School of Contemporary Chinese Studies

School of Contemporary Chinese StudiesA18 Si Yuan Centre - University of Nottingham - NG8 1BB

Productivity and Output Loss with Scale Diseconomies and

Resource Misallocation in China

Carlo MilanaBirkbeck College, University of London

Email: [email protected]

Harry X. Wu IER, Hitotsubashi University

Email: [email protected]

Tuesday 25th February 2014 (17:00-19:00)

Contents

5. Conclusion

2. Motivation

3. The research problem3a. Methodology

3b. Data

4. Main findings

1. Main message

The results obtained using a new sectorial dataset indicate that a strong technical change accompanied by a high growth of (imported) intermediate inputs has indeed occurred in China throughout the period 1980-2010,

1. Main message

A number of recent empirical studies report a modest TFP growth in China during the last two decades, which does not match the observed high-speed economic growth.

Beyond data problems, this puzzling feature of China’s economic growth can be explained by decomposing productivity growth into its main constituents using an appropriate methodology.

but resource misallocation and severe scale diseconomies dwarfed its effects on productivity growth.

Whether such government-engineered growth has been accompanied by healthy productivity performance however remains very controversial (Yanrui Wu, 2011; Wu, 2013)

2. Motivation (I)

China’s post-reform rapid growth has been mainly attributed to competition between local governments for a faster growth and the political promotion of officials (Li and Zhou, JPE, 2005, Xu, JEL, 2011; Huang, JEP, 2012).

2. Motivation (II)

Hence, data problems have been indicated as the main reason for such highly differentiated TFP estimates

Despite all studies adopting the same Solow-type of growth accounting approach, estimates of China’s post-reform TFP growth range from less than 1% to over 4% p.a.

Frictions of various kinds and sub-optimal choices may affect TFP performance through scale diseconomies and misallocation of resources causing rising costs.

2. Motivation (III)

Government interventions are industry-specific by nature, which are made through various industry policies that are deemed growth-promoting or/and strategically important (for future growth)

Second, a proper methodology has to be set up to take into account the scale (dis)economies and misallocation of resources in the case of imperfect market or institutional deficiencies

3. The research problem

How to adequately and accurately measure the effects ofscale (dis)economies and resource misallocation on TFP in China?

To answer this question, two important efforts have to be made:

First, all inputs and output have to be properly measured at industry level as well as coherent parts of the national accounting system

3a. Methodology (I)

In order to account for the growth of a highly distorted economy, Afriat’s index-number approach is particularly useful as it incorporates cost-inefficiency as one of the main cost components.

The search for meaningful economic measures

Growth accounting is typically based on the economic theory of index numbers

Its typical assumptions include the perfect market, rational behavior of agents, and efficient allocation of resources, which is not meaningful in the case of China.

Scale diseconomies may also be measured as other important factors reducing TFP.

3a. Methodology (II)The search for meaningful economic measures

Afriat (1967, 1972, 1981) deliberately gave up any index-number formula and founded its method on his theorem stating that a piece-wise linear utility or production function exists which rationalizes the data if and only if a “cyclical consistency” of homotheticity inequality conditions (i.e. chained Laspeyres and Paasche inequalities) are satisfied.

The minimum requirements for quantity and price indexes to provide meaningful economic measures include certain axioms such as those regarding consistency in aggregation, transitivity, and linear homogeneity (Irving Fisher 1922; Samuelson and Swamy 1974)

Unfortunately, such index number formulas satisfying these minimum requirements are generally nonexistent (Frisch 1936). A similar impossibility theorem arose in Van Veelen (2002).

The HARP conditions yield the optimized chained Laspeyres and Paasche index numbers, which define, respectively, the tight upper and lower bounds of the “true” indexes.

3a. Methodology (III)The search for meaningful economic measures

This path-breaking theorem is later re-proposed as GARP (general axiom of revealed preference) by Varian (1982), and then following Afriat’s further development under more stringent conditions, as HARP (homothetic axiom of revealed preference).

Testing HARP conditions have become a standard procedure to establish if a particular data set is fully consistent with aggregation without imposing any further joint hypothesis concerning a specific functional form of the rationalizing utility (or production) function.

3a. Methodology (IV)The search for meaningful economic measures

The starting point for this more general treatment is the recognition of allocative inefficiency signaled by the violation of the Laspeyres-Paasche inequality condition which is regarded as inefficiency in input allocation.

More importantly, Afriat’s approach allows the construction of consistent index numbers in the heterogeneity case with data violating HARP.

Afriat (1972) introduced an inefficiency index, later called “Afriat’s inefficiency index” by Varian (1990, 1993), as a way to generalize the accounting context thus widening considerably the set of the data that can be rationalized by utility (or production) functions up to a certain degree of efficiency.

3a. Methodology (V)The search for meaningful economic measures

Optimizing chained indexes of upper and lower bounds of aggregate inputs by minimizing chained Laspeyres and maximizing chained Paasche indexes.

Correction of these indexes for cost-inefficiency that can be revealed by the violation of Laspeyres-Paasche inequality condition, i.e. Las ≥ Paa.

The inefficiency parameter et is found in the interval

The starting decomposition of the minimum costs are therefore obtained as cost-inefficiency parameter multiplied by observed total costs:

3a. Methodology (VI)The search for meaningful economic measures

simultaneously along the decomposition

( , )t t t t ti ii

e w x w x

( , ) ( ) ( )t t t t w x w x

0 1te

O

F

E B

y1

y0

D

C

Input x2

Input x1

A

Within industry misallocation reduces productivity

A1

A0

Output y2

.

Output y1

Between industry misallocation causes output loss

....A

C

BP

BL

P ray

L rayq1

q0

Commodity 2

Upper bound (Laspeyres-type)

Lower bound (Paasche-type)

Keynes’ method of limits

Upper bound (Laspeyres-type)

Lower bound (Paasche-type)

Commodity 2

Paasche limit

Laspeyres limit

P ray

L ray

Tightbounds

....A

C

BL

q1

q0

q2

Samuelson-Afriat tight bounds

...min ...ij ik kl mjkl m

M L L L

...max ...ij ik kl mjkl m

H K K K

1

jiM

=

1

jiM

Scale elasticity and misallocation effect

for every i ≥ j

Finding Afriat efficiency index

Finding the scale elasticity :

where p is the output price and m is the pure profit margin

where L and K are respectively the Laspeyres and Paasche indexes

11,0 1 0 1,0

0

( ) / ( )e

EX Xe

x x

1,0

1,0 1,01( )SE EX

1,0 1,0

1,01,0 1,0

1,0

1,0

1 and increasing returns to scale ( >1) 1 with

1 and decreasing returns to scale ( <1)

any (positive) and constant returns to 1 with

EXSE

EX

EXSE

1,0

1,0

1,0 1,0

1,01,0 1,0

scale ( =1)

1 and any returns to scale

1 and decreasing returns to scale ( <1) 1 with

1 and increasing returns to scale ( >1)

EX

EXSE

EX

Scale effects on TFP

Output

InputseX X

TC

TFP

TFP, TC, misallocation and scale effects

3a. Methodology (VII)Decomposing TFP growth

1,01,0 1,0 1,0 0 1

1,0

= Y

TFP TC SE OC eX

where

0 0 0 1 1 11,0 1,0 1,0

0 0 1 1

(1 )e e

p y p y

w x w x

Output index Actual input index = Efficiency input index ∙Afriat efficiency index

Scale effect indexTechnical change index

Marginal elasticity of scale Potential gain in TFP level from removal of base-period “within” misallocation

3b. Methodology (I)Data work

Our data work follows the KLEMS standard industry-level accounting principles outlined by Jorgenson and Landefeld (2006, 2009), which require the homogenization of all heterogeneous input measures by their user costs.

This relies on substantial efforts to re-establish consistency in the official data in terms of concept, classification and coverage

We first develop a new data set for the Chinese economy in the Jorgenson and associates growth accounting framework that covers the entire economy in 37 industries for 1980-2010,

which is controlled by the national accounts and input-output tables to ensure the complete coherence of sectors

3b. Methodology (II)Data work

For each industry, data on prices and quantities of 131 different types of inputs of production have been constructed. In particular, 2 types of durable capital goods (structures and machinery), 70 different types of labour inputs (cross classified by gender (2), age (5), and education (7)), and 59 different types of intermediate inputs.

The industry-level data on nominal levels, volume and price indices for outputs and inputs used in this study are part of a new data set for the Chinese economy constructed by one of us (Harry Wu, 2012) complementing national accounts with other statistical sources.

We have therefore 4,847 different industry-specific factor inputs for each year to be considered in the overall economy.

1980-84 84-91 91-96 96-01 01-07 07-10 80-10

1. Technical and organizational change 12.1 5.9 8.9 8.9 12.0 9.3 9.4

1a. Technical change 5.5 1.4 5.7 4.9 7.8 5.8 5.0

1b. Within-industry potential reallocation 6.2 3.8 2.8 3.7 3.9 3.1 3.9

1c. Between-industry potential reallocation 0.4 0.7 0.4 0.3 0.3 0.4 0.5 2. Scale effects

-3.6 -4.0 -4.5 -3.0 -5.8 -4.4 -4.3

3. Within-industry misallocation effects -6.1 -3.5 -2.6 -4.1 -3.7 -3.2 -3.8

4. Aggregate TFP growth (1a+1b+2+3) 2.0 -2.3 1.4 1.5 2.2 1.3 0.8

5. Between-industry misallocation -0.6 -0.7 -0.4 -0.3 -0.3 -0.4 -0.5

6. Output loss (misallocation & scale) -10.3 -8.2 -7.5 -7.4 -9.8 -8.0 -8.6

TABLE 1DECOMPOSITION OF AGGREGATE TFP GROWTH AND OUTPUT LOSS (AVERAGE PERCENT P.A.)

FIGURE 1TFP GROWTH DECOMPOSED INTO TECHNICAL CHANGE (TC),

SCALE EFFECT (SE) AND ALLOCATIVE EFFECT (AE) (% P.A.)

ASSA/CES, Philadelphia, January 3, 2014

25

FIGURE 2ACCOUNTING FOR TOTAL LOSSES IN THE CHINESE

ECONOMY DUE TO MISALLOCATION OF RESOURCES(% P.A.)


26

FIGURE 3 PRODUCTION INDEX OF THE CHINESE ECONOMY:

ACTUAL VERSUS POTENTIAL (1980 = 1)


27

4. Main findings (I)

Our results are also consistent with those of Cao, Ho, Jorgenson, Ren, and Sun (2009), who have found a deceleration of TFP growth below 1.0% per year during the period 1994-2000.

The TFP growth at industry level and in the overall economy has turned out to be rather low (0.8% per year) during the entire period 1980-2010.

Our estimated modest TFP growth rates are similar to those obtained by Young (2003), who applied the Jorgenson, Gollop, Fraumeni’s (1987) approach on quantity data from national statistics

4. Main findings (II)

We should also consider that the misallocation of resources between industries accounts at least for another -0.4% of the final output to the economic welfare, thus bringing the welfare gain from TFP growth not higher than +0.4% per year.

This outcome has been due to a substantial reduction of the respectable technical change (near 5% per year) mainly by the worsening effects of the diseconomies of scale (-4.3%) and misallocation of resources (-0.7%).

Over the years, misallocation effects have accumulated output losses that amount in total around 15% of the effective productivity level.

4. Main findings (III)

This can be considered a huge loss, but is still lower than the “between-firm” misallocation effect found by Hsieh and Klenow (2009) in the size of 40% of the distance in the TFP level between China and the U.S.

The total output loss due to “within” and “between” misallocation effects is -4.3% on the TFP level while the reducing effect of both scale diseconomies and misallocation sum up to an average -8.6% per year.

Over the years, misallocation effects have accumulated output losses that amount in total around 15% of the effective productivity level.

More recently, Midrigan and Xu (2014) on the AER have found results for China consistent with ours. Inter-plant misallocation does not affect TFP growth very much just because of its persistence over time, but can potentially raise TFP level by about 5 per cent if it is removed.

4. Main findings (IV)

We argue that the fundamentals of the scale diseconomies can partly explain this puzzle. Recent analyses have pointed the importance of urbanization process, congestion of resource utilization leading to negative marginal productivity, and insufficient infrastructure development (Brockett, Cooper, and Wang, 1998; Fan, Morck, and Yeung, 2011; Özyurt and Guironnet, 2011; Fan, Kanbur, Wei, and Zhang, 2013).

These results are puzzling compared with the high growth rates of the real economy. An explanation can be found in the different meaning itself between TFP and technical change, which is still very pronounced in China.

4. Main findings (V)

In general, however, the contribution of TFP and TC to output growth has been less important than the factor input growth. This was already noted in some earlier empirical studies, which have focused their attention to the inadequate and inefficient investment of capital in China.

The second additional explanation comes from the significant contribution of intermediate input quantities, in part imported, with an average 8.1% increase per year and only a marginal contribution from an average increase in capital by 1.7% and labour by 0.1% per year.

• Trade services, Paper products, Rubber and plastics are, in decreasing order, the industries that are more affected by misallocation costs.

4. Main findings (VI)

• Agriculture, Financial intermediation, and ICT are the industries that have benefited from the highest technical change and opportunity of increasing productivity.

• The industries that showed a technical regress are Oil and gas

extraction, Petroleum and coal products and strategic services like Education and Health and Social Security.

• Other industries remained technically stagnant like Construction, Business services, Energy, Metal products and Textiles.

• Financial intermediaries, Real estate, Tobacco, Oil and gas extraction, ICT, Business services are most affected by the diseconomies of scale.

FIGURE 4

TFP GROWTH AGAINST TECHNICAL CHANGE AT THE SECTORAL LEVEL OF THE CHINESE ECONOMY, 1980-2010

(% p.a.)


34

The results support our argument that government has been able to promote growth in China, but not to solve the efficiency problem, and growth without efficiency cannot be sustainable.

5. Conclusion

A new way to account for total factor productivity has been presented here.

Appropriate index numbers were constructed using a procedure that takes also into account scale effects and allocative inefficiencies simultaneously.

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