scholarly journals PROCYCLICAL SOLOW RESIDUALS WITHOUT TECHNOLOGY SHOCKS

2009 ◽  
Vol 13 (3) ◽  
pp. 366-389 ◽  
Author(s):  
Andrew J. Clarke ◽  
Alok Johri
Keyword(s):  
Standard Deviation ◽  
Business Cycle ◽  
Total Factor Productivity ◽  
Learning By Doing ◽  
Real Business Cycle ◽  
Technology Shocks ◽  
Factor Productivity ◽  
Hard Time ◽  
Second Moments ◽  
Key Innovation

Most real business cycle models have a hard time jointly explaining the twin facts of strongly procyclical Solow residuals and extremely low correlations between wages and hours. We present a model that delivers both these results without using exogenous variation in total factor productivity (technology shocks). The key innovation of the paper is to add learning-by-doing to firms' technology. As a result, firms optimally vary their prices to control the amount of learning, which in turn influences future productivity. We show that exogenous variation in labor wedges (preference shocks) measured from aggregate data deliver around 50% of the standard deviation in the efficiency wedge (Solow residual) as well as realistic second moments for key aggregate variables, which is in sharp contrast to the model without learning-by-doing.

scholarly journals Impact of Migrant Workers on Total Factor Productivity in Chinese Construction Industry

2019 ◽  
Vol 11 (3) ◽  
pp. 926 ◽  
Author(s):  
Gui Ye ◽  
Yuhe Wang ◽  
Yuxin Zhang ◽  
Liming Wang ◽  
Houli Xie ◽  
...  
Keyword(s):  
Construction Industry ◽  
Total Factor Productivity ◽  
Migrant Workers ◽  
Annual Report ◽  
Learning By Doing ◽  
Construction Sector ◽  
Factor Productivity ◽  
The Sustainable Development ◽  
Statistical Yearbook ◽  
The Impact

Total factor productivity (TFP) is of critical importance to the sustainable development of construction industry. This paper presents an analysis on the impact of migrant workers on TFP in Chinese construction sector. Interestingly, Solow Residual Approach is applied to conduct the analysis through comparing two scenarios, namely the scenario without considering migrant workers (Scenario A) and the scenario with including migrant workers (Scenario B). The data are collected from the China Statistical Yearbook on Construction and Chinese Annual Report on Migrant Workers for the period of 2008–2015. The results indicate that migrant workers have a significant impact on TFP, during the surveyed period they improved TFP by 10.42% in total and promoted the annual average TFP growth by 0.96%. Hence, it can be seen that the impact of migrant workers on TFP is very significant, whilst the main reason for such impact is believed to be the improvement of migrant workers’ quality obtained mainly throughout learning by doing.

scholarly journals The Procyclical Behavior of Total Factor Productivity in the United States, 1890–2004

2010 ◽  
Vol 70 (2) ◽  
pp. 326-350 ◽  
Author(s):  
Alexander J. Field
Keyword(s):  
United States ◽  
Total Factor Productivity ◽  
Productivity Growth ◽  
The United States ◽  
Real Business Cycle ◽  
Factor Productivity ◽  
Tfp Growth ◽  
Capital Services ◽  
Labor Hoarding ◽  
Statistical Artifact

Between 1890 and 2004 total factor productivity (TFP) growth in the United States has been strongly procyclical, while labor productivity growth has been mildly so. This article argues that these results are not simply a statistical artifact, as Mathew Shapiro and others have argued. Procyclicality resulted principally from demand shocks interacting with capital services which are relatively invariant over the cycle. This account contrasts with explanations emphasizing labor hoarding as well as those offered by the real business cycle (RBC) program, in which TFP shocks (deviations from trend) are themselves the cause of cycles.

Technology Shocks and the Great Depression

2016 ◽  
Vol 76 (3) ◽  
pp. 909-933 ◽  
Author(s):  
Shingo Watanabe
Keyword(s):  
Business Cycles ◽  
Business Cycle ◽  
Great Depression ◽  
Imperfect Competition ◽  
Real Business Cycle ◽  
Technology Shocks ◽  
The Great Depression ◽  
Large Fluctuations ◽  
Technology Growth

Standard productivity measures indicate large fluctuations in technology during the Great Depression. This article's historical technology series (1892–1966), controlled for aggregation effects, varying input utilization, non-constant returns, and imperfect competition, does not indicate technology regress such that could trigger the downturn. In contrast, technology improvements in the recovery were so rapid that, over the whole Great Depression period, technology growth was highest among pre-WWII decades. This article also finds that output changed little and inputs fell when technology improved in the pre-WWII period. Real-business-cycle models have difficulty in explaining pre-WWII business cycles characterized by such responses.

scholarly journals Did Technology Shocks Drive the Great Depression? Explaining Cyclical Productivity Movements in U.S. Manufacturing, 1919–1939

2011 ◽  
Vol 71 (4) ◽  
pp. 827-858 ◽  
Author(s):  
ROBERT INKLAAR ◽  
HERMAN DE JONG ◽  
REITZE GOUMA
Keyword(s):  
Business Cycle ◽  
Great Depression ◽  
Returns To Scale ◽  
The United States ◽  
Increasing Returns ◽  
Real Business Cycle ◽  
Technology Shocks ◽  
Business Cycle Theory ◽  
Increasing Returns To Scale ◽  
The Great Depression

Technology shocks and declining productivity have been advanced as important factors driving the Great Depression in the United States, based on real business cycle theory. We estimate an improved measure of technology for interwar manufacturing, using data from the U.S. census reports. There is clear evidence of increasing returns to scale and we find no statistical proof that technology shocks led to changes in hours worked or other inputs. This contradicts a key prediction of real business cycle theory. We find that increasing returns to scale are not due to market power but to labor and capital hoarding.

scholarly journals Technology, Employment, and the Business Cycle: Do Technology Shocks Explain Aggregate Fluctuations?

1999 ◽  
Vol 89 (1) ◽  
pp. 249-271 ◽  
Author(s):  
Jordi Galí
Keyword(s):  
Simple Model ◽  
Business Cycles ◽  
Business Cycle ◽  
Monopolistic Competition ◽  
Real Business Cycle ◽  
Technology Shocks ◽  
Aggregate Fluctuations ◽  
G7 Countries ◽  
Technology Shock ◽  
The Business Cycle

I estimate a decomposition of productivity and hours into technology and non-technology components. Two results stand out: (a) the estimated conditional correlations of hours and productivity are negative for technology shocks, positive for nontechnology shocks; (b) hours show a persistent decline in response to a positive technology shock. Most of the results hold for a variety of model specifications, and for the majority of G7 countries. The picture that emerges is hard to reconcile with a conventional real-business-cycle interpretation of business cycles, but is shown to be consistent with a simple model with monopolistic competition and sticky prices. (JEL E32, E24)

scholarly journals Read All about It!! What Happens Following a Technology Shock?

2011 ◽  
Vol 101 (4) ◽  
pp. 1144-1179 ◽  
Author(s):  
Michelle Alexopoulos
Keyword(s):  
Information Technology ◽  
Total Factor Productivity ◽  
Technical Change ◽  
Economic Activity ◽  
New Technologies ◽  
Technology Shocks ◽  
Factor Productivity ◽  
Technology Shock ◽  
Impact Of Technology ◽  
The Impact

Existing indicators of technical change are plagued by shortcomings. I present new measures based on books published in the field of technology that resolve many of these problems and use them to identify the impact of technology shocks on economic activity. They are positively linked to changes in R&D and scientific knowledge, and capture the new technologies' commercialization dates. Changes in information technology are found to be important sources of economic fluctuations in the post-WWII period, and total factor productivity, investment, and, to a lesser extent, labor are all shown to increase following a positive technology shock. (JEL E22, E23, E32, O33, O34, O47)

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