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Algorithms for Labour Income Share Forecasting: Detecting of Intersectoral Nonlinearity

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Algorithms and Solutions Based on Computer Technology

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 387))

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Abstract

We examine different algorithms to forecast labour share for 18 KLEMS-classified economic sectors, 12 European countries. The choice is driven by data availability. For each sector 11 specifications of time component in CES production function with factor-augmenting technical change are tested. This includes comparing models with linear, nonlinear time and the same with structural breaks. Then, three degrees of models ‘power’ are proposed to characterize whether a model is consistent and valid for prediction. Here, residuals stationarity and autocorrelation as well as regressors and structural breaks statistical significance are investigated. To sum up main results, models with structural break in nonlinear time component show better predictive power according to the derived criteria. Next, overall labour share decline cannot be stated as only 7 sectors out of 18 have decreasing trend in more than one third of cases (countries). Additionally, each country sectors are grouped by LS forecast average value into four interval categories.

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Notes

  1. 1.

    Labour Share or Labour Income Share is referred to as LS hereafter.

  2. 2.

    The contribution of the factor in total LS decline (since 1998) is indicated in parentheses [2, p. 2].

    “Supercycle and boom-bust effects” (33%).

    “Rising and faster depreciation due to higher capital stocks and a shift to intangible assets with shorter life cycles” (26%).

    “Superstar effects—which see a small proportion of large firms capturing a disproportionately larger share of economic profit than their peers” (18%).

    “Capital substitution of labor and automation” (12%).

    “Globalization and decreased labor bargaining power” (11%).

  3. 3.

    The authors exclude self-employed and residential activities from their analysis, which is based on historical data only. France, Italy, Germany and the UK are referred to as “four major European economies” [7, p. 9].

  4. 4.

    “Let the data speak for themselves”.

  5. 5.

    Biased Technical Change is referred to as technical progress bias to highly qualified labor. Education here is complementary to new technologies [19]

  6. 6.

    RSS—Residual Sum of Squares.

  7. 7.

    This was done by searching for the local minimum in RSS vector of the models estimated on \(\lambda\) interval.

  8. 8.

    ADF with trend, AFD with drift, ADF without trend and drift (moving from relatively more strictly stationarity conditions weaker ones respectively)—see [22].

  9. 9.

    Durbin-Watson and Breusch-Godfrey tests may further be referred to as DW and BG tests respectively.

  10. 10.

    \(wL = COMP \times \frac{GVA\_2010}{{GVA}}\), where GVA is in current prices.

  11. 11.

    \(r = \frac{GVA\_2010 - COMP\_2010}{{Kq\_GFCF}}\).

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Author information

Authors and Affiliations

  1. National Research University Higher School of Economics, Kantemirovskaya 3, St. Petersburg, Russia

    Stanislav Rogachev

  2. International University of Kyrgyzstan, Bishkek, Kyrgyzstan

    Bakytbek Akaev

Authors
  1. Stanislav Rogachev
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  2. Bakytbek Akaev
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Corresponding author

Correspondence to Stanislav Rogachev .

Editor information

Editors and Affiliations

  1. Institute of Maritime Logistics, Hamburg University of Technology, Hamburg, Germany

    Carlos Jahn

  2. Rektor Wildau, Deutsch-Kasachische Universität, Wildau, Brandenburg, Germany

    László Ungvári

  3. Graduate School of Business and Management, Peter the Great Saint Petersburg Polytechnic University, St. Petersburg, Russia

    Igor Ilin

Appendices

Appendix 1. Estimation Example

See (Tables 6, 7 and 8).

Table 6 Regression (8) estimation—UK, \({\varvec{\varphi }}\left( t \right) = \frac{1}{\beta_t }\left( {\beta_{t_b } e^{\lambda_b t_b } + \beta_{t_a } e^{\lambda_a t_a } } \right)\)
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Table 7 Residuals stationarity and autocorrelation for regression (8)
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Table 8 Critical values for respective ADF-test
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Appendix 2. Intersectoral LS Forecasts

See (Figs. 5 and 6).

Fig. 5
figure 5

Intersectoral labour share forecasts, averaged by countries with upward, constant, and downward labour share trend (sectors A-I)

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Fig. 6
figure 6

Intersectoral labour share forecasts, averaged by countries with upward, constant,

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Appendix 3. Aggregate LS forecasts

See (Fig. 7).

Fig. 7
figure 7

Aggregate labour share forecasts convoluted from intersectoral forecasts for Czech Republic, Denmark, France, and Italy

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Rogachev, S., Akaev, B. (2022). Algorithms for Labour Income Share Forecasting: Detecting of Intersectoral Nonlinearity. In: Jahn, C., Ungvári, L., Ilin, I. (eds) Algorithms and Solutions Based on Computer Technology. Lecture Notes in Networks and Systems, vol 387. Springer, Cham. https://doi.org/10.1007/978-3-030-93872-7_14

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