Indexing Structural Distortion: Sectoral Productivity, Structural Change and Growth

Ando, Sakai; Nassar, Koffie Ben

doi:10.2139/ssrn.3053201

Cited by 5 publications

(9 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Although structural transformations have a significant effect in the labor market and the economy in OECD, the growth in labor productivity has a negative influence over employment in OECD affecting the long-term unemployment in an increasing rate. Meanwhile, Ando and Nassar (2017) found that education can boost the successful rate of structural change to generate jobs. They emphasized that higher educational attainment is important because skills are necessary for labor mobility.…”

Section: Discussionmentioning

confidence: 99%

“…This happens because most of the labor force will be coming from the low-skilled agricultural sector to be absorbed by the labor market of the high-skilled industrial and service sectors during a structural transformation. This explains the importance of higher educational attainment and trainings and seminars during a fast-paced structural transformation because skills are necessary for labor mobility (Ando & Nassar, 2017). This also confers countries with more flexible labor markets experience greater growth-enhancing structural change .…”

Section: Tablementioning

confidence: 99%

See 1 more Smart Citation

The Effect of Structural Change on Labor Productivity Growth and Employment in the Philippines

Laurente¹

2022

IJAIR

View full text Add to dashboard Cite

Philippines is considered one of the fastest developing economies because of the growing service sector. This growth brought a significant change in the economic structure of the country which previously relied on the agricultural sector. This paper conducted a study about the significant impact of structural change on labor productivity growth and employment. The paper localized the decomposition analysis used in literatures to extract the share of “within” sector and “structural change” to total changes in labor productivity in the Philippines from 2004-2018, and Applied Pooled Least Square, to obtain the impact of structural change to labor productivity growth and employment. Based on Durbin-Watson test results, both Panel Regression Equation and Seemingly Unrelated Equation were utilized because there is no contemporaneous autocorrelation found in Pooled Least Square. Using Breusch-Pagan LM Test, Panel Regression is deemed more appropriate than Seemingly Unrelated Regression. Furthermore, the decomposition analysis showed that higher share of service sector in employment makes the contribution of “structural change” lesser to labor productivity growth due to labor market that becomes less flexible as service sector dominates the labor market because of higher skillsets needed by the sector. The regression analysis showed that structural change is a significant determinant of employment and labor productivity; structural change has a positive relationship to labor productivity due to the transfer of labor to high-productivity sector; and structural change has a negative relationship to employment because the employment brought by the structural change cannot be absorbed by the labor force because of skills mismatch.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Tablementioning

confidence: 99%

The Effect of Structural Change on Labor Productivity Growth and Employment in the Philippines

Laurente¹

2022

IJAIR

View full text Add to dashboard Cite

show abstract

“…When this difference is reflected in different departments, it will worsen the overall allocation efficiency of resources. In the allocation of production factors, Ando and Nassar [23] found through the non-competitive equilibrium model that the transfer rate of production factors among various departments is equal. When production resources can be effectively allocated, the industrial structure is the best.…”

Section: Research Hypothesismentioning

confidence: 99%

“…In factor allocation, Ando and Nassar [23] argued that the rate at which factors of production are transferred between sectors is equal and that the industrial structure is optimal when productive resources can be allocated efficiently. However, when there are deviations in output and employment in one sector of the economy and the equilibrium between industries is broken, the other sectors deviate in the opposite direction, creating factor market distortions that lead to increased energy intensity in the production process.…”

Section: Research Hypothesismentioning

confidence: 99%

“…Drawing on Ando and Nassar [23] this paper uses Euclidean distances to measure the degree of distortion in industrial structure, starting from the output and employment shares between sectors in disequilibrium, as follows. In equation ( 2), di represents the distance between output share and employment share, and d denotes the Euclidean distance between value added and employment share of the economy.…”

Section: Carbon Emission Intensity Measurementmentioning

confidence: 99%

See 1 more Smart Citation

Heterogeneous Dynamic Correlation Research Among Industrial Structure Distortion, Two-way FDI, and Carbon emission intensity

You¹,

Ding²,

Zhang³

2022

Preprint

View full text Add to dashboard Cite

In this paper, industrial structure distortion, two-way FDI and carbon emission intensity are brought into a unified research framework, and based on China's panel data from 2011 to 2020, empirical tests are conducted employing Exploratory Spatial Data Analysis (ESDA), spatial econometric model and intermediary effect test. The results show the following. Firstly, China's industrial structure distortion index shows a downward trend. The industrial structure distortion index is the highest in the west, followed by the middle, and the lowest in the East. Secondly, the relationship between carbon emission intensity and economic development shows a "decoupling" effect and keeps decreasing year by year. The spatial disparity is remarkable, showing the pattern of "the east leading, the middle catching up and the west lagging ". At the provincial level, except in Xinjiang province, the carbon emission intensity of other provinces showed different degrees of decline. In terms of spatial distribution, the polarization characteristics of carbon emission intensity are significant, and the traditional spatial distribution pattern has been broken. Thirdly, there is a positive spatial correlation between China's industrial structure distortion, two-way FDI and carbon emission intensity. The distortion of industrial structure will not only lead to the increase of local carbon emission intensity but also produce reverse spillover to adjacent areas. IFDI and OFDI provide a strong driving force for the decline of carbon emission intensity. IFDI promotes the decline of carbon emission intensity in adjacent areas, while OFDI will increase the carbon emission intensity in surrounding areas. The interaction of IFDI and OFDI can significantly reduce the carbon emission intensity of local and adjacent areas. Fourthly, the results of intermediary effect analysis show that two-way FDI is the two channels of industrial structure distortion affecting carbon emission intensity. Industrial structure distortion can affect the transmission mechanism of carbon emission intensity by affecting two-way FDI.

show abstract

Carbon emission efficiency in the age of digital economy: New insights on green technology progress and industrial structure distortion

Yu,

Liu,

Yan

et al. 2024

Bus Strat Env

View full text Add to dashboard Cite

There is a dual impact of the digital economy (DE) on carbon emission efficiency (CE). An in‐depth study of the impact of the DE on “local‐adjacent” CE at different spatial scales can support the debate on whether the digital economy is empowerment or impairment. This study reassesses the DE at the urban level from a micro‐perspective by cross‐referencing the data of listed companies with the information obtained from city yearbooks. Considering the potential spatial correlation of the digital economy, we use the spatial Durbin model to examine 281 cities from 2011 to 2019 and study the influence of the DE on “local‐adjacent” CE. We further explain the pathways of this influence from the perspectives of green technological progress and industrial structural distortion. The study findings indicate that the impact of DE on local CE exhibits a “U”‐shaped pattern overall while its impact on neighboring CE shows an inverted “U”‐shaped pattern. These conclusions are still valid even after conducting a series of robustness tests, including spatial difference‐in‐differences and spatial replacement matrix. Further research reveals that the impact of DE on neighboring CE weakens with increasing spatial distance. The promoting effect of DE on neighboring CE peaks at 250 km, and the inverted “U”‐shaped pattern holds within a range of 300 km. In the initial stages, green technological progress can mask the negative impact of DE on local CE while simultaneously improving CE within a 250‐km range of neighboring areas. However, in the later stages, DE exacerbates the distortion of neighboring industrial structures, thereby reducing neighboring CE. The lack of synergy in the scale and growth rate of DE development in different regions significantly reduces the enhancement effect of the digital economy on CE.

show abstract

Indexing Structural Distortion: Sectoral Productivity, Structural Change and Growth

Cited by 5 publications

References 0 publications

The Effect of Structural Change on Labor Productivity Growth and Employment in the Philippines

The Effect of Structural Change on Labor Productivity Growth and Employment in the Philippines

Heterogeneous Dynamic Correlation Research Among Industrial Structure Distortion, Two-way FDI, and Carbon emission intensity

Carbon emission efficiency in the age of digital economy: New insights on green technology progress and industrial structure distortion

Contact Info

Product

Resources

About