Policy Drivers of Inter-Regional Investment in China
Abstract
:1. Introduction
2. Methodology
2.1. Literature Review
2.2. Data
2.3. Model
3. Results
4. Discussion
- A focus on improving communications infrastructure is likely to reap very high rewards in the form of inward investment.
- Reducing government ownership of the means of production, particularly if the proportion of state-owned enterprises currently exceeds 45%, will also attract investment.
- Reducing administrative burden, such as compliance costs, will pay dividends across the entire range of observed values, so it would be worthwhile to benchmark administrative performance against best practices.
- These points apply most strongly to the Central and Eastern provincial groupings.
- Our results are in accord with some of the findings from the literature that we have reviewed above. Bjorvatn (2000) noted the importance of the provision of infrastructure. Becker and Fuest (2010) drew attention to how infrastructure provision interacts with government policy at both the national and regional levels. This point is important in the Chinese context as regional governments have a degree of autonomy over policies (Jin et al. 2005), which may lead to policy competition amongst regions cancelling out any positive effects, as noted by Head et al. (1999) in the US context. In summary, our main conclusion is that the most important contribution government can make to economic development in terms of attracting investment is to carefully consider its role. Our findings indicate that government should focus on the provision of public goods in the form of improved infrastructure while, at the same time, both reducing its role in direct ownership of productive activities and minimising the compliance costs of firms. That these findings remain relevant is underscored by the reform plans of the Third Plenum of the Chinese Communist Party in 2013, which noted “the need for markets to play a decisive role in allocating resources, as well as limiting government focus to effective regulation and preserving macroeconomic stability, rather than micromanaging economic decisions” (Dieppe et al. 2018, p. 30).
Author Contributions
Funding
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Category | Measures |
---|---|
Government policies | Infrastructure provision Tax burden State-owned enterprise proportion Administrative expenses |
Market-related factors | Labour cost Land cost Export share Market size |
Firm-specific characteristics | Size Profitability Capital structure |
Name | Definition |
---|---|
INVjikt | Dependent variable measuring industrial relocation; takes the value 1 if company j in region i invests in region k at time t; otherwise, 0. |
Local government GOV | Each variable measured for region k at time t. |
INT | Regional transport infrastructure, measured as regional road density in km of highway/surface area of region (km/km2). |
INC | Regional telecommunications infrastructure, measured as the natural log of the value of post and telecommunications infrastructure (CNY billion). |
TAX | Regional tax burden, measured as the ratio of local taxes and surcharges to the value of regional industrial output. |
SOE | The proportion of businesses in a region that are state-owned enterprises. |
ADM | An index that measures the administrative expenses of the regional government as a fraction of the total. |
Market characteristics MKT | Each variable measured for region k at time t. |
WGE | The natural log of the regional average wage as a measure of the cost of labour. |
LND | The natural log of the price of an average commercial house, which is a proxy for the cost of land. |
EXP | Exports from the region as a share of regional GDP. |
LMS | A measure of local market size to capture potential demand. where GDPs is the gross domestic product of region s, GDPr is the gross domestic product of region r, drs is the distance between the two provinces, and is the intra-province distance (with arear being the province’s area). |
Firm characteristics FRM | Each variable measured for firm j in region i at time t. |
SZE | Firm size, measured as the natural log of total assets. |
ROA | Return on assets, as a measure of profitability, defined as net profit/total assets. |
CAP | Capital structure, measured as asset-to-liability ratio. |
Categorical (Dummy) variables | |
INVREG | Location of the investing firms. 1: Guangdong. 2: Jiangsu. 3: Shanghai. 4: Zhejiang. |
MAJORREG | Major provincial grouping receiving investment. 1: East. 2: North. 3: Central. 4: West. |
INDYCLASS | Major industry classifications. 1: Labour-intensive industries. 2: Capital-intensive industries. 3: Technology-intensive industries. 4: Service industries. |
YEAR | Year of observation (2000–2010). |
Variable | Mean | Standard Deviation | Minimum | Maximum |
---|---|---|---|---|
INV | 0.0588378 | N/A | 0 | 1 |
INT | 0.6444056 | 0.4283969 | 0.0208343 | 1.900635 |
INC | 5.951272 | 1.064577 | 2.163323 | 8.423625 |
TAX | 0.0211205 | 0.0182539 | 0.0057701 | 0.164441 |
SOE | 0.4538201 | 0.2017989 | 0.0920957 | 0.8910649 |
ADM | 0.1245857 | 0.0378919 | 0.0371712 | 0.2592029 |
WGE | 9.9753 | 0.4908811 | 8.841882 | 11.18267 |
LND | 7.963653 | 0.5472049 | 6.855409 | 9.785942 |
EXP | 0.1692471 | 0.2044167 | 0.0052584 | 0.9051254 |
LMS | 5.76994 | 0.6463238 | 3.647182 | 7.136748 |
SZE | 21.49668 | 1.061312 | 17.92033 | 26.1563 |
ROA | 0.0495136 | 0.0608682 | 0.5237361 | 0.3897035 |
CAP | 0.4385126 | 0.1880797 | 0.0127279 | 2.258085 |
Variable | Model (1) | Model (2) | Model (3) |
---|---|---|---|
INT | 0.43660464 *** | 0.20400277 | 0.19796123 |
INC | 0.55900919 *** | 0.49505422 *** | 0.50471382 *** |
TAX | −4.279756 * | −2.8156894 | −2.8311528 |
SOE | −0.80141995 *** | −1.0362848 *** | −1.0465065 *** |
ADM | −4.801426 *** | −3.1808374 ** | −3.2037023 ** |
WGE | −0.44269669 | −0.43134058 | |
LND | 0.53034116 *** | 0.53366381 *** | |
EXP | 0.37742587 | 0.38130881 | |
LMS | 0.3588615 ** | 0.36748048 ** | |
SZE | 0.14014136 *** | ||
ROA | 0.32573334 | ||
CAP | −0.02272468 | ||
constant | −4.2348581 *** | −5.8536407 *** | −8.9992135 *** |
Variable | Model (1) | Model (2) | Model (3) |
---|---|---|---|
INT | 0.82950528 *** | 0.39431354 | 0.3805061 |
INC | 0.10590548 *** | 0.9438466 *** | 0.96150437 *** |
TAX | −8.7166736 * | −5.709996 | −5.6842483 |
SOE | −1.5437591 *** | −2.0739192 *** | −2.0983987 *** |
ADM | −9.1593013 *** | −6.0791368 ** | −6.1234514 ** |
WGE | −0.83200279 | −0.80745708 | |
LND | 1.0419983 *** | 1.0454238 *** | |
EXP | 0.61154122 | 0.61585561 | |
LMS | 0.69001749 ** | 0.70958825 ** | |
SZE | 0.26991016 *** | ||
ROA | 0.58583844 | ||
CAP | −0.06225517 | ||
constant | −7.8661387 *** | −11.301255 *** | −17.354669 *** |
Variable | IV Probit | Probit |
---|---|---|
INT | 0.0164266 | 0.19796123 |
INC | 0.2269846 *** | 0.50471382 *** |
TAX | 0.5147498 | −2.8311528 |
SOE | −1.002299 *** | −1.0465065 *** |
ADM | 0.2672673 | −3.2037023 ** |
WGE | −0.5901624 * | −0.43134058 |
LND | 0.5566808 *** | 0.53366381 *** |
EXP | 0.3800976 | 0.38130881 |
LMS | 0.4889643 ** | 0.36748048 ** |
SZE | 0.1182275 *** | 0.14014136 *** |
ROA | 0.4731584 | 0.32573334 |
CAP | −0.2319111 | −0.02272468 |
constant | −7.265602 ** | −8.9992135 *** |
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Sang, R.; Alexander, W.R.J.; Anwar, S. Policy Drivers of Inter-Regional Investment in China. Economies 2023, 11, 150. https://doi.org/10.3390/economies11050150
Sang R, Alexander WRJ, Anwar S. Policy Drivers of Inter-Regional Investment in China. Economies. 2023; 11(5):150. https://doi.org/10.3390/economies11050150
Chicago/Turabian StyleSang, Ruicong, W. Robert J. Alexander, and Sajid Anwar. 2023. "Policy Drivers of Inter-Regional Investment in China" Economies 11, no. 5: 150. https://doi.org/10.3390/economies11050150