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Wages and productivity in Argentinian manufacturing. A structuralist and distributional firm-level analysis

María Gómez and Maria Enrica Virgillito

LEM Papers Series from Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy

Abstract: Wages and productivity represent two of the most relevant variables to consider in economic development. Given the low productivity levels that emerging countries reveal, the accumulation of productive capabilities and a narrower dispersion across sectors would enable emerging countries to overcome the middle-income trap. Yet, this positive trend in productivity should translate into higher wages. Thus, we pose the following questions applied to a middle-income trapped country: is there a link between labour productivity and wages in the Argentine manufacturing sector? Does it differ across techno-productive classes or wage levels? Which factors affect this nexus, considering premature deindustrialisation? Using a firm-level dataset from 2010 to 2016, we perform quantile regression estimates to evaluate the link between productivity and wages across the conditional wage distribution among manufacturing firms. Based on a structural analysis, we identify the differences in these elasticities at 2-ISIC code levels and across Pavitt taxonomies. Our results confirm a positive, but extremely low, pass-through between productivity and wages in the Argentinian manufacturing firms, different across sectors according to their techno-productive capabilities, robust under different empirical strategies.

Keywords: Gains from productivity; Development; Asymmetries. (search for similar items in EconPapers)
Date: 2022-12-11
New Economics Papers: this item is included in nep-eff, nep-sea and nep-tid
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Related works:
Working Paper: Wages and Productivity in Argentinian Manufacturing. A Structuralist and Distributional firm-level Analysis (2023) Downloads
Working Paper: Wages and productivity in Argentinian manufacturing: A structuralist and distributional firm-level analysis (2023) Downloads
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