Human capital and productivity for Korea's sustained economic growth

The Republic of Korea (henceforth Korea) has shown a remarkable economic performance during the past half century. Although the economy was devastated suddenly by the financial crises in 1997–1998 and 2008–2009, it has managed to recover quickly. The startling performance of the Korean economy has raised many challenging questions on the main features of Korea's development strategy. Noland (2012) provides a succinct but informative analysis of Korea's growth performance and analyzes the applicability of its development strategy to other developing countries. The paper also discusses the major challenges facing the Korean economy in sustaining its superior economic performance. Noland's analysis of Korea's growth performance is built on a proposition that it has followed a process of “catching-up.” As in other developing countries, Korea started its development process with a low initial level of output relative to its own long-run potential (or steady-state) level of output. The gap of the existing physical and human capital stock and productivity from their long-run levels provided the opportunity for rapid catching-up that could take place via high rates of physical and human capital accumulation and technology diffusion from advanced economies. However, an important question is, what are the factors that help realize the large potential for catching-up? The growth literature shows that they consist of “good fundamentals” such as a high saving rate, strong human capital, a high degree of trade openness, the maintenance of good institutions (less corruption), and prudent fiscal and monetary management. A number of papers, including my own work (Radelet et al., 2001; Lee, 2005), show that Korea's rapid catch-up over the last 40 years is largely attributed to these “good fundamentals.” Noland discusses the role that some of these “good fundamentals,” such as the high saving rate and strong human capital, have played for Korea's economic growth, but provides little consideration of other factors – notably international trade. The Korean government's policies toward export orientation were largely effective in pushing the pace of change in comparative advantage. Free trade provided access to cheap imported intermediate goods, larger markets, and advanced technologies that were critical for its rapid industrialization. The Korean economy has also maintained relative macroeconomic stability, although it has suffered from severe crises as its high openness made the economy vulnerable to external shocks. Noland (2012) argues that the “Korean model” is “unlikely to be reproducible elsewhere” due to (irreproducible) initial conditions such as Korea's high human capital endowment, its lack of natural resources, and its equal income distribution due to land reform. However, to the extent that Korea's growth performance is explained within the framework of a standard neoclassical growth model or an extended model incorporating the role of technology absorption and innovation, Korea's rapid growth cannot be considered as the result of a unique model of economic development. It would be reproducible in many other developing countries if they were able to embrace policies to build the same “good fundamentals.” It is true that with good human capital, Korea was well positioned for rapid economic growth. But Korea's high level of human capital endowment in 1960 was also observed in other Asian economies. The average years of schooling of the population over 15 years old in Korea in 1960 was similar to that in the Philippines. Human capital can be built up, albeit over a long time, by government efforts. The physical characteristics of a country such as its natural resource intensity and its political environment such as that of instigating land reform are not easily replicable. Nevertheless, I believe that the Korean or East Asian record is broadly transferable to many other developing countries. Noland rightly points out productivity growth is a key challenge for Korea's sustained growth in the future. Korea's swift “catch-up” process is attributed to physical and human capital accumulation for the most part, rather than to total factor productivity growth. Specifically, the poor productivity performance in the service industries, such as finance, construction, and wholesale and retail trade sectors, hampers overall productivity growth in the Korean economy. Thus, as pointed out, service sector liberalization and capital and labor market reform are needed to increase productivity in the service sectors. How the Korean economy can sustain strong growth and job creation must depend on small- and medium-size enterprises in the service industries. The paper also provides good assessments of the possible impacts of unification with North Korea. It would be useful to first discuss whether unification could occur in the immediate future, and explain what risk factors from North Korea can significantly affect the South Korean economy, especially considering that the North Korean leadership succession process may pose significant challenges and risks for both Koreas.

Comment

Despite the intensive debates on the East Asian economic miracle that persisted during the last decade, the verdict on the source of output growth is inconclusive. There can be no dispute over the importance of total factor productivity (TFP) growth in the process of economic development and raising the level of living standards. But, the question of whether TFP growth played a significant role in East Asian economic growth remains contentious, especially in Singapore's case. This paper provides an updated review on productivity growth in the East Asian manufacturing and that of Hong Kong, Japan, Korea, Singapore and Taiwan, and recommends options for further research to improve understanding on the issue of TFP growth in East Asian manufacturing. Copyright # 2005 John Wiley & Sons, Ltd. To uncover the source of the East Asian economic miracle, the debates on productivity growth in East Asia have been widespread since the 1990s. Due to differences in data, methodology and sample period selected, recent empirical TFP studies have, not surprisingly, revealed mixed results. In an influential paper by Young (1995), he pointed out that the spectacular economic performance in East Asia was not as impressive as previously thought and claimed the economic success was nothing more than intensive factor accumulation. Using growth accounting and breaking down output growth into components that can be attributed to the observable factors of the growth of capital stock and labour force, Young showed that TFP growth (or Solow residual) in East Asian countries was comparable with those of developed economies. Young's finding further predicted that high economic growth is unlikely to be maintained in East Asian economies due to scant progress in the level of TFP. While the findings of Kim and Lau (1994), Krugman (1994), and Collins and Bosworth (1996) are generally consistent with Young's, Chen (1997) raised concerns over possible

PurposeThe purpose of this paper is to examine whether innovation plays a significant role in the total factor productivity (TFP) growth in India at an aggregate level.Design/methodology/approachThis study first estimates the TFP growth using a growth accounting framework. In the second stage, the authors examine the long-run and short-run impact of innovation on TFP growth using the ARDL bound testing approach.FindingsThe results indicate a cointegrating relationship between innovation and TFP growth. Further, coefficients of long-run elasticity show that the increase in overall innovation activities improves the TFP growth. Other factors such as human capital, financial development and FDI do not affect the TFP growth in the long run; however, these variables significantly affect the productivity growth in the short run.Practical implicationsFindings of the study suggest that the innovation-friendly policies such as the strengthening of intellectual property rights, R&D subsidies and innovation rebates may spur the productivity growth, and hence, good growth and prosperity as well.Originality/valueHaving devoted a large volume of literature to address the sources of economic growth, the present study focuses on the determinants of TFP growth in India which may fall in similar category but differ in several angles: First, the authors construct a TFP index using a growth accounting framework. Second, the authors construct an innovation index using principal component analysis which is new to the literature and also an innovation index. Third, given the scanty innovation activities in low developed countries like India and its widening role in the contemporary literature, special emphasis will be given to this aspect. Finally, the effect of the examined relationship on TFP growth in the long run and short run provides several implications for policy purpose to the developing nations like India.

The study examines the resources of economic growth in the Czech Republic in the course of years from 1992 until 2004. Using the growth accounting method, it analyses the contribution of individual factors to economic growth. Special attention is given to total factor productivity, which, apart from labour, also includes a fixed capital stock at constant prices. Compared to the previous period, the acceleration of the growth of total factor productivity decisively contributed to the speeding up of economic growth in the years 1999-2004. Furthermore, the study examines growth resources in six national economy sectors and analyses the contribution of individual sectors to the growth of macroeconomic total factor productivity. The analysis has shown that namely industry, transport, communications, and other services were involved in the speeding up of the growth of macroeconomic total factor productivity. A comparison of the dynamics of total factor productivity of the CR and EU-15 at the macroeconomic level has shown that while in 1992-1998, the growth of total factor productivity was slower in the CR, after 1998, it was faster (in 1999-2004, the average annual growth rate in the CR was 2.2% and 0.6% in EU-15). In the years 1996-2004, for which revised data are available for the CR, the average annual growth rate of total factor productivity in the CR was 1.5%, compared to 0.7% in EU-15. The analysis indicated that since 1999, total factor productivity in the CR has been converging to the EU-15 level, accelerating in 2003 and 2004, thereby achieving 63% of the EU-15 level in 2004.

The study examines the resources of economic growth in the Czech Republic in the course of years from 1992 until 2004. Using the growth accounting method, it analyses the contribution of individual factors to economic growth. Special attention is given to total factor productivity, which, apart from labour, also includes a fixed capital stock at constant prices. Compared to the previous period, the acceleration of the growth of total factor productivity decisively contributed to the speeding up of economic growth in the years 1999-2004. Furthermore, the study examines growth resources in six national economy sectors and analyses the contribution of individual sectors to the growth of macroeconomic total factor productivity. The analysis has shown that namely industry, transport, communications, and other services were involved in the speeding up of the growth of macroeconomic total factor productivity. A comparison of the dynamics of total factor productivity of the CR and EU-15 at the macroeconomic level has shown that while in 1992-1998, the growth of total factor productivity was slower in the CR, after 1998, it was faster (in 1999-2004, the average annual growth rate in the CR was 2.2% and 0.6% in EU-15). In the years 1996-2004, for which revised data are available for the CR, the average annual growth rate of total factor productivity in the CR was 1.5%, compared to 0.7% in EU-15. The analysis indicated that since 1999, total factor productivity in the CR has been converging to the EU-15 level, accelerating in 2003 and 2004, thereby achieving 63% of the EU-15 level in 2004.

This study uses industrial panel data for Japanese manufacturing to estimate the sources of productivity growth by simultaneously considering embodied technical progress, spillover effects, and openness, after controlling for returns to scale, imperfect competition, and capacity utilization. Estimation results show the existence of considerable embodied technical progress and interindustry externalities of capital investments positively affecting productivity growth. Furthermore, embodied technical progress causes research and development (R&D) capital to affect productivity growth insignificantly, suggesting that the impact of R&D is realized only after being embodied into other capitals. From sector‐wise estimations, we notice differences in factors affecting productivity growth between the durable and nondurable manufacturing sectors. (JELD24, O30)

This study aimed to analyze the trends and causes of productivity growth in the Philippine agriculture by estimating the total factor productivity (TFP) level and growth in the sector using the Tornqvist index number approach. Likewise, the factors causing the movements in TFP over a period of time, and the policy alternatives for increasing productivity growth were identified. The most recent agricultural data set covering 12 administrative regions and years 1974–2004 was used to determine the TFP level and growth. Results showed that output growth in Philippine agriculture was mainly driven by productivity, and minimally by the inputs of production. Productivity gaps were observed among the different regions. Central Luzon was the most productive region, whereas Bicol was the least productive. TFP growth was at its peak in the late 1970s, followed by a deceleration in the 1980s, and resurgence in the 1990s until the early part of the recent decade. The highest TFP growth rate recorded has not been paralleled despite government efforts and initiatives to revive the less dynamic agricultural sector of the Philippines. Across time, revenue growth was also seen to be declining, which may be due to the decrease in growth contribution of output prices (which remained relatively large), and to the decrease in growth contribution of input quantities (which was relatively small). Lastly, output prices contributed substantially to agricultural revenues, and TFP growth accounted mostly for growth in quantities of agricultural outputs. The TFP growth substantiated the importance of infrastructure, rural electrification, and investments in research and development to enhance agricultural productivity. Overall, this study recommends further examination of the role of agricultural output prices in determining farm incomes, and for initiatives to be undertaken to boost agricultural productivity through investments in infrastructure and research and development.

Research background: The issues of finance-growth nexus and financial instability have attracted considerable attention, but have been studied in isolation. This paper aims at filling this gap by providing insights into the implications of financial instability for long term productivity growth.
 Purpose of the article: This paper sheds light on the relationship between credit-to-GDP ratio volatility and the total factor productivity (TFP) growth rate. The impact of systemic banking crises and financial depth on productivity growth is also studied.
 Methods: The System GMM estimation of panel data for over 100 countries and spanning the period of 1970?2009 is used. The decomposition of credit-to-GDP ratio into trend and cyclical component is performed using the Hodrick-Prescott filter and a regression analysis with country-specific intercepts and slopes. The data on TFP comes from the Penn World Tables database.
 Findings & Value added: TFP growth is negatively affected by credit volatility, mainly in less technologically advanced countries, while financial depth exerts a negative influence on TFP growth in economies with superior technology. Systemic banking crises and the concomitant credit crunches have a negative impact on productivity growth, regardless of the level of technological development. Moreover, the level of human capital, patents and globalization fuel productivity growth. Macroeconomic instability, measured by the rate of inflation, hampers TFP growth.

Economic theory has identified a number of channels through which openness to international financial flows could raise productivity growth. However, while there is a vast empirical literature analyzing the impact of financial openness on output growth, far less attention has been paid to its effects on productivity growth. We provide a comprehensive analysis of the relationship between financial openness and total factor productivity (TFP) growth using an extensive dataset that includes various measures of productivity and financial openness for a large sample of countries. We find that de jure capital account openness has a robust positive effect on TFP growth. The effect of de facto financial integration on TFP growth is less clear, but this masks an important and novel result. We find strong evidence that FDI and portfolio equity liabilities boost TFP growth while external debt is actually negatively correlated with TFP growth. The negative relationship between external debt liabilities and TFP growth is attenuated in economies with higher levels of financial development and better institutions.

I. Introduction Total factor productivity (TFP) growth is an important measure of potential output growth given the nature of the diminishing returns to input use in the long run. Thus, Malaysia in her drive to enjoy sustainable growth to raise its living standards is set on focusing on TFP growth as stated in Malaysia's Second Industrial Master Plan 1996-2005. In fact, the manufacturing sector which has increased its contribution to gross domestic product (GDP) output from 19.3 per cent in 1979 to 34.2 per cent in 1996 has been identified as a key growth engine in this transformation process. Hence, it is imperative and timely for an analysis on the productivity growth performance of this sector to be undertaken. This study adds to the existing empirical literature in three ways. First, previous studies on Malaysian manufacturing have only considered the nonfrontier measure using the divisia translog index approach. To date, using the nonfrontier approach, Tham (1996, 1997) and the Productivity Report 1999 provide evidence of declining TFP growth for the Malaysian manufacturing sector in the 1990s (see Table 3). (1) How would this result compare with the use of the frontier approach? Will the frontier models also provide low TFP growth measures? This is one of the issues addressed in this article. As for the earlier studies, the nonparametric technique adopted computes TFP growth as a residual since it measures anything and everything of output growth that is not accounted by input growth. More importantly, the translog index TFP growth measure ignores the concept of technical inefficiency (by unrealistically assuming that all industries are technically efficient) and inaccurately interprets technical change as TFP growth. Thus in this study, frontier measures are used to overcome these major drawbacks. In the productivity literature, TFP growth is shown to be composed of both technical change (frontier shift) and technical efficiency (catching up effect). While the frontier effect indicates how far the efficient frontier itself has shifted over time due to the use of better technology and equipment, the catching up effect reflects how far the industry has moved towards the efficient frontier due to the better use of technology and equipment. The second difference in this study is that empirical robustness is ensured by the use of both the parametric and nonparametric frontier approaches to calculate TFP growth. Under the parametric approach, a stochastic production frontier model incorporating non-parallel shifts is estimated. With the nonparametric approach, the data envelope analysis (DEA) technique is used. Using a panel data set of twenty-eight manufacturing industries (see Appendix 1 for a list) from 1981 to 1996, a measure of TFP growth is first obtained and then decomposed to technical change and change in technical efficiency for both models. The results are then compared to previous studies with a focus on the Malaysian manufacturing sector as TFP growth studies on the aggregate economy may have broad implications that are not necessarily reflective of the TFP growth performance of specific sectors in the economy. The third contribution of this article is that the comparative performance of the results from alternative methodologies would add to similar work by Bjurek and Hjalmarsson (1990), Coelli and Perelman (1999), and Kumbhakar, Heshmati, and Hjalmarsson (1999) which provide mixed evidence of similarities in the results from the use of various models. Often, the choice of the method is said to depend on a range of factors. For instance, if the researcher simply wants to know if output growth is TFP or input-driven growth, then either approach would suffice. However, to answer questions on maximum productive or best practice output levels, the stochastic frontier can be used to understand the industries' catching up behaviour with respect to its own maximum potential, while DEA allows for the study of the performance of each industry relative to efficient industries in the sample. …

Productivity growth over the past six decades have been recognised as a major source of economic growth for many countries. And with increasing globalization efforts across all regions, the emphasis on improving productivity and competitiveness of domestic industries has then become the forefront of many government policies. The main purpose of this research is therefore to analyze what factors affect the growth of Total Factor Productivity (TFP) across different income grouped countries and identify possible policy implications based on the relevant findings. A special focus is made on how the quality of governance, as a determinant of productivity, affects TFP growth using specific governance indicators. This aspect of the research is deemed necessary as though the quality of governance is generally expected to have a positive impact on economic growth and development, there is very limited explanation and evidence as to how specific indicators of governance quality will affect TFP growth. The research will therefore bridge the gap in knowledge by explaining how the quality of governance affects the growth of total factor productivity. In order to achieve the research objective, the study investigates three specific questions: 1. Whether the indicators used for the quality of governance significantly impact productivity growth across the income groups; 2. Which of the determinants affect productivity growth the most among high income countries and lower income groups; and lastly, 3. Does FDI, human capital, R&D, and the indicators for the quality of governance have greater impact on productivity growth in lower income or high income countries. Using Pooled OLS and Fixed Effects regression analysis, this study analyzes the determinants of Total Factor Productivity (TFP) growth for 35 countries across different income groups for the period 2002 to 2014. The main results reveal that Government Effectiveness, Rule of Law, FDI, and Trade Openness, all had statistically significant effect on the growth of Total Factor Productivity. In regards to the sample of High Income countries, the evidence suggests that Government Effectiveness and R&D negatively impacts growth in Total Factor Productivity, whereas Trade Openness and FDI showing positive impact. Whereas, in Lower Income, R&D impacts productivity growth positively, whilst Human Capital has a negative impact. An additional and unique finding of the research, was that Government Effectiveness and Rule of Law, both being used as proxies to measure the effect of the Quality of Governance on productivity growth, had totally opposite impact. Specifically, Government Effectiveness was negatively related to productivity growth across all samples, whereas Rule of Law was positive.

In this book we have documented and analysed Europe's productivity performance since the mid 1990s and compared it to growth since the 1970s, as well as to the productivity record of the United States. On both counts, Europe's performance has been disappointing as labour productivity growth has seriously slowed since 1995, while it has accelerated in the United States. In this book we have analysed the determinants of Europe's poor productivity performance using a new database on productivity at the industry level, the EU KLEMS database.

This paper analyzes the Republic of Korea's rapid and sustained growth experience for the past six decades from the perspective of the neoclassical growth model (the workhorse model of the World Bank’s Long Term Growth Model (LTGM) project). Overall, the sources of Korea's growth were balanced among labor market and demographic factors, capital investment, human capital accumulation, and productivity growth. However, the main engine of growth evolved sequentially, e.g., labor and human capital factors in the 1960s, capital deepening in the 1970s, and then productivity growth for the following periods. The major sources of sustained growth over six decades were human capital accumulation and productivity growth rather than labor or capital investment. A counterfactual calibration of the model explains Korea's actual growth experience well, and shows why gaps between the model’s predictions and the data arise. This illustrates that an appropriate calibration of a simple neoclassical growth model provides useful lessons and tools for policy makers in developing countries in designing their national development strategies.

Purpose – China and India have made significant strides in transforming their agricultural sectors to cut hunger and poverty for the masses through improved agricultural productivity. Given limited land and shift of labor to non-agricultural sector, increasing productivity will continue to be central in agricultural growth in the twenty-first century. The purpose of this paper is to provide comparative analysis of the agricultural total factor productivity (TFP) growth in the two countries. It complements existing literature by examining the evolution and drivers of TFP at disaggregated sub-national level. Richer data allows a deeper understanding of the nature and drivers of TFP growth in the two countries. Design/methodology/approach – This paper applies different analytical framework to address different research questions using data since 1980. China study estimates a parametric output-based distance function using a translog stochastic frontier function. Productivity growth index and its multiple components are calculated using parameters derived from the parametric approach to identify the characteristics of technology such as structural bias. India study first applies data envelopment analysis to estimate the aggregate productivity growth index, technical change (TC), and efficiency change. Next productivity indexes by for traditional crops are estimated using growth accounting framework at state level. Finally, a panel regression links TFP on its determinants. Findings – Several common themes emerge from this comparative study. Faced with similar challenges of limited resources and growing demand, improving productivity is the only way to meet long-term food security. Agriculture sector has performed impressively with annual TFP growth beyond 2 percent in China and between 1 and 2 percent in India since the 1980s. The TFP growth is mainly propelled by technological advance but efficiency had been stagnant or even deteriorated. This study provides a granular picture of within country heterogeneity: fast growth in the North and Northeast part of China, South and East of India. Research limitations/implications – The study suggests some possible policy interventions to improve agricultural productivity, including investment in agricultural R & D to create advanced production technology, effective extension programs and supportive policies to increase efficiency, and diversification from staple crops for sector-wide growth. The India study suggests certain policies may not be contributing much to productivity growth in the long run due to a negative impact on environment. Further studies are needed to expand the productivity analysis to take into consideration of the negative externalities to the society. Data enhancement to account for quality-adjusted inputs could improve the estimation of productivity growth. Originality/value – Each country study reveals certain prospects of the agricultural sector and production technology. China analysis statistically confirms the existence of technical inefficiency and technology progress, suggests the translog form is appropriate to capture the production technology and satisfies conditions stipulated in theoretical models. The results indicate TC does not influence the contribution of output or input to the production process. India study pinpoints the lagging productivity growth of traditional crops, which still derives growth from input expansion. Although different states benefited from different crops, sector-wide productivity gain is primarily the result of diversification to high-value crops and livestock products.