Three Essays on Resource Allocation in China

(1)

Thesis

Reference

Three Essays on Resource Allocation in China

OUYANG, Difei

Abstract

Over the past two decades, China experienced a gradual transformation from a central planning economy to a market-oriented economy and cultivated the largest manufacturing sector in the world, which involves a drastic reallocation of labor and capital across firms and across industries. We provide three chapters to study the resource reallocation within the Chinese economy from the late 1990s. In Chapter 1, we study what determines overall sizable misallocation within the Chinese manufacturing sector and explores its evolution. In Chapter 2, we study how the ownership reform of Chinese state-owned banks affects their credit allocation to nonfinancial firms. In Chapter 3, we study how real estate booms could crowd financial resources out of the manufacturing sector and hurt manufacturing firms.

OUYANG, Difei. Three Essays on Resource Allocation in China. Thèse de doctorat : Univ.

Genève, 2021, no. GSEM 97

URN : urn:nbn:ch:unige-1523539

DOI : 10.13097/archive-ouverte/unige:152353

Available at:

http://archive-ouverte.unige.ch/unige:152353

Disclaimer: layout of this document may differ from the published version.

1 / 1

(2)

THREE ESSAYS ON RESOURCE ALLOCATION IN CHINA

(Trois essais sur l’allocation des ressources en Chine)

THESIS

submitted to the

Geneva School of Economics and Management, University of Geneva, Switzerland,

by

Difei OUYANG

Under the direction of Prof. Harald HAU, supervisor

in fulfillment of the requirements for the degree of

Docteur ès économie et management mention economics

Jury members:

Prof. Marcelo OLARREAGA, Chair, University of Geneva Prof. Harald HAU, supervisor, University of Geneva

Prof. Ugo PANIZZA, Institut des Hautes Etudes Internationales et du Développement (IHEID)

Thesis no 97 Geneva, June 2021

(3)

La Faculté d’économie et de management, sur préavis du jury, a autorisé l’impression de la présente thèse, sans entendre, par-là, émettre aucune opinion sur les propositions qui s’y trouvent énoncées et qui n’engagent que la responsabilité de leur auteur.

Geneva, le 9 juin 2021

Dean

Marcelo OLARREAGA

Impression d’après le manuscrit de l’auteur

(4)

Acknowledgements

First and foremost, I would like to thank professor Harald Hau, my advisor and also my co-author, for guiding and supporting me throughout my master’s and Ph.D. career in Geneva in the past seven years. You are open to a broad range of research topics and always offer me inspiring ideas and suggestions. I am glad to have the chance to cooperate with you in studying Chinese real estate booms, you have set an example of excellence as a researcher, mentor, instructor, and role model in this process. You always recommend excellent books and research papers to me, which contribute significantly to my academic thinking from different disciplines. Moreover, I benefit a lot from talking with you about politics, history, arts, and literature during our lunch and coffee time. I am glad to be one of your Ph.D. students, and I hope we can keep in touch after I go back to China.

Second, I want to thank my thesis committee members for all of your guidance through this process: Prof. Marcelo Olarreaga and Prof. Ugo Panizza. Thanks for taking time to read my long thesis with lengthy tables and lengthy appendices. Your discussion, ideas, and feedback have been absolutely invaluable.

Third, I thank my colleagues at the Institute of Economics and Econometrics and Geneva Finance Research Institute. Especially, I learned a lot about how to do a good research from Prof. Marcelo Olarreaga, Prof. Frédéric Robert-Nicoud, Prof. Giacomo De Giorgi, Prof. Jérémy Lucchetti, Prof. Michele Pellizzari, and Prof. Tony Berrada, which is crucially important for my long-term academic career. I also thank my co-authors, Weidi Yuan, and Zhicheng Zhang. We not only spend many hours discussing research ideas together but also share and support each other’s lives when we all study abroad.

Without your help and inspiration, there would be no my dissertation.

Fourth, I am deeply thankful for the immeasurable support and unconditional love of my family. My major was accounting in college and when I decided to change my major to economics for master degree, you supported me without hesitation.

Fifth, I would like to thank Prof. Binkai Chen at the Central University of Finance and Economics. My major was accounting when I was in college, you taught me Macroe- conomics and especially focused on Chinese economy. I gradually be interested in Eco- nomics, and you were always glad to discuss with me, we finally had two publications on top Chinese journals. Without your enlightenment, probably I will not be on this path.

Last but not least, I want to share this important moment with my girlfriend, Siwei Yu. Thank you for your accompany in Geneva for the past six years. Without your love, support and constant encouragement, I certainly will not make it here today.

(5)

(6)

Abstract

Over the past two decades, China experienced a gradual transformation from a central planning economy to a market-oriented economy and cultivated the largest manufacturing sector in the world, which involves a drastic reallocation of labor and capital across firms and across industries. We provide three chapters to study the resource reallocation within the Chinese economy from the late 1990s. In Chapter 1, we study what determines overall sizable misallocation within the Chinese manufacturing sector and explores its evolution.

In Chapter 2, we study how the ownership reform of Chinese state-owned banks affects their credit allocation to nonfinancial firms. In Chapter 3, we study how real estate booms could crowd financial resources out of the manufacturing sector and hurt manufacturing firms.

(7)

(8)

R´ esum´ e

Au cours des deux dernières décennies, la Chine a connu une transformation progressive d’une économie de planification centrale à une économie de marché et a cultivé le plus grand secteur manufacturier du monde, ce qui implique une redistribution drastique de la main-d’œuvre et du capital entre les entreprises et entre les industries. Nous proposons trois chapitres pour étudier la réallocation des ressources au sein de l’économie chinoise

à partir de la fin des années 1990. Dans le chapitre 1, nous étudions ce qui détermine une mauvaise allocation globale importante au sein du secteur manufacturier chinois et explorons son évolution. Dans le chapitre 2, nous étudions comment la réforme de la propriété des banques publiques chinoises affecte leur allocation de crédit aux entreprises non financières. Au chapitre 3, nous étudions comment les booms immobiliers pourraient

évincer les ressources financières du secteur manufacturier et nuire aux entreprises man- ufacturières.

(9)

(10)

C.1 Additional tables and figures . . . 131 C.2 Supplementary materials . . . 148 C.2.1 Model Generalization to Price Elastic Factor Supplies . . . 148 C.2.2 Persistence of Corporate Loans Rate Differences Across Cities . . . 149 C.2.3 Sample Construction . . . 150 C.2.4 Real Investment and Capital Stock Calculation . . . 150

(13)

(14)

To my family.

(15)

(16)

Introduction

China experienced a gradual transformation from a central planning economy to a market- oriented economy and has achieved fast economic growth over the past decades. Due to the legacy of the central planning state, there exists sizable misallocation of labor and capital and there was a drastic resource reallocation during this period. In this dissertation, I study various events that cause resource reallocation across firms and industries and thus affect China’s economic development from the reallocation channel.

My thesis consists of three individual essays on resource reallocation in China. In the first essay (Chapter 1), we try to provide a comprehensive picture of the factor misallocation within the Chinese manufacturing sector between 1998-2007, an important transition period. First, we show that high productivity firms face greater factor distortions which cause them inefficiently small. This so-called productivity-correlated factor distortion explains most of the labor and capital misallocation. Meanwhile, we find that the productivity-correlated distortion is especially prominent in regions with worse economic institutions and more government interference. During 1998-2007, the improved aggregate allocative efficiency is driven by a convergence pattern that initially more distorted industries experienced a larger decline in misallocation. The privatization reform of the state sector started in the late 1990s contributes to this convergence pattern whereas the WTO accession in the early 2000s only has a small effect.

The main contribution of Chapter 1 is that we quantify the importance of productivity- correlated distortion in explaining overall misallocation in China. The traditional focus in the Chinese context is the state-ownership associated policy distortions, yet our analysis shows that ownership explains little of the productivity-correlated distortion and overall misallocation. Moreover, we show that economic institutions are important in generating productivity-correlated distortions, which contributes to understanding why this type of distortion is more pronounced in poor and developing countries than in developed countries. Last but not least, this chapter identifies the key dynamic pattern of improved aggregate efficiency and how the most important two economic reforms-state sector privatization and entering WTO can explain this pattern.

In Chapter 2, my coauthors Weidi Yuan, Zhicheng Zhang, and I study the impact of bank ownership reform (partial privatization) on China’s three largest state-owned banks (SOBs) in 2004. We find that the ownership reform reduces SOBs’ lending to firms with a larger amount of debts, firms with lower productivity, and firms that are state-owned.

These results imply that ownership reform causes SOBS to value credit risks more in the lending process. Moreover, the credit cut only hurts the growth of low productivity firms and SOEs but not firms with a larger amount of debts. The main contribution of Chapter 2 is that we complete our understanding of how ownership change affects SOBs’ performance and benefits the aggregate economy through credit allocation. Most of the existing research finds relatively consistent results that ownership change improves SOBs’ performance in terms of profitability and efficiency, yet they can hardly uncover the

(17)

2

underlying mechanism. Our study relates firm-level information and regional variation in exposure to SOBs’ reform and provides direct evidence that ownership change will change SOBs’ lending behaviors.

In Chapter 3, my author Harald Hau and I study the impact of real estate booms in the early 2000s on Chinese manufacturing firms. In geographically segmented credit markets, local real estate booms can cause investment substitution: driving resources from the manufacturing sector to the real estate sector. We find that real estate booms push up capital cost, reduce lending to manufacturing firms, and further cause the decline in their investment, output, profitability, and productivity. These effects are more pronounced in financially constrained small firms and private firms, and firms in more bank-dependent regions. The main contribution of Chapter 3 is that we provide evidence on how asset price inflation can generate large harmful effects on firm investment and growth through credit supply channels and demonstrate how asset price inflation can hurt aggregate industry competitiveness.

(18)

Chapter 1 Factor Distortions and Resource Misallocation Across Chinese

Manufacturing Firms ¹

Abstract

This paper quantifies the degree of labor and capital misallocation across the productivity spectrum of Chinese manufacturing firms and confirms the excessive allocation of resources to low-productivity firms. This misallocation is only partially proxied by private versus state ownership status. The degree of resource misallocation across the productivity spectrum features pronounced heterogeneity across provinces with a strong legacy component — suggesting incomplete economic reforms to remove policy distortions. The improvement in the aggregate resource allocative efficiency during the period 1998–2007 is largely driven by improved factor allocation within the most distorted industries. A declining output share for the state sector positively contributes to efficiency improvement, whereas international trade does not appear to foster more efficiency.

Key words: Resource misallocation, policy distortions, TFP JEL codes: D24, O11, O47

1.1 Introduction

New research relates the allocation of resources across firms of different productivity to aggregate productivity and per capita incomes. An influential paper by Hsieh and Klenow (2009) finds that the manufacturing sector’s total-factor-productivity (TFP) would be 30-50 percent higher if China had achieved the same level of efficiency in its labor and capital allocation across firms as the United States. This large gap is often attributed to widespread policy distortions that cause excessive allocation of resources to low productivity establishments. Yet the precise structure of factor misallocation in China is

1I am grateful to Harald Hau, Giacomo De Giorgi, Fr´ed´eric Robert-Nicoud, Marcelo Olarreaga, Michele Pellizzari, and Aleksey Tetenov. I also thank participants at the Swiss Meeting of Young Economists and seminar participants at the University of Geneva and Central University of Finance and Economics.

(19)

4 Chapter 1. Factor Distortions and Resource Misallocation Across Chinese Manufacturing Firms still an open research question. What are the important determinants of such factor misallocation? It is hard to evaluate Chinese policy reform without a clear quantitative understanding of the key dimensions of allocative inefficiency in China.

The first contribution of this paper is to explore the role of factor misallocation along the productivity spectrum as the key feature of China’s aggregate productivity shortfall.

We show that there exist productivity-correlated distortions, which cause high productivity firms generally face a relative factor scarcity in both labor and capital, whereas low productivity firms tend to use both factors abundantly. This pattern is widespread within narrowly-defined industries in China and we call it the depressed productivity- factor nexus (DPFN). The productivity-correlated distortions account for roughly 78%

of the aggregate TFP shortfall of China’s manufacturing sector and is therefore its most eminent feature. By contrast, in an economy with relatively efficient factor allocation, the relationship between factor use and firm productivity tends to be more positive, i.e.

more capital and labor are used by the most productive firms within industries.

Much previous research has focused on private versus state ownership as a proxy for differential firm productivity and factor misallocation. Indeed, state-owned-enterprises (SOEs) shown to have the lowest marginal productivity of capital, which is consistent with the common observation that SOEs can acquire cheap credits from national bank- ing systems and thus are less subject to capital distortions. Yet, we show that firm ownership status-associated policy distortions are only a very imperfect proxy of factor misallocation— accounting for no more than 6% of the aggregate TFP shortfall. Mean- while, though SOEs are on average less productive, the ownership difference has only a small explanatory power on the productivity-correlated distortions and the DPFN. A key policy insight is that narrow policy measures around firm ownership fall short of addressing the wider issues of economic inefficiency.

The second contribution of the paper is to relate productivity-correlated distortions and the DPFN to China’s economic history. The productivity-correlated distortion features large regional heterogeneity, which points to the incompleteness of China’s economic reforms and the persistent role of economic legacies. We measure economic legacy issues at the province level in two different dimensions: (i) employment in heavy industries established between 1964–1978 (Third Front Construction movement) in which the central government relocated firms to inland regions due to the potential conflicts with the So- viet Union and the United States; (ii) share of city bank branches to total bank branches in 1998. While the first measure is a negative legacy factor as regions with more heavy industries established in the central planning period should have greater government interference on economic activities, the second measure is a positive legacy factor as city bank density in early periods is related to financial development in pre-revolutionary China.

The second contribution of the paper is to relate productivity-correlated distortions and the DPFN to China’s economic history. The productivity-correlated distortion features large regional heterogeneity, which points to the incompleteness of China’s economic reforms and the persistent role of economic legacies. We measure economic legacy issues at the province level in two different dimensions: (i) employment in heavy industries established between 1964–1978 (Third Front Construction movement) in which the central government relocated firms to inland regions due to the potential conflicts with the So- viet Union and the United States; (ii) share of city bank branches to total bank branches in 1998. While the first measure is a negative legacy factor as regions with more heavy industries established in the central planning period should have greater government interference on economic activities, the second measure is a positive legacy factor as city bank

(20)

1.1. Introduction 5 density in early periods is related to financial development in pre-revolutionary China.

We find that the productivity-correlated distortions and DPFN is more pronounced in provinces which are marked strongly by the negative legacy factor and only weakly by the positive legacy factor. For example, the productivity elasticity of capital distortion would decrease from 0.68 to 0.25 in provinces with no city banks to provinces fully dominated by city banks, and productivity elasticity of capital would increase from 0.05 to 1.18 in parallel. Specifically, this pattern is weaker on young firms with age below 5 years old, which is in line with the intuition that economic legacies shape the DPFN gradually.

Moreover, we provide anecdotal evidence that high productivity firms are more negatively affected by government interventions proxied by fewer subsidies and worse credit access in provinces with stronger negative legacy factors and weaker positive legacy factors. These results imply that economic institutions can cause productivity-correlated distortion and thus provide suggestive evidence on why the productivity-factor use relationship can vary greatly across countries.

The third contribution of the paper is to explore the drivers of improved aggregate factor efficiency during the period of 1998–2007. This analysis takes firm-level changes in productivity as given and demonstrates that the reduced misallocation within initially more distorted industries dominates the aggregate change. For comparison, the decline in factor misallocation is 30 and 12 percentage points for industries with initial misallocation levels above and below the sample median, respectively. Consistently, the DPFN improves more within initially more distorted industries especially in terms of the capital use-productivity relationship. This pattern is attributed to the change in within-region distortions rather than between-region distortions, implying that the underlying forces are industry-specific shocks rather than relaxed restrictions on inter-region factor flows.

We further specifically explore the effect of SOEs (privatization) reform starting from the late 1990s and the WTO accession in 2002 on misallocation as both affect the Chinese economy greatly in recent decades and can generate differential responses at the industry level. We find that the declining output share of SOEs can partially explain the aggregate pattern, whereas the latter one does not appear to be an important driver.

This paper mainly uses the static framework by Hsieh and Klenow (2009) that infers firm-level distortions using marginal revenue product of labor and capital. When firms face distortions that increase input prices relatively, they have higher marginal productivity and thus revenue productivity (TFPR). The key result of Hsieh and Klenow (2009)’s framework is that the aggregate TFP shortfall due to misallocation is proportional to the within-industry dispersion in the logarithm of TFPR. ² While the reduced-form approach shows that high productivity firms face stronger factor distortions and are rendered smaller than their optimal size, this also enables us to quantifies the importance of productivity- correlated distortions on explaining aggregate misallocation.³ However, other factors such as adjustment costs and uncertainty in a dynamic framework can also generate dispersion in revenue productivity and may affect its joint-distribution with firm productivity. As a robustness check, we implement the structural quantification method proposed by David and Venkateswaran (2019) and confirm that productivity-correlated distortions indeed are the major source of both labor and capital misallocation.

2Revenue productivity (TFPR) is the product of firm productivity and a firm’s idiosyncratic output price, which in essence is the geometric average of marginal productivity of labor and marginal productivity of capital.

3Firm productivity in the theoretical framework is defined as the combination of physical productivity and idiosyncratic demand, which abstracts the individual price effect.

(21)

6 Chapter 1. Factor Distortions and Resource Misallocation Across Chinese Manufacturing Firms The counterfactual experiments also confirm the importance of distortion-productivity nexus as an alternative method. Unlike the focus on the dispersion in (log) TFPR, this method relaxes the assumption about the joint distribution of firm productivity and TFPR. For example, if we eliminate factor distortions within two productivity quantiles:

below and above the sample median, the resulting counterfactual TFP growth is only 81% of the benchmark level with all distortions removed. The large remaining gap of 19% can thus be attributed to the difference in average factor distortions between two productivity quantiles. This gap increases with the number of productivity quantiles applied (e.g. 33% using productivity quartiles), as it captures more of the difference in distortions across the productivity spectrum. Additionally, this method shows that the positive distortion-productivity nexus is more important for labor misallocation, which is in line with the reduced-form results that firm productivity has a larger explanation power on within-industry dispersion of (log) marginal revenue product of labor (MRPL) than of capital (MRPK).

The evolution of quantified factor misallocation allows us to relate improved aggregate efficiency with SOEs reform. The decline of the state sector is a key feature that characterizes the Chinese economy in the transition period, yet estimating its effect on misallocation using the OLS method could be confounded by other contemporaneous policies or measurement errors. We thus construct an instrumental variable for the within-industry change in SOEs output share using the initial spatial distribution of SOEs and change in SOEs output share of other manufacturing industries within each city following the barkit method. This instrument satisfies the exclusion restriction as local governments’

strategies in privatizing or closing local SOEs in other industries should be orthogonal to industry-specific shocks. We find that SOEs reform contributes to the reduction in capital misallocation in a large economic magnitude: One standard deviation of the decline in SOEs output share reduces capital misallocation by 11.9 percentage points between 1998-2007, which represents 65% of its sample standard deviation. Moreover, this effect becomes statistically insignificant once conditioning on the initial misallocation level, while initially more distorted industries still show a significantly larger decline in misallocation. This implies that SOEs reform reduces (capital) misallocation especially within initially more distorted industries, and there are other economic reforms contributing to the aggregate pattern.

A similar analysis at the industry level finds little evidence of the international trade fostering aggregate efficiency. The integration into the world market undoubtedly boosts the Chinese economy, yet its impact on misallocation is far from clear. For example, misallocation could amplify if low productivity firms increase foreign sales due to favorable policies or decline if governments reduce constraints on high productivity exporters (i.e.

issue more export licenses), which makes it more of an empirical relevant question. We focus specifically on two dimensions associated with the WTO accession: First is the removed uncertainty of import tariffs on products shipped to the U.S. that increases Chinese exports, second is the tariff cut on Chinese imports. We solve the endogeneity concerns by using difference-in-difference methodology that uses ex-ante uncertainty on exports to the U.S. and imports tariff rates as the industry-level exposure. We find that uncertainty cut reduces labor misallocation slightly and import competition has little effect.

This paper relates to a strand of literature on misallocation in China. Most studies on this subject focus on the state ownership as which represent explicit government interference. For example, Brandt et al. (2013) find an increasing capital misallocation between

(22)

1.1. Introduction 7 state and non-state sectors within provinces after the mid-1990s, which is related to government policies that encourage investment in the state sector. Curtis (2016) finds that financial frictions impede the entry of less talented entrepreneurs in the private sector but not in the financially unconstrained state sector, which explains the difference in TFP between sectors. Wu (2018) uses the ownership as a proxy for the policy distortions to identify its effect on average MRPK dispersion and finds that policy distortions play a much bigger role than financial frictions in capital misallocation. One of the few excep- tions is David and Venkateswaran (2019) that allows firm-level capital distortion to be determined by firm productivity in a dynamic structural model and finds a larger contribution of firm productivity than permanent factors to the dispersion of average capital productivity in China than in the U.S.

This paper is also related to the literature on the distortion-productivity nexus. A number of studies have found that if distortions correlate with firm productivity positively, the quantitative effect of misallocation on aggregate productivity will be much larger than the uncorrelated case (Restuccia and Rogerson 2008; Bartelsman et al. 2013; Bento and Restuccia, 2017). Buera and Fattal-Jaef (2018) claim that productivity dependence of idiosyncratic distortions is a pervasive feature of misallocation in developing countries.

Bento and Restuccia (2017) find a large variation of productivity elasticity of distortions across countries, and which inversely correlates with GDP per capita. Yet, it’s still open to debate why high productivity firms are more constrained by policy distortions. This paper provides suggestive evidence that economic institutions can partly contribute to this relationship, and thus also explains why distortion-productivity nexus is more common in developing countries as governments play a much larger in allocating resources. Since we use a single source of micro-firm level data, this paper also mitigates the concern about measurement errors which could appear in cross-country analysis.

This paper provides new insights into the evolution of misallocation in China during its transition period. This paper first characterizes that the improvement in aggregate allocative efficiency between 1998–2007 is largely driven by initially more distorted industries. Further, the main results find that the decline of SOEs can partially explain the aggregate change of capital misallocation. This is in line with a strand of literature that relates China’s economic growth with the rise of the productive private sector (Song et al. 2011; Hsieh and Song 2015; Curtis 2016). However, SOEs reform appears to have little effect on labor misallocation and might not be the only driver of capital misallocation. Therefore, this paper suggests that a broad range of economic reforms caused more distorted industries to converge faster to the efficient level. More importantly, it provides policy implications that improving the depressed productivity-factor nexus in addition to the narrow focus on the state ownership is key to reduce overall factor misallocation.

This paper finally contributes to a growing body of literature on trade liberalization and intra-industry reallocation in the presence of factor distortions. While the new new trade theories predict that trade liberalization can induce the reallocation of resources from less to more productive firms (Melitz 2003; Pavcnik 2002), this effect is ambiguous in the presence of ex-ante weak institutions, market failures and distortions in developing countries (Atkin and Khandelwal, 2019). A number of studies confirm this argument (Almeida and Poole 2017; Bai et al. 2019; Berthou et al. 2019). A direct related one is Lu and Yu (2015) which finds that import competition induced by China’s WTO accession reduces markup dispersion, yet this paper does not find any significant effect on overall misallocation. Morever, Brandt et al. (2017) find that WTO accession associated output tariff decline improves aggregate productivity through the reallocation channel-entry of

(23)

8 Chapter 1. Factor Distortions and Resource Misallocation Across Chinese Manufacturing Firms high productivity firms, this paper does not account for this channel as it takes the number of firms and their productivity distribution as given.

1.2 Theoretical Framework

In this section, we describe the measure of misallocation within the manufacturing sector in China between 1998 and 2007. We mainly use the static framework developed by Hiseh and Klenow (2009). We consider a three-digit manufacturing industry s populated by an exogenous large number N_s of monopolistically competitive firms.

Total industry output is given by a CES production function:

Y_si = [^X^N^s

i=1

D_si(Y_si)^θ−1^θ ]^θ−1^θ (1.1) where Y_si denotes real output of firm i in industry s, and D_si denotes a demand shifted for firmi’s variety, andθ is the elasticity of substitution between varieties (θ >1).

Further, we usePsi as the price of varietyiandPsas the aggregate price index of industry output Y_s. Firms face an isoelastic demand for their output given by Y_si = (D_si_P^P^s

si)^θY_s, ⁴ so firms with greater demand or lower prices will have larger output.

Firm’s output is given by a Cobb-Douglas production function:

Y_si =A_siK_si^α^sL^1−α_si ^s (1.2) where K_si is the capital, L_si is labor, A_si is physical productivity, and α_s (1−α_s) is the share of capital (labor) input to total output of industry s which are allowed to be differed across industries.

Firms choose their prices, capital, and labor use to maximize their profits:

π_si =P_siY_si−τ_si^kRK_si−τ_si^lwL_si (1.3) whereR denotes the capital cost (real interest rate plus depreciation rate) that is the same across all industries, and w denotes the average wage. τ_si^k denotes a firm-specific wedge that distorts capital use such as access to (cheap) external credits and collateral requirements; and τ_si^l denotes a firm-specific wedge that distorts labor use such as policy restrictions on migrations or labor market frictions and regulations.

The first-order conditions with respect to labor and capital are given by:

M RP L_si= θ−1

θ (1−α_s)P_siY_si

Lsi =τ_si^lw (1.4)

M RP K_si = θ−1

θ α_sP_siY_si

Ksi =τ_si^kR (1.5)

θ−1

θ represents the fixed markup over firm’s marginal cost. Equation 1.4 shows that the marginal revenue product of labor (MRPL) is proportional to revenue-based average labor productivity ^P^si_L_si^Y^si and equal to the wage times the labor wedgeτ_si^l, and equation 1.5 shows that the marginal revenue product of capital (MRPK) is proportional to revenue- based average capital productivity ^P^si_K^Y_si^si and equal to the capital cost times the capital wedge τ_si^k. This implies that in the absence of distortions, all firms within the same

4The aggregate price indexPsis given by [PNs

i=1D^θ_siP_si^1−θ]^(1/(1−θ)).

(24)

1.2. Theoretical Framework 9 industry have the same level of revenue-based factor productivity, which contrasts with the observed pattern that Chinese manufacturing firms with larger labor (capital) size have significantly lower average labor (capital) productivity.

Meanwhile, the capital-labor ratio is given by:

K_si

L_si = α_s 1−α_s

w R

τ_si^l

τ_si^k (1.6)

so the capital-labor ratio variation across firms within the same industry is entirely driven by the relative strength of labor and capital distortions. For example, if firms are relatively more labor distorted, then firms have larger capital-labor ratio.

The firm’s input and output are given by:

L_si =ψ_s1 D^θ_siA^θ−1_si

(τ_si^l)^θ+α^s^−α^s^θ(τ_si^k)^α^s^θ−α^s (1.7) K_si =ψ_s2 D^θ_siA^θ−1_si

(τ_si^l)^θ+α^s^−α^s^θ−1(τ_si^k)^α^s^θ−α^s⁺¹ (1.8) Y_si =ψ_s3 D^θ_siA^θ_si

(τ_si^l)^θ−α^s^θ−1(τ_si^k)^α^s^θ (1.9) whereψ_s1, ψ_s2, and ψ_s3 represent three industry aggregate which are equalized across firms within the same industry s. Further, the revenue-based total factor productivity T F P R_si at the firm level is given by:

T F P R_si =P_siA_si = θ

θ−1(M RP Lsi

1−α_s )^1−α^s(M RP Ksi

α_s )^α^s ∝(τ_si^l)^1−α^s(τ_si^k)^α^s (1.10) which is the product of firm-specific price P_si and physical productivity A_si. It’s clearly that firm’s TFPR is proportional to a geometric average of firm’s marginal revenue products of labor and capital, and thus the geometric average of labor and capital distortions.

total factor productivity at the industry level is defined as T F P_s = _Kαs^Y^s

s L^1−αss , where K_s =^P^Ni ^sK_si is the sum of capital used in the industry, andL_s =^P^Ni ^sL_si is the sum of labor used in the industry. Industry’s TFP is further given by:

T F P_s= T F P R_s

P_s = [^X^N^s

i

(D

θ θ−1

si A_siT F P R_s

T F P R_si)^θ−1]^θ−1¹ (1.11) where industry’s TFPR is the geometric average of average MRPL and MRPK in the industry, given by T F P R_s = ^θ−1_θ (^{M RP L}_1−α_s^s)^1−α^s(^{M RP K}_α_s ^s)^α^s ∝ [(^P^Ni ^s 1

M RP Lsi

PsiYsi

PsYs )⁻¹]^1−α^s × [(^P^Ni ^s 1

M RP Ksi

PsiYsi

PsYs )⁻¹]^α^s, P_siy_si denotes firm nominal value added output and P_sY_s denotes the industry nominal value added output. In the absence of distortions, MRPL, MRPK, and TFPR are equalized across firms within the same industry, so theT F Pef f icient

s =

[^P^Ni ^s(D

θ θ−1

si Asi)^θ−1]^1−θ¹ , which represents the industry’s TFP at the most efficient level.

Once there are policy distortions, revenue-based productivity is not equalized across firms, resource misallocation occurs which generates aggregate TFP loss.

(25)

10 Chapter 1. Factor Distortions and Resource Misallocation Across Chinese Manufacturing Firms The within-industry misallocation or industry TFP shortfall due to misallocation is expressed as:

∆lnT F Pˆ _s= lnT F Pˆ ef f icient

s −lnT F Pˆ ^actual_s

= 1

θ−1[ln

Ns

X

i

Z_si^θ−1−ln

Ns

X

i

(Z_siT F P R_s

T F P Rsi)^θ−1] (1.12) where Z_si represents firm productivity (combination of physical productivity and demand shifter), and we estimate it as follows:

Z_si =D

θ θ−1

si A_si = (P_sY_s)⁻^θ−1¹ Ps

(P_siY_si)^θ−1^θ

K_si^α^sL^1−α_si ^s (1.13) as A_si = _Kαs^Y^si

siL¹⁻_si^αs and D

θ θ−1

si = (^Y_Y^si_s)^θ−1^θ (^P_P^si_s)^θ−1¹ derived from the isoelastic demand function. Following Hsieh and Klenow (2009), we use an elasticity of substitution between varieties equal to θ = 3 for comparability.

Under the assumption that revenue productivity (TFPR) and firm productivity (Z) are jointly log-normal distributed, the misallocation is proportional to the within-industry dispersion of lnTFPR, and which is further proportional to the within-industry dispersion of lnMRPL, lnMRPK, and their covariance:

∆lnT F Pˆ _s= ∝ θ

2var(lnT F P R_si)

∝κ_s1var(lnM RP L_si) +κ_s2var(lnM RP K_si) +κ_s3cov(lnM RP L_si, lnM RP K_si)

(1.14)

whereκ_s1,κ_s2, and κ_s3 are three positive industry-specific terms. This approximation allows us to quantify the contribution of different distortion components on the dispersion of revenue-based productivity and thus on overall misallocation.

1.3 Data

The data for a universe of Chinese manufacturing firms is from the Annual Surveys of Industrial Firms (ASIF) over the period of 1998–2007 conducted by China’s National Bureau of Statistics. The ASIF covers all non-state firms with annual revenues above five million yuan (approximately $ 600,000) and all state-owned firms with no less than eight employees in the mining, manufacturing, and utility sectors. Appendix A.2.1 reports the procedures to obtain the final sample for analysis which consists of 1,614,954 firm-year observations distributed in 155 three-digit industries and 30 provinces.

The information we use from the ASIF includes firm labor and capital use, value-added output, wage payments, age, ownership, geographic location, and the affiliated industry.

The ASIF only reports the nominal value of fixed assets so the nominal capital stock is not comparable across firms and years due to inflation. Following Brandt et al. (2012), we assume that firms start purchasing fixed assets from the starting periods with a certain pattern so we can deflate the book value to obtain the real capital stock (see Appendix A.2.2). We use the total number of employees as the labor use, as the ASIF does not report the number of working hours and workers’ skill levels. To mitigate the problem of measurement errors due to outliers, we trim the top and bottom 2% of T F P R_si and

(26)

1.4. Stylized Facts 11 Zsi within each three-digit industry. It is also worth mentioning that we use both paid-in capital and ownership registration to define state-owned enterprises (SOEs), as this is the broadest way to capture the policy distortions associated with state ownership.

Similar to Hiseh and Klenow (2009), we set the industry factor sharesα_sto those in the corresponding U.S. manufacturing industry in 1997, as it provides the benchmark which is less distorted. Specifically, we use the CES Manufacturing Industry Database and match U.S. NAICS six-digit industry with the Chinese three-digit industry (GB/T 4754–2002 standard) manually. Since this database only reports total payroll, it understates the labor share due to missing fringe benefits. The CES manufacturing labor share is about third-fourths what it is in manufacturing according to the Census of Manufacturers. We thus scale up each industry’s labor share by 4/3 to arrive at the labor elasticity assumed for the Chinese industry.

When exploring the dynamic change in overall misallocation between 1998–2007, this paper studies the effect of WTO accession in 2002. Before entering WTO, the U.S.

granted NTR (Normal Trade Relationship) tariff rates to China but which required annual approval by U.S. Congress. If the annual renewal is rejected, then Chinese products face non-NTR tariff rates which are several times higher than NTR tariff rates, so this annual renewal created uncertainty that impedes Chinese exports to the U.S. In October 2000, the U.S. Congress granted permanent NTR status to China which became effective upon China’s accession to the WTO. We use the ad valorem equivalent NTR and non-NTR tariff rates at the HS6 product level from Feenstra et al. (2002) to compute the policy uncertainty of import tariff rates on Chinese exports to the U.S. at the industry level in 1999. Industries subject to greater policy uncertainty should experience a larger surge in export growth after entering WTO. Moreover, we use the simple average of MFN applied tariff rates at the HS6 product level from the UN TRAINS database to compute the import tariff rates at the industry level in 2000. Industries subject to larger import tariffs before the WTO accession experienced a larger decline in import tariff rates, and thus face greater import competition after the WTO accession.

1.4 Stylized Facts

According to the theoretical framework, in the absence of policy distortions, marginal (average) productivity should be equated across firms, and firm’s input/output size should be purely determined by firm productivity (Z_it in eq.(1.13)) and thus there is no relationship between marginal (average) productivity and firm size. Yet when firms face distortions that increase input prices relatively, they are rendered smaller and have higher marginal (average) productivity. To sketch the basic features of resource misallocation within Chinese manufacturing sector, Figure 1.1 provides an overview of the relationship between average productivity and firm size quantile. Figures 1.1a and 1.1b confirm the latter case by showing a negative relationship between average productivity and factor use.⁵ However, Figures 1.1c and 1.1d present a different scenario as there exists a positive relationship between average productivity and output. Figure 1.1 thus conveys a con- flicting message of how distortions and firm size are jointly distributed and how policies should step in to reduce misallocation. Since firm output is determined by firm input and productivity jointly, one natural explanation is that the positive output-distortion

5Under a Cobb-Douglas production function, marginal factor productivity is proportional to average factor productivity.

(27)

12 Chapter 1. Factor Distortions and Resource Misallocation Across Chinese Manufacturing Firms relationship is attributed to positive productivity-distortion relationship. That is, firms with high productivity are subject to greater factor distortions.

Figure 1.1: Average factor productivity and firm size

Figure 1.1a plots the relationship between (log) average labor productivity and employment size quantiles. Figure 1.1b plots the relationship between (log) average capital productivity and capital stock size quantiles. Figure 1.1c plots the relationship between (log) average labor productivity and value-added output size quantiles. Figure 1.1d plots the relationship between (log) average capital productivity and capital size quantiles. All figures control for three-digit industry-year fixed effects.

The benchmark is the firms in the 0-10th percentile of input or output size.

Table 1.1 further provides an overview using formal regressions. Panel A, Column (1) shows there is a negative relationship between revenue productivity and labor use.

Quantitatively, doubling labor use is associated with a 20.7% decline in TFPR. Columns (2) and (3) further show that firms with more labor use have significantly lower MRPL and MRPK in a similar magnitude. Consistently, Column (4) shows that the capital- labor ratio does not vary with labor use, as the coefficient is close to zero and statistically insignificant. This suggests that firms with different amounts of labor use face the same level of labor and capital distortions.

Panel B instead looks at the joint distribution of revenue-based productivity and capital use. Column (1) finds a similar pattern that firms with more capital use have significantly lower revenue productivity. On the contrary, Columns (2) and (3) show that these firms have significantly lower MRPK but slightly higher MRPL.⁶ Specifically, doubling capital use is associated with a 21.2% decline in TFPR and a 38.9% decrease in MRPK. Unlike Panel A which finds no relationship between capital-labor ratio and labor use, Column (4) instead shows that capital-labor ratio increases with capital use

6The quantile regression reveals that firms in the 5th quintile of capital use have a large increase in ln MRPL, suggesting that firms with extremely large capital stock face somehow stronger labor distortions (e.g. tighter labor regulations).

(28)

1.4. Stylized Facts 13

Table 1.1: Revenue-based Productivity, Capital Intensity, and Size

This table reports the relationship between revenue-based (marginal) productivity, capital-labor ratio, firm’s input, and output size. All regressions control three-digit industry-year fixed effects. The robust standard errors (clustered at the firm level) are in bracket. We use ***, **, and * to denote statistical significance at the 1%, 5%, and 10% level, respectively.

(1) (2) (3) (4)

Dependent variables lnT F P Rit lnM RP Lit lnM RP Kit ln^K_{L it} Panel A: labor size

lnLit -0.232*** -0.227*** -0.226*** -0.001

(0.001) (0.001) (0.002) (0.002)

R-squared 0.374 0.276 0.181 0.150

Panel B: capital size

lnKit -0.238*** 0.069*** -0.493*** 0.563***

(0.001) (0.001) (0.001) (0.001)

R-squared 0.447 0.232 0.528 0.628

Panel C: output size

lnPitYit 0.221*** 0.392*** 0.090*** 0.302***

(0.001) (0.001) (0.001) (0.001)

R-squared 0.382 0.431 0.153 0.227

Panel D: output size conditioning on intermediate input

lnPitYit 0.449*** 0.571*** 0.356*** 0.215***

(0.002) (0.001) (0.002) (0.002)

lnPmtMit -0.280*** -0.219*** -0.327*** 0.107***

(0.002) (0.002) (0.002) (0.002)

R-squared 0.412 0.451 0.182 0.230

Industry×year fixed effects Yes Yes Yes Yes

Observations 1,614,954 1,614,954 1,614,954 1,614,954

in a large magnitude. This implies that firms with less capital use are heavily financially constrained and face a disproportionate strong capital distortion.

Overall, Panel A and B confirm the existence of policy distortions which generate higher marginal productivity and depress factor use simultaneously. Panel C however shows that larger (small) firms in terms of value-added output exhibit significantly higher (lower) revenue-based productivity. This thus indicates that large firms face greater policy distortions, contradicting with the policy implications of Panel A and Panel B. As we have discussed, since output is either attributed to input use or idiosyncratic productivity, this paradox suggests that high productivity firms somehow face greater policy distortions, which generates a positive bias when regressing revenue-based productivity on output.

Although we use value-added output which nets intermediate input, it is possible that firms with more intermediate inputs have higher productivity and somehow face more distortions. Panel D further confirms the intermediate input use (lnP_mtM_it), which however shows that the positive relationship between output and revenue productivity becomes even stronger.

In the absence of policy distortions (τ_si^l = τ_si^k = 1), there should be a linear positive relationship between (log) productivity and (log) input use, which solely depends on the elasticity of substitution between varieties θ. Table 1.2 then demonstrates the joint- distribution of firm size and productivity, which is the mirror of the joint-distribution of distortions and productivity. Column (1) shows that the labor-productivity relationship is significantly positive, and the quantile regression in Column (2) shows that labor use increases monotonically with firm productivity. Although high productivity firms indeed acquire more labor, this point estimate of 0.119 is far below the theoretical level predicted by the model (2 =θ−1) in the most efficient case. Column (3) then finds an even weaker relationship between capital use and productivity as the corresponding coefficient is only

(29)

14 Chapter 1. Factor Distortions and Resource Misallocation Across Chinese Manufacturing Firms

Table 1.2: Firm Size and Productivity

This table reports the relationship between firm productivity and input-output size. All regressions control three-digit industry-year fixed effects. The robust standard errors (clustered at the firm level) are in bracket. We use ***, **, and * to denote statistical significance at the 1%, 5%, and 10% level, respectively.

(1) (2) (3) (4) (5) (6) (7)

Dependent variables lnLit lnKit lnPitYit

lnZit 0.119*** 0.027*** 0.708***

(0.001) (0.002) (0.001)

2nd Quintile ofZit 0.023*** -0.211*** 0.568*** 1.238***

(0.003) (0.004) (0.002) (0.002)

3rd Quintile ofZit 0.111*** -0.209*** 1.015*** 2.077***

(0.003) (0.005) (0.003) (0.003)

4th Quintile ofZit 0.216*** -0.138*** 1.509*** 2.956***

(0.004) (0.005) (0.003) (0.004)

5th Quintile ofZit 0.369*** 0.041*** 2.303*** 4.328***

(0.005) (0.007) (0.004) (0.006)

lnM RP Lit -0.235***

(0.002)

lnM RP Kit -0.694***

(0.001)

Industry×year fixed effects Yes Yes Yes Yes

R-squared 0.141 0.140 0.110 0.114 0.548 0.500 0.723

Observations 1,614,954 1,614,954 1,614,954 1,614,954 1,614,954 1,614,954 1,614,954

slightly above zero. Especially, Column (4) shows a U-shaped relationship between capital use and productivity: Firms in the 2nd quintile is 19.0% smaller whereas firms in the 5th quintile is 4.2% larger than firms with productivity in the 1st quintile, suggesting that some least productive firms are occupying abundant financial resources.

The combined results show that high productivity firms have acquire more labor and capital, yet they still face a relative factor scarcity as they are rendered smaller than the optimal size, which we call the depressed productivity-factor use nexus (DPFN).

Unsurprisingly, Columns (5) and (6) find that high productivity firms have more output.

Column (7) instead shows the relationship between output and firm productivity when conditioning on policy distortions. It shows that high productivity firms are also rendered smaller in terms of output: Firms in the 5th quintile should be 75 times [= e^4.328−1] larger than the firms in the 1st quintile of productivity yet they are only 9 times [= e^2.303−1]

larger in the data.

Table A.1 implements counterfactual experiments that equalize policy distortions across firms within industries to quantify resource misallocation ∆lnT F P. Specifically, lnT F P_s is the (log) counterfactual TFP if factor distortions are eliminated, and lnT F P_s^a is the (log) actual TFP of industry s. We use the time-invariant industrial value-added share between 1998 and 2007 w_s to calculate the economy’s aggregate TFP (lnT F P), so the evolution of quantified misallocation does not reflect a shift in industry composition:

∆lnT F Pˆ =^X

s

(lnT F Pˆ _s−lnT F P_s^a)×weight_s (1.15) Columns (1) and (2) eliminate only within-industry labor distortion (τ_si^l = τ_s^l). In 1998, this will increase aggregate TFP by 55 percent and this magnitude declines to 42 percent in 2007, with average at 47 percent during this period. Columns (3) and (4) instead eliminate within-industry capital distortion (τ_si^k = τ_s^k), and it shows that aggregate TFP will increase by 116 percent in 1998 and still large at 89 percent in 2007, with average at 116 percent. The larger capital misallocation is in line with a more pronounced DPFN on the capital-productivity relationship. Columns (5) and (6) finally

Three Essays on Resource Allocation in China

Thesis

Reference

Three Essays on Resource Allocation in China

THREE ESSAYS ON RESOURCE ALLOCATION IN CHINA

(Trois essais sur l’allocation des ressources en Chine)

THESIS

Difei OUYANG

Docteur ès économie et management mention economics

Acknowledgements

Abstract

R´ esum´ e

Contents

I Appendices 107

Introduction

Chapter 1

Factor Distortions and Resource Misallocation Across Chinese

Manufacturing Firms ¹

1.1 Introduction

1.2 Theoretical Framework

1.3 Data

1.4 Stylized Facts

Three Essays on Resource Allocation in China

Thesis

Reference

Three Essays on Resource Allocation in China

THREE ESSAYS ON RESOURCE ALLOCATION IN CHINA

(Trois essais sur l’allocation des ressources en Chine)

THESIS

Difei OUYANG

Docteur ès économie et management mention economics

Acknowledgements

Abstract

R´ esum´ e

Contents

I Appendices 107

Introduction

Chapter 1

Factor Distortions and Resource Misallocation Across Chinese

Manufacturing Firms 1

1.1 Introduction

1.2 Theoretical Framework

1.3 Data

1.4 Stylized Facts

Manufacturing Firms ¹