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2018 | OriginalPaper | Buchkapitel

What are the Drivers of TFP Growth? An Empirical Assessment

verfasst von : Iván Kataryniuk, Jaime Martínez-Martín

Erschienen in: International Macroeconomics in the Wake of the Global Financial Crisis

Verlag: Springer International Publishing

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Abstract

This chapter builds upon the related research that grapples with determinants of TFP, as the driving force of potential growth. In particular, we empirically estimate, in a homogenous and systematic manner, cross-country contributions of cyclical and structural determinants of aggregate TFP growth. Under a growth accounting framework, we compute TFP growth estimates for 41 economies over the 1992–2014 period. After selecting its main drivers by means of a Bayesian Model Averaging (BMA) approach, we exploit panel estimates to conclude that a substantial share of the growth underperformance in recent years was related to cyclical factors, mainly the output gap, but also: (i) over-indebtedness for advanced economies; and (ii) the decline in commodity prices for commodity exporters. In addition, the growth of IT capital and the convergence towards the technological frontier appear to be significant structural drivers of TFP productivity growth in emerging market economies.

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Fußnoten
1
See Basu and Fernald (2009).
 
2
Bear in mind that one caveat of our empirical strategy has to do with sectoral data constraints. We are not able to construct TFP series at a sectoral level but at an aggregate, country-specific level, observing the output of resource reallocation (if any).
 
3
The emerging countries included are ARG, BOL, BRA, CHL, CHN, COL, CZE, ECU, HUN, IDN, IND, KOR, MEX, PER, POL, ZAF, TUR, and URY. In addition, advanced economies under consideration are: AUS, AUT, BEL, CAN, DNK, FIN, FRA, GER, GRE, IRL, ISR, ITA, JPN, NDL, NZL, NOR, PRT, RUS, ESP, SWE, CHE, GBR, and the USA.
 
4
Both variables are computed by applying a two-sided HP filter with a λ parameter of 100 over the annual GDP, in the case of the output gap; and over credit-to-GDP, in the case of the credit gap. In this chapter, we do not attempt to find a causal relationship between the different cyclical variables and TFP growth. For an extended view in this matter, which adds an identification strategy based on the theoretical DSGE framework of Ferraro and Peretto (2017), please refer to Kataryniuk and Martínez-Martín (2017).
 
5
The Economic Complexity Index (ECI) is a holistic measure of the production characteristics of a country. The goal of this index is to explain an economic system as a whole rather than the sum of its parts. The ECI aims to explain the knowledge accumulated in a country’s population and that is expressed in the country’s industrial composition. It combines metrics of the diversity of countries and the ubiquity of products to create measures of the relative complexity of a country’s exports. For further details, see Hidalgo and Hausmann (2009).
 
6
For an overview of model averaging in economics, see Moral-Benito (2015).
 
7
Four different price indices have been employed based on IMF Global Commodities Watch. Each product has been allocated to every single price category: [1] PFANDB: index of food and beverages (base 2005). It includes cereals, vegetables, fruits, oils, meat, sea products, sugar, coffee, tea and cacao. [2] PRAWM: index of raw agricultural materials (base 2005). Includes wood, cotton, wool, rubber and leather. [3] PMETA: metals index (base 2005). Includes copper, aluminium, iron, tin, nickel, zinc, lead and uranium. [4] PNRG: energy index (base 2005). It includes prices for petroleum, natural gas and coal. Consistent data on both prices and export shares are available from 1992 onwards.
 
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Metadaten
Titel
What are the Drivers of TFP Growth? An Empirical Assessment
verfasst von
Iván Kataryniuk
Jaime Martínez-Martín
Copyright-Jahr
2018
DOI
https://doi.org/10.1007/978-3-319-79075-6_4