Essays on industry dynamics and misallocation
- Autores: Shangyu Liu
- Directores de la Tesis: Jaume Ventura Fontanet (dir. tes.), Manuel García-Santana (codir. tes.)
- Lectura: En la Universitat Pompeu Fabra ( España ) en 2020
- Idioma: español
- Tribunal Calificador de la Tesis: Giacomo Ponzetto (presid.), Carolina Villegas Sanchez (secret.), Raül Santaeulàlia-Llopis (voc.)
- Materias:
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- Resumen
China's considerable economic growth in the last three decades has attracted significant attentions and discussions. It is widely accepted that such miracle has been achieved through the collective effects of several factors; yet, among these factors, the improvement of resource allocation efficiency is particularly vital to understand the engeines of growth. Starting from this point, I document new facts and explore the linkages among industry dynamics, resource allocations and asset price bubbles in China. The thesis comprises two chapters. The first chapter investigates the sources of misallocation and conducts a series of quantitative exercises, while the second chapter focuses on the resource reallocation effects of asset price bubbles.
In the first chapter, I introduce uncertainty and inter-group TFP measurement to analyze China's intra-sector misallocation problem. Misallocation is usually believed to be a result of distortion, which is measured as the wedge between factor price and marginal revenue productivity across firms. The traditional framework, however, does not incorporate uncertainty and is weak in decomposition. Using China's Industrial Survey data, I construct the TFP based allocation efficiency measurement. In the counter factual analysis, my model suggests that uncertainty explains around 26% of TFP loss, ownership differences contributes 15%, and regional differences accounts for 14% to the loss. The remaining 45% of TFP loss is determined by traditional intra-group distortions. In all cases, the TFP loss induced by labor market frictions is greater than the loss caused by capital market frictions. Finally, I show that these results are robust to dynamic settings with adjustment cost and inter-temporal optimizations.
The second chapter identifies the aggregate effects of asset bubbles at the sector level and their capital reallocation channels within sectors. First, I apply the discounted dividend model to China' s stock market data to construct bubble indicators by sector. Empirically, the movement of bubble components is primarily controlled by interest rates. Next, based on the Industrial Survey and the efficiency measures from chapter 1, I create a series of variables related to industry dynamics. The results of the regression tests show that asset bubbles 1) lower allocative efficiency; 2) raise up leverage; 3) crowd out aggregate investment; 4) increase exit rates; 5) cut firms' profit; 6) reduce firms' mobility; and 7) narrow down the difference between large and small firms. These findings solve the ambiguity in theoretical models that fail to answer whether bubbles are good or bad to the economy. Further, the empirical evidence helps us link bubbles' macro impacts and their micro mechanisms at the sector level.
