Structural transformation out of manufacturing : evidence of push and pull effects

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Structural transformation out of manufacturing:

Evidence of push and pull effects

Mémoire

Chantal Princesse Yasenzia Yangunyo

Maîtrise en économique

Maître ès arts (M.A.)

Québec, Canada

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Résumé

Ce document examine les facteurs qui déterminent la transformation structurelle ou réalloca-tion des travailleurs du secteur manufacturier vers celui des services en utilisant un échantil-lon de 45 pays. Comme suggéré par le modèle d’équilibre général développé dans ce travail, l’analyse des tendances du prix relatif des services nous permet d’identifier deux principaux mécanismes de transformation structurelle: les effets labor pull et labor push. Pour les États-Unis, par exemple, le canal « pull » domine avant 1953, signifiant que c’est la croissance technologique plus rapide du secteur des services qui « attire » les travailleurs des manufac-tures vers les services. Le canal « push » domine depuis 1953, suggérant que c’est plutôt la croissance technologique plus rapide des manufactures qui « pousse » les travailleurs vers les services. L’analyse de tout l’échantillon de 1970 à 2011 suggère également des périodes de dominance de ces canaux pour une poignée d’autres pays analysés.

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Abstract

This paper examines the factors driving structural transformation, or worker reallocation, from manufacturing to services using a sample of 45 countries. As suggested by the general equilibrium model developed in this work, examining the trends in the relative price of services to manufacturing goods allows us to identify two main engines of structural transformation: a labor pull and a labor push effect. In the case of the United States, for example, the “pull” channel dominates before 1953, meaning that it is higher technological growth in services which is “pulling” workers to move out of manufacturing and into services. The “push” channel is the main engine at work since 1953, suggesting that it is instead higher technological growth in manufacturing which is “pushing” workers towards the services sector. A cross-country analysis over the 1970-2011 period also suggests periods of dominance of both channels for a handful of other countries analyzed.

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List of Tables

2.1 Sources of data by country . . . 18

3.1 Structural transformation out of manufacturing: Annual change in the share

of employment in manufacturing, LM (in percentage points) . . . 26 3.2 The average annualized percentage change of the relative price of services . . . 28 3.3 Periods of dominance of pull and push effects . . . 30 3.4 Effects of time and stage of structural change on growth rate of the relative

price of services . . . 32

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List of Figures

3.1 The share of employment in services and the relative price of services to

man-ufactured goods, USA, 1850/1889-2005. . . 22 3.2 The share of employment in services and the relative price of services to

man-ufactured goods, UK, 1841/1855-2005 . . . 23 3.3 The employment share in services. . . 25 3.4 The evolution of the relative price of services (ps/pm) across countries . . . 29

B.1 Countries having reached services employment share above 70% during the 1990s 39 B.2 The relative price of services (p_s/pm) . . . 40

B.3 Countries with a downward trend in the relative price of services over the entire

1970-2011 period . . . 41 B.4 Countries with an upward trend in the relative price of services over the entire

covered period, 1970/1990-2011 . . . 42 B.5 Countries with multiple trends in the relative price of services during the period

1970-2011 . . . 43

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List of abbreviations and acronyms

e.g. exempli gratia (for example)

et al. et alii (and others)

etc. et cetera (and other things)

GDP Gross Domestic Product

GGDC Groningen Growth and Development Centre

GLS General Least Squares

HP Hodrick-Prescott

i.e. id est (in other words)

ISIC International Standard Industrial Classification

NBSC National Bureau of Statistics of China

OECD Organisation for Economic Co-operation and Development

OLS Ordinary Least Squares

TFP Total Factor Productivity

UK United Kingdom

UN United Nations

US(A) United States (of America)

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A vous, très cher papa, Achille Gere Ngakula, pour tous les sacrifices consentis pour mon épanouissement. Je regretterai éternellement de n’avoir pas su vous les rendre.

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Acknowledgments

The most sincere thanks are extended to my research director, Professor Radek Stefanski, who provided guidance throughout the process of developing this work.

I would also like to thank Professor Roland Pongou for his moral support.

I must express my gratitude to the Rawji Foundation for the financial support I had received for my training.

I am also grateful for all the support I have received from different people and in the translation industry.

I warmly thank my dear mother, Béatrice Masika Muhasa, who always encourages me in the pursuit of excellence.

I also thank God for providing me with health and strength necessary to complete my studies.

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Foreword

Structural transformation is the reallocation of workers across sectors associated with eco-nomic development. One particular important aspect of this reallocation – especially in richer countries – is the movement of workers into the services sector. This paper’s goal is to identify the key factor driving the reallocation of workers towards services. Pining these forces down can potentially be useful in the choice and orientation of economic growth policies.

Several researchers have developed theories to explain the structural transformation that has occurred between agricultural and non-agricultural sectors. They consider technological progress as one of the determinants of this structural transformation. Given the available data, we perform a similar analysis but focus on the manufacturing and the services sectors. We use technological progress as the main driver of workers moving from manufacturing to services. This work does not seek to develop a new theory of this process. But instead, it aims to identify what the data can tell us concerning the importance of sectoral technological progress in explaining the structural change out of manufacturing.

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Introduction

“Structural transformation between manufacturing and services in modern economies is a well established fact” (Moro(2012), p.410). It is characterized by the increase in the share of employment in services relative to the manufacturing sector.1 Has this structural transforma-tion been driven by technological developments in services (pull effect) or rather by those in manufacturing (push effect)? The purpose of this study is to identify, using historical data, whether technological progress in one or both of these sectors promotes structural change.

This research topic is interesting because several studies (e.g., Gollin et al. (2002); Gylfa-son and Zoega (2006); and Alvarez-Cuadrado and Poschke (2011)) argue that technological progress drives the reallocation of labor across sectors in the economy. However, none of these studies specifically explores the importance of pull and push effects in the case of the structural transformation between manufacturing and services. For this reason, this study is relevant and complements the literature.

To achieve this goal, the present paper adopts a methodology based on a general equilibrium model that highlights the relative importance of pull and push effects on structural transfor-mation. After presenting the theoretical model, we analyze structural change in the United States (USA) and the United Kingdom (UK) to find empirical evidence of these effects in the long term. In the case of the USA, the analysis of the relative price of services to manufac-tured goods indicates a dominant pull effect before 1953, while the push effect is found to dominate since then. In the case of UK, the pull effect dominates from 1902 to 1916, and also for the periods 1933-1934 and 1953-1955, while the push effect is identified during the periods 1855-1887, 1916-1933 and 1955-2005. Furthermore, a cross-country analysis with recent data suggests a dominant push effect after 1970 for most of the countries analyzed. The pull effect dominance is also identified before and after 1980 for some countries.

This work is organized into three chapters, apart from the introduction and the conclusion. The first chapter presents the theoretical framework. It starts with a section that clarifies the meaning of some basic concepts and presents the main theories of structural transformation. The next section of this chapter provides a brief review of the recent literature on structural

1_{The countries analyzed in this work corroborate this assertion by}_Moro₍₂₀₁₂_{). Most of them exhibit an}

upward trend in the employment share of services (see Figure3.3).

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transformation. The second chapter presents the methodology and describes the data. The third chapter produces the empirical analysis. The first section of this third chapter evaluates the model against the experiences of the USA and UK. Then, a second section examines data across a panel of 45 countries for the period 1950-2012.

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Chapter 1

Theoretical framework

This chapter is organized into two sections. Before presenting the review of previous stud-ies, a first section provides definitions of some basic concepts of the theories of structural transformation.

1.1 Definitions and theories of structural transformation

In the first point of this section we define some concepts which are necessary to understand the methodology used in this work. The second point presents the main theories of structural transformation.

1.1.1 Definitions

The four following concepts are defined: structural transformation, productivity, technological progress, and homothetic preferences. One of the definitions is taken from a dictionary while others are provided in articles.

Structural transformation is defined as a reallocation of labor across sectors (see Kural-bayeva and Stefanski(2013), p.273). Considering the evolution of economic activity, structural transformation can also be defined as “the reallocation of economic activity across three broad sectors (agriculture, manufacturing, and services) that accompanies the process of modern economic growth” (Herrendorf et al. (2013), p.5).

Productivity is a measure which represents the efficiency of a process, person, machine,

etc. to transform one or several inputs to outputs.1 Duarte and Restuccia (2010) define the aggregate labor productivity as “the sum of labor productivity across sectors weighted by the share of hours in each sector” (p.131).

1_{Business Dictionary, definition of productivity, accessed from}

http://www.businessdictionary.com/definition/productivity.html#ixzz386iv33MU, on July 15, 2014.

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Technological progress refers to the “total productivity of inputs” or the “total factor

productivity” (see for details Martínez-García (2013)). Formally, it is given by the ratio of output to the combination of factors of production used to produce it (Martínez-García (2013), p.2).

Homothetic preferences are used to express the behaviour of a consumer who considers

all goods as superior and purchases them at the same proportion no matter what his income (see Schotter (2008), p.72). The preferences are non-homothetic when this condition is not satisfied.

1.1.2 Theories of structural transformation

Two major theories can be presented in explaining the structural transformation observed in an economy. One is based on preferences and the other on technological progress.2

The theory based on preferences uses the concept of non-homotheticity to reproduce the fact that the budget share of a particular good is influenced by the level of income. Many researchers used non-homotheticities in agriculture to introduce structural transformation into a model. Gollin et al.(2002), for instance, introduce subsistence requirements in agriculture as non-homothetic preferences. These imply that the household will not consume the agricultural good beyond the subsistence level. In such an environment, the surplus of labor not needed to produce this subsistence quantity of agricultural output moves into the non-agricultural sector. Other studies use homothetic preferences and focus on other elements to explain the process of structural transformation. This is the case in Kuralbayeva and Stefanski (2013). These authors use a model with homothetic preferences and combine the role of windfall revenues with the existence of a non-traded sector as the channel driving labor reallocation. Two sectors are considered in their model: manufacturing and non-manufacturing. Windfall revenues generate an increase in the demand for goods. The higher demand for manufactured goods is presumed to be satisfied by imports. As non-manufacturing is assumed to be a non-traded sector in their study, it follows that labor flowing out of manufacturing allows the economy to meet the higher food demand in the non-manufacturing sector.

Regarding the theory based on technological progress, two principal sources of structural transformation are distinguished: labor pull and labor push. Labor pull is observed when technological improvements in a sector result in higher wages in that sector, with the out-come of less labor supplied to other sectors.3 Meanwhile, the labor push channel comes into play when productivity gains in a sector are associated with a transfer of labor out of this sec-tor.4 This occurs when technological improvements effectively reduce the number of workers

2_{Buera and Kaboski}₍₂₀₀₉_{) consider these theories as the two traditional explanations of structural change.} 3

This idea emerged from the explanations given byGylfason and Zoega(2006) andAlvarez-Cuadrado and Poschke(2011).

4

Alvarez-Cuadrado and Poschke(2011) used labor push to refer to the hypothesis that technological

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required to produce higher quantities of output (Matsuyama (2009), p.479).

Several theoretical studies suggest other mechanisms to complement these two traditional theories. For example, the mechanism presented in Ngai and Pissarides (2007) attributes structural change to different elasticities of substitution between goods and differences in productivity growth rates across sectors. In their analysis, these authors consider a model of growth with many sectors that can produce consumption and intermediate goods, and they allow only one sector to produce capital goods (one of the extensions of their model allows many sectors to produce capital goods). If the elasticity of substitution between the final goods is low (below one), labor moves to the sector with the lowest total factor productivity (TFP) growth rate. Hence, the economy converges to a situation in which the employment share vanishes except for the sector with the smallest TFP growth rate and for the ones which produce capital and intermediate goods. However, if the elasticity of substitution exceeds unity, there would be structural change in favor of the high TFP sector.

The literature review by Dabla-Norris et al. (2013) mentions other mechanisms. They note, for instance, that the rising skills intensity in services and scale technologies caused structural reallocation as shown byBuera and Kaboski(2012a) andBuera and Kaboski(2012b). Open-ness to trade, combined with the “differential productivity growth rates across sectors”,5 has also influenced structural transformation as shown in the work ofMatsuyama (2009).

Referring toSchettkat and Yocarini(2006)’s analysis, differential productivity growth, differ-ences in the inter-industry division of labor and differdiffer-ences in the structure of final demand are used as hypotheses that explain the rising share of services employment. Concerning this hypothesis based on the demand, Haksever and Render (2013) argue that the “demand for increased technical knowledge and skills in the workplace makes higher education a prerequi-site to entry into postindustrial society and good life”; this lead to the increase in the demand of “services such as healthcare, education, arts, and so on” (p.10).6 Other reasons and mech-anisms explaining the growth of services include: “a rise in per capita income, an increase in urbanization, deregulation, demographic shifts, an increase in international trade, joint sym-biotic growth of services with manufacturing, advances in information, telecommunication technologies”, etc. (Haksever and Render (2013), pp.11-12).

1.2 Technological progress and structural transformation

Two points are discussed in this section. The first is a review of the literature and the second presents a model of structural transformation.

provements free up resources which can then flow from the agricultural to the non-agricultural sector.

5

Dabla-Norris et al.(2013), p.5.

6_{In the case of USA,}_{Haksever and Render}₍₂₀₁₃_{) mention that the government has contributed to meet}

these higher demands.

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1.2.1 Review of previous studies

Many studies have been conducted on the role of technological progress, i.e. productivity improvements, in structural transformation processes. This literature review focuses on five recent works.

Gollin et al. (2002) develop a model of development which is associated with a declining role of the agriculture sector. A subsistence consumption of agricultural goods is imposed so that once agricultural output corresponding to this level is reached, all remaining labor is reallocated to non-agricultural sector. The agricultural goods can be produced by traditional or modern technology. The rise of agriculture technology is associated with the decrease of labor needed to produce a quantity of output. This corresponds to the notion that produc-tivity improvements in agricultural sector release resources to other activities. To support this substitution effect, regressions are conducted using data for 62 developing countries for the period 1960-1990. The cross section analysis shows that agricultural productivity and the share of employment in agriculture are negatively related. Moreover, the panel analysis reveals that there is a positive relationship between the growth of agricultural productivity and the movement of labor out of agriculture in each country.

Gollin et al.(2007) use a similar model but allow feedback from industry to agriculture. This feedback corresponds to the mechanization of farming. As mentioned by these authors,Schultz (1953)’s idea about food problems is incorporated into the model to analyze the evolution of international incomes. In fact, the expression “food problem” is used by Schultz to refer to the situation of “food drain” that many poor countries suffer, “in which they have a level of income so low that a critically large proportion of the income is required for food” (Gollin et al. (2007), p.1231). According to this idea, countries can begin the process of modern economic growth only after meeting their subsistence needs and the reallocation of labor from agriculture to industry is therefore constrained by the need to satisfy the demand for food. In this study, the pace of the structural change is initially entirely driven by improvements in agricultural technology. Then, on-farm use of capital produced in the industrial sector accelerates the process of structural transformation. Thus, food scarcity offers a reason which explains the relatively slow convergence in some countries with regard to the beginning of their industrialization process.

In the study byDuarte and Restuccia (2010), a general equilibrium model is developed with agricultural, industrial, and services sectors. Income and substitution effects are incorporated into the model as the main channels of the structural transformation.7 The model is first calibrated using United States data from 1956 to 2004. This makes it possible to measure sectoral productivity differences across countries at a given point in time. These measures are

7_{Income and substitution effects result respectively from the non-homothetic preferences and from the}

differential productivity growth across sectors (Duarte and Restuccia(2010), p.132).

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used to evaluate the effect of sectoral productivity on labor reallocation across countries. Their panel dataset includes data series of output and hours worked, by sector, across 29 countries. For most of these countries, the data used covers the period 1956-2004. Their analysis shows that the process of structural transformation is common for all the countries despite the fact that they were at different stages of structural transformation in 1960.8 This process seems to be characterized by the shares of labor hours which are declining in agriculture and increasing in services for all countries analyzed. Regarding industry, a “hump-shaped share of hours”9 is exhibited among countries which are at an early stage of their structural change. But this share of hours is declining in the countries which are at a more advanced stage. In addition, they find that there is no movement of labor out of agriculture when productivity growth in this sector is set to zero. But when the industry productivity is set to zero, labor moves from industry to services and the share of hours in agriculture does not change. Finally, in the case of no productivity growth in services, they observe a small reallocation of labor across sectors. These counterfactuals and others allowed them to conclude that sectoral productivity growth has a substantial impact on labor reallocation.

Alongside the technological progress in the agricultural sector, Gylfason and Zoega (2006) show how technological improvements in the industrial sector can also promote structural transformation towards modern agriculture. Using historical data from Iceland, they found that economic growth began around 1870 in this country and was accompanied by a structural change from agriculture to fishing, and later to an economy dominated by the services sector. Labor push and labor pull are shown as mechanisms which drive the process of economic growth. In the case of labor push, economic growth arises from the introduction of new technologies in the agricultural sector, which is accompanied with a transfer of labor from agriculture to other sectors. In the case of labor pull, the growth is due to the attraction of cities, where technological improvements in urban areas increase urban wages and thereby pull workers out of agriculture. This also generates an increase in rural wages because few workers would accept to work for the prevailing wages of rural areas, thus inducing farmers to adopt new technologies (labor saving technologies), hence promoting economic growth. The authors conclude that it is especially the pull effect which promoted the movement of workers from agriculture to fishing in this country. In addition, they use historical data to find evidence for the role of both channels. Their empirical results reveal a negative relationship between structural change and economic growth. But this has not allowed them to identify the relative importance of each effect.

Recently, Alvarez-Cuadrado and Poschke(2011) proposed a method based on the analysis of the relative price of non-agricultural goods to overcome this difficulty. They develop a model which captures both labor push and labor pull channels. Their theoretical results suggest

8

Their sample includes countries with high shares of employment in agriculture, about 70%, as well as those with shares of less than 10%.

9

Duarte and Restuccia(2010), p.132.

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that if there is no technological regress, the decreases in the relative price of non-agricultural goods are associated with faster change in non-agricultural technology. This means that the labor pull effect unambiguously dominates. Using data from 12 industrialized countries, their empirical results indicate that the pull effect dominated until 1920 and the push effect became dominant after 1960.

1.2.2 A model of structural transformation with labor push and labor

pull effects: Alvarez-Cuadrado and Poschke (2011)’s model

Alvarez-Cuadrado and Poschke(2011) develop a general equilibrium model of structural trans-formation with two sectors. One produces agricultural goods and the other produces industrial goods and services. The parameters of technology are assumed to be initially constant. Labor is the only production factor. It is normalized to 1 and its mobility across sectors is allowed. There are two groups of economic agents, firms and consumers.

A. Firms’ problem

Firms in each sector produce goods according to the following weakly diminishing returns production functions:

Y_tA= AGLA_t (1.1)

and

Y_tM = M F1 − LA_t, (1.2)

where A > 0, G0 > 0, G00 ≤ 0, M > 0, F0 > 0, and F00 ≤ 0. YA

t is the level of food or

agriculture production, A is the parameter of agricultural technology, LA_t is the share of labor employed in agriculture, Y_tM is the level of non-agricultural production, M is the parameter of non-agricultural technology, and t is an index representing each date.

Competition between firms implies this non-arbitrage condition:

wA_t = AG0LA_t = ptM F0 1 − LA_t = w_tM (1.3) with pt= AG0LA_t M F0 1 − LA_t. (1.4)

In the equation (1.3), w_tAis the wage in agriculture sector, wM_t is the wage in non-agriculture sector, and p_t, the relative price of non-agricultural commodities and services to agricultural goods.

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B. Consumers’ problem

Consumers are identical and live infinitely. They supply their labor force to firms. Their preferences are non-homothetic. The representative consumer solves the following problem:

max cA t, cMt UcA_t, cM_t = blncA_t − µ1 + lncM_t + µ2 (1.5) subject to w_tAlA_t + w_tM1 − lM_t + πA_t + πM_t = cA_t + p_tcM_t , (1.6)

where b > 0, µ1 > 0, and µ2 > 0. cAt is the individual consumption of food, cMt is the individual

consumption of non-agricultural commodities and services, b is the relative weight of food in preferences, µ₁ is the subsistence level of food consumption, lA_t is the time spent working in the agricultural sector, lM_t is the time spent working in the non-agricultural sector, π_tAis the profit received from agricultural sector, π_tM is the profit received from non-agricultural sector, and µ₂ is the exogenous endowment of non-agricultural goods.10 In line with Engel’s Law, the income elasticity of food demand is less than one.11 The quantity of food produced by the entire labor force is assumed to be greater than µ₁. Formally, this latter condition is given by AG (1) > µ₁. This assumes that productivity in the agricultural sector is high enough to operate above the level of the subsistence consumption (µ1) if the entire labor endowment is

allocated to food production.

With λ the shadow value of an additional unit of income, the first order conditions yield the equations b cA_t − µ1 = λ (1.7) and 1 cM t + µ2 = λp_t. (1.8)

Combining equations (1.7) and (1.8), the relation between the two levels of consumption demand is

cA_t − µ₁ = p_tbcM_t + µ₂. (1.9)

As the equilibrium in each market implies cA_t = Y_tAand cM_t = Y_tM, the relation (1.9) becomes

Y_tA− µ1 = ptb Y_tM+ µ2 . (1.10) 10

µ1 and µ2 are the non-homothetic components in the utility function, implying that the income elasticity

of agricultural goods is less than one and that of non-agricultural goods is greater than one (Alvarez-Cuadrado and Poschke(2011), p.132).

11_{Engel’s Law is an economic theory stating that the proportion of income spent on food falls as income}

increases (seeMokyr(2003), p.199).

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Using equations (1.1), (1.2), (1.4) and (1.10), we obtain the relation AGLA_t− µ 1 = b AG0LA_t M F0 _{1 − L}A t h M F1 − LA_t+ µ2 i . (1.11)

After dividing by A, the equation (1.11) can be rewritten as

GLA_t −µ1 A − b G0LA_t F0 1 − LA t F1 − LA_t+ µ2 M = 0 ≡ φLA_t, A, M. (1.12)

Differentiating LA_t with respect to A and M , we obtain the effects of productivity increases on the sectoral allocation of labor:

∂LA_t ∂A = − φA φ_LA t = − µ1 A2_φ LA t < 0 (1.13) and ∂LA_t ∂M = − φM φ_LA t < 0, (1.14) with φ_LA

t , φAand φM the partial derivative of equation (1.12) with respect to L

A

t, A and M

respectively.

The inequalities (1.13) and (1.14) suggest that productivity increases in agricultural and in non-agricultural sectors lead to the reallocation of labor out of the agricultural sector. So, the model captures labor push and labor pull channels. Here, the values of LA_t , φ_LA

t , φAand

φM correspond to those of equilibrium.

The partial derivatives of equation (1.4) with respect to A and M imply

∂pt

∂A > 0 (1.15)

and

∂pt

∂M < 0. (1.16)

According to these inequalities, the relative price of non-agricultural goods is positively related to the technology used in agriculture and negatively to that of non-agricultural sector.

In the case of continuous technological change, the total derivative of the equation (1.4) with respect to time implies

˙ pt= _˙ AtG 0 LAt + AtL˙AttG 00 LAt MtF 0 1 − LAt −_M˙_t_F0 _{1 − L}A t − AtL˙AttF 00 LAt AtG 0 LAt MtF0 1 − LA_t 2 , (1.17) 10

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where the change in a variable x relative to time is denoted by ˙x. Dividing the equation (1.17) by the expression of the relative price, we obtain:

˙ pt pt = ˙ At At −M˙t Mt + ˙LA_t   G00LA_t G0 LA_t + F001 − LA_t F0 1 − LA_t  > ˙ At At −M˙t Mt (1.18) with ˙ LA_t   G00LA_t G0 LA_t + F001 − LA_t F0 1 − LA_t  > 0. (1.19)

Under the hypothesis of no technological regress, i.e. ˙At> 0 and ˙Mt> 0, the relation (1.18)

holds. Thus, decreases in the relative price of non-agricultural goods are associated with faster technological change in the non-agricultural sector. These decreases of the relative price unambiguously indicate that the labor pull effect dominates, i.e., with ˙pt/pt < 0, we

have ˙Mt/Mt> ˙At/At. But in the case of relative price increases, “the situation is less clear”

(Alvarez-Cuadrado and Poschke(2011), p.135). Because of this ambiguity, they conclude that the identification of the pull effect is very robust compared to that of the push effect.

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Chapter 2

Methodology and data

Two sections are presented in this chapter. The first section presents the methodology and the second describes the data.

2.1 Modeling structural transformation between

manufacturing and services

We start this chapter by presenting the model by giving the main problem of each agent. Then we present the equilibrium and the comparative statics.

2.1.1 Model

Our model is an application of Alvarez-Cuadrado and Poschke (2011)’s model to the case of manufacturing and services. The technologies of production and the mathematical de-velopment are the same, but the agricultural sector is replaced by manufacturing while the non-agricultural sector is replaced by services. The term manufacturing is used to refer to all industrial goods. Here, we have:

A. Firms’ problem

Firms’ production technologies are given by

Y_tM = M GLM_t (2.1)

and

Y_tS = SF1 − LM_t , (2.2)

with M > 0, G0 > 0, G00 ≤ 0, S > 0, F0 > 0, and F00 ≤ 0. YM

t is the level of manufacturing

production, M is the level of manufacturing technology, LM_t is the share of labor employed in manufacturing, Y_tS is the level of services production, and S is the level of services technology.

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In this case, the relative price of services can be expressed as1 pt= M G0LM_t SF0 _{1 − L}M t . (2.3) B. Consumers’ problem

Preferences of the representative consumer are still non-homothetic with a few changes as follows:

UcM_t , cS_t=αlncM_t −m+ (1−α) lncS_t+s, (2.4)

where, as at point B of sub-section1.2.2, cM_t is the individual consumption of manufacturing goods, cS_t is the individual consumption of services, α is the weight of manufactured goods in preferences (α ∈ (0, 1)), and the parameters m and s are strictly positive. We only examine

the allocation of time across market sectors. Exactly as in the paper byDuarte and Restuccia (2010), since we are not interested by a possible allocation of time between the market and the non-market sector (home sector), this latter is modeled in a reduced-form (s represents the home sector for services).

As noted before, the income elasticity for agricultural goods is low, that of industrial goods, in particular for manufactured goods is higher, and that for services still higher (see for instance Clark(1960) and Kuznets (1971)). The additional restriction we impose is that the income elasticity of manufacturing is assumed to be less than one and that of services greater than one. We do this in order to reproduce the fact that the richest countries tend to have some of the lowest manufacturing shares as a fraction of gross domestic product (GDP).2 In equation (2.4), m is interpreted as a requirement level of manufactured goods consumption and s is the domestic production of services. The maximization of the function (2.4) subject to the budget constraint

wM_t lM_t + w_tS1 − lM_t + π_tM + πS_t = cM_t + p_tcS_t (2.5)

yields a relation between the demand for manufacturing goods cM_t and that for services cS_t:

(1−α)cM_t − m= ptα

cS_t + s. (2.6)

In equation (2.5), lM_t is the time spent working in the manufacturing sector, lS_t is the time spent working in the services sector, πM_t is the profit received from the manufacturing sector, and πS_t is the profit received from the services sector.

1

With competition between firms, wMt = M G

0

LMt

= ptSF0 1 − LMt

= wtS. In this equality, wMt and

wtSdenote the wages in the manufacturing and the services sectors respectively.

2_{For example, data on household expenditures for 48 US states for the period 1939-1958 indicates an}

income elasticity of 0.97 for goods and 1.12 for services (Fuchs(1968), p.42), consistent with our additional restriction. Using French data for the period 1959-1978,Kravis et al.(1983) also found an income elasticity which is lower (below one) for commodities (0.87) than for services (1.23).

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2.1.2 Market equilibrium and comparative statics

Market clearing implies, for each good produced and at each period, the relations cM_t = Y_tM and cS

t = YtS. The market clearing conditions, with equations (2.1), (2.2), (2.3) and (2.6),

yield the relation

GLM_t − m M − α 1−α G0LM_t F0 1 − LM_t F1 − LM_t + s S = 0 ≡ φLM_t , M, S. (2.7) From (1.11), we have ∂LM_t ∂M = − φM φ_LM t = − m M2_φ LM t < 0 (2.8) and ∂LM t ∂S = − φS φ_LM t < 0, (2.9) with φ_LM

t , φM and φS the partial derivative of equation (2.7) with respect to L

M

t , M and

S respectively. The relation expressed in (2.8) indicates the push effect while the equation (2.9) refers to the pull effect.3 The values of LM_t , φ_LM

t , φM and φS considered here are those

obtained at the equilibrium.

The partial derivative of equation (2.3) with respect to LM_t , M , and S implies

∂pt ∂LM_t = M G00LM_t SF01 − LM_t + SF001 − LM_t M G0LM_t SF0 1 − LM_t 2 < 0, (2.10) ∂pt ∂M > 0 (2.11) and ∂pt ∂S < 0. (2.12)

In the presence of continuous technological change, the total derivative of the equation (2.3) relative to time implies

˙ pt= _˙ MtG 0 LM t + MtL˙M_t tG 00 LM t StF 0 1 − LM t −_S˙_t_F0 1 − LM t − MtL˙M_t tF 00 LM t MtG 0 LM t MtF0 1 − LAt 2 , (2.13)

3_{With homothetic preferences given by U c}M t , cSt =α cM t ρ + (1−α) cS t

ρ1ρ_{, the expression (}_2.7₎

be-comes G LM t − α 1−α G0(LM_t ) F0(1−LM t ) M S ρ 1 1−ρ F 1 − LM t = 0 ≡ φ LM t , M, S

. With ρ ∈ (0, 1), only the in-equality ∂LMt

∂S < 0 holds, meaning that it is only the labor pull effect which is captured.

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where the derivative of a variable x relative to time is denoted by ˙x. Dividing equation (2.13) by the relative price of services, we obtain

˙ pt pt = ˙ Mt Mt −S˙t St + ˙LM_t   G00LM t G0 LM_t + F001 − LM_t F0 1 − LM_t  > ˙ Mt Mt −S˙t St . (2.14)

As in sub-section1.2.2, the inequality in (2.14) holds under the hypothesis of no technological regress. This implies that decreases in the relative price of services to manufactured goods are associated with relatively faster technological change in the services sector. But there is ambiguity in the case of increases in the relative price. In other words, if 0 > ˙pt/pt >

˙

Mt/Mt− ˙St/St, then ˙St/St > ˙Mt/Mt. But, if 0 < ˙pt/pt, then we can not say much about

the relationship between growth of M and S. To obtain a clear situation, we suppose that

G(LM_t ) = LM_t and F (1 − LM_t ) = 1 − LM_t . In that case, the relation (2.14) becomes ˙ pt pt = ˙ Mt Mt −S˙t St . (2.15)

So, the equation (2.15) implies that increases in the relative price are always due to higher growth in M and decreases are always due to higher growth in S. In this condition, the pull effect dominates when faster productivity growth in the services sector drives the process of structural transformation. This empirically corresponds to the decline in the relative price of services. Meanwhile, the domination of the push effect indicates that faster productivity growth in manufacturing drives the process of structural change. This is empirically associated with an increase in the relative price of services.

2.2 Data

This section begins with notes on data collected for the empirical section. The idea is to describe the procedure used to obtain the indicators required for the empirical evaluation of our model.

2.2.1 Notes on data

This type of study is best performed using long-term panel data. However, given that long series of historical data are not available by sector of activity for most countries, only the USA and the UK data are analysed in the very long term, specifically, from the 19th century to the beginning of the 21st century. Three sources were used to put together the series of long-term data: i) the Groningen Growth and Development Centre (GGDC), especially the GGDC 10-Sector Database and the Historical National Accounts, ii) the Historical Statistics of the United States (Millennial Edition), especially the Table Ca9-19 by Sutch (2006), and

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iii)Mitchell(2003a) (Tables B1 and J1) andMitchell(2003b) (Table B1). From these different sources, GGDC database provides series of production and employment by sector of activity, Table Ca9-19 provides values of production in current prices for USA, Table B1 presents the economically active population by major industrial groups, and Table J1 offers national accounts totals.

In fact, two types of data were collected. The first is comprised of very long term series and the second is comprised of more recent data. Table2.1summarizes the sources of data by country, specifying the periods of data availability. The historical series of production data provided by the GGDC database go back to the 19th century. The USA data covers the period 1869-2005 and the UK data extends from 1855 to 1869-2005. The values of total national production at current prices provided by Sutch (2006) and Mitchell (2003a) were used to complete the GGDC series. In the case of employment, Mitchell (2003a) and Mitchell (2003b) provide series of active population by sector of activity for the period 1820-1995 in the case of the USA and from 1841 to 2001 for the UK. Details on procedures used to obtain complete series of production and employment by sector of activity are presented in AppendixA. The modern production series cover the period 1970-2011 for a larger number of countries. We have used the data series named GDP and its breakdown at current prices in national currency and at constant 2005 prices as provided by the National Accounts Main Aggregrates Database of the United Nations (UN). While other sources can be used to reach back as far as 1940 for some countries, we have chosen this data because information across the panel of production data comes from the same sources for all the other 43 countries considered for this study. This is more suitable for cross-country comparisons. In terms of employment, the more recent data on employed persons by industry is taken from the Organisation for Economic Co-operation and Development (OECD) database for 26 countries. Data for China is from the National Bureau of Statistics of China while data for the USA, the UK and the other 16 countries is taken from the GGDC 10-Sector Database.

2.2.2 Determining the indicators of structural transformation analysis

The relative price of services to manufactured goods and the share of employment in the manufacturing sector are the two indicators needed for the emprical analysis. As proposed by Alvarez-Cuadrado and Poschke (2011), analysis of the share of employment is used to highlight the process of structural transformation while the relative price of services allows us to identify the mechanism driving this transformation.

In fact, series of price indices of services are calculated using data on GDP at current and constant prices.4 For the manufacturing sector, the following components are included in the

4_{From this paragraph, the expression relative price refers to the price index of services relative to}

manu-factured goods.

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Table 2.1: Sources of data by country

Countries Data sources

Employed personsa,b Values addedc

GGDC NBSCd OECD GGDC GGDC/Historical UN/National Accounts

10-Sector 10-Sector National Accounte Main Aggregrates

Argentina 1950-2005 1970-2011 Australia 1956-2012 1970-2011 Austria 1956-2011 1970-2011 Bolivia 1950-2005 1970-2011 Brazil 1950-2005 1970-2011 Belgium 1956-2012 1970-2011 Canada 1956-2012 1970-2011 Chile 1950-2005 1970-2011 China 1952-2006 1970-2011 Colombia 1950-2005 1970-2011 Costa Rica 1950-2005 1970-2011 Czech Republic 1975-2012 1990-2011 Denmark 1956-2011 1970-2011 Finland 1956-2012 1970-2011 France 1956-2011 1970-2011 Germany 1956-2012 1970-2011 Greece 1956-2012 1970-2011 Hong Kong 1974-2005 1970-2011 Hungary 1992-2012 1970-2011 Iceland 1956-2012 1970-2011 India 1960-2005 1970-2011 Indonesia 1961-2005 1970-2011 Ireland 1956-2012 1970-2011 Italy 1956-2012 1970-2011 Japan 1956-2011 1970-2011

Korea (Republic of) 1973-2012 1970-2011

Malaysia 1975-2005 1970-2011 Mexico 1950-2005 1970-2011 Netherlands 1956-2012 1970-2011 New Zealand 1956-2012 1970-2011 Norway 1956-2011 1970-2011 Peru 1960-2005 1970-2011 Philippines 1971-2005 1970-2011 Poland 1993-2011 1970-2011 Portugal 1960-2012 1970-2011 Singapore 1970-2005 1970-2011 Slovakia 1994-2011 1990-2011 Spain 1956-2012 1970-2011 Sweden 1956-2012 1970-2011 Switzerland 1960-2009 1970-2011 Thailand 1960-2005 1970-2011 Turkey 1956-2011 1970-2011 United Kingdom 1948-2005 1947-2005 1855-1965 United States 1950-2005 1947-2005 1869-1957 Venezuela 1950-2005 1970-2011 Notes:

a_{The economically active population by major industrial groups provided in}_Mitchell₍_2003a_{) and}_Mitchell₍_2003b_{) is also}

used to extend the employment series provided by GGDC 10-Sector for USA and UK respectively.

b_{Linear interpolation was used to complete missing data for Germany (2008), and Indonesia (1962-1970).}

c_{The historical Statistics of the United States provided by}_Sutch₍₂₀₀₆_{) and national accounts totals from}_Mitchell₍_2003b₎

are respectively used to extend until 2005 the GGDC series of value added for USA and UK.

d_{NBSC is the abbreviation we used for National Bureau of Statistics of China.}

e_{Linear interpolation was used to complete missing data for UK (1913-1920 and 1938-1946) and USA (some years between}

1869 and 1948).

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data: Mining, Manufacturing and Utilities (ISIC C-E),5 _{and Construction (ISIC F). Services} sector includes Wholesale, retail trade, restaurants, and hotels (ISIC G-H), Transport, stor-age, and communication (ISIC I), and Other Activities (ISIC J-P). Three steps are taken to calculate the relative price of services for each period. First, we calculate for each sector i the sum of the values of products in current prices (V_i) and the sum of the values of products in constant prices (V_iconstant). Then, the values obtained are used to calculate the series of price indices in each sector. For each period, the price is obtained by taking the ratio of V_i to V_iconstant. So, for each period, the ratio between the sum of production at current price in the manufacturing sector and the sum of production at constant price yields the price of manufactured goods (p_m), i.e., p_m = V_m/V_mconstant (m denotes the manufacturing sector). Prices of services (ps) are similarly obtained by taking the sums of production in this sector.

Finally, the relative price of services to manufactured goods (p) is obtained, for each period, as the ratio of the price of services and the price of manufactured goods, i.e., p = p_s/pm.

The shares of employment are calculated using data on the population active in each sector. To calculate the share in the manufacturing sector, we first calculate the total number of persons, i.e., the sum of workers employed in the respective components of the two sectors, or

lm+ ls(with lm as the total number of persons employed in the branches of the manufacturing

sector and ls for the services sector). The share of employment in the manufacturing sector

(LM) is obtained by dividing the sum of employment in the manufacturing sector by total employment in the two sectors, i.e., LM = l_m/(lm+ ls).

5

ISIC is the abbreviation of International Standard Industrial Classification.

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Chapter 3

Empirical analysis

The goal of this chapter is to find empirical support for the importance of pull and push effects addressed in the theoretical model. To do this, the first part is devoted to analysis of long-term trends in the relative price of services in the case of the USA and UK. The second deals with analysis of structural transformation over a set of 45 countries.

3.1 Structural transformation between manufacturing and

services: What can the long series of historical data say?

Two points are developed in this section. The first is devoted to the analysis of the USA and the second to the UK.

3.1.1 United States’ experience

The USA is selected as the point of reference in our model for two reasons. First, in most studies, the USA is used as a point of reference due to the quality of its data. Also, the increase in the rate of employment in services that occurred in this country is well documented (see, for instance, Rogerson(2008) and Haksever and Render(2013)).

Indeed, Figure 3.1 shows the evolution of the share of employment in services from 1850 to 2005 and the price of services relative to manufactured goods from 1889 to 2005. During this period, the share of employment in services increased from 54 to 92%. Monotonic increase is found during the periods 1860-1870, 1880-1910 and 1920-1952. After 1952, this share con-tinued to increase annually until 2005 with the exception of a few years. Trends components presented in Figure3.1are obtained after using Hodrick-Prescott (HP) filter with λ = 1600.1 In terms of relative price, which is an index that equals here to 1 in 1995 (the base year), two trends can be clearly identified. On the one hand, we have the relative price of services which tends to fall between 1889 and 1952, with a strong volatility in the period of the Great

1

λ is the smoothing parameter of HP filter.

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Figure 3.1: The share of employment in services and the relative price of services to man-ufactured goods, USA, 1850/1889-2005

Depression and the beginning of the Second World War, i.e., between 1929 and 1939. On the other hand, we have an upward trend in this relative price over the 1952-2005 period.2

As suggested byAlvarez-Cuadrado and Poschke(2011), changes in the directions observed in Figure3.1indicate that both mechanisms, labor pull and labor push, matter. Thus, according to equation (2.15), the decline of the relative price before 1953 – which coincided with the fall in the employment share in manufacturing – corresponds to the dominance of the labor pull channel. This means that higher technological growth in the services sector is “pulling” workers to move out of manufacturing and into services. In constrast, the increase in the relative price since the year 1953 is associated with the dominance of the labor push channel, meaning that faster technological growth which takes place within the manufacturing sector is “pushing” labor to the services sector.

2_{According to}_Ott₍₁₉₈₇_{)’s analysis, the US share of services in total output has risen; “a rise in the relative}

price of services, then, can only mean that the demand for them also has increased relative to commodities” (Ott(1987), p.12). Two explanations are given concerning the rise of demand for services. First, it is supposed to result from the increase in the quality of services. Second, development economists and economic historians argue that “as economies mature, rising income is progressively directed toward purchases of “higher-order” or luxury goods of which services predominate” (Ott(1987), p.12).

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3.1.2 United Kingdom’s experience

The choice of the United Kingdom is not only motivated by the availability of data, but also by the fact that this country was the first country to undergo the process of industrialization in the 18th century. Therefore, we find it interesting to evaluate our model with respect to the experience of this country.

In effect, the long historical series put together for the UK covers the period 1841-2005 for the sectoral shares of employment and 1855-2005 for the relative price of services. Figure3.2 plots the share of employment in the services sector, which increased over 1841-2005 from 47 to 81%. This increase is monotonic during the periods 1851-1871, 1881-1891, 1911-1931, and 1955-2005 (with the exception of 1960, 1964, and 1998). This figure also presents the relative price of services, its trend component and that of the employment share of services. These trend series are also obtained after using HP filter with λ = 1600.

Figure 3.2: The share of employment in services and the relative price of services to man-ufactured goods, UK, 1841/1855-2005

On the relative price of services,3 the original series is strongly volatile during the period of Great Depression as in the case of USA. The period of volatility is specifically from 1923 to 1935. But the results of HP filter suggest an increasing trend of this relative price for

3

As an index, this relative price equals to 1 in the base year 1995.

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the period 1923-1933. Our model identifies the dominance of pull effect only for the periods 1902-1916, 1933-1934 and 1953-1955 because during these years the employment share in manufacturing also declines. But for the periods 1855-1887, 1916-1933 and 1955-2005, this relative price tends to rise in correspondence with the dominance of the push effect.4

The main evidence resulting from this time series analysis is that the determinant of structural transformation is technology growth in both manufacturing and services sectors. On the one hand, the higher technology growth in services is the principal determinant during the period 1889-1952 in USA, and for the periods 1902-1916, 1933-1934 and 1953-1955 in UK. This is what we qualify as the dominance of the pull effect because it is higher productivity growth in services which is pulling workers to move out of manufacturing and into the services sector. On the other hand, the higher technology growth in the manufacturing sector is the main determinant of structural change after 1952 in USA and during the periods 1855-1887, 1916-1933 and 1955-2005 in UK. This phenomenon is called the dominance of the push effect because it is higher technology growth in manufacturing which leads to release workers who become available for the services sector. Furthermore, this analysis showed that, during the Great Depression, the relative price series is strongly volatile for both countries. However, its trend component from HP filter reveals a dominance of the pull effect during this period in the case of USA, but UK data suggests a dominance of the push effect.

3.2 Structural transformation across countries

Two points are developed in this section. The first deals with the analysis of structural transformation across a set of 45 countries. The second studies the relationship between the relative price of services and structural transformation in these countries.

3.2.1 Evidence of structural transformation

Structural transformation is analyzed using data from a sample of 45 countries selected ac-cording to data availability. As found in the literature, Figure 3.3 shows that structural transformation between manufacturing and services has occurred in modern economies. With the exceptions of Indonesia, Malaysia, and Thailand, other countries analyzed present a clear upward trend in the share of employment in services between 1950 and 2012. Consequently, the share of employment in manufacturing sector declines.5 Table 3.1 confirms this trend of negative growth rates in the employment shares of manufacturing for the periods covered

4

Hunt(2007) presents some factors which can explain the increase in the UK relative price of services. These factors include the globalization, specifically, the importation of “competitively priced goods from emerging Asia” during the period 1997-2006, the gap between sectoral productivity within the country, the pound appreciation (e.g., during the 1996–1997 period), and the efficiency improvement in the sector of distribution (Hunt(2007), p.3).

5_{It is the non-agricultural manufacturing share. The decline of this manufacturing employment share is}

commonly attributed to productivity gains in manufacturing (Matsuyama(2009)).

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with the exception of the countries mentioned above (see column 1). Even when considering 10-yearly sub-periods, cases of increasing employment shares in manufacturing are few in number.

Figure 3.3: The employment share in services

In effect, for the 10-yearly periods, the rates of change in employment shares are generally negative with limited exceptions. The process is slower in the 1950s for most countries, but this accelerates through the 1970s. Overall, the countries with the fastest structural change are Hong Kong (average change of employment share in manufacturing: -3.78% for the 1974-2005 period) and the Netherlands (average change of employment share in manufacturing: -2.39% for the 1956-2012 period). Of the 45 countries in the sample, 24 attained services employment shares of more than 70% in the 1990s (see Figure B.1 in Appendix B). These shares continued to increase above 80% during the 2000s in 6 countries: Denmark, France, Hong Kong, Netherlands, UK, and USA.

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Table 3.1: Structural transformation out of manufacturing: Annual change in the share of employment in manufacturing, LM (in percentage points)

Entire period 1950-1959 1960-1969 1970-1979 1980-1989 1990-1999 2000- Period covered

Argentina -1.14 0.35 -0.90 -0.79 -2.65 -3.27 0.27 1950-2005 Australia -1.29 -0.53 -1.00 -1.87 -1.93 -1.90 -0.58 1956-2012 Austria -1.22 -1.06 -0.51 -1.11 -1.24 -2.27 -1.56 1956-2011 Belgium -1.55 -1.39 -0.88 -2.18 -2.22 -0.76 -1.62 1956-2012 Bolivia -0.73 -0.99 0.49 -1.36 -4.31 1.57 -0.49 1950-2003 Brazil -1.20 -1.23 0.26 -1.51 -1.52 -2.44 -0.85 1950-2005 Canada -1.35 -2.54 -1.13 -0.95 -1.25 -1.06 -1.33 1956-2011 Chile -1.12 0.10 -0.25 -0.38 0.04 -2.87 -1.04 1950-2005 China -0.04 2.83 0.75 1.02 -0.80 -1.67 -0.43 1952-2006 Colombia -0.85 -0.23 -0.49 -2.22 -0.17 -2.36 -0.60 1950-2005 Costa Rica -0.47 -0.62 0.16 0.67 0.19 -2.00 -1.50 1950-2005 Czech Republic -1.00 -0.71 -0.38 -2.11 -0.52 1975-2012 Denmark -1.60 -0.08 -0.56 -2.19 -1.30 -0.61 -3.27 1956-2011 Finland -1.58 -3.98 -1.26 -1.16 -1.96 -1.30 -1.76 1956-2012 France -1.77 -0.13 -0.70 -1.49 -3.06 -2.33 -2.17 1956-2011 Germany -1.16 -0.33 -0.32 -1.60 -1.30 -1.45 -1.51 1956-2012 Greece -1.36 -0.30 0.32 0.31 -1.79 -2.97 -2.96 1956-2012 Hong Kong -3.78 0.46 -2.88 -6.01 -5.70 1974-2005 Hungary -1.20 -1.07 -1.24 1992-2012 Iceland -1.60 -1.46 -0.59 -0.46 -2.15 -3.05 -2.25 1956-2012 India -0.01 0.17 -0.26 -0.58 1.72 -1.73 1960-2004 Indonesia 0.36 -0.21 0.33 0.96 0.09 -0.02 1961-2005 Ireland -1.24 -1.60 1.03 -0.47 -1.80 -0.91 -3.86 1956-2012 Italy -0.96 -0.43 -0.07 -1.19 -2.33 -0.18 -1.32 1956-2012 Japan -0.79 0.62 0.61 -1.04 -0.69 -1.05 -2.23 1956-2011

Korea (Republic of) -0.97 2.77 0.14 -3.59 -1.55 1973-2012

Malaysia 0.43 2.92 -0.06 1.71 -2.41 1975-2005 Mexico -0.34 -0.20 0.98 -0.66 -0.69 -0.92 -1.31 1950-2005 Netherlands -2.39 -0.83 -0.75 -2.08 -1.84 -2.53 -4.68 1956-2012 New Zealand -1.24 0.61 -0.24 -1.63 -3.72 -0.41 -1.33 1956-2012 Norway -1.49 -1.23 -0.56 -2.90 -1.97 -1.42 -0.89 1956-2011 Peru -1.81 -1.40 -3.26 -1.55 -2.45 0.31 1960-2005 Philippines -0.77 -0.04 -1.19 -0.78 -0.65 1971-2005 Poland -1.00 -1.69 -0.65 1993-2011 Portugal -1.10 -0.81 0.84 -1.57 -0.62 -2.65 1960-2012 Singapore -0.09 2.14 0.01 -1.01 -2.55 1970-2005 Slovakia -1.93 -1.21 -2.07 1994-2011 Spain -1.61 0.78 -0.35 -1.81 -1.49 -1.48 -3.50 1956-2012 Sweden -1.59 -0.55 -0.85 -2.11 -1.22 -1.76 -1.91 1956-2012 Switzerland -1.69 -0.67 -1.83 -1.87 -2.66 -1.30 1960-2009 Thailand 0.57 1.71 0.93 -0.07 -1.18 0.22 1960-2005 Turkey -0.48 -1.94 -0.07 -0.24 -0.66 -0.12 -0.49 1956-2011 United Kingdom -1.65 -0.19 -0.67 -0.07 -2.39 -2.33 -3.17 1950-2005 United States -1.29 -0.95 -0.60 -0.03 -1.88 -1.30 -2.27 1950-2005 Venezuela -0.97 -0.20 -0.35 0.60 -1.58 0.17 -6.87 1950-2004

Note: The average change in percentage (γLM) of a variable LM between periods 0 and T is calculated as follows:

γ_LM= _LM T LM 0 T1 − 1 × 100. 26

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3.2.2 Identification of main channels: Labor pull versus labor push and the role of time

This part is devoted to identification of the principal channels driving structural transfor-mation of the countries retained for study and the role that technological developments in a certain time period play in this process. Next we identify these principal channels and then address the role of time.

A. Labor pull versus labor push

As suggested in the theoretical framework, the analysis of growth in the relative price of services is used to determine the main engine of the structural change in each country. Table 3.2 presents the average change of the relative price of services, in percent, for a period from 1970 to 2011, and for sub-periods of at least 10 years. As we use values added series from the United Nations database, which are in 2005 prices, the value of the relative price equals to 1 in that base year.6 For all periods with data availability, 13 countries recorded a decline in relative price of services during the period covered. The other 32 countries recorded positive variations during the period covered.

To better identify the trend across the entire panel with respect to time and stages of structural transformation, the relative prices series of each country was divided by the value in the first year of data availability. These standardized series are used to construct Figure3.4. In effect, in part (a) of this figure, the relative price is presented relative to time. It can be seen that the price series of certain countries ends above the line corresponding with a value of 1 while for other countries it ends below this line. Furthermore, observing part (b), where the relative price is plotted against employment share in services, an upward trend can be observed for some series. So, the relative price seems to be influenced by time periods and stages of structural change.

To complete this analysis, we use the HP filter to capture the trend of the relative price of services. Figure B.2 in Appendix B presents both the relative price of services and its trend component from the HP filter (λ = 1600) for all the panel. According to trends observed, three groups of countries can be considered. The first is comprised of Colombia, India and Indonesia. These countries exhibit a downward trend in the relative price of services over the entire period covered, i.e., 1970-2011 (see Figure B.3 in Appendix B). But the dominance of the pull effect is identified only for Colombia and for India over the periods 1970-2005 and 1970-1985 respectively because during these periods the employment share in manufacturing also tends to decline. By the dominance of the pull effect, we mean that higher technology growth in the services sector pulls workers to move out of manufacturing and into services.

6_{The USA and UK series of the relative price which were in 1995 prices are divided by the value in the}

year 2005 to obtain 1 in this year.

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Table 3.2: The average annualized percentage change of the relative price of services

Entire period 1970-1979 1980-1989 1990-1999 2000- Period covered

Argentina 1.05 0.92 -2.58 4.99 -2.17 1970-2011 Australia 0.46 0.61 2.50 0.44 -1.36 1970-2011 Austria 1.24 2.82 0.91 0.60 0.79 1970-2011 Belgium 2.09 4.17 1.65 1.34 1.34 1970-2011 Bolivia -0.27 0.53 0.08 3.40 -2.23 1970-2011 Brazil 0.36 -1.93 2.04 6.73 -0.65 1970-2011 Canada -0.47 -1.33 2.19 -0.26 -1.56 1970-2011 Chile -0.80 0.55 -2.67 3.49 -3.71 1970-2011 China 1.87 -1.15 3.27 3.74 1.47 1970-2011 Colombia -1.46 -1.51 -0.57 1.36 -3.15 1970-2011 Costa Rica 1.28 1.49 0.32 -0.01 1.74 1970-2011 Czech Republic 1.10 -1.11 2.61 1990-2011 Denmark -0.05 1.28 0.90 -0.16 -0.52 1970-2011 Finland 1.50 -0.42 1.56 0.67 3.05 1970-2011 France 0.99 0.61 0.75 1.67 1.04 1970-2011 Germany 0.13 0.66 -0.19 0.45 -0.38 1970-2011 Greece 0.28 -0.55 -0.07 2.31 0.06 1970-2011 Hong Kong 0.13 -0.01 0.05 0.00 0.39 1970-2011 Hungary 2.13 0.54 1.85 5.14 0.94 1970-2011 Iceland -0.19 -0.17 -1.50 0.87 -1.26 1970-2011 India -0.47 -1.67 -0.36 0.37 -0.64 1970-2011 Indonesia -2.15 -5.93 3.15 -0.32 -3.12 1970-2011 Ireland 3.51 1.51 3.84 1.86 6.53 1970-2011 Italy 1.11 1.26 1.82 1.15 0.33 1970-2011 Japan 1.07 1.74 0.39 0.93 1.50 1970-2011

Korea (Republic of) 2.17 3.58 1.83 1.41 1.57 1970-2011

Malaysia -2.01 -6.50 5.16 -1.21 -2.50 1970-2011 Mexico -0.40 -1.23 0.56 1.38 -1.82 1970-2011 Netherlands 0.31 1.33 0.89 1.26 -0.90 1970-2011 New Zealand 0.22 -0.56 2.87 0.28 -1.30 1970-2011 Norway -0.66 -0.23 4.78 0.73 -3.11 1970-2011 Peru -0.32 -6.92 7.58 1.56 -2.01 1970-2011 Philippines 0.36 0.15 -0.53 2.13 0.02 1970-2011 Poland 4.24 0.04 1.13 19.05 1.44 1970-2011 Portugal 0.05 -0.67 0.82 -0.10 0.12 1970-2011 Singapore 0.36 -1.21 -0.42 0.31 2.97 1970-2011 Slovakia 1.60 -2.22 4.81 1990-2011 Spain 0.92 0.99 1.22 2.05 -0.56 1970-2011 Sweden 1.50 0.37 1.00 3.32 1.42 1970-2011 Switzerland 0.76 -0.01 0.38 2.19 0.47 1970-2011 Thailand 0.02 -0.57 0.77 1.45 -0.92 1970-2011 Turkey 1.78 -0.06 -1.38 5.28 0.94 1970-2011 United Kingdom 0.90 -0.98 2.78 1.52 0.80 1970-2005 United States 0.82 -0.57 2.58 1.52 -0.20 1970-2005 Venezuela -1.34 -6.50 -0.66 8.99 -4.00 1970-2011

Note: The average change in percentage of relative price p between periods 0 and T is calculated as follows:

γp= h pT p0 _T1 − 1i× 100.

In the case of Indonesia, the pull effect is not identified because its series of employment share in manufacturing tends to increase over the entire period covered. The second group is comprised of 23 countries which present an upward trend in the relative price of services over the entire 1970-2011 period. Trend component series of this relative price of services and those of employment share in manufacturing for these countries are presented in Figure B.4in Appendix B. The push effect dominates for all these countries according to the period of employment data covered by country. This means that labor is pushed to services sector by higher technological developments that are taking place within the manufacturing sector. The third group of countries presents multiple trends in the relative price during the period 1970-2011. Some of them begin with a downward trend. This is the case of 10 countries presented in the part (a) of the FigureB.5 in Appendix B. Meanwhile, the other 9 countries, presented in the part (b) of this figure, begin with an upward trend. Both pull and push effects

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Figure 3.4: The evolution of the relative price of services (p_s/pm) across countries

Note: In part (b) of this figure, the graph for Poland does not cross the line corresponding to “p standardized = 1” because data on employment share in manufacturing covers only the 1993-2011 period for this country and all values of p standardized corresponding to this period are greater than 1.

are identified for some of these countries (e.g., Argentina, Australia, and Bolivia). Table 3.3 presents the periods corresponding to the dominance of each effect by country.