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“It should also be noted that the objective of convergence and equal distribution, including across under-performing areas, can hinder efforts to generate growth. Contrariwise, the objective of competitiveness can exacerbate regional and social inequalities, by targeting efforts on zones of excellence where projects achieve greater returns (dynamic major cities, higher levels of general education, the most advanced projects, infrastructures with the heaviest traffic, and so on). If cohesion policy and the Lisbon Strategy come into conflict, it must be borne in mind that the former, for the moment, is founded on a rather more solid legal foundation than the latter” European Commission (2005, p. 9)Adaptation of Cohesion Policy to the Enlarged Europe and the Lisbon and Gothenburg Objectives.

This chapter applies the five drivers of productivity framework to regional microdata for Egypt and extends it by introducing an index of industrial clusters as an explanatory factor of the productivity performance of local private sector firms. Applying structural equation models, the geographic concentration of sectoral economic activity is found to have a positive and statistically significant effect on labor productivity. The transmission mechanism is conjectured to be the positive spillovers that are created, which local firms can tap into. In contrast, a higher concentration of skilled workers in an industrial sector in a region is associated with lower levels of labor productivity – a finding that suggests there may be structural deficiencies in the allocation of skilled workers. Regional policy should focus on net investments in gross capital formation throughout the country, for which the national and regional governments should improve how public investments are managed and the institutional framework – including the rule of law, bureaucracy and red tape, conflict of interest, transparency, and governance – so that private investment (both local and foreign) may substantially increase.

This chapter does three things. First, it estimates regional gross domestic product (GDP) for three different geographical levels in Switzerland (97 micro regions, 16 labor market basins, and 3 large regions). Second, it analyzes the evolution of regional inequality relying on a heuristic model inspired by Williamson (1965), which features an initial growth impulse in one or several core regions and subsequent diffusion. Third, it uses index number theory to decompose regional inequality into three different effects: sectoral structure, productivity, and comparative advantage.

The results can be summarized as follows: As a consequence of the existence of multiple core regions, Swiss regional inequality has been comparatively low at higher geographical levels. Spatial diffusion of economic growth occurred across different parts of the country and within different labor market regions. This resulted in a bell-shaped evolution of regional inequality at the micro regional level and convergence at higher geographical levels. In early and in late stages of the development process, productivity differentials were the main drivers of inequality, whereas economic structure was determinant between 1888 and 1941. The poorest regions suffered from comparative disadvantage, that is, they were specialized in the vary sector (agriculture), where their relative productivity was comparatively lowest.

When spillovers are measured at a national level, the regional benefits may not be identified if they are too small. Very few studies that examined how FDI impacts the regional productivity of the host nations have shown mixed results. The evidence is still scarce and little is known about how the regional penetration of FDI affects the regional productivity performance. The trajectory of regional productivity growth in India has been a subject of scrutiny and intense debates and remains less systematically investigated. The purpose of this paper is to fill this lacuna by investigating the effect of FDI spillover on regional productivity in Indian states.

Using data supplied by the Central Statistical Organization, National Statistical Organization, National Sample Survey Office, and National Accounts Statistics, Government of India at the Indian Ministry of Statistics and Programme Implementation, first, the study employs stochastic frontier model to explore the extent to which FDI spillover contributes to the regional productivity from panel data of 28 Indian states over 1993-2013. Second, the study examines the roles of absorptive ability and technology gap on productivity effect of FDI. Third, by adopting SFA, we measure productivity growth of Indian states in terms of Malmquist productivity index. Fourth, India’s development is imbalanced. To analyze the imbalance due to skewed distribution of FDI among Indian states, Indian states are divided into three regions, and the spillover effects of FDI on TFP in these regions are explored.

The results on the effects of FDI spillover on regional productivity in India using stochastic frontier and panel data from 28 states over 1993-2013 show that R&D, technology import, human capital, and various specifications of FDI have a significant impact on the regional productivity in India except technology gap. Study does not find support for the resource curse hypothesis in Indian states. Productivity growth for India using the Malmquist TFP index based on the stochastic frontier shows positive impact. The TFP growth in the three regions of India is turned to be differently attributed by the FDI spillover.

Little is known about how the regional penetration of FDI affects the regional productivity performance. This research aims to fill this lacuna by investigating the effect of FDI spillover on regional productivity in Indian states which has been a subject of scrutiny and intense debates.

The aim of this paper is to disentangle the contemporaneous and non-contemporaneous relationship between regional business cycles and manufacturing productivity in a developing country, namely Colombia.

The methodology is quantitative. To deal with the problems of endogeneity in the production function and with the law motion of productivity (the Markov process), the authors obtain Total Factor Productivity (TFP) through the Wooldridge’s two equations system that can be jointly estimated under the generalized method of moments framework (GMM). Secondly, to avoid bias we estimate regional business cycles through the Kalman filter. Subsequently, we implement an instrumental variables/generalized method of moments regression (IV/GMM) to capture the contemporaneous and endogenous TFP–GDP cycles’ linkage at the regional level. Lastly, to deal with the non-contemporaneous link, the authors estimate a vector autoregressive model with exogenous variables (VARX) for each region. We also present the corresponding impulse–response functions.

The authors’ general results suggest a remarkable causality, both contemporary and non-contemporary, from productivity to GDP (but not vice versa) in the most developed regions of the country. This implied productivity could influence in the economic growth of regions in short and long runs. These results are different than those expected by economic theory and should be considered by local economic policy makers.

The authors consider that a more detailed analysis should be carried out at the level of each sector within the manufacturing industry to further clarify these findings.

The policy should be oriented to obtaining cutting-edge technologies through subsidies, and also should facilitate the access to financial capital and the investment in R&D laboratories. On the other hand, the link with international trade also must be reinforced because the importing of intermediate inputs and exporting of output allow the firms to obtain embodied technologies, also to incur on learning by exporting and importing processes and finally to gain experience and competitiveness in foreign markets.

The causality in the region that provides more than 50% of economic activity within the country (Third region) is only in one directional, from TFP towards gross domestic product (GDP) and not vice versa. As the influence from GDP towards TFP is minimal in the remaining regions, the manufacturing productivity influences both short and long run regional economic growth in Colombia. This implies that economic policy at the level of macro-region must be modified; the government should give additional support to the manufacturing sector, especially in developed regions and for the small and medium-sized enterprises (SMEs) (wich represent 92% of manufacturing firms) to increase economic growth in the future.

The authors’ contribution is threefold. First, they pay special attention to the contemporaneous cyclical relationship (i.e. pro-cyclical, counter-cyclical or acyclic) and the non-contemporaneous causality with productivity. Second, they estimate productivity with the GMM two equation system considering an endogenous Markov process. Third, to the best of their knowledge, at least in the case of Latin America, there are no studies in this direction combining these statistic methods, including that of Colombia.

Understanding and simulating construction activities is a vital issue from a macro-perspective, since construction is an important contributor in economic development. Although the construction labor productivity frontier has attracted much research effort, the temporal and regional characteristics have not yet been explored. The purpose of this paper is to investigate the long-run equilibrium and dynamics within construction development under a conditional frontier context.

Analogous to the simplified production function, this research adopts the conditional frontier theory to investigate the convergence of construction labor productivity across regions and over time. Error correction models are implemented to identify the long-run equilibrium and dynamics of construction labor productivity against three types of convergence hypotheses, while a panel regression method is used to capture the regional heterogeneity. The developed models are applied to investigate and simulate the construction labor productivity in the Australian states and territories.

The results suggest that construction labor productivity in Australia should converge to stable frontiers in a long-run perspective. The dynamics of the productivity are mainly caused by the technology utilization efficiency levels of the local construction industry, while the influences of changes in technology level and capital depending appear limited. Five regional clusters of the Australian construction labor productivity are suggested by the simulation results, including New South Wales; Australian Capital Territory; Northern Territory, Queensland, and Western Australia; South Australia; and Tasmania and Victoria.

Three types of frontier of construction labor productivity is proposed. An econometric approach is developed to identify the convergence frontier of construction labor productivity across regions over time. The specified model can provides accurate predictions of the construction labor productivity.

This study aims to propose a refined dynamic network slacks-based measure (DNSBM) to evaluate the efficiency of China's regional green innovation system which consists of basic research, applied research and commercialization stages and explore the influencing factors of the stage efficiency.

A two-step procedure is employed. The first step proposes an improved DNSBM model with flexible settings of stages' input or output efficiency and uses second order cone programming (SOCP) to solve the non-linear problem. In the second step, least absolute shrinkage and selection operator (LASSO) and Tobit models are used to explore the influencing factors of the stage efficiency. Global Dynamic Malmquist Productivity Index (GDMPI) and Dagum Gini coefficient decomposition method are introduced for further discussion of the productivity change and regional differences.

On average, Chinese provincial green innovation efficiency should be improved by 24.11% to become efficient. The commercialization stage outperforms the stages of basic research and applied research. Comparisons between the proposed model and input-oriented, output-oriented and non-oriented DNSBM models show that the proposed model is more advanced because it allows some stages to have output-oriented model characteristics while the other stages have input-oriented model characteristics. The examination of the influencing factors reveals that the three stages of the green innovation system have quite diverse influencing factors. Further discussion reveals that Chinese green innovation productivity has increased by 39.85%, which is driven mainly by technology progress, and the increasing tendency of regional differences between northern and southern China should be paid attention to.

This study proposes an improved dynamic three-stage slacks-based measure (SBM) model that allows calculating output efficiency in some stages and input efficiency in the other stages with the application of SOCP approach. In order to capture productivity change, this study develops a GDMPI based on the DNSBM model. In practice, the efficiency of regional green innovation in China and the factors that influence each stage are examined.

The purpose of this paper is to assess the effect of human capital development on regional productivity growth and convergence in the Spanish provinces over the period 1991-2006.

The stochastic frontier analysis (SFA) methodology is used to estimate production inefficiencies. This approach enables the assessment of the degree to which a given region’s observed output deviates from the maximal possible. Therefore, the resulting region specific productive efficiencies are modelled as outcomes of the level of human capital. A β-convergence equation for the regional efficiency levels is also estimated to detect any signs of regional catching-up.

The results show that increasing levels of human capital development are associated with lower regional inefficiency. All levels of education contributed to reduce the inefficiency levels, however, secondary schooling played a stronger role than primary and even higher education. There is also evidence of regional convergence towards the best practice frontier through a process that is beneficially aided by human capital development.

The paper combines the use of the SFA to study the effect of human capital on regional productivity with the estimation of a β-convergence equation for the obtained regional efficiency levels.

Purpose – The chapter investigates the effects of knowledge on economic growth at the regional level.

Methodology/approach – We elaborate a view on knowledge as the result of a combinatorial search activity and implement indicators synthesizing the network architecture of knowledge structure.

Findings – Empirical estimations corroborate the hypothesis that knowledge coherence and variety, besides the traditional measure of knowledge stock, matter in shaping regional economic performances.

Social implications – Important policy implications stem from the analysis, in that regional innovation strategies, to trigger economic performances, should be carefully coordinated so as to foster exploration strategies, but taking into full account the technological competences accumulated in the course of time.

Originality/value of the paper – The originality of the chapter lies mainly in the methodological approach, which provides operational translation to the view of knowledge as an outcome of a combinatorial search. In this perspective, the chapter also sheds light on previously unexplored aspects of the relationships between knowledge and growth.

As one of the pillar sectors, China’s construction industry (CE) is not efficient in productivity with large regional gaps over the past decades. It is crucial for stakeholders to have insightful information on regional input of resources and output of productive efficiency for making policies and investment decisions. The purpose of this paper is to develop an efficiency measurement for the CE and explore the regional differences of construction productive efficiency across the three regions of China.

Data envelopment analysis (DEA) is an objective benchmarking methodology used for measuring the performance of construction productivity. Distance friction minimization (DFM) approach, based on DEA model, is applied to identify the causes of inefficiency, sources of growth and the optimal paths to efficient frontier for regional CE. Further studies are conducted to provide insightful information for efficiency improvement, according to DFM modeling results and empirical analysis.

The results indicate that eastern region leads construction development due to strong performance of coastal provinces. Faced with decreasing supply of skilled workers in developed region, investing more on construction plants and equipment for labor savings is more efficient to the long-term productivity growth of CE in the east. For developing midland region, heavy reliance on cheap manpower should be gradually relieved by allocating more budgets to vocational training and education program to boost quality labor supply, as well as making steady investment on construction equipment and advanced technology. In underdeveloped western region, raising construction labor wages is recommended to attract more workers to meet the market demand and achieve an optimal production efficiency in the CE.

The findings provide insights into the causes of inefficiency, the sources of growth and the best strategies for efficiency improvement in regional CE, recommendations are made for policy making and strategic planning to enhance the overall performance of China’s construction productive efficiency.