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Multifactor productivity growth enhancers across industries and countries: firm-level evidence

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Abstract

Multifactor productivity (MFP) growth is an imperative economic engine. MFP dynamism across five advanced and seven developing countries from 1996 to 2015 is analyzed, elucidating its association with financing and intangible assets. Debt is manifested by its inverted U-shaped nonlinear relationship with MFP advancement, while corporate cash holdings are negatively (positively) associated with MFP development in five (three) countries. The heterogeneous relationships between intangible assets and MFP growth are identified across industries, countries, and time; intangible assets are requisite MFP growth enhancers for manufacturing in developing countries, for service businesses in advanced countries, and for the period after the global financial crisis. The greater the productivity effect of intangible assets is, the higher a country’s per-capita income and/or governance quality becomes. Additionally, the results evince the catching-up of MFP to the technological frontier. Moreover, older firms exhibit slower MFP growth than their peers, whilst the positive effects of firm size on MFP growth are larger in high-tech and knowledge-intensive industries.

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Notes

  1. There are a few studies that used Orbis data for a particular country to analyze firm-level productivity or performance (e.g., Gopinath et al., 2017; Nakatani, 2019a). For instance, Gopinath et al. (2017) used the same Orbis database to analyze manufacturing firms in Spain, although their focus was resource allocation, which differs from our focus. In our analysis, the empirical analysis is done at the firm level, so the panel regression would not be contaminated by resource allocation effects (Castiglionesi & Ornaghi, 2013).

  2. Although Şeker and Saliola (2018) estimated the MFP in developing countries using business surveys, they did not conduct an econometric analysis to study the drivers of firm-level MFP growth.

  3. Nakatani (2021a, 2023a, 2024) studied the productivity drivers of specific industries (the information and communication technology sector, the infrastructure sector, and the food sector, respectively), but the author did not study more broad categories of industries. Therefore, in this paper, we study all industries, including manufacturing, service, and the knowledge and technology intensive sectors.

  4. In economics, MFP has been traditionally treated as a residual of production function (i.e., what we call Solow residual) and a shock to the economy and financial markets (e.g., Nakatani, 2014, 2017a, 2019b).

  5. Anderton et al. (2020) found that competition-enhancing product market regulation is associated with a higher rate of firm churning, which in turn is positively related to higher MFP. See also Polemis (2020) for the relationship between product market competition and productivity.

  6. Since we do not have information about the status of entry and exit of firms in the markets, it might be possible that there is a potential sample selection bias. For example, when firms exit from the markets due to a failure of business, their data might disappear from the Orbis database. In that sense, only successful firms tend to be included in the data sample.

  7. See Schankerman (1981) for discussions on the double-counting issues (or omission bias) regarding the inclusion of R&D or intangible assets in the estimation of MFP.

  8. International Financial Reporting Standards says “The costs of generating other internally generated intangible assets are classified into whether they arise in a research phase or a development phase. Research expenditure is recognized as an expense. Development expenditure that meets specified criteria is recognized as the cost of an intangible asset. Intangible assets are measured initially at cost. After initial recognition, an entity usually measures an intangible asset at cost less accumulated amortization.”.

  9. Some empirical studies use total number of employees as a proxy for firm size. However, in the Orbis database, the data on the number of employees are missing for some countries, and therefore, we are not able to explore this method.

  10. We believe that potential omitted variable bias is not a serious problem despite the following missing factors: trade (Newman et al., 2023), foreign investment (Belderbos et al., 2021), market regulations (Anderton et al., 2020), corporate tax (Bournakis & Mallick, 2021; Liu et al., 2022), corruption (Lambsdorff, 2003), staff training (Yang et al., 2010), and managerial ownership (Palia & Lichtenberg, 1999). Unfortunately, there is no information on these factors in our database, so we cannot include them. Nevertheless, these omitted variables are controlled by the industry-specific time-varying and firm-specific fixed effects, \({\mu }_{j,t}\) and \({v}_{i}\), respectively. For instance, omitted variables common for the same industry, such as market regulations and corporate tax rates, are controlled by the industry-specific time-varying fixed effects, while others such as trade/foreign investment/training/managerial ownership are broadly captured by the firm fixed effects.

  11. For example, an adverse MFP shock can increase credit constraints of firms and banks (Nakatani, 2016, 2017b).

  12. Tajika and Nakatani (2008) provided evidence of the repatriation of royalties from foreign affiliates to parent companies of Japanese multinational corporations that share the same intellectual property.

  13. One caveat is that the negative correlation between lagged MFP level and MFP growth might just capture the mean-reversion behavior of firm-level productivity. That is, a high current productivity is followed by a lower productivity tomorrow, which could easily generate a negative correlation between lagged MFP level and future MFP growth.

  14. This resembles the inverted U-shaped relationship between public debt and fiscal balance (Nakatani, 2021b). The idea is that net benefits to debt financing arise for countries with low debt levels but decrease as leverage reaches high levels.

  15. This finding is consistent with the recent findings by Nakatani et al. (2023, 2024), who found that quality of governance, including rule of law and regulatory quality, matters for efficiency of public health and education services, which are one of the large main sectors of knowledge-intensive industries in many countries, including developing economies.

  16. Our variable focuses on the amount of cash held by a firm, and it is not the same as the one often used by pecking order models such as cashflow (i.e., net income plus depreciation) (López-Gracia & Sogorb-Mira, 2008).

  17. Almeida et al. (2015) documented how the institutional features of Chaebols helped them survive the Asian financial crisis.

  18. https://ec.europa.eu/eurostat/statistics-explained/index.php/Glossary:High-tech_classification_of_manufacturing_industries

  19. https://ec.europa.eu/eurostat/statistics-explained/index.php/Glossary:Knowledge-intensive_services_(KIS).

  20. This result is in line with findings by Bloch et al. (2023), who found that the MFP effects of broad R&D increased slightly in the period after the crisis in two Nordic countries (Denmark and Finland).

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Funding

No funding was received for conducting this study. The author is grateful for the helpful comments from two anonymous reviewers.

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  1. International Monetary Fund, Washington, DC, USA

    Ryota Nakatani

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Appendix: Technical explanation of MFP estimation and supplementary tables

Appendix: Technical explanation of MFP estimation and supplementary tables

Gandhi et al. (2020) developed a nonparametric identification method for gross output production functions when additional sources of variation in the demand for flexible inputs are unavailable. Their identification method regresses revenue shares on inputs to identify flexible input elasticity, solves the partial differential equation, and integrates this into the dynamic panel/proxy variable structure to identify the remainder of the production function. The output function for firm \(j\) in year \(t\) is

$${Y}_{jt}=F\left({k}_{jt},{l}_{jt},{m}_{jt}\right){e}^{{v}_{jt}}$$

where \({Y}_{jt}\) is the output, \({k}_{jt}\) is the log value of capital input, \({l}_{jt}\) is the log value of labor input, \({m}_{jt}\) is the log value of intermediate input, and \({v}_{jt}\) is the Hicks neutral productivity shock (\({v}_{jt}={\omega }_{jt}+{\varepsilon }_{jt}\)), which is decomposed into the Markovian component \({\omega }_{jt}\) and ex-post productivity shock \({\varepsilon }_{jt}\). The production function is differentiable for all inputs and strictly concave for intermediate inputs. The intermediate-input demand \({m}_{jt}={M}_{t}\left({k}_{jt},{l}_{jt},{m}_{jt}\right)\) is assumed to be strictly monotone in a single unobservable \({\omega }_{jt}\). Firms are price takers in the output/intermediate input markets. The authors demonstrated that the first-order condition of a firm’s problem is used to solve the demand for intermediate inputs, which can also be inverted to solve for productivity:

$${\omega }_{jt}={M}^{-1}\left({k}_{jt},{l}_{jt},{m}_{jt}\right)+{d}_{t}$$

where \({d}_{t}\equiv ln\left({\rho }_{t}/{P}_{t}\right)-ln\upvarepsilon\) is defined by the common intermediate-input price \({\rho }_{t}\) and the common output price facing all firms \({P}_{t}\). In the proxy variable framework, they note that appropriately lagged input decisions can be used as instruments. By replacing productivity in the intermediate-input demand equation, the only sources of variation left in \({m}_{jt}\) are unobservable and \({d}_{t}\). Identification of the production function by instrumental variables is based on projecting output onto the exogenous variables. They showed that the restrictions implied by the firm’s optimizing behavior, integrated with the idea of using lagged inputs as instruments employed by the dynamic panel and proxy variable literature, are sufficient to nonparametrically identify the production function and MFP, even when additional sources of exogenous variation in flexible inputs are absent. This is because input demand is implicitly defined by the production function through the firm’s first-order condition. Under these assumptions, the share regression equation nonparametrically identifies flexible input elasticity. Then, we use the information from the share regression to recover the rest of the production function nonparametrically. Combining these two steps, the estimating equation is written with a complete polynomial degree \(r\) as follows.

We estimate a gross output production function using a complete polynomial series of degree two and a polynomial of degree three for the Markovian process.

Tables 12, 13, and 14

Table 12 Correlation matrix
Full size table
Table 13 Two-year lags for intangible assets
Full size table
Table 14 Three-year lags for intangible assets
Full size table

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Nakatani, R. Multifactor productivity growth enhancers across industries and countries: firm-level evidence. Eurasian Bus Rev (2024). https://doi.org/10.1007/s40821-024-00265-8

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