Detecting multiple factors in panel data: an application on the growth of local regions in China

ABSTRACT

Due to unbalanced growth in China’s local regions, we construct a panel data model with multiple common factors to examine the differences among the growth factors in these areas. This article shows the various impacts from the supply and demand sides on economic growth. Different from the demand side, the supply-side impacts have permanent influences. This article focuses on these deep and profound impacts to explain the reasons behind China’s fast economic growing. By using data on 27 regions from 1958 to 2013, we summarize the main permanent influences along three lines. The first comes from the coastal regions, which have learned modern technology and systems from foreign companies, such as in Guangdong, Zhejiang, Fujian and Liaoning. The second comes from big cities, such as Beijing and Shanghai, in which a huge migration has given the companies opportunities to recruit excellent workers, making the resource allocation specialized and more efficient. The third is from the government’s major public works, which have improved areas’ infrastructure and assisted long-run economic growth, such as for Sichuan, Guangxi and Yunnan.

KEYWORDS:

• Panel data model
• common factors
• unit root test
• whittle method
• canonical correlation
• temporary and permanent effects

JEL CLASSIFICATION:

• C23
• C52

Disclosure statement

No potential conflict of interest was reported by the author.

Supplementary-material

Supplemental data for this article can be accessed here.

Notes

¹ If we take the stationary part, we can leave the stationary elements and replace the non-stationary series by zero. We can do the same thing for the non-stationary part.

² The regions include Anhui, Beijing, Fujian, Gansu, Guangdong, Guangxi, Guizhou, Hebei, Heilongjiang, Henan, Hubei, Hunan, Jiangsu, Jiangxi, Jilin, Liaoning, Ningxia, Qinghai, Shaanxi, Shandong, Shanghai, Shanxi, Sichuan, Tianjin, Yunnan, Zhejiang and Inner Mongolia.

³ According to Osterwald-Lenum (1992), the last 10 critical values for the trace statistics of likelihood ratio tests are 12.25, 25.32, 42.44, 62.99, 87.31, 114.90, 146.76, 182.82, 222.21, and 263.42.

⁴ Here, the lag length used is according to the BIC criterion.

⁵ The critical value according to the Dickey–Fuller test based on the estimated OLS t-statistics is –3.43 at the significance level of 5%.

⁶ The correlations between the first component and the top five cities are 0.3396, 0.3365, 0.2877, 0.2551 and 0.2463, respectively.