Naturally, more foreigners were in managerial positions

and western management practices quickly spread across the economy.

!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!1 A large literature examines the causes behind the Asian Financial Crisis and why it spread so quickly to many countries, including South Korea, which was geographically detached and generally perceived as a more stronger economy compared to Southeast Asia. Potential causes for the financial crisis include weak corporate governance, moral hazard in the financial sector, speculative attacks on currencies by hedge funds, as well as the grouping of countries under the so called emerging market funds. (Krugman 1999, Faccio et al. 2001).

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Some indication of this can be seen in how pay practices evolved in South Korea. Most

firms in Korea paid their employees based on a set wage table before the crisis. That is, worker

pay was primarily determined by the number of years one worked at the firm, along with a few

observable characteristics, such as, education, gender, military experience, etc. The notion that

individual pay among workers in the same entering cohort could differ substantially based on

performance was alien to many workers and firms at that time. However, with the spread of

western management practices, firms started to adopt salary systems where individual pay would

be negotiated based on performance. Figure 2 illustrates the year firms first adopted a salary

system based on a survey of South Korean establishments.2 Most establishments started to adopt

a salary system soon after the crisis and the number peaks in 2000 during the height of the

structural reforms.

The delayering of organizations and the introduction of teams are also aspects of

organizational change in modern firms (Caroli and Van Reenen 2001). The team structure

reduces the hierarchy within organizations and help expedite the decision making process.

Moreover, evaluating worker contribution is easier in smaller teams, since performance can more

easily be tracked within small groups. Figure 3 presents the year team systems were introduced.

It exhibits a similar pattern to Figure 2. The year teams were introduced also peaks at 2000. As

Figures 2 and 3 illustrate, two features often associated with modern management practices -

performance pay and delayering - were widely instituted in Korea soon after the financial crisis.

The share of occupation groups in the economy also changed soon after the financial

crisis. Figure 4 presents the employment patterns of four occupational skill groups - (a) managers,

(b) technical and professional workers, (c) office, service, and sales workers, and (d) production

and simple task workers - for all South Korean firms with 5 or more employees. The

employment dip in 1998 is the aftermath of the financial crisis, which negatively affected overall

employment. The dip is particularly sharp for the production and simple task workers and their

employment level does not reach its pre-crisis level until 2003. On the other hand, employment

of the technical and professional workers jumps after the crisis. The number of managers jumps

after the financial crisis as well, which is more clearly illustrated in Figure 6.

!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!2 Figures 3 and 4 are based on the workplace survey used in the empirical analysis of this paper. I provide more details on the survey in the following data section.

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Figure 5 presents the employment dynamics of each occupation group in terms of its

share relative to total employment. The share of production and simple task workers decrease by

about 10 percentage points from 50% to 40% after the crisis, reaching a new steady state. The

relatively lower skilled white-collar workers, i.e., office workers and service and sales workers,

also drop to a slightly lower steady state. On the other hand, the share of technical and

professional workers jumps to a higher steady state. Manager share also increases after the crisis,

which again is better illustrated in Figure 6. Figures 4 through 6 suggest that the demand for

managers, technical and professional workers may have increased after the financial crisis.

Though these figures are purely descriptive, they do hint that modern management practices may

be related to the composition of the occupational skill groups in the economy.

One thing to note is that there were shifts in the institutional arrangement related to South

Korea’s labor market after the Asian Financial Crisis. South Korea’s labor market was quite

inflexible. Firms generally hired workers at the entry level, and maintained them until retirement.

Labor laws made layoffs extremely difficult and hence the notion of life-time employment was

widely practiced. However, the massive bankruptcies and lay-offs following the Asian Financial

Crisis and the structural changes requested by the IMF resulted in labor market regulation

changes that eased some of the inflexibilities. In particular, the regulation on how long

businesses could use temporary workers was relaxed. Despite the effort to increase labor market

mobility, however, laying-off workers remained quite difficult and costly. Hence, the major

change in the labor market was the increased share of non-regular workers (temporary and part-

time employees). Such change was also accompanied by the increase in the gap of earnings,

especially benefits, between regular and non-regular workers. Such increase in the duality of the

labor market was often manifested through sub-contracting. For instance, when an auto

manufacturer needs to increase labor input to meet demand, it would increase temporary workers,

primarily by sub-contracting to firms that maintain a pool of temporary workers. Since firing

regular workers is costly and the cost of temporary workers is lower, companies maintain labor

flexibility and keep costs down by sub-contracting on a needs basis. The current paper focuses

on management practices and employees within the firm. The impact of modern management on

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temporary workers outside of the firm may be an important phenomenon, but in this paper those

effects are not captured and I focus on the within firm variation.3

3. Data

This paper’s empirical analysis uses a nationally representative workplace survey that

samples all South Korean establishments with 30 or more employees in 2002 and 2003. The

survey was conducted by the Korea Labor Institute as a pre-survey to the biannual Workplace

Panel Survey that starts in 2005. The 2002 and 2003 survey collected data on when firms first

adopted certain management practices after the financial crisis. It also provides detailed industry

classification, enabling it to be merged with the World Management Survey. These questions

were not asked in the main biannual panel survey that started several years later.4

The survey interviews human resource managers and employee representatives, and asks

about management practices, compensation policy, labor relations, and worker benefits. I

benchmark the World Management Survey to construct South Korea’s management index based

on questions that ask about employee evaluation, operations, and human resource management

strategies.5 These questions are listed in Table 1. Evaluation questions ask how important

individual performance is in promotion decisions and whether the establishment implements

Management by Objective (MBO) practices, where individuals set goals at the beginning of the

year and are evaluated based on performance on these goals at the end of the year. Operations

related question is whether the firm implements Six Sigma, which is a set of practices that aims

for process improvement.6 Human resource management questions ask about the organization’s

emphasis on the individual versus the team, performance versus harmony, and how flexible the

organization can hire and fire workers. I standardize each response and take the sum to construct

a standardized management index.

In addition, the survey provides the number of employees by occupational skill groups,

i.e., managers, technical workers, office workers, service and sales workers, production line

!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!3 The increasing labor market duality, i.e., the divergence of regular workers and non-regular workers (temporary workers and part-time workers) is an important characteristic of the South Korean labor market that warrants separate examination. 4 More information on the survey and the data can be accessed at the Korea Labor Institute’s website 5 The World Management Survey does not yet cover South Korea. 6 One thing to note is that these management concepts were all devised or popularized in the west. MBO was popularized by the management guru Peter Drucker in 1954. Six Sigma was introduced by Motorola and Jack Welch used it as a central business strategy of General Electric.

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workers, and simple manual laborers. The manager category does not include executives, such as

the owner, CEO, or top executives. Technical workers are employees involved in technology,

professional, or research and development work. They include (1) science, technology, and

engineering professionals, (2) finance, accounting, and insurance professionals, and (3)

administrative and legal professionals. Office workers are other desk workers that are not

managers or technical workers. Simple manual laborers are low-skilled non-production line

workers, such as cleaning and janitorial personnel. I construct the relative demand of four

occupational skill groups, i.e., managers, technical workers, office workers, and service and sales

workers, relative to unskilled workers. Unskilled workers are comprised of the production line

workers and simple manual laborers.

I also construct the relative earnings. However, information on earnings is more limited

in the survey. The survey collects the average earnings of all six occupational skill groups, but

only for temporary workers in a subsample of the establishments. The survey does collect data

on the average earnings of managers and all non-managers combined, i.e., the rest of the workers,

for each establishment. I use these information to impute the average earnings of each

occupational group for each establishment. I first calculate the industry level relative earnings

based on the temporary worker information. I then use the number of employees in each

occupational skill group and the average earnings of managers and non-managers to impute the

average earnings of each occupational group for each establishment.7 Such imputation assumes

that the differences between temporary and permanent workers are constant across the non-

managerial occupations. In Appendix Table 1, I use data from the Korean Labor Income and

Panel Study (KLIPS) to examine the earnings differences between temporary and permanent

(regular contract) workers across non-managerial occupations. Earnings are the total

compensation that employees receive from the company and include wages/salaries, bonuses,

and benefits. In particular, I regress log earnings on the occupation group dummies, a dummy for

temporary work status, and the interactions terms between temporary work status and the

occupation group dummies. I also control for individual characteristics, region, industry, and

!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!7 For example, I impute the average wage of technical workers as below

!!"#! =!!"!!!"#"$%&'×!!"!!!"#"$%&'

!!"#! + !!×!!!"#!!"#!!"#!!!"",!"#$,!"#$,!"#$

where ! denotes wage, !!"# the industry average wage from the temporary worker information, and ! employment, the subscripts indicate the occupational skill groups.

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business characteristics. Since KLIPS is an individual level panel, I also present results that use

individual fixed effects in lieu of the individual characteristics. The omitted category is the sales

and service workers, so the coefficient estimates indicate the earnings differential between each

occupation group relative to this omitted category. The coefficient estimate on each interaction

term indicates the difference between the earnings differential between temporary and permanent

workers for each occupation group and the earnings differential between temporary and

permanent workers for service and sales workers. If the differences between temporary and

permanent workers are constant across the non-managerial occupations, the estimates on the

interaction terms would not be statistically different from zero. Indeed none of the interactions

terms are significantly different from zero in Appendix Table 1.

Despite the paper’s focus on manufacturing establishments, there are establishments with

unusually high ratios of skilled to unskilled workers, often due to the very low number of low-

skilled workers. For example, quite a few establishments have zero or only one production

worker. I drop establishments with zero or one production worker. I also drop establishments if

the ratio of any skilled occupation group to unskilled workers is greater than 10.8 The final

sample size is 1,430 establishments and Table 2 presents the descriptive statistics.

Establishments on average have 390 employees. Production workers comprise 52% of the

employment, followed by office workers at 13%, and simple manual laborers at 12%. Managers

comprise 10 % of the employment and technical workers 7%. Service and sales workers

comprise the lowest share at 6%. The earnings of managers relative to unskilled workers in log

differences, i.e., ln(manager earnings) – ln(unskilled earnings), is 0.5. The relative premium is

0.46 for technical workers, 0.01 for office workers, and 0.06 for sales and service workers. In

other words, manager pay is over 50% higher than that of unskilled workers and is closely

followed by technical workers. The average pay of office workers is very close to that of

unskilled workers.

4. The Empirical Framework

I consider the following equation to examine the relationship between modern

management practices and the demand for different occupational skill groups:

!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!8 There were 280 establishments with zero production worker, and 10 establishments with exactly one production worker. This process in total drops 308 observations. !

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!!"# = !!!!" + !!"Π+ !!"#. (1)

! indexes the occupational skill group, ! the establishment, and ! the industry. !!"# is

establishment i's share of employees in occupation group ! relative to the unskilled occupation

group. !!" is the set of control variables that include establishment age and log employee size, as

well as region dummies, and a year dummy for 2003 (since the survey spans two years). The

dummy variables capture unobserved region and time characteristics that affects the demand of

each occupational skill group. !!" is the establishment's management index. In addition to

examining labor share !!"# , I examine earnings share !!"# , i.e., the relative earnings of

occupational skill group ! relative to that of the unskilled occupation group. By examining both

relative employment and earnings, one can check whether the responses are driven by the

demand for or supply of the different occupational skill groups. If modern management increases

the relative demand for a certain occupational skill, then both the relative employment and

earnings of that occupational skill group would increase.

The main challenge in examining the impact of modern management practices on the

demand for occupational skill groups is the fact that management practices are endogenous.

Firms weigh the benefit against the cost of introducing modern management practices and

choose whether or not to adopt new practices. There would be firm specific characteristics that

factor into that decision, many of which the econometrician may not be able to control for. In

addition to firm level OLS regressions of equation (1), this paper examines the industry level

variation in management practices and the demand for occupational skill. Modern management

practices would vary across industries due to various reasons that render modern management

practices more beneficial to some industries relative to others.9 Industries that often use

complicated machineries and production processes may reap the benefits of modern management

more than others. Industries that use a multitude of intermediate goods sourced from many

regions may benefit more from modern management than industries that do not. Multinational

firm activity or labor unionization could also generate industry level variation in management

practices. As Figure 1 illustrates there indeed is substantial variation in management practices

across industries within each of the five advanced economies. I construct the industry !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!9 The idea of utilizing industry level variation for identification has been used to examine the impacts of financial dependence. Rajan and Zingales (1998) identify the industry’s need for external finance to examine the impact of external financing on growth. Manova (2004) examines how financial market imperfections distort trade across industries and countries.

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management frontier by averaging this group of 5 - the US, Britain, France, Germany, and Italy-

countries' industry level management indexes from the World Management Survey. These five

economies are generally at the frontier of production technologies and business practices.

Modern management practices were widely established among firms in these countries and many

multinational firms originate from these advanced economies. Moreover, firms and funds from

these western economies invested in South Korea’s firms and assets after the financial crisis.

Hence, the industry management frontier is likely to be more correlated with the management

practices in countries with large western firm presence, which was increasingly the case for post-

financial crisis South Korea.

Figure 7 presents the correlation between the industry management frontier (the G5

industry management index) and South Korea’s management index. The x-axis indicates that

there is substantial industry level variation in management practices in the Group of 5 countries.

Also, there is a positive correlation between the industry management frontier and South Korea’s

management index. Table 3 presents the regression results of this relationship. In panel A of

column (1), South Korea’s management index is regressed on the G5 industry management index

and the control variables. Standard errors are clustered at the industry level. A standard deviation

increase in the G5 index increases South Korea’s management index by about 0.13 standard

deviation, and the effect is statistically significant at the 1 percent level. The other panels in

column (1) examine the correlation using each country’s industry management index. The

estimates are similar across all 5 countries. I next split Korea’s management index into two

components – an industry average and the establishment specific residual– and examine how

each component is related to the foreign management indexes. The foreign industry level

management indexes are strongly correlated with South Korea’s industry level index but not with

the establishment specific residual. Finally, I examine the scatterplots between the employment

share of each occupational skill group and the G5 industry management index in Figure 8. The

positive relation between the share of technical workers and the G5 index, and the negative

relation between the share of production workers and the G5 index particularly stands out. This

potentially suggests that modern management practices may be complementary to technical skill

while substituting production workers.

These evidence all illustrate that the G5 index can potentially serves as an instrument for

the Korean management index. The other condition for the G5 index to be a valid instrumental

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variable is that there are no additional omitted variables, i.e., the G5 index is not correlated with

the error term in equation (1). It is worth emphasizing that the G5 index must be uncorrelated

with the error term conditional on the control variables in the regression. Hence, in the

sensitivity analysis I include potential omitted variables - such as, industry level characteristics,

variables that proxy for technology, trade, corporate governance and labor unions - to the base

regression and examine the consistency of the estimates.

5. The Empirical Results

5.1 Modern management and the level of employment and wages by skill groups

I first examine how modern management is related to the level of employment and

earnings of each occupational skill group in Table 4. The base OLS results in Panel A indicate

that modern management is positively related to the number of managers, technical workers, and

office workers, but negatively related to the number of production workers. On the other hand,

modern management is positively and significantly related to the earnings of all six occupational

skill groups.

In Panel B, I separate the management index into two components - the industry average

and the establishment specific residual component. In general, employment is more strongly and

significantly related to the industry average than the residual component. Establishments in

industries with a higher average management index have significantly more managers, technical

workers, and office workers, but less production workers. On the other hand, earnings tends to be

more significantly related to the establishment specific residual. An interesting feature from the

earnings result is that the coefficient estimates on the establishment specific residual index for all

six occupation groups are quite similar, whereas the estimates on the industry average index are

substantially more varied. One explanation may be that the establishment specific residual index

is closely related to the establishment's total productivity that affects all workers similarly,

whereas the industry average index better captures the differential demand for the occupational

skill groups.

Panel C examines how the industry management frontier, i.e., the G5 industry

management index, is related to the level of employment and earnings. The industry

management frontier is most strongly and positively related to the number of technical workers,

as well as managers, but negatively related to the number of production workers. The industry

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management frontier is also positively and significantly related to the earnings of skilled workers,

especially, technical workers and office workers.

Finally, Panel D presents the regression results that use the variation in South Korea’s

management index generated by the industry management frontier, i.e., the 2SLS results. As

Table 3 illustrated the first stage is strong with an F-statistic of 13.47. Overall, the 2SLS results

are qualitatively similar to the reduced form results of Panel C. The evidence is consistent with a

positive impact of modern management practices on the employment and earnings of skilled

workers, notably, the technical workers, managers, and office workers.

5.2 Modern management and the relative demand for skill

Table 5 examines the relative demand for the different occupational skill groups. The

dependent variables in columns (1) through (4) are the natural logarithms of the employment of

managers, technical workers, office workers, and sales and service workers relative to the

employment of unskilled workers, i.e., production workers and simple manual laborers. Columns

(5) to (8) present the relative earnings results for the corresponding occupation groups. The OLS

results in Panel A indicate that modern management is most strongly associated with the relative

demand for technical workers and office workers. For managers the estimate is significant only

for relative employment, and for sales and service workers only the relative earnings result is

significant.

As before, I separate out the industry average and establishment specific residual

management index in Panel B. Again the industry average, compared to the establishment

specific residual, has a substantially larger relation with the relative employment of each skill

group, and the estimate on the industry average is largest for the technical workers. The relative

earnings results are not as strong statistically, except for one estimate on the industry average

index for sales and service workers. However, similar to before the magnitude of the estimate for

technical workers is larger than that for managers or office workers. There is no establishment

specific residual effect on the relative earnings of any skill group.

Finally, the reduced form results of Panel C and the 2SLS results of Panel D all return

positive and significant estimates for the relative employment shares of skilled workers. Similar

to previous results, the effect is strongest for the technical workers. Moreover, the positive

estimates for both relative employment and relative earnings suggest that the effects are driven

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by modern management’s demand for skilled workers, rather than the relative supply of skilled

workers. The coefficient estimates for the technical worker group is consistently the largest in

Table 5, regardless of whether one examines relative employment or relative earnings, or

whether one examines the OLS result, reduced form result, or 2SLS result.

The dataset used in this paper does not have information on hours worked. However,

there could be substantial variation in hours worked across the different occupation groups

across industries. To better account for hours worked in the analysis, I use the KLIPS data to

calculate the average work hours of each occupation group by industry and weight employment

and normalize earnings. However, individuals in KLIPS do not populate all manufacturing

industries and occupation groups, and hence, there are many missing weights. In such cases, I

use the average across industries as the weights for each occupation group. I rerun the

regressions in Table 5 and present the results in Appendix 2. The results are overall quite similar

to that of Table 5.

5.3 Sensitivity analysis and controlling for technology

There are likely unobserved factors that correlate with the industry level management

index and the demand for skill in the Table 5 Panel D results. Especially, there could be other

industry level omitted variables, such as industry level competition and productivity. I construct

the industry level revenue based Herfindahl-Hirschman index to control for the degree of

competition and concentration. I construct industry labor productivity to capture productivity

differences across industries. Table 6 Panel A presents the results when I include these industry

level variables. Appendix Table 3 presents the first stage regression. Industries that are more

competitive and more productive have significantly better management practices. In fact, the

first-stage statistic of the 2SLS regression actually increases to 15.54. However, including these

industry variables barely changes the 2SLS estimates in the second stage. The estimates on Table

5 Panel D and Table 6 Panel A are quite similar. The inclusion of these variables may not fully

alleviate omitted variable concerns, but the consistency of the 2SLS estimates is reassuring.

Technology adoption varies across industries and influences the demand for skill.

Moreover, there is evidence that organizational characteristics, such as delayering and

multitasking, are complementary to technology. The rest of Table 6 presents the 2SLS results

with additional controls for technology as well as other potential channels that could influence

! 16!

the demand for technical skill. In Panel B, I additionally control for the log number of computers

at the establishment to proxy for technology usage. The relative employment share results in

columns (1) through (4) indeed indicate that technology increases the demand for skill,

especially managers and office workers. The coefficient estimates on the management index

become smaller in magnitude, but only the estimate for the technical workers remains significant.

On the other hand, the relative earnings results in columns (5) through (8) indicate that, the

number of computers is not significantly related to the relative earnings of skill groups, and if

any tends to reduce the earnings gap. However, the estimates on the management index, as well

as their standard errors become larger. Overall, Table 6 Panel B indicates that modern

management increases the demand for technical skill even when controlling for a proxy for

technology.

I further explore how controlling for exports, corporate governance, and labor unions

change the Table 5 Panel D results. I include the export share of revenue as an additional control

in Panel C. The literature has found exports to be associated with better quality and the demand

for skilled workers (Verhoogen 2008). However, the export share variable has no significant

relationship with the relative employment or wage share of all occupational skill groups when

management is controlled for. Moreover, the coefficient estimates on the management index is

very similar in terms of both magnitude and significance to the 2SLS results in Table 5.

I next examine whether controlling for an aspect of corporate governance, i.e., whether

the firm had a Chief Executive Officer (CEO) independent from the owner, changes the impact

of modern management on the demand for skill in Panel D. Many firms in Korea were directly

governed by their owners rather than independent CEOs. Holding management practices

constant, having a CEO tends to be negatively associated with the relative demand of skill.

However, the coefficient estimates on the management index barely changes. Panel E examines

whether having a labor union influences the relative demand for skill. I find no impact of union

status on relative employment and earnings. Again, the coefficient estimates on the management

index are virtually unchanged. Finally, I control for all of the above potential channels in Panel F.

All coefficient estimates are very similar to that from Panel B. This suggests that technology is

the critical factor that needs to be accounted for in the 2SLS regressions that uses the industry

management frontier as an instrumental variable. However, once technology is controlled for,

other factors identified in the literature are not major concerns, and the 2SLS regression that

! 17!

controls for technology suggests that modern management increase the demand for skill. The

magnitude of the effect is quite large. A 0.2 standard deviation increase in the management index

increases the earnings difference between technical workers and low-skilled workers by about a

full standard deviation.

While controlling for the number of computers at the establishment, I examine whether

modern management’s relative demand for technical skill is primarily due to the increase in the

demand for technical workers and/or the decrease in the demand for unskilled workers. Table 7

Panels A and B present the results on employment and wages. Modern management has a

significant and positive effect on the demand for technical workers. However, modern

management's effects on the employment and earnings of unskilled workers are not significant.

This indicates that modern management's relative demand for skill is primarily driven by the

increased demand for technical workers.

If establishments had temporary employees, those numbers were included in the

employment numbers. In Table 7 Panels C and D, I separate out the temporary employees from

the regular employees. First of all, column (7) shows no evidence that modern management

practices substitute regular employees for temporary employees. Columns (1) through (6)

indicate that modern management’s demand for the technical workers is for the regular

employees and not the temporary employees.

6. Organizational change and firm performance

6.1 Modern management and organizational change

Why might modern management increase the demand for technical skill? Caroli and Van Reenen

(2001) show that organizational delayering and multi-tasking increases the demand for skill. One

mechanism by which modern management could increase the demand for technical skill is

through the complementarity between modern management and technical skill to achieve various

organizational changes, e.g., digitization of administrative, financial, and production tasks,

decentralization and delayering of the organization, and information sharing.

As previously noted, technical workers perform a wide range of high-skill activities and

include not only the science, technology, and engineering professionals, but also finance,

accounting, and insurance professionals, and administrative and legal professionals. Modern

management practices aim to improve firm performance through better measurement of workers

! 18!

and tasks. Firms are adopting information technology into its organization to improve

performance and reduce costs. This is where modern management practices and technical skill

can complement each other, i.e., in achieving IT based organizational changes. For example,

information sharing within the firm can reduce redundant tasks and errors, and ultimately reduce

costs. To set up a system of information sharing, the firm needs IT technicians to set up and

manage the computer system, administrative professionals to implement the system, and

management practices that can measure and assess the workers and their tasks. In other words,

new organizational objectives like information sharing, decentralization, and digitization often

mobilize various technology, financial, and administrative professionals and accompany

management practices that emphasize measurement.

In Table 8, I examine whether modern management practices are positively related with

how well organizational change objectives that utilize information technology are achieved.

Several questions in the 2003 survey examine information technology use. I compile a set of

questions that asks how effective information technology is in helping accomplish various

organizational objectives. I regress each of these variables on the modern management index

including the same set of control variables used in the previous tables.

Establishments with better management practices utilize information technology to

digitize modular tasks, e.g., accounting, inventory management and sales management (Panel A),

employ Enterprise Resource Planning (ERP) or systems that comprehensively support workplace

tasks (Panel B), and Electronic Data Interchange (EDI) or systems that help connect and support

clients and suppliers (Panel C). Establishments with better management practices were also more

likely to say that information technology helped in setting up task force teams (Panel D),

delayering and decision making (Panel E), increasing multi-tasking (Panel F), encouraging

employee involvement in company operations (Panel G), and sharing information (Panel H).

And as more direct evidence, establishments with better management practices were more likely

to achieve what the company had intended to achieve through its investment in information

technology personnel (Panel I). The results in Table 8 suggest that modern management practices

complement technical skill to help achieve organizational changes that utilize information

technology. The ultimate goal of such organizational changes would be to improve firm

performance. In Table 9, I directly examine the complementarity between modern management

and technical workers in terms of firm performance.

! 19!

6.2 Modern management and firm performance

I first examine the relationship between modern management and firm performance

measured as the return on assets (ROA). ROA is constructed as net income, i.e., profits post-tax,

divided by total assets. ROA is one measure of firm performance and indicates how well

management is utilizing all of the company’s resources to generate profit. Table 9 columns (1)

and (2) present the OLS results where I include the same set of control variables as before. The

estimates on modern management are positive but not statistically significant. When I instrument

using the industry management frontier in columns (3) and (4), modern management positively

and significantly increases the ROA. In column (3), A standard deviation increase in the

management index increases the ROA by 0.304, and the impact is statistically significant at the

10% level. Given that the average ROA is about 0.12 with a standard deviation of 0.57, this

amounts to more than half a standard deviation increase. Once I control for the number of

computers in column (4) a standard deviation increase in the management index increases the

ROA by 0.64, which is greater than a full standard deviation, and the impact is statistically

significant at the 5 percent level.

Finally, I examine the complementarity between modern management and technical

workers in columns (5) and (6). I run an OLS regression that additionally includes the log

number of technical workers, and the management index interacted with the log number of

technical workers. The coefficient estimates on the interaction term are positive and statistically

significant and are similar regardless of whether I control for log computers in column (6).

Overall, Table 9 indicates that modern management and firm performance are positively related

and that there is complementarity between modern management and technical workers.

7. Conclusion

This paper examined the relationship between modern management practices and the

demand for different occupational skills. Focusing on the industry level variation in management

practices generated by the industry management frontier, i.e., the average industry management

index of 5 advanced economies (the US, Britain, France, Germany, and Italy), I examine how

modern management practices affect to the demand for managers, technical workers (R&D

workers and technicians), office workers, sales and service workers relative to the unskilled

! 20!

production workers and simple manual laborers. As widely found in the literature, controlling for

technology is important for the analysis. I find that modern management has a positive effect on

the demand for workers with technical skill, but has relatively little effects on the other

occupational skill groups. I show that the complementarity between modern management

practices and technical skill helps achieve organizational changes that utilize information

technology. I also find that performance measured as the return on assets increases with modern

management practices, and that modern management practices complement technical workers in

increasing the return on assets. A relatively large literature finds that modern management

practices increase the efficiency of firms and organization. However, what this paper finds is that

such practices may increase inequality between skilled - in particular, technically skilled - and

unskilled workers.

The complementarity between modern management practices and technical workers

suggests why modern management might increase the demand for technical skill - the marginal

productivity of technical workers might be higher than that of other occupational skill groups,

and modern management practices are better at identifying that. Firms adopt modern

management practices to better monitor and track the performance of workers. These firms are

making the additional effort to identify the types of workers that generate relatively more value

to the firm, and eventually hire and pay more for such workers. If workers with technical skill

create more value to the firm, by contributing to organizational change objectives and ultimately

firm performance, firms that implement modern management practices would more likely

increase the demand for technical workers relative to the other occupational skill groups. It

seems that more firms around the world are adopting modern management practices, either

because of guidance by foreign investors, multinational firms, management consultancy, MBA

education, and even international organizations like the World Bank. Future research that further

examines how modern management practices increase the demand for technical skill using

detailed data in other settings could help shed light on how labor market inequality and

polarization may evolve in the future.

! 21!

References

Acemogul, Daron and David Autor. 2011. “Skills, Tasks, and Technologies: Implications for Employment and Earnings.” In Handbook of Labor Economics, vol. 4, ed. David Card and Orley Ashenfelter, 1043-1166. Amsterdam: Elsevier.

Autor, David and David Dorn. (2013). "The Growth of Low-Skill Service Jobs and the Polarization of the U.S. Labor Market," American Economic Review, 103(5): 1553-97.

Autor, David, Frank Levy, and Richard Murnane. 2003. "The Skill Content of Recent Technological Change: An Empirical Exploration." Quarterly Journal of Economics, 118(4): 1279-1333.

Autor, David H., Lawrence F. Katz and Melissa S. Kearney. 2006. "The Polarization Of The U.S. Labor Market," American Economic Review 96(2): 189-194.

Bandiera, Oriana, Iwan Barankay, and Imran Rasul. 2007. Incentives for Managers and Inequality among Workers: Evidence from a Firm-Level Experiment. Quarterly Journal of Economics, 122 (2): 729-773 Bloom, Nicholas and John Van Reenen (2007). “Measuring and Explaining Management Practices Across Firms and Countries.” Quarterly Journal of Economics, 122, 1351–1408.

Bloom, Nicholas and John Van Reenen (2011). “Human Resource Management and Productivity.” In Handbook of Labor Economics, Vol. 4B, edited by Orley Ashenfelter and David Card. North-Holland, Chapter 19, pp. 1697–1769.

Bloom, N., Sadun, R. and van Reenen, J. (2012). ‘Americans do IT better: US multinationals and the productivity miracle’, American Economic Review, 102(1): 167–201. Bloom, N., Eifert, B., Mahajan, A., Mckenzie, D. and Roberts, J. (2013). ‘Does management matter: evidence from India’, Quarterly Journal of Economics, vol. 128(1), pp. 1–51. Bloom, N., Propper, C., Seiler, S. and van Reenen, J. (2015). ‘The impact of competition on management practices in public hospitals’, Review of Economic Studies, vol. 82(2), pp. 457–89. Bloom, N., Renata Lemos, Raffaella Sadun and John Van Reenen. 2015. “Does management matter in schools?” Economic Journal, 125: 647-674. Bresnahan, Timothy, Erik Brynjolfsson, and Lorin Hitt (2002). “Information Technology, Workplace Organization and the Demand for Skilled Labor: Firm-Level Evidence.” Quarterly Journal of Economics, 117(1): 339–376.

Caroli, Eve, and John Van Reenen. 2001. “Skill-Biased Organizational Change? Evidence from a Panel of British and French Establishments.” Quarterly Journal of Economics, 116(4): 1449-1492.

! 22!

Choi, Jongmoo Jay, Sae Woon Park, and Sean Sehyun Yoo. 2007. "The value of outside directors: Evidence from corporate governance reform in Korea." Journal of Financial and Quantitative Analysis 42(4): 941-962. Faccio, Mara, Larry H.P. Lang, and Leslie Young. 2001. “Dividends and Expropriation.” American Economic Review 91(1): 54-78. Khan, Adnan, Asim Khwaja, and Ben Olken. 2016. “Making Moves Matter: Experimental Evidence on Incentivizing Bureaucrats through Performance-Based Postings.” Unpublished. Kim, Kihwan. 2006. "The 1997-98 Korean financial crisis: Causes, policy response, and lessons." In IMF Seminar on Crisis Prevention in Emerging Markets. Krugman, Paul. 1999. The Return of Depression Economics. New York: W. W. Norton & Company. Lazear, Edward (2000). “Performance Pay and Productivity.” American Economic Review, 90, 1346–1361. Lemieux, Thomas, W. Bentley MacLeod, Daniel Parent. 2009. “Performance Pay and Wage Inequality.” Quarterly Journal of Economics, 124(1): 1-49. Manova, Kalina. 2013. “Credit Constraints, Heterogeneous Firms, and International Trade.” Review of Economic Studies 80: 711-744. McConnell, John, Rich Lindrooth, Doug Wholey, Tom Maddox, and Nicholas Bloom. 2013 “Management practices and the quality of care in Cardiac units.” Journal of the American Medical Association: Internal Medicine, 173(8):684-692. McCormack, John, Carol Popper and Sarah Smith. 2014. “Herding Cats? Management and University Performance.” Economic Journal, 124: 534-564. Michaels, Guy, Ashwini Natraj, and John Van Reenen. 2014. “Has ICT Polarized Skill Demand? Evidence from Eleven Countries over 25 Years.” Review of Economics and Statistics 96(1): 60-77. Rajan, Raghuram G. and Luigi Zingales. 1998. “Financial Dependence and Growth.” American Economic Review, 88(3): 559-586. Rasul, Imran and Daniel Rogger. 2017. “Management of Bureaucrats and Public Service Delivery: Evidence from the Nigerian Civil Service.” Economic Journal, doi:10.1111/ecoj.12418 Verhoogen, Eric. 2008. "Trade, Quality Upgrading, and Wage Inequality in the Mexican Manufacturing Sector." Quarterly Journal of Economics 123(2): 489-530.

! 23!

Figure 1. The distribution of industry level management practices across countries

Source: World Management Survey.

0.2

.4.6

.81

Density

-3 -2 -1 0 1 2US

0.2

.4.6

.81

Density

-2 -1 0 1 2Britain

0.5

1Density

-4 -2 0 2France

0.2

.4.6

.8Density

-2 -1 0 1 2Germany

0.2

.4.6

.81

Density

-3 -2 -1 0 1 2Italy

! 24!

Figure 2. Number of establishments that introduce performance pay by year

Figure 3. Number of establishments that introduce team systems by year

0!

50!

100!

150!

200!

250!

1977!

1979!

1982!

1983!

1986!

1987!

1988!

1989!

1990!

1991!

1992!

1993!

1994!

1995!

1996!

1997!

1998!

1999!

2000!

2001!

2002!

2003!

salary!system!start!year!

0!

50!

100!

150!

200!

250!

1977!1982! 1986!1988!1990!1992!1994! 1996!1998!2000!2002!

team!system!start!year!

! 25!

Figure 4. Employment by skill groups

Figure 5. Employment share by skill groups

0!

500000!

1000000!

1500000!

2000000!

2500000!

3000000!

1994! 1995! 1996! 1997! 1998! 1999! 2000! 2001! 2002! 2003!

Managers!

Technical!and!professional!workers!

Office,!service,!and!sales!workers!

ProducGon!and!simple!task!workers!

0!

0.1!

0.2!

0.3!

0.4!

0.5!

1994! 1995! 1996! 1997! 1998! 1999! 2000! 2001! 2002! 2003!

%!Manager!

%!Technical!and!professional!

%!Office,!service,!and!sales!

%!ProducGon!and!simple!task!

! 26!

Figure 6. Employment and employment share of the managers

Figure 7. Korea management index and the Group of 5 countries industry management index

170000!

220000!

270000!

320000!

370000!

420000!

0.036!

0.041!

0.046!

0.051!

0.056!

0.061!

0.066!

1994! 1995! 1996! 1997! 1998! 1999! 2000! 2001! 2002! 2003!

% Manager Managers

-20

24

Kore

a m

anag

emen

t ind

ex

-1 -.5 0 .5Group of 5 industry management index

! 27!

Figure 8. Employment share by skill group and the G5 industry management index

! 28!

Table 1. Questions used in constructing the management index for Korea Category Survey question

Evaluation

How important are individual performance evaluation scores in promotion decisions? (0 to 100 scale)

Do you implement Management by Objectives (MBO)? (Yes/No)

MBO is a practice where individuals set goals at the beginning of the year and are evaluated based on performance on these goals at the end of the year.

Operations Do you implement Sigma 6 practices? (Yes/No)

Human resource management

HRM’s main objective is to reduce labor costs, as opposed to promoting loyalty to the firm (1 to 7 scale) Hire and fire qualified personnel based on firm needs, as opposed to develop personnel by hiring new recruits and maintaining long-term employment. (1 to 7 scale) Utilize temporary workers as much as possible, as opposed to use permanent workers as much as possible. (1 to 7 scale)

HRM is based on individual performance, as opposed to teamwork. (1 to 7 scale)

HRM focuses on maximizing employee’s short-term performance, as opposed to long-term development and nurturing of employees

Notes: Management by Objectives (MBO) is a practice where employees set goals at the beginning of the year and are evaluated based on performance towards the set goals at the end of the year.

! 29!

Table 2. Descriptive statistics

Variable Mean Std. Dev. Min Max Obs

Mangement index 0.18 1.07 -2.10 3.53 1430

G5 industry management index 0.02 0.39 -1.02 0.61 1430

Total employment 390.14 1726.89 6 42150 1430

Manager share 0.10 0.08 0 0.5 1430

Technical (R&D and tech) worker share 0.07 0.09 0 0.63 1430

Office worker share 0.13 0.09 0 0.72 1430

Service and sales worker share 0.06 0.10 0 0.74 1430

Production worker share 0.52 0.26 0.008 0.98 1430

Simple task worker share 0.12 0.21 0 0.91 1430

Log(manager earnings relative to unskilled earnings) 0.50 0.23 -0.12 1.85 1430

Log(technical worker earnings relative to unskilled earnings) 0.46 0.29 -0.08 1.17 1430

Log(office worker earnings relative to unskilled earnings) 0.01 0.12 -0.41 0.46 1430

Log(service and sales worker earnings relative to unskilled earnings) 0.06 0.19 -0.34 0.56 1430

Manager earnings (1,000 KRW) 33308.46 9640.78 2000 97000 1430

R&D and technical worker earnings (1,000 KRW) 33633.58 15545.59 1915 135452 1430

Office worker earnings (1,000 KRW) 20868.58 7509.21 1488 53899 1430

Service and sales worker earnings (1,000 KRW) 22359.40 9331.58 1422 70032 1430

Return on capital 0.04 5.28 -172.78 78.93 1326

Log(revenue) 10.60 1.98 4.28 17.52 1359

Number of computers 138.34 400.84 0 8000 1353

Use team system 0.58 0.49 0 1 1430

Use salary system 0.45 0.50 0 1 1424

Hired consultants 0.38 0.49 0 1 1430

Has a CEO separate from the owner 0.49 0.50 0 1 1430

Notes: Salary and revenue numbers are adjusted to 2002 values. Primary source of data is the 2002 and 2003 establishment level pilot survey for the Korea Workplace Panel. The G5 industry management index is constructed based on the US, Britain, France, Germany, and Italy data in the World Management Surveys.

! 30!

Table 3. The correlation of management indexes (1) (2) (3)

Management index Industry average management index

Firm specific management index

Foreign management indexes

G5: US, Bri, Fra, Ger, Ita 0.368*** 0.406*** -0.0376 (0.100) (0.127) (0.0621)

Observations 1,430 1,430 1,430 R-squared 0.229 0.268 0.153

US 0.162** 0.148* 0.0135 (0.0653) (0.0753) (0.0345)


Britain 0.145* 0.175* -0.0297 (0.0765) (0.0986) (0.0439)


France 0.170** 0.196*** -0.0260 (0.0656) (0.0674) (0.0597)


Germany 0.169** 0.225** -0.0566 (0.0830) (0.0992) (0.0452)


Italy 0.147 0.238** -0.0916** (0.0896) (0.0926) (0.0427)


Notes: Each regression includes as control variables the age and size (log employment) of the establishment, dummy variable for survey year 2003, and province fixed effects. Standard errors clustered at the industry level are in parentheses. *, **, *** denote significance at the 10, 5, and 1 percent level.

! 31!

Table 4. Employment and salary effects from modern management - by skill group (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12)

log(# of managers)

log(# of technical workers)

log(# of office workers)

log(# of service and

sales workers)

log(# of production workers)

log(# of simple task workers)

log(manager earnings)

log(technical worker

earnings)

log(office worker

earnings)

log(service and sales worker

earnings)

log(production worker

earnings)

log(simple task worker earnings)

Panel A: Establishment level management index

Management index 0.0999*** 0.215*** 0.101*** -0.0755 -0.0613*** 0.00701 0.0385*** 0.0582*** 0.0475*** 0.0632*** 0.0282*** 0.0394***

(0.0233) (0.0539) (0.0203) (0.0540) (0.0227) (0.0460) (0.00837) (0.0188) (0.0129) (0.0169) (0.00892) (0.00924)

R-squared 0.645 0.394 0.648 0.217 0.718 0.078 0.289 0.219 0.257 0.264 0.266 0.279 Panel B: Industry average index and establishment specific residual

Industry average management index

0.227*** 0.952*** 0.251*** 0.0127 -0.138* -0.223 0.0661** 0.177 0.125* 0.288*** -0.00383 0.0769

(0.0670) (0.123) (0.0815) (0.162) (0.0734) (0.138) (0.0266) (0.113) (0.0698) (0.0947) (0.0470) (0.0495)


0.0828*** 0.117** 0.0812*** -0.0873* -0.0509** 0.0378 0.0348*** 0.0423*** 0.0371*** 0.0330*** 0.0326*** 0.0344***

(0.0237) (0.0497) (0.0191) (0.0490) (0.0203) (0.0477) (0.00862) (0.0120) (0.0105) (0.0115) (0.00919) (0.00930)

R-squared 0.647 0.427 0.651 0.218 0.719 0.080 0.290 0.230 0.264 0.307 0.267 0.280 Panel C: G5 industry index (Reduced form results)

G5 management index 0.164** 0.845*** 0.139 0.297* -0.146** 0.0222 0.0427* 0.261** 0.172*** 0.169 -0.0245 0.00716

(0.0667) (0.131) (0.0861) (0.154) (0.0704) (0.191) (0.0249) (0.101) (0.0580) (0.126) (0.0393) (0.0611)

R-squared 0.641 0.416 0.644 0.220 0.718 0.078 0.277 0.253 0.272 0.266 0.261 0.268 Panel D: Predicted index (2SLS results using G5 industry index as IV)

Management index 0.447*** 2.294*** 0.378* 0.807 -0.397* 0.0602 0.116* 0.708*** 0.468*** 0.458 -0.0666 0.0195

(0.144) (0.547) (0.208) (0.494) (0.222) (0.520) (0.0608) (0.263) (0.150) (0.280) (0.114) (0.161)

First stage F-statistic 13.47

Notes: Each regression includes age, size (log employment) of establishment, a 2003 year dummy, and province fixed effects as control variables. The number of observations is 1,430, except for Panel E which is 1,353. Kleibergen-Paap F-statistics are reported for the first stage regressions in Panel D. Standard errors clustered by industry are in parentheses. *, **, *** denote significance at the 10, 5, and 1 percent level.

! 32!

Table 5. Modern management and the demand for skill (1) (2) (3) (4) (5) (6) (7) (8)

ln!( !"#$%&!!"!!

!"#$%&!!"!!"#$%&&'(!!"#$%#&)

where X=

ln!( !"#$%&#!!"#$%$&'!!"!!

!"#. !"#$%$&'!!"!!"#$%&&'(!!"#$%#&)

where X=

Managers Technical workers

Office workers

Sales and

service workers


Office workers

Sales and service workers


Management index 0.144*** 0.260*** 0.146*** -0.0310 0.00533 0.0251* 0.0143** 0.0301**

(0.0335) (0.0609) (0.0297) (0.0616) (0.00775) (0.0149) (0.00714) (0.0117)

R-squared 0.266 0.163 0.181 0.320 0.059 0.074 0.086 0.086

Panel B: Industry average index and establishment specific residual


0.394*** 1.119*** 0.419*** 0.180 0.0334 0.144 0.0922* 0.255***

(0.0992) (0.150) (0.106) (0.187) (0.0364) (0.102) (0.0489) (0.0713) Establishment specific management index

0.111*** 0.144** 0.109*** -0.0594 0.00156 0.00914 0.00390 -

0.000157

(0.0331) (0.0560) (0.0266) (0.0562) (0.00791) (0.00657) (0.00324) (0.00462)

R-squared 0.275 0.209 0.192 0.322 0.061 0.102 0.126 0.256 Panel C: G5 industry index (Reduced form results)

G5 management index

0.279*** 0.959*** 0.254** 0.412** 0.0507 0.268*** 0.180*** 0.177*

(0.102) (0.162) (0.121) (0.187) (0.0369) (0.0973) (0.0389) (0.0966)

R-squared 0.259 0.189 0.173 0.326 0.065 0.190 0.266 0.161

Panel D: Predicted management index (2SLS results using G5 industry index as IV)

Management index 0.758*** 2.606*** 0.689** 1.119* 0.138 0.729** 0.489*** 0.480**

(0.258) (0.652) (0.312) (0.609) (0.116) (0.297) (0.161) (0.214) First stage F-statistic 13.47

Notes: Each regression includes age, size (log employment) of establishment, a 2003 year dummy, and province fixed effects as control variables. Unskilled workers include production workers and simple manual laborers. The number of observations is 1,430. Kleibergen-Paap F-statistics are reported for the first stage regressions in Panel D. Standard errors clustered by industry are in parentheses. *, **, *** denote significance at the 10, 5, and 1 percent level.

! 33!

Table 6. Sensitivity of the 2SLS results (1) (2) (3) (4) (5) (6) (7) (8)

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where X=


Office workers

Sales and

service workers


Office workers

Sales and service workers

A. Control for industry level competition and labor productivity

Management index 0.782*** 2.903*** 0.723** 1.254** 0.166 0.740** 0.543*** 0.530** (0.280) (0.646) (0.338) (0.618) (0.123) (0.314) (0.148) (0.227)

HHI (revenue) 0.114 1.600** 0.169 0.764 0.143 0.0610 0.310* 0.265 (0.290) (0.672) (0.313) (0.501) (0.0903) (0.230) (0.167) (0.236)

Labor productivity 0.00758 -0.327 -0.00219 -0.228 -0.0128 -0.0215 -0.107** -0.0455 (0.0781) (0.207) (0.0826) (0.168) (0.0302) (0.0802) (0.0446) (0.0475)

First stage F-statistic 15.54 B. Additionally control for technology

Management index 0.327 2.765*** 0.127 0.958 0.211 1.124* 0.811** 0.783** (0.345) (1.017) (0.475) (0.911) (0.199) (0.603) (0.326) (0.392)

ln(computer) 0.452*** 0.0400 0.596*** 0.227 -0.0319 -0.320 -0.232** -0.219 (0.120) (0.360) (0.173) (0.313) (0.0658) (0.204) (0.113) (0.140)

First stage F-statistic 7.12 C. Additionally control for export


Export share 0.165 0.285 0.0732 -0.0914 0.00700 -0.0536 -0.00327 -0.0853 (0.123) (0.300) (0.131) (0.285) (0.0426) (0.105) (0.0578) (0.0573)

First stage F-statistic 15.24 D. Additionally control for governance


Has a CEO -0.0770 -0.537** -0.0739 -0.361* -0.0533 -0.201** -

0.128*** -0.109* (0.0998) (0.246) (0.107) (0.205) (0.0377) (0.0991) (0.0459) (0.0624) First stage F-statistic 15.15 E. Additionally control for labor unions


Has a labor union 0.0335 0.0825 0.0122 -0.0489 -6.50e-05 0.0280 0.00910 0.0154 (0.0517) (0.129) (0.0420) (0.0828) (0.0119) (0.0313) (0.0224) (0.0230) First stage F-statistic 16.28 F. Control all of the above

Management index 0.394 1.731** -0.00108 0.437 0.114 1.579* 0.841* 1.063** (0.436) (0.815) (0.576) (1.005) (0.217) (0.908) (0.468) (0.508)

First stage F-statistic 9.3 Notes: Each regression includes age, size (log employment) of establishment, a 2003 year dummy, and province fixed effects as base control variables. Unskilled workers include production workers and simple manual laborers. Kleibergen-Paap F-statistics are reported for the first stage regressions. Standard errors clustered by industry are in parentheses. *, **, *** denote significance at the 10, 5, and 1 percent level.

! 34!

Table 7. Additional results (1) (2) (3) (4) (5) (6) (7)

Panel A. Employment

log(# of managers)

log(# technical workers)

log(# of office workers)

log(# of service and sales workers)



Management index 0.236 2.340*** -0.00269 0.740 -0.438 0.800 (0.174) (0.898) (0.322) (0.763) (0.384) (0.801)

ln(computer) 0.228*** -0.120 0.379*** 0.0142 -0.0165 -0.541* (0.0647) (0.330) (0.116) (0.268) (0.150) (0.287)

Panel B. Earnings

log(manager earnings)

log(technical worker earnings)

log(office worker earnings)

log(service and sales worker

earnings)

log(production worker earnings)

log(simple task worker earnings)

Management index 0.0428 0.980* 0.620** 0.590 -0.179 -0.0742 (0.0866) (0.502) (0.270) (0.437) (0.190) (0.246)

ln(computer) 0.0737** -0.220 -0.129 -0.108 0.108 0.0854 (0.0308) (0.175) (0.0980) (0.157) (0.0674) (0.0861)

Panel C. Employment - regular employees

log(# of

managers) log(# technical

workers) log(# of office

workers)




log(# of employees

Management index 0.214 2.366*** 0.118 0.750 -0.427 0.727 0.0721 (0.173) (0.917) (0.343) (0.717) (0.382) (0.721) (0.0692)

ln(computer) 0.238*** -0.124 0.328*** 0.0101 0.0162 -0.572** 0.00455 (0.0638) (0.340) (0.123) (0.250) (0.145) (0.270) (0.0287)

Panel D. Employment - temporary employees

log(# of

managers) log(# technical

workers) log(# of office

workers)




log(# of employees

Management index 0.139 -0.0333 -0.118 0.0936 -0.570 0.392 -0.427 (0.127) (0.144) (0.251) (0.209) (0.384) (0.464) (0.500)

ln(computer) -0.0549 0.0340 0.161* -0.0374 0.111 -0.153 0.150

(0.0529) (0.0663) (0.0922) (0.0832) (0.172) (0.163) (0.196) Notes: The number of observation is 1,345. The regressions in this table use the full specification in Table 6 Panel F and controls for the age and size (log employment) of the establishment, the industry competition index (HHI) and labor productivity, measures of technology, export, governance, and unions, a dummy variable for the survey year 2003, and province fixed effects. Results are from 2SLS regressions that use the industry management frontier as the instrumental variable. The Kleibergen-Paap F-statistics is 9.3. In Panel D, when there are zero part-time employees I add one before taking logs. Standard errors are clustered at the industry level. *, **, *** denote significance at the 10, 5, and 1 percent level.

! 35!

Table 8. Modern management and the efficacy of information technology in achieving organizational objectives

Dependent variable Coefficient estimate

on management index

Observations R-squared

A. Use information technology to digitize modular tasks, e.g., accounting, inventory management, sales management.

0.0559**

(0.0248) 613 0.091

B. Use Enterprise Resource Planning (ERP) or systems that comprehensively support your workplace's tasks

0.101***

(0.0169) 613 0.096 C. Use Electronic Data Interchange (EDI) or information technology systems that help connect and support clients and suppliers

0.0488**

(0.0225) 613 0.062 D. Did information technology at your workplace help with the organization and operation of project based task force teams

3.334**

(1.611) 611 0.150

E. Did information technology at your workplace facilitate organizational delayering and decision making?

0.0640** (0.0271) 613 0.089

F. Did information technology at your workplace help increases workers that can multitask?

0.0407** (0.0200) 613 0.074

G. Did information technology at your workplace encourage worker involvement in company operations?

0.0615** (0.0249) 613 0.083

H. Degree to which on the job experiences are collected and shared among employees through information technology

0.0978***

(0.0321) 557 0.088 I. Degree to which information technology capital and personnel investment achieved what the company had intended to achieve

0.114***

(0.0359) 558 0.128 Notes: Each row represents an OLS regression where the first column describes the dependent variable, and the second column indicates the coefficient estimate on the management index. All regressions controls for the age and size (log employment) of the establishment, the industry competition index (HHI) and labor productivity, and province fixed effects. Standard errors clustered by industry are in parentheses. *, **, *** denote significance at the 10, 5, and 1 percent level.

! 36!

Table 9. Complementarity between modern management and technical skill

(1) (2) (3) (4) (5) (6) OLS OLS 2SLS 2SLS OLS OLS

Return on assets

Management index 0.00346 0.0272 0.304* 0.639** -0.0436* -0.0145 (0.0182) (0.0171) (0.172) (0.322) (0.0227) (0.0228)

Management index*ln(tech workers) 0.0252*** 0.0203** (0.00729) (0.00791)

ln(tech workers) -0.0294 -0.00209 (0.0212) (0.0150)

ln(computer) -0.0972** -0.297*** -0.0944** (0.0455) (0.110) (0.0416)

First stage F-statistic 16.02 16.02 Observations 1,324 1,260 1,324 1,260 1,324 1,260 R-squared 0.013 0.022 0.016 0.024

Notes: Each regression controls for the age and size (log employment) of the establishment, the industry competition index (HHI) and labor productivity, measures of technology, export, governance, and unions, a dummy variable for the survey year 2003, and province fixed effects. Kleibergen-Paap F-statistics are reported for the first stage regressions in columns (3) and (4). Standard errors clustered by industry are in parentheses. *, **, *** denote significance at the 10, 5, and 1 percent level.

! 37!

Appendix Table 1. Earnings differences between regular and temporary workers across occupation groups

(1) (2) (3) (4) (5) (6)

Log (earnings)

Technical worker 0.132*** 0.129*** 0.0530* 0.0626 0.0608 0.0449 (0.0203) (0.0206) (0.0290) (0.0499) (0.0497) (0.119)

Office worker 0.121*** 0.115*** 0.104*** 0.0764** 0.0738** -0.00458 (0.0136) (0.0135) (0.0218) (0.0357) (0.0357) (0.0744)

Production worker -0.0351** -0.0392*** -0.0513** -0.0224 -0.0247 -0.0712 (0.0145) (0.0143) (0.0239) (0.0351) (0.0351) (0.0933)

Simple task worker -0.225*** -0.229*** -0.277*** -0.105*** -0.103*** -0.169 (0.0188) (0.0187) (0.0362) (0.0354) (0.0354) (0.106)

Temporary work -0.287*** -0.284*** -0.277*** -0.0604** -0.0598** -0.0592 (0.0227) (0.0227) (0.0355) (0.0247) (0.0247) (0.0456)

Temporary * Technical worker -0.0677 -0.0937 0.0599 -0.0300 -0.0304 0.0168 (0.108) (0.112) (0.111) (0.102) (0.101) (0.161)

Temporary * Office worker -0.0506 -0.0472 -0.0664 0.00279 0.00385 -0.0366 (0.0320) (0.0320) (0.0429) (0.0397) (0.0396) (0.0615)

Temporary * Production worker -0.00426 0.00592 -0.0164 -0.00532 -0.000657 -0.00872 (0.0261) (0.0266) (0.0460) (0.0309) (0.0309) (0.0588)

Temporary * Simple task worker

0.00695 0.0153 0.00913 0.000125 0.00128 0.0719 (0.0305) (0.0307) (0.0510) (0.0348) (0.0348) (0.0668)

Gender, education level, and age fixed effects Yes Yes Yes No No No Individual fixed effects No No No Yes Yes Yes Region fixed effects Yes Yes Yes Yes Yes Yes Industry (2 digit) fixed effects Yes Yes Yes Yes Yes Yes Business type fixed effects No Yes Yes No Yes Yes Business size fixed effects No No Yes No No Yes Observations 15,591 15,584 6,512 14,268 14,261 5,140 R-squared 0.470 0.474 0.510 0.835 0.835 0.868

Notes: The above regressions use data from the Korean Labor and Income Panel Study (KLIPS) covering the years 2000 to 2006. The sample is comprised of workers holding non-managerial position. The omitted category, i.e., the reference occupation group is the service and sales workers. Business types are personal business, incorporated company, foreign company, government enterprise, self-employed, and others. Business size is categorized into eleven bins. Robust standard errors are in parentheses. *, **, *** denote significance at the 10, 5, and 1 percent level.

! 38!

Appendix Table 2. Results that account for average hours worked (1) (2) (3) (4) (5) (6) (7) (8)

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where X=

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where X=


Office workers

Sales and service workers Managers Technical

workers Office

workers Sales and service workers


Management index 0.173*** 0.507*** 0.159*** -0.100 -0.00663 0.0187 0.0172* 0.0374**

(0.0375) (0.119) (0.0337) (0.121) (0.0135) (0.0126) (0.0100) (0.0144)

R-squared 0.233 0.114 0.129 0.159 0.050 0.061 0.049 0.088

Panel B: Industry average index and residual establishment management index


0.499*** 2.070*** 0.415*** 0.292 -0.0207 0.117 0.136** 0.352***

(0.126) (0.295) (0.126) (0.399) (0.0796) (0.0768) (0.0620) (0.0875)


0.130*** 0.299*** 0.125*** -0.152 -0.00476 0.00564 0.00143 -0.00437

(0.0360) (0.109) (0.0324) (0.108) (0.00926) (0.00729) (0.00557) (0.00634)

R-squared 0.246 0.154 0.136 0.160 0.051 0.079 0.094 0.280 Panel C: G5 industry index (Reduced form results)

G5 management index

0.321** 1.856*** 0.188 0.797** 0.0531 0.254*** 0.253*** 0.229**

(0.124) (0.312) (0.135) (0.371) (0.0622) (0.0713) (0.0533) (0.103)

R-squared 0.223 0.140 0.118 0.165 0.055 0.163 0.226 0.163

Panel D: Predicted management index (2SLS results using G5 industry index as IV)

Management index 0.876*** 5.055*** 0.511 2.172* 0.145 0.693*** 0.689*** 0.625***


Panel E: Panel D + additional control variables

Management index 0.377 4.942*** -0.144 1.999 0.217 1.035** 1.087*** 0.941**


Notes: In this table employment is weighted by the industry average hours worked and earnings is normalized by the industry average hours worked for each occupation group. Each regression includes age, size (log employment) of establishment, a 2003 year dummy, and province fixed effects as control variables. Unskilled workers include production workers and simple manual laborers. The number of observations is 1,426. Kleibergen-Paap F-statistics are reported for the first stage regressions in Panel D and E. Panel E additional controls for all the variables used in Table 6 Panel F. Standard errors clustered by industry are in parentheses. *, **, *** denote significance at the 10, 5, and 1 percent level.

! 39!

Appendix Table 3. First-stage regression that includes industry level competition and productivity variables

Management index

G5 management index 0.328*** (0.0833)

HHI (revenue) -0.594** (0.236)

Labor productivity 0.140*** (0.0515)

Establishment size Yes Establishment age Yes Year dummy Yes Region fixed effects Yes Observations 1,430 R-squared 0.241

Notes: The regression reports the first stage of the 2SLS regression that corresponds to Table 6 Panel A. Standard errors clustered by industry are in parentheses. *, **, *** denote significance at the 10, 5, and 1 percent level.

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MODERN MANAGEMENT AND THE DEMAND FOR ......modern management practices and the demand for different occupational skills, and in particular technical skill, in the manufacturing sector