Firm productivity, heterogeneity and macroeconomic dynamics: a data-driven investigation

被引:1
|
作者
Constantinescu, Mihnea [1 ]
Proskute, Aurelija [2 ]
机构
[1] PrepayWay AG, Haldenstr 5, CH-6340 Baar, Switzerland
[2] Bank Lithuania, Econ Dept, Vilnius, Lithuania
关键词
Productivity; firm dynamism; job creation and destruction; firm heterogeneity; Lithuanian economy; CROSS-COUNTRY DIFFERENCES; SIZE; EXIT; ENTRY; AGE; REALLOCATION; EVOLUTION; MARGINS; EXPORT; TRADE;
D O I
10.1080/1406099X.2019.1633897
中图分类号
F [经济];
学科分类号
02 ;
摘要
In this paper, we offer a unique firm-level view of the empirical regularities underlying the evolution of the Lithuanian economy over the period of 2000-2014. Employing a novel dataset, we investigate key distributional moments of real and financial variables of Lithuanian firms. We focus in particular on the issues related to productivity, firm birth and death and the associated employment creation and destruction across firm sizes, industry classification and trade status (exporting vs. non-exporting). We refrain from any structural modelling attempt in order to map out the key economic processes across industries and selected firm characteristics. Nevertheless, existing theoretical and empirical findings guide our analysis and the selection of the main variables to investigate. We uncover similar regularities as already highlighted in the literature: trade participation and firm productivity are strongly positively linked, the 2008 recession has had a cleansing effect on the non-tradable sector, firm birth (death) is highly pro(counter)-cyclical. The richness of the dataset allows us to produce additional insights: for example, we observe an increasing share of exporting but a constant share of importing firms since 2000.
引用
收藏
页码:216 / 247
页数:32
相关论文
共 50 条
  • [41] Heterogeneity of the GFP fitness landscape and data-driven protein design
    Somermeyer, Gonzalez Louisa
    Fleiss, Aubin
    Mishin, Alexander S.
    Bozhanova, Nina G.
    Igolkina, Anna A.
    Meiler, Jens
    Alaball Pujol, Maria-Elisenda
    Putintseva, Ekaterina, V
    Sarkisyan, Karen S.
    Kondrashov, Fyodor A.
    ELIFE, 2022, 11
  • [42] Integrating Macroeconomic and Technical Indicators into Forecasting the Stock Market: A Data-Driven Approach
    Latif, Saima
    Aslam, Faheem
    Ferreira, Paulo
    Iqbal, Sohail
    ECONOMIES, 2025, 13 (01)
  • [43] An investigation on the coupling of data-driven computing and model-driven computing
    Yang, Jie
    Huang, Wei
    Huang, Qun
    Hu, Heng
    COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING, 2022, 393
  • [44] FLEXIBILITY AND PRODUCTIVITY: TOWARD THE UNDERSTANDING OF FIRM HETEROGENEITY
    Macedoni, Luca
    Xu, Mingzhi
    INTERNATIONAL ECONOMIC REVIEW, 2022, 63 (03) : 1055 - 1108
  • [45] Management, productivity and firm heterogeneity in international trade
    Serrano, Javier
    Myro, Rafael
    APPLIED ECONOMIC ANALYSIS, 2020, 28 (82): : 1 - 18
  • [46] Macroeconomic labour productivity and its impact on firm's profitability
    Choi, K.
    Haque, M.
    Lee, H. W.
    Cho, Y. K.
    Kwak, Y. H.
    JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY, 2013, 64 (08) : 1258 - 1268
  • [47] Data-driven respiratory gating for the uEXPLORER with fast dynamics
    Feng, Tao
    Yang, Gang
    Li, Hongdi
    Shi, Hongcheng
    Cherry, Simon
    Badawi, Ramsey
    Dong, Yun
    JOURNAL OF NUCLEAR MEDICINE, 2020, 61
  • [48] Data-driven learning and control of nonlinear system dynamics
    Becerra-Mora, Yeyson A.
    Acosta, Jose angel
    NONLINEAR DYNAMICS, 2024,
  • [49] Understanding Business Ecosystem Dynamics: A Data-Driven Approach
    Basole, Rahul C.
    Russell, Martha G.
    Huhtamaki, Jukka
    Rubens, Neil
    Still, Kaisa
    Park, Hyunwoo
    ACM TRANSACTIONS ON MANAGEMENT INFORMATION SYSTEMS, 2015, 6 (02)
  • [50] Data-driven discovery of emergent behaviors in collective dynamics
    Zhong, Ming
    Miller, Jason
    Maggioni, Mauro
    PHYSICA D-NONLINEAR PHENOMENA, 2020, 411