ERIA-DP-2015-48

ERIA Discussion Paper Series

How Labour Market Policies Affect

Innovation and Trade Competitiveness

Siwage Dharma NEGARA*

The Indonesian Institute of Sciences (LIPI), The Institute of

Southeast Asian Studies (ISEAS)

July 2015

Abstract: Endogenous growth theory postulates that innovation is one of the key

drivers of technological progress and productivity growth of a country.

Technological improvements stemming from firms’ innovative activities can

contribute to a country’s overall productivity and export competitiveness. For

innovation to flourish, it necessitates an environment that is conducive to firms

conducting risky innovative activities. Studies show that public policies, including

labour market policies, can influence the operating conditions and institutional

structures of firms to foster innovation that leads to productivity gains. However, the

literature indicates that there is mixed empirical evidence on the impact of labour

market policies on firms’ incentives to innovate.

This paper argues that more flexible labour market policies that do not constrain

workers’ adjustments and mobility will have positive associations with a country’s

technological innovation competitiveness. In addition, innovation competitiveness

affects a country’s productivity and trade competitiveness. Using a balanced panel

of OECD and non-OECD countries, this study offers simple empirical models to

measure the relationship between labour market policies and innovation capacity;

and between innovation capacity and trade competitiveness. The main findings show

that countries with more flexible labour market policies have higher levels of

innovation competitiveness. In addition, the paper finds evidence of a positive

correlation between innovation competitiveness and trade competitiveness.

Key words: Labour market policies, innovation, trade, competitiveness, labour

market flexibility

JEL Classification: F16, J08, J38, J63, O31, O38

* Researcher at the Economic Research Center of the Indonesian Institute of Sciences (P2E-LIPI)

and visiting fellow at the Institute of Southeast Asian Studies (ISEAS). Email:

siwage@yahoo.com. This study is commissioned by the Economic Research Institute for ASEAN

and East Asia (ERIA).

1

1. Introduction

Endogenous growth theory postulates that innovation is one of the key drivers of

technological progress and productivity growth of a country (Romer, 1990; Aghion

and Howitt, 2005). Technological improvement stemming from a firm’s innovative

activities can lead to more efficient production processes and generate better quality

outputs. In an increasingly competitive global economy, innovation capacity is crucial

for firms to improve productivity and hence sustain their export competitiveness (Aw

et al., 2009; Cassiman et al., 2010).

Innovation requires an environment that is conducive to support firms to conduct

innovative activities. Studies show that public policies, including labour market

policies, can influence the operating conditions and institutional structures of firms to

foster innovation that leads to productivity gains. Several OECD studies in this area

suggest that different regulatory environments may explain performance differentials

between countries, or between industries with regard to innovation (OECD, 2002;

OECD, 2009).

Certain regulatory policies may affect firms’ incentives to innovate. For example,

regulations that restrict competition (e.g., creating entry barriers or operational

restrictions), or limit the ability of firms to adjust their workforce (e.g., stricter hiring

and firing rules), can adversely affect incentives to innovate. This in turn could hinder

the process of technological transfer and deny the economy potential productivity

improvements.

Labour market policies can influence the allocation of resources across firms and

sectors in the economy. They affect firms’ choices over labour inputs, investments,

technology, and outputs (Boeri et al., 2008). Different labour market policies induce

greater or lesser adjustment costs for firms to reallocate workers, thus affecting overall

economic efficiency. An efficient labour market is defined as one that ensures the

flexibility to shift workers from one production activity to another at low cost (World

Economic Forum, 2014). Efficiency in the labour market is critical in ensuring that

labour is assigned to its most effective use in the production process. An efficient

labour market provides opportunities for firms to foster innovation activities, which

may involve labour replacement or job reallocation, without excessive social

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disruption. In this paper, the term ‘efficient and flexible labour market’ is used

interchangeably.

The literature on the impact of labour market policies on firms’ incentives to

innovate provides mixed empirical evidence. Some studies find that less-stringent

labour market policies are associated with stronger innovation competitiveness

(OECD, 2002; Saint-Paul, 2002; Barbosa and Faria, 2011; Murphy, et al., 2013;

Griffith and Macartney, 2014). Conversely, other studies also find that stricter labour

market policies can foster innovation and lead to greater country-level economic

growth. They argue that stringent labour market policies that limit firms’ ability to

dismiss workers, act as an ‘insurance device’ for firms not to punish employees when

certain innovative activities fail. In turn, this increases workers’ incentives to engage

in innovative activity (Acharya et al., 2010; Tang, 2012).

Countries at different development stages have different labour market

requirements, which influence their respective labour market policies. This paper

attempts to construct a set of balanced panels consisting of OECD and non-OECD

countries to investigate the relationship between labour market policies and countries’

innovation capacity. In addition, it also looks at how the latter relates to countries’

trade competitiveness. It argues that a more efficient labour market characterised by

less-rigid labour market policies stimulates innovation. In turn, higher innovation

positively impacts countries’ trade competitiveness.

The paper is organised as follows. Section 2 reviews selected studies examining

the relationship between labour market policies, and innovation intensity and

performance. Section 3 proposes simple empirical models to test the relationship

between labour market policies and countries’ innovation competitiveness. Section 4

discusses the data used for the estimation and presents some stylised facts. Section 5

discusses the empirical findings, whilst Section 6 concludes the paper.

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2. Literature Review

There are several studies assessing the relationship between labour market policies

and innovation. This literature survey was not meant to be exhaustive, but rather to

provide an assessment of the current state of knowledge and highlight the existing

knowledge gaps where future research might be focused. It is important to note that

most, if not all, of the studies assessing the relationship between labour market policies

and innovation primarily focus on Organisation for Economic Co-operation and

Development (OECD) member countries. Availability of good quality firm- and

industrial-level data in OECD countries may explain this trend. However, for non-

OECD countries, especially for developing countries in Southeast Asia, there is lack

of (or in many cases non-existent) good quality microdata on innovation and

productivity (Lee and Narjoko, 2015).

Labour market policies vary from country to country. In general, these policies

aim to achieve social (rather than economic) objectives, such as providing employment

protection measures for workers. Employment protection policies are primarily

introduced to protect workers from adverse labour market risks, such as lay-offs or

low-paid earnings. Empirically, these policies vary widely across and within countries

(see Botero et al., 2004 for detailed discussion of each policy). To protect the earnings

of the lowest level of workers, some governments set minimum wages.1 In addition,

some governments also set regulations such that employers must provide extra benefits

to their workers, including training, health care, paid vacations, maternity leave, etc.

To protect workers from lay-off, some governments provide unemployment insurance

for those who lose their jobs.2 Alternatively, some governments prefer to set

regulations that restrict firms’ ability to dismiss employees at will.

1For examples, several countries, such as Australia, the United States, France, Chinese Taipei,

Japan, South Korea, China, the United Kingdom, Thailand, Malaysia, and Indonesia, implement

minimum wage systems. The arguments for minimum wage policy are to ensure low wage workers

having a minimum living standard; to prevent employers from exploiting low-skilled workers; to

increase purchasing power of low wage workers; and to compel employers to raise efficiency and

productivity, amongst others. 2 The US, Chinese Taipei, Japan, South Korea, and China also operate unemployment insurance

benefits, which are not linked to the minimum wages. Australia operates an unemployment

assistance system, in which benefit rates are set below the earnings of minimum wage workers.

France operates both systems.

4

The existing literature suggests that the relationship between labour market

policies and innovation capacity can be both positive and negative. On the one hand,

a stricter labour market policy provides job security; hence this should increase

incentives for workers to invest in firm-specific human capital and to be more engaged

in innovation activities (see Acharya et al., 2010). On the other hand, a stricter labour

market policy, which causes higher hiring and firing costs, will increase the cost for

firms that need to adjust their workforce in relation to innovation activities. This in

turn will discourage firms from conducting significant innovative activities (see Saint-

Paul, 2002; Bassinini and Ernst, 2002; Barbosa and Faria, 2011).

Murphy et al. (2013) identify two channels through which labour market policies

(employment protection regulations) may affect innovation. The first channel is linked

through human capital investment. In this channel, labour market policy is likely to

increase the probability of workers engaging in firm-specific or industry-specific

skills. It also increases the probability of workers engaging in innovation activities

(Murphy et al., 2013, p.5). Furthermore, a stricter labour market policy is likely to

increase workers’ bargaining power and their incentives to invest in firm-specific

skills. Acharya et al. (2010) found that stronger employment protection regulations

had a positive impact on innovation at the industry level. It also led to relatively more

innovation in the innovation-intensive industries than in traditional industries.

Acharya et al. (2010) find that stronger employment protection laws not only

foster innovation but also lead to greater country-level economic growth. They argue

that regulations governing the dismissal of workers are the only dimension of labour

laws that enhance firm-level innovation and country-level economic growth (Acharya

et al., p.23). They explain that employment protection laws impose limits on firms’

ability to dismiss workers. This acts as an ‘insurance device’ encouraging firms not to

punish their workers as a result of an unsuccessful innovative project. Consequently,

this incentivises workers to increase their investment in innovative projects relative to

their investment in routine projects. Therefore, stringent employment protection laws

encourage firms to find innovative projects in order to be more value-enhancing than

routine projects.

Tang (2012) shows that cross-country differences in labour market policies shape

the pattern of international trade, with a focus on workers’ skill acquisition. Similar to

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Acharya et al. (2010), Tang also finds that countries with more protective labour laws

export relatively more in firm-specific skill-intensive sectors through both the

intensive and extensive margins of trade.3

Contrary to Acharya et al. (2010) and Tang (2012) findings, Barbosa and Faria

(2011) find that stricter labour market policy leads to less innovation intensity at the

industry level in European countries. They find that in European countries that have

rigid labour market policy, the innovation intensity decreases by 1.89 for a unit

increase in the indicator of employment rigidity holding other variables constant (see

Barbosa and Faria, 2011, p. 20). These findings are in line with Bassanini and Ernst

(2002), who investigated the impact of product and labour market regulations on

innovation by using a set of OECD indicators on the regulatory framework on a cross-

section of 18 OECD countries and 18 manufacturing industries. Their results show a

positive association between more flexible labour market policies and research and

development (R&D) intensity, which is used as proxy for innovation.

The second channel through which labour market policy may affect innovation is

through firms’ adjustment costs when they need to adjust against idiosyncratic shocks

(Murphy et al., 2013).4 In this case, a more rigid labour market policy inflates hiring

and firing costs for firms. As a result, this is likely to discourage innovative activities

that require adjustments or reallocations of labour. The adjustment cost will be

relatively higher in technologically advanced industries (Saint-Paul, 2002). Stricter

employment protection regulations discourage firms from experimenting with new

technologies with higher returns, but also with higher adjustment costs. Empirically,

employment protection restrictions are more costly in industries with rapid

technological change, such as ICT. Therefore, countries with stricter employment

protection regulations are likely to specialise in industries with a lower rate of technical

change (Murphy, et al., p.5).

A study by Pierre and Scarpetta (2006) find that innovative firms are the most

negatively affected by stricter labour market policies. Griffith and Macartney (2014)

find that multinational firms are likely to locate more innovation activities in countries

3 The intensive margin refers to the export volume per firm, whilst the extensive margin refers to

the number of exporting firms. 4 Idiosyncratic shocks that affect individual firm can be natural, social, economic, political, or

environmental.

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with stricter employment protection policies. However, the same firms locate more

technologically advanced innovation activities in countries with less-stringent

employment protection policies. Furthermore, Murphy et al. (2013) find that the

effects of labour market policies on innovation vary by industry and country depending

on factors such as lay-off propensity, technological intensity, skills intensity,

competition pressures, openness, and other labour market institutions, such as wage-

setting institutions. They find that a more rigid labour market policy leads to

significantly lower innovation intensity in industries with higher job reallocation rates

or higher probability of dismissal. In addition, Murphy et al. find that innovation

intensity was higher in industries with higher import competition and in industries with

less rigid product market regulations. Furthermore, in countries with abundant human

capital, innovation intensity was higher in human capital-intensive industries.

Meanwhile, in countries with abundant physical capital, innovation intensity was

higher in physical capital-intensive industries. These results are aligned with Saint-

Paul (2002) and Griffith and Macartney (2014). In summary, the empirical literature

on the relationship between labour market policies and innovation provides a mixed

and complicated picture. The overall evidence leans slightly towards a positive

association between more flexible labour market policies and innovation capacity.

With regard to the relationship between innovation and trade competitiveness,

studies find a positive association between innovation capacity and firms’ exports. For

examples, Wagner (2007), Aw et al. (2009) and Cassiman, et al. (2010) find evidence

on the link between product innovation, productivity and exports. In this case,

innovation policy targeted at enhancing productivity is likely to be important for

improving firms’ exporting competitiveness. However, using the Slovenian microdata,

Damijan, et al. (2010) find a reverse causal direction in which exporting induces

process innovation and, in turn, affects the productivity growth relationship. This

finding suggests that firms with past exporting experience have a higher probability of

being more productive due to process innovation. Therefore, the link between

innovation and exporting seems fairly robust, and existing studies show that the

direction of causality can be both ways. Along these lines, Lee and Narjoko 2015

review some micro-data studies on the relationship between innovation and trade

competitiveness measured by exporting capability for five developing countries from

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Southeast Asia, namely, Indonesia, Malaysia, the Philippines, Thailand, and Viet Nam.

They conclude that most existing studies in the region are not able to pinpoint the

direction of causality between innovation and exporting capability, most likely due to

data constraints. However, there is strong evidence that the incidence of innovation is

positively correlated with firms’ exporting performance. In addition, they find that

foreign competition in domestic markets is also important for innovation.

3. Data, Empirical Model, and Variables

3.1. Data

To investigate whether cross-country variations in labour market policies can

explain differences in innovation intensity, we constructed a set of panel data of 32

countries covering the period 2009-13. The data set includes seven ASEAN member

states, all members of the BRICS (Brazil, Russia, India, China, and South Africa), 16

OECD countries, plus Argentina, Hong Kong, Mexico and Pakistan (Table 1). The

selection of countries tries to capture all different stages of development as discussed

in the Global Competitiveness Report (WEF, 2014).

As can be seen from Table 1, ASEAN member states’ levels of development vary

from factor-driven economies such as Cambodia to innovation-driven economies such

as Singapore. As mentioned above, countries with different development levels have

different labour market requirements. Therefore, this influences their respective labour

market policies. In addition, countries with different development levels have different

innovation intensities and capacities. In view of this, combining such a wide

development level within a panel increases the variation in our data. Due to the lack

of data, three ASEAN member states, namely, Brunei Darussalam, Lao PDR, and

Myanmar, cannot be included in the panel estimation.

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Table 1: Countries Covered in the Panel Data Set

Stage 1:

Factor-driven

economies

Transition

from stage 1

to stage 2

Stage 2:

Efficiency-

driven

economies

Transition

from stage 2

to stage 3

Stage 3:

Innovation-driven

economies

Cambodia

(CMB)

India

(IND)

Pakistan

(PAK)

Viet Nam

(VNM)

Philippines

(PHP)

China

(CHN)

Indonesia

(IDN)

South Africa

(SAF)

Thailand

(THL)

Argentina

(ARG)

Brazil

(BRZ)

Malaysia

(MLY)

Mexico

(MEX)

Russia

(RUS)

Turkey

(TUR)

Australia (AUS)

Canada (CAN)

Finland (FIN)

France (FRA)

Germany (GER)

Greece (GRE)

Hong Kong (HKG)

Italy (ITA)

Japan (JPN)

South Korea (KOR)

New Zealand (NZ)

Portugal (POR)

Singapore (SIN)

Spain (SPN)

Sweden (SWE)

United Kingdom (UK)

United States (US)

Note: The classification of countries follows the development stages in the Global

Competitiveness Report 2014–2015 published by the World Economic Forum. Country codes are

shown in parentheses.

As a proxy for innovation intensity, this paper uses scores data for innovation

competitiveness published by the World Economic Forum (WEF) since 2005. The

Global Competitiveness Index (GCI) published by WEF is designed to measure both

microeconomic and macroeconomic foundations of national competitiveness. It is a

composite index consisting of 12 pillars of competitiveness, namely, institutions,

infrastructure, macroeconomic environment, health and primary education, higher

education and training, goods market efficiency, labour market efficiency, financial

market development, technological readiness, market size, business sophistication,

and innovation. Each pillar consists of several indicators. The indicators are derived

using a standardised survey targeted to over 14,000 business executives in 144

countries. Similar to other perception surveys, there is a potential subjectivity bias in

the data. Nevertheless, the Global Competitiveness Index is a valuable data set that

can easily be used in a quantitative way, especially when other alternative standardised

measures on innovation and labour market policy are not readily available as in most

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developing countries. Moreover, the opinions of business executives are invaluable in

understanding the factors that determine business competitiveness.

In addition to innovation scores data, the paper also uses alternative proxies for

innovation, including the number of patents filed by residents at the national office,

the number of venture capital deals, and the number of people employed in knowledge-

intensive services from the Global Innovation Index published by Cornell University,

INSEAD, and the World Intellectual Property Organization (WIPO).

It is important to note that comparable cross-country firm-level data on innovation

are relatively limited in the non-OECD countries, including in the Southeast Asian

region. So far, there is no standardised industry- or firm-level survey on innovation

activity and intensity within ASEAN member states. In view of this, it is critical for

countries in ASEAN to improve the availability and quality of their micro-data on

innovation to be on a par with OECD countries.

Lee and Narjoko (2015) explain that there are two types of innovation measures,

namely, innovation input and innovation output. The most widely used measure of

innovation input is R&D expenditure. As for innovation outputs, the patents count data

have been widely used to measure innovation intensity. However, there are drawbacks

to using either R&D expenditure or the number of patents to measure innovation

intensity. R&D expenditure cannot capture the output side of the innovation process.

In addition, R&D expenditure is only one of several inputs for innovation. Meanwhile,

patents only measure invention rather than innovation and cannot capture many non-

patented inventions and innovations.5 In addition, the use of patents to protect

inventions varies across industries. Nevertheless, the advantage of using innovation

indicators from GCI is that they capture both input and output measures of innovation,

namely, the capacity for innovation, the quality of scientific research institutions,

company spending on R&D, university–industry R&D collaboration, government

procurement of advanced technology products, availability of scientists and engineers,

patent applications, and intellectual property protection.

As a proxy for labour market policies, this paper uses scores data for labour market

efficiency published by the GCI and cost of redundancy dismissal from the World

5 See Kleinknecht et al. 2002 for detailed discussion about the weakness of each innovation

indicator.

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Bank’s Doing Business. Specifically, the labour market policies index covers two

components, namely, the flexibility and the efficiency of the labour market. The

flexibility of a labour market is measured by indicators of: cooperation in labour-

employer relations; flexibility of wage determination; hiring and firing practices; costs

of redundancy dismissal; and the effect of taxation on incentives to work. The higher

the score of labour market efficiency, the more efficient the labour market is, implying

a less-rigid labour market policy. Meanwhile, the cost of redundancy dismissals

measures the strictness of regulations in terms of dismissal for a regular and a

temporary contract (Murphy et al., 2013, p. 3).

Table 2 describes each variable used in the empirical model, data sources and the

expected impact on innovation.

Table 2: Variables Acronyms, Description and Expected Impact

on Innovation

Variable Description Expected Impact

Innov Dependent variable, measured by the innovation

scores (weighted average of capacity for innovation;

quality of scientific research institutions; company

spending on R&D; university–industry collaboration

in R&D; government procurement of advanced

technology products; availability of scientists and

engineers; PCT patent applications and intellectual

property protection), values vary between 1 and 7

from least to most innovative environment.

Source: Global Competitiveness Index, World

Economic Forum

Patent Dependent variable, measured by weighted scores of

number of patent filed by resident at national office

(per billion GDP, 2005 PPP$), values vary between 0

and 100 from least to most patent application.

Source: World Intellectual Property Organization

(WIPO), Global Innovation Index.

Empkw Dependent variable, measured by weighted scores of

employment in knowledge-intensive services (% of

workforce), values vary between 0 and 100 from

least to most knowledge employment.

Source: ILO, Global Innovation Index.

Vencap Dependent variable, measured by weighted scores of

venture capital per investment location: number of

deals (per trillion GDP, 2005 PPP$), values vary

between 0 and 100 from least to most venture deals.

Source: Thomson Reuters, Global Innovation Index.

11

Lxport Dependent variable, measured by log of country

exports.

Source: World Bank, World Integrated Trade

Solution (WITS) database.

Lbreffi Independent variable (alternative for lmefi),

measured by weighted scores of cost of redundancy

dismissal (sum of notice period and severance pay

for redundancy dismissal in salary weeks), values

vary between 0 and 100 from most to least costly.

Source: World Bank, Global Innovation Index.

+/-

Lmefi Independent variable, measured by weighted scores

of labour market efficiency (cooperation in labour-

employer relations; flexibility of wage

determination; hiring and firing practices;

redundancy costs; effect of taxation on incentives to

work; pay and productivity; reliance on professional

management; country capacity to retain and attract

talent, and female participation in labour force),

values vary between 1 and 7 from least to most

efficient and flexible labour market.

Source: Global Competitiveness Index, World

Economic Forum

+/-

Gmefi Independent variable, measured by weighted scores

of goods market efficiency (domestic and foreign

competition), values vary between 1 and 7 from least

to most healthy market competition. Source: Global

Competitiveness Index, World Economic Forum

+

Institut Independent variable (alternative for gmefi),

measured by weighted scores of institutional

environment (weighted average of property rights,

ethics and corruption, undue influence, government

efficiency, security, corporate ethics, and

accountability), values vary between 1 and 7 from

least to most conducive institutional environment.

Source: Global Competitiveness Index, World

Economic Forum

+

Highed Independent variable, measured by weighted scores

of higher education and training quality, values vary

between 1 and 7 from low to high quality of higher

education and training environment.

Source: Global Competitiveness Index, World

Economic Forum

+

Macro Independent variable, measured by weighted scores

of stability of macroeconomic environment, values

vary between 1 and 7 from least to most stable

macroeconomic environment.

Source: Global Competitiveness Index, World

Economic Forum

+

Mktsize Independent variable, measured by weighted scores

of market size (domestic and foreign market size),

+/-

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values vary between 1 and 7 from smallest to largest

market size.

Source: Global Competitiveness Index, World

Economic Forum

Techred Independent variable (alternative for highed),

measured by weighted scores of technological

readiness (technological adoption and ICT use),

values vary between 1 and 7 from least to most agile

economy to adopt existing technologies to enhance

the productivity of its industries. Source: Global

Competitiveness Index, World Economic Forum

+

Infras Independent variable (alternative for techred),

measured by weighted scores of infrastructure

quality (weighted average of transport, electricity

and telephony infrastructure), values vary between 1

and 7 from least to most extensive and efficient

infrastructure.

Source: Global Competitiveness Index, World

Economic Forum

+

Openness Independent variable, measured by weighted scores

of applied tariff rate (weighted mean, all products

(%)), values vary between 0 and 100 from least to

most open economy.

Source: World Bank, Global Innovation Index.

+

It is important to note that the innovation scores from the GCI focus on

technological innovation capacity. Technological innovation is not related to skills,

know-how, or organisational conditions. The importance of technological innovation

in improving standards of living is well documented. Technological breakthroughs

have been the basis of many of productivity gains that modern economies currently

enjoy. For example, technological innovation in ICT has led the digital revolution

which, in turn, has benefitted modern economies in terms of increasing productivity

and efficiency. The digital revolution would have never happened without

technological innovation, which has significantly transformed the ways in which

things are done, and opened a wider range of new opportunities in terms of products

and services development.

Technological innovation is particularly important for economies approaching the

frontiers of knowledge. At this stage of development, the possibility of generating

more value by merely integrating and adapting exogenous technologies tends to

disappear. For firms in those countries that have reached this innovation stage of

development, they must design and develop cutting-edge products and processes to

maintain a competitive edge and move towards even higher value-added activities.

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This advancement requires an environment that is conducive to innovative activities

and supported by both the public and the private sectors. It needs sufficient investment

in R&D, especially by the private sector; the presence of high-quality scientific

research institutions that can generate the basic knowledge needed to build the new

technologies; extensive collaboration in research and technological developments

between universities and industry; and the protection of intellectual property (WEF

2014, pp.8–9).

Table 3 reports some descriptive statistics of the dependent and independent

variables used in the empirical analysis. Part I of the table reports the summary

statistics, whereas Part II reports the summary of correlation coefficients amongst

selected variables.

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Table 3: Descriptive Statistics of Dependent and Independent Variables

Part I: Summary statistics

Part II: Correlation coefficients amongst selected variables

The correlation matrix shows that there is a high positive correlation between the

institutional environment (institute) and goods market efficiency (gmefi). The

institutional environment is determined by the legal and administrative framework

within which firms and government interact to generate wealth. Meanwhile, goods

market efficiency is determined by healthy market competition and demand conditions

(customer orientation and buyer sophistication).

There is also a high positive correlation between infrastructure quality (infra) and

higher education quality (highed); between infrastructure quality and technological

readiness (techred); and between higher education quality and technological readiness.

Infrastructure quality is measured by how extensive and efficient the infrastructure a

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country has. A well-developed transport and communications infrastructure network,

quality of roads, railroads, ports, electricity supplies and telecommunications network,

all are critical for effective functioning of an economy. Considering the correlation

between variables, our choice of explanatory variable in the estimation avoids putting

together highly correlated variables. All in all, the correlation matrix shows that none

of these variables are perfectly collinear.

3.2. Some stylised facts

Figure 1 depicts the trend scores of labour market efficiency based on the GCI.

Overall, the global trend shows that employment protection has been increasing in

some countries and declining in others. In the data, Argentina, Italy, Pakistan, and

Turkey are classified as the strictest countries in terms of labour market policies

(measured by lower scores for labour market efficiency), whilst Singapore, Hong

Kong, the US, and the UK are classified as the least strict countries for labour market

policies (higher scores for labour market efficiency).

Figure 1: Cross-country Labour Market Efficiency Trend, 2009–2013

Source: Author’s calculation based on Global Competitiveness Index 2009-2013.

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2009 2010 2011 2012 2013 2009 2010 2011 2012 2013 2009 2010 2011 2012 2013 2009 2010 2011 2012 2013

2009 2010 2011 2012 2013 2009 2010 2011 2012 2013

ARG AUS BRZ CAN CHN CMB

FIN FRA GER GRE HKG IDN

IND ITA JPN KOR MEX MLY

NZ PAK PHP POR RUS SAF

SIN SPN SWE THL TUR UK

US VNM

lmefi

yearGraphs by country

16

Figure 2 shows the trend scores of innovation capacity based on the GCI. Overall,

the global trend shows that innovation intensity has been increasing in some countries

and also declining in others. Finland, Japan, Germany, and the US lead in global

innovation competitiveness, whilst Cambodia, Argentina and Pakistan seem to be

laggards in innovation.

Figure 2: Cross-country Innovation Scores Trend, 2009–2013

Source: Author’s calculation based on Global Competitiveness Index 2009–2013.

Figure 3 depicts a positive association between labour market efficiency (lmefi)

and innovation capacity. The figure shows that countries such as Singapore, the US,

and the UK, are relatively advanced in terms of innovation performance and relatively

flexible in terms of labour market policies. This pattern remains unchanged when we

use a different proxy for labour market efficiency, namely, the cost of redundancy

dismissal (lbrefi).

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2009 2010 2011 2012 2013 2009 2010 2011 2012 2013 2009 2010 2011 2012 2013 2009 2010 2011 2012 2013

2009 2010 2011 2012 2013 2009 2010 2011 2012 2013

ARG AUS BRZ CAN CHN CMB

FIN FRA GER GRE HKG IDN

IND ITA JPN KOR MEX MLY

NZ PAK PHP POR RUS SAF

SIN SPN SWE THL TUR UK

US VNM

inn

ov

yearGraphs by country

17

Figure 3: Labour Market Efficiency and Innovation Capacity, 2013

Source: Authors’ calculation based on Global Competitiveness Index.

Figures 4 to 6 show that labour market efficiency has a positive association with

the number of patents filed in the national office, the number of venture capital deals,

and the number of people employed in knowledge-intensive services.

THL

SAF

ARG

RUS

CHN

KOR

US

TURMEX

SWE

SIN

POR

PAK

NZ

BRZ

VNM

ITA

UK

HKG

JPN

MLY

GER

SPN

FIN

IDN

PHP

FRA

CAN

GRE

IND

CMB

AUS

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Inn

ova

tion

3 4 5 6labour market efficiency

innov Fitted values

18

Figure 4: Labour Market Efficiency and Number of Patents Filed, 2013

Source: Authors’ calculation based on Global Innovation Index and Global Competitiveness

Index.

Figure 5: Labour Market Efficiency and Number of Venture Deals, 2013

Source: Authors’ calculation based on Global Innovation Index and Global Competitiveness

Index.

THLSAFARG

RUS

CHNKOR US

TUR

MEX

SWE

SIN

POR

PAK

NZ

BRZ

VNM

ITA

UK

HKG

JPN

MLY

GER

SPN

FIN

IDN PHP

FRA

CAN

GREIND

CMB

AUS

02

04

06

08

01

00

num

ber

of p

ate

nt

3 4 5 6labour market efficiency

patent Fitted values

THLSAFARGRUSCHNKOR

US

TUR MEX

SWESIN

POR

PAK

NZ

BRZVNMITA

UK

HKGJPNMLY

GER

SPN

FIN

IDN

PHP

FRA

CAN

GRE

IND

CMB

AUS

-50

05

01

00

num

ber

of ve

ntu

re d

eals

3 4 5 6labour market efficiency

vencap Fitted values

19

Figure 6: Labour Market Efficiency and Employment

in Knowledge-intensive Services, 2013

Source: Authors’ calculation based on Global Innovation Index and Global Competitiveness

Index.

Figure 7 shows that the relationship between labour market efficiency (lmefi) and

trade openness also seems positive. A more efficient labour market is associated with

greater openness to trade. Similarly, Figure 8 shows that the relationship between

innovation and trade competitiveness (measured by the natural logarithm of export

value) is also positive. A more innovative country is associated with greater export

competitiveness.

THL

SAFARG

RUS

CHN

KOR

US

TUR

MEX

SWE

SIN

POR

PAK

NZ

BRZ

VNM

ITA

UK

HKG

JPN

MLY

GER

SPN

FIN

IDN

PHP

FRA CAN

GRE

CMB

AUS

02

04

06

08

01

00

num

ber

of e

mp

loym

en

t in

kno

wle

dge

secto

r

3 4 5 6labour market efficiency

empkw Fitted values

20

Figure 7: Trade Openness and Labour Market Efficiency, 2013

Source: Authors’ calculation based on Global Innovation Index and Global Competitiveness

Index.

Figure 8: Trade Competitiveness and Innovation, 2013

Source: Authors’ calculation based on Global Innovation Index and Global Competitiveness

Index.

THLSAF

ARGRUS

CHN

KOR

US

TUR

MEX

SWE

SIN

POR

PAK

NZ

BRZ

VNM

ITA UK

HKG

JPN

MLY

GERSPN FIN

IDN

PHP

FRACAN

GRE

IND

CMB

AUS

60

70

80

90

100

trad

e o

pen

ne

ss

3 4 5 6labour market efficiency

open Fitted values

THL

SAFARG

RUS

CHN

KOR

US

TUR

MEX

SWE

SIN

POR

PAK

NZ

BRZ

VNM

ITA UKHKGJPN

MLY

GER

SPN

FIN

IDN

PHP

FRACAN

GRE

IND

CMB

AUS

81

01

21

41

6

log

expo

rt

3 4 5 6innovation

lxprt Fitted values

21

3.3. Empirical Model

We are interested in examining how much variation in labour market policies

relate to variation in innovation capacity. For this purpose, we propose a simplified

version of Acharya, et al. (2010) fixed effect model, as follows:

Innovit = αit +β*Lmefiit +γ*Xit +μi + εit (1)

The dependent variable, Innovit, represents innovation competitiveness in country i at

time t. Lmefiit represents labour market efficiency in country i at time t. Higher values

of this variable correspond to more efficient labour markets, implying more flexible

labour market policies. Xit is a set of control variables including institutional

environment, infrastructure quality, macroeconomic condition, higher education

quality, goods market efficiency, and market size of country i at time t. μit controls for

unobserved country-specific characteristics, and εit is a disturbance term capturing

unobservable variables affecting innovation. The country fixed effects control for

time-invariant unobserved factors at the country level.6

The parameters of interest are β coefficients on the labour market efficiency

indicators. Model 1 assumes that labour market policies affect the way resources are

allocated, and hence the efficiency and flexibility of the overall labour market. This in

turn leads to either higher or lower country innovation capacity.

Table 3 shows the estimation results for equation (1) using the innovation scores

measure as the dependent variables. The β coefficient for the labour market efficiency

(lmefi) is positive and significant. This result indicates that as the score of labour

market efficiency increases (less rigid labour market policies), a country’s innovation

score increases, holding other things constant. The estimate for β coefficient remains

positive and significant when we control for additional variables, such as higher

education quality, the institutional environment, infrastructure quality, market size,

openness and interaction term (openness*lmefi). The latter tries to capture the

possibility of non-linearity in the model specification. As column 4 and 5 show that

the effect of labour market efficiency on innovation is higher when we include the

6 Due to lack of standardised industrial data, the model cannot control for industrial

heterogeneity.

22

openness and interaction terms. In this case, the degree of openness matters for

innovation.

The estimated coefficient for higher education is positive and significant for

different model specification. This means higher education quality is positively

associated with innovation. In view of this, policy that supports the quality of higher

education is crucial for improving country’s innovation competitiveness. Other

significant determinant for innovation is infrastructure quality. Columns 2–4 show that

better infrastructure quality is associated with higher innovation competitiveness.

Meanwhile, columns 1, 2, and 5 show that estimated coefficients on goods market

efficiency are positive and significant, meaning that more efficient goods market

policies (more domestic and foreign competition) are associated with higher

innovation capacity. This result is in line with other previous findings (see Aghion et

al., 2005; Barbosa and Faria, 2011).

Table 3: Fixed Effect Regressions Results: Innovation Capacity

as Dependent Variable

Dependent variable:

Innovation capacity scores

1 2 3 4 5

Lmefi

Gmefi

Highed

Infra

Mktsize

Openness

Open*Lmefi

Techred

0.232**

(0.099)

0.362**

(0.136)

0.371***

(0.112)

0.225***

(0.079)

0.290**

(0.109)

0.451***

(0.089)

0.296***

(0.082)

0.108

(0.122)

0.288**

(0.112)

0.388 ***

(0.098)

0.158

(0.286)

1.718***

(0.401)

0.150

(0.117)

0.284**

(0.112)

0.378***

(0.091)

0.019***

(0.006)

-0.323***

(0.089)

1.643***

(0.409)

0.311**

(0.144)

0.312***

(0.109)

0.019***

(0.006)

-0.308***

(0.090)

0.114**

(0.066)

Constant

-0.395

(0.786)

-0.469

(0.626)

-1.847

(1.514)

-2.744***

(0.877)

-2.170**

(0.984)

N

Rho

Prob > F

160

0.946

0.000

160

0.952

0.000

160

0.950

0.000

160

0.953

0.000

160

0.945

0.000 Notes: Robust standard errors are in parentheses. Based on them ***, **,* mean coefficients statistically significant at 1%, 5%, and 10% level, respectively. See Table 2 for the detailed description of the variables. Source: Author’s calculation.

23

The Hausman test is conducted to choose between the fixed effect and random

effect model. The Hausman test rejects the null hypothesis, i.e., that the unique error

(ui) is not correlated with the regressors.7 Therefore, the fixed effect is selected for

equation (1).

Different proxies for innovation are also tested in the estimation of equation (1).

Table 4 shows the results of fixed effect regression on equation (1) using different

proxies for dependent variable, namely the number of venture deals (vencap) and the

number of people employed in knowledge-intensive services (empkw) as the

dependent variables. Similar to previous results, the β coefficient for the labour market

efficiency (lmefi) remains positive and significant. However, we cannot find a

significant association between labour market policies and the number of patents filed

in the national office. This is probably due to weaknesses of the patent data.8

7 See the Appendix for the Hausman test results. 8 Firms in different industries and countries have different propensities to patent and that the value

of a patent is heterogeneous across countries (see Griffith and Macartney, 2014, p.141).

24

Table 4: Fixed Effect Regressions Results: Venture Capital Deals and

Employment in Knowledge Services as Dependent Variable

Dependent variable:

Venture capital deals

Dependent variable:

Employment in knowledge

services

1 2 3 4

Lmefi

Gmefi

Mktsize

Highed

Infra

2.490***

(0.471)

-3.247***

(0.656)

-5.038***

(1.804)

2.136***

(0.472)

-2.059***

(0.774)

-4.032**

(1.781)

-1.552***

(0.546)

-0.379

(0.531)

0.357**

(0.150)

-0.532***

(0.197)

2.708***

(0.482)

0.341**

(0.156)

-0.535**

(0.247)

2.677***

(0.486)

-0.196

(0.177)

0.199

(0.173)

Constant

33.818***

(9.482)

34.175***

(9.185)

9.417***

(2.536)

-9.205***

(2.554)

N

Rho

Prob > F

145

0.973

0.000

145

0.974

0.000

155

0.989

0.000

155

0.989

0.000

Notes: Robust standard errors are in parentheses. Based on them ***, **,* mean coefficients

statistically significant at 1%, 5%, and 10% level, respectively. See Table 2 for the detailed

description of the variables.

Source: Author’s calculation

It is important to note that our fixed-effect model is likely to suffer from an

endogeneity problem. An important concern stems from the fact that changes in a

country's labour market policies are likely to be correlated with changes in other

unobserved factors. To address this endogeneity problem, we run a dynamic panel-

data using the Arellano-Bond method by including the lag of the dependent variable

to control for other unobserved factors.

Table 5 shows the results of the Arellano-Bond dynamic panel-data estimation.

The results corroborate the findings from previous models (compared with Table 3).

Our estimation results show that a more efficient labour market is associated positively

with innovation capacity. The estimate for β coefficient remains positive and

significant when we control for additional variables, such as higher education quality,

the institutional environment, infrastructure quality, institutional quality and openness

(columns 3-5). We find that improved goods market efficiency (more competition),

better infrastructure quality, and improved institutional quality, are all positively

correlated with a country’s innovation performance. It is important to note that a higher

25

education variable becomes non-significant after including more control variables,

namely, infrastructure and institutional quality. This is probably due to the strong

positive correlation between higher education and these two variables. The

insignificant sign of the openness variable and the lag of innovation are rather

puzzling.

Table 5: Arellano-Bond Dynamic Panel-data Estimation

Dependent variable:

Innovation capacity scores

1 2 3 4 5

Lmefi

Gmefi

Mktsize

Highed

Infra

Institut

Macro

Openness

Innov (t-1)

0.222***

(0.074)

0.549***

(0.125)

0.181*

(0.097)

-0.181

(0.154)

0.243***

(0.085)

0.537***

(0.134)

0.227

(0.346)

0.190*

(0.103)

-0.253

(0.197)

0.210**

(0.092)

0.237*

(0.134)

0.168

(0.306)

0.105

(0.090)

0.436***

(0.117)

0.195*

(0.105)

-0.220

(0.182)

0.234**

(0.095)

0.230*

(0.139)

0.294

(0.317)

0.100

(0.094)

0.468***

(0.119)

0.192*

(0.109)

-0.056

(0.043)

-0.315*

(0.186)

0.201**

(0.085)

0.243*

(0.133)

0.101

(0.088)

0.430***

(0.114)

0.199*

(0.103)

0.001

(0.002)

-0.195

(0.154)

Constant

0.457

(0.711)

-0.505

(1.625)

-1.423

(1.472)

-1.617

(1.517)

-0.700

(0.611)

N

Prob>Chi2

96

0.000

96

0.000

96

0.000

96

0.000

96

0.000

Notes: ***, **,* mean coefficients statistically significant at 1%, 5%, and 10% level,

respectively. See Table 2 for the detailed description of the variables.

Source: Author’s calculation

In addition, we also test whether labour market policies and innovation will have

an effect on a country’s trade competitiveness.

Lxportit = αit +β*Innovit-1 + γ*Lmefiit +δ*Xit +μit + εit (2)

The dependent variable, Lxportit, represents trade competitiveness measured by the

natural logarithm of country i’s exports at time t. Innovit-1 represents innovation

26

intensity in country i at time t-1. A potential caveat that should be kept in mind, due to

data constraints, is that we can only use a one-year lag. This one-year lag is probably

too short for innovation to show any meaningful impact on a country’s trade

competitiveness. Xit is a set of control variables of country i at time t. μit controls for

unobserved country-specific characteristics and εit is an idiosyncratic error term.

Our hypothesis is that a more efficient labour market is associated with greater

innovation. And greater innovation is, in turn, associated with a country’s trade

competitiveness measured by its exports. Again, this is a rather bold simplification of

the reality in which innovation may indirectly affect productivity and export

competitiveness. Nevertheless, we are interested in finding preliminary evidence that

overall innovation scores could be higher or lower in countries with more efficient

labour markets.

To capture the dynamic nature of innovation relative to export competitiveness,

we then estimate equation (2) with fixed and random effect models and perform the

Hausman test. As stated above, trade competitiveness is measured by the natural

logarithm of a country’s exports value (see Table 2 description). Table 6 shows that a

different model specification leads to different choices between the fixed or random

effect. The Hausman test selects the random effect for the model without goods market

policy variable (gmefi) (see column 1 and 2). While it selects the fixed effect for the

model with goods market policy variable (see column 3 and 4). Overall, the estimation

results show that the lag variable of innovation is positively associated with export

competitiveness. The estimated coefficient for innovation variable remains consistent,

positive and significant across different model specifications. In addition,

technological readiness and goods market competition policy are associated with

higher exports. Meanwhile, a more flexible labour market policy is associated with

lower exports. This result contradicts our hypothesis about the impact of labour market

policy on trade competitiveness.

27

Table 6: Exploring the Relationship between Innovation

and Trade Competitiveness

Dependent variable:

Log exports

1

(F.E)

2

(R.E)

3

(F.E)

4

(R.E)

Lmefi

Gmefi

Techred

Innov (t-1)

-0.212**

(0.082)

0.178***

(0.066)

0.195**

(0.083)

-0.186**

(0.080)

0.194***

(0.064)

0.237***

(0.080)

-0.237***

(0.080)

0.319**

(0.127)

0.103

(0.071)

0.165**

(0.081)

-0.216***

(0.079)

0.303**

(0.125)

0.121*

(0.069)

0.204***

(0.079)

Constant

11.547***

(0.542)

11.190***

(0.536)

10.676***

(0.630)

10.407***

(0.620)

N

Rho

Prob > F or

Prob>Chi2

128

0.991

0.000

128

0.991

0.000

128

0.992

0.000

128

0.991

0.000

Hausman Test Prob >Chi2 = 0.173

Cannot reject Ho. Use random

effect

Prob >Chi2 = 0.000

Reject Ho. Use fixed effect

Notes: Standard errors are in parentheses. Based on them ***, **,* mean coefficients statistically

significant at 1%, 5%, and 10% level, respectively.

Source: Author’s calculation

Conclusions

This paper investigates the relationship between labour market policies and

innovation. Most of the studies in this area have been undertaken for OECD member

countries. This study attempts to fill in the knowledge gap by expanding the analysis

to include both OECD and non-OECD countries, including some of the Southeast

Asian countries. The data used in the estimation come from the Global

Competitiveness Report and the Global Innovation Index. A set of balanced panel data

is constructed including 32 countries covering the period 2009–2013. Data availability

limits our empirical model in two ways. First, it is related to the difficulty of finding

reliable and standardised innovation data at the firm or industry level for developing

countries. Second, it is related to the difficulty of finding good proxies for labour

market policy, as there is substantial heterogeneity in labour market policies across

28

countries. Due to institutional complexity, there is no perfect proxy for labour market

policy. The use of labour market efficiency indicators from the Global Competivenesss

Index (GCI) should therefore be treated with caution.

Despite these challenges, GCI data nevertheless provide valuable information

when better alternative standardised measures on innovation and labour market policy

are not easily available in most countries. In this case, opinions from business

executives are instrumental in understanding the factors behind business

competitiveness. This study finds some preliminary evidence that a more efficient

labour market (more flexible labour market policy) is positively correlated with

innovation. Innovation competitiveness in turn leads to a country’s trade

competitiveness. Our results are robust even after controlling for other explanatory

variables. The quality of higher education is positively associated with a country’s

innovation competitiveness. Therefore, policies that support improving higher

education quality are crucial for economies that want to develop their innovation

competitiveness. Other important determinants for innovation are infrastructure

quality and goods market policies (more domestic and foreign competition). The two

are positively correlated with innovation. The country-level analysis in this paper

demonstrates the average correlation between labour market efficiency and

innovation. However, it does not take into account structural differences across

industries/sectors. Subject to data availability, future research should be directed

towards understanding variances of labour market efficiency and innovation between

different industries and sectors across countries.

On trade aspects, we find some preliminary evidence that past innovation is

positively associated with trade competitiveness. This is in line with some previous

studies that find a positive link between innovation and exporting (Wagner, 2007; Aw

et al. 2009; Cassiman et al. 2010). We cannot find a significant positive association

between labour market policies and trade competitiveness. However, we postulate that

there may be an indirect link between labour market policies and trade

competitiveness—one that our simple model fails to capture.

Research on labour market policies and innovation is still limited in the region.

We hope that future research will fully explore the relationship between labour market

policies and innovation in developing countries in the Southeast Asian region using

29

good quality firm-level data. As ASEAN will soon start implementing the ASEAN

Economic Community characterised by the free flow of skilled labour amongst others,

there is a greater need to assess how labour market policies in one member state may

affect other countries, and also how they affect innovation in the region. In addition, it

is important to collect better quality micro-data to test the relationship between

innovation, productivity and firms’ exporting competitiveness. Innovation policy

targeted at enhancing productivity, especially in export-oriented sectors, is likely to be

important, but empirical evidence in this area is still lacking in ASEAN.

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Appendix: Hausman Test Results

Dependent variable:

Innovation capacity

1

(F.E)

2

(R.E)

3

(F.E)

4

(R.E)

Lmefi

Gmefi

Mktsize

Highed

Infra

Macro

0.296***

(0.071)

0.108

(0.114)

0.158

(0.219)

0.288***

(0.082)

0.388**

(0.080)

0.275***

(0.060)

0.198**

(0.093)

0.135

(0.086)

0.307***

(0.073)

0.297***

(0.067)

0.297***

(0.072)

0.109

(0.115)

0.165

(0.232)

0.288 ***

(0.082)

0.387***

(0.081)

-0.003

(0.034)

0.276***

(0.060)

0.204**

(0.099)

0.139

(0.086)

0.307***

(0.074)

0.294***

(0.067)

-0.009

(0.032)

Constant

-1.847

(1.160)

-1.685***

(0.565)

-1.868

(1.189)

-1.681***

(0.565)

N

Rho

Prob > F or

Prob>Chi2

160

0.950

0.000

160

0.935

0.000

160

0.950

0.000

160

0.934

0.000

Hausman Test Prob >Chi2 = 0.000

Reject Ho. Use fixed effect

Prob >Chi2 = 0.000

Reject Ho. Use fixed effect

Notes: Standard errors are in parentheses. Based on them ***, **,* mean coefficients statistically

significant at 1%, 5%, and 10% level, respectively.

33

Dependent variable:

Number of venture deals

1

(F.E)

2

(R.E)

3

(F.E)

4

(R.E)

Lmefi

Gmefi

Mktsize

Highed

Infra

2.490***

(0.471)

-3.247***

(0.656)

-5.038***

(1.804)

2.880***

(0.332)

-2.486***

(0.415)

-0.107

(0.290)

2.136***

(0.472)

-2.059***

(0. 774)

-4.032**

(1.781)

-1.552***

(0.546)

-0.379

(0.531)

2.826***

(0.073)

-2.753***

(0.515)

-0.239

(0.293)

-0.601

(0.388)

0.679**

(0.332)

Constant

33.818***

(9.482)

2.357***

(2.250)

34.175***

(9.185)

4.043*

(2.369)

N

Rho

Prob > F or

Prob>Chi2

145

0.973

0.000

145

0.701

0.000

145

0.974

0.000

145

0.693

0.000

Hausman Test Prob >Chi2 = 0.000

Reject Ho. Use fixed effect

Prob >Chi2 = 0.000

Reject Ho. Use Fixed effect

Notes: Standard errors are in parentheses. Based on them ***, **,* mean coefficients statistically

significant at 1%, 5%, and 10% level, respectively.

34

Dependent variable:

Employment in knowledge services

1

(F.E)

2

(R.E)

3

(F.E)

4

(R.E)

Lmefi

Gmefi

Mktsize

Highed

Infra

0.357**(0.150)

-

0.532***(0.19

7)

2.708***

(0.482)

0.157 (0.137)

0.002 (0.172)

0.648***(0.19

4)

0.341**(0.156)

-0.535**

(0.247)

2.677***

(0.486)

-0.196 (0.177)

0.199 (0.173)

0.135 (0.134)

-0.369*

(0.215)

0.316*(0.162)

0.147 (0.161)

0.383***(0.14

7)

Constant

-9.417***

(2.536)

-0.351 (1.273) -

9.205***(2.55

4)

0.534 (1.108)

Observation

s

Rho

Prob > F or

Prob>Chi2

155

0.989

0.000

155

0.935

0.006

155

0.989

0.000

155

0.884

0.000

Hausman

Test

Prob >Chi2 = 0.000 (Reject Ho)

Use fixed effect

Prob >Chi2 = 0.000 (Reject Ho)

Use fixed effect Notes: Standard errors are in parentheses. Based on them ***, **,* mean coefficients statistically

significant at 1%, 5%, and 10% level, respectively.

35

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Feb

2015

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Feb

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2015

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Jan

2015

2014-26 Hikari ISHIDO Coverage of Trade in Services under ASEAN+1 FTAs

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2014

2014-25 Junianto James

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2014

2014-24 Dayong ZHANG and

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Nov

2014

2014-23 Dandan ZHANG,

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Enhanced Measurement of Energy Market Integration in East Asia: An Application of Dynamic Principal Component Analysis

Nov

2014

2014-22 Yanrui WU Deregulation, Competition, and Market Integration in China’s Electricity Sector

Nov

2014

2014-21 Yanfei LI and

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Infrastructure Investments for Power Trade and Transmission in ASEAN+2: Costs, Benefits, Long-Term Contracts, and Prioritised Development

Nov

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2014-20 Yu SHENG, Yanrui

WU, Xunpeng SHI,

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Nov

2014

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2014

2014-18 Olivier CADOT, Lili

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Sep

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2014-17 Sadayuki TAKII Import Penetration, Export Orientation, and Plant Size in Indonesian Manufacturing

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2014

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No. Author(s) Title Year

2014-16 Tomoko INUI, Keiko

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2014

2014-15 Han PHOUMIN and

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June

2014

2014-14 Cassey LEE The Exporting and Productivity Nexus: Does Firm Size Matter?

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2014

2014-13 Yifan ZHANG Productivity Evolution of Chinese large and Small Firms in the Era of Globalisation

May

2014

2014-12

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May

2014

2014-11 Inkyo CHEONG Korea’s Policy Package for Enhancing its FTA

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May

2014

2014-10 Sothea OUM,

Dionisius NARJOKO,

and Charles HARVIE

Constraints, Determinants of SME Innovation, and

the Role of Government Support

May

2014

2014-09 Christopher

PARSONS and Pierre-

Louis Vézina

Migrant Networks and Trade: The Vietnamese

Boat People as a Natural Experiment

May

2014

2014-08

Kazunobu

HAYAKAWA and

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MATSUURA

Dynamic Tow-way Relationship between

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May

2014

2014-07 DOAN Thi Thanh Ha

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Firm-level Evidence on Productivity Differentials

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Apr

2014

2014-06 Larry QIU and

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Multiproduct Firms, Export Product Scope, and

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Apr

2014

2014-05 Han PHOUMIN and

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Analysis on Price Elasticity of Energy Demand in

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Apr

2014

2014-04 Youngho CHANG and

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Non-renewable Resources in Asian Economies:

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Feb

2014

2014-03 Yasuyuki SAWADA

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41

No. Author(s) Title Year

2014

2014-02 Cassey LEE Competition Law Enforcement in Malaysia Jan

2014

2014-01 Rizal SUKMA ASEAN Beyond 2015: The Imperatives for Further

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Jan

2014

2013-38 Toshihiro OKUBO,

Fukunari KIMURA,

Nozomu TESHIMA

Asian Fragmentation in the Global Financial Crisis Dec

2013

2013-37 Xunpeng SHI and

Cecilya MALIK

Assessment of ASEAN Energy Cooperation within

the ASEAN Economic Community

Dec

2013

2013-36 Tereso S. TULLAO,

Jr. And Christopher

James CABUAY

Eduction and Human Capital Development to

Strengthen R&D Capacity in the ASEAN

Dec

2013

2013-35 Paul A. RASCHKY

Estimating the Effects of West Sumatra Public

Asset Insurance Program on Short-Term Recovery

after the September 2009 Earthquake

Dec

2013

2013-34

Nipon

POAPONSAKORN

and Pitsom

MEETHOM

Impact of the 2011 Floods, and Food Management

in Thailand

Nov

2013

2013-33 Mitsuyo ANDO Development and Resructuring of Regional

Production/Distribution Networks in East Asia

Nov

2013

2013-32 Mitsuyo ANDO and

Fukunari KIMURA

Evolution of Machinery Production Networks:

Linkage of North America with East Asia?

Nov

2013

2013-31 Mitsuyo ANDO and

Fukunari KIMURA

What are the Opportunities and Challenges for

ASEAN?

Nov

2013

2013-30 Simon PEETMAN Standards Harmonisation in ASEAN: Progress,

Challenges and Moving Beyond 2015

Nov

2013

2013-29 Jonathan KOH and

Andrea Feldman

MOWERMAN

Towards a Truly Seamless Single Windows and

Trade Facilitation Regime in ASEAN Beyond 2015

Nov

2013

2013-28 Rajah RASIAH

Stimulating Innovation in ASEAN Institutional

Support, R&D Activity and Intelletual Property

Rights

Nov

2013

42

No. Author(s) Title Year

2013-27 Maria Monica

WIHARDJA

Financial Integration Challenges in ASEAN

beyond 2015

Nov

2013

2013-26 Tomohiro MACHIKIT

A and Yasushi UEKI

Who Disseminates Technology to Whom, How,

and Why: Evidence from Buyer-Seller Business

Networks

Nov

2013

2013-25 Fukunari KIMURA

Reconstructing the Concept of “Single Market a

Production Base” for ASEAN beyond 2015

Oct

2013

2013-24

Olivier CADOT

Ernawati MUNADI

Lili Yan ING

Streamlining NTMs in ASEAN:

The Way Forward

Oct

2013

2013-23

Charles HARVIE,

Dionisius NARJOKO,

Sothea OUM

Small and Medium Enterprises’ Access to Finance:

Evidence from Selected Asian Economies

Oct

2013

2013-22 Alan Khee-Jin TAN Toward a Single Aviation Market in ASEAN:

Regulatory Reform and Industry Challenges

Oct

2013

2013-21

Hisanobu SHISHIDO,

Shintaro SUGIYAMA,

Fauziah ZEN

Moving MPAC Forward: Strengthening Public-

Private Partnership, Improving Project Portfolio

and in Search of Practical Financing Schemes

Oct

2013

2013-20

Barry DESKER, Mely

CABALLERO-

ANTHONY, Paul

TENG

Thought/Issues Paper on ASEAN Food Security:

Towards a more Comprehensive Framework

Oct

2013

2013-19

Toshihiro KUDO,

Satoru KUMAGAI, So

UMEZAKI

Making Myanmar the Star Growth Performer in

ASEAN in the Next Decade: A Proposal of Five

Growth Strategies

Sep

2013

2013-18 Ruperto MAJUCA

Managing Economic Shocks and Macroeconomic

Coordination in an Integrated Region: ASEAN

Beyond 2015

Sep

2013

2013-17

Cassy LEE and

Yoshifumi

FUKUNAGA

Competition Policy Challenges of Single Market

and Production Base

Sep

2013

2013-16 Simon TAY Growing an ASEAN Voice? : A Common Platform

in Global and Regional Governance

Sep

2013

43

No. Author(s) Title Year

2013-15

Danilo C. ISRAEL

and Roehlano M.

BRIONES

Impacts of Natural Disasters on Agriculture, Food

Security, and Natural Resources and Environment

in the Philippines

Aug

2013

2013-14 Allen Yu-Hung LAI

and Seck L. TAN

Impact of Disasters and Disaster Risk Management

in Singapore: A Case Study of Singapore’s

Experience in Fighting the SARS Epidemic

Aug

2013

2013-13 Brent LAYTON Impact of Natural Disasters on Production

Networks and Urbanization in New Zealand

Aug

2013

2013-12 Mitsuyo ANDO

Impact of Recent Crises and Disasters on Regional

Production/Distribution Networks and Trade in

Japan

Aug

2013

2013-11 Le Dang TRUNG Economic and Welfare Impacts of Disasters in East

Asia and Policy Responses: The Case of Vietnam

Aug

2013

2013-10

Sann VATHANA,

Sothea OUM, Ponhrith

KAN, Colas

CHERVIER

Impact of Disasters and Role of Social Protection

in Natural Disaster Risk Management in Cambodia

Aug

2013

2013-09

Sommarat

CHANTARAT, Krirk

PANNANGPETCH,

Nattapong

PUTTANAPONG,

Preesan RAKWATIN,

and Thanasin

TANOMPONGPHAN

DH

Index-Based Risk Financing and Development of

Natural Disaster Insurance Programs in Developing

Asian Countries

Aug

2013

2013-08 Ikumo ISONO and

Satoru KUMAGAI

Long-run Economic Impacts of Thai Flooding:

Geographical Simulation Analysis

July

2013

2013-07

Yoshifumi

FUKUNAGA and

Hikaru ISHIDO

Assessing the Progress of Services Liberalization in

the ASEAN-China Free Trade Area (ACFTA)

May

2013

2013-06

Ken ITAKURA,

Yoshifumi

FUKUNAGA, and

Ikumo ISONO

A CGE Study of Economic Impact of Accession of

Hong Kong to ASEAN-China Free Trade

Agreement

May

2013

2013-05 Misa OKABE and

Shujiro URATA The Impact of AFTA on Intra-AFTA Trade May

44

No. Author(s) Title Year

2013

2013-04 Kohei SHIINO How Far Will Hong Kong’s Accession to ACFTA

will Impact on Trade in Goods?

May

2013

2013-03

Cassey LEE and

Yoshifumi

FUKUNAGA

ASEAN Regional Cooperation on Competition

Policy

Apr

2013

2013-02

Yoshifumi

FUKUNAGA and

Ikumo ISONO

Taking ASEAN+1 FTAs towards the RCEP:

A Mapping Study

Jan

2013

2013-01 Ken ITAKURA

Impact of Liberalization and Improved

Connectivity and Facilitation in ASEAN for the

ASEAN Economic Community

Jan

2013

2012-17

Sun XUEGONG, Guo

LIYAN, Zeng

ZHENG

Market Entry Barriers for FDI and Private

Investors: Lessons from China’s Electricity Market

Aug

2012

2012-16 Yanrui WU Electricity Market Integration: Global Trends and

Implications for the EAS Region

Aug

2012

2012-15 Youngho CHANG,

Yanfei LI

Power Generation and Cross-border Grid Planning

for the Integrated ASEAN Electricity Market: A

Dynamic Linear Programming Model

Aug

2012

2012-14 Yanrui WU, Xunpeng

SHI

Economic Development, Energy Market

Integration and Energy Demand: Implications for

East Asia

Aug

2012

2012-13

Joshua AIZENMAN,

Minsoo LEE, and

Donghyun PARK

The Relationship between Structural Change and

Inequality: A Conceptual Overview with Special

Reference to Developing Asia

July

2012

2012-12

Hyun-Hoon LEE,

Minsoo LEE, and

Donghyun PARK

Growth Policy and Inequality in Developing Asia:

Lessons from Korea

July

2012

2012-11 Cassey LEE Knowledge Flows, Organization and Innovation:

Firm-Level Evidence from Malaysia

June

2012

2012-10

Jacques MAIRESSE,

Pierre MOHNEN,

Yayun ZHAO, and

Feng ZHEN

Globalization, Innovation and Productivity in

Manufacturing Firms: A Study of Four Sectors of

China

June

2012

45

No. Author(s) Title Year

2012-09 Ari KUNCORO

Globalization and Innovation in Indonesia:

Evidence from Micro-Data on Medium and Large

Manufacturing Establishments

June

2012

2012-08 Alfons

PALANGKARAYA

The Link between Innovation and Export: Evidence

from Australia’s Small and Medium Enterprises

June

2012

2012-07 Chin Hee HAHN and

Chang-Gyun PARK

Direction of Causality in Innovation-Exporting

Linkage: Evidence on Korean Manufacturing

June

2012

2012-06 Keiko ITO Source of Learning-by-Exporting Effects: Does

Exporting Promote Innovation?

June

2012

2012-05 Rafaelita M.

ALDABA

Trade Reforms, Competition, and Innovation in the

Philippines

June

2012

2012-04

Toshiyuki

MATSUURA and

Kazunobu

HAYAKAWA

The Role of Trade Costs in FDI Strategy of

Heterogeneous Firms: Evidence from Japanese

Firm-level Data

June

2012

2012-03

Kazunobu

HAYAKAWA,

Fukunari KIMURA,

and Hyun-Hoon LEE

How Does Country Risk Matter for Foreign Direct

Investment?

Feb

2012

2012-02

Ikumo ISONO, Satoru

KUMAGAI, Fukunari

KIMURA

Agglomeration and Dispersion in China and

ASEAN:

A Geographical Simulation Analysis

Jan

2012

2012-01 Mitsuyo ANDO and

Fukunari KIMURA

How Did the Japanese Exports Respond to Two

Crises in the International Production Network?:

The Global Financial Crisis and the East Japan

Earthquake

Jan

2012

2011-10

Tomohiro

MACHIKITA and

Yasushi UEKI

Interactive Learning-driven Innovation in

Upstream-Downstream Relations: Evidence from

Mutual Exchanges of Engineers in Developing

Economies

Dec

2011

2011-09

Joseph D. ALBA,

Wai-Mun CHIA, and

Donghyun PARK

Foreign Output Shocks and Monetary Policy

Regimes in Small Open Economies: A DSGE

Evaluation of East Asia

Dec

2011

2011-08

Tomohiro

MACHIKITA and

Yasushi UEKI

Impacts of Incoming Knowledge on Product

Innovation: Econometric Case Studies of

Technology Transfer of Auto-related Industries in

Developing Economies

Nov

2011

46

No. Author(s) Title Year

2011-07 Yanrui WU Gas Market Integration: Global Trends and

Implications for the EAS Region

Nov

2011

2011-06 Philip Andrews-

SPEED

Energy Market Integration in East Asia: A

Regional Public Goods Approach

Nov

2011

2011-05 Yu SHENG,

Xunpeng SHI

Energy Market Integration and Economic

Convergence: Implications for East Asia

Oct

2011

2011-04

Sang-Hyop LEE,

Andrew MASON, and

Donghyun PARK

Why Does Population Aging Matter So Much for

Asia? Population Aging, Economic Security and

Economic Growth in Asia

Aug

2011

2011-03 Xunpeng SHI,

Shinichi GOTO

Harmonizing Biodiesel Fuel Standards in East Asia:

Current Status, Challenges and the Way Forward

May

2011

2011-02 Hikari ISHIDO

Liberalization of Trade in Services under

ASEAN+n :

A Mapping Exercise

May

2011

2011-01

Kuo-I CHANG,

Kazunobu

HAYAKAWA

Toshiyuki

MATSUURA

Location Choice of Multinational Enterprises in

China: Comparison between Japan and Taiwan

Mar

2011

2010-11

Charles HARVIE,

Dionisius NARJOKO,

Sothea OUM

Firm Characteristic Determinants of SME

Participation in Production Networks

Oct

2010

2010-10 Mitsuyo ANDO Machinery Trade in East Asia, and the Global

Financial Crisis

Oct

2010

2010-09 Fukunari KIMURA

Ayako OBASHI

International Production Networks in Machinery

Industries: Structure and Its Evolution

Sep

2010

2010-08

Tomohiro

MACHIKITA, Shoichi

MIYAHARA,

Masatsugu TSUJI, and

Yasushi UEKI

Detecting Effective Knowledge Sources in Product

Innovation: Evidence from Local Firms and

MNCs/JVs in Southeast Asia

Aug

2010

2010-07

Tomohiro

MACHIKITA,

Masatsugu TSUJI, and

Yasushi UEKI

How ICTs Raise Manufacturing Performance:

Firm-level Evidence in Southeast Asia

Aug

2010

47

No. Author(s) Title Year

2010-06 Xunpeng SHI

Carbon Footprint Labeling Activities in the East

Asia Summit Region: Spillover Effects to Less

Developed Countries

July

2010

2010-05

Kazunobu

HAYAKAWA,

Fukunari KIMURA,

and

Tomohiro

MACHIKITA

Firm-level Analysis of Globalization: A Survey of

the Eight Literatures

Mar

2010

2010-04

Tomohiro

MACHIKITA

and Yasushi UEKI

The Impacts of Face-to-face and Frequent

Interactions on Innovation:

Upstream-Downstream Relations

Feb

2010

2010-03

Tomohiro

MACHIKITA

and Yasushi UEKI

Innovation in Linked and Non-linked Firms:

Effects of Variety of Linkages in East Asia

Feb

2010

2010-02

Tomohiro

MACHIKITA

and Yasushi UEKI

Search-theoretic Approach to Securing New

Suppliers: Impacts of Geographic Proximity for

Importer and Non-importer

Feb

2010

2010-01

Tomohiro

MACHIKITA

and Yasushi UEKI

Spatial Architecture of the Production Networks in

Southeast Asia:

Empirical Evidence from Firm-level Data

Feb

2010

2009-23 Dionisius NARJOKO

Foreign Presence Spillovers and Firms’ Export

Response:

Evidence from the Indonesian Manufacturing

Nov

2009

2009-22

Kazunobu

HAYAKAWA,

Daisuke

HIRATSUKA, Kohei

SHIINO, and Seiya

SUKEGAWA

Who Uses Free Trade Agreements? Nov

2009

2009-21 Ayako OBASHI Resiliency of Production Networks in Asia:

Evidence from the Asian Crisis

Oct

2009

2009-20 Mitsuyo ANDO and

Fukunari KIMURA Fragmentation in East Asia: Further Evidence

Oct

2009

2009-19 Xunpeng SHI The Prospects for Coal: Global Experience and

Implications for Energy Policy

Sept

2009

48

No. Author(s) Title Year

2009-18 Sothea OUM Income Distribution and Poverty in a CGE

Framework: A Proposed Methodology

Jun

2009

2009-17

Erlinda M.

MEDALLA and Jenny

BALBOA

ASEAN Rules of Origin: Lessons and

Recommendations for the Best Practice

Jun

2009

2009-16 Masami ISHIDA Special Economic Zones and Economic Corridors Jun

2009

2009-15 Toshihiro KUDO Border Area Development in the GMS: Turning the

Periphery into the Center of Growth

May

2009

2009-14

Claire HOLLWEG

and Marn-Heong

WONG

Measuring Regulatory Restrictions in Logistics

Services

Apr

2009

2009-13 Loreli C. De DIOS Business View on Trade Facilitation Apr

2009

2009-12

Patricia SOURDIN

and Richard

POMFRET

Monitoring Trade Costs in Southeast Asia Apr

2009

2009-11 Philippa DEE and

Huong DINH

Barriers to Trade in Health and Financial Services

in ASEAN

Apr

2009

2009-10 Sayuri SHIRAI

The Impact of the US Subprime Mortgage Crisis on

the World and East Asia: Through Analyses of

Cross-border Capital Movements

Apr

2009

2009-09 Mitsuyo ANDO and

Akie IRIYAMA

International Production Networks and

Export/Import Responsiveness to Exchange Rates:

The Case of Japanese Manufacturing Firms

Mar

2009

2009-08 Archanun

KOHPAIBOON

Vertical and Horizontal FDI Technology

Spillovers:Evidence from Thai Manufacturing

Mar

2009

2009-07

Kazunobu

HAYAKAWA,

Fukunari KIMURA,

and Toshiyuki

MATSUURA

Gains from Fragmentation at the Firm Level:

Evidence from Japanese Multinationals in East

Asia

Mar

2009

2009-06 Dionisius A.

NARJOKO

Plant Entry in a More

LiberalisedIndustrialisationProcess: An Experience

of Indonesian Manufacturing during the 1990s

Mar

2009

2009-05 Kazunobu

HAYAKAWA,

Fukunari KIMURA,

Firm-level Analysis of Globalization: A Survey Mar

2009

49

No. Author(s) Title Year

and Tomohiro

MACHIKITA

2009-04 Chin Hee HAHN and

Chang-Gyun PARK

Learning-by-exporting in Korean Manufacturing:

A Plant-level Analysis

Mar

2009

2009-03 Ayako OBASHI Stability of Production Networks in East Asia:

Duration and Survival of Trade

Mar

2009

2009-02 Fukunari KIMURA

The Spatial Structure of Production/Distribution

Networks and Its Implication for Technology

Transfers and Spillovers

Mar

2009

2009-01 Fukunari KIMURA

and Ayako OBASHI

International Production Networks: Comparison

between China and ASEAN

Jan

2009

2008-03

Kazunobu

HAYAKAWA and

Fukunari KIMURA

The Effect of Exchange Rate Volatility on

International Trade in East Asia

Dec

2008

2008-02

Satoru KUMAGAI,

Toshitaka GOKAN,

Ikumo ISONO, and

Souknilanh KEOLA

Predicting Long-Term Effects of Infrastructure

Development Projects in Continental South East

Asia: IDE Geographical Simulation Model

Dec

2008

2008-01

Kazunobu

HAYAKAWA,

Fukunari KIMURA,

and Tomohiro

MACHIKITA

Firm-level Analysis of Globalization: A Survey Dec

2008