nep-gro New Economics Papers
on Economic Growth
Issue of 2021‒12‒20
five papers chosen by
Marc Klemp
University of Copenhagen

  1. Inequality, living standards, and growth: two centuries of economic development in Mexico† By Bleynat, Ingrid; Challú, Amílcar E.; Segal, Paul
  2. Industrialization in developing countries: is it related to poverty reduction? By Abdul A. Erumban; Gaaitzen de Vries
  3. Globalization and the Ladder of Development: Pushed to the Top or Held at the Bottom? By David Atkin; Arnaud Costinot; Masao Fukui
  4. Deindustrialization and Industry Polarization By Michael Sposi; Kei-Mu Yi; Jing Zhang
  5. The Fallacy in Productivity Decomposition By Simon Bruhn; Thomas Grebel; Lionel Nesta

  1. By: Bleynat, Ingrid; Challú, Amílcar E.; Segal, Paul
    Abstract: Historical wage and income data provide both normative measures of living standards, and indicators of patterns of economic development. This study shows that, given limited historical data, median incomes are most appropriate for measuring welfare and inequality, while urban unskilled wages can be used to test dualist models of development. We present new estimates of these series for Mexico from 1800 to 2015 and find that both have historically failed to keep up with aggregate growth: GDP per worker is now over eight times higher than in the nineteenth century, while unskilled urban real wages are only 2.2 times higher, and national median incomes only 2.0 times higher. From the perspective of inequality and social welfare, our findings confirm that there is no automatic positive relationship between economic growth and rising living standards for the majority. From the perspective of development, we argue that these findings are explained by a dual economy model incorporating Lewis's assumption of a reserve army of labour, and we explain why the decline in inequality predicted by Kuznets has not occurred.
    JEL: N36
    Date: 2021–08–01
  2. By: Abdul A. Erumban; Gaaitzen de Vries
    Abstract: This paper proposes an empirical framework that relates poverty reduction to production growth. We use the GGDC/UNU-WIDER Economic Transformation Database to measure the contribution to growth of productivity improvements within sectors and structural change—the reallocation of workers across sectors—for 42 developing countries from 1990 to 2018. Next, the contributions are used in a regression analysis, which indicates that poverty reduction is significantly related to structural change and productivity growth in manufacturing.
    Keywords: Poverty, Production, Growth, Manufacturing, Structural change, Developing countries
    Date: 2021
  3. By: David Atkin; Arnaud Costinot; Masao Fukui
    Abstract: We study the relationship between international trade and development in a model where countries differ in their capability, goods differ in their complexity, and capability growth is a function of a country’s pattern of specialization. Theoretically, we show that it is possible for international trade to increase capability growth in all countries and, in turn, to push all countries up the development ladder. This occurs because: (i) the average complexity of a country’s industry mix raises its capability growth, and (ii) foreign competition is tougher in less complex sectors for all countries. Empirically, we provide causal evidence consistent with (i) using the entry of countries into the World Trade Organization as an instrumental variable for other countries’ patterns of specialization. The opposite of (ii), however, appears to hold in the data. Through the lens of our model, these two empirical observations imply dynamic welfare losses from trade that are small for the median country, but pervasive and large among a number of African countries.
    JEL: F1 O1 O4
    Date: 2021–11
  4. By: Michael Sposi; Kei-Mu Yi; Jing Zhang
    Abstract: We add to recent evidence on deindustrialization and document a new pattern: increasing industry polarization over time. We assess whether these patterns can be explained by a dynamic open economy model of structural change in which the two primary driving forces are sector-biased productivity growth and sectoral trade integration. We calibrate the model to the same countries used to document our patterns. We find that sector-biased productivity growth is important for deindustrialization, and sectoral trade integration is important for industry polarization through specialization. The interaction of these two driving forces is also essential. The key transmission channel is the declining relative price of manufacturing goods to services over time.
    JEL: F11 F43 O11 O41
    Date: 2021–11
  5. By: Simon Bruhn (Ilmenau University of Technology, Ilmenau, Germany); Thomas Grebel (Ilmenau University of Technology, Ilmenau, Germany); Lionel Nesta (Université Côte d'Azur, France; GREDEG CNRS; OFCE, SciencesPo; SKEMA Business School)
    Abstract: This paper argues that the typical practice of performing growth decompositions based on log-transformed productivity values induces fallacious conclusions: using logs may lead to an inaccurate aggregate growth rate, an inaccurate description of the microsources of aggregate growth, or both. We identify the mathematical sources of this log-induced fallacy in decomposition and analytically demonstrate the questionable reliability of log results. Using firm-level data from the French manufacturing sector during the 2009-2018 period, we empirically show that the magnitude of the log-induced distortions is substantial. Depending on the definition of accurate log measures, we find that around 60-80% of four-digit industry results are prone to mismeasurement. We further find significant correlations of this mismeasurement with commonly deployed industry characteristics, indicating, among other things, that less competitive industries are more prone to log distortions. Evidently, these correlations also affect the validity of studies that investigate the role of industry characteristics in productivity growth.
    Keywords: productivity decomposition, growth, log approximation, geometric mean, arithmetic mean
    JEL: C18 L22 L25 O47
    Date: 2021–12

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