Trade openness, structural change, and productivity growth in China

This dissertation explores the growth mechanisms and regional development patterns of the Chinese economy during the period of 1997 to 2015 through the prism of labor productivity growth and its sectoral components. Chapter 1 studies China's economic structure and structural change across 31 provinces and 9 sectors. Methodologically, the empirical analysis is based on decompositions of aggregate labor productivity growth into its sectoral components. It indicates that China's economic growth was mainly driven by the within-sector effect, rather than the labor reallocation effect. The decomposition results of labor productivity growth by sectors within regions at the provincial level provide a reliable foundation to further investigate sources of economic growth and economic convergence in the following chapters. Chapter 2 explores the determinants of labor productivity growth and its components. Its objective is to improve our understanding of sources of economic growth in China with a strong focus on globalization and industrialization. Empirical results are based on panel data analysis. The estimation addresses issues of serial correlation, cross-sectional dependence, and heteroskedasticity. It concludes that trade openness and inward foreign direct investment have had positive effects on the manufacturing and service sectors. The manufacturing sector has demonstrated a spillover effect on the service and agricultural sectors. Moreover, trade openness has not contributed significantly to productivity growth in the agricultural sector. iv Chapter 3 studies labor productivity convergence and its components in China at both the aggregate and provincial levels across sectors. Because regional disparity has accompanied China's rapid economic growth, this research aims to understand which sectors have demonstrated a stronger conditional convergence effect that could potentially make the poorer inland provinces grow faster compared to the richer coastal provinces. Panel regressions show that trade openness following China's membership in the World Trade Organization in 2001 immediately caused a dramatic structural change in all sectors within the country's aggregate and regional economies. Therefore, by removing these deviations from the baseline in the period of 2002 to 2003, the manufacturing and highly regulated modern service sectors become suitable catalysts to reduce regional dispersion.

TRADE OPENNESS, STRUCTURAL CHANGE, AND PRODUCTIVITY GROWTH IN CHINA by Sophie Yun-Chen Wu A dissertation submitted to the faculty of The University of Utah in partial fulfillment of the requirements for the degree of Doctor of Philosophy Department of Economics The University of Utah December 2019 Copyright © Sophie Yun-Chen Wu 2019 All Rights Reserved The University of Utah Graduate School STATEMENT OF DISSERTATION APPROVAL The dissertation of Sophie Yun-Chen Wu has been approved by the following supervisory committee members: Codrina Rada , Chair 07/25/2019 Date Approved Thomas N. Maloney , Member 07/25/2019 Date Approved Minqi Li , Member 07/25/2019 Date Approved Richard Fowles , Member 07/25/2019 Date Approved Tracy Mott , Member Date Approved and by Norman J. Waitzman the Department of and by David B. Kieda, Dean of The Graduate School. , Chair of Economics ABSTRACT This dissertation explores the growth mechanisms and regional development patterns of the Chinese economy during the period of 1997 to 2015 through the prism of labor productivity growth and its sectoral components. Chapter 1 studies China’s economic structure and structural change across 31 provinces and 9 sectors. Methodologically, the empirical analysis is based on decompositions of aggregate labor productivity growth into its sectoral components. It indicates that China’s economic growth was mainly driven by the within-sector effect, rather than the labor reallocation effect. The decomposition results of labor productivity growth by sectors within regions at the provincial level provide a reliable foundation to further investigate sources of economic growth and economic convergence in the following chapters. Chapter 2 explores the determinants of labor productivity growth and its components. Its objective is to improve our understanding of sources of economic growth in China with a strong focus on globalization and industrialization. Empirical results are based on panel data analysis. The estimation addresses issues of serial correlation, crosssectional dependence, and heteroskedasticity. It concludes that trade openness and inward foreign direct investment have had positive effects on the manufacturing and service sectors. The manufacturing sector has demonstrated a spillover effect on the service and agricultural sectors. Moreover, trade openness has not contributed significantly to productivity growth in the agricultural sector. iii Chapter 3 studies labor productivity convergence and its components in China at both the aggregate and provincial levels across sectors. Because regional disparity has accompanied China’s rapid economic growth, this research aims to understand which sectors have demonstrated a stronger conditional convergence effect that could potentially make the poorer inland provinces grow faster compared to the richer coastal provinces. Panel regressions show that trade openness following China’s membership in the World Trade Organization in 2001 immediately caused a dramatic structural change in all sectors within the country’s aggregate and regional economies. Therefore, by removing these deviations from the baseline in the period of 2002 to 2003, the manufacturing and highly regulated modern service sectors become suitable catalysts to reduce regional dispersion. iv To My Dearest Grandparents 張清冷 & 張黃唱 v TABLE OF CONTENTS ABSTRACT....................................................................................................................... iii LIST OF FIGURES ......................................................................................................... viii ACKNOWLEDGMENTS ................................................................................................. ix Chapters 1. THE PATTERNS OF LABOR PRODUCTIVITY IN CHINA ..................................... 1 1.1 Abstract ................................................................................................................ 1 1.2 Introduction .......................................................................................................... 2 1.3 Discussion of Regional and Sectoral Data ........................................................... 4 1.4 Economic Structure of China ............................................................................... 7 1.5 The Decomposition Formula ............................................................................. 12 1.6 An Analysis of the Decomposition Results ....................................................... 14 1.7 Conclusion ......................................................................................................... 17 1.8 Appendix. Derivation of Equation 1.1 ............................................................... 20 1.9. Acronyms in Figures and Tables in Chapter 1 ................................................. 21 2. DETERMINANTS OF LABOR PRODUCTIVITY GROWTH IN CHINA ............... 34 2.1 Abstract .............................................................................................................. 34 2.2 Introduction ........................................................................................................ 35 2.3 The Economic Structure of China’s Regional Economies ................................ 38 2.4 Literature Reviews on Determinants of Labor Productivity Growth................. 40 2.5 Model Specifications, Data Sources, and Diagnostic Tests ............................... 45 2.6 Estimation Results of the Determinants of Labor Productivity Growth ............ 49 2.7 Conclusion ......................................................................................................... 53 3. LABOR PRODUCTIVITY CONVERGENCE IN CHINA ......................................... 72 3.1 Abstract .............................................................................................................. 72 3.2 Introduction ........................................................................................................ 73 3.3 China’s Development Policies for Economic Convergence .............................. 75 3.4 An Overview of Data ......................................................................................... 76 3.5 Methodology ...................................................................................................... 78 3.6 The Empirical Results of the Convergence Tests .............................................. 81 3.7 Conclusion ......................................................................................................... 89 REFERENCES ................................................................................................................. 97 vii LIST OF FIGURES 1.1. Map of China. ............................................................................................................ 22 1.2. Income per capita growth of Chinese provinces. ....................................................... 22 1.3. Labor productivity growth of Chinese provinces. ..................................................... 23 1.4. Income per capita of Chinese provinces in USD. ...................................................... 23 1.5. Labor productivity levels of Chinese provinces in USD. .......................................... 24 1.6. Geographic regions' changes in output shares in sectors. .......................................... 25 1.7. Geographic regions' changes in employment shares in sectors. ................................ 26 1.8. Labor productivity growth decompositions by regions of China, 1997–2015 .......... 27 1.9. Labor productivity gorwht decompositions by sectors of China., 1997–2015 .......... 27 ACKNOWLEDGMENTS Doing a Ph.D. in economics presented me with a lot of challenges but was also fun. I feel very fortunate to have lived a privileged life over the past few years as a doctoral student in North America. The Department of Economics at the University of Utah granted me great opportunities as a funded student, allowing me to embark on this meaningful journey to deepen my spiritual and intellectual growth. Thanks to the tremendous financial support of my committee and my family, I have been able to complete my dream to travel around the world during school breaks, all while pursuing an advanced degree and establishing my own family. My footsteps have been set on many UNESCO historical sites on almost all continents, transforming me into a world citizen with a big heart. I will remember all the favors that I have received from others and will pass them down to my students or those in need in the future. I want to extend my gratitude to the following professors: My advisor, Dr. Codrina Rada, has been very patient with me and fully allowed me to explore my research ideas. I owe her special thanks for guiding my research projects step by step. I also want to thank Dr. Stephen Reynolds (who passed away in 2015) and Dr. Thomas Maloney for opening their arms to welcome me back to the school, so I could achieve my dream of obtaining a Ph.D. and teaching at a university. My committee, Dr. Minqi Li, Dr. Richard Fowles, and Dr. Tracy Mott, who was also the chair of my master’s thesis at the University of Denver, have x generously shared their insights on my dissertation. Without their help, I would not have been able to get ready to publish my dissertation. Before I moved to the U.S. to start my doctoral study, I was working in ASUS’s mobile wireless team in Taiwan as a business development manager. This job gave me the opportunity to closely observe the dynamics of world economic flows in the telecom operator sector, which later inspired me to pursue the highest degree in economics in North America to further inform my research. Therefore, I want to thank my former employer and colleagues for their trust and support in giving me the greatest latitude to develop the company’s new businesses. Chloe, my best friend, and a long-time pen pal before she passed away at the end of 2016, always encouraged me to be who I am. Her insights have helped me live through the setbacks in the transitional periods of time. Our sincere friendship and our many conversations will remain with me for the rest of my life. I also owe special thanks to our family friend, Dr. Shiaw C. Tseng, who was my father’s roommate at National Taiwan University four decades ago. His generous financial support and career guidance have smoothed my doctoral life in the States. I thank my parents, K.C. Wu and Billie Chang, for their huge sacrifice in financing my master’s and doctoral programs in North America and looking after my lovely daughter, Emma, as the best grandparents she could have. My dearest sister Yun-Jung Wu, who is now in Tokyo, Japan with her husband and kids, has donated her savings to my tuition fund and helped me survive. My humorous and smart husband, Dr. Chih-Wei Chang, has helped me take care of our whole family in Taiwan, so I was able to fully concentrate on my studies. I will always feel grateful for his unconditional support and love. Last but not least, I dedicate my dissertation to my grandparents, my mom’s father x xi and mother, Mr. and Mrs. Chang, who were another set of parents in my eyes. They did not have English names, so please pardon me listing their names in Mandarin in the dedication to show my greatest respect and to honor their memory. Although they passed away over two decades ago, it was actually the intention I have always had to dedicate my dissertation to them that motivated me to persist and complete the degree. With tears rolling down my cheeks, I hope I have made them proud. Sophie Wu (吳昀真) at Coffee Garden in Salt Lake City, Utah 08/01/2019 xi 1 CHAPTER 1 THE PATTERNS OF LABOR PRODUCTIVITY IN CHINA 1.1 Abstract This chapter investigates the economic structure and structural change in China during the postreform period of 1997–2015 through the prism of labor productivity growth across 31 provinces and 9 sectors. The empirical results suggest that during this period, China’s economic growth was mainly due to productivity growth within sectors, rather than labor reallocation across sectors. Rapidly growing service sectors have attracted labor from the primary and secondary sectors, while regional disparity has been maintained. One partial conclusion is that the Chinese industrial policies during the postreform period targeted structural transformation to stimulate balanced regional growth. Nonetheless, due to heterogeneous regional economies, geographic and climate patterns, less developed inland provinces have not yet caught up with the more developed coastal province. The manufacturing sector is found to act as an economic engine because of its largest labor productivity growth compared to other sectors. 2 1.2 Introduction The development path of China, one of the very few centrally planned economies remaining in the world, is, in many ways, unique. Since 1978, the Chinese communist government has implemented a series of market openness reforms and institutional changes (Oi, 1999). China’s economic growth was remarkable at 9.7% per annum during 1997–2015. Although the Chinese province economies rapidly grew from absolute poverty to the upper middle income level according to the 2015 United Nations classifications,1 regional disparity gaps continue to exist during this process of industrialization and modernization. Previous research suggests several reasons for the existence of regional disparities within China, such as policy distortion (Démurger et al., 2002; Zheng & Chen, 2008), financial development (Liang, 2006), infrastructure investment (Liao & Wei, 2015; Wang & Zhang, 2003; Yu, Xin, Guo, & Liu, 2011), and geographic or climate patterns. In addition to regional disparities, significant structural transformation within China has been observed during this period. Nonetheless, patterns of structural change have continued to vary across provinces. China’s inland provinces are in the stage of industrialization, whereas its coastal provinces are moving toward mature economies in which the services sector has outgrown the manufacturing sector. Overall, China’s labor force has moved from primary and secondary sectors to the tertiary sector. An overarching view suggests that each Chinese province has its unique economic structure and structural patterns. It then follows that inland provinces should not be viewed as the underdeveloped versions of the coastal provinces. 1 World Economic Situation and Prospects 2015 (https://www.un.org/en/development/desa/policy/wesp/wesp_archive/2015wesp_full_en.pdf) 3 This chapter adopts the structuralist approach to study the economic structure and structural change within China during the period of 1997 to 2015. It investigates regional and sectoral patterns of labor productivity growth across 31 provinces and 9 sectors. Labor productivity growth, in particular, is important because all evidence indicates a positive association between labor productivity growth and long-run income per capita growth within an economy; more importantly, labor productivity growth is affected by the economy’s structure as measured by the sectoral composition of employment and output. From a structuralist viewpoint, structural transformation is an important source of economic growth in underdeveloped economies (Kuznets, 1966; Lewis, 1954; Lin, 2012). Economic restructuring relies on industrial diversification and labor reallocation from low-productivity sectors to high-productivity sectors until a certain threshold of income per capita is reached. Labor becomes, then, fully employed in the modern sectors (Imbs & Wzcziarg, 2003; Samaniego & Sun, 2016). Man-made borders or natural barriers (Nagy, 2015), trade liberalization (Lardy, 2002), human capital (Fleisher, Li, & Zhao, 2011; Heckman & Yi, 2014), and foreign investment (Wei, Yao, & Liu., 2009; Zhang, 2006) have all played critical roles in driving the process of structural transformation and spatial distribution within China. Moreover, after China joined the World Trade Organization in 2001, its regional economies increased more their international dependence than their domestic linkages (Kumer, 1994; Poncet, 2003; Young, 2000). This is reflected and relevant also for the rise in interprovincial trade that has occurred mostly in the manufacturing sector, but not in the agricultural and service sectors (Xin & Qin, 2011). Domestic market segmentation is another issue identified to hinder regional equality (Li , Gu, & Zhang, 2015). 4 Methodologically, this paper employs a decomposition of labor productivity growth to investigate patterns and sources of aggregate and regional growth following the approach in the structuralist literature (Ocampo, Rada, & Taylor, 2009; Syrquin, 1986). As a major recipient of foreign direct investment (FDI), China’s economic growth during 1997–2015 was mainly driven by productivity growth within the export-oriented manufacturing sector (Chen, Chang, & Zhang, 1995; Li, Liu, & Parker, 2001; Liu & Song, 1997), which was faster than that in the agricultural or service sectors. This chapter is organized as follows. Section 1.3 discusses sources of sectoral and regional data in China from 1997 to 2015. Section 1.4 discusses the economic structure and structural change in China during 1997–2015, and Section 1.5 presents the decomposition technique. Section 1.6 presents analytical results by sectors within regions. Section 1.7 concludes with a few remarks on possible policy initiatives that could address regional disparities through redistribution of resources across provinces in order to achieve a more balanced regional development in the long term. 1.3 Discussion Regional and Sectoral Data In this research, labor productivity growth is examined across 31 provinces and 9 sectors. The 31 provinces are further classified into seven geographic regions: North, Northeast, East, Central, South, Southwest, and Northwest. Figure 1.1 shows the map of China, and Table 1.1 presents the links between a province and a geographic region. The 9 sectors, according to the database of the National Bureau of Statistics of China (NBSC), include (a) agriculture, fishery, forestry, and animal husbandry; (b) manufacturing; (c) construction; (d) retail and wholesale service; (e) transport, transmission, and post; (f) 5 hotel and catering service; (g) banking and finance; (h) real estate; and (i) the other social and community services in the industries of social welfare, scientific research, environmental protection, and health care. All Chinese provincial-level data by sectors during 1997–2015 in this analysis come from the database of the National Bureau of Statistics of China (NBSC) and the Chinese Statistical Yearbooks (CSYs). All output data are initially posted in nominal terms in the national currency, the Chinese yuan (RMB). Following the method suggested by Alcala and Ciccone (2004), output data have then been adjusted to real USD according to the annual average exchange rate between U.S. dollars (USD) and RMB as well as the inflation rate in RMB. Labor productivity growth by regions within sectors is defined by the ratio of each province’s real GDP over the employed persons in a sector. The data for employed persons by provinces within sectors prior to 2004 can be found in the Chinese Statistical Yearbooks. Official statistics on the number of sectoral classification categories and the way employed persons are counted are inconsistent between the 1997–2003 period and since 2004 (Chen, 1992; Rawski & Mead, 1998). Starting in 2004, China’s provinciallevel economic data have been classified into 9 sectors, whereas province-level employment data were classified into 16 sectors prior to 2004. Consequently, this chapter primarily focuses on the 9 sectors to study China’s labor productivity growth and its components by 31 provinces. Output data by provinces, despite being available for 9 sectors throughout the entire period under study, have a slightly different classification for the wholesale and retail sectors between the two periods. For example, the hotel and catering service sector was merged into the wholesale and retail service sectors prior to 2004, while it became an independent category starting in 2004. Out of concern for the 6 above issues, further arrangements are required to make the provincial-level sectoral employment data match with their corresponding provincial-level sectoral output data, which is necessary for calculating labor productivity and its growth rate. The approach to merge employment data for 1997–2003 from 16 sectors into 9 sectors is explained as follows. The manufacturing sector’s employment data by provinces during 1997–2003 have included workers in the mining and quarry industries. The construction sector’s employment data by provinces during 1997–2003 have absorbed the workers from “electricity, gas, and water production and supply” and “geological prospecting and water conservancy” activities. Because the addition of “mining and quarry” to the manufacturing sector accounts for only a relatively small part in certain subperiods of this study, and the discrepancy of China’s data classifications is found throughout the entire study period, the manufacturing sector is still adopted as an umbrella term to include these sectors, rather than using “industry.” A similar reason exists behind the definition of the construction sector. The community and social service sector’s employment data by provinces have included workers in the service sectors such as “social services,” “health care, sports, and social welfare,” “education, culture, art, radio, film, and television,” “scientific research and polytechnical services,” “government agencies, party agencies, and social organizations,” and “other services.” The employment data in the subsector such as “government agencies, party agencies, and social organizations” are included in the other service sectors because of lack of detailed specifications about what kind of social organizations are included in this category and the missing output data for the government sector in the Chinese official database. More importantly, despite China allowing a market-oriented 7 economy to be fitted into its newly transformed communist system during the postreform period, its industrial and service development plans across sectors and regions remain at the discretion of the central policy planners. Therefore, it becomes meaningless to further investigate whether the subsector of “government agencies, party agencies, and social organizations” should be classified as the government sector and whether its employment data should be left out or not. In addition to the problems mentioned thus far, the reliability of Chinese economic data has been questioned in the literature (Koch-Weser, 2013). China’s official statistics are collected through reports from local governments or through surveys conducted every several years. Interprovincial competition inevitably has made some provinces overstate their actual economic performance. Therefore, labor productivity growth or economic growth data presented in this chapter could suffer from an overestimation bias. Holz (2006, 2013a, 2013b) has verified the reliability of China’s officially posted sectoral output and employment data back to 1952 and made the adjustment to reflect the most accurate situations. Therefore, this chapter uses a similar approach as suggested by Holz to simulate the output and employment series. 1.4 Economic Structure of China During 1997–2015, China’s income per capita in USD increased 9.8 fold and labor productivity expanded 10.2 fold. China’s remarkable aggregate economic performance is largely the result of contributions by several regional economies. Figures 1.2 and 1.3, respectively, present income per capita growth and labor productivity growth in USD for the Chinese provincial-level economies during 1997–2015. To sum up, the 8 patterns of regional income per capita growth are consistent with regional labor productivity growth, suggesting that China’s economic growth at the aggregate and regional levels is mainly driven by productivity growth, especially of labor. Each province’s income per capita and labor productivity levels in 1997 and 2015 are summarized in Figures 1.4 and 1.5, respectively. Figure 1.4 shows income per capita in terms of real GDP per capita in USD for all 31 provinces for 1997 and 2015. The graph is organized to show the ranking of income per capita in 2015. A comparison between the wealthiest and the poorest provinces in both years reveals that regional disparities have persisted despite rapid economic growth during this period. In 1997, Shanghai, the wealthiest directly controlled city, had an income per capita seven times larger than Tibet, the poorest province in China. In 2015, Beijing, the wealthiest directly controlled city, still had an income per capita four times larger than Gansu, the poorest province in this more recent year. The pattern of income inequalities in China has been maintained such that the poorest and the wealthiest few provinces remained largely the same over the period examined. In 2015, Beijing, Tianjin, Shanghai, Jiangsu, and Zhejiang were the wealthiest provinces in China, of which only Jiangsu was not ranked as one of the top five wealthiest provinces in 1997. Guangdong. Guangxi, Tibet, Guizhou, Yunnan, and Gansu were the poorest provinces in 2015, of which only Yunnan was not ranked as one of the bottom five poorest provinces in 1997. This phenomenon also indicates that China’s regional development levels are largely clustered geographically. Nonetheless, the middle-ranked provinces fluctuated in their rankings. Many of these middle-ranked provinces in terms of income per capita are located in the central and western territories of China. 9 Figure 1.5 shows labor productivity of each province in 1997 and 2015 ordered by their ranking in 2015. Provinces with the highest labor productivity levels in 2015 included Tianjin, Beijing, Shanghai, and Jiangsu, all of which were ranked as the wealthiest provinces in terms of their income per capita. The provinces with the lowest labor productivity levels in 2015 included Gansu, Tibet, Guizhou, and Guangxi, all of which were the poorest provinces in terms of income per capita as well. In summary, the poorest and the wealthiest Chinese provinces showed a direct connection between income per capita and labor productivity level. Thus, labor productivity growth is essential for overall income growth. From 1997 to 2015, each province maintained very similar output and employment shares of the aggregate for China. Similar provincial output shares over this period suggest that regional disparity has been maintained in the course of economic development (Nagy, 2015). Conversely, similar provincial employment shares imply that the household registration system has largely restrained labor mobility across China (Nagi, Pissarides, & Wang, 2016). To achieve balanced regional growth, it becomes critical to facilitate structural transformation within the regional and aggregate economies by fostering industrial and service sectors in the less developed areas and, perhaps, considering the free flow of resources, especially of labor, across provincial borders. Before going into a discussion of the decomposition and estimation results, it is worth taking a moment to examine the sectoral structure of China and of its regional economies during the period of analysis. Few relevant observations emerge. The agricultural output share decreased from 20% to 9%, whereas the output share in the industrial sectors, including construction, mining and quarrying, remained at 10 approximately 45%. Output shares in the service sectors increased from 35% to 46% at the national level. Concomitantly, the agricultural sector’s employment share shrank from 52% to 39%, implying that China is rapidly moving away from being an agricultural economy as it has been for several thousands of years. The industrial sectors at the aggregate level experienced a slight decline in their employment shares from 23% to 22%, while most gains have been recorded by the services sector where employment shares rose from 25% to 39% of the total. Given these trends, it can be said that structural change in China during the 1997–2015 period favored the service sectors, while reducing its reliance on the agricultural sector. At the same time, output and employment shares in the manufacturing sector reflect the fact that the Chinese economy in aggregate was already an industrialized economy. The data for the service sectors at a more disaggregated level show several interesting patterns. In short, traditional service sectors2 had declining output shares, whereas the modern service and government service sectors have become more important. On one hand, output shares decreased across sub-sectors in the traditional service sectors: from 10% to 9% in the retail and wholesale service sector, and from 7% to 5% in the sector of transport, transmission, and post services. On the other hand, modern service sectors saw a rise in their output shares: from 0% to 2% for hotel and catering services; from 5% to 7% for banking and finance; and from 3% to 5% for real estate services sector.3 In terms of the government service sector, the output share in the 2 In this paper, the traditional service sectors include “retail and wholesale trade” and “transport, transmission, and post.” The modern service sectors include “hotel and catering services,” “banking and finance,” and “real estate.” 3 The hotel and catering service sector had 0% output and employment shares in 1997 because this sector’s output data were merged into the wholesale and retail trade service sector during 1997–2003. Not until 2004 was the hotel and catering service sector singled out as an independent sector. 11 sector of the community, social, personal, and government services increased from 10% to 18%. Among all these service sectors, the hotel and catering service sector should receive additional attention because China’s domestic tourism was highly related to its open-door policies during the postreform period (Wu, Zhu, & Xu, 2000). In the late 1980s, China opened its territories to attract foreign visitors (Tisdell & Wen, 1991). However, it was not until 1995 that the Chinese inbound domestic tourism was incorporated in its 5-year national economic guidelines for its use as a pillar industry to stimulate its regional economic development (He, 1999; Zhang, 1995). Shaw and William (1994) indicated that tourism is only an optional tool to stimulate the economic development of any country, and thus tourism development is a policy choice. Because China’s institution remains largely centralized, labor mobility is strictly restricted. Other aspects, captured by the data, reflect the impact of institutions on economic structure. For example, tourism as a sector was largely undeveloped before the market openness reforms. After the reforms, tourism was gradually supported as a means of economic development to stimulate regional growth. Therefore, China’s increasing output share in the hotel and catering services reflects the impact of market openness. Figures 1.6 and 1.7 describe regional economic structures and present sectoral changes of output and employment shares, respectively, within each regional economy for the same period, i.e., 1997–2015. Each geographical region is treated in these figures as an independent economy. Consequently, the sectoral output share in a region is defined as the ratio of the region’s sectoral output over the region’s total output. Similarly, sectoral employment share in a region is defined as the ratio of the region’s employed persons in sector i to the region’s total employed persons. 12 The change in output shares within regions is demonstrated in Figure 1.6. Output shares in almost all regions increased in the modern and social service sectors, whereas output shares in the manufacturing sector increased only in Central, South, and Northwest China. Output shares of the agricultural sector, unsurprisingly, largely decreased across all geographic regions. Overall, the data suggest that regional economies across China have undergone significant structural transformation between 1997 and 2015 given their stage of development. The most advanced regions have transitioned from industrialization to services. Industrial sectors had seen positive changes in their output shares in Northeast, East, Central, Southwest, and Northwest China. The rise in the output shares of these sectors in the western territory of China reflects the country’s long-term economic plans for regional balance by integrating the economies of its inland provinces with Eurasia. Finally, Figure 1.7 shows changes in employment shares of each region. Overall, the agricultural sector’s employment share increased only in Northeast China, and the manufacturing sector’s employment share increased only in East and South China. The employment shares of “wholesale and retail services” and “hotel and catering services” rose in all regions, suggesting rapidly growing domestic demand for nontradables, and, perhaps, an improved standard of living within all Chinese regional economies. 1.5 The Decomposition Formula Labor productivity growth decompositions have been widely used to study the effects of changes in economic structures during the course of economic development (Chenery, 1979; Kuznets 1966; Syrquin 1986). This section presents insights into sources 13 of labor productivity growth in China during the period 1997–2015, using a decomposition of aggregate labor productivity growth by 31 provinces and 9 sectors. The decomposition formula below is based on Syrquin’s approach (1986) as well as that of Ocampo, Rada, and Tylor (2009). 𝜉(𝑡,𝑡+1) = ∑𝑖,𝑗 [𝜃𝑖,𝑗,𝑡 𝜉𝑖,𝑗,(𝑡,𝑡+1) + 𝑞𝑖,𝑗,𝑡+1 𝑞𝑡 (𝜀𝑖,𝑗,𝑡+1 − 𝜀𝑖,𝑗,𝑡 )] (1.1) where ξ(t,t+1) is China’s labor productivity growth during period t and t+1, 𝜃𝑖,𝑗,𝑡 is the output share of sector i in province j at time t, 𝜀𝑖,𝑗,𝑡 is the employment share in sector i province j at time t, 𝜉𝑖,𝑗,(𝑡,𝑡+1) is labor productivity growth in sector i and province j between t and t+1, 𝑞𝑖,𝑗,𝑡+1 is the labor productivity level at time t+1 in sector i province j, and 𝑞𝑡 is China’s labor productivity level at time t. The first term in this formula, 𝜃𝑖,𝑗,𝑡 𝜉𝑖,𝑗,(𝑡,𝑡+1), is the within-sector effect that captures the contribution to aggregate productivity growth from intrasectoral productivity gains. The second term, 𝑞𝑖,𝑗,𝑡+1 𝑞𝑡 (𝜀𝑖,𝑗,𝑡+1 − 𝜀𝑖,𝑗,𝑡 ), is the reallocation effect that captures the contribution to labor productivity growth from changes in employment across sectors within regions. The derivation of the decomposition equation is shown in the appendix following Roncolato and Kucera’s approach (2014). Following the decomposition approach outlined above, aggregate labor productivity growth is the sum of the within-sector effect and the reallocation effect. The within-sector effect captures productivity growth within sectors and thus largely contributes to economic growth through economic integration. The reallocation effect 14 contributes to economic growth through structural change within a transitional economy from the traditional rural sector to the modern urban sector. The relevance of the reallocation effect has been highlighted by theories of structural change that point out that an underperforming economy can benefit from a transfer of labor from low to high productivity sectors (Lewis, 1954). The sign of the reallocation effect at the regional level is determined by the change of employment shares and reflects the direction of labor flow. For example, when a sector or a region has a negative labor reallocation effect, its employment share decreases. Conversely, the reallocation term is positive when the employment share of the sector or the province rises. At the aggregate level, however, the reallocation effect is positive only if employment shares increased in sectors or provinces with higher than average labor productivity. 1.6 An Analysis of the Decomposition Results This section examines the labor productivity growth decomposition results for the 1997–2015 period. The objective is to identify patterns of structural change and sectoral sources of productivity growth across all provinces. China’s aggregate labor productivity grew 1020 percentage points, of which 1000 percentage points came from productivity growth within sectors and 20 percentage points came from labor reallocation across sectors and provinces. Agriculture, fishery, forestry, and animal husbandry contributed 8% to China’s aggregate labor productivity growth, and the secondary sector of manufacturing and construction industries contributed 45%. The remaining 47% was contributed by the tertiary sector. Complete results for each region and each sector’s contribution to China’s aggregate labor productivity growth are summarized in Table 1.2, 15 while the distribution of the within-sector effect and labor reallocation effect is presented in Table 1.3 and Table 1.4, respectively. A detailed version of the decomposition results across 31 provinces and 9 sectors is presented in Table 1.5 and Table 1.6. In terms of regional contributions to aggregate labor productivity, East China had the largest contribution with approximately 36.69%, about two and a half times more than North China’s, the second in place. Within regions, there are, however, major differences across provinces and their economies. For example, South China ranks fourth in terms of its contribution to China’s overall labor productivity growth with approximately 12.89%. However, a significant part of its contribution was largely made by Guangdong province itself, with the special economic zones of Shantou, Zhuhai, and Shenzhen playing critical roles in driving the economic growth during the period. Therefore, South China was ranked fourth only as a result of much lower economic performance of the Hainan and Guangxi provinces. Overall, South China acted as a labor attractor to migrant workers from other regions. Figures 1.8 to 1.9 visualize sectoral and regional patterns of the within-sector and labor-reallocation effects of China during 1997–2015 following Table 1.3 and Table 1.4. Results show that in all regions and sectors, the within-sector effects dominate, whereas contributions from labor reallocation are either very minor or negative. At the regional level, only the South and East have a positive labor reallocation effect, suggesting that South and East China have been absorbing labor from other regions during this period. In terms of decomposition results by sectors, agriculture and manufacturing sectors have had a negative labor reallocation effect as these sectors have experienced a decline in their employment shares. Service sectors of “wholesale and retail,” “hotel and 16 catering,” “financial and banking,” and “real estate” industries have had an increase in employment and thus appear to have contributed to the aggregate productivity growth by absorbing labor from other sectors. The positive sectoral labor reallocation effect is a welcome development if the sector experiences dynamic growth in terms of both labor productivity and employment. Employment can also rise in sectors that act as employers of last resort but are stagnating overall. The negative effect of such a trend would then be captured by the overall reallocation effect. As China’s secondary sector still has much higher labor productivity growth than the tertiary or primary sectors, the empirical evidence indicates more complicated patterns of labor shift across provinces in China. The observed sectoral patterns of labor productivity growth in China explain a few stylized facts. First, China’s economy has experienced similar patterns of change as other mature economies in Western Europe or North America – contingent only upon its national economic data – due to its rapidly growing service sector during 1997–2015. Clark-Fisher’s model (Fisher, 1939; Clark, 1940) suggests that the tertiary sector is a product of industrialization due to higher elasticity demand of consumer goods and lower labor productivity growth in the tertiary sector. Therefore, labor outflows to the tertiary sector suggest deep industrialization within China during 1997–2015 despite issues of disparity at the regional level. Second, the Lewis dual-sector model (1954) points to labor outflows from low to high productivity sectors in the process of structural change. That said, the labor movement toward the tertiary sector in China can be explained from an institutional perspective. China, as a former centrally planned economy, already has had a sizable heavy industrial sector during the prereform period. It is therefore not a surprise that a rapidly expanding 17 tertiary sector has followed industrialization during the postreform period. In addition, the manufacturing sector in the coastline regions contributed to China’s rapid economic growth during 1997–2015, implying that China’s landlocked regions still had room to improve through industrialization. The service sectors, when looked at individually, did not show consistent signs with respect to labor reallocation effects. “Retail and wholesale,” “banking and finance,” “hotel and catering,” and “real estate” had contributed positively through the reallocation effect. “Transportation, transmission, and post” and “community, social, personal, and government services” had a negative labor reallocation effect. This pattern indicates that the rapidly developed modern service sectors during the postreform period were more likely to expand their employment shares. 1.7 Conclusion To summarize, this chapter examines regional and sectoral patterns of labor productivity growth within China by 31 provinces and 9 sectors during 1997–2015. The scope of the analysis complements the work of Valli and Saccone (2011, 2015), who investigate structural change in China using the approaches suggested by Syrquin (1986) and Ocampo et al. (2009). However, these authors did not investigate patterns of structural change at the provincial level. Sectoral patterns of aggregate labor productivity growth obtained from the decomposition show that China’s labor productivity growth was mainly driven by growth within sectors, rather than by labor reallocation across provinces and sectors. The manufacturing sector in South and East China primarily absorbed labor from other regions and thus contributed positively through reallocation 18 effects. Still, during 1997–2015, structural change in terms of labor reallocation was observed across all provinces and sectors. Although the manufacturing sector still acted as an engine of economic growth within Chinese regional economies during the period of analysis, its employment share has been decreasing during this period. Dichotomous patterns between the coastal and inland provinces have also been observed. In the coastal provinces, industrial upgrading to capital-intensive and high-technology industries, pushed by the central government’s industrial policies, released labor to the tertiary sector (Wei, 2000; Zhang & Hu, 2004). Thus, four decades after the implementation of economic reforms, the coastal provinces, especially the areas where the special economic zones were located, have shared more characteristics with some developed and uppermiddle-income economies than with inland provinces. China’s national economic policies during 1997–2015 have followed the 5-year national economic guidelines, which have continued to adjust to the goal of overall economic development as well as the facilitation of regional convergence during the more recent period. Although China’s national economic policies favored the coastline regions in the beginning stages of the economic reform processes, which might have accentuated regional disparities, this solution was consistent with the structuralist view that perpetual growth would have been better maintained when partial reforms were implemented (Hausmann, Rodrik, & Velasco, 2005, 2007). Because China’s economic goal during the early postreform period was to improve the country’s economic performance and address absolute poverty, the country took a shortcut to concentrate the nation’s resources on the coastline regions rather than equally allocate them across all provinces. The descriptive results presented in this chapter, therefore, indicate significant 19 heterogeneity across Chinese provinces. For the last two decades, however, the development policy in China has been to address regional disparity. The One Belt One Road Campaign in 2015 can be viewed as an extension of the Western Development Campaign, which aims to stimulate economic activities within its landlocked regions through interregional integration with the neighbor countries. Therefore, regional disparities in China could be reduced through more government investment and financial support for the inland provinces’ vital economic sectors with comparative advantages. 20 1.8 Appendix. Derivation of Equation 1.1 1. Aggregate labor productivity is defined as total value added (real term) over total 𝑋 employment in time t, or 𝑞𝑡 = 𝐿𝑡 . 𝑡 2. Aggregate labor productivity growth between time t and time t+1 is defined as 𝜉(𝑡,𝑡+1) = 𝑋 𝑋 ( 𝑡+1 )−( 𝑡) 𝐿𝑡+1 𝑋𝑡 𝐿𝑡 𝐿𝑡 . 𝑥𝑖,𝑗,𝑡 3. Labor productivity growth in sector i, province j, and time t is defined as 𝑞𝑖,𝑗,𝑡 = 𝑙𝑖,𝑗,𝑡 4. Equation (1.1) is derived as follows. For easier visualization, time t is denoted as 0, and time t+1 is denoted as 1. ∑ 𝑥𝑖,𝑗,𝑡+1 ∑ 𝑥𝑖,𝑗,𝑡 ∑ 𝑥𝑖,𝑗,𝑡 ∑ 𝑥𝑖,𝑗,𝑡 𝑋𝑡+1 𝑋 − + − ) − ( 𝑡) ∑ 𝑙𝑖,𝑗,𝑡+1 ∑ 𝑙𝑖,𝑗,𝑡 ∑ 𝑙𝑖,𝑗,𝑡 ∑ 𝑙𝑖,𝑗,𝑡 𝐿𝑡+1 𝐿𝑡 = = 𝑋𝑡 ∑ 𝑥𝑖,𝑗,𝑡 𝐿𝑡 ∑𝑙 ( 𝜉(𝑡,𝑡+1) 𝑖,𝑗,𝑡 ∑𝑥 ∑𝑙 =[ ∑ 𝑙 𝑖,𝑗,𝑡+1 ∑ 𝑥𝑖,𝑗,𝑡 𝑖,𝑗,𝑡+1 = 𝑖,𝑗,𝑡 ∑ 𝑥𝑖,𝑗,𝑡 𝑋𝑡 ∑𝑥 ∑ 𝑥𝑖,𝑗,𝑡 ∑ 𝑥𝑖,𝑗,𝑡+1 ∑ 𝑙𝑖,𝑗,𝑡 [∑𝑙 𝑋𝑡 𝑖,𝑗,𝑡+1 ∑ 𝑥𝑖,𝑗,𝑡 𝑖,𝑗,𝑡 − = ∑𝑖,𝑗[𝜃𝑖,𝑗,𝑡 𝜉𝑖,𝑗,(𝑡,𝑡+1) + Let 𝑋𝑡 ≈ ∑ 𝑥𝑖,𝑗,𝑡+1 𝐿𝑡 ≈ ∑ 𝑙𝑖,𝑗,𝑡+1 𝜃𝑖,𝑗,𝑡 = 𝑥𝑖,𝑗,𝑡 𝑋𝑡 𝜀𝑖,𝑗,𝑡 = 𝑙𝑖,𝑗,𝑡 𝐿𝑡 ∑𝑙 − ∑ 𝑙 𝑖,𝑗,𝑡 ∑ 𝑥𝑖,𝑗,𝑡 𝑖,𝑗,𝑡 ∑ 𝑥𝑖,𝑗,𝑡 𝑋𝑡 ∑𝑥 𝑖,𝑗,𝑡 ∑ 𝑥𝑖,𝑗,𝑡 ∑ 𝑙𝑖,𝑗,𝑡 ∑ 𝑥𝑖,𝑗,𝑡+1 𝐿𝑡 ]+[ ∑ 𝑙𝑖,𝑗,𝑡 ∑ 𝑥𝑖,𝑗,𝑡 ∑ 𝑙𝑖,𝑗,𝑡+1 𝑋𝑡 𝑞𝑖,𝑗,𝑡+1 𝑞𝑡 ∑𝑙 ]+ [ ∑ 𝑙 𝑖,𝑗,𝑡 ∑ 𝑥𝑖,𝑗,𝑡 − (𝜀𝑖,𝑗,𝑡+1 − 𝜀𝑖,𝑗,𝑡 )] 𝑖,𝑗,𝑡 ∑𝑙 ∑ 𝑥𝑖,𝑗,𝑡 ∑ 𝑙𝑖,𝑗,𝑡 ∑ 𝑙𝑖,𝑗,𝑡 ∑ 𝑥𝑖,𝑗,𝑡 ∑𝑙 𝑋 ] ∑𝑙 ∑𝑙 𝑋 (∑ 𝑥𝑖,𝑗,𝑡+1 ∑ 𝑙𝑖,𝑗,𝑡+1 𝐿𝑡 − ∑ 𝑥𝑖,𝑗,𝑡+1 ∑ 𝑙𝑖,𝑗,𝑡 𝐿𝑡 )] 𝑖,𝑗,𝑡+1 𝑖,𝑗,𝑡+1 𝑡 𝑖,𝑗,𝑡+1 𝑖,𝑗,𝑡 𝑡 . 21 1.9. Acronyms in Figures and Tables in Chapter 1 S1: agriculture, fishery, forestry, and animal husbandry sector S2: manufacturing sector S3: construction sector S4: wholesale and retail sector S5: transportation, transmission, and post sector S6: hotel and catering service sector S7: financial and banking service sector S8: real estate service sector S9: community and social service sector WC1: the within-sector effect in S1 WC2: the within-sector effect in S2 WC3: the within-sector effect in S3 WC4: the within-sector effect in S4 WC5: the within-sector effect in S5 WC6: the within-sector effect in S6 WC7: the within-sector effect in S7 WC8: the within-sector effect in S8 WC9: the within-sector effect in S9 RA1: the labor reallocation effect in S1 RA2: the labor reallocation effect in S2 RA3: the labor reallocation effect in S3 RA4: the labor reallocation effect in S4 RA5: the labor reallocation effect in S5 RA6: the labor reallocation effect in S6 RA7: the labor reallocation effect in S7 RA8: the labor reallocation effect in S8 RA9: the labor reallocation effect in S9 Beijing Tianjin Hebei Shanxi Inner Mongolia Liaoning Jilin Heilongjiang Shanghai Jiangsu Zhejiang Anhui Fujian Jiangxi Shandong Henan Hubei Hunan Guangdong Guangxi Hainan Chongqing Sichuan Guizhou Yunnan Tibet Shaanxi Gansu Qinghai Ningxia Xinjiang 22 Figure 1.1 Map of China. 20 18 16 14 12 10 8 6 4 2 0 Figure 1.2 Income per capita growth of Chinese provinces during 1997–2015. Beijing Tianjin Shanghai Jiangsu Zhejiang Inner Mongolia Liaoning Guangdong Fujian Shandong Jilin Chongqing Hubei Shaanxi Ningxia Hunan Qinghai Hainan Hebei Heilongjiang Xinjiang Henan Sichuan Jiangxi Shanxi Anhui Guangxi Tibet Guizhou Yunnan Gansu Beijing Tianjin Hebei Shanxi Inner Mongolia Liaoning Jilin Heilongjiang Shanghai Jiangsu Zhejiang Anhui Fujian Jiangxi Shandong Henan Hubei Hunan Guangdong Guangxi Hainan Chongqing Sichuan Guizhou Yunnan Tibet Shaanxi Gansu Qinghai Ningxia Xinjiang 23 18 16 14 12 10 8 6 4 2 0 Figure 1.3 Labor productivity growth of Chinese provinces during 1997–2015. 16000 14000 12000 10000 8000 6000 4000 2000 0 2015 income per capita in USD 1997 income per capita in USD Figure 1.4 Income per capita of Chinese provinces in USD in 1997 and 2015. Tianjin Beijing Shanghai Liaoning Jiangsu Inner Mongolia Shandong Fujian Guangdong Heilongjiang Zhejiang Jilin Hebei Shaanxi Qinghai Hubei Ningxia Xinjiang Chongqing Hunan Shanxi Hainan Jiangxi Henan Anhui Sichuan Guangxi Guizhou Tibet Gansu Yunnan 24 50000 45000 40000 35000 30000 25000 20000 15000 10000 5000 0 2015 labor productivity level 1997 labor productivity level Figure 1.5 Labor productivity levels of Chinese provinces in USD in 1997 and 2015. 25 North Northeast 0.1 0.1 0.05 0.05 0 0 -0.05 -0.05 -0.1 S1 S2 S3 S4 S5 S6 S7 S8 S9 -0.1 East S1 S2 S3 S4 S5 S6 S7 S8 S9 Central 0.1 0.1 0.05 0.05 0 0 -0.05 -0.05 -0.1 -0.15 -0.1 S1 S2 S3 S4 S5 S6 S7 S8 S9 -0.15 -0.2 South S1 S2 S3 S4 S5 S6 S7 S8 S9 Southwest 0.1 0.1 0.05 0.05 0 0 -0.05 -0.05 -0.1 -0.1 -0.15 S1 S2 S3 S4 S5 S6 S7 S8 S9 -0.15 -0.2 S1 S2 S3 S4 S5 S6 S7 S8 S9 Northwest 0.1 0.05 0 -0.05 -0.1 -0.15 S1 S2 S3 S4 S5 S6 S7 S8 S9 Figure 1.6 Geographic regions’ changes in output share in sectors during 1997–2015. 26 North 0.1 Northeast 0.1 0.05 0.05 0 0 -0.05 -0.05 S1 S2 S3 S4 S5 S6 S7 S8 S9 -0.1 -0.1 S1 S2 S3 S4 S5 S6 S7 S8 S9 -0.15 East 0.2 Central 0.1 0.1 0.05 0 0 -0.1 -0.05 -0.2 S1 S2 S3 S4 S5 S6 S7 S8 S9 -0.3 S1 S2 S3 S4 S5 S6 S7 S8 S9 -0.1 South 0.2 Southwest 0.15 0.1 0.1 0.05 0 0 -0.05 -0.1 S1 S2 S3 S4 S5 S6 S7 S8 S9 -0.2 -0.1 S1 S2 S3 S4 S5 S6 S7 S8 S9 -0.15 Northwest 0.1 0.05 0 -0.05 S1 S2 S3 S4 S5 S6 S7 S8 S9 -0.1 Figure 1.7 Geographic regions’ changes in employment share in sectors during 1997– 2015. 27 China Northwest Southwest South Central East Northeast North -2 0 2 4 the labor reallocation effect 6 8 10 12 the within-seccor effect Figure 1.8 Labor productivity growth decompositions by regions of China during1997– 2015. China S9 S8 S7 S6 S5 S4 S3 S2 S1 -2 0 2 4 the labor reallocation effect 6 8 10 12 the within-sector effect Figure 1.9 Labor productivity growth decompositions by sectors of China during 1997– 2015. Table 1.1 Chinese Provinces and Regions North Northeast East Central South Southwest Northwest Beijing Tianjin Hebei Shanxi Inner Mongolia Liaoning Jilin Heilongjiang Shanghai Jiangsu Zhejiang Anhui Fujian Jiangxi Shandong Henan Hubei Hunan Guangdong Guangxi Hainan Chongqing Sichuan Guizhou Yunnan Tibet Shaanxi Gansu Qinghai Ningxia Xinjiang Table 1.2 Regional and Sectoral Contributions to Aggregate Labor Productivity Growth of China, 1997–2015 North Northeast East Central South Southwest Northwest S1 8.51% 9.12% 22.55% 14.49% 8.90% 11.51% 6.39% S2 51.32% 30.95% 149.60% 55.09% 53.08% 33.31% 19.92% S3 7.80% 5.18% 23.17% 8.53% 5.64% 9.42% 6.03% S4 13.37% 7.83% 40.68% 9.79% 12.46% 7.13% 3.84% S5 8.19% 3.88% 14.40% 5.74% 4.95% 4.49% 2.40% S6 3.12% 2.21% 7.70% 3.52% 3.05% 3.11% 1.33% S7 12.92% 4.71% 27.30% 7.10% 10.31% 7.56% 3.70% S8 6.58% 3.22% 18.43% 5.01% 8.79% 3.77% 2.00% S9 31.34% 14.73% 70.36% 25.02% 24.30% 19.05% 10.51% 28 Table 1.3 Regional and Sectoral Patterns of the Within-Sector Effect Across China, 1997–2015 North Northeast East Central South Southwest Northwest S1 10.20% 9.56% 41.02% 20.44% 10.40% 17.24% 7.63% S2 88.40% 68.53% 121.90% 78.63% 33.48% 45.39% 26.77% S3 13.14% 7.47% 17.78% 10.45% 7.30% 8.01% 7.03% S4 7.92% 6.86% 11.55% 5.13% 2.49% 1.82% 1.55% S5 13.28% 5.00% 17.82% 11.86% 4.82% 5.53% 3.39% S6 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% S7 4.31% 3.25% 10.62% 5.72% 6.57% 4.35% 2.12% S8 1.39% 1.99% 3.27% 0.86% 1.26% 0.27% 0.16% S9 25.69% 16.31% 70.73% 31.86% 25.55% 24.23% 9.74% S8 5.19% 1.24% 15.16% 4.16% 7.54% 3.49% 1.84% S9 5.65% -1.58% -0.37% -6.84% -1.25% -5.18% 0.76% Table 1.4 Regional and Sectoral Patterns of the Labor Reallocation Effect Across China, 1997–2015 S2 -37.08% -37.57% 27.70% -23.54% 19.60% -12.08% -6.85% S3 -5.34% -2.29% 5.39% -1.92% -1.66% 1.40% -1.00% S4 5.45% 0.97% 29.13% 4.67% 9.97% 5.31% 2.28% S5 -5.09% -1.11% -3.42% -6.13% 0.13% -1.04% -0.99% S6 3.12% 2.21% 7.70% 3.52% 3.05% 3.11% 1.33% S7 8.61% 1.46% 16.68% 1.38% 3.74% 3.21% 1.57% 29 North Northeast East Central South Southwest Northwest S1 -1.70% -0.45% -18.46% -5.95% -1.50% -5.73% -1.25% Table 1.5 The Within-Sector Effect of All Sector Within Provinces Beijing Tianjin Hebei Shanxi InnerMongolia Liaoning Jilin Heilongjiang Shanghai Jiangsu Zhejiang Anhui Fujian Jiangxi Shandong Henan Hubei Hunan Guangdong Guangxi Hainan WC1 0.23% 0.32% 6.20% 1.18% 2.27% 3.57% 2.50% 3.49% 0.29% 12.92% 5.28% 4.99% 4.07% 3.62% 9.84% 7.05% 7.68% 5.72% 5.28% 3.94% 1.18% WC2 6.38% 12.97% 40.16% 10.76% 18.13% 30.84% 15.35% 22.33% 10.11% 23.53% 12.87% 17.52% 10.96% 9.90% 37.01% 28.59% 20.61% 29.43% 20.83% 11.67% 0.99% WC3 0.93% 0.98% 6.41% 2.09% 2.73% 3.54% 1.13% 2.79% 0.27% 2.39% 1.10% 3.31% 1.80% 1.59% 7.32% 5.13% 2.40% 2.91% 4.40% 2.52% 0.37% WC4 0.61% 3.65% 2.07% 0.70% 0.88% 2.71% 0.95% 3.20% 0.94% 2.79% 1.79% 1.07% 0.59% 0.53% 3.84% 2.18% 0.99% 1.96% 1.89% 0.39% 0.21% WC5 0.10% 1.41% 7.60% 2.63% 1.53% 2.21% 0.99% 1.79% 0.25% 4.48% 2.57% 2.27% 2.58% 0.96% 4.70% 5.94% 2.09% 3.83% 3.07% 1.52% 0.23% WC6 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% WC7 0.70% 0.79% 1.02% 0.88% 0.92% 1.80% 0.71% 0.74% 0.75% 3.58% 1.35% 0.94% 1.07% 0.83% 2.10% 2.55% 2.23% 0.94% 5.22% 1.10% 0.24% 30 WC8 WC9 0.36% 6.42% 0.28% 4.45% 0.38% 7.78% 0.24% 4.02% 0.13% 3.02% 1.05% 7.67% 0.34% 3.27% 0.60% 5.36% 0.59% 4.16% 1.21% 20.13% 0.54% 9.49% 0.18% 9.51% 0.15% 5.66% 0.16% 6.51% 0.44% 15.26% 0.33% 11.50% 0.42% 8.24% 0.11% 12.12% 1.00% 19.21% 0.19% 5.80% 0.07% 0.55% (to be continued) Table 1.5 Continued. Chongqing Sichuan Guizhou Yunnan Tibet Shaanxi Gansu Qinghai Ningxia Xinjiang WC1 2.89% 7.87% 3.19% 3.17% 0.13% 3.46% 1.40% 0.39% 0.48% 1.91% WC2 8.50% 25.16% 6.64% 5.04% 0.05% 15.06% 4.24% 1.77% 1.64% 4.05% WC3 1.51% 3.45% 1.15% 1.71% 0.20%. 3.34% 1.03% 0.67% 0.81% 1.19% WC4 WC5 WC6 WC7 WC8 WC9 0.47% 0.88% 0.00% 0.70% 0.11% 4.69% 0.69% 1.97% 0.00% 1.98% 0.12% 12.39% 0.25% 1.90% 0.00% 0.52% 0.03% 3.43% 0.41% 0.69% 0.00% 1.08% 0.01% 3.55% 0.01% 0.09% 0.00% 0.07% 0.00% 0.17% 0.97% 1.46% 0.00% 0.81% 0.07% 3.98% 0.20% 0.69% 0.00% 0.47% 0.03% 3.30% 0.09% 0.21% 0.00% 0.24% 0.01% 0.54% 0.03% 0.43% 0.00% 0.18% 0.02% 0.51% 0.26% 0.61% 0.00% 0.43% 0.03% 1.42% Sources: Author’s calculation based on the Chinese Statistical Yearbooks. 31 Table 1.6 The Labor Reallocation Effect of All Sectors Within Provinces Beijing Tianjin Hebei Shanxi InnerMongolia Liaoning Jilin Heilongjiang Shanghai Jiangsu Zhejiang Anhui Fujian Jiangxi Shandong Henan Hubei Hunan Guangdong Guangxi Hainan RA1 -0.07% -0.07% -1.40% -0.10% -0.06% -0.21% -0.40% 0.16% -0.17% -7.52% -3.06% -1.86% -1.30% -1.32% -3.23% -1.43% -3.24% -1.28% -1.08% -0.45% 0.02% RA2 -1.21% -3.05% -22.09% -4.17% -6.57% -13.95% -6.27% -17.35% -0.34% 15.92% 11.60% -4.08% 4.63% 0.15% -0.17% -6.11% -3.95% -13.49% 22.42% -2.53% -0.29% RA3 0.38% 0.07% -4.03% -0.90% -0.86% -0.83% 0.21% -1.67% 0.81% 3.32% 2.40% -0.91% 1.37% 0.54% -2.15% -2.17% 0.50% -0.26% -1.28% -0.57% 0.18% RA4 2.65% -0.66% 1.06% 0.77% 1.64% 1.29% 0.61% -0.93% 4.36% 6.96% 5.32% 1.10% 2.08% 1.11% 8.20% 1.38% 2.09% 1.20% 8.55% 1.04% 0.38% RA5 1.19% -0.45% -4.40% -1.45% 0.01% 0.12% -0.33% -0.90% 1.15% -0.81% -0.45% -1.29% -0.63% 0.01% -1.40% -3.55% -0.44% -2.14% 0.57% -0.45% 0.01% RA6 0.62% 0.38% 0.61% 0.53% 0.98% 0.97% 0.51% 0.73% 0.57% 1.82% 1.50% 0.63% 0.59% 0.60% 1.99% 1.56% 1.05% 0.91% 2.22% 0.57% 0.27% RA7 5.04% 1.58% 0.95% 0.71% 0.33% 0.96% 0.09% 0.41% 5.04% 4.14% 2.84% 0.84% 1.30% 0.42% 2.11% 0.27% 0.49% 0.62% 3.19% 0.43% 0.12% 32 RA8 RA9 1.81% 7.16% 0.63% 0.43% 1.51% -2.38% 0.71% -0.22% 0.53% 0.66% 0.68% -0.93% 0.30% 0.21% 0.25% -0.87% 1.80% 4.19% 4.27% 0.45% 2.85% 2.22% 1.04% -4.46% 1.34% -0.13% 0.61% -2.61% 3.25% -0.03% 2.02% -3.51% 1.21% -0.65% 0.93% -2.69% 6.39% 0.81% 0.77% -2.34% 0.37% 0.28% (to be continued) Table 1.6 Continued. Chongqing Sichuan Guizhou Yunnan Tibet Shaanxi Gansu Qinghai Ningxia Xinjiang WC1 -1.45% -3.02% -0.87% -0.38% -0.01% -1.16% -0.09% -0.11% -0.14% 0.26% WC2 -0.86% -9.32% -1.98% 0.04% 0.04% -4.13% -1.85% -0.46% -0.22% -0.19% WC3 0.66% -0.16% 0.06% 0.58% 0.26% -0.75% 0.03% -0.21% -0.20% 0.14% WC4 WC5 WC6 WC7 WC8 WC9 1.39% 0.16% 0.53% 1.28% 1.12% -0.75% 1.75% -0.29% 1.32% 1.27% 1.64% -4.41% 0.68% -0.54% 0.54% 0.33% 0.30% -0.77% 1.41% -0.32% 0.67% 0.30% 0.39% 0.44% 0.09% -0.05% 0.05% 0.03% 0.05% 0.31% 1.15% -0.52% 0.66% 0.79% 0.94% -0.03% 0.46% -0.32% 0.30% 0.16% 0.32% -0.93% 0.13% -0.09% 0.07% 0.08% 0.07% 0.10% 0.15% -0.15% 0.08% 0.19% 0.12% 0.29% 0.40% 0.08% 0.23% 0.35% 0.38% 1.33% Sources: Author’s calculation based on the Chinese Statistical Yearbooks. 33 CHAPTER 2 DETERMINANTS OF LABOR PRODUCTIVITY GROWTH IN CHINA 2.1 Abstract This chapter studies factors that are associated with labor productivity growth in China during the period 1997 to 2015. In light of the rapid integration of the Chinese economy into the global markets and deep structural changes, this chapter provides an empirical analysis of China’s economic growth with two objectives in mind. First, it undertakes a comprehensive analysis of China’s economic growth by investigating determinants of productivity growth, and its components, at the sectoral and regional level. Specifically, the empirical specification takes components from the productivity decomposition presented in the first chapter as the dependent variables. Second, this chapter tests the degree of association between aggregate labor productivity growth, and its decomposition terms across 31 provinces and 9 sectors, and the usual candidates for determinants of growth. Among these, the degree of openness, capital and human accumulation, and industrial spillovers are considered prime candidates. Empirical results suggest that industrialization spillovers from the manufacturing sector to the service sectors play an important role in boosting labor productivity growth across sectors. Trade openness and foreign direct investment both contribute to China’s economic growth 35 across sectors; however, despite a significant increase in physical and human capital stock, the analysis does not show their substantial impact on labor productivity growth and its components. 2.2 Introduction Recent decades have witnessed intense debates about China’s economic power in the world (Li, 2008; Zhu, 2012). Institutional change during the postreform period is widely considered by many as having been the major catalyst for economic development through ensuring the “right” incentives and therefore efficient allocation of resources. In particular, the dual-track pricing system has been singled out as acting as a trigger of growth. Government-controlled prices have led to the coexistence of low and high market prices within the same sector, thereby providing a smooth transitional platform to accommodate entrepreneurial spirit (Zou, 1993) within a socialist political regime. Political centralization and allocative efficiency have managed to sustain the unique mechanism of the Chinese economy. In a nutshell, economic development policies during the reform period have been based on a mixture of central design and local experimentation (Heilmann, 2008). The 5-year national economic guidelines clearly delineated the nation’s economic goals in the current cycle and also served as the roadmap for the future economic development agenda. In the meantime, local governments were encouraged to have tailored economic policies in accordance with local conditions. Economic growth theories are broadly divided into two schools of thought: the conventional view that identifies and focuses on total factor productivity growth (TFPG) 36 as the main source of growth, and the structuralist view, which also takes into account the role of economic structure and structural change. The total factor productivity growth approach builds on the Solow growth model (1957). It adopts an aggregate production function and the growth accounting framework with the aim of identifying contributions to growth from capital, labor, and technical progress. Total factor productivity growth becomes a residual that absorbs all unexplained factors and assigns them to technical progress, which is the main driver of sustainable growth (formally, it is the force that causes a shift in the aggregate production function). The structuralist view (Chenery 1979; Kuznets, 1957; Lewis, 1954) starts from an examination of economic structure and structural change with the goal of understanding the mechanisms of growth as they relate to the existing structural constraints that the economy faces. Hence, microeconomic assumptions of rational economic agents and constant returns to scale are not embedded in its theoretical foundation. Given China’s economic development patterns during the postreform period, the structural change theory appears more applicable to contextualize the driving force behind its remarkable growth. During 1997–2015, economic growth was mainly the result of the rapid expansion of the manufacturing sector, a pattern that fits with the socalled Kaldor’s laws (Kaldor, 1957). Kaldor identifies the manufacturing sector as the engine of economic growth because of several important features – the existence of increasing returns to scale, spillover effects, and dynamic economies of scale (Ocampo, Rada, & Taylor, 2009). In addition to identifying and distinguishing among the qualitative features of different sectors, structuralist theories have pointed out that 37 structural change has been widely observed in the course of economic development. Transfer of labor from low productivity to high productivity sectors is by itself a powerful source of economic growth. Overall, structural transformation can be incentivized through policy interventions in both laissez-faire markets and other economic regimes (Lei & Nugent, 2018). Structural transformation, growth, and economic development can form a virtuous circle and take an economy on a higher path. Output growth, for example, can further help the economy expand in a sustainable manner as it provides scope for expansion of labor skills from learning by doing. From a supply-side perspective, skilled labor is a required ingredient for the development of both capital-intensive industries and service sectors. The manufacturing sector, in particular, together with manufacturing-related service sectors, facilitates such dynamics among growth, learning by doing, and structure change. Unlike the structuralist perspective, the new growth literature, building on the neoclassical growth model, emphasizes that an outward economy would outperform an inward economy. Thus, trade openness is viewed as a key driver of economic growth (Grossman & Helpman, 1991a, 1991b; Romer, 1990; Young, 1991), and therefore policies that facilitate integration in the global economy can benefit an economy. A somewhat sizable body of literature has supported the idea that trade openness is indeed a successful strategy for China’s economic development (Lin, Yao, & Yeuh, 2007; Sally, 2006; Song & Yao, 2006; Wu, 2008). However, empirical evidence for this hypothesis as applied across developing countries remains ambiguous (Madison, 2009; Yanikkaya, 2003). In contrast, import substitution industrializations that encourage countries to 38 develop their domestic industries, and, in particular, manufacturing, rather than imports from foreign countries have, historically, had greater success in stimulating growth in the least developed countries (Meltz, 2005). This chapter follows the structuralist approach in order to examine the determinants of labor productivity growth and its components across 31 Chinese provinces within 9 sectors during the period of 1997 to 2015. The provinces and the sectors are the same as those analyzed in Chapter 1. The rest of the chapter is structured as follows: Section 2.3 briefly reviews the economic structure of regional economies in China. Section 2.4 presents selected determinants of labor productivity growth in the context of the relevant literature. Section 2.5 discusses the model specification and data structures. Section 2.6 summarizes key results, and Section 2.7 presents the conclusions. 2.3 The Economic Structure of China’s Regional Economies China’s market reforms since 1978 have favored coastal provinces over inland provinces. With the hope of using coastal provinces as catalysts to drive aggregate economic growth, the spillover effect has not necessarily panned out as expected (Zheng & Chen, 2008). Regional disparity across provinces is still widely observed. The Gini coefficient indicates that income gaps have widened in the postreform period, despite the fact that all provincial economies have moved from lower income economies to uppermiddle income economies in recent years. Geographic and demographic patterns are also major factors that drive regional inequality. The eastern territories outperformed the western territories even before the communist era. Therefore, each province should be 39 viewed as an isolated economy with a unique economic structure. Under the wave of globalization, the Chinese provinces did not increase their mutual dependence. Instead, they appear to have become more dependent on linkages with international markets (Kumer, 1994; Poncet, 2003; Young, 2000). Interprovincial transactions mainly took place in the manufacturing sector because of the supply chains in the upstream and downstream areas within the industry. During the period 1997–2015, China’s economic growth rate was on average 9.7% per year while the country continued to experience significant changes in its economic structure. Almost all provinces had decreasing shares of output and employment in the agricultural sector. Part of the labor force released from agriculture during the postreform period was absorbed by the manufacturing sector. Nonetheless, during the period of analysis, the aggregate output and employment shares in the manufacturing sector remained somewhat constant as other nonagricultural sectors began to grow and absorb labor. Only a few provinces in Southeast China such as Guangdong, Zhejiang, Jiangsu, and Fujian had increasing employment shares in manufacturing, given the advantage and specialization permitted by their status as the home to special economic zones. Overall, however, the service sectors outgrew the manufacturing sector in their output and employment shares. The construction sector also had expanding employment and output shares in many provinces, suggesting that infrastructure investment continued to sustain regional development. The increasing employment shares in the modern service or social service sectors indicate labor reallocation to the sectors that required higher knowledge and skills. 40 2.4 Literature Reviews on Determinants of Labor Productivity Growth The existing literature has used a variety of methods to identify potential sources of labor productivity growth in China. Su and Heshmati (2011), for example, have included wage and profit as proxies to study the employment perspective of labor productivity growth. Yang and Lahr (2010) used input-output-based analysis to investigate sources of labor productivity growth in China within the primary, secondary, and tertiary sectors. Ding and Knight (2008) found that structural transformation along with physical and human capital formation has resulted in rapid economic growth in China. This chapter contributes to this diverse literature by examining factors that are associated with components of aggregate labor productivity growth. Specifically, labor productivity growth can be decomposed into a within-sector effect and a labor reallocation effect as detailed in the first chapter (Syquirn, 1986). Briefly, the withinsector effect refers to productivity growth within sectors, whereas the labor reallocation effect refers to productivity growth across sectors. Understanding the determinants of labor productivity growth and their impacts on the within-sector and labor reallocation effects will be critical to understanding the patterns and mechanisms of economic growth. In what follows, I discuss the main factors that have been found to be associated with economic growth, and which will be included in the empirical analysis in this chapter. These are the following: trade openness, foreign direct investment, spillovers from an expansion of manufacturing, and human and capital accumulation. 41 2.4.1 Trade Openness The link between economic growth and trade openness was first examined explicitly in the new growth literature using cross-country data (Edwards, 1998; Harrison, 1996; Sachs & Warner, 1995). Trade liberalization rewards the comparative advantages of a human-capital rich country if it specializes in exporting products with high-technological content while importing labor-intensive traditional products (Grossman & Helpman, 1989). According to Grossman and Helpman (1991a, 1991b), the reduction of trade barriers generates spillovers within an underperforming local economy through its engagement with foreign business entities. The economy can access a larger pool of human capital within the shortest time (Romer, 1990), while, at the same time, importing intermediate goods to develop higher value-added industries (Coe & Helpman, 1995; Keller, 1998). Merlitz (2003) points out that trade openness facilitates inputallocative efficiencies through industrial shifts, whereas Hausmann, Rodrik, and Velasco (2005, 2007) suggest that institutional quality improves. Finally, supporters of global integration as a strategy for growth point out its benefits for firm-level productivity growth given positive externalities firms are exposed to and stiffer competition that is expected to incentivize firms to find more efficient techniques of production (Acemoglu, Johnson, & Robinson, 2002; Krugman & Venables, 1995; Tybout, 2001). As the new millennium rolled in, globalization and international trade continued to increase crosscountry dependence (Dao, 2014), new voices that questioned the validity of the link between trade liberalization and economic growth emerged (Rodrik & Rodriguez, 2001; Yannikaya, 2003). This new empirical and theoretical literature raises doubts about whether economic growth driven by trade openness substantially improves social welfare 42 (Hausmann et al., 2005, 2007). With these diverse bodies of literature in mind, I test the association between trade openness, as measured by the share of exports and imports to GDP, and labor productivity growth and its components. 2.4.2 Foreign Direct Investment Foreign direct investment (FDI) is another indicator of the degree of globalization and openness of an economy. Policymakers and economic theory alike highlight the benefits from FDI: knowledge spillovers and diffusion of technology, provision of muchneeded financing of capital accumulation (Alfargo, 2003), and establishment of backward and forward linkages with firms on global markets. Although FDI is viewed as a means of capital accumulation in the least developed countries, some cross-country studies have found inconclusive evidence about its impact on different economies. FDI also appears to have different impacts across sectors. The primary sectors with local supplies are usually least affected by FDI (World Investment Report, 2001, pp.138), whereas manufacturing and service sectors benefit the most from technological diffusion following FDI (Findlay, 1978; Wang & Bloomstrom, 1992). 2.4.3 The Role of the Manufacturing Sector as an Economic Engine The empirical literature has identified a positive link between the degree of industrialization and GDP per capita in developing countries (Kaldor, 1966, 1967; Rodrik, 2009). Theoretically, the Kaldor-Verdoorn law suggests the important role of the manufacturing sector as an engine of growth through dynamic economies of scales and 43 positive spillovers to other economic activities (Kaldor, 1957; Verdoorn, 1949). A successful process of structural transformation is expected to shift resources, and especially labor, from agriculture to manufacturing, and further from manufacturing to services (Chenery, 1979; Clark, 1941; Kuznets, 1957; Su & Yao, 2016). The manufacturing sector has unique properties that are usually not found in other sectors. Technical progress often originates in the manufacturing sector before it advances to the agricultural or service sectors. The vehicle of overall technological progress is often regarded as the investment and subsequent capital and human accumulation taking place in the manufacturing sector (Arrow, 1962; Romer, 1986), giving rise to positive externalities of knowledge (Su & Yao, 2016). Stylized facts about patterns of development suggest that developed countries tend to have a much larger tertiary sector. The rise of the information and communications technology (ICT) sector in developed economies has secured their economic influence and their position as the core of the world economic system. In other words, the ICT sector has taken the lead in driving economic growth within the developed world. Whether ICT and other such dynamic sectors can be used as an economic engine in the developing countries remains open to debate (Maroto- Sánchez & Cuadrado-Rourara, 2009; Szirmai & Verspagen, 2015). During 1997–2015, China’s service sectors saw a significant rise in their output and employment shares, overcoming the manufacturing sector, although labor productivity growth in the manufacturing sector remained much larger (Wu, 2015). This chapter thereby chooses the output growth rate of the manufacturing sector as an explanatory variable of labor productivity growth for all sectors except the manufacturing sector itself. 44 2.4.4 Human Capital Human capital refers to innate and acquired abilities, and to the stock of knowledge accumulated as a result of learning by doing (Schultz, 1961). Because human capital is developed and accumulated via education, it becomes an important factor apart from traditional labor as input to the production process (Mankiew, Romer, & Weil, 1992). A large body of literature has found a positive relationship between human capital and income per capita (Barro & Lee, 1994; Becker, 1967; Mankiew, Romer, & Weil, 1992). However, the relationship between human capital and economic growth becomes unclear when educational attainment is introduced as a variable in regression models adopting the growth accounting approach (Barro & Sala-i-Martin, 1995; Benhabib & Spiegel, 1994; Islam, 1995; Pritchett, 1996). Although educational attainment increased from 1997 to 2015, the strict household registration system and labor market segmentation led to divergent human capital levels across provinces (Fleisher, Li, & Zhao, 2011; Fleisher & Wang, 2004; Meng & Zhang, 2001). Birthplace plays a critical role in the quality of education, especially in China (Heckman, 2003, 2005). As a result, it tends to maintain and even reinforce existing income gaps across provinces. Moreover, empirical evidence indicates that investing in infrastructures generates high returns in the developed eastern territory, whereas investing in human capital generates high or comparable returns in the less developed western territory (Fleisher et al., 2011). The empirical literature also points out that education at the secondary or college level resulted in different growth rates across provinces (Chen & Fleisher, 1996; Démurger, 2001; Fleisher & Chen, 1997). Therefore, this chapter includes human capital as an 45 explanatory variable for labor productivity growth using Barro and Li’s (1993) indicators on human capital, which is measured by the average years of schooling. 2.4.5 Fixed Gross Capital Formation The association between physical capital accumulation and economic growth has been studied extensively since the birth of modern economic growth theory. de Long and Summers (1991, 1992) find that the rate of gross capital formation is associated with the economic growth rate. However, Benhabib and Jovanovic (1991) maintain the position that the engine of economic growth is not primarily fueled by physical capital. A strand of the literature suggests the reverse causal link from economic growth to investment rates through the effect that economic growth has on saving rates (Dooley, Frankel, & Mathieson, 1987). In China’s case, acceleration in fixed gross capital formation during the postreform period has resulted in rapid economic growth (Chow & Li, 2002; Heckman & Yi, 2012). Therefore, despite the stylized fact that high rates of gross capital formation accompany rapid growth in income per capita (Blomström, Lipsey, & Zejan, 1996), it is important to further investigate whether the fixed investment is a major driving force to economic growth at the sectoral level. 2.5 Model Specifications, Data Sources, and Diagnostic Tests This chapter contributes to a large body of empirical literature that explores patterns and sources of labor productivity growth in different settings (Griffith, Redding, & Reenen, 2004; Heshmati & Shiu, 2006; Islam, 1995; Su & Heshmati, 2011). The methodology is, broadly described, panel data analysis, which allows us to control for 46 heterogeneity across regional economies through fixed effects at the unit and/or time level. The model specification is as follows: 2 2 2 𝜉𝑖,𝑗,𝑡 = 𝛼0 + 𝜍𝑡 + ∑ 𝜑𝑛 ∆𝑇𝑅𝐴𝐷𝐸𝑖,𝑗,𝑡−𝑛 + ∑ 𝛾𝑛 ∆𝐹𝐷𝐼𝑖,𝑗,𝑡−𝑛 + ∑ 𝛿𝑛 ∆𝐻𝐶𝑖,𝑗,𝑡−𝑛 𝑛=1 2 𝑛=1 𝑛=1 2 + ∑ 𝜆𝑛 ∆𝑃𝐶𝑖,𝑗,𝑡−𝑛 + ∑ 𝜏𝑛 𝑀𝐺𝐷𝑃𝑖,𝑗,𝑡−𝑛 + 𝑢𝑖,𝑗,𝑡 𝑛=1 𝑛=1 (2.1) The subscript i stands for sectors and j stands for province in year t. The dependent variables are labor productivity growth ( ) and its components: the withinsector effect (WC) and the labor reallocation effect (RA). The error term in the fixedeffect model can be decomposed into a time-specific effect (𝜍𝑡), region-specific effect (α0), and random error components (u), which are assumed to follow a normal distribution. Table 2.1 provides the definition and acronyms for the explanatory variables used in the analysis. Globalization is proxied by trade openness (TRADE) and inward foreign direct investment (FDI) as a share of GDP. More specifically, following the suggestions of Caselli et al. (1996) and Dollar and Krayy (2004), trade openness is measured by three indicators: the share of trade volume to GDP (TRADE), the share of exports to GDP (EX), and the share of imports to GDP (IM). Industrialization is proxied by the growth rate of output in the manufacturing sector (MGDP), whereas human capital (HC) and physical capital (PC) are measured by mean years of schooling and the ratio of gross fixed capital formation to GDP. All explanatory variables are introduced in terms of 47 first differences. Note that equation 2.1 is applied to all sectors except the manufacturing sector because the growth rate of output in the manufacturing sector (MGDP) is a proxy for the spillover effect of industrialization in other sectors. In terms of data, the original output and employment data came from the National Bureau of Statistics of China (NBSC) and the Chinese Statistical Yearbooks (CSYs). The output series, initially posted in RMB, have been converted to real USD. Employed persons before 2004 were directly posted in the Chinese Statistical Yearbooks, but after 2004 they are merged by “the employed persons in urban units,” “the private enterprises or self-employed individuals,” and “the employed persons in suburban area.” Labor productivity growth is defined as real output per worker. The within-sector and labor reallocation effects are, as discussed in equation 1.1 of the first chapter, obtained using the decomposition of aggregate labor productivity growth suggested by Syquirn (1986) and Ocampo, Rada, and Taylor (2009). To remind the reader, the panel data series in all sectors are balanced and cover the period 1997 to 2015. The data for hotel and catering services, however, only start in 2004. Given the nature of the data, with the results summarized in Table 2.2 through Table 2.10, issues of cross-sectional dependence, serial correlation, and heteroskedasticity are tested for (Baltagi, 2012) with R. The plmtest command is used first to check time-specific and region-specific effects to examine the applicability of adopting the simple OLS regression. The bptest command is used to test whether the residuals have constant variance (Torres-Renya, 2010). The diagnostic test results show that heterogeneity exist in sectors across regions, except for a few series in the sectors of “transport, transmission and post” and “hotel and catering services.” The data series that 48 are classified as the pooling data are suggested using the simple OLS regression. The next step is to decide between a random and a fixed-effect model specification by the Hausmann test using the phtest command. The test results indicate that not all of these sectors are suitable for the fixed-effect model. Moreover, because the socioeconomic data are subject to the issue of cross-sectional dependence (Sarafidis & Wansbeek, 2010), the Breusch-Pagan Lagrange Multiplier test is conducted using the pcdtest command. It suggests that cross-sectional dependence is found in all sectors under various model specifications. Endogeneity broadly refers to a situation where an explanatory variable is correlated with the error term. A first step to address this problem is by taking the first difference in an attempt to eliminate omitted variables that are invariant over time. In this chapter, the time-invariant variables are absorbed into the fixed effects, causing their disappearance after the first-difference or de-mean transformation. The second step in my approach is to use lags of explanatory variables, thus addressing the broader endogeneity issue without necessarily resorting to instrumental variables. To check if a given variable is correlated with its lagged version, the BreuschGodfrey autocorrelation test is performed using the pbgtest command. The results show that serial correlation mainly occurred in the traditional service sectors such as “wholesale and retail trade” and “transmission, transport, and post.” Heteroskedasticity is another issue of panel data that one needs to check for and address if present. The Breusch-Pagan test is performed with the bptest command. The results show that no significant issue of heteroskedasticity is found using the data and the model specifications in this chapter. 49 2.6 Estimation Results of the Determinants of Labor Productivity Growth Table 2.11 to Table 2.19 present estimates for the model for the 9 sectors. The dependent variables cover aggregate productivity growth, as well as its components provided by the decomposition. In each sector, degree of openness is measured by various indicators mentioned above: the share of total trade volume to GDP, the share of exports to GDP, the share of imports to GDP, and the share of FDI to GDP. These indicators reflect China’s economic development patterns that have become more dependent on integration in the global economy and appear to facilitate rapid industrial development within its local economies through international and interprovincial trade. During the postreform period, the Chinese central government created a business-friendly environment using its low-cost labor force to encourage foreign enterprises to switch their manufacturing to China. Benefiting from technology transfers and foreign direct investment, China’s manufacturing sector grew rapidly , and it has now begun to occupy a very important position in global supply chain. Economic integration with the world economy gradually facilitated institutional change to allow Chinese citizens to own their private assets and means of production, driving incentives to improve production efficiency and productivity levels. Moreover, China set financial regulations to ensure that foreign capital and its gains could be continuously used for its reproduction within its territories.The rapidly growing foreign exchange reserves in the country was further used for capital and technology accumulation with the goal of sustaining the economy’s rapid growth. Overall, for China, globalization has become a key mechanism behind its rapid labor productivity growth. As a result, both trade openness and foreign direct investment 50 show a positive impact on labor productivity growth, although the magnitude of the estimates differs across sectors. The manufacturing sector, unsurprisingly, has been an economic engine of growth for China and its regional economies. The spillover effect of the manufacturing sector is observed in agriculture, construction, and service sectors, implying that industrialization has had a beneficial effect on labor productivity growth in other sectors. Human capital and physical capital, however, did not show any significant effect on labor productivity growth from 1997 to 2015. The detailed interpretation of the empirical results is listed in the following paragraphs. Estimates at the sectoral level indicate that trade openness has no direct impact on labor productivity growth or its components in agriculture, fishery, forestry, and animal husbandry. However, the manufacturing sector appeared to have positive spillovers to the agricultural sector – the estimate for manufacturing growth is positive, suggesting a positive association between the expansion of manufacturing and the within-sector effect in agriculture. In the manufacturing and construction sectors, trade openness is positively associated with both the within-sector and labor reallocation effects. For the manufacturing sector, exports have had a positive impact on the within-sector effect and a negative impact on the labor reallocation effect. Trade openness has not shown a consistent impact across the service sectors on labor productivity growth and its components, despite the fact that both exports and imports have had a positive impact on the within-sector effect and a negative impact on the labor reallocation effect. Overall, trade openness has played a critical role in stimulating China’s labor productivity growth through sectoral expansion, but the bonus 51 of trade liberalization is offset by the labor shortage in certain areas that are not primarily involved in the supply chain. Moving on to the other measure of global integration, foreign direct investment as a share of GDP has a positive impact on labor reallocation in agriculture, fishery, forestry, and animal husbandry, but did not have any significant impact on the withinsector effect. This finding further validates the view that foreign direct investment did not contribute much to productivity growth in the primary sector, which tends to rely on local supplies (World Investment Report, 2001, p.138). In the manufacturing and construction sectors, foreign direct investment associates positively with the within-sector effect and negatively with the labor reallocation effect. This finding is consistent with the observation about patterns of structural transformation: that the manufacturing sector has the largest labor productivity growth among sectors, but its employment shares has decreased in most provinces. Foreign direct investment take different roles across service sectors. In the traditional service and social service sectors, foreign direct investment had a positive impact on the within-sector effect and a negative impact on the labor reallocation effect when trade openness is measured in terms of exports and imports, respectively. In the social and community service sectors, such as education and scientific research, foreign direct investment positively associates with labor productivity growth and its withinsector effect. Zhong (2008) points out that the spillover effect of foreign direct investment on human capital accumulation across Chinese provinces was observed especially in the export-processing sectors. Therefore, a rising share of capital-andtechnology-intensive FDI has resulted in a higher percentage of the population with a 52 college or university education. Foreign direct investment did not have, however, any significant impact on the banking and financial sector. One possible explanation is that this is due to the banking system in China being mostly controlled by governmentassociated organizations. In the real estate service sector, it should be noted that foreign direct investment had a positive impact on the within-sector effect and negative impact on the labor reallocation effect with its second lagged versions. Human capital shows, at best, scattered significant association with labor productivity growth and its components. In the model specification with trade openness defined in terms of exports and imports over GDP, increasing years of schooling has a significant estimate only in the case of construction and real estate services. In the construction sector, human capital accumulation has had a negative impact on the withinsector effect and a positive impact on the labor reallocation effect. Contrarily, in the real estate services sector, human capital accumulation appears to have had a positive impact on the within-sector effect and a negative impact on the labor reallocation effect. This finding suggests that human capital accumulation would benefit especially sectors that require advanced know-how. Moreover, human capital has had a positive impact on the second lag of the within-sector effect in government-related service sectors when trade openness is measured by the ratio of imports to GDP, reflecting long-term positive externalities of education on scientific research. Historically, economic growth has been viewed as tightly connected to rising capital stock. The empirical evidence in this chapter shows the positive impact of physical capital accumulation only on the within-sector effect in the agricultural sector. 53 The results indicate no substantial correlation between physical capital accumulation and labor productivity growth and its components in the manufacturing or the service sectors. Overall, we find that the manufacturing sector remains an important engine of growth for the Chinese economy. The growth rate of the manufacturing output show significant labor productivity growth and the within-sector effect in the agricultural, modern service and government-related service sectors due to its export-led economy. However, no spillovers are observed in the traditional service sectors. 2.7 Conclusion In closing, this chapter studies determinants of labor productivity growth and its components. The objective is to understand sources and patterns of economic growth within China in the period 1997–2015. Using the fixed-effect panel data framework, I examine the association between labor productivity growth and its sectoral components and variables associated with globalization, industrialization, and human and physical capital. The empirical results indicate that both globalization and industrialization have positively contributed to China’s economic growth. However, regression by sectors did not show any substantial relationships between physical or human capital education and labor productivity growth and its components. One of the intended contributions of this chapter is to provide a comprehensive analysis of sources of economic growth across sectors given regional economies. Although regional disparity in terms of labor productivity levels has decreased slightly, income inequality within the country has risen (Riskin, Zhao, & Li, 2001). Overall, the 54 analysis suggests that trade liberalization and the expansion of the manufacturing sector can become potential tools to stimulate balanced regional development. Still, trade liberalization has increased regional dependence on international markets rather than strengthen linkages among provinces and regional economies in China (Kumer, 1994; Poncet, 2003; Young, 2000). Interprovincial trade primarily occurs in the manufacturing sector due to its downstream and upstream networks among industries (Xin et al., 2015). Sectors that mainly rely on local supplies, such as agriculture or services, have not seen a rise in trade across provincial boundaries. Since 2000, China’s economic growth has been heavily influenced by integration in the global markets. Trade openness has further caused changes in the economic structure of China. Among rapidly expanded modern service sector, the financial sector stands out, which is an expected outcome given China’s rapid and sustained economic growth. Interestingly, it should also be noted that despite integration in the global markets and the fast-rising trade volumes, indicators of trade openness used in this chapter did not appear to have a significant association with labor productivity growth and its components within the manufacturing sector. Foreign direct investment overall had a positive impact on labor productivity growth and its components in almost all sectors. This finding is consistent with previous research that shows foreign direct investment has had a fundamental role in driving China’s economic growth during the postreform period (Berthelemy & Démurger, 2002). Also, trade openness, foreign direct investment, and economic growth are said to mutually reinforced one another under the open-door policies (Liu, Burridge, & Sinclair, 2010). 55 Official statistics show a rise in the mean years of schooling during 1997–2015 period. The accumulation of human capital stock took place at the same time with the rapid increase in income per capita across provinces. However, when controlling for other important factors of growth, the empirical evidence in this chapter does not indicate any substantial relationship between human capital accumulation and labor productivity growth and its components. The lack of the role of human capital in the manufacturing sector does not imply that human capital is not an important factor for China’s growth strategy. Instead, it simply means that China has moved out of the stage in which it relies only on this factor as a driver of economic growth. Previous research that retained a positive view of human capital accumulation and economic growth mainly was based on the growth accounting approach (Fleisheir et al., 2008; Li et al., 2017), and thus model specifications may generate different interpretations of the role of human capital. Moreover, the growth model of China has been associated with several phases throughout the postreform period (Yang, 2016): China’s economic growth was first driven by its resources during its prereform period, followed by capital investment and human capital accumulation in the early stage of market reform processes, while it was mainly driven by labor productivity growth from 1997 onward. In conclusion, the manufacturing sector remains an engine of growth at both national and regional development. The stock of physical and human capital across provinces still varies greatly. Despite the fact that China’s economic structure has been transitioning rapidly to the tertiary sectors, labor productivity in the service sectors is, as expected, behind that in the manufacturing sector. This is true even for sectors such as 56 ICT, which are considered potential engines of sustained growth on par with the manufacturing sector. 57 Table 2.1 The Definition and Acronyms for the Explanatory Variables Variable Exports/GDP Imports/GDP Exports+Imports/GDP Manufacture output growth FDI/GDP Mean years of schooling Gross Fixed Capital Formation Label EX IM TRADE MGDP FDI HC PC Table 2.2 Agriculture, Fishery, Forestry, and Animal Husbandry LPG WC RA TRADE EX IM TRADE EX IM TRADE EX IM panel panel panel panel panel panel panel panel panel random fixed fixed random cross-sectional dependence No Yes Yes No No Yes No Yes Yes serial correlation Yes No No Yes No No Yes No No homoskedasticity No No No No No No No No No OLS or panel fixed or random random random fixed random random Table 2.3 Manufacturing LPG OLS or panel fixed or random WC RA TRADE EX IM TRADE EX IM TRADE EX IM panel panel panel panel panel panel panel panel panel random random random random random random random random random cross-sectional dependence Yes No No Yes No No Yes Yes No serial correlation No No No Yes Yes No No No No homoskedasticity No No Yes No No Yes No No Yes 58 Table 2.4 Construction LPG OLS or panel fixed or random WC RA TRADE EX IM TRADE EX IM TRADE EX IM panel panel panel panel panel panel panel panel panel random random random random random random random random fixed cross-sectional dependence Yes Yes Yes Yes Yes Yes Yes Yes No serial correlation Yes Yes Yes Yes Yes Yes Yes Yes No homoskedasticity No No No No No Yes No No Yes Table 2.5 Retail and Wholesale LPG OLS or panel fixed or random WC RA TRADE EX IM TRADE EX IM TRADE EX IM panel panel panel panel panel panel panel panel panel random random random random random random random random random cross-sectional dependence Yes Yes No Yes Yes Yes Yes Yes Yes serial correlation Yes Yes Yes Yes Yes Yes Yes Yes Yes homoskedasticity No No No No No No No No No 59 Table 2.6 Transport, Transmission, and Post LPG WC RA TRADE EX IM TRADE EX IM TRADE EX IM panel OLS panel panel panel panel panel panel panel random N/A cross-sectional dependence Yes No Yes Yes Yes No No Yes Yes serial correlation Yes No Yes Yes No Yes No No Yes homoskedasticity No No No No No No No No No OLS or panel fixed or random random random random random fixed random random Table 2.7 Hotel and Catering LPG WC TRADE EX IM TRADE OLS or panel OLS panel panel fixed or random N/A fixed fixed cross-sectional dependence No Yes serial correlation No homoskedasticity Yes RA EX IM TRADE EX IM OLS panel panel N/A random fixed panel panel panel fixed random fixed No No No No No No No No No No No No No No No No No Yes Yes No Yes Yes No 60 Table 2.8 Financial and Banking LPG WC RA TRADE EX IM TRADE EX IM TRADE EX IM OLS or panel panel panel panel panel panel panel panel panel panel fixed or random fixed fixed fixed fixed fixed fixed fixed cross-sectional dependence No No Yes Yes No No No No No serial correlation No No No No No No No No No homoskedasticity Yes Yes No Yes Yes Yes Yes No No random random Table 2.9 Real Estate Services LPG OLS or panel fixed or random WC RA TRADE EX IM TRADE EX IM TRADE EX panel panel panel panel panel panel panel panel panel fixed random random random random random random random random IM cross-sectional dependence Yes No No No Yes Yes No No Yes serial correlation No Yes No No Yes No No No No homoskedasticity No No No No No No No No No 61 Table 2.10 Community and Social Service Sector LPG WC RA TRADE EX IM TRADE EX IM TRADE EX IM panel panel panel panel panel panel panel panel panel random fixed fixed fixed cross-sectional dependence Yes Yes Yes No No No Yes Yes Yes serial correlation Yes Yes Yes Yes Yes Yes Yes Yes Yes homoskedasticity No No No No No No No No No OLS or panel fixed or random random random fixed random random 62 63 Table 2.11 Detailed Results in Agriculture LPG TRADE intercept L1_∆PC L2_∆PC L1_∆HC L2_∆HC WC Estimate Std. Error Estimate 0.074418*** 0.01427 N/A 0.09022 0.05440 -0.01532 0.06598 0.01990 0.01489 RA Std. Error Estimate -0.00001 N/A 0.00058* 0.00055 -0.00006 Std. Error 0.00010 0.00029 0.00007 0.000269 0.00035 -0.00016 0.000328 0.00008 -0.00009 0.000074 0.000075 0.01499 -0.00012 0.00008 0.00001 L1_∆TRADE -0.64467 1.57576 0.00334 0.00797 0.00841 0.007794 L2_∆TRADE -1.07132 1.56094 -0.00191 0.00784 -0.00647 0.007695 L1_MGDP 0.11355*** 0.04620 0.00014 0.00027 -0.00009 0.000238 L2_MGDP 0.171379*** 0.04894 0.00063* 0.00028 -0.00009 0.000249 0.00026 0.00051* 0.0002463 0.00027 -0.00041* 0.000249 L1_∆FDI L2_∆FDI EX 0.00728 0.02333 0.05083 0.05915 0.05168 0.00007 0.00046* LPG Estimate WC RA Std. Error Estimate Std. Error Estimate Std. Error intercept 0.074418*** 0.0142653 N/A N/A -0.00001 0.00010 L1_∆PC 0.09022 0.0543967 0.00058*** 0.00028 0.00008 0.00027 L2_∆PC -0.01532 0.065983 0.00035 -0.00017 0.00033 L1_∆HC 0.01990 0.0148924 -0.00006 0.00007 -0.00010 0.00007 L2_∆HC 0.00728 0.0149949 -0.00011 0.00007 0.00001 0.00008 L1_∆EX -0.64467 1.5757623 0.00504 0.01515 0.00842 0.00779 L2_∆EX -1.07132 1.5609399 -0.00774 0.01495 -0.00648 0.00770 L1_MGDP 0.1135553*** 0.0461986 0.00014 0.00026 -0.00009 0.00024 L2_MGDP 0.171379*** 0.0489423 0.00063*** 0.00027 -0.00009* 0.00025 L1_∆FDI 0.02333 0.0508322 0.00006 0.00026 0.00052 0.00025 L2_∆FDI 0.05914 0.0516753 0.00046* 0.00026 -0.00041* 0.00025 0.00055 LPG IM WC RA Estimate Std. Error Estimate Std. Error Estimate Std. Error intercept 0.07692*** 0.01581 N/A N/A -0.00001 0.00009 L1_∆PC 0.09459 0.05396 0.00058*** 0.00029 0.00007 0.00027 L2_∆PC -0.01871 0.06574 0.00055 0.00035 -0.00016 0.00033 L1_∆HC 0.01997 0.01487 -0.00006 0.00008 -0.00009 0.00008 L2_∆HC 0.00732 0.01496 -0.00012 0.00008 0.00002 0.00008 L1_∆IM -1.26439 2.71869 0.00580 0.01384 0.01631 0.01365 L2_∆IM -1.55506 2.66684 0.00098 0.01353 -0.00277 0.01338 L1_MGDP 0.10021*** 0.04676 0.00014 0.00027 -0.00011 0.00024 L2_MGDP 0.16952*** 0.04928 0.00063*** 0.00028 -0.00010 0.00025 L1_∆FDI 0.02105 0.05007 0.00008 0.00026 0.00052*** 0.00025 L2_∆FDI 0.06049 0.05083 0.00045* 0.00027 -0.00041 significance p<0.01***, p<0.05**, p<0.1* 0.00025 64 Table 2.12 Detailed Results in Manufacturing LPG TRADE WC intercept Estimate 0.16922*** Std. Error 0.05524 Estimate 0.00240*** L1_∆PC -0.05834 0.15115 L2_∆PC 0.21800 RA Std. Error -0.00040 Std. Error Estimate 0.00090 -0.00016 0.00433 0.00080 0.18478 -0.00408 0.00525 0.00230 0.00377 L1_∆HC -0.04181 0.04195 -0.00040 0.00117 -0.00014 0.00086 L2_∆HC 0.044304 0.04225 0.00014 0.00118 -0.00039 0.00086 4.36198 0.23642* 0.12474 -0.13292 0.09254 4.31786 -0.2614** 0.1235 0.11646 0.09174 L1_∆TRADE 1.57027 L2_∆TRADE 1.36508 0.00055 0.00313 L1_MGDP N/A N/A N/A N/A N/A N/A L2_MGDP N/A N/A N/A N/A N/A N/A L1_∆FDI 0.27796** 0.13731 0.00988** 0.00404 -0.00613** 0.00294 L2_∆FDI -0.09645 0.13975 -0.00170 0.00413 0.00227 EX LPG 0.00300 WC RA Std. Error Estimate Std. Error Estimate Std. Error Estimate intercept 0.16957*** 0.05653 0.00239*** 0.00092 -0.00015 0.00056 L1_∆PC -0.05364 0.15105 -0.00062 0.00434 0.00079 0.00314 L2_∆PC 0.21841 0.18420 -0.00380 0.00525 0.00198 0.00377 L1_∆HC -0.04405 0.04184 -0.00042 0.00118 -0.00011 0.00086 L2_∆HC 0.04321 0.04219 0.00024 0.00119 -0.00041 0.00086 L1_∆EX 1.25718 8.25816 0.38159 0.23702 -0.19462 0.17508 L2_∆EX -2.89343 8.22428 -0.56888** 0.23624 0.30417* 0.17469 L1_MGDP N/A N/A N/A N/A N/A N/A L2_MGDP N/A N/A N/A N/A N/A N/A L1_∆FDI 0.27485** 0.13714 0.00996** 0.00404 -0.00614** 0.00294 L2_∆FDI -0.09199 0.13965 -0.00163 0.00413 0.00219 LPG IM WC 0.00300 RA Estimate Std. Error Estimate Std. Error Estimate Std. Error intercept 0.16871*** 0.05439 0.00239*** 0.00088 -0.00015 0.00053 L1_∆PC -0.06111 0.15099 -0.00062 0.00434 0.00093 0.00313 L2_∆PC 0.22167 0.18456 -0.00378 0.00526 0.00224 0.00377 L1_∆HC -0.03889 0.04204 -0.00033 0.00118 -0.00019 0.00086 L2_∆HC 0.04638 0.04228 0.00018 0.00119 -0.00044 0.00086 L1_∆IM 4.06029 7.60432 0.37444* 0.21920 -0.23108 L2_∆IM 6.54984 7.46211 -0.28176 0.21506 0.08225 0.15958 L1_MGDP N/A N/A N/A N/A N/A N/A L2_MGDP N/A N/A N/A N/A N/A L1_∆FDI 0.28212** 0.13732 0.01007** 0.00406 N/A -0.00624** 0.00295 L2_∆FDI -0.09876 0.13964 -0.00174 0.00415 0.00230 significance p<0.01***, p<0.05**, p<0.1* 0.16249 0.00301 65 Table 2.13 Detailed Results in Construction LPG TRADE WC RA Std. Error 0.12440 Estimate Std. Error Estimate Std. Error intercept Estimate 0.21066 0.00051 0.00037 -0.00023 0.00029 L1_∆PC 0.25059 0.29580 0.00025 0.00133 -0.00018 0.00117 L2_∆PC -0.06571 0.36189 -0.00216 0.00161 0.00179 0.00142 L1_∆HC -0.13257 0.08225 -0.00039 0.00037 0.00035 0.00032 L2_∆HC -0.01248 0.08275 0.00002 0.00037 -0.00003 0.00032 8.55863 0.06305 0.03850 -0.05633 0.03426 8.44493 -0.04497 0.03811 0.02687 0.03396 L1_∆TRADE 4.64032 L2_∆TRADE 7.18274 L1_MGDP -0.10542 0.26392 0.00001 0.00114 0.00020 0.00099 L2_MGDP 0.36662 0.27586 0.00048 0.00120 -0.00034 0.00105 L1_∆FDI 0.869214*** 0.27003 0.00378 0.00123 -0.00293** 0.00109 L2_∆FDI 0.00760 0.00001*** 0.00125 0.00006 0.00111 EX 0.27362 LPG WC Std. Error Estimate RA Std. Error Estimate Std. Error Estimate intercept 0.20825 0.13675 0.00050 0.00039 -0.00022 0.00031 L1_∆PC 0.25513 0.29500 0.00021 0.00133 -0.00017 0.00117 L2_∆PC -0.05335 0.36023 -0.00204 0.00161 0.00167 0.00142 L1_∆HC -0.14223 0.08188 -0.00043 0.00037 0.00040 0.00032 L2_∆HC -0.02140 0.08250 0.00001 0.00037 0.00000 0.00033 L1_∆EX -0.01776 16.18558 0.08441 0.07297 -0.07535 0.06488 L2_∆EX 0.73247 16.04280 -0.11514 0.07257 0.07377 0.06463 L1_MGDP -0.09988 0.26431 0.00007 0.00115 0.00012 0.00100 L2_MGDP 0.38616 0.27635 0.00053 0.00121 -0.00039 0.00106 L1_∆FDI 0.86690*** 0.26888 0.00380*** 0.00123 -0.00296 0.00109 L2_∆FDI 0.02085 0.27253 0.00005 0.00124 0.00002*** 0.00111 LPG IM WC RA Estimate Std. Error Estimate Std. Error Estimate Std. Error intercept 0.21120 0.11627 0.00052 0.00034 -0.00024 0.00028 L1_∆PC 0.25302 0.29602 0.00021 0.00133 -0.00017 0.00117 L2_∆PC -0.07194 0.36195 -0.00213 0.00161 0.00177 0.00141 L1_∆HC -0.12170 0.08253 -0.00033 0.00037 0.00029 0.00032 L2_∆HC -0.00316 0.08293 0.00008 0.00037 -0.00009 0.00032 L1_∆IM 15.08974 14.92365 0.11633 0.06760 -0.10482 0.06019 L2_∆IM 21.34113 14.62658 -0.03137 0.06636 0.01307 0.05913 L1_MGDP -0.10875 0.26324 -0.00009 0.00113 0.00032 0.00097 L2_MGDP 0.35620 0.27494 0.00048 0.00119 -0.00032 0.00103 L1_∆FDI 0.87474*** 0.27079 0.00380*** 0.00124 -0.00293*** 0.00110 L2_∆FDI 0.00242 0.27418 0.00001 0.00126 0.00005 0.00112 significance p<0.01***, p<0.05**, p<0.1* 66 Table 2.14 Detailed Results in Retail and Wholesale LPG TRADE WC RA Estimate Std. Error Estimate Std. Error Estimate Std. Error intercept 0.07596 0.04890 0.00041 0.00022 0.00014 0.00012 L1_∆PC 0.03489 0.14139 0.00016 0.00079 -0.00032 0.00056 L2_∆PC -0.12364 0.17257 -0.00114 0.00096 -0.00024 0.00067 L1_∆HC -0.01097 0.03925 0.00001 0.00022 -0.00007 0.00015 L2_∆HC 0.076488* 0.03948 0.00031 0.00022 -0.00023 0.00015 L1_∆TRADE 1.52896 4.09921 0.01983 0.02278 0.00003 0.01639 L2_∆TRADE 4.52536 4.04939 -0.04044 0.02256 -0.01444 0.01627 L1_MGDP -0.02236 0.12466 -0.00036 0.00068 0.00021 0.00045 L2_MGDP 0.11088 0.13065 -0.00022 0.00071 -0.00005 0.00048 L1_∆FDI 0.19182 0.12969 0.00167 0.00074 -0.00082 0.00053 L2_∆FDI -0.06553 0.13145 -0.00097 0.00075 0.00033 0.00054 EX LPG WC RA Std. Error Estimate Std. Error Estimate Std. Error Estimate intercept 0.07641 0.05218 0.00041 0.00023 0.00014 0.00012 L1_∆PC 0.03774 0.14120 0.00017 0.00079 -0.00031 0.00056 L2_∆PC -0.12252 0.17205 -0.00105 0.00096 -0.00026 0.00067 L1_∆HC -0.01469 0.03913 0.00000 0.00022 -0.00006 0.00015 L2_∆HC 0.07301 0.03942 0.00031 0.00022 -0.00022 0.00015 L1_∆EX -0.38413 7.75904 0.013201*** 0.04315 0.02540 0.03099 L2_∆EX 5.00435 7.70061 -0.08911 0.04292 -0.01962 0.03097 L1_MGDP -0.02225 0.12512 -0.00037 0.00068 0.00021 0.00045 L2_MGDP 0.11259 0.13113 -0.00022 0.00072 -0.00005 0.00048 L1_∆FDI 0.19113 0.12927 0.00169*** 0.00073 -0.00082 0.00053 L2_∆FDI -0.06207 0.13106 -0.00095 0.00075 0.00031 0.00054 LPG IM WC RA Estimate Std. Error Estimate Std. Error Estimate Std. Error intercept 0.07518 0.04648 0.00040* 0.00021 0.00014 0.00012 L1_∆PC 0.03595 0.14147 0.00012 0.00079 -0.00031 0.00055 L2_∆PC -0.12633 0.17257 -0.00114 0.00096 -0.00017 0.00066 L1_∆HC -0.00733 0.03936 0.00003 0.00022 -0.00009 0.00015 L2_∆HC 0.079316*** 0.03955 0.00032 0.00022 -0.00024 0.00015 L1_∆IM 5.44761 7.15667 0.04551 0.03997 -0.02223 0.02871 L2_∆IM 9.56234 7.01853 -0.04346 0.03923 -0.02784 0.02821 L1_MGDP -0.02108 0.12419 -0.00038 0.00067 0.00025 0.00044 L2_MGDP 0.11111 0.13015 -0.00019 0.00071 -0.00003 0.00047 L1_∆FDI 0.19221 0.13009 0.00167*** 0.00074 -0.00081 0.00053 L2_∆FDI -0.06564 0.13175 -0.00099 0.00075 0.00034 0.00054 significance p<0.01***, p<0.05**, p<0.1* 67 Table 2.15 Detailed Results in Transmission, Transport, and Post LPG TRADE WC RA Estimate Std. Error Estimate Std. Error Estimate Std. Error Intercept 0.13859 0.08602 0.00041*** 0.00014 -0.00016 0.00020 L1_∆PC 0.09875 0.24579 -0.00041 0.00101 0.00031 0.00085 L2_∆PC 0.08336 0.30004 -0.00057 0.00115 0.00079 0.00103 L1_∆HC 0.09490 0.06823 0.00062*** 0.00024 0.00005 0.00023 L2_∆HC 0.06788 0.06865 0.00113*** 0.00024 -0.00011 0.00023 L1_∆TRADE 1.15258 7.12489 0.04241 0.03240 -0.04028 0.02503 L2_∆TRADE 9.39511 7.03770 -0.08478*** 0.03168 0.01625 0.02484 L1_MGDP -0.23194 0.21691 -0.00060 0.00064 0.00026 0.00070 L2_MGDP 0.37231 0.22729 -0.00058 0.00074 -0.00040 0.00075 L1_∆FDI 0.46875* 0.22537 0.00100 0.00109 -0.0020*** 0.00080 L2_∆FDI 0.14375 0.22842 0.00006 0.00111 0.00018 0.00082 EX LPG WC RA Std. Error Estimate Std. Error Estimate Std. Error Estimate Intercept 0.13581 0.09568 0.00032 0.00028 -0.00014 0.00021 L1_∆PC 0.10975 0.24529 -0.00026 0.00095 0.00032 0.00085 L2_∆PC 0.08830 0.29907 -0.00105 0.00116 0.00070 0.00103 L1_∆HC 0.08674 0.06801 -0.00017 0.00026 0.00008 0.00023 L2_∆HC 0.05616 0.06852 0.00000 0.00026 -0.00008 0.00024 L1_∆EX -5.52165 13.47224 0.06926 0.05185 -0.05548 0.04727 L2_∆EX 1.46894 13.36574 -0.11877 0.05155 0.07268 0.04716 L1_MGDP -0.22093 0.21807 0.00010 0.00083 0.00018 0.00071 L2_MGDP 0.39092* 0.22838 0.00066 0.00087 -0.00042 0.00076 L1_∆FDI 0.46649*** 0.22426 0.00285*** 0.00089 -0.00201*** 0.00080 L2_∆FDI 0.15714 0.22735 -0.00048 0.00090 0.00016 LPG IM WC 0.00081 RA Estimate Std. Error Estimate Std. Error Estimate Std. Error Intercept N/A N/A 0.00035 0.00024 -0.00017 0.00018 L1_∆PC -0.12213 0.25137 -0.00031 0.00096 0.00028 0.00085 L2_∆PC -0.14665 0.31158 -0.00111 0.00116 0.00080 0.00102 L1_∆HC 0.09110 0.06834 -0.00009 0.00026 0.00000 0.00023 L2_∆HC 0.05627 0.06894 0.00011 0.00026 -0.00021 0.00023 L1_∆IM 7.682831* 12.19161 0.07967 0.04856 -0.075271* 0.04417 L2_∆IM 28.44471 11.91676 -0.02137 0.04767 -0.01390 0.04340 L1_MGDP -0.29597 0.23340 -0.00011 0.00080 0.00039 0.00068 L2_MGDP 0.34404 0.24454 0.00060 0.00085 -0.00036 0.00073 L1_∆FDI 0.527078* 0.23194 0.00282*** 0.00091 -0.0019847* 0.00081 L2_∆FDI 0.10459 0.23681 -0.00050 0.00092 0.00018 significance p<0.01***, p<0.05**, p<0.1* 0.00082 68 Table 2.16 Detailed Results in Hotel and Catering Services LPG TRADE WC RA Estimate Std. Error Estimate Std. Error Estimate Std. Error Intercept -0.02251 0.02673 -0.00001 0.11222 N/A N/A L1_∆PC -0.10347 0.17042 -0.00008 0.72797 -0.00006 0.00016 L2_∆PC 0.21441 0.20838 0.00010 0.88067 -0.00005 0.00021 L1_∆HC -0.06152 0.04946 -0.00002 0.20483 -0.00005 0.00005 L2_∆HC -0.04180 0.04879 -0.00003 0.20097 -0.00005 0.00005 L1_∆TRADE -1.47065 5.88791 -0.00130 25.25 0.00444*** 0.00499 L2_∆TRADE -0.92753 5.71955 -0.00082 24.52 0.00887 0.00467 L1_MGDP 0.61109*** 0.11711 0.00038 0.49632 -0.00002*** 0.00016 L2_MGDP 0.00472 0.14332 0.00001 0.59816 0.00029 0.00017 L1_∆FDI 0.05104 0.36755 -0.00009 1.57570 -0.00034 0.00030 L2_∆FDI 0.16808 0.42192 0.00002 1.80700 0.00041 0.00016 Estimate Std. Error LPG Estimate Std. Error Estimate WC Std. Error Intercept -0.02306 0.02680 -0.00001 0.00002 N/A N/A L1_∆PC -0.10220 0.17043 -0.00008 0.00012 -0.00006 0.00016 L2_∆PC 0.21423 0.20863 0.00010 0.00014 -0.00006 0.00021 L1_∆HC -0.06052 0.04959 -0.00002 0.00003 -0.00005 0.00005 L2_∆HC -0.04161 0.04876 -0.00003 0.00003 -0.00005 0.00005 L1_∆EX -0.24534 10.83634 -0.00053 0.00743 0.00855 0.00926 L2_∆EX -0.85175 10.61176 -0.00212 0.00727 0.014593* 0.00871 L1_MGDP 0.607453*** 0.11682 0.00037*** 0.00008 -0.00002 0.00016 L2_MGDP 0.01096 0.14312 0.00002 0.00010 0.00028* 0.00017 L1_∆FDI 0.05151 0.36792 -0.00009 0.00025 -0.00036 0.00030 L2_∆FDI 0.16074 0.42370 0.00002 0.00029 0.00040*** 0.00016 EX LPG IM RA WC RA Estimate Std. Error Estimate Std. Error Estimate Std. Error Intercept -0.02253 0.02664 -0.00001 0.00002 N/A N/A L1_∆PC -0.10409 0.17037 -0.00008 0.00012 -0.00006 0.00016 L2_∆PC 0.21801 0.20849 0.00010 0.00014 -0.00004 0.00021 L1_∆HC -0.06261 0.04900 -0.00003 0.00003 -0.00005 0.00005 L2_∆HC -0.04219 0.04878 -0.00003 0.00003 -0.00005 0.00005 L1_∆IM -4.55842 10.57024 -0.00361 0.00724 0.00748 0.00869 L2_∆IM -2.52853 10.27216 -0.00098 0.00704 0.01483* 0.00832 L1_MGDP 0.613701*** 0.11701 0.00037*** 0.00008 -0.00002 0.00016 L2_MGDP 0.00140 0.14262 0.00001 0.00010 0.00029* 0.00017 L1_∆FDI 0.04549 0.36759 -0.00009 0.00025 -0.00033 0.00030 L2_∆FDI 0.16483 0.42037 0.00001 0.00029 0.00041*** 0.00016 significance p<0.01***, p<0.05**, p<0.1* 69 Table 2.17 Detailed Results in Financial and Banking Services LPG TRADE WC RA Estimate Std. Error Estimate Std. Error Estimate Std. Error Intercept N/A N/A N/A N/A 0.00014*** 0.00005 L1_∆PC 0.06117 0.12212 -0.00004 0.00029 -0.00020 0.00020 L2_∆PC -0.05419 0.15153 0.00014 0.00036 -0.00061*** 0.00024 L1_∆HC 0.06907*** 0.03310 0.00009 0.00008 -0.00002 0.00005 L2_∆HC 0.01976 0.03343 0.00018*** 0.00008 0.00000 0.00005 L1_∆TRADE -1.70733 3.40854 -0.02867*** 0.00811 0.00498 0.00572 L2_∆TRADE -1.31786 3.35209 -0.03437*** 0.00798 -0.02279*** 0.00567 L1_MGDP 0.25704*** 0.11336 -0.00046 0.00027 -0.00008 0.00017 L2_MGDP 0.04946 0.11917 0.00001 0.00028 -0.00019 0.00018 L1_∆FDI 0.00445 0.11262 -0.00024 0.00027 0.00026 0.00018 L2_∆FDI 0.10676 0.11511 0.00008 0.00027 -0.00001 0.00018 EX LPG WC RA Estimate Std. Error Estimate Std. Error Estimate Std. Error Intercept 0.078175*** 0.027592 N/A N/A N/A N/A L1_∆PC 0.143274 0.116142 -0.00003 0.00029 0.00008 0.00019 L2_∆PC 0.084555 0.139868 0.00017 0.00036 -0.00008 0.00024 L1_∆HC 0.055761 0.031639 0.00011 0.00008 -0.00004 0.00005 L2_∆HC 0.011204 0.031863 0.00019*** 0.00008 -0.00002 0.00005 L1_∆EX -5.445540 6.420998 -0.056482*** 0.01547 0.01048 0.01016 L2_∆EX -4.779471 6.407926 -0.057547*** 0.01527 -0.02591*** 0.01002 L1_MGDP 0.319781*** 0.096338 -0.00046 0.00027 0.00021 0.00018 L2_MGDP 0.162383 0.102572 0.00004 0.00029 0.00016 0.00019 L1_∆FDI -0.041797 0.109392 -0.00020 0.00027 0.00025 0.00018 L2_∆FDI 0.056883 0.111228 0.00009 0.00027 -0.00002 0.00018 LPG IM WC RA Estimate Std. Error Estimate Std. Error Estimate Std. Error Intercept N/A N/A N/A N/A N/A N/A L1_∆PC 0.06132 0.12206 -0.00005 0.00029 0.00010 0.00019 L2_∆PC -0.05936 0.15130 0.00008 0.00036 -0.00012 0.00023 L1_∆HC 0.0690084* 0.03319 0.00008 0.00008 -0.00006 0.00005 L2_∆HC 0.01955 0.03348 0.00018* 0.00008 -0.00004 0.00005 L1_∆IM -1.82693 5.92004 -0.04208*** 0.01416 0.00373 0.00905 L2_∆IM -2.03458 5.78658 -0.05648*** 0.01384 -0.04549*** 0.00884 L1_MGDP 0.257375* 0.11334 -0.00046 0.00027 0.00021 0.00017 L2_MGDP 0.04540 0.11874 -0.00004 0.00028 0.00017 0.00018 L1_∆FDI 0.00301 0.11262 -0.00026 0.00027 0.00023 0.00017 L2_∆FDI 0.10454 0.11499 0.00005 0.00027 0.00000 0.00018 significance p<0.01***, p<0.05**, p<0.1* 70 Table 2.18 Detailed Results in Real Estate Services LPG TRADE WC RA Estimate Std. Error Estimate Std. Error Estimate Std. Error Intercept 0.11402 0.15909 0.00021 0.00012 0.00007 0.00014 L1_∆PC 0.92394 0.82757 0.00000 0.00060 -0.00042 0.00062 L2_∆PC 1.69341 0.98269 -0.00008 0.00071 -0.00044 0.00074 L1_∆HC 0.09458 0.21707 0.00019 0.00016 -0.00018 0.00017 L2_∆HC 0.16042 0.21873 -0.00008 0.00016 0.00011 0.00017 L1_∆TRADE -11.27368 23.39994 0.00826 0.01775 -0.00451 0.01808 L2_∆TRADE -4.60297 23.24887 0.087122*** 0.01763 -0.11227*** 0.01794 L1_MGDP 0.86377 0.62946 0.00011 0.00047 0.00004 0.00051 L2_MGDP -0.64786 0.68197 0.00005 0.00051 -0.00031 0.00054 L1_∆FDI 0.02821 0.75886 -0.00031 0.00058 0.00079 0.00058 L2_∆FDI 0.65983 0.77096 0.00158*** 0.00058 -0.00183*** 0.00059 EX LPG WC RA Estimate Std. Error Estimate Std. Error Estimate Std. Error Intercept 0.11599 0.15760 0.00020*** 0.00012 0.00006 0.00014 L1_∆PC 0.92814 0.82602 0.00001 0.00060 -0.00043 0.00062 L2_∆PC 1.6865*** 0.97878 -0.00013 0.00071 -0.00033 0.00074 L1_∆HC 0.10071 0.21616 0.00017 0.00016 -0.00014 0.00017 L2_∆HC 0.15833 0.21765 -0.00011 0.00016 0.00015 0.00017 L1_∆EX -13.14363 44.23721 0.02347 0.03364 -0.01809 0.03448 L2_∆EX -8.58522 44.25501 0.15903*** 0.03364 -0.19895*** 0.03443 L1_MGDP 0.86700 0.62591 0.00012 0.00047 0.00003 0.00051 L2_MGDP -0.66156 0.67874 0.00006 0.00051 -0.00030 0.00054 L1_∆FDI 0.03716 0.75842 -0.00036 0.00058 0.00085 0.00058 L2_∆FDI 0.64784 0.77105 0.00158*** 0.00059 -0.00183*** 0.00059 LPG IM WC Estimate Std. Error Estimate Intercept 0.10283 0.16774 (NA) L1_∆PC 0.92266 0.83566 0.00037 L2_∆PC 1.65222 0.99757 0.00067 L1_∆HC 0.08555 0.22089 L2_∆HC 0.16785 L1_∆IM RA Std. Error Estimate Std. Error 0.00007 0.00014 0.00062 -0.00048 0.00062 0.00077 -0.00048 0.00075 0.00026 0.00017 -0.00019 0.00017 0.22247 -0.00007 0.00017 0.00011 0.00017 -25.07703 41.27290 0.00057*** 0.03028 -0.00903 0.03207 L2_∆IM -10.19154 40.57842 0.12024 0.02960 -0.16973*** 0.03151 L1_MGDP 0.82517 0.64771 -0.00017 0.00058 -0.00001 0.00051 L2_MGDP -0.54617 0.69877 0.00121*** 0.00061 -0.00026 0.00054 L1_∆FDI -0.00787 0.75985 -0.00050 0.00058 0.00079 0.00059 L2_∆FDI 0.69647 0.77088 0.00184*** 0.00059 -0.00185*** 0.00060 significance p<0.01***, p<0.05**, p<0.1* 71 Table 2.19 Detailed Results in Community and Social Services LPG TRADE WC RA Estimate Std. Error Estimate Std. Error Estimate Std. Error Intercept 0.22322* 0.09158 N/A N/A -0.00027 0.00034 L1_∆PC 0.00087 0.23271 0.00041 0.00153 0.00007 0.00116 L2_∆PC 0.28879 0.28451 0.00024 0.00189 0.00006 0.00141 L1_∆HC -0.04954 0.06468 -0.00084*** 0.00041 0.00053 0.00032 L2_∆HC 0.00032 0.07474 0.06507 -0.00037 0.00042 0.00001 L1_∆TRADE 1.74157 6.73690 0.13145*** 0.04258 -0.09423*** 0.03370 L2_∆TRADE 8.77069 6.64960 -0.02011 0.04187 0.00563 0.03334 L1_MGDP 0.08643 0.20694 0.00039 0.00142 -0.00029 0.00100 L2_MGDP -0.12727 0.21647 -0.00002 0.00149 0.00039 0.00106 L1_∆FDI 0.50376*** 0.21272 0.00385*** 0.00141 -0.00269*** 0.00107 L2_∆FDI -0.11220 0.21556 0.00011 0.00144 0.00004 EX LPG 0.00109 WC RA Std. Error Estimate Std. Error Estimate Std. Error Estimate Intercept 0.22201*** 0.09930 N/A N/A -0.00026 0.00035 L1_∆PC 0.00420 0.23250 0.00033 0.00153 0.00012 0.00116 L2_∆PC 0.28985 0.28373 0.00047 0.00189 -0.00013 0.00141 L1_∆HC -0.05663 0.06451 -0.00087*** 0.00041 0.00057* 0.00032 L2_∆HC 0.06669 0.06499 -0.00034 0.00042 0.00001 0.00032 L1_∆EX -1.27949 12.76217 0.21100*** 0.08123 -0.13664*** 0.06399 L2_∆EX 8.46962 12.65454 -0.05225 0.08014 0.03148 0.06362 L1_MGDP 0.09136 0.20764 0.00035 0.00142 -0.00032 0.00101 L2_MGDP -0.11516 0.21725 -0.00002 0.00150 0.00035 0.00106 L1_∆FDI 0.50059*** 0.21219 0.00382*** 0.00141 -0.0026*** 0.00107 L2_∆FDI -0.10400 0.21509 0.00012 0.00144 0.00000 0.00109 LPG IM WC RA Estimate Std. Error Estimate Std. Error Estimate Std. Error Intercept 0.22431*** 0.08334 N/A N/A N/A N/A L1_∆PC 0.00810 0.23302 0.00036 0.00153 0.00015 0.00123 L2_∆PC 0.28073 0.28461 0.00040 0.00189 -0.00013 0.00152 L1_∆HC -0.04133 0.06491 -0.00081 0.00042 0.000629* 0.00033 L2_∆HC 0.08446 0.06522 -0.00038 0.00042 0.00020 0.00034 L1_∆IM 7.46393 11.7661*** 0.21472 0.07411 -0.16079*** 0.05959 L2_∆IM 19.16926 11.53527 -0.00261 0.07244 -0.02183 0.05825 L1_MGDP 0.08453 0.20600 0.00034 0.00142 -0.00003 0.00114 L2_MGDP -0.14179 0.21548 0.00019 0.00149 0.00053 0.00120 L1_∆FDI 0.50344*** 0.21366*** 0.00396 0.00141 -0.00312*** 0.00113 L2_∆FDI -0.11440 0.21637 0.00017 0.00144 -0.00012 significance p<0.01***, p<0.05**, p<0.1* 0.00116 72 CHAPTER 3 LABOR PRODUCTIVITY CONVERGENCE IN CHINA 3.1 Abstract This chapter studies labor productivity convergence in China across 31 provinces and 9 sectors during 1997–2015. The results are robust to changes in the model’s specifications at both aggregate and provincial levels. The coefficient of unconditional aggregate convergence is quite large at 11.6% per year in the baseline specification. Conditional aggregate convergence, nonetheless, is estimated at 1.2% per year in the baseline specification and has a consistent convergence rate with its agricultural sector. Overall, the manufacturing and modern service sectors show a strong and systematic tendency for convergence and thus their development can be enlisted to address China’s regional disparity. The empirical evidence also shows that trade openness following China’s membership in the World Trade Organization in 2001 immediately caused dramatic structural changes, causing a structural break in the data from 2002 to 2003. Therefore, systematic convergence across the Chinese regional economies become even more apparent if the break in the data from 2002 to 2003 is addressed. 73 3.2 Introduction This chapter investigates labor productivity convergence and its components in China during the period of 1997 to 2015 across 31 provinces and 9 sectors. Economic convergence has been the focus of growth and development economics. Neoclassical economic models argue that poor countries grow faster than rich countries, and over time, the dispersion of income per capita is reduced. However, among the East Asian economies, despite rapid economic growth overall, there has been no systematic tendency for the poorer countries to grow faster than the richer countries unless exports became the main driving force (Fukuda & Toya, 1994). Economic convergence depends on specific social-economic conditions such as institutions and establishments. Therefore, measuring economic convergence in terms of income per capita and its growth may not fully reflect the structural characteristics of the selected countries. The existing empirical evidence that has supported presumed economic convergence patterns has mainly come from the United States (Barro & Sala-i-Martin, 1991). A recent large-scale study of cross-country convergence, nevertheless, did not show a systematic pattern of convergence across countries and sectors (Rodrik, 2011). Instead, Rodrik (2011) finds that countries with similar initial income per capita tended to converge to a similar steady-state, pointing out to convergence clubs. These findings indicate that institutions play an important role in economic development. Moreover, trade patterns and technological progress can increase the magnitude of structural changes through sectoral changes in employment shares feeding back into the growth performance of economies studies (Naveed & Ahmad, 2016). Therefore, it is critical to study economic convergence within the prism of sectoral labor productivity growth and 74 its components. As in the previous chapters, I focus on the within-sector effect and the labor reallocation effect (Ocampo, Rada, & Taylor, 2009; Syquirn, 1986). The objective is to investigate convergence of labor productivity growth and its components across regions and sectors in order to better understand regional patterns of structural transformation in China. Structural change theories suggest that sustained economic growth can be achieved only through continuous structural transformation from low to high productivity sectors (Kuznets, 1966; Lewis, 1954; Lin 2012, to name a few). Structural transformation can be stimulated through policy targeting integration in the global economy, especially through trade. In the process of structural change, production shifts from the primary sector to the manufacturing sector and, later, from the manufacturing sector to the service sector (Chenery & Syrquin, 1975; Kuznet, 1975). The manufacturing sector and service sectors become the economic engines for developing countries and developed countries, respectively. Rodrik (2013) indicated that unconditional economic convergence does not exist in a cross-country study of labor productivity convergence, but it does exist in the manufacturing sector. Kinfemichael (2019) extended Rodrik’s study to conclude that unconditional economic convergence does exist in the service sectors in various countries. Therefore, this chapter studies labor productivity convergence in China in different model specifications, to understand which sectors show a systematic tendency for economic convergence, and by extension, can be used as economic engines. The rest of this chapter will be structured as follows. Section 3.3 illustrates China’s development policies for economic convergence. Section 3.4 discusses panel data structures. Section 75 3.5 presents model specifications. Section 3.6 presents empirical evidence. Section 3.7 is the conclusion. 3.3 China’s Development Policies for Economic Convergence During 1997–2015, China’s economic growth was 9.7% per year. Despite the recent economic slowdown, its growth has been maintained at 6.3% annually (Bai & Zhang, 2017). A large body of literature in the past decade has focused on China’s regional development patterns (Cheong & Wu, 2013; Kanbur & Zhang, 1999; Meng, Gregory, & Wang, 2005), because the country’s rapid economic growth since the market reform period has been based on its distorted resource distribution across regions and sectors. During the entire postreform period, the coastal provinces have continued to stand out among the rest of the regional economies. The inward foreign direct investment primarily went to the coastal provinces due to the preferential policies that aimed to promote economic prosperity in the eastern territory of China (Zheng & Chen, 2007). Therefore, China’s economic overall improvement originated in its coastal provinces. China’s economic development scheme has been relying on the coastal provinces as the catalysts to facilitate institutional change and the mode of production in the aggregate economy. Nonetheless, the spillover effects from the richer coastal provinces to the poorer inland provinces did not pan out as expected (Fu, 2012). Unequal regional development caused the coastal provinces to become the final destinations of migrant workers, further aggravating the intraprovincial regional disparity (Cheong & Wu, 2013). The wave of globalization limited interprovincial trade and fostered instead international dependence rather than interprovincial linkages (Tisdell, 2013). 76 To overcome the issues of unbalanced regional growth, China’s national economic policies switched gears in 1996. The goal was to promote regional equality, as income inequality would undermine central control (Hu, 1996; Xue, 1997). In the ninth cycle of the 5-year national economic plans (1996–2000), a cross-province economic development strategy was highlighted for the first time. It targeted, for example, regional growth and development in China’s western territory. The following 10th cycle of the 5year national economic plans (2001–2005) was extended to increase infrastructure investment in transportation and hydraulic south-north water transfer projects in the inland provinces (Lai, 2007; Naughton & Chen, 2004). Although these large infrastructure investments successfully promoted regional economic growth in the inland provinces, they only slightly reduced the income gaps between the coastal and inland provinces (Cui, 1998). This outcome indicates that China’s regional economic growth may still be largely constrained by its heterogeneous economic structures across regions. One aspect of the Chinese development policy led by the central government is that it directs and distributes resources to sectors and regions that have a higher priority in the overall economic development agenda. To address regional inequality, the ideal strategy is indeed to identify sectors that can act as engines of growth across provinces, despite their various economic structures and geographic patterns. 3.4 An Overview of Data The data used in this chapter come from the National Bureau of Statistics of China and Chinese Statistical Yearbooks during 1997–2015. Labor productivity growth is the ratio of GDP per capita in terms of US dollars to employed persons after adjustment 77 to the real term. The 9 sectors, as before, are (a) agriculture, fishery, forestry, and animal husbandry; (b) manufacturing; (c) construction; (d) wholesale and retail; (e) transmission, transport, and post; (f) hotel and catering services; (g) finance and banking; (h) real estate services; and (i) other social and community services. Except for the hotel and catering service sector, which has data only from 2004, the rest of the sectors have data for the entire period of 1997–2015. The within-sector and labor reallocation effects of labor productivity growth across provinces and sectors are recorded in Tables 1.5 and 1.6 in Chapter 1. The literature on cross-country convergence indicates that empirical results may be affected by model specifications and sample selections. For example, Baumol (1986) used Maddison’s (1982) data to study convergence in labor productivity, income per capita, and exports in a group of 16 industrialized economies during 1870–1979. Baumol concludes that these developed countries exhibited a tendency to converge. However, de Long (1988) argued that Baumol’s work might have demonstrated convergence patterns among the industrialized countries, but it did not sufficiently support the transitional paths of developing or underperforming economies. Also, the study of cross-country convergence indicates that convergence was mainly discovered in the high-income countries in the western world, but not in the group of the less developed countries in the rest of the world, such as East Asian economies (Zhang, 2001). Therefore, economic convergence should be studied with a much larger dataset where variations among samples are recognized. This has come to be known as conditional converegence. 78 3.5 Methodology The primary purpose of this study is to examine convergence of labor productivity using three measures: overall labor productivity growth, its within-sector effect, and its labor reallocation effect. The decomposition of labor productivity growth into its withinsector and labor reallocation components is presented in Chapter 1 using Syquirn’s (1986) method. The concept of economic convergence is formalized as the hypothesis that low-income economies would tend to grow faster than high-income economies, suggesting a negative relationship between the initial income per capita and its growth rate. The equation of economic convergence was derived by Barro and Sala-i-Martin (1991) from Solow’s growth model. In a nutshell, it shows the transitional path to the steady-state over a long period of time. In the equation of economic convergence, - is the convergence parameter measuring the speed of convergence. If the absolute value of  is large, the so-called “-convergence” exhibits a greater response to the gap with its steady-state value, implying a faster convergence speed to the steady-state. The literature points to two concepts of -convergence. These are absolute or conditional convergence. Absolute -convergence (Abramovitz, 1986; Barro & Sala-iMartin, 1991) describes the convergence of income per capita in the sample regardless of the heterogenous economic structures and institutions across countries. Conditional convergence (Barro & Sala-i-Martin, 1992; Sala-i-Martin, 1995) controls for differences across economies. Therefore, economies with similar institutions, population growth rates, and saving rates would eventually reach the same steady-state, even though they start from different initial output levels. Furthermore, in addition to the -convergence, the existence of the so-called -convergence implies that dispersion of the mean income 79 per capita across economies would decrease over time. Empirical evidence indicates that at sectoral level unconditional convergence in the cross-country particularly exists within the manufacturing (Rodrik, 2013) and service sectors (Kinfemichael, 2019), and it does not exist for aggregate economies when sectoral boundaries are blurred. In the 1980s, the emerging concept of unconditional economic convergence was introduced, primarily based on the development stories of developed countries in North America and Western Europe (Baumol, 1986; de Long, 1988). However, the western countries that show unconditional convergence still tend to share similar economic structures and development patterns. Therefore, GDP per worker should be a more appropriate indicator to replace GDP per capita (Jones, 1997). Furthermore, the Chinese provincial-level economies showed divergence in the short run only due to shocks, but in the long run, they are forecasted to converge to two different income levels (Andersson, Edgerton & Opper, 2013). Based on the findings of the literature, this chapter, therefore, focuses on labor productivity and its components in order to capture the structural characteristics of the Chinese provincial-level economies. Within-sector convergence across provinces in a sector can be used as a proxy to detect how likely a sector can be used as a catalyst across economies with heterogeneous structures to further promote growth and, ultimately, structural transformation (Lee et al., 2016; Lei, 2000). The negative estimates of withinsector convergence, therefore, can be viewed as a sector’s potential to be used as an economic engine of growth. Conversely, labor reallocation convergence across provinces in a sector can be used as a proxy to detect the degree of labor misallocation resulting 80 from the within-sectoral expansion. The negative estimates of labor reallocation convergence suggest a better allocation of labor (McMillan & Rodrik, 2014). This chapter analyzes economic convergence using three model specifications that draw on Rodrik’s approach (2011). In each model specification, the dependent variable  is measured by labor productivity growth (LPG), its within-sector effect (WC), and its labor reallocation effect (RA). The subscripts i, j, and t represent sector, province, and the initial year of the entire period, respectively. The first model studies economic convergence of the aggregate Chinese economy, controlling for provinces (𝐷𝑗 ) and sectors multiplied by time (𝐷𝑖,𝑡 ). Because of the region-invariant effect (𝐷𝑗 ), the first model specification measures conditional convergence: 𝜉𝑖,𝑗,(𝑡,𝑡+1) = −𝛽 ln 𝑞𝑖,𝑗,𝑡 + 𝐷𝑖,𝑡 + 𝐷𝑗 + 𝑢𝑖,𝑗,𝑡 (3.1) where 𝜉𝑖,𝑗,(𝑡,𝑡+1) can be treated as an umbrella term for labor productivity growth in sector i and province j between time t and t+1, or specifically refers to labor productivity growth and its within-sector and labor reallocation effects. 𝑞𝑖,𝑗,𝑡 refers to labor productivity level of the initial year t in sector i and province j for the growth period. 𝐷𝑖,𝑡 refers to the fixed effect of industry times time, 𝐷𝑗 refers to the regional fixed effects, and 𝑢𝑖,𝑗,𝑡 refers to the error term. A negative value of  would suggest the existence of conditional convergence. Model specification 3.2 is similar to Model specification 3.1. Nevertheless, Model specification 3.2 studies unconditional convergence, because the region-invariant effect (𝐷𝑗 ) is dropped. In this circumstance, economic convergence takes place irrespective of 81 the structural characteristics of the provincial-level economies. Model specification 3.2 is described as follows: 𝜉𝑖,𝑗,(𝑡,𝑡+1) = −𝛽 ln 𝑞𝑖,𝑗,𝑡 + 𝐷𝑖,𝑡 + 𝑢𝑖,𝑗,𝑡 (3.2) The third model specification studies economic convergence in 9 sectors, to find out which sectors exhibit higher rates of convergence to the steady state, given the timeinvariant effect (𝐷𝑡 ) and the region-invariant effect (𝐷𝑗 ). Equivalently, the third model specification examines conditional -convergence in sectors because the region-invariant effect (𝐷𝑗 ) leads to labor productivity convergence irrespective of regional economic structures. Equation 3.3 is constructed as follows: 𝜉𝑗,(𝑡,𝑡+1) = −𝛽 ln 𝑞𝑗,𝑡 + 𝐷𝑡 + 𝐷𝑗 + 𝑢𝑗,𝑡 (3.3) 3.6 The Empirical Results of the Convergence Tests This section discusses the empirical evidence on convergence using the above model specifications. Table 3.1 shows the conditional convergence of labor productivity growth and its components in China during 1997–2015 across 31 provinces and 9 sectors using Model 3.1. The negative estimates of the coefficients of the natural logarithm of the initial labor productivity levels suggest strong conditional labor productivity convergence of approximately 1.2% per year. The within-sector effect shows a much smaller tendency to converge compared with aggregate labor productivity growth, whereas the labor reallocation effect shows a tendency to diverge. The contradictive convergence patterns 82 of the within-sector and labor reallocation effects across 31 provinces and 9 sectors suggest that China’s rapid economic development during 1997–2015 resulted in a reduction of regional disparity across sectors and inefficient labor reallocation. Therefore, economic development policies should be tailored to the provincial levels to allocate labor in more efficient ways. Currently, China has implemented a restrictive household registration system to prevent labor outflow from the less developed regions that lack industrial development and natural resources. However, the conditional divergence of the labor reallocation effect across provinces and sectors suggests that the current solution of keeping sufficient labor in the poor regions is not efficient because labor migration actually increases the social burden. The empirical evidence also reflects the shocks of trade openness and its influences on regional Chinese economies. Once the structural break for 2002 to 2003 is controlled in the regression, there is systematic convergence of labor productivity growth and its components, suggesting a dramatic structural transformation after China joined the WTO in 2001. Table 3.2 shows the estimates of unconditional convergence of provincial labor productivity growth and its components based on equation 3.2, controlling for the period and sector effects. Because there is no provincial-specific effect, this model studies unconditional convergence in China. Unconditional labor productivity convergence was 11.6% per year during the period 1997–2015. Also, despite a smaller impact, both the within-sector and labor reallocation effects showed a slight divergence during this period. The divergence of the within-sector effect comes from a few extreme positive deviations (outliers) of the coastal provinces in 2002, mainly because of their fast rising exports. Labor reallocation from the lower productive inland provinces to higher productive 83 coastal provinces in the transition to economic integration also shows divergence. The inconsistent patterns of labor productivity convergence and its components may seem a fallacy: how can two components with divergent patterns eventually turn into unconditional convergence once they are merged into one? This observation is consistent with the findings from previous research that unconditional economic convergence is rarely found in a cross-country study when the countries share dissimilar structural characteristics (Baumol, 1986; de Long, 1988; Kinfemichael, 2019; Rodrik, 2013). Also, it should be further noted that the convergence patterns of labor productivity growth should not be treated as the agglomeration of convergence patterns of the within-sector and labor reallocation effects. Within-sector convergence and labor reallocation convergence reflect different proxies of economic phenomena than labor productivity convergence, as explained in Section 3.5. As each province has a unique economic structure, the divergence of the within-sector and labor reallocation effects simply indicates the complexity of the mechanisms of the Chinese economy. Regional disparity remains embedded in the aggregate Chinese economy as geographic borders play a central role in limiting the market size of the provincial-level economies in China. The agricultural and service sectors, therefore, were largely present in linkages among provinces, whereas the manufacturing sector was linked in with both interprovincial and international trade. These trade patterns show as fostering systematic convergence, conditionally or unconditionally, in the manufacturing sector (Lemonie, Mayo, Poncet, & Ünal, 2014). The limits of interprovincial trade combined with the existing economies structures imply that some Chinese provinces that lagged integration in the global markets might have not been able to benefit from spillovers effects and specialization 84 necessary to grow as fast as coastal provinces. Therefore, although all provincial-level economies had rapid labor productivity growth during 1997–2015, the divergence of the within-sector and labor reallocation effects still existed. Table 3.3 through Table 3.11 show conditional convergence in 9 sectors based on equation 3.3, controlling for both time and province. Because the province is controlled, this model studies conditional convergence across sectors. In the agricultural sector, neither labor productivity growth nor the within-sector and labor reallocation effect exhibited a tendency to converge. This phenomenon further validates the empirical evidence in Chapter 2, which shows that China’s agricultural sector was the least impacted by trade openness in the postreform period. Thereby, labor productivity growth in the agricultural sector was an expected outcome with the redundant labor flowing from the agriculture to the manufacturing sector (Rey et al., 2016). The agricultural sector had the lowest rate of convergence compared to other sectors during the 1997–2015 period. This finding aligns with the empirical results from previous research using the TFP approach (Li, Zheng, & Zhang, 2008; Wang, Huang, Wang, & Tuan, 2018). The insignificant convergence pattern in the agricultural sector suggests that geography plays an important role in local agricultural development across provinces. In the manufacturing sector, the estimated rate of conditional labor productivity convergence during 1997–2015 was approximately 8.24% per year. The within-sector effect did not show significant divergence, but the labor reallocation did, suggesting an inefficient labor reallocation and a lag in development in some provinces. Nevertheless, Rodrik (2013) previously documented unconditional convergence in the manufacturing sector across different regions and types of manufacturing activities. Manufacturing that 85 requires more advanced technology and capital would converge faster than that with less advanced technology and less capital. Although convergence speed varies across industries, the four-digit manufacturing industries since the 1990s have shown unconditional labor productivity convergence at all income levels. Rodrik (2013) also suggests competition and diffusion of technology are the main reasons for labor productivity convergence in the manufacturing sector. Assuncao, Bruity, and Medeiros (2012) extend their research to further examine the speed of convergence in Rodrik’s (2011) work. They find that the speed of convergence for industrial productivity growth varies systematically with political institutions across countries, and that it does not monotonically link to trade openness and education. Therefore, structural change of industrial composition within the less-developed inland provinces may expedite the overall process of industrialization and contribute towards achieving regional balance. In the construction sector, the negative estimated rate of strong conditional convergence during 1997–2015 was 16.7% per year. The within-sector effect slightly converged by 0.3% per year, aligning with labor productivity convergence patterns. In contrast, the labor reallocation effect slightly diverged by 0.3% per year. Overall, these findings suggest that workers in the construction sector across regions have converged sufficiently in skills to increase labor productivity growth following rapid market expansion and infrastructural development in most regions. More recently, infrastructural development fostered by the One Belt One Road Initiatives in the western territory of China has facilitated labor inflow to these less-developed areas. Still, the distribution of foreign direct investment across regions remains unequal. The coastal provinces with special economic zones have received larger inward foreign direct investment, making 86 them leaders in terms of their infrastructure for industrial development. Infrastructural development is viewed as a prerequisite for other types of investments across theories and policy strategies. During 1997–2015, infrastructure was used as a catalyst for wider economic integration in China (Roland-Host, 2006) in order to reduce the cost of trade, which granted its provincial-level economies comparatively more advantages with respect to their foreign competitors. However, some of the literature points out that infrastructural investment also caused the spatial crowding-out effect of private capital, leading to negative impacts on some provinces (Démurger, 2000; Li, Loyalka, Rozelle, & Wu, 2017). As for the service sectors, almost all types of service activities in China during 1997–2015 exhibit strong conditional convergence. Kinfemichael et al. (2019) extended Rodrik’s research to investigate unconditional labor productivity convergence in the service sectors using the panel data from 101 countries. This chapter presents empirical evidence similar to the findings of Kinfemichael (2019). It is striking that the withinsector and labor reallocation effects diverged in almost all types of service activities. The inconsistent convergence patterns between labor productivity growth and its components indicate that trade openness may have different impact across provincial economies in China. As described in Chapter 2, the wave of globalization further strengthened the dichotomous structural transformation patterns in the coastal and inland provinces. Therefore, the coastal provinces are in the process of transitioning from the manufacturing to the service sectors, whereas the inland provinces are transitioning from primary to manufacturing sectors. Eichengreen and Gupta (2009) indicate that service sectors growth follows two phases: (a) the expansion of a traditional service sectors in the 87 early stage of economic development, and (b) the expansion of modern service sectors of information and communication technology (ICT) at later stages of developemnt. To sum up, China’s highly regulated service sectors tended to show stronger labor productivity convergence. Across sub-sectors, social services, transmission, and real estate service sectors have had a higher convergence rate compared to other service sectors. The only exception was the financial service sector, a relatively small sector during 1997–2015. The labor convergence patterns in the service sectors during 1997–2015 are summarized as follows. In the wholesale and retail service sectors, conditional labor productivity convergence was 2.7% per year, much weaker than other service sectors during the same period. The within-sector effect slightly diverged, while the labor reallocation effect slightly converged. As the wholesale and retail service sector existed in the prereformed communist era and is viewed as a traditional sector, the divergence of the within-sector effect indicates that more developed provinces actually have an advantage for fostering productivity growth within the sector. At the same time, they may have much less room to improve their labor productivity growth through structural transformation in this sector. In the transmission, transport, and post services sector, the negative estimated rate of labor productivity growth showed strong conditional convergence at 15.6% per year. The small divergence of labor reallocation across provinces in this sector simply shows that some provinces have undergone more structural transformation than others. In the hotel and catering service sector, the negative estimated rate of strong conditional convergence was 11.1% per year. The within-sector effect did not show significant convergence, and the labor reallocation effect showed strong conditional divergence. It should be noted that the hotel and catering service sector 88 has become an independent sector only since 2004; the divergence of labor reallocation, therefore, suggests heterogenous experiences across more and less developed provinces. In the banking and financial service sector, the negative estimated rate of convergence showed a strong conditional convergence of labor productivity growth of 9.5% per year. However, both the within-sector and labor reallocation effects showed strong conditional divergence of 0.01% per year, indicating that the banking and financial service sector which sheds doubt on the aggregate converegence parameter. The community and social service sector showed strong conditional convergence of 17.1% each year. Because the community and social service sectors are heavily reliant on government subsidies to expand, the divergent within-sector and labor reallocation patterns are difficult to interpret along the same lines as other sectors. It is striking to observe that the real estate service sectors had the largest convergence rate of 21.4%. The real estate market in China was used as an economic engine to stimulate its aggregate economic growth during 1978–2014. Since 2014, a series of housing market regulations have been implemented to ensure macroeconomic stability. The housing markets in China are treated as one of the largest investment vehicles, partially because the country’s financial markets have very limited investment objectives in the portfolio (Liu & Xiong, 2018). Moreover, local governments finance their debt through land sales, which more or less encourage speculative investments in the real estate markets. 89 3.7 Conclusion In conclusion, this chapter investigates labor productivity convergence and its components in China to understand which sectors should be promoted through policy for the goal of achieving sustainable growth and regional balance. Heterogeneous economic structures of province economies open the door to central policymakers to select a few sectors that can quickly achieve fast economic growth even at different stages of development of local economies. To reduce regional disparity, the diverging patterns of the within-sector and labor reallocation effects along with the mixed role of trade openness should also be taken into account. The coefficient orf unconditional labor productivity convergence in China across regions and sectors was quite large at 11.6% per year during 1997–2015, which is comparable to the conditional convergence rate in the industrial and service sectors. The coefficient for conditional labor convergence in China across regions and sectors was much smaller at 1.2% per year during the same period, which was comparable to the conditional convergence rate only in the agricultural sector. These findings suggest that both the industrial and service sectors are better candidates than the agricultural sector to serve as economic engines for China’s future development. Manufacturing, construction, and real estate sectors appear to be the engines of growth in China (Barnett and Brooks, 2006). These sectors can, therefore, continue to be used as catalysts for spurring economic growth in the inland provinces. The empirical findings in this research also show that the service sectors that are strictly regulated by the central government, such as telecom and financial markets, had a strong conditional rate of convergence. The central policymakers can, thus, introduce reforms that relax the market constraints of these 90 modern service sectors in order to increase their employment and output shares. The social service sector, which is mainly funded by the government to improve the quality of healthcare and education, is another option to catalyze economic growth in the lagging inland economies. Overall, only sectors with strong labor productivity convergence irrespective of regions are considered as suitable economic engines for sustained regional growth. 91 Table 3.1 The Conditional Convergence of China During 1997–2015 log initial labor productivity standard error Significance p-value province fixed effects period times sector fixed effects period fixed effects number of provinces number of sectors number of observations LPG -0.012833 0.00471 *** 0.006559 Yes Yes No 31 9 5070 WC -0.0000694 0.0000264 *** 0.008613 Yes Yes No 31 9 5070 RA 0.00007 0.0000191 *** 0.0002607 Yes Yes No 31 9 5070 p<0.01 ***, p<0.05**, p<0.1* Table 3.2 The Unconditional Convergence of China During 1997–2015 LPG WC log initial labor productivity -0.1165055 -0.015135 standard error 0.0090433 0.000064 significance *** ** p-value 0 0.064346 province fixed effects No No Period times by sector fixed effects Yes Yes period fixed effects No No number of provinces 31 31 number of sectors 9 9 number of observations 5070 5070 p<0.01 ***, p<0.05**, p<0.1* RA 0.00024 0.000047 *** 0.0000004 No Yes No 31 9 5070 92 Table 3.3 The Conditional Convergence in Agriculture, Fishery, Forestry, and Animal Husbandry During 1997–2015 LPG -0.01748 0.0059602 WC 0.0000658 0.00004 log initial labor productivity standard error significance p-value 0.42277676 0.136 province fixed effects Yes Yes period times sector fixed effects No No period fixed effects Yes Yes number of provinces 31 31 number of sectors 1 1 number of observations 589 589 p<0.01 ***, p<0.05**, p<0.1* RA -0.0000323 0.0002 0.286 Yes No Yes 31 1 589 Table 3.4 The Conditional Convergence in Manufacturing During 1997–2015 LPG WC RA initial labor productivity -0.082447 0.00098054 0.00084544 standard error 0.02369 0.00070944 0.00050786 significance *** * p-value 0.0005372 0.1675 0.09652 province fixed effects Yes Yes Yes period times sector fixed effects No No No period fixed effects Yes Yes Yes number of provinces 31 31 31 number of sectors 1 1 1 number of observations 589 589 589 p<0.01 ***, p<0.05**, p<0.1* 93 Table 3.5 The Conditional Convergence in Construction During 1997–2015 LPG WC RA log initial labor productivity -0.167088 -0.0003694 0.00032857 standard error 0.035393 0.00070944 0.00050786 significance *** *** ** p-value 0.0000003 0.00016187 0.02284 province fixed effects Yes Yes Yes period times sector fixed effects No No No period fixed effects Yes Yes Yes number of provinces 31 31 31 number of sectors 1 1 1 number of observations 589 589 589 p<0.01 ***, p<0.05**, p<0.1* Table 3.6 The Conditional Convergence in Wholesale and Retail During 1997–2015 LPG WC RA log initial labor productivity -0.027266 0.00018183 -0.000284 standard error 0.014841 0.000083 0.000058 significance * ** *** p-value 0.0667 0.0282 0.00000133 province fixed effects Yes Yes Yes period times sector fixed effects No No No period fixed effects Yes Yes Yes number of provinces 31 31 31 number of sectors 1 1 1 number of observations 589 589 589 p<0.01 ***, p<0.05**, p<0.1* 94 Table 3.7 The Conditional Convergence in Transmission, Transport, and Post During 1997–2015 LPG WC log initial labor productivity -0.156192 0.0000296 standard error 0.025804 0.000105 significance *** p-value 0.00000002 0.7782 province fixed effects Yes Yes period times sector fixed effects No No period fixed effects Yes Yes number of provinces 31 31 number of sectors 1 1 number of observations 589 589 p<0.01 ***, p<0.05**, p<0.1* RA 0.000181 0.0000926 * 0.05133 Yes No Yes 31 1 589 Table 3.8 The Conditional Convergence in Hotel and Catering During 1997–2015 LPG WC log initial labor productivity -0.111851 -0.000000272 standard error 0.026603 0.00001853 significance *** p-value 0.00003308 0.9883 province fixed effects Yes Yes period times sector fixed effects No No period fixed effects Yes Yes number of provinces 31 31 number of sectors 1 1 number of observations 372 372 p<0.01 ***, p<0.05**, p<0.1* RA 0.0000869 0.0001537 *** 0.00000003 Yes No Yes 31 1 372 95 Table 3.9 The Conditional Convergence in Financial and Banking During 1997–2015 LPG WC log initial labor productivity -0.095704 0.00012709 standard error 0.011168 0.00002774 significance *** *** p-value 0 0.00000568 province fixed effects Yes Yes Period times sector fixed effects No No period fixed effects Yes Yes number of provinces 31 31 number of sectors 1 1 number of observations 589 589 p<0.01 ***, p<0.05**, p<0.1* RA 0.00018719 0.00001644 *** 0 Yes No Yes 31 1 589 Table 3.10 The Conditional Convergence in Real Estate Services During 1997–2015 LPG WC log initial labor productivity -0.214901 0.000052 standard error 0.046228 0.000057 Significance *** p-value 0.00000418 0.3617 province fixed effects Yes Yes period times sector fixed effects No No period fixed effects Yes Yes number of provinces 31 31 number of sectors 1 1 number of observations 589 589 p<0.01 ***, p<0.05**, p<0.1* RA 0.000131 0.0000587 *** 0.02569 Yes No Yes 31 1 589 96 Table 3.11 The Conditional Convergence in 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