Following the work of Lavopa (2015) and many others, we construct a large data set with internationally comparable information on employment and value added at the industry level. The construction procedure is briefly explained as follows. First, we gather the value-added data of manufacturing
Figure 5.3: The Pattern of Manufacturing Development in the PRC
1950 1960
Manufacturing share: Employment
1970 1980 1990 2000 2010
40
30
20
10 10
15 20
5 0
Year
Manufacturing share: Value-added
Manufacturing share: Employment Manufacturing share: Value-added PRC = People’s Republic of China.
Note: This figure plots the manufacturing employment and real value-added share in the PRC since 1950.
Source: Authors. See section 5.2.1.
and service industries and transform them into constant terms based on a variety of sources.5 Then we use the World Bank’s World Development Indicators (WDI) data as the primary source, and extrapolate these series by using the corresponding growth rate calculated from other databases.
our final data set covers 158 economies from 1950 to 2013.6 We use real value-added growth rate and labor productivity growth rate of each sector as two proxies for its economic performance. Throughout this chapter, labor productivity is defined as real value added per person employed.
For detailed descriptions about data sources, as well the country classification method used in this chapter, please refer to the Internet Appendix.7
Descriptive statistics for all variables used in our main regression are reported in Table 5.1. In panels A and B, we present the statistics of two country classification results according to the relative and absolute criterion, respectively. Although there are modest discrepancies in the observations and economies included, statistical characteristics do not change much when we adopt different classification methods. For example, in the relative criterion sample, the means of the manufacturing and services sector value-added growth rate are 3.98% and 4.29%, respectively, and we observe sizable standard deviations on these two variables. When we use the absolute criterion, the means of manufacturing and services sector value-added growth rate are 3.90% and 4.20%, respectively, with only slight differences from those in the relative criterion sample. Similar conclusions hold for other variables used in this chapter.
5 It includes the World Bank’s WDI database, the World Input–output Database, the World KLEMS Database, the European Union KLEMS Database, the Asian KLEMS Database, the organisation for Economic Co-operation and Development’s Structural Analysis Database (STAN), the Groningen Growth and Development Center 10-sector Database, and the UNIDo INDSTAT2 Database.
6 For most economies, the value-added data of the manufacturing and services sectors start in the 1970s.
7 https://www.tandfonline.com/doi/suppl/10.1080/13547860.2016.1261481/suppl_file/
rjap_a_1261481_sm2703.pdf.
Table 5.1: Statistical Description
Panel A: Relative Criterion
Variable Obs Mean St. Dev. Min Max
Agriculture value-added
growth (%) 2,068 1.911 7.613 –22.56 28.22
Manufacturing value-added
growth (%) 2,271 3.975 8.134 –23.00 33.44
Non-manufacturing industry
value-added growth (%) 2,052 3.882 9.411 –27.41 39.33
Services value-added growth (%) 2,007 4.289 4.336 –10.30 19.65 Agriculture labor productivity
growth (%) 1,002 –3.576 15.18 –15.43 10.31
Manufacturing labor
productivity growth (%) 1,002 –1.069 14.49 –8.287 9.050 Non-manufacturing industry
labor productivity growth (%) 1,002 –2.136 14.45 –11.32 9.033 Services labor
productivity growth (%) 1,002 –1.355 13.65 –3.211 4.322 Gross private savings ratio (%) 677 29.71 11.06 –40.08 99.94
TFP growth (%) 1,076 0.729 0.206 0.261 1.359
GDP per capita 1,095 12,804 6,772 1,624 35,828
Population 1,094 2.930e+07 5.440e+07 370,433 1.340e+09
Inflation (%) 699 28.75 170.1 –1.380 2948
Chinn–Ito Index 839 0.474 0.360 0 1
Deposit money banks’ assets
of GDP (%) 857 49.72 37.08 5.102 297.7
Domestic Credit to
private sector of GDP (%) 909 76.90 640.2 1.126 13957
Real interest rate (%) 637 26.56 338.4 –98.93 6447
Dependency ratio (%) 961 61.02 16.23 34.49 102.2
Urban population of total (%) 961 65.67 15.81 16.48 100 Public expenditures on
education and health of GDP (%) 345 8.896 2.797 2.585 24.74 Total investment share (%) 677 29.71 11.06 –40.08 99.94
Rule of law 1,582 4.248 6.650 –10 10
Human capital index 1,496 2.377 0.505 1.222 3.536
continued next page
Table 5.1: Continued Panel B: Absolute Criterion
Variable Obs Mean St. Dev. Min Max
Agriculture value-added
growth (%) 2,243 2.046 7.573 –22.56 29.82
Manufacturing value-added
growth (%) 2,401 3.900 8.007 –24.05 31.53
Non-manufacturing industry
value-added growth (%) 2,198 3.957 9.047 –27.44 38.95
Services value-added growth (%) 2,172 4.196 4.405 –11.68 21.38 Agriculture labor productivity
growth (%) 975 –3.131 14.27 –14.61 10.24
Manufacturing labor productivity
growth (%) 975 –0.740 13.15 –7.637 8.567
Non-manufacturing industry
labor productivity growth (%) 965 –1.657 13.10 –9.252 8.828 Services labor productivity
growth (%) 975 –0.859 13.52 –2.894 3.997
Gross private savings ratio (%) 659 29.24 12.47 –40.08 99.94
TFP growth (%) 1,052 0.736 0.212 0.261 1.359
GDP per capita 1,059 12,505 5031 2,207 30,691
Population 1,059 3.850e+07 9.730e+07 370,433 1.340e+09
Inflation (%) 648 32.28 181.2 –1.380 2,948
Chinn–Ito Index 782 0.464 0.354 0 1
Deposit money banks’ assets
of GDP (%) 801 46.03 29.48 5.102 164.2
Domestic Credit to
private sector of GDP (%) 858 76.08 658.6 1.126 13,957
Real interest rate (%) 608 25.98 348.9 –1014 6,447
Dependency ratio (%) 915 59.99 14.70 34.49 102.2
Urban population of total (%) 915 66.95 15.09 24.14 100 Public expenditures on
education and health of GDP (%) 313 8.549 2.823 2.585 24.74 Total investment share (%) 659 29.24 12.47 –40.08 99.94
Rule of law 1,657 4.027 6.672 –10 10
Human capital index 1,542 2.375 0.456 1.338 3.536
GDP = gross domestic product, TFP = total factor productivity.
Source: Authors. See section 5.2.1.
We also compute the correlation coefficients between each pair of our main variables of interest. Table 5.2 shows the correlation coefficients between manufacturing development and the development of other sectors as well as several growth determinants. The “annual” term here means we implement our computation by using all the available country–year observations.
Table 5.2: Correlations of Growth Determinants and Sectoral Development
Manufacturing
Agriculture Other Industries Services Corr.
Coeff Rank Corr.
Coeff Corr.
Coeff Rank Corr.
Coeff Corr.
Coeff Rank Corr.
Coeff Panel A: Relative criterion
Annual 0.0916***
(0.000) 0.1296***
(0.000) 0.0623***
(0.003) 0.3246***
(0.000) 0.3391***
(0.000) 0.4287***
(0.000) Country averages 0.2714***
(0.000) 0.3625***
(0.000) 0.1313
(0.170) 0.2395**
(0.011) 0.4731***
(0.000) 0.5353***
(0.000) Panel B: Absolute criterion
Annual 0.0903***
(0.000) 0.1517***
(0.000) 0.0567***
(0.001) 0.3281***
(0.000) 0.2396***
(0.000) 0.4296***
(0.000) Country averages 0.2613**
(0.011) 0.3544
(0.001) 0.1001
(0.342) 0.3069***
(0.003) 0.4918***
(0.000) 0.6310***
(0.000)
Manufacturing
Savings TFP
Corr.
Coeff Rank Corr.
Coeff Corr.
Coeff Rank Corr.
Coeff Panel A: Relative criterion
Annual 0.4140***
(0.000) 0.2171***
(0.000) 0.0864***
(0.000) 0.0953***
(0.000) Country averages 0.1641***
(0.000) 0.2683**
(0.026) 0.0817***
(0.000) 0.1396***
(0.009) Panel B: Absolute criterion
Annual 0.4073***
(0.000) 0.1933***
(0.000) 0.1218***
(0.000) 0.1451***
(0.000) Country averages 0.1481***
(0.000) 0.2373**
(0.032) 0.0814***
(0.000) 0.0813***
(0.001)
Note: ***, **, * denote statistical significance at 1%, 5%, and 10%, respectively. Calculated p-values are shown in parentheses.
Source: Authors. See section 5.2.1.
As for “country average” correlations, we first average the available information for each country, and then compute the cross-sectional correlation coefficients. We provide estimated coefficients of both Pearson correlation and Spearman Rank correlation. Again, panels A and B present the results of using relative criterion and absolute criterion, respectively.
Based on Table 5.2, three general conclusions stand out.
First, manufacturing sector development is significantly related to each of the selected variables. Moreover, the correlation coefficients are always positive, no matter which calculation method we adopt. It indicates that a higher growth rate of the manufacturing sector is positively correlated with a larger private savings ratio, technological accumulation speed, and more rapid growth of all the other sectors. Second, we find that saving–
manufacturing growth correlations are higher than total factor productivity (TFP)–manufacturing growth correlations, although they are all statistically significant. Also, services–manufacturing growth relationships are somewhat greater than agriculture–manufacturing and other industry–manufacturing correlations. This indicates that manufacturing is more closely related to the services sector. Third, when we investigate the sample of year-averaged observations, correlation coefficients slightly decrease for manufacturing–
saving and manufacturing–TFP correlations. In contrast, sectoral relationships are much stronger when we use cross-sectional data.