Sectoral Productivity: Differences Across Countries
Explore the complexities of measuring sectoral productivity and the impact on income differences across countries, with insights on tradability and various sector roles. Discusses the challenges, theories, and benefits of development accounting in 84 covered countries.
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Presentation Transcript
Tradability and sectoral productivity differences across countries Robert Inklaar, Ryan Marapin & Kaira Gr ler Groningen Growth and Development Centre, University of Groningen
Productivity drives income differences 61.6% of income variation
Sectoral perspective: many hypotheses Role of: Agriculture (e.g., Gollin et al. 2014) Manufacturing (e.g., Rodrik, 2013) Investment versus consumption sector (e.g., Hsieh & Klenow, 2007) Traditional versus non-traditional services (e.g., Duarte & Restuccia, 2020) Traded versus non-traded sector (e.g., Balassa,1964 & Samuelson, 1964)
Sectoral perspective: many hypotheses Role of: Agriculture (e.g., Gollin et al. 2014) Manufacturing (e.g., Rodrik, 2013) Investment versus consumption sector (e.g., Hsieh & Klenow, 2007) Traditional versus non-traditional services (e.g., Duarte & Restuccia, 2020) Traded versus non-traded sector (e.g., Balassa,1964 & Samuelson, 1964) Preview: Key distinction
So why is the role of sectors not resolved yet? Measuring sectoral productivity is hard Theory is not new (Caves, Christensen, Diewert, 1982) Trade-off in empirical literature: country coverage vs. measurement sophistication (e.g., Fadinger et al. 2022 for 38 countries) Development accounting benefits from broad coverage
84 countries covered in this paper GDP/worker All countries (PWT) Covered 84 Median $33 000 $44 000 p90/p10 19 15 Slope TFP 0.359 0.349
This paper 1. Develop new measures of relatives prices for sectoral (gross)output, intermediate inputs and value added 84 countries 12 sectors 2005, 2011 & 2017 2. Development accounting for 12 sectors and relevant combinations of sectors Traded vs. non-traded key distinction 3. Demonstrate effect of improved measurement Notably better employment data (see GGDC ETD, Kruse et al. 2022) See for the GGDC Productivity Level Database 2023
Measuring sectoral productivity is hard Measurement area Previous approach Industry output Final expenditure price Labor input Efficient labor allocation Production function Cobb-Douglas Capital input Efficient capital allocation
Measuring sectoral productivity is hard Measurement area Previous approach This paper Industry output Final expenditure price Sectoral value added price Labor input Efficient labor allocation Observed employment data Production function Cobb-Douglas T rnqvist Capital input Efficient capital allocation Robust findings to using observed capital data for half the sample
Measurement framework ???=??? Sectoral productivity=output/input: ,sector ?,country ? ??? ???= ???/??? Real output=Nominal output/PPP: ???? ???? 1 2 Real input=aggregate of relative factor inputs: ???/?= exp ????+ ???? log ? ??? ????? Multilateral comparisons, USA=1:
Sectoral output PPPs Final expenditure => Value added Producer price data for two sectors Agriculture (FAO) and mining (WB, CWN) Other sectors: consumption & investment prices (ICP), adjusted for distribution margins, product taxes, and terms of trade Estimate sectoral intermediate input prices using Supply-Use Tables (distinguishing domestic and imported products) => Relative prices for value added
Price variation within & between sectors Non-traded services with lowest average prices Goods sectors closer to LOP
Factor inputs Labor: employment from GGDC Economic Transformation Database Based on Census and LFS, see Kruse et al. (2022) for details Capital: Human capital: assume same level of schooling across sectors Produced capital: assume efficient capital allocation (capital stock share = capital income share) Land: arable land for agriculture; other land, assume efficient allocation (land stock share = land income share)
Factor income shares Typical approach: use US income shares, assume Cobb-Douglas production function Here: cross-country variation in production function (T rnqvist index) and estimates of country-sector income shares Country-level data on labor shares from PWT Adding-up constraints within & across sectors
No evidence for efficient labor allocation See also Gollin et al. (QJE, 2014) ? ??? ?????= ??? ??? ??????=????? ???? Plot shows median ?????? See also Philippon & Reshef (JEP, 2013)
Sectoral development accounting Does sectoral productivity systematically vary with GDP/worker? log ???? = ??+ ??+ ??log ??? + ????
What is the pattern? Group Productivity level i 0.409 [0.377 0.441] 0.380 [0.336 0.423] 0.032 [-0.016 0.079] Goods & transport Broad traded Also tradable services Narrow traded Non-traded Agriculture 0.582 [0.518 0.646] 0.255 [0.218 0.292] Non-agriculture Manufacturing 0.366 [0.305 0.426] 0.342 [0.300 0.383] Non-manufacturing Consumption 0.363 [0.322 0.403] 0.242 [0.160 0.324] Investment
What is the pattern? Group Productivity level i 0.409 [0.377 0.441] 0.380 [0.336 0.423] 0.032 [-0.016 0.079] Balassa/Samuelson & Baumol Broad traded Narrow traded Non-traded Agriculture 0.582 [0.518 0.646] 0.255 [0.218 0.292] Non-agriculture Manufacturing 0.366 [0.305 0.426] 0.342 [0.300 0.383] Non-manufacturing Consumption 0.363 [0.322 0.403] 0.242 [0.160 0.324] Investment
Extensions Difference versus earlier studies: using employment data instead of assuming efficient labor allocation Using capital stock data (available for half the sample) changes little Traded/non-traded result also in time series
Concluding remarks Sectoral development accounting: Renewed focus on Balassa/Samuelson & Baumol But what about tradability is conducive for productivity growth? (e.g., Rodrik, 2016 on manufacturing s features) Shows the development path of currently rich countries But is this path (still) open for emerging economies? => need for analysis of convergence to determine the paths that are being taken
Comments This paper made a significant progress in comparing sectoral productivity levels in a large number of countries. Additional challenges remain: PPPs for services (e.g. wholesale and retails) PPPs for non-market services (quality adjustment) Lack of PPPs for products used for intermediate inputs (chemicals)
Questions One of the main findings of the paper is that the productivity of non- traded sector (public admin, health and education) is not related to GDP per capita. To what extent is this finding driven by the current measurement practice for those sectors? Output is largely measured by inputs in the sector. The productivity is close to one. What are the next big challenges and priorities for the international productivity level/growth comparison?
