Aggregate productivity and the allocation of resources over the business cycle

Osotimehin, Sophie

doi:10.1016/j.red.2019.02.003

Cited by 23 publications

(19 citation statements)

References 38 publications

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“…To measure the misallocation degree of resources, that is, labor and capital among regions, first, we need to measure the output elasticity of labor and capital. This paper refers to the practice of Osotimehin [50], using the Solow residual value to measure the elasticity. Suppose the production function is a C-D production function with the same scale return, i.e.,…”

Section: Calculating the Output Elasticity Of Resourcementioning

confidence: 99%

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Can Industrial Co-Agglomeration between Producer Services and Manufacturing Reduce Carbon Intensity in China?

Han

Feng

et al. 2019

Sustainability

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Climate change poses unprecedented challenges for humanity. Reducing carbon intensity is an inevitable choice for tackling climate change and promoting sustainable development. China has made some emission reduction commitments in the international community to promote the decoupling of China’s economic development from carbon emissions. The realization of the industrial structure from the “single-wheel drive” of the manufacturing to the “two-wheel drive” economic development model of the service industry and the manufacturing has become a key measure to achieve China’s economic intensive development. According to resource misallocation situation in different regions, this paper explored the impact of the collaborative agglomeration between producer services and manufacturing (hereinafter referred to as industrial co-agglomeration) on carbon intensity. The research results show that the carbon intensity is decreasing year by year, and the degree of intensification of China’s economic growth continues to increase. Moreover, the effect of industrial co-agglomeration to promote carbon emission reduction is significantly limited by the degree of misallocated resources, and there is a double threshold effect. Specifically, in areas where resource allocation is reasonable, industrial co-agglomeration can produce significant agglomeration effects and promote carbon intensity reduction. Once the degree of misallocated resources exceeds a threshold level, the agglomeration effect will turn into a crowding effect, resulting in an inability to reduce carbon intensity. We comprehensively analyzed the driving factors for reducing carbon intensity and proposed policy pathways for achieving China’s carbon intensity target.

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Section: Calculating the Output Elasticity Of Resourcementioning

confidence: 99%

“…where CC represents carbon emissions of cement consumption; Q denotes total cement production; β denotes the carbon emissions coefficients, refer to Zhang H [50,55], the value is 0.5270tCO 2 /t. Hence, the formula for calculating the total amount of CO 2 emissions in each region is CO 2 = EC + CC.…”

Section: Calculating the Carbon Intensitymentioning

confidence: 99%

Can Industrial Co-Agglomeration between Producer Services and Manufacturing Reduce Carbon Intensity in China?

Han

Feng

et al. 2019

Sustainability

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“…In focusing on the consequences of barriers to labor reallocation on aggregate productivity growth, our analysis goes one step beyond the recent literature on misallocation that focuses on the level effects, following the seminal work of Restuccia and Rogerson (2008) and Hsieh and Klenow (2009). Empirical studies that evaluate the contribution of reallocation to productivity changes, such as Foster et al (2001) and Osotimehin (2016), are designed to analyze the sources of productivity growth, rather than the level; in that sense, our analysis is more comparable to that literature. We highlight that barriers to reallocation affect not only the allocation of resources across firms with different productivity levels, but also the productivity process itself as it modifies the firms' incentives to innovate.…”

Section: Introductionmentioning

confidence: 99%

Barriers to Reallocation and Economic Growth: The Effects of Firing Costs

Mukoyama

Osotimehin

2019

American Economic Journal: Macroeconomics

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We study how factors that hinder the reallocation of inputs across firms influence aggregate productivity growth. We extend Hopenhayn and Rogerson’s (1993) firm-dynamics model to allow for endogenous innovation. We evaluate the effects of firing taxes on reallocation, innovation, and productivity growth. We find firing taxes can have opposite effects on entrants’ innovation and incumbents’ innovation, and the overall outcome depends on the relative strengths of these forces. In the entrant-driven growth calibration, firing taxes reduce aggregate productivity growth, whereas aggregate productivity growth increases in the incumbent-driven growth calibration. (JEL D24, E23, E24, J23, J24, J62, K31, O31, O47)

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“…The study by Fontagné & Santoni (2015) takes a similar position, showing that misallocation applies particularly to small and old firms. Osotimehin (2016) considers, on the one hand, the significance of reallocation towards the most productive continuing firms and, on the other, the process of creative destruction. The author shows that, over the period 1989-2007, the contribution of reallocation towards the most productive continuing firms to the evolution of French sectoral total factor productivity (TFP) is greater than that resulting from a Schumpeterian process of creative destruction.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The decomposition of the average annual growth rate of sectoral TFP does not take into account reallocations between sectors. Osotimehin (2016) shows that cross-sectoral reallocations play a limited role in aggregate productivity trends.…”

Section: * * *mentioning

confidence: 99%